B vdJ?@sdZddlmZddlmZddlZddlmZddlmZddlm Z ddl Z dd l m Z mZd d Zd d ZeeedZddZddZddZddZddZdddZd ddZddZdS)!zX Multi-class / multi-label utility function ========================================== )Sequence)chainN)issparse) dok_matrix) lil_matrix) check_array_assert_all_finitecCs&t|drtt|St|SdS)N __array__)hasattrnpuniqueasarrayset)yre/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/sklearn/utils/multiclass.py_unique_multiclasss rcCstt|dddgdjdS)Ncsrcsccoo) accept_sparser)r Zarangershape)rrrr_unique_indicatorsr)binary multiclasszmultilabel-indicatorcs|s tdtdd|D}|ddhkr0dh}t|dkrHtd||}|dkrzttd d|Ddkrztd t|d std t|ttfd d|D}ttdd|Ddkrtdt t |S)awExtract 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 : ndarray 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]) zNo argument has been passed.css|]}t|VqdS)N)type_of_target).0xrrr Ksz unique_labels..rrrz'Mix type of y not allowed, got types %szmultilabel-indicatorcss&|]}t|dddgdjdVqdS)rrr)rrN)rr)rrrrrrYszCMulti-label binary indicator input with different numbers of labelsNzUnknown label type: %sc3s|]}|VqdS)Nr)rr)_unique_labelsrrrgscss|]}t|tVqdS)N) isinstancestr)rlabelrrrrjsz,Mix of label input types (string and number)) ValueErrorrlenpop_FN_UNIQUE_LABELSgetreprr from_iterabler arraysorted)ZysZys_typesZ label_typeZ ys_labelsr)r r unique_labels(s,    r-cCs |jjdkot|t|kS)Nf)dtypekindr allastypeint)rrrr_is_integral_floatpsr4c Cst|dst|trjtHtdtjyt|}Wn$tjk r^tj |t d}YnXWdQRXt|dr|j dkr|j ddksdSt |rt|ttfr|}t|jd kpt|jjdko|jjd kptt|jSt|}t|d ko|jjd kpt|SdS) a~Check if ``y`` is in a multilabel format. Parameters ---------- y : ndarray of shape (n_samples,) Target values. Returns ------- out : bool Return ``True``, if ``y`` is in a multilabel format, else ```False``. Examples -------- >>> import numpy as np >>> from sklearn.utils.multiclass import is_multilabel >>> is_multilabel([0, 1, 0, 1]) False >>> is_multilabel([[1], [0, 2], []]) False >>> is_multilabel(np.array([[1, 0], [0, 0]])) True >>> is_multilabel(np.array([[1], [0], [0]])) False >>> is_multilabel(np.array([[1, 0, 0]])) True r error)r/NrrFrZbiu)r r!rwarningscatch_warnings simplefilterr VisibleDeprecationWarningrr+objectndimrrrrZtocsrr%datar sizer/r0r4)rlabelsrrr is_multilabelts& "  rAcCs t|}|dkrtd|dS)a*Ensure that target y is of a non-regression type. Only the following target types (as defined in type_of_target) are allowed: 'binary', 'multiclass', 'multiclass-multioutput', 'multilabel-indicator', 'multilabel-sequences' Parameters ---------- y : array-like )rrzmulticlass-multioutputzmultilabel-indicatorzmultilabel-sequenceszUnknown label type: %rN)rr$)rZy_typerrrcheck_classification_targetss rBc Cst|tst|st|do&t|t }|s8td||jjdk}|rPtdt|r\dSt Ht dt j yt |}Wn$t j k rt j|td}YnXWdQRXy6t|d dst|d trt|d tstd Wntk rYnX|jd ks2|jtkr6t|r6t|jd ts6d S|jd krV|jd d krVd S|jd krx|jd d krxd}nd}|jjdkrt ||tkrt|d|Stt |d ks|jd krt|d d krd|SdSdS)a# Determine the type of data indicated by the target. Note that this type is the most specific type that can be inferred. For example: * ``binary`` is more specific but compatible with ``multiclass``. * ``multiclass`` of integers is more specific but compatible with ``continuous``. * ``multilabel-indicator`` is more specific but compatible with ``multiclass-multioutput``. Parameters ---------- y : array-like Returns ------- target_type : str 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 -------- >>> from sklearn.utils.multiclass import type_of_target >>> 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]]) 'multilabel-indicator' >>> 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 %r)Z SparseSeriesZ SparseArrayz1y cannot be class 'SparseSeries' or 'SparseArray'zmultilabel-indicatorr5)r/NrzYou 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 - the MultiLabelBinarizer transformer can convert to this format.r6unknownrz -multioutputr.Z continuousrr)r!rrr r"r$ __class____name__rAr8r9r:r r;rr< IndexErrorr=r/r%Zflatrr0anyr2r3r r )rZvalidZ sparse_pandassuffixrrrrsJB     4$2rcCsrt|dddkr"|dkr"tdnL|dk rnt|dddk r`t|jt|sntd||jfnt||_dSdS)a"Private helper function for factorizing common classes param logic. Estimators that implement the ``partial_fit`` API need to be provided with the list of possible classes at the first call to partial_fit. Subsequent calls to partial_fit should check that ``classes`` is still consistent with a previous value of ``clf.classes_`` when provided. This function returns True if it detects that this was the first call to ``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also set on ``clf``. classes_Nz8classes must be passed on the first call to partial_fit.zD`classes=%r` is not the same as on last call to partial_fit, was: %rTF)getattrr$r Z array_equalrJr-)Zclfclassesrrr_check_partial_fit_first_callMs  rMcCsg}g}g}|j\}}|dk r(t|}t|rv|}t|j}xt|D]}|j|j||j|d} |dk r|| } t |t | } nd} |jd||} tj |j |j||j|ddd\} } tj | | d}d| kr || dk| 7<d| krD|||jdkrDt | dd} t |d| }|| || jd||| qRWnlxjt|D]^}tj |dd|fdd\} } || || jdtj | |d}||| qW|||fS)a{Compute class priors from multioutput-multiclass target data. Parameters ---------- y : {array-like, sparse matrix} of size (n_samples, n_outputs) The labels for each example. sample_weight : array-like of shape (n_samples,), default=None Sample weights. Returns ------- classes : list of size n_outputs of ndarray of size (n_classes,) List of classes for each column. n_classes : list of int of size n_outputs Number of classes in each column. class_prior : list of size n_outputs of ndarray of size (n_classes,) Class distribution of each column. NrrT)Zreturn_inverse)weights)rr rrZtocscdiffZindptrrangeindicessumr r>Zbincountinsertappend)rZ sample_weightrL n_classesZ class_prior n_samplesZ n_outputsZy_nnzkZ col_nonzeroZnz_samp_weightZzeros_samp_weight_sumZ classes_kZy_kZ class_prior_krrrclass_distributionpsD    (   rXc Cs |jd}t||f}t||f}d}xt|D]}xt|d|D]}|dd|f|dd|f8<|dd|f|dd|f7<||dd|fdk|fd7<||dd|fdk|fd7<|d7}qHWq4W|dt|d} || S)ayCompute a continuous, tie-breaking OvR decision function from OvO. It is important to include a continuous value, not only votes, to make computing AUC or calibration meaningful. Parameters ---------- predictions : array-like of shape (n_samples, n_classifiers) Predicted classes for each binary classifier. confidences : array-like of 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``. rrNr7)rr ZzerosrPabs) Z predictionsZ confidencesrUrVZvotesZsum_of_confidencesrWijZtransformed_confidencesrrr_ovr_decision_functions $$$$ r\)N)N)__doc__collections.abcr itertoolsrr8Z scipy.sparserrrnumpyr Z validationrr rrr'r-r4rArBrrMrXr\rrrrs,     H> # K