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ubi.py
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ubi.py
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# coding = utf-8
import xlrd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
# 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]
# 返回训练集数据
return X, y
if __name__ == '__main__':
X, y = loadData('e:/python/data/20170309嘉兴人保数据.xlsx')
# 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)
# 预测概率
preb_proba = clf.predict_proba(X)[:, 1]
# for ii in range(len(preb_proba)):
# print(preb_proba[ii])
# 将计算的结果按照0.01的间隔进行划分
axis_x = np.linspace(0, 0.7, 71)
# print(axis_x)
# 按照0.01的间隔进行统计
num_proba_space = []
num_proba = []
for ii in axis_x:
num_proba.append(np.sum(preb_proba >= ii))
for ii in range(1, len(num_proba)):
num_proba_space.append(num_proba[ii - 1] - num_proba[ii])
num_proba_space1 = np.array(num_proba_space) / 3071
num_proba_space = []
num_proba = []
for ii in axis_x:
num_proba.append(np.sum(preb_proba[320:] >= ii))
for ii in range(1, len(num_proba)):
num_proba_space.append(num_proba[ii - 1] - num_proba[ii])
num_proba_space2 = np.array(num_proba_space) / (3071 - 320)
num_proba_space = []
num_proba = []
for ii in axis_x:
num_proba.append(np.sum(preb_proba[0:320] >= ii))
for ii in range(1, len(num_proba)):
num_proba_space.append(num_proba[ii - 1] - num_proba[ii])
num_proba_space3 = np.array(num_proba_space) / 320
# print(np.array(num_proba_space)/3071)
set_ch()
# # plt.plot(axis_x[0:len(axis_x)-1],num_proba_space1,'b*')
# plt.plot(axis_x[0:len(axis_x)-1],num_proba_space1,'r')
# # plt.plot(axis_x[0:len(axis_x)-1],num_proba_space1,'b-')
# plt.plot(axis_x[0:len(axis_x)-1],num_proba_space2,'g')
# # plt.plot(axis_x[0:len(axis_x)-1],num_proba_space1,'b+')
# plt.plot(axis_x[0:len(axis_x)-1],num_proba_space3,'b')
# num_proba_space4 = (num_proba_space3*320)/(num_proba_space1*3071)
# num_proba_space5 = (num_proba_space2*(3071-320))/(num_proba_space1*3071)
# plt.plot(axis_x[0:len(axis_x)-1],num_proba_space4,'b*')
# plt.plot(axis_x[0:len(axis_x)-1],num_proba_space5,'g+')
plt.title('出险概率以0.01为间隔')
plt.ylabel('同一间隔中出险和未出险所占比例')
plt.xlabel('出险概率0.01为间隔')
plt.xticks(np.linspace(0, 0.70, 36))
plt.yticks(np.linspace(0, 1.0, 21))
plt.grid()
plt.show()