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- #!/usr/bin/python
- import os
- import pickle
- import re
- import sys
- 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.
- """
- 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
- ### 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
- 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") )
- ### in Part 4, do TfIdf vectorization here
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