def test_fit(self):
"""Tests GaussianMixture.fit()"""
x = np.array([[1, 2], [2, 1], [-3, -3], [-1, -2], [-2, -1], [3, 3]])
ds_x = ds.array(x, block_size=(3, 2))
gm = GaussianMixture(n_components=2, random_state=666)
gm.fit(ds_x)
expected_weights = np.array([0.5, 0.5])
expected_means = np.array([[-2, -2], [2, 2]])
expected_cov = np.array([[[0.66671688, 0.33338255],
[0.33338255, 0.66671688]],
[[0.66671688, 0.33338255],
[0.33338255, 0.66671688]]])
expected_pc = np.array([[[1.22469875, -0.70714834],
[0., 1.4141944]],
[[1.22469875, -0.70714834],
[0., 1.4141944]]])
gm.weights_ = compss_wait_on(gm.weights_)
gm.means_ = compss_wait_on(gm.means_)
gm.covariances_ = compss_wait_on(gm.covariances_)
gm.precisions_cholesky_ = compss_wait_on(gm.precisions_cholesky_)
self.assertTrue((np.allclose(gm.weights_, expected_weights)))
self.assertTrue((np.allclose(gm.means_, expected_means)))
self.assertTrue((np.allclose(gm.covariances_, expected_cov)))
self.assertTrue((np.allclose(gm.precisions_cholesky_, expected_pc)))
def test_fit_predict_vs_fit_and_predict(self):
"""Tests GaussianMixture fit_predict() eq. fit() and predict() for both
converged and not converged runs (and a fixed random_state)."""
x0 = np.random.normal(size=(1000, 2))
x1 = np.random.normal(size=(2000, 2))
x0 = np.dot(x0, [[1.2, 1], [0, 0.5]]) + [0, 3]
x1 = np.dot(x1, [[0.4, 0], [1, 2.5]]) + [1, 0]
x = np.concatenate((x0, x1))
x_ds = ds.array(x, (1500, 2))
# We check the cases with and without convergence
with warnings.catch_warnings():
warnings.simplefilter("ignore", ConvergenceWarning)
for max_iter, converges in ((5, False), (100, True)):
gm1 = GaussianMixture(n_components=2,
max_iter=max_iter,
random_state=0)
gm1.fit(x_ds)
labels1 = gm1.predict(x_ds)
gm2 = GaussianMixture(n_components=2,
max_iter=max_iter,
random_state=0)
labels2 = gm2.fit_predict(x_ds)
self.assertTrue(np.all(labels1.collect() == labels2.collect()))
self.assertEqual(gm1.n_iter, gm2.n_iter)
self.assertEqual(converges, gm1.converged_)
self.assertEqual(gm1.converged_, gm2.converged_)
self.assertEqual(gm1.lower_bound_, gm2.lower_bound_)
gm1.weights_ = compss_wait_on(gm1.weights_)
gm1.means_ = compss_wait_on(gm1.means_)
gm1.covariances_ = compss_wait_on(gm1.covariances_)
gm2.weights_ = compss_wait_on(gm2.weights_)
gm2.means_ = compss_wait_on(gm2.means_)
gm2.covariances_ = compss_wait_on(gm2.covariances_)
self.assertTrue(np.all(gm1.weights_ == gm2.weights_))
self.assertTrue(np.all(gm1.means_ == gm2.means_))
self.assertTrue(np.all(gm1.covariances_ == gm2.covariances_))
