topicModelingTickets/corporization.py

# -*- coding: utf-8 -*-

import csv
import sys
from miscellaneous import *
import time
from datetime import datetime
import re
import textacy
from textacy.preprocess import normalize_whitespace
from scipy import *
import os

csv.field_size_limit(sys.maxsize)
FILEPATH = os.path.dirname(os.path.realpath(__file__)) + "/"

# load config
config_ini = FILEPATH + "config.ini"

config = ConfigParser.ConfigParser()
with open(config_ini) as f:
    config.read_file(f)



def ticketcsv_to_textStream(path2csv, content_collumn_name):
    """
    :param path2csv: string
    :param content_collumn_name: string

    :return: string-generator
    """
    stream = textacy.fileio.read_csv(path2csv, delimiter=";")  # ,encoding='utf8')
    content_collumn = 0  # standardvalue

    for i, lst in enumerate(stream):
        if i == 0:
            # look for desired column
            for j, col in enumerate(lst):
                if col == content_collumn_name:
                    content_collumn = j
        else:
            yield lst[content_collumn]


def ticket_csv_to_DictStream(path2csv,content_collumn_name):
    """
    :param path2csv: string
    :param content_collumn_name: string

    :return: dict-generator
    """
    stream = textacy.fileio.read_csv(path2csv, delimiter=";")  # ,encoding='utf8')

    content_collumn = 0  # standardvalue
    metaindices = []
    metalist = []

    metadata_template = {}
    for i, lst in enumerate(stream):
        if i == 0:
            for j, col in enumerate(lst):
                if "icketNumb" in col: #korrigieren der .csv todo wenn hier sowieso hardgecodet werden muss, dann gleich auch config.ini raus?
                    col = "TicketNumber" 
                metalist.append(str(col))
                metaindices.append(j)
            metadata_template = dict(
                zip(metalist, metaindices))  # zB {'Subject' : 1, 'categoryName' : 3, 'Solution' : 10}

        else:
            metadata = metadata_template.copy()
            for key, value in metadata.items():
                metadata[key] = lst[value]
            yield metadata



##################################################################################################



content_collumn_name = config.get("tickets","content_collumn_name")


path2de_csv = FILEPATH + config.get("de_corpus","input")
corpus_de_path = FILEPATH + config.get("de_corpus", "path")





def ticketcsv2Corpus(path2_csv, corpus_path, content_collumn_name, lang, printrandom=0): #todo bla das kann hier die main sein
    """
    Use textacy to create a Corpus out of the ITMC-Ticket.csv

    :param path2_csv: str
    :param corpus_path: str
    :param content_collumn_name: str    the Collumn which is used as the Docs text
    :param lang: str       standard 2-letter language
    :param printrandom: print n random Documents
    :return: textacy.Corpus
    """

    # print paths
    path_csv_split = path2_csv.split("/")
    filename = path_csv_split[len(path_csv_split) - 1]

    logprint("Corporization of {0}".format(filename))#, datetime.now()))


    raw_corpus = textacy.Corpus(lang)

    ## add files to textacy-corpi, todo bla hier cleanen, dict nich vergessn
    raw_corpus.add_texts(
        ticketcsv_to_textStream(path2_csv, content_collumn_name), 
        ticket_csv_to_DictStream(path2_csv,content_collumn_name)
    )
   

    

    # leere docs aus corpi kicken
    raw_corpus.remove(lambda doc: len(doc) == 0)

    logprint("corpus-length: {}".format(len(raw_corpus)))



    # save corpus
    raw_name = lang + "_raw"
    save_corpus(corpus=raw_corpus, corpus_path=corpus_path, corpus_name=raw_name)

    return raw_corpus


def main():
    start = time.time()



    raw_corpus = ticketcsv2Corpus(path2de_csv,corpus_de_path,content_collumn_name,lang="de")




    end = time.time()
    logprint("Time Elapsed Corporization:{0} min".format((end - start) / 60))
    return raw_corpus


if __name__ == "__main__":
    main()