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test_naive_bayes.cpython-35.pyc
��(X�M � @ sF d d l Z d d l m Z d d l Z d d l Z d d l m Z m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z d d l m Z m Z m Z e j dR dS g dT dU g dV dW g d d g d d g d d g g � Z e j d d d d d d g � Z e j j d � Z e j d dX � Z! e j d d � d k j" e j# � Z$ e j% d d dY �Z& e j d d d d d d g � 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& d' � Z0 d( d) � Z1 d* d+ � Z2 d, d- � Z3 d. d/ � Z4 d0 d1 � Z5 d2 d3 � Z6 d4 d5 � Z7 d6 d7 � Z8 d8 d9 � Z9 d: d; � Z: d< d= � Z; d> d? � Z< d@ dA � Z= dB dC � Z> dD dE � Z? dF dG � Z@ dH dI � ZA dJ dK � ZB dL dM � ZC dN dO � ZD dP dQ � ZE d S)Z� N)�BytesIO)� load_digits� load_iris)�train_test_split)�cross_val_score)�zip)�assert_almost_equal)�assert_array_equal)�assert_array_almost_equal)� assert_equal)� assert_raises)�assert_greater)� GaussianNB� BernoulliNB� MultinomialNB� � �size� � � � �d c C s� t � } | j t t � j t � } t | t � | j t � } | j t � } t t j | � | d � t t t � j t t d d d g �d S)N� �classesr r )r �fit�X�y�predictr � predict_proba�predict_log_probar �np�logr � ValueError� partial_fit)�clf�y_pred� y_pred_proba�y_pred_log_proba� r) �/test_naive_bayes.py�test_gnb$ s r+ c C se t � j t t � } t t j d d g � d | j d � | j t t � t | j j � d � d S)Nr g @r r ) r r r r r r! �array� class_prior_�X1�y1�sum)r% r) r) r* �test_gnb_prior7 s r1 c C s� t j d � } t � j t t � } t � j t t | � } t | j | j � t | j | j � t j t j d � } t � j t t d | �} t � j t t d d d g d | d �} | j t t d | d �t | j | j � t | j | j � t j d t j d d � } t j | d t j d �} t � j t | t | � } t � j t t | � } t | j | j � t | j | j � d S) z6Test whether sample weights are properly used in GNB. r r � sample_weightr r r � Z minlengthN)r! Zonesr r r r r �theta_�sigma_�rngZrand�shaper$ �randintZbincount)Zswr% Zclf_sw�clf1�clf2Zindr2 Zclf_duplr) r) r* �test_gnb_sample_weightA s" + r; c C s8 t d t j d d g � � } t t | j t t � d S)z:Test whether an error is raised in case of negative priors�priorsg �?g @Ng �)r r! r, r r# r r r )r% r) r) r* �test_gnb_neg_priorsa s r= c C s� t d t j d d g � � j t t � } t | j d d g g � t j d d g g � d � t | j t j d d g � � d S) z6Test whether the class prior override is properly usedr< g333333�?gffffff�?g�������?g[ �9�h�?g���s\�?r Ng��������g��������) r r! r, r r r r r r r- )r% r) r) r* �test_gnb_priorsg s * r> c C s> t d t j d d d d g � � } t t | j t t � d S)za Test whether an error is raised if the number of prior is different from the number of classr< g �?N)r r! r, r r# r r r )r% r) r) r* �test_gnb_wrong_nb_priorsp s $r? c C s8 t d t j d d g � � } t t | j t t � d S)z?Test if an error is raised if the sum of prior greater than oner< g @g �?N)r r! r, r r# r r r )r% r) r) r* �test_gnb_prior_greater_onew s r@ c C s] t d t j d d g � � } | j t t � t | j d d g g � t j d g � � d S) z@Test if good prediction when class prior favor largely one classr< g{�G�z�?g�G�z��?g�������?r Ng��������g��������)r r! r, r r r r r )r% r) r) r* �test_gnb_prior_large_bias} s rA c C sj d } d } d } t j d t j d f � } t j | | | | � \ } } t | | � t | | � d S)z5 Test when the partial fit is called without any datar g g �?r r N)r! �emptyr r7 r Z_update_mean_variancer )Z prev_points�mean�varZx_emptyZtmeanZtvarr) r) r* � test_check_update_with_no_data� s rE c C sB t � } | j t t � t t | j t j t t f � t � d S)z^Test whether an error is raised when the number of feature changes between two partial fitN) r r r r r r# r$ r! 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__init__.cpython-35.opt-1.pyc
95 bytes
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__init__.cpython-35.pyc
95 bytes
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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
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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
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test_pipeline.cpython-35.opt-1.pyc
21679 bytes
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test_pipeline.cpython-35.pyc
21679 bytes
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test_random_projection.cpython-35.opt-1.pyc
9420 bytes
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test_random_projection.cpython-35.pyc
9607 bytes
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