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text_analysis_B_case_unigram.py
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text_analysis_B_case_unigram.py
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import os
import csv
import spacy
import nltk
import sys
from operator import itemgetter
from collections import OrderedDict
from scipy import sparse
from scipy.sparse import *
from scipy import *
import numpy as np
from numpy import array
from nltk import word_tokenize
from nltk.stem.porter import PorterStemmer
import string
from sklearn.feature_extraction.text import CountVectorizer
import codecs
reload(sys)
sys.setdefaultencoding('utf8')
current_file_path = __file__
foldername = os.path.dirname(os.path.realpath(__file__))
stemmer = PorterStemmer()
class text_analysis():
def __init__(self):
self.csvfile1 = codecs.open('sheet1.csv','w',encoding = 'utf-8')
self.csvfile2 = codecs.open('sheet2_B_bigram.csv','w',encoding = 'utf-8')
self.fieldnames1 = ['Stemmed Term', 'Set A occurences', 'Set B occurences', 'ratio DocFreq(A) to DocFreq(B)']
self.writer1 = csv.DictWriter(self.csvfile1,fieldnames=self.fieldnames1)
self.writer1.writeheader()
def main(self):
print('start working...')
set_A = self.convert_A_to_list(foldername)
set_B = self.convert_B_to_list(foldername)
set_A_case_pre, set_A_case_dict = self.convert_A_to_caseslist(foldername)
set_B_case_pre, set_B_case_dict = self.convert_B_to_caseslist(foldername)
set_A_case = set_A_case_pre.values()
set_B_case = set_B_case_pre.values()
vect = CountVectorizer(min_df = 10, stop_words = 'english',ngram_range=(2, 2), decode_error='ignore',tokenizer = self.tokenize)
sparse_matrix_A = vect.fit_transform(set_A)
sparse_matrix_B = vect.transform(set_B)
sparse_matrix_A_cases = vect.transform(set_A_case)
sparse_matrix_B_cases = vect.transform(set_B_case)
print shape(sparse_matrix_A)
print shape(sparse_matrix_B)
print shape(sparse_matrix_A_cases)
print("Vocabulary size: {}".format(len(vect.vocabulary_)))
t_A = sparse_matrix_A.nonzero()
sparse_matrix_A[t_A] = 1
t_B = sparse_matrix_B.nonzero()
sparse_matrix_B[t_B] = 1
t_A_case = sparse_matrix_A_cases.nonzero()
sparse_matrix_A_cases[t_A_case] = 1
res_vector_A = sparse_matrix_A.sum(axis = 0)
res_vector_B = sparse_matrix_B.sum(axis = 0)
index, keys = self.key(vect)
print len(keys)
for index in xrange(len(keys)):
stem = keys[index]
A_DocFreq = float(res_vector_A[0,index])/len(set_A)
B_DocFreq = float(res_vector_B[0,index])/len(set_B)
if B_DocFreq == 0:
ratio = 'inf'
else:
ratio = A_DocFreq/float(B_DocFreq)
self.writer1.writerow({'Stemmed Term': stem, 'Set A occurences': A_DocFreq, 'Set B occurences': B_DocFreq, 'ratio DocFreq(A) to DocFreq(B)': ratio})
print('sheet2_B working...')
filedname2 = ["Stemmed Term"]
#column_number = len(set_A)
case_name = set_B_case_pre.keys()
for i in xrange(len(set_B_case)):
case = ''
case = 'Case B-{} DocFreq'.format(case_name[i])
filedname2.append(case)
writer2 = csv.DictWriter(self.csvfile2, fieldnames = filedname2)
writer2.writeheader()
number_of_docs = set_B_case_dict.values()
print len(number_of_docs)
for j in xrange(len(keys)):
#for j in xrange(5):
stems = keys[j]
new_dict = {'Stemmed Term': stems}
print new_dict
w = sparse_matrix_B_cases.getcol(j).toarray()
print len(w)
for v in xrange(len(w)):
new_key = ''
new_key = 'Case B-{} DocFreq'.format(case_name[v])
new_dict[new_key] = w[v,0]/float(number_of_docs[v])
print new_dict
writer2.writerow(new_dict)
def convert_B_to_caseslist(self, foldername):
data_dir = os.path.join(foldername, 'SET_B_subsidies')
set_B_case_dict = {}
set_B_case_list = {}
test_set = set()
i = 1
pre_file = ''
counter = 0
case = ''
current_file_name = 'WTDS46'
for filename in os.listdir(data_dir):
if filename != '.DS_Store':
item = ''
pre_file = filename.split('-')[0]
test_set.add(pre_file)
f = open(data_dir+'/'+filename,'r')
item = f.read()
f.close()
if len(test_set) == i:
case = case + item
counter = counter + 1
else:
set_B_case_list[current_file_name] = case
set_B_case_dict[current_file_name] = counter
current_file_name = pre_file
i = i + 1
case = ''
case = case + item
counter = 1
if case != '':
set_B_case_list[pre_file] = case
set_B_case_dict[pre_file] = counter
#print counter, len(set_A_case_list), len(test_set)
return set_B_case_list, set_B_case_dict
def convert_A_to_caseslist(self, foldername):
data_dir = os.path.join(foldername, 'SET_A_txt_zeroing_cases')
set_A_case_dict = {}
set_A_case_list = {}
test_set = set()
i = 1
pre_file = ''
counter = 0
case = ''
current_file_name = 'WTDS179'
for filename in os.listdir(data_dir):
item = ''
pre_file = filename.split('-')[0]
test_set.add(pre_file)
f = open(data_dir+'/'+filename,'r')
item = f.read()
f.close()
if len(test_set) == i:
case = case + item
counter = counter + 1
else:
set_A_case_list[current_file_name] = case
set_A_case_dict[current_file_name] = counter
current_file_name = pre_file
i = i + 1
case = ''
case = case + item
counter = 1
if case != '':
set_A_case_list[pre_file] = case
set_A_case_dict[pre_file] = counter
#print counter, len(set_A_case_list), len(test_set)
return set_A_case_list, set_A_case_dict
def convert_A_to_list(self, foldername):
data_dir = os.path.join(foldername, 'SET_A_txt_zeroing_cases')
set_A = []
for filename in os.listdir(data_dir):
item = ''
f = open(data_dir+'/'+filename,'r')
item = f.read()
f.close()
item = item.replace('\n','')
#item = item.encode('utf-8').strip()
set_A.append(item)
set_A = [doc.replace(b"<br />", b" ") for doc in set_A]
return set_A
def convert_B_to_list(self, foldername):
data_dir = os.path.join(foldername,'SET_B_subsidies')
set_B = []
for filename in os.listdir(data_dir):
item = ''
f = open(data_dir+'/'+filename,'r')
item = f.read()
f.close()
item = item.replace('/n','')
#item = item.encode('utf-8').strip()
set_B.append(item)
set_B = [doc.replace(b"<br />", b" ") for doc in set_B]
return set_B
#stemmer = PorterStemmer()
def stem_tokens(self, tokens, stemmer):
stemmed = []
for item in tokens:
stemmed.append(stemmer.stem(item))
return stemmed
def tokenize(self, text):
text = "".join([ch for ch in text if ch not in string.punctuation])
tokens = nltk.word_tokenize(text)
stems = self.stem_tokens(tokens, stemmer)
return stems
def key(self, vect):
dic = vect.vocabulary_
sorted_x = OrderedDict(sorted(dic.items(), key=lambda t: t[1]))
index = sorted_x.values()
keys = sorted_x.keys()
return index, keys
o = text_analysis()
o.main()