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Corpus.py
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Corpus.py
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# /usr/bin/env python
# encoding: utf-8
"""
Corpus.py
Created by Andrew Tulloch on 2010-04-27.
Copyright (c) 2010 Andrew Tulloch. All rights reserved.
"""
from utils import *
from Message import *
import Stemmer
#------------------------------------------------------------------------------
class Corpus():
"""Corpus class. A superclass for the classes SubjectCorpus and BodyCorpus"""
def csv_write(self):
"""Writes a csv file
201 columns - 200 features and class identifier
601 rows - header (f1, f2...f200, class) and 600 examples"""
headers = []
for index in xrange(len(self.top200)):
# Create the list [f1, f2, ..., f200]
headers.append("f" + str(index + 1) )
headers.append("Spam Class")
# Create the list [f1, f2..., f200, Spam Class]
csv_file = []
csv_file.append(headers)
for message in self.messages:
# Append the row of tf-idf scores for each feature
msg_scores = [scores[1] for scores in message.tf_idf_scorelist]
# Append the spam class in the last column
msg_scores.append(message.spam)
# Append the row to the file
csv_file.append(msg_scores)
csv_filename = self.type + ".csv"
writer = csv.writer(open(csv_filename, "wb"))
for row in csv_file:
writer.writerow(row)
# Write the CSV file
def get_length(self):
"""Find the number of examples in the corpus"""
self.length = len(self.data)
def tf_idf_scores(self):
"""Calculate tf-idf scores for all messages in the corpus"""
for message in self.messages:
message.tf_idf(self)
def DF_score(self):
"""Calculate the document frequency score for all words in the corpus"""
self.DF_counts = {}
for message in self.cleaned_data:
for word in message.split():
# Initialise the dictionary
self.DF_counts[word] = 0
for message in self.cleaned_data:
word_added_already = []
for word in message.split():
if word not in word_added_already:
# Avoids double counting a word if it appears twice in a message
self.DF_counts[word] += 1
word_added_already.append(word)
word_list = sorted((value,key) for (key,value) in self.DF_counts.items())
# Sort our list, in order of least prevalent to most prevalent
word_list.reverse()
# Reverse this list
self.top200 = word_list[:200]
# Return the top 200 words
return self.top200
def word_count(self):
"""Counts the number of unique words in the corpus"""
word_string = []
for message in self.data:
for words in message.split():
word_string.append(words)
word_counts = counter(word_string)
return word_counts
def remove_stop_words(self):
"""Performs the filtering described in the data preprocessing
section of the report.
Removes stop words
Removes punctuation
Stems words
Filters numeric data
"""
self.cleaned_data = []
stemmer = Stemmer.Stemmer('english')
for data in self.data:
words = data.split()
stemmed_words = [stemmer.stemWord(word) for word in words \
if word not in stop_words \
and word not in forbidden_words]
# Perhaps an overly complex line - returns a list of stemmed words,
# if the word is not a stop word or forbidden
words = []
for word in stemmed_words:
# Filters out our numeric features
# e.g "112" --> "NUMERIC"
if word.isdigit():
words.append("NUMERIC")
else:
words.append(word)
clean_data = " ".join(words)
# Converts list to string
self.cleaned_data.append(clean_data)
return self.cleaned_data
def creation(self):
"""A container method, performing the following operations:
filtering out stop words and punctuation
performing the stemming algorithm
calculates tf-idf scores
writes the CSV file
"""
self.remove_stop_words()
self.DF_score()
self.tf_idf_scores()
self.csv_write()
print "DONE! - {0} CSV file created".format(self.type)
#------------------------------------------------------------------------------
class SubjectCorpus(Corpus):
"""Subject Corpus
Message data is the subjects of the individual messages
"""
def __init__(self, message_list):
self.messages = message_list
self.data = [message.subject for message in message_list]
self.get_length()
self.type = "subject"
class BodyCorpus(Corpus):
"""Body Corpus
Message data is the body of the individual messages
"""
def __init__(self, message_list):
self.messages = message_list
self.data = [message.body for message in message_list]
self.get_length()
self.type = "body"
#------------------------------------------------------------------------------
def Create_BC_SC_CSV():
file_list = [(file, file[-3:]) for file in os.listdir("./Data")]
proper_files = [file for file, extension in file_list if extension == "txt"]
# Filters out files that are not text files
message_list = [Message(file) for file in proper_files]
# Our list of message objects
SC = SubjectCorpus(message_list)
SC.creation()
BC = BodyCorpus(message_list)
BC.creation()
if __name__ == '__main__':
Create_BC_SC_CSV()