in model_card_toolkit/model_card_toolkit.py [0:0]
def _annotate_eval_results(self, model_card: ModelCard) -> ModelCard:
"""Annotates a model card with info from TFMA evaluation results.
The eval results are annotated as PerformanceMetrics in the model_card.
Graphics are also generated and appended to the QuantitativeAnalysis
section.
EvalResults are read from both TfmaSource or MlmdSource, whichever is
provided. Using both may cause duplicates to be recorded. If neither is
provided, this function will be a no-op.
Args:
model_card: The model card object to annotate with TFMA EvalResult
metrics.
Returns:
The model_card with eval result metrics annotated.
"""
if self._source and self._source.tfma:
for eval_result_path in self._source.tfma.eval_result_paths:
eval_result = tfma.load_eval_result(
output_path=eval_result_path,
output_file_format=self._source.tfma.file_format)
if eval_result:
logging.info('EvalResult found at path %s', eval_result_path)
if self._source.tfma.metrics_include or self._source.tfma.metrics_exclude:
eval_result = tfx_util.filter_metrics(
eval_result, self._source.tfma.metrics_include,
self._source.tfma.metrics_exclude)
tfx_util.annotate_eval_result_metrics(model_card, eval_result)
graphics.annotate_eval_result_plots(model_card, eval_result)
else:
logging.info('EvalResult not found at path %s', eval_result_path)
if self._store:
metrics_artifacts = tfx_util.get_metrics_artifacts_for_model(
self._store, self._artifact_with_model_uri.id)
for metrics_artifact in metrics_artifacts:
eval_result = tfx_util.read_metrics_eval_result(metrics_artifact.uri)
if eval_result is not None:
tfx_util.annotate_eval_result_metrics(model_card, eval_result)
graphics.annotate_eval_result_plots(model_card, eval_result)
return model_card