code: def plot_3(data, ss, *args):
      
  
      if len(data) <= 1:
          warnings.warn("Only one datapoint. Could not compute t-SNE embedding.")
          return None
  
      scores = np.array([d['mean_test_score'] for d in data])
      # maps each parameters to a vector of floats
      warped = np.array([ss.point_to_unit(d['parameters']) for d in data])
  
      # Embed into 2 dimensions with t-SNE
      X = TSNE(n_components=2).fit_transform(warped)
  
      e_scores = np.exp(scores)
      mine, maxe = np.min(e_scores), np.max(e_scores)
      color = (e_scores - mine) / (maxe - mine)
      mapped_colors = list(map(rgb2hex, cm.get_cmap('RdBu_r')(color)))
  
      p = bk.figure(title='t-SNE (unsupervised)', tools=TOOLS)
  
      df_params = nonconstant_parameters(data)
      df_params['score'] = scores
      df_params['x'] = X[:, 0]
      df_params['y'] = X[:, 1]
      df_params['color'] = mapped_colors
      df_params['radius'] = 1
      p.circle(
          x='x', y='y', color='color', radius='radius',
          source=ColumnDataSource(data=df_params), fill_alpha=0.6,
          line_color=None)
      cp = p
      hover = cp.select(dict(type=HoverTool))
      format_tt = [(s, '@%s' % s) for s in df_params.columns]
      hover.tooltips = OrderedDict([("index", "$index")] + format_tt)
  
      xax, yax = p.axis
      xax.axis_label = 't-SNE coord 1'
      yax.axis_label = 't-SNE coord 2'
      return p
docs: t-SNE embedding of the parameters, colored by score