Plotting#

Plotting grouped metrics#

The simplest way to visualize grouped metrics from the MetricFrame is to take advantage of the inherent plotting capabilities of pandas.DataFrame:

metrics = {
    "accuracy": accuracy_score,
    "precision": zero_div_precision_score,
    "false positive rate": false_positive_rate,
    "false negative rate": false_negative_rate,
    "selection rate": selection_rate,
    "count": count,
}
metric_frame = MetricFrame(
    metrics=metrics, y_true=y_test, y_pred=y_pred, sensitive_features=A_test
)
metric_frame.by_group.plot.bar(
    subplots=True,
    layout=[3, 3],
    legend=False,
    figsize=[12, 8],
    title="Show all metrics",
)
../../_images/sphx_glr_plot_quickstart_001.png

It is possible to customize the plots. Here are some common examples.

Customize Plots: ylim#

The y-axis range is automatically set, which can be misleading, therefore it is sometimes useful to set the ylim argument to define the yaxis range.

metric_frame.by_group.plot(
    kind="bar",
    ylim=[0, 1],
    subplots=True,
    layout=[3, 3],
    legend=False,
    figsize=[12, 8],
    title="Show all metrics with assigned y-axis range",
)
../../_images/sphx_glr_plot_quickstart_002.png

Customize Plots: colormap#

To change the color scheme, we can use the colormap argument. A list of colorschemes can be found here.

metric_frame.by_group.plot(
    kind="bar",
    subplots=True,
    layout=[3, 3],
    legend=False,
    figsize=[12, 8],
    colormap="Accent",
    title="Show all metrics in Accent colormap",
)
../../_images/sphx_glr_plot_quickstart_003.png

Customize Plots: kind#

There are different types of charts (e.g. pie, bar, line) which can be defined by the kind argument. Here is an example of a pie chart.

metric_frame.by_group[["count"]].plot(
    kind="pie",
    subplots=True,
    layout=[1, 1],
    legend=False,
    figsize=[12, 8],
    title="Show count metric in pie chart",
)
../../_images/sphx_glr_plot_quickstart_004.png

There are many other customizations that can be done. More information can be found in pandas.DataFrame.plot().

In order to save a plot, access the matplotlib.figure.Figure as below and save it with your desired filename.

fig = metric_frame.by_group[["count"]].plot(
    kind="pie",
    subplots=True,
    layout=[1, 1],
    legend=False,
    figsize=[12, 8],
    title="Show count metric in pie chart",
)

# Don't save file during doc build
if "__file__" in locals():
    fig[0][0].figure.savefig("filename.png")

Plotting ROC curves by sensitive feature#

To assess how well a binary classifier separates the positive and negative classes for each subgroup, plot_roc_curve_by_group() draws one Receiver Operating Characteristic (ROC) curve per group defined by the sensitive feature(s), along with the overall curve and a chance-level baseline. Curves that lie on top of one another indicate similar ranking performance across groups, while diverging curves indicate that the model discriminates between the classes better for some groups than for others.

plot_roc_curve_by_group(y_test, y_score, sensitive_features=A_test)
plt.show()
../../_images/sphx_glr_plot_roc_auc_001.png

The function only produces the plot. To obtain the AUC scores themselves, use MetricFrame with sklearn.metrics.roc_auc_score, passing the scores as y_pred. See the full Plotting ROC curves by sensitive feature example for details.