code: def radviz(
      X,
      y=None,
      ax=None,
      features=None,
      classes=None,
      colors=None,
      colormap=None,
      alpha=1.0,
      show=True,
      **kwargs,
  ):
      """
      Displays each feature as an axis around a circle surrounding a scatter
      plot whose points are each individual instance.
  
      This helper function is a quick wrapper to utilize the RadialVisualizer
      (Transformer) for one-off analysis.
  
      Parameters
      ----------
  
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n, default:None
          An array or series of target or class values
  
      ax : matplotlib Axes, default: None
          The axes to plot the figure on.
  
      features : list of strings, default: None
          The names of the features or columns
  
      classes : list of strings, default: None
          The names of the classes in the target
  
      colors : list or tuple of colors, default: None
          Specify the colors for each individual class
  
      colormap : string or matplotlib cmap, default: None
          Sequential colormap for continuous target
  
      alpha : float, default: 1.0
          Specify a transparency where 1 is completely opaque and 0 is completely
          transparent. This property makes densely clustered points more visible.
  
      show: bool, default: True
          If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot
          call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply
          calls ``finalize()``
  
      kwargs : dict
          Keyword arguments passed to the visualizer base classes.
  
      Returns
      -------
      viz : RadViz
          Returns the fitted, finalized visualizer
      """
      # Instantiate the visualizer
      visualizer = RadialVisualizer(
          ax=ax,
          features=features,
          classes=classes,
          colors=colors,
          colormap=colormap,
          alpha=alpha,
          **kwargs,
      )
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y, **kwargs)
      visualizer.transform(X)
  
      if show:
          visualizer.show()
      else:
          visualizer.finalize()
  
      # Return the visualizer object
      return visualizer
  
file_path: yellowbrick/yellowbrick/features/radviz.py
imports: import numpy
  import matplotlib.patches
  from yellowbrick.draw import manual_legend
  from yellowbrick.utils import is_dataframe
  from yellowbrick.utils import nan_warnings
  from yellowbrick.features.base import DataVisualizer
label_desc: Use image node_id 1 for calling a global function with example usage: radviz(X, y, ax, features, classes, colors, colormap, alpha, show) and returns: visualizer