component.py

# Lint as: python2, python3
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""TFX ExampleValidator component definition."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from typing import Optional, Text

import absl

from tfx import types
from tfx.components.base import base_component
from tfx.components.base import executor_spec
from tfx.components.example_validator import executor
from tfx.types import standard_artifacts
from tfx.types.standard_component_specs import ExampleValidatorSpec


class ExampleValidator(base_component.BaseComponent):
  """A TFX component to validate input examples.

  The ExampleValidator component uses [Tensorflow Data
  Validation](https://www.tensorflow.org/tfx/data_validation) to
  validate the statistics of some splits on input examples against a schema.

  The ExampleValidator component identifies anomalies in training and serving
  data. The component can be configured to detect different classes of anomalies
  in the data. It can:
    - perform validity checks by comparing data statistics against a schema that
      codifies expectations of the user.
    - detect data drift by looking at a series of data.
    - detect changes in dataset-wide data (i.e., num_examples) across spans or
      versions.

  Schema Based Example Validation
  The ExampleValidator component identifies any anomalies in the example data by
  comparing data statistics computed by the StatisticsGen component against a
  schema. The schema codifies properties which the input data is expected to
  satisfy, and is provided and maintained by the user.

  Please see https://www.tensorflow.org/tfx/data_validation for more details.

  ## Example
  ```
  # Performs anomaly detection based on statistics and data schema.
  validate_stats = ExampleValidator(
      statistics=statistics_gen.outputs['statistics'],
      schema=infer_schema.outputs['schema'])
  ```
  """

  SPEC_CLASS = ExampleValidatorSpec
  EXECUTOR_SPEC = executor_spec.ExecutorClassSpec(executor.Executor)

  def __init__(self,
               statistics: types.Channel = None,
               schema: types.Channel = None,
               output: Optional[types.Channel] = None,
               stats: Optional[types.Channel] = None,
               instance_name: Optional[Text] = None):
    """Construct an ExampleValidator component.

    Args:
      statistics: A Channel of type `standard_artifacts.ExampleStatistics`. This
        should contain at least 'eval' split. Other splits are currently
        ignored.
      schema: A Channel of type `standard_artifacts.Schema`. _required_
      output: Output channel of type `standard_artifacts.ExampleAnomalies`.
      stats: Backwards compatibility alias for the 'statistics' argument.
      instance_name: Optional name assigned to this specific instance of
        ExampleValidator. Required only if multiple ExampleValidator components
        are declared in the same pipeline.  Either `stats` or `statistics` must
        be present in the arguments.
    """
    if stats:
      absl.logging.warning(
          'The "stats" argument to the StatisticsGen component has '
          'been renamed to "statistics" and is deprecated. Please update your '
          'usage as support for this argument will be removed soon.')
      statistics = stats
    anomalies = output or types.Channel(
        type=standard_artifacts.ExampleAnomalies,
        artifacts=[standard_artifacts.ExampleAnomalies()])
    spec = ExampleValidatorSpec(
        statistics=statistics, schema=schema, anomalies=anomalies)
    super(ExampleValidator, self).__init__(
        spec=spec, instance_name=instance_name)