-
Notifications
You must be signed in to change notification settings - Fork 8
/
model.py
250 lines (197 loc) · 8.22 KB
/
model.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
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
# -*- coding: utf-8 -*-
# model
# author = 'huangth'
import os
import time
import numpy as np
import pandas as pd
import lightgbm as lgb
import xgboost as xgb
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.metrics import mean_absolute_error
BASE_PATH = '/home/wolfkin/ImageOfConsumers/'
ETL_DATA_PATH = os.path.join(BASE_PATH, "EtlData")
def get_feature(name):
data_name = os.path.join(ETL_DATA_PATH, "{}.csv".format(name))
df = pd.read_csv(data_name)
return df
def lgb_mae_model(train_df, test_df, params):
NFOLDS = 10
train_label = train_df['信用分']
kfold = KFold(n_splits=NFOLDS, shuffle=False, random_state=2019)
kf = kfold.split(train_df, train_label)
train = train_df.drop(['用户编码', '信用分'], axis=1)
test = test_df.drop(['用户编码'], axis=1)
cv_pred = np.zeros(test.shape[0])
valid_best_l2_all = 0
count = 0
for i, (train_fold, validate) in enumerate(kf):
print("model: lgb_mae. fold: ", i , "training...")
X_train, label_train = train.iloc[train_fold], train_label.iloc[train_fold]
X_validate, label_validate = train.iloc[validate], train_label.iloc[validate]
dtrain = lgb.Dataset(X_train, label_train)
dvalid = lgb.Dataset(X_validate, label_validate, reference=dtrain)
bst = lgb.train(params, dtrain, num_boost_round=10000, valid_sets=dvalid, verbose_eval=-1, early_stopping_rounds=50)
cv_pred += bst.predict(test, num_iteration=bst.best_iteration)
valid_best_l2_all += bst.best_score['valid_0']['l1']
count += 1
cv_pred /= NFOLDS
valid_best_l2_all /= NFOLDS
print("lgb_mae cv score for valid is: ", 1/(1+valid_best_l2_all))
print("----------------------------------------")
print("----------------------------------------")
print("lgb_mae feature importance：")
fea_importances = pd.DataFrame({
'column': train.columns,
'importance': bst.feature_importance(importance_type='split', iteration=bst.best_iteration)
}).sort_values(by='importance', ascending=False)
print(fea_importances)
print("----------------------------------------")
print("----------------------------------------")
return cv_pred
def lgb_mse_model(train_df, test_df, params):
NFOLDS = 10
train_label = train_df['信用分']
kfold = KFold(n_splits=NFOLDS, shuffle=False, random_state=2019)
kf = kfold.split(train_df, train_label)
train = train_df.drop(['用户编码', '信用分'], axis=1)
test = test_df.drop(['用户编码'], axis=1)
cv_pred = np.zeros(test.shape[0])
valid_best_l2_all = 0
count = 0
for i, (train_fold, validate) in enumerate(kf):
print("model:lgb_mse. fold: ", i , "training...")
X_train, label_train = train.iloc[train_fold], train_label.iloc[train_fold]
X_validate, label_validate = train.iloc[validate], train_label.iloc[validate]
dtrain = lgb.Dataset(X_train, label_train)
dvalid = lgb.Dataset(X_validate, label_validate, reference=dtrain)
bst = lgb.train(params, dtrain, num_boost_round=10000, valid_sets=dvalid, verbose_eval=-1, early_stopping_rounds=50)
cv_pred += bst.predict(test, num_iteration=bst.best_iteration)
valid_best_l2_all += bst.best_score['valid_0']['l1']
count += 1
cv_pred /= NFOLDS
valid_best_l2_all /= NFOLDS
print("lgb_mse cv score for valid is: ", 1/(1+valid_best_l2_all))
print("----------------------------------------")
print("----------------------------------------")
print("lgb_mse feature importance：")
fea_importances = pd.DataFrame({
'column': train.columns,
'importance': bst.feature_importance(importance_type='split', iteration=bst.best_iteration)
}).sort_values(by='importance', ascending=False)
print(fea_importances)
print("----------------------------------------")
print("----------------------------------------")
return cv_pred
def xgb_mae_model(train_df, test_df, params):
NFOLDS = 5
train_label = train_df['信用分']
kfold = KFold(n_splits=NFOLDS, shuffle=False, random_state=2019)
kf = kfold.split(train_df, train_label)
train = train_df.drop(['用户编码', '信用分'], axis=1)
test = test_df.drop(['用户编码'], axis=1)
cv_pred = np.zeros(test.shape[0])
count = 0
preds_list = list()
oof = np.zeros(train_df.shape[0])
for i, (train_fold, validate) in enumerate(kf):
print("model: xgb_mae. fold: ", i , "training...")
X_train, label_train = train.iloc[train_fold], train_label.iloc[train_fold]
X_validate, label_validate = train.iloc[validate], train_label.iloc[validate]
gbm = xgb.XGBRegressor(**params)
bst = gbm.fit(X_train, label_train, eval_set=[(X_train, label_train), (X_validate, label_validate)],
early_stopping_rounds=200, verbose=500)
k_pred = bst.predict(X_validate)
oof[validate] = k_pred
preds = gbm.predict(test)
preds_list.append(preds)
count += 1
fold_mae_error = mean_absolute_error(train_label, oof)
preds_columns = ['preds_{id}'.format(id=i) for i in range(NFOLDS)]
preds_df = pd.DataFrame(data=preds_list)
preds_df = preds_df.T
preds_df.columns = preds_columns
preds_list = list(preds_df.mean(axis=1))
cv_pred = preds_list
print("xgb_mae cv score for valid is: ", 1/(1+fold_mae_error))
# print("----------------------------------------")
# print("----------------------------------------")
# print("xgb_mae feature importance：")
# fea_importances = pd.DataFrame({
# 'column': train.columns,
# 'importance': bst.feature_importance(importance_type='split', iteration=bst.best_iteration)
# }).sort_values(by='importance', ascending=False)
# print(fea_importances)
# print("----------------------------------------")
# print("----------------------------------------")
return cv_pred
def model_bagging(pred1, pred2):
cv_pred = (pred1 + pred2 ) / 3
return cv_pred
def model_main():
train_data = get_feature(name="train_data")
test_data = get_feature(name="test_data")
print('Gen train shape: {}, test shape: {}'.format(train_data.shape, test_data.shape))
print('features num: ', test_data.shape[1] - 1)
# lgb_mae参数
params_mae_lgb = {
'learning_rate': 0.01,
'boosting_type': 'gbdt',
'objective': 'regression_l1',
'metric': 'mae',
'feature_fraction': 0.66,
'bagging_fraction': 0.8,
'bagging_freq': 2,
'num_leaves': 31,
'verbose': -1,
'max_depth': 5,
'lambda_l2': 5, 'lambda_l1': 0, 'nthread': 8
}
# lgb_mse参数
params_mse_lgb = {
'learning_rate': 0.01,
'boosting_type': 'gbdt',
'objective': 'regression_l2',
'metric': 'mae',
'feature_fraction': 0.66,
'bagging_fraction': 0.8,
'bagging_freq': 2,
'num_leaves': 31,
'verbose': -1,
'max_depth': 5,
'lambda_l2': 5, 'lambda_l1': 0, 'nthread': 8,
'seed': 89
}
# xgb_mae参数
params_mae_xgb = {
'booster': 'gbtree',
'learning_rate': 0.01,
'max_depth': 5,
'subsample': 0.66,
'colsample_bytree': 0.8,
'objective': 'reg:linear',
'n_estimators': 10000,
'min_child_weight': 3,
'gamma': 0,
'silent': True,
'n_jobs': -1,
'random_state': 2019,
'reg_alpha': 2,
'reg_lambda': 0.1,
'alpha': 1,
'verbose': 1
}
# xgb_mae_pred = xgb_mae_model(train_data, test_data, params_mae_xgb)
lgb_mae_pred = lgb_mae_model(train_data, test_data, params_mae_lgb)
lgb_mse_pred = lgb_mse_model(train_data, test_data, params_mse_lgb)
bagging_pred = model_bagging(lgb_mae_pred, lgb_mse_pred)
test_data_sub = test_data[['用户编码']]
test_data_sub['score'] = bagging_pred
test_data_sub.columns = ['id', 'score']
test_data_sub['score'] = test_data_sub['score'].apply(lambda x: int(np.round(x)))
test_data_sub[['id', 'score']].to_csv('0216_2143result.csv', index=False)
if __name__ == "__main__":
t0 = time.time()
model_main()
print("Model has trained!")
print("Cost {} s.".format(time.time() - t0))