Simplify executables structure

executables/bestest_hydronic_heat_pump/P_hp.py

@@ -1,45 +1,11 @@
-import os
-
-from physXAI.preprocessing.preprocessing import PreprocessingSingleStep
-from physXAI.preprocessing.constructed import Feature
 from physXAI.models.models import LinearRegressionModel
 from physXAI.models.ann.ann_design import ClassicalANNModel, CMNNModel, LinANNModel
-from physXAI.utils.logging import Logger
-
+from configuration import *
 
 """
 Creates standard models to predict the power of the heat pump using the Boptest data.
 """
 
-# Setup up logger for saving
-Logger.setup_logger(folder_name='P_hp', override=True)
-
-# File path to data
-file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
-
-# List of input features. Can include constructed features
-inputs = ['oveHeaPumY_u', 'Func(logistic)', 'weaSta_reaWeaTDryBul_y', 'reaTZon_y']
-# Output feature
-output = 'reaPHeaPum_y'
-
-""" 
-Example how to use constructed features. 
-The constructed features are automatically added to the data via 'physXAI.preprocessing.constructed.py' 
-The names of the constructed features should be added to the input list
-"""
-# x = Feature('weaSta_reaWeaTDryBul_y')  # Create Feature for calculation
-# x.lag(1)  # Lags can be added directly based on the feature
-# x.exp()
-# y = Feature('reaTZon_y')
-# z = x + y  # Arithmetic operations can be performed on Features to create constructed Features
-# z.rename('test')  # Constructed features derive a name based on the arithmetic operation.
-# It is recommended to rename features, so that they can be easily added to the input list
-
-# Create Training data
-prep = PreprocessingSingleStep(inputs, output)
-# Process Training data
-td = prep.pipeline(file_path)
-
 """Example usages of different models"""
 # m = LinearRegressionModel()  # Linear Regression
 m = ClassicalANNModel(epochs=50)  # Classical ANN
@@ -52,14 +18,4 @@
 # epochs=50)  # Constrained Monotonic Neural Network (CMNN)
 # m = LinANNModel(epochs=50)  # A hybrid model combining a Linear Regression model with an ANN (RBF)
 
-# Training pipeline
-model = m.pipeline(td)
-
-"""Example usage of online learning"""
-m.epochs = 5
-model_ol = m.online_pipeline(td, os.path.join(Logger._logger, 'model.keras'))
-
-# Log setup of preprocessing and model as json
-Logger.log_setup(prep, m)
-# Log training data as pickle
-Logger.save_training_data(td)
+bestest_php.pipeline(m, online_epochs=5)

executables/bestest_hydronic_heat_pump/P_hp_FeatureSelection.py

@@ -1,42 +1,23 @@
 from physXAI.feature_selection.recursive_feature_elimination import recursive_feature_elimination_pipeline
 from physXAI.models.models import LinearRegressionModel
-from physXAI.preprocessing.constructed import Feature
-from physXAI.preprocessing.preprocessing import PreprocessingSingleStep
-from physXAI.utils.logging import Logger
-
+from configuration import *
 
 """
 Feature selection for linear regression to predict the power of the heat pump using the Boptest data
 """
-# Setup up logger for saving
-Logger.setup_logger(folder_name='P_hp_feature', override=True)
-
-# File path to data
-file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
-
-# List of all possible input features candidates.
-inputs = ['oveHeaPumY_u', 'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2', 'Func(logistic)', 'Func(logistic)_lag1',
-          'Func(logistic)_lag2', 'weaSta_reaWeaTDryBul_y', 'weaSta_reaWeaTDryBul_y_lag1',
-          'weaSta_reaWeaTDryBul_y_lag2', 'reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2']
-# Output feature
-output = 'reaPHeaPum_y'
 
+# List of all possible input features candidates (overwrite default set in configuration.py)
+bestest_php.inputs.extend(['oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2', 'Func(logistic)_lag1', 'Func(logistic)_lag2',
+                           'weaSta_reaWeaTDryBul_y_lag1', 'weaSta_reaWeaTDryBul_y_lag2', 'reaTZon_y_lag1',
+                           'reaTZon_y_lag2'])
 # Create lags
-x1 = Feature('oveHeaPumY_u')
-x1.lag(2)
-x2 = Feature('Func(logistic)')
-x2.lag(2)
-x3 = Feature('weaSta_reaWeaTDryBul_y')
-x3.lag(2)
-x4 = Feature('reaTZon_y')
-x4.lag(2)
-
-# Generic Preprocessing Pipeline
-# Model is output model, so single step evaluation is choosen
-prep = PreprocessingSingleStep(inputs, output)
+u_hp.lag(2)
+u_hp_logistic.lag(2)
+t_amb.lag(2)
+TAirRoom.lag(2)
 
 # Generic Model
 m = LinearRegressionModel()
 
 # Feature Selection
-fs = recursive_feature_elimination_pipeline(file_path, prep, m, ascending_lag_order=True)
+fs = bestest_php.recursive_feature_elimination_pipeline(m, ascending_lag_order=True)

executables/bestest_hydronic_heat_pump/P_hp_pinn.py

@@ -1,35 +1,17 @@
-from physXAI.preprocessing.preprocessing import PreprocessingSingleStep
-from physXAI.preprocessing.constructed import Feature
 from physXAI.models.ann.ann_design import PINNModel
-from physXAI.utils.logging import Logger
+from configuration import *
 
 
 """
 Creates a physics-informed model (PINN) to predict the power of the heat pump using the Boptest data
 """
-# Setup up logger for saving
-Logger.setup_logger(folder_name='P_hp_pinn', override=True)
 
-# File path to data
-file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
-
-# List of input features. Can include constructed features
-inputs = ['oveHeaPumY_u', 'Func(logistic)', 'weaSta_reaWeaTDryBul_y', 'reaTZon_y']
 
 """
 Output feature(s). In addition to the standard data-driven loss, the PINN includes a physical loss function, 
 which is created as a constructed feature
 """
-output = ['reaPHeaPum_y', 'pinn']
-
-""" 
-The constructed features are automatically added to the data via 'physXAI.preprocessing.constructed.py' 
-The names of the constructed features should be added to the input list
-"""
-u_hp = Feature('oveHeaPumY_u')
-u_hp_logistic = Feature('Func(logistic)')
-t_amb = Feature('weaSta_reaWeaTDryBul_y')
-TAirRoom = Feature('reaTZon_y')
+bestest_php.outputs.append('pinn')
 
 """
 Arithmetic operations can be performed on Features to create constructed Features
@@ -38,22 +20,11 @@
 pinn = (u_hp * 10000 * ((TAirRoom + 15 - t_amb) / ((TAirRoom + 15) * 0.55)) + (1110 + 500) * u_hp_logistic)
 pinn.rename('pinn')
 
-# Create Training data
-prep = PreprocessingSingleStep(inputs, output)
-# Process Training data
-td = prep.pipeline(file_path)
-
 """
 Create PINN model. 
 pinn_weights are used to balance the individual loss therms of the PINN, their length should be `num_outputs - 1`
 This ´PINN implementation uses a CMNN as its base architecture
 """
 m = PINNModel(pinn_weights=[1], epochs=50)
 
-# Training pipeline
-model = m.pipeline(td)
-
-# Log setup of preprocessing and model as json
-Logger.log_setup(prep, m)
-# Log training data as pickle
-Logger.save_training_data(td)
+bestest_php.pipeline(m)

executables/bestest_hydronic_heat_pump/TAir.py

@@ -1,39 +1,9 @@
-from physXAI.preprocessing.constructed import Feature
 from physXAI.models.ann.ann_design import CMNNModel
-from physXAI.preprocessing.preprocessing import PreprocessingSingleStep
-from physXAI.utils.logging import Logger
-
+from configuration import *
 
 """
 Creates a constrained monotonic neural network to predict the air temperature using the Boptest data
 """
-# Setup up logger for saving
-Logger.setup_logger(folder_name='TAir', override=True)
-
-# File path to data
-file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
-
-# List of input features. Can include constructed features and lagged inputs
-inputs = ['reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2', 'weaSta_reaWeaTDryBul_y', 'weaSta_reaWeaTDryBul_y_lag1',
-          'weaSta_reaWeaHDirNor_y', 'oveHeaPumY_u', 'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2']
-# Output feature
-output = 'Change(T_zone)'
-
-""" 
-The constructed features are automatically added to the data via 'physXAI.preprocessing.constructed.py' 
-Lagged inputs can be added directly based on the feature
-"""
-x1 = Feature('reaTZon_y')
-x1.lag(2)  # reaTZon_y_lag1, reaTZon_y_lag2
-x2 = Feature('weaSta_reaWeaTDryBul_y')
-x2.lag(1)  # weaSta_reaWeaTDryBul_y_lag1
-x3 = Feature('oveHeaPumY_u')
-x3.lag(2)  # oveHeaPumY_u_lag1, oveHeaPumY_u_lag2
-
-# Create Training data
-prep = PreprocessingSingleStep(inputs, output)
-# Process Training data
-td = prep.pipeline(file_path)
 
 # Constrained Monotonic Neural Network (CMNN)
 m = CMNNModel(monotonies={
@@ -49,10 +19,4 @@
 
 }, activation_split=[1, 1, 1], epochs=100)
 
-# Training pipeline
-model = m.pipeline(td)
-
-# Log setup of preprocessing and model as json
-Logger.log_setup(prep, m)
-# Log training data as pickle
-Logger.save_training_data(td)
+bestest_tair.pipeline(m)

executables/bestest_hydronic_heat_pump/TAir_FeatureSelection.py

@@ -1,44 +1,34 @@
-from physXAI.feature_selection.recursive_feature_elimination import recursive_feature_elimination_pipeline
 from physXAI.models.models import LinearRegressionModel
-from physXAI.preprocessing.constructed import Feature
 from physXAI.preprocessing.preprocessing import PreprocessingMultiStep
-from physXAI.utils.logging import Logger
+from configuration import *
 
 
 """
 Feature selection for linear regression to predict the air temperature using the Boptest data
 """
-# Setup up logger for saving
-Logger.setup_logger(folder_name='TAir_feature', override=True)
 
-# File path to data
-file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
-
-# List of all possible input features candidates.
-inputs = ['reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2', 'weaSta_reaWeaTDryBul_y', 'weaSta_reaWeaTDryBul_y_lag1',
-          'weaSta_reaWeaTDryBul_y_lag2', 'weaSta_reaWeaHDirNor_y', 'weaSta_reaWeaHDirNor_y_lag1',
-          'weaSta_reaWeaHDirNor_y_lag2', 'oveHeaPumY_u', 'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2']
-# Output feature
-output = 'Change(T_zone)'
+# List of all possible input features candidates (overwrite default set in configuration.py)
+bestest_tair.inputs = ['reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2', 'weaSta_reaWeaTDryBul_y',
+                       'weaSta_reaWeaTDryBul_y_lag1', 'weaSta_reaWeaTDryBul_y_lag2', 'weaSta_reaWeaHDirNor_y',
+                       'weaSta_reaWeaHDirNor_y_lag1', 'weaSta_reaWeaHDirNor_y_lag2', 'oveHeaPumY_u',
+                       'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2']
 
 # Create lags
-x1 = Feature('reaTZon_y')
-x1.lag(2)
-x2 = Feature('weaSta_reaWeaTDryBul_y')
-x2.lag(2)
-x3 = Feature('weaSta_reaWeaHDirNor_y')
-x3.lag(2)
-x4 = Feature('oveHeaPumY_u')
-x4.lag(2)
+TAirRoom.lag(2)
+t_amb.lag(2)
+q_sol = Feature('weaSta_reaWeaHDirNor_y')
+q_sol.lag(2)
+u_hp.lag(2)
 
 # Generic Preprocessing Pipeline
 # Model is state model, so multi-step evaluation is choosen
 # See example TAir_evaluateMultiStep.py for more information
-prep = PreprocessingMultiStep(inputs, output, 48, 0, init_features=['reaTZon_y'],
-                              overlapping_sequences=False, batch_size=1)
+bestest_tair.warmup_width = 0  # overwrite default set in configuration.py
+prep = bestest_tair.get_preprocessing_multi_step(init_features=['reaTZon_y'], overlapping_sequences=False, batch_size=1)
 
 # Generic Model
 m = LinearRegressionModel()
 
 # Feature Selection
-fs = recursive_feature_elimination_pipeline(file_path, prep, m, use_multi_step_error=True, ascending_lag_order=True)
+fs = bestest_tair.recursive_feature_elimination_pipeline(m, prep=prep, use_multi_step_error=True,
+                                                         ascending_lag_order=True)

executables/bestest_hydronic_heat_pump/TAir_evaluateMultiStep.py

@@ -1,36 +1,12 @@
 from physXAI.models.models import LinearRegressionModel
-from physXAI.preprocessing.constructed import Feature
-from physXAI.models.ann.ann_design import ClassicalANNModel
 from physXAI.preprocessing.preprocessing import PreprocessingMultiStep
-from physXAI.utils.logging import Logger
-
+from configuration import *
 
 """
 Creates a linear regression to predict the air temperature using the Boptest data
 The model is trained as a single step model, but evaluated as a multi step model
 """
-# Setup up logger for saving
-Logger.setup_logger(folder_name='TAir', override=True)
-
-# File path to data
-file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
 
-# List of input features. Can include constructed features and lagged inputs
-inputs = ['reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2', 'weaSta_reaWeaTDryBul_y', 'weaSta_reaWeaTDryBul_y_lag1',
-          'weaSta_reaWeaHDirNor_y', 'oveHeaPumY_u', 'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2']
-# Output feature
-output = 'Change(T_zone)'
-
-""" 
-The constructed features are automatically added to the data via 'physXAI.preprocessing.constructed.py' 
-Lagged inputs can be added directly based on the feature
-"""
-x1 = Feature('reaTZon_y')
-x1.lag(2)  # reaTZon_y_lag1, reaTZon_y_lag2
-x2 = Feature('weaSta_reaWeaTDryBul_y')
-x2.lag(1)  # weaSta_reaWeaTDryBul_y_lag1
-x3 = Feature('oveHeaPumY_u')
-x3.lag(2)  # oveHeaPumY_u_lag1, oveHeaPumY_u_lag2
 
 # Create Training data
 """
@@ -42,18 +18,10 @@
 overlapping_sequence should be False to avoid duplicate labels for single step prediction
 batch_size should be 1 as batches are processes differently in single step models
 """
-prep = PreprocessingMultiStep(inputs, output, 48, 0, init_features=['reaTZon_y'],
-                              overlapping_sequences=False, batch_size=1)
-# Process Training data
-td = prep.pipeline(file_path)
+bestest_tair.warmup_width = 0  # overwrite default set in configuration.py
+prep = bestest_tair.get_preprocessing_multi_step(init_features=['reaTZon_y'], overlapping_sequences=False, batch_size=1)
 
 # Linear Regression
 m = LinearRegressionModel()
 
-# Training pipeline
-model = m.pipeline(td)
-
-# Log setup of preprocessing and model as json
-Logger.log_setup(prep, m)
-# Log training data as pickle
-Logger.save_training_data(td)
+bestest_tair.pipeline(m, prep)