random forest python 实现

一、实验数据

实验数据来自http://sci2s.ugr.es/keel/category.php?cat=clas 的mushroom数据集

二、设计思路：

1、首先，实现一个单一的决策树算法；
2、设定训练树的数目，默认为值为10；
3、根据训练数据集进行采样，此处采样大小为原始数据的一半，相同数据进行合并；对采样之后的数据抽取一半的特征，然后开始训练第一棵决策树；
4、重复步骤3生成其他的决策树；
5、利用生成的决策树对测试数据进行多数表决，决定其类别；

__author__ = 'hxw'
#-*- coding=utf-8 -*-
import numpy as np
"""
Note:this random forest can only process discrete data
if you want process continue data,you should disperse your data,before using it
"""
class randomforest():
    def __init__(self,train_data,n_estimators=10):
        self.data=train_data
        self.n_estimators=n_estimators
        self.decision_trees=[]
        self.labels=np.unique(self.data[:,:-1])
    def cal_entropy(self,y):
        elements={}
        total=len(y)
        for ele in y:
            elements[ele]=elements.get(ele,0)+1
        entropy=0
        for ele in elements:
            p=elements.get(ele)*1.0/total
            entropy-=p*np.log2(p)
        return entropy
    def split_data(self,data,i,value):
        "split data x in the dimension i with value "
        split_data=[]
        for row in data:
            if row[i]==value:
                split_data.append(row[:i].tolist()+row[i+1:].tolist())
        return np.array(split_data)
    def select_feature(self,data):
        x=data[:,:-1]
        y=data[:,-1]
        base_entropy=self.cal_entropy(y)
        max=-1
        select_feature=-1
        row_num,col_num=x.shape
        for col in range(col_num):
            elements=np.unique(x[:,col])
            entropy=0
            for ele in elements:
                split_data=self.split_data(data,col,ele)
                entropy+=split_data.shape[0]*1.0/row_num*self.cal_entropy(split_data[:,-1])
            info_gain=base_entropy-entropy
            #print "featurn %d increase is %f"%(col,info_gain)
            if maxreturn select_feature
    def build_decision_tree(self,data,features_list):
        "train the data and build an decision tree"
        x=data[:,:-1]
        y=data[:,-1]
        #conditions for the termination of the recursive function
        #condition one:when all lables is the
        if len(np.unique(y))==1:
            return y[0]
        #condition two :when there are no feature to split
        if data.shape[1]==1:
            dic={}
            for i in y:
                dic[i]=dic.get(i,0)+1
            l=sorted(dic.iteritems(),key=lambda xx:xx[1],reverse=True)
            return l[0][0]
        select_feature=self.select_feature(data)
        values=np.unique(data[:,select_feature])
        raw_feature=features_list[select_feature]
        features_list.remove(raw_feature)
        myTree={raw_feature:{}}
        for value in values:
            myTree[raw_feature][value]=self.build_decision_tree(self.split_data(data,select_feature,value),features_list[:])
        return myTree
    def fit(self):
        print self.data.shape
        row,col=self.data.shape
        for i in range(self.n_estimators):
            #sample data
            samples=np.random.randint(0,row,(row+1)/2)
            samples=np.unique(samples)
            samples_data=self.data[samples.tolist()]
            #sample feature
            features=np.random.randint(0,col-1,col/2)
            features=np.unique(features)
            features_list=features.tolist()
            features_list.append(-1)
            sample_feature_data=samples_data[:,features_list]
            #print sample_feature_data
            decision_tree=self.build_decision_tree(sample_feature_data,features_list)
            #print decision_tree
            self.decision_trees.append(decision_tree)
    def classify(self,decision_tree,test_x):
        first_feature=decision_tree.keys()[0]
        secondDict=decision_tree[first_feature]
        key=test_x[first_feature]
        value=secondDict.get(key,"false")
        #if we can not find the key ,the we will return a label randomly
        if value=="false":
            return "cannot calssify"
            values=secondDict.values();
            r=np.random.randint(0,len(values))

            return values[r]
        if isinstance(value,dict):
            classLabel=self.classify(value,test_x)
        else:classLabel=value
        return classLabel
    def predict(self,test_data):
        predict_labels=[]
        for test_x in test_data:
            dic={}
            for decision_tree in self.decision_trees:
                predict_label=self.classify(decision_tree,test_x)
                if predict_label=="cannot calssify":
                    continue
                #print predict_label
                dic[predict_label]=dic.get(predict_label,0)+1

            l=sorted(dic.iteritems(),key=lambda x:x[1],reverse=True)
            if len(l)==0:
                r=np.random.randint(0,self.labels.shape[0])
                label=self.labels[r]
                predict_labels.append(label)
            else:
                predict_labels.append(l[0][0])
        return np.array(predict_labels)
    def accuracy(self,test_data):
        predict_labels=RF.predict(test_data[:,:-1])
        length=test_data.shape[0]
        num=0
        for i in range(length):
            if predict_labels[i]==test_data[i,-1]:
                num+=1
        print"test data accuracy is %f"%(num*1.0/length)
def loaddata(path):
    data=np.loadtxt(path,dtype="S20",delimiter=",")
    return data
path="D:\\pycharm_project\\RandomForest\\mushroom.dat"
data=loaddata(path)
train_data=data[:4000]
test_data=data[4000:]
RF=randomforest(train_data)
RF.fit()
RF.accuracy(test_data)

实验结果：


test data accuracy is 0.946472