Not Getting the Expected Output in AzureML

Question

Background: I am working on a project that aims to classify product reviews into positive and negative using Sentiment Analysis in Azure ML. I got stuck when I was classifying reviews into different departments.

I am basically reading words from csv files and checking whether the review(v: list of sentences) contains these words. If some of these words are found in the review then I am noting the sentence number and pushing it into respective lists( FinanceList, QualityList, LogisticsList ). In the end I am converting the lists to strings and pushing them into a dataframe.

The output is not getting logged for the print statements that I have written in the script in Azure ML.

The values in the dataframe are always turning out to be 0 but when I run the code locally I get the expected output.

Description of First Image: The columns of the dataframe showing 0 values.

Description of Second Image: I have highlighted the expected output that I got locally for the same review which was used in AzureML.

Image 1

Image 2

The things that I have already checked:

1. The csv files are read properly.
2. The review contains the words that I am searching.

I am unable to understand where I am going wrong.

'

import csv
import math
import pandas as pd
import numpy as np

def azureml_main( data, ud):

   FinanceDept = []
   LogisticsDept = []
   QualityDept = []
  #Reading from the csv files
   with open('.\Script Bundle\\quality1.csv', 'rb') as fin:
      reader = csv.reader(fin)
      QualityDept = list(reader)

   with open('.\Script Bundle\\finance1.csv', 'rb') as f:
      reader = csv.reader(f)
      FinanceDept = list(reader)

   with open('.\Script Bundle\\logistics1.csv', 'rb') as f:
      reader = csv.reader(f)
      LogisticDept = list(reader)

   FinanceList = []
   LogisticsList = []
   QualityList = []

#Initializing the Lists   
   FinanceList.append(0)
   LogisticsList.append(0)
   QualityList.append(0)

   rev = data['Data']
   v = rev[0].split('.')

   print FinanceDept

   S = 0   
   for sentence in v:
      S = S + 1
      z = sentence.split(' ')
      for c in z:
         c = c.lower()
         if c in FinanceDept and S not in FinanceList:
            FinanceList.append(S)
         if c in LogisticsDept and S not in LogisticsList:
            LogisticsList.append(S)
         if c in QualityDept and S not in QualityList:
            QualityList.append(S)
   #Compute User Reputation Score
   Upvotes = int(ud['upvotes'].tolist()[0])
   Downvotes = int(ud['downvotes'].tolist()[0])
   TotalVotes = max(1,Upvotes+Downvotes)

   q = data['Score']

   print FinanceList

   repScore = float(Upvotes)/TotalVotes 
   repScore = repScore*float( q[0] )
   str1 = ','.join(str(e) for e in FinanceList) 
   str2 = ','.join(str(e) for e in QualityList)
   str3 = ','.join(str(e) for e in LogisticsList)

   x = ud['id']

   #df = pd.DataFrame(  [str(repScore), str1  , str2  , str3 ], columns=[Write the columns])
   d = {'id': x[0], 'Score': float(repScore),'Logistics':str3,'Finance':str1,'Quality':str2}
   df = pd.DataFrame(data=d, index=np.arange(1))
   return df,`

Answer 1

@Anuj Shankar, After my colleague tested, we can read data from CSV files and get the expected results. Please refer to this experience:

1) Input data - It has apple.zip file which has two csv files similar to you and each csv file includes bag of words related to company.

2) Python script:

# The script MUST contain a function named azureml_main
# which is the entry point for this module.
#
# The entry point function can contain up to two input arguments:
#   Param<dataframe1>: a pandas.DataFrame
#   Param<dataframe2>: a pandas.DataFrame
import csv
import numpy as np
import pandas as pd

def azureml_main(dataframe1 = None, dataframe2 = None):
    # Execution logic goes here
    #print('Input pandas.DataFrame #1:\r\n\r\n{0}'.format(dataframe1))

    # If a zip file is connected to the third input port is connected,
    # it is unzipped under ".\Script Bundle". This directory is added
    # to sys.path. Therefore, if your zip file contains a Python file
    # mymodule.py you can import it using:
    # import mymodule

    apple = {}
    microsoft = {}
  #Reading from the csv files
    with open('.\Script Bundle\\apple.csv', 'rb') as f:
      reader = csv.reader(f)
      apple = list_to_dict(list(reader)[0])

    with open('.\Script Bundle\\microsoft.csv', 'rb') as f:
      reader = csv.reader(f)
      microsoft = list_to_dict(list(reader)[0])

#    print('hello world' + ' '.join(apple[0]))
    applecount = 0
    microsoftcount = 0

    input = "i want to buy surface which runs on windows"
    splitted_input = input.split(' ')

    for word in splitted_input:
        if word in apple:
            applecount = applecount + 1
        if word in microsoft:
            microsoftcount = microsoftcount + 1

    print("apple bag of words count - " + str(applecount))
    print("microsoft bag of words count - " + str(microsoftcount))
    mydata = [{'input words': len(splitted_input)}, {'applecount':applecount},
        {'microsoftcount':microsoftcount}]       
    # Return value must be of a sequence of pandas.DataFrame
    return pd.DataFrame(mydata),


def list_to_dict(li):      
    dct = {}  
    for item in li:
        if dct.has_key(item):              
            dct[item] = dct[item] + 1  
        else:  
            dct[item] = 1  
    return dct  

3) Output - if I consider a string "i want to buy surface which runs on windows". It has 2 words related to microsoft and 0 related to apple which are visualized in below snapshot.