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318 lines
13 KiB
Python
318 lines
13 KiB
Python
#!/usr/bin/python
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# coding:utf8
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'''
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Created 2017-04-25
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Random Forest Algorithm on Sonar Dataset
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@author: Flying_sfeng/片刻
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---
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源代码网址：http://www.tuicool.com/articles/iiUfeim
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Flying_sfeng博客地址：http://blog.csdn.net/flying_sfeng/article/details/64133822
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在此表示感谢你的代码和注解， 我重新也完善了你的注解
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'''
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from random import seed, randrange, random
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# 导入csv文件
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def loadDataSet(filename):
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dataset = []
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with open(filename, 'r') as fr:
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for line in fr.readlines():
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if not line:
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continue
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lineArr = []
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for featrue in line.split(','):
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# strip()返回移除字符串头尾指定的字符生成的新字符串
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str_f = featrue.strip()
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if str_f.isdigit():
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# 将数据集的第column列转换成float形式
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lineArr.append(float(str_f))
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else:
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# 添加分类标签
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lineArr.append(str_f)
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dataset.append(lineArr)
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return dataset
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def cross_validation_split(dataset, n_folds):
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"""cross_validation_split(将数据集进行抽重抽样 n_folds 份，数据可以重复重复抽取，每一次list的元素是无重复的)
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Args:
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dataset 原始数据集
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n_folds 数据集dataset分成n_flods份
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Returns:
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dataset_split list集合，存放的是：将数据集进行抽重抽样 n_folds 份，数据可以重复重复抽取，每一次list的元素是无重复的
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"""
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dataset_split = list()
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dataset_copy = list(dataset) #复制一份dataset,防止dataset的内容改变
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fold_size = len(dataset) / n_folds
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for i in range(n_folds):
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fold = list() #每次循环fold清零，防止重复导入dataset_split
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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
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# 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
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index = randrange(len(dataset_copy))
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# 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。
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# 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。
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fold.append(dataset_copy.pop(index))
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dataset_split.append(fold)
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# 由dataset分割出的n_folds个数据构成的列表，为了用于交叉验证
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return dataset_split
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# Split a dataset based on an attribute and an attribute value #根据特征和特征值分割数据集
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def test_split(index, value, dataset):
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left, right = list(), list()
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for row in dataset:
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if row[index] < value:
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left.append(row)
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else:
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right.append(row)
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return left, right
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# Calculate the Gini index for a split dataset
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def gini_index(groups, class_values): #个人理解：计算代价，分类越准确，则gini越小
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gini = 0.0
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for class_value in class_values: #class_values =[0,1]
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for group in groups: #groups=(left,right)
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size = len(group)
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if size == 0:
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continue
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proportion = [row[-1] for row in group].count(class_value) / float(size)
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gini += (proportion * (1.0 - proportion)) #个人理解：计算代价，分类越准确，则gini越小
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return gini
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# 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）
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def get_split(dataset, n_features):
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class_values = list(set(row[-1] for row in dataset)) #class_values =[0,1]
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b_index, b_value, b_score, b_groups = 999, 999, 999, None
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features = list()
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while len(features) < n_features:
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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
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if index not in features:
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features.append(index)
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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
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for row in dataset:
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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
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gini = gini_index(groups, class_values)
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if gini < b_score:
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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。
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#print b_score
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return {'index':b_index, 'value':b_value, 'groups':b_groups}
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# Create a terminal node value #输出group中出现次数较多的标签
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def to_terminal(group):
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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;
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return max(set(outcomes), key=outcomes.count) # 输出group中出现次数较多的标签
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# 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
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def split(node, max_depth, min_size, n_features, depth): #max_depth = 10，min_size = 1，n_features = int(sqrt(len(dataset[0])-1))
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left, right = node['groups']
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del(node['groups'])
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# check for a no split
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if not left or not right:
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node['left'] = node['right'] = to_terminal(left + right)
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return
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# check for max depth
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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
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node['left'], node['right'] = to_terminal(left), to_terminal(right)
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return
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# process left child
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if len(left) <= min_size:
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node['left'] = to_terminal(left)
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else:
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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
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split(node['left'], max_depth, min_size, n_features, depth+1) #递归，depth+1计算递归层数
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# process right child
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if len(right) <= min_size:
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node['right'] = to_terminal(right)
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else:
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node['right'] = get_split(right, n_features)
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split(node['right'], max_depth, min_size, n_features, depth+1)
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# Build a decision tree
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def build_tree(train, max_depth, min_size, n_features):
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"""build_tree(创建一个决策树)
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Args:
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train 训练数据集
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max_depth 决策树深度不能太深，不然容易导致过拟合
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min_size 叶子节点的大小
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n_features 选取的特征的个数
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Returns:
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root 返回决策树
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"""
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# 返回最有列和相关的信息
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root = get_split(train, n_features)
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# 对左右2变的数据 进行递归的调用，由于最优特征使用过，所以在后面进行使用的时候，就没有意义了
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# 例如： 性别-男女，对男使用这一特征就没任何意义了
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split(root, max_depth, min_size, n_features, 1)
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return root
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# Make a prediction with a decision tree
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def predict(node, row): #预测模型分类结果
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if row[node['index']] < node['value']:
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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。
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return predict(node['left'], row)
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else:
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return node['left']
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else:
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if isinstance(node['right'], dict):
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return predict(node['right'], row)
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else:
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return node['right']
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# Make a prediction with a list of bagged trees
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def bagging_predict(trees, row):
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"""bagging_predict(bagging预测)
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Args:
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trees 决策树的集合
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row 测试数据集的每一行数据
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Returns:
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返回随机森林中，决策树结果出现次数做大的
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"""
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# 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
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predictions = [predict(tree, row) for tree in trees]
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return max(set(predictions), key=predictions.count)
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# Create a random subsample from the dataset with replacement
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def subsample(dataset, ratio): #创建数据集的随机子样本
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"""random_forest(评估算法性能，返回模型得分)
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Args:
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dataset 训练数据集
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ratio 训练数据集的样本比例
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Returns:
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sample 随机抽样的训练样本
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"""
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sample = list()
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# 训练样本的按比例抽样。
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# round() 方法返回浮点数x的四舍五入值。
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n_sample = round(len(dataset) * ratio)
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while len(sample) < n_sample:
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# 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
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index = randrange(len(dataset))
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sample.append(dataset[index])
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return sample
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# Random Forest Algorithm
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def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features):
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"""random_forest(评估算法性能，返回模型得分)
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Args:
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train 训练数据集
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test 测试数据集
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max_depth 决策树深度不能太深，不然容易导致过拟合
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min_size 叶子节点的大小
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sample_size 训练数据集的样本比例
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n_trees 决策树的个数
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n_features 选取的特征的个数
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Returns:
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predictions 每一行的预测结果，bagging 预测最后的分类结果
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"""
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trees = list()
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# n_trees表示决策树的数量
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for i in range(n_trees):
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# 随机抽样的训练样本， 随机采样保证了每棵决策树训练集的差异性
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sample = subsample(train, sample_size)
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# 创建一个决策树
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tree = build_tree(sample, max_depth, min_size, n_features)
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trees.append(tree)
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# 每一行的预测结果，bagging 预测最后的分类结果
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predictions = [bagging_predict(trees, row) for row in test]
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return predictions
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# Calculate accuracy percentage
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def accuracy_metric(actual, predicted): #导入实际值和预测值，计算精确度
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correct = 0
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for i in range(len(actual)):
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if actual[i] == predicted[i]:
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correct += 1
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return correct / float(len(actual)) * 100.0
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# 评估算法性能，返回模型得分
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def evaluate_algorithm(dataset, algorithm, n_folds, *args):
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"""evaluate_algorithm(评估算法性能，返回模型得分)
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Args:
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dataset 原始数据集
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algorithm 使用的算法
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n_folds 树的个数
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*args 其他的参数
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Returns:
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scores 模型得分
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"""
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# 将数据集进行抽重抽样 n_folds 份，数据可以重复重复抽取，每一次list的元素是无重复的
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folds = cross_validation_split(dataset, n_folds)
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scores = list()
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# 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
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for fold in folds:
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train_set = list(folds)
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train_set.remove(fold)
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# 将多个fold列表组合成一个train_set列表, 类似 union all
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"""
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In [20]: l1=[[1, 2, 'a'], [11, 22, 'b']]
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In [21]: l2=[[3, 4, 'c'], [33, 44, 'd']]
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In [22]: l=[]
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In [23]: l.append(l1)
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In [24]: l.append(l2)
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In [25]: l
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Out[25]: [[[1, 2, 'a'], [11, 22, 'b']], [[3, 4, 'c'], [33, 44, 'd']]]
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In [26]: sum(l, [])
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Out[26]: [[1, 2, 'a'], [11, 22, 'b'], [3, 4, 'c'], [33, 44, 'd']]
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"""
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train_set = sum(train_set, [])
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test_set = list()
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# fold表示从原始数据集dataset提取出来的测试集
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for row in fold:
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row_copy = list(row)
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test_set.append(row_copy)
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row_copy[-1] = None
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predicted = algorithm(train_set, test_set, *args)
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actual = [row[-1] for row in fold]
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# 计算随机森林的预测结果的正确率
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accuracy = accuracy_metric(actual, predicted)
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scores.append(accuracy)
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return scores
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if __name__ == '__main__':
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# 加载数据
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dataset = loadDataSet('input/7.RandomForest/sonar-all-data.txt')
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# print dataset
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n_folds = 5 # 分成5份数据，进行交叉验证
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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
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min_size = 1 # 决策树的叶子节点最少的元素数量
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sample_size = 1.0 # 做决策树时候的样本的比例
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# n_features = int(sqrt(len(dataset[0])-1))
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n_features =15 # 调参（自己修改） #准确性与多样性之间的权衡
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for n_trees in [1, 5, 10]: # 理论上树是越多越好
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scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
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# 每一次执行本文件时都能产生同一个随机数
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seed(1)
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print 'random=', random()
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print 'Trees: %d' % n_trees
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print 'Scores: %s' % scores
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print 'Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores)))
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