content: """
  ======================================================
  Out-of-core classification of text documents
  ======================================================
  
  This is an example showing how scikit-learn can be used for classification
  using an out-of-core approach: learning from data that doesn't fit into main
  memory. We make use of an online classifier, i.e., one that supports the
  partial_fit method, that will be fed with batches of examples. To guarantee
  that the features space remains the same over time we leverage a
  HashingVectorizer that will project each example into the same feature space.
  This is especially useful in the case of text classification where new
  features (words) may appear in each batch.
  
  The dataset used in this example is Reuters-21578 as provided by the UCI ML
  repository. It will be automatically downloaded and uncompressed on first run.
  
  The plot represents the learning curve of the classifier: the evolution
  of classification accuracy over the course of the mini-batches. Accuracy is
  measured on the first 1000 samples, held out as a validation set.
  
  To limit the memory consumption, we queue examples up to a fixed amount before
  feeding them to the learner.
  """
  
  # Authors: Eustache Diemert <eustache@diemert.fr>
  #          @FedericoV <https://github.com/FedericoV/>
  # License: BSD 3 clause
  
  from __future__ import print_function
  
  from glob import glob
  import itertools
  import os.path
  import re
  import tarfile
  import time
  
  import numpy as np
  import matplotlib.pyplot as plt
  from matplotlib import rcParams
  
  from sklearn.externals.six.moves import html_parser
  from sklearn.externals.six.moves import urllib
  from sklearn.datasets import get_data_home
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn.linear_model import SGDClassifier
  from sklearn.linear_model import PassiveAggressiveClassifier
  from sklearn.linear_model import Perceptron
  from sklearn.naive_bayes import MultinomialNB
  
  
  def _not_in_sphinx():
      # Hack to detect whether we are running by the sphinx builder
      return '__file__' in globals()
  
  
  ###############################################################################
  # Reuters Dataset related routines
  ###############################################################################
  
  
  class ReutersParser(html_parser.HTMLParser):
      """Utility class to parse a SGML file and yield documents one at a time."""
  
      def __init__(self, encoding='latin-1'):
          html_parser.HTMLParser.__init__(self)
          self._reset()
          self.encoding = encoding
  
      def handle_starttag(self, tag, attrs):
          method = 'start_' + tag
          getattr(self, method, lambda x: None)(attrs)
  
      def handle_endtag(self, tag):
          method = 'end_' + tag
          getattr(self, method, lambda: None)()
  
      def _reset(self):
          self.in_title = 0
          self.in_body = 0
          self.in_topics = 0
          self.in_topic_d = 0
          self.title = ""
          self.body = ""
          self.topics = []
          self.topic_d = ""
  
      def parse(self, fd):
          self.docs = []
          for chunk in fd:
              self.feed(chunk.decode(self.encoding))
              for doc in self.docs:
                  yield doc
              self.docs = []
          self.close()
  
      def handle_data(self, data):
          if self.in_body:
              self.body += data
          elif self.in_title:
              self.title += data
          elif self.in_topic_d:
              self.topic_d += data
  
      def start_reuters(self, attributes):
          pass
  
      def end_reuters(self):
          self.body = re.sub(r'\s+', r' ', self.body)
          self.docs.append({'title': self.title,
                            'body': self.body,
                            'topics': self.topics})
          self._reset()
  
      def start_title(self, attributes):
          self.in_title = 1
  
      def end_title(self):
          self.in_title = 0
  
      def start_body(self, attributes):
          self.in_body = 1
  
      def end_body(self):
          self.in_body = 0
  
      def start_topics(self, attributes):
          self.in_topics = 1
  
      def end_topics(self):
          self.in_topics = 0
  
      def start_d(self, attributes):
          self.in_topic_d = 1
  
      def end_d(self):
          self.in_topic_d = 0
          self.topics.append(self.topic_d)
          self.topic_d = ""
  
  
  def stream_reuters_documents(data_path=None):
      """Iterate over documents of the Reuters dataset.
  
      The Reuters archive will automatically be downloaded and uncompressed if
      the `data_path` directory does not exist.
  
      Documents are represented as dictionaries with 'body' (str),
      'title' (str), 'topics' (list(str)) keys.
  
      """
  
      DOWNLOAD_URL = ('http://archive.ics.uci.edu/ml/machine-learning-databases/'
                      'reuters21578-mld/reuters21578.tar.gz')
      ARCHIVE_FILENAME = 'reuters21578.tar.gz'
  
      if data_path is None:
          data_path = os.path.join(get_data_home(), "reuters")
      if not os.path.exists(data_path):
          """Download the dataset."""
          print("downloading dataset (once and for all) into %s" %
                data_path)
          os.mkdir(data_path)
  
          def progress(blocknum, bs, size):
              total_sz_mb = '%.2f MB' % (size / 1e6)
              current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6)
              if _not_in_sphinx():
                  print('\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb),
                        end='')
  
          archive_path = os.path.join(data_path, ARCHIVE_FILENAME)
          urllib.request.urlretrieve(DOWNLOAD_URL, filename=archive_path,
                                     reporthook=progress)
          if _not_in_sphinx():
              print('\r', end='')
          print("untarring Reuters dataset...")
          tarfile.open(archive_path, 'r:gz').extractall(data_path)
          print("done.")
  
      parser = ReutersParser()
      for filename in glob(os.path.join(data_path, "*.sgm")):
          for doc in parser.parse(open(filename, 'rb')):
              yield doc
  
  
  ###############################################################################
  # Main
  ###############################################################################
  # Create the vectorizer and limit the number of features to a reasonable
  # maximum
  vectorizer = HashingVectorizer(decode_error='ignore', n_features=2 ** 18,
                                 non_negative=True)
  
  
  # Iterator over parsed Reuters SGML files.
  data_stream = stream_reuters_documents()
  
  # We learn a binary classification between the "acq" class and all the others.
  # "acq" was chosen as it is more or less evenly distributed in the Reuters
  # files. For other datasets, one should take care of creating a test set with
  # a realistic portion of positive instances.
  all_classes = np.array([0, 1])
  positive_class = 'acq'
  
  # Here are some classifiers that support the `partial_fit` method
  partial_fit_classifiers = {
      'SGD': SGDClassifier(),
      'Perceptron': Perceptron(),
      'NB Multinomial': MultinomialNB(alpha=0.01),
      'Passive-Aggressive': PassiveAggressiveClassifier(),
  }
  
  
  def get_minibatch(doc_iter, size, pos_class=positive_class):
      """Extract a minibatch of examples, return a tuple X_text, y.
  
      Note: size is before excluding invalid docs with no topics assigned.
  
      """
      data = [(u'{title}\n\n{body}'.format(**doc), pos_class in doc['topics'])
              for doc in itertools.islice(doc_iter, size)
              if doc['topics']]
      if not len(data):
          return np.asarray([], dtype=int), np.asarray([], dtype=int)
      X_text, y = zip(*data)
      return X_text, np.asarray(y, dtype=int)
  
  
  def iter_minibatches(doc_iter, minibatch_size):
      """Generator of minibatches."""
      X_text, y = get_minibatch(doc_iter, minibatch_size)
      while len(X_text):
          yield X_text, y
          X_text, y = get_minibatch(doc_iter, minibatch_size)
  
  
  # test data statistics
  test_stats = {'n_test': 0, 'n_test_pos': 0}
  
  # First we hold out a number of examples to estimate accuracy
  n_test_documents = 1000
  tick = time.time()
  X_test_text, y_test = get_minibatch(data_stream, 1000)
  parsing_time = time.time() - tick
  tick = time.time()
  X_test = vectorizer.transform(X_test_text)
  vectorizing_time = time.time() - tick
  test_stats['n_test'] += len(y_test)
  test_stats['n_test_pos'] += sum(y_test)
  print("Test set is %d documents (%d positive)" % (len(y_test), sum(y_test)))
  
  
  def progress(cls_name, stats):
      """Report progress information, return a string."""
      duration = time.time() - stats['t0']
      s = "%20s classifier : \t" % cls_name
      s += "%(n_train)6d train docs (%(n_train_pos)6d positive) " % stats
      s += "%(n_test)6d test docs (%(n_test_pos)6d positive) " % test_stats
      s += "accuracy: %(accuracy).3f " % stats
      s += "in %.2fs (%5d docs/s)" % (duration, stats['n_train'] / duration)
      return s
  
  
  cls_stats = {}
  
  for cls_name in partial_fit_classifiers:
      stats = {'n_train': 0, 'n_train_pos': 0,
               'accuracy': 0.0, 'accuracy_history': [(0, 0)], 't0': time.time(),
               'runtime_history': [(0, 0)], 'total_fit_time': 0.0}
      cls_stats[cls_name] = stats
  
  get_minibatch(data_stream, n_test_documents)
  # Discard test set
  
  # We will feed the classifier with mini-batches of 1000 documents; this means
  # we have at most 1000 docs in memory at any time.  The smaller the document
  # batch, the bigger the relative overhead of the partial fit methods.
  minibatch_size = 1000
  
  # Create the data_stream that parses Reuters SGML files and iterates on
  # documents as a stream.
  minibatch_iterators = iter_minibatches(data_stream, minibatch_size)
  total_vect_time = 0.0
  
  # Main loop : iterate on mini-batchs of examples
  for i, (X_train_text, y_train) in enumerate(minibatch_iterators):
  
      tick = time.time()
      X_train = vectorizer.transform(X_train_text)
      total_vect_time += time.time() - tick
  
      for cls_name, cls in partial_fit_classifiers.items():
          tick = time.time()
          # update estimator with examples in the current mini-batch
          cls.partial_fit(X_train, y_train, classes=all_classes)
  
          # accumulate test accuracy stats
          cls_stats[cls_name]['total_fit_time'] += time.time() - tick
          cls_stats[cls_name]['n_train'] += X_train.shape[0]
          cls_stats[cls_name]['n_train_pos'] += sum(y_train)
          tick = time.time()
          cls_stats[cls_name]['accuracy'] = cls.score(X_test, y_test)
          cls_stats[cls_name]['prediction_time'] = time.time() - tick
          acc_history = (cls_stats[cls_name]['accuracy'],
                         cls_stats[cls_name]['n_train'])
          cls_stats[cls_name]['accuracy_history'].append(acc_history)
          run_history = (cls_stats[cls_name]['accuracy'],
                         total_vect_time + cls_stats[cls_name]['total_fit_time'])
          cls_stats[cls_name]['runtime_history'].append(run_history)
  
          if i % 3 == 0:
              print(progress(cls_name, cls_stats[cls_name]))
      if i % 3 == 0:
          print('\n')
  
  
  ###############################################################################
  # Plot results
  ###############################################################################
  
  
  def plot_accuracy(x, y, x_legend):
      """Plot accuracy as a function of x."""
      x = np.array(x)
      y = np.array(y)
      plt.title('Classification accuracy as a function of %s' % x_legend)
      plt.xlabel('%s' % x_legend)
      plt.ylabel('Accuracy')
      plt.grid(True)
      plt.plot(x, y)
  
  rcParams['legend.fontsize'] = 10
  cls_names = list(sorted(cls_stats.keys()))
  
  # Plot accuracy evolution
  plt.figure()
  for _, stats in sorted(cls_stats.items()):
      # Plot accuracy evolution with #examples
      accuracy, n_examples = zip(*stats['accuracy_history'])
      plot_accuracy(n_examples, accuracy, "training examples (#)")
      ax = plt.gca()
      ax.set_ylim((0.8, 1))
  plt.legend(cls_names, loc='best')
  
  plt.figure()
  for _, stats in sorted(cls_stats.items()):
      # Plot accuracy evolution with runtime
      accuracy, runtime = zip(*stats['runtime_history'])
      plot_accuracy(runtime, accuracy, 'runtime (s)')
      ax = plt.gca()
      ax.set_ylim((0.8, 1))
  plt.legend(cls_names, loc='best')
  
  # Plot fitting times
  plt.figure()
  fig = plt.gcf()
  cls_runtime = []
  for cls_name, stats in sorted(cls_stats.items()):
      cls_runtime.append(stats['total_fit_time'])
  
  cls_runtime.append(total_vect_time)
  cls_names.append('Vectorization')
  bar_colors = rcParams['axes.color_cycle'][:len(cls_names)]
  
  ax = plt.subplot(111)
  rectangles = plt.bar(range(len(cls_names)), cls_runtime, width=0.5,
                       color=bar_colors)
  
  ax.set_xticks(np.linspace(0.25, len(cls_names) - 0.75, len(cls_names)))
  ax.set_xticklabels(cls_names, fontsize=10)
  ymax = max(cls_runtime) * 1.2
  ax.set_ylim((0, ymax))
  ax.set_ylabel('runtime (s)')
  ax.set_title('Training Times')
  
  
  def autolabel(rectangles):
      """attach some text vi autolabel on rectangles."""
      for rect in rectangles:
          height = rect.get_height()
          ax.text(rect.get_x() + rect.get_width() / 2.,
                  1.05 * height, '%.4f' % height,
                  ha='center', va='bottom')
  
  autolabel(rectangles)
  plt.show()
  
  # Plot prediction times
  plt.figure()
  #fig = plt.gcf()
  cls_runtime = []
  cls_names = list(sorted(cls_stats.keys()))
  for cls_name, stats in sorted(cls_stats.items()):
      cls_runtime.append(stats['prediction_time'])
  cls_runtime.append(parsing_time)
  cls_names.append('Read/Parse\n+Feat.Extr.')
  cls_runtime.append(vectorizing_time)
  cls_names.append('Hashing\n+Vect.')
  bar_colors = rcParams['axes.color_cycle'][:len(cls_names)]
  
  ax = plt.subplot(111)
  rectangles = plt.bar(range(len(cls_names)), cls_runtime, width=0.5,
                       color=bar_colors)
  
  ax.set_xticks(np.linspace(0.25, len(cls_names) - 0.75, len(cls_names)))
  ax.set_xticklabels(cls_names, fontsize=8)
  plt.setp(plt.xticks()[1], rotation=30)
  ymax = max(cls_runtime) * 1.2
  ax.set_ylim((0, ymax))
  ax.set_ylabel('runtime (s)')
  ax.set_title('Prediction Times (%d instances)' % n_test_documents)
  autolabel(rectangles)
  plt.show()
  