sklearn.utils.shuffle(*arrays, **options)

sklearn.feature_selection.mutual_info_regression(X, y, discrete_features='auto', n_neighbors=3, copy=True, ran

sklearn.metrics.silhouette_samples(X, labels, metric='euclidean', **kwds)

sklearn.metrics.log_loss(y_true, y_pred, eps=1e-15, normalize=True, sample_weight=None, labels=None)

class sklearn.decomposition.KernelPCA(n_components=None, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1

class sklearn.preprocessing.FunctionTransformer(func=None, inverse_func=None, validate=True, accept_sparse=False, pass_y=False

sklearn.metrics.mutual_info_score(labels_true, labels_pred, contingency=None)

class sklearn.gaussian_process.kernels.Product(k1, k2)

sklearn.datasets.make_s_curve(n_samples=100, noise=0.0, random_state=None)

sklearn.utils.estimator_checks.check_estimator(Estimator)