import sys
sys.path.append("C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/tools/")
sys.path.append('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/choose_your_own')
sys.path.append('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/datasets_questions')
import os
os.chdir('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/feature_selection')
import pickle
import numpy
numpy.random.seed(42)
words_file = "../text_learning/your_word_data.pkl"
authors_file = "../text_learning/your_email_authors.pkl"
def my_func(words_file, authors_file):
'''
I will use this code later in the lesson so I made it a function
'''
### The words (features) and authors (labels), already largely processed.
### These files should have been created from the previous (Lesson 10)
### mini-project.
word_data = pickle.load( open(words_file, "r"))
authors = pickle.load( open(authors_file, "r") )
### test_size is the percentage of events assigned to the test set (the
### remainder go into training)
### feature matrices changed to dense representations for compatibility with
### classifier functions in versions 0.15.2 and earlier
from sklearn import cross_validation
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(word_data, authors, test_size=0.1, random_state=42)
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
stop_words='english')
features_train = vectorizer.fit_transform(features_train)
features_test = vectorizer.transform(features_test).toarray()
### a classic way to overfit is to use a small number
### of data points and a large number of features;
### train on only 150 events to put ourselves in this regime
features_train = features_train[:150].toarray()
labels_train = labels_train[:150]
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
clf.fit(features_train, labels_train)
return clf, vectorizer, features_train, features_test, labels_train, labels_test
(clf, vectorizer, features_train, features_test, labels_train, labels_test) = my_func(words_file, authors_file)
print 'Number of training points = {0}'.format(len(features_train))
print 'Accuracy on test set = {0}'.format(clf.score(features_test, labels_test))
TfIdf to Get the Most Important Word¶top_features = [(number, feature, vectorizer.get_feature_names()[number]) for number, feature in
zip(range(len(clf.feature_importances_)), clf.feature_importances_) if feature > 0.2]
print top_features
import string
from nltk.stem.snowball import SnowballStemmer
def parseOutText(f):
'''
Input: a file containing text
Output: the stemmed words in the input text, all separated by a single space
'''
f.seek(0) ### go back to beginning of file (annoying)
all_text = f.read()
### split off metadata
content = all_text.split("X-FileName:")
# the stemmer
stemmer = SnowballStemmer('english')
# the string of words
words = ""
if len(content) > 1:
### remove punctuation
text_string = content[1].translate(string.maketrans("", ""), string.punctuation)
### split the text string into individual words, stem each word,
### and append the stemmed word to words (make sure there's a single
### space between each stemmed word)
for word in text_string.split():
# stem the word and add it to words
words += stemmer.stem(word) + ' '
return words[:-1]
ff = open("../text_learning/test_email.txt", "r")
text = parseOutText(ff)
from sklearn.feature_extraction.text import TfidfVectorizer
"""
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.
"""
def sara_and_chris(sw):
'''
I'm going to reuse this code later so I'm making it a function
'''
with open("../text_learning/from_sara.txt", "r") as from_sara, open("../text_learning/from_chris.txt", "r") as from_chris:
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])
with open(path, 'r') as email:
### use parseOutText to extract the text from the opened email
text = parseOutText(email)
### use str.replace() to remove any instances of the words
### ["sara", "shackleton ", "chris", "germani"]
for word in sw:
if(word in text):
text = text.replace(word, "")
### append the text to word_data
word_data.append(text.replace('\n',' ').strip())
### 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)
pickle.dump( word_data, open("your_word_data.pkl", "w") )
pickle.dump( from_data, open("your_email_authors.pkl", "w") )
return None
sw = ["sara", "shackleton", "chris", "germani", "sshacklensf"]
sara_and_chris(sw)
words_file = 'your_word_data.pkl'
authors_file = 'your_email_authors.pkl'
(clf, vectorizer, features_train, features_test, labels_train, labels_test) = my_func(words_file, authors_file)
top_features = [(number, feature, vectorizer.get_feature_names()[number]) for number, feature in
zip(range(len(clf.feature_importances_)), clf.feature_importances_) if feature > 0.2]
print top_features
# remove the 2 outlier words
sw = ["sara", "shackleton", "chris", "germani", "sshacklensf", "cgermannsf"]
sara_and_chris(sw)
# re-fit the tree
words_file = 'your_word_data.pkl'
authors_file = 'your_email_authors.pkl'
(clf, vectorizer, features_train, features_test, labels_train, labels_test) = my_func(words_file, authors_file)
print 'Accuracy on test set = {0}'.format(clf.score(features_test, labels_test))