基于cart树的随机森林实现matlab,机器学习之随机森林——CART模型的PYTHON实现

#-*- coding: utf-8 -*-

# Random Forest Algorithm on Sonar Dataset

from random import seed

from random import randrange

from csv import reader

from math import sqrt

from math import log

# Load data file

def load_data(filename, ty): #导入csv文件

dataset = list()

with open(filename, 'r') as file:

if ty == 'csv':

readers = reader(file)

for row in readers:

if not row:

continue

dataset.append(row)

else:

while True: # txt文件

readers = file.readline()

if not readers:

break

pass

p_tmp = [float(i) for i in readers.split(',')]

dataset.append(p_tmp)

pass

### wine数据集默认类别标签在第一列，统一放入最后一列

length = len(dataset[0])-1

sets = []

for data in dataset:

temp = data[1: length]+ [data[0]]

sets.append(temp)

#### --------------------

return sets

# Convert string column to float

def str_column_to_float(dataset, column): #将数据集的第column列转换成float形式

for row in dataset:

row[column] = float(row[column]) #strip()返回移除字符串头尾指定的字符生成的新字符串。

# Convert string column to integer

def str_column_to_int(dataset, column): #将最后一列表示标签的值转换为Int类型0,1,...

class_values = [row[column] for row in dataset]

unique = set(class_values)

lookup = dict()

for i, value in enumerate(unique):

lookup[value] = i

for row in dataset:

row[column] = lookup[row[column]]

return lookup

# 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()

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

dataset_copy = list(dataset) #

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): #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))

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(1,len(dataset[0])) #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:

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)

# row_copy[-1] = None

test_set.append(row_copy)

predicted = algorithm(train_set, test_set, *args)

actual = [row[-1] for row in fold]

accuracy = accuracy_metric(actual, predicted)

scores.append(accuracy)

return scores

# Test the random forest algorithm

seed(1) #每一次执行本文件时都能产生同一个随机数

# load and prepare data

filename = 'wine.txt'

dataset = load_data(filename, 'txt')

# convert string attributes to integers

for i in range(0, len(dataset[0])-1):

str_column_to_float(dataset, i)

# convert class column to integers

#str_column_to_int(dataset, len(dataset[0])-1) ##将最后一列表示标签的值转换为Int类型0,1(可以不用转换，标签可以为str型)

n_folds = 2 #分成5份数据，进行交叉验证

max_depth = 4 #决策树最大深度，不能太深，不然容易导致过拟合

min_size = 1 # 最小深度

sample_size = 0.5 # 每次生成决策树所用子数据集占总数据比重

n_features = int(sqrt(len(dataset[0])-1)) # 每棵决策树的最大特征数

for n_trees in [1,5,10]: #树的数量的选择并非越多越好，得自己调

scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)

print('Trees: %d' % n_trees)

print('Scores: %s' % scores)

print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))