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base.cpython-35.opt-1.pyc
��(XjM � @ s� d Z d d l Z d d l Z d d l 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 e d � Gd d � d e � � Z d d � Z d d d � Z d e d d � Z Gd d � d e � Z Gd d � d e � Z Gd d � d e � Z Gd d � d e � Z Gd d � d e � Z Gd d � d e � Z Gd d! � d! e � Z Gd" d# � d# e � Z d$ d% � Z d& d' � Z d S)(z Base classes for all estimators.� N)�sparse� )�six)� signature)� deprecated)�ChangedBehaviorWarning)� __version__zyChangedBehaviorWarning has been moved into the sklearn.exceptions module. It will not be available here from version 0.19c @ s e Z d Z d S)r N)�__name__� __module__� __qualname__� r r �/base.pyr s r c C sm t | t j � s! t | d � rW t j | � r9 | j n | } | j d | j d f S| d | d f Sd S) z?Returns first and last element of numpy array or sparse matrix.�datar r N���)r r r r )r r )� isinstance�np�ndarray�hasattrr �issparser Zflat)Zarrr r r r �_first_and_last_element s ! r Tc s t | � } | t t t t f k rA | � f d d � | D� � St | d � s� � sc t j | � St d t | � t | � f � � | j } | j d d � } x3 t j | � D]" \ } } t | d d �| | <q� W| | � } | j d d � } x | D]} | | } | | } | | k r$q� t | t j � rt | t | � � sTd } q�| j d k r�| j d d k r�t | t j � r�| j d k r�| j d d k r�| j | j k o�| j | j k o�t | � t | � k } q�t j | | k � } n� t j | � r�t j | � s(d } q�| j d k sF| j d k ry| j | j k os| j d k os| j d k } q�| j | j k o�t | � t | � k o�| j | j k o�| j | j k } n | | k } | r�t j d t | � j t � q� t d | | f � � q� W| S) a Constructs a new estimator with the same parameters. Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data. Parameters ---------- estimator: estimator object, or list, tuple or set of objects The estimator or group of estimators to be cloned safe: boolean, optional If safe is false, clone will fall back to a deepcopy on objects that are not estimators. c s" g | ] } t | d � �� q S)�safe)�clone)�.0�e)r r r � <listcomp>9 s zclone.<locals>.<listcomp>� get_paramsz�Cannot clone object '%s' (type %s): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.�deepFr r z�Estimator %s modifies parameters in __init__. This behavior is deprecated as of 0.18 and support for this behavior will be removed in 0.20.zLCannot clone object %s, as the constructor does not seem to set parameter %s) �type�list�tuple�set� frozensetr �copy�deepcopy� TypeError�repr� __class__r r � iteritemsr r r r �ndim�shapeZdtyper �allr r �sizeZnnz�warnings�warnr �DeprecationWarning� RuntimeError) � estimatorr Zestimator_type�klassZnew_object_params�nameZparamZ new_objectZ params_setZparam1Zparam2Z equality_testr )r r r % sf r c C s� t j � } t j d d d d d d � t � } | } d d | d d } x"t t t j | � � � D]\ } \ } } t | � t k r� d | t | � f } n d | | | � f } t | � d k r� | d d � d | d d � } | d k rQ| t | � d k s d | k r:| j | � t | � } n | j d � | d 7} | j | � | t | � 7} qi Wt j | � d j | � } d j d d � | j d � D� � } | S)aA Pretty print the dictionary 'params' Parameters ---------- params: dict The dictionary to pretty print offset: int The offset in characters to add at the begin of each line. printer: The function to convert entries to strings, typically the builtin str or repr Z precision� Z threshold�@ Z edgeitems� z, r � z%s=%si� Ni, z...�d r �K � z, � c s s | ] } | j d � Vq d S)r6 N)�rstrip)r �lr r r � <genexpr>� s z_pprint.<locals>.<genexpr>i����)r Zget_printoptionsZset_printoptionsr � enumerate�sortedr r' r �float�str�len�append�join�split) �params�offsetZprinterZoptionsZ params_listZthis_line_lengthZline_sep�i�k�vZ this_repr�linesr r r �_pprint� s. ." " %rL c @ sg e Z d Z d Z e d d � � Z d d d � Z d d � Z d d � Z d d � Z d d � Z d S)� BaseEstimatorz�Base class for all estimators in scikit-learn Notes ----- All estimators should specify all the parameters that can be set at the class level in their ``__init__`` as explicit keyword arguments (no ``*args`` or ``**kwargs``). c C s� t | j d | j � } | t j k r+ g St | � } d d � | j j � D� } x6 | D]. } | j | j k rZ t d | | f � � qZ Wt d d � | D� � S)z%Get parameter names for the estimatorZdeprecated_originalc S s7 g | ]- } | j d k r | j | j k r | � q S)�self)r2 �kindZ VAR_KEYWORD)r �pr r r r � s z2BaseEstimator._get_param_names.<locals>.<listcomp>z�scikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). %s with constructor %s doesn't follow this convention.c S s g | ] } | j � q Sr )r2 )r rP r r r r � s ) �getattr�__init__�objectr � parameters�valuesrO ZVAR_POSITIONALr/ r? )�clsZinitZinit_signaturerT rP r r r �_get_param_names� s z BaseEstimator._get_param_namesTc s� t � } x� | j � D]� � t j d t � zT t j d d � � } t | � d � } Wd QRXt | � r | d j t k r w Wd t j j d � X| r� t | d � r� | j � j � } | j � f d d � | D� � | | � <q W| S) ao Get parameters for this estimator. Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. �always�recordTNr r c 3 s) | ] \ } } � d | | f Vq d S)�__Nr )r rI �val)�keyr r r= � s z+BaseEstimator.get_params.<locals>.<genexpr>)�dictrW r, � simplefilterr. �catch_warningsrQ rB �category�filters�popr r �items�update)rN r �out�w�valueZ deep_itemsr )r\ r r � s zBaseEstimator.get_paramsc K s� | s | S| j d d � } x� t j | � D]� \ } } | j d d � } t | � d k r� | \ } } | | k r� t d | | f � � | | } | j | | i � q, | | k r� t d | | j j f � � t | | | � q, W| S)a\ Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object. Returns ------- self r TrZ r zsInvalid parameter %s for estimator %s. Check the list of available parameters with `estimator.get_params().keys()`.) r r r' rE rB � ValueError� set_paramsr& r �setattr) rN rF Z valid_paramsr\ rg rE r2 Zsub_nameZ sub_objectr r r ri s" zBaseEstimator.set_paramsc C s8 | j j } d | t | j d d � d t | � �f S)Nz%s(%s)r FrG )r& r rL r rB )rN � class_namer r r �__repr__' s zBaseEstimator.__repr__c C sH t | � j j d � r1 t | j j � d t �St | j j � � Sd S)Nzsklearn.�_sklearn_version)r r � startswithr] �__dict__rc r )rN r r r � __getstate__, s zBaseEstimator.__getstate__c C so t | � j j d � r[ | j d d � } | t k r[ t j d j | j j | t � t � | j j | � d S)Nzsklearn.rm zpre-0.18z�Trying to unpickle estimator {0} from version {1} when using version {2}. This might lead to breaking code or invalid results. Use at your own risk.) r r rn rb r r, r- �formatr& r � UserWarningro rd )rN �stateZpickle_versionr r r � __setstate__2 s zBaseEstimator.__setstate__N) r r r �__doc__� classmethodrW r ri rl rp rt r r r r rM � s % & rM c @ s+ e Z d Z d Z d Z d d d � Z d S)�ClassifierMixinz0Mixin class for all classifiers in scikit-learn.� classifierNc C s, d d l m } | | | j | � d | �S)a� Returns the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters ---------- X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. Returns ------- score : float Mean accuracy of self.predict(X) wrt. y. r )�accuracy_score� sample_weight)�metricsry �predict)rN �X�yrz ry r r r �scoreD s zClassifierMixin.score)r r r ru �_estimator_typer r r r r rw @ s rw c @ s+ e Z d Z d Z d Z d d d � Z d S)�RegressorMixinz:Mixin class for all regression estimators in scikit-learn.� regressorNc C s2 d d l m } | | | j | � d | d d �S)a� 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. 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_scorerz Z multioutputZvariance_weighted)r{ r� r| )rN r} r~ rz r� r r r r e s zRegressorMixin.score)r r r ru r� r r r r r r� a s r� c @ s+ e Z d Z d Z d Z d d d � Z d S)� ClusterMixinz7Mixin class for all cluster estimators in scikit-learn.Z clustererNc C s | j | � | j S)a Performs clustering on X and returns cluster labels. Parameters ---------- X : ndarray, shape (n_samples, n_features) Input data. Returns ------- y : ndarray, shape (n_samples,) cluster labels )�fitZlabels_)rN r} r~ r r r � fit_predict� s zClusterMixin.fit_predict)r r r ru r� r� r r r r r� � s r� c @ sL e Z d Z d Z e d d � � Z d d � Z d d � Z d d � Z d S) �BiclusterMixinz8Mixin class for all bicluster estimators in scikit-learnc C s | j | j f S)z{Convenient way to get row and column indicators together. Returns the ``rows_`` and ``columns_`` members. )�rows_�columns_)rN r r r � biclusters_� s zBiclusterMixin.biclusters_c C s> | j | } | j | } t j | � d t j | � d f S)a� Row and column indices of the i'th bicluster. Only works if ``rows_`` and ``columns_`` attributes exist. Returns ------- row_ind : np.array, dtype=np.intp Indices of rows in the dataset that belong to the bicluster. col_ind : np.array, dtype=np.intp Indices of columns in the dataset that belong to the bicluster. r )r� r� r Znonzero)rN rH Zrows�columnsr r r � get_indices� s zBiclusterMixin.get_indicesc C s& | j | � } t d d � | D� � S)z�Shape of the i'th bicluster. Returns ------- shape : (int, int) Number of rows and columns (resp.) in the bicluster. c s s | ] } t | � Vq d S)N)rB )r rH r r r r= � s z+BiclusterMixin.get_shape.<locals>.<genexpr>)r� r )rN rH �indicesr r r � get_shape� s zBiclusterMixin.get_shapec C sX d d l m } | | d d �} | j | � \ } } | | d d � t j f | f S)z�Returns the submatrix corresponding to bicluster `i`. Works with sparse matrices. Only works if ``rows_`` and ``columns_`` attributes exist. r )� check_arrayZ accept_sparseZcsrN)Zutils.validationr� r� r Znewaxis)rN rH r r� Zrow_indZcol_indr r r � get_submatrix� s z BiclusterMixin.get_submatrixN) r r r ru �propertyr� r� r� r� r r r r r� � s r� c @ s% e Z d Z d Z d d d � Z d S)�TransformerMixinz1Mixin class for all transformers in scikit-learn.Nc K sE | d k r% | j | | � j | � S| j | | | � j | � Sd S)a� Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. Returns ------- X_new : numpy array of shape [n_samples, n_features_new] Transformed array. N)r� Z transform)rN r} r~ Z fit_paramsr r r � fit_transform� s z TransformerMixin.fit_transform)r r r ru r� r r r r r� � s r� c @ s+ e Z d Z d Z d Z d d d � Z d S)� DensityMixinz7Mixin class for all density estimators in scikit-learn.ZDensityEstimatorNc C s d S)z�Returns the score of the model on the data X Parameters ---------- X : array-like, shape = (n_samples, n_features) Returns ------- score: float Nr )rN r} r~ r r r r � s zDensityMixin.score)r r r ru r� r r r r r r� � s r� c @ s e Z d Z d Z d S)�MetaEstimatorMixinz4Mixin class for all meta estimators in scikit-learn.N)r r r ru r r r r r� s r� c C s t | d d � d k S)z?Returns True if the given estimator is (probably) a classifier.r� Nrx )rQ )r0 r r r � is_classifier s r� c C s t | d d � d k S)z>Returns True if the given estimator is (probably) a regressor.r� Nr� )rQ )r0 r r r � is_regressor s r� )!ru r" r, Znumpyr Zscipyr Z externalsr Z utils.fixesr Zutils.deprecationr � exceptionsr Z_ChangedBehaviorWarningr: r r r r% rL rS rM rw r� r� r� r� r� r� r� r� r r r r �<module> s0 _3�!&5!
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__init__.cpython-35.opt-1.pyc
2267 bytes
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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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