我不确定我做错了什么,因为我几乎可以肯定地确定我已经引用了变量,并且都是正确的。课程与功能 -
我对使用函数还不太熟悉,并且刚刚开始学习如何在一天前使用Python类。
所以,当我运行代码,我得到这个错误信息:
line 37, in pathlist
while self.no_of_files > 0: #self.number_of_files
AttributeError: 'int' object has no attribute 'no_of_files'
我猜它是与我的代码顺序步骤,或者是因为我已经转换输入到代码第20行中的int()的numfiles。
我附上我的代码如下。请帮我在此先感谢:)
import csv
import numpy as np
''' DEFINING MAIN CONTROL'''
def main():
no_of_files # = number_of_files()
a = Calculate_RMSE_Assess_Models()
a.no_of_files() # = no_of_files
a.pathlist()
a.out_path()
a.open_read_write_files()
''' DEFINING CLASS OF ALL '''
class Calculate_RMSE_Assess_Models:
def __init__(self, no_of_files):
self.no_of_files = no_of_files
def number_of_files():
numfiles = input("Enter the number of files to iterate through: ")
numfilesnumber = int(numfiles)
return numfilesnumber
no_of_files = number_of_files()
def pathlist(self):
filepathlist = []
while self.no_of_files > 0: #self.number_of_files
path = input("Enter the filepath of the input file: ")
filepathlist.append(path)
no_of_files = no_of_files - 1
return filepathlist
list_filepath = pathlist(no_of_files)
def out_path():
path = input("Enter the file path of output path: ")
return path
file_out_path = outpath()
def open_read_write_files():
with open('{d[0]}'.format(d=list_filepath), 'r') as csvinput, open('{d[1]}'.format(d=list_filepath), 'r') as csvinput2, open('d{[2]}'.format(d=list_filepath), 'r') as csvinput3, open('{d}'.format(d=file_out_path), 'w') as csvoutput:
reader, reader2, reader3 = csv.reader(csvinput, csvinput2, csvinput3) #1: Decision Forest, 2: Boosted Decision Tree, 3: ANN
writer = csv.DictWriter(csvoutput, lineterminator='\n', fieldnames = ['oldRMSE', 'Decision Forest Regression RMSE', 'Boosted Decision Tree Regression RMSE', 'Neural Network Regression RMSE', 'Old Accurate Predictions', 'Old Inaccurate Predictions', 'Decision Forest Accurate Predictions', 'Decision Forest Inaccurate Predictions', 'Boosted Decision Tree Accurate Predictions', 'Boosted Decision Tree Inaccurate Predictions', 'Neural Network Accurate Predictions', 'Neural Network Inaccurate Predictions'])
#######################################
#For Decision Forest Predictions
headerline = next(reader)
emptyl=[]
for row in reader:
emptyl.append(row)
#Calculate RMSE
DecFSqResidSum = 0
for row in emptyl:
for cell in row:
if cell == row[-3]:
DecFSqResidSum = float(cell) + DecFSqResidSum
DecFSqResidAvg = DecFSqResidSum/len(emptyl)
DecForest_RMSE = np.sqrt(DecFSqResidAvg)
#Constructing No. of Correct/Incorrect Predictions
DecisionForest_Accurate = 0
DecisionForest_Inaccurate = 0
Old_Accurate = 0
Old_Inaccurate = 0
for row in emptyl:
for cell in row:
if cell == row[-2] and 'Accurate' in cell:
Old_Accurate += 1
else:
Old_Inaccurate += 1
if cell == row[-1] and 'Accurate' in cell:
DecisionForest_Accurate += 1
else:
DecisionForest_Inaccurate += 1
######################################
#For Boosted Decision Tree
headerline2 = next(reader2)
emptyl2=[] #make new csv file(list) from csv reader
for row in reader2:
emptyl2.append(row)
#Calculate RMSE
OldSqResidSum = 0
BoostDTSqResidSum = 0
for row in emptyl2: #make Sum of Squared Residuals
for cell in row:
if cell == row[-4]:
OldSqResidSum = float(cell) + OldSqResidSum
if cell == row[-3]:
BoostDTSqResidSum = float(cell) + BoostDTSqResidSum
OldSqResidAvg = OldSqResidSum/len(emptyl2) #divide by N to get average
BoostDTResidAvg = BoostDTSqResidSum/len(emptyl2)
oldRMSE = np.sqrt(OldSqResidAvg) #calculate RMSE of ESTARRTIME & Boosted Decision Tree
BoostedDecTree_RMSE = np.sqrt(BoostDTResidAvg)
#Constructing Correct/Incorrect Predictions
BoostedDT_Accurate = 0
BoostedDT_Inaccurate = 0
for row in emptyl2:
if cell == row[-1] and 'Accurate' in cell:
BoostedDT_Accurate += 1
else:
BoostedDT_Inaccurate += 1
######################################
#For Artificial Neural Network (ANN) Predictions
headerline3 = next(reader3)
emptyl3=[]
for row in reader3:
emptyl3.append(row)
#Calculate RMSE
ANNSqResidSum = 0
for row in emptyl3:
for cell in row:
if cell == row[-3]:
ANNSqResidSum = float(cell) + ANNSqResidSum
ANNSqResidAvg = ANNSqResidSum/len(emptyl3)
ANN_RMSE = np.sqrt(ANNSqResidAvg)
#Constructing Correct/Incorrect Predictions
ANN_Accurate = 0
ANN_Inaccurate = 0
for row in emptyl3:
for cell in row:
if cell == row[-1] and 'Accurate' in cell:
ANN_Accurate += 1
else:
ANN_Inaccurate += 1
#Compile the Error Measures
finalcsv = []
finalcsv.append(oldRMSE)
finalcsv.append(DecForest_RMSE)
finalcsv.append(BoostedDecTree_RMSE)
finalcsv.append(ANN_RMSE)
finalcsv.append(Old_Accurate)
finalcsv.append(Old_Inaccurate)
finalcsv.append(DecisionForest_Accurate)
finalcsv.append(DecisionForest_Inaccurate)
finalcsv.append(BoostedDT_Accurate)
finalcsv.append(BoostedDT_Inaccurate)
finalcsv.append(ANN_Accurate)
finalcsv.append(ANN_Inaccurate)
#Write the Final Comparison file
writer.writeheader()
writer.writerows({'oldRMSE': row[0], 'Decision Forest Regression RMSE': row[1], 'Boosted Decision Tree Regression RMSE': row[2], 'Neural Network Regression RMSE': row[3], 'Old Accurate Predictions': row[4], 'Old Inaccurate Predictions': row[5], 'Decision Forest Accurate Predictions': row[6], 'Decision Forest Inaccurate Predictions': row[7], 'Boosted Decision Tree Accurate Predictions': row[8], 'Boosted Decision Tree Inaccurate Predictions': row[9], 'Neural Network Accurate Predictions': row[10], 'Neural Network Inaccurate Predictions': row[11]} for row in np.nditer(finalcsv))
main()
'回溯(最近最后调用): 文件 “”,第22行,在 类Calculate_RMSE_Assess_Models: 文件 “”,43行,在Calculate_RMSE_Assess_Models list_filepath = pathlist(no_of_files) 文件 “” ,第37行,在路径列表 while self.no_of_files> 0: AttributeError:'int'object has no attribute'no_of_files'' –
Christoph
对不起,我没有在OG文章中包含完整的回溯,认为它并不重要就像我不得不手动删除文件目录一样,因为它包含一些机密的东西。 – Christoph
我的答案解释了为什么你会得到这个特定的错误。在看回溯时,试着在脑海中走过它。 – Galen