U 9%eTy@sdZddlZddlZddlmZmZddlmZddlZddl m Z ddl m Z mZm Z ddlmZddlmZdd lmZmZmZmZmZdd lmZdd lmZmZdd lmZdd l m!Z!m"Z"m#Z#m$Z$ddl%m&Z&m'Z'ddl(m)Z)m*Z*ddl+m,Z,m-Z-ddl.m/Z/m0Z0m1Z1dZ2ddZ3d+ddZ4d,ddZ5d-ddZ6GdddeedZ7Gd d!d!eZ8Gd"d#d#Z9Gd$d%d%eee7Z:d.d'd(Z;d/d)d*Zd }|dkr`|r`td ||tn|sptd t|S) aBNormalize is to be deprecated from linear models and a use of a pipeline with a StandardScaler is to be recommended instead. Here the appropriate message is selected to be displayed to the user depending on the default normalize value (as it varies between the linear models and normalize value selected by the user). Parameters ---------- normalize : bool, normalize value passed by the user estimator_name : str name of the linear estimator which calls this function. The name will be used for writing the deprecation warnings Returns ------- normalize : bool, normalize value which should further be used by the estimator at this stage of the depreciation process Notes ----- This function should be completely removed in 1.4. )TF deprecatedzALeave 'normalize' to its default value or set it to True or Falser!FzIf you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior: from sklearn.pipeline import make_pipeline model = make_pipeline(StandardScaler(with_mean=False), z()) If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows: kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps} model.fit(X, y, **kwargs) Z LassoLarsz=Set parameter alpha to: original_alpha * np.sqrt(n_samples). zF'normalize' was deprecated in version 1.2 and will be removed in 1.4. a3'normalize' was deprecated in version 1.2 and will be removed in 1.4. Please leave the normalize parameter to its default value to silence this warning. The default behavior of this estimator is to not do any normalization. If normalization is needed please use sklearn.preprocessing.StandardScaler instead.) ValueErrorwarningswarn FutureWarning) normalizeZestimator_name _normalizeZ pipeline_msgZ alpha_msgr)Y/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/linear_model/_base.py_deprecate_normalize;s6    r+c Cst|}|dttjj}|jtjkr4t}t }nt }t }t |rf||j|j|j|||d}t} nt|}|||||d}d} || fS)aDCreate ``Dataset`` abstraction for sparse and dense inputs. This also returns the ``intercept_decay`` which is different for sparse datasets. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data y : array-like, shape (n_samples, ) Target values. sample_weight : numpy array of shape (n_samples,) The weight of each sample random_state : int, RandomState instance or None (default) Determines random number generation for dataset random sampling. It is not used for dataset shuffling. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Returns ------- dataset The ``Dataset`` abstraction intercept_decay The intercept decay )seed?)rrandintnpZiinfoZint32maxdtypefloat32rrrrspissparsedataZindptrindicesSPARSE_INTERCEPT_DECAYZascontiguousarray) Xy sample_weightZ random_staterngr-ZCSRDataZ ArrayDataZdatasetZintercept_decayr)r)r* make_datasets   r=FTcCst|tjrd}|dk r"t|}|rNt||ddgtd}t||j|dd}n4|j|j|d}|rt |rv| }n |j dd }|rt |rt |d |d \}} nF|rt |d d d |d \}} } ntj|d |d }|j|jdd}||8}|rv| j|jdd} t| ||jd } |dkr.| |jd 9} n | |9} tj| | d} d| | <t |rlt|d| n|| }ntj|jd|jd} tj|d |d } || 8}n\tj|jd|jd}tj|jd|jd} |jdkr|jd } ntj|jd|jd} |||| | fS)a9Center and scale data. Centers data to have mean zero along axis 0. If fit_intercept=False or if the X is a sparse matrix, no centering is done, but normalization can still be applied. The function returns the statistics necessary to reconstruct the input data, which are X_offset, y_offset, X_scale, such that the output X = (X - X_offset) / X_scale X_scale is the L2 norm of X - X_offset. If sample_weight is not None, then the weighted mean of X and y is zero, and not the mean itself. If fit_intercept=True, the mean, eventually weighted, is returned, independently of whether X was centered (option used for optimization with sparse data in coordinate_descend). This is here because nearly all linear models will want their data to be centered. This function also systematically makes y consistent with X.dtype Returns ------- X_out : {ndarray, sparse matrix} of shape (n_samples, n_features) If copy=True a copy of the input X is triggered, otherwise operations are inplace. If input X is dense, then X_out is centered. If normalize is True, then X_out is rescaled (dense and sparse case) y_out : {ndarray, sparse matrix} of shape (n_samples,) or (n_samples, n_targets) Centered version of y. Likely performed inplace on input y. X_offset : ndarray of shape (n_features,) The mean per column of input X. y_offset : float or ndarray of shape (n_features,) X_scale : ndarray of shape (n_features,) The standard deviation per column of input X. Ncsrcsc)copy accept_sparser2F)r2r@Z ensure_2d)r@K)orderr)axisweights)Z last_meanZ last_varianceZlast_sample_countr;outr.r,r2) isinstancenumbersNumberr0Zasarrayrrr2astyper4r5r@rrZaveragershapesumsqrtroneszerosndimtype)r9r: fit_interceptr'r@Zcopy_yr; check_inputX_offsetZX_var_Z constant_maskX_scaley_offsetr)r)r*_preprocess_datas\+            r[cCs|jd}t|}t|s(t|r>tj|df||fd}t|rTt||}n2|rp||ddtjf9}n||ddtjf}t|rt||}nZ|r|j dkr||9}q||ddtjf9}n*|j dkr||}n||ddtjf}|||fS)aRescale data sample-wise by square root of sample_weight. For many linear models, this enables easy support for sample_weight because (y - X w)' S (y - X w) with S = diag(sample_weight) becomes ||y_rescaled - X_rescaled w||_2^2 when setting y_rescaled = sqrt(S) y X_rescaled = sqrt(S) X Returns ------- X_rescaled : {array-like, sparse matrix} y_rescaled : {array-like, sparse matrix} r)rNNr,) rNr0rPr4r5rZ dia_matrixrZnewaxisrS)r9r:r;inplace n_samplessample_weight_sqrtZ sw_matrixr)r)r* _rescale_data/s,          r_c@s<eZdZdZeddZddZddZdd Zd d Z d S) LinearModelzBase class for Linear ModelscCsdS)z Fit model.Nr))selfr9r:r)r)r*fitjszLinearModel.fitcCs6t||j|dddgdd}t||jjdd|jS)Nr>r?cooFrAresetTZ dense_output)r _validate_datarcoef_T intercept_rar9r)r)r*_decision_functionnszLinearModel._decision_functioncCs ||S)a! Predict using the linear model. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Samples. Returns ------- C : array, shape (n_samples,) Returns predicted values. )rlrkr)r)r*predicttszLinearModel.predictcCs>|jr4tj|j||jd|_|t||jj|_nd|_dS)zSet the intercept_rIrFN)rUr0dividerhr2dotrirj)rarWrZrYr)r)r*_set_interceptszLinearModel._set_interceptcCsddiS)NZ requires_yTr))rar)r)r* _more_tagsszLinearModel._more_tagsN) __name__ __module__ __qualname____doc__rrbrlrmrprqr)r)r)r*r`gs  r`) metaclassc@s(eZdZdZddZddZddZdS) LinearClassifierMixinzRMixin for linear classifiers. Handles prediction for sparse and dense X. cCsZt|t|\}}|j|ddd}t||jjdd|j}|jddkrV||dS|S)a Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data matrix for which we want to get the confidence scores. Returns ------- scores : ndarray of shape (n_samples,) or (n_samples, n_classes) Confidence scores per `(n_samples, n_classes)` combination. In the binary case, confidence score for `self.classes_[1]` where >0 means this class would be predicted. r>FrdTrfr,)) r rrgrrhrirjrNreshape)rar9xprXscoresr)r)r*decision_functions  z'LinearClassifierMixin.decision_functioncCsRt|\}}||}t|jdkr6||dkt}n|j|dd}||j|S)a~ Predict class labels for samples in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data matrix for which we want to get the predictions. Returns ------- y_pred : ndarray of shape (n_samples,) Vector containing the class labels for each sample. r,rrD) rr|lenrNrMintZargmaxZtakeZclasses_)rar9rzrXr{r7r)r)r*rms   zLinearClassifierMixin.predictcCs\||}t||d|jdkr4td||gjS||jdd|jddf}|SdS)zProbability estimation for OvR logistic regression. Positive class probabilities are computed as 1. / (1. + np.exp(-self.decision_function(X))); multiclass is handled by normalizing that over all classes. rGr,r}rrxN) r|r rSr0vstackrirOryrN)rar9Zprobr)r)r*_predict_proba_lrs     z'LinearClassifierMixin._predict_proba_lrN)rrrsrtrur|rmrr)r)r)r*rwsrwc@s eZdZdZddZddZdS)SparseCoefMixinzlMixin for converting coef_ to and from CSR format. L1-regularizing estimators should inherit this. cCs,d}t||dt|jr(|j|_|S)a Convert coefficient matrix to dense array format. Converts the ``coef_`` member (back) to a numpy.ndarray. This is the default format of ``coef_`` and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op. Returns ------- self Fitted estimator. z6Estimator, %(name)s, must be fitted before densifying.msg)r r4r5rhZtoarrayrarr)r)r*densifys    zSparseCoefMixin.densifycCs"d}t||dt|j|_|S)a Convert coefficient matrix to sparse format. Converts the ``coef_`` member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation. The ``intercept_`` member is not converted. Returns ------- self Fitted estimator. Notes ----- For non-sparse models, i.e. when there are not many zeros in ``coef_``, this may actually *increase* memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with ``(coef_ == 0).sum()``, must be more than 50% for this to provide significant benefits. After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify. z7Estimator, %(name)s, must be fitted before sparsifying.r)r r4Z csr_matrixrhrr)r)r*sparsifys zSparseCoefMixin.sparsifyN)rrrsrtrurrr)r)r)r*rsrc@sZeZdZUdZdgdgdegdgdZeed<ddddddd Ze dd d d d Z dS)LinearRegressiona^ Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation. Parameters ---------- fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). copy_X : bool, default=True If True, X will be copied; else, it may be overwritten. n_jobs : int, default=None The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly `n_targets > 1` and secondly `X` is sparse or if `positive` is set to `True`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. positive : bool, default=False When set to ``True``, forces the coefficients to be positive. This option is only supported for dense arrays. .. versionadded:: 0.24 Attributes ---------- coef_ : array of shape (n_features, ) or (n_targets, n_features) Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features. rank_ : int Rank of matrix `X`. Only available when `X` is dense. singular_ : array of shape (min(X, y),) Singular values of `X`. Only available when `X` is dense. intercept_ : float or array of shape (n_targets,) Independent term in the linear model. Set to 0.0 if `fit_intercept = False`. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- Ridge : Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization. Lasso : The Lasso is a linear model that estimates sparse coefficients with l1 regularization. ElasticNet : Elastic-Net is a linear regression model trained with both l1 and l2 -norm regularization of the coefficients. Notes ----- From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares (scipy.optimize.nnls) wrapped as a predictor object. Examples -------- >>> import numpy as np >>> from sklearn.linear_model import LinearRegression >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) >>> # y = 1 * x_0 + 2 * x_1 + 3 >>> y = np.dot(X, np.array([1, 2])) + 3 >>> reg = LinearRegression().fit(X, y) >>> reg.score(X, y) 1.0 >>> reg.coef_ array([1., 2.]) >>> reg.intercept_ 3.0... >>> reg.predict(np.array([[3, 5]])) array([16.]) booleanNrUcopy_Xn_jobspositive_parameter_constraintsTFcCs||_||_||_||_dSNr)rarUrrrr)r)r*__init__}szLinearRegression.__init__)Zprefer_skip_nested_validationcs|j}|jrdndddg}|j|ddd\|dk }|rPt|jdd}|jo`t }t|j ||d \}} } |rt ||d \|jrj d krt d |_n>t|d fddtjdD} tdd| D|_ntr|| |r:fdd} fdd} nfdd} fdd} tjjj| | dj d krtd |_n>t|d fddtjdD} tdd| D|_n$t\|_}|_|_|jj|_j dkrt|j|_||| | |S)ap Fit linear model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data. y : array-like of shape (n_samples,) or (n_samples, n_targets) Target values. Will be cast to X's dtype if necessary. sample_weight : array-like of shape (n_samples,), default=None Individual weights for each sample. .. versionadded:: 0.17 parameter *sample_weight* support to LinearRegression. Returns ------- self : object Fitted Estimator. Fr>r?rcT)rAZ y_numericZ multi_outputN)r2Zonly_non_negative)rUr@r;)r\r r)rc3s*|]"}ttjdd|fVqdSr)rrnnls.0j)r9r:r)r* sz'LinearRegression.fit..r,cSsg|] }|dqSrr)rrHr)r)r* sz(LinearRegression.fit..cs||Srrobr9X_offset_scaler^r)r*matvecsz$LinearRegression.fit..matveccsj||Sr)rirorrr)r*rmatvecsz%LinearRegression.fit..rmatveccs||Srrrr9rr)r*rscsj||Sr)rirorOrrr)r*rs)rNrrc3s,|]$}ttdd|fVqdSr)rrravelr) X_centeredr:r)r*rscSsg|] }|dqSrr)rr)r)r*rs)rrrgrr2rr4r5r[rUr_rSrrrhrrangerNr0rrrZLinearOperatorrZlstsqZrank_Z singular_rirrp)rar9r:r;Zn_jobs_rAZhas_swZcopy_X_in_preprocess_datarWrZrYZoutsrrrXr))r9rrr^r:r*rbs~         zLinearRegression.fit)N) rrrsrtrurrdict__annotations__rrrbr)r)r)r*rs `  rh㈵>c Cs|jd}|d}t|d|d}|dd|f||||} |dd|f||||} t| | } |||f} |j| jg} |dkrdd| D}t|}tj| | ||dstd|d|d | d | d dS) a^Computes a single element of the gram matrix and compares it to the corresponding element of the user supplied gram matrix. If the values do not match a ValueError will be thrown. Parameters ---------- X : ndarray of shape (n_samples, n_features) Data array. precompute : array-like of shape (n_features, n_features) User-supplied gram matrix. X_offset : ndarray of shape (n_features,) Array of feature means used to center design matrix. X_scale : ndarray of shape (n_features,) Array of feature scale factors used to normalize design matrix. rtol : float, default=None Relative tolerance; see numpy.allclose If None, it is set to 1e-4 for arrays of dtype numpy.float32 and 1e-7 otherwise. atol : float, default=1e-5 absolute tolerance; see :func`numpy.allclose`. Note that the default here is more tolerant than the default for :func:`numpy.testing.assert_allclose`, where `atol=0`. Raises ------ ValueError Raised when the provided Gram matrix is not consistent. r,r NcSsg|]}|tjkrdndqS)g-C6?gHz>)r0r3)rr2r)r)r*r*sz2_check_precomputed_gram_matrix..)rtolatolzGram matrix passed in via 'precompute' parameter did not pass validation when a single element was checked - please check that it was computed properly. For element (,z) we computed z! but the user-supplied value was .)rNminr0ror2r1iscloser#)r9 precomputerWrYrr n_featuresf1f2Zv1Zv2expectedactualZdtypesZrtolsr)r)r*_check_precomputed_gram_matrixs&      rc  Cs|j\} } t|r:d}t||||d||d\}}} } } nsN      M 9 r 8-H:d H