mirror of
https://github.com/apachecn/ailearning.git
synced 2026-02-13 23:35:28 +08:00
223 lines
10 KiB
Python
223 lines
10 KiB
Python
#!/usr/bin/python
|
||
# coding:utf8
|
||
|
||
'''
|
||
Created 2017-04-25
|
||
Random Forest Algorithm on Sonar Dataset
|
||
@author: Flying_sfeng/jiangzhonglian
|
||
'''
|
||
from random import seed, randrange, random
|
||
from math import sqrt
|
||
from math import log
|
||
|
||
|
||
# 导入csv文件
|
||
def loadDataSet(filename):
|
||
dataset = []
|
||
with open(filename, 'r') as fr:
|
||
for line in fr.readlines():
|
||
if not line:
|
||
continue
|
||
lineArr = []
|
||
for featrue in line.split(','):
|
||
# strip()返回移除字符串头尾指定的字符生成的新字符串
|
||
str_f = featrue.strip()
|
||
if str_f.isdigit():
|
||
# 将数据集的第column列转换成float形式
|
||
lineArr.append(float(str_f))
|
||
else:
|
||
# 添加分类标签
|
||
lineArr.append(str_f)
|
||
dataset.append(lineArr)
|
||
return dataset
|
||
|
||
|
||
# Split a dataset into k folds
|
||
def cross_validation_split(dataset, n_folds): #将数据集dataset分成n_flods份，每份包含len(dataset) / n_folds个值，每个值由dataset数据集的内容随机产生，每个值被使用一次
|
||
dataset_split = list()
|
||
dataset_copy = list(dataset) #复制一份dataset,防止dataset的内容改变
|
||
fold_size = len(dataset) / n_folds
|
||
for i in range(n_folds):
|
||
fold = list() #每次循环fold清零，防止重复导入dataset_split
|
||
while len(fold) < fold_size: #这里不能用if，if只是在第一次判断时起作用，while执行循环，直到条件不成立
|
||
index = randrange(len(dataset_copy))
|
||
fold.append(dataset_copy.pop(index)) #将对应索引index的内容从dataset_copy中导出，并将该内容从dataset_copy中删除。pop() 函数用于移除列表中的一个元素（默认最后一个元素），并且返回该元素的值。
|
||
dataset_split.append(fold)
|
||
return dataset_split #由dataset分割出的n_folds个数据构成的列表，为了用于交叉验证
|
||
|
||
|
||
# Calculate accuracy percentage
|
||
def accuracy_metric(actual, predicted): #导入实际值和预测值，计算精确度
|
||
correct = 0
|
||
for i in range(len(actual)):
|
||
if actual[i] == predicted[i]:
|
||
correct += 1
|
||
return correct / float(len(actual)) * 100.0
|
||
|
||
|
||
# Split a dataset based on an attribute and an attribute value #根据特征和特征值分割数据集
|
||
def test_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
|
||
|
||
|
||
# Calculate the Gini index for a split dataset
|
||
def gini_index(groups, class_values): #个人理解：计算代价，分类越准确，则gini越小
|
||
gini = 0.0
|
||
for class_value in class_values: #class_values =[0,1]
|
||
for group in groups: #groups=(left,right)
|
||
size = len(group)
|
||
if size == 0:
|
||
continue
|
||
proportion = [row[-1] for row in group].count(class_value) / float(size)
|
||
gini += (proportion * (1.0 - proportion)) #个人理解：计算代价，分类越准确，则gini越小
|
||
return gini
|
||
|
||
|
||
# Select the best split point for a dataset #找出分割数据集的最优特征，得到最优的特征index，特征值row[index]，以及分割完的数据groups（left,right）
|
||
def get_split(dataset, n_features):
|
||
class_values = list(set(row[-1] for row in dataset)) #class_values =[0,1]
|
||
b_index, b_value, b_score, b_groups = 999, 999, 999, None
|
||
features = list()
|
||
while len(features) < n_features:
|
||
index = randrange(len(dataset[0])-1) #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: #在n_features个特征中选出最优的特征索引，并没有遍历所有特征，从而保证了每课决策树的差异性
|
||
for row in dataset:
|
||
groups = test_split(index, row[index], dataset) #groups=(left,right)；row[index]遍历每一行index索引下的特征值作为分类值value，找出最优的分类特征和特征值
|
||
gini = gini_index(groups, class_values)
|
||
if gini < b_score:
|
||
b_index, b_value, b_score, b_groups = index, row[index], gini, groups #最后得到最优的分类特征b_index,分类特征值b_value,分类结果b_groups。b_value为分错的代价成本。
|
||
#print b_score
|
||
return {'index':b_index, 'value':b_value, 'groups':b_groups}
|
||
|
||
|
||
# Create a terminal node value #输出group中出现次数较多的标签
|
||
def to_terminal(group):
|
||
outcomes = [row[-1] for row in group] #max()函数中，当key参数不为空时，就以key的函数对象为判断的标准;
|
||
return max(set(outcomes), key=outcomes.count) # 输出group中出现次数较多的标签
|
||
|
||
|
||
# Create child splits for a node or make terminal #创建子分割器，递归分类，直到分类结束
|
||
def split(node, max_depth, min_size, n_features, depth): #max_depth = 10，min_size = 1，n_features = int(sqrt(len(dataset[0])-1))
|
||
left, right = node['groups']
|
||
del(node['groups'])
|
||
# check for a no split
|
||
if not left or not right:
|
||
node['left'] = node['right'] = to_terminal(left + right)
|
||
return
|
||
# check for max depth
|
||
if depth >= max_depth: #max_depth=10表示递归十次，若分类还未结束，则选取数据中分类标签较多的作为结果，使分类提前结束，防止过拟合
|
||
node['left'], node['right'] = to_terminal(left), to_terminal(right)
|
||
return
|
||
# process left child
|
||
if len(left) <= min_size:
|
||
node['left'] = to_terminal(left)
|
||
else:
|
||
node['left'] = get_split(left, n_features) #node['left']是一个字典，形式为{'index':b_index, 'value':b_value, 'groups':b_groups}，所以node是一个多层字典
|
||
split(node['left'], max_depth, min_size, n_features, depth+1) #递归，depth+1计算递归层数
|
||
# process right child
|
||
if len(right) <= min_size:
|
||
node['right'] = to_terminal(right)
|
||
else:
|
||
node['right'] = get_split(right, n_features)
|
||
split(node['right'], max_depth, min_size, n_features, depth+1)
|
||
|
||
|
||
# Build a decision tree
|
||
def build_tree(train, max_depth, min_size, n_features):
|
||
#root = get_split(dataset, n_features)
|
||
root = get_split(train, n_features)
|
||
split(root, max_depth, min_size, n_features, 1)
|
||
return root
|
||
|
||
|
||
# Make a prediction with a decision tree
|
||
def predict(node, row): #预测模型分类结果
|
||
if row[node['index']] < node['value']:
|
||
if isinstance(node['left'], dict): #isinstance是Python中的一个内建函数。是用来判断一个对象是否是一个已知的类型。
|
||
return predict(node['left'], row)
|
||
else:
|
||
return node['left']
|
||
else:
|
||
if isinstance(node['right'], dict):
|
||
return predict(node['right'], row)
|
||
else:
|
||
return node['right']
|
||
|
||
|
||
# Make a prediction with a list of bagged trees
|
||
def bagging_predict(trees, row):
|
||
predictions = [predict(tree, row) for tree in trees] #使用多个决策树trees对测试集test的第row行进行预测，再使用简单投票法判断出该行所属分类
|
||
return max(set(predictions), key=predictions.count)
|
||
|
||
|
||
# Create a random subsample from the dataset with replacement
|
||
def subsample(dataset, ratio): #创建数据集的随机子样本
|
||
sample = list()
|
||
n_sample = round(len(dataset) * ratio) #round() 方法返回浮点数x的四舍五入值。
|
||
while len(sample) < n_sample:
|
||
index = randrange(len(dataset)) #有放回的随机采样，有一些样本被重复采样，从而在训练集中多次出现，有的则从未在训练集中出现，此则自助采样法。从而保证每棵决策树训练集的差异性
|
||
sample.append(dataset[index])
|
||
return sample
|
||
|
||
|
||
# Random Forest Algorithm
|
||
def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
|
||
trees = list()
|
||
for i in range(n_trees): #n_trees表示决策树的数量
|
||
sample = subsample(train, sample_size) #随机采样保证了每棵决策树训练集的差异性
|
||
tree = build_tree(sample, max_depth, min_size, n_features) #建立一个决策树
|
||
trees.append(tree)
|
||
predictions = [bagging_predict(trees, row) for row in test]
|
||
return(predictions)
|
||
|
||
|
||
# Evaluate an algorithm using a cross validation split
|
||
def evaluate_algorithm(dataset, algorithm, n_folds, *args): #评估算法性能，返回模型得分
|
||
folds = cross_validation_split(dataset, n_folds)
|
||
scores = list()
|
||
for fold in folds: #每次循环从folds从取出一个fold作为测试集，其余作为训练集，遍历整个folds，实现交叉验证
|
||
train_set = list(folds)
|
||
train_set.remove(fold)
|
||
train_set = sum(train_set, []) #将多个fold列表组合成一个train_set列表
|
||
test_set = list()
|
||
for row in fold: #fold表示从原始数据集dataset提取出来的测试集
|
||
row_copy = list(row)
|
||
test_set.append(row_copy)
|
||
row_copy[-1] = None
|
||
predicted = algorithm(train_set, test_set, *args)
|
||
actual = [row[-1] for row in fold]
|
||
accuracy = accuracy_metric(actual, predicted)
|
||
scores.append(accuracy)
|
||
return scores
|
||
|
||
|
||
if __name__ == '__main__':
|
||
|
||
# 加载数据
|
||
dataset = loadDataSet('input/7.RandomForest/sonar-all-data.txt')
|
||
# print dataset
|
||
|
||
n_folds = 5 #分成5份数据，进行交叉验证
|
||
#max_depth = 10 #递归十次
|
||
max_depth = 20 #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_size = 1.0
|
||
#n_features = int(sqrt(len(dataset[0])-1))
|
||
n_features =15 #调参（自己修改） #准确性与多样性之间的权衡
|
||
for n_trees in [1,5,10]: #理论上树是越多越好
|
||
scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
|
||
# 每一次执行本文件时都能产生同一个随机数
|
||
seed(1)
|
||
print 'random=', random()
|
||
print('Trees: %d' % n_trees)
|
||
print('Scores: %s' % scores)
|
||
print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))
|