|
1
|
|
|
from collections import defaultdict |
|
2
|
|
|
|
|
3
|
|
|
import seaborn as sns |
|
4
|
|
|
from matplotlib import pylab as plt |
|
5
|
|
|
|
|
6
|
|
|
from ethically.fairness.metrics.score import ( |
|
7
|
|
|
roc_auc_score_by_attr, roc_curve_by_attr, |
|
8
|
|
|
) |
|
9
|
|
|
|
|
10
|
|
|
|
|
11
|
|
|
def _groupby(x, by): |
|
12
|
|
|
d = defaultdict(list) |
|
13
|
|
|
for key, val in zip(by, x): |
|
14
|
|
|
d[key].append(val) |
|
15
|
|
|
return d |
|
16
|
|
|
|
|
17
|
|
|
|
|
18
|
|
|
def distplot_by(a, by, bins=None, hist=True, kde=True, rug=False, |
|
19
|
|
|
fit=None, hist_kws=None, kde_kws=None, rug_kws=None, |
|
20
|
|
|
fit_kws=None, vertical=False, norm_hist=False, |
|
21
|
|
|
ax=None): |
|
22
|
|
|
|
|
23
|
|
|
axes = [sns.distplot(a_group, |
|
24
|
|
|
bins=bins, hist=hist, kde=kde, rug=rug, |
|
25
|
|
|
fit=fit, hist_kws=hist_kws, kde_kws=kde_kws, |
|
26
|
|
|
rug_kws=rug_kws, fit_kws=fit_kws, |
|
27
|
|
|
vertical=vertical, norm_hist=norm_hist, |
|
28
|
|
|
ax=ax, label=group) |
|
29
|
|
|
for group, a_group in _groupby(a, by).items()] |
|
30
|
|
|
plt.legend() |
|
31
|
|
|
return axes |
|
32
|
|
|
|
|
33
|
|
|
|
|
34
|
|
|
# Soruce: https://github.com/reiinakano/scikit-plot/blob/master/scikitplot/metrics.py#L332 |
|
35
|
|
|
def plot_roc_curves(roc_curves, aucs=None, |
|
36
|
|
|
title='ROC Curves by Attribute', |
|
37
|
|
|
ax=None, figsize=None, |
|
38
|
|
|
title_fontsize='large', text_fontsize='medium'): |
|
39
|
|
|
"""Generate the ROC curves by attribute from (fpr, tpr, thresholds). |
|
40
|
|
|
|
|
41
|
|
|
Based on :func:`skplt.metrics.plot_roc` |
|
42
|
|
|
|
|
43
|
|
|
:param roc_curves: Receiver operating characteristic (ROC) |
|
44
|
|
|
by attribute. |
|
45
|
|
|
:type roc_curves: dict |
|
46
|
|
|
:param aucs: Area Under the ROC (AUC) by attribute. |
|
47
|
|
|
:type aucs: dict |
|
48
|
|
|
:param str title: Title of the generated plot. |
|
49
|
|
|
:param ax: The axes upon which to plot the curve. |
|
50
|
|
|
If `None`, the plot is drawn on a new set of axes. |
|
51
|
|
|
:param tuple figsize: Tuple denoting figure size of the plot |
|
52
|
|
|
e.g. (6, 6). |
|
53
|
|
|
:param title_fontsize: Matplotlib-style fontsizes. |
|
54
|
|
|
Use e.g. 'small', 'medium', 'large' |
|
55
|
|
|
or integer-values. |
|
56
|
|
|
:param text_fontsize: Matplotlib-style fontsizes. |
|
57
|
|
|
Use e.g. 'small', 'medium', 'large' |
|
58
|
|
|
or integer-values. |
|
59
|
|
|
:return: The axes on which the plot was drawn. |
|
60
|
|
|
:rtype: :class:`matplotlib.axes.Axes` |
|
61
|
|
|
|
|
62
|
|
|
""" |
|
63
|
|
|
|
|
64
|
|
|
if ax is None: |
|
65
|
|
|
fig, ax = plt.subplots(1, 1, figsize=figsize) # pylint: disable=unused-variable |
|
66
|
|
|
|
|
67
|
|
|
ax.set_title(title, fontsize=title_fontsize) |
|
68
|
|
|
|
|
69
|
|
|
for x_sens_value in roc_curves: |
|
70
|
|
|
|
|
71
|
|
|
label = 'ROC curve of group {0}'.format(x_sens_value) |
|
72
|
|
|
if aucs is not None: |
|
73
|
|
|
label += ' (area = {:0.2f})'.format(aucs[x_sens_value]) |
|
74
|
|
|
|
|
75
|
|
|
ax.plot(roc_curves[x_sens_value][0], |
|
76
|
|
|
roc_curves[x_sens_value][1], |
|
77
|
|
|
lw=2, |
|
78
|
|
|
label=label) |
|
79
|
|
|
|
|
80
|
|
|
ax.plot([0, 1], [0, 1], 'k--', lw=2) |
|
81
|
|
|
ax.set_xlim([0.0, 1.0]) |
|
82
|
|
|
ax.set_ylim([0.0, 1.05]) |
|
83
|
|
|
ax.set_xlabel('False Positive Rate', fontsize=text_fontsize) |
|
84
|
|
|
ax.set_ylabel('True Positive Rate', fontsize=text_fontsize) |
|
85
|
|
|
ax.tick_params(labelsize=text_fontsize) |
|
86
|
|
|
ax.legend(loc='lower right', fontsize=text_fontsize) |
|
87
|
|
|
|
|
88
|
|
|
return ax |
|
89
|
|
|
|
|
90
|
|
|
|
|
91
|
|
|
def plot_roc_by_attr(y_true, y_score, x_sens, |
|
92
|
|
|
title='ROC Curves by Attribute', |
|
93
|
|
|
ax=None, figsize=None, |
|
94
|
|
|
title_fontsize='large', text_fontsize='medium'): |
|
95
|
|
|
"""Generate the ROC curves by attribute from targets and scores. |
|
96
|
|
|
|
|
97
|
|
|
Based on :func:`skplt.metrics.plot_roc` |
|
98
|
|
|
|
|
99
|
|
|
:param y_true: Binary ground truth (correct) target values. |
|
100
|
|
|
:param y_score: Estimated target score as returned by a classifier. |
|
101
|
|
|
:param x_sens: Sensitive attribute values corresponded to each |
|
102
|
|
|
estimated target. |
|
103
|
|
|
:param str title: Title of the generated plot. |
|
104
|
|
|
:param ax: The axes upon which to plot the curve. |
|
105
|
|
|
If `None`, the plot is drawn on a new set of axes. |
|
106
|
|
|
:param tuple figsize: Tuple denoting figure size of the plot |
|
107
|
|
|
e.g. (6, 6). |
|
108
|
|
|
:param title_fontsize: Matplotlib-style fontsizes. |
|
109
|
|
|
Use e.g. 'small', 'medium', 'large' |
|
110
|
|
|
or integer-values. |
|
111
|
|
|
:param text_fontsize: Matplotlib-style fontsizes. |
|
112
|
|
|
Use e.g. 'small', 'medium', 'large' |
|
113
|
|
|
or integer-values. |
|
114
|
|
|
:return: The axes on which the plot was drawn. |
|
115
|
|
|
:rtype: :class:`matplotlib.axes.Axes` |
|
116
|
|
|
|
|
117
|
|
|
""" |
|
118
|
|
|
|
|
119
|
|
|
roc_curves = roc_curve_by_attr(y_true, y_score, x_sens) |
|
120
|
|
|
aucs = roc_auc_score_by_attr(y_true, y_score, x_sens) |
|
121
|
|
|
return plot_roc_curves(roc_curves, aucs, |
|
122
|
|
|
title, ax, figsize, |
|
123
|
|
|
title_fontsize, text_fontsize) |
|
124
|
|
|
|
ResponsiblyAI /
responsibly
