def test_automatic_stop_training():
""" Test that verifies that iGSFA automatically calls stop training when
trained on single batch mode.
"""
x = numx.random.normal(size=(300, 15))
n = iGSFANode(output_dim=15, reconstruct_with_sfa=True,
slow_feature_scaling_method=None)
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
n = iGSFANode(output_dim=15, reconstruct_with_sfa=True,
slow_feature_scaling_method="data_dependent")
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
n = iGSFANode(output_dim=15, reconstruct_with_sfa=True,
slow_feature_scaling_method="sensitivity_based")
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
n = iGSFANode(output_dim=15, reconstruct_with_sfa=True,
slow_feature_scaling_method="QR_decomposition")
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
def test_automatic_stop_training():
""" Test that verifies that iGSFA automatically calls stop training when
trained on single batch mode.
"""
x = numx.random.normal(size=(300, 15))
n = iGSFANode(output_dim=15,
reconstruct_with_sfa=True,
slow_feature_scaling_method=None)
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
n = iGSFANode(output_dim=15,
reconstruct_with_sfa=True,
slow_feature_scaling_method="data_dependent")
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
n = iGSFANode(output_dim=15,
reconstruct_with_sfa=True,
slow_feature_scaling_method="sensitivity_based")
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
n = iGSFANode(output_dim=15,
reconstruct_with_sfa=True,
slow_feature_scaling_method="QR_decomposition")
n.train(x, train_mode="regular")
with py.test.raises(mdp.TrainingFinishedException):
n.train(x, train_mode="regular")
def test_no_automatic_stop_training():
""" Test that verifies that iGSFA does not call stop training when
multiple-train is used.
"""
x = numx.random.normal(size=(300, 15))
n = iGSFANode(output_dim=5, reconstruct_with_sfa=False,
slow_feature_scaling_method=None)
n.train(x, train_mode="regular")
n.train(x, train_mode="regular")
n.stop_training()
n = iGSFANode(output_dim=5, reconstruct_with_sfa=False,
slow_feature_scaling_method="data_dependent")
n.train(x, train_mode="regular")
n.train(x, train_mode="regular")
n.stop_training()
def test_enforce_int_delta_threshold_le_output_dim():
x = numx.random.normal(size=(300, 15))
# No automatic stop_training. Since delta_threshold > output_dim this
# should rise an exception
n = iGSFANode(output_dim=5, reconstruct_with_sfa=False,
slow_feature_scaling_method=None, delta_threshold=6)
n.train(x, train_mode="regular")
n.train(x**3, train_mode="regular")
with py.test.raises(Exception):
n.stop_training()
# Automatic stop_training. Since delta_threshold > output_dim this
# should rise an exception
n = iGSFANode(output_dim=5, reconstruct_with_sfa=True,
slow_feature_scaling_method=None, delta_threshold=6)
with py.test.raises(Exception):
n.train(x, train_mode="regular")
def test_no_automatic_stop_training():
""" Test that verifies that iGSFA does not call stop training when
multiple-train is used.
"""
x = numx.random.normal(size=(300, 15))
n = iGSFANode(output_dim=5,
reconstruct_with_sfa=False,
slow_feature_scaling_method=None)
n.train(x, train_mode="regular")
n.train(x, train_mode="regular")
n.stop_training()
n = iGSFANode(output_dim=5,
reconstruct_with_sfa=False,
slow_feature_scaling_method="data_dependent")
n.train(x, train_mode="regular")
n.train(x, train_mode="regular")
n.stop_training()
def test_enforce_int_delta_threshold_le_output_dim():
x = numx.random.normal(size=(300, 15))
# No automatic stop_training. Since delta_threshold > output_dim this
# should rise an exception
n = iGSFANode(output_dim=5,
reconstruct_with_sfa=False,
slow_feature_scaling_method=None,
delta_threshold=6)
n.train(x, train_mode="regular")
n.train(x**3, train_mode="regular")
with py.test.raises(Exception):
n.stop_training()
# Automatic stop_training. Since delta_threshold > output_dim this
# should rise an exception
n = iGSFANode(output_dim=5,
reconstruct_with_sfa=True,
slow_feature_scaling_method=None,
delta_threshold=6)
with py.test.raises(Exception):
n.train(x, train_mode="regular")
def test_equivalence_GSFA_PCA_for_DT_0():
""" Test of iGSFA and PCA when delta_threshold is smaller than 0.0
"""
x = numx.random.normal(size=(300, 15))
n = iGSFANode(output_dim=5, slow_feature_scaling_method=None,
delta_threshold=0.0)
n.train(x, train_mode="regular")
# n.stop_training() has been automatically called
y = n.execute(x)
deltas_igsfa = comp_delta(y)
n2 = mdp.nodes.PCANode(output_dim=5)
n2.train(x)
n2.stop_training()
y2 = n2.execute(x)
deltas_pca = comp_delta(y2)
assert_array_almost_equal(deltas_igsfa, deltas_pca, decimal)
def test_equivalence_GSFA_PCA_for_DT_0():
""" Test of iGSFA and PCA when delta_threshold is smaller than 0.0
"""
x = numx.random.normal(size=(300, 15))
n = iGSFANode(output_dim=5,
slow_feature_scaling_method=None,
delta_threshold=0.0)
n.train(x, train_mode="regular")
# n.stop_training() has been automatically called
y = n.execute(x)
deltas_igsfa = comp_delta(y)
n2 = mdp.nodes.PCANode(output_dim=5)
n2.train(x)
n2.stop_training()
y2 = n2.execute(x)
deltas_pca = comp_delta(y2)
assert_array_almost_equal(deltas_igsfa, deltas_pca, decimal)
def test_slow_feature_scaling_methods():
""" Test that executes each feature scaling method and verifies that
(most of them) only change the scale of the slow features in the slow
part but do not mix them.
"""
x = numx.random.normal(size=(300, 15))
all_slow_feature_scaling_methods = [
"QR_decomposition", "sensitivity_based", None, "data_dependent"
]
num_slow_feature_scaling_methods = len(all_slow_feature_scaling_methods)
output_features = []
for slow_feature_scaling_method in all_slow_feature_scaling_methods:
n = iGSFANode(output_dim=15,
reconstruct_with_sfa=True,
slow_feature_scaling_method=slow_feature_scaling_method)
n.train(x, train_mode="regular")
if n.is_training():
n.stop_training()
output_features.append(n.execute(x))
size_slow_part = 15
print("size_slow_part:", size_slow_part)
for i in range(num_slow_feature_scaling_methods):
output_features[i] = output_features[i][:, :size_slow_part]
first_sample_y_data_dependent = \
output_features[num_slow_feature_scaling_methods-1][0]
for i in range(1, len(all_slow_feature_scaling_methods) - 1):
print(
"checking feature equivalence between",
all_slow_feature_scaling_methods[i], "and",
all_slow_feature_scaling_methods[num_slow_feature_scaling_methods -
1])
first_sample_y_i = output_features[i][0]
y = output_features[i] \
* first_sample_y_data_dependent / first_sample_y_i
assert_array_almost_equal(
y, output_features[num_slow_feature_scaling_methods - 1], decimal)
def test_slow_feature_scaling_methods():
""" Test that executes each feature scaling method and verifies that
(most of them) only change the scale of the slow features in the slow
part but do not mix them.
"""
x = numx.random.normal(size=(300, 15))
all_slow_feature_scaling_methods = ["QR_decomposition",
"sensitivity_based",
None,
"data_dependent"]
num_slow_feature_scaling_methods = len(all_slow_feature_scaling_methods)
output_features = []
for slow_feature_scaling_method in all_slow_feature_scaling_methods:
n = iGSFANode(output_dim=15, reconstruct_with_sfa=True,
slow_feature_scaling_method=slow_feature_scaling_method)
n.train(x, train_mode="regular")
if n.is_training():
n.stop_training()
output_features.append(n.execute(x))
size_slow_part = 15
print("size_slow_part:", size_slow_part)
for i in range(num_slow_feature_scaling_methods):
output_features[i] = output_features[i][:, :size_slow_part]
first_sample_y_data_dependent = \
output_features[num_slow_feature_scaling_methods-1][0]
for i in range(1, len(all_slow_feature_scaling_methods)-1):
print("checking feature equivalence between",
all_slow_feature_scaling_methods[i], "and",
all_slow_feature_scaling_methods[num_slow_feature_scaling_methods-1])
first_sample_y_i = output_features[i][0]
y = output_features[i] \
* first_sample_y_data_dependent / first_sample_y_i
assert_array_almost_equal(y,
output_features[num_slow_feature_scaling_methods-1],
decimal)