class_docstr: RadViz is a multivariate data visualization algorithm that plots each
  axis uniformely around the circumference of a circle then plots points on
  the interior of the circle such that the point normalizes its values on
  the axes from the center to each arc.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  features : list, default: None
      a list of feature names to use
      The names of the features specified by the columns of the input dataset.
      This length of this list must match the number of columns in X, otherwise
      an exception will be raised on ``fit()``.
  
  classes : list, default: None
      a list of class names for the legend
      The class labels for each class in y, ordered by sorted class index. These
      names act as a label encoder for the legend, identifying integer classes
      or renaming string labels. If omitted, the class labels will be taken from
      the unique values in y.
  
      Note that the length of this list must match the number of unique values in
      y, otherwise an exception is raised. This parameter is only used in the
      discrete target type case and is ignored otherwise.
  
  colors : list or tuple, default: None
      optional list or tuple of colors to colorize lines
      A single color to plot all instances as or a list of colors to color each
      instance according to its class. If not enough colors per class are
      specified then the colors are treated as a cycle.
  
  colormap : string or cmap, default: None
      optional string or matplotlib cmap to colorize lines
      The colormap used to create the individual colors. If classes are
      specified the colormap is used to evenly space colors across each class.
  
  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.
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Examples
  --------
  
  >>> visualizer = RadViz()
  >>> visualizer.fit(X, y)
  >>> visualizer.transform(X)
  >>> visualizer.show()
  
  Attributes
  ----------
  features_ : ndarray, shape (n_features,)
      The names of the features discovered or used in the visualizer that
      can be used as an index to access or modify data in X. If a user passes
      feature names in, those features are used. Otherwise the columns of a
      DataFrame are used or just simply the indices of the data array.
  
  classes_ : ndarray, shape (n_classes,)
      The class labels that define the discrete values in the target. Only
      available if the target type is discrete. This is guaranteed to be
      strings even if the classes are a different type.
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code: def __init__(
      self,
      ax=None,
      features=None,
      classes=None,
      colors=None,
      colormap=None,
      alpha=1.0,
      **kwargs,
  ):
      if "target_type" not in kwargs:
          kwargs["target_type"] = "discrete"
      super(RadialVisualizer, self).__init__(
          ax=ax,
          features=features,
          classes=classes,
          colors=colors,
          colormap=colormap,
          **kwargs,
      )
      self.alpha = alpha
  
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 to create a new RadialVisualizer object from inherited base classes: DataVisualizer with example: obj = RadialVisualizer(ax, features, classes, colors, colormap, alpha)