python数据分析（十四）

# -*- coding: utf-8 -*-

#逻辑回归 自动建模

import pandas as pd

#参数初始化

filename = 'd:/data/bankloan.xls'

data = pd.read_excel(filename)

x = data.iloc[:,:8].as_matrix()

y = data.iloc[:,8].as_matrix()

from sklearn.linear_model import LogisticRegression as LR

from sklearn.linear_model import RandomizedLogisticRegression as RLR

rlr = RLR() #建立随机逻辑回归模型，筛选变量

rlr.fit(x, y) #训练模型

rlr.get_support() #获取特征筛选结果，也可以通过.scores_方法获取各个特征的分数

print(u'通过随机逻辑回归模型筛选特征结束。')

print(u'有效特征为：%s' % ','.join(data.columns[rlr.get_support()]))

x = data[data.columns[rlr.get_support()]].as_matrix() #筛选好特征

lr = LR() #建立逻辑回归模型

lr.fit(x, y) #用筛选后的特征数据来训练模型

print(u'逻辑回归模型训练结束。')

print(u'模型的平均正确率为：%s' % lr.score(x, y)) #给出模型的平均正确率，本例为81.4%

#非线性回归

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

from sklearn import metrics

x=pd.DataFrame([1.5,2.8,4.5,7.5,10.5,13.5,15.1,16.5,19.5,22.5,24.5,26.5])

y=pd.DataFrame([7.0,5.5,4.6,3.6,2.9,2.7,2.5,2.4,2.2,2.1,1.9,1.8])

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1)

ax.scatter(x,y)

fig.show()

from sklearn.linear_model import LinearRegression

linreg = LinearRegression()

linreg.fit(x,y)

# The coefficients

print('Coefficients: \n', linreg.coef_)

y_pred = linreg.predict(x)

# The mean square error

print "MSE:",metrics.mean_squared_error(y,y_pred)

# Explained variance score: 1 is perfect prediction

print('Variance score: %.2f' % linreg.score(x, y))

#多项式模型

x1=x

x2=x**2

x1['x2']=x2

linreg = LinearRegression()

linreg.fit(x1,y)

# The coefficients

print('Coefficients: \n', linreg.coef_)

y_pred = linreg.predict(x)

# The mean square error

print "MSE:",metrics.mean_squared_error(y,y_pred)

#对数模型

x2=pd.DataFrame(np.log(x[0]))

linreg = LinearRegression()

linreg.fit(x2,y)

# The coefficients

print('Coefficients: \n', linreg.coef_)

y_pred = linreg.predict(x2)

# The mean square error

print "MSE:",metrics.mean_squared_error(y,y_pred)

#指数

y2=pd.DataFrame(np.log(y))

linreg = LinearRegression()

linreg.fit(pd.DataFrame(x[0]),y2)

# The coefficients

print('Coefficients: \n', linreg.coef_)

y_pred = linreg.predict(pd.DataFrame(x[0]))

# The mean square error

print "MSE:",metrics.mean_squared_error(y2,y_pred)

#幂函数

linreg = LinearRegression()

linreg.fit(x2,y2)

# The coefficients

print('Coefficients: \n', linreg.coef_)

y_pred = linreg.predict(x2)

# The mean square error

print "MSE:",metrics.mean_squared_error(y2,y_pred)