def test_preprocessing_network(self):
feature_value_map = read_data()
normalization_parameters = {}
name_preprocessed_blob_map = {}
for feature_name, feature_values in feature_value_map.items():
normalization_parameters[
feature_name] = normalization.identify_parameter(
feature_name,
feature_values,
feature_type=self._feature_type_override(feature_name),
)
feature_values[
0] = MISSING_VALUE # Set one entry to MISSING_VALUE to test that
preprocessor = Preprocessor(
{feature_name: normalization_parameters[feature_name]}, False)
feature_values_matrix = torch.from_numpy(
np.expand_dims(feature_values, -1))
normalized_feature_values = preprocessor(
feature_values_matrix,
(feature_values_matrix != MISSING_VALUE))
name_preprocessed_blob_map[
feature_name] = normalized_feature_values.numpy()
test_features = NumpyFeatureProcessor.preprocess(
feature_value_map, normalization_parameters)
for feature_name in feature_value_map:
normalized_features = name_preprocessed_blob_map[feature_name]
if feature_name != ENUM_FEATURE_ID:
normalized_features = np.squeeze(normalized_features, -1)
tolerance = 0.01
if feature_name == BOXCOX_FEATURE_ID:
# At the limit, boxcox has some numerical instability
tolerance = 0.5
non_matching = np.where(
np.logical_not(
np.isclose(
normalized_features.flatten(),
test_features[feature_name].flatten(),
rtol=tolerance,
atol=tolerance,
)))
self.assertTrue(
np.all(
np.isclose(
normalized_features.flatten(),
test_features[feature_name].flatten(),
rtol=tolerance,
atol=tolerance,
)),
"{} does not match: {} \n!=\n {}".format(
feature_name,
normalized_features.flatten()[non_matching],
test_features[feature_name].flatten()[non_matching],
),
)
def test_type_override(self):
# Take a feature that should be identified as probability
feature_value_map = read_data()
probability_values = feature_value_map[PROBABILITY_FEATURE_ID]
# And ask for a binary anyways
parameter = normalization.identify_parameter(
"_", probability_values, feature_type=identify_types.BINARY)
self.assertEqual(parameter.feature_type, "BINARY")
def test_type_override_quantile(self):
# Take a feature that should be identified as CONTINUOUS
feature_value_map = read_data()
probability_values = feature_value_map[BOXCOX_FEATURE_ID]
# And ask for a QUANTILE anyways
parameter = normalization.identify_parameter(
"_", probability_values, feature_type=identify_types.QUANTILE
)
self.assertEqual(parameter.feature_type, "QUANTILE")
def test_type_override_boxcox(self):
# Take a feature that should be identified as CONTINUOUS
feature_value_map = read_data()
probability_values = feature_value_map[CONTINUOUS_FEATURE_ID]
# And ask for a BOXCOX anyways
parameter = normalization.identify_parameter(
"_", probability_values, feature_type=identify_types.BOXCOX
)
self.assertEqual(parameter.feature_type, "BOXCOX")
def test_type_override_continuous(self):
# Take a feature that should be identified as BOXCOX
feature_value_map = read_data()
probability_values = feature_value_map[BOXCOX_FEATURE_ID]
# And ask for a CONTINUOUS anyways
parameter = normalization.identify_parameter(
"_", probability_values, feature_type=identify_types.CONTINUOUS
)
self.assertEqual(parameter.feature_type, "CONTINUOUS")
def test_persistency(self):
feature_value_map = read_data()
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
name, values, feature_type=self._feature_type_override(name))
values[
0] = MISSING_VALUE # Set one entry to MISSING_VALUE to test that
s = normalization.serialize(normalization_parameters)
read_parameters = normalization.deserialize(s)
# Unfortunately, Thrift serializatin seems to lose a bit of precision.
# Using `==` will be false.
self.assertEqual(read_parameters.keys(),
normalization_parameters.keys())
for k in normalization_parameters:
self.assertEqual(
read_parameters[k].feature_type,
normalization_parameters[k].feature_type,
)
self.assertEqual(
read_parameters[k].possible_values,
normalization_parameters[k].possible_values,
)
for field in [
"boxcox_lambda",
"boxcox_shift",
"mean",
"stddev",
"quantiles",
"min_value",
"max_value",
]:
if getattr(normalization_parameters[k], field) is None:
self.assertEqual(
getattr(read_parameters[k], field),
getattr(normalization_parameters[k], field),
)
else:
npt.assert_allclose(
getattr(read_parameters[k], field),
getattr(normalization_parameters[k], field),
)
def test_prepare_normalization_and_normalize(self):
feature_value_map = read_data()
normalization_parameters = {}
for name, values in feature_value_map.items():
normalization_parameters[name] = normalization.identify_parameter(
name,
values,
10,
feature_type=self._feature_type_override(name))
for k, v in normalization_parameters.items():
if id_to_type(k) == CONTINUOUS:
self.assertEqual(v.feature_type, CONTINUOUS)
self.assertIs(v.boxcox_lambda, None)
self.assertIs(v.boxcox_shift, None)
elif id_to_type(k) == BOXCOX:
self.assertEqual(v.feature_type, BOXCOX)
self.assertIsNot(v.boxcox_lambda, None)
self.assertIsNot(v.boxcox_shift, None)
else:
assert v.feature_type == id_to_type(k)
preprocessor = Preprocessor(normalization_parameters, False)
sorted_features, _ = sort_features_by_normalization(
normalization_parameters)
input_matrix = torch.zeros([10000, len(sorted_features)])
for i, feature in enumerate(sorted_features):
input_matrix[:, i] = torch.from_numpy(feature_value_map[feature])
normalized_feature_matrix = preprocessor(
input_matrix, (input_matrix != MISSING_VALUE))
normalized_features = {}
on_column = 0
for feature in sorted_features:
norm = normalization_parameters[feature]
if norm.feature_type == ENUM:
column_size = len(norm.possible_values)
else:
column_size = 1
normalized_features[
feature] = normalized_feature_matrix[:,
on_column:(on_column +
column_size)]
on_column += column_size
self.assertTrue(
all([
np.isfinite(parameter.stddev) and np.isfinite(parameter.mean)
for parameter in normalization_parameters.values()
]))
for k, v in six.iteritems(normalized_features):
v = v.numpy()
self.assertTrue(np.all(np.isfinite(v)))
feature_type = normalization_parameters[k].feature_type
if feature_type == identify_types.PROBABILITY:
sigmoidv = special.expit(v)
self.assertTrue(
np.all(
np.logical_and(np.greater(sigmoidv, 0),
np.less(sigmoidv, 1))))
elif feature_type == identify_types.ENUM:
possible_values = normalization_parameters[k].possible_values
self.assertEqual(v.shape[0], len(feature_value_map[k]))
self.assertEqual(v.shape[1], len(possible_values))
possible_value_map = {}
for i, possible_value in enumerate(possible_values):
possible_value_map[possible_value] = i
for i, row in enumerate(v):
original_feature = feature_value_map[k][i]
if abs(original_feature - MISSING_VALUE) < 0.01:
self.assertEqual(0.0, np.sum(row))
else:
self.assertEqual(
possible_value_map[original_feature],
np.where(row == 1)[0][0],
)
elif feature_type == identify_types.QUANTILE:
for i, feature in enumerate(v[0]):
original_feature = feature_value_map[k][i]
expected = NumpyFeatureProcessor.value_to_quantile(
original_feature,
normalization_parameters[k].quantiles)
self.assertAlmostEqual(feature, expected, 2)
elif feature_type == identify_types.BINARY:
pass
elif (feature_type == identify_types.CONTINUOUS
or feature_type == identify_types.BOXCOX):
one_stddev = np.isclose(np.std(v, ddof=1), 1, atol=0.01)
zero_stddev = np.isclose(np.std(v, ddof=1), 0, atol=0.01)
zero_mean = np.isclose(np.mean(v), 0, atol=0.01)
self.assertTrue(
np.all(zero_mean),
"mean of feature {} is {}, not 0".format(k, np.mean(v)),
)
self.assertTrue(np.all(np.logical_or(one_stddev, zero_stddev)))
elif feature_type == identify_types.CONTINUOUS_ACTION:
less_than_max = v < 1
more_than_min = v > -1
self.assertTrue(
np.all(less_than_max),
"values are not less than 1: {}".format(
v[less_than_max == False]),
)
self.assertTrue(
np.all(more_than_min),
"values are not more than -1: {}".format(
v[more_than_min == False]),
)
else:
raise NotImplementedError()
