-
Notifications
You must be signed in to change notification settings - Fork 0
/
JO-validation_pred2table.py
142 lines (98 loc) · 5.12 KB
/
JO-validation_pred2table.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
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
seedSize = 10
batchSize = 10
num_iters_test = 20 # manual input of test loop iterations chosen
df_data = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/MLPlatform/data/auto_mpg.csv', header=0)
arr_data = np.asarray(df_data)
trainingSetSizes = np.arange(seedSize, arr_data.shape[0], batchSize)
splits = trainingSetSizes / arr_data.shape[0]
testSetSizes = arr_data.shape[0] - trainingSetSizes
lenTestcycle = np.sum(testSetSizes)
# --------> FUNCTIONS <------------------
# RMSE CALCULATION
def rmse(true_values, predicted_values):
n = len(true_values)
residuals = 0
for i in range(n):
residuals += (true_values[i] - predicted_values[i]) ** 2.
return np.sqrt(residuals / n)
# --------> DATA IMPORT <------------------
print('Importing files...')
# data
df_rr_mcs = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/preds_rr_mcs.csv', header=None)
df_mlp_mcs = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/preds_mlp_mcs.csv', header=None)
df_xgb_mcs = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/preds_xgb_mcs.csv', header=None)
df_rr_qbc = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/preds_rr_qbc.csv', header=None)
df_mlp_qbc = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/preds_mlp_qbc.csv', header=None)
df_xgb_qbc = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/preds_xgb_qbc.csv', header=None)
df_targets_mcs = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/targets_mlp_mcs.csv', header=None)
df_targets_qbc = pd.read_csv(
'/Users/philippnoodt/Jobs_Bewerbungen/IMA/Python/AL_Kunststoff/2019-03-14/3/targets_mlp_qbc.csv', header=None)
df_mcs = pd.concat([df_rr_mcs, df_mlp_mcs, df_xgb_mcs], axis=1)
df_qbc = pd.concat([df_rr_qbc, df_mlp_qbc, df_xgb_qbc], axis=1)
print('Converting to np-arrays...')
preds_mcs = np.asarray(df_mcs)
preds_qbc = np.asarray(df_qbc)
targets_mcs = np.asarray(df_targets_mcs)
targets_qbc = np.asarray(df_targets_qbc)
def calculator_rmse_std_r2(preds_mcs, preds_qbc, targets_mcs, targets_qbc):
results_table = np.zeros([1+3*3*3, int(len(splits) + 2)])
# --------> INPUT SPLITS <------------------
for i in range(len(splits)):
results_table[0, i] = splits[i]
# --------> RMSE <------------------
# --------> MCS <------------------
print(targets_mcs.shape[0], np.sum(testSetSizes) * num_iters_test)
assert (targets_mcs.shape[0] == np.sum(testSetSizes) * num_iters_test)
for idx, testSetSize in enumerate(testSetSizes):
for model in range(3):
list_rmse = []
for test in range(num_iters_test):
test_loop = test * lenTestcycle
counter = np.sum(testSetSizes[:idx])
start_idx = test_loop + counter
end_idx = start_idx + testSetSize
rmse_value = rmse(targets_mcs[start_idx: end_idx],
preds_mcs[start_idx: end_idx, model])
print('MCS split ' + str(idx + 1) + ', test ' + str(test + 1) + ', model ' + str(model + 1) + ', preds '+
str(start_idx) + ' - ' + str(end_idx) + ', testSetSize: ' + str(len(targets_mcs[start_idx: end_idx]))
+ ', RMSE: ' + str(np.round(rmse_value, decimals=2)))
print(' ')
list_rmse.append(rmse_value)
results_table[model + 1, idx] = np.mean(list_rmse)
# --------> QBC <------------------
print(targets_qbc.shape[0], np.sum(testSetSizes) * num_iters_test)
assert (targets_qbc.shape[0] == np.sum(testSetSizes) * num_iters_test)
for idx, testSetSize in enumerate(testSetSizes):
for model in range(3):
list_rmse = []
for test in range(num_iters_test):
test_loop = test * lenTestcycle
counter = np.sum(testSetSizes[:idx])
start_idx = test_loop + counter
end_idx = start_idx + testSetSize
rmse_value = rmse(targets_qbc[start_idx: end_idx],
preds_qbc[start_idx: end_idx, model])
print('QBC split ' + str(idx + 1) + ', test ' + str(test + 1) + ', model ' + str(model + 1) + ', preds ' +
str(start_idx) + ' - ' + str(end_idx) + ', testSetSize: ' + str(
len(targets_qbc[start_idx: end_idx]))
+ ', RMSE: ' + str(np.round(rmse_value, decimals=2)))
print(' ')
list_rmse.append(rmse_value)
results_table[model + 7, idx] = np.mean(list_rmse)
return results_table
print('Calculating...')
table = calculator_rmse_std_r2(preds_mcs=preds_mcs, preds_qbc=preds_qbc,
targets_mcs=targets_mcs, targets_qbc=targets_qbc)
print('Saving...')
pd.DataFrame(table[1:, :], columns=[table[0]]).to_csv('results.csv')