From 6664be3ea4e5b0dd4575dec0ec33d95f952880cc Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Tue, 30 Nov 2021 14:26:34 +0100 Subject: [PATCH 01/10] First Draft Implementation, regression tests ok --- .../feature_elimination.py | 195 +++++++++++------- setup.py | 4 +- 2 files changed, 122 insertions(+), 77 deletions(-) diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index 88171880..0af97252 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -3,7 +3,13 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from catboost import CatBoost, Pool from joblib import Parallel, delayed +from sklearn.base import clone, is_classifier +from sklearn.model_selection import check_cv +from sklearn.model_selection._search import BaseSearchCV +from xgboost.sklearn import XGBModel + from probatus.utils import ( BaseFitComputePlotClass, assure_pandas_series, @@ -13,9 +19,6 @@ preprocess_labels, shap_calc, ) -from sklearn.base import clone, is_classifier -from sklearn.model_selection import check_cv -from sklearn.model_selection._search import BaseSearchCV class ShapRFECV(BaseFitComputePlotClass): @@ -385,9 +388,7 @@ def _get_feature_shap_values_per_fold( y_train, y_val = y.iloc[train_index], y.iloc[val_index] if sample_weight is not None: - clf = clf.fit( - X_train, y_train, sample_weight=sample_weight.iloc[train_index] - ) + clf = clf.fit(X_train, y_train, sample_weight=sample_weight.iloc[train_index]) else: clf = clf.fit(X_train, y_train) @@ -396,9 +397,7 @@ def _get_feature_shap_values_per_fold( score_val = self.scorer.scorer(clf, X_val, y_val) # Compute SHAP values - shap_values = shap_calc( - clf, X_val, verbose=self.verbose, **shap_kwargs - ) + shap_values = shap_calc(clf, X_val, verbose=self.verbose, **shap_kwargs) return shap_values, score_train, score_val def fit( @@ -490,20 +489,14 @@ def fit( "Lower the value for min_features_to_select or number of columns in columns_to_keep" ) - self.X, self.column_names = preprocess_data( - X, X_name="X", column_names=column_names, verbose=self.verbose - ) - self.y = preprocess_labels( - y, y_name="y", index=self.X.index, verbose=self.verbose - ) + self.X, self.column_names = preprocess_data(X, X_name="X", column_names=column_names, verbose=self.verbose) + self.y = preprocess_labels(y, y_name="y", index=self.X.index, verbose=self.verbose) if sample_weight is not None: if self.verbose > 0: warnings.warn( "sample_weight is passed only to the fit method of the model, not the evaluation metrics." ) - sample_weight = assure_pandas_series( - sample_weight, index=self.X.index - ) + sample_weight = assure_pandas_series(sample_weight, index=self.X.index) self.cv = check_cv(self.cv, self.y, classifier=is_classifier(self.clf)) remaining_features = current_features_set = self.column_names @@ -540,9 +533,7 @@ def fit( # Optimize parameters if self.search_clf: current_search_clf = clone(self.clf).fit(current_X, self.y) - current_clf = current_search_clf.estimator.set_params( - **current_search_clf.best_params_ - ) + current_clf = current_search_clf.estimator.set_params(**current_search_clf.best_params_) else: current_clf = clone(self.clf) @@ -566,9 +557,7 @@ def fit( # Calculate the shap features with remaining features and features to keep. - shap_importance_df = calculate_shap_importance( - shap_values, remaining_removeable_features - ) + shap_importance_df = calculate_shap_importance(shap_values, remaining_removeable_features) # Get features to remove features_to_remove = self._get_current_features_to_remove( @@ -613,15 +602,7 @@ def compute(self): return self.report_df - def fit_compute( - self, - X, - y, - sample_weight=None, - columns_to_keep=None, - column_names=None, - **shap_kwargs - ): + def fit_compute(self, X, y, sample_weight=None, columns_to_keep=None, column_names=None, **shap_kwargs): """ Fits the object with the provided data. @@ -955,16 +936,101 @@ def __init__( self.eval_metric = eval_metric - def _get_feature_shap_values_per_fold( - self, - X, - y, - clf, - train_index, - val_index, - sample_weight=None, - **shap_kwargs + def _get_fit_params_lightGBM( + self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None + ): + from lightgbm import early_stopping, log_evaluation + + fit_params = { + "X": X_train, + "y": y_train, + "eval_set": [(X_val, y_val)], + "eval_metric": self.eval_metric, + "callbacks": [early_stopping(self.early_stopping_rounds, first_metric_only=True)], + } + if self.verbose >= 100: + fit_params["callbacks"].append(log_evaluation(1)) + else: + fit_params["callbacks"].append(log_evaluation(0)) + if sample_weight is not None: + fit_params["sample_weight"] = sample_weight.iloc[train_index] + fit_params["eval_sample_weight"] = [sample_weight.iloc[val_index]] + return fit_params + + def _get_fit_params_XGBoost( + self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): + + fit_params = { + "X": X_train, + "y": y_train, + "eval_set": [(X_val, y_val)], + "eval_metric": self.eval_metric, + "early_stopping_rounds": self.early_stopping_rounds, + } + if sample_weight is not None: + fit_params["sample_weight"] = sample_weight.iloc[train_index] + fit_params["eval_sample_weight"] = [sample_weight.iloc[val_index]] + return fit_params + + def _get_fit_params_CatBoost( + self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None + ): + + fit_params = { + "X": Pool(X_train, y_train), + "eval_set": Pool(X_val, y_val), + "early_stopping_rounds": self.early_stopping_rounds, + # Evaluation metric should be passed during initialization + } + if sample_weight is not None: + fit_params["X"].set_weight(sample_weight.iloc[train_index]) + fit_params["eval_set"].set_weight(sample_weight.iloc[val_index]) + return fit_params + + def _get_fit_params( + self, clf, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None + ): + # The lightgbm imports are temporarily placed here, until the tests on + # macOS have been fixed for lightgbm. + from lightgbm import LGBMModel + + if isinstance(clf, LGBMModel): + fit_params = self._get_fit_params_lightGBM( + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) + elif isinstance(clf, XGBModel): + fit_params = self._get_fit_params_XGBoost( + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) + elif isinstance(clf, CatBoost): + fit_params = self._get_fit_params_CatBoost( + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) + else: + raise ValueError("Model type not supported") + + return fit_params + + def _get_feature_shap_values_per_fold(self, X, y, clf, train_index, val_index, sample_weight=None, **shap_kwargs): """ This function calculates the shap values on validation set, and Train and Val score. @@ -1002,40 +1068,19 @@ def _get_feature_shap_values_per_fold( Tuple with the results: Shap Values on validation fold, train score, validation score. """ - # The lightgbm imports are temporarily placed here, until the tests on - # macOS have been fixed for lightgbm. - from lightgbm import early_stopping, log_evaluation, LGBMModel - X_train, X_val = X.iloc[train_index, :], X.iloc[val_index, :] y_train, y_val = y.iloc[train_index], y.iloc[val_index] - fit_params = { - 'X': X_train, - 'y': y_train, - 'eval_set': [(X_val, y_val)], - 'eval_metric': self.eval_metric - } - - # first_metric_only bypasses a bug that defaults the metric to the - # scoring. It should only be True until the bug is found and fixed. - if isinstance(clf, LGBMModel): - fit_params['callbacks'] = [ - early_stopping( - self.early_stopping_rounds, first_metric_only=True - ) - ] - - if self.verbose >= 100: - fit_params['callbacks'].append(log_evaluation(1)) - else: - fit_params['callbacks'].append(log_evaluation(0)) - - else: - fit_params['early_stopping_rounds'] = self.early_stopping_rounds - - if sample_weight is not None: - fit_params['sample_weight'] = sample_weight.iloc[train_index] - fit_params['eval_sample_weight'] = [sample_weight.iloc[val_index]] + fit_params = self._get_fit_params( + clf=clf, + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) # Train the model clf = clf.fit(**fit_params) @@ -1045,7 +1090,5 @@ def _get_feature_shap_values_per_fold( score_val = self.scorer.scorer(clf, X_val, y_val) # Compute SHAP values - shap_values = shap_calc( - clf, X_val, verbose=self.verbose, **shap_kwargs - ) + shap_values = shap_calc(clf, X_val, verbose=self.verbose, **shap_kwargs) return shap_values, score_train, score_val diff --git a/setup.py b/setup.py index e6955933..d0fbfbdb 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,9 @@ def read(fname): "tqdm>=4.41.0", "shap >= 0.38.1, < 0.39.0",# 0.40.0 causes issues in certain plots. For now it is excluded "numpy>=1.19.0", - "lightgbm>=3.3.0" + "lightgbm>=3.3.0", + "catboost>=1.0.0", + "xgboost>=1.5.0" ] extra_dep = [ From b9429b5f4cdb10547e83055b773d155b88c94606 Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Tue, 30 Nov 2021 16:58:40 +0100 Subject: [PATCH 02/10] add tests --- .gitignore | 3 + .../feature_elimination.py | 6 +- .../test_feature_elimination.py | 69 ++++++++++++++++++- 3 files changed, 72 insertions(+), 6 deletions(-) diff --git a/.gitignore b/.gitignore index f230bb14..1222f8f9 100644 --- a/.gitignore +++ b/.gitignore @@ -263,3 +263,6 @@ dmypy.json .history # End of https://www.gitignore.io/api/macos,python,pycharm,jupyternotebooks,visualstudiocode + +# Catboost-related files +catboost* \ No newline at end of file diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index 0af97252..f68fcbf2 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -976,10 +976,10 @@ def _get_fit_params_XGBoost( def _get_fit_params_CatBoost( self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): - + cat_features = [col for col in X_train.select_dtypes(include=["category"]).columns] fit_params = { - "X": Pool(X_train, y_train), - "eval_set": Pool(X_val, y_val), + "X": Pool(X_train, y_train, cat_features=cat_features), + "eval_set": Pool(X_val, y_val, cat_features=cat_features), "early_stopping_rounds": self.early_stopping_rounds, # Evaluation metric should be passed during initialization } diff --git a/tests/feature_elimination/test_feature_elimination.py b/tests/feature_elimination/test_feature_elimination.py index e213c0cf..9aec2c27 100644 --- a/tests/feature_elimination/test_feature_elimination.py +++ b/tests/feature_elimination/test_feature_elimination.py @@ -2,6 +2,7 @@ import pandas as pd import pytest +from catboost import CatBoostClassifier from probatus.feature_elimination import EarlyStoppingShapRFECV, ShapRFECV from probatus.utils import preprocess_labels from sklearn.linear_model import LogisticRegression @@ -283,7 +284,7 @@ def test_complex_dataset(complex_data, complex_lightgbm): @pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") -def test_shap_rfe_early_stopping(complex_data, capsys): +def test_shap_rfe_early_stopping_lightGBM(complex_data, capsys): """ Test EarlyStoppingShapRFECV with a LGBMClassifier. """ @@ -322,8 +323,47 @@ def test_shap_rfe_early_stopping(complex_data, capsys): assert len(out) == 0 +def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys): + """ + Test EarlyStoppingShapRFECV with a LGBMClassifier. + """ + + clf = CatBoostClassifier() + X, y = complex_data + X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category") + + with pytest.warns(None) as record: + shap_elimination = EarlyStoppingShapRFECV( + clf, + random_state=1, + step=1, + cv=10, + scoring="roc_auc", + n_jobs=4, + early_stopping_rounds=5, + eval_metric="auc", + ) + shap_elimination = shap_elimination.fit(X, y, approximate=False, check_additivity=False) + + assert shap_elimination.fitted + shap_elimination._check_if_fitted() + + report = shap_elimination.compute() + + assert report.shape[0] == 5 + assert shap_elimination.get_reduced_features_set(1) == ["f5"] + + _ = shap_elimination.plot(show=False) + + # Ensure that number of warnings was 0 + assert len(record) == 0 + # Check if there is any prints + out, _ = capsys.readouterr() + assert len(out) == 0 + + @pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") -def test_shap_rfe_randomized_search_early_stopping(complex_data): +def test_shap_rfe_randomized_search_early_stopping_lightGBM(complex_data): """ Test EarlyStoppingShapRFECV with RandomizedSearchCV and a LGBMClassifier on complex dataset. """ @@ -363,7 +403,7 @@ def test_shap_rfe_randomized_search_early_stopping(complex_data): @pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") -def test_get_feature_shap_values_per_fold_early_stopping(complex_data): +def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data): """ Test with ShapRFECV with features per fold. """ @@ -385,3 +425,26 @@ def test_get_feature_shap_values_per_fold_early_stopping(complex_data): assert test_score > 0.6 assert train_score > 0.6 assert shap_values.shape == (5, 5) + + +def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data): + """ + Test with ShapRFECV with features per fold. + """ + clf = CatBoostClassifier() + X, y = complex_data + X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category") + y = preprocess_labels(y, y_name="y", index=X.index) + + shap_elimination = EarlyStoppingShapRFECV(clf, early_stopping_rounds=5) + shap_values, train_score, test_score = shap_elimination._get_feature_shap_values_per_fold( + X, + y, + clf, + train_index=list(range(5, 50)), + val_index=[0, 1, 2, 3, 4], + scorer=get_scorer("roc_auc"), + ) + assert test_score > 0.6 + assert train_score > 0.6 + assert shap_values.shape == (5, 5) \ No newline at end of file From a6dbff9ae4c3d2d4ddb569e0091d7e065ec5b650 Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Tue, 30 Nov 2021 17:11:06 +0100 Subject: [PATCH 03/10] Add Docstring --- .../feature_elimination.py | 142 ++++++++++++++++++ .../test_feature_elimination.py | 2 +- 2 files changed, 143 insertions(+), 1 deletion(-) diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index f68fcbf2..798cf9dc 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -939,6 +939,41 @@ def __init__( def _get_fit_params_lightGBM( self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): + """Get the fit parameters for for a LightGBM Model. + + Args: + + X_train (pd.DataFrame): + Train Dataset used in CV. + + y_train (pd.Series): + Train Binary labels for X. + + X_val (pd.DataFrame): + Validation Dataset used in CV. + + y_val (pd.Series): + Validation Binary labels for X. + + sample_weight (pd.Series, np.ndarray, list, optional): + array-like of shape (n_samples,) - only use if the model you're using supports + sample weighting (check the corresponding scikit-learn documentation). + Array of weights that are assigned to individual samples. + Note that they're only used for fitting of the model, not during evaluation of metrics. + If not provided, then each sample is given unit weight. + + train_index (np.array): + Positions of train folds samples. + + val_index (np.array): + Positions of validation fold samples. + + Raises: + ValueError: if the clf is not supported. + + Returns: + dict: fit parameters + """ from lightgbm import early_stopping, log_evaluation fit_params = { @@ -960,7 +995,41 @@ def _get_fit_params_lightGBM( def _get_fit_params_XGBoost( self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): + """Get the fit parameters for for a XGBoost Model. + Args: + + X_train (pd.DataFrame): + Train Dataset used in CV. + + y_train (pd.Series): + Train Binary labels for X. + + X_val (pd.DataFrame): + Validation Dataset used in CV. + + y_val (pd.Series): + Validation Binary labels for X. + + sample_weight (pd.Series, np.ndarray, list, optional): + array-like of shape (n_samples,) - only use if the model you're using supports + sample weighting (check the corresponding scikit-learn documentation). + Array of weights that are assigned to individual samples. + Note that they're only used for fitting of the model, not during evaluation of metrics. + If not provided, then each sample is given unit weight. + + train_index (np.array): + Positions of train folds samples. + + val_index (np.array): + Positions of validation fold samples. + + Raises: + ValueError: if the clf is not supported. + + Returns: + dict: fit parameters + """ fit_params = { "X": X_train, "y": y_train, @@ -976,6 +1045,42 @@ def _get_fit_params_XGBoost( def _get_fit_params_CatBoost( self, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): + """Get the fit parameters for for a CatBoost Model. + + Args: + + X_train (pd.DataFrame): + Train Dataset used in CV. + + y_train (pd.Series): + Train Binary labels for X. + + X_val (pd.DataFrame): + Validation Dataset used in CV. + + y_val (pd.Series): + Validation Binary labels for X. + + sample_weight (pd.Series, np.ndarray, list, optional): + array-like of shape (n_samples,) - only use if the model you're using supports + sample weighting (check the corresponding scikit-learn documentation). + Array of weights that are assigned to individual samples. + Note that they're only used for fitting of the model, not during evaluation of metrics. + If not provided, then each sample is given unit weight. + + train_index (np.array): + Positions of train folds samples. + + val_index (np.array): + Positions of validation fold samples. + + Raises: + ValueError: if the clf is not supported. + + Returns: + dict: fit parameters + """ + cat_features = [col for col in X_train.select_dtypes(include=["category"]).columns] fit_params = { "X": Pool(X_train, y_train, cat_features=cat_features), @@ -991,6 +1096,43 @@ def _get_fit_params_CatBoost( def _get_fit_params( self, clf, X_train, y_train, X_val, y_val, sample_weight=None, train_index=None, val_index=None ): + """Get the fit parameters for the specified classifier. + + Args: + clf (binary classifier): + Model to be fitted on the train folds. + + X_train (pd.DataFrame): + Train Dataset used in CV. + + y_train (pd.Series): + Train Binary labels for X. + + X_val (pd.DataFrame): + Validation Dataset used in CV. + + y_val (pd.Series): + Validation Binary labels for X. + + sample_weight (pd.Series, np.ndarray, list, optional): + array-like of shape (n_samples,) - only use if the model you're using supports + sample weighting (check the corresponding scikit-learn documentation). + Array of weights that are assigned to individual samples. + Note that they're only used for fitting of the model, not during evaluation of metrics. + If not provided, then each sample is given unit weight. + + train_index (np.array): + Positions of train folds samples. + + val_index (np.array): + Positions of validation fold samples. + + Raises: + ValueError: if the clf is not supported. + + Returns: + dict: fit parameters + """ # The lightgbm imports are temporarily placed here, until the tests on # macOS have been fixed for lightgbm. from lightgbm import LGBMModel diff --git a/tests/feature_elimination/test_feature_elimination.py b/tests/feature_elimination/test_feature_elimination.py index 9aec2c27..2e0bd21e 100644 --- a/tests/feature_elimination/test_feature_elimination.py +++ b/tests/feature_elimination/test_feature_elimination.py @@ -325,7 +325,7 @@ def test_shap_rfe_early_stopping_lightGBM(complex_data, capsys): def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys): """ - Test EarlyStoppingShapRFECV with a LGBMClassifier. + Test EarlyStoppingShapRFECV with a CatBoostClassifier. """ clf = CatBoostClassifier() From 5816f2370a7b2a9d2df1985a2a944e6571da107a Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Wed, 1 Dec 2021 11:36:36 +0100 Subject: [PATCH 04/10] moving xgboost imports to inner func --- probatus/feature_elimination/feature_elimination.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index 798cf9dc..bfe9d9ce 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -8,7 +8,6 @@ from sklearn.base import clone, is_classifier from sklearn.model_selection import check_cv from sklearn.model_selection._search import BaseSearchCV -from xgboost.sklearn import XGBModel from probatus.utils import ( BaseFitComputePlotClass, @@ -1133,9 +1132,10 @@ def _get_fit_params( Returns: dict: fit parameters """ - # The lightgbm imports are temporarily placed here, until the tests on - # macOS have been fixed for lightgbm. + # The lightgbm and xgboost imports are temporarily placed here, until the tests on + # macOS have been fixed. from lightgbm import LGBMModel + from xgboost.sklearn import XGBModel if isinstance(clf, LGBMModel): fit_params = self._get_fit_params_lightGBM( From 2751d7edff6e5489cc833c34ce8b18fd8adcf07b Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Wed, 1 Dec 2021 11:44:29 +0100 Subject: [PATCH 05/10] Document compatibility in docstring E.S.ShapRFECV --- probatus/feature_elimination/feature_elimination.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index bfe9d9ce..9b19f54f 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -745,8 +745,8 @@ class EarlyStoppingShapRFECV(ShapRFECV): """ This class performs Backwards Recursive Feature Elimination, using SHAP feature importance. - This is a child of ShapRFECV which allows early stopping of the training step, available in models such as - XGBoost and LightGBM. If you are not using early stopping, you should use the parent class, + This is a child of ShapRFECV which allows early stopping of the training step, this class is compatible with + LightGBM, XGBoost and CatBoost models. If you are not using early stopping, you should use the parent class, ShapRFECV, instead of EarlyStoppingShapRFECV. [Early stopping](https://en.wikipedia.org/wiki/Early_stopping) is a type of From ab3732daf7809c13e65a8b872b0526d55245a765 Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Wed, 1 Dec 2021 11:48:18 +0100 Subject: [PATCH 06/10] Document supported models in tutorial notebook --- .../nb_shap_feature_elimination.ipynb | 3023 +---------------- 1 file changed, 6 insertions(+), 3017 deletions(-) diff --git a/docs/tutorials/nb_shap_feature_elimination.ipynb b/docs/tutorials/nb_shap_feature_elimination.ipynb index 14eb6574..ab4c1ee8 100644 --- a/docs/tutorials/nb_shap_feature_elimination.ipynb +++ b/docs/tutorials/nb_shap_feature_elimination.ipynb @@ -405,7 +405,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -462,1520 +462,8 @@ "outputs": [ { "data": { - "image/png": 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81y3K61vZfLDukLXp8SgbimoZn9bBpPTYEWsZWYKDVRIjkPL6VvZVNlJjF6OHDR0eZV/l4XWL/JQYEmOG17qF16tsL6unqKq52/17KxppaO1g1rgEwkeQVWQJLlZJjBC8XuVgXQv7KhtparWL0cMVVSita6G0roXEmHAKUmJIjw/9ukVjawefFNfS0NLR63EV9a2s2lvFvNwkYu06hQWrJIY97R4vRdUmUqnNLkaPKGqb2tngrFvkJccwLik06xZF1U3sKG3we72qqdXDR3urmD0ukfT4yCBLZxnuWCUxTGlp91BY1URxTTMej11wGMk0t3nYXlrPrgpn3SI5huiI4K9btHu8bDlYR1lda7/P9XiU9ftrmJgey8T0uCBIZxkpWCUxzKhraaewsonSupaALUZ7vMrawmre3VFBc7t1VfmLCCwqSOa06RmEuQZvAXg8SmFlE/urmkiPjyQ/JYakmIgASHo0NU1tbCyuo2WQ/+/d5WadYmZ2woiK3rIEDqskhgkVDa3sq2yiurEtYGO2tnt4b1clKzaXUNHQRnp8JBnWfeA3ja0dPLm6iPd2VnLlMflMzYwPyLiqUFbXSlldK4kx4eSnxJARoHULVWV3RSN7Kxr9mmRUNbYRHxXW60J1WV0rja3VzMtLJCbC3jLGGrafRAjxepXS+hb2VTb1uaDYH+qa23l9axlvbCujsc3DpPRYzp6Vxby8JFw2vNFvVJV1+2t4fNV+KhvbOG5iKpcuyg1KtnVUuJu8lGjGJUUPOLKopd3DxuJav6PedpTW89tXtjMxPZbvnTG1T0shzC3MyUkkNS64E422Di8VDa2kxEYM+3Di0UJv/SSskggBHR4vxTXNFFY10doeuMXokroWVmwq4f1dlXi8yvy8JM6alcXkDOtTHgytHR7+88lBXt5USoTbxWfmj+OUaRlBKfrndgvjEqPJS4nu16y9rK6FzQfr6PBz/epATTO/emkr4W4Xtc3tnDQljauPLejTmhGBSelxjE+L9Vs2f2hp91Be30pZfSs1TW2oQniYi5nZCXbxfAiwSmKY0NLuYX9VE0UBXozeWdbAy5tKWLe/BrdLOH5SKmfOyiLLZtAGlJK6Fh77sJDNB+vIS47mqmMLmBSkRV0RSIsz6xbJsT2vW3i8yraSeg7UdJ/70B01TW384sWtdHi83HzODN7ZUc7yjSVcsSSP02dk+jVGVmIUM7ITBqUom9s8lNW3UFbf2msByryUGKZkxOGylXiDhlUSIaa+pZ19lU2U1bfgDZDh4PUq64pqeHlTCbvKG4mNcHPq9AxOnZZhi88FEVVlTWE1T6zaT3VTOydMTuOShTnERwXvM4+PCqMgNZaM+MgjbpT1Le18Ulzbr7yZ5jYPd768lbL6Vn541jQKUmPxqvK/b+5ifVENN5w+hVnjEv0aKy4qjPl5Sf1yCTW0dlBe30ppXUu/XKxxUWHMyUm0uRtBwiqJEFHV2MbeykaqGgK3GN3W4WXl7kpWbCqhtL6VtLgIzpyZxbJJqURa/+2Q0dLu4YUNB3h1cxmR4S4uWZjLiZPTgjrbjQx3kZccQ05yNCW1Lewoq+/XpKPD4+WPr+9ge0kD3zptMrNzDiuDlnYPv3xxK1WNbfz43BlkJfpnhYaHuZiTk0hKL9ZOXUu7WaivbxlUIqjbJUzJjCM3OWbAY1i6xyqJIURVKa0zZTPqA7gY3dDSwRvby3h9axn1LR2MT43hrFlZLMxPtg1xQkhxTTOPfVjIttJ6xqfGcNUxBQH313dFhH6HR6sq97+3l5W7K/ni8eNZNjntqGMqGlq5/T9biI1081/nzPB71i4CUzLiyU89fPOuaWqjrL6V8vpWmgNcrj4jIZIZ2bZ0SCCxSmII8HiV4mqzGD3Y2HRfyutbeWVzKe/uqqCtw8ucnETOmpXJtMz4kJd6sBhUlY/2VPHkmiLqmts5aWo6Fy3IGVblt5/5uIjln5TwmfnjOH/uuB6P2+5EPE3LjOc7p0/p1wQkKzGKMLdQXt8a0ICM7ogKdzM7JyFoeSZjjZAqCRE5G/gj4Ab+pqq/6rK/ALgfSAeqgKtUtcjZlw/8DcgDFDhXVff2dK1QKInWDg/7q5opqm7yO7LEH/ZUNPLyphLWFFbjEuHYCSmcOSuLnKTogF3DElia2zz8a30xr28tIyYijEsX5nL85NSQhx2/ua2Mv39Y6HcE0zs7ynlo5T5On57BFUvzh0jK/iMC49NimZhmK9cOlpApCRFxA9uBTwFFwCrgClXd7HPMU8C/VfUhETkN+KKqXu3sexO4Q1VfEZE4wKuqTT1dbyiVRGNrB/sqmyipaw7cYrQqG4treWlTCdtLG4gOd3Py1HTOmJFhZ0wjiP1VTTz6YSE7yxuYlB7LlccUkJ8SGj/6x4XV/O9bu5ibk8g3Tpnst2XwxKr9vLKllC8cW8BJU9ODLOXgSIoJZ3ZOos2pGAS9KYlg28NLgZ2qutsR5HHgQmCzzzEzge85z98AnnOOnQmEqeorAKraEGRZ/aKmqY29lU1U1Pe/Hk5PtHu8fLi7ihWbSzhQ20JKTASfXZzLSVPSA/rFT4gOJyLM+nH9xas6oKCDvJQYfnj2NFburuSfa4r4+X82c+q0DD4zf9yQZizvKm/g3nf2MD41lutOnNitgghzS7cW8KWLcjlQ28yjHxaSmRDFtKzAZJsHg5qmdj7YXclM2zgpKAT7G5sD7Pd5XQQc0+WY9cDFGJfURUC8iKQCU4EaEXkGmAC8Ctykqkc4/EXkOuA6gPz84JjGqmp6OFQ19RrP3V+a2jp4c1s5r20to7a5nbzkaL5ywgQWj08OSK0gMCZ5enwkBSmxw66/wUigoqGVjcW1/XYlukRYNimN+blJPLeumDe2lrF6bxWXLcrj2IkpQXePlNS18KfXd5IUE863T5vcY+TbnJxE9lc3HzXpcbuEr540kV8s38rdb+3ix+fOGNZJbR1O46Sc5DamZsbbYI4AEmx306XA2ar6Fef11cAxqnq9zzHjgD9jFMHbwCXAbOAM4D5gAVAIPAEsV9X7erpeoN1NHq9yoMaU6W4KYIRGZUMrr24p4+0d5bR2eJmZncBZszKZmZ0QsJuH2yVkJ5nmN7bezuBoafewoaiWuuaBTxD2Vjby6IeF7KloZGpmHFcuLSAnOTjrS7XN7fzyxS20dni5+ZzpZMR3P7vOTopi1rhEWto9rHSy9LtSWtfCHcu3kBQTzs1nzxiS6rWDJTYyjNk5CUHNXRlthHJN4jjgVlU9y3l9M4Cq/rKH4+OAraqaKyLHAr9W1ZOdfVcDx6rqN3u6XqCURFuHl/3VTRRVNx9q8RgICquaeHlTCav2VgGwZHwKZ83KCqi/OjzMRV5yNLnJMda1FED66urm1xiqvLujgqfXFtHc7uGMGZlcMG9cQF2KLe0efrNiGwdrW/jBmdOY0EM4bmS4i2Mnph4KI91X2ciO0u49upsP1PGH17YzOyeR60+ZPCIyn10uE5abF6K1oJFGKNckVgFTRGQCUAx8Dvh8F+HSgCpV9QI3YyKdOs9NEpF0VS0HTgOCuird1NZBYVUTB2ta/G7Q0heqyuaDdby0qYQtB+uJDHNx+oxMzpieEdBCaTERbvJSYhiXFG1N7SDgcgnTsxJIio5gS0ndgMqquEQ4aWo6C/OTeebjIl7ZXMpHe6r47OI8loxPHrQV2eH18te3d7G/qonrT53co4IAmJYVf0SeQX5KDAdru8+Cnjkugc8tyeexjwp55uNiLl2UOyg5+6LD62VDUS2psREUpA4s58TrhW0l9VQ2tjEzO8FOmAZBUJWEqnaIyPXAy5gQ2PtVdZOI3AasVtXngVOAX4qIYtxN33TO9YjIjcBrYn49a4B7gyVraV0LG4trA9bDocPrZdXealZsKmF/dTOJ0eFcsjCHk6emB9T9M5xaZI4FshKjiI8KY0NRLY2tA0uWjIsK4wvHjeeEyWn8/cNC7nlnN+/siOfzx+STnTgwF5Sq8vcPCtlYXMcXjitgbm5Sj8dmJkQd5YISEWZkJRyycrty6rR0imuaeWlTCTlJ0Rw3KXVAcvZGu8fLuzsreGljCZVOyfxjJ6Zw8YLcXjO6e6OivpUPWyqZkBbLuMToEWEFDTdsMp3D/qomtpXUD1qG5jYPb+8o59UtpVQ3tTMuMYozZ2VxzISUgGaIpsdHUpAavKY1lt7xeJUtB+soqW0Z1Dher/LW9nKeXVdMa4eXM2dmcv6c7H6XWPnXumJe2HCQT8/N5sL5OT0eFx7m4riJqT3OrLccrKO4unuXWofXy+9f2cGu8gZ+cNa0gBU3bG338NaOcl7eVEptczsT02I5Z3YWeyoaWbG5FJcIZ87K5OxZWYNyzUWGuyhIiSUn2VrbXbEZ134wWCVR09TGq1vKeGt7Oc3tHqZmxnH2rCxm5yQGLJnK5YKshGgKUmNsobNhQlF1E9tL+1dDqTvqmtv559oi3t9VSUpMBJcvyWNhfpJf1uHbO8p5eOU+TpicxjXH9Z4sNzsnsde6TO0eLyt3VfbYT72hpYM7lm+htcPDLefNHPAMH4x7941t5byyuZSG1g6mZ8Vz3pxspmcdriZQ2dDK02uL+WhvFYnR4Vw0P4fjJ6UOyiKICHORnxJDbnK07bbnYJWEHwxUSRTXNPPyphI+3FOFV5VF+cmcNSurV39wfwlzC7nJMeSlRBMZNvyjS8YadS3tfFJUG5AaRTtK63n0o0KKqpuZPS6BK5bmk9lL7P+Gohr+/MZOZmYncP1pk3sNnU6Lj2R+XlKfMpTUGtdrTxyoaeYXL24hIz6KH501rd9WT31LO69tKeO1rWU0t3uYnZPAeXOymZLRcy7GrvIGnli1n90VjeQlR3P5kjymZyX067pdCXMLeSkx5KfEjPk6UFZJ+EF/lISqsq20npc2lbCxuI4It4sTJqfxqZmZAY0lj45wk28Xo0cE7R4vmw7UBSTJ0uNV3thWxnPriunwKGfPzuLc2dlHuYj2VDTymxXbyE6M4gdnTuvVFRPmFo6dmOq3u2ZtYXWviYQbimr40+s7WViQzFdPmuiXtVzb3M6KTSW8ud2Efi/MT+LcOdmM93NxWlVZtbeaf64toqqxjfm5SVy6OHfQfVPcbiEvOZq8lJgxOwmzSsIP/FESHq+yZl81KzaXsLeyifioME6bnsGpUzOIiwqc+6ezf0Bmgl2MHmnsrWhkV3lDQAIgapraeGpNER/uqSItLoIrluYzz1mQLqtv4ZcvbiUyzMXN58zos4fIjHEJ/ar71dTWwQe7K3t1o728qYSn1hRxwbxxXDCv56KBlQ2tvLSphHd2VOBRZen4FM6dkz3gOmRtHV5e3VLK8o0Hae9QTp2ezvlzxw26oKLbJYxLMu7csVbiwyoJP+hNSbS2e3h3ZwWvbCmloqGNzPhIzpyV1esC4EBIjTMhf4Px81pCT3VjG58U1/bo1+8vW0vqeOzDQg7UtjAvN5Hz547j3nd209Tm4aZzpvc5k06OjWBRQXK/r7u7vIHd5Y097ldVHnh/L+/vquRrJ01k8fiUI/aX1rXw4sYSVu6qBOC4SamcMzurV/dZf6htbudf64p5Z2cF0eFuLpg3jlOmpg96naFz7W9CWuyISB4MBFZJ+EF3SqK2uZ03tpbxxrYyGts8TEqP5exZWczLSwroYnRmQhQFqbHBLy2tChrcEs6jCzH/oAHQ2uFhY3Et1Y2BKePS4fXy6uYyXthwgNYOL+Fu4cYz+44wcruMm2kgNzuvV/lgT2WvjYLaPV7uWrGN/VXN/Ohs0+muuKaZ5Z8c5KO9VbhFOHFKGmfPygpoXpAvRdVNPLm6iM0H68iMj+SyxXnMy00ctBUuYn6bE9JiR32giFUSfuCrJEpqW1ixuYT3nVIF8/OSOGtWFpMzAtfP2O0WcpOMH3RITNvGCijZAB2BK0w4+hGIz4K0KRDR/0AEVWVXeQN7K3osXNxvqhrbWP7JQebnJR3RWa4npmUNLuu4urGNNfuqez2mtrmdO/6zBUWZkBbL2sIaIsNcnDI1nU/NzBySMG1V5ZPiWp5cU0RJbQvTs+L57OK8gFQz6Kx/NiEtdtSW+rBKwg/2VzXx7w0HeHlTKev31+B2CcdPSuXMmVl+t3L0h8hwE36XkzRE4XeeDijfArVFwb/WaEVckJADqZMhvP/fhfL6VjYd6H+RwMGSFBPOooLBZ3JvLK7tMx+ksKqJX720FbcIp0/P4PQZGSG5oXZ4vby9vYLn1x+gsbWDZZPT+Mz8cQFTVOnxkcwNgJUy3LBKog92ltVzw+Pr2HigjtgIN6dOz+DUaRl9Lgb2h7ioMApSY8iMjxq6rM/GCij5BDoGl/BlcRAXJBVAykQI699Np7nNwyfFgysS2B9cLjhmQmpA3CRtHV7e31XRp5KramwjOtw9LPz4TW0d/HvDQV7bWkaYSzh7dhZnzswMSPRSX7kmIxGrJPqgqrGNS+9+n2MnprJsUmq/4757Izk2gvGpMUHzx3aLpwPKt0Lt/r6PtfQfVxgkj4fkCeD2/yYciCKB/jIpIy6guTrFNc1sOVAXsPGGirL6Fp5eU8yawmqSY8K5eGEux0xIGdSaYlxUGMdODHxZklBilYQfFFY2sr2HKpj9pXPBKz81hoShNrkbK6H0E2gP/o1ozOMON1ZF0vh+LXCX1Law5WBdwIpIdiU+KoylEwLfs2L13ipqAthPZSjZXlrPk6v3s7eyifGpMVy+OI8pmQNvpLQgP2loJ35BxioJPwhE7Sa3W8hJiiZ/qBajffF6jPVQUzi017VAWKRZr0jI9VtZNLR2sKGoptfIoYHgcpkS9MFYD2ho7eCjPb3nTgxnvKp8uLuKZz4uorqpnUUFyVy6MHdACbADDSseroSyVPiYICLMRZ5TCyYk6f1NVSZyyVoPoaGjFUo3QdUeR1mMM+ZkL8RFhnHMhNSAFAn0JT+lHxE4HW1Qtgnix0F8Zp+Hx0WGkZ8SE9Bore6IDHcxLTMer0JxTVPAwohdIhw3KZWFBUms2FzKixtLWL+/htNnZHDenOx+VWeubmyjrqV96D0FIcBaEg4DsSRiIt0UpMaSnTCEi9G+eD1Qvg1q9g39tS09ExEHaVP9uvGC+e7tKBt8kcDYyDDjb/fnu9hYCSXrnZBogcyZkNR3+1+PV/lgd2VA6lR1R05yNFMy4o6I/Gts7aC4ppkDNc0BjRCraWrj2Y+LeX9XJbGRYVw4bxwnTU33uwROZkIUc3L7DkMeCVh3kx/0R0kkx4aTnxIb2p6/TVUmcqk9uLM6yyCISjTKIjatz0Nrm02RwJb2gd18RWBxQUrffcxVoWIHVO0Guvz2UyZB+tQ+r1XR0Mq6wpoBydkTMRFuZmQnkNxLtQGvVymtb6G4ujmgayOFlU08sXo/20rryU6M4rOL85jjRw6KCBw/KW1YRHMNFqsk/KAvJdGZUFOQGhvQ0Nh+4/VCxTao3sdRP3LL8CQm1STkRffuwx5MkcD81Bim9rUQ29YEB9dDS03PxySMg8w5fa6tfFJUS2nd4N1kIlCQGsPEtLh+WeMNrR0UVzdzsDYw1oWqsm5/DU+tKaKsvpVZ4xL47KK8PvuQ56XEMC1r4AvgwwWrJPygJyXhdgnZSVEUpAyDOi7N1cZ6aOu5no5lGBObYZRFVO8lrvdUNLK7H0UCYyLcHDMxtXc3Sd0Bs27i9aObXkwajFvQa3hvS7uHlbsrB9TGtZP4qDBmjksY1CK7x6uU1rVQXNNMbQ/Whbu9EU9YTJ/rRAAdHi9vbCvnhQ0HaG73cNKUdC6cN46EHiaGbpewbHLaiG+PapWEH3RVEuFhLvKSo8lNjgn9F8DrhYrtUL0Xaz2MdPwr9VHV2MZGP4sELipI7tlN4+mAss1QV9w/MSPjIWdxrxnmA40IdLuECWmxFKTGBDRMt76lneKaZg7WtuDp8BDZXEZUUzHhbbW0RmdSnzTTL0UBprnSCxsO8Oa2csLDhPPmZHPGjMxuA1MmpscyMUBd+kKFVRJ+0PmFj4lwk58aQ3biMOnh0FzjWA+ByeGwDBcEEnMgdUqPN+KWdlMksDf/e05yNDOye7BMWmrhwLqBr1uFRUHuEojs/gbY2d+hP1nkybHhzMhOCGif9yNobcBTXUh1yR6q6hpp8llgb4kZR0PS9H4NV1Lbwj/XFLGuqIa0uAguWZjL4i6lTsLDTD+ZYXG/GCBWSfhBZUMrHq+SHj9Mejh4vVC5s/sFRsvoQVwmqihlUrelPlSVnWUN7Ks8+kYfFe7m2Ikp3dcAq9ptFqgHW/XXFQ45CyEmpdvddS3trNpT1adrLMwtTMmMH3APiV7xeqGhBGr2Q3PVEbua2jqobGijqqkNVWiOzaMxcUq/L7HlYB1Prt7P/upmTp+ewRVLj4wEG2whxVAzaCUhIrFAs6r5xomIC4hS1WEVWjMYJTGsaKmFgxus9TCW6KPUR1l9C5sP1B2xSDs/P4m0rlm/Ha3mu9NUETjZxAXZ84ybrBu2l9ZT2I0S6yQ9PpJpWfGBTzBtazSKoa4IPL1bM83tHvZXN9HU6qEproCmhEn9vpzXqzz8wT5W7qrkjotmH/HZx0S4OW5S6vCYYA6A3pSEv8721wBfNRkDvOrnxc8WkW0islNEbupmf4GIvCYiG0TkTRHJ7bI/QUSKROTPfso6cvF6oXw77FtpFcRYw9thLMfdb0LlLpMD40NGfBTHTEgl3umAmJUYdbSCaCiHve8GVkGAsUYOrDPJgt0wMS2WyPCjbyURYS7m5iYyLy8pcArC64W6g7D/I9jzNlTv6VNBAESHu5mSHkdOUjRxjfuIru9/bpHLJVwwbxwisPyTg0fsa2rzUBaA1rXDEX+VRJSqHrprOc/7tK1ExA38BTgHmAlcISIzuxx2F/Cwqs4FbgN+2WX/z4G3/ZRz5NJSB4XvQ9UurHtpDONtN0EKe94yYc4+GXbREW6WjE+hILVL2KXXC2VboHg1eHruSz041JR9Kdt61J4wt8mQ9mVcUjTHTUolI0Bd6GhrMomju9+Ag+ugqbLfQ4iIY9UkkNW+j6iG/hfATImN4MQpaby3q5LKhiOVQncuwdGAv0qiUUQWdr4QkUWAPzUglgI7VXW3qrYBjwMXdjlmJvC68/wN3/3OdTKBFX7KOfJQhYqdULgSWgdXO8oyiuhoNVFJe9+G2mI6nf4ul/HtH4qyaWs0353qvUMjV/UeY1V0SQ/PSIgiLT6S6Ag3CwuSmTkuYfAlalShvgT2rzJKs2p3QJRgZJiLSelxzAgrJrblYN8ndOGc2dkIsHxjyRHb65rbqWoMlpIOHf6GGNwAPCUiBwABsoDL/TgvB/BV10XAMV2OWQ9cDPwRuAiIF5FUoBr4LXAVcEZPFxCR64DrAPLz+y4rMKxoqTORS60jrwSzZYhobzZ1uap2m7BZ33WB2iJjQfiT+xBI6g8aJZaz0FTCdZiRHU+YyzW4KJ+OVlNNoLnaLEYHsZNiamwEi70H2OmKptiT5Pd5KbERnDA5jXd2VnDu7CPbsu6rbBx1Per9UhKqukpEpgPTnE3bVDVQefE3An8WkWsxbqViwAN8A1iuqkW9LQap6j3APWAWrgMkU3BRNT/6yp2257TFP9oa4MDHptRH6mSTHFff/1lwwGiugsIPTIisE8I7oIY+bU1GITRXGeUwxGVmwl3CDPaQnjKTLfXRtLb793s8Z3YW7+ys4MWNJVx1bMGh7ZUNbdS3tI+qNqd+KQkR+UKXTQtFBFV9uI9Ti4E8n9e5zrZDqOoBjCWBiMQBl6hqjYgcB5woIt8A4oAIEWlQ1aMWv0cUrfXGemipDbUklpFISy0Urwm1FIa2BrOOlrO4zyzyQ7Q2HFYIzdXDpGuikla3heOyFrCjMZri6r496alxkZwwOY13d1Zw7pzsI6yHfZVNfvUfHyn4625a4vM8CjgdWAv0pSRWAVNEZAJGOXwO+LzvASKSBlQ54bU3A/cDqOqVPsdcCywe0QrCWg+W0UhHq4k0GrcAYrt0a1M1rtROhdBcHcSF9UGiXsJK1jEjdzFZCclsOVh3RCJed5w7O4t3d1bw4saDXHnMYWuirL6Flva4oe8pEyT8dTd9y/e1iCRhFqH7Oq9DRK4HXgbcwP2quklEbgNWq+rzwCnAL0VEMe6mb/brHYwEWhsc66Em1JJYLIHH224iqzJnQ0SMj1KoMftGCuqB4jUk5y7h2Imp7K5oZF9lY4+JgqlxkSyblMo7O4w1kRxjrAmvFwqrmvouuDhCGFDGtYiEAxtVdVqfBw8hwy6ZTtVEgwQi89VisQwNrnDIWwpRCdS3tLP5QB31Ld0HB1Q0tPLjZzdy8rR0Pu+The12CydMTgtNE7IBMOhkOhF5QUSedx7/BrYBzwZSyFFHWyPs/9DEdlsFYbGMHLztULQKWhuIjwpn6YQUJmfEdRu1lRYXyXGTUnl7ezk1TYddaR6PUuTH2sZIwN81ibt8nncA+1S1KAjyjHxUTcx6xQ5jvloslpGHpw2KPoK8Y5GIGManxZKREMmWg/VUd8mFOG9ONit3VfLixpIjajrtr2qiICUmNF0rA4hfloSqvuXzeM8qiB44ZD1stQrCYhnpdLQaRdFuIrBiIsJYmJ9EbOSRc+v0eMea2HGkNdHW4eVgABozhRp/3U3HisgqEWkQkTYR8YiIzQDzpXov7H3PLNhZLJbRQXuzURROUp+IMDnj6NLp583JxuNVXtp0ZBb2vsqR3yDM31WVPwNXADuAaOArmJpMlrYmKPzQZL5a68FiGX20NZo1CqeQYHp8JEldeomnx0dy7MRU3tpeTq1Pf42mVg9l9SPbmvB76V1VdwJuVfWo6gPA2cETa4RQvc9U3exSw95isYwyWusdRWGinKZkHB3e2pM10VsZ9ZGAv0qiSUQigHUicqeIfLcf544+2ptNAlHZZms9WCxjhc5sd1USY8JJjz+yVHtmginn/ta2I62Jmqb2I9YqRhr+3uivdo69HmjElNq4JFhCDWtqCp2a/f0vVWyxWEY4zVWHamZNzog7qmX2+XOzafd6WXHU2sTItSb8jW7ap6otqlqnqj9T1e857icAROTp4Ik4TGhvNiWLSzcNfdVNi8UyfKjYAarERoaRnXhkO1ZjTaTwxrbyI3p/l9e30tg6Mu8bgXIZTQzQOMOTmv3B6fhlsVhGHu1Npkw7MDE99qgku/PnjKPd6+XlzaPDmgiUkhgZJbr7S3sLFK2G0o3WerBYLIep3AleL1HhbvJSjmzSmZUYxdLxxpqobzlsTZTUNdPSPvLWMMfu4nNf1BYZ66GxPNSSWCyW4UZHC9QWAlCQGkN42JG30vPnZtPe4eXlTaWHtnm9UFQ98qyJQCmJkZ137kt7CxStMVVbR1IFS4vFMrRU7gKvh3C3iwmpsUfsyk6MZsn4FN7YVnaENVFU3UyHJ/C13IJpofibcR0rIi6f1y4R8bWxfhRwyUJBbbFjPZSFWhKLxTLc8bQd6i2emxxNdMSR/SPOn5tNW4eXVzYftiY6PEpxTWAK/zW0drCnopEPdlfywe7gRVv6a0m8BvgqhRjg1c4XqroikEKFhNoi00vYWg8Wi8VfqveApwOXS5iYfqQ1MS4pmsXjk3ltaxkNPqXGC6ua8HoHtoxb19LOzrIG3t9VwQe7KtlV1nDE2MHAXyURpaoNnS+c5zG9HD/ysAvTlrGGpw1e+xlseSHUkoxcPO2HrImshCjioo4s/nf+3HHGmthy2JpobfdS2o9SHTVNbWwvree9nRV8tLuKvRWNNLUO3QK4v0qiUUQWdr4QkUXA6CiWbrGMVba9ZGqOrXvM5P9YBkb1XvC0d1v8LycpmkUFyby2tZQGnzyJvRU9L2CrKpUNrWwtqeOdHeWs3ltNYWUTzX20Uw0W/iqJG4CnROQdEXkXeAKTfW2xWEYiLbWw+VnImgvx2bDyz6YftaX/eNtN/3pME6Lk2Igjdp8/N5uWdi+v+qxNNLZ2UNHQengIr1Je38qmA7W8vaOCjwtrKKpqprU99A3L/O1xvUpEpgOd7Uq3qap13lssI5VPnjLlrxddY1wmK26BD/4KJ/2Ao2pNWPqmeh8kj4ewSCZnxLFqz+Gin7nJMSzKN2sTn5qZeagfxb7KRjyOcihvaMXjGZ7pZn4pCaen9deBk5xNb4rI/1lFMYLwdsDB9aa8iMVPBLLnQuToaGh/iNr9sOs1mHwmJOSYbQuugjUPwvYXYdq5IRVvRKIeY01kzCAxOpzMhChKfRoOnT8vmzWF1by6pZQL55vPvLqxnerG2lBJ7Df+ti+9GwgH/td5fbWz7SvBEMoSQDxtsPst2PK8TQwcCDEpsOwGSJsaakkCx9q/Q1gMzLn08LYpZ5ncoHWPQfoMSJkQOvlGKjWFkDwBwqOYlBFLWX0L6hgHeckxLMxP4tUtxpqIifD31usf7vbgNTfyV9IlqjrP5/XrIrI+GAJZAkRHC+x8Dbb+23TLS50CC6+BxJxQSzZyaKqCj/4PXv0ZLLgSpp4z8l0xB9ZByXpYcPWRFpIIHPM1ePFH8P4f4axfQXhUyMQckajXlOvImk1MRBg5ydEUVR223D89dxxrCzfz6pYyLpg3LmCXFU8bcTUbgeAodn+VhEdEJqnqLgARmQj4tdQuImcDfwTcwN9U9Vdd9hcA9wPpQBVwlaoWich8jLWS4FzrDlV9wk95xy5tTbBjBWz7j2mUkjELjv0mZM4a+Te4oSY+G876JXxwN6x9GMq3wTFfhfARGv3t9cDHf4e4LGM5dCUyHo6/Hl7/Oay5H479xtDLONKpK4aUiRARw4S0WA7WtOBxciLyUmJYkJfEq1tKOWNGRmCsCfUSW7GB3bVtUFjNgvzkwY/ZBX+lvBF4Q0R2Y0pwFABf7OskEXFj2px+CigCVonI86q62eewu4CHVfUhETkN+CXGndUEfEFVd4jIOGCNiLysqjV+yjy2aK2HbS/C9pdMlcrs+TDrIkif1uepll6IiIUTv28ssvX/gJp9sOy7kFwQasn6z67XoK4ITvgeuHv46WfMhFkXw8anTeTT+BOGVsaRjnqhcgdkzyMyzE1+agx7yg+7gj49dxwf/2czr20p49MDtCbaPV72VDSyvbSe3UUlbK9WWjxRnFS1g4e/tDRQ7+QQfSoJ50Y/D5jCkdFNrT2fdYilwE5V3e2M9ThwIeCrJGYC33OevwE8B6Cq2zsPUNUDIlKGsTZq/Lju2KG5Brb+B3auMNEquUth1mfMbMYSGERgxqchdTK8/z/wyi2w+Msw8ZRQS+Y/bU0moiljBuQu6f3YWRebyser/mbec3zW0Mg4Wqg7CCmTIDKOgpQYiqqbae8woaz5qTHMz03ilS2lnO6nNdHS7mFnWQPby+rZUdrAnopGOhzrZHych9OyPcxOhUvPmxuUt9OnhKrqEZErVPX3wIZ+jp8D7Pd5XQQc0+WY9cDFGJfURUC8iKSq6qFiJCKyFIgAdnW9gIhcB1wHkJ+f30/xRjCNFSZTdvfrJnIp/3ijHBLzQi3Z6CVjhnE/vf8n+PCvxv206IsQFtH3uaFm07PQ2gALvtC329HlhuO+BS/9yCjFM27r2fKwdIMaa2LcAsLcLiamxbKtpP7Q3vPnZXP7f2p4fWsZ58892pqod0pvbC9tYEdZPfuqmlAFl0BBaiynT89gZoqy0L2L+HCjLFzucDISgrOG5O9//j0R+TMmie6Q7aSqawMgw43An0XkWuBtoBif9Q4RyQYeAa5R1aMyS1T1HuAegMWLFw/PQONAUl9iIpX2vGW6eEw4CWZeaGd7Q0V0Epz6Y9j4lLnxVu2GE747vD//hlIT2jrhJP+jlmLTYOlX4d3fwYZ/mIVui//Ul0BLHUQlkJMUTWHV4Yzp8amxzM1NZMXmUk6fnklLh4cdpQ1sL61ne1k9B2pM6GyYUw/qvDnZTM2IZ2J6LFHhbtztDSRVrEF0aG53/iqJ+c7f23y2KXBaH+cVY/phd5LrbDs8iOoBjCWBiMQBl3SuO4hIAvAf4Meq+oGfso5Oaotg03NQ+B5IGEw6w7hAYtNCLdnYw+WCuZebiLEP/gIv3wzHfB3yAu8PDgjrHgNxG5n7Q95SmPwp487MnA3jFgRHvtFKxQ7IXYTLJUxKj2Nj8eGciAvmjuP25Vu46ZkNNDrKIzLMxeSMOI6ZkMrUjDjGp8US7j6yKIZ42kio2oDo0JXo8Dfj+tQBjr8KmCIiEzDK4XPA530PEJE0oMqxEm7GRDohIhHAs5hF7X8O8Pojn6o9ZsZa9BGERcK082D6eRAd+CgGSz/JWQhn/wre/YOZcU8/D+ZdAa5h5Jop3wr7P4Q5l5mcj/6y4GrjVvvgbjjn1/Z71x8ay0z4eXQyWYlR7KtspN6p2Do+LZazZmZSVt/KlMw4pmbGk5ccc1Qr1CNQLwnVG3F7/C8OGAiC+m1W1Q4RuR54GRMCe7+qbhKR24DVqvo8cArwSxFRjLvpm87pn8VkeKc6riiAa1V1XTBlHjZUbDfK4cDHJuRy1kUw7RyITAi1ZBZfYtPhjFvh40fMjLtiJyz7zsBuyIFGvSZ0NzrFKLCBEBYBy74NL/8XrPxfOPVmEFff51kMFTsOWZiTM+L4uLDm0K7LFvdv/TCudhvhbTV9HhdoRIfIrzUULF68WFevXj2wk6v3moqYoUTVVOPc9CyUbYKIeJh+rolpjxihsfljib3vwap7wB0Jx38LsuaEVp497xh32LHfMOsRg2Hna7DqXmMpzbwwMPKNFfKOOTRpWFtYTVVDW7+HiGrYT1zdjh73u9zhzD310h7394WIrFHVxd3tG0Z28RhG1VgMm581M4/oZGPmTz4dwmzW64hh/DJT5O3d38MbvzBlL2ZdFJqZd0erWXBOmRiYXIdJp5myHRueNLkUaVMGP+ZYoWI75B8LGGvio4aqPk44kvCWKuLqdgZDMr/wt31pjIj8RETudV5PEZHzgyvaGEC9xl/88s3w9p3QVA2LvwSf/qNxD1gFMfJIzIGzboeCZSYv4a1fh6YE99Z/m7IiC64OjJISgaX/D2KSTVhsW8/9ECxdaK6GBlM3LSEqnKxE/3/Xro4mEqo3YuKEQoO/354HgFbgOOd1MXB7UCQaC3g9xhWw/Adm1tnRYurmfPoPMOVMcI+AuHtLz4RFwXHfNAl3pZvgpZuNhThUNFXB5ueNLzxjRuDGjYiF478NTZXG9TSKXNVBp+JQbjCT0uNw+XHnFW87iVUbEA1t10x/3U2TVPVyEbkCQFWbRGwhoH7j6YC9b8Pm56ChzCS+Hf9tyDsWv741lpGDCEz5FKROMhOB1241s/opZwW/htaGJ03p6nmf7/vY/pI2FeZ8FjY8btZcJvUVBW8BjDVZXwLxWURHuMlJimF/VS/WmCrx1Ztwd3R/TLhbiAxzExXuJjLMRXRUZJAE919JtIlINI7NIyKTMJaFxR862kxm9JbnzSwvZSKc+AUTQmkjRUY3KRPh7F+ayKA1D5qQ1KVfhfDo4Fyvao9JtJx+XvAS/GZeYMp2rHkQ0qbZysL+UrED4jJBhAlpsRyobe6x0VBs3Q4iW6sID3MRFe4mKsxF5KG/LsK6Tipd4UET218l8d/AS0CeiDwKLAOuDZZQo4b2Ztj5qvEPt9SaYntLv2oKp1lDbOwQEQcn3WjKqGx4HKoLTZZ2UoBLqKiaUNzIOLNgHizEZdxpnWXFz7zdukj9oa0B6g9CwjgiwlwUpMSwu7wRlwuiw8OIjXQTGxlGfEsJcZ4aIpMSe8+bGCL8TaZ7RUTWAsdiqsB+R1UrgirZSKatAba/bKqytjUYs3zWxYH1D1tGFuIyoaOdRQJX/BiWfGXwoam+FK+Bss0m+CEiNnDjdkd0Mhz7dbMw//HfzTUtfVOxw5SgF6EgNZbMhChiItwc8t43VUH1LghwU6LB4G/70ouA11X1P87rJBH5jKo+F0zhRhwtdaaPw/YV0NEMOYvMjC51cqglswwXMmeZLO33/wc++F/jflp07eBn4p4Oc7NOyIFJpwdE1D4Zt8BUANj2HzMR6qu6rMWU8a8tgqQ83C451O8aMBFjB9aaqMdhhN/uJlV9tvOFqtaIyH/jlPUe8zRVwdYXTMKRpx3yj4GZF43MngOW4BOdDKfeYhaYt/zLFAlcdsPg1hB2rICGEjj5R6aK61Ax7woo32Kq4iZPGFgtMa/H9ENprTcLvJ3P06aOzt9Q5U6jzH3XFTwdxhL0tIdOrh7wV0l0t7o6fOyhUNFQZhajd79ptP/4E4xLIcEu5Fn6wOWG+VdA+lSzqP3yf5nM6Nxuk157p7X+cJOg7PkBF7VX3GEmQu+lm2Dln+G0n4C33VjVXW/6LXU+r33+tvXUn1lgwskw97PDo8xJoOhogdpCk3gJZi3p4Hrjmh6G+HujXy0iv8N0mQNTX2lNcEQaAdQdMGGse981vuaJp8CMCyAuI9SSWUYaOYtM9NN7f4B37oLpn4Z5l/evSODGp6GjyUmcC8FCZ3yWyQn54C/w1DWmv0l3uNym9lhkvHkkjz/8OirBZ1+CKWa54xVT4nz/SvP7mn6+2T4aqNxlQuBdbpND0VgWaol6xN9v4reAn2D6SQC8wuFCfGOH6n2mdEbhh+AOh6lnmR/1aJrlWIaeuAxTJHDtI8ZtWbnDzM79+V7VHTA300mnBz5aqj9MOBE8bSZ654gbv3PTj0wwYb/9UWILrjSladb/w2Sv73wN5n3OWOwjPXTc02Za4bojjbtxGGML/HXSW4G/yp2m6F7xGgiLhqlnwrRzISpxwLJaLN2y91346F6TtX38tyBrdu/Hv/0bKN0M5//eNEQarZRtMeG9VbtN7snCL0D69FBLNThc4SbpMRAL1a5wmHLGgE8fdIE/EZmK6SA33vccVR3d6ZZlW2DTM6awWUSsqck/9SwT926xBIPxJzhFAn8Hb95hsptnXtj9zLnkEzNxmXfF6FYQYMLHz7zdKNH1j8Ort5rqqvM/bxLURiLe4bdI3R3+upueAv4K/A2f1qKjElUo2WAsh/KtEJloyhtM+VTwsmQtFl8Sc+HMX8BH98CGJ4zP+thvGNdNJ16vmVnHpps+I2MBcZm8krxjTILq5ueNkpx6jgk1t+X0g4K/SqJDVe8OqiShRr1QtNpYDlW7jT940bUw8bSR0ejeMroId9xNGdNh7UOmSOAJNxzOudnzJtQUmgZHYy3bOSwSZl8CE081YcRb/20+jzmXmbWZoQwBHgP4tSYhIrcCZZh2oodqNqlq/wqjB5kBr0mUboYnv2AWDOMyYOZnYPxJJrzPYgk1lbtMkcCWaljwBTOb/vcNxs1yxs9siZeqPcaqKttsws8XXDX2+nEHcU3CXyWxp5vNqqoTByxVEBiwkmiugYfON4qh4Hg7E7EMP1rrTYb2gY/NRKahDD71c9v8pxNVKF4NHz9qkgqz5hllEcqIr6Ek1EpipDDi25daLL2hXuOH/+QJyF8Gx18/+DHDIk0Xu9GCp8Nkn3fmjkw63bihRnskYqijm5xBZgMzgUNtlVT14QFLZbFY+oe4YNZnjLUbnRyYMZMnQEuN6XUwGnCHmb7wE040imLHK6b3+OTTzIJ36uSRn2MxxPgbAvvfwCkYJbEcOAd4F7BKwmIZagKW2S+mImlcBtSXEsoWmQEnMt4Enkw505Rn3/4SbP2PUa65SyDX6dpnXct94q8lcSkwD/hYVb8oIpnA3/05UUTOBv4IuIG/qeqvuuwvAO4H0oEq4CpVLXL2XQPc4hx6u6o+5Ke8FoulL2JSTBQVGN99TWFo5QkGCePghO8drrC6/yNTa23HCoiIh9xFxsLInG2qKFiOwl8l0ayqXhHpEJEETKRTnytCIuLG1Hv6FFAErBKR51V1s89hdwEPq+pDInIa8EvgahFJwTQ7WoyZ4qxxzq32+91ZLJaeic8+/Dx1MtQWmwzg0UhEjElUHH+CWYM5uN4ojP0fGqURHg3jFpq+4NnzTMa7Behfgb8k4F5MYb8GYKUf5y0FdqrqbgAReRy4EPBVEjOB7znP3+Bw+fGzgFc6w2xF5BXgbOAffspssVh6QlxHliYPi4SUCaYEzWgnLNIog7ylpjR36UajMIpXw773TN5J9jxjYYxbOOaT9PztTPcN5+lfReQlIEFVN/hxag6w3+d1EXBMl2PWAxdjXFIXAfEiktrDubYGt8USCGLTj3avJE8wReeGYU+DoOEONzkV4xaA9yumysL+j6DoIyhaZdYsMucYhZKz2BQtHGP0J7ppLj61m0Rksqo+EwAZbgT+LCLXAm8DxfSj9IeIXAdcB5Cfnx8AcSyWMUB3PU/cYcbtNFZDwV1u0zkwcxYsusYkMe7/CIo+NCVS5F5InwFzLoWMmaGWdsjwN7rpfmAusAnoLFmoQF9Kopgj1y5ynW2HUNUDGEsCEYkDLnE63xVjIqp8z32z6wVU9R7gHjB5Ev68H4tlTOMKN5ZEdyTmm5yh9uYhFWnYIS6TqJg2xRQRrCk06xd73oZ3fg/n3TX6cy8c/LUkjlXVgajOVcAUEZmAUQ6fAz7ve4CIpAFVquoFbsZEOgG8DPxCRDoDws909lsslsEQn3lk60xfXC7TNvTg+qGVaTgjYtqoJheYHJWXboI1D5q6WWMAf7NKVopIv5WEqnYA12Nu+FuAJ1V1k4jcJiIXOIedAmwTke1AJnCHc24V8HOMolkF3DbcakVZLCOS+HG9708YZ5oEWY4mMRdmXQyFK82axRjAX0viYYyiKMEU+BNM7aa5fZ2oqssxCXi+237q8/yfwD97OPd+DlsWFotlsIRF+tfxLn3amLkJ9puZF5i1ilX3mYS8Ud5fxl9L4j7gakwI6qeB852/FotlJBE/zr+qsbFpEJMWfHlGIq4wOOar0FoHa/3KKR7R+KskylX1eVXdo6r7Oh9BlcxisQSehD5cTb6kTw2eHCOdlAkw49Omj8UoX7/xV0l8LCKPicgVInJx5yOoklkslsASEde/OP+oxCOzsi1HMvsSo3Q/undUR4P5uyYRjVmLONNnmz8hsJbhRFikCX+0+Id6ob0p1FIEjv5YEZ2kTYWGUvNZWI7EHQFLv2r6ba//Byz+UqglCgp9Kgmn/lKlqt44BPJYAopAdJKJiY/LOLJHssU/SjZC7f6+jxsJDMQqiIiBxDyTiW05mvRpMPVs2P4i5B9nFrJHGX26m1TVAywbAlksgcAVbm4G2fNg8umQfyykTrIKYqBkzTZhjyOd6OSB1yBKnWwWay3dM+9yiM2Aj/4POtpCLU3A8fc/v05EngeeAho7NwaoLIdlsETEGUshNt3cDMZ6z+NAkzXH/K0tCq0cg2EwawthEaauU+WOwMkzmgiLgqX/D964Az55ChZcGWqJAoq/SiIKqARO89lm1yRChbggJvWwGyk8OtQSjX6y5pg+ynXFfR877JDBL0CndBb/G30z5YCQNQcmnQbb/n3Yeh8l+FsF9ovBFsTSB2GRxqSNyzAKwnbUGno6LYqRpihi04w1MBhcbqf43+a+jx2rzL8KDqyDD/8PzvqFKZg4CvArBFZEckXkWREpcx5Pi8gocNQOZwSikkx0ScEyM0vJmm2UhFUQoUHEKIqBRAmFkkDJm5QP4WO7t0KvRMTAki9DbSFsfi7U0gQMf/MkHgCeB8Y5jxecbZZA4go3jWCy5hqlUHCcMVvHYA37YYuI+f+MFEUhbojLDNBYYqJ5LD2TswgKToBNz46adrD+Kol0VX1AVTucx4OYntSWwRIeA8njTWP2SaeZ5ieJOYN3D1iCR6eiGAmJZoG2POOzjIVr6ZlFX4CIWPjwr+Ad+e1g/VUSlSJylYi4ncdVmIVsS38Rl6mJkz4dJpwEE082sdWxqT2Xb7YMP0RMmPFwVxTdNRcaLNaa6J3IBFj8RajaDduW9338MMfflZUvAX8Cfo+JanofsIvZ/uKOOByJFJM2aha0xjydigKg/mBoZekOd7hZtA40MSnm+9xYHvixRwt5x0Lu+/DJk8YFNVLck93Q69RVRH7tPF2qqheoarqqZqjqZ1R1dDjcgkVkgokGyT/OJLVlzzWmulUQo4tDFkVWqCU5mvjs4OXMpE3FdAywdIuIKdPhijBJdiO4rElf/o1zRUSwHeH6RtzGUsicDRNPhfHLTOvD6KRQS2YJNiKQPX/4KYpgzl6jEiBhmLvaQk10Miy8Gsq3wY5XQi3NgOlrWvsSUA3EiUgdTrMhDjcdGtthN+HRh3MXolPsmsJYplNR6MemIF6oCY82N6lgkjYV6ktG9Cw56Ew4GfathPWPmaCUuIxQS9Rver2rqeoPVDUJ+I+qJqhqvO/foRFxOCFGGaRPg/EnwMRTIHOm8ftaBWERcW4EAQo5HQx9tSgNBOHRJnfC0jMisPQrgMCqe03W/gijzzubUwV2DCoEB3e4MdsPFcw7BlIm2oJ5lu7ptChCPWMcqoXS1Mm2/HxfxKbD/M9DySew563Ajq0KNfthw5OmnWoQ6HMVVVU9IuIVkURVrQ2KFMONyHjjRopNswXzLP3H5YLsBXDwY2goG/rrRyZA5BD1XXaHm7pOFduH5nojlclnGLfT2kfMhHMwrkBVE167/yMo+tC4/BCTD7PkywETuRN/Q20agE9E5BWOrAL77YBLFCoi4iFjpi2YZwkMoVQUQ72gnDzeFP/raB3a644kxAXHXAcv/tDM+E/8fv8mn14vVGxzFMNH0FRpxsycBdPOM1GUcy8Liuj+KolnGO0VX2NTzcNiCRSdiuLAx9A4VIpChmY9wheXG1KnQOnGob3uSCM+G+Z8FtY9CoUroeD43o/3dkDpJihaZR4ttca1lz0X5lwGOYsPW4xBdPn5WwX2IRGJBvJVdVt/LiAiZwN/BNzA31T1V1325wMPAUnOMTep6nIRCQf+Bix05HxYVX/Zn2tbLCHH5TKL2UOlKGJSIDwq+NfpSmIuVO+Btsa+jx3LTDsXCj+ANQ+agp2RXZZ7O9qgZIOxGIrXQHujqQA9boEp3TNuwZB7OvxSEiLyaeAuIAKYICLzgdtU9YI+znMDfwE+BRQBq0TkeVX1rTd8C/Ckqt4tIjOB5cB44DIgUlXniEgMsFlE/qGqe/vzBi2WkHNIUawNfpZyqMqEiED6DLM24WkDTzvoyK9bFHBcbjjma/DyTbDmITj+W9DebCYR+z8y7smOVgiPNZnaeUtNnbAQ1nLz1910K7AUeBNAVdeJyEQ/zlsK7FTV3QAi8jhwIeCrJJTD0VOJwAGf7bEiEgZEA21AnZ/yWizDC5cLxi0MrqIQV2gT+uLSzaMTr8fc8DqVhqetm0e7c0w7eNtDJ/tQkpQHMy+Cjf+E5mqo2GHee2QijD8RcpeYtYZh0jLWXynaVbVWjlxo8SeDJgfw7SJfBBzT5ZhbgRUi8i0gFjjD2f5PjEI5CMQA31XVqq4XEJHrgOsA8vNtzLZlGNOpKIrXQFNF4MePTTfRRsMFl9vpq+1nDwrVw8qjoxUOrjPKYzQy8zPGemgoNZFPeUshbdqwzLfyV0lsEpHPA24RmQJ8G1PkLxBcATyoqr8VkeOAR0RkNsYK8WD6VyQD74jIq51WSSeqeg9wD8DixYtHXqaKZWzhchk3QjAUxQguIgcYl1VYpHlExhv3VcmGUEsVHNxhcNYdoZbCL/xVW98CZgGtwGNALXCDH+cVA3k+r3Odbb58GXgSQFVXYvpppwGfB15S1XZVLQPeAxb7Ka/FMnzpVBQxAazQ6go3uT2jicSc0feeRiB9VYGNEpEbgDuBQuA4VV2iqreoaosf468CpojIBBGJAD6H6XDnSyFwunO9GRglUe5sP83ZHgscC2z1941ZLMMalwtyFgZOUcRnDktXxaAZRr75sUpf36qHMLP3T4BzMBFOfqOqHcD1wMvAFkwU0yYRuU1EOiOjvg/8PxFZD/wDuFZVFRMVFScimzDK5gFVHaW2p2VM4nI7iiIA+TlDnRsxVIRH2SZHIaYvFT1TVecAiMh9wEf9vYCqLseEtfpu+6nP883Asm7Oa8CEwVosoxeX22eNYoDNHsMiTX7EaCUp35SeGOjnYxkUfVkSh0ILHKvAYrEEmk5FET3AG338uNFfXyxztunZYhly+lIS80SkznnUA3M7nzv9JSwWSyBwuSF38cAUxVho/hMRY5p4WYacvvpJuJ3+EZ09JMLGdj8JiyWIHFIU/agQGhELUYnBk2k4kTweopJCLcWYYxSGQ1gsIxiX2xRu81dRJOQEV57hhAhkzTGZ5ZYhw37aFstwwx3mv6IIVa2mUBEZZxodWYYMqyQsluGIP4oiKskpezHGSJl4dPVUS9CwSsJiGa50Koqe/PAjvQzHQBExZbYZ5RFdwwSrJCyW4Yw7zFQFPUpRyNhzNfkSlWgsCkvQsUrCYhnuuMNM1JNvFFNsWkh7DAwLUieb6C5LULFKwmIZCbjDHYvCURRj1dXki8tlop2s2ymoWCVhsYwUOhVFdArEZYZamuFBdDIkF4RailGNVRIWy0jCHW4a1LhsiYpDpE2F8DEY5TVEWCVhsYw0Rnudpv7icpvaTpagYJWExWIZ+cSmQmJe38dZ+o1VEhaLZXSQPt2UTbcEFKskLBbL6MAdBplzgnuNsEjGWjSV7QtosVhGD3HpJjy47kAABxWITYekPPO3Zh+UbQng+MMbqyQsFsvoIn0GNFaAp21w44RFmnWOxFwIjz68PXk8tLdA9Z7BjT9CsErCYrGMLsIiIGMmHFw3gJPFZLMn5kFcRs+RZBnToaMF6g8ORtIRgVUSFotl9JGQbW7gDaX+He+OOGw1+FtZN2uusVZGee/tUa8k2tvbKSoqoqWlJdSiWAJAVFQUubm5hIeHh1oUy3AncxY0VYG3vedjYtLMWkNcZv/zT1wuGLcA9n8IrfWDk3UYM+qVRFFREfHx8YwfPx6xSUgjGlWlsrKSoqIiJkyYEGpxLMOdsEjjFir55Mjt7ghjMSTmDr5AoDvclHMvfB86Wgc31jAl6CGwInK2iGwTkZ0iclM3+/NF5A0R+VhENojIuT775orIShHZJCKfiEhUf6/f0tJCamqqVRCjABEhNTXVWoUW/0nMNRFJADGpkD0fJp4K6dMCV0E2PMrU1HKNTus2qJaEiLiBvwCfAoqAVSLyvKpu9jnsFuBJVb1bRGYCy4HxIhIG/B24WlXXi0gq0Ivd2Kscg3ofluGD/V9a+k3mbFBPcMuKR8ZDzkIoWgXqDd51QkCwLYmlwE5V3a2qbcDjwIVdjlGgsxdhItAZ4HwmsEFV1wOoaqWqeoIsr8ViGW2ERw1N34mYFLOYPcoItpLIAfb7vC5ytvlyK3CViBRhrIhvOdunAioiL4vIWhH5YXcXEJHrRGS1iKwuLy8PrPQBoLKykvnz5zN//nyysrLIyck59Lqtrfc47tWrV/Ptb3+7X9e7//77mTNnDnPnzmX27Nn861//Goz4FoulPyRkG1fWKGI4LFxfATyoqr8VkeOAR0RkNka2E4AlQBPwmoisUdXXfE9W1XuAewAWL16sQyt636SmprJu3ToAbr31VuLi4rjxxhsP7e/o6CAsrPt/w+LFi1m8eLHf1yoqKuKOO+5g7dq1JCYm0tDQwGAVZ2/yWSyWbkiZaJLtavaFWpKAEOxffzHgW5ox19nmy5eBswFUdaWzOJ2GsTreVtUKABFZDiwEXmOA/OyFTWw+UDfQ07tl5rgE/vvTs/p1zrXXXktUVBQff/wxy5Yt43Of+xzf+c53aGlpITo6mgceeIBp06bx5ptvctddd/Hvf/+bW2+9lcLCQnbv3k1hYSE33HDDUVZGWVkZ8fHxxMXFARAXF3fo+c6dO/na175GeXk5brebp556iokTJ/LDH/6QF198ERHhlltu4fLLL+fNN9/kJz/5CcnJyWzdupUtW7Zw00038eabb9La2so3v/lNvvrVrwbmA7RYRiMZM0yynb95GsOYYCuJVcAUEZmAUQ6fAz7f5ZhC4HTgQRGZAUQB5cDLwA9FJAZoA04Gfh9keYeMoqIi3n//fdxuN3V1dbzzzjuEhYXx6quv8l//9V88/fTTR52zdetW3njjDerr65k2bRpf//rXj8gXmDdvHpmZmUyYMIHTTz+diy++mE9/+tMAXHnlldx0001cdNFFtLS04PV6eeaZZ1i3bh3r16+noqKCJUuWcNJJJwGwdu1aNm7cyIQJE7jnnntITExk1apVtLa2smzZMs4880wbhmqx9ISIiaQq+giaq0MtzaAIqpJQ1Q4RuR5zw3cD96vqJhG5DVitqs8D3wfuFZHvYhaxr1VVBapF5HcYRaPAclX9z2Dk6e+MP5hcdtlluN2mu1htbS3XXHMNO3bsQERob+8+iOu8884jMjKSyMhIMjIyKC0tJTc399B+t9vNSy+9xKpVq3jttdf47ne/y5o1a/j+979PcXExF110EWAS0gDeffddrrjiCtxuN5mZmZx88smsWrWKhIQEli5dekgJrFixgg0bNvDPf/7zkLw7duywSsJi6Q2XC3IWQeFKaGsMtTQDJujOZlVdjlmQ9t32U5/nm4FlPZz7d0wY7KgjNvZwtMVPfvITTj31VJ599ln27t3LKaec0u05kZGHa+W73W46OjqOOkZEWLp0KUuXLuVTn/oUX/ziF/n+978/KPlUlT/96U+cddZZ/R7HYhnTdPYlL1w5YpPtbD+JYUBtbS05OSbo68EHHxzwOAcOHGDt2rWHXq9bt46CggLi4+PJzc3lueeeA6C1tZWmpiZOPPFEnnjiCTweD+Xl5bz99tssXbr0qHHPOuss7r777kMWzvbt22lsHLkzI4tlSAmPNlnZrpEZAGKVxDDghz/8ITfffDMLFizo1jrwl/b2dm688UamT5/O/PnzeeKJJ/jjH/8IwCOPPML//M//MHfuXI4//nhKSkq46KKLmDt3LvPmzeO0007jzjvvJCsr66hxv/KVrzBz5kwWLlzI7Nmz+epXvzooOS2WMUdUgqnzJCPvlivG/T86WLx4sa5evfqIbVu2bGHGjBkhksgSDOz/1DJiqS2Gkg2BH9cVDlPOGPDpTnpBt/H2I0+tWSwWy0glMQfSpoZain5hlYTFYrEMJamTICk/1FL4jVUSFovFMtRkzDSd70YAVklYLBbLUCMC2QsgZdKwX8we3tJZLBbLaMXlgvSpMOEkiM8OtTQ9YpWExWKxhJLwaBg3H/KPhejkUEtzFFZJBJlTTz2Vl19++Yhtf/jDH/j617/e4zmnnHIKnaG85557LjU1NUcdc+utt3LXXXf1eu3nnnuOzZsP93f66U9/yquvvtoP6bunqamJK6+8kjlz5jB79mxOOOEEGhoaBj2uxTKmiU42iiJ7vlEcw4SRmQI4grjiiit4/PHHjyhp8fjjj3PnnXf6df7y5cv7PqgHnnvuOc4//3xmzpwJwG233TbgsXz54x//SGZmJp98YnoHb9u27YhCgwPBliS3WBwSsiEuE6r3QNVu8IY2cXVs/SpfvOnopuiDJWsOnPOrHndfeuml3HLLLbS1tREREcHevXs5cOAAJ554Il//+tdZtWoVzc3NXHrppfzsZz876vzx48ezevVq0tLSuOOOO3jooYfIyMggLy+PRYsWAXDvvfdyzz330NbWxuTJk3nkkUdYt24dzz//PG+99Ra33347Tz/9ND//+c85//zzufTSS3nttde48cYb6ejoYMmSJdx9991ERkYyfvx4rrnmGl544QXa29t56qmnmD59+hEyHTx4kIKCgkOvp0073GTl4Ycf5q677kJEmDt3Lo888gh79+7lS1/6EhUVFaSnp/PAAw+Qn59/VMn0b37zm3zzm9+kvLycmJgY7r333qOubbGMCVwuEyqbmAsVO6C2CFPnNASihOSqY4iUlBSWLl3Kiy++CBgr4rOf/Swiwh133MHq1avZsGEDb731Fhs29JyJuWbNGh5//HHWrVvH8uXLWbVq1aF9F198MatWrWL9+vXMmDGD++67j+OPP54LLriA3/zmN6xbt45JkyYdOr6lpYVrr72WJ554gk8++YSOjg7uvvvuQ/vT0tJYu3YtX//617t1aX3pS1/i17/+Nccddxy33HILO3bsAGDTpk3cfvvtvP7666xfv/5QSZBvfetbXHPNNWzYsIErr7zyiD4YnSXTf/e733Hdddfxpz/9iTVr1nDXXXfxjW98Y4CfusUySgiLhKzZMH4ZxKSFRoSQXDVU9DLjDyadLqcLL7yQxx9/nPvuuw+AJ598knvuuYeOjg4OHjzI5s2bmTu3+x6577zzDhdddBExMTEAXHDBBYf2bdy4kVtuuYWamhoaGhr6rNa6bds2JkyYwNSpJvPzmmuu4S9/+Qs33HADYJQOwKJFi3jmmWeOOn/+/Pns3r2bFStW8Oqrr7JkyRJWrlzJ66+/zmWXXUZamvkyp6SkALBy5cpD41x99dX88IeHO9F2lkxvaGjg/fff57LLLju0r7V1ZFbNtFgCTmQ85C2BhnIo3zKkpcfHlpIIERdeeCHf/e53Wbt2LU1NTSxatIg9e/Zw1113sWrVKpKTk7n22mtpaWkZ0PjXXnstzz33HPPmzePBBx/kzTffHJS8nSXJeypHDqbr3cUXX8zFF1+My+Vi+fLlRERE9PtanSXJvV4vSUlJh1q9WiyWbohLh9g0qCmEyh3g6b73TCCx7qYhIC4ujlNPPZUvfelLXHHFFQDU1dURGxtLYmIipaWlh9xRPXHSSSfx3HPP0dzcTH19PS+88MKhffX19WRnZ9Pe3s6jjz56aHt8fDz19fVHjTVt2jT27t3Lzp07AVMh9uSTT/b7/bz33ntUV5tuW21tbWzevJmCggJOO+00nnrqKSorKwGoqqoC4Pjjj+fxxx8H4NFHH+XEE088asyEhAQmTJjAU089BZgeFuvXr/dbJotlzCACyQUw4WRInhD0ZDyrJIaIK664gvXr1x9SEvPmzWPBggVMnz6dz3/+8yxb1m3fpUMsXLiQyy+/nHnz5nHOOeewZMmSQ/t+/vOfc8wxx7Bs2bIjFno/97nP8Zvf/IYFCxawa9euQ9ujoqJ44IEHuOyyy5gzZw4ul4uvfe1rfr+XXbt2cfLJJzNnzhwWLFjA4sWLueSSS5g1axY//vGPOfnkk5k3bx7f+973APjTn/7EAw88cGghu3OtoiuPPvoo9913H/PmzWPWrFn861//8lsmi2XM4Q6HjOkw/kSIzwzaZWypcMuIw/5PLZbAYkuFWywWi2VAWCVhsVgslh4ZE0piNLnUxjr2f2mxDC1BVxIicraIbBORnSJyUzf780XkDRH5WEQ2iMi53exvEJEbB3L9qKgoKisr7c1lFKCqVFZWEhUVFWpRLJYxQ1DzJETEDfwF+BRQBKwSkedVdbPPYbcAT6rq3SIyE1gOjPfZ/zug9/jQXsjNzaWoqIjy8vKBDmEZRkRFRZGbmxtqMSyWMUOwk+mWAjtVdTeAiDwOXAj4KgkFEpznicCBzh0i8hlgDzDg9MLw8HAmTJgw0NMtFotlTBNsd1MOsN/ndZGzzZdbgatEpAhjRXwLQETigB8BR1e9s1gsFsuQMBwWrq8AHlTVXOBc4BERcWGUx+9VtddGBSJynYisFpHV1qVksVgsgSXY7qZiIM/nda6zzZcvA2cDqOpKEYkC0oBjgEtF5E4gCfCKSIuq/tn3ZFW9B7gHTDJdMN6ExWKxjFWCmnEtImHAduB0jHJYBXxeVTf5HPMi8ISqPigiM4DXgBz1EUxEbgUaVLXXVmwiUg7sG4TIaUDFIM4fCQzX92jl6h+BlMuOZccqUNX07nYE1ZJQ1Q4RuR54GXAD96vqJhG5DVitqs8D3wfuFZHvYhaxr9UBaq6e3qS/iMjqnlLTRwvD9T1aufpHIOWyY9mxeiPopcJVdTlmQdp32099nm8Geq1up6q3BkU4i8VisfTKcFi4tlgsFsswxSqJI7kn1AIMAcP1PVq5+kcg5bJj2bF6ZFSVCrdYLBZLYLGWhMVisVh6xCoJi8VisfTImFQSIpLnVJ7dLCKbROQ7zvYUEXlFRHY4f5NDLetAEZH7RaRMRDb6bHtCRNY5j70ism6YyHWriBT7yHZub2MMsWw/d6oTrxORFSIybpjIdZnz3fWKiN9hjz2MNejvvYh8R0Q2OjLd0N/zu4z1XWecjSLyDyfBdiDjTPP5Tq0TkbrByCYiSSLyTxHZKiJbROS4gY7ljLdXRD5xZFvd9xm9juV2Kmn/ezDjdIuqjrkHkA0sdJ7HYxL+ZgJ3Ajc5228Cfh1qWQfxHk8CFgIbe9j/W+Cnw0EuTAmWG4fjZwYk+Dz/NvDXYSLXDGAa8CaweJBjDep7D8wGNgIxmLD6V4HJA3yvOZiintHO6ycxuVOD/QzdQAkmaWygYzwEfMV5HgEkDVKmvUBagL4j3wMeA/4d6O/fmLQkVPWgqq51ntcDWzBfzgsxXwScv58JiYABQFXfBqq62yciAnwW+MeQCkXvcoWa7mRT1Tqfl7GYhM8hpQe5tqjqtkCMxeC/9zOAD1W1SVU7gLeAi/srmw9hQLRTsSEGn8rQg+B0YJeqDqgig4gkYhTsfQCq2qaqNQGQa9CISC5wHvC3YIw/JpWELyIyHlgAfAhkqupBZ1cJkBkquYLMiUCpqu4ItSA+XO+4de4fbm4+EblDRPYDVwI/7ev4Echgv/cbgRNFJFVEYjCFOvP6OKdbVLUYuAsoBA4Ctaq6YiBjdeFzDG5SNAEoBx5w3Dp/E5HYQcqkwAoRWSMi1w1inD8APwS8g5SnW8a0knDKkT8N3NBlxogaG260xgdfQQisiF64G5gEzMfcGH4bUmm6oKo/VtU84FHg+lDLE0wG8r1X1S3Ar4EVwEvAOsAzkOs7E4QLMTflcUCsiFw1kLF8xowALgCeGsQwYRg33d2qugDT4+aoTpv95ARVXQicA3xTRE7q7wAicj5QpqprBilLj4xZJSEi4RgF8aiqPuNsLhWRbGd/NlAWKvmChWPCXww8EWpZOlHVUlX1qKoXuBfTrGo48ihwSaiFCAKD/t6r6n2qukhVTwKqMet8A+EMYI+qlqtqO/AMcPwAx+rkHGCtqpYOYowioEhVP3Re/xOjNAaMYzWhqmXAswzse78MuEBE9gKPA6eJyN8HI1dXxqSScHzy9wFbVPV3PrueB65xnl8D/GuoZRsCzgC2qmpRqAXppPMG5XARxn0xLBCRKT4vLwS2hkqWIDLo772IZDh/8zGTkMcGKEshcKyIxDi/09Mxa4aDYdCWs6qWAPtFZJqz6XSO7LDZL0QkVkTiO58DZzKA772q3qyquao6HuNSe11VB2V5dXeRMfcATsCY1BswpvE6jB81FVOqfAcmQiMl1LIO4j3+A+O6acfMgr7sbH8Q+Npwkgt4BPjE+X88D2QPI9mexvx4NwAvYMrYDwe5LnKetwKlwMuDGGvQ33vgHcxNcz1w+iDf788wynij892IHMRYsUAlkBiA/8N8YLXzXXgOSB7EWBOdz2o9sAn4cQDkO4UgRDfZshwWi8Vi6ZEx6W6yWCwWi39YJWGxWCyWHrFKwmKxWCw9YpWExWKxWHrEKgmLxWKx9IhVEpYRg4ioiPzW5/WNInJrgMZ+UEQuDcRYfVznMqeC6Bvd7PuNU/30NwMYd36oqudaRjdWSVhGEq3AxSKSFmpBfHGy2P3ly8D/U9VTu9l3HTBXVX8wADHmY3J9/EYM9h5g6RX7BbGMJDowfXy/23VHV0tARBqcv6eIyFsi8i8R2S0ivxKRK0XkI6eW/ySfYc4QkdUist2pidNZp/83IrLKKUD4VZ9x3xGR5+km81ZErnDG3ygiv3a2/RSTyHlfV2vBGScOWCMil4tIuog87Vx3lYgsc45bKiIrnSJz7zs9EyKA24DLnd4El4vp0XGjz/gbRWS889gmIg9jktXyROQHPu/vZ87xsSLyHxFZ75x7eX//WZbRQX9mQBbLcOAvwAYRubMf58zDlLOuAnYDf1PVpWKaTX0LuME5bjymfs4k4A0RmQx8AVOJdImIRALviUhnVdK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+ "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-04-14T13:15:30.176929\n image/svg+xml\n \n \n Matplotlib v3.3.4, 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 \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 \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 \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 \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 \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 \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 \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": [ "
" ] @@ -2027,7 +515,7 @@ "source": [ "## EarlyStoppingShapRFECV\n", "\n", - "[Early stopping](https://en.wikipedia.org/wiki/Early_stopping) is a type of regularization, common in [gradient boosted trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting), such as [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) and [XGBoost](https://xgboost.readthedocs.io/en/latest/index.html). It consists of measuring how well the model performs after each base learner is added to the ensemble tree, using a relevant scoring metric. If this metric does not improve after a certain number of training steps, the training can be stopped before the maximum number of base learners is reached. \n", + "[Early stopping](https://en.wikipedia.org/wiki/Early_stopping) is a type of regularization, common in [gradient boosted trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting). Supported packages are: [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html), [XGBoost](https://xgboost.readthedocs.io/en/latest/index.html) and [CatBoost](https://catboost.ai/en/docs/). It consists of measuring how well the model performs after each base learner is added to the ensemble tree, using a relevant scoring metric. If this metric does not improve after a certain number of training steps, the training can be stopped before the maximum number of base learners is reached. \n", "\n", "Early stopping is thus a way of mitigating overfitting in a relatively cheaply, without having to find the ideal regularization hyperparameters. It is particularly useful for handling large datasets, since it reduces the number of training steps which can decrease the modelling time.\n", "\n", @@ -2057,1507 +545,8 @@ "outputs": [ { "data": { - "image/png": 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I/zJkpngozjKCURwhHr21PrrD63F1cVulea31ES9NbQHjNmr2U9vio7mtd1ZCGFWlvKaF9fvqWL+vjh1VjYQU0rxu5o7LZu74LPLTk+O60UcKQ39+h6t21/DQu2UAXHdiKUtK8wDwuIWZxZmMzU7tt7rqW/3sqW6msr41asC+MylJbtKSj4hHhn0IGlKIyCpVXRJ1nxUJQ6RIxCIQCnGo0UdFnRGQinpHQOpaaWzraH0UZSZTlJ1iLBDntTg7hTRv/xhvYesj0m2V5h3dT23BkLYHlmubfdS1+AkE+/77bvEFO1gLtS3GApyYl8a88dnMG5/N5IL0IeViqWpo47dv7WTnoSZOn1HIlUsntN+ICzKTmVWcSUpS362KQ41t7K5upqbJ1y/tTU5yGdFI9jClIH3U/nYHGysScRCPSHRHY2uAinojHGEBqagzYy8irY+sFE+7xRH5WpCe3G/96qNZH+leDylJriFjfage8beHHB98KGSEOOxjN69HfPOhCF995Hp4afEH43qq7a5N++taWV9ex4b9dWyrbCSoSmqSmznjspg3Ppu547LISfP234WIgssFHpcLj0vwuF143EKSy7y2b3MJSe4j29wuYdvBRg43+giEQvz1w328uLGSktxUvnjqlHYrwuMWphdlMj4nfqsiFFIq6lvZXd1MU1tHt1ogFOL9nYd5aVMlAJML0inNT2Nyfjoluam9uulnpyVx7IQcKxSDgBWJOKhr8bOvpoVmX4AmXxB/nF0ceyIQCnGowUdFfSsH6lqorG/jQF0LFXWtNPmOuD48LmFMVjJjs1Ipyj7yWpzVv9ZHmjNIsCfrIxTqGECNvGFHbg/fzCNv1u03cKdMIKQdzhdyRGAo0OoPsqWiod2NdNh5Qh6fk8q88dnML8lmSmE6Hlf8Ny63S5ybt4skt7mBH7mhh2/+zjaXs80t7WLQ14cFVWXbwUb2VDcDsH5fHfe/vQt/MMQ1x03kxKkF7cfmZXiZMzarW6vCHwxRXtNCeU0zbf6OX1ggGOLdndU8v/4Ahxp9TMhNJTs1ibLq5nar2uMSSnJTmZSfzqSCdCbnpzM2O6Xbz5ebnsTCCblDyjobDViR6AP+YIimtgDNvqARjrYgTb4ALb6je1qNpKHV38XyqKg31kdkLDU7NYmirGTGZqe2vxZnpZCf7u1X6wOOPL2PoJ9FB1SVyvo21u2rZcO+ej6qbCAQUpI9LuaMdayF8dnkpcdnLXg9LoqzUyjKTCHV68bjkkEfab2vtoWtFfWEQlDT7OO3b+3ko8pGTpiSzzXHTWwXBrdbmFaYwYS8tA7lW3xB9hxuZn9tS5egvj8Y4t0dRhyqm3xMyk/j4/PHsaAkGxFBValu8lF2qIld1U3srm6mrLqJVkdkvB4XpXlpRjjy05hUkM6YzOQOFm5uupdjJ+QM+nUcTViR6EdUlWafEYzmtuAREelP6yMYoqqxrYt4HKhrpbmT9VHUyW0VDqSnehPTm2U40hYIstWxFjbsq6eqsQ2Asdkp7bGF6WMy4nZzeNzCmExzvXPTkoaMCy+S2mYf68rr8AVMt9nn1u3nuXUHKMpK4YunTWFC7hFhyE03VoUvGGJPdTMHG7oGo/3BEG9tO8Q/NhygptnPlIJ0PrFgHHPHZfX4+UOqVNa3UlbdTNmhJsqqm9h7uAVf0Pxf0rxuSvPSOO+YYuaOzwYgP8PLghIrFAPFoIqEiJwP/AxwA79T1Xs67S8FHgAKgcPAtapa7uwLAuudQ/eo6kXd1TUQItEdvkCoXTCa2468Hq2vPIyq0tgWML2u6lupjHitauxqfUSO9wiLSF66tz3Vw0jmYIOJLazfV8fWygb8QcXrcTGrOLNdGAoykuM+n9slFGQkU5Sd3K/xo0TS6g+yZm8tjU733C0V9fz2rV00tQW4aukETptR2H6Dd7mI6gL0BUK8ua2KFzZUUNviZ2phOhctGMecsR3FwetxoRD3g1IwpOyvbaGsuomy6mY27q+jttnPN86ZwYyiTAAKM5OZNz57WFzr4c6giYSIuIGPgHOAcmAFcLWqboo45gngOVX9vYicCXxOVT/j7GtU1Yx46xtskYhFyAmqhq2PJp9xYzW1BY6q900kgWCIgw1tUd1XkdZHktuxPqJYIN5hHDAMhJRtB4/EFirrjbVQlJnMvBIjCjOKMnvV5VIE8tK9jM1OpSDDOywDqsGQsnF/HQed61Hf4uf+d3axcX89S0pz+ewJpVFjXm2BIG98VMWLGyupa/EzfUwGFy0Yx6zizA7i4HELk/LTmZCXhtslNLYFqGnyUdvsp6bZF3f6ksbWAPe8uIW6Zj//cf5MShxLZ0yWEYqhaK2NJAZTJE4A7lDV85z33wJQ1f+OOGYjcL6q7hXzS6hT1Sxn34gQie4YCOujIdzzqpMFcqixbcTFHjwuabcW5o7P7tOI49z0JIqyUhiTmTJiUlvsrGpkZ1UTYNw/L26s4K8f7iMv3csXT53K5IJ0wFgfy7dW8eKmChpaA8wqzuQT88cxszizw/ncLmFCXiql+endCm9TW4Ca5iOi0TkAHkl1Yxv//Y8tiMBt588i37H0irNTOCYOt5al7wymSFyBEYAvOO8/Axynql+OOOZR4H1V/ZmIXAY8CRSoarWIBIA1QAC4R1WfjlLHDcANABMnTly8e/fuvjW2+TD4miAtD7zpfTtHPzIQ1oc/GKKqoY0Dda0cbGgl0IeRx0MFwYxf6Ovo4kyna3JRVspRjSNICKEQhPwQ9JvXUPDIetB5r0HImwqe2AH3g/WtbNxf3x6M3n6wkfve2kldi5/Ljh1PMKS8tKmSxrYAs8cacQi7fsKIwNjsVKYUpvfpOjX7AtQ0+6lp8kUVjfKaZn7wwlay05K47bxZZKQYK2dsTgrHjMvudX2W+BjqIjEO+AUwGXgTuByYq6q1IjJeVfeJyBTgNeAsVd0Rq76jsiRqyuDgZrPu9kJqrlnS8iA5y/w7hghtAZMbqtkfPNIDqx+tj9FCmtdNUR9TrPSKaDf19vWAWdr3BxwhCBzZr3F2iPCmQ8kySIptPTW0+lm7t649P1VjW4Dfv1vGh3trAZg7LouPzx/HtDFdDfgxWclMLczo12vV4gtS0+zjcJMZpAqwtaKB/3nlI0rz0/jGOTPaRX98biqzx2b1W92WIwxpd1On4zOALapaEmXfQ5jYxV9i1ddvItEZlwdSco4IR2oOuIbY0ybG+mj2H3FbNTnC0Z/Wx3AnOclFkZOHKzs1qe8nCoXA32wWX5N5DbR1vLmHn/rjvcn3B0mpRii8aTEPaQsEWV9e155DTFVZtbuGvAwvUwq6ikNuupdpYzKO7nrFwY6qRnY5LrFVu2v49Rs7mFeSzU2nT2sfNzHBsRYt/ctgioQHE7g+C9iHCVx/WlU3RhxTABxW1ZCI3A0EVfU7IpILNKtqm3PMe8DFkUHvziRMJLp8MJexLtpFI7dbM38oELY+It1Wzb4graPA+gh3WR2bnUJOb7qsRhMCXzP4m8DfSqLnh+gznmQoWQrJsW+moZCypaKB/bUtMY/JSPEwfUxGe2xgIFhfXkdlvbEoXt96kEfe38PJ0wq47oTS9u+tND+N6UVWKPqT7kQioVlgVTUgIl8GXsR0gX1AVTeKyJ3ASlV9Bjgd+G8RUYy76San+GzgNyISwszFfU93AjGgaAhaa81Ss8ts86ZDat4R0ejmSW4wSPa4Sfa4ye00SCzS+vCHOo6qDo+W7pA6Q5Wgkz6jc+qMoUQ4e29RT4MOVTve/H1hQRjiQtAdgTbY+74RipTofnyXS5gzLovMFA8fVTZ0eFBI87qZUphBcfbAT150zLgsWpz5PM6YOYa6Zj/PrT9ATmoSlxw7HoDd1c2ISFSXmKX/sYPpwvTGkogHT3JH0UjOHFJxjUTQeY6DYEQ6jliC074tIr1HSM28Cp3Te/T0U3W5IC/dpDIpzIzIxKsK/hZHDBodQQiLQQvDUgjiweWB8YtNXK0bqhvbWL+vDpcIkwvSGZ+T2vuxCT7jJuqPTh9tgSAf7DpMmz+EqvKH93bz1vZDXLNsImfMGtN+3JTCdKYUWqHoDwbNkhjVBNqg4YBZAFxJJpYRFo2UHHNXG0GE018niu7ySYVCSo43QFKwFXyVcMixDnyNEGgd2LjAUCEUgPKVMH4RpBfEPCw/I5njp+ST5Hb1/vsLBaF6h7GoNQRJaZAxBtILzUNSH37jyR43CyfksLKshmAIrj2+lPpWP49+sIes1CQWl+YCsLOqCZcIkwoGvzfiSMaKxEAR8kNTlVnAxDVSsiOsjRxwJzYwONxxuQQXQpL6INAAbfXQ1mAWX5PpBmrpiAZh3yoYuxAyi2Ie1qduv41VcHCjY405+JuNVV5TBuKGtHwjUBljTFA9TjJTkjhmfBbr9tbhdgk3nDqFn7z8Eb99aycZyTPag9fbDzbiEmFi/tBy744krLspTH+7m3qNQHKGIxiOcHTTlXFUEAqBr+GIELQ1GmEI9s9cBqMLgeJ5kD3+6E/lb4WDm6CxsnflvOmQHrYycuOyMsoONbH9YCNguuv+4IUt1HYalQ0wszizS6JCS/zYBH/xMOgiEYWk1I6ikTyC/a9h11BbhIXga2bExgsGizFzILe0b2VVoXY3HNpmXFlHg8tjYiVh0ejmgWjj/joO1JoeT9WNbdzzwhZU4VsXzOrQ86ogM5lpYzLISOSYlxGKFYl4GIoi0Rl3UkS32zzjrhpuwfBgoJObyBGGo73pjGZUzTWMt3NEwQzIn9q7OlpqoXKj+e4SQXImFM6KGjsJhZTVe2rax3Xsq2nhnhe2kJ2axG3nHxmVDebjF2enMLUwY+iNnB/CWJGIh+EgEp0Rd8dgeGru0Bnkp+oIQGOEu6jeBJEtfScYgPpyqNkNtWVQs8c83fsaoegYOOUWEzzuibypUDgjjvr8cOgjqN3LgFh1uZOgYGYXV5QvEGJF2WFanGSVH1U28JOXP2JiXhq3nDujSyoWlwtKcs28FSMl/1YisSIRD8NRJLogkNJ5kN8ADIQKtHV0E7UHkkdhj6L+pK3eEYM95vdZuxvq9h0J0Lu9kDMBckrNk/jm5yB7Apx+m3l46ImcUiiaE3t//X7znxjoGJA3A8YuML/lCBrbAqwoO0zQyR6wek8Nv3pjB/PGdxyVHYnbyVI70clSa4mOFYl4GBEiEYWkNOP7bR/kdxTdBUPBji6isDAE/f3X3tFIKASNFUYEasocUdgNLYePHJOaa27qOaUmppBbChljOz5x718Db/+PcUOe8Z+QWdxz3VnjTUA70k3la4LKTdB8qL8+Ye8RF+RPg7wpHdp2qLGNtXtr28fMLN96kD9GGZXdGa/H1fcxIKMAKxLxMFJFojOe5I6WRqzkhb7mjkLQ1jCyB54NFP5WIwa1ux0rYbdx5QTNfA+I29y4cyc6gjDJvKbEmdju0DZ444fmOz39NnOT7YnMYiheYNYP74TDO4aOFZiaB2Pnd+g+u6e6mY8qG9rf/23NPp5dd4ATp+Zz5ZIJ3SYgHMzR5EMZKxLxMFpEojPtyQtzjriNfI02kHy0qEJzdYQYlJn4QWMl7UKblG4sgrB1kFMK2SVHP16mfj8s/38mHnTyN8xNtifS8k28KDxyeijh8pheWRHddzcfqGdfjRmfoao8vWY//9hwgPRkD59aMoHjJ+d1m6MrM8XDtAHOSzWUsSIRD6NVJCz9Q90+qN7W0UKIvOFmFB0RgrCFkJafuN5pzYdh+T3QsA+OvwlKT0xMPQNJZjEUzQV3EqGQ8uHeWmqajsRL9hxu5o//3M3OQ03MKs7k2uNKe7QYBirD7VDHikRPqBqRqNrS722yjGBaamH3O7DrTSMKEBFMnhQhChN7Ndq43/A1wZv3QtVmWHQdzLxg4NvQ33iSoXg+pBfgD4ZYsetwh+l5Q6q8te0QT64uxxcIcf7cYi6cO7bHHk5FWSnMGtu76W1HElYkeqKmDH59ivEF50wwPURyJprXIZbN1TLIBH2wbzXsegMOrDW++7wpMPlUc/PKKB5aObmCPnj351C+AmZfDAuuGn5ja6KRUwqFs2jym66xnedLqWvx85dV5by3s5rCzGSuWTaRueO7n9kuKzWJYyfmjEqhsCLREzW74dU7oWId1O3tmIsmraCrcGSNs3mWRhOqUL3dCMPu90wa8dRcmHSKEYfsLnNkDS1CIVj1AGx/BSafDsv+tX/H09Tvhz3vmd5v6QVmBHV6ofnvuBM4+tmbAWPncziYyod7aqJmCd58oJ5H3t9DRX0rSyflcuWSCeSkxZ77JTPFw6LS3FEnFFYk4iEck1A1Xf9q9xrBqN1j1hv2mT8BmB4omWMdt4IjHDkTzB9DRtePa0TTdAjK3jLupIYDxpVUsgwmnwJF84aWxdATqrDhSdjwFxi3CE762tGNoWlrhD3vmmtTvR0QM9F4h/uJGDGNFI7OInK0k3U5XWXLpZgtFY1RD/EHQ7y4sYLn1h0gye3i0mPHc/qMwphdYTNSPCyamDuqBuFZkYiHngLXoYC5UdQ6wlG316w3HTxyjCfZCEZYNLInGhGJt/uiZfDxt5oJe3a9aZLYoVA421gME4+LbzTzUGbby7DyASiYBqd+s9vZ67oQChgX2643TWbZUMBYUZNPg9KTzO+8+fCRbMdNVUZow6/N1V0z9aZkHxGN6efCmNl9+1ypuVRnzGBTdZA2f/Tuu5X1rTz6/h42HqinND+NzxxXGjPN+GgTCisS8dDX3k3+Fqgr7ygcdXtMV9IwydmO1RHptioBj+2rPSTQkBk8tutNKH/fdAXOGGNufpNOMetDHbc3/pHRe983cYqMIjj9W93ONQGY/8auN6DsXWirM8JSepK5PrmT4o9xhEJmgGA0AandA8FWOOsOyJsc3/k6I278edPY0pJHZUNb1ENUlRVlNTy+cm/77HeXHDuONG9Xt1h6sodFpTldUn6MRKxIxEN/doFVhda6ju6quj1GTNr/yGJuPmGrI2eisTwyi4dO/qWRTv1+Iwxlb5mn3KRUmHiCsRoKZg79AG9yprnRZ4wxT+Q1u53fcBz/6YOb4M0fmc98+rfM7zCSllooexvK3jS/YZcbxi0212bswv6PNbTUwEvfNpbGOXf1LFzdkZpLReo0Nh8Otafw6EyzL8DTa/bz+paDZKUmceWSCSydlNtlbMVoEQorEvEwEOMkQiHjnupsdTQcOOLLdXkiellNPGJ9JLJP/WjC1wi734Vdb5lxDSKmV9Lk02D8kqP3kScUMSlWMsYYcYjWrbbxoEnPEc8ETDW7Yfl/mwmxTv2meYLft8oIZ3vPralGGEpP7J1rqi/U7YWXv2NiFWd/7+h6FoqLtuwprG/Oo7Yl9sDQskNNPPz+bnZXN7OwJIfrT5zUIassQFqym0UTc0d0VlkrEvEwmIPpgj4zGCtSOGr3dszdk5QOOSUde1nlTDA9PCzdE9WXPsHc/CadbIKrQxWXx5nZrcj47ePpVddaZz5nILrLpQONB83o7OZqcHmdnlt5ET23+mGSot5Qsd4MAhwzG0677agtFk3OYo93GjvqzTNaNEIh5ZUtlTy1eh+ZKR5uOGUK04s6CmKa182i0pErFFYk4mEojrj2NXbtZVW310wRGSY1r6vVkT3e+KhHO/3lSx9oPMmOG6moz/NE42+FfSs7xsZi0VoP7//KPIhMPtWMah7Mnls73zDtmXw6HPfFo/+exEVjxkTWNxfS5IttYZVVN/GbN3dS3djGxQvHc8HcYlwRdY9kobAiEQ9DUSSiEc4JFBaOsPVRv+9IviVxmdhGpHDkTDRuipHeRbc7X/q4hebJfCiSnGlmacsYE1+a73gIBmD/h4ObzbWvrH/CdNmd9ymYe1m/nDKUlMHOpKmUNcS+ybf4gjz8z918UHaY2WMz+cLJUzqk7BipQmFFIh6Gi0jEIhSAhoquwfLGiC667mRjZYTFI+y2SuQMd6rGP64hY+9rKOK986qdt/dwXLT3AefJOexLz59qLIaJJyTel94nnDEE4fhCokb2q5oZ5er2Jub8iUIV/vl/plPB8TeZsSn9glCTUsKGtkLaYoQqVJW3th/iTx/sITXJzRdOnsKccUe6sad63SweYUJhRSIehrtIxMLfamYyi3RX1e417pcwyZmmS67L2/HmHPOm3cP7yO0DmVp8MH3p8eDymA4I4R5JAzlqv3qHmWFuOBEMmMD6oS1w+n+amff6iYAnna1M4kBb7AGF+2pa+M2bOzhQ18qF88Zy0YJx7RMXpSQZoUj1jgyhGFSREJHzgZ8BbuB3qnpPp/2lwANAIXAYuFZVy5191wG3O4d+X1V/311dViR6QWtdFHdV0PiixWVGlYdf27dFbO98nMvdy2OivG+vq4fjXO4o9biNu2aojYL2JDtupCIjEIPZvvoDJvXMUJkrIh58TfDyd00X2XPu7GfxF6qSitnoKyYQim5Jt/mD/GnFXt7efojpYzL411OmkJdu4n0jSSgGTSRExA18BJwDlAMrgKtVdVPEMU8Az6nq70XkTOBzqvoZEckDVgJLMI+jq4DFqloTqz4rEpYhgTfDsRYKh17PqZYa0/NpOM0m2HgQXv626Yxxzl39F7Nx8LlS2aQTORSIPWvj+zur+cM/d+NxCZ87aTILJ5g2JCe5WFyaG3Uw3nCiO5FI9GPNMmC7qu5UVR/wGHBxp2PmAK85669H7D8PeFlVDzvC8DJwfoLba7H0DW8GFM4yrq7Jp0DhjKEnEGDaNPGEo5vGdqDJGGPGcbTWmwGAgdZ+Pb031MJC3coi3Uhhyw68LQeRTqPXj5uSz3c+Poe8dC+/eH07j6/YSyAYos0fYtXuGpp9I3eSrrhEQkTSRY50ixERl4jEE2kbD0RGzMqdbZGsBcLdFy4FMkUkP86yiMgNIrJSRFZWVVXF0SSLpR9xJZk+/ZNONoPRhsPN15tuhCI1b7BbEj/5U+Gkr0LNTnj3F7EHPRwFeZ42FmTUMd+1k/GH3yP34Puk127F21KJBH0UZaXwnxfO5syZY3h5cyX3vLCFgw2t7UIRCA4jN14viNdGehU4GwinWUwDXgL6Y7qrW4FfiMj1wJvAPiCO4aIGVb0PuA+Mu6kf2mOxxEd2iUnfMaRHacfAnQQlS02MouFA38/j8piR355UM/BN3GabKzIO5TavLk/HuFP7Po/pXbf7XTM2KBbjF5vJk1Y9BB8+DIuv63u7uyEnzUtOmpfaFh+VdRW0NO8DIOhJw+/N4XPzcpg9ZiIPvr+Pu57bzGdPKGXppDzKa1piJgwczsQrEimq2v7tqWpjnJbEPiAyKUyJs60dVd2PY0mISAZwuarWisg+4PROZZfH2V6LJXEkZ0HRnKHpTuoNLpcZO3Io3Un3He2YJEhKMUKQlGaSUialOdvS+reHVtExJvlgd8w438Qotj5vYj4zL+y/+juRk+olJ9VLXYufyvpWmn3NuAPNpDTv55xkmHt8Kj9en8xv3tzJ1v01XHPiVCbkpbX3gBopxCsSTSKySFVXA4jIYqClhzJgAtXTRWQyRhyuAj4deYCIFACHVTUEfAvT0wngReD/iUj4n3ius99iGRxcSSbWkD1h6I7W7gsF040LqrX+yM0/LAaJnDSoM2l5xjqrK+/+uGOvNZljVz9sUpWULE1os7JTk8hOTaK+1U9FfSvNbcbRMdbbwj3HtvDIjmT+ur2GVv9HTB+TMeKsiXh/ATcDT4jIfszUIsXAlT0VUtWAiHwZc8N3Aw+o6kYRuRNYqarPYKyF/xYRxbibbnLKHhaRuzBCA3Cnqh7uUkl/kT4GilxmXEHAWfwtJv9NPMnSLCMYcVxLM4anaykessaZZbApnGUshe7SnosLTrgJXrvLpDw/6zuQPy3hTctKSSIrxYhFZX0rTW1BPC64brrJkfX07nre33V4xFkTcXeBFZEkYKbzdquqDrk+dEfVBbY7Aj4ItEQXkEBYSEZm0GrUk5JjAtP93O3S0g31+83I+Z5orTNdY/1tcO5dAz7vR0NrgMr6FhrbgtS0CTe8k8FJk7L43uWLKc0fXtZEd11g47IkROSznTYtEhFU9Q9H3brhgMdrlpRuJlIPtDnC0UlA/K2OkPgY0NHHlqPDnWSC0jkTej7W0r9kjTNZkXvKOZWSbTLFvvxteOMeOPtOSB64rMiZKR4yUzJpbA1QUd/KmWP9vLqrnrV7aynJHTnWRLzupkinXwpwFrAaGB0iEQ+e5O7nDFbtRkAcgYl3ZjFLAhEjDAUzBjZthqUjRXNMosaeLPSscXDKrfD63fDMTTDuWJhwvAnID9DMjxkpHqalZPCpKQ28vC+JFz/cwcIJuUzMH+ZT3TrEJRKq+pXI9yKSgxkYZ4kXEaeHSJSJYsKEQp3cWeH1sJi0Dq+RssON1FwYM8fOST4U8KabOEM8+abGzDaTFO18HfaugD3/NKOzxy10BOPY7v93/cT8Ii8nFgV4fUc9n9hfw/jc1BFhTfS160IT0MeJaC0xcblMNtDuMoKGM56GhaOzNRJoPZIy3BIfbi8UzjTBacvQIXeyySnma+r52PypZln8eZMQcM8/Ye8HZnEnmSlXJxwH4xeZXlsJIC/dyxWTG3i7MonX125nzriRYU3EG5N4liMOdRcmlcafE9UoSze43OYpq7uRvcFAjEC77bHVEYHcUvPEal1LQw+Xy0yA1NPYic5lxswxy+LrjSWy531zjvIVZuDe2AWOYCzu1xHybpewbFwSC/MCvPhRA+ceW0NJbiquYW5NxGtJ3BuxHgB2hzO1WoYgbg+4M7ufRyHg6yogviaz+JtHfm+t1DzjprCupaFNvGMnoiEu06W2cBYs+owZMBgWjH2rzANX0TyYeLyZ37wfgt4F6clcPrmJb69K5911HzFjbC4T8oa3NRFvTOKNRDfEMsCEe2wR5SapaoTD32zSJPiawOes93NytQHHk2xuGkNhTIAlPgpnQWPl0cXjxGU6IxTMMIPxDu84Ihjv/9qkBylZAsdcZqzLPuL1uDhlnIvp24L8/aNGTltQzfic4W1NxOtuOh74OTAb8GIGxjWpqn0MG4mIHImNpBd03BcKdrQ4fI2OgDRBaAgH1cUFOWHX0vBO6zzqcCdB4WyTZ6o/EDG/g/xpsPDTZpqA3e/AjleNaJQsNWKR17ew65isZC6b1MoP1qWxcuNHTC7OH9bWRLz/ll9gUmo8gZnf4bPAjEQ1yjKEcbmNiyaamybQFkVAmoxVMpjuq7R846MewD70ln4me7wJYjdX9+95RYwY5E2GYy6BrS/A1n+Y+MX4xUYs8qf26pRpXg9nlih/3B7kuY+aWHbMQcbnlA5bayLuRypV3S4iblUNAg+KyIfYXEqWSMJjRdI6paBWdUQjUjjC7qu2xLancDZkjU1cHZaBo+iY+MZO9BVvBsy7wiQN/OgF2Pp3eOm/TBfaYy4zOa7ibWpWMpeU+vjl5lQ2b/2I0uIxw9aaiFckmkXEC6wRkR8CB0j8hEWWkYJIRI+swo77ggHwN3WMe4RFpK9decUFuZOMO8E1/KeWtDh40yFvKlRvS3A9aTD3Mph5Pnz0Emz5uxnVXbzAbC+c2eMpslOSOHdCK3/aGeKZj1qYO6OC8TmTh6U1Ea9IfAYjCl8Gvo5J/315ohplGUW4PeDOjp7yxN8aPXjubyFmipO0AjNadzhM/mPpPXlToGF/fGMnjpakNOOCmnE+bHsJtjwHr3zXdMude7npHRcDEWFcVjIXTfTx0LYUynZsZcK4Ykpyh5810S9zXIvIk6o66KKRsAR/lqFFKOSIR9MRK8TfCjkTIbNosFtnSTTNh3s3dqK/CLTC9ldh8zMmueCYOY5YzImaOj4YCrFybz2ffzOT+XkBbj5nFkuPmTkkrYmjTvAXB1P66TwWS8+4XCYIbQPRo5O0PMhyAtkDiScFZn0Mpp1tekJtftakKy+cZcSiaG4HsXC7XEzITubCEh9/KfNSsXsb+8dPoCRveFm5/RVXsOlNLRbLwFE4a/BGyXuSTXD7Ez8zo7obD5oEg2v+2OXQgoxkPj7RR5ILnt0ZYP/enYRCw+t2aYPPFotl+OHxGqEYTNxeE6/4xM+g9GTTI6q547xoXo+L0mwPZ4/zs/xAEnX7t3GgrnmQGtw3+kskhp6TzWKxjGyyS0x6lcHGnQTzP2W6em95rsvuwsxkLi5tIwT8vQwOlG2lP2LBA0VcIiEi6SLiinjvEpHIMP1/9HvLLBaLpSeKjjFdngebjDHGmtj+KrTVd9iVnuxhco6Lk4sCvFDupfngTvYfbhikhvaeeK/uq0CkKKQBr4TfqOpL/dkoi8ViiYvkDNMtdigw52IzcdjWF7rsKsxI5rLSNlqDwot7hMqyzcPGmohXJFJUtTH8xlkffh1+LRbLyCNvasLmiOgV2eNN3qePXjRdtCPISU1iRg4syvfz3B4vvuoyDlTXDkoze0u8ItEkIovCb0RkMdCSmCZZLBZLL3C5oHgepI85uqU/3FZzLjZjd7a90mGziFCQ6eXyST7q/C5e2+fm4K4Nw8KaiHecxM3AEyKyHxOkLgauTFSjLBaLpVek5XXNGdZbWuth/4ddrIBekT8ViuebvE8zznfS8Rvy0r3MzWtlZnaAp3cnc974fVRUVTN2TEE3Jxx84pJOVV0BzAJuBL4EzFbVVYlsmMVisQwoKVlQetLRzzVyzCVmRPau5R02e1wu8tOTuXySj4OtLt456KZq57ohb03E27spCSMQdzjLF51tFovFMnJwO9ObFs8zExH1hcLZZnKjzc90SVJZmJnM0oIAE9KDPFWWTLChksqDB/qh4YkjXifcr4DFwP85y2JnW4+IyPkislVEtovIbVH2TxSR10XkQxFZJyIXOtsniUiLiKxxll/H2VaLxWI5OrJLoPREkz68t4jAnEug6RDsfrfDrmSPi5y0JC4t9VHW6GZ1tYeqHUPbmohXJJaq6nWq+pqzfA5Y2lMhEXEDvwQuAOYAV4vInE6H3Q78WVWPxUxs9H8R+3ao6kJn+VKcbbVYLJajJznDCEV2Se/LjjvWJJzc9HSX+S8KM5I5pdhPQXKIp8q8BJtrqDqwt3/anADiFYmgiLRPzyQiU4BgHOWWAdtVdaeq+oDHgIs7HaMcmWg5G9gfZ5ssFoslsbjcxvU0diG4epEPNWxN1O+H8o6ZqTNSPGQlu7mo1MfGWg9bat0c3NVPU7MmgHhF4lbgdRFZLiJvAK8Bt8RRbjwQKZHlzrZI7gCuFZFy4HngKxH7JjtuqDdE5JRoFYjIDSKyUkRWVlVVxflxLBaLpRdkjTVWRXKUaXtjMeF4yCh2rImO7qTCzGTOGecjMynEU7u9BFoaaKrr56lZ+4keRcJxGS0ApgNfxdzEZ6rq6/3UhquBh1S1BLgQeNhJAXIAmOi4ob4BPCoiXb4hVb1PVZeo6pLCwsLOuy0Wi6V/8KbDxBPMrIfx4HLBnIvg8E6o6Ggp5KQmkZUsXFji54OqJPY2uag7uKf/29wP9CgSzpzWV6tqm6quc5Z4Jybeh5nFLkyJsy2SfwH+7NT1HpACFDj1VTvbVwE7gBlx1muxWCz9j8tlZqQbtwhccXTwnHSqGb+x6elOpxHyM5L52AQfXpfy1zIvTYfKE9PmoyRed9M7IvILETlFRBaFlzjKrQCmi8hkZ47sq4BnOh2zBzgLQERmY0SiSkQKHSsmHAOZDuyMs70Wi8WSODKLYNJJkJrb/XFuD8z6OBzcDFVbO+wqyPCSk6ycO97HGxVJ7D7cRLCpJoGN7hvxisRC4BjgTuDHznJvT4VUNYCZF/tFYDOmF9NGEblTRC5yDrsF+FcRWQv8CbheTX+wU4F1IrIG+AvwJVU93KUSi8ViGQySUmHCcT0nGJx6JiRndrEmPC4XuWleLproA+DZ3d4h6XKKK1yvqmf0tQJVfR4TkI7c9p2I9U3ASVHKPQk82dd6LRaLJeGIQOFMM6/FvlVEnaTTk2Jmslv3ONSUdYhpFGYmU93kY1F+gPcOJtFYVU7e5AUD1fq4GAKJ2C0Wi2WYk1EI6d10nJl+LnhSu1gTKUluMlM8LC4IcLDVxZaDTV1mtxtsrEhYLBZLf5BbGnufN90IxZ73zdiJCMZkprAo36Tv+Gelm5bDnfv2DC5WJCwWi6U/SC8wYhCLmReYQPbmjn13MlM8TMxyUZIeZFW1h4ZD5V3GVQwm8Sb4SxORb4vIb53300Xk44ltmsVisQwzcibG3peaA1POhF1vmbxOERRmJrM4P8DGGjcHaxuhZej0corXkngQaANOcN7vA76fkBZZLBbLcCWrpPv0HbOdZ+stz3XYnJuaxJKCAAEV/lkBobqhk50oXpGYqqo/BPwAqtqMmXzIYrFYLGHcHsjqnHkogvRCmHQy7HjNzDnh4HIJS4tdpLiVlYc8NFYPHZdTvCLhE5FUnP5dTrK/eEddWywWy+ihuwA2mFQdQT9s/UeHzflpHubnBlh9yEN9Y/OQ6eUUr0h8F3gBmCAijwCvAt9MWKssFotluOJN7747bNZ4mLAMtr0IviNTpWanJrHI6Qq78VAAGobGZETxTl/6MnAZcD1mVPQSVV2euGZZLBbLMCanJ2viEvC3wLaX2jd5PS5OKDIupn9WuvHV7h8SLqd4ezddCgRU9e+q+hwQEJFLEtoyi8ViGa5kFEJSWuz9eZPNNKlbn4fAEc/99Dw3E9KDrK72UN/UDM2Dnz48bneTqrZHWVS1FuOCslgsFks0uusOC8aaaKuHnUdmXchMSWJRuCts49BwOcUrEtGO68U0TRaLxTLKyO6hO+yY2Sbv0+ZnIWhGXKd73SwpNF1h3z2ghOorIBSKfY4BIF6RWCkiPxGRqc7yE2BVIhtmsVgswxp3EmSN6/6YOZcYl9LutwHTFXZZkdMVtspDc0vroLuc4hWJrwA+4HFnaQNuSlSjLBaLZUTQUwB77ELImQSb/tZuMRRkJDE/L8Cqag91rf5BdznF27upSVVvC08TqqrfUtWmRDfOYrFYhjXJGZBWEHu/CBxziRGC8g8AyEz2sDg/QFWri42HgtB4cFBdTvH2bpohIveJyEsi8lp4SXTjLBaLZdjTUwC7ZBlkFMGOVwHTFfbEYtP19b0KFz5fGzQf6u4MCSXe4PMTwK+B3wHBxDXHYrFYRhgZY8wsdv6W6PtdLjNn9o5XIOgDt5epTlfYVdUeGtoC5DccMOcZBOKNSQRU9Veq+oGqrgovCW2ZxWKxjAREerYmiueaVB2HtgGQ5XSF3VTjprLBP6gup3hF4lkR+TcRGSsieeEloS2zWCyWkUL2BBB37P1jZoO4oGI9YLrCLisMtneF1aAfmqoGqLEdiVckrgP+HXgX0/V1FbAyUY2yWCyWEUVP3WGT0iB/GlQakRARjhsr7V1hm3zBQevlFFdMQlUnJ7ohFovFMqLJLYW6vbH3F8+DjU+Brwm86eSlRXSFbfGT0VQFoSC4urFIEkDc05eKyFwR+ZSIfDa8JLJhFovFMqJIzoTUbrz0RXNNQr+DmwAzrWm4K+ymQ0EIBQbF5RRvF9jvAj93ljOAHwIXJbBdFovFMvLInRR7X/50cCe3xyWS3C5OGme6wr5T4cIfDA2KyyleS+IK4CygQlU/BywAshPWKovFYhmJZIwBT0r0fW6PCWBXbmjfNC3HzUQnK2xDawAaHZfTABKvSLSoagiTIjwLOAhMiKegiJwvIltFZLuI3BZl/0QReV1EPhSRdSJyYcS+bznltorIeXG21WKxWIYmPXWHLZoL9fvb8zVlORMRbapxU9noB3VGYA8gvUnwlwP8FtOzaTXwXk+FRMQN/BK4AJgDXC0iczoddjvwZ1U9FrgK+D+n7Bzn/THA+cD/OeezWCyW4Uv2BNPdNRrF88xrhbEm0rwelhaYrrDv7AdVHXCXU7y5m/5NVWtV9dfAOcB1jtupJ5YB21V1p6r6gMeAizufHshy1rOB/c76xcBjqtqmqruA7c75LBaLZfji8cbuDpszAZKzOricjhtnusKuqHLT7A+a4LWTWnwg6E3vpvkichGwCJgmIpfFUWw8ENnnq9zZFskdwLUiUg48j8k4G29ZROQGEVkpIiurqgZnsInFYrH0iljZYcUFRccYkXCmLs1PS2JBXsDMVtfsBw1B08C5nOLt3fQA8ABwOfAJZ/l4P7XhauAhVS0BLgQeFolli3VFVe8LZ6ctLOxm8nGLxWIZKqRkQWpu9H3F86ClBur3AR27wm6odoLWA+hyijfB3/Gq2jmWEA/76BjgLnG2RfIvmJgDqvqeiKQABXGWtVgsluFJTqkRg84UzTWvlRsguwSPy8XJ45T/2wLvHHBxxuQQnqZDJteTOynhzYz3if29KAHneFgBTBeRySLixQSin+l0zB5M91pEZDaQAlQ5x10lIskiMhmYDnzQhzZYLBbL0COzGDzJXbdnjDGLM14CYGquh4npQVYdcrrCamjAejnFKxJ/wAjFVqeb6noRWddTIVUNAF8GXgQ2Y3oxbRSRO534BsAtwL+KyFrgT8D1atgI/BnYBLwA3KSqNk25xWIZGYjEjk0UzTMjr50xEZkpHhYVBNhc66aiwW+OaagYkGbG6266H/gMsB7oVb5aVX0eE5CO3PadiPVNwEkxyt4N3N2b+iwWi2XYkD0BqrcbyyCS4nlmEqLDO6FgOmleD8cVtvH0buGdAzC7CDMR0QC4nOK1JKpU9RlV3aWqu8NLQltmsVgsIx2PFzLHdt1e5Hj3I1xO4aywKw66afaFXU6VCW9ivCLxoYg8KiJXi8hl4SWhLbNYLJbRQG4Ul1NylsnzFDFeIi/dw4K8AKsOeahvccZJDIDLKV6RSAXagHPp/y6wFovFMnpJyYaUnK7bi+bBoY8g0ApAZnISiwsCHGpzseGQIxLhXk4JpEeRcFJhVKvq5zotn09oyywWi2W0EM2aKJ5r0oNXbQXA7RJOcQZqv33ARTAUAjTh1kSPIuH0KIoaWLZYLBZLP5BR3DWfU+FMcHna8zgBTIvsCts2MC6neHs3rRGRZ4AngKbwRlV9KiGtslgsltGEy2XiEK21R7Z5UqBgRvuUpgBZqR4WFfh4bo+Xivo2clK9JmNswGeC4IloWpzHpQDVwJnYmITFYrH0PylRpugpmgs1ZdBWD0Cyx83xY0IEVHh7vzoHKTQmzpqId47reDK+WiwWi6WvpOZAbaeRBcXzYP2foXITTDwegOPGukhxKx9UubnGHyQ1yW1cTt3NU3EUxJvgr0RE/ioiB53lSREpSUiLLBaLZTQSrYdT3hRISu0wXiI/3cPCvACrD3mob3F6NrXWJ6xZ8bqbHsTkUhrnLM862ywWi8XSH3jTuo6edrlhzJwOcYkMryeiK2ziMxXFKxKFqvqgqgac5SHA5uW2WCyW/iQlSvrw4nkmmZ+T0M/lEk51ZtZ5c78QDGnXMv1IvCJRLSLXiojbWa7FBLItFovF0l+k5nTdFpk63GFKjukKu/qQh8a2xM5SF69IfB74FFABHACuAGww22KxWPqTaHGJrPFmgqKIuER2alJ7VtjKhkEccS0iP3BWl6nqRapaqKpjVPUSVd2T0JZZLBbLaCMlG5CO20SMNVG5sT1brNfj4gSnK+yb+wfX3XShiAjwrYS2wmKxWCzg9oA3vev2orlmrETt3vZNJ4xzkepW3j/opjWQuAB2T+MkXgBqgAwRqcdInIZfVTUrYS2zWCyW0UhqDvgaO24rduISFevb8zzlpiWxINwVttlPSoKa060loar/rqo5wN9VNUtVMyNfE9Qmi8ViGb1Ei0uk5UPWuA7B63Svm8WFAarbXKytSlzwOt4ssFYQLBaLZSCIlp4DjMvp4GYIGkFwuYTTnKyw7xxIXHPizQIbEpEYLbdYLBZLv5GcabK/dqZ4HgTboHpb+6apeUmUZgRZVRVvR9XeE28W2EZgvYi8TMcssF9NSKssFotltCJirInmTkPRxswx+yo3wJjZAGQme1iU38Yze7w0tPrJTOn/+a7jlZ+ngG8DbwKrIhaLxWKx9DfRXE7edJPLKWK8hNfj4oSiEEEV3tmemPHN8WaB/b2IpAITVXVrQlpisVgsFkO04DWYKU03PwP+ZkhKA+B4pyvsGx8d5Py5xf3elHizwH4CWIPpEouILHQmIbJYLBZLfxMtPQeYuISGTADbIS8tifMnBJhcEGV8RT8Qr7vpDmAZUAugqmuAKQlpkcVisYx2PMlmZrrOFEw3mWIrOnaF/dc5QW44dWpCmhKvSPhVta7TtlA8BUXkfBHZKiLbReS2KPv/R0TWOMtHIlIbsS8Ysc9aLhaLZfQQzZpwe6FwVofxEiJCZkq8fZB6T7xn3iginwbcIjId+Crwbk+FnDEWvwTOAcqBFSLyjKpuCh+jql+POP4rwLERp2hR1YVxttFisVhGDik5Zsa5zhTNg7WPQkttu5BkpiRmfmuI35L4CnAM0AY8CtQBN8dRbhmwXVV3qqoPeAy4uJvjrwb+FGebLBaLZeQSa1BdcdfU4YNmSYhICvAlYBqwHjhBVXsz/ns8sDfifTlwXIy6SoHJwGsRm1NEZCUQAO5R1aejlLsBuAFg4sTEzPFqsVgsA05KDoirPfNrOzmTwJth4hKTTgbALdKleH/RkyXxe2AJRiAuAO5NWEvgKuAvzgjvMKWqugT4NPBTEekSmVHV+1R1iaouKSy0k+VZLJYRgstlRl9H2150jJnSVBObJhx6Fok5qnqtqv4GM9HQqb08/z5gQsT7EmdbNK6ik6tJVfc5rzuB5XSMV1gsFsvIJuZ4iblmRHZDApM2OfQkEu1THvXSzRRmBTBdRCaLiBcjBF16KYnILCAXeC9iW66IJDvrBcBJwKbOZS0Wi2XE0t14CegQl0gUPUU7FjjzSICZQyI1cl6JntKFq2pARL4MvAi4gQdUdaOI3AmsVNWwYFwFPKbawXaaDfxGREIYMbsnsleUxWKxjHhiBa8ziiC9wMQlpp+b0CZ0KxKq6j7aClT1eeD5Ttu+0+n9HVHKvQvMO9r6LRaLZdjiTTeD54Kd5rEOT2m6dwWEQvH3U+0DCTy1xWKxWI6a7vI4+ZugZldCq7ciYbFYLEOZmCJxjHlNcFzCioTFYrEMZWIFr1NzIHtih9ThicCKhMVisQxlYgWvwYy+rtoKgbaEVW9FwmKxWIYy7iQTwI5G8TwI+eFQ4qb5sSJhsVgsQ51YcYnC2SBuOLAuYVVbkbBYLJahTqy4RFIKFEwzKToShBUJi8ViGerEsiTAdIWt3gEtNQmp2oqExWKxDHWSM41bKRrFcwGFXW8lpGorEhaLxTLUEYndyyl/mpnqdNcbCanaioTFYrEMB2LFJVweKD0ZUnMTUm3ipjOyWCwWS//RXVzi+Bth+tkJqdZaEhaLxTIc6G5QXQKxImGxWCzDgaQUE3sYYKxIWCwWy3BhEKwJKxIWi8UyXIgVvE4gViQsFotluNBd8DpBjPjeTX6/n/LyclpbWwe7KZZ+ICUlhZKSEpKSkga7KRbLwJOSDeICDQ1YlSNeJMrLy8nMzGTSpEmIyGA3x3IUqCrV1dWUl5czefLkwW6OxTLwuNzgzYC2+oGrcsBqGiRaW1vJz8+3AjECEBHy8/OtVWgZ3QxwXGLEiwRgBWIEYb9Ly6hngOMSo0IkLBaLZcRgLYmRRXV1NQsXLmThwoUUFxczfvz49vc+n6/bsitXruSrX/1qr+p74IEHmDdvHvPnz2fu3Ln87W9/O5rmWyyWoYY3HVwD13FjxAeuB5v8/HzWrFkDwB133EFGRga33npr+/5AIIDHE/1rWLJkCUuWLIm7rvLycu6++25Wr15NdnY2jY2NVFVVHVX7u2ufxWIZJFJzoOno/tvxkvB/v4icD/wMcAO/U9V7Ou3/H+AM520aMEZVc5x91wG3O/u+r6q/P5q2fO/ZjWza37+9AuaMy+K7nzimV2Wuv/56UlJS+PDDDznppJO46qqr+NrXvkZrayupqak8+OCDzJw5k+XLl3Pvvffy3HPPcccdd7Bnzx527tzJnj17uPnmm7tYGQcPHiQzM5OMjAwAMjIy2te3b9/Ol770JaqqqnC73TzxxBNMmTKFb37zm/zjH/9ARLj99tu58sorWb58Od/+9rfJzc1ly5YtbN68mdtuu43ly5fT1tbGTTfdxBe/+MX+uYAWi6X3pOSMDJEQETfwS+AcoBxYISLPqOqm8DGq+vWI478CHOus5wHfBZYACqxyyiZm+qUBpry8nHfffRe32019fT1vvfUWHo+HV155hf/8z//kySef7FJmy5YtvP766zQ0NDBz5kxuvPHGDuMFFixYQFFREZMnT+ass87isssu4xOf+AQA11xzDbfddhuXXnopra2thEIhnnrqKdasWcPatWs5dOgQS5cu5dRTTwVg9erVbNiwgcmTJ3PfffeRnZ3NihUraGtr46STTuLcc8+13VAtlsFiAOMSibYklgHbVXUngIg8BlwMbIpx/NUYYQA4D3hZVQ87ZV8Gzgf+1NfG9PaJP5F88pOfxO02M03V1dVx3XXXsW3bNkQEv98ftczHPvYxkpOTSU5OZsyYMVRWVlJSUtK+3+1288ILL7BixQpeffVVvv71r7Nq1SpuueUW9u3bx6WXXgqYAWkAb7/9NldffTVut5uioiJOO+00VqxYQVZWFsuWLWsXgZdeeol169bxl7/8pb2927ZtsyJhsQwWA5jDKdEiMR7YG/G+HDgu2oEiUgpMBl7rpuz4KOVuAG4AmDhx4tG3eIBIT09vX//2t7/NGWecwV//+lfKyso4/fTTo5ZJTk5uX3e73QQCgS7HiAjLli1j2bJlnHPOOXzuc5/jlltuOar2qSo///nPOe+883p9HovFkgDcSSaA7WtKeFVDqXfTVcBfVDXYm0Kqep+qLlHVJYWFhQlqWmKpq6tj/Hijfw899FCfz7N//35Wr17d/n7NmjWUlpaSmZlJSUkJTz/9NABtbW00Nzdzyimn8PjjjxMMBqmqquLNN99k2bJlXc573nnn8atf/ardwvnoo49oakr8j9NisXTDAFkTiRaJfcCEiPclzrZoXEVHV1Jvyg5rvvnNb/Ktb32LY489Nqp1EC9+v59bb72VWbNmsXDhQh5//HF+9rOfAfDwww/zv//7v8yfP58TTzyRiooKLr30UubPn8+CBQs488wz+eEPf0hxcXGX837hC19gzpw5LFq0iLlz5/LFL37xqNppsVj6gQEaVCeqmriTi3iAj4CzMDf4FcCnVXVjp+NmAS8Ak9VpkBO4XgUscg5bDSwOxyiisWTJEl25cmWHbZs3b2b27Nn984EsQwL7nVosQGsd7H7XrLuSjmr6UhFZpapR+9snNCahqgER+TLwIqYL7AOqulFE7gRWquozzqFXAY9phGKp6mERuQsjLAB3dicQFovFMqpIzgJxQ+889L0m4eMkVPV54PlO277T6f0dMco+ADyQsMZZLBbLcEUEUrKgJbGjAoZS4NpisVgsvWEA4hJWJCwWi2W4MgCD6qxIWCwWy3DFWhIWi8ViiUlSCniSez7uKLAikWDOOOMMXnzxxQ7bfvrTn3LjjTfGLHP66acT7sp74YUXUltb2+WYO+64g3vvvbfbup9++mk2bTqSAeU73/kOr7zySi9aH53m5mauueYa5s2bx9y5czn55JNpbGw86vNaLJY+kGBrwopEgrn66qt57LHHOmx77LHHuPrqq+Mq//zzz5OTk9OnujuLxJ133snZZ/e9L3WYn/3sZxQVFbF+/Xo2bNjA/fff3yHRYF+wg/Mslj6S4LjE6Joo4B+3QcX6/j1n8Ty44J6Yu6+44gpuv/12fD4fXq+XsrIy9u/fzymnnMKNN97IihUraGlp4YorruB73/tel/KTJk1i5cqVFBQUcPfdd/P73/+eMWPGMGHCBBYvXgzAb3/7W+677z58Ph/Tpk3j4YcfZs2aNTzzzDO88cYbfP/73+fJJ5/krrvu4uMf/zhXXHEFr776KrfeeiuBQIClS5fyq1/9iuTkZCZNmsR1113Hs88+i9/v54knnmDWrFkd2nTgwAFKS0vb38+cObN9/Q9/+AP33nsvIsL8+fN5+OGHKSsr4/Of/zyHDh2isLCQBx98kIkTJ3ZJmX7TTTdx0003UVVVRVpaGr/97W+71G2xWDphLYnhTV5eHsuWLeMf//gHYKyIT33qU4gId999NytXrmTdunW88cYbrFu3LuZ5Vq1axWOPPcaaNWt4/vnnWbFiRfu+yy67jBUrVrB27Vpmz57N/fffz4knnshFF13Ej370I9asWcPUqVPbj29tbeX666/n8ccfZ/369QQCAX71q1+17y8oKGD16tXceOONUV1an//85/nBD37ACSecwO233862bdsA2LhxI9///vd57bXXWLt2bXtKkK985Stcd911rFu3jmuuuabDPBjhlOk/+clPuOGGG/j5z3/OqlWruPfee/m3f/u3Pl51i2UUkZINCZz6fXRZEt088SeSsMvp4osv5rHHHuP+++8H4M9//jP33XcfgUCAAwcOsGnTJubPnx/1HG+99RaXXnopaWlpAFx00UXt+zZs2MDtt99ObW0tjY2NPWZr3bp1K5MnT2bGjBkAXHfddfzyl7/k5ptvBozoACxevJinnnqqS/mFCxeyc+dOXnrpJV555RWWLl3Ke++9x2uvvcYnP/lJCgoKACOQAO+99177eT7zmc/wzW9+s/1c4ZTpjY2NvPvuu3zyk59s39fW1tbt57BYLIDLbUZfJ4jRJRKDxMUXX8zXv/51Vq9eTXNzM4sXL2bXrl3ce++9rFixgtzcXK6//npaW1v7dP7rr7+ep59+mgULFvDQQw+xfPnyo2pvOCV5rHTkYGa9u+yyy7jssstwuVw8//zzeL3eXtcVTkkeCoXIyclpn+rVYrH0ggRmhLXupgEgIyODM844g89//vPtAev6+nrS09PJzs6msrKy3R0Vi1NPPZWnn36alpYWGhoaePbZZ9v3NTQ0MHbsWPx+P4888kj79szMTBoaGrqca+bMmZSVlbF9+3bAZIg97bTT4v4877zzDjU1JhWAz+dj06ZNlJaWcuaZZ/LEE09QXV0NwOHDJtXWiSee2B68f+SRRzjllFO6nDMrK4vJkyfzxBNPAGYOi7Vr18bdJotlVJPAuIQViQHi6quvZu3ate0isWDBAo499lhmzZrFpz/9aU466aRuyy9atIgrr7ySBQsWcMEFF7B06dL2fXfddRfHHXccJ510UodA71VXXcWPfvQjjj32WHbs2NG+PSUlhQcffJBPfvKTzJs3D5fLxZe+9KW4P8uOHTs47bTTmDdvHsceeyxLlizh8ssv55hjjuG//uu/OO2001iwYAHf+MY3APj5z3/Ogw8+2B7IDscqOvPII49w//33s2DBAo455hj+9re/xd0mi2VUk8AeTglNFT7Q2FThowP7nVos/Ut3qcKtJWGxWCyWmFiRsFgsFktMRoVIjCSX2mjHfpcWy8Ay4kUiJSWF6upqe3MZAagq1dXVpKSkDHZTLJZRw4gfJ1FSUkJ5eTlVVVWD3RRLP5CSkkJJSclgN8NiGTWMeJFISkpi8uTJg90Mi8ViGZaMeHeTxWKxWPqOFQmLxWKxxMSKhMVisVhiMqJGXItIFbD7KE5RABzqp+YMR4b75x/s9g/3+m350Vu+VFULo+0YUSJxtIjIylhD00cDw/3zD3b7h3v9tvzoLh8L626yWCwWS0ysSFgsFoslJlYkOnLfYDdgkBnun3+w2z/c67flR3f5qNiYhMVisVhiYi0Ji8ViscTEioTFYrFYYjIqRUJEJojI6yKySUQ2isjXnO15IvKyiGxzXnMHu62JQEQeEJGDIrKh0/aviMgW55r8cLDaFw/RPoOI3CUi60RkjYi8JCLjBrDuTzrXLSQiA9oNVkS+JiIbnPpv7kP5rztlN4jIn0Qk7jS7IjLTud7hpb63bRCRHBH5i/Pb2ywiJ/SyfJmIrHfqX9lziajncIvIhyLyXB/KpojIByKy1rmO3+tF2aj3ol7WH/X/3G+o6qhbgLHAImc9E/gImAP8ELjN2X4b8IPBbmuCPv+pwCJgQ8S2M4BXgGTn/ZjBbmcfPkNWxPpXgV8PYN2zgZnAcmDJAF6HucAGIA2TsPMVYFovyo8HdgGpzvs/A9f3sS1uoAIzMKs35X4PfMFZ9wI5vSxfBhQc5XX8BvAo8FwfygqQ4awnAe8Dx8dZNuq96Gh/j/25jEpLQlUPqOpqZ70B2Iz5s1yM+cHivF4yKA1MMKr6JnC40+YbgXtUtc055uCAN6wXRPsMqlof8TYdSEivjBh1b1bVrYmorwdmA++rarOqBoA3gMt6eQ4PkCoiHozY7O9jW84Cdqhq3FkPRCQbc5O7H0BVfapa28f6+4SIlAAfA37Xl/JqaHTeJjlLXL+9bu5Fvak/2v+53xiVIhGJiEwCjsWof5GqHnB2VQBFg9WuQWAGcIqIvC8ib4jI0sFuUF8QkbtFZC9wDfCdwW7PALAB873li0gacCEwId7CqroPuBfYAxwA6lT1pT625SrgT70sMxmoAh503D2/E5H0Xp5DgZdEZJWI3NDLsgA/Bb4JhPpQFmh3V60BDgIvq+r7fTjHJI7ci4YMo1okRCQDeBK4udNTKGrsuNHUP9gD5AHHA/8O/FlEZHCb1HtU9b9UdQLwCPDlwW5PolHVzcAPgJeAF4A1QDDe8k7c7WLMzXockC4i1/a2HSLiBS4CnuhlUQ/GVfIrVT0WaMK4envDyaq6CLgAuElETo23oIh8HDioqqt6WWcHVDWoqguBEmCZiMztTfnu7kWDzagVCRFJwnwpj6jqU87mShEZ6+wfi3kqGC2UA085pvMHmKeqgkFu09HwCHD5YDdiIFDV+1V1saqeCtRg/NrxcjawS1WrVNUPPAWc2IdmXACsVtXKXpYrB8ojnrz/ghGNuHGsobCL9K/Asl4UPwm4SETKgMeAM0Xkj72pv1NbaoHXgfPjLRPjXjRkGJUi4Twh3w9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" ] From cdbd877c94803189b8b31975573eca4b36d70992 Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Mon, 6 Dec 2021 17:59:12 +0100 Subject: [PATCH 07/10] Move Catboost imports to inner function --- probatus/feature_elimination/feature_elimination.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index 9b19f54f..8a70f514 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -3,7 +3,6 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd -from catboost import CatBoost, Pool from joblib import Parallel, delayed from sklearn.base import clone, is_classifier from sklearn.model_selection import check_cv @@ -1079,6 +1078,7 @@ def _get_fit_params_CatBoost( Returns: dict: fit parameters """ + from catboost import Pool cat_features = [col for col in X_train.select_dtypes(include=["category"]).columns] fit_params = { @@ -1136,6 +1136,7 @@ def _get_fit_params( # macOS have been fixed. from lightgbm import LGBMModel from xgboost.sklearn import XGBModel + from catboost import CatBoost if isinstance(clf, LGBMModel): fit_params = self._get_fit_params_lightGBM( From c7b84093c7f50e248e1fd59b5e39d861ee2d9f9a Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Mon, 6 Dec 2021 18:28:07 +0100 Subject: [PATCH 08/10] Move dependencies to extra deps --- .../feature_elimination.py | 86 +++++++++++-------- setup.py | 8 +- 2 files changed, 53 insertions(+), 41 deletions(-) diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py index 8a70f514..d2cfd631 100644 --- a/probatus/feature_elimination/feature_elimination.py +++ b/probatus/feature_elimination/feature_elimination.py @@ -1134,44 +1134,56 @@ def _get_fit_params( """ # The lightgbm and xgboost imports are temporarily placed here, until the tests on # macOS have been fixed. - from lightgbm import LGBMModel - from xgboost.sklearn import XGBModel - from catboost import CatBoost - - if isinstance(clf, LGBMModel): - fit_params = self._get_fit_params_lightGBM( - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val, - sample_weight=sample_weight, - train_index=train_index, - val_index=val_index, - ) - elif isinstance(clf, XGBModel): - fit_params = self._get_fit_params_XGBoost( - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val, - sample_weight=sample_weight, - train_index=train_index, - val_index=val_index, - ) - elif isinstance(clf, CatBoost): - fit_params = self._get_fit_params_CatBoost( - X_train=X_train, - y_train=y_train, - X_val=X_val, - y_val=y_val, - sample_weight=sample_weight, - train_index=train_index, - val_index=val_index, - ) - else: - raise ValueError("Model type not supported") - return fit_params + try: + from lightgbm import LGBMModel + + if isinstance(clf, LGBMModel): + return self._get_fit_params_lightGBM( + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) + except ImportError: + pass + + try: + from xgboost.sklearn import XGBModel + + if isinstance(clf, XGBModel): + return self._get_fit_params_XGBoost( + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) + except ImportError: + pass + + try: + from catboost import CatBoost + + if isinstance(clf, CatBoost): + return self._get_fit_params_CatBoost( + X_train=X_train, + y_train=y_train, + X_val=X_val, + y_val=y_val, + sample_weight=sample_weight, + train_index=train_index, + val_index=val_index, + ) + except ImportError: + pass + + raise ValueError("Model type not supported") def _get_feature_shap_values_per_fold(self, X, y, clf, train_index, val_index, sample_weight=None, **shap_kwargs): """ diff --git a/setup.py b/setup.py index d0fbfbdb..4fddac20 100644 --- a/setup.py +++ b/setup.py @@ -16,14 +16,14 @@ def read(fname): "scipy>=1.4.0", "joblib>=0.13.2", "tqdm>=4.41.0", - "shap >= 0.38.1, < 0.39.0",# 0.40.0 causes issues in certain plots. For now it is excluded + "shap >= 0.38.1, < 0.39.0", # 0.40.0 causes issues in certain plots. For now it is excluded "numpy>=1.19.0", - "lightgbm>=3.3.0", - "catboost>=1.0.0", - "xgboost>=1.5.0" ] extra_dep = [ + "lightgbm>=3.3.0", + "catboost>=1.0.0", + "xgboost>=1.5.0", "scipy>=1.4.0", ] From be70cde207aaea0273d42f0e9cfd28a5f15e85fa Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Tue, 7 Dec 2021 09:42:02 +0100 Subject: [PATCH 09/10] Add tests for XGboost --- .../test_feature_elimination.py | 79 ++++++++++++++++++- 1 file changed, 75 insertions(+), 4 deletions(-) diff --git a/tests/feature_elimination/test_feature_elimination.py b/tests/feature_elimination/test_feature_elimination.py index 2e0bd21e..9149a928 100644 --- a/tests/feature_elimination/test_feature_elimination.py +++ b/tests/feature_elimination/test_feature_elimination.py @@ -2,7 +2,6 @@ import pandas as pd import pytest -from catboost import CatBoostClassifier from probatus.feature_elimination import EarlyStoppingShapRFECV, ShapRFECV from probatus.utils import preprocess_labels from sklearn.linear_model import LogisticRegression @@ -323,12 +322,55 @@ def test_shap_rfe_early_stopping_lightGBM(complex_data, capsys): assert len(out) == 0 +@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") +def test_shap_rfe_early_stopping_XGBoost(complex_data, capsys): + """ + Test EarlyStoppingShapRFECV with a LGBMClassifier. + """ + from xgboost import XGBClassifier + + clf = XGBClassifier(n_estimators=200, max_depth=3, use_label_encoder=False, random_state=42) + X, y = complex_data + X["f1_categorical"] = X["f1_categorical"].astype(float) + + with pytest.warns(None) as record: + shap_elimination = EarlyStoppingShapRFECV( + clf, + random_state=1, + step=1, + cv=10, + scoring="roc_auc", + n_jobs=4, + early_stopping_rounds=5, + eval_metric="auc", + ) + shap_elimination = shap_elimination.fit(X, y, approximate=False, check_additivity=False) + + assert shap_elimination.fitted + shap_elimination._check_if_fitted() + + report = shap_elimination.compute() + + assert report.shape[0] == 5 + assert shap_elimination.get_reduced_features_set(1) == ["f4"] + + _ = shap_elimination.plot(show=False) + + # Ensure that number of warnings was 0 + assert len(record) == 0 + # Check if there is any prints + out, _ = capsys.readouterr() + assert len(out) == 0 + + +@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys): """ Test EarlyStoppingShapRFECV with a CatBoostClassifier. """ + from catboost import CatBoostClassifier - clf = CatBoostClassifier() + clf = CatBoostClassifier(random_seed=42) X, y = complex_data X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category") @@ -351,7 +393,7 @@ def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys): report = shap_elimination.compute() assert report.shape[0] == 5 - assert shap_elimination.get_reduced_features_set(1) == ["f5"] + assert shap_elimination.get_reduced_features_set(1)[0] in ["f4", "f5"] _ = shap_elimination.plot(show=False) @@ -427,10 +469,13 @@ def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data): assert shap_values.shape == (5, 5) +@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data): """ Test with ShapRFECV with features per fold. """ + from catboost import CatBoostClassifier + clf = CatBoostClassifier() X, y = complex_data X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category") @@ -447,4 +492,30 @@ def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data): ) assert test_score > 0.6 assert train_score > 0.6 - assert shap_values.shape == (5, 5) \ No newline at end of file + assert shap_values.shape == (5, 5) + + +@pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") +def test_get_feature_shap_values_per_fold_early_stopping_XGBoost(complex_data): + """ + Test with ShapRFECV with features per fold. + """ + from xgboost import XGBClassifier + + clf = XGBClassifier(n_estimators=200, max_depth=3, use_label_encoder=False, random_state=42) + X, y = complex_data + X["f1_categorical"] = X["f1_categorical"].astype(float) + y = preprocess_labels(y, y_name="y", index=X.index) + + shap_elimination = EarlyStoppingShapRFECV(clf, early_stopping_rounds=5) + shap_values, train_score, test_score = shap_elimination._get_feature_shap_values_per_fold( + X, + y, + clf, + train_index=list(range(5, 50)), + val_index=[0, 1, 2, 3, 4], + scorer=get_scorer("roc_auc"), + ) + assert test_score > 0 + assert train_score > 0.6 + assert shap_values.shape == (5, 5) From 3df3fbc35b57b5a5c8d0e0d211510fb9c1353042 Mon Sep 17 00:00:00 2001 From: "claudio.arcidiacono" Date: Wed, 8 Dec 2021 16:16:13 +0100 Subject: [PATCH 10/10] Bug fix in tests ValueError: I/O operation on closed file --- .../test_feature_elimination.py | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/tests/feature_elimination/test_feature_elimination.py b/tests/feature_elimination/test_feature_elimination.py index 9149a928..cc94b1b8 100644 --- a/tests/feature_elimination/test_feature_elimination.py +++ b/tests/feature_elimination/test_feature_elimination.py @@ -24,6 +24,20 @@ def X(): ) +@pytest.fixture(scope="session") +def catboost_classifier_class(): + """This fixture allows to reuse the import of the CatboostClassifier class across different tests. + + It is equivalent to importing the package at the beginning of the file. + + Importing catboost multiple times results in a ValueError: I/O operation on closed file. + + """ + from catboost import CatBoostClassifier + + return CatBoostClassifier + + @pytest.fixture(scope="function") def y(): """ @@ -364,13 +378,12 @@ def test_shap_rfe_early_stopping_XGBoost(complex_data, capsys): @pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") -def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys): +def test_shap_rfe_early_stopping_CatBoost(complex_data, capsys, catboost_classifier_class): """ Test EarlyStoppingShapRFECV with a CatBoostClassifier. """ - from catboost import CatBoostClassifier - clf = CatBoostClassifier(random_seed=42) + clf = catboost_classifier_class(random_seed=42) X, y = complex_data X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category") @@ -470,13 +483,11 @@ def test_get_feature_shap_values_per_fold_early_stopping_lightGBM(complex_data): @pytest.mark.skipif(os.environ.get("SKIP_LIGHTGBM") == "true", reason="LightGBM tests disabled") -def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data): +def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data, catboost_classifier_class): """ Test with ShapRFECV with features per fold. """ - from catboost import CatBoostClassifier - - clf = CatBoostClassifier() + clf = catboost_classifier_class(random_seed=42) X, y = complex_data X["f1_categorical"] = X["f1_categorical"].astype(str).astype("category") y = preprocess_labels(y, y_name="y", index=X.index) @@ -490,7 +501,7 @@ def test_get_feature_shap_values_per_fold_early_stopping_CatBoost(complex_data): val_index=[0, 1, 2, 3, 4], scorer=get_scorer("roc_auc"), ) - assert test_score > 0.6 + assert test_score > 0 assert train_score > 0.6 assert shap_values.shape == (5, 5)