def log_table()

in src/smexperiments/tracker.py [0:0]

• 18 lines of code
• 9 McCabe index (conditional complexity)

    def log_table(self, title=None, values=None, data_frame=None, output_artifact=True):
        """Record a table of values to an artifact. Rendering in Studio is not currently supported.

        Examples
            .. code-block:: python

                table_data = {
                    "x": [1,2,3],
                    "y": [4,5,6]
                }
                my_tracker.log_table('SampleData',table_data)

                # or log a data frame
                df = pd.DataFrame(data=table_data)
                my_tracker.log_table('SampleData',df)

        Args:
            title (str, optional): Title of the table. Defaults to None.
            values ([type], optional): A dictionary of values. i.e. {"x": [1,2,3], "y": [1,2,3]}.
                Defaults to None.
            data_frame (DataFrame, optional): Pandas dataframe alternative to values.
                Defaults to None.
            output_artifact (bool): Determines direction of association to the trial component.
                Defaults to output artifact. If False will be an input artifact.

        Raises:
            ValueError: If values or data_frame are invalid.
        """
        if values is None and data_frame is None:
            raise ValueError("Either values or data_frame must be supplied.")

        if values is not None and data_frame is not None:
            raise ValueError("Only one of values or data_frame may be supplied.")

        if values is not None:
            for key in values:
                if "list" not in str(type(values[key])):
                    raise ValueError(
                        'Table values should be list. i.e. {"x": [1,2,3]}, instead was ' + str(type(values[key]))
                    )

        if data_frame is not None:
            values = _ArtifactConverter.convert_data_frame_to_values(data_frame)
            fields = _ArtifactConverter.convert_data_frame_to_fields(data_frame)
        else:
            fields = _ArtifactConverter.convert_dict_to_fields(values)

        data = {"type": "Table", "version": 0, "title": title, "fields": fields, "data": values}

        self._log_graph_artifact(title, data, "Table", output_artifact)