ptyhon 随机森林算法及其优化 RandomForest

优化随机森林算法，正确率提高1%~5%（已经有90%+的正确率，再调高会导致过拟合）

论文当然是参考的，毕竟出现早的算法都被人研究烂了，什么优化基本都做过。而人类最高明之处就是懂得利用前人总结的经验和制造的工具（说了这么多就是为偷懒找借口。hhhh）

优化思路

1. 计算传统模型准确率
2. 计算设定树木颗数时最佳树深度，以最佳深度重新生成随机森林
3. 计算新生成森林中每棵树的AUC，选取AUC靠前的一定百分比的树
4. 通过计算各个树的数据相似度，排除相似度超过设定值且AUC较小的树
5. 计算最终的准确率

主要代码粘贴如下（注释比较详细，就不介绍代码了）

#-*- coding: utf-8 -*-
import time
from csv import reader
from random import randint
from random import seed

import numpy as np
from numpy import mat

from group_11 import caculateAUC_1, plotTree

# 建立一棵CART树
'''试探分枝'''
def data_split(index, value, dataset):
    left, right = list(), list()
    for row in dataset:
        if row[index] < value:
            left.append(row)
        else:
            right.append(row)
    return left, right

'''计算基尼指数'''
def calc_gini(groups, class_values):
    gini = 0.0
    total_size = 0
    for group in groups:
        total_size += len(group)
    for group in groups:
        size = len(group)
        if size == 0:
            continue
        for class_value in class_values:
            proportion = [row[-1] for row in group].count(class_value) / float(size)
            gini += (size / float(total_size)) * (proportion * (1.0 - proportion))# 二分类执行两次，相当于*2
    return gini

'''找最佳分叉点'''
def get_split(dataset, n_features):
    class_values = list(set(row[-1] for row in dataset))# 类别标签集合
    b_index, b_value, b_score, b_groups = 999, 999, 999, None

    # 随机选取特征子集，包含n_features个特征
    features = list()
    while len(features) < n_features:
        # 随机选取特征
        # 特征索引
        index = randint(0, len(dataset[0]) - 2)  # 往features添加n_features个特征（n_feature等于特征数的根号），特征索引从dataset中随机取
        if index not in features:
            features.append(index)
    for index in features:      # 对每一个特征
        # 计算Gini指数
        for row in dataset:   # 按照每个记录的该特征的取值划分成两个子集，计算对于的Gini（D，A），取最小的
            groups = data_split(index, row[index], dataset)
            gini = calc_gini(groups, class_values)
            if gini < b_score:
                b_index, b_value, b_score, b_groups = index, row[index], gini, groups
    return {'index': b_index, 'value': b_value, 'groups': b_groups}  # 每个节点由字典组成

'''多数表决'''
def to_terminal(group):
    outcomes = [row[-1] for row in group]
    return max(set(outcomes), key=outcomes.count)

'''分枝'''
def split(node, max_depth, min_size, n_features, depth):
    left, right = node['groups']  # 自动分包/切片
    del (node['groups'])
    if not left or not right:    # left或者right为空时
        node['left'] = node['right'] = to_terminal(left + right)  # 叶节点不好理解
        return

    if depth >= max_depth:
        node['left'], node['right'] = to_terminal(left), to_terminal(right)
        return
    # 左子树
    if len(left) <= min_size:
        node['left'] = to_terminal(left)
    else:
        node['left'] = get_split(left, n_features)
        split(node['left'], max_depth, min_size, n_features, depth + 1)
    # 右子树
    if len(right) <= min_size:   # min_size最小的的分枝样本数
        node['right'] = to_terminal(right)
    else:
        node['right'] = get_split(right, n_features)
        split(node['right'], max_depth, min_size, n_features, depth + 1)

'''建立一棵树'''
def build_one_tree(train, max_depth, min_size, n_features):
    # 寻找最佳分裂点作为根节点
    root = get_split(train, n_features)
    split(root, max_depth, min_size, n_features, 1)
    return root

'''用森林里的一棵树来预测'''
def predict(node, row):
    if row[node['index']] < node['value']:
        if isinstance(node['left'], dict):
            return predict(node['left'], row)
        else:
            return node['left']
    else:
        if isinstance(node['right'], dict):
            return predict(node['right'], row)
        else:
            return node['right']


# 随机森林类
class randomForest:
    def __init__(self,trees_num, max_depth, leaf_min_size, sample_ratio, feature_ratio):
        self.trees_num = trees_num                # 森林的树的数目
        self.max_depth = max_depth                # 树深
        self.leaf_min_size = leaf_min_size        # 建立树时，停止的分枝样本最小数目
        self.samples_split_ratio = sample_ratio   # 采样，创建子集的比例（行采样）
        self.feature_ratio = feature_ratio        # 特征比例（列采样）
        self.trees = list()                       # 森林

    '''有放回的采样，创建数据子集'''
    def sample_split(self, dataset):
        sample = list()
        n_sample = round(len(dataset) * self.samples_split_ratio)  #每棵树的采样数
        while len(sample) < n_sample:
            index = randint(0, len(dataset) - 2)  #随机有放回的采样
            sample.append(dataset[index])
        return sample

    ##############***Out-of-Bag***################################
    # 进行袋外估计等相关函数的实现,需要注意并不是每个样本都可能出现在随机森林的袋外数据中
    # 因此进行oob估计时需要注意估计样本的数量
    def OOB(self, oobdata, train, trees):
        '''输入为：袋外数据dict,训练集,tree_list
        return oob准确率'''

        n_rows = []
        count = 0
        n_trees = len(trees)  # 森林中树的棵树

        for key, item in oobdata.items():
            n_rows.append(item)

        # print(len(n_rows))  # 所有trees中的oob数据的合集

        n_rows_list = sum(n_rows, [])

        unique_list = []
        for l1 in n_rows_list:  # 从oob合集中计算独立样本数量
            if l1 not in unique_list:
                unique_list.append(l1)

        n = len(unique_list)
        # print(n)

        # 对训练集中的每个数据，进行遍历，寻找其作为oob数据时的所有trees,并进行多数投票
        for row in train:
            pre = []
            for i in range(n_trees):
                if row not in oobdata[i]:
                    # print('row: ',row)
                    # print('trees[i]: ', trees[i])
                    pre.append(predict(trees[i], row))
            if len(pre) > 0:
                label = max(set(pre), key=pre.count)
                if label == row[-1]:
                    count += 1

        return (float(count) / n) * 100

    '''建立随机森林'''
    def build_randomforest(self, train):
        temp_flag = 0
        max_depth = self.max_depth           # 树深
        min_size = self.leaf_min_size        # 建立树时，停止的分枝样本最小数目
        n_trees = self.trees_num             # 森林的树的数目
        n_features = int(self.feature_ratio * (len(train[0])-1))    #列采样，从M个feature中，选择m个(m<<M)
        # print('特征值为 : ',n_features)
        oobs = {}  # ----------------------
        for i in range(n_trees):            # 建立n_trees棵决策树
            sample = self.sample_split(train)     # 有放回的采样，创建数据子集
            oobs[i] = sample  # ----------------
            tree = build_one_tree(sample, max_depth, min_size, n_features)   # 建立决策树
            self.trees.append(tree)
            temp_flag += 1
            # print(i,tree)
        oob_score = self.OOB(oobs, train, self.trees)  # oob准确率---------
        print("oob_score is ", oob_score)  # 打印oob准确率---------
        return self.trees

    '''随机森林预测的多数表决'''
    def bagging_predict(self, onetestdata):
        predictions = [predict(tree, onetestdata) for tree in self.trees]
        return max(set(predictions), key=predictions.count)

    '''计算建立的森林的精确度'''
    def accuracy_metric(self, testdata):
        correct = 0
        for i in range(len(testdata)):
            predicted = self.bagging_predict(testdata[i])
            if testdata[i][-1] == predicted:
                correct += 1
        return correct / float(len(testdata)) * 100.0


# 数据处理
'''导入数据'''
def load_csv(filename):
    dataset = list()
    with open(filename, 'r') as file:
        csv_reader = reader(file)
        for row in csv_reader:
            if not row:
                continue
            # dataset.append(row)
            dataset.append(row[:-1])
    # return dataset
    return dataset[1:], dataset[0]

'''划分训练数据与测试数据'''
def split_train_test(dataset, ratio=0.3):
    #ratio = 0.2  # 取百分之二十的数据当做测试数据
    num = len(dataset)
    train_num = int((1-ratio) * num)
    dataset_copy = list(dataset)
    traindata = list()
    while len(traindata) < train_num:
        index = randint(0,len(dataset_copy)-1)
        traindata.append(dataset_copy.pop(index))
    testdata = dataset_copy
    return traindata, testdata

'''分析树，将向量内积写入list'''
def analyListTree(node, tag, result):
    # 叶子节点的父节点
    if (isinstance(node['left'], dict)):
        # 计算node与node[tag]的内积
        tag="left"
        re = Inner_product(node, tag)
        result.append(re)
        analyListTree(node['left'], 'left', result)
        return
    elif (isinstance(node['right'], dict)):
        # 计算node与node[tag]的内积
        tag = "right"
        re = Inner_product(node, tag)
        result.append(re)
        analyListTree(node['right'], 'right', result)
        return
    else:
        return

'''求向量内积'''
# 计算node与node[tag]的内积
def Inner_product(node ,tag):
    a = mat([[float(node['index'])], [float(node['value'])]])
    b = mat([[float(node[tag]['index'])], [float(node[tag]['value'])]])
    return (a.T * b)[0,0]

'''相似度优化'''
''' same_value = 20      # 向量内积的差（小于此值认为相似）
    same_rate = 0.63     # 树的相似度（大于此值认为相似）
    返回新的森林（已去掉相似度高的树）'''
def similarity_optimization(newforest, samevalue, samerate):
    res = list()                # 存储森林的内积
    result = list()             # 存储某棵树的内积
    i = 1
    for tree in newforest:
        # 分析树，将向量内积写入list
        # result 存储tree的内积
        analyListTree(tree, None, result)
        res.append(result)
        # print('第',i,'棵树：',len(result),result)
        result = []
    # print('res = ',len(res),res)
    # 取一棵树的单个向量内积与其他树的单个向量内积做完全对比（相似度）
    # 遍历列表的列
    for i in range(0, len(res) - 1):
        # 保证此列未被置空、
        if not newforest[i] == None:
            # 遍历做对比的树的列
            for k in range(i + 1, len(res)):
                if not newforest[k] == None:
                    # time用于统计相似的次数，在每次更换对比树时重置为0
                    time = 0
                    # 遍历列表的当前行
                    for j in range(0, len(res[i])):
                        # 当前两颗树对比次数
                        all_contrast = (res[ i].__len__() * res[k].__len__())
                        # 遍历做对比的树的行
                        for l in range(0, len(res[k])):
                            # 如果向量的内积相等，计数器加一
                            if res[i][j] - res[k][l] < samevalue:
                                time = time + 1
                        # 如果相似度大于设定值
                    real_same_rate = time / all_contrast
                    if (real_same_rate > samerate):
                        # 将对比树置空
                        newforest[k] = None
    result_forest = list()
    for i in range(0, newforest.__len__()):
        if not newforest[i] == None:
            result_forest.append(newforest[i])
    return result_forest


'''auc优化method'''
def auc_optimization(auclist,trees_num,trees):
    # 为auc排序，获取从大到小的与trees相对应的索引列表
    b = sorted(enumerate(auclist), key=lambda x: x[1], reverse=True)
    index_list = [x[0] for x in b]
    auc_num = int(trees_num * 2 / 3)
    # 取auc高的前auc_num个
    print('auc: ', auc_num, index_list)
    newTempForest = list()
    for i in range(auc_num):
        # myRF.trees.append(tempForest[i])
        # newTempForest.append(myRF.trees[index_list[i]])
        newTempForest.append(trees[index_list[i]])
    return newTempForest

'''得到森林中决策树的最佳深度'''
def getBestDepth(min_size,sample_ratio,trees_num,feature_ratio,traindata,testdata):
    max_depth = np.linspace(1, 15, 15, endpoint=True)
    # max_depth=[5,6,7,8,9,10,11,12,13,14,15]
    scores_final = []
    i=0
    for depth in max_depth:
        # 初始化随机森林
        # print('=========>',i,'<=============')
        myRF_ = randomForest(trees_num, depth, min_size, sample_ratio, feature_ratio)
        # 生成随机森林
        myRF_.build_randomforest(traindata)
        # 测试评估
        acc = myRF_.accuracy_metric(testdata[:-1])
        # print('模型准确率：', acc, '%')
        # scores_final.append(acc.mean())
        scores_final.append(acc*0.01)
        i=i+1
    # print('scores_final: ',scores_final)
    # 找到深度小且准确率高的值
    best_depth = 0
    temp_score = 0
    for i in range(len(scores_final)):
        if scores_final[i] > temp_score:
            temp_score = scores_final[i]
            best_depth = max_depth[i]
    # print('best_depth:',np.mean(scores_final),best_depth)
    # plt.plot(max_depth, scores_final, 'r-', lw=2)
    # # plt.plot(max_depth, list(range(0,max(scores_final))), 'r-', lw=2)
    # plt.xlabel('max_depth')
    # plt.ylabel('CV scores')
    # plt.ylim(bottom=0.0,top=1.0)
    # plt.grid()
    # plt.show()
    return best_depth


'''对比不同树个数时的模型正确率'''
def getMyRFAcclist(treenum_list):
    seed(1)  # 每一次执行本文件时都能产生同一个随机数
    filename = 'DataSet3.csv'            #SMOTE处理过的数据
    min_size = 1
    sample_ratio = 1
    feature_ratio = 0.3  # 尽可能小，但是要保证 int(self.feature_ratio * (len(train[0])-1)) 大于1
    same_value = 20  # 向量内积的差（小于此值认为相似）
    same_rate = 0.63  # 树的相似度（大于此值认为相似）

    # 加载数据
    dataset, features = load_csv(filename)
    traindata, testdata = split_train_test(dataset, feature_ratio)
    # 森林中不同树个数的对比
    # treenum_list = [20, 30, 40, 50, 60]
    acc_num_list = list()
    acc_list=list()
    for trees_num in treenum_list:
        # 优化1-获取最优深度
        max_depth = getBestDepth(min_size, sample_ratio, trees_num, feature_ratio, traindata, testdata)
        print('max_depth is ', max_depth)

        # 初始化随机森林
        myRF = randomForest(trees_num, max_depth, min_size, sample_ratio, feature_ratio)
        # 生成随机森林
        myRF.build_randomforest(traindata)

        print('Tree_number: ', myRF.trees.__len__())
        # 计算森林中每棵树的AUC
        auc_list = caculateAUC_1.caculateRFAUC(testdata, myRF.trees)
        # 选取AUC高的决策数形成新的森林(auc优化)
        newTempForest = auc_optimization(auc_list,trees_num,myRF.trees)
        # 相似度优化
        myRF.trees = similarity_optimization(newTempForest, same_value, same_rate)
        # 测试评估
        acc = myRF.accuracy_metric(testdata[:-1])
        print('myRF1_模型准确率：', acc, '%')
        acc_num_list.append([myRF.trees.__len__(), acc])
        acc_list.append(acc)
    print('trees_num from 20 to 60: ', acc_num_list)
    return acc_list


if __name__ == '__main__':
    start = time.clock()
    seed(1)  # 每一次执行本文件时都能产生同一个随机数
    filename = 'DataSet3.csv'       # 这里是已经利用SMOTE进行过预处理的数据集
    max_depth = 15  # 调参（自己修改） #决策树深度不能太深，不然容易导致过拟合
    min_size = 1
    sample_ratio = 1
    trees_num = 20

    feature_ratio = 0.3     # 尽可能小，但是要保证 int(self.feature_ratio * (len(train[0])-1)) 大于1
    same_value = 20        # 向量内积的差（小于此值认为相似）
    same_rate = 0.82     # 树的相似度（大于此值认为相似）
    # 加载数据
    dataset,features = load_csv(filename)
    traindata,testdata = split_train_test(dataset, feature_ratio)

    # 优化1-获取最优深度
    # max_depth = getBestDepth(min_size, sample_ratio, trees_num, feature_ratio, traindata, testdata)
    # print('max_depth is ',max_depth)

    # 初始化随机森林
    myRF = randomForest(trees_num, max_depth, min_size, sample_ratio, feature_ratio)
    # 生成随机森林
    myRF.build_randomforest(traindata)

    print('Tree_number: ', myRF.trees.__len__())
    acc = myRF.accuracy_metric(testdata[:-1])
    print('传统RF模型准确率：',acc,'%')

    # 画出某棵树用以可视化观察（这里是第一棵树）
    # plotTree.creatPlot(myRF.trees[0], features)
    # 计算森林中每棵树的AUC
    auc_list = caculateAUC_1.caculateRFAUC(testdata,myRF.trees)
    # 画出每棵树的auc——柱状图
    # plotTree.plotAUCbar(auc_list.__len__(),auc_list)

    # 选取AUC高的决策数形成新的森林(auc优化)
    newTempForest = auc_optimization(auc_list,trees_num,myRF.trees)
    # 相似度优化
    myRF.trees=similarity_optimization(newTempForest, same_value, same_rate)

    print('优化后Tree_number: ', myRF.trees.__len__())
    # 测试评估
    acc = myRF.accuracy_metric(testdata[:-1])
    # print('优化后模型准确率：', acc, '%')
    print('myRF1_模型准确率：', acc, '%')
    # 画出某棵树用以可视化观察（这里是第一棵树）
    # plotTree.creatPlot(myRF.trees[0], features)
    # 计算森林中每棵树的AUC
    auc_list = caculateAUC_1.caculateRFAUC(testdata, myRF.trees)
    # 画出每棵树的auc——柱状图
    plotTree.plotAUCbar(auc_list.__len__(), auc_list)
    end = time.clock()
    print('The end!')
    print(end-start)