mvpa2.misc.plot.plot_decision_boundary_2d¶
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mvpa2.misc.plot.
plot_decision_boundary_2d
(dataset, clf=None, targets=None, regions=None, maps=None, maps_res=50, vals=None, data_callback=None)¶ Plot a scatter of a classifier’s decision boundary and data points
Assumes data is 2d (no way to visualize otherwise!!)
Parameters: dataset :
Dataset
Data points to visualize (might be the data
clf
was train on, or any novel data).clf :
Classifier
, optionalTrained classifier
targets : string, optional
What samples attributes to use for targets. If None and clf is provided, then
clf.params.targets_attr
is used.regions : string, optional
Plot regions (polygons) around groups of samples with the same attribute (and target attribute) values. E.g. chunks.
maps : string in {‘targets’, ‘estimates’}, optional
Either plot underlying colored maps, such as clf predictions within the spanned regions, or estimates from the classifier (might not work for some).
maps_res : int, optional
Number of points in each direction to evaluate. Points are between axis limits, which are set automatically by matplotlib. Higher number will yield smoother decision lines but come at the cost of O^2 classifying time/memory.
vals : array of floats, optional
Where to draw the contour lines if maps=’estimates’
data_callback : callable, optional
Callable object to preprocess the new data points. Classified points of the form samples = data_callback(xysamples). I.e. this can be a function to normalize them, or cache them before they are classified.