def render_time_series(
results: view_types.EvalResults,
slicing_spec: Optional[Union[slicer.SingleSliceSpec,
config_pb2.SlicingSpec]] = None,
display_full_path: bool = False
) -> Optional[visualization.TimeSeriesViewer]: # pytype: disable=invalid-annotation
"""Renders the time series view as widget.
Args:
results: An tfma.EvalResults.
slicing_spec: A tfma.SlicingSpec determining the slice to show time series
on. Show overall if not set.
display_full_path: Whether to display the full path to model / data in the
visualization or just show file name.
Returns:
A TimeSeriesViewer object if in Jupyter notebook; None if in Colab.
"""
if slicing_spec and isinstance(slicing_spec, config_pb2.SlicingSpec):
slicing_spec = slicer.SingleSliceSpec(spec=slicing_spec)
slice_spec_to_use = slicing_spec if slicing_spec else slicer.SingleSliceSpec(
)
data = util.get_time_series(results, slice_spec_to_use, display_full_path)
cfg = {
'isModelCentric': results.get_mode() == constants.MODEL_CENTRIC_MODE
}
return visualization.render_time_series(data, cfg)
def get_time_series(
results: view_types.EvalResults, slicing_spec: slicer.SingleSliceSpec,
display_full_path: bool
) -> List[Dict[Text, Union[Dict[Union[float, Text], Any], Text]]]:
"""Util function that extracts time series data for the specified slice.
Args:
results: A collection of EvalResult whose metrics should be visualized in a
time series.
slicing_spec: The spec specifying the slice to show in the time series.
display_full_path: Whether to display the full path or just the file name.
Returns:
A list of dictionaries, where each dictionary contains the config and the
metrics for the specified slice for a single eval run.
Raises:
ValueError: if the given slice spec matches more than one slice for any eval
run in results or if the slicing spec matches nothing in all eval runs.
"""
data = []
for result in results.get_results():
matching_slices = find_all_slices(result.slicing_metrics, slicing_spec)
slice_count = len(matching_slices)
if slice_count == 1:
data.append({
'metrics': matching_slices[0]['metrics'],
'config': {
'modelIdentifier':
_get_identifier(result.model_location, display_full_path),
'dataIdentifier':
_get_identifier(result.data_location, display_full_path),
}
})
elif slice_count > 1:
raise ValueError('Given slice spec matches more than one slice.')
run_count = len(data)
if not run_count:
raise ValueError('Given slice spec has no matches in any eval run.')
return data # pytype: disable=bad-return-type