109 lines
4.5 KiB
Python
109 lines
4.5 KiB
Python
from kfp.components import InputPath, OutputPath
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def ExampleValidator(
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statistics_path: InputPath('ExampleStatistics'),
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schema_path: InputPath('Schema'),
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anomalies_path: OutputPath('ExampleAnomalies'),
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):
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"""
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A TFX component to validate input examples.
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The ExampleValidator component uses [Tensorflow Data
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Validation](https://www.tensorflow.org/tfx/data_validation) to
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validate the statistics of some splits on input examples against a schema.
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The ExampleValidator component identifies anomalies in training and serving
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data. The component can be configured to detect different classes of anomalies
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in the data. It can:
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- perform validity checks by comparing data statistics against a schema that
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codifies expectations of the user.
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- detect data drift by looking at a series of data.
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- detect changes in dataset-wide data (i.e., num_examples) across spans or
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versions.
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Schema Based Example Validation
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The ExampleValidator component identifies any anomalies in the example data by
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comparing data statistics computed by the StatisticsGen component against a
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schema. The schema codifies properties which the input data is expected to
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satisfy, and is provided and maintained by the user.
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Please see https://www.tensorflow.org/tfx/data_validation for more details.
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Args:
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statistics: A Channel of 'ExampleStatistics` type. This should contain at
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least 'eval' split. Other splits are ignored currently.
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schema: A Channel of "Schema' type. _required_
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Returns:
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anomalies: Output channel of 'ExampleAnomalies' type.
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Either `stats` or `statistics` must be present in the arguments.
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"""
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from tfx.components.example_validator.component import ExampleValidator as component_class
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#Generated code
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import json
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import os
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import tensorflow
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from google.protobuf import json_format, message
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from tfx.types import Artifact, channel_utils, artifact_utils
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arguments = locals().copy()
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component_class_args = {}
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for name, execution_parameter in component_class.SPEC_CLASS.PARAMETERS.items():
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argument_value_obj = argument_value = arguments.get(name, None)
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if argument_value is None:
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continue
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parameter_type = execution_parameter.type
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if isinstance(parameter_type, type) and issubclass(parameter_type, message.Message): # Maybe FIX: execution_parameter.type can also be a tuple
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argument_value_obj = parameter_type()
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json_format.Parse(argument_value, argument_value_obj)
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component_class_args[name] = argument_value_obj
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for name, channel_parameter in component_class.SPEC_CLASS.INPUTS.items():
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artifact_path = arguments[name + '_path']
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if artifact_path:
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artifact = channel_parameter.type()
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artifact.uri = artifact_path + '/' # ?
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if channel_parameter.type.PROPERTIES and 'split_names' in channel_parameter.type.PROPERTIES:
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# Recovering splits
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subdirs = tensorflow.io.gfile.listdir(artifact_path)
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artifact.split_names = artifact_utils.encode_split_names(sorted(subdirs))
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component_class_args[name] = channel_utils.as_channel([artifact])
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component_class_instance = component_class(**component_class_args)
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input_dict = {name: channel.get() for name, channel in component_class_instance.inputs.get_all().items()}
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output_dict = {name: channel.get() for name, channel in component_class_instance.outputs.get_all().items()}
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exec_properties = component_class_instance.exec_properties
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# Generating paths for output artifacts
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for name, artifacts in output_dict.items():
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base_artifact_path = arguments[name + '_path']
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# Are there still cases where output channel has multiple artifacts?
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for idx, artifact in enumerate(artifacts):
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subdir = str(idx + 1) if idx > 0 else ''
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artifact.uri = os.path.join(base_artifact_path, subdir) # Ends with '/'
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print('component instance: ' + str(component_class_instance))
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#executor = component_class.EXECUTOR_SPEC.executor_class() # Same
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executor = component_class_instance.executor_spec.executor_class()
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executor.Do(
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input_dict=input_dict,
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output_dict=output_dict,
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exec_properties=exec_properties,
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)
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if __name__ == '__main__':
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import kfp
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kfp.components.func_to_container_op(
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ExampleValidator,
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base_image='tensorflow/tfx:0.21.4',
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output_component_file='component.yaml'
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)
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