def test_metric(self):
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = lambda x: np.array(x, dtype=np.float64)
random_state = np.random.RandomState(0)
scores = dci.compute_dci(ground_truth_data, representation_function,
random_state, None, 1000, 1000)
self.assertBetween(scores["disentanglement"], 0.9, 1.0)
self.assertBetween(scores["completeness"], 0.9, 1.0)
def test_bad_metric(self):
gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
gin.bind_parameter("discretizer.num_bins", 10)
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = np.zeros_like
random_state = np.random.RandomState(0)
scores = mig.compute_mig(ground_truth_data, representation_function,
random_state, None, 3000)
self.assertBetween(scores["discrete_mig"], 0.0, 0.2)
def test_metric(self):
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = lambda x: x
random_state = np.random.RandomState(0)
scores = beta_vae.compute_beta_vae_sklearn(
ground_truth_data, representation_function, random_state, None, 5,
2000, 2000)
self.assertBetween(scores["train_accuracy"], 0.9, 1.0)
self.assertBetween(scores["eval_accuracy"], 0.9, 1.0)
def test_metric(self):
gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
gin.bind_parameter("discretizer.num_bins", 10)
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = lambda x: np.array(x, dtype=np.float64)
random_state = np.random.RandomState(0)
scores = modularity_explicitness.compute_modularity_explicitness(
ground_truth_data, representation_function, random_state, None,
3000, 3000)
self.assertBetween(scores["modularity_score"], 0.9, 1.0)
def test_bad_metric(self):
gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
gin.bind_parameter("discretizer.num_bins", 10)
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = lambda x: np.zeros_like(x, dtype=np.float64)
random_state = np.random.RandomState(0)
scores = irs.compute_irs(ground_truth_data, representation_function,
random_state, None, 0.99, 3000, 3000)
self.assertBetween(scores["IRS"], 0.0, 0.1)
def test_bad_metric(self):
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = np.zeros_like
random_state = np.random.RandomState(0)
scores = factor_vae.compute_factor_vae(ground_truth_data,
representation_function,
random_state, None, 5, 3000,
2000, 2500)
self.assertBetween(scores["train_accuracy"], 0.0, 0.2)
self.assertBetween(scores["eval_accuracy"], 0.0, 0.2)
def test_bad_metric(self):
ground_truth_data = dummy_data.IdentityObservationsData()
representation_function = lambda x: np.zeros_like(x, dtype=np.float64)
random_state = np.random.RandomState(0)
scores = sap_score.compute_sap(ground_truth_data,
representation_function,
random_state,
None,
3000,
3000,
continuous_factors=True)
self.assertBetween(scores["SAP_score"], 0.0, 0.2)
def test_duplicated_latent_space(self):
ground_truth_data = dummy_data.IdentityObservationsData()
def representation_function(x):
x = np.array(x, dtype=np.float64)
return np.hstack([x, x])
random_state = np.random.RandomState(0)
scores = dci.compute_dci(ground_truth_data, representation_function,
random_state, None, 1000, 1000)
self.assertBetween(scores["disentanglement"], 0.9, 1.0)
target = 1. - np.log(2) / np.log(10)
self.assertBetween(scores["completeness"], target - .1, target + .1)
def test_duplicated_latent_space(self):
gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
gin.bind_parameter("discretizer.num_bins", 10)
ground_truth_data = dummy_data.IdentityObservationsData()
def representation_function(x):
x = np.array(x, dtype=np.float64)
return np.hstack([x, x])
random_state = np.random.RandomState(0)
scores = mig.compute_mig(ground_truth_data, representation_function,
random_state, None, 3000)
self.assertBetween(scores["discrete_mig"], 0.0, 0.1)
def test_duplicated_latent_space(self):
ground_truth_data = dummy_data.IdentityObservationsData()
def representation_function(x):
x = np.array(x, dtype=np.float64)
return np.hstack([x, x])
random_state = np.random.RandomState(0)
scores = sap_score.compute_sap(ground_truth_data,
representation_function,
random_state,
None,
3000,
3000,
continuous_factors=True)
self.assertBetween(scores["SAP_score"], 0.0, 0.2)
def test_bad_metric(self):
ground_truth_data = dummy_data.IdentityObservationsData()
random_state_rep = np.random.RandomState(0)
# The representation which randomly permutes the factors, should have equal
# non-zero importance which should give a low modularity score.
def representation_function(x):
code = np.array(x, dtype=np.float64)
for i in range(code.shape[0]):
code[i, :] = random_state_rep.permutation(code[i, :])
return code
random_state = np.random.RandomState(0)
scores = dci.compute_dci(ground_truth_data, representation_function,
random_state, None, 1000, 1000)
self.assertBetween(scores["disentanglement"], 0.0, 0.2)
self.assertBetween(scores["completeness"], 0.0, 0.2)
def test_bad_metric(self):
gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
gin.bind_parameter("discretizer.num_bins", 10)
ground_truth_data = dummy_data.IdentityObservationsData()
random_state_rep = np.random.RandomState(0)
# The representation which randomly permutes the factors, should have equal
# non-zero MI which should give a low modularity score.
def representation_function(x):
code = np.array(x, dtype=np.float64)
for i in range(code.shape[0]):
code[i, :] = random_state_rep.permutation(code[i, :])
return code
random_state = np.random.RandomState(0)
scores = modularity_explicitness.compute_modularity_explicitness(
ground_truth_data, representation_function, random_state, None,
20000, 20000)
self.assertBetween(scores["modularity_score"], 0.0, 0.2)
