更新Sklearn 决策树的使用Demo

src/python/tools/DecisionTree.py

@@ -0,0 +1,124 @@
+#!/usr/bin/python
+# coding: utf8
+
+from math import log
+
+
+def calcShannonEnt(dataSet):
+    """calcShannonEnt(calculate Shannon entropy 计算label分类标签的香农熵)
+
+    Args:
+        dataSet 数据集
+    Returns:
+        返回香农熵的计算值
+    Raises:
+
+    """
+    # 求list的长度，表示计算参与训练的数据量
+    numEntries = len(dataSet)
+    # print type(dataSet), 'numEntries: ', numEntries
+
+    # 计算分类标签label出现的次数
+    labelCounts = {}
+    # the the number of unique elements and their occurance
+    for featVec in dataSet:
+        currentLabel = featVec[-1]
+        if currentLabel not in labelCounts.keys():
+            labelCounts[currentLabel] = 0
+        labelCounts[currentLabel] += 1
+        # print '-----', featVec, labelCounts
+
+    # 对于label标签的占比，求出label标签的香农熵
+    shannonEnt = 0.0
+    for key in labelCounts:
+        prob = float(labelCounts[key])/numEntries
+        # log base 2
+        shannonEnt -= prob * log(prob, 2)
+        # print '---', prob, prob * log(prob, 2), shannonEnt
+    return shannonEnt
+
+
+def splitDataSet(dataSet, axis, value):
+    """splitDataSet(通过遍历dataSet数据集，求出axis对应的colnum列的值为value的行)
+
+    Args:
+        dataSet 数据集
+        axis 表示每一行的axis列
+        value 表示axis列对应的value值
+    Returns:
+        axis列为value的数据集【该数据集需要排除axis列】
+    Raises:
+
+    """
+    retDataSet = []
+    for featVec in dataSet:
+        # axis列为value的数据集【该数据集需要排除axis列】
+        if featVec[axis] == value:
+            # chop out axis used for splitting
+            reducedFeatVec = featVec[:axis]
+            '''
+            请百度查询一下： extend和append的区别
+            '''
+            reducedFeatVec.extend(featVec[axis+1:])
+            # 收集结果值 axis列为value的行【该行需要排除axis列】
+            retDataSet.append(reducedFeatVec)
+    return retDataSet
+
+
+def getFeatureShannonEnt(dataSet, labels):
+    """chooseBestFeatureToSplit(选择最好的特征)
+
+    Args:
+        dataSet 数据集
+    Returns:
+        bestFeature 最优的特征列
+    Raises:
+
+    """
+    # 求第一行有多少列的 Feature
+    numFeatures = len(dataSet[0]) - 1
+    # label的信息熵
+    baseEntropy = calcShannonEnt(dataSet)
+    # 最优的信息增益值, 和最优的Featurn编号
+    bestInfoGain, bestFeature, endEntropy = 0.0, -1, 0.0
+    # iterate over all the features
+    for i in range(numFeatures):
+        # create a list of all the examples of this feature
+        # 获取每一个feature的list集合
+        featList = [example[i] for example in dataSet]
+        # get a set of unique values
+        # 获取剔重后的集合
+        uniqueVals = set(featList)
+        # 创建一个临时的信息熵
+        newEntropy = 0.0
+        # 遍历某一列的value集合，计算该列的信息熵
+        for value in uniqueVals:
+            subDataSet = splitDataSet(dataSet, i, value)
+            prob = len(subDataSet)/float(len(dataSet))
+            newEntropy += prob * calcShannonEnt(subDataSet)
+        # gain[信息增益] 值越大，意味着该分类提供的信息量越大，该特征对分类的不确定程度越小
+        # gain[信息增益]=0, 表示与类别相同，无需其他的分类
+        # gain[信息增益]=baseEntropy, 表示分类和没分类没有区别
+        infoGain = baseEntropy - newEntropy
+        # print infoGain
+        if (infoGain > bestInfoGain):
+            endEntropy = newEntropy
+            bestInfoGain = infoGain
+            bestFeature = i
+    else:
+        if numFeatures < 0:
+            labels[bestFeature] = 'null'
+
+    return labels[bestFeature], baseEntropy, endEntropy, bestInfoGain
+
+
+if __name__ == '__main__':
+    labels = ['no surfacing', 'flippers']
+    dataSet1 = [['yes'], ['yes'], ['no'], ['no'], ['no']]
+    dataSet2 = [['a', 1, 'yes'], ['a', 2, 'yes'], ['b', 3, 'no'], ['c', 4, 'no'], ['c', 5, 'no']]
+    dataSet3 = [[1, 'yes'], [1, 'yes'], [1, 'no'], [3, 'no'], [3, 'no']]
+    infoGain1 = getFeatureShannonEnt(dataSet1, labels)
+    infoGain2 = getFeatureShannonEnt(dataSet2, labels)
+    infoGain3 = getFeatureShannonEnt(dataSet3, labels)
+    print '香农熵: \n\t%s, \n\t%s, \n\t%s' % (infoGain1, infoGain2, infoGain3)
+