MetricFrame visualizations

import pandas as pd
from fairlearn.datasets import fetch_adult
from sklearn.metrics import accuracy_score, precision_score
from sklearn.tree import DecisionTreeClassifier

from fairlearn.metrics import (
    MetricFrame,
    count,
    false_negative_rate,
    false_positive_rate,
    selection_rate,
)

data = fetch_adult()
X = pd.get_dummies(data.data)
y_true = (data.target == ">50K") * 1
sex = data.data["sex"]

classifier = DecisionTreeClassifier(min_samples_leaf=10, max_depth=4)
classifier.fit(X, y_true)
y_pred = classifier.predict(X)

# Analyze metrics using MetricFrame
metrics = {
    "accuracy": accuracy_score,
    "precision": 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_true, y_pred=y_pred, sensitive_features=sex
)
metric_frame.by_group.plot.bar(
    subplots=True,
    layout=[3, 3],
    legend=False,
    figsize=[12, 8],
    title="Show all metrics",
)

# Customize plots with ylim
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",
)

# Customize plots with colormap
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",
)

# Customize plots with kind (note that we are only plotting the "count" metric here because we are showing 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",
)

# Saving plots
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")

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