scikit-learn机器学习RandomForest实例（含类别属性处理）
在工作中进行机器学习的数据一般都包含Nominal属性和Numric属性，在scikit-learn中提供了处理numric方法像Normalization方法等，也提供了处理Nominal的方法（OneHotEncoder方法）。本文将展示OneHotEncoder方法处理Nominal数据，并将数据应用到机器学习的流程。
一、准备数据
本文使用的数据是csv格式的，数据中的属性有Numric型和Nominal型。属性描述如下
@attribute ‘birthday’ numeric
@attribute ‘astrology’ {‘1′,’2′,’3′,’4′,’5′,’6′,’7′,’8′,’9′,’10’,’11’,’12’}
@attribute ‘animalsign’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8′,’9′,’10’,’11’,’12’}
@attribute ‘height’ numeric
@attribute ‘degree’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8’}
@attribute ‘housing’ {‘0′,’1′,’2′,’3′,’4’}
@attribute ‘marriage’ {‘0′,’1′,’2′,’3′,’4’}
@attribute ‘income’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8′,’9′,’10’,’11’,’12’}
@attribute ‘haveChildren’ {‘1′,’2′,’3′,’4’}
@attribute ‘hasMainPhoto’ {‘0′,’1’}
@attribute ‘nationality’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8′,’9′,’10’,’11’,’12’}
@attribute ‘religion’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8′,’9′,’10’,’11’,’12’}
@attribute ‘bodyType’ numeric
@attribute ‘physicalLooking’ numeric
@attribute ‘newNature’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8’}
@attribute ‘industry’ {‘0′,’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’}
@attribute ‘newWorkStatus’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8′,’9’}
@attribute ‘newCar’ {‘0′,’1′,’2′,’3′,’4’}
@attribute ‘isCreditedBySfz’ {‘0′,’1’}
@attribute ‘cregisterTime’ numeric
@attribute ‘age’ numeric
@attribute ‘housestatus’ {‘0′,’1′,’2′,’3′,’4′,’5′,’6′,’7′,’8’}
@attribute ‘photonum’ numeric
@attribute ‘msgcnt’ numeric
@attribute ‘himsgcnt’ numeric
@attribute ‘huifumsgcnt’ numeric
@attribute ‘receivemsg’ numeric
@attribute ‘viewcnt’ numeric
@attribute ‘beviewcnt’ numeric
@attribute ‘focuscnt’ numeric
@attribute ‘befocuscnt’ numeric
@attribute ‘class’ {‘0′,’1’}
数据如下所示,仅仅展示了几条数据
@data
1973,6,2,162,6,1,1,6,11,1,1,8613,1,1,3,8,2,23,0,0,1,113,42,1,3,0,2,0,5,0,27,0,0,1
1979,7,8,172,4,4,2,5,11,1,1,8651,3,6,7,6,7,1,7,0,1,113,36,4,2,0,1,8,20,28,98,0,0,1
1980,3,9,175,6,1,1,7,11,1,1,8637,1,1,7,?,0,24,0,0,0,113,35,1,0,0,0,1,3,1,20,0,1,0
1981,7,10,175,6,4,1,7,11,1,0,8623,1,1,4,8,0,5,0,2,1,113,34,4,0,0,0,0,0,0,0,0,0,0
1977,9,6,165,0,1,4,0,11,1,0,8632,1,1,4,7,7,7,8,0,1,113,38,1,0,0,0,0,9,0,20,0,2,0.
二、将数据读入内存，将训练集特征，训练集目标类，测试集特征，测试集目标类分别提取出来，以本文为例： train_data = open(“../../data/data/train.csv”,”r”)test_data = open(“../../data/data/test.csv”,”r”)##train datatrain_feature=[]train_target=[]for line in train_data: temp = line.strip().split(‘,’) train_feature.append(map(int,temp[0:-1])) train_target.extend(map(int,temp[-1]))train_data.close()##test datatest_feature=[]test_target=[]for line in test_data: temp = line.strip().split(‘,’) test_feature.append(map(int,temp[0:-1])) test_target.extend(map(int,temp[-1]))test_data.close()
三、使用OneHotEncoder将数据中的类别特征进行转化。以本文为例：
enc = OneHotEncoder(categorical_features=np.array([1,2,4,5,6,7,8,9,10,11,14,15,16,17,18,21]),n_values=[13,13,9,5,5,13,5,2,13,13,9,31,10,5,2,9])
categorical_features代表类别属性的索引数值，n_values代表categorical_features中每个属性含有多少个类别。
注意：类别属性尽量处理为从0开始的整数，像（0,1,2,3,4,5），不可取的实例像（17,19,50,100,1000），这与OneHotEncoder处理类别属性采取的方式有关，此处不细讲了。
四、使用OneHotEncoder将训练特征和测试特征进行转化。以本文为例：
注意：此处一定将训练特征和测试特征一起转化，因为转化之后数组的维度将会发生变化，有一个不转化，就会出错。
enc.fit(train_feature)train_feature = enc.transform(train_feature).toarray()test_feature = enc.transform(test_feature).toarray()
五、声明一个分类器，设置分类器参数，使用分类器进行训练，预测，评估等。 以下是以RandomForest为例源码： from sklearn.preprocessing import OneHotEncoderfrom sklearn.ensemble import RandomForestClassifierfrom numpy import shapeimport numpy as npfrom sklearn.metrics.metrics import classification_reportfrom sklearn.metrics import confusion_matrixtrain_data = open(“../../data/data/train.csv”,”r”)test_data = open(“../../data/data/test.csv”,”r”)##train datatrain_feature=[]train_target=[]for line in train_data: temp = line.strip().split(‘,’) train_feature.append(map(int,temp[0:-1])) train_target.extend(map(int,temp[-1]))train_data.close()##test datatest_feature=[]test_target=[]for line in test_data: temp = line.strip().split(‘,’) test_feature.append(map(int,temp[0:-1])) test_target.extend(map(int,temp[-1]))test_data.close()train_feature = np.array(train_feature)test_feature = np.array(test_feature)##OneHotEncoder usedenc = OneHotEncoder(categorical_features=np.array([1,2,4,5,6,7,8,9,10,11,14,15,16,17,18,21]),n_values=[13,13,9,5,5,13,5,2,13,13,9,香港vps31,10,5,2,9])enc.fit(train_feature)train_feature = enc.transform(train_feature).toarray()test_feature = enc.transform(test_feature).toarray()clf = RandomForestClassifier(n_estimators=10)clf = clf.fit(train_feature,train_target)##resultprint clf.predict(test_feature)target_names = [‘losing’, ‘active’]print classification_report(test_target, clf.predict(test_feature),target_names=target_names)
实验结果如下所示 ?? ? ? ? ? ? ?precision ? ?recall ?f1-score ? support
? ? ?losing ? ? ? 0.85 ? ? ?0.91 ? ? ?0.88 ? ? 31138
? ? ?active ? ? ? 0.84 ? ? ?0.75 ? ? ?0.79 ? ? 19725
avg / total ? ? ? 0.85 ? ? ?0.85 ? ? ?0.84 ? ? 50863
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