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__init__.cpython-35.pyc
��(X�4 � @ s� d Z d d l m Z d d l Z d d l m Z d d l Z d d l m Z d d l m Z m Z m Z m Z m Z m Z m Z m Z m Z d d l m Z d d l m Z 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 d d d d d d d d d d d d g Z d d � Z d! d" � Z! d# d � Z" d$ d% � Z# d& d' � Z$ d( d) d* � Z% d+ d, � Z& d d- d. � Z' d/ d0 � Z( d1 d2 � Z) d3 d � Z* d S)4z= The :mod:`sklearn.utils` module includes various utilities. � )�SequenceN)�issparse� )�murmurhash3_32) �as_float_array�assert_all_finite�check_random_state� column_or_1d� check_array�check_consistent_length� check_X_y� indexable�check_symmetric)� deprecated)�compute_class_weight�compute_sample_weight� )� cpu_count)�ConvergenceWarning)�DataConversionWarningzuConvergenceWarning 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 � /__init__.pyr s r r r r r r r r r � safe_indexingr r r r �indices_to_maskc C s^ t j | � } t j | j t j � r+ | St | d � rZ t j | j d � } | | } | S)z�Return a mask which is safe to use on X. Parameters ---------- X : {array-like, sparse matrix} Data on which to apply mask. mask: array Mask to be used on X. Returns ------- mask Ztoarrayr )�np�asarrayZ issubdtype�dtype�int�hasattr�arange�shape)�X�maskZindr r r � safe_mask& s r& c C sF | d k r) | t | | � d d � f St j d d | j d f � S)a7 This mask is safer than safe_mask since it returns an empty array, when a sparse matrix is sliced with a boolean mask with all False, instead of raising an unhelpful error in older versions of SciPy. See: https://github.com/scipy/scipy/issues/5361 Also note that we can avoid doing the dot product by checking if the len_mask is not zero in _huber_loss_and_gradient but this is not going to be the bottleneck, since the number of outliers and non_outliers are typically non-zero and it makes the code tougher to follow. r Nr# r )r& r �zerosr# )r$ r% Zlen_maskr r r �axis0_safe_slice? s r( c s� t � d � rW y � j | SWq� t k rS t j d t � � j � j | SYq� Xnt t � d � r� t � d � r� t | d � r� | j j d k r� � j | d d �S� | Sn � f d d � | D� Sd S) a1 Return items or rows from X using indices. Allows simple indexing of lists or arrays. Parameters ---------- X : array-like, sparse-matrix, list. Data from which to sample rows or items. indices : array-like, list Indices according to which X will be subsampled. �ilocz$Copying input dataframe for slicing.r# �taker �iZaxisr c s g | ] } � | � q Sr r )�.0�idx)r$ r r � <listcomp>r s z!safe_indexing.<locals>.<listcomp>N) r! r) � ValueError�warnings�warnr �copyr �kindr* )r$ �indicesr )r$ r r S s c s� t | j d d � � } | j d d � } | j d d � } | rX t d | j � � � t | � d k rn d S| d } t | d � r� | j d n t | � } | d k r� | } n) | | k r� | r� t d | | f � � t | � | r | j d | d | f �� n, t j | � � | j � � � d | � � d d � | D� } � f d d � | D� } t | � d k r~| d S| Sd S)a� Resample arrays or sparse matrices in a consistent way The default strategy implements one step of the bootstrapping procedure. Parameters ---------- *arrays : sequence of indexable data-structures Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension. replace : boolean, True by default Implements resampling with replacement. If False, this will implement (sliced) random permutations. n_samples : int, None by default Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays. If replace is False it should not be larger than the length of arrays. random_state : int or RandomState instance Control the shuffling for reproducible behavior. Returns ------- resampled_arrays : sequence of indexable data-structures Sequence of resampled views of the collections. The original arrays are not impacted. Examples -------- It is possible to mix sparse and dense arrays in the same run:: >>> X = np.array([[1., 0.], [2., 1.], [0., 0.]]) >>> y = np.array([0, 1, 2]) >>> from scipy.sparse import coo_matrix >>> X_sparse = coo_matrix(X) >>> from sklearn.utils import resample >>> X, X_sparse, y = resample(X, X_sparse, y, random_state=0) >>> X array([[ 1., 0.], [ 2., 1.], [ 1., 0.]]) >>> X_sparse # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE <3x2 sparse matrix of type '<... 'numpy.float64'>' with 4 stored elements in Compressed Sparse Row format> >>> X_sparse.toarray() array([[ 1., 0.], [ 2., 1.], [ 1., 0.]]) >>> y array([0, 1, 0]) >>> resample(y, n_samples=2, random_state=0) array([0, 1]) See also -------- :func:`sklearn.utils.shuffle` � random_stateN�replaceT� n_sampleszUnexpected kw arguments: %rr r# z?Cannot sample %d out of arrays with dim %dwhen replace is False�sizec S s. g | ]$ } t | � r$ | j � n | � q Sr )r Ztocsr)r, �ar r r r. � s z resample.<locals>.<listcomp>c s g | ] } t | � � � q Sr )r )r, r9 )r4 r r r. � s r ) r �popr/ �keys�lenr! r# r �randintr r" �shuffle)�arrays�optionsr5 r6 Z max_n_samples�firstr7 Zresampled_arraysr )r4 r �resampleu s4 D ( rB c O s d | d <t | | � S)a Shuffle arrays or sparse matrices in a consistent way This is a convenience alias to ``resample(*arrays, replace=False)`` to do random permutations of the collections. Parameters ---------- *arrays : sequence of indexable data-structures Indexable data-structures can be arrays, lists, dataframes or scipy sparse matrices with consistent first dimension. random_state : int or RandomState instance Control the shuffling for reproducible behavior. n_samples : int, None by default Number of samples to generate. If left to None this is automatically set to the first dimension of the arrays. Returns ------- shuffled_arrays : sequence of indexable data-structures Sequence of shuffled views of the collections. The original arrays are not impacted. Examples -------- It is possible to mix sparse and dense arrays in the same run:: >>> X = np.array([[1., 0.], [2., 1.], [0., 0.]]) >>> y = np.array([0, 1, 2]) >>> from scipy.sparse import coo_matrix >>> X_sparse = coo_matrix(X) >>> from sklearn.utils import shuffle >>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0) >>> X array([[ 0., 0.], [ 2., 1.], [ 1., 0.]]) >>> X_sparse # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE <3x2 sparse matrix of type '<... 'numpy.float64'>' with 3 stored elements in Compressed Sparse Row format> >>> X_sparse.toarray() array([[ 0., 0.], [ 2., 1.], [ 1., 0.]]) >>> y array([2, 1, 0]) >>> shuffle(y, n_samples=2, random_state=0) array([0, 1]) See also -------- :func:`sklearn.utils.resample` Fr6 )rB )r? r@ r r r r> � s = r> Tc C sr t | d d d d g d d �} t | � rQ | r? | j � } | j d C_ n | rd | d } n | d C} | S)ad Element wise squaring of array-likes and sparse matrices. Parameters ---------- X : array like, matrix, sparse matrix copy : boolean, optional, default True Whether to create a copy of X and operate on it or to perform inplace computation (default behaviour). Returns ------- X ** 2 : element wise square Z accept_sparseZcsrZcscZcooZ ensure_2dFr )r r r2 �data)r$ r2 r r r �safe_sqr s ! rD c c sc d } x< t t | | � � D]$ } | | } t | | � V| } q W| | k r_ t | | � Vd S)a� Generator to create slices containing batch_size elements, from 0 to n. The last slice may contain less than batch_size elements, when batch_size does not divide n. Examples -------- >>> from sklearn.utils import gen_batches >>> list(gen_batches(7, 3)) [slice(0, 3, None), slice(3, 6, None), slice(6, 7, None)] >>> list(gen_batches(6, 3)) [slice(0, 3, None), slice(3, 6, None)] >>> list(gen_batches(2, 3)) [slice(0, 2, None)] r N)�ranger �slice)�nZ batch_size�start�_�endr r r � gen_batches< s rK c c s� d } | d k r" t d | � � x� t | � D]r } | | } | | | k rY | d 7} | d k r/ | | } | d k r� t | | � } t | | d � V| } q/ Wd S)a� Generator to create n_packs slices going up to n. Pass n_samples when the slices are to be used for sparse matrix indexing; slicing off-the-end raises an exception, while it works for NumPy arrays. Examples -------- >>> from sklearn.utils import gen_even_slices >>> list(gen_even_slices(10, 1)) [slice(0, 10, None)] >>> list(gen_even_slices(10, 10)) #doctest: +ELLIPSIS [slice(0, 1, None), slice(1, 2, None), ..., slice(9, 10, None)] >>> list(gen_even_slices(10, 5)) #doctest: +ELLIPSIS [slice(0, 2, None), slice(2, 4, None), ..., slice(8, 10, None)] >>> list(gen_even_slices(10, 3)) [slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)] r r z+gen_even_slices got n_packs=%s, must be >=1N)r/ rE �minrF )rG Zn_packsr7 rH Zpack_numZthis_nrJ r r r �gen_even_slicesU s rM c C sG | d k r$ t t � d | d � S| d k r? t d � � n | Sd S)a� Get number of jobs for the computation. This function reimplements the logic of joblib to determine the actual number of jobs depending on the cpu count. 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. Parameters ---------- n_jobs : int Number of jobs stated in joblib convention. Returns ------- n_jobs : int The actual number of jobs as positive integer. Examples -------- >>> from sklearn.utils import _get_n_jobs >>> _get_n_jobs(4) 4 >>> jobs = _get_n_jobs(-2) >>> assert jobs == max(cpu_count() - 1, 1) >>> _get_n_jobs(0) Traceback (most recent call last): ... ValueError: Parameter n_jobs == 0 has no meaning. r r z%Parameter n_jobs == 0 has no meaning.N)�maxr r/ )Zn_jobsr r r � _get_n_jobsw s rO c C s@ t | t j � r t j | � St | t � r2 | St | � Sd S)z;Cast iterable x to a Sequence, avoiding a copy if possible.N)� isinstancer Zndarrayr r �list)�xr r r � tosequence� s rS c C sG | t j | � k r! t d � � t j | d t j �} d | | <| S)aY Convert list of indices to boolean mask. Parameters ---------- indices : list-like List of integers treated as indices. mask_length : int Length of boolean mask to be generated. Returns ------- mask : 1d boolean nd-array Boolean array that is True where indices are present, else False. z-mask_length must be greater than max(indices)r T)r rN r/ r' �bool)r4 Z mask_lengthr% r r r r � s )+�__doc__� collectionsr Znumpyr Z scipy.sparser r0 Z murmurhashr Z validationr r r r r r r r r Z deprecationr Z class_weightr r Zexternals.joblibr � exceptionsr Z_ConvergenceWarningr �__all__r& r( r rB r> rD rK rM rO rS r r r r r �<module> s> @ " j A " '
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__init__.cpython-35.opt-1.pyc
13339 bytes
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__init__.cpython-35.pyc
13339 bytes
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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
51813 bytes
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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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