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()
code: def __init__(
      self,
      ax=None,
      labels=None,
      classes=None,
      colors=None,
      colormap=None,
      random_state=None,
      alpha=0.7,
      **kwargs,
  ):
      if UMAP is None:
          raise YellowbrickValueError(
              (
                  "umap package doesn't seem to be installed."
                  "Please install UMAP via: pip install umap-learn"
              )
          )
  
      # Visual Parameters
      self.alpha = alpha
      self.labels = labels
      self.colors = colors
      self.colormap = colormap
      self.random_state = random_state
  
      # Fetch UMAP kwargs from kwargs by popping only keys belonging to UMAP params
      umap_kwargs = {
          key: kwargs.pop(key)
          for key in UMAP().get_params()
          if key in kwargs
      }
  
      # UMAP doesn't require any pre-processing before embedding and thus doesn't
      # require a pipeline.
      self.transformer_ = self.make_transformer(umap_kwargs)
  
      # Call super at the end so that size and title are set correctly
      super(UMAPVisualizer, self).__init__(ax=ax, **kwargs)
  
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 1 to create a new UMAPVisualizer object from inherited base classes: TextVisualizer with example: obj = UMAPVisualizer(ax, labels, classes, colors, colormap, random_state, alpha)