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multioutput.cpython-35.opt-1.pyc
��(Xj# � @ s# d Z d d l Z d d l m Z d d l m Z m Z d d l m Z m Z d d l m Z m Z d d l m Z d d l m Z m Z d d l m Z m Z d d l m Z d d g Z d d d � Z Gd d � d e j e e � � Z Gd d � d e e � Z Gd d � d e e � Z d S)a This module implements multioutput regression and classification. The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. The meta-estimator extends single output estimators to multioutput estimators. � N)�ABCMeta� )� BaseEstimator�clone)�RegressorMixin�ClassifierMixin)� check_array� check_X_y)�parallel_helper)�check_is_fitted�has_fit_parameter)�Parallel�delayed)�six�MultiOutputRegressor�MultiOutputClassifierc C sE t | � } | d k r1 | j | | d | �n | j | | � | S)N� sample_weight)r �fit)� estimator�X�yr � r �/multioutput.py�_fit_estimator s r c @ s: e Z d Z d d d � Z d d d � Z d d � Z d S) �MultiOutputEstimatorr c C s | | _ | | _ d S)N)r �n_jobs)�selfr r r r r �__init__* s z MultiOutputEstimator.__init__Nc s� t � j d � s t d � � t � � d d d d �\ � � � j d k rZ t d � � � d k r� t � j d � r� t d � � t d � j � � � � � f d d � t � j d � D� � � _ � S)a� Fit the model to data. Fit a separate model for each output variable. Parameters ---------- X : (sparse) array-like, shape (n_samples, n_features) Data. y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets. An indicator matrix turns on multilabel estimation. sample_weight : array-like, shape = (n_samples) or None Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights. Returns ------- self : object Returns self. r z0The base estimator should implement a fit methodZ multi_outputT� accept_sparser zQy must have at least two dimensions for multi target regression but has only one.Nr z5Underlying regressor does not support sample weights.r c 3 s= | ]3 } t t � � j � � d d � | f � � Vq d S)N)r r r )�.0�i)r r r r r r � <genexpr>V s z+MultiOutputEstimator.fit.<locals>.<genexpr>) �hasattrr � ValueErrorr �ndimr r r �range�shape� estimators_)r r r r r )r r r r r r . s ' zMultiOutputEstimator.fitc s{ t | d � t | j d � s+ t d � � t � d d �� t d | j � � f d d � | j D� � } t j | � j S) a� Predict multi-output variable using a model trained for each target variable. Parameters ---------- X : (sparse) array-like, shape (n_samples, n_features) Data. Returns ------- y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor. r' �predictz4The base estimator should implement a predict methodr Tr c 3 s' | ] } t t � | d � � Vq d S)r( N)r r )r �e)r r r r! o s z/MultiOutputEstimator.predict.<locals>.<genexpr>) r r" r r# r r r r' �npZasarray�T)r r r r )r r r( Z s z MultiOutputEstimator.predict)�__name__� __module__� __qualname__r r r( r r r r r ( s ,r c s: e Z d Z d Z d � f d d � Z d d d � Z � S)r a� Multi target regression This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression. Parameters ---------- estimator : estimator object An estimator object implementing `fit` and `predict`. n_jobs : int, optional, default=1 The number of jobs to run in parallel for `fit`. If -1, then the number of jobs is set to the number of cores. When individual estimators are fast to train or predict using `n_jobs>1` can result in slower performance due to the overhead of spawning processes. r c s t t | � j | | � d S)N)�superr r )r r r )� __class__r r r � s z MultiOutputRegressor.__init__Nc C s2 d d l m } | | | j | � d | d d �S)aE Returns the coefficient of determination R^2 of the prediction. The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0. Notes ----- R^2 is calculated by weighting all the targets equally using `multioutput='uniform_average'`. Parameters ---------- X : array-like, shape (n_samples, n_features) Test samples. y : array-like, shape (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape [n_samples], optional Sample weights. Returns ------- score : float R^2 of self.predict(X) wrt. y. r )�r2_scorer Z multioutputZuniform_average)Zmetricsr1 r( )r r r r r1 r r r �score� s !zMultiOutputRegressor.score)r, r- r. �__doc__r r2 r r )r0 r r u s c sC e Z d Z d Z d � f d d � Z d d � Z d d � Z � S) r a� Multi target classification This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification Parameters ---------- estimator : estimator object An estimator object implementing `fit`, `score` and `predict_proba`. n_jobs : int, optional, default=1 The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. The number of jobs to use for the computation. It does each target variable in y in parallel. Attributes ---------- estimators_ : list of `n_output` estimators Estimators used for predictions. r c s t t | � j | | � d S)N)r/ r r )r r r )r0 r r r � s z MultiOutputClassifier.__init__c sT t | d � t | j d � s+ t d � � t j � f d d � | j D� � } | S)a� Probability estimates. Returns prediction probabilites for each class of each output. Parameters ---------- X : array-like, shape (n_samples, n_features) Data Returns ------- T : (sparse) array-like, shape = (n_samples, n_classes, n_outputs) The class probabilities of the samples for each of the outputs r' � predict_probaz7The base estimator should implementpredict_proba methodc s g | ] } | j � � � q Sr )r4 )r r )r r r � <listcomp>� s z7MultiOutputClassifier.predict_proba.<locals>.<listcomp>)r r" r r# r* Zdstackr' )r r �resultsr )r r r4 � s z#MultiOutputClassifier.predict_probac C s� t | d � t | j � } | j d k r7 t d � � | j d | k ri t d j | | j d � � � | j | � } t j t j | | k d d �� S)a "Returns the mean accuracy on the given test data and labels. Parameters ---------- X : array-like, shape [n_samples, n_features] Test samples y : array-like, shape [n_samples, n_outputs] True values for X Returns ------- scores : float accuracy_score of self.predict(X) versus y r' r zTy must have at least two dimensions for multi target classification but has only onezCThe number of outputs of Y for fit {0} and score {1} should be sameZaxis) r �lenr' r$ r# r& �formatr( r* Zmean�all)r r r Z n_outputs_Zy_predr r r r2 � s zMultiOutputClassifier.score)r, r- r. r3 r r4 r2 r r )r0 r r � s ) r3 Znumpyr* �abcr �baser r r r Zutilsr r Z utils.fixesr Zutils.validationr r Zexternals.joblibr r Z externalsr �__all__r Zwith_metaclassr r r r r r r �<module> s "M<
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
2267 bytes
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base.cpython-35.opt-1.pyc
17198 bytes
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base.cpython-35.pyc
17198 bytes
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calibration.cpython-35.opt-1.pyc
18382 bytes
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calibration.cpython-35.pyc
18382 bytes
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cross_validation.cpython-35.opt-1.pyc
68580 bytes
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cross_validation.cpython-35.pyc
68580 bytes
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discriminant_analysis.cpython-35.opt-1.pyc
26569 bytes
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discriminant_analysis.cpython-35.pyc
26569 bytes
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dummy.cpython-35.opt-1.pyc
15443 bytes
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dummy.cpython-35.pyc
15443 bytes
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exceptions.cpython-35.opt-1.pyc
5892 bytes
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exceptions.cpython-35.pyc
5892 bytes
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grid_search.cpython-35.opt-1.pyc
37096 bytes
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grid_search.cpython-35.pyc
37096 bytes
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isotonic.cpython-35.opt-1.pyc
12665 bytes
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isotonic.cpython-35.pyc
12665 bytes
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kernel_approximation.cpython-35.opt-1.pyc
16865 bytes
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kernel_approximation.cpython-35.pyc
16865 bytes
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kernel_ridge.cpython-35.opt-1.pyc
6644 bytes
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kernel_ridge.cpython-35.pyc
6644 bytes
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lda.cpython-35.opt-1.pyc
802 bytes
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lda.cpython-35.pyc
802 bytes
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learning_curve.cpython-35.opt-1.pyc
13957 bytes
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learning_curve.cpython-35.pyc
13957 bytes
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multiclass.cpython-35.opt-1.pyc
28997 bytes
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multiclass.cpython-35.pyc
28997 bytes
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multioutput.cpython-35.opt-1.pyc
9728 bytes
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multioutput.cpython-35.pyc
9728 bytes
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naive_bayes.cpython-35.opt-1.pyc
26877 bytes
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naive_bayes.cpython-35.pyc
26877 bytes
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pipeline.cpython-35.opt-1.pyc
28874 bytes
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pipeline.cpython-35.pyc
28874 bytes
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qda.cpython-35.opt-1.pyc
815 bytes
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qda.cpython-35.pyc
815 bytes
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random_projection.cpython-35.opt-1.pyc
20389 bytes
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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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