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")
