tfma.evaluators.MetricsPlotsAndValidationsEvaluator

Creates an Evaluator for evaluating metrics and plots.

tfma.evaluators.MetricsPlotsAndValidationsEvaluator(
    eval_config: tfma.EvalConfig,
    eval_shared_model: Optional[tfma.types.EvalSharedModel] = None,
    metrics_key: str = constants.METRICS_KEY,
    plots_key: str = constants.PLOTS_KEY,
    attributions_key: str = constants.ATTRIBUTIONS_KEY,
    run_after: str = slice_key_extractor.SLICE_KEY_EXTRACTOR_STAGE_NAME,
    schema: Optional[schema_pb2.Schema] = None,
    random_seed_for_testing: Optional[int] = None,
    tensor_adapter_config: Optional[tensor_adapter.TensorAdapterConfig] = None
) -> tfma.evaluators.Evaluator

Args
eval_config	Eval config.
eval_shared_model	Optional shared model (single-model evaluation) or list of shared models (multi-model evaluation). Only required if there are metrics to be computed in-graph using the model.
metrics_key	Name to use for metrics key in Evaluation output.
plots_key	Name to use for plots key in Evaluation output.
attributions_key	Name to use for attributions key in Evaluation output.
run_after	Extractor to run after (None means before any extractors).
schema	A schema to use for customizing metrics and plots.
random_seed_for_testing	Seed to use for unit testing.
tensor_adapter_config	Tensor adapter config which specifies how to obtain tensors from the Arrow RecordBatch. The model's signature will be invoked with those tensors (matched by names). If None, an attempt will be made to create an adapter based on the model's input signature otherwise the model will be invoked with raw examples (assuming a signature of a single 1-D string tensor).

Returns
Evaluator for evaluating metrics and plots. The output will be stored under 'metrics' and 'plots' keys.