tfx.components.ModelValidator

DEPRECATED: Please use Evaluator instead.

Inherits From: BaseComponent, BaseNode

View aliases

Main aliases

tfx.components.model_validator.component.ModelValidator

tfx.components.ModelValidator(
    examples: tfx.types.Channel,
    model: tfx.types.Channel,
    blessing: Optional[tfx.types.Channel] = None,
    instance_name: Optional[Text] = None
)

The model validator component can be used to check model metrics threshold and validate current model against a previously validated model. If there isn't a prior validated model, model validator will just make sure the threshold passed. Otherwise, ModelValidator compares a newly trained models against a known good model, specifically the last model "blessed" by this component. A model is "blessed" if the exported model's metrics are within predefined thresholds around the prior model's metrics.

Note: This component includes a driver to resolve last blessed model.

Possible causes why model validation fails

Model validation can fail for many reasons, but these are the most common:

• problems with training data. For example, negative examples are dropped or features are missing.
• problems with the test or evaluation data. For example, skew exists between the training and evaluation data.
• changes in data distribution. This indicates the user behavior may have changed over time.
• problems with the trainer. For example, the trainer was stopped before model is converged or the model is unstable.

Example

  # Performs quality validation of a candidate model (compared to a baseline).
  model_validator = ModelValidator(
      examples=example_gen.outputs['examples'],
      model=trainer.outputs['model'])

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. required
blessing	Output channel of type standard_artifacts.ModelBlessing that contains the validation result.
instance_name	Optional name assigned to this specific instance of ModelValidator. Required only if multiple ModelValidator components are declared in the same pipeline.

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