-
Notifications
You must be signed in to change notification settings - Fork 2
/
unittest_tests.py
123 lines (109 loc) · 5.25 KB
/
unittest_tests.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
import unittest
from forecaster import forecaster
from batchless_VanillaLSTM_pytorch import batchless_VanillaLSTM_pytorch
from batchless_VanillaLSTM_keras import batchless_VanillaLSTM_keras
from VanillaLSTM_keras import VanillaLSTM_keras
from ABBA import ABBA as ABBA
import numpy as np
class test_LSTM(unittest.TestCase):
##################################################
# batchless_VanillaLSTM_keras
##################################################
def test_VanillaLSTM_stateful_numeric_keras(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_keras(stateful=True), abba=None)
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_stateless_numeric_keras(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_keras(stateful=False), abba=None)
f.train(max_epoch=2000, patience=200)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_stateful_symbolic_keras(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_keras(stateful=True), abba=ABBA(max_len=2, verbose=0))
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_stateless_symbolic_keras(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_keras(stateful=False), abba=ABBA(max_len=2, verbose=0))
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
##################################################
# batchless_VanillaLSTM_pytorch
##################################################
def test_VanillaLSTM_stateful_numeric_pytorch(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_pytorch(stateful=True), abba=None)
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_stateless_numeric_pytorch(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_pytorch(stateful=False), abba=None)
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_stateful_symbolic_pytorch(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_pytorch(stateful=True), abba=ABBA(max_len=2, verbose=0))
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_stateless_symbolic_pytorch(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=batchless_VanillaLSTM_pytorch(stateful=False), abba=ABBA(max_len=2, verbose=0))
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
##################################################
# VanillaLSTM_keras
##################################################
def test_VanillaLSTM_batch_numeric_keras(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=VanillaLSTM_keras(), abba=None)
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
def test_VanillaLSTM_batch_symbolic_keras(self):
time_series = [1, 2, 3, 2]*100 + [1]
k = 10
f = forecaster(time_series, model=VanillaLSTM_keras(), abba=ABBA(max_len=2, verbose=0))
f.train(max_epoch=500, patience=50)
prediction = f.forecast(k).tolist()
prediction = [round(p) for p in prediction]
print(prediction)
self.assertTrue(prediction == [2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
if __name__ == "__main__":
unittest.main()