tfx.components.SchemaGen

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

Inherits From: BaseComponent

View aliases

Main aliases

tfx.components.schema_gen.component.SchemaGen

tfx.components.SchemaGen(
    statistics: Optional[tfx.types.Channel] = None,
    infer_feature_shape: Optional[Union[bool, tfx.orchestration.data_types.RuntimeParameter]] = False,
    exclude_splits: Optional[List[Text]] = None,
    output: Optional[tfx.types.Channel] = None,
    stats: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

Used in the notebooks

Used in the tutorials
TFX Estimator Component Tutorial TFX Keras Component Tutorial Graph-based Neural Structured Learning in TFX Fairness Indicators Case Study to showcase Lineage

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 is consumed by the other pipeline components.

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

Example

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

Args
statistics	A Channel 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.
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.
output	Output Schema channel for schema result.
stats	Backwards compatibility alias for the 'statistics' argument.
instance_name	Optional name assigned to this specific instance of SchemaGen. Required only if multiple SchemaGen components are declared in the same pipeline. Either statistics or stats must be present in the input arguments.

Attributes
component_id	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: component_id is deprecated, use id instead
component_type	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: component_type is deprecated, use type instead
downstream_nodes
exec_properties
id	Node id, unique across all TFX nodes in a pipeline. If instance name is available, node_id will be: . otherwise, node_id will be:
inputs
outputs
type
upstream_nodes

Child Classes

class DRIVER_CLASS

class SPEC_CLASS

Methods

add_downstream_node

View source

add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node	a component that must run after this node.

add_upstream_node

View source

add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node	a component that must run before this node.

from_json_dict

View source

@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_id

View source

@classmethod
get_id(
    instance_name: Optional[Text] = None
)

Gets the id of a node.

This can be used during pipeline authoring time. For example: from tfx.components import Trainer

resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))

Args
instance_name	(Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns
an id for the node.

to_json_dict

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

Class Variables

• EXECUTOR_SPEC