text: import sys
  import os
  import numpy as np
  from string import punctuation
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords, wordnet
  from nltk.stem import WordNetLemmatizer
  from copy import deepcopy
  from sklearn.metrics import f1_score
  from sklearn.feature_extraction.text import TfidfTransformer
  
  stopwords = stopwords.words("english")
  lemmatizer = WordNetLemmatizer()
  
  DEBUG = True
  
  """
      Rocchio classifier
      Extracts tokens, computes feature matrices, class averages and trains & tests the Rocchio classifier
  """
  
  
  class Rocchio_classifier:
      def __init__(self, data_path, out_file):
          self.data_path = data_path
          self.out_file = out_file
          self.tokens = []
          self.feature_idx_map = {}
          self.mean_c1 = []
          self.mean_c2 = []
          self.X_train = []
          self.y_train = []
          self.X_test = []
          self.y_test = []
  
      """ Maps features (tokens) to integers """
  
      def prepare_feature_map(self):
          for pos, token in enumerate(self.tokens):
              self.feature_idx_map[token] = pos
  
      """ Computes the feature matrix of size (n_samples, n_features) """
  
      def prepare_count_matrix(self):
          classes = os.listdir(self.data_path)
          for className in classes:
              class_path = os.path.join(self.data_path, className)
              if className == "class1" or className == "class2":
                  classID = int(className[-1])
                  data_folders = os.listdir(class_path)
                  for data_folder in data_folders:
                      data_path = os.path.join(class_path, data_folder)
                      files = os.listdir(data_path)
                      files.sort(key=lambda x: int(x))
                      X = []
                      y = []
                      for file_ in files:
                          text = open(os.path.join(data_path, file_), errors="replace").read()
                          text = text.lower()
                          feature_vector = [0] * len(self.tokens)
                          for _ in punctuation:
                              text = text.replace(_, " ")
                          text = text.replace("  ", " ")
                          tokens = word_tokenize(text)
                          for token in tokens:
                              if token not in stopwords:
                                  token = lemmatizer.lemmatize(token)
                                  try:
                                      pos = self.feature_idx_map[token]
                                      feature_vector[pos] += 1
                                  except KeyError:
                                      pass
                          X.append(feature_vector)
                          y.append(classID)
  
                      if data_folder == "train":
                          self.X_train.extend(X)
                          self.y_train.extend(y)
                      else:
                          self.X_test.extend(X)
                          self.y_test.extend(y)
  
          if DEBUG:
              print("Construction of feature matrix complete")
          self.X_train = np.array(self.X_train)
          self.y_train = np.array(self.y_train)
          self.X_test = np.array(self.X_test)
          self.y_test = np.array(self.y_test)
  
      """ Uses TfidfTransformer to compute the tf-idf matrices from the feature matrices """
  
      def prepare_tf_idf_vectors(self):
          transformer = TfidfTransformer(sublinear_tf=True, smooth_idf=False)
          self.X_train = transformer.fit_transform(self.X_train).toarray()
          self.X_test = transformer.transform(self.X_test).toarray()
  
      """ Computes the class centroids for both classes """
  
      def compute_class_centroids(self):
          class_1_vectors = []
          class_2_vectors = []
  
          for pos in range(self.X_train.shape[0]):
              if self.y_train[pos] == 1:
                  class_1_vectors.append(self.X_train[pos])
              else:
                  class_2_vectors.append(self.X_train[pos])
  
          self.mean_c1 = np.average(np.array(class_1_vectors), axis=0)
          self.mean_c2 = np.average(np.array(class_2_vectors), axis=0)
  
      """ Reads the dataset folder"""
  
      def read_dataset(self):
          classes = os.listdir(self.data_path)
          for className in classes:
              class_path = os.path.join(self.data_path, className)
              if className == "class1" or className == "class2":
                  self.tokens.extend(self.read_class(class_path))
          self.tokens = list(set(self.tokens))
          self.tokens.sort()
          if DEBUG:
              print("Total Features: {}".format(len(self.tokens)))
  
      """ Reads data files for each class (class1 and class2) """
  
      def read_class(self, class_path):
          data_folders = os.listdir(class_path)
          for data_folder in data_folders:
              data_path = os.path.join(class_path, data_folder)
              if data_folder == "train":
                  return self.process_data(data_path)
  
      """ Computes tokens from the texts in all files pointed by 'data_path' """
  
      @staticmethod
      def process_data(data_path):
          files = os.listdir(data_path)
          features = []
          cache = {}
          files.sort(key=lambda x: int(x))
          for file_ in files:
              text = open(os.path.join(data_path, file_), errors="replace").read()
              text = text.lower()
              for _ in punctuation:
                  text = text.replace(_, " ")
              text = text.replace("  ", " ")
              for token in word_tokenize(text):
                  if token not in stopwords:
                      token = lemmatizer.lemmatize(token)
                      if token not in cache.keys():
                          features.append(token)
                          cache[token] = 1
          return features
  
      """ Returns Euclidean distance between two doc vectors a and b """
  
      def distance(self, a, b):
          return np.linalg.norm(a - b)
  
      """ Runs the Rocchio classifier for different values of b """
  
      def run_Rocchio(self, b, out_file):
  
          results = []
  
          for b_ in b:
              y_predict = []
              for test_file in self.X_test:
                  p = self.distance(self.mean_c1, test_file)
                  q = self.distance(self.mean_c2, test_file)
                  if p < q - b_:
                      y_predict.append(1)
                  else:
                      y_predict.append(2)
  
              score = f1_score(self.y_test, y_predict, average="macro")
              results.append(score)
              if DEBUG:
                  print("b = {}, f1_score = {}".format(b_, score))
  
          result_file = open(out_file, "w", encoding="utf-8")
          result_file.write("b       ")
  
          for b_ in b:
              result_file.write("{}".format(b_))
          result_file.write("\n")
  
          result_file.write("Rocchio")
          for result in results:
              result_file.write(" {0:.6f}".format(result))
  
          result_file.close()
  
  
  if __name__ == "__main__":
      data_path, out_file = sys.argv[1], sys.argv[2]
      b = [0]
      Rocchio = Rocchio_classifier(data_path, out_file)
      Rocchio.read_dataset()
      Rocchio.prepare_feature_map()
      Rocchio.prepare_count_matrix()
      Rocchio.prepare_tf_idf_vectors()
      Rocchio.compute_class_centroids()
      Rocchio.run_Rocchio(b, out_file)
  