-
Notifications
You must be signed in to change notification settings - Fork 0
/
02_fbeta_curves.py
118 lines (86 loc) · 3.04 KB
/
02_fbeta_curves.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
import numpy as np
import matplotlib.pyplot as plt
import os
from config import *
RESULTS = os.listdir("results")
CLF = 0
clf_names = [s.__name__ for s in SAMPLING]
# betas = np.hstack([np.linspace(0.05, 1.0, 1000), np.linspace(1.0, 50, 1000)])
betas = np.geomspace(0.1, 10, 1000)
def f_beta(TPR, PPV, beta=1.0):
if not hasattr(beta, "__iter__"):
beta = np.array([beta])
beta_sqr = np.power(beta, 2)
return np.nan_to_num(
(1 + beta_sqr)
* PPV[..., np.newaxis]
* TPR[..., np.newaxis]
/ ((beta_sqr * PPV[..., np.newaxis]) + TPR[..., np.newaxis])
)
def f_beta_zero_point(TPR_A, PPV_A, TPR_B, PPV_B):
return np.sqrt(
(TPR_A * TPR_B * (PPV_B - PPV_A)) / (PPV_A * PPV_B * (TPR_A - TPR_B))
)
for res_file in RESULTS:
results = np.load(os.path.join("results", res_file))
ds_name = res_file.split('.')[0]
print(ds_name)
data = results[:, CLF, :, :]
recall = data[:, :, 0]
precision = data[:, :, 1]
n_splits = data.shape[1]
fig, axs = plt.subplots(
n_splits, 2, figsize=(15, 3 * n_splits), gridspec_kw={"width_ratios": [1, 3]}
)
# Splits
for n in range(n_splits):
f_betas = f_beta(recall[:, n], precision[:, n], beta=betas).T
best = np.argmax(f_betas, axis=-1)
a = np.pad(best[1:], (0, 1), "edge") - best
best_map = a != 0
best_map[-1] = True
unique_best = best[best_map]
ax = axs[n, 0]
ax.grid(ls=":")
ax.set_xlim(0.0, 1.05)
ax.set_ylim(0.0, 1.05)
ax.set_aspect("equal")
ax.set_xlabel("TPR")
ax.set_ylabel("PPV")
ax.spines[["right", "top"]].set_visible(False)
ax.scatter(recall[:, n], precision[:, n], s=10, c="#DDDDDD")
for b in unique_best:
ax.scatter(recall[b, n], precision[b, n], s=50, marker="*")
ax = axs[n, 1]
ax.grid(ls=":")
ax.set_xscale("log")
ax.set_ylim(0.0, 1.0)
ax.set_xlim(np.min(betas), np.max(betas))
ax.set_ylabel("$F_\\beta$ score")
if n == n_splits - 1:
ax.set_xlabel("$\\beta$")
ax.spines[["right", "top"]].set_visible(False)
ax.plot(betas, f_betas, c="#DDDDDD", lw=1)
for b in unique_best:
ax.plot(betas[best == b], f_betas[:, b][best == b], label=clf_names[b])
ax.legend(loc="lower center", prop={"size": "x-small"}, ncol=8)
best_iter = iter(unique_best)
last = next(best_iter)
while x := next(best_iter, None):
a = f_beta_zero_point(
recall[last, n], precision[last, n], recall[x, n], precision[x, n]
)
ax.vlines(a, 0.0, 1.0, color="k", lw=1, ls=":")
ax.text(
a,
0.92,
f"{a:.2f}",
rotation=90,
fontsize="x-small",
horizontalalignment="right",
)
last = x
plt.tight_layout()
plt.savefig(f"figures/02_{ds_name}.png")
plt.savefig(f"figures/02_{ds_name}.pdf")
plt.close()