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ZdZG ddä dâZdS )Ú )┌ensemble)┌feature_selection)┌tree)┌svm)┌SVR)┌RandomizedLogisticRegressionN)┌ CONSTANTSzMohsen Mesgarpourz-Copyright 2016, https://github.com/mesgarpour┌GPLz1.xzmohsen.mesgarpour@gmail.com┌Developmentc @ s^ e Zd Zddä Zddä Zddä Zddä Zd d
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ddä Zddä ZdS )┌FeatureSelectionc C s t jtjâ| _| jjtâ d S )N)┌logging┌ getLoggerr ┌app_name┌_FeatureSelection__logger┌debug┌__name__)┌selfę r ˙VC:\Users\eagle\Documents\GitHub\UoW_Docobo\IntegratedCare_py\Stats\FeatureSelection.py┌__init__ s zFeatureSelection.__init__c K s4 | j jtâ | j jdâ tjf |Ä}|j||â}|S )a÷ random forest classifier (Brieman)
model.estimators_
model.classes_
model.n_classes_
model.n_features_
model.n_outputs_
model.feature_importances_
n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, bootstrap=True,
oob_score=False, n_jobs=-1, random_state=None, verbose=0, warm_start=False, class_weight=None
z*Running Random Forest Classifier (Brieman))r r r ┌infor ZRandomForestClassifier┌fit)r ┌features_indep┌feature_target┌kwargs┌
classifier┌modelr r r ┌rank_random_forest_breiman s
z+FeatureSelection.rank_random_forest_breimanc K s2 | j jtâ | j jdâ tf |Ä}|j||â}|S )aú random forest classifier (Brieman)
model.estimators_
model.classes_
model.n_classes_
model.n_features_
model.n_outputs_
model.feature_importances_
C=1, scaling=0.5, sample_fraction=0.75, n_resampling=200, selection_threshold=0.25, tol=0.001,
fit_intercept=True, verbose=False, normalize=True, random_state=None, n_jobs=1, pre_dispatch='3*n_jobs'
z"Running Random Logistic Regression)r r r r r r )r r r r r r r r r ┌rank_random_logistic_regression/ s
z0FeatureSelection.rank_random_logistic_regressionc K s4 | j jtâ | j jdâ tjf |Ä}|j||â}|S )a┬ C-Support Vector Classification
model.support_
model.support_vectors_
model.n_support_
model.dual_coef_
model.coef_
model.intercept_
C=1.0, kernel='linear', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,
tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None,
random_state=None, sample_weight=None
z'Running C-Support Vector Classification)r r r r r ┌SVCr )r r r r r r r r r ┌rank_svm_c_supportB s
z#FeatureSelection.rank_svm_c_supportc K s4 | j jtâ | j jdâ tjf |Ä}|j||â}|S )a decision tree classifier (Brieman)
model.classes_
model.feature_importances_
model.max_features_
model.n_classes_
model.n_features_
model.n_outputs_
model.tree_
criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, class_weight=None,
presort=True, sample_weight=None, check_input=True, X_idx_sorted=None
z*Running Decision Tree Classifier (Brieman))r r r r r ZDecisionTreeClassifierr )r r r r r r r r r ┌rank_tree_briemanV s
z"FeatureSelection.rank_tree_briemanc K s4 | j jtâ | j jdâ tjf |Ä}|j||â}|S )aÎ Gradient Boosted Regression Trees (GBRT)
model.feature_importances_
model.train_score_
model.loss_
model.init
model.estimators_
loss='ls', learning_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=2, min_samples_leaf=1,
min_weight_fraction_leaf=0.0, max_depth=3, init=None, random_state=None, max_features=None, alpha=0.9,
verbose=0, max_leaf_nodes=None, warm_start=False, presort=True
z0Running Gradient Boosted Regression Trees (GBRT))r r r r r ZGradientBoostingRegressorr )r r r r r r r r r ┌rank_tree_gbrtk s
zFeatureSelection.rank_tree_gbrt┌linearc C sF | j jtâ | j jdâ t|dŹ}tj|dddddŹ}|j||â}|S )z═Feature ranking with recursive feature elimination
model.n_features_
model.support_ # selected features
model.ranking_
model.grid_scores_
model.estimator_
z:Running Feature Ranking with Recursive Feature Elimination)┌kernelÚ Nr )┌ estimator┌step┌cv┌scoring┌verbose)r r r r r r ┌RFECVr )r r r r$ r&