-
Notifications
You must be signed in to change notification settings - Fork 0
/
ubi20170308V1.0.py
165 lines (129 loc) · 6.09 KB
/
ubi20170308V1.0.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
# coding = utf-8
import xlrd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from scipy import stats
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.metrics import precision_recall_curve, roc_curve, auc
from sklearn.metrics import classification_report
# 下面的函数计算出险概率
def sigmoid(h):
return 1.0 / (1.0 + np.exp(-h))
# 下面的函数用于设置画图时能够显示汉字
def set_ch():
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
# datemode = 0,使用1900为基础的时间戳；
# datemode = 1,使用1904为基础的时间戳
def dateMap(excelDate):
return xlrd.xldate.xldate_as_datetime(excelDate, 0)
def loadData(xlsFileName):
sheet_index = 1 # 风险因子数据所在的页
x_rows_index = 1 # 风险因子数据起始行
# 打开文件
workbook = xlrd.open_workbook(xlsFileName)
# 根据sheet索引或者名称获取sheet内容
sheet1 = workbook.sheet_by_index(sheet_index) # sheet索引从0开始
print('该页基本信息（页名，行数，列数）', sheet1.name, sheet1.nrows, sheet1.ncols)
# 读取所有行,并将数据从字符串转化为浮点数,map完后要转成list，否则会报错
ubiData = []
for ii in range(x_rows_index, sheet1.nrows):
ubiData.append(list(map(float, sheet1.row_values(ii))))
ubiData = np.array(ubiData)
ubiDataType = ubiData.shape
print('UBI原始样本值的大小：', ubiDataType)
X = ubiData[:, [0,1,3,5,7,9,11,12,13,14,15]]
y = ubiData[:, ubiDataType[1] - 1]
# 返回训练集的大小
return X, y
if __name__ == '__main__':
# set_ch() #设置中文显示
X, y = loadData('e:/python/data/20170309嘉兴人保数据.xlsx')
# print(X)
# print(y)
# 下面的代码用于抽取参数重要性
# model = ExtraTreesClassifier()
# model.fit(X, y)
# # # display the relative importance of each attribute
# print('参数重要性：', model.feature_importances_)
# # 对数据进行预处理
# # normalize the data attributes
# normalized_X = preprocessing.normalize(X)
# # standardize the data attributes
# standardized_X = preprocessing.scale(X)
# X = StandardScaler().fit_transform(X)
# 进行Logistic学习，也就是训练train
# X,y以矩阵的方式传入
clf = LogisticRegression()
clf.fit(X, y)
# print(clf)
# 得到训练之后的系数
# print('模型参数:', clf.coef_)
print('Ravel训练后得到的参数值:', clf.coef_.ravel()) # 多维数组转化为一维数组
print('单位因子的增加对发生比的影响：', np.exp(clf.coef_.ravel())) # 多维数组转化为一维数组
# print('系数之和：', np.sum(clf.coef_.ravel()))
print('截距：', clf.intercept_)
# 对输入的因子进行粉线预测,返回预测值
# sample = (np.array([39,1,1.6136,7.9091,0.1364,0.0455,85.7544,148.6852,0.09658,34.61,8.3837])).reshape(1,-1)
# print(sample)
# prob = clf.predict(sample)
# print(prob)
# prob = clf.predict(X)
# for ii in range(1,len(prob)):
# if(prob[ii] != 0 ):
# print(prob[ii])
# print(ii)
# print(prob)
preb_proba = clf.predict_proba(X)[:,1]
# for ii in range(len(preb_proba)):
# print(preb_proba[ii])
print('总体均值',np.mean(preb_proba))
preb_proba_mean = preb_proba-np.mean(preb_proba)
for ii in range(len(preb_proba_mean)):
print(preb_proba_mean[ii])
print('出险的平均概率：',np.mean(preb_proba[0:319]))
print('最大出险的概率：',np.max(preb_proba[0:319:]))
print('出险概率中位数：',np.median(preb_proba[0:319:]))
print('出险概率众数：',stats.mode(preb_proba[0:319:]))
print('出险概率>0.11的个数：',np.sum(preb_proba[0:319]>0.12))
print('出险的平均概率标准偏差：',np.std(preb_proba[0:319]))
print('非出险的平均概率：',np.mean(preb_proba[320:]))
print('最大非出险的概率：',np.max(preb_proba[320:]))
print('非出险概率中位数：',np.median(preb_proba[320:]))
print('非出险概率众数：',stats.mode(preb_proba[320:]))
print('非出险概率>0.11的个数：',np.sum(preb_proba[320:]>0.12))
print('非出险的平均概率标准偏差：',np.std(preb_proba[320:]))
print('最大出险概率：', np.max(preb_proba))
max_index = np.where( preb_proba == np.max(preb_proba))
print('最大出险概率对应的UBI因子', X[max_index[0][0],:])
print('最小出险概率：', np.max(preb_proba))
min_index = np.where( preb_proba == np.min(preb_proba))
print('最小出险概率对应的UBI因子', X[min_index[0][0],:])
# 下面的代码验证了概率公式的有效性
# coef = clf.coef_.ravel()
# print(X[0,:])
# print(X[0,:]*coef)
# h = np.sum(X[0,:]*coef)+clf.intercept_
# print(h)
# print(sigmoid(h))
# prob = preb_proba > 0.17
# print(metrics.classification_report(y, prob))
# print(metrics.confusion_matrix(y, prob))
# 评分函数，将返回一个小于1的得分，可能会小于0
# 这里的得分没有任何的意义，概率才是最重要的
# score = clf.score(X, y)
# print('模型得分：',score)
# #准确率与召回率
# answer = clf.predict_proba(x_test)[:,1]
# precision, recall, thresholds = precision_recall_curve(y_test, answer)
# report = answer > 0.5
# print(classification_report(y_test, report, target_names = ['neg', 'pos']))
# print("average precision:", average/testNum)
# print("time spent:", time.time() - start_time)
# plot_pr(0.5, precision, recall, "pos")