content: """
  Benchmark scikit-learn's Ward implement compared to SciPy's
  """
  
  import time
  
  import numpy as np
  from scipy.cluster import hierarchy
  import matplotlib.pyplot as plt
  
  from sklearn.cluster import AgglomerativeClustering
  
  ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
  
  n_samples = np.logspace(.5, 3, 9)
  n_features = np.logspace(1, 3.5, 7)
  N_samples, N_features = np.meshgrid(n_samples,
                                      n_features)
  scikits_time = np.zeros(N_samples.shape)
  scipy_time = np.zeros(N_samples.shape)
  
  for i, n in enumerate(n_samples):
      for j, p in enumerate(n_features):
          X = np.random.normal(size=(n, p))
          t0 = time.time()
          ward.fit(X)
          scikits_time[j, i] = time.time() - t0
          t0 = time.time()
          hierarchy.ward(X)
          scipy_time[j, i] = time.time() - t0
  
  ratio = scikits_time / scipy_time
  
  plt.figure("scikit-learn Ward's method benchmark results")
  plt.imshow(np.log(ratio), aspect='auto', origin="lower")
  plt.colorbar()
  plt.contour(ratio, levels=[1, ], colors='k')
  plt.yticks(range(len(n_features)), n_features.astype(np.int))
  plt.ylabel('N features')
  plt.xticks(range(len(n_samples)), n_samples.astype(np.int))
  plt.xlabel('N samples')
  plt.title("Scikit's time, in units of scipy time (log)")
  plt.show()
  