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A TFX component to evaluate models trained by a TFX Trainer component.
Used in the tutorials:
The Evaluator component performs model evaluations in the TFX pipeline and the resultant metrics can be viewed in a Jupyter notebook. It uses the input examples generated from the ExampleGen component to evaluate the models.
Specifically, it can provide: - metrics computed on entire training and eval dataset - tracking metrics over time - model quality performance on different feature slices
Exporting the EvalSavedModel in Trainer
In order to setup Evaluator in a TFX pipeline, an EvalSavedModel needs to be exported during training, which is a special SavedModel containing annotations for the metrics, features, labels, and so on in your model. Evaluator uses this EvalSavedModel to compute metrics.
As part of this, the Trainer component creates eval_input_receiver_fn, analogous to the serving_input_receiver_fn, which will extract the features and labels from the input data. As with serving_input_receiver_fn, there are utility functions to help with this.
Please see https://www.tensorflow.org/tfx/model_analysis for more details.
# Uses TFMA to compute a evaluation statistics over features of a model. model_analyzer = Evaluator( examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
__init__( examples=None, model=None, feature_slicing_spec=None, fairness_indicator_thresholds=None, output=None, model_exports=None, instance_name=None )
Construct an Evaluator component.
examples: A Channel of 'ExamplesPath' type, usually produced by ExampleGen component. required
model: A Channel of 'ModelExportPath' type, usually produced by Trainer component. Will be deprecated in the future for the
feature_slicing_spec: evaluator_pb2.FeatureSlicingSpec instance that describes how Evaluator should slice the data.
fairness_indicator_thresholds: Optional list of float threshold values for use with TFMA fairness indicators. Experimental functionality: this interface and functionality may change at any time. TODO(b/142653905): add a link to additional documentation for TFMA fairness indicators here.
output: Channel of
ModelEvalPathto store the evaluation results.
model_exports: Backwards compatibility alias for the
instance_name: Optional name assigned to this specific instance of Evaluator. Required only if multiple Evaluator components are declared in the same pipeline.
model must be present in the input arguments.
Node id, unique across all TFX nodes in a pipeline.
If instance name is available, node_id will be:
from_json_dict( cls, dict_data )
Convert from dictionary data to an object.
Convert from an object to a JSON serializable dictionary.