-
Notifications
You must be signed in to change notification settings - Fork 2
/
common_plot.py
174 lines (151 loc) · 6.13 KB
/
common_plot.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
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import re
import os
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import scipy.spatial as spatial
def get_test_accuracy(log, top_k):
iteration = re.findall(r'Iteration (\d*), Testing net \(#0\)', log)
accuracy = re.findall(r'Test net output #\d: accuracy/top-{top_k} = (\d*.\d*)'.format(top_k=top_k), log)
if len(accuracy)==0:
accuracy = re.findall(r'Test net output #\d: loss/top-{top_k} = (\d*.\d*)'.format(top_k=top_k), log)
iteration = [int(i) for i in iteration]
accuracy = [float(i) for i in accuracy]
return iteration, accuracy
def get_test_loss(log):
iteration = re.findall(r'Iteration (\d*), Testing net ', log)
loss = re.findall(r'Test net output #\d: loss = (\d*.\d*)', log)
if len(loss)==0:
loss = re.findall(r'Test net output #\d: loss/loss = (\d*.\d*)', log)
iteration = [int(i) for i in iteration]
loss = [float(i) for i in loss]
return iteration, loss
def get_net_name(log):
return re.findall(r"Solving (.*)\n", log)[0]
def parse_files(files, top_k=1, separate=False):
data = {}
for file in files:
with open(file, 'r') as fp:
log = fp.read()
net_name = os.path.basename(file) if separate else get_net_name(log)
if net_name not in data.keys():
data[net_name] = {}
data[net_name]["accuracy"] = {}
data[net_name]["accuracy"]["accuracy"] = []
data[net_name]["accuracy"]["iteration"] = []
data[net_name]["loss"] = {}
data[net_name]["loss"]["loss"] = []
data[net_name]["loss"]["iteration"] = []
iteration, accuracy = get_test_accuracy(log, top_k)
data[net_name]["accuracy"]["iteration"].extend(iteration)
data[net_name]["accuracy"]["accuracy"].extend(accuracy)
iteration, loss = get_test_loss(log)
data[net_name]["loss"]["iteration"].extend(iteration)
data[net_name]["loss"]["loss"].extend(loss)
return data
def fmt(x, y):
return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x=x, y=y)
class FollowDotCursor(object):
"""Display the x,y location of the nearest data point.
http://stackoverflow.com/a/4674445/190597 (Joe Kington)
http://stackoverflow.com/a/20637433/190597 (unutbu)
"""
def __init__(self, ax, x, y, formatter=fmt, offsets=(-20, 20)):
try:
x = np.asarray(x, dtype='float')
except (TypeError, ValueError):
x = np.asarray(mdates.date2num(x), dtype='float')
y = np.asarray(y, dtype='float')
mask = ~(np.isnan(x) | np.isnan(y))
x = x[mask]
y = y[mask]
self._points = np.column_stack((x, y))
self.offsets = offsets
y = y[np.abs(y - y.mean()) <= 3 * y.std()]
self.scale = x.ptp()
self.scale = y.ptp() / self.scale if self.scale else 1
self.tree = spatial.cKDTree(self.scaled(self._points))
self.formatter = formatter
self.ax = ax
self.fig = ax.figure
self.ax.xaxis.set_label_position('top')
self.dot = ax.scatter(
[x.min()], [y.min()], s=130, color='green', alpha=0.7)
self.annotation = self.setup_annotation()
plt.connect('motion_notify_event', self)
def scaled(self, points):
points = np.asarray(points)
return points * (self.scale, 1)
def __call__(self, event):
ax = self.ax
# event.inaxes is always the current axis. If you use twinx, ax could be
# a different axis.
if event.inaxes == ax:
x, y = event.xdata, event.ydata
elif event.inaxes is None:
return
else:
inv = ax.transData.inverted()
x, y = inv.transform([(event.x, event.y)]).ravel()
annotation = self.annotation
x, y = self.snap(x, y)
annotation.xy = x, y
annotation.set_text(self.formatter(x, y))
self.dot.set_offsets((x, y))
event.canvas.draw()
def setup_annotation(self):
"""Draw and hide the annotation box."""
annotation = self.ax.annotate(
'', xy=(0, 0), ha = 'right',
xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
bbox = dict(
boxstyle='round,pad=0.5', fc='yellow', alpha=0.75),
arrowprops = dict(
arrowstyle='->', connectionstyle='arc3,rad=0'))
return annotation
def snap(self, x, y):
"""Return the value in self.tree closest to x, y."""
dist, idx = self.tree.query(self.scaled((x, y)), k=1, p=1)
try:
return self._points[idx]
except IndexError:
# IndexError: index out of bounds
return self._points[0]
def plot_accuracy(top_k, data, value_at_hover=False):
nets = data.keys()
colors = iter(cm.rainbow(np.linspace(0, 1, len(nets))))
fig = plt.figure()
ax = fig.add_subplot(111)
for net in nets:
iteration = data[net]["accuracy"]["iteration"]
accuracy = data[net]["accuracy"]["accuracy"]
iteration, accuracy = (np.array(t) for t in zip(*sorted(zip(iteration, accuracy))))
ax.plot(iteration, accuracy*100, color=next(colors), linestyle='-')
if value_at_hover:
cursor = FollowDotCursor(ax, iteration, accuracy*100)
plt.legend(nets, loc='lower right')
plt.title("Top {}".format(top_k))
plt.xlabel("Iteration")
plt.ylabel("Accuracy [%]")
plt.ylim(0,100)
plt.grid()
return plt
def plot_loss(data, value_at_hover=False):
nets = data.keys()
colors = iter(cm.rainbow(np.linspace(0, 1, len(nets))))
fig = plt.figure()
ax = fig.add_subplot(111)
for net in nets:
iteration = data[net]["loss"]["iteration"]
loss = data[net]["loss"]["loss"]
iteration, loss = (list(t) for t in zip(*sorted(zip(iteration, loss))))
ax.scatter(iteration, loss, color=next(colors))
if value_at_hover:
cursor = FollowDotCursor(ax, iteration, loss)
plt.legend(nets, loc='upper right')
plt.title("Log Loss")
plt.xlabel("Iteration")
plt.ylabel("Log Loss")
plt.xlim(0)
plt.grid()
return plt