def render_plot(
    result: view_types.EvalResult,
    slicing_spec: Optional[Union[slicer.SingleSliceSpec,
                                 config_pb2.SlicingSpec]] = None,
    output_name: Optional[Text] = None,
    class_id: Optional[int] = None,
    top_k: Optional[int] = None,
    k: Optional[int] = None,
    label: Optional[Text] = None,
) -> Optional[visualization.PlotViewer]:  # pytype: disable=invalid-annotation
    """Renders the plot view as widget.

  Args:
    result: An tfma.EvalResult.
    slicing_spec: The tfma.SlicingSpec to identify the slice. Show overall if
      unset.
    output_name: A string representing the output name.
    class_id: A number representing the class id if multi class.
    top_k: The k used to compute prediction in the top k position.
    k: The k used to compute prediciton at the kth position.
    label: A partial label used to match a set of plots in the results.

  Returns:
    A PlotViewer 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, cfg = util.get_plot_data_and_config(result.plots, slice_spec_to_use,
                                              output_name, class_id, top_k, k,
                                              label)
    return visualization.render_plot(data, cfg)
Exemple #2
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def render_plot(result, slicing_spec=None):
    """Renders the plot view as widget.

  Args:
    result: An tfma.EvalResult.
    slicing_spec: The slicing spec to identify the slice. Show overall if unset.

  Returns:
    A PlotViewer object if in Jupyter notebook; None if in Colab.
  """
    slice_spec_to_use = slicing_spec if slicing_spec else SingleSliceSpec()
    data, config = util.get_plot_data_and_config(result.plots,
                                                 slice_spec_to_use)
    return visualization.render_plot(data, config)
Exemple #3
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def render_plot(
    result: model_eval_lib.EvalResult,
    slicing_spec: Optional[SingleSliceSpec] = None,
    label: Optional[Text] = None,
) -> Optional[visualization.PlotViewer]:
    """Renders the plot view as widget.

  Args:
    result: An tfma.EvalResult.
    slicing_spec: The slicing spec to identify the slice. Show overall if unset.
    label: A partial label used to match a set of plots in the results.

  Returns:
    A PlotViewer object if in Jupyter notebook; None if in Colab.
  """
    slice_spec_to_use = slicing_spec if slicing_spec else SingleSliceSpec()
    data, config = util.get_plot_data_and_config(result.plots,
                                                 slice_spec_to_use, label)
    return visualization.render_plot(data, config)