-
Notifications
You must be signed in to change notification settings - Fork 0
/
ball49_xgboost_train.py
259 lines (223 loc) · 11.5 KB
/
ball49_xgboost_train.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
251
252
253
254
255
256
257
258
259
#!/usr/bin/env python
# _*_ coding:utf-8 _*_
# ============================================
# @Time : 2020/01/15 22:45
# @Author : WanDaoYi
# @FileName : ball49_xgboost_train.py
# ============================================
import os
import numpy as np
from sklearn import metrics
from sklearn.model_selection import GridSearchCV
from xgboost.sklearn import XGBClassifier
import joblib
from config import cfg
import matplotlib.pyplot as plt
import matplotlib
# 用于解决画图中文乱码
font = {"family": "SimHei"}
matplotlib.rc("font", **font)
class Ball49Train(object):
def __init__(self):
self.train_data_path = cfg.TRAIN.TRAIN_DATA_INFO_PATH
self.val_data_path = cfg.TRAIN.VAL_DATA_INFO_PATH
self.model_save_path = cfg.TRAIN.MODEL_SAVE_PATH
self.x_train, self.y_train = self.read_data(self.train_data_path)
self.x_val, self.y_val = self.read_data(self.val_data_path)
self.roc_flag = cfg.TRAIN.ROC_FLAG
pass
# 读取数据
def read_data(self, data_path):
with open(data_path, "r") as file:
data_info = file.readlines()
data_list = [data.strip().split(".") for data in data_info]
data_list = np.array(data_list, dtype=int)
data_info = data_list[:, 1:-1]
label_info = data_list[:, -1:]
print(data_info[: 5])
# ravel() 是列转行，用于解决数据转换警告。
return data_info, label_info.ravel()
pass
def best_estimators_depth(self):
# np.arange 可以生成 float 类型，range 只能生成 int 类型
best_param = {'n_estimators': range(10, 201, 5),
'max_depth': range(1, 20, 1)
}
best_gsearch = GridSearchCV(estimator=XGBClassifier(learning_rate=0.1,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
nthread=4,
min_child_weight=5,
seed=27
),
param_grid=best_param, scoring='roc_auc', iid=False, cv=10)
best_gsearch.fit(self.x_train, self.y_train)
print("best_param:{0}".format(best_gsearch.best_params_))
print("best_score:{0}".format(best_gsearch.best_score_))
# best_param: {'max_depth': 3, 'n_estimators': 20}
# best_score: 0.5851190476190475
return best_gsearch.best_params_
pass
def best_lr_gamma(self):
# np.arange 可以生成 float 类型，range 只能生成 int 类型
best_param = {'learning_rate': np.arange(0.1, 1.1, 0.1),
'gamma': np.arange(0.1, 5.1, 0.2)
}
best_gsearch = GridSearchCV(estimator=XGBClassifier(n_estimators=20,
max_depth=3,
# learning_rate=0.1,
# gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
nthread=4,
min_child_weight=5,
seed=27
),
param_grid=best_param, scoring='roc_auc', iid=False, cv=10)
best_gsearch.fit(self.x_train, self.y_train)
print("best_param:{0}".format(best_gsearch.best_params_))
print("best_score:{0}".format(best_gsearch.best_score_))
# best_param: {'gamma': 1.7000000000000004, 'learning_rate': 0.5}
# best_score: 0.6467261904761905
return best_gsearch.best_params_
pass
def best_subsmaple_bytree(self):
# np.arange 可以生成 float 类型，range 只能生成 int 类型
# 调整subsample(行),colsample_bytree（列）
best_param = {'subsample': np.arange(0.1, 1.1, 0.1),
'colsample_bytree': np.arange(0.1, 1.1, 0.1)
}
best_gsearch = GridSearchCV(estimator=XGBClassifier(n_estimators=20,
max_depth=3,
learning_rate=0.5,
gamma=1.7,
# subsample=1.0,
# colsample_bytree=0.8,
objective='binary:logistic',
nthread=4,
min_child_weight=5,
seed=27
),
param_grid=best_param, scoring='roc_auc', iid=False, cv=10)
best_gsearch.fit(self.x_train, self.y_train)
print("best_param:{0}".format(best_gsearch.best_params_))
print("best_score:{0}".format(best_gsearch.best_score_))
# best_param: {'colsample_bytree': 0.8, 'subsample': 0.8}
# best_score: 0.6467261904761905
return best_gsearch.best_params_
pass
def best_nthread_weight(self):
# np.arange 可以生成 float 类型，range 只能生成 int 类型
best_param = {'nthread': range(1, 20, 1),
'min_child_weight': range(1, 20, 1)
}
best_gsearch = GridSearchCV(estimator=XGBClassifier(n_estimators=20,
max_depth=3,
learning_rate=0.5,
gamma=1.7,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
# nthread=4,
# min_child_weight=5,
seed=27
),
param_grid=best_param, scoring='roc_auc', iid=False, cv=10)
best_gsearch.fit(self.x_train, self.y_train)
print("best_param:{0}".format(best_gsearch.best_params_))
print("best_score:{0}".format(best_gsearch.best_score_))
# best_param: {'min_child_weight': 5, 'nthread': 1}
# best_score: 0.6467261904761905
return best_gsearch.best_params_
pass
def best_seek(self):
# np.arange 可以生成 float 类型，range 只能生成 int 类型
best_param = {'seed': range(1, 1000, 1)}
best_gsearch = GridSearchCV(estimator=XGBClassifier(n_estimators=20,
max_depth=3,
learning_rate=0.5,
gamma=1.7,
subsample=0.8,
colsample_bytree=0.8,
nthread=1,
min_child_weight=5,
# seed=27,
objective='binary:logistic'
),
param_grid=best_param, scoring='roc_auc', iid=False, cv=10)
best_gsearch.fit(self.x_train, self.y_train)
print("best_param:{0}".format(best_gsearch.best_params_))
print("best_score:{0}".format(best_gsearch.best_score_))
# best_param: {'seed': 27}
# best_score: 0.6467261904761905
return best_gsearch.best_params_
pass
# 绘制 ROC 曲线
def plt_roc(self, model):
if self.roc_flag:
y_proba = model.predict_proba(self.x_val)
# 预测为 0 的概率
y_zero = y_proba[:, 0]
# 预测为 1 的概率
y_one = y_proba[:, 1]
print("AUC Score2: {}".format(metrics.roc_auc_score(self.y_val, y_one)))
# 得到误判率、命中率、门限
fpr, tpr, thresholds = metrics.roc_curve(self.y_val, y_one)
# 计算auc
roc_auc = metrics.auc(fpr, tpr)
# 对ROC曲线图正常显示做的参数设定
# 用来正常显示中文标签, 上面设置过
# plt.rcParams['font.sans-serif'] = ['SimHei']
# 用来正常显示负号
plt.rcParams['axes.unicode_minus'] = False
plt.plot(fpr, tpr, label='{0}_AUC = {1:.5f}'.format("xgboost", roc_auc))
plt.title('ROC曲线')
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.ylabel('命中率: TPR')
plt.xlabel('误判率: FPR')
plt.show()
pass
# 较好的 模型参数 进行训练
def best_param_xgboost(self):
best_model = XGBClassifier(n_estimators=20,
max_depth=3,
learning_rate=0.5,
gamma=1.7,
subsample=0.8,
colsample_bytree=0.8,
nthread=1,
min_child_weight=5,
seed=27,
objective='binary:logistic'
)
best_model.fit(self.x_train, self.y_train)
y_pred = best_model.predict(self.x_val)
acc_score = metrics.accuracy_score(self.y_val, y_pred)
print("acc_score: {}".format(acc_score))
print("score: {}".format(best_model.score(self.x_val, self.y_val)))
save_path = self.model_save_path + "acc={:.6f}".format(acc_score) + ".m"
# 判断模型是否存在，存在则删除
if os.path.exists(save_path):
os.remove(save_path)
pass
# 保存模型
joblib.dump(best_model, save_path)
print("AUC Score: {}".format(metrics.roc_auc_score(self.y_val, y_pred)))
# 绘制 ROC 曲线
self.plt_roc(best_model)
pass
if __name__ == "__main__":
demo = Ball49Train()
# demo.best_estimators_depth()
# demo.best_lr_gamma()
# demo.best_subsmaple_bytree()
# demo.best_nthread_weight()
# demo.best_seek()
demo.best_param_xgboost()
pass