-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathlgb+light+sel.py
203 lines (171 loc) · 8.04 KB
/
lgb+light+sel.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
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import VarianceThreshold
import lightgbm as lgb
from lightgbm import plot_importance
import matplotlib.pyplot as plt
import seaborn as sns
import os
import sklearn
import time
import random
from pandas import DataFrame as DF
from sklearn.ensemble import GradientBoostingRegressor as GBR
import xgboost as xgb
from sklearn.cross_validation import cross_val_score as cv
'''
模拟退火算法计算 最佳特征的选择
'''
train = pd.read_csv('./data/do_feat_data_2/train.csv')
test = pd.read_csv('./data/do_feat_data_2/test.csv')
# def get_model(nums, cv_fold):
# '''
# 这部分是指定迭代次数和 cv次数下的 多模型融合算法 传入参数可以得到多模型融合的结果。
#
# 使用了 LGBMClassifier XGBClassifier GBC 三个模型分别做cv后再做融合
# '''
# feature_name1 = train_data[feature_name].columns
# get_ans_face = list(set(get_pic(gbc_model, feature_name1).head(nums)['name']) & set(
# get_pic(xgb_model, feature_name1).head(nums)['name']) & set(
# get_pic(lgb_model, feature_name1).head(nums)['name']))
# print('New Feature: ', len(get_ans_face))
# if 'SNP32*SNP34' not in get_ans_face:
# get_ans_face.append('SNP32*SNP34')
# print('New Feature: ', len(get_ans_face))
# new_lgb_model = lgb.LGBMClassifier(objective='binary', n_estimators=300, max_depth=3, min_child_samples=6,
# learning_rate=0.102, random_state=1)
# cv_model = cv(new_lgb_model, train_data[get_ans_face], train_label, cv=cv_fold, scoring='f1')
# new_lgb_model.fit(train_data[get_ans_face], train_label)
# m1 = cv_model.mean()
#
# new_xgb_model1 = xgb.XGBClassifier(objective='binary:logistic', n_estimators=300, max_depth=4, learning_rate=0.101,
# random_state=1)
# cv_model = cv(new_xgb_model1, train_data[get_ans_face].values, train_label, cv=cv_fold, scoring='f1')
# new_xgb_model1.fit(train_data[get_ans_face].values, train_label)
# m2 = cv_model.mean()
#
# new_gbc_model = GBC(n_estimators=310, subsample=1, min_samples_split=2, max_depth=3, learning_rate=0.1900,
# min_weight_fraction_leaf=0.1)
# kkk = train_data[get_ans_face].fillna(7)
# cv_model = cv(new_gbc_model, kkk[get_ans_face], train_label, cv=cv_fold, scoring='f1')
# new_gbc_model.fit(kkk.fillna(7), train_label)
#
# m3 = cv_model.mean()
# print((m1 + m2 + m3) / 3)
# pro1 = new_lgb_model.predict_proba(test_data[get_ans_face])
# pro2 = new_xgb_model1.predict_proba(test_data[get_ans_face].values)
# pro3 = new_gbc_model.predict_proba(test_data[get_ans_face].fillna(7).values)
# ans = (pro1 + pro2 + pro3) / 3
# return ans
# temp = [140,160,180,200,220,240,260,280,300,320]
# ans = []
# for i in range(len(temp)):
# print('Now All Feature:',temp[i])
# ans = get_model(temp[i],5)
# if i == 0:
# ans1 = ans
# else:
# ans1 += ans
# ans1 /= len(temp)
def find_best_feature(feature_name, cv_fold,train_data,train_label):
# 为了寻找最佳的特征组合，这里是对LGBMClassifier XGBClassifier GBC三个模型的得分进行平均，来代表这个特征所代表的分数
get_ans_face = feature_name
new_lgb_model = lgb.LGBMRegressor(n_estimators=300,random_state=1)
cv_model = cv(new_lgb_model, train_data[get_ans_face], train_label, cv=cv_fold, scoring='r2')
new_lgb_model.fit(train_data[get_ans_face], train_label)
m1 = cv_model.mean()
new_xgb_model1 = xgb.XGBRegressor(n_estimators=300,random_state=1)
cv_model = cv(new_xgb_model1, train_data[get_ans_face].values, train_label, cv=cv_fold, scoring='r2')
new_xgb_model1.fit(train_data[get_ans_face].values, train_label)
m2 = cv_model.mean()
new_gbc_model = GBR(n_estimators=310)
cv_model = cv(new_gbc_model, train_data[get_ans_face].values, train_label, cv=cv_fold, scoring='r2')
new_gbc_model.fit(train_data[get_ans_face].values, train_label)
m3 = cv_model.mean()
return (m1 + m2 + m3) / 3
#
#
# def train_best_feature(feature_name):
# # 这里是三个模型的预测结果取平均
# get_ans_face = feature_name
# new_lgb_model = lgb.LGBMClassifier(objective='binary', n_estimators=300, max_depth=3, min_child_samples=6,
# learning_rate=0.102, random_state=1)
# new_lgb_model.fit(train_data[get_ans_face], train_label)
#
# new_xgb_model1 = xgb.XGBClassifier(objective='binary:logistic', n_estimators=300, max_depth=4, learning_rate=0.101,
# random_state=1)
# new_xgb_model1.fit(train_data[get_ans_face].values, train_label)
#
# new_gbc_model = GBC(n_estimators=310, subsample=1, min_samples_split=2, max_depth=3, learning_rate=0.1900,
# min_weight_fraction_leaf=0.1)
# kkk = train_data[get_ans_face].fillna(7)
# new_gbc_model.fit(kkk.fillna(7), train_label)
#
# pro1 = new_lgb_model.predict_proba(test_data[get_ans_face])
# pro2 = new_xgb_model1.predict_proba(test_data[get_ans_face].values)
# pro3 = new_gbc_model.predict_proba(test_data[get_ans_face].fillna(7).values)
# ans = (pro1 + pro2 + pro3) / 3
#
# return ans
def get_pic(model, feature_name):
ans = DF()
ans['name'] = feature_name
ans['score'] = model.feature_importances_
# print(ans[ans['score']>0].shape)
return ans.sort_values(by=['score'], ascending=False).reset_index(drop=True)
def get_start():
'''
:return:使用单模型做5折cv
'''
lgbm_submission = pd.read_csv('./data/base_data/submit_example.csv',header=None)
lgbm_submission.columns=['A','B']
print('###:',)
#train_flg = pd.read_csv(os.getcwd() + '/data/train/train_flg.csv', sep='\t')
train = pd.read_csv( './data/do_feat_data_2/train.csv')
test = pd.read_csv( './data/do_feat_data_2/test.csv')
train.pop('板温**1/2')
train.pop('现场温度**1/2')
test.pop('板温**1/2')
test.pop('现场温度**1/2')
train=train.interpolate()
train=train[~train.isin([np.nan,np.inf,-np.inf]).any(1)]
# is_nan_air = np.isnan(train).any()
# print(is_nan_air)
# for index,i in enumerate(list(is_nan_air)):
# if(i==True):
# print('::::',train.iloc[:,index])
y=train.pop('发电量')
lgb_model = lgb.LGBMRegressor(n_estimators=120)
# cv_model = cv(lgb_model, train_data[feature_name], train_label, cv=10, scoring='f1')
lgb_model.fit(train, y)
#
xgb_model = xgb.XGBRegressor(n_estimators=120)
# cv_model = cv(xgb_model, train_data[feature_name].values, train_label, cv=10, scoring='f1')
xgb_model.fit(train, y)
gbc_model = GBR(n_estimators=200)
# cv_model = cv(gbc_model, kkk[feature_name], train_label, 1cv=10, scoring='f1')
gbc_model.fit(train, y)
nums = 100
feature_name1 = train.columns
#这里的|运算是分别获取lgb_model 认为最重要的45个特征，xgb_model、gbc_model等各自认为最重要的45个特征进行
get_ans_face = list(set(get_pic(lgb_model,feature_name1).head(nums)['name'])|set(get_pic(xgb_model,feature_name1).head(nums)['name'])|set(get_pic(gbc_model,feature_name1).head(nums)['name']))
print('New Feature: ',len(get_ans_face))
now_feature = []
check = 0
for i in range(len(get_ans_face)):
#贪心方法找到最佳的feature组合 这里是使用不断添加feature的方式，
now_feature.append(get_ans_face[i])
jj = find_best_feature(now_feature,6,train,y)
print('vvvv:',jj)
if jj>check:
print('目前特征长度为',len(now_feature),' 目前帅气的cv值是',jj,' 成功加入第',i+1,'个','增值为',jj-check)
check = jj
else:
now_feature.pop()
# print('目前特征长度为',len(now_feature),'第',i+1,'个拉闸了')
#
train[now_feature].to_csv('./data/do_feat_data/train_tuihuo.csv')
if __name__=='__main__':
get_start()