随机森林算法构建红酒口感预测模型

# -*- coding:utf-8 -*-import numpyimport urllib.requestfrom sklearn.model_selection import train_test_splitfrom sklearn import ensemblefrom sklearn.metrics import mean_squared_errorimport pylab as plot#从网页中读取数据url="http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"data=urllib.request.urlopen(url)#将数据中第一行的属性读取出来放在names列表中，将其他行的数组读入row中，并将row中最后一列提取#出来放在labels中作为标签，并使用pop将该列从row去去除掉，最后将剩下的属性值转化为float类型存入xList中xlist=[]labels=[]names=[]firstline=Truefor line in data:    if firstline:        names=line.strip().split(b';')        firstline=False    else:        row=line.strip().split(b';')        labels.append(float(row[-1]))        row.pop()        floatrow=[float(num) for num in row]        xlist.append(floatrow)#计算几行几列nrows=len(xlist)ncols=len(xlist[1])#转化为numpy格式x=numpy.array(xlist)y=numpy.array(labels)winenames=numpy.array(names)#随机抽30%的数据用于测试，随机种子为531固定值，确保多次运行结果相同便于优化算法xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.30,random_state=531)mseoos=[]#测试50棵~500棵决策树的方差(步长10)ntreelist=range(50,500,10)for itrees in ntreelist:    depth=None    maxfeat=4    #随机森林算法生成训练    winerandomforestmodel=ensemble.RandomForestRegressor(n_estimators=itrees,max_depth=depth,max_features=maxfeat,oob_score=False,random_state=531)    winerandomforestmodel.fit(xtrain,ytrain)    #测试方差放入列表    prediction=winerandomforestmodel.predict(xtest)    mseoos.append(mean_squared_error(ytest,prediction))print("MSE")print(mseoos[-1])plot.plot(ntreelist,mseoos)plot.xlabel("number of trees")plot.ylabel("fang cha")plot.show()#用feature_importances_方法提取属性重要性numpy数组featureimportance=winerandomforestmodel.feature_importances_#归一化？featureimportance=featureimportance/featureimportance.max()#argsort方法返回array类型的索引sorted_idx=numpy.argsort(featureimportance)#函数说明：arange([start,] stop[, step,], dtype=None)根据start与stop指定的范围以及step设定的步长，生成一个 ndarraybarpos=numpy.arange(sorted_idx.shape[0]) + .5plot.barh(barpos,featureimportance[sorted_idx],align='center')plot.yticks(barpos,winenames[sorted_idx])plot.xlabel("variable importance")plot.show()

注意：

from sklearn.cross_validation import train_test_split 改为from sklearn.model_selection import train_test_split上面那个已经不用了