def load_eval_result(output_path: Text,
model_name: Optional[Text] = None) -> EvalResult:
"""Creates an EvalResult object for use with the visualization functions."""
metrics_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_metrics(
path=os.path.join(output_path, _METRICS_OUTPUT_FILE),
model_name=model_name))
plots_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_plots(
path=os.path.join(output_path, _PLOTS_OUTPUT_FILE)))
eval_config = load_eval_config(output_path)
return EvalResult(slicing_metrics=metrics_proto_list,
plots=plots_proto_list,
config=eval_config)
def load_eval_result(output_path: Text,
model_name: Optional[Text] = None) -> EvalResult:
"""Creates an EvalResult object for use with the visualization functions."""
eval_config = load_eval_config(output_path)
output_spec = _get_output_data_spec(eval_config, model_name)
metrics_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_metrics(
path=output_filename(output_spec, constants.METRICS_KEY),
model_name=model_name))
plots_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_plots(
path=output_filename(output_spec, constants.PLOTS_KEY)))
return EvalResult(slicing_metrics=metrics_proto_list,
plots=plots_proto_list,
config=eval_config)
def load_eval_result(output_path: Text) -> EvalResult:
"""Creates an EvalResult object for use with the visualization functions."""
metrics_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_metrics(
path=os.path.join(output_path, _METRICS_OUTPUT_FILE)))
plots_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_plots(
path=os.path.join(output_path, _PLOTS_OUTPUT_FILE)))
slicing_metrics = [(key, _convert_proto_map_to_dict(metrics_data))
for key, metrics_data in metrics_proto_list]
plots = [(key, _convert_proto_map_to_dict(plot_data))
for key, plot_data in plots_proto_list]
eval_config = load_eval_config(output_path)
return EvalResult(
slicing_metrics=slicing_metrics, plots=plots, config=eval_config)
def _get_evaluation_result_from_remote_path(self, request):
evaluation_output_path = request.args.get('evaluation_output_path')
try:
evaluation_output_path = six.ensure_text(evaluation_output_path)
except (UnicodeDecodeError, AttributeError):
pass
try:
metrics = (
metrics_and_plots_serialization.load_and_deserialize_metrics(
path=evaluation_output_path))
data = widget_view.convert_slicing_metrics_to_ui_input(metrics)
except (KeyError, json_format.ParseError) as error:
logging.info('Error while fetching evaluation data, %s', error)
data = []
return http_util.Respond(request,
data,
content_type='application/json')
def load_eval_result(output_path: Text,
model_name: Optional[Text] = None) -> EvalResult:
"""Creates an EvalResult object for use with the visualization functions."""
eval_config, data_location, file_format, model_locations = (
_load_eval_run(output_path))
metrics_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_metrics(
path=os.path.join(output_path, constants.METRICS_KEY),
model_name=model_name))
plots_proto_list = (
metrics_and_plots_serialization.load_and_deserialize_plots(
path=os.path.join(output_path, constants.PLOTS_KEY)))
if model_name is None:
model_location = list(model_locations.values())[0]
else:
model_location = model_locations[model_name]
return EvalResult(slicing_metrics=metrics_proto_list,
plots=plots_proto_list,
config=eval_config,
data_location=data_location,
file_format=file_format,
model_location=model_location)
