RadViz is a multivariate data visualization algorithm that plots each
feature dimension uniformly 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. This
mechanism allows as many dimensions as will easily fit on a circle,
greatly expanding the dimensionality of the visualization.
Data scientists use this method to detect separability between classes. E.g. is there an opportunity to learn from the feature set or is there just too much noise?
If your data contains rows with missing values (
numpy.nan), those missing
values will not be plotted. In other words, you may not get the entire
picture of your data.
RadViz will raise a
DataWarning to inform you of the
If you do receive this warning, you may want to look at imputation strategies. A good starting place is the scikit-learn Imputer.
from yellowbrick.datasets import load_occupancy from yellowbrick.features import RadViz # Load the classification dataset X, y = load_occupancy() # Specify the target classes classes = ["unoccupied", "occupied"] # Instantiate the visualizer visualizer = RadViz(classes=classes) visualizer.fit(X, y) # Fit the data to the visualizer visualizer.transform(X) # Transform the data visualizer.poof() # Draw/show/poof the data
For regression, the
RadViz visualizer should use a color sequence to
display the target information, as opposed to discrete colors.
Implements radviz for feature analysis.
RadialVisualizer(ax=None, features=None, classes=None, color=None, colormap=None, alpha=1.0, **kwargs)¶
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.
- 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 If a DataFrame is passed to fit and features is None, feature names are selected as the columns of the DataFrame.
- classes : list, default: None
a list of class names for the legend If classes is None and a y value is passed to fit then the classes are selected from the target vector.
- color : list or tuple, default: None
optional list or tuple of colors to colorize lines Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale.
- colormap : string or cmap, default: None
optional string or matplotlib cmap to colorize lines Use either color to colorize the lines on a per class basis or colormap to color them on a continuous scale.
- 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.
These parameters can be influenced later on in the visualization process, but can and should be set as early as possible.
>>> visualizer = RadViz() >>> visualizer.fit(X, y) >>> visualizer.transform(X) >>> visualizer.poof()
draw(self, X, y, **kwargs)¶
Called from the fit method, this method creates the radviz canvas and draws each instance as a class or target colored point, whose location is determined by the feature data set.
Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize.
- kwargs: generic keyword arguments.
fit(self, X, y=None, **kwargs)¶
The fit method is the primary drawing input for the visualization since it has both the X and y data required for the viz and the transform method does not.
- X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
- y : ndarray or Series of length n
An array or series of target or class values
- kwargs : dict
Pass generic arguments to the drawing method
- self : instance
Returns the instance of the transformer/visualizer
MinMax normalization to fit a matrix in the space [0,1] by column.