diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 6d22b92..0000000 --- a/.gitignore +++ /dev/null @@ -1,20 +0,0 @@ -.DS_Store -vtacML/output/models/test_model.pkl -vtacML/output/models/knn_grid_search.pkl -vtacML/output/models/svc_best_model.pkl -vtacML/output/models/0.968_ada_best_model.pkl -vtacML/output/models/0.865_rfc_best_model.pkl -vtacML/output/models/0.731_test1_best_model.pkl -vtacML/output/models/knn_best_model.pkl -vtacML/output/models/rfc_grid_search.pkl -vtacML/output/models/ada_grid_search.pkl -vtacML/output/models/ada_best_model.pkl -vtacML/output/models/0.977_knn_best_model.pkl -vtacML/output/models/0.842_test2_best_model.pkl -vtacML/output/models/lr_grid_search.pkl -vtacML/output/models/svc_grid_search.pkl -vtacML/output/models/rfc_best_model.pkl -vtacML/output/models/lr_best_model.pkl -vtacML/output/models/0.978_svc_best_model.pkl -vtacML/output/models/0.869_lr_best_model.pkl -vtacML/output/models/0.967_dt_best_model.pkl diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 13566b8..0000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,8 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml -# Editor-based HTTP Client requests -/httpRequests/ -# Datasource local storage ignored files -/dataSources/ -/dataSources.local.xml diff --git a/.idea/VTAC_ML.iml b/.idea/VTAC_ML.iml deleted file mode 100644 index 580eff1..0000000 --- a/.idea/VTAC_ML.iml +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2d..0000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index 01f7288..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml deleted file mode 100644 index 3189d63..0000000 --- a/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index 21a0fea..0000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - \ No newline at end of file diff --git a/GRB-hunter/utils/__pycache__/preparation.cpython-312.pyc b/GRB-hunter/utils/__pycache__/preparation.cpython-312.pyc deleted file mode 100644 index 3d3b28b..0000000 Binary files a/GRB-hunter/utils/__pycache__/preparation.cpython-312.pyc and /dev/null differ diff --git a/GRB-hunter/utils/__pycache__/run_pipeline.cpython-312.pyc b/GRB-hunter/utils/__pycache__/run_pipeline.cpython-312.pyc deleted file mode 100644 index 5da7920..0000000 Binary files a/GRB-hunter/utils/__pycache__/run_pipeline.cpython-312.pyc and /dev/null differ diff --git a/GRB-hunter/utils/__pycache__/training.cpython-312.pyc b/GRB-hunter/utils/__pycache__/training.cpython-312.pyc deleted file mode 100644 index ff44e8c..0000000 Binary files a/GRB-hunter/utils/__pycache__/training.cpython-312.pyc and /dev/null differ diff --git a/GRB-hunter/~/Repos/VTAC_ML/GRB-hunter/output/plots/corr/.png b/GRB-hunter/~/Repos/VTAC_ML/GRB-hunter/output/plots/corr/.png deleted file mode 100644 index dc62bff..0000000 Binary files a/GRB-hunter/~/Repos/VTAC_ML/GRB-hunter/output/plots/corr/.png and /dev/null differ diff --git a/LICENSE b/LICENSE deleted file mode 100644 index dd3fcab..0000000 --- a/LICENSE +++ /dev/null @@ -1,28 +0,0 @@ -BSD 3-Clause License - -Copyright (c) 2024, jerbeario - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index a7a8644..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1,17 +0,0 @@ -include README.md -include LICENSE -include vtacML/config/config.yaml -include vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet -include vtacML/output/models/0.978_svc_best_model.pkl -#include vtacML/output/models/0.977_knn_best_model.pkl -#include vtacML/output/models/0.974_rfc_best_model.pkl -#include vtacML/output/models/0.968_ada_best_model.pkl -#include vtacML/output/models/0.869_lr_best_model.pkl -#include vtacML/output/models/0.967_dt_best_model.pkl - - -exclude vtacML/output/models/ada_grid_search.pkl -exclude vtacML/output/models/rfc_grid_search.pkl -exclude vtacML/output/models/svc_grid_search.pkl -exclude vtacML/output/models/knn_grid_search.pkl -exclude vtacML/output/models/lr_grid_search.pkl diff --git a/README.md b/README.md deleted file mode 100644 index 1ba197d..0000000 --- a/README.md +++ /dev/null @@ -1,251 +0,0 @@ - -# vtacML - -vtacML is a machine learning package designed for the analysis of data from the Visible Telescope (VT) on the SVOM mission. This package uses machine learning models to analyze a dataframe of features from VT observations and identify potential gamma-ray burst (GRB) candidates. The primary goal of vtacML is to integrate into the SVOM data analysis pipeline and add a feature to each observation indicating the probability that it is a GRB candidate. - -## Table of Contents - -- [Overview](#overview) -- [Installation](#installation) -- [Usage](#usage) - - [Quick Start](#quick-start) - - [Grid Search and Model Training](#grid-search-and-model-training) - - [Loading and Using the Best Model](#loading-and-using-the-best-model) - - [Using Pre-trained Model for Immediate Prediction](#using-pre-trained-model-for-immediate-prediction) - - [Config File](#config-file) -- [Documentation](#documentation) -- [License](#license) -- [Contact](#contact) - -## Overview - -The SVOM mission, a collaboration between the China National Space Administration (CNSA) and the French space agency CNES, aims to study gamma-ray bursts (GRBs), the most energetic explosions in the universe. The Visible Telescope (VT) on SVOM plays a critical role in observing these events in the optical wavelength range. - -vtacML leverages machine learning to analyze VT data, providing a probability score for each observation to indicate its likelihood of being a GRB candidate. The package includes tools for data preprocessing, model training, evaluation, and visualization. - -## Installation - -To install vtacML, you can use `pip`: - -```sh -pip install vtacML -``` - -Alternatively, you can clone the repository and install the package locally: - -```sh -git clone https://github.com/yourusername/vtacML.git -cd vtacML -pip install . -``` - -## Usage - -### Quick Start - -Here’s a quick example to get you started with vtacML: - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize the pipeline -pipeline = VTACMLPipe() - -# Load configuration -pipeline.load_config('path/to/config.yaml') - -# Train the model -pipeline.train() - -# Evaluate the model -pipeline.evaluate('evaluation_name', plot=True) - -# Predict GRB candidates -predictions = pipeline.predict(observation_dataframe, prob=True) -print(predictions) -``` - -### Grid Search and Model Training - -vtacML can perform grid search on a large array of models and parameters specified in the configuration file. Initialize the `VTACMLPipe` class with a specified config file (or use the default) and train it. Then, you can save the best model for future use. - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize the pipeline with a configuration file -pipeline = VTACMLPipe(config_file='path/to/config.yaml') - -# Train the model with grid search -pipeline.train() - -# Save the best model -pipeline.save_best_model('path/to/save/best_model.pkl') -``` - -### Loading and Using the Best Model - -After training and saving the best model, you can create a new instance of the `VTACMLPipe` class and load the best model for further use. - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize a new pipeline instance -pipeline = VTACMLPipe() - -# Load the best model -pipeline.load_best_model('path/to/save/best_model.pkl') - -# Predict GRB candidates -predictions = pipeline.predict(observation_dataframe, prob=True) -print(predictions) -``` - -### Using Pre-trained Model for Immediate Prediction - -If you already have a trained model, you can use the quick wrapper function `predict_from_best_pipeline` to predict data immediately. A pre-trained model is available by default. - -```python -from vtacML.pipeline import predict_from_best_pipeline - -# Predict GRB candidates using the pre-trained model -predictions = predict_from_best_pipeline(observation_dataframe, model_path='path/to/pretrained_model.pkl') -print(predictions) -``` - -### Config File - -The config file is used to configure the model searching process. - -```yaml -# Default config file, used to search for best model using only first two sequences (X0, X1) from the VT pipeline -Inputs: - file: 'combined_qpo_vt_all_cases_with_GRB_with_flags.parquet' # Data file used for training. Located in /data/ -# path: 'combined_qpo_vt_with_GRB.parquet' -# path: 'combined_qpo_vt_faint_case_with_GRB_with_flags.parquet' - columns: [ - "MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - 'EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_B0', - 'EFLAG_B1', - "NEW_SRC", - "DMAG_CAT" - ] # features used for training - target_column: 'IS_GRB' # feature column that holds the class information to be predicted - -# Set of models and parameters to perform GridSearchCV over -Models: - rfc: - class: RandomForestClassifier() - param_grid: - 'rfc__n_estimators': [100, 200, 300] # Number of trees in the forest - 'rfc__max_depth': [4, 6, 8] # Maximum depth of the tree - 'rfc__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'rfc__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - 'rfc__bootstrap': [True, False] # Whether bootstrap samples are used when building trees - ada: - class: AdaBoostClassifier() - param_grid: - 'ada__n_estimators': [50, 100, 200] # Number of weak learners - 'ada__learning_rate': [0.01, 0.1, 1] # Learning rate - 'ada__algorithm': ['SAMME'] # Algorithm for boosting - svc: - class: SVC() - param_grid: - 'svc__C': [0.1, 1, 10, 100] # Regularization parameter - 'svc__kernel': ['poly', 'rbf', 'sigmoid'] # Kernel type to be used in the algorithm - 'svc__gamma': ['scale', 'auto'] # Kernel coefficient - 'svc__degree': [3, 4, 5] # Degree of the polynomial kernel function (if `kernel` is 'poly') - knn: - class: KNeighborsClassifier() - param_grid: - 'knn__n_neighbors': [3, 5, 7, 9] # Number of neighbors to use - 'knn__weights': ['uniform', 'distance'] # Weight function used in prediction - 'knn__algorithm': ['ball_tree', 'kd_tree', 'brute'] # Algorithm used to compute the nearest neighbors - 'knn__p': [1, 2] # Power parameter for the Minkowski metric - lr: - class: LogisticRegression() - param_grid: - 'lr__penalty': ['l1', 'l2', 'elasticnet'] # Specify the norm of the penalty - 'lr__C': [0.01, 0.1, 1, 10] # Inverse of regularization strength - 'lr__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'] # Algorithm to use in the optimization problem - 'lr__max_iter': [100, 200, 300] # Maximum number of iterations taken for the solvers to converge - dt: - class: DecisionTreeClassifier() - param_grid: - 'dt__criterion': ['gini', 'entropy'] # The function to measure the quality of a split - 'dt__splitter': ['best', 'random'] # The strategy used to choose the split at each node - 'dt__max_depth': [4, 6, 8, 10] # Maximum depth of the tree - 'dt__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'dt__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - -# Output directories -Outputs: - model_path: '/output/models' - viz_path: '/output/visualizations/' - plot_correlation: - flag: True - path: 'output/corr_plots/' - - -``` - -## Documentation - -See documentation at - - -### Setting Up Development Environment - - -To set up a development environment, you can use the provided `requirements-dev.txt`: - - -```sh - -conda create --name vtacML-dev python=3.8 - -conda activate vtacML-dev - -pip install -r requirements-dev.txt - -``` - - -### Running Tests - - -To run tests, use the following command: - - -```sh - -pytest - -``` - - -## License - - -This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details. - - -## Contact - - -For questions or support, please contact: - - -- Your Name - [palmerio.jeremy@gmail.com](mailto:palmerio.jeremy@gmail.com) - -- Project Link: [https://github.com/jerbeario/VTAC_ML](https://github.com/jerbeario/VTAC_ML) diff --git a/build/lib/vtacML/__init__.py b/build/lib/vtacML/__init__.py deleted file mode 100644 index 5349d64..0000000 --- a/build/lib/vtacML/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .pipeline import VTACMLPipe, predict_from_best_pipeline -from .utils import ROOTDIR, get_path diff --git a/build/lib/vtacML/config/config.yaml b/build/lib/vtacML/config/config.yaml deleted file mode 100644 index 018962d..0000000 --- a/build/lib/vtacML/config/config.yaml +++ /dev/null @@ -1,78 +0,0 @@ -# Default config file, used to search for best model using only first two sequences (X0, X1) from the VT pipeline -Inputs: - file: 'combined_qpo_vt_all_cases_with_GRB_with_flags.parquet' # Data file used for training. Located in /data/ -# path: 'combined_qpo_vt_with_GRB.parquet' -# path: 'combined_qpo_vt_faint_case_with_GRB_with_flags.parquet' - columns: [ - "MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - 'EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_B0', - 'EFLAG_B1', - "NEW_SRC", - "DMAG_CAT" - ] # features used for training - target_column: 'IS_GRB' # feature column that holds the class information to be predicted - -# Set of models and parameters to perform GridSearchCV over -Models: - rfc: - class: RandomForestClassifier() - param_grid: - 'rfc__n_estimators': [100, 200, 300] # Number of trees in the forest - 'rfc__max_depth': [4, 6, 8] # Maximum depth of the tree - 'rfc__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'rfc__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - 'rfc__bootstrap': [True, False] # Whether bootstrap samples are used when building trees - ada: - class: AdaBoostClassifier() - param_grid: - 'ada__n_estimators': [50, 100, 200] # Number of weak learners - 'ada__learning_rate': [0.01, 0.1, 1] # Learning rate - 'ada__algorithm': ['SAMME'] # Algorithm for boosting - svc: - class: SVC() - param_grid: - 'svc__C': [0.1, 1, 10, 100] # Regularization parameter - 'svc__kernel': ['poly', 'rbf', 'sigmoid'] # Kernel type to be used in the algorithm - 'svc__gamma': ['scale', 'auto'] # Kernel coefficient - 'svc__degree': [3, 4, 5] # Degree of the polynomial kernel function (if `kernel` is 'poly') - knn: - class: KNeighborsClassifier() - param_grid: - 'knn__n_neighbors': [3, 5, 7, 9] # Number of neighbors to use - 'knn__weights': ['uniform', 'distance'] # Weight function used in prediction - 'knn__algorithm': ['ball_tree', 'kd_tree', 'brute'] # Algorithm used to compute the nearest neighbors - 'knn__p': [1, 2] # Power parameter for the Minkowski metric - lr: - class: LogisticRegression() - param_grid: - 'lr__penalty': ['l1', 'l2', 'elasticnet'] # Specify the norm of the penalty - 'lr__C': [0.01, 0.1, 1, 10] # Inverse of regularization strength - 'lr__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'] # Algorithm to use in the optimization problem - 'lr__max_iter': [100, 200, 300] # Maximum number of iterations taken for the solvers to converge - dt: - class: DecisionTreeClassifier() - param_grid: - 'dt__criterion': ['gini', 'entropy'] # The function to measure the quality of a split - 'dt__splitter': ['best', 'random'] # The strategy used to choose the split at each node - 'dt__max_depth': [4, 6, 8, 10] # Maximum depth of the tree - 'dt__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'dt__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - -# Output directories -Outputs: - model_path: '/output/models' - viz_path: '/output/visualizations/' - plot_correlation: - flag: True - path: 'output/corr_plots/' diff --git a/build/lib/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet b/build/lib/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet deleted file mode 100644 index bc5c512..0000000 Binary files a/build/lib/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet and /dev/null differ diff --git a/build/lib/vtacML/evaluate_best_model.py b/build/lib/vtacML/evaluate_best_model.py deleted file mode 100644 index b1897f8..0000000 --- a/build/lib/vtacML/evaluate_best_model.py +++ /dev/null @@ -1,17 +0,0 @@ -from .pipeline import VTACMLPipe - -if __name__ == '__main__': - vtac_ml = VTACMLPipe() - - best_model_paths = [ - # '../output/models/ada_grid_search.pkl', - '../output/models/knn_grid_search.pkl', - '../output/models/lr_grid_search.pkl', - '../output/models/rfc_grid_search.pkl', - '../output/models/svc_grid_search.pkl', - ] - # for model in best_model_paths: - # vtac_ml.load_best_model(model_path=model, is_grid_search=True) - # vtac_ml.evaluate(name='seq0-1_' + model.split('/')[-1].split('_')[0], plot_extra=True) - vtac_ml.load_best_model(model_name='test') - vtac_ml.evaluate(name='test') \ No newline at end of file diff --git a/build/lib/vtacML/output/models/0.978_svc_best_model.pkl b/build/lib/vtacML/output/models/0.978_svc_best_model.pkl deleted file mode 100644 index 8772f33..0000000 Binary files a/build/lib/vtacML/output/models/0.978_svc_best_model.pkl and /dev/null differ diff --git a/build/lib/vtacML/pipeline.py b/build/lib/vtacML/pipeline.py deleted file mode 100644 index 103f377..0000000 --- a/build/lib/vtacML/pipeline.py +++ /dev/null @@ -1,647 +0,0 @@ -import logging -import os -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import yaml -import joblib - - -from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier -from sklearn.neighbors import KNeighborsClassifier -from sklearn.linear_model import LogisticRegression -from sklearn.tree import DecisionTreeClassifier -from sklearn.svm import SVC -from sklearn.metrics import mean_absolute_error, mean_squared_error, f1_score, confusion_matrix -from sklearn.model_selection import train_test_split, GridSearchCV -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler, Normalizer - -from imblearn.over_sampling import SMOTE - -from yellowbrick import ROCAUC -from yellowbrick.model_selection import FeatureImportances, ValidationCurve -from yellowbrick.classifier import ClassificationReport, ConfusionMatrix, PrecisionRecallCurve, ClassPredictionError -from yellowbrick.model_selection import RFECV - -from .preparation import Cleaner -from .utils import get_path - -log = logging.getLogger(__name__) - - -def predict_from_best_pipeline(X: pd.DataFrame, prob_flag=False, model_name='0.974_rfc_best_model.pkl', model_path=None, - config_path=None): - """ - Predict using the best model pipeline. - - Parameters - ---------- - X : array-like - Features to predict. - prob_flag : bool, optional - Whether to return probabilities, by default False. - model_name : str, optional - Name of the model to use, by default '0.974_rfc_best_model.pkl' - model_path : str, optional - Path to the model to use for prediction, by default 'None' - config_path : str, optional - Path to the configuration file, by default '../config/config.yaml'. - - Returns - ------- - array-like - Predicted values or probabilities. - """ - - vtac_ml_pipe = VTACMLPipe() - print(model_name) - vtac_ml_pipe.load_best_model(model_name=model_name) - print(vtac_ml_pipe.best_model) - y = vtac_ml_pipe.predict(X, prob=prob_flag) - return y - - -class VTACMLPipe: - """ - A machine learning pipeline for training and evaluating an optimal model for optical identification of GRBs for the SVOM mission. - - Parameters - ---------- - config_path : str - Path to the configuration file. - - Methods - ------- - _load_data() - Load the data from the source. - _create_pipe() - Create the machine learning pipeline. - train(X, y) - Train the pipeline. - resample(X, y) - Resample the data using SMOTE. - save_best_model(model_path) - Save the best model to disk. - load_best_model(model_path) - Load the best model from disk. - predict(X, prob=False) - Predict using the trained pipeline. - evaluate(X, y) - Evaluate the pipeline on the given data. - hyperparameter_valid_curve(param_name, param_range, X, y) - Plot validation curve for a hyperparameter. - recursive_feature_elimination_plot(X, y) - Plot recursive feature elimination. - _split_data(X, y) - Split the data into training and test sets. - _load_config() - Load the configuration from the file. - - """ - - def __init__(self, config_file='config/config.yaml'): - - """ - Initialize the VTACMLPipe. - - Parameters - ---------- - config_path : str - Path to the configuration file. - """ - - # initialize attributes - self.config = None - self.X = None - self.y = None - self.X_columns = None - self.y_columns = None - self.X_train = None - self.y_train = None - self.X_test = None - self.y_test = None - self.preprocessing = Pipeline(steps=[], verbose=True) - self.full_pipeline = Pipeline(steps=[], verbose=True) - self.models = {} - self.best_model = None - self.y_predict = None - self.y_predict_prob = None - - # Load configs from config file - self.load_config(config_file) - - # Defining Steps of the preprocessing pipeline - cleaner = Cleaner(variables=self.X_columns) - scaler = StandardScaler() - normalizer = Normalizer() - self.steps = [ - ('cleaner', cleaner), - # ('ohe', ohe), - ('scaler', scaler), - ('normalizer', normalizer) - ] - self._create_pipe(self.steps) - - def _load_data(self, data: pd.DataFrame, columns: list, target: str, test_size: float = 0.2): - """ - Load the data from the source specified in the config. - - Parameters - ---------- - data : pd.DataFrame - The data to load. - columns : list - The columns to load. - target: str - The target column. - test_size: float, optional - The size of the test sample as a fraction of the total sample. Default is 0.2. - - Returns - ------- - DataFrame - Loaded data. - """ - X = data[columns] - y = data[target] - self._split_data(X, y, test_size) - - def _create_pipe(self, steps): - """ - Create the machine learning pipeline from the given steps. - - Parameters - ------- - steps : list - The steps to use for the machine learning preprocessing pipeline. - - Returns - ------- - Pipeline - The created machine learning pipeline. - """ - for step in steps: - self.preprocessing.steps.append(step) - - def train(self, save_all_model=False, resample_flag=False, scoring='f1', cv=5): - """ - Train the pipeline with the given data. - - Parameters - ---------- - model_path : str, optional - The path to the model to load. Default is None - resample_flag : bool, optional - Whether to resample the data. Default is False - scoring : str, optional - The scoring function to use. Default is 'f1'. - - Returns - ------- - Pipeline - Trained machine learning pipeline. - """ - - models = self.models - - if resample_flag: - self.X_train, self.y_train = self._resample(self.X_train, self.y_train) - - if self.preprocessing.steps is None: - print("No preprocessing steps") - model_path = None - best_score = 0 - for name, model in models.items(): - - param_grid = self.config['Models'][name]['param_grid'] - - log.info("Model: {}".format(name)) - - self.full_pipeline = Pipeline(steps=self.preprocessing.steps.copy(), verbose=True) - self.full_pipeline.steps.append((name, model)) - log.info(self.full_pipeline.steps) - - # model fitting - grid_search = GridSearchCV(self.full_pipeline, param_grid, scoring=scoring, verbose=2, cv=cv) - grid_search.fit(self.X_train, self.y_train) - - model_filename = f'{round(grid_search.best_score_, 3)}_{name}_best_model.pkl' - model_path = get_path(f'output/models/{model_filename}') - if save_all_model: - joblib.dump( - grid_search.best_estimator_, - model_path) - - if grid_search.best_score_ > best_score: - best_score = grid_search.best_score_ - self.best_model = grid_search.best_estimator_ - - log.info('*' * 50) - log.info(f'Best {name} Pipeline:') - log.info(grid_search.best_estimator_) - log.info(f'Best Score: {grid_search.best_score_}') - log.info('*' * 50) - log.info(f'Overall best model: {self.best_model}') - - # self.save_best_model(model_path=model_path) - - @staticmethod - def _resample(X, y): - """ - Resamples the input data - - Parameters - ------- - X : pd.DataFrame - input data - y : pd.Series - input label - - Returns - ------- - X_ : pd.DataFrame - resampled data - y_ : pd.Series - resampled label - """ - sm = SMOTE(sampling_strategy='minority', random_state=42) - X_, y_ = sm.fit_resample(X, y) - return X_, y_ - - def save_best_model(self, model_name='best_model', model_path=None): - """ - Saves best model from training to the specified path in the config file. Optionally change name and/or path - of the model. - - Parameters - ------- - model_name : str (optional, default='best_model') - Name of the model to be saved. - model_path : str (optional default='model_path' in config file) - Path to the model to be saved. - ------- - - """ - if model_path is None: - model_path = get_path(f'{self.config['Outputs']['model_path']}/{model_name}') - else: - print(model_path) - model_path = get_path(model_path) - print(model_path) - - joblib.dump(self.best_model, model_path) - logging.info(f'Saved model to {model_path}') - - def load_best_model(self, model_name): - """ - Loads 'model_name' into current pipeline. - - Parameters - ------- - model_name : str - The name of the model from the Outputs/models/ directory to be loaded. - - - """ - model_path = get_path(f'{self.config['Outputs']['model_path']}/{model_name}') - self.best_model = joblib.load(model_path) - logging.info(f'Loaded {model_path}') - - def predict(self, X, prob=False): - """ - Predict using the best model. - - Parameters - ---------- - X : DataFrame - The input features for prediction. - prob : bool, optional - If True, returns the probability of the predictions, by default False. - - Returns - ------- - ndarray - The predicted values or probabilities. - """ - X = X[self.X_columns] - if prob is True: - self.y_predict_prob = self.best_model.predict_proba(X) - return self.y_predict_prob - else: - self.y_predict = self.best_model.predict(X) - return self.y_predict - - # predict with proba - def evaluate(self, name, plot=False, score=f1_score, plot_extra=False): - """ - Evaluate the best model with various metrics and visualization. - - Parameters - ---------- - name : str - The name for the evaluation output. - plot : bool, optional - If True, generates and saves evaluation plots, by default False. - score : callable, optional - The scoring function to use for evaluation, by default f1_score. - plot_extra : bool, optional - If True, generates additional plots such as hyperparameter validation curve, by default False. - - Returns - ------- - None - """ - viz_path = self.config['Outputs']['viz_path'] - output_path = get_path(f'{viz_path}/{name}') - - print(self.best_model.steps) - if not os.path.exists(output_path): - os.makedirs(output_path) - print(f"Folder '{output_path}' created.") - else: - print(f"Folder '{output_path}' already exists.") - # INCLUDE case in titles - # model scoring - - if self.best_model.steps[-1][0] == 'knn' and plot: - print('plotting') - self.preprocessing.fit(self.X) - X = pd.DataFrame(self.preprocessing.transform(self.X)) - self.plot_knn_neighbors(knn=self.best_model.steps[-1][1], X=X, y=self.y, features=self.X_columns) - print('done plotting') - else: - print(self.best_model.steps[-1][1]) - - train_pred = self.best_model.predict(self.X_train) - test_pred = self.best_model.predict(self.X_test) - - train_conf_matrix = confusion_matrix(self.y_train, train_pred) - test_conf_matrix = confusion_matrix(self.y_test, test_pred) - - # Evaluate model performance - print('*' * 50) - print('Training score:') - print( - f'MAE: {round(mean_absolute_error(self.y_train, train_pred), 4)} ' - f'| RMSE: {round(mean_squared_error(self.y_train, train_pred, squared=False), 4)} ' - f'| F1: {round(f1_score(self.y_train, train_pred), 4)}' - ) - print('Confusion Matrix:') - print(train_conf_matrix) - print('-' * 20) - print('Validation score:') - print( - f'MAE: {round(mean_absolute_error(self.y_test, test_pred), 4)} ' - f'| RMSE: {round(mean_squared_error(self.y_test, test_pred, squared=False), 4)} ' - f'| F1: {round(f1_score(self.y_test, test_pred), 4)}' - ) - print('Confusion Matrix:') - print(test_conf_matrix) - - if plot: - - _, ax_report = plt.subplots() - - report_viz = ClassificationReport(self.best_model, classes=["NOT_GRB", "IS_GRB"], support=True, - ax=ax_report) - report_viz.fit(self.X_train, self.y_train) # Fit the visualizer and the model - report_viz.score(self.X_test, self.y_test) # Evaluate the model on the test data - report_viz.show(outpath=output_path + '/classification_report.pdf') - - _, ax_cm_test = plt.subplots() - - cm_test_viz = ConfusionMatrix(self.best_model, classes=["NOT_GRB", "IS_GRB"], percent=True, axes=ax_cm_test) - cm_test_viz.fit(self.X_train, self.y_train) - cm_test_viz.score(self.X_test, self.y_test) - cm_test_viz.show(outpath=output_path + '/confusion_matrix_test.pdf') - - _, ax_cm_train = plt.subplots() - - cm_train_viz = ConfusionMatrix(self.best_model, classes=["NOT_GRB", "IS_GRB"], percent=True, ax=ax_cm_train) - cm_train_viz.fit(self.X_train, self.y_train) - cm_train_viz.score(self.X_train, self.y_train) - cm_train_viz.show(outpath=output_path + '/confusion_matrix_train.pdf') - - _, ax_roc = plt.subplots() - - roc_viz = ROCAUC(self.best_model, classes=["NOT_GRB", "IS_GRB"], ax=ax_roc) - roc_viz.fit(self.X_train, self.y_train) # Fit the training data to the visualizer - roc_viz.score(self.X_test, self.y_test) # Evaluate the model on the test data - roc_viz.show(outpath=output_path + '/ROC_AUC.pdf') - - _, ax_pr_curve = plt.subplots() - - pr_curve_viz = PrecisionRecallCurve(self.best_model, classes=["NOT_GRB", "IS_GRB"], ax=ax_pr_curve) - pr_curve_viz.fit(self.X_train, self.y_train) - pr_curve_viz.score(self.X_test, self.y_test) - pr_curve_viz.show(outpath=output_path + '/PR_curve.pdf') - - _, ax_class_pred = plt.subplots() - ax_class_pred.semilogy() - - class_pred_viz = ClassPredictionError(self.best_model, classes=["NOT_GRB", "IS_GRB"], ax=ax_class_pred) - class_pred_viz.fit(self.X_train, self.y_train) - class_pred_viz.score(self.X_test, self.y_test) - class_pred_viz.show(outpath=output_path + '/class_predictions.pdf') - # - if self.best_model.steps[-1][1] == RandomForestClassifier(): - _, ax_feature_imp = plt.subplots() - - feature_imp_viz = FeatureImportances(self.best_model.steps[-1][1], ax=ax_feature_imp) - feature_imp_viz.fit(self.X, self.y) - feature_imp_viz.show(outpath=output_path + '/feature_importances.pdf') - if plot_extra: - self.hyperparameter_valid_curve(outpath=output_path) - # self.recursive_feature_elimination_plot(outpath=output_path) - - def hyperparameter_valid_curve(self, outpath): - """ - Validate hyperparameters and generate validation curves. - - Parameters - ---------- - outpath : str - The output path where the validation curve plots will be saved. - - Returns - ------- - None - """ - best_model_name = self.best_model.steps[-1][0] - param_grid = self.config['Models'][best_model_name]['param_grid'] - for param in param_grid: - # self.preprocessing.fit(self.X, self.y) - # processed_X = self.preprocessing.transform(self.X) - # processed_y = self.y - param_range = param_grid[param] - param_name = param.split('__')[1] - print(f'Validating {param_name} over range {param_range}') - _, ax_valid_curve = plt.subplots() - - valid_curve_viz = ValidationCurve(self.best_model, - param_name=param, - param_range=param_range, - cv=5, - scoring="f1", - ax=ax_valid_curve - ) - valid_curve_viz.fit(self.X, self.y) - valid_curve_viz.show(outpath=f'{outpath}/{param_name}_valid_curve.pdf') - - def recursive_feature_elimination_plot(self, outpath): - """ - Generate a recursive feature elimination plot. - - Parameters - ---------- - outpath : str - The output path where the feature elimination plot will be saved. - - Returns - ------- - None - """ - _, ax_feature_elimination = plt.subplots() - visualizer = RFECV(self.best_model.steps[-1][1], cv=5, scoring='f1_weighted', ax=ax_feature_elimination) - visualizer.fit(self.X, self.y) # Fit the data to the visualizer - visualizer.show(outpath=outpath + 'feature_elimination.pdf') - - @staticmethod - def plot_knn_neighbors(knn, X, y, features): - """ - Plot the KNN neighbors for a given dataset. - - Parameters - ---------- - knn : KNeighborsClassifier - The KNN classifier. - X : DataFrame - The dataset containing the features. - y : array-like - The target values. - features : list - List of feature names to plot. - - Returns - ------- - None - """ - - # Select a random point - random_index = np.random.randint(0, len(X)) - random_point = X.iloc[random_index] - - # Find the neighbors of the random point - neighbors = knn.kneighbors([random_point], return_distance=False) - - # Plot each pair of features - num_features = len(features) - for i in range(num_features): - for j in range(i + 1, num_features): - plt.figure(figsize=(8, 6)) - plt.scatter(X.iloc[:, i], X.iloc[:, j], c=y, cmap='viridis', marker='o', edgecolor='k', s=50) - plt.scatter(random_point[i], random_point[j], c='red', marker='x', s=200, label='Random Point') - plt.scatter(X.iloc[neighbors[0], i], X.iloc[neighbors[0], j], c='red', marker='o', edgecolor='k', s=100, - facecolors='none', label='Neighbors') - plt.xlabel(features[i]) - plt.ylabel(features[j]) - plt.title(f'KNN Neighbors with {features[i]} vs {features[j]}') - plt.legend() - plt.savefig( - f'/output/visualizations/knn_plots/{features[i]}_vs_{features[j]}_knn_neighbors.pdf' - ) - - @staticmethod - def _get_data(data_file: str): - """ - Load data from a parquet file. - - Parameters - ---------- - data_file : str - The name of the data file to load. - - Returns - ------- - DataFrame - The loaded data. - """ - data_path = get_path(f'/data/{data_file}') - print(data_path) - data = pd.read_parquet(data_path, engine='fastparquet') - return data - - def _split_data(self, X, y, test_size): - """ - Split data into training and testing sets. - - Parameters - ---------- - X : DataFrame - The input features. - y : array-like - The target values. - test_size : float - The proportion of the dataset to include in the test split. - - Returns - ------- - None - """ - (self.X_train, - self.X_test, - self.y_train, - self.y_test) = train_test_split(X, y, - test_size=test_size, - random_state=123) - - # _, ax_class_balance = plt.subplots() - # ax_class_balance.semilogy() - # class_balance_visualizer = ClassBalance(labels=["NOT_GRB", "GRB"], ax=ax_class_balance, - # kwargs={'verbose': 2}) - # class_balance_visualizer.fit(self.y_train, self.y_test) # Fit the data to the visualizer - # class_balance_visualizer.show(outpath='/output/visualizations/class_balance.pdf') - - def load_config(self, config_file): - """ - Load the configuration file and prepare the data. - - Parameters - ---------- - config_file : str - The path to the configuration file. - - Returns - ------- - None - """ - config_path = get_path(config_file) - - with open(config_path, 'r') as file: - self.config = yaml.safe_load(file) - - # loading config file and prepping data - data_file = self.config['Inputs']['file'] - data = self._get_data(data_file=data_file) - - self.X_columns = self.config['Inputs']['columns'] - self.X = data[self.X_columns] - self.y_columns = self.config['Inputs']['target_column'] - self.y = data[self.y_columns] - self._load_data(data, columns=self.X_columns, target=self.y_columns, test_size=0.2) - - # building models attribute - for model in self.config['Models']: - if model == 'rfc': - self.models[model] = RandomForestClassifier() - if model == 'svc': - self.models[model] = SVC() - if model == 'knn': - self.models[model] = KNeighborsClassifier() - if model == 'lr': - self.models[model] = LogisticRegression() - if model == 'dt': - self.models[model] = DecisionTreeClassifier() - if model == 'ada': - self.models[model] = AdaBoostClassifier() diff --git a/build/lib/vtacML/preparation.py b/build/lib/vtacML/preparation.py deleted file mode 100644 index ef85e38..0000000 --- a/build/lib/vtacML/preparation.py +++ /dev/null @@ -1,92 +0,0 @@ -import os -import shutil -import pandas as pd -import numpy as np -from matplotlib import pyplot as plt -import seaborn as sns -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.utils.class_weight import compute_class_weight -from sklearn.preprocessing import OneHotEncoder -from sklearn.impute import SimpleImputer - - -class OneHotEncoderCustom(BaseEstimator, TransformerMixin): - def __init__(self, variables): - self.variables = variables - self.ohe = OneHotEncoder( - handle_unknown='ignore') - - def fit(self, X, y=None): - X_ = X.loc[:, self.variables] - self.ohe.fit(X_) - return self - - def transform(self, X): - X_ = X.loc[:, self.variables] - X_transformed = pd.DataFrame(self.ohe.transform(X_).toarray(), - columns=self.ohe.get_feature_names_out()) - X.drop(self.variables, axis=1, inplace=True) - X[self.ohe.get_feature_names_out()] = X_transformed[self.ohe.get_feature_names_out()].values - return X - - -class Cleaner(BaseEstimator, TransformerMixin): - def __init__(self, variables): - self.variables = variables - self.fitted = False - self.y = None - - def handle_nans(self, X, y=None): - X_ = X.copy() - for column in self.variables: - if X[column].isna().sum(): - # Create the isna column - new_column_name = f'{column}_isnan' - X_[new_column_name] = X_[column].isna().astype(int) - # Impute missing values with the mean - impute_value = X_[column].median() - X_[column].fillna(impute_value, inplace=True) - - return X_ - - def clean_binary(self, X, col, y=None): - X[col] = X[col].str.decode("utf-8").apply(lambda x: 1 if x else 0) - return X - - def handle_outliers(self, X, y=None): - - return self - - def fit(self, X, y=None): - self.y = y - self.fitted = True - return self # Nothing to fit, just return self - - def transform(self, X, y=None): - e_flags = ['EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_R2', - 'EFLAG_R3', - 'EFLAG_B0', - 'EFLAG_B1', - 'EFLAG_B2', - 'EFLAG_B3'] - - e_flags_in_cols = [x for x in self.variables if x in e_flags] - - if not self.fitted: - raise ValueError("The transformer has not been fitted yet. " - "Call 'fit' before 'transform'.") - - X_ = X.loc[:, self.variables] - transformed_X = self.handle_nans(X_) # Store the transformed data - - if len(e_flags_in_cols) > 0: - for e_flag in e_flags_in_cols: - transformed_X[e_flag] = transformed_X[e_flag].fillna(-1) - if 'NEW_SRC' in self.variables: - transformed_X = self.clean_binary(transformed_X, 'NEW_SRC') - if 'DMAG_CAT' in self.variables: - transformed_X = self.clean_binary(transformed_X, 'DMAG_CAT') - - return transformed_X.dropna() # Return the transformed data diff --git a/build/lib/vtacML/search_best_model.py b/build/lib/vtacML/search_best_model.py deleted file mode 100644 index 80a9577..0000000 --- a/build/lib/vtacML/search_best_model.py +++ /dev/null @@ -1,7 +0,0 @@ -from .pipeline import VTACMLPipe - -if __name__ == "__main__": - vtac_ml = VTACMLPipe() - - vtac_ml.train() - vtac_ml.save_best_model() \ No newline at end of file diff --git a/build/lib/vtacML/utils.py b/build/lib/vtacML/utils.py deleted file mode 100644 index 3106833..0000000 --- a/build/lib/vtacML/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -from pathlib import Path - - -# Define the root directory -ROOTDIR = Path(__file__).parent - - -def get_path(subpath): - """Utility function to get the path to a subpath relative to ROOTDIR.""" - print(f'rootdir: {ROOTDIR}/{subpath}') - return f'{ROOTDIR}/{subpath}' diff --git a/dev/.ipynb_checkpoints/RandomForestModel-checkpoint.ipynb b/dev/.ipynb_checkpoints/RandomForestModel-checkpoint.ipynb deleted file mode 100644 index 4ec7f53..0000000 --- a/dev/.ipynb_checkpoints/RandomForestModel-checkpoint.ipynb +++ /dev/null @@ -1,6426 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2550aabc-ee57-4355-a271-f427930db157", - "metadata": {}, - "source": [ - "# Optical Identification of GRBs Using Machine Learning Techniques on Simulated Data " - ] - }, - { - "cell_type": "markdown", - "id": "e1b0e41e-bb18-4cb7-a156-42ee28792e5f", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "59ec5447-3dc3-435e-818e-43c88d93cda9", - "metadata": {}, - "source": [ - "### Importing Data & Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "837b8335-34a3-479b-87cc-c436992f2844", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CASE\n", - "bright_case2 64572\n", - "bright_case3 64572\n", - "bright_case4 62356\n", - "bright_case1a 61192\n", - "bright_case1 61106\n", - "Name: count, dtype: int64\n" - ] - }, - { - "data": { - "text/html": [ - "
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12142.61547842.54990013.0613.0413.0213.0413.5913.5813.58...12200222223222242222010.00.00.00.0
213142.62468042.59956413.3113.2713.2713.3119.7819.8019.78...12200222223222242222010.00.00.00.0
33142.64456242.57300914.7614.7114.7214.7215.2115.2215.21...12200222223222242222010.00.00.00.0
44142.67117342.60656414.9614.9514.9714.9815.4515.4615.45...12200222223222242222010.00.00.00.0
..................................................................
313793158162.30543532.411057NaNNaNNaNNaN22.76NaNNaN...1130021111311114111103640.00.00.01.0
313794170162.34373532.406399NaNNaNNaNNaN23.03NaNNaN...1130021111311114111103640.00.00.01.0
313795175162.24961832.405460NaNNaNNaNNaN23.03NaNNaN...1120021111311114111103640.00.00.01.0
313796177162.25523432.399235NaNNaNNaNNaN22.91NaNNaN...1130021111311114111103640.00.00.01.0
313797178162.25482232.395775NaNNaNNaNNaN23.12NaNNaN...1130021111311114111103640.00.00.01.0
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313798 rows × 43 columns

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" - ], - "text/plain": [ - " VT_ID RA DEC MAGCAL_R0 MAGCAL_R1 MAGCAL_R2 \\\n", - "0 1 142.660568 42.577675 12.94 12.97 12.96 \n", - "1 2 142.615478 42.549900 13.06 13.04 13.02 \n", - "2 13 142.624680 42.599564 13.31 13.27 13.27 \n", - "3 3 142.644562 42.573009 14.76 14.71 14.72 \n", - "4 4 142.671173 42.606564 14.96 14.95 14.97 \n", - "... ... ... ... ... ... ... \n", - "313793 158 162.305435 32.411057 NaN NaN NaN \n", - "313794 170 162.343735 32.406399 NaN NaN NaN \n", - "313795 175 162.249618 32.405460 NaN NaN NaN \n", - "313796 177 162.255234 32.399235 NaN NaN NaN \n", - "313797 178 162.254822 32.395775 NaN NaN NaN \n", - "\n", - " MAGCAL_R3 MAGCAL_B0 MAGCAL_B1 MAGCAL_B2 ... SEQFLAG0 SEQFLAG1 \\\n", - "0 12.97 13.39 13.35 13.35 ... 12200 22222 \n", - "1 13.04 13.59 13.58 13.58 ... 12200 22222 \n", - "2 13.31 19.78 19.80 19.78 ... 12200 22222 \n", - "3 14.72 15.21 15.22 15.21 ... 12200 22222 \n", - "4 14.98 15.45 15.46 15.45 ... 12200 22222 \n", - "... ... ... ... ... ... ... ... \n", - "313793 NaN 22.76 NaN NaN ... 11300 21111 \n", - "313794 NaN 23.03 NaN NaN ... 11300 21111 \n", - "313795 NaN 23.03 NaN NaN ... 11200 21111 \n", - "313796 NaN 22.91 NaN NaN ... 11300 21111 \n", - "313797 NaN 23.12 NaN NaN ... 11300 21111 \n", - "\n", - " SEQFLAG2 SEQFLAG3 IS_GRB SIM_ID CASE_bright_case1a \\\n", - "0 32222 42222 0 1 0.0 \n", - "1 32222 42222 0 1 0.0 \n", - "2 32222 42222 0 1 0.0 \n", - "3 32222 42222 0 1 0.0 \n", - "4 32222 42222 0 1 0.0 \n", - "... ... ... ... ... ... \n", - "313793 31111 41111 0 364 0.0 \n", - "313794 31111 41111 0 364 0.0 \n", - "313795 31111 41111 0 364 0.0 \n", - "313796 31111 41111 0 364 0.0 \n", - "313797 31111 41111 0 364 0.0 \n", - "\n", - " CASE_bright_case2 CASE_bright_case3 CASE_bright_case4 \n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "313793 0.0 0.0 1.0 \n", - "313794 0.0 0.0 1.0 \n", - "313795 0.0 0.0 1.0 \n", - "313796 0.0 0.0 1.0 \n", - "313797 0.0 0.0 1.0 \n", - "\n", - "[313798 rows x 43 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import os\n", - "import shutil \n", - "data = pd.read_parquet('/Users/jeremypalmerio/Repos/VTAC_ML/combined_qpo_vt_with_GRB.parquet', engine='fastparquet')\n", - "data.head()\n", - "\n", - "print(data['CASE'].value_counts())\n", - "ohe = OneHotEncoderCustom(variables=['CASE'])\n", - "ohe.fit(data)\n", - "ohe.transform(data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "765c6e68-c608-434a-ba64-44c072482a4c", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.base import BaseEstimator, TransformerMixin\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "class OneHotEncoderCustom(BaseEstimator, TransformerMixin):\n", - " # take from https://medium.com/@benlc77/how-to-write-clean-and-scalable-code-with-custom-transformers-sklearn-pipelines-ecb8e53fe110\n", - " def __init__(self, variables):\n", - " self.variables = variables\n", - " self.ohe = OneHotEncoder(handle_unknown='ignore')\n", - "\n", - " def fit(self, X, y=None):\n", - " X_ = X.loc[:, self.variables]\n", - " self.ohe.fit(X_)\n", - " return self\n", - "\n", - " def transform(self, X):\n", - " X_ = X.loc[:, self.variables]\n", - " # get one-hot encoded feature in df format\n", - " X_transformed = pd.DataFrame(self.ohe.transform(X_).toarray(), columns=self.ohe.get_feature_names_out())\n", - "\n", - " # Remove columns that are one hot encoded in original df\n", - " X.drop(self.variables, axis=1, inplace=True)\n", - "\n", - " # Add one hot encoded feature to original df\n", - " X[self.ohe.get_feature_names_out()] = X_transformed[self.ohe.get_feature_names_out()].values\n", - " return X\n" - ] - }, - { - "cell_type": "markdown", - "id": "df43cac7-48e2-4609-b95f-f9de0cc3786f", - "metadata": {}, - "source": [ - "### Exploring Table" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8e99f3b1-d8ba-4a2c-bed5-2d2f1e6aa497", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['VT_ID', 'RA', 'DEC', 'MAGCAL_R0', 'MAGCAL_R1', 'MAGCAL_R2',\n", - " 'MAGCAL_R3', 'MAGCAL_B0', 'MAGCAL_B1', 'MAGCAL_B2', 'MAGCAL_B3',\n", - " 'MAGERR_R0', 'MAGERR_R1', 'MAGERR_R2', 'MAGERR_R3', 'MAGERR_B0',\n", - " 'MAGERR_B1', 'MAGERR_B2', 'MAGERR_B3', 'MAGVAR_R1', 'MAGVAR_R2',\n", - " 'MAGVAR_R3', 'MAGVAR_B1', 'MAGVAR_B2', 'MAGVAR_B3', 'EFLAG_R0',\n", - " 'EFLAG_R1', 'EFLAG_R2', 'EFLAG_R3', 'EFLAG_B0', 'EFLAG_B1', 'EFLAG_B2',\n", - " 'EFLAG_B3', 'SEQFLAG0', 'SEQFLAG1', 'SEQFLAG2', 'SEQFLAG3', 'IS_GRB',\n", - " 'SIM_ID'],\n", - " dtype='object')\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'NoneType' object has no attribute 'columns'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m data \u001b[38;5;241m=\u001b[39m clean_data(data)\n\u001b[0;32m----> 2\u001b[0m data\u001b[38;5;241m.\u001b[39mcolumns\n", - "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'columns'" - ] - } - ], - "source": [ - "data = clean_data(data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "21af26ab-afef-4ff8-9f66-525634f32c77", - "metadata": {}, - "outputs": [], - "source": [ - "def clean_data(self,data):\n", - " # Get list of numeric column names\n", - " numeric_columns = data.select_dtypes(include=['number']).columns\n", - " \n", - " # Filter DataFrame to keep only numeric columns\n", - " \n", - " \n", - " return data[numeric_columns]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d03ec45c-32ac-4dda-a4d2-92e17c414e08", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 313798 entries, 0 to 313797\n", - "Data columns (total 40 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 VT_ID 313798 non-null int64 \n", - " 1 RA 313798 non-null float64\n", - " 2 DEC 313798 non-null float64\n", - " 3 MAGCAL_R0 229753 non-null float64\n", - " 4 MAGCAL_R1 234744 non-null float64\n", - " 5 MAGCAL_R2 233169 non-null float64\n", - " 6 MAGCAL_R3 234242 non-null float64\n", - " 7 MAGCAL_B0 189306 non-null float64\n", - " 8 MAGCAL_B1 212247 non-null float64\n", - " 9 MAGCAL_B2 212579 non-null float64\n", - " 10 MAGCAL_B3 213286 non-null float64\n", - " 11 MAGERR_R0 229753 non-null float64\n", - " 12 MAGERR_R1 234744 non-null float64\n", - " 13 MAGERR_R2 233169 non-null float64\n", - " 14 MAGERR_R3 234242 non-null float64\n", - " 15 MAGERR_B0 189306 non-null float64\n", - " 16 MAGERR_B1 212247 non-null float64\n", - " 17 MAGERR_B2 212579 non-null float64\n", - " 18 MAGERR_B3 213286 non-null float64\n", - " 19 MAGVAR_R1 212775 non-null float64\n", - " 20 MAGVAR_R2 218663 non-null float64\n", - " 21 MAGVAR_R3 218964 non-null float64\n", - " 22 MAGVAR_B1 178573 non-null float64\n", - " 23 MAGVAR_B2 199108 non-null float64\n", - " 24 MAGVAR_B3 199256 non-null float64\n", - " 25 EFLAG_R0 229753 non-null Int64 \n", - " 26 EFLAG_R1 234744 non-null Int64 \n", - " 27 EFLAG_R2 233169 non-null Int64 \n", - " 28 EFLAG_R3 234242 non-null Int64 \n", - " 29 EFLAG_B0 189306 non-null Int64 \n", - " 30 EFLAG_B1 212247 non-null Int64 \n", - " 31 EFLAG_B2 212579 non-null Int64 \n", - " 32 EFLAG_B3 213286 non-null Int64 \n", - " 33 SEQFLAG0 313798 non-null int64 \n", - " 34 SEQFLAG1 313798 non-null int64 \n", - " 35 SEQFLAG2 313798 non-null int64 \n", - " 36 SEQFLAG3 313798 non-null int64 \n", - " 37 IS_GRB 313798 non-null int64 \n", - " 38 CASE 313798 non-null object \n", - " 39 SIM_ID 313798 non-null int64 \n", - "dtypes: Int64(8), float64(24), int64(7), object(1)\n", - "memory usage: 98.2+ MB\n" - ] - } - ], - "source": [ - "data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0f748aa6-8ca6-4c4a-ad61-177c2ca746c4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CASE\n", - "bright_case2 64572\n", - "bright_case3 64572\n", - "bright_case4 62356\n", - "bright_case1a 61192\n", - "bright_case1 61106\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['CASE'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "d3235ebc-4fc1-4b11-824c-bea6bdd9cae4", - "metadata": {}, - "source": [ - "Splitting dataframe by GRB cases: \n", - "- b1 : bright_case1\n", - "- b1a : bright_case1a\n", - "- b2 : bright_case2\n", - "- b3 : bright_case3\n", - "- b4 : bright_case4" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f54b4c8d-1f3c-42aa-99ce-1ffc83fd1273", - "metadata": {}, - "outputs": [], - "source": [ - "b1 = data[data['CASE']=='bright_case1'].drop([\"CASE\"],axis = 1)\n", - "b1a = data[data['CASE']=='bright_case1a'].drop([\"CASE\"], axis = 1)\n", - "b2 = data[data['CASE']=='bright_case2'].drop([\"CASE\"], axis = 1)\n", - "b3 = data[data['CASE']=='bright_case3'].drop([\"CASE\"], axis = 1)\n", - "b4 = data[data['CASE']=='bright_case4'].drop([\"CASE\"], axis = 1)\n", - "cases_dict = {'bright_case1':b1,\n", - " 'bright_case1a':b1a,\n", - " 'bright_case2':b2,\n", - " 'bright_case3':b3,\n", - " 'bright_case4':b4}" - ] - }, - { - "cell_type": "markdown", - "id": "54621ac2-5140-4ee0-8772-525a91e0dc94", - "metadata": {}, - "source": [ - "Exploring the distributions of positive and negative classes for 'IS_GRB' columns" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3565116c-5644-4ab0-9db8-19a132c68d1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bright_case1|29389|178\n", - "bright_case1|60894|212\n", - "bright_case1a|29234|180\n", - "bright_case1a|60981|211\n", - "bright_case2|30139|181\n", - "bright_case2|64359|213\n", - "bright_case3|30139|181\n", - "bright_case3|64359|213\n", - "bright_case4|29545|185\n", - "bright_case4|62142|214\n" - ] - } - ], - "source": [ - "for case, sub_data in cases_dict.items():\n", - " drop_counts = list(sub_data.dropna()[\"IS_GRB\"]\n", - " .value_counts())\n", - " counts = list(sub_data[\"IS_GRB\"].value_counts())\n", - " print(case+\"|\"+str(drop_counts[0])+\"|\"+str(drop_counts[1]))\n", - " print(case+\"|\"+str(counts[0])+\"|\"+str(counts[1]))\n" - ] - }, - { - "cell_type": "markdown", - "id": "38d5ba61-9ad8-410a-bde4-d5bc94e1649f", - "metadata": {}, - "source": [ - "### Getting Class Weigts\n" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "98df29fe-9963-430c-bb1a-7c0ba0650cb0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: 0.5016995219594864, 1: 147.60018814675448}\n" - ] - }, - { - "data": { - "text/plain": [ - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "313793 0\n", - "313794 0\n", - "313795 0\n", - "313796 0\n", - "313797 0\n", - "Name: IS_GRB, Length: 313798, dtype: int64" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.utils.class_weight import compute_class_weight\n", - "y = data['IS_GRB']\n", - "class_weights = compute_class_weight(\n", - " class_weight=\"balanced\", \n", - " #class_weight=None, \n", - " classes=np.unique(y), \n", - " y=y\n", - " )\n", - "\n", - "class_dict = dict(zip([0, 1], class_weights))\n", - "print(class_dict)\n", - "y" - ] - }, - { - "cell_type": "markdown", - "id": "b0081447-88d1-4de5-8dee-06636d66b365", - "metadata": {}, - "source": [ - "### Missing Values" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b22e953b-874e-467f-a79e-94c7d4f437a8", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VT_ID False\n", - "RA False\n", - "DEC False\n", - "MAGCAL_R0 True\n", - "MAGCAL_R1 True\n", - "MAGCAL_R2 True\n", - "MAGCAL_R3 True\n", - "MAGCAL_B0 True\n", - "MAGCAL_B1 True\n", - "MAGCAL_B2 True\n", - "MAGCAL_B3 True\n", - "MAGERR_R0 True\n", - "MAGERR_R1 True\n", - "MAGERR_R2 True\n", - "MAGERR_R3 True\n", - "MAGERR_B0 True\n", - "MAGERR_B1 True\n", - "MAGERR_B2 True\n", - "MAGERR_B3 True\n", - "MAGVAR_R1 True\n", - "MAGVAR_R2 True\n", - "MAGVAR_R3 True\n", - "MAGVAR_B1 True\n", - "MAGVAR_B2 True\n", - "MAGVAR_B3 True\n", - "EFLAG_R0 True\n", - "EFLAG_R1 True\n", - "EFLAG_R2 True\n", - "EFLAG_R3 True\n", - "EFLAG_B0 True\n", - "EFLAG_B1 True\n", - "EFLAG_B2 True\n", - "EFLAG_B3 True\n", - "SEQFLAG0 False\n", - "SEQFLAG1 False\n", - "SEQFLAG2 False\n", - "SEQFLAG3 False\n", - "IS_GRB False\n", - "CASE False\n", - "SIM_ID False\n", - "dtype: bool\n" - ] - } - ], - "source": [ - "print(data.isnull().any())\n", - "data = data.copy().dropna()\n" - ] - }, - { - "cell_type": "markdown", - "id": "be89e6c0-0786-4b21-b97b-5ebaf7353309", - "metadata": {}, - "source": [ - "### Feature Engineering\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "85e910dd-9ef0-49e6-9826-f948949b4299", - "metadata": {}, - "outputs": [], - "source": [ - "data[\"R0_B0\"] = data[\"MAGCAL_R0\"]-data[\"MAGCAL_B0\"]\n", - "data[\"R1_B1\"] = data[\"MAGCAL_R1\"]-data[\"MAGCAL_B1\"]" - ] - }, - { - "cell_type": "markdown", - "id": "0081265b-31e1-47ce-819b-40337f211f84", - "metadata": {}, - "source": [ - "### Pair plot distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "604e2ea4-cc2b-41b3-8d53-edeb50003116", - "metadata": { - "editable": true, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "#if os.path.isdir('plots/'):\n", - "# shutil.rmtree('plots/')\n", - "#os.mkdir('plots/')\n", - "#sns.pairplot(\n", - "# data.dropna(),\n", - "# vars = [\"MAGCAL_R0\",\n", - "# \"MAGCAL_B0\",\n", - "# \"MAGCAL_R1\",\n", - "# \"MAGCAL_B1\",\n", - "# \"MAGVAR_R1\",\n", - "# \"MAGVAR_B1\",\n", - "# \"R0_B0\",\n", - "# \"R1_B1\"], \n", - "# hue=\"IS_GRB\",\n", - "# height=3\n", - "#)\n", - "#plt.savefig('plots/pairplot.png')\n", - "#plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "803632e9-4440-4d2e-9983-cd0ad009e5c6", - "metadata": {}, - "source": [ - "### Plotting Corrolations" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "dab10307-a886-4b33-bcd8-44fa7da177b7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if os.path.isdir('plots/corr/'):\n", - " shutil.rmtree('plots/corr/')\n", - "os.mkdir('plots/corr/')\n", - "\n", - "for case, sub_data in cases_dict.items():\n", - " plt.figure(figsize=(12,12))\n", - " corr_matrix = sub_data.corr()\n", - " sns.heatmap(abs(corr_matrix), cmap='Blues')\n", - " plt.savefig('plots/corr/'+case+'corrolation')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3f0480a1-c9fb-4990-953b-a03e8e3d340d", - "metadata": {}, - "source": [ - "### Principal Component Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "07b0e97f-70bc-4426-9e1c-7ce71c7502ad", - "metadata": {}, - "outputs": [], - "source": [ - "# from sklearn.preprocessing import StandardScaler\n", - "# from numpy.linalg import eig\n", - "# \n", - "# data_prediction = data.drop([\"CASE\", \"IS_GRB\"],\n", - " # axis=1, inplace=False)\n", - "# \n", - "# # centering adn normalizing the columns \n", - "# x = StandardScaler().fit_transform(\n", - " # data_prediction)\n", - "# # calculating covariance matrix\n", - "# x_cov = np.cov(x.T)\n", - "# \n", - "# # getting eigenvalues and eigenvectors\n", - "# eig_vals, eig_vecs = eig(x_cov)\n", - "# \n", - "# # sort values in increasing order \n", - "# idx = eig_vals.argsort()[::-1] \n", - "# eig_vals = eig_vals[idx]\n", - "# eig_vecs = eig_vecs[:,idx]\n", - "# \n", - "# # make a list of (eigenvalue, eigenvector) tuples\n", - "# eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[i,:]) for i in range(len(eig_vals))]\n", - "# \n", - "# # Then, we sort the tuples from the highest to the lowest based on eigenvalues magnitude\n", - "# eig_pairs.sort(key=lambda x: x[0], reverse=True)\n", - "# \n", - "# # For further usage\n", - "# eig_vals_sorted = np.array([x[0] for x in eig_pairs])\n", - "# eig_vecs_sorted = np.array([x[1] for x in eig_pairs])\n", - "# \n", - "# # find thereshold value for explaining variance \n", - "# explained_variance = eig_vals_sorted/np.sum(eig_vals_sorted)\n", - "# cum_explained_variance = np.cumsum(explained_variance)\n", - "# threshold = 0.9\n", - "# n_components = np.argmax(cum_explained_variance >= threshold) + 1\n", - "# \n", - "# eig_vals_reduced = eig_vals_sorted[:n_components]\n", - "# eig_vecs_reduced = eig_vecs_sorted[:, :n_components]\n", - "# \n", - "# # project the reduced eigenvector matrix on data\n", - "# projected_data = np.dot(data_prediction, eig_vecs_reduced)\n", - "# " - ] - }, - { - "cell_type": "markdown", - "id": "c017a971-e34f-4723-b4ca-49e1767b54b7", - "metadata": {}, - "source": [ - "## Random Forest Model\n" - ] - }, - { - "cell_type": "markdown", - "id": "edfc6f38-0b14-48f4-ac9b-956851760015", - "metadata": {}, - "source": [ - "Classifying using a random forest model. See doc: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "2107b8a8-3bb1-468e-b1fc-6cc071d6d502", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.datasets import make_classification\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.metrics import confusion_matrix\n", - "from sklearn.metrics import classification_report\n" - ] - }, - { - "cell_type": "markdown", - "id": "b3374d3c-4b43-459f-8a12-603a6209fa4c", - "metadata": {}, - "source": [ - "### Tuning Model" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "312adb70-bb1b-4a3f-8355-5444145fe399", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.3s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.4s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "Best parameters: {'max_depth': 4, 'min_samples_split': 2, 'n_estimators': 100}\n", - "Best score: 0.9999263495232379\n" - ] - } - ], - "source": [ - "\n", - "# X = StandardScaler().fit_transform(\n", - " # projected_data)\n", - "#X = data[[\n", - "# \"MAGCAL_R0\",\n", - "# \"MAGCAL_B0\",\n", - "# \"MAGCAL_R1\",\n", - "# \"MAGCAL_B1\",\n", - "# \"MAGVAR_R1\",\n", - "# \"MAGVAR_B1\",b\n", - "# \"MAGERR_R0\",\n", - "# \"MAGERR_R1\",\n", - "# \"MAGERR_B0\",\n", - "# \"MAGERR_B1\",\n", - "# \"R0_B0\", \n", - "# \"R1_B1\",\n", - "# \"IS_GRB\"]]\n", - "#X = data.drop([\"CASE\"], axis = 1).dropna()\n", - "#X = data[[\n", - "#\"MAGCAL_R0\", \n", - "#\"MAGCAL_B0\", \n", - "#\"MAGCAL_R1\", \n", - "#\"MAGCAL_B1\", \n", - "#\"IS_GRB\" \n", - "#]].dropna()\n", - "#X = data.drop(\"CASE\", axis=1).dropna() # Not sure this is the best way to handle missing values \n", - "X = data.dropna()[[\n", - " \"MAGCAL_R0\",\n", - " \"MAGCAL_B0\",\n", - " \"MAGERR_R0\",\n", - " \"MAGERR_B0\",\n", - " \"MAGCAL_R1\",\n", - " \"MAGCAL_B1\",\n", - " \"MAGERR_R1\",\n", - " \"MAGERR_B1\",\n", - " \"MAGVAR_R1\",\n", - " \"MAGVAR_B1\",\n", - "\n", - "]]\n", - "y = data.dropna()[\"IS_GRB\"]\n", - "\n", - "param_grid = {\n", - " 'n_estimators': [100],\n", - " 'max_depth': [4],\n", - " 'min_samples_split': [2, 3]\n", - "}\n", - "\n", - "grid_search = GridSearchCV(RandomForestClassifier(class_weight=class_dict), param_grid=param_grid, cv=5, verbose=2)\n", - "grid_search.fit(X, y)\n", - "\n", - "# Print the best hyperparameters and corresponding score\n", - "print(\"Best parameters: \", grid_search.best_params_)\n", - "print(\"Best score: \", grid_search.best_score_)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e4036b49-c267-4822-99e6-6ca775ac2363", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "48197914-228d-4769-a86a-383ba30c869e", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"None of [Index(['CASE'], dtype='object')] are in the [columns]\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m ohe \u001b[38;5;241m=\u001b[39m OneHotEncoderCustom(variables\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCASE\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m----> 2\u001b[0m ohe\u001b[38;5;241m.\u001b[39mfit(data)\n\u001b[1;32m 3\u001b[0m ohe\u001b[38;5;241m.\u001b[39mtransform(data)\n", - "Cell \u001b[0;32mIn[12], line 10\u001b[0m, in \u001b[0;36mOneHotEncoderCustom.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m---> 10\u001b[0m X_ \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mloc[:, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables]\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mohe\u001b[38;5;241m.\u001b[39mfit(X_)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1147\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m-> 1147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[1;32m 1148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m 1150\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1339\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 1336\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take_opportunity(tup):\n\u001b[1;32m 1337\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take(tup)\n\u001b[0;32m-> 1339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple_same_dim(tup)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:994\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_tuple_same_dim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(key):\n\u001b[1;32m 992\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 994\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(retval, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39m_getitem_axis(key, axis\u001b[38;5;241m=\u001b[39mi)\n\u001b[1;32m 995\u001b[0m \u001b[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001b[39;00m\n\u001b[1;32m 996\u001b[0m \u001b[38;5;66;03m# be handled by the _getitem_lowerdim call above.\u001b[39;00m\n\u001b[1;32m 997\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m retval\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1382\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m key\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 1380\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index with multidimensional key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_iterable(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m 1384\u001b[0m \u001b[38;5;66;03m# nested tuple slicing\u001b[39;00m\n\u001b[1;32m 1385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_nested_tuple(key, labels):\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1322\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_iterable\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m 1321\u001b[0m \u001b[38;5;66;03m# A collection of keys\u001b[39;00m\n\u001b[0;32m-> 1322\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_listlike_indexer(key, axis)\n\u001b[1;32m 1323\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[1;32m 1324\u001b[0m {axis: [keyarr, indexer]}, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1325\u001b[0m )\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1520\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1517\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m 1518\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[0;32m-> 1520\u001b[0m keyarr, indexer \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39m_get_indexer_strict(key, axis_name)\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexes/base.py:6115\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m 6112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6113\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 6115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[1;32m 6117\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m 6118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[1;32m 6119\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n", - "File \u001b[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexes/base.py:6176\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m 6174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[1;32m 6175\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 6176\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6178\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[1;32m 6179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['CASE'], dtype='object')] are in the [columns]\"" - ] - } - ], - "source": [] - }, - { - "cell_type": "markdown", - "id": "8ad1a843-0d1b-42db-91d3-6274a178b846", - "metadata": {}, - "source": [ - "### Fitting Model" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "ccb95c0c-497c-47c2-aca4-ed7e7c40f287", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "\n", - "RFmodel = RandomForestClassifier(**grid_search.best_params_, class_weight=class_dict)\n", - "\n", - "\n", - "RFmodel.fit(X_train, y_train)\n", - "y_pred = RFmodel.predict(X_test)\n", - "y_pred_proba = RFmodel.predict_proba(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8b1497f5-e421-4d62-a688-26c8b03ac384", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.96033075 0.03966925]\n", - " [1. 0. ]\n", - " [1. 0. ]\n", - " ...\n", - " [1. 0. ]\n", - " [1. 0. ]\n", - " [1. 0. ]]\n" - ] - } - ], - "source": [ - "print(y_pred_proba)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "1630b046-f7fb-4c49-b051-a1f543e2f0fc", - "metadata": {}, - "outputs": [], - "source": [ - "def run_grid_search(X, y, model, param_grid, cv=5):\n", - " grid_search = GridSearchCV(model,\n", - " param_grid=param_grid,\n", - " cv=cv,\n", - " verbose=2)\n", - " grid_search.fit(X, y)\n", - " return grid_search" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "0bdafba6-8804-4cf3-ad73-6fb9cd3e58b2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.8s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 1.8s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.0s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.0s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 1.8s\n" - ] - }, - { - "data": { - "text/html": [ - "
GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
-       "             param_grid={'max_depth': [4], 'min_samples_split': [2, 3],\n",
-       "                         'n_estimators': [100]},\n",
-       "             verbose=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n", - " param_grid={'max_depth': [4], 'min_samples_split': [2, 3],\n", - " 'n_estimators': [100]},\n", - " verbose=2)" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_grid_search(X_train, y_train, RandomForestClassifier(), param_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b77ce934-b9e7-4f9a-931e-bdc92c85ba0d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[29644 0]\n", - " [ 0 182]]\n" - ] - } - ], - "source": [ - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "report = classification_report(y_test, y_pred)\n", - "print(conf_matrix)" - ] - }, - { - "cell_type": "markdown", - "id": "de69cb46-b7e4-421b-82ae-0d3af4d88e3e", - "metadata": {}, - "source": [ - "### Feature Importance\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "f114501c-7850-4d50-89ff-699c216734d6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "importances = RFmodel.feature_importances_\n", - "std = np.std([tree.feature_importances_ for tree in RFmodel.estimators_], axis=0)\n", - "feature_names = list(X.columns)\n", - "forest_importances = pd.Series(importances, index=feature_names)\n", - "forest_importances = forest_importances.sort_values(ascending=False)\n", - "\n", - "fig, ax = plt.subplots(figsize = (5,6))\n", - "forest_importances.head(15).plot.barh(ax=ax)\n", - "ax.set_title(\"Feature importances using MDI\")\n", - "ax.set_xlabel(\"Mean decrease in impurity\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5272ca7c-eb5f-4afb-b85a-a176444e7c4d", - "metadata": {}, - "source": [ - "### Visualizing the Random Forestfrom sklearn.tree import export_graphviz" - ] - }, - { - "cell_type": "markdown", - "id": "bed5031c-3e68-4555-9ade-e524d6bef0bf", - "metadata": {}, - "source": [ - "The RandomForestModel consists of `n_estimators` DecisionTreeModel. To visualize each tree the `plot_tree` attribute can be used, see below:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "23862b07-a1bf-456c-91bf-6e2c086a4c63", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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2rXGH82AAAAAAQLD68ssv5dSpU8Ydp9NpPgYhJTEx0bjh8XgkKytLYQ0AWOf8+fOye/du4w7XWnxfgwcPlsjISOMO78MG4O/u3r0rGzZsMO5wrcX39fDDD0uNGjWMO1xr4UslJSVy9OhRo0bdunWlcuXKSosCi8PhMG643W6FJQDKgt3XAwAAoWXUqFGSl5en0mrUqJG88sorKi0A+D4GDx4sjz32mFpv9OjRag8rAIBAVVJSItnZ2cYdbsjFv9OtWzepXr26cYebcgEAAAAAAL7d1KlT5dy5cyqtChUqyJIlS1RaAPB93HvvvTJu3Di13tSpU1UeegEAgU7jjG3IkCESERGhsAbBqGHDhtKxY0fjDufBAAAAAAAA327lypXy2WefqbRsNpusXLlSwsPDVXoA8F3VqlVL5syZo9Z74403ZNeuXWo9AAhUGmdsnTp1knr16imsQTCKioqSQYMGGXc4DwYAAAAAAPh2GzdulHfffVett2jRopB90QsA34mKipLU1FS13tatW2XVqlVqPQAIVBpnbDVr1pQuXboorEGw0nim+Jo1a6SkpERhDQAAAAAA/sXlchk3ypcvLwMGDFBYg1DSpEkTlee6paenK6wBAOtkZGQYN2w2myQkJBh3EFoqVaok/fr1M+5o/LwIAFbasGGD5OfnG3eSkpIU1iCUhIeHq3weYfXq1eLxeBQWAd/fiRMnpLi42KjhcDiU1gQejf/2w4cPKywBUBbsvh4AAAgdmZmZkpWVpdZbtmyZxMTEqPUA4PtYtGiRVKxYUaV14cIFmTJlikoLAALVjh075ObNm8YdPoSCfyc8PFxiY2ONOxofmgIAAAAAAAg2X375pSxevFitN336dGnQoIFaDwC+jylTpkiTJk1UWvn5+TJy5Ejxer0qPQAIRKdPn5Z9+/YZdzRueENw0/geWbdunRQVFSmsAQAAAAAACB4XL16UF198Ua03fPhweeihh9R6APB9PPXUU9K9e3e1XnJysvHDzgAgkBUUFMj69euNO9wfjP9E43vk888/l3PnzpmPAQAAAAAACCL5+fkyfPhwtd7AgQPlRz/6kVoPAL6PRx99VH7xi1+o9Z577jmV56QCQCDTeAZvXFychIWFmY9B0NI4D75+/brs2rXLfAwAAAAAAH4mPT3duDFo0CApX768whqEGqfTadzYsmWL3L59W2ENAFjD5XIZN7p27Sp169ZVWINQo3Gt3bdvn5w8eVJhDQBYQ+Na26xZM7n//vsV1iDUJCUlGTcuXLggn376qcIa4Ptzu93GDYfDobAkMGn8t2t8DQCUDbuvBwAAQkNubq6MGjVKrZeYmChDhw5V6wHA91W/fn2ZMWOGWm/p0qWyd+9etR4ABJrMzEzjRsOGDeWBBx5QWINgpnFT7qFDh+T48ePmYwAAAAAAAIJEaWmpJCcnS2lpqUqvffv2qufLAPB9lS9fXpYvX67WW7t2raxevVqtBwCBJisry7gRGRkpAwcOVFiDYKZxHnznzh3Ztm2bcQcAAAAAACCYjB07Vu7cuaPSqlu3rrz22msqLQD4Iex2u6SlpUl4eLhK7+DBg7Jw4UKVFgAEoq1bt8rdu3eNO/Hx8QprEMwGDRqkcv3W+AwDAAAAAABAMHnttdfkxIkTKq1y5crJ8uXLxWazqfQA4IeYP3++VK1aVaV19epVmThxokoLAALR0aNH5ejRo8YdzoPxnzz44IMqL7fXeMY5AAAAAAD+xO12i9vtNu44nU6FNQhFGt87xcXFsnbtWoU1AKDv2rVrsn37duNOUlKSwhqEori4OAkLCzPuuFwuhTUAoK+wsFCys7ONO06nk8+m4gd59NFHpVKlSsad9PR0hTXA96fxu0GHw6GwJDA1btxYoqOjjRoaXwMAZcPu6wEAgNAwdepUOXv2rEqrQoUKsnjxYpUWAJgYMWKEdOzYUaXl8XhUX24OAIHE6/Wq3OAYFxfHwRj+o379+km5cuWMO9yUCwAAAAAA8D9++9vfyqeffqrSstlssnLlSrUXZQLADzV48GDVG2BHjx4tOTk5aj0ACCQaZ2taN7shuN17773StGlT4w7nwQAAAAAAAP9j48aN8u6776r1Fi1aJJUrV1brAcAPce+998q4cePUelOnTpVTp06p9QAgkGicrbVo0SKkH/yI76ZKlSrSq1cv4w7nwQAAAAAAAP/D7XbLnDlz1HqTJ0+WZs2aqfUA4IeoVauWvP7662q93/72t7Jr1y61HgAEEo2ztfLly0vfvn0V1iCY2e12iYuLM+5kZmaK1+tVWAQAAAAAgH9YvXq1cSMiIkKGDBmisAahqE2bNir3e7hcLoU1AKAvKytL5f3BiYmJCmsQimrUqCE9e/Y07nCtBeCvtm7dKnfu3DHuOJ1OhTUIRVFRURIbG2vccblcfB4BPuF2u40bofwsD7vdLq1btzZqXLt2Ta5du6a0CICV7L4eAAAIfvv375dFixap9aZPny4NGzZU6wHADxUWFiZpaWlit+v8WP35559LamqqSgsAAsmBAwdUHlKfkJBg3EDwi4mJkX79+hl3MjIyzMcAAAAAAAAEgUuXLsmLL76o1hs2bJh06tRJrQcAJhYvXiwVK1ZUaZ0/f16mTJmi0gKAQHLz5k3Ztm2bcSc+Pt58DIKezWZT+V7hIc0AAAAAAAD/LT8/X4YPH67WGzBggPzoRz9S6wGAicmTJ0vjxo1VWvn5+TJq1Ch+twwg5Hg8HsnKyjLuJCQkiM1mU1iEYKdxL/mHH34ot2/fNh8DAAAAAAAQ4LxerwwbNkyKi4tVem3btpXx48ertADA1NNPPy3dunVT66WkpKj9fQkAgUTj2bsDBgyQ6Oho8zEIehrnwcePH5dDhw6ZjwEAAAAAwE+4XC7jRt++faVy5coKaxCqkpKSjBvr16+XvLw8hTUAoEvjWvvAAw9I06ZNFdYgVDmdTuPGrl275OLFiwprAEBXenq6caNevXrSuXNnhTUIVRrX2hMnTsiBAwcU1gDfj9vtNm60bdtWYUngcjgcxg2NrwMA69l9PQAAENxKS0slOTlZSktLVXrt27eXUaNGqbQAQMODDz4oI0aMUOtNmjRJLly4oNYDgECQmZlp3KhcubL07NlTYQ1CgcZNuR9//LFcu3bNfAwAAAAAAECAGzt2rNrLrOrUqSMzZ85UaQGAhvr168uMGTPUekuWLJG9e/eq9QAgEKxbt07l84NxcXEKaxAKNM6Dz58/L1988YX5GAAAAAAAgAD32muvyYkTJ1Ra5cqVkxUrVojNZlPpAYCpmJgYWb58uVovOztbVq9erdYDgEDw6aefyqVLl4w78fHxCmsQCjQ+O1BcXCzr169XWAMAAAAAABDY3n77bdm+fbtaLzU1VSIjI9V6AGDCbrdLWlqahIeHq/S++uorWbRokUoLAALF5cuXZffu3cYdzoPxXfXu3VsqVqxo3NF41jkAAAAAAL7m9Xpl/fr1Ks/BSkpKUliEUOZ0Oo0beXl5snHjRoU1AKBn7969smHDBuOOxt+TCG2JiYkqnVmzZklxcbFKCwA0lJSUSEZGhnEnMTFR7Ha7+SCErIEDB0p0dLRxZ9asWVJQUKCwCPhuvF6vuN1uo0aVKlWkdu3aSosCU9u2bY0bpl8HAGWDnxgBAJZ64403ZM+ePSotm80mK1euVLvxDQC0zJgxQ+rWravSysnJkbFjx6q0ACBQaNzYOHjwYImIiFBYg1AQGxtr/OIXj8cj2dnZSosAAAAAAAAC06ZNm+Svf/2rWm/RokVSuXJltR4AaBgxYoR06NBBpeXxeCQlJUVKS0tVegAQCDTOgzt16iT16tVTWINQ0L17d6lWrZpxh4c0AwAAAACAUOd2u2XOnDlqvcmTJ0uzZs3UegCgYciQIaovIhg9erTk5OSo9QDA32mcqdWsWVO6du2qsAahoGHDhiqf5eI8GAAAAAAAhLrr16/L+PHj1Xq/+tWv5JFHHlHrAYCG++67T5577jm13tSpU+X06dNqPQDwd2vWrBGv12vUsNvtMmTIEKVFCHZRUVEycOBA4w7nwQAAAACAQHfx4kUZMGCADB482Lhlt9slLi5OYRVCWfv27aVJkybGnVGjRslf//pX4987AoCpmzdvyuOPPy4dO3ZUeUa05n3KCE316tVTucdy6dKl0qJFC9m5c6fCKgAw43a75Uc/+pFcv37duOV0OhUWIZTFxMSofB7h3XfflUaNGvEea5SZc+fOSW5urlHD4XAYv7890DkcDuOG2+1WWALAanZfDwAABK9Lly7JxIkT1XrDhg2TTp06qfUAQEulSpVk8eLFar2//e1vsmHDBrUeAPizM2fOyN69e407CQkJ5mMQMmrVqiXdunUz7mRkZJiPAQAAAAAACFD5+fkyfPhwtV7//v3lxz/+sVoPALSEhYXJypUrxW7X+bjlZ599JmlpaSotAPB3hYWFsn79euNOfHy8whqEivDwcImNjTXucB4MAAAAAABCmdfrlWHDhklxcbFKz+FwqL4AGAA0LVq0SCpUqKDSOn/+vEyZMkWlBQCBQONMbejQoRIWFmY+BiFD457ydevWSVFRkfkYAAAAAACAADVhwgSVF2OJiFSvXl3mzJmj0gIAbVOmTJHGjRurtPLy8mTUqFG8WBxAyNA4D+7Ro4fUqFHDfAxChsZ58GeffSbnz583HwMAAAAAgA98/PHHcv/998vmzZtVeo888ojUrFlTpYXQZbPZJCkpybhz/vx5+elPfyoJCQly7do1hWUA8P199dVX0r59e/nb3/6m0mvTpo04HA6VFkKb0+lU6Zw5c0Z69uwpc+fOVekBwPfl9XplxYoV0qFDB5XPHVSvXl0eeeQR82EIeVrX2qtXr8rQoUPl+eef57OEsJzb7TZu8O8VnT+Dw4cPKywBYDWdt4oBAPAtnnvuObl9+7ZKq06dOjJz5kyVFgBY4bHHHpNBgwap9YYPHy55eXlqPQDwV1lZWcaNiIgIGThwoMIahBKNm3I3bdrE9RoAAAAAAISsmTNnyvHjx1Va5cqVkxUrVojNZlPpAYC2Bx98UEaMGKHWmzRpkly4cEGtBwD+auvWrZKbm2vc0TjbQ2jR+J45ePCgnDhxwnwMAAAAAABAAHr77bdl+/btar20tDSJjIxU6wGApgYNGsiMGTPUekuWLJG9e/eq9QDAX3399ddy5MgR4058fLzCGoQSje+ZO3fuyLZt28zHAAAAAAAABKAdO3bIm2++qdabN2+e1KhRQ60HAJpiYmJk2bJlar01a9aovFwQAPxdbm6ubNmyxbjDeTC+r8GDB0t4eLhxR+OZ5wAAAAAAlLUrV67IY489JteuXVNrOp1OtRZCm+b3UlZWlnTq1Elu3bql1gSA7yI3N1cee+wxOXPmjFqTay20aH4veTwemTBhgqxfv16tCQDf1QsvvCAjRoyQgoIClV58fLzKGTIQGxsrERERar158+bJ22+/rdYDvo3b7TZuOBwOhSWBrUWLFhIWFmbU0PhaALCe3dcDAADBafPmzfLOO++o9RYuXCiVK1dW6wGANpvNJsuXL5dy5cqp9E6ePCmvvfaaSgsA/FlmZqZx49FHH5VKlSoprEEo0biROz8/X+WmcgAAAAAAgEBz5MgRef3119V6L7/8sjRv3lytBwBWePXVV6Vu3boqrTt37shzzz2n0gIAf6ZxHtyiRQtu8sH31r9/f5XPcWl8DwMAAAAAAASa69evy/jx49V6v/rVr+SRRx5R6wGAFUaMGCEdOnRQaXk8HklJSZHS0lKVHgD4K42ztOjoaOnbt6/CGoSS++67T5o0aWLc4TwYAAAAAACEoqKiIklJSVHrPfLII/Jf//Vfaj0AsEJsbKzqy05Hjx4tOTk5aj0A8EebNm2SwsJC447Gs38RWqpUqSI9e/Y07nAeDAAAAAAIRE8//bRcunRJtZmYmKjaQ+jq0qWL1KlTR6138uRJ+fWvfy1er1etCQD/ydixY+Xrr79WbSYlJan2ELqaNWsm7du3V20+8cQTcu3aNdUmAPw769atk7lz56o2NT/3hdBWpUoV6dOnj2pzxIgRcuLECdUm8I/cbrdxg/c7iERGRkqLFi2MGmfPnpXc3FylRQCsYvf1AABA8CkoKJDhw4er9fr37y+PP/64Wg8ArNK0aVOZMmWKWm/27NkyYcIE2bRpk3g8HrUuAPiLW7duybZt24w7CQkJxg2EnpYtW6ocBmRkZJiPAQAAAAAACCBer1dSUlKkuLhYpdemTRvVFwADgFUqV64sixcvVuu9++678pvf/EYyMjKkqKhIrQsA/sLj8ag84DY+Pl5sNpvCIoSSmJgYlZc9cx4MAAAAAABC0YQJE+T69esqrerVq8ucOXNUWgBgpfDwcElLS1M7k/jss88kKSlJ/vKXv/AALgBBS+MsbcCAAVK+fHnzMQgpNptN5d7yzMxMXvgCAAAAAABCzvz58+Xw4cMqrYiICNXzFQCw0uLFi6VChQoqrXPnzsmgQYPkrbfeUvuMDQD4G43z4Pvuu0+aNWtmPgYhR+M8eOvWrXL79m3zMQAAAAAAlJHTp09Ldna2arNx48bSoEED1SZCl91ul8TERNVmenq6rF27VrUJAP/KnTt35I9//KNqs1KlSvLAAw+oNhHaunXrptq7du2avPfee6pNAPhXSktL5de//rVqU+v50sDf9ezZU7WXl5cnb7/9tmoT+Edut9u4ofHe9mCg8edw5MgRhSUArGT39QAAQHAoLi6W9PR0GTJkiNSvX1+++eYblW5UVJSsWLGCG3IBBIxx48ZJ27ZtVVoej0fmzp0rAwYMkObNm8v8+fOltLRUpQ0A/mDdunVSUlJi3ImLi1NYg1CkcVNudnY212cAAAAAABD0PB6PbN68WX784x9LvXr1ZPv27WrttLQ0iYqKUusBgJUee+wxGTRokFrvd7/7nSQmJkqDBg1k0qRJkp+fr9YGAF/77LPP5NKlS8YdjTM9hCaN752dO3fKtWvXzMcAAAAAAAD4Ma/XK3v27JGnn35amjZtKm+++aZae+7cuVKjRg21HgBY6aGHHpIRI0ao9TIzM+XnP/+51KtXT4YNG8bLewEElcuXL8snn3xi3ImPj1dYg1Ck8b1z/vx5+eKLLxTWAAAAAAAA+De32y1jx46VNm3ayKRJk9S6EyZM4KUuAAJGgwYN5NVXX1Xrffzxx/LUU09JvXr15Oc//7mcPHlSrQ0AvlZSUiJr16417nAejB9K41njxcXFsmHDBoU1AAAAAACUjfT0dPXmuXPn5MCBA+pdhK5q1aqpN//617+qNwHg22RnZ0tRUZFq886dO7J161bVJkJXUVGRrF+/Xr37/vvvqzcB4Nt89NFHcvHiRdVmxYoVJSIiQrWJ0OX1emX16tXqXa61sJLb7Tb630dHR0vjxo2V1gQ2jfseTL8eAKxn9/UAAEDg27VrlzRt2lQee+wxWbdundy4cUOt/fLLL0vz5s3VegBgtcjISElLS1Pvnjp1SsaPHy+JiYmSk5Oj3gcAX8jMzDRuPPTQQ1KvXj2FNQhFGjd0X716VeVh4wAAAAAAAP7q66+/lvvvv1/69+8v7733nly6dEmt/eSTT0rPnj3VegBgNZvNJsuWLZNy5cqpdq9evSqzZs2SXr16qd9kBAC+onEeXLNmTenatavCGoSi2NhYsdlsRg2Px6PysHEAAAAAAAB/dfnyZendu7d06dJF3nzzTTl16pRa+5FHHpEnn3xSrQcAZWHGjBlSt25d1WZOTo6kpaVJ586deRgXgKCRnZ0tXq/XqGG32yU2NlZpEULNww8/rPICGI3PNgAAAAAAAPir3Nxcefzxx6Vt27ayaNEiOXr0qFq7WbNm8tJLL6n1AKAsjBw5Uh544AHVZlFRkfzlL3+RTp06yY4dO1TbAOArO3fuVHnXiMYzfxGaGjVqpHLN5jwYAAAAABBIPv30U/VmaWmp/Pa3v1XvIjTl5eXJ0qVL1btZWVlSXFys3gWAf2bFtVZEZMWKFZZ0EXq2bNkiJ0+eVO/u2bNHvQkA32b16tXqzUuXLsmqVavUuwhNX375pSU/Ex4+fFhyc3PVu8D169fl6tWrRo3WrVtLWFiY0qLA1rZtW+MGz40C/J/d1wMAAIHtz3/+s/Tu3VvOnz+v3m7Tpo08//zz6l0AsFqPHj3kqaeesqS9Zs0aGThwoJSWllrSB4CyUlhYKOvWrTPuJCQkmI9ByHrooYdUXqyQkZFhPgYAAAAAAMAPbd26VTp37iyHDh1Sb1erVk3mzp2r3gUAqzVr1kymTJliSfvTTz+V7t27S05OjiV9AChLGmdosbGx3OCDH6x27drStWtX4w7nwQAAAAAAIFh99dVX0qlTJ9m+fbt6OyIiQlJTU8Vms6m3AcBKlStXlkWLFlnSPn78uHTt2lXOnDljSR8AypLGGdrDDz8sNWrUMB+DkBQeHi6xsbHGHc6DAQAAAABAsDp37pz06NFD/va3v1nSX7FihURHR1vSBgCrhIeHS1pamiWfZ7l27Zr06dOHl58CCAoaZ2j169eXjh07mo9ByNJ45vjatWulqKjIfAwAAAAAAGXA4/FY0n3//fd51yFUrF+/Xu7cuaPezcnJkfz8fPUuAPwzq66169at4znSUGHV57y8Xq8lXQD4ZxcvXrSk+84771jSReh59913LWtzvYUV3G63ccPhcCgsCQ4afxYaXxMA1rL7egAAIHDt379fnnzyScs+fJ6WliZRUVGWtAHAanPmzLHsoaG7du2S1NRUS9oAUFY+/PBDyc3NNe7Ex8crrEGostvtEhcXZ9zJzMzk4AsAAAAAAASdS5cuSVJSkty6dcuS/ty5c3kBG4CANW7cOGnbtq0l7ZMnT8qUKVMsaQNAWTl27JjKzSQaD9hFaNP4Htq0aRMP+AEAAAAAAEEnPz9fnE6nnDlzxpL+888/b9lZCgBY7Uc/+pEMHDjQkvbt27dlxIgR3IcEIKDdvXtXtmzZYtzh/mCY0vgeOnjwoJw4cUJhDQAAAAAAgP/wer3yy1/+Ur788ktL+o8//rgMGDDAkjYAWK1Tp04yfPhwS9rFxcXyzDPPSHFxsSV9ACgLXq9XMjMzjTvx8fFis9kUFiFUaZwH37lzR7Zv366wBgAAAACAwHX58mU5deqUr2cgCKSnp1vW5l47AIGsoKBADhw44OsZCAJ79uyxpMt1FkBZservm82bN1v2vhyEFquutSJcb2GNw4cPGzccDofCkuDQpk0b44bGezcAWMvu6wEAgMBUWloqzzzzjJSWllrS/6//+i/p2bOnJW0AKAvVq1eXefPmWdafNGmS5ObmWtYHAKtp3JDbvHlzXuoBYxo35X7zzTf8MhwAAAAAAASdsWPHWvaB9B49esiTTz5pSRsAykJkZKSkpaVZ1l+yZIkcO3bMsj4AWE3jPDg6Olr69u2rsAahTOM8OC8vT+Ul1AAAAAAAAP7ktddek2+++caSdtOmTeWll16ypA0AZcFms8ny5culXLlylvSzs7P5vTOAgLZp0yYpKCgw7mic5SG09e/fX6Kioow7Gp9xAAAAAAAA8Cd/+MMfZNu2bZa0K1WqJAsXLrSkDQBl5bXXXpM6depY0j5w4ID8/ve/t6QNAGXhq6++klOnThl3OA+Gqfvvv18aN25s3OE8GAAAAAAQKBo0aGBZ26p3zSK0XLhwwbJ2ZGSkZW0A+DuutfB3JSUllnQbNmxoSRcA/plVz+coKSmR69evW9JGaLHqWlupUiWpWLGiJW2ELq/XK+vWrTPuOBwOhTXBISYmRho1amTU+Prrr+XSpUtKiwBYwe7rAQCAwPTRRx/Jp59+akm7WrVqMm/ePEvaAFCWnnjiCenZs6cl7ZycHNm5c6clbQCw2qlTp2TVqlXGnYSEBLHZbAqLEMoeffRRqVChgnFn1qxZUlhYqLAIAAAAAADA986ePSt//etfLWmHh4dLamqq2O18bAlAYOvRo4c89dRTlrQ9Ho9s3LjRkjYAWO3q1auSlpZm3Onfv7+UL19eYRFCWatWraRNmzbGnfnz50tOTo7CIgAAAAAAAN8rKiqy9KW6K1as4Hd7AAJes2bNZPLkyZb1NR6UBgC+kJOTIwsWLDDu3HvvvdK8eXOFRQhlFSpUkH79+hl3UlNT5dq1awqLAAAAAAAA/MOcOXMsa8+cOVPq1q1rWR8AykLlypVl0aJFlvU5DwYQqAoKCmT27NnGnUqVKkmvXr3MByGk2Ww2SUhIMO78+c9/ljNnzpgPAgAAAADAYq1bt/b1BODfKi0ttaTboEEDiY6OtqQNAP+Iay1CFd/7AMpKixYtLGtb9e8RhBar3n3eunVr3qsOVTk5OdKvXz/JzMw0bjkcDoVFwcP0z8Pr9UqLFi1k7dq1SosAaONtiACAH+TDDz+0rD137lypUaOGZX0AKCs2m01SU1MlIiLCkr6VfxcDgBUKCwtl7Nix0rRpU5WXm8bHxyusQqiLioqSQYMGGXf+/Oc/S9OmTWXr1q0KqwAAAAAAAHzLyjOI559/Xu655x7L+gBQlubMmSPVq1e3pM15MIBA4/F4ZO7cuVKvXj05fvy4cU/jwbqAiM730vbt26V+/fryl7/8xXwQAAAAAACAj3322WeSl5dnSfvHP/6xDBw40JI2AJS18ePHW/YwMs6DAQSiVatWSb169WTnzp3GLe4PhhaN76Vjx45JvXr1ZP78+eLxeBRWAQAAAAAA+M7FixflyJEjlrQfeughSUlJsaQNAGXtxz/+sQwYMMCS9vbt23lxIICAs2XLFmnatKm88847xq3BgwdLZGSkwiqEOo3z4Nu3b0vjxo1l3LhxUlRUpLAKAAAAAABrtG7d2pJurVq1pHnz5pa0EVqqVKliSbdNmzaWdAHgn1n1901UVJQ88MADlrQRWrp162ZJl2stgLJi5d83lSpVsqyN0GHVtdaq3+kgNN2+fVv69+8vH3zwgXHLbrdLy5YtFVYFD43nSN29e1cSExMlMzNTYREAbXZfDwAABKYdO3ZY0n344YflySeftKQNAL7gcDhkwoQJlrQPHTpkSRcArHDlyhXp1q2bLFq0SKVXo0YNyw4xEHq0Hvh98eJF6du3r8yePVulBwAAAAAA4CtWnQc3bdpUXn75ZUvaAOAL1atXl3nz5lnS5jwYQCDJz8+X2NhYmTBhgpSUlBj37Ha7xMbGKiwD9M6Dc3Jy5Oc//7mkpKSI1+tVaQIAAAAAAPiCVefBlSpVkoULF1rSBgBfiIyMlLS0NEvahw8fFo/HY0kbALR5PB75zW9+I7/4xS8kNzdXpal1hgcMHTpUbDabcae4uFjGjx8vcXFxUlBQoLAMAAAAAADAN3bu3GlJ1263S1pamoSFhVnSB4CyZrPZZPny5RIREaHevnPnjpw/f169CwBWmTlzpvTv318uXbqk0uM8GFp69OghVatWVWktWLBAunfvLlevXlXpAQAAAACgrU2bNpZ04+LiOOODiiFDhljSbd26tSVdAPhnzZo1k/DwcPVu3759pWLFiupdhJ6EhARLulxrAZQVq/6+efDBB6VOnTqWtBFarLrWWvU7HYSmMWPGyO7du1VaYWFhfE7mH3i9Xjl27JhKq7i4WB5//HE5d+6cSg+AHruvBwAAAtOZM2fUmzabTdLS0sRu5/IEILg8+eSTlnS1bmoDAKt5vV558sknZe/evWrNoUOH8iFPqBk8eLDa95PX65UXX3xR1q5dq9IDAAAAAADwBSvOg0VEli5dKuXLl7ekDQC+8vjjj1vykGbOgwEEkgkTJsj69evVet27d5caNWqo9RDaOnXqpHqz7cqVK2X58uVqPQAAAAAAgLJm1XnwlClTpF69epa0AcBXevToIdWqVVPvFhcXy82bN9W7AGCFJUuWyO9+9zu1Xv369aVjx45qPYS22rVrS9euXdV6a9eulRdffFGtBwAAAAAAUNasOg9+4oknpEOHDpa0AcBXmjdvLq1atbKkzT3CAALFmjVr5KWXXhKv16vSi4iIkEGDBqm0gPDwcImNjVXrff755/LUU0+pfb8DAAAAAKCpdu3aUq5cOdVmeHi4DBs2TLWJ0BUXF2fJO4bbtGmj3gSAbxMRESG1atVS744aNUq9idA0cOBAadmypXqXay2AsmLV57ASExMt6SL0dOrUSTp37qzebd26tXoToenw4cPypz/9Sa1XXFwsSUlJfE7m/3n//fdV3zNfWFgor776qloPgA7932ADAEKCFTeBeb1e9Q8AAIA/2LRpkyXdy5cvW9IFAG1paWmqL+wVEYmPj1ftIbRVrVpVevXqpdr81a9+JdevX1dtAgAAAAAAlBWrHgoaFRVlSRcAfGnHjh1SXFys3r19+7YUFBSodwFA28aNG2XZsmWqzYSEBNUeQpvdbpe4uDjV5vjx48Xtdqs2AQAAAAAAyopV58Hly5e3pAsAvnTixAm5ceOGJW3uEQYQCA4dOiQvvPCCatOqF3cgdGnfc75o0SLZsmWLahMAAAAAAKCsWHUeXKlSJUu6AOBLxcXFcuTIEUvanAcDCARXr16Vp556SrXZu3dvqVy5smoToU37PDg7O1t++9vfqjYBAAAAANBgs9nUz+SmT58uHTp0UG0idNWrV08mTZqk3m3durV6EwD+Fe1r7ahRo2TAgAGqTYSu6OhoWbVqlYSHh6t2udYCKCsxMTHqnxdo0aKFjB49WrWJ0GW322XVqlVSoUIF1W6rVq1UewhdixYtEo/Ho9rcs2ePZGdnqzYDUUlJiUyePFm9++abb8rNmzfVuwB+OJ5mBAD43nJzcyUvL8+S9ueff25JFwB8yaoHGVy+fFm8Xq8lbQDQtHz5ctWezWaTHj16qDYB7cOrq1evyvvvv6/aBAAAAAAAKCtWPRT0008/taQLAL508eJFy9pXrlyxrA0AWlasWKHefPjhh9WbCG3t2rVT7RUWFspbb72l2gQAAAAAACgrVp0Hc38wgGBk1f3BIry0F0Bg+P3vfy9FRUWqzfbt26v2gO7du6s3U1NT1ZsAAAAAAABlwarzhy+//NKSLgD40vXr16W0tNSSNufBAALBe++9J9euXVNt8hJoaHvkkUfEZrOpNq24Nx4AAAAAAA01a9ZUa8XGxsqECRPUeoCIyJQpU6RTp06qTX6nCKAs1atXT63VtWtXef3119V6gIjIQw89JIsWLVLrhYWFSY0aNdR6APCfVKxYUa0VFhYmq1atkgoVKqg1gebNm8ubb74pdrtdran9HmyErn379lnSnT17tiXdQLJmzRo5evSoerekpEQOHjyo3gXww+ld4QEAIcPKG8CuXr1qWRsAfEX7RrS/Ky4ulps3b1rSBgAtR48elUOHDqk2vV6vzJo1S7WJ0Hbnzh1599131bsul0u9CQAAAAAAYLXS0lLLzm05DwYQjKw6Dxax9oXAAKAhNzdXNm7cqN7lPBiavF6vJQ9Udrlc4vV61bsAAAAAAABWu3DhgiVdzoMBBCPOgwGEMq/Xa8k9ksuWLeOcDarmzJmj3ly/fr3k5eWpdwEAAAAAAKxm1fkD58EAgpGV58EXL160rA0AWqw4D37nnXckNzdXvYvQNWvWLPXPGBw4cECOHz+u2gQAAAAAQEOHDh1UOgMGDJD33ntPwsLCVHrA30VEREhGRoa0atVKpWez2aRBgwYqLQD4Lrp3767S6dSpk6xfv16io6NVesA/GjFihMydO1elVbNmTZUOAHxXDRs2VOnY7Xb505/+JJ06dVLpAf/oRz/6kbz11ltis9mMWzExMVKuXDmFVQh1Ho9HDh8+bEl737594vF4LGkHii+++MKy9qFDhyxrA/j+7L4eAAAIPFY+EJQHmAEIRlq/BP423JQLwN99+umnlnTfeustKS4utqSN0JORkSE3btxQ71r1/Q8AAAAAAGCla9euWfYBSs6DAQQjK8+DT506ZVkbADR89dVXUlhYqN7NysrixeVQ88knn1hyI9Px48ctOWcGAAAAAACw2oULFyzpch4MIBhZeR5s1cPTAEDL1atX5fTp0+rdr776insvoebChQuyZs0a9W5+fr4cPHhQvQsAAAAAAGC1Y8eOWdLlPBhAMKpfv77Ki9i+jZUvfAIALXv27FFvXrt2TbKystS7CE1FRUXyhz/8wZI2n1sAAAAAAPijp59+2rjRp08fWb16tZQrV05hEfB/1a1bV7Zu3SrNmzc3btWuXVvsdrvCKgD4bn71q18ZNzp06CAbNmyQypUrKywCvt348ePltddeM+706NFDYQ0AfHd9+vQxbthsNnnrrbfkpz/9qcIi4Ns98cQT8sYbbxh32rdvbz4GkP/+u8+q35Hk5+db8tyQQGLlM57CwsIsawP4/vhtMwDge9u/f79l7QcffNCyNgD4ipWHT59//rllbQDwZ9evX5ctW7b4egaCxDvvvOPrCQAAAAAAAH7j5MmTlrU5DwYQjKw8D96xY4dlbQDwZx6PR9577z1fz0CQ4DwYAAAAAADgf+Tm5kpRUZElbc6DAQSj+++/XypWrGhJ+5NPPrGkCwCBgDM8aPnb/8feXcdJXW9/HH9PbHcHtQtLd4eAgqiglEGIca2r17z+7M7rtTuuddVrkIoIJogiICHd3bXFdu/E749Zdhlme2dY4vV8PHz42Jnv53zPzM4yM9/zPec7fbrsdntjpwEAAAAAAHDKOHz4sEfiUg8GcCYKCwtTp06dPBJ706ZNHokLAKcD6sFwl19++UWZmZmNnQYAAAAAACfNueeeq8jIyHqv79evn2bPni0/Pz83ZgW4atKkiX777Tc1b968QXFGjRrlpowAoHYSExOVlJRU7/Vt27bV3LlzFRYW5sasgMo98sgjevLJJxsU48UXX3RTNgBQO3fccUeDY3z88ce69tpr3ZANUL0bb7xR//nPfxoU4/nnn3dTNjjbGQwGtWnTxmPxS0tLPRb7dGCxWDwW25O/NwB1Z2zsBAAAp5/169d7JK7ZbFa/fv08EhsAGlO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A/v2aPOkBHcz/YxPhLh3a6sSTB+n5l/8jSVr45RcaddoQvfLaNK1csUJvvvGqcrKz1bP3CXr8n0+rfYe00nOvueIyff3Vl1q+ZkOdfV8ff/iB2ndI0wn9+pe+5nQ6df4FF+qBe+/W9t9/V7PmzavMufuOW5WYlKxx192gF555qtzXv128SDabTcNOG1Hm9dPPGKlXX/mXPvpwpm6+7Y7SY4ePOENRUX+MEzdo0EADTjxJH838QDt37FBS8vHHxPCH1HZttXxZ3Y1n1pXP5s7XmX86TyefOFA3XDtOxe5iTX3yae3avVt79+4r8z237dhFg04+Uf956XlJ0hdfLdSQ00cpLMypQSefqAvOP0+Hsg7pznvul91u10/fLZLDUfLQ6cuuukZffvV1lT9Dj8cjj8dTZd02m600+3iat+mgEwf213+nvVbm9U9mz9Hoc87XU1OnaPjQIZVmzP/fAo06+zx9u3CBunXtorYdu6hTRro+en966THjrr9R/37tDf28dLGczj+235k7/3ONGP0nnXrKIL356iuSpMhGTXTWmaP06r9eKPP/XHjp5Xpn+gw9NfXRKmuyWpdefbVy1Wq/1hDqmKdxvPNZtw8AAAAAAICK7fthjjYf6VOLS1KzoZerOPegtn44VSf+548+taW3luwZ3eGKJyVJB1Yv0q+PnKe0q59VzuE+NXd+jmLadle7Y/aMXvNyyZ7RfR6rwz2jl1W8Z3ST6u4ZvaziPrWEXiO0+5g+tYqENWhc+n9WxdkgXoVZ9Kn5KtjnaATzvIzjYb/oijH3om4x9nS8860be9qz+D0d+m2Juk9coC0fhO5eGvVNILWzwdxmMpexLNpEAAAAAABgCvsq+yaQ+g+PCOR+xKHP/exTv1ggvl81EcjvbUV9xL72g7ZJaqRFj11R6/Pr0uc/bdCfH5mhAektddWIXnK7PXrm4++052Cu9mXllfk+ul//vAZmtNKz146UJH29YovOevBthTkcGpjRSucOzFBWXoH+8daXsttsapPYSL/9zrwMf8r8cY62zDxq3PXw/KRts6ZqwCtHjbve3lcN044Zd51Sybhr0jHjrr8tVq9HA2Mtc7Dfj48WDPdmX+/HwfR++/P9ZHzQd/uOmQfT7PA8mK2zpmrgUfNgvr+tZL7o0ffjXx89Tx0O3493HXM/jjpmHszB1YvVuw7nwez7oeL5ok2rOV/UF4F0fQfD/fh4Wv1jsdxVb7NxXIH0PlYk0N7boc//rLUhfj/f9t1s/fL2I8ravl6R8UlKG3mlCnMO6tfpj+mC93aVHjdrXG817TRA/W4o2T9p16/f6H/3/Un9//68Dm5ZrY0L3lFRfo4ap/ZQ7ysnq2HzP+7HS57+m3avWKTRL3xfZ9/X1m8/VYNmqUpI61P6mt3hVOtB5+iXNx9S7r4dimocmn1Rxwq06/YI5rj5jt9pAYSCqlfSAQFs//IFWv3MFWqY1ldp456X11Os3z97QUWH9lY7Y/P7k9UwtY9SL5sid162Ns+YpFVPXaqek76UzV755pDH8no8krcafxHbbFVm5/7+mxq2P6Hc61Et0g9/fc1xO1jchXnK37NZjXuOKPe16BYZ2lX4pvL3bFZkUruqaz1K04Hnafu8l7X+jfFqfe7dcjZorEO/LdbOL6Yp+dRL5QiPqjoEIe2xBVuV1NClty5Ol8tZssDtlNRG6vvkD9XOaN8kUk+f077033a7TeOmr9FPv2erV8sGlZxZ3hNfbtPUL7ZVeVyLRuH69qaeNco+Yn9esVLiyj/8ulFkSRO9P7e4yvOPHFvh+XmVn4/QdnQ72eFwO7m9hu3klvcnq8FR7eSmw+1kj3reTuZVo52Mr6CdjGqRIU8t28kjjnyfxTkHtff7j3RgxZdqc+HEWmUhdAVzm+lL23Ywzy2vVxWfH0XbCAAAAAAAEGz2/bJAy5+6Qo3S+qnztc/L63Fry+wXVHhwT7UzNsyYrNj2fdTxr4/JnZelddMn6ZcnLlW/ybXr5/ZWo5/bVo1+7pxtv6lRh77lXo9pWdLPnf37b8ddbOwuzFPe7k1q0uv0Cs/3FOYrf89mRSW1U2RCCyX0PE1b5vxLDdp0VYM23VWQuV1rpt2j8MbNldjvrDLnFx7aq7Vv3at259+liPhmVX6vCC2PfrZaybGReufq/qV916emN1WfB+dXO6NDYoyevahX6b/tdpuueu17/bRlv3q1jq9RPY/P/U2Pz1lT5XEt4iL1/b3DKj1mf26h4qLCyr0eFx1W+nUrHMwr0qfLd2hQWhO1iCs752LnwXzlFbl15avf629D2+uB1nH6acsBPfbZb1q9M0sf3jBQNlvtNpdGcGA82D/jwUcrzj2knM2/KjZ9oM9ZCC2MB1eM8WAAAAAAAAAcD31qFaNPDabNn/uZLvq/czXwxJP1n2lvq7i4WE8/OVW7d+2q+uTD/nHvPerXf4Cefv4lHTp0SPfffaf+709naenPK+Rw1Gz8yePxyOOp3jyNqrJXrVyh/gNPLPd65y5dSr/erHnzSjMWfD5f77z5hhZ88+1x/7/CwkLZ7XaFhZUdf3a5StYZr1j+S5ljw13l1x8feW3Fr8uVlGzNZo0IDPc/OEnNmzXT7Fnvy+VySZJOHzZUbdO7VDsjvWNHvfHvf5X+2+Fw6M8XXaqly35QvxP6VHJmeQ8+9IgeeGhylceltGqlDauXV3rMvsxMxcfFlXs9Pj7u8Nf3V3p+dna2rr7ub7rlxhvUrevxfx4Z6R3ldru15LulOnFA/9LXv1m0pLSO0mM7punb75bK4/HIfvhBv8XFxfpu6ffVqgnBj3karNsHAAAAAABAzexfvkCrnrlCsWl9lXLN8/K6S/aMLqxBn9qm9w7vGX3pFLnzs7Xp3Ula+c9L1esh//ap5fz+m2I7lO9Ti25Zgz2je1XQp9YyQ54vK+5T87qL5SkuVM6WFdr8wRQ1bH+CGrYv39dfrk/t1y/Vlj41VCGY52UcD/tFw98Ye6r7safCQ3u18e371OrcOxXOXhqopWBuM5nLCAAAAAAAYB77Kofm2rhA7keMCrPXKDvUBPJ7W9tx1WDx8LtfKzk+Ru/edb5czpL726nd26rHDS9UOyOtRWO9eP2Zpf922O3665MfqlWTWOP1ovr2L1+g1c9eoYYd+qrD1YfHXefUctz10mPGXSf6d9wV1cO9ObjUt/cT1Xf0PJi0w/Ngfq/hPJjN7x+eL3p4Hszmw/fjnvV8Hkxl80WrVUOQqG/Xr8n7sTt03sYKBep7G6q2//g/fT3lr2qS3k9db35RHo9bqz98Tvk16Iv65a2HlNDxBJ1wzVQV5WXppzcm6quHL9YZ//xa9hruHVXdvijJVmX2wS2r1SS9X7nXG6VklHx962+KahyafVHHCtTrljluAIDqKH8XB4LIlpmPyRWXpE43vyW7s2TjybjOp+j728sPyh5PVLP26nDV06X/ttnt+u35ccre+JMatOtVyZnlbZ31hLbOmlrlceGNW6j3lG8rPaY4e7+cMY3Kve6MblT69eOem3NQ8npLj63o/KLs/YqsstKyIhJaquvds7T6mcu1bPyA0teTh16uNhf8o4ZpCDW5hW79vD1Hl52QVPqHlyRFhzs0rEOcpv9UvT/Eh6eVfXBvemLJg2y3HSyo8R9fF/ZK1NAO5Td4PZbLWfmDb71eb7kOMafjj3NsOv751XmmbmWH8EheVGbr4XYy45h2clkN2snIY9pJ2e1a40M7ua2a7WSvWraTYUe1c8fjrkY7WVk7W5UN0+7Uri+mSZJsTpfajH1ASYMvrnUeQk8wt5lH+Nq20TYCAAAAAACEhg3vT1F4XJK63/ZHP3d8l1O0+JbyE9WPJ7p5B3Ua90zpv212h3599mod2vCTYlNr1s+98cOp2jSz6n7uiIQWGvD4d5UeU3S8fu7Dr1XWz13d8eAjOl/3kta8fpd+nHxe6WsxLTPU8873S/vVj/jttTsVldROzU/9S6X1I/TkFBTr560H9NcT2xzTd+3UsE6J+u93W6uVc1rnpDL/zmjWUJK0dX+eerWuWU0X92+tYRlJVR4X7qzmIvBKOpgrG/P1xXvLtim/yKOxfVPKfc3j9Sq/yKO7R6bphqElE5QHpibI5bBrwsxftXDNXp2c1sSSuhAYGA+umNXjwUfbMO1ueQpz1WLU34zkITQwHmz9+QAAAAAAAAgu9KlZfz5wxKQH7lezZs31/sez5XKVjD8NHX66uqS1rXZGx/R0/evVN0r/7XA4dOmFf9YP3y9Vn77lN8KqzCOTHtTkSQ9UeVyrVilavmZDpcdk7tunuLj4cq8feW1/5r5Kz8/Oztbfrr1aN/z9FnXp2u24x3VMz5Db7dbSb5eo/8ATS19fsuibkjqO+n/S0jO09Ltv5fF4ZLeX3N+Ki4v1/dLvqlUTgltOTo6+/+FHXTfuqtLrUZJiYmI0asQIvTbtzWrlnDmy7INtu3buLEnavGWL+p1Q/sHulbnyr5dq5IjTqjwuPLz8w64rYqtkkX1V6+/vnHC/wsLCNOGuOyo9buz/na8HH35E466/Ua+88JzSOqRq9tz5evr5FyVJdtsfv1tcd83VuvKa63XDTbfqrttvlcfr0QOTJmvzlpI5MXY7rWqoY55GxVi3DwAAAAAAgOPZ/MFjCo9LUqdbjupT63KKlt5Wsz2j064uu2f06ufGKWvjT2pYwz61LbOe0NYPq9en1uexavSp1bJPrLI1wmHH2TP60Ppl+mXi6NJ/x3U9VWnjnq/w4WHr37hTO4/qU2s79gEln0KfGo4vmOdlsF806jPGnipm5djThjfuVGRSOyWdwl4aqJ1gbjOPYC4jAAAAAACAWeyrHHoCvR/x2nfX1ig7lAT6exvKcvIL9dOGHbritF5yOf+YZxET4dJpPdvp7S9/rVbO6b3al/l3RquSPaGLjx2URp3a+uFxxl3vqMG4a3J7dbjymHHXF2o57vpRDcZdH6183BVV494cXOrj+4nq23J4HkynY+7H39dgHkzUMfNgbHa7fvNhHszWat6Pe9dyHkx15rF4PaHxe0J9vH65H5sRiO/tuHfXaMv+ghplBpPl7zyiyPhkDZ7wXznCSu7Hyd1P1axrelc7I7ZFmgbc+Fzpv212h755/Eplrv9RCR2qnyNJv777uH6d/liVx0U3aanRL3xf6TGF2fsV3qBRudfDD9+jC7Iya1RbsArE6/YI5rgBAKrD6e8CAKu4C3KVvelnJZ96WWnniiQ5IqIV322Ydn8zvVo58d2Hl/l3VIt0SVL+vm017mBJHHSh4roNrfI4e5irymOkKh4CWq2VfJUtBKz5r3L5e7dq1T//orCGTZR27UsKa9BYWRt+1LaP/yl3fo7a//XxGmcidBzIK5bXKyVEh5X7WpOY8q8dT1xU2aYt/PDi1/yimncsNo0Jq7CeY1V1uSzedEjnvbqyzGtL/t5DLeMiFBfp1P68onLnHMgrliQ1iqy8qS45v7jW5yN0HWknkypoJ+O6DdOeWraT0YfbyYJatJNJgy5UfDXaSVs128nKuiWq1c5Vdkwt2skjWoz8mxJPGquirL3K/GmeNky7R+6CPDU/fVytMxFagrnNlHxr22IjHbLZVPH5ubSNAAAAAAAAwcRdkKusTT+rxdCy/dzOiGg17j5MO7+uXj93Qo9j+rlb/jEeXNPFxs0HX6SE7sOqPO7oeitT+cauVXe2VX7MH1/77bXx2rPsM7Uf+w/FpHRR4cHd2jL7ef34yHnqMX6GIhNaSJJ2L/1Ee3+apxMemFur8WQEt4N5RfJ6pSYNyj9os0lM9R6+KUlxUWWvjyMTd/OL3DWuqWmDcCXEVH29VefzHBfl0v6c8uO6R15rFFX9/vmaeHvJFjWOcen0LkkV1iTlaHDHpmVePzW9qSbMlH7ZdkAnpzWxpC7Uf4wH+288+Igt7z+qvUveV5sLJyqmdVef8xA6GA9mPBgAAAAAAAA1Q58afWqoGzk5Ofpx2fe66prr5HL9MZ4TExOjEWeM0ptvvFatnBEjzyzz785dSsZRtmzZrD59+9Wopksvv1KnnTGyyuPCw6s3Zl3pGFMVF+z999ypsLAw3XH3hEqPO//PY/XIQw/qxuvG6bmXXlFqhzTNnzNbLz5Xslmk3f7H5lZXX3Odrh93pW79+w269Y675PF4NHnSA9q6ZXNJSUcdi9Czf/8Beb1eJTYtPy8gMbH6cwUaNy67KVp4eMn1nZeXX+OakpIS1bSCeo5VnfHcxvHx2pdZfgO8zMySTUrj446/+dp3S5fp+Zde1oy3pyk/P1/5+SXfi8fjUXFxsQ4cOKDIyEiFh4crIaGxPp35ni676hoNGDyk5P9uHK/HJk/Slddcr+bNkktz//qXi7V3715NeuQxvfCvVyRJ/fueoFv+foMeffxJNW/WrMrvC8GLeRqs2wcAAAAAAEDNlO4ZPaSCPaO7D9Puaq4RPt6e0QV7t0kW9alVd42wr31iNdlzOrpFurrd+6k8hXnK2bJC2z59Vise+7M63/6uHOGRZY5tMepvSjx5rIoO7VXmz/O0/nCfWosR9KmhYsE8L4P9olFfMfZU92NP+77/RPt/nq9u981hLw3UWjC3mRJzGQEAAAAAAExjX+XQ7IsN9n7EUMZ7G7gO5OSX7KkdG1Xua01io6udEx8TUebf4c6S99Lj9fpWIGrNsnHXlj6Mu55czXHX6s5PQqW4NweX+vh+onpK54tWcD+O7zZMu2t5Pz4yXzS/FvfjxEEXKq4680WrOQ+mJvM9Q1F9vH5N3o8ddskdoreQQHxvw52hu1dPcX6OMtf/rA4j/irHUfe3sMhoNe89XBsXvFOtnOZ9Tivz70YpGZKknD3blNChd41qajfsYjXrVXVflKOu5iWGgEC8bo9gjhsAoDq4UyNoFecckLxehTVMKPe1sIbV33jSGVN2A0d7WMlGrZ7Cmm886YptKlcF9ZRTjd/knDFxKsreX+714pwDJV+PbnT8c6NjJZtNxbU8/3g2z3hI7vxsdf/HPDnCSwaRYtP6KSwmXuv+c7OaDjxXsWn9a5yL0NAo0imbTdpbwYNvd2eXf60uPPHlNk39YluVx7VoFK5vb+p53K93bRajT6/qUua1xAYlf7inJ0Zp5vK9KnZ75XT8ce2v3pUrSUprWn5A9mgdE6NKjz1adc9H6DrSTlbULrl8aCdtPrSTYbFNK2y3y6lmO1lRO1dUjXbOYVE7eUR44+YKb9xckhTXtWRT5y3vPaymA85TWMPGtc5F6AjmNlPyrW2LDHOodXxEheev2p2riDC7UuIiKjgTAAAAAAAAgaaotJ+7fJ+2K7b6/dxhx44HO30bD65OP3d1JmeGHWc8uCj7gKTqjQdXNp4cFlNy/r5fFmjHV2+r8/UvqWmfUaXHxXcepMW39tXGDx5TxpVPqjg/R2veuEsthl4mV6NEFeUclCR5iwtL6so5KLszrHScGKEnNjJMNpu0J6ug3Nd2V/BaXXh87m96fM6aKo9rERep7++tfHJ2enJDrd5xqNzrR17rmNywdkVWYvm2g1r++0GNG9xOYY7yE/ozmjXUss3lr/MjS3PtdiaChzLGgxsd91yrx4MlaeuHU7Xt43+q1Z/uUPKQy3zKQuhhPJjxYAAAAAAAANQMfWr0qaFuHNi/X16vV02aJpb7WpPE8q8dT3zjsuvnXK6S8af8vLwa15SYlKQmTZtWeVx15mnEN26szMx95V7fvz9TkhQXF3/cc5ct/U4vv/i8pv13hvLz85WfXzKW5vF4VFxcrAMHDigyMlLh4eFqnJCg92Z9qmuuuExDTh5Q+n9PeuQxXT/uSiU3a16ae/Glf9XevXv12ORJeuWlFyRJJ/TrrxtuukVPPvaomh11LEJPXFwj2Ww27dq9p9zXdu7a7YeKpAcfekQPPDS5yuNSWrXShtXLKz2mc+cM/bpiZbnXf12xouTrGRnHPXfl6tXyer06588Xlvva1m3b1LhZiqY++rBuvP5aSVKf3r306w/fadPmzcrJyVX71HZa9uNPkqSTThxY5vzbb7lJN15/rdauW68GDWKU0qqVxl1/o6Kjo9WrR/dKvycEN+ZpNDruuazbBwAAAAAAQEVM9amVWyN8pE+tyP97Rte2T6x0jXDO8fvkwo453xEepQZtukkq2Qe6Qdse+nnimdr5xRtqftpVZY6NaNxcEYf71OK7lfSpbX7vYSUOpE8NFQvmeRnsF436irGnRsc914qxJ3d+jjZMu1vJQy6TKy5RxblH9tIouccV5x6UzcFeGqhaMLeZEnMZAQAAAAAATGNf5UY1LS8oBHo/YlRY+b1xUSLQ39uq+oiDWaPoiJI9tQ+W78PefSDHDxXBFMvGXX1oa02Ou6Jq3JuDS318P1E9R+7HFd3/wny4H9t9mAdjer5oZc+EqexvH5s9NH6/ro/Xr8n7cbjDrlyPx1RpASWQ39tQVJhzUPJ6FVFBv1NEbDXuiYe5Ghx7Py6Zd+suqPn9OLJR02r+31Xfj10xcSrIKn8/LjjcF+U6ph0JVYF83TLHDQBQHU5/FwBYxRndqGTR6aG95b5WdMg/G09unfWEts6aWuVx4Y1bqPeUbys9JrpFR+X+vrrc67nbSl6Lap523HMdrkhFNG2tnG3lz8/5fZXsrghFNEmpss5y525Zochm7cstJoo5vJA3d9tvik3rX+NchIYol0PdmkXrs9WZmjA8RS5nSUdgToFb89eU/+O1LlzYK1FDO1T9x7HLWfkf4THhDnVrHlPh105Pj9eby3brk1X7dFbnP/7gf/enPUpqEKaeLSo+r/T8jvG665ON+mFblnq2aCBJKnZ79f4ve9SjRYySGrqqrB+h6Ug7WVhBO1nox3ZyWzXbyV5VtJNRBtrJ3ArayVwf2snjiWnTQ7u+eEP5ezazcQWqJZjbTMn3tm1Ex3i9vGSHfj9YoOaxJYOj2QVuzV61T8PT4spsggEAAAAAAIDAFVbaz13+QYSFB8u/Vhc2fjhVm2ZW3c8dkdBCAx7/rtJjolt0VPa2VeVeP/JaTIuOxz3X4YpUZNM2yt5avp87e9vqMv3cWVt+lSQ1aNO9zHFh0bGKbNpaOb//JkkqyspU4cE92vrZi9r62Yvlchdem66Enqep643/qfT7QvCKDneqW8tG+mz5Tt03utNRfdfFmr9yl19qurh/aw3LSKryuHBn1YtURnRN0vgZy/XD5v3qmVLSH17s9mjGsm3qmRKnpFjzk2Xf+nazJGlsv1YVfn1k12S9sXiz/rdql7q0iC19/fPDP+9eKUwED2WMB/tvPHjrh1O19cPH1fKsW9Ri1N9qnYPQxXgw48EAAAAAAACoGfrU6FND3WgUFyebzaY9u8uP/+7eudMPFUmPTHpQkyc9UOVxrVqlaPmaDZUek9Gps1b++mu511ccfi2jU+fjnrt61Up5vV5deP455b62bdtWpSQ11sNTpuraG26UJPXq3Uff/fSrNm/apNzcHLVLba+fflgmSRp44kllzr/p1tt17Q03av26tYqJaaBWKSm68bpxio6OVveevSr/xhHUoqOj1btnD3340Sea8vBEuVwl7UF2drY+mf2ZX2q68q+XauSI06o8Ljw8vMpjxpw5Stf//RZ9+9336ntCb0lScXGx3nxnuvr26a1mzZKPe+7pw4bq888+Lvf62Ev+qjatUzTpgfuU2rZtua+3TikZJ/Z6vZr6z6fVLDlZ5/1pTIX1d+6UIUnasnWrpr/3ga647C+KjIys8vtC8GKeBuv2AQAAAAAAUDOV9qkd9E+f2pZZT2jrh9XrU+vzWNV7Rle457OvfWrbqtenFtOmm2SzK29X5WNkktSgTQ/tXECfGo4vmOdlsF806ivGnup27KkoO1NFh/Zo+5wXtX1O+b00vrs+Q/E9TlPHG/5do1yEnmBuMyXmMgIAAAAAAJjGvsrH31c5mAV6P+K1766tg4oCU6C/t6EsOsKl7m2T9enSNXrgolPkcjokSdn5hZr743o/Vwdf1Mf5SVs/qsG466OVj7uiatybg0t9fD9RPUfux0UV3I+L/DgPZms178e9q5gHE+3DPBjZqn4+RjCoj9evyftxTLhDuUUeE2UFnEB8b8e9u0Zb9hfUYUX1hys6VrLZlF9Bv1P+Af/0Rf367uP6dfpjVR4X3aSlRr/wfaXHNEpJ18Et5fuiDm4ueS22VWj2RR0rEK/bI5jjBgCoDqe/CwCs4giPUkzrbtr342dq/X8TZHeW/GLjzs9R5s/z/VJT4qALFddtaJXH2cOqXkQX3/N0bXjjLmWt/0EN2vWUJHndxdqz5H3FtO2h8LjKH0TauOcIbZ/3sgoyf1d4fHNJUnFetjKXzVZ89+GyOWp+e3A1SlTu77/JnZ8jR0R06etZ60s2jnXFH38zTECSbj2lpS55c7XGvrFKV/RLltvr1fPfbFe0y6EDecV1Xk9SQ5fli1pPbR+nk9vF6q6PNyg7363WjSP04fK9WrDugJ4+J1UO+x9/VN0yc53e/XmPFt3YUy0alfwB9ueeTfXa0p26evoa3TW0lRpHh+n1pbu0fm++3vlLuqW1I7AdaSczK2gn9/upnUwadKHiq9FO2qrRTjauop10VdFOxvccoR3HtJPuvGztWzZbcbVsJ4/n0OpvJJvd6AazCH7B3GbWpG07/9UVWrL5kLbc17/0tXEDm+m9X/boL2+u1q2ntJTLadOzC39XQbFXtwxuaaRGAAAAAAAA+J8jPEoNWnfTnmWfKfXP95b2cxfn52jfT/P8UlPzwRcpofuwKo87UmtlmvQaoTWv36mD639Q7OF+bo+7WLsWva+G7XpWOR7cpNfp2jr3ZeXv+10Rjf8YD97z/adK6DFc9sP93OGNSnIOrV+myIQWpecXZWcqd+cGxWWcKElyxTZRj/Ezyv0/mz95RgdWL1G3W6YprEF8ld8Xgtvtp3fURf9aoj+/uFhXntxWbo9Xzy1YryiXQ/tz676epNgIJcVGGMm6oG8r/efrTbry1e9196h0JcSE69VvNmr97mxNv6Z/mWPPfW6RFq/fp98fP7P0tdzCYn2+smQx0LLNJZOPF6/bp8zsQkWFOzQkPbFMRn6RWx/88Lv6tI5Th8QGFdY0uGNTDe+UqKlz18jjlXqlxOnnrf/P3n0GRlF1ARh+k2x67yE9IYQQCF16L4KINEXABooN24eIolKUoqIUEQsWUFFBKdIUUEGagCCdFDrppPeeTbLfjw1JNh3Sk/P8IrN3Zu/ssHOTc869k8yKv64w3Neenp6yYHpLJvnghskHh+/6mPCdK3Ae/T9cxs66q2MIAZIPvk3ywUIIIYQQQgghhBCiuiSmpiYxNVGXjI2N6dKtO7t37WTJ0mXo6an/j6enp/PH3t0N0qdp059hxKj7q2ynr69fZZvRY8fx2isvcfq/k3Tv0ROAvLw8Nv+8ge49etLK0bHCfYfdO5Lf//y7zPannngEN3cP3ln0Hp6tvcq87ubuDoBKpeLTT1bSytGRcQ9OLLf/vu07ABAeFsb2rZuZ+tTTGBoaVnleonl7d/5cHpgwkfvGTODlF54nvyCfFR+vxsTYhMTE+l8YzdGxFY6OtbOexFNTH2fN12uZ9NhU3l/8Lna2Nqz5ei1Xrl7jr907NdoOH/UAh/85Rm5aIgAODvY4ONiXOaaBgQFWVlYMGtBfY/u8dxbRoYMvrRwcCAuP4PsffuLkqdP8tm2zxvcsIDCIbTt20a1rF/T19bjoH8CHKz6mTWtPFi2YWyvnLZouqdOQeftCCCGEEEIIIYQQ4s4UxdTO/oFHI1kzuroxterMEbbuOpIb5cXU/t2G6Z2sGZ0QiX6JOcIJ1VwzOuXyv6AqwMDOvcq+JktMTVRDc67LqIisFy0akuSe6jf3pGduS/s3tpTZHrnnc1KvnKDdqz+iMJG1NET1NOcxU2oZhRBCCCGEEEIIIWqXrKtceSy4OWvOccSWTq5t0/XWxH5M/nArE9/fzLP3dSO/QMVnv53EWF+XpPSshu6euEsaedeHS+VdLzZQ3nVANfOu1RhrRfXIvbl5aWzXU1TP7ftxQjl1MA1VL2o/8FEsq1MvWo06GKsq6mBa8t8+JTW2729t3o/NDRTEpitr5VhNUVO7tgYK7XrsTeOiMDDGqnUnIv77g85PvItO4T1OmZVB5JmGiUW1Hv44jt2qjkXpVON+7NxjFKe/mUP81TPYeHcD1LGokCNbsW7TFSMruR/f1tS+t3dCatyEEELU3ipWQjRCruNmE7TqCQJXPoLjsKdRFeQT+ccadPSNyctIrvf+6Fs61Frgw77fZKIPrOfymudwf+htdE2tiTr4A1nRN2g/+xeNtgHLHiblygn6rg0r2uY04nlij/9K0KqpuI6bjbZCj4g9n1OgzMFl7Gsa+2dGXiUz6ioAuSlxFORmEX/6dwCMWnlj5OQNQKvhz3D5s6cIXDEZx+HPoDC1Iu3GWSJ2f4ahozeWfoNr5dxF8zW4jSXfTGrL8oPhzNhyFVsTXabe40BMWi6/Xoxr6O7VmbWT2vLh32EsPxhOclYerW0M+eKhNoz1s9Fol6+C/AL1gsy36Su02TTVlyV/hTFvTwjZynx8HYz58TEferub1/epiCbGZdxsLq16gqCVj9Bq2NPQwOOknqVDlRNlq8uu32SiDqznyprncCscJ6MP/kB29A18S42TgYXjZJ9S42Tc8V+5tGoqLoXjZOSez1FVY5zMr2CcvPH9G+gYmmDi0Rldc1vy0hKJP/07Cf/twnHkDHTN5EHaovqa85h5J2NbQeHYWJK1sS7bnurA4j9DmLn9OnkFKrq5mLJ1mi9etvJwAiGEEEIIIYQQQojmxHPC61xY+Tjnlz2Cy73TURUUELZ3DToGxiibeD7YccBkIv/+noDPnqX1w2+jZ2ZD5N/ryYy+Qec3Nmm0PffhwyRf/pfB34UXbXO9bwbRx3/lwson8JwwG22FPqG7P6NAmYPHuOI4t233UQRvX8aV9W+RkxiFqbsfOckxhO1ZQ0FuFi73Pg2Ajp4Blu36lOln1NHNaGlrl/uaaHmGtLNj3ZP38NHeyzy3/gy2ZvpM6+tOTEo2W89ENHT3akRfocPWF3qzaFcQc7f5k6XMp72jORuf7UUfr1J53QIV+QUqjW3x6bk8s/60xrblf14BwNnSkNMLNIvD91yMIjlTySO9Kl/4/KsnurPizyv89G8oK/68gr25Ac8ObM1rI7zv9lRFMyL5YLX6ygdH/vEl4TuWY9FhMJadhpJ244zGcUxbd6uVcxctg+SD1SQfLIQQQgghhBBCCCGqS2JqahJTE3Vt7oJ3mTjuASaMvo/nX3qZ/Px8Vn+8AhNjE5ISE+u9P60cHWnl6Fgrx3p86lOs/XINUx+ZxLtL3sfG1o61X6/h2tUr7Nzzl0bbB0YO59g/h0nMyAXA3sEBe4eyeTADfQOsrKzoP3CQxvZFC+bh26EDDg6tiAgP46f133P61Ek2b/8NQ8Pi72VQYAC7tm+jS7du6OnpE+B/kY+Xf4inVxvmvrOoVs5bNG0j7x3Glo0/8u6S95nyxJM42Nsz49np3IqK5qeff6n6AI2Yvr4++3bvYs68BfzvtdfJzMyic0c/du/YysD+/TTa5ucXkJ+ff9fvlZSczFvz3iU6JgYzM1MG9OvLv4f/xq9De412enp6HDx8hE/XfEl6egauLs48N/0p5sx+FWNj47t+f9F8SJ2GmszbF0IIIYQQQgghhBDV5TZ+NoEfP0HgikdwHF64ZvTeZrJmdH91TO3yF+qYmp6ZNVEH1GtGdygVU/P/SB1T67euOKbmPPJ54v79lcBVU3EbPxutEmtGu5aYI5x4fh/Rhzdi1eVeDKydKMjPIz3kArf+WouBnTsOAx4panvt+zdQGJhg4tkZPTNblOmJxJ/6nfj/duF0n8TUROWac11GZWS9aNGQJPekVh+5J21dA8x9yq6XEXtsM2hrl/uaEBVpzmOm1DIKIYQQQgghhBBC1D5ZV7llas5xxJZOrm3TNbSzJ9/PGsfSLUd5+pNd2FkY89TwLkQnpbP5n8CG7p6oAZexs7n0Sam865/NI+8qqkfuzc2LXM+my3XcbIJWPUHgykdwHFZYL9qAdTC1Wi/abzLRB9Zzec1zuBfWwUQdVNeLti9VBxNQWAfTt0QdTEvRnL+/WloN3YOG1ZyvbXPkN3kOR95/lEOLJ9H2/mdQFRRwaefn6BoYk5ueVO/9MbJywMiqdu7HnkOncO2Pbzm24hk6PTYXAzMbrv35Pam3bjDknS218h7NRXP+3kqNmxBCCEVDd0CIumTpNxifF78hbMdyrnw5Az1zWxwGTyU3OYa4f39t6O7ViLauPu1nbyJkyxJubphHQW42xq6++M78EfO2vTXaqgoKoEBz4UldM2v83tpGyKbFXFs3E1V+Hqatu9FhzlaMWnlptI0/9Rvhu1ZqbLvyxXMAuIyZhauTemKSdZd7aT97E5F7Pufmz++Qn5WKnqUjDoMew/n+l9BW6NX2xyCaoZHtrBjZzqroZ2V+AfeuuUhHRxONdidf7arxcx8PcyIXav7fB3CxNCizfdV4rzLtGpKxvg6LRnmwaJRHpe1Wjfcqt++2Jnp8MqFxnZNoGiz9BtP2xW8I37Gcq1/OQLdwnFQ2o3EydMsSggvHSSNXX9rdxTh5vcQ42b6CcTKi1Dh5tXCcdC4xTpq07kbcsU3EHttCflYq2vrGGLv40uaZ1dj2frC2PwLRAjTnMbO6Y9vWJ9uXu93dyoB1U3xqu1tCCCGEEEIIIYQQopGx7jgYv5e/4ea25QR8oc4HOw+dSk5SDNHHm36cu8uczVzftISrP86nIDcLE9f2dHrtJyx9Sse581GVinPrmVnT9e3tXP9lEUHfqOPc5l7d6PrWVowd2xS1UxgY023+74T+9gmRB38kJzEKhbEFpm4daDt1KeZe3erlfEXzcZ9fK+7za1X0szK/gKHLDtPJ2UKj3ekFwzV+7utlQ/THY8ocz9XKqMz21Y90qb0O3wFbUwM+fbRrle22v9S3zLbyzqMyE7o5M6Gbc5XtDPV0mPeAL/Me8K32sUXLIflgtfrKBydd2A9AcsBBkgMOlulzn28ja3bSosWRfLDkg4UQQgghhBBCCCHEnZGYmsTURN0bdu9IfvxlC+8vepcnH5uCvb0D05+bQXTULX7Z+FNDd69G9PX12bV3HwvensPrs/5HVmYmfp06s3XnbvoNGKjRtiA/n/z8/AqOVLXk5CTenfsWMTHRmJqZ0bf/AP7+51/ad/DTaKenp8eRQwf58otPyUhPx9nFlaeefo5XX5+DsbHxXb+/aF7GjRnNuDGji35WKpV07dWPbl01aytuXvbX+HnQgP7kZ6aUOZ67m1uZ7d99vaYWe1x99vZ2fP/Nl1W2O/Dn7modr/RncNvnn6wsd3tp3m28OPjXnmq1FS2T1Gmoybx9IYQQQgghhBBCCFFdln6DaffSN4RuX87lNeo5wq0GTyWnmcTUOry+iZDNmmtGt3/1R8zLzBGuOKYWvGkxV9cWxtS8uuFXKqZmYO+BlkKX8F2rUKbGA6Bv44z9gCk4j3oRhZFZUVuz1t2IObqJmFIxNe9nVmPXR2JqomrNuS6jIrJetGhIkntSq6/ckxC1qTmPmVLLKIQQQgghhBBCCFG7ZF3llqs5xxFbOrm2Tdf993hz/z3eRT8r8/IZ+OZ3dPZ00Gh3/rMZGj/3a+9Kwi9zyhzP1c6chF/m0Gf2WqKT0uum06JKln6DafvCN4TvXM7Vr2aga9Z88q6i+uTe3LzI9WyaLP0G4/PiN4TtWM6VL9V/+zgMnkpuM7gf366DCdmiWS/qW806mJZEvr/Nl1zbpsOxyxD6zV7HxV8+4tjK5zCwsKPNyGlkJUYTcmRrQ3evRnR09Rn87lbO/7iYM2vnkp+bhYV7ewbN3Yhd+z4N3b1Gpzl/b6XGTQghWjYtlUqlauhOiNrRvn17goKC7np/Q0dvui4p+wC55qYgT8n5d+9F39KB9q/93NDdEc3A2XmDybp1tV7ey9vWkIMvda6X92oor+24zoDWFtiZ6hKbruTHUzGcCE1l4+PtGNDaoqG7J+rB4M/OczUu6673N3T0pksLGM/qSkGekgvv3ouejJOikTtXB+NvUxtnZcxs/mo6Jvr6+hIYGFiLPRJCCCGEEEIIIURjU9McsbGjNz0/OFR7HWoECvKUnJo/HD1LB7q88UtDd0c0YiffGkRGDeLM3vamHHlzcC32qHl49ZfzDPS2xc5Mn9i0HH44HsK/NxLY9FxvBrS1bejuiXo2YOlBrsak3fX+kvutfZIPFg2ppjneppbPrSnJB4uqSD5ZCCGEEEIIIYRo/mqaD5aYmsTUhKaaxtTuhE87X06e86+X92poSqWSfj260srRkR27/2zo7ohmoGcXPy5fuvvxrzy+7XzwP3OyVo/ZWDz9/IsMGzqYVg4ORMfE8PXa7zj8z1H++G07w4ZIXUtL49etJ0GXLt/1/lKnUfukTkM0VTWt8ZB8tBBCCCGEEEIIoammuV8jR2+6vtcyYncFeUrOv6OOqXWYLTE1UXNn5w4msx7WjG4JNRpSlyFA1oxuaJJ7Ek2BrBctY2ZLIXN8hRBCCCGEEEKIislayTXT0tZVrvHawE0sfnhbU40j1jQu1lSv151oqte2PDW93m2drTm+/Ola7FHdeuXLPQzq6I69hQmxyRl8v/88xy6FsfXtSQzyc7/r4/aZvZYrEQl3vb+hozddFkuOtTYV5Cm5sPBe9Cyabt713HxZ3/dONPV7s4y/mhrqetZGzUxXqZnRUJCn5Py796Lfgutgzraw9dqb+v24InKfblrXtqbXy8y5Lfd/cqQWe9TwCvKU7H1tKEbWDgxesLmhu1Pndv9vAKkRV+56/+bwnYWm9b0VtUdq3oQQLYGioTsgRF279u1rWLQfgJ6FHbkpsUQf+pGsqGt4TlnY0F0TQpQjPbeARX+FkpihRKGjhV8rY3581Ef+8BKijlwvHCd1LexQlhgnPWScFKLRkzFTCCGEEEIIIYQQQgi4tG4WVu0HoGdhT25KLJEHfiQj6hptHlvU0F0TokVKz85j4a5AEtJzUeho0dHZnA3P9GJAW9uG7poQLZLkg4VouiQfLIQQQgghhBBCCCHEnZGYmhD148Xnnmbw0GE4OLQiJiaa7775miuXL7F0+ccN3TUhWqS09HTeeGs+cfHx6Orq0rVzJ37fvpVhQwY3dNeEaJGkTkMIIYQQQgghhBBCiDtTtGa0efGa0ZlR1/B8RGJqQjQ2UpchRP2T3JMQTZOMmUIIIYQQQgghhBDiTsi6yi2TxBGbL7m2TVd6di4LfjpIQmoWujradPSwZ9OciQzyc2/orokauv7da1j4St61JZN7c/Mi17PpKqoXtSiuF82Kuoan3I9bDPn+Nl9ybZuWk5/PxKHTQAwt7clKjuX6n+tJjbxKt6cWN3TXRD2S760QQojmStHQHRCiruVnpxOyeRHKtES0dBSYuPnhO/NHLNoPaOiuCSHK8dXD3g3dBSFalPLGyXYyTgrRJMiYKYQQQgghhBBCCCEE5GdlcP2XxeSmJaBdGOfuNOsnrCTOLUSD+GZa94bughCiBMkHC9F0ST5YCCGEEEIIIYQQQog7IzE1IepHenoa8996g/i4OHR1denUuStbd/7O4KHDGrprQrRIm35a39BdEEKUIHUaQgghhBBCCCGEEELcmfzsdII3acbU2r8qMTUhGiOpyxCi/knuSYimScZMIYQQQgghhBBCCHEnZF3llkniiM2XXNum69uZ4xq6C6KO5GenE7KlRN7V1Y92//sRC18Za1sKuTc3L3I9m67y6mB8pQ6mRZHvb/Ml17ZpUWZlcG79QnJSE9BWKLD06MjAuRtx6DSwobsm6pF8b4UQQjRXiobugBB1zeeFrxq6C0IIIUSj1VbGSSGEEEIIIYQQQgghhBBNWIeXJM4thBBCVETywUIIIYQQQgghhBBCCCGEEKI2rd+wqaG7IIQQQjRaUqchhBBCCCGEEEIIIcSdkTWjhRBCiIpJ7kkIIYQQQgghhBBCCCGaP1lXWQghhKhbbWfIWCuEEI2B1IsKIUTj0G/2Nw3dBSGEEEKIOqNo6A4IIYQQdWnFwXBWHoogcmHvhu7KXQmIyuDDv8O4HJtJYoYSA11tPK0NmdbDgQc72Va4n0ql4sHvAjkZmsa0Hva8d7+nxuuxabl8ciSCA9eSiU3LxdpYl/6e5swa5IKThX5RuyM3kvni6C2uxmWSlJmHqYEObe2MeL6PI0O9LevsvIUQQjSspj5+pufks+pwBIHRGQREZZCYmcesQc68Ntil3Pb+t9JZsi+MsxFpKLS16Othzvx73XCzMtBoV93xUwghhBBCCCGEEEIIIUT9WPbHZVb8eZXoj8c0dFfuyrHr8Tz4+fFyX9v9v350c7cq+vnkzQQ2/RdOQGQKl6PSyM0v4L/5w3C1Mip3//DETFb8eYWDl2NJylBiZaJHF1cLvnuqh0a7uLQcFv8WxP6gGLJy8/F1NOPNUT709644Hy2EEKLpaOq53zupnVKpVHx7Mpr1p6IJT8rBwlDBCB8r3hzmioVhcbn0jfgsNpyJ4XhwKqFJ2WhrgZeNEc/0bsXo9tZl+nDoejIfH4ogICodXR1terqZ8fZwV9ralT8GCyGEEEIIIYQQQoimranH1I7eTGHbxThOh6dxKyUXcwMdOjqa8OogZzo6mmi0rW5MDSAmLZcP9odx4FoS6Tn5eFobMr2nA1O62Wu0k/mIQgjR8ixc8gGL3l9KfmZKQ3flrpy/cJF57y4mIDCIuPh4DA0NaNumDTOee4bHpkzSaKtSqfhszVes+XotwSGhWFlaMvaB+3lv4QIsLTXHuaioaN5+ZyF7//yL1NQ0vNt48fILzzN92hP1eXpCCCGEEEIIIYQQQgghhBCNVlOv0WjoNaNvpeTw5fFbBEZlEBSTSWp2PivHtWZSF7s6O2chhBANq6mPnXW1XjRARHIOKw+Fc+h6MkmZeVgZKejsZMK6KT51fVpCCCGEEEIIIYQQQjQpTT3O2NLI9RJCiMZD7skti1xvIZov+X43LXK9hGgemtt3eeOZGF7fdRMjPW2uze2p8drM7dfZcj6uzD6tbQw48nKX+uqiEEK0WIqqmwgh6lPYjhWE71pJ328jG7ordy3t5jnCti8j9fppQIWJRyfcxs/BrM09Dd01IZqc1Ow8HM31Gedng4OZHpm5BWy/GMcr264TnpzDzIHO5e73/X/RhCRml/taTl4BE74LJCUrj9cGu+Bta8iN+GxWHArn0I0UDr/UGRN9HQCSsvLwtjNkSjc77Ex0Sc7K48dTMTyx4TKrJ3hVOilYiPKE7VhBxK6V9Gmi41x+Vjrhv60iIzyQjNAA8tITcR4zC9dxr5VpG7VvHXEnt5MdG0J+dga6ZjaYenXH5YGZGDm1LT5mTibXv3uNjNAAcpNjQFWAvo0LNj3H4njvs+joFz/0MznwCJF7vyAz8ip5GUnoGJpi5NQWp5HPY9lxaL18BkI0BUmZSjacicHX3oiRPlZsPBtbYdvrcVk89H0Q7R2M+HKiNzl5BSw/GM6EbwP4a0YnrI11gTsbP4UQQgghhBBCCCGEALi5fTkhO1YyZP2thu7KXUkMOkrM8V9JuX6a7IRbKIzMMfPoiPvYWZh5dCzTPi3kItc3LSH1xlm0dBRYtuuL1+QFGNq5NUDvhWha3r6/HX29rDW2+bQy0/j5n6vxHLkah5+zOSYGCo5fT6jweJeiUnnw8+O4Whvxzpj2tLIwJCY1m0OXNePlOXn5TFxznNQsJYvHd8DGRI/vjoYw5asTbJ7Rmz5eNrV3kqLFaCk5YVVBPlH71pIceITMyMvkZSSjb+2MVecRON3/Igoj8zLHjtr/LdEHvic7Phw9C3vs+j6M0/0vo63Q1WiXcukYEbs/JSM8iILcLAxs3bDvPwWHodPQ0pZ8lGhZ7qR2atGfoaw9EcXzfRzp39qcq7FZLD8YzoVb6ex6ugO6OtoAHLmRzN9Xk3mwkw2dnUzIK1CxKyCB5zZfZfZgZ14dVLzY85+XE5n+yxVGtLXk60ltScvJZ+UhdT5597MdcS9noWchhBBCCCGEEEIIIRrSD6eiScrKY3rPVnjbGZKQkcdXx2/xwDcBbHi8Hf08i+PX1Y2ppWbnMX5dALn5KuYOd8PeVI8d/vHM3nWT1Jx8nuvjWHRMmY8o6sMHixey9L1FpGTnN3RX7kpaWhoffbAE/wvnuXjhPAnx8bw5dwFvzX+nTFtzg4pzQ22823L6YlC5r12+FET/nt3Izc3l4LGTdO3Wvdb6L0Rzk5ySgouzE5Mffggnx1ZkZGSycdNmpk5/ltDQMOa++XpR29ffmscnn33BazNfZujgQVy6fIV3Fr/P6TNnOXZoP7q66txvSkoKA4aNIDdXydIli2jlYM8vW37l2RdeJjU1lVdfeamBzlY0N1KjUX6NRm5yDKG/fkDSxQPkZ6Vj6OBJq2HTsR8wRaOdzNsXQgghhBBCCCGEEBUJ3bGC8J0r6fdd04y95d2OvYUFkl4Ye3MZOwu3cmJvt/atJSlAM/Zm3WUEzhXMjxJCFGvoNaNDErPZfjGe9g7GDGljwQ7/iucdC1EdknuS3JMQda0u1osGuByTyUPfB+Jmqc/8e91oZaZPTHouh68n18NZCSGEEEIIIYQQQjRPTX1NZSGEEKIxCdtZmItd1zRzsUII0dyF7VhB+K6V9G2iNTNCCNFc+G9aRsDm5Uz5NaahuyJEhaJSc1j8VygOprqk5pS//pmBrjabp/qW2SaEEKLuKRq6A0KI5iUt+Dz+Sx/E1LMz3s+sBpWKyD++IGDZJDq8sRkzL1nkVYg70cfDnD4emhP3hre1JCzZnw1nYsqdkBuelM0H+8P4ZIIXT/9ytczrJ0NTCU7IZvkYT6Z0sy96H1MDHV7ceo1/biZzXzv1A4LHdrBhbAfNB/MO87ak16pzbDgTI4umixZHmZFEzOENGLv4YtV1JLFHNlba1tJvCEYuviiMzcmODSNyz2dcXDKaTgv2YtjKCwBVvhJUKhxHPIuBjStoaZF69SQRu1aRevlf2r++qeiYeRlJGDl6Yz9gCrrmduSlJxNz6EcurXqCNs+sxrb3g3X+GQjRFDhb6BP05j1oaWmRmKGsdFLusoPh6Olosf4RH0wN1H8id3Q0od/qc3x57BZz73UD7mz8FEIIIYQQQgghhBCiOYg88APK9CSchz+NsZM3yrQEwvZ+xZnFo+k0eyNWvv2K2mbcusbZDx7CxLU97V/8igJlDsHblnHmvfH0WLwPPTOJnQlRGQ8bY7q5W1XaZta93swe2RaALw5e5/j18hdTVqlUvLzhLI4WBux8uS/6iuKH+o7r4qTRduOJMC5HpfH7//rRvfD9+3rZMGTZYRb/FsTeVwfU5LSEaJKqmxMuyM0mfOdKbHqOxW7AFHRNrMgI9Sfi99UkXthHxwV70NEzLGof8dsnhO1YhtOoF7FoP5D04AuEbf+I3KRoWk/7qKhdcuARglY+ilnbnnhNW4a2niGJ5/cR/PMCsuNC8XhkUZ1/BkI0JtWtnYpKzWHdySim9XAoyvEOaG2BjYkuL269xuZzcTzaXZ3nHdvBhmk9HNDS0io65pA2liRmKvn86C1e6OeEvkI9meW9faH42BmxdnLbovbdXUzpv/ocyw+E89lDber8MxBCCCGEEEIIIYQQ4k68f78nNia6GtsGe1nQd/U5Pv0nkn6e6njbncTUfjgVQ2hSDnuf86OjowkAg7wsiE3LZfnBcCZ3scPcUD0fQ+YjClG1xIQE1q/7hg5+nbj/gbH88N26CtvuP3yszLbTp/7jzdmvMnrMuHL3yc/P58XnnsbaxoaoW7JIvhBVGTSgP4MG9NfYNnrUSEJCQvnm2++Z++brAERG3mL152t44blnWLpEnbcdPnQItra2PDZtOt//uIFnnpoGwJffrONmcAj/HT1Et65dABgxfBhR0dG8s/h9nnziMSwsLOrtHIVorOqiRiMvMxX/D8ajysvFfeJcdC3siT+5gxvfzyY/KxXHEc8VHVfm7QshhBBCCCGEEEKI5iovI4noQxswdvXFuutIYiqJvYXtWIltz7E4DJiCwlQdewv/bTWJ5/fR6R3N+VFCCE0NvWZ0Lzcz/OfcA8CFyHR2+Jc/71iIlkJyT0I0fnWxXrRKpeKVbddwNNNj21MdiuYHA2VqGYUQQgghhBBCCCGEEEIIIYQQQgghhBBCiLv15m/B9HQzw8JQwe6g8mu3tbWgm4tpPfdMCCEEgKKhOyCEaF7Cti9DYWSG76sb0NFXTzQy9+3PmTm9Cdm8mI5v72zgHoq7lZCh5MO/wzhwLZmEDCUm+jp4Whvy2mBnBrS2AODIjWS+OxnNxagMkjKVtDLTo6+HOW8OdcXKuHjh7xUHw1l5KIJ9Mzqy6nAER26koKMNEzvbMW+4GyFJ2byzN5hTYWlYGuky9R57XuhX/DDZ48EpTPw+iNUTvPCPymCHfzxp2Xl0djJh4X0edGhlXOX57AyIZ+2/UVyKyURLC+5xMeXt4W4a+4YmZrP07zBOhqaSlJmHmYGCtnaGLBjhXq33qEtWRgoSMpTlvvbGbzcZ0NqiaFJtabo66gcK3p54dJuZgfrhvSUnGZW/vzbmBjroaGtV2k6I5kjf2pkenwWhpaWFMi2xwgm5AK7jZmv8bN62N6atu3J+3iDiTmzHdbx64WaFkTltZ3yp0dai/QAK8nK5tfcLsmNDMbBTTwq06TEWmx5jNdpadhrG2Tm9iDm8QSbkNkIyfjbM+FnyYbuVyctXsf9qEg91stUYF50t9Onjbsbey4lFk3JrOn4KIYQQQgghhBBCCNHUtH3iffTMNBeks/IbzIk3+hD622qsfPsVbQ/ethxtXT06zfoBhaG6ENDUvSMn3uhL2N41eE2aV699F3cuPj2Hpbsvc+ByDPFpuZgYKGhta8zrI30Y0Fb9IOXDV2L59mgIF8OTSczIpZWFAf3b2PLmKB+sTfSLjrXsj8us+PMqB14fyMq/rnL4Shw62lpMuseF+Q/4EpKQyfzt/vwXnIilkR7T+rrz0tA2Rfsfux7Pg58f57NHu+AfkcK2s5GkZSvp7GrJ4nEd8HM2L9P/0naci+SbwzcJikpFC+jhYcXc0b4a+4bGZ/D+nkucuJFAUoYSM0MFPq3MeHdsezo4Vf0e9U27mvnZf28kEBCZyidTOqOv0Km07V7/KLzsTOjublW0TaGjzUPdnXl/9yWikrNoZSGLrIuWpbo5YW09A7p+9C+6JsXfH3OfPuhZO3H1i+dIPLOnKH+rTE8k4vfV2A94BLcH3ypqq8pXErb9I1oNfxojJ28AYo9tQUtHl3b/+wEdfSNAnT/Ojr5B7LHNeDyyqC5PX1RCcr+Nu3bqbEQ6+QUwpI2FRrth3pYA7LmUwKPd1Q8uKHktSuriZMLfV5NJzsrD3lSPxEwlN+KzebGfo0YO2tlCn7Z2RvxxOZH8ApXUUAkhhBBCCCGEEEJUQGJqDRNTszEpG/8y1tehja0ht1JyirbdSUztVFgatia6dHQ0KdP28I0UDl1PZqxfxQ87k/mIQmhydXMjNDoBLS0tEuLj+eG7dRW2vadnrzLbvlv7NVpaWjz+5FPl7vP56lXcioxg5mtvMOe1mbXVbVEP4uLimffuIv74az+xcXGYmZni7eXFO/PeYtiQwQDs+/sAn3/5DWfPnSc+IQFnJ0eGDBrIkncXYGNTPK984ZIPWPT+Us6dPMaSpR+x7++D6OhoM/WxR/nwvUXcuBnMzNlzOH7iJNZWVsx4djqvz5pZtP+hI/8wdORo1q/7mnPnL/Dzpi2kpKbSo3s3Vn70AV06d6ryfDZt/ZXVn63hYkAgWlpa9Ondkw8Wvaux783gYOa+s4h/jh4nITERCwtzOvi2Y9kH79G5U8da+2zvhrW1NbFxcUU/nzh1ivz8fO4bca9Gu9H3jQBg245dPPPUNACO/XsSezs7unXtUqrtSPbtP8Af+/YzeeJDdXsCQjQBdVGjEX3wB3LiQum4YC8m7ur7iGWHQSiTYwnbsRy7/pNRGKlrw2TevhBCCCGEEEIIIYRorvStnen1eXHsLaaS2Fv3ZZqxNwufPuhbOXH5i+dIOL0Huz4SJ2sKpEajcc97Kqk214yu7rxjIVoKyT2JOyFjZ/NZL/pEaCqB0Zl8PL61rA0thBBCCCGEEEIIIepVS4wzKvNVtfcB1rOWeL0aQ1y4PsSnZvLeL0fYf/4m8amZmBrq0bqVFXMm9mOQnzsABy8Gs/bPs1wIjiYxLQtHK1MGdHBj7qQBWJsZFR3rwy1H+ejXYxz58EmWbzvOwYvB6GhrM2VgB959dDDBMUm8vf5vTl6JxMrEgKfu7corY3oW7X80MIyxi39mzYujuRgcza/HLhGXklHfH4kQjZ7ck5vvPbk8ze16CyGKNbfv993ezzNy82vvQ61Dcr2a1vUSoiLyXW7Y36V/vRDHidBUDr7YiY8OhNfrewshhKgeRdVNhGialKkJhG77kCT/AyjTEtAxMMHQwRPXsa9h0X4AAMmBR4j6+zvSQy+iTEtC36oV5u364jbhTXRNiyfQhO1YQfiulXReuI/w31aRHHgELW0d7PpOxH3iPLJiQwj++R1Sr51C18QShyFTcb7vhaL9Uy4fJ+CjibR5ZjUZof7EndhBflYaJh6d8ZiyEBO3DlWeT9x/O4nat5aM8EugpYWZ1z24PfS2xr7ZsaGEbltKypWT5GUkoTAyw8ipLe6TFmDiWvV71IbUa6ew6jQUHf3ih3gqDE0wb9uLhDN7yE2OQc/Cvl76ImrXK9uu4R+VwZwhrnjaGJCanY//rQySsvKK2oQkZtPNxZQp3eww01cQnpzD1//eYty3Afz9Qid0dTQnsDy/5SoTOtryWHd7/rmRwhfHbpGXr+KfmylMvcee5/o4ssM/nvf2heFuZcAoX80Jpkv/DsOvlTHLxrQmLSePFQcjeOi7QP58viNuVgYVnsvqIxF8dCCcSZ1t+d9AZ5T5Baw5dovx3waw+xk/vO3UydDHN1wivwDmDnfDyVyfxEwlp8PTSM3Oq/DYACqVivyC6n2uCp3qTRoqKFBRoIKU7Dx+C0zg8PUUlozyKNNu45kYzkemc/DFzhUe6x4XMzo6GrPyUDjOFvq0sTHkZkIWS/erP8/+nmUfSHz7/eMzlPx0OoabCdm8Pdyteicp6oQyNYGwcsY5l3LGuYxS45xrOeNcxK6VdFq4j4gS45xt4TiXXc4451RqnAssHOfSQ/2JLzHOuVdznIv/bye39q0ls8Q451rBOJdazjhnXE/jXHUn+lVE11R9H9PSqfrX8Oq21VboomNkDtqVP4RbNAwZPxt2/KxKSFI22coC2tkblXmtnb0RR26mkK0swEBX+67GTyGEEEIIIYQQQghRO3JTE7i5dSkJ/gfITU1AYWiCkb0nHhNmY1UYE08MOEzE/u9IC/FHmZ6IvmUrLH374fnQHPRMi2NkN7cvJ2THSnos3k/wrlUkBhxGS1uHVv0epvUkde732oYFpBTGxJ2GTMXt/heL9k+6dJxzSx/C99lPSQv1J+bEdvIy0zDz7EybRxdi6uZX5fnEnNxJ+J9rSQ9XLwBp3uYeWj/8tsa+WbGh3Nj6AclXTqJMV8fETZx98Jq8ANNqxN1rg55Z2YfsKgyMMXL0JifxVtG2gvw84i/sw6HvRBSGpkXbDW2csWzXh7gzf+A1aV699FncvZc3nOViRApvjWqHp60xqVlKLkakkJiZW9QmJD6T7m6WPNrTFVNDXcITM/nq0A3GfnqMg28MKhPPfnb9GR7s5swTvd05fDWOzw9cR5mv4p+rcUzt686MQV5sOxvBkt8v4WFrzP0dHTX2/2D3ZfyczVkxqRNp2Xks/+MKEz4/xv7XBuJmU3Ex7Cf7rrJ072Um93Bl5nBvlPkFfHHwOmM/PcreVwfQ1kH9//TRb06SX6Bi/gO+OFkakZiRw6ngJFKyyl8Y+TZ1PLt6E8cVOtVb5PGtXy/y/I9nMNTVobu7Ja/e601Pz/IXYK7KiZsJAJgYKHjk6xMcuxaPjrYWfbxseGeML23si7+nl6PSyn2fdq3MALgSnUYrC8Myr4v6ITnhxp0T1tLW0Vig+TZTD/VDeEuOlcn+hyhQZmPXb5JGW7t+kwjb9iGJ5/7AyMlbfVwdBdoKXbR1NfN2OkZmaOvq38mpiFomud/GXTt1e1EXvVILLCu0tdDSgksxmVW+x7HgVKyNFdgUTiIqOmY547meQossZQEhidm0tpGxUgghhBBCCCGEEKI8ElNrPPMpUrPzCIjKoK9H8byHO4mpKfML0CvnfW/vGxSTydhSqXKZj9g0xMfFseideez/8w/i4mIxNTPDy8ubt+a/w+ChwwA4sH8f33z5OefPnSUhPh5HJ2cGDh7CgoVLsLYpriv4YPFClr63iGOnzvHRB0s4uH8f2jo6PPr4VBa9/yHBN28w57WZnPz3OFZW1kx/bgYzX3u9aP9/Dh9i9IihfP3tei6cP8eWTT+TmpJCt+49+GD5Sjp17lLl+fy6ZRNrPltNoP9FtLS06Nm7D+8u+UBj3+CbN1n0zlyOH/2HxIQEzC0saOfbgfc+XEbHTp1r78OtRE3mKKalpbFj21b69R9I69ZeZV6/cf0a7y96h29/3EhSYmJNuikawBPTn+XchQssfmc+3m28SE5O4dz5CySUuJY3bgbTu+c9TJ/2BObmZoSEhrFq9ecMGDaCC6f+RVdXV+OYkx+fxqOTJ/Hs9CfZf+Agy1Z+glKp5O+Dh5jx7NO8NvNlft60lTfnvUNrT08mjBujsf+8dxbRpXMnvv7iU1JSU1n03gcMGTmaM/8ewdOj7Dz32z74aDnzFy5h2uOP8vac18nNzWXFqtUMHH4fJ44cwLedDwCjx08kPz+fpe8twtXFmfj4BP49eZLklJRKPyuVSkV+fvUWR1QoqresTkFBAQUFBSQlJbN123b+2v83q1cuK3o9N1ddT6Kvr6exn66uLlpaWvgHBBa3Veair182v6unp97X3z+QyRMfqla/RP2QGo3mU6ORdv0Uuma2RQ/Rvs2y0zCSAw+T7H8Im56aD88uSebtCyGEEEIIIYQQQtQuZWoCIds+JOmiZuzNbVxx7C0p8AhR+zXXhrZo1xe3BzVjb6E7VhC+cyVdFu0jbJfm2tAeD6vnB9/c+A5p10+hMLak1ZCpOI8qjr0lXz5OwIcT8S6MvRWtDe3ZGc/qrg19Uh17K7k2tPvEsrG3kF+Xknq1eH6wkVNbPCbX39rQNY29mXiWjb2Jxk1qNBr3vKfb6mrNaNH4SO5Jck+3Se6p8ZKxs/HUN5bnTtaLPhGSBoCJng6P/3SJY8Ep6Ghr0dvdjAX3uuNlK3OAhRBCCCGEEEII0bBkTeWGWVO5PrTEOGNESk4tfoL1qyVer5rGhau38nLDm/H571wMjmHupP60bmVFSmYOF4OjSUrLKmoTEpPMPd5OPD6kE2ZG+oTFpbBm9ylGvbuBox89ha5CM5fz1Cc7mdivPVOHdebQxRA+/e0kyvwCDvuH8NS9XXlxdA9+PRbEwo2H8HCw4IEebTX2X/LLYTp6OLDq2ZG8tu5PohLT6+WzaAmUaZXkYn1L5GIPlJOLHV8qF7uzMBf7bmEuNugIWlolcrFxpXKxg8vJxS6bSJunC3OxJ0vkYiffYS42okQu9sFSudi4UEILa5Fu52LzszNq8VOtf3JPvrN7clO5H1ekOV1vUTVlagKh5dynXcupmUkvdZ92K6dmJnzXSjov3Ef4b5o1o+4T1X//lK6ZcS51nw4orJnJKFkz6tEZj+rWjP63k6hSNaNuFdTMpJRTM1NfNaMNpTl9v2tyP39j102ylNUsrGhAcr2a1vWqqeyUeC5u/ICocwfITolH18gU01ae+E16HYdOAwGIunCYa3vXkXjjIjlpiRhZt8Lerz+dHnkLfbPia+W/aRkBm5czcsUBArd+TPSFQ2hp6+AxaBKdn1hAenQIZ76dR/zl/9A3tcJr5DR8x71UtH9MwDEOvDOBXq98TtLNi4Qe3YYyMw0rry50fXIxVp5Vx6NCj+3gyu9fkxyqjkfZtO1Bp8fmaeybHh3ChY3vExd0gpy0JHSNzbBw9aHL1IVYejSf+7F8lxuu7i0+Xck7f4Tw1jBXHM0rf2ZMtrKAzstOk5ChxM5Uj5E+lswe7IKlkW6l+wkhhKi56q02J0QTdHXtK2SE+uM6YQ6GDp7kZaaSEeqPMiOpqE1WbAimrbthP2AKOoZm5MSHc+uvr/H/YBydF/2NtkLzl5Era57HtvcEHAY+RnLQP0Tu/QJVfh7JQf+oA+IjniPu5A5Ct7yHoZ071t1Gaewf+utSTNz88Jq2jLysNMJ3riDgo4fo/M6fGNhVvPhw+O+rCdv+EXZ9J+E8+n+o8pRE/rEG/6Xj6TRvd9FD/IJWPY6qIB/3h+eib+VEXnoiqddPk5+ZWulnpVKpoKB6i1Rq6VR+21DlK9FS6JXZfntbRsRl9Czsq/VeonE5FZbGlK72PNq9+PqN8NGcbPbEPQ5F/1apVHR3MaWPuxk9Pj7LwWvJ3Fuq/aPd1H9AAQxobcHhG8l89180ayd7c1879R9SfdzN2X81ie0X48v8cWVtpMu6yW2LJsj1cDWj3+pzfPZPJMvGti73PCJTclhxMIIneziwuMSE1gGeFvRbfY6VhyL48mFvEjOV3IjPZuFIdx7sZFvUrnQfyrP5fByzdtyosh1A5MLe1Wr31u5gfjodA4CejhaL7nPn8Xs0v0tRqTks/iuUucPdcDAr+z28TaGjxZZp7Xnp12vc/7V/0fbe7mZ8M8m7zB/BoP5D89B19YK3pvo6rJnYhmHeltXqu6gb19a+QnqoP24T5mDg4El+Zirpof7klRjnsisY5wI+GEencsa5q2uex6b3BOwLx7lbheNcSuE45zjiOeILxzmDSsa51tOWkV84zgV+9BCdqhjnIsoZ5279sYaApePpWGKcu1RinNMrHOfSrp8mrx7HubuhKshHlZ9Hdnw4YVvfR9fMBrt+D5dtV9jP/Nws0q6f5tafX2HTcxz61k7lHLMAVAUoU+OJPvwT2dE3cXvo7Vrvu6g5GT/VGmr8rEpSpjpgamlY9rtvYaiLqnAxDANdvbsaP4UQQgghhBBCCCFE7Qj6+mXSQvzxfOhNjBw8yctMKZxYXDL3G4qZVzccBz6CjqEZ2fHhhP/5FWffG0ePJQfKxMQDvngO+94P4jT4MRIDjhC25wtU+UoSA//BaehUXO97nph/t3Nj83sY2ntg110zJn5j6weYuvvh89Ry8jLTCN6xgrMfPESPRX9hWElMPOS31dz89UNa9Z+E+xh1TDx07xecfW883d/Zg3FhTPzCysdQFRTg9fA89K2dUKYnknLtVLVi4qpqxsS17yImnleYj7Bs17doW1ZsCAW52Zi4tCvT3sSlHYmBR8jPzUZHTyaeNGb/BSfyaE83Hutd/P93pF8rjTZT+7oX/VulUnGPuyV9vKzpvmg/By7FMqKDg0b7x3q78fwgddx5QFtbDl+J5dujwXz75D2M6qg+dh8va/YFxbDtTCT3d3TU2N/aRI/vnrqnOJ7tYUWf9/9m9d/XWDGpc7nnEZmUxbI/rvBUPw/em1BcND2wrS293/ubFX9e4eup3UnMyOV6bDqLx3Xgoe4uRe1K96E8m06FM/Pn81W2A4j+eEylr5sZ6PLMAE/6eFljaaRHcHwGXxy8zoTPj/PTMz0Z7GNXrffReM/kbABm/nyeBzo58tMzPYlJzWbpnsuM/fQYB18fhL25+vuYlJlbbqGupbFu0eui4UhOuOnkhEtKuXQUACPH4kn0mZFX1NucNcdKPQt7FCZWRa8DOAx6nPiTOwneOB/n+19GW9+QxPP7SDz7B64PvlmnfReVk9yvWmOtnWpTuIjyqbA0+noUP4zgdHgaKlVxbrgiG8/E8G9IKovuc0dHW/152hrrYmGo4FR4mkbblKw8rsSqF+UoOTFJCCGEEEIIIYQQQmiSmJpaY5hPMXd3MJm5BbwyoHiO0p3E1NrYGvHPzRQik3NwsihePOa/MHX8PilTWeY9ZT5i0/DsU09w4fw55r+7GK823qSkJHPh3DkSExOK2gQH3+Cenr154snpmJmZExYawuerVzFiyAD+PXMBXV3NfNS0RyczacqjPDn9WQ4e2M8nK5ahVCo5dOBvnn5uBi/PfI2tm37mnblv4tm6NWPGTdDYf9GCeXTq0oVP13xNakoKHyxZxOh7h3DkxBk8PD0rPJflH37Aknfn8+gT03j9zbfJzc1l9ccruG/oQA4cPYFPO18AJo4bTX5+PoveW4qziysJCfGcPPEvKcnJlX5WKpWK/Pzq5aMUirrLR/26+RcyMjJ44smnyrymUql4+flnGTHqfkaNHsOGH76vs36IunH8xEmmT3uCZ56aVrRt7AP3a7R5/pnpRf9WqVT06dWTQQP64dG2A3v/3MeY0Zo53meemsarr6gXtxs2ZDD79h/g8y+/ZuvPPzF+7AMADBrQn91//MHGTZuZME6zzsHWxpptmzYUjZ39+vSirV9Xli5byddffFrueYRHRPDukg948fln+WTFR0Xbhw8dTFu/rix6fym//Pg9CQmJXLl6jY+XLeWxKZOK2pXuQ3nW/7SR6c+9UGU7gPzMlGq1e/F/s/h63XcA6OnpsWr5hzz3dPF3zddHnQM+/u9JBg8cULT9+ImTqFQqEhITS7T14e8DhwgLD8fVpbgu5di/JwA02orGQWo0mk+NhipPiZZuZWveBJV5kLbM2xdCCCGEEEIIIYSoO1e+Ua8N7fbgHAztPYvmqJacH5wdG4KpVzfsB05BUTg/+NafX3Px/XF0WVw29nb5C/Xa0K0GPUZSoOba0K2GTMV55HPEnthBSGHszaZ72dibsZsfbZ5Urw0dtmMF/h8+RJd3q14bOnTbR9j3m4TLA/+jIE9J5N41XPxgPJ3nF8feAj8ujL1NnKueH5zWhGNvTm2raCkaC6nRUGus856gbteMFo2P5J4k9yS5p8ZPxk61xlDfWJ47WS86Ok29JsasHTcY3d6a9Y+2IzYtl48OhDP+2wD2v9AJe9OKx14hhBBCCCGEEEKIuiZrKld/TeWmpiXGGZX5qjv9mBqNlni96iMu3BicvBLJ44M78sTQzkXbRnVvo9HmyeFdiv6tUqno4e1EP19XOr20hv3nb3JfqfZTh3bihft7ADDIz51D/sGs/fMs62eNZ3QP9b2un68rf529wdajQTzQQ7O+wsbMiB9fG4+WlhYLfz5EVGJ6bZ5yi1aUix1fKhdbshYprjAX278wF5tQmItdOo5OC8vJxX75PDa9JmA/6DGSA//h1h+FudhLJXKxJ3YQurWCXOy2pZi4lsrFLivMxdpWkovdXSoXm1+Yi/2wMBfrWCoXO7E4F3tj/RsU5GbV4idbv+SerFbde/K1uKZ7raH5XG9lQdP9Pag+XV2rrhl1nTAHQwd1zWhGqD/KjJJ//5RfM+P/wTg6l1Mzc2WNumbUobBmpmTNqMPgqTiNeI64wpoZw0pqZrymqWtGw3euIOCjh+hcRc1MeDk1M5F/rMF/6Xg6laiZCSpRM6NfeJ9OvX6a/OrUzKia9v+r5vL9run9fNmB8CaxRrlcL7Wmcr1q6t/VL5F08yIdH3kLM8fW5GakkHTTn5wSvzenR4dg492d1kMfRdfIjIy4cC7/9iX75o5h1MeHytyPj614FveBD+I1/HGiLx7h0o7PKMhXEnPxH9qMnEa7MTMIObqNCz8uxtTBA5demuvlXNz4PpYefvSYsRJlZir+m5Zz4J3xjFy2HxMH9wrPJfDXVVz8eSmegyfT/qFXKchTcnnn5/w9fwz3Lv0Dcxf132OH3nsUVUE+nR5fgLGNEzlpicRfOUVuRvXWnGkq5Lus1hB1b2/tvklra0OmlqoRL83X3gjfe93wsTcC4N+QVL75N4qjN1PZ86wfxvo61eqTEEKIu1O3Mw6EaECp105hP2AKDgMfLdpm3WWERptWg58o+rdKpQKv7pj79OH06z1I8j+IdZd7NdrbD3wUpxHPAWDRfgDJgYeJ+vs7fF5ci3W3+wAw9+lD0oX9xJ3YXiboomtqjc9L64p+ETRr04Ozb/UjYs9neE1bVu555CRGEr5zBa2GPInno4uLtlu0H8CZt/oRtmslPjO+RJmeSFb0DTymLMSu94PF51yqD+WJPbaZ69/OqrIdQN9vIyt93cixDWk3zqIqKEBLWz2pT5WfR/rNcwAayQnRtHR2MmHL+VgsjRT09zSno6NxmYmb8elKlh0M5++rScSk5VIyRnwtLot7fTSPWXrR7Ta2hgTFZDLYq3i7QkcLdysDIlJyyvRpnJ9N0fcJwNlCn+4uphwPqfgP28PXk8krUPFQJ1vySiTz9RXa9HI343iwOkhpaajA3UqfNcdvka9S0dfDHF97I7S1tSo6dJHhbS3Z86xfle3uxCv9nXikqx3xGUr2XUli3p5gspQFPN+3+CHCb/4WjK+9EY92q/xBvsr8AmZsucrl2EyWjfGktY0hYUk5fHIkgik/XGLzNF/MDDR/RVg8yoPU7Hxi03L59WI8M7ZcY9V4FeP8bGr1PEX13R7n7EuMc1alxjmHCsa5M6/3INn/IFbljHOOJca5lMDDRP/9HW1LjXOJlYxzbUuMc6ZtenCumuOcQznj3Nm3+hG+ayVtS4xz7lMWYnuH41zcHYxzfaoY5+7GiefboMpT38MM7D1p/8ZW9K2cyrRL+G8XV78qXjTart8kWk/9qEw7UCfEkwMOAaBjaIr382uw6jSs1vsuak7Gz4YdP6utku7dfuluxk8hhBBCCCGEEEIIUTtSrp3CccAjOA0qjonbdh2p0cZpiGZMXNWmO5btenN8Vg8SLh7AtqtmDN1x0GO4jlTHxK3aDyAx4DAR+7/D7+V12HZXx8QtfPoQf34/Mf9uKzPhWM/UGr9Xvi2KtZl79+DEG30J+f1T2j21vNzzyE6IJHj7cpyHPYn3Y0uKtlt2GMCJN/oSvGMFHV78CmV6IplRN2jz6CIc+hbHxEv3oTzRRzdzae2rVbYDGLL+VrXalXTlh7fJz8nE7YH/FW27nQPWNbYo015hbAkqFXmZKejoGdzx+4n608XVkk2nwrE01mOAtw0dXSzKxLPj0nJYtvcy+y/FEJ2SrRHPvhqTxogODhrth/tqFo+2sTcl8FYqQ9oV5zMVOtp42BgTnphZpk/juzppxLNdrIzo7m7FsevxFZ7HoSux5BWomNjdhbz8gqLt+gptentZc+yael9LI13cbYz44uB1dTzby4b2jmbVimff296BP14dUGW76vBzNsfPufih2L1aWzOqYysGf3SQxb8FMdin8txveW5fl+7ulqyc3Llou08rM4YtP8x3x4J5c1S74h0qjZFX/XmIuiM5YbWmkBO+LScpitCtH2Ds3gnLEvnbvPQktBT66OgbldlHYWyhUU9l2ror7d/YxNUvnif6wPfqjdo6uD34Fk4jn6+zvouqSe63cddOtXcwppebKV8eu0VrG0MGeJpzNS6LN3+/iY42VNbtA9eSmLs7mPt9rXiqZ/HvM9raWkzrYc+qw5F8fCiCx7vbk5aTxzt/hJClVC8MX42PQwghhBBCCCGEEKLFkpha45hP8dHfYWy7GM+SUe50dDQp2n4nMbXHutvx4+loXvr1Gksf8MTWRJdd/gn8FpAAgLZW2XOU+YhNw8l/j/PEk9OZNv2Zom33P6D5YMnpzxTnJ1QqFT1796HfgEF08PZg3597GTV6jEb7adOf4aX/qesWBg8dxoH9+/h6zef8tGkrD4wdD0D/gYP4Y+9uNv+ykTHjJmjsb21ry4bN24q+q7369KNrh7asXLaUT9d8Xe55RISH88Hid3l2xot8tPKTou2Dhw6na4e2LH1vEd//9AuJCQlcu3qFpcs/ZtIjjxW1K92H8mz8cT0vPDu9ynYAKdnVe7jo3fhx/XeYW1gwZvyDZV775ssvCAz059SGwDp7f1G3enTvyvqfNmBtZcXQIYPo1qUzurqai9rFxsbxzuL32PPHX9yKiqKgoLg24vKVK4wZrZlfvf8+zRorH5+2XPAP4L4Rw4u2KRQKvDw9CQsLL9OnKZMmaoydbq6u9OnVk0NH/qnwPP7c9zd5eXk8/shk8vKKFy80MDBgQP++RftaWVnS2tOD5R+vJj8/n0ED+tOpox/a2lU/ePqBUfdx8p+DVba7E2+98RrTpz1BbFw8v+/ZyyuzXiczM5PXZr4CQKeOfgzo15flq1bj7e3F8CGDCbp8hRdeeRUdHR2Nfj/z1DS+/GYdjz/5DF+s/hgHe3s2bfmVzVu3AVTrHEX9khoNteZQo2Ho2IbkoH/ISYhE37p4Pn/atf+A8te8kXn7QgghhBBCCCGEEHUn7Xo5a0N3rXxtaLPba0PPLn9taIdBFawN/dJabMpZG9qm1Nxchak17V7WXBv6zJv9CN/9GW2erCD2lhBJ2I4VtBr6JK1LxN4s2w/g9Jv9CNu5Ep8XSq0N3ac49la6D+WJPbaZa+uqF3vr913dxt5CtnyAiXsniZM1IVKj0bjnPUHdrhktGh/JPalJ7klyT42ZjJ2No76xStVYL1pV+FDbbi4mLC/x0L+2dkaM+PIi3/8XzZyhrnXYSSGEEEIIIYQQQojKyZrKatVZU7mpaYlxRl0dLZQl2jQlLfF61TQu/PyWq4Qlle13Y9O1dSt+PuKPpakhA/3c6OzhgK5CR6NNXEoGS7cc5a+zN4hOSqdAVfzZXY1M4L7ubTTa39vFS+Nnb0drAkJjGdbZs2ibQkcbD3sLwuNTy/Tpwb6+GtdW1J5q5WIHlcrFFnTHvG0fzrzRg+SAg1h1LpWLHVAiF+tbmIs9UJiL7VqYi23bh8SL1czFevXg3Nv9iNhdzVzsIyVysb4DOPt2YS72+RK52MmaudiwHcvIy2i6z4aXe/Kd3ZNdLfWbxP24Is3lemfm1t0aEs3J7fu0Rs1ol8prRm/XzJx+vfyaUfuBFdSMlqqZSaqkZsbnJc2a0bPVrJlpVU7NzJm3+hG2ayU+M0rVjN5hzUzssc1kRV2rsl1j1ly+3zW9nzcVcr2a1vWqqfjL/9F62KN4DX+8aJtzj/s02rQZMbXo3yqVCpuCe7Br34ddz3fj1rm/cb5HM37lNfxxfMao16Fy6DSQqAuHuLb3W/q98R0uPdX3PbsOfbl1eh8hR37Fpdf9Gvvrm1nTf873Rdfc1qcnv7/cm6Dtq+kxY2W555ERH4n/pmV43/cU3aa/X7TdoeNAfn+pFwGbl9P3tW/ISUsk7dZ1uj65GI+BDxW1K92H5kC+yw1T97Y7KIH9V5L48/mOVf69/2wfR42fB7S2oEMrY57ddJUNZ2LKvC6EEKJ2yWwb0WyZenYm9tgWdE0ssfDtj7FbR7QVmotU5qbGE7ZjGUkX/iY3OQZUxYtUZkVdg1JBl9ITXQxbtSEjPAjLjoOLtmnpKDCwcyc7PqJMn2x7jtP45cjAxhlTr+6kXD5e4XkkBRxGlZ+HbZ+HUOUXL1KprauPedteRfsqjC0xsHMn8o81qAryMffpi7GLL1rVWMDRqvNwOs7fU2W76mg19Cmuf/caNzfMxXn0K6AqIGznx2QnFH4e8tSyJuvLid6sOhLBz2djWXYgHGM9bUa2s2LecDfsTPUoKFAx5ccgYtJymTnAGR97I4z0dChQqXjgmwCy8wrKHNPSUHMY0tXRxlBXGwNdzf+3ejrapOcoy+xvZ6pbZputiS5B0RkVnkdcuvo4o772L/f12/9FtbS02DS1PR8fCmfNsVss+jMUC0MFEzraMGeoKyb6OuXuf/u8zPRrd4h1stDHyUIfgKGFf5R+sD+MiZ1tsTbW5ffABA5dT2b79PakllrgWZmvIiUrDyM9bXR1tPn5bCwHriWz51k/OjmpF17v6QY9XE3p88k51v4bxazBLhrH8LQ2LPr3vT5WPPbjJebuvsmY9tbV+oNT1L7b45yiinEuvIJxLjPqWpkJuZbVHOcM7dzJKWecs6lgnEutZJxLLhzn7MoZ58za9ira9/Y4d+uPNVCQj9kdjHOWtTjO3Q2/uTtR5eWSHRvKrb++IfCjibR/fRNGTm012ll0GEjH+XvIz04n7cYZIvd+gTI9SZ2wKXWeHo8uJi8zFWVyLHH//srVL2fgNX0Vtr3G1eOZieqQ8bNhx8+qWBqp3y8pM6/Ma8lZSrS0KFqk4m7GTyGEEEIIIYQQQghRO8w8uxB1dDMKE0us2vfH1L38mPjNbctIuLCfnKSyMXHQLNC36awZEzd2bEN6eBBWJWLi2joKDO3Lz/3a9x6vERM3tHHGvE13ki9VHBNP9D+EKj8Ph74TKSgVE7fw6UXSpeKYuKGdO2F71Llfy3Z9MHFpX62YuE2X4XR/d2+V7e7GzV8/IubfbXg/tgQzj45lG1RaKCj5pMbuqye6sWrfNTacCOXDvZcx1tfhPr9WLHjAFzszAwoKVEz+8l+iU7N5dbg37RzNMNLTQaWCUav+IVtZdgKRhZHm91Qdz9bBQFenzPa07LJxWjszg3K26RN0q+zk1Nvi0tRFuyM/PlLu6yXj2Vtm9GHlX1f5/MB13t0ZiKWRLhO6OfPWqHaYVLKAsaWRLmYGZhW+XlPmhroM87Xnh+OhZOXmY6hXcWy9PJbG6s99UFvNhZw7OJljb6bPxYjiYmVLIz2SMsrmEm5vK30NRf2SnHDTyQkDKNOTuPTx44CKts+vKdPvSgvqS7yWHnKRK589jYlnFzwHLEVH34iUS8cI2/4RBcpsXMa8WkdnIKoiud/GXTsF8NXDbXl1x3We33wVAD0dLZ7p3Yp/bqaQWs7vGgCHrifzzC9X6N/anM8ebFPmu/rqQBcycgtYfSSC5QfDC9/fgkmd7dh4NhYHU71aPU8hhBBCCCGEEEKI5kRiag0/n2LlwXA+ORLJnKEuPNmzVZnXqxtTa2NrxNrJbZnz202GfH4BAEdzPRaMcGPenhAczMrGyWQ+YtPQtXsPNvy4HisrawYNGUrnrt3Q1dX8nsTFxvLeonf4a+8eoqJuUVBQ/N28cvkyo0aP0Wg/cpTm4lVt2/oQcPECw0cUL+SlUCjwbO1FeFhYmT5NnDRFI1br6uZGz159+OfwoQrP4+99f5KXl8fkRx8nL6/4/66BgQF9+w8o2tfSygoPz9as/ng5+fn59B84CL+OndCuRj7qvvsf4OCxk1W2q0uXggI5/d9Jnnn+BQwMNPPpYaGhLJz/Nh8sW4mdvX0D9VDU1M8/fM97Hy5j3fc/sGDREkxMTBg3ZjQfLlmEg4M9BQUFjHxgPLeio5j35ht0aO+LsZERBQUq+gwaSlZWdpljWllqLpSmp6eHkZFRmf9Denp6pKalldnfvpz/T/b2dlzwL39cBIiNjQWgZ//B5b5++zunpaXFvj27WPz+hyxb+Qmz35yLlZUlj0x6mCXvzsfU1LTC97CyssTcvHZrN1xdXHB1Uc8PHDVSnWt/e8FCnnj0EWxtbQDY9NN6nnx2BpMfmwaoP7eZL7/A3wcOkZxSXI/Rzqctv/6ygRkvz6Rj914AuDg7s/yD93jltddxcpQF1RobqdFoPjUa9gMfI/rgj1z9+iVaP7EUXXNb4k/uIv7Ub+oGWmXPUebtCyGEEEIIIYQQQtQdE4/OxBzdgsLYEov2/TGpaG3o7ctILG9t6FtVrw1tVBh7sypnbeichHLWhu5192tDlxd7q2htaFR3vjZ0pwUNH3sLLIy9+cwoOz9KNF5So9G45z3V9ZrRovGR3JPkniT31PjJ2Nnw9Y2VuZP1om9/7gNbW2i069DKGHtTXQKiKv78hBBCCCGEEEIIIeqDrKlc/TWVm5qWGGd0NtcnOLHsHLamoCVer5rGhfUVTeN7u+5/Y1ix/Tg/HbjAB5v/wdhAj/vvacO7jw7C3sKEggIVD72/meikdF6b0AdfF1uMDHQpKFAxYv6PZOeWjUVbmmjOv9RV6GCkp4uBnuY111PokJaVW2Z/Owvj2j1JUcTUozAXa1yNXOzFcnKxt65h1bmKXKxjGzIigrD0KycXW04tUoW52Ct3mYv17lW0b1Eu9k/NXGxTJ/fkO7snN5X7cUWay/XOK1BV95RbtNs1M7pV1MyEVVAzkxVVdc1oRTUzBnbl//1jW8F9ujo1o7Z3UDOqKrjzmlF9G1dy4suuu9JUNJfvd03v502FXK+mdb1qyrpNF24e3ISeqSUOHQdg5dmpzP04OyUO/18+4taZ/WQlRaMqsaZUasQ1uGekRnvH7sM1fjZ3akNySCCOXYYUbdPWUWDayoPMcu7Hbv0naNyPje1csGl7DzEBxyo8j+jzB1Hl5+E+8GGNeJSOnj527XsX7atnYomJgzuXdn6BqqAAuw59sXSXeFRz/i7XZ91bRk4+c3cH82RPB+xN9UjJUv9fVOarP8uUrDx0dbQwquS5Uvf5WGGkp83ZiPQa90cIIUTlWs5vfKLFafv8l4T/toqYIz8Ttn0Z2vrGWHcdifvD89Azt0NVUEDQiinkJsfg/MBMjJ190NY3goICLr73AAW5ZRN8CmPNRSq1Fbpo6xmirauZJNFS6KHKLvuLjK65XZlteua2ZIQHVXgeypQ4AC4uHlV+A63iRSrbz95E+K6Pidy7hpBNi1AYW2DbawKuE+agMDSp8D0UxpaYGNbOIpX2/SejTEsg/LdPiD74AwCmrbvhNOJ5Ivd+jr5F2YWeRdNgZazLovs8WHSfB5HJOfx1JZH394eRkKFkw+O+XI7NJCg6k4/Ht+bhzsX/14MTsuqsT7FpZf/giktXFk2wKY9V4UNmv57kjbO5fqXHd7bQZ8U4LwBuxGfxW2ACKw+Fk5uv4sMHPCvcb/P5OGbtuFGdUyByYe9qtSuti7MJP56OITQpG2tjXa7EZpJXoP5DtrQNZ2LZcCaWdZPbMrKdFYHRmehog18rzQStm5UBlkYKLsdmVvn+nZ1MOHg9mYRMJbYm8jDChuD9/JdE/LaK2CM/E15inHMrZ5xzeWAmRiXGOf9qjnNalYxzBeWMc3rljHO6tTjO+c7eRESpcc6m1wTcJsxBp4pxTlFL49zdMHHzA9TjoVXnezn7Vl9Cf11Ku1e+02inMLbAxMMCAPN2fTGwc+Pqly+QeO5PrLvdp9HW0L74HmTV5V6CVj5G8Ia52PQY0ywDfE2ZjJ+Na/wszd3SAANdbS7HlB37Lsdm4m5lUBT0rY3xUwghhBBCCCGEEELcnfYvrCFk1ydEHdlI8LaP0DEwxrbrfbSeNA99C3VM/PyyyeQkxeA+diYmzu3Q0TdCpSrgzKLR5JcbE7fQ+FlLoYeOniE6epoxcW2FHsqs6sXE9cxtSQ+rOCaemxoPwOl37yu/QYmYeOc5mwnZuZKwPV9w/eeFKIwtcegzHs8H36y33G9JwdtXELJrFZ4PvYnz8Kc039NEnV9QpieV2S8vIwm0tFAYNVycXlSPtYk+i8d3YPH4DkQkZfJnQAzv/R5EQnoOPz/Xm8vRqQTeSuWTKZ2Z1MO1aL/guLor8oxNLfvdjU3NwdK4bBHubVbG6tzl2mndcbY0qvT4LlZGfDy5MwA3YtPZdf4Wy/+8gjKvgI8e7lThfptOhTPz5/NVnwAQ/fGYqhuVp3A+ltZdPL/a17Hi75tKBdolDtqulRmXo1LLtLu9zaeVfHcbkuSEm05OOC8jufBaRNP+9c0Y2LlpvK4wsaRAmU1+ThY6+oZl9lW4dyz6+eZPb6NrZovPS+vQ0lYX25u36wva2oTvXIltrwllji/qh+R+G1fut3TtFICNiS4/PtaO+HQlsem5OFvoY6DQZv2pGO73tSpzjEPXk5n+82V6uZvxzaS26JUzMV2ho8W7I915fbALYcnZWBnpYm+qxyM/BOFqqY9jFZ+hEEIIIYQQQgghREsmMbWGjamtPBjOikMRvDbImVcGOJfb5k5iakPaWPLfq10JTswmv0CFp7UhvwUmANDLreoYvcxHbJy+/+lnli19jx++W8eShQswMTFh9JhxLHr/Q+wdHCgoKGD86JFERd3ijbfm4du+A0bGxqgKChg6oA/ZWWW/r5aWmv939PT0MDIywsBAMx+lp6tHWmrZXKW9vX2ZbXYO9vj7X6jwPGJjYwEY3Ldnua9raxfno3bt3ceH7y/mk5XLmDtnNpZWVjw8+RHmL1yCqalphe9haWWFmbl5ha/Xhx+//xaAJ56cXua12TNfol37DowZ/yDJyckAZBZen4z0dFJSUjBv4P6LqtnYWPPxsqV8vGwpYeHh/Pb7Xt5a8C6xsXHs3bWNgMAgLvj78+3Xa5j62CNF+12/Ub0x5G7ExMSUsy0Wa6uyuZfbrK2tAdi88QfcXCp/gLSbqytrv/wcgKvXrrPl1+0sfO8DcnNzWfPpqgr3W//TRqY/90I1zgDyM1Oq1a60e7p346u133IzOARbWxsA7Oxs2b1jK7GxcUTHxODm6oKhoSFrvl7Hg+PGaux/34jhBF8J4PqNm+Tl5eHdxovNv24HoH+/PnfVJ1F3pEaj+dRoGDm2weeltdz4YQ7n56sX/NSzcsR90gKCN8xDz9KhzDFl3r4QQgghhBBCCCFE3fGZobk2tI6BMVZdR+JRIvYWuLww9jZGHXvT0TMCVQEXljxAvrJmsbf8asbeqlwbOlUde7uwqOrYW4fX1WtDR+xZQ/AvhWtD91bH3hpifnB15WUkE7B8CrlJ0fi9UTb2Jho3qdFo3POe6nvNaNHwJPckuafbJPfUeMnY2bjGztLuZL3odg7GZdrcplLd3ZodQgghhBBCCCGEELVJ1lSu/prKTU1LjDPq6jTdgFtLvF71ERduDKzNjHh/6jDenzqMiPhU9p6+xuKfDxOfmsmWtx7mUngcAaGxfDZjFFMG+hXtdzO67FrqtSU2OaPOjt3SeT//JRG/ryL2n58J31EiFzuxRC52ZYlcrFNhLlZVmIutTi2STiW52GqOq7pm1atFqlYu9rVNRPz2MZF/rCFk86Iyvwc0RXJPvrN78rW4ujvv+tBcrreOJB6rpe3zmjWjt+/T7uXUzDg/MBPjEjUzF6tZM6NdSc2MqpyaGd27qRm9g5qZ9rPVNaMla2Zse03AtRo1o9q6TXtd6+by/a7p/XzZgfAa9Lj+yPVqWterpvrO+pqArR9zc/9G/H/+EIWBMc49R9H58QUYWqrvxwcXTSIrMZoOE2dh7toOhYERqoIC9r01qtx4lL6JhcbP2go9FPoVxKMy08rsb2hhW2abgYUtSSGBFZ5HdrL6fvzXnBHlvq5VYk2pIe9sJWDLCi7t/Ixz699Bz8QS9wET6PjI2+hKPKrZfZfrs+4tMVNJXLqSr45H8dXxqDKv+y49xQgfS76d4lPpe6ifFVWt7gghhKiBikcfIZo4XVMrPB9ZhOcji8hJiCTx/F+EbH0fZVoC7WdtIDPyMhnhQbSZ/jF2fR8u2i8rJrjO+qRMiS2zLTclDl0Ty3Jaq+maqhewbPvC1+hbl79Q8m0GNs60eWoFAFnRN4g/9RthO1dSkJ+L1xMfVrhf7LHNXP92VnVOgb7fRlbZxnnUizgOf5qsmGB0DEwwsHHm+vo30NY3wtjdr8r9RePnZKHPkz1bcfRmCqfC1X/Q3o4H6+toTgT76XTZxVlry86AeJ7r0wqtwjePSM7hdHgaD3Uq+wf1bYO8zFFoaxGamM39vtbVfq/WNobMHOjMnqAEAqIqfxDx8LaW7Hm2bv+vHwtOQVsL3CzVQYaHO9vS273shMOJ3wcx0seS6b1a4WOnfkixg6ku+QVw/lY6XZ2LF5W+EZ9FUmYercwq/6NTpVJxIjQVcwMdLA0rfkCyqFu6plZ4PLIIjxLjXGjhOOdbOM5lhgfhVY/jXG4545yyinFOcYfjnFepcS5850pU+bm0rmSci7uDca5PNca5mtAxNMHQwYvsmJtVtjXx6AJAVnXaenYmOeAgyrQE9MwrvgeKhiXjZ8OPn6UpdLQY7m3J3kuJzLvXDRN99YOwI5NzOB6cyjO9WxW1ren4KYQQQgghhBBCCCHunp6pNd6PLsL70UVkJ0QQf/Yvbmx5n9y0eDrP3khGxGXSw4Jo98wqWvUrjoln1nNMvLq53w4vfYOBTeUxcUMbZ9pNXwlAZvQNYk7+RsiOFRTkKfGZVnFMPProZi6tfbU6p8CQ9beq1S54+wqCd6zAY9xruD/wStm+2rmjrWdARvjlMq+lR1zG0M6jTOGsaNycLY2Y3t+Do9fi+C84EQAt1DFlfYWORtsf/g2ts35sPxvJ84NaF8WzwxMzOR2SyMTuFT/gc5CPHQptLULiMxjdybHa79XazoRX7/Vm98VbXIys/OGd97Z34I9XB1T72HcqOTOXfUExdHAyw0BXp+odShnSzh5DPR0OXIrluUGti7ZfDE8mNi2Hbm7F96n7Ojrw5lZ/zoYm0bVwe15+AVvPRNDVzRIHc/nuNiTJCTeNnHBeRjKByyeTEx+O7+xNGLv4lmlj5Kwums+MuIRp665F23NTYslLT8TIqW3RtoywIGx6jkVLW/P7b+LeCVQFZEZdk4cgNAKS+2343G/p2qmSbEx0sTFR1zStOxFFZm4+T/bQXAz98PVkpv98mXtczVg32Qd9ReWLnxvr69DOXr2Qs/+tdI4Gp7DgXvfaORkhhBBCCCGEEEKIFkBiavUbU/v4UAQrDkXwvwFOzBpccW7tturE1EC9WJGntSEAuXkFrD0RRXsHI3qVM6+xJJmP2HhZ29iwdPnHLF3+MeFhYezd/RvvznuLuLhYtv22l6DAAPwvXmDNN9/yyONTi/a7ceN6nfUpJqbsPSA2OgYrq4q/f9bW6td++HkzLq6V51Fc3dz4/Ku1AFy/dpXtW7fwwZKF5ObmsuqzNRXut/HH9bzw7PTqnAIp2fnVancncnNz+WXjT3Tu2o2OnTqXef1SYCBhYaG4OZT9nEaPGIq5uTlhMYm13i9Rd1xdXHhxxrP8fegwx0+cACgav/T19TTafr3uuzrrxy+bt/LqKy8VvXdoWBjHT5zk8UcmV7jPiOFDUSgU3LwZzIPjxlb7vbzbeDH3zdfZtmMn585fqLTtA6Pu4+Q/B6t97Ltx6Mg/aGtr4+nhXuY1Oztb7OzUvz98+sWXZGRk8OLzz5Rpp6WlRRsvde1Gbm4un36+hs4d/RjQr2+d9l3cOanRaD41GgCWHYfQbdl/ZMcGo8rPx9DBk/j/fgPAzLtXle8j8/aFEEIIIYQQQgghak/JtaGzEyJJPFe4NnRqAh1e01wb2r5fw8XeqpofrDBRx958XrzztaHj/lOvDa3Ky8VrauVrQ19bV73YW7/vaj/25r9MHXvr8HrFsTfRNEiNRuOb91Sfa0aLxkFyT5J7KklyT42fjJ0NP3aWdifrRQ/2ssBQV5uD15N5tk/xGiP+t9KJTVdqjKdCCCGEEEIIIYQQDUHWVK7+mspNWUuMMzZlLfF63U1c+PktVwlLyqn2ezQGzjZmPDOyG0cCQ/nvijq3VDQvs9Qaz+v3n6+zfvx6/BIv3H9P0XuL2qNraoXHlEV4TCmRi/21MBf7aolc7FMNnItNrV4tUtsZX6NfxbhqYOOM15OlcrE7ltWgx42L3JOrvie7Wuo3uftxRZry9TbW0wGUddan5qJkzejt+3RIYc1M+1maNaP1dZ9W1uDvn+rWzLQpVTMTtnMlBfm5eFVSMxN7bDNZUdeqcwpNQlP+ftf0fr76SAQ5eaoa970+yfVqWtfrbuibWdPtqSV0e2oJGXERRJ76kwsblpCdEs/g+b+QHHaJ5JBAer60Gs/Bk4r2S4uqu/txVnJcmW3ZyXHom1Z8P9Y3U1/jfrPXYWRb+f3Y2M6Fni+uAiD11g3Cju8kYNNyCvKU3PNc8/n9uaSW/F2uz7o3WxM9tkwrW1/6+dFIToSk8uNj7bAyqnxdvd+DEshSFkgtmxBC1ANFQ3dAiPqgb+1Eq6FPkhx0lLTrp9QbC38Z01JoTjyLPvxTnfUj7uROHEc8V/SLYHZ8BGnXT2Pb56EK97HoMAgtHQXZcaHYdL+/2u9l6NAalwdmknBmDxmhAZW2teo8nI7z91T72NWhrauPceEDCHMSIon/7zfsBzyCjp5hrb6PqB+p2XlM/D6I8X42tLYxxERfmwuRGRy6nsx97dR/oHjZGOJupc/7+8NQARaGCvZdSeTIjcofcFsT8RlKpv9yhUe62ZOWncfygxHoK7R5qb9Thfu4WBowe7ALH/4dTmhSDoO9LDA31CEuXcn5yHSMdHWYPcSFoOgM5u0JZrSvNR7WBujqaHMsOIVLMZm82K/i4wNYGelW+UdPdb2x6wYm+jp0djLB1kSXxMw8fg9MYFdAAjP6OmJtrFt0Xi7lPJQQwMFMjz4e5kU/P9zFjm/+jeKZTVf53wAnWtsYEpaYzaf/RGKkp83j99gXtX1y42V8HYxo72CMpZGCmDQlm8/F8m9IKu/d74FCR5K7jcHtcS4l6CippcY57VLjXEwdjnPxNRznrO9inEusxjhnWQfj3N1SpiWSGXkZU6/uVbZNuXwcAEM790rbqVQqUq+cQMfIvNJEjqh/Mn423PgJcOBaEpm5BWTkqh98cDUui98DEwAY2sYCQz11UdTswS6M+voiUzdc4sX+TuTkFbD8QDhWRgqeKzH59k7GTyGEEEIIIYQQQghRdwysnXEe/hSJQUdJuVY6Jq75gMJbB3+ss37EnNiBy8jimHhWfAQp107j0LfimLhVYUw8KzYEu3uqHxM3cmiNx9iZxJ3eTVqIf6VtbboMp/u7e6t97KoE7/yY4B0rcB8zE4/xr5XbRltHgU3n4cSe2UPrSfNQGJoAkJ0QQdKl47iMKPuAQtG4pGYpefDz44zv5oSXnQkm+grOhydz8HIso/zUixd62ZvgbmPEkt+DUKHCwkiPvwKjOXKlbLFzbUlIz+XJb0/xaC830rKVLPvjCvoKHV4Z1qbCfVytjHjjPh+W7rlMWEImg9vZYW6oS1xaDufCkjHS0+GN+3wIupXC27/6M7qTI562JugptDh6LZ6gW6m8PLTi4wNYGethZaxXaZvqmvHjGZwsDOnkYoGViR7BcRl8eegGcWk5fDKli0bb+PQc/r2ujnNfupUKwIFLMVgb62NtokcfLxsAzA11eWNkWxbuCuKVjecY39WJ2NRsPtp7BSdLQ6b1dS865pSernx3NIRnvj/N3NHtsDHR5/tjwdyITWfzjN61co6idkhOuHHmhG8v0JwdF0b7137GxK1Due0sOgxCW9eA2GObMW3dtWh77NHNoKWFVZeRRdv0LOzJCLmIqiAfLe3iSf5pN84AoG9ZvKisqD+S+238tVMAGwonAblZGZCancfBa8n8fC6WN4e64udoUtTuv9BUnvrlCrYmerw8wInA6AyN9/S2NcTUQF1efTw4hQuR6bRzMEalUnE+Mp3Pj95ikJcFT/Z0qJVzFEIIIYQQQgghhGiOJKbWcDG1L4/dYvnBcAZ7WTDU25IzhYvr3NbNpXgRl+rG1ADm7Q6mt4cZloYKwpJyWHcyiqjUXH59sr1GO5mP2HS5uLry7IwXOXzwb078q57XdjsnpKevmY/6bu3XddaPrZt/4aX/vVr03mGhoZw8cZzJjz5e4T5Dh49AoVAQfPMmY8c/WO338mrjzetvzWXnjm1cOH+u0rb33f8AB4+drPaxa9ue33eREB/P2wveLff1b3/cSHZ2tsa2/fv+ZNXyj/j40y9o59u+3P1E45GSksLQkaOZMmkibb29MTU14fSZs/y5bz/jxz4AgE9bb1p7evD2/IWoVGBlacHve/5g/4GDddav2Lh4Jkx6lKefnEpKaioLl7yPgYEBb75e8UN03d3cWDj/bea9u5ibwSGMGD4MS0sLYmJjOXX6DMZGxrw7/20u+gfwyqzXeWjCOLxat0ZPT5eDh45wMSCQOa+9Wmm/rK2tsLa2qpVzfO7FVzAzM+We7t2wt7MjPiGBrdt2sHnrNma/+gq2tjZFbb/59nsAWnt6kJycwh9/7ePb9T/y3sIFdO3SWeO4r8x6nYED+mFtZcXN4BA+++IrIm5FcvDPxjHfWVRMajSado3GbVpaWhjaewJQkJdL1P61GLu2x7xt5Q/Slnn7QgghhBBCCCGEEHXHwNoJx2FPknzpKGm35wdTGHvTLbU29KG6jb05lRN7s6sk9mbpVxh7i73ztaFdx6jXhk6vxtrQnRY0TOzNf5k69tZhdtWxN9H4SI1G45/3VFdrRgNF62uGJanzxRdvpRc+NBJGt28ZD9lu7CT3JLknyT01PjJ2Nq/1os0NFcwe7MLiv0KZuf064/xsiE3PZdmBcJzM9Zgq60ULIYQQQgghhBCiEZE1lStfU7kpaalxxvDknDrre11qqderpnFhA4V2bZ9yrUvNzGHsop95sG872jhaY2Kox7kbURw4H8zoHt4AtHG0wsPegkUbD6NSgaWJAX+cucEh/5A661d8SgaPr9jOE0M6kZrZNL83TUFRLvZSI8jF3lu/udiI31ejymua/7fknnzn9+SmcD+uSHO63rqyVsodu32fTg46Slqp+7RWqft0dB3ep+NqWDNzpzWjLg+oa0arqpmx6jwcfRtXcuLDqn38xqQ5fb9rej/PyVPV2fnUFrleTet61TZjW2e8R00nxv8f4i7/BxSvKaWjqxmPuv7XD3XWj7Cj2/F54Pmi986IDSf+yincB06scB+Hzur7cVpMCC69R1f7vcwcW9PhoVmEn9hN4s2LNe57YyHf5YapezPQ1dao9b5t8/k4tLW1NF6LSM7hxa3XGNvBGndrA7SAEyGprD0RRVs7Qx7pZlfj/gghhKicoqE7IERdyMtMJeCjidj2Go+hQ2t0DE1ID75AcsAhrLrdB4ChgxcGdu6Ebn0fVCoUJhYknt9HcuCROuuXMi2ey59Nx37AI+RnpRG2Yznauvo43/9ShfsY2LjgOm42Yds+JDsuFMsOg1EYm6NMiSMt+Dw6+ka4jptNRngQNzfMw7r7aAztPdBS6JJy6RgZ4ZdwHvVipf3SNbFC16R2FqnMiLhMwpk9mLh3RFuhR0Z4EBF7PsfQ3gO38W/UynuI+qev0KaLkwlbL8QRkZyDskCFk7keL/Rz4oW+6skqujrafP+IDwv2hjDnt5sotKGfpwW/TPWlx8qzddKvN4e6ciEynVk7rpOek09nJxPWTGyDu1X5E1Nve3mAE952hqw9EcVO/3hy8wuwNdGjk6Mxj9+jfrienYkebpYGrD8Vw63UHLQAV0sDFoxw56l6fABfNxdTNp2LZcv5OFKz8zHW08bXwZjVE7x4sJPtXR3TyVyf3c/68fHhCL44eovY9FxsjHXp5mLKqwOd8bIxLGp7j6spu4MS+P6/aNJy8jEzUNDJ0Zj1j/owzFsm/jWUvMxUAj+aiE01xzlV4TiXVA/j3JXPpmNXOM6FV3OccykxzlmUGOfSg8+jXWKcCy4c5wzsPdAuMc451eM4B5B08QD5uZnkZ6kf4pl16yrxp38HwNJvKDr6huRlphK0Ygo2PcdhYO+Bjp4BWdE3idq/jgJlDi5jiheojj70I6lX/8Oi/QD0rRzJz8kk9dp/RP/9LaZe3bHqMqKo7aXVT2Ls4ouxa3sUJpbkJsUQd2wzqVf+xeOx99DSkV/vGxMZPxtu/AR46/dgIkoU7v0emFA0KffEzC64FE7K9bI1ZOuT7XlvXxjPbrqKQluLvh5mzB/hrvHA3zsZP4UQQgghhBBCCCFE7cnLTOXs0odw6DUeI0cvdAxMSLt5nkT/Q9h2V8fEjVp5YWjnzo0t6pi4rokl8ef+IqkOY+K5qfH4r34Kx4GPkpeVSvD2FWjr6uM2+uUK9zG0dcFj/Ovc/PVDsuLCsPYbhMLYgtyUOFJvnkNH3wjPCa+THhbElR/nYtdjNEb2nmgpdEkKOkZ6+CXcRlccc4fajYmH7f2S4G3LsPIbjHWnoaRcP6PxurlXt6J/e4yfTcLCUVz8+Anc7n+JAmUON7cvQ9fECteRz9dKf0Td0dfVpoubBVtPRxCemElevgonS0NeHOLFi0O8AHU8+4enezJ/WwCvb76IQkeL/m1s2TyjD90W7auTfr11vw/nw5KZ+cs50rPz6OJqwZdPdMPdxrjS/V4Z1gZvexO+ORLM9nPnyM0rwNZUn86uFkzt4w6ArakBbjbGrD8WQmRyFlpa4GZtzLtj2zO9v2ednE95fFuZsfN8JD8cDyEjNx8LI116eljx6aNd6OKqmY+9Ep3GM+tPa2x7c6t6EYLera3Z/lLxQ0NnDPbCzFCXb47cZMfZSEwMFAz2sWXuaF8sjYuL4fUVOmx9oTeLdgUxd5s/Wcp82juas/HZXvTxskE0HMkJN/6ccH5uFkErHyUjLACPyQtRFeSRdqN4rNQ1tcbAzr2wf5Y4j36FsB3LUBhbYNFhIOnBFwjfuRL7/o9g5ORdtJ/jvc8QvHE+lz6ZhsOgx9DWMyQl6Ci3/voKc9/+GLvKA7YbguR+m0btlApYeyKKiOQctLWgQytj1k1uywgfzfvDPzdTyFYWEJ6cw8PfB5V5zy3TfIsmvejqaLP7UiKfHIkkN78ADyv1xJ7pvRzQ0ZYJ1EIIIYQQQgghhBAVkZhaw8XU9l9NAuDg9WQOXk8u83rkwt5F/65uTA3gVmoO8/cEk5iZh6WhgkFeFnw3xQdnC82F8WQ+YtORkpLC6BFDmThpCt5t22JiYsrZM6fZ/9efPDB2PADebX3w8GzNwnlvg0qFhZUVf+z+nYN/76+zfsXHxvLowxOY+tTTpKak8P7ihRgYGDDr9Tcr3MfN3Z23Fyxk8TvzCAm+ybB7R2BhYUlsbAxnTp/C2MiYtxe8S4D/RV6f+QrjHnyI1l5e6OrqceTQQQL9L/Lq7DmV9svK2hor69p7wO2+P/eSkZFBeloaAJcvB7Fj21YA7h05CiMjI432P37/LYaGhkyc9Ei5x7unZ9mHgoaFhgDQuWs3unbrXmt9F3XDwMCAHvd056efNxESGoZSqcTVxZk3Zs3k9Vn/A0BXV5edWzcx8/U5zHh5JgqFDkMHD+Kv3Ttx966bPOKShQs4feYs059/gdTUNHp078bG9d/S2rPy2oo3X3+Ndj4+fPrFGn7Z8is5OTk42NvTvVsXnnv6KQAc7O3x9PBgzddrCY+IREtLC08Pd5YvfY+XZjxXJ+dTnl49e7D+xw38sOFnkpNTMDExppOfH+vXfc1jUyZptFWpVKz+fA2hYeFoa2vTpVNHfv1lA2MfKLv4anhEJP+b9QbxCQlYW1kxYvgwtm/ZiJura32dmqgmqdFoXjUaADc3zMO8bW8UJpZkx4URtX8duUlRdJjzq8Z7y7x9IYQQQgghhBBCiLqVl5mK/4fqtaGNWrVWzw8OvkCy/yGsb8feWqljbyFbCteGNi5cGzqoDmNvqfFc+nQ6DgMfIS+zxNrQlczfvb02dOjttaH9BqMwMic3NY70m+rYm9t4deztxk/zsLmncG1onRJrQ99fv7G3xIsHKMjJJD+7ROztVGHsrWNx7C1ghTr25jlFHXtLLRV7MywRexONk9RoNI15T9V1p2tePrf5qsbP3/8Xw/f/xQCaNSKi/kjuSXJPkntq/GTsbF7rRQM839cRMwMd1hV+Bsb6Ogz2suCtYa5Y1sKD+IQQQgghhBBCCCHulqypfGdrKjclLTXOqKujRZayTrpep1rq9WosceG6pK+rQ1evVmz+J5CwuBTy8gtwsjHjlTE9eXlMTwB0FTpseONB3v7+b2at/ROFtjYD/dzYPncSHV9aUyf9mjd5IOduRPHyl3tISM2sk/doifIyUwlcNhGbnuMxLKxFKsrFdi2Vi/31fVSoa5GSLtRzLnZnYS52VBW52LGzCdteKhdbohZJIxd7z2gM7Ipzsaq8nAqP3djJPbn53pPL09yut6hcXmYqAR+pa0arUzNDYc1MYj3UzFz+bDr2hffpsGrWzLiWqJmxLFEzkxZ8Hp0S9+mbhTUzhvYeaJWomXGuRs2Mtl7T/T/X3L7fNbmf25roEpfeuP9QkuvVtK5XTeVmpHLgnQm49Z+AmZMXuoYmJFw/T9T5gzj3HAWAmVMbTBzcufDTe6hUKvRNLIk8/RfRFw/XWb+yU+L558NptB7+GMrMVPx/WYa2rj6+E/5X4T4mdq74TX6Dixs/ICMmlFadB6NnYkF2chwJ18+h0DfCb/IbJIUEcmbt27j2eQCTVp5oK3SJ9T9KSmgQ7cZVHO9qauS73Ph/lzbR18HWRJev/40iLkNJQYEKJwt9nurZipcHOGFUWBsnhBCi7mipVCpVQ3dC1I727dsTFFT2gVLVZejoTdclB2uxRw2nQJlD8M/vkHr9NDkJEajylehbOWHTYyxO972Ajr56slnmrWsE/7yAtBtn0dJRYN6uHx6TF3B6dg9cxszCddxrAITtWEH4rpX0+MQfXdPiCT3X1s0k/vRueq+5pvH+/h8+RF56Il0WHwAg5fJxAj6aSJtnVpMefIH4kzvIy0rH1LMzHpPfxcS9Y9G+t9+r77eRGsdMOPcnUfvWkh7qT4EyFz1zW0w8OuEw6HEsfPuTmxpP6Jb3SLt5lpzEW4AWBrau2PebRKthT6GlXT+/WGVF3+D692+QGXmZ/JxM9K0csekxFuf7X0JH36jqAzRBZ+cNJuvW1aob1gJvW0MOvtS5Xt6rMTsenMLE74P46mFvRrevvUWPReMw+LPzXI3Luuv9DR296dJMxrOK3B7n0kqNc9YVjHPpJcY598kLODO7B86lxrmIXSu5p5xxLuH0bnqVGucCPnwIZalxLrDEOBd3cgf5heOceznjXMSulfQpNc4lnvuTW/vWklFqnLMvNc6llxrn7Op5nAM483pPchIiyn2t60cnMLBxUT+cfsM80q6dIifxFqq8HHTNbDH36Y3TqJc1HsKbev0Ukbs/IyM0AGV6IlraOhjYe2Dd/X4c732u6HoCRO79goTTu8mODSEvKw2FkRkm7p1wGPokVp2G1fm5Nybn6mD8be7jrIyfTU9Nx0RfX18CAwNrsUdCCCGEEEIIIYRobGqaIzZ29KbnB4dqr0N1oECZw7UN75By/TRZ8eGo8pUYWDth13MsbqNeKMpBZty6xtWf5pNaGBO38u2P15QFHJ91D+7jZuE5fjYAN7cvJ2THSvp95o+eaXGcLOibmcSd+p2BX1/XeP+zHzyIMi2Rnu+rcw9Jl45zbulD+D77KanB54k5sYP87HRMPTrT5pGFmHkUx8Rvv9eQ9bc0jhl39g/C/1pHWshFVHnqmLipR2ecBj+OVXt1TPz6piWk3iiMiWtpYWjrRqv+k3AeXn8x8bMfPEjy5X8rfL30eaUGX+TG5iWkXD+Dlo4Cy3Z98Zq8ACN79zru6d05+dYgMmoQZ/a2N+XIm4NrsUfitmPX43nw8+N8M7U7D3R2bOjuiBoYsPQgV2PS7nr/lpD7rYjkhBt/Tjg7Ppyzb5R9GPZttn0n0mb6Ko1tUfvWEXXge3ISItA1t8Wu7yScR7+CtkJz8deEM3u49dc3ZEVdp0CZjb6NCzY9xuB477NNuv6qpjne5p7PvROS+xUg+WQhhBBCCCGEEKIlqGk+WGJqxSSmJqDmMbU74dPOl5Pn/Ovlve5WTk4Ob85+lf9O/EtYaAhKpRJnF1cenDiJ/732OkZG6pzElcuXmPPaTE7/dxIdhYJBQ4by3tLltG/jzptzF/DW/HcA+GDxQpa+t4ibETFY29gUvc+Mp59k5/ZfuZWQqvH+9w8fQkJCPCfOXgTgn8OHGD1iKF9/u56zZ06zdfMvpKWm0q17D95ftoIuXbsV7Xv7vVKy8zWOufu3naz57FMunDtDTk4O9vYOdOnenaeefo5BQ4YSFxvLO3Pf5NR/J4mMCEdLSwt3D08efWIaz73wEjo69ZeP8vP2JCwstNzXLl6+gZu7e9HPEeHh+LX1ZNKUR/ly3ffVfo8NP3zPC89O5+Cxk3Tt1r2GPa4/Pbv4cfnS3Y9/5fFt54P/mZO1eszm7tCRfxg6cjSbNqznofHjGro7opr8uvUk6NLlu96/pdZpSI1G86vRuPzpU6TdPE9eeiIKE0ssOgzCZexrGNg4a+wn8/YrVtMaD8lHCyGEEEIIIYQQmmqa+zVy9Kbre00vdlegzOHm7bWh40usDd1zLM6lYm83NxavDW3h2w+PSQs4NbsHLmNn4VYYewvdsYLwnSvpuVoz9nZ1rXpt6D5fasbeLi5Vrw3ddYk69pZ8+TgBH07E+5nVpJVaG9pzimbs7fZ79fuu1NrQZ9Wxt9JrQ7caXBx7C9nyHmmF84O1tLTQL1wb2rGeY2+nZlcce+u+rDj2dvr1imNvdn0n4v30qjrqYf04O3cwmfWwZrTUaBSTGo3mTdaMrprkniT31NJzT7Je9J2TsbNpkjm+QgghhBBCCCFExVrCWsnVJWsqV72mco3XBm7m8cPqqq84Y03jYnK91JpKXLim17utszXHlz9diz1q/I4GhjF28c98N3MsY3r5ANBn9lquRCTc9TENHb3psrh551irq0CZQ/AvFeRiR5aTi715Fi1tBea+/XCftIAzrxfmYscW5mJ3FuZiV5WTiz2zm15flMrFfvQQyrRSudhlE2nzdGEu9r/CXKxHObnYwvfqs66cXOz+UrlY91K52K1lc7HK1HiUqXF3/VnK/bhYU7gny/hbe2pyvWujZqZrC6mZSS11n7apoGYmrUTNjMfkBZye3QOXUjUz4btW0qOcmpn407vpXapmxv9Ddc1oyft0QImamZI1ox7l1MyE71pJ31I1Mwnn/iSqnJpRh1I1M2ml7tP21ayZOSvrtdeahryfy336zjXl62Xm3Jb7PzlSiz2qffnKHM5+O5/4K6fIiAunIC8PIxsn3PqNo924F1EUxqNSIq5y9tt5xF89g7aOAnu/AXSZ9i67nutKh4dn4zfpdQD8Ny0jYPNyJnwXhL5Z8fU68ekrhJ/4jYkbgjXe/+8F48lJTWDUKvXnFBNwjAPvTKDXK5+TeP0coUe3o8xKx9qrC12fXIRV605F+95+rym/xmgcM+K/vVzZ/Q1JNy+Sr8zF0MIOK6/OeN37BA4dB5CdEsf5H5eQcPUMmQmRgBYm9m54DJmM931Po11iTand/xtAasSVu/58W+J3tiJN4XdpoUlq3oQQLYGWSqVSNXQnRO2oadK5JQRjGsrtoEvbF77Cpvvohu6OqGU1DVjdCfkDS03+uGreZEJu03N7Qq63jHOiHsmk3Dsn42fTI8FJIYQQQgghhBBCVEUmJte/2xOOO7z0NXb3SEy8KavxRG17U468ObgWeyRuO3Y9ngc/P843U7vzQGfHhu6OqIEBSw9yNSbtrveX3G/jITlhURtqmuNt7vncOyG5XwGSTxZCCCGEEEIIIVqCmuaDJaZWTGJqAmoeU7sTPu18OXnOv17eq7n45/AhRo8YyvqNmxg34aGG7o5oQD27+HH50t2Pf+XxbeeD/5mTtXrM5u7QkX8YOnI0mzas56Hx4xq6O6Ka/Lr1JOjS5bveX+o0Ggep0RCNQU1rPCQfLYQQQgghhBBCaKpp7tfI0Zuu70nsrqaSLx8n4MOJ+LzwFTYyP7jFODt3MJn1sGa01GgUkxqN5k3WjG56JPck6pusF33nZOxsmmSOrxBCCCGEEEIIUTFZK7nxaoxrKtd4beBmHj+srvqKM9Y0LibXS62pxIVrer3bOltzfPnTtdijxu9oYBhjF//MdzPHMqaXDwB9Zq/lSkTCXR/T0NGbLoslx9oYpVw+TuCyiXjPqP9c7Ln5sr5vbWkK92QZf2tPTa53bdTMdJWamXqVcvk4AR9NpG0jrpk5K+u115qGvJ/LffrONeXrZebclvs/OVKLPWr+YgKOceCdCfSdvRbX3g80dHfY/b8BpEZcuev9W+J3tiJN4XdpoUlq3oQQLYF2Q3dACCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEqo2joDgghhBB3qo+HOZELezd0N4QQQogmRcZPIYQQQgghhBBCCCGEEE1BXy8boj8e09DdEEIIIRotyf0KIYQQQgghhBBCCHFnJKYmhBBC3JlBA/qTn5nS0N0QQgghhBBCCCGEEEIIIYQQTYzUaAghhBB3RsZOIYQQQgghhBBCCCFETUmcsWmR69V89WvvSsIvcxq6G0KIOyD35JZFrrcQzZd8v5sWuV5CNA/yXRZCCNEYKRq6A0K0BOY+fej7bWRDd0MIIYSoE+Y+fegj45wQQgghhBBCCCGEEEKIFsCyXR+GrL/V0N0QQggh6pXkhIUQQgghhBBCCCGEEEIIIURd6j9wECnZ+Q3dDSGEEKLBSY2GEEIIIYQQQgghhBB1w8KnD/2+k9ibEEKIlkVyT0IIIYQQQgghhBBCCCFukzWVhRBCiNpj7tOHPuskFyuEEI2VuU8f+krNjBBCNDj7Dn2Z8mtMQ3dDCCGEEC2EdkN3QAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghKqNo6A4IIYRoOMeDU5j4fRBbpvnSx8O8obtz12Zuv86W83EAtLUz5MCLnQFIy87j25PR/HMzhevxWWTk5uNqoc+EjrZM79UKA13tomOsOBjOykMRFb7HFw+1YayfTaVt9RVa3Jzfq+jnmLRcvv8vmqM3UwhOzEaZr8LDyoDHutszpasdOtpaVZ5bbFounxyJ4MC1ZGLTcrE21qW/pzmzBrngZKFf1G5PUAK/ByZw4VY60am52JjocY+LKbMGO+NpbahxzIe+C+TfkNQy7zXIy5wNj/tW2aeKhCZms/ivUI4Fp5BXoKKbsylzh7vi52hSrf39b6WzZF8Y1+Kz7roPQgjRmDT3cRYgJ6+Ab09EseVCHGFJORjr6eDXypj/DXTmHlfTonYXb6Xzy9lYToamEp6cg6GuDj72Rrzc34l+nlV/NiX7UJ5dT3egm4v6/VQqFRvPxPLj6RiCE7NRaGvhY2fIjH5ODPO2vMtPAeLTlSzZF8r+q0lkKQvwtTfijaGu9K9G/6H642S7D/4jNTsfAHMDnbvurxBCCCGEEEIIIYQQomk5dj2eBz8/zq8v9qGvl01Dd+euvbLxHJtPhQPQ1sGUw3MGl9suKzefocsPcTMugwVjfHlhsJfG68r8Aj7Zd41f/gsjNjUHV2sjpvV15+kBnmWO9fuFW3x16AbXYtMpKFDhaWfC9H4eTLzHpajN7c+3Ik/0duOjhztV+HpGTh6zNp0nICKF6NRsClTgYmnE+K5OPDvQE2P9ikuwlu65xKp91yr8PDJy8vjswHV2noskIjELY30d2jmasfzhTnjaVi/XWpkXfzrDr2ciGeZrz0/P9KywXVxaNgOWHiQpU8k3U7vzQGfHoteOXotj6+kITockEZmchbmhLp1cLJh1rzedXCxq3EchhGgILSGfu3R/GAeuJRGZkkOWsgB7Uz36e5rzygBnnEvUHQF8+HcYgdEZBEZlEJ2mZGJnW1aN1xyfATaeiWH/1SQCozOJT8/FwUyP/p4WvDrIGXtTvTLtd/rH89nRSG7EZ2FhqGB0e2vmDHHFWL/qXKjTO/+Wu/2tYa681N+p6OeKaqFuOze7G3aFfdt3JYnfAuMJjMrgenw2eQUqIhf2rrIvVamtuqmzEWkotLXo62HO/HvdcLMyKGqTkpWH79JTRT/bGEsJuBBCCCGEEEIIIepXc4+p3clcxNKO3Ehmyg+XAPB/oztWxrpFr1V3LuJtNYmpVXcuIlRvjkRNPpOqSExNCNGUHTryD0NHjubvP35n0ID+Dd2du/bkszP44aeNALT3bcfF0yeKXpv3ziL2/vkXoeHhZGZm4djKgaGDB/H2nNm4ubpqHGf+u4s5f9Gf8xcucisqiicee4Tvvl5T5v08ffwIDQsrty/6+vpkJsVWu78ltfVuQ9D500U/f//jBqY/90KFx3l/0TvMmT1LY9vO33az6tPPOXfhIvn5+bi7ufLyC8/z7PQnK+1Tec6cPcd3P/zEP8eOExIahpGRIX7tfXnz9dcYMmhgmfYbftnMylWruXTlKiYmxgwfOoSlSxbi4uys0a6iz+/Z6U+y5tNVd9xPIYQQQgghhBBCCCGEEEI0XlKjcXfrRYN6LcrN5+P44VQ0V2Kz0NHWwt3KgFmDnBnhYwUUf74Veay7PR8+UHY+c0nVrdG4k3lPAJm5+Xx+NJKdAQlEJudgpKdDO3sjPhrjWWZ96eqqzRqNbGXBXfVBCCEak+Y+zoKsF13X60VP62F/1/0VQgghhBBCCCGEEI1Xc48dhidl02vVuQr3K/0M0/LWJmxsmvs1g+qvJ3mnedXdQQl8fTyK6/GZFKjA09qAJ3u24qFOxdc5r0BVi2dZc0cDwxi7+Gd2zp9Cv/auVe8ghKg3LeF+fKdr2V6OyWTloXD+DUklPScfO1M9hrSx4IPRxfUoPT8+S0RyTrn7V7QeSWPQEq53SVnKfIavuUhwQnY99k6IhtESvt81XZu8snt3YyHXsVhkSg4L9gQTFJNJXLoShbYWuflSAykal5bwnS2p5O9W8+914/m+jhqvV/dZGeVJz8ln1eEIAqMzCIjKIDEzj1mDnHltsEuZtutORLHdP56QxGwycvKxMdGlu4spMwc609bOSKNtTFouHxTGZ9Jz8vG0NmR6TwemdKtZDdmRG8ksOxBOUEwmhrraDPO2ZN5wN2xMdKvemeqtVfjHpUSm/3Kl6GfXUusQCiFEcySrjYo6kXL5OAEfTaTDG1sw9+nT0N25a9fWzST22BYAjJza0mXxgXLb5edmcf6d4WTHBOP+8HycRj5frePHndxJ5J7PyIy6gcLYApt7RuM2YQ46BsZFbfKy0on4bRUZ4YGkhwaQl56Iy5hZuI57rdJjq1QqAj58kNSrJ3EYMo3Wj71XzbOuXOi2D4n4fXWlnwdU/pmkh1wk5p9fSL16kuz4cHT0DTFy8sF59MtYtOtXrX6khwYQvmslaTfPk5+Vgr6VEza9xuM04nl09A1LtfUnZPMS0m6eRUtbgXm7vng8PB8DOzeNdrf++oaUqyfJCAsgJz4cs7a98Zuztcx73/rrG4J/ebfoZwP71tXqs6hbdia6rJ3cFsMSE20jU3JZeyKKBzvZ8mzvVhjp6fBfaCorD4Vz5GYKvzzRDi0tLQCmdLVjkJdFmeO+sesGIUk55b624fF2mJb4g0JbS/P1i7cy2Hohjoc62TJzoDO6OlocuJbMW7/f5GxEGivHVf7HW05eARO+CyQlK4/XBrvgbWvIjfhsVvyfvfMOj6Jq+/C9Jdn03js1JEAIvSO9o4gggqggiF1RxN47iordV5Dee1FAQHovoUMCCZDee0+2fH9s2mZ3k90U9X2/c18XF1fOnDlzZnZ2zuxTfs+heA7F5HL4uXDsKo7/07Ek3O0seGGAHwHOCpJyy/j+aCIjf7nMzic66v1wC3RW8P0DbXTaHK0a/lqQWVjOhCVXcbSW89V9rVDIpfxwNJGJy67zx+yOtHarO9E3Or2Yicuu097LBh8HSxJzyxo8l6YmN/IE176YRPv/gTUtvWJNszbwDFeVFpG460cyzmynNDMRmcIGG/8QWj32Bdae1c664pQYYjd/Tu6N42iUpdj4huA39gVcOg/XGa8oMYrkA8sojLtKUfwN1GXFZl3D5H2/kX56KyVpd1GVFGLh4IZ96274j5uDjW+w0f2KEm9y6YMRaJRlhL2zC7sW1cW2M8/vIvPs7xTcvURZdop2zDbd8b/vZZ1zNJe4LfMpjLtGYdw1ynJScO87iTYzFxrsW5IWy90NH2mvn1qJfauuBE56C7vAjlV9ynJSSTmwjJwbxyhJvYNGWY6VZws875mG54ApSKTVz53cG8dIP7mF/OhzlGYlIbdxxDYoDP97X8IuKEzn2BqNhrQja0g5tJKS1DtIZHJsfNvhM+ppXDoN1Z9rRgIJO74m+8ohlIXZWNi5YNcinHbP/1bvNTH1Pkk/tY3UQysoTolBWZSH3NYZu6CO+I55DofW3XX6nn42BFWxVnzAa/D0eucgaFoMrbMA83bEsPVyBs/196VvC0dyipX8eCyRiUuvsW1mezr7aRNlt13J4GJiAZM7exDqZUtRuYqVZ1OZvPw6C+9vXW+Q3Zx7/Hikm75xcfqaSCzlUsJ9qxNbFxyMZ+HhRB7p5smbwwIoUapZejqFx1ZHsmhyW0aHupp9/qVKNZOXXyO3RMWHo4Jws7Vg2ZkUpq28wbrHQugdVLeh2px1ct2joSjVGu5dfNXseQoEAoFAIBAIBAKBQCAQ/NNk3zjBhc8n0vn1TTiH/Pfa1K8vmkPKsQ0A2PoG0/PTg1XbMi7uI+30TvLjrlKUHI1GpWTw8iS9MUoyE7m56h0K4q9TlpuORCbH2j0AnwFT8Bn8KFJZ3X6ZymtpiK7v7MSxdVeD2zQaDRGfTiD35ml8h0wn+NFP9foUZyRwd9tXZF45RHmB1v7r0DKcsBeX1DknQyiL80nYt4Ssa0coSo5GVVKIlXsAXn0m4DdsFjJLK53+RSkxxGz8jOzrx1ErS7HzCyFw3Au4dxmh0+/21gXc3fa13vGkFgoGLr5T55w0ahXxexeTdfUwhQlRlBdmY+Xqh1uXEQSOeQ4L2//ewNP/FjzsFSx9vDvWlsYLSc/fHUlRmcro9tc3XWbTuQReHdWO8AAnDkWm8c62qxSWKnlxWNuqfmtOx/HyuouMCfNmzvC2SJCw4Ww8z6+5QFZhGU8O1Przw/wc+eNF/XiEZcfvsvFcAqPCvOs8J6Vag0YDTw5sRYCLDVKJhJO3M/l6bxQnojPY+IzhZ97VxFx+PhiDu73hYNTCUiUTfjxBal4Jzw1uTaiPA/klSs7eyaK4jutjKvuupbL7Sgr2JviCX990BYWFDCjX27bs+F2yC8uZNaAFbT3tySwo45dDMYxZeJR1T/WiX5t/XzK9IYTvV9f3a6qfM/XIGrIv7acw7hrleRlYOnvhGNof/3tfwtKp/uDwmvOtibVXKzp/eqTq77Rj64le8rJev0oCHngDvzHP1du32zcXsHT0qHdehmhq329tynLTufj2QJSFObR95j+4dRurs11VUkjclvlknP0dZWEO1t6t8Bv9HG4979PpZ+o11R4zjYSd35J9+QDluWnIHVxxCumP/30vo3D1bbJrYo7vV9D0GPPn5pUoGd/Rjdbu1thZyriZXsx3RxLYG5XNgWc74WJTnYix6GQyIZ42DAt2Yf0F4wWyvzoYT58Wjrw+xB8vB0tiMkpYeDiBvVFZ/PlUGO521UUDtlxO5/nN0Uzt4sH7I4O4nVHCp/tjuZVezNpHQ40eoyZjQl14so9uEo2vo+66+umYFuSX6q6bxeVqpq26QUdvW51CBntuZBKRUEAHL1ss5VIuJxWaNI+6aMq4qV8mtaVUqWbBwXgmLLnK3qc74Wqr/ZzsFDJ2zOpAWkEZs9bdbPS8BQKBQCAQCAQCgUAgEAj+P9PYXMSaFJaqeHXHbbzsLUjJ1/ezVFJfLiI0zqZmTi6iqTkSDb0m9SFsauZz9PAhxo4Ywu9//kX/ewb+09NpME/PmsGaVSsACAltz6mIy1Xb9uz6na2bNnL50kVuRkWiVCrJLdH3mSbEx/Pa3Be5cvky6WmpyOVyAlu05NHpj/P4E08il1f7Rju2bUlcXKzBuSgUCtJyi0yeu0ajYfTQQZw4fpQnnnqGBQu/1+sTFxvL/E8+ZP++P8nKzMTVzY2u3bqzesMWvb5/7NzOj98u5PKlC6hUKgICg3jq2eeZMWu2yXOqyUfvvcOVyxe5fOkiyUlJTJ32KD8vXlrvfk9Mf4QN69YwYtRoNmzdWdVeec8ZY8as2Sz84ed6xzflmvz0/be8Ma/a99e6TVtDQwmaCS9PTzavX42Nte6zNyc3l4cenEi74GDs7e24cSOST+YvYOcfu7ly/jSuri5VfRf+8BNhHdozbswolq5YZfRYm9etorRMN388Lj6eKY/MYPy9Y43spYu1tTX7d+/UbbPSjU8aM3IExw/t19v3vY8+Yf9fBxl/7zid9vkLvubt9z/iyVmP89orL2NhIScq6hblZcbX9bpYt3ETZ8+dZ8aj0wjr2IHCwiL+s3gJw0bfy9JFv/Dow1Oq+v7w8394ce6rzJz+KJ9+9D4JiUm89+En3DN0FOdPHsHZWbdIad/evfjis4912jw9/p0xGyJGozpGI27bVyTs0I+Fq6Ttkz9VxQU0Jj9fo1aRvG8xOdeOUJQYibIwB4WrHy7hI/Ad8yxym+q4OXNiNGoTV6E5Y+h6mMM/mZ9vLO6iko5v7cC+VVezrmnlHGI3f0b25QOoiguw9mqJ99CZeA6YYuRINa6HGfeJOTEaacc3kn3lAIWxVylJu4PCxZeuX57WGz8zYg9RP8ys+lvhJopZCAQCgUAgEAgEAoFA8L9MTuQJrs6fRIfXNuL0X2y/u7lYVzO6y8dae1VJRjzn5hkvYOfUYSAd5q6uc+yzr/SkNFO/uKnXwGm0fmx+1d8514+RVksL0i4ojID79LUga863JtZerej62RG99oYQu3k+8RWa0ZXXwxCqsmIuvFutGe03Sl8zOjfqNKWZ8UgtrbHxa4f/mOdxCjVdMzpu+9cU3LmIskirGe3e6358RxrQjL57hTsbPyY/RqsZ7RTSlxaT9TWjE/cuIi/qNAU1NKPDXtfXjE7cu4g7a9+v+ttaaEb/4/zdetGv/36HjRfTeKKXN68PDUSl1hCZWkRxeXXhrY7etuyY1UFvzBVnU9l0KZ1RIS5622piToyGOXlPhaUqJi27Rmp+Gc/28yXEy4b8EhXn4vN15m8OTR2j8drO2yTnCc3opubv1owGyDz3B0l7f6U4ORqNWo21V0u8hszAo49hjYuaNIe+8dX5E8mLOmn0mA3NHTbnmqSf3ELSn79QlBSNzMoGp/b3EDjpTRQu+rm4Gae3k7DrB4pr1JYIqFVbojDuKrFb5lOUEIkyPwuppRVWXi3xHjwd994P6Iyn0WhI2b+ElIPLKcmIR27rhEvnEQQ+8DpyW6c6z7EkI56IV+t+9wl9WfvuU3D3MmlGakM4GqgNodFoSDu2gdRDKyhKjEIilWHlEYT/fS/j0rlaI6SmZrTMRuh5/J0IvWihFy0QCAQCgUAgEAgEAoGgbv7XdZLN1QRWK8uJ3fkdycfWU5qThrW7P75DpuM/bKbeMQ2hLCnk9ub5pJ3ZibIwBxvvVgSOeQ7PXuP1+qqV5STsX0Ly0fUUp95FamGJjU8b2jz0Lo5tquPe1WplA6+KoC4M2Q497C0N+sj+jMzix2NJjGyna6MzVZtQ0DQ0Vk/SHL/quog05m6PYXSoCy/e0wYJsPFiOi9uiSarsJzZFdqPJcqG+SgF/15yI09w7ctJtJ/3P+BfPVHhX/XR9a/GbvmcnMsHKM1MRFVWjKWTJ06h/fEd8wJWbn4641T5EiNr+RLDjfhX9/1KcVI0Gk2Ff3Wwaf7V5P0GavK2MqEmb1KtmrxBnYz2FTQdxp7H5mjZHr+Ty6OrI+kZYM/n41riYiMnMbeMq8m6+/z2UDCltZ61ibmlPL3xFiPb1R27ImgajH3eNfnyQHyT1ApoSnIjT3D1i0l0+B+Il6kZA1r5PFcW55O8fwk5149SnByNqrQQK7cA3HtPwGfYTKQWVkbHzLl2hGtfaXN6e3x7BQv7+r9LqpJCYmtowtt4t8J39HO419KE12g0pBqIl/GtFS9TnBJDyuHV5EaeoCQtFolUirV3a3yGP6GnRy9oPprieW6M7gH2vDO8Osb12U23iM8pbfScBfo09nMsLlNjp5Az5x4/fBwVlKvUPL85mlLlv8cWkXr1OAfem8DgD7bg2aHvPz2dBnPq+xe4c2g9AI7+wYxeqI2NLy/K5+auxaRcPkJe4i2UJYXYegQQNGAiwWOe0LNd1STl0mEOfvggABOWXkfhUH9sUXlxIZfXfkb8iR2UFuTg4Nua0PufJ7Df/U1wloL6aIp3q8bYo7KLyll9PpVQTxtGtnNhTYTx/bOLlAxu7USoly2O1jLiskv54WgiYxddYfeTYVVxY3klSu7/7SplKg1vDQvE096SbVcyeGXHbfJKVXr1M0zl5N1cHlkVyZC2TiwdHExGYTmf7Itj8vJr7HoyDIXc+DUE07UKewU5sGNWB/66mc23RxIbNFeBQCD4b6P+Cn0Cwf9zLBw9CHluMVJL4wllcVu/RF1abNa4aSe3cGvR83gOmErQQ+9Tknqbuxs/pTj5Fu3nrq3qpyzMJuXwamz9Q3HtMpLUI2tMGj/lwDKKU++aNaf6KIi7SuKe/2DhUL+QY13XJP30NgruXMSj/2Rs/UNRlxaRcmgl176cTJuZC/HoO6nOsYsSb3Ll0/uw9mpJyynvI7d3IS/qNPE7vqHw7mVCXqgWlS1Kjubq/InYBrQn+Olf0JSXErdtAVc+n0D4+3uxqPHDKeXQSqQKGxzb9SX70j6jx3freR/2rbqQcmQtaUfXGu0n+HuxlEvp6m+v0xbgrOD0S12wqVEcuF9LR2wsZXy0N5azcfn0CHQAwMdRgU+tAn/x2SVEpRdzf0c3HK31l8wwb1tcbC302ivpHmDPiRc7YyGr/sEyoJUT5So1y86kMneQv15RwZqcjs3jTmYJC+5tyZSu2kSkPi0csbeS8eymWxy9ncOoEO09vGxqO9zsdOfSt4UjvRZGsOhkMgvu000ct7LQv16N4efjSWQWKdk+qyN+Ttpz6hFgT59vL7DgQDy/PFi3aPKXB+OxlElYPrWdSEJqRiwcPWhnYE1TlRRy9YtJlOek4jv6WWz8Q1AV5ZMffU7nWV6SEc+VT+7FwsmTVo9+hlRhS+rBFUT+8DjBT/8H125jqvoW3L1EVsSf2Aa2xzG0H9kXjT9XDVFemI1zx8HY+Icit3WkJC2OxF0/cPnjsXR6dzfW3q319tGoVUQvfRkLOxfKclL0tifu+gkLR3etI9wjgLKsJBJ+/55L748k7O2ddTqi6yJp7yJs/ENwDh9G2rH1xs8pL5Orn09AZutI68e/QmqhIOGPH7g2fyJh7/xRdU4Fdy+TdmITHn0mYj9uDhKZBdlXDnB75RsU3I6g9ePVAqwpB1dQXpCN97CZWPu0pTw/k6Q//8OVT8YR+vJqnQTY+G0LSNi5EM+BjxA48U3U5SUk/7WUyG8fI/jZRbh2HV3VtzAhkmtfTMTKPZCgye9g6exNeW4qOVcP13s9zLlPlIXZ2LfujvfQmcjtXSjPSSNp769c/fwB2s9bj2Nw76q+7V9Zh0at5Mon95r2wQiaFEPrbKlSzbYrGYzv6MZrQ6rFeLsH2NNlwXm2Xs6oSsp9pq8v744I0tl/SBtnRvxymYWH4+tNyg1ysSLIRdc4fvJuLllFSl4c4IusRvWSdRfS6VEReFDJgJZOdF5wjo0X0xuUlLs2Io3ItGK2z+pAt4rr0CfIkWE/X+KTvXH8Ptt4MW4wb53sVCPBWCAQCAQCgUAgEAgEAoFA8M9h6ehBxxd+Q1bLpp5+fje5MRHYB7ZHKrck/+5lg/urSouQW9sTdO8crFx90SjLybz8FzdXvU1+3DVCZn5l0jxaTnxDL9Hb1q+d0f6J+5dSnHbX6PaChEgufPYAVu6BtH7oHRTOPpTlppJ15ZBJ86lNSWYi8XsX4dVnIv4jZiOzsiU36jR3tn5N1tUjhL+6vkoktzg9nnMf3ovCyYPg6Z8jU9iSeGAFV757nA7P/opH9zF643d6ZQ1y62rbpERSd6AggKqshDtbv8Kz13h87pmKhZ0L+bFXuLvjWzIv7KPbB7v1PldB02Ipl9I1yHhiSkRsNkuO3uHHaV14Yvk5ve2RyXmsOR3HG6NDeHaw1n/St7Ub2YXlLNx3i0f7BOFsqxUlXnc6Dj9naxY91g1pha14UDt3riXmsv5sPE8O1PpJ7a0s9Oak0Wh4ZlUEfs7W3NO2bju1o7UFvz7WTadtQLA7ZUo1Px6IJjajkEA3W53tSpWaOWsv8EjvQK4l5ZFVqC9K/PmuSG6l5nNw3kCd/Ud08KpzPqaQV1zOqxsv8dqodiw6crvOvr9fSuJQVBqfPRDGC2su6G3/7IEw3O11fduDQzzo9clffLvvFv3a/DsLgv4v01jfL5ju54zf9hUO7foQ8MDrWDp7UZwcQ8LOhWRd2Eun9//E0rH+z19qaUX7eRt022oluzmHDaXjWzv09o3btoDca0dw7TJSb1vrx7/W8x3LbZ31+plKU/t+a3N71Zt1JvlF/jiLgjuXCJz4BtaeLUk/vY2b/3kGjUaNey/dBA9Trqm6vJSrn09AWZiL//i52Pi0pTglhvhtX5Fz7RCdPz6MzLpu35Sp18Qc36+g6THkzwX4dGxLnb/7tHAkwFnBI6si2RuZzUNdqkXLb77Zo2ot3Xw53eix/nyqk06MUu8gRzp62zL61yusPp/GnHu0wgQqtYaP98ZyTytHvqyIW+rbwhE7hZTnNkdz4FY2g9vU/311t7OoN8aprYeNXtuGi2mUqzRM7aIrzP7lva2qzvOtP243KFGyNk0ZN2VvpY1RC/Oxo993F/jleBJvVSRlyqQSuvrbE59d0ug5CwQCgUAgEAgEAoFAIBD8f6exuYg1+XR/LI7Wcoa0capTpKS+XMTG2tTMyUU0NUeiodekPoRN7f83nl5erF6/GWsbXdvuzu3bOHvmNGHh4VgqFFyMOG9w/6KiQuztHXj1jbfw8w+gvLyMvXt2M++lF7h86SI//LKoqu+qDZspK9UVuouPj2PGtCmMvXe8WfNe9MtP3L4dbXT79WtXGTN8MEEtWvLxZ1/g4+tHSkoyB/bt1ev79Zfz+ei9t3n8iSd5+dXXkMstuHUzivLycrPmVJOfvl9I+45hjBozjlXLl9a/A/Dn7j/4Y+d2HBz0v8edOndh/+Hjeu2//foLa1evZOx94+sd39Rr8sCkyXTv0ZPlS39j5bIlJs1d0HQoFAp69eiu1/7DQt3YpoED+hMUFMTY+yey/fc/ePyxR6q25aYlIpVqY3pWrTXuU+wcri8QvXffXwDMnP6oSfOVSqUG51sTd3c33N3ddNoKCws5dfos/fr0Jrhtm6r28xEXePv9j/j0w/eY9/KcqvYhgwaaNB9DzHtpDl9+9olO2+iRw+nWZwAffzafRx/WCr+Wlpby7oefMHb0KH796fuqvqEh7eg3aBhfLfyejz94V2ccR0fHes9f0PQ0NkbDc8AUnDsO1Bs3ZtmrlKTfxanGtsbk56vLSojf/jVuPe/DY8AULOxcKIy9QsLv35F1aR9h7+6qiptrSIwGaAtNm6o5Ux//ZH6+/7g5eA18RO9YN76djtTCErsW4YB511RZlMeVz+5HoywjaNJbWDh5knF6GzHLXkFVnIfPiCfrvB7m3CfmxGikn9xMeW469i3DQaNGozIsKusY3IuOb+0g+9JfJPz+bZ1zFQgEAoFAIBAIBAKBQCD4N2Hh6EHI84t1ckYtHT0Ie1vf/pUV8ScJu340av+qjUOb7gRNfkenzbKWbSz54AqUBdn4DJuJTYUWZOKf/+HSx+No//JqnEL76fSXWlrR4dW684EaSkHcVRJM1YzeUodm9Klt5N++iGeFZrSqrIiUgyu5+uVk2sxaiKcJmtGXP9FqRreY8j4Wdi7k3jxN3I5vKLh7mdAXdTWjr1RoRrd7+hfUFZrRlz+bQOcPamlGH9RqRjuF9CWrDtupe4VmdOrhtaQKzeh/BX+nXvSeG1msOpfKz5PacG+Han9R7WLF9lZyvTlpNBqe33wLPycFA1o61nlO5sRomJP3NP9AHLcyitn/dCcCa2hvDm9EYc6mjtGwV8hIbvBsBHXxd2pGpx5dR8zSubh0HY3f2BdBIiH9+EaiF7+IsiALn+Gz65xrc+gbt5z2KaqSfJ02dVkx17+ehm1QRywddb8vpmDONUnev4Q7a97BY8BUAia+SVlWMnFbv+TqZxPo9P6fyG2dqvqmV9SW8BgwlRYPvU9x6m1iN35KUe3aEkV5KFx8cO85HksnL1SlRWSc2sqtRS9QkhGP/7g5VX3vrv+Q5H2L8Rn5FE6h/SlKukn8tgUU3LlEx7d2IJUbjzuzdPQw6PvLiviTxN0/4lLj3Sfj9Dbya9SGUNWoDdHaQG2I2ytfJ+3YRnyGP0HAA6+jUasoSohEVes9QmhG/3MIvWihFy0QCAQCgUAgEAgEAoHg/w+GdJLN0QQGiFrxBqknNtNiwjwcWoSTdeUQt1a/i6qkkKBxL9Q7hyvfzST/ziVaPfgmNl4tSTm5lWs/a7USvXpPqOqnUau48t1Mcm+dIWD0Mzi27oaqtIj8u5dRlRZV9VOXl1KSHt80F0iggyHbocKIXuFn++OwtpAyvqOujc5UbUJB09BYPUlz/KrrLqTh56TgP5PaVn3GA1s7cS2liA0X05ndxwcAa4v6dcAFgn8KC0cP2j1rwL9alIdbz/FYe7dGZmVHUdJNbS7Yxb2Ef3QACzut/70kI54rn96LhaMnrR6p8CUeqvAlPmXAv7qshn8VCeknNhL9m2n+1fKCbJw71KjJm15Rk/eTipq8XubX5FWVFDTgqglMwdjz2FQt2+IyFc9vvkXfFg4sn9pO511sYidd31sHb9vau3M4OgegKh4FoEylMfs8BKZh7POu5EJCPktPp/D9A214csPNv3Fm/3+wcPQgpFa8TGlmIkn7FuPR5wF8hs9GZmVD3s0zxG//mpxrR2j/yjqd71YlqpJCope/iqWTl8FnpzEif5xF/p1LBE18AyvPlmRUaMJTSxM+riJexmvgIwTViJe58e1jtKsRL5Nz7QjZl//CvfcD2LcIR6NSknF2B1E/PUnR+FcIuPelRlwxgak09nleFw5WMp2xxXtz89HYz7G1uzXfTtB91/KwsyCn2HC+vaBxWDl50P+1pTq2q8KMBKL+WETQgIkEj3sSuZUt6TdOc3XDAlIuHWbQexsNPtPLiws588tcrF28KM4y/Zl+7MsZZEZfJHza29h7tyT22BZOfPMUGo2aoP4PNMl5CozTFO9WjbFH+TkpuP56dyQSCVmF5ayJSDPa95XB/jp/9w6CLn52DPzhElsvpzNvsDbubsXZVGKzS9n9ZEfCfLTxYwNbO5GWX8aCg/E81NlDx+ZiKh/vjaWlqxW/PhiMXKY9X38nK8b/dpV1EWk81sN4vSpztAqdrLUx89EZhnNHBAKB4H8R85/KAsH/M6RyS+xbdTW6Pf/2BZL/Wkrb2d8T9VPdommVaNQq7m74GKf299B6+pfaxpC+yKzsuPnrc2RfPoBz2GAAFK5+9PzhOhKJhPL8LFKPrKl3/JKMeO5u+oy2s74l8sdZJs2p3jmrlEQveRmvgdMojL+OsiDLaN/6ronvqGdoMVlXMNI5bAgX3x9B/E79RJ3apJ/eirq8hOBnF2HtEQSAU0g/ynJTST28GmVhTlVSU9zWL5HILQl5cXlVcV/bwDAi3uhH4p+/EDTprapxO398CEmFQOiFdwYbPb6loweWjh5kXzlU5zybg5vpxRy9nUv/Wsmcy8+k8OYfd9j3dBihXrZcSizglxNJRCQUkFFQhpudJV397HhzWGBVIogxJi69BsCmGe112udsjebk3TxOv9Slqq1Mqean40lsuZxOfHYpdgoZQ9s68/bwQFzrEBT/u6iZjFuT8Ipkl6Q8/SK5NVl3IQ2NBqZ2NT9JD7Q/MAwf3x5IJTmvDF9H45+HRcWPn0oh8UocrLTnpZBXG9lqFkesxMvBEm8HS5JyS/W2NTW7b2TRt4WDzv1lbyVnVIgLmy9noFRpqn7M1Uap0rD/ZjYTO7nrnaugaTG2psVtmU9x8i3CP9iPlUdgVbtL5+E6/RJ3/YC6rITQl1ejcPYGwLnjIC6+M4Q7697Hpcuoqueoe++JePR9EICMc7+bJRILEDD+FZ2/HYN7Y9+qCxffHkj6qa0E3D9Pb5+kvYsoy07Gd9Qz3Fn7rt72di8uw9JBV3jZsV1fzr/ai6S9i2g9Y4FZc6yk5083q847/eRmo/0S9/xMeX4mHd7cjpWbtmipfZseRLzWh7htCwh++hdAK9jR5fMTOgmyTu0HoFGWk3JgGf7j56Jw8QWgxbRP9c7JucMgIl7vS8Lv3+MYUi3ckXZsHfZtetDq0c91xj07pzNpxzdWOW00Gg3Ri19A4eJDh9e3ILWo/l679biv3uthzn3iPWSG3v5OHQdx9sUw0o6s1RGJtWuhL9Dd1Ih11jykEpBKJFXrUiX2ChlSSf3rlEwqIczHlq2XMxp0/LURaUgk6BQWBrCQSrCvNScrCykKuVRnTuaw50YWrdysqhJyAeQyCRPC3Pn8rziS80rxdjD+2TdmnRQIBAKBQCAQCAQCgUAgEPwzSC0scWytb1NvN2NBlY0zasWb5N+9bHB/W582hM7WLWTl2mkwZXkZpBzfSPCjn+rYX41h49XC4DwMUZweT8ymzwh94juufD9Tb7tGo+H6f55H4eJD17e26hzfs2f99l9DWLsH0OerM8gU1QKyLqH9kClsiF7/Ebm3zuDUticAsX/8gLqsmPBX1qBw0dqPXcMGcfqtwdxa8x7uXavtx5XYB3XE0t48oT2ZpRV9vjpVlRAH4BzSBytXX67+MJv0s7vw6tv4ANmbqfkcvZlO/7a6iU/Ljt/h9U1XODDvHkJ9HLkYl8PPh6I5fzebjIJS3OwUdAty4a2xIfi76Avv1uT+H7QFUbc+11en/YU1FzgRncG5d4dVtZUp1fx4IJrN5xOIyyzCzkrOsPaevDMuFDe7+u+1v4sypZqX1l1kRr8gOgU4Geyz52oKGg081EM3cPWhnv6sOhXLwcg0JnTV+lrkMgm2CnlVIC2ARCLBzgS/4/HoDGIzi3hlRLDO/ubgamcJgMyAjff7v6LJLirnjTEhTFt0Wm97UZmS1adiGRfuQ6CbfsJcY3l/+zU8HKyYNaAli47cNtovu7CMNzZf4Y3RIfg6Wxvs426vfw/ZKuS09bQnKUcE3P4TNNb3C6b7OcPe/1PHJ+kY3Bu7wI5c/mg0qUdW64gEG0UirTP+CsDCwVVHWB/Qim1En8e+TY+qops1sfFt16R+xKb2/dYk89wf5Fw9TMtpnxD92xy97dmX/yL32hHazP4R917jAXAM6UtpZgKxGz7Grce9SKQ1fGAmXNO8m6cpSb1Dq+kL8BygLQTs2K4PMit7bv36LDnXj+LadVSdY5h6Tczx/TYU4c9tGlxttHOT1Vr7TF0LDfl+w3xskUnRiVGKSMgnNb+c90bo+nPHtnfltZ232XMjqyqJozlYF5GGraVUp+ACmH6e5tAccVN+Tgr6BDmwOzKLt4YHGtxXIBAIBAKBQCAQCAQCgaA+hE3NPBqSi3g6No/V59PYOasDe6OyG3X8xtrUzMlFNDVHorH5mcYQNrX/3ygsFXTv2Uuv/fuff0Va4ZN4Zc7zXIw4b3D/tsHt+OW3ZTptw0aMIj09jbWrVvDVtz+gUGjvrU7hnfX2/2vfXgAenaEfV2GM2Lt3+eCdN/nlt2VMmzxRb7tGo2H244/h6+fPnr8OVx0f4IFJk3X6Xog4z0fvvc17H33KnLnVOZIDBw8xeT6GSMzIrbp+69esqrd/bm4uc557mrfe+5BffvhOb7uDg4Pe56TRaJg1/RECAgIZPGSY3j61+5p6TTy9vPD08mL/3j/rnXdDuX4jkr8OHmLIoIE67T//upjn5szlwunjhHXswLnzEXz17fecPnOO1LQ0PD086NWzO5999D6BAQEGx65k8AitIPOBP//QaZ8x+2kOHznG7cgrVW1lZWV8+fW3rF63njt3Y3FwsGfMyJHM/+RD3N11fRv/JtzdtP5cuVx3rZFKG5azp9FoWLZyNS1bBDF44D2Nnl9drN+0hYKCAh6f/qhO+4+/LEKhUPDc06bphJiCh4d+AVOZTEaX8HDWrN9Q1Xb12nVyc3MZNUL3+9S7Zw9cXJzZsn0HH3+gnyst+PtpbIyGwsUHhYuPTltJRjxFSVG497ofuU31+3pj8vOlllZ0+eKkTtycY7s+WLr6cvOnJ8k6vwv33tq4uYbEaNTWnCmvQ3PGFP7J/HwrjyCsKrRqKsmNOomyIAu/sS9WxWeYc01TDq6gND2WsHd3YxcUBoBzh4GU56QRt20BHv0f0vmsa2POfWJOjEboy2uqrvONhY9SlBhl8PhyWyfsW3WlODna6BwFAoFAIBAIBAKBQCAQCP6NSOWWONSy30ktFHptALGbPkNqaV2Vt1MfMhsHg+PUpNUjBrQgOw7i3Gt9if/je5xC++nuIJHWO2ZD0KiU3PrNdM3opL+WEjz7eyINaUaPfoYWD+nap13ChnDh/RHE71iIZ32a0ae0mtHtnquhGR3aj/KcVFKMaEaHzqnWjLYLCuP86/1I2PMLLR6s1ozu8km1ZnTE2/8+zWgRo2EezaUXvfhUMv5OCr18IlM4fieP2OxS5g70qzf/yJwYDUMYynsqLlOxNiKNsaGuBLpYmT1/Ywi96P8e/k7N6LSj61C4+hH89H+q2pw6DKQw/hppxzbUW2S4OfSNbXzb6h/n+AY0qnI8+0+tcz7GMPWaqMtLidv6Jc7hw6rrRQA2Pm248ul9JO75hcAHXteek4HaEo4VtSVu1aot4diuD47t+ujMySV8GCUZcaQers7PLs1OJnn/b3gNnl5VL8Gp/QAs7N249euzpB/fgOc9Dxs9T6mFwuC9E7tZ++7j1nN8VZvPqGcIMlAb4tL7I0ioVRsiM2IPqYdW0fapn3HrcW91/w4D9Y7V3JrRYp01D6EXLfSiBQKBQCAQCAQCgUAgEPxvYkgn2RxN4IKEKJKPrKXlA68TOPoZQKvVW16Qzd0dC/Ed9AgWdsZzWDMu/UX2tSOEPvUjXr3vr9i/LyUZCUSv+xjPnvdVxeIn7FtC5uUDdH17u86c3cKH6oyZc/M0GlV5I64KFJWp9NqE7dB07maVcCo2j0kG/EHNodkn7L1NgzE9ydoY86vKpRJsLaV6Ws72Cl0brkzSuHsgKiGTw1fuck/HIJ32JXsvMG/JXo7Mn0H7QA8uxCTz4+9nOHcrifTcItwdbeje1pd3p9yDv7vxXByAez/Q1vbe8Z6uP+nZn/7g+PU4Lv7wdFVbmVLFdztOs/HYNeLScrG3tmR4l1a8//Ag3Bzq1ggX/Psw5l9tOe1Tnb8d2/XByj2AGwsfIevCXjz7PwTU4Ut8dwh31tfyrx6r8K8+ZcC/erx+/6rBmrwtu3DxnYqavOPNr8lbnpde5zHrQzyPzcfUdXHn9UxS88v5caIPEjOfoxqNhvUX0wl0VtCvhUNVe2NXZPF5N4wypZq522N4rIcXnXyavn6AQIuh57mVewDdvjyt8zvHKUT7O+fuho/Iv3UWh7Y99Ma6u+lT5DaOOIcNIeH3b/W2GyLr8l/kXDtC2xqa8E4VmvB3a2nCpx1bh4OBeJkzteJl3Hrch9fg6TrPAOewwZTnZ5G460f8Rj1jUo0aUxDfb/Npjt85jUV8jubzb/wcBSCzUODWtptOm51HAPf+fBa5VfVa6tWxP3KFDRdXfEBG5BncQ3rqjXVp1cdY2jrh03Uo1zZ9Y9Lxk87vJ+XSYXrP+Zmg/hMA8OzYj8L0BC6u+JCAPuORygzHL5uD+M42DFPfrRrz/Tb3/bs2lfYWeY05nI3Lx93OgjAfO52+Q9s6czgml0PROdzX0by49eS8Ui4mFvLG0ACd2LTuAfa0dLViT2QWj/XwMrr/P13/QyAQCP7tNCwiWfA/iaq0SK8t+eByjj/uS2H8dQDy71wi6penOTevJyefbMW5eT2J+uUZSjIS6h3/yvyJXJmvL0x667c5nJun+5KrVpYRv3MhEW8O4MTsFpx+oSO3fnuJ8rzMBp5d86BWlhG9dC7egx/DLsj0JJH8mAjKc1Px6KcrMurabSxShS2ZEXuq2iQSidkvbjHLX8Wp/YB6i+yZQ8KuH1AW5hA44bU6+5lyTWonGANIpDLsgsIozUqqdy4SWcWLqLWDTrvcxhEkUiRybZFUjUpJ9uX9uHYbXZWMC2Dl5odjuz5kRuyuNYd//yNRJoENF9L02jdeTKejty2hXtofD/E5pbRyteaDkUGsfiSUN4cFkFZQzuhfL5NV2LiAg0rUag2Pr43ix2OJ3N/RjeUPt+PNoQEcuZ3LxKXXKC7XD0yoiUajQaky7V9Tc/xOLgBt3Q0XoAXt+W24mE6QixW9gww7YIf8dAn/90/S6YtzvLDlFok5pQb7GTq+XCqhpWvdSbHd/R0I87Hl60PxXEwsoLBUxZWkAj7fH0dHb1u9H9q1ic0qISGnlLYe+o7d2KwS2n9+hoAPTtJnYQSf74+r9zMzRnG5itjsEkI89X+8hnraUlKuJja7xOj+d7NLKClXE+LZNA7o4qSb5Fw/qteecnA5J2qsaQUVa9r5eT059WQrzs/ryU0T17Sr8ydy1ciadt7ImnbhzQGcnN2CM/+yNU1VWkzq0bW4dhurk4xriPxb57DxD6lyFoP2+e3UcRBlWUkU3LlQo73pn6kWFUXaJTL95O3i1NvEb/uSltM+Q2Ztp7cdDK8/ls5eWDp7U2bC+mMMU881K2I3jiF9qwRiAeTW9rh2HUX2pX1oVEptm62TjkBsJXYtwgEoy0qunr+Bc5JZ2WLt00ZvTZXILHTWQwCphRVSC4WOEybv5ikK467hPWxWg5wz5twnhpBZ2SG1UBj8nJsbsc6ah4VMyqPdPdl4MZ09N7LIL1ESn13CvB0xOFjJ9IKvaqNUaTgdm29wnaqPvBIlf1zPol8LRwKcddfTmb28ORSdw9rzqeQUK0nNL+P9PXfJL1Eys5dxI2JdRKUVGVznQry0c7+ZZrxAfWPXSYFAIBAIBAKBQCAQCASC5qQw6SZZ1/Rt6gl/LefAYz4UxGlt6nl3LnH1p6c4MbcHh2a15MTcHlz96WmKTbCpR3z2ABGfPaDXfn3RHE7M1Q1wVyvLuLN9Iade78/BmUEcfa4D1xfNoexfYlOHxtu/LexdQSIFaeODM2sTuXQeLu0H4N7NsJ84J+oUBXHX8B/xRJMF58sUNjpJDJXYt9QWQi3JrLZV5946i11AKAoXXfuxa9hgSrOSyLtdt/3YVCRSmU7RtEocWoZr59QIn0BNZFIJ687E6bWvPxNPmJ8joT5aX158VhGt3O346P4OrH2yN2+PCyU1r4SR3xwhs8A0/2J9qNUaHvvtDN//dYv7u/iy6omevD02hCNR6Uz44QTFBpLKa6K1KatN+tdYvt4bRVGZildHtTPaJzI5H1c7SzwcdO2/Id7aGIHIlPyqtpn9W3ArNZ+F+26SUVBKZkEpPx2M5nJCLk8P0i8mWJM1p+KQSuChnv4mz7/yWuWXlHPgRhq/HIzh/i6++Dnrfg+iUrRzmj8xDFuFYZ/H5fhcispUtHSz5dWNlwh+czf+r+xk+FeH2Xct1eQ5GeJIVDobz8Xz9eRO9SZVv731CgEuNjzer4VZx8grLudKQg7BXvb1d24gwvdrHub4fsH0Nc2QT9I2KAykskb5WU0h48wO1KWFeA6Y0qzHqaSpfb+VlBdkc3vVWwROeA2Fq6/BMTMj9iBV2OLWfaxOu0ffyZTlpJB/O8LMswFJhd9ZZqP7PZXbaJ+nprwPNObdp6l9v8Kf23CUKg3F5SquJhfy3p67tHS1YnSI/vtSQzl5Nw+VGoJr+H4jU7U+1NrxQBYyKa3drIlM049XNcS2Kxm0+ugULT48xchfLrPewD1Qm9uZxZyOzee+Dm7YKpr+vbsmzRk3FeJpw90s7XaBQCAQCAQCgUAgEAgEgoYgbGpNg7FcxOJyFXO3xzCrlzcdfQznMtWkvlzExtrUzMlFbEyOBJiWn2mM/y82tcgb1zl04C+99sW//oyjlYyrVy4DEHH+HDMemULHti3xdLKlY9uWPP7IVOJiY+s9xphhgxkzTL848dOzZtCxbUudtrKyMr787BO6hYXi7mBNSz9PnnnicTLSGyfQ2pRIGxmP4ebmjlQqRVaHWJZGo2H1imUEtWjJPYOMF3auzYvPPsnAIUMZd9/9BrcfP3qEK5cu8sxzL6BQ1O1/WfTzjygUCp585jmTj28K5l6/t157BU8vb5569nmT9zly6CB379zm4Uen13s8c67J34FMJmPZytV67StWraFLeCfCOnYA4G5cHMFt2vD1F5+xe8cWPvv4fVJSUunZfxAZGU3jF1ar1YyfNIX5X33DlAcnsXPLBj798H32HzjI4JFjKC6u+xms0WhQKpUm/WsKlEolxcXFXLh4iZdffYO2bVoz4b5xTTL2/gMHiY2LY8aj00zW0yguLsYnqA0Wds4EtA7h+ZdeISvLeCH6SpYuX4mDgwOTJozXaT96/DghwW3ZvG0HIZ26Vo37xjvvUVZWd3Fwc1AqlRw7cYL2IdXxMmXl2vcsQ98RhaWCW9ExlJTorolHj5/A0cMXhYMrHbr04Otvv0elapg+gCEMac6IGA3DmBujUZu0o+tAo8FjgG6BgMb46I3Fzdm30Mby1aflUl+MRsKuHygvzCGgHs0ZU/kn8/MNkXZkLUgkeFQI8WvnaPo1zY8+i4WDO3ZBYTp9nTsNRV1aRM6VQ3Ue3+CcjNwnhjAWo/HfoKMjEAgEAoFAIBAIBAKBoH6KjORYJR9YzrEZuprRkT8/zdlXenJidivOvtKTSBPtd5c/n8jlz/XtdzcXz+HsK/r2u7gdCzn/xgCOP9GCU8935Oa/yH5niOK0u+RGncKtxzg9PcbGYEwL0sanTbPnXdUk/g+tZnTQA/VrRt9aUqEZ3cJMzejAxmlGywxoRmdd2o/b/4hmtIjRaBoaoxetVGmISMing7ct/zmRRI+vz+P//kl6L4zgl+NJaDR1z3ddRBpSCUzuXLeGJjROL9pY3tPl5EKKytS0cLXi9Z23Cf3sDEEfnmLUfy6z/2Z2vXMyxL9NLxpE3rC5NJtmtFyO1MpW59kqkUiQWdkjtahbLx3+Pn3jtKPrtHm3Pe41e18w/ZoUJUahKs7DuaNuLIl9627IbZ3IOr+resx6aktk1agtYQwLOxck0mqfTkFMBKhVOIfpHt+l01AAMmsc31RK0u6SF3UK1+667z7m1IZI3r8YhZt/g69/UyLWWfMQetFCL1ogEAgEAoFAIBAIBIL/dgzlNgidZMOYowmcEbEHNBq8++vaN70HTEZdVkLmlYN1Hiv9/G5kVrZ49NDNK/Lur9VKzIup1kqM37sYp+BeOLbuWueYlb7FxpBbom+TE7ZD01kXkYZGQ712w6ZC2Hsbjrl6knXV4X28pxe30ov59nACmYXlZBWW88vxJC4nF/BkX58mmS9otbvXHL6i17728BU6tfCkfaD2votPz6W1jwufPDaEjW8+yHtTB5KaXcDQt1aQmWeaZmV9qNUapn25me+2n2Ji31DWvjaRd6fcw6Erd7n3wzUUl9V9X/2d2t21MZrzN7OGf/VuhX/11Z6ceqoV51/tyc3/mOhf/WIiV78w4l991Yh/9a0BnHyyBWde7MitJS9Rnv/vWBeNUV3/ttpHnx9thn9VJkeqaLh/tc45SY3U5N1eUZPXSl/HorwgC42ycc9C8TxuPk7fzQNApYbxv10l6MNThH52hmc23iQlr+685aO3c0nIKWVyZw+dvGsLmWk52MYQn3fD+OZwAkVlal4dbHr9grow9DxPPric44/rxoBG/fI05+b15OSTrTg3rydRJsbLXJk/kStG4mXOGYmXiXhzACdmt+D0vyxextjvnMp8YUNxjLk3T5N6eDWtZyxAYkYNmCwzNOElMgtkRuJlJDXiYizsXQxqJ9i17Iy6rBhlYY7J86sP8f3+Zzgdm0/bT04T+MEpBv5wkayixml7iM+x+amcV06xkvzSxulz5CVEkXL5iF77rT3LWPuAJ9l3rwGQGX2R41/PZsdT3dgwJZAdT3WCzpWCAAEAAElEQVTj+NdPUpgWX+8x/nr3fv56V1/j6NT3L7DjqW46baryMq5u+prfn+/L+sn+bJkRyqkfXqQkN6OBZ9i0yK1skVvpx+a4ttbargozEvW2pV0/Rcz+lfR45muznukJZ3Yht7IloI9uvFuLQQ9RnJVC5i3z63wYQnxnG0ZTv1s1FSq1hlKlmuj0Yl7ZEYObrQUP1oglL1epsTTwPm4p1/4+vJ5qvt0iKs2w1mBlW1Q9WoNNVf9DIBAI/ldpmupYgv8JVEW5em1pxzZiG9gRW/9QAEoz47H2aoVbj/uQ2zpRlptKysGVXP5oNJ0/PoSFfeMLe2nUam589zh5t07jO+oZHFp1pTQzkbjtC7jyxUQ6vbsLmaXxRDqNRgNq037INLZAXPyOb1CVFhFw/6tmGf6LEiMBsPUL0WmXyi2w8W5dtb0hpBxZQ/7ti3T+uG5ntjkUJd4kfud3tHtuETIDP1hq0tBrolEpybt5GhuftvX29eg7iaR9i4lZ+TpBE99Cbu9KXtRJUg6twnvw9CpDWXHaXdRlJXrXGcDGP4Sc60dQl5c02HHxT+BgJWfXjSw+KVHiYKW9f2+lF3EhsYCPRwdV9Rvb3pWx7V2r/lapNQxr60ynL8+x9UoGM3t51x7abHZey+RgdA6LJrdldGj1sUK9bBn96xU2XEjnsR7GE182XEzn5W0xJh0r8YPejZ5vJddTCvnpeBKjQlyqfpAa4nBMDkm5ZbwxNEBvW6CLFa8N8aeDty0KuZSLCQX8dDyJIzG57H6yI94OxpMCD0fnsPlSOjN7eeNiU3cAiFwmYeP09jy3+RZjfq12IvcOcmDR5LZYyIwnkStVGuZuj8HWUsYTvXU/7+4B9oxr70prd2tKytUcvJXDz8eTOBuXx8bp7ZHWU8S3NrnFKjQacLLWf6Y62WjbsouNGwSzK4yFzgb2bxBSGWnHNuAU2l+nufaaVlJrTStvpjUtssaaZl+xpsVvX8DVLyYS9i9Y0wpjL6MuLcLaswUxK16vEFgtwsYvBP/xc6sSNkHrLJHbOumNIbXQCiEUxt/AvlXdgU/molGr0KiUlGTEE7fpUywc3PDo96BuH42GmKXzcA4bikvn4aQdW2/y+CVpsZRmJuDSZUSTzrs2qrJiStJjcekySm+bjV8o6rLVlKTHYu3VyugYuZHHkcjkWHm1NNoHQFmUR2HsVRxD+uq0ew+dyd0NH5F6ZC2uXUehLi8lcc/PqIrz8R46s6pfXtQpQCvWev2bR8i9cRyJTIZjcG8CJ7+LjXfdBc8bcp9o1Co0ajVlOSkk/vE9Go0Gr8HT6zxOcyDWWfP5YGQQDgo5T6yPQl1hE/V1tGT9Y+1p4Vp3MY6vDsVzN6uEJVOCzT7utisZlJSrmdJFP4Dvid7eWMmlvLXrDq/suA1o16ilU9vRPcBBr78pZBcrDa9z1vWvc41dJwUCgUAgEAgEAoFAIBAImhWpjOSj63Fpr2tTTzm2AfvAjtgFVNjUM+Kx8WqFZ8/7kNs6U5abSuKBFZx7fxQ9PzuEpb2rodHNQqNWc3nhDHJvniZg9DM4tulGSUYCd7Z+xYXPHqDbB7vrtalrTLSpSxvpJzaHynmpSgrIunqYlGMbCBj5pMlziFrxJtd+ehqppTWOrbsSdN8cnNr21OuXdGg1+Xcu0vPTQ0bHyqlh/7301TSybxzXJoa160Prh97B1qdNg87RENk3jgFg51tt/9Moy5Aash9XCC4XxN/QS7A+89YQyvIysLB3wbXDQFpOfBUrVz+9MUya0/XjANj61u+TNgUHKzm7LqeQV1yOg7XW73czNZ8LcTl8MqFjVb9x4T6MC69OQFWpNQwL9aTju3+yNSKRWQPqtvubwo6LSRyMTOO3Gd0YE1Z9rFAfR0Z+c4T1Z+OY3reF0f3Xn41nztqLJh0r5ZuGi2xeTczlxwPRrJjVE1uFnMxCw0lj2YVlONtY6rXbKuRYyqRk19hvTJgPS2Z054U1F/h8lzbWwtpCxndTO3NvuPHE39zicnZdSeaeYHf8nE0Xj9x+IYmnVp6v+vuhHv4seFBXrFyt1vDSuouMDvNmaKin0bFScrWCjT8ciCbE24Hvp3ZGIpHwy6EYHv3tNGtm92JQO/MT2QtLlczdcImnB7Wmva9xoWeAfddS2XExib1z7zHbT/zG5ssUlal4cVjTfKcMIny/ZmGO77ex5EWdBLUKGx/T/DzqshLOzgmnPD8TSycPXDqPxH/8K1jYOde5X9rRtcis7XHtZrgI8I1vH6M8PxO5jQMOwb3xH/8Ktn7tDPZtKhri+72z5l0U7v54DZlB3s1TBsctSozExqeN3udv66+NeypKiMKhdfeqdlOuqUPr7tgGhhG//WsUrn7YeLehOPU2cVs+xzawI461vltNQXP6foU/t2Gk5ZfReUH12tXZz46N09vriPw3hoJSFW/+cQcfR0seqpFYkl2sTQIy5meNzymtd+z7O7oxpK0TPo4KMgrLWReRxsvbYojNKuHVIfoxXZWsi9AmMD1kwJ/c1DRn3JSTtQUaDeSWKLGy0H83EggEAoFAIBAIBAKBQCCoD2FTazx15SJ+eSAetVrD3EF1+y9NzUVsrE3NnFzExuRImJqfaYz/LzY1mUzG6hXLGDh4iE77mpUr6NS5Cx06hgEQF3uXNm2CeWDSZJydXUhJSWbJr/9hUL+enLlwFVc3/cKJ5qJWq5kycTwnjx/jxZfn0aNXb+LjYvns4w8YM3wwh06cwdq6bj+XSmWan0su/3vjMVQqFfn5+RzYv5c1K5fz3Isv1zmHg3/tJy4ulrff/8ig6KEhli9ZTMS5s5y+cNVonxPHtAVm7eztmXjfGI4cOohcLqffgHv4+PMvaRtc7b86fuwobduFsGPrZr747BNux0Tj5e3Ng1Me5q13P8DSsvnv3YN/7Wfd6pUcPH4amcx0f8HKZUuQSqU8/Oj0evuac03+DhwdHdi6fSe5ubk4Omr96Dciozhz7jzfffVlVb+J949n4v3jq/5WqVSMHTUS76A2rN2wkeefearRc9mweQt/7tvPxjUrmTC+OgajU8cO9Ow/iGUrV/P07FlG91++ag0zn3zGpGMZ0g8xh5SUVHxbVscE9Ozejb92/46dnb7oc0NYsnwlMpmMx6Y9bFL/Th070Omzj2kfqvWhHjl6nIU//MSBQ4c5ffSg0XlFRt3kxKnTzJ45Axsb3RiRxKRk0jMymfPKq3z4ztuEhARz4OBh5n/1DfEJiaxaurhxJ1nBBx9/RnTMbbasX1PVFtymNVKplBMnTzHj0WlV7TG3b5OckgJAdnYO3t7ad6TRI4fTtUtnWrVsQXZ2Dpu2bmPeG29z8fIVVvz2a5PM0xTNGRGjoaUxMRoatZq04xuw8gjCMbjp3l2NkVsRy1dfjEddMRpFiTdJMFFzpin5u/LzlUV5ZJ7/A8eQfli5G/dFV41p4JpqlOVIDLyDSiriIwsTruPW8756x64az4T75N+Sny8QCAQCgUAgEAgEAoHgb0AqI/Wofo5V6vFamtEZWvude88KzeicVJIPruTSh6Pp8knTakbn3jyN36hncGjdlZLMROK2LeDK7Yl0eu+ft98ZIvXoOtBo8Bow1eR98qJOc/LptqjLS7HybIFn/4fwHf5EvcVglEV5FMRexamWFiRo84FOv1idD+TaeSQB99efY1UXlZrRIaZoRm/XakYHTmhGzeh+k0jct5joFa/TYlItzeghBjSj/fU1o23/CzWjRYxG42msXnRWUTmlSg3HbudyMbGA14YE4O1gye/XMvlobyw5xUpeN6AxDZBbrGT3jUwGtHLE18m4pnQljdGLNpb3VFmo86djSbTztGHhhNZIJRL+cyKJ6WsiWTUthIGtneqdm+55/cv0okHkDZtJc2lGew95nKifniRh57d43jMNJBLSjm+gIPYybZ/8sd55/R36xsWpt8m7eRqPAVMb7J8y9ZpolNrvn0Su//2XyBUUp96pWpMqa0fYGKgtYW2ktoRGrQaNGmVhLhnndpJz7TAtHv64xjzLK8bQ9TVJ5HKQSCiMv2HGWWupfPfxNOHdx9A6r1EpyY+OwDlsMEl//oek/b9RlpWMwtUPr8GP4TPiSZNjb5oCsc6aj9CLFnrRAoFAIBAIBAKBQCAQ/DdTbiC3Qegkm4chTeCChEgs7F1ROOnabuwqbPOFCVF1jlmYEIWNdxu9uVbuX5AQhWOb7pRkJlKSEY9b52HEbPyMpCNrURZkY+PVioAxz+Bdo/agY5vuSC0UqMvr16QzRkGpijxhO2wQKrWGjZfSae1m3WAbnbkIe2/DaIieZF11eEeHurL4oWDmbI3miwPxAFhZSFl4f2vGtW/887MSBxsFf5y5SV5RKQ42Wj9MVGIGETHJfD692s91b6923NurOudUpVYzvEsrQp78gU3Hr/PkqG6Nnsu2Uzf469Idlr08nnE9qp+N7QM9GPrWCtYeusrjwzsb3X/t4as8/8suk46Vue61Rs+3JgZz/mrFDJVkGPCvHlrJ5Y9H0/mjJvSvfl/hXx35DPata/lX3/nn/as6x1MpUavKKU6O4c7a97DybIlrl9FV2436EqtywWr5V39+koTfK/yr1PCvzq7fv1o1J1Nr8i6rqMkbbrgmr6bCv9gYxPO4+UjJ1/qfZ6+P4uGunswb7M/tjBLmH4jjgaXX2P90GNaWhp/fayPSkEnhwc7uTTon8Xmbz9XkQn4+nsSyqe2wsZSRWdj4750pOdylteJlypopXuZGjXgZh4p4mbjtC7jyxUQ6/QviZYyRe0NbZ8SmVp0RVVkx0Uvn4jNsFnaBHcm6sNfkMc3RhPcZOpM7RuJlfGrEyxidf+RxLOxdsXBovI5MJeL7/fczpI0TnXztCHS2IrdEye/XMtlyOaNRY4rPsfn58VgSn+2Pa5KxJFIZtw+uwytsgE77nUPrcG4ZhnNQewAK0+Nx8GlNYN/xWNo5U5yTSvSfy/nztRGM+fYoCoemsV0dnf8Y6TdOEXLfc7gFd6MwPYEr67/kr1sXGPHFn8gV/07bVepVre3K0V83PklZWsyZn16i7ZjZuLQMI/HsnyaPmRMXiYOfvu3KOVC7zubG3cC9XXdDu5qF+M6aT3O8WzUVbT45TalSG2DX0tWKTTNC8XWsjiVt427D0du5JOaU6sSYn4nLAyC7yPxzqdzHWNxbZfy20f2boP6HQCAQ/C/z9ylhCv71qEoKUBblIbfROsGKkm5RcOcCLWskc7h1Gwvdxlb9rVGrcOk0jDNzOpF+ais+w+r/wV8fGWd3knP1IO2eXYRr12ojtY1/KJc/Gk3a8Q14D3rM6P5pxzcQveRlk47Vd0lig+dZEHeVxN0/E/LiMmQKG7OST8sLsgGQ2znpbZPbOlGSEd+gOZVmJ3N3/UcETXoLhbPxF1tz0KjV3Fr6Mq5dR+ESNqTOvo25JnHbv6Ik7S7tnl9Sb18rN3/C3tpB5A8zOf96n6p276EzaTHlg6q/lYUV19lWPxnZwtYJNBqUhblYOv13JOQCOFjJyM5WsuNqJtO6aYu9rr+QjkIuYXzHaiNaYamKhYcT2HUjk/icUlTq6jFupRc3yVz238zG0UrGsGBnlCpNVXt7L1s87Cw4eTevzh9Yw4Kd2TW7o9HtzUF8dgmPrYnEx8GSL++tuxjy2og05FIJD4brOyAmdtJt69vCkT4tHLh38VV+PpbEh6MNF0C+klTAUxtv0sXP3qBTujblKjVPb7xJZFoRX97bklZu1sRll/LtkQSmrLjBhumhVT+0a6LRaJi7PYYzcXksmhys86MN4LVaRQyHtHXGz0nBR3tj+TMqi1EhDTPC1JU2Z1JKXRPl3cmtHciK2IWy6BO9Na1FPWuac6dhnJ3TiYxTW/FugjUts2JNC661ptlWrGnpxzfgVcealm7GmtangWtaWbZWyDdx90/Y+LajzayFIJGS9Od/iPxuOiEvrcK5w0AAbHzakht1ElVJoU4Ca/6ts0D1c7cpOfVUGzRK7Q93K8+WtH91EwoXX50+KQeWUZQYSfjTh8waW6NSEr1sLjIrW3yGP9FUUzaIqjAXNBqDDvfKNmWB8euXc/Uw6Sc24z1sJhZ2dTu/bq96C3VZEX5jX9Bp9xn+BFJLK26veouYZa9UHbvdC0txaFNtBCzL0d4T0UtexrXbWELmLKc8N424LV9w9bP7Cf9wP5ZOxguON+Q+ufj2IIpTtEY3C0dPQl9ejV1QWJ3n2RyIddZ8vj2SyC8nknh5oD89A+0pKFWx9EwKU1ZcZ+2joXTwNpzsvuZ8Kt8dSeTJPt6MaGe+Q3ddRBrONnJGhujvu/5CGu/tucP0Hl4MauNMuUrNpovpPL42isUPBZstPlFJY9e5Rq+TAoFAIBAIBAKBQCAQCATNgNzagfTzu3T8xIVJt8i7fYG206pt6h7dx+LRXdem7hY+jGPPh5F6civ+w40XDjSVtDM7yLpykA7PL8ajW7VN3S6gPefeH0Xy0Q34DTFuU085toEbi18y6ViDlyc1er6mEvvHD9ze+Jn2D4mEwLEv0Gpi/Yl0cht7/IbPwrldbyzsXChKvUPc7p+58NlEwl5eiWvHgVV9S7OSiV7/Ea0efLtOP3FphU/gxuKX8Og+lrCXVlCWk8btLfOJ+PR+enz8F4o67L+mUhB3nbhdP+HedVRVgjto7cc5kSdRlhQir2E/zrl5Bqj2owNYewTRcuLr2Ad0QGqpIO/2BWL/+Jmsa4fp/v4eFC7mBZSWZiUTs/FT7Ft0wi18WCPPUIuDtQXZRUVsv5DII32CAFh3Og6FXMqELtW+jMJSJV/vvckfl5OIzypGpa62+d5MzW+Suey7noKjtQXD23uhrGG07uDrgIe9ghPRmUzva9iHCTC8vRd7XhpgdHtToFSpeWntRe4L92VQO33BxdqYalM9cCONZ1dHMK6TD/eG+yKXSvjzWgovrrlAmVLNlJ6G/bKbzydQUq5mas9As85jYDt39rw0gIJSJefvZvHDgWiyi8pZOqM7Uql2Zr8cjuFOegHLZ/aocyy1RnsvWMikrJndC7sK32/fNq70+fQA3+y9adK1qs3Hv1/HQibh5eF1C6LnFZfz6sZLPDe4DSHe5iXMz98VyebziXwyoSOd/J3MnqOpCN+veZjj+20MquICbq98E0sXHzz6P1Rvf1v/UGwnh2Ljq02Kz4s6SdLeReTeOKZNsjYinlyUHE1+9Dk8B05DVivJw8LRA7+xL2DXsgsya3uKEiJJ3PUDVz4ZR8c3tmEb0L7R52kMc32/WZf2k3l2J2Hv7UEiNS6+rizIxspd/5lUNWYNP6up11Qik9Ph1Y3cXPQcVz4aU7W/Q7veBD+zCKncwuTzNpXm9P0Kf27DcLGxYNfsjpSp1NxKL+an40lMWnaNTTPa42nfuELwJeVqZq2LIiGnlA3TQw0KeRjT4TZFn/uHiW10/h4T6spjqyP58VgSM3t542qrfw8rVRo2Xkwn2MOarv72Jp1HU9CccVPCnywQCAQCgUAgEAgEAoGgoQibWuOoKxfxQkI+i08ls2paCNYWdRfoNTcXsaE2NXNzERti0zInP7M+/tdtag6OjuzcvpXc3FwcHR0BiIq8wfmzZ/jym++q+o2fMJHxEyZW/a1SqRg5eixtArzZuH4tTz37fKPnsmXTBvbv/ZOV6zZy7/gJVe0dwjoxqG9PVq9cxqzZTxvdf83K5Twz2zR/W26JacJcTcE3C77gg3feBEAikTD31Td454OP6txn5fIlyGQyHn7EuF+vJkmJibzzxqt88MnnePv4GO+XpPXvPTt7JvdNmMiGrTtJTUnm4/ffZeSQezhx9iJe3to4h+SkRDIz0nl17hzefu9DgtuFcPjgAb5ZMJ/E+HgWL19l0twaSkFBAS888yTPz5lLx7BOJu+Xk5PDzu1bGTRkKP4B9edHm3NN/g6cnJzIyrrD+k1bmD1zBgDLVq5CoVAwZXL1d7CgoICPP/+CLdt2cDc2DpWq+p6+EVl3IQJT+WP3nzg5OTJuzCiUymrRrPBOYXh5enL46DGenm08Fmvc6FGcPnqwSeZSH25urpw+epDSsjJuREax4JtvGTJqLAf2/IG3d+O0LLKysti+8w9GDBuKr6/x71dN5jz/rM7fw4YMJjw8jAenPsripcv1tleyZPkKAGZOf1Rvm1qtJj8/n9XLf+OhSdp7YdA9AygsKuLbH37i/bffoHWrVuacmh6Lly7n0y8W8PKLz3HfuGq/rYuLC1MfepCVa9bRrWsXJk0YT0JiEk899yIymQyVSoW0ho/5h4Vf6Yx737gxODs58eMvv/LS88/SOdz077QxjGnOiBgNfRoTo5Fz7TBlWUkEPPBGg45tDqXZycRu+gzboE441yjiXZu6YjQ0ajXRFZozzvVozjQ1f1d+fsbpbajLSvDsP6XeORm7ptY+bci5fpTSzEQUrtXxg/m3ztQ7T4NzN+E++bfk5wsEAoFAIBAIBAKBQCBofuTWDmQayrG6XUszuvtY3GrlAruED+P0i02rGZ19RasZ7dZN13536cPRpB3bgPfgujWjb/1mmv2u39KGa0bXRKNWkXZ8I9berXU0F+vCpdMQ7II6YeURiLIwl4yzv3N3/UcUxl0jePb3de4bs1KrBek/TlcL0tY/lBaTQ7Hxq8gHijxJ4t5F5Nw4Rvi7xnOs6j43NbeWvIxb11G4dKpfMzph98+ENkQzeptWMzrERM3oTm/v4Mb3Mzn3mq5mdMupNTSjC4xrRsv/CzWjRYxG42gKvejK9O38UhWrHwmpyivq19KR9IIyFp1M4vn+vgbzn7ZezqBEqWFKF9Py7BuqF11X3lN1zrGEVdNCsKuYZ98gB/p9d4GFhxP+MR3NpgzEEHnD5tFcmtGuXUfT7rnF3Fo8h7itXwAgtbSizcyFuHUfV++8/g5947Qj6wDwNCGP2RimXhMrr1YgkZIffRbP/pOr+pWk3aU8N7Wir3ZNUtZRW8LCSG2J26veIPWQNh5EIrekxdQP8Rr4SI15avMo86LP4hjSt3qe0ecq1kPz/EwatYr0E6a/+8QbqA1RXpCFRllK7o1jFNy5SMCE11C4eJNx9ndiN3yEsjCHwAdeN2tejUGss+Yj9KLr39ZU+wsEAoFAIBAIBAKBQCBoemrnNgidZPMwpglcXpCNhQHbpkxhg0RuqaMJbIjygmysPfTzyirHrNy/Uvs4+dhGrJy9CX7kE2TW9iQdXsONRXNQK8vxHfgwAFKZHCs3f4qSoxtyqgBoQNgOG8jB6BxS8sp4Z7h5uryNQdh7G0ZD9CTrqsN78FY2z2++xdj2roxr74pMKmFfVDYvbY2hXKVhcmfztZAN4WirILughK0nbvDY0HDtvA5dQWEhY2K/6udTQUkZCzaf4PczUcSl5+pqdyeaHttQF39GxOBoq2Bkl9Y62t0dgzzxdLLl+PU4Hh/e2ej+I7u2Zv8n+vmJfwdGc/6mmuBffakTGae34j20Cf2rzzTCv7rURP/qb42LGSrLTePcy9Wfp13LznSYt1HHZ1ivL7Ggln/12cXc+q1h/tVKTj1dqybvPCM1eRMiCf/4kNFxLBzcQSIFjdpon/oQz+Pmo/IRNq6DG29VrLF9WzjiYW/B42uj2Holg6ld9f3k2UXl/BmZxcDWTng7KPS2NwbxeZuHUqWtBz+uvWuDfZiGMPY8b1lPvIxLp2GcmdO0MaA5V7UxoDWf5zYVz/O04xvwruN5nmZGvEzfBsbLGKIw/jqJe37CpcsobP1DdbbFbf0S1Gr8x881e9xyMzThK+NlYla9RXSNeJmQWvEyhkg5soa8yJO0mPIhEmndOkXmIL7ffz+fjtWNbxzRzoUDt3LIKVYa2aN+xOfY/DwY7k7/lo7kFCt5dvMtsosa/nlZ2DiQcGoXZbPysLTVPtNzE26SeesCXWd+WtUvoPc46F39rqhWqfDtOoytMztw9+gWgsc80fATqiDuxHaSLxyg37wl+Peq1jJxCmrP3tdGcOfgetqMnG50/zsH13P6xxdNOtaUzamNnW4V2XevcWPbj/j1HI1zkG49kSvr5qNRq+g4eZ7Z45blZ2Prqf9Mt7TXxqqXmqn3YAzxnTWP5nq3aiq2z+xAmUpDbHYJi04mM2nZddY/Fkqwhw0A07p5sPJcCs9tvsXn41ribmfBjiuZ7LyqtVdITSm2YYTG1O9oiv0FAoHgfxX9TB7B/180GjLO7MBr4DQA0o6tRyJX4NZzfFUXVUkh8TsXknl+lzYZRF0tPFmcfKtJppF9aT8yG0ecOw1Do6r+MWIX0B4LRw9yI0/WaYxxCR9G2Du7mmQuxtColEQvmYtbj3GNLG5o+E1E0sA3lJgVr2PrH4rnPQ83Yk66JO39lZLUO4S8sKzOfo25JilH1pDw+3f4jHgS184j6u1fkhHPjW8fw8LBneBnfsXC3pX82xdI+P1bVCWFtHlcV4Cy7gyI/663QYVcSrivLesvpDGtmycqtYYtl9MZHuyCs011UbpnN9/i2O1c5tzjRydfO+wVMiTAI6tvUKJsuLOkJukF5eSWqAj68LTB7VlF5XXu72wtx0Hx9y1DCTmlTFp2HblUwvrHQnWuV22yCsvZF5XNkLZOeJhYrLCznz0tXa2JSCgwuP1qciEPrbhBCxcrVk5rh0JuvDBnJWsj0jhwK4ddszvSydcOgJ6B0CPAnj7fXmDxyWReHuSvs49Go+GV7TFsuZzOwvtbm5zo9EAnNz7aG0tEfAGjQlxN2qcSR2sZEglkGzD45VQYlZysjX/WzjbabY0xQNVEZuOAMj3b5DUty8CaVtREa1qWkTXNtsaaVpfD2PnvWNMqHKgSmQWhL61CZq291xzb9eXCG/1I2LGw6rnuNWQ6WRf/5NbiFwl68B2kChtS/lpKXvQ57WCS+u9rc+n41nY0yjJK0mJJ2ruIa19Mov289dj4BgNQkpFA7KbPaDHlAywd9YM9jKHRaIheOpe8m2do9+wiPSd0s1HXumNkW0HsFaJ+fgr7Vl0IrEeQN27LF2Sc2kKLhz/WE1lNPbqeO2vew3vIdJw6DkKjLCf9xCYiv3+c4OcWV6/fFd5b+1ZdaT1jQdX+Nr7BXHp/BCkHlhEw4TWjc2jIfRL87CJUpUWUZiWSenAlN76ZRrsXluLYro9e3+ZErLPmcSu9iAUH43l7WCBP9a0WSh/UxomBP1zi/T132TRDv0D2+gtpvLbzNg939WhQEN71lEIuJRUys5eX3nqaU6zkzT/uMKWLB++OCKpqH9zGmYlLr/H6ztuceqmL2cd0tpYbXueK61/nGrtOCgQCgUAgEAgEAoFAIBA0JxY2DhSnZ5N6ege+g7Q29eSj65BaKPDsfX9VP2VJIXe3f0P6Oa1NXVPTpp7U8ETXmmRc3I/cxhG38GGoa/mJLR09yIk8UWdyslvnYXR7f3eTzKUp8e43GZf2AygvyCb7xnHidv+MqjiPto98Uud+9oEdsQ+sDlZ0Cu6Je9dRnHl7MDHrP8a148CqbZHLX8POPxSfgfX4idVa+6Vj666EzKz2qdr6BXP23eEk7F9Gq4nG7b+mUJwez6VvHsXKxYd2jy/Q2eY3dAYZF/7kxq8v0Pqhd5EpbEjYX20/lkir7eTefSfq7Osc0hfndn0599E4Ynf9RNtpdRderUl5QTaXvn4EjUZDh2d+QSJtGn+GQi4jPMCJdWfieaRPECq1hs3nExjRwQtn22of49Mrz3P0VgYvD2tLeIATdlZyJEh4eNEpSsqbyKacX0pucTn+r/xucHtWYVmd+zvbWOBg5dAkczHGr0duE5tZyK+PdSW3WGvjLijR/l9aria3uBw7hRyZVIKzrSVXk3L1xigsVVKmUuNUcX01Gg0vrbtAr5auLJxSnRg5INidvOJy3tpyhXvDfbA1YC9feyoOVztLRnY0r6iok40l4QHa4/dr40agmy1PrTjPnqspjA7zJiG7iC93R/HW2BAs5dKqc1WpNag1GnKLy7GUSbG2lFXdJ91buGBXQ4zZxlJO71au7LmSYtbcACJis1l2/C5LZnSnVKmmtMJvodGASq29zlYWUhRyGZ/tuoFcJuXx/kFV8yws1T5/i8tV5BaX42Al14thWbAnim/23eSN0e2Y2V+3qHhTI3y/5mGO77ehqMtLiPxxFqWZCbSft8Gk4gA+w2fr/O3UfgC2AR2I+mk2qUdW622vJO3oWgA8+0/V2+bccRDOHQdV/e0Y3AvnTkO4+M4Q4rYtIOSFpeacVsMwwferLMrj9orX8B31DLYVBRbqGdSkbaZeU7WynKhfnqYoMZJW07/E2qsVJelxJPz+Lde/mkL7eRuqEjebiub0/Qp/bsOQyyRVcUfdAxwY2NqJ3gsv8OPRRD4c3fDneKlSzax1UZyNy2PZwyF08dMtHOBsrf1MsouUuNvpxl/lFCsb7COd0MmN/TezuZRUwOA2+oU6DtzKJq2gnGf6/T1xIM0ZN5VTXI5EgsGiDQKBQCAQCAQCgUAgEAgEpiBsag2nvlzEudtjGBXiSicfO3IrbEOVfpn8UhWWcmlVMU1DGMpFbKxNzZxcxIbkSJiTn1kX/19sak5OTtzJymLLxvXMmKW1269asQyFQsHEyVOq+hUUFPDFZx+zY+sW4mLvolJV+7miIm80yVz+3PUHjk5OjBozDqWy+rqFdQrH08uLY0cOM2v200b3HzVmHAePG/7u/pM8/MhjDBw8hOysLI4cPsh33ywgLy+XL7/5zmD/rKws/tixnaHDR+Dja5oN+aXnn6ZDx05Mn1m38Jm6Ih6je89e/PDLoqr2kPYd6N+zK4t++Yl3Pvioqm9+fj6/rVjNxAe1hUYHDBxEUVEhP33/LW+8+z6tWrU2aX4N4f2338DCwoLX3nrHrP02rltDSUkJj84wTdjTnGvyd2ClUNC9axeWr1zN7JkzUKlUrF67gXvHjsbFpToP++Hpszhw6DBvvz6Pbl274GBvj0QiYeyESRSXlDTJXFLT0sjJycXK0c3g9szMukXFXVyccXRs3hiPSuRyOd26anPx+vbuxchhQ2kVGsb8r75h4YL5jRp79boNlJaWMnN644TL7793HLa2tpw6c9bg9vLyclauWUenjh2rzqUmri4upKSmMmKobmH7kcOH8u0PPxFx8RKtW7Vq8PyWrljF08/P4YnHp/PFpx/rbf/p26/RaDQ8N2cuz7zwElKplGlTH8LDw4O9+//C1bVunYCHH5rMj7/8yqkzZ+kc3qnB86zCDM0ZEaPR8BiNtKNrkcjkePR7sFnnWF6QzY1vHgE0BD/1c51xc3XFaFRqzrSrR3OmWWnm/PzUo+uQ2znj0mVknf3quqae90wj5eBKbv76HK0e/RwLR3cyTu8g4+zOinmaF7doyn3yb8nPFwgEAoFAIBAIBAKBQND8yG0cKKmVY5VaYb9z7zW+qp+qpJC4HYY1o5vMfndRa79zCa9DM3pw3ZrRnd5tXvtdbbKvHKQsO4WgB033j7R65FOdv127jEBu60jyX0vxHfEkdoEdDO4Xu+UL0k9toaUBLUjfEbr5QM7tB2Ab2IHIH2eTcni13nZTSPzzV4pT7xDy4rI6+2lUSm79VqEZXSM/2RRSDq8h/vfv8B3xJK5dTNOMvr7wMSwd3Ql8tlozOn7nt6hLDWhG18V/kWa0iNFoOE2lF10Zj2BnKaOrv26e06A2zuyJzOZmehGda+VAAay9kIarrZwR7fRzlQzREL1oqDvvqfK8u/nb68ScWFvK6BXkwJ5I84tH/dv0okHkDZtLc2lGZ185yM1fn8et+1hcu49DIpWRdXEf0UteQq0sx7P/5Drn1dz6xhqVkrQTG7H2Dca+VdeGXDqzromFnTPuve4n/cQm7Fp0wrXbWMqyk4lZ/ipIZdr7T8/XY3ptCb8xL+DZfyrl+RlkXdzH7VVvoyotxnfkU4D2nnNo24ukPb9g7dUKp/YDKEq6ScyK10EqQ2Kmn6ny3SfQhHef1Bq1IVxq1oaoiPtQFecT+vLqqs/BMaQf5bnpJO9dhN+Y503KM28KxDprHkIvWuhFCwQCgUAgEAgEAoFA8F+PRiN0khtIXZrAWsyPzTd9/4r/K2zb6vJSwuauwtrNDwCXDvdw7r2R3N3+Nb4V+slqZTmlWckmHNc4CrlE2A4byLqINCxkEiZ2Mr3uY2MR9t6GYa6eZF1+VY1Gw9ztMfQMdODr8dW5rANaOZFXouLtXXcY194VG0vjefKmorCQ07mVN2sOX+GxoeGo1Go2HLvOqK5tcLazrur35Pc7OXI1lrkT+tC5pRf2NgokwEPzN1FS1jS+yvTcQnILS/GaZujZCJn5xXXu72xnhYONZ5PMxWw0GjLO7sDrngr/6vE6/KsRzehfvWzEv+pf4V+N+uf9q5VY2LkQ9s4u1OVlFCffInHPT1z9chIdXt2EpZP2c/QaXOFL/K3Cl2hpQ8qBpeTFGPGvLnoet24V/lVZLf9qv7r9q5V0fLOiJm96RU3eLyfR/pVaNXk311+TVyKVIrd1QlmQ1cArJJ7HzYlzhX9pYGtHnfZ7WjkhkcCV5EKD+225nEGpUsPULvrPmsIylYE9TEd83uax+FQycdkl/DKpTZWeTH5p4z4DwKwcbkMxoMVN9DzPNhIvoxMDWsfz3OVvfJ5XUpIRz/VvH8PS2YfW07/U2ZZ/+wJJ+xbT/qVVyCytjYxQH6ZpwqceXc/tingZ54p4mbQTm7jx/eO0qxkvU4vsywe4veotXLuNwXvo4w2co2HE9/vfgb1CVhWn0BDE59j8eNhbVv02cre1aFRMpIWtI2UF2cQd30br4Vq9lNsHtLarwP4TqvqVFxdybdPXxJ/6ncI0XdtVXkLTPNMTz+/DwtYR327DdWxXzi06YOXkQdq147QZOd3o/r7dhzN8/p9NMhdTKUiL48hnj2Dj5kOPp7/W2ZZ5K4Ko339l4NtrkSsa9kw3FL9Xta3O573piO+sedT1blWiVJNbrMROIUMm/WfyBjr6aO0tXf3tGR7sQt9vL/D5/jiWTtXWlWnjbsPih4J5bedtBv94CQAfR0veHRHI27vu4uVgaXRsY1TeJ4Y19OrXGmyu+h8CgUDwv4J4CgqqkFgoSDu+Hq+B09CoVaSd3IJL5+FY2FUnj0X9+iy514/hP24Odi06IbO2ByTcWPgI6vKmEZ4sy0tHVZTLydlBBrfXZ+iV2zpjZ928wpNJ+xZTkh5H8NO/oCzSFg5VFecD2mKFyqJcZFZ2SKSGnUeV11RZkK1n3FYW5iC3dTJ7Thnnfifn6iE6vr4VVXGezjaNshxlUS5SSxukctOFkUszE4nb9iWBD7yJVG5Rda4alRKNWo2yKBeJ3BKZpXWDr0nq0fXELH8Nz3seNjmhOXbTp6hKCgj/YB8yhQ2gLcZoYedC9NKX8eg7Ecfg3shtq69zbcoLc0AiafJChH8HD4Z78OYfd7iVXkRsdimp+eVM7lx9H+WVKNl/M5uX7/Hjuf7VSZmlSrVJBhmFXEJeib6Bt/YPJhcbOc42clZPCzE4jm0dIuMAGy6m8/K2mHrnA5D4QW+T+hkjIaeUiUuvoQE2TW+Pj6Oizv6bLqVTptIwxYADoi40aAzGkVxNLmTy8uv4OSlY82ioySLg11KKkEmho7duMlqgixXONnIi04p0j6/R8Mr2GNZfTOer+1rxQAMCGxryW9PaQkaQixWRqUV6226kFWFlISXQ2cro/kHOVlhZSA3u3xCkFgrsWoTrrGnpBta0mxVrml+NNU2ChOtNuKaVV6xppxqxpsmbeU2TV1wT+9bdqpJxAWQKaxyCe5F1YU9Vm1Nof1o//jV3139IxOta8U5rn7YE3D+PuM2fo3A2r8i1KdhVFKi3b9UVl/DhRLzRl9jNn1cV3b2z6k1s/IJx7Ta6ev0p0wYzqEoLURbl6T3rNRoNMUtfIf3kFtrMWqibJNpMyGwdQSIxuCYpC3MADK7/BbFXub7gIaw8WxAyZyVSC+PPr/jtX5Pw+7cETHgN7yEz9I5xZ/WbeA6YQtDkd6vancMGc3X+RG6veJ2uX5zSzqPinnDqcI/OGLYBHbBw9KQg9mqd59qQ+6QyAMC+ZWdcO4/k0vsjuLPmXcI/3F/nsZoDsc6azvWUIjQa6OSru05ZyKSEetlw6m6e3j7rL6TxyvYYJoW7M39cyzoN1MZYF5EGYDBQICajmJJydVXQWE3CfGw5eTePwlJVvdevNu08bQyuU5VtwR42Rvdt7DopEAgEAoFAIBAIBAKBQNCcSC0U2LcIJ/noenwHaW3qKSe24FbLpn7952fIun6MoPvm4NAiHLm1PUgkXPpqGqom9BMri3I5NNOwkFu5AftqTf4OP3FDUDh5oHDyAMC140AsbB2J2fAp3gMewr7CBm4qFraOuHUaRuLBFajKipFZWpN29neyrhyiy1vbUBbp+4nLC3ORKbR+4srP1KVWEL99YAcsnTwpiL3S4PMEKM5I4MLnE5HI5IS/tkHnHgJwad+fkFnfEL32A07O09olbX3a0mLCq9ze9BkKJ+86x3do1Rkbr5bkxUSYPKfywhwufvEQpdnJdH5tI9Ye5gsF1sVDPfx5fdMVbqbmE5tRRGpeKQ/1CKjanldczr7rqcwdHszzQ9tUtZcqVeTUE7AKYGUhJc+A7TmrsEznbxdbBS62lqyZ3cvgOHb1+CfXn41nztqL9c4HIOWbe03qV5vI5HzySpT0/vSA3rb5uyOZvzuS/a/cQwdfR0K87dl2IZG0vBI8HKxqjKG9x9t5aQWS0/NLSc0r5ZHe+qLI4QHObDyXQHxWEe28dZ8NVxJyuZKYy1MDW2EhM0+0tDadA5wAiEnXFtiOzSyiuFzF21uv8vZWfZ9K8Ju7eWJASz66vwOh3safWRpNw/TFb6bmo9HAjCX6xUkTc4oJfnM3H45vz+x7WhGZnE98VhEd392r1/eFNRcAiPp0FI7W1XEmC/ZEseDPKF4ZEcyLw9qaP0EzEb5f8zDH99sQ1OWlRH4/i/xbZ2n34jLsW5kvhFqJS5dRSBU25Bt5pqsrBJtt/EOxa2FaoVkrN38c2vSg4Lbp60RDMMf3G7dlPhKZBV5DZlT7s0u0ybLq0mJtPJW1AxKJBLmdM8pC8/zJNTF0TdOOriXnygHC3tlVdR0d2vbEoU0PIl7vQ/K+xfjf97I5p18vze37Ff7cxuPjqMDT3oLbmQ1/PpYq1cxcG8WJu7ksmdKO/i0d9fq089T6UG+kFdG2hj9VqdIQnVHM+I6GC5PXi1a3HamRhXJtRBqWMgkPdGrg+GbSnHFTkWlFBLlotwsEAoFAIBAIBAKBQCAQNBRhUzMfU3IRo9KKiUor5vdrmXrb+nx7gVAvG/Y9Xbd9u3YuYmNtaubkIpqbI2FufmZd/H+xqSkUVnTp1p3VK5czY9ZsVCoVG9auZvS4e3FxcanqN+uxhzl88ADz3nibLl27Ye+g9RtMGj+WkuK6BX9NJS0tldycHNzsDV/XzAz9+7gmzi4uODjq24H/aTy9vPD00uapDRk2HCcnZ95/+w2mPTaDTuGd9fpvWLua0tJSHp0x06Txt23ZxP69f/LngSPk5ubqbCsrKyMnJwdbW1ssLCxwcXGtmIdunmJYp3C8vL25dLHaf+Pi6kpqSope36HDR/LT999y6UIErVq1pjk4f/YMi//zM6vWb6KkpISSEq2vQK1Wo1QqycnJwdraGoVC/zu+YtkS3NzdGTPuPpOOZc41+bt47JGHeW7OXG5ERnH7zl2SU1KY/si0qu25ubn8sXsP7775Oq+9Uu1DKy0tJSur/oLICoWCvDz9fLqMWt8xN1dXXF1d2LVts8Fx7O31i0bXZPmqNcx88pl65wOgKsqtv5MZ+Pn54uPtzc1bjS9msWT5Sjw9PBg7emSjx9JoNEilhp/9v+/aQ1paOm+/9qrB7R07ticlNdXgmIDRcU1h6YpVzH7meR59eCo/f7/QYC6lra0tK377lW8XzCc+IREfb2/c3FwJDe9Gn149kMvrjvnR0Ph51qS25oyI0ajjGA2M0SjPzyLr4j6cw4Zg6ejRbPNTFuZw/asplOWk0H7eBqzqiJurK0ajNDOReCOaM9TSnGkO/o78/ML46xTevYT30Jl19qvvmtr4tKHdc4uJWfEaF98ZDICliw9Bk9/lzuq3sTRDh8HU++TflJ8vEAgEAoFAIBAIBAKBoHmpzLFKPaZrv3OtrRn9n2fJuX4M/3vnYN+iEzIrbS7w9W8eQV3WtPa7E08EGd7+L9CMrk3qkXVIZBZ49J3YqHE8ek8g+a+l5Mecxy6wg972uG1fE7/zWwIfeA2foTMMjKCPaz05VnVRUqEZHTRR136HWolGo2u/S9yr1Yxu94z5mtHRy1/D656HCZpsmmb03Y1azej2H+prRt9a8jIefSbi2K53lZ3VaD7Vf6FmtIjRMJ+m1Iu2tpDRwsWK9AL9/G1NHflIV5MLuZpcyJN9vE3OOTZXL7qSuvKeQjyN61lqNP8betEg8obNpTk0ozUaDdFL5uIQ3JPWj1cXQXNqPwBVcR531ryNW49xVc/w2vwd+sbZlw9QnpuG7yjTfOPGMEcfueWjnwMabq98k9srXgeJFPc+D2Dh4E7O1cNV96e8jtoS5UZqSyhcfVG4ap/7zmFDAIjb/BkefSZh4aCNr2j7zH+I/u0lbv78FAASuSU+w54g5/pRvXoT9ZF2VPvu496n7nefmrUhAmvVhqj008ms7LBv1VVnm1PHQWRd2ENR0k3sW+rH6DQXYp01HaEXLfSiBQKBQCAQCAQCgUAg+G9HaqEQOskNoD5NYAs7ZwriruntpyotQqMsw6IerUMLO2eD51xekKPdbqtrR7X1bo21m19VP4lEgkvHgcT+/j1leRlYOriRfGQtqtJCc05TD0crOREJBcJ2aCYZBeXsv5nNsGBn3OxMr23cFAh7b+OpT0+yLr9qekE5qfnlTOuqb68N97Vl06V04nNK67SvmsPUezoyb8leohIziE3NJTW7gKkDqzXZ84pK+TMimlcf6Muc+6p1tUvLlWQX1J9nrbCUk1dUqteela+7r6u9DS721mx4fZLBceysLes8ztrDV3n+l131zgcgc91rJvUzFYmFgrRj6/G6px7/6o0K/2pQhX9VUuFfbeKYoVNPBhncrsz/5/2rlUhkcuyCKjR423THqcNAIl7vTeKuH2kx9UOgwpc442vubqjlSxw/j7gttfyrS+fi0LaWfzW0hn+1u3H/ak10avJ2Gk7Em32J3fI5Ic9X1ORd/SY2vsG4djVQk7dEtyavhb1rvT7t+hDP4+YhxNOW7VeN6zcYi/9YG5GGu50FQ9vqa+2XKjWNnpf4vE0nMq2IvBIV/b672KD9jVE7hzvNwPM8qiJexr9GvAxIuNGE8TJlFc/zk42Il/k7f+eUZCRw9YtJSKRyOsxbr/c7J3rpXFy7jMKuRaeqZ6e6XPtuoCrORyK3RG6t/95TiYWJmvDKwhxuV8TLtKgVL3Nl/kRiVrxOt4p4mZpkXz1E5I9P4BTan7azf2iQL74+xPf7fwPxOf73ILNQ4NI6nNsH19F6+KOoVSpij2zCr/sIFPbVz6iTC58i5cpROkx8GZfW4VjYaJ/phz+ZiqqJ3tFLctIpL8xl/WQ/g9tL63lHt7RzxvlvjNUuTIvnwHsTkEhlDH5/k871Ajj94xz8eo7BpXU4ZYWV78Paa1VelI/UQoFFHc90S3tng+dclq99zlvaOQGgUet/F8xFfGdNp653qy8PxPPlgXj+fCqMDrVisv8J7BQyWrtZ6dlbBrdx5sxLXbiTVYJKraGlqzU7K7QHewWa/x0K9tDqs0SmFjGk1vt/ZFpRvTaZZqv/IRAIBP8j1K3YJvh/hdzGkfyYCIqSblGSHkt5biqe/SZXbVcW5ZF9aT/+976M35jnqtrV5aWUVxgG6kJqoTCY7FFe66XUws4FuZ0zoS+tNjiOzKruF6G04xuIXmJacbm+SxJN6lebosRIVMV5RLzRT29b3NYvidv6JZ3e/xO7AP2kWgAbv3YAFCbewMa3uqilRqWkKDka957jzZ9TQhQalZLLn4zT25Z6ZDWpR1bT7rnfcO1iupBlSXos6rIS7qx9lztr39Xbfvq5ULyHzqTl1A8bdE1Sj64netkrePSdRKtH55tsCCqMu4a1Txs9R0OlIGBRQhSOwb2x9ghCamlFUUKk3hhFCZFYeQQhtfjvS4AY39GND/+8y4YL6cRml+DlYMk9rZyqtkvQJldaynUTP9eeT0Olrn98Pycrfr+WSalSjaJijKyics7H52OnqF42hgY7s/1qJiqNhi5+dQvCGmJYsDO7ZptXmLohJFYk5Ko0GjZNb4+fU/0C4esupOFlb8HgNk4mH+d8fD53Mkt4vKducemryYVMXn4dbwdL1j4agpO16Uuvl70FKjVcTCrQucYxGcVkFynxdqg+F41Gw7wdt1l/MZ35Y1syubN5op0bL6YD0MXf/M8SYFQ7FxafSiYxtxTfiqTnglIVu29kMjzYGbnM+PdbLpMwrK0zu29k8fbwpimc7dHvQW6vfFNnTfMwcU1TmrimKQ2sabUdwI1d09LNWNP6NHBNs/UzbCQBKqpD6z5LPPo+iHuvCRSn3kEik2Pt2YKEP74HiQT7Nj0bNAdTkVnbYe3VmpLU21VtRYlRlGYmcOa5UL3+176YhMzagZ4/3qhq02g0xCx9hbTj62k1/Svcez/QrHOumrulNVYeQYbXpMQbSC2tsHLXvf+1ArGTUbj60X7umjpFJOK3f0389q/wv28ufmNf0NtenBKDuqzEYIFju6Aw8qJOoiopRGZli01d9wQak9brxtwnEpkc28AOZJ79vd7jNAdinTUdT3tt4FBEfAG9g6qLMZQq1VxNLsTbQTewqDIhd0KYOwvubdUgJ2CpUs3myxl09rWrMvrVxKvGnB4Mr14LNRoNEQkFOFnLsLE0X5B8ZDsX3vzjDhEJ+VWfh1KlYcvldDr72eHlUHcQVWPWSYFAIBAIBAKBQCAQCASC5sa7/2RurniDwqRbFKfFUpaTinf/h6q2K4vyyLi0nxb3vUzQ2Oer2s2yqRfl67Ub8hNb2DnT6ZU1Bsepz6aecmwDNxa/VO98AAYvTzKpX3PgUCFCWJRyG/tA8214lYX3tNZKKEiIRKNScv7DsXp9kw6vJunwajq+8BvuXUdh62+eT8AcijMSuPDZA4CGzq9vxsrFx2A/734P4tm72n5s49mCuzu19mPHYFP8DKbPs7wwh4vzJ1OcEU/n19ZjF6DvS2gs93fx4/3t11h/Jp7YzEK8Ha0YGFwdHCuRaC+topZNefWpOFTq+hOf/J1t2BmXRKlShUKuDXDNKizj3N0sHZvysPaebLuQiFqjoUugfpJVfQxv78WelwaYvZ85PD+kNZN7+Ou0peeV8tTK8zzaJ5D7OvvSwk37PR/RwYvPd0ey/mw8zw9pU9V//Zl4rC1kDGqntf862lhgZSHlfKx+gsv5u1lIJeDpoB8TsOZ0LABTewU0+ryOR2uDYyvn3sHXkc3P9tHr9+7Wq+SVKFk4JRwfR+2cPB2t6BbkzNk7WeSXlGNvpU1cLypTcjImg64N+CwHt/MwePynVpwnwMWGN8eGVM31o/s7kFusG2h9LTGXd7dd45URwfRu7YqtZXVg9dd7o1jwZxQvDWvLKyODzZ5bQxG+X9Mx1/drDuryUiK/n0lu5AnavbAEp9D+DR6r5pwkRuaUfWk/5XkZ+I2bY+6gjTpPUzDH91uUGEVpRjznXgrX6xv92xwAevxwHbmNI7a+IaSf3oZGpUQiq37GVx7Hxs+E712ta1oYfw2kMmxrvXNYeQQit3OmKFH/HJqS5vD9Cn9u47mTWUxyXhnDg10atH+pUs2sdVEcv5PLooeCGdjayWC/Ln52eNpbsPFCOvd1qE7Y+ON6JoVlakaFNOz4my6lYyGTEGYgoSYtv4wDt3IYFeKCi83fJwjTlHFTdhVJTYk5pZy4k8cTvb2N7isQCAQCgUAgEAgEAoFAYArCpmYepuYibpyu73vccDGdjRfTWTIluCq3wRiGchEba1MzJxfRnByJhuRn1sf/F5vaw48+xtwXniMq8gZ379wmJTmZaY9Or9qem5vLnl1/8Ppb7/LyvGpB3tLSUrKz6hdNVVgpyMvT93NlZmbo/O3q6oaLqyubdxgWB7a3r/s7uWblcp6ZPbPe+QDkGhB/+rvo2q07ANG3btIpXL9A5MplS/Dw9GTkaP34CkPcuHYNpVLJkAH6/tflSxazfMliVm/YzNh7x9Oho/Hnk0ajQSqtfsa279CR1JQU/X4V8SA1+zY1kTeuo9FoePhB/ZzKhIR4Ar1c+ezLr3nm+Rd1tl26eIHLFy/w3JyXsbAwzf5uzjX5u5jy4EReef0tlq9aze07d/H18WH40MFV2yUSCRqNBoVC9xn+29IVqFT139tBgQFs2rqN0tJSFArtsy0zM4uTp0/jYF+dGzpm1EjWb9yMSqWmZ49uZp/HuNGjOH30oNn7NQXRMTEkJCYybsyoRo1z7nwEl69cZd7LLyKXN07yZtPWbRQVFdGrR3eD25csX4mVlRVTHzIsrj7hvnvZt/8Au/fuZ+rk6j67/9yHVCqle9cuDZrXspWrmf3M8zw8ZTKLfv6+3lxKZ2dnnJ21cSI7ft9F1M1bfPbRB/UeZ+XqtQBGz99c5DaOFNTSnBExGoZpaIxG+slNaJRlePSf0qDjmoKyMIdrCx6iNCOe0FfWY+tfd9xcXTEa9WnOnKnQnKkUr29qmjs/H7RFrAE8Bkw12sfUa+ocNpiuX56hJO0OGpUKa6+WZJzZCYBD214G9zFEQ+6Tfzo/XyAQCAQCgUAgEAgEAkHz49nvQWJq5FiV5aTi2V/Xfpd1aT8B976Mf1NqRtcqyCW319rv2r/ccM3oW7+ZZr/rt7Rh9rualOVlkHVpPy6dh2Hp0LhiFFX5vQbsf3HbviZu+1cE3DcXfwNakHUPrIEG+E4q7Xe317zL7TX69rtTz4biM0xXM/r868Y1o8M/0NeMvrW0QjP6MfM0o23q0IwuTIzCsV21ZnShAftf4X+pZrSI0TCP5tCLHh3qyg9HEzkbl0/3gOpzP3ArG1tLKW0rit7UZG1EKgBTupiu42xOjEYl9eU9edpb0tXfjrPx+eSXKLG30n6mxWUqTsXmNeizhOaJ0WgsIm/YdJpDM7o8L53y3FTsW0zTG9KuRTjpJzZRmhGPja/hnNa/Q9849ehaJHJL3Ps0XjPaVH1kmcKGNk98T4upH1GalYSlkxcW9i5ceHMA9q27VuX9VtaWKDJQW6LYxNoSdi06k3poJSXpsVg4uAJg6eBG6EsrKcvLoDw3DYWrH1JLK1IOLse12xiTz7csL4PsS/txDq/73Sf16HpiKmpDtDRQG0Lrp2tBeV66gb2170TGcsSbC7HOmo7QixZ60QKBQCAQCAQCgUAgEPy3I7dxJC/mvNBJNgNTNIHt/ENIO72d0pw0FE7VNpqCeG1tP9t6tA7t/NqRenobapUSaQ2txMKEGxXbtftr/YD6PhlAa8QDrVgvkB93zaTzqwt7hYy8EqWwHZrJpkvplKs0ZvnImgph72089elJ1uVXdbSWYyWXEJFQoLftfHwBUgl42DWdjuMDfUN4Z+UB1h66SmxaDt4udgwKa6HTR6MBSwvdPMOVBy6bpN0d4O7I9lORlJYrUVSMkZVfzJmbidhbV9uIh3dpxZYTN1CpNXRrY1g3vS5Gdm3N/k8eNXu/pkBu40jB7Vo5f31r+Vcv78d/3Mv4jW6gf7XIgH+1oBn8q0tN9K/+1viYoZooXHywcPSkuEb9W2gC/2pQ/f5VY9RZk/d5AzV5v6yoyftDRU3eBvhzaiOex83DqBAX5h+I4+CtHEaFuFa1H4zORqPB4DleSizgRmoRz/T1MeifkkvF513J3/F5P9fPlwfD3XXa0gvKeWbTrUaNK7dxJL9WDrenifEyjYoBbeJ4mTQz4mX6NjBeppKSjASufjERNBo6vLYJhYHfOUWJURQlRpF5Tj+39vzrfbD1DyX8g31Gj2FjRBO+sJYmfGW8jL0J8TKVZF89ROT3M3EI7kW7Zxchldftv24o4vv9z5NXomz0GOJz/O+i5aApnFv0GrkJNylIjaU4O5UWg6t1AcoK80g8v48OD75C6ITquHJVeSllBTn1ji+1UFBu4B29ND9T52+FvQuW9i4MfHutwXEsrO3qPM6dg+s5/eOLdfapZMrmVJP6GaMwLZ6/3rsfNDDkwy3YuOo/03Pjo8iNjyL+5A69bTuf7YlTUHtGfXXA6DGcAkKIPbZVz3aVE6d9h3YM0MYzqpVljToXEN9Zc6jr3eqRbp7c28GVFi7/jpyBrMJyItOK6Oavr1EikUho6aq1eZYp1Sw+lUx7Lxt6BdWtZ2IIbwcFnX3t2HI5naf6+iCreN8/H59PTEYJs3rVraHXXPU/BAKB4H+FxinaCf6nkFnboyrKI+34BkrSY7F09sKp/T3VHSqqi9b+wZ56ZC2o6xeeVLj5kXn2d9TlpUgttIHo5QVZ5MecR2ZV/TLu3GkoGWe2g1qFfSvzRQ1dwocR9o5hIdmmwnf0c3j0fVCnrSw3nZv/eQavgY/g1uNerD1aGNkb7Ft2wcLRk7RjG3HvcV9Ve8a5P1CXFuLa1XzhSo9+D+LYrrde+9UvJuHSeSQ+w2Zi49vOrDFtA9rT4dWNeu131r6HsjifNo9/jaWz9mXM3GuSemw90ctewb33BFpPX2BWUoalkydFiVF6xqX8mPPa7S7aOUlkcpw7DSMzYjeBk95GXvGjrzQzkdzIE/gMe8LkY/6bcLSWMzLEhQ0X08krUfJkHx+kNYzi9lZyegXa8/PxJFxs5Pg5KTh1N491F9JwtKo/2XFiJzdWnUvl+c23eLirJ9nFSn46lqjz4wrgvg5ubL2cwSOrIpnVy4twXzvkMinJeVqx6xHtnHWM/rVxsbFo9mJ5GQXlTFp2jbSCMhbc14qMwnIyCquL0Ho7WOLjqJvYGpGQT1RaMc/396368VGboT9d4oEwN1q726CQS7iYWMAvx5PwsLPgmX7VP+CjM4p5aMV1AF4bEsCdzBLuZJZUbQ9yscLVVnsNTt7NZfLy67x0jx8vDdQWL36wsweLTibzxPqbvDjAl1Zu1sRllfD90URsLKU80t2zaqx3dt1lbUQaD3V2p52nDefjq4N8FHIpHSoKF56OzeO7IwmMbOdKoIuCEqWag7dyWH0+lb4tHBjWVrcQcM9vIrT7vVT38/ipvj5svpzOY6sjeWWQP5ZyCT8eTaRUqWHuQN1izH2/1Y55/MXqMV8Z5M/oXy/z2OobFJY1Xqjbred47q77kLTjGyitY02TNGJNKzBjTdM0cE1z/hvWNEsnT+xbdSU/+izK4nzk1lqDiaq0mNyoU9i31J+3RCbHxkdbMFtZlEfq4dW4hI/Ays2vWedanp9FUWIk9q2rhanbPvUT6vJSnX45Vw6RuPtHWj76OTY+1U5pjUZDzLJ5pB1fT6tH5+sIj/wduHQZRfK+xZRmJaJw8QVAVVxA5vndOIcP13W6xGkFYi2dvQl9ZS1yWyej48bv+Ib47V/hN/ZF/O8z7JCydPICID8mQme91mg05MdEILd1QlohdOEcNgippTXZVw7iM3x2Vd+C2CuU56ZhZ+K93ND7RF1eQn5MBFYeQSYdp6kR66zp9AiwJ9zXlq8PxVNcrqZnkAP5JUqWnE4hLruU7ya0ruq781omr2yPob2XLdO6eXIhUTcIq4O3bZVh95tD8XxzOIH1j4XqJPsC7InMIqdYaTSAz9dJwegQF1afT8VSLmFIG2dKVWo2XkznbFw+8wb767x3Tlx6jZN380j8QP89uiYPdfFg+dkUntxwkzeHBuBqa8GKs6nEZJSw7jFdYYAHl13jVGwece9Vj2nOOikQCAQCgUAgEAgEAoFA8Hfj2Ws80Ws/IPnoeorT41A4e+PSwYBN3ULXpp50eA0aE2zqVm7+pJ3Rt6nnRp+r8t8BuIUPI+201qbu2ACbulvnYXR7f7fZ+/3dZN84DlCnP9kY5YU5ZF7cj11Ae2SW2iBC736TcW6nXzD0wucTcesyEv/hs6oSoV3DBiO1tCbz8gECRlbbf/PvXqYsNw2HBlx3gJJMbbK2Rq2iyxubsa7HDiyVybGtYT9OOrQKt84j6t0vN/o8RSl38B9Wf3HY8sIcLs6fTHF6HOGvrsM+sHkCPx2tLRjV0Zv1Z+LIK1by9KBWtWzKFvRq5cpPB6NxsbPE39mGkzGZrDkdi6N1/Tbcid38WHEylmdXRTCtVyDZRWX8eCBaz6Y8vrMvm88n8PCvp5g1oCWdA5yxkElIyinmeHQmIzt4MTrMeKCli60lLrbNk7xRSRtPe9p46gbuxmUVARDkZkvf1tXBne28HZjaM4AFe6KQSSWE+ztxOCqdladieX1UO5wr5qqQy3isbxD/OXSb51ZHcF+41se750oyWyISmdozoKpvJSXlKrZGJNI9yJm2noYDiU9EZzDp55O8PLwtc0dovz8rTtzl9O1M7gn2wMfJmqIyJadvZ7Hk6B26BzkzsoPWF+NobaFzLpU4WFugVGv0tr13b3se+PEED/1yiueGtEYC/HIohqzCMl4brRvj0e1DbaLPuXeHGb3OHg5WeDjoBxkr5FKcbS11jt/B11GvXyXBXvY6fX8+GM0Xu6MY1M6DoaEenL+rm3jVNag6EPeldRfZcDaeU28NMTq+OQjfr+k0xPdrCuryUiJ/mEVu5HGCn12Ec4eBjZ5r5rnfUZcVG/U9ph1di9TCCvde95s8Zkl6HHm3zuIU2r/R86sPU32/Laa8r5eMXhh3jbvr3sf/vrk4BPdCprCtGHMkqUdWk3n+D9xqxJOlHd+IpZNXvZ+foWtq6eQFahUFdy7q3LfFKTEoC7Kr4r6ai+bw/Qp/rulcTynk/T13GRPqSqCLFVIJ3EgtYtHJZJxt5DzVVzdJ6eTdXDILtQl/arWGhJxSfr+mTcrqHeRQFeM0e/1NDtzK4YUBvjhby3VilOwVMtp6aGMPZFIJbw0L5IUt0by6I4bxHd24k1nCJ/tiGdDKkUFtnHWOXTtu6udjidxML6ZfS0e8HSzJKCxnXUQah2NymTvQDxdb/eu38WI6SrWGqV2NC8Ik5JRyscJfHZuljTepPE9/JwWdfKuf/f9E3NSz/X0pVapZcCAeFxs5T/YxX+RDIBAIBAKBQCAQCAQCgaAmwqZmOubkIvZpoe9nOXlXaw/u7m+vY78yNRexsTY1c3IRTc2RMDc/U9jUdJn44BTeevUVVq9czt07t/Hx9WXw0OFV2yUSCRqNBkuFbo7riqW/oVLV7+cKCAxi25ZNlJaWoqgYIyszk9OnTuJgXy3uM3L0GDZvXI9apaJbj57GhjPKqDHjOHj8tNn7/d0cPXwIgJatWuttizh/jqtXLvPi3HnI5abJa0x95DH6DbhHr33siCGMvfc+nnr2BULba4tYDx0xChsbG/b9uZtnX5hT1ffihQhSU1Lo3qNXVdu94ydwYP8+9v+5m0kPTa1q37dnN1KplC5du5s0v4YwdPhIfv/zL732xx+dSmBQC9778BOD12/lsiUAPDr9cdOPZcY1+btwcnJi/L1jWb5qDTk5ucx98XmkNQqiOzg4MKBfXxYs/A5XV1eCAgM4cvQ4S5avxMnJuH+9kmlTH+LX35byyONPMGvGY2RmZbPg64U630eAhyY9wJp1Gxg7YSIvPPM03bt1xcJCTkJiEoeOHOXeMaO5/75xRo/j6uqCq2vzimVdvnKVua+9wYTx99GyRRBSqZSr166z8PufcHV1Ye6c53X6Hz56jPSMDABUKhVxcfFs2roNgHv69cPdXTdWYsnylQA8/phx0fK2HcIBuHn1IgCxcXFMmz6LyZMeoFXLlkgkEo4cO8a3P/xM+9AQZk7XHyspKZk/9+3nwYkTcHZ21tsOMOPRaSz6bSnPzZlLZmYmIe2C+evgIX76zyKenj2LwICAqr4ffPwZH376OX/t+Z2BA4z7pTdu2coTTz9HeFhHZs+cwZlz53W2d+4UVvXc3rxtO8nJKbQLbktJSSmHjx7lux9/4clZj3PfuOpCvGvWb2Tr9h2MHjmCwAB/cnJy2bR1G+s3buaxaQ/TKaxpYqNqas6IGI26aWiMRurRdVg6eeEcNrhZ5qUszOHagocoSY+j/dy12AV2qHefumI0bAPa096I5oyqOJ/WNTRnmovmys8HbUxM+snN2LXojK2fYT0ec6+pRCLB2rOldnxlGcn7F2Mb0B7HYNPXvobcJ/90fr5AIBAIBAKBQCAQCASC5se913jurPuQ1GN1a0bXzgVOMdF+Z+XmR4Yh+120rv3OpdNQMk43TjO607vNa7+rSdrxTWhU5Xj2n1J/ZxPGAvTOO27HN8Rt/wr/cS8SMN604mSVZFTkAzUk78suoD0dXjNgv1tToRk982sUFfY7vzHP4dFPVzO6PDedqF+ewWuQYc3oW0tfwaP3BNrMaFrN6Mo5SWRyXDoNI/P8boJqaEaXVGhG+w7/79OMFjEaptNcetFP9fFm6+V0ntoQxbzBAXg7WPLH9Uz2RmXzzvBArC10r3NJuZptVzLo5m9PG3cbg2M2NkajElPynt4ZHsSDy64xdeUNnu3ni0QC/zmRRFaRknmDdeMp/qkYjYJSoRldtf+/1CdVnxaw3MYRqYUV+TERevvmx5wHiRQLR+P3aXPrG5flppFz5QAuXUZhYdc0vnFz9JHltk5VPqasC3spTokhcNJbVdtr1paomQucWVFbwsWE2hJ5kcdBIsXKPVBvm6WDG5YOWh978r7fUJUW4TVkhsnnmn6i/neftGPriamoDdGqjtoQrt1Gk/jHD+RFn8WhdXU8S/blA0gVtlj7tjV5Xk2BWGdNR+hFC71ogUAgEAgEAoFAIBAI/tuRW9ujLMoTOskmYqomsFvnEdzePJ+U4xsIHPNcVXvysQ1ILa1w7TiozuO4dx1F0uHVpJ/7A8+e1fbR5GNarcRKTWOpTI57lxGknf2D4vR4rN21dhaNRkPmlYNYewRhaa+1oSmc9H0q5iKTSoTtsAGsjUjDx9GSga2cjPYxpE2Y3wS+ImHvNR1z9SShfr+qQi7l0e5e/HoymRe23OLeDm7IJLAnMputVzKY0sUD54rzKlNpGn0OjrZWjOnehnVHrpBbWMqzY3vofN4ONgr6hPjzw87TuNpb4+/uyInrcaw6dAVHW0UdI2t5sH97lu2/yFM//M6jgzuRVVDM9ztPY2+tu0ZM6BPCpmPXeWj+Rp4c1Y0urbyRy6QkZeVz7Foco7q1YWwP474PF3trXOytG34hGoHJOX+11sXUoyb6V139KLhjJGbI2oh/tQFxLn+Hf9UYxal3+D/27jqwqbMLA/iTJm2aurs7tLi7DzZkwNBtbMyVDTYYjMGAocOZfEyY4w5Dx4a7S71Aoe7ubZrvj5S0adM2FZrK8/urae69eW9uc9/mnHPPLUiNhVn7Zyo8VyGXeLaS/OrD2uVXK1OYmYKcqGAYepa5J+87Ku7J619yT94pK6Bn711+M3XC83HNqNvL1sNSgqldbPDHtTjo6wgxwNMED5PzsOpkBPxs9THSt+K+bLuZAECe+1LFQCwEMlU+pTYeb/V5WErgYal8zo9MzatkafWVPZ9XVQOqVYd6meQa1MvUpQa0Ic7n+cnR8F85DrJiKdp8trvSe2D7qbgGOuHCTiRc2AWfab8q6lwqY17SEz7pxmFYVtETvib1MgCQ6n8Gwd++AUPPLmg17RfFMXka+PmuGXXP51Fp+ei54SbGt7PEmtHyOot9dxNxJCgFg71MYW8sRkZeEQ4FJCM9j9+Tnmhsx3H1yUgkZheiu7MhbIx0kJEnRXxmQZ1f37nPGNz6YwHCT+1AVvxjSMxsYduuv+J5Qck5XVjunP7g3y1qxa4MrBwRcfFvSAvzISw5f+RnpiAp5Dq0JaX317HvPAQRF/ZDViyFhVenGu+HfZdn8MzXx2u8Xk1lJ0bhvwVjICsuxqBFe6FvpbomZ+CivRV+F35qB8JP70Cf2b9DYlZ1XwuHbs/hwb+bEXn5EJx7jS7dxumdkJjZwNyzNHZVV/zMqq+q/61czHQr9PJT914ZldWynQxLRU5BMbIL5J+10MRcxfqDPE0g0REiI68Ik/8MxOg2lnA114WuSAsPk3Pxy+U45BfJ8El/5f875h0ORw9XI5hKRIhIzccvV2IRm1GAPa/5Vtiv7utvYXx7S6wfU7GHV1lzhzhh8p9BeGdnKF7pYo3k7EIs+zcCPlYSTOxQ+r1A1XxUk16FREQtUd1nemo2BFpCmHUchoQLO1GUkwH7oe9AUKbxpEhiCCOv7og+thEiQzPomjsgPeQy4s9th1Cv+saTVj3GIf70ZoT+PA3WfV9CUVYqoo/9TykQAwCW3Z5H0uV9CFw/BbZD3oSha3sIhCIUpMYiPfgizNoPhXkVF6JoG5jV28U1ldGz9QBslf+ByUuKBADoWrnAuMxNcvOSonBjTk9Y9RwPz9fXAJC/1y4TvkDYzx/h/h+fwbLbaOTGh+PRrqUw8e0L03KJ6NS7JyEtyIE0NxsAkBMTiqTrhwAApm0GQSiWQNfCEboWqr886JjaKI0JAO59PQ4ZIZfQ69foSvdTpGdcYT0AEOoZy4sCyjxXk/ck6drfuP/bTOg7+cKm/8vICr+ltJ6+k58iOBRxcB0iD66D36wdMPaWX9hgO+QtBH/3OgLWTILdkLcgMjRD5oObiDr8HSR2Xkrvn9Pombjz1XMI2vAqHJ77AMWF+YjYvxraBmawH/qO0utmht9BfrJ8zEW5mZDJZIr32cClfaUBN02Y2MEK++/J/3Gf0MGywvPfjfPEl0cfYemJxygqljf+3vZKa7yyJbjabXdxMsL6MR74/nw0Xt8WDCdTXczo74CTYWmKRuKA/B/t3yb7YNPlWOy5m4jvzkVDqCWArZEY3V2M4GOl+uLThhSamIPHqfJAyLQ99ys8/0l/B3w6QPlzs/1mAgQCVHqxDwB4WUqw+UYCErIKUCiVwdpQB6P8LDCjvwOsDUuDGzciM5GaI//CNnVrxfd+7Wh3xRcamQyQFgPFZfLN9sZiHH67DdadicL/zscgIasAFvra6ORoiBn9HOBhUfrl8URoqnz8txKx/Vai0us4mIgVF9VaGWhDSyDA+rNRSM0phACAq7kEMwc4VviyDgA5BVK4mFW8yW955vra2Pu6HxYff4Tp++6jqFiGTo6G2D21dYUvuUXFFdf3sJRg92u+WHoiAjHpdQ9GifSMYdZxGBJL5jS7Sua0mGMboW1oBrG5AzJqMKdZlsxpYSVzWmFWKmJUzGkW3Z5H4uV9CCqZ0wwa4ZwGAM4T5yNg5QQErn0R9s9+AIFAgJjjP6IoKwVOY2YplivISELM8R9h5NEFQl195MbdR/TRjYBAC24vL1XapjQ/F6n35A29s0ouzM0IuYzCrBQIdfSUmobenNMLANBxhfzG9UU5GQhcMxkW3UZD19oVQh1d5MY9ROy/v6C4MB+Oo0qbUxi6Vwzy5ZfMPwbObWHg2k7x+/Ct85Fwbhusek+CnoOPopEDAAhEYqWmqRH71yDq4Fr4frZL5VxYVnrIJRRmys/JKC5GfnKUYv4w9uoBbSN5sMl+6LtIvLgHQetfhePomdAS6SD6yPeQFebD8flPFdvLjb2PgNWTAABOY2cjLz4cefHhiud1LV0U24w+9gMi96+Gid8AmLYbpLRPZd8fsbk9zDo9h/gzW6Al0oFp20EoLspHwoVdyLx/DY5jZikuVBTpGcNx9Ew83rkYYb9Mh0W30ShMT0DEvlXQMbeHzYBXFdtPuLAL93/7FB6vrYFVr/EAavZ3cm/pKJi2fwZ6dp4QSgyRnxSJuFN/IS/xMXw+3FTl+/40cZ5Vj5aWANteaY2NF2JwKDAZP1yMgb6OEJ6WEvz1sg8Glgm6/ReaimIZcC82G6N/8a+wrcvTO8DRVD7fFJfMiTIVNVjbbyZAT0cLz7exqPhkiW9f8MTvV+Ow+04idtxKhEhLADdzXXz7ggfGlFsvu0AKK4PqA75ikRZ2vNoaS/6JwLwjj5BXKEVrG3389bJPhQuHn4y/rJrMk0REREREREREDU1b3xiWnYYh9rw8pu707LsVYuom3t0RceQH6BiYQdfSEWnBlxFzdhtEasTUbXq+gJhTfyHgx2mw6/ciCrNSEXHkf0oXJgOAdffnEX9pL+6smQLHZ96AkVsHCIQi5KfGIjXoIiw7DIVlZ83H1HOTopAZflv+c8JjAEDCNXlMWNfCEUYlcemHe1ehICMJJt7dIDa1RVFOOlLunkbMmS2w6jISRq5tFdsM378Wjw6sQ/vZO2HqI8+JBmx8H2Jzexi5toO2gRly4sMReewHFGQkotVb6xTrSiwdFRcklyc2tYFpq9IYt7a+MdzGzsL97V8h8OfpsO4+GgXpCXi4ZyXE5vZwGFQa/81NjMSlmd1g03sCWr+1vtL3oyAjCTeXj0dBegJ8Xl+DgowkFGQklY7BzBa6ZnaKZSOO/gBjzy4QSQyQHXMfEUf+Bwi04P3KMqXtXp03GNY9x0LfzhNa2mJkPLyNiCMboWNsBafh7yste2mWfB97rLoIAJAW5OL2qheRGeEPzxcXQSYtQvr90vi5tqE59KxdKt2nmprU1RH7bsrz7hO7VjwWG1/uiHn7/LH470AUSWXo4mqGHe/2wMs/V3+T265u5vjmxQ749r8wTP31KpzM9PHpUC/8F5SAi/dL32ehlgB/vtEVP599iN3Xo/Dtf2EQaglgZyxBD3dztLI1rOJVGqcV49rCxlgXv5wLR2JGPhzNJFg82g9v9nVTWm7BSF94WRviz4uP8eGWmyiWyeBiro9lL7TBlB4VG5ceuRuLtJxCvNi94nNPyABIi2UoLhOobmVrhBMB8Vh2OAgpWQUQCgVws9DHR4M98U5/N4iEWpVurypdXM2w6/0eWHEkGO9vlufUOjmbYu8HvdDZRfmcllMghauFvqrNPHX/BMQDAE4FJ+BUcEKF5+PWjVL8LC2WQVosQ90vtZZj7rdm1M39AurnOUP+9zbS7p2Ew4iPoG1gqpSTFOoaQq9Mk93yud+8pCiE/fQBLLo+L79JpECAjJDLiD2xCRJ7b1j3Lb0x9RMFqXFI9T8Ni66jKr2ZZsCqiTDy7g49h1byOSUqGDFHN0IgEMCx3H5qMver71T5jTsl9l5K4zFtOxDGvn3x8K+5kOZmQdfKBUlXDiDN/xQ83/oWAi1hjd9Tq94TEPPPzwj531twGPExJDbuyEuMQNThb6El1oNN/ymKZVXlfmvynjRk7pf5XPVYGmjD2lAHP12KQXxmIaTFMtga6WCwlymm9bWHfbkbGaw5FaW0j5ceZSge75raWnEhy78lNUrfnI3GN2eVaw97uBhhd5mLRl5oZwmhlgDfnYvGrtuJMJGIMK6dJWYPclJaT1XdlLulBP+EpOK/sFSk50qhq60FXxs9/G+cZ6X54u23EuBoIkYft8rP/xfC0/HJ/gdKv3tnZygAVLiwRRN1U2/vCIVIS4BerkaYP9RFcUEQEREREREREVFdMKamntpci6gOda9FBOoWU6vJtYjqXiNR0/eEMTVlJiYmGDFqNLb+9QfS09Iwbfqn0CqT5zIyMkKv3n3xzbrVMDc3h5OzCy6cO4u//vgVxiYm1W5/0osv47dNP+Gt16bg1dfeRGpKMtavXQ0jQyOl5V6YMAk7t2/FuNEj8N4HH6FTly4QibQREx2Fc2dO47mRozDy+TGVvo6ZuTnMzCtvxlRfIh4/xs0b1wAA4Q/lcdz9e+U3wnZydkHHTvImtMu+WoiEhHj06t0Htnb2SE9Pw7//HMcfv27C6BfGoUPHitcb/vX7rwCAV6a+Xunrt28tz3ndDpTHjJ1dXODs4qJyWVs7e/Tp11/x2MTEBHO/XIR5c2bhvTdfwwsTJyEhLg5LFy2Ao6MT3njnPcWyL7/6Gn775Wd8+vGHSE5OhrdPK5w+9R9+/uF/ePOd9+DkXJrLXr54EVYs/QqHjv+n9HqqnD97BklJ8muLpVIpIiMiFO9f7z79YGFpCWsbG1jbVGwyqSvWhZmZmcrXyMvLw64d29Cte094+7Sq8PyT1x717BDMnjsfs7+YX+P3pCFNnfIStu+Uvy+vTnmpwvObf9uE6bNmY868L1FUJEXP7t1w/NB+jBw7ocKy5fXq0R2//fwDVq5ZhzETXoSbqwvmz52No8dP4MzZ84rlhEIhDuzejm++34jNW3dgxeq1EIlEcLC3Q9/evdDGr3X97XAtWVtZwdbWFuu++Q6xcfEoKiqCg70dhj87FJ9/9ikcHZR7LixashxnzpXu4+mz53D67DkAwH/HDqG/ZR/Fc7m5udi+aw/69u4FL8/KG44VFSk3HjQyNISVlSXWffMd4hMSIZVK4ezkiGnvv4PPZ30Kff2K9RR/bN4CqVSKN6a+UunraGtr4/ih/fhiwVdYvnINUlJT4erijOWLF2LGRx8qLZuVnQWBQAAb66pvEHHk2D8oLi7Gzdt30GdgxabeD4LuwqXksy7UEuK3P/5C2IOHKC4uhm8rH2z8dj1ee+VlpXXcXF2Qnp6OeQu+QnJKCrS1teHbygffrV+Dd96s/NxWU096zrBGQz01qdEA5I15c6NDYD98mqIGoLy6XJ8vLchF4NqXkB3hD9dJiyArLlKq8dA2NJfXGZRRXY1GZT1nRCp6zgBN5/r8J1JuHUNRdhqs+6q+iXVN39OHW+bB2LsHRAamyEuMQOy/v6AgNRZ+s/cobbeyGg1Avb+TmtRo5ESHIidW/v9NQXoipAW5ivdZz9ZLqe6HiIiIiIiIiBo3kZ4xzMv2jB6mIn7n3R3RRzdC28AMuhY17BndcxziTm9GyE/TYNNP3jM66qjqntGJl/YhYN0U2A15E4Zu8vhdfkpJ/K7DUFg0gvjdE/HntkFsZgdTv/4qn89LisL12T1h3au0Z3TCpX1IvnkEZm0HQ2xuj6KcDCRdP4SkKwdg1XsCDJxKr5uIOvYDIvathmkbeS/IjHK9II1KekHmJUUh5McPYNlNfj2QQCBAevBlxJzYBD17b9j0U77G6u4Kec/o3r9V3TPapIqe0SblekbrVdYz2tJFadmka38j7NfSntGZ5XpGG5TtGX1gHSIOrkObWTtgXHJ9tN0zbyHo29fhv3oS7J55C9oGZsh8KO8ZrWfnBdO2ZXpGj5H3jA4s2zN6XxU9o0vGLM3NBGQyJJVc323g2nh6RrNGQz1Pq1+0qZ429r3hh6X/PMbifx4hp6AY7hYSpf7PZR0NSkZarhQvdqp8m3Wt0VCMX43rnro4GWLHq62x8mQkPtwTBgDo6GCAXVN90dlR+VpxTdVo5KtaqIZ43XDN1HfPaC1tMawHvILYf35C2M8fwbzrKAi0hEi5dQxJl/fBqs9kaBuU9pItn5N6Wv2Nn0i4sAsyaZHK64+fUDcnVZP+yMnXD6MgLR4SWw8UF+YjI+QSYv/9Bdb9p8Csw1DFcmXvLfHgj89g0W008kruLWFc7t4SD37/DEKJAQxc20Pb2BJFmSlIun4IyVcPwm7Ye0q5q/gzWwAAulbOKMrJQOq9U0g4tw1OY+fAwLmN0lgvvukEY+/u8J21s+L7d24bdMzsYFLJ/z5l7w1hXc29IeyGvovEy/sQ+r934ThmFsSmtki+fhipt/+B84T5EOo0fC9dzrPqYb9o9osmIiIiIiIiauoEWkL2SVZTTXoCGzh4w7bvZITvWwOBQAhDt/ZI8T+DmNOb4fbCbKXYsKo+yebtBsLUty9C/vgcRblZ0LN2Qfyl/Ui5dwqt3/lOqf7ddexnSL57CnfWvAjX0Z9CKDFE7JmtyIoMhN8HPyqWs+0zEeH71wKyuuUfGDusmWsRmbiflIsZ/Rwq3LO0LFW9CesLj5l6atpPElAvrzr/GWd4Wkqw+Xo8PtoThmIZ4Gymi6XPueKlzqXr5RZKK91GTUzu3wZ7LgbJf+5XsUfrj9NGYu7v/2LhltMoKi5GNy977J07AZO+3l3ttrt5O+D794djw4HLeHn1XjhbG+OzF3rhxK2HuBAYoVhOqKWFLbNewI9Hr2PnuQCs338ZQqEW7MwM0Ku1E1o7Vfw7bCwU1/xdVCO/amAGsUUt8qtnyuRXs1MRc/R/EJabFy26lsmvDlaRX+0wFOYdNTsvZkcG4tH2hTDvPBxiS2cIBFrIiQpCzImfITIwhd3QdxXLKnKJniW5xNj7iD5Wkkt8qZL86qaPYN6lTH71ior86ucl+dXlZe7Ju7bknrxWJffkjS+5J29RDe7J69IWBi6l9+Qtzs+pl/eM52P11aSX7aJnXWBrpIOtNxPw29U4mOmJMMrPAnMGOUFHpNzHPrdQigP+SejubAh3FfUmAFDFdF0jPN6a9eR8rqgBreR8Hn1sI0SGZtA1r2ENaEm9TGjJ+bwoKxXRKuplLLs9j6TL+xBYUi9j2AjrZQoykuC/cjwK0hLg8dpqFGYmoTCz9HuOjqktxCXfc1TVjaQHXwIAGHl0gbZh6VgjDq5D5MF18Ju1A8be8u85pm0HwkSNnvBic3uYd3oOcWe2QFBSLyMrUy/jVKZeJiP0KoK/ex3axpZwGD4N2REBSuOT2HlBJJHXgsmkhfXynvHzrT51z+cymQzSYkBapkbCyVQXGXlSrPg3Aqm5RRBpCeBtJYGVgTYSsup+LHkc1afucWxrr49fL8fheHAKUnOKIBYJKtSO1IaOvjEcuj6Lh6e2ozA7Az6j3lM6p2vrGcKydQ8EHfgfxEZm0Ld0RELgJTz8byu09as/p7v0G4/7//yJSxs+gPvgl5GfmYKgA99DW6JcR+rUawwend2LM0tfgvfwt2Dm2QFaQhFykmOR4H8B9l2HwbHbc5W+jtjQDGLDp3tOz0tPxMkFY5GbmoBu769FXkYS8srErvTM7aBnLj+nW/v1qrB+QoD8HlSWPl0hLlNv579zDfx3rcHAhbth5SufC+w6DoJNu364/tNsFOZkwtDGFY/P70PsrZPo8fH30BLKz+kCYf30J+Nn9ulQ914ZldWyfX4oHFFp+YrHhwKScShAfpwuT+8ARx0hxCIttLbWx5Yb8YhJz0d+kQyWBtro4WKEnyd6wavc+xaTkY/5R8KRklMEU4kI/T1M8NtkHziYKMdlsgvkJxhrNereeroa46+XfbDqZCRe2xoMXW0hBnuZYv4zzhCX+c6gaj4C1O9VSETUEglkMlWlztQU+fr6IjAwsNbrS+y84DZ5EQLWyJuLdVx+DhJr5Ztf5qfGInzrl0gPvgiZtAiGHl3gOnE+Ate/AmOfHvB8Yz0AID34IvxXjodfuYtYEi7sQtSR75GfFAmxpROcRs1A6r2TSA++hM6rSm9QKpMWIebEJiRe2oPcuIeAlhBiM1sYeXeH/dB3IbF2rfV+1kTYL9MrjK0yeUmRuPFZd7hMmA/7Ye9W+L1Vr/GK9+eJxMv7EXXkO+TGPYBI3wQWXUbCeexsCHWVm0Ren9UN+clRKl+308rL0LWovPHzhdftYTNwKtzLXRR0e9GzKEiLQ9d1typZs3L3vh6HoqwUdFh8ssrlKntPwn6ZjoQLuypdr+w+Rexfg8iDayv8LaUFXUD0ke+RHRUEaW4GdEztYNZ+CByGf1ghGJf16C4e7VqKzAc3IBCKYOzTCy4T50NSrsFgVePyeH0trHtPVPrdk7HpWrsjL/6ByvXqm5elBKc+bN8gr9WUTN93H5ceZeDCRx0gEMi//FHdhSbkYMD3d/DHSz4Y7GVa/Qr1ZMB3txGamFvr9SV2Xuiw5BTSAs4isGRO61DFnJZRZk5zmTgfQetfgVG5OS1g5fgKF2YmXNiF6DJzmmPJnJYRfAmdKpnT8srNaXYNPKeVH1tZGaFXEbFvJbJKbjxv6N4RjmNmwciji2KZwqxUhP34AbIi/CHNyYC2iRXM2g+F46gZSskFQD4H3Pysu8rXEps7KI3jxqxuAKD4XXFhPh5umYfMsGvIT4mBrCgf2kaWMPbpAfvnplXbDDTh/A7c//UTtJ1/BAaupQnjG1XMp+XH9GjHV4j55ye0X3K6QsOJ8vy/lje5UKX8301ewiM82rEY6cEX5H937p3gPGGe0gWxT8ZfGY/X18KqZE6q6rUBoOevpY03igvzEPvf70i8uBv5SZEQCEXQtXGD7cDXYNF9jCJp80T82a2IPfELcuMfQqirDxO/AXAe97ki4VR2rGXHVJO/k0c7vkJa4DnkJ0WhuCAHIgMzGLp3gt2wd5T+9sq6WPK/TXrwReTGhFa677XBeVa15jrPZuVL4bviGhYNc8HUbhVvQKAp0mIZZDLA+avLMNYVIj2v9sVorVu3RkBAQPULEhEREREREVGTVdccsb6dF7otP40U/zO4vUoeU+/+9Xno2ZSLqafEInTLl0gNksc2jT27wGPSl7i7bgpMfHqi9VvrAQCpQRdxa8U4dJizG6atSmOjsed34fHh75CXGAmJpRNcRn+C5LsnkRZ8ET3XXFUsVywtQtQ/mxB3cTdyYh9CIBRCbGoLE58ecHr2Xeg1UEw98OfpFcam2JdzOxC0aYbK9Wx6T1C8F0m3/kHkiV+QHRWMwqxUaGmLoW/nBeueY2E/8FVoCUWK9R7uW41H+9cqvW+PDn2LhCsHkZcUCWleNkT6JjDx6grnEdNg5Na+2n04+aod7AdNhfcryyo8F3N6CyJP/IKcuIcQ6erDrM0AuE+Yq7iwGgCyooJx9YuBcB7+IdwnzK30dZ4c88q4jP4EbmNmApDHjwM2vo/Mx/4oysmAjokVLDsOhcvzM6BjqHxzNP//vYfMR3dRkBaP4qJCiE2tYd5mIFxGz4DYRPlGiBc/7QoAiuOVmxiJSzO7VTqmJ8fpyuf9kV2HOLOXtSHOzhlQ/YItzEdbb+Hi/SRc/mIQBAJBs4kpa1pIXCb6fX0Kf73ZDUN8q74ZaGPSd8UphMZn1np95n5Vq4/cL6B+nvPi6/aVjsXIuwf8Zpde2F4+91uUnYb7v81EdoQ/CjMSISsuljdo7vgsHIZPg0jPqMI2ow59g4i9X8N31k4Yt6p4wQQAhG9bgLSAsyhIiUFxYR60DS1g3KonHEZOh8TGXWlZTeZ+VXnyd+j1/o+w6DxC6TlpXjYi9n6NpGt/oyg7DRIbdzgMnwaLbs8rlqnpe5obH46og+uQEXYVBWkJ0Da2gKF7JziOnKGUe1eV+63Je6JO7vfWvAF1yvEyn6tac83napqm6qaqUySVISo9H7023IKFvghJ2UW13hbzyURERERERESNX13zwYypqcaY2tPREmJqNeHTqjWu3LqHk/+ewJgRwwAAN/2D4e7hqbRcTHQ0Zs+cjrOnT0FaVIRuPXpiyYpVmDB6JHr37YeNm34DAJw7cxojhg7CoeP/oU+//or1t23+E+tWr8TjR+FwcXXD7C/m48Sxozh/9gzuhT5ULFdUVISN332DHVs3Iyw0BCKRCHb2DujVpy+mzfgU7u5V51Dqy3tvvlZhbE9s+fN3vP/2GyrXe/HlVxTvxdHDf+OH779FYIA/UpKToaurC+9WrTFx8kt44+13IRKJlNbNzc2Ft4s9/Nq0w5F/T1U6tjZe8hykqrGVZawrxFvvvo/V67+t8Nwfv27CD99/i/thoTAwNMTgIUOxcMly2Dso3zA6JSUFX335BQ7/fQCpKSlwdnHFq6+/iQ8/ngGtMk3ZvpgzC99vWIert/3h5e1T5biGDxmI8+fOqHyu/N9NeW283NDK1xc79/1d4bld27fizalT8P2Pm/Dyq6+pXP/J3+ecL77E5/MXKD2n7nsCAMsXL8KKpV/Bw9ML98Pq97rB1q18cO9G9f0iWprX3n4PZ86eR6j/LXmNh1BY/UotWPc+A+Dk5IidW/7U9FAq1aZTNwQGVd+wrzISOy+4Tl7EGo0y6qtGA5DfpDn+3FZ0XH4BulbOKrdXl+vzq1oXACxV9LxRp0ZDFf+vx6FQRc+ZpnJ9/hMBayYj8/51dFl7q8KNBYCav6fB376OzIe3UZSVApGBKUz8+sPx+U+ha6E871VWowGo93dSk+vzn9zEXBWHUZ/AafSnKscmtnBCflKEyvXUwXw0ERERERERkbK65n717LzQcekppAacRcBqefyu0/JzkJS/Fjg1Fg+3fon0IHn8zshTHr8LXCfvGe315noAQFrwRfh/PR5+s3fBpEwcJv5Jz+jESOhaOsHx+RlIvSvvGd1ltXL8LvrEJiRelPeMFgiF0DG1hbF3d9gPa7j4Xeim6RXGVlZG2DXcXTYajqNmwLnkOtfy8pIicX2WvGf0k/cn48ENPN67EjnRoSjKSoVAKIKevTese0+ATf8pSjfZubui6l6QvX+T94Isyk5D2K8zkVXueiDzjs/CcUTF64FuL3oW+alx6La+5j2j766Q94zuuKT6ntHXZ8l7Rjs8W9ozOnRT1T2jO68q7Rn9eP8aRB5YW+FvKS3oAqIOl/aMFpvZwazdEDiMUN0zOnzXUmTeL+kZ3aoXXFX0jK5qXJ5vVOwZ/WRsEmt35DZAz2jWaKjGGo2nQ5M1GuwZ/XQ0dM9oWXExEs5vR9zpzchLeATIiqFr6QyrPpNh3e8laIlKbyBVPicFPJ3+xk/c/LwPZNJCdPz6UoXtPKFuTqom70nyzWOIOrgWufHhgKwYEjtv2Ax4BVa9J6ocR+Ll/Ygud28Jp3L3log/twOJF3YgJyYM0twMaIn1oe/YGtZ9J8OyxwtK24s7vRmxJzbJ+2YLtGDg7Ae7oe/ArMPQCq998XX7Ctd3A0DG/WvwXzYaDqNmwGm06v99wn6ZjsQq5vmO5e53kZ8Sjcc7lyIt4Ayk+TmQ2LjDdshbsO4zUeX6F1+3h1DPGNKc9EpfozY4z6rWXOfZptAvempXa1wMz6jTnMicKhERERERETVn9dEr2fOlReyTXEZlfZJr0hMYAIqLCvHo7w2IPbcDBemJkFg4wH7wa3Aconxdn6o+yQBQlJeNh7tXIOHq3yjMToO+rQecR3wI6+6jK7x2VlQwHuxchrSQy5BJi2Dg5AuXUR/Bov0QpeUufdYTufGPavBuKGP8sKKnGTusa66Ix0u1xhrvrevx9nYwx8XVb9bjiJqmnjM3ISQqudbrS+y84DppEQLXluRXl1WSX932JTKCLkJWXJJfnTAfQRtegZF3ufzqqvHwnVUuv3pRRX717klkhFxCp5Xl8qv/qsivemkgv1pubABQkJ6Ixzu/QubDmyhIi4esWAodU1uY+PaFw/BpEJuV9lcuzEpF2E9lconGVjDrMBSOI6vIr55RkV/tWy6/+llJfnVluXvy3i93T17vHrAfPg16dmrck/e3knvyupS5J+/s7shPiqzV+wfwfFyZ5no+5vFWrabHuz5qZtwmL0JASb1MxyrqZdLL1Mu4TpyPwPXyGtCy53P/lePhp6JeJqrM+dyppF4mPfgSOldSL5Nbrl7GvoHP5+XHVnb/KuOo4trYsiL2r0HkwbXouuGe0jn9ye/Lv2/SvGw8VtET3rJMT3igtF4moUy9jMTGDTYDX4NlmXqZJ69TmbKvf2N2T+QlPq502erw861aQ5/PeZ5+Op7Wcazr8TJy8MbwDWcRe+cMTn81AQAw4ttLMLRTPqfnJMfi5q/zEO9/HsXSIlj6dEWHVxbgzLKXYeXbE92nfQMAiPe/gJMLxmLgor2w9ivt9xB+eicC932L7IQI6Fs5o82ETxFz8z8kBFzEqB+uK5YrlhYh5PDPeHRmFzJjHkCgJYSeuR2sfHvAZ9T7MLRtmHP65W8/qjC2svtXGb8JM9Fm4qxKn7+3YxX8d67G2N8CITYyr/D78u9bYW427m5dhoiLB1GQlQYjew+0HvsRnHuPUSxz+OO+yIgKqc1uAuBntjKN9X/phvT71TgsPfEYFz/uAEsDHU0PBwAgk8kgLQZ230nEpwcewMlEjIi0/FpvjzVvRNQUCGQymUzTg6D6Udeks8TOCx2XVN58tCV6EozptOICIBBAoNU8Gk8W5Wbh6ke+cJ20CLaDpmp6OE2OTCYDiqWIOLgOUX+vh661O/Ia4IJcgF+wKjN9333sup0IAPC2kuDkB+01O6Bm4vcrcdh7LxEH36z6BqP1rb4uyCVlTy7I7djM5rSn5e7i4RCb28P7/Z80PRQqISuWAjIZLr3lDJuBU5EefLFON/hVhfOsas11nv03NBVfHA7HuWntoSPSqn6FBtJq+VVk5EkByG/skF7yc20wOElERERERETU/NXHhcndlp+uvwE1E08uTu6+8iIELTSmHvXf73iwYwl6rLoEHWNLTQ/nqbjyeX9k1yHO7GVtiLNzBtTjiJqHj7bews5r8gv4vG0McWY236P68Ov5cOy9EYVDH/fR9FBqpO+KUwiNz6z1+sz9qsbcb80w99t43Jo3oE45XuZzVWuu+VxN01TdVFXSc4vQesU1xWMLfRGSsotqvT3mk4mIiIiIiIgav7rmgxlTU40xtaejJcTUasKnVWtcuXWvQV6rKXnvzddw/uwZ3AoMhUAggFDIPFdVBvTuDkcnJ/y5daemh/JUyWQySKVSfL10MVYuXwIPTy/cD6vf6wZbt/LBvRuqb3bbkr329nv4c/NWAIBv61a4e/2yhkfUeGVkZMDayR03L59HKx9vTQ+nUm06dUNgUHCt12edRkWs0agZ1mg0XU/66CRc3I0Hv30KsYUT8pMiar095qOJiIiIiIiIlNU196tn54WOSxm7Ky90U8kNvL5uXvG7otwsXJnmC9fJi2DHntE1VrZndOTB9ZBYuyO3AXpGs0ZDNdZoPB2arNFgz+ingzmpmmFOqvEp2zNaqGcMaU56vW6f86xqzXWebQr9oqd2tcbF8Iw6zYnMqRIREREREVFzxl7J9a8590muc29gxg8reJqxw7rmini8VGus8d66Hm9vB3NcXP1mPY6oaeo5cxNCopJrvb7EzgsdFjPHWlbYL9OREXIJHZe33Pzqrfns7/s0NNfzMY+3ajU93vVRM9ORNTNKwn6R14B2asH1MldndEBhekKt1+fnW7WGPp/zPP10PK3jWNfjZeTgjeEbztbLWJqTy99+hISAixjx/WUAAmg14p5Shz/ui4yokFqvz8+sao31f+mG9PaOELiaS/D5YCdND0XhWFAK3the+vfuZCJGRFp+rbfHmjciagpEmh4AUWOXnxyFi285Q8/eGx0Wn9T0cOpFRuhl6JjYwLrfi5oeSpMUe2ITwrcv1PQwqIxP+zvgta42AABd7cZzUU1TN7WbDaZ2s9H0MKge5SdH4dJbzpA0ozntaSjKzUR2ZCA83liv6aFQGVen+UGam6HpYbRIzXWeHexlisFeppoeRgW7pvpCWiwDAHywOxTpJRfoEhERERERERFRw8pLisLp152gb++Nbsta3kUNqUEX4TDkDegYW2p6KNTEzBzmjdd7uwJoXjFlTXu9t6vifSUCmPtVF3O/1BI013yupjXGuikDsRBH3i69ucKHe8KQlF2kwRERERERERERETVNjKk9HYypkboiIh7D3ECMVq19cfnmXU0Pp9HKyMiA/907+GHTb5oeylO38btv8PmsTzQ9jBZpwRdz8ME7bwEAJBKJhkfTuBkZGSE3LVHTwyANYY2Gelij0bSl3DqOkO/e0PQwiIiIiIiIiIhqLD85ChfelPeM7rikecTvnvSMtmHP6FqJObEJ4dsWanoYVII1Gk9HY6zRoLpjTko9zEk1TuwZrRnNdZ5tCv2iLfS1cTGcf/NERERERERE1LBaep9kUl9zjR02ZzxmRDWXnxyFS287Q2LH/CrVH56PWxYe78YhPzkKF9+S14C2xPN5cX6OpofQLPHz3TzwODY92YmR2DHBHsaO3nhu/VlND4caGD+zwE8TvTU9hAp6uBgp9eD7aG+YBkdDRNQwRJoeAFFj5vj8p7AZ+BoAQKijq+HR1B+zdoNh1m6wpofRZFl0HwNDz66Kx6GbPtbgaAgAHE114dj4rqkhalQcn/8UtiVzmlYzmtOeBpHEED1+Ctf0MKgcv9m7IJNKAQDaRhZID76o4RG1HJxnG5afrb7iZ21hywwcExERERERERFpmuvoT+EwuCSmrt0yY+ptPvxJ00OgJsrJTA9OZnqaHgZRs8bcr/qY+6WWgPnclkOoJUA7ewPFY5GWQIOjISIiIiIiIiJquhhTazkYU2t85sxbgLfe+wAAIJFINDyaxs3IyAiJGbmaHkaDGD9xMrr37KV4/O4bUzU3mBbGxdkZLs7Omh4GUaPGGg31sUajaTP26YG2848oHof+/JEGR0NEREREREREpB6n0Z/CdlDzi9+xZ3TdWHYfA6OyPaN/Zs9oTWKNBpF6mJNSH3NSjVPZntEhP34AaU66hkfUMnCebVhl+0UTERERERERETU09kmmmmDssOnhMSOqGcfny9QMcV6kesTzccvC4615js9/CpuSehlhC62X0TGzQ25MqKaH0ezw89088Dg2LX4TZ8Lz2dcBAKIWek5v6fiZbZyMJSKlHnxaAvbgI6LmT6TpARA1ZroWjtC1cNT0MKiR0TGygI6RheKxgP80ElEToGvhCHBOoyZM38lP00MgIiIiIiIiIiKiFkJi6QiJJWPqRETUODH3S0RERERERERERERERE2Rs4sLnF1cND0MamQsraxgaWWleMzr9omoMWGNBrUUIj1jGLi2UzwWaGlpcDREREREREREROphz2hShT2jiagpYk6KmrqyPaO1RNoaHAkREREREREREVHzxD7JREREpZhfJSJqHlgDSkTUfBhYOcHAyknTwyAiIiKCSNMDICKi5uNieDrG/x6IXVNbo6ersaaH02T8fjUOEm0tTOxgVf3CKhQXy7DvXhL23EmEf1w20nOl0NPRgruFBCNam2NCe0uY6ZdevNZt3U1EpeUrHku05ctO6mCJqV1tlC4yf3JMyzLWFcLNXIJXulpjQvvajZmIqCXiPFk7nCeJiIiIiIiIiIiIGocL95PwwvcXseeDnujlYVH9CgQA+O18OCQ6QkzqWrvC8eJiGfbejMLu61G4F52O9JxC6OkI4WFtiBHtbDGxiyPMDcSK5Tt/dQJRqbmKxxIdITwsDTCpmxNe7+2iFOd+ckzLMpZow81SH1N7uWBiLcdMRNQSMR9cO8wHExERERERERG1XIyp1Q5jakRELcPps+cwaNgI/HfsEPr37aPp4TQZG3/8GRI9PUyd8lKt1i8uLsbWHbuwedt23L5zF6mpadDX14ePlydeGPM8Xn35JVhYmCuWd/Npg8cREYrHenp68Pb0wGuvTMH7776lNE8+OaZlmZgYw8vDA+++/SZeffnFWo2ZiIiIiIiIiIiIiIioOqzRqB3WaBARtQycJ2uH8yQREREREREREbVUjCk2Xzy2zdf5gAg8v3gbDsyfjN6+7LVM1NjxfNx88dgSNS38zDZfPLZETQ8/t7XDGjciopZHpOkBEDUG6cEX4b9yPPw+2wVjn56aHs5TlR0ZiJgTvyAj5BIK0uIBADqmNjBu1QvWfV+CoWs7xbIR+9cg8uBaxWOBUARtYyuY+vWH05hZ0DFW/gfs+qxuyE+OKl1eJIbYzBZm7YfAYfhH0DY0e8p7R9Q0/XktDmZ62rX6IpZbKMXr20Jw7mE6RvmaY/GzrrA21EFmvhTXIzOx8UIMjgenYN8bfkrrdXEyxPxnnAEA8ZkF+OlSLOYdeYTMfCk+6utQ4XXmDHJCT1cjAEBKThF230nEjH0PkJUnxevdbWux19SUpAdfRMDK8fBtIfNk7IlfkB5yCYUq5kmDcvNklIp50qSSefJGJfOkKedJoipxniQiIiIiIiIiorpKDbqIWyvGocOc3TBt1bzj3FkRgYg8sQmpQaX5YLGZLUxb9YJd/5dgVCbO/XDfajzarxzn1jG2glmb/nAb+xnEJsoxuYufdkVeUmmcW0tbDLGpLSw6PAPnkdOgY2gOIqro9wuPYKavg0lda36Rbm6BFFN/uYqzYYl4vr09lo5pA2tjXWTmFeL6o1T87+R9HL8XhwMf9VZar6urGRaMag0AiMvIw4+nH+KLvfeQlVeIj4d4VXiducNboZeH/DOcnF2AXdei8PG228jMK8Kbfd1qsdfUlDAfzHwwkSYxH0xERERERERERFQzjKlRY3fuzGmMGDoIh47/hz79+mt6OE+V/7272PjdNzh/9gziYmMAAHb2DujbfwBeff1NdOzUWbHs8sWLsGLpV4rHIpEINja2GDjkGcxb8BWsbWyUtt3Gyw0REY8Vj8ViMezsHfDciJH49LPPYW5h8ZT3jqhp2vjzL7AwN8fUKS/VeN3c3FyMnjAZ/508jQnjxmL96q9hZ2uLjIxMXLp8BavXf4ODh47gzL/HlNbr1aM7Vi5fAgCIiYnF+m+/x0efzkJGZgY+/2xmhddZuuhL9O/XFwCQlJSMv7Zuw+tvv4fMzEx8+N47tdhrakpYo8EaDSIiIiIiIiIiqpm04Ivw/3o8/GbvgkkLiKnFnPgF6cHK/aJNWvWCdT/lftGP969B5IGK1web+PWH89iKMbVrMyvvF+04gjE1osqwRoMaO+aemHsi0iTOk0REREREREREDaMl9VQmIiLShPTgiwhYNR6+s5p/3pWIqDFLD74I/5Xj4ddC6mBiTvyCvPhwTQ+FiEileP8LOLlgLAYu2gtrv16aHg5RrbHGjYio5RFpegBE1HDiTv+Fh1vmQWLjDtvBb0DP3gsQCJAbcx+JV/bj7uLn0HHFBUisXJTWa/3JFogkhpDmZSMt4Cyij/+IjPvX0X7hP9ASaSsta+jRBa4T5wMApAV5yHp0F5EH1iA95AraLzjaULtK1GIsOPoIZx+k4/txnhjdRrnp8hBvU0zva4+9d5MqrGekK0QnR0PF4z5uxui67iY2X49X+UXM1VxXaflBnia4E52F/f5J/CJGzUbc6b8QvmUedG3cYTf4DUjsvSAQCJATcx9JZeZJ3XLzZKty82TM8R+Ref862lUyT7qUzJPFZebJjJAraMd5kqjecZ4kIiIiIiIiIqKWJPrUXwj96wvo2bjD8Zk3oG/vDQgEyIkJQ/zl/bi+8Fl0X3kRetYuSuu1m7lVHufOz0aK/xlEHP0RGWHX0WXxiQpxbmPPLvCY9CUAoLgwDxnhdxC+bw3SQi6jyyLlGxQSUd19ud8fZ0ITsXFKR4zpqByffsbXBtOHeGLvjegK6xlJtNHJpbQhbF8vS3RadAJ/XnqMj4d4VVje1UJfafnBraxxe2ka9t+Kxpt93epxj4g0h/lgouaH+WAiIiIiIiIiIqKaYUyNqP78+vOPmDXjI3h6eeO9Dz+CT6vWEAgECA0Jxu4d2zGgVzfcCgiFm7u70np7/j4CYyNjZGVl4eR/J/Dd+rW4evkSzl+9CW1t5dxT9x69sGTFSgBAbm4ubt28gRVLFuHC+XM4c/Fqg+0rUUsxY9Yc/PvfKWz+/RdMnjBO6bkRzw3DF3NmYcv2nRXWMzY2RveuXRSPBw/sDxdvP/z0y+/4/LOZFZb38HBXWv65Yc/g+o2b2L5zNz5875163CMizWGNBhERERERERERUc3EnirtF203pKRfNATIiZXH1O589Rw6fV2xX7TvJ1sg1DNEcV42UgPOIvqYPKbWflHFmJqRZ8WYWsT+NcgIZb9ooqeBNRpE9Ye5J6Lmh/MkERERERERERERERERERG1RHGnS+tFRfomKMxI1PSQiIiISAXWuBERNU0iTQ+AiBpGRtg1PPhrLkzbDYLP+z9BS6SjeM6kVW/YDpqKpGt/Q0tbt8K6Bs5toW0ov1mniW9fFGalIOH8DmSEXYVJq15Ky4r0jGDo3qnMtntBmpeFqL/XIzfuASQ2ys1mqWm5n5iLtacjcSE8Axl5RbAw0EYPFyOsGuUOsUhL5Tp3orPww8UY3IzKQlJWASwMdNDJwQBzhzjDwUSsWC63QIpVpyJxJDAFCVkFkGhrwclUF+/0tFN8wXickocV/0XgyuMMpOYUwUhXBG8rCb4c6gI/W/0GeQ/WnIrE2tNROPlBO2w4E4WTYWkQi7QwyMsEC4e5wEi3dGrNKyzGutOROOCfjLjMApjriTDUxwyzBznBWCJfrtu6m4hKyweQC/sFlwAADiZiXJnRsdqxxGcWYMetRAzyMqnwJewJiY4QL3W2rnZbhroiuJnrIig+R413AdDSEkBfRwvaWgK1lidq7DLCruFhyTzpXW6eNK7hPFlUMk9mhl2FcTXzpDHnyWaF8yTnySc4TxIRERERERERUUNLC72KkD8+h0W7QfCb9rNSnNusdW84DH4NCVf/hlCnYpzb0KUNdAzN5cv69kVhZgpiz+1AethVmFaIcxvD2KM0zm1aEud+dGA9cuIeQI9x7iYtLD4Ta46H4nxYIjJyi2BpKEYPD3OsmdgOYpFQ5Tq3I9Kw8fR93HiUiqSsfFgYiNHZxQxfjGgFRzM9xXI5BUVYeTQEh+/GIiEjDxIdIZzM9PDeAHeM6SgvFH2clI1lR4Jw+UEyUrMLYSQRwcfWCAuf94WfvXGDvAerjgVjzfFQnP6sP9afCMN/QfEQi7QwuLU1vhrtByNJaUPWvEIp1hwPwf5bMYhLz4W5gRjD/Gzw+fBWMC5ZrvNXJxCVmgsAsJlxEADgYCrB9S+HVDuW+PQ8bLsSgcGtrRXvUXl6OiK83MO52m0Z6mrDzcoAQTEZ1S4LlMS5xUKIhKrzG0RNDfPBVB+YD2Y++Anmg4mIiIiIiIiI1MOYGmNqTzCmRs3N5YsX8OnHH2Los8/hz227oKNTmnvqN2Ag3nr3fezbswsSiaTCuh06dIK5hfwzOGDQYKQkJWHzn7/j0oXz6Nt/gNKyxibG6NKtu+Jx3/4DkJWZiZXLl+B+WCg8PL2e0h5SQwgOCcVXy1bg1JmzSEtLh7WVFfr17Y2fvv8GYrFY5TrXb9zEmg3f4srV64hPSIC1lRW6d+uC5YsXwtnJSbFcTk4OFixeir37DyI2Lh56ehK4ubhgxsfTMHnCOADAw/BwfLHgK5w7fxHJKSkwMTGGX+tWWLV8Kdq3a9sg78GiJcvx1bIVuHv9MpZ+vQpHj5+Arq4Yzw0dirUrl8HYuLRWJC8vD18tW4Edu/YgOiYWlhYWeH7kcCxZOB8mJiYAADefNngcEQEAEOrJ13V2csLD4HvVjiU2Ng6//bkZzw0bqniPytPT08Nbr0+tdltGRkbw8nDHXf+AapcFAC0tLRgYGEBbW7v6hYmaANZoEBERERERERER1cyTftFm7QbB54Ny/aJb94ZdVTE1l3L9ojMr7xct1DOCkYp+0ZEHGVNrDlijwRqNJ1ijQc0Nc09UHzhPcp58gvMkERERERERERGpgzHFymOKBdLiBnn9p4XHtmbxYpmsQYZUL0Kjk7FqzwWcC3iM9Ox8WBrroVdrJ6x/exjE2iKV69x6EIvvD13F9bAYJKbnwNJYD1287PHl5H5wtCy9tjEnvxArdp7D31dDEZ+WBYmONpytTfDB8C54oVdrAMCj+DQs2X4GF4OjkJqZC2N9MXwcLLF4ygCVr03U0vF83HzPxzy2RFV7Ui9q2m4QfN7/CbcXDkVhRqLGxsPPbP3UU0gb4Ymax7b5fq+l5oufW9a4PcEaNyKiulEdDSZqZnJi7yPywFqkB19AUU4GtI0sYOzdAx5TV0FLW3UTy8zwO4g5/gMyH9xEYUYStI0sYOjeCc7j5kLXovTmmNL8XETsX4XkG0dQkJYAoVgCsYUT7Ie+A8vuowEAeQmP8XjvCqSHXEFRdipEekbQs/eGy8QvYeDk1xBvAaIOfwOBlhAer3ytdKFRWRZdRqq1LQOXtkg4vwOFGUlqLS+SGAIABEI2jmzKAuKyMeYXf5jpaWPmAEe4musiIasA/wSnolAqg7iSGSUyLR/u5hI872cBE4kI8VkF+OtaPJ776S5Of9AeZvryv4uFxx9jz51EfDbQEX62+sgpLEZIfA5ScwoV25qyJQjSYuCLIc6wNxYjJacQ1yMzkZFXVOXYZTIZ1I1viITqfbF4a0cIRvlaYFJHKwTH52DFf/KmrmtHeyhe843twTj/MAMf9rFDN2cjBMblYM3pSNyIysLBN/0gFmnhl0neeHtHCAx1RVg23BUAKv1SW97F8HQUFcvwjLeZejtXhSKpDDHpBXAzr9gUGgCKZTIUSeVBvdTcQuy4lYjghFysHOlW59cmzcuJvY8oFfOkexXzZFb4HUQf/wFZ5eZJJxXzZKSKedJOxTyZoWKe1G/gedK9HuZJfZe2QA3mSSHnyWaB86QyzpOcJ4mIiIiIiIiIGlJ2TBjC969FapA8zq1jZAFTn57web3yOHdG+B1EHN2IjAc3UZCeBB1jCxi5d4L7hC8gUYpz5+Dh3lVIvH4EBekJ0NKRQGLpBMdh78CmxxgAQG7CYzzYvRxpIVdQmCWPcxs4+MBj0pcwdG6YOPfjQ99CoCWE92srK41zW3VVL85t6NoOsed2oCBdvaJ9kcQIAOPcTV1AdDpGfXseZvpifPasD9ws9RGfkY/j/nEoLKoizp2SA3dLA4zuYA8TPR3EZ+ThjwuPMGzdWZydPQDmBvLP4ML9Adh1IwpznvWBn4MxcvKlCI7LQEp2aZz7pZ+vQFosw/yRrWFvqoeU7HxcC09Fem6h6hcvIY9zq3eBgkioXoz5zd+vY1R7O7zYrQuCYjOw7HAQAGD95A6K13zt16s4F5qEaYM90d3NDIExGVh1LAQ3HqXi0PTeEIuE+O31rnjz92sw0tXGinFtAAA6IqFaY7hwPwlFxTIM9a2+8LY6RdJixKTmws1SdYGzPM4tTxak5hRg+5VIBMdmYvWEdnV+bdI85oOZD6a6Yz5YGfPBzAcTEREREREREVWHMTVljKkxptachIYEY8WSr3D2zCmkp6XBysoavfv2wzcbf4JYrDr3dPPGdXy7fg2uX7mChIR4WFlZo0u37li4ZDmcnJ0Vy+Xk5GDpVwtwcN9exMfFQqKnBxdXN0z7eAbGTZwMAAh/+BBfLfgCF8+fQ0pyMoxNTNCqtR+Wfr0Kbdu1b4i3AGtWLodQKMT6736Ajo7q3NOYF8arta32nTpj85+/IyEhXq3ljYzljZ9FIuaemrI7d++h7+BhsDA3w8J5c+Hp4Y7YuHj8ffgICgoKKv0sPYqIgLenJyaOewFmZqaIjYvDjz//im59BsD/xlVYWJgDAD6dPRebt+3A4gXz0L5dW2Rn5yAgMBApySmKbY0YMx5SqRQrln4FJ0cHJCUl49KVK0hLT69y7DKZDFKpVK39FInUa2cz/sUpmPDCWLz+6hT4BwRi7peLAAC//Pi94jXHTHgRJ0+fwZyZn6B3rx645x+AhUuW4/KVq7hw+l+IxWLs2b4ZE156BcZGRvhuw1oAgLiSz2h5p86eQ1FREUYOf1at5atSVFSEyKhoeHl6qHy+uLgYRUXy/0eSk1Pw+1+b4R8QiB++21Dn1ybNY40GazSIiIiIiIiIiKhmcmLvI+LAWqSXuT7YyKcHPKvpFx19rFy/aI9OcFHVL3rfKiSVianpWlbsF/1ozwpkhJZeH6xn7w3XSQ3XLzryUEm/6FfrHlMzdK1Zv2jG1JoH1mgoY40GazSaE+aemHuiuuM8qYzzJOdJIiIiIiIiImp82FO5cWFMUVn5mOJXxx+r9wKNEI+tMnXixdEZ+eq9qIb5P07Acwu2wNxQgjnj+8DNxhTxaVk4dv0+CoqkEGurPriRienwsDPDmJ6tYGogQXxqFn47cQuDv/gTF1e/AXMjPQDA/L9OYue5AMyd0AdtXKyRk1+IoMhEpGTmKrY18etdKC6WYeGL/eFgYYTkzBxcC4lGenbTeA+bu5zY+4g6qCLv+moVeddH8vqkrIfl8q4vVJJ3vVmSd9WRQGxZknftNhoAkJf4GI9L6pMUeVe7hs27NiY8HytrTufj5n5s1bsDAlUlJ/Y+IlXUwXhUUy8ac7xcvah7JzirqhdVUQdTvl708d4VSFdRB9NQ9aJP6mA8qqiDaSjN/TP7REPUU6h7j5SGwmOrrDl9r61PGVFhuLdzNeL9z6MwOwO6Jpaw8u2Jru+tgbCSc3Ly/dsIPvg/JIfeRF56InSNLWHu1QntX54HfStHxXJF+Tm4t30lIi8fQm5qAkRiCQysneE98l249BkLAMiKe4Q7W5chMfAy8jNToa1vBBMnH3R4dRFMXfk/Mj+3rHFjjRsRUe2p1/WOqAnLjgjA3eVjoG1oBqfRM6Fr7YqCtASk3P4HxUWFlQZZ8pMjIbFxh0XX5yHSN0FBejziTv2Fu4ufQ4clp6FtKP/HJ3zHQiRe3AOnsZ/BwMkP0vwc5ESHoCg7VbGtwPVTICuWwmXCFxCb2aMoKwUZ969DmpNR5dhlMhlQrF4TS4Gw8o+zrFiK9OCLMHBpCx2Tut/cMy8pEgAgsVH1D5gMMqn8n+Liwnxkht9GzIlNMPToAl1Lpzq/NmnOomOPINIS4PDbbWCuX3rh2Ni2llWuN8LXHCN8zRWPpcUyDPEyRbtV17HvXhLe6G4LALgekYF+7sZ4u6edYtnBXqaKn1NyCvEgKQ+LhrnghXalr/lc69JtV2bn7UR8sv9B9TsJIHpRD7WWm9zBCu/1tgcA9HU3waOUPOy4lYA1z7tDIBDgzIN0nL6fjnlDnJSWszPWwXu7wrD7diJe6mwNP1t96GprwVAsRCdHQ7Ve+4mY9AIAgINJxfPYky9NT5T/gimTlS4Tn1mADWejkJpbhFXPu6t8rfd2hQEIUzzWEgAf9bXHS53rfk4hzcqOCMC9knnScfRMSMrMk7KiQqCSeTKv3DxZWMk8+ajMPKlfyTwZVGae1CmZJzPvX0dRA86TGfU4T+aXzJO6KuZJWbl5Miv8NmI5TzYLnCeVcZ7kPElERERERERE1FAyIwJwc+loaBuYwW3sLEWcO+nW8SrzwXlJkdCzcYd1t+ch0jdFQXo8ok/+iesLn0W35aehYyiPrYVtW4S4C7vh9sJsGDrL49zZ0cEoyiqNc99Z+zJkxcXwmDAPYnN7FGalID3smlpxbpmacW6tauLcqUEXYOjaFuL6yAcnygsQ9SrJBxeXywdH/vMzjD27QMI4d5O24EAARFpaODqjDywMSj83L3RyqGItYGR7O4xsXxq7lhbLMKS1Ndp8eRz7bkbjzb7yv6Orj1LQ39sS7/QvjbMO8S39e03JLsD9hCwsHu2HcZ1Li6yHty3ddmV2XIvE9G23q10OAOLWjVJrucndnPDBQHkRbl9vS4QnZWP71Qism9QeAoEAp0MScSo4EfNHtlYs18/bCnYmErzz5w3suhaFl3s4o42DMXS1hTDQFaGTS80KbKPT5BfjOpjpVXiuqFwlskioXOwrk8kUy8Rl5GH9P2FIzSnAmontVb7WO3/eUHqsJQCmD/HEyz2cVS5PTQfzwcwHU/1gPlgZ88HMBxMRERERERERVYcxNWWMqTGm1lzcu3sHwwb2hZm5BebOXwh3D0/Ex8XiyOG/UVBQALFYde4p4vEjeHp644XxE2Fqaoa4uFj8+tOPGNC7G67e8oe5hQUAYO5nn2LH1s2Yt3Ax2rZrj5ycbAQGBCAlJUWxrfGjR0AqleKrpSvg4OiE5OQkXLl8CelpaVWOXSaTQSpVL/ckElWee5JKpTh35jQ6dOoMG1tbtbZXlcePwgEAHp5eFZ6TyWQoKpLnnvLy8nDz+jVs/HYDuvfoBRdX1zq/NmnOzDlzIRIJcfnsKVhaWih+/9KkCVWuN27MaIwbM1rxWCqVYsSzw2Dr4oltO3dh2vvvAgAuXr6CIYMGYvq0DxTLDn92qOLn5OQUhISGYd2qFXh58kTF78eOrr6m4o/NW/HGO+9XuxwASHPS1Vru9VenYOaMjwEAgwcOwP0HD/Hbn5ux6YfvIBAI8M+//+Gff//D10u/Uiw3ZNBAODjYY/KU1/Dnlm146/Wp6NC+HSQSCYyMjNC9axe1XvuJqKgoAICzk2OF5558Dp8of44o+1mNiY3F0hWrkJySgp/+963K15o85TWlx1paWpj72Uy89frUGo2ZGh/WaLBGg4iIiIiIiIiIaiarJKYmMpD3i5ZYu6IgPQEpt6rpF50kj6lZdivpF50Wj9hTf+HOV8+h49Iy/aK3L0TCxT1wHvsZ9J39UJyfg5yoEBSWuT44YF1JTG38F/LrgzMbPqZWr/2iE0v6RVuruD5YpqJf9D+bYOTJmFpTxxoNZazRYI1Gc8HcE3NPVD84TyrjPMl5koiIiIiIiIgaF/ZUBmSVPqMZjCkqKx9TXH8mCul56h33xobHVpk68eKcguJqttI4zPvzJERCAU4sfQUWRqU9nMf39q1yvVHdfTCqu4/isbS4GM90dEerd77D7guBeOfZzgCAKyFR6N/WBe8NL71m8ZmOpXHplMxc3I9JwbJXBmFCn9LXHNnVu877RnWXHVkm7/p8mfqk6vKuSSryrqf/wt0lz6HD4nJ510t74DSmXN41S0XedXw95l1ljW0GVR/Px8qa0/m4pRxbqp3siADcLTkfO42eCd0ydTBV1ouWq4MpqKQOJrxMHYxBJXUwgWXqYMQl5+OM+9chbaL1onXVUj6zDVVP0Zjw2CprTt9r60vqowD8+8VIiI3M0HbSbBjYuiIvNR7R1+TxKGEl5+TsxEgY2XnAuddo6BiYIjctHveP/4Hjs4di+IZzEBvJj/Gt3xcg/MwutJ08B6aubVCUn4P0iGAUZJaek08vfQmyYinaTfkS+hb2yM9MQVLINRRkV92npr7iUY0NP7fKWOPGGjciorpoOv8BENVS+I5FEAhFaDfvMLSNSv9hs+oxtsr1LDqPADqPUDyWFUth1m4Irk5vh8TL+2A35A0AQGbYdZj49oP9M28rljVrN1jxc2FWCnLjHsB18iJY9XhB8XvzTs9VO/aECztx/9dPqt9JAL1+ja70ucLMFBQX5EFsXvHmqrJiqXIAXUsIgUBQYRmZtAjS/GykBZxF3Kk/YdFtNAyc21TYXurdk7j4lvJNPA1cO8Dng5/U2g9qnHILpLj8OAOTOlgpfQlTR3a+FOvPROFIUDIi0/JR9l6yYYm5ip/b2xtg370kLDvxGP09TNDBwQASbaHieVOJCC5mYmy8GAOpTIZersZoba0HLS3lv1dVhnib4sjbFf9e62KIj/INdltZ6yOvSIak7EJYGujgwkP5F/YJHayUlhvpa45PDzzA+fD0p/Ylxj82G0N/uKv0u3ufdYZZmWN3MiwNzl9dVlpmxQg3pS+/ZX0xxAm9XI0BAOl5RbjwMB3fn49GQVEx5g91qd8doAb1qGSebFtunrSsxTxp2m4Irk1vh6TL+2Bbbp60q2aedJm8CJY1nCcTazBP9mwk82Ta3ZO4pGKe9OY82aRxnlSxTc6TnCeJiIiIiIiIiBrI/W2LINASofOCI9ApE+e26Vl1nNuqywhYdVGOc1u0H4Lz09oi/tI+OD7zJgAgPewazPz6wWlYaZzbor1ynDsn9gE8X/oKNr1K49xWnauPc8ed34mgTTOq30kAA/+IqfS5J3Fu3Uri3LIycW6Bijg3iotRLC2CNC8bqQFnEX3yT1h3Hw1Dl7YVtpd85z+cfl25yaSRWwf4ffizWvtBjVNOQREuPUjGi92cYGGguii6Mtn5RVj7TygO341BZEoupMWlf2+h8ZmKnzs4mWLvjSgs+TsQA1pZoaOTKSQ6ZeLcetpwsdDD/07dl8e5PSzga2ekVpz7GV8bHJvRt0bjrs5QPxulx63tjJBXWIykrHxYGurifFgSAGBiV+Wbd45qb4cZ22/jXFgiXu6hnBOqL/7R6Ri8+ozS7wIWD4V5mWP3X1ACHGYeUlpm5fi2GOKrOvY+b0Qr9PaUF0Rn5BbiXFgivv3vPvKLirFgVNUXHVPjxnww88FUd8wHq9gm88HMBxMRERERERERVYExNRXbZEyNMbVmYu5nMyEUiXDq/GVYWJY2XJow+aUq1xs9dhxGjx2neCyVSjHsuRHwdLLFrh3b8O4H0wAAVy5dxMDBQ/DBR9MVyw59drji55TkZISFhmDF6nWY+OLLit+PGl117gsAtv71B95/+41qlwNQZbO15KQk5ObmwtGx4g06pVLlGg2hsGLuSSqVoqioCFlZWTj13wn8+tMPGDdhEtp36Fhhe/8cO6qUBwaATl264s9tO9XaD2qccnJycObcBbz+6hRYWlrUaN2srCwsWbESe/cfxKPHEZBKS/9Wg4JDFD936dQRW3fswufzF2DokMHo1qUzJBKJ4nkzM1O4u7li9bpvIJVK0b9vH7Rr2wZaWlrVjmHkc8/iyrlTNRp3tdscrpw/buPni7y8PCQkJMLa2gqnzpwFALz6svK5ZvzYMXjz3Q9x8vQZvPX61Hod0xO379xFpx59lH4XH/EQFhal+fejx/9RNBN84n/frMPwZ4eq3OaKJYswoJ+8ziUtPR0nT5/B12vWIb8gHyuXLannPaCGxBoN1mgQEREREREREVHNhG9fBGiJ0H5+DftFdxkBi3LXB5u1H4IrHyv3i84Iuw4Tv36wH6pGv+iepTE1CzWuD064sBNhv6gXU+v9mxoxNYv6ianFnvoTlt1Gw8BFdb/oC2+Wi6m5sV90U8caDRXbZI0GazSaCeaemHuiuuM8qWKbnCc5TxIRERERERFRo8KeygCKi9TaRkNgTFHFNsvFFMUiLQCVX3/ZWPHYqtimmvHixi4nvxAXgyLw8oC2sDDSq9G6WXkFWL3nIg5dDUFEYrpyT+3oZMXPHd1tsftCIBZtPY1B7dzQydMWEp3SvyNTA124Wpvg20NXIJUVo3drZ/g5W6l1bOnpe7S9TN7VsEzetXst864z2iHpyj7YDi7Ju95XM+86qZZ519/Uy7s2FTwfq9hmMzkft4Rj++6uUESk5tdo36hUeEkdTLt5NawXVXE+Nms3BFenK9eLPqmDsa/mfOw6eRGsang+TqhBHUyvWtbBNLSW8JmtqbrUU7y/O0ypjEmTeGxVbLOZfK+tT7d+/xICoQjPrDgGXePSvjcufcdVsRbg1GMk0GOk4nGxVAr7TkOw7w0/PDq3F97D3wIAJAZfhW27/vAZ+a5iWftOQxQ/52emIDPmPjq+thiu/Upf07F7aY+pyoSf2oEr339c/U4CmLwnXq3lNI2fWxXbZI0ba9yIiOpApOkBED1N0vxcpIdchnWfSUoBFrXWzctG5N/rkXzjCPKSIoHi0i9GubFhip8NXNsj8fI+PNq1DKZt+sPArQOEOqVNLEX6ptC1ckH0sY2QFUth7NML+o6tIVCjiaVZ+yFoO/9IjcZdU3cWDUN2ZKDiscuE+bAf9q7SMtdmtFd6bOTVHZ5vrFe5PSPPrnCZtBAAIJMWIDf2ASL/3gD/lRPQZs5eaBuaqVyPGre0vCJIiwFbo5rdsBcAPtgThvMP0zG9nwPa2RvAUCyEAMCULUHIKyr9Vrb4OVfYGolxMCAJ35+Pga5IgH4eJpj3jDPczCUQCATY8aov1p2OxMYLMfjq+GOYSEQY29YCswc5wUAsrHQMphIRjMT1O+WZSZS3pyOSfyHMK5TvU2puEURaggpfXAUCAawMtJGaU/fCD3sT+fGISlMOxLtb6Cq+eG65EY8tNxIqrNvVyRALh7lAKpMhPDkPq05GYt6RcHhbSdDV2ajC8k6mumhnb6B43NfdBGl5Uvx4KRaTO1rDw1JSYR1q/OpjnkxRMU/mqJgnH+9aBpMq5smYYxuBYimMajBPmjbQPJlTZp50VjFPXlcxT3pUMk8aenaFa8k8WVwyT0b9vQEBKyfAj/Nkk8V5siLOk5wniYiIiIiIiIgagjQ/B2nBl2Dbd7LSxcbqKMrLxqMD65B4XR7nlpWNc8fcV/xs5NYe8Zf24f7OpTBvMwBG7hXj3BIrF0QckeeDTVv1hIGjr1pxbosOQ9B54dEajbumri0YiqyI0ji3x8T5cHruPaVlzn/UTumxiXd3tHprg8rtGXt1heeLiwAAsqICZMfex6ODG3Dr6/HoOHcvdAxrdhyocUjPKYS0WAZbE90ar/veXzdwLiwJnwzxQnsnExjoiiCAAC/9fFkREwaAJWP8YGusi4O3Y/DdyfvQ1dZCf28rfDmqNdwsDSAQCLDrvZ5Y+08ovj95HwsPBMBUTxtjOzng8+dawUC38ji2qZ42jHQrxm7rwlRPOX6tI5J/pnOfxLmzCyDSEsCi3A1zBQIBrIzESM0uqPMYHEzl55qolByl37tbGuDYDPnNPDdfeozNlx9XWLebqxkWjfZDsUyGh4lZ+PpoCObuuQdvG0N0c6v4OXU210d7JxPF477elkjPKcQPpx/gxW5O8LQ2rPP+UMNjPrh6zAeTOpgProj5YOaDiYiIiIiIiIiqwphaRYypMabWHOTk5ODCuTOYMvV1WFha1mjdrKwsrFy+BAf37UXE40eQSktzTyHBQYqfO3bugl3bt2LBvM8xeMhQdO7aDRJJ6d+LqZkZXN3c8c261ZBKpejTrz/atG0HLTVyT88OH4lTF67UaNw11a9HF9y7e0fxePHylfhoxqdKy3g62yk97tW7L3745XeV2+vRszeWrVoDACgsKEBoSDBWrViGkcMG4+i/p2FuYaFyPWrcUlPTIJVK4WBvX+N1X5r6Jk6ePoN5c2ahc6eOMDI0hEAgwIix45Gbl6dYbsOalXCwt8fOPXuxcs166Orq4pnBg7By2WJ4erhDIBDgxJGDWLzsa6xauwEz53wBMzNTvDhxApYsnA9Dw8prBMzMTGFsXL81GuZm5ZoeiuVzVm6evCFccnIKRCIRLC2V/+YFAgFsrK2QnJJS5zE4OjoCAB5HRCr93tvLE1fOnQIA/Pzr79j02x8V1u3dswfWfL0MUmkxwh48wIKvluCjT2bBt3Ur9O7Zo8Lyrq4u6Nypo+Lx4IEDkJqahrUbvsPrr74CH2+vOu8PNTzWaFSPNRpERERERERERFTWk5iaTS1jahEHVfeLLhtTM3Rrj4RL8n7RJm36w7CKftGQ1bxfdLsvn25M7fbCiv2iHZ5Vjqldnd5e6bGRd3d4vrle5faMPLvCdfJCAPLrg3NiHyDy0AbcWzkBbRlTa7JYo1ERazRYo9EcMPdUPeaeSB2cJyviPMl5koiIiIiIiIgaD/ZUbnwYU6yofExRIKjXzTcYHtuK1IkXNwVp2XmQFstgZ1bzvsnvfPs3zvo/xqdje6KDmw0M9cQQAJj09W7kFZTu//Kpg2Fnboh9l4LxzcEr0NUWYUA7Vyx6qT/cbc0gEAiwb94krNpzAd8evIr5f52CqYEuxvX2xRcT+9Tj3lJNSfNzkR5aknetYd98Rd71ppp5193LYOJXRd71eP3mXUM2vov8pIga7VNjwPNxRc3mfNwCjq1YVP3nllSrjzoYVfWiuSrOx492LYNpFXUw0cfk331qWi/6tOtgGlpL+MzWVF3qKURaAhRKZfU6ntrisa2ouXyvrS9F+TlICLgEt0EvQte4Zn2LCnOzEbB7LSIvH0J2gnI8KiOq9Jxs7tEBj87txe2/FsO2w0CYe3aESFx6TtYxMIWBjQuCDvwPsuJiWPn1gqmLevEo+y7P4Jmvj9do3I0dP7cVscaNNW5ERHVRv7MSUSNTlJMGFEshNrWt8bohP32A9MDzcBw5HQau7SCUGAIQIGj9FBQXljaxdHtpMcRmtki6ehDRR7+HlrYuTPz6wWXCPEis3SAQCOA7cwciD65D9NGNeLTjK4j0TWDZfSycxs6GSGJQ6RhE+qYwkNS9iaW2oRm0dHSRnxxV4Tmvt7+HtCAXhenxCPrmNZXr+87cDqHECEXZaYg/sxnJN47g4dZ58Hjl6wrLCiWGMHQtvcmvkUcX6Nl54u7SUYg+/iNcxn1e5/2hhmciEUGoBcRm5Fe/cBkZeUX4NzQVn/RzwId9SpvJ5hcVI61cMF9PR4iZAx0xc6AjErMKcDIsDcv/jcDUrcE4O60DAMDBRIw1oz0AAA+ScvF3QDLWno5EgVSGr0e6VTqOnbcT8cn+B2qNOXpRxWaotWEqEaGoWIbk7EKlL2MymQwJWYVoZ1f5Z19dPVyMINIS4J+QFLzc2Vrxe4m2UPGl6d/QVJXrGuqWLtPRwRAdHAwwZONdzD0cjn/ebQstreojQq2s9SCTAYHx2fwi1kQ9mSd1ajFPhpbMkw5l5kkBBAgsN0+6vrQYOma2SC43TzqXmSdbz9yBqHLzpEX3sXAeOxvCauZJUQPMk8UFuShIj0dwJfNk65nbISqZJ+PObEbKjSMI3zoP7irmSZHEEAYq5sl7S0ch5viPcOY82SRxnqw5zpNERERERERERFQfirLTISuWQmxW8zh34Mb3kRJ4Hi7PT4eRa3uIJIaAQIA7a16GtEyc2+vlJRCb2iLh6kFEHJbHuc3a9IPHxPnQs5HHudvP3olHB9Yi4sj/cH/bIoj0TWHTcwzcXpjToPngPBVxbt93/wdpQS4K0uJxd/1Uleu3/2wHRHpGKMxKRczpLUi8fhihm+fBZ6qqOLcRjMrEuY09u0Dfzgs3Fo9E5NEf4T5hbp33hxqeiZ4OhFoCxKblVb9wGRm5hTgRGI9Pn/HGtMGeit/nF0mRllOotKy+WITPnvXBZ8/6IDEzD/8FJWDpoSC8sukqzn8+EADgaKaHdZPaAwAeJGTh4O0YrD4egsKiYqyc0A6V2XEtEtO33VZrzHHrRtVoHytjqq+DomIZkrLyYWFQWtAsk8mQkJGP9o6mdX6Nnu4WEGkJcDwgHlN6uih+L9ERor2TCQDgRGCcynUNJdqKZTo6m6KjsykGrTqDObvv4r+Z/dWLc9sZyePcMRnwtK75xcikecwHyzEfTHXFfHDNMR9MRERERERERNSyMaZWc4ypUVOQlpoKqVQKe3uHGq/75qsv4cypk5j1+Tx07NQZhkZGEAgEGD96BPJycxXLrVy7Afb2Dti7eyfWr14JXV1dDBryDBYvXwl3D08IBAIcPHoCXy9bjA1rV+GL2TNhamaGCZNexPxFS2BoWHle09TMDEbGxrXa97LMLSwgkUgQGVmxse2mPzYjJycH8XGxmPTCaJXrHzjyD4yMjZGakoLff/0ZB/ftxawZH2H9dxsrLGtkbISOnTorHnfr0RM+rVpjcL9e+Hb9WixcsqzO+0MNz8zMFEKhEFHR0TVaLz09HYePHsOXc+dg9sxPFL/Pz89HSory+VtfXx8L58/FwvlzER+fgKP/nMDc+Qvx/LiJCLx9HQDg7OSETT98DwAIDbuPXXv2YdHS5SgoKMDGb9dXOo4/Nm/FG++8r9aYpTnpNdrHypibm6GoqAiJiUmwtCxtAiiTyRAXn4DOnTrW+TX69+kNkUiEvw8fxdtvlOaOJRKJYvuHj6pu5GdkZKRYplvXzujWpTM6dOuFD6d/ipuXz0NLjUaBbf18IZPJcNffHz7eXnXeH2p4rNGQY40GERERERERERGpSxFTq8X1wSE/foC0wPNwHDUdhq7tINSVXx8cuG4KiguU+0XrmMr7RUcdKY2puU6YB0nJ9cF+s+T9oqOObET49pJ+0T3kMbUG7RedVDGm5v2OvF90QXo8gjaojqn5zSrtFx13ZjOSrx/Bwy3z4PGqin7ReuX6RXt2gZ69J+4uGYXoYz/CZTxjak0RazRqjjUa1BQw9yTH3BPVFefJmuM8SURERERERETUcNhTuYRW47mdNmOKzRePbc2ZShrPZ7Mqpga6EGoJEJOSWaP1MnLycfzmfXz2Qi9Mf7674vf5hUVIzcpVWlZfVwdzxvfBnPF9kJCWjX9vP8TibWfw4qo9uLL2LQCAo6Uxvnn3OQDA/ZgU7L8cjJW7z6OwSFrHPaS6qHPeNagk7+pSkncVlORdy9Qnub4or09KvlYm7+pbLu/66Q5E/b0O0cc24tHOMnnXMbXPu2ppi1X+vrHj+bjmmsr5uCUdW6q5J+djcS3OxyEldTCOZepgAAGCytXBuL20GGIzeb1o2ToYlzLnY9+Z8nrRsnUwlt3Hwqmh60VV1ME0tJb0mW2IeoqiYlm9vEZ94LGl6hRkyeNReuZ2NV730vp3EXfvHPzGfQIzj/bQ1pOfk88sfRHSMv8jd3pjKfTM7fD4wgEE7f8OQh1d2LTrjw6vLIChnfycPHDBbvjvWoOgA9/h1h8LoGNgCpe+Y9H2xbnQruKcrGNgClO9eohHNSL83NYca9yIiKgqTSOSQlRLIn0TQEuI/NTYGq1XlJOB1Dv/wnHUJ3AY/qHi98WF+SjMTlNaVijWg9PomXAaPRMF6YlIvXcSj3cvR9CGqei47CwAQNfCAZ6vrwEA5MY9QNK1vxFxYC2KpQXwUHHBzhMJF3bi/q+fVPp8Wb1+rbxRp0BLCGOfXkgLOIuCtHjomJT+w6ZnL2/imJcUWen6+o6+0DY0AwCY+PZF4NoXEX9mC6z7TIaha/tqx6bn0BoAkBMZqM6uUCMk0Raiu7MRDgcmY84gJ5iV+WJRFQEAmQzQESk3F912IwHS4srXszTQwcQOVgiMy8Gmy7HILZBCoiNUWsbdQoLp/RxwJDAZ/rFZVY5jiLcpjrzdRq0x15febsb434UY7LmTiLd7lgYVDgemIKegGL3dSps96wi1kFdYxRtSCWtDHUzsYIktNxJw4F4Snm9jUf1KlXAzl+D9XnZYezoKBwOSMVqNbQXGZQMALNT8e6DG58k8WVCP82RRNfNkWsk8GbxhKjqUmSc9ys2TkQfWQiYtUHlh6xOJNZgnezbQPGlcZp60quE8mc15ssniPFlznCeJiIiIiIiIiKg+iAxMINASIj+l5nHupDv/wvX5T+AyYpri95XFud3GzoLb2FkoSE9E8t2TeLBrGe6ufxXdV5wDAEgsHNDqjbUAgJy4B4i/8jce7V+D4qJC+EytPM4dd34ngjbNUGvMA/+IqfQ5gZYQpq16I8X/DPLT4iEuE+fWL4lz5yZWHuc2cGoNHUNzAICZXz/cXjUZMac3w67vZBi5ta92bAaOrQAAWYxzN1kSHSF6uJvj7zsxmPOcD8wN1LsITyCQx7nF5eLcWy5HQFrFBQOWhrqY1NUJgdEZ+OnsQ+QUFEFPR7mEyd3KADOe8cLhuzG4G131TT2f8bXBsRl91RpzfenjaYHvT97HnutReKe/u+L3h+7EIqdAij5epXFksUgLeYU1v2jW2lgXk7s54a9Lj7H/ZjRGd7SvfqVKuFka4P2B7lhzPBQHbkdjTMfqb6wcEJ0BALAwbJoXZRLzwU8wH0x1xXxwzTEfTERERERERETUsjGmVnOMqVFTYGpmBqFQiOjomjUETE9Px7EjhzHniy/xyazZit/n5+cjNSVFaVl9fX3M/XIh5n65EAnx8Thx/CgWzp+LiWOfx/W78lyLk7Mzvv9xEwDgflgo9u3eheVLFqGgoADrv9tY6Ti2/vUH3n/7DfXGnFd5flcoFKJv/wE4+e8JxMXGwsa2tFGkTyt5Xujxo0eVrt+mbTuYW8g/MwMHD8GYEcPw+y8/Y8rU19Gpc5dqx+bbpi0AwP/eHXV2hRohiUSCfn16Yfe+/Viy8EtYWJirtZ5AIIBMJoNYrKP0+19++xNSaeV/s9bWVpg65SXcveePDd/9Dzk5OdDT01NaxsvTA1/MmYW9+w/g1u2q/7ZGPvcsrpw7pdaY68vA/v2wau0GbNm+A9OnfaD4/Z79B5CdnY2B/fspfifW0UFubq6qzVTJ1tYGr73yMn7+9Xds37Ubk8aPq/V4PT3cMWvGx/hq2Qrs2L0XkydUv63bd+8BAKwsLWv9uqRZrNGQY40GERERERERERGpSxFTq8X1wSl3/oXTqE/gqEa/aOcxM+E8prRf9KNdyxG4YSo6LVfdLzrxqrxftKyoAB6vVt0vOuwX9WJqvX+rJqbWqhfS/OunX3TAmhcR96RftBrXB+szptbksUaj5lijQU0Bc09yzD1RXXGerDnOk0REREREREREDYc9leUEam2hYTCm2Hzx2Nbck3hxYyfR0UbPVk44cDkEX0zsC3MjvepXKiGTATrayv2w/zp5t8qe2lYm+nixfxsEPE7AD0evIye/EHpi5b8nDzszzBzbE4euhuBOeHzNdojqVZ3yrnf/hePIT+DwXC3yrnuWI/ibqeiwtEze9bVyedeD8vqkavOuv6mXd20qeD6uuSZzPm4Bx/bdXaGISM2vchlS7cn5OL8e62BU1YuWPR+nltTBBG2Yio7LVNeLJl2T14sWSwvgUcX5OKEGdTC9alkH09Bawme2vlVVTyGr/N/HBsdjS9XRKYlH5STX7P+LguwMRN84Ab8JM9F67EeK30sL81GQlaa0rEhXH20mfYY2kz5DbloCYm+exJ0tS3Bm+RSM+PYCAEDfyhHdPlgPAMiIeYCIiwfgv2M1iosK0eWdVZWOI/zUDlz5/mO1xjx5T9P4PsbPbc2xxo2IiKoiqn4RoqZLqCOBsXd3JF8/DOexcxQXzFSr5E6jWiLlJpbxZ7cBxZU3sdQxtoR174nIjgxE7IlNkObnQiiWKC0jsXGH48jpSL5xBNmP/aschln7IWg7/4h6Y66Gw/BpSL13Cg/+nAPv93+Clqh2/zgJBAK4vbwUt+YNQMTelfD9dGu162RHyvdT20i9JqLUOC0Y5oIxv/hjxM/38EFve7iY6yIpqxD/hKTg65HuMBALK6xjqCtCd2dDbLwQAzM9ERxMxLj8KAPbbyXAWFd5+RE/3cMgL1O0ttGDsa4IYYm52HM3EZ0cDSDRESIwLhvzjoRjRGtzuJrrQluohQvh6QiKz8EHvau+Wa2ZnjbM9Br2y0Jfd2P09zDGsn8jkJUvRWcnQwTF52DNqUj42erjhXalTVR9rPVw0D8JB/yT4GyqC7FIgFbW+mq9zqJnXRCZlo8P94Thn5AUPONjBhtDHeQWFuN+Yi4O+CdBVySASFh9qcm7Pe3w1/V4rD0diZG+5hBqla4TnpyHG5GZAIDMfCnOPUjDtpsJaGenj27ORjV8d6ixKDtPOtVinhTUYp60UnOeTFFjnjStx3nSvp7nydvzBiBy70q05jzZYnCerBnOk0REREREREREVB+EOhKY+PRAwrVDcBs3GzqGasZZn8S5tZXj3DFntkJWTZzbts9EZEUEIvKfnyHNz4FQrHxhnp6NO1yfn47E64eR+ehelcOw6DAEnRceVW/M1XAe8SGS755EyO+z4ffhz3WKc3u/sgxX5vbHwz1fo/2sbdWukxURAADQNqp90SBp3qLnfTHq2/N4bv05TBvkCRcLfSRl5uN4QBxWjW8HA92KJUaGutro7m6O/526DzMDHTia6uHSg2RsvfIYxhLlv8Fn153FEF9rtLY1grGeDsLiM7H7RhQ6u5hCT0eEwJh0zN1zDyPa2cHN0gA6IgHOhyUhMCYD0wZ5Vjl2M30dmOnrVLlMfevnbYkBPpZYcigQmXlF6OpqhsDYDKw6Fow29sYY19lBsayPrREO3IrG/lvRcDbXg65IiFZ26sWOvxrth4jkHLy/+QaOB8RhqJ8NbIx0kVsoxf34LBy4FQNdbS1oC7Wq3db7Azzw58XHWHM8FKPa2yvHuZOyceOR/AbJGXlFOBuaiK1XHqOdowm6uzGH1VQxH1yK+WCqK+aDa4b5YCIiIiIiIiIiYkytZhhTo6ZAIpGgV59+2L93N75ctATmFurVCAgEAshkMuiIxUq///O3XyCVVp57srK2xkuvTIX/vbv437cbkJOTAz095RoND08vzPr8CxzYvxd3bt+qchzPDh+JUxeuqDXm6nwyaw5OHD+GGdPew5/bdkFbu/a5pzUbvkPX9n5YsnA+9h06Vu069+7cBgBYWlrV6jWpcVi9Yhn6Dh6GHv0G4rNPZ8DD3Q3xCQn4+/BR/PDtehgaGlZYx8jICH1798Lq9d/A3NwcLs5OOHvuAn794y+YmBgrLduj70AMf3YY2vj5wtTUBEHBIdi8bTt6dOsKPT093L3nj48+mYVxY0fDw90dOjraOHX6LO76B2D2p1XfPMHc3Azm5mrmnuvJkEED8czgQZgzbwEyMjLRs0c33PMPwMIly9GhXVtMeXGSYlk/P1/s2LUHO3bvgZuLC3R1ddHGz1et11m3agXCHz/GlNfewt+Hj2LUiOdgZ2uLnJwchISGYcfuPdDV1YW2dvVtej6dPg0/bvoVi5etwIQXxkAoLP1f5v79B7h89RoAID09Hf+dOo1f//gLnTt2QJ9ePWv47lBjwRqNUqzRICIiIiIiIiIidTyJqSVdPwznF2oRUyt3fXBcDfpFx/xTeUzNaZS8X3SWGv2i231ZPzE1x+HTkHr3FO7/MQc+H9Qtpub+8lLcnDcAj/euhN9MNWJqEYypNQes0agZ1mhQU8DcUynmnqiuOE/WDOdJIiIiIiIiIqKGw57KjRNjis0Xj23N9HU3hp6OFnIKihv0dWtjySsD8dyCLXhm3l/4+PlucLUxRWJ6No5dv481bw2FoURcYR0jPTF6tnLEd39fgbmhBI6WxrgYGIHNp+/BWF95+SFf/ImhHd3R2tkKJvq6CI1Oxs7zAejiaQc9sTYCHidg9m8nMKq7D9xtTKEtEuJcwGMEPE7Ex893x5HroQ31VlA5Qh0JjL26I/naYTiNqXt9Uvw5NfOuUWrmXSNqn3cN2fgu8pMi1NufRobn45ppSufj5n5sdUXV3wOAVCtbB+NcizoYrVrUwVirWQeTrEYdjFk91sE4lKuD0aTm/pmtb1XVU4hFAuQXyRp0PFXhsaWqiMQSWPn2QOSlv9Huxc8hVrM2TlByThaWOyc/+HdLlfEoiYkV3AZOQtrjAIQc+glF+TkQlYtHGdm5w2/cJ4i8fBgpD+9WOQ77Ls/gma+PqzXmpoSf25phjRsREVWl+u5wRE2c68QFuLt8DO4uGQH75z6ArpULCjOSkHL7H7i/8jVEEoMK64gkhjDy6o7oYxshMjSDrrkD0kMuI/7cdgj1lJtY3lk8AmbtBkHPsTVEesbIjQ1D4qU9MHTvBKFYguzIQDzcMg/mnUdAYu0KgUgb6UEXkB0ZBIfnPqhy7NoGZtA2qJ8mlkaeXeD28lKEb52PO4uGwbrfS9Cz94JAoIWCtAQk3zgMABDqVnw/ypNYu8G670uIO/UHMkKvwsirq+K5opwMZD64AQAolhYhNyYMUYe/hUAkhs3AqfWyL6QZvjb6OPx2G6w+FYXl/0UgO18KSwNt9HI1hnYV/+R/N84TXx59hKUnHqOoGOjiaIhtr7TGK1uClZbr5WqEEyEp2HQ5BrmFxbAx1MG4dpb4qK/8S5aVgQ6cTXXxx7V4xGTkQwDAyVQXXw51wevdbJ7mrteKQCDAL5N8sPZ0JHbcTsQ356JhpifCC+0sMWeQE8RlAugzBzgiIbMAnx18iKx8KRxMxLgyo6NaryPRFmLLy62w914S9txJxLzD4cjIk0JPRwvu5hKM9DXHy52tYaTipsrl6YuFmN7PAV8cDsfeu0kY3770y+KK/0qTbHo6WnAwFmN6Pwe83cNW6QsbNT0uExfgXsk86aBinhRWMU/GHNsIbUMziM0dkFHJPHl38QiYthsEfcfWEFYyT4aXzJO61q7QKjNP2mtonrxbZp7EU5wnZdIi5MSEIZrzZLPAebJmOE8SEREREREREVF98Zi8ADeXjsb1RSPgPOJD6Fm7oCA9EUm3/oH31JWV5oNNvLsj4sgP0DEwg66lI9KCLyPm7DaIysW5ry8aDvP2g2Hg2AoiPRPkxIYh7uJuGHl0glCsh6yIQIT89QWsuo6AnrUbBCJtpAZeQFZkEJxHfFjl2Oszzm3i1RXeryxD6OZ5uLZgKOz6vwR9e28IBFrIT4tH4nV5cb9QUvEmj+Xp2bjBrv9LiP7vD6SFXoGJVzfFc0U56Ui/Xxrnzo4Jw+ND30BLWwyHQVPrZV9IM3ztjXF0Rl+sOhqCZYeDkJVXBEsjMXp7WkBbVHmcc+PLHTFvnz8W/x2IIqkMXVzNsOPdHnj5Z+Wb3/b2tMBx/3j8dOYhcguksDHWxfjODvh4iBcAwNJQF84W+vjjwiNEp+VCIACczfWx8HlfvNHH7anue20IBAL89npXrD4Wgu1XI7Dh31CY6etgXGdHzB3eCmJRaWHyrGHeSMjIw8wdd5CVXwQHUwmufzlErdeR6Aix7Z3u2HMjCruvR2HunnvIyC2Eno4Q7lYGGNneDq/0cIaRpPrCZH2xCJ8844XP99zDnhtRmNDFUfHcssNBip/1dIRwMNXDjGe88G5/d8a5mzjmg+WYD6a6Yj64ZpgPJiIiIiIiIiIixtRqhjE1aiqWrVyNYQP7YmCfHpgx8zO4uXsgISEeRw/9jfXf/wBDw4o1CUZGRujVuy++Wbca5ubmcHJ2wYVzZ/HXH7/C2MREadmBfXpg2LPD4dumDUxMTBESHITtWzeja/ce0NPTg/+9u5g1/SOMfmEc3D08oK2tg7OnTyHg3l3MmDm7yrGbmZvDzLx+bqrZvWcvrN7wLT6b8TH6du+MqW+8BZ9WraGlpYX4uFgc2L8XAFS+H+W5e3hi6htvYdOPG3Hpwnn06NVb8Vx6WjquXbkMACgsLERIcBDWrlwBsViMN999v172hTSjXds2uHz2JBYuWYYvFixCZmYWbKytMaBfX+jo6FS63ubfNmH6rNmYM+9LFBVJ0bN7Nxw/tB8jx05QWm5A/774+/ARrP/ue+Tk5MLezhZTXpyMuZ/NBADYWFvDzdUVG3/ahMioaPnNal1dsHrFUnz43jtPdd9rQyAQYN/OrVi0dDl+/2sLlq1cDQtzc7w8eSKWLvoSYnFpU/SF8z5HbFwc3vngY2RmZsLZyQkPg6u+acQTEokERw/sxZbtO7F523Z89MkspKWlQ19fH96eHhg/dgzefuM1GBsbV7stAwMDzP/8M3w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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn import tree\n", - "fn=list(X.columns)\n", - "cn=[\"not_GRB\", \"GRB\"]\n", - "fig, axes = plt.subplots(nrows = 1,ncols = 5,figsize = (10,2), dpi=1200)\n", - "for index in range(0, 5):\n", - " tree.plot_tree(RFmodel.estimators_[index],\n", - " feature_names = fn, \n", - " class_names=cn,\n", - " filled = True,\n", - " ax = axes[index]);\n", - "\n", - " axes[index].set_title('Estimator: ' + str(index), fontsize = 11)\n", - "fig.savefig('rf_5trees.png')" - ] - }, - { - "cell_type": "markdown", - "id": "07e8ce32-6a3c-4e3f-8380-580eccd6c894", - "metadata": {}, - "source": [ - "Moreover, the `dtreeviz` package can provide a more detailed and visual representation of the decision tree" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "e5e61132-54c8-4c9e-bd4a-379396163319", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "G\n", - "\n", 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node0->node1\n", - "\n", - "\n", - "  ≤\n", - "\n", - "\n", - "\n", - "node0->node16\n", - "\n", - "\n", - "  >\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2024-03-16T12:16:25.620898\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.8.0, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import dtreeviz \n", - "for index in range(0, 5):\n", - " trees=[]\n", - " trees.append(dtreeviz.model(RFmodel.estimators_[index], X, y,\n", - " target_name=\"target\",\n", - " feature_names=fn,\n", - " class_names=cn))\n", - "\n", - "tree_index = 0 # the index of the tree to visualize \n", - "\n", - "trees[tree_index].view()\n", - "#trees[tree_index].view().save('Trees.svg')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "67b7d545-a765-47b3-bc5c-20fdee88f345", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = data[data[\"MAGCAL_R0\"]>1]\n", - "data[\"R0_B0\"] = data[\"MAGCAL_R0\"]-data[\"MAGCAL_B0\"]\n", - "\n", - "#plt.scatter(data[\"R0_B0\"], data[\"MAGCAL_R0\"], )\n", - "mask = data[\"IS_GRB\"] == 1 \n", - "sns.scatterplot(data=data, y = \"R0_B0\", x = \"MAGCAL_R0\")\n", - "sns.scatterplot(data=data[mask], y = \"R0_B0\", x = \"MAGCAL_R0\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "1d764786-af61-406d-ad51-4ff15cd88aba", - "metadata": {}, - "source": [ - "- Look at PCA (done not great)\n", - "- New flags ??\n", - "- Class imbalance: dealt with using class weights class from sklearn\n", - "\n", - "- Cleanup pipeline\n" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "6ac21b62-90b9-43e4-99e4-b8480077a24e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'paths': {'plots': 'plots/corr/', 'data': '~/Repos/VTAC_ML/combined_qpo_vt_with_GRB.parquet', 'model': 'RFModel'}, 'Inputs': {'path': '~/Repos/VTAC_ML/combined_qpo_vt_with_GRB.parquet', 'columns': ['MAGCAL_R0', 'MAGCAL_B0', 'MAGERR_R0', 'MAGERR_B0', 'MAGCAL_R1', 'MAGCAL_B1', 'MAGERR_R1', 'MAGERR_B1', 'MAGVAR_R1', 'MAGVAR_B1', 'R0_B0', 'R1_B1'], 'target_column': ['IS_GRB']}, 'RFModel': {'param_grid': {'n_estimators': [100], 'max_depth': [4], 'min_samples_split': [2, 3]}}, 'OtherModel': [None]}\n" - ] - } - ], - "source": [ - "import yaml\n", - "import os\n", - "\n", - "with open('/Users/jeremypalmerio/Repos/VTAC_ML/GRB-hunter/config/config.yaml', 'r') as file:\n", - " data = yaml.safe_load(file)\n", - "config = yaml.safe_load(open('/Users/jeremypalmerio/Repos/VTAC_ML/GRB-hunter/config/config.yaml', 'r'))\n", - "\n", - "print(data) \n", - "\n", - "\n", - "# Output:\n", - "# {'example': 'data'}" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "7e4b35e0-fe99-478e-867a-1e2cd0e8fff1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'n_estimators': [100], 'max_depth': [4], 'min_samples_split': [2, 3]}" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config['RFModel']['param_grid']" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/dev/.ipynb_checkpoints/Untitled-checkpoint.ipynb deleted file mode 100644 index 363fcab..0000000 --- a/dev/.ipynb_checkpoints/Untitled-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/dev/.ipynb_checkpoints/Untitled1-checkpoint.ipynb deleted file mode 100644 index 363fcab..0000000 --- a/dev/.ipynb_checkpoints/Untitled1-checkpoint.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/.ipynb_checkpoints/initial_tests-checkpoint.ipynb b/dev/.ipynb_checkpoints/initial_tests-checkpoint.ipynb deleted file mode 100644 index 04b74b6..0000000 --- a/dev/.ipynb_checkpoints/initial_tests-checkpoint.ipynb +++ /dev/null @@ -1,2373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "64b14aa8-39f6-419a-bc27-789e59798dfa", - "metadata": {}, - "source": [ - "# Initial Data Exploration" - ] - }, - { - "cell_type": "markdown", - "id": "2041d222-49fe-477f-80a1-56e1977f5d24", - "metadata": {}, - "source": [ - "## Fetching Data" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "8af00bfa-ce1e-4e19-a3e8-ad2cabbaca01", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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...................................................................................................................................................................................................................................................
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\n", - "

149 rows × 80 columns

\n", - "
" - ], - "text/plain": [ - " VT_ID RA DEC RADEC_OG ... SEQFLAG3 NEW_SRC DMAG_CAT MAG_VAR\n", - "0 False False False False ... False True True True\n", - "1 False False False False ... False True True True\n", - "2 False False False False ... False True True True\n", - "3 False False False False ... False True False True\n", - "4 False False False False ... False True True True\n", - ".. ... ... ... ... ... ... ... ... ...\n", - "144 False False False False ... False False True True\n", - "145 False False False False ... False True True True\n", - "146 False False False False ... False False True True\n", - "147 False False False False ... False False True True\n", - "148 False False False False ... False True True True\n", - "\n", - "[149 rows x 80 columns]" - ] - }, - "execution_count": 174, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from astropy.table import Table\n", - "from astropy.io import fits\n", - "qpo_vt = fits.open('qpo_vt_example.fits')\n", - "tab = Table.read(qpo_vt, hdu='COMBINED')\n", - "df = tab.to_pandas()\n", - "df.isna()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "id": "189ab9dd-713e-4d97-9ef4-272adda7fba9", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "XTENSION= 'BINTABLE' / binary table extension \n", - "BITPIX = 8 / array data type \n", - "NAXIS = 2 / number of array dimensions \n", - "NAXIS1 = 705 / length of dimension 1 \n", - "NAXIS2 = 149 / length of dimension 2 \n", - "PCOUNT = 0 / number of group parameters \n", - "GCOUNT = 1 / number of groups \n", - "TFIELDS = 80 / number of table fields \n", - "EXTNAME = 'COMBINED' \n", - "R_XMATCH= 1.0 / Combination crossmatch radius in arcsec \n", - "MXT_RA = 212.6408177188752 / RA from QPO_MXT used for filtering in deg \n", - "MXT_DEC = 29.69708513294715 / Dec from QPO_MXT used for filtering in deg \n", - "MXT_ERR = 0.01138888888888889 / Error from QPO_MXT used for filtering in deg \n", - "TTYPE1 = 'VT_ID ' / Unique VT identifier \n", - "TFORM1 = 'K ' \n", - "TTYPE2 = 'RA ' / Right Ascension from VT \n", - "TFORM2 = 'D ' \n", - "TUNIT2 = 'deg ' \n", - "TTYPE3 = 'DEC ' / Declination from VT \n", - "TFORM3 = 'D ' \n", - "TUNIT3 = 'deg ' \n", - "TTYPE4 = 'RADEC_OG' / Seq. where RA and DEC originate \n", - "TFORM4 = '2A ' \n", - "TTYPE5 = 'IN_MXT ' / 1: True, 0: False, -1: no QPO_MXT \n", - "TFORM5 = 'K ' \n", - "TTYPE6 = 'OBJID_R0' / Object identifier in R0 seq. \n", - "TFORM6 = 'K ' \n", - "TNULL6 = -99999 \n", - "TTYPE7 = 'OBJID_R1' / Object identifier in R1 seq. \n", - "TFORM7 = 'K ' \n", - "TNULL7 = -99999 \n", - "TTYPE8 = 'OBJID_R2' / Object identifier in R2 seq. \n", - "TFORM8 = 'K ' \n", - "TTYPE9 = 'OBJID_R3' / Object identifier in R3 seq. \n", - "TFORM9 = 'K ' \n", - "TTYPE10 = 'OBJID_B0' / Object identifier in B0 seq. \n", - "TFORM10 = 'K ' \n", - "TNULL10 = -99999 \n", - "TTYPE11 = 'OBJID_B1' / Object identifier in B1 seq. \n", - "TFORM11 = 'K ' \n", - "TNULL11 = -99999 \n", - "TTYPE12 = 'OBJID_B2' / Object identifier in B2 seq. \n", - "TFORM12 = 'K ' \n", - "TTYPE13 = 'OBJID_B3' / Object identifier in B3 seq. \n", - "TFORM13 = 'K ' \n", - "TTYPE14 = 'RA_R0 ' / Right Ascension in R0 seq. \n", - "TFORM14 = 'D ' \n", - "TUNIT14 = 'deg ' \n", - "TTYPE15 = 'RA_R1 ' / Right Ascension in R1 seq. \n", - "TFORM15 = 'D ' \n", - "TUNIT15 = 'deg ' \n", - "TTYPE16 = 'RA_R2 ' / Right Ascension in R2 seq. \n", - "TFORM16 = 'D ' \n", - "TUNIT16 = 'deg ' \n", - "TTYPE17 = 'RA_R3 ' / Right Ascension in R3 seq. \n", - "TFORM17 = 'D ' \n", - "TUNIT17 = 'deg ' \n", - "TTYPE18 = 'RA_B0 ' / Right Ascension in B0 seq. \n", - "TFORM18 = 'D ' \n", - "TUNIT18 = 'deg ' \n", - "TTYPE19 = 'RA_B1 ' / Right Ascension in B1 seq. \n", - "TFORM19 = 'D ' \n", - "TUNIT19 = 'deg ' \n", - "TTYPE20 = 'RA_B2 ' / Right Ascension in B2 seq. \n", - "TFORM20 = 'D ' \n", - "TUNIT20 = 'deg ' \n", - "TTYPE21 = 'RA_B3 ' / Right Ascension in B3 seq. \n", - "TFORM21 = 'D ' \n", - "TUNIT21 = 'deg ' \n", - "TTYPE22 = 'DEC_R0 ' / Declination in R0 seq. \n", - "TFORM22 = 'D ' \n", - "TUNIT22 = 'deg ' \n", - "TTYPE23 = 'DEC_R1 ' / Declination in R1 seq. \n", - "TFORM23 = 'D ' \n", - "TUNIT23 = 'deg ' \n", - "TTYPE24 = 'DEC_R2 ' / Declination in R2 seq. \n", - "TFORM24 = 'D ' \n", - "TUNIT24 = 'deg ' \n", - "TTYPE25 = 'DEC_R3 ' / Declination in R3 seq. \n", - "TFORM25 = 'D ' \n", - "TUNIT25 = 'deg ' \n", - "TTYPE26 = 'DEC_B0 ' / Declination in B0 seq. \n", - "TFORM26 = 'D ' \n", - "TUNIT26 = 'deg ' \n", - "TTYPE27 = 'DEC_B1 ' / Declination in B1 seq. \n", - "TFORM27 = 'D ' \n", - "TUNIT27 = 'deg ' \n", - "TTYPE28 = 'DEC_B2 ' / Declination in B2 seq. \n", - "TFORM28 = 'D ' \n", - "TUNIT28 = 'deg ' \n", - "TTYPE29 = 'DEC_B3 ' / Declination in B3 seq. \n", - "TFORM29 = 'D ' \n", - "TUNIT29 = 'deg ' \n", - "TTYPE30 = 'MAGCAL_R0' / Calib. mag. in SVOM phot. system in R0 seq. \n", - "TFORM30 = 'D ' \n", - "TTYPE31 = 'MAGCAL_R1' / Calib. mag. in SVOM phot. system in R1 seq. \n", - "TFORM31 = 'D ' \n", - "TTYPE32 = 'MAGCAL_R2' / Calib. mag. in SVOM phot. system in R2 seq. \n", - "TFORM32 = 'D ' \n", - "TTYPE33 = 'MAGCAL_R3' / Calib. mag. in SVOM phot. system in R3 seq. \n", - "TFORM33 = 'D ' \n", - "TTYPE34 = 'MAGCAL_B0' / Calib. mag. in SVOM phot. system in B0 seq. \n", - "TFORM34 = 'D ' \n", - "TTYPE35 = 'MAGCAL_B1' / Calib. mag. in SVOM phot. system in B1 seq. \n", - "TFORM35 = 'D ' \n", - "TTYPE36 = 'MAGCAL_B2' / Calib. mag. in SVOM phot. system in B2 seq. \n", - "TFORM36 = 'D ' \n", - "TTYPE37 = 'MAGCAL_B3' / Calib. mag. in SVOM phot. system in B3 seq. \n", - "TFORM37 = 'D ' \n", - "TTYPE38 = 'MAGERR_R0' / Calib. mag. err. in R0 seq. \n", - "TFORM38 = 'D ' \n", - "TTYPE39 = 'MAGERR_R1' / Calib. mag. err. in R1 seq. \n", - "TFORM39 = 'D ' \n", - "TTYPE40 = 'MAGERR_R2' / Calib. mag. err. in R2 seq. \n", - "TFORM40 = 'D ' \n", - "TTYPE41 = 'MAGERR_R3' / Calib. mag. err. in R3 seq. \n", - "TFORM41 = 'D ' \n", - "TTYPE42 = 'MAGERR_B0' / Calib. mag. err. in B0 seq. \n", - "TFORM42 = 'D ' \n", - "TTYPE43 = 'MAGERR_B1' / Calib. mag. err. in B1 seq. \n", - "TFORM43 = 'D ' \n", - "TTYPE44 = 'MAGERR_B2' / Calib. mag. err. in B2 seq. \n", - "TFORM44 = 'D ' \n", - "TTYPE45 = 'MAGERR_B3' / Calib. mag. err. in B3 seq. \n", - "TFORM45 = 'D ' \n", - "TTYPE46 = 'MAGVAR_R1' / Mag. variation between R1 and R0 seq. \n", - "TFORM46 = 'D ' \n", - "TTYPE47 = 'MAGVAR_R2' / Mag. variation between R2 and R1 seq. \n", - "TFORM47 = 'D ' \n", - "TTYPE48 = 'MAGVAR_R3' / Mag. variation between R3 and R2 seq. \n", - "TFORM48 = 'D ' \n", - "TTYPE49 = 'MAGVAR_B1' / Mag. variation between B1 and B0 seq. \n", - "TFORM49 = 'D ' \n", - "TTYPE50 = 'MAGVAR_B2' / Mag. variation between B2 and B1 seq. \n", - "TFORM50 = 'D ' \n", - "TTYPE51 = 'MAGVAR_B3' / Mag. variation between B3 and B2 seq. \n", - "TFORM51 = 'D ' \n", - "TTYPE52 = 'EFLAG_R0' / Enhanced VVPP flag in R0 seq. >0 may be noise \n", - "TFORM52 = 'K ' \n", - "TNULL52 = -99999 \n", - "TTYPE53 = 'EFLAG_R1' / Enhanced VVPP flag in R1 seq. >0 may be noise \n", - "TFORM53 = 'K ' \n", - "TNULL53 = -99999 \n", - "TTYPE54 = 'EFLAG_R2' / Enhanced VVPP flag in R2 seq. >0 may be noise \n", - "TFORM54 = 'K ' \n", - "TTYPE55 = 'EFLAG_R3' / Enhanced VVPP flag in R3 seq. >0 may be noise \n", - "TFORM55 = 'K ' \n", - "TTYPE56 = 'EFLAG_B0' / Enhanced VVPP flag in B0 seq. >0 may be noise \n", - "TFORM56 = 'K ' \n", - "TNULL56 = -99999 \n", - "TTYPE57 = 'EFLAG_B1' / Enhanced VVPP flag in B1 seq. >0 may be noise \n", - "TFORM57 = 'K ' \n", - "TNULL57 = -99999 \n", - "TTYPE58 = 'EFLAG_B2' / Enhanced VVPP flag in B2 seq. >0 may be noise \n", - "TFORM58 = 'K ' \n", - "TTYPE59 = 'EFLAG_B3' / Enhanced VVPP flag in B3 seq. >0 may be noise \n", - "TFORM59 = 'K ' \n", - "TTYPE60 = 'XFLAG_R0' / Crossmatch flag in R0 seq. \n", - "TFORM60 = 'K ' \n", - "TTYPE61 = 'XFLAG_R1' / Crossmatch flag in R1 seq. \n", - "TFORM61 = 'K ' \n", - "TTYPE62 = 'XFLAG_R2' / Crossmatch flag in R2 seq. \n", - "TFORM62 = 'K ' \n", - "TTYPE63 = 'XFLAG_R3' / Crossmatch flag in R3 seq. \n", - "TFORM63 = 'K ' \n", - "TTYPE64 = 'XFLAG_B0' / Crossmatch flag in B0 seq. \n", - "TFORM64 = 'K ' \n", - "TTYPE65 = 'XFLAG_B1' / Crossmatch flag in B1 seq. \n", - "TFORM65 = 'K ' \n", - "TTYPE66 = 'XFLAG_B2' / Crossmatch flag in B2 seq. \n", - "TFORM66 = 'K ' \n", - "TTYPE67 = 'XFLAG_B3' / Crossmatch flag in B3 seq. \n", - "TFORM67 = 'K ' \n", - "TTYPE68 = 'VFLAG_R1' / Variability flag between R1 and R0 seq. \n", - "TFORM68 = 'K ' \n", - "TTYPE69 = 'VFLAG_R2' / Variability flag between R2 and R1 seq. \n", - "TFORM69 = 'K ' \n", - "TTYPE70 = 'VFLAG_R3' / Variability flag between R3 and R2 seq. \n", - "TFORM70 = 'K ' \n", - "TTYPE71 = 'VFLAG_B1' / Variability flag between B1 and B0 seq. \n", - "TFORM71 = 'K ' \n", - "TTYPE72 = 'VFLAG_B2' / Variability flag between B2 and B1 seq. \n", - "TFORM72 = 'K ' \n", - "TTYPE73 = 'VFLAG_B3' / Variability flag between B3 and B2 seq. \n", - "TFORM73 = 'K ' \n", - "TTYPE74 = 'SEQFLAG0' / Flag for 1st seq. see doc. for more info \n", - "TFORM74 = 'K ' \n", - "TTYPE75 = 'SEQFLAG1' / Flag for 2nd seq. see doc. for more info \n", - "TFORM75 = 'K ' \n", - "TTYPE76 = 'SEQFLAG2' / Flag for 3rd seq. see doc. for more info \n", - "TFORM76 = 'K ' \n", - "TTYPE77 = 'SEQFLAG3' / Flag for 4th seq. see doc. for more info \n", - "TFORM77 = 'K ' \n", - "TTYPE78 = 'NEW_SRC ' / Seq. where source is new (not in catalogs) \n", - "TFORM78 = '30A ' \n", - "TTYPE79 = 'DMAG_CAT' / Seq. where source mag varied wrt catalogs \n", - "TFORM79 = '30A ' \n", - "TTYPE80 = 'MAG_VAR ' / Seq. pairs between which the source varied \n", - "TFORM80 = '35A ' \n", - "CHECKSUM= 'Rl2gUl0fRl0fRl0f' / HDU checksum updated 2023-12-19T03:31:08 \n", - "DATASUM = '1216721935' / data unit checksum updated 2023-12-19T03:31:08 " - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qpo_vt[\"COMBINED\"].header" - ] - }, - { - "cell_type": "markdown", - "id": "d2670c37-a2a8-4992-b472-d022762ec9fa", - "metadata": {}, - "source": [ - "# Proto-Pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "7ad80fa9-5b14-4bb3-9348-22edcca76b96", - "metadata": {}, - "source": [ - "## Proto-DataFrame\n", - "The dataset used here is from the Sloan Digital Sky Survey taken from: \n", - "\n", - "fedesoriano. (January 2022). Stellar Classification Dataset - SDSS17. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17." - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "id": "9008af59-7ae0-4b32-9e35-095dbb490211", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
01.237661e+18135.68910732.49463223.8788222.2753020.3950119.1657318.7937136063012796.543777e+18GALAXY0.634794581256354171
11.237665e+18144.82610131.27418524.7775922.8318822.5844421.1681221.61427451830151191.176014e+19GALAXY0.7791361044558158427
21.237661e+18142.18879035.58244425.2630722.6638920.6097619.3485718.94827360630121205.152200e+18GALAXY0.644195457655592299
31.237663e+18338.741038-0.40282822.1368223.7765621.6116220.5045419.25010419230132141.030107e+19GALAXY0.932346914958039775
41.237680e+18345.28259321.18386619.4371817.5802816.4974715.9771115.54461810230131376.891865e+18GALAXY0.116123612156187842
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" - ], - "text/plain": [ - " obj_ID alpha delta ... plate MJD fiber_ID\n", - "0 1.237661e+18 135.689107 32.494632 ... 5812 56354 171\n", - "1 1.237665e+18 144.826101 31.274185 ... 10445 58158 427\n", - "2 1.237661e+18 142.188790 35.582444 ... 4576 55592 299\n", - "3 1.237663e+18 338.741038 -0.402828 ... 9149 58039 775\n", - "4 1.237680e+18 345.282593 21.183866 ... 6121 56187 842\n", - "\n", - "[5 rows x 18 columns]" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "\n", - "df = pd.read_csv('star_classification.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "52e52d93-959a-4255-b9a6-44d6d1a70435", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis\n" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "fc018812-b683-44bf-996d-6aa5633d58af", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100000, 18)" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "7abfea97-88db-4df8-a7bc-a7e8a270a8c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['obj_ID', 'alpha', 'delta', 'u', 'g', 'r', 'i', 'z', 'run_ID',\n", - " 'rerun_ID', 'cam_col', 'field_ID', 'spec_obj_ID', 'class', 'redshift',\n", - " 'plate', 'MJD', 'fiber_ID'],\n", - " dtype='object')" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "0ee37ced-219b-4cf6-b218-fa07642f80a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 100000 entries, 0 to 99999\n", - "Data columns (total 18 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 obj_ID 100000 non-null float64\n", - " 1 alpha 100000 non-null float64\n", - " 2 delta 100000 non-null float64\n", - " 3 u 100000 non-null float64\n", - " 4 g 100000 non-null float64\n", - " 5 r 100000 non-null float64\n", - " 6 i 100000 non-null float64\n", - " 7 z 100000 non-null float64\n", - " 8 run_ID 100000 non-null int64 \n", - " 9 rerun_ID 100000 non-null int64 \n", - " 10 cam_col 100000 non-null int64 \n", - " 11 field_ID 100000 non-null int64 \n", - " 12 spec_obj_ID 100000 non-null float64\n", - " 13 class 100000 non-null object \n", - " 14 redshift 100000 non-null float64\n", - " 15 plate 100000 non-null int64 \n", - " 16 MJD 100000 non-null int64 \n", - " 17 fiber_ID 100000 non-null int64 \n", - "dtypes: float64(10), int64(7), object(1)\n", - "memory usage: 13.7+ MB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "d2ba20bc-b553-49cf-88c7-b2f7fd1a47d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GALAXY\n", - "1 GALAXY\n", - "2 GALAXY\n", - "3 GALAXY\n", - "4 GALAXY\n", - "5 QSO\n", - "6 QSO\n", - "7 GALAXY\n", - "8 GALAXY\n", - "9 STAR\n", - "10 GALAXY\n", - "11 GALAXY\n", - "12 STAR\n", - "13 GALAXY\n", - "14 GALAXY\n", - "Name: class, dtype: object" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['class'].head(15)" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "id": "4e81f847-3027-4229-9dd6-1c575fdb9f9c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 1\n", - "2 1\n", - "3 1\n", - "4 1\n", - "5 0\n", - "6 0\n", - "7 1\n", - "8 1\n", - "9 0\n", - "10 1\n", - "11 1\n", - "12 0\n", - "13 1\n", - "14 1\n", - "Name: class, dtype: int64" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['class'] = (df['class'] =='GALAXY').astype(int)\n", - "df['class'].head(15)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "57fbe2ef-e1a6-49a6-8d1f-c48b47641440", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
obj_ID1.0000000.0137350.3012370.0153100.0157100.1538910.1476700.0138111.000000NaN0.0469970.0314980.2394610.0199940.0654000.2394600.2626870.067178
alpha0.0137351.0000000.1386910.0015320.0024230.0220830.0235800.0029180.013737NaN0.0195820.1655770.0025530.0040850.0016670.0025540.0199430.030464
delta0.3012370.1386911.0000000.0020740.0035230.0068350.0044800.0036300.301238NaN0.0325650.1734160.1123290.0382970.0316380.1123290.1073330.028250
u0.0153100.0015320.0020741.0000000.9993110.0541490.0457300.9980930.015309NaN0.0035480.0083740.0299970.0231290.0143090.0299970.0319970.016305
g0.0157100.0024230.0035230.9993111.0000000.0623870.0562710.9991610.015710NaN0.0035080.0088520.0394430.0142890.0229540.0394430.0402740.017470
.........................................................
class0.0199940.0040850.0382970.0231290.0142890.0379970.1603790.0083820.019996NaN0.0205690.0396540.1097531.0000000.2569250.1097530.1105840.005923
redshift0.0654000.0016670.0316380.0143090.0229540.4332410.4923830.0303800.065400NaN0.0000970.0213310.3886420.2569251.0000000.3886410.3871090.127044
plate0.2394600.0025540.1123290.0299970.0394430.6552430.6616400.0378130.239459NaN0.0019490.0834711.0000000.1097530.3886411.0000000.9701660.241258
MJD0.2626870.0199430.1073330.0319970.0402740.6711800.6725230.0374690.262687NaN0.0067450.0950640.9701670.1105840.3871090.9701661.0000000.256970
fiber_ID0.0671780.0304640.0282500.0163050.0174700.2231060.2147870.0146680.067165NaN0.1215970.0123370.2412790.0059230.1270440.2412580.2569701.000000
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18 rows × 18 columns

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" - ], - "text/plain": [ - " obj_ID alpha delta ... plate MJD fiber_ID\n", - "obj_ID 1.000000 0.013735 0.301237 ... 0.239460 0.262687 0.067178\n", - "alpha 0.013735 1.000000 0.138691 ... 0.002554 0.019943 0.030464\n", - "delta 0.301237 0.138691 1.000000 ... 0.112329 0.107333 0.028250\n", - "u 0.015310 0.001532 0.002074 ... 0.029997 0.031997 0.016305\n", - "g 0.015710 0.002423 0.003523 ... 0.039443 0.040274 0.017470\n", - "... ... ... ... ... ... ... ...\n", - "class 0.019994 0.004085 0.038297 ... 0.109753 0.110584 0.005923\n", - "redshift 0.065400 0.001667 0.031638 ... 0.388641 0.387109 0.127044\n", - "plate 0.239460 0.002554 0.112329 ... 1.000000 0.970166 0.241258\n", - "MJD 0.262687 0.019943 0.107333 ... 0.970166 1.000000 0.256970\n", - "fiber_ID 0.067178 0.030464 0.028250 ... 0.241258 0.256970 1.000000\n", - "\n", - "[18 rows x 18 columns]" - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "corr_matrix = abs(df.corr())\n", - "sns.heatmap(corr_matrix, cmap='Blues')\n", - "corr_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "id": "c19d346f-3e82-4565-9331-eecd850dc2e5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeremypalmerio/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RVlaG+fPno6CgAMuWLdNaR32dqa5duxo8r6urq9Zjpa7VEvXU7d69G6tWrTJYk4iIqKNgsNKhT58++OGHH9C7d2+t+z/66CO89957+Nvf/gbg8duIo0ePxrBhwxrMLSsrE8c2NjYGz21rayuOVSpVi9VqiXpERETmjM9Y6dCtWzedoQoArKys8PHHH+O1114DAAiCgDVr1rRWewAeX1EjIiKitoNXrJppzZo12Lt3LwRBwDfffIPy8nKNqzoA4ODggPv37wMAKioq4ODgoLdmeXm5OJbL5Q1qaZvXlFpS91ZfZGQkxo0bZ7BHba5du4aFCxc26VgiIiJTYbBqpu7du+Opp57CrVu3UFlZiczMTPj5+WnMcXZ2FsNLUVGRwfBSXFyscWz9WnWKiooM9qevltS91eft7W3wUz9EREQdCW8FSkD9oW9tD3T36dNHHGdmZhqspz5H/Vipa7VEPSIiInPGYCUB9fWctF3FCQgIEMfJycl6a+Xn54vLGbi7uzdYzsDPzw8WFo//ZUtNTW3w0en61M/Xv3//Fu2NiIjI3DFYNVNubi5u374NALC2thY/IaPO0Orn6o4fPy6Ota2ELpfLERQUBODxW3kJCQk6a9XW1mp802/SpEkt2hsREZG5Y7Bqpvfffx+CIAAAxowZAzs7uwZzgoOD4enpCQA4f/48UlJStNaqqanBhg0bxO05c+Zonaf++/Xr1+vs7ejRo+Ktu2HDhmkNfVL3RkREZM4YrLS4ffs21q1bB6VSqXNOVVUV3nnnHezdu1f83fvvv691rqWlJVasWCFuz5s3DwUFBQ3mLVu2DGlpaQCAoKAgTJgwQWu9yMhI8aHw2NhYbN68ucGcH3/8UeOtOl1LQUjdGxERkTmTCXWXW5rp22+/BfD4LTlfX99GH5+ZmSk+v/Pcc881uY/MzEzs2rVL43fXrl1DbGwsAODpp5/G5MmTNfYHBgZixowZ4nZaWhoGDRoEa2trjB07FkOHDkWvXr0gl8s1Vl6v6xcAPvjgA7z33ns6+6qurkZISAji4uIAAJ6enoiKioKfnx9KSkoQHR0t3tZzcnJCYmIi/P39ddY7c+YMQkJCUFVVBQAICwvDlClTYG9vj5SUFOzcuROlpaUAgKioKGzfvr3VepNCQkKCxgem4+PjMXLkSL3HlJaWYv2xVLwVNghOTk4t2h8REZFWgkRkMplgYWEh/O53v2vS8W+//bZgYWEhWFpaNquPc+fOCQAa9RMREaFRIzU11ehjHR0dhV27dhnVm1KpFMLCwvTW8/LyEhITE42qd/jwYcHZ2VlvvaioKKG6urrVe2uu+Ph4jXPHx8cbPEahUAjvf3ZOUCgUrdAhERFRQ21qHStBmotnzdavXz+cPHkSSUlJuHTpErKyslBcXIz79+/D2toaXbt2xYABAzB+/Hi88sorcHR0NKquXC5HbGwsYmJisG/fPiQnJ6OgoAByuRy+vr6YMWMGFixYYPTVlunTp2PYsGHYunUrYmNjkZWVhYqKCnTr1g0jR47E/PnzERwcbJLeiIiIzFGbClZSGD16dLMDmrW1NSZMmNBizxFNnToVU6dOlaRWt27dsHr1aqxevVqSelL2RkREZG7azMPrDx8+BGDch4CJiIiI2qI2E6zqXvNXX8WciIiIqD1p0q3A7OxsnftUKpXe/eqqqqpw9+5dfP7557h06RJkMhkGDhzYlJaIiIiITK5Jwapnz56QyWQNfi8IAvbt24d9+/Y1uaGXXnqpyccSERERmVKzHl7X9pB4cx4cf/nllzFr1qzmtERERERkMk0KVt7e3g2uWN25cwcymQwODg5wcXExWEMmk8HGxgaurq4ICAhAeHg4nn/++aa0Q0RERNQmNClYZWVlNfidhcXj5+AjIiI0vilHREREZC4kfSuwrSzwSURERGQKki0QmpmZCQBGr0JORERE1NFIFqx8fHykKkVERETULrWZBUKJiIiI2rsW/1bgw4cPUVpaiqqqKqOP8fb2bsGOiIiIiFqG5MGqpqYG0dHRiI6ORnJyMoqLixt1vEwmQ3V1tdRtEREREbU4SYNVVlYWpk2bhvT0dAB8S5CIiIjMi2TB6uHDh3j++eeRlZWlEahsbW3RpUsXWFlZSXUqIiIiojZJsmD1ySefIDMzEzKZDFZWVliyZAkiIyPRt29fqU5BRERE1KZJFqyOHj0qjv/zn/8gPDxcqtJERERE7YJkyy38+OOPkMlkCAwMZKgiIiIisyRZsKqsrAQADBw4UKqSRERERO2KZMHKy8sLABq1XhURERFRRyJZsHruuecgCIK41AIRERGRuZEsWL3++uuwsLBAWloarly5IlVZIiIionZDsmA1ePBg/OlPf4IgCJg7dy7y8/OlKk1ERETULkj6EeZVq1Zh5cqVuH37NgICAvDJJ5/g7t27Up6CiIiIqM2SbB2rJ598UhxbWVmhqKgIS5cuxdKlS+Hk5AQnJyfIZDKDdWQyGX766Sep2iIiIiJqNZIFq6ysLI3gVDcWBAEKhQKlpaUGawiCYFT4IiIiImqLJL0VKAhCgx99+3TNJSIiIuP8+c9/hkwmg0wmw5///GdTt2P2JLtilZmZKVUpIiIionZJsmDl4+MjVSkiIiKidknSW4FERERE5ozBioiIiEgiDFZEREREEmGwIiIiIpJIiywQ2hxcIJSIiMyRUqnEp59+itOnTyM9PR1FRUWoqqqCq6sr+vXrh+eeew4zZ86Ev79/k+rX1tYiMTERcXFxSEpKwg8//ICioiLU1tbCxcUFffv2xbhx4/Cb3/wGXbt2NapmTk4Odu/ejW+++QYZGRlQKBTo3LkzunbtCjc3N/j7++OFF17A+PHjddasqqrCwYMHcfjwYaSlpaGgoED8u7t27YoePXpg7NixmDBhAvr379+kv71VCRKRyWSChYVFo35kMpn4U7dtYWEhVUvUjsXHxwsAxJ/4+HiDxygUCuH9z84JCoWiFTokIpLO1q1bhS5dumj8956unxMnTmgcu3LlSnHfypUrtdZ/9OiR0L17d6Pq29vbC/v37zfY87Zt2wRbW1ujagYFBWmtkZGRIfTr18+oGgCEH3/8sdH/bFubZFes/i+kNfoYmUzGBUKJiMhsLV68GBs3bhS3LS0tMXToUDz11FOwsbFBYWEh0tLSkJWVBQCoqKho9DlqamrEb/c6ODjA398fTz75JBwdHVFVVYXc3FwkJSVBqVTiwYMHeOWVV2BlZYXZs2drrXf06FG8/vrr4rajoyOGDx8OLy8vdOrUCaWlpbh16xauX7+OR48eaa2hUqkwbtw45OTkAAAsLCwwaNAg9OvXDw4ODnj48CHu3r2Lq1evoqioqNF/s6m0+gKhtbW1KC0tRXp6Oj7//HN8/fXXsLGxwebNmzF27Fip2iEiImrztm3bphGqZs2ahfXr18PLy6vB3OvXr2PHjh2ws7Nr9HksLCzw2muvYd68eQgKCoKVlVWDOZWVldiwYQPee+89VFdX4/XXX0doaCgcHBwazFVf4X3RokVYu3at1r7Kyspw4sQJXLlypcG+Xbt2iaHKz88Phw8fRp8+fRrMEwQBly9fxp49e2Btbd2YP9s0THvBTBCOHTsm2NvbC1ZWVsLRo0dN3Q61EbwVSEQdXUlJiSCXy8X/nnv99debVMeYW4GN8dFHH4n1tmzZ0mC/SqUS9/fo0UOora1t0nnCw8PFOnFxcc1tu80w+VuBoaGh2Lp1K6qrq/Hqq68iNzfX1C0RERG1uO3bt0OlUgF4/PWSf/7zn6Zt6P+89tpr4vjMmTMN9iuVSnHs6uoKmUzWpPOo13Fzc2tSjbZI0mesmuqVV17B+++/j5ycHGzbtg1/+ctfTN0SERFRizp58qQ4joqKarXbXLW1tbhy5QrS0tKQm5sLpVKJqqoqrXPT0tIa/M7NzQ22trYoLy9Heno6vv32Wzz33HON7sPb21scb9myBf/6178aXaMtahPBCgBGjBiBAwcO4KuvvmKwIiKiDu/SpUvieMyYMS1+vurqamzcuBF///vfjb47pO2hcSsrK0yfPh3/+c9/UFNTg3HjxmHmzJmYOXMmgoOD4erqalTt2bNnY9euXQAeX71LTk5GREQEJkyYgL59+xr/h7UxJr8VWMfR0REAkJ2dbeJOiIiIWpZSqUR5ebm4LdVakLpUVlYiNDQUS5cubdQjN3W3Kuv7xz/+IYafqqoqREdHIzw8HG5ubvDz88OCBQtw6NAhvW8wjh8/Hm+++aa4nZqait///vfo168f3NzcMG3aNPzzn/8UH3BvL9pMsPr5558BPH4llIiIqCOrH1i0vXknpVWrVuH06dMAHr8hOHfuXHzxxRe4efMmSktL8ejRI3HpI0Ft+SNBx1JI7u7uSE5Oxp///Gc88cQTGvNv3ryJ7du3Y+bMmXjiiSfw0Ucf6fzf9r///e/46quvMGLECI3fFxUVISYmBm+++SZ8fHwQHh6OO3fuNPcfQ6toE8Hq5s2buHDhAmQyGXr06GHqdoiIiFqUXC7X2C4rK2uxc1VWVmos6bBv3z78+9//xsyZM9G3b184OjpqLL+g6ypVfQ4ODli5ciVycnKQnJyM9evXY9q0aRorrN+/fx/vvvsuwsPDdYa0yZMnIzExEbm5ufjss8+wYMEC+Pn5ifsFQcDhw4cxePBg3Lp1q7F/fqszebCKi4vDpEmTxAfnJkyYYOKOiIiIWpajoyNsbW3FbWPXgmyK//73v2JwCwgIwEsvvaR3fmOvDFlYWGDIkCFYunQpjhw5gvz8fCQkJGDatGninJiYGBw6dEhvne7du+Oll17Ctm3b8P333yMnJwdr1qyBvb09AKC4uBhLly5tVG+mINnD65GRkUbPra6uRnFxMa5evYp79+6Jv7ezs8Pbb78tVUtERERt1rPPPovz588DAM6ePYvhw4e3yHl++eUXcWzMdwa//fbbZp3PwsICQUFBGDFiBCZNmoRTp04BAL766ivMnDnT6DpeXl5Yvnw5fH19MXfuXADA6dOnUVlZ2aYXCpUsWO3du7dJa1nUXRq0t7fHl19+ie7du0vVEhERUZs1adIkMVjt2LEDb7/9dosEBguL/7859fDhQ71za2trsX37dknOK5PJEBoaKgar/Pz8JtUJCwsTx1VVVSgpKUG3bt0k6bElSHorUP3BN2N/5HI5IiMjcf36dd4GJCIisxEVFSU+tH7nzh38/ve/b5HzqL9xeOHCBZSWluqc+/HHH+Pq1at666lUKp3f/6tP/U3/+ouAGvv9P/UaFhYWcHFxMeo4U5HsitWePXuMnmtlZQW5XI5evXqhX79+sLS0lKoNIiKidqFLly5Yu3YtFi5cCODxdwNLSkp0fivw+++/x/bt2xEaGooXXnjB6PMMGjQI3bt3x927d1FaWooXX3wRe/fu1Xibr7KyEh988IH4TNODBw901rty5Qp+/etf4ze/+Q1mzZql9fZiTU0NDhw4oPHQfEhIiMac4cOHY8iQIXjllVcwbtw4dO7cuUGdmzdvYt68eeL2888/36ZvAwISBquIiAipShEREZmFN954A9evX8fWrVsBAJ9//jkOHTqEoUOHonfv3rCxsUFhYSFSU1ORlZUFoPGLiVpYWGDNmjXis9BxcXHo3bs3RowYAR8fHxQXF+P8+fO4f/8+gMeLdRp6wD0vLw+rV6/G6tWr4eHhgUGDBsHT0xOdOnVCXl4erly5ovEM9ahRozBnzhyNGlVVVThw4AAOHDgAW1tbPP3003jyySfh6OiI+/fv46efftL4eLOtrS3+9re/NepvN4U2s/I6ERGROdqyZQv69OmDFStWQKlUoqamBklJSUhKSmowVyaTwc7OrtHneO2113D79m389a9/BQA8ePAAcXFxGnNsbGzwj3/8A3PnztUbrGxtbdGpUydUV1cDePzslPrneeqbOXMmdu/erfGsF6C55ER5eTkuXbqksRq9ul69euGzzz7D008/rf8PbQMYrIiIiExsyZIlePnll7F3716cOnUKN27cEJ9B6tq1K/r164fg4GDMnj0bTz31VJPO8cEHH2DSpEnYtGkTEhISUFhYCLlcDi8vL0ycOBHz5883qvazzz6LgoICnDlzBgkJCUhNTcVPP/2E4uJi1NTUwNHREb6+vhg2bBhefvllPPPMM1rrpKWlISkpCefOncN///tfZGRk4JdffsHDhw9hZ2cHT09PDBw4EFOmTMGsWbPa/C3AOjJB14pd7VRNTQ1u3ryJy5cv48qVK7h8+TKuXr0qfjogIiICe/fubVTNe/fuYdu2bYiNjUVWVhYqKirg6emJUaNGITIyEsHBwY2qFxMTg/379yM5ORn5+fnivwmnT5+OBQsWwMnJqUP01hwJCQkYNWqUuB0fH4+RI0fqPaa0tBTrj6XirbBBrdYnERGRBqEFnT59Wnj33XeFCRMmCIMHDxZ69+4tDB48WJgwYYLw7rvvCnFxcZKfc8aMGQIAnT8RERGNqnf48GHB2dlZb82oqCihurraYC2lUimEhYXpreXl5SUkJia2+96aKz4+XuPc8fHxBo9RKBTC+5+dExQKRSt0SERE1FCL3Ao8cuQI/vjHP+Knn37SOScuLg5r166Fr68v1q1bp7FCa3PU/x6Ri4sLXF1d8eOPPza61pkzZzB79mxxVfjQ0FBMmTIF9vb2SElJwc6dO6FUKrFjxw4A0Lv2R3V1NcLDw8V72h4eHoiKioKfnx9KSkoQHR0tLukfGhqKhIQEvQu5teXeiIiIzJbUSW3p0qWChYWFYGFhIchkMqN+LCwshLfeekuS83/wwQfCsmXLhC+++EL4+eefBUEQhD179jT6ilV5ebng7e0tHrdx48YGczIyMgRPT09xzunTp3XW27JlizjPz89PyMvLazDnrbfeEucEBQW1y96kwitWRETUHkkarD766CMxKMlkMqFz587ClClThLVr1woHDx4Ujh07Jhw8eFBYt26dMHXqVMHa2lojXK1du1bKdkRNCVabNm0Sj5k8ebLOeYcOHRLnDRs2TOuc6upqjZBz5coVnfMGDhwozjt58mS7600qDFZERNQeSRascnJyBFtbWzFUTZs2Tbh7967eY/Ly8oQXX3xRDFd2dnZCTk6OVC2JmhKsRo0aJR5z7tw5nfNqamqEnj17inMzMzMbzPnmm2/E/cHBwXrPu3v3boO9tuXepMJgRURE7ZFkn7TZuXMnKioqAACvvPIKjhw5orGqqzYeHh74/PPP8eqrrwIAKioqsGvXLqlaajKVSoXExEQAj9fZUH87rT4LCwtMnDhR3D5x4kSDOerre9RfebY+9f3aarXl3oiIiMydZA+v131k0cHBAZs3b27UsRs2bMChQ4dQVlaGkydPYuXKlVK11SQ3btxAbW0tgMefAjD0yZ2hQ4di27ZtAIDr16832J+enq4xVx8PDw/06NEDOTk5KCgoQGFhocb3ldpyb/VlZ2drfOOpMa5du9ak44iIiExJsmCVmZkJmUyGMWPGiB+VNJaDgwPGjh2LmJgY/Pzzz1K11GQZGRniuFevXgbnq89RP7Y59XJycsRj1cNLW+6tvt27d2PVqlUGaxIREXUUkt0KrPvGkKenZ5OOd3d3BwAoFAqpWmoy9R66du1qcL6rq6vWY1uiXlvujYiIyNxJFqy6dOkC4PGHGZsiPz8fAODs7CxVS01WVlYmjm1sbAzOt7W1FccqlapF67Xl3oiIiMydZLcCe/XqhYKCApw9exZlZWWNuh344MEDnDt3DjKZzKjbUa1JJpOZugWd2nJvABAZGYlx48Y16dhr165h4cKFEndERETUsiQLVi+88AIuXbqEBw8eYPHixdi9e7fRx7755ptQqVSQyWSYMGGCVC01mXoorPvGoD7qc9S/1q1er+5WaUVFhcHQqa9eW+6tPm9vb3h7exvskYiIqKOQ7Fbg//zP/4i3kj799FPMnj1bvL2nS1FREV5++WVxiQVra2v8z//8j1QtNZn67ci6r4vrU1xcrPXYlqjXlnsjIiIyd5JdserRoweWL1+O5cuXQyaT4csvv8RXX32FSZMmISgoCD4+PrC3t8eDBw+QnZ2Nixcv4vjx46isrIQgCJDJZFi+fDl69OghVUtN1qdPH3GcmZlpcL76HPVj1X9XNyczMxM9e/Zscr223BsREZG5k/QjzO+99x5++eUXbNmyBTKZDJWVlYiJiUFMTIzW+YIgiOOFCxfivffek7KdJvPz84OFhQVqa2uRmpqKmpoavetFJScni+P+/fs32B8QECAuxJmcnIwxY8borJWfny8uZ+Du7t5gOYO23BsREZG5k+xWYJ1NmzbhwIED4pUP4fFnc7T+AI8fej948CA2bNggdStNJpfLERQUBODxm28JCQk659bW1oqLowLApEmTGswxtPq5uuPHj4tjbSuht+XeiIiIzJ3kwQoAZs2ahdu3b+PEiRN45513MG7cOAwYMAC+vr4YMGAAxo0bh3feeQcnTpzA7du38eKLL7ZEG80yZ84ccbx+/Xqd844ePSreHhs2bJjWW2nBwcHi+l7nz59HSkqK1lo1NTUaAVO9h/bSGxERkVkz2VcKW1FTPsJcXl4ueHt7i8dt2rSpwZxbt24Jnp6e4py4uDid9bZs2SLO8/f3F/Lz8xvMefvtt8U5QUFB7bI3qfAjzERE1B7JBEHtQacOIDMzs8GHnK9du4bY2FgAwNNPP43Jkydr7A8MDMSMGTMa1Dpz5gxCQkJQVVUFAAgLC8OUKVNgb2+PlJQU7Ny5E6WlpQCAqKgobN++XWdf1dXVCAkJQVxcHIDHK9RHRUXBz88PJSUliI6OFm/rOTk5ITExEf7+/jrrteXepJCQkKDxgen4+HiMHDlS7zGlpaVYfywVb4UNgpOTU4v2R0REpFVTE9nHH38srFq1Sli1apVw8eLFJtW4ePGiWOPvf/97U1vRcO7cOY0rHcb86LuKdfjwYcHZ2Vnv8VFRUUJ1dbXB3pRKpRAWFqa3lpeXl5CYmGjU39qWe2suXrEiIqL2qElvBR44cAB/+MMfIJPJ0L9/f7zzzjtNKYMBAwbgN7/5DW7cuAEA8PX1xZQpU5pUq6VMnz4dw4YNw9atWxEbG4usrCxUVFSgW7duGDlyJObPn4/g4GCjasnlcsTGxiImJgb79u1DcnIyCgoKIJfL4evrixkzZmDBggVGX21py70RERGZoybdCuzbty9u3boFS0tLXL16FX5+fk1uID09HYMGDUJtbS0GDRqEK1euNLkWdRy8FUhE7cGQIUOa/I3c9s7T0xOXL182dRttTqOvWCUlJeHWrVuQyWR46aWXmhWqgMfrKL300kvYv38/0tLSkJaWhoEDBzarJhERUWvIy8vD3bt3Td2G2YiJicH+/fuRnJyM/Px8ODo6wtfXF9OnT28zd1UaHazqHgIHgN/97neSNPG73/0O+/fvB/B4iQAGKyIiak9kFhZwdDGPRZOVJYUQamtb9ZwqlQpz587FsWPHNH5fWFiIwsJCJCUlYePGjTh48CBGjBjRqr3V1+hgVbeSt4eHBwYPHixJE0OGDIGHhwcKCgpw6dIlSWoSERG1FkcXN6z8z7embqNVrJr7HEqL9H8LWErV1dUIDw8X31z38PBo8OZ6YmIicnNzERoaioSEhBZ/c12fRi8QmpGRAZlMhgEDBkjayMCBAyEIAn744QdJ6xIREVH7tWPHDjFU+fn54erVq1izZg1+/etfY+HChUhISMBbb70FAFAoFFiwYIEp2218sLp//z4AiKt1S6WuXklJiaR1iYiIqH2qqanB6tWrxe39+/fDw8Ojwby1a9eKjxElJiZqfM6ttTU6WD169AgA9H74tynq6tXVJyIiIvN24cIF8a3L4OBgBAYGap1naWmJxYsXi9vR0dGt0p82jQ5WXbt2BfD4gTEpFRUVAQBcXFwkrUtERETt08mTJ8VxSEiI3rnq+0+cONFiPRnS6GDl5uYGQRBw7do1SRu5du0aZDIZ3NzM460KIiIi0i89PV0cDx06VO9cDw8P9OjRAwBQUFAg+QUgYzU6WNW9CZidnY2bN29K0sQPP/yArKwsjfpERERk3jIyMsRxr169DM5Xn6N+bGtqdLAaP368OP7www8laUK9jnp9IiIiMl8KhUIc1z2KpI+rq6vWY1tTo4PVpEmT0KVLFwiCgP/85z8aC4Y2xbFjx/Dvf/8bAODs7IzQ0NBm1SMiIqKOoaysTBzb2NgYnG9rayuOVSpVi/RkSKODlaOjo7heRG1tLebMmYNDhw416eRHjhzBnDlzUFtbC5lMhqVLl0IulzepFhEREZGpNTpYAcCbb76JgIAAyGQylJeXY9asWZg1axa+++47o45PSkrCrFmzMHPmTDx8+BAymQz9+/fHm2++2ZR2iIiIqANycHAQxxUVFQbnl5eXi2NTXahp9CdtgMeX2mJjY/Hss8+ioKAAgiDg0KFDOHToELy9vfHss8/C398fzs7OcHBwQFlZGRQKBW7cuIFLly7hzp07AABBEAAA7u7uiI2NhZ2dnXR/GREREbVrzs7O4sLkRUVFGkFLm+LiYo1jTaFJwQoAvL298d133+HFF1/ElStXADwOSnfu3EF2dja++OILrcfVhSmZTAYACAwMxBdffAFvb++mtkJEREQdUJ8+fZCZmQkAyMzMRM+ePfXOr5tbd6wpNOlWYJ2ePXvi4sWLWL16tcbT+nXhqT7137u6umL16tW4ePGiUa9QEhERkXkJCAgQx8nJyXrn5ufnIycnB8DjO2GmWhezWcEKAKysrLB8+XLcuXMH+/btw6uvvgpfX19YWlpCEATxx9LSEr6+voiIiMCnn36K7OxsLF++HJ07d5bi7yAiIqIOZuLEieLY0Grqx48fF8eGVmlvSU2+FVifjY0NXn75Zbz88svi71QqFVQqFeRyOd/2IyIiokYJDg6Gp6cn8vLycP78eaSkpGj9XmBNTQ02bNggbs+ZM6c129TQ7CtW+sjlcjzxxBMMVURERNRolpaWWLFihbg9b948FBQUNJi3bNkypKWlAQCCgoIwYcKE1mqxAcmuWBERERFJLSoqCkeOHEFcXBy+//57DBgwAFFRUfDz80NJSQmio6ORkJAAAHBycsK//vUvk/bLYEVERERtVqdOnXDo0CHMnTsXx44dQ15eHtasWdNgnpeXFw4ePAh/f38TdPn/GKyIiIiaSVlSiFVznzN1G61CWVLY6ueUy+WIjY1FTEwM9u3bh+TkZBQUFEAul8PX1xczZszAggUL4OTk1Oq91cdgRURE1ExCbS1Ki/JN3UaHN3XqVEydOtXUbejFYEVERNREnp6epm7BZMz5b9eHwYqIiKiJLl++bOoWqI1p0eUWiIiIiMwJgxURERGRRBisiIiIiCTCYEVEREQkEQYrIiIiIokwWBERERFJhMGKiIiISCIMVkREREQSYbAiIiIikgiDFREREZFEGKyIiIiIJMJgRURERCQRBisiIiIiiTBYEREREUmEwYqIiIhIIgxWRERERBJhsCIiIiKSCIMVERERkUQ6mboBIiKi9mrIkCHIy8szdRsm4enpicuXL5u6jTaHwYqIiKiJ8vLycPfuXVO3QW0IgxUREVEzWVjI0M1Fbuo2WsW9EhVqa4VWPWdNTQ1u3ryJy5cv48qVK7h8+TKuXr2K8vJyAEBERAT27t3bqj3pwmBFRETUTN1c5Mj9fJmp22gVXrM+wt0iZauec9asWTh8+HCrnrOp+PA6ERERtWk1NTUa2y4uLnjqqadM1I1+vGJFREREbdozzzyDfv36YfDgwRg8eDB69eqFvXv34rXXXjN1aw0wWBEREVGb9t5775m6BaPxViARERGRRBisiIiIiCTCYKXH6NGjIZPJjP7JysoyWPPevXtYuXIlAgMD4eLiAjs7Ozz55JOIiIjAhQsXGt1jTEwMZs6cCR8fH9jY2MDd3R3Dhw/HunXrUFpa2qhaUvdGRERkbviMVSs6cuQIIiMjoVAoNH6fmZmJzMxM7Nu3D1FRUdi6dSssLS311lKpVJg7dy6OHTum8fvCwkIUFhYiKSkJGzduxMGDBzFixIhW7Y2IiMhcMVgZ6ciRIwbnuLu769x35swZzJ49G1VVVQCA0NBQTJkyBfb29khJScHOnTuhVCqxY8cOAMD27dt11qqurkZ4eDji4uIAAB4eHoiKioKfnx9KSkoQHR2NxMRE5ObmIjQ0FAkJCfD392+V3oiIiMwZg5WRpk2b1uRjKyoqMH/+fDG4bNy4EYsWLRL3v/TSS1iwYAGCg4ORl5eHHTt24MUXX8T48eO11tuxY4cYqvz8/HD27Fl4eHiI+xcuXIi3334b69evh0KhwIIFC5CQkNAqvREREZkzPmPVCnbt2oXs7GwAwOTJkzWCS53evXtj8+bN4vaKFSu01qqpqcHq1avF7f3792uEqjpr167FwIEDAQCJiYk4depUi/dGRERk7hisWsHBgwfF8dKlS3XOmzZtGnr27AkASEpK0vow/IULF8QvqQcHByMwMFBrLUtLSyxevFjcjo6ObvHeiIiIzB2DVQtTqVRITEwEAMjlcowaNUrnXAsLC0ycOFHcPnHiRIM5J0+eFMchISF6z62+X1stqXsjIiIyd3zGykhhYWFITU1FYWEh7O3t8cQTT2DEiBGYO3cuxowZo/O4GzduoLa2FgAwaNAgg2/UDR06FNu2bQMAXL9+vcH+9PR0jbn6eHh4oEePHsjJyUFBQQEKCwvh5ubWYr3Vl52dLd5mbKxr16416TgiIiJTYrAy0tdffy2OFQoFFAoFbty4gZ07d2Ls2LH47LPP0K1btwbHZWRkiONevXoZPI/6HPVjm1MvJydHPFY9WEndW327d+/GqlWrDM4jIiLqKBisDOjSpQvGjx+PIUOGoHv37rC0tMTdu3dx9uxZnDhxArW1tTh79iyGDx+OpKQkeHp6ahyvvi5U165dDZ7P1dVV67EtUU/q3oiIiMwdg5UeH374IQYPHozOnTs32Ld06VKkpKQgPDwcWVlZuHPnDiIjI3H8+HGNeWVlZeLYxsbG4DltbW3FsUqlarBfynpS90ZERGTuGKz0GD58uN79gYGBOHXqFJ5++mlUVlbixIkTSE5O1vnsk0wma4k2JdESvUVGRmLcuHFNOvbatWtYuHChxB0RERG1LAarZurduzfmzZsnrkp+7NgxjWDl4OAgjsvLyw3WU58jl8sb7HdwcMD9+/cBPF7cU71+Y+tJ3Vt93t7e8Pb2NjiPiIhIn8zMTOzatUvjd+ovOaWmpmL58uUa+wMDAzFjxoxW6U8dg5UExowZIwarmzdvauxzdnYWx0VFRQZrFRcXaz1W/Xd1waqoqMhgsNJXT+reiIjM1b0SFbxmfWTqNlrFvZLWfxTkzp07+OCDD3Tuv3btWoO3ySMiIhis2iv1B7/rP9Tdp08fcZyZmWmwlvoc9WPVf1c3JzMzU1y0syn1pO6NiMhc1dYKuFukNHUb1AYwWEmgsLBQHNe/kuPn5wcLCwvU1tYiNTUVNTU1eteLSk5OFsf9+/dvsD8gIEBcJDQ5OVnvGlr5+fniUgvu7u4aSy20RG9EROam/pvg5qQ1//bRo0dDEIRWO19zMFhJ4Ny5c+K4/pUcuVyOoKAgxMfHQ6VSISEhAcHBwVrr1NbWanzTb9KkSQ3mTJw4ER9//DGAx6uf/+EPf9DZl/obitpWaZe6NyIic3P58mVTt0BtDD9p00wZGRnYv3+/uB0WFtZgzpw5c8Tx+vXrddY6evSoeLtt2LBhWm/zBQcHi/9fwvnz55GSkqK1Vk1NDTZs2KC1h5bqjYiIyNwxWOmwYcMGXLx4Ue+c1NRUTJw4EZWVlQCAF154Ac8++2yDeZGRkeLbcbGxsdi8eXODOT/++KPG8gJr1qzRek5LS0usWLFC3J43bx4KCgoazFu2bBnS0tIAAEFBQZgwYYLWelL2RkREZO54K1CHs2fPYsmSJfD19cW4cePQv39/uLq6wtLSEr/88gu++eYbHD9+XPzWno+PD/bs2aO1lo2NDXbt2oWQkBBUVVVh0aJFOHnyJKZMmQJ7e3ukpKRg586dKC0tBQBERUXpXf8pKioKR44cQVxcHL7//nsMGDAAUVFR8PPzQ0lJCaKjo5GQkAAAcHJywr/+9S+dtaTujYiIyKwJpNXUqVMFAEb9TJgwQbh7967BmocPHxacnZ311oqKihKqq6sN1lIqlUJYWJjeWl5eXkJiYqJRf6+UvUkhPj5e49zx8fEGj1EoFML7n50TFApFK3RIRETUEK9Y6bB+/XpMnjwZSUlJuHr1KgoLC1FUVITKyko4OTmhZ8+eGD58OObOnYthw4YZVXP69OkYNmwYtm7ditjYWGRlZaGiogLdunXDyJEjMX/+fJ0Pj9cnl8sRGxuLmJgY7Nu3D8nJySgoKIBcLoevry9mzJiBBQsWwMnJqdV7IyIiMlcyQWgn7y+SWUlISMCoUaPE7fj4eIwcOVLvMaWlpVh/LBVvhQ0yOlASERFJiQ+vExEREUmEwYqIiIhIIgxWRERERBJhsCIiIiKSCIMVERERkUQYrIiIiIgkwmBFREREJBEGKyIiIiKJMFgRERERSYTBioiIiEgiDFbU4QiCgNLSUvBrTURE1NoYrKjDUSqVyDm0AkqlstHHMpQREVFzMFhRh+RoZ9Ok45RKJbK/fB85OTkMV0RE1GgMVkT1yGQWKDz+YZOueBERkXljsCLSwsG2M28JEhFRozFYUYfU3Gelysof8aoVERE1GoMVdUgqCYKR3M5awo6IiMgcMFhRh9XYYCRe5QJv/xERUdN0MnUDRG2FUqnE3a/+is6dZKZuhYiI2ilesaIOR6lUovJRZZOOlTdxmQYiIiKAwYqIiIhIMgxW1GE1981ArsJORESNxWBFHVZzl0zgkgtERNRYDFbUoTV3yQQuuUBERI3BYEVEREQkEQYrIiIiIokwWBERERFJhMGKiIiISCIMVkTQ/TkbLrlARESNwWBFhP//nM2jR480fs8lF4iIqDEYrIj+j67P2XDJBSIiMhaDFREREZFEGKyogxGgUqkAPhJFREQmwGBFHcqjykfIj9vU4FkpIiKi1sBgRR2Ove3/PxPFt/qIiKg1MVhRh6brrb7GBC6GMyIiMhaDFXV42t7qUyqVyDm0wqhlFLjkAhERGYvBisyW3NYapaWlqK2t1bo4qMZcLrlARERGYLAis6X6vytRubm5WhcHJSIiaiwGKzJrdVeidC0OWofPWRERkTEYrKjDqx+K1L8LKAgClEql3tuAAJ+zIiIi4zBYUYenHooEQUBOTg5y/+/WX1n5IxTH/d2o24B8zoqIiAxhsCKzUBeK6j62bN1JJu5zsGVgIiIiaXQydQNErUEQBCgUCgCAg501H1QnIqIWwWBFZqGs/BHyDq8C0PQrVMoHFXiUkwNHR0fIZDLDBxARkdnhrUAyGw621s267feg4hGUZ/6O7OxsKBQKviFIREQNMFgRNYJMJkPW4VX46cC7fEOQiIgaYLAiaiQHW2u+IUhERFoxWFGHIQgCKisfGVyTSqpzccFQIiKqj8GKOgylUomyn/+LqqqqFj9XWfkjFHz9V+Tk5DBcERGRiMGKOhSrTpatdi6ZTMbV2ImISAODFTUQExODmTNnwsfHBzY2NnB3d8fw4cOxbt06lJaWmrq9NsXBtjNvCRIRkYjBikQqlQqTJ0/GtGnTcOjQIWRnZ6OyshKFhYVISkrCH//4R/Tv3x8XL140dattBr8hSERE6rhAKAEAqqurER4ejri4OACAh4cHoqKi4Ofnh5KSEkRHRyMxMRG5ubkIDQ1FQkIC/P39Tdx121B31YoLhxIREa9YEQBgx44dYqjy8/PD1atXsWbNGvz617/GwoULkZCQgLfeegsAoFAosGDBAlO226bwqhUREdVhsCLU1NRg9erV4vb+/fvh4eHRYN7atWsxcOBAAEBiYiJOnTrVWi22eQ62naFQKIxakZ1LNRARdVy8FUi4cOEC8vLyAADBwcEIDAzUOs/S0hKLFy9GZGQkACA6OhoTJkxotT7bsrpvEdrbdIZbyLvibUEnJyfIZDKNMKVUKrHp5FUsmjgAjo6OBmvLZDI4OjpCqVRqDWPq5wH+P7gB0Ph9nboeeOuSiEh6DFaEkydPiuOQkBC9c9X3nzhxosV6aosEQYDq4SNYVdfonKN6WIF7+9/DUdlY2Ds64a0pQ8VQ9Pej3+FR1SMAgI2NAz764gIAoLL8IWQWndHZWvt/HDvb2CFqdF/s+Oa6eHz9/UsnDxVDWt25YNlJ4/d1tAW7+uGtflgjIiLjMFgR0tPTxfHQoUP1zvXw8ECPHj2Qk5ODgoICFBYWws3NraVbbDGCIOBBxSPYWVvhYWUV7Kyt8KDikdYrQ1l5Chwr7gZLS8NrZXXu/D1gOQCrPzsDq85WkMkehykby87iHBsbBwCATJABllaw7txZVzlsPpnS4Hh1dSFNvXZl+QOs/uwbrYFNPdgBDcNb/bCmCwMYEZEmBitCRkaGOO7Vq5fB+b169UJOTo54rK5glZ2djezs7Cb1dOnSJY3ta9euGTwmPz8f93Ky8b1dAeQ2VqiuFQx+3qa8shrn7rtihEMBLpa5Y4RDAeLvu6CmtrbB3IqKKljbFMHKyrhHE6tzc9HJUgYr91+hcyfN/6h1suqMuihSVVkOWHSClZWVUXWN1di6y2+moXNnW3F7ybo0HXUrAJkVrDpbwqqzNWYH9YO9vb0ULcPBwYEhjRotICAATk5Opm6DCACDFeHxW351unbtanC+q6ur1mPr2717N1atWtWc1kQLFy40eu7RJtQ/Ve//kvG+/KepOyBzFx8fj5EjR5q6DSIAfCuQAJSVlYljGxsbg/Ntbf//qoZKpWqRnoiIiNojBisiIiIiifBWIMHBwQH3798HAFRUVMDBwUHv/PLycnEsl8t1zouMjMS4ceOa1FN+fj4SExPh4uICFxcXPPnkk3r7unbtmsbtws2bN+Ppp59u0rmJWhL/vSq9gIAAU7dAJGKwIjg7O4vBqqioyGCwKi4u1jhWF29vb3h7eze5r/Dw8CYf+/TTT/OZC2oX+O9Voo6FtwIJffr0EceZmZkG56vPUT+WiIjI3DFYkcZl9OTkZL1z8/PzxaUW3N3d2/UaVkRERFJjsCJMnDhRHBtaTf348ePi2NAq7UREROaGwYoQHBwMT09PAMD58+eRkpKidV5NTQ02bNggbs+ZM6dV+iMiImovGKwIlpaWWLFihbg9b948FBQUNJi3bNkypKWlAQCCgoL4AWYiIqJ6+FYgAQCioqJw5MgRxMXF4fvvv8eAAQMQFRUFPz8/lJSUIDo6GgkJCQAAJycn/Otf/zJxx0RERG0PgxUBADp16oRDhw5h7ty5OHbsGPLy8rBmzZoG87y8vHDw4EH4+/uboEsiIqK2jbcCSSSXyxEbG4ujR49ixowZ6NGjB6ytrdG1a1c8++yzWLt2La5fv44RI0aYulUiIqI2iVesqIGpU6di6tSppm6DiIio3eEVKyIiIiKJMFgRERERSYTBioiIiEgiDFZEREREEuHD69QheHt7Y+XKlRrbRG0R/71K1LHJBEEQTN0EERERUUfAW4FEREREEmGwIiIiIpIIgxURERGRRBisiIiIiCTCYEXtWkxMDGbOnAkfHx/Y2NjA3d0dw4cPx7p161BaWmrq9sjMjR49GjKZzOifrKwsU7dMRM3E5RaoXVKpVJg7dy6OHTum8fvCwkIUFhYiKSkJGzduxMGDB/nRaCIiajUMVtTuVFdXIzw8HHFxcQAADw8PREVFwc/PDyUlJYiOjkZiYiJyc3MRGhqKhIQE+Pv7m7hrMndHjhwxOMfd3b0VOiGilsR1rKjd2bp1K9544w0AgJ+fH86ePQsPDw+NOW+//TbWr18PAAgKCkJCQkKr90k0evRoXLhwAQDA/6olMg8MVtSu1NTUwMvLC3l5eQCAK1euIDAwUOu8IUOGIC0tDQBw8uRJTJgwoTVbJWKwIjJDfHid2pULFy6IoSo4OFhrqAIAS0tLLF68WNyOjo5ulf6IiMi8MVhRu3Ly5ElxHBISoneu+v4TJ060WE9ERER1GKyoXUlPTxfHQ4cO1TvXw8MDPXr0AAAUFBSgsLCwRXsj0icsLAzdu3dH586d0aVLF/j7+yMqKgrnzp0zdWtEJCEGK2pXMjIyxHGvXr0Mzlefo34sUWv7+uuv8csvv6CqqgoKhQI3btzAzp07MXbsWDz//PO4d++eqVskIglwuQVqVxQKhTju2rWrwfmurq5ajyVqLV26dMH48eMxZMgQdO/eHZaWlrh79y7Onj2LEydOoLa2FmfPnsXw4cORlJQET09PU7dMRM3AYEXtSllZmTi2sbExON/W1lYcq1SqFumJSJcPP/wQgwcPRufOnRvsW7p0KVJSUhAeHo6srCzcuXMHkZGROH78uAk6JSKp8FYgEVELGT58uNZQVScwMBCnTp2CtbU1gMcvWSQnJ7dWe0TUAhisqF1xcHAQxxUVFQbnl5eXi2O5XN4iPRE1R+/evTFv3jxxu/5nmoiofWGwonbF2dlZHBcVFRmcX1xcrPVYorZkzJgx4vjmzZsm7ISImovBitqVPn36iOPMzEyD89XnqB9L1Jaov4jBlyyI2jcGK2pXAgICxLGhZ1Hy8/ORk5MD4PHHbd3c3Fq0N6KmUl9jjVdWido3BitqVyZOnCiODa2mrv52laFV2olMSX2RUF5ZJWrf+BFmalf4EWbqaDIyMjBgwABUVlYCAJKSkvDss8+auCsiaipesaJ2xdLSEitWrBC3582bh4KCggbzli1bJoaqoKAghipqdRs2bMDFixf1zklNTcXEiRPFUPXCCy8wVBG1c7xiRe1OdXU1QkJCEBcXBwDw9PREVFQU/Pz8UFJSgujoaCQkJAAAnJyckJiYCH9/f1O2TGZo2rRpiImJga+vL8aNG4f+/fvD1dUVlpaW+OWXX/DNN9/g+PHjqK2tBQD4+Pjg4sWLeOKJJ0zcORE1B4MVtUsqlQpz587Vu+aPl5cXDh48iBEjRrRiZ0SP1QUrY0yYMAG7d+9mqCLqABisqF2LiYnBvn37kJycjIKCAsjlcvj6+mLGjBlYsGABnJycTN0imamffvoJ58+fR1JSEq5evYrCwkIUFRWhsrISTk5O6NmzJ4YPH465c+di2LBhpm6XiCTCYEVEREQkET68TkRERCQRBisiIiIiiTBYEREREUmEwYqIiIhIIgxWRERERBJhsCIiIiKSCIMVERERkUQYrIiIiIgkwmBFREREJBEGKyIiIiKJMFgRERERSYTBiohazd69eyGTySCTyfDqq6+26LnqziOTySSrOXr0aLHm+fPnDc7/8ssvMWXKFHTv3h3W1tbisaNHj5asJyJqWzqZugEioo5o3rx52L9/v6nbIKJWxmBFRCSx6OhojVD1zDPPwM/PD/b29gCAp556CsDjK3ivvfYaACAiIgJ79+5t9V6JSFoMVkREEvv000/F8apVq7BixQoTdkNErYnBiojISMY8VwUAKSkp4nj+/Pkt1A0RtUV8eJ2ISGL3798Xx926dTNhJ0TU2hisiIgkVl1dLY4tLPhfs0TmhP+JJ2qntC0ncPXqVSxZsgT9+/eHi4sLZDIZpk2bpvX44uJirF+/HuPHj0ePHj1gY2MDZ2dn+Pn5YeHChbh8+XKj+rl58yYWLVqEp556CnZ2dujatSuGDBmCDz/8EEVFRY3++86ePYv58+cjICAAzs7OsLKyQteuXdGvXz+MHj0af/rTn/Dtt9+iqqrK6JoZGRn4/e9/j379+sHBwQGOjo4YMGAA3n33XaN61LfcQs+ePbUu76D+r5NMJkPPnj3x6quvQiaTiQ+uA4+fy6o/l0szELVDAhG1SwDEH0EQhJUrVwqWlpYavwcgTJ06tcGxmzZtEpycnBrMVf+RyWRCZGSkUFlZabCXTz75ROjcubPOWt26dRMSEhKEPXv2iL+LiIjQWkulUglTpkzR25v6z44dO4z657N161bB2tpaZx1XV1chOTlZ798ZHBwszj937pzGPh8fH6P69fHxESIiIoz++4KDgw3+8yeitoMPrxN1AB9//DFWrVoFAPD19cUzzzwDOzs7ZGVlwcrKSmPum2++iX/+85/itqurK4YNG4YnnngCFRUVSE1NxfXr1yEIAnbv3o1ffvkFX3/9tc5bWps3b8aSJUvEbWtra4wZMwZeXl4oLCzE2bNnce/ePYSFheH3v/+9wb/llVdewVdffSVu/+pXv8KgQYPg4uKCqqoqFBYWIj09HVlZWUb/89m7dy9++9vfAgD69OmDIUOGwNbWFj/88AMSExMhCAKKi4sxefJk3Lx5E87OzkbXrhMREYHi4mIAj/+Z1Fm4cKHGPFdXVzz11FNwcHDADz/8gG+++QYA0LdvXzz//PMN6tYtzUBE7YSpkx0RNQ3Urmp06tRJcHJyEo4cOdJgXkVFhTjetWuXeIyDg4OwdetWrVekzp49K3Tv3l2cu3btWq09/PDDDxpXgcaNGyfcu3dPY45SqRReeeUVAYDGVS1tV6xSU1M1+jt+/LjOv/+nn34S/vKXvwhfffWVwX8+1tbWgpubm3DixIkG8y5cuCA4OjqKc1etWqXznPquWOk6tz7GXMEjovaFwYqonVL/H2+ZTCZcuHBB73ylUik4OzsLAARLS0uD82/cuCHY2NiIt8kePHjQYM6vf/1rsYeAgADh4cOHWmvV1NQIISEhGj1rCxIbN24U9//pT3/S258h9YPV1atXdc7dtGmTOLdv37465zFYEZEhfHidqAN48cUX8dxzz+mds3v3bigUCgDAq6++anB+v379EBERAeDxg+4nT57U2H///n0cPnxY3P74449ha2urtZaFhQU++eQTg9/tUyqV4tjNzU3v3Mb4zW9+g6efflrn/nnz5qFTp8dPRmRkZGj0QUTUGAxWRB3AnDlzDM45fvx4o+YDwNixY8VxQkKCxr6LFy+isrISAODh4YHx48frrfWrX/0KI0aM0DvH29tbHH/66ad48OCBUX0a8uKLL+rdL5fL4evrCwAQBAHZ2dmSnJeIzA8fXifqAAYPHmxwznfffSeO9+3bh6NHjxo8Jjc3Vxzn5ORo7EtLSxPHQ4cONWq9pmHDhiExMVHn/pCQEDg4OKCsrAypqano06cPXnvtNYSGhmLw4MENHsQ3VkBAgME5rq6u4ri0tLRJ5yEiYrAi6gAM3TYrKyuDSqUSt9U/EGws9dXEAaCwsFAcq19p0qdHjx5697u4uGDPnj146aWX8OjRI9y9exd/+ctf8Je//AW2trZ45plnEBwcjClTphgVJus4OTkZnKMe2hqzNhYRkTreCiTqAHQ921RHiisw6quJA4/DWh07Ozujatjb2xucM3PmTFy+fBkvvvgiOnfuLP6+vLwcFy5cwOrVqzFkyBAMGTIE3377rVHnNfRsFxGRVBisiMxA/UCjUCggPH4r2Oif+iuNOzg4iOOHDx8a1Yexz0wFBATg888/R2FhIY4dO4Y//OEPGD58uMZVpStXrmDMmDH44osvjKpJRNQaGKyIzICzszOsra3F7R9//LHZNdVvPxr7sHf957QMcXR0RGhoKNauXYuLFy+iqKgIe/fuRc+ePQEAtbW1eOONN1BeXt6oukRELYXBishMPPPMM+L41KlTza43cOBAcZycnIza2lqDxyQlJTXrnI6OjoiIiMDZs2fFoFhUVKTxYH57wluURB0PgxWRmQgLCxPH27ZtQ0VFRbPqjRgxQnwGKj8/H3FxcXrn3759GxcvXmzWOev06tUL/v7+4nZ+fr4kdVubjY2NOOYD80QdA4MVkZlYsGCB+A283NxcvPHGGxAEwahji4qKUFNTo/G7Ll26YMaMGeL2O++8o/OWnCAIWLJkicHzFRUVGdVPdXU17t27J25LuZhoa1Jf4uHu3bsm7ISIpMJgRWQmnJyc8I9//EPc3rNnDyZPnowffvhB63xBEPDdd99h0aJF8PHx0RqaVqxYId6SS09Px9SpU5GXl6cxR6VSISIiAsePH9d4y0+bd955B6NGjcKnn37aYHmHOoWFhYiMjBSDlaOjI4KCgvTWbavU19e6dOkSFyYl6gC4jhWRGXn11Vfx888/Y82aNQCAr7/+GsePH0f//v3Rv39/ODo64sGDB7h79y5SU1PFT+Do0q9fP6xbtw5LliwBAMTFxaFnz54YO3YsvLy8UFhYiLNnz0KpVMLZ2RlLlizBqlWrdNYTBAEJCQlISEiApaUl+vTpAz8/P3Tp0gXl5eXIzc3FxYsX8ejRI/GYv/3tbwaXm2irPDw8EBQUhMTERFRUVGDAgAGYOHEiunXrJi646uvri9/+9rcm7pSIjMVgRWRmVq9ejf79++PNN9/EL7/8AkEQkJ6ejvT0dJ3HPPPMMzpXPV+8eDGqq6vx7rvv4tGjR6isrMSJEyc05nh4eODLL7/E7du39fYml8vFcU1NDW7cuIEbN27onLt+/XpERUXprdnWbdiwAWPGjIFSqYRCocCBAwc09gcHBzNYEbUjDFZEZmjWrFmYOnUqDhw4gFOnTiE5ORmFhYUoKyuDvb09unfvjn79+mHUqFEICQlB79699dZbunQpJk6ciE2bNuH06dO4e/cu7Ozs4OPjg+nTp2PBggVwd3c3GKw2btyIN954A2fOnEFSUhK+//57ZGdnQ6VSoVOnTnB1dYW/vz9eeOEFvPLKK3B3d5fyH4tJBAYG4tq1a9i4cSPOnj2Ln3/+GWVlZQ2eaSOi9kEmGPv0KhERERHpxYfXiYiIiCTCYEVEREQkEQYrIiIiIokwWBERERFJhMGKiIiISCIMVkREREQSYbAiIiIikgiDFREREZFEGKyIiIiIJMJgRURERCQRBisiIiIiiTBYEREREUmEwYqIiIhIIgxWRERERBJhsCIiIiKSyP8C0ISJdq1kCeQAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_z = df[['class', 'redshift']]\n", - "sns.displot(data = df, x = 'redshift', hue='class')\n", - "#plt.xscale('log')\n" - ] - }, - { - "cell_type": "markdown", - "id": "dbd0e244-c279-46c4-9899-face0a77808b", - "metadata": {}, - "source": [ - "## Playing with Different Models " - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "id": "9560fafd-bd7e-4dfd-9dfd-6938cd2bd373", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.metrics import confusion_matrix\n", - "\n", - "X = df.drop(columns='class')\n", - "y = df['class']" - ] - }, - { - "cell_type": "markdown", - "id": "3934602e-4785-42e6-840b-0649106eeefd", - "metadata": {}, - "source": [ - "### Logistic Regression\n" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "b6cd41cd-7d81-4228-a8b6-6aede6102e0f", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LogisticRegression" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "id": "b3043f7a-3629-4250-be47-a39fbd83d0a8", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test,y_train, y_test = train_test_split(X, y, test_size=0.2)\n", - "\n", - "model = LogisticRegression()\n", - "\n", - "model.fit(X_train, y_train)\n", - "\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "id": "3a617608-39ac-4cec-aa47-2cf32d77c357", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training accuracy: 0.6072\n", - "Testing accuracy: 0.611\n" - ] - } - ], - "source": [ - "train_acc =model.score(X_train, y_train)\n", - "test_acc = model.score(X_test, y_test)\n", - "\n", - "print(\"Training accuracy:\", train_acc)\n", - "print(\"Testing accuracy:\", test_acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "4e9dce3e-049d-4081-85cc-c609c0baf473", - "metadata": {}, - "outputs": [], - "source": [ - "###manually making confusion table from a different threshold\n", - "def t_repredict(est, t, xtest):\n", - " probs = est.predict_proba(xtest)\n", - " p0 = probs[:,0]\n", - " p1 = probs[:,1]\n", - " ypred = (p1 > t)*1\n", - " return ypred" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "db258f98-ed81-4f83-8c56-d79989a2ba9d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training CM: \n", - " [[ 2981 29544]\n", - " [ 1880 45595]]\n", - "Testing CM: \n", - " [[ 730 7300]\n", - " [ 480 11490]]\n" - ] - } - ], - "source": [ - "threshold = 0.5\n", - "print(\"Training CM: \\n\", confusion_matrix(y_train,t_repredict(model, threshold, X_train)))\n", - "print(\"Testing CM: \\n\",confusion_matrix(y_test,t_repredict(model, threshold, X_test)))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "id": "a7477257-597b-4986-8c7b-073b19700384", - "metadata": {}, - "outputs": [], - "source": [ - "#making ROC curves for this model\n", - "from sklearn.metrics import roc_curve, auc\n", - "\n", - "#name: name of your model to appear on the figure (can be arbitrary)\n", - "#clf: the model as you named it - will be used for getting the predictions\n", - "#ytest, xtest: your test data\n", - "#skip, labe: steps that control how many points you see in the ROC curve and how many labels are there\n", - "\n", - "def make_roc(name, clf, ytest, xtest, ax=None, labe=5, proba=True, skip=0):\n", - " initial=False\n", - " if not ax:\n", - " ax=plt.gca()\n", - " initial=True\n", - " if proba:#for stuff like logistic regression\n", - " fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n", - " else:#for stuff like SVM? (but double-check this pleaseee)\n", - " fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n", - " \n", - " #this is the single value for the AUC score\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " if skip: \n", - " l=fpr.shape[0]\n", - " ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n", - " else:\n", - " ax.plot(fpr, tpr, '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n", - " \n", - " label_kwargs = {}\n", - " label_kwargs['bbox'] = dict(\n", - " boxstyle='round,pad=0.3', alpha=0.2,\n", - " )\n", - " \n", - " #add labels to the curve\n", - " if labe!=None:\n", - " for k in range(0, fpr.shape[0],labe):\n", - " #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n", - " threshold = str(np.round(thresholds[k], 2))\n", - " ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)\n", - " \n", - " if initial:\n", - " ax.plot([0, 1], [0, 1], 'k--')\n", - " ax.set_xlim([0.0, 1.0])\n", - " ax.set_ylim([0.0, 1.05])\n", - " ax.set_xlabel('False Positive Rate')\n", - " ax.set_ylabel('True Positive Rate')\n", - " ax.set_title('ROC')\n", - " ax.legend(loc=\"lower right\")\n", - " return ax" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "f26dad69-409a-47c3-ac31-75a6f07229ef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#This is how the above function should be used\n", - "\n", - "sns.set_context(\"poster\")\n", - "fig, ax = plt.subplots(figsize = (10,10))\n", - "ax=make_roc(\"logistic\",model, y_test, X_test, labe=1000, skip=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d8ea00fa-d871-4771-a08e-06595debffa5", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Random Forest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d8d23eb-89ab-4d59-803d-593dc8d0e032", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.datasets import make_classification\n", - "\n", - "RFmodel = RandomForestClassifier()\n", - "\n", - "param_grid = {\n", - " 'n_estimators': [10, 20, 30, 40 ],\n", - " 'max_depth': [None, 1, 2, 3],\n", - " 'min_samples_split': [2, 3, 4, 5]\n", - "}\n", - "\n", - "grid_search = GridSearchCV(RFmodel, param_grid=param_grid, cv=5)\n", - "grid_search.fit(X, y)\n", - "\n", - "# Print the best hyperparameters and corresponding score\n", - "print(\"Best parameters: \", grid_search.best_params_)\n", - "print(\"Best score: \", grid_search.best_score_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "079c08c4-bf66-46df-b8dc-48000ece5cae", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "\n", - "RFmodel = RandomForestClassifier(**grid_search.best_params_)\n", - "\n", - "\n", - "RFmodel.fit(X_train, y_train)\n", - "y_pred = RFmodel.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a81fe9b6-a26e-4ce1-b2e0-bc61e11890f0", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report\n", - "\n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "report = classification_report(y_test, y_pred)\n", - "print(report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ae6908e-f049-49ef-8b3b-5f257a45d80d", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "start_time = time.time()\n", - "importances = RFmodel.feature_importances_\n", - "std = np.std([tree.feature_importances_ for tree in RFmodel.estimators_], axis=0)\n", - "elapsed_time = time.time() - start_time\n", - "\n", - "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c257c479-15fa-4e60-aaf1-c0264e4568c5", - "metadata": {}, - "outputs": [], - "source": [ - "feature_names = [name for name in X.columns]\n", - "forest_importances = pd.Series(importances, index=feature_names)\n", - "forest_importances = forest_importances.sort_values(ascending=False)\n", - "\n", - "fig, ax = plt.subplots(figsize = (5,6))\n", - "forest_importances.head(15).plot.barh(ax=ax)\n", - "ax.set_title(\"Feature importances using MDI\")\n", - "ax.set_ylabel(\"Mean decrease in impurity\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/.ipynb_checkpoints/log-checkpoint.txt b/dev/.ipynb_checkpoints/log-checkpoint.txt deleted file mode 100644 index 89547ba..0000000 --- a/dev/.ipynb_checkpoints/log-checkpoint.txt +++ /dev/null @@ -1 +0,0 @@ -log.txt \ No newline at end of file diff --git a/dev/.ipynb_checkpoints/out-checkpoint.txt b/dev/.ipynb_checkpoints/out-checkpoint.txt deleted file mode 100644 index 09d3674..0000000 --- a/dev/.ipynb_checkpoints/out-checkpoint.txt +++ /dev/null @@ -1,3590 +0,0 @@ - -Fitting 5 folds for each of 162 candidates, totalling 810 fits -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 48.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 46.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.1s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 46.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 47.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 46.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 47.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 48.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 48.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 47.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 50.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 49.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 51.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 50.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 51.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 50.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 51.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 46.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 50.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 49.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 50.1s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 46.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 46.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 48.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 46.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 49.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 50.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 46.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 53.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 54.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 53.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 54.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 53.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 59.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 53.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 53.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 59.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 53.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 54.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 51.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 52.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 59.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 59.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 53.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 51.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 52.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 56.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 57.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 56.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 57.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 57.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 58.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 55.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 56.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 56.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 57.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 54.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 55.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 55.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 56.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 54.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 55.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 54.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 55.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s diff --git a/dev/RandomForestModel.ipynb b/dev/RandomForestModel.ipynb deleted file mode 100644 index 87274a5..0000000 --- a/dev/RandomForestModel.ipynb +++ /dev/null @@ -1,6996 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2550aabc-ee57-4355-a271-f427930db157", - "metadata": {}, - "source": [ - "# Optical Identification of GRBs Using Machine Learning Techniques on Simulated Data " - ] - }, - { - "cell_type": "markdown", - "id": "e1b0e41e-bb18-4cb7-a156-42ee28792e5f", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "59ec5447-3dc3-435e-818e-43c88d93cda9", - "metadata": {}, - "source": [ - "### Importing Data & Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "837b8335-34a3-479b-87cc-c436992f2844", - "metadata": { - "scrolled": true, - "is_executing": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import os\n", - "import shutil \n", - "faint_path = '/Users/jeremypalmerio/Repos/VTAC_ML/data/combined_qpo_vt_faint_case_with_GRB_with_flags.parquet'\n", - "bright_path = '/Users/jeremypalmerio/Repos/VTAC_ML/data/combined_qpo_vt_with_GRB.parquet'\n", - "data_faint = pd.read_parquet(faint_path, engine='fastparquet')\n", - "data_bright = pd.read_parquet(bright_path, engine='fastparquet')\n", - "data.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "51efba19-28e3-4e1a-95fd-645f0194ecf7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "IS_GRB\n", - "0 250150\n", - "1 846\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_faint['IS_GRB'].value_counts()" - ] - }, - { - "cell_type": "code", - "outputs": [], - "source": [ - "data_bright['IS_GRB'].value_counts()" - ], - "metadata": { - "collapsed": false, - "is_executing": true - }, - "id": "d06c42dfbfa95cbf" - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "765c6e68-c608-434a-ba64-44c072482a4c", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.base import BaseEstimator, TransformerMixin\n", - "from sklearn.preprocessing import OneHotEncoder\n", - "class OneHotEncoderCustom(BaseEstimator, TransformerMixin):\n", - " # take from https://medium.com/@benlc77/how-to-write-clean-and-scalable-code-with-custom-transformers-sklearn-pipelines-ecb8e53fe110\n", - " def __init__(self, variables):\n", - " self.variables = variables\n", - " self.ohe = OneHotEncoder(handle_unknown='ignore')\n", - "\n", - " def fit(self, X, y=None):\n", - " X_ = X.loc[:, self.variables]\n", - " self.ohe.fit(X_)\n", - " return self\n", - "\n", - " def transform(self, X):\n", - " X_ = X.loc[:, self.variables]\n", - " # get one-hot encoded feature in df format\n", - " X_transformed = pd.DataFrame(self.ohe.transform(X_).toarray(), columns=self.ohe.get_feature_names_out())\n", - "\n", - " # Remove columns that are one hot encoded in original df\n", - " X.drop(self.variables, axis=1, inplace=True)\n", - "\n", - " # Add one hot encoded feature to original df\n", - " X[self.ohe.get_feature_names_out()] = X_transformed[self.ohe.get_feature_names_out()].values\n", - " return X\n" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "26df46b6-88b5-42f2-82ec-4d135ff4dd44", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " VT_ID RA DEC MAGCAL_R0 MAGCAL_R1 MAGCAL_R2 \\\n", - "0 1 142.660568 42.577675 12.94 12.97 12.96 \n", - "1 2 142.615478 42.549900 13.06 13.04 13.02 \n", - "2 13 142.624680 42.599564 13.31 13.27 13.27 \n", - "3 3 142.644562 42.573009 14.76 14.71 14.72 \n", - "4 4 142.671173 42.606564 14.96 14.95 14.97 \n", - "... ... ... ... ... ... ... \n", - "313793 158 162.305435 32.411057 NaN NaN NaN \n", - "313794 170 162.343735 32.406399 NaN NaN NaN \n", - "313795 175 162.249618 32.405460 NaN NaN NaN \n", - "313796 177 162.255234 32.399235 NaN NaN NaN \n", - "313797 178 162.254822 32.395775 NaN NaN NaN \n", - "\n", - " MAGCAL_R3 MAGCAL_B0 MAGCAL_B1 MAGCAL_B2 ... SEQFLAG1 SEQFLAG2 \\\n", - "0 12.97 13.39 13.35 13.35 ... 22222 32222 \n", - "1 13.04 13.59 13.58 13.58 ... 22222 32222 \n", - "2 13.31 19.78 19.80 19.78 ... 22222 32222 \n", - "3 14.72 15.21 15.22 15.21 ... 22222 32222 \n", - "4 14.98 15.45 15.46 15.45 ... 22222 32222 \n", - "... ... ... ... ... ... ... ... \n", - "313793 NaN 22.76 NaN NaN ... 21111 31111 \n", - "313794 NaN 23.03 NaN NaN ... 21111 31111 \n", - "313795 NaN 23.03 NaN NaN ... 21111 31111 \n", - "313796 NaN 22.91 NaN NaN ... 21111 31111 \n", - "313797 NaN 23.12 NaN NaN ... 21111 31111 \n", - "\n", - " SEQFLAG3 IS_GRB SIM_ID CASE_bright_case1 CASE_bright_case1a \\\n", - "0 42222 0 1 1.0 0.0 \n", - "1 42222 0 1 1.0 0.0 \n", - "2 42222 0 1 1.0 0.0 \n", - "3 42222 0 1 1.0 0.0 \n", - "4 42222 0 1 1.0 0.0 \n", - "... ... ... ... ... ... \n", - "313793 41111 0 364 0.0 0.0 \n", - "313794 41111 0 364 0.0 0.0 \n", - "313795 41111 0 364 0.0 0.0 \n", - "313796 41111 0 364 0.0 0.0 \n", - "313797 41111 0 364 0.0 0.0 \n", - "\n", - " CASE_bright_case2 CASE_bright_case3 CASE_bright_case4 \n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "313793 0.0 0.0 1.0 \n", - "313794 0.0 0.0 1.0 \n", - "313795 0.0 0.0 1.0 \n", - "313796 0.0 0.0 1.0 \n", - "313797 0.0 0.0 1.0 \n", - "\n", - "[313798 rows x 44 columns]" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "dea8366f-fee1-4632-b1df-08b382d7dd53", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.base import BaseEstimator, TransformerMixin\n", - "\n", - "class Cleaner(BaseEstimator, TransformerMixin):\n", - " def __init__(self, variables):\n", - " self.variables = variables\n", - "\n", - " def handle_nans(self, X, y, nans=\"drop\"):\n", - " data = X.join(y)\n", - " if nans == 'drop':\n", - " data = data.dropna()\n", - " X_ = data[self.variables]\n", - " y_ = data.drop(columns=self.variables)\n", - " return X_, y_ # Return the result of dropping missing values\n", - " elif nans == 'impute':\n", - " # Implement imputation strategy if needed\n", - " pass\n", - " elif nans == 'ignore':\n", - " # Implement strategy to ignore missing values if needed\n", - " pass\n", - " else:\n", - " raise ValueError(\"Invalid value for 'nans' argument.\")\n", - "\n", - " def fit(self, X, y=None):\n", - " return self # Nothing to fit, just return self\n", - "\n", - " def transform(self, X, y=None):\n", - " X_ = X.loc[:, self.variables]\n", - " transformed_X, transformed_y = self.handle_nans(X_, y, 'drop') # Store the transformed data\n", - " return transformed_X, transformed_y # Return the transformed data\n" - ] - }, - { - "cell_type": "markdown", - "id": "df43cac7-48e2-4609-b95f-f9de0cc3786f", - "metadata": {}, - "source": [ - "### Exploring Table" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "id": "f87098f9-be00-40be-946e-80460b8fab6e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VT_ID False\n", - "RA False\n", - "DEC False\n", - "MAGCAL_R0 True\n", - "MAGCAL_R1 True\n", - "MAGCAL_R2 True\n", - "MAGCAL_R3 True\n", - "MAGCAL_B0 True\n", - "MAGCAL_B1 True\n", - "MAGCAL_B2 True\n", - "MAGCAL_B3 True\n", - "MAGERR_R0 True\n", - "MAGERR_R1 True\n", - "MAGERR_R2 True\n", - "MAGERR_R3 True\n", - "MAGERR_B0 True\n", - "MAGERR_B1 True\n", - "MAGERR_B2 True\n", - "MAGERR_B3 True\n", - "MAGVAR_R1 True\n", - "MAGVAR_R2 True\n", - "MAGVAR_R3 True\n", - "MAGVAR_B1 True\n", - "MAGVAR_B2 True\n", - "MAGVAR_B3 True\n", - "EFLAG_R0 True\n", - "EFLAG_R1 True\n", - "EFLAG_R2 True\n", - "EFLAG_R3 True\n", - "EFLAG_B0 True\n", - "EFLAG_B1 True\n", - "EFLAG_B2 True\n", - "EFLAG_B3 True\n", - "SEQFLAG0 False\n", - "SEQFLAG1 False\n", - "SEQFLAG2 False\n", - "SEQFLAG3 False\n", - "IS_GRB False\n", - "CASE False\n", - "SIM_ID False\n", - "dtype: bool\n" - ] - } - ], - "source": [ - "print(data.isnull().any())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "b906199d-23cb-4a3e-a44d-ba073b346ece", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Cannot use mean strategy with non-numeric data:\ncould not convert string to float: 'bright_case1'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[76], line 4\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mimpute\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m SimpleImputer\n\u001B[1;32m 2\u001B[0m imputer \u001B[38;5;241m=\u001B[39m SimpleImputer(strategy\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmean\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m----> 4\u001B[0m X_imputed \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame(imputer\u001B[38;5;241m.\u001B[39mfit_transform(X), columns\u001B[38;5;241m=\u001B[39mX\u001B[38;5;241m.\u001B[39mcolumns, index\u001B[38;5;241m=\u001B[39mX\u001B[38;5;241m.\u001B[39mindex)\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/sklearn/utils/_set_output.py:140\u001B[0m, in \u001B[0;36m_wrap_method_output..wrapped\u001B[0;34m(self, X, *args, **kwargs)\u001B[0m\n\u001B[1;32m 138\u001B[0m \u001B[38;5;129m@wraps\u001B[39m(f)\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mwrapped\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m--> 140\u001B[0m data_to_wrap \u001B[38;5;241m=\u001B[39m f(\u001B[38;5;28mself\u001B[39m, X, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 141\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(data_to_wrap, \u001B[38;5;28mtuple\u001B[39m):\n\u001B[1;32m 142\u001B[0m \u001B[38;5;66;03m# only wrap the first output for cross decomposition\u001B[39;00m\n\u001B[1;32m 143\u001B[0m return_tuple \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 144\u001B[0m _wrap_data_with_container(method, data_to_wrap[\u001B[38;5;241m0\u001B[39m], X, \u001B[38;5;28mself\u001B[39m),\n\u001B[1;32m 145\u001B[0m \u001B[38;5;241m*\u001B[39mdata_to_wrap[\u001B[38;5;241m1\u001B[39m:],\n\u001B[1;32m 146\u001B[0m )\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/sklearn/base.py:915\u001B[0m, in \u001B[0;36mTransformerMixin.fit_transform\u001B[0;34m(self, X, y, **fit_params)\u001B[0m\n\u001B[1;32m 911\u001B[0m \u001B[38;5;66;03m# non-optimized default implementation; override when a better\u001B[39;00m\n\u001B[1;32m 912\u001B[0m \u001B[38;5;66;03m# method is possible for a given clustering algorithm\u001B[39;00m\n\u001B[1;32m 913\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m y \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 914\u001B[0m \u001B[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001B[39;00m\n\u001B[0;32m--> 915\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit(X, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\u001B[38;5;241m.\u001B[39mtransform(X)\n\u001B[1;32m 916\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 917\u001B[0m \u001B[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001B[39;00m\n\u001B[1;32m 918\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\u001B[38;5;241m.\u001B[39mtransform(X)\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/sklearn/base.py:1151\u001B[0m, in \u001B[0;36m_fit_context..decorator..wrapper\u001B[0;34m(estimator, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1144\u001B[0m estimator\u001B[38;5;241m.\u001B[39m_validate_params()\n\u001B[1;32m 1146\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m config_context(\n\u001B[1;32m 1147\u001B[0m skip_parameter_validation\u001B[38;5;241m=\u001B[39m(\n\u001B[1;32m 1148\u001B[0m prefer_skip_nested_validation \u001B[38;5;129;01mor\u001B[39;00m global_skip_validation\n\u001B[1;32m 1149\u001B[0m )\n\u001B[1;32m 1150\u001B[0m ):\n\u001B[0;32m-> 1151\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m fit_method(estimator, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/sklearn/impute/_base.py:366\u001B[0m, in \u001B[0;36mSimpleImputer.fit\u001B[0;34m(self, X, y)\u001B[0m\n\u001B[1;32m 348\u001B[0m \u001B[38;5;129m@_fit_context\u001B[39m(prefer_skip_nested_validation\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m 349\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, y\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m 350\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Fit the imputer on `X`.\u001B[39;00m\n\u001B[1;32m 351\u001B[0m \n\u001B[1;32m 352\u001B[0m \u001B[38;5;124;03m Parameters\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 364\u001B[0m \u001B[38;5;124;03m Fitted estimator.\u001B[39;00m\n\u001B[1;32m 365\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 366\u001B[0m X \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_input(X, in_fit\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m 368\u001B[0m \u001B[38;5;66;03m# default fill_value is 0 for numerical input and \"missing_value\"\u001B[39;00m\n\u001B[1;32m 369\u001B[0m \u001B[38;5;66;03m# otherwise\u001B[39;00m\n\u001B[1;32m 370\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfill_value \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/sklearn/impute/_base.py:327\u001B[0m, in \u001B[0;36mSimpleImputer._validate_input\u001B[0;34m(self, X, in_fit)\u001B[0m\n\u001B[1;32m 321\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcould not convert\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mstr\u001B[39m(ve):\n\u001B[1;32m 322\u001B[0m new_ve \u001B[38;5;241m=\u001B[39m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 323\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCannot use \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m strategy with non-numeric data:\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\n\u001B[1;32m 324\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstrategy, ve\n\u001B[1;32m 325\u001B[0m )\n\u001B[1;32m 326\u001B[0m )\n\u001B[0;32m--> 327\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m new_ve \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 328\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 329\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m ve\n", - "\u001B[0;31mValueError\u001B[0m: Cannot use mean strategy with non-numeric data:\ncould not convert string to float: 'bright_case1'" - ] - } - ], - "source": [ - "from sklearn.impute import SimpleImputer\n", - "imputer = SimpleImputer(strategy='mean')\n", - "\n", - "X_imputed = pd.DataFrame(imputer.fit_transform(X), columns=X.columns, index=X.index)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "8e99f3b1-d8ba-4a2c-bed5-2d2f1e6aa497", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VT_ID False\n", - "RA False\n", - "DEC False\n", - "MAGCAL_R0 False\n", - "MAGCAL_R1 False\n", - "MAGCAL_R2 False\n", - "MAGCAL_R3 False\n", - "MAGCAL_B0 False\n", - "MAGCAL_B1 False\n", - "MAGCAL_B2 False\n", - "MAGCAL_B3 False\n", - "MAGERR_R0 False\n", - "MAGERR_R1 False\n", - "MAGERR_R2 False\n", - "MAGERR_R3 False\n", - "MAGERR_B0 False\n", - "MAGERR_B1 False\n", - "MAGERR_B2 False\n", - "MAGERR_B3 False\n", - "MAGVAR_R1 False\n", - "MAGVAR_R2 False\n", - "MAGVAR_R3 False\n", - "MAGVAR_B1 False\n", - "MAGVAR_B2 False\n", - "MAGVAR_B3 False\n", - "EFLAG_R0 False\n", - "EFLAG_R1 False\n", - "EFLAG_R2 False\n", - "EFLAG_R3 False\n", - "EFLAG_B0 False\n", - "EFLAG_B1 False\n", - "EFLAG_B2 False\n", - "EFLAG_B3 False\n", - "SEQFLAG0 False\n", - "SEQFLAG1 False\n", - "SEQFLAG2 False\n", - "SEQFLAG3 False\n", - "CASE False\n", - "SIM_ID False\n", - "dtype: bool (149351, 39)\n" - ] - } - ], - "source": [ - "X = data.drop(['IS_GRB'], axis=1)\n", - "y = data[\"IS_GRB\"]\n", - "cln = Cleaner(variables=X.columns)\n", - "\n", - "X, y = cln.transform(X=X, y=y)\n", - "print(X.isnull().any(), X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "21af26ab-afef-4ff8-9f66-525634f32c77", - "metadata": {}, - "outputs": [], - "source": [ - "def clean_data(data):\n", - " # Get list of numeric column names\n", - " numeric_columns = data.select_dtypes(include=['number']).columns\n", - " \n", - " # Filter DataFrame to keep only numeric columns\n", - " \n", - " \n", - " return data[numeric_columns]" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d03ec45c-32ac-4dda-a4d2-92e17c414e08", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 313798 entries, 0 to 313797\n", - "Data columns (total 40 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 VT_ID 313798 non-null int64 \n", - " 1 RA 313798 non-null float64\n", - " 2 DEC 313798 non-null float64\n", - " 3 MAGCAL_R0 229753 non-null float64\n", - " 4 MAGCAL_R1 234744 non-null float64\n", - " 5 MAGCAL_R2 233169 non-null float64\n", - " 6 MAGCAL_R3 234242 non-null float64\n", - " 7 MAGCAL_B0 189306 non-null float64\n", - " 8 MAGCAL_B1 212247 non-null float64\n", - " 9 MAGCAL_B2 212579 non-null float64\n", - " 10 MAGCAL_B3 213286 non-null float64\n", - " 11 MAGERR_R0 229753 non-null float64\n", - " 12 MAGERR_R1 234744 non-null float64\n", - " 13 MAGERR_R2 233169 non-null float64\n", - " 14 MAGERR_R3 234242 non-null float64\n", - " 15 MAGERR_B0 189306 non-null float64\n", - " 16 MAGERR_B1 212247 non-null float64\n", - " 17 MAGERR_B2 212579 non-null float64\n", - " 18 MAGERR_B3 213286 non-null float64\n", - " 19 MAGVAR_R1 212775 non-null float64\n", - " 20 MAGVAR_R2 218663 non-null float64\n", - " 21 MAGVAR_R3 218964 non-null float64\n", - " 22 MAGVAR_B1 178573 non-null float64\n", - " 23 MAGVAR_B2 199108 non-null float64\n", - " 24 MAGVAR_B3 199256 non-null float64\n", - " 25 EFLAG_R0 229753 non-null Int64 \n", - " 26 EFLAG_R1 234744 non-null Int64 \n", - " 27 EFLAG_R2 233169 non-null Int64 \n", - " 28 EFLAG_R3 234242 non-null Int64 \n", - " 29 EFLAG_B0 189306 non-null Int64 \n", - " 30 EFLAG_B1 212247 non-null Int64 \n", - " 31 EFLAG_B2 212579 non-null Int64 \n", - " 32 EFLAG_B3 213286 non-null Int64 \n", - " 33 SEQFLAG0 313798 non-null int64 \n", - " 34 SEQFLAG1 313798 non-null int64 \n", - " 35 SEQFLAG2 313798 non-null int64 \n", - " 36 SEQFLAG3 313798 non-null int64 \n", - " 37 IS_GRB 313798 non-null int64 \n", - " 38 CASE 313798 non-null object \n", - " 39 SIM_ID 313798 non-null int64 \n", - "dtypes: Int64(8), float64(24), int64(7), object(1)\n", - "memory usage: 98.2+ MB\n" - ] - } - ], - "source": [ - "data.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0f748aa6-8ca6-4c4a-ad61-177c2ca746c4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CASE\n", - "bright_case2 64572\n", - "bright_case3 64572\n", - "bright_case4 62356\n", - "bright_case1a 61192\n", - "bright_case1 61106\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['CASE'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "d3235ebc-4fc1-4b11-824c-bea6bdd9cae4", - "metadata": {}, - "source": [ - "Splitting dataframe by GRB cases: \n", - "- b1 : bright_case1\n", - "- b1a : bright_case1a\n", - "- b2 : bright_case2\n", - "- b3 : bright_case3\n", - "- b4 : bright_case4" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f54b4c8d-1f3c-42aa-99ce-1ffc83fd1273", - "metadata": {}, - "outputs": [], - "source": [ - "b1 = data[data['CASE']=='bright_case1'].drop([\"CASE\"],axis = 1)\n", - "b1a = data[data['CASE']=='bright_case1a'].drop([\"CASE\"], axis = 1)\n", - "b2 = data[data['CASE']=='bright_case2'].drop([\"CASE\"], axis = 1)\n", - "b3 = data[data['CASE']=='bright_case3'].drop([\"CASE\"], axis = 1)\n", - "b4 = data[data['CASE']=='bright_case4'].drop([\"CASE\"], axis = 1)\n", - "cases_dict = {'bright_case1':b1,\n", - " 'bright_case1a':b1a,\n", - " 'bright_case2':b2,\n", - " 'bright_case3':b3,\n", - " 'bright_case4':b4}" - ] - }, - { - "cell_type": "markdown", - "id": "54621ac2-5140-4ee0-8772-525a91e0dc94", - "metadata": {}, - "source": [ - "Exploring the distributions of positive and negative classes for 'IS_GRB' columns" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3565116c-5644-4ab0-9db8-19a132c68d1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "bright_case1|29389|178\n", - "bright_case1|60894|212\n", - "bright_case1a|29234|180\n", - "bright_case1a|60981|211\n", - "bright_case2|30139|181\n", - "bright_case2|64359|213\n", - "bright_case3|30139|181\n", - "bright_case3|64359|213\n", - "bright_case4|29545|185\n", - "bright_case4|62142|214\n" - ] - } - ], - "source": [ - "for case, sub_data in cases_dict.items():\n", - " drop_counts = list(sub_data.dropna()[\"IS_GRB\"]\n", - " .value_counts())\n", - " counts = list(sub_data[\"IS_GRB\"].value_counts())\n", - " print(case+\"|\"+str(drop_counts[0])+\"|\"+str(drop_counts[1]))\n", - " print(case+\"|\"+str(counts[0])+\"|\"+str(counts[1]))\n" - ] - }, - { - "cell_type": "markdown", - "id": "38d5ba61-9ad8-410a-bde4-d5bc94e1649f", - "metadata": {}, - "source": [ - "### Getting Class Weigts\n" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "id": "98df29fe-9963-430c-bb1a-7c0ba0650cb0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: 0.5016995219594864, 1: 147.60018814675448}\n" - ] - }, - { - "data": { - "text/plain": [ - "0 0\n", - "1 0\n", - "2 0\n", - "3 0\n", - "4 0\n", - " ..\n", - "313793 0\n", - "313794 0\n", - "313795 0\n", - "313796 0\n", - "313797 0\n", - "Name: IS_GRB, Length: 313798, dtype: int64" - ] - }, - "execution_count": 116, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.utils.class_weight import compute_class_weight\n", - "y = data['IS_GRB']\n", - "class_weights = compute_class_weight(\n", - " class_weight=\"balanced\", \n", - " #class_weight=None, \n", - " classes=np.unique(y), \n", - " y=y\n", - " )\n", - "\n", - "class_dict = dict(zip([0, 1], class_weights))\n", - "print(class_dict)\n", - "y" - ] - }, - { - "cell_type": "markdown", - "id": "b0081447-88d1-4de5-8dee-06636d66b365", - "metadata": {}, - "source": [ - "### Missing Values" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b22e953b-874e-467f-a79e-94c7d4f437a8", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VT_ID False\n", - "RA False\n", - "DEC False\n", - "MAGCAL_R0 True\n", - "MAGCAL_R1 True\n", - "MAGCAL_R2 True\n", - "MAGCAL_R3 True\n", - "MAGCAL_B0 True\n", - "MAGCAL_B1 True\n", - "MAGCAL_B2 True\n", - "MAGCAL_B3 True\n", - "MAGERR_R0 True\n", - "MAGERR_R1 True\n", - "MAGERR_R2 True\n", - "MAGERR_R3 True\n", - "MAGERR_B0 True\n", - "MAGERR_B1 True\n", - "MAGERR_B2 True\n", - "MAGERR_B3 True\n", - "MAGVAR_R1 True\n", - "MAGVAR_R2 True\n", - "MAGVAR_R3 True\n", - "MAGVAR_B1 True\n", - "MAGVAR_B2 True\n", - "MAGVAR_B3 True\n", - "EFLAG_R0 True\n", - "EFLAG_R1 True\n", - "EFLAG_R2 True\n", - "EFLAG_R3 True\n", - "EFLAG_B0 True\n", - "EFLAG_B1 True\n", - "EFLAG_B2 True\n", - "EFLAG_B3 True\n", - "SEQFLAG0 False\n", - "SEQFLAG1 False\n", - "SEQFLAG2 False\n", - "SEQFLAG3 False\n", - "IS_GRB False\n", - "CASE False\n", - "SIM_ID False\n", - "dtype: bool\n" - ] - } - ], - "source": [ - "print(data.isnull().any())\n", - "data = data.copy().dropna()\n" - ] - }, - { - "cell_type": "markdown", - "id": "be89e6c0-0786-4b21-b97b-5ebaf7353309", - "metadata": {}, - "source": [ - "### Feature Engineering\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "85e910dd-9ef0-49e6-9826-f948949b4299", - "metadata": {}, - "outputs": [], - "source": [ - "data[\"R0_B0\"] = data[\"MAGCAL_R0\"]-data[\"MAGCAL_B0\"]\n", - "data[\"R1_B1\"] = data[\"MAGCAL_R1\"]-data[\"MAGCAL_B1\"]" - ] - }, - { - "cell_type": "markdown", - "id": "0081265b-31e1-47ce-819b-40337f211f84", - "metadata": {}, - "source": [ - "### Pair plot distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "604e2ea4-cc2b-41b3-8d53-edeb50003116", - "metadata": { - "editable": true, - "scrolled": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "#if os.path.isdir('plots/'):\n", - "# shutil.rmtree('plots/')\n", - "#os.mkdir('plots/')\n", - "#sns.pairplot(\n", - "# data.dropna(),\n", - "# vars = [\"MAGCAL_R0\",\n", - "# \"MAGCAL_B0\",\n", - "# \"MAGCAL_R1\",\n", - "# \"MAGCAL_B1\",\n", - "# \"MAGVAR_R1\",\n", - "# \"MAGVAR_B1\",\n", - "# \"R0_B0\",\n", - "# \"R1_B1\"], \n", - "# hue=\"IS_GRB\",\n", - "# height=3\n", - "#)\n", - "#plt.savefig('plots/pairplot.png')\n", - "#plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "803632e9-4440-4d2e-9983-cd0ad009e5c6", - "metadata": {}, - "source": [ - "### Plotting Corrolations" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "dab10307-a886-4b33-bcd8-44fa7da177b7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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ZAwYMwMSJE/HUU0/9psdEREREREREt5bMarVaO7oSdGMie7BF/mbVa9hzwmKd279WWKw7gdki7prorHcBkT+wyp3E/lorF1g5kadf5FF20stC6DGK1CzwhHXWa79Z6EGKDCX2qhB5/kWeMyeB/5ci7/uWZnGfR85ycU8vNgs8SCeBF0WTyPeRQEI7NQUeosjrHgDyT1TduFAbPTbYV1is35LbQ+kdXQWHNBS+3NFVaBP2YBMREREREd0pulDKq66IZ5eIiIiIiIhIADawiYiIiIiIiARgA5uIiIiIiIhIAD6DTUREREREdKdgmq5bij3YRERERERERAKwB7uLeHrbEWGx9In3CoslMrVWj6GpwmIJ5SQXF0vgrI33/PEPwmKJTNciMp2GyFgThvYVFgsA+nu5CYvVWVPJKJzEXa89XF2ExeqsenoohcWqqr0kLFZIbw9hsbxV4v4fv/+pXlis3mpXYbEAoKzqV2GxRL6PPNzEfW0T2X91vs4sLJZcLq5mItO3icwU1Ufg54fIjkiFwHMvUmOT2LRmD4X0EhqP6FrswSYiIiIiIiISgD3YREREREREdwrmwb6leHaJiIiIiIiIBGADm4iIiIiIiEgADhEnIiIiIiK6UzBN1y3lUA92cnIyZDIZpk+fbrdtxowZkMlkSE5Olqzfu3cv5HI5tFptizHNZjNWrlyJiIgIuLu7Q61WIzw8HIsWLcLZs2clZauqqpCamoqgoCAolUr4+/tj/PjxKCwstIubnp4OuVyO119/3W5bVlYWunfv3vYDv8qVcyCTyaBQKBAQEIBnn30W1dXVknImkwmpqanw8fGBu7s7Hn30UfznP/9xaJ9ERERERETUeTk8RNzf3x85OTloaGiwrbt06RKys7MREBBgV16v1yM1NRXFxcWoqKiQbDOZTIiJiUF6ejqSk5NRVFSEQ4cOYcWKFTh37hzWrv1vKqjy8nJERkbiq6++wooVK3D06FHk5+cjOjoaM2fOtNuvwWDAiy++CL1e7+ihXpdWq0VlZSXKy8vx3nvv4ZNPPsGMGTMkZWbPno0PP/wQOTk5KC4uRl1dHcaNG4empibh9SEiIiIiIqKO4/AQ8YiICJw+fRq5ubmYNGkSACA3Nxf+/v4ICgqSlK2vr8f27dtx4MABVFVVISsrC4sXL7ZtX716NYqLi3Hw4EEMGTLEtl6j0SA2NhbWq3LEXukh379/P9zd3W3rBw0ahJSUFMl+d+/ejYaGBqSlpWHTpk0oKirCyJEjHT1kO0qlEr6+vgCAu+66CwkJCcjKyrJtr6mpgU6nw9///neMGTMGALB582b4+/vjyy+/RGxsrLC6EBERERERUcdq1yRnU6ZMgcFgsP2t1+vtGrkAsG3bNoSEhCAkJARJSUkwGAySRnN2djZiYmIkjeuryf7/5wTOnz+P/Px8zJw5U9K4vuLa4d46nQ6JiYlwdnZGYmIidDqdI4fZJqdPn0Z+fj6cnZ1t6w4dOoTGxkaMHTvWtq5Pnz4ICwvD3r17b1ldiIiIiIiIWiRz6ppLF9Gumk6ePBnFxcUoLy/HmTNnsGfPHiQlJdmV0+l0tvVarRZ1dXWS56XLysoQEhIiec2ECROgUqmgUqkQFRUFAPj2229htVoRGhp6w7rV1tZix44dtv0mJSXhgw8+QG1trcPHe628vDyoVCq4ublhwIABOHHiBBYsWGDbXlVVBRcXF3h5eUle17t3b1RVVQmrBxEREREREXW8djWwfXx8EBcXh40bN8JgMCAuLg4+Pj6SMqdOncL+/fvxxBNPAAAUCgUSEhLsnomWXTObXUZGBoxGI1JSUnDx4kUAsPV6X1u2JVu3bkVQUBDCw8MBAPfeey+CgoKQk5Pj2MG2IDo6GkajEfv27UNqaipiY2ORmpp6w9dZrdZWj8FkMqG2tlayNDWahdWbiIiIiIiIxGt3X3tKSgqysrKwcePGFoeH63Q6WCwW9O3bFwqFAgqFApmZmcjNzbXNuB0cHIyTJ09KXufn5weNRgNvb2/buuDgYMhkMpSWlt6wXnq9HsePH7ftU6FQ4Pjx40KHibu7u0Oj0WDw4MFYs2YNTCYTli5datvu6+sLs9lsN7P4Tz/9hN69e1837vLly6FWqyVLaZ74SdqIiIiIiOgOI5N1zaWLaHcDW6vVwmw2w2w2203aZbFYsGnTJqxatQpGo9G2HDlyBIGBgdiyZQsAIDExEQUFBTh8+HCr+/L29kZsbCzWrVuH+vp6u+0XLlwAABw9ehQHDx7Erl27JPstKirCgQMHcOzYsfYedoteffVVvPnmm7a0YpGRkXB2dkZBQYGtTGVlJY4dO2Yb9t6ShQsXoqamRrIMHGf/4wURERERERF1Hg7PIn6FXC639SjL5XLJtry8PFRXV2Pq1KlQq9WSbRMnToROp8OsWbMwZ84cfPrppxg9ejSWLFmCBx54AF5eXigrK8POnTslcTMyMhAVFYWhQ4ciLS0NgwcPhsViQUFBATIzM1FaWgqdToehQ4e2OGP4sGHDoNPpsHr1agBAU1MTjEajpIyLiwvuvvvumz4Xo0aNwqBBg5Ceno63334barUaU6dOxbx589CjRw94e3tj/vz5uOeee2yzirdEqVRCqVRK1smdXW66PkRERERERPTbETIdm6enJzw9Pe3W63Q6jBkzxq5xDQDx8fEwGo0oKSmBq6srCgsL8dJLL8FgMGDEiBEYOHAgZs+ejeHDh+Ojjz6yva5///4oKSlBdHQ05s2bh7CwMMTExKCwsBCZmZkwm83YvHkz4uPjW6xrfHw8Nm/eDLP58jPNdXV1GDJkiGR55JFHHD4Xc+fOxYYNG/Dvf/8bwOUUZI899hgef/xxDB8+HN26dcMnn3xi92MEERERERERdW0y69X5sqjTemJj68Pnb4Y+8V5hsSDwcYgeQ288QVyHcBL4Y4jAFAP3/PEPwmKJvAs4OYm7KETGmjC0r7BYANDfy01YrGaB/wFOAp9RUjiJu157uN7+o3B6eihvXKiNqmovCYsV0ttDWCxvlbj/x+9/sn/Uy1G91a7CYgFAWdWvwmKJfB95uLV74KGNyKcZz9eJm4hVLhdXM5nAoxT4cYQ+Aj8/RD6WqhB47kVqbBLbVHFXivte56HsOqmjrub2yN86ugoOafjs+Y6uQpt0zauCiIiIiIiIqJNhA7sFFRUVthzcLS0VFRUdXUUiIiIiIiLqZMSNNbqN9OnTx27is2u3ExEREREREV2NDewWKBQKaDSajq4GERERERGRWF0op3RXxCHiRERERERERAKwgU1EREREREQkAIeIExERERER3SkEpo0le2xgdxGWZnE5AAWGEpoX8o5gbRYWSmSO6GaRF0Un5aIQ+2FS32gRFstZYJ5cmUzc/6Vc4DNaTQJzfYvMbSuSh6u4j9SqWmGhhN4rmgTeK4TmOxZ8SYjMXV1jEpcj2lnROa99kZ8gzeI+JoXeD0VmDv/5V5OwWOpuzsJiWa0CPydF5ucW/GWzs36G0O2DP18QERERERERCcAGNhEREREREZEAHCJORERERER0p+Az2LcUzy4RERERERGRAGxgExEREREREQngUAM7OTkZMpkM06dPt9s2Y8YMyGQyJCcnS9bv3bsXcrkcWq22xZhmsxkrV65EREQE3N3doVarER4ejkWLFuHs2bOSslVVVUhNTUVQUBCUSiX8/f0xfvx4FBYW2sVNT0+HXC7H66+/brctKysL3bt3b/uBX+XKOZDJZFAoFAgICMCzzz6L6upqSbl3330Xo0aNgqenJ2QyGS5cuODQ/oiIiIiIiNpNJuuaSxfhcA+2v78/cnJy0NDQYFt36dIlZGdnIyAgwK68Xq9HamoqiouLUVFRIdlmMpkQExOD9PR0JCcno6ioCIcOHcKKFStw7tw5rF271la2vLwckZGR+Oqrr7BixQocPXoU+fn5iI6OxsyZM+32azAY8OKLL0Kv1zt6qNel1WpRWVmJ8vJyvPfee/jkk08wY8YMSZmLFy9Cq9Xi5ZdfFr5/IiIiIiIi6jwcnuQsIiICp0+fRm5uLiZNmgQAyM3Nhb+/P4KCgiRl6+vrsX37dhw4cABVVVXIysrC4sWLbdtXr16N4uJiHDx4EEOGDLGt12g0iI2NhfWq/KlXesj3798Pd3d32/pBgwYhJSVFst/du3ejoaEBaWlp2LRpE4qKijBy5EhHD9mOUqmEr68vAOCuu+5CQkICsrKyJGVmz54NANi1a5ew/RIREREREVHn065nsKdMmQKDwWD7W6/X2zVyAWDbtm0ICQlBSEgIkpKSYDAYJI3m7OxsxMTESBrXV5P9/0MCzp8/j/z8fMycOVPSuL7i2uHeOp0OiYmJcHZ2RmJiInQ6nSOH2SanT59Gfn4+nJ2db9k+iIiIiIiIqPNqVwN78uTJKC4uRnl5Oc6cOYM9e/YgKSnJrpxOp7Ot12q1qKurkzwvXVZWhpCQEMlrJkyYAJVKBZVKhaioKADAt99+C6vVitDQ0BvWrba2Fjt27LDtNykpCR988AFqa2sdPt5r5eXlQaVSwc3NDQMGDMCJEyewYMECYfGJiIiIiIiEkjl1zaWLaFdNfXx8EBcXh40bN8JgMCAuLg4+Pj6SMqdOncL+/fvxxBNPAAAUCgUSEhLsnomWXfPgekZGBoxGI1JSUnDx4kUAsPV6X1u2JVu3bkVQUBDCw8MBAPfeey+CgoKQk5Pj2MG2IDo6GkajEfv27UNqaipiY2ORmpra7rgmkwm1tbWSpanRLKDGREREREREdKu0+6eAlJQUZGVlYePGjS0OD9fpdLBYLOjbty8UCgUUCgUyMzORm5trm3E7ODgYJ0+elLzOz88PGo0G3t7etnXBwcGQyWQoLS29Yb30ej2OHz9u26dCocDx48eFDhN3d3eHRqPB4MGDsWbNGphMJixdurTdcZcvXw61Wi1ZTn1quPELiYiIiIiIqMO0u4Gt1WphNpthNpsRGxsr2WaxWLBp0yasWrUKRqPRthw5cgSBgYHYsmULACAxMREFBQU4fPhwq/vy9vZGbGws1q1bh/r6ervtV1JgHT16FAcPHsSuXbsk+y0qKsKBAwdw7Nix9h52i1599VW8+eabdmnFbtbChQtRU1MjWULipgiqJREREREREd0KDs8ifoVcLrf1KMvlcsm2vLw8VFdXY+rUqVCr1ZJtEydOhE6nw6xZszBnzhx8+umnGD16NJYsWYIHHngAXl5eKCsrw86dOyVxMzIyEBUVhaFDhyItLQ2DBw+GxWJBQUEBMjMzUVpaCp1Oh6FDh7Y4Y/iwYcOg0+mwevVqAEBTUxOMRqOkjIuLC+6+++6bPhejRo3CoEGDkJ6ejrfffhvA5ZzdVVVV+PbbbwFcbvx7eHggICBA0jt/NaVSCaVSKVknd3a56foQERERERFJdKGc0l2RkKfFPT094enpabdep9NhzJgxdo1rAIiPj4fRaERJSQlcXV1RWFiIl156CQaDASNGjMDAgQMxe/ZsDB8+HB999JHtdf3790dJSQmio6Mxb948hIWFISYmBoWFhcjMzITZbMbmzZsRHx/fYl3j4+OxefNmmM2Xn2muq6vDkCFDJMsjjzzi8LmYO3cuNmzYgH//+98AgPXr12PIkCH43//9XwDAyJEjMWTIEHz88ccO74OIiIiIiIg6H5n16nxZ1GlNNJQIi5X1ZMvp0BzhJHBCvx5D2z9B3C3hJL9xmQ4Q/qc/CovV3CzuNtCWSQjbyslJXKyEKH9hsQCgu6u468JZ4BtJ5I/SrnJxx+jlKm4Ujgyd85d3TS/79JGO+r+f6oTFGuhn/wO4ozxc2z3wzeaH6gZhsXp6KG9c6CZ896P9Y2iOqjGJm6TUx13scYpy0dQkLJaTwJuYyPuhyHopncXd89XdxKWHdZEL/FIn8NwrBH4XAABXZ3GfbSpl5/w8uhG3x97t6Co4pOGjpzu6Cm0i7pOSiIiIiIiIOrculPKqK+LZbUFFRYUtB3dLS0VFRUdXkYiIiIiIiDoZ9mC3oE+fPnYTn127nYiIiIiIiOhqbGC3QKFQQKPRdHQ1iIiIiIiIqAthA5uIiIiIiOhOwTRdtxSfwSYiIiIiIiISgA1sIiIiIiIiIgE4RLyLUCo6Z55coTppvmk0i8vvKZKLi7i3b1NTc6eMZbWKy8/tLDiPZjdncef/YqNFWCylQtz7SC7wnCkEpgSxQtx1IZLIHPB9u7sJiyUXWC9To7j3t8jc1SLzCgOAyNuFu8B7hcjzL/KcicwR7STyv1LgrULkfcdF4Hc6N4E5nc/Xi8vZ3stT3Pu7035vJboONrCJiIiIiIjuECJ/ECZ7HCJOREREREREJAAb2EREREREREQCcIg4ERERERHRHYJDxG8t9mATERERERERCeBQAzs5ORkymQzTp0+32zZjxgzIZDIkJydL1u/duxdyuRxarbbFmGazGStXrkRERATc3d2hVqsRHh6ORYsW4ezZs5KyVVVVSE1NRVBQEJRKJfz9/TF+/HgUFhbaxU1PT4dcLsfrr79uty0rKwvdu3dv+4Ff5co5kMlkUCgUCAgIwLPPPovq6mpbmfPnzyM1NRUhISHo1q0bAgIC8Nxzz6GmpsahfRIREREREVHn5XAPtr+/P3JyctDQ0GBbd+nSJWRnZyMgIMCuvF6vR2pqKoqLi1FRUSHZZjKZEBMTg/T0dCQnJ6OoqAiHDh3CihUrcO7cOaxdu9ZWtry8HJGRkfjqq6+wYsUKHD16FPn5+YiOjsbMmTPt9mswGPDiiy9Cr9c7eqjXpdVqUVlZifLycrz33nv45JNPMGPGDNv2s2fP4uzZs3jzzTdx9OhRZGVlIT8/H1OnThVeFyIiIiIiIupYDj+DHRERgdOnTyM3NxeTJk0CAOTm5sLf3x9BQUGSsvX19di+fTsOHDiAqqoqZGVlYfHixbbtq1evRnFxMQ4ePIghQ4bY1ms0GsTGxkpy4V7pId+/fz/c3d1t6wcNGoSUlBTJfnfv3o2GhgakpaVh06ZNKCoqwsiRIx09ZDtKpRK+vr4AgLvuugsJCQnIysqybQ8LC8OOHTtsfw8YMADLli1DUlISLBYLFAo+Ak9ERERERL8hPoJ9S7XrGewpU6bAYDDY/tbr9XaNXADYtm0bQkJCEBISgqSkJBgMBkmjOTs7GzExMZLG9dWuPIh//vx55OfnY+bMmZLG9RXXDvfW6XRITEyEs7MzEhMTodPpHDnMNjl9+jTy8/Ph7Ozcarmamhp4enqycU1ERERERHSbaVcDe/LkySguLkZ5eTnOnDmDPXv2ICkpya6cTqezrddqtairq5M8L11WVoaQkBDJayZMmACVSgWVSoWoqCgAwLfffgur1YrQ0NAb1q22thY7duyw7TcpKQkffPABamtrHT7ea+Xl5UGlUsHNzQ0DBgzAiRMnsGDBguuWP3fuHP7yl7/gmWeeEVYHIiIiIiIi6hza1Y3q4+ODuLg4bNy4EVarFXFxcfDx8ZGUOXXqFPbv34/c3NzLO1QokJCQAL1ejzFjxtjKXTtdfEZGBurr67FmzRoUFRUBgK3Xuy1Ty2/duhVBQUEIDw8HANx7770ICgpCTk4Onn76accP+irR0dHIzMzExYsX8d5776GsrAypqaktlq2trUVcXBzuvvtuvPrqq63GNZlMMJlMknVNjWbInV2E1JuIiIiIiO5MTNN1a7U7TVdKSgqysrKwcePGFoeH63Q6WCwW9O3bFwqFAgqFApmZmcjNzbXNuB0cHIyTJ09KXufn5weNRgNvb2/buuDgYMhkMpSWlt6wXnq9HsePH7ftU6FQ4Pjx40KHibu7u0Oj0WDw4MFYs2YNTCYTli5dalfu119/hVarhUqlwocffnjDYeTLly+HWq2WLMc/ET9JGxEREREREYnT7ga2VquF2WyG2WxGbGysZJvFYsGmTZuwatUqGI1G23LkyBEEBgZiy5YtAIDExEQUFBTg8OHDre7L29sbsbGxWLduHerr6+22X7hwAQBw9OhRHDx4ELt27ZLst6ioCAcOHMCxY8fae9gtevXVV/Hmm29K0orV1tZi7NixcHFxwccffwxXV9cbxlm4cCFqamoky6Dx9j9eEBERERERUefR7pm25HK5rUdZLpdLtuXl5aG6uhpTp06FWq2WbJs4cSJ0Oh1mzZqFOXPm4NNPP8Xo0aOxZMkSPPDAA/Dy8kJZWRl27twpiZuRkYGoqCgMHToUaWlpGDx4MCwWCwoKCpCZmYnS0lLodDoMHTq0xRnDhw0bBp1Oh9WrVwMAmpqaYDQaJWVcXFxw99133/S5GDVqFAYNGoT09HS8/fbb+PXXXzF27FhcvHgRmzdvRm1tre0Z8J49e9qdryuUSiWUSqVkHYeHExERERERdW7t7sEGAE9PT3h6etqt1+l0GDNmjF3jGgDi4+NhNBpRUlICV1dXFBYW4qWXXoLBYMCIESMwcOBAzJ49G8OHD8dHH31ke13//v1RUlKC6OhozJs3D2FhYYiJiUFhYSEyMzNhNpuxefNmxMfHt1jX+Ph4bN68GWazGQBQV1eHIUOGSJZHHnnE4XMxd+5cbNiwAf/+979x6NAh7Nu3D0ePHoVGo4Gfn59t+fe//+3wPoiIiIiIiBwhk8m65NJVyKxX58uiTmvS343CYr2bEC4slshrvcf9s8UFE6m5qaNr0KL/mZwoLFZTU3OnjCXS5AcChcbr6S5uVMnFRouwWEpFyyNjHNFNYCzva0bltIcVnfNja0AvlbBY9SZx14RXN3HXqtxJ3E1f5OeH0llIf4HNyR9+FRbLLPCe6CTwpIk8Z5fMAo9R5H+lyFuFwOvV0631uXhuhrfAz6Lz9WZhsXp5irvni25XKQReZCpl12n0Xc0jYWNHV8Ehv257qqOr0CZiP5GIiIiIiIiI7lBsYLegoqLCloO7paWioqKjq0hERERERESdTLsnObsd9enTx27is2u3ExERERERdTVd6XnmrogN7BYoFApoNJqOrgYRERERERF1IRwiTkRERERERCQAe7CJiIiIiIjuEBwifmuxB5uIiIiIiIhIAPZgdxFOAnOPdloygb/3WDtnLmaRRF4TVqu4WE2dM2248DyaIjkLzMkpMueup7O4XK0ib2HNAq9XkUQeo8h8xypXcR/1IvNzKxXirnuR56szc3UWl5v+olnc/6VMZJLozkpkTm2RBJ56kbmrq2ouCYvl4yGuXgAgd+qkCdLptsEebCIiIiIiIiIB2INNRERERER0p2DH+y3FHmwiIiIiIiIiAdjAJiIiIiIiIhKAQ8SJiIiIiIjuEEzTdWs51IOdnJwMmUyG6dOn222bMWMGZDIZkpOTJev37t0LuVwOrVbbYkyz2YyVK1ciIiIC7u7uUKvVCA8Px6JFi3D27FlJ2aqqKqSmpiIoKAhKpRL+/v4YP348CgsL7eKmp6dDLpfj9ddft9uWlZWF7t27t/3Ar3LlHMhkMigUCgQEBODZZ59FdXW1pNwzzzyDAQMGwM3NDT179sQf/vAHnDx50qF9EhERERERUefl8BBxf39/5OTkoKGhwbbu0qVLyM7ORkBAgF15vV6P1NRUFBcXo6KiQrLNZDIhJiYG6enpSE5ORlFREQ4dOoQVK1bg3LlzWLt2ra1seXk5IiMj8dVXX2HFihU4evQo8vPzER0djZkzZ9rt12Aw4MUXX4Rer3f0UK9Lq9WisrIS5eXleO+99/DJJ59gxowZkjKRkZEwGAwoLS3F559/DqvVirFjx6Kps+YyIiIiIiIiIoc4PEQ8IiICp0+fRm5uLiZNmgQAyM3Nhb+/P4KCgiRl6+vrsX37dhw4cABVVVXIysrC4sWLbdtXr16N4uJiHDx4EEOGDLGt12g0iI2NhdX633x1V3rI9+/fD3d3d9v6QYMGISUlRbLf3bt3o6GhAWlpadi0aROKioowcuRIRw/ZjlKphK+vLwDgrrvuQkJCArKysiRlnn76adu/+/Xrh9deew3h4eEoLy/HgAEDhNWFiIiIiIiIOla7JjmbMmUKDAaD7W+9Xm/XyAWAbdu2ISQkBCEhIUhKSoLBYJA0mrOzsxETEyNpXF/tynMC58+fR35+PmbOnClpXF9x7XBvnU6HxMREODs7IzExETqdzpHDbJPTp08jPz8fzs7O1y1TX18Pg8GA/v37w9/f/5bVhYiIiIiIqCVXHnPtaktX0a4G9uTJk1FcXIzy8nKcOXMGe/bsQVJSkl05nU5nW6/ValFXVyd5XrqsrAwhISGS10yYMAEqlQoqlQpRUVEAgG+//RZWqxWhoaE3rFttbS127Nhh229SUhI++OAD1NbWOny818rLy4NKpYKbmxsGDBiAEydOYMGCBXblMjIybMeSn5+PgoICuLi4XDeuyWRCbW2tZGlqNAurNxEREREREYnXrga2j48P4uLisHHjRhgMBsTFxcHHx0dS5tSpU9i/fz+eeOIJAIBCoUBCQoLdM9HX/iqRkZEBo9GIlJQUXLx4EQBsvd5t+QVj69atCAoKQnh4OADg3nvvRVBQEHJychw72BZER0fDaDRi3759SE1NRWxsLFJTU+3KTZo0CYcPH8bu3bsRHByMxx9/HJcuXbpu3OXLl0OtVkuWYx/fut53IiIiIiIiar9258FOSUlBVlYWNm7c2OLwcJ1OB4vFgr59+0KhUEChUCAzMxO5ubm2GbeDg4PtZtb28/ODRqOBt7e3bV1wcDBkMhlKS0tvWC+9Xo/jx4/b9qlQKHD8+HGhw8Td3d2h0WgwePBgrFmzBiaTCUuXLrUrp1arERwcjJEjR+KDDz7AyZMn8eGHH1437sKFC1FTUyNZwh6dKqzeREREREREJF67G9harRZmsxlmsxmxsbGSbRaLBZs2bcKqVatgNBpty5EjRxAYGIgtW7YAABITE1FQUIDDhw+3ui9vb2/ExsZi3bp1qK+vt9t+4cIFAMDRo0dx8OBB7Nq1S7LfoqIiHDhwAMeOHWvvYbfo1VdfxZtvvmmXVuxaVqsVJpPputuVSiU8PT0li9z5+kPKiYiIiIiI2qKjn6W+3Z/BdngW8SvkcrmtR1kul0u25eXlobq6GlOnToVarZZsmzhxInQ6HWbNmoU5c+bg008/xejRo7FkyRI88MAD8PLyQllZGXbu3CmJm5GRgaioKAwdOhRpaWkYPHgwLBYLCgoKkJmZidLSUuh0OgwdOrTFGcOHDRsGnU6H1atXAwCamppgNBolZVxcXHD33Xff9LkYNWoUBg0ahPT0dLz99ts4ffo0tm3bhrFjx6Jnz5744Ycf8MYbb8DNzQ2PPPLITccnIiIiIiKizqvdPdgAbL2s19LpdBgzZoxd4xoA4uPjYTQaUVJSAldXVxQWFuKll16CwWDAiBEjMHDgQMyePRvDhw/HRx99ZHtd//79UVJSgujoaMybNw9hYWGIiYlBYWEhMjMzYTabsXnzZsTHx7dY1/j4eGzevBlm8+VJw+rq6jBkyBDJ0p7G79y5c7Fhwwb8+9//hqurK7755hs88sgj0Gg0ePzxx+Hu7o69e/eiV69eDu+DiIiIiIiIOh+Z9ep8WdRpTd5yRFis9X8aLCyWyNEaPYbNFRfM2iwuVnOTuFgC3ffUk8JiNTWJO1+NjZ3zfP15ZKDQeD7dxD220Sjw/FsE3tJ9XJXCYnVXXj+F4c1q7qSfWpre9ukjHVVvEvc+6uvlJixWvckiLJbSWchv/AAAZ7m4WABw4j/iMo6YBb6/u7m0e+ChzUWzuP9LGcR9GXAS+V/ZSe8Vnt3E3Q+9VeI+ixRO4v4fq2quP5nvzfLxEPdZBACuAu89Hkqx957fSo8/Z3d0FRxyblNiR1ehTbrmVUFERERERETUybCB3YKKigpb3uqWloqKio6uIhEREREREXUy4sYa3Ub69OljN/HZtduJiIiIiIiIrsYGdgsUCgU0Gk1HV4OIiIiIiEisrpPxqkviEHEiIiIiIiIiAdjAJiIiIiIiIhKAQ8S7iMJvvhMWqyn+HmGxmgWmBLrnj38QFstJYKoJF4FpUUTWa9/GrcJiQSbwtzZngek0BNarp/Z5YbEAwNtVXGoUkSluRKZZ+fy7X4TFGuDjKiyWyOSScoHD5Nyc5cJi/VgvLsWNyGtC5SrufniwvFpYrIF+nsJiAYC3h7j3d7PArJFe7uLSOzkJzLO5/V//Fharn6e4dHcCL33IRX4eCfycNFvEXWBmYZEAtZu4a/VCfaOwWADg211s2q+uSCYyzy7ZYQ82ERERERERkQBsYBMREREREREJwAY2ERERERERkQB8BpuIiIiIiOgOwWewby32YBMREREREREJ4FADOzk5GTKZDNOnT7fbNmPGDMhkMiQnJ0vW7927F3K5HFqttsWYZrMZK1euREREBNzd3aFWqxEeHo5Fixbh7NmzkrJVVVVITU1FUFAQlEol/P39MX78eBQWFtrFTU9Ph1wux+uvv263LSsrC927d2/7gV/lyjm4svTo0QNarRb/+te/JOWqq6sxefJkqNVqqNVqTJ48GRcuXHBon0RERERERNR5OdyD7e/vj5ycHDQ0NNjWXbp0CdnZ2QgICLArr9frkZqaiuLiYlRUVEi2mUwmxMTEID09HcnJySgqKsKhQ4ewYsUKnDt3DmvXrrWVLS8vR2RkJL766iusWLECR48eRX5+PqKjozFz5ky7/RoMBrz44ovQ6/WOHup1abVaVFZWorKyEoWFhVAoFBg3bpykzJNPPgmj0Yj8/Hzk5+fDaDRi8uTJwutCREREREREHcvhZ7AjIiJw+vRp5ObmYtKkSQCA3Nxc+Pv7IygoSFK2vr4e27dvx4EDB1BVVYWsrCwsXrzYtn316tUoLi7GwYMHMWTIENt6jUaD2NhYWK9Kenqlh3z//v1wd/9vrsRBgwYhJSVFst/du3ejoaEBaWlp2LRpE4qKijBy5EhHD9mOUqmEr68vAMDX1xcLFizAyJEj8fPPP6Nnz54oLS1Ffn4+/vnPf+K+++4DAGzYsAHDhg3DqVOnEBISIqwuREREREREN8JnsG+tdj2DPWXKFBgMBtvfer3erpELANu2bUNISAhCQkKQlJQEg8EgaTRnZ2cjJiZG0ri+2pWL4Pz588jPz8fMmTMljesrrh3urdPpkJiYCGdnZyQmJkKn0zlymG1SV1eHLVu2QKPRoEePHgCAf/zjH1Cr1bbGNQDcf//9UKvV2Lt37y2rCxEREREREf322tXAnjx5MoqLi1FeXo4zZ85gz549SEpKsiun0+ls67VaLerq6iTPS5eVldn15k6YMAEqlQoqlQpRUVEAgG+//RZWqxWhoaE3rFttbS127Nhh229SUhI++OAD1NbWOny818rLy7PV0cPDAx9//DG2bdsGJ6fLp7Wqqgq9evWye12vXr1QVVUlrB5ERERERETU8drVwPbx8UFcXBw2btwIg8GAuLg4+Pj4SMqcOnUK+/fvxxNPPAEAUCgUSEhIsHsm+tqhChkZGTAajUhJScHFixcBwNbr3ZZhDVu3bkVQUBDCw8MBAPfeey+CgoKQk5Pj2MG2IDo6GkajEUajEfv27cPYsWPx8MMP48yZM7YyLdXVarW2egwmkwm1tbWSxdrUKKzeRERERER0h5J10aWLaHce7JSUFMyaNQsAsG7dOrvtOp0OFosFffv2ta2zWq1wdnZGdXU1vLy8EBwcjJMnT0pe5+fnBwDw9va2rQsODoZMJkNpaSkee+yxVuul1+tx/PhxKBT/PcTm5mbodDo8/fTTN32cLXF3d4dGo7H9HRkZCbVajQ0bNuC1116Dr68vfvzxR7vX/fzzz+jdu/d14y5fvhxLly6VrFNFPg6P3ycIqTcRERERERGJ1+482FqtFmazGWazGbGxsZJtFosFmzZtwqpVq2w9vUajEUeOHEFgYCC2bNkCAEhMTERBQQEOHz7c6r68vb0RGxuLdevWob6+3m77lfRXR48excGDB7Fr1y7JfouKinDgwAEcO3asvYfdIplMBicnJ9vM6sOGDUNNTQ32799vK7Nv3z7U1NTYhr23ZOHChaipqZEsqiF/vCV1JiIiIiIiIjHa3YMtl8tRWlpq+/fV8vLyUF1djalTp0KtVku2TZw4ETqdDrNmzcKcOXPw6aefYvTo0ViyZAkeeOABeHl5oaysDDt37pTEzcjIQFRUFIYOHYq0tDQMHjwYFosFBQUFyMzMRGlpKXQ6HYYOHdrijOHDhg2DTqfD6tWrAQBNTU0wGo2SMi4uLrj77rtveOwmk8n2LHV1dTXefvtt1NXVYfz48QCAgQMHQqvV4n//93/xzjvvAACefvppjBs3rtUZxJVKJZRKpWSdTO58w/oQERERERFRx2l3DzYAeHp6wtPT0269TqfDmDFj7BrXABAfHw+j0YiSkhK4urqisLAQL730EgwGA0aMGIGBAwdi9uzZGD58OD766CPb6/r374+SkhJER0dj3rx5CAsLQ0xMDAoLC5GZmQmz2YzNmzcjPj6+xbrGx8dj8+bNMJvNAC7P/j1kyBDJ8sgjj7TpuPPz8+Hn5wc/Pz/cd999OHDgAN5//32MGjXKVmbLli245557MHbsWIwdOxaDBw/G3//+9zbFJyIiIiIiEkkmk3XJxREZGRno378/XF1dERkZiW+++abV8lu2bEF4eDi6desGPz8/TJkyBefOnbu582u9Ol8WdVp9pucKi3XyrceExWoWePmMXd36BX8znJzEzYTg4tLugR42Iuu1b+NWYbEgE/Jb22XOyhuXaSuB9dq0/nlhsQDA29VFWCyZwJk7FAKvsc+/+0VYrAE+rsJiifzUkgucNCXMp7uwWD/WXxIWa5Cv/Q/gjlK5irsfHv2hRlisgX7ijhEALjU2CYvV3CwsFLzcxY1mcxKYB3f7v/4tLFY/T/s0rI4SeDuEXODnUT+fbsJiOcsFfn4LJHI+qnqTuPcjAPh2F/c9xUPZOc//jfSe9n5HV8EhP773p5sqv23bNkyePBkZGRkYPnw43nnnHbz33ns4ceIEAgIC7MoXFxfjwQcfxOrVqzF+/Hj88MMPmD59OoKDg/Hhhx+2eb9d86ogIiIiIiIiuo6//vWvmDp1KqZNm4aBAwfirbfegr+/PzIzM1ss/89//hP9+vXDc889h/79+2PEiBF45plncPDgwZvaLxvYLaioqLDlt25pqaio6OgqEhERERER3bSOHurt6NJSKmOTydTiMZrNZhw6dAhjx46VrB87diz27t3b4muioqLwn//8B5999hmsVit+/PFHfPDBB4iLi7up8yturNdtpE+fPnYTn127nYiIiIiIiH4bLaUyfvXVV7FkyRK7sr/88guamprsUiP37t3bNkn1taKiorBlyxYkJCTg0qVLsFgsePTRR7F27dqbqicb2C1QKBSS/NZERERERETUcRYuXIi5c+dK1l2beela106OZrVarzth2okTJ/Dcc89h8eLFiI2NRWVlJV544QVMnz4dOp2uzfVkA5uIiIiIiIg6tZZSGV+Pj48P5HK5XW/1Tz/9ZNerfcXy5csxfPhwvPDCCwCAwYMHw93dHQ888ABee+01+Pn5tWnffAabiIiIiIjoDtHRz1L/Fmm6XFxcEBkZiYKCAsn6goICREVFtfiaixcvwslJ2jyWy+UALvd8txUb2ERERERERHRbmTt3Lt577z3o9XqUlpZizpw5qKiowPTp0wFcHnL+5z//2VZ+/PjxyM3NRWZmJk6fPo09e/bgueeew9ChQ29qDi4OEe8iuqnchMUSmUO2s8ZqbhYXrKlJXBJTq1VgZkiRuautAhO1NgvMVykTeFF0YlYIvF476SmzCKyYReD7W2TecGeFuFiuCnHvb7nAYxQaS2AeZpGxAKBJ4DUm8rOtUeDn0c32BrVGKTAXc3ltvbBYamXnzBvua3YVFsvFTdy5bxZ4sVoFZsIWeW8FAIEfudTJJSQk4Ny5c0hLS0NlZSXCwsLw2WefITAwEABQWVkpyQ6VnJyMX3/9FW+//TbmzZuH7t27Y/To0XjjjTduar9sYBMREREREdFtZ8aMGZgxY0aL27KysuzWpaamIjU1tV37ZAObiIiIiIjoDiFyBAvZ4zPYRERERERERAKwgU1EREREREQkAIeIExERERER3Sk4QvyWcqgHOzk5GTKZzDbF+dVmzJgBmUyG5ORkyfq9e/dCLpdDq9W2GNNsNmPlypWIiIiAu7s71Go1wsPDsWjRIpw9e1ZStqqqCqmpqQgKCoJSqYS/vz/Gjx+PwsJCu7jp6emQy+V4/fXX7bZlZWWhe/fubT/wq1w5B1eWHj16QKvV4l//+pek3LJlyxAVFYVu3bo5vC8iIiIiIiLq/BweIu7v74+cnBw0NDTY1l26dAnZ2dkICAiwK6/X65Gamori4mLJdOgAYDKZEBMTg/T0dCQnJ6OoqAiHDh3CihUrcO7cOaxdu9ZWtry8HJGRkfjqq6+wYsUKHD16FPn5+YiOjsbMmTPt9mswGPDiiy9Cr9c7eqjXpdVqUVlZicrKShQWFkKhUGDcuHGSMmazGX/605/w7LPPCt8/ERERERERdR4ODxGPiIjA6dOnkZubi0mTJgEAcnNz4e/vj6CgIEnZ+vp6bN++HQcOHEBVVRWysrKwePFi2/bVq1ejuLgYBw8exJAhQ2zrNRoNYmNjYb0qL9+VHvL9+/fD3d3dtn7QoEFISUmR7Hf37t1oaGhAWloaNm3ahKKiIowcOdLRQ7ajVCrh6+sLAPD19cWCBQswcuRI/Pzzz+jZsycAYOnSpQBangaeiIiIiIiIbh/tmuRsypQpMBgMtr/1er1dIxcAtm3bhpCQEISEhCApKQkGg0HSaM7OzkZMTIykcX21K1PJnz9/Hvn5+Zg5c6akcX3FtUOwdTodEhMT4ezsjMTEROh0OkcOs03q6uqwZcsWaDQa9OjR45bth4iIiIiIyFFXP+balZauol0N7MmTJ6O4uBjl5eU4c+YM9uzZg6SkJLtyOp3Otl6r1aKurk7yvHRZWRlCQkIkr5kwYQJUKhVUKhWioqIAAN9++y2sVitCQ0NvWLfa2lrs2LHDtt+kpCR88MEHqK2tdfh4r5WXl2ero4eHBz7++GNs27YNTk6cnJ2IiIiIiOhO066WoI+PD+Li4rBx40YYDAbExcXBx8dHUubUqVPYv38/nnjiCQCAQqFAQkKC3TPR1/4qkZGRAaPRiJSUFFy8eBEAbL3ebfkFY+vWrQgKCkJ4eDgA4N5770VQUBBycnIcO9gWREdHw2g0wmg0Yt++fRg7diwefvhhnDlzpl1xTSYTamtrJYvV0iio1kRERERERHQrtDtNV0pKCmbNmgUAWLdund12nU4Hi8WCvn372tZZrVY4OzujuroaXl5eCA4OxsmTJyWv8/PzAwB4e3vb1gUHB0Mmk6G0tBSPPfZYq/XS6/U4fvw4FIr/HmJzczN0Oh2efvrpmz7Olri7u0Oj0dj+joyMhFqtxoYNG/Daa685HHf58uW2Z7ev8Br2JLyj7EcHEBERERERUefQ7rHMWq0WZrMZZrMZsbGxkm0WiwWbNm3CqlWrbD29RqMRR44cQWBgILZs2QIASExMREFBAQ4fPtzqvry9vREbG4t169ahvr7ebvuFCxcAAEePHsXBgwexa9cuyX6Liopw4MABHDt2rL2H3SKZTAYnJyfJzOqOWLhwIWpqaiSL19AEQbUkIiIiIqI7VUc/S327P4Pd7h5suVyO0tJS27+vlpeXh+rqakydOhVqtVqybeLEidDpdJg1axbmzJmDTz/9FKNHj8aSJUvwwAMPwMvLC2VlZdi5c6ckbkZGBqKiojB06FCkpaVh8ODBsFgsKCgoQGZmJkpLS6HT6TB06NAWZwwfNmwYdDodVq9eDQBoamqC0WiUlHFxccHdd999w2M3mUyoqqoCAFRXV+Ptt99GXV0dxo8fbytTUVGB8+fPo6KiQrIvjUYDlUrVYlylUgmlUilZJ1M437A+RERERERE1HHa3cAGAE9PzxbX63Q6jBkzxq5xDQDx8fFIT09HSUkJIiIiUFhYiLfeegsGgwELFy5Ec3Mz+vfvj4cffhhz5syxva5///4oKSnBsmXLMG/ePFRWVqJnz56IjIxEZmYmzGYzNm/ejAULFrRYp/j4eCxfvhxvvPEGgMuzf187e3lgYCDKy8tveNz5+fm2oeweHh4IDQ3F+++/j1GjRtnKLF68GBs3brT9fWVfX3/9taQcERERERERdW0y69X5sqjT0szfKSzWob/E3rhQGzULvHzGrv5GWCyRo0icneU3LtRGTk7iKnZwy3ZhsWBtFhdL4SIulkzcjPyb3p0rLBYAeLsKPE6BnARe/F+ePicsVqCX8saF2sjSLO6+oxD4nryvr/eNC7XRL/UmYbF+18tDWCyVq5Df5QEAR/9TIyzWQL+Wf+h3VO0lcROLivyW5ekm7vyLHG6582SlsFimJnGfR2qluNF/Iu+tYb3sO54cJfKaEPmdDhB3vizNAr+jAOjhLu7z28O1a2YO8p/5/zq6Cg7597o/dHQV2qRrXhVEREREREREnQwb2C2oqKiw5bduaamoqOjoKhIREREREVEnI25cyW2kT58+dhOfXbudiIiIiIiI6GpsYLdAoVBI8lsTERERERHdFrpOxqsuiUPEiYiIiIiIiARgA5uIiIiIiIhIAA4RJyIiIiIiukOITNNH9tjA7iJcBeYeFfqeEpgyUWSOaJGaBObkbGoSFgpwFpdXGM0CK2Yxi4slMA+26A8TgamYheYeFfk2Epn3tbN+los89yJzRHdzERfLQ2CeXJFpcjW9VMJiic3fC8gEPqBoFflBKVCTwJuYyHuFyNzVv5otwmKpnOXCYink4s6XyHMvsl4iU1eru4nLWw1A6HdXopZwiDgRERERERGRAGxgExEREREREQnAIeJERERERER3CD6DfWuxB5uIiIiIiIhIADawiYiIiIiIiARwqIGdnJwMmUyG6dOn222bMWMGZDIZkpOTJev37t0LuVwOrVbbYkyz2YyVK1ciIiIC7u7uUKvVCA8Px6JFi3D27FlJ2aqqKqSmpiIoKAhKpRL+/v4YP348CgsL7eKmp6dDLpfj9ddft9uWlZWF7t27t/3Ar3LlHFxZevToAa1Wi3/961+2MuXl5Zg6dSr69+8PNzc3DBgwAK+++irMZoGzLBMREREREVGn4HAPtr+/P3JyctDQ0GBbd+nSJWRnZyMgIMCuvF6vR2pqKoqLi1FRUSHZZjKZEBMTg/T0dCQnJ6OoqAiHDh3CihUrcO7cOaxdu9ZWtry8HJGRkfjqq6+wYsUKHD16FPn5+YiOjsbMmTPt9mswGPDiiy9Cr9c7eqjXpdVqUVlZicrKShQWFkKhUGDcuHG27SdPnkRzczPeeecdHD9+HKtXr8b69evx8ssvC68LERERERHRjVzdSdiVlq7C4UnOIiIicPr0aeTm5mLSpEkAgNzcXPj7+yMoKEhStr6+Htu3b8eBAwdQVVWFrKwsLF682LZ99erVKC4uxsGDBzFkyBDbeo1Gg9jYWFivym95pYd8//79cHd3t60fNGgQUlJSJPvdvXs3GhoakJaWhk2bNqGoqAgjR4509JDtKJVK+Pr6AgB8fX2xYMECjBw5Ej///DN69uwJrVYr6bEPCgrCqVOnkJmZiTfffFNYPYiIiIiIiKjjtesZ7ClTpsBgMNj+1uv1do1cANi2bRtCQkIQEhKCpKQkGAwGSaM5OzsbMTExksb11a78YnH+/Hnk5+dj5syZksb1FdcO99bpdEhMTISzszMSExOh0+kcOcw2qaurw5YtW6DRaNCjR4/rlqupqYG3t/ctqwcRERERERF1jHY1sCdPnozi4mKUl5fjzJkz2LNnD5KSkuzK6XQ623qtVou6ujrJ89JlZWUICQmRvGbChAlQqVRQqVSIiooCAHz77bewWq0IDQ29Yd1qa2uxY8cO236TkpLwwQcfoLa21uHjvVZeXp6tjh4eHvj444+xbds2ODm1fFq/++47rF27tsVn169mMplQW1srWZotfG6biIiIiIjap6OHet/uQ8Tb1cD28fFBXFwcNm7cCIPBgLi4OPj4+EjKnDp1Cvv378cTTzwBAFAoFEhISLB7Jvrak5aRkQGj0YiUlBRcvHgRAGy93m05wVu3bkVQUBDCw8MBAPfeey+CgoKQk5Pj2MG2IDo6GkajEUajEfv27cPYsWPx8MMP48yZM3Zlz549C61Wiz/96U+YNm1aq3GXL18OtVotWX7ZK67eREREREREJJ7Dz2BfkZKSglmzZgEA1q1bZ7ddp9PBYrGgb9++tnVWqxXOzs6orq6Gl5cXgoODcfLkScnr/Pz8AEAynDo4OBgymQylpaV47LHHWq2XXq/H8ePHoVD89xCbm5uh0+nw9NNP3/RxtsTd3R0ajcb2d2RkJNRqNTZs2IDXXnvNtv7s2bOIjo7GsGHD8O67794w7sKFCzF37lzJuvvTvxFSZyIiIiIiIro12p0HW6vVwmw2w2w2IzY2VrLNYrFg06ZNWLVqla2n12g04siRIwgMDMSWLVsAAImJiSgoKMDhw4db3Ze3tzdiY2Oxbt061NfX222/cOECAODo0aM4ePAgdu3aJdlvUVERDhw4gGPHjrX3sFskk8ng5OQkmVn9hx9+wKhRoxAREQGDwXDd4eNXUyqV8PT0lCxOCpdbUmciIiIiIiISo9092HK5HKWlpbZ/Xy0vLw/V1dWYOnUq1Gq1ZNvEiROh0+kwa9YszJkzB59++ilGjx6NJUuW4IEHHoCXlxfKysqwc+dOSdyMjAxERUVh6NChSEtLw+DBg2GxWFBQUIDMzEyUlpZCp9Nh6NChLc4YPmzYMOh0OqxevRoA0NTUBKPRKCnj4uKCu++++4bHbjKZUFVVBQCorq7G22+/jbq6OowfPx7A5Z7rUaNGISAgAG+++SZ+/vln22uvzD5ORERERET0m+k6jzN3Se1uYAOAp6dni+t1Oh3GjBlj17gGgPj4eKSnp6OkpAQREREoLCzEW2+9BYPBgIULF6K5uRn9+/fHww8/jDlz5the179/f5SUlGDZsmWYN28eKisr0bNnT0RGRiIzMxNmsxmbN2/GggULWqxTfHw8li9fjjfeeAPA5dm/r529PDAwEOXl5Tc87vz8fNtQdg8PD4SGhuL999/HqFGjAABffPEFvv32W3z77be46667JK+9ehZ1IiIiIiIi6vpkVrb0uoSwRQXCYu1d9JCwWE3N4i4f7d+KhcUSqbNOWnjkwzxxwZqbxMUSOeO9rN1Psdj83bBQWCwAULs4C4vVLPA27CTwgi06Uy0sVoCXuMdcGptEni9hoRCj6S0sVnOzsFDo6Snu3Iv8xtBgFnffkYv8jwRQbxJXN5Hvb083If0iAACBH9/48v9+FBbLRS7uvv+r2SIslspZfuNCbRTZV1y6VpVS3DXRhqcY20zkPczdVdy5BwAIvPY9XAWetN9Q/zmfdnQVHPL96riOrkKbiHtXEhERERERUafWlVJedUVd82eXW6yiosKW37qlpaKioqOrSERERERERJ0Me7Bb0KdPH7uJz67dTkRERERERHQ1NrBboFAoJPmtiYiIiIiIiG6EDWwiIiIiIqI7BJ/BvrX4DDYRERERERGRAOzB7iLkAtNWiPzNSuQPYE6C06yI0mkz2QlMYQWZwGMUWS+rwDwfgom8XDvpFXZHEJmqSGSqKLnA60tkvcwWce9Jkde9m4vYND4i03R1WiJv++JCCeXhIu5rrsiUXyLTKYpMrSWSQuBNrNEi9lPSWdFZr1i6XXTStyURERERERFR18IebCIiIiIiojsEH8G+tdiDTURERERERCQAG9hEREREREREAnCIOBERERER0R2CabpuLYd6sJOTkyGTyTB9+nS7bTNmzIBMJkNycrJk/d69eyGXy6HValuMaTabsXLlSkRERMDd3R1qtRrh4eFYtGgRzp49KylbVVWF1NRUBAUFQalUwt/fH+PHj0dhYaFd3PT0dMjlcrz++ut227KystC9e/e2H/hVrpyDK0uPHj2g1Wrxr3/9S1Lu0UcfRUBAAFxdXeHn54fJkyfbHQ8RERERERF1fQ4PEff390dOTg4aGhps6y5duoTs7GwEBATYldfr9UhNTUVxcTEqKiok20wmE2JiYpCeno7k5GQUFRXh0KFDWLFiBc6dO4e1a9faypaXlyMyMhJfffUVVqxYgaNHjyI/Px/R0dGYOXOm3X4NBgNefPFF6PV6Rw/1urRaLSorK1FZWYnCwkIoFAqMGzdOUiY6Ohrbt2/HqVOnsGPHDnz33XeYOHGi8LoQERERERFRx3J4iHhERAROnz6N3NxcTJo0CQCQm5sLf39/BAUFScrW19dj+/btOHDgAKqqqpCVlYXFixfbtq9evRrFxcU4ePAghgwZYluv0WgQGxsryUN8pYd8//79cHd3t60fNGgQUlJSJPvdvXs3GhoakJaWhk2bNqGoqAgjR4509JDtKJVK+Pr6AgB8fX2xYMECjBw5Ej///DN69uwJAJgzZ46tfGBgIF566SU89thjaGxshLOzs7C6EBERERERUcdq1yRnU6ZMgcFgsP2t1+vtGrkAsG3bNoSEhCAkJARJSUkwGAySRnN2djZiYmIkjeurXXlO4Pz588jPz8fMmTMljesrrh3urdPpkJiYCGdnZyQmJkKn0zlymG1SV1eHLVu2QKPRoEePHi2WOX/+PLZs2YKoqCg2romIiIiI6Dcnk3XNpatoVwN78uTJKC4uRnl5Oc6cOYM9e/YgKSnJrpxOp7Ot12q1qKurkzwvXVZWhpCQEMlrJkyYAJVKBZVKhaioKADAt99+C6vVitDQ0BvWrba2Fjt27LDtNykpCR988AFqa2sdPt5r5eXl2ero4eGBjz/+GNu2bYOTk/S0LliwAO7u7ujRowcqKirw//7f/xNWByIiIiIiIuoc2tXA9vHxQVxcHDZu3AiDwYC4uDj4+PhIypw6dQr79+/HE088AQBQKBRISEiweyb62tnsMjIyYDQakZKSgosXLwKArde7LTPfbd26FUFBQQgPDwcA3HvvvQgKCkJOTo5jB9uC6OhoGI1GGI1G7Nu3D2PHjsXDDz+MM2fOSMq98MILOHz4ML744gvI5XL8+c9/lvTgX8tkMqG2tlayNFvMwupNRERERERE4rU7TVdKSgpmzZoFAFi3bp3ddp1OB4vFgr59+9rWWa1WODs7o7q6Gl5eXggODsbJkyclr/Pz8wMAeHt729YFBwdDJpOhtLQUjz32WKv10uv1OH78OBSK/x5ic3MzdDodnn766Zs+zpa4u7tDo9HY/o6MjIRarcaGDRvw2muv2db7+PjAx8cHv/vd7zBw4ED4+/vjn//8J4YNG9Zi3OXLl2Pp0qWSdb1GTobvqKeE1JuIiIiIiO5MTNN1a7WrBxu4POTbbDbDbDYjNjZWss1isWDTpk1YtWqVrafXaDTiyJEjCAwMxJYtWwAAiYmJKCgowOHDh1vdl7e3N2JjY7Fu3TrU19fbbb9w4QIA4OjRozh48CB27dol2W9RUREOHDiAY8eOtfewWySTyeDk5CSZWf1aV3quTSbTdcssXLgQNTU1kqXXiETh9SUiIiIiIiJx2t2DLZfLUVpaavv31fLy8lBdXY2pU6dCrVZLtk2cOBE6nQ6zZs3CnDlz8Omnn2L06NFYsmQJHnjgAXh5eaGsrAw7d+6UxM3IyEBUVBSGDh2KtLQ0DB48GBaLBQUFBcjMzERpaSl0Oh2GDh3a4ozhw4YNg06nw+rVqwEATU1NMBqNkjIuLi64++67b3jsJpMJVVVVAIDq6mq8/fbbqKurw/jx4wEA+/fvx/79+zFixAh4eXnh9OnTWLx4MQYMGHDd3mvg8uzkSqVSss5J4XLD+hAREREREVHHaXcPNgB4enrC09PTbr1Op8OYMWPsGtcAEB8fD6PRiJKSEri6uqKwsBAvvfQSDAYDRowYgYEDB2L27NkYPnw4PvroI9vr+vfvj5KSEkRHR2PevHkICwtDTEwMCgsLkZmZCbPZjM2bNyM+Pr7FusbHx2Pz5s0wmy8/01xXV4chQ4ZIlkceeaRNx52fnw8/Pz/4+fnhvvvuw4EDB/D+++9j1KhRAAA3Nzfk5ubioYceQkhICFJSUhAWFobdu3fbNaCJiIiIiIioa5NZW5ttizqN8FcLb1yojYoXRguL1STw8nl4zR5hsUTqrG+RIx99Ji5Yc5O4WCIn5LM2Cwv196xXhMUCAC+luFR7It9HMoh7rqroTLWwWAFe4kbhNDZ1zvfkIyG+4oIJPMQeHuLOvdki7j15qVFcLA/Xdg/Ik/jlV3H3sWaB729PN3HH2Szu9KPw2x+FxXKWC+n7Ee5Xs0VYrOj+vYTFUrnKb1yoAzgJfMZXZCwAcFaIi+eh7JzX642EvvR5R1fBISdfj71xoU6ga14VRERERERERJ0MG9gtqKiosOW3bmmpqKjo6CoSERERERFRJyN2TNVtok+fPnYTn127nYiIiIiIiOhqbGC3QKFQSPJbExERERER3Q6cnJgH+1biEHEiIiIiIiIiAdjAJiIiIiIiIhKAQ8SJiIiIiIjuEIIzn9E12MDuIl6ZMFBYLGeFwIELTeISaU4Y2ldYLBeBx+gs8DkVkTe0ntrnxQUTSNZJ79qTk5eJDajyFhtPFIW4/NyB9w0VFkskZ+fOOfjqD3eLmwDz1wZxOXdNAvNNd3MRl3NXZE5t0VwE5smVycRdryJzV4v0kKa3sFhOAt/eAlOQC/38dhP4PhKZs13kp7fAUw9vdxeB0cTn1Sa6Vuf8lkJERERERETUxbCBTURERERERCQAh4gTERERERHdITrr43y3C/ZgExEREREREQnABjYRERERERGRADfVwE5OToZMJsP06dPtts2YMQMymQzJycmS9Xv37oVcLodWq20xptlsxsqVKxEREQF3d3eo1WqEh4dj0aJFOHv2rN2+r12ujtuvXz/bejc3N4SGhmLlypWwXjWNZHl5ueT1arUa999/Pz755JM2n4esrCxJjN69e2P8+PE4fvy4XdmMjAz0798frq6uiIyMxDfffNPm/RAREREREYkkk3XNpau46R5sf39/5OTkoKGhwbbu0qVLyM7ORkBAgF15vV6P1NRUFBcXo6KiQrLNZDIhJiYG6enpSE5ORlFREQ4dOoQVK1bg3LlzWLt2raS8VqtFZWWlZMnOzpaUSUtLQ2VlJUpLSzF//ny8/PLLePfdd+3q9eWXX6KyshL79u3D0KFDER8fj2PHjrX5PHh6eqKyshJnz57Fp59+ivr6esTFxcFs/m+6hG3btmH27Nl45ZVXcPjwYTzwwAN4+OGH7c4DERERERERdX033cCOiIhAQEAAcnNzbetyc3Ph7++PIUOGSMrW19dj+/btePbZZzFu3DhkZWVJtq9evRrFxcX46quv8NxzzyEyMhIajQaxsbHIzMxEenq6pLxSqYSvr69k8fLykpTx8PCAr68v+vXrh2nTpmHw4MH44osv7I6jR48e8PX1RWhoKJYtW4bGxkZ8/fXXbT4PMpkMvr6+8PPzw+9//3vMmTMHZ86cwalTp2xl/vrXv2Lq1KmYNm0aBg4ciLfeegv+/v7IzMxs836IiIiIiIioa3DoGewpU6bAYDDY/tbr9UhJSbErt23bNoSEhCAkJARJSUkwGAyS4drZ2dmIiYmxa5hf0Z4Z7qxWK3bt2oXS0lI4Oztft1xjYyM2bNgAAK2Wa82FCxewdetWSQyz2YxDhw5h7NixkrJjx47F3r17HdoPERERERERdV4ONbAnT56M4uJilJeX48yZM9izZw+SkpLsyul0Ott6rVaLuro6FBYW2raXlZUhJCRE8poJEyZApVJBpVIhKipKsi0vL8+27cryl7/8RVJmwYIFUKlUUCqViI6OhtVqxXPPPWdXt6ioKKhUKri6umLevHno168fHn/88Tafg5qaGqhUKri7u8PLyws5OTl49NFHERoaCgD45Zdf0NTUhN69e0te17t3b1RVVbV5P0RERERERKK0NK9VV1i6CofyYPv4+CAuLg4bN26E1WpFXFwcfHx8JGVOnTqF/fv324aSKxQKJCQkQK/XY8yYMbZy156sjIwM1NfXY82aNSgqKpJsi46Othte7e3tLfn7hRdeQHJyMn7++We88sorGD16tF1DHbjcux4aGoqysjLMnj0b69evt4vVGg8PD5SUlMBisWD37t1YuXIl1q9fb1fu2uOzWq03vEBMJhNMJpNkXaPZBGcXZZvrR0RERERERL8thxrYAJCSkoJZs2YBANatW2e3XafTwWKxoG/fvrZ1VqsVzs7OqK6uhpeXF4KDg3Hy5EnJ6/z8/ADYN5wBwN3dHRqNptV6+fj4QKPRQKPRYMeOHdBoNLj//vsljXrg8mRtwcHBCA4OhkqlQnx8PE6cOIFevXq16fidnJxsdQkNDUVVVRUSEhJsPwr4+PhALpfb9Vb/9NNPdr3a11q+fDmWLl0qWTfxmbn40/R5baobERERERER/fYczoOt1WphNpthNpsRGxsr2WaxWLBp0yasWrUKRqPRthw5cgSBgYHYsmULACAxMREFBQU4fPhw+47iOry8vJCamor58+dLnv2+1oMPPoiwsDAsW7bM4X3NmTMHR44cwYcffggAcHFxQWRkJAoKCiTlCgoKWuxRv9rChQtRU1MjWR5LmeVw3YiIiIiIiOjWc7iBLZfLUVpaitLSUsjlcsm2vLw8VFdXY+rUqQgLC5MsEydOhE6nA3C5UTps2DCMHj0af/vb31BSUoLvv/8en3/+OXbu3GkX12QyoaqqSrL88ssvrdZz5syZOHXqFHbs2NFquXnz5uGdd97BDz/84MDZuJy2a9q0aXj11Vdtjfm5c+fivffeg16vR2lpKebMmYOKiooW84hfTalUwtPTU7JweDgREREREbVXRz9Lfbs/g+1wAxuArfF3LZ1OhzFjxkCtVttti4+Ph9FoRElJCVxdXVFYWIiXXnoJBoMBI0aMwMCBAzF79mwMHz4cH330keS1+fn58PPzkywjRoxotY49e/bE5MmTsWTJEjQ3N1+33Lhx49CvX7929WI///zzKC0txfvvvw8ASEhIwFtvvYW0tDTce++9KCoqwmeffYbAwECH90FERERERESdk8za2thp6jS2G88Ki/VoWB9hsRqbrv+jxc3K3Pu9sFguinb9diTh7CTuFzORP7717OYiLphAnfUXxsnJjv941iJV2ydF/E0pHEs32JLA+4YKiyWSs7O497dI+fMeFBbr1waLsFg+HuLuFd1c5Dcu1EY1DY3CYrkrHZ5SpkW1Ausm8p4o76T3V5GcBL69RX7DFXnq3QS+j3751SwslsirS2Tjwttd7PcdhVzckbq7dM33ZPirhTcu1AkdWfpQR1ehTcR+IhEREREREVGndQf8VtehOmc3QAcbNGiQXb7tK8uVCdqIiIiIiIiIrsYe7BZ89tlnaGxseXjYjVJsERERERER0Z2JDewWcBIyIiIiIiIiullsYBMREREREd0hOuuEtLcLPoNNREREREREJAAb2EREREREREQCcIh4F+Gu6Jz/VSJzcvb3chMWq75RXA7Zbs6d89x7u3bOPNjNApNfCkxBLj5vdd15cbG6qcXFEnfpC80ha7E0C4vl1Elz03u5i8tBXm8S9x8pMueryNy2CoEJj0WPdlQqxOUpNgm89oV2iwj8z3RWCHxPCs3GLI7Ia6zilwZhsfx7iPvuVF0vLv+7yA8QkbnRAbH5vola0jlbDkRERERERCQcH8G+tThEnIiIiIiIiEgANrCJiIiIiIiIBOAQcSIiIiIiojsE03TdWuzBJiIiIiIiIhLgphrYycnJkMlkmD59ut22GTNmQCaTITk5WbJ+7969kMvl0Gq1LcY0m81YuXIlIiIi4O7uDrVajfDwcCxatAhnz5612/e1y9Vx+/XrZ1vv5uaG0NBQrFy5EtarZjIsLy+XvF6tVuP+++/HJ5980ubzkJWVJYnRu3dvjB8/HsePH5eUKyoqwvjx49GnTx/IZDJ89NFHbd4HERERERERdS033YPt7++PnJwcNDT8N8XApUuXkJ2djYCAALvyer0eqampKC4uRkVFhWSbyWRCTEwM0tPTkZycjKKiIhw6dAgrVqzAuXPnsHbtWkl5rVaLyspKyZKdnS0pk5aWhsrKSpSWlmL+/Pl4+eWX8e6779rV68svv0RlZSX27duHoUOHIj4+HseOHWvzefD09ERlZSXOnj2LTz/9FPX19YiLi4PZbLaVqa+vR3h4ON5+++02xyUiIiIiIqKu6aafwY6IiMDp06eRm5uLSZMmAQByc3Ph7++PoKAgSdn6+nps374dBw4cQFVVFbKysrB48WLb9tWrV6O4uBgHDx7EkCFDbOs1Gg1iY2MlPc8AoFQq4evr22r9PDw8bGWmTZuGzMxMfPHFF3jmmWck5Xr06AFfX1/4+vpi2bJlWLt2Lb7++muEhYW16TzIZDLbfvz8/DBnzhw8+uijOHXqFO655x4AwMMPP4yHH364TfGIiIiIiIhuNT6CfWs59Az2lClTYDAYbH/r9XqkpKTYldu2bRtCQkIQEhKCpKQkGAwGSaM5OzsbMTExksb11drzAL7VasWuXbtQWloKZ2fn65ZrbGzEhg0bAKDVcq25cOECtm7d2q4YRERERERE1LU51MCePHkyiouLUV5ejjNnzmDPnj1ISkqyK6fT6WzrtVot6urqUFhYaNteVlaGkJAQyWsmTJgAlUoFlUqFqKgoyba8vDzbtivLX/7yF0mZBQsWQKVSQalUIjo6GlarFc8995xd3aKioqBSqeDq6op58+ahX79+ePzxx9t8DmpqaqBSqeDu7g4vLy/k5OTg0UcfRWhoaJtjEBERERER0e3DoTRdPj4+iIuLw8aNG2G1WhEXFwcfHx9JmVOnTmH//v3Izc29vCOFAgkJCdDr9RgzZoyt3LW91BkZGaivr8eaNWtQVFQk2RYdHY3MzEzJOm9vb8nfL7zwApKTk/Hzzz/jlVdewejRo+0a6sDl3vXQ0FCUlZVh9uzZWL9+vV2s1nh4eKCkpAQWiwW7d+/GypUrsX79+ja/vjUmkwkmk0myrtFsgrOLUkh8IiIiIiK6MzFN163lcB7slJQUzJo1CwCwbt06u+06nQ4WiwV9+/a1rbNarXB2dkZ1dTW8vLwQHByMkydPSl7n5+cHwL7hDADu7u7QaDSt1svHxwcajQYajQY7duyARqPB/fffL2nUA5cnawsODkZwcDBUKhXi4+Nx4sQJ9OrVq03H7+TkZKtLaGgoqqqqkJCQYPejgCOWL1+OpUuXStYlPjsfk2a80O7YREREREREdGs4nAdbq9XCbDbDbDYjNjZWss1isWDTpk1YtWoVjEajbTly5AgCAwOxZcsWAEBiYiIKCgpw+PDh9h3FdXh5eSE1NRXz58+3mzDtag8++CDCwsKwbNkyh/c1Z84cHDlyBB9++KHDMa5YuHAhampqJMvj0+yHuRMREREREVHn4XADWy6Xo7S0FKWlpZDL5ZJteXl5qK6uxtSpUxEWFiZZJk6cCJ1OB+Byo3TYsGEYPXo0/va3v6GkpATff/89Pv/8c+zcudMurslkQlVVlWT55ZdfWq3nzJkzcerUKezYsaPVcvPmzcM777yDH374wYGzcTlt17Rp0/Dqq6/aGvN1dXW2HxcA4Pvvv4fRaLRLV3YtpVIJT09PycLh4URERERERJ2bww1sALbG37V0Oh3GjBkDtVptty0+Ph5GoxElJSVwdXVFYWEhXnrpJRgMBowYMQIDBw7E7NmzMXz4cHz00UeS1+bn58PPz0+yjBgxotU69uzZE5MnT8aSJUvQ3Nx83XLjxo1Dv3792tWL/fzzz6O0tBTvv/8+ANjSj12ZJX3u3LkYMmSIJFUZERERERHRb0Um65pLVyGztjZ2mjqNT4/9JCzWQ6Fte868LZqbxV0+n56oFBarvtEiLFY3Z4enKrileri6dHQVWiTwkoCTwJvpuP9dIy4YANSdFxerm/2PkQ5zkt+4TBsFRA0XFstiuf4PnDdLqRR3jCI/sA8siREW6z/nG4TF6uUpbgSUs7xdv8tLXDQ1CYvl6iKuXgBwySzuejUJvPYVcoEXrMB7tbNCXL1k6JzfokXeK85WXxIWy7+Hm7BY1fWNwmKJbF708BD7fUcu8D+zm0vnvF5vZGj6ro6ugkP2vzyqo6vQJmI/kYiIiIiIiIjuUGxgt2DQoEF2+bavLFcmaCMiIiIiIiK6Wucc+9rBPvvsMzQ2tjxMpnfv3r9xbYiIiIiIiMRgHuxbiw3sFgQGBnZ0FYiIiIiIiKiL4RBxIiIiIiIiIgHYg01ERERERHSH4AjxW4sN7C7CKjKfhkAia9UsMKWDs5PAVDICU36JrJfIVCYiry+R/4+d86r//4lMrXWxRlwsN09hoUSmWREZq7FRXNojkZqaxB2jh6u4j2eLwHrJBL4rRabWkovM6SeYyNRaliaBKb866eeRyC/+IhPRdtaktiJTa4m87/x6Sdx3p878/iZqCYeIExEREREREQnABjYRERERERGRABwiTkREREREdIdgmq5biz3YRERERERERAKwgU1EREREREQkwE01sJOTkyGTyTB9+nS7bTNmzIBMJkNycrJk/d69eyGXy6HValuMaTabsXLlSkRERMDd3R1qtRrh4eFYtGgRzp49a7fva5er4/br18+23s3NDaGhoVi5cqVk9try8nLJ69VqNe6//3588sknbT4PWVlZkhi9e/fG+PHjcfz4cUm55cuX43/+53/g4eGBXr164bHHHsOpU6favB8iIiIiIiKRZLKuuXQVN92D7e/vj5ycHDQ0NNjWXbp0CdnZ2QgICLArr9frkZqaiuLiYlRUVEi2mUwmxMTEID09HcnJySgqKsKhQ4ewYsUKnDt3DmvXrpWU12q1qKyslCzZ2dmSMmlpaaisrERpaSnmz5+Pl19+Ge+++65dvb788ktUVlZi3759GDp0KOLj43Hs2LE2nwdPT09UVlbi7Nmz+PTTT1FfX4+4uDiYzWZbmd27d2PmzJn45z//iYKCAlgsFowdOxb19fVt3g8RERERERF1DTc9yVlERAROnz6N3NxcTJo0CQCQm5sLf39/BAUFScrW19dj+/btOHDgAKqqqpCVlYXFixfbtq9evRrFxcU4ePAghgwZYluv0WgQGxtrlzdVqVTC19e31fp5eHjYykybNg2ZmZn44osv8Mwzz0jK9ejRA76+vvD19cWyZcuwdu1afP311wgLC2vTeZDJZLb9+Pn5Yc6cOXj00Udx6tQp3HPPPQCA/Px8yWsMBgN69eqFQ4cOYeTIkW3aDxEREREREXUNDj2DPWXKFBgMBtvfer0eKSkpduW2bduGkJAQhISEICkpCQaDQdJozs7ORkxMjKRxfbX2zHBntVqxa9culJaWwtnZ+brlGhsbsWHDBgBotVxrLly4gK1bt94wRk1NDQDA29vbof0QERERERFR5+VQA3vy5MkoLi5GeXk5zpw5gz179iApKcmunE6ns63XarWoq6tDYWGhbXtZWRlCQkIkr5kwYQJUKhVUKhWioqIk2/Ly8mzbrix/+ctfJGUWLFgAlUoFpVKJ6OhoWK1WPPfcc3Z1i4qKgkqlgqurK+bNm4d+/frh8ccfb/M5qKmpgUqlgru7O7y8vJCTk4NHH30UoaGhLZa3Wq2YO3cuRowY0eZeciIiIiIiIpFamteqKyxdhUN5sH18fBAXF4eNGzfCarUiLi4OPj4+kjKnTp3C/v37kZube3lHCgUSEhKg1+sxZswYW7lrT1ZGRgbq6+uxZs0aFBUVSbZFR0cjMzNTsu7a3uAXXngBycnJ+Pnnn/HKK69g9OjRdg114HLvemhoKMrKyjB79mysX7/+pnqWPTw8UFJSAovFgt27d2PlypVYv379dcvPmjUL//rXv1BcXHzD2CaTCSaTSbKu0WyCs4uyzfUjIiIiIiKi35ZDDWwASElJwaxZswAA69ats9uu0+lgsVjQt29f2zqr1QpnZ2dUV1fDy8sLwcHBOHnypOR1fn5+AFoeRu3u7g6NRtNqvXx8fKDRaKDRaLBjxw5oNBrcf//9kkY9cHmytuDgYAQHB0OlUiE+Ph4nTpxAr1692nT8Tk5OtrqEhoaiqqoKCQkJdj8KAEBqaio+/vhjFBUV4a677rph7OXLl2Pp0qWSdYnPzsOTM15oU92IiIiIiIjot+dwHmytVguz2Qyz2YzY2FjJNovFgk2bNmHVqlUwGo225ciRIwgMDMSWLVsAAImJiSgoKMDhw4fbdxTX4eXlhdTUVMyfP99uwrSrPfjggwgLC8OyZcsc3tecOXNw5MgRfPjhh7Z1VqsVs2bNQm5uLr766iv079+/TbEWLlyImpoayfKnafbD3ImIiIiIiKjzcLiBLZfLUVpaitLSUsjlcsm2vLw8VFdXY+rUqQgLC5MsEydOhE6nA3C5UTps2DCMHj0af/vb31BSUoLvv/8en3/+OXbu3GkX12QyoaqqSrL88ssvrdZz5syZOHXqFHbs2NFquXnz5uGdd97BDz/84MDZuJy2a9q0aXj11VdtjfmZM2di8+bN2Lp1Kzw8PGx1vjrFWUuUSiU8PT0lC4eHExERERFRe3V0PmvmwW7FlcbftXQ6HcaMGQO1Wm23LT4+HkajESUlJXB1dUVhYSFeeuklGAwGjBgxAgMHDsTs2bMxfPhwfPTRR5LX5ufnw8/PT7KMGDGi1Tr27NkTkydPxpIlS9Dc3HzdcuPGjUO/fv3a1Yv9/PPPo7S0FO+//z4AIDMzEzU1NRg1apSkztu2bXN4H0RERERERNQ5yaytjZ2mTiPv2I/CYo0J7S0sVlOzuMsn7/hZYbEsAuvV2MoPMzfL2aldv2lJ+Lm7CYtlhbjzJfLci/y18g9PrxUXDACam8TFulgjLpab/Y+ejvIfMVJYLItF3PtILhf3PhKp5C9jhcWqM1mExXIWeL5ExhJ4O4TcSWzXRv0lce/vJoFfsyxN4t5HCoH/AS4KcbFE3vc76zfcyguXhMVSuTo8nZIdD4Gxfr0k7h7W09NFWCzRujl3oW7Vq4x485uOroJDiuc/0NFVaBNx7yQiIiIiIiLq1LpSyquuqHN2A3SwQYMG2eXbvrJcmaCNiIiIiIiI6GrswW7BZ599hsbGxha39e4tbng1ERERERER3T7YwG5BYGBgR1eBiIiIiIiIuhg2sImIiIiIiO4QfAb71uIz2EREREREREQCsIFNREREREREJACHiHcRCpm430JE5oUUOcDESeBwFZlM3EEqFXJhscxCc5iKO19NAq8JweloxVE4i40nLsWn0NzVaKgVFspJ4H9mZx2OJvIYRd4QRZ6vbkpx97BGi7ibhcic2ncKhcBzZhaYm17pzP/LmyHytiMy1kWzuPzvnm7imhhNIr+kQPB9v4vqpB/Jtw3eEYmIiIiIiIgEYAObiIiIiIiISAA2sImIiIiIiIgE4DPYREREREREd4jOOi/K7YI92EREREREREQC3FQDOzk5GTKZDNOnT7fbNmPGDMhkMiQnJ0vW7927F3K5HFqttsWYZrMZK1euREREBNzd3aFWqxEeHo5Fixbh7Nmzdvu+drk6br9+/Wzr3dzcEBoaipUrV8J61bTZ5eXlkter1Wrcf//9+OSTT9p8HrKysiQxevfujfHjx+P48eOScpmZmRg8eDA8PT3h6emJYcOGYefOnW3eDxEREREREXUdN92D7e/vj5ycHDQ0NNjWXbp0CdnZ2QgICLArr9frkZqaiuLiYlRUVEi2mUwmxMTEID09HcnJySgqKsKhQ4ewYsUKnDt3DmvXrpWU12q1qKyslCzZ2dmSMmlpaaisrERpaSnmz5+Pl19+Ge+++65dvb788ktUVlZi3759GDp0KOLj43Hs2LE2nwdPT09UVlbi7Nmz+PTTT1FfX4+4uDiYzWZbmbvuuguvv/46Dh48iIMHD2L06NH4wx/+YNcQJyIiIiIioq7vpp/BjoiIwOnTp5Gbm4tJkyYBAHJzc+Hv74+goCBJ2fr6emzfvh0HDhxAVVUVsrKysHjxYtv21atXo7i4GAcPHsSQIUNs6zUaDWJjYyU9zwCgVCrh6+vbav08PDxsZaZNm4bMzEx88cUXeOaZZyTlevToAV9fX/j6+mLZsmVYu3Ytvv76a4SFhbXpPMhkMtt+/Pz8MGfOHDz66KM4deoU7rnnHgDA+PHjJa9ZtmwZMjMz8c9//hODBg1q036IiIiIiIhE4SPYt5ZDz2BPmTIFBoPB9rder0dKSopduW3btiEkJAQhISFISkqCwWCQNJqzs7MRExMjaVxfrT0P4FutVuzatQulpaVwdna+brnGxkZs2LABAFot15oLFy5g69atrcZoampCTk4O6uvrMWzYMIf2Q0RERERERJ2XQw3syZMno7i4GOXl5Thz5gz27NmDpKQku3I6nc62XqvVoq6uDoWFhbbtZWVlCAkJkbxmwoQJUKlUUKlUiIqKkmzLy8uzbbuy/OUvf5GUWbBgAVQqFZRKJaKjo2G1WvHcc8/Z1S0qKgoqlQqurq6YN28e+vXrh8cff7zN56CmpgYqlQru7u7w8vJCTk4OHn30UYSGhkrKHT161Faf6dOn48MPP8Tdd9/d5v0QERERERFR1+BQmi4fHx/ExcVh48aNsFqtiIuLg4+Pj6TMqVOnsH//fuTm5l7ekUKBhIQE6PV6jBkzxlbu2l7qjIwM1NfXY82aNSgqKpJsi46ORmZmpmSdt7e35O8XXngBycnJ+Pnnn/HKK69g9OjRdg114HLvemhoKMrKyjB79mysX7/eLlZrPDw8UFJSAovFgt27d2PlypVYv369XbmQkBAYjUZcuHABO3bswFNPPYXdu3e32sg2mUwwmUySdWazCS4uyjbXj4iIiIiI6FpM03VrOZwHOyUlBbNmzQIArFu3zm67TqeDxWJB3759beusViucnZ1RXV0NLy8vBAcH4+TJk5LX+fn5AbBvOAOAu7s7NBpNq/Xy8fGBRqOBRqPBjh07oNFocP/990sa9cDlydqCg4MRHBwMlUqF+Ph4nDhxAr169WrT8Ts5OdnqEhoaiqqqKiQkJNj9KODi4mIr9/vf/x4HDhzA3/72N7zzzjvXjb18+XIsXbpUsm7Ss/ORNPPFNtWNiIiIiIiIfnsO58HWarUwm80wm82IjY2VbLNYLNi0aRNWrVoFo9FoW44cOYLAwEBs2bIFAJCYmIiCggIcPny4fUdxHV5eXkhNTcX8+fPtJky72oMPPoiwsDAsW7bM4X3NmTMHR44cwYcffthqOavVatc7fa2FCxeipqZGsjz+v887XDciIiIiIiK69RxuYMvlcpSWlqK0tBRyuVyyLS8vD9XV1Zg6dSrCwsIky8SJE6HT6QBcbpQOGzYMo0ePxt/+9jeUlJTg+++/x+eff46dO3faxTWZTKiqqpIsv/zyS6v1nDlzJk6dOoUdO3a0Wm7evHl455138MMPPzhwNi6n7Zo2bRpeffVVW2P+5ZdfxjfffIPy8nIcPXoUr7zyCnbt2mWbff16lEqlLXf2lYXDw4mIiIiIiDo3hxvYAGyNv2vpdDqMGTMGarXablt8fDyMRiNKSkrg6uqKwsJCvPTSSzAYDBgxYgQGDhyI2bNnY/jw4fjoo48kr83Pz4efn59kGTFiRKt17NmzJyZPnowlS5agubn5uuXGjRuHfv36tasX+/nnn0dpaSnef/99AMCPP/6IyZMnIyQkBA899BD27duH/Px8xMTEOLwPIiIiIiIiR8lkXXPpKmTW1sZOU6eRf/xnYbEe/F1PYbFEXj6fnqgUFquxlR9TbpbIiSDMTeLq1d/TXVisJoH/j5bmznlLeXRG5o0L3QxLo7hYTRZxsRpqhYUKjB0vLFZjo7hr38lJ3HtSZKyDaeJ+PK03NQmL5enm8HQrdhot4t7fSud2/cZ/S/3aIO49KfL+KpLZIu496eEq7hoTqZOeevxYc0lYLJXAcy/y+467Un7jQm2kEHifBsTe991dulCr7yoPrf1HR1fBIYWpXSPVcef9dCMiIiIiIiLqQtjAbsGgQYPs8m1fWa5M0EZERERERER0tc45pqeDffbZZ2hsbHn4Z+/evX/j2hAREREREYnh1JUeaO6C2MBuQWBgYEdXgYiIiIiIiLoYDhEnIiIiIiIiEoA92ERERERERHcIjhC/tdiDTURERERERCQAe7C7iG8qqoXFeiDYR1gskRRO4n7vkQv8aU4uMF+ip7OzsFiff/eLsFgiddaJMwLvGyo0nsj8qiLzyYvM73nm80+ExUJ3gRNEOonLryrShfpRwmJdEpg3XOQ9zEUu7j5dJTAXcPdu4u6tANBNYA5fkfcKhbxz3l9/rDEJi+Wi6JzHKIO4enm5uwiLJfIjV2QsS5O4C7+pWWxCc5G5w4lawh5sIiIiIiIiIgH4Ew4REREREdEdQtZJRxveLtiDTURERERERCQAG9hEREREREREAnCIOBERERER0R1C4NyX1IKb7sFOTk6GTCbD9OnT7bbNmDEDMpkMycnJkvV79+6FXC6HVqttMabZbMbKlSsREREBd3d3qNVqhIeHY9GiRTh79qzdvq9dro7br18/23o3NzeEhoZi5cqVkll6y8vLJa9Xq9W4//778cknbZ8xNysrSxJDpVIhMjISubm5knJWqxVLlixBnz594ObmhlGjRuH48eNt3g8RERERERF1DQ4NEff390dOTg4aGhps6y5duoTs7GwEBATYldfr9UhNTUVxcTEqKiok20wmE2JiYpCeno7k5GQUFRXh0KFDWLFiBc6dO4e1a9dKymu1WlRWVkqW7OxsSZm0tDRUVlaitLQU8+fPx8svv4x3333Xrl5ffvklKisrsW/fPgwdOhTx8fE4duxYm8+Dp6enrQ6HDx9GbGwsHn/8cZw6dcpWZsWKFfjrX/+Kt99+GwcOHICvry9iYmLw66+/tnk/RERERERE1Pk51MCOiIhAQECApLc2NzcX/v7+GDJkiKRsfX09tm/fjmeffRbjxo1DVlaWZPvq1atRXFyMr776Cs899xwiIyOh0WgQGxuLzMxMpKenS8orlUr4+vpKFi8vL0kZDw8P+Pr6ol+/fpg2bRoGDx6ML774wu44evToAV9fX4SGhmLZsmVobGzE119/3ebzIJPJbHUIDg7Ga6+9BicnJ/zrX/8CcLn3+q233sIrr7yCP/7xjwgLC8PGjRtx8eJFbN26tc37ISIiIiIios7P4UnOpkyZAoPBYPtbr9cjJSXFrty2bdsQEhKCkJAQJCUlwWAwSIZrZ2dnIyYmxq5hfkV7ppG3Wq3YtWsXSktL4ezsfN1yjY2N2LBhAwC0Wq41TU1N2LhxI4DLP0AAwPfff4+qqiqMHTvWVk6pVOLBBx/E3r17HdoPERERERGRo1p65LYrLF2Fww3syZMno7i4GOXl5Thz5gz27NmDpKQku3I6nc62XqvVoq6uDoWFhbbtZWVlCAkJkbxmwoQJUKlUUKlUiIqKkmzLy8uzbbuy/OUvf5GUWbBgAVQqFZRKJaKjo2G1WvHcc8/Z1S0qKgoqlQqurq6YN28e+vXrh8cff7zN56CmpsZWBxcXFzz77LN49913MWDAAABAVVUVAKB3796S1/Xu3du2jYiIiIiIiMTLyMhA//794erqisjISHzzzTetljeZTHjllVcQGBgIpVKJAQMGQK/X39Q+HZ5F3MfHB3Fxcdi4cSOsVivi4uLg4+MjKXPq1Cns37/fNpRcoVAgISEBer0eY8aMsZW79heJjIwM1NfXY82aNSgqKpJsi46ORmZmpmSdt7e35O8XXngBycnJ+Pnnn/HKK69g9OjRdg114HLvemhoKMrKyjB79mysX7/eLlZrPDw8UFJSAgC4ePEivvzySzzzzDPo0aMHxo8ff93js1qtrf4KYzKZYDKZJOssjWYonF3aXDciIiIiIqI71bZt2zB79mxkZGRg+PDheOedd/Dwww/jxIkTLc4bBgCPP/44fvzxR+h0Omg0Gvz000+wWCw3td92pelKSUnBrFmzAADr1q2z267T6WCxWNC3b1/bOqvVCmdnZ1RXV8PLywvBwcE4efKk5HV+fn4A7BvOAODu7g6NRtNqvXx8fKDRaKDRaLBjxw5oNBrcf//9kkY9cHmytuDgYAQHB0OlUiE+Ph4nTpxAr1692nT8Tk5Okrpcedb7jTfewPjx4+Hr6wvgck/2lWMCgJ9++smuV/tqy5cvx9KlSyXrRjw5CyOTUttULyIiIiIiojvZX//6V0ydOhXTpk0DALz11lv4/PPPkZmZieXLl9uVz8/Px+7du3H69GlbO7Rfv343vV+Hh4gDl4d8m81mmM1mxMbGSrZZLBZs2rQJq1atgtFotC1HjhxBYGAgtmzZAgBITExEQUEBDh8+3J6qXJeXlxdSU1Mxf/58ybPf13rwwQcRFhaGZcuWtWt/crncNrt6//794evri4KCAtt2s9mM3bt3t9ijfsXChQtRU1MjWaISnmlXvYiIiIiIiGSyrrmYTCbU1tZKlmtH/V5hNptx6NAhyVxYADB27NjrzoX18ccf4/e//z1WrFiBvn374ne/+x3mz58vyZzVFu1qYMvlcpSWlqK0tBRyuVyyLS8vD9XV1Zg6dSrCwsIky8SJE6HT6QAAc+bMwbBhwzB69Gj87W9/Q0lJCb7//nt8/vnn2Llzp11ck8mEqqoqyfLLL7+0Ws+ZM2fi1KlT2LFjR6vl5s2bh3feeQc//PBDm47farXa6vD999/j3Xffxeeff44//OEPAC4PDZ89ezbS09Px4Ycf4tixY0hOTka3bt3w5JNPXjeuUqmEp6enZOHwcCIiIiIiulMtX74carVasrTUEw0Av/zyC5qamm5qLqzTp0+juLgYx44dw4cffoi33noLH3zwAWbOnHlT9WzXEHHgci7oluh0OowZMwZqtdpuW3x8PNLT01FSUoKIiAgUFhbirbfegsFgwMKFC9Hc3Iz+/fvj4Ycfxpw5cySvzc/Plwy3BoCQkBC7YeZX69mzJyZPnowlS5bgj3/843XLjRs3Dv369cOyZcuQkZHR2mEDAGpra211USqVCAwMRFpaGhYsWGAr8+KLL6KhoQEzZsxAdXU17rvvPnzxxRfw8PC4YXwiIiIiIiK6PMp37ty5knVKpbLV19zMXFjNzc2QyWTYsmWLrQ3717/+FRMnTsS6devg5ubWpnrKrK2Nm6ZO45WdZcJivfxQsLBYIn1x8kdhsURe1nIncWkBFLJ2DRqR2PvvC8JiieTUSdMovL/rtNB4Iu+cIq9XJ4HX65nPPxEWC92vP+/ETXOS37hMByh7f56wWJcam4XF6u7uWPrJlrjIxd3Dqi+ahcXq3k3cMQKAs8DjFHmvUMg75/31x5qWh2g6wkXROY9Rhk5aL4HVEhpL4PkS/bVC5dru/kUbgaF+U+PeOdDRVXBI3jP/0+ayZrMZ3bp1w/vvv48JEybY1j///PMwGo3YvXu33Wueeuop7NmzB99++61tXWlpKe6++26UlZUhOLhtbShxnyBEREREREREHczFxQWRkZGSubAAoKCg4LpzYQ0fPhxnz55FXV2dbV1ZWRmcnJxw1113tXnfbGBfx6BBg+zybV9ZrkzQRkRERERERJ3P3Llz8d5770Gv16O0tBRz5sxBRUUFpk+fDuDykPM///nPtvJPPvkkevTogSlTpuDEiRMoKirCCy+8gJSUlDYPDwcEPIN9u/rss8/Q2NjY4rbWUmwRERERERFRx0pISMC5c+eQlpaGyspKhIWF4bPPPkNgYCAAoLKyEhUVFbbyKpUKBQUFSE1Nxe9//3v06NEDjz/+OF577bWb2i8b2Ndx5cQTERERERHdLgRO19LpzZgxAzNmzGhxW1ZWlt260NBQu2HlN4tDxImIiIiIiIgEYAObiIiIiIiISAAOEe8iAr1az/F2M0SmOxCZfqSHq4uwWE0CKyYytZbIITkDfFyFxbI0iTtfnTRLl3AWi7g0SiLTdF0vt6NDRKbWuiAuDR+6qcXFEvj+VjqLi9UsMrWTwBuPWeB1LzKNj1zwjaezpj7qrFwFXvsWgRe/yGtfJJFp+ESe+yaB517kmXd1EdsfyATFgr8rkB32YBMREREREREJwAY2ERERERERkQBsYBMREREREREJwGewiYiIiIiI7hB8BPvWYg82ERERERERkQA33cBOTk6GTCbD9OnT7bbNmDEDMpkMycnJkvV79+6FXC6HVqttMabZbMbKlSsREREBd3d3qNVqhIeHY9GiRTh79qzdvq9dro7br18/23o3NzeEhoZi5cqVkll6y8vLJa9Xq9W4//778cknn7T5PGRlZUliqFQqREZGIjc3V1IuNzcXsbGx8PHxgUwmg9FobPM+iIiIiIiIqOtwqAfb398fOTk5aGhosK27dOkSsrOzERAQYFder9cjNTUVxcXFqKiokGwzmUyIiYlBeno6kpOTUVRUhEOHDmHFihU4d+4c1q5dKymv1WpRWVkpWbKzsyVl0tLSUFlZidLSUsyfPx8vv/wy3n33Xbt6ffnll6isrMS+ffswdOhQxMfH49ixY20+D56enrY6HD58GLGxsXj8/2Pv3uOiqvb+gX+GAYbLyISgMprIUQwNf3ntmGbnqClQ0OkxFFTQB0HKC5mlpuBdEx5vp04lnpPPzKApF03Sc7yUl/JRlNSUMU8ppoWYAocDCAo6w+33h4/7cTugAy4R9PPutV+v2Gvt7157u+eyZq29vyEhyM7OluqUl5fjxRdfxH/9139ZHZeIiIiIiIhankbdg92nTx/88ssvSE9PR1hYGIBbI7UdO3ZE586dZXXLy8uxefNmHD9+HPn5+UhKSsKCBQuk8g8//BAZGRn4/vvv0bt3b2m9t7c3/P39LfLDqlQqeHh43LN9rVq1kupMnDgRa9euxZ49e/DWW2/J6rm5ucHDwwMeHh5YtmwZPvnkE3z77bfo0aOHVedBoVBI+/Hw8MAHH3yAVatW4YcffoCPjw8AYNy4cQBujZoTERERERE9Sja8CfuhavQ92BMmTIDBYJD+1uv1iIyMtKiXlpYGHx8f+Pj4IDw8HAaDQdZpTklJwfDhw2Wd6zs9SCL02tpaHDhwAGfOnIGdnV299SorK7Fu3ToAuGe9e6mursb69esB3PoBgoiIiIiIiJ4sje5gjxs3DhkZGcjJycHFixdx+PBhhIeHW9TT6XTS+oCAAFy/fh379++Xys+dOyeN9t42YsQIqNVqqNVqDBw4UFa2Y8cOqez2snTpUlmd2bNnQ61WQ6VSYciQIaitrcW0adMs2jZw4ECo1Wo4ODhgxowZ8PLyQkhIiNXnoLS0VGqDvb09Jk+ejM8++wxdunSxOgYRERERERE9Hhqdpsvd3R2BgYFYv349amtrERgYCHd3d1md7OxsHDt2THrwl62tLUJDQ6HX6zFs2DCp3t2j1ImJiSgvL8fHH3+MgwcPysqGDBmCtWvXyta1bt1a9vesWbMQERGBwsJCzJ07F0OHDrXoqAO3Rte7deuGc+fOYfr06fjrX/9qEeteWrVqhZMnTwIAKioqsG/fPrz11ltwc3PDa6+9ZnWcu5lMJphMJtm6SrMJdvaqRsckIiIiIiLiDPGH64HyYEdGRiImJgYAsGbNGotynU6HqqoqdOjQQVpXW1sLOzs7lJSUwNXVFV27dsXZs2dl22m1WgCWHWcAcHZ2hre39z3b5e7uDm9vb3h7e2Pr1q3w9vbGCy+8IOvUA7ce1ta1a1d07doVarUawcHB+Omnn9C2bVurjt/GxkbWlueeew579uzB8uXLH6iDnZCQgMWLF8vWBUW9g9cmvtvomERERERERPRwPVAe7ICAAJjNZpjNZvj7+8vKqqqqsGHDBqxevRpGo1FaTp06hU6dOmHTpk0AgDFjxmDv3r3Iysp6kKbUy9XVFW+//TZmzpxp8cC0O/3xj39Ejx49sGzZsgfan1KplD1dvTFiY2NRWloqWwLGT3mgmERERERERPRwPdAItlKpxJkzZ6T/v9OOHTtQUlKCqKgoaDQaWdnIkSOh0+kQExODd999Fzt37sTQoUOxaNEivPTSS3B1dcW5c+ewe/dui7gmkwn5+fnyg7C1tZiefqepU6di+fLl2Lp1K0aOHFlvvRkzZmDUqFF4//33ZaPu9amtrZXacuPGDezduxdff/217CnpxcXFyM3NlfJ5307hdfvp5XVRqVRQqeTTwe3sS+7bHiIiIiIiInp0HmgEG7iVC9rFxcVivU6nw7Bhwyw61wAQHBwMo9GIkydPwsHBAfv378ecOXNgMBgwaNAgdO/eHdOnT8eLL76Ibdu2ybb96quvoNVqZcugQYPu2cY2bdpg3LhxWLRoEWpqauqtFxQUBC8vL6tHscvKyqQ2dO/eHatXr8aSJUswd+5cqc7f//539O7dG4GBgQCA0aNHo3fv3vjrX/9q1T6IiIiIiIhEUSgULXJpKRS195o3Tc3GZ99dFBYrvK+nsFgir54TOeJG6asFNsxW8cC/Q0lsBL43ZF+9JixWVbW489Vc3/9Wf3n2/pUaoLKy/h/rGkrk27DID6ArJ78XFgtXC8TFcrL84bbRBL6+L+5ecP9KVrphFnd9uTg+0GQ1GZHvFRXmamGxWjs3LsVmfZTK5vlG1lxz15ZWVAqLVVUj8PNb5IeuQDcFfn442Il7D6sR+VkEcefewV7cMQKAylZ5/0pWchT71tNkRhpOPuomNMoXE1pGKmSxVywRERERERHRE4od7Hr4+vpa5Nu+vdx+QBsREREREVFLolC0zKWlEDdv7DGza9cuVFbWPeWpXbt2TdwaIiIiIiIiau7Ywa5Hp06dHnUTiIiIiIiIqAXhFHEiIiIiIiIiATiCTURERERE9IRorhkJHhccwSYiIiIiIiISgCPYLYR9M83J2TxbJTb/Yi3E5YWsqRXYLoE5yEXmHW2u7ATmCgUAG4H5VUXm1BbKRlyuUKG5qytKxcVydhUWSinwmlAJvF5tBX5+iBz1EHmMIvMKA4CzUuC1Tw0i8nUkNK+zwGtf5DGKfE2KfK8Q+R1FZN5qoGU9jZpaJo5gExEREREREQnAEWwiIiIiIqInBAfxHy6OYBMREREREREJwA42ERERERERkQCcIk5ERERERPSEEPnQPrLU4BHsiIgIKBQKTJo0yaJsypQpUCgUiIiIkK0/cuQIlEolAgIC6oxpNpuxcuVK9OnTB87OztBoNOjZsyfmzZuHK1euWOz77uXOuF5eXtJ6R0dHdOvWDStXrkTtHY8zzMnJkW2v0Wjwwgsv4B//+IfV5yEpKUkWQ61Wo2/fvkhPT5fqVFZWYvbs2fh//+//wdnZGe3bt8f48eNlx0RERERERESPh0ZNEe/YsSNSU1Nx48YNad3NmzeRkpICT09Pi/p6vR5vv/02MjIykJubKyszmUwYPnw44uPjERERgYMHD+LEiRNYsWIFioqK8Mknn8jqBwQEIC8vT7akpKTI6ixZsgR5eXk4c+YMZs6cibi4OHz22WcW7dq3bx/y8vJw9OhR/P73v0dwcDD++c9/Wn0eXFxcpDZkZWXB398fISEhyM7OBgBUVFTg5MmTmD9/Pk6ePIn09HScO3cOf/rTn6zeBxEREREREbUMjZoi3qdPH/zyyy9IT09HWFgYACA9PR0dO3ZE586dZXXLy8uxefNmHD9+HPn5+UhKSsKCBQuk8g8//BAZGRn4/vvv0bt3b2m9t7c3/P39ZSPPAKBSqeDh4XHP9rVq1UqqM3HiRKxduxZ79uzBW2+9Javn5uYGDw8PeHh4YNmyZfjkk0/w7bffokePHladB4VCIe3Hw8MDH3zwAVatWoUffvgBPj4+0Gg02Lt3r2ybTz75BL///e+Rm5tb548RRERERERE1DI1+iFnEyZMgMFgkP7W6/WIjIy0qJeWlgYfHx/4+PggPDwcBoNB1mlOSUnB8OHDZZ3rOz3IPQK1tbU4cOAAzpw5Azs7u3rrVVZWYt26dQBwz3r3Ul1djfXr1wO49QNEfUpLS6FQKPDUU081aj9ERERERESNZaNomUtL0egO9rhx45CRkYGcnBxcvHgRhw8fRnh4uEU9nU4nrQ8ICMD169exf/9+qfzcuXPw8fGRbTNixAio1Wqo1WoMHDhQVrZjxw6p7PaydOlSWZ3Zs2dDrVZDpVJhyJAhqK2txbRp0yzaNnDgQKjVajg4OGDGjBnw8vJCSEiI1eegtLRUaoO9vT0mT56Mzz77DF26dKmz/s2bNzFnzhyMHTsWLi4uVu+HiIiIiIiImr9GP0Xc3d0dgYGBWL9+PWpraxEYGAh3d3dZnezsbBw7dkx68JetrS1CQ0Oh1+sxbNgwqd7do9SJiYkoLy/Hxx9/jIMHD8rKhgwZgrVr18rWtW7dWvb3rFmzEBERgcLCQsydOxdDhw616KgDt0bXu3XrhnPnzmH69On461//ahHrXlq1aoWTJ08CuHW/9b59+/DWW2/Bzc0Nr732mqxuZWUlRo8ejZqaGiQmJt4zrslkgslkkm9vNsHOXmV124iIiIiIiKhpPVCarsjISMTExAAA1qxZY1Gu0+lQVVWFDh06SOtqa2thZ2eHkpISuLq6omvXrjh79qxsO61WC8Cy4wwAzs7O8Pb2vme73N3d4e3tDW9vb2zduhXe3t544YUXZJ164NbD2rp27YquXbtCrVYjODgYP/30E9q2bWvV8dvY2Mja8txzz2HPnj1Yvny5rINdWVmJkJAQ/Prrr/jmm2/uO3qdkJCAxYsXy9a9PnE6/uPNd61qFxERERERUV2YpuvhavQUceDWlG+z2Qyz2Qx/f39ZWVVVFTZs2IDVq1fDaDRKy6lTp9CpUyds2rQJADBmzBjs3bsXWVlZD9KUerm6uuLtt9/GzJkzLR6Ydqc//vGP6NGjB5YtW/ZA+1MqlbKnq9/uXP/888/Yt28f3Nzc7hsjNjYWpaWlsiUwYsoDtYuIiIiIiIgergcawVYqlThz5oz0/3fasWMHSkpKEBUVBY1GIysbOXIkdDodYmJi8O6772Lnzp0YOnQoFi1ahJdeegmurq44d+4cdu/ebRHXZDIhPz9ffhC2thbT0+80depULF++HFu3bsXIkSPrrTdjxgyMGjUK77//vmzUvT61tbVSW27cuIG9e/fi66+/lp6SXlVVhZEjR+LkyZPYsWMHqqurpfqtW7eGvb19nXFVKhVUKvl0cDv7q/dtDxERERERET06DzSCDdzKBV3XlGedTodhw4ZZdK4BIDg4GEajESdPnoSDgwP279+POXPmwGAwYNCgQejevTumT5+OF198Edu2bZNt+9VXX0Gr1cqWQYMG3bONbdq0wbhx47Bo0SLU1NTUWy8oKAheXl5Wj2KXlZVJbejevTtWr16NJUuWYO7cuQCA3377DX//+9/x22+/oVevXrI2HzlyxKp9EBERERERUcugqL3XvGlqNpKO5wqLFdKro7BYEHj1fJ9TIixWjcB2iUwLoIC4YOeuXhMWy1RV/w9Pj4u1u38WGq+6WtxFVlnZPM//b//7EEchbpaLi1VRKi6Ws6uwUL99vVBYrEqB15ezSnn/Sla6x2/UDSbyFkCT4NeQs4O4cyaSTTO9b7K0olJYLJFfSkV+xRV5z2qVwNe3yvaBx8okNuJCQWTvwln1QBNuLYh8GTmIbVqTGbfp1KNuQqN8HtbzUTfBKgJfSkRERERERERPLnaw6+Hr62uRb/v2cvsBbURERERERES3tdCJDQ/frl27UFlZ95Sndu3aNXFriIiIiIiIqLljB7senTp1etRNICIiIiIiEop5sB8uThEnIiIiIiIiEoAdbCIiIiIiIiIBOEWciIiIiIjoCSEyBS1ZYge7hbAVmZyQHgtKgW+OtgLfaWsEJr8Umc9ctOZ6+5JNc/3UVAh8DxOYuxrlJcJCKQWee5E5ZJUi8/fWiss3bQNx7bIXmAv4SSHyGhNJ6DuYwGtfZE5tpcDLtbl+Fol9PxR7sfL+Y3rY+IlEREREREREJAA72EREREREREQCcIo4ERERERHRE4LT5B8ujmATERERERERCcAONhEREREREZEADe5gR0REQKFQYNKkSRZlU6ZMgUKhQEREhGz9kSNHoFQqERAQUGdMs9mMlStXok+fPnB2doZGo0HPnj0xb948XLlyxWLfdy93xvXy8pLWOzo6olu3bli5cqXsCYQ5OTmy7TUaDV544QX84x//sPo8JCUlyWKo1Wr07dsX6enpsnqLFi1Ct27d4OzsDFdXVwwbNgxHjx61ej9ERERERETUMjRqBLtjx45ITU3FjRs3pHU3b95ESkoKPD09Lerr9Xq8/fbbyMjIQG5urqzMZDJh+PDhiI+PR0REBA4ePIgTJ05gxYoVKCoqwieffCKrHxAQgLy8PNmSkpIiq7NkyRLk5eXhzJkzmDlzJuLi4vDZZ59ZtGvfvn3Iy8vD0aNH8fvf/x7BwcH45z//afV5cHFxkdqQlZUFf39/hISEIDs7W6rzzDPP4NNPP8Xp06eRkZEBLy8v+Pn5obCw0Or9EBERERERiaBooUtL0agOdp8+feDp6SkbrU1PT0fHjh3Ru3dvWd3y8nJs3rwZkydPRlBQEJKSkmTlH374ITIyMvDNN99g2rRp6Nu3L7y9veHv74+1a9ciPj5eVl+lUsHDw0O2uLrKc6K2atUKHh4e8PLywsSJE/Hcc89hz549Fsfh5uYGDw8PdOvWDcuWLUNlZSW+/fZbq8+DQqGQ2tC1a1d88MEHsLGxwQ8//CDVGTt2LIYNG4bOnTvD19cXf/7zn1FWViarQ0RERERERC1fo+/BnjBhAgwGg/S3Xq9HZGSkRb20tDT4+PjAx8cH4eHhMBgMsunaKSkpGD58uEXH/LYHecpdbW0tDhw4gDNnzsDOzq7eepWVlVi3bh0A3LPevVRXV2P9+vUAbv0AURez2YzPPvtMmgJPREREREREj49Gd7DHjRuHjIwM5OTk4OLFizh8+DDCw8Mt6ul0Oml9QEAArl+/jv3790vl586dg4+Pj2ybESNGQK1WQ61WY+DAgbKyHTt2SGW3l6VLl8rqzJ49G2q1GiqVCkOGDEFtbS2mTZtm0baBAwdCrVbDwcEBM2bMgJeXF0JCQqw+B6WlpVIb7O3tMXnyZHz22Wfo0qVLnW12cHDAhx9+iL1798Ld3d3q/RAREREREYlgo1C0yKWlaHQebHd3dwQGBmL9+vWora1FYGCgRacxOzsbx44dk6aS29raIjQ0FHq9HsOGDZPq3T1KnZiYiPLycnz88cc4ePCgrGzIkCFYu3atbF3r1q1lf8+aNQsREREoLCzE3LlzMXToUIuOOnBrdL1bt244d+4cpk+fjr/+9a8Wse6lVatWOHnyJACgoqIC+/btw1tvvQU3Nze89tprsjYbjUb8+9//xrp16xASEoKjR4+ibdu2dcY1mUwwmUyydZVmE+zsVVa3jYiIiIiIiJpWozvYABAZGYmYmBgAwJo1ayzKdTodqqqq0KFDB2ldbW0t7OzsUFJSAldXV3Tt2hVnz56VbafVagFYdpwBwNnZGd7e3vdsl7u7O7y9veHt7Y2tW7fC29sbL7zwgqxTD9x6WFvXrl3RtWtXqNVqBAcH46effqq343s3GxsbWVtu3+u9fPlyWQf7dptvt6Nr167Q6XSIjY2tM25CQgIWL14sWzci+l288dZ7VrWLiIiIiIiImt4D5cEOCAiA2WyG2WyGv7+/rKyqqgobNmzA6tWrYTQapeXUqVPo1KkTNm3aBAAYM2YM9u7di6ysrAdpSr1cXV3x9ttvY+bMmbJ7v+/2xz/+ET169MCyZcseaH9KpVL2dPW61NbWWoxQ3yk2NhalpaWy5bUJUx+oXURERERERPRwPdAItlKpxJkzZ6T/v9OOHTtQUlKCqKgoaDQaWdnIkSOh0+kQExODd999Fzt37sTQoUOxaNEivPTSS3B1dcW5c+ewe/dui7gmkwn5+fnyg7C1vec9zVOnTsXy5cuxdetWjBw5st56M2bMwKhRo/D+++/LRt3rU1tbK7Xlxo0b2Lt3L77++mssWLAAwK0nqC9btgx/+tOfoNVqUVRUhMTERPz2228YNWpUvXFVKhVUKvl0cDv7svu2h4iIiIiI6F5a0O3MLdIDjWADt3JBu7i4WKzX6XQYNmyYRecaAIKDg2E0GnHy5Ek4ODhg//79mDNnDgwGAwYNGoTu3btj+vTpePHFF7Ft2zbZtl999RW0Wq1sGTRo0D3b2KZNG4wbNw6LFi1CTU1NvfWCgoLg5eVl9Sh2WVmZ1Ibu3btj9erVWLJkCebOnQvg1o8OZ8+eRXBwMJ555hkEBQWhsLAQhw4dgq+vr1X7ICIiIiIiopZBUXuvedPUbGw88ZuwWG88d//ReasJvHq+zykRFqtGYLtsBP7Kp4C4YBdKrwmLZa4Wd8JqBL6liPx3XLv7Z3HBANQIbJzZXP8Pfw1lI/CCzf3+hLBYMN371plHplzc+07ekb8Ii1Ul8DXpZK+8fyUrmaoEXqvNeAjFzrZ5tk3kORP57a/sRqW4YAKJ/ILbXL8u2ykfeKxMIvIlKfJaVdmKO0ZA7OekwwPNBX50ojf/81E3oVHWhfR41E2wSgu9LIiIiIiIiKih7s7gRGKJ/UnoMeLr62uRb/v2cvsBbURERERERES3cQS7Hrt27UJlZd1Tntq1a9fErSEiIiIiIqLmjh3senTq1OlRN4GIiIiIiIhaEHawiYiIiIiInhC8Bfvh4j3YRERERERERAKwg01EREREREQkAKeItxCDO7cRFktkLuZqgXkh27RSCYvVSmBiQpGpDETm1Ha0E5fbVmTOV7XAc68UeMJef7a9sFgA4OpsJyxWtcCcxwJf3rhaPlhYLJWduN9zRV4XImNpB74jLNaxf/yXsFhCc8gKvL4OXfy3sFgvd24rLBYAXDdVCYulshX3Xt1OI+5zsrK6eeY0F/maFDkNVuTrqLhcXN7wmlpx/44iv1fUCPx++I8frwiLBQCv+Yr8PsC51mSJHWwiIiIiIqInhMgfxcgSp4gTERERERERCcAONhEREREREZEAnCJORERERET0hOAM8YeLI9hEREREREREAjS4gx0REQGFQoFJkyZZlE2ZMgUKhQIRERGy9UeOHIFSqURAQECdMc1mM1auXIk+ffrA2dkZGo0GPXv2xLx583Dlyq0nB7722msYNmxYndtnZmZCoVDg5MmT0ro333wTSqUSqampFvUXLVoEhUIBhUIBGxsbtG/fHmFhYbh06ZK1pwGDBw+WYtjb26NLly6IjY2FyWSS1Vu2bBkGDhwIJycnPPXUU1bHJyIiIiIiopalUSPYHTt2RGpqKm7cuCGtu3nzJlJSUuDp6WlRX6/X4+2330ZGRgZyc3NlZSaTCcOHD0d8fDwiIiJw8OBBnDhxAitWrEBRURE++eQTAEBUVBS++eYbXLx4sc74vXr1Qp8+fQAAFRUVSEtLw6xZs6DT6eo8Bl9fX+Tl5eG3335DWloaTp8+jZCQkAadh+joaOTl5eH8+fNYsWIF1qxZg0WLFsnqmM1mjBo1CpMnT25QbCIiIiIiImpZGnUPdp8+ffDLL78gPT0dYWFhAID09HR07NgRnTt3ltUtLy/H5s2bcfz4ceTn5yMpKQkLFiyQyj/88ENkZGTg+++/R+/evaX13t7e8Pf3R+3/5tELCgpC27ZtkZSUhIULF0r1bnem4+PjpXVbtmzBs88+i9jYWGi1WuTk5MDLy0t+4La28PDwAAC0b98e0dHRmDZtGsrKyuDi4mLVeXBycpJieHp6Ijk5GXv27EFCQoJUZ/HixQCApKQkq2ISERERERE9LArehP1QNfoe7AkTJsBgMEh/6/V6REZGWtRLS0uDj48PfHx8EB4eDoPBIHWaASAlJQXDhw+Xda7vdPsCsLW1xfjx45GUlCTbfsuWLTCbzVJHHwB0Oh3Cw8Oh0Wjw6quvytpZl/z8fKSnp0OpVEKpVFp3Au5y6tQpHD58GHZ2do3anoiIiIiIiFq2Rnewx40bh4yMDOTk5ODixYs4fPgwwsPDLerd7uwCQEBAAK5fv479+/dL5efOnYOPj49smxEjRkCtVkOtVmPgwIHS+sjISOTk5ODAgQPSOr1ejzfeeAOurq4AgJ9//hnfffcdQkNDAUDq1NfU1Mj2cfr0aajVajg5OUGr1eLAgQOYOnUqnJ2drT4HiYmJUKvVUKlU6NWrFwoLCzFr1iyrt6+PyWRCWVmZbLn73m4iIiIiIiJqXhrdwXZ3d0dgYCDWr18Pg8GAwMBAuLu7y+pkZ2fj2LFjGD16NIBbo9ChoaHQ6/WyendPU0hMTITRaERkZCQqKiqk9d26dcPAgQOl7S9cuIBDhw7JRs51Oh38/f2ltrz66qsoLy/Hvn37ZPvw8fGB0WjE8ePHsWzZMvTq1QvLli1r0DkICwuD0WhEZmYmQkJCEBkZieDg4AbFqEtCQgI0Go1sWfPhigeOS0RERERETzabFrq0FA+UBzsyMhIxMTEAgDVr1liU63Q6VFVVoUOHDtK62tpa2NnZoaSkBK6urujatSvOnj0r206r1QIAWrdubREzKioKMTExWLNmDQwGAzp16oSXX34ZAFBdXY0NGzYgPz8ftrb/d2jV1dXQ6XTw8/OT1tnb28Pb2xvArQee/fzzz5g8eTI+//xzq49fo9FIMTZu3AhfX1/odDpERUVZHaMusbGxeO+992TrCivqqUxERERERETNwgP9GBAQEACz2Qyz2Qx/f39ZWVVVFTZs2IDVq1fDaDRKy6lTp9CpUyds2rQJADBmzBjs3bsXWVlZVu0zJCQESqUSycnJWL9+PSZMmCCNgO/atQvXrl1DVlaWbJ9btmzBtm3bUFRUVG/c+fPnIyUlRZbqqyHs7OwQFxeHefPmyUbdG0OlUsHFxUW2qFSqB4pJRERERERED9cDdbCVSiXOnDmDM2fOWDwcbMeOHSgpKUFUVBR69OghW0aOHCmlz3r33XcxYMAADB06FH/5y19w8uRJ/Prrr/j666+xe/dui7hqtRqhoaGIi4vDlStXZDm3dTodAgMD0bNnT9n+goOD0aZNG2zcuLHeY+ncuTNef/112RPOG2rs2LFQKBRITEyU1uXm5sJoNCI3NxfV1dVSp//69euN3g8RERERERE1Pw88nf32COvddDodhg0bBo1GY1EWHBwMo9GIkydPwsHBAfv378ecOXNgMBgwaNAgdO/eHdOnT8eLL76Ibdu2WWwfFRWFkpISDBs2TMq7XVBQgJ07d9Z5D7RCocAbb7xRb07s22bMmIGdO3fi6NGjVh69nL29PWJiYrBixQqpA71gwQL07t0bCxcuxPXr19G7d2/07t0b33//faP2QURERERE1FgKhaJFLi2FovbOnFfUbP1WIu4p4m5qcdPNq2vEXT6XisXdaN7K4YEeLyAj8gVtI/C94XLxTWGx7GzFNUwt8NwrBZ4wO6XYx2O4OotLyVddLfBtWOA1drW8UlgslZ248y/yuhAZSzvwHWGxjv3jv4TFUtkKvPYFXl+HLv5bWKyXO7cVFgsArpuqhMVS2TYu/Wdd2mnEfX5XVtfcv5KVasSFEvqaFPl9XOTrqFjge6vIY3S0E3et1kLc59q+cwXCYgHAa77thcVysm85nb47Tdt29v6VmqGP/6Pbo26CVVrSA9mIiIiIiIiImi12sOtw6NAhKQ93XQsRERERERHR3cTN5XyM9OvXD0aj8VE3g4iIiIiISCiRtyySJXaw6+Do6CjltyYiIiIiIiKyBqeIExEREREREQnAEWwiIiIiIqInBKeIP1zsYLcQIlMyiUzTJTI9RH6ZuGPMLxMWCh2echQWy0bgCSsoF3e+HASmH3GyF5imS+D1de2GuLQ7AFAuMI1Pc00rd7NSYBofgZnIRKb8EpmoUmRqrd+/NkdYrOz9q4XFuvhvcekUF23IEhary9svCYsFAN5tnYXFKhGYkklkukFbgd+w/1UmLpWojcB2iUytZS8wVtE1cedLKfCD8pqNuGMU+f1w8p//R1wwAEG6MULjEd2NU8SJiIiIiIiIBGAHm4iIiIiIiEgAThEnIiIiIiJ6Qoi8nYwscQSbiIiIiIiISAB2sImIiIiIiIgEaHAHOyIiAgqFApMmTbIomzJlChQKBSIiImTrjxw5AqVSiYCAgDpjms1mrFy5En369IGzszM0Gg169uyJefPm4cqVKwCA1157DcOGDatz+8zMTCgUCpw8eVJa9+abb0KpVCI1NdWi/qJFi6BQKKBQKGBjY4P27dsjLCwMly5dsvY0YPDgwVIMe3t7dOnSBbGxsTCZ/u/JkDk5OYiKisLvfvc7ODo6okuXLli4cCHMZrPV+yEiIiIiIhLFRtEyl5aiUSPYHTt2RGpqKm7cuCGtu3nzJlJSUuDp6WlRX6/X4+2330ZGRgZyc3NlZSaTCcOHD0d8fDwiIiJw8OBBnDhxAitWrEBRURE++eQTAEBUVBS++eYbXLx4sc74vXr1Qp8+fQAAFRUVSEtLw6xZs6DT6eo8Bl9fX+Tl5eG3335DWloaTp8+jZCQkAadh+joaOTl5eH8+fNYsWIF1qxZg0WLFknlZ8+eRU1NDf72t7/hxx9/xIcffoi//vWviIuLa9B+iIiIiIiIqPlr1EPO+vTpg19++QXp6ekICwsDAKSnp6Njx47o3LmzrG55eTk2b96M48ePIz8/H0lJSViwYIFU/uGHHyIjIwPff/89evfuLa339vaGv78/av83SWlQUBDatm2LpKQkLFy4UKp3uzMdHx8vrduyZQueffZZxMbGQqvVIicnB15eXvIDt7WFh4cHAKB9+/aIjo7GtGnTUFZWBhcXF6vOg5OTkxTD09MTycnJ2LNnDxISEgAAAQEBslH7zp07Izs7G2vXrsWqVaus2gcRERERERG1DI2+B3vChAkwGAzS33q9HpGRkRb10tLS4OPjAx8fH4SHh8NgMEidZgBISUnB8OHDZZ3rO91+yp2trS3Gjx+PpKQk2fZbtmyB2WyWOvoAoNPpEB4eDo1Gg1dffVXWzrrk5+cjPT0dSqUSSqXSuhNwl1OnTuHw4cOws7O7Z73S0lK0bt26UfsgIiIiIiKi5qvRHexx48YhIyMDOTk5uHjxIg4fPozw8HCLerc7u8CtEd3r169j//79Uvm5c+fg4+Mj22bEiBFQq9VQq9UYOHCgtD4yMhI5OTk4cOCAtE6v1+ONN96Aq6srAODnn3/Gd999h9DQUACQOvU1NTWyfZw+fRpqtRpOTk7QarU4cOAApk6dCmdnZ6vPQWJiItRqNVQqFXr16oXCwkLMmjWr3voXLlzAJ598Uuf960RERERERA+bQtEyl5ai0R1sd3d3BAYGYv369TAYDAgMDIS7u7usTnZ2No4dO4bRo0cDuDUKHRoaCr1eL6t3dy62xMREGI1GREZGoqKiQlrfrVs3DBw4UNr+woULOHTokGzkXKfTwd/fX2rLq6++ivLycuzbt0+2Dx8fHxiNRhw/fhzLli1Dr169sGzZsgadg7CwMBiNRmRmZiIkJASRkZEIDg6us+6VK1cQEBCAUaNGYeLEifeMazKZUFZWJlvMdzw8jYiIiIiIiJqfRt2DfVtkZCRiYmIAAGvWrLEo1+l0qKqqQocOHaR1tbW1sLOzQ0lJCVxdXdG1a1ecPXtWtp1WqwWAOqdSR0VFISYmBmvWrIHBYECnTp3w8ssvAwCqq6uxYcMG5Ofnw9b2/w6turoaOp0Ofn5+0jp7e3t4e3sDuPXAs59//hmTJ0/G559/bvXxazQaKcbGjRvh6+sLnU6HqKgoWb0rV65gyJAhGDBgAD777LP7xk1ISMDixYvlx/32bES/E2t124iIiIiIiKhpPVAe7ICAAJjNZpjNZvj7+8vKqqqqsGHDBqxevRpGo1FaTp06hU6dOmHTpk0AgDFjxmDv3r3Iysqyap8hISFQKpVITk7G+vXrMWHCBGkEfNeuXbh27RqysrJk+9yyZQu2bduGoqKieuPOnz8fKSkpslRfDWFnZ4e4uDjMmzdPNup++fJlDB48GH369IHBYICNzf1PeWxsLEpLS2XLf056r1HtIiIiIiIioqbxQB1spVKJM2fO4MyZMxYPB9uxYwdKSkoQFRWFHj16yJaRI0dK6bPeffddDBgwAEOHDsVf/vIXnDx5Er/++iu+/vpr7N692yKuWq1GaGgo4uLicOXKFVnObZ1Oh8DAQPTs2VO2v+DgYLRp0wYbN26s91g6d+6M119/XfaE84YaO3YsFAoFEhMTAdwauR48eDA6duyIVatWobCwEPn5+cjPz79nHJVKBRcXF9lir1I1ul1EREREREQAYKNQtMilpXigDjYAqQN4N51Oh2HDhkGj0ViUBQcHw2g04uTJk3BwcMD+/fsxZ84cGAwGDBo0CN27d8f06dPx4osvYtu2bRbbR0VFoaSkBMOGDZPybhcUFGDnzp113gOtUCjwxhtv1JsT+7YZM2Zg586dOHr0qJVHL2dvb4+YmBisWLEC169fx549e3D+/Hl88803ePrpp6HVaqWFiIiIiIiIHi+K2jtzXlGzdfRCqbBYz3la/ujRWDUCL59jvxYLiyVSh6cchcUS+etb9r+uCYvlYPvAv7VJPF2tfxL//dgpxZ2vm5U196/UAFbc7WG1Vg4P9DgMmbsfGvkgrt2oEhbLXuA1prITF8vWRtz5yrt6U1is3782R1is7P2rhcW6+O+K+1ey0tiPDwqL9fnbLwmLBQDebcW9j5WUVwqL5d1OLSyWyK9//yoT9yBWG4GvSZXA9x21wPfp8/nXhcVSCvyctBX4wSZysHHArG3iggG4pBsjLJazfcsZVb3TnF3nHnUTGuW/Xn3mUTfBKuLeLYiIiIiIiKhZEzhGQHXg+a3DoUOHpDzcdS1EREREREREd+MIdh369esHo9H4qJtBRERERERELQg72HVwdHSU8lsTERERERERWYMdbCIiIiIioidEC8p41SLxHmwiIiIiIiIiAdjBJiIiIiIiIhKAU8RbCK82To+6CXVSCsxX6dOulbBYIvNoKgXOoxGZR1Nk/l6R/46tHMUdo8h2mQTnwbYVmHu0qlpcPlonlVJYLJHnX+T1KvLci3x9i8y5KzJ3tc/LM4TF+ufXK4XF+iCij7BYnq3Ffka6OtsLi6VxtBMWK7dIXB5ykTw0DsJi1QjMz20v8DUpckqt1lXc+RJJ4KmHjcATtua9PwqLBYhtW0vFc/BwcQSbiIiIiIiISAB2sImIiIiIiIgEYAebiIiIiIiISADeg01ERERERPSE4C3YDxdHsImIiIiIiIgEaHAHOyIiAgqFApMmTbIomzJlChQKBSIiImTrjxw5AqVSiYCAgDpjms1mrFy5En369IGzszM0Gg169uyJefPm4cqVKwCA1157DcOGDatz+8zMTCgUCpw8eVJa9+abb0KpVCI1NdWi/qJFi6BQKKBQKGBjY4P27dsjLCwMly5dsvY0YPDgwVIMe3t7dOnSBbGxsTCZTLJ6f/rTn+Dp6QkHBwdotVqMGzdOOiYiIiIiIiJ6fDRqBLtjx45ITU3FjRs3pHU3b95ESkoKPD09Lerr9Xq8/fbbyMjIQG5urqzMZDJh+PDhiI+PR0REBA4ePIgTJ05gxYoVKCoqwieffAIAiIqKwjfffIOLFy/WGb9Xr17o0+dWyo+KigqkpaVh1qxZ0Ol0dR6Dr68v8vLy8NtvvyEtLQ2nT59GSEhIg85DdHQ08vLycP78eaxYsQJr1qzBokWLZHWGDBmCzZs3Izs7G1u3bsWFCxcwcuTIBu2HiIiIiIiImr9G3YPdp08f/PLLL0hPT0dYWBgAID09HR07dkTnzp1ldcvLy7F582YcP34c+fn5SEpKwoIFC6TyDz/8EBkZGfj+++/Ru3dvab23tzf8/f1R+79J+YKCgtC2bVskJSVh4cKFUr3bnen4+Hhp3ZYtW/Dss88iNjYWWq0WOTk58PLykh+4rS08PDwAAO3bt0d0dDSmTZuGsrIyuLi4WHUenJycpBienp5ITk7Gnj17kJCQINV59913pf/v1KkT5syZg//4j/9AZWUl7OzE5cYkIiIiIiK6Hxveg/1QNfoe7AkTJsBgMEh/6/V6REZGWtRLS0uDj48PfHx8EB4eDoPBIHWaASAlJQXDhw+Xda7vpPjfu/BtbW0xfvx4JCUlybbfsmULzGaz1NEHAJ1Oh/DwcGg0Grz66quydtYlPz8f6enpUCqVUCqV1p2Au5w6dQqHDx++Z6e5uLgYmzZtwsCBA9m5JiIiIiIiesw0uoM9btw4ZGRkICcnBxcvXsThw4cRHh5uUe92ZxcAAgICcP36dezfv18qP3fuHHx8fGTbjBgxAmq1Gmq1GgMHDpTWR0ZGIicnBwcOHJDW6fV6vPHGG3B1dQUA/Pzzz/juu+8QGhoKAFKnvqamRraP06dPQ61Ww8nJCVqtFgcOHMDUqVPh7Oxs9TlITEyEWq2GSqVCr169UFhYiFmzZlnUmz17NpydneHm5obc3Fxs377d6n0QERERERFRy9DoDra7uzsCAwOxfv16GAwGBAYGwt3dXVYnOzsbx44dw+jRowHcGoUODQ2FXq+X1VPc9az4xMREGI1GREZGoqKiQlrfrVs3DBw4UNr+woULOHTokGzkXKfTwd/fX2rLq6++ivLycuzbt0+2Dx8fHxiNRhw/fhzLli1Dr169sGzZsgadg7CwMBiNRmRmZiIkJASRkZEIDg62qDdr1ixkZWVhz549UCqVGD9+vGwU/m4mkwllZWWy5e6HpxERERERETWUjULRIpeW4oHyYEdGRiImJgYAsGbNGotynU6HqqoqdOjQQVpXW1sLOzs7lJSUwNXVFV27dsXZs2dl22m1WgBA69atLWJGRUUhJiYGa9asgcFgQKdOnfDyyy8DAKqrq7Fhwwbk5+fD1vb/Dq26uho6nQ5+fn7SOnt7e3h7ewO49cCzn3/+GZMnT8bnn39u9fFrNBopxsaNG+Hr6wudToeoqChZPXd3d7i7u+OZZ55B9+7d0bFjR3z33XcYMGBAnXETEhKwePFi2boZc+ZhVuyCOusTERERERHRo/dAebADAgJgNpthNpvh7+8vK6uqqsKGDRuwevVqGI1GaTl16hQ6deqETZs2AQDGjBmDvXv3Iisry6p9hoSEQKlUIjk5GevXr8eECROkEfBdu3bh2rVryMrKku1zy5Yt2LZtG4qKiuqNO3/+fKSkpMhSfTWEnZ0d4uLiMG/ePNmo+91uj1zfa0Q6NjYWpaWlsmXae7Mb1S4iIiIiIiJqGg/UwVYqlThz5gzOnDlj8XCwHTt2oKSkBFFRUejRo4dsGTlypJQ+691338WAAQMwdOhQ/OUvf8HJkyfx66+/4uuvv8bu3bst4qrVaoSGhiIuLg5XrlyR5dzW6XQIDAxEz549ZfsLDg5GmzZtsHHjxnqPpXPnznj99ddlTzhvqLFjx0KhUCAxMREAcOzYMXz66acwGo24ePEivv32W4wdOxZdunSpd/QaAFQqFVxcXGSLSqVqdLuIiIiIiIjo4XugDjYAqQN4N51Oh2HDhkGj0ViUBQcHw2g04uTJk3BwcMD+/fsxZ84cGAwGDBo0CN27d8f06dPx4osvYtu2bRbbR0VFoaSkBMOGDZPybhcUFGDnzp113gOtUCjwxhtv1JsT+7YZM2Zg586dOHr0qJVHL2dvb4+YmBisWLEC169fh6OjI9LT0/Hyyy/Dx8cHkZGR6NGjB/7nf/6HHWYiIiIiImpyCkXLXFoKRe29nrZFzUZBWaWwWC6O4lKEibzYi6+bhcWyEZjgTynwINUOD/TYA5mC0pvCYikFnq9WjuKOUWS7TJU196/UALZKcW2rqhb3NuykalyqwbqUm6qFxbIV+G8p8tyLfH3/VnxDWCwHe3H/jj4vzxAW659frxQWKyP338JivdSpjbBYANBOI+5H8Joaca/vK1fFve+L5KFxEBarRuDXUnvbBx5Hkoj8vnP9ZpW4YAKJ7BGIfCDVvvMFwmIBwH/06HD/SlYS+JW6SS3dd/5RN6FR5g/zftRNsIq4dx4iIiIiIiKiJxg72HU4dOiQlIe7roWIiIiIiIjobuLmcj5G+vXrB6PR+KibQUREREREJJTAu7aoDuxg18HR0VHKb01ERERERERkDU4RJyIiIiIiIhKAI9hERERERERPCAU4R/xh4gg2ERERERERkQAcwW4hWgnMnywyl6PI/J6t1fbCYlULbJfI/MnlJnG5L0Xm1BaZb1pkHk1zlbhz7yQwrzAACDxMKARGq6wSmENWKe43WJH/liLzq1bVCsyPLvC99eK/K4TFEpm7uof/LGGxcg9+JCyWyPd8QOxnm63A15HIfNPmanHXfkGpuPzchddMwmL9ro2zsFgirzGNU/NMntxcxzTdHMR9PwTEfg8mqgtHsImIiIiIiIgE4Ag2ERERERHRE4Jpuh4ujmATERERERERCcAONhEREREREZEAnCJORERERET0hOAU8YerwSPYERERUCgUmDRpkkXZlClToFAoEBERIVt/5MgRKJVKBAQE1BnTbDZj5cqV6NOnD5ydnaHRaNCzZ0/MmzcPV65cAQC89tprGDZsWJ3bZ2ZmQqFQ4OTJk9K6N998E0qlEqmpqRb1Fy1aBIVCAYVCARsbG7Rv3x5hYWG4dOmStacBgwcPlsVo164dRo0ahYsXL8rqvfPOO+jbty9UKhV69epldXwiIiIiIiJqWRo1Rbxjx45ITU3FjRs3pHU3b95ESkoKPD09Lerr9Xq8/fbbyMjIQG5urqzMZDJh+PDhiI+PR0REBA4ePIgTJ05gxYoVKCoqwieffAIAiIqKwjfffGPRgb0dv1evXujTpw8AoKKiAmlpaZg1axZ0Ol2dx+Dr64u8vDz89ttvSEtLw+nTpxESEtKg8xAdHY28vDxcvnwZ27dvx6VLlxAeHi6rU1tbi8jISISGhjYoNhEREREREbUsjZoi3qdPH/zyyy9IT09HWFgYACA9PR0dO3ZE586dZXXLy8uxefNmHD9+HPn5+UhKSsKCBQuk8g8//BAZGRn4/vvv0bt3b2m9t7c3/P39Ufu/SXWDgoLQtm1bJCUlYeHChVK9253p+Ph4ad2WLVvw7LPPIjY2FlqtFjk5OfDy8pIfuK0tPDw8AADt27dHdHQ0pk2bhrKyMri4uFh1HpycnKQYWq0WU6dOtRjZ//jjjwEAhYWF+OGHH6yKS0RERERERC1Pox9yNmHCBBgMBulvvV6PyMhIi3ppaWnw8fGBj48PwsPDYTAYpE4zAKSkpGD48OGyzvWdFP+bDd7W1hbjx49HUlKSbPstW7bAbDZLHX0A0Ol0CA8Ph0ajwauvviprZ13y8/ORnp4OpVIJpVJp3Qm4S3FxMbZs2YL+/fs3ansiIiIiIqKH7fZtri1taSka3cEeN24cMjIykJOTg4sXL+Lw4cMW06OB/+vsAkBAQACuX7+O/fv3S+Xnzp2Dj4+PbJsRI0ZArVZDrVZj4MCB0vrIyEjk5OTgwIED0jq9Xo833ngDrq6uAICff/4Z3333nTQl+3anvqamRraP06dPQ61Ww8nJCVqtFgcOHMDUqVPh7Oxs9TlITEyEWq2Gs7Mz3NzckJ2dDb1eb/X29TGZTCgrK5MtJpPpgeMSERERERHRw9PoDra7uzsCAwOxfv16GAwGBAYGwt3dXVYnOzsbx44dw+jRowHcGoUODQ216ITe/YtEYmIijEYjIiMjUVFRIa3v1q0bBg4cKG1/4cIFHDp0SDZyrtPp4O/vL7Xl1VdfRXl5Ofbt2yfbh4+PD4xGI44fP45ly5ahV69eWLZsWYPOQVhYGIxGI06dOoWMjAx4e3vDz88P165da1CcuyUkJECj0ciWVSsSHigmERERERERPVwPlKYrMjISMTExAIA1a9ZYlOt0OlRVVaFDhw7SutraWtjZ2aGkpASurq7o2rUrzp49K9tOq9UCAFq3bm0RMyoqCjExMVizZg0MBgM6deqEl19+GQBQXV2NDRs2ID8/H7a2/3do1dXV0Ol08PPzk9bZ29vD29sbwK0Hnv3888+YPHkyPv/8c6uPX6PRSDG8vb2h0+mg1WqRlpaGiRMnWh3nbrGxsXjvvfdk66oV9o2OR0RERERERA9fo0ewgVtTvs1mM8xmM/z9/WVlVVVV2LBhA1avXg2j0Sgtp06dQqdOnbBp0yYAwJgxY7B3715kZWVZtc+QkBAolUokJydj/fr1mDBhgjQCvmvXLly7dg1ZWVmyfW7ZsgXbtm1DUVFRvXHnz5+PlJQUWaqvhrp9//adT1dvDJVKBRcXF9miUqkeKCYREREREZGNomUuLcUDjWArlUqcOXNG+v877dixAyUlJYiKioJGo5GVjRw5EjqdDjExMXj33Xexc+dODB06FIsWLcJLL70EV1dXnDt3Drt377aIq1arERoairi4OJSWlspybut0OgQGBqJnz56ybXx9fTF9+nRs3LgR77zzTp3H0rlzZ7z++utYsGABduzYYdXxV1RUID8/HwBQUFCADz74AA4ODrKR8vPnz+P69evIz8/HjRs3YDQaAQDPPvss7O05Kk1ERERERPS4eKARbADSCOvddDodhg0bZtG5BoDg4GAYjUacPHkSDg4O2L9/P+bMmQODwYBBgwahe/fumD59Ol588UVs27bNYvuoqCiUlJRg2LBhUt7tgoIC7Ny5E8HBwRb1FQoF3njjjXpzYt82Y8YM7Ny5E0ePHrXq2NetWwetVgutVoshQ4agsLAQu3btkj20beLEiejduzf+9re/4dy5c+jduzd69+6NK1euWLUPIiIiIiIiahkUtXfmvKJmq8Is7p/JRuAci5qa5tmuaoHtMlXW3L+SlWqa6ctN2Uzn3Yg8X452jUvBVx+R/5JV1c3zuhDJXCXudWRv+8C/DUtEXmOF18RleygoFRer/VMOwmL18J8lLFbuwY+ExRL5ng8Azipx7xe2SnHXq8jXkblaXKyS62ZhsUS+jn7XxvrMMPcj8hrTONkJiyVS8/wmAGT+Wv8tno3xh65thMVyeKC5wI/Onw/+8qib0Cjv/aHzo26CVcS96xMRERERERE9wdjBrsOhQ4ekPNx1LURERERERER3a6ETGx6ufv36SQ8jIyIiIiIiIrIGO9h1cHR0lPJbExERERERPS5sFM31jvvHA6eIExEREREREQnADjYRERERERGRAJwi3kKcLygXFusZrcAHtQmcYfLrv8Qdo1IprmFtWqmExVIJTC/0fU6JsFhKgVOFvNuKu75EJt4Rmd4GAGxtxP1bOtiLi2UnMCVQfulNYbEUAt8sVHbijtFGYLsOXfy3sFiLNmQJi/VBRB9hsUSm1vL8w3RhsYqOfSIsFiA6PaO4WLYCP9sqq4WFgpNK3NdJtVlcw0Sm9FMLzMeUW1QhLJbI99ZmmrETYxL2CY13STdaYLRmetLuo7n+Wz8uOIJNREREREREJAA72EREREREREQCsINNREREREREJADvwSYiIiIiInpCMEvXw8URbCIiIiIiInrsJCYm4ne/+x0cHBzQt29fHDp0yKrtDh8+DFtbW/Tq1avB+2xwBzsiIgIKhQKTJk2yKJsyZQoUCgUiIiJk648cOQKlUomAgIA6Y5rNZqxcuRJ9+vSBs7MzNBoNevbsiXnz5uHKlSsAgNdeew3Dhg2rc/vMzEwoFAqcPHlSWvfmm29CqVQiNTXVov6iRYugUCigUChgY2OD9u3bIywsDJcuXbL2NGDw4MGyGO3atcOoUaNw8eJFqc6pU6cwZswYdOzYEY6OjujevTv+8pe/WL0PIiIiIiIiari0tDRMnz4dc+fORVZWFl566SW88soryM3Nved2paWlGD9+PF5++eVG7bdRI9gdO3ZEamoqbty4Ia27efMmUlJS4OnpaVFfr9fj7bffRkZGhsUBmUwmDB8+HPHx8YiIiMDBgwdx4sQJrFixAkVFRfjkk1upN6KiovDNN9/IOrB3xu/Vqxf69LmViqSiogJpaWmYNWsWdDpdncfg6+uLvLw8/Pbbb0hLS8Pp06cREhLSoPMQHR2NvLw8XL58Gdu3b8elS5cQHh4ulZ84cQJt2rTBxo0b8eOPP2Lu3LmIjY3Fp59+2qD9EBERERERkfX+/Oc/IyoqChMnTkT37t3x0UcfoWPHjli7du09t3vrrbcwduxYDBgwoFH7bdQ92H369MEvv/yC9PR0hIWFAQDS09PRsWNHdO7cWVa3vLwcmzdvxvHjx5Gfn4+kpCQsWLBAKv/www+RkZGB77//Hr1795bWe3t7w9/fH7W1tzLhBgUFoW3btkhKSsLChQulerc70/Hx8dK6LVu24Nlnn0VsbCy0Wi1ycnLg5eUlP3BbW3h4eAAA2rdvj+joaEybNg1lZWVwcXGx6jw4OTlJMbRaLaZOnSob2Y+MjJTV79y5MzIzM5Geno6YmBir9kFERERERCSKTQvN390QZrMZJ06cwJw5c2Tr/fz8cOTIkXq3MxgMuHDhAjZu3IgPPvigUftu9D3YEyZMgMFgkP7W6/UWHUrg1tC8j48PfHx8EB4eDoPBIHWaASAlJQXDhw+Xda7vpPjfu/BtbW0xfvx4JCUlybbfsmULzGaz1NEHAJ1Oh/DwcGg0Grz66quydtYlPz8f6enpUCqVUCqV1p2AuxQXF2PLli3o37//PeuVlpaidevWjdoHERERERHRk8hkMqGsrEy2mEymOuv++9//RnV1Ndq1aydb365dO+Tn59e5zc8//4w5c+Zg06ZNsLVt/LPAG93BHjduHDIyMpCTk4OLFy/i8OHDsunRt93u7AJAQEAArl+/jv3790vl586dg4+Pj2ybESNGQK1WQ61WY+DAgdL6yMhI5OTk4MCBA9I6vV6PN954A66urgBunZjvvvsOoaGhACB16mtqamT7OH36NNRqNZycnKDVanHgwAFMnToVzs7OVp+DxMREqNVqODs7w83NDdnZ2dDr9fXWz8zMxObNm/HWW29ZvQ8iIiIiIqInXUJCAjQajWxJSEi45zaKux6ZXltba7EOAKqrqzF27FgsXrwYzzzzzAO1s9EdbHd3dwQGBmL9+vUwGAwIDAyEu7u7rE52djaOHTuG0aNHA7g1Ch0aGmrRCb37IBMTE2E0GhEZGYmKigppfbdu3TBw4EBp+wsXLuDQoUOykXOdTgd/f3+pLa+++irKy8uxb98+2T58fHxgNBpx/PhxLFu2DL169cKyZcsadA7CwsJgNBpx6tQpZGRkwNvbG35+frh27ZpF3R9//BGvv/46FixYgOHDh98zbl2/zpjr+XWGiIiIiIjIWgpFy1xiY2NRWloqW2JjY+s8Rnd3dyiVSovR6n/9618Wo9oAcO3aNXz//feIiYmBra0tbG1tsWTJEpw6dQq2trb45ptvrD6/D5QHOzIyUrqXeM2aNRblOp0OVVVV6NChg7SutrYWdnZ2KCkpgaurK7p27YqzZ8/KttNqtQBQ51TqqKgoxMTEYM2aNTAYDOjUqZP0hLfq6mps2LAB+fn5smH96upq6HQ6+Pn5Sevs7e3h7e0N4NYDz37++WdMnjwZn3/+udXHr9FopBje3t7Q6XTQarVIS0vDxIkTpXo//fQThg4diujoaMybN+++cRMSErB48WLZuknTYzH5vTir20ZERERERPS4UKlUUKlUVtW1t7dH3759sXfvXowYMUJav3fvXrz++usW9V1cXHD69GnZusTERHzzzTf44osv8Lvf/c7qdj5QBzsgIABmsxkA4O/vLyurqqrChg0bsHr1alnHFgCCg4OxadMmxMTEYMyYMZg3bx6ysrLqvQ/7TiEhIXjnnXeQnJyM9evXIzo6WhoB37VrF65du4asrCzZvdRnz55FWFgYioqK4ObmVmfc+fPn45lnnsG7774rPY28oW7v886nq//4448YOnQo/vM//9PqEfLY2Fi89957snXn/lXZqDYRERERERE9ad577z2MGzcO/fr1w4ABA/DZZ58hNzdXeih1bGwsLl++jA0bNsDGxgY9evSQbd+2bVs4ODhYrL+fB+pgK5VKnDlzRvr/O+3YsQMlJSWIioqCRqORlY0cORI6nQ4xMTF49913sXPnTgwdOhSLFi3CSy+9BFdXV5w7dw67d++2iKtWqxEaGoq4uDiUlpbKcm7rdDoEBgaiZ8+esm18fX0xffp0bNy4Ee+8806dx9K5c2dpCveOHTusOv6Kigpp2kFBQQE++OADODg4SD8o/PjjjxgyZAj8/Pzw3nvvSXWVSiXatGlTb9y6fp2xL71uVZuIiIiIiIiedKGhoSgqKsKSJUuQl5eHHj16YNeuXejUqRMAIC8v7745sRuj0fdg3+bi4lJnWiudTodhw4ZZdK6BWyPYRqMRJ0+ehIODA/bv3485c+bAYDBg0KBB6N69O6ZPn44XX3wR27Zts9g+KioKJSUlGDZsmJR3u6CgADt37kRwcLBFfYVCgTfeeKPenNi3zZgxAzt37sTRo0etOvZ169ZBq9VCq9ViyJAhKCwsxK5du6SHtm3ZsgWFhYXYtGmTVE+r1eL555+3Kj4REREREZFINoqWuTTGlClTkJOTA5PJhBMnTuAPf/iDVJaUlCR7ePbdFi1aBKPR2OB9KmrvzHlFzdYPl8SNYD+jVQuLVSPw8rlYWHH/SlZSKsXl92vTyrp7PaxhK7Bd3+eUCIulrONpio3l3Vbc9SXyzamxb8z1sbV54N8nJQ724mLZKcXFyi+9KSyWQmDOTVdnO2GxRH4CfnH6N2GxFm3IEhbrg4jG3fZUl1d9tMJief5hurBYRcc+ERYLAEyVNfevZCUbge+vIj9DbpirhcW6KfB8FV0T91DXDq0dhcVSOzzQpE+Z3CJx33dEvreK/pwU5fcztwmNd0k3WlgsJ7tmetLu46+ZOY+6CY0yaYDXo26CVcR9EyMiIiIiIiJ6gon7Oe4xcujQIbzyyiv1ll+/zvuhiYiIiIio5RE5s4YssYNdh379+jVqvj0RERERERE9udjBroOjo6OU35qIiIiIiIjIGrwHm4iIiIiIiEgAjmATERERERE9IXgL9sPFEWwiIiIiIiIiAZgHu4UoqRCXr9LRXikslsirR2ROTpG/zKnsxP0OJfKpjVcrKoXFEpkHW2RudJHXqmgirzGlwOSjIq+xm5XiXpMirzGROXftbcW9vkvKzcJi5QjMk+vZ2klYLCeBr0kXJ3GT6Nx+/7awWABQfOxTYbFEvieKfK+orBKY61tgu0R+rziXf01YLJHt6tzWWVwwgUQeo8hr9cTFEmGxAKB/59bCYjnbt8yh4HVHLz7qJjRKdP9Oj7oJVuEINhEREREREZEAvAebiIiIiIjoCcE82A8XR7CJiIiIiIiIBGAHm4iIiIiIiEgAThEnIiIiIiJ6QnCG+MPV4BHsiIgIKBQKTJo0yaJsypQpUCgUiIiIkK0/cuQIlEolAgIC6oxpNpuxcuVK9OnTB87OztBoNOjZsyfmzZuHK1euAABee+01DBs2rM7tMzMzoVAocPLkSWndm2++CaVSidTUVIv6ixYtgkKhgEKhgI2NDdq3b4+wsDBcunTJ2tOAwYMHy2K0a9cOo0aNwsWL//dUvqKiIgQEBKB9+/ZQqVTo2LEjYmJiUFZWZvV+iIiIiIiIqGVo1BTxjh07IjU1FTdu3JDW3bx5EykpKfD09LSor9fr8fbbbyMjIwO5ubmyMpPJhOHDhyM+Ph4RERE4ePAgTpw4gRUrVqCoqAiffPIJACAqKgrffPONrAN7Z/xevXqhT58+AICKigqkpaVh1qxZ0Ol0dR6Dr68v8vLy8NtvvyEtLQ2nT59GSEhIg85DdHQ08vLycPnyZWzfvh2XLl1CeHi4VG5jY4PXX38df//733Hu3DkkJSVh3759df44QURERERERC1bo6aI9+nTB7/88gvS09MRFhYGAEhPT0fHjh3RuXNnWd3y8nJs3rwZx48fR35+PpKSkrBgwQKp/MMPP0RGRga+//579O7dW1rv7e0Nf39/3E7THRQUhLZt2yIpKQkLFy6U6t3uTMfHx0vrtmzZgmeffRaxsbHQarXIycmBl5eX/MBtbeHh4QEAaN++PaKjozFt2jSUlZXBxcXFqvPg5OQkxdBqtZg6daqs8+zq6orJkydLf3fq1AlTpkzBypUrrYpPRERERERELUejH3I2YcIEGAwG6W+9Xo/IyEiLemlpafDx8YGPjw/Cw8NhMBikTjMApKSkYPjw4bLO9Z0U/3uTgK2tLcaPH4+kpCTZ9lu2bIHZbJY6+gCg0+kQHh4OjUaDV199VdbOuuTn5yM9PR1KpRJKpdK6E3CX4uJibNmyBf3796+3zpUrV5Ceno4//vGPjdoHERERERHRg7BpoUtL0ei2jhs3DhkZGcjJycHFixdx+PBh2fTo2253dgEgICAA169fx/79+6Xyc+fOwcfHR7bNiBEjoFaroVarMXDgQGl9ZGQkcnJycODAAWmdXq/HG2+8AVdXVwDAzz//jO+++w6hoaEAIHXqa2pqZPs4ffo01Go1nJycoNVqceDAAUydOhXOzs5Wn4PExESo1Wo4OzvDzc0N2dnZ0Ov1FvXGjBkDJycndOjQAS4uLvjv//5vq/dBRERERERELUOjO9ju7u4IDAzE+vXrYTAYEBgYCHd3d1md7OxsHDt2DKNHjwZwaxQ6NDTUohOquOtRdomJiTAajYiMjERFRYW0vlu3bhg4cKC0/YULF3Do0CHZyLlOp4O/v7/UlldffRXl5eXYt2+fbB8+Pj4wGo04fvw4li1bhl69emHZsmUNOgdhYWEwGo04deoUMjIy4O3tDT8/P1y7dk1W78MPP8TJkyexbds2XLhwAe+9994945pMJpSVlckWk8nUoLYRERERERFR03qgNF2RkZGIiYkBAKxZs8aiXKfToaqqCh06dJDW1dbWws7ODiUlJXB1dUXXrl1x9uxZ2XZarRYA0Lp1a4uYUVFRiImJwZo1a2AwGNCpUye8/PLLAIDq6mps2LAB+fn5sLX9v0Orrq6GTqeDn5+ftM7e3h7e3t4Abj3w7Oeff8bkyZPx+eefW338Go1GiuHt7Q2dTgetVou0tDRMnDhRqufh4QEPDw9069YNbm5ueOmllzB//nzpOO+WkJCAxYsXy9a9Hzcfc+YurLM+ERERERERPXoPNJ09ICAAZrMZZrMZ/v7+srKqqips2LABq1evhtFolJZTp06hU6dO2LRpE4Bb06f37t2LrKwsq/YZEhICpVKJ5ORkrF+/HhMmTJBGwHft2oVr164hKytLts8tW7Zg27ZtKCoqqjfu/PnzkZKSIkv11VC379++8+nqd7t9//i9RqRjY2NRWloqW96dOafR7SIiIiIiIgIgpRpuaUtL8UAj2EqlEmfOnJH+/047duxASUkJoqKioNFoZGUjR46ETqdDTEwM3n33XezcuRNDhw7FokWL8NJLL8HV1RXnzp3D7t27LeKq1WqEhoYiLi4OpaWlspzbOp0OgYGB6Nmzp2wbX19fTJ8+HRs3bsQ777xT57F07twZr7/+OhYsWIAdO3ZYdfwVFRXIz88HABQUFOCDDz6Ag4ODNFK+a9cuFBQU4Pnnn4darcZPP/2E999/Hy+++KLFU83vpFKpoFKpZOuqK6qtahMRERERERE9Gg/8QDYXF5c601rpdDoMGzbMonMNAMHBwTAajTh58iQcHBywf/9+zJkzBwaDAYMGDUL37t0xffp0vPjii9i2bZvF9lFRUSgpKcGwYcOkvNsFBQXYuXMngoODLeorFAq88cYb9ebEvm3GjBnYuXMnjh49atWxr1u3DlqtFlqtFkOGDEFhYSF27dolPbTN0dER69atkx1TUFCQ1R14IiIiIiIiajkUtXfmvKJmq0TgCLajfeNSkdVF5NVzwyzuGEXOIlHZiUsMYCOwYVcrKoXFUgpsV43Ai0LktSqayGtMaSMumMhr7GaluNekyGvsZmXN/StZyd5W3Ou7pNwsLFZOUcX9K1nJs7WTsFhOAl+TLk4PNIlOxu33bwuLBQDFxz4VFkvke6LI94rKKnGvIxuB7RL5veJc/rX7V7KSyHZ1bmt9xpqmJPIYRV6rJy6WCIsFAP07Wz7jqbGc7VvOtOU7bfj+0qNuQqOM79fxUTfBKi0ppRgRERERERFRs8UOdh0OHTok5eGuayEiIiIiIiK6m7j5WY+Rfv36wWg0PupmEBERERERUQvCDnYdHB0dpfzWREREREREjwuRz2shS5wiTkRERERERCQAO9hEREREREREAnCKOBERERER0ROCE8QfLnawW4izV8TlcnzOUyMslsg8hyLzVdraiMxdLSyUUK1b2QuLVV0jLvmlQuDbdrlJXB5me1ux/5Aq2+abo1sUJ5W4YxR5u5ezsnme++umKmGxvAXmyXV1FvdeUSPwvcIkMJ+5yLzVAND69zHCYm1PXigslklg7urKGnGxurdxERarWmAyZke75vleITLftEi1ENewanGXF57/nau4YBD73ZWoLpwiTkRERERERCQAO9hEREREREREAnCKOBERERER0ROCWboeLo5gExEREREREQnADjYRERERERGRAE3awY6IiIBCobBYAgICAABeXl4WZU8//bS0vZeXFz766KP77ic5ORlKpRKTJk2qs7ysrAzz58+Hr68vHB0d4ebmhueffx4rVqxASUmJVccyePBgqY329vbo0qULYmNjYTKZZPVKSkowbtw4aDQaaDQajBs3DlevXrVqH0RERERERNRyNPk92AEBATAYDLJ1KpVK+v8lS5YgOjpa+lvZiHQser0e77//PtauXYs///nPcHJyksqKi4sxaNAglJWVYenSpejbty/s7e1x/vx5JCcnIzk5GVOnTrVqP9HR0ViyZAnMZjOOHz+OCRMmAAASEhKkOmPHjsVvv/2Gr776CgDw5ptvYty4cfjHP/7R4OMiIiIiIiJ6EArehP1QNXkHW6VSwcPDo97yVq1a3bP8fnJycnDkyBFs3boV3377Lb744guMHz9eKo+Li0Nubi6ys7PRoUMHaX23bt0QFBSE2gYkJ3RycpLa6unpieTkZOzZs0fqYJ85cwZfffUVvvvuO/Tv3x8AsG7dOgwYMADZ2dnw8fFp9HESERERERFR8/LY3YOt1+sRGBgIjUaD8PBw6HQ6qaympgZpaWkIDw+Xda7v1NhfdE6dOoXDhw/Dzs5OWpeZmQmNRiN1rgHghRdegEajwZEjRxq1HyIiIiIiImqemryDvWPHDqjVatmydOlSqXz27Nmyso8//tjq2DU1NUhKSkJ4eDgAYPTo0cjMzMT58+cBAIWFhbh69arFyHHfvn2l/Y0ZM8bq/SUmJkKtVkOlUqFXr14oLCzErFmzpPL8/Hy0bdvWYru2bdsiPz/f6v0QERERERGJYNNCl5aiyaeIDxkyBGvXrpWta926tfT/s2bNQkREhPS3u7u71bH37NmD8vJyvPLKK9K2fn5+0Ov1iI+Pl+rdPUr95Zdfwmw2Y/bs2bhx44bV+wsLC8PcuXNRVlaG5cuXw8XFBcHBwbI6dY2I19bW3nOk3GQyWTwszWwywf6Oe9WJiIiIiIioeWnyDrazszO8vb3rLXd3d79n+b3o9XoUFxfLHmpWU1ODrKwsLF26FG3atMFTTz2Fs2fPyrbz9PQEcOv+74Y84Vuj0Uht3bhxI3x9faHT6RAVFQUA8PDwQEFBgcV2hYWFaNeuXb1xExISsHjxYtm6yLdnY+K0OVa3jYiIiIiIiJpWSxptv6eioiJs374dqampMBqNsuX69evYvXs3bGxsEBISgo0bN+Ly5ctC929nZ4e4uDjMmzcPFRUVAIABAwagtLQUx44dk+odPXoUpaWlGDhwYL2xYmNjUVpaKlvGv/Wu0PYSERERERGRWE0+gm0ymSzuP7a1tbV6Kvjly5dhNBpl6zw9PfH555/Dzc0No0aNgo2N/HeDoKAg6HQ6BAUFIT4+HgcOHED//v2xZMkS9OvXD87Ozvjhhx+QmZmJHj16NPrYxo4di7i4OCQmJmLmzJno3r07AgICEB0djb/97W8AbqXpCgoKuucTxFUqlSx1GQDYq2oa3S4iIiIiIiKAaboetibvYH/11VfQarWydT4+PhbTtuuzatUqrFq1SrbOYDBAr9djxIgRFp1rAAgODkZoaCgKCgrQrl07HDt2DMuXL8fKlSvx66+/wsbGBl27dkVoaCimT5/e6GOzt7dHTEwMVqxYgUmTJkGtVmPTpk2YNm0a/Pz8AAB/+tOf8OmnnzZ6H0RERERERNQ8KWobkviZHpnM81eFxXrOUyMsltJG3C9gxotXhcWyreOHlsYSeIhCtW5lLyxWdY24twEFmucJs7cV2y6VrVJovObISSXuGJ+EH8t/+Ve5sFhPOdndv5KVXJ3FvVfUCHyvqBIYy8FO7Oux9e9jhMXanrxQWCxTlbjZbJU14mJ1b+MiLFa1wK+lymb6xtNO4/Com1CnWjTP7wJ2gj+/7ZTiviM6NPlQpRibjVcedRMaJaRX+0fdBKu00MuCiIiIiIiIGqp5/vT0+HhsHnIm0qFDhyxydd+5EBEREREREd2NI9h16Nevn8WD1IiIiIiIiIjuhR3sOjg6OjY6FzcRERERERE9mdjBJiIiIiIiekIwTdfDxXuwiYiIiIiIiATgCHYLUWIyC4sl8jcrkSlbRKbWKhV4vpztmufLRGCGFYhM1icyzYdICoXY3xNFpsuxVTbPX5JFXhdPwo/lIlO3lZRXCoulcRSX8stWYHqbmlpxr6EawRlHRabWen3sYmGxkvSxwmKJTIclksjPNqXIj2+Bp+vf103CYolM6Sfy3EMh7oTZKcWm4auqFviPKTiFGD0eOIJNREREREREJEDzHJojIiIiIiIi4TjC+nDx/BIREREREREJwA42ERERERERkQCcIk5ERERERPSEYJquh6tJR7AjIiKgUCgsloCAAACAl5eXRdnTTz8tbe/l5YWPPvrovvtJTk6GUqnEpEmT6iwvKyvD/Pnz4evrC0dHR7i5ueH555/HihUrUFJSYtWxDB48WGqjvb09unTpgtjYWJhM8idDLlu2DAMHDoSTkxOeeuopq2ITERERERFRy9PkI9gBAQEwGAyydSqVSvr/JUuWIDo6Wvpb2YhH8+v1erz//vtYu3Yt/vznP8PJyUkqKy4uxqBBg1BWVoalS5eib9++sLe3x/nz55GcnIzk5GRMnTrVqv1ER0djyZIlMJvNOH78OCZMmAAASEhIkOqYzWaMGjUKAwYMgE6na/CxEBERERERUcvQ5B1slUoFDw+PestbtWp1z/L7ycnJwZEjR7B161Z8++23+OKLLzB+/HipPC4uDrm5ucjOzkaHDh2k9d26dUNQUBBqG5AX0snJSWqrp6cnkpOTsWfPHlkHe/HiW/kvk5KSGn1MRERERERE1Pw9dg850+v1CAwMhEajQXh4uGzUuKamBmlpaQgPD5d1ru/U2HsSTp06hcOHD8POzq5R2xMRERERET1siha6tBRN3sHesWMH1Gq1bFm6dKlUPnv2bFnZxx9/bHXsmpoaJCUlITw8HAAwevRoZGZm4vz58wCAwsJCXL16FT4+PrLt+vbtK+1vzJgxVu8vMTERarUaKpUKvXr1QmFhIWbNmmX19kRERERERPT4aPIp4kOGDMHatWtl61q3bi39/6xZsxARESH97e7ubnXsPXv2oLy8HK+88oq0rZ+fH/R6PeLj46V6d49Sf/nllzCbzZg9ezZu3Lhh9f7CwsIwd+5clJWVYfny5XBxcUFwcLDV29fHZDJZPCyt0myCnb2qni2IiIiIiIjoUWvyDrazszO8vb3rLXd3d79n+b3o9XoUFxfLHmpWU1ODrKwsLF26FG3atMFTTz2Fs2fPyrbz9PQEcOv+76tXr1q9P41GI7V148aN8PX1hU6nQ1RUVKPaf1tCQoJ07/ZtYyfPRNhUjo4TEREREVHjMUvXw/XY3INdVFSE7du3IzU1FUajUbZcv34du3fvho2NDUJCQrBx40ZcvnxZ6P7t7OwQFxeHefPmoaKi4oFixcbGorS0VLaERE8T1FIiIiIiIiJ6GJp8BNtkMiE/P1/eCFtbq6eCX758GUajUbbO09MTn3/+Odzc3DBq1CjY2Mh/NwgKCoJOp0NQUBDi4+Nx4MAB9O/fH0uWLEG/fv3g7OyMH374AZmZmejRo0ejj23s2LGIi4tDYmIiZs6cCQDIzc1FcXExcnNzUV1dLbXd29sbarW6zjgqlUqWugwA7OxvNrpdRERERERE9PA1eQf7q6++glarla3z8fGxmLZdn1WrVmHVqlWydQaDAXq9HiNGjLDoXANAcHAwQkNDUVBQgHbt2uHYsWNYvnw5Vq5ciV9//RU2Njbo2rUrQkNDMX369EYfm729PWJiYrBixQpMmjQJarUaCxYswPr166U6vXv3BgB8++23GDx4cKP3RURERERERM2LorYhiZ/pkdn147+ExRrctY2wWCL9dPmasFilJrOwWM52Tf47lFXaujgIi1XzBLwNONorhcYTecpslc3zZii1Sty1X8dvn4+dy8XiZhrdrKwWFsvL3en+laxkqxT3D2muqhEWS/Rr6PCFfwuL9frYxfevZKUkfaywWNUC38R6e7gKi1VZLa5ddrYCrwuB7/lKgdfrU07i0sPWiHtJCs2p5Cz487uxKXnrolY1z8/v+/nH6YJH3YRGee3/tXvUTbDKE/CVh4iIiIiIiOjhYwe7DocOHbLI1X3nQkRERERERHS35jn39RHr16+fxYPUiIiIiIiIiO6FHew6ODo6NjoXNxERERERUXPFPNgPF6eIExEREREREQnADjYRERERERGRAJwiTkRERERE9IRQiMyjRhbYwW4hRObcFZn/T+Q9HK0cxV2OInNfmirFJYZ0sBOXy9HVWVzuy8pqkckvmyeh+T0BofN/qgSef5F5iptrfu7mqp1GJSyWncB/x9yiCmGxPDQOwmKJvL6UNmKvVZPAHN0ic1dHRCYIizVv1XRhsZ7vIO56VSjEfeGprhEXy0bge77Iz6MqgXnDRV73jgK/79gIfn0TPWycIk5EREREREQkADvYRERERERERAJwijgREREREdETgmm6Hi6OYBMREREREREJwA42ERERERERkQBN2sGOiIiAQqGwWAICAgAAXl5eFmVPP/20tL2Xlxc++uij++4nOTkZSqUSkyZNqrO8rKwM8+fPh6+vLxwdHeHm5obnn38eK1asQElJiVXHMnjwYKmN9vb26NKlC2JjY2EymaQ6OTk5iIqKwu9+9zs4OjqiS5cuWLhwIcxms1X7ICIiIiIiEskGiha5tBRNfg92QEAADAaDbJ1K9X+pTZYsWYLo6Gjpb6Wy4Y/51+v1eP/997F27Vr8+c9/hpOTk1RWXFyMQYMGoaysDEuXLkXfvn1hb2+P8+fPIzk5GcnJyZg6dapV+4mOjsaSJUtgNptx/PhxTJgwAQCQkHArjcbZs2dRU1ODv/3tb/D29sY///lPREdHo7y8HKtWrWrwcREREREREVHz1eQdbJVKBQ8Pj3rLW7Vqdc/y+8nJycGRI0ewdetWfPvtt/jiiy8wfvx4qTwuLg65ubnIzs5Ghw4dpPXdunVDUFAQahuQcNrJyUlqq6enJ5KTk7Fnzx6pgx0QECCNzgNA586dkZ2djbVr17KDTURERERE9Jh57O7B1uv1CAwMhEajQXh4OHQ6nVRWU1ODtLQ0hIeHyzrXd1I08rF6p06dwuHDh2FnZ3fPeqWlpWjdunWj9kFERERERETNV5N3sHfs2AG1Wi1bli5dKpXPnj1bVvbxxx9bHbumpgZJSUkIDw8HAIwePRqZmZk4f/48AKCwsBBXr16Fj4+PbLu+fftK+xszZozV+0tMTIRarYZKpUKvXr1QWFiIWbNm1Vv/woUL+OSTT+q9N5yIiIiIiOhhUiha5tJSNPkU8SFDhmDt2rWydXeO6M6aNQsRERHS3+7u7lbH3rNnD8rLy/HKK69I2/r5+UGv1yM+Pl6qd/co9Zdffgmz2YzZs2fjxo0bVu8vLCwMc+fORVlZGZYvXw4XFxcEBwfXWffKlSsICAjAqFGjMHHixHvGNZlMsoelAUCl2QQ7e1U9WxAREREREdGj1uQdbGdnZ3h7e9db7u7ufs/ye9Hr9SguLpY91KympgZZWVlYunQp2rRpg6eeegpnz56Vbefp6Qng1v3fV69etXp/Go1GauvGjRvh6+sLnU6HqKgoWb0rV65gyJAhGDBgAD777LP7xk1ISMDixYtl68ZMnomwKfWPjhMREREREdGj9djcg11UVITt27cjNTUVRqNRtly/fh27d++GjY0NQkJCsHHjRly+fFno/u3s7BAXF4d58+ahoqJCWn/58mUMHjwYffr0gcFggI3N/U95bGwsSktLZUvIxGlC20tERERERERiNfkItslkQn5+vrwRtrZWTwW/fPkyjEajbJ2npyc+//xzuLm5YdSoURad2KCgIOh0OgQFBSE+Ph4HDhxA//79sWTJEvTr1w/Ozs744YcfkJmZiR49ejT62MaOHYu4uDgkJiZi5syZuHLlCgYPHgxPT0+sWrUKhYWFUt17PSldpVLJUpcBgJ39zUa3i4iIiIiICGhZ9zO3RE3ewf7qq6+g1Wpl63x8fCymbddn1apVFimuDAYD9Ho9RowYUecIcXBwMEJDQ1FQUIB27drh2LFjWL58OVauXIlff/0VNjY26Nq1K0JDQzF9+vRGH5u9vT1iYmKwYsUKTJo0CXv27MH58+dx/vx5PP3007K6DUkHRkRERERERM2fopY9vRZh5z//JSzWUJ+2wmKJ/AXs4r8r7l/JSjcrq4XFMlXWCIvlYKcUFqujm6OwWJXV4o6xuaoRfYgCr/0qgeffVinuzp+nnO6ddpDkzFXi/h3tBP475haJe2/10DgIi2WrFPciUtqIHY7Ze6ZAWKyKqiphsSIiE4TFmrdqurBYIT3qTn3aGNU14r6WioxlxR1+VlMI/ABxcRQ3VmYS+B7mKPD7TiuBxyias33LHArec6bw/pWaIb/ubR51E6zSfK9YIiIiIiIiEkrkjzxk6bF5yJlIhw4dssjVfedCREREREREdDeOYNehX79+Fg9SIyIiIiIiIroXdrDr4Ojo2Ohc3ERERERERPRkYgebiIiIiIjoCSH4uZB0F96DTURERERERCQAO9hEREREREREAnCKeAtRVSsuN2EtBKY+rxU3x6S5zlZR2Yn7HarCLC4fqo3AJOQKgbFE5h0VeakKJ7BttgITrIrMxfwkqBX47ygyn7xtM52/ZxZ4jJXVwkJBZSt2vKCyRtxxVgu8yETmrv5g5kfCYoV+vVJYLJEEfrQJ/WwT+equEXh92QnMTV92s1JYLJHfwwDx7xdEd2MHm4iIiIiI6AnBPNgPF3/CISIiIiIiIhKAHWwiIiIiIiIiAThFnIiIiIiI6Akh8vkEZIkj2EREREREREQCNGkHOyIiAgqFwmIJCAgAAHh5eVmUPf3009L2Xl5e+Oijj+67n+TkZCiVSkyaNKnO8rKyMsyfPx++vr5wdHSEm5sbnn/+eaxYsQIlJSVWHcvgwYOlNtrb26NLly6IjY2FyWSS1fvTn/4ET09PODg4QKvVYty4cbhy5YpV+yAiIiIiIqKWo8lHsAMCApCXlydbUlJSpPIlS5bIyrKyshq8D71ej/fffx+pqamoqKiQlRUXF+OFF16AwWDAzJkzcfToURw+fBgLFy6E0WhEcnKy1fuJjo5GXl4ezp8/jxUrVmDNmjVYtGiRrM6QIUOwefNmZGdnY+vWrbhw4QJGjhzZ4GMiIiIiIiKi5q3J78FWqVTw8PCot7xVq1b3LL+fnJwcHDlyBFu3bsW3336LL774AuPHj5fK4+LikJubi+zsbHTo0EFa361bNwQFBaG2AfkEnZycpLZ6enoiOTkZe/bsQUJCglTn3Xfflf6/U6dOmDNnDv7jP/4DlZWVsLOza/RxEhERERERNRTTdD1cj9092Hq9HoGBgdBoNAgPD4dOp5PKampqkJaWhvDwcFnn+k6KRt71f+rUKRw+fPienebi4mJs2rQJAwcOZOeaiIiIiIjoMdPkHewdO3ZArVbLlqVLl0rls2fPlpV9/PHHVseuqalBUlISwsPDAQCjR49GZmYmzp8/DwAoLCzE1atX4ePjI9uub9++0v7GjBlj9f4SExOhVquhUqnQq1cvFBYWYtasWRb1Zs+eDWdnZ7i5uSE3Nxfbt2+3eh9ERERERETUMjT5FPEhQ4Zg7dq1snWtW7eW/n/WrFmIiIiQ/nZ3d7c69p49e1BeXo5XXnlF2tbPzw96vR7x8fFSvbtHqb/88kuYzWbMnj0bN27csHp/YWFhmDt3LsrKyrB8+XK4uLggODjYot6sWbMQFRWFixcvYvHixRg/fjx27NhR72i5yWSyeFhapdkEO3uV1W0jIiIiIiK6mw1niD9UTd7BdnZ2hre3d73l7u7u9yy/F71ej+LiYjg5OUnrampqkJWVhaVLl6JNmzZ46qmncPbsWdl2np6eAG7d/3316lWr96fRaKS2bty4Eb6+vtDpdIiKirI4Jnd3dzzzzDPo3r07OnbsiO+++w4DBgyoM25CQgIWL14sWxc6aQbGTJlpdduIiIiIiIioaT0292AXFRVh+/btSE1NhdFolC3Xr1/H7t27YWNjg5CQEGzcuBGXL18Wun87OzvExcVh3rx5Fk8uv9Pth6jdPUJ9p9jYWJSWlsqWkRPfFtpeIiIiIiIiEqvJR7BNJhPy8/PljbC1tXoq+OXLl2E0GmXrPD098fnnn8PNzQ2jRo2CjY38d4OgoCDodDoEBQUhPj4eBw4cQP/+/bFkyRL069cPzs7O+OGHH5CZmYkePXo0+tjGjh2LuLg4JCYmYubMmTh27BiOHTuGQYMGwdXVFb/88gsWLFiALl261Dt6Ddx60rpKJZ8Obmdff6ediIiIiIiIHr0m72B/9dVX0Gq1snU+Pj4W07brs2rVKqxatUq2zmAwQK/XY8SIERadawAIDg5GaGgoCgoK0K5dOxw7dgzLly/HypUr8euvv8LGxgZdu3ZFaGgopk+f3uhjs7e3R0xMDFasWIFJkybB0dER6enpWLhwIcrLy6HVahEQEIDU1FSLDjQREREREdHDxjRdD5eitiGJn+mR2X46//6VrDS8WzthsUS+QC8ViRulv1FZLSyWSDfM4trVvb2LsFjm6hphsaprBL6l8N2pwUT+W7Z1efx/CBT5CVhhrhIWy9FOKSzWpWLrH955P63V9sJiifx6p7IVe8fb3uwCYbFuVot73/+15KawWB/M/EhYrNNfrxQWq6pa3IuyRuALXGQskd+dNE5NPlZmlQqB33fatBL7WSTy/cLJvmV2VA+dK3nUTWiUl55xfdRNsMpjcw82ERERERER0aPEDnYdDh06ZJGr+86FiIiIiIiI6G7Nc17JI9avXz+LB6kRERERERG1dIqWObO9xWAHuw6Ojo6NzsVNRERERERETyZOESciIiIiIiISgCPYRERERERETwjOEH+42MFuIfZfuCos1svPiEvTVQtxKYGKr5uFxRKZ3clG4I0qIlNzbP7hkrBYKqW4ySxiz5c4L3uLu+4BwM62eV4XKjtx/5YFpSZhsRwEtqu5Ennt/6tM3Ln30DgIi1VQKi5NlJNK3FcQR3txac0AoHsbcWkQRXq+g7jXUajA1Fr/z3+WsFjrDXHCYokkMqtt/45uwmIJzMwolMpW7GtSJAVvQKaH7PH/xkNERERERETUBNjBJiIiIiIiIhKAU8SJiIiIiIieECJvaSJLHMEmIiIiIiIiEoAdbCIiIiIiIiIBmrSDHRERAYVCYbEEBAQAALy8vCzKnn76aWl7Ly8vfPTRR/fdT3JyMpRKJSZNmlRneVlZGebPnw9fX184OjrCzc0Nzz//PFasWIGSkhKrjmXw4MFSG21sbNCuXTuMGjUKFy9elNXLzc3Fa6+9BmdnZ7i7u2PatGkwm8U9LZuIiIiIiMhaiha6tBRNPoIdEBCAvLw82ZKSkiKVL1myRFaWlZXV4H3o9Xq8//77SE1NRUVFhaysuLgYL7zwAgwGA2bOnImjR4/i8OHDWLhwIYxGI5KTk63eT3R0NPLy8nD58mVs374dly5dQnh4uFReXV2NwMBAlJeXIyMjA6mpqdi6dStmzJjR4GMiIiIiIiKi5q3JH3KmUqng4eFRb3mrVq3uWX4/OTk5OHLkCLZu3Ypvv/0WX3zxBcaPHy+Vx8XFITc3F9nZ2ejQoYO0vlu3bggKCmpQnkMnJyeprVqtFlOnTpWNmu/Zswc//fQTLl26hPbt2wMAVq9ejYiICCxbtgwuLs0zzyYRERERERE13GN3D7Zer0dgYCA0Gg3Cw8Oh0+mkspqaGqSlpSE8PFzWub5TY5PPFxcXY8uWLejfv7+0LjMzEz169JA61wDg7+8Pk8mEEydONGo/RERERERE1Dw1eQd7x44dUKvVsmXp0qVS+ezZs2VlH3/8sdWxa2pqkJSUJE3THj16NDIzM3H+/HkAQGFhIa5evQofHx/Zdn379pX2N2bMGKv3l5iYCLVaDWdnZ7i5uSE7Oxt6vV4qz8/PR7t27WTbuLq6wt7eHvn5+Vbvh4iIiIiISIhHfTP1Y34TdpNPER8yZAjWrl0rW9e6dWvp/2fNmoWIiAjpb3d3d6tj79mzB+Xl5XjllVekbf38/KDX6xEfHy/Vu3uU+ssvv4TZbMbs2bNx48YNq/cXFhaGuXPnAgAKCgoQHx8PPz8/nDhxAq1atapzXwBQW1t7z5Fyk8kEk8kkW1ddaYbSzt7qthEREREREVHTavIOtrOzM7y9vestd3d3v2f5vej1ehQXF8PJyUlaV1NTg6ysLCxduhRt2rTBU089hbNnz8q28/T0BHDr/u+rV69avT+NRiO11dvbGzqdDlqtFmlpaZg4cSI8PDxw9OhR2TYlJSWorKy0GNm+U0JCAhYvXixb9/vQqeg/OsbqthEREREREVHTemzuwS4qKsL27duRmpoKo9EoW65fv47du3fDxsYGISEh2LhxIy5fviy8DUqlEgCkUfABAwbgn//8J/Ly8qQ6e/bsgUqlQt++feuNExsbi9LSUtnSL/hN4e0lIiIiIiIicZp8BNtkMlncf2xra2v1VPDLly/DaDTK1nl6euLzzz+Hm5sbRo0aBRsb+e8GQUFB0Ol0CAoKQnx8PA4cOID+/ftjyZIl6NevH5ydnfHDDz9IDyWzVkVFhXQsBQUF+OCDD+Dg4AA/Pz8AgJ+fH5599lmMGzcOK1euRHFxMWbOnIno6Oh7PkFcpVJBpVLJ1nF6OBERERERPShFS7qhuQVq8g72V199Ba1WK1vn4+NjMW27PqtWrcKqVatk6wwGA/R6PUaMGGHRuQaA4OBghIaGoqCgAO3atcOxY8ewfPlyrFy5Er/++itsbGzQtWtXhIaGYvr06VYfy7p167Bu3ToAtx5e9txzz2HXrl3SQ9SUSiV27tyJKVOm4MUXX4SjoyPGjh1r0X4iIiIiIiJq+RS1DUn8TI/MtG3W/QBhjfhXfO5fyUq1EHf5/HipTFgskRe1TSNTtz1sP/z7qrBYKqW4u0VEni+RZ/5l7/qfe9AYdrYij1NgLIEnrexGlbBYDnaPzR1J9RJ57ZebxJ17V2dxM6AKSm8Ki+WkEvcbv6uznbBYAJD77wqh8USxFfheLfK94v/5zxIWa70hTlgskUR+Xe7f0U1YrMaml21JXBzFjgc62CmFxXIU+9bTZI5eKH3UTWiU/l00j7oJVmnyEWwiIiIiIiJ6NJ6A32Ueqcd/SKERDh06ZJGr+86FiIiIiIiI6G4cwa5Dv379LB6kRkRERERERHQv7GDXwdHRsdG5uImIiIiIiOjJxA42ERERERHRE4K3YD9cvAebiIiIiIiISAB2sImIiIiIiIgE4BTxFqK1k7icfbZKkRNDxMVSCmxXTY2wULBppj9Debk4C4uVU1YuLJZG1TyTQor+d2yuuatFsheY67uqRlwOWaVN88y1LrJdNgJj1QjM31t4zSQsltpcLSzWU05i33eqBZ4zkZ9HCoW4dokkMnf1f06IFxbr07+9LyyWyNeRqUrcReEsMJ98tcD36VqIi2Uj+ENSZNta7GTrFtrslqKZdh2IiIiIiIiIWhZ2sImIiIiIiIgEYAebiIiIiIiISADeg01ERERERPSEEPkcGbLEEWwiIiIiIiIiAZq0gx0REQGFQmGxBAQEAAC8vLwsyp5++mlpey8vL3z00Uf33U9ycjKUSiUmTZpUZ3lZWRnmz58PX19fODo6ws3NDc8//zxWrFiBkpISq45l8ODBUhttbGzQrl07jBo1ChcvXpTVe+edd9C3b1+oVCr06tXLqthERERERETU8jT5CHZAQADy8vJkS0pKilS+ZMkSWVlWVlaD96HX6/H+++8jNTUVFRUVsrLi4mK88MILMBgMmDlzJo4ePYrDhw9j4cKFMBqNSE5Otno/0dHRyMvLw+XLl7F9+3ZcunQJ4eHhsjq1tbWIjIxEaGhog4+DiIiIiIiIWo4mvwdbpVLBw8Oj3vJWrVrds/x+cnJycOTIEWzduhXffvstvvjiC4wfP14qj4uLQ25uLrKzs9GhQwdpfbdu3RAUFITaBuQ5dHJyktqq1WoxdepUi1Hzjz/+GABQWFiIH374odHHRURERERE9KAEpxanuzx292Dr9XoEBgZCo9EgPDwcOp1OKqupqUFaWhrCw8Nlnes7KRp5xRUXF2PLli3o379/o7YnIiIiIiKilq3JO9g7duyAWq2WLUuXLpXKZ8+eLSu7PQJsjZqaGiQlJUnTtEePHo3MzEycP38ewK1R5KtXr8LHx0e2Xd++faX9jRkzxur9JSYmQq1Ww9nZGW5ubsjOzoZer7d6eyIiIiIiInp8NPkU8SFDhmDt2rWyda1bt5b+f9asWYiIiJD+dnd3tzr2nj17UF5ejldeeUXa1s/PD3q9HvHx8VK9u0epv/zyS5jNZsyePRs3btywen9hYWGYO3cuAKCgoADx8fHw8/PDiRMn0KpVK6vj3M1kMsFkMsnWVZnNsLW3b3RMIiIiIiIizhB/uJq8g+3s7Axvb+96y93d3e9Zfi96vR7FxcVwcnKS1tXU1CArKwtLly5FmzZt8NRTT+Hs2bOy7Tw9PQHcuv/76tWrVu9Po9FIbfX29oZOp4NWq0VaWhomTpzYqGMAgISEBCxevFi27o/hMRg8blqjYxIREREREdHD9djcg11UVITt27cjNTUVRqNRtly/fh27d++GjY0NQkJCsHHjRly+fFl4G5RKJQA0aBS8LrGxsSgtLZUtg0LrTjlGREREREREzUOTj2CbTCbk5+fLG2Fra/VU8MuXL8NoNMrWeXp64vPPP4ebmxtGjRoFGxv57wZBQUHQ6XQICgpCfHw8Dhw4gP79+2PJkiXo168fnJ2d8cMPPyAzMxM9evSw+lgqKiqkYykoKMAHH3wABwcH+Pn5SXXOnz+P69evIz8/Hzdu3JDa/uyzz8K+ninfKpUKKpVKto7Tw4mIiIiIiJq3Ju9gf/XVV9BqtbJ1Pj4+FtO267Nq1SqsWrVKts5gMECv12PEiBEWnWsACA4ORmhoKAoKCtCuXTscO3YMy5cvx8qVK/Hrr7/CxsYGXbt2RWhoKKZPn271saxbtw7r1q0DALi6uuK5557Drl27ZA9RmzhxIv7nf/5H+rt3794AgF9//RVeXl5W74uIiIiIiOiB8Sbsh0pR25DEz/TILNrzs7BYc4Z2FRZLpNOXSoXFqqkRFgp1/GbTLFy9WSksVk5ZubBYGpWdsFgiDe7SRmg8W4EXRnPNR3mzslpYrBqBnzRKG3EnTOSpt1OKuyaum6qExWrlIO639J8ulwmLpVaJa1eXdmphsQAg59/i3hNFfh7Z2zbPD6QfCq4Ki/WfE+LvX8lKn/7tfWGxagR+Xf5DJ3GfR84CX0fVAt+oayEulquT2Fmcdrbi3vmd7JrpB/h9nLwo7r28KfXp5PKom2CV5vlOTURERERERNTCNPkU8Zbg0KFDUqqvuly/fr0JW0NERERERCSGgnPEHyp2sOvQr18/iwepEREREREREd0LO9h1cHR0bHQubiIiIiIiInoy8R5sIiIiIiIiIgE4gk1ERERERPSEaK7ZSx4XHMEmIiIiIiIiEoAj2C2ETTP9qUlkXkiRTzRUKAQm3W2mmeIFpgIWmrv6mllg/l57cW9RAi9V4Zpz20SxFXjBinzfEfkzvsi3aZXAfMcicyf/ro2zsFgi23Uu/5qwWADgaKcUFksp8JuWyDzFzfRrhdDc1TFvrRAWa9WnM4XFEqlKYKJ1pcCL17Z2jgAAdoVJREFUolrg27TIvNVA8/1OTQ9HYmIiVq5ciby8PPj6+uKjjz7CSy+9VGfd9PR0rF27FkajESaTCb6+vli0aBH8/f0btE+OYBMREREREdFjJS0tDdOnT8fcuXORlZWFl156Ca+88gpyc3PrrH/w4EEMHz4cu3btwokTJzBkyBC89tpryMrKatB+OYJNRERERET0hHhSxvD//Oc/IyoqChMnTgQAfPTRR/j666+xdu1aJCQkWNT/6KOPZH/Hx8dj+/bt+Mc//oHevXtbvV+OYBMREREREdFjw2w248SJE/Dz85Ot9/Pzw5EjR6yKUVNTg2vXrqF169YN2jdHsImIiIiIiKhZM5lMMJlMsnUqlQoqlcqi7r///W9UV1ejXbt2svXt2rVDfn6+VftbvXo1ysvLERIS0qB2cgSbiIiIiIjoSaFomUtCQgI0Go1sqWuqt+xQ73qoXW1trcW6uqSkpGDRokVIS0tD27Zt71v/Tk3awY6IiIBCobBYAgICAABeXl4WZU8//bS0vZeXl8Xc+LokJydDqVRi0qRJdZaXlZVh/vz58PX1haOjI9zc3PD8889jxYoVKCkpsepYBg8eLLXRxsYG7dq1w6hRo3Dx4kWpzqlTpzBmzBh07NgRjo6O6N69O/7yl79YFZ+IiIiIiIhuiY2NRWlpqWyJjY2ts667uzuUSqXFaPW//vUvi1Htu6WlpSEqKgqbN2/GsGHDGtzOJh/BDggIQF5enmxJSUmRypcsWSIra+hT2wBAr9fj/fffR2pqKioqKmRlxcXFeOGFF2AwGDBz5kwcPXoUhw8fxsKFC2E0GpGcnGz1fqKjo5GXl4fLly9j+/btuHTpEsLDw6XyEydOoE2bNti4cSN+/PFHzJ07F7Gxsfj0008bfExERERERERPKpVKBRcXF9lS1/RwALC3t0ffvn2xd+9e2fq9e/di4MCB9e4jJSUFERERSE5ORmBgYKPa2eT3YKtUKnh4eNRb3qpVq3uW309OTg6OHDmCrVu34ttvv8UXX3yB8ePHS+VxcXHIzc1FdnY2OnToIK3v1q0bgoKCUNuA/KpOTk5SW7VaLaZOnSobNY+MjJTV79y5MzIzM5Geno6YmJjGHiIRERERERHdw3vvvYdx48ahX79+GDBgAD777DPk5uZK/bXY2FhcvnwZGzZsAHCrcz1+/Hj85S9/wQsvvCCNfjs6OkKj0Vi938fuHmy9Xo/AwEBoNBqEh4dDp9NJZTU1NUhLS0N4eLisc30na+bk16W4uBhbtmxB//7971mvtLS0wU+iIyIiIiIiEkHRQv9rqNDQUHz00UdYsmQJevXqhYMHD2LXrl3o1KkTACAvL0+WE/tvf/sbqqqqMHXqVGi1Wml55513GrTfJu9g79ixA2q1WrYsXbpUKp89e7as7OOPP7Y6dk1NDZKSkqRp2qNHj0ZmZibOnz8PACgsLMTVq1fh4+Mj265v377S/saMGWP1/hITE6FWq+Hs7Aw3NzdkZ2dDr9fXWz8zMxObN2/GW2+9ZfU+iIiIiIiIqOGmTJmCnJwcmEwmnDhxAn/4wx+ksqSkJBw4cED6+8CBA6itrbVYkpKSGrTPJp8iPmTIEKxdu1a27s4R3VmzZiEiIkL6293d3erYe/bsQXl5OV555RVpWz8/P+j1esTHx0v17h6l/vLLL2E2mzF79mzcuHHD6v2FhYVh7ty5AICCggLEx8fDz88PJ06cQKtWrWR1f/zxR7z++utYsGABhg8ffs+4dT2Cvspsgq193fcYEBERERER0aPX5B1sZ2dneHt711vu7u5+z/J70ev1KC4uhpOTk7SupqYGWVlZWLp0Kdq0aYOnnnoKZ8+elW3n6ekJ4Nb931evXrV6fxqNRmqrt7c3dDodtFot0tLSMHHiRKneTz/9hKFDhyI6Ohrz5s27b9yEhAQsXrxYtm5w+NsYMn6a1W0jIiIiIiKipvXY3INdVFSE7du3IzU1FUajUbZcv34du3fvho2NDUJCQrBx40ZcvnxZeBuUSiUAyEbBf/zxRwwZMgT/+Z//iWXLllkVp65H0L80mtPKiYiIiIjowSgULXNpKZp8BNtkMlnkI7O1tbV6Kvjly5dhNBpl6zw9PfH555/Dzc0No0aNgo2N/HeDoKAg6HQ6BAUFIT4+HgcOHED//v2xZMkS9OvXD87Ozvjhhx+QmZmJHj16WH0sFRUV0rEUFBTggw8+gIODA/z8/AD8X+faz88P7733nlRXqVSiTZs29cZVqVQWj5zn9HAiIiIiIqLmrck72F999RW0Wq1snY+Pj8W07fqsWrUKq1atkq0zGAzQ6/UYMWKERecaAIKDgxEaGoqCggK0a9cOx44dw/Lly7Fy5Ur8+uuvsLGxQdeuXREaGorp06dbfSzr1q3DunXrAACurq547rnnsGvXLukhalu2bEFhYSE2bdqETZs2Sdt16tQJOTk5Vu+HiIiIiIiImj9FbUMSP9Mjs2TveWGx3h/SuHvc61Ij8PL56bdrwmKJbFdznZFSZq4UFqvopllYrGvmKmGxWtmL+w3wj53rnzXSGHbKx+YOm3qZqqqFxbIROLdL6OtbYLtUtuKuCXNVjbBYagdxr6PSCnHvO/YCz1duUYWwWADgaKcUF0zgh0iNuMtC6HTLf/6rVFgskZ8hMW+tEBZr1aczhcUa1qWtsFgqO3GvI6XAi6Ja4Pt0WxexszhFfh4JfHttUv/87fqjbkKj9Hha/aibYJXH/xsiERERERERURNgB7sOhw4dssjVfedCREREREREdLcWOrHh4erXr5/Fg9SIiIiIiIiI7oUd7Do4Ojo2Ohc3ERERERFRs9VcHzD0mOAUcSIiIiIiIiIB2MEmIiIiIiIiEoBTxImIiIiIiJ4QCs4Rf6jYwW4hHGyb5wtBZC5BG6GHKC5YLQSmihcYSqkQNwFF5L+jWmD+WJH5UEXmfBUdT2C6UKGa6wewyNzVtQJPvsg82CJzRIu8VqtrxJ0vkfm5m+trCIDQ930bgfMORf5binwdicxzLzJ39cyYVcJi/fDVSmGxbIV+eRIXy05gu26aBSaAh9jc4byZmerCKeJEREREREREArCDTURERERERCQAp4gTERERERE9IUTfNkdyHMEmIiIiIiIiEoAdbCIiIiIiIiIBmrSDHRERAYVCYbEEBAQAALy8vCzKnn76aWl7Ly8vfPTRR/fdT3JyMpRKJSZNmlRneVlZGebPnw9fX184OjrCzc0Nzz//PFasWIGSkhKrjmXw4MFSG21sbNCuXTuMGjUKFy9elOoUFRUhICAA7du3h0qlQseOHRETE4OysjKr9kFEREREREQtR5OPYAcEBCAvL0+2pKSkSOVLliyRlWVlZTV4H3q9Hu+//z5SU1NRUVEhKysuLsYLL7wAg8GAmTNn4ujRozh8+DAWLlwIo9GI5ORkq/cTHR2NvLw8XL58Gdu3b8elS5cQHh4uldvY2OD111/H3//+d5w7dw5JSUnYt29fvR1/IiIiIiKih0nRQpeWoskfcqZSqeDh4VFveatWre5Zfj85OTk4cuQItm7dim+//RZffPEFxo8fL5XHxcUhNzcX2dnZ6NChg7S+W7duCAoKalAuRycnJ6mtWq0WU6dOlXWeXV1dMXnyZOnvTp06YcqUKVi5Ulz+QyIiIiIiImoeHrt7sPV6PQIDA6HRaBAeHg6dTieV1dTUIC0tDeHh4bLO9Z0UjXysXnFxMbZs2YL+/fvXW+fKlStIT0/HH//4x0btg4iIiIiIiJqvJu9g79ixA2q1WrYsXbpUKp89e7as7OOPP7Y6dk1NDZKSkqRp2qNHj0ZmZibOnz8PACgsLMTVq1fh4+Mj265v377S/saMGWP1/hITE6FWq+Hs7Aw3NzdkZ2dDr9db1BszZgycnJzQoUMHuLi44L//+7/vGddkMqGsrEy2VJlNVreLiIiIiIioTo96rvdjPke8yTvYQ4YMgdFolC1Tp06VymfNmiUru3N69/3s2bMH5eXleOWVVwAA7u7u8PPzs+j03j1K/eWXX8JoNMLf3x83btywen9hYWEwGo04deoUMjIy4O3tDT8/P1y7dk1W78MPP8TJkyexbds2XLhwAe+999494yYkJECj0ciWb5P/anW7iIiIiIiIqOk1+T3Yzs7O8Pb2rrfc3d39nuX3otfrUVxcDCcnJ2ldTU0NsrKysHTpUrRp0wZPPfUUzp49K9vO09MTwK37v69evWr1/jQajdRWb29v6HQ6aLVapKWlYeLEiVI9Dw8PeHh4oFu3bnBzc8NLL72E+fPnQ6vV1hk3NjbWohP+aeZvVreLiIiIiIiImt5jcw92UVERtm/fjtTUVIsR8uvXr2P37t2wsbFBSEgINm7ciMuXLwtvg1KpBIB7joLffoiayVT/lG+VSgUXFxfZYmuvEttYIiIiIiIi+v/t3XlcVPX+P/DXLDCMzLCICy4silvmxTXccslUKjQ0FzKXzCWvZdqmFhlWVzPNrmuWKQiVLVZel6yulWH6FU1LxLTc0tQUFxARlP39+8Pr/BoBPYMfZAZeTx/n8fDM55zXfM7hzJn5nO2j1G0/g52bm4vU1FT7ShiNqFGjhqb5//rrLyQnJ9u9FhgYiA8++AB+fn4YNGgQ9Hr74wZ9+vRBbGws+vTpg9dffx2JiYlo3749XnvtNbRr1w6enp5ISUlBUlISWrRooXlZLl++bFuWM2fOYMaMGfDw8EDv3r0BAF999RXOnDmDu+66CxaLBfv378eUKVPQuXNnBAcHa34fIiIiIiIiFXSudEOzC7rtDexvvvmm2KXRTZs2LXbZdmnmzp2LuXPn2r22YsUKxMXFoX///sUa1wAwYMAAREVF4cyZM6hduzZ++uknzJ49G2+++SaOHj0KvV6Pxo0bIyoqCk8//bTmZVm2bBmWLVsG4GqXXKGhofjqq69sD1Ezm81YtmwZnnnmGeTm5iIgIAAPPfQQXnjhBc3vQURERERERK5BJ450/EwVZs4PR5RlTewSoixLpf0nM5VlFSncqgVKw5TJzi9UlnU+R91T6guLipRlZSlcxr53lPzMg7JyM6i7w8ZZ98J5Ber+lmXsAbFEKleXyq9Ab7ObsiyVy6hy3addylOWVdNL3a1P+xR+fwBANXeD0jxlFP4tCxV+Ue47e1FZVmZegbKsvAJ1y/j8hLk3n0ijlG/eVJZldld5t6e6DUzlfsfspvbzaHJTt8483V3zTPDvpy9XdBXKpFmdajefyAnc9jPYREREREREVDFUHgCh4irNQ85U2rJlS7G+uv8+EBEREREREV2PZ7BL0K5du2IPUiMiIiIiIiK6ETawS2A2m8vcFzcRERERERFVTWxgExERERERVRG8Bbt88R5sIiIiIiIiIgV4BttF1La6K8tS2SVQkcKwur5mZVnnLqnrdsrd6JzHoWq6qevixj/PQ1mW0aDuuKhe4WMuzYq73Tl+/orSPFX0Cg9L+3qq2+/k5Kvr8sugcCEV9raG9Ox8ZVlpCvdhdXzVfb69q6nriux4mrpuYhrW8lSWBaj9njyfpe5vqbAXRKVnsNoH+CnLylXYPaBKKrvWCr1vsrKsPpNGKctyV7hDdFP42+m18KbKsgCgmknd7yeikjhny4GIiIiIiIjIxfAMNhERERERUVXBm7DLFc9gExERERERESnABjYRERERERGRArxEnIiIiIiIqIrQ8RrxcsUz2EREREREREQKlHsD++zZsxg3bhwCAwNhMpng7++P8PBwJCUlAQCCg4Oh0+mKDW+88YZdTkJCAsLCwuDp6Qmr1YquXbviyy+/tJsmMTGxxKxp06bZlWdkZNy03r1794bBYMD27dtLLN+9ezeioqJQp04dmEwmBAUFoU+fPli/fj3kb/17HD9+HH379oWnpydq1KiBiRMnIi8vz5FVSERERERERC6g3C8RHzBgAPLz85GQkICGDRvizJkz+P7775Genm6b5rXXXsPYsWPt5rNarbb/P//881i8eDFmzJiBfv36IT8/Hx9++CEiIyOxYMECTJgwwW7eAwcOwMvLyzZusVgcqvPx48eRlJSECRMmIDY2Fh06dLArX7t2LQYPHoyePXsiISEBISEhSEtLQ0pKCqZNm4YuXbrAx8cHhYWFiIiIQM2aNbF161akpaXh0UcfhYhg0aJFDtWJiIiIiIiInFu5NrAzMjKwdetWJCYmolu3bgCAoKAghIWF2U1ntVrh7+9fYsb27dvx1ltvYeHChXjqqadsr8+cORM5OTl49tlnERkZiYCAAFtZrVq14OPjU+Z6r1ixAn369MH48eMRFhaG+fPnw9PTEwCQnZ2N0aNHIyIiAqtXr7bNExISgrCwMIwZM8Z2Bnvjxo3Yv38/Tpw4gbp16wIA3nrrLYwcORIzZ860OwhARERERERU3nS8Bbtclesl4haLBRaLBWvWrEFubm6ZMj7++GNYLBaMGzeuWNlzzz2H/Px8fPHFF7daVRsRwYoVKzBs2DA0a9YMTZo0wapVq2zlGzduRFpaGqZMmVJqhu5/W21SUhJatGhha1wDQHh4OHJzc/Hzzz8rqzMRERERERFVvHJtYBuNRsTHxyMhIQE+Pj7o3LkzoqOjkZKSYjfd1KlTbY3xa0NiYiIA4ODBgwgJCYG7u3ux/Lp168Lb2xsHDx60e71+/fp2WWlpaZrr/N133+Hy5csIDw8HAAwbNgyxsbG28mvv1bRpU9trO3futHu/a/eGp6amonbt2nb5vr6+cHd3R2pqaql1yM3NRWZmpt2Qn1e2AxRERERERER0e5T7Q84GDBiAU6dOYd26dQgPD0diYiLatGmD+Ph42zSTJ09GcnKy3dC+fXtN+SJSrPG9ZcsWuyxfX1/N9Y2NjUVUVBSMxqtXzw8ZMgQ7duzAgQMHSp0nNDTU9l7Z2dkoKCiwlelKuAZDREp8/ZpZs2bB29vbbvgy/m3Ny0BERERERFQSnYsOruK2dNPl4eGBXr16ISYmBtu2bcPIkSMxffp0W3mNGjXQqFEju8FsNgMAGjdujCNHjpT45O1Tp04hMzMTTZo0sXu9QYMGdll6vbbFTE9Px5o1a7BkyRIYjUYYjUbUq1cPBQUFiIuLs9UHgF2D22Qy2d7r7/z9/Yudqb5w4QLy8/OLndn+uxdffBEXL160G/qMfFLTMhAREREREVHFqJB+sJs3b47s7GxN0w4ZMgRZWVlYunRpsbK5c+fCw8MDUVFRSuq1cuVK1K9fH3v27LE7Az5//nwkJCSgoKAAvXv3RvXq1TF79uyb5nXs2BG//vorTp8+bXtt48aNMJlMaNu2banzmUwmeHl52Q1u7iYly0hERERERETlo1yfIp6WloZBgwZh1KhRCA0NhdVqxa5duzBnzhxERkbaprt06VKxM73VqlWDl5cXOnbsiEmTJmHy5MnIy8uz66Zr4cKFiI+Ph5+fn0P12rt3r103YADQqlUrxMbGYuDAgWjRooVdWVBQEKZOnYoNGzYgMjISy5cvR1RUFCIiIjBx4kQ0btwYWVlZ+OabbwAABoMBwNW+tJs3b47hw4fjzTffRHp6Op5//nmMHTuWTxAnIiIiIiKqZMq1gW2xWNC+fXvMmzcPR44cQX5+PgICAjB27FhER0fbpouJiUFMTIzdvOPGjcO7774LAJg/fz5CQ0OxZMkSTJs2DTk5OXB3d8emTZvQtWtXh+tV0jy7du3Cnj17sGzZsmJlVqsVvXv3RmxsLCIjI9G/f39s27YNs2fPxogRI5Ceng5vb2+0a9cOn3zyCfr06QPgakN7w4YNeOKJJ9C5c2eYzWY88sgjmDt3rsN1JiIiIiIiumWudEOzC9LJtU6bXcixY8fQrVs3dOzYEStXrrSdMa7MEnadUJY1uGXAzSfSqEjh5nPpSsHNJ9Lo3CV1T113N1bInRQ3ZXJTV6+cvCJlWUaDur22XmFHjTW9ivdEcCuOn7+iNE8VvcIvTV9Pdesst0DdNmZQuJAGhR/vQnWLiDSF+7A6vh7Ksjzc1H3fpl7MUZZV06r2NiqVv4zOZ6n7WxYp3MYKi9QtpKdJ3Xahcl+hksrvo9D7JivL6jNplLIsd4U7RDeFv51eC29684kcUMtL3f7Co1xPVZafI+ec8zfMzYTUNFd0FTRxzpbDTQQHByMxMRHNmjVDcnJyRVeHiIiIiIiIqHwvES9PDRo0wCuvvFLR1SAiIiIiIiIC4MINbCIiIiIiInKMjjdhlyuXvESciIiIiIiIyNmwgU1ERERERESkAC8RJyIiIiIiqiIUPhSfSsAGtoto5G1RluWsPbOp/LB7V3NTlmVW2C2Nylte8hR2ZeJuVncxi8quTPQKr7E5fylPXRiAAD91XUVcyM5XlqWymy6Vn0kPhd3KqdzGVC5jkSjsikxhd3fOqqrcA+ij8PuooFDd97fKbjZVdlHnaVL307RAYb9mRoU7V5Vda325IE5ZVpcxw5RlqeymSzWV2z47lKaSOO/WT0RERERERORC2MAmIiIiIiIiUoCXiBMREREREVURvLC9fPEMNhEREREREZECbGATERERERERKVDuDeyzZ89i3LhxCAwMhMlkgr+/P8LDw5GUlAQACA4Ohk6nKza88cYbdjkJCQkICwuDp6cnrFYrunbtii+//NJumsTExBKzpk2bZleekZFx03r37t0bBoMB27dvL7F89+7diIqKQp06dWAymRAUFIQ+ffpg/fr1dk/pnjRpEtq2bQuTyYRWrVo5sOaIiIiIiIjIlZT7PdgDBgxAfn4+EhIS0LBhQ5w5cwbff/890tPTbdO89tprGDt2rN18VqvV9v/nn38eixcvxowZM9CvXz/k5+fjww8/RGRkJBYsWIAJEybYzXvgwAF4eXnZxi0Wx7q4On78OJKSkjBhwgTExsaiQ4cOduVr167F4MGD0bNnTyQkJCAkJARpaWlISUnBtGnT0KVLF/j4+AC42iXWqFGjsGPHDqSkpDhUDyIiIiIiIqV4E3a5KtcGdkZGBrZu3YrExER069YNABAUFISwsDC76axWK/z9/UvM2L59O9566y0sXLgQTz31lO31mTNnIicnB88++ywiIyMREBBgK6tVq5atgVsWK1asQJ8+fTB+/HiEhYVh/vz58PT0BABkZ2dj9OjRiIiIwOrVq23zhISEICwsDGPGjLE7g71w4UIAwLlz59jAJiIiIiIiqsTK9RJxi8UCi8WCNWvWIDc3t0wZH3/8MSwWC8aNG1es7LnnnkN+fj6++OKLW62qjYhgxYoVGDZsGJo1a4YmTZpg1apVtvKNGzciLS0NU6ZMKTVDp+NhISIiIiIioqqmXBvYRqMR8fHxSEhIgI+PDzp37ozo6OhiZ3KnTp1qa4xfGxITEwEABw8eREhICNzd3Yvl161bF97e3jh48KDd6/Xr17fLSktL01zn7777DpcvX0Z4eDgAYNiwYYiNjbWVX3uvpk2b2l7buXOn3ftdf284ERERERGRM9C56D9XcVvuwY6IiMCWLVuQlJSEb775BnPmzMHy5csxcuRIAMDkyZNt/7+mXr16mvJFpFjje8uWLXb3cPv6+mqub2xsLKKiomA0Xl01Q4YMweTJk3HgwAG7RvXfhYaGIjk5GQDQuHFjFBQUaH6/kuTm5hY745+Xlwt3d9Mt5RIREREREVH5uS3ddHl4eKBXr16IiYnBtm3bMHLkSEyfPt1WXqNGDTRq1MhuMJvNAK42WI8cOYK8vLxiuadOnUJmZiaaNGli93qDBg3ssvR6bYuZnp6ONWvWYMmSJTAajTAajahXrx4KCgoQFxdnqw9w9UFq15hMJtt7qTBr1ix4e3vbDR+8O09JNhEREREREZWPCukHu3nz5sjOztY07ZAhQ5CVlYWlS5cWK5s7dy48PDwQFRWlpF4rV65E/fr1sWfPHiQnJ9uG+fPnIyEhAQUFBejduzeqV6+O2bNnK3nPkrz44ou4ePGi3TD8n8+U2/sRERERERHRrSvXS8TT0tIwaNAgjBo1CqGhobBardi1axfmzJmDyMhI23SXLl1Camqq3bzVqlWDl5cXOnbsiEmTJmHy5MnIy8uz66Zr4cKFiI+Ph5+fn0P12rt3r90l5ADQqlUrxMbGYuDAgWjRooVdWVBQEKZOnYoNGzYgMjISy5cvR1RUFCIiIjBx4kQ0btwYWVlZ+OabbwAABoPBNu/hw4eRlZWF1NRUXLlyxXYpefPmzUu8rxy4ekbcZLK/HNzdvdChZSQiIiIiIroen8dcvsq1gW2xWNC+fXvMmzcPR44cQX5+PgICAjB27FhER0fbpouJiUFMTIzdvOPGjcO7774LAJg/fz5CQ0OxZMkSTJs2DTk5OXB3d8emTZvQtWtXh+tV0jy7du3Cnj17sGzZsmJlVqsVvXv3RmxsLCIjI9G/f39s27YNs2fPxogRI5Ceng5vb2+0a9cOn3zyCfr06WObd8yYMdi8ebNtvHXr1gCAo0ePIjg42OG6ExERERERkXPSyd87bXYRx44dQ7du3dCxY0esXLnS7oxxZfV/hy4oy2od5KMsS+XGk5Vzaw+H+7vcgiJlWWY3hduXwiOGeQqXUeWBTL3Cw6IaH5+gSXau2qtA/CwlX4FSFhey85Vl6RX+Md2NFXIX0U2p3MZUHsVXud+5kF38uSNlVctL3QMyPRTuD89cLFv3nSWpYVX3eQQAlb+MCorUbRcFheoqVqRwIQvVLSIMCndiKte9UWG9nlm7T1nWlwvilGV1GTNMWZabwu+Pdwa1VJYFqN1fVHNzzVPBx9PV7X9vp8DqrvHAZ+f89XQTwcHBSExMRLNmzWyXXBMREREREdGN6Vx0cBXl3k1XeWnQoAFeeeWViq4GEREREREREQAXPYNNRERERERE5GzYwCYiIiIiIiJSwGUvESciIiIiIiLHsJuu8sUz2EREREREREQKsIFNREREREREpAAvEXcRKvt9Vfmce5VXmBgN6tJE1B07SnfS/mjV1Uptf6gq/44qqa6Vyr6rrR7qdsWX89T1961yt1NYVPm3MbPCPqIvKewEXmWfzirXvMo+21UuIwAIFPY3rbCPaJV9rbs56edI5b7CoPQ6WHVZ7gZ1n2+VfVdvWf6hsqz2jz6iLMs5t1Si0rGBTUREREREVGXwsEV54iXiRERERERERAqwgU1ERERERESkAC8RJyIiIiIiqiLYTVf54hlsIiIiIiIiIgXKvYF99uxZjBs3DoGBgTCZTPD390d4eDiSkpIAAMHBwdDpdMWGN954wy4nISEBYWFh8PT0hNVqRdeuXfHll1/aTZOYmFhi1rRp0+zKMzIyblrv3r17w2AwYPv27SWW7969G1FRUahTpw5MJhOCgoLQp08frF+/HvK/x5nu2bMHQ4YMQUBAAMxmM+644w4sWLDA0VVIRERERERELqDcLxEfMGAA8vPzkZCQgIYNG+LMmTP4/vvvkZ6ebpvmtddew9ixY+3ms1qttv8///zzWLx4MWbMmIF+/fohPz8fH374ISIjI7FgwQJMmDDBbt4DBw7Ay8vLNm6xWByq8/Hjx5GUlIQJEyYgNjYWHTp0sCtfu3YtBg8ejJ49eyIhIQEhISFIS0tDSkoKpk2bhi5dusDHxwc///wzatasiQ8//BABAQHYtm0bHn/8cRgMhmJ1JiIiIiIiItdWrg3sjIwMbN26FYmJiejWrRsAICgoCGFhYXbTWa1W+Pv7l5ixfft2vPXWW1i4cCGeeuop2+szZ85ETk4Onn32WURGRiIgIMBWVqtWLfj4+JS53itWrECfPn0wfvx4hIWFYf78+fD09AQAZGdnY/To0YiIiMDq1att84SEhCAsLAxjxoyxncEeNWqUXW7Dhg2RlJSE1atXs4FNRERERES3HW/BLl/leom4xWKBxWLBmjVrkJubW6aMjz/+GBaLBePGjStW9txzzyE/Px9ffPHFrVbVRkSwYsUKDBs2DM2aNUOTJk2watUqW/nGjRuRlpaGKVOmlJqhu8GTAy5evIjq1asrqy8RERERERE5h3JtYBuNRsTHxyMhIQE+Pj7o3LkzoqOjkZKSYjfd1KlTbY3xa0NiYiIA4ODBgwgJCYG7u3ux/Lp168Lb2xsHDx60e71+/fp2WWlpaZrr/N133+Hy5csIDw8HAAwbNgyxsbG28mvv1bRpU9trO3futHu/6+8NvyYpKQmrVq0q8WDB3+Xm5iIzM9NuyCvjAQoiIiIiIiK6Pcr9IWcDBgzAqVOnsG7dOoSHhyMxMRFt2rRBfHy8bZrJkycjOTnZbmjfvr2mfBEp1vjesmWLXZavr6/m+sbGxiIqKgpG49Wr54cMGYIdO3bgwIEDpc4TGhpqe6/s7GwUFBQUm2bfvn2IjIxETEwMevXqdcM6zJo1C97e3nbD+0vnaV4GIiIiIiKikuh0rjm4itvSTZeHhwd69eqFmJgYbNu2DSNHjsT06dNt5TVq1ECjRo3sBrPZDABo3Lgxjhw5gry8vGK5p06dQmZmJpo0aWL3eoMGDeyy9Hpti5meno41a9ZgyZIlMBqNMBqNqFevHgoKChAXF2erDwC7BrfJZLK9V0n279+PHj16YOzYsbYnmt/Iiy++iIsXL9oNI8Y9o2kZiIiIiIiIqGJUSD/YzZs3R3Z2tqZphwwZgqysLCxdurRY2dy5c+Hh4YGoqCgl9Vq5ciXq16+PPXv22J0Bnz9/PhISElBQUIDevXujevXqmD17tqbMffv24Z577sGjjz6KmTNnaprHZDLBy8vLbnA3mW5l0YiIiIiIiKicletTxNPS0jBo0CCMGjUKoaGhsFqt2LVrF+bMmYPIyEjbdJcuXUJqaqrdvNWqVYOXlxc6duyISZMmYfLkycjLy7PrpmvhwoWIj4+Hn5+fQ/Xau3evXTdgANCqVSvExsZi4MCBaNGihV1ZUFAQpk6dig0bNiAyMhLLly9HVFQUIiIiMHHiRDRu3BhZWVn45ptvAAAGgwHA/29c9+7dG88++6xtGQ0GA2rWrOlQnYmIiIiIiMi5lWsD22KxoH379pg3bx6OHDmC/Px8BAQEYOzYsYiOjrZNFxMTg5iYGLt5x40bh3fffRcAMH/+fISGhmLJkiWYNm0acnJy4O7ujk2bNqFr164O16ukeXbt2oU9e/Zg2bJlxcqsVit69+6N2NhYREZGon///ti2bRtmz56NESNGID09Hd7e3mjXrh0++eQT9OnTBwDw2Wef4dy5c1i5ciVWrlxpywsKCsKxY8ccrjcREREREdGt0LGjrnKlk2udNruQY8eOoVu3bujYsSNWrlxpO2NcmSUdzlCW1TLIW1mWSlfyCpVl5Reo26wvXslXllXLS92l/pcVri+VuwF3Y4XceXJTl3PVrS/gxt3xOcrqoe5Yp8rtwt2obhkLiyr/NqbyB8vZTHU9R/h6uinLquau7vtW7TIW72nkVgjUba8qv4+u5Kv7fLsZVH6+lUUppVfYhlC5z5/65X5lWecyc5RlbVn+obKs9o8+oizrgxFtlWUBgJ9V3f6imptrNlRTL6r7bXs7+Xur+z4rT875K+UmgoODkZiYiGbNmiE5Obmiq0NERERERERUvpeIl6cGDRrglVdeqehqEBEREREREQFw4QY2EREREREROcg1r2x3GS55iTgRERERERGRs2EDm4iIiIiIiEgBXiJORERERERURfAK8fLFBraLcDM46cUGztrJm8I9h8qutVIvqutOw9usrqsCUbjCihR212JU2I2M8k1VYddml3IKlGV5mdXt1gsK1S2jyi9zlZ1LGhT241OksGIKewSCXmWYk1L5dwQUdzulU7ddmN3UdZOWmaOumx6TUV29VHaRpnAXBjeF25ibwq4GVWap7FprR8JHyrJ0j6rtpsv1OigmV+OkrTYiIiIiIiIi18IGNhEREREREZECvESciIiIiIioiqgCdw5VKJ7BJiIiIiIiIlKADWwiIiIiIiIiBXiJOBERERERURWhY0dd5arcz2CfPXsW48aNQ2BgIEwmE/z9/REeHo6kpCQAQHBwMHQ6XbHhjTfesMtJSEhAWFgYPD09YbVa0bVrV3z55Zd20yQmJpaYNW3aNLvyjIyMm9a7d+/eMBgM2L59e4nlu3fvRlRUFOrUqQOTyYSgoCD06dMH69evh/zv+f9paWm47777ULduXZhMJgQEBGDChAnIzMx0dDUSERERERGRkyv3M9gDBgxAfn4+EhIS0LBhQ5w5cwbff/890tPTbdO89tprGDt2rN18VqvV9v/nn38eixcvxowZM9CvXz/k5+fjww8/RGRkJBYsWIAJEybYzXvgwAF4eXnZxi0Wi0N1Pn78OJKSkjBhwgTExsaiQ4cOduVr167F4MGD0bNnTyQkJCAkJARpaWlISUnBtGnT0KVLF/j4+ECv1yMyMhIzZsxAzZo1cfjwYTz55JNIT0/HRx+p6x+QiIiIiIiIKl65NrAzMjKwdetWJCYmolu3bgCAoKAghIWF2U1ntVrh7+9fYsb27dvx1ltvYeHChXjqqadsr8+cORM5OTl49tlnERkZiYCAAFtZrVq14OPjU+Z6r1ixAn369MH48eMRFhaG+fPnw9PTEwCQnZ2N0aNHIyIiAqtXr7bNExISgrCwMIwZM8Z2BtvX1xfjx4+3TRMUFIQnnngCb775ZpnrRkRERERERM6pXC8Rt1gssFgsWLNmDXJzc8uU8fHHH8NisWDcuHHFyp577jnk5+fjiy++uNWq2ogIVqxYgWHDhqFZs2Zo0qQJVq1aZSvfuHEj0tLSMGXKlFIzdKU8+/7UqVNYvXq17WADERERERHRbaVz0cFFlGsD22g0Ij4+HgkJCfDx8UHnzp0RHR2NlJQUu+mmTp1qa4xfGxITEwEABw8eREhICNzd3Yvl161bF97e3jh48KDd6/Xr17fLSktL01zn7777DpcvX0Z4eDgAYNiwYYiNjbWVX3uvpk2b2l7buXOn3ftdf2/4kCFDUK1aNdSrVw9eXl5Yvnz5DeuQm5uLzMxMuyGvjAcoiIiIiIiI6PYo94ecDRgwAKdOncK6desQHh6OxMREtGnTBvHx8bZpJk+ejOTkZLuhffv2mvJFpFjje8uWLXZZvr6+musbGxuLqKgoGI1Xr54fMmQIduzYgQMHDpQ6T2hoqO29srOzUVBQYFc+b948/PLLL1izZg2OHDmCZ5999oZ1mDVrFry9ve2G+Hf+rXkZiIiIiIiI6Pa7Ld10eXh4oFevXujVqxdiYmIwZswYTJ8+HSNHjgQA1KhRA40aNSpx3saNG2Pr1q3Iy8sr1pA+deoUMjMz0aRJE7vXGzRoUKZ7sNPT07FmzRrk5+fjnXfesb1eWFiIuLg4zJ49G40bNwZw9UFq1x5+ZjKZSq0/APj7+8Pf3x/NmjWDn58funTpgpdffhl16tQpcfoXX3yxWCP811M8g01EREREROTMyv0MdkmaN2+O7OxsTdMOGTIEWVlZWLp0abGyuXPnwsPDA1FRUUrqtXLlStSvXx979uyxOwM+f/58JCQkoKCgAL1790b16tUxe/bsMr3HtQeg3eiedJPJBC8vL7vB3WQq0/sRERERERFdU9G3UlfyW7DL9wx2WloaBg0ahFGjRiE0NBRWqxW7du3CnDlzEBkZaZvu0qVLSE1NtZu3WrVq8PLyQseOHTFp0iRMnjwZeXl5dt10LVy4EPHx8fDz83OoXnv37rXrBgwAWrVqhdjYWAwcOBAtWrSwKwsKCsLUqVOxYcMGREZGYvny5YiKikJERAQmTpyIxo0bIysrC9988w0AwGAwAAC++uornDlzBnfddRcsFgv279+PKVOmoHPnzggODnaozkREREREROTcyrWBbbFY0L59e8ybNw9HjhxBfn4+AgICMHbsWERHR9umi4mJQUxMjN2848aNw7vvvgsAmD9/PkJDQ7FkyRJMmzYNOTk5cHd3x6ZNm9C1a1eH61XSPLt27cKePXuwbNmyYmVWqxW9e/dGbGwsIiMj0b9/f2zbtg2zZ8/GiBEjkJ6eDm9vb7Rr1w6ffPIJ+vTpAwAwm81YtmwZnnnmGeTm5iIgIAAPPfQQXnjhBYfrTERERERERM5NJ9euWXYhx44dQ7du3dCxY0esXLnSdsa4Mtt1NFNZVvP61ptPpJXCredKfqGyrPxCdRUzGdXdSZF6MUdZlrfZTVlWkcK/o1Gv7iIeo0Fd1qWcgptP5ACVlyqV1rVfWXiZ1R03LVD4OSpUuJGp3C4MCrfXIoVfp+cv5SnLqu5ZvBeOsvJwU7c/PJup7tkiNaxqb6NSub3mFRYpy1L5nZuZk68sy2RU9ztMVC6kQm4Gddv+S1//rizrXKa63xXZV9R9T+5I+EhZ1qFNbynLAgBfhftET3dXunD5/0vLVvub6Hbx87wtjw+7ZRVyD/atCg4ORmJiIpo1a4bk5OSKrg4RERERERHR7XmKeHlo0KABXnnllYquBhEREREREREAFz2DTURERERERORsXPYMNhERERERETlG51KdXrkensEmIiIiIiIiUoANbCIiIiIiIiIFXLKbrqoo9aK67jR8PNV176RSxmV1y+isXUWp7D4sI1vd+nIzqltGP4u67i/yC9StL5XdMQGAXuHhSZV1K1S4jeUWqOteSOXnSGmXQAq/AtfvO6Usa/y/NyvLevvZbsqy/DzUfb6HzPpOWdZn0b2VZQHAXQ181YUp/JWlV7ivyFP4+VZJr7DbQpXfbTl56tZXVq5zdpGktvtJdVmNezynLgzAhZ2LlWV5uOjNthcuq+sa93byreYaXTPzDDYRERERERGRAmxgExERERERESnABjYRERERERGRAmxgExERERERESnABjYRERERERGRAi7dwB45ciT69esHADh79izGjRuHwMBAmEwm+Pv7Izw8HElJSZrzdu/ejaioKNSpUwcmkwlBQUHo06cP1q9fb3vS7LFjx6DT6WyDt7c3OnTogPXr19tlxcfH201nsVjQtm1brF69WtnyExERERERkfNw6Qb23w0YMAB79uxBQkICDh48iHXr1qF79+5IT0/XNP/atWvRoUMHZGVlISEhAfv378dnn32Gfv36Ydq0abh48aLd9N999x1Onz6NHTt2ICwsDAMGDMCvv/5qN42XlxdOnz6N06dPY/fu3QgPD8fgwYNx4MABZctNREREREREzsFFe2+zl5GRga1btyIxMRHdul3t7zMoKAhhYWGa5s/Ozsbo0aMRERFhd4Y5JCQEYWFhGDNmTLG+Uv38/ODv7w9/f3/MnDkTixYtwg8//IAWLVrYptHpdPD39wcA+Pv7Y8aMGZg7dy5SUlLQtGnTW11sIiIiIiIih6jsp5yKqxRnsC0WCywWC9asWYPc3FyH59+4cSPS0tIwZcqUUqfRlbIl5ufnY9myZQAANze3UucvLCxEQkICAKBNmzYO15GIiIiIiIicW6VoYBuNRsTHxyMhIQE+Pj7o3LkzoqOjkZKSomn+gwcPAoDdWeWdO3faGu4WiwVffvml3TydOnWCxWKBh4cHnnvuOQQHB2Pw4MF201y8eNE2v7u7O8aPH4/33nsPISEht7jERERERERE5GwqRQMbuHoP9qlTp7Bu3TqEh4cjMTERbdq0QXx8fJnyQkNDkZycjOTkZGRnZ6OgoMCu/NNPP8Xu3buxbt06NGrUCMuXL0f16tXtprFarbaM3bt34/XXX8e4ceOKPRDterm5ucjMzLQbynJmnoiIiIiI6O90LvrPVVSaBjYAeHh4oFevXoiJicG2bdswcuRITJ8+/abzNW7cGADsHj5mMpnQqFEjNGrUqMR5AgIC0LhxY0RERGD58uWIiorC2bNn7abR6/W2jNDQUDz77LO45557MHv27BvWZ9asWfD29rYbFv37xvMQERERERFRxapUDezrNW/eHNnZ2Tedrnfv3qhevfpNG76l6datG1q0aIGZM2fedFqDwYArV67ccJoXX3wRFy9etBueenZqmepGREREREREt0eleIp4WloaBg0ahFGjRiE0NBRWqxW7du3CnDlzEBkZedP5LRaL7Sx0REQEJk6ciMaNGyMrKwvffPMNgKsN4xt57rnnMGjQIEyZMgX16tUDAIgIUlNTAQBXrlzBt99+i//+97+IiYm5YZbJZILJZLJ77bLk33Q5iIiIiIiIqOJUiga2xWJB+/btMW/ePBw5cgT5+fkICAjA2LFjER0drSmjf//+2LZtG2bPno0RI0YgPT0d3t7eaNeuHT755BP06dPnhvP36dMHwcHBmDlzJpYsWQIAyMzMRJ06dQBcbTQHBQXhtddew9SpPBtNRERERES3H7vpKl86ub6DZ3JKqRfVncH28Sy9O7GKlHFZ3TIa9er2HEaDuqz8QnUft4xsdevLzahuGf0s7sqy8gvUrS+Dwm0CAPQKb7BRWbdChdtYbkGRsiyVnyOT8cZXFDlC5Vfg+n2nlGWN//dmZVlvP9tNWZafh7rP95BZ3ynL+iy6t7IsALirga+6MIW/svQK9xV5Cj/fKukV/vJX+d2Wk6dufWXlFtx8ogqg8ltSZQOucY/n1IUBuLBzsbIsDxc9VZmZ45yf/5vx8nCNu5tdo5ZERERERERETq5KNLBXrlxp16f134c777yzoqtHRERERERElYCLXtjgmAcffBDt27cvsczNzTkvlyYiIiIiIlKNt2CXryrRwLZarbBarRVdDSIiIiIiIqrEqsQl4kRERERERETlrUqcwSYiIiIiIiLwGvFyxjPYRERERERERAqwH2wXkZWr7s908PQlZVnHMy8ry7q3aS1lWTonPTQnKjtEVclJq6Xyz6iyb1VA8cFfhWEq9+gq++dWWS/Ff0pliorULaTKj6TKbV/lui9SuFGo/iWjctsvUNg3vcr1r7bPY4X7CoVbv8ptv1Dh51vl9qXyc6SSs34XAYDvXROUZV3Zra5P7dvpUq5r9oNtNbnGuWHXqCURERERERGRk+M92ERERERERFWEs17pWVnwDDYRERERERGRAmxgExERERERESnAS8SJiIiIiIiqCGd9WGhlUanOYJ89exbjxo1DYGAgTCYT/P39ER4ejqSkJABAcHAw5s+fb5s+ODgYOp0On3zySbGsO++8EzqdDvHx8Zreu7RsnU4Hs9mM4OBgDB48GJs2bbqVRSQiIiIiIiInVaka2AMGDMCePXuQkJCAgwcPYt26dejevTvS09NLnScgIAArVqywe2379u1ITU2Fp6fnLdXntddew+nTp3HgwAG8//778PHxQc+ePTFz5sxbyiUiIiIiIiLnU2kuEc/IyMDWrVuRmJiIbt26AQCCgoIQFhZ2w/mGDh2KefPm4cSJEwgICAAAxMXFYejQoXj//fdvqU5WqxX+/v4AgMDAQHTt2hV16tRBTEwMBg4ciKZNm95SPhERERERETmPSnMG22KxwGKxYM2aNcjNzdU8X+3atREeHo6EhAQAwOXLl/Hpp59i1KhR5VLPSZMmQUSwdu3acsknIiIiIiIqjc5FB1dRaRrYRqMR8fHxSEhIgI+PDzp37ozo6GikpKTcdN5Ro0YhPj4eIoLPP/8cISEhaNWqVbnUs3r16qhVqxaOHTtWLvlERERERERUMSpNAxu4eg/2qVOnsG7dOoSHhyMxMRFt2rS56YPKIiIikJWVhR9//BFxcXHldvb6GhGB7gaP78vNzUVmZqbd4MhZeSIiIiIiIrr9KlUDGwA8PDzQq1cvxMTEYNu2bRg5ciSmT59+w3mMRiOGDx+O6dOnY8eOHRg6dGi51S8tLQ3nzp1DgwYNSp1m1qxZ8Pb2thvemjOr3OpEREREREREt67SNbCv17x5c2RnZ990ulGjRmHz5s2IjIyEr69vudVnwYIF0Ov16NevX6nTvPjii7h48aLd8NyUF8utTkREREREVEVU9M3Ulfwm7ErzFPG0tDQMGjQIo0aNQmhoKKxWK3bt2oU5c+YgMjLypvPfcccdOH/+PKpVq6asTpcuXUJqairy8/Nx9OhRfPjhh1i+fDlmzZqFRo0alTqfyWSCyWSyey0rV5TVi4iIiIiIiNSrNA1si8WC9u3bY968eThy5Ajy8/MREBCAsWPHIjo6WlOGn5+f0jrFxMQgJiYG7u7u8Pf3R4cOHfD999/jnnvuUfo+REREREREVPF0IsJToy5A5Rnsg6cvKcs6nnlZWda9TWspy9I56XUkAif9uDlptVT+GfU3eLBgWShNUximco9u0KurmMp6Kf5TKlNUpG4hVX4kVW77Ktd9kcKNQvUvGZXbfkGhusqpXP8qP0Y3enCro1R+T6rc9gsVfr5Vbl8qP0cqOet3EQD43jVBWdaV3YuVZd1OV/IrugZlY3ar6BpoU+nvwSYiIiIiIiK6HdjA1mDlypWwWCwlDnfeeWdFV4+IiIiIiIicQKW5B7s8Pfjgg2jfvn2JZW5uLnKtAhEREREREZUrNrA1sFqtsFqtFV0NIiIiIiKiW+KszzKpLHiJOBEREREREZECbGATERERERERqSBUKeTk5Mj06dMlJyeHWbc5S3Ues5jFLOfJUp3HLGYxy3myVOcxi1lEIiLsB7uSyMzMhLe3Ny5evAgvLy9m3cYsZ64bs5jFLH6+mcUsZrle3ZhVObKoauIl4kREREREREQKsIFNREREREREpAAb2EREREREREQKsIFdSZhMJkyfPh0mk4lZtzlLdR6zmMUs58lSnccsZjHLebJU5zGLWUQAwIecERERERERESnAM9hERERERERECrCBTURERERERKQAG9hERERERERECrCBTURERERERKQAG9hERERERERECrCBTURERERERKQAG9guKDs7GzExMWjRogUsFgusVitCQ0Px2muv4fLly8rfLy0tDfPnz9c07YULF7Bo0SJkZmYWK7t48WKpZURERERVWVZWFjZv3oxPP/0Uq1atwubNm5GVlaX0PQoKCnD8+HGlmbfizJkzTlGfQ4cOYe7cuZgwYQKeeuop/Pvf/8Yff/xR0dUiF8V+sF1MXl4eOnXqhF9//RX3338/mjVrBhHBb7/9hm+++QZt2rTBjz/+CDc3t1t6HxHBxo0bERsbi7Vr18LLywvnzp276Xz/+te/kJKSgs8++6zE8sGDB6Nly5Z46aWXNNfju+++w7Zt25CamgqdTofatWujc+fOuPfee6HT6RxartJcuHAB69evx4gRIzTPU1RUBL2++DGqoqIinDx5EoGBgZpyRATHjh1DQEAAjEYj8vLy8J///Ae5ubl44IEHUKNGDc11Aq5+SVy/vjp16oTGjRs7lHMj2dnZ+Pnnn9G1a1dlmY4oLCyEwWCwje/YsQO5ubno2LGjQ9v+yZMn8c4775S4vv75z38iICBASX1PnDiB6dOnIy4uTvM8V65cwc8//4zq1aujefPmdmU5OTlYtWqV5u31t99+w/bt29GxY0c0a9YMv//+OxYsWIDc3FwMGzYMPXr00Fyv7OxsfPTRRyV+JocMGQJPT0/NWTdy5swZLF26FDExMZrnOXnyJHx8fGCxWOxez8/PR1JSkubtNS0tDSkpKWjZsiWqV6+O8+fPIzY2Frm5uRg0aBDuuOMOh5alNJXlc3QjBQUFOHXqlOb9YXk7c+YMcnNzy1Sf27FvBSrXdpGVlYWff/7Zbp21bdu22Gf0VlSGbaygoADPPfccli1bhpycHLi7u0NEkJ+fDw8PDzz++ON48803lXwu9+zZgzZt2qCwsFDT9EuWLMHq1atRvXp1/POf/7T7vjh//jzCwsI0NUQvXbqE8ePHY8uWLejevTuWLVuGZ555Bu+88w50Oh3uvvturF+/Hl5eXprqdejQIaSkpKBNmzZo0KABNmzYgNmzZ+PKlSvo168foqOjNf9GnDVrFmJiYlBUVIRatWpBRHDu3DkYDAa8/vrreP755zXlENkIuZT58+dL7dq15ffffy9W9ttvv0nt2rVl4cKFZc4/evSovPzyyxIQECB6vV6GDx8u3377rRQUFGiav2XLlvLdd9+VWv7dd99Jq1atNGWdPHlSWrVqJQaDQVq2bCm9e/eWXr16ScuWLcVgMEibNm3k5MmTmrJuJjk5WfR6vaZpL168KIMGDRIPDw+pVauWxMTE2K2f1NRUzVm///67BAUFiV6vl0aNGskff/whbdu2FU9PT6lWrZrUqFFDDh48qCkrIyNDHnzwQdHpdOLj4yNNmjSRxo0bi4+Pj+j1eomMjJSLFy9qyroZR9ZXXl6eTJ48WUJCQuSuu+6SuLg4u3JH1tepU6ekc+fOYjAYpGvXrpKeni4RERGi0+lEp9NJkyZN5NSpU5qytmzZIhaLRe644w6ZNGmSvP766zJz5kyZNGmSNG/eXKxWq2zdulVT1s04sr5ERA4cOCBBQUGi0+lEr9dLt27d7JbLkXX29ddfi7u7u1SvXl08PDzk66+/lpo1a0rPnj3l3nvvFaPRKN9//72mrH379kndunXFx8dHIiMj5fHHH5exY8dKZGSk+Pj4SL169WTfvn2al/NGHFlnp06dkrvuukv0er0YDAYZMWKEXLp0yVbuyPrasWOHeHt7i06nE19fX9m1a5c0aNBAGjduLI0aNRKz2Sw///xzmZbpepXhc3Qzjm77b7/9ttx7770yaNCgYtvluXPnpEGDBppyMjMzZejQoRIYGCgjRoyQ3NxceeKJJ2yfqa5du2reH97OfatI5dgu8vPzZeLEiWI2m0Wn04nJZBJ3d3fR6XRiNptl0qRJkpeXpynrZirDNjZx4kSpV6+efPLJJ3LhwgXb6xcuXJBPPvlEAgICZNKkSVoX8YYcWV8LFiyQatWqyZNPPinDhg0Tk8kkr7/+uq3cke1rwoQJ0qxZM1m4cKF0795dIiMjpUWLFrJ161b58ccfpUWLFhIdHa0pa/Xq1WI0GsXd3V1MJpMkJCSIyWSS++67TyIiIsRoNMobb7yhKWvTpk2i1+tl+vTpkp6ebns9LS1NXn75ZTEYDLJ582ZNWUTXsIHtYrp27SqLFy8utXzhwoXStWtXhzJzcnLko48+kh49eoiHh4f0799fPvvsMzEajQ7/WLZYLPLnn3+WWv7nn3+K1WrVlPXggw9Kjx49SvxCP3XqlPTo0UMiIyM1ZV28ePGGw5YtWzR/SUycOFGaNGkin332mSxbtkyCgoIkIiJCcnNzReTqF45Op9OUFRkZKQ8++KCkpKTI008/Lc2bN5fIyEjJy8uT3NxciYyMlGHDhmnKGj58uPzjH/+Q7du3Fyvbvn27hIaGyogRIzRl3YwjX9DTp0+X2rVry5tvvikvvfSSeHt7y+OPP24rd2R9DR8+XDp16iTr1q2TqKgo6dSpk3Tp0kVOnjwpx48fly5dusiTTz6pKatdu3by9NNPl1r+9NNPS7t27TRlrV279obDvHnzHPoB2K9fP+nTp4+cO3dODh06JH379pUGDRrYPluO/Kjp2LGjvPTSSyIi8vHHH4uvr6/dj5jo6Gjp1auXpqzu3bvLww8/bNvW/y43N1eGDBki3bt315S1Z8+eGw6ffvqp5mUcMWKEdOjQQXbu3CnffvuttGvXTtq2bWv7seTINtazZ08ZM2aMZGZmyptvvin169eXMWPG2MpHjx4t/fr105R1M5Xhc3QzleHH/O3ct4pUju2CDUbHtrEaNWrc8EDnd999JzVq1NCU1bp16xsOzZo107yMzZs3l5UrV9rGt23bJrVq1ZKXX35ZRBxbXwEBAbJp0yYREfnrr79Ep9PJunXrbOUbNmyQpk2baspq27atREdHS1FRkcTFxYnZbJZ58+bZypcuXSrNmjXTlDV48GC7z8z1xo4dKw8//LCmLKJr2MB2MTVq1JBff/211PK9e/dq3glf4+fnJ126dJGlS5faHb0rSwPb29tbkpKSSi1PSkoSb29vTVmenp6SnJxcavkvv/winp6emrKuHVEubbhWrkVgYKD88MMPtvHz589L+/btpXfv3pKTk+PQF07NmjVl9+7dIiKSlZUlOp1OtmzZYivftm2bBAYGasry9vYu8QfgNY6se19f3xsOXl5empexUaNGsn79etv44cOHpXHjxjJy5EgpKipyaH3VqVPHtn2lpaWJTqezu2Ji06ZN0rBhQ01ZHh4eJV4Jcs1vv/0mHh4emrKubT/XzvSUNDjSwK5Vq5akpKTYvfbEE09IYGCgHDlyxKF15uXlJYcOHRIRkcLCQjEajXZnYPfu3Su1a9fWlGU2m2+4T9i7d6+YzWZNWTdaZ45+JuvWrSs7duywjefk5EhkZKS0atVK0tLSHFpfvr6+sn//fhG5enZQr9fbZf/yyy9Sr149zVmV/XNUFX7Mq9y3ilSN7YINRse2MU9PT9mzZ0+p5bt379b8e8dkMsmjjz4qr7zySonDuHHjNC+j2WyWo0eP2r3266+/Su3ateWFF15waH2ZTCY5fvy4bbxatWpy4MAB2/ixY8ekWrVqmrIsFoscPnxYRK5+rxkMBtm7d6+t/OjRo5q/i4KDg+1+d13vxx9/lODgYE1ZRNcYK/oSdXJMRkYG/Pz8Si338/PDxYsXHcosLCyETqeDTqezu++qLFq3bo01a9agQ4cOJZb/5z//QevWrTVlmc1mpKenl1p+4cIFmM1mTVlWqxUvvfQS2rdvX2L5oUOHMG7cOE1Z58+fR1BQkG3cz88P3377LcLDw/HAAw9g+fLlmnKAq/enVa9eHQDg6ekJT09P1KlTx1Zev359nDlzRnPeje43cuR+9dzcXIwfPx7/+Mc/Siz/888/8eqrr2rK+uuvv9CiRQvbeEhICBITE9GjRw8MHz4cc+bM0VyvCxcuoF69egCA6tWro1q1anZ/i5CQEJw+fVpTVp06dbBt2zY0bdq0xPKkpCS7v8XNst5++23069evxPLk5GS0bdtWUxZw9f5ro9F+9/z2229Dr9ejW7du+OijjzRn/Z1er4eHhwd8fHxsr1mtVs37DF9fXxw6dKjYPeHXHD58GL6+vpqy/Pz8MHv2bNx7770llu/btw99+/bVlHXx4kW79zWZTPj8888xaNAg3HPPPfjwww815QBXn3Nxbb/i5uaGatWq2T0Hwc/PD2lpaZqyqsLnaP/+/Xj44YfRoEGDEstPnz6NgwcPaso6evQoOnXqZBvv2LEjNm3ahHvvvRf5+fl4+umnNeUAwNmzZ9GoUSMAQN26dWE2m+0+63feeSdOnDihOU/VvhWoGtvFlStXbvj8ED8/P1y5ckVTVlXYxu655x48++yzWLlyJWrXrm1XdubMGUyZMkXzszJatGiB9u3bY/z48SWWJycnY9myZZqyatSogRMnTiA4ONj22p133olNmzahR48e+OuvvzTlAFf/5ufOnbM92yQyMtLuuygrKwsmk0lTVnZ2NqxWK4Cr32tmsxnVqlWzlZvNZuTm5mrKOnPmjN3yXa9BgwZITU3VlEVkU9EtfHKMXq+Xs2fPllruyNHEa65cuSIffvih3HPPPWI2m+Whhx6S1atXi5ubm8NnsD///HMxGo2yaNEiu/uSCwoKZOHCheLm5iafffaZpqwJEyZIQECAfPbZZ5KRkWF7PSMjQz777DMJDAyUiRMnasrq3r27zJ49u9Ty5ORkzZfRNW3aVDZs2FDs9UuXLknHjh2lZcuWmv8GISEhdkdOlyxZIpmZmbbxn3/+Wfz9/TVlDRs2TEJDQ2Xnzp3Fynbu3CmtWrWS4cOHa8rq1KmTzJ8/v9RyRy7Ja9CgQYn35f/111/SpEkT6dmzp0NXD/z9bOLUqVMlLS3Nrl5az4q8/fbb4u7uLk8++aSsWbNGkpKSZPv27bJmzRp58sknxWQyyTvvvKMpq2/fvrYzICVxZPsSEbnrrrvk/fffL7HsySeftN37qUVoaKh8/fXXtvG9e/dKfn6+bXzLli2a7zmcPn26eHt7y5tvvinJycly+vRpSU1NleTkZHnzzTfF19dXXn31VU1Z4eHh8q9//avUckfW2T/+8Q/5/PPPi72en58v/fr1k8DAQM3rq1mzZnZn3r788ku5fPmybXz79u1Sv359TVlV4XPUtm1bWbJkSanlu3fvduiM4I8//ljs9X379knt2rVl+PDhDl3V8PcrNYYMGSJnzpyxjf/666/i6+urKUvlvlWkamwXffr0kXvvvVdSU1OLlaWmpkqvXr2kb9++mrKqwjZ2/PhxadGihRiNRmnVqpWEh4fLfffdJ61atRKj0SihoaFy4sQJTVmTJk264eX3hw8f1nwrz5AhQ0rN+vXXX6VmzZqa19d9990n7777bqnlK1askE6dOmnKuv63sNVqlT/++MM27shvYZ1OZ/d3u15ZflcT8Qy2ixER3HvvvcXObl1TUFDgcKaHhweGDh2KoUOH4siRI1ixYgUmTpyIgoICzJw5EyNHjkSPHj00nd0eMGAApkyZgokTJ+Kll15Cw4YNodPpcOTIEWRlZWHy5MkYOHCgpnq99dZbKCgowNChQ1FQUAB3d3cAV88wGY1GjB49WvPR+UceeeSGR8v9/f0xffp0TVm9e/fGihUr8MADD9i9brFY8N///he9evXSlAMAPXv2xO+//467774bAIodcd64cSPatGmjKWvRokUYMmQIwsLC4OPjg1q1akGn0+HMmTO4ePEiwsPDsXDhQk1ZERERyMjIKLW8evXqmp9g3aNHD3z00UfFzlTWrVsXmzZtQvfu3TXlAECrVq2QlJSEsLAwAMAbb7xhV75161aEhoZqynriiSfg5+eHefPmYenSpbYnqhoMBrRt2xbvv/8+Bg8erClr8uTJyM7OLrW8UaNG+OGHHzRlAUD//v3x8ccfY/jw4cXKFi9ejKKiIrz77ruassaPH2/3tNi/n+0CgK+//lrzmZFXXnkFZrMZ//73vzFlyhTbmTsRgb+/P1544QVMmTJFU9a4ceNuuM4CAwOxYsUKTVn3338/3nvvPQwYMMDudaPRiM8++wwDBgzAyZMnNWU9/PDDOHv2rG08IiLCrnzdunW27e9mqsLn6O6778aBAwdKLbdarZqfhn333Xfjiy++QJcuXexeb968Ob7//nvcc889mnIAIDQ0FDt37rTtP6+/6mPnzp2anwavct8KVI3tYsmSJXjggQdQv359tGjRArVr14ZOp0Nqaip+/fVXNG/eHBs2bNCUVRW2sYCAAOzZswf//e9/sX37dtsZ07CwMMyaNQu9e/cusdeSktysW9WQkBDN30cvvPACfv755xLL7rzzTvzwww/4/PPPNWWtXLnyhstQu3ZtzJw5U1OWiKBJkya276CsrCy0bt3ali8OdpC0fPnyUp9sf+nSJYeyiAB20+VytF42prWxWJqioiJ88803iIuLw/r161FYWOhQ4/2nn37CypUrcfjwYduO8JFHHtH8w/TvMjMzbd18AFcbw23bttXclYNqFy5cwKlTp3DnnXeWWH6tW5Ju3brd8nsdPXoUHh4emi9VBoDff/8dSUlJduvrWvdMFeHPP//E77//jvDw8BLLT58+jY0bN+LRRx+95ffauXMnzGZzsUbkzeTn5+P8+fMArl4Sp6qLosrs6NGjdttYaZdv3g4FBQW4fPlyqfuEwsJCnDx50u5y17K6fPkyDAaD5ksZVXGFz9GtSklJwc8//4zHHnusxPJ9+/bh888/1/T9lp6eDr1eb3cJ6t99/fXXMJvNDjVAnW3fCjj3dlFUVFSswXhtnTnSYFTJ2bcx0iYhIUHTdFq2++DgYE23eRw9elTTexIBbGDTTZw+fRozZ87Ee++9h7y8vIquTjF//fWX7Z4xZpFqn3/+ueYrLm5nluo81XUj7S5fvmx372BlzKoKVK8vZ/1bcrsoP5mZmbaDhF999ZXdSQ2DwVDsapqqnkXk1Crs4nRyGhcuXJBHHnlEatSoIXXq1JEFCxZIYWGhvPzyy1KtWjW566675KOPPtKUNXv2bLv7FTdv3iw5OTm28czMTBk/fvwt1/n06dMyYcIEzU95rkpZ58+ft/3/+PHj8vLLL8vzzz9fpn4cS8sq6R42V8zKz8+XX3/91e5JpiIia9askdDQUHF3d6+QLGeu28GDB+Xzzz+33e/25ZdfSpcuXaRdu3YyY8YMKSoqYpYGV65ckblz52p+gvvtzHrrrbduKSsrK0tiY2Nl8eLFcvDgwVuqj7NmXaNy3avO43bhOlnr16+XVq1a2cYtFkuxnhW0Pr+mKmTdzKlTp5R1NUhUFmxgu5hWrVrdtMuK1q1bO5Q5fvx4qV+/vjz33HNy5513il6vl/vvv1/uueceSUxMdChLr9fbPSzCarXKkSNHbOOOPCziRg1/s9ks7dq109zwrwpZKSkpEhQUJHq9Xpo2bSq7d++W2rVri8ViES8vLzEYDPKf//yHWf+zb98+adCgga2rtv79+0tqaqp07dpVvL295bnnnrPrUuR2ZTlz3VavXi1Go1Hc3d3FZDJJQkKCmEwmue+++yQiIkKMRqO88cYbzPqf3NxciY6Olnbt2knHjh1t22ZcXJzUqVNH6tata9cHrytm/fnnn9K1a1exWCzSs2dP+fPPP6VJkya2H83VqlXTfHDPWbNUri/Vec6a5ax/S2fN6tu3ryxfvtw2brFY7H47zZ49W+6//35m/c2+fftk8eLFsnTpUltf6+fOnZOnn35aPDw85I477tCUs2DBAk0DkSPYwHYxpfVreP3giMDAQPn2229FROTIkSOi0+lu+ATKG7n+aYzX70AdaWCrbPhXhaz77rtP+vTpI1u2bJFx48ZJvXr15LHHHpPCwkIpLCyUJ554Qtq3b8+s/+nbt6/06NFD1q9fLw8//LDodDpp3LixvPrqq3ZPcr/dWc5ct7Zt20p0dLQUFRVJXFycmM1mmTdvnq186dKl0qxZM2b9z4svviheXl4yYMAA8ff3F6PRKI8//rg0adJE4uPjJS8vT1OOM2cNGjRIOnToIB988IE8+OCD0qxZM4mIiJDU1FQ5e/asDBw4UO655x6XzlK5vlTnOWuWs/4tnTUrKCjI7in11/92SklJkZo1azLrf9avXy/u7u62gxkhISGyadMmqVGjhnTv3t2ub/ibCQ4OvumgtacNomvYwK7ktm7daneJdkmMRqP89ddftnGz2Sx79+4t0/upbGCrbPhXhSw/Pz/Zs2ePiFztMkyn09l9mf3222/i7e3NrP+pXbu2rYuVCxcuiE6nk/fee0/TvOWZ5cx1s1gscvjwYRERKSwsFIPBYLevOHr0qJjNZmb9T0hIiKxevVpE/n+3Yw8//LBdN2laOWtW7dq1bV07paWliU6nk23bttnKk5OTxc/Pz6WzVK4v1XnOmuWsf0tnzTKZTHbdTO3cudPugMYff/yh+VaeqpDVoUMHmThxoly6dEneeust0el00qRJkzLdCkdUHtjAruSuv0S7JNf3J2ixWOx2go5Q2cBW2fCvClkq131Vyfp7H62enp7F7lHWSmWWM9fNmf+Wzpjl7u5u13etyWSS3bt3a5rXVbL0en2x7aus68tZs1SuL9V5zprlrH9LZ82qU6eO7WB7Sf773/+Kv78/s/7H29vb9j2Wn58vBoNBvvrqK03z3qoWLVo4dMsXVU3sB7uSEw0PiRcRjBw50tbtTE5ODv75z3/C09PTbrrVq1dres+/9ydYUFCA+Ph41KhRA4Bj/QkWFRXZdZdkMBiK1YlZ9q7vakJL1xNVNUun09l1E6PX68vcPZfKLGeum06ns1vf148zy15+fj7c3d1t425ubvD29q5UWSJSbH2VlbNmqVxfqvOcNctZ/5bOmtW1a1csXLgQPXv2LLF84cKFmvv6rgpZmZmZtu7RjEYjzGYzmjRpomneW3Xs2DHk5+fflvci18UGNhXrJ3DYsGFlzgoMDMSyZcts4/7+/vjggw+KTaOFyoZ/VcgCcMOs3NxcTRlVJUv+1z/7tR9FWVlZaN26dbG+WdPT029rljPX7WZZWg7oVaUsAIiJibF1a5SXl4cZM2YUa7T8+9//rlRZM2fOtGVdvnxZU4arZd3K+lKd5ypZzvq3dIasqVOnomPHjhg0aBCmTJliayweOHAAs2fPxnfffYdt27Yx62/2799v619dRHDgwAFkZ2fbTRMaGqo5j0gl9oNdyVmtVuzZswcNGzas6Ko47LHHHtM03YoVK5jFLIezEhISNGVdfwCqvLNU5zGr4rK6d+9+07NaOp0OmzZtqtRZAPDDDz9U6iyt60t1nitnAc75t6yILABYu3YtxowZU+xgp6+vL5YvX45+/fppyqkKWXq9HjqdrsQDntde1+l0KCws1Fw3rVz5dzXdPmxgV3IVsSMoKipCfHw8Vq9ejWPHjkGn06Fhw4YYMGAAhg8ffkuXUd3IyZMnUbdu3WJn5pjFLBVZH3/8MR588MFbuoS/PLJU5zGr4rKIqGq7fPky/vvf/+LQoUMAgMaNG6N3795l2r9U5qw///xT03RBQUEO1+9m2MAmTcr/Nm+qSFoecqZSUVGRPPDAA6LT6aRVq1by8MMPS1RUlISGhopOp5PIyMhye2+Vy8osZrlKluo8ZjGLWc6TpTqPWa6XVVhYKOvWrVPy+6kqZJW36x98SVQS3oPtYo4fP46AgADNZ4HlNl+gEB8fjy1btuD777/HPffcY1e2adMm9OvXD++//z5GjBih/L1VLiuzmOUqWarzmMUsZjlPluo8ZrlO1qFDhxAXF4eEhARcuHAB4eHhZX7vypiVmZkJLy8vAMBXX32FgoICW5nBYEBERESZ60V0q9jAdjENGjTA6dOnUatWLU3TO/LUbhU+/vhjREdHF2tcA0CPHj3wwgsvYOXKleXSwCYiIiJyVVeuXMGqVasQGxuL7du3o7CwEPPmzcOoUaNsvbMwC/jyyy/x8ssvY/fu3QCAqKgouwec6XQ6fPrppxg4cKBDddNi6dKlqF27tvJcqlzYwHYxt/uMtKNSUlIwZ86cUsvvv/9+LFy48DbWiIiIiMh5/fTTT1i+fDk+/fRTNGnSBMOGDcNnn32G+vXro2fPng41YqtC1nvvvYcJEybYvXb48GHbfdFz5sxBXFycpgb2+++/r+k9r50YeuSRRzTXk6ouNrBJqfT09Bse2atduzYuXLhwG2tERERE5Lw6deqEp556Cj/99BOaNm3KrJtISUlBTExMqeX3338/5s6dqylr5MiRsFgsMBqNpZ7E0ul0vPKSHMIGtgtavnz5TY/0TZw48TbVxl5hYSGMxtI3K4PBYHefjEoqn07OLGYR3W7Ouu0zq+KyVOcxyzmzevTogdjYWJw9exbDhw9HeHh4md+7KmSlpqbCz8/PNv7DDz8gICDANm6xWHDx4kVNWXfccQfOnDmDYcOGYdSoUew7m5RgA9sFvfvuuzAYDKWW63S6CmtgiwhGjhwJk8lUYnlubm65vjezmFVeWUFBQXBzc3O6LNV5zKq4LGfd9plVcVmq85jlnFkbN27EiRMnsGLFCowfPx5XrlxBVFQUAMcb+VUhq3r16jhy5AgaNGgAAGjXrp1d+aFDh1C9enVNWfv27cOOHTsQFxeHrl27olGjRhg9ejSGDh1qe4gakcPUPZCcbgedTidnzpyp6GqUauTIkZoGFfbv3y8NGjSwjR8/flwKCgqYxSwlWadOnZInn3yyTPOWZ5bqPGZVXJaIyE8//WT7/5YtWyQnJ4dZLpj1dyqzVOcxyzWyNm7cKA8//LB4eHhI48aN5cUXX5Sff/65TO9bGbOioqKkb9++pZZHRETI4MGDHa7T5cuXJSEhQbp37y7VqlWTRx55ROlnmaoONrBdjLM3sG+n5ORk0ev1zGJWmbP27dsnixcvlqVLl8qFCxdEROTcuXPy9NNPi4eHh9xxxx0VkuXMdWOW43/LS5cuyeXLl+1e2717t/Tp08fhbZ9Ztz/rwoULsmTJEtv4I488Iv3797cNAwcOtG0ntzuPWZUjqzTp6emycOFCadWq1S1/T1amrF9++UVMJpMMHDhQfvrpJ8nIyJCMjAzZsWOHPPTQQ2Iymcrc8BcR2bx5s3Tv3l30er2kp6eXOYeqLjawXYxer5ezZ89WdDWcQmVp5DGrYrLWr18v7u7uotPpRKfTSUhIiGzatElq1Kgh3bt3l/Xr12t+X5VZzlw3ZjmWdeLECenUqZPo9Xpxc3OTZ555RrKzs2X48OFiNBplwIABsm3bNmY5edacOXNk6NChtnGLxSIDBgywXZHVtGlTmT59uqYs1XnMqhxZWtxKg7EyZq1Zs0Zq1Kgher3ebvDz85P//Oc/Dr/3yZMnZebMmdKoUSOpU6eOTJ48WX777TeHc4hE2MB2OTqdTtq0aSNLliyRjIyMiq5OhaoMjTxmVVxWhw4dZOLEiXLp0iV56623RKfTSZMmTWTz5s0Ov6/KLGeuG7McM3ToUAkNDZVFixbZzoa0adNGHnvsMfnjjz+Y5SJZYWFhsmHDBtu4xWKRI0eO2MZXr14trVq1qpA8ZlWOrJupDN+55ZGVnZ0tq1evltmzZ8vs2bNl9erVkpWV5VDGp59+Kvfdd5+YzWbp16+frF27tsy3lhFdwwa2i0lKSpIxY8aIl5eXmM1mGTp0qGzatKmiq1UhKnrHzizXzvL29pYDBw6IiEh+fr4YDAb56quvyvS+KrOcuW7MckzdunVl69atIiJy+vRp0el0MmvWLGa5WJafn59tmxARadu2rZw4ccI2fuTIEfH09KyQPGZVjqybqQzfubcz63otWrSQ48ePl1im0+kkKChIoqOjZcGCBaUORI7gU8RdTIcOHdChQwcsXLgQq1atwooVK9CzZ08EBwdj1KhRePTRR1G/fv2KrqYSvr6+N3yypCPdfTGLWdfLzMyEj48PAMBoNMJsNqNJkyaa5y+vLGeuG7Mck5qaipCQEACAv78/zGYzIiMjmeViWZcvX0ZeXp5tfNeuXXbl2dnZKCoqqpA8ZlWOLCpfx44dQ35+follgYGB0Ol0+Oijj0qdvyJ75yHXxAa2izKbzXj00Ufx6KOP4siRI1ixYgWWLl2KV155Bb169cJXX31V0VW8ZfPnz2cWs8otCwD279+P1NRUAFe7UDlw4ACys7PtptHaJ6bKLGeuG7Mcy/p7l4p6vR4eHh6a5mOW82Q1bNgQv/zyC1q0aFFi+a5du2zdBd3uPGZVjiyqOMeOHavoKlAlpBNR3GEjVYisrCysXLkS0dHRyMjIQGFhYUVX6bYoKCiA0ajmOBGzqlaWXq+HTqcrsW/Sa6/rdDpNnyWVWc5cN2Y5ntWiRQvb9piSkoJmzZrB3d3dbrpffvmFWU6c9fLLLyMhIQE//fQT/P397cpOnz6N9u3bY8SIEZgxY8ZNs1TnMatyZGVmZt6wPCUlBd26ddO036kKWY6yWq3Ys2cPGjZsqDybqCRsYLu4zZs3Iy4uDl988QUMBgMGDx6M0aNHo0OHDhVdtXK1f/9+xMbG4sMPP8SZM2eYxSyHs/78809NuUFBQbc1S3Uesyou69VXX9WUNX36dGY5cdalS5fQvn17nDx5EsOHD0eTJk2g0+nw+++/48MPP0S9evXw008/wWq1anpPlXnMqhxZ1w7slaYsBwkrc5ajbtTAXrhwoaYMXiJOjmAD2wWdOHEC8fHxiI+Px9GjR9GpUyeMHj0agwcPhqenZ0VXr9xkZWXhk08+QWxsLHbu3IkOHTpgwIABeOaZZ5jFrFvOIiIqzYULF/Diiy9i1apVyMjIAAD4+Phg8ODBeP3111G9evUKy2OW62clJibesPF5Tbdu3ZhVBjdqYGu5jF+n0+GPP/5QXi+qxMr7KWqkVs+ePcVgMIi/v79MmTJFfv/994quUrnbsmWLPProo2KxWOQf//iHGAwG29NhmcWsW80qzRdffCH/+Mc/nC5LdR6zKiYrPT1dFi5cKC1btmSWC2UVFRXJmTNn5MyZM1JUVGR7/ezZs2Wqi8o8ZlWOLFLv+i7UiMobG9gupm/fvrJmzZoq0Uff7NmzpWnTplKvXj15/vnnJTk5WUREjEaj7Nu3j1nMuqUsEZH33ntPBg4cKEOGDJHt27eLiMj3338vrVq1ErPZLI8//niFZDlz3Zjl+N/y77799lt5+OGHxcPDQ+rXry8TJ05klgtmiVxtVG3YsEH69+8v7u7ut5SlOo9ZrpX18ssvS3Z2tm08PT29zO9fFbIctXLlylL7x96+fXuxrhgTEhIkODhYatasKWPHjpWcnJzbUU2qRNjAJqdlMBgkOjq62MGEsjSmmMWs67355pvi5uYmbdu2lWrVqkm1atVk5syZ4ufnJ6+88oqcO3euQrKcuW7McvxvKSLy559/yiuvvCJBQUHi5+cner1ePv/8c4dzmFWxWdccOXJEXnrpJalfv774+PjI0KFDZfXq1U6RxyzXzNLr9XLmzBnbuNVqLfMZ16qQpbJRHB4eLm+88YZtPCUlRYxGo4wZM0beeust8ff3l+nTp5epnlR1sYFNTmvmzJnSuHFjCQgIkClTpsjevXtFpGyNKWYx63rNmjWT2NhYERH54YcfRKfTyb333isXLlxwKEd1ljPXjVmO+fTTT6VXr15SrVo1GThwoKxZs0Zyc3PLtL0yq+KyRESuXLkiH3zwgXTr1k1MJpP06dNHDAaDbR9UkXnMcv0snU5n1/i8lUuaq0LWfffdp6xR7O/vLzt37rSNR0dHS+fOnW3jq1atkjvuuKNM9aSqiw1scnqJiYkyYsQI8fT0lNDQ0Fu635ZZzLrGbDbLn3/+aRt3d3e3XQ7sKJVZzlw3ZjnGYDDIiy++KJmZmXavl/XqDWZVTNb48ePF19dXOnToIIsXL5bz58+XOUt1HrMqR5azNmSdNUtlo9hkMsnx48dt4507d5Z//etftvGjR4+KxWIpUz2p6mIDm1xGZmamvPPOOxIWFiYGg0E6duwob731FrOYVaYsZ/3h4Mx1Y5Zjxo4dK97e3tKpUyd55513bPccluUHOLMqLuvarSkqGuuq85hVObL0er0cPnxYLl68KBkZGWK1WmXPnj1y8eJFu4FZV6lsFAcGBsrmzZtFRCQ3N1fMZrN89913tvKUlBTx9fXVlEV0DbvpIpe0d+9exMbG4qOPPsLZs2eZxSyHs/R6PWbMmAGLxQIAmDp1KiZPnowaNWrYTael70uVWc5cN2Y5lgUAV65cwapVqxAXF4cdO3YgPDwcGzZsQHJyMlq0aKEpg1kVm/XRRx9hxYoVSEpKQkREBIYPH4777rsPZrMZe/bsQfPmzR2ql8o8ZlWOrOv7iJb/9Ql9/XhZ+puujFlBQUH44IMP0LVrV+Tl5cHHxwfr16/HvffeC+Dq74Fu3bohPT39plnjxo3D3r17MXv2bKxZswYJCQk4deoU3N3dAQArV67E/PnzsXPnzptmEV3DBja5tPz8fLi5uTGLWQ5nBQcH37RPTq19X6rMcua6McuxrOsdOnQIcXFxeP/995GVlYWIiAgMHDgQDz30ELNcIOvYsWNYsWIF4uPjcfnyZaSnp+PTTz/FwIEDHa6P6jxmuXbW5s2bNU2npY/oqpClslF87tw5PPTQQ/i///s/WCwWJCQkoH///rbye++9Fx06dMDMmTM11Z8IAPvBJuf1/fffyx133FHiJUMZGRnSvHlz+fHHH5nFrDJlEZW33bt3l/h6YWGhrFu3TiIjIzV348Osisu6XlFRkXz99dcyaNAgMZlMUq9ePXnqqafKlKU6j1mVI4tu7OzZs3L33XeLTqcTq9UqX3zxhV15jx49JDo62qHMjIyMErvATUtLk9zc3FuqL1U9bGCT0+rbt6/8+9//LrV8wYIF0q9fP2Yxq0xZROVNp9NJmzZtZMmSJZKRkVHiNH+/35tZzpl1I2lpaTJv3jxp2bLlLWepzmOW62WdPHlSFixYIE8++aQ888wz8u6775a5v+iqkHWjRnFeXl6ZMolUYAObnFZgYKDs37+/1PLffvtNAgICmMWsMmXdf//9dj+8Z8yYYdcV0/nz5zU/hVRlljPXjVmOZW3btk3GjBkjXl5eYjabZejQobJp0yZN8zLLebJuJj09XRISEpwyj1muk/X222+LyWQSnU4nPj4+4u3tLTqdTqpVqyYfffSRiFw9S/7LL79U+az+/ftrGogqChvY5LRMJpMcOnSo1PJDhw6Jh4cHs5hVpiy9Xm93Bstqtdo9LTo1NVX0ev1tz3LmujHL8b+liMjly5clPj5eunXrJnq9Xho2bCgzZsyQEydOOJTDrIrNKk1ycrLD28TtymOWa2R9+eWXYjAY5LnnnpNTp07ZXj916pQ888wz4ubmJlu2bJEhQ4bIq6++WuWzRo4cqWkgqihsYJPTatiwoaxevbrU8i+++EIaNGjALGaVKetm3TE50pBSmeXMdWNW2RrYf3f48GF56aWXJCAgQIxGo9x///3McsGsv2MDm1m3mtW1a1d56aWXSi1/6aWXxMPDQ4KDg+XYsWNVPovI2bGBTU5rwoQJ0qJFC7ly5UqxssuXL0uLFi00P0CEWcy6njM3ypy1bsy69Qa2iMilS5fk3XfflerVqzPLhbOuYQObWbeaZbVa5ffffy+1/PfffxedTid//vkns4hcgLGin2JOVJpp06Zh9erVaNKkCSZMmICmTZtCp9Pht99+w9tvv43CwkK89NJLzGJWmbJ0Ol2x7phu1j3T7chy5rox69Zs3rwZcXFx+OKLL2AwGDB48GCMHj2aWS6WRaRaUVHRDbuXdHNzg9lsRmBgILOIXAAb2OS0ateujW3btmH8+PF48cUXIf/rsl2n0yE8PBxLlixB7dq1mcWsMmWJCEaOHAmTyQQAyMnJwT//+U94enoCAHJzczXlqM5y5roxy/G/5YkTJxAfH4/4+HgcPXoUnTp1wqJFizB48GBbJrOcO2vhwoU3LP/rr78cqpfKPGZVjqw777wTa9euxTPPPFNi+Zo1a3DnnXcyi8hF6OTaL1QiJ3bhwgUcPnwYIoLGjRvD19eXWcy6pazHHntM03QrVqy4rVmq85hVcVm9evXCDz/8gJo1a2LEiBEYNWoUmjZtqimfWc6T1aBBA03THT169LbnMatyZCUkJGD8+PGYO3cuHn/8cRiNV89/FRQUYOnSpZg8eTKWLFmCkSNHMovIFdyua9GJiIiqkr59+8qaNWtK7KeVWa6TRXQ7PPfcc6LT6cTLy0tat24trVu3Fi8vL9Hr9fL0008zi8iF8Aw2Oa3Tp09j8eLFmDlzJgDg7rvvxuXLl23lBoMBa9asQb169ZjFLIeziIiInMn27dvx8ccf49ChQwCAxo0bY8iQIejQoQOziFyIvqIrQFSaJUuWICMjwza+Z88edOnSBZGRkYiMjITBYMC8efOYxawyZZ0+fdrugWh333032rRpYxvuuusuzffQqcxy5roxi1lVMSswMBBpaWm28cWLFyMzM1PTvOWdx6zKkXVNhw4dsGDBAnz11Vf46quvsGDBgjI3PKtCFpGzYgObnNb69evRr18/u9cmTZqE6dOnY/r06Xj11Vfx9ddfM4tZZcpy1oa/M9eNWcyqilknT55EYWGhbTw6Ohrnz5/XNG955zGrcmTNmTMHV65csY3/+OOPdg9UvHTpEp544glmEbmKir5Gnag03t7edv3P9u/fX1JTU23jR48eFbPZzCxmlSmrZcuWsnHjRtv49f0df/PNN9K8efPbnuXMdWMWs6pi1s36RneUyjxmVY4svV5vl2W1Wu2yUlNTNfepXRWyiJwdu+kip1VQUICLFy/axlevXm1XfuHCBej12i7CYBazrnfs2DGEhITYxnv16mXXdU/Tpk01PxVYZZYz141ZzKqKWUTlTa57HNL148wici1sYJPTatq0KbZt24bWrVuXWL5lyxY0adKEWcwqU5azNvyduW7MYlZVzAKA5cuXw2Kx2LLj4+NRo0YNu2kmTpxYIXnMqhxZRFR5sIFNTuvhhx9GTEwMunTpgtDQULuyPXv24NVXX8ULL7zALGaVKctZG/7OXDdmMasqZgUGBmLZsmW2cX9/f3zwwQd20+h0Os0NKZV5zKocWURUyVTc1elEN5aXlyddu3YVo9Eo999/vzz99NPyzDPPyP333y9Go1G6dOkieXl5zGJWmbLmzJkj1atXlz179hQrS05OlurVq8ucOXNue5Yz141ZzKqKWUTlTafTycyZM2XBggWyYMEC8fDwkJdfftk2PmPGDM33J1eFLCJnxwY2ObXc3FyZNWuWtGzZUsxms5jNZgkNDZVZs2ZJTk4Os5hV5ixnbfg7c92YxayqmHX//fdLRkaGbXzGjBly4cIF2/j58+fljjvu0JSlOo9ZlSMrKChIgoODbzowi8g1sIFNLm337t3MYlaZs5yx4e/sdWMWs6pa1vVPi77Vpx+rzGNW5cgiosqFDWxyORkZGfL2229L69atb/nLi1nMuhFnPYigOo9ZzGJW6W7WHdOtNrBvJY9ZlSNr+/bt8tVXX9m9lpCQIMHBwVKzZk0ZO3as5oNCVSGLyNlpf4QmUQXbtGkThg4dijp16mDRokV44IEHsGvXLmYxS0nWNRcvXsSSJUvQpk0btG3b1mmynLluzGJWVcwiUmX69OlISUmxje/duxejR49Gz5498cILL2D9+vWYNWsWs4hcRUW38Ilu5MSJE/Kvf/1LGjRoILVq1ZIJEyaI0WiUffv2MYtZt5z1d99//7088sgjYjabpVmzZvLSSy/JL7/8UuFZzlw3ZjGrKmTp9Xo5e/asbdxiscgff/xhG3f0DLbKPGZVjix/f3/ZuXOnbTw6Olo6d+5sG1+1apXm+7mrQhaRs2M3XeS0HnjgAWzduhV9+vTBokWLcN9998FgMODdd99lFrNuOQsATp48ifj4eMTFxSE7OxuDBw9Gfn4+vvjiCzRv3rzCspy5bsxiVlXLEhGMHDkSJpMJAJCTk4N//vOf8PT0BADk5uY6VC+VecyqHFkXLlxA7dq1beObN2/GfffdZxu/6667cOLECWYRuQheIk5Oa+PGjRgzZgxeffVVREREwGAwMItZyrIeeOABNG/eHPv378eiRYtw6tQpLFq0qMKznLluzGJWVcx69NFHUatWLXh7e8Pb2xvDhg1D3bp1beO1atXCiBEjKiSPWZUjq3bt2jh69CgAIC8vD7/88gs6duxoK7906RLc3NyYReQqKu7kOdGNbdu2TcaMGSNeXl4SFhYmixYtkrNnz5bpcmBmMet6BoNBnnnmGTl48KDd6xWd5cx1YxazqmIWUXl7/PHHpWPHjvLjjz/Ks88+K35+fpKbm2sr//DDD6Vdu3bMInIRPINNTqtjx45YtmwZTp8+jXHjxuGTTz5BvXr1UFRUhG+//RaXLl1iFrPKnLVlyxZcunQJ7dq1Q/v27bF48WKcO3dO8/zlleXMdWMWs6piFlF5mzFjBgwGA7p164Zly5Zh2bJlcHd3t5XHxcWhd+/ezCJyFRXdwidyxO+//y6TJ08Wf39/8fDwkL59+zKLWbeUlZ2dLbGxsdK5c2dxc3MTvV4v8+fPl8zMTIfrojLLmevGLGZVxSyi8paRkSEFBQXFXk9LS7M728ssIufGBja5pIKCAlmzZo08+OCDzGKWsixnavi7St2YxayqmEVERFQanYhIRZ9FJyrJqFGjNE0XFxfHLGY5nHUjhYWF+PLLLxEXF4e1a9c6TZYz141ZzKqKWURERNdjN13ktOLj4xEUFITWrVujtONAOp2OWcwqU5aWxrqfn99tz1KdxyxmMevWsoiIiBzBM9jktJ544gl88sknCAwMxKhRozBs2DBUr16dWcxSkqXX6zU11levXn1bs5y5bsxiVlXMIiIicsjtuhadqCxycnLko48+kp49e0q1atVk0KBB8s0330hRURGzmHVLWePHjxdfX19p2bKlLFiwQNLS0hyuS3lkOXPdmMWsqphFRETkCDawyWUcO3ZMXnnlFWnYsKEEBATIpUuXmMWsW8pyxoa/s9eNWcyqillERERasR9schk6nQ46nQ4igqKiImYx65azTCYThgwZgm+//Rb79+/HnXfeiSeeeAJBQUHIysqqsCxnrhuzmFUVs4iIiLRiA5ucWm5uLj7++GP06tULTZs2xd69e7F48WIcP34cFouFWcy6pay/c6aGv6vUjVnMqopZREREN3Q7T5cTOeLv99DNnz9fzp8/zyxmKcsSsb+E1MPDQwYOHCgbNmyQwsLCCs1y5roxi1lVMYuIiEgrPkWcnJZer0dgYCBat259w66XtD5RllnM+ru/P5H8sccew7Bhw8rcbY/KLGeuG7OYVRWziIiIHMEGNjmtkSNHaurTeMWKFcxilsNZztrwd+a6MYtZVTGLiIjIEcaKrgBRaeLj45nFrHLLGjFihKbG+u3OUp3HLGYxi4iI6PbhGWwiIiIiIiIiBfgUcSIiIiIiIiIF2MAmIiIiIiIiUoANbCIiIiIiIiIF2MAmIiIiIiIiUoANbCIiIiIiIiIF2MAmIiIiIiIiUoANbCIiIiIiIiIF2MAmIiIiIiIiUuD/ASMYOld7SqdhAAAAAElFTkSuQmCC", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if os.path.isdir('plots/corr/'):\n", - " shutil.rmtree('plots/corr/')\n", - "os.mkdir('plots/corr/')\n", - "\n", - "for case, sub_data in cases_dict.items():\n", - " plt.figure(figsize=(12,12))\n", - " corr_matrix = sub_data.corr()\n", - " sns.heatmap(abs(corr_matrix), cmap='Blues')\n", - " plt.savefig('plots/corr/'+case+'corrolation')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "3f0480a1-c9fb-4990-953b-a03e8e3d340d", - "metadata": {}, - "source": [ - "### Principal Component Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "07b0e97f-70bc-4426-9e1c-7ce71c7502ad", - "metadata": {}, - "outputs": [], - "source": [ - "# from sklearn.preprocessing import StandardScaler\n", - "# from numpy.linalg import eig\n", - "# \n", - "# data_prediction = data.drop([\"CASE\", \"IS_GRB\"],\n", - " # axis=1, inplace=False)\n", - "# \n", - "# # centering adn normalizing the columns \n", - "# x = StandardScaler().fit_transform(\n", - " # data_prediction)\n", - "# # calculating covariance matrix\n", - "# x_cov = np.cov(x.T)\n", - "# \n", - "# # getting eigenvalues and eigenvectors\n", - "# eig_vals, eig_vecs = eig(x_cov)\n", - "# \n", - "# # sort values in increasing order \n", - "# idx = eig_vals.argsort()[::-1] \n", - "# eig_vals = eig_vals[idx]\n", - "# eig_vecs = eig_vecs[:,idx]\n", - "# \n", - "# # make a list of (eigenvalue, eigenvector) tuples\n", - "# eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[i,:]) for i in range(len(eig_vals))]\n", - "# \n", - "# # Then, we sort the tuples from the highest to the lowest based on eigenvalues magnitude\n", - "# eig_pairs.sort(key=lambda x: x[0], reverse=True)\n", - "# \n", - "# # For further usage\n", - "# eig_vals_sorted = np.array([x[0] for x in eig_pairs])\n", - "# eig_vecs_sorted = np.array([x[1] for x in eig_pairs])\n", - "# \n", - "# # find thereshold value for explaining variance \n", - "# explained_variance = eig_vals_sorted/np.sum(eig_vals_sorted)\n", - "# cum_explained_variance = np.cumsum(explained_variance)\n", - "# threshold = 0.9\n", - "# n_components = np.argmax(cum_explained_variance >= threshold) + 1\n", - "# \n", - "# eig_vals_reduced = eig_vals_sorted[:n_components]\n", - "# eig_vecs_reduced = eig_vecs_sorted[:, :n_components]\n", - "# \n", - "# # project the reduced eigenvector matrix on data\n", - "# projected_data = np.dot(data_prediction, eig_vecs_reduced)\n", - "# " - ] - }, - { - "cell_type": "markdown", - "id": "c017a971-e34f-4723-b4ca-49e1767b54b7", - "metadata": {}, - "source": [ - "## Random Forest Model\n" - ] - }, - { - "cell_type": "markdown", - "id": "edfc6f38-0b14-48f4-ac9b-956851760015", - "metadata": {}, - "source": [ - "Classifying using a random forest model. See doc: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "2107b8a8-3bb1-468e-b1fc-6cc071d6d502", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.datasets import make_classification\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.metrics import confusion_matrix\n", - "from sklearn.metrics import classification_report\n" - ] - }, - { - "cell_type": "markdown", - "id": "b3374d3c-4b43-459f-8a12-603a6209fa4c", - "metadata": {}, - "source": [ - "### Tuning Model" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "312adb70-bb1b-4a3f-8355-5444145fe399", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.3s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.4s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "Best parameters: {'max_depth': 4, 'min_samples_split': 2, 'n_estimators': 100}\n", - "Best score: 0.9999263495232379\n" - ] - } - ], - "source": [ - "\n", - "# X = StandardScaler().fit_transform(\n", - " # projected_data)\n", - "#X = data[[\n", - "# \"MAGCAL_R0\",\n", - "# \"MAGCAL_B0\",\n", - "# \"MAGCAL_R1\",\n", - "# \"MAGCAL_B1\",\n", - "# \"MAGVAR_R1\",\n", - "# \"MAGVAR_B1\",b\n", - "# \"MAGERR_R0\",\n", - "# \"MAGERR_R1\",\n", - "# \"MAGERR_B0\",\n", - "# \"MAGERR_B1\",\n", - "# \"R0_B0\", \n", - "# \"R1_B1\",\n", - "# \"IS_GRB\"]]\n", - "#X = data.drop([\"CASE\"], axis = 1).dropna()\n", - "#X = data[[\n", - "#\"MAGCAL_R0\", \n", - "#\"MAGCAL_B0\", \n", - "#\"MAGCAL_R1\", \n", - "#\"MAGCAL_B1\", \n", - "#\"IS_GRB\" \n", - "#]].dropna()\n", - "#X = data.drop(\"CASE\", axis=1).dropna() # Not sure this is the best way to handle missing values \n", - "X = data.dropna()[[\n", - " \"MAGCAL_R0\",\n", - " \"MAGCAL_B0\",\n", - " \"MAGERR_R0\",\n", - " \"MAGERR_B0\",\n", - " \"MAGCAL_R1\",\n", - " \"MAGCAL_B1\",\n", - " \"MAGERR_R1\",\n", - " \"MAGERR_B1\",\n", - " \"MAGVAR_R1\",\n", - " \"MAGVAR_B1\",\n", - "\n", - "]]\n", - "y = data.dropna()[\"IS_GRB\"]\n", - "\n", - "param_grid = {\n", - " 'n_estimators': [100],\n", - " 'max_depth': [4],\n", - " 'min_samples_split': [2, 3]\n", - "}\n", - "\n", - "grid_search = GridSearchCV(RandomForestClassifier(class_weight=class_dict), param_grid=param_grid, cv=5, verbose=2)\n", - "grid_search.fit(X, y)\n", - "\n", - "# Print the best hyperparameters and corresponding score\n", - "print(\"Best parameters: \", grid_search.best_params_)\n", - "print(\"Best score: \", grid_search.best_score_)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e4036b49-c267-4822-99e6-6ca775ac2363", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "48197914-228d-4769-a86a-383ba30c869e", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"None of [Index(['CASE'], dtype='object')] are in the [columns]\"", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[17], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m ohe \u001B[38;5;241m=\u001B[39m OneHotEncoderCustom(variables\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mCASE\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m----> 2\u001B[0m ohe\u001B[38;5;241m.\u001B[39mfit(data)\n\u001B[1;32m 3\u001B[0m ohe\u001B[38;5;241m.\u001B[39mtransform(data)\n", - "Cell \u001B[0;32mIn[12], line 10\u001B[0m, in \u001B[0;36mOneHotEncoderCustom.fit\u001B[0;34m(self, X, y)\u001B[0m\n\u001B[1;32m 9\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, y\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[0;32m---> 10\u001B[0m X_ \u001B[38;5;241m=\u001B[39m X\u001B[38;5;241m.\u001B[39mloc[:, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mvariables]\n\u001B[1;32m 11\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mohe\u001B[38;5;241m.\u001B[39mfit(X_)\n\u001B[1;32m 12\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1147\u001B[0m, in \u001B[0;36m_LocationIndexer.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m 1145\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_is_scalar_access(key):\n\u001B[1;32m 1146\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_value(\u001B[38;5;241m*\u001B[39mkey, takeable\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_takeable)\n\u001B[0;32m-> 1147\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_tuple(key)\n\u001B[1;32m 1148\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1149\u001B[0m \u001B[38;5;66;03m# we by definition only have the 0th axis\u001B[39;00m\n\u001B[1;32m 1150\u001B[0m axis \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxis \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;241m0\u001B[39m\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1339\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_tuple\u001B[0;34m(self, tup)\u001B[0m\n\u001B[1;32m 1336\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_multi_take_opportunity(tup):\n\u001B[1;32m 1337\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_multi_take(tup)\n\u001B[0;32m-> 1339\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_tuple_same_dim(tup)\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:994\u001B[0m, in \u001B[0;36m_LocationIndexer._getitem_tuple_same_dim\u001B[0;34m(self, tup)\u001B[0m\n\u001B[1;32m 991\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m com\u001B[38;5;241m.\u001B[39mis_null_slice(key):\n\u001B[1;32m 992\u001B[0m \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[0;32m--> 994\u001B[0m retval \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(retval, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname)\u001B[38;5;241m.\u001B[39m_getitem_axis(key, axis\u001B[38;5;241m=\u001B[39mi)\n\u001B[1;32m 995\u001B[0m \u001B[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001B[39;00m\n\u001B[1;32m 996\u001B[0m \u001B[38;5;66;03m# be handled by the _getitem_lowerdim call above.\u001B[39;00m\n\u001B[1;32m 997\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m retval\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mndim\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1382\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_axis\u001B[0;34m(self, key, axis)\u001B[0m\n\u001B[1;32m 1379\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(key, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mndim\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;129;01mand\u001B[39;00m key\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m 1380\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCannot index with multidimensional key\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m-> 1382\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_iterable(key, axis\u001B[38;5;241m=\u001B[39maxis)\n\u001B[1;32m 1384\u001B[0m \u001B[38;5;66;03m# nested tuple slicing\u001B[39;00m\n\u001B[1;32m 1385\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_nested_tuple(key, labels):\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1322\u001B[0m, in \u001B[0;36m_LocIndexer._getitem_iterable\u001B[0;34m(self, key, axis)\u001B[0m\n\u001B[1;32m 1319\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_key(key, axis)\n\u001B[1;32m 1321\u001B[0m \u001B[38;5;66;03m# A collection of keys\u001B[39;00m\n\u001B[0;32m-> 1322\u001B[0m keyarr, indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_listlike_indexer(key, axis)\n\u001B[1;32m 1323\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_reindex_with_indexers(\n\u001B[1;32m 1324\u001B[0m {axis: [keyarr, indexer]}, copy\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, allow_dups\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m 1325\u001B[0m )\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexing.py:1520\u001B[0m, in \u001B[0;36m_LocIndexer._get_listlike_indexer\u001B[0;34m(self, key, axis)\u001B[0m\n\u001B[1;32m 1517\u001B[0m ax \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_axis(axis)\n\u001B[1;32m 1518\u001B[0m axis_name \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_get_axis_name(axis)\n\u001B[0;32m-> 1520\u001B[0m keyarr, indexer \u001B[38;5;241m=\u001B[39m ax\u001B[38;5;241m.\u001B[39m_get_indexer_strict(key, axis_name)\n\u001B[1;32m 1522\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m keyarr, indexer\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexes/base.py:6115\u001B[0m, in \u001B[0;36mIndex._get_indexer_strict\u001B[0;34m(self, key, axis_name)\u001B[0m\n\u001B[1;32m 6112\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 6113\u001B[0m keyarr, indexer, new_indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_reindex_non_unique(keyarr)\n\u001B[0;32m-> 6115\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001B[1;32m 6117\u001B[0m keyarr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtake(indexer)\n\u001B[1;32m 6118\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(key, Index):\n\u001B[1;32m 6119\u001B[0m \u001B[38;5;66;03m# GH 42790 - Preserve name from an Index\u001B[39;00m\n", - "File \u001B[0;32m~/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/pandas/core/indexes/base.py:6176\u001B[0m, in \u001B[0;36mIndex._raise_if_missing\u001B[0;34m(self, key, indexer, axis_name)\u001B[0m\n\u001B[1;32m 6174\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m use_interval_msg:\n\u001B[1;32m 6175\u001B[0m key \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(key)\n\u001B[0;32m-> 6176\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNone of [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mkey\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m] are in the [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00maxis_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m]\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 6178\u001B[0m not_found \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(ensure_index(key)[missing_mask\u001B[38;5;241m.\u001B[39mnonzero()[\u001B[38;5;241m0\u001B[39m]]\u001B[38;5;241m.\u001B[39munique())\n\u001B[1;32m 6179\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnot_found\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m not in index\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", - "\u001B[0;31mKeyError\u001B[0m: \"None of [Index(['CASE'], dtype='object')] are in the [columns]\"" - ] - } - ], - "source": [] - }, - { - "cell_type": "markdown", - "id": "8ad1a843-0d1b-42db-91d3-6274a178b846", - "metadata": {}, - "source": [ - "### Fitting Model" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "ccb95c0c-497c-47c2-aca4-ed7e7c40f287", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "\n", - "RFmodel = RandomForestClassifier(**grid_search.best_params_, class_weight=class_dict)\n", - "\n", - "\n", - "RFmodel.fit(X_train, y_train)\n", - "y_pred = RFmodel.predict(X_test)\n", - "y_pred_proba = RFmodel.predict_proba(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8b1497f5-e421-4d62-a688-26c8b03ac384", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.96033075 0.03966925]\n", - " [1. 0. ]\n", - " [1. 0. ]\n", - " ...\n", - " [1. 0. ]\n", - " [1. 0. ]\n", - " [1. 0. ]]\n" - ] - } - ], - "source": [ - "print(y_pred_proba)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "1630b046-f7fb-4c49-b051-a1f543e2f0fc", - "metadata": {}, - "outputs": [], - "source": [ - "def run_grid_search(X, y, model, param_grid, cv=5):\n", - " grid_search = GridSearchCV(model,\n", - " param_grid=param_grid,\n", - " cv=cv,\n", - " verbose=2)\n", - " grid_search.fit(X, y)\n", - " return grid_search" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "0bdafba6-8804-4cf3-ad73-6fb9cd3e58b2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.8s\n", - "[CV] END .max_depth=4, min_samples_split=2, n_estimators=100; total time= 1.9s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 1.8s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.0s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.0s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 2.2s\n", - "[CV] END .max_depth=4, min_samples_split=3, n_estimators=100; total time= 1.8s\n" - ] - }, - { - "data": { - "text/html": [ - "
GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
-       "             param_grid={'max_depth': [4], 'min_samples_split': [2, 3],\n",
-       "                         'n_estimators': [100]},\n",
-       "             verbose=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n", - " param_grid={'max_depth': [4], 'min_samples_split': [2, 3],\n", - " 'n_estimators': [100]},\n", - " verbose=2)" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "run_grid_search(X_train, y_train, RandomForestClassifier(), param_grid)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b77ce934-b9e7-4f9a-931e-bdc92c85ba0d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[29644 0]\n", - " [ 0 182]]\n" - ] - } - ], - "source": [ - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "report = classification_report(y_test, y_pred)\n", - "print(conf_matrix)" - ] - }, - { - "cell_type": "markdown", - "id": "de69cb46-b7e4-421b-82ae-0d3af4d88e3e", - "metadata": {}, - "source": [ - "### Feature Importance\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "f114501c-7850-4d50-89ff-699c216734d6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "importances = RFmodel.feature_importances_\n", - "std = np.std([tree.feature_importances_ for tree in RFmodel.estimators_], axis=0)\n", - "feature_names = list(X.columns)\n", - "forest_importances = pd.Series(importances, index=feature_names)\n", - "forest_importances = forest_importances.sort_values(ascending=False)\n", - "\n", - "fig, ax = plt.subplots(figsize = (5,6))\n", - "forest_importances.head(15).plot.barh(ax=ax)\n", - "ax.set_title(\"Feature importances using MDI\")\n", - "ax.set_xlabel(\"Mean decrease in impurity\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5272ca7c-eb5f-4afb-b85a-a176444e7c4d", - "metadata": {}, - "source": [ - "### Visualizing the Random Forestfrom sklearn.tree import export_graphviz" - ] - }, - { - "cell_type": "markdown", - "id": "bed5031c-3e68-4555-9ade-e524d6bef0bf", - "metadata": {}, - "source": [ - "The RandomForestModel consists of `n_estimators` DecisionTreeModel. To visualize each tree the `plot_tree` attribute can be used, see below:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "23862b07-a1bf-456c-91bf-6e2c086a4c63", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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FChWUnJxsctHhP2RmZioyMtL0oD4AAN4kJSXFceOee+5RhQoVnA8TgO69916Tx1ws/h4BAAAAAAD83cWLF9WtWzddvHjRrBkcHKwVK1aYfJAfwH9WrFgxrVixQlWrVjXt9uvXTz/++KNpEwAAb2GxR7zqqqvUuHFj58MEoGuvvVYNGzZ03GEfDAAAAAAA8N/l5eWpd+/eOnLkiGl31qxZJr/jAfCfuVyuAvn/7bHHHtOHH35o2gQAwFtYPPYaGhqq9u3bG0wTeEqVKqX77rvPcYdHewEAAAAAAP6YkSNHavfu3abN8ePHq23btqZNAP/e888/b/7/25tvvqmlS5eaNgEA8BabNm3S5cuXHXfCw8MNpgk8LpdLERERjjtpaWmm794AAAAAQKB78skndfToUbPerFmzFBoaatYDitJLL72kWrVqmfViYmKUkZFh1gMAAAD+quzsbM2bN89xJzQ0VP379zeYCAUtJCREgwcPdtzJysrSggULDCYCABQEd1EPAAAAAPir7OxsRUdH68SJE6bdhQsXql69eqZNAL/vlltu0YwZM0ybhw4dUv/+/TnoAgDwOxcuXNCWLVscdziQ+9eFhoaqY8eOjjtc0gwAAAAAAPCfeTwexcTEaN++fabdyZMnq2XLlqZNAL/vyiuv1LJly+R2232k+ty5c4qMjOSCAACA3/F4PEpLS3PcCQ8Pl8vlMpgoMFns01evXq28vDyDaQAAAAAAAPzXs88+q02bNpk2R44cqV69epk2Afy+0qVLKzk5WaVLlzZr5ubmqnv37vrtt9/MmgAAeAuLc6Vt27Y1/bc30Fjsgz/44AOdOnXKYBoAAAAAAAD/NXfuXM2ePdu02aVLF40ZM8a0CeD3BQUFacmSJapZs6Zpd+jQofrmm29MmwAAeAOLfXD9+vV1/fXXG0wTmCz2wb/++qt27dplMA0AAAAAYMeOHZo5c6ZZb9iwYbrzzjvNekBRK1OmjN5++22z3g8//KDx48eb9QAAAIC/KiUlRSdOnHDc6d69uypWrGgwEQpDTEyMyX3YCQkJ8ng8BhMBAKzZvTgEAAAA4H8ZPXq0PvroI9Pm008/rS5dupg2Afx3gwYN0tChQ02bq1ev1sSJE02bAAAUtfXr1ys7O9txJywszGCawGVxKPezzz7Tr7/+ajANAAAAAACAf5o+fbqWLVtm2uzRo4cefPBB0yaA/+7ee+/VSy+9ZNrcu3evhg8fzkEaAIBf2bNnj3755RfHHYt9ZiCz+PqdPHlSO3fuNJgGAAAAAADAP61evVoTJkwwbTZv3lyTJ082bQL47+rXr6+5c+eaNo8ePaqePXsqNzfXtAsAQFE6ffq03n//fccd9sHOdO3a1fEl0Pn5+Vq9erXRRAAAAAAAAP5nz549Gj58uGmzbt26euedd+R28/QLUJiqVKmixMREhYSEmDUvX76syMhInT9/3qwJAEBRy8vL06pVqxx32Ac707JlS5NHnFNTUw2mAQAAAIDAlpmZqZiYGLNezZo1eYsQfqlz587q1auXWe/VV1/V3r17zXoAAADAX5GQkGDSiY2NNemgcNStW1ft2rVz3Pnuu++0fft2g4kAANb4JD8AAABQAJYtW6Zp06aZNtu0aaPnn3/etAngj3vzzTd16623mjbHjh2rjRs3mjYBAChKKSkpjhu33HKL6tSp43yYANapUycFBwc77qSlpRlMAwAAAAAA4H927typUaNGmTYbNGig2bNnO358C8BfM3r0aPMLI+fNm6c5c+aYNgEAKEoW++AKFSqoVatWzocJYE2aNFH16tUdd7ikGQAAAAAA4N/78ccf1a9fP9NmlSpVtGLFChUrVsy0C+CPiY6O1iOPPGLa3LZtm5555hnTJgAARWn16tXKz8931HC5XOratavRRIGpatWqatGiheMO+2AAAAAAAIB/7+zZs4qMjFRmZqZZMzQ0VMnJyapQoYJZE8Af16xZM02ZMsW0+d1332nIkCHyeDymXQAAisrHH3+sEydOOO5Y38kRaIKDg0126uyDAQAAAMC58ePH6/vvvzfrvf322ypbtqxZD/Am06ZNU6VKlUxaeXl5Gjp0qHJzc016AAAAwJ914MABbd682XGnYcOGatmypcFEKExxcXEmnYSEBJMOAMCWu6gHAAAAAPzNt99+q6FDh5o2a9asqaVLlyooKMi0C+CPK168uJKSkkwPxns8HvXu3Vu//PKLWRMAgKKSk5OjtWvXOu5wINe58uXL65577nHc4VAuAAAAAADAvzpx4oSio6NND/yWKlVKycnJKl26tFkTwJ/jcrm0YMECXXPNNabdESNGaNeuXaZNAACKisX+sEuXLgoODjaYJnC53W6FhYU57qSmpvKYBAAAAAAAwP+RkZGhyMhIpaenmzXdbreWLl2qmjVrmjUB/HmvvPKK7rzzTvNmSkqKaRMAgKJisQ9u1qyZrrjiCoNpApvFOesNGzbo8uXLBtMAAAAAAAD4j/z8fPXv31+HDh0y7c6YMUO33HKLaRPAnzNs2DD16dPHtJmUlKSpU6eaNgEAKCoW++Bq1aqpadOmBtMENot98Ndff62DBw8aTAMAAAAAgemLL77Qq6++atbr0aOHunTpYtYDvE2VKlU0ZcoUs96uXbvYwQAAAKDIJCQkmHTi4uLkcrlMWig8Xbp0UfXq1R13kpOTderUKYOJAACW3EU9AAAAAOBPLly4oMjISF26dMmsGRISosTERFWpUsWsCeCvqVu3rhYtWmTaPHXqlKKjo5WVlWXaBQCgsO3YscPkoRKLw6Sw+Tpu2bJF58+fN5gGAAAAAADAP+Tm5qpnz546evSoaXfu3Llq0KCBaRPAn1euXDklJyerRIkSZs2srCxFRUXpzJkzZk0AAIrCoUOH9OWXXzrusA+2YfF1PHDggPbt22cwDQAAAAAAgP8YMWKE9u7da9qcMGGC2rRpY9oE8OeFhIRo+fLlqlatmml3wIAB+uGHH0ybAAAUtoyMDG3YsMFxh32wjYiICMeNjIwMbdq0yfkwAAAAAAAAfuTll1/W6tWrTZtDhw7V4MGDTZsA/jyXy6X4+Hg1atTItPv444/r/fffN20CAFDYPB6PUlJSHHfCwsLkdvPUoVPt2rVT8eLFHXdSU1MNpgEAAACAwJObm6uhQ4cqNzfXpFexYkW98cYbJi3Am/Xt21ft27c3640bN04HDx406wEAAAB/RFZWlubNm+e4U7x4cfXr189gIhS2kJAQk898Zmdna/78+c4HAgCY4pNNAAAAgBGPx6MhQ4Zo//79pt0pU6aoWbNmpk0Af12nTp00duxY0+Ynn3yiRx991LQJAEBhsziQW6dOHd10003Oh4HJZdc5OTlat26dwTQAAAAAAAD+YezYsdq6datp8+GHH1b37t1NmwD+usaNG2vmzJmmzcOHD6tv377Kz8837QIAUJgsLvMNDQ01vYQnkLVu3VqlS5d23LHY8wMAAAAAAPiL2bNna+7cuabNsLAwjR492rQJ4K+rUaOGli9frqCgILPm+fPn/z/2/vzP57r///+vz1kZjDW7LBFlqSzpEKns68xhxr6HGSSiBUVHB4p0pKgwY5R9mxnNjDUkoU1UaLGEJGv2bWbM8vz+cHyO76vjeB914HGbmedz5nL+Ay6n68lTYu7Px/2hsLAwXb9+3awJAEB227Rpk8n/y0JDQ52PgapVq6Z77rnHcYeX9gIAAAAAAPyfTZs2md8jW7duXb399tumTQC3r0CBAlq5cqUKFSpk1szIyFCXLl106tQpsyYAANlt3759OnjwoOOOxT3H+OffWVq0aOG4w3kwAAAAANyeGTNmaOfOnWa9N998UyVLljTrAZ7K5XIpKipKBQoUMOklJycrIiJCbrfbpAcAAADcjA8++EBnz5513OnatauKFi1qsAg5YdCgQXK5XI470dHR/JsGADyMT04PAAAAAHKL6dOnKzY21rTZo0cPDR061LQJwLm//e1vatmypWnz3Xff1eLFi02bAABkF7fbbfLwZkhIiMmhJKQKFSqobt26jjs8lAsAAAAAAPBPiYmJmjJlimnz4Ycf1tSpU02bAJzr06ePBg8ebNpct26dXnnlFdMmAADZyeLcsHnz5ipYsKDBGgQGBqpNmzaOO5wHAwAAAAAA/NOuXbs0bNgw0+Zdd92l+fPny8eHq30AT9K0aVPz73/s3btXgwcP5oJFAIDXsjg3rF69uqpXr26wBpLNC5BXrVqljIwMgzUAAAAAAADe7dixY+revbsyMzPNmkWLFlVcXJzy5ctn1gTg3N1336158+aZNk+dOqWuXbsqPT3dtAsAQHaxOA8uUKCAmjVrZrAGks158Pbt201eMA0AAAAAecnhw4c1btw4s17Lli3Vu3dvsx7g6SpWrGh6p+rmzZvNz3UAAACAPxMVFWXSiYyMNOkgZ1SsWFGtW7d23Dl48KA+/vhjg0UAACvc9AYAAAAY2L59u5577jnTZs2aNRUdHS2Xy2XaBeCcr6+vFi9erAoVKph2IyIi9N1335k2AQDIDt9++62OHTvmuGPxECn+j8Wv59q1a3Xjxg2DNQAAAAAAAN7rp59+Ut++fU2bJUuW1IoVK+Tv72/aBWDjrbfeUv369U2bf/vb3/Thhx+aNgEAyA7nzp3Ttm3bHHc4D7Zl8eu5Y8cOnThxwmANAAAAAACA9zp//rzCw8OVmppq1syXL5/i4+NVpEgRsyYAO88884w6depk2ly4cKGio6NNmwAAZIeMjAytWrXKcYfzYFuhoaGOG7/99ps+//xz52MAAAAAAAC82I0bN9S5c2edPXvWtLto0SJVrlzZtAnARqdOnfTss8+aNrdu3aoXXnjBtAkAQHZJTEx03GjdurXy5ctnsAaS1L59e8fvn8nMzNTq1auNFgEAAABA7ud2uxUZGank5GSTXlBQkGbPns37RZHnDBs2TA0bNjTrjRo1SqdOnTLrAQAAAH/kwIED2rJli+NOrVq19NBDDzkfhBwVGRlp0omKijLpAABs+OT0AAAAAMDbnTp1Sl26dFF6erpZs1ChQoqPj1eBAgXMmgBslShRQnFxcQoICDBrXr9+XWFhYbp8+bJZEwCA7JCQkOC4UbRoUTVp0sT5GPz/WVzSfOnSJX3yySfOxwAAAAAAAHipf53fXLp0yazp4+Oj5cuXq2zZsmZNALYCAwMVFxenYsWKmTXdbrd69Oiho0ePmjUBAMgOa9asUUZGhqOGy+VShw4djBZBktq2bSs/Pz/HnaSkJIM1AAAAAAAA3ikzM1O9e/fWzz//bNqdPXu27rvvPtMmADsul0vvvfeeqlWrZtodPny4vvrqK9MmAABZ7YsvvtCZM2ccdyyeZ8X/adCggcqUKeO4Y/FCZgAAAAAAAG/2zDPP6MsvvzRtjh8/Xm3btjVtArA1efJkPfLII6bN119/XStXrjRtAgCQ1U6ePKkvvvjCcSckJMRgDf6lVKlSatSokeMO58EAAAAAcPPmz5+vTZs2mfUmTZqkypUrm/UAb+Hr66uYmBj5+/ub9C5evKjhw4ebtAAAAIA/Ex0dbdKJjIyUy+UyaSHntGvXTuXKlXPc+eCDD0ye0QYA2PDJ6QEAAACAN0tPT1e3bt108uRJ0+7777+v6tWrmzYB2HvwwQc1ffp00+aBAwfUv39/ud1u0y4AAFnJ4qHNdu3amX3JEv9Uu3ZtVapUyXGHh3IBAAAAAEBe5Xa7NWTIEO3Zs8e0O3nyZD366KOmTQD2KlasqCVLlpg+EHX+/Hl17txZqampZk0AALKaxXnhQw89pNKlSxuswb8ULVpUTZs2ddzhPBgAAAAAAORlr7zyitauXWvajIiIUN++fU2bAOwVLlxYK1euVFBQkFnzxo0bCg8P19mzZ82aAABkNYvzwlKlSqlhw4YGa/AvPj4+6tChg+NOQkICd5cAAAAAAIA8a/HixXrnnXdMmy1bttTf/vY30yYAe35+flq+fLn5s0z9+vXTgQMHTJsAAGSlVatWOW74+vqqXbt2BmvweyEhIY4bH374oa5fv26wBgAAAAByt9OnT2vUqFFmvQYNGmj48OFmPcDb1KpVS2PHjjXrxcbGcg8aAAAAslRKSormzZvnuJM/f3716tXL+SDkOD8/Pw0YMMBxJy0tzeT3FgDAhk9ODwAAAAC82YsvvqhPPvnEtPnss88qLCzMtAkg60RGRqp3796mzZUrV2ratGmmTQAAssrPP/+s3bt3O+5YPDyKf+dyuUx+XRMTE7mkGQAAAAAA5Elz5szRggULTJuhoaF67rnnTJsAsk6rVq308ssvmza/+uorPf3006ZNAACySnJysj788EPHHc6Ds4bFr+vmzZt1+fJlgzUAAAAAAADeZcOGDeYv161fv76mT59u2gSQdWrVqqXo6GjT5i+//KKePXsqIyPDtAsAQFZwu91KSEhw3OnQoYN8fLjS0lpoaKjjxk8//aQff/zR+RgAAAAAAAAv89133ykiIsK0WaFCBS1evFi+vr6mXQBZo3Tp0lqxYoXpf7NXrlxRWFiYrl27ZtYEACArJSYmOm488sgjKlasmMEa/J7F88HJycnatGmTwRoAAAAAyN2GDx+uCxcumLT8/PwUExPDeRHyvBdeeEH33HOPWW/o0KG6dOmSWQ8AAAD4vZUrV+rcuXOOO926dVORIkWcD4JHGDBggMmz0dHR0crMzDRYBABwihsvAAAAgNv0wQcfaOrUqabNRx55RJMnTzZtAshaLpdLs2fPVu3atU27o0eP1tatW02bAABkBYsHcgMDA9WqVSuDNfhPFpc0//rrr/r666+djwEAAAAAAPAiX331lZ566inTZtWqVTVv3jy5XC7TLoCsNW7cOLVp08a0OXv2bC1YsMC0CQBAVvjoo49MXixgcW6J/5fFJc03btzQ+vXrDdYAAAAAAAB4j6NHj6pHjx5yu91mzWLFiik2Nlb58uUzawLIej179tTQoUNNmxs2bNCECRNMmwAAZIV9+/bp4MGDjjucB2eNxx9/XAULFnTcsXgOHAAAAAAAwJtcvnxZYWFhun79ulnT399fcXFxKlGihFkTQNZr0qSJ+f3x3333nSIjI02/cwIAQFa4cuWKNm3a5Lhj8Rwr/l9333237rnnHscdzoMBAAAA4M8lJSVpxYoVZr0xY8aoTp06Zj3AWwUGBiomJsbsfuUTJ05o9OjRJi0AAADgP0VFRZl0IiMjTTrwDHfeeafJuycOHTqkzZs3GywCADjlk9MDAAAAAG904MAB9e3b17RZunRpLVu2TH5+fqZdAFkvKChI8fHxCg4ONmtmZGSoS5cuOnnypFkTAICsYPGwZrNmzVSoUCGDNfhPjRs3VrFixRx3eCgXAAAAAADkJefOnVN4eLhu3Lhh1syfP7/i4+NVuHBhsyaA7OHj46OFCxeqYsWKpt3Bgwdrz549pk0AAKxZnBNWr15d1atXN1iD/3TnnXfqgQcecNzhPBgAAAAAAOQlqamp6ty5s86dO2fWdLlcWrx4sSpVqmTWBJB9pk2bpoYNG5o2J0yYoLVr15o2AQCwZnFOWKBAATVr1sxgDf5TYGCgWrdu7biTkJDgfAwAAAAAAICXcLvd6t+/vw4cOGDanT59uh588EHTJoDsMXLkSIWHh5s2Fy9erFmzZpk2AQCw9uGHH5rcmxMSEmKwBv+Nxa/tqlWrlJGRYbAGAAAAAHKfS5cuaciQIWa96tWr68UXXzTrAd6uUaNGGjp0qFkvKipKn3zyiVkPAAAAkKR9+/Zp69atjjv33Xcf3yHMhSIjI006UVFRJh0AgDM+OT0AAAAA8DbXrl1TWFiYrly5Ytb09fXVihUrVKZMGbMmgOxVrVo1zZ8/37R5+vRpdenSRWlpaaZdAACsnD9/3uRgmQdys46fn5/atWvnuMNLewEAAAAAQF6RkZGhnj176pdffjHtRkdHq06dOqZNANmnePHiio+PV0BAgFkzOTlZYWFhunjxolkTAABLGRkZSkpKctzhPDhrWfz6rlmzhu+oAQAAAACAPGPkyJH66quvTJt/+9vf1Lp1a9MmgOwTGBio2NhYFS9e3LTbq1cv/fzzz6ZNAAAsWTw32qpVK+XLl89gDf6b0NBQx40dO3boxIkTzscAAAAAAAB4gWnTpmnlypWmzV69emnw4MGmTQDZx+Vyae7cuapevbpp9+mnn9aXX35p2gQAwJLFefB9992nSpUqOR+D/8ri+eDffvtNn3/+ucEaAAAAAMh9xowZY/od2piYGL43DfyHyZMnq3z58ma9QYMGKSUlxawHAAAAREdHm3QiIyPlcrlMWvAcbdq0Mfk3TUJCgk6dOmWwCADghE9ODwAAAAC8idvt1uDBg/Xdd9+ZdqdOnaomTZqYNgFkv9DQUD3//POmze3bt2vMmDGmTQAArKxZs0YZGRmOGi6XSx07djRahP/G4pLmPXv26MiRI87HAAAAAAAAeLiJEyfqww8/NG0OGTJEvXr1Mm0CyH716tXTO++8Y9r86aef1K9fP7ndbtMuAAAWvvzyS505c8Zxx+K8En/M4tf30qVL+uSTT5yPAQAAAAAA8HALFy7UrFmzTJutW7fW+PHjTZsAsl+FChW0dOlS04szL1y4oPDwcC6OBwB4pJMnT+qLL75w3OE8OGu1bdtWvr6+jjurVq0yWAMAAAAAAODZtm7dqtGjR5s2a9euraioKF6+Bni54OBgxcfHKygoyKyZlpam8PBw/fbbb2ZNAACspKWlafXq1Y47ISEhBmvwRx588EGVLl3acScxMdFgDQAAAADkLtu2bdPs2bPNekOHDlXjxo3NekBuUahQIdP/1g4ePKgJEyaY9QAAAJC3paSkaP78+Y47QUFB6tGjh8EieBo/Pz8NHDjQcSc9PV3z5s1zPggA4IhPTg8AAAAAvMns2bO1aNEi02ZYWJhGjhxp2gSQc1555RU9+uijps1p06YpLi7OtAkAgAWLhzQbNmxo8sAo/ljLli0VGBjouMNDuQAAAAAAILdbt26d+cO6Dz74oN58803TJoCcM3DgQPXr18+0mZiYqNdff920CQCABYvzwVKlSqlhw4YGa/BH6tSpo4oVKzrucB4MAAAAAAByuz179igyMtK0WbFiRS1atEg+PlzfA+QGLVq0MP/eyK5duzR8+HDTJgAAFlatWuW44evrq3bt2hmswR8pWrSomjZt6riTkJDgfAwAAAAAAIAHO3nypLp27aqMjAyzZnBwsOLj4xUUFGTWBJBzatasqZiYGNPmr7/+qh49epj+2QMAgIVt27bp4sWLjjuhoaGOG/hjPj4+6tixo+NOQkKC3G63wSIAAAAAyB1SUlI0cOBAs1758uU1efJksx6Q27Rr107du3c3602dOlW7d+826wEAACDviouL0/nz5x13unfvrsKFCxssgicaMGCAyb1Bc+bMUWZmpsEiAMDt4hY4AAAA4Cbt2LFDI0aMMG1Wr15d7733nlwul2kXQM7x8/PTsmXLVKZMGdNu//79tX//ftMmAABOpKSkaP369Y47ISEhBmvwZwoWLKjmzZs77vDSXgAAAAAAkJv9/PPP6tWrl+mlZMWLF1dsbKwCAwPNmgBylsvl0syZM3XfffeZdseOHauPP/7YtAkAgFMW54MdOnQweRAVf8zlcpmcuycmJnJJMwAAAAAAyLUuXbqksLAwJScnmzUDAgIUFxen4sWLmzUB5LwXXnhB7dq1M23OmTNH77//vmkTAACnLM6DmzRpomLFihmswZ+xeDHy5s2bdeXKFedjAAAAAAAAPFBaWpq6dOmiU6dOmXbnzZunatWqmTYB5Kzu3bvrqaeeMm1u2rRJL7/8smkTAACnLM6D77zzTt1///3Ox+BPWTwf/NNPP+nHH380WAMAAAAAucOkSZN04MABs96sWbMUHBxs1gNyo7feesvs2YKMjAwNGDBA6enpJj0AAADkXVFRUSadyMhIkw48U/ny5U3uGDl8+LA2bdpksAgAcLt4EwIAAABwE86ePavw8HClpaWZNYOCghQfH8+XK4BcqFSpUoqNjZWfn59Z8+rVq+rUqZOuXr1q1gQAwImPPvpI165dc9yxuDwY/5vFr/PWrVt17tw552MAAAAAAAA8TEpKisLDw3X+/Hmzpsvl0pIlS3TnnXeaNQF4hvz58ys+Pl6FCxc2a2ZmZqpbt246fvy4WRMAACf27dun/fv3O+5wHpw9LH6djx07pm+++cb5GAAAAAAAAA/jdrvVr18//fTTT6bdt99+W/Xr1zdtAsh5Pj4+WrhwoSpVqmTaHTp0qL799lvTJgAAt+vKlSsmFwFzHpw9Onbs6Lhx48YNrV+/3mANAAAAAACA5xk7dqy2b99u2nz++ef117/+1bQJwDP84x//0EMPPWTanDRpklavXm3aBADgdrndbiUkJDjudOzYUS6Xy/kg/KnHH39cBQsWdNxJTEw0WAMAAAAA3m/Pnj167bXXzHpdu3ZV+/btzXpAblWyZEm99dZbZr1du3Zp+vTpZj0AAADkPd9//73J9wofeOAB7pbJAyIjI006UVFRJh0AwO3xyekBAAAAgKfLyMhQjx49dOzYMdPunDlzVLNmTdMmAM/x8MMP6/XXXzdt/vDDD4qIiJDb7TbtAgBwOywezrz77rtVo0YNgzX4Xzp06OD44efMzEytWbPGaBEAAAAAAIDnGDFihHbt2mXa/Pvf/66WLVuaNgF4jrvuuksLFy40bZ45c0ZdunRRWlqaaRcAgNthcR5coEABNWvWzGAN/pcmTZqoaNGijjtc0gwAAAAAAHKj119/3eSFZL/Xt29fDRo0yLQJwHMULVpU8fHxCgwMNGumpKQoLCxMFy5cMGsCAHC7PvzwQ924ccNxJyQkxGAN/peKFSvq/vvvd9zhPBgAAAAAAORGcXFxeuONN0ybTZs21SuvvGLaBOA5AgICFBsbqzvuuMO027t3bx0+fNi0CQDA7di9e7d++eUXx53Q0FDnY/A/5cuXT61bt3bcsf6eLAAAAAB4o4yMDA0YMEDp6ekmvWLFimnGjBkmLSAv6NWrl+k9zOPHj9ehQ4fMegAAAMhb5syZY9KJjIx0/P5XeL7WrVvrzjvvdNxJTEzUyZMnDRYBAG6HT04PAAAAADzd3//+d23cuNG0OWzYMPXo0cO0CcDzjBgxQl26dDFtLl26VO+++65pEwCAW5WZmamkpCTHHS5ozj6lSpXSQw895LjDJc0AAAAAACC3mTdvnqKjo02bbdu21YsvvmjaBOB5OnTooBdeeMG0+dlnn+m5554zbQIAcDsszgVbtWqlfPnyGazB/+Ln56d27do57nAeDAAAAAAAcpstW7Zo7Nixps377rtPM2fO5JI9IJerW7euZs6cado8fPiw+vbtq8zMTNMuAAC3yuJcsE6dOqpUqZLzMbgpFi9IXrNmjdLS0pyPAQAAAAAA8BD79+9X//79TZtlypTRsmXL5OfnZ9oF4FnKly+vpUuXysfH7nVNFy9eVHh4uJKTk82aAADcDovz4CJFiuiRRx4xWIObYXE/944dO3TixAmDNQAAAADgvaZPn66dO3ea9aZNm6aSJUua9YDczuVyKSoqSgUKFDDpJScnKzIyUm6326QHAACAvCM5OVnz58933ClQoIB69OhhsAieztfXVwMHDnTcycjI0HvvvWewCABwO+y+FQwAAADkQmvWrNHEiRNNmw899JDeeOMN0yYAz+RyuRQTE6MaNWqYdkeNGqXPP//ctAkAwK348ssvdfr0accdi0uDcfMsfr3Xr1/P5SAAAAAAACDX+PbbbzVkyBDTZqVKlbRw4ULTS1sBeK4JEyaoWbNmps3p06dr+fLlpk0AAG7FqVOn9MUXXzjucB6cvSx+vXfv3q0jR444HwMAAAAAAOABjh8/rq5duyozM9OsWbhwYcXFxSkoKMisCcBzPfHEExowYIBpc9WqVZoyZYppEwCAW5GWlqbVq1c77nAenL0sXtp78eJFbd261WANAAAAAABAzrt69ao6deqkq1evmjX9/PwUGxur0qVLmzUBeK5mzZpp0qRJps1vvvlGw4YNM20CAHCrEhISHDfatm0rf39/52NwU9q1aydfX1/HnVWrVhmsAQAAAADvdPjwYY0bN86s16JFC/Xp08esB+QVlSpV0iuvvGLW++ijjzRv3jyzHgAAAPKG2NhYXbx40XGnR48eKlSokPNB8AoDBgwwObedM2eO6V1HAICbx1vCAAAAgD9w5MgR9erVy7RZokQJxcbGKiAgwLQLwHMVKlRIK1euVIECBcyaaWlp6ty5s86cOWPWBADgViQmJjpulCxZUg0bNjRYg5tlcUnz9evX9dFHHxmsAQAAAAAAyFkXL15UWFiYUlJSzJqBgYGKj49XsWLFzJoAPJuvr6+WLFmicuXKmXYHDBigH3/80bQJAMDNWrVqldxut6OGr6+v2rVrZ7QIN6NVq1YKDAx03ElKSjJYAwAAAAAAkLPS0tLUpUsX8+fvFixYoKpVq5o2AXi2t99+Ww888IBpc/z48dq0aZNpEwCAm7Vt2zaTy6ctnlfFzbvvvvtUsWJFxx2L58MBAAAAAABymtvtVkREhH744QfT7uuvv66HH37YtAnAs40ePVodOnQwbb733nuaO3euaRMAgJt19OhRffvtt447oaGhjhu4eUWLFlXTpk0ddxISEpyPAQAAAAAv5Ha7FRkZqeTkZJNeUFCQoqKi5HK5THpAXjNs2DDT95E988wzOnXqlFkPAAAAuV9UVJRJJzIy0qQD71C2bFmT75IdPXpUGzZsMFgEALhVPjk9AAAAAPBEKSkpCgsLM7l47l98fHy0bNkylS9f3qwJwDvcc8895g/QHj9+XN27d1dGRoZpFwCAm2FxSW+HDh3k6+trsAY3q3r16qpevbrjDpc0AwAAAAAAb5eZmak+ffro8OHDpt13331XdevWNW0C8HwlS5ZUXFyc/P39zZrXrl1Tp06ddOXKFbMmAAA3y+I8sEmTJipWrJjBGtysggULqlmzZo47nAcDAAAAAIDc4Pnnn9dnn31m2hw7dqw6duxo2gTg+fLnz6/4+HgVKVLErJmZmanu3bvr119/NWsCAHCzLM4DK1SooAceeMBgDW6Wy+VSSEiI405CQoLcbrfBIgAAAAAAgJzz7rvvaunSpabNLl26aMSIEaZNAJ7Px8dHCxYsUJUqVUy7Tz75pL7++mvTJgAANyMpKclxIyAgQK1btzZYg1thcR68efNm7igBAAAAkCctWLBAmzZtMutNmjRJlStXNusBeY2vr69iYmLk5+dn0rtw4YKGDx9u0gIAAEDu991335ncN1OvXj3Vq1fPYBG8SUREhEknKirKpAMAuDU+OT0AAAAA8ETDhg3TN998Y9qcNGmSyQuyAHinrl27mj+Uv3nzZo0fP960CQDA/7J//37t27fPcSc0NNT5GNwyi1/3pKQkZWRkOB8DAAAAAACQQ1577TWtWrXKtPnEE09owIABpk0A3uOhhx7StGnTTJv79u3TwIEDeWkeACBbXb161eRCKs6Dc4bFr/vWrVt1/vx552MAAAAAAAByyIoVK/TWW2+ZNh9//HFNmDDBtAnAe1SuXFmLFi0ybZ49e1adO3fWjRs3TLsAAPwZt9uthIQEx52QkBC5XC7ng3BLLF7ae+zYMX377bfOxwAAAAAAAOSQzz//XKNGjTJt1qhRQzExMfzMC8ijihQpovj4eOXLl8+smZqaqrCwMJ7PAQBkO4vz4Mcff1yFChVyPga3xOI8+MaNG1q/fr3BGgAAAADwHqdPn9bIkSPNeg0aNNDw4cPNekBeVatWLY0dO9asFxsbq8TERLMeAAAAcq+oqCiTTmRkpEkH3qVly5aqWLGi486qVat04sQJg0UAgFvhk9MDAAAAAE8zd+5czZ0717TZoUMHjR492rQJwPtMnTpVjRo1Mm1OnjxZSUlJpk0AAP6MxZcSg4KC1KxZM4M1uFUWD+WeOXNGX375pcEaAAAAAACA7PfRRx9p3Lhxps0HHnhA77zzjmkTgPd58skn1b17d9PmihUrNGPGDNMmAAB/5sMPP1RqaqrjjsW5JG5dhw4dHL84JiMjQ2vWrDFaBAAAAAAAkL1+/PFHPfHEE6bNcuXKaenSpfLz8zPtAvAu7dq1M/++yRdffKFnnnnGtAkAwJ/ZvXu3fvnlF8cdzoNzRpMmTVSkSBHHHV5eAwAAAAAAvNWZM2fUuXNnpaWlmTULFCig+Ph4FSpUyKwJwPvcf//9mjVrlmnz559/Vu/evZWZmWnaBQDgj1y4cEGffPKJ405oaKjzMbhlFStW1P333++4k5CQ4LgBAAAAAN5kxIgRunDhgknLz89PMTEx8vX1NekBed2LL76oGjVqmPWGDh2qS5cumfUAAACQ+1y/fl0LFy503ClUqJD5uwbgHXx9fTVo0CDHnYyMDM2dO9dgEQDgVvjk9AAAAADAk3zzzTd68sknTZtVqlTRggUL5OPDX7+BvC4gIEArVqxQyZIlTbt9+vTRoUOHTJsAAPwRi8t5W7Vqpfz58xuswa1q2LChSpUq5bjDJc0AAAAAAMAb/frrr+revbvpZadFihRRXFwcP+8CIJfLpTlz5qhmzZqm3WeffVaffvqpaRMAgD9icQ5Yp04dVapUyfkY3LLSpUurYcOGjjucBwMAAAAAAG905coVderUSdeuXTNr+vn5KTY21vx5QADe6eWXX1aLFi1Mm++8846WLFli2gQA4I9YnAMWLlxYTZs2NViDW+Xv76/27ds77vDSXgAAAAAA4I0yMjLUvXt3HT9+3LQ7d+5c3XvvvaZNAN6pX79+ioiIMG2uXbtWr776qmkTAIA/snbtWmVkZDjudOjQwWANbkdISIjjxtq1a5WWlmawBgAAAAA836pVq7R8+XKz3ujRo1WnTh2zHpDXBQYGKiYmRi6Xy6R34sQJjRkzxqQFAACA3GnFihW6dOmS407Pnj1VsGBBg0XwRk888YT8/Pwcd2JiYkzO8AEAN88npwcAAAAAnuLChQsKCwtTamqqWTNfvnyKj49XkSJFzJoAvFu5cuW0bNky+fjY/ZP80qVLCgsL0/Xr182aAAD8N6dPn9bnn3/uuBMaGup8DG6Lj4+POnbs6LjDJc0AAAAAAMDb3LhxQ507d9Zvv/1m2l24cKGqVKli2gTgvQoUKKD4+HjTB6zS09PVuXNnnT592qwJAMB/k5aWptWrVzvucB6csyx+/devX6+UlBTnYwAAAAAAALKJ2+3WwIEDtW/fPtPutGnT9Je//MW0CcB7+fr6asmSJapQoYJpd9CgQfruu+9MmwAA/DcWz4W2a9dO/v7+zsfgtli8tHf37t36+eefnY8BAAAAAADIRi+99JI2b95s2hwxYoS6du1q2gTg3aZPn6569eqZNl966SVt2LDBtAkAwH9jcR784IMPqmzZss7H4LZYPB988eJFbd261fkYAAAAAPBwly5d0pAhQ8x61atX17hx48x6AP7p4Ycf1tChQ816s2fP5mcfAAAA+ENRUVEmnYiICJMOvFOZMmVM3vX7yy+/6MMPPzRYBAC4WT45PQAAAADwBJmZmerdu7eOHDli2p01a5buv/9+0yYA7/fYY4/p1VdfNW3u3r1bQ4cOldvtNu0CAPB7q1atcvz/Gl9fX7Vr185oEW6HxSXNBw4cMH/BDQAAAAAAQFZ69tln9cUXX5g2X3zxRbVv3960CcD7Va9eXe+//75p8+TJk+rWrZvS09NNuwAA/N727dt14cIFxx2L80jcPotf/2vXrumjjz4yWAMAAAAAAJA9ZsyYoRUrVpg2u3fvrmHDhpk2AXi/EiVKKDY2Vv7+/mbN69evKywsTJcvXzZrAgDwn44ePapvv/3WcYfz4JzVqlUrBQQEOO4kJSUZrAEAAAAAAMgeSUlJ5ve3NmrUSFOnTjVtAvB++fLlU1xcnIoVK2bWdLvd6tGjh3755RezJgAA/yk1NVXr16933AkNDXU+BrftvvvuU8WKFR13EhISnI8BAAAAAA83duxYHT9+3KwXExOjfPnymfUA/J9XX31V5cuXN+sNGjRIKSkpZj0AAADkDnv27DF5D0mDBg30wAMPGCyCN4uMjDTpREVFmXQAADfHJ6cHAAAAAJ5g8uTJWrNmjWlz0KBB6tevn2kTQO7x/PPPmz+QNH/+fMXExJg2AQD4vcTERMeNxo0bq3jx4gZrcLuaNWumAgUKOO5Y/H4AAAAAAADIDkuXLtXbb79t2mzevLn+/ve/mzYB5B7h4eEaNWqUaXPLli168cUXTZsAAPyexflfhQoVeNg4h9WoUUN333234w7nwQAAAAAAwFt8+umnevbZZ02b9957r6Kjo+VyuUy7AHKHhg0bavr06abNAwcO6IknnpDb7TbtAgDwL0lJSY4b/v7+at26tcEa3K5ChQqpefPmjju8tBcAAAAAAHiLQ4cOqU+fPqbNkiVLasWKFQoICDDtAsgdKlWqpMWLF5t+Z+TcuXPq3LmzUlNTzZoAAPze5s2bdfXqVcedkJAQgzW4XS6XSx07dnTcSUxM5HtoAAAAAHK1bdu2adasWWa9IUOGqHHjxmY9AP8uODjY9L/ZAwcOaOLEiWY9AAAA5A5RUVEmncjISJMOvFvz5s1VuXJlx53Vq1fr119/NVgEALgZPjk9AAAAAMhpGzdu1Pjx402b9erV04wZM0ybAHIXl8ulefPmqWrVqqbdYcOGaefOnaZNAAAk6erVq9q4caPjDg/k5rx8+fKpVatWjjtc0gwAAAAAALzB999/r4EDB5o2y5cvryVLlsjX19e0CyB3mTJlipo0aWLanDp1qj744APTJgAAkuR2u03O/zp27Gj6kgLcHotz+aSkJGVmZhqsAQAAAAAAyDqnT59Wly5dlJ6ebtYsWLCg4uPjVbBgQbMmgNxn8ODB6tWrl2kzPj5e06ZNM20CAPAvFufBzZo1U3BwsPMxcMTiPHjr1q06f/68wRoAAAAAAICsk5ycrLCwMF26dMms6ePjo2XLlqlcuXJmTQC5T+vWrfXSSy+ZNnfs2KGRI0eaNgEA+BeL8+CqVavqnnvucT4GjoSGhjpuHDt2TN9++63jDgAAAAB4opSUFA0aNMisV65cOU2ZMsWsB+C/a9++vbp162bWmzp1qnbv3m3WAwAAgHe7du2aFi1a5LgTHBxs+vdWeC8fHx+Tnz9kZmZq7ty5BosAADfDJ6cHAAAAADnpl19+Uffu3eV2u82aRYsWVVxcnPLly2fWBJA7FS5cWPHx8cqfP79Z88aNGwoPD9e5c+fMmgAASNKGDRuUmprquGNxOTCcs3go98svv9SpU6ecjwEAAAAAAMgily9fVqdOnXT9+nWzpr+/v+Li4nTHHXeYNQHkTv7+/lq+fLlKlSpl2u3Xr58OHjxo2gQAYM+ePTp69KjjjsU5JJyz+BxOnz6tL7/80vkYAAAAAACALJKenq5u3brpxIkTpt33339fNWrUMG0CyH1cLpdmz56tWrVqmXZHjx6trVu3mjYBALhw4YI++eQTxx2eD/YMHTp0cNzIyMjQ2rVrDdYAAAAAAABkDbfbraFDh5q/ePfVV1/VY489ZtoEkDuNHz9erVq1Mm3OmjVLCxcuNG0CAJCZmamkpCTHndDQULlcLoNFcKJJkyYqUqSI405CQoLjBgAAAAB4okmTJmn//v1mvVmzZik4ONisB+CPTZ8+XcWKFTNppaena+DAgUpPTzfpAQAAwLstX75cly9fdtzp1auXChQoYLAIuUH//v3l5+fnuBMTE8O/XQAgm/jk9AAAAAAgp6Smpqpz5846d+6cWdPlcmnx4sWqVKmSWRNA7lanTh1FRUWZNo8ePapevXopMzPTtAsAyNsSExMdN2rXrq0qVaoYrIFT7dq1k6+vr6OG2+3WqlWrjBYBAAAAAADYcrvdGjBggA4cOGDafeutt9SwYUPTJoDcq0yZMlqxYoXjn8f+3uXLlxUWFqZr166ZNQEAsDgPLly4sJo2bWqwBk41bNhQJUuWdNyx+H0BAAAAAACQVcaNG6ctW7aYNkeNGqXw8HDTJoDcq0CBAlq5cqXpyxoyMjLUtWtXnTx50qwJAMDatWuVkZHhuNOxY0eDNXCqTJkyeuihhxx3eGkvAAAAAADwZDExMZo3b55pMyQkRM8//7xpE0Du5evrq8WLF+vOO+807UZGRmrv3r2mTQBA3vbVV1/p1KlTjjshISEGa+CUv7+/2rVr57jD88EAAAAAcqM9e/botddeM+t17dpVHTp0MOsB+HMlS5bUm2++adbbuXOnpk+fbtYDAACA97J6J3xERIRJB7lD6dKlFRoa6rjz66+/av369c4HAQD+J5+cHgAAAADklFGjRmnHjh2mzZdeeklt2rQxbQLI/Xr37q3BgwebNtevX69JkyaZNgEAeVd6erpWr17tuMMDuZ6jWLFiatKkieMOlzQDAAAAAABP9eabbyouLs602bNnTw0ZMsS0CSD3e+SRR0wv/JCkvXv3avDgwXK73aZdAEDeZXHu17ZtW/n7+zsfA8d8fX1NLgjjPBgAAAAAAHiqhIQE8/OXxo0ba8qUKaZNALlftWrVzF8gfurUKXXt2lVpaWmmXQBA3mVx7vfggw+qbNmyzsfAhMXz2uvXr1dKSorBGgAAAAAAAFs7d+7UsGHDTJtVq1bV/Pnz5XK5TLsAcrfixYsrLi5OAQEBZs3k5GSFhYXp0qVLZk0AQN5mcR58xx136C9/+YvzMTBh8SLg3bt36+eff3bcAQAAAABPkZGRoYEDByo9Pd2kV7RoUU2fPt2kBeDm9e7dWy1btjTrjR8/XocPHzbrAQAAwPt8++232rFjh+NOw4YNdd999xksQm4SGRlp0omKijLpAAD+nE9ODwAAAABywqJFizRz5kzTZqtWrTR+/HjTJoC846233lKDBg1Mmy+//LLWr19v2gQA5E3bt2/X+fPnHXcsHgKFHYvP46OPPtLVq1edjwEAAAAAADC0bds2Pf/886bNmjVrKioqiguaAdyWUaNGqVOnTqbNRYsW8eANAMDEL7/8om+++cZxh/Ngz2Lxeezfv1/79+93PgYAAAAAAMDQwYMH1bdvX9NmqVKltHz5cvn7+5t2AeQNf/3rX/Xcc8+ZNrdt26axY8eaNgEAeVNqaqrJnRMhISEGa2DF4vO4du2aNm/ebLAGAAAAAADAzrlz5xQeHq4bN26YNfPnz6/4+HgVLlzYrAkg72jQoIFmzJhh2jx48KD69+8vt9tt2gUA5E2JiYmOGx06dJCvr6/BGlho1aqVAgICHHcsfm8AAAAAgKeYMWOGvvrqK7Pem2++qVKlSpn1ANwcl8ulqKgoBQUFmfSSk5MVGRnJmQsAAEAeZnVHf2RkpEkHucvjjz+uu+66y3Fn7dq1OnbsmMEiAMCf8cnpAQAAAEB227t3ryIiIkybd955pxYvXswDBgBuW2BgoOLi4lS8eHGzptvtVs+ePXX06FGzJgAgb7J46LJ8+fKqW7euwRpYsbikOTU1VR9++KHBGgAAAAAAABunTp1Sly5dlJGRYdYsVKiQVq5cqQIFCpg1AeQtLpdL77//vu6++27T7ogRI7Rjxw7TJgAg70lKSnLc8Pf3V+vWrQ3WwEqzZs1MLirikmYAAAAAAOBJrl+/rrCwMF2+fNms6evrq+XLl6ts2bJmTQB5z6uvvqqmTZuaNt944w3FxcWZNgEAec/mzZt19epVxx2L51Fhp0aNGibfRUtISHA+BgAAAAAAwEhmZqZ69eplfn/q7NmzVadOHdMmgLwlIiJCffv2NW1+8MEH+sc//mHaBADkPQcOHNCPP/7ouMN5sGcpVKiQmjVr5rjD88EAAAAAcosjR45o3LhxZr0WLVqoT58+Zj0At6ZSpUp65ZVXzHqbNm3S/PnzzXoAAADwHlevXtXixYsddwoXLqyuXbsaLEJu4+Pjo4iICMedzMxMxcTEGCwCAPwZn5weAAAAAGSnS5cuKSwsTMnJyWbNgIAAxcXFqXjx4mZNAHnTnXfeqcWLF8vlcpk1z58/r/DwcKWmppo1AQB5i9vtNrmEt2PHjqb/j4NzlSpVMrlYiEuaAQAAAACAp0hLS1PXrl116tQp0+68efNMXnYFIG8LDg5WfHy8goKCzJo3btxQeHi4zp49a9YEAOQ9Fud9jz/+uIKDg52PgZn8+fOrVatWjjucBwMAAAAAAE/hdrs1ePBg7d2717Q7ZcoUNW3a1LQJIO/x8/PTsmXLVKZMGdPuE088of3795s2AQB5i8VLWatWrap7773XYA2suFwukxcpr1q1SpmZmQaLAAAAAAAAnJs0aZLWr19v2hw8eDAv4QbgmMvl0syZM03ubfy9MWPGaMuWLaZNAEDeYnEenD9/fjVv3txgDSyFhoY6bmzdulXnz593PgYAAAAAcpDb7VZkZKSuX79u0gsKClJUVBTvTgJy2FNPPaUHH3zQrDdq1CidPn3arAcAAADvsHTpUl25csVxp3fv3qbvDUDu0q9fP/n7+zvuxMTEKD093WARAOCP+OT0AAAAACC7uN1u9e/fXwcPHjTtzpgxQw0aNDBtAsi7WrVqpZdfftm0uXPnTo0YMcK0CQDIO/bu3auff/7Zccfi4U/Ys/hc1qxZo7S0NOdjAAAAAAAAHHrhhRe0detW0+azzz6rTp06mTYB5F21atVSdHS0afPYsWPq2bOnMjIyTLsAgLzh4sWL+uSTTxx3OA/2TBafyxdffMHlRAAAAAAAwCNERUVp4cKFps1OnTrpmWeeMW0CyLtKly6tFStWyM/Pz6x55coVhYWF6dq1a2ZNAEDekZmZafLS3pCQEF5U5IFCQkIcN06dOqUdO3YYrAEAAAAAAHDmww8/NL+HtX79+nrrrbdMmwDyrqCgIMXHx6tw4cJmzczMTHXr1k0nTpwwawIA8haL8+BWrVrxQmIP1KFDB8eNjIwMrVmzxmANAAAAAOScBQsWaOPGjWa9iRMnqnLlymY9ALfH19dXMTExZs/hXbhwQcOHDzdpAQAAwHtY3fUfGRlp0kHuVLJkSf31r3913Dlx4gTntwCQxXxyegAAAACQXd544w198MEHps0+ffooIiLCtAkA48aNU5s2bUybUVFRmj9/vmkTAJA3WDyQGxwcrKZNmxqsgTWLS5ovXLig7du3G6wBAAAAAAC4fStXrtQ//vEP0+YjjzyiyZMnmzYBoGfPnnryySdNmxs2bNCECRNMmwCAvGHt2rVKT0933OnYsaPBGlhr166dfH19HTXcbrdWrVpltAgAAAAAAOD27NixQyNGjDBtVqtWTe+9955cLpdpF0De1rhxY73++uumze+//16DBg2S2+027QIAcr+vvvpKp06dctyxeA4V9h566CGVLFnScSchIcH5GAAAAAAAAAeOHj2qHj16mJ6FFCtWTHFxcQoMDDRrAkDVqlXN73Y+ffq0unTporS0NNMuACD3O3PmjD777DPHHc6DPVOZMmXUsGFDxx2Le8UBAAAAIKecPn1aI0eONOs1aNDA/BlFALevdu3aGjt2rFlvxYoVSkpKMusBAADAs3399dfauXOn406jRo1Uq1Ytg0XIzSIjI0060dHRJh0AwH/nk9MDAAAAgOzwySefaMyYMabNOnXqaNasWVzQDMCcj4+PFi1apIoVK5p2Bw8erN27d5s2AQC5n8Xlu23btlVAQIDzMTD3wAMPqEKFCo47XNIMAAAAAABy0v79+9WvXz/TZunSpbV8+XL5+fmZdgFAkqZNm2ZyYeLvTZgwQWvXrjVtAgByP4tzvgYNGqhs2bLOx8Bc8eLF1bhxY8cdzoMBAAAAAEBOOnv2rMLDw3Xjxg2zZlBQkFauXKnChQubNQHgX0aMGKHOnTubNpcuXaqZM2eaNgEAuZ/Fy1hLlCihRo0aGayBNV9fX3Xo0MFxh5f2AgAAAACAnJSamqrw8HCdP3/erOlyubRkyRLz+1wBQJJCQkLM77n/9NNPNXr0aNMmACD3W7Vqldxut6OGj4+P2rdvb7QI1kJCQhw31q9fr5SUFIM1AAAAAJD9RowYoQsXLpi0/Pz8FBMTI19fX5MeABsvvviiatSoYdYbMmSILl26ZNYDAACA54qKijLpREZGmnSQuz322GOqVq2a4866det09OhRg0UAgP/GJ6cHAAAAAFntxIkT6tq1qzIyMsyawcHBio+PV1BQkFkTAH6vWLFiio+PV0BAgFkzJSVFYWFhunjxolkTAJC7HTt2TF9//bXjTmhoqPMxyBIul8vkodzExETHD28DAAAAAADcjmvXriksLExXrlwxa/r6+mrFihUqXbq0WRMAfi8gIECxsbEqUaKEabdXr146cuSIaRMAkHulpqZq3bp1jjucB3s2i89n06ZNunr1qvMxAAAAAAAAtygjI0M9e/bUsWPHTLvR0dGqVauWaRMA/sXlcmnu3LmqXr26aXfkyJH64osvTJsAgNwtISHBcaNDhw68rMiDWTwfvG/fPu3fv99gDQAAAAAAwK17+umntXPnTtPmyy+/rFatWpk2AeD3Jk6cqMcee8y0+eabb2rFihWmTQBA7paYmOi40bhxY/M7L2DH4vnga9eu6aOPPnI+BgAAAACy2apVq7R8+XKz3ujRo1WnTh2zHgAbgYGBiomJMeudOHFCY8aMMesBAADAM125ckVLlixx3ClSpIg6d+5ssAi5ncvlUkREhOOO2+02/TcQAODf+eT0AAAAACArpaWlqWvXrjp9+rRpd8GCBapatappEwD+U7169fTOO++YNg8dOqS+ffsqMzPTtAsAyJ2SkpIcN/z9/dWmTRuDNcgqFpc0Hz16VHv27DFYAwAAAAAAcPPcbrciIiL0/fffm3anTp2qJk2amDYB4D9VqFBBS5culcvlMmteuHBB4eHhSklJMWsCAHKvjz/+WFevXnXcsThvRNax+HxSU1O1YcMGgzUAAAAAAAC3ZsKECeY/lxg6dKh69uxp2gSA/1SoUCGtXLlSBQoUMGumpaUpPDxcv/32m1kTAJB7HTx4UD/++KPjDufBnq158+YKCgpy3LF4wTMAAAAAAMCtWrBggWbPnm3abNOmjcaNG2faBID/5Ofnp6VLl6ps2bKm3QEDBpj8bB8AkPtdu3ZNGzdudNzhPNiz1ahRQ9WqVXPc4TwYAAAAgLe5fPmyhgwZYtarXr0650eAB3v44Yc1dOhQs97s2bO1bds2sx4AAAA8z5IlS0zuU+7Tp4/y589vsAh5Qb9+/RQQEOC4M3fuXKWlpRksAgD8J5+cHgAAAABkpTFjxmj79u3mTR4qAJBdBg4cqP79+5s2k5KS9Prrr5s2AQC5U0JCguPGY489puDgYOdjkGWaNm2qwoULO+5Y/H4BAAAAAAC4FbNmzdKSJUtMm+Hh4Ro5cqRpEwD+SPPmzTVx4kTT5tdff62nnnrKtAkAyJ0szvfuuusu3Xvvvc7HIMtUrlxZtWvXdtzhPBgAAAAAAGS3tWvXasKECabNhg0batq0aaZNAPgj9957r2JiYkybx48fV/fu3ZWRkWHaBQDkPhYvYc2fP79atGhhsAZZJX/+/GrVqpXjDi/tBQAAAAAA2W337t2KjIw0bVasWFGLFi2Sjw+vXwGQ9UqVKqXY2Fj5+fmZNa9evaqwsDCTl0wCAHK3DRs2KCUlxXGH9614NpfLZfIZJSUlKTMz02ARAAAAAGSPMWPG6Pjx42a9OXPmKF++fGY9APYmT56s8uXLm/UGDhxo8vMzAAAAeB63262oqCiTlvV3GJG7lShRQp06dXLcOXnypFavXm2wCADwn3iKAAAAALlWbGys+WXKjz32mPnLMwHgz7hcLr377ru67777TLsvvPCCNm/ebNoEAOQuFy9e1JYtWxx3QkNDHTeQtfz9/dWuXTvHHS5pBgAAAAAA2emLL77Q008/bdqsXr265s6dK5fLZdoFgD8zduxYtW/f3rQZExOj9957z7QJAMhdMjMzlZSU5LgTGhrK35+9gMW5/Zo1a5Senu58DAAAAAAAwE04cuSIevXqZdosXry4YmNjFRgYaNoFgD/TrVs3DR8+3LT50Ucf6aWXXjJtAgByn4SEBMeNli1bKigoyPkYZCmLl/Z+/vnnOn36tMEaAAAAAACA/+3ixYsKCwszfWluQECA4uPjVaxYMbMmAPwvjRo10htvvGHa/PHHHzVw4EC53W7TLgAgd7G4/7dWrVq66667DNYgK1k8H3z69Gl9+eWXzscAAAAAQDbYvn27Zs2aZdYbMmSImjRpYtYDkDWCg4NN/9s/cOAA7xsGAADIpXbt2qVvvvnGcadx48a69957DRYhL4mMjDTpREVFmXQAAP/OJ6cHAAAAAFlh3759euKJJ0ybZcuW1dKlS+Xn52faBYD/JX/+/IqPj1fhwoXNmpmZmerWrZuOHz9u1gQA5C7r1q0zeQFrx44dDdYgq1lc0vzNN9/ol19+MVgDAAAAAADw53777Td17txZaWlpZs2goCDFx8crODjYrAkAN8PHx0cLFixQ5cqVTbtPPvmkycNkAIDcaefOnTp58qTjjsU5I7Kexed0/vx5bd++3WANAAAAAADAn0tJSVF4eLguXLhg1nS5XFq6dKkqVKhg1gSAm/X666/rL3/5i2nz1Vdf1apVq0ybAIDc48yZM/rss88cdzgP9g7t27eXj4+zK0Xdbjd/twAAAAAAANkiMzNT/fr106FDh0y777zzjurVq2faBICb8dRTT6lr166mzeXLl+vtt982bQIAco/09HStXr3acYfzYO/w0EMP6Y477nDcSUxMNFgDAAAAAFkrJSVFAwcONOuVK1dOU6ZMMesByFrt27dXt27dzHpTp07V7t27zXoAAADwDFFRUSadyMhIkw7ylqZNm6p69eqOOxs2bNCRI0cMFgEAfs/ZjQsAAACAB7p69arCwsJ09epVs6afn59iY2NVqlQpsyYA3Iq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333/ftHny5El169ZN6enppl0AgGezeCA3X758atGihcEa5BSLh3JPnjypr776ymANAAAAAADIizIzM9W7d2/9/PPPpt3Zs2fr/vvvN20CQFYrUaKE4uLiFBAQYNb81/duLl++bNYEAHi+DRs2KDk52XHH4jwROadjx46OG+np6Vq7dq3BGgAAAAAAkFc988wz+vLLL02b48ePV7t27UybAJDV8uXLp7i4OBUrVsys6Xa71aNHDx09etSsCQDwfN9//70OHz7suBMaGup8DHJM06ZNFRwc7Lhj8bw5AAAAAADIu6ZPn67Y2FjTZvfu3fXkk0+aNgEgq7lcLr3//vuqVq2aaXf48OHc8wgAecylS5f08ccfO+7wfLB3q1KlimrVquW4w3kwAAAAgJy2evVqLVu2zKw3evRo1alTx6wHwLMEBgZqzpw5Zr3jx49r7NixZj0AAABkraioKJNOZGSkSQf4vUaNGunee+913Pnoo4/0008/GSwCgLzNJ6cHAAAAALfK7XZr6NCh2rNnj2l38uTJevTRR02bAJBdwsLC9Mwzz5g2P/nkE73wwgumTQCAZ7N4iLJly5YqUKCAwRrklEaNGqlEiRKOOzyUCwAAAAAAbtcrr7yitWvXmjYjIiLUt29f0yYAZJcHH3xQ06dPN20eOHBA/fv3l9vtNu0CADyXxfldzZo1VbVqVYM1yCnlypVTgwYNHHc4DwYAAAAAALdr8eLFeuedd0ybLVq00N/+9jfTJgBkl4oVK2rx4sVyuVxmzfPnz6tz585KTU01awIAPFtCQoLjRqFChbh3x8sFBASobdu2jjucBwMAAAAAgNu1fft2Pffcc6bNmjVras6cOaZnKQCQXQoXLqz4+Hjlz5/frHnjxg2Fh4fr7NmzZk0AgGdbt26d0tLSHHc6duxosAY5KTQ01HFj9erVSk9Pdz4GAAAAAG7D5cuXNWTIELNe9erVNW7cOLMeAM/UuHFjDR061Kw3a9Ysbdu2zawHAACArHHhwgUtX77ccadEiRL661//arAI+Hcul0uRkZEmrejoaJMOAORlPjk9AAAAALhVc+bM0fz5802boaGhevbZZ02bAJDdJk+erCZNmpg2X3/9da1cudK0CQDwTL/99ps+/fRTx52QkBCDNchJvr6+6tChg+MOlzQDAAAAAIDbsWHDBvOX69arV0/Tp083bQJAdouMjFTv3r1NmytXrtS0adNMmwAAz5Senq5Vq1Y57nAenDtYfI7r1q1TamqqwRoAAAAAAJCXfPfdd4qIiDBtVqhQQUuWLJGvr69pFwCyU+vWrc2/L/PVV1/p6aefNm0CADyXxfOcbdq0UWBgoMEa5CSLl/bu2rVLx44dcz4GAAAAAADkKadOnVKXLl2Unp5u1ixYsKDi4+NVoEABsyYAZLfatWubv2Tvl19+Uc+ePZWRkWHaBQB4Jovz4Hr16qlChQoGa5CTLJ4PPn/+vLZv326wBgAAAABu3dixY/Xrr7+a9ebMmaN8+fKZ9QB4rsmTJ6t8+fJmvUGDBiklJcWsBwAAAHuLFi0y+Ttb//79eXYWWaZ3794mP5t4//33uecZABzyyekBAAAAwK3YuXOnnnrqKdNmtWrVNG/ePLlcLtMuAGQ3f39/LV++XKVLlzbt9uvXTwcOHDBtAgA8z+rVq5WZmemo4XK51L59e6NFyEkWD+X+8MMPOnjwoMEaAAAAAACQV/zyyy/q0aOH3G63WbNYsWKKi4vjwXoAXs/lcmn27NmqXbu2aXf06NHaunWraRMA4Hk+++wznTt3znHH4hwROc/ic7xy5Yo+/vhjgzUAAAAAACCvuHz5ssLCwnT9+nWzpr+/v+Li4lSiRAmzJgDklPHjx6tNmzamzdmzZ2vBggWmTQCA5/n111+1c+dOx53Q0FDnY5Dj2rRpI39/f8edpKQkgzUAAAAAACCvSE9PV7du3XTy5EnT7vvvv6/q1aubNgEgJ/Tq1UtDhw41bW7YsEETJ040bQIAPM+NGze0du1axx2eD84d6tWrp3LlyjnuJCYmGqwBAAAAgFuzfft2zZw506w3ePBgNWnSxKwHwLMFBwdr1qxZZr39+/dr0qRJZj0AAADYcrvdioqKMmlFRESYdID/pmjRouratavjztmzZ5WQkOB8EADkYT45PQAAAAC4WefOnVN4eLhu3Lhh1syfP7/i4+NVuHBhsyYA5KQyZcpo+fLl8vX1NWteuXJFYWFhunbtmlkTAOB5LB6efPjhh1WyZEmDNchpLVq0UP78+R13eCgXAAAAAADcrNTUVIWHh+vcuXNmTZfLpcWLF6tSpUpmTQDISUFBQYqPj1dwcLBZMyMjQ126dDG/IB8A4Fkszu3Kli2r+vXrG6xBTqtZs6buuusuxx3OgwEAAAAAwM1yu93q37+/Dhw4YNqdPn26HnzwQdMmAOQUHx8fLVy4UBUrVjTtDh48WHv27DFtAgA8S1JSkuOGn5+f2rRpY7AGOS04OFiPPfaY4w7nwQAAAAAA4Fa8+OKL+uSTT0ybzzzzjMLDw02bAJCTpk2bZv49lwkTJmjdunWmTQCAZ9myZYsuX77suBMaGup8DHKcy+VSSEiI405iYqLcbrfBIgAAAAC4OSkpKRo0aJBZr1y5cpoyZYpZD4B3aN++vbp27WrWe+2113juDgAAwEN99tln+v777x13mjVrpqpVqxosAv5YZGSkSScqKsqkAwB5lU9ODwAAAABuRkZGhnr27KmjR4+adqOjo1W7dm3TJgDktEceeUSvvfaaafO7777T4MGDeagGAHKp69eva8OGDY47Fg9xwjMEBQWpZcuWjjtc0gwAAAAAAG7WyJEj9dVXX5k2X3rpJbVu3dq0CQA5rVq1apo/f75p8/Tp0+rSpYvS0tJMuwAAz+B2u03O7Tp27CgfHx5Dyg2sLmlOSkpSZmamwSIAAAAAAJDbTZs2TStXrjRt9urVS4MHDzZtAkBOK168uOLi4hQQEGDWTE5OVlhYmC5evGjWBAB4Fovz4EcffVRFihRxPgYeweIFzB9//DF/fwAAAAAAADflgw8+0NSpU02bTZo00eTJk02bAJDTAgMDFRsbq+LFi5s13W63evbsqZ9//tmsCQDwLBbnwZUrV1atWrUM1sATWDwffOTIEe3du9dgDQAAAADcnFdeeUX79u0z682cOVOFCxc26wHwHjNmzFCxYsVMWunp6RowYIAyMjJMegAAALATFRVl0omIiDDpAH/moYceUu3atR13Pv74Yx04cMBgEQDkTbw9AQAAAF5h0qRJ+vDDD02bQ4YMUa9evUybAOApRo0apbCwMNPmokWLNHv2bNMmAMAzbNy4UcnJyY47Fg9xwnNYfJ6ffvqpzpw5Y7AGAAAAAADkZgsXLtSsWbNMm61bt9ZLL71k2gQATxEaGqrnn3/etLl9+3aNGTPGtAkA8Azff/+9Dh065LjDeXDuYvF5njhxQjt37jRYAwAAAAAAcrOtW7dq9OjRps3atWsrKipKLpfLtAsAnqB+/fp65513TJs//fST+vXrJ7fbbdoFAOS8S5cu6eOPP3bcCQ0NdT4GHqNjx46OG+np6Vq3bp3BGgAAAAAAkJsdOHBAffv2NW2WLl1ay5cvl7+/v2kXADzBnXfeqaVLl5p+5+XChQsKDw9XSkqKWRMA4BncbrcSExMdd0JCQvi+ZS7y6KOPKjg42HHH4vcWAAAAANyMvXv3asqUKWa9Ll26mHxXFoB3KlmypKZNm2bW27lzp6ZPn27WAwAAgHPnz5/XihUrHHdKlizJs7PIFi6XSxERESat6Ohokw4A5EU+OT0AAAAA+F/Wr1+vv//976bNBx98UG+++aZpEwA8icvl0nvvvae7777btDtixAjt2LHDtAkAyHkWD03ee++9qlatmsEaeIr27dvLx8fZMYLb7dbq1auNFgEAAAAAgNxoz549ioyMNG3eeeedWrRokeOfbQCAJ3vllVf06KOPmjanTZumuLg40yYAIOdZnAcXKlRIjz32mMEaeIpGjRqpRIkSjjtc0gwAAAAAAP7MyZMn1bVrV2VkZJg1g4ODFR8fr6CgILMmAHiagQMHql+/fqbNxMREvf7666ZNAEDOW7dundLS0hx3eJFR7lKuXDnVr1/fcYfzYAAAAAAA8GeuXbumsLAwXblyxazp6+ur5cuXq0yZMmZNAPA0LVq00IQJE0ybu3bt0ogRI0ybAICct2vXLh0/ftxxhxcT5y4BAQFq27at4w7nwQAAAACyQ0ZGhgYOHKj09HSTXtGiRTVjxgyTFgDv1adPH7Vo0cKsN27cOB0+fNisBwAAAGcWLlyo1NRUx53+/fsrICDAYBHwv/Xq1Uv58+d33Jk3b55SUlIMFgFA3sMbygAAAODRfv75Z/Xs2VNut9usWbx4ccXGxiowMNCsCQCeKDg4WCtXrjS9kD4tLU3h4eE6e/asWRMAkLMyMjK0atUqx52QkBCDNfAkd9xxhx5++GHHHR7KBQAAAAAAf+TSpUsKCwtTcnKyWTMgIEBxcXEqXry4WRMAPJGfn5+WLVtmfiF9//79tX//ftMmACBnWZzXtWnThu9c5jJ+fn5q37694w7nwQAAAAAA4I+kpaWpS5cuOnXqlGl33rx5qlatmmkTADyNy+XSu+++q/vuu8+0O3bsWH388cemTQBAzrI4r6tbt64qVKhgsAaexOJFzGvXrjW57BwAAAAAAOQ+brdbgwcP1nfffWfafe211/TII4+YNgHAE73wwgtq166daTM6Olrz5s0zbQIAcpbFeXCxYsVM7haGZ7G4B3zXrl06duyYwRoAAAAA+GNvv/22duzYYdabNm2aSpUqZdYD4J1cLpeioqLM3s2ZnJysyMhI0/clAwAA4Pa43W5FRUWZtAYNGmTSAW5GkSJF1K1bN8edc+fO6YMPPjBYBAB5j09ODwAAAAD+SEpKisLDw3X+/Hmzpsvl0pIlS3TnnXeaNQHAk9WsWVNz5swxbR47dkw9evRQRkaGaRcAkDM+++wznT171nHH4uFNeB6Lz3XDhg26du2awRoAAAAAAJCbuN1u9evXTz/99JNp9+2331aDBg1MmwDgqUqVKqXY2Fj5+fmZNa9evapOnTrp6tWrZk0AQM45fvy4vvrqK8cdzoNzJ4vP9fvvvzf/dx0AAAAAAMgdxo4dq+3bt5s2n3/+ef31r381bQKApwoKClJ8fLwKFy5s1szMzFS3bt10/PhxsyYAIOfcuHFDa9euddwJDQ11PgYex+I8+MqVK9qyZYvzMQAAAAAAINeZPXu2Fi1aZNoMCwvTqFGjTJsA4Kl8fHy0cOFCVapUybQ7ZMgQffvtt6ZNAEDOSUhIcNxo37696X0U8Axt2rSRv7+/405SUpLBGgAAAAD4744cOaIXX3zRrNe8eXP17dvXrAfAu1WuXFmTJk0y623atEkLFiww6wEAAOD2bN++XT/++KPjTosWLXTXXXcZLAJuXmRkpEknKirKpAMAeY1PTg8AAAAA/siIESO0a9cu0+aECRPUsmVL0yYAeLoePXpo2LBhps2NGzfq73//u2kTAJAzEhMTHTfKlCmjBg0aGKyBp7G4pDklJUUbN240WAMAAAAAAHKT119/3eSyuN/r27evBg0aZNoEAE/38MMP6/XXXzdt/vDDD4qIiJDb7TbtAgCyn8XluX5+fmrbtq3BGniali1bKn/+/I47Ft87AAAAAAAAuUtcXJzeeOMN02bTpk31yiuvmDYBwNPddddd5pe/nzlzRl26dFFaWpppFwCQ/bZs2aLLly877lg8RwrPU7NmTVWpUsVxh/NgAAAAAADwn3bs2KERI0aYNu+++2699957crlcpl0A8GRFixZVfHy8AgMDzZopKSkKCwvTxYsXzZoAgJxx+PBhfffdd447nAfnToULF9Zjjz3muMN5MAAAAICs4na7NXjwYF2/ft2klz9/fkVFRXGWBODfDB8+3PR9aSNHjtTp06fNegAAALh1UVFRJp3IyEiTDnArHnzwQd13332OO5988on27dtnsAgA8hafnB4AAAAA/Dfz5s1TdHS0abNt27Z64YUXTJsA4C3eeOMNNWzY0LQ5ceJErVmzxrQJAMhebrdbCQkJjjsdO3aUjw8/bs6Nqlatqpo1azru8FAuAAAAAAD4vS1btmjs2LGmzTp16mjmzJk8VA8gTxoxYoS6dOli2ly6dKneffdd0yYAIPtZnNM9+uijKlKkiPMx8DhBQUFq0aKF4w7nwQAAAAAA4Pf279+v/v37mzbLlCmjZcuWyc/Pz7QLAN6gY8eO5t+z+eyzz/Tcc8+ZNgEA2c/inK5SpUqqXbu2wRp4GpfLpdDQUMedxMREZWZmOh8EAAAAAAByhbNnzyo8PFxpaWlmzaCgIMXHxys4ONisCQDeom7duubP8h4+fFh9+vThZ7sA4OUszoPz5cunVq1aGayBJwoJCXHc+Pjjj3Xx4kXnYwAAAADgPyxcuFAbNmww602cOFFVqlQx6wHIHXx9fRUTE2P27PGFCxc0YsQIkxYAAABu3blz5xQXF+e4U6pUKXXs2NFgEXBrXC6XIiMjTVrR0dEmHQDIS3xyegAAAADwn3bv3q0hQ4aYNitVqqSFCxfKx4e/AgPImwICAhQbG6sSJUqYdnv16qUjR46YNgEA2eeHH37QoUOHHHcsHtqE57L4fFetWqX09HSDNQAAAAAAwNudOHFCXbt2Nb30s3DhwoqPj1dQUJBZEwC8icvlUkxMjGrUqGHaHTVqlD7//HPTJgAg+1y6dEmbN2923OE8OHez+Hw//fRT/fbbbwZrAAAAAACAt7t69ao6deqkq1evmjX9/PwUGxur0qVLmzUBwNtMmDBBjz/+uGlz+vTpWr58uWkTAJB93G63yUt7Q0ND5XK5DBbBE1mcB584cUK7du0yWAMAAAAAALxdRkaGevTooWPHjpl258yZo1q1apk2AcCbDBgwQE888YRpc9WqVXrttddMmwCA7JWQkOC40bx5cxUoUMD5GHgkixdPp6ena926dQZrAAAAAOD/nDlzRiNHjjTr1a9fXyNGjDDrAchd6tSpozFjxpj1li9frlWrVpn1AAAAcPPmz5+v1NRUx50nnnhC/v7+BouAW9ejRw+Td+bMnz9fKSkpBosAIO/wyekBAAAAwO9dvHhRYWFhpv/ADwwMVHx8vIoVK2bWBABvVKFCBS1btkw+PnY/DsiKP7cBANnH4oLmggULmr8EAJ4lNDTUcePcuXP67LPPnI8BAAAAAABeLS0tTV26dNGZM2dMu/Pnz1fVqlVNmwDgbQoVKqSVK1eaXqKZlpamzp07m/+5DQDIHuvXr1daWprjjsVLXeG5OnTo4Pj7ZJmZmVq9erXRIgAAAAAA4K3cbrciIiL0ww8/mHZff/11Pfzww6ZNAPA2fn5+Wrp0qcqVK2faHTBggH788UfTJgAge+zatUvHjx933OE8OHdr1KiRihcv7rhj8Tw6AAAAAADwfn//+9+1ceNG0+awYcPUo0cP0yYAeKN33nlHDzzwgGlz3Lhx+uijj0ybAIDscfbsWW3fvt1xh/Pg3K18+fKqX7++4w7nwQAAAACsjRgxQufPnzdp+fn5KSYmRn5+fiY9ALnTiy++qOrVq5v1hgwZosuXL5v1AAAA8L+53W5FR0c77rhcLg0aNMhgEXB7ChcurO7duzvunD9/XnFxcQaLACDvcHbLPgAAAGAoMzNTffr00aFDh0y77777rurWrWvaBABv1axZM02cONG0+c0332jYsGGmTQBA9rB4SLJNmzYKDAw0WANPVa9ePZUtW9Zxh4dyAQAAAADA888/r08//dS0OWbMGC6NA4D/zz333KO5c+eaNo8fP67u3bsrIyPDtAsAyHoW53N169ZVhQoVDNbAU91xxx1q1KiR4w7nwQAAAAAA4N1339XSpUtNm126dNGIESNMmwDgrUqWLKnY2FjTF09cu3ZNnTp10pUrV8yaAIDsYXE+V6xYMTVu3NhgDTyVn5+fOnTo4LiTkJDgfAwAAAAAAPBqa9asMb/HtGHDhnrjjTdMmwDgrfLnz6+4uDgVKVLErJmZmanu3bvr119/NWsCALLHmjVrlJmZ6ajhcrlMzgrh2SzuXFq7dq1SU1MN1gAAAACAtHr1ai1btsys9/zzz+u+++4z6wHInfLly6eYmBiz3vHjxzVmzBizHgAAAP63rVu3av/+/Y47LVu2VOXKlQ0WAbcvMjLSpBMdHW3SAYC8wienBwAAAAD/MnXqVK1atcq0+cQTT2jAgAGmTQDwdmPGjDF/eGru3LnmLwMGAGStEydOaMeOHY47Fg9rwrP5+PioY8eOjjsJCQlyu90GiwAAAAAAgDdasWKF3nrrLdPm448/bn7pMwB4u65du5q/zHzz5s0aP368aRMAkLVu3LihNWvWOO5wHpw3WHzOGzZs0PXr1w3WAAAAAAAAb/T5559r1KhRps0aNWooJiZGLpfLtAsA3uwvf/mLpk2bZtrct2+fBg4cyDM/AOBlEhMTHTfat28vPz8/gzXwZBbnwd9//70OHTpksAYAAAAAAHijI0eOqHfv3qbNEiVKKDY2VgEBAaZdAPBmVapU0cKFC02bv/32mzp37qwbN26YdgEAWSshIcFx4y9/+YtKlSrlfAw8msV58JUrV7RlyxbnYwAAAADkeZcvX9aQIUPMenfffTf3HwK4aY0bNzb9M2jWrFnavn27WQ8AAAB/LioqyqQTGRlp0gGcqF+/vh544AHHnW3btumHH34wWAQAeYNPTg8AAAAAJOmjjz7Siy++aNp84IEH9M4775g2ASA38PHx0fz581WlShXT7pNPPqlvvvnGtAkAyDpJSUmOG35+fmrbtq3BGni60NBQx43Dhw/r+++/dz4GAAAAAAB4nR9//FFPPPGEabNs2bJaunQpLxADgP9i6tSpatSokWlz8uTJJmcLAIDs8cknn+jy5cuOOxbnhPB8Fpc0Jycna+PGjQZrAAAAAACAtzlz5ow6d+6stLQ0s2aBAgUUHx+vQoUKmTUBILcYNmyYunfvbtpcsWKFZsyYYdoEAGSdw4cPa+/evY47FueE8HwtWrRQvnz5HHcSExMN1gAAAAAAAG+TkpKisLAwXbhwwazp4+OjZcuWqUKFCmZNAMgt2rdvr3Hjxpk2v/jiCz377LOmTQBA1klOTtaGDRscdzgPzhtq1apl8q4JzoMBAAAAWBg7dqx+/fVXs96cOXNMvgMLIO+YMmWKypUrZ9YbOHCgUlJSzHoAAAD4786ePav4+HjHnTJlyqh9+/YGiwBnXC6XIiMjTVrR0dEmHQDIC3xyegAAAADw66+/qnv37srMzDRrFilSRHFxccqfP79ZEwByk6JFiyo+Pt70i2apqanmFywAALKOxcORTZs2VdGiRQ3WwNM9+uijJi/A4aFcAAAAAADynitXrqhTp066du2aWdPPz0+xsbEqWbKkWRMAcpOAgACtWLHC/M/JPn366NChQ6ZNAEDWsDiXq1SpkmrXrm2wBp6uWrVquvfeex13OA8GAAAAACDvycjIUPfu3XX8+HHT7ty5c01+XgEAuZHL5VJ0dLT5n5PPPvusPv30U9MmACBrWJzLBQYGqmXLlgZr4OkKFChg8lknJCQ4HwMAAAAAALzOU089pW+++ca0OXHiRDVr1sy0CQC5ycsvv6zmzZubNt9++20tXbrUtAkAyBqbNm3S9evXHXdCQ0Odj4HHc7lcCgkJcdxJTEw0fVcQAAAAgLzn008/1cyZM816gwcP1iOPPGLWA5A3BAcHa9asWWa9/fv3a9KkSWY9AAAA/Hfz5s3TjRs3HHeeeOIJ+fv7GywCnOvRo4cKFizouDN//nwlJycbLAKA3M8npwcAAAAgb7tx44a6dOmi3377zbS7cOFCValSxbQJALnN/fffb/rlNUk6cuSIevfuzcM2AODhLl++rI8++shxx+IhTXiHwMBAtWnTxnGHS5oBAAAAAMhb3G63Bg0apH379pl2p02bpkaNGpk2ASC3KVeunJYtWyYfH7uvi1+6dElhYWEml34CALKO2+02eWlvSEiIXC6XwSJ4A4vz/1WrVikjI8NgDQAAAAAA8BYvvfSSNm/ebNocMWKEunbtatoEgNymYMGCio+PN7m08V/S09PVuXNnnT592qwJAMgaFufBLVq0MP3/CDybxXnwp59+qrNnzxqsAQAAAAAA3uK9995TTEyMabN9+/YaM2aMaRMAchtfX18tWbJEFSpUMO0OHDhQ33//vWkTAGDP4t7eGjVq6O6773Y+Bl7B4jz4xIkT2rVrl8EaAAAAAHlRSkqKBg4caNYrW7aspkyZYtYDkLd06NDB9Bnl1157TXv27DHrAQAA4N+53W5FR0c77rhcLg0aNMhgEWCjUKFC6tGjh+POxYsXFRsba7AIAHI/u7fyAAAAALfh2Wef1eeff27afPHFF9W+fXvTJgDkVv379zc/KFizZo0mT55s2gQA2Fq/fr3S0tIcdywe0oT3CA0NddzYuXOnjh8/7nwMAAAAAADwCjNmzNDy5ctNm926ddOwYcNMmwCQWz322GN69dVXTZu7d+/W0KFD5Xa7TbsAADtff/21fv31V8cdi/NBeA+Lz/vs2bP67LPPnI8BAAAAAABeISkpyfwcolGjRpo6dappEwByqxo1auj99983bZ48eVLdunVTenq6aRcAYOfs2bPatm2b4w7PB+ct7du3l8vlctTIzMzU6tWrjRYBAAAAAABP980332jo0KGmzSpVqmjBggXy8eH1KADwv9xxxx2KjY2Vv7+/WfP69evq1KmTLl++bNYEANjKyMjQqlWrHHc4D85bHn74YRUvXtxxJzEx0WANAAAAgLzo1Vdf1b59+8x6s2bNUuHChc16APKe6dOnq2jRoiat9PR0DRw4UBkZGSY9AAAA/LstW7bo4MGDjjutW7dWxYoVDRYBdiIiIkw6UVFRJh0AyO14SgEAAAA5ZunSpXr77bdNm82bN9ff//530yYA5HYzZsxQ3bp1TZvjx4/Xxo0bTZsAADsWD0U+8MADuvPOOw3WwFu0adNGfn5+jjtJSUkGawAAAAAAgKf79NNP9eyzz5o27733Xs2ZM8fxi6QAIC95/vnnzS/ZnD9/vmJiYkybAAA7FufBxYoVU+PGjQ3WwFvUr19fZcqUcdzhkmYAAAAAAPKGQ4cOqU+fPqbNkiVLasWKFQoICDDtAkBuFh4erlGjRpk2t2zZohdffNG0CQCws2bNGmVmZjpquFwudejQwWgRvEHJkiX18MMPO+4kJCQ4HwMAAAAAADzehQsXFBYWptTUVLNmvnz5FB8fb/aiXADICxo2bKi33nrLtHngwAE98cQTcrvdpl0AgI0vvvhCv/32m+NOaGio8zHwGn5+fmrfvr3jDufBAAAAAG7H3r17NXnyZLNe586d1bFjR7MegLypVKlSevPNN816X331lWbMmGHWAwAAwP+Jiooy6URGRpp0AEv16tVTvXr1HHc+++wzfffddwaLACB388npAQAAAMibvv/+ew0cONC0Wb58eS1ZskS+vr6mXQDI7fLly6e4uDjTCw3cbre6d++uX375xawJALCRlpamNWvWOO5Yv/Adnq9IkSJ69NFHHXd4KBcAAAAAgNzv9OnT6tKli9LT082aBQsWVHx8vAoWLGjWBIC8wOVyaf78+apatappd9iwYdq5c6dpEwBgw+I8rl27dvLz83M+Bl7Dx8fH5AKzhIQEXuQAAAAAAEAul5ycrLCwMF26dMms6ePjo2XLlqlcuXJmTQDIK6ZMmaLGjRubNqdOnaoPPvjAtAkAsJGYmOi48Ze//EWlSpUyWANvYvFc+IYNG3T9+nWDNQAAAAAAwFNlZmaqd+/eOnLkiGl35syZuv/++02bAJAXDBkyRD179jRtxsfHm764HABgx+L54NKlS+vBBx90PgZexeI8+Pvvv9ehQ4cM1gAAAADIKzIyMjRw4ECze2eLFi2qt99+26QFAH369FGLFi3MeuPGjTM/RwcAAMjrzpw5o5UrVzrulCtXTu3atTNYBNiLjIw06URHR5t0ACA388npAQAAAMh7Ll++rLCwMNNLufz9/RUXF6c77rjDrAkAeUnlypW1ePFiuVwus+a5c+fUuXNnpaammjUBAM598sknJi9LCQ0NdT4GXsfic//4449NX9gDAAAAAAA8S3p6urp166YTJ06Ydt977z3VqFHDtAkAeUXhwoUVHx+v/PnzmzVv3Lih8PBwnTt3zqwJAHDu8OHD2rt3r+MO58F5k8XnfujQIf3www/OxwAAAAAAAI/kdrs1dOhQ7d6927T76quv6rHHHjNtAkBe4e/vrxUrVqhUqVKm3X79+ungwYOmTQCAM8nJyfrwww8ddyxe3grvY/G5Jycna9OmTQZrAAAAAACAp5o8ebLWrFlj2hw4cKD69+9v2gSAvMLlcikqKkq1atUy7T7//PPatm2baRMA4Izb7VZCQoLjTocOHeTjwysJ85qWLVsqX758jjuJiYkGawAAAADkFW+//bZ27Nhh1nvjjTfMn40BkHf964wlKCjIpHf9+nVFRkbK7Xab9AAAACDNmzdPaWlpjjsDBgyQn5+fwSLAXvfu3VWoUCHHnQULFuj69esGiwAg9+IbUwAAAMhWbrdbAwYM0P79+027b731lho2bGjaBIC8pk2bNho/frxpc8eOHRo1apRpEwDgjMUDuRUrVlSdOnWcj4HX6dixo+NGWlqa1q1bZ7AGAAAAAAB4onHjxmnLli2mzZEjR6pz586mTQDIa+rUqaPZs2ebNo8ePapevXopMzPTtAsAuH0Wl+MGBgaqZcuWBmvgbR577DGTB3stvpcAAAAAAAA8U0xMjObNm2faDAkJ0fPPP2/aBIC8pkyZMlqxYoV8fX3NmpcvX1ZYWJiuXbtm1gQAOLNp0yaTC3ZDQkIM1sDbVKtWTffee6/jDufBAAAAAADkXhs3bjS/j7Ru3bp6++23TZsAkNcUKFBA8fHxJs/8/EtGRoa6dOmikydPmjUBAM78+OOP+umnnxx3QkNDnY+B1ylQoIBatGjhuMN5MAAAAICb9fPPP+vFF1806zVr1kz9+vUz6wGAJFWuXFkTJ040623cuFELFiww6wEAAORlmZmZio6Odtzx8fHRgAEDDBYBWaNgwYLq2bOn486lS5e0YsUKg0UAkHv55PQAAAAA5C1vvfWW4uLiTJs9e/bUkCFDTJsAkFe99NJL5i9hnDlzphYtWmTaBADcHrfbraSkJMedkJAQuVwug0XwNhUqVFDdunUddyxeHg0AAAAAADxPQkKCXnvtNdNm48aNzZsAkFf16dNHgwcPNm2uX79ekyZNMm0CAG6fxTlc8+bNVbBgQYM18DaBgYFq3bq14w7nwQAAAAAA5E47d+7UsGHDTJtVq1bV/PnzeT4BAAw88sgjmjJlimlz7969Gjx4sNxut2kXAHB7LM7hatSooerVqxusgTcKCQlx3Fi9erUyMjIM1gAAAAAAAE9y7Ngx9ejRw/RMoGjRooqLi1O+fPnMmgCQV919992aN2+eafPUqVPq2rWr0tLSTLsAgNtjcR5coEABPf744wZr4I0szoM//fRTnT171mANAAAAgNzM7XYrMjJS169fN+nlz59f0dHRPGMIIEuMGDFCDRo0MOuNHDlSp0+fNusBAADkVZs3b9ahQ4ccd9q0aaM777zTYBGQdSIjI006UVFRJh0AyK18cnoAAAAA8o5t27bpueeeM23WqlVLUVFRfHkCAIz4+vpq8eLF5ocIERER2rt3r2kTAHDrvvnmGx07dsxxx+KhTHgvi89/7dq1unHjhsEaAAAAAADgKQ4ePKi+ffuaNkuVKqXly5fL39/ftAsAedlbb72l+vXrmzZffvllrV+/3rQJALh1586d07Zt2xx3OA/O2yw+/6+++krHjx83WAMAAAAAADzFuXPnFB4ebvod8Pz58ys+Pl6FCxc2awJAXvfMM8+oU6dOps1FixZxmSMAeICMjAwlJSU57nAenLdZfP6//fabPv/8c4M1AAAAAADAU6Smpio8PFxnz541a7pcLi1atEiVK1c2awJAXtepUyfz+/23bdumF154wbQJALg9iYmJjhutW7dWvnz5DNbAG3Xo0MHxO3syMzO1evVqo0UAAAAAcqtFixZpw4YNZr2JEyeqSpUqZj0A+D1fX1/FxMTIz8/PpHfhwgWNGDHCpAUAAJCXRUdHm3QiIyNNOkBWuv/++9WgQQPHnS+++EJ79uwxWAQAuZNPTg8AAABA3nDq1Cl16dJFGRkZZs1ChQopPj5eBQoUMGsCAKQSJUooLi5OAQEBZs3k5GSFhYXp0qVLZk0AwK1LSEhw3ChatKiaNGnifAy8VmhoqOPG5cuXtWXLFscdAAAAAADgGa5fv66wsDBdvnzZrOnr66vly5erbNmyZk0AgBQYGKi4uDgVK1bMrOl2u9WzZ08dPXrUrAkAuHWrV69WZmamo4bL5VKHDh2MFsEbtW3b1uSyIYsXSAMAAAAAAM+QmZmpXr16mZ8DzJ49W3Xq1DFtAkBe53K59P7776tatWqm3REjRmjHjh2mTQDArfniiy/022+/Oe6EhIQYrIG3atCggcqUKeO4Y/G8OgAAAAAA8BzPPPOM+TnA+PHj1bZtW9MmAEB69dVX9cgjj5g2//GPfyg+Pt60CQC4NSdOnNCXX37puGNxXzC8V8mSJdWoUSPHHc6DAQAAAPyZM2fO6Omnnzbr1a9fXyNGjDDrAcB/U6dOHY0ePdqst3z5cq1atcqsBwAAkNecPn1aH3zwgeNO+fLl1aZNG4NFQNaLjIw06URHR5t0ACA38snpAQAAAMj90tPT1bVrV506dcq0O2/ePN19992mTQDAPzVo0EDTp083bR48eFD9+/eX2+027QIAbl5iYqLjRrt27eTv72+wBt6qdu3aqlSpkuOOxe9HAAAAAACQ89xutwYPHqy9e/eadqdMmaKmTZuaNgEA/1SxYkUtWbJELpfLrHn+/HmFh4crNTXVrAkAuDUW528PPfSQSpcubbAG3qpo0aIm/xbjPBgAAAAAgNxj0qRJWr9+vWlz8ODB6tOnj2kTAPBPwcHBWrlypYKCgsyaN27cUHh4uM6ePWvWBADcGovzt1KlSqlhw4YGa+CtfHx81LFjR8edxMRE7g0BAAAAACCXWLx4sd59913TZsuWLfXSSy+ZNgEA/+Tn56fly5erTJkypt3+/ftr//79pk0AwM1btWqV44avr6/atm1rsAbeLCQkxHFjw4YNun79usEaAAAAALnR008/rfPnz5u0/Pz8FBMTIz8/P5MeAPyZcePGqXr16ma9oUOH6vLly2Y9AACAvOT9999Xenq6487AgQP5NyW8Rrdu3RQcHOy4s3DhQl27ds1gEQDkPj45PQAAAAC539ixY7V161bT5rPPPqtOnTqZNgEA/y4yMtL8MvwPPvhAb7zxhmkTAHBzjhw5oj179jjuWDyMCe/mcrlMfh9wSTMAAAAAALlDVFSUFi5caNrs1KmTnnnmGdMmAODftWrVSi+//LJpc+fOnRoxYoRpEwBwc5KTk/Xhhx867nAeDMnm98HmzZu5YAgAAAAAgFxg/fr15ucJ9evX11tvvWXaBAD8u1q1aik6Otq0eezYMfXs2VMZGRmmXQDA/+Z2u5WQkOC407FjR/n4cPVkXmdxHvzTTz/pxx9/NFgDAAAAAABy0t69ezVo0CDT5p133qnFixfL19fXtAsA+D+lS5fWihUrTP+svXLlisL+f+zdZ4CU9bnw4XtmCyy9CYgooCIgKhILGkWNaKwUA1bsDU8SS8xrSzQac4zx2KJRI7FXUIEAlqhBDZbYC9h7AwuCIH3rvB80loCozH/Ydl2fzoGZ33O7GdreTxk+3AP+AGrJpEmT8m5ss8020a5duwTTUJ8NGzYs78aSJUtiypQp+Q8DAAA0OHfeeWeMGTMmWe/EE0+Mfv36JesBrEjTpk3jiiuuSNabMWNGnHLKKcl6AACNRU1NTZK/l2Wz2TjssMMSTASrRvPmzWP//ffPuzN//vy45ZZbEkwE0PC4kwYAAAU1YcKEOO+885I2t9lmmzj77LOTNgFYViaTib/+9a+x4YYbJu2efPLJMXXq1KRNAL5bigtymzRpEjvttFOCaajvUlyUO3PmzHj66afzHwYAAACoNU888UQce+yxSZs9e/aMq6++OjKZTNIuAMs69dRTY5dddknaHD16dFx33XVJmwB8tylTpsTixYvz7qTYA1L/pXhob2VlZfzjH/9IMA0AAABQW959990YOXJk5HK5ZM127drFuHHjokmTJsmaACzfyJEj4xe/+EXS5r333htnnnlm0iYA3+2VV16J119/Pe9Oij0g9d/2228fLVq0yLszceLE/IcBAAAAas38+fNj+PDhsWTJkmTN0tLSGDduXHTo0CFZE4Dl23rrrePcc89N2nzxxRfjyCOPTHquEADfbcGCBXHffffl3XF9MBGf37epT58+eXfsgwEAgP82f/78OOqoo5L11ltvvTjttNOS9QC+j4EDB8b//M//JOtddtll8fDDDyfrAQA0BlOmTIm33nor787uu+8eXbt2TTARrDqjRo1K0hk9enSSDkBDk63tAQAAaLhee+21OPjgg5M2O3fuHLfccksUFxcn7QKwfM2aNYvx48dHq1atkjWrq6tj7733jg8++CBZE4DvNmnSpLwbgwYNipYtWyaYhvpu6623jnbt2uXdSfG5BAAAAGrH7NmzY88994yKiopkzWbNmsWECROidevWyZoAfLtsNhs33nhjdOvWLWn3qKOOimnTpiVtArBiKfZuvXr1il69eiWYhvpurbXWiv79++fdsQ8GAACA+qu8vDxGjBgRn376abJmJpOJm2++OfleAoBvd/7558eAAQOSNs8888y46667kjYBWLEUe7fmzZvHoEGDEkxDfdekSZPYZZdd8u7YBwMAAED9lcvl4pBDDonXX389afeiiy6KzTbbLGkTgG933HHHxYgRI5I2b7755rjsssuSNgFYsbvvvjvJvXuGDh2aYBoaghSfhTvuuCOqq6sTTAMAADQUv/nNb2LGjBnJeldccUU0bdo0WQ/g+/rTn/4Ua6yxRrLeEUccEUuXLk3WAwBo6EaPHp2kc+SRRybpwKq00UYbxRZbbJF354knnojnnnsu/4EAGphsbQ8AAEDDtGjRohg+fHgsWLAgWbOoqChuvfXW6Ny5c7ImAN+tZ8+ecf311ydtfvzxx7H33ntHZWVl0i4Ayzdnzpx46KGH8u64IJf/KC4ujt122y3vjps0AwAAQP1UXV0dI0eOjPfeey9p929/+1tssMEGSZsArFi7du1i/PjxUVpamqy5dOnSGD58eMybNy9ZE4BvV11dHbfffnveHftgvi7F5+Guu+5KcvNwAAAAYNU79thj46mnnkraPOOMM2KnnXZK2gRgxZo0aRK33XZbdOjQIWl3//33j7fffjtpE4Bvl+I6zJ133tmDjvhSin3wE088ER988EGCaQAAAIBV7fzzz48JEyYkbR5wwAExatSopE0AViyTycTVV18dvXr1Str91a9+FY899ljSJgDfLsU+uF+/ftGtW7cE09AQpNgHf/LJJ/Hoo48mmAYAAGgIHnnkkbjsssuS9UaNGhXbbLNNsh7AD9GqVaukv6e98sorcdZZZyXrAQA0ZB9++GFMnjw5785aa60VO++8c4KJYNU78sgjk3RGjx6dpAPQkGRrewAAABqeXC4XRx55ZLzwwgtJu+eee24MHDgwaROA72fo0KFx0kknJW0+/PDDcfLJJydtArB8d955Z1RXV+fVyGQyMWTIkEQT0RAMGzYs78bzzz8fb731Vv7DAAAAAKvUmWeeGffee2/S5s9//vMYOXJk0iYA388mm2wSl1xySdLmm2++GQcddFDU1NQk7QKwrMceeyxmzZqVdyfF/o+GI8Xn4bPPPoupU6fmPwwAAACwSl133XXJb9S1yy67xKmnnpq0CcD3s+aaa8aYMWMik8kka86dOzdGjBgRS5cuTdYEYPk+/PDDJA9KT/GQVhqOXXfdNYqLi/PupLhhOgAAALBqTZ06Nfk9QDfccMO4/PLLk+4iAPh+WrZsGRMmTIjmzZsna1ZWVsaee+4Zn3zySbImAMtXWVkZd955Z94d1wfzdZtvvnl07tw5787EiRPzHwYAAKj3ysvL4/DDD49cLpek16VLlzjnnHOStABW1pAhQ2KvvfZK1vvTn/4U06dPT9YDAGiorrnmmqiqqsq7c/jhh0dRUVGCiWDV23vvvaN169Z5d2666aZYuHBhgokAGo5sbQ8AAEDD89e//jVuvvnmpM0RI0bEcccdl7QJwA/zv//7v/GTn/wkafOCCy6I2267LWkTgGVNmjQp78aAAQOSXIBJw/HTn/40mjRpkncnxecTAAAAWHXuuuuuOPPMM5M2BwwYEBdccEHSJgA/zOGHHx6HHHJI0ubkyZPj3HPPTdoEYFkp9m2dOnWKAQMGJJiGhmKjjTaKbt265d2xDwYAAID6Zdq0aXHUUUclbXbr1i1uvPHGyGbd4gagtuywww7xhz/8IWnzmWeeiaOPPjppE4Bl3X777Xk3ioqKYrfddkswDQ1F27ZtY9ttt827Yx8MAAAA9csHH3wQe++9d1RXVydrtmrVKsaPHx/NmjVL1gTgh1l//fXjyiuvTNqcMWNG7Lfffkn/zABgWQ8++GDMmzcv787QoUPzH4YGI5vNxpAhQ/LuTJo0KXK5XIKJAACA+uyss86KV155JVnvsssui9atWyfrAaysiy++ONq2bZukVVVVFYcffri9CgDACtTU1MQVV1yRd6eoqCgOO+ywBBNB7WjWrFkccMABeXcWLFgQY8eOTTARQMPhDncAACT12GOPxXHHHZe02atXr7j66qsjk8kk7QLwwxQXF8eYMWOiS5cuSbuHHnpo0pPtAPimJUuWxD333JN3xwW5/LcWLVrEDjvskHfHTZoBAACg/nj77bdj//33T9ps37593HbbbdGkSZOkXQB+mEwmE5deemn069cvafc3v/lN3H///UmbAHwll8vFxIkT8+4MHjw4slmXGPGVTCaT5DwBN2kGAACA+mPevHkxfPjwWLp0abJmaWlpjB8/Ptq1a5esCcDKOeWUU2L33XdP2rzyyivj6quvTtoE4JtSXH+5zTbb+Ds5y0ixD77//vtj/vz5CaYBAAAACq2ysjL23nvv+Pjjj5N2r7vuuujZs2fSJgA/3D777BPHHHNM0uaUKVPi9NNPT9oE4JtS7IPXWmut2HjjjfMfhgYlxT74jTfeiJdffjnBNAAAQH31/PPPx9lnn52st+eee3ruEVBndOrUKS644IJkvSeffDIuvvjiZD0AgIbm3nvvjXfeeSfvzuDBg5M/Ux1WtVGjRiXpjB49OkkHoKHwhAUAAJL55JNPYs8994zKyspkzebNm8eECROiZcuWyZoArLxOnTrFrbfeGsXFxcmaCxcujOHDh8fChQuTNQH4yn333ReLFi3KuzNs2LD8h6HBSfG5eOihh2L27Nn5DwMAAAAU1NKlS2PEiBExd+7cZM1MJhNjxoyJNddcM1kTgJVXVlYW48ePj9atWydr1tTUxD777BMzZ85M1gTgK6+88kq8/vrreXfsg1meFJ+LGTNmxDPPPJP/MAAAAEBB1dTUxEEHHRRvvvlm0u4ll1wSm2yySdImACsnm83G9ddfHz169Eja/cUvfhHPPvts0iYAn1uwYEFMmTIl746HHbE8KT4XFRUVcffddyeYBgAAACi0k08+OR5++OGkzZNOOsm1CAB1yLnnnhtbbrll0uZZZ50Vd9xxR9ImAJ/L5XIxceLEvDtDhw6NTCaT/0A0KNtvv320aNEi706KzygAAFA/VVdXxxFHHBFVVVVJem3bto2LL744SQsglYMOOih22GGHZL1TTz013n777WQ9AICGZPTo0Uk6o0aNStKB2rTBBhvEj3/847w7Tz31lHs+A3xNtrYHAACgYaiuro799tsvZsyYkbR7xRVXxPrrr5+0CUB+ttpqqzjvvPOSNl966aU44ogjIpfLJe0CEDFp0qS8G+utt1707t07wTQ0NIMHD877Yu2ampq48847E00EAAAAFMoxxxyT/CTsP/zhD7HjjjsmbQKQn3XWWSduuOGGpM1PPvkk9txzz6ioqEjaBSDNPrh58+YxaNCgBNPQ0AwcODDatm2bdyfF5xQAAAAorHPPPTcmT56ctHnIIYfE4YcfnrQJQH7atm0b48ePjyZNmiRrLl26NIYPHx5z585N1gTgc/fcc0+S822GDh2aYBoamrXWWiv69++fd8c+GAAAAOq+2267LS644IKkze222y7+93//N2kTgPyUlpbGrbfeGquttlrS7gEHHBBvvfVW0iYAEc8991y8//77eXfsg1mepk2bxs4775x3xz4YAAAar0suuSQef/zxZL3zzz8/OnfunKwHkEImk4nRo0dHs2bNkvQWL14co0aN8hxOAID/8sEHH8Ttt9+ed6dbt26ea0KDMWrUqCSd0aNHJ+kANATZ2h4AAICG4fTTT48pU6YkbR599NGx7777Jm0CkMYxxxwTe++9d9Lm2LFj45JLLknaBGjsqqurkzxIxQW5fJtOnTrFFltskXfHRbkAAABQt11zzTVxxRVXJG3utttuccoppyRtApDG4MGD4ze/+U3S5qOPPhonnHBC0iYAafZsO+20UzRt2jTBNDQ0xcXFsdtuu+XdsQ8GAACAuu3+++9Pvhfo169fXHrppZHJZJJ2Achf//7947LLLkvafPvtt+PAAw+MmpqapF2Axi7Fnq1fv37RvXv3/IehQUpx/fidd94ZlZWVCaYBAAAACuGVV16JQw89NGmzS5cuMXbs2CguLk7aBSB/Xbt2jbFjx0Y2m+5RVPPmzYvhw4fHkiVLkjUBSLMPbtOmTWyzzTYJpqEhSrEPfuKJJ+KDDz5IMA0AAFCfvPPOO0mvNxw0aFAcfPDByXoAKa299trxhz/8IVnvn//8Z9xwww3JegAADcHVV18d1dXVeXeOOOKIKCoqSjAR1L4999wz2rRpk3fn5ptvjgULFuQ/EEADkO7MWQAAGq077rgjzjrrrKTNLbfcMs4777ykTQDSyWQyceWVV0afPn2Sdo8//vh49NFHkzYBGrPHH388Zs2alXdn2LBh+Q9Dg5Xi83HPPfe4MQcAAADUUc8++2z8/Oc/T9rs0aNH3HDDDUlv/glAWmeeeWYMGjQoafPiiy+OsWPHJm0CNGYffvhhPPbYY3l37INZkRSfj+nTp8fbb7+d/zAAAABAcjNnzox99tknampqkjVbt24d48ePj7KysmRNANI69NBD4/DDD0/avOOOO+JPf/pT0iZAY1ZZWRl33HFH3p0UD2Wl4UqxD/7ss89i6tSp+Q8DAAAAJLdw4cIYPnx4LFy4MFmzuLg4br311ujUqVOyJgBpbb/99smfF/Dcc8/FL3/5y6RNgMZu4sSJeTd22223KCkpyX8YGqTddtstyUOsJ0+enGAaAACgvsjlcnHUUUfF4sWLk/TKysrib3/7W2QymSQ9gEI45phjYtNNN03W+9WvfpXkeXEAAA1BdXV1XHHFFXl3iouL49BDD00wEdQNZWVlceCBB+bdWbhwYYwZMybBRAD1nyegAQCQl7feeisOOOCApM3VVlstbr311igtLU3aBSCtFi1axPjx46N58+bJmlVVVbHnnns6gQQgkUmTJuXd6NixYwwYMCDBNDRUKW7ivXjx4pgyZUqCaQAAAICU5s6dGyNGjIilS5cmazZp0iTGjx8fbdu2TdYEIL2ioqK4+eabY4011kjaPfzww+Oll15K2gRorG6//fa8G0VFRbHbbrslmIaGaqeddoomTZrk3Ulx/gIAAACQVkVFRey5557xySefJO3ecMMNsc466yRtApDeX/7yl/jRj36UtHnaaae5PgggkYceeijmzZuXd2fYsGF5N2i4Ntpoo+jWrVveHftgAAAAqHtyuVwcccQRya/jOu+882KrrbZK2gQgvRNPPDGGDBmStHn11VfHlVdembQJ0Fi98847MW3atLw7Ke4HTMPVtm3b2HbbbfPu2AcDAEDjcuONN8Y999yTrPeHP/wh1l577WQ9gEIoLi6Oq666KoqLi5P0Pv300zj22GOTtAAA6rt77rkn3nvvvbw7Q4YMidVXXz3BRFB3jBo1Kkln9OjRSToA9V22tgcAAKD+WrJkSQwfPjzJTd/+I5vNxtixY6Nr167JmgAUTp8+feLqq69O2pw5c2bss88+UVVVlbQL0BiluMhx8ODBUVRUlGAaGqpevXpFr1698u64KBcAAADqlpqamjjwwAPjrbfeStq97LLLon///kmbABRGx44dY9y4cVFSUpKsuWjRohg+fHgsWLAgWROgsUqxXxs4cGC0a9cuwTQ0VC1atIhBgwbl3bEPBgAAgLrnhBNOiEcffTRp8ze/+U0MHjw4aROAwmjatGmMGzcu2rZtm6xZU1MT++67b7z//vvJmgCNVYr92lprrRUbb7xx/sPQYGUymSQPdp40aVLkcrkEEwEAAACpXHLJJTF27Nikzb333juOOeaYpE0ACiObzcZ1110X66yzTtLuL3/5y3j66aeTNgEao8mTJ+fdKC0tjZ133jnBNDRkKfbB999/f8yfPz/BNAAAQF03a9asOO6445L1Ntlkkzj22GOT9QAKaaONNoqTTjopWW/s2LFxxx13JOsBANRXo0ePTtIZNWpUkg7UJeuvv35svfXWeXeeeeaZeOqppxJMBFC/ZWt7AAAA6q9f/vKX8dxzzyVtnnXWWbH99tsnbQJQWHvttVfSE+giIh544IH43e9+l7QJ0Ni88sor8eqrr+bdGTZsWP7D0OCl+JxMnjw5qqur8x8GAAAASOJPf/pT8os9DzvssDj00EOTNgEorC222CIuuOCCpM1XXnklDjvsMA/uA8jDggULYsqUKXl37IP5PlJ8Th588MGYM2dO/sMAAAAASYwdOzYuvvjipM1BgwbFmWeembQJQGH16NEjbrzxxqTN2bNnx5577hkVFRVJuwCNSS6Xi4kTJ+bdGTJkSGQymfwHokFLsQ9+//3349lnn81/GAAAACCJRx99NI4//vikzT59+sSVV17p+00A9UibNm1i/Pjx0bRp02TN8vLyGDFiRHz66afJmgCNUYp98KBBg6Jly5b5D0ODNnTo0LwbFRUVcffddyeYBgAAqOuOO+64ZN/7KyoqiquuuiqKi4uT9ABWhVNPPTV69eqVrPc///M/MX/+/GQ9AID6ZsaMGUmeddKjR4/YYYcdEkwEdc+oUaOSdEaPHp2kA1CfZWt7AAAA6qerrroqrr766qTNIUOGxIknnpi0CcCq8X//93+x1VZbJW2effbZMWnSpKRNgMYkxe+hzZo1i0GDBiWYhoYuxUW5n3zySTz22GMJpgEAAADyNWXKlDjttNOSNn/0ox/FJZdckrQJwKrxi1/8Ivbdd9+kzdtuuy0uuuiipE2AxuSee+5J8vDzFHs+Gr7Bgwfn/bCdmpqauPPOOxNNBAAAAOTjpZdeisMPPzxps2vXrjFmzJgoKipK2gWg8Hbdddfk5wk9/vjjyR8OD9CYTJs2Ld577728O8OGDct/GBq8gQMHRtu2bfPuuDcIAAAA1A2zZs2KPffcM6qqqpI1mzdvHuPHj48WLVokawKwavTr1y8uv/zypM133nknDjjggKipqUnaBWgsPv3003jwwQfz7rg+mO+jW7dusfHGG+fdsQ8GAICG784774wxY8Yk65144onRr1+/ZD2AVaFp06ZxxRVXJOvNmDEjTjnllGQ9AID65qqrrkpyfskRRxwR2Ww2wURQ94wYMSLJNb5jxoyJ+fPnJ5gIoP7ytwUAAH6wp59+On7xi18kba6zzjpx3XXX+YYWQD1VUlISt9xyS3Ts2DFp96CDDoo33ngjaROgsUhxceNOO+0UZWVlCaahoRswYEB06tQp746LcgEAAKD2vf/++7HvvvsmvWlmmzZtYty4cdG0adNkTQBWnUwmE1dccUX07ds3afeEE06Ihx9+OGkToLFIsVfbaKONonv37vkPQ4PXuXPnGDBgQN4d+2AAAACofQsWLIjhw4fHokWLkjVLSkritttui9VWWy1ZE4BV6/TTT4+f/vSnSZuXXnpp3HTTTUmbAI1Fir1amzZtYptttkkwDQ1dcXFx7Lbbbnl37IMBAACg9lVVVcW+++4bM2fOTNq9+uqro0+fPkmbAKw6Bx10UBx55JFJm3fddVecddZZSZsAjcVdd90V1dXVeXcGDx6cYBoag6FDh+bduPPOO6OysjLBNAAAQF20YMGCOOqoo5L11ltvvfjd736XrAewKg0cODDp74mXXXZZPPLII8l6AAD1RXV1dVx55ZV5d4qLi+OQQw5JMBHUTU2bNo2DDjoo786iRYvi5ptvTjARQP2Vre0BAACoXz799NMYMWJElJeXJ2s2bdo0xo8fH23atEnWBGDVW2ONNeKWW26JbDbdtxs+++yzGD58eCxevDhZE6Ax+Oijj+Kxxx7LuzNs2LD8h6FRyGazMWTIkLw7EydOjFwul2AiAAAAYGVUVFTEnnvuGbNnz07avfHGG6NHjx5JmwCsWs2bN4/x48dHixYtkjWrqqpir732io8++ihZE6AxqKysjDvuuCPvjn0wP0SKz8vdd98dS5YsyX8YAAAAYKXkcrk47LDD4pVXXknaveCCC2KLLbZI2gRg1SoqKoqbbrop1lxzzaTdI488Ml544YWkTYDGYOLEiXk3dt111ygpKcl/GBqFFPvgadOmxdtvv53/MAAAAMBK+93vfhf3339/0uZxxx0Xe+21V9ImAKveRRddFJtuumnS5umnnx733ntv0iZAY5BiHzxgwIDo0qVL/sPQKKTYB3/22WcxderU/IcBAADqpFNOOSVmzJiRrHfFFVdE06ZNk/UAVrU//elPscYaayTrHX744bF06dJkPQCA+uAf//hHkn9rDhs2LDp37pxgIqi7jjzyyCSd0aNHew4w0Khla3sAAADqj5qamjjggAPinXfeSdq9/PLLo1+/fkmbANSO7bbbLs4+++ykzenTp8fPf/5z38gF+AFuv/32vH/fLCoqit122y3RRDQGQ4cOzbvx+uuvJ38oEAAAAPD9/frXv47HH388afO0007zfSaABqJXr15xzTXXJG1++OGHsc8++0RVVVXSLkBD9tBDD8W8efPy7qTY79F4pPi8LF68OO67774E0wAAAAAr46KLLorbbrstaXO//faLX/ziF0mbANSODh06xLhx46KkpCRZc/HixTF8+PCYP39+siZAQ/fuu+/Gc889l3cnxUNYaTx22mmnaNKkSd6dyZMnJ5gGAAAAWBmTJ09Ofh/QrbbaKv7v//4vaROA2tG0adO47bbbol27dsmauVwu9ttvv3j33XeTNQEauqVLl8bdd9+dd8f1wfwQ/fr1i27duuXdmTRpUoJpAACAuuaRRx6Jyy67LFlv1KhRsc022yTrAdSG1q1bJ/298ZVXXok//vGPyXoAAHXdxx9/HKeffnqS1qhRo5J0oC7r06dPku+nPPfcc3HttddGZWVlgqkA6p9sbQ8AAED9cdZZZ8Vdd92VtHnkkUfGQQcdlLQJQO064YQTkt/U87rrrosrrrgiaROgIUtxUePWW28d7du3TzANjcWgQYOiefPmeXdclAsAAAC146abbopLLrkkaXPHHXdMdpEEAHXDiBEj4te//nXS5tSpU+M3v/lN0iZAQ5Zin7bmmmtG//79E0xDY9G7d+9Yb7318u7YBwMAAEDtePjhh+OEE05I2uzbt2/87W9/i0wmk7QLQO3ZfPPN46KLLkrafO211+KQQw6JXC6XtAvQUE2ePDnvRmlpaey8884JpqGxaNGiRQwaNCjvjn0wAAAA1I433ngjDjzwwKTNjh07xi233BIlJSVJuwDUnu7du8dNN92U9FyfOXPmxJ577hnl5eXJmgAN2f333x+LFi3KuzN06NAE09BYZDKZGDJkSN6dSZMmOQcMAAAamPLy8jjiiCOS/V2/S5cucc455yRpAdS2IUOGxF577ZWsd/bZZ8fzzz+frAcAUBfNmzcvfvGLX8Tqq68ezzzzTN69ddZZJ7bffvsEk0HdN2rUqCSdQw89NHr16hV33313kh5AfZKt7QEAAKgf7r333uQP191kk02S38QTgNqXyWTi2muvjXXXXTdp9+ijj46nnnoqaROgIVq4cGFMmTIl786wYcPyH4ZGpWnTpklu7D1x4sT8hwEAAAB+kBdeeCGOPPLIpM0111wzbr755igqKkraBaD2nX322TFw4MCkzXPPPTcmTJiQtAnQEOVyuST7tKFDhya94T6NQ4rzCCZPnhzV1dX5DwMAAAB8bx999FHstddeUVVVlazZsmXLGD9+fDRv3jxZE4C64aijjooDDjggaXPChAlxwQUXJG0CNFQp9sHbb799tGzZMv9haFRS7IMffPDB+PTTT/MfBgAAAPjeFi9eHMOHD4/PPvssWTObzcbYsWNjjTXWSNYEoG7Yeeedkz9n4Mknn4xf/epXSZsADVWKfXDPnj2jT58++Q9Do5JiH/z+++/Hs88+m/8wAABAnfHHP/4xXn755WS9yy67LFq3bp2sB1DbLr744mjbtm2SVlVVVRx++OHuvwYANFjPPvts9OnTJy677LLI5XJJmkceeWRks9kkLajrhg8fHu3bt0/Sevvtt2OXXXaJI444ImpqapI0AeoDf2sAAOA7vffee7Hffvsl+wZWRES7du1i3Lhx0bRp02RNAOqO1q1bx/jx46OsrCxZs6KiIkaMGBFz5sxJ1gRoiO65554oLy/PuzN06NAE09DYpPjcPP744/Hhhx8mmAYAAAD4PubPnx/Dhw+PxYsXJ2uWlJTEuHHjokOHDsmaANQdJSUlccstt0Tnzp2Tdg8++OB47bXXkjYBGppp06bFe++9l3fHPpiVkeJzM2vWrHj88ccTTAMAAAB8H1VVVbHPPvskPz/7mmuuiV69eiVtAlA3ZDKZuPzyy2PDDTdM2j3ppJPiwQcfTNoEaGjmzp0bU6dOzbuT4uGrND6DBw+OTCaTV6O6ujruvPPORBMBAAAA3yWXy8XPf/7zmD59etLu2WefHT/5yU+SNgGoO0477bTYeeedkzb/+te/xg033JC0CdDQ1NTUxO233553Z+jQoXnv9Wh8Bg4cGG3atMm7M2nSpPyHAQAA6oQXXnghzj777GS9ESNGuKcV0OB06tQpLrjggmS9J554Iv7yl78k6wEA1BUzZ86MwYMHx0cffZSsWVJSEgcffHCyHtR1TZo0Sf6Zv/LKK+OMM85I2gSoy7K1PQAAAHVbeXl5jBgxIubMmZOsmclk4qabboru3bsnawJQ92y00UYxevTopM133303Ro4cGdXV1Um7AA1JiosZN9xww+jRo0eCaWhsdttttygqKsq7k+LCcgAAAOC75XK5OOSQQ+K1115L2r3oooti8803T9oEoG5ZffXV45ZbbknyPeH/WLBgQQwfPjwWLVqUrAnQ0KTYB7du3Tq23XbbBNPQ2AwYMCA6duyYd8dNmgEAAGDV+c1vfhNTp05N2vz1r38dw4cPT9oEoG5p1qxZjB8/Plq1apWsWV1dHXvttVd8+OGHyZoADc1dd92V5D4KgwcPTjANjU3nzp1jwIABeXfsgwEAAGDVueKKK+K6665L2hw2bFiccMIJSZsA1C3ZbDZuvPHG6NatW9LuqFGjYvr06UmbAA3JE088keShxUOHDk0wDY1NSUlJ7Lbbbnl37IMBAKBhqKqqikMOOSQqKyuT9Nq0aRN/+ctfkrQA6pqDDjoodthhh2S9//f//l/sv//+cdVVV8WCBQuSdQEAatPIkSNj5syZSZt77LFHkvvfQn1yxBFHJG/+4Q9/iH/+85/JuwB1Uba2BwAAoG771a9+FU8++WTS5u9+97vYeeedkzYBqJsOOOCA+J//+Z+kzXvuuSf+93//N2kToKGorKyMO+64I+/OsGHD8h+GRqldu3axzTbb5N2ZOHFi/sMAAAAA3+mCCy6ICRMmJG3uv//+cdRRRyVtAlA3bbPNNnHOOeckbb7wwgtx1FFHRS6XS9oFaChS7NF22223KCkpyX8YGp2ioqIYMmRI3h37YAAAAFg1JkyYEOeee27S5sCBA+Pss89O2gSgburZs2fyB79//PHHsddeeyV70AdAQ5Nij7b55ptHly5d8h+GRinF9eV33313LF26NP9hAAAAgBV66qmn4uijj07aXHfddePaa6+NTCaTtAtA3dO+ffsYN25clJaWJmsuWbIkhg8fHp999lmyJkBDkmIfvNpqq8WWW26Z/zA0Sin2wdOmTYu33347/2EAAIBV7pNPPokLL7ww+vXrF02aNImnnnoqWfv888+Pzp07J+sB1CWZTCZGjx4dZWVlSXrV1dVx0003xeGHHx6dO3eOY445xvn3AEC99txzz8XUqVOTd0eNGpW8CXVdr169Yrvttkveveiii5I3AeqibG0PAABA3XXDDTfEX//616TNnXfeOX73u98lbQJQt1144YWx2WabJW3+/ve/j7vvvjtpE6AhePjhh2Pu3Ll5d4YOHZpgGhqrFJ+f++67LxYsWJBgGgAAAODbPPjgg3HSSSclbW644YYxevRoN2gGaESOP/74+NnPfpa0eeONN8bll1+etAnQELz77rvx3HPP5d2xDyYfKT4/r732WrzyyisJpgEAAAC+zWuvvRYHH3xw0mbnzp3jlltuiZKSkqRdAOquYcOGxYknnpi0+fDDD8fJJ5+ctAnQEJSXlye5f0KKh67SeKXYBy9atCjuu+++BNMAAAAA32bOnDkxYsSIqKioSNYsKyuL8ePHR+vWrZM1AajbNt100/jLX/6StPnGG2/EwQcfHLlcLmkXoCGYNGlS3o3BgwdHUVFRgmlojHbaaacoLS3NuzN58uQE0wAAAKvS7bffHuuss04cf/zxMX369KipqUnWHjRoUBxyyCHJegB10dprrx1/+MMfkncXL14cf/nLX2KbbbaJGTNmJO8DAKwKo0ePTt7s1KlT/OQnP0nehfrg0EMPTd6866674v3330/eBahrsrU9AAAAdcdrr70WZ511Vmy99dbRpk2bOPDAA5P211prrbjxxhsjm/XXUIDGpEmTJjFu3Lho3759smYul4u99tor/va3v8X9998f5eXlydoA9VmKC3K7du0aP/rRjxJMQ2OV4ibNFRUVcc899ySYBgAAAPiPmTNnxoUXXhg//elPo127dvGTn/wkqqurk/VbtWoV48ePj2bNmiVrAlD3ZTKZuOaaa2K99dZL2j366KPj3HPPjbvvvjsWLlyYtA1QX6W4qW1JSUnsvPPOCaahsRo0aFCSf/elOL8BAAAA+MrcuXNj9OjRMWzYsFhttdWib9++sWDBgmT9oqKiuOWWW2L11VdP1gSgfjjrrLNiu+22S9q84IIL4pRTTonbb7895syZk7QNUF/df//9Sc6RSXF9J41X7969k5wHZh8MAAAAaS1ZsiSuv/762G+//aJLly7RpUuXePfdd5MeY/To0bHRRhslbQJQ9x1xxBFx0EEHJW1OnDgxDj300Jg4cWLMnDkzaRugvnr11VfjlVdeybtjH0w+WrZsGYMGDcq7Yx8MAAD1ywUXXBBDhw5Neq3hf5SVlcXo0aMjk8kkbwPUNccee2xsuummBWk/+eSTscsuu0RFRUVB+gAAhfTCCy8kb86fPz8++eST5F2oD1599dXkzVwuFy+//HLyLkBdk63tAQAAqH1VVVVx1llnxQYbbBCnnnpqPPLII/HZZ58lPUZpaWmMGzcu2rdvn7QLQP2w1lprxc0335z0pLkFCxbEqFGjYtCgQdGuXbsYNmyYb+oCjVoul4uJEyfm3Rk6dKiTnMlL9+7do1+/fnl3UnyeAQAAgM+/b3TVVVdFnz594vjjj49//vOfMXfu3KipqUl6nGuvvTZ69uyZtAlA/dCqVasYP358NGvWLFmzuro6TjzxxNhll12iXbt2MWjQoHjkkUeS9QHqoxT7s0GDBkWrVq3yH4ZGq6ysLHbeeee8O/bBAAAAkM4dd9wRffv2jaOOOiomTZoUs2fPjqqqqqTHOOecc2KbbbZJ2gSgfiguLo6xY8fG6quvnrT7pz/9KYYMGRIdO3aMLbfc0veNgUYvxe+D6667bvTp0yf/YWjUhg0blndj8uTJyc9TBgAAgMbqsccei/79+8dBBx0UY8aMiQ8//DD5g1uPOuqoOOCAA5I2AagfMplMXHbZZUnuHfl11157beyxxx7RtWvX6NevX4wePTpyuVzSYwDUJ5MmTcq70axZs9hxxx0TTENjlmIf/OCDD8ann36a/zAAAEDBPfHEE3HCCScU7HtzZ555ZqyzzjoFaQPUNcXFxXHllVdGcXFxQfovvPBCnH322QVpAwAU0pw5c5I3lyxZEn//+9+Td6Guy+Vyce211xakPXv27IJ0AeqSbG0PAABA7aqqqopddtklTj311KisrCzYcf7yl7/EZpttVrA+AHXfT3/60/j9739fkPbixYtj0qRJ0b9//zj33HNdmAs0StOmTYt33303787QoUMTTENjl+JzdPvttxf036kAAADQGORyuTjyyCPj8MMPjwULFhTsOCeeeGLsscceBesDUPdtsMEG8be//a0g7crKyrj//vtj4MCBcfzxx0d5eXlBjgNQl82dOzf+9a9/5d2xDyaFFJ+jxx57LD766KME0wAAAEDjdvbZZ8fgwYPjww8/LNgxhg8fHscff3zB+gDUfZ06dYrbbrutIDeZr6mpicceeyz22GOPGDlyZMybNy/5MQDqupqamiQ3lB42bFhkMpkEE9GYpdgHf/zxx/HYY48lmAYAAAAatzFjxsRWW20Vr776asGOsdlmm8Wf//zngvUBqPuaNWsW48aNi9atWxekP3369DjqqKNixx13jBkzZhTkGAB13fjx4/Nu/PSnP42ysrIE09CYDR48OO9GdXV13H777QmmAQAACqmysjIOPfTQqKmpKUh/k002ieOOO64gbYC6ql+/fnHiiScWrH/WWWfF/PnzC9YHACiERYsWFaR73333FaQLddlrr70WM2fOLEh74cKFBekC1CXZ2h4AAIDaddJJJ8WUKVMKeoyDDjoojjjiiIIeA4D64be//W3suuuuBeuXl5fHiSeeGOeff37BjgFQ19TU1MTFF18cO+64Y96tVq1axbbbbptgKhq7FDdpnj9/fvzoRz+Kf/zjHwkmAgAAgMbp8ssvjyuvvLKgx9h2223jrLPOKugxAKgfRo4cGb/4xS8K1s/lcnHhhRfGscceW7BjANRFY8eOjU022STJDbGGDBmSYCIau9122y2Kiory7my22WZx3XXXRS6XSzAVAAAAND7/+Mc/4re//W1Bj7HeeuvF1VdfHZlMpqDHAaDu22qrreLcc88t6DFuvvnm2HvvvaO6urqgxwGoS+6///7YdNNN45NPPsm7leK6ThgwYEB07Ngx787uu+8e5513nj/XAQAAYCVNnz49DjvssII9WDsion379jFu3Lho0qRJwY4BQP2w7rrrxvXXX1/QY9x3332xyy67FOzhnQB10bRp02KHHXaIJ554Iu+WfTAprL766jFgwIC8O0cddVSccsopsWTJkgRTAQAAhfD444/Hiy++WJB2UVFRXHnllVFcXFyQPkBddtppp8V6661XkHZlZWVMnTq1IG0AgEJp165dQbrPPPNMQbpQlz377LMFa7dv375gbYC6IlvbAwAAUHv+/e9/xwUXXFDQY2y00UZx2WWXuUEzABERkc1m44Ybboju3bsX9DgnnXRSPPDAAwU9BkBdMGvWrNhtt93i2GOPjdmzZ+fd22233aK0tDTBZDR2/fv3jzXXXDPvzgsvvBC77rpr/L//9/+ioqIiwWQAAADQeMyYMSOOPfbYgh5j9dVXj7Fjx7p4HoAvXXDBBUlu2rgio0ePjmuuuaagxwCoCxYtWhSHHHJI7LvvvvH222/n3dt8882jS5cuCSajsWvfvn0MHDgw786MGTPi4IMPjpEjR8b8+fMTTAYAAACNx9KlS+OAAw6IXC5XsGM0a9YsJkyYEK1atSrYMQCoX4499tjYa6+9CnqMe++9N04//fSCHgOgLqiqqopTTz01dthhhyQ31V1ttdViyy23TDAZjV1RUVEMGTIk787cuXPjhBNOiB133DE++OCDBJMBAABA45HL5eKggw6KJUuWFOwYmUwmbr755lhrrbUKdgwA6pchQ4bEKaecUtBjvPDCC3H44YcX9BgAdUEul4tLL700BgwYEPfdd1/evWw2G7vvvnuCySBi2LBheTeWLl0af/rTn2LAgAHx8ssv5z8UAACQ3D//+c+CtU888cTYeOONC9YHqMuaNm0aV1xxRcH6U6ZMKVgbAKAQVltttYJ0mzdvXpAu1GXNmjUrWLtQv1YB6pJsbQ8AAEDtKfTDDFu3bh3jx48v6D/eAah/2rVrF+PGjYsmTZoU7Bg1NTVx2mmnFawPUBeUl5fHTjvtFHfffXey5tChQ5O1aNwymUzSz9P5558fxxxzTLIeAAAANAY33XRTVFZWFqxfXFwct912W3Tu3LlgxwCg/iktLY3bbrstOnToUNDjnHrqqVFdXV3QYwDUplwuF/vtt19ce+21yZr2waSU8vM0ZsyYGD58eNTU1CRrAgAAQEN3xx13xJw5cwp6jCuuuCL69u1b0GMAUL9kMpm48soro3fv3gU9zrnnnhuffvppQY8BUNtOPPHEOOussyKXyyXpDR48OIqKipK0IOU++IEHHogddtghFi9enKwJAAAADd1zzz0Xzz33XEGP8fvf/z5++tOfFvQYANQ/Z555Zmy//fYFPcbYsWNj+vTpBT0GQG277LLL4pe//GWUl5cn6W299dYFv38DjUfKffDzzz8f22yzTXz00UfJmgAAQBqPPPJIQbo9e/b0TDig0dtmm23iqKOOKkj7tddeK0gXAKBQVltttYJ0W7duXZAu1GWF/NwX6tcqQF2Sre0BAACoHeXl5XHrrbcW9BjXXXddrLvuugU9BgD10yabbBKXXHJJQY/xyCOPxJtvvlnQYwDUpt/+9rdJb7JTUlISu+yyS7IepH4I9OjRo2PixIlJmwAAANCQ3XDDDQXtn3vuubHVVlsV9BgA1E9rrrlmjBkzJrLZwp2q/sEHH8T9999fsD5AbRs9enRMnjw5aTP1/o7GLfXnacqUKXHhhRcmbQIAAEBDVuh98C9/+cvYb7/9CnoMAOqnli1bxoQJE6J58+YFO0ZFRUXB74UBUJvuvffe5Lsx+2BSGjRoUDRr1ixZ7+WXX47/9//+X7IeAAAANHSF3gfvsssu8dvf/ragxwCgfiouLo4xY8bEGmusUdDjXH/99QXtA9SmF198MfluzD6YlHr37h09e/ZM1ps9e3YcfPDBUVNTk6wJAADk77333itI94orroiysrKCtAHqkxNOOKEg3ZkzZxakCwBQKKuttlpBugMHDixIF+qy/v37J7229+sK9WsVoC4p3JNxAACo06ZNmxbz588vWP/kk092Qj8AK3T44YfHoYceWtBjjBs3rqB9gNry/vvvx/nnn5+0udlmm0WrVq2SNmnctt122+Qn0B9//PGRy+WSNgEAAKAhmjNnTrz44osF6++1115x7LHHFqwPQP23ww47xB/+8IeCHuO2224raB+gtpSXl8dJJ52UtLn66qvH+uuvn7RJ49ajR49Ye+21kzZPPfXUWLhwYdImAAAANES5XC4eeuihgvW32GKL5NcrANCw9OnTJ6666qqCHsM+GGjIjj/++KS9kpKS2GGHHZI2adzKyspiq622Str861//Gq+99lrSJgAAADRUDz74YMHa3bt3jxtvvDGyWY8oAWD5OnbsGLfddlsUFxcX7Bj2wUBD9pvf/CaWLl2atOmZL6SUyWRip512Stq855574t57703aBAAAVl4ul4uZM2cWpN2tW7eCdAHqm6lTpxakO2PGjIJ0AQAKpXnz5gXpHnbYYQXpQl3WqlWr2GeffZJ3M5lMtGvXLnkXoK5xhQQAQCP1zDPPFKy9/fbbF/whjAA0DJdccklsvPHGBes/9dRTBWsD1KaJEycmb7755psehkpSr7/+elRUVCRtvv322zFt2rSkTQAAAGiInn322YK1e/fuHVdeeWVkMpmCHQOAhuHkk0+O3XffvWD9J598smBtgNp0//33x/z585M2586dW7Aba9E4zZo1K2bNmpW0uXTp0rj77ruTNgEAAKAheuedd2Lu3LkFaXfo0CFuu+22KC0tLUgfgIZj7733jmOPPbZg/aeeeipqamoK1geoLa+99lq8+OKLSZvV1dXxyiuvJG3SuC1ZsqQgn6m///3vyZsAAADQ0FRUVMTzzz9fkHaTJk1i3LhxHsQEwHfacsst44ILLihY/7333kt+XRJAXbBw4cK45557kndfeOGF5E0ar1wuF08//XTy7oQJE5I3AQCAlTNv3rxYvHhxQdpPPPFEQboA9c3HH39ckO7cuXML9ns4AEAhLFmyJHlz7733jrXXXjt5F+qDX/3qV9G0adOkzaKioshms0mbAHWR3+kAABqpQpwYHRHRsWPHGDNmTBQXFxekD0DDUlZWFuPHj482bdoUpF+oP+8Aattdd92VvPnxxx/HmDFjkndpvM4555yorq5O3r3jjjuSNwEAAKChKdT3x5s2bRrjx4+Pli1bFqQPQMOSzWbj+uuvL9gFby+88EKUl5cXpA1Qm+68887kzaVLl8Yll1ySvEvjdfnll8fChQuTdwvx+QcAAICGplD74EwmE2PHjo2uXbsWpA9Aw/N///d/8eMf/7gg7fnz58dbb71VkDZAbSrE9cE1NTXxxz/+MXmXxmvChAnx/vvvJ+/aBwMAAMB3e/HFF6OioqIg7UsuuSQ22WSTgrQBaHh++ctfxj777FOw/jPPPFOwNkBteeCBBwpy/4MzzjgjeZPG64knnohHH300edf9ogEAoO6YMWNGwdpz584tWBugPink74czZ84sWBsAILVOnTol7W200UZx5ZVXJm1CfbLBBhvEVVddlbTZpEmTpD2Auipb2wMAAFA7CnWT5gMPPDA6duxYkDYADdPaa68dN9xwQ0Hab7/9dnz66acFaQPUpgULFhSke/PNNxekS+OzZMmSmDBhQkHahXgQMAAAADQ0hdoHDxo0KNZff/2CtAFomNq2bRvjx4+PbDb9aetVVVXx/PPPJ+8C1LZC7YPHjBkTNTU1BWnTuORyuYKdX2AfDAAAAN+tUPvg3r17x6BBgwrSBqBhKi0tjVtvvTXKysoK0i/Un3kAtalQ++A77rgj5s+fX5A2jc+YMWMK0rUPBgAAgO/2zDPPFKTbpk2bOOywwwrSBqBhymQyccUVVyR/2OZ/2AcDDVGh9sHPPfdcvPzyywVp0/i4PhgAABq+999/v2DtxYsXF6wNUJ906dKlYO0XX3yxYG0AgNS22mqrZK0ePXrEpEmTokWLFsmaUB/tt99+cfbZZyfrdezYMVkLoC5L/zQcAADqvPLy8pg+fXpB2h9//HFBugA0bL169SpY+9lnny1YG6C2ZDKZgnSnTp0aS5YsKUibxuXRRx8t2MWzhfr8AwAAQEPy0EMPFaT76aefFqQLQMPWs2fPyGYLc9r6U089VZAuQG0q1D7svffei1dffbUgbRqXQn6W7IMBAADgu/3rX/8qSHfOnDkF6QLQsK2xxhrRunXrgrTtg4GGqFD7sPLy8njwwQcL0qZxyeVyce+99xakbR8MAAAA3+3hhx8uSHfevHmxaNGigrQBaLhatGgRa6+9dkHaTz75ZEG6ALWpkPuwe+65p2BtGpdCfZbsgwEAoO647777CtbebLPNCtYGqE+23377grUfffTRgrUBAFIbMGBAFBUV5d3ZdNNN49FHH43u3bvnPxQ0ACeffHJcddVVSX59bb755gkmAqj7CvM0HAAA6rTHHnssqqurC9Iu1AMXAWjYZsyYUbB2oR5MAFCbCnVj+1wuF+Xl5QVp07gsXry4YO1WrVoVrA0AAAANwezZs+Ojjz4qSDvFSdoAND5z5syJqqqqgrT/+c9/FqQLUJsKtQ+OKOwej8bDPhgAAABqTy6Xi6effrogbftgAFbW7NmzC9L18EmgIbIPpq6rqqqKysrKgrTtgwEAAOC7/eMf/yhY2z2jAVgZc+fOLUj3wQcfLEgXoDbZB1MfFOqzZB8MAAB1x+uvv16QbrNmzWLzzTcvSBugvunbt2907969IO1C3VccAKAQSkpKYsCAAXk1dt1113jggQeiU6dOiaaChuHQQw+N22+/PZo1a5ZX5/TTT080EUDd5moJAIBGaNy4cQVrl5aWFqwNQMNVyD8/CvnnHkBt6dOnT8HaFRUVBWvTeBTqBs0REeuvv37B2gAAANAQ3H777QVr2wcDsDIK+efH/fffH7lcrmB9gNpgH0xdZx8MAAAAtee1114r2L/N7YMBWFmF+jPkpZdesg8GGhz7YOo6+2AAAACoPZWVlfHxxx8XrF9SUlKwNgANV6H2wXPnzi3on3sAtcE+mPqgUDth+2AAAKg7Fi5cWJDuoYce6hpEgC9ks9m49NJLC9J2PR0AUN9cd911kclkVuq9+++/f0yaNClatGiReCpoGHbZZZeYOnXqSv8a2W677Qp6LgNAXZKt7QEAAFj1pk+fXrD2FltsUbA2AA1X69atC9Z+9913C9YGqC2FuiixQ4cO0b59+4K0aVx69+5dsLaLcgEAAGDFHnvssYK17YMBWBktWrSIbLYwp63Pmzcvqqqq3GgAaFAKtQ/LZrPRs2fPgrRpXHr06FGwG6rZBwMAAMCK/etf/ypYe8CAAQVrA9CwtWnTpiDd6urqePnllwvSBqgthdyHFfK6ThqPsrKyWHPNNQvStg8GAACAFXv11VcL1t5kk02ipKSkYH0AGq5C3jP6jjvuKFgboDZ069YtmjVrVpC2h6qSSqHOLbAPBgCAuqN58+bJm2ussUacddZZybsA9dmuu+4aBxxwQPKu77MAAPXNuuuuG2efffYPft/ee+8d119/fRQXFxdgKmg4Nt1003jyySejadOmP+h9bdu2jbvuuqtAUwHUPYV5Gg4AAHXanDlzCtItLS2Nn/3sZwVpA9Cw9e3bN9Zee+2CtJcuXRo1NTUFaQPUlr59+xaku9tuu0VRUVFB2jQuvXv3jnXWWSd5t0mTJgX7OwMAAAA0FB988EHB2vvuu2/B2gA0XC1atIhBgwYVrP/+++8XrA1QGwq1D/7xj38cHTp0KEibxqVly5ax3XbbFaRdqM8/AAAANBTvvPNOwdr77bdfwdoANGxDhw4tWPuJJ54oWBugNqy++uoFedj56quvHj/60Y+Sd2l8MplM7L777gVp2wcDAADAir3++usFa7s+GICVVch98LRp0wrWBqgN2Ww2+vTpk7xbXFwcO++8c/IujdPgwYML0rUPBgCAumPAgAFJe23atImbb745WrVqlbQL0BBcffXVcdxxxyVtHnzwwUl7AACrwkknnRR/+MMfIpPJfK/Xn3LKKTF27Njv/Xpo7Hr37h2vv/56dOnS5Xu9fvXVV49XX301ysrKCjwZQN2Rre0BAABY9Qr1D9+DDjoo2rRpU5A2AA1bJpMp2I3+c7lcQboAtakQF+RGROy///4F6dL4ZDKZOOCAA5J3u3fvHkVFRcm7AAAA0JC0bNmyIN3tt98+Nthgg4K0AWj4Cvng93nz5kUmk7EbBhqM9u3bF+RcTPtgUirEPrisrCy6du2avAsAAAANSaGu4e3Zs6cHfAGw0gq5D16wYEHB2gC1IZPJxDrrrJO8O3LkyMhm3VaSNAp1fsH6669fkC4AAAA0FO3atStYd+TIkQVpA9Dw7b333gV7YOaiRYsK0gWoTYW4Z/TgwYOjdevWybs0TsOHD4+mTZsm79oHAwBA3fE///M/yVo9e/aMxx9/PLbZZptkTYCGpLi4OC688MK44YYbokOHDnn31l577ejcuXOCyQAAVr1TTz01nn766RU+z6R3797x+OOPxx//+MdVOBk0DF27do33338/jjvuuCgrK1vua8rKyuLYY4+NGTNmxGqrrbaKJwSoXe62AQDQCPXv3z95s2/fvnHhhRcm7wLQeBx33HGx1lprJe8WFRW54SjQ4LRs2TJatmyZtHn44YfHDjvskLRJ43byySfHRhttlLTZqVOnpD0AAABoiLbeeuvkzU6dOsWNN96YvAtA47HvvvvGZpttVpB2jx49vvy/c7lcQY4BsKp17NgxaW/77bePI444ImmTxm3kyJGx2267JW2uttpqBXuoAwAAADQUO+20U/Jm06ZNY9y4cdGkSZPkbQAah6222iqGDx9ekPa6665bkC5AbUq9D15vvfXi97//fdImjduPf/zjOOaYY5I2mzZt6obPAAAA8B223HLLgnRvvPFGD3kFYKV17do1TjjhhIK0C3EfaoDa1qVLl6S9du3axaWXXpq0SePWrVu3OOecc5J3+/Tpk7wJAACsnPbt28cWW2yRV6O0tDROOeWUeOaZZ2K99dZLNBlAw7X//vvHa6+9FkcffXRez8u85pprEk4FALDq9e/fP55//vmYN29eXHbZZXHUUUfFUUcdFZdeemnMnj07Xn755dh8881re0yot7LZbFx44YWxePHimDp1apx00klxxBFHxMknnxxTp06NxYsXx5///Oe8/l0CUF9lcp5UAwDQ6Dz//POx0UYbJev17Nkz7rjjDidKAJC3J554IrbeeuuorKxM1lxzzTXjvffeS9YDqCu6du0aM2fOTNLq1atXPPnkk9GyZcskPfiP559/PrbccstYtGhRkt4ee+wREyZMSNICAACAhmrhwoXRqlWrSHVqYKdOnWL8+PGx1VZbJekB0Hi9++670b9//5g7d26yZrNmzb7xPej//PmXyWSSHQOgNmyxxRbx+OOPJ2l16NAhnn76aTe1J7mPPvooNtlkk/jggw+S9DbaaKOYNm1akhYAAAA0VLlcLlq0aBGLFy9O0mvRokVcc801MWLEiCQ9ABqvzz77LDbddNN44403kjUzmUyUl5dHSUlJsiZAXXDggQfGDTfckKTVtGnTeOihh2LTTTdN0oP/WLJkSWyxxRYxffr0JL3VVlstZs2alaQFAAAADdlaa60V77//fpJWcXFxnHfeeXHssccm6QHQeFVVVcWgQYPiwQcfTNp94YUXom/fvkmbALXtwgsvjOOPPz5JK5PJxIQJE2LYsGFJevAfNTU1MXjw4LjrrruS9EpKSqKioiJJCwAASGPWrFnRtWvXlXoG3C677BIXXXRR9OzZswCTATR806ZNiyOPPDKeeOKJH/S+448/Ps4///wCTQUAAADQsGVrewAAAFa9DTfcMNZZZ50krZ///Ofx7LPPxnrrrZekB0Djtvnmm8eUKVOiW7duyZoHHnhgshZAXZLqhOUePXrElClTomXLlkl68HUbbrhh3HHHHdG0adMkvS233DJJBwAAABqyFi1axE9+8pMkrZ/97Gfx/PPPx1ZbbZWkB0Dj1q1bt3jooYdi4403Ttbccccdv/H/ZzKZiPj8IfYA9Vn//v2TdNq0aRNTpkyJtdZaK0kPvq5z584xZcqUWG211ZL0NtpooyQdAAAAaMgymUwcfPDBSVpbb711TJs2LUaMGJGkB0Dj1rp163jggQdihx12SNbs27dvlJSUJOsB1BUDBw5M0ikpKYm///3vsemmmybpwdeVlZXFvffem+yeUj169EjSAQAAgIbu9NNPT9Lp06dPPPbYY3Hssccm6QHQuBUXF8e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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn import tree\n", - "fn=list(X.columns)\n", - "cn=[\"not_GRB\", \"GRB\"]\n", - "fig, axes = plt.subplots(nrows = 1,ncols = 5,figsize = (10,2), dpi=1200)\n", - "for index in range(0, 5):\n", - " tree.plot_tree(RFmodel.estimators_[index],\n", - " feature_names = fn, \n", - " class_names=cn,\n", - " filled = True,\n", - " ax = axes[index]);\n", - "\n", - " axes[index].set_title('Estimator: ' + str(index), fontsize = 11)\n", - "fig.savefig('rf_5trees.png')" - ] - }, - { - "cell_type": "markdown", - "id": "07e8ce32-6a3c-4e3f-8380-580eccd6c894", - "metadata": {}, - "source": [ - "Moreover, the `dtreeviz` package can provide a more detailed and visual representation of the decision tree" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "e5e61132-54c8-4c9e-bd4a-379396163319", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "G\n", - "\n", 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node0->node1\n", - "\n", - "\n", - "  ≤\n", - "\n", - "\n", - "\n", - "node0->node16\n", - "\n", - "\n", - "  >\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2024-03-16T12:16:25.620898\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.8.0, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import dtreeviz \n", - "for index in range(0, 5):\n", - " trees=[]\n", - " trees.append(dtreeviz.model(RFmodel.estimators_[index], X, y,\n", - " target_name=\"target\",\n", - " feature_names=fn,\n", - " class_names=cn))\n", - "\n", - "tree_index = 0 # the index of the tree to visualize \n", - "\n", - "trees[tree_index].view()\n", - "#trees[tree_index].view().save('Trees.svg')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "67b7d545-a765-47b3-bc5c-20fdee88f345", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data = data[data[\"MAGCAL_R0\"]>1]\n", - "data[\"R0_B0\"] = data[\"MAGCAL_R0\"]-data[\"MAGCAL_B0\"]\n", - "\n", - "#plt.scatter(data[\"R0_B0\"], data[\"MAGCAL_R0\"], )\n", - "mask = data[\"IS_GRB\"] == 1 \n", - "sns.scatterplot(data=data, y = \"R0_B0\", x = \"MAGCAL_R0\")\n", - "sns.scatterplot(data=data[mask], y = \"R0_B0\", x = \"MAGCAL_R0\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "1d764786-af61-406d-ad51-4ff15cd88aba", - "metadata": {}, - "source": [ - "- Look at PCA (done not great)\n", - "- New flags ??\n", - "- Class imbalance: dealt with using class weights class from sklearn\n", - "\n", - "- Cleanup pipeline\n" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "6ac21b62-90b9-43e4-99e4-b8480077a24e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'paths': {'plots': 'plots/corr/', 'data': '~/Repos/VTAC_ML/combined_qpo_vt_with_GRB.parquet', 'model': 'RFModel'}, 'Inputs': {'path': '~/Repos/VTAC_ML/combined_qpo_vt_with_GRB.parquet', 'columns': ['MAGCAL_R0', 'MAGCAL_B0', 'MAGERR_R0', 'MAGERR_B0', 'MAGCAL_R1', 'MAGCAL_B1', 'MAGERR_R1', 'MAGERR_B1', 'MAGVAR_R1', 'MAGVAR_B1', 'R0_B0', 'R1_B1'], 'target_column': ['IS_GRB']}, 'RFModel': {'param_grid': {'n_estimators': [100], 'max_depth': [4], 'min_samples_split': [2, 3]}}, 'OtherModel': [None]}\n" - ] - } - ], - "source": [ - "import yaml\n", - "import os\n", - "\n", - "with open('/Users/jeremypalmerio/Repos/VTAC_ML/GRB-hunter/config/config.yaml', 'r') as file:\n", - " data = yaml.safe_load(file)\n", - "config = yaml.safe_load(open('/Users/jeremypalmerio/Repos/VTAC_ML/GRB-hunter/config/config.yaml', 'r'))\n", - "\n", - "print(data) \n", - "\n", - "\n", - "# Output:\n", - "# {'example': 'data'}" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "id": "7e4b35e0-fe99-478e-867a-1e2cd0e8fff1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'n_estimators': [100], 'max_depth': [4], 'min_samples_split': [2, 3]}" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "config['RFModel']['param_grid']" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/Untitled.ipynb b/dev/Untitled.ipynb deleted file mode 100644 index b7a5c59..0000000 --- a/dev/Untitled.ipynb +++ /dev/null @@ -1,673 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "75207a47-53e2-4540-b822-48a3b0accc34", - "metadata": {}, - "source": [ - "# Optical Identification of GRBs Using Machine Learning Techniques on Simulated Data " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "298e0dbb-81d5-403c-b910-72c051888334", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51f55ea5-1c2c-4b6f-a375-a67baefaffd4", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5afe3b35-86d9-4963-9fea-0fba7e225b19", - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_parquet('data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet', engine='fastparquet')\n", - "# df.to_csv(path_or_buf='data/combined_qpo_vt_all_cases_with_GRB_with_flags.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4570771-32ed-416a-a451-87129c748393", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "f36d363d-652f-43aa-a79d-0a89ba2b5042", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 564794 entries, 0 to 564793\n", - "Data columns (total 43 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 VT_ID 564794 non-null int64 \n", - " 1 RA 564794 non-null float64\n", - " 2 DEC 564794 non-null float64\n", - " 3 MAGCAL_R0 413758 non-null float64\n", - " 4 MAGCAL_R1 423094 non-null float64\n", - " 5 MAGCAL_R2 420718 non-null float64\n", - " 6 MAGCAL_R3 433421 non-null float64\n", - " 7 MAGCAL_B0 343293 non-null float64\n", - " 8 MAGCAL_B1 384444 non-null float64\n", - " 9 MAGCAL_B2 382679 non-null float64\n", - " 10 MAGCAL_B3 398935 non-null float64\n", - " 11 MAGERR_R0 413758 non-null float64\n", - " 12 MAGERR_R1 423094 non-null float64\n", - " 13 MAGERR_R2 420718 non-null float64\n", - " 14 MAGERR_R3 433421 non-null float64\n", - " 15 MAGERR_B0 343293 non-null float64\n", - " 16 MAGERR_B1 384444 non-null float64\n", - " 17 MAGERR_B2 382679 non-null float64\n", - " 18 MAGERR_B3 398935 non-null float64\n", - " 19 MAGVAR_R1 383321 non-null float64\n", - " 20 MAGVAR_R2 393443 non-null float64\n", - " 21 MAGVAR_R3 408733 non-null float64\n", - " 22 MAGVAR_B1 323844 non-null float64\n", - " 23 MAGVAR_B2 358961 non-null float64\n", - " 24 MAGVAR_B3 376558 non-null float64\n", - " 25 EFLAG_R0 413758 non-null Int64 \n", - " 26 EFLAG_R1 423094 non-null Int64 \n", - " 27 EFLAG_R2 420718 non-null Int64 \n", - " 28 EFLAG_R3 433421 non-null Int64 \n", - " 29 EFLAG_B0 343293 non-null Int64 \n", - " 30 EFLAG_B1 384444 non-null Int64 \n", - " 31 EFLAG_B2 382679 non-null Int64 \n", - " 32 EFLAG_B3 398935 non-null Int64 \n", - " 33 SEQFLAG0 564794 non-null int64 \n", - " 34 SEQFLAG1 564794 non-null int64 \n", - " 35 SEQFLAG2 564794 non-null int64 \n", - " 36 SEQFLAG3 564794 non-null int64 \n", - " 37 NEW_SRC 564794 non-null object \n", - " 38 DMAG_CAT 564794 non-null object \n", - " 39 MAG_VAR 564794 non-null object \n", - " 40 IS_GRB 564794 non-null int64 \n", - " 41 CASE 564794 non-null object \n", - " 42 SIM_ID 564794 non-null int64 \n", - "dtypes: Int64(8), float64(24), int64(7), object(4)\n", - "memory usage: 189.6+ MB\n" - ] - }, - { - "data": { - "text/plain": [ - "(VT_ID False\n", - " RA False\n", - " DEC False\n", - " MAGCAL_R0 True\n", - " MAGCAL_R1 True\n", - " MAGCAL_R2 True\n", - " MAGCAL_R3 True\n", - " MAGCAL_B0 True\n", - " MAGCAL_B1 True\n", - " MAGCAL_B2 True\n", - " MAGCAL_B3 True\n", - " MAGERR_R0 True\n", - " MAGERR_R1 True\n", - " MAGERR_R2 True\n", - " MAGERR_R3 True\n", - " MAGERR_B0 True\n", - " MAGERR_B1 True\n", - " MAGERR_B2 True\n", - " MAGERR_B3 True\n", - " MAGVAR_R1 True\n", - " MAGVAR_R2 True\n", - " MAGVAR_R3 True\n", - " MAGVAR_B1 True\n", - " MAGVAR_B2 True\n", - " MAGVAR_B3 True\n", - " EFLAG_R0 True\n", - " EFLAG_R1 True\n", - " EFLAG_R2 True\n", - " EFLAG_R3 True\n", - " EFLAG_B0 True\n", - " EFLAG_B1 True\n", - " EFLAG_B2 True\n", - " EFLAG_B3 True\n", - " SEQFLAG0 False\n", - " SEQFLAG1 False\n", - " SEQFLAG2 False\n", - " SEQFLAG3 False\n", - " NEW_SRC False\n", - " DMAG_CAT False\n", - " MAG_VAR False\n", - " IS_GRB False\n", - " CASE False\n", - " SIM_ID False\n", - " dtype: bool,\n", - " None)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.isna().any(), df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "fdffbc8a-3857-4ef6-9ed3-3792d823711c", - "metadata": {}, - "outputs": [], - "source": [ - "def handle_nans( X, y=None):\n", - " X_ = X.copy()\n", - " for column in X.columns:\n", - " if X[column].isna().sum():\n", - " # Create the isna column\n", - " new_column_name = f'{column}_isnan'\n", - " X_[new_column_name] = X_[column].isna().astype(int)\n", - " # Impute missing values with the mean \n", - " impute_value = X_[column].median()\n", - " X_[column].fillna(impute_value, inplace=True)\n", - " return X_" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "0d7ef086-f28e-4473-bd1c-4700403d48da", - "metadata": {}, - "outputs": [], - "source": [ - "cols =['MAGCAL_R0', \n", - " 'MAGCAL_R1',\n", - " 'MAGCAL_R2',\n", - " 'MAGCAL_R3',\n", - " 'MAGCAL_B0',\n", - " 'MAGCAL_B1',\n", - " 'MAGCAL_B2',\n", - " 'MAGCAL_B3',\n", - " 'MAGERR_R0',\n", - " 'MAGERR_R1',\n", - " 'MAGERR_R2',\n", - " 'MAGERR_R3',\n", - " 'MAGERR_B0',\n", - " 'MAGERR_B1',\n", - " 'MAGERR_B2',\n", - " 'MAGERR_B3',\n", - " 'MAGVAR_R1',\n", - " 'MAGVAR_R2',\n", - " 'MAGVAR_R3',\n", - " 'MAGVAR_B1',\n", - " 'MAGVAR_B2', \n", - " 'MAGVAR_B3']\n", - "clean_df = handle_nans(df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20b5fbf0-45e2-4ea6-bf2e-99667b073052", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "798a5b1b-9e90-46a7-acec-525b97ecdc9a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MAGCAL_R0 False\n", - "MAGCAL_R1 False\n", - "MAGCAL_R2 False\n", - "MAGCAL_R3 False\n", - "MAGCAL_B0 False\n", - "MAGCAL_B1 False\n", - "MAGCAL_B2 False\n", - "MAGCAL_B3 False\n", - "MAGERR_R0 False\n", - "MAGERR_R1 False\n", - "MAGERR_R2 False\n", - "MAGERR_R3 False\n", - "MAGERR_B0 False\n", - "MAGERR_B1 False\n", - "MAGERR_B2 False\n", - "MAGERR_B3 False\n", - "MAGVAR_R1 False\n", - "MAGVAR_R2 False\n", - "MAGVAR_R3 False\n", - "MAGVAR_B1 False\n", - "MAGVAR_B2 False\n", - "MAGVAR_B3 False\n", - "dtype: bool" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clean_df[cols].isna().any()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "dcbcc29c-6684-46ae-9cfe-8359b3b4bca4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_features(df, 'MAGVAR_B2', 'MAGVAR_B3', 'IS_GRB')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2a9356ef-1cd7-414d-84f6-8057382034e9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeremypalmerio/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/jeremypalmerio/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", - "sns.kdeplot(data=df, x=\"MAGVAR_B1\", y=\"MAGVAR_B2\", hue=\"IS_GRB\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c9c89799-0cd0-402d-afe4-7dc4487c8806", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['EFLAG_R2', 'EFLAG_B0', 'EFLAG_B1']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cols = ['MAGCAL_R3', 'MAGCAL_B0', 'MAGCAL_B1', 'MAGCAL_B2', 'MAGCAL_B3',\n", - " 'MAGERR_R0', 'MAGERR_R1', 'MAGERR_R2', 'MAGERR_R3', 'MAGERR_B0',\n", - " 'MAGERR_B1', 'MAGERR_B2', 'EFLAG_R2', 'EFLAG_B0', 'EFLAG_B1',]\n", - "\n", - "list1 = ['EFLAG_R1', 'EFLAG_R2', 'EFLAG_R3', 'EFLAG_B0', 'EFLAG_B1', 'EFLAG_B2',\n", - " 'EFLAG_B3']\n", - "\n", - "[x for x in cols if x in list1]" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/Untitled1.ipynb b/dev/Untitled1.ipynb deleted file mode 100644 index 8136a20..0000000 --- a/dev/Untitled1.ipynb +++ /dev/null @@ -1,163 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 15, - "id": "4ec07d13-b571-418f-9403-45502f8182ec", - "metadata": {}, - "outputs": [], - "source": [ - "import time \n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "0967ef53-d7aa-4b05-b64f-ef6fc411183c", - "metadata": {}, - "outputs": [], - "source": [ - "data = [0]\n", - "t = [0]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "a2503454-8739-46ba-b58e-2c26b037a61c", - "metadata": {}, - "outputs": [], - "source": [ - "def add_data_point(data, time):\n", - " start_time = 1717357531\n", - " current_time = time.time()\n", - " delta_time = (current_time-start_time)/3600\n", - " n_jobs = 810\n", - " out = open('out.txt').read()\n", - " out_split = out.split()\n", - " df_out = pd.DataFrame(data=out_split)\n", - " # index = df_out[df_out[0] == 'rfc'].index[0]\n", - " # df_trimmed = df_out.iloc[index:]\n", - " solved_models = df_out.value_counts()['[CV]']\n", - " print(f\"Progress: {round(100*solved_models/n_jobs, 1)}%\\n\"\n", - " \n", - " )\n", - " if delta_time - t[-1]>0.2:\n", - " data.append(solved_models)\n", - " t.append(delta_time)\n", - "\n", - " avg_models_per_hour = (data[-1]-data[-2])/(t[-1]-t[-2])\n", - " estiamted_completion = current_time + 3600*(n_jobs - data[-1])/avg_models_per_hour\n", - "\n", - " print(datetime.fromtimestamp(estiamted_completion))\n", - " \n", - " fig, ax = plt.subplots()\n", - " \n", - " plt.plot(t, data, marker='x', label='models solved')\n", - " plt.axhline(y=n_jobs, color='red', label='total models')\n", - " plt.xlabel('time (hours)')\n", - " plt.ylabel('Models solved')\n", - " plt.title('Progress of models grid search over time')\n", - " plt.ylim(0, n_jobs*1.1)\n", - " plt.legend()\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "6a158dd2-b8a3-4e85-882a-60268f115214", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Progress: 88.5%\n", - "\n", - "2024-06-04 05:38:51.527531\n" - ] - }, - { - "data": { - "text/plain": [ - "([0,\n", - " 4.274272164702415,\n", - " 5.7548765977885985,\n", - " 15.230073993603389,\n", - " 16.040863393611378,\n", - " 17.67927683889866,\n", - " 20.81412830306424,\n", - " 26.864809267454678],\n", - " [0, 177, 223, 485, 505, 545, 605, 717])" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "add_data_point(data, time)\n", - "t, data" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "8327c35e-d57e-4760-a0b4-b6cecee232a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([0, 4.274272164702415], [0, 177])" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t, data" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/dev_tests/_test_config.py b/dev/dev_tests/_test_config.py deleted file mode 100644 index d412ff9..0000000 --- a/dev/dev_tests/_test_config.py +++ /dev/null @@ -1,14 +0,0 @@ -import yaml - -config_path = "../../vtacML/config/config.yaml" -with open(config_path, 'r') as f: - config = yaml.safe_load(f) - -model_dict = [] -for model in config['models']: - print(model) - model_name = model - model_class = model['class'] - print(model_class) - # model_params = model['param_grid'] - # model_dict.append((model_name, model_class, model_params)) \ No newline at end of file diff --git a/dev/dev_tests/_test_resampler.py b/dev/dev_tests/_test_resampler.py deleted file mode 100644 index e5bf7fc..0000000 --- a/dev/dev_tests/_test_resampler.py +++ /dev/null @@ -1,32 +0,0 @@ -import pandas as pd -from imblearn.over_sampling import SMOTE - -data = pd.read_parquet('/vtacML/data/combined_qpo_vt_with_GRB.parquet') -print(data['IS_GRB'].value_counts()) -columns = ["MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - # "CASE", - # "EFLAG_B1" - "IS_GRB" - ] - -data = data[columns].dropna() - - -def resample(X): - sm = SMOTE(sampling_strategy='minority', random_state=42) - X_, y_ = sm.fit_resample(X.drop('IS_GRB', axis=1), X['IS_GRB']) - X_resampled = pd.concat([pd.DataFrame(y_), pd.DataFrame(X_)], axis=1) - return X_resampled - - -resampled_data = resample(data) -print(resampled_data['IS_GRB'].value_counts()) diff --git a/dev/dev_tests/_test_training.py b/dev/dev_tests/_test_training.py deleted file mode 100644 index 91670cb..0000000 --- a/dev/dev_tests/_test_training.py +++ /dev/null @@ -1,32 +0,0 @@ -import yaml -from sklearn.ensemble import RandomForestClassifier -from sklearn.model_selection import GridSearchCV - - - -def make_model(model_type, class_weights): - model = None - if model_type == 'RFClassifier': - model = RandomForestClassifier(class_weight=class_weights) - return model - - -def run_grid_search(X, y, model, param_grid, cv=5): - grid_search = GridSearchCV(model, - param_grid=param_grid, - cv=cv, - verbose=2) - grid_search.fit(X, y) - return grid_search - - -def train_model(X_train, y_train, weights, model=None, config_path='./config/config.yaml'): - config = yaml.safe_load(open(config_path, 'r')) - model_type = config['Inputs']['model'] - if model is None: - model = make_model(config['Inputs']['model'], class_weights=weights ) - print("SHAPE: ", X_train.shape, y_train.shape,) - grid_search = run_grid_search(X_train, y_train, model, param_grid=config[model_type]['param_grid']) - model.set_params(**grid_search.best_params_) - model.fit(X_train, y_train) - return model diff --git a/dev/dev_tests/_test_validation.py b/dev/dev_tests/_test_validation.py deleted file mode 100644 index 2a42608..0000000 --- a/dev/dev_tests/_test_validation.py +++ /dev/null @@ -1,5 +0,0 @@ -from sklearn.datasets import make_classification -from sklearn.model_selection import train_test_split -from sklearn.metrics import accuracy_score -from sklearn.metrics import confusion_matrix -from sklearn.metrics import classification_report diff --git a/dev/dev_tests/_test_wrapper.py b/dev/dev_tests/_test_wrapper.py deleted file mode 100644 index 4532dd3..0000000 --- a/dev/dev_tests/_test_wrapper.py +++ /dev/null @@ -1,17 +0,0 @@ -from vtacML.pipeline import predict_from_best_pipeline -import pandas as pd -import os - -cdir = os.getcwd() -print(cdir) -config_path = "../../vtacML/config/config.yaml" - -data = pd.read_parquet(f'{cdir}/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet') -print(data.head()) -X_GRB = data[data['IS_GRB'] == 1] - -y = predict_from_best_pipeline(X=X_GRB) -# -# print(sum(y), len(y)) - - diff --git a/dev/initial_tests.ipynb b/dev/initial_tests.ipynb deleted file mode 100644 index 04b74b6..0000000 --- a/dev/initial_tests.ipynb +++ /dev/null @@ -1,2373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "64b14aa8-39f6-419a-bc27-789e59798dfa", - "metadata": {}, - "source": [ - "# Initial Data Exploration" - ] - }, - { - "cell_type": "markdown", - "id": "2041d222-49fe-477f-80a1-56e1977f5d24", - "metadata": {}, - "source": [ - "## Fetching Data" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "8af00bfa-ce1e-4e19-a3e8-ad2cabbaca01", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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149 rows × 80 columns

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" - ], - "text/plain": [ - " VT_ID RA DEC RADEC_OG ... SEQFLAG3 NEW_SRC DMAG_CAT MAG_VAR\n", - "0 False False False False ... False True True True\n", - "1 False False False False ... False True True True\n", - "2 False False False False ... False True True True\n", - "3 False False False False ... False True False True\n", - "4 False False False False ... False True True True\n", - ".. ... ... ... ... ... ... ... ... ...\n", - "144 False False False False ... False False True True\n", - "145 False False False False ... False True True True\n", - "146 False False False False ... False False True True\n", - "147 False False False False ... False False True True\n", - "148 False False False False ... False True True True\n", - "\n", - "[149 rows x 80 columns]" - ] - }, - "execution_count": 174, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from astropy.table import Table\n", - "from astropy.io import fits\n", - "qpo_vt = fits.open('qpo_vt_example.fits')\n", - "tab = Table.read(qpo_vt, hdu='COMBINED')\n", - "df = tab.to_pandas()\n", - "df.isna()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "id": "189ab9dd-713e-4d97-9ef4-272adda7fba9", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "XTENSION= 'BINTABLE' / binary table extension \n", - "BITPIX = 8 / array data type \n", - "NAXIS = 2 / number of array dimensions \n", - "NAXIS1 = 705 / length of dimension 1 \n", - "NAXIS2 = 149 / length of dimension 2 \n", - "PCOUNT = 0 / number of group parameters \n", - "GCOUNT = 1 / number of groups \n", - "TFIELDS = 80 / number of table fields \n", - "EXTNAME = 'COMBINED' \n", - "R_XMATCH= 1.0 / Combination crossmatch radius in arcsec \n", - "MXT_RA = 212.6408177188752 / RA from QPO_MXT used for filtering in deg \n", - "MXT_DEC = 29.69708513294715 / Dec from QPO_MXT used for filtering in deg \n", - "MXT_ERR = 0.01138888888888889 / Error from QPO_MXT used for filtering in deg \n", - "TTYPE1 = 'VT_ID ' / Unique VT identifier \n", - "TFORM1 = 'K ' \n", - "TTYPE2 = 'RA ' / Right Ascension from VT \n", - "TFORM2 = 'D ' \n", - "TUNIT2 = 'deg ' \n", - "TTYPE3 = 'DEC ' / Declination from VT \n", - "TFORM3 = 'D ' \n", - "TUNIT3 = 'deg ' \n", - "TTYPE4 = 'RADEC_OG' / Seq. where RA and DEC originate \n", - "TFORM4 = '2A ' \n", - "TTYPE5 = 'IN_MXT ' / 1: True, 0: False, -1: no QPO_MXT \n", - "TFORM5 = 'K ' \n", - "TTYPE6 = 'OBJID_R0' / Object identifier in R0 seq. \n", - "TFORM6 = 'K ' \n", - "TNULL6 = -99999 \n", - "TTYPE7 = 'OBJID_R1' / Object identifier in R1 seq. \n", - "TFORM7 = 'K ' \n", - "TNULL7 = -99999 \n", - "TTYPE8 = 'OBJID_R2' / Object identifier in R2 seq. \n", - "TFORM8 = 'K ' \n", - "TTYPE9 = 'OBJID_R3' / Object identifier in R3 seq. \n", - "TFORM9 = 'K ' \n", - "TTYPE10 = 'OBJID_B0' / Object identifier in B0 seq. \n", - "TFORM10 = 'K ' \n", - "TNULL10 = -99999 \n", - "TTYPE11 = 'OBJID_B1' / Object identifier in B1 seq. \n", - "TFORM11 = 'K ' \n", - "TNULL11 = -99999 \n", - "TTYPE12 = 'OBJID_B2' / Object identifier in B2 seq. \n", - "TFORM12 = 'K ' \n", - "TTYPE13 = 'OBJID_B3' / Object identifier in B3 seq. \n", - "TFORM13 = 'K ' \n", - "TTYPE14 = 'RA_R0 ' / Right Ascension in R0 seq. \n", - "TFORM14 = 'D ' \n", - "TUNIT14 = 'deg ' \n", - "TTYPE15 = 'RA_R1 ' / Right Ascension in R1 seq. \n", - "TFORM15 = 'D ' \n", - "TUNIT15 = 'deg ' \n", - "TTYPE16 = 'RA_R2 ' / Right Ascension in R2 seq. \n", - "TFORM16 = 'D ' \n", - "TUNIT16 = 'deg ' \n", - "TTYPE17 = 'RA_R3 ' / Right Ascension in R3 seq. \n", - "TFORM17 = 'D ' \n", - "TUNIT17 = 'deg ' \n", - "TTYPE18 = 'RA_B0 ' / Right Ascension in B0 seq. \n", - "TFORM18 = 'D ' \n", - "TUNIT18 = 'deg ' \n", - "TTYPE19 = 'RA_B1 ' / Right Ascension in B1 seq. \n", - "TFORM19 = 'D ' \n", - "TUNIT19 = 'deg ' \n", - "TTYPE20 = 'RA_B2 ' / Right Ascension in B2 seq. \n", - "TFORM20 = 'D ' \n", - "TUNIT20 = 'deg ' \n", - "TTYPE21 = 'RA_B3 ' / Right Ascension in B3 seq. \n", - "TFORM21 = 'D ' \n", - "TUNIT21 = 'deg ' \n", - "TTYPE22 = 'DEC_R0 ' / Declination in R0 seq. \n", - "TFORM22 = 'D ' \n", - "TUNIT22 = 'deg ' \n", - "TTYPE23 = 'DEC_R1 ' / Declination in R1 seq. \n", - "TFORM23 = 'D ' \n", - "TUNIT23 = 'deg ' \n", - "TTYPE24 = 'DEC_R2 ' / Declination in R2 seq. \n", - "TFORM24 = 'D ' \n", - "TUNIT24 = 'deg ' \n", - "TTYPE25 = 'DEC_R3 ' / Declination in R3 seq. \n", - "TFORM25 = 'D ' \n", - "TUNIT25 = 'deg ' \n", - "TTYPE26 = 'DEC_B0 ' / Declination in B0 seq. \n", - "TFORM26 = 'D ' \n", - "TUNIT26 = 'deg ' \n", - "TTYPE27 = 'DEC_B1 ' / Declination in B1 seq. \n", - "TFORM27 = 'D ' \n", - "TUNIT27 = 'deg ' \n", - "TTYPE28 = 'DEC_B2 ' / Declination in B2 seq. \n", - "TFORM28 = 'D ' \n", - "TUNIT28 = 'deg ' \n", - "TTYPE29 = 'DEC_B3 ' / Declination in B3 seq. \n", - "TFORM29 = 'D ' \n", - "TUNIT29 = 'deg ' \n", - "TTYPE30 = 'MAGCAL_R0' / Calib. mag. in SVOM phot. system in R0 seq. \n", - "TFORM30 = 'D ' \n", - "TTYPE31 = 'MAGCAL_R1' / Calib. mag. in SVOM phot. system in R1 seq. \n", - "TFORM31 = 'D ' \n", - "TTYPE32 = 'MAGCAL_R2' / Calib. mag. in SVOM phot. system in R2 seq. \n", - "TFORM32 = 'D ' \n", - "TTYPE33 = 'MAGCAL_R3' / Calib. mag. in SVOM phot. system in R3 seq. \n", - "TFORM33 = 'D ' \n", - "TTYPE34 = 'MAGCAL_B0' / Calib. mag. in SVOM phot. system in B0 seq. \n", - "TFORM34 = 'D ' \n", - "TTYPE35 = 'MAGCAL_B1' / Calib. mag. in SVOM phot. system in B1 seq. \n", - "TFORM35 = 'D ' \n", - "TTYPE36 = 'MAGCAL_B2' / Calib. mag. in SVOM phot. system in B2 seq. \n", - "TFORM36 = 'D ' \n", - "TTYPE37 = 'MAGCAL_B3' / Calib. mag. in SVOM phot. system in B3 seq. \n", - "TFORM37 = 'D ' \n", - "TTYPE38 = 'MAGERR_R0' / Calib. mag. err. in R0 seq. \n", - "TFORM38 = 'D ' \n", - "TTYPE39 = 'MAGERR_R1' / Calib. mag. err. in R1 seq. \n", - "TFORM39 = 'D ' \n", - "TTYPE40 = 'MAGERR_R2' / Calib. mag. err. in R2 seq. \n", - "TFORM40 = 'D ' \n", - "TTYPE41 = 'MAGERR_R3' / Calib. mag. err. in R3 seq. \n", - "TFORM41 = 'D ' \n", - "TTYPE42 = 'MAGERR_B0' / Calib. mag. err. in B0 seq. \n", - "TFORM42 = 'D ' \n", - "TTYPE43 = 'MAGERR_B1' / Calib. mag. err. in B1 seq. \n", - "TFORM43 = 'D ' \n", - "TTYPE44 = 'MAGERR_B2' / Calib. mag. err. in B2 seq. \n", - "TFORM44 = 'D ' \n", - "TTYPE45 = 'MAGERR_B3' / Calib. mag. err. in B3 seq. \n", - "TFORM45 = 'D ' \n", - "TTYPE46 = 'MAGVAR_R1' / Mag. variation between R1 and R0 seq. \n", - "TFORM46 = 'D ' \n", - "TTYPE47 = 'MAGVAR_R2' / Mag. variation between R2 and R1 seq. \n", - "TFORM47 = 'D ' \n", - "TTYPE48 = 'MAGVAR_R3' / Mag. variation between R3 and R2 seq. \n", - "TFORM48 = 'D ' \n", - "TTYPE49 = 'MAGVAR_B1' / Mag. variation between B1 and B0 seq. \n", - "TFORM49 = 'D ' \n", - "TTYPE50 = 'MAGVAR_B2' / Mag. variation between B2 and B1 seq. \n", - "TFORM50 = 'D ' \n", - "TTYPE51 = 'MAGVAR_B3' / Mag. variation between B3 and B2 seq. \n", - "TFORM51 = 'D ' \n", - "TTYPE52 = 'EFLAG_R0' / Enhanced VVPP flag in R0 seq. >0 may be noise \n", - "TFORM52 = 'K ' \n", - "TNULL52 = -99999 \n", - "TTYPE53 = 'EFLAG_R1' / Enhanced VVPP flag in R1 seq. >0 may be noise \n", - "TFORM53 = 'K ' \n", - "TNULL53 = -99999 \n", - "TTYPE54 = 'EFLAG_R2' / Enhanced VVPP flag in R2 seq. >0 may be noise \n", - "TFORM54 = 'K ' \n", - "TTYPE55 = 'EFLAG_R3' / Enhanced VVPP flag in R3 seq. >0 may be noise \n", - "TFORM55 = 'K ' \n", - "TTYPE56 = 'EFLAG_B0' / Enhanced VVPP flag in B0 seq. >0 may be noise \n", - "TFORM56 = 'K ' \n", - "TNULL56 = -99999 \n", - "TTYPE57 = 'EFLAG_B1' / Enhanced VVPP flag in B1 seq. >0 may be noise \n", - "TFORM57 = 'K ' \n", - "TNULL57 = -99999 \n", - "TTYPE58 = 'EFLAG_B2' / Enhanced VVPP flag in B2 seq. >0 may be noise \n", - "TFORM58 = 'K ' \n", - "TTYPE59 = 'EFLAG_B3' / Enhanced VVPP flag in B3 seq. >0 may be noise \n", - "TFORM59 = 'K ' \n", - "TTYPE60 = 'XFLAG_R0' / Crossmatch flag in R0 seq. \n", - "TFORM60 = 'K ' \n", - "TTYPE61 = 'XFLAG_R1' / Crossmatch flag in R1 seq. \n", - "TFORM61 = 'K ' \n", - "TTYPE62 = 'XFLAG_R2' / Crossmatch flag in R2 seq. \n", - "TFORM62 = 'K ' \n", - "TTYPE63 = 'XFLAG_R3' / Crossmatch flag in R3 seq. \n", - "TFORM63 = 'K ' \n", - "TTYPE64 = 'XFLAG_B0' / Crossmatch flag in B0 seq. \n", - "TFORM64 = 'K ' \n", - "TTYPE65 = 'XFLAG_B1' / Crossmatch flag in B1 seq. \n", - "TFORM65 = 'K ' \n", - "TTYPE66 = 'XFLAG_B2' / Crossmatch flag in B2 seq. \n", - "TFORM66 = 'K ' \n", - "TTYPE67 = 'XFLAG_B3' / Crossmatch flag in B3 seq. \n", - "TFORM67 = 'K ' \n", - "TTYPE68 = 'VFLAG_R1' / Variability flag between R1 and R0 seq. \n", - "TFORM68 = 'K ' \n", - "TTYPE69 = 'VFLAG_R2' / Variability flag between R2 and R1 seq. \n", - "TFORM69 = 'K ' \n", - "TTYPE70 = 'VFLAG_R3' / Variability flag between R3 and R2 seq. \n", - "TFORM70 = 'K ' \n", - "TTYPE71 = 'VFLAG_B1' / Variability flag between B1 and B0 seq. \n", - "TFORM71 = 'K ' \n", - "TTYPE72 = 'VFLAG_B2' / Variability flag between B2 and B1 seq. \n", - "TFORM72 = 'K ' \n", - "TTYPE73 = 'VFLAG_B3' / Variability flag between B3 and B2 seq. \n", - "TFORM73 = 'K ' \n", - "TTYPE74 = 'SEQFLAG0' / Flag for 1st seq. see doc. for more info \n", - "TFORM74 = 'K ' \n", - "TTYPE75 = 'SEQFLAG1' / Flag for 2nd seq. see doc. for more info \n", - "TFORM75 = 'K ' \n", - "TTYPE76 = 'SEQFLAG2' / Flag for 3rd seq. see doc. for more info \n", - "TFORM76 = 'K ' \n", - "TTYPE77 = 'SEQFLAG3' / Flag for 4th seq. see doc. for more info \n", - "TFORM77 = 'K ' \n", - "TTYPE78 = 'NEW_SRC ' / Seq. where source is new (not in catalogs) \n", - "TFORM78 = '30A ' \n", - "TTYPE79 = 'DMAG_CAT' / Seq. where source mag varied wrt catalogs \n", - "TFORM79 = '30A ' \n", - "TTYPE80 = 'MAG_VAR ' / Seq. pairs between which the source varied \n", - "TFORM80 = '35A ' \n", - "CHECKSUM= 'Rl2gUl0fRl0fRl0f' / HDU checksum updated 2023-12-19T03:31:08 \n", - "DATASUM = '1216721935' / data unit checksum updated 2023-12-19T03:31:08 " - ] - }, - "execution_count": 175, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qpo_vt[\"COMBINED\"].header" - ] - }, - { - "cell_type": "markdown", - "id": "d2670c37-a2a8-4992-b472-d022762ec9fa", - "metadata": {}, - "source": [ - "# Proto-Pipeline" - ] - }, - { - "cell_type": "markdown", - "id": "7ad80fa9-5b14-4bb3-9348-22edcca76b96", - "metadata": {}, - "source": [ - "## Proto-DataFrame\n", - "The dataset used here is from the Sloan Digital Sky Survey taken from: \n", - "\n", - "fedesoriano. (January 2022). Stellar Classification Dataset - SDSS17. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17." - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "id": "9008af59-7ae0-4b32-9e35-095dbb490211", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
01.237661e+18135.68910732.49463223.8788222.2753020.3950119.1657318.7937136063012796.543777e+18GALAXY0.634794581256354171
11.237665e+18144.82610131.27418524.7775922.8318822.5844421.1681221.61427451830151191.176014e+19GALAXY0.7791361044558158427
21.237661e+18142.18879035.58244425.2630722.6638920.6097619.3485718.94827360630121205.152200e+18GALAXY0.644195457655592299
31.237663e+18338.741038-0.40282822.1368223.7765621.6116220.5045419.25010419230132141.030107e+19GALAXY0.932346914958039775
41.237680e+18345.28259321.18386619.4371817.5802816.4974715.9771115.54461810230131376.891865e+18GALAXY0.116123612156187842
\n", - "
" - ], - "text/plain": [ - " obj_ID alpha delta ... plate MJD fiber_ID\n", - "0 1.237661e+18 135.689107 32.494632 ... 5812 56354 171\n", - "1 1.237665e+18 144.826101 31.274185 ... 10445 58158 427\n", - "2 1.237661e+18 142.188790 35.582444 ... 4576 55592 299\n", - "3 1.237663e+18 338.741038 -0.402828 ... 9149 58039 775\n", - "4 1.237680e+18 345.282593 21.183866 ... 6121 56187 842\n", - "\n", - "[5 rows x 18 columns]" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "\n", - "df = pd.read_csv('star_classification.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "52e52d93-959a-4255-b9a6-44d6d1a70435", - "metadata": {}, - "source": [ - "## Exploratory Data Analysis\n" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "fc018812-b683-44bf-996d-6aa5633d58af", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100000, 18)" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "7abfea97-88db-4df8-a7bc-a7e8a270a8c9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['obj_ID', 'alpha', 'delta', 'u', 'g', 'r', 'i', 'z', 'run_ID',\n", - " 'rerun_ID', 'cam_col', 'field_ID', 'spec_obj_ID', 'class', 'redshift',\n", - " 'plate', 'MJD', 'fiber_ID'],\n", - " dtype='object')" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "0ee37ced-219b-4cf6-b218-fa07642f80a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 100000 entries, 0 to 99999\n", - "Data columns (total 18 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 obj_ID 100000 non-null float64\n", - " 1 alpha 100000 non-null float64\n", - " 2 delta 100000 non-null float64\n", - " 3 u 100000 non-null float64\n", - " 4 g 100000 non-null float64\n", - " 5 r 100000 non-null float64\n", - " 6 i 100000 non-null float64\n", - " 7 z 100000 non-null float64\n", - " 8 run_ID 100000 non-null int64 \n", - " 9 rerun_ID 100000 non-null int64 \n", - " 10 cam_col 100000 non-null int64 \n", - " 11 field_ID 100000 non-null int64 \n", - " 12 spec_obj_ID 100000 non-null float64\n", - " 13 class 100000 non-null object \n", - " 14 redshift 100000 non-null float64\n", - " 15 plate 100000 non-null int64 \n", - " 16 MJD 100000 non-null int64 \n", - " 17 fiber_ID 100000 non-null int64 \n", - "dtypes: float64(10), int64(7), object(1)\n", - "memory usage: 13.7+ MB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "d2ba20bc-b553-49cf-88c7-b2f7fd1a47d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 GALAXY\n", - "1 GALAXY\n", - "2 GALAXY\n", - "3 GALAXY\n", - "4 GALAXY\n", - "5 QSO\n", - "6 QSO\n", - "7 GALAXY\n", - "8 GALAXY\n", - "9 STAR\n", - "10 GALAXY\n", - "11 GALAXY\n", - "12 STAR\n", - "13 GALAXY\n", - "14 GALAXY\n", - "Name: class, dtype: object" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['class'].head(15)" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "id": "4e81f847-3027-4229-9dd6-1c575fdb9f9c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 1\n", - "2 1\n", - "3 1\n", - "4 1\n", - "5 0\n", - "6 0\n", - "7 1\n", - "8 1\n", - "9 0\n", - "10 1\n", - "11 1\n", - "12 0\n", - "13 1\n", - "14 1\n", - "Name: class, dtype: int64" - ] - }, - "execution_count": 181, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['class'] = (df['class'] =='GALAXY').astype(int)\n", - "df['class'].head(15)" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "57fbe2ef-e1a6-49a6-8d1f-c48b47641440", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
obj_ID1.0000000.0137350.3012370.0153100.0157100.1538910.1476700.0138111.000000NaN0.0469970.0314980.2394610.0199940.0654000.2394600.2626870.067178
alpha0.0137351.0000000.1386910.0015320.0024230.0220830.0235800.0029180.013737NaN0.0195820.1655770.0025530.0040850.0016670.0025540.0199430.030464
delta0.3012370.1386911.0000000.0020740.0035230.0068350.0044800.0036300.301238NaN0.0325650.1734160.1123290.0382970.0316380.1123290.1073330.028250
u0.0153100.0015320.0020741.0000000.9993110.0541490.0457300.9980930.015309NaN0.0035480.0083740.0299970.0231290.0143090.0299970.0319970.016305
g0.0157100.0024230.0035230.9993111.0000000.0623870.0562710.9991610.015710NaN0.0035080.0088520.0394430.0142890.0229540.0394430.0402740.017470
.........................................................
class0.0199940.0040850.0382970.0231290.0142890.0379970.1603790.0083820.019996NaN0.0205690.0396540.1097531.0000000.2569250.1097530.1105840.005923
redshift0.0654000.0016670.0316380.0143090.0229540.4332410.4923830.0303800.065400NaN0.0000970.0213310.3886420.2569251.0000000.3886410.3871090.127044
plate0.2394600.0025540.1123290.0299970.0394430.6552430.6616400.0378130.239459NaN0.0019490.0834711.0000000.1097530.3886411.0000000.9701660.241258
MJD0.2626870.0199430.1073330.0319970.0402740.6711800.6725230.0374690.262687NaN0.0067450.0950640.9701670.1105840.3871090.9701661.0000000.256970
fiber_ID0.0671780.0304640.0282500.0163050.0174700.2231060.2147870.0146680.067165NaN0.1215970.0123370.2412790.0059230.1270440.2412580.2569701.000000
\n", - "

18 rows × 18 columns

\n", - "
" - ], - "text/plain": [ - " obj_ID alpha delta ... plate MJD fiber_ID\n", - "obj_ID 1.000000 0.013735 0.301237 ... 0.239460 0.262687 0.067178\n", - "alpha 0.013735 1.000000 0.138691 ... 0.002554 0.019943 0.030464\n", - "delta 0.301237 0.138691 1.000000 ... 0.112329 0.107333 0.028250\n", - "u 0.015310 0.001532 0.002074 ... 0.029997 0.031997 0.016305\n", - "g 0.015710 0.002423 0.003523 ... 0.039443 0.040274 0.017470\n", - "... ... ... ... ... ... ... ...\n", - "class 0.019994 0.004085 0.038297 ... 0.109753 0.110584 0.005923\n", - "redshift 0.065400 0.001667 0.031638 ... 0.388641 0.387109 0.127044\n", - "plate 0.239460 0.002554 0.112329 ... 1.000000 0.970166 0.241258\n", - "MJD 0.262687 0.019943 0.107333 ... 0.970166 1.000000 0.256970\n", - "fiber_ID 0.067178 0.030464 0.028250 ... 0.241258 0.256970 1.000000\n", - "\n", - "[18 rows x 18 columns]" - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "corr_matrix = abs(df.corr())\n", - "sns.heatmap(corr_matrix, cmap='Blues')\n", - "corr_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "id": "c19d346f-3e82-4565-9331-eecd850dc2e5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeremypalmerio/opt/miniconda3/envs/VTAC/lib/python3.12/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 183, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_z = df[['class', 'redshift']]\n", - "sns.displot(data = df, x = 'redshift', hue='class')\n", - "#plt.xscale('log')\n" - ] - }, - { - "cell_type": "markdown", - "id": "dbd0e244-c279-46c4-9899-face0a77808b", - "metadata": {}, - "source": [ - "## Playing with Different Models " - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "id": "9560fafd-bd7e-4dfd-9dfd-6938cd2bd373", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.metrics import confusion_matrix\n", - "\n", - "X = df.drop(columns='class')\n", - "y = df['class']" - ] - }, - { - "cell_type": "markdown", - "id": "3934602e-4785-42e6-840b-0649106eeefd", - "metadata": {}, - "source": [ - "### Logistic Regression\n" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "b6cd41cd-7d81-4228-a8b6-6aede6102e0f", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LogisticRegression" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "id": "b3043f7a-3629-4250-be47-a39fbd83d0a8", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test,y_train, y_test = train_test_split(X, y, test_size=0.2)\n", - "\n", - "model = LogisticRegression()\n", - "\n", - "model.fit(X_train, y_train)\n", - "\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 187, - "id": "3a617608-39ac-4cec-aa47-2cf32d77c357", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training accuracy: 0.6072\n", - "Testing accuracy: 0.611\n" - ] - } - ], - "source": [ - "train_acc =model.score(X_train, y_train)\n", - "test_acc = model.score(X_test, y_test)\n", - "\n", - "print(\"Training accuracy:\", train_acc)\n", - "print(\"Testing accuracy:\", test_acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "4e9dce3e-049d-4081-85cc-c609c0baf473", - "metadata": {}, - "outputs": [], - "source": [ - "###manually making confusion table from a different threshold\n", - "def t_repredict(est, t, xtest):\n", - " probs = est.predict_proba(xtest)\n", - " p0 = probs[:,0]\n", - " p1 = probs[:,1]\n", - " ypred = (p1 > t)*1\n", - " return ypred" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "id": "db258f98-ed81-4f83-8c56-d79989a2ba9d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training CM: \n", - " [[ 2981 29544]\n", - " [ 1880 45595]]\n", - "Testing CM: \n", - " [[ 730 7300]\n", - " [ 480 11490]]\n" - ] - } - ], - "source": [ - "threshold = 0.5\n", - "print(\"Training CM: \\n\", confusion_matrix(y_train,t_repredict(model, threshold, X_train)))\n", - "print(\"Testing CM: \\n\",confusion_matrix(y_test,t_repredict(model, threshold, X_test)))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "id": "a7477257-597b-4986-8c7b-073b19700384", - "metadata": {}, - "outputs": [], - "source": [ - "#making ROC curves for this model\n", - "from sklearn.metrics import roc_curve, auc\n", - "\n", - "#name: name of your model to appear on the figure (can be arbitrary)\n", - "#clf: the model as you named it - will be used for getting the predictions\n", - "#ytest, xtest: your test data\n", - "#skip, labe: steps that control how many points you see in the ROC curve and how many labels are there\n", - "\n", - "def make_roc(name, clf, ytest, xtest, ax=None, labe=5, proba=True, skip=0):\n", - " initial=False\n", - " if not ax:\n", - " ax=plt.gca()\n", - " initial=True\n", - " if proba:#for stuff like logistic regression\n", - " fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n", - " else:#for stuff like SVM? (but double-check this pleaseee)\n", - " fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n", - " \n", - " #this is the single value for the AUC score\n", - " roc_auc = auc(fpr, tpr)\n", - " \n", - " if skip: \n", - " l=fpr.shape[0]\n", - " ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n", - " else:\n", - " ax.plot(fpr, tpr, '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n", - " \n", - " label_kwargs = {}\n", - " label_kwargs['bbox'] = dict(\n", - " boxstyle='round,pad=0.3', alpha=0.2,\n", - " )\n", - " \n", - " #add labels to the curve\n", - " if labe!=None:\n", - " for k in range(0, fpr.shape[0],labe):\n", - " #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n", - " threshold = str(np.round(thresholds[k], 2))\n", - " ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)\n", - " \n", - " if initial:\n", - " ax.plot([0, 1], [0, 1], 'k--')\n", - " ax.set_xlim([0.0, 1.0])\n", - " ax.set_ylim([0.0, 1.05])\n", - " ax.set_xlabel('False Positive Rate')\n", - " ax.set_ylabel('True Positive Rate')\n", - " ax.set_title('ROC')\n", - " ax.legend(loc=\"lower right\")\n", - " return ax" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "id": "f26dad69-409a-47c3-ac31-75a6f07229ef", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#This is how the above function should be used\n", - "\n", - "sns.set_context(\"poster\")\n", - "fig, ax = plt.subplots(figsize = (10,10))\n", - "ax=make_roc(\"logistic\",model, y_test, X_test, labe=1000, skip=0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d8ea00fa-d871-4771-a08e-06595debffa5", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Random Forest" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d8d23eb-89ab-4d59-803d-593dc8d0e032", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.datasets import make_classification\n", - "\n", - "RFmodel = RandomForestClassifier()\n", - "\n", - "param_grid = {\n", - " 'n_estimators': [10, 20, 30, 40 ],\n", - " 'max_depth': [None, 1, 2, 3],\n", - " 'min_samples_split': [2, 3, 4, 5]\n", - "}\n", - "\n", - "grid_search = GridSearchCV(RFmodel, param_grid=param_grid, cv=5)\n", - "grid_search.fit(X, y)\n", - "\n", - "# Print the best hyperparameters and corresponding score\n", - "print(\"Best parameters: \", grid_search.best_params_)\n", - "print(\"Best score: \", grid_search.best_score_)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "079c08c4-bf66-46df-b8dc-48000ece5cae", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "\n", - "\n", - "RFmodel = RandomForestClassifier(**grid_search.best_params_)\n", - "\n", - "\n", - "RFmodel.fit(X_train, y_train)\n", - "y_pred = RFmodel.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a81fe9b6-a26e-4ce1-b2e0-bc61e11890f0", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report\n", - "\n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "conf_matrix = confusion_matrix(y_test, y_pred)\n", - "report = classification_report(y_test, y_pred)\n", - "print(report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ae6908e-f049-49ef-8b3b-5f257a45d80d", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "start_time = time.time()\n", - "importances = RFmodel.feature_importances_\n", - "std = np.std([tree.feature_importances_ for tree in RFmodel.estimators_], axis=0)\n", - "elapsed_time = time.time() - start_time\n", - "\n", - "print(f\"Elapsed time to compute the importances: {elapsed_time:.3f} seconds\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c257c479-15fa-4e60-aaf1-c0264e4568c5", - "metadata": {}, - "outputs": [], - "source": [ - "feature_names = [name for name in X.columns]\n", - "forest_importances = pd.Series(importances, index=feature_names)\n", - "forest_importances = forest_importances.sort_values(ascending=False)\n", - "\n", - "fig, ax = plt.subplots(figsize = (5,6))\n", - "forest_importances.head(15).plot.barh(ax=ax)\n", - "ax.set_title(\"Feature importances using MDI\")\n", - "ax.set_ylabel(\"Mean decrease in impurity\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/log.txt b/dev/log.txt deleted file mode 100644 index 89547ba..0000000 --- a/dev/log.txt +++ /dev/null @@ -1 +0,0 @@ -log.txt \ No newline at end of file diff --git a/dev/out.txt b/dev/out.txt deleted file mode 100644 index 09d3674..0000000 --- a/dev/out.txt +++ /dev/null @@ -1,3590 +0,0 @@ - -Fitting 5 folds for each of 162 candidates, totalling 810 fits -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 48.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 46.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.1s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 46.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 47.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 46.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 47.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 48.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 48.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 47.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 50.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 49.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 51.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 50.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 51.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 50.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 51.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 46.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 50.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 49.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 50.1s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 46.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.0s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 46.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 48.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 49.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.0s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 46.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 49.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 48.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 49.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 50.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 45.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 46.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 47.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 48.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 48.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 49.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=True, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 53.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 54.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 53.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 54.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 53.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 59.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 53.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 53.2s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 59.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 53.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 54.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 51.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 52.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 59.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 58.1s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 59.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 52.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 53.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.9s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 51.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 52.4s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 59.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.6min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.0min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 56.3s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 57.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 56.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 57.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 57.7s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 58.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 55.5s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 56.6s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 56.4s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 57.5s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 54.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 55.8s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.0min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 55.2s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 56.3s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 54.8s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 55.9s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 54.6s -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 55.7s -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.9min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 1.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.1min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.3min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=True, rfc__max_depth=8, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=4, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=2, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.1min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.1min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.2min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.2min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=6, rfc__min_samples_leaf=4, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 4.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=2, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.7min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 4.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.4min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=5, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.4min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.7min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 1.8min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=100; total time= 1.8min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.6min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 2.9min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 2.9min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.6min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 3.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=200; total time= 3.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 5.3min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 5.3min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.4s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.2s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s -[Pipeline] ............... (step 4 of 4) Processing rfc, total= 4.5min -[CV] END rfc__bootstrap=False, rfc__max_depth=8, rfc__min_samples_leaf=1, rfc__min_samples_split=10, rfc__n_estimators=300; total time= 4.5min -[Pipeline] ........... (step 1 of 4) Processing cleaner, total= 0.5s -[Pipeline] ............ (step 2 of 4) Processing scaler, total= 0.3s -[Pipeline] ........ (step 3 of 4) Processing normalizer, total= 0.0s diff --git a/dev/plots/.ipynb_checkpoints/pairplot-checkpoint.png b/dev/plots/.ipynb_checkpoints/pairplot-checkpoint.png deleted file mode 100644 index 33b6397..0000000 Binary files a/dev/plots/.ipynb_checkpoints/pairplot-checkpoint.png and /dev/null differ diff --git a/dev/plots/corr/bright_case1acorrolation.png b/dev/plots/corr/bright_case1acorrolation.png deleted file mode 100644 index d660b10..0000000 Binary files a/dev/plots/corr/bright_case1acorrolation.png and /dev/null differ diff --git a/dev/plots/corr/bright_case1corrolation.png b/dev/plots/corr/bright_case1corrolation.png deleted file mode 100644 index 2c37a31..0000000 Binary files a/dev/plots/corr/bright_case1corrolation.png and /dev/null differ diff --git a/dev/plots/corr/bright_case2corrolation.png b/dev/plots/corr/bright_case2corrolation.png deleted file mode 100644 index 1c45127..0000000 Binary files a/dev/plots/corr/bright_case2corrolation.png and /dev/null differ diff --git a/dev/plots/corr/bright_case3corrolation.png b/dev/plots/corr/bright_case3corrolation.png deleted file mode 100644 index 1c45127..0000000 Binary files a/dev/plots/corr/bright_case3corrolation.png and /dev/null differ diff --git a/dev/plots/corr/bright_case4corrolation.png b/dev/plots/corr/bright_case4corrolation.png deleted file mode 100644 index 2fe8c5f..0000000 Binary files a/dev/plots/corr/bright_case4corrolation.png and /dev/null differ diff --git a/dev/plots/pairplot.png b/dev/plots/pairplot.png deleted file mode 100644 index 33b6397..0000000 Binary files a/dev/plots/pairplot.png and /dev/null differ diff --git a/dev/rf_5trees.png b/dev/rf_5trees.png deleted file mode 100644 index 1de7c2a..0000000 Binary files a/dev/rf_5trees.png and /dev/null differ diff --git a/dev/star_classification.csv b/dev/star_classification.csv deleted file mode 100644 index 7b7eed8..0000000 --- a/dev/star_classification.csv +++ /dev/null @@ -1,100001 +0,0 @@ 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-1237663788487409664,111.513340183329,42.598480668202,16.73921,15.76221,15.37943,15.21267,15.15262,4264,301,4,40,4119804735258908672,"STAR",-0.0005008617,3659,55181,498 -1237678858460201472,333.434448886385,10.1728721252561,21.32664,19.02131,17.82425,17.24615,16.60153,7773,301,2,136,5704022604651976704,"GALAXY",0.1000437,5066,55749,777 -1237662306187478272,209.201009087868,39.6715265134814,24.41582,22.82964,21.25132,20.12857,19.49338,3919,301,3,50,9962180486045652992,"GALAXY",0.6326444,8848,57875,793 -1237667211585520384,140.498498978728,22.5990080143612,22.43314,21.66414,20.27876,19.44785,19.04612,5061,301,4,180,6497805133115840512,"GALAXY",0.5396467,5771,56011,861 -1237680299428545024,359.736398242624,23.4150551182036,21.83221,21.13558,20.71169,20.26156,19.97177,8108,301,6,240,8553736312301312000,"QSO",0.6872038,7597,56973,999 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-1237661361833705728,198.493779228394,46.9015267699891,24.05576,21.68063,20.91656,20.80649,20.8103,3699,301,4,83,8344075384497920000,"STAR",-0.0002531843,7411,56776,113 -1237678858473374208,4.12981826759523,12.0527632844423,22.87833,22.27461,21.73857,21.94935,21.35565,7773,301,2,337,13021214534534453248,"QSO",0.6627138,11565,58507,662 -1237678598073221120,320.470200337719,2.51878848042579,23.61598,20.07312,18.55815,20.79692,20.08831,7712,301,5,66,10314609144032942080,"STAR",1.59147e-05,9161,57691,873 -1237663784194212352,352.695084411867,0.0823997795485288,23.36559,23.85368,22.20703,21.52391,21.27602,4263,301,4,67,10360558010204706816,"GALAXY",0.7258884,9202,57713,98 -1237659154215928064,242.496730629321,37.8075276751998,20.41779,20.29264,20.2078,19.9683,20.4048,3185,301,4,13,12084449314954565632,"QSO",1.544978,10733,58244,602 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-1237680333780353280,340.931835790468,28.6772963777851,23.19604,23.0048,20.65484,19.5733,19.25694,8116,301,6,119,7086572726971029504,"GALAXY",0.5258356,6294,56482,577 -1237663782600245504,30.7581526563276,-1.16908601254608,19.5591,18.51028,17.85034,17.46817,17.27019,4263,301,1,321,1209295439182981120,"GALAXY",0.1815001,1074,52937,287 -1237663782600704256,31.7456863870076,-1.15070834352593,21.04457,20.10737,19.481,19.08935,18.98766,4263,301,1,328,1209250634084149248,"GALAXY",0.1980699,1074,52937,124 -1237663782601097472,32.6629196129704,-1.10040192057477,21.20298,19.01739,17.75465,17.28885,16.90243,4263,301,1,334,790393041138509824,"GALAXY",0.1793306,702,52178,41 -1237663782595396608,19.7348393638173,-1.09060355708494,23.88128,24.13506,21.74703,20.17141,19.5092,4263,301,1,247,8868875035261294592,"GALAXY",0.7115706,7877,56898,587 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-1237665442061746944,218.082911424074,25.9189435559266,23.76943,22.78716,20.75975,19.58295,18.96601,4649,301,4,222,6774729782028687360,"GALAXY",0.6576617,6017,56075,691 -1237663782599393536,28.782735757199,-1.20355857888102,22.02785,21.18041,20.78345,20.72976,20.48866,4263,301,1,308,4765997619003807744,"QSO",2.450556,4233,55449,230 -1237663782601097472,32.7117229528605,-1.14774201156988,20.5417,18.81514,17.63516,17.13223,16.74529,4263,301,1,334,1208168434932672512,"GALAXY",0.1719708,1073,52649,283 -1237663782593888512,16.2111232782242,-1.14137354611502,20.99657,18.94558,17.82341,17.36242,16.98203,4263,301,1,224,445890203559684096,"GALAXY",0.1438422,396,51816,123 -1237663782589694208,6.64579587285514,-1.22680295681303,26.03585,22.50772,20.84194,19.48344,19.10729,4263,301,1,160,4751366142861531136,"GALAXY",0.6448491,4220,55447,249 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-1237659154219729408,250.084856262695,31.6086455315523,21.15788,20.84668,20.74517,20.46881,20.58642,3185,301,4,71,12295011839121840128,"QSO",2.02076,10920,58252,672 -1237664835927541248,129.885807486665,23.3675455585464,23.95721,23.9595,23.294,22.51353,21.62273,4508,301,3,116,10802103745057675264,"GALAXY",1.056659,9594,58135,800 -1237665535471452672,247.615499795652,15.8434034062339,19.46829,19.21473,19.28602,19.15874,19.21588,4671,301,2,133,2484940044054325248,"QSO",0.6303637,2207,53558,287 -1237667209982443776,159.100930938162,26.3653950142744,21.42895,20.52416,20.40507,20.37941,19.8137,5061,301,1,295,7269984183195555840,"QSO",2.580263,6457,56330,176 -1237667209983099136,160.722320716749,26.6626836505159,21.11295,20.57676,20.45014,20.56223,20.2232,5061,301,1,305,7265527038521661440,"QSO",2.463068,6453,56365,345 -1237667209986113792,168.304453860998,27.7219956310552,20.03079,20.00471,19.97858,19.74656,19.74791,5061,301,1,351,7245174253255153664,"QSO",1.834513,6435,56341,30 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-1237666301629170176,49.908205349845,0.691045140334193,23.58682,22.8382,20.92051,19.87798,19.29417,4849,301,5,788,1762180739132057600,"GALAXY",0.5240234,1565,53002,536 -1237663462608273408,353.069942786828,0.787075631284524,19.99702,18.36795,17.55502,17.09883,16.82333,4188,301,5,63,432449223749822464,"GALAXY",0.1285602,384,51821,377 -1237663462608732672,354.029013201519,0.653515525433384,22.56851,22.36273,20.40817,19.42452,19.10652,4188,301,5,70,4743569505942525952,"GALAXY",0.5063643,4213,55449,557 -1237654391105913088,125.286853948382,44.5204510546384,20.75058,20.81625,20.50318,20.38755,20.53902,2076,301,4,146,9321487900091897856,"QSO",1.3825,8279,57063,591 -1237678858475274752,8.57956764803663,12.0208998046492,22.54328,20.97296,19.32745,18.40921,17.8148,7773,301,2,366,6981867878959306752,"GALAXY",0.493555,6201,56186,591 -1237659326019993600,223.678743721785,54.0059943892848,23.67327,20.95563,19.19339,18.58738,18.26036,3225,301,4,95,7565158251161212928,"GALAXY",0.391589,6719,56390,861 -1237662301899587840,219.563714221247,46.9982995628168,20.54196,20.24887,19.95152,19.63796,19.39499,3918,301,3,158,9560213596967032832,"QSO",1.97112,8491,57488,718 -1237662301900833280,223.198211857109,45.5608833592253,22.13395,21.7585,21.27113,20.68648,20.42113,3918,301,3,177,9557835353232267264,"GALAXY",0.6264784,8489,57483,258 -1237662301901553920,225.140810440753,44.6704083472132,22.12845,21.91453,21.53,21.55039,21.42378,3918,301,3,188,9559113254263085056,"QSO",1.177506,8490,57104,811 -1237665441525137920,218.536660328239,25.4446127531249,24.80733,21.77143,20.09014,19.26006,18.84669,4649,301,3,226,6774615432819398656,"GALAXY",0.4837005,6017,56075,275 -1237665441528414464,226.382114540865,23.1256484250062,20.69464,18.48297,17.13704,16.58242,16.22126,4649,301,3,276,2422958929630750720,"GALAXY",0.1817902,2152,53874,81 -1237665441539228160,250.291991887463,13.3252366969234,18.94777,16.99748,16.10991,15.71294,15.38205,4649,301,3,441,2481493080453179392,"GALAXY",0.05139576,2204,53877,35 -1237651737375342848,197.75881639647,3.26077885728273,18.87629,17.32848,16.48254,16.07764,15.74466,1458,301,5,487,591211838805403648,"GALAXY",0.1174276,525,52295,416 -1237670965384708352,138.092544107458,14.1211200247508,22.26999,22.35811,21.58266,21.64061,22.07717,5935,301,4,146,10749048455207081984,"GALAXY",0.7399712,9547,57806,298 -1237670965385167616,139.177276794629,14.3224567356332,22.19381,22.88575,22.02776,21.15624,20.40883,5935,301,4,153,10748984683532670976,"GALAXY",0.9088994,9547,57806,66 -1237665441532346368,235.349819789102,19.9622735447854,24.5152,21.71014,20.0934,19.24047,18.99828,4649,301,3,336,4432857413910288384,"GALAXY",0.4190447,3937,55352,689 -1237665441521008640,208.368986023853,27.5404147922961,18.78319,16.78113,15.72555,15.30556,14.94575,4649,301,3,163,2384721212845615104,"GALAXY",0.132348,2118,53820,237 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-1237663462605718016,347.12470893891,0.748132915033127,24.70788,22.0901,20.5458,19.76737,19.3103,4188,301,5,24,4738028517547333632,"GALAXY",0.3333176,4208,55476,879 -1237665537618608384,247.729466615964,17.6867634097679,25.67451,24.01755,21.28139,20.06444,19.31341,4671,301,6,128,4575732902860707840,"GALAXY",0.5941142,4064,55366,275 -1237667538008866816,153.633729491601,21.7435117129424,19.08648,17.73911,17.19394,16.98487,16.8744,5137,301,4,269,3579403010654183424,"STAR",-8.192181e-05,3179,54830,608 -1237657628967699712,117.398711177659,25.9182910607637,23.10577,22.57926,21.02249,19.96296,19.33228,2830,301,3,44,5023766302919251968,"GALAXY",0.5588685,4462,55600,3 -1237680302104707328,341.072933967622,25.0214288654377,21.90497,21.56584,21.113,20.66256,21.12251,8109,301,3,115,8621171839971121152,"QSO",1.539473,7657,57309,568 -1237680302103331328,337.691807679104,24.5360121418251,21.61229,21.30327,21.14924,21.22335,20.76272,8109,301,3,94,7093217350061938688,"STAR",-0.0001057969,6300,56449,174 -1237678858472521984,2.13201376415287,11.960820137975,21.49159,20.71998,20.49632,20.40524,20.12008,7773,301,2,324,6359243301917579264,"QSO",2.56065,5648,55923,584 -1237665442066662144,229.731277395533,22.428186743668,24.09829,21.91394,20.99827,19.99868,19.58677,4649,301,4,297,4453125816726083584,"GALAXY",0.6275545,3955,55678,697 -1237657628966192384,114.850485490292,23.301857847267,24.29854,22.39092,20.92126,19.77907,19.1416,2830,301,3,21,5032869434345412608,"STAR",0.0001106811,4470,55587,352 -1237663785282437632,25.8617995406631,0.87618132334956,22.9418,22.21128,21.81069,21.53728,21.73154,4263,301,6,288,4097399987222370304,"QSO",0.6960714,3639,55205,910 -1237663785276604928,12.4880589190885,1.00991566560102,22.5972,21.91602,21.44204,20.85808,21.24322,4263,301,6,199,8861092419061700608,"QSO",1.551315,7870,57016,946 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-1237665441524351232,216.582922320727,25.7757162803463,20.68751,17.68265,17.1439,17.13243,17.80476,4649,301,3,214,2403891747949144064,"QSO",1.480877,2135,53827,347 -1237650371012395520,165.390702522413,-1.99933645159503,24.14896,22.3966,20.6665,19.5963,19.29886,1140,301,4,83,4263825921533040640,"GALAXY",0.5271025,3787,55565,156 -1237651495221854976,122.063801335108,46.8948896773527,25.79216,23.61226,22.63904,20.6513,19.96422,1402,301,2,110,8.250745537974e+18,"GALAXY",0.7879923,7328,56715,549 -1237667782837010432,190.50184198449,22.4803023616997,16.93892,15.81469,15.42584,15.31579,15.26918,5194,301,4,498,2981502600454629376,"STAR",-6.907342e-05,2648,54485,435 -1237661382231262464,135.716769744669,30.0833058087959,24.20778,22.98926,22.60811,21.89841,21.92569,3704,301,2,29,11756788800835311616,"GALAXY",0.9789821,10442,58141,516 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-1237662701327811328,247.550298334516,51.3044407548508,25.98218,21.25481,19.47643,18.77759,18.27408,4011,301,3,101,7107823812465678336,"GALAXY",0.3637112,6313,56460,64 -1237662701328007680,247.829279455584,50.9789703089108,26.05274,21.33595,19.77056,18.74616,18.32873,4011,301,3,104,7107819689297074176,"GALAXY",0.5212418,6313,56460,49 -1237680299420942848,341.025157424463,21.6640012267248,21.45039,20.74121,20.50935,20.4584,20.11127,8108,301,6,124,8542295899250120704,"QSO",2.42803,7587,57297,339 -1237680299421532160,342.364659578752,21.9540964864176,18.73099,17.67674,17.30174,17.14908,17.10711,8108,301,6,133,8567134685884602368,"STAR",-0.0003412955,7609,56959,590 -1237680299424154368,348.884691822207,22.7025183686507,24.1361,21.32963,20.68373,20.46352,20.30515,8108,301,6,173,6883914587449874432,"STAR",-0.0005169419,6114,56209,591 -1237667782836814080,190.0884908546,22.5392667536761,21.49224,21.39737,21.08684,20.7785,20.55849,5194,301,4,495,6738728747913205760,"QSO",1.47282,5985,56089,792 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-1237663785277849600,15.3419664991732,0.992855887997957,19.5587,19.25438,19.02693,19.02377,18.94979,4263,301,6,218,3503955346920153088,"QSO",0.9646628,3112,54802,563 -1237650371017572608,177.187058255557,-2.07886374712678,19.48917,17.87901,17.06806,16.66376,16.35079,1140,301,4,162,371644040921245696,"GALAXY",0.1030408,330,52370,353 -1237650371018818304,180.015451420702,-2.17402515160547,24.07417,22.67374,20.73364,19.73242,19.27372,1140,301,4,181,4251427547567642624,"GALAXY",0.5419022,3776,55209,107 -1237650372095508736,186.828236549959,-1.32803502280422,20.01841,19.45146,19.59652,19.49501,19.21138,1140,301,6,226,4331383214864357376,"QSO",2.34268,3847,55588,168 -1237658206127194368,203.048382641181,49.6928079595338,21.05265,20.32254,20.31834,20.29478,20.14788,2964,301,6,399,7592204862731081728,"QSO",2.466267,6743,56385,952 -1237652947990151424,3.13187654850351,-9.63302480971376,22.62941,22.06703,20.58231,19.62001,19.05092,1740,301,4,43,8071664229407807488,"GALAXY",0.5442542,7169,56628,319 -1237662501081120768,243.073988531492,35.9560876039018,18.17137,17.1273,16.74708,16.53895,16.39143,3964,301,6,195,1189021541375961088,"GALAXY",0.03137255,1056,52764,259 -1237662307263709952,216.444426629895,38.504988489755,26.73586,22.22511,20.70761,19.75039,19.38956,3919,301,5,88,5306566198590461952,"GALAXY",0.5166692,4713,56044,727 -1237662307260039168,206.000821451936,41.1876012090321,20.0259,18.12749,17.25697,16.87989,16.57459,3919,301,5,32,1550478846980024320,"GALAXY",0.06199552,1377,53050,417 -1237662307260628992,207.823867637423,40.8156138483601,21.49272,20.17628,18.98144,18.52569,18.10865,3919,301,5,41,1550536021584668672,"GALAXY",0.3102617,1377,53050,625 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-1237678854161892352,325.979750773827,7.25789071315876,26.71788,23.25559,21.50304,20.29582,19.78767,7772,301,2,85,4607278992835500032,"GALAXY",0.6465083,4092,55477,351 -1237660765380018432,142.288807702489,37.0179279302486,24.61688,24.09369,21.71282,20.67152,19.87728,3560,301,5,234,9977745715458496512,"GALAXY",0.5801,8862,57461,75 -1237660765375037952,130.62384449198,30.8626272006929,23.10911,23.8938,21.77528,20.61771,19.94069,3560,301,5,158,12007881802654177280,"GALAXY",0.5933899,10665,58456,579 -1237680241986306304,357.816836954364,-7.84126935080341,21.41883,20.59656,20.37371,20.26729,20.08582,8095,301,3,285,8045765506970703872,"STAR",-0.0006106031,7146,56573,308 -1237680299421467136,342.285298065712,21.9622446311114,20.37449,20.14895,19.97295,20.04562,20.08885,8108,301,6,132,8567122316378789888,"QSO",0.9474875,7609,56959,545 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-1237660961860682496,126.938716557698,27.8127560062633,22.35789,20.78005,20.48558,20.41765,20.22551,3606,301,3,19,5015948499344447488,"STAR",0.0001879562,4455,55539,234 -1237667735047045376,188.297595018983,22.4277797411816,21.42961,19.18971,17.78827,17.29557,16.93321,5183,301,3,369,6.736338963098e+18,"GALAXY",0.2130074,5983,56310,290 -1237655108362961152,131.035110323151,45.7604238507269,22.59804,22.38232,21.68148,21.70765,21.2424,2243,301,4,108,8249599022224136192,"GALAXY",0.2432715,7327,56715,474 -1237667551413862656,152.559869045621,22.2597838235051,19.81404,18.68159,18.24694,18.09616,18.07071,5140,301,5,13,3579358755311165440,"STAR",3.228067e-05,3179,54830,447 -1237667551417925888,162.473424802525,23.9749116825832,21.90869,19.24414,19.00879,18.94533,18.84127,5140,301,5,75,1.1785962967321e+19,"QSO",3.299934,10468,58131,155 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-1237678601310241024,11.9146918188131,17.2189361358384,21.81887,21.29002,19.404,18.76603,18.36079,7713,301,3,307,6985250252174678016,"GALAXY",0.3079669,6204,56220,608 -1237667211048518144,140.422454563279,22.0778999903122,22.68822,21.85145,20.5919,19.65663,19.36321,5061,301,3,178,6497800735069329408,"QSO",0.1888542,5771,56011,845 -1237665128010023680,193.097293926525,34.4522561983794,24.39352,23.42783,21.69473,20.35882,19.60406,4576,301,3,493,11540696013745707008,"GALAXY",0.7081423,10250,58472,807 -1237665126933790720,186.183105571983,33.863380234971,21.38235,20.50026,20.44475,20.30212,20.06545,4576,301,1,455,4465420828830619648,"QSO",2.343472,3966,55571,370 -1237665126922453248,155.850399196721,30.142847802806,24.65342,21.50217,20.64362,20.22646,19.74098,4576,301,1,282,7268959988265277440,"QSO",2.345887,6456,56339,546 -1237667536929947904,141.709335911865,17.9821590919915,22.33475,20.38987,18.70121,18.04852,17.7031,5137,301,2,190,2657162050723145728,"GALAXY",0.3468007,2360,53728,139 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-1237659327106253056,253.533782322786,34.3120943638718,20.00529,18.25244,17.38909,16.9818,16.69036,3225,301,6,286,1094471782204729344,"GALAXY",0.06855103,972,52435,353 -1237659327110776320,260.287047711786,25.84360313689,26.01073,22.85554,20.70281,19.65042,19.12231,3225,301,6,355,5647715514010851328,"GALAXY",0.6622276,5016,55709,733 -1237648705122337024,202.535275455734,0.557158290156819,21.89062,21.28067,21.1344,21.03292,20.82895,752,301,5,394,4560154751149561856,"QSO",2.556971,4050,55599,946 -1237678619552776704,331.386519985492,2.64590257350155,22.33463,20.39806,18.86861,18.25382,17.86536,7717,301,5,138,4859594097013577728,"GALAXY",0.3049603,4316,55505,764 -1237679582158848000,31.2715755583426,30.7911896040385,19.62828,19.53794,19.38874,19.10419,18.91457,7941,301,6,85,8711115746342885376,"QSO",2.056244,7737,57722,102 -1237679582159503616,32.9282441849173,30.2648398886499,20.69339,20.53261,20.41369,19.95858,20.16944,7941,301,6,95,8712306523777554432,"QSO",1.872421,7738,58100,338 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-1237654601562260224,147.430772926425,4.03275936866783,24.20482,22.02506,20.34161,19.37049,18.94544,2125,301,4,191,5403309471429842944,"GALAXY",0.5041602,4799,55656,421 -1237679998237344000,348.030986526764,-1.80393258224696,23.22044,22.96015,22.05923,21.2962,20.46425,8038,301,5,139,10373008055227144192,"GALAXY",0.6948956,9213,57712,335 -1237656495651094784,1.31813670569329,15.106259328118,20.20845,18.09462,16.87207,16.33302,15.89229,2566,301,4,317,845612990211319808,"GALAXY",0.1463237,751,52251,226 -1237656495652405504,4.41787907216775,15.0225709636628,22.59921,20.69192,19.02521,18.40393,18.02068,2566,301,4,337,6962730055803099136,"GALAXY",0.3013549,6184,56267,600 -1237651540331790848,230.425247759296,60.8679134851463,25.18906,22.60765,21.04616,19.96119,19.55875,1412,301,6,305,7860178662259447808,"GALAXY",0.5935153,6981,56443,987 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-1237665530635550976,207.432694658743,24.7859995534672,18.49204,17.27511,16.64206,16.24964,15.99931,4670,301,1,71,2381337735907534848,"GALAXY",0.07351929,2115,53535,216 -1237665530632733440,200.432365601748,25.6914063435853,25.94514,21.86047,20.33731,19.35526,18.83173,4670,301,1,28,6751083685314058240,"GALAXY",0.5979242,5996,56096,683 -1237657627905098240,141.314138458692,41.995388535118,22.04284,21.10262,19.67862,18.91966,18.53897,2830,301,1,214,5223098420957566976,"QSO",0.4463439,4639,55944,177 -1237668331491558144,221.189067929057,13.7147042160144,22.52425,20.51507,18.84281,18.24215,17.9017,5322,301,2,78,6162235284199331840,"GALAXY",0.3366171,5473,56033,673 -1237668349754671360,233.76547100667,11.8659864947887,18.39755,16.94573,16.29809,15.90762,15.59176,5326,301,4,223,3100749580178843648,"GALAXY",0.04346794,2754,54240,77 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-1237680306395088128,329.784200247808,22.8248270712094,22.30405,21.42617,21.17934,21.29247,21.52086,8110,301,3,45,8534437958786504704,"QSO",2.611505,7580,56955,424 -1237658203448017920,218.476264520523,43.7935979373531,24.4241,24.78186,22.21474,21.0875,19.63424,2964,301,1,478,9565720787090167808,"GALAXY",0.8701758,8496,58159,273 -1237678598097404160,15.8708821446312,3.10300364878504,21.63205,21.96285,22.07334,21.84795,21.23715,7712,301,5,435,9858492413223655424,"QSO",2.124637,8756,57687,410 -1237655501884424704,250.547514634621,39.9375083327875,22.04329,21.60796,20.79057,20.54037,20.31725,2335,301,1,33,12078864345708386304,"QSO",1.171834,10728,58248,764 -1237672024632394496,328.476963927391,44.563168131816,21.02346,19.07061,18.25914,17.94757,17.72111,6182,301,1,167,2889139498002704384,"STAR",-0.0001353426,2566,54333,292 -1237678598625100288,354.846195002127,3.59899836378236,23.1087,21.24231,21.01553,20.95498,21.07252,7712,301,6,295,4820073806373869568,"QSO",2.90549,4281,55836,350 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-1237678598633423104,13.867391591985,3.67019384145627,21.06157,20.79873,20.75358,20.62082,20.52836,7712,301,6,422,9855097390926944256,"QSO",1.91824,8753,57373,347 -1237678621697311232,15.6097163863579,4.07536961674498,22.45352,23.04309,22.21659,20.60607,19.94843,7718,301,1,93,9855005031950211072,"GALAXY",0.5261329,8753,57373,11 -1237662335715836416,234.030142598136,34.7428481668813,24.27094,23.12413,21.53668,21.2735,21.01567,3926,301,2,57,5600328767396665344,"QSO",4.150959,4974,56038,373 -1237678597540807680,330.811624544945,2.30844715651326,22.16833,22.11172,21.78726,21.84303,21.18559,7712,301,4,134,10320205664258119680,"QSO",0.9088884,9166,58051,753 -1237659132207759872,213.643920519894,51.2760334065062,24.4207,22.06115,20.21202,19.32441,18.85966,3180,301,3,67,7591019039943841792,"GALAXY",0.4910066,6742,56415,734 -1237661137956438272,144.50499224247,34.6789461496706,19.03877,18.16185,17.9337,17.71903,17.60598,3647,301,3,51,1794746898989475840,"GALAXY",0.07952199,1594,52992,227 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-1237665535469486080,243.278943652072,17.8246850650833,18.33023,17.20601,16.79073,16.62834,16.58803,4671,301,2,103,2451199622371108864,"STAR",-0.0001341305,2177,54557,420 -1237665535470075904,244.600339098381,17.1588002157791,18.72992,16.92716,16.2974,16.0811,15.98997,4671,301,2,112,2451245526981568512,"STAR",-3.057908e-05,2177,54557,587 -1237661360768024832,222.407500481556,40.2165431019292,19.97472,19.06704,18.85448,18.74485,18.73361,3699,301,2,206,3812446701231382528,"STAR",0.0006247531,3386,54952,544 -1237661360768811264,224.375378503605,39.38170840042,21.23142,20.49998,20.41175,20.49517,20.36353,3699,301,2,218,9563486836640471040,"QSO",2.612872,8494,57104,338 -1237661360770188032,228.024541479651,37.7743644085402,23.14207,22.16012,21.98789,21.95615,21.64688,3699,301,2,239,12095678901717850112,"QSO",2.220911,10743,58222,495 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-1237679319627137792,2.1514648705662,10.7761816480912,25.79776,23.26037,21.62145,20.50523,20.35691,7880,301,5,57,6359240278260602880,"GALAXY",0.5772329,5648,55923,573 -1237679434526621952,18.6105040606088,-2.77813429088397,23.65453,23.04338,22.70753,22.17653,21.43625,7907,301,3,196,10506904663152154624,"GALAXY",0.8933983,9332,58072,24 -1237664869208424960,129.027621814077,33.915184243389,23.2684,22.55788,20.63308,19.66916,19.27986,4516,301,1,38,5000231805141669888,"GALAXY",0.522715,4441,55532,401 -1237664869210915328,135.824577346743,33.2317938991125,24.65595,21.56331,19.91724,19.12618,18.72039,4516,301,1,76,6543770222469601280,"GALAXY",0.4218483,5812,56354,145 -1237662224601711104,177.173783977501,39.4451139850413,22.27382,22.1854,21.65349,21.70539,21.4773,3900,301,3,334,9955432500741560320,"QSO",2.285382,8842,57424,820 -1237662224601972992,177.850489478128,39.4240612088376,21.96444,21.26622,21.00686,21.14967,20.53936,3900,301,3,338,5288471254411794432,"QSO",2.417627,4697,55660,434 -1237662224603480064,182.393252927864,39.6256328362494,18.57349,17.70845,17.4385,17.31944,17.28642,3900,301,3,361,9953143317549307904,"STAR",-0.0003743141,8840,57425,684 -1237650796212388352,128.800922829156,0.186694865285923,21.75019,21.0025,21.26728,21.28592,20.65759,1239,301,4,56,5396567540872009728,"GALAXY",0.03787447,4793,55648,470 -1237666300018163968,49.0493571058121,-0.600981788014859,21.31956,19.5142,18.57213,18.19104,17.87796,4849,301,2,782,904098213840054272,"GALAXY",0.1810665,803,52318,2 -1237666300019147008,51.2846895487579,-0.544926225873603,19.64262,17.95175,17.06969,16.60949,16.23162,4849,301,2,797,466185539830179840,"GALAXY",0.03755179,414,51869,229 -1237666300020130048,53.5982435759274,-0.465722460338184,20.57404,19.19945,18.1555,17.65068,17.21032,4849,301,2,812,907514675942418432,"GALAXY",0.2603219,806,52592,143 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-1237657628989981440,176.313762762428,51.9368666300373,22.36132,20.6887,18.98891,18.27178,17.78341,2830,301,3,384,990823843570411520,"GALAXY",0.4307207,880,52367,116 -1237678598075843072,326.513757205322,2.746902595801,21.93347,22.27607,21.72007,21.43203,22.26733,7712,301,5,106,12749750609315649536,"QSO",3.996939,11324,58395,218 -1237651539800163072,244.841424065652,51.7322128075297,23.22536,22.27212,20.78471,19.84248,19.46622,1412,301,5,385,7111442580496472064,"GALAXY",0.5189393,6316,56483,941 -1237651539776438784,137.596206001259,59.643119127783,22.50833,22.07313,20.86448,19.94538,19.59666,1412,301,5,23,6426909183078324224,"GALAXY",0.6910703,5708,56605,991 -1237651539777945856,143.031489567527,61.927156600028,19.15149,19.11183,18.71611,18.54552,17.89108,1412,301,5,46,547281120136292352,"QSO",0.2922775,486,51910,341 -1237673706650862336,111.596206075896,41.4189206964787,24.67739,23.08862,21.55337,20.40165,19.71258,6573,301,6,196,11997698670797805568,"STAR",0.000177066,10656,58163,397 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-1237678858472325376,1.70604056803029,12.0519154484261,21.11714,21.45862,20.80847,20.61367,20.90043,7773,301,2,321,13016733199648446464,"QSO",1.40651,11561,58485,743 -1237659161195512576,226.72519559617,48.2466902991287,23.94696,22.07644,21.51188,20.45214,19.64292,3187,301,1,17,9492594456391866368,"GALAXY",0.8741506,8431,57482,481 -1237662337862010368,232.190914870784,37.7246543662294,26.54461,23.43497,21.93997,20.73046,19.86745,3926,301,6,37,12093364429875599360,"QSO",0.7756236,10741,58230,267 -1237651539793412608,224.297496869005,62.6658187703976,22.53437,21.22311,19.34265,18.73538,18.3012,1412,301,5,282,7862169603006552064,"GALAXY",0.3831736,6983,56447,38 -1237667783901577216,167.813965399307,22.5162650533159,23.93754,21.34784,21.18946,21.13959,21.30153,5194,301,6,358,7238563438469076992,"STAR",-0.0001383479,6429,56274,556 -1237652934030656000,311.630837057954,-6.65945033880841,20.58014,18.67975,17.63789,17.22885,16.90657,1737,301,2,30,715011348018259968,"GALAXY",0.1556319,635,52145,236 -1237678858480910336,21.7516408172701,11.3584416452227,24.04203,21.47874,20.09603,19.03499,18.52523,7773,301,2,452,12468217085171095552,"STAR",2.755553e-05,11074,58488,5 -1237680243061490176,1.07092161911862,-6.99302828472132,22.48121,20.84691,19.06483,18.42281,17.9304,8095,301,5,307,8048124509470087168,"GALAXY",0.3854028,7148,56591,698 -1237680243062801152,4.05864259074534,-7.01709104651059,24.04575,23.05486,21.24736,19.95569,19.38444,8095,301,5,327,8049324901323266048,"QSO",1.891646,7149,56593,969 -1237657627892384256,115.307970299541,22.7251372713448,18.82606,17.13158,16.3147,15.92494,15.65014,2830,301,1,20,1043773578308249600,"GALAXY",0.0764102,927,52577,234 -1237657627912896768,164.67544033092,49.58493880539,19.7004,18.13146,17.30578,16.85578,16.64528,2830,301,1,333,986291111951624192,"GALAXY",0.177366,876,52669,10 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-1237678894426817536,22.7756758237949,34.1776445232332,24.14883,24.27513,21.68585,20.51377,19.84403,7781,301,5,79,8702257812720605184,"GALAXY",0.6028698,7729,58135,645 -1237678894428717824,27.8025521085452,33.297511833979,25.78146,21.06277,19.34536,18.58256,18.21944,7781,301,5,108,7435679486107211776,"GALAXY",0.4254224,6604,56337,860 -1237678618497057024,12.4627092709363,2.23642255796553,21.10615,21.13027,20.95117,20.56122,20.77492,7717,301,3,413,8846383156900943872,"QSO",1.882468,7857,57278,682 -1237667227686273536,33.6522463289023,5.80370234966025,26.11632,22.48985,20.86491,19.64845,19.15836,5065,301,2,98,4954053965656643584,"GALAXY",0.6056138,4400,55509,343 -1237661361839342336,216.206752762638,43.2480071988859,25.59885,21.52053,20.55411,20.41257,20.44251,3699,301,4,169,9571306570299758592,"QSO",3.680971,8501,57519,114 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-1237679318017704448,4.88543319905517,9.39985788529422,22.85675,22.36868,22.36792,22.13307,21.59567,7880,301,2,75,12736343714919897088,"STAR",-0.0001754568,11312,58433,596 -1237662337867318016,244.019694155353,30.666963898093,25.51195,21.96809,21.64926,21.65152,21.26347,3926,301,6,118,5580042497936349184,"STAR",-0.0005077625,4956,55737,300 -1237663789030966016,121.774623895488,56.3925860935195,25.42436,21.35229,19.57279,18.78127,18.41007,4264,301,5,142,5797387088040318976,"GALAXY",0.4174452,5149,55944,467 -1237663457778861312,322.657271644672,0.0261145082853398,22.04832,21.58966,20.25917,19.36722,18.84721,4187,301,4,100,4722216990147893248,"GALAXY",0.5655168,4194,55450,701 -1237663232838730240,357.168108143626,35.0915607414561,20.97586,20.46862,19.98824,19.69738,19.63071,4135,301,1,234,8736081818510710784,"QSO",1.450979,7759,58401,816 -1237660962937307904,132.4541689573,32.2927575431992,23.75072,22.36562,20.55705,19.4439,18.80354,3606,301,5,63,5838040981291948032,"GALAXY",0.6154441,5185,55978,909 -1237658204492267776,128.688410842328,33.4505174306395,21.01048,18.64754,17.34011,16.83092,16.44242,2964,301,3,28,1050534751275870208,"GALAXY",0.196097,933,52642,255 -1237659324408529152,219.63172042799,53.7699310675918,21.84799,19.97292,18.51842,17.89842,17.52184,3225,301,1,82,7556043574611302400,"GALAXY",0.2786251,6711,56388,470 -1237664852043563776,238.847297743165,23.2057427984114,24.61826,21.57999,21.12365,21.03225,21.46655,4512,301,1,267,4437220550407116800,"STAR",-0.001284164,3941,55321,178 -1237664852026786048,196.707284362281,36.0053553822507,22.66134,21.85299,21.64023,21.54913,21.12175,4512,301,1,11,9986790028565829632,"QSO",0.7599329,8870,57779,210 -1237678617972769536,41.3129450826551,1.23284233819666,24.16671,23.30081,22.79452,22.58466,22.12848,7717,301,2,605,8260942408323454976,"QSO",0.7035394,7337,56686,781 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-1237660962940453632,139.782222073915,36.084446015423,23.7818,23.32023,21.03031,19.85728,19.56496,3606,301,5,111,5228901093204056064,"QSO",0.5879495,4644,55922,807 -1237667142861980416,128.547670050778,19.6791693714261,24.31602,22.32102,20.41183,19.40941,18.98444,5045,301,4,118,5048630658282575872,"GALAXY",0.4791623,4484,55565,347 -1237679322860945920,34.0999620586115,-4.58972235512651,23.70726,22.74196,22.14711,21.12098,20.82261,7881,301,3,249,10495654454640269312,"GALAXY",0.8478692,9322,57742,56 -1237665026515796480,185.268841548632,36.0639655444408,23.1096,21.89879,21.37389,20.79707,20.35055,4552,301,6,102,5194051241974716416,"QSO",0.7474824,4613,55591,1000 -1237658204514812416,196.524013053882,49.3008598787375,24.93,24.07074,22.07511,20.96726,19.94773,2964,301,3,372,8343132009024215040,"GALAXY",0.7486923,7410,57104,777 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-1237657629528162560,180.944040856435,52.5290547319688,20.79115,19.13829,17.77582,17.26336,16.96544,2830,301,4,404,993195490201856000,"GALAXY",0.2256997,882,52370,552 -1237662305120419840,226.471854966108,33.1753190300518,17.83992,16.90575,16.64804,16.54148,16.52026,3919,301,1,152,3276490852381255680,"STAR",6.495871e-05,2910,54630,444 -1237680507184611584,346.514212167427,32.4869077231251,21.21568,21.3134,20.84513,20.7323,20.6004,8157,301,1,42,8741659634975397888,"QSO",1.19205,7764,58042,628 -1237657611268260096,188.597476105894,56.9894576548382,22.33951,21.52137,21.1409,20.83844,20.99012,2826,301,2,308,9268655559728715776,"QSO",1.272749,8232,58135,900 -1237678894429570048,30.1059336709881,32.8440161132269,24.90023,21.79436,20.15442,19.27482,18.84076,7781,301,5,121,7436752888159033344,"GALAXY",0.4804432,6605,56565,669 -1237667292657090816,149.787651787781,22.6758059872895,19.98444,18.59747,17.80238,17.41968,17.18585,5080,301,3,242,2587360379818502144,"GALAXY",0.147718,2298,53737,154 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-1237661355928322560,167.127059862988,44.8924884324657,23.34346,21.81198,20.35585,19.53611,19.07404,3698,301,1,86,7486159989536741376,"GALAXY",0.3875938,6649,56364,187 -1237662301916497152,256.47722905357,22.2358018999081,24.26843,21.64306,21.22875,21.30107,21.43617,3918,301,3,416,4699685526500038656,"QSO",1.827094,4174,55659,652 -1237648703518475264,217.967283068231,-0.689023034014074,23.29546,23.00945,21.08615,19.87409,19.36985,752,301,2,497,4531868985713776640,"GALAXY",0.6147627,4025,55350,443 -1237667781743345920,142.552807365915,16.1415460897868,23.27371,22.11226,20.55186,19.54715,19.11471,5194,301,2,194,5980872489928251392,"GALAXY",0.4927213,5312,56001,335 -1237680508258550528,346.925827005717,33.3139245325525,26.58755,20.91068,19.99245,19.55455,19.32729,8157,301,3,45,8039051339148580864,"STAR",-0.0001701695,7140,56569,458 -1237680276871905792,3.59176232533235,27.5842770342494,26.31214,22.58419,20.7367,19.63066,19.29264,8103,301,4,117,7067390093850597376,"GALAXY",0.4979263,6277,56273,423 -1237673703969915136,122.386782061925,41.1785393546549,18.42999,16.94332,16.19846,15.78625,15.43012,6573,301,1,248,613772716335458304,"GALAXY",0.06446669,545,52202,572 -1237673703966769152,113.111382760318,39.6478005213522,20.95011,18.93105,17.74087,17.25928,16.90934,6573,301,1,200,1951351440567986176,"GALAXY",0.1779467,1733,53047,607 -1237655126093332736,208.172893870141,5.1378045182008,20.68998,18.73734,17.83526,17.43831,17.14685,2247,301,5,316,962781899149830144,"GALAXY",0.0770959,855,52375,500 -1237655126076030976,168.527172160614,5.26777864946163,19.18318,17.18336,16.16402,15.73202,15.35937,2247,301,5,52,940256753365313536,"GALAXY",0.09648739,835,52326,474 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-1237660958106976512,121.728192054672,24.6063622752915,19.4073,19.30128,19.09954,19.18722,19.4779,3605,301,4,86,12520221511632836608,"QSO",1.120984,11120,58510,780 -1237680267203117824,330.646779717724,15.8953822643485,22.48888,21.90238,20.28934,19.25369,19.3364,8101,301,2,39,5698189696422926336,"GALAXY",0.5269495,5061,55806,37 -1237661137958142208,148.924811177347,36.42418905365,22.0829,21.84365,21.3097,21.55503,21.06417,3647,301,3,77,9966487540822464512,"GALAXY",0.2676248,8852,57449,78 -1237661137958338816,149.372579815456,36.5153969050077,21.89369,21.56754,21.51071,21.37925,20.74151,3647,301,3,80,5149007823930415104,"QSO",2.071546,4573,55587,973 -1237659324418818816,246.084791287811,38.5864410588051,24.6623,21.07442,19.21682,18.29558,17.86148,3225,301,1,239,1319624281218377728,"GALAXY",0.4983014,1172,52759,253 -1237661355932254976,179.903492177648,46.2496850883361,25.96054,23.60272,21.94213,20.64583,19.69697,3698,301,1,146,8336166047603513344,"QSO",6.572865,7404,56776,11 -1237673704507835136,125.566917922262,42.0323588228486,22.20325,22.01333,21.66407,21.59577,21.53001,6573,301,2,264,9337309327659390976,"QSO",1.790321,8293,57361,805 -1237659324954116352,243.337163247544,41.624538318353,19.4498,17.58491,16.62712,16.23512,15.90814,3225,301,2,215,1318534115338774528,"GALAXY",0.1038613,1171,52753,383 -1237661966895808512,192.756618143229,41.818293792318,18.66578,18.47606,18.36512,18.50361,18.46567,3840,301,3,214,9441988334815827968,"QSO",0.8986349,8386,57518,697 -1237651714263810816,252.32656294835,41.9826490268983,23.47031,21.4883,19.70581,18.91504,18.34053,1453,301,2,89,6797246681174005760,"GALAXY",0.4552952,6037,56106,687 -1237648721753538816,147.905370928718,0.412108660205203,19.64714,18.28406,17.62482,17.22549,16.915,756,301,4,214,300746143988475904,"GALAXY",0.0935129,267,51608,476 -1237661070869201664,174.066573175906,15.7359260759336,23.31001,22.1213,20.90443,19.8099,19.55787,3631,301,6,381,6049730505280346112,"GALAXY",0.6698323,5373,56010,983 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-1237660962940715008,140.548780926534,36.291705221338,19.62715,19.52819,19.60576,19.47713,19.4179,3606,301,5,115,9977825430051510272,"QSO",2.060963,8862,57461,365 -1237679322327154944,41.1245855954341,-4.74865890069138,20.69975,20.68847,20.39932,20.56982,20.29451,7881,301,2,296,8871097155995326464,"QSO",1.322665,7879,57359,479 -1237662224611082752,204.660075375622,38.0829986357162,24.56671,22.11635,20.5951,19.54774,19.01934,3900,301,3,477,9993749112603039744,"STAR",7.546635e-05,8876,57783,951 -1237662224611868928,206.863606963629,37.5473792649105,20.91714,20.40835,20.09806,19.97013,20.08225,3900,301,3,489,4337133105133672448,"STAR",8.134374e-05,3852,55243,606 -1237680246810543360,350.699579584536,19.81241226883,26.9248,23.45986,21.90181,20.36104,19.68445,8096,301,4,169,8561501062980458496,"QSO",1.092434,7604,56947,575 -1237670962165448960,42.8589107764221,35.5840523023098,21.02447,19.20675,18.57305,18.34546,18.23074,5934,301,6,176,8167438564084439040,"STAR",5.550694e-05,7254,56625,584 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-1237656242237473792,340.368179270052,13.8077328779631,22.4902,22.30401,20.70362,19.65628,19.10594,2507,301,4,156,5679251439643547648,"GALAXY",0.5954452,5044,56186,772 -1237653664716029952,136.428618988523,1.50083289243828,18.09176,17.09563,16.7983,16.69001,16.62389,1907,301,3,92,14123425470419851264,"STAR",0.001314285,12544,58928,498 -1237666210343420928,320.967459490613,-2.78006187788695,26.6236,21.02255,19.29132,18.49194,18.13993,4828,301,3,518,4934854574731450368,"GALAXY",0.3578669,4383,55881,128 -1237661362370838784,199.588797104495,47.1188879862282,21.38903,21.05516,20.85499,20.66212,20.23112,3699,301,5,87,7458231020154017792,"QSO",1.275045,6624,56385,982 -1237658191072199680,122.734926778769,28.1083100006077,22.44363,22.57355,20.50052,19.36975,18.76419,2961,301,2,54,5018293213242611712,"GALAXY",0.5964086,4457,55858,572 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-1237653664722518272,151.194244544579,1.99475585157746,21.16154,20.09031,19.98397,19.83809,19.52822,1907,301,3,191,3551248091128635392,"QSO",2.429485,3154,54821,581 -1237662337331954432,246.669720706869,27.9921264105773,22.32212,21.97795,20.40549,19.46736,18.80893,3926,301,5,141,5636504343647901696,"GALAXY",0.5074535,5006,55706,907 -1237664093975806208,136.053864757147,29.3570591967314,19.36247,19.01902,19.04058,18.95912,18.94109,4335,301,5,175,11756772308160894976,"QSO",0.6552244,10442,58141,456 -1237664093976461568,137.506099370051,30.1148988252265,20.35592,20.13697,19.97296,19.89428,20.02916,4335,301,5,185,12807235826514941952,"QSO",1.475609,11375,58426,452 -1237664673787544064,189.477782694442,37.9567297918709,21.80947,21.80998,21.58332,21.56715,20.90862,4470,301,5,40,9983594292667766784,"QSO",1.396665,8867,57460,872 -1237668296597045760,193.545724129104,18.6565801479992,26.34715,21.34058,19.36691,18.54705,18.17758,5314,301,1,110,2944265715001092096,"GALAXY",0.4186775,2615,54483,136 -1237668296604254464,210.736214114511,17.0460945332108,23.64226,21.77448,20.19467,19.42063,18.86098,5314,301,1,220,6135071024751073280,"GALAXY",0.435251,5449,56030,154 -1237668296596914176,193.30017994981,18.6541171029729,19.81203,18.04368,16.90997,16.37882,15.89394,5314,301,1,108,2944284406698764288,"GALAXY",0.1146177,2615,54483,204 -1237668296605565440,213.799603268514,16.4209313482788,25.5049,22.37866,20.50258,19.71224,19.30008,5314,301,1,240,6140670837219678208,"GALAXY",0.4350679,5454,56015,46 -1237651539798851584,241.598168138572,54.0988235265208,17.93959,17.69229,17.62518,17.69847,17.66697,1412,301,5,365,8516477158935517184,"QSO",0.8792845,7564,56804,619 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-1237663239272464640,42.3780083290417,0.427599458377349,25.44279,21.58155,20.09847,19.14135,18.63747,4136,301,5,101,1203738776472414208,"STAR",-0.0001132893,1069,52590,552 -1237663239272923392,43.526198384626,0.623980771445368,24.55017,22.1224,20.44892,18.97875,18.17444,4136,301,5,108,1202576867962415104,"STAR",8.468692e-05,1068,52614,421 -1237678579295453952,9.04399867094264,24.2156284516094,26.71766,22.24214,20.56991,19.58838,19.16456,7708,301,2,260,7074271386802608128,"GALAXY",0.5125418,6283,56239,881 -1237679433450258688,12.5983332768308,-3.65300305131608,22.8426,22.25071,21.85383,21.18218,20.57391,7907,301,1,156,10513832960326916096,"QSO",0.6601806,9338,58044,653 -1237651539776110592,136.445921122189,59.1066249498102,19.02551,18.8514,18.67504,18.66854,18.67316,1412,301,5,18,545026846371112960,"QSO",1.143496,484,51907,332 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-1237664093976723968,138.215656273044,30.3964022217673,21.24445,20.33031,19.76097,19.51701,19.48026,4335,301,5,189,2703393491685238784,"STAR",0.0002537763,2401,53768,392 -1237664093977510144,139.956447369678,31.2995079586653,22.40736,21.90103,21.69967,21.18896,20.90584,4335,301,5,201,12808387839890053120,"QSO",2.120744,11376,58430,547 -1237666338113912832,13.6090259901097,-0.928245743136728,18.04445,16.29841,15.54024,15.18968,14.91471,4858,301,1,445,443608168803624960,"GALAXY",0.04225965,394,51913,13 -1237666338116403456,19.3185484783124,-0.910598395972256,19.17546,17.24506,16.21007,15.77306,15.40742,4858,301,1,483,446986141405571072,"GALAXY",0.1042563,397,51794,14 -1237661362370576640,198.615247331227,47.1720646285122,20.83541,20.57544,20.34274,20.22591,20.06219,3699,301,5,83,8340821105024718848,"QSO",0.742425,7408,56780,562 -1237661362373984512,209.453793800605,45.2635429484731,21.18409,20.98904,20.70135,20.53525,20.01994,3699,301,5,135,8363198640567900160,"QSO",1.41426,7428,56781,51 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-1237664877808910336,159.053883818065,33.5913114943219,19.21821,17.24138,16.1382,15.67163,15.30309,4518,301,1,199,2231667263671396352,"GALAXY",0.1439455,1982,53436,486 -1237678847182767360,325.414721183416,8.34415726260673,23.62815,21.3363,19.51832,18.86022,18.5498,7770,301,5,88,4608336448109959168,"GALAXY",0.3864766,4093,55475,102 -1237663783128662272,11.4314158592508,-0.745963136607961,21.89893,19.46076,18.15051,17.51659,17.14398,4263,301,2,192,1272350227516385280,"STAR",7.302788e-05,1130,52669,303 -1237663783128728064,11.5355518740928,-0.817029265100441,22.57022,22.03901,22.21469,21.64931,21.48099,4263,301,2,193,8860901378916374528,"QSO",1.646339,7870,57016,251 -1237663783128989952,12.1704598580529,-0.752286820286066,23.6397,20.89997,19.36729,18.17269,17.50315,4263,301,2,197,1272343905324525568,"STAR",0.0001458543,1130,52669,280 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-1237659324406824960,213.718436741265,55.6933069118453,23.84124,21.92649,21.51384,21.29002,21.20261,3225,301,1,56,9487995480237692928,"GALAXY",0.1420675,8427,57846,134 -1237663786882105856,121.420218397434,52.7212462296507,21.19891,20.9246,20.80937,20.98982,21.23486,4264,301,1,121,8219153545116801024,"STAR",0.0024165,7300,56707,306 -1237668495773598976,156.299167524238,15.6302832252286,26.69464,22.52109,21.75553,20.80362,20.88076,5360,301,4,71,10781656122769299456,"GALAXY",0.937452,9576,57864,140 -1237658203430453504,160.453170070761,46.3200170468262,22.45901,21.41915,20.50536,19.75213,19.17124,2964,301,1,210,7495327167766878208,"GALAXY",0.6086046,6657,56366,769 -1237679998229218304,329.392560675106,-1.4653805578924,23.52723,24.28302,22.55306,21.76475,22.26663,8038,301,5,15,10394403732322539520,"GALAXY",0.8528416,9232,58037,348 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-1237665128552595968,208.668902077699,32.74814690343,23.02657,21.7981,20.13319,19.253,18.81536,4576,301,4,580,4347298915286276096,"GALAXY",0.5198869,3861,55274,725 -1237679322322764032,31.0993637629829,-5.22321638423199,25.30395,21.91261,20.84366,20.65833,20.75418,7881,301,2,229,8149453852976109568,"QSO",3.767797,7238,56660,692 -1237679322324992256,36.3034438251421,-5.02747692460881,19.36567,17.60376,16.84895,16.48062,16.26309,7881,301,2,263,7170904164863858688,"STAR",7.367197e-05,6369,56217,173 -1237661362372214784,204.095141408463,46.4469458469727,18.20564,18.15002,17.94956,17.9591,18.08054,3699,301,5,108,8339796360053413888,"QSO",1.216527,7407,56772,930 -1237658191072133888,122.599387741675,27.9428680216769,23.6869,22.29365,20.48922,19.66975,19.24689,2961,301,2,53,5011413838480037888,"GALAXY",0.4750946,4451,55537,121 -1237679318017049344,3.44768970545857,9.40130951630338,22.55411,21.91797,20.59729,19.76328,19.24215,7880,301,2,65,5107283561209419776,"GALAXY",0.5112165,4536,55857,733 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-1237662337324089600,229.313376547721,38.6072186469177,19.96362,18.75971,17.85271,17.3818,17.07141,3926,301,5,21,1575298674756446208,"GALAXY",0.2034235,1399,53172,599 -1237678832147431936,16.2084959058587,8.84227798668821,24.73615,21.15818,19.40505,18.67155,18.28963,7767,301,1,43,5119481268934039552,"GALAXY",0.4063682,4547,55893,52 -1237680276868301568,354.351795343284,27.2190737658038,23.21349,22.58831,20.73516,19.70179,18.88759,8103,301,4,62,7334156907106818048,"GALAXY",0.5507055,6514,56487,163 -1237680276873937152,8.75308777681977,27.5444052227143,26.23782,23.37523,20.98882,19.80549,19.16526,8103,301,4,148,7073127350540720128,"GALAXY",0.6038434,6282,56572,815 -1237660962939732480,138.143532826479,35.3022321011433,20.08649,20.22794,20.56865,20.93358,20.94498,3606,301,5,100,1433372061572556800,"QSO",1.890004,1273,52993,369 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-1237669766546850048,7.94878022956948,7.25738557308753,21.39794,20.61389,20.42221,20.46459,20.42064,5656,301,3,68,5112847365343303680,"QSO",2.579703,4541,55882,494 -1237669766548619520,12.0606888053161,7.04234146880621,20.57521,20.15462,20.14684,19.96829,20.12332,5656,301,3,95,5115004057501849600,"QSO",0.6269339,4543,55888,148 -1237669766549930240,15.0426808861958,7.09716348307752,20.71721,20.59999,20.11214,20.03552,19.91449,5656,301,3,115,12455888810004469760,"QSO",1.216893,11063,58426,211 -1237669766549930240,15.0619784583772,7.05273974300387,20.63364,20.53395,20.53526,20.23748,20.06049,5656,301,3,115,12455890734149818368,"QSO",1.684247,11063,58426,218 -1237679580548956672,32.8407238526041,29.0814594482601,25.87646,20.14645,18.48984,17.95332,17.43393,7941,301,3,96,7061870821229484032,"GALAXY",0.2634511,6272,56325,824 -1237669766551699712,19.0999731305707,6.89367997316643,22.30551,22.04935,21.82038,21.874,21.65448,5656,301,3,142,9859769233533327360,"QSO",2.656629,8757,58428,959 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-1237662337326055936,233.995616078589,36.1449690814927,24.81699,22.97002,21.55603,20.22045,19.48166,3926,301,5,51,12094628867710670848,"GALAXY",0.6411632,10742,58198,771 -1237669766547243008,8.80311430346075,7.21012439675781,17.90728,16.53038,15.9937,15.81711,15.74321,5656,301,3,74,2603133973160814592,"STAR",-7.079637e-05,2312,53709,194 -1237669766547898368,10.3129333484453,7.0541512025064,20.03032,18.58031,18.00937,17.79724,17.70066,5656,301,3,84,2619974917902329856,"STAR",-0.0003649071,2327,53710,21 -1237669766548553984,11.8773265345066,7.1666924834795,22.28147,21.83503,21.35158,21.05483,20.7011,5656,301,3,94,12445889026594199552,"QSO",1.43959,11054,58424,696 -1237669766549274880,13.5747395444602,7.03814345962816,21.98953,21.19633,20.87078,20.86233,20.4849,5656,301,3,105,12455977595568412672,"QSO",2.405871,11063,58426,534 -1237679580546531584,26.5873984963921,30.2588208194852,20.46088,18.35498,17.26286,16.65561,16.20997,7941,301,3,59,7432100025969432576,"GALAXY",0.1237627,6601,56335,126 -1237651754020439040,203.175134656455,2.47563754966282,22.64208,22.77587,20.54253,19.5226,19.18166,1462,301,4,519,4554508484448901120,"GALAXY",0.5922276,4045,55622,885 -1237658204499804672,146.893855532443,43.0545534849769,23.47474,21.57788,20.83723,20.48675,20.18224,2964,301,3,143,9329326868225349632,"STAR",0.0008483298,8286,57062,437 -1237680276870201856,359.113681620657,27.5308151842923,21.07054,21.10459,20.78647,20.66527,20.7541,8103,301,4,91,8666322739655890944,"QSO",1.42975,7697,57574,986 -1237662529530626304,217.302146774043,12.4262237752729,19.5846,17.71704,16.65748,16.19997,15.84701,3971,301,3,124,1923053867210139648,"GALAXY",0.1315569,1708,53503,61 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-1237651539778207744,144.142625539596,62.0922322815605,18.04578,16.34898,15.73075,15.50719,15.4382,1412,301,5,50,2683127843654756352,"STAR",-0.0001470703,2383,53800,394 -1237664877809041920,159.424332919733,33.703113416072,25.84696,21.38888,19.55102,18.89611,18.49865,4518,301,1,201,7258867571419797504,"GALAXY",0.3436516,6447,56362,694 -1237664877803143424,144.387017371652,29.2019481423133,20.66886,18.72237,17.80417,17.40861,17.06093,4518,301,1,111,2188851730274347008,"GALAXY",0.1278577,1944,53385,372 -1237664877809369344,160.266954205446,33.9222789149791,18.96811,17.03428,16.13635,15.69195,15.33258,4518,301,1,206,2280019936686401536,"GALAXY",0.08349957,2025,53431,264 -1237678859545870592,1.13033563212502,12.6829597371174,21.72005,21.60507,20.10479,19.21271,19.07267,7773,301,4,318,6953742372829419520,"GALAXY",0.4793732,6176,56264,671 -1237678859552031232,15.6742196320941,12.5421975739568,22.5202,23.01512,21.17656,20.08007,19.81186,7773,301,4,412,5246765138011510784,"GALAXY",0.5527447,4660,56191,260 -1237660765912171008,130.97117879388,31.4742562374957,23.26034,21.96214,20.33126,19.41895,19.01428,3560,301,6,162,5837967863768700928,"GALAXY",0.5015212,5185,55978,643 -1237678832148546048,18.7821680311264,8.72062811169166,25.84188,21.96026,20.33747,19.12903,18.66175,7767,301,1,60,5127576978082519040,"GALAXY",0.586253,4554,56193,832 -1237655349965095424,239.010767393767,50.2626292909001,22.04578,21.1905,21.00168,21.27134,21.36043,2299,301,6,264,9468859573744195584,"QSO",3.096577,8410,57481,150 -1237655349965423104,239.620624988187,49.5681390022128,22.25265,21.86181,21.7513,21.90608,22.88196,2299,301,6,269,9499446344890472448,"QSO",2.603813,8437,57894,832 -1237655349965685248,240.455003777277,49.1998040882925,22.65964,21.51959,21.11733,21.03229,20.72133,2299,301,6,273,7115878834533324800,"QSO",1.280109,6320,56453,696 -1237678918589612544,349.690870630876,11.4107538810945,22.05963,22.42775,20.58982,19.68299,19.29761,7787,301,2,134,6931194137703372800,"GALAXY",0.4932663,6156,56239,561 -1237680306404131328,352.082836981586,26.8795593644332,21.97774,21.3769,21.13597,21.088,20.68579,8110,301,3,183,7416303345802172416,"QSO",1.041011,6587,56537,2 -1237680306404590080,353.285206259293,26.9647806861047,20.59261,20.55963,20.5893,20.42903,20.11829,8110,301,3,190,8670818639044433920,"QSO",1.801727,7701,57356,958 -1237672764446343680,352.515799371499,12.3034030656038,23.50397,21.68674,20.18435,19.18149,18.64242,6354,301,3,256,6922033556073240576,"GALAXY",0.4991557,6148,56209,3 -1237665570368913920,252.400159954602,46.3589373457148,21.82179,21.15797,20.84629,20.71919,20.43257,4679,301,3,146,9614136130036586496,"STAR",-0.001474477,8539,58021,279 -1237665570369962496,254.082775416038,44.2692030507324,21.32187,21.17574,21.26806,20.96436,21.43745,4679,301,3,162,9610702630000285696,"QSO",1.630161,8536,58015,76 -1237665570370093568,254.383462269905,43.9874326196153,22.25495,21.89459,21.85101,21.57198,21.4012,4679,301,3,164,9606315853600806912,"QSO",1.789708,8532,58022,501 -1237665570370290432,254.467557905866,43.6190369664621,22.76308,21.77897,21.49274,21.32501,20.80083,4679,301,3,167,9606324649693829120,"QSO",2.357028,8532,58022,533 -1237664092897607936,126.750795284062,23.5425640681201,16.94067,15.49387,14.96476,14.78275,14.70679,4335,301,3,107,2606540260720535552,"STAR",-7.37076e-05,2315,53741,298 -1237680276868301312,354.35909243985,27.1359183204577,21.37414,21.3361,20.67905,20.82302,20.93096,8103,301,4,62,8668481080981215232,"QSO",1.333339,7699,57574,646 -1237660962940322048,139.604865026809,35.9026393206335,21.86157,20.78945,20.59485,20.67383,20.54897,3606,301,5,109,5.228744687675e+18,"QSO",2.950487,4644,55922,238 -1237678579285230080,343.54543392873,22.7562273114003,21.61346,21.34822,21.12514,20.85452,22.75503,7708,301,2,104,8571582210905495552,"QSO",1.015435,7613,56988,386 -1237667536923459840,127.06048637503,13.0653193074307,22.71898,20.59779,18.61534,17.97453,17.60842,5137,301,2,91,2730362868871489536,"GALAXY",0.3818392,2425,54139,202 -1237663783118307584,347.761023426667,-0.671495019373772,22.21879,22.13176,21.97784,21.82063,21.19715,4263,301,2,34,10306619273314064384,"QSO",1.035307,9154,58013,478 -1237663783120077312,351.845370040584,-0.687207199981121,21.65056,20.55341,19.82523,19.58587,19.29075,4263,301,2,61,4741235517584332800,"QSO",2.377655,4211,55446,258 -1237663783121125632,354.25694924521,-0.665509988852284,19.67495,18.30487,17.74763,17.48874,17.38705,4263,301,2,77,3537591055757189120,"STAR",-0.0007260878,3142,54735,49 -1237678859531321600,327.318876005124,10.186289119779,19.85735,17.83669,16.76011,16.25526,15.81859,7773,301,4,96,4609643767536048128,"GALAXY",0.07749251,4094,55481,762 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-1237651539256148480,222.27673419482,62.9046506137402,24.05792,21.68527,20.69593,19.77877,19.49757,1412,301,4,276,7862369164366993408,"GALAXY",0.6015992,6983,56447,764 -1237661382776586240,156.812946802696,38.6117941680421,19.10991,18.19935,17.89215,17.79669,17.78042,3704,301,3,158,3672839234810650624,"STAR",5.459029e-05,3262,54884,559 -1237661382768132352,135.541685120905,30.4130418235052,20.10802,19.90634,19.91298,19.76715,19.78549,3704,301,3,29,12010103360982374400,"QSO",1.596116,10667,58163,469 -1237665442591408384,199.825227347201,29.7596990728953,22.49693,21.71349,21.46145,20.90346,20.69063,4649,301,5,112,12836497680311277568,"QSO",0.6462441,11401,58491,410 -1237678859551834368,15.094648409517,12.5868468576929,24.20226,22.60317,20.84801,19.80083,19.24989,7773,301,4,409,5246797298726623232,"GALAXY",0.518692,4660,56191,377 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-1237680245734900224,346.169584513246,18.5036841367418,22.5328,21.98937,20.28294,19.41275,18.84615,8096,301,2,140,6.900782471753e+18,"GALAXY",0.4986544,6129,56301,516 -1237680245743943936,7.96557974030577,19.4572000062294,21.90012,19.61994,18.13401,17.56647,17.21257,8096,301,2,278,6966016770550552576,"GALAXY",0.2323464,6187,56244,269 -1237662662138135040,227.851602909609,31.0282082053589,23.10644,22.75305,20.82052,19.59774,18.94834,4002,301,2,130,4363996373257050112,"GALAXY",0.7968625,3876,55245,30 -1237679998229218048,329.471911271339,-1.45350466456364,24.59594,22.04231,20.29031,19.19292,18.51263,8038,301,5,15,4927111263790848000,"GALAXY",0.525086,4376,55863,630 -1237666339727081728,19.4949130759959,0.30699964608533,24.07342,21.13559,19.36582,18.48938,17.98568,4858,301,4,484,448259100908742656,"STAR",0.000211116,398,51789,549 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-1237667430103056640,162.861461655076,25.6099513966018,24.81741,21.87023,21.28424,20.69687,20.08738,5112,301,3,349,11792773892300625920,"GALAXY",0.7574648,10474,58143,357 -1237661136887153152,156.565971659192,37.8497035807389,25.27587,22.7559,20.8271,19.65374,19.11285,3647,301,1,119,5133097890844268544,"GALAXY",0.5813958,4559,55597,437 -1237655504031186944,251.095588629764,42.1253397642098,17.07621,15.9729,15.58947,15.58141,15.56707,2335,301,5,22,707085792287156224,"STAR",-0.0002089742,628,52083,75 -1237680298889315328,353.866680738357,22.6038478965117,26.01035,23.39219,22.21544,20.64705,19.66549,8108,301,5,204,8560485664210311168,"GALAXY",0.8934467,7603,56960,977 -1237657070088684288,34.9994518234051,-0.545424992482656,23.57191,22.62992,23.01453,22.31318,21.05745,2700,301,2,99,10555521768864634880,"GALAXY",1.131398,9375,58076,764 -1237667143396884736,124.220871083383,18.0064580872929,22.67789,23.27056,17.58843,22.04938,22.97666,5045,301,5,88,2557016057730590720,"STAR",-0.001038165,2271,53726,354 -1237672796107047680,2.14390733451455,-4.76539997475289,23.62055,21.69394,21.39374,21.32328,21.8969,6361,301,6,32,8892391947513387008,"QSO",2.921346,7898,57363,125 -1237655504569107200,253.408535646699,40.4797427566004,24.53413,21.5794,20.60608,20.26998,19.81631,2335,301,6,38,709484101471594496,"GALAXY",0.2723388,630,52050,608 -1237655504568844544,252.974759968517,40.9676903271246,20.66845,19.35865,18.87339,18.69877,18.69357,2335,301,6,34,3705387253816729600,"STAR",-0.0009876372,3291,54939,184 -1237655504569434112,253.90603897724,39.8923623332407,19.21552,18.88527,18.84167,18.56885,18.69837,2335,301,6,43,712788959033321472,"QSO",1.772655,633,52079,343 -1.237664092898e+18,127.565323505854,23.8626756851716,22.84711,22.7661,21.94659,21.41695,21.218,4335,301,3,113,10809949309405255680,"GALAXY",0.8636968,9601,58083,670 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-1237671257974178048,55.8729003832272,9.8187171675862,22.04786,20.80791,19.95621,19.64275,19.43648,6003,301,5,67,3036638708930471936,"STAR",-0.0006685432,2697,54389,315 -1237669701585011200,9.77884403759447,7.0316196983628,23.65528,20.82924,19.24136,18.52415,18.04724,5641,301,2,61,5112762153192151040,"GALAXY",0.4424751,4541,55882,184 -1237669701588484864,17.8001450547428,6.7973818052517,24.73138,23.68782,21.55726,20.54398,19.81333,5641,301,2,114,9859701613568219136,"GALAXY",0.5583145,8757,58428,713 -1237661362908299776,201.618180329865,47.3073684069875,25.55429,24.91524,21.89072,20.58991,19.90273,3699,301,6,96,8341818087679744000,"GALAXY",0.7265124,7409,56809,93 -1237680504507663360,345.631973964679,33.6659419610576,21.2482,20.22243,20.02668,19.91393,19.74438,8156,301,4,155,7324229142966458368,"STAR",-0.0006198098,6505,56560,910 -1237667539086016768,161.709287876234,24.1019626848586,24.04763,22.44323,20.82803,19.9467,19.47012,5137,301,6,321,7224996290186467328,"GALAXY",0.5819892,6417,56308,351 -1237667539085361920,160.108991910767,23.9782544455054,24.82018,22.59271,21.99583,21.32383,21.36116,5137,301,6,311,11778103933268154368,"GALAXY",0.8548197,10461,58142,236 -1237678859555504640,23.7601148541254,12.1228339378302,22.03228,20.56171,18.6944,17.94161,17.42318,7773,301,4,465,5253691505487665152,"GALAXY",0.4121043,4666,55832,882 -1237678859553538560,19.0955576792089,12.4922713187075,22.30993,21.4408,19.78427,18.99172,18.63968,7773,301,4,435,5255811645142489088,"GALAXY",0.3847333,4668,56211,403 -1237678859535908864,337.96274883451,11.4353698699629,24.82299,21.91017,20.07367,19.16519,18.86999,7773,301,4,166,5683553004546185216,"GALAXY",0.4384492,5048,56218,37 -1237678859536761344,339.881824696961,11.5745875749143,23.80377,21.14629,20.04254,19.27994,18.80089,7773,301,4,179,5684708589337597952,"GALAXY",0.4896166,5049,56103,145 -1237678859545739264,0.901235401223119,12.7219401604792,19.04116,17.37127,16.53251,16.05979,15.76728,7773,301,4,316,6953728903811979264,"GALAXY",0.07433062,6176,56264,622 -1237678855234979072,324.456109509796,8.0496116841797,27.46361,20.61513,18.926,18.17893,18.04882,7772,301,4,75,4602910632702138368,"GALAXY",0.6569992,4088,55451,843 -1237656242237014528,339.305827288989,13.7394132127219,21.62774,19.07951,17.66974,17.10347,16.74334,2507,301,4,149,832147825574832128,"GALAXY",0.2232365,739,52520,392 -1237659161198788864,235.215697404216,43.6593412706772,23.47637,21.83512,21.79804,21.88908,21.58158,3187,301,1,67,9582569973823592448,"QSO",2.853529,8511,57901,130 -1237651505952850432,200.69660983784,1.07677852904402,22.7485,21.96182,20.56713,19.77146,19.44353,1404,301,6,12,4510413665196070912,"GALAXY",0.5081415,4006,55328,213 -1237665443124740352,190.608557323713,30.9091386506211,23.36436,21.15501,19.34163,18.67816,18.23893,4649,301,6,58,7293820495958661120,"GALAXY",0.3878638,6478,56341,876 -1237667542290858496,150.60845899603,21.378691899701,23.45529,22.55221,21.7393,21.27704,21.05502,5138,301,4,71,10825653080655419392,"GALAXY",0.2580627,9615,57870,456 -1237658192148038144,126.145365577044,31.9182509088913,21.24309,21.11583,21.03446,20.58839,20.551,2961,301,4,86,12005642367413803008,"QSO",1.778203,10663,58162,624 -1237680251633533440,27.6342050465415,28.6128001376042,23.25508,22.26032,20.5477,19.58932,19.09502,8097,301,5,34,7057335059544823808,"GALAXY",0.5000283,6268,56245,707 -1237662224066281728,181.373993476822,39.1251442543345,22.24722,21.48293,20.24023,19.41849,19.13902,3900,301,2,356,5291817343995107328,"GALAXY",0.5105523,4700,55709,319 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-1237665128027062272,236.118164628489,23.4279546313007,18.72395,17.90016,17.5678,17.25236,17.19022,4576,301,3,753,2442182510717724672,"GALAXY",0.1208336,2169,53556,384 -1237664285093659648,122.928691837838,6.0789865457013,23.4522,21.55352,20.06644,19.29084,18.81847,4380,301,1,50,5362843598810535936,"GALAXY",0.4896264,4763,55869,663 -1237671767998530304,227.338357659442,57.2116811408881,25.41774,23.47315,22.06904,20.7856,19.85998,6122,301,3,21,9475818664643287040,"GALAXY",0.7402591,8416,57894,891 -1237662700792054016,249.120858115065,48.9816623163553,19.78008,19.66846,19.58479,19.26606,19.26141,4011,301,2,118,9070314091516811264,"QSO",1.698456,8056,57186,234 -1237678579292635136,1.94168748433719,24.2503408794602,18.96012,17.76591,17.33217,17.1459,17.08898,7708,301,2,217,3153661929524848640,"STAR",-0.0004060478,2801,54331,59 -1237678579282215168,336.30105211127,21.4365222952471,19.83328,19.62658,19.40437,19.40466,19.47204,7708,301,2,58,8536750231823603712,"QSO",1.096949,7582,56960,644 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-1237668494705230336,169.087203902013,16.0360969063545,24.0922,22.08335,20.62789,19.60734,19.05463,5360,301,2,153,6042860206471534592,"GALAXY",0.5370615,5367,55986,565 -1237668494701167360,159.533803829595,15.2097606901169,23.22268,22.158,20.48817,19.52165,18.9261,5360,301,2,91,6020276238006114304,"GALAXY",0.5294508,5347,56008,325 -1237661150304403456,194.39188373256,48.9554847138892,19.05452,18.10486,17.85345,17.76517,17.76044,3650,301,2,50,3646877016566898688,"STAR",-2.162867e-05,3239,54888,317 -1237664877804781824,148.456819438008,30.6856029537993,21.82052,21.77813,21.34182,21.15598,21.15856,4518,301,1,136,12811681152227366912,"QSO",1.468351,11379,58438,240 -1237659163353351680,251.126760499215,33.1861241935427,23.6081,24.45681,23.98132,22.11904,21.44767,3187,301,5,175,12289365846997096448,"QSO",1.229179,10915,58257,612 -1237662702402863616,251.017306228574,49.2021279821255,21.97985,21.35043,21.17899,20.77904,20.36575,4011,301,5,121,9070259115935422464,"QSO",1.703428,8056,57186,34 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-1237666339727081728,19.4297216156322,0.40677506010893,19.65756,17.99503,17.11997,16.64243,16.30544,4858,301,4,484,4759424739029833728,"GALAXY",0.1330903,4227,55481,894 -1237665566072505088,220.122403582973,20.7058271507343,22.84189,22.02584,22.16345,22.11449,22.5863,4678,301,3,124,6647407434557315072,"STAR",-0.0002783031,5904,56046,343 -1237661972805845248,207.26368524475,7.95228466618916,21.01792,19.05537,17.72799,17.18663,16.83243,3841,301,6,282,6127221060612151296,"GALAXY",0.2269998,5442,55978,268 -1237657630056579584,151.442962765268,47.9671644051541,22.81054,22.26374,20.79977,19.5599,19.03074,2830,301,5,275,7499729887202400256,"GALAXY",0.6452484,6661,56366,402 -1237651754024698112,212.834856197011,2.28923040986456,23.90901,22.50721,20.72721,19.68905,19.18283,1462,301,4,584,4541027917271160832,"GALAXY",0.4671953,4033,55332,995 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-1237664846122516736,200.749599844348,37.6239875075848,19.62851,18.71182,18.38128,18.27989,18.23256,4510,301,6,31,4484598786057066496,"STAR",-0.000995661,3983,55603,507 -1237652947989627136,1.92717420067315,-9.69754972028485,25.19596,20.79807,19.06407,18.29892,17.86013,1740,301,4,35,733095015693182976,"GALAXY",0.4254842,651,52141,488 -1237679436129239296,0.321293530879487,-1.48362669967717,25.73304,21.9763,20.23214,19.31268,19.03368,7907,301,6,74,4902376099567392768,"GALAXY",0.4741608,4354,55810,756 -1237679436129632768,1.28827622868118,-1.554598711692,22.27438,20.94211,19.06905,18.44874,18.07415,7907,301,6,80,4902430525392967680,"GALAXY",0.3976817,4354,55810,954 -1237662662143246592,239.528811913844,25.3176886573914,19.23368,18.09227,17.74095,17.4332,17.29336,4002,301,2,208,1863401513448138752,"GALAXY",0.06402723,1655,53523,135 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-1237671260121727488,126.430298751046,8.36652402542902,21.41713,18.77956,17.34751,16.1511,15.48208,6004,301,1,68,1979463479609812992,"STAR",0.0001966302,1758,53084,478 -1237680508258878208,347.706779548187,33.4070018937117,23.50498,21.97748,22.31988,21.78496,22.26586,8157,301,3,50,7323112038817093632,"QSO",2.384494,6504,56540,942 -1237680508262023680,356.354107064157,34.4709731949682,21.49191,21.01568,20.60914,20.72578,20.10331,8157,301,3,98,8042470545483388928,"QSO",2.729439,7143,56572,609 -1237655504033939968,255.504818853564,36.873915627396,18.62598,17.51186,17.0123,16.67625,16.51191,2335,301,5,64,923398493211486208,"GALAXY",0.08372875,820,52438,584 -1237668365847887872,232.775978712429,15.0077843035856,17.73411,16.43286,15.81699,15.49648,15.24505,5330,301,2,26,3132340204417869824,"GALAXY",0.03079554,2782,54592,315 -1237667735032430848,152.693835963619,19.1556358188871,21.7715,19.88183,18.32867,17.72507,17.35637,5183,301,3,146,6623828682359461888,"GALAXY",0.306744,5883,56033,580 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-1237662301371040256,240.389131103964,35.914275329446,22.03258,21.84421,22.00962,21.74621,21.89526,3918,301,2,285,12091303945504577536,"QSO",1.756791,10739,58255,963 -1237678825169094144,320.435177430526,3.73455417417531,22.47328,21.61928,21.40645,21.43521,20.63721,7765,301,4,58,4590550746106451968,"QSO",0.8880931,4077,55361,934 -1.237663789029e+18,117.861569883144,52.5189558831444,22.97871,20.92598,20.39063,20.21009,20.17238,4264,301,5,112,4142358467993425920,"STAR",-0.0001871628,3679,55209,628 -1237663230684168448,315.297850429794,76.3623338717915,19.48771,17.51257,16.52684,16.01704,15.68992,4134,301,5,126,2453489613403088896,"STAR",-0.0001382161,2179,53555,559 -1237661967427895808,178.039157329701,42.3659650570908,23.76712,21.96679,20.17907,19.37177,18.84119,3840,301,4,141,7474973558588069888,"GALAXY",0.5086271,6639,56385,451 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+0,0 @@ - -# vtacML -vtacML is a Python package designed for the real-time analysis of data from the Visible Telescope (VT) of the SVOM satellite. This package uses machine learning models to analyze features from a list of observed VT sources and identify potential gamma-ray burst (GRB) optical afterglow candidates. vtacML is integrated into the real-time SVOM VT VHF pipeline and flags each source detected, indicating the probability that it is a GRB candidate. This information is then used by Burst Advocates (BAs) on shift to help them identify which source is the real GRB counterpart. -## Table of Contents - -- [Overview](#overview) -- [Installation](#installation) -- [Usage](#usage) - - [Quick Start](#quick-start) - - [Grid Search and Model Training](#grid-search-and-model-training) - - [Loading and Using the Best Model](#loading-and-using-the-best-model) - - [Using Pre-trained Model for Immediate Prediction](#using-pre-trained-model-for-immediate-prediction) - - [Config File](#config-file) -- [Documentation](#documentation) - - -## Overview - -The SVOM mission, a collaboration between the China National Space Administration (CNSA) and the French space agency CNES, aims to study gamma-ray bursts (GRBs), the most energetic explosions in the universe. The Visible Telescope (VT) on SVOM plays a critical role in observing these events in the optical wavelength range. - -vtacML leverages machine learning to analyze VT data, providing a probability score for each observation to indicate its likelihood of being a GRB candidate. The package includes tools for data preprocessing, model training, evaluation, and visualization. - -## Installation - -To install vtacML, you can use `pip`: - -```sh -pip install vtacML -``` - -Alternatively, you can clone the repository and install the package locally: - -```sh -git clone https://github.com/jerbeario/VTAC_ML.git -cd vtacML -pip install . -``` - -## Usage - -### Quick Start - -Here’s a quick example to get you started with vtacML: - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize the pipeline -pipeline = VTACMLPipe() - -# Load configuration -pipeline.load_config('path/to/config.yaml') - -# Train the model -pipeline.train() - -# Evaluate the model -pipeline.evaluate('evaluation_name', plot=True) - -# Predict GRB candidates -predictions = pipeline.predict(observation_dataframe, prob=True) -print(predictions) -``` - -### Grid Search and Model Training - -vtacML can perform grid search on a large array of models and parameters specified in the configuration file. Initialize the `VTACMLPipe` class with a specified config file (or use the default) and train it. Then, you can save the best model for future use. - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize the pipeline with a configuration file -pipeline = VTACMLPipe(config_file='path/to/config.yaml') - -# Train the model with grid search -pipeline.train() - -# Save the best model -pipeline.save_best_model('path/to/save/best_model.pkl') -``` - -### Loading and Using the Best Model - -After training and saving the best model, you can create a new instance of the `VTACMLPipe` class and load the best model for further use. - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize a new pipeline instance -pipeline = VTACMLPipe() - -# Load the best model -pipeline.load_best_model('path/to/save/best_model.pkl') - -# Predict GRB candidates -predictions = pipeline.predict(observation_dataframe, prob=True) -print(predictions) -``` - -### Using Pre-trained Model for Immediate Prediction - -If you already have a trained model, you can use the quick wrapper function `predict_from_best_pipeline` to predict data immediately. A pre-trained model is available by default. - -```python -from vtacML.pipeline import predict_from_best_pipeline - -# Predict GRB candidates using the pre-trained model -predictions = predict_from_best_pipeline(observation_dataframe, model_path='path/to/pretrained_model.pkl') -print(predictions) -``` - -### Config File - -The config file is used to configure the model searching process. - -```yaml -# Default config file, used to search for best model using only first two sequences (X0, X1) from the VT pipeline -Inputs: - file: 'combined_qpo_vt_all_cases_with_GRB_with_flags.parquet' # Data file used for training. Located in /data/ -# path: 'combined_qpo_vt_with_GRB.parquet' -# path: 'combined_qpo_vt_faint_case_with_GRB_with_flags.parquet' - columns: [ - "MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - 'EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_B0', - 'EFLAG_B1', - "NEW_SRC", - "DMAG_CAT" - ] # features used for training - target_column: 'IS_GRB' # feature column that holds the class information to be predicted - -# Set of models and parameters to perform GridSearchCV over -Models: - rfc: - class: RandomForestClassifier() - param_grid: - 'rfc__n_estimators': [100, 200, 300] # Number of trees in the forest - 'rfc__max_depth': [4, 6, 8] # Maximum depth of the tree - 'rfc__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'rfc__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - 'rfc__bootstrap': [True, False] # Whether bootstrap samples are used when building trees - ada: - class: AdaBoostClassifier() - param_grid: - 'ada__n_estimators': [50, 100, 200] # Number of weak learners - 'ada__learning_rate': [0.01, 0.1, 1] # Learning rate - 'ada__algorithm': ['SAMME'] # Algorithm for boosting - svc: - class: SVC() - param_grid: - 'svc__C': [0.1, 1, 10, 100] # Regularization parameter - 'svc__kernel': ['poly', 'rbf', 'sigmoid'] # Kernel type to be used in the algorithm - 'svc__gamma': ['scale', 'auto'] # Kernel coefficient - 'svc__degree': [3, 4, 5] # Degree of the polynomial kernel function (if `kernel` is 'poly') - knn: - class: KNeighborsClassifier() - param_grid: - 'knn__n_neighbors': [3, 5, 7, 9] # Number of neighbors to use - 'knn__weights': ['uniform', 'distance'] # Weight function used in prediction - 'knn__algorithm': ['ball_tree', 'kd_tree', 'brute'] # Algorithm used to compute the nearest neighbors - 'knn__p': [1, 2] # Power parameter for the Minkowski metric - lr: - class: LogisticRegression() - param_grid: - 'lr__penalty': ['l1', 'l2', 'elasticnet'] # Specify the norm of the penalty - 'lr__C': [0.01, 0.1, 1, 10] # Inverse of regularization strength - 'lr__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'] # Algorithm to use in the optimization problem - 'lr__max_iter': [100, 200, 300] # Maximum number of iterations taken for the solvers to converge - dt: - class: DecisionTreeClassifier() - param_grid: - 'dt__criterion': ['gini', 'entropy'] # The function to measure the quality of a split - 'dt__splitter': ['best', 'random'] # The strategy used to choose the split at each node - 'dt__max_depth': [4, 6, 8, 10] # Maximum depth of the tree - 'dt__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'dt__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - -# Output directories -Outputs: - model_path: '/output/models' - viz_path: '/output/visualizations/' - plot_correlation: - flag: True - path: 'output/corr_plots/' - - -``` - -## Documentation diff --git a/docs/__init__.py b/docs/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle deleted file mode 100644 index d3537f2..0000000 Binary files a/docs/build/doctrees/environment.pickle and /dev/null differ diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree deleted file mode 100644 index 0ce437a..0000000 Binary files a/docs/build/doctrees/index.doctree and /dev/null differ diff --git a/docs/build/doctrees/modules.doctree b/docs/build/doctrees/modules.doctree deleted file mode 100644 index 11ef7db..0000000 Binary files a/docs/build/doctrees/modules.doctree and /dev/null differ diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index dc1312a..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/source/__init__.py b/docs/source/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 1f4c47d..0000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,54 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html -import os -import sys - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -sys.path.insert(0, os.path.abspath('../../')) - -project = 'vtacML' -copyright = '2024, Jeremy Palmerio' -author = 'Jeremy Palmerio' - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [ - 'sphinx.ext.coverage', - 'sphinx.ext.autodoc', - 'sphinx.ext.napoleon', - 'sphinx.ext.viewcode', - 'sphinx.ext.doctest', - 'recommonmark' -] -# Autodoc configuration -autodoc_default_options = { - 'members': True, - 'undoc-members': True, - 'private-members': False, # Do not include private members - 'special-members': False, # Do not include special members - 'inherited-members': False, # Do not include inherited members - 'show-inheritance': True, - 'exclude-members': '_split_data, _get_data, _load_data, _create_pipe, _load_config, _resample' -} -templates_path = ['_templates'] -exclude_patterns = [] - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = 'alabaster' -html_static_path = ['_static'] - -from recommonmark.transform import AutoStructify - - -def setup(app): - app.add_config_value('recommonmark_config', { - 'auto_toc_tree_section': 'Contents', - 'enable_eval_rst': True, - }, True) - app.add_transform(AutoStructify) diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index c2e6267..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. vtacML documentation master file, created by - sphinx-quickstart on Wed Jun 12 23:30:54 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to vtacML's homepage! -================================== -.. include:: ../README_DOCS.md - :parser: recommonmark - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - modules - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/modules.rst b/docs/source/modules.rst deleted file mode 100644 index 859060a..0000000 --- a/docs/source/modules.rst +++ /dev/null @@ -1,13 +0,0 @@ -Docs vtacML -=========== - -Pipeline --------- - -.. automodule:: vtacML.pipeline - :members: - :undoc-members: - :show-inheritance: - :exclude-members: _split_data, _get_data, _load_data, _create_pipe, _load_config, _resample - - diff --git a/docs/build/html/.buildinfo b/html/.buildinfo similarity index 100% rename from docs/build/html/.buildinfo rename to html/.buildinfo diff --git a/docs/build/html/_modules/index.html b/html/_modules/index.html similarity index 100% rename from docs/build/html/_modules/index.html rename to html/_modules/index.html diff --git a/docs/build/html/_modules/vtacML/pipeline.html b/html/_modules/vtacML/pipeline.html similarity 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similarity index 100% rename from docs/build/html/index.html rename to html/index.html diff --git a/docs/build/html/modules.html b/html/modules.html similarity index 100% rename from docs/build/html/modules.html rename to html/modules.html diff --git a/docs/build/html/objects.inv b/html/objects.inv similarity index 100% rename from docs/build/html/objects.inv rename to html/objects.inv diff --git a/docs/build/html/py-modindex.html b/html/py-modindex.html similarity index 100% rename from docs/build/html/py-modindex.html rename to html/py-modindex.html diff --git a/docs/build/html/search.html b/html/search.html similarity index 100% rename from docs/build/html/search.html rename to html/search.html diff --git a/docs/build/html/searchindex.js b/html/searchindex.js similarity index 100% rename from docs/build/html/searchindex.js rename to html/searchindex.js diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index 8fe2f47..0000000 --- a/pyproject.toml +++ /dev/null @@ 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100644 index d0876a7..0000000 --- a/pythonProject/.idea/pythonProject.iml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/pythonProject/.idea/vcs.xml b/pythonProject/.idea/vcs.xml deleted file mode 100644 index 6c0b863..0000000 --- a/pythonProject/.idea/vcs.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/pythonProject/.idea/workspace.xml b/pythonProject/.idea/workspace.xml deleted file mode 100644 index 5dfe832..0000000 --- a/pythonProject/.idea/workspace.xml +++ /dev/null @@ -1,54 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - 1710788261126 - - - - - - \ No newline at end of file diff --git a/requirements-dev.txt b/requirements-dev.txt deleted file mode 100644 index 2f77584..0000000 --- a/requirements-dev.txt +++ /dev/null @@ -1,143 +0,0 @@ -anyio @ file:///private/var/folders/c_/qfmhj66j0tn016nkx_th4hxm0000gp/T/abs_646d7rz2z6/croot/anyio_1706220176177/work -appnope @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/appnope_1699320997592/work -argon2-cffi @ file:///opt/conda/conda-bld/argon2-cffi_1645000214183/work -argon2-cffi-bindings @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/argon2-cffi-bindings_1699264521425/work -astropy @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/astropy_1699264604004/work -asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work -async-lru @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/async-lru_1701806219228/work -attrs @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/attrs_1699282013651/work -Babel @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/babel_1699247663878/work -beautifulsoup4 @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/beautifulsoup4-split_1699283371893/work -black==24.4.2 -bleach @ file:///opt/conda/conda-bld/bleach_1641577558959/work -Bottleneck @ 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file:///Users/builder/cbouss/perseverance-python-buildout/croot/websocket-client_1699251014005/work -wheel==0.41.2 -xgboost==2.0.3 -yellowbrick==1.5 -zipp @ file:///Users/builder/cbouss/perseverance-python-buildout/croot/zipp_1707361439201/work diff --git a/setup.py b/setup.py deleted file mode 100644 index 1e33824..0000000 --- a/setup.py +++ /dev/null @@ -1,47 +0,0 @@ -from setuptools import setup, find_packages - -setup( - name='vtacML', - version='0.1.17', - packages=find_packages(include=['vtacML', 'vtacML.*']), - install_requires=[ - 'numpy>=1.26.3', - 'matplotlib>=3.8.0', - 'pandas>=2.1.4', - 'scikit-learn==1.3.0', - 'seaborn>=0.12.2', - 'yellowbrick>=1.5', - 'pyyaml>=6.0.1', - 'imblearn>=0.0', - 'fastparquet>=2023.8.0', - 'joblib>=1.2.0' - # List your dependencies here - # Example: 'numpy', 'pandas', 'scikit-learn', - ], - extras_require={ - 'dev': [ - 'pytest>=8.0.1' - ] - - }, - include_package_data=True, - entry_points={ - 'console_scripts': [ - # Define any command-line scripts here - # Example: 'vtac_classifier=pipeline:main', - ], - }, - url='https://github.com/jerbeario/VTAC_ML', # Replace with your project's URL - license='MIT', - author='Jeremy Palmerio', - author_email='jeremypalmerio05@gmail.com', - description='A machine learning pipeline to classify objects in VTAC dataset as GRB or not.', - long_description=open('README.md').read(), - long_description_content_type='text/markdown', - classifiers=[ - 'Programming Language :: Python :: 3', - 'License :: OSI Approved :: MIT License', - 'Operating System :: OS Independent', - ], - python_requires='>=3.10', -) diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py deleted file mode 100644 index 02144c2..0000000 --- a/tests/test_pipeline.py +++ /dev/null @@ -1,37 +0,0 @@ -from sklearn.ensemble import RandomForestClassifier -import pytest -from vtacML.pipeline import VTACMLPipe -from vtacML import get_path -import pandas as pd - -data_path = get_path('/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet') -test_df = pd.read_parquet(data_path) -def test_vtacMLPipe_init(): - vtacML_pipe = VTACMLPipe(config_file='config/test_config.yaml') - assert vtacML_pipe.config is not None - assert vtacML_pipe.preprocessing is not None - -def test_vtacMLPipe_train(): - vtacML_pipe = VTACMLPipe(config_file='config/test_config.yaml') - assert vtacML_pipe.best_model is None - vtacML_pipe.train(cv=2) - assert vtacML_pipe.best_model is not None - - -def test_vtacMLPipe_save_model(): - vtacML_pipe = VTACMLPipe(config_file='config/test_config.yaml') - vtacML_pipe.train(cv=2) - vtacML_pipe.save_best_model(model_name='test_model.pkl') - assert vtacML_pipe.best_model.steps[-1][0] == 'rfc' - -def test_vtacMLPipe_load_model(): - vtacML_pipe = VTACMLPipe(config_file='config/test_config.yaml') - vtacML_pipe.load_best_model(model_name='test_model.pkl') -def test_vtacMLPipe_predict(): - vtacML_pipe = VTACMLPipe(config_file='config/test_config.yaml') - vtacML_pipe.load_best_model(model_name='test_model.pkl') - test_X = test_df.drop('IS_GRB', axis=1) - test_y = test_df['IS_GRB'] - pred_y = vtacML_pipe.predict(test_X, prob=False) - assert len(pred_y) == len(test_y) - diff --git a/vtacML.egg-info/PKG-INFO b/vtacML.egg-info/PKG-INFO deleted file mode 100644 index b5ade5c..0000000 --- a/vtacML.egg-info/PKG-INFO +++ /dev/null @@ -1,278 +0,0 @@ -Metadata-Version: 2.1 -Name: vtacML -Version: 0.1.17 -Summary: A machine learning pipeline to classify objects in VTAC dataset as GRB or not. -Home-page: https://github.com/jerbeario/VTAC_ML -Author: Jeremy Palmerio -Author-email: jeremypalmerio05@gmail.com -License: MIT -Classifier: Programming Language :: Python :: 3 -Classifier: License :: OSI Approved :: MIT License -Classifier: Operating System :: OS Independent -Requires-Python: >=3.10 -Description-Content-Type: text/markdown -License-File: LICENSE -Requires-Dist: numpy>=1.26.3 -Requires-Dist: matplotlib>=3.8.0 -Requires-Dist: pandas>=2.1.4 -Requires-Dist: scikit-learn==1.3.0 -Requires-Dist: seaborn>=0.12.2 -Requires-Dist: yellowbrick>=1.5 -Requires-Dist: pyyaml>=6.0.1 -Requires-Dist: imblearn>=0.0 -Requires-Dist: fastparquet>=2023.8.0 -Requires-Dist: joblib>=1.2.0 -Provides-Extra: dev -Requires-Dist: pytest>=8.0.1; extra == "dev" - - -# vtacML - -vtacML is a machine learning package designed for the analysis of data from the Visible Telescope (VT) on the SVOM mission. This package uses machine learning models to analyze a dataframe of features from VT observations and identify potential gamma-ray burst (GRB) candidates. The primary goal of vtacML is to integrate into the SVOM data analysis pipeline and add a feature to each observation indicating the probability that it is a GRB candidate. - -## Table of Contents - -- [Overview](#overview) -- [Installation](#installation) -- [Usage](#usage) - - [Quick Start](#quick-start) - - [Grid Search and Model Training](#grid-search-and-model-training) - - [Loading and Using the Best Model](#loading-and-using-the-best-model) - - [Using Pre-trained Model for Immediate Prediction](#using-pre-trained-model-for-immediate-prediction) - - [Config File](#config-file) -- [Documentation](#documentation) -- [License](#license) -- [Contact](#contact) - -## Overview - -The SVOM mission, a collaboration between the China National Space Administration (CNSA) and the French space agency CNES, aims to study gamma-ray bursts (GRBs), the most energetic explosions in the universe. The Visible Telescope (VT) on SVOM plays a critical role in observing these events in the optical wavelength range. - -vtacML leverages machine learning to analyze VT data, providing a probability score for each observation to indicate its likelihood of being a GRB candidate. The package includes tools for data preprocessing, model training, evaluation, and visualization. - -## Installation - -To install vtacML, you can use `pip`: - -```sh -pip install vtacML -``` - -Alternatively, you can clone the repository and install the package locally: - -```sh -git clone https://github.com/yourusername/vtacML.git -cd vtacML -pip install . -``` - -## Usage - -### Quick Start - -Here’s a quick example to get you started with vtacML: - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize the pipeline -pipeline = VTACMLPipe() - -# Load configuration -pipeline.load_config('path/to/config.yaml') - -# Train the model -pipeline.train() - -# Evaluate the model -pipeline.evaluate('evaluation_name', plot=True) - -# Predict GRB candidates -predictions = pipeline.predict(observation_dataframe, prob=True) -print(predictions) -``` - -### Grid Search and Model Training - -vtacML can perform grid search on a large array of models and parameters specified in the configuration file. Initialize the `VTACMLPipe` class with a specified config file (or use the default) and train it. Then, you can save the best model for future use. - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize the pipeline with a configuration file -pipeline = VTACMLPipe(config_file='path/to/config.yaml') - -# Train the model with grid search -pipeline.train() - -# Save the best model -pipeline.save_best_model('path/to/save/best_model.pkl') -``` - -### Loading and Using the Best Model - -After training and saving the best model, you can create a new instance of the `VTACMLPipe` class and load the best model for further use. - -```python -from vtacML.pipeline import VTACMLPipe - -# Initialize a new pipeline instance -pipeline = VTACMLPipe() - -# Load the best model -pipeline.load_best_model('path/to/save/best_model.pkl') - -# Predict GRB candidates -predictions = pipeline.predict(observation_dataframe, prob=True) -print(predictions) -``` - -### Using Pre-trained Model for Immediate Prediction - -If you already have a trained model, you can use the quick wrapper function `predict_from_best_pipeline` to predict data immediately. A pre-trained model is available by default. - -```python -from vtacML.pipeline import predict_from_best_pipeline - -# Predict GRB candidates using the pre-trained model -predictions = predict_from_best_pipeline(observation_dataframe, model_path='path/to/pretrained_model.pkl') -print(predictions) -``` - -### Config File - -The config file is used to configure the model searching process. - -```yaml -# Default config file, used to search for best model using only first two sequences (X0, X1) from the VT pipeline -Inputs: - file: 'combined_qpo_vt_all_cases_with_GRB_with_flags.parquet' # Data file used for training. Located in /data/ -# path: 'combined_qpo_vt_with_GRB.parquet' -# path: 'combined_qpo_vt_faint_case_with_GRB_with_flags.parquet' - columns: [ - "MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - 'EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_B0', - 'EFLAG_B1', - "NEW_SRC", - "DMAG_CAT" - ] # features used for training - target_column: 'IS_GRB' # feature column that holds the class information to be predicted - -# Set of models and parameters to perform GridSearchCV over -Models: - rfc: - class: RandomForestClassifier() - param_grid: - 'rfc__n_estimators': [100, 200, 300] # Number of trees in the forest - 'rfc__max_depth': [4, 6, 8] # Maximum depth of the tree - 'rfc__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'rfc__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - 'rfc__bootstrap': [True, False] # Whether bootstrap samples are used when building trees - ada: - class: AdaBoostClassifier() - param_grid: - 'ada__n_estimators': [50, 100, 200] # Number of weak learners - 'ada__learning_rate': [0.01, 0.1, 1] # Learning rate - 'ada__algorithm': ['SAMME'] # Algorithm for boosting - svc: - class: SVC() - param_grid: - 'svc__C': [0.1, 1, 10, 100] # Regularization parameter - 'svc__kernel': ['poly', 'rbf', 'sigmoid'] # Kernel type to be used in the algorithm - 'svc__gamma': ['scale', 'auto'] # Kernel coefficient - 'svc__degree': [3, 4, 5] # Degree of the polynomial kernel function (if `kernel` is 'poly') - knn: - class: KNeighborsClassifier() - param_grid: - 'knn__n_neighbors': [3, 5, 7, 9] # Number of neighbors to use - 'knn__weights': ['uniform', 'distance'] # Weight function used in prediction - 'knn__algorithm': ['ball_tree', 'kd_tree', 'brute'] # Algorithm used to compute the nearest neighbors - 'knn__p': [1, 2] # Power parameter for the Minkowski metric - lr: - class: LogisticRegression() - param_grid: - 'lr__penalty': ['l1', 'l2', 'elasticnet'] # Specify the norm of the penalty - 'lr__C': [0.01, 0.1, 1, 10] # Inverse of regularization strength - 'lr__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'] # Algorithm to use in the optimization problem - 'lr__max_iter': [100, 200, 300] # Maximum number of iterations taken for the solvers to converge - dt: - class: DecisionTreeClassifier() - param_grid: - 'dt__criterion': ['gini', 'entropy'] # The function to measure the quality of a split - 'dt__splitter': ['best', 'random'] # The strategy used to choose the split at each node - 'dt__max_depth': [4, 6, 8, 10] # Maximum depth of the tree - 'dt__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'dt__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - -# Output directories -Outputs: - model_path: '/output/models' - viz_path: '/output/visualizations/' - plot_correlation: - flag: True - path: 'output/corr_plots/' - - -``` - -## Documentation - -See documentation at - - -### Setting Up Development Environment - - -To set up a development environment, you can use the provided `requirements-dev.txt`: - - -```sh - -conda create --name vtacML-dev python=3.8 - -conda activate vtacML-dev - -pip install -r requirements-dev.txt - -``` - - -### Running Tests - - -To run tests, use the following command: - - -```sh - -pytest - -``` - - -## License - - -This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details. - - -## Contact - - -For questions or support, please contact: - - -- Your Name - [palmerio.jeremy@gmail.com](mailto:palmerio.jeremy@gmail.com) - -- Project Link: [https://github.com/jerbeario/VTAC_ML](https://github.com/jerbeario/VTAC_ML) diff --git a/vtacML.egg-info/SOURCES.txt b/vtacML.egg-info/SOURCES.txt deleted file mode 100644 index 1d70d8f..0000000 --- a/vtacML.egg-info/SOURCES.txt +++ /dev/null @@ -1,20 +0,0 @@ -LICENSE -MANIFEST.in -README.md -pyproject.toml -setup.py -tests/test_pipeline.py -vtacML/__init__.py -vtacML/evaluate_best_model.py -vtacML/pipeline.py -vtacML/preparation.py -vtacML/search_best_model.py -vtacML/utils.py -vtacML.egg-info/PKG-INFO -vtacML.egg-info/SOURCES.txt -vtacML.egg-info/dependency_links.txt -vtacML.egg-info/requires.txt -vtacML.egg-info/top_level.txt -vtacML/config/config.yaml -vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet -vtacML/output/models/0.978_svc_best_model.pkl \ No newline at end of file diff --git a/vtacML.egg-info/dependency_links.txt b/vtacML.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/vtacML.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/vtacML.egg-info/requires.txt b/vtacML.egg-info/requires.txt deleted file mode 100644 index 45ba2b5..0000000 --- a/vtacML.egg-info/requires.txt +++ /dev/null @@ -1,13 +0,0 @@ -numpy>=1.26.3 -matplotlib>=3.8.0 -pandas>=2.1.4 -scikit-learn==1.3.0 -seaborn>=0.12.2 -yellowbrick>=1.5 -pyyaml>=6.0.1 -imblearn>=0.0 -fastparquet>=2023.8.0 -joblib>=1.2.0 - -[dev] -pytest>=8.0.1 diff --git a/vtacML.egg-info/top_level.txt b/vtacML.egg-info/top_level.txt deleted file mode 100644 index eed3e67..0000000 --- a/vtacML.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -vtacML diff --git a/vtacML/__init__.py b/vtacML/__init__.py deleted file mode 100644 index 5349d64..0000000 --- a/vtacML/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .pipeline import VTACMLPipe, predict_from_best_pipeline -from .utils import ROOTDIR, get_path diff --git a/vtacML/config/config.yaml b/vtacML/config/config.yaml deleted file mode 100644 index 018962d..0000000 --- a/vtacML/config/config.yaml +++ /dev/null @@ -1,78 +0,0 @@ -# Default config file, used to search for best model using only first two sequences (X0, X1) from the VT pipeline -Inputs: - file: 'combined_qpo_vt_all_cases_with_GRB_with_flags.parquet' # Data file used for training. Located in /data/ -# path: 'combined_qpo_vt_with_GRB.parquet' -# path: 'combined_qpo_vt_faint_case_with_GRB_with_flags.parquet' - columns: [ - "MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - 'EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_B0', - 'EFLAG_B1', - "NEW_SRC", - "DMAG_CAT" - ] # features used for training - target_column: 'IS_GRB' # feature column that holds the class information to be predicted - -# Set of models and parameters to perform GridSearchCV over -Models: - rfc: - class: RandomForestClassifier() - param_grid: - 'rfc__n_estimators': [100, 200, 300] # Number of trees in the forest - 'rfc__max_depth': [4, 6, 8] # Maximum depth of the tree - 'rfc__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'rfc__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - 'rfc__bootstrap': [True, False] # Whether bootstrap samples are used when building trees - ada: - class: AdaBoostClassifier() - param_grid: - 'ada__n_estimators': [50, 100, 200] # Number of weak learners - 'ada__learning_rate': [0.01, 0.1, 1] # Learning rate - 'ada__algorithm': ['SAMME'] # Algorithm for boosting - svc: - class: SVC() - param_grid: - 'svc__C': [0.1, 1, 10, 100] # Regularization parameter - 'svc__kernel': ['poly', 'rbf', 'sigmoid'] # Kernel type to be used in the algorithm - 'svc__gamma': ['scale', 'auto'] # Kernel coefficient - 'svc__degree': [3, 4, 5] # Degree of the polynomial kernel function (if `kernel` is 'poly') - knn: - class: KNeighborsClassifier() - param_grid: - 'knn__n_neighbors': [3, 5, 7, 9] # Number of neighbors to use - 'knn__weights': ['uniform', 'distance'] # Weight function used in prediction - 'knn__algorithm': ['ball_tree', 'kd_tree', 'brute'] # Algorithm used to compute the nearest neighbors - 'knn__p': [1, 2] # Power parameter for the Minkowski metric - lr: - class: LogisticRegression() - param_grid: - 'lr__penalty': ['l1', 'l2', 'elasticnet'] # Specify the norm of the penalty - 'lr__C': [0.01, 0.1, 1, 10] # Inverse of regularization strength - 'lr__solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'] # Algorithm to use in the optimization problem - 'lr__max_iter': [100, 200, 300] # Maximum number of iterations taken for the solvers to converge - dt: - class: DecisionTreeClassifier() - param_grid: - 'dt__criterion': ['gini', 'entropy'] # The function to measure the quality of a split - 'dt__splitter': ['best', 'random'] # The strategy used to choose the split at each node - 'dt__max_depth': [4, 6, 8, 10] # Maximum depth of the tree - 'dt__min_samples_split': [2, 5, 10] # Minimum number of samples required to split an internal node - 'dt__min_samples_leaf': [1, 2, 4] # Minimum number of samples required to be at a leaf node - -# Output directories -Outputs: - model_path: '/output/models' - viz_path: '/output/visualizations/' - plot_correlation: - flag: True - path: 'output/corr_plots/' diff --git a/vtacML/config/test_config.yaml b/vtacML/config/test_config.yaml deleted file mode 100644 index 4ae3708..0000000 --- a/vtacML/config/test_config.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# Default config file, used to search for best model using only first two sequences (X0, X1) from the VT pipeline -Inputs: - file: 'combined_qpo_vt_all_cases_with_GRB_with_flags.parquet' # Data file used for training. Located in /data/ -# path: 'combined_qpo_vt_with_GRB.parquet' -# path: 'combined_qpo_vt_faint_case_with_GRB_with_flags.parquet' - columns: [ - "MAGCAL_R0", - "MAGCAL_B0", - "MAGERR_R0", - "MAGERR_B0", - "MAGCAL_R1", - "MAGCAL_B1", - "MAGERR_R1", - "MAGERR_B1", - "MAGVAR_R1", - "MAGVAR_B1", - 'EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_B0', - 'EFLAG_B1', - "NEW_SRC", - "DMAG_CAT" - ] # features used for training - target_column: 'IS_GRB' # feature column that holds the class information to be predicted - -# Set of models and parameters to perform GridSearchCV over -Models: - rfc: - class: RandomForestClassifier() - param_grid: - 'rfc__n_estimators': [10] # Number of trees in the forest - 'rfc__max_depth': [4] # Maximum depth of the tree - 'rfc__min_samples_split': [2] # Minimum number of samples required to split an internal node - 'rfc__min_samples_leaf': [1] # Minimum number of samples required to be at a leaf node - 'rfc__bootstrap': [True] # Whether bootstrap samples are used when building trees - -Outputs: - model_path: '/output/models' - viz_path: '/output/visualizations/' - plot_correlation: - flag: True - path: 'output/corr_plots/' diff --git a/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet b/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet deleted file mode 100644 index bc5c512..0000000 Binary files a/vtacML/data/combined_qpo_vt_all_cases_with_GRB_with_flags.parquet and /dev/null differ diff --git a/vtacML/data/combined_qpo_vt_faint_case_with_GRB_with_flags.parquet b/vtacML/data/combined_qpo_vt_faint_case_with_GRB_with_flags.parquet deleted file mode 100644 index 56aa1a6..0000000 Binary files a/vtacML/data/combined_qpo_vt_faint_case_with_GRB_with_flags.parquet and /dev/null differ diff --git a/vtacML/data/combined_qpo_vt_with_GRB.parquet b/vtacML/data/combined_qpo_vt_with_GRB.parquet deleted file mode 100644 index b96ba28..0000000 Binary files a/vtacML/data/combined_qpo_vt_with_GRB.parquet and /dev/null differ diff --git a/vtacML/data/environment.yml b/vtacML/data/environment.yml deleted file mode 100644 index 4b5cfaa..0000000 --- a/vtacML/data/environment.yml +++ /dev/null @@ -1,235 +0,0 @@ -name: VTAC -channels: - - conda-forge - - defaults -dependencies: - - anyio=4.2.0=py312hecd8cb5_0 - - appnope=0.1.3=py312hecd8cb5_1001 - - argon2-cffi=21.3.0=pyhd3eb1b0_0 - - argon2-cffi-bindings=21.2.0=py312h6c40b1e_0 - - astropy=5.3.4=py312h32608ca_0 - - asttokens=2.0.5=pyhd3eb1b0_0 - - async-lru=2.0.4=py312hecd8cb5_0 - - attrs=23.1.0=py312hecd8cb5_0 - - babel=2.11.0=py312hecd8cb5_0 - - beautifulsoup4=4.12.2=py312hecd8cb5_0 - - blas=1.0=openblas - - bleach=4.1.0=pyhd3eb1b0_0 - - boost-cpp=1.82.0=ha357a0b_2 - - bottleneck=1.3.5=py312h32608ca_0 - - brotli=1.0.9=hca72f7f_7 - - brotli-bin=1.0.9=hca72f7f_7 - - bzip2=1.0.8=h6c40b1e_5 - - c-ares=1.19.1=h6c40b1e_0 - - ca-certificates=2023.12.12=hecd8cb5_0 - - cairo=1.16.0=h3ce6f7e_5 - - certifi=2024.2.2=py312hecd8cb5_0 - - cffi=1.16.0=py312h6c40b1e_0 - - 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colour==0.1.5 - - docutils==0.21.2 - - dtreeviz==2.2.2 - - example-package-jerbeario==0.0.0 - - imbalanced-learn==0.12.2 - - imblearn==0.0 - - iniconfig==2.0.0 - - jaraco-classes==3.4.0 - - jaraco-context==5.3.0 - - jaraco-functools==4.0.1 - - keyring==25.2.0 - - markdown-it-py==3.0.0 - - mdurl==0.1.2 - - more-itertools==10.2.0 - - mypy-extensions==1.0.0 - - nh3==0.2.17 - - pathspec==0.12.1 - - pip==24.0 - - pkginfo==1.10.0 - - pluggy==1.4.0 - - pyproject-hooks==1.1.0 - - pytest==8.0.1 - - python-graphviz==0.20.1 - - readme-renderer==43.0 - - requests-toolbelt==1.0.0 - - rfc3986==2.0.0 - - rich==13.7.1 - - twine==5.0.0 - - xgboost==2.0.3 - - yellowbrick==1.5 -prefix: /Users/jeremypalmerio/opt/miniconda3/envs/VTAC diff --git a/vtacML/evaluate_best_model.py b/vtacML/evaluate_best_model.py deleted file mode 100644 index b1897f8..0000000 --- a/vtacML/evaluate_best_model.py +++ /dev/null @@ -1,17 +0,0 @@ -from .pipeline import VTACMLPipe - -if __name__ == '__main__': - vtac_ml = VTACMLPipe() - - best_model_paths = [ - # '../output/models/ada_grid_search.pkl', - '../output/models/knn_grid_search.pkl', - '../output/models/lr_grid_search.pkl', - '../output/models/rfc_grid_search.pkl', - '../output/models/svc_grid_search.pkl', - ] - # for model in best_model_paths: - # vtac_ml.load_best_model(model_path=model, is_grid_search=True) - # vtac_ml.evaluate(name='seq0-1_' + model.split('/')[-1].split('_')[0], plot_extra=True) - vtac_ml.load_best_model(model_name='test') - vtac_ml.evaluate(name='test') \ No newline at end of file diff --git a/vtacML/output/models/0.974_rfc_best_model.pkl b/vtacML/output/models/0.974_rfc_best_model.pkl deleted file mode 100644 index 3f5d2fb..0000000 Binary files a/vtacML/output/models/0.974_rfc_best_model.pkl and /dev/null differ diff --git a/vtacML/output/rfc_ada_knn_FullGridSearch/best_model b/vtacML/output/rfc_ada_knn_FullGridSearch/best_model deleted file mode 100644 index ffbaacd..0000000 Binary files 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sklearn.metrics import mean_absolute_error, mean_squared_error, f1_score, confusion_matrix -from sklearn.model_selection import train_test_split, GridSearchCV -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler, Normalizer - -from imblearn.over_sampling import SMOTE - -from yellowbrick import ROCAUC -from yellowbrick.model_selection import FeatureImportances, ValidationCurve -from yellowbrick.classifier import ClassificationReport, ConfusionMatrix, PrecisionRecallCurve, ClassPredictionError -from yellowbrick.model_selection import RFECV - -from .preparation import Cleaner -from .utils import get_path - -log = logging.getLogger(__name__) - - -def predict_from_best_pipeline(X: pd.DataFrame, prob_flag=False, model_name='0.974_rfc_best_model.pkl', - config_path=None): - """ - Predict using the best model pipeline. - - Parameters - ---------- - X : array-like - Features to predict. - prob_flag : bool, optional - Whether to return probabilities, by default False. - model_name : str, optional - Name of the model to use, by default '0.974_rfc_best_model.pkl' - model_path : str, optional - Path to the model to use for prediction, by default 'None' - config_path : str, optional - Path to the configuration file, by default '../config/config.yaml'. - - Returns - ------- - ndarray - Predicted values or probabilities. - """ - - vtac_ml_pipe = VTACMLPipe(config_file=config_path) - print(model_name) - vtac_ml_pipe.load_best_model(model_name=model_name) - print(vtac_ml_pipe.best_model) - y = vtac_ml_pipe.predict(X, prob=prob_flag) - return y - - -class VTACMLPipe: - """ - A machine learning pipeline for training and evaluating an optimal model for optical identification of GRBs for the SVOM mission. - - Parameters - ---------- - config_path : str, optional - Path to the configuration file. Default 'config/config.yaml' - - """ - - def __init__(self, config_file='config/config.yaml'): - - """ - Initialize the VTACMLPipe. - - Parameters - ---------- - config_path : str - Path to the configuration file. - """ - - # initialize attributes - self.config = None - self.X = None - self.y = None - self.X_columns = None - self.y_columns = None - self.X_train = None - self.y_train = None - self.X_test = None - self.y_test = None - self.preprocessing = Pipeline(steps=[], verbose=True) - self.full_pipeline = Pipeline(steps=[], verbose=True) - self.models = {} - self.best_model = None - self.y_predict = None - self.y_predict_prob = None - - # Load configs from config file - self.load_config(config_file) - - # Defining Steps of the preprocessing pipeline - cleaner = Cleaner(variables=self.X_columns) - scaler = StandardScaler() - normalizer = Normalizer() - self.steps = [ - ('cleaner', cleaner), - # ('ohe', ohe), - ('scaler', scaler), - ('normalizer', normalizer) - ] - self._create_pipe(self.steps) - - def load_config(self, config_file): - """ - Load the configuration file and prepare the data. - - Parameters - ---------- - config_file : str - The path to the configuration file. - - """ - config_path = get_path(config_file) - - with open(config_path, 'r') as file: - self.config = yaml.safe_load(file) - - # loading config file and prepping data - data_file = self.config['Inputs']['file'] - data = self._get_data(data_file=data_file) - - self.X_columns = self.config['Inputs']['columns'] - self.X = data[self.X_columns] - self.y_columns = self.config['Inputs']['target_column'] - self.y = data[self.y_columns] - self._load_data(data, columns=self.X_columns, target=self.y_columns, test_size=0.2) - - # building models attribute - for model in self.config['Models']: - if model == 'rfc': - self.models[model] = RandomForestClassifier() - if model == 'svc': - self.models[model] = SVC() - if model == 'knn': - self.models[model] = KNeighborsClassifier() - if model == 'lr': - self.models[model] = LogisticRegression() - if model == 'dt': - self.models[model] = DecisionTreeClassifier() - if model == 'ada': - self.models[model] = AdaBoostClassifier() - - def train(self, save_all_model=False, resample_flag=False, scoring='f1', cv=5): - """ - Train the pipeline with the given data. - - Parameters - ---------- - save_all_model : bool, optional - Whether to save best model of each model type to output directory. Default is False. - resample_flag : bool, optional - Whether to resample the data. Default is False - scoring : str, optional - The scoring function to use. Default is 'f1'. - cv : int, optional - The cross-validation split to use. Default is 5. - - Returns - ------- - Pipeline - Trained machine learning pipeline. - """ - - models = self.models - - if resample_flag: - self.X_train, self.y_train = self._resample(self.X_train, self.y_train) - - if self.preprocessing.steps is None: - print("No preprocessing steps") - model_path = None - best_score = 0 - for name, model in models.items(): - - param_grid = self.config['Models'][name]['param_grid'] - - log.info("Model: {}".format(name)) - - self.full_pipeline = Pipeline(steps=self.preprocessing.steps.copy(), verbose=True) - self.full_pipeline.steps.append((name, model)) - log.info(self.full_pipeline.steps) - - # model fitting - grid_search = GridSearchCV(self.full_pipeline, param_grid, scoring=scoring, verbose=2, cv=cv) - grid_search.fit(self.X_train, self.y_train) - - model_filename = f'{round(grid_search.best_score_, 3)}_{name}_best_model.pkl' - model_path = get_path(f'output/models/{model_filename}') - if save_all_model: - joblib.dump( - grid_search.best_estimator_, - model_path) - - if grid_search.best_score_ > best_score: - best_score = grid_search.best_score_ - self.best_model = grid_search.best_estimator_ - - log.info('*' * 50) - log.info(f'Best {name} Pipeline:') - log.info(grid_search.best_estimator_) - log.info(f'Best Score: {grid_search.best_score_}') - log.info('*' * 50) - log.info(f'Overall best model: {self.best_model}') - - # self.save_best_model(model_path=model_path) - - def save_best_model(self, model_name='best_model', model_path=None): - """ - Saves best model from training to the specified path in the config file. Optionally change name and/or path - of the model. - - Parameters - ------- - model_name : str, optional - Name of the model to be saved. Default='best_model'. - model_path : str, optional - Path to the model to be saved. Default='model_path' in config file - - - """ - if model_path is None: - model_path = get_path(f'{self.config['Outputs']['model_path']}/{model_name}') - else: - print(model_path) - model_path = get_path(model_path) - print(model_path) - - joblib.dump(self.best_model, model_path) - logging.info(f'Saved model to {model_path}') - - def load_best_model(self, model_name): - """ - Loads 'model_name' into current pipeline. - - Parameters - ------- - model_name : str - The name of the model from the Outputs/models/ directory to be loaded. - - - """ - model_path = get_path(f'{self.config['Outputs']['model_path']}/{model_name}') - self.best_model = joblib.load(model_path) - logging.info(f'Loaded {model_path}') - - def evaluate(self, name, plot=False, score=f1_score): - """ - Evaluate the best model with various metrics and visualization. - - Parameters - ---------- - name : str - The name for the evaluation output. - plot : bool, optional - If True, generates and saves evaluation plots, by default False. - score : callable, optional - The scoring function to use for evaluation, by default f1_score. - - """ - viz_path = self.config['Outputs']['viz_path'] - output_path = get_path(f'{viz_path}/{name}') - - print(self.best_model.steps) - if not os.path.exists(output_path): - os.makedirs(output_path) - print(f"Folder '{output_path}' created.") - else: - print(f"Folder '{output_path}' already exists.") - # INCLUDE case in titles - # model scoring - # - # if self.best_model.steps[-1][0] == 'knn' and plot: - # print('plotting') - # self.preprocessing.fit(self.X) - # X = pd.DataFrame(self.preprocessing.transform(self.X)) - # self.plot_knn_neighbors(knn=self.best_model.steps[-1][1], X=X, y=self.y, features=self.X_columns) - # print('done plotting') - # else: - print(self.best_model.steps[-1][1]) - - # Evaluate model performance - self._print_model_eval() - - if plot: - - _, ax_report = plt.subplots() - - report_viz = ClassificationReport(self.best_model, classes=["NOT_GRB", "IS_GRB"], support=True, - ax=ax_report) - report_viz.fit(self.X_train, self.y_train) # Fit the visualizer and the model - report_viz.score(self.X_test, self.y_test) # Evaluate the model on the test data - report_viz.show(outpath=output_path + '/classification_report.pdf') - - _, ax_cm_test = plt.subplots() - - cm_test_viz = ConfusionMatrix(self.best_model, classes=["NOT_GRB", "IS_GRB"], percent=True, axes=ax_cm_test) - cm_test_viz.fit(self.X_train, self.y_train) - cm_test_viz.score(self.X_test, self.y_test) - cm_test_viz.show(outpath=output_path + '/confusion_matrix_test.pdf') - - _, ax_cm_train = plt.subplots() - - cm_train_viz = ConfusionMatrix(self.best_model, classes=["NOT_GRB", "IS_GRB"], percent=True, ax=ax_cm_train) - cm_train_viz.fit(self.X_train, self.y_train) - cm_train_viz.score(self.X_train, self.y_train) - cm_train_viz.show(outpath=output_path + '/confusion_matrix_train.pdf') - - _, ax_roc = plt.subplots() - - roc_viz = ROCAUC(self.best_model, classes=["NOT_GRB", "IS_GRB"], ax=ax_roc) - roc_viz.fit(self.X_train, self.y_train) # Fit the training data to the visualizer - roc_viz.score(self.X_test, self.y_test) # Evaluate the model on the test data - roc_viz.show(outpath=output_path + '/ROC_AUC.pdf') - - _, ax_pr_curve = plt.subplots() - - pr_curve_viz = PrecisionRecallCurve(self.best_model, classes=["NOT_GRB", "IS_GRB"], ax=ax_pr_curve) - pr_curve_viz.fit(self.X_train, self.y_train) - pr_curve_viz.score(self.X_test, self.y_test) - pr_curve_viz.show(outpath=output_path + '/PR_curve.pdf') - - _, ax_class_pred = plt.subplots() - ax_class_pred.semilogy() - - class_pred_viz = ClassPredictionError(self.best_model, classes=["NOT_GRB", "IS_GRB"], ax=ax_class_pred) - class_pred_viz.fit(self.X_train, self.y_train) - class_pred_viz.score(self.X_test, self.y_test) - class_pred_viz.show(outpath=output_path + '/class_predictions.pdf') - # - if self.best_model.steps[-1][1] == RandomForestClassifier(): - _, ax_feature_imp = plt.subplots() - - feature_imp_viz = FeatureImportances(self.best_model.steps[-1][1], ax=ax_feature_imp) - feature_imp_viz.fit(self.X, self.y) - feature_imp_viz.show(outpath=output_path + '/feature_importances.pdf') - # if plot_extra: - # self.hyperparameter_valid_curve(outpath=output_path) - # # self.recursive_feature_elimination_plot(outpath=output_path) - # - - def predict(self, X, prob=False): - """ - Predict using the best model. - - Parameters - ---------- - X : DataFrame - The input features for prediction. - prob : bool, optional - If True, returns the probability of the predictions, by default False. - - Returns - ------- - ndarray - The predicted values or probabilities. - """ - X = X[self.X_columns] - if prob is True: - self.y_predict_prob = self.best_model.predict_proba(X) - return self.y_predict_prob - else: - self.y_predict = self.best_model.predict(X) - return self.y_predict - - @staticmethod - def _get_data(data_file: str): - """ - Load data from a parquet file. - - Parameters - ---------- - data_file : str - The name of the data file to load. - - Returns - ------- - DataFrame - The loaded data. - """ - data_path = get_path(f'/data/{data_file}') - print(data_path) - data = pd.read_parquet(data_path, engine='fastparquet') - return data - - def _load_data(self, data: pd.DataFrame, columns: list, target: str, test_size: float = 0.2): - """ - Load the data from the source specified in the config. - - Parameters - ---------- - data : pd.DataFrame - The data to load. - columns : list - The columns to load. - target: str - The target column. - test_size: float, optional - The size of the test sample as a fraction of the total sample. Default is 0.2. - - Returns - ------- - DataFrame - Loaded data. - """ - X = data[columns] - y = data[target] - self._split_data(X, y, test_size) - - def _split_data(self, X, y, test_size): - """ - Split data into training and testing sets. - - Parameters - ---------- - X : DataFrame - The input features. - y : array-like - The target values. - test_size : float - The proportion of the dataset to include in the test split. - - Returns - ------- - None - """ - (self.X_train, - self.X_test, - self.y_train, - self.y_test) = train_test_split(X, y, - test_size=test_size, - random_state=123) - - # _, ax_class_balance = plt.subplots() - # ax_class_balance.semilogy() - # class_balance_visualizer = ClassBalance(labels=["NOT_GRB", "GRB"], ax=ax_class_balance, - # kwargs={'verbose': 2}) - # class_balance_visualizer.fit(self.y_train, self.y_test) # Fit the data to the visualizer - # class_balance_visualizer.show(outpath='/output/visualizations/class_balance.pdf') - - @staticmethod - def _resample(X, y): - """ - Resamples the input data - - Parameters - ------- - X : pd.DataFrame - input data - y : pd.Series - input label - - Returns - ------- - X_ : pd.DataFrame - resampled data - y_ : pd.Series - resampled label - """ - sm = SMOTE(sampling_strategy='minority', random_state=42) - X_, y_ = sm.fit_resample(X, y) - return X_, y_ - - def _create_pipe(self, steps): - """ - Create the machine learning pipeline from the given steps. - - Parameters - ------- - steps : list - The steps to use for the machine learning preprocessing pipeline. - - Returns - ------- - Pipeline - The created machine learning pipeline. - """ - for step in steps: - self.preprocessing.steps.append(step) - - def _print_model_eval(self): - """ - Prints the evaluation of the model, mean average error (MAE), root mean squared error (RMSE), - f1 score and confusion matrices for training and testing datasets. - """ - - train_pred = self.best_model.predict(self.X_train) - test_pred = self.best_model.predict(self.X_test) - - train_conf_matrix = confusion_matrix(self.y_train, train_pred) - test_conf_matrix = confusion_matrix(self.y_test, test_pred) - print('*' * 50) - print('Training score:') - print( - f'MAE: {round(mean_absolute_error(self.y_train, train_pred), 4)} ' - f'| RMSE: {round(mean_squared_error(self.y_train, train_pred, squared=False), 4)} ' - f'| F1: {round(f1_score(self.y_train, train_pred), 4)}' - ) - print('Confusion Matrix:') - print(train_conf_matrix) - print('-' * 20) - print('Validation score:') - print( - f'MAE: {round(mean_absolute_error(self.y_test, test_pred), 4)} ' - f'| RMSE: {round(mean_squared_error(self.y_test, test_pred, squared=False), 4)} ' - f'| F1: {round(f1_score(self.y_test, test_pred), 4)}' - ) - print('Confusion Matrix:') - print(test_conf_matrix) - -# def hyperparameter_valid_curve(self, outpath): -# """ -# Validate hyperparameters and generate validation curves. -# -# Parameters -# ---------- -# outpath : str -# The output path where the validation curve plots will be saved. -# -# Returns -# ------- -# None -# """ -# best_model_name = self.best_model.steps[-1][0] -# param_grid = self.config['Models'][best_model_name]['param_grid'] -# for param in param_grid: -# # self.preprocessing.fit(self.X, self.y) -# # processed_X = self.preprocessing.transform(self.X) -# # processed_y = self.y -# param_range = param_grid[param] -# param_name = param.split('__')[1] -# print(f'Validating {param_name} over range {param_range}') -# _, ax_valid_curve = plt.subplots() -# -# valid_curve_viz = ValidationCurve(self.best_model, -# param_name=param, -# param_range=param_range, -# cv=5, -# scoring="f1", -# ax=ax_valid_curve -# ) -# valid_curve_viz.fit(self.X, self.y) -# valid_curve_viz.show(outpath=f'{outpath}/{param_name}_valid_curve.pdf') -# -# def recursive_feature_elimination_plot(self, outpath): -# """ -# Generate a recursive feature elimination plot. -# -# Parameters -# ---------- -# outpath : str -# The output path where the feature elimination plot will be saved. -# -# Returns -# ------- -# None -# """ -# _, ax_feature_elimination = plt.subplots() -# visualizer = RFECV(self.best_model.steps[-1][1], cv=5, scoring='f1_weighted', ax=ax_feature_elimination) -# visualizer.fit(self.X, self.y) # Fit the data to the visualizer -# visualizer.show(outpath=outpath + 'feature_elimination.pdf') -# -# @staticmethod -# def plot_knn_neighbors(knn, X, y, features): -# """ -# Plot the KNN neighbors for a given dataset. -# -# Parameters -# ---------- -# knn : KNeighborsClassifier -# The KNN classifier. -# X : DataFrame -# The dataset containing the features. -# y : array-like -# The target values. -# features : list -# List of feature names to plot. -# -# Returns -# ------- -# None -# """ -# -# # Select a random point -# random_index = np.random.randint(0, len(X)) -# random_point = X.iloc[random_index] -# -# # Find the neighbors of the random point -# neighbors = knn.kneighbors([random_point], return_distance=False) -# -# # Plot each pair of features -# num_features = len(features) -# for i in range(num_features): -# for j in range(i + 1, num_features): -# plt.figure(figsize=(8, 6)) -# plt.scatter(X.iloc[:, i], X.iloc[:, j], c=y, cmap='viridis', marker='o', edgecolor='k', s=50) -# plt.scatter(random_point[i], random_point[j], c='red', marker='x', s=200, label='Random Point') -# plt.scatter(X.iloc[neighbors[0], i], X.iloc[neighbors[0], j], c='red', marker='o', edgecolor='k', s=100, -# facecolors='none', label='Neighbors') -# plt.xlabel(features[i]) -# plt.ylabel(features[j]) -# plt.title(f'KNN Neighbors with {features[i]} vs {features[j]}') -# plt.legend() -# plt.savefig( -# f'/output/visualizations/knn_plots/{features[i]}_vs_{features[j]}_knn_neighbors.pdf' -# ) - - diff --git a/vtacML/preparation.py b/vtacML/preparation.py deleted file mode 100644 index ef85e38..0000000 --- a/vtacML/preparation.py +++ /dev/null @@ -1,92 +0,0 @@ -import os -import shutil -import pandas as pd -import numpy as np -from matplotlib import pyplot as plt -import seaborn as sns -from sklearn.base import BaseEstimator, TransformerMixin -from sklearn.utils.class_weight import compute_class_weight -from sklearn.preprocessing import OneHotEncoder -from sklearn.impute import SimpleImputer - - -class OneHotEncoderCustom(BaseEstimator, TransformerMixin): - def __init__(self, variables): - self.variables = variables - self.ohe = OneHotEncoder( - handle_unknown='ignore') - - def fit(self, X, y=None): - X_ = X.loc[:, self.variables] - self.ohe.fit(X_) - return self - - def transform(self, X): - X_ = X.loc[:, self.variables] - X_transformed = pd.DataFrame(self.ohe.transform(X_).toarray(), - columns=self.ohe.get_feature_names_out()) - X.drop(self.variables, axis=1, inplace=True) - X[self.ohe.get_feature_names_out()] = X_transformed[self.ohe.get_feature_names_out()].values - return X - - -class Cleaner(BaseEstimator, TransformerMixin): - def __init__(self, variables): - self.variables = variables - self.fitted = False - self.y = None - - def handle_nans(self, X, y=None): - X_ = X.copy() - for column in self.variables: - if X[column].isna().sum(): - # Create the isna column - new_column_name = f'{column}_isnan' - X_[new_column_name] = X_[column].isna().astype(int) - # Impute missing values with the mean - impute_value = X_[column].median() - X_[column].fillna(impute_value, inplace=True) - - return X_ - - def clean_binary(self, X, col, y=None): - X[col] = X[col].str.decode("utf-8").apply(lambda x: 1 if x else 0) - return X - - def handle_outliers(self, X, y=None): - - return self - - def fit(self, X, y=None): - self.y = y - self.fitted = True - return self # Nothing to fit, just return self - - def transform(self, X, y=None): - e_flags = ['EFLAG_R0', - 'EFLAG_R1', - 'EFLAG_R2', - 'EFLAG_R3', - 'EFLAG_B0', - 'EFLAG_B1', - 'EFLAG_B2', - 'EFLAG_B3'] - - e_flags_in_cols = [x for x in self.variables if x in e_flags] - - if not self.fitted: - raise ValueError("The transformer has not been fitted yet. " - "Call 'fit' before 'transform'.") - - X_ = X.loc[:, self.variables] - transformed_X = self.handle_nans(X_) # Store the transformed data - - if len(e_flags_in_cols) > 0: - for e_flag in e_flags_in_cols: - transformed_X[e_flag] = transformed_X[e_flag].fillna(-1) - if 'NEW_SRC' in self.variables: - transformed_X = self.clean_binary(transformed_X, 'NEW_SRC') - if 'DMAG_CAT' in self.variables: - transformed_X = self.clean_binary(transformed_X, 'DMAG_CAT') - - return transformed_X.dropna() # Return the transformed data diff --git a/vtacML/search_best_model.py b/vtacML/search_best_model.py deleted file mode 100644 index 80a9577..0000000 --- a/vtacML/search_best_model.py +++ /dev/null @@ -1,7 +0,0 @@ -from .pipeline import VTACMLPipe - -if __name__ == "__main__": - vtac_ml = VTACMLPipe() - - vtac_ml.train() - vtac_ml.save_best_model() \ No newline at end of file diff --git a/vtacML/utils.py b/vtacML/utils.py deleted file mode 100644 index 3106833..0000000 --- a/vtacML/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -from pathlib import Path - - -# Define the root directory -ROOTDIR = Path(__file__).parent - - -def get_path(subpath): - """Utility function to get the path to a subpath relative to ROOTDIR.""" - print(f'rootdir: {ROOTDIR}/{subpath}') - return f'{ROOTDIR}/{subpath}'