Klassen & Funktionen -

Question

Ich bin mir nicht sicher, was genau ich falsch mache, da ich fast absolut sicher bin, dass ich auf Variablen und alle richtig verwiesen habe.

Ich bin ziemlich neu in der Verwendung von Funktionen, und habe gerade angefangen zu lernen, Python-Klassen vor einem Tag zu verwenden.

Also, wenn ich den Code ausführen, bekomme ich diese Fehlermeldung:

line 37, in pathlist 
    while self.no_of_files > 0:    #self.number_of_files 
AttributeError: 'int' object has no attribute 'no_of_files' 

Ich vermute, dass es etwas mit meinem aufeinanderfolgenden Stufen Code zu tun, oder ist das, weil ich umgewandelt habe die numfiles Eingabe in ein int() in Zeile 20 des Codes.

Ich habe meinen Code unten beigefügt. Bitte helfen Sie mir Vielen Dank im Voraus :)

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() 

Answer 1

Sie sollten eine no_of_files params geben, wenn ein durch den Aufruf def init Calculate_RMSE_Assess_Models Instanz erstellen (self, no_of_files).

Answer 2

Sie müssen self zur Unterzeichnung number_of_files() hinzuzufügen, out_path() und open_read_write_file():

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 

    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 

    def out_path(self): 
     path = input("Enter the file path of output path: ") 
     return path 


    def open_read_write_files(self): 
     pass 

Wenn Sie jedoch die Eigenschaften einer Funktion innerhalb der Klasse behalten möchten, können Sie den classmethod Dekorateur verwenden:

class Calculate_RMSE_Assess_Models: 

    def __init__(self, no_of_files): 
     self.no_of_files = no_of_files 
    @classmethod 
    def number_of_files(cls): 
     numfiles = input("Enter the number of files to iterate through: ") 
     numfilesnumber = int(numfiles) 
     return numfilesnumber 

    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 
    @classmethod 
    def out_path(cls): 
     path = input("Enter the file path of output path: ") 
     return path 

    @classmethod 
    def open_read_write_files(cls): 
     pass 

Answer 3

In Ihrer Klassendefinition haben Sie list_filepath = pathlist(no_of_files). Dies ruft pathlist mit no_of_files als self. no_of_files ist ein int, so versucht while self.no_of_files > 0: auf das no_of_files Attribut eines int zuzugreifen.

Der vollständige Traceback zeigt dies an. Es ist hilfreich, den vollständigen Traceback zu senden, wenn Sie ein Problem wie dieses betrachten.

Traceback (most recent call last): 
    File "redacted", line 17, in <module> 
    class Calculate_RMSE_Assess_Models: 
    File "redacted", line 38, in Calculate_RMSE_Assess_Models 
    list_filepath = pathlist(no_of_files) 
    File "redacted", line 32, in pathlist 
    while self.no_of_files > 0:    #self.number_of_files 
AttributeError: 'int' object has no attribute 'no_of_files'