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)
優化思路
計算傳統模型準確率
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
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
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
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<
# 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 = []鄭州婦科醫院哪家好 yiyuan.120ask.com/art/
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)