def test_make_blobs_n_samples_list(setup): n_samples = [50, 30, 20] X, y = make_blobs(n_samples=n_samples, n_features=2, random_state=0) X, y = mt.ExecutableTuple((X, y)).execute().fetch() assert X.shape == (sum(n_samples), 2) assert all(np.bincount(y, minlength=len(n_samples)) == n_samples) is True
def testMakeBlobsNSamplesList(self): n_samples = [50, 30, 20] X, y = make_blobs(n_samples=n_samples, n_features=2, random_state=0) X, y = mt.ExecutableTuple((X, y)).execute() self.assertEqual(X.shape, (sum(n_samples), 2), "X shape mismatch") self.assertTrue( all(np.bincount(y, minlength=len(n_samples)) == n_samples), "Incorrect number of samples per blob")
def test_make_blobs_n_samples_centers_none(setup): for n_samples in [[5, 3, 0], np.array([5, 3, 0]), tuple([5, 3, 0])]: centers = None X, y = make_blobs(n_samples=n_samples, centers=centers, random_state=0) X, y = mt.ExecutableTuple((X, y)).execute().fetch() assert X.shape == (sum(n_samples), 2) assert all( np.bincount(y, minlength=len(n_samples)) == n_samples) is True
def testMakeBlobsNSamplesCentersNone(self): for n_samples in [[5, 3, 0], np.array([5, 3, 0]), tuple([5, 3, 0])]: centers = None X, y = make_blobs(n_samples=n_samples, centers=centers, random_state=0) X, y = mt.ExecutableTuple((X, y)).execute() self.assertEqual(X.shape, (sum(n_samples), 2), "X shape mismatch") self.assertTrue( all(np.bincount(y, minlength=len(n_samples)) == n_samples), "Incorrect number of samples per blob")
def test_make_blobs_n_samples_list_with_centers(setup): n_samples = [20, 20, 20] centers = np.array([[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]]) cluster_stds = np.array([0.05, 0.2, 0.4]) X, y = make_blobs(n_samples=n_samples, centers=centers, cluster_std=cluster_stds, random_state=0) X, y = mt.ExecutableTuple((X, y)).execute().fetch() assert X.shape == (sum(n_samples), 2) assert all(np.bincount(y, minlength=len(n_samples)) == n_samples) is True for i, (ctr, std) in enumerate(zip(centers, cluster_stds)): assert_almost_equal((X[y == i] - ctr).std(), std, 1, "Unexpected std")
def test_make_blobs(setup): cluster_stds = np.array([0.05, 0.2, 0.4]) cluster_centers = np.array([[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]]) X, y = make_blobs(random_state=0, n_samples=50, n_features=2, centers=cluster_centers, cluster_std=cluster_stds) X, y = mt.ExecutableTuple((X, y)).execute().fetch() assert X.shape == (50, 2) assert y.shape == (50, ) assert np.unique(y).shape == (3, ) for i, (ctr, std) in enumerate(zip(cluster_centers, cluster_stds)): assert_almost_equal((X[y == i] - ctr).std(), std, 1, "Unexpected std")
def testMakeBlobs(self): cluster_stds = np.array([0.05, 0.2, 0.4]) cluster_centers = np.array([[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]]) X, y = make_blobs(random_state=0, n_samples=50, n_features=2, centers=cluster_centers, cluster_std=cluster_stds) X, y = mt.ExecutableTuple((X, y)).execute() self.assertEqual(X.shape, (50, 2), "X shape mismatch") self.assertEqual(y.shape, (50, ), "y shape mismatch") self.assertEqual( np.unique(y).shape, (3, ), "Unexpected number of blobs") for i, (ctr, std) in enumerate(zip(cluster_centers, cluster_stds)): assert_almost_equal((X[y == i] - ctr).std(), std, 1, "Unexpected std")
def testMakeBlobsNSamplesListWithCenters(self): n_samples = [20, 20, 20] centers = np.array([[0.0, 0.0], [1.0, 1.0], [0.0, 1.0]]) cluster_stds = np.array([0.05, 0.2, 0.4]) X, y = make_blobs(n_samples=n_samples, centers=centers, cluster_std=cluster_stds, random_state=0) X, y = mt.ExecutableTuple((X, y)).execute() self.assertEqual(X.shape, (sum(n_samples), 2), "X shape mismatch") self.assertTrue( all(np.bincount(y, minlength=len(n_samples)) == n_samples), "Incorrect number of samples per blob") for i, (ctr, std) in enumerate(zip(centers, cluster_stds)): assert_almost_equal((X[y == i] - ctr).std(), std, 1, "Unexpected std")