tfma.default_extractors
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Returns the default extractors for use in ExtractAndEvaluate.
tfma.default_extractors(
eval_shared_model: Optional[tfma.types.EvalSharedModel
] = None,
eval_config: Optional[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, an attempt will be made to
create the tensors using default TensorRepresentations.
|
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.
|
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Last updated 2024-04-26 UTC.
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