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