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dummy.cpython-35.pyc
��(X�C � @ s� d d l m Z d d l Z d d l Z d d l j Z d d l m Z 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 Gd d � d e e � Z Gd d � d e e � Z d S)� )�divisionN� )� BaseEstimator�ClassifierMixin�RegressorMixin)�check_random_state)� check_array)�check_consistent_length)�check_is_fitted)�random_choice_csc)�_weighted_percentile)�class_distributionc @ s^ e Z d Z d Z d d d d d � Z d d d � Z d d � Z d d � Z d d � Z d S)�DummyClassifieraO DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems. Read more in the :ref:`User Guide <dummy_estimators>`. Parameters ---------- strategy : str, default="stratified" Strategy to use to generate predictions. * "stratified": generates predictions by respecting the training set's class distribution. * "most_frequent": always predicts the most frequent label in the training set. * "prior": always predicts the class that maximizes the class prior (like "most_frequent") and ``predict_proba`` returns the class prior. * "uniform": generates predictions uniformly at random. * "constant": always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class .. versionadded:: 0.17 Dummy Classifier now supports prior fitting strategy using parameter *prior*. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use. constant : int or str or array of shape = [n_outputs] The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. Attributes ---------- classes_ : array or list of array of shape = [n_classes] Class labels for each output. n_classes_ : array or list of array of shape = [n_classes] Number of label for each output. class_prior_ : array or list of array of shape = [n_classes] Probability of each class for each output. n_outputs_ : int, Number of outputs. outputs_2d_ : bool, True if the output at fit is 2d, else false. sparse_output_ : bool, True if the array returned from predict is to be in sparse CSC format. Is automatically set to True if the input y is passed in sparse format. � stratifiedNc C s | | _ | | _ | | _ d S)N)�strategy� random_state�constant)�selfr r r � r � /dummy.py�__init__P s zDummyClassifier.__init__c s� � j d k r t d � � � j d k rU t j | � rU | j � } t j d t � t j | � � _ � j s t j | � } | j d k � _ | j d k r� t j | d � } | j d � _ � j d k r6� j d k r� t d � � nG t j t j � j � d � � � j d � j k r6t d � j � � t | | � \ � _ � _ � _ � j d k r�t � � f d d � t � j � D� � r�t d � � � j d k r�� j r�� j d � _ � j d � _ � j d � _ � S)a$ Fit the random classifier. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_outputs] Target values. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- self : object Returns self. � most_frequentr �uniformr �priorzUnknown strategy type.z�A local copy of the target data has been converted to a numpy array. Predicting on sparse target data with the uniform strategy would not save memory and would be slower.� r NzMConstant target value has to be specified when the constant strategy is used.r z0Constant target value should have shape (%d, 1).c 3 s& | ] } � | � j | k Vq d S)N)�classes_)�.0�k)r r r r � <genexpr>� s z&DummyClassifier.fit.<locals>.<genexpr>z:The constant target value must be present in training data)r r r r r ���)r r r )r r )r � ValueError�spZissparseZtoarray�warnings�warn� UserWarning�sparse_output_�npZ atleast_1d�ndim� output_2d_�reshape�shape� n_outputs_r r r � n_classes_� class_prior_�any�range)r �X�y� sample_weightr )r r r �fitV s> ! zDummyClassifier.fitc s� t | d � t | d d d d g �} t | j d � � t | j � � | j � | j � | j � | j } | j d k r� � g � � g � � g � | g } | j d k r� | j | � � | j d k r� � g � | j r~d } | j d k r d d � � D� � nX | j d k r#� } n@ | j d k rAt d � � n" | j d k rcd d � | D� � t � � | | j � } n-| j d k r�t j � � f d d � t | j � D� � d g � } n� | j d k r t j � � f d d � t | j � D� � j } ny | j d k rY� � � � f d d � t | j � D� } t j | � j } n* | j d k r�t j | j � d f � } | j d k r�| j r�t j | � } | S)a� Perform classification on test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X. r � accept_sparse�csr�csc�coor r r Nr r c S s( g | ] } t j | j � g � � q Sr )r& �array�argmax)r �cpr r r � <listcomp>� s z+DummyClassifier.predict.<locals>.<listcomp>r zCSparse target prediction is not supported with the uniform strategyr c S s" g | ] } t j | g � � q Sr )r& r8 )r �cr r r r; � s c s( g | ] } � | � | j � � q Sr )r9 )r r )r- r r r r; � s c 3 s- | ]# } � | � | j d d � Vq d S)�axisr N)r9 )r r )r �probar r r � s z*DummyClassifier.predict.<locals>.<genexpr>c s1 g | ]' } � | � j � | d � �� q S)�size)�randint)r r )r r, � n_samples�rsr r r; � s )r r )r r )r r �intr* r r r, r r- r r+ r � predict_probar% r r r& Ztiler/ Zvstack�Tr( �ravel)r r0 r Z class_probr1 Zretr )r- r r, rA r> rB r �predict� sX " zDummyClassifier.predictc C s\ t | d � t | d d d d g �} t | j d � } t | j � } | j } | j } | j } | j } | j d k r� | j r� | g } | g } | g } | g } g } x�t | j � D]p} | j d k r!| | j � } t j | | | f d t j �} d | d d � | f <n| j d k rPt j | d f � | | } n� | j d k r~| j d | | d | �} n� | j d k r�t j | | | f d t j �} | | | } nd | j d k r$t j | | | | k � } t j | | | f d t j �} d | d d � | f <| j | � q� W| j d k rX| j rX| d } | S)aA Return probability estimates for the test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- P : array-like or list of array-lke of shape = [n_samples, n_classes] Returns the probability of the sample for each class in the model, where classes are ordered arithmetically, for each output. r r4 r5 r6 r7 r r r Zdtypeg �?Nr r r? r r )r r rC r* r r r, r r- r r+ r( r/ r r9 r& ZzerosZfloat64�onesZ multinomialZwhere�append) r r0 rA rB r, r r- r �Pr Zind�outr r r rD � sD " " " z DummyClassifier.predict_probac C s@ | j | � } | j d k r+ t j | � Sd d � | D� Sd S)aI Return log probability estimates for the test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- P : array-like or list of array-like of shape = [n_samples, n_classes] Returns the log probability of the sample for each class in the model, where classes are ordered arithmetically for each output. r c S s g | ] } t j | � � q Sr )r& �log)r �pr r r r; @ s z5DummyClassifier.predict_log_proba.<locals>.<listcomp>N)rD r+ r& rL )r r0 r> r r r �predict_log_proba+ s z!DummyClassifier.predict_log_proba) �__name__� __module__� __qualname__�__doc__r r3 rG rD rN r r r r r s 9H M @r c @ sF e Z d Z d Z d d d d d � Z d d d � Z d d � Z d S) �DummyRegressora� DummyRegressor is a regressor that makes predictions using simple rules. This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems. Read more in the :ref:`User Guide <dummy_estimators>`. Parameters ---------- strategy : str Strategy to use to generate predictions. * "mean": always predicts the mean of the training set * "median": always predicts the median of the training set * "quantile": always predicts a specified quantile of the training set, provided with the quantile parameter. * "constant": always predicts a constant value that is provided by the user. constant : int or float or array of shape = [n_outputs] The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. quantile : float in [0.0, 1.0] The quantile to predict using the "quantile" strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum. Attributes ---------- constant_ : float or array of shape [n_outputs] Mean or median or quantile of the training targets or constant value given by the user. n_outputs_ : int, Number of outputs. outputs_2d_ : bool, True if the output at fit is 2d, else false. �meanNc C s | | _ | | _ | | _ d S)N)r r �quantile)r r r rU r r r r o s zDummyRegressor.__init__c s� | j d k r" t d | j � � t � d d �� t � � d k rR t d � � � j d k | _ � j d k r� t j � d � � � j d | _ t | � � � | j d k r� t j � d d d � �| _ n�| j d k r6� d k r t j � d d �| _ q�� � f d d � t | j � D� | _ nL| j d k r�| j d k sgt j | j � rzt d | j � � | j d � � d k r�t j � d d d � �| _ q�� � � f d d � t | j � D� | _ n� | j d k r�| j d k r t d � � t | j d d d d g d d d d �| _ | j rv| j j d � j d k rvt d � j d � � | j | _ t j | j d � | _ | S)!a# Fit the random regressor. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_outputs] Target values. sample_weight : array-like of shape = [n_samples], optional Sample weights. Returns ------- self : object Returns self. rT �medianrU r zNUnknown strategy type: %s, expected 'mean', 'median', 'quantile' or 'constant'Z ensure_2dFr zy must not be empty.r r r= ZweightsNc s5 g | ]+ } t � d d � | f � d d �� q S)N� percentileg I@)r )r r )r2 r1 r r r; � s z&DummyRegressor.fit.<locals>.<listcomp>z>Quantile must be a scalar in the range [0.0, 1.0], but got %s.g Y@�qc s5 g | ]+ } t � d d � | f � d � �� q S)NrW )r )r r )rW r2 r1 r r r; � s zMConstant target value has to be specified when the constant strategy is used.r4 r5 r6 r7 Zensure_min_samplesz0Constant target value should have shape (%d, 1).)rT rV rU r r )r r r )r r )r r r �lenr' r( r& r) r* r+ r Zaverage� constant_rV r/ rU ZisscalarrW r � TypeError)r r0 r1 r2 r )rW r2 r1 r r3 t sP ! " ! & zDummyRegressor.fitc C s} t | d � t | d d d d g �} | j d } t j | d f � | j } | j d k ry | j ry t j | � } | S)a� Perform classification on test vectors X. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Input vectors, where n_samples is the number of samples and n_features is the number of features. Returns ------- y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X. rZ r4 r5 r6 r7 r r ) r r r* r& rH rZ r+ r( rF )r r0 rA r1 r r r rG � s zDummyRegressor.predict)rO rP rQ rR r r3 rG r r r r rS C s *PrS )Z __future__r r" Znumpyr& Z scipy.sparseZsparser! �baser r r Zutilsr Zutils.validationr r r Z utils.randomr Z utils.statsr Zutils.multiclassr r rS r r r r �<module> s � /
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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base.cpython-35.opt-1.pyc
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base.cpython-35.pyc
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calibration.cpython-35.opt-1.pyc
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calibration.cpython-35.pyc
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cross_validation.cpython-35.opt-1.pyc
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cross_validation.cpython-35.pyc
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discriminant_analysis.cpython-35.opt-1.pyc
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discriminant_analysis.cpython-35.pyc
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dummy.cpython-35.opt-1.pyc
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dummy.cpython-35.pyc
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exceptions.cpython-35.opt-1.pyc
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exceptions.cpython-35.pyc
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grid_search.cpython-35.opt-1.pyc
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grid_search.cpython-35.pyc
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isotonic.cpython-35.opt-1.pyc
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isotonic.cpython-35.pyc
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kernel_approximation.cpython-35.opt-1.pyc
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kernel_approximation.cpython-35.pyc
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kernel_ridge.cpython-35.opt-1.pyc
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kernel_ridge.cpython-35.pyc
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lda.cpython-35.opt-1.pyc
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lda.cpython-35.pyc
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learning_curve.cpython-35.opt-1.pyc
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learning_curve.cpython-35.pyc
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multiclass.cpython-35.opt-1.pyc
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multiclass.cpython-35.pyc
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multioutput.cpython-35.opt-1.pyc
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multioutput.cpython-35.pyc
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naive_bayes.cpython-35.opt-1.pyc
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naive_bayes.cpython-35.pyc
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pipeline.cpython-35.opt-1.pyc
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pipeline.cpython-35.pyc
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qda.cpython-35.opt-1.pyc
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qda.cpython-35.pyc
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random_projection.cpython-35.opt-1.pyc
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random_projection.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
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setup.cpython-35.pyc
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