From 4615df371365fdab9f2a51ae270c754595891353 Mon Sep 17 00:00:00 2001 From: stephanbreimann Date: Thu, 5 Oct 2023 19:18:30 +0200 Subject: [PATCH] Add _backend to project structure --- aaanalysis/__init__.py | 33 ++-- .../__pycache__/__init__.cpython-39.pyc | Bin 809 -> 754 bytes .../__pycache__/utils_cpp.cpython-39.pyc | Bin 10638 -> 10638 bytes aaanalysis/_utils/utils_cpp.py | 2 +- aaanalysis/data_handling/__init__.py | 9 +- .../__pycache__/__init__.cpython-39.pyc | Bin 376 -> 330 bytes ...on-39.pyc => _load_dataset.cpython-39.pyc} | Bin 9123 -> 9125 bytes ...hon-39.pyc => _load_scales.cpython-39.pyc} | Bin 6371 -> 6373 bytes ...data_read_write.py => _data_read_write.py} | 0 .../{load_dataset_.py => _load_dataset.py} | 0 .../{load_scales_.py => _load_scales.py} | 0 ...ales_filtering.py => _scales_filtering.py} | 0 .../__init__.py | 0 .../tree_based.py | 4 +- aaanalysis/feature_engineering/__init__.py | 21 ++- .../__pycache__/__init__.cpython-39.pyc | Bin 657 -> 499 bytes 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-from aaanalysis.config import options +from .config import options -__all__ = ["load_dataset", "load_scales", - "AAclust", "AAclustPlot", - "CPP", "CPPPlot", "SequenceFeature", "SplitRange", - "dPULearn", "plot_get_clist", "plot_get_cmap", "plot_get_cdict", - "plot_settings", "plot_legend", "plot_gcfs", "options"] +__all__ = [ + "load_dataset", + "load_scales", + "AAclust", + "AAclustPlot", + "CPP", + "CPPPlot", + "SequenceFeature", + "SplitRange", + "dPULearn", + "plot_get_clist", + "plot_get_cmap", + "plot_get_cdict", + "plot_settings", + "plot_legend", + "plot_gcfs", + "options" +] diff --git a/aaanalysis/__pycache__/__init__.cpython-39.pyc b/aaanalysis/__pycache__/__init__.cpython-39.pyc index 0d25d2962b0a7adf963b12eca9618b6193105279..00b90d02c12caea178f2af12a4e1a452e39ea239 100644 GIT binary patch delta 118 zcmZ3<_KB4@k(ZZ?0SIBNiLV8j(xxX(JtS*?Me}E&X$pvN^)7#Z7E69WLnNam9&u@yhHjKeoM|c z&CG+^5bQN;{dc5Gca9}8|-hXX<8?(P%>X50jRe; 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from aaanalysis.template_classes import Wrapper import aaanalysis.utils as ut -from aaanalysis.feature_engineering.aaclust._aaclust import (estimate_lower_bound_n_clusters, optimize_n_clusters, merge_clusters, - compute_centers, compute_medoids) -from aaanalysis.feature_engineering.aaclust._aaclust_bic import bic_score -from aaanalysis.feature_engineering.aaclust._aaclust_comp_corr import compute_correlation -from aaanalysis.feature_engineering.aaclust._aaclust_name_clusters import name_clusters - +from ._backend.aaclust.aaclust_fit import estimate_lower_bound_n_clusters, optimize_n_clusters, merge_clusters +from ._backend.aaclust.aaclust_eval import bic_score +from ._backend.aaclust.aaclust_methods import (compute_centers, compute_medoids, + name_clusters, + compute_correlation) # I Helper Functions # Check parameter functions @@ -159,7 +158,7 @@ def fit(self, X: ut.ArrayLike2D, n_clusters: Optional[int] = None, on_center: bool = True, - min_th: float = 0, + min_th: float = 0.3, merge_metric: Union[str, None] = "euclidean", names: Optional[List[str]] = None) -> "AAclust": """ @@ -229,8 +228,7 @@ def fit(self, check_match_X_n_clusters(X=X, n_clusters=n_clusters, accept_none=True) check_match_X_names(X=X, names=names, accept_none=True) - args = dict(model=self.model_class, model_kwargs=self._model_kwargs, min_th=min_th, on_center=on_center, - verbose=self._verbose) + args = dict(model=self.model_class, model_kwargs=self._model_kwargs, min_th=min_th, on_center=on_center) # Clustering using given clustering models if n_clusters is not None: @@ -240,15 +238,21 @@ def fit(self, # Clustering using AAclust algorithm else: # Step 1.: Estimation of lower bound of k (number of clusters) + if self._verbose: + ut.print_out("1. Estimation of lower bound of k (number of clusters)", end="") n_clusters_lb = estimate_lower_bound_n_clusters(X, **args) # Step 2. Optimization of k by recursive clustering + if self._verbose: + objective_fct = "min_cor_center" if on_center else "min_cor_all" + ut.print_out(f"2. Optimization of k by recursive clustering ({objective_fct}, min_th={min_th}, k={n_clusters_lb})", end="") n_clusters = optimize_n_clusters(X, n_clusters=n_clusters_lb, **args) self.model = self.model_class(n_clusters=n_clusters, **self._model_kwargs) labels = self.model.fit(X).labels_.tolist() # Step 3. Cluster merging (optional) if merge_metric is not None: - labels = merge_clusters(X, labels=labels, min_th=min_th, on_center=on_center, - metric=merge_metric, verbose=self._verbose) + if self._verbose: + ut.print_out(f"3. Cluster merging (k={len(labels)})", end="") + labels = merge_clusters(X, labels=labels, min_th=min_th, on_center=on_center, metric=merge_metric) n_clusters = len(set(labels)) # Obtain cluster centers and medoids diff --git a/aaanalysis/feature_engineering/aaclust_plot_.py b/aaanalysis/feature_engineering/_aaclust_plot.py similarity index 93% rename from aaanalysis/feature_engineering/aaclust_plot_.py rename to aaanalysis/feature_engineering/_aaclust_plot.py index bb532bce..d7b69cfe 100644 --- a/aaanalysis/feature_engineering/aaclust_plot_.py +++ b/aaanalysis/feature_engineering/_aaclust_plot.py @@ -1,18 +1,15 @@ """ This is a script for the plotting class of AAclust. """ -import time -import pandas as pd -import numpy as np from sklearn.decomposition import PCA from typing import Optional, Dict, Union, List, Tuple, Type from sklearn.base import TransformerMixin -import matplotlib.pyplot as plt -import seaborn as sns import aaanalysis as aa import aaanalysis.utils as ut -from aaanalysis.feature_engineering.aaclust_plot._aaclust_plot_eval import plot_eval + +from ._backend.aaclust_plot.aaclust_plot_eval import plot_eval + # I Helper Functions def _get_components(data=None, model_class=None): diff --git a/aaanalysis/feature_engineering/aaclust/__init__.py b/aaanalysis/feature_engineering/_backend/__init__.py similarity index 100% rename from aaanalysis/feature_engineering/aaclust/__init__.py rename to aaanalysis/feature_engineering/_backend/__init__.py diff --git a/aaanalysis/feature_engineering/_backend/__pycache__/__init__.cpython-39.pyc b/aaanalysis/feature_engineering/_backend/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 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zu9vpjN$&6ur-Xbl;D=y%l*&hpieSb^bDV3KqHuK8dSsoE&{K<_XrOqZ)0Mq;^rpZU gd>n0Cw6Um|6|qz-7t2*c;P315bLA(>rSigm0DAq2%>V!Z literal 0 HcmV?d00001 diff --git a/aaanalysis/feature_engineering/_backend/aaclust/_utils_aaclust.py b/aaanalysis/feature_engineering/_backend/aaclust/_utils_aaclust.py new file mode 100644 index 00000000..5fbec81d --- /dev/null +++ b/aaanalysis/feature_engineering/_backend/aaclust/_utils_aaclust.py @@ -0,0 +1,23 @@ +""" +This is a script for utility functions for aaclust object. +""" +import numpy as np + + +# II Main Functions +def _cluster_center(X): + """Compute cluster center (i.e., arithmetical mean over all data points/observations of a cluster)""" + return X.mean(axis=0)[np.newaxis, :] + + +def _cluster_medoid(X): + """Obtain cluster medoids (i.e., scale closest to cluster center used as representative scale for a cluster)""" + # Create new array with cluster center and given array + center_X = np.concatenate([_cluster_center(X), X], axis=0) + # Get index for scale with the highest correlation with cluster center + ind_max = np.corrcoef(center_X)[0, 1:].argmax() + return ind_max + + + + diff --git a/aaanalysis/feature_engineering/aaclust/_aaclust_bic.py b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_eval.py similarity index 95% rename from aaanalysis/feature_engineering/aaclust/_aaclust_bic.py rename to aaanalysis/feature_engineering/_backend/aaclust/aaclust_eval.py index 34196bf6..3126da17 100644 --- a/aaanalysis/feature_engineering/aaclust/_aaclust_bic.py +++ b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_eval.py @@ -1,12 +1,14 @@ """ -This is a script for computing the Bayesian Information Criterion (BIC). +This is a script for computing the Bayesian Information Criterion (BIC) used in the AAclust.eval() method. """ import numpy as np from scipy.spatial import distance -from aaanalysis.feature_engineering.aaclust._aaclust import compute_centers + +from .aaclust_methods import compute_centers # I Helper Functions + # II Main function def bic_score(X, labels=None): """Computes the BIC metric for given clusters. @@ -53,5 +55,4 @@ def bic_score(X, labels=None): bic_components = size_clusters * (log_size_clusters - log_n_samples) - 0.5 * size_clusters * n_features * log_bcv - 0.5 * (size_clusters - 1) * n_features bic = np.sum(bic_components) - const_term - return bic \ No newline at end of file diff --git a/aaanalysis/feature_engineering/aaclust/_aaclust.py b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_fit.py similarity index 80% rename from aaanalysis/feature_engineering/aaclust/_aaclust.py rename to aaanalysis/feature_engineering/_backend/aaclust/aaclust_fit.py index c19eff32..88a5bdbd 100644 --- a/aaanalysis/feature_engineering/aaclust/_aaclust.py +++ b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_fit.py @@ -13,39 +13,9 @@ from sklearn.metrics.pairwise import pairwise_distances import aaanalysis.utils as ut - +from ._utils_aaclust import _cluster_center # I Helper Functions -def _cluster_center(X): - """Compute cluster center (i.e., arithmetical mean over all data points/observations of a cluster)""" - return X.mean(axis=0)[np.newaxis, :] - -def compute_centers(X, labels=None): - """Obtain cluster centers and their labels""" - center_labels = list(OrderedDict.fromkeys(labels)) - list_masks = [[True if i == label else False for i in labels] for label in center_labels] - centers = np.concatenate([_cluster_center(X[mask]) for mask in list_masks]).round(3) - return centers, np.array(center_labels) - -def _cluster_medoid(X): - """Obtain cluster medoids (i.e., scale closest to cluster center used as representative scale for a cluster)""" - # Create new array with cluster center and given array - center_X = np.concatenate([_cluster_center(X), X], axis=0) - # Get index for scale with the highest correlation with cluster center - ind_max = np.corrcoef(center_X)[0, 1:].argmax() - return ind_max - - -def compute_medoids(X, labels=None): - """Obtain cluster medoids and their labels""" - unique_labels = list(OrderedDict.fromkeys(labels)) - list_masks = [[True if i == label else False for i in labels] for label in unique_labels] - list_ind_max = [_cluster_medoid(X[mask]) for mask in list_masks] - indices = np.array(range(0, len(labels))) - medoid_ind = [indices[m][i] for m, i in zip(list_masks, list_ind_max)] - medoid_labels = np.array([labels[i] for i in medoid_ind]) - medoids = np.array([X[i, :] for i in medoid_ind]) - return medoids, medoid_labels, medoid_ind # Compute minimum correlation on center or all scales @@ -99,7 +69,7 @@ def get_max_dist(X, on_center=True, metric="euclidean"): # II Main Functions # 1. Step (Estimation of n clusters) @ut.catch_convergence_warning() -def _estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0.6, on_center=True, verbose=True): +def _estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0.3, on_center=True): """ Estimate the lower bound of the number of clusters (k). @@ -119,16 +89,12 @@ def _estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0. on_center : bool, optional, default = True Whether the minimum correlation is computed for all observations within a cluster or just for the cluster center. - verbose : bool, optional, default = False - A flag to enable or disable verbose outputs. Returns ------- n_clusters : int Estimated lower bound for the number of clusters (k). """ - if verbose: - ut.print_out("1. Estimation of lower bound of k (number of clusters)", end="") f = lambda c: get_min_cor(X, labels=model(n_clusters=c, **model_kwargs).fit(X).labels_, on_center=on_center) # Create range between 10% and 90% of all scales (10% steps) as long as minimum correlation is lower than threshold n_samples, n_features = X.shape @@ -144,16 +110,13 @@ def _estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0. # Select second highest lower bound (highest lower bound is faster but might surpass true bound) nclust_mincor.sort(key=lambda x: x[0], reverse=True) n_clusters = nclust_mincor[1][0] if len(nclust_mincor) > 1 else nclust_mincor[0][0] # Otherwise, only existing one - if verbose: - ut.print_out(f": k={n_clusters}") return n_clusters -def estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0.6, on_center=True, verbose=True): +def estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0.6, on_center=True): """Wrapper for _estimate_lower_bound_n_clusters to catch convergence warnings""" try: n_clusters = _estimate_lower_bound_n_clusters(X, model=model, model_kwargs=model_kwargs, - min_th=min_th, on_center=on_center, - verbose=verbose) + min_th=min_th, on_center=on_center) except ut.ClusteringConvergenceException as e: n_clusters = e.distinct_clusters return n_clusters @@ -161,7 +124,7 @@ def estimate_lower_bound_n_clusters(X, model=None, model_kwargs=None, min_th=0.6 # 2. Step (Optimization of n clusters) @ut.catch_convergence_warning() -def _optimize_n_clusters(X, model=None, model_kwargs=None, n_clusters=None, min_th=0.5, on_center=True, verbose=True): +def _optimize_n_clusters(X, model=None, model_kwargs=None, n_clusters=None, min_th=0.3, on_center=True): """ Optimize the number of clusters using a recursive algorithm. @@ -185,18 +148,12 @@ def _optimize_n_clusters(X, model=None, model_kwargs=None, n_clusters=None, min_ on_center : bool, optional, default = True Whether the minimum correlation is computed for all observations within a cluster or just for the cluster center. - verbose : bool, optional, default = False - A flag to enable or disable verbose outputs. Returns ------- n_clusters : int Optimized number of clusters (k) after the recursive clustering. """ - if verbose: - objective_fct = "min_cor_center" if on_center else "min_cor_all" - ut.print_out(f"2. Optimization of k by recursive clustering ({objective_fct}, min_th={min_th})", end="") - n_samples, n_features = X.shape f = lambda c: get_min_cor(X, labels=model(n_clusters=c, **model_kwargs).fit(X).labels_, on_center=on_center) min_cor = f(n_clusters) @@ -210,17 +167,14 @@ def _optimize_n_clusters(X, model=None, model_kwargs=None, n_clusters=None, min_ n_clusters = max(1, n_clusters - step * 2) step = 1 min_cor = f(n_clusters) - if verbose: - ut.print_out(f": k={n_clusters}") return n_clusters -def optimize_n_clusters(X, model=None, model_kwargs=None, n_clusters=None, min_th=0.5, on_center=True, verbose=True): +def optimize_n_clusters(X, model=None, model_kwargs=None, n_clusters=None, min_th=0.5, on_center=True): """Wrapper for _optimize_n_clusters to catch convergence warnings""" try: n_clusters = _optimize_n_clusters(X, model=model, model_kwargs=model_kwargs, - n_clusters=n_clusters, min_th=min_th, on_center=on_center, - verbose=verbose) + n_clusters=n_clusters, min_th=min_th, on_center=on_center) except ut.ClusteringConvergenceException as e: n_clusters = e.distinct_clusters return n_clusters @@ -252,7 +206,7 @@ def _get_best_cluster(dict_clust_qm=None, metric=None): return min(dict_clust_qm, key=dict_clust_qm.get) -def merge_clusters(X, n_max=5, labels=None, min_th=0.5, on_center=True, metric="correlation", verbose=True): +def merge_clusters(X, n_max=5, labels=None, min_th=0.5, on_center=True, metric="correlation"): """ Merge small clusters into other clusters optimizing a given quality measure. @@ -277,16 +231,13 @@ def merge_clusters(X, n_max=5, labels=None, min_th=0.5, on_center=True, metric=" metric : str, optional, default = 'correlation' Quality measure used to optimize merging. Can be 'correlation' for maximum correlation or any valid distance metric like 'euclidean' for minimum distance. - verbose : bool, optional, default = False - A flag to enable or disable verbose outputs. Returns ------- labels : array-like, shape (n_samples,) Cluster labels for observations after merging. """ - if verbose: - ut.print_out("3. Cluster merging (optional)", end="") + unique_labels = list(OrderedDict.fromkeys(labels)) for n in range(1, n_max): s_clusters = [x for x in unique_labels if labels.count(x) == n] # Smallest clusters @@ -307,7 +258,5 @@ def merge_clusters(X, n_max=5, labels=None, min_th=0.5, on_center=True, metric=" sorted_labels = pd.Series(labels).value_counts().index # sorted in descending order of size dict_update = {label: i for label, i in zip(sorted_labels, range(0, len(set(labels))))} labels = [dict_update[label] for label in labels] - if verbose: - ut.print_out(f": k={len(set(labels))}") return labels diff --git a/aaanalysis/feature_engineering/aaclust/_aaclust_name_clusters.py b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_methods.py similarity index 53% rename from aaanalysis/feature_engineering/aaclust/_aaclust_name_clusters.py rename to aaanalysis/feature_engineering/_backend/aaclust/aaclust_methods.py index ac549802..973d249c 100644 --- a/aaanalysis/feature_engineering/aaclust/_aaclust_name_clusters.py +++ b/aaanalysis/feature_engineering/_backend/aaclust/aaclust_methods.py @@ -1,14 +1,40 @@ """ -This is a script for the AAclust name_cluster method. +This is a script for the AAclust methods. """ import pandas as pd +import numpy as np +from collections import OrderedDict -from aaanalysis.feature_engineering.aaclust._aaclust import compute_medoids import aaanalysis.utils as ut +from ._utils_aaclust import _cluster_center, _cluster_medoid # Settings -# 1. Helper function +# I Helper function + + +# II Main Functions +def compute_centers(X, labels=None): + """Obtain cluster centers and their labels""" + center_labels = list(OrderedDict.fromkeys(labels)) + list_masks = [[True if i == label else False for i in labels] for label in center_labels] + centers = np.concatenate([_cluster_center(X[mask]) for mask in list_masks]).round(3) + return centers, np.array(center_labels) + + +def compute_medoids(X, labels=None): + """Obtain cluster medoids and their labels""" + unique_labels = list(OrderedDict.fromkeys(labels)) + list_masks = [[True if i == label else False for i in labels] for label in unique_labels] + list_ind_max = [_cluster_medoid(X[mask]) for mask in list_masks] + indices = np.array(range(0, len(labels))) + medoid_ind = [indices[m][i] for m, i in zip(list_masks, list_ind_max)] + medoid_labels = np.array([labels[i] for i in medoid_ind]) + medoids = np.array([X[i, :] for i in medoid_ind]) + return medoids, medoid_labels, medoid_ind + + +# Name clusters def _get_cluster_names(list_names=None, name_medoid=None, name_unclassified="Unclassified", shorten_names=True): @@ -45,8 +71,6 @@ def remove_2nd_info(name_): names_cluster = [name_unclassified] return names_cluster - -# 2. Main function def name_clusters(X, labels=None, names=None, shorten_names=True): """""" medoids, medoid_labels, medoid_ind = compute_medoids(X, labels=labels) @@ -70,4 +94,45 @@ def name_clusters(X, labels=None, names=None, shorten_names=True): if not assigned: dict_cluster_names[clust] = ut.STR_UNCLASSIFIED cluster_names = [dict_cluster_names[label] for label in labels] - return cluster_names \ No newline at end of file + return cluster_names + + +# Compute correlation +def _sort_X_labels_names(X, labels=None, names=None): + """Sort labels""" + sorted_order = np.argsort(labels) + labels = [labels[i] for i in sorted_order] + X = X[sorted_order] + if names: + names = [names[i] for i in sorted_order] + return X, labels, names + +def _get_df_corr(X=None, X_ref=None): + """Get df with correlations""" + # Temporary labels to avoid any confusion with potential duplicates + X_labels = range(len(X)) + X_ref_labels = range(len(X), len(X) + len(X_ref)) + combined = np.vstack((X, X_ref)) + df_corr_full = pd.DataFrame(combined.T).corr() + # Select only the rows corresponding to X and columns corresponding to X_ref + df_corr = df_corr_full.loc[X_labels, X_ref_labels] + return df_corr + +def compute_correlation(X, X_ref=None, labels=None, labels_ref=None, names=None, names_ref=None): + """Computes Pearson correlation of given data with reference data.""" + # Sort based on labels + X, labels, names = _sort_X_labels_names(X, labels=labels, names=names) + if X_ref is not None: + X_ref, labels_ref, names_ref = _sort_X_labels_names(X_ref, labels=labels_ref, names=names_ref) + # Compute correlations + if X_ref is None: + df_corr = pd.DataFrame(X.T).corr() + else: + df_corr = _get_df_corr(X=X, X_ref=X_ref) + # Replace indexes and columns with names or labels + df_corr.index = names if names else labels + if X_ref is None: + df_corr.columns = names if names else labels + else: + df_corr.columns = names_ref if names_ref else labels_ref + return df_corr diff --git 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labels=None, names=None): - """""" - sorted_order = np.argsort(labels) - labels = [labels[i] for i in sorted_order] - X = X[sorted_order] - if names: - names = [names[i] for i in sorted_order] - return X, labels, names - -def _get_df_corr(X=None, X_ref=None): - """""" - # Temporary labels to avoid any confusion with potential duplicates - X_labels = range(len(X)) - X_ref_labels = range(len(X), len(X) + len(X_ref)) - combined = np.vstack((X, X_ref)) - df_corr_full = pd.DataFrame(combined.T).corr() - # Select only the rows corresponding to X and columns corresponding to X_ref - df_corr = df_corr_full.loc[X_labels, X_ref_labels] - return df_corr - - -# II Main Functions -def compute_correlation(X, X_ref=None, labels=None, labels_ref=None, names=None, names_ref=None): - """Computes Pearson correlation of given data with reference data.""" - # Sort based on labels - X, labels, names = _sort_X_labels_names(X, labels=labels, names=names) - if X_ref is not None: - X_ref, labels_ref, names_ref = _sort_X_labels_names(X_ref, labels=labels_ref, names=names_ref) - # Compute correlations - if X_ref is None: - df_corr = pd.DataFrame(X.T).corr() - else: - df_corr = _get_df_corr(X=X, X_ref=X_ref) - # Replace indexes and columns with names or labels - df_corr.index = names if names else labels - if X_ref is None: - df_corr.columns = names if names else labels - else: - df_corr.columns = names_ref if names_ref else labels_ref - return df_corr diff --git a/aaanalysis/feature_engineering/cpp/__pycache__/__init__.cpython-39.pyc b/aaanalysis/feature_engineering/cpp/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index 4ade8c78e3a338560718da40d8e674c64475c307..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 209 zcmY+8F%H5o3`JAHfRH!=Ct$&1M~D-kN*o|Etw}>0H*!*>9*HY4uy6!6CXxyG(to!9 z@MT;q<|=9OSW=$Pj9(S$*e8c+LamN!RUB?ga{R^n3Qg|_LL}cHpMxlD;&jR`)JB3$iB*e+0w5w|HwOE+o(bb1q=}7XBFQC{_n@_98?|2h@jlzSz8*T- J<$qqu>I0XaI;{Wz diff --git a/aaanalysis/pertubation/aamut_.py b/aaanalysis/pertubation/_aamut.py similarity index 100% rename from aaanalysis/pertubation/aamut_.py rename to aaanalysis/pertubation/_aamut.py diff --git a/tests/unit/test_dpulearn.py b/aaanalysis/pertubation/_backend/__init__.py similarity index 93% rename from tests/unit/test_dpulearn.py rename to aaanalysis/pertubation/_backend/__init__.py index 2463d63c..4c94af15 100644 --- a/tests/unit/test_dpulearn.py +++ b/aaanalysis/pertubation/_backend/__init__.py @@ -5,6 +5,7 @@ import pandas as pd import numpy as np +import scripts._utils as ut # Settings pd.set_option('expand_frame_repr', False) # Single line print for pd.Dataframe diff --git a/aaanalysis/pu_learning/dpulearn/__init__.py b/aaanalysis/pertubation/_backend/aamut/__init__.py similarity index 100% rename from aaanalysis/pu_learning/dpulearn/__init__.py rename to aaanalysis/pertubation/_backend/aamut/__init__.py diff --git a/aaanalysis/plotting/__init__.py b/aaanalysis/plotting/__init__.py index fadc6a6b..f4842c64 100644 --- a/aaanalysis/plotting/__init__.py +++ b/aaanalysis/plotting/__init__.py @@ -1,16 +1,16 @@ -from aaanalysis.plotting.plot_get_clist_ import plot_get_clist -from aaanalysis.plotting.plot_get_cmap_ import plot_get_cmap -from aaanalysis.plotting.plot_get_cdict_ import plot_get_cdict -from aaanalysis.plotting.plot_settings_ import plot_settings -from aaanalysis.plotting.plot_gcfs_ import plot_gcfs -from aaanalysis.plotting.plot_legend_ import plot_legend +from ._plot_get_clist import plot_get_clist +from ._plot_get_cmap import plot_get_cmap +from ._plot_get_cdict import plot_get_cdict +from ._plot_settings import plot_settings +from ._plot_gcfs import plot_gcfs +from ._plot_legend import plot_legend - -__all__ = ["plot_get_clist", - "plot_get_cmap", - "plot_get_cdict", - "plot_settings", - "plot_legend", - "plot_gcfs", - ] +__all__ = [ + "plot_get_clist", + "plot_get_cmap", + "plot_get_cdict", + "plot_settings", + "plot_legend", + "plot_gcfs", +] diff --git a/aaanalysis/plotting/__pycache__/__init__.cpython-39.pyc b/aaanalysis/plotting/__pycache__/__init__.cpython-39.pyc index 9ae7cc53f00f1d7267c11b2a8e148d773a1eb23b..2a53daca6d27c0978eff554960143ebd3664ffb7 100644 GIT binary patch delta 161 zcmbQsx|D@4k(ZZ?0SLmM$fedXPUI_RWSrR6Sk6(q<7)t{VJT*L+v;DHF_q^76lr4+FP Sg>JFO$0z1YW@DPh$P55#bui)p delta 289 zcmZ3=GMAMvk(ZZ?0SI`kMN*}hCi0atGE8jitXED+OeVvmnc%*lz5U&&C!Ie98$KO+kO%ei9z diff --git a/aaanalysis/plotting/__pycache__/plot_gcfs_.cpython-39.pyc b/aaanalysis/plotting/__pycache__/_plot_gcfs.cpython-39.pyc similarity index 82% rename from aaanalysis/plotting/__pycache__/plot_gcfs_.cpython-39.pyc rename to aaanalysis/plotting/__pycache__/_plot_gcfs.cpython-39.pyc index 11e8cad5a53dd68999860d63f4c8104154e36e30..8fde1be2b0e49d05d697cb03e12c98f137857be5 100644 GIT binary patch delta 94 zcmdnay`6i*Mi%8;{K=(7MX7lu@yYplC8-r9w|MdkN;32F;>!|qN>f)d6md?z!ji}p lUyzeu5}%%&Ry^6BRfUmzatf=8C^rKGP#%@wnmm_P3IKA19Q6PI delta 94 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a/aaanalysis/plotting/plot_get_cdict_.py b/aaanalysis/plotting/_plot_get_cdict.py similarity index 100% rename from aaanalysis/plotting/plot_get_cdict_.py rename to aaanalysis/plotting/_plot_get_cdict.py diff --git a/aaanalysis/plotting/plot_get_clist_.py b/aaanalysis/plotting/_plot_get_clist.py similarity index 100% rename from aaanalysis/plotting/plot_get_clist_.py rename to aaanalysis/plotting/_plot_get_clist.py diff --git a/aaanalysis/plotting/plot_get_cmap_.py b/aaanalysis/plotting/_plot_get_cmap.py similarity index 100% rename from aaanalysis/plotting/plot_get_cmap_.py rename to aaanalysis/plotting/_plot_get_cmap.py diff --git a/aaanalysis/plotting/plot_legend_.py b/aaanalysis/plotting/_plot_legend.py similarity index 100% rename from aaanalysis/plotting/plot_legend_.py rename to aaanalysis/plotting/_plot_legend.py diff --git a/aaanalysis/plotting/plot_settings_.py b/aaanalysis/plotting/_plot_settings.py similarity index 100% rename from aaanalysis/plotting/plot_settings_.py rename to aaanalysis/plotting/_plot_settings.py diff --git a/aaanalysis/pu_learning/__init__.py b/aaanalysis/pu_learning/__init__.py index f823cebf..050751f7 100644 --- a/aaanalysis/pu_learning/__init__.py +++ b/aaanalysis/pu_learning/__init__.py @@ -1,3 +1,3 @@ -from aaanalysis.pu_learning.dpulearn_ import dPULearn +from ._dpulearn import dPULearn __all__ = ["dPULearn"] \ No newline at end of file diff --git a/aaanalysis/pu_learning/__pycache__/__init__.cpython-39.pyc b/aaanalysis/pu_learning/__pycache__/__init__.cpython-39.pyc index 95d816f3c0fb3a99918d552c8f2463da7ede3767..b8e42e851e18749afe91e9395969b3630e9467de 100644 GIT binary patch delta 60 zcmbQk)XK!0$ji&c00eQb(e~0y&zDw>VM)LVZ#bi}H$?fC7G+%u$^2DFvlD JAfbtkvjLAP4|V_m delta 83 zcmZo=n#07K$ji&c00cpIrBi(;@>*GeIGT*NI8p*aeNq#P@`{*%0)CpzQ3{EPiFt`R cmBpFGdIhENIUvc*ymY;kf>JO$eqz>a09eczn*aa+ diff --git a/aaanalysis/pu_learning/__pycache__/dpulearn_.cpython-39.pyc b/aaanalysis/pu_learning/__pycache__/_dpulearn.cpython-39.pyc similarity index 98% rename from aaanalysis/pu_learning/__pycache__/dpulearn_.cpython-39.pyc rename to aaanalysis/pu_learning/__pycache__/_dpulearn.cpython-39.pyc index 99c3a84c7846c307a0eea1b2e9b0c5b04c989154..3ce6bb63559b4b389ab326b4907b8ba3c0b4dfdd 100644 GIT binary patch delta 22 dcmbOkH#2U-0w&J*l!DTn)Wo8^%`2G-bO39}2>t*7 delta 22 dcmbOkH#2U-0w&Isg3_GS#G<_T%`2G-bO3BC2>t*7 diff --git a/aaanalysis/feature_engineering/aaclust_plot/_aaclust_plot_corr.py b/aaanalysis/pu_learning/_backend/__init__.py similarity index 50% rename from aaanalysis/feature_engineering/aaclust_plot/_aaclust_plot_corr.py rename to aaanalysis/pu_learning/_backend/__init__.py index 3bc183ce..4c94af15 100644 --- a/aaanalysis/feature_engineering/aaclust_plot/_aaclust_plot_corr.py +++ b/aaanalysis/pu_learning/_backend/__init__.py @@ -4,7 +4,8 @@ import time import pandas as pd import numpy as np -import seaborn as sns + +import scripts._utils as ut # Settings pd.set_option('expand_frame_repr', False) # Single line print for pd.Dataframe @@ -14,15 +15,18 @@ # II Main Functions -def plot_correlation(df_corr=None): - """""" - ax = sns.heatmap(df_corr, cmap="viridis", vmin=-1, vmax=1) - ax.set_yticklabels(ax.get_yticklabels(), rotation=0) - ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right") # III Test/Caller Functions # IV Main +def main(): + t0 = time.time() + + t1 = time.time() + print("Time:", t1 - t0) + +if __name__ == "__main__": + main() diff --git a/aaanalysis/pu_learning/dpulearn_plot/__init__.py b/aaanalysis/pu_learning/_backend/dpulearn/__init__.py similarity index 100% rename from aaanalysis/pu_learning/dpulearn_plot/__init__.py rename to aaanalysis/pu_learning/_backend/dpulearn/__init__.py diff --git a/aaanalysis/pu_learning/_backend/dpulearn_plot/__init__.py b/aaanalysis/pu_learning/_backend/dpulearn_plot/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/aaanalysis/pu_learning/dpulearn_.py b/aaanalysis/pu_learning/_dpulearn.py similarity index 100% rename from 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