fairlearn.metrics._plotter.plot_metric_frame

fairlearn.metrics._plotter.plot_metric_frame(metric_frame, *, kind='point', metrics=None, conf_intervals=None, subplots=True, plot_ci_labels=False, ci_labels_precision=4, ci_labels_fontsize=8, ci_labels_color='black', ci_labels_ha='center', ci_labels_legend='Conf. Intervals', **kwargs)

Visualization for metrics with and without confidence intervals.

Plots a given metric and its given error (as described by conf_intervals)

This function takes in a fairlearn.metrics.MetricFrame with precomputed metrics and metric errors and a conf_intervals array to interpret the columns of the fairlearn.metrics.MetricFrame.

The items at each index of the given metrics array and given errors or conf_intervals array should correspond to a pair of the same metric and metric error, respectively.

Parameters:

metric_framefairlearn.metrics.MetricFrame

The collection of disaggregated metric values, along with the metric errors.

kindstr, default=”point”

The type of plot to display, e.g., “point”, “bar”, “line”, etc. The supported values are “point” and those listed in pandas.DataFrame.plot()

metricslist[str] | str | None

The name of the metrics to plot. Should match columns from the given fairlearn.metrics.MetricFrame.

conf_intervalslist[str] | str | None

The name of the confidence intervals to plot. Should match columns from the given fairlearn.metrics.MetricFrame.

Note:

The return of the error function should be an array of the lower and upper bounds. e.g. [0.59, 0.62]

subplotsbool, default=True

Whether or not to plot metrics on separate subplots

plot_ci_labelsbool, default=False

Whether or not to plot numerical labels for the confidence intervals

ci_labels_precisionint, default=4

The number of digits of precision to show for confidence interval labels

ci_labels_fontsizeint, default=8

The font size to use for confidence interval labels

ci_labels_colorstr, default=”black”

The font color to use for confidence interval labels

ci_labels_hastr, default=”center”

The horizontal alignment modifier to use for confidence interval labels

ci_labels_legendstr, default=”Conf. Intervals”

The label corresponding to the confidence interval bars

**kwargs

Keyword arguments that are passed in to pandas.DataFrame.plot()

Returns:

matplotlib.axes.Axes or numpy.ndarray of them