U _{jK@sddlZddlmZddlmZddlmZ ddlm Z ddlm Z dd Zd d Zd d ZddZd6ddZddddZddZddZddZddZddZd d!Zd"d#Zd$d%Zd&d'Zd(d)Zd*d+Zd7d,d-Zd8d.d/Z d0d1Z!d2d3Z"d4d5Z#dS)9N)_check_sample_weight)csc_mean_variance_axis0)csr_mean_variance_axis0)incr_mean_variance_axis0cCs,t|r|jnt|}d|}t|dS)z2Raises a TypeError if X is not a CSR or CSC matrixz,Expected a CSR or CSC sparse matrix, got %s.N)spissparseformattype TypeError)XZ input_typeerrrX/var/www/html/bwave/python/venv/lib/python3.8/site-packages/sklearn/utils/sparsefuncs.py_raise_typeerrorsrcCs|dkrtd|dS)N)rrz8Unknown axis value: %d. Use 0 for rows, or 1 for columns) ValueErroraxisrrr_raise_error_wrong_axissrcCs6|jd|jdkst|j|j|jdd9_dS)a Inplace 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. rrZclip)modeN)shapeAssertionErrordataZtakeindicesr scalerrrinplace_csr_column_scale#srcCs:|jd|jdkst|jt|t|j9_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)rrrnprepeatdiffindptrrrrrinplace_csr_row_scale6sr"FcCst|t|rD|jdkrD|dkr2t|||dSt|j||dSnDt|r|jdkr|dkrnt|||dSt|j||dSnt|dS)a4Compute 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`. csrr)weightsreturn_sum_weightscscN)rrr r _csr_mean_var_axis0_csc_mean_var_axis0Tr)r rr$r%rrrmean_variance_axisHs4$r*)r$cCs*t|t|r|jdks$t|t|dkrFtj|j||j d}t|t|krlt|ksvnt d|dkrt||jdkrt d|jddt|dn6t||jdkrt d |jddt|d|dkr|j n|}|d k rt |||j d}t |||||d S) a7 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. )r&r#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$)rrr r rrsizefullrr+rr)r_incr_mean_var_axis0)r rr,r-r.r$rrrincr_mean_variance_axiss4<( r2cCsNt|r"|jdkr"t|j|n(t|rB|jdkrBt||nt|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. r&r#N)rr r r"r)rrrrrrinplace_column_scales  r3cCsNt|r"|jdkr"t|j|n(t|rB|jdkrBt||nt|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. r&r#N)rr r rr)r"rrrrrinplace_row_scales  r4cCsv||fD]}t|tjrtdq|dkr8||jd7}|dkrN||jd7}|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) isinstancerndarrayr rr)r mntZm_maskrrrinplace_swap_row_cscs    r;c Csx||fD]}t|tjrtdq|dkr8||jd7}|dkrN||jd7}||kr`||}}|j}||}||d}||}||d}||} ||} | | kr|j|d|| | 7<|| |j|d<|| |j|<t|jd||j|||j|||j|||j|dg|_t|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. r5rrrN) r6rr7r rr!Z concatenaterr) r r8r9r:r!Zm_startZm_stopZn_startZn_stopZnz_mZnz_nrrrinplace_swap_row_csr,sH                 r<cCsPt|r"|jdkr"t|||n*t|rD|jdkrDt|||nt|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. r&r#N)rr r r;r<rr r8r9rrrinplace_swap_rowks r>cCs||dkr||jd7}|dkr,||jd7}t|rN|jdkrNt|||n*t|rp|jdkrpt|||nt|dS)ag 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. rrr&r#N)rrr r r<r;rr=rrrinplace_swap_columnsr?cCsLtt|j}t||j|j|jf|jd}||j|j|}||fS)N)r) rZ flatnonzeror r!r rrrZreduceat)r Zufunc major_indexvaluerrr _minor_reducesrBc Cs|j|}|dkrtd|jd|}|dkr8|n|}|t||\}}t|j||k}|||d||<|dk} t | |}t | |}|dkrt j |t t ||ff|jd|fd} n(t j ||t t |ff|j|dfd} | jS)Nr&zero-size array to reduction operationr)r+r)rrZtocscZtocsrZsum_duplicatesrBrr r!compressrZ coo_matrixzeroslenr+Aravel) r r min_or_maxNMZmatr@rAnot_fullmaskresrrr_min_or_max_axiss0   rOcCs|dkrdd|jkrtd|jd}|jdkr4|S||j}|jt |jkr`|||}|S|dkrt|d7}|dks|dkrt |||StddS)NrrCrrz.invalid axis, use 0 for rows, or 1 for columns) rrr+r nnzreducerrHrprodrO)r rrIzeror8rrr_sparse_min_or_maxs     rTcCst||tjt||tjfSN)rTrminimummaximumr rrrr_sparse_min_maxs  rYcCst||tjt||tjfSrU)rTrZfminZfmaxrXrrr_sparse_nan_min_maxsrZcCs>t|r2|jdkr2|r$t||dSt||dSnt|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#r&rN)rr r rZrYr)r rZ ignore_nanrrr min_max_axiss  r[cCs|dkrd}n(|dkrd}n|jdkr6td|j|dkrb|dkrL|jStt|j|Sn|dkrt|j}|dkr|dS||S|dkr|dkrtj|j |j dd St |t|j}tj|j |j d|d Snt d |dS) 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}NZintp) minlength)r^r$zUnsupported axis: {0}) r r rPrdotr r!ZastypeZbincountrrrr)r rZ sample_weightoutr$rrr count_nonzeros*   racCspt||}|stjSt|dk}t|d\}}||rLt||||St|d|||t||||dS)zCompute the median of data with n_zeros additional zeros. This function is used to support sparse matrices; it modifies data in-place. rrrg@)rFrnanradivmodsort_get_elem_at_rank)rn_zerosZn_elems n_negativemiddleZis_oddrrr _get_median;s  ricCs,||kr||S|||kr dS|||S)z@Find the value in data augmented with n_zeros for the given rankrr)ZrankrrgrfrrrreQs  rec Cst|r|jdks"td|j|j}|j\}}t|}tt |dd|ddD]8\}\}}t |j ||}||j } t || ||<qZ|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. r&z%Expected matrix of CSC format, got %sNr\r)rr r r r!rrrE enumeratezipcopyrr/ri) r r!Z n_samplesZ n_featuresZmedianZf_indstartendrZnzrrrcsc_median_axis_0Zs  * ro)NF)F)NN)$numpyrZ scipy.sparsesparserZutils.validationrZsparsefuncs_fastrr(rr'rr1rrrr"r*r2r3r4r;r<r>r?rBrOrTrYrZr[rarirerorrrrs6      <^?  $ 6