tfma.run_model_analysis

Runs TensorFlow model analysis.

tfma.run_model_analysis(
    eval_shared_model: Optional[tfma.types.EvalSharedModel] = None,
    eval_config: Optional[tfma.EvalConfig] = None,
    data_location: str = '',
    file_format: str = 'tfrecords',
    output_path: Optional[str] = None,
    extractors: Optional[List[extractor.Extractor]] = None,
    evaluators: Optional[List[evaluator.Evaluator]] = None,
    writers: Optional[List[writer.Writer]] = None,
    pipeline_options: Optional[Any] = None,
    slice_spec: Optional[List[slicer.SingleSliceSpec]] = None,
    write_config: Optional[bool] = True,
    compute_confidence_intervals: Optional[bool] = False,
    min_slice_size: int = 1,
    random_seed_for_testing: Optional[int] = None,
    schema: Optional[schema_pb2.Schema] = None
) -> Union[tfma.EvalResult, view_types.EvalResults]

Used in the notebooks

Used in the guide	Used in the tutorials
Using Counterfactual Logit Pairing with Keras	TensorFlow Model Analysis

It runs a Beam pipeline to compute the slicing metrics exported in TensorFlow Eval SavedModel and returns the results.

This is a simplified API for users who want to quickly get something running locally. Users who wish to create their own Beam pipelines can use the Evaluate PTransform instead.

Args
eval_shared_model	Optional shared model (single-model evaluation) or list of shared models (multi-model evaluation). Only required if needed by default extractors, evaluators, or writers.
eval_config	Eval config.
data_location	The location of the data files.
file_format	The file format of the data, can be either 'text' or 'tfrecords' for now. By default, 'tfrecords' will be used.
output_path	The directory to output metrics and results to. If None, we use a temporary directory.
extractors	Optional list of Extractors to apply to Extracts. Typically these will be added by calling the default_extractors function. If no extractors are provided, default_extractors (non-materialized) will be used.
evaluators	Optional list of Evaluators for evaluating Extracts. Typically these will be added by calling the default_evaluators function. If no evaluators are provided, default_evaluators will be used.
writers	Optional list of Writers for writing Evaluation output. Typically these will be added by calling the default_writers function. If no writers are provided, default_writers will be used.
pipeline_options	Optional arguments to run the Pipeline, for instance whether to run directly.
slice_spec	Deprecated (use EvalConfig).
write_config	Deprecated (use EvalConfig).
compute_confidence_intervals	Deprecated (use EvalConfig).
min_slice_size	Deprecated (use EvalConfig).
random_seed_for_testing	Provide for deterministic tests only.
schema	Optional tf.Metadata schema of the input data.

Returns
An EvalResult that can be used with the TFMA visualization functions.

Raises
ValueError	If the file_format is unknown to us.