def _compare_with_sklearn(self, cov_type):
# sklearn version.
iterations = 40
np.random.seed(5)
sklearn_assignments = np.asarray([0, 0, 1, 0, 0, 0, 1, 0, 0, 1])
sklearn_means = np.asarray([[144.83417719, 254.20130341],
[274.38754816, 353.16074346]])
sklearn_covs = np.asarray([[[395.0081194, -4.50389512],
[-4.50389512, 408.27543989]],
[[385.17484203, -31.27834935],
[-31.27834935, 391.74249925]]])
# skflow version.
gmm = gmm_lib.GMM(self.num_centers,
initial_clusters=self.initial_means,
covariance_type=cov_type,
config=run_config.RunConfig(tf_random_seed=2))
gmm.fit(input_fn=self.input_fn(), steps=iterations)
points = self.points[:10, :]
skflow_assignments = []
for item in gmm.predict_assignments(
input_fn=self.input_fn(points=points, batch_size=10)):
skflow_assignments.append(item)
self.assertAllClose(sklearn_assignments,
np.ravel(skflow_assignments).astype(int))
self.assertAllClose(sklearn_means, gmm.clusters())
if cov_type == 'full':
self.assertAllClose(sklearn_covs, gmm.covariances(), rtol=0.01)
else:
for d in [0, 1]:
self.assertAllClose(
np.diag(sklearn_covs[d]), gmm.covariances()[d, :], rtol=0.01)
def test_infer(self):
gmm = gmm_lib.GMM(self.num_centers,
initial_clusters=self.initial_means,
random_seed=4,
config=run_config.RunConfig(tf_random_seed=2))
gmm.fit(input_fn=self.input_fn(), steps=60)
clusters = gmm.clusters()
# Make a small test set
num_points = 40
points, true_assignments, true_offsets = (self.make_random_points(
clusters, num_points))
assignments = []
for item in gmm.predict_assignments(
input_fn=self.input_fn(points=points, batch_size=num_points)):
assignments.append(item)
assignments = np.ravel(assignments)
self.assertAllEqual(true_assignments, assignments)
# Test score
score = gmm.score(input_fn=self.input_fn(points=points,
batch_size=num_points),
steps=1)
self.assertNear(score, np.sum(true_offsets), 4.05)
def test_clusters(self):
"""Tests the shape of the clusters."""
gmm = gmm_lib.GMM(self.num_centers,
initial_clusters=self.initial_means,
random_seed=4,
config=run_config.RunConfig(tf_random_seed=2))
gmm.fit(input_fn=self.input_fn(), steps=0)
clusters = gmm.clusters()
self.assertAllEqual(list(clusters.shape), [self.num_centers, self.num_dims])
def test_fit(self):
gmm = gmm_lib.GMM(self.num_centers,
initial_clusters='random',
random_seed=4,
config=run_config.RunConfig(tf_random_seed=2))
gmm.fit(input_fn=self.input_fn(), steps=1)
score1 = gmm.score(input_fn=self.input_fn(batch_size=self.num_points),
steps=1)
gmm.fit(input_fn=self.input_fn(), steps=10)
score2 = gmm.score(input_fn=self.input_fn(batch_size=self.num_points),
steps=1)
self.assertLess(score1, score2)
def test_random_input_large(self):
# sklearn version.
iterations = 5 # that should be enough to know whether this diverges
np.random.seed(5)
num_classes = 20
x = np.array([[np.random.random() for _ in range(100)]
for _ in range(num_classes)], dtype=np.float32)
# skflow version.
gmm = gmm_lib.GMM(num_classes,
covariance_type='full',
config=run_config.RunConfig(tf_random_seed=2))
def get_input_fn(x):
def input_fn():
return constant_op.constant(x.astype(np.float32)), None
return input_fn
gmm.fit(input_fn=get_input_fn(x), steps=iterations)
self.assertFalse(np.isnan(gmm.clusters()).any())
def test_queues(self): gmm = gmm_lib.GMM(2, covariance_type='diag') gmm.fit(input_fn=self.input_fn(), steps=1)
