-
Notifications
You must be signed in to change notification settings - Fork 2
/
utils.py
158 lines (121 loc) · 4.78 KB
/
utils.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
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from skopt.acquisition import gaussian_ei
from skopt.acquisition import gaussian_lcb
def plot_main(opt,x,acq_name='EI',n_iter=-1):
model=opt.models[n_iter]
x_model=opt.space.transform(x.tolist())
y_pred, sigma = model.predict(x_model, return_std=True)
plt.plot(x, y_pred, "g--", label=r"$\mu(x)$")
plt.plot(opt.Xi[0:n_iter+3], opt.yi[0:n_iter+3],
"ro", label="Observations")
plt.grid()
def plot_optimizer_part(opt, x, acq_name='EI',n_iter=1):
model = opt.models[n_iter]
x_model = opt.space.transform(x.tolist())
# Plot Model(x) + contours
y_pred, sigma = model.predict(x_model, return_std=True)
plt.plot(x, y_pred, "g--", label=r"$\mu(x)$")
plt.fill(np.concatenate([x, x[::-1]]),
#np.concatenate([y_pred - 1.9600 * sigma,
# (y_pred + 1.9600 * sigma)[::-1]]),
np.concatenate([y_pred - sigma,
(y_pred + sigma)[::-1]]),
alpha=.2, fc="g", ec="None")
# Plot sampled points
plt.plot(opt.Xi[:n_iter+3], opt.yi[:n_iter+3],
"ro", label="Observations")
if acq_name == 'EI':
acq = gaussian_ei(x_model, model, y_opt=np.min(opt.yi),
**opt.acq_func_kwargs)
acq /= acq.max()
elif acq_name == 'LCB':
acq = gaussian_lcb(x_model, model, **opt.acq_func_kwargs)
acq /= acq.min()
# shift down to make a better plot
acq = acq - 2
plt.plot(x, acq, "b", label="%s(x)" % acq_name)
plt.fill_between(x.ravel(), -2.0, acq.ravel(), alpha=0.3, color='blue')
# Adjust plot layout
plt.grid()
plt.legend(loc='best')
def plot_optimizer(opt, x, acq_name='EI'):
model = opt.models[-1]
x_model = opt.space.transform(x.tolist())
# Plot Model(x) + contours
y_pred, sigma = model.predict(x_model, return_std=True)
plt.plot(x, y_pred, "g--", label=r"$\mu(x)$")
plt.fill(np.concatenate([x, x[::-1]]),
#np.concatenate([y_pred - 1.9600 * sigma,
# (y_pred + 1.9600 * sigma)[::-1]]),
np.concatenate([y_pred - sigma,
(y_pred + sigma)[::-1]]),
alpha=.2, fc="g", ec="None")
# Plot sampled points
plt.plot(opt.Xi, opt.yi,
"ro", label="Observations")
if acq_name == 'EI':
acq = gaussian_ei(x_model, model, y_opt=np.min(opt.yi),
**opt.acq_func_kwargs)
acq /= acq.max()
elif acq_name == 'LCB':
acq = gaussian_lcb(x_model, model, **opt.acq_func_kwargs)
acq /= acq.min()
# shift down to make a better plot
acq = acq - 2
plt.plot(x, acq, "b", label="%s(x)" % acq_name)
plt.fill_between(x.ravel(), -2.0, acq.ravel(), alpha=0.3, color='blue')
# Adjust plot layout
plt.grid()
plt.legend(loc='best')
def midpoint(x):
return x[0] + (x[1] - x[0])/2
def spread(x):
return np.power((x[1] - x[0]) / 2., 2)
def beer_gauss(ABV, IBU):
means = midpoint(ABV), midpoint(IBU)
cov = ((spread(ABV), 0.), (0, spread(IBU)))
return stats.multivariate_normal(mean=means, cov=cov)
def beer_score(points):
# alcohol and IBU dimensions
# session beers
session = beer_gauss((3.5, 5.), (10, 35))
# american IPA
american_ipa = beer_gauss((6.3, 7.5), (50, 70))
# american imperial red ale
american_imperial = beer_gauss((8, 10.6), (55, 85))
american_wheat_ale = beer_gauss((8.5, 12.2), (45, 85))
# english bitter
bitter = beer_gauss((3, 4.2), (20, 35))
# belgian pale ale
belgian_pale_ale = beer_gauss((4, 6), (20, 30))
# belgian dubble
belgian_dubble = beer_gauss((6.3, 7.6), (20, 35))
belgian_triple = beer_gauss((7.1, 10.1), (20, 45))
# belgian quadruple
belgian_quad = beer_gauss((7.2, 11.2), (25, 50))
belgian_golden = beer_gauss((7, 11), (20, 50))
# vienna lager
vienna_lager = beer_gauss((4.5, 5.5), (22, 28))
# weizen bock
weizen_bock = beer_gauss((7, 9.5), (15, 35))
maibock = beer_gauss((6, 8), (20, 38))
weizen_dunkel = beer_gauss((4.8, 5.4), (10, 15))
background = beer_gauss((2, 14), (0, 80))
Z = (10 * background.pdf(points) +
3 * session.pdf(points) +
14.2 * american_ipa.pdf(points) +
2 * american_imperial.pdf(points) +
15 * american_wheat_ale.pdf(points) +
5 * bitter.pdf(points) +
5 * belgian_pale_ale.pdf(points) +
1 * belgian_dubble.pdf(points) +
7 * belgian_triple.pdf(points) +
5 * belgian_quad.pdf(points) +
6 * belgian_golden.pdf(points) +
1 * vienna_lager.pdf(points) +
3.2 * weizen_bock.pdf(points) +
2.9 * weizen_dunkel.pdf(points) +
7 * maibock.pdf(points))
return Z / 0.66