def get_word_data_from_email(email_addr, n_counter=10):
"Get word data given email address"
# print email
DIR = "../dataset/emails_by_address"
try:
f_emails = open("{dir}/from_{addr}.txt".format(addr=email_addr, dir=DIR), "r")
except IOError:
return "NaN"
counter = 0
word_data = []
for path in f_emails:
counter += 1
if counter > n_counter:
break
path = "{dir}/".format(dir=DIR) + path[:-1]
# print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
words = parseOutText(email).replace("\n", "").replace("\r", "")
word_data.append(words)
document = " ".join(word_data)
return document
def addFeatures(data_dict, features_list):
emaillistings = os.listdir(emails_by_address_path)
for person, features in data_dict.iteritems():
mentions = 0
eaddr = features['email_address']
listing = "from_" + eaddr + ".txt"
if listing in emaillistings:
emailfiles = open(emails_by_address_path + '/' + listing, 'r')
for emailfilepath in emailfiles:
#emailfilepath = emailfilepath.replace('enron_mail_20110402/','../')
email = open(emailfilepath.strip())
words = parseOutText(email)
# contains email address or names
mentions += num_mentions(words, poiIdentifiers(), person)
print person + " : " + str(mentions)
features['mentions'] = mentions
features_list.append('mentions')
return (data_dict, features_list)
def getSingleWordData(email):
fn = "from_" + email + ".txt"
words = ""
try:
from_file = open("./emails_by_address/" + fn, 'r')
for path in from_file:
path = path[path.index('maildir'):]
path = os.path.join('..', path[:-1])
email = open(path, 'r')
words = parseOutText(email)
email.close()
from_file.close()
except IOError:
return []
return [words]
def updateWordData(email,label,word_data,word_data_label):
fn = "from_" + email + ".txt"
try:
from_file = open("./emails_by_address/" + fn, 'r')
for path in from_file:
path = path[path.index('maildir'):]
path = os.path.join('..', path[:-1])
email = open(path, 'r')
words = parseOutText(email)
word_data.append(words)
word_data_label.append(label)
email.close()
from_file.close()
except IOError:
pass
def get_emails(email_addr, sig_words, n_emails, to=True):
'''get email word data for a person using email address.
To get emails *to* this person, set to=True,
To get emails *from* this person, set to=False.
'''
emails = []
addr_dir = 'emails_by_address'
pre = 'to_' if to else 'from_'
filename = pre + email_addr + '.txt'
path = addr_dir + '/' + filename
with open(path, 'r') as f:
temp_email_locs = f.readlines()
# get lines that have valid email locations
email_locs = []
for loc in temp_email_locs:
if 'maildir' in loc:
maildir_idx = loc.index('maildir')
# You may need to modify this line if you have emails in a
# different location or with a different naming format
new_loc = 'C:/enron/' + loc[maildir_idx:-1].replace('.','_')
email_locs.append(new_loc)
# if n_emails is specified as 'all', check all the emails
# if there aren't n_emails to check, check as many as we can
if n_emails == 'all' or len(email_locs) < n_emails:
n_emails = len(email_locs)
# shuffle the email list so we can randomly choose n_emails
shuffle(email_locs)
email_locs = email_locs[:n_emails]
# get email contents
for loc in email_locs:
try:
email = open(loc, 'r')
except:
# just going to skip to the next email if one won't open
print "\nTried and failed to open %s" % loc
continue
text = parseOutText(email)
# close the file after we got the text out of it
email.close()
# remove instances of signature words
for sig_word in sig_words:
text = text.replace(sig_word, '')
emails.append(text)
# return the parsed emails as one big string
return ' '.join(emails)
def vectorize_emails(from_sara, from_chris, words_to_remove = [], max_emails = False):
'''
This function can be used independently. It takes as input the file pointers
from_sara and from_chris, mandatory, and optionally:
a list of words to remove (if not sent, it won't remove
any words from the emails)
and a max_emails (if parameter not sent, it uses all emails available,
if max_emails is larger than the number of emails available,
it uses all available emails)
'''
from_data = []
word_data = []
### temp_counter is a way to speed up the development--there are
### thousands of emails from Sara and Chris, so running over all of them
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
temp_counter = 0
for path in from_person:
if max_emails and temp_counter == max_emails:
break
temp_counter += 1
path = os.path.join('..', path[:-1])
# print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
text = parseOutText(email)
### use str.replace() to remove any instances of the words
for word in words_to_remove:
text = text.replace(word, '')
### append the text to word_data
word_data.append(text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(str(int(name == 'sara')))
email.close()
print "emails processed"
from_sara.close()
from_chris.close()
return word_data, from_data
def extractTextData(email_list):
from_data = []
word_data = []
emails_nf = []
person_emails = {}
temp_counter = 0
for email_address, poi in email_list:
try:
from_person = open(os.path.join(emails_by_address_path, "from_"+email_address+".txt"), "r")
word_data_from_person = []
for path in from_person:
temp_counter += 1
if (temp_counter % 5000) == 0:
print "{} parsed emails".format(temp_counter)
path = os.path.join(mail_dir_path , path[:-1])
### using parseOutText to extract the text from the opened email
email = open(path, "r")
text = parseOutText(email)
### append the text to word_data
word_data.append(text)
word_data_from_person.append(text)
### append a 0 to from_data if email is not from POI, and 1 if email is from a POI
from_data.append(poi)
email.close()
person_emails[email_address] = word_data_from_person
except:
emails_nf.append(email_address)
from_person.close()
print "all emails processed: %s total" %temp_counter
print "%s emails not found" %len(emails_nf)
pickle.dump( word_data, open("enron_word_data.pkl", "w") )
pickle.dump( from_data, open("enron_email_authors.pkl", "w") )
pickle.dump( person_emails, open("emails_from_person.pkl", "w") )
def get_word_data():
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_parsed = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
remove = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
for word in remove:
email_parsed = email_parsed.replace(word,"")
### append the text to word_data
word_data.append(email_parsed)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
from_data.append(1)
email.close()
print "emails processed"
from_sara.close()
from_chris.close()
pickle.dump( word_data, open("your_word_data.pkl", "w") )
pickle.dump( from_data, open("your_email_authors.pkl", "w") )
return word_data
#ler emails de cada colaborador que possui um endereco de email no dicionario
#nesse processo a funcao parseOutText ja retira os stopwords
print "Iniciando leitura dos emails"
for name in data_dict:
try:
from_person = open(
"emails_by_address/from_" + data_dict[name]["email_address"] +
".txt", "r")
temp_counter = 0
words = ""
for path in from_person:
#por questao de tempo de processamento, so li 100 emails de cada pessoa
if temp_counter < 100:
path = os.path.join('..', path[20:len(path) - 1:])
email = open(path, "r")
w = parseOutText(email)
words = words + w
email.close()
temp_counter += 1
word_data.append(words)
from_data.append(name)
if data_dict[name]["poi"] == 1:
p.append(1)
else:
p.append(0)
from_person.close()
except:
continue
print "emails processados"
#transformar a lista de emails de cada colaborador em um vetor de palavras
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
#if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_parsed = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
email_parsed = string.replace(email_parsed, "sara", "")
email_parsed = string.replace(email_parsed, "shackleton", "")
email_parsed = string.replace(email_parsed, "chris", "")
email_parsed = string.replace(email_parsed, "germani", "")
### append the text to word_data
word_data.append(email_parsed)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0) if name == "sara" else from_data.append(1)
email.close()
print "emails processed"
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
#if temp_counter < 20000000000:
if temp_counter < 5:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
emailText = parseOutText(email).replace("sara","").replace("shackleton","").replace("chris","").replace("germani","").replace("sshacklensf","").replace("cgermannsf","")
### use parseOutText to extract the text from the opened email
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
### append the text to word_data
word_data.append(emailText)
if name == 'sara':
from_data.append(0)
else:
from_data.append(1)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
email.close()
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
# print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsed_email = parseOutText(email).encode('ascii')
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for outcast in ["sara", "shackleton", "chris", "germani"]:
parsed_email = parsed_email.replace(outcast, "")
### append the text to word_data
word_data.append(parsed_email)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append((0 if name == "sara" else 1))
email.close()
print "emails processed"
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
from nltk.corpus import *
sw = stopwords.words('english')
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
##temp_counter += 1
##if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
content = parseOutText(email)
#for i in ["sara", "shackleton", "chris", "germani"]:
# text.replace(i, "")
stop_words = [
"sara", "shackleton", "chris", "germani", "sshacklensf",
"cgermannsf"
]
for w in stop_words:
content = content.replace(w, "")
### append the text to word_data
word_data.append(content)
if name == "sara":
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
### temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
#print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
extract = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
identifier_words = ["sara", "shackleton", "chris", "germani"]
for i in identifier_words:
extract = extract.replace(i, "")
### append the text to word_data
word_data.append(extract)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
from_data.append(1)
email.close()
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
stemmed_words = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for word in ["sara", "shackleton", "chris", "germani"]:
stemmed_words = stemmed_words.replace(word, "")
### append the text to word_data
word_data.append(stemmed_words)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
from_data.append(1)
email.close()
print "emails processed"
print '[\033[91m LOADING\033[0m ] \033[94m\033[1mEmails are processing right now...\033[0m'
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=17578).start()
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
# only look at first 200 emails when developing
# once everything is working, remove this line to run over full dataset
# temp_counter += 1
# if temp_counter < 200: #remove comment if you need 200
path = os.path.join('..', path[:-1])
# print '[\033[91m OK\033[0m ]' + path
email = open(path, "r")
# use parseOutText to extract the text from the opened email
raw_txt = parseOutText(email)
# use str.replace() to remove any instances of the words
# ["sara", "shackleton", "chris", "germani"]
unwanted_words = ["sara", "shackleton", "chris", "germani"]
for word in unwanted_words:
raw_txt = raw_txt.replace(word, "")
# append the text to word_data
word_data.append(raw_txt)
# append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0) if (name == "sara") else from_data.append(1)
email.close()
pbar.update(len(word_data))
pbar.finish()
print "[\033[92m OK\033[0m ] \033[94m\033[1mEmails Processed\033[0m"
from_sara.close()
word_data = []
### temp_counter - limit no. emails processed
#temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
#if temp_counter < 100:
path = os.path.join('..', path[:-1])
print(path)
email = open(path, "r")
### use parseOutText to extract the text from the opened email
stemmed_email = parseOutText(email)
### use str.replace() to remove any instances of the words
words_to_replace = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
for word in words_to_replace:
if (word in stemmed_email):
stemmed_email = stemmed_email.replace(word, "")
#stemmed_removed_email = " ".join([word for word in stemmed_email.split() if word not in words_to_replace])
### append the text to word_data
word_data.append(stemmed_email)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
from_data.append(1)
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
# if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsedLine = parseOutText(email)
### use str.replace() to remove any instances of the words
removeArr = ["sara", "shackleton", "chris", "germani"]
for elem in removeArr:
parsedLine = parsedLine.replace(elem, "")
### append the text to word_data
word_data.append(parsedLine)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if from_person == "sara":
from_data.append(0)
else:
from_data.append(1)
email.close()
print "emails processed"
def dump_email_data(data_dict):
directory = "emails_by_address/"
counter = 0
word_data = {}
ls = poiEmails()
for key in ls:
email = ls[key]
path1 = directory + "from_" + email + ".txt"
path2 = directory + "to_" + email + ".txt"
words = ""
try:
f1 = open(path1, "r")
ls1 = f1.readlines()
f1.close()
for path in ls1:
path = "../" + path[:-1]
f2 = open(path, "r")
words = words + " " + (parseOutText(f2))
f2.close()
except Exception:
pass
try:
f1 = open(path2, "r")
ls1 = f1.readlines()
f1.close()
for path in ls1:
path = "../" + path[:-1]
f2 = open(path, "r")
words = words + " " + (parseOutText(f2))
f2.close()
except Exception:
pass
if words != "":
if key in word_data:
word_data[key] = word_data[key] + " " + words
else:
word_data[key] = words
del words
for key in data_dict:
email = data_dict[key]['email_address']
path1 = directory + "from_" + email + ".txt"
path2 = directory + "to_" + email + ".txt"
words = ""
try:
f1 = open(path1, "r")
ls = f1.readlines()
f1.close()
for path in ls:
path = "../" + path[:-1]
f2 = open(path, "r")
words = words + " " + (parseOutText(f2))
f2.close()
except Exception:
pass
try:
f1 = open(path2, "r")
ls = f1.readlines()
f1.close()
for path in l1:
path = "../" + path[:-1]
f2 = open(path, "r")
words = words + " " + (parseOutText(f2))
f2.close()
except Exception:
pass
if words != "":
if key in word_data:
word_data[key] = word_data[key] + " " + words
else:
word_data[key] = words
del words
counter += 1
print counter
pickle_out = open("email_data.pkl", "wb")
pickle.dump(word_data, pickle_out)
pickle_out.close()
print "\nSuccess!\nEmail Data Fetched."
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
# only look at first 200 emails when developing
# once everything is working, remove this line to run over full dataset
path = os.path.join('../..', 'enron_dataset/' + path[:-1])
# use parseOutText to extract the text from the opened email
# use str.replace() to remove any instances of the words
# ["sara", "shackleton", "chris", "germani"]
# append the text to word_data
# append a 0 to from_data if email is from Sara, and 1 if email is from Chris
try:
email = open(path, "r")
text = parseOutText(
email) # parse every word of email with stemming
text = str(text)
text = text.replace('sara ', '')
text = text.replace('shackleton ', '')
text = text.replace('chris ', '')
text = text.replace('germani ', '')
word_data.append(text)
if name == 'sara':
from_data.append(0)
else:
from_data.append(1)
email.close()
except IOError:
pass
print word_data[147]
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
#if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
stemmed_text = parseOutText(email)
### use str.replace() to remove any instances of the words
delete_words = [
"sara", "shackleton", "chris", "germani", 'sshacklensf',
'cgermannsf'
]
for word in delete_words:
stemmed_text = stemmed_text.replace(word, '').strip()
### append the text to word_data
word_data.append(stemmed_text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == 'sara':
from_data.append(0)
else:
from_data.append(1)
def processMails(p):
np.set_printoptions(threshold=np.nan)
sys.path.append("../tools/")
from parse_out_email_text import parseOutText
"""
Starter code to process the emails from Sara and Chris to extract
the features and get the documents ready for classification.
The list of all the emails from Sara are in the from_sara list
likewise for emails from Chris (from_chris)
The actual documents are in the Enron email dataset, which
you downloaded/unpacked in Part 0 of the first mini-project. If you have
not obtained the Enron email corpus, run startup.py in the tools folder.
The data is stored in lists and packed away in pickle files at the end.
"""
f_list = open(p, "r")
from_data = []
word_data = []
### temp_counter is a way to speed up the development--there are
### thousands of emails from Sara and Chris, so running over all of them
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
n = 0
for path in f_list:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
if temp_counter < 100:
temp_counter += 1
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsed_email = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
#words_to_remove = ["sara", "shackleton", "chris", "germani"]
#print parsed_email
#for word in words_to_remove:
# parsed_email = parsed_email.replace(word+' ', "")
#print parsed_email
### append the text to word_data
word_data.append(parsed_email)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
n += 1
email.close()
#print "Email not found"
print n, "emails processed"
f_list.close()
return word_data
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
#if temp_counter < 200:
path = os.path.join('..', path[:-1])
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_text = parseOutText(email)
### use str.replace() to remove any instances of the words
remove = ["sara", "shackleton", "chris", "germani"]
for word in remove:
email_text = email_text.replace(word, "")
### append the text to word_data
word_data.append(email_text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0 if name == "sara" else 1)
email.close()
print "emails processed"
from_sara.close()
### thousands of emails from Sara and Chris, so running over all of them
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
#if temp_counter < 200:
path = os.path.join('..', path[:-1])
print "In: ", path
email = open(path, "r")
words_stem = str(parseOutText(email))
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for word in sw:
if(word in words_stem):
words_stem = words_stem.replace(word, "")
word_data.append(words_stem)### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name is "sara":
from_data.append("0")
elif name is "chris":
from_data.append("1")
email.close()
print "All emails processed"
from_sara.close()
from_chris.close()
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter > 0:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
extract_text = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for w in [
"sara", "shackleton", "chris", "germani", "sshacklensf",
"cgermannsf"
]:
extract_text = extract_text.replace(w, '')
### append the text to word_data
word_data.append(extract_text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == 'sara':
from_data.append(0)
else:
from_data.append(1)
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_body = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
signature_words = ["sara", "shackleton", "chris", "germani","sshacklensf","cgermannsf"]
### append the text to word_data
for word in signature_words:
if word in email_body:
email_body=email_body.replace(word,"")
word_data.append(email_body)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if from_person == from_sara :
from_data.append(0)
else:
from_data.append(1)
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
path = os.path.join('..', path[:-1])
print (path)
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsed_email = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
parsed_email.replace("sara", "")
parsed_email.replace("shackleton", "")
parsed_email.replace("chris", "")
parsed_email.replace("germani", "")
word_data.append(parsed_email)
if from_person == "sara":
from_data.append(0)
elif from_person == "chris":
from_data.append(1)
###temp_counter += 1
###if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
### append the text to word_data
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
test = parseOutText(email)
test = test.replace("sara", "").replace("shackleton", "").replace("chris", "").replace("germani", "")
test = test.replace("sshacklensf", "").replace("cgermannsf", "")
word_data.append(test)
from_data.append(0 if name == "sara" else 1)
email.close()
print "emails processed"
from_sara.close()
from_chris.close()
pickle.dump( word_data, open("your_word_data.pkl", "w") )
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter < 500000:
path = os.path.join('..', path[:-1])
print path
try:
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsedText = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani, sshacklensf, cgermannsf"]
parsedText = parsedText.replace("sara", "")
parsedText = parsedText.replace("shackleton", "")
parsedText = parsedText.replace("chris", "")
parsedText = parsedText.replace("germani", "")
parsedText = parsedText.replace("sshacklensf", "")
parsedText = parsedText.replace("cgermannsf", "")
### append the text to word_data
word_data.append(parsedText)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == 'sara':
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
# if temp_counter < 200:
path = os.path.join('..', path[:-1])
email = open(path, "r")
### use parseOutText to extract the text from the opened email
import parse_out_email_text
stememail = parse_out_email_text.parseOutText(email)
# print stememail
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
removeWrods = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
for rw in removeWrods:
stememail = stememail.replace(rw,'')
# print stememail
### append the text to word_data
word_data.append(stememail)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name=='sara':
from_data.append(0)
else:
from_data.append(1)
### thousands of emails from Sara and Chris, so running over all of them
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
path = os.path.join('../../enron_mail_20150507/enron_mail_20150507',
path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
words = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
words = words.replace("sara", "")
words = words.replace("shackleton", "")
words = words.replace("chris", "")
words = words.replace("germani", "")
words = words.replace("sshacklensf", "")
words = words.replace("cgermannsf", "")
### append the text to word_data
word_data.append(words)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
if temp_counter < 10000:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
string = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for i in ["sara", "shackleton", "chris", "germani", 'sshacklensf', 'cgermannsf']:
string = string.replace(i, '')
# string = string.replace('\r\n', ' ')
# string = string.replace(' ', ' ')
### append the text to word_data
word_data.append(string)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_spammed = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for i in ["sara", "shackleton", "chris", "germani"]:
email_spammed.replace(i, '')
### append the text to word_data
word_data.append(email_spammed)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0 if name == 'sara' else 1)
email.close()
print "emails processed"
### thousands of emails from Sara and Chris, so running over all of them
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
path = os.path.join('..', path[:-1])
print(path)
email = open(path, "r")
text = parseOutText(email) ### use parseOutText to extract the text from the opened email
words_to_remove = ["sara", "shackleton", "chris",
"germani", "sshacklensf", "cgermannsf"]
for word in words_to_remove:
text = text.replace(word, "")
word_data.append(text)### append the text to word_data
from_data.append(0 if name =="sara" else 1)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
email.close()
print("emails processed")
from_sara.close()
from_chris.close()
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
# temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
# if temp_counter < 200:
path = os.path.join('..', path[:-1])
# print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsed_email = str((parseOutText(email)))
# print (parsed_email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
stopwords = [
"sara", "shackleton", "chris", "germani", "sshacklensf",
"cgermannsf"
]
new = parsed_email
for stopword in stopwords:
if stopword in new:
new = new.replace(stopword, '')
word_data.append(new)
def dump_data():
"""
Starter code to process the emails from Sara and Chris to extract
the features and get the documents ready for classification.
The list of all the emails from Sara are in the from_sara list
likewise for emails from Chris (from_chris)
The actual documents are in the Enron email dataset, which
you downloaded/unpacked in Part 0 of the first mini-project. If you have
not obtained the Enron email corpus, run startup.py in the tools folder.
The data is stored in lists and packed away in pickle files at the end.
"""
from_sara = open("from_sara.txt", "r")
from_chris = open("from_chris.txt", "r")
from_data = []
word_data = []
### temp_counter is a way to speed up the development--there are
### thousands of emails from Sara and Chris, so running over all of them
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
# if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsed_out_text = parseOutText(email)
### use str.replace() to remove any instances of the words
removewords = ["sara", "shackleton", "chris", "germani", "sshacklensf","cgermannsf"]
for word in removewords:
parsed_out_text = parsed_out_text.replace(word,"")
### append the text to word_data
word_data.append(parsed_out_text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0 if name == "sara" else 1)
email.close()
print "emails processed"
from_sara.close()
from_chris.close()
print "Answer to Lesson 10 Quiz, content of word_data[152]", word_data[152]
pickle.dump( word_data, open("your_word_data.pkl", "w") )
pickle.dump( from_data, open("your_email_authors.pkl", "w") )
return word_data, from_data
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsed_email = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
noise_words = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
for word in noise_words:
parsed_email = parsed_email.replace(word,'')
### append the text to word_data
word_data.append(parsed_email)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0 if name == "sara" else 1)
email.close()
temp_counter = 0
replaceWords = ["sara", "shackleton", "chris", "germani"]
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
# if temp_counter < 101:
path = os.path.join('..', path[:-1])
# print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
parsedEmail = parseOutText(email)
### use str.replace() to remove any instances of the words
for word in replaceWords:
parsedEmail = parsedEmail.replace(word, "")
### append the text to word_data
word_data.append(parsedEmail)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if(name == 'sara'):
from_data.append(0)
elif(name == 'chris'):
from_data.append(1)
email.close()
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
stemmed_email = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
remove = ["sara", "shackleton", "chris", "germani","sshacklensf","cgermannsf"]
for word in remove:
stemmed_email = stemmed_email.replace(word,'')
### append the text to word_data
word_data.append(stemmed_email)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == 'sara':
from_data.append(0)
if name == 'chris':
from_data.append(1)
email.close()
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
if temp_counter < 200:
path = './' + path[:-1]
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
texto = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for word in ["sara", "shackleton", "chris", "germani"]:
texto = texto.replace(word, '')
### append the text to word_data
word_data.append(texto)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == 'sara': from_data.append(0)
else: from_data.append(1)
email.close()
print "emails processed"
from_sara.close()
from_chris.close()
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_temp = str(parseOutText(email))
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
rem = ["sara", "shackleton", "chris", "germani"]
for word in rem:
if (word in email_temp):
email_temp = email_temp.replace(word, "")
### append the text to word_data
word_data.append(email_temp)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
from_data.append(1)
email.close()
# temp_counter = 0
words_to_remove = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
# if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_contents = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
for w in words_to_remove:
email_contents = email_contents.replace(w, "")
### append the text to word_data
word_data.append(email_contents)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
from_data.append(0)
else:
from_data.append(1)
### can iterate your modifications quicker
#temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
#if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
body_message = parseOutText(email)
# print "1: ", body_message
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
replace_list = [
"sara", "shackleton", "chris", "germani", "sshacklensf",
"cgermannsf"
]
for word in replace_list:
body_message = body_message.replace(word, "")
# print "2: ", body_message
### append the text to word_data
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
# temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
text = parseOutText(email)
### use str.replace() to remove any instances of the words
words = ["sara", "shackleton", "chris", "germani","sshacklensf","cgermannsf"]
for word in words:
text = text.replace(word, '')
### append the text to word_data
word_data.append(text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
print name
if name=='sara':
from_data.append(0)
else:
from_data.append(1)
pattern=re.compile('\s+')
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
#if temp_counter < 200:
if(True):
path = os.path.join('..', path[:-1])#remove the '\n'
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
temp = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
temp=temp.replace("sara","")
temp=temp.replace("shackleton","")
temp=temp.replace("chris","")
temp=temp.replace("germani","")
temp=temp.replace("sshacklensf","")
temp=temp.replace("cgermannsf","")
temp=re.sub(pattern,' ',temp)
### append the text to word_data
word_data.append(temp)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list so you
### can iterate your modifications quicker
counter=0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
#if counter<100:
counter+=1
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
email_text=str(parseOutText(email))
#email_text=parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
email_text=email_text.replace("sara", "").replace("shackleton", "").replace("chris", "").replace("germani", "").replace("sshacklensf", "").replace("cgermannsf", "")
email_text=email_text.strip()
### append the text to word_data
word_data.append(email_text)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
#print name=="sara"
#print name
from_data.append((0 if name=="sara" else 1))
email.close()
# temp_counter helps you only look at the first 200 emails in the list so you
# can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
# only look at first 200 emails when developing
# once everything is working, remove this line to run over full dataset
# temp_counter += 1
# if temp_counter < 200:
path = os.path.join('../../..', path[:-1])
print(path)
email = open(path, "r")
# use parseOutText to extract the text from the opened email
text = parse_out_email_text.parseOutText(email)
# use str.replace() to remove any instances of the words
# ["sara", "shackleton", "chris", "germani"]
# print(type(text))
text = text.replace('sara', '')
text = text.replace('shackleton', '')
text = text.replace('chris', '')
text = text.replace('germani', '')
# append the text to word_data
word_data.append(text)
# append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == 'sara':
from_data.append(0)
else:
from_data.append(1)
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
words = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
words.replace("sara", "").replace("shackleton", "").replace("chris", "").replace("germani", "")
### append the text to word_data
word_data.append(words)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
from_data.append(0 if name == "sara" else 1)
email.close()
print "emails processed"
from_sara.close()
from_chris.close()
pickle.dump( word_data, open("your_word_data.pkl", "w") )
### can iterate your modifications quicker
temp_counter = 0
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
temp_counter += 1
if temp_counter > 0:
path = os.path.join("..", path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
stemmedString = parseOutText(email)
remove_words = ["sara", "shackleton", "chris", "germani"]
for rw in remove_words:
stemmedString = stemmedString.replace(rw, "")
stemmedString = " ".join(stemmedString.split())
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
### append the text to word_data
word_data.append(stemmedString)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
if name == "sara":
### can take a long time
### temp_counter helps you only look at the first 200 emails in the list
temp_counter = 0
from nltk.corpus import *
sw = stopwords.words('english')
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
##temp_counter += 1
##if temp_counter < 200:
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
content = parseOutText(email)
#for i in ["sara", "shackleton", "chris", "germani"]:
# text.replace(i, "")
stop_words = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
for w in stop_words:
content = content.replace(w, "")
### append the text to word_data
word_data.append( content )
if name == "sara":
from_data.append(0)
else:
word_data = []
from_data = []
from parse_out_email_text import parseOutText
for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
for path in from_person:
### only look at first 200 emails when developing
### once everything is working, remove this line to run over full dataset
#temp_counter += 1
#if temp_counter < 200:
origPath = path
path = os.path.join('..', path[:-1])
print path
email = open(path, "r")
### use parseOutText to extract the text from the opened email
string1 = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton", "chris", "germani"]
stringList = [
"sshacklensf", "cgermannsf", "sara", "shackleton", "chris",
"germani"
]
#"houectect", "houston", "houect", "fax", "smith", "1400", "forward", "germany", "street", "77002"
for string in stringList:
string1 = string1.replace(string, "")
### append the text to word_data
word_data.append(string1)
### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
