tfx.v1.components.SchemaGen

A TFX SchemaGen component to generate a schema from the training data.

tfx.v1.components.SchemaGen(
    statistics: types.BaseChannel,
    infer_feature_shape: Optional[Union[bool, tfx.v1.dsl.experimental.RuntimeParameter]] = True,
    exclude_splits: Optional[List[str]] = None
)

Used in the notebooks

Used in the tutorials
Recommending Movies: Recommender Models in TFX TFX Estimator Component Tutorial TFX Keras Component Tutorial Data validation using TFX Pipeline and TensorFlow Data Validation Better ML Engineering with ML Metadata

The SchemaGen component uses TensorFlow Data Validation to generate a schema from input statistics. The following TFX libraries use the schema:

• TensorFlow Data Validation
• TensorFlow Transform
• TensorFlow Model Analysis

In a typical TFX pipeline, the SchemaGen component generates a schema which is consumed by the other pipeline components.

Example

  # Generates schema based on statistics files.
  infer_schema = SchemaGen(statistics=statistics_gen.outputs['statistics'])

Component outputs contains:

• schema: Channel of type standard_artifacts.Schema for schema result.

See the SchemaGen guide for more details.

Args
statistics	A BaseChannel of ExampleStatistics type (required if spec is not passed). This should contain at least a train split. Other splits are currently ignored. required
infer_feature_shape	Boolean (or RuntimeParameter) value indicating whether or not to infer the shape of features. If the feature shape is not inferred, downstream Tensorflow Transform component using the schema will parse input as tf.SparseTensor. Default to True if not set.
exclude_splits	Names of splits that will not be taken into consideration when auto-generating a schema. Default behavior (when exclude_splits is set to None) is excluding no splits.

Attributes
outputs	Component's output channel dict.