""" The :mod:`sklearn.model_selection._validation` module includes classes and functions to validate the model. """ # Author: Alexandre Gramfort # Gael Varoquaux # Olivier Grisel # Raghav RV # License: BSD 3 clause from __future__ import print_function from __future__ import division import warnings import numbers import time import numpy as np import scipy.sparse as sp from ..base import is_classifier, clone from ..utils import indexable, check_random_state, safe_indexing from ..utils.deprecation import DeprecationDict from ..utils.validation import _is_arraylike, _num_samples from ..utils.metaestimators import _safe_split from ..externals.joblib import Parallel, delayed, logger from ..externals.six.moves import zip from ..metrics.scorer import check_scoring, _check_multimetric_scoring from ..exceptions import FitFailedWarning from ._split import check_cv from ..preprocessing import LabelEncoder __all__ = ['cross_validate', 'cross_val_score', 'cross_val_predict', 'permutation_test_score', 'learning_curve', 'validation_curve'] def cross_validate(estimator, X, y=None, groups=None, scoring=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', return_train_score="warn"): """Evaluate metric(s) by cross-validation and also record fit/score times. Read more in the :ref:`User Guide `. 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, list/tuple, dict or None, default: None A single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`) to evaluate the predictions on the test set. For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values. NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each. See :ref:`multimetric_grid_search` for an example. If None, the estimator's default scorer (if available) is used. 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' return_train_score : boolean, optional Whether to include train scores. Current default is ``'warn'``, which behaves as ``True`` in addition to raising a warning when a training score is looked up. That default will be changed to ``False`` in 0.21. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. Returns ------- scores : dict of float arrays of shape=(n_splits,) Array of scores of the estimator for each run of the cross validation. A dict of arrays containing the score/time arrays for each scorer is returned. The possible keys for this ``dict`` are: ``test_score`` The score array for test scores on each cv split. ``train_score`` The score array for train scores on each cv split. This is available only if ``return_train_score`` parameter is ``True``. ``fit_time`` The time for fitting the estimator on the train set for each cv split. ``score_time`` The time for scoring the estimator on the test set for each cv split. (Note time for scoring on the train set is not included even if ``return_train_score`` is set to ``True`` Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_validate >>> from sklearn.metrics.scorer import make_scorer >>> from sklearn.metrics import confusion_matrix >>> from sklearn.svm import LinearSVC >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() Single metric evaluation using ``cross_validate`` >>> cv_results = cross_validate(lasso, X, y, return_train_score=False) >>> sorted(cv_results.keys()) # doctest: +ELLIPSIS ['fit_time', 'score_time', 'test_score'] >>> cv_results['test_score'] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE array([ 0.33..., 0.08..., 0.03...]) Multiple metric evaluation using ``cross_validate`` (please refer the ``scoring`` parameter doc for more information) >>> scores = cross_validate(lasso, X, y, ... scoring=('r2', 'neg_mean_squared_error')) >>> print(scores['test_neg_mean_squared_error']) # doctest: +ELLIPSIS [-3635.5... -3573.3... -6114.7...] >>> print(scores['train_r2']) # doctest: +ELLIPSIS [ 0.28... 0.39... 0.22...] See Also --------- :func:`sklearn.model_selection.cross_val_score`: Run cross-validation for single metric evaluation. :func:`sklearn.metrics.make_scorer`: Make a scorer from a performance metric or loss function. """ X, y, groups = indexable(X, y, groups) cv = check_cv(cv, y, classifier=is_classifier(estimator)) scorers, _ = _check_multimetric_scoring(estimator, scoring=scoring) # We clone the estimator to make sure that all the folds are # independent, and that it is pickle-able. parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch) scores = parallel( delayed(_fit_and_score)( clone(estimator), X, y, scorers, train, test, verbose, None, fit_params, return_train_score=return_train_score, return_times=True) for train, test in cv.split(X, y, groups)) if return_train_score: train_scores, test_scores, fit_times, score_times = zip(*scores) train_scores = _aggregate_score_dicts(train_scores) else: test_scores, fit_times, score_times = zip(*scores) test_scores = _aggregate_score_dicts(test_scores) # TODO: replace by a dict in 0.21 ret = DeprecationDict() if return_train_score == 'warn' else {} ret['fit_time'] = np.array(fit_times) ret['score_time'] = np.array(score_times) for name in scorers: ret['test_%s' % name] = np.array(test_scores[name]) if return_train_score: key = 'train_%s' % name ret[key] = np.array(train_scores[name]) if return_train_score == 'warn': message = ( 'You are accessing a training score ({!r}), ' 'which will not be available by default ' 'any more in 0.21. If you need training scores, ' 'please set return_train_score=True').format(key) # warn on key access ret.add_warning(key, message, FutureWarning) return ret 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 by cross-validation Read more in the :ref:`User Guide `. 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 : array of float, shape=(len(list(cv)),) Array of scores of the estimator for each run of the cross validation. Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_val_score >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> print(cross_val_score(lasso, X, y)) # doctest: +ELLIPSIS [ 0.33150734 0.08022311 0.03531764] See Also --------- :func:`sklearn.model_selection.cross_validate`: To run cross-validation on multiple metrics and also to return train scores, fit times and score times. :func:`sklearn.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) cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups, scoring={'score': scorer}, cv=cv, return_train_score=False, n_jobs=n_jobs, verbose=verbose, fit_params=fit_params, pre_dispatch=pre_dispatch) return cv_results['test_score'] def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score=False, return_parameters=False, return_n_test_samples=False, return_times=False, error_score='raise'): """Fit estimator and compute scores for a given dataset split. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like, optional, default: None The target variable to try to predict in the case of supervised learning. scorer : A single callable or dict mapping scorer name to the callable If it is a single callable, the return value for ``train_scores`` and ``test_scores`` is a single float. For a dict, it should be one mapping the scorer name to the scorer callable object / function. The callable object / fn should have signature ``scorer(estimator, X, y)``. train : array-like, shape (n_train_samples,) Indices of training samples. test : array-like, shape (n_test_samples,) Indices of test samples. verbose : integer The verbosity level. error_score : 'raise' (default) or numeric Value to assign to the score if an error occurs in estimator fitting. If set to 'raise', the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. parameters : dict or None Parameters to be set on the estimator. fit_params : dict or None Parameters that will be passed to ``estimator.fit``. return_train_score : boolean, optional, default: False Compute and return score on training set. return_parameters : boolean, optional, default: False Return parameters that has been used for the estimator. return_n_test_samples : boolean, optional, default: False Whether to return the ``n_test_samples`` return_times : boolean, optional, default: False Whether to return the fit/score times. Returns ------- train_scores : dict of scorer name -> float, optional Score on training set (for all the scorers), returned only if `return_train_score` is `True`. test_scores : dict of scorer name -> float, optional Score on testing set (for all the scorers). n_test_samples : int Number of test samples. fit_time : float Time spent for fitting in seconds. score_time : float Time spent for scoring in seconds. parameters : dict or None, optional The parameters that have been evaluated. """ if verbose > 1: if parameters is None: msg = '' else: msg = '%s' % (', '.join('%s=%s' % (k, v) for k, v in parameters.items())) print("[CV] %s %s" % (msg, (64 - len(msg)) * '.')) # Adjust length of sample weights fit_params = fit_params if fit_params is not None else {} fit_params = dict([(k, _index_param_value(X, v, train)) for k, v in fit_params.items()]) test_scores = {} train_scores = {} if parameters is not None: estimator.set_params(**parameters) start_time = time.time() X_train, y_train = _safe_split(estimator, X, y, train) X_test, y_test = _safe_split(estimator, X, y, test, train) is_multimetric = not callable(scorer) n_scorers = len(scorer.keys()) if is_multimetric else 1 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_time = time.time() - start_time score_time = 0.0 if error_score == 'raise': raise elif isinstance(error_score, numbers.Number): if is_multimetric: test_scores = dict(zip(scorer.keys(), [error_score, ] * n_scorers)) if return_train_score: train_scores = dict(zip(scorer.keys(), [error_score, ] * n_scorers)) else: test_scores = error_score if return_train_score: train_scores = error_score warnings.warn("Classifier fit failed. The score on this train-test" " partition for these parameters will be set to %f. " "Details: \n%r" % (error_score, e), FitFailedWarning) 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_time = time.time() - start_time # _score will return dict if is_multimetric is True test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric) score_time = time.time() - start_time - fit_time if return_train_score: train_scores = _score(estimator, X_train, y_train, scorer, is_multimetric) if verbose > 2: if is_multimetric: for scorer_name, score in test_scores.items(): msg += ", %s=%s" % (scorer_name, score) else: msg += ", score=%s" % test_scores if verbose > 1: total_time = score_time + fit_time end_msg = "%s, total=%s" % (msg, logger.short_format_time(total_time)) print("[CV] %s %s" % ((64 - len(end_msg)) * '.', end_msg)) ret = [train_scores, test_scores] if return_train_score else [test_scores] if return_n_test_samples: ret.append(_num_samples(X_test)) if return_times: ret.extend([fit_time, score_time]) if return_parameters: ret.append(parameters) return ret def _score(estimator, X_test, y_test, scorer, is_multimetric=False): """Compute the score(s) of an estimator on a given test set. Will return a single float if is_multimetric is False and a dict of floats, if is_multimetric is True """ if is_multimetric: return _multimetric_score(estimator, X_test, y_test, scorer) else: 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 if not isinstance(score, numbers.Number): raise ValueError("scoring must return a number, got %s (%s) " "instead. (scorer=%r)" % (str(score), type(score), scorer)) return score def _multimetric_score(estimator, X_test, y_test, scorers): """Return a dict of score for multimetric scoring""" scores = {} for name, scorer in scorers.items(): 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 scores[name] = score if not isinstance(score, numbers.Number): raise ValueError("scoring must return a number, got %s (%s) " "instead. (scorer=%s)" % (str(score), type(score), name)) return scores def cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', method='predict'): """Generate cross-validated estimates for each input data point Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' and 'predict' The object to use to fit the data. X : array-like The data to fit. Can be, for example a list, or an array at least 2d. 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. 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' method : string, optional, default: 'predict' Invokes the passed method name of the passed estimator. For method='predict_proba', the columns correspond to the classes in sorted order. Returns ------- predictions : ndarray This is the result of calling ``method`` Notes ----- In the case that one or more classes are absent in a training portion, a default score needs to be assigned to all instances for that class if ``method`` produces columns per class, as in {'decision_function', 'predict_proba', 'predict_log_proba'}. For ``predict_proba`` this value is 0. In order to ensure finite output, we approximate negative infinity by the minimum finite float value for the dtype in other cases. Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_val_predict >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data[:150] >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> y_pred = cross_val_predict(lasso, X, y) """ X, y, groups = indexable(X, y, groups) cv = check_cv(cv, y, classifier=is_classifier(estimator)) if method in ['decision_function', 'predict_proba', 'predict_log_proba']: le = LabelEncoder() y = le.fit_transform(y) # We clone the estimator to make sure that all the folds are # independent, and that it is pickle-able. parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch) prediction_blocks = parallel(delayed(_fit_and_predict)( clone(estimator), X, y, train, test, verbose, fit_params, method) for train, test in cv.split(X, y, groups)) # Concatenate the predictions predictions = [pred_block_i for pred_block_i, _ in prediction_blocks] test_indices = np.concatenate([indices_i for _, indices_i in prediction_blocks]) if not _check_is_permutation(test_indices, _num_samples(X)): raise ValueError('cross_val_predict only works for partitions') inv_test_indices = np.empty(len(test_indices), dtype=int) inv_test_indices[test_indices] = np.arange(len(test_indices)) # Check for sparse predictions if sp.issparse(predictions[0]): predictions = sp.vstack(predictions, format=predictions[0].format) else: predictions = np.concatenate(predictions) return predictions[inv_test_indices] def _fit_and_predict(estimator, X, y, train, test, verbose, fit_params, method): """Fit estimator and predict values for a given dataset split. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' and 'predict' The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like, optional, default: None The target variable to try to predict in the case of supervised learning. train : array-like, shape (n_train_samples,) Indices of training samples. test : array-like, shape (n_test_samples,) Indices of test samples. verbose : integer The verbosity level. fit_params : dict or None Parameters that will be passed to ``estimator.fit``. method : string Invokes the passed method name of the passed estimator. Returns ------- predictions : sequence Result of calling 'estimator.method' test : array-like This is the value of the test parameter """ # Adjust length of sample weights fit_params = fit_params if fit_params is not None else {} fit_params = dict([(k, _index_param_value(X, v, train)) for k, v in fit_params.items()]) X_train, y_train = _safe_split(estimator, X, y, train) X_test, _ = _safe_split(estimator, X, y, test, train) if y_train is None: estimator.fit(X_train, **fit_params) else: estimator.fit(X_train, y_train, **fit_params) func = getattr(estimator, method) predictions = func(X_test) if method in ['decision_function', 'predict_proba', 'predict_log_proba']: n_classes = len(set(y)) if n_classes != len(estimator.classes_): recommendation = ( 'To fix this, use a cross-validation ' 'technique resulting in properly ' 'stratified folds') warnings.warn('Number of classes in training fold ({}) does ' 'not match total number of classes ({}). ' 'Results may not be appropriate for your use case. ' '{}'.format(len(estimator.classes_), n_classes, recommendation), RuntimeWarning) if method == 'decision_function': if (predictions.ndim == 2 and predictions.shape[1] != len(estimator.classes_)): # This handles the case when the shape of predictions # does not match the number of classes used to train # it with. This case is found when sklearn.svm.SVC is # set to `decision_function_shape='ovo'`. raise ValueError('Output shape {} of {} does not match ' 'number of classes ({}) in fold. ' 'Irregular decision_function outputs ' 'are not currently supported by ' 'cross_val_predict'.format( predictions.shape, method, len(estimator.classes_), recommendation)) if len(estimator.classes_) <= 2: # In this special case, `predictions` contains a 1D array. raise ValueError('Only {} class/es in training fold, this ' 'is not supported for decision_function ' 'with imbalanced folds. {}'.format( len(estimator.classes_), recommendation)) float_min = np.finfo(predictions.dtype).min default_values = {'decision_function': float_min, 'predict_log_proba': float_min, 'predict_proba': 0} predictions_for_all_classes = np.full((_num_samples(predictions), n_classes), default_values[method]) predictions_for_all_classes[:, estimator.classes_] = predictions predictions = predictions_for_all_classes return predictions, test def _check_is_permutation(indices, n_samples): """Check whether indices is a reordering of the array np.arange(n_samples) Parameters ---------- indices : ndarray integer array to test n_samples : int number of expected elements Returns ------- is_partition : bool True iff sorted(indices) is np.arange(n) """ if len(indices) != n_samples: return False hit = np.zeros(n_samples, dtype=bool) hit[indices] = True if not np.all(hit): return False return True def _index_param_value(X, v, indices): """Private helper function for parameter value indexing.""" if not _is_arraylike(v) or _num_samples(v) != _num_samples(X): # pass through: skip indexing return v if sp.issparse(v): v = v.tocsr() return safe_indexing(v, indices) def permutation_test_score(estimator, X, y, groups=None, cv=None, n_permutations=100, n_jobs=1, random_state=0, verbose=0, scoring=None): """Evaluate the significance of a cross-validated score with permutations Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object implementing 'fit' The object to use to fit the data. X : array-like of shape at least 2D The data to fit. y : array-like The target variable to try to predict in the case of supervised learning. groups : array-like, with shape (n_samples,), optional Labels to constrain permutation within groups, i.e. ``y`` values are permuted among samples with the same group identifier. When not specified, ``y`` values are permuted among all samples. When a grouped cross-validator is used, the group labels are also passed on to the ``split`` method of the cross-validator. The cross-validator uses them for grouping the samples while splitting the dataset into train/test set. scoring : string, callable or None, optional, default: None A single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`) to evaluate the predictions on the test set. If None the estimator's default scorer, if available, is used. 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_permutations : integer, optional Number of times to permute ``y``. n_jobs : integer, optional The number of CPUs to use to do the computation. -1 means 'all CPUs'. random_state : int, RandomState instance or None, optional (default=0) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. verbose : integer, optional The verbosity level. Returns ------- score : float The true score without permuting targets. permutation_scores : array, shape (n_permutations,) The scores obtained for each permutations. pvalue : float The p-value, which approximates the probability that the score would be obtained by chance. This is calculated as: `(C + 1) / (n_permutations + 1)` Where C is the number of permutations whose score >= the true score. The best possible p-value is 1/(n_permutations + 1), the worst is 1.0. Notes ----- This function implements Test 1 in: Ojala and Garriga. Permutation Tests for Studying Classifier Performance. The Journal of Machine Learning Research (2010) vol. 11 """ X, y, groups = indexable(X, y, groups) cv = check_cv(cv, y, classifier=is_classifier(estimator)) scorer = check_scoring(estimator, scoring=scoring) random_state = check_random_state(random_state) # We clone the estimator to make sure that all the folds are # independent, and that it is pickle-able. score = _permutation_test_score(clone(estimator), X, y, groups, cv, scorer) permutation_scores = Parallel(n_jobs=n_jobs, verbose=verbose)( delayed(_permutation_test_score)( clone(estimator), X, _shuffle(y, groups, random_state), groups, cv, scorer) for _ in range(n_permutations)) permutation_scores = np.array(permutation_scores) pvalue = (np.sum(permutation_scores >= score) + 1.0) / (n_permutations + 1) return score, permutation_scores, pvalue permutation_test_score.__test__ = False # to avoid a pb with nosetests def _permutation_test_score(estimator, X, y, groups, cv, scorer): """Auxiliary function for permutation_test_score""" avg_score = [] for train, test in cv.split(X, y, groups): X_train, y_train = _safe_split(estimator, X, y, train) X_test, y_test = _safe_split(estimator, X, y, test, train) estimator.fit(X_train, y_train) avg_score.append(scorer(estimator, X_test, y_test)) return np.mean(avg_score) def _shuffle(y, groups, random_state): """Return a shuffled copy of y eventually shuffle among same groups.""" if groups is None: indices = random_state.permutation(len(y)) else: indices = np.arange(len(groups)) for group in np.unique(groups): this_mask = (groups == group) indices[this_mask] = random_state.permutation(indices[this_mask]) return safe_indexing(y, indices) def learning_curve(estimator, X, y, groups=None, train_sizes=np.linspace(0.1, 1.0, 5), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=1, pre_dispatch="all", verbose=0, shuffle=False, random_state=None): """Learning curve. Determines cross-validated training and test scores for different training set sizes. A cross-validation generator splits the whole dataset k times in training and test data. Subsets of the training set with varying sizes will be used to train the estimator and a score for each training subset size and the test set will be computed. Afterwards, the scores will be averaged over all k runs for each training subset size. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression; None for unsupervised learning. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. train_sizes : array-like, shape (n_ticks,), dtype float or int Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class. (default: np.linspace(0.1, 1.0, 5)) 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. 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)``. exploit_incremental_learning : boolean, optional, default: False If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes. n_jobs : integer, optional Number of jobs to run in parallel (default 1). pre_dispatch : integer or string, optional Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like '2*n_jobs'. verbose : integer, optional Controls the verbosity: the higher, the more messages. shuffle : boolean, optional Whether to shuffle training data before taking prefixes of it based on``train_sizes``. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``shuffle`` is True. Returns ------- train_sizes_abs : array, shape = (n_unique_ticks,), dtype int Numbers of training examples that has been used to generate the learning curve. Note that the number of ticks might be less than n_ticks because duplicate entries will be removed. train_scores : array, shape (n_ticks, n_cv_folds) Scores on training sets. test_scores : array, shape (n_ticks, n_cv_folds) Scores on test set. Notes ----- See :ref:`examples/model_selection/plot_learning_curve.py ` """ if exploit_incremental_learning and not hasattr(estimator, "partial_fit"): raise ValueError("An estimator must support the partial_fit interface " "to exploit incremental learning") X, y, groups = indexable(X, y, groups) cv = check_cv(cv, y, classifier=is_classifier(estimator)) # Store it as list as we will be iterating over the list multiple times cv_iter = list(cv.split(X, y, groups)) scorer = check_scoring(estimator, scoring=scoring) n_max_training_samples = len(cv_iter[0][0]) # Because the lengths of folds can be significantly different, it is # not guaranteed that we use all of the available training data when we # use the first 'n_max_training_samples' samples. train_sizes_abs = _translate_train_sizes(train_sizes, n_max_training_samples) n_unique_ticks = train_sizes_abs.shape[0] if verbose > 0: print("[learning_curve] Training set sizes: " + str(train_sizes_abs)) parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch, verbose=verbose) if shuffle: rng = check_random_state(random_state) cv_iter = ((rng.permutation(train), test) for train, test in cv_iter) if exploit_incremental_learning: classes = np.unique(y) if is_classifier(estimator) else None out = parallel(delayed(_incremental_fit_estimator)( clone(estimator), X, y, classes, train, test, train_sizes_abs, scorer, verbose) for train, test in cv_iter) else: train_test_proportions = [] for train, test in cv_iter: for n_train_samples in train_sizes_abs: train_test_proportions.append((train[:n_train_samples], test)) out = parallel(delayed(_fit_and_score)( clone(estimator), X, y, scorer, train, test, verbose, parameters=None, fit_params=None, return_train_score=True) for train, test in train_test_proportions) out = np.array(out) n_cv_folds = out.shape[0] // n_unique_ticks out = out.reshape(n_cv_folds, n_unique_ticks, 2) out = np.asarray(out).transpose((2, 1, 0)) return train_sizes_abs, out[0], out[1] def _translate_train_sizes(train_sizes, n_max_training_samples): """Determine absolute sizes of training subsets and validate 'train_sizes'. Examples: _translate_train_sizes([0.5, 1.0], 10) -> [5, 10] _translate_train_sizes([5, 10], 10) -> [5, 10] Parameters ---------- train_sizes : array-like, shape (n_ticks,), dtype float or int Numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of 'n_max_training_samples', i.e. it has to be within (0, 1]. n_max_training_samples : int Maximum number of training samples (upper bound of 'train_sizes'). Returns ------- train_sizes_abs : array, shape (n_unique_ticks,), dtype int Numbers of training examples that will be used to generate the learning curve. Note that the number of ticks might be less than n_ticks because duplicate entries will be removed. """ train_sizes_abs = np.asarray(train_sizes) n_ticks = train_sizes_abs.shape[0] n_min_required_samples = np.min(train_sizes_abs) n_max_required_samples = np.max(train_sizes_abs) if np.issubdtype(train_sizes_abs.dtype, np.floating): if n_min_required_samples <= 0.0 or n_max_required_samples > 1.0: raise ValueError("train_sizes has been interpreted as fractions " "of the maximum number of training samples and " "must be within (0, 1], but is within [%f, %f]." % (n_min_required_samples, n_max_required_samples)) train_sizes_abs = (train_sizes_abs * n_max_training_samples).astype( dtype=np.int, copy=False) train_sizes_abs = np.clip(train_sizes_abs, 1, n_max_training_samples) else: if (n_min_required_samples <= 0 or n_max_required_samples > n_max_training_samples): raise ValueError("train_sizes has been interpreted as absolute " "numbers of training samples and must be within " "(0, %d], but is within [%d, %d]." % (n_max_training_samples, n_min_required_samples, n_max_required_samples)) train_sizes_abs = np.unique(train_sizes_abs) if n_ticks > train_sizes_abs.shape[0]: warnings.warn("Removed duplicate entries from 'train_sizes'. Number " "of ticks will be less than the size of " "'train_sizes' %d instead of %d)." % (train_sizes_abs.shape[0], n_ticks), RuntimeWarning) return train_sizes_abs def _incremental_fit_estimator(estimator, X, y, classes, train, test, train_sizes, scorer, verbose): """Train estimator on training subsets incrementally and compute scores.""" train_scores, test_scores = [], [] partitions = zip(train_sizes, np.split(train, train_sizes)[:-1]) for n_train_samples, partial_train in partitions: train_subset = train[:n_train_samples] X_train, y_train = _safe_split(estimator, X, y, train_subset) X_partial_train, y_partial_train = _safe_split(estimator, X, y, partial_train) X_test, y_test = _safe_split(estimator, X, y, test, train_subset) if y_partial_train is None: estimator.partial_fit(X_partial_train, classes=classes) else: estimator.partial_fit(X_partial_train, y_partial_train, classes=classes) train_scores.append(_score(estimator, X_train, y_train, scorer)) test_scores.append(_score(estimator, X_test, y_test, scorer)) return np.array((train_scores, test_scores)).T def validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None, n_jobs=1, pre_dispatch="all", verbose=0): """Validation curve. Determine training and test scores for varying parameter values. Compute scores for an estimator with different values of a specified parameter. This is similar to grid search with one parameter. However, this will also compute training scores and is merely a utility for plotting the results. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape (n_samples) or (n_samples, n_features), optional Target relative to X for classification or regression; None for unsupervised learning. param_name : string Name of the parameter that will be varied. param_range : array-like, shape (n_values,) The values of the parameter that will be evaluated. groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. 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. 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)``. n_jobs : integer, optional Number of jobs to run in parallel (default 1). pre_dispatch : integer or string, optional Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The string can be an expression like '2*n_jobs'. verbose : integer, optional Controls the verbosity: the higher, the more messages. Returns ------- train_scores : array, shape (n_ticks, n_cv_folds) Scores on training sets. test_scores : array, shape (n_ticks, n_cv_folds) Scores on test set. Notes ----- See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` """ X, y, groups = indexable(X, y, groups) cv = check_cv(cv, y, classifier=is_classifier(estimator)) scorer = check_scoring(estimator, scoring=scoring) parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch, verbose=verbose) out = parallel(delayed(_fit_and_score)( clone(estimator), X, y, scorer, train, test, verbose, parameters={param_name: v}, fit_params=None, return_train_score=True) # NOTE do not change order of iteration to allow one time cv splitters for train, test in cv.split(X, y, groups) for v in param_range) out = np.asarray(out) n_params = len(param_range) n_cv_folds = out.shape[0] // n_params out = out.reshape(n_cv_folds, n_params, 2).transpose((2, 1, 0)) return out[0], out[1] def _aggregate_score_dicts(scores): """Aggregate the list of dict to dict of np ndarray The aggregated output of _fit_and_score will be a list of dict of form [{'prec': 0.1, 'acc':1.0}, {'prec': 0.1, 'acc':1.0}, ...] Convert it to a dict of array {'prec': np.array([0.1 ...]), ...} Parameters ---------- scores : list of dict List of dicts of the scores for all scorers. This is a flat list, assumed originally to be of row major order. Example ------- >>> scores = [{'a': 1, 'b':10}, {'a': 2, 'b':2}, {'a': 3, 'b':3}, ... {'a': 10, 'b': 10}] # doctest: +SKIP >>> _aggregate_score_dicts(scores) # doctest: +SKIP {'a': array([1, 2, 3, 10]), 'b': array([10, 2, 3, 10])} """ out = {} for key in scores[0]: out[key] = np.asarray([score[key] for score in scores]) return out