tfma.run_model_analysis( eval_shared_model, data_location, file_format='tfrecords', slice_spec=None, output_path=None, extractors=None, evaluators=None, writers=None, write_config=True, pipeline_options=None, num_bootstrap_samples=1 )
Runs 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.
eval_shared_model: Shared model parameters for EvalSavedModel including any additional metrics (see EvalSharedModel for more information on how to configure additional metrics).
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.
slice_spec: A list of tfma.slicer.SingleSliceSpec. Each spec represents a way to slice the data. If None, defaults to the overall slice. Example usages: # TODO(xinzha): add more use cases once they are supported in frontend.
- tfma.SingleSiceSpec(): no slice, metrics are computed on overall data.
- tfma.SingleSiceSpec(columns=['country']): slice based on features in column "country". We might get metrics for slice "country:us", "country:jp", and etc in results.
- tfma.SingleSiceSpec(features=[('country', 'us')]): metrics are computed on slice "country:us".
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.
write_config: True to write the config along with the results.
pipeline_options: Optional arguments to run the Pipeline, for instance whether to run directly.
num_bootstrap_samples: Optional, set to at least 20 in order to calculate metrics with confidence intervals.
An EvalResult that can be used with the TFMA visualization functions.
ValueError: If the file_format is unknown to us.