U f{jH @sdZddlZddlZddlZddlmZddlm Z m Z m Z ddl m Z mZGddde Zdd d Zdd d ZdddZdddZed ddZd!ddZddZdS)"z"Base class copy from sklearn.base.N) parallel_func) BaseEstimator is_classifier_get_check_scoring)warnverbosec@s^eZdZdZdddZddZeddZd d Zd d Z d dZ ddZ ddZ ddZ dS) LinearModelaCompute and store patterns from linear models. The linear model coefficients (filters) are used to extract discriminant neural sources from the measured data. This class computes the corresponding patterns of these linear filters to make them more interpretable :footcite:`HaufeEtAl2014`. Parameters ---------- model : object | None A linear model from scikit-learn with a fit method that updates a ``coef_`` attribute. If None the model will be LogisticRegression. Attributes ---------- filters_ : ndarray, shape ([n_targets], n_features) If fit, the filters used to decompose the data. patterns_ : ndarray, shape ([n_targets], n_features) If fit, the patterns used to restore M/EEG signals. See Also -------- CSP mne.preprocessing.ICA mne.preprocessing.Xdawn Notes ----- .. versionadded:: 0.10 References ---------- .. footbibliography:: NcCs6|dkrddlm}|dd}||_t|dd|_dS)Nr)LogisticRegressionZ liblinear)Zsolver_estimator_type)Zsklearn.linear_modelr modelgetattrr )selfr r rP/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/decoding/base.py__init__6s   zLinearModel.__init__cKst|t|}}|jdkr0td|jf|jdkrJtd|jf|jj||f|d}||jddd}|jdkr|jddkr||jddd}tj t |j }t |j  |j j |j |_|S) aEstimate the coefficients of the linear model. Save the coefficients in the attribute ``filters_`` and computes the attribute ``patterns_``. Parameters ---------- X : array, shape (n_samples, n_features) The training input samples to estimate the linear coefficients. y : array, shape (n_samples, [n_targets]) The target values. **fit_params : dict of string -> object Parameters to pass to the fit method of the estimator. Returns ------- self : instance of LinearModel Returns the modified instance. rz9LinearModel only accepts 2-dimensional X, got %s instead.z?LinearModel only accepts up to 2-dimensional y, got %s instead.g?rT)Zkeepdims)npZasarrayndim ValueErrorshaper fitZmeanZlinalgZpinvZcovTdotfilters_Z patterns_)rXy fit_paramsZinv_Yrrrr>s"   zLinearModel.fitcCs\t|jdr|jj}n"t|jjdr0|jjj}ntd|jdkrX|jddkrX|d}|S)Ncoef_z(model does not have a `coef_` attribute.rrr)hasattrr rZbest_estimator_rrr)rfiltersrrrrhs   zLinearModel.filters_cCs |j|S)aTransform the data using the linear model. Parameters ---------- X : array, shape (n_samples, n_features) The data to transform. Returns ------- y_pred : array, shape (n_samples,) The predicted targets. )r transformrrrrrr!vs zLinearModel.transformcCs||||S)aFit the data and transform it using the linear model. Parameters ---------- X : array, shape (n_samples, n_features) The training input samples to estimate the linear coefficients. y : array, shape (n_samples,) The target values. Returns ------- y_pred : array, shape (n_samples,) The predicted targets. )rr!rrrrrr fit_transformszLinearModel.fit_transformcCs |j|S)a Compute predictions of y from X. Parameters ---------- X : array, shape (n_samples, n_features) The data used to compute the predictions. Returns ------- y_pred : array, shape (n_samples,) The predictions. )r predictr"rrrr%s zLinearModel.predictcCs |j|S)a:Compute probabilistic predictions of y from X. Parameters ---------- X : array, shape (n_samples, n_features) The data used to compute the predictions. Returns ------- y_pred : array, shape (n_samples, n_classes) The probabilities. )r predict_probar"rrrr&s zLinearModel.predict_probacCs |j|S)a@Compute distance from the decision function of y from X. Parameters ---------- X : array, shape (n_samples, n_features) The data used to compute the predictions. Returns ------- y_pred : array, shape (n_samples, n_classes) The distances. )r decision_functionr"rrrr's zLinearModel.decision_functioncCs|j||S)a^Score the linear model computed on the given test data. Parameters ---------- X : array, shape (n_samples, n_features) The data to transform. y : array, shape (n_samples,) The target values. Returns ------- score : float Score of the linear model. )r scorer#rrrr(szLinearModel.score)N)__name__ __module__ __qualname____doc__rrpropertyrr!r$r%r&r'r(rrrrr s$ * r c Cs|dkr|dk}nt|}ddlm}ddlm}m}m}t|tt j frb|rR|n|} | |d}n,t|t rt ||s~t dt||}|}||||d}d d |jt ||d D} t d d | Dst d || fS)zDSet the default CV depending on whether clf is classifier/regressor.) classifierZ regressorr.r)model_selection)check_cvStratifiedKFoldKFold)Zn_splitszUnknown cross-validation)cvrr.cSsg|]\}}||fqSrr.0traintestrrr sz_set_cv..)rrcSsg|]\}}t|qSr)len)r5r6_rrrr8sz(Some folds do not have any train epochs.)rZsklearnr/Zsklearn.model_selectionr0r1r2 isinstanceintrZint64strrrr splitZ zeros_likeall) r3 estimatorrrZest_is_classifiermodelsr0r1r2ZXFoldZ cv_splitsrrr_set_cvs(       rBTcsFd}tdr$tfdd|Ds,td|rBtdsBtddS) zsz#_check_estimator..zestimator must be a scikit-learn transformer or an estimator with the fit and a predict-like (e.g. predict_proba) or a transform method. get_paramszlestimator must be a scikit-learn transformer or an estimator with the get_params method that allows cloning.N)ranyr)r@rFZ valid_methodsrrDr_check_estimatorsrHcCsdg}t|dr:t}|jD]\}}|t|ddqnt|drL|jg}|r|ddk}d|ddk}|r|r|dd}nt}|S)z>Retrieve the inverse functions of an pipeline or an estimator.Fsteps)terminalinverse_transformN)rlistrIextend_get_inverse_funcsrK)r@rJ inverse_funcr:estZlast_is_estimatorZall_invertiblerrrrOs   rOrFcCs|}t|dr|jdd}qd}t|drjt}|jD]}|t|||q8t|}|tj}d}n&t||st d||fn t ||}|j dkr|tj}d}|rt|dst|dst dt |d d dD] }||}q|r|d }|S) aRetrieve the coefficients of an estimator ending with a Linear Model. This is typically useful to retrieve "spatial filters" or "spatial patterns" of decoding models :footcite:`HaufeEtAl2014`. Parameters ---------- estimator : object | None An estimator from scikit-learn. attr : str The name of the coefficient attribute to retrieve, typically ``'filters_'`` (default) or ``'patterns_'``. inverse_transform : bool If True, returns the coefficients after inverse transforming them with the transformer steps of the estimator. Returns ------- coef : array The coefficients. References ---------- .. footbibliography:: rIrLrF estimators_Tz/This estimator does not have a %s attribute: %sz?inverse_transform can only be applied onto pipeline estimators.Nr) rrIrMrRappendget_coefrZ transposeZnewaxisrr rrO)r@attrrKrQZsqueeze_first_dimZcoefZthis_estrPrrrrTs6          rT2*n_jobsc sddlmddlm} ddlm} t} | |\}| |td}t| |} | |dt t || d\}}|fdd | D}t |d d dd fS) a Evaluate a score by cross-validation. Parameters ---------- estimator : instance of sklearn.base.BaseEstimator The object to use to fit the data. Must implement the 'fit' method. X : array-like, shape (n_samples, n_dimensional_features,) The data to fit. Can be, for example a list, or an array at least 2d. y : array-like, shape (n_samples, n_targets,) The target variable to try to predict in the case of supervised learning. groups : array-like, with shape (n_samples,) Group labels for the samples used while splitting the dataset into train/test set. scoring : str, callable | None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``. Note that when using an estimator which inherently returns multidimensional output - in particular, SlidingEstimator or GeneralizingEstimator - you should set the scorer there, not here. cv : int, cross-validation generator | iterable Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a ``(Stratified)KFold``, - An object to be used as a cross-validation generator. - An iterable yielding train, test splits. For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`sklearn.model_selection.StratifiedKFold` is used. In all other cases, :class:`sklearn.model_selection.KFold` is used. %(n_jobs)s %(verbose)s fit_params : dict, optional Parameters to pass to the fit method of the estimator. pre_dispatch : int, or str, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: - None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs - An int, giving the exact number of total jobs that are spawned - A string, giving an expression as a function of n_jobs, as in '2*n_jobs' Returns ------- scores : array of float, shape (n_splits,) | shape (n_splits, n_scores) Array of scores of the estimator for each run of the cross validation. r)clone) indexable)r0)r.)scoring) pre_dispatchc 3s.|]&\}}||ddVqdS)N)r@rrscorerr6r7 parametersrrr4rrWr@rZp_funcr[rrrrEsz'cross_val_multiscore..N.)Z sklearn.baserWZ sklearn.utilsrXZsklearn.model_selection._splitr0rrrMr>r_fit_and_scorerarray)r@rrgroupsrYr3Zn_jobsrrrZrXr0Z check_scoringZcv_iterparallelZscoresrr]rcross_val_multiscorebsB     rbraisec  Csddlm} ddlm}ddlm}|dk r0|ni}| |||}|dk rT|jf|tj }|||||\}}||||||\}}z,|dkr|j |f|n|j ||f|Wn~t k r.}z^tj |}t d}| dkrn6t | tjr| }|r| }td| |fntd W5d}~XYnDXtj |}t||||}tj ||}|rrt||||}|r||gn|g}| r|||| r|||g| r|||S) z;Fit estimator and compute scores for a given dataset split.r)_check_fit_paramsr) _safe_split) _num_samplesNrczrClassifier fit failed. The score on this train-test partition for these parameters will be set to %f. Details: %rzerror_score must be the string 'raise' or a numeric value. (Hint: if using 'raise', please make sure that it has been spelled correctly.))fixesrdZsklearn.utils.metaestimatorsreZsklearn.utils.validationrfZ set_paramsdtdatetimenowr Exception timedeltar;numbersNumberrr_scorerSrN total_seconds)r@rrr[r6r7r\rZreturn_train_scoreZreturn_parametersZreturn_n_test_samplesZ return_timesZ error_scorerdrerf start_timeZX_trainZy_trainX_testy_testeZ fit_durationZscore_durationZ test_scoreZ train_scoreretrrrr^sV        r^cCsP|dkr|||}n ||||}t|drLz |}Wntk rJYnX|S)zCompute the score of an estimator on a given test set. This code is the same as sklearn.model_selection._validation._score but accepts to output arrays instead of floats. Nitem)rrvr)r@rrrsr[r(rrrros    ro)NNN)T)T)rF)NNNNNNNrV)FFFFrc)r,numpyrrirhrmrarrgrrrutilsrrr rBrHrOrTrbr^rorrrrs6 E    C Z C