我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用sklearn.model_selection.validation_curve()。
def plot_validation_curve(estimators, X, y, cv=10, **kwargs): figsize = (6.4 * len(estimators), 4.8) fig, axes = plt.subplots(nrows=1, ncols=len(estimators), figsize=figsize) param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0] if len(estimators) == 1: axes = [axes] for ax, estimator in zip(axes, estimators): train_scores, test_scores = validation_curve( estimator=estimator, X=X, y=y, param_name='clf__C', param_range=param_range, cv=cv, **kwargs) xlabel = 'Parameter C' _plot_curve(ax, param_range, train_scores, test_scores, xlabel, 'log') ax.set_title(pipeline_name(estimator)) # fig.tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None) return fig
def validation_crv(estimator, X, y, title, n_jobs=1): param_range = np.logspace(-6, -1, 5) train_scores, test_scores = validation_curve( estimator, X, y, param_name="max_features", param_range=param_range, cv=10, scoring="accuracy", n_jobs=n_jobs) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.title(title) plt.xlabel("$\gamma$") plt.ylabel("Score") plt.ylim(0.0, 1.1) lw = 2 plt.semilogx(param_range, train_scores_mean, label="Training score", color="darkorange", lw=lw) plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2, color="darkorange", lw=lw) plt.semilogx(param_range, test_scores_mean, label="Cross-validation score", color="navy", lw=lw) plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color="navy", lw=lw) plt.legend(loc="best") return plt
def test_validation_curve(): X, y = make_classification(n_samples=2, n_features=1, n_informative=1, n_redundant=0, n_classes=2, n_clusters_per_class=1, random_state=0) param_range = np.linspace(0, 1, 10) with warnings.catch_warnings(record=True) as w: train_scores, test_scores = validation_curve( MockEstimatorWithParameter(), X, y, param_name="param", param_range=param_range, cv=2 ) if len(w) > 0: raise RuntimeError("Unexpected warning: %r" % w[0].message) assert_array_almost_equal(train_scores.mean(axis=1), param_range) assert_array_almost_equal(test_scores.mean(axis=1), 1 - param_range)
def validation_curve(estimator, epochs, y, param_name, param_range, cv=None): """Validation curve on epochs. Parameters ---------- estimator : object that implements "fit" and "predict" method. the estimator whose Validation curve must be found epochs : instance of mne.Epochs. The epochs. y : array The labels. param_name : str Name of the parameter that will be varied. param_range : array The values of the parameter that will be evaluated. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation strategy. Returns ------- train_scores : array The scores in the training set test_scores : array The scores in the test set """ from sklearn.model_selection import validation_curve if not isinstance(estimator, GlobalAutoReject): msg = 'No guarantee that it will work on this estimator.' raise NotImplementedError(msg) BaseEpochs = _get_epochs_type() if not isinstance(epochs, BaseEpochs): raise ValueError('Only accepts MNE epochs objects.') data_picks = _handle_picks(epochs.info, picks=None) X = epochs.get_data()[:, data_picks, :] n_epochs, n_channels, n_times = X.shape estimator.n_channels = n_channels estimator.n_times = n_times train_scores, test_scores = \ validation_curve(estimator, X.reshape(n_epochs, -1), y=y, param_name="thresh", param_range=param_range, cv=cv, n_jobs=1, verbose=0) return train_scores, test_scores
