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tfma.default_extractors

Returns the default extractors for use in ExtractAndEvaluate.

tfma.default_extractors(
    eval_shared_model: Optional[tfma.types.EvalSharedModel] = None,
    eval_config: tfma.EvalConfig = None,
    slice_spec: Optional[List[slicer.SingleSliceSpec]] = None,
    materialize: Optional[bool] = None,
    tensor_adapter_config: Optional[tensor_adapter.TensorAdapterConfig] = None,
    custom_predict_extractor: Optional[tfma.extractors.Extractor] = None,
    config_version: Optional[int] = None
) -> List[tfma.extractors.Extractor]

eval_shared_model Shared model (single-model evaluation) or list of shared models (multi-model evaluation). Required unless the predictions are provided alongside of the features (i.e. model-agnostic evaluations).
eval_config Eval config.
slice_spec Deprecated (use EvalConfig).
materialize True to have extractors create materialized output.
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).
custom_predict_extractor Optional custom predict extractor for non-TF models.
config_version Optional config version for this evaluation. This should not be explicitly set by users. It is only intended to be used in cases where the provided eval_config was generated internally, and thus not a reliable indicator of user intent.

NotImplementedError If eval_config contains mixed serving and eval models.