def testGetFeatureValuesForModelSpecFieldNoValues(self):
model_spec = config_pb2.ModelSpec(name='model1',
example_weight_key='feature2')
extracts = {}
got = model_util.get_feature_values_for_model_spec_field(
[model_spec], 'example_weight', 'example_weights', extracts)
self.assertIsNone(got)
def testGetFeatureValuesForModelSpecFieldWithMultiModelTransforedFeatures(
self, model_specs, field, multi_output_field, expected_values):
extracts = {
# Only need the num_rows from RecordBatch so use fake array of same len
# as features.
constants.ARROW_RECORD_BATCH_KEY:
pa.RecordBatch.from_arrays([pa.array([1])], ['dummy']),
constants.FEATURES_KEY: [
{
'feature1': [1.0, 1.1, 1.2],
'feature2': [2.0, 2.1, 2.2],
},
],
constants.TRANSFORMED_FEATURES_KEY: [
{
'model1': {
'feature2': [4.0, 4.1, 4.2],
'feature3': [5.0, 5.1, 5.2]
},
'model2': {
'feature2': [6.0, 6.1, 6.2],
'feature3': [7.0, 7.1, 7.2]
}
},
]
}
got = model_util.get_feature_values_for_model_spec_field(
model_specs, field, multi_output_field, extracts)
self.assertAlmostEqual(expected_values, got)
def extract_labels( # pylint: disable=invalid-name
batched_extracts: types.Extracts) -> types.Extracts:
"""Extract labels from extracts containing features."""
result = copy.copy(batched_extracts)
result[constants.LABELS_KEY] = (
model_util.get_feature_values_for_model_spec_field(
list(eval_config.model_specs), 'label_key', 'label_keys',
result, True))
return result
def extract_example_weights( # pylint: disable=invalid-name
batched_extracts: types.Extracts) -> types.Extracts:
"""Extract example weights from extracts containing features."""
result = copy.copy(batched_extracts)
example_weights = model_util.get_feature_values_for_model_spec_field(
list(eval_config.model_specs), 'example_weight_key',
'example_weight_keys', result)
if example_weights is not None:
result[constants.EXAMPLE_WEIGHTS_KEY] = example_weights
return result
def testGetFeatureValuesForModelSpecFieldNoValues(self):
model_spec = config_pb2.ModelSpec(name='model1',
example_weight_key='feature2')
extracts = {
constants.ARROW_RECORD_BATCH_KEY:
pa.RecordBatch.from_arrays([pa.array([1])], ['dummy']),
}
got = model_util.get_feature_values_for_model_spec_field(
[model_spec], 'example_weight', 'example_weights', extracts)
self.assertIsNone(got)
def extract_predictions( # pylint: disable=invalid-name
batched_extracts: types.Extracts) -> types.Extracts:
"""Extract predictions from extracts containing features."""
result = copy.copy(batched_extracts)
predictions = model_util.get_feature_values_for_model_spec_field(
list(eval_config.model_specs), 'prediction_key', 'prediction_keys',
result)
if predictions is not None:
result[constants.PREDICTIONS_KEY] = predictions
return result
def testGetFeatureValuesForModelSpecField(self, model_specs, field,
multi_output_field,
expected_values):
extracts = {
constants.FEATURES_KEY: {
'feature1': [1.0, 1.1, 1.2],
'feature2': [2.0, 2.1, 2.2],
'feature3': [3.0, 3.1, 3.2],
}
}
got = model_util.get_feature_values_for_model_spec_field(
model_specs, field, multi_output_field, extracts)
self.assertAlmostEqual(expected_values, got)
def testGetFeatureValuesForModelSpecFieldWithSingleModelTransforedFeatures(
self, model_specs, field, multi_output_field, expected_values):
extracts = {
constants.FEATURES_KEY: {
'feature1': [1.0, 1.1, 1.2],
'feature2': [2.0, 2.1, 2.2],
},
constants.TRANSFORMED_FEATURES_KEY: {
'feature2': [4.0, 4.1, 4.2],
}
}
got = model_util.get_feature_values_for_model_spec_field(
model_specs, field, multi_output_field, extracts)
self.assertAlmostEqual(expected_values, got)
def testGetFeatureValuesForModelSpecField(self, model_specs, field,
multi_output_field,
expected_values):
extracts = {
# Only need the num_rows from RecordBatch so use fake array of same len
# as features.
constants.ARROW_RECORD_BATCH_KEY:
pa.RecordBatch.from_arrays([pa.array([1])], ['dummy']),
constants.FEATURES_KEY: [
{
'feature1': [1.0, 1.1, 1.2],
'feature2': [2.0, 2.1, 2.2],
'feature3': [3.0, 3.1, 3.2],
},
]
}
got = model_util.get_feature_values_for_model_spec_field(
model_specs, field, multi_output_field, extracts)
self.assertAlmostEqual(expected_values, got)
def _extract_features_and_labels(self, batched_extract):
"""Extract features from extracts containing arrow table."""
# This function is a combination of
# _ExtractFeatures.extract_features in extractors/features_extractor.py
# and _ExtractLabels.extract_labels in extractors/labels_extractor.py
result = copy.copy(batched_extract)
(record_batch, serialized_examples) = (
features_extractor.
_drop_unsupported_columns_and_fetch_raw_data_column( # pylint: disable=protected-access
batched_extract[constants.ARROW_RECORD_BATCH_KEY]))
features = result[
constants.
FEATURES_KEY] if constants.FEATURES_KEY in result else {}
features.update(util.record_batch_to_tensor_values(record_batch))
result[constants.FEATURES_KEY] = features
result[constants.INPUT_KEY] = serialized_examples
labels = (model_util.get_feature_values_for_model_spec_field(
list(self._eval_config.model_specs), "label_key", "label_keys",
result, True))
result[constants.LABELS_KEY] = self._transform_labels(labels)
return result
