*YHhl ^dZddlZddlmZmZddlmZmZddlZ ddl m Z m Z ddl mZmZmZmZmZmZddlmZdd lmZmZdd lmZmZdd lmZdd lmZm Z m!Z!gd Z"dZ# ddZ$dZ%ddZ&dZ'GddeeeeeeZ(Gdde(Z)Gdde(Z*Gdde(Z+GddeeeZ,y) zG The :mod:`sklearn.pls` module implements Partial Least Squares (PLS). N)ABCMetaabstractmethod)IntegralReal)pinvsvd) BaseEstimatorClassNamePrefixFeaturesOutMixinMultiOutputMixinRegressorMixinTransformerMixin _fit_context)ConvergenceWarning) check_arraycheck_consistent_length)Interval StrOptions)svd_flip) FLOAT_DTYPEScheck_is_fitted validate_data)PLSSVD PLSCanonical PLSRegressionc t|dd\}}}|jjj}ddd}t j |||zt j |jz}t j||kD}|ddd|f}||d|z}t jt jt j||d|S)NF) full_matrices check_finiteg@@g.A)fd) rdtypecharlowernpmaxfinfoepssum transpose conjugatedot)ausvhtfactorcondranks Y/var/www/html/planif/env/lib/python3.12/site-packages/sklearn/cross_decomposition/_pls.py _pinv2_oldr5 s 1E>HAq"  AS !F 66!9vay 288A;?? 2D 66!d( D !UdU( A5DMA << RVVAr%4y%9: ;;ctj|jj t fd|j D}d}|dk(rt|t|} } t|D]} |dk(rtj |} n7tj|j |tj||z } | tjtj| | zz} tj|| } |dk(rtj | }nAtj|j | tj| j | z }|r/|tjtj||zz}tj||tj||zz }| |z }tj|||ks|jddk(rn| } dz}||k(rtjdt |fS#t $r}t d|d}~wwxYw)a?Return the first left and right singular vectors of X'y. Provides an alternative to the svd(X'y) and uses the power method instead. With norm_y_weights to True and in mode A, this corresponds to the algorithm section 11.3 of the Wegelin's review, except this starts at the "update saliences" part. c3zK|]2}tjtj|kDs/|4ywN)r$anyabs).0colr's r4 z;_get_first_singular_vectors_power_method..?s)GsRVVBFF3K#4E-FsGs0;;y residual is constantNdBz$Maximum number of iterations reached)r$r&r!r'nextT StopIterationr5ranger+sqrtshapewarningswarnr)Xymodemax_itertolnorm_y_weightsy_scoree x_weights_oldX_pinvy_pinvi x_weightsx_score y_weightsx_weights_diffn_iterr's @r4(_get_first_singular_vectors_power_methodr\2s ((177   C=GaccGGM s{$A 1  8_" 3;vw/IqssG,rvvgw/GGIRWWRVVIy9:S@@ &&I& 3;vw/IqssG,rvvgii/III   9!=>D DI&&I&"&&I*F*LM"]2 66.. 1C 71771:? ! -"0UF  <>PQ i ''U =451<=sH,, I5 IIctj|j|}t|d\}}}|dddf|dddffS)zbReturn the first left and right singular vectors of X'y. Here the whole SVD is computed. FrNr)r$r+rDr)rKrLCU_Vts r4_get_first_singular_vectors_svdrcmsD qssAA1E*HAq" QT7Bq!tH r6c||jd}||z}|jd}||z}|rA|jdd}d||dk(<||z}|jdd}d||dk(<||z}nDtj|jd}tj|jd}||||||fS)z{Center X, y and scale if the scale parameter==True Returns ------- X, y, x_mean, y_mean, x_std, y_std raxisrB)rfddofg?)meanstdr$onesrH)rKrLscalex_meany_meanx_stdy_stds r4_center_scale_xyrqwsVVV^FKA VVV^FKA 11%!esl U 11%!esl U  # # a --r6ctjtj|}tj||}||z}||z}y)z7Same as svd_flip but works on 1d arrays, and is inplaceN)r$argmaxr;sign)r-vbiggest_abs_val_idxrts r4 _svd_flip_1drwsA))BFF1I. 771() *DIAIAr6c $eZdZUdZeedddgdgeddhged d hged d hgeedddgeed ddgdgdZe e d<e dddd d dddddZ e ddZddZddZddZddZfdZxZS) _PLSaPartial Least Squares (PLS) This class implements the generic PLS algorithm. Main ref: Wegelin, a survey of Partial Least Squares (PLS) methods, with emphasis on the two-block case https://stat.uw.edu/sites/default/files/files/reports/2000/tr371.pdf rBNleftclosedboolean regression canonicalArArnipalsr n_componentsrldeflation_moderM algorithmrNrOcopy_parameter_constraintsTư>)rlrrMrrNrOrct||_||_||_||_||_||_||_||_yr9)rrrMrlrrNrOr) selfrrlrrMrrNrOrs r4__init__z _PLS.__init__s>),  "   r6prefer_skip_nested_validationc  t||t||tjd|jd}t |dtjd|jd}|j dk(rd|_|jdd}nd|_|jd }|jd}|jd}|j}|jd k(r t||n t|||}||kDrtd |d |d |jdk(|_|j}t|||j \} } |_|_|_|_tj*||f|_tj*||f|_tj*||f|_tj*||f|_tj*||f|_tj*||f|_g|_tj:| j<j>} tA|D]q} |jBdk(rtjDtjF| d| zkd } d| dd| f< tI| | |jJ|jL|jN|\}}}|j8jY|n|jBdk(rt[| | \}}t]tj^| |}|rd}ntj^||}tj^| ||z }tj^|| tj^||z }| tj`||z} |jdk(rFtj^|| tj^||z }| tj`||z} |jd k(rFtj^|| tj^||z }| tj`||z} ||j,dd| f<||j.dd| f<||j0dd| f<||j2dd| f<||j4dd| f<|j6dd| f<ttj^|j,tctj^|j4jd|j,d|_3tj^|j.tctj^|j6jd|j.d|_4tj^|jf|j6jd|_5|jj|j(zjd|j&z |_5|j$|_6|jfjd|_7|S#tP$r3}tS|dk7rtUjVd| Yd}~d}~wwxYw)Fit model to data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. y : array-like of shape (n_samples,) or (n_samples, n_targets) Target vectors, where `n_samples` is the number of samples and `n_targets` is the number of response variables. Returns ------- self : object Fitted model. Tr r!force_writeablerensure_min_samplesrLF input_namer!rr ensure_2drBrr~`n_components` upper bound is . Got instead. Reduce `n_components`.rr rerhN)rMrNrOrPr?z$y residual is constant at iteration r)r)8rrr$float64rrndim _predict_1dreshaperHrrmin ValueError_norm_y_weightsrqrl_x_mean_y_mean_x_std_y_stdzeros x_weights_ y_weights_ _x_scores _y_scores x_loadings_ y_loadings_n_iter_r&r!r'rFrallr;r\rMrNrOrEstrrIrJappendrcrwr+outerrrD x_rotations_ y_rotations_coef_ intercept__n_features_out)rrKrLnpqrrank_upper_boundrPXkyky_epskyk_maskrWrYrrRx_scoresy_ssy_scores x_loadings y_loadingss r4fitz_PLS.fitsf& 1%   **     **    66Q;#D  "a A$D  GGAJ GGAJ GGAJ(( ,, ))At'7'7'9'9:4;;&4<<' =A#\BA ii4+;+;+=+=>O t{{ *O t|| +O"O3 3r6ct|t|||td}||jz}||jj z|j z}|jr|jS|S)aLPredict targets of given samples. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples. copy : bool, default=True Whether to copy `X` or perform in-place normalization. Returns ------- y_pred : ndarray of shape (n_samples,) or (n_samples, n_targets) Returns predicted values. Notes ----- This call requires the estimation of a matrix of shape `(n_features, n_targets)`, which may be an issue in high dimensional space. Fr) rrrrrrDrrravel)rrKry_preds r4predictz _PLS.predicts`,  $L N T\\TZZ\\!DOO3!%!1!1v||~=v=r6cF|j||j||S)aLearn and apply the dimension reduction on the train data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. y : array-like of shape (n_samples, n_targets), default=None Target vectors, where `n_samples` is the number of samples and `n_targets` is the number of response variables. Returns ------- self : ndarray of shape (n_samples, n_components) Return `x_scores` if `y` is not given, `(x_scores, y_scores)` otherwise. rrrrKrLs r4 fit_transformz_PLS.fit_transform!$xx1~''1--r6cht|}d|j_d|j_|S)NTF)super__sklearn_tags__regressor_tags poor_score target_tagsrequired)rtags __class__s r4rz_PLS.__sklearn_tags__s1w'))-&$)! r6r )NTr9T)__name__ __module__ __qualname____doc__rrrrrdict__annotations__rrrrrrrrr __classcell__rs@r4ryrys"(AtFCD%|[&ABCS#J'( %!234h4?@q$v67  $D #   *5`6`D%N*X>:.(r6ry) metaclassceZdZUdZiej Zeed<dD]Zeje d dddddfd Z fd Z xZ S) ra PLS regression. PLSRegression is also known as PLS2 or PLS1, depending on the number of targets. For a comparison between other cross decomposition algorithms, see :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, n_features]`. scale : bool, default=True Whether to scale `X` and `y`. max_iter : int, default=500 The maximum number of iterations of the power method when `algorithm='nipals'`. Ignored otherwise. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `y` in :term:`fit` before applying centering, and potentially scaling. If `False`, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `y`. x_scores_ : ndarray of shape (n_samples, n_components) The transformed training samples. y_scores_ : ndarray of shape (n_samples, n_components) The transformed training targets. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_targets, n_components) The projection matrix used to transform `y`. coef_ : ndarray of shape (n_target, n_features) The coefficients of the linear model such that `y` is approximated as `y = X @ coef_.T + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `y` is approximated as `y = X @ coef_.T + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. n_features_in_ : int Number of features seen during :term:`fit`. 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 -------- PLSCanonical : Partial Least Squares transformer and regressor. Examples -------- >>> from sklearn.cross_decomposition import PLSRegression >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> pls2 = PLSRegression(n_components=2) >>> pls2.fit(X, y) PLSRegression() >>> y_pred = pls2.predict(X) For a comparison between PLS Regression and :class:`~sklearn.decomposition.PCA`, see :ref:`sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py`. rrrMrTrrrlrNrOrc 4t|||ddd|||y)Nr~rrrrrrrrlrNrOrrs r4rzPLSRegression.__init__ms/ %'  r6clt||||j|_|j|_|S)r)rrr x_scores_r y_scores_)rrKrLrs r4rzPLSRegression.fit{s.$  Aq r6r) rrrrryrrrparampoprrrrs@r4rrsbeN$Cd&A&A#BDB8*""5)*  '+cu4  r6rceZdZUdZiej Zeed<dD]Zeje d ddddddfd Z xZ S) ra^ Partial Least Squares transformer and regressor. For a comparison between other cross decomposition algorithms, see :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `y`. algorithm : {'nipals', 'svd'}, default='nipals' The algorithm used to estimate the first singular vectors of the cross-covariance matrix. 'nipals' uses the power method while 'svd' will compute the whole SVD. max_iter : int, default=500 The maximum number of iterations of the power method when `algorithm='nipals'`. Ignored otherwise. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `y` in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `y`. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_targets, n_components) The projection matrix used to transform `y`. coef_ : ndarray of shape (n_targets, n_features) The coefficients of the linear model such that `y` is approximated as `y = X @ coef_.T + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `y` is approximated as `y = X @ coef_.T + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. Empty if `algorithm='svd'`. n_features_in_ : int Number of features seen during :term:`fit`. 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 -------- CCA : Canonical Correlation Analysis. PLSSVD : Partial Least Square SVD. Examples -------- >>> from sklearn.cross_decomposition import PLSCanonical >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]] >>> y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> plsca = PLSCanonical(n_components=2) >>> plsca.fit(X, y) PLSCanonical() >>> X_c, y_c = plsca.transform(X, y) r)rrMTrrr)rlrrNrOrc 4t|||dd||||y)Nrrrr)rrrlrrNrOrrs r4rzPLSCanonical.__init__s/ %&  r6r rrrrryrrrrrrrrs@r4rrse`D$Cd&A&A#BDB+*""5)*     r6rceZdZUdZiej Zeed<dD]Zeje d dddddfd Z xZ S) CCAa Canonical Correlation Analysis, also known as "Mode B" PLS. For a comparison between other cross decomposition algorithms, see :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`. Read more in the :ref:`User Guide `. Parameters ---------- n_components : int, default=2 Number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `y`. max_iter : int, default=500 The maximum number of iterations of the power method. tol : float, default=1e-06 The tolerance used as convergence criteria in the power method: the algorithm stops whenever the squared norm of `u_i - u_{i-1}` is less than `tol`, where `u` corresponds to the left singular vector. copy : bool, default=True Whether to copy `X` and `y` in fit before applying centering, and potentially scaling. If False, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the cross-covariance matrices of each iteration. y_weights_ : ndarray of shape (n_targets, n_components) The right singular vectors of the cross-covariance matrices of each iteration. x_loadings_ : ndarray of shape (n_features, n_components) The loadings of `X`. y_loadings_ : ndarray of shape (n_targets, n_components) The loadings of `y`. x_rotations_ : ndarray of shape (n_features, n_components) The projection matrix used to transform `X`. y_rotations_ : ndarray of shape (n_targets, n_components) The projection matrix used to transform `y`. coef_ : ndarray of shape (n_targets, n_features) The coefficients of the linear model such that `y` is approximated as `y = X @ coef_.T + intercept_`. intercept_ : ndarray of shape (n_targets,) The intercepts of the linear model such that `y` is approximated as `y = X @ coef_.T + intercept_`. .. versionadded:: 1.1 n_iter_ : list of shape (n_components,) Number of iterations of the power method, for each component. n_features_in_ : int Number of features seen during :term:`fit`. 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 -------- PLSCanonical : Partial Least Squares transformer and regressor. PLSSVD : Partial Least Square SVD. Examples -------- >>> from sklearn.cross_decomposition import CCA >>> X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]] >>> y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]] >>> cca = CCA(n_components=1) >>> cca.fit(X, y) CCA(n_components=1) >>> X_c, y_c = cca.transform(X, y) rrTrrrc 4t|||ddd|||y)NrrArrrrs r4rz CCA.__init__xs/ %&  r6rrrs@r4rrs]Xt$Cd&A&A#BDB8*""5)*  '+cu4   r6rceZdZUdZeedddgdgdgdZeed<dd d d d Z e d d Z ddZ ddZ y)raPartial Least Square SVD. This transformer simply performs a SVD on the cross-covariance matrix `X'y`. It is able to project both the training data `X` and the targets `y`. The training data `X` is projected on the left singular vectors, while the targets are projected on the right singular vectors. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- n_components : int, default=2 The number of components to keep. Should be in `[1, min(n_samples, n_features, n_targets)]`. scale : bool, default=True Whether to scale `X` and `y`. copy : bool, default=True Whether to copy `X` and `y` in fit before applying centering, and potentially scaling. If `False`, these operations will be done inplace, modifying both arrays. Attributes ---------- x_weights_ : ndarray of shape (n_features, n_components) The left singular vectors of the SVD of the cross-covariance matrix. Used to project `X` in :meth:`transform`. y_weights_ : ndarray of (n_targets, n_components) The right singular vectors of the SVD of the cross-covariance matrix. Used to project `X` in :meth:`transform`. n_features_in_ : int Number of features seen during :term:`fit`. 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 -------- PLSCanonical : Partial Least Squares transformer and regressor. CCA : Canonical Correlation Analysis. Examples -------- >>> import numpy as np >>> from sklearn.cross_decomposition import PLSSVD >>> X = np.array([[0., 0., 1.], ... [1., 0., 0.], ... [2., 2., 2.], ... [2., 5., 4.]]) >>> y = np.array([[0.1, -0.2], ... [0.9, 1.1], ... [6.2, 5.9], ... [11.9, 12.3]]) >>> pls = PLSSVD(n_components=2).fit(X, y) >>> X_c, y_c = pls.transform(X, y) >>> X_c.shape, y_c.shape ((4, 2), (4, 2)) rBNrzr{r}rrlrrT)rlrc.||_||_||_yr9r)rrrlrs r4rzPLSSVD.__init__s(  r6rc&t||t||tjd|jd}t |dtjd|jd}|j dk(r|jdd}|j}t|jd |jd|jd}||kDrtd |d |d t|||j\}}|_|_|_|_tj$|j&|}t)|d \}}}|ddd|f}|d|}t+||\}}|j&} ||_| |_|j,jd|_|S)aJFit model to data. Parameters ---------- X : array-like of shape (n_samples, n_features) Training samples. y : array-like of shape (n_samples,) or (n_samples, n_targets) Targets. Returns ------- self : object Fitted estimator. Tr rrLFrrBrrrrrr^N)rrr$rrrrrrrrHrrqrlrrrrr+rDrrrrr) rrKrLrrr_r`r.rbVs r4rz PLSSVD.fits" 1%   **     **    66Q; "a A (( qwwqz1771:qwwqzB * *01A0BC#n$DF  FV q$**F B1dlDL$+t{ FF133Nq.1b a,    B2 DD#44Q7 r6ct|t||tjd}||jz |j z }tj ||j}|~t|ddtj}|jdk(r|jdd}||jz |jz }tj ||j}||fS|S)a  Apply the dimensionality reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to be transformed. y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Targets. Returns ------- x_scores : array-like or tuple of array-like The transformed data `X_transformed` if `y is not None`, `(X_transformed, y_transformed)` otherwise. F)r!rrL)rrr!rBr)rrr$rrrr+rrrrrrr)rrKrLXrryrrs r4rzPLSSVD.transforms&  $5 A$,,$++ -66"doo. =A#bjjQAvv{IIb!$dll"dkk1Bvvb$//2HX% %r6cF|j||j||S)aLearn and apply the dimensionality reduction. Parameters ---------- X : array-like of shape (n_samples, n_features) Training samples. y : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Targets. Returns ------- out : array-like or tuple of array-like The transformed data `X_transformed` if `y is not None`, `(X_transformed, y_transformed)` otherwise. rrs r4rzPLSSVD.fit_transform7rr6rr9)rrrrrrrrrrrrrrr6r4rrshAH"(AtFCD $D 4 5>6>@@.r6r)rrrFr)-rrIabcrrnumbersrrnumpyr$ scipy.linalgrrbaser r r r rr exceptionsrutilsrrutils._param_validationrr utils.extmathrutils.validationrrr__all__r5r\rcrqrwryrrrrrr6r4rs'"",8:$KK 5<&=B8(v.4]# ]@ VDVrB 4B Jk $k \B. ,.> B.r6