python - 如何解决google colab上python代码中的错误“KeyError：'Class'”？
问题描述
我正在尝试为决策树运行我的代码，这里出现错误是我在执行决策树时经常面临这个关键错误问题的回溯。
据说有一些关键错误，但它显示的关键名称是“类”。这怎么可能？
这是代码
它是带有后修剪的决策树的实现。
import warnings
import pandas as pd
#import numpy as np
import math
import random
import copy
import sys
warnings.simplefilter("ignore")
print("Please wait to complete!")
def entropyCalculator(labels):
total = labels.shape[0]
ones = labels.sum().sum()
zeros = total - ones
if total == ones or total == zeros:
return 0
entropy = -(ones/total)*math.log(ones/total, 2) - (zeros/total)*math.log(zeros/total,2)
# print ( "ones : " + str(ones) + "zeros : " + str(zeros) + "entropy : " + str(entropy))
return entropy
# print(entropyCalculator(df[['Class']]))
def informationGain(featurelabels):
total = featurelabels.shape[0]
ones = featurelabels[featurelabels[featurelabels.columns[0]] == 1].shape[0]
zeros = featurelabels[featurelabels[featurelabels.columns[0]] == 0].shape[0]
parentEntropy = entropyCalculator(featurelabels[['Class']])
entropyChildWithOne = entropyCalculator(featurelabels[featurelabels[featurelabels.columns[0]] == 1][['Class']])
entropyChildWithZero = entropyCalculator(featurelabels[featurelabels[featurelabels.columns[0]] == 0][['Class']])
# print ("left entropy : " + str(entropyChildWithZero))
# print ("right entropy : " + str(entropyChildWithOne))
infoGain = parentEntropy - (ones/total)*entropyChildWithOne - (zeros/total)*entropyChildWithZero
return infoGain
# informationGain(df[['XB', 'Class']])
def findBestAttribute(data):
maxInfoGain = -1.0
for x in data.columns:
if x == 'Class':
continue
currentInfoGain = informationGain(data[[x, 'Class']])
# print(str(currentInfoGain) + " " + x)
if maxInfoGain < currentInfoGain:
maxInfoGain = currentInfoGain
bestAttribute = x
return bestAttribute
# findBestAttribute(df)
class Node():
def __init__(self):
self.left = None
self.right = None
self.attribute = None
self.nodeType = None # L/R/I leaf/Root/Intermidiate
self.value = None # attributes split's value 0 or 1
self.positiveCount = None
self.negativeCount = None
self.label = None
self.nodeId = None
def setNodeValue(self, attribute, nodeType, value = None, positiveCount = None, negativeCount = None):
self.attribute = attribute
self.nodeType = nodeType
self.value = value
self.positiveCount = positiveCount
self.negativeCount = negativeCount
class Tree():
def __init__(self):
self.root = Node()
self.root.setNodeValue('$@$', 'R')
def createDecisionTree(self, data, tree):
global nodeCount
total = data.shape[0]
ones = data['Class'].sum()
zeros = total - ones
if data.shape[1] == 1 or total == ones or total == zeros:
tree.nodeType = 'L'
if zeros >= ones:
tree.label = 0
else:
tree.label = 1
return
else:
bestAttribute = findBestAttribute(data)
tree.left = Node()
tree.right = Node()
tree.left.nodeId = nodeCount
nodeCount=nodeCount+1
tree.right.nodeId = nodeCount
nodeCount=nodeCount+1
tree.left.setNodeValue(bestAttribute, 'I', 0, data[(data[bestAttribute]==0) & (df['Class']==1) ].shape[0], data[(data[bestAttribute]==0) & (df['Class']==0) ].shape[0])
tree.right.setNodeValue(bestAttribute, 'I', 1, data[(data[bestAttribute]==1) & (df['Class']==1) ].shape[0], data[(data[bestAttribute]==1) & (df['Class']==0) ].shape[0])
self.createDecisionTree( data[data[bestAttribute]==0].drop([bestAttribute], axis=1), tree.left)
self.createDecisionTree( data[data[bestAttribute]==1].drop([bestAttribute], axis=1), tree.right)
def printTreeLevels(self, node,level):
if(node.left is None and node.right is not None):
for i in range(0,level):
print("| ",end="")
level = level + 1
print("{} = {} (ID:{}) : {}".format(node.attribute, node.value,(node.nodeId if node.nodeId is not None else ""),(node.label if node.label is not None else "")))
self.printTreeLevels(node.right,level)
elif(node.right is None and node.left is not None):
for i in range(0,level):
print("| ",end="")
level = level + 1
# print("{} = {} : {}".format(node.attribute, node.value,(node.label if node.label is not None else "")))
print("{} = {} (ID:{}) : {}".format(node.attribute, node.value,(node.nodeId if node.nodeId is not None else ""),(node.label if node.label is not None else "")))
self.printTreeLevels(node.left,level)
elif(node.right is None and node.left is None):
for i in range(0,level):
print("| ",end="")
level = level + 1
print("{} = {} (ID:{}) : {}".format(node.attribute, node.value,(node.nodeId if node.nodeId is not None else ""),(node.label if node.label is not None else "")))
else:
for i in range(0,level):
print("| ",end="")
level = level + 1
print("{} = {} (ID:{}) : {}".format(node.attribute, node.value,(node.nodeId if node.nodeId is not None else ""),(node.label if node.label is not None else "")))
self.printTreeLevels(node.left,level)
self.printTreeLevels(node.right,level)
def printTree(self, node):
self.printTreeLevels(node.left,0)
self.printTreeLevels(node.right,0)
def predictLabel(self, data, root):
if root.label is not None:
return root.label
elif data[root.left.attribute][data.index.tolist()[0]] == 1:
return self.predictLabel(data, root.right)
else:
return self.predictLabel(data, root.left)
def countNodes(self,node):
if(node.left is not None and node.right is not None):
return 2 + self.countNodes(node.left) + self.countNodes(node.right)
return 0
def countLeaf(self,node):
if(node.left is None and node.right is None):
return 1
return self.countLeaf(node.left) + self.countLeaf(node.right)
def searchNode(tree, x):
tmp = None
res = None
if(tree.nodeType != "L"):
if(tree.nodeId == x):
return tree
else:
res = searchNode(tree.left,x)
if (res is None):
res = searchNode(tree.right,x)
return res
else:
return tmp
def postPruning(pNum,newTree):
for i in range(pNum):
x = random.randint(2,pruneTree.countNodes(pruneTree.root)-1)
tempNode = Node()
tempNode = searchNode(newTree,x)
if(tempNode is not None):
tempNode.left = None
tempNode.right = None
tempNode.nodeType = "L"
if(tempNode.negativeCount >= tempNode.positiveCount):
tempNode.label = 0
else:
tempNode.label = 1
def calculateAccuracy(data, tree):
correctCount = 0
for i in data.index:
val = tree.predictLabel(data.iloc[i:i+1, :].drop(['Class'], axis=1),tree.root)
if val == data['Class'][i]:
correctCount = correctCount + 1
return correctCount/data.shape[0]*100
pruneFactor = 0.05
df = df_train
dtest = df_test
dvalidation = df_val
# remove empty rows
df = df.dropna()
dtest = dtest.dropna()
dvalidation = dvalidation.dropna()
nodeCount = 0 # for node id
dtree = Tree()
dtree.createDecisionTree(df, dtree.root)
maxAccuracy = calculateAccuracy(dvalidation, dtree)
bestTree = copy.deepcopy(dtree)
countOfNodes = bestTree.countNodes(bestTree.root)
c = 0
while c < 10:
c += 1
pruneNum = round(countOfNodes*pruneFactor)
# print(pruneNum)
pruneTree = Tree()
pruneTree = copy.deepcopy(bestTree)
postPruning(pruneNum,pruneTree.root)
temp = calculateAccuracy(dvalidation, pruneTree)
if temp > maxAccuracy:
# print("Accuracy Improved")
maxAccuracy = temp
bestTree = copy.deepcopy(pruneTree)
countOfNodes = bestTree.countNodes(bestTree.root)
print("-------------------------------------")
print("Pre-Pruned Tree")
print("-------------------------------------")
dtree.printTree(dtree.root)
print("")
print("-------------------------------------")
print("Pre-Pruned Accuracy")
print("-------------------------------------")
print("Number of training instances = " +str(df.shape[0]))
print("Number of training attributes = " +str(df.shape[1] -1))
print("Total number of nodes in the tree = "+ str(dtree.countNodes(dtree.root)))
print("Number of leaf nodes in the tree = " +str(dtree.countLeaf(dtree.root)))
print("Accuracy of the model on the training dataset = " + str(calculateAccuracy(df,dtree))+"%")
print("")
print("Number of validation instances = " +str(dvalidation.shape[0]))
print("Number of validation attributes = " +str(dvalidation.shape[1]-1))
print("Accuracy of the model on the validation dataset before pruning = "+ str(calculateAccuracy(dvalidation,dtree))+"%")
print("")
print("Number of testing instances = "+str(dtest.shape[0]))
print("Number of testing attributes = "+str(dtest.shape[1]-1))
print("Accuracy of the model on the testing dataset = "+ str(calculateAccuracy(dtest,dtree))+"%")
print("")
print("-------------------------------------")
print("Post-Pruned Tree")
print("-------------------------------------")
bestTree.printTree(bestTree.root)
print("")
print("-------------------------------------")
print("Post-Pruned Accuracy")
print("-------------------------------------")
print("Number of training instances = " + str(df.shape[0]))
print("Number of training attributes = " + str(df.shape[1] - 1))
print("Total number of nodes in the tree = " + str(bestTree.countNodes(bestTree.root)))
print("Number of leaf nodes in the tree = " + str(bestTree.countLeaf(bestTree.root)))
print("Accuracy of the model on the training dataset = " + str(calculateAccuracy(df, bestTree)) + "%")
print("")
print("Number of validation instances = " + str(dvalidation.shape[0]))
print("Number of validation attributes = " + str(dvalidation.shape[1] - 1))
print("Accuracy of the model on the validation dataset after pruning = " + str(calculateAccuracy(dvalidation, bestTree)) + "%")
print("")
print("Number of testing instances = " + str(dtest.shape[0]))
print("Number of testing attributes = " + str(dtest.shape[1] - 1))
print("Accuracy of the model on the testing dataset = " + str(calculateAccuracy(dtest, bestTree)) + "%")
print("")
if(maxAccuracy > calculateAccuracy(dvalidation, dtree)):
print("Successfully Pruned with improvement in Accuracy on validation data set.")
else:
print("Pruned but Accuracy didn't improved after 10 attempts so returning same tree.")
这是错误
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2897 try:
-> 2898 return self._engine.get_loc(casted_key)
2899 except KeyError as err:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'Class'
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
3 frames
<ipython-input-15-babebb234de5> in <module>()
1 dtree = Tree()
----> 2 dtree.createDecisionTree(df, dtree.root)
3
4 maxAccuracy = calculateAccuracy(dvalidation, dtree)
5 bestTree = copy.deepcopy(dtree)
<ipython-input-13-188b2bef3d66> in createDecisionTree(self, data, tree)
78 global nodeCount
79 total = data.shape[0]
---> 80 ones = data['Class'].sum()
81 zeros = total - ones
82 if data.shape[1] == 1 or total == ones or total == zeros:
/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py in __getitem__(self, key)
2904 if self.columns.nlevels > 1:
2905 return self._getitem_multilevel(key)
-> 2906 indexer = self.columns.get_loc(key)
2907 if is_integer(indexer):
2908 indexer = [indexer]
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2898 return self._engine.get_loc(casted_key)
2899 except KeyError as err:
-> 2900 raise KeyError(key) from err
2901
2902 if tolerance is not None:
KeyError: 'Class'
解决方案
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