def compute_unified_score_on_fixed_data(
observations, labels, representation_function,
train_percentage=gin.REQUIRED, matrix_fns=gin.REQUIRED, batch_size=100):
"""Computes the unified scores on the fixed set of observations and labels.
Args:
observations: Observations on which to compute the score. Observations have
shape (num_observations, 64, 64, num_channels).
labels: Observed factors of variations.
representation_function: Function that takes observations as input and
outputs a dim_representation sized representation for each observation.
train_percentage: Percentage of observations used for training.
matrix_fns: List of functions to relate factors of variations and codes.
batch_size: Batch size used to compute the representation.
Returns:
Unified scores.
"""
mus = utils.obtain_representation(observations, representation_function,
batch_size)
assert labels.shape[1] == observations.shape[0], "Wrong labels shape."
assert mus.shape[1] == observations.shape[0], "Wrong representation shape."
mus_train, mus_test = utils.split_train_test(
mus,
train_percentage)
ys_train, ys_test = utils.split_train_test(
labels,
train_percentage)
return unified_scores(mus_train, ys_train, mus_test, ys_test, matrix_fns)
def compute_sap_on_fixed_data(observations, labels, representation_function,
train_percentage=gin.REQUIRED,
continuous_factors=gin.REQUIRED,
batch_size=100):
"""Computes the SAP score on the fixed set of observations and labels.
Args:
observations: Observations on which to compute the score. Observations have
shape (num_observations, 64, 64, num_channels).
labels: Observed factors of variations.
representation_function: Function that takes observations as input and
outputs a dim_representation sized representation for each observation.
train_percentage: Percentage of observations used for training.
continuous_factors: Whether factors should be considered continuous or
discrete.
batch_size: Batch size used to compute the representation.
Returns:
SAP computed on the provided observations and labels.
"""
mus = utils.obtain_representation(observations, representation_function,
batch_size)
assert labels.shape[1] == observations.shape[0], "Wrong labels shape."
assert mus.shape[1] == observations.shape[0], "Wrong representation shape."
mus_train, mus_test = utils.split_train_test(
mus,
train_percentage)
ys_train, ys_test = utils.split_train_test(
labels,
train_percentage)
return _compute_sap(mus_train, ys_train, mus_test, ys_test,
continuous_factors)
def test_split_train_test(self):
xs = np.zeros([10, 100])
xs_train, xs_test = utils.split_train_test(xs, 0.9)
shouldbe_train = np.zeros([10, 90])
shouldbe_test = np.zeros([10, 10])
np.testing.assert_allclose(xs_train, shouldbe_train)
np.testing.assert_allclose(xs_test, shouldbe_test)