Python随机森林例子源码分享

ID:780408 · 发表于 2020-12-18 11:04

"#测试gini\n",
"gini=calGini((l,r),classLabels)\n",
"print(gini)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def getBestSplit(dataSet,featureNumbers):\n",
" '''\n",
" 对于一个数据集，选择featureNumber个特征进行简单划分，得到最好的特征和划分结果\n",
" args:\n",
" dataSet:数据集,类型：list\n",
" featureNumbers:选择的特征值数,类型：int\n",
" classLabels：所有分类，类型：list\n",
" ''' \n",
" \n",
" #样本数\n",
" m=len(dataSet)\n",
" if m==0:\n",
" return None\n",
" #样本特征值数+1（因为最后有一个标签）\n",
" totalColumnNumber=len(dataSet[0])\n",
" #随机选择的特征索引\n",
" randomSelectedFeatures=[]\n",
" \n",
" \n",
" \n",
" #选择数目必须在特征数目范围内\n",
" if totalColumnNumber-1>=featureNumbers: \n",
" #借助这个变量防止选择重复的特征进入\n",
" indexList=list(range(totalColumnNumber-1)) \n",
" for j in range(featureNumbers):\n",
" #索引序列长度\n",
" leftSize=len(indexList)\n",
" #随机数\n",
" randIndex=random.randrange(leftSize)\n",
" #索引学列随机数处数据弹出，放入选择特征列表\n",
" origIndex=indexList.pop(randIndex)\n",
" #存入的是原始数据特征索引\n",
" randomSelectedFeatures.append(origIndex)\n",
" else:\n",
" randomSelectedFeatures=range(totalColumnNumber-1)#特征全部被选择\n",
" \n",
" \n",
" # print(\"current select features\")\n",
" # print(randomSelectedFeatures)\n",
"\n",
" #当前数据集的标签序列\n",
" class_values=list(set(item[-1] for item in dataSet))\n",
" \n",
" #对于每个特征以及每个特征值进行简单划分\n",
" #保留最小的基尼系数\n",
" minGini=9999\n",
" #存入最好的信息\n",
" bestInfor={}\n",
" #外层循环，对于每个特征\n",
" for index in randomSelectedFeatures:\n",
" #内层循环对于每个特征值\n",
" tempFeatureValueList=list(set(item[index] for item in dataSet))\n",
" #print(len(tempFeatureValueList))\n",
" for tempValue in tempFeatureValueList:\n",
" #简单分类\n",
" groups=simpleSplit(dataSet,index,tempValue) \n",
" #print(\"currentIndex:%d,CurrentTempValue:%f\"%(index,tempValue))\n",
" #计算基尼系数\n",
" gini=calGini(groups,class_values)\n",
" #print(\"computed gini:\",gini) \n",
" if gini<minGini:\n",
" minGini=gini\n",
" #保存目前最后的信息\n",
" bestInfor[\"index\"]=index#存入原来索引 \n",
" bestInfor[\"indexValue\"]=tempValue\n",
" bestInfor[\"groups\"]=groups\n",
" bestInfor[\"gini\"]=gini\n",
" \n",
" return bestInfor"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"52 0.017\n"
]
}
],
"source": [
"#测试最好分类函数\n",
"bestInfor=getBestSplit(dataSet,3)\n",
"print(bestInfor[\"index\"],bestInfor[\"indexValue\"])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def terminalLabel(subSet):\n",
" '''\n",
" 树叶点对应的标签\n",
" args:\n",
" subSet:当前数据集，最后列是标签列，类型：list\n",
" returns:\n",
" 当前列中最多的标签，类型：原标签类型\n",
" '''\n",
" #得到最后一列\n",
" labelList=[item[-1] for item in subSet]\n",
" #max函数，key后是函数，代表对前面的进行那种运算，这里是技术\n",
" #max返回值是第一个参数，这里set是把labelList转换成集合，即去掉重复项\n",
" #key:相当于循环调用labelList.count（set(labelList))中的每个元素，然后max取得最大值\n",
" #返回set(labelList)中对应最大的那个标签\n",
" return max(set(labelList), key=labelList.count) # 输出 subSet 中出现次数较多的标签 \n",
"\n",
" #下面的写法也是成立的，利用lambda表达式，表达式中x从全面取，这种写法可能更好理解些\n",
" #return max(set(labelList), key=lambda x:labelList.count(x)) "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R\n"
]
}
],
"source": [
"#测试\n",
"label=terminalLabel(l)\n",
"print(label)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#对得到的最好分类信息进行分割\n",
"def split(node, max_depth, min_size, n_features, depth): # 创建子分割器递归分类直到分类结束\n",
" '''\n",
" :param node: 节点，类型：字典\n",
" bestInfor[\"index\"]=index#存入原来索引 \n",
" bestInfor[\"indexValue\"]=tempValue\n",
" bestInfor[\"groups\"]=groups\n",
" bestInfor[\"gini\"]=gini\n",
" :param max_depth: 最大深度,int\n",
" :param min_size: 最小,int\n",
" :param n_features: 特征选取个数,int\n",
" :param depth: 深度,int\n",
" :return:\n",
" '''\n",
" left, right = node['groups']\n",
" del (node['groups'])\n",
"\n",
" if not left or not right: # 如果只有一个子集\n",
" node['left'] = node['right'] = terminalLabel(left + right) # 投票出类型\n",
" return\n",
"\n",
" if depth >= max_depth: # 如果即将超过\n",
" node['left'], node['right'] = terminalLabel(left), terminalLabel(right) # 投票出类型\n",
" return\n",
"\n",
" if len(left) <= min_size: # 处理左子集\n",
" node['left'] = terminalLabel(left)\n",
" else:\n",
" node['left'] = getBestSplit(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\n",
" split(node['left'], max_depth, min_size, n_features, depth + 1) # 递归，depth+1计算递归层数\n",
"\n",
" if len(right) <= min_size: # 处理右子集\n",
" node['right'] = terminalLabel(right)\n",
" else:\n",
" node['right'] = getBestSplit(right, n_features)\n",
" split(node['right'], max_depth, min_size, n_features, depth + 1)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"#构建一个决策树\n",
"def buildTree(train, max_depth, min_size, n_features):\n",
" '''\n",
" 创建一个决策树\n",
" :param train: 训练数据集\n",
" :param max_depth: 决策树深度不能太深不然容易导致过拟合\n",
" :param min_size: 叶子节点的大小\n",
" :param n_features: 选择的特征的个数\n",
" :return\n",
" root 返回决策树\n",
" '''\n",
" root = getBestSplit(train, n_features) # 获取样本数据集\n",
" split(root, max_depth, min_size, n_features, 1) # 进行样本分割，构架决策树\n",
" return root # 返回决策树\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'index': 55, 'indexValue': 0.0114, 'gini': 0.0, 'left': {'index': 35, 'indexValue': 0.2288, 'gini': 0.0, 'left': 'R', 'right': {'index': 33, 'indexValue': 0.2907, 'gini': 0.0, 'left': 'R', 'right': {'index': 58, 'indexValue': 0.0057, 'gini': 0.0, 'left': {'index': 12, 'indexValue': 0.0493, 'gini': 0.0, 'left': 'R', 'right': 'R'}, 'right': 'R'}}}, 'right': {'index': 54, 'indexValue': 0.0063, 'gini': 0.0, 'left': {'index': 21, 'indexValue': 0.8384, 'gini': 0.0, 'left': 'M', 'right': 'M'}, 'right': {'index': 32, 'indexValue': 0.558, 'gini': 0.0, 'left': 'M', 'right': {'index': 58, 'indexValue': 0.0332, 'gini': 0.0, 'left': 'M', 'right': 'M'}}}}\n"
]
}
],
"source": [
"#测试决策树\n",
"#选择一个子集\n",
"s=putBackSample(dataSet,10)\n",
"tempTree=buildTree(s,10,1,3)\n",
"print(tempTree)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"#根据决策树进行预测\n",
"def predict(node, row): # 预测模型分类结果\n",
" '''\n",
" 在当前节点进行预测，row是待预测样本\n",
" args:\n",
" node:树节点\n",
" row:待分类样本\n",
" return:\n",
" 分类标签\n",
" '''\n",
" if row[node['index']] < node['indexValue']:\n",
" 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。\n",
" return predict(node['left'], row)\n",
" else:\n",
" return node['left']\n",
" else:\n",
" if isinstance(node['right'], dict):\n",
" return predict(node['right'], row)\n",
" else:\n",
" return node['right']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R R\n"
]
}
],
"source": [
"#测试下\n",
"label=predict(tempTree,s[0])\n",
"print(label,s[0][-1])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"#多个树的决策，多数服从少数\n",
"def baggingPredict(trees, row):\n",
" \"\"\"\n",
" 多个树的决策，多数服从少数\n",
" Args:\n",
" trees 决策树的集合\n",
" row 测试数据集的每一行数据\n",
" Returns:\n",
" 返回随机森林中，决策树结果出现次数做大的\n",
" \"\"\"\n",
"\n",
" # 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\n",
" predictions = [predict(tree, row) for tree in trees]\n",
" return max(set(predictions), key=predictions.count)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def subSample(dataSet, ratio): \n",
" '''\n",
" 按比例随机抽取数据，有重复抽样\n",
" args:\n",
" dataSet:数据集，类型:list\n",
" ratio:0-1之间的数\n",
" '''\n",
" if ratio<0.0:\n",
" return None\n",
" if ratio>=1:\n",
" return dataSet\n",
" sampleNumber=int(len(dataSet)*ratio)\n",
" subSet=putBackSample(dataSet,sampleNumber)\n",
" return subSet"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"41\n"
]
}
],
"source": [
"#测试\n",
"subSet=subSample(dataSet,0.2)\n",
"print(len(subSet))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"#随机森林主函数\n",
"def buildRandomForest(train, max_depth=10, min_size=1, sample_size=0.2, n_trees=10, n_features=3):\n",
" \"\"\"\n",
" random_forest(评估算法性能，返回模型得分)\n",
" Args:\n",
" train 训练数据集，类型:list \n",
" max_depth 决策树深度不能太深，不然容易导致过拟合\n",
" min_size 叶子节点的大小\n",
" sample_size 训练数据集的样本比例,0,1之间的数\n",
" n_trees 决策树的个数\n",
" n_features 选取的特征的个数\n",
" Returns:\n",
" trees:树序列\n",
" \"\"\"\n",
"\n",
" trees = list()\n",
" # n_trees 表示决策树的数量\n",
" for i in range(n_trees):\n",
" # 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\n",
" sample = subSample(train, sample_size)\n",
" # 创建一个决策树\n",
" tree = buildTree(sample, max_depth, min_size, n_features)\n",
" trees.append(tree)\n",
" return trees\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def predictByForest(trees,test):\n",
" '''\n",
" predictions 每一行的预测结果，bagging 预测最后的分类结果\n",
" '''\n",
" # 每一行的预测结果，bagging 预测最后的分类结果\n",
" predictions = [baggingPredict(trees, row) for row in test]\n",
" return predictions"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def calQuota(predictions,labelClass,OrigClassLabels):\n",
" '''\n",
" 计算分类指标\n",
" args:\n",
" predictions:预测值，类型：list\n",
" labelClass:真实标签，类型：list\n",
" OrigClassLabels:数据可能的标签库，一个正例一个负例标签\n",
" '''\n",
" \n",
" Pos=OrigClassLabels[0]\n",
" Nev=OrigClassLabels[1] \n",
" #真正例 \n",
" #TP=len([item for item in labelClass if item==Pos and predictions[labelClass.index(item)]==Pos])\n",
" TP=0\n",
" TN=0\n",
" FP=0\n",
" FN=0\n",
" for j in range(len(predictions)): \n",
" if predictions[j]==Pos and labelClass[j]==Pos:\n",
" TP+=1\n",
" if predictions[j]==Nev and labelClass[j]==Nev:\n",
" TN+=1\n",
" if predictions[j]==Pos and labelClass[j]==Nev:\n",
" FP+=1\n",
" if predictions[j]==Nev and labelClass[j]==Pos:\n",
" FN+=1\n",
"# #真负例,下面的做法不行，原因是index可能得到不同的索引\n",
"# TN=len([item for item in labelClass if item==Nev and predictions[labelClass.index(item)]==Nev])\n",
"# #伪正例\n",
"# FP=len([item for item in labelClass if item==Nev and predictions[labelClass.index(item)]==Pos])\n",
"# #伪负例\n",
"# FN=len([item for item in labelClass if item==Pos and predictions[labelClass.index(item)]==Nev])\n",
"\n",
" #Recall,TruePosProp=TP/(TP+FN)#识别的正例占整个正例的比率\n",
" #FalsPosProp=FP/(FP+TN)#识别的正例占整个负例的比率\n",
" #Precition=TP/(TP+FP)#识别的正确正例占识别出所有正例的比率\n",
" \n",
" return TP,TN,FP,FN"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"#测试下：\n",
"trees=buildRandomForest(dataSet)\n",
"testSet=nonPutBackSample(dataSet,100)\n",
"prediction=predictByForest(trees,testSet)\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(44, 39, 12, 5)\n"
]
}
],
"source": [
"labelClass=[item[-1] for item in testSet]\n",
"\n",
"tp=calQuota(prediction,labelClass,list(classLabels))\n",
"print(tp)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"def accuracy( predicted,actual): \n",
" correct = 0\n",
" for i in range(len(actual)):\n",
" if actual[i] == predicted[i]:\n",
" correct += 1\n",
" return correct / float(len(actual)) * 100.0\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"83.0\n"
]
}
],
"source": [
"a=accuracy(prediction,labelClass)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"def createCrossValideSets(trainSet,n_folds,bPutBack=True):\n",
" '''\n",
" 产生交叉验证数据集\n",
" Args:\n",
" dataset 原始数据集 \n",
" n_folds 数据的份数，数据集交叉验证的份数，采用无放回抽取\n",
" bPutBack 是否放回\n",
" '''\n",
" subSetsList=[]\n",
" subLen=int(len(trainSet)/n_folds)\n",
" if bPutBack:\n",
" for j in range(n_folds):\n",
" subSet=putBackSample(trainSet,subLen)\n",
" subSetsList.append(subSet)\n",
" else:\n",
" for j in range(n_folds):\n",
" subSet=nonPutBackSample(trainSet,subLen)\n",
" subSetsList.append(subSet)\n",
" return subSetsList"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"def randomForest(trainSet,testSet,max_depth=10, min_size=1, sample_size=0.2, n_trees=10, n_features=3):\n",
" '''\n",
" 构造随机森林并测试\n",
" Args:\n",
" train 训练数据集，类型:list \n",
" testSet 测试集，类型：list\n",
" max_depth 决策树深度不能太深，不然容易导致过拟合\n",
" min_size 叶子节点的大小\n",
" sample_size 训练数据集的样本比例,0,1之间的数\n",
" n_trees 决策树的个数\n",
" n_features 选取的特征的个数\n",
" Returns:\n",
" predition 测试集预测值，类型：list\n",
" '''\n",
" trees=buildRandomForest(trainSet,max_depth, min_size, sample_size, n_trees, n_features)\n",
" predition=predictByForest(trees,testSet)\n",
" return predition"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def evaluteAlgorithm(trainSet,algorithm,n_folds,*args):\n",
" '''\n",
" 评价算法函数\n",
" Args:\n",
" dataset 原始数据集\n",
" algorithm 使用的算法\n",
" n_folds 数据的份数，数据集交叉验证的份数，采用无放回抽取\n",
" *args 其他的参数\n",
" Returns:\n",
" scores 模型得分\n",
" '''\n",
" folds = createCrossValideSets(trainSet, n_folds)\n",
" scores = list()\n",
" # 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\n",
" for fold in folds:\n",
" train_set = list(folds)\n",
" train_set.remove(fold)\n",
" # 将多个 fold 列表组合成一个 train_set 列表, 类似 union all\n",
" \"\"\"\n",
" In [20]: l1=[[1, 2, 'a'], [11, 22, 'b']]\n",
" In [21]: l2=[[3, 4, 'c'], [33, 44, 'd']]\n",
" In [22]: l=[]\n",
" In [23]: l.append(l1)\n",
" In [24]: l.append(l2)\n",
" In [25]: l\n",
" Out[25]: [[[1, 2, 'a'], [11, 22, 'b']], [[3, 4, 'c'], [33, 44, 'd']]]\n",
" In [26]: sum(l, [])\n",
" Out[26]: [[1, 2, 'a'], [11, 22, 'b'], [3, 4, 'c'], [33, 44, 'd']]\n",
" \"\"\"\n",
" train_set = sum(train_set, [])\n",
" test_set = list()\n",
" # fold 表示从原始数据集 dataset 提取出来的测试集\n",
"# for row in fold:\n",
"# row_copy = list(row)\n",
"# row_copy[-1] = None\n",
"# test_set.append(row_copy)\n",
" predicted = algorithm(train_set, fold, *args)\n",
" \n",
" actual = [row[-1] for row in fold]\n",
"\n",
" # 计算随机森林的预测结果的正确率\n",
" accuracyValue = accuracy(predicted,actual)\n",
" scores.append(accuracyValue)\n",
" return scores"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"随机因子= 0.13436424411240122\n",
"决策树个数: 1\n",
"模型得分: [87.8048780487805, 90.2439024390244, 92.6829268292683, 85.36585365853658, 95.1219512195122]\n",
"平均准确度: 90.244%\n",
"随机因子= 0.13436424411240122\n",
"决策树个数: 10\n",
"模型得分: [92.6829268292683, 92.6829268292683, 87.8048780487805, 78.04878048780488, 100.0]\n",
"平均准确度: 90.244%\n"
]
}
],
"source": [
" \n",
" #综合测试函数\n",
" n_folds = 5 # 分成5份数据，进行交叉验证\n",
" 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\n",
" min_size = 1 # 决策树的叶子节点最少的元素数量\n",
" sample_size = 1.0 # 做决策树时候的样本的比例\n",
" # n_features = int((len(dataset[0])-1))\n",
" n_features = 15 # 调参（自己修改） #准确性与多样性之间的权衡\n",
" for n_trees in [1, 10]: # 理论上树是越多越好\n",
" scores = evaluteAlgorithm(dataSet, randomForest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)\n",
" # 每一次执行本文件时都能产生同一个随机数\n",
" random.seed(1)\n",
" print('随机因子=', random.random()) # 每一次执行本文件时都能产生同一个随机数\n",
" print('决策树个数: %d' % n_trees) # 输出决策树个数\n",
" print('模型得分: %s' % scores) # 输出五份随机样本的模型得分\n",
" print('平均准确度: %.3f%%' % (sum(scores)/float(len(scores)))) # 输出五份随机样本的平均准确度\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"随机因子= 0.13436424411240122\n",
"决策树个数: 1\n",
"模型得分: [80.48780487804879, 75.60975609756098, 73.17073170731707, 75.60975609756098, 78.04878048780488]\n",
"平均准确度: 76.585%\n",
"随机因子= 0.13436424411240122\n",
"决策树个数: 10\n",
"模型得分: [87.8048780487805, 85.36585365853658, 90.2439024390244, 78.04878048780488, 92.6829268292683]\n",
"平均准确度: 86.829%\n"
]
}
],
"source": [
" sample_size =0.5 # 做决策树时候的样本的比例\n",
" \n",
" for n_trees in [1, 10]: # 理论上树是越多越好\n",
" scores = evaluteAlgorithm(dataSet, randomForest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)\n",
" # 每一次执行本文件时都能产生同一个随机数\n",
" random.seed(1)\n",
" print('随机因子=', random.random()) # 每一次执行本文件时都能产生同一个随机数\n",
" print('决策树个数: %d' % n_trees) # 输出决策树个数\n",
" print('模型得分: %s' % scores) # 输出五份随机样本的模型得分\n",
" print('平均准确度: %.3f%%' % (sum(scores)/float(len(scores)))) # 输出五份随机样本的平均准确度"
]
}
],
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