UserWarning: findfont: Font family ['sans-serif'] not found.错误？
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题目描述
这个怎么弄？按照网上改了改一个mat里面的文本，还是在报错
题目来源及自己的思路
ID3决策树
相关代码
import numpy as npimport pandas as pd
from utils.plotDecisionTree import *
from sklearn.metrics import accuracy_score
import time
#计算经验熵
def calcEntropy(dataSet):
mD = len(dataSet) # mD表示数据集的数据向量个数
dataLabelList = [x[-1] for x in dataSet] # 数据集最后一列 标签
dataLabelSet = set(dataLabelList) # 转化为标签集合，集合不重复，所以转化
ent = 0
for label in dataLabelSet: # 对于集合中的每一个标签
mDv = dataLabelList.count(label) # 统计它出现的次数
prop = float(mDv) / mD # 计算频率
ent = ent - prop * np.math.log(prop, 2) # 计算条件熵，见算法预备知识
return ent
#计算条件熵
def calcCondEntropy(dataSet,featureSet,i):
mD = len(dataSet)
ent=0
for feature in featureSet:
# 拆分数据集，去除第i行数据特征
splitedDataSet = splitDataSet(dataSet, i, feature)
mDv = len(splitedDataSet)
# 计算条件熵
ent = ent + float(mDv) / mD * calcEntropy(splitedDataSet)
return ent
# 拆分数据集
# index 要拆分的特征的下标
# feature 要拆分的特征
# 返回值 dataSet中index所在特征为feature，且去掉index一列的集合
def splitDataSet(dataSet, index, feature):
splitedDataSet = []
mD = len(dataSet)
for data in dataSet:
if(data[index] == feature): # 将数据集拆分
sliceTmp = data[:index] # 取[0,index)
sliceTmp.extend(data[index + 1:]) # 扩展(index,len]
splitedDataSet.append(sliceTmp)
return splitedDataSet
# 根据信息增益 - 选择最好的特征
# 返回值 - 最好的特征的下标
def chooseBestFeature(dataSet):
entD = calcEntropy(dataSet) # 计算经验熵
mD = len(dataSet)
featureNumber = len(dataSet[0]) - 1
maxGain = -100 # 最大增益
maxIndex = -1 # 最大增益的下标
Gain=0
for i in range(featureNumber):
featureI = [x[i] for x in dataSet] # 数据集合中的第i列特征
featureSet = set(featureI) # 特征集合
Gain = entD - calcCondEntropy(dataSet,featureSet,i) # 计算信息增益
if(maxIndex == -1):
maxGain = Gain
maxIndex = i
elif(maxGain < Gain): # 记录最大的信息增益和下标
maxGain = Gain
maxIndex = i
return maxIndex # 返回下标
# 寻找最多的，作为标签
def mainLabel(labelList):
labelRec = labelList[0]
maxLabelCount = -1
labelSet = set(labelList)
for label in labelSet:
if(labelList.count(label) > maxLabelCount):
maxLabelCount = labelList.count(label)
labelRec = label
return labelRec
# 生成决策树，注意，是列表的形式存储
# dataSet：数据集, featureNames：数据属性类别, featureNamesSet：属性类别集合, labelListParent：父节点标签列表
def createFullDecisionTree(dataSet, featureNames, featureNamesSet, labelListParent):
labelList = [x[-1] for x in dataSet]
if(len(dataSet) == 0): # 如果数据集为空，返回父节点标签列表的主要标签
return mainLabel(labelListParent)
elif(len(dataSet[0]) == 1): # 没有可划分的属性，选出最多的label作为该数据集的标签
return mainLabel(labelList)
elif(labelList.count(labelList[0]) == len(labelList)): # 全部都属于同一个Label，返回labList[0]
return labelList[0]
# 不满足上面的边界情况则需要创建新的分支节点
bestFeatureIndex = chooseBestFeature(dataSet) # 根据信息增益，选择数据集中最好的特征下标
bestFeatureName = featureNames.pop(bestFeatureIndex) # 取出属性类别
myTree = {bestFeatureName: {}} # 新建节点，一个字典
featureList = featureNamesSet.pop(bestFeatureIndex) # 取出最佳属性的类别
featureSet = set(featureList) # 剔除属性类别集合
for feature in featureSet: # 遍历最佳属性所有取值
featureNamesNext = featureNames[:]
featureNamesSetNext = featureNamesSet[:][:]
splitedDataSet = splitDataSet(dataSet, bestFeatureIndex, feature) # 剔除最佳特征
# 递归地生成新的节点
# featureNames：数据属性类别, featureNamesSet：属性类别集合, labelListParent：父节点标签列表
# 一个二叉树
myTree[bestFeatureName][feature] = createFullDecisionTree(splitedDataSet, featureNamesNext, featureNamesSetNext, labelList)
return myTree
# 读取数据集
def readDataSet(path):
ifile = open(path, encoding='utf-8_sig', errors='ignore')
#表头
featureName = ifile.readline()
featureName = featureName.rstrip("\n")
#类别,属性
featureNames = (featureName.split(' ')[0]).split(',')
#读取文件
lines = ifile.readlines()
#数据集
dataSet = []
for line in lines:
tmp = line.split('\n')[0]
tmp = tmp.split(',')
dataSet.append(tmp)
#获取标签
labelList = [x[-1] for x in dataSet]
#获取featureNamesSet
featureNamesSet = []
for i in range(len(dataSet[0]) - 1):
col = [x[i] for x in dataSet]
colSet = set(col)
featureNamesSet.append(list(colSet))
#返回 数据集，属性名，所有属性的取值集合，以及标签列表
return dataSet, featureNames, featureNamesSet,labelList
def tree_predict(tree, data):
#print(data)
feature = list(tree.keys())[0] #取树第一个结点的键（特征）
#print(feature)
label = data[feature] #该特征下的属性
next_tree = tree[feature][label] #取下一个结点树
if type(next_tree) == str: #如果是个字符串，说明已经到达叶节点返回分类结果
return next_tree
else: # 否则继续如上处理
return tree_predict(next_tree, data)
def main():
#获取训练集,所有属性名称，每个属性的类别，所有标签(最后一列)
dataTrain, featureNames, featureNamesSet,labelList = readDataSet("D:\MachineLearning-master\lab4\data\ex3dataEn.csv")
print("dataTrain: \n",dataTrain,"featureNames:\n",featureNames,"featureNamesSet:\n",featureNamesSet,"labelList:\n",labelList)
#获取测试集
train= pd.read_csv("D:\MachineLearning-master\lab4\data\ex3dataEn.csv")
test = pd.read_csv("D:\MachineLearning-master\lab4\data\ex3dataEn.csv")
print("train:\n",train[:10])
print("test:\n",test[:10])
#生成决策树
t0 = time.time()
tree=createFullDecisionTree(dataTrain, featureNames,featureNamesSet,labelList)
t1 = time.time()
print("ID3算法生成决策树的时间开销：",(t1 - t0)*(10**6),"us")
createPlot(tree,r"D:\MachineLearning-master\lab4\fig\ID3.jpg")
predictTrain = train.apply(lambda x: tree_predict(tree, x), axis=1)
label_list = train.iloc[:, -1]
score = accuracy_score(label_list, predictTrain)
print('训练补全分支准确率为：' + repr(score * 100) + '%')
#预测
y_predict = test.apply(lambda x: tree_predict(tree, x), axis=1)
label_list = test.iloc[:, -1]
score = accuracy_score(label_list, y_predict)
print('测试集补全分支准确率为：' + repr(score * 100) + '%')
if __name__ == "__main__":
main()
你期待的结果是什么？实际看到的错误信息又是什么？
C:\Users\Faker\Anaconda3\python.exe C:/Users/Faker/PycharmProjects/pythonProject/main.pydataTrain:
[['bad', 'true', 'true', 'high'], ['bad', 'true', 'true', 'high'], ['bad', 'false', 'true', 'high'], ['bad', 'true', 'true', 'high'], ['bad', 'false', 'true', 'high'], ['bad', 'true', 'false', 'high'], ['good', 'true', 'true', 'high'], ['good', 'true', 'false', 'high'], ['good', 'true', 'true', 'high'], ['good', 'true', 'true', 'high'], ['good', 'true', 'true', 'high'], ['good', 'true', 'true', 'high'], ['bad', 'true', 'true', 'low'], ['good', 'false', 'true', 'high'], ['good', 'false', 'true', 'high'], ['good', 'false', 'true', 'high'], ['good', 'false', 'true', 'high'], ['good', 'false', 'false', 'high'], ['bad', 'false', 'false', 'low'], ['bad', 'false', 'true', 'low'], ['bad', 'false', 'true', 'low'], ['bad', 'false', 'true', 'low'], ['bad', 'false', 'false', 'low'], ['bad', 'true', 'false', 'low'], ['good', 'false', 'true', 'low'], ['good', 'false', 'true', 'low'], ['bad', 'false', 'false', 'low'], ['bad', 'false', 'false', 'low'], ['good', 'false', 'false', 'low'], ['bad', 'true', 'false', 'low'], ['good', 'false', 'true', 'low'], ['good', 'false', 'false', 'low'], ['good', 'false', 'false', 'low']] featureNames:
['bad', 'true', 'true', 'high'] featureNamesSet:
[['bad', 'good'], ['true', 'false'], ['true', 'false']] labelList:
['high', 'high', 'high', 'high', 'high', 'high', 'high', 'high', 'high', 'high', 'high', 'high', 'low', 'high', 'high', 'high', 'high', 'high', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low', 'low']
train:
bad true true.1 high
0 bad True True high
1 bad True True high
2 bad False True high
3 bad True True high
4 bad False True high
5 bad True False high
6 good True True high
7 good True False high
8 good True True high
9 good True True high
test:
bad true true.1 high
0 bad True True high
1 bad True True high
2 bad False True high
3 bad True True high
4 bad False True high
5 bad True False high
6 good True True high
7 good True False high
8 good True True high
9 good True True high
ID3算法生成决策树的时间开销： 0.0 us
C:\Users\Faker\Anaconda3\lib\site-packages\matplotlib\font_manager.py:1331: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
(prop.get_family(), self.defaultFamily[fontext]))
Traceback (most recent call last):
File "C:/Users/Faker/PycharmProjects/pythonProject/main.py", line 176, in <module>
main()
File "C:/Users/Faker/PycharmProjects/pythonProject/main.py", line 164, in main
predictTrain = train.apply(lambda x: tree_predict(tree, x), axis=1)
File "C:\Users\Faker\Anaconda3\lib\site-packages\pandas\core\frame.py", line 6014, in apply
return op.get_result()
File "C:\Users\Faker\Anaconda3\lib\site-packages\pandas\core\apply.py", line 142, in get_result
return self.apply_standard()
File "C:\Users\Faker\Anaconda3\lib\site-packages\pandas\core\apply.py", line 248, in apply_standard
self.apply_series_generator()
File "C:\Users\Faker\Anaconda3\lib\site-packages\pandas\core\apply.py", line 277, in apply_series_generator
results[i] = self.f(v)
File "C:/Users/Faker/PycharmProjects/pythonProject/main.py", line 164, in <lambda>
predictTrain = train.apply(lambda x: tree_predict(tree, x), axis=1)
File "C:/Users/Faker/PycharmProjects/pythonProject/main.py", line 140, in tree_predict
next_tree = tree[feature][label] #取下一个结点树
KeyError: (True, 'occurred at index 0')
Process finished with exit code 1
回答：
看你标题是找不到字体的错误，请下载该字体并解决。
看你代码是键错误，可以尝试使用静态的键来调试错误，或进入debug查看当时的键值表。
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