class_docstr: Word correlation illustrates the extent to which words in a corpus appear in the
  same documents.
  
  WordCorrelationPlot visualizes the binary correlation between words across
  documents as a heatmap. The correlation is defined using the mean square
  contingency coefficient (phi-coefficient) between any two words m and n. The
  coefficient is a value between -1 and 1, inclusive. A value close to 1 or -1
  indicates strong positive or negative correlation between m and n, while a value
  close to 0 indicates little or no correlation. The constructor takes one required
  argument, which is the list of words or n-grams to be plotted.
  
  Parameters
  ----------
  words : list of str
      The list of words or n-grams to be plotted. The words must be present in the
      provided corpus on fit().
  
  ignore_case : bool, default: False
      If True, all words will be converted to lowercase before processing.
  
  ax : matplotlib Axes, default: None
      The axes to plot the figure on.
  
  cmap : str or cmap, default: "RdYlBu"
      Colormap to use for the heatmap.
  
  colorbar : bool, default: True
      If True, a colorbar will be added to the heatmap.
  
  fontsize : int, default: None
      Font size to use for the labels on the axes.
  
  kwargs : dict
      Pass any additional keyword arguments to the super class.
  
  Attributes
  ----------
  self.doc_term_matrix_ : array of shape (n_docs, n_features)
      The computed sparse document-term matrix containing binary values indicating if
      a word is present in a document.
  
  self.num_docs_ : int
      The number of observed documents in the corpus.
  
  self.vocab_ : dict
      A dictionary mapping words to their indices in the document-term matrix.
  
  self.num_features_ : int
      The number of features (word labels) in the resulting plot.
  
  self.correlation_matrix_ : ndarray of shape (n_features, n_features)
      The computed matrix containing the phi-coefficients between all features.
docstr: Prepares the figure for rendering by adding the title. This method is usually
  called from show() and not directly by the user.
docstr_tok: ["Prepares","the","figure","for","rendering","by","adding","the","title",".","This","method","is","usually","called","from","show","(",")","and","not","directly","by","the","user","."]
code: def finalize(self):
      """
      Prepares the figure for rendering by adding the title. This method is usually
      called from show() and not directly by the user.
      """
      self.set_title("Word Correlation Plot")
      self.fig.tight_layout()
  
file_path: yellowbrick/yellowbrick/text/correlation.py
imports: import numpy
  from sklearn.feature_extraction.text import CountVectorizer
  from yellowbrick.style import find_text_color
  from yellowbrick.text.base import TextVisualizer
  from yellowbrick.style.palettes import color_sequence
  from yellowbrick.exceptions import YellowbrickValueError
label_desc: Use image node_id 7 for calling the WordCorrelationPlot obj's underlying member method code with example usage: obj.finalize() without return types