def _feature_type_override(self, feature_id):
"""
This should only be used to test CONTINUOUS_ACTION
"""
if id_to_type(feature_id) == identify_types.CONTINUOUS_ACTION:
return identify_types.CONTINUOUS_ACTION
return None
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()