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multiclass.cpython-35.opt-1.pyc
��(X�9 � @ s\ d 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 Z d d l m Z d d l m Z d d l m Z d d l m Z d d � Z d d � Z d e d e d e i Z d d � Z d d � Z d d � Z d d � Z d d � Z d d! d" � Z d d# d$ � Z d% d& � Z d S)'zX Multi-class / multi-label utility function ========================================== � )�division)�Sequence)�chain)�issparse)�spmatrix)� dok_matrix)� lil_matrixN� )� string_types� )� check_array)�bincount)� array_equalc C s3 t | d � r% t j t j | � � St | � Sd S)N� __array__)�hasattr�np�unique�asarray�set)�y� r �/multiclass.py�_unique_multiclass s r c C s& t j t | d d d g � j d � S)N�csr�csc�coor )r Zaranger �shape)r r r r �_unique_indicator! s r �binary� multiclasszmultilabel-indicatorc sW | s t d � � t d d � | D� � } | t d d g � k rR t d g � } t | � d k rt t d | � � | j � } | d k r� t t d d � | D� � � d k r� t d � � t j | d � � � s� t d t | � � � t t j � f d d � | D� � � } t t d d � | D� � � d k rDt d � � t j t | � � S)az Extract an ordered array of unique labels We don't allow: - mix of multilabel and multiclass (single label) targets - mix of label indicator matrix and anything else, because there are no explicit labels) - mix of label indicator matrices of different sizes - mix of string and integer labels At the moment, we also don't allow "multiclass-multioutput" input type. Parameters ---------- *ys : array-likes, Returns ------- out : numpy array of shape [n_unique_labels] An ordered array of unique labels. Examples -------- >>> from sklearn.utils.multiclass import unique_labels >>> unique_labels([3, 5, 5, 5, 7, 7]) array([3, 5, 7]) >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]) array([1, 2, 3, 4]) >>> unique_labels([1, 2, 10], [5, 11]) array([ 1, 2, 5, 10, 11]) z No argument has been passed.c s s | ] } t | � Vq d S)N)�type_of_target)�.0�xr r r � <genexpr>O s z unique_labels.<locals>.<genexpr>r r r z'Mix type of y not allowed, got types %szmultilabel-indicatorc s s. | ]$ } t | d d d g � j d Vq d S)r r r r N)r r )r! r r r r r# Z s zCMulti-label binary indicator input with different numbers of labelsNzUnknown label type: %sc 3 s | ] } � | � Vq d S)Nr )r! r )�_unique_labelsr r r# d s c s s | ] } t | t � Vq d S)N)� isinstancer )r! Zlabelr r r r# g s z,Mix of label input types (string and number)) � ValueErrorr �len�pop�_FN_UNIQUE_LABELS�get�reprr � from_iterabler �array�sorted)ZysZys_typesZ label_typeZ ys_labelsr )r$ r � unique_labels, s& (% r/ c C s. | j j d k o- t j | j t � | k � S)N�f)�dtype�kindr �all�astype�int)r r r r �_is_integral_floatm s r6 c C s t | d � r t j | � } t | d � oL | j d k oL | j d d k sS d St | � r� t | t t f � r� | j � } t | j � d k p� t j | j � j d k o� | j j d k p� t t j | j � � St j | � } t | � d k o| j j d k pt | � Sd S) a� Check if ``y`` is in a multilabel format. Parameters ---------- y : numpy array of shape [n_samples] Target values. Returns ------- out : bool, Return ``True``, if ``y`` is in a multilabel format, else ```False``. Examples -------- >>> import numpy as np >>> from sklearn.utils.multiclass import is_multilabel >>> is_multilabel([0, 1, 0, 1]) False >>> is_multilabel([[1], [0, 2], []]) False >>> is_multilabel(np.array([[1, 0], [0, 0]])) True >>> is_multilabel(np.array([[1], [0], [0]])) False >>> is_multilabel(np.array([[1, 0, 0]])) True r r r r Fr Zbiu� N)r r r �ndimr r r% r r Ztocsrr' �datar �sizer1 r2 r6 )r Zlabelsr r r � is_multilabelq s 1 0$r; c C s, t | � } | d k r( t d | � � d S) a* Ensure that target y is of a non-regression type. Only the following target types (as defined in type_of_target) are allowed: 'binary', 'multiclass', 'multiclass-multioutput', 'multilabel-indicator', 'multilabel-sequences' Parameters ---------- y : array-like r r �multiclass-multioutput�multilabel-indicator�multilabel-sequenceszUnknown label type: %rN)r r r< r= r> )r r& )r Zy_typer r r � check_classification_targets� s r? c C s t | t t f � s$ t | d � o1 t | t � } | sJ t d | � � t | � rZ d Sy t j | � } Wn t k r� d SYn XyK t | d d � r� t | d t � r� t | d t � r� t d � � Wn t k r� Yn X| j d k s'| j t k r+t | � r+t | j d t � r+d S| j d k rQ| j d d k rQd S| j d k r|| j d d k r|d } n d } | j j d k r�t j | | j t � k � r�d | St t j | � � d k s�| j d k rt | d � d k rd | Sd Sd S)as Determine the type of data indicated by target `y` Parameters ---------- y : array-like Returns ------- target_type : string One of: * 'continuous': `y` is an array-like of floats that are not all integers, and is 1d or a column vector. * 'continuous-multioutput': `y` is a 2d array of floats that are not all integers, and both dimensions are of size > 1. * 'binary': `y` contains <= 2 discrete values and is 1d or a column vector. * 'multiclass': `y` contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector. * 'multiclass-multioutput': `y` is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1. * 'multilabel-indicator': `y` is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values. * 'unknown': `y` is array-like but none of the above, such as a 3d array, sequence of sequences, or an array of non-sequence objects. Examples -------- >>> import numpy as np >>> type_of_target([0.1, 0.6]) 'continuous' >>> type_of_target([1, -1, -1, 1]) 'binary' >>> type_of_target(['a', 'b', 'a']) 'binary' >>> type_of_target([1.0, 2.0]) 'binary' >>> type_of_target([1, 0, 2]) 'multiclass' >>> type_of_target([1.0, 0.0, 3.0]) 'multiclass' >>> type_of_target(['a', 'b', 'c']) 'multiclass' >>> type_of_target(np.array([[1, 2], [3, 1]])) 'multiclass-multioutput' >>> type_of_target([[1, 2]]) 'multiclass-multioutput' >>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]])) 'continuous-multioutput' >>> type_of_target(np.array([[0, 1], [1, 1]])) 'multilabel-indicator' r z:Expected array-like (array or non-string sequence), got %rzmultilabel-indicator�unknownr z�You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead.r r z -multioutput� r0 Z continuousr r N)r% r r r r r& r; r r � IndexErrorr8 r1 �objectr' Zflatr r2 �anyr4 r5 r )r Zvalid�suffixr r r r � s< 6$ ' *"" 0@r c C s� t | d d � d k r3 | d k r3 t d � � nk | d k r� t | d d � d k r� t | j t | � � s� t d | | j f � � n t | � | _ d Sd S)a! Private helper function for factorizing common classes param logic Estimators that implement the ``partial_fit`` API need to be provided with the list of possible classes at the first call to partial_fit. Subsequent calls to partial_fit should check that ``classes`` is still consistent with a previous value of ``clf.classes_`` when provided. This function returns True if it detects that this was the first call to ``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also set on ``clf``. �classes_Nz8classes must be passed on the first call to partial_fit.zD`classes=%r` is not the same as on last call to partial_fit, was: %rTF)�getattrr& r rF r/ )Zclf�classesr r r � _check_partial_fit_first_call s $ rI c C sm g } g } g } | j \ } } t | � r�| j � } t j | j � } xt | � D]u} | j | j | | j | d � } | d k r� t j | � | } t j | � t j | � } n d } | j d | | } t j | j | j | | j | d � d d �\ } } t | d | �} d | k rH| | d k | 7<d | k r�| | | j d k r�t j | d d � } t j | d | � } | j | � | j | j d � | j | | j � � qX Wn� x� t | � D]{ } t j | d d � | f d d �\ } } | j | � | j | j d � t | d | �} | j | | j � � q�W| | | f S)az Compute class priors from multioutput-multiclass target data Parameters ---------- y : array like or sparse matrix of size (n_samples, n_outputs) The labels for each example. sample_weight : array-like of shape = (n_samples,), optional Sample weights. Returns ------- classes : list of size n_outputs of arrays of size (n_classes,) List of classes for each column. n_classes : list of integers of size n_outputs Number of classes in each column class_prior : list of size n_outputs of arrays of size (n_classes,) Class distribution of each column. r Nr Zreturn_inverseTZweights)r r Ztocscr ZdiffZindptr�range�indicesr �sumr r9 r �insert�append)r Z sample_weightrH � n_classesZ class_prior� n_samplesZ n_outputsZy_nnz�kZ col_nonzeroZnz_samp_weightZzeros_samp_weight_sumZ classes_kZy_kZ class_prior_kr r r �class_distribution< sD % + # + rR c C s� | j d } t j | | f � } t j | | f � } d } x� t | � D]� } x� t | d | � D]� } | d d � | f | d d � | f 8<| d d � | f | d d � | f 7<| | d d � | f d k | f d 7<| | d d � | f d k | f d 7<| d 7} qd WqJ W| j � } | j � } | | k rL| St j | j � j } t t | � t | � � } d | | } | | | S)ak Compute a continuous, tie-breaking ovr decision function. It is important to include a continuous value, not only votes, to make computing AUC or calibration meaningful. Parameters ---------- predictions : array-like, shape (n_samples, n_classifiers) Predicted classes for each binary classifier. confidences : array-like, shape (n_samples, n_classifiers) Decision functions or predicted probabilities for positive class for each binary classifier. n_classes : int Number of classes. n_classifiers must be ``n_classes * (n_classes - 1 ) / 2`` r r Ng �?) r r ZzerosrJ �max�minZfinfor1 �eps�abs)Z predictionsZ confidencesrO rP ZvotesZsum_of_confidencesrQ �i�jZmax_confidencesZmin_confidencesrU Zmax_abs_confidenceZscaler r r �_ovr_decision_function� s& **** rY )!�__doc__Z __future__r � collectionsr � itertoolsr Z scipy.sparser Zscipy.sparse.baser r r Znumpyr Z externals.sixr Z validationr Z utils.fixesr r r r r) r/ r6 r; r? r rI rR rY r r r r �<module> s2 A - i#J
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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_scipy_sparse_lsqr_backport.cpython-35.opt-1.pyc
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_scipy_sparse_lsqr_backport.cpython-35.pyc
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arpack.cpython-35.opt-1.pyc
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arpack.cpython-35.pyc
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bench.cpython-35.opt-1.pyc
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bench.cpython-35.pyc
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class_weight.cpython-35.opt-1.pyc
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class_weight.cpython-35.pyc
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deprecation.cpython-35.opt-1.pyc
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deprecation.cpython-35.pyc
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estimator_checks.cpython-35.opt-1.pyc
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estimator_checks.cpython-35.pyc
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extmath.cpython-35.opt-1.pyc
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extmath.cpython-35.pyc
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fixes.cpython-35.opt-1.pyc
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fixes.cpython-35.pyc
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graph.cpython-35.opt-1.pyc
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graph.cpython-35.pyc
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linear_assignment_.cpython-35.opt-1.pyc
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linear_assignment_.cpython-35.pyc
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metaestimators.cpython-35.opt-1.pyc
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metaestimators.cpython-35.pyc
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mocking.cpython-35.opt-1.pyc
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mocking.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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optimize.cpython-35.opt-1.pyc
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optimize.cpython-35.pyc
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random.cpython-35.opt-1.pyc
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random.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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sparsefuncs.cpython-35.opt-1.pyc
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sparsefuncs.cpython-35.pyc
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stats.cpython-35.opt-1.pyc
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stats.cpython-35.pyc
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testing.cpython-35.opt-1.pyc
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testing.cpython-35.pyc
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validation.cpython-35.opt-1.pyc
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validation.cpython-35.pyc
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