0Ph@XdZddlZddlmZddlmZddlm Z m Z ddl m Z ddl mZdd l mZdd l mZd Zd Zd ZdZddZdddZdZdZdZdZdZdZd dZ d!dZ!dZ"dZ#dZ$dZ%dS)"zBA collection of utilities to work with sparse matrices and arrays.N)LinearOperator)_sparse_min_max_sparse_nan_min_max)_check_sample_weight)csc_mean_variance_axis0)csr_mean_variance_axis0)incr_mean_variance_axis0c~tj|r|jnt|}d|z}t |)z2Raises a TypeError if X is not a CSR or CSC matrixz,Expected a CSR or CSC sparse matrix, got %s.)spissparseformattype TypeError)X input_typeerrs Y/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/utils/sparsefuncs.py_raise_typeerrorrs6[^^8aJ 8: EC C..c2|dvrtd|zdS)N)rrz8Unknown axis value: %d. Use 0 for rows, or 1 for columns) ValueErroraxiss r_raise_error_wrong_axisrs/ 6 F M   rc|jd|jdksJ|xj||jdzc_dS)aInplace column scaling of a CSR matrix. Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to normalize using the variance of the features. It should be of CSR format. scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed feature-wise values to use for scaling. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 3, 4, 4, 4]) >>> indices = np.array([0, 1, 2, 2]) >>> data = np.array([8, 1, 2, 5]) >>> scale = np.array([2, 3, 2]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 1, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>> sparsefuncs.inplace_csr_column_scale(csr, scale) >>> csr.todense() matrix([[16, 3, 4], [ 0, 0, 10], [ 0, 0, 0], [ 0, 0, 0]]) rrclip)modeN)shapedatatakeindicesrscales rinplace_csr_column_scaler&%sHJ ;q>QWQZ ' ' ' 'FFejjj000FFFFrc|jd|jdksJ|xjtj|tj|jzc_dS)aInplace row scaling of a CSR matrix. Scale each sample of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to be scaled. It should be of CSR format. scale : ndarray of float of shape (n_samples,) Array of precomputed sample-wise values to use for scaling. rN)r r!nprepeatdiffindptrr$s rinplace_csr_row_scaler,NsM ;q>QWQZ ' ' ' 'FFbirwqx00111FFFFrFczt|tj|r:|jdkr/|dkrt |||St |j||Stj|r:|jdkr/|dkrt |||St |j||St|dS)a{Compute mean and variance along an axis on a CSR or CSC matrix. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Input data. It can be of CSR or CSC format. axis : {0, 1} Axis along which the axis should be computed. weights : ndarray of shape (n_samples,) or (n_features,), default=None If axis is set to 0 shape is (n_samples,) or if axis is set to 1 shape is (n_features,). If it is set to None, then samples are equally weighted. .. versionadded:: 0.24 return_sum_weights : bool, default=False If True, returns the sum of weights seen for each feature if `axis=0` or each sample if `axis=1`. .. versionadded:: 0.24 Returns ------- means : ndarray of shape (n_features,), dtype=floating Feature-wise means. variances : ndarray of shape (n_features,), dtype=floating Feature-wise variances. sum_weights : ndarray of shape (n_features,), dtype=floating Returned if `return_sum_weights` is `True`. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 3, 4, 4, 4]) >>> indices = np.array([0, 1, 2, 2]) >>> data = np.array([8, 1, 2, 5]) >>> scale = np.array([2, 3, 2]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 1, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>> sparsefuncs.mean_variance_axis(csr, axis=0) (array([2. , 0.25, 1.75]), array([12. , 0.1875, 4.1875])) csrr)weightsreturn_sum_weightscscN)rr rr_csr_mean_var_axis0_csc_mean_var_axis0Tr)rrr/r0s rmean_variance_axisr5`slD!!! {1~~!(e++ 199&77I 'W9K  Q AH-- 199&77I 'W9K  r)r/cpt|tj|r |jdvst |t j|dkr!t j|j||j }t j|t j|cxkrt j|ksntd|dkrWt j||jdkr3td|jddt j|dnVt j||jdkr3td |jddt j|d|dkr|j n|}|t|||j }t||||| S) a Compute incremental mean and variance along an axis on a CSR or CSC matrix. last_mean, last_var are the statistics computed at the last step by this function. Both must be initialized to 0-arrays of the proper size, i.e. the number of features in X. last_n is the number of samples encountered until now. Parameters ---------- X : CSR or CSC sparse matrix of shape (n_samples, n_features) Input data. axis : {0, 1} Axis along which the axis should be computed. last_mean : ndarray of shape (n_features,) or (n_samples,), dtype=floating Array of means to update with the new data X. Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1. last_var : ndarray of shape (n_features,) or (n_samples,), dtype=floating Array of variances to update with the new data X. Should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1. last_n : float or ndarray of shape (n_features,) or (n_samples,), dtype=floating Sum of the weights seen so far, excluding the current weights If not float, it should be of shape (n_features,) if axis=0 or (n_samples,) if axis=1. If float it corresponds to having same weights for all samples (or features). weights : ndarray of shape (n_samples,) or (n_features,), default=None If axis is set to 0 shape is (n_samples,) or if axis is set to 1 shape is (n_features,). If it is set to None, then samples are equally weighted. .. versionadded:: 0.24 Returns ------- means : ndarray of shape (n_features,) or (n_samples,), dtype=floating Updated feature-wise means if axis = 0 or sample-wise means if axis = 1. variances : ndarray of shape (n_features,) or (n_samples,), dtype=floating Updated feature-wise variances if axis = 0 or sample-wise variances if axis = 1. n : ndarray of shape (n_features,) or (n_samples,), dtype=integral Updated number of seen samples per feature if axis=0 or number of seen features per sample if axis=1. If weights is not None, n is a sum of the weights of the seen samples or features instead of the actual number of seen samples or features. Notes ----- NaNs are ignored in the algorithm. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 3, 4, 4, 4]) >>> indices = np.array([0, 1, 2, 2]) >>> data = np.array([8, 1, 2, 5]) >>> scale = np.array([2, 3, 2]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 1, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>> sparsefuncs.incr_mean_variance_axis( ... csr, axis=0, last_mean=np.zeros(3), last_var=np.zeros(3), last_n=2 ... ) (array([1.3..., 0.1..., 1.1...]), array([8.8..., 0.1..., 3.4...]), array([6., 6., 6.])) )r1r.r)dtypez8last_mean, last_var, last_n do not have the same shapes.rzHIf axis=1, then last_mean, last_n, last_var should be of size n_samples z (Got z).zIIf axis=0, then last_mean, last_n, last_var should be of size n_features N) last_meanlast_varlast_nr/)rr rrrr(sizefullr r7rr4r_incr_mean_var_axis0)rrr8r9r:r/s rincr_mean_variance_axisr>sbD!!! KNNqx>99 wv!& HHH GI  "'("3"3 F F F Frwv F F F FSTTT qyy 79   + +K"#'!*KK46GI4F4FKKK  , 79   + +L#$71:LL57WY5G5GLLL  qyyaA&wAAA  Y&'   rctj|r"|jdkrt|j|dStj|r|jdkrt ||dSt |dS)aInplace column scaling of a CSC/CSR matrix. Scale each feature of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to normalize using the variance of the features. It should be of CSC or CSR format. scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed feature-wise values to use for scaling. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 3, 4, 4, 4]) >>> indices = np.array([0, 1, 2, 2]) >>> data = np.array([8, 1, 2, 5]) >>> scale = np.array([2, 3, 2]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 1, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>> sparsefuncs.inplace_column_scale(csr, scale) >>> csr.todense() matrix([[16, 3, 4], [ 0, 0, 10], [ 0, 0, 0], [ 0, 0, 0]]) r1r.N)r rrr,r4r&rr$s rinplace_column_scaler@!sJ {1~~!(e++ac5))))) QAH-- E*****rctj|r"|jdkrt|j|dStj|r|jdkrt ||dSt |dS)aInplace row scaling of a CSR or CSC matrix. Scale each row of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix to be scaled. It should be of CSR or CSC format. scale : ndarray of shape (n_features,), dtype={np.float32, np.float64} Array of precomputed sample-wise values to use for scaling. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 2, 3, 4, 5]) >>> indices = np.array([0, 1, 2, 3, 3]) >>> data = np.array([8, 1, 2, 5, 6]) >>> scale = np.array([2, 3, 4, 5]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 1, 0, 0], [0, 0, 2, 0], [0, 0, 0, 5], [0, 0, 0, 6]]) >>> sparsefuncs.inplace_row_scale(csr, scale) >>> csr.todense() matrix([[16, 2, 0, 0], [ 0, 0, 6, 0], [ 0, 0, 0, 20], [ 0, 0, 0, 30]]) r1r.N)r rrr&r4r,rr$s rinplace_row_scalerBNsH {1~~!(e++ e,,,,, QAH--a'''''rc||fD]+}t|tjrtd,|dkr||jdz }|dkr||jdz }|j|k}||j|j|k<||j|<dS)aKSwap two rows of a CSC matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two rows are to be swapped. It should be of CSC format. m : int Index of the row of X to be swapped. n : int Index of the row of X to be swapped. m and n should be valid integersrN) isinstancer(ndarrayrr r#)rmntm_masks rinplace_swap_row_cscrKzsV@@ a $ $ @>?? ? @ 1uu QWQZ1uu QWQZ Y!^F !AIai1nAIfrc <||fD]+}t|tjrtd,|dkr||jdz }|dkr||jdz }||kr||}}|j}||}||dz}||}||dz}||z } ||z } | | kr:|j|dz|xx| | z z cc<|| z|j|dz<|| z |j|<tj|jd||j|||j|||j|||j|dg|_tj|jd||j|||j|||j|||j|dg|_dS)aKSwap two rows of a CSR matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two rows are to be swapped. It should be of CSR format. m : int Index of the row of X to be swapped. n : int Index of the row of X to be swapped. rDrrrN) rEr(rFrr r+ concatenater#r!) rrGrHrIr+m_startm_stopn_startn_stopnz_mnz_ns rinplace_swap_row_csrrTsV@@ a $ $ @>?? ? @ 1uu QWQZ1uu QWQZ 1uu!1 XFQiG AE]FQiG AE]F G D G D t|| Qtd{*!D.Qtm  Ihwh  Igfn % IfWn % Igfn % Ifgg   AI^ F8G8  F76> " F6'> " F76> " F677O  AFFFrctj|r|jdkrt|||dStj|r|jdkrt |||dSt |dS)a Swap two rows of a CSC/CSR matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two rows are to be swapped. It should be of CSR or CSC format. m : int Index of the row of X to be swapped. n : int Index of the row of X to be swapped. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 2, 3, 3, 3]) >>> indices = np.array([0, 2, 2]) >>> data = np.array([8, 2, 5]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 0, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>> sparsefuncs.inplace_swap_row(csr, 0, 1) >>> csr.todense() matrix([[0, 0, 5], [8, 0, 2], [0, 0, 0], [0, 0, 0]]) r1r.N)r rrrKrTrrrGrHs rinplace_swap_rowrWsJ {1~~!(e++Q1%%%%% QAH--Q1%%%%%rcD|dkr||jdz }|dkr||jdz }tj|r|jdkrt |||dStj|r|jdkrt |||dSt |dS)a Swap two columns of a CSC/CSR matrix in-place. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Matrix whose two columns are to be swapped. It should be of CSR or CSC format. m : int Index of the column of X to be swapped. n : int Index of the column of X to be swapped. Examples -------- >>> from sklearn.utils import sparsefuncs >>> from scipy import sparse >>> import numpy as np >>> indptr = np.array([0, 2, 3, 3, 3]) >>> indices = np.array([0, 2, 2]) >>> data = np.array([8, 2, 5]) >>> csr = sparse.csr_matrix((data, indices, indptr)) >>> csr.todense() matrix([[8, 0, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) >>> sparsefuncs.inplace_swap_column(csr, 0, 1) >>> csr.todense() matrix([[0, 8, 2], [0, 0, 5], [0, 0, 0], [0, 0, 0]]) rrr1r.N)r r rrrTrKrrVs rinplace_swap_columnrYsJ 1uu QWQZ1uu QWQZ {1~~!(e++Q1%%%%% QAH--Q1%%%%%rctj|r-|jdvr$|rt||St ||St |dS)aCompute minimum and maximum along an axis on a CSR or CSC matrix. Optionally ignore NaN values. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Input data. It should be of CSR or CSC format. axis : {0, 1} Axis along which the axis should be computed. ignore_nan : bool, default=False Ignore or passing through NaN values. .. versionadded:: 0.20 Returns ------- mins : ndarray of shape (n_features,), dtype={np.float32, np.float64} Feature-wise minima. maxs : ndarray of shape (n_features,), dtype={np.float32, np.float64} Feature-wise maxima. )r.r1rN)r rrrrr)rr ignore_nans r min_max_axisr\4sb6 {1~~!(n44  1&qt444 4"14000 0rc|dkrd}n;|dkrd}n2|jdkr'td|j|5||jStjtj|j|S|dkr5tj|j}||dS||zS|dkr{|&tj|j |j d Stj |tj|j}tj|j |j d| Std |) aA variant of X.getnnz() with extension to weighting on axis 0. Useful in efficiently calculating multilabel metrics. Parameters ---------- X : sparse matrix of shape (n_samples, n_labels) Input data. It should be of CSR format. axis : {0, 1}, default=None The axis on which the data is aggregated. sample_weight : array-like of shape (n_samples,), default=None Weight for each row of X. Returns ------- nnz : int, float, ndarray of shape (n_samples,) or ndarray of shape (n_features,) Number of non-zero values in the array along a given axis. Otherwise, the total number of non-zero values in the array is returned. rrr.z#Expected CSR sparse format, got {0}Nintp) minlength)rar/zUnsupported axis: {0}) rrnnzr(dotr*r+astypebincountr#r r)r)rr sample_weightoutr/s r count_nonzerorhXsD, rzz  U  =DDQXNNOOO  |  5L6"'!(++];; ; gah  ::f%% %]""   ;qyAGAJ??? ?i rwqx/@/@AAG;qyAGAJPPP P077==>>>rc>t||z}|s tjStj|dk}t |d\}}||rt ||||St |dz |||t ||||zdz S)zCompute the median of data with n_zeros additional zeros. This function is used to support sparse matrices; it modifies data in-place. rrrg@)lenr(nanrhdivmodsort_get_elem_at_rank)r!n_zerosn_elems n_negativemiddleis_odds r _get_medianrts $ii'!G v !$(++JGQ''NFFIIKKK D z7CCC &1*dJ@@ FD*g > > ?   rcJ||kr||S||z |krdS|||z S)z@Find the value in data augmented with n_zeros for the given rankr)rankr!rqros rrnrns; jDz j7""q w rctj|r |jdkstd|jz|j}|j\}}t j|}tt|dd|ddD]F\}\}}t j |j ||}||j z } t|| ||<G|S)aCFind the median across axis 0 of a CSC matrix. It is equivalent to doing np.median(X, axis=0). Parameters ---------- X : sparse matrix of shape (n_samples, n_features) Input data. It should be of CSC format. Returns ------- median : ndarray of shape (n_features,) Median. r1z%Expected matrix of CSC format, got %sNr^r)r rrrr+r r(zeros enumeratezipcopyr!r;rt) rr+ n_samples n_featuresmedianf_indstartendr!nzs rcsc_median_axis_0rs KNNLqx500?!(JKKK XFGIz Xj ! !F(VCRC[&*)E)EFF..|swqveCi())  "#D"--u MrcdddfjtfdfdfdfdjjS)aACreate an implicitly offset linear operator. This is used by PCA on sparse data to avoid densifying the whole data matrix. Params ------ X : sparse matrix of shape (n_samples, n_features) offset : ndarray of shape (n_features,) Returns ------- centered : LinearOperator Nc|z|zz SNrvxroffsets rz)_implicit_column_offset..Q!+rc|z|zz Srrvrs rrz)_implicit_column_offset..rrc>|z|zz Sr)sumrXTrs rrz)_implicit_column_offset..s"q&FQUUWW$45rc`|zj|ddddfzz S)Nrr)r4rrs rrz)_implicit_column_offset..s0"q&68aeeemmD!!!G.D#DDr)matvecmatmatrmatvecrmatmatr7r )r4rr7r )rrrs``@r_implicit_column_offsetrs|D!!!G_F B ++++++++++55555DDDDDgg    r)NF)F)NN)&__doc__numpyr( scipy.sparsesparser scipy.sparse.linalgr utils.fixesrrutils.validationrsparsefuncs_fastr r3r r2r r=rrr&r,r5r>r@rBrKrTrWrYr\rhrtrnrrrvrrrs HH ......>>>>>>>>333333    &1&1&1R222$KKKK\NRpppppf***Z)))X:<<<~***Z...b!!!!H3?3?3?3?l   ,   >r