-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path1_singlelgb.py
188 lines (132 loc) · 6.52 KB
/
1_singlelgb.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 20 18:00:21 2018
@author: kenn
"""
import pandas as pd
import sklearn as skl
import numpy as np
import lightgbm as lgb
import matplotlib.pyplot as plt
missing_90 = ['x_0'+str(i) for i in range(62,73+1)] + ['x_0'+str(i) for i in range(81,87+1)] + ['x_0'+str(i) for i in range(92,99+1)] + ['x_'+str(i) for i in range(100,120+1)] + ['x_'+str(i) for i in range(128,130+1)]
pos_neg_rate = 0
importance = []
num_leaves = 20
max_depth = 9
feature_fraction = 0.6
bagging_fraction = 0.5
num_trees = 150
learning_rate = 0.05
update_var_score = True
use_var_score = False
var_score_drop = 0.01
def balance_data(data):
global pos_neg_rate
pos_neg_rate = 0
dataset_1 = data.loc[data['y']==1,]
dataset_0 = data.loc[data['y']==0,]
data_0_split_index = np.array((list(range(int(len(dataset_0)/len(dataset_1))))*len(dataset_0))[:len(dataset_0)])
train_split = []
for i in set(data_0_split_index):
temp = dataset_0[data_0_split_index==i]
pos_neg_rate += len(temp)/(len(temp)+len(dataset_1))/len(set(data_0_split_index))
train_split.append(dataset_0[data_0_split_index==i].append(dataset_1))
return train_split
def lgb_splitdata_train_balanced(i, param = {'num_leaves':num_leaves,'max_depth':max_depth, 'feature_fraction':feature_fraction,
'bagging_fraction':bagging_fraction , 'num_trees':num_trees, 'learning_rate':learning_rate,
'objective':'binary','is_unbalance':True}, num_round = num_trees,):
global importance
train_X = i.drop(['y'],axis=1)
train_Y = i['y']
train_data = lgb.Dataset(train_X, label=train_Y, categorical_feature=categorical_feature)
bst = lgb.train(param, train_data, num_round)
importance.append(pd.DataFrame(bst.feature_importance(),index=train_X.columns, columns=["name"]))
return bst
def lgb_splitdata_train(i, param = {'num_leaves':num_leaves,'max_depth':max_depth, 'feature_fraction':feature_fraction, 'bagging_fraction':bagging_fraction ,
'num_trees':num_trees, 'learning_rate':learning_rate, 'objective':'binary'}, num_round = num_trees,):
global importance
train_X = i.drop(['y'],axis=1)
train_Y = i['y']
train_data = lgb.Dataset(train_X, label=train_Y, categorical_feature=categorical_feature)
bst = lgb.train(param, train_data, num_round)
importance.append(pd.DataFrame(bst.feature_importance(),index=train_X.columns, columns=["name"]))
return bst
def model_train_f(split_dataset):
model_set = []
for i in split_dataset:
model_set.append(lgb_splitdata_train(i))
return model_set
def model_pred_f(data_test,model):
test_pred = []
for i in model:
test_pred.append(i.predict(data_test).reshape(-1,1))
test_pred = np.concatenate(test_pred,axis=1)
test_pred = np.mean(test_pred,axis=1).reshape(-1,1)
return test_pred
def cv_process(model_data, model_train_f, model_pred_f, k):
global cv_res
cv_res = []
cv_index = np.array((list(range(k))*len(model_data))[:len(model_data)])
global F1
F1 = []
global AUC
AUC = []
for cv_i in set(cv_index):
dataset_train = model_data[cv_index!=cv_i]
dataset_test = model_data[cv_index==cv_i]
data_test_X = dataset_test.drop(['y'],axis=1)
data_test_Y = dataset_test['y']
split_dataset = balance_data(dataset_train)
model_fit = model_train_f(split_dataset)
test_prob = model_pred_f(data_test_X, model_fit)
print(pos_neg_rate)
test_pred = skl.preprocessing.binarize(test_prob,pos_neg_rate)
cv_res.append(np.column_stack((data_test_Y,
test_prob.reshape(-1),
test_pred.reshape(-1))))
F1.append(skl.metrics.f1_score(data_test_Y, test_pred))
AUC.append(skl.metrics.roc_auc_score(data_test_Y, test_prob))
return np.mean(F1), np.sqrt(np.var(F1)), np.mean(F1)-np.sqrt(np.var(F1)), np.mean(F1)+np.sqrt(np.var(F1)) ,np.mean(AUC), np.sqrt(np.var(AUC))
def cv_process_balanced(model_data, model_train_f, model_pred_f, k):
global cv_res
cv_res = []
cv_index = np.array((list(range(k))*len(model_data))[:len(model_data)])
global F1
F1 = []
global AUC
AUC = []
for cv_i in set(cv_index):
dataset_train = model_data[cv_index!=cv_i]
dataset_test = model_data[cv_index==cv_i]
data_test_X = dataset_test.drop(['y'],axis=1)
data_test_Y = dataset_test['y']
model = lgb_splitdata_train_balanced(dataset_train)
test_prob = model.predict(data_test_X).reshape(-1,1)
print(pos_neg_rate)
test_pred = skl.preprocessing.binarize(test_prob,0.5)
cv_res.append(np.column_stack((data_test_Y,
test_prob.reshape(-1),
test_pred.reshape(-1))))
F1.append(skl.metrics.f1_score(data_test_Y, test_pred))
AUC.append(skl.metrics.roc_auc_score(data_test_Y, test_prob))
print(np.mean(F1), np.sqrt(np.var(F1)), np.mean(F1)-np.sqrt(np.var(F1)), np.mean(F1)+np.sqrt(np.var(F1)) ,np.mean(AUC))
return [np.mean(F1), np.sqrt(np.var(F1)), np.mean(F1)-np.sqrt(np.var(F1)), np.mean(F1)+np.sqrt(np.var(F1)) ,np.mean(AUC)]
dataset = pd.read_csv('model_sample.csv')
categorical_feature = ['x_001','x_010','x_011','x_027','x_033'] + ['x_00'+str(i) for i in range(3,9+1)] + ['x_0'+str(i) for i in range(13,19+1)]
dataset.drop(['user_id','x_012'],axis=1,inplace=True)
dataset.drop(missing_90,axis=1,inplace=True)
if use_var_score:
var_score = pd.read_csv("var_score.csv")
var_score.columns=['name','score']
drop_var = list(var_score.loc[var_score['score']<=var_score_drop,'name'])
categorical_feature = list(set(categorical_feature).difference(set(drop_var)))
dataset.drop(drop_var,axis=1,inplace=True)
model_data = dataset
print(cv_process(model_data, model_train_f, model_pred_f, 10))
if update_var_score:
importance_matrix = pd.concat(importance,axis=1)
importance_matrix = (importance_matrix - np.min(importance_matrix))/(np.max(importance_matrix) - np.min(importance_matrix))
var_score = pd.DataFrame(np.mean(importance_matrix,axis=1),columns=["score"])
var_score.sort_values(by="score",ascending=False,inplace=True)
var_score.to_csv("var_score.csv")