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test_calibration.cpython-35.opt-1.pyc
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Z! d d l" m# Z# d d l" m$ Z$ m% Z% d d l" m& Z& e d d � � Z' d d � Z( d d � Z) d d � Z* d d � Z+ d d � Z, d d � Z- d d � Z. d d! � Z/ d S)"� )�divisionN)�sparse)� LeaveOneOut)�assert_array_almost_equal� assert_equal�assert_greater�assert_almost_equal�assert_greater_equal�assert_array_equal� assert_raises�ignore_warnings)�make_classification� make_blobs)� MultinomialNB)�RandomForestClassifier�RandomForestRegressor)� LinearSVC)�Pipeline)�Imputer)�brier_score_loss�log_loss)�CalibratedClassifierCV)�_sigmoid_calibration�_SigmoidCalibration)�calibration_curvec C sD d } t d d | d d d d � \ } } t j j d d � j d | j � } | | j � 8} | d | � | d | � | d | � } } } | | d � | | d � } } t � j | | d | �} | j | � d d � d f } t | d | j d �} t t | j | | � x"| | f t j | � t j | � f g D]�\ } } x�d d g D]�} t | d | d d �} | j | | d | �| j | � d d � d f } t t | | � t | | � � | j | | d d | �| j | � d d � d f } t | | � | j | d | d d | �| j | � d d � d f } t | | � | j | | d d d | �| j | � d d � d f } | d k r�t | d | � q_t t | | � t | d d | � � q_Wt | d d �} t t | j | | � t t � d d �} t t | j | | � qFWd S)z2Test calibration objects with isotonic and sigmoid�d � n_samples� � n_features� � random_state�* �seed�sizeN� sample_weight� �cv�isotonic�sigmoid�methodZfoo)r �np�random� RandomState�uniformr# �minr �fit� predict_probar r � ValueErrorr � csr_matrixr r r r � RuntimeError)r �X�yr$ �X_train�y_train�sw_train�X_test�y_test�clf� prob_pos_clf�pc_clfZ this_X_train� this_X_testr) �prob_pos_pc_clfZprob_pos_pc_clf_relabeledZclf_invalid_methodZclf_base_regressor� r@ �/test_calibration.py�test_calibration sR $2! rB c C s7 d } t d d | d d d d � \ } } t j j d d � j d t | � � } | d | � | d | � | d | � } } } | | d � } x� d d g D]� } t d d � } t | d | �} | j | | d | �| j | � } | j | | � | j | � } t j j | | � } t | d � q� Wd S)Nr r r r r r r! r" r# r( r' r) r$ g�������?) r r* r+ r, r- �lenr r r/ r0 ZlinalgZnormr )r r4 r5 r$ r6 r7 r8 r9 r) Zbase_estimatorZcalibrated_clfZ probs_with_swZprobs_without_swZdiffr@ r@ rA �test_sample_weightg s '2rD c C s� t � } t d d d d d d d d d d � \ } } t j d d d g � } | | } | d d d � | d d d � } } | d d d � | d d d � } } | j | | � x� d d g D]� } t | d | d d �} | j | | � | j | � } t t j | d d �t j t | � � � d d � } t | | | j | � � � } t | | � } t | | � q� Wt d d d d d d d d � \ } } | d d d � | d d d � } } | d d d � | d d d � } } t d d d d � } | j | | � | j | � } t | | � } xg d d g D]Y } t | d | d d �} | j | | � | j | � } t | | � } t | | � q:Wd S)z Test calibration for multiclass r r r r r r! Zcenters� Z cluster_stdg @�a�b�cNr% r' r( r) r& �axisc S s0 t j | � } | | j d d � j d d � S)NrI r% ���)r* �exp�sumZreshape)�y_pred�er@ r@ rA �softmax� s z,test_calibration_multiclass.<locals>.softmax� n_estimators� )r r r* �arrayr/ r r0 r rL �onesrC r Zdecision_functionr r r )r; r4 Zy_idxZ target_namesr5 r6 r7 r9 r: r) �cal_clfZprobasrO Zuncalibrated_log_lossZcalibrated_log_lossZ clf_probsZlossZ cal_clf_probsZcal_lossr@ r@ rA �test_calibration_multiclass� s> ''+ ''rU c C sP d } t d d | d d d d � \ } } t j j d d � j d | j � } | | j � 8} | d | � | d | � | d | � } } } | | d | � | | d | � | | d | � } } } | d | d � | d | d � } } t � } | j | | | � | j | � d d � d f } x| | f t j | � t j | � f g D]� \ } } x� d d g D]� } t | d | d d �} x� | d g D]� } | j | | d | �| j | � } | j | � } | d d � d f } t | t j d d g � t j | d d �� t t | | � t | | � � q�WqwWq^Wd S)z*Test calibration for prefitted classifiers�2 r rE r r r r! r" r# Nr r% r' r( r) r& Zprefitr$ r rI )r r* r+ r, r- r# r. r r/ r0 r r2 r �predictr rR Zargmaxr r )r r4 r5 r$ r6 r7 r8 ZX_calibZy_calibZsw_calibr9 r: r; r< Z this_X_calibr> r) r= ZswZy_probrM r? r@ r@ rA �test_calibration_prefit� s4 $2" ) )rX c C s� t j d d d g � } t j d d d g � } t j d d g � } t | t | | � d � d d t j | d | | d � } t � j | | � j | � } t | | d � t t t � j t j | | f � | � d S)z0Test calibration values with Platt sigmoid model� � g �?r% g�j�=��?gY90�(��?rE r r N���rJ rJ g�j�=�ɿ) r* rR r r rK r r/ rW r r1 Zvstack)ZexFZexYZ AB_lin_libsvmZlin_probZsk_probr@ r@ rA �test_sigmoid_calibration� s ' r\ c C s t j d d d d d d g � } t j d d d d d d g � } t | | d d �\ } } t | | d d d d d �\ } } t t | � t | � � t t | � d � t | d d g � t | d d g � t | | � t | | � t t t d g d g d d �d S)z Check calibration_curve functionr r% g g�������?g�������?g�������?g�������?g �?Zn_binsr Z normalizeTg�������?FNg��������)r* rR r r rC r r r1 )Zy_truerM Z prob_trueZ prob_predZprob_true_unnormalizedZprob_pred_unnormalizedr@ r@ rA �test_calibration_curve� s !!% r] c C s� t d d d d d d d d d d � \ } } t j | d <t d t � f d t d d � f g � } t | d d d d �} | j | | � | j | � d S)z$Test that calibration can accept nanr rQ r r Z n_informativeZ n_redundantr r r! ZimputerZrfrP r% r& r) r' N)r r ) r r* �nanr r r r r/ rW )r4 r5 r; Zclf_cr@ r@ rA � test_calibration_nan_imputer s r_ c C s� d } t d d d d d | � \ } } t d d � } t | d d d t � �} | j | | � | j | � } t | j d d � t j | j d � � d S)Nr r rQ r rY Z n_classes�Cg �?r) r( r& rI r% r ) r r r r r/ r0 r rL r* rS �shape)Z num_classesr4 r5 r; Zclf_probZprobsr@ r@ rA �test_calibration_prob_sum s rb c C s t j j d d � } t j d � } t d d � } t | d d d t � �} | j | | � x� t | j � D]� \ } } | j | � } t | d d � | f t j t | � � � t t j t j | d d � d | � f | d d � | d d � f g � � d � qn Wd S) NrQ rY r` g �?r) r( r& r% T)r* r+ ZrandnZaranger r r r/ � enumerateZcalibrated_classifiers_r0 r ZzerosrC r �allZhstack)r4 r5 r; rT �iZcalibrated_classifierZprobar@ r@ rA � test_calibration_less_classes" s ,(rf )0Z __future__r Znumpyr* Zscipyr Zsklearn.model_selectionr Zsklearn.utils.testingr r r r r r r r Zsklearn.datasetsr r Zsklearn.naive_bayesr Zsklearn.ensembler r Z sklearn.svmr Zsklearn.pipeliner Zsklearn.preprocessingr Zsklearn.metricsr r Zsklearn.calibrationr r r r rB rD rU rX r\ r] r_ rb rf r@ r@ r@ rA �<module> s. :L 7 )
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__init__.cpython-35.opt-1.pyc
95 bytes
0644
__init__.cpython-35.pyc
95 bytes
0644
test_base.cpython-35.opt-1.pyc
12215 bytes
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test_base.cpython-35.pyc
12215 bytes
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test_calibration.cpython-35.opt-1.pyc
8939 bytes
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test_calibration.cpython-35.pyc
8939 bytes
0644
test_check_build.cpython-35.opt-1.pyc
443 bytes
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test_check_build.cpython-35.pyc
443 bytes
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test_common.cpython-35.opt-1.pyc
5752 bytes
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test_common.cpython-35.pyc
5752 bytes
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test_cross_validation.cpython-35.opt-1.pyc
38584 bytes
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test_cross_validation.cpython-35.pyc
38634 bytes
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test_discriminant_analysis.cpython-35.opt-1.pyc
9768 bytes
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test_discriminant_analysis.cpython-35.pyc
9872 bytes
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test_dummy.cpython-35.opt-1.pyc
17534 bytes
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test_dummy.cpython-35.pyc
17534 bytes
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test_grid_search.cpython-35.opt-1.pyc
26280 bytes
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test_grid_search.cpython-35.pyc
26810 bytes
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test_init.cpython-35.opt-1.pyc
477 bytes
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test_init.cpython-35.pyc
477 bytes
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test_isotonic.cpython-35.opt-1.pyc
13663 bytes
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test_isotonic.cpython-35.pyc
13832 bytes
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test_kernel_approximation.cpython-35.opt-1.pyc
6144 bytes
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test_kernel_approximation.cpython-35.pyc
6144 bytes
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test_kernel_ridge.cpython-35.opt-1.pyc
3488 bytes
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test_kernel_ridge.cpython-35.pyc
3488 bytes
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test_learning_curve.cpython-35.opt-1.pyc
11671 bytes
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test_learning_curve.cpython-35.pyc
11740 bytes
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test_metaestimators.cpython-35.opt-1.pyc
5841 bytes
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test_metaestimators.cpython-35.pyc
5841 bytes
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test_multiclass.cpython-35.opt-1.pyc
21261 bytes
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test_multiclass.cpython-35.pyc
21261 bytes
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test_multioutput.cpython-35.opt-1.pyc
6100 bytes
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test_multioutput.cpython-35.pyc
6100 bytes
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test_naive_bayes.cpython-35.opt-1.pyc
15582 bytes
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test_naive_bayes.cpython-35.pyc
15582 bytes
0644
test_pipeline.cpython-35.opt-1.pyc
21679 bytes
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test_pipeline.cpython-35.pyc
21679 bytes
0644
test_random_projection.cpython-35.opt-1.pyc
9420 bytes
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test_random_projection.cpython-35.pyc
9607 bytes
0644
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