def is_old_model_artifact(model_artifact: artifact.Artifact) -> bool:
"""Check whether the model artifact is generated by old TFX version."""
assert model_artifact.type == standard_artifacts.Model, ('Wrong artifact '
'type, only accept '
'Model.')
return artifact_utils.is_artifact_version_older_than(
model_artifact, artifact_utils._ARTIFACT_VERSION_FOR_MODEL_UPDATE) # pylint: disable=protected-access
def is_old_model_artifact(model_artifact: artifact.Artifact) -> bool:
"""Check whether the model artifact is generated by old TFX version."""
if model_artifact.type != standard_artifacts.Model:
absl.logging.warning(
f'Artifact type is not Model: {model_artifact.type}.')
return artifact_utils.is_artifact_version_older_than(
model_artifact, artifact_utils._ARTIFACT_VERSION_FOR_MODEL_UPDATE) # pylint: disable=protected-access
def display(self, artifact: types.Artifact):
from IPython.core.display import display # pylint: disable=g-import-not-at-top
from IPython.core.display import HTML # pylint: disable=g-import-not-at-top
for split in artifact_utils.decode_split_names(artifact.split_names):
display(HTML('<div><b>%r split:</b></div><br/>' % split))
stats_path = io_utils.get_only_uri_in_dir(
artifact_utils.get_split_uri([artifact], split))
if artifact_utils.is_artifact_version_older_than(
artifact, artifact_utils._ARTIFACT_VERSION_FOR_STATS_UPDATE): # pylint: disable=protected-access
stats = tfdv.load_statistics(stats_path)
else:
stats = tfdv.load_stats_binary(stats_path)
tfdv.visualize_statistics(stats)
def display(self, artifact: types.Artifact):
from IPython.core.display import display # pylint: disable=g-import-not-at-top
from IPython.core.display import HTML # pylint: disable=g-import-not-at-top
for split in artifact_utils.decode_split_names(artifact.split_names):
display(HTML('<div><b>%r split:</b></div><br/>' % split))
anomalies_path = io_utils.get_only_uri_in_dir(
artifact_utils.get_split_uri([artifact], split))
if artifact_utils.is_artifact_version_older_than(
artifact, artifact_utils._ARTIFACT_VERSION_FOR_ANOMALIES_UPDATE): # pylint: disable=protected-access
anomalies = tfdv.load_anomalies_text(anomalies_path)
else:
anomalies = anomalies_pb2.Anomalies()
anomalies_bytes = io_utils.read_bytes_file(anomalies_path)
anomalies.ParseFromString(anomalies_bytes)
tfdv.display_anomalies(anomalies)
def testIsArtifactVersionOlderThan(self):
examples = standard_artifacts.Examples()
self.assertFalse(
artifact_utils.is_artifact_version_older_than(examples, '0.1'))
examples.mlmd_artifact.state = metadata_store_pb2.Artifact.LIVE
self.assertTrue(
artifact_utils.is_artifact_version_older_than(examples, '0.1'))
examples.set_string_custom_property(
artifact_utils.ARTIFACT_TFX_VERSION_CUSTOM_PROPERTY_KEY, '0.2')
self.assertTrue(
artifact_utils.is_artifact_version_older_than(examples, '0.10'))
self.assertTrue(
artifact_utils.is_artifact_version_older_than(examples, '0.3'))
self.assertFalse(
artifact_utils.is_artifact_version_older_than(examples, '0.2'))
self.assertFalse(
artifact_utils.is_artifact_version_older_than(examples, '0.1'))
def Do(self, input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any]) -> None:
"""TensorFlow ExampleValidator executor entrypoint.
This validates statistics against the schema.
Args:
input_dict: Input dict from input key to a list of artifacts, including:
- statistics: A list of type `standard_artifacts.ExampleStatistics`
generated by StatisticsGen.
- schema: A list of type `standard_artifacts.Schema` which should
contain a single schema artifact.
output_dict: Output dict from key to a list of artifacts, including:
- output: A list of 'standard_artifacts.ExampleAnomalies' of size one.
It will include a single binary proto file which contains all
anomalies found.
exec_properties: A dict of execution properties.
- exclude_splits: JSON-serialized list of names of splits that the
example validator should not validate.
Returns:
None
"""
self._log_startup(input_dict, output_dict, exec_properties)
# Load and deserialize exclude splits from execution properties.
exclude_splits = json_utils.loads(
exec_properties.get(standard_component_specs.EXCLUDE_SPLITS_KEY,
'null')) or []
if not isinstance(exclude_splits, list):
raise ValueError(
'exclude_splits in execution properties needs to be a '
'list. Got %s instead.' % type(exclude_splits))
# Setup output splits.
stats_artifact = artifact_utils.get_single_instance(
input_dict[standard_component_specs.STATISTICS_KEY])
stats_split_names = artifact_utils.decode_split_names(
stats_artifact.split_names)
split_names = [
split for split in stats_split_names if split not in exclude_splits
]
anomalies_artifact = artifact_utils.get_single_instance(
output_dict[standard_component_specs.ANOMALIES_KEY])
anomalies_artifact.split_names = artifact_utils.encode_split_names(
split_names)
schema = io_utils.SchemaReader().read(
io_utils.get_only_uri_in_dir(
artifact_utils.get_single_uri(
input_dict[standard_component_specs.SCHEMA_KEY])))
for split in artifact_utils.decode_split_names(
stats_artifact.split_names):
if split in exclude_splits:
continue
logging.info(
'Validating schema against the computed statistics for '
'split %s.', split)
stats_uri = io_utils.get_only_uri_in_dir(
artifact_utils.get_split_uri([stats_artifact], split))
if artifact_utils.is_artifact_version_older_than(
stats_artifact,
artifact_utils._ARTIFACT_VERSION_FOR_STATS_UPDATE): # pylint: disable=protected-access
stats = tfdv.load_statistics(stats_uri)
else:
stats = tfdv.load_stats_binary(stats_uri)
label_inputs = {
standard_component_specs.STATISTICS_KEY: stats,
standard_component_specs.SCHEMA_KEY: schema
}
output_uri = artifact_utils.get_split_uri(
output_dict[standard_component_specs.ANOMALIES_KEY], split)
label_outputs = {labels.SCHEMA_DIFF_PATH: output_uri}
self._Validate(label_inputs, label_outputs)
logging.info(
'Validation complete for split %s. Anomalies written to '
'%s.', split, output_uri)
def Do(self, input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any]) -> None:
"""TensorFlow SchemaGen executor entrypoint.
This infers the schema using tensorflow_data_validation on the precomputed
stats of 'train' split.
Args:
input_dict: Input dict from input key to a list of artifacts, including:
- 'statistics': A list of 'ExampleStatistics' type which must contain
split 'train'.
output_dict: Output dict from key to a list of artifacts, including:
- schema: A list of 'Schema' artifact of size one.
exec_properties: A dict of execution properties, includes:
- infer_feature_shape: Whether or not to infer the shape of the feature.
- exclude_splits: Names of splits that will not be taken into
consideration when auto-generating a schema.
Returns:
None
"""
infer_feature_shape = bool(
exec_properties.get(
standard_component_specs.INFER_FEATURE_SHAPE_KEY, True))
# Load and deserialize exclude splits from execution properties.
exclude_splits = json_utils.loads(
exec_properties.get(standard_component_specs.EXCLUDE_SPLITS_KEY,
'null')) or []
if not isinstance(exclude_splits, list):
raise ValueError(
'exclude_splits in execution properties needs to be a '
'list. Got %s instead.' % type(exclude_splits))
# Only one schema is generated for all splits.
schema = None
stats_artifact = artifact_utils.get_single_instance(
input_dict[standard_component_specs.STATISTICS_KEY])
for split in artifact_utils.decode_split_names(
stats_artifact.split_names):
if split in exclude_splits:
continue
logging.info('Processing schema from statistics for split %s.',
split)
stats_uri = io_utils.get_only_uri_in_dir(
artifact_utils.get_split_uri([stats_artifact], split))
if artifact_utils.is_artifact_version_older_than(
stats_artifact,
artifact_utils._ARTIFACT_VERSION_FOR_STATS_UPDATE): # pylint: disable=protected-access
stats = tfdv.load_statistics(stats_uri)
else:
stats = tfdv.load_stats_binary(stats_uri)
if not schema:
schema = tfdv.infer_schema(stats, infer_feature_shape)
else:
schema = tfdv.update_schema(schema, stats, infer_feature_shape)
output_uri = os.path.join(
artifact_utils.get_single_uri(
output_dict[standard_component_specs.SCHEMA_KEY]),
DEFAULT_FILE_NAME)
io_utils.write_pbtxt_file(output_uri, schema)
logging.info('Schema written to %s.', output_uri)