def render_fairness_indicator( eval_result: model_eval_lib.EvalResult, slicing_column: Optional[Text] = None, slicing_spec: Optional[slicer.SingleSliceSpec] = None, output_name: Text = '', multi_class_key: Text = '', event_handlers: Optional[Dict[Text, Callable[..., Any]]] = None, ) -> Optional[Any]: """Renders the Fairness Indicator view. Args: eval_result: An tfma.EvalResult. slicing_column: The slicing column to to filter results. If both slicing_column and slicing_spec are None, show all eval results. slicing_spec: The slicing spec to filter results. If both slicing_column and slicing_spec are None, show all eval results. output_name: The output name associated with metric (for multi-output models). multi_class_key: The multi-class key associated with metric (for multi-class models). event_handlers: The event handler callback. Returns: A FairnessIndicatorViewer object if in Jupyter notebook; None if in Colab. """ data = convert_eval_result_to_ui_input(eval_result, slicing_column, slicing_spec, output_name, multi_class_key) return visualization.render_fairness_indicator(data, event_handlers)
def render_fairness_indicator( eval_result: Optional[view_types.EvalResult] = None, multi_eval_results: Optional[Dict[str, view_types.EvalResult]] = None, slicing_column: Optional[str] = None, slicing_spec: Optional[slicer.SingleSliceSpec] = None, output_name: str = '', multi_class_key: str = '', event_handlers: Optional[Dict[str, Callable[..., Any]]] = None, ) -> Optional[Any]: """Renders the Fairness Indicator view. Args: eval_result: An tfma.EvalResult. multi_eval_results: A map {string: tfma.EvalResult} of length 2, mapping an eval or model name to an EvalResult. This can be used for model comparison. slicing_column: The slicing column to to filter results. If both slicing_column and slicing_spec are None, show all eval results. This is ignored for cross slice comparison based results. slicing_spec: The slicing spec to filter results. If both slicing_column and slicing_spec are None, show all eval results. This is ignored for cross slice comparison based results. output_name: The output name associated with metric (for multi-output models). multi_class_key: The multi-class key associated with metric (for multi-class models). event_handlers: The event handler callback. Returns: A FairnessIndicatorViewer object if in Jupyter notebook; None if in Colab. """ if ((eval_result and multi_eval_results) or (not eval_result and not multi_eval_results)): raise ValueError( 'Exactly one of the "eval_result" and "multi_eval_results" ' 'parameters must be set.') if (multi_eval_results and len(multi_eval_results) != 2): raise ValueError( '"multi_eval_results" parameter only accepts 2 eval results.') data = None multi_data = None if eval_result: data = convert_slicing_metrics_to_ui_input(eval_result.slicing_metrics, slicing_column, slicing_spec, output_name, multi_class_key) else: multi_data = {} for eval_name in multi_eval_results: multi_data[eval_name] = convert_slicing_metrics_to_ui_input( multi_eval_results[eval_name].slicing_metrics, slicing_column, slicing_spec, output_name, multi_class_key) return visualization.render_fairness_indicator(data, multi_data, event_handlers)
def render_fairness_indicator( eval_result: Optional[model_eval_lib.EvalResult] = None, multi_eval_results: Optional[Dict[Text, model_eval_lib.EvalResult]] = None, slicing_column: Optional[Text] = None, slicing_spec: Optional[slicer.SingleSliceSpec] = None, output_name: Text = '', multi_class_key: Text = '', event_handlers: Optional[Dict[Text, Callable[..., Any]]] = None, ) -> Optional[Any]: """Renders the Fairness Indicator view. Args: eval_result: An tfma.EvalResult. multi_eval_results: A map of {string: tfma.EvalResult}. The key is the eval or model name. slicing_column: The slicing column to to filter results. If both slicing_column and slicing_spec are None, show all eval results. slicing_spec: The slicing spec to filter results. If both slicing_column and slicing_spec are None, show all eval results. output_name: The output name associated with metric (for multi-output models). multi_class_key: The multi-class key associated with metric (for multi-class models). event_handlers: The event handler callback. Returns: A FairnessIndicatorViewer object if in Jupyter notebook; None if in Colab. """ if ((eval_result and multi_eval_results) or (not eval_result and not multi_eval_results)): raise ValueError( 'Exactly one of the "eval_result" and "multi_eval_results" ' 'parameters must be set.') if (multi_eval_results and len(multi_eval_results) != 2): raise ValueError( '"multi_eval_results" parameter only accept 2 eval results.') data = None multi_data = None if eval_result: data = convert_eval_result_to_ui_input(eval_result, slicing_column, slicing_spec, output_name, multi_class_key) else: multi_data = {} for eval_name in multi_eval_results: multi_data[eval_name] = convert_eval_result_to_ui_input( multi_eval_results[eval_name], slicing_column, slicing_spec, output_name, multi_class_key) return visualization.render_fairness_indicator(data, multi_data, event_handlers)