前言

  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)

  优化思路

  计算传统模型准确率

  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

  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

  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

  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<

  # 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 = []郑州妇科医院哪家好 yiyuan.120ask.com/art/

  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)