content: """
  ================================================================
  Biclustering documents with the Spectral Co-clustering algorithm
  ================================================================
  
  This example demonstrates the Spectral Co-clustering algorithm on the
  twenty newsgroups dataset. The 'comp.os.ms-windows.misc' category is
  excluded because it contains many posts containing nothing but data.
  
  The TF-IDF vectorized posts form a word frequency matrix, which is
  then biclustered using Dhillon's Spectral Co-Clustering algorithm. The
  resulting document-word biclusters indicate subsets words used more
  often in those subsets documents.
  
  For a few of the best biclusters, its most common document categories
  and its ten most important words get printed. The best biclusters are
  determined by their normalized cut. The best words are determined by
  comparing their sums inside and outside the bicluster.
  
  For comparison, the documents are also clustered using
  MiniBatchKMeans. The document clusters derived from the biclusters
  achieve a better V-measure than clusters found by MiniBatchKMeans.
  
  """
  from __future__ import print_function
  
  from collections import defaultdict
  import operator
  from time import time
  
  import numpy as np
  
  from sklearn.cluster.bicluster import SpectralCoclustering
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.externals.six import iteritems
  from sklearn.datasets.twenty_newsgroups import fetch_20newsgroups
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics.cluster import v_measure_score
  
  print(__doc__)
  
  
  def number_normalizer(tokens):
      """ Map all numeric tokens to a placeholder.
  
      For many applications, tokens that begin with a number are not directly
      useful, but the fact that such a token exists can be relevant.  By applying
      this form of dimensionality reduction, some methods may perform better.
      """
      return ("#NUMBER" if token[0].isdigit() else token for token in tokens)
  
  
  class NumberNormalizingVectorizer(TfidfVectorizer):
      def build_tokenizer(self):
          tokenize = super(NumberNormalizingVectorizer, self).build_tokenizer()
          return lambda doc: list(number_normalizer(tokenize(doc)))
  
  
  # exclude 'comp.os.ms-windows.misc'
  categories = ['alt.atheism', 'comp.graphics',
                'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware',
                'comp.windows.x', 'misc.forsale', 'rec.autos',
                'rec.motorcycles', 'rec.sport.baseball',
                'rec.sport.hockey', 'sci.crypt', 'sci.electronics',
                'sci.med', 'sci.space', 'soc.religion.christian',
                'talk.politics.guns', 'talk.politics.mideast',
                'talk.politics.misc', 'talk.religion.misc']
  newsgroups = fetch_20newsgroups(categories=categories)
  y_true = newsgroups.target
  
  vectorizer = NumberNormalizingVectorizer(stop_words='english', min_df=5)
  cocluster = SpectralCoclustering(n_clusters=len(categories),
                                   svd_method='arpack', random_state=0)
  kmeans = MiniBatchKMeans(n_clusters=len(categories), batch_size=20000,
                           random_state=0)
  
  print("Vectorizing...")
  X = vectorizer.fit_transform(newsgroups.data)
  
  print("Coclustering...")
  start_time = time()
  cocluster.fit(X)
  y_cocluster = cocluster.row_labels_
  print("Done in {:.2f}s. V-measure: {:.4f}".format(
      time() - start_time,
      v_measure_score(y_cocluster, y_true)))
  
  print("MiniBatchKMeans...")
  start_time = time()
  y_kmeans = kmeans.fit_predict(X)
  print("Done in {:.2f}s. V-measure: {:.4f}".format(
      time() - start_time,
      v_measure_score(y_kmeans, y_true)))
  
  feature_names = vectorizer.get_feature_names()
  document_names = list(newsgroups.target_names[i] for i in newsgroups.target)
  
  
  def bicluster_ncut(i):
      rows, cols = cocluster.get_indices(i)
      if not (np.any(rows) and np.any(cols)):
          import sys
          return sys.float_info.max
      row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]
      col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]
      # Note: the following is identical to X[rows[:, np.newaxis],
      # cols].sum() but much faster in scipy <= 0.16
      weight = X[rows][:, cols].sum()
      cut = (X[row_complement][:, cols].sum() +
             X[rows][:, col_complement].sum())
      return cut / weight
  
  
  def most_common(d):
      """Items of a defaultdict(int) with the highest values.
  
      Like Counter.most_common in Python >=2.7.
      """
      return sorted(iteritems(d), key=operator.itemgetter(1), reverse=True)
  
  
  bicluster_ncuts = list(bicluster_ncut(i)
                         for i in range(len(newsgroups.target_names)))
  best_idx = np.argsort(bicluster_ncuts)[:5]
  
  print()
  print("Best biclusters:")
  print("----------------")
  for idx, cluster in enumerate(best_idx):
      n_rows, n_cols = cocluster.get_shape(cluster)
      cluster_docs, cluster_words = cocluster.get_indices(cluster)
      if not len(cluster_docs) or not len(cluster_words):
          continue
  
      # categories
      counter = defaultdict(int)
      for i in cluster_docs:
          counter[document_names[i]] += 1
      cat_string = ", ".join("{:.0f}% {}".format(float(c) / n_rows * 100, name)
                             for name, c in most_common(counter)[:3])
  
      # words
      out_of_cluster_docs = cocluster.row_labels_ != cluster
      out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
      word_col = X[:, cluster_words]
      word_scores = np.array(word_col[cluster_docs, :].sum(axis=0) -
                             word_col[out_of_cluster_docs, :].sum(axis=0))
      word_scores = word_scores.ravel()
      important_words = list(feature_names[cluster_words[i]]
                             for i in word_scores.argsort()[:-11:-1])
  
      print("bicluster {} : {} documents, {} words".format(
          idx, n_rows, n_cols))
      print("categories   : {}".format(cat_string))
      print("words        : {}\n".format(', '.join(important_words)))
  