Source code for skpro.model_selection

import numpy as np
from sklearn.model_selection import cross_validate
from sklearn.metrics.scorer import check_scoring

[docs]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
[docs]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 <cross_validation>` 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']))