"""Utilities to evaluate models with respect to a variable """ # Author: Alexander Fabisch # # License: BSD 3 clause import warnings import numpy as np from .base import is_classifier, clone from .cross_validation import check_cv from .externals.joblib import Parallel, delayed from .cross_validation import _safe_split, _score, _fit_and_score from .metrics.scorer import check_scoring from .utils import indexable from .utils.fixes import astype __all__ = ['learning_curve', 'validation_curve'] def learning_curve(estimator, X, y, 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): """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. 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. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. For integer/None inputs, if ``y`` is binary or multiclass, :class:`StratifiedKFold` used. If the estimator is a classifier or if ``y`` is neither binary nor multiclass, :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. 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 = indexable(X, y) # Make a list since we will be iterating multiple times over the folds cv = list(check_cv(cv, X, y, classifier=is_classifier(estimator))) scorer = check_scoring(estimator, scoring=scoring) # HACK as long as boolean indices are allowed in cv generators if cv[0][0].dtype == bool: new_cv = [] for i in range(len(cv)): new_cv.append((np.nonzero(cv[i][0])[0], np.nonzero(cv[i][1])[0])) cv = new_cv n_max_training_samples = len(cv[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 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) else: out = parallel(delayed(_fit_and_score)( clone(estimator), X, y, scorer, train[:n_train_samples], test, verbose, parameters=None, fit_params=None, return_train_score=True) for train, test in cv for n_train_samples in train_sizes_abs) out = np.array(out)[:, :2] 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.float): 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 = astype(train_sizes_abs * n_max_training_samples, 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 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, 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. 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. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. For integer/None inputs, if ``y`` is binary or multiclass, :class:`StratifiedKFold` used. If the estimator is a classifier or if ``y`` is neither binary nor multiclass, :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:`examples/model_selection/plot_validation_curve.py ` """ X, y = indexable(X, y) cv = check_cv(cv, X, 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)( estimator, X, y, scorer, train, test, verbose, parameters={param_name: v}, fit_params=None, return_train_score=True) for train, test in cv for v in param_range) out = np.asarray(out)[:, :2] 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]