python 隨機森林算法及其優化詳解

這篇文章主要介紹了ptyhon 隨機森林算法及其優化詳解,文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值,需要的朋友可以參考下

前言

優化隨機森林算法，正確率提高1%~5%（已經有90%+的正確率，再調高會導致過擬合）

論文當然是參考的，畢竟出現早的算法都被人研究爛了，什麼優化基本都做過。而人類最高明之處就是懂得利用前人總結的經驗和製造的工具（說了這麼多就是爲偷懶找藉口。hhhh）

優化思路

1. 計算傳統模型準確率

2. 計算設定樹木顆數時最佳樹深度，以最佳深度重新生成隨機森林

3. 計算新生成森林中每棵樹的AUC，選取AUC靠前的一定百分比的樹

4. 通過計算各個樹的數據相似度，排除相似度超過設定值且AUC較小的樹

5. 計算最終的準確率

主要代碼粘貼如下（註釋比較詳細，就不介紹代碼了）

#-*- 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樹
'''試探分枝'''
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

'''計算基尼指數'''
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))# 二分類執行兩次，相當於*2
 return gini

'''找最佳分叉點'''
def get_split(dataset, n_features):
 class_values = list(set(row[-1] for row in dataset))# 類別標籤集合
 b_index, b_value, b_score, b_groups = 999, 999, 999, None

 # 隨機選取特徵子集，包含n_features個特徵
 features = list()
 while len(features) < n_features:
  # 隨機選取特徵
  # 特徵索引
  index = randint(0, len(dataset[0]) - 2) # 往features添加n_features個特徵（n_feature等於特徵數的根號），特徵索引從dataset中隨機取
  if index not in features:
   features.append(index)
 for index in features:  # 對每一個特徵
  # 計算Gini指數
  for row in dataset: # 按照每個記錄的該特徵的取值劃分成兩個子集，計算對於的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} # 每個節點由字典組成

'''多數表決'''
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'] # 自動分包/切片
 del (node['groups'])
 if not left or not right: # left或者right爲空時
  node['left'] = node['right'] = to_terminal(left + right) # 葉節點不好理解
  return

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

'''建立一棵樹'''
def build_one_tree(train, max_depth, min_size, n_features):
 # 尋找最佳分裂點作爲根節點
 root = get_split(train, n_features)
 split(root, max_depth, min_size, n_features, 1)
 return root

'''用森林裏的一棵樹來預測'''
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']


# 隨機森林類
class randomForest:
 def __init__(self,trees_num, max_depth, leaf_min_size, sample_ratio, feature_ratio):
  self.trees_num = trees_num    # 森林的樹的數目
  self.max_depth = max_depth    # 樹深
  self.leaf_min_size = leaf_min_size  # 建立樹時，停止的分枝樣本最小數目
  self.samples_split_ratio = sample_ratio # 採樣，創建子集的比例（行採樣）
  self.feature_ratio = feature_ratio  # 特徵比例（列採樣）
  self.trees = list()      # 森林

 '''有放回的採樣，創建數據子集'''
 def sample_split(self, dataset):
  sample = list()
  n_sample = round(len(dataset) * self.samples_split_ratio) #每棵樹的採樣數
  while len(sample) < n_sample:
   index = randint(0, len(dataset) - 2) #隨機有放回的採樣
   sample.append(dataset[index])
  return sample

 ##############***Out-of-Bag***################################
 # 進行袋外估計等相關函數的實現,需要注意並不是每個樣本都可能出現在隨機森林的袋外數據中
 # 因此進行oob估計時需要注意估計樣本的數量
 def OOB(self, oobdata, train, trees):
  '''輸入爲：袋外數據dict,訓練集,tree_list
  return oob準確率'''

  n_rows = []
  count = 0
  n_trees = len(trees) # 森林中樹的棵樹

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

  # print(len(n_rows)) # 所有trees中的oob數據的合集

  n_rows_list = sum(n_rows, [])

  unique_list = []
  for l1 in n_rows_list: # 從oob合集中計算獨立樣本數量
   if l1 not in unique_list:
    unique_list.append(l1)

  n = len(unique_list)
  # print(n)

  # 對訓練集中的每個數據，進行遍歷，尋找其作爲oob數據時的所有trees,並進行多數投票
  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

 '''建立隨機森林'''
 def build_randomforest(self, train):
  temp_flag = 0
  max_depth = self.max_depth   # 樹深
  min_size = self.leaf_min_size  # 建立樹時，停止的分枝樣本最小數目
  n_trees = self.trees_num    # 森林的樹的數目
  n_features = int(self.feature_ratio * (len(train[0])-1)) #列採樣，從M個feature中，選擇m個(m<<M)
  # print('特徵值爲 : ',n_features)
  oobs = {} # ----------------------
  for i in range(n_trees):   # 建立n_trees棵決策樹
   sample = self.sample_split(train)  # 有放回的採樣，創建數據子集
   oobs[i] = sample # ----------------
   tree = build_one_tree(sample, max_depth, min_size, n_features) # 建立決策樹
   self.trees.append(tree)
   temp_flag += 1
   # print(i,tree)
  oob_score = self.OOB(oobs, train, self.trees) # oob準確率---------
  print("oob_score is ", oob_score) # 打印oob準確率---------
  return self.trees

 '''隨機森林預測的多數表決'''
 def bagging_predict(self, onetestdata):
  predictions = [predict(tree, onetestdata) for tree in self.trees]
  return max(set(predictions), key=predictions.count)

 '''計算建立的森林的精確度'''
 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


# 數據處理
'''導入數據'''
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]

'''劃分訓練數據與測試數據'''
def split_train_test(dataset, ratio=0.3):
 #ratio = 0.2 # 取百分之二十的數據當做測試數據
 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

'''分析樹，將向量內積寫入list'''
def analyListTree(node, tag, result):
 # 葉子節點的父節點
 if (isinstance(node['left'], dict)):
  # 計算node與node[tag]的內積
  tag="left"
  re = Inner_product(node, tag)
  result.append(re)
  analyListTree(node['left'], 'left', result)
  return
 elif (isinstance(node['right'], dict)):
  # 計算node與node[tag]的內積
  tag = "right"
  re = Inner_product(node, tag)
  result.append(re)
  analyListTree(node['right'], 'right', result)
  return
 else:
  return

'''求向量內積'''
# 計算node與node[tag]的內積
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]

'''相似度優化'''
''' same_value = 20  # 向量內積的差（小於此值認爲相似）
 same_rate = 0.63  # 樹的相似度（大於此值認爲相似）
 返回新的森林（已去掉相似度高的樹）'''
def similarity_optimization(newforest, samevalue, samerate):
 res = list()    # 存儲森林的內積
 result = list()    # 存儲某棵樹的內積
 i = 1
 for tree in newforest:
  # 分析樹，將向量內積寫入list
  # result 存儲tree的內積
  analyListTree(tree, None, result)
  res.append(result)
  # print('第',i,'棵樹：',len(result),result)
  result = []
 # print('res = ',len(res),res)
 # 取一棵樹的單個向量內積與其他樹的單個向量內積做完全對比（相似度）
 # 遍歷列表的列
 for i in range(0, len(res) - 1):
  # 保證此列未被置空、
  if not newforest[i] == None:
   # 遍歷做對比的樹的列
   for k in range(i + 1, len(res)):
    if not newforest[k] == None:
     # time用於統計相似的次數，在每次更換對比樹時重置爲0
     time = 0
     # 遍歷列表的當前行
     for j in range(0, len(res[i])):
      # 當前兩顆樹對比次數
      all_contrast = (res[ i].__len__() * res[k].__len__())
      # 遍歷做對比的樹的行
      for l in range(0, len(res[k])):
       # 如果向量的內積相等，計數器加一
       if res[i][j] - res[k][l] < samevalue:
        time = time + 1
      # 如果相似度大於設定值
     real_same_rate = time / all_contrast
     if (real_same_rate > samerate):
      # 將對比樹置空
      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優化method'''
def auc_optimization(auclist,trees_num,trees):
 # 爲auc排序，獲取從大到小的與trees相對應的索引列表
 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個
 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

'''得到森林中決策樹的最佳深度'''
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:
  # 初始化隨機森林
  # print('=========>',i,'<=============')
  myRF_ = randomForest(trees_num, depth, min_size, sample_ratio, feature_ratio)
  # 生成隨機森林
  myRF_.build_randomforest(traindata)
  # 測試評估
  acc = myRF_.accuracy_metric(testdata[:-1])
  # print('模型準確率：', acc, '%')
  # scores_final.append(acc.mean())
  scores_final.append(acc*0.01)
  i=i+1
 # print('scores_final: ',scores_final)
 # 找到深度小且準確率高的值
 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


'''對比不同樹個數時的模型正確率'''
def getMyRFAcclist(treenum_list):
 seed(1) # 每一次執行本文件時都能產生同一個隨機數
 filename = 'DataSet3.csv'   #SMOTE處理過的數據
 min_size = 1
 sample_ratio = 1
 feature_ratio = 0.3 # 儘可能小，但是要保證 int(self.feature_ratio * (len(train[0])-1)) 大於1
 same_value = 20 # 向量內積的差（小於此值認爲相似）
 same_rate = 0.63 # 樹的相似度（大於此值認爲相似）

 # 加載數據
 dataset, features = load_csv(filename)
 traindata, testdata = split_train_test(dataset, feature_ratio)
 # 森林中不同樹個數的對比
 # treenum_list = [20, 30, 40, 50, 60]
 acc_num_list = list()
 acc_list=list()
 for trees_num in treenum_list:
  # 優化1-獲取最優深度
  max_depth = getBestDepth(min_size, sample_ratio, trees_num, feature_ratio, traindata, testdata)
  print('max_depth is ', max_depth)

  # 初始化隨機森林
  myRF = randomForest(trees_num, max_depth, min_size, sample_ratio, feature_ratio)
  # 生成隨機森林
  myRF.build_randomforest(traindata)

  print('Tree_number: ', myRF.trees.__len__())
  # 計算森林中每棵樹的AUC
  auc_list = caculateAUC_1.caculateRFAUC(testdata, myRF.trees)
  # 選取AUC高的決策數形成新的森林(auc優化)
  newTempForest = auc_optimization(auc_list,trees_num,myRF.trees)
  # 相似度優化
  myRF.trees = similarity_optimization(newTempForest, same_value, same_rate)
  # 測試評估
  acc = myRF.accuracy_metric(testdata[:-1])
  print('myRF1_模型準確率：', 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) # 每一次執行本文件時都能產生同一個隨機數
 filename = 'DataSet3.csv'  # 這裏是已經利用SMOTE進行過預處理的數據集
 max_depth = 15 # 調參（自己修改） #決策樹深度不能太深，不然容易導致過擬合
 min_size = 1
 sample_ratio = 1
 trees_num = 20

 feature_ratio = 0.3  # 儘可能小，但是要保證 int(self.feature_ratio * (len(train[0])-1)) 大於1
 same_value = 20  # 向量內積的差（小於此值認爲相似）
 same_rate = 0.82  # 樹的相似度（大於此值認爲相似）
 # 加載數據
 dataset,features = load_csv(filename)
 traindata,testdata = split_train_test(dataset, feature_ratio)

 # 優化1-獲取最優深度
 # max_depth = getBestDepth(min_size, sample_ratio, trees_num, feature_ratio, traindata, testdata)
 # print('max_depth is ',max_depth)

 # 初始化隨機森林
 myRF = randomForest(trees_num, max_depth, min_size, sample_ratio, feature_ratio)
 # 生成隨機森林
 myRF.build_randomforest(traindata)

 print('Tree_number: ', myRF.trees.__len__())
 acc = myRF.accuracy_metric(testdata[:-1])
 print('傳統RF模型準確率：',acc,'%')

 # 畫出某棵樹用以可視化觀察（這裏是第一棵樹）
 # plotTree.creatPlot(myRF.trees[0], features)
 # 計算森林中每棵樹的AUC
 auc_list = caculateAUC_1.caculateRFAUC(testdata,myRF.trees)
 # 畫出每棵樹的auc——柱狀圖
 # plotTree.plotAUCbar(auc_list.__len__(),auc_list)

 # 選取AUC高的決策數形成新的森林(auc優化)
 newTempForest = auc_optimization(auc_list,trees_num,myRF.trees)
 # 相似度優化
 myRF.trees=similarity_optimization(newTempForest, same_value, same_rate)

 print('優化後Tree_number: ', myRF.trees.__len__())
 # 測試評估
 acc = myRF.accuracy_metric(testdata[:-1])
 # print('優化後模型準確率：', acc, '%')
 print('myRF1_模型準確率：', acc, '%')
 # 畫出某棵樹用以可視化觀察（這裏是第一棵樹）
 # plotTree.creatPlot(myRF.trees[0], features)
 # 計算森林中每棵樹的AUC
 auc_list = caculateAUC_1.caculateRFAUC(testdata, myRF.trees)
 # 畫出每棵樹的auc——柱狀圖
 plotTree.plotAUCbar(auc_list.__len__(), auc_list)
 end = time.clock()
 print('The end!')
 print(end-start)

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