From 7d55f04b33dd0480b95d6de61c61e98e85c5a70e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Franz=20Kir=C3=A1ly?= Date: Fri, 8 Sep 2023 21:20:39 +0100 Subject: [PATCH] Delete model_selection.py --- skpro/regression/model_selection.py | 144 ---------------------------- 1 file changed, 144 deletions(-) delete mode 100644 skpro/regression/model_selection.py diff --git a/skpro/regression/model_selection.py b/skpro/regression/model_selection.py deleted file mode 100644 index 6f7217c39..000000000 --- a/skpro/regression/model_selection.py +++ /dev/null @@ -1,144 +0,0 @@ -# -*- coding: utf-8 -*- -# LEGACY MODULE - TODO: remove or refactor - -import numpy as np -from sklearn.model_selection import cross_validate - -# from sklearn.metrics.scorer import check_scoring - - -class RetrievesScores: - def __init__(self, scorer, score=True, std=False): - self.scorer = scorer - self.score = score - self.std = std - - def __call__(self, estimator, X, y): - score, std = self.scorer(estimator, X, y, return_std=True) - - if self.score and self.std: - return score, std - elif self.std: - return std - else: - return score - - -def cross_val_score( - estimator, - X, - y=None, - groups=None, - scoring=None, - cv=None, - n_jobs=1, - verbose=0, - fit_params=None, - pre_dispatch="2*n_jobs", -): - """Evaluate a score using cross-validation - - Parameters - ---------- - estimator : estimator object implementing 'fit' - The object to use to fit the data. - X : array-like - The data to fit. Can be for example a list, or an array. - y : array-like, optional, default: None - The target variable to try to predict in the case of - supervised learning. - groups : array-like, with shape (n_samples,), optional - Group labels for the samples used while splitting the dataset into - train/test set. - scoring : string, callable or None, optional, default: None - A string (see model evaluation documentation) or - a scorer callable object / function with signature - ``scorer(estimator, X, y)``. - cv : int, cross-validation generator or an iterable, optional - Determines the cross-validation splitting strategy. - Possible inputs for cv are: - - None, to use the default 3-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:`StratifiedKFold` is used. In all - other cases, :class:`KFold` is used. - Refer :ref:`User Guide ` for the various - cross-validation strategies that can be used here. - n_jobs : integer, optional - The number of CPUs to use to do the computation. -1 means - 'all CPUs'. - verbose : integer, optional - The verbosity level. - fit_params : dict, optional - Parameters to pass to the fit method of the estimator. - pre_dispatch : int, or string, 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 : numpy.array, shape=(len(list(cv)), 2) - Array of scores of the estimator for each run of the cross validation - with their corresponding uncertainty. - - See Also - --------- - :func:`skpro.metrics.make_scorer`: - Make a scorer from a performance metric or loss function. - """ - - # To ensure multimetric format is not supported - scorer = check_scoring(estimator, scoring=scoring) - - if n_jobs == 1: - # If we are not multiprocessing it's possible to - # use a wrapper function to retrieve the std values - test_scores = [] - - def scoring_task(estimator, X, y): - score, std = scorer(estimator, X, y, return_std=True) - test_scores.append([score, std]) - - return score - - else: - # We allow multiprocessing by passing in two scoring functions. - # That is far from ideal since we call the scorer twice, - # so any improvement is welcome - score_scorer = RetrievesScores(scorer, score=True, std=False) - std_scorer = RetrievesScores(scorer, score=False, std=True) - scoring_task = {"score": score_scorer, "std": std_scorer} - - cv_results = cross_validate( - estimator=estimator, - X=X, - y=y, - groups=groups, - scoring=scoring_task, - cv=cv, - return_train_score=False, - n_jobs=n_jobs, - verbose=verbose, - fit_params=fit_params, - pre_dispatch=pre_dispatch, - ) - - if n_jobs == 1: - return np.array(test_scores) - else: - return np.column_stack((cv_results["test_score"], cv_results["test_std"]))