"""Base class copy from sklearn.base.""" # Authors: The MNE-Python contributors. # License: BSD-3-Clause # Copyright the MNE-Python contributors. import datetime as dt import numbers import numpy as np from scipy.sparse import issparse from ..fixes import BaseEstimator, _check_fit_params, _get_check_scoring from ..parallel import parallel_func from ..utils import _pl, logger, verbose, warn class LinearModel(BaseEstimator): """Compute 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:: """ _model_attr_wrap = ( "transform", "predict", "predict_proba", "_estimator_type", "decision_function", "score", "classes_", ) def __init__(self, model=None): if model is None: from sklearn.linear_model import LogisticRegression model = LogisticRegression(solver="liblinear") self.model = model def _more_tags(self): return {"no_validation": True} def __getattr__(self, attr): """Wrap to model for some attributes.""" if attr in LinearModel._model_attr_wrap: return getattr(self.model, attr) elif attr == "fit_transform" and hasattr(self.model, "fit_transform"): return super().__getattr__(self, "_fit_transform") return super().__getattr__(self, attr) def _fit_transform(self, X, y): return self.fit(X, y).transform(X) def fit(self, X, y, **fit_params): """Estimate 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. """ # Once we require sklearn 1.1+ we should do: # from sklearn.utils import check_array # X = check_array(X, input_name="X") # y = check_array(y, dtype=None, ensure_2d=False, input_name="y") if issparse(X): raise TypeError("X should be a dense array, got sparse instead.") X, y = np.asarray(X), np.asarray(y) if X.ndim != 2: raise ValueError( f"LinearModel only accepts 2-dimensional X, got {X.shape} instead." ) if y.ndim > 2: raise ValueError( f"LinearModel only accepts up to 2-dimensional y, got {y.shape} " "instead." ) # fit the Model self.model.fit(X, y, **fit_params) # Computes patterns using Haufe's trick: A = Cov_X . W . Precision_Y inv_Y = 1.0 X = X - X.mean(0, keepdims=True) if y.ndim == 2 and y.shape[1] != 1: y = y - y.mean(0, keepdims=True) inv_Y = np.linalg.pinv(np.cov(y.T)) self.patterns_ = np.cov(X.T).dot(self.filters_.T.dot(inv_Y)).T return self @property def filters_(self): if hasattr(self.model, "coef_"): # Standard Linear Model filters = self.model.coef_ elif hasattr(self.model.best_estimator_, "coef_"): # Linear Model with GridSearchCV filters = self.model.best_estimator_.coef_ else: raise ValueError("model does not have a `coef_` attribute.") if filters.ndim == 2 and filters.shape[0] == 1: filters = filters[0] return filters def _set_cv(cv, estimator=None, X=None, y=None): """Set the default CV depending on whether clf is classifier/regressor.""" # Detect whether classification or regression from sklearn.base import is_classifier if estimator in ["classifier", "regressor"]: est_is_classifier = estimator == "classifier" else: est_is_classifier = is_classifier(estimator) # Setup CV from sklearn import model_selection as models from sklearn.model_selection import KFold, StratifiedKFold, check_cv if isinstance(cv, (int, np.int64)): XFold = StratifiedKFold if est_is_classifier else KFold cv = XFold(n_splits=cv) elif isinstance(cv, str): if not hasattr(models, cv): raise ValueError("Unknown cross-validation") cv = getattr(models, cv) cv = cv() cv = check_cv(cv=cv, y=y, classifier=est_is_classifier) # Extract train and test set to retrieve them at predict time cv_splits = [(train, test) for train, test in cv.split(X=np.zeros_like(y), y=y)] if not np.all([len(train) for train, _ in cv_splits]): raise ValueError("Some folds do not have any train epochs.") return cv, cv_splits def _check_estimator(estimator, get_params=True): """Check whether an object has the methods required by sklearn.""" valid_methods = ("predict", "transform", "predict_proba", "decision_function") if (not hasattr(estimator, "fit")) or ( not any(hasattr(estimator, method) for method in valid_methods) ): raise ValueError( "estimator must be a scikit-learn transformer or " "an estimator with the fit and a predict-like (e.g. " "predict_proba) or a transform method." ) if get_params and not hasattr(estimator, "get_params"): raise ValueError( "estimator must be a scikit-learn transformer or an " "estimator with the get_params method that allows " "cloning." ) def _get_inverse_funcs(estimator, terminal=True): """Retrieve the inverse functions of an pipeline or an estimator.""" inverse_func = list() estimators = list() if hasattr(estimator, "steps"): # if pipeline, retrieve all steps by nesting for _, est in estimator.steps: inverse_func.extend(_get_inverse_funcs(est, terminal=False)) estimators.append(est.__class__.__name__) elif hasattr(estimator, "inverse_transform"): # if not pipeline attempt to retrieve inverse function inverse_func.append(estimator.inverse_transform) estimators.append(estimator.__class__.__name__) else: inverse_func.append(False) estimators.append("Unknown") # If terminal node, check that that the last estimator is a classifier, # and remove it from the transformers. if terminal: last_is_estimator = inverse_func[-1] is False logger.debug(f" Last estimator is an estimator: {last_is_estimator}") non_invertible = np.where( [inv_func is False for inv_func in inverse_func[:-1]] )[0] if last_is_estimator and len(non_invertible) == 0: # keep all inverse transformation and remove last estimation logger.debug(" Removing inverse transformation from inverse list.") inverse_func = inverse_func[:-1] else: if len(non_invertible): bad = ", ".join(estimators[ni] for ni in non_invertible) warn( f"Cannot inverse transform non-invertible " f"estimator{_pl(non_invertible)}: {bad}." ) inverse_func = list() return inverse_func @verbose def get_coef(estimator, attr="filters_", inverse_transform=False, *, verbose=None): """Retrieve 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. %(verbose)s Returns ------- coef : array The coefficients. References ---------- .. footbibliography:: """ # Get the coefficients of the last estimator in case of nested pipeline est = estimator logger.debug(f"Getting coefficients from estimator: {est.__class__.__name__}") while hasattr(est, "steps"): est = est.steps[-1][1] squeeze_first_dim = False # If SlidingEstimator, loop across estimators if hasattr(est, "estimators_"): coef = list() for ei, this_est in enumerate(est.estimators_): if ei == 0: logger.debug(" Extracting coefficients from SlidingEstimator.") coef.append(get_coef(this_est, attr, inverse_transform)) coef = np.transpose(coef) coef = coef[np.newaxis] # fake a sample dimension squeeze_first_dim = True elif not hasattr(est, attr): raise ValueError(f"This estimator does not have a {attr} attribute:\n{est}") else: coef = getattr(est, attr) if coef.ndim == 1: coef = coef[np.newaxis] squeeze_first_dim = True # inverse pattern e.g. to get back physical units if inverse_transform: if not hasattr(estimator, "steps") and not hasattr(est, "estimators_"): raise ValueError( "inverse_transform can only be applied onto pipeline estimators." ) # The inverse_transform parameter will call this method on any # estimator contained in the pipeline, in reverse order. for inverse_func in _get_inverse_funcs(estimator)[::-1]: logger.debug(f" Applying inverse transformation: {inverse_func}.") coef = inverse_func(coef) if squeeze_first_dim: logger.debug(" Squeezing first dimension of coefficients.") coef = coef[0] return coef @verbose def cross_val_multiscore( estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=None, verbose=None, fit_params=None, pre_dispatch="2*n_jobs", ): """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. """ # This code is copied from sklearn from sklearn.base import clone, is_classifier from sklearn.model_selection._split import check_cv from sklearn.utils import indexable check_scoring = _get_check_scoring() X, y, groups = indexable(X, y, groups) cv = check_cv(cv, y, classifier=is_classifier(estimator)) cv_iter = list(cv.split(X, y, groups)) scorer = check_scoring(estimator, scoring=scoring) # We clone the estimator to make sure that all the folds are # independent, and that it is pickle-able. # Note: this parallelization is implemented using MNE Parallel parallel, p_func, n_jobs = parallel_func( _fit_and_score, n_jobs, pre_dispatch=pre_dispatch ) position = hasattr(estimator, "position") scores = parallel( p_func( estimator=clone(estimator), X=X, y=y, scorer=scorer, train=train, test=test, fit_params=fit_params, verbose=verbose, parameters=dict(position=ii % n_jobs) if position else None, ) for ii, (train, test) in enumerate(cv_iter) ) return np.array(scores)[:, 0, ...] # flatten over joblib output. # This verbose is necessary to properly set the verbosity level # during parallelization @verbose def _fit_and_score( estimator, X, y, scorer, train, test, parameters, fit_params, return_train_score=False, return_parameters=False, return_n_test_samples=False, return_times=False, error_score="raise", *, verbose=None, position=0, ): """Fit estimator and compute scores for a given dataset split.""" # This code is adapted from sklearn from sklearn.utils.metaestimators import _safe_split from sklearn.utils.validation import _num_samples # Adjust length of sample weights fit_params = fit_params if fit_params is not None else {} fit_params = _check_fit_params(X, fit_params, train) if parameters is not None: estimator.set_params(**parameters) start_time = dt.datetime.now() X_train, y_train = _safe_split(estimator, X, y, train) X_test, y_test = _safe_split(estimator, X, y, test, train) try: if y_train is None: estimator.fit(X_train, **fit_params) else: estimator.fit(X_train, y_train, **fit_params) except Exception as e: # Note fit time as time until error fit_duration = dt.datetime.now() - start_time score_duration = dt.timedelta(0) if error_score == "raise": raise elif isinstance(error_score, numbers.Number): test_score = error_score if return_train_score: train_score = error_score warn( "Classifier fit failed. The score on this train-test partition for " f"these parameters will be set to {error_score}. Details: \n{e!r}" ) else: raise ValueError( "error_score must be the string 'raise' or a numeric value. (Hint: if " "using 'raise', please make sure that it has been spelled correctly.)" ) else: fit_duration = dt.datetime.now() - start_time test_score = _score(estimator, X_test, y_test, scorer) score_duration = dt.datetime.now() - start_time - fit_duration if return_train_score: train_score = _score(estimator, X_train, y_train, scorer) ret = [train_score, test_score] if return_train_score else [test_score] if return_n_test_samples: ret.append(_num_samples(X_test)) if return_times: ret.extend([fit_duration.total_seconds(), score_duration.total_seconds()]) if return_parameters: ret.append(parameters) return ret def _score(estimator, X_test, y_test, scorer): """Compute 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. """ if y_test is None: score = scorer(estimator, X_test) else: score = scorer(estimator, X_test, y_test) if hasattr(score, "item"): try: # e.g. unwrap memmapped scalars score = score.item() except ValueError: # non-scalar? pass return score