content: """
  ===========================
  Random projection benchmark
  ===========================
  
  Benchmarks for random projections.
  
  """
  from __future__ import division
  from __future__ import print_function
  
  import gc
  import sys
  import optparse
  from datetime import datetime
  import collections
  
  import numpy as np
  import scipy.sparse as sp
  
  from sklearn import clone
  from sklearn.externals.six.moves import xrange
  from sklearn.random_projection import (SparseRandomProjection,
                                         GaussianRandomProjection,
                                         johnson_lindenstrauss_min_dim)
  
  
  def type_auto_or_float(val):
      if val == "auto":
          return "auto"
      else:
          return float(val)
  
  
  def type_auto_or_int(val):
      if val == "auto":
          return "auto"
      else:
          return int(val)
  
  
  def compute_time(t_start, delta):
      mu_second = 0.0 + 10 ** 6  # number of microseconds in a second
  
      return delta.seconds + delta.microseconds / mu_second
  
  
  def bench_scikit_transformer(X, transfomer):
      gc.collect()
  
      clf = clone(transfomer)
  
      # start time
      t_start = datetime.now()
      clf.fit(X)
      delta = (datetime.now() - t_start)
      # stop time
      time_to_fit = compute_time(t_start, delta)
  
      # start time
      t_start = datetime.now()
      clf.transform(X)
      delta = (datetime.now() - t_start)
      # stop time
      time_to_transform = compute_time(t_start, delta)
  
      return time_to_fit, time_to_transform
  
  
  # Make some random data with uniformly located non zero entries with
  # Gaussian distributed values
  def make_sparse_random_data(n_samples, n_features, n_nonzeros,
                              random_state=None):
      rng = np.random.RandomState(random_state)
      data_coo = sp.coo_matrix(
          (rng.randn(n_nonzeros),
          (rng.randint(n_samples, size=n_nonzeros),
           rng.randint(n_features, size=n_nonzeros))),
          shape=(n_samples, n_features))
      return data_coo.toarray(), data_coo.tocsr()
  
  
  def print_row(clf_type, time_fit, time_transform):
      print("%s | %s | %s" % (clf_type.ljust(30),
                             ("%.4fs" % time_fit).center(12),
                             ("%.4fs" % time_transform).center(12)))
  
  
  if __name__ == "__main__":
      ###########################################################################
      # Option parser
      ###########################################################################
      op = optparse.OptionParser()
      op.add_option("--n-times",
                    dest="n_times", default=5, type=int,
                    help="Benchmark results are average over n_times experiments")
  
      op.add_option("--n-features",
                    dest="n_features", default=10 ** 4, type=int,
                    help="Number of features in the benchmarks")
  
      op.add_option("--n-components",
                    dest="n_components", default="auto",
                    help="Size of the random subspace."
                         " ('auto' or int > 0)")
  
      op.add_option("--ratio-nonzeros",
                    dest="ratio_nonzeros", default=10 ** -3, type=float,
                    help="Number of features in the benchmarks")
  
      op.add_option("--n-samples",
                    dest="n_samples", default=500, type=int,
                    help="Number of samples in the benchmarks")
  
      op.add_option("--random-seed",
                    dest="random_seed", default=13, type=int,
                    help="Seed used by the random number generators.")
  
      op.add_option("--density",
                    dest="density", default=1 / 3,
                    help="Density used by the sparse random projection."
                         " ('auto' or float (0.0, 1.0]")
  
      op.add_option("--eps",
                    dest="eps", default=0.5, type=float,
                    help="See the documentation of the underlying transformers.")
  
      op.add_option("--transformers",
                    dest="selected_transformers",
                    default='GaussianRandomProjection,SparseRandomProjection',
                    type=str,
                    help="Comma-separated list of transformer to benchmark. "
                         "Default: %default. Available: "
                         "GaussianRandomProjection,SparseRandomProjection")
  
      op.add_option("--dense",
                    dest="dense",
                    default=False,
                    action="store_true",
                    help="Set input space as a dense matrix.")
  
      (opts, args) = op.parse_args()
      if len(args) > 0:
          op.error("this script takes no arguments.")
          sys.exit(1)
      opts.n_components = type_auto_or_int(opts.n_components)
      opts.density = type_auto_or_float(opts.density)
      selected_transformers = opts.selected_transformers.split(',')
  
      ###########################################################################
      # Generate dataset
      ###########################################################################
      n_nonzeros = int(opts.ratio_nonzeros * opts.n_features)
  
      print('Dataset statics')
      print("===========================")
      print('n_samples \t= %s' % opts.n_samples)
      print('n_features \t= %s' % opts.n_features)
      if opts.n_components == "auto":
          print('n_components \t= %s (auto)' %
                johnson_lindenstrauss_min_dim(n_samples=opts.n_samples,
                                              eps=opts.eps))
      else:
          print('n_components \t= %s' % opts.n_components)
      print('n_elements \t= %s' % (opts.n_features * opts.n_samples))
      print('n_nonzeros \t= %s per feature' % n_nonzeros)
      print('ratio_nonzeros \t= %s' % opts.ratio_nonzeros)
      print('')
  
      ###########################################################################
      # Set transformer input
      ###########################################################################
      transformers = {}
  
      ###########################################################################
      # Set GaussianRandomProjection input
      gaussian_matrix_params = {
          "n_components": opts.n_components,
          "random_state": opts.random_seed
      }
      transformers["GaussianRandomProjection"] = \
          GaussianRandomProjection(**gaussian_matrix_params)
  
      ###########################################################################
      # Set SparseRandomProjection input
      sparse_matrix_params = {
          "n_components": opts.n_components,
          "random_state": opts.random_seed,
          "density": opts.density,
          "eps": opts.eps,
      }
  
      transformers["SparseRandomProjection"] = \
          SparseRandomProjection(**sparse_matrix_params)
  
      ###########################################################################
      # Perform benchmark
      ###########################################################################
      time_fit = collections.defaultdict(list)
      time_transform = collections.defaultdict(list)
  
      print('Benchmarks')
      print("===========================")
      print("Generate dataset benchmarks... ", end="")
      X_dense, X_sparse = make_sparse_random_data(opts.n_samples,
                                                  opts.n_features,
                                                  n_nonzeros,
                                                  random_state=opts.random_seed)
      X = X_dense if opts.dense else X_sparse
      print("done")
  
      for name in selected_transformers:
          print("Perform benchmarks for %s..." % name)
  
          for iteration in xrange(opts.n_times):
              print("\titer %s..." % iteration, end="")
              time_to_fit, time_to_transform = bench_scikit_transformer(X_dense,
                transformers[name])
              time_fit[name].append(time_to_fit)
              time_transform[name].append(time_to_transform)
              print("done")
  
      print("")
  
      ###########################################################################
      # Print results
      ###########################################################################
      print("Script arguments")
      print("===========================")
      arguments = vars(opts)
      print("%s \t | %s " % ("Arguments".ljust(16),
                             "Value".center(12),))
      print(25 * "-" + ("|" + "-" * 14) * 1)
      for key, value in arguments.items():
          print("%s \t | %s " % (str(key).ljust(16),
                                 str(value).strip().center(12)))
      print("")
  
      print("Transformer performance:")
      print("===========================")
      print("Results are averaged over %s repetition(s)." % opts.n_times)
      print("")
      print("%s | %s | %s" % ("Transformer".ljust(30),
                              "fit".center(12),
                              "transform".center(12)))
      print(31 * "-" + ("|" + "-" * 14) * 2)
  
      for name in sorted(selected_transformers):
          print_row(name,
                    np.mean(time_fit[name]),
                    np.mean(time_transform[name]))
  
      print("")
      print("")
  