class_docstr: Display a projection of a vectorized corpus in two dimensions using TSNE,
  a nonlinear dimensionality reduction method that is particularly well
  suited to embedding in two or three dimensions for visualization as a
  scatter plot. TSNE is widely used in text analysis to show clusters or
  groups of documents or utterances and their relative proximities.
  
  TSNE will return a scatter plot of the vectorized corpus, such that each
  point represents a document or utterance. The distance between two points
  in the visual space is embedded using the probability distribution of
  pairwise similarities in the higher dimensionality; thus TSNE shows
  clusters of similar documents and the relationships between groups of
  documents as a scatter plot.
  
  TSNE can be used with either clustering or classification; by specifying
  the ``classes`` argument, points will be colored based on their similar
  traits. For example, by passing ``cluster.labels_`` as ``y`` in ``fit()``, all
  points in the same cluster will be grouped together. This extends the
  neighbor embedding with more information about similarity, and can allow
  better interpretation of both clusters and classes.
  
  For more, see https://lvdmaaten.github.io/tsne/
  
  Parameters
  ----------
  
  ax : matplotlib axes
      The axes to plot the figure on.
  
  decompose : string or None, default: ``'svd'``
      A preliminary decomposition is often used prior to TSNE to make the
      projection faster. Specify ``"svd"`` for sparse data or ``"pca"`` for
      dense data. If None, the original data set will be used.
  
  decompose_by : int, default: 50
      Specify the number of components for preliminary decomposition, by
      default this is 50; the more components, the slower TSNE will be.
  
  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 tSNE.
  
  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 TSNE transformer.
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docstr: The fit method is the primary drawing input for the TSNE projection
  since the visualization requires both X and an optional y value. The
  fit method expects an array of numeric vectors, so text documents must
  be vectorized before passing them to this method.
  
  Parameters
  ----------
  X : ndarray or DataFrame of shape n x m
      A matrix of n instances with m features representing the corpus of
      vectorized documents to visualize with tsne.
  
  y : ndarray or Series of length n
      An optional array or series of target or class values for
      instances. If this is specified, then the points will be colored
      according to their class. Often cluster labels are passed in to
      color the documents in cluster space, so this method is used both
      for classification and clustering methods.
  
  kwargs : dict
      Pass generic arguments to the drawing method
  
  Returns
  -------
  self : instance
      Returns the instance of the transformer/visualizer
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code: def fit(self, X, y=None, **kwargs):
      """
      The fit method is the primary drawing input for the TSNE projection
      since the visualization requires both X and an optional y value. The
      fit method expects an array of numeric vectors, so text documents must
      be vectorized before passing them to this method.
  
      Parameters
      ----------
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features representing the corpus of
          vectorized documents to visualize with tsne.
  
      y : ndarray or Series of length n
          An optional array or series of target or class values for
          instances. If this is specified, then the points will be colored
          according to their class. Often cluster labels are passed in to
          color the documents in cluster space, so this method is used both
          for classification and clustering methods.
  
      kwargs : dict
          Pass generic arguments to the drawing method
  
      Returns
      -------
      self : instance
          Returns the instance of the transformer/visualizer
      """
  
      # Store the classes we observed in y
      if y is not None:
          self.classes_ = np.unique(y)
      elif y is None and self.labels is not None:
          self.classes_ = np.array([self.labels[0]])
      else:
          self.classes_ = np.array([self.NULL_CLASS])
  
      # Fit our internal transformer and transform the data.
      vecs = self.transformer_.fit_transform(X)
      self.n_instances_ = vecs.shape[0]
  
      # Draw the vectors
      self.draw(vecs, y, **kwargs)
  
      # Fit always returns self.
      return self
  
file_path: yellowbrick/yellowbrick/text/tsne.py
imports: 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.manifold import TSNE
  from sklearn.pipeline import Pipeline
  from sklearn.decomposition import TruncatedSVD, PCA
label_desc: Use image node_id 3 for calling the TSNEVisualizer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self