old_contents: #!/usr/bin/python
  
  import pickle
  import numpy
  
  from sklearn import cross_validation
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_selection import SelectPercentile, f_classif
  
  
  
  def preprocess(words_file = "../tools/word_data.pkl", authors_file="../tools/email_authors.pkl"):
      """ 
          this function takes a pre-made list of email texts (by default word_data.pkl)
          and the corresponding authors (by default email_authors.pkl) and performs
          a number of preprocessing steps:
              -- splits into training/testing sets (10% testing)
              -- vectorizes into tfidf matrix
              -- selects/keeps most helpful features
  
          after this, the feaures and labels are put into numpy arrays, which play nice with sklearn functions
  
          4 objects are returned:
              -- training/testing features
              -- training/testing labels
  
      """
  
      ### the words (features) and authors (labels), already largely preprocessed
      ### this preprocessing will be repeated in the text learning 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 (remainder go into training)
      features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(word_data, authors, test_size=0.1, random_state=42)
  
  
  
      ### text vectorization--go from strings to lists of numbers
      vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                   stop_words='english')
      features_train_transformed = vectorizer.fit_transform(features_train)
      features_test_transformed  = vectorizer.transform(features_test)
  
  
  
      ### feature selection, because text is super high dimensional and 
      ### can be really computationally chewy as a result
      selector = SelectPercentile(f_classif, percentile=10)
      selector.fit(features_train_transformed, labels_train)
      features_train_transformed = selector.transform(features_train_transformed).toarray()
      features_test_transformed  = selector.transform(features_test_transformed).toarray()
  
      ### info on the data
      print "no. of Chris training emails:", sum(labels_train)
      print "no. of Sara training emails:", len(labels_train)-sum(labels_train)
  
  
      return features_train_transformed, features_test_transformed, labels_train, labels_test
  
new_contents: #!/usr/bin/python
  
  import pickle
  import cPickle
  import numpy
  
  from sklearn import cross_validation
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_selection import SelectPercentile, f_classif
  
  
  
  def preprocess(words_file = "../tools/word_data.pkl", authors_file="../tools/email_authors.pkl"):
      """ 
          this function takes a pre-made list of email texts (by default word_data.pkl)
          and the corresponding authors (by default email_authors.pkl) and performs
          a number of preprocessing steps:
              -- splits into training/testing sets (10% testing)
              -- vectorizes into tfidf matrix
              -- selects/keeps most helpful features
  
          after this, the feaures and labels are put into numpy arrays, which play nice with sklearn functions
  
          4 objects are returned:
              -- training/testing features
              -- training/testing labels
  
      """
  
      ### the words (features) and authors (labels), already largely preprocessed
      ### this preprocessing will be repeated in the text learning mini-project
      authors_file_handler = open(authors_file, "r")
      authors = pickle.load(authors_file_handler)
      authors_file_handler.close()
  
      words_file_handler = open(words_file, "r")
      word_data = cPickle.load(words_file_handler)
      words_file_handler.close()
  
      ### test_size is the percentage of events assigned to the test set (remainder go into training)
      features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(word_data, authors, test_size=0.1, random_state=42)
  
  
  
      ### text vectorization--go from strings to lists of numbers
      vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                   stop_words='english')
      features_train_transformed = vectorizer.fit_transform(features_train)
      features_test_transformed  = vectorizer.transform(features_test)
  
  
  
      ### feature selection, because text is super high dimensional and 
      ### can be really computationally chewy as a result
      selector = SelectPercentile(f_classif, percentile=10)
      selector.fit(features_train_transformed, labels_train)
      features_train_transformed = selector.transform(features_train_transformed).toarray()
      features_test_transformed  = selector.transform(features_test_transformed).toarray()
  
      ### info on the data
      print "no. of Chris training emails:", sum(labels_train)
      print "no. of Sara training emails:", len(labels_train)-sum(labels_train)
      
      return features_train_transformed, features_test_transformed, labels_train, labels_test
  
subject: Fix to ValueError: insecure string pickle