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]

Args
`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. If None, we feed the raw examples to the model.
`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.

Raises
`NotImplementedError`	If eval_config contains mixed serving and eval models.

tfma.default_extractors

Args

Raises