-
Notifications
You must be signed in to change notification settings - Fork 74
/
6_2_split_ffm_to_subfolds.py
65 lines (42 loc) · 1.44 KB
/
6_2_split_ffm_to_subfolds.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
import feather
import numpy as np
df_all = feather.read_dataframe('tmp/clicks_train_50_50.feather')
df_train_0 = df_all[df_all.fold == 0].reset_index(drop=1)
df_train_1 = df_all[df_all.fold == 1].reset_index(drop=1)
del df_train_0['fold'], df_train_1['fold'], df_all
# define subfolds for each fold
np.random.seed(1)
uniq0 = df_train_0.display_id.unique()
uniq1 = df_train_1.display_id.unique()
np.random.shuffle(uniq0)
np.random.shuffle(uniq1)
n0 = len(uniq0) // 2
fold_0_0 = set(uniq0[:n0])
n1 = len(uniq1) // 2
fold_1_0 = set(uniq1[:n1])
df_train_0['subfold'] = df_train_0.display_id.isin(fold_0_0).astype('uint8')
df_train_1['subfold'] = df_train_1.display_id.isin(fold_1_0).astype('uint8')
np.save('tmp/fold_0_split.npy', df_train_0.fold.values)
np.save('tmp/fold_1_split.npy', df_train_1.fold.values)
# split fold 0 into subfolds
f_0 = open('ffm/ffm_xgb_0_0.txt', 'w')
f_1 = open('ffm/ffm_xgb_0_1.txt', 'w')
with open('ffm/ffm_xgb_0.txt', 'r') as f_in:
for fold, line in tqdm(zip(df_train_0.fold, f_in)):
if fold == 0:
f_0.write(line)
else:
f_1.write(line)
f_0.close()
f_1.close()
# split fold 1 into subfolds
f_0 = open('ffm/ffm_xgb_1_0.txt', 'w')
f_1 = open('ffm/ffm_xgb_1_1.txt', 'w')
with open('ffm/ffm_xgb_1.txt', 'r') as f_in:
for fold, line in tqdm(zip(df_train_1.fold, f_in)):
if fold == 0:
f_0.write(line)
else:
f_1.write(line)
f_0.close()
f_1.close()