def data_info(enron_data):
num_people = len(enron_data)
print("Size of Enron dataset: " + str(num_people))
num_features = [len(v) for v in enron_data.itervalues()]
num_features = list(dict.fromkeys(num_features))
print("Total features in Enron dataset: " + str(num_features))
features_list = [v for v in enron_data.itervalues()][0].keys()
print("Features:")
for f in features_list:
print("\t" + f)
poi_list = poiEmails()
def poiFlagEmail(f):
""" given an email file f,
return a trio of booleans for whether that email is
to, from, or cc'ing a poi """
import sys
sys.path.append("../final_project/")
from poi_email_addresses import poiEmails
to_emails, from_emails, cc_emails = getToFromStrings(f)
### poi_emails.poiEmails() returns a list of all POIs' email addresses.
poi_email_list = poiEmails()
to_poi = False
from_poi = False
cc_poi = False
### to_poi and cc_poi are boolean variables which flag whether the email
### under inspection is addressed to a POI, or if a POI is in cc,
### respectively. You don't have to change this code at all.
### There can be many "to" emails, but only one "from", so the
### "to" processing needs to be a little more complicated
if to_emails:
ctr = 0
while not to_poi and ctr < len(to_emails):
if to_emails[ctr] in poi_email_list:
to_poi = True
ctr += 1
if cc_emails:
ctr = 0
while not cc_poi and ctr < len(cc_emails):
if cc_emails[ctr] in poi_email_list:
cc_poi = True
ctr += 1
#################################
######## your code below ########
### set from_poi to True if #####
### the email is from a POI #####
#################################
if from_emails:
ctr = 0
while not from_poi and ctr < len(from_emails):
if from_emails[ctr] in poi_email_list:
from_poi = True
ctr += 1
#################################
return to_poi, from_poi, cc_poi
def text_classifier(data_dict):
'''
This function is the main function appealing to other functions
in the dependencies folder. This function creates a dict of all
the emails of all poi and non-poi. Then it iterate through the dict
and feed the results to the email_open function. In turn this
function feed a list of email_path to email_process which will
return a list of emails. This will finally be fed to a
vectorizing function which will root the words and return them
for the classification.
The function classify the list of emails and test them using
different metrics.
'''
email_dict = poiEmails()
for key in email_dict:
email_dict[key] = {"email": email_dict[key], "poi": True}
for key in data_dict:
if key in email_dict:
continue
else:
email_dict[key] = {
"email": [data_dict[key]["email_address"]],
"poi": data_dict[key]["poi"]
}
for key in email_dict:
try:
for i in email_dict[key]["email"]:
None
except:
print key
email_list, poi_list = email_open(email_dict)
word_list = email_process(email_list)
features_train, features_test, labels_train, labels_test = email_vectorizer(
word_list, poi_list)
clf = DecisionTreeClassifier()
clf.fit(features_train, labels_train)
predict = list(clf.predict(features_test))
print "Number of POI emails in test set: " + str(predict.count(1.))
print "Number of emails in test set: " + str(len(features_test))
print "Number of real POI emails in test set: " + str(labels_test.count(1))
print "Precision is: " + str(metrics.precision_score(labels_test, predict))
print "Recall is: " + str(metrics.recall_score(labels_test, predict))
def process_text_learning_features():
poi_emails = poiEmails()
_all = False
data_dict = pickle.load(open("final_project_dataset.pkl", "r") )
emls = []
eml_key = {}
for key in data_dict.keys():
emls.append(data_dict[key]['email_address'])
eml_key[data_dict[key]['email_address']] = key
author_dict = get_text_from_emails(emls,eml_key,poi_emails, data_dict,from_pkl=True, combined=_all)
print 'Got the Author Text Dictionary!!!'
#look_for_common_words(author_dict, 'body', data_dict)
subj_scrub = []
body_scrub = ['ddelainnsf','delainey','regard','david','houect','delaineyhouect','christoph', 'jeff', 'product', 'kitchen',
'jdasovicnsf','allegheni','pastoria','jdasovicnsf','neuner','jacobi','catalytica','calpin',
'ect','dave','tim','ena','62602pst','belden','guy','chris','calger','valu','salisburi',
'swerzbin','kelli','paula','motley','chapman','johnson','frevertna','presto','ben','ray','janet','wes','dietrich',
'deal','holden','kay','floor','thxs','portland','manag','plan','turbin','enron','board','meet','forward','year',
'term','pdx','market','goal','lavoratocorp','2702pst','desk','unit','discuss','mid','2000','kellycom','7138532534',
'7138536485','execut','power','cost','busi','complet','ensur','howev','provid','sheet','short','right','structur',
'trade','organ','peopl','jskillinpst','corp','generat','kevin','dash','rob','view','sale','need']
#body_scrub = ['ddelainnsf','delaineyhouect','hang','court','advis','delainey','david'
# 'ect','0921','ray','todaytonight','lavoratoenron','let'
# ]
#body_scrub = ['pastoria','calpin','amita','ecc','turbin','las','calgerpdxectect','calgerpdxect','allegheni',
#'dwr','catalytica','cdwr','2mm','qf','02062001','vega','creditworthi','psco','calger',
#'5mm','jacobi','erc','01262000','7mm','10mm','jdasovicnsf','pcg','parquet','goldendal',
#'ae','eix','neuner','4mm','5034643735','helpdesk','christoph','louis', 'product', 'kitchen',
#'christoph', 'jeff', 'david', 'delainey', 'ben']
s_vectorizer, scrubs_subj, recur, workset = vectorize_text_first(author_dict, 'subject',subj_scrub,test_size=0.4, all_together=_all)
store_results(workset, author_dict, 'text_subj_score')
b_vectorizer, scrubs_body, recur, workset = vectorize_text_first(author_dict, 'body',body_scrub,test_size=0.4, all_together=_all)
store_results(workset, author_dict, 'text_score')
print scrubs_subj
print scrubs_body
pickle.dump({'all':_all,'dict':author_dict}, open('author_dict.pkl', "w") )
# print "lenron_data=", enron_data
print "len(enron_data)=", len(enron_data)
print "# of features=", len(enron_data.items()[0][1])
cnt = 1
for f in enron_data.items()[0][1]:
print cnt, "-", f
cnt += 1
import sys
sys.path.append("../final_project/")
from poi_email_addresses import poiEmails
emails = poiEmails()
cnt = 0
tscnt = 0
ptcnt = 0
ecnt = 0
scnt = 0
mcnt = 0
pcnt = 0
for i in enron_data.items():
if enron_data[i[0]]["poi"] == True:
cnt += 1
if enron_data[i[0]]["total_payments"] == "NaN":
ptcnt +=1
if enron_data[i[0]]["total_stock_value"] == "NaN":
tscnt +=1
print len(enron_data.keys())
print len(enron_data[enron_data.keys()[0]].keys())
poi_count = 0
for person in enron_data:
if enron_data[person]['poi'] == True:
poi_count += 1
else:
pass
print poi_count
from poi_email_addresses import poiEmails
print len(poiEmails())
print enron_data['PRENTICE JAMES']['total_stock_value']
print enron_data['COLWELL WESLEY']['from_this_person_to_poi']
print enron_data['SKILLING JEFFREY K']['exercised_stock_options']
print enron_data['FASTOW ANDREW S']['total_payments']
print enron_data['LAY KENNETH L']['total_payments']
print enron_data['SKILLING JEFFREY K']['total_payments']
type(poi_count)
salary_count = 0
email_address_count = 0
for person in enron_data:
for (key, val) in enron_data.items():
if (val['poi']):
count = count + 1
print 'Numbe of poi: ', count
#avoid loop and use lambda
newDict = dict(filter(lambda (key, val): val['poi'], enron_data.items()))
count = len(newDict)
print 'Numbe of poi: ', count
import sys
sys.path.append('../final_project/')
from poi_email_addresses import poiEmails
print '# of Poi emails: ', len(poiEmails())
names = open("../final_project/poi_names.txt", "r")
#first three lines are headers
print "Number of names ", len(names.readlines()[2:])
print "James Prince, total stock value: ", enron_data['PRENTICE JAMES'][
'total_stock_value']
#find the keys
print enron_data['PRENTICE JAMES'].keys()
print "Email from this to poi: ", enron_data['COLWELL WESLEY'][
'from_this_person_to_poi']
print enron_data['SKILLING JEFFREY K']
enron_data["SKILLING JEFFREY K"]["bonus"] = 5600000
"""
import sys
import pickle
import pandas as pd
import numpy
sys.path.append("../final_project/")
from poi_email_addresses import poiEmails
enron_data = pickle.load(
open("../final_project/final_project_dataset.pkl", "rb"))
x = enron_data["ALLEN PHILLIP K"]
y = poiEmails()
jp = enron_data["PRENTICE JAMES"]["total_stock_value"]
wc = enron_data["COLWELL WESLEY"]["from_this_person_to_poi"]
jks = enron_data["SKILLING JEFFREY K"]["exercised_stock_options"]
names = ["SKILLING JEFFREY K", "LAY KENNETH L", "FASTOW ANDREW S"]
for n in names:
earnings = enron_data[n]["total_payments"]
print(earnings)
enron_pd = pd.DataFrame.from_dict(enron_data)
enron_pd = enron_pd.T
def getNumberOfPersonsOfInterestsInTxt():
f = open("../final_project/poi_names.txt", "r")
lines = f.readlines()
return len(poiEmails())
salry_cnt += 1
if enron_data[x]['email_address'] !='NaN':
email_cnt += 1
# In[49]:
print(salry_cnt)
print(email_cnt)
# In[40]:
poiEmails_list = poiEmails()
poiEmails_list
# In[43]:
len(poiEmails_list)
# In[45]:
i = 0;
lenth = 0;
for x in enron_data:
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."
The dataset has the form:
enron_data["LASTNAME FIRSTNAME MIDDLEINITIAL"] = { features_dict }
{features_dict} is a dictionary of features associated with that person.
You should explore features_dict as part of the mini-project,
but here's an example to get you started:
enron_data["SKILLING JEFFREY K"]["bonus"] = 5600000
"""
import pickle
import math
from poi_email_addresses import poiEmails
knownEmails = poiEmails()
enron_data = pickle.load(open("../final_project/final_project_dataset.pkl", "r"))
print "Number of People:", len(enron_data)
print "Number of Features:", len(enron_data["SKILLING JEFFREY K"])
print "Avaliable features:", dict.keys(enron_data["SKILLING JEFFREY K"])
poiNum = 0
peopleWithSalary = 0
peopleWithKnownEmail = 0
peopleWithTotalPayments = 0
poiWithTotalPayments = 0
for people in enron_data:
if (enron_data[people]["poi"] == 1):
poiNum+=1
if (enron_data[people]["salary"] != "NaN"):
peopleWithSalary+=1
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectPercentile, f_classif
from parse_out_email_text import parseOutText
from poi_email_addresses import poiEmails
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
from collections import defaultdict
# In[3]:
poi_emails = poiEmails()
from_emails_folder = r'C:\Users\jcsmi329\Documents\Homework\Udacity\Projects\Machine Learning\ud120-projects-master\final_project\emails_by_address'
base_folder = r'C:\Users\jcsmi329\Documents\Homework\Udacity\Projects\Machine Learning\ud120-projects-master'
word_data = defaultdict(list)
final_project_emails = []
email_names = set()
### Load the dictionary containing the dataset
with open(
r"C:\Users\jcsmi329\Documents\Homework\Udacity\Projects\Machine Learning\final_project\final_project_dataset.pkl",
"rb") as data_file:
data_dict = pickle.load(data_file)
for x in data_dict.values():
#### parameters to be tuned #########
debug_mode = True
debug_counter = 200
person_filter_percentage = 0
nonpoi_filter_percentage = 60
poi_filter_percentage = 40
email_feature_percentage = 10
n_pca_components = 40
n_kfolds = 10
######################################
addr_list = []
label_list = []
email_list = []
poiEmailList = poiEmails()
path = 'emails_by_address'
t0 = time()
# generate label_list and email_list for training
for file_name in os.listdir(path):
# for debug run only
if debug_mode:
print "debug_counter = ", debug_counter
debug_counter -= 1
if (debug_counter < 0):
break
# go to each person's folder, get his label, and the path to his emails
try:
ToOrFrom, email_addr = file_name.split('_', 1)
"""
#%%
import pickle
import sys
sys.path.append("../final_project/")
from poi_email_addresses import poiEmails
enron_data = pickle.load(
open("../final_project/final_project_dataset_unix.pkl", "rb"))
poi_count = {k: v for k, v in enron_data.items() if v['poi'] == 1}
print('POI Count: ', len(poi_count))
print('Email Count: ', len(poiEmails()))
print(
f"James Prentice stock value: ${enron_data['PRENTICE JAMES']['total_stock_value']}"
)
print(
f"Skilling profits: ${enron_data['SKILLING JEFFREY K']['total_payments']}")
print(f"Lay profits: ${enron_data['LAY KENNETH L']['total_payments']}")
print(f"Fastow profits: ${enron_data['FASTOW ANDREW S']['total_payments']}")
with_salary = {k: v for k, v in enron_data.items() if v['salary'] != 'NaN'}
print('Employees w/ quantified salary: ', len(with_salary))
with_email = {
k: v
import sys
sys.path.append("../final_project/")
from poi_email_addresses import poiEmails
enron_data = pickle.load(
open("../final_project/final_project_dataset.pkl", "rb"))
f = open("../final_project/poi_names.txt", "rb")
poi_names = []
poi_lines = f.readlines()
print(enron_data["SKILLING JEFFREY K"]["bonus"])
print(len(enron_data))
print(list(enron_data.keys()))
print(len(enron_data["SKILLING JEFFREY K"].keys()))
pois = [x for x, y in enron_data.items() if y['poi']]
print('Number of POI\'s: {0}'.format(len(pois)))
email_lst = poiEmails()
print(email_lst)
for line in poi_lines:
line = line.rstrip()
print(line)
print(enron_data['PRENTICE JAMES']['total_stock_value'])
print(enron_data['COLWELL WESLEY']['from_this_person_to_poi'])
print(enron_data['SKILLING JEFFREY K']['exercised_stock_options'])
print(enron_data['SKILLING JEFFREY K']['total_payments'])
print(enron_data['FASTOW ANDREW S']['total_payments'])
print(enron_data['LAY KENNETH L']['total_payments'])
print(enron_data)
count_salary = 0
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
### Task 1: Select what features you'll use.
### features_list is a list of strings, each of which is a feature name.
### The first feature must be "poi".
features_list = ['poi','salary'] # You will need to use more features
### Load the dictionary containing the dataset
with open("../final_project/final_project_dataset.pkl", "r") as data_file:
data_dict = pickle.load(data_file)
print(len(data_dict))
print(len(poiEmails()))
poi_email_in_data=[]
all_email_in_data=[]
for name, features in data_dict.iteritems():
print(name)
email = features['email_address']
all_email_in_data.append(email)
if(features['poi']):
if(email in poiEmails()):
poi_email_in_data.append(email)
#print(email)
#print('1: poi in poi_email_list')
else:
print('2: poi not in poi_email_list')
email_exist_count = 0
none_count = 0
true_count = 0
true_contain_count = 0
false_count = 0
for name in enron_data:
row = enron_data[name]
if row['salary'] != 'NaN':
salary_exist_count += 1
if row['email_address'] != 'NaN':
email_exist_count += 1
if row['poi'] is None:
none_count += 1
elif row['poi']:
if row['email_address'] in poiEmails():
true_contain_count += 1
true_count += 1
else:
false_count += 1
print("none_count:{}, true_count:{}, false_count:{}, total_count:{}".format(none_count, true_count, false_count,
none_count + true_count + false_count))
print("true_contain_count:{}".format(true_contain_count))
print(enron_data['PRENTICE JAMES']['total_stock_value'])
print(enron_data['COLWELL WESLEY']['from_this_person_to_poi'])
print(enron_data['SKILLING JEFFREY K']['exercised_stock_options'])
# print(enron_data.keys())
'loan_advances'] >= 1000000:
print key, value['long_term_incentive'], value['loan_advances']
print(
"******** End Possible Outliers and POIs - ( Long Term Incentive & loan Advance) **********"
)
# REMOVING THE OUTLIER
data_dict.pop("TOTAL", 0)
### Task 3: Create new feature(s)
### Store to my_dataset for easy export below.
import os
from poi_email_addresses import poiEmails
EMAILS = "./emails_by_address/"
POI_EMAILS = poiEmails()
def process_emails():
''' extract filename of each file in emails_by_address folder
and pass it to process_email method
'''
for _, _, files in os.walk(EMAILS):
for filename in files:
process_email(filename)
def process_email(email):
''' Will extract from title and verify if it was a sender or recipient
It will open the file and count the amount of deleted emails by that person
'''
{features_dict} is a dictionary of features associated with that person.
You should explore features_dict as part of the mini-project,
but here's an example to get you started:
enron_data["SKILLING JEFFREY K"]["bonus"] = 5600000
"""
import cPickle
import sys
sys.path.append("/Users/shubhamgupta/shubham/udacity_ml/final_project")
from poi_email_addresses import poiEmails
import pdb
enron_data = cPickle.load(
open("../final_project/final_project_dataset.pkl", "r"))
address_list = poiEmails()
valid_email_count = 0
valid_salary_count = 0
valid_poi = 0
for obj in enron_data.iteritems():
x, y = obj
if y.get("email_address") in address_list:
valid_poi += 1
if y.get("email_address") != "NaN":
valid_email_count += 1
if y.get("salary") != "NaN":
valid_salary_count += 1
print(len(enron_data))
print(valid_poi)
print(valid_email_count)
with open(line, "r") as text:
#open email message
msg = email.message_from_file(text)
match1 = re.findall('\w+',msg['X-From']) # considering names of email sender
match2 = re.findall('\w+',msg['X-To']) # and recipient as stopwords
if len(match1) >= 2:
#condition to ignore data that do not in include name in 'X-From' and 'X-To'
stp_words.append(match1[0])
stp_words.append(match1[1])
if len(match2) >=2:
stp_words.append(match2[0])
stp_words.append(match2[1])
for key, value in msg.items(): # condition for the email to be send to poi
if key == 'To':
if value in poiEmails():
parsed_text = parseOutText(text)
for item in stp_words:
text = parsed_text.replace(item, '')
merge_text.append(text)
wrd = ' '.join([merge_text[i] for i in range(len(merge_text))])
wrd = ''.join(i for i in wrd if not i.isdigit())
word_data.update({name: wrd})
else:
#
"""
Starter code for exploring the Enron dataset (emails + finances);
loads up the dataset (pickled dict of dicts).
The dataset has the form:
enron_data["LASTNAME FIRSTNAME MIDDLEINITIAL"] = { features_dict }
{features_dict} is a dictionary of features associated with that person.
You should explore features_dict as part of the mini-project,
but here's an example to get you started:
enron_data["SKILLING JEFFREY K"]["bonus"] = 5600000
"""
import pickle
from poi_email_addresses import poiEmails
enron_data = pickle.load(
open("../final_project/final_project_dataset.pkl", "rb"))
email_addresses = poiEmails()
counter = 0
for x in enron_data:
if (enron_data[x]["poi"] == 1):
counter = counter + 1
print "POIs: {}".format(counter)
print "POI Emails: {}".format(len(email_addresses))
'\w+', msg['X-From']
) # considering names of email sender
match2 = re.findall(
'\w+',
msg['X-To']) # and recipient as stopwords
if len(match1) >= 2:
#condition to ignore data that do not in include name in 'X-From' and 'X-To'
stp_words.append(match1[0])
stp_words.append(match1[1])
if len(match2) >= 2:
stp_words.append(match2[0])
stp_words.append(match2[1])
for key, value in msg.items(
): # condition for the email to be send to poi
if key == 'To':
if value in poiEmails():
parsed_text = parseOutText(text)
for item in stp_words:
text = parsed_text.replace(
item, '')
merge_text.append(text)
wrd = ' '.join([merge_text[i] for i in range(len(merge_text))])
wrd = ''.join(i for i in wrd if not i.isdigit())
word_data.update({name: wrd})
else:
#
from_email = '(?<=from_)' + data_point['email_address']
for i in list_of_emails:
