class_docstr: Display a projection of a vectorized corpus in two dimensions using UMAP (Uniform
  Manifold Approximation and Projection), a nonlinear dimensionality reduction method
  that is particularly well suited to embedding in two or three dimensions for
  visualization as a scatter plot. UMAP is a relatively new technique but is often
  used to visualize clusters or groups of data points and their relative proximities.
  It typically is fast, scalable, and can be applied directly to sparse matrices
  eliminating the need to run a ``TruncatedSVD`` as a pre-processing step.
  
  The current default for UMAP is Euclidean distance. Hellinger distance would be a
  more appropriate distance function to use with CountVectorize data. That will be
  released in a forthcoming version of UMAP. In the meantime cosine distance is likely
  a better text default that Euclidean and can be set using the keyword argument
  ``metric='cosine'``.
  
  For more, see https://github.com/lmcinnes/umap
  
  Parameters
  ----------
  
  ax : matplotlib axes
      The axes to plot the figure on.
  
  labels : list of strings
      The names of the classes in the target, used to create a legend.
      Labels must match names of classes in sorted order.
  
  colors : list or tuple of colors
      Specify the colors for each individual class
  
  colormap : string or matplotlib cmap
      Sequential colormap for continuous target
  
  random_state : int, RandomState instance or None, optional, default: None
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by np.random. The random state is applied to the preliminary
      decomposition as well as UMAP.
  
  alpha : float, default: 0.7
      Specify a transparency where 1 is completely opaque and 0 is completely
      transparent. This property makes densely clustered points more visible.
  
  kwargs : dict
      Pass any additional keyword arguments to the UMAP transformer.
  
  Examples
  --------
  
  >>> model = MyVisualizer(metric='cosine')
  >>> model.fit(X)
  >>> model.show()
docstr: Finalize the drawing by adding a title and legend, and removing the
  axes objects that do not convey information about UMAP.
docstr_tok: ["Finalize","the","drawing","by","adding","a","title","and","legend",",","and","removing","the","axes","objects","that","do","not","convey","information","about","UMAP","."]
code: def finalize(self, **kwargs):
      """
      Finalize the drawing by adding a title and legend, and removing the
      axes objects that do not convey information about UMAP.
      """
      self.set_title(
          "UMAP Projection of {} Documents".format(self.n_instances_)
      )
  
      # Remove the ticks
      self.ax.set_yticks([])
      self.ax.set_xticks([])
  
      # Add the legend outside of the figure box.
      if not all(self.classes_ == np.array([self.NULL_CLASS])):
          box = self.ax.get_position()
          self.ax.set_position(
              [box.x0, box.y0, box.width * 0.8, box.height]
          )
          manual_legend(
              self,
              self.classes_,
              self.color_values_,
              loc="center left",
              bbox_to_anchor=(1, 0.5),
          )
  
file_path: yellowbrick/yellowbrick/text/umap_vis.py
imports: import warnings
  import numpy
  from collections import defaultdict
  from yellowbrick.draw import manual_legend
  from yellowbrick.text.base import TextVisualizer
  from yellowbrick.style.colors import resolve_colors
  from yellowbrick.exceptions import YellowbrickValueError
  from sklearn.pipeline import Pipeline
label_desc: Use image node_id 5 for calling the UMAPVisualizer obj's underlying member method code with example usage: obj.finalize() without return types