tfx.components.Evaluator

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A TFX component to evaluate models trained by a TFX Trainer component.

Inherits From: BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.evaluator.component.Evaluator

tfx.components.Evaluator(
    examples: tfx.types.Channel = None,
    model: tfx.types.Channel = None,
    baseline_model: Optional[tfx.types.Channel] = None,
    feature_slicing_spec: Optional[Union[evaluator_pb2.FeatureSlicingSpec, Dict[Text, Any]]] = None,
    fairness_indicator_thresholds: Optional[List[Union[float, data_types.RuntimeParameter]]] = None,
    example_splits: Optional[List[Text]] = None,
    evaluation: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None,
    eval_config: Optional[tfma.EvalConfig] = None,
    blessing: Optional[tfx.types.Channel] = None,
    schema: Optional[tfx.types.Channel] = None,
    module_file: Optional[Text] = None,
    module_path: Optional[Text] = None
)

Used in the notebooks

Used in the tutorials
TFX Estimator Component Tutorial TFX Keras Component Tutorial Fairness Indicators Lineage Case Study MLMD Model Card Toolkit Demo Better ML Engineering with ML Metadata

See Evaluator for more information on what this component's required inputs are, how to configure it, and what outputs it produces.

Args
examples	A Channel of type standard_artifacts.Examples, usually produced by an ExampleGen component. required
model	A Channel of type standard_artifacts.Model, usually produced by a Trainer component.
baseline_model	An optional channel of type 'standard_artifacts.Model' as the baseline model for model diff and model validation purpose.
feature_slicing_spec	Deprecated, please use eval_config instead. Only support estimator. evaluator_pb2.FeatureSlicingSpec instance that describes how Evaluator should slice the data. If any field is provided as a RuntimeParameter, feature_slicing_spec should be constructed as a dict with the same field names as FeatureSlicingSpec proto message.
fairness_indicator_thresholds	Optional list of float (or RuntimeParameter) threshold values for use with TFMA fairness indicators. Experimental functionality: this interface and functionality may change at any time. to additional documentation for TFMA fairness indicators here.
example_splits	Names of splits on which the metrics are computed. Default behavior (when example_splits is set to None or Empty) is using the 'eval' split.
evaluation	Channel of ModelEvaluation to store the evaluation results.
instance_name	Optional name assigned to this specific instance of Evaluator. Required only if multiple Evaluator components are declared in the same pipeline. Either model_exports or model must be present in the input arguments.
eval_config	Instance of tfma.EvalConfig containg configuration settings for running the evaluation. This config has options for both estimator and Keras.
blessing	Output channel of 'ModelBlessing' that contains the blessing result.
schema	A Schema channel to use for TFXIO.
module_file	A path to python module file containing UDFs for Evaluator customization. The module_file can implement following functions at its top level. def custom_eval_shared_model( eval_saved_model_path, model_name, eval_config, **kwargs, ) -> tfma.EvalSharedModel: def custom_extractors( eval_shared_model, eval_config, tensor_adapter_config, ) -> List[tfma.extractors.Extractor]:
module_path	A python path to the custom module that contains the UDFs. See 'module_file' for the required signature of UDFs. Note this can not be set together with module_file.

Attributes
component_id
component_type
downstream_nodes
exec_properties
id	Node id, unique across all TFX nodes in a pipeline. If id is set by the user, return it directly. otherwise, if instance name (deprecated) 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

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

Convert from dictionary data to an object.

get_id

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@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

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to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.

with_id

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with_id(
    id: Text
) -> "BaseNode"

with_platform_config

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with_platform_config(
    config: message.Message
) -> "BaseComponent"

Attaches a proto-form platform config to a component.

The config will be a per-node platform-specific config.

Args
config	platform config to attach to the component.

Returns
the same component itself.

Class Variables
EXECUTOR_SPEC	tfx.dsl.components.base.executor_spec.ExecutorClassSpec