import numpy as np from numpy.testing import assert_equal, assert_approx_equal, \ assert_array_almost_equal from nose.tools import assert_true from sklearn.utils.testing import assert_less from .. import make_classification from .. import make_multilabel_classification from .. import make_hastie_10_2 from .. import make_regression from .. import make_blobs from .. import make_friedman1 from .. import make_friedman2 from .. import make_friedman3 from .. import make_low_rank_matrix from .. import make_sparse_coded_signal from .. import make_sparse_uncorrelated from .. import make_spd_matrix from .. import make_swiss_roll from .. import make_s_curve def test_make_classification(): X, y = make_classification(n_samples=100, n_features=20, n_informative=5, n_redundant=1, n_repeated=1, n_classes=3, n_clusters_per_class=1, hypercube=False, shift=None, scale=None, weights=[0.1, 0.25], random_state=0) assert_equal(X.shape, (100, 20), "X shape mismatch") assert_equal(y.shape, (100,), "y shape mismatch") assert_equal(np.unique(y).shape, (3,), "Unexpected number of classes") assert_equal(sum(y == 0), 10, "Unexpected number of samples in class #0") assert_equal(sum(y == 1), 25, "Unexpected number of samples in class #1") assert_equal(sum(y == 2), 65, "Unexpected number of samples in class #2") def test_make_multilabel_classification(): for allow_unlabeled, min_length in zip((True, False), (0, 1)): X, Y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=3, random_state=0, allow_unlabeled=allow_unlabeled) assert_equal(X.shape, (100, 20), "X shape mismatch") if not allow_unlabeled: assert_equal(max([max(y) for y in Y]), 2) assert_equal(min([len(y) for y in Y]), min_length) assert_true(max([len(y) for y in Y]) <= 3) def test_make_hastie_10_2(): X, y = make_hastie_10_2(n_samples=100, random_state=0) assert_equal(X.shape, (100, 10), "X shape mismatch") assert_equal(y.shape, (100,), "y shape mismatch") assert_equal(np.unique(y).shape, (2,), "Unexpected number of classes") def test_make_regression(): X, y, c = make_regression(n_samples=100, n_features=10, n_informative=3, effective_rank=5, coef=True, bias=0.0, noise=1.0, random_state=0) assert_equal(X.shape, (100, 10), "X shape mismatch") assert_equal(y.shape, (100,), "y shape mismatch") assert_equal(c.shape, (10,), "coef shape mismatch") assert_equal(sum(c != 0.0), 3, "Unexpected number of informative features") # Test that y ~= np.dot(X, c) + bias + N(0, 1.0) assert_approx_equal(np.std(y - np.dot(X, c)), 1.0, significant=2) def test_make_blobs(): X, y = make_blobs(n_samples=50, n_features=2, centers=[[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]], random_state=0) assert_equal(X.shape, (50, 2), "X shape mismatch") assert_equal(y.shape, (50,), "y shape mismatch") assert_equal(np.unique(y).shape, (3,), "Unexpected number of blobs") def test_make_friedman1(): X, y = make_friedman1(n_samples=5, n_features=10, noise=0.0, random_state=0) assert_equal(X.shape, (5, 10), "X shape mismatch") assert_equal(y.shape, (5,), "y shape mismatch") assert_array_almost_equal(y, 10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 \ + 10 * X[:, 3] + 5 * X[:, 4]) def test_make_friedman2(): X, y = make_friedman2(n_samples=5, noise=0.0, random_state=0) assert_equal(X.shape, (5, 4), "X shape mismatch") assert_equal(y.shape, (5,), "y shape mismatch") assert_array_almost_equal(y, (X[:, 0] ** 2 + (X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5) def test_make_friedman3(): X, y = make_friedman3(n_samples=5, noise=0.0, random_state=0) assert_equal(X.shape, (5, 4), "X shape mismatch") assert_equal(y.shape, (5,), "y shape mismatch") assert_array_almost_equal(y, np.arctan((X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) / X[:, 0])) def test_make_low_rank_matrix(): X = make_low_rank_matrix(n_samples=50, n_features=25, effective_rank=5, tail_strength=0.01, random_state=0) assert_equal(X.shape, (50, 25), "X shape mismatch") from numpy.linalg import svd u, s, v = svd(X) assert_less(sum(s) - 5, 0.1, "X rank is not approximately 5") def test_make_sparse_coded_signal(): Y, D, X = make_sparse_coded_signal(n_samples=5, n_components=8, n_features=10, n_nonzero_coefs=3, random_state=0) assert_equal(Y.shape, (10, 5), "Y shape mismatch") assert_equal(D.shape, (10, 8), "D shape mismatch") assert_equal(X.shape, (8, 5), "X shape mismatch") for col in X.T: assert_equal(len(np.flatnonzero(col)), 3, 'Non-zero coefs mismatch') assert_equal(np.dot(D, X), Y) assert_array_almost_equal(np.sqrt((D ** 2).sum(axis=0)), np.ones(D.shape[1])) def test_make_sparse_uncorrelated(): X, y = make_sparse_uncorrelated(n_samples=5, n_features=10, random_state=0) assert_equal(X.shape, (5, 10), "X shape mismatch") assert_equal(y.shape, (5,), "y shape mismatch") def test_make_spd_matrix(): X = make_spd_matrix(n_dim=5, random_state=0) assert_equal(X.shape, (5, 5), "X shape mismatch") assert_array_almost_equal(X, X.T) from numpy.linalg import eig eigenvalues, _ = eig(X) assert_equal(eigenvalues > 0, np.array([True] * 5), "X is not positive-definite") def test_make_swiss_roll(): X, t = make_swiss_roll(n_samples=5, noise=0.0, random_state=0) assert_equal(X.shape, (5, 3), "X shape mismatch") assert_equal(t.shape, (5,), "t shape mismatch") assert_equal(X[:, 0], t * np.cos(t)) assert_equal(X[:, 2], t * np.sin(t)) def test_make_s_curve(): X, t = make_s_curve(n_samples=5, noise=0.0, random_state=0) assert_equal(X.shape, (5, 3), "X shape mismatch") assert_equal(t.shape, (5,), "t shape mismatch") assert_equal(X[:, 0], np.sin(t)) assert_equal(X[:, 2], np.sign(t) * (np.cos(t) - 1))