from collections import defaultdict
import seaborn as sns
from matplotlib import pylab as plt
from responsibly.fairness.metrics.score import (
roc_auc_score_by_attr, roc_curve_by_attr,
)
def _groupby(x, by):
d = defaultdict(list)
for key, val in zip(by, x):
d[key].append(val)
return d
def distplot_by(a, by, bins=None, hist=True, kde=True, rug=False,
fit=None, hist_kws=None, kde_kws=None, rug_kws=None,
fit_kws=None, vertical=False, norm_hist=False,
ax=None):
axes = [sns.distplot(a_group,
bins=bins, hist=hist, kde=kde, rug=rug,
fit=fit, hist_kws=hist_kws, kde_kws=kde_kws,
rug_kws=rug_kws, fit_kws=fit_kws,
vertical=vertical, norm_hist=norm_hist,
ax=ax, label=group)
for group, a_group in _groupby(a, by).items()]
plt.legend()
return axes
# Soruce: https://github.com/reiinakano/scikit-plot/blob/master/scikitplot/metrics.py#L332
[docs]def plot_roc_curves(roc_curves, aucs=None,
title='ROC Curves by Attribute',
ax=None, figsize=None,
title_fontsize='large', text_fontsize='medium'):
"""Generate the ROC curves by attribute from (fpr, tpr, thresholds).
Based on :func:`skplt.metrics.plot_roc`
:param roc_curves: Receiver operating characteristic (ROC)
by attribute.
:type roc_curves: dict
:param aucs: Area Under the ROC (AUC) by attribute.
:type aucs: dict
:param str title: Title of the generated plot.
:param ax: The axes upon which to plot the curve.
If `None`, the plot is drawn on a new set of axes.
:param tuple figsize: Tuple denoting figure size of the plot
e.g. (6, 6).
:param title_fontsize: Matplotlib-style fontsizes.
Use e.g. 'small', 'medium', 'large'
or integer-values.
:param text_fontsize: Matplotlib-style fontsizes.
Use e.g. 'small', 'medium', 'large'
or integer-values.
:return: The axes on which the plot was drawn.
:rtype: :class:`matplotlib.axes.Axes`
"""
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=figsize) # pylint: disable=unused-variable
ax.set_title(title, fontsize=title_fontsize)
for x_sens_value in roc_curves:
label = 'ROC curve of group {0}'.format(x_sens_value)
if aucs is not None:
label += ' (area = {:0.2f})'.format(aucs[x_sens_value])
ax.plot(roc_curves[x_sens_value][0],
roc_curves[x_sens_value][1],
lw=2,
label=label)
ax.plot([0, 1], [0, 1], 'k--', lw=2)
ax.set_xlim([0.0, 1.0])
ax.set_ylim([0.0, 1.05])
ax.set_xlabel('False Positive Rate', fontsize=text_fontsize)
ax.set_ylabel('True Positive Rate', fontsize=text_fontsize)
ax.tick_params(labelsize=text_fontsize)
ax.legend(loc='lower right', fontsize=text_fontsize)
return ax
[docs]def plot_roc_by_attr(y_true, y_score, x_sens,
title='ROC Curves by Attribute',
ax=None, figsize=None,
title_fontsize='large', text_fontsize='medium'):
"""Generate the ROC curves by attribute from targets and scores.
Based on :func:`skplt.metrics.plot_roc`
:param y_true: Binary ground truth (correct) target values.
:param y_score: Estimated target score as returned by a classifier.
:param x_sens: Sensitive attribute values corresponded to each
estimated target.
:param str title: Title of the generated plot.
:param ax: The axes upon which to plot the curve.
If `None`, the plot is drawn on a new set of axes.
:param tuple figsize: Tuple denoting figure size of the plot
e.g. (6, 6).
:param title_fontsize: Matplotlib-style fontsizes.
Use e.g. 'small', 'medium', 'large'
or integer-values.
:param text_fontsize: Matplotlib-style fontsizes.
Use e.g. 'small', 'medium', 'large'
or integer-values.
:return: The axes on which the plot was drawn.
:rtype: :class:`matplotlib.axes.Axes`
"""
roc_curves = roc_curve_by_attr(y_true, y_score, x_sens)
aucs = roc_auc_score_by_attr(y_true, y_score, x_sens)
return plot_roc_curves(roc_curves, aucs,
title, ax, figsize,
title_fontsize, text_fontsize)
