python学习:两个py文件间的函数调用

　　本例子是测试一些数据分析模型的R值,R值越接近1,表明该模型越适合分析该数据集.

　　本例子是在集成开发环境Aptana Studio 3 中创建 一个dataAnaly ,然后创建modelTest.py调用modelChose.py中的函数;在modelTest.py中需要import modelChose

　　格式:from 模块名 import 函数名1,函数名2....

'''
Created on 2015-1-19

@author: xuzhengzhu
'''
#input files
import xlrd,openpyxl
import pandas as pd
from sklearn import cross_validation
from dataAnaly import modelChose
from sklearn.metrics import r2_score
import numpy as np

file=pd.ExcelFile('e:\report.xlsx')
data=file.parse('Sheet1')
n=len(data)
#init data
x=data[['myjg','tjg']]
y=data['byjg']
models=['linear_model.SGDRegressor','GradientBoostingRegressor','RandomForestRegressor','AdaBoostRegressor','BaggingRegressor','linear_model.LinearRegression','linear_model.LogisticRegression','svm.svr','svm.NuSVR']
m=len(models)
k=10
R2=np.zeros(k)
z=2
count=0
modelCount=0   
#lookup get model object 
for modelCount in range(m-1):
    clf=modelChose.modelChose(models[modelCount])
    R2=np.zeros(k)
    count=0
    #lookup folds
    for train_index,test_index in cross_validation.KFold(n-z,n_folds=k):
        x_train,x_test=x.ix[train_index],x.ix[test_index]
        y_train,y_test=y[train_index],y[test_index]
        clf.fit(x_train,y_train)
        y_predict=clf.predict(x_test);
        r2=r2_score(y_test,y_predict)
        #print 'computed %d time(s) and R square is:%f ' %(count+1,r2)
        R2[count]=r2
        count+=1

    print 'model choose is :',models[modelCount],'the mean of R2 is :',np.mean(R2)
    y_validation = clf.predict(x.ix[(n-z):n])
    r2_val=r2_score(y.ix[(n-z):n],y_validation)
    print 'model choose is :',models[modelCount],'the validation ser R square is :%f ',r2_val
    #print pd.DataFrame({'y_true':y.ix[(n-z):n,],'y_validation':y_validation})
    modelCount+=1

'''
Created on 2015-1-19
@author: xuzhengzhu
'''
from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import linear_model
from sklearn.svm import SVR
from sklearn.svm import NuSVR

def modelChose(modelName):       
    if(cmp(modelName,'linear_model.SGDRegressor')==0):
            #print modelName
            clf = linear_model.SGDRegressor()
            return clf
    elif  (cmp(modelName,'GradientBoostingRegressor')==0):
            #print modelName
            clf = GradientBoostingRegressor()
            return clf
    elif (cmp(modelName,'RandomForestRegressor')==0):
            #print modelName
            clf = RandomForestRegressor()
            return clf
    elif (cmp(modelName,'AdaBoostRegressor')==0):
            #print modelName
            clf = AdaBoostRegressor()
            return clf
    elif (cmp(modelName,'BaggingRegressor')==0):
            #print modelName
            clf = BaggingRegressor()
            return clf
    elif (cmp(modelName,'linear_model.LinearRegression')==0):
            #print modelName
            clf = linear_model.LinearRegression()
            return clf
    elif (cmp(modelName,'linear_model.LogisticRegression')==0):
            #print modelName
            clf = linear_model.LogisticRegression()
            return clf
    elif  (cmp(modelName,'svm.svr')==0):
            #print modelName
            clf = SVR()
            return clf
    elif  (cmp(modelName,'svm.NuSVR')==0):
            #print modelName
            clf = NuSVR()
            return clf
    else: 
            #print modelName,count,'dddd',models[count]
            return 1
    

测试结果:

model choose is : linear_model.SGDRegressor the mean of R2 is : -4.40149514377e+158
model choose is : linear_model.SGDRegressor the validation ser R square is :%f  -1.69950873171e+175
model choose is : GradientBoostingRegressor the mean of R2 is : 0.06842532769
model choose is : GradientBoostingRegressor the validation ser R square is :%f  -0.706828939678
model choose is : RandomForestRegressor the mean of R2 is : 0.0656454293629
model choose is : RandomForestRegressor the validation ser R square is :%f  -1.62440546968
model choose is : AdaBoostRegressor the mean of R2 is : 0.0678670360111
model choose is : AdaBoostRegressor the validation ser R square is :%f  -0.743162901308
model choose is : BaggingRegressor the mean of R2 is : 0.0913739612188
model choose is : BaggingRegressor the validation ser R square is :%f  -1.11141498216
model choose is : linear_model.LinearRegression the mean of R2 is : 0.0976952970181
model choose is : linear_model.LinearRegression the validation ser R square is :%f  -15.3631379961
model choose is : linear_model.LogisticRegression the mean of R2 is : -0.224099722992
model choose is : linear_model.LogisticRegression the validation ser R square is :%f  0.588585017836
model choose is : svm.svr the mean of R2 is : -0.243679440381
model choose is : svm.svr the validation ser R square is :%f  -1.21033155027