Source code for secml.figure._plots.c_plot_classifier

.. module:: CPlotClassifier
   :synopsis: Plot a classifier's decision regions on 2D feature spaces.

.. moduleauthor:: Battista Biggio <>
.. moduleauthor:: Marco Melis <>

from secml.figure._plots import CPlotFunction
from import CClassifier
from secml.array import CArray

[docs]class CPlotClassifier(CPlotFunction): """Plot a classifier. Custom plotting parameters can be specified. Currently parameters default: - grid: False. See Also -------- .CPlot : basic subplot functions. .CFigure : creates and handle figures. """
[docs] def apply_params_clf(self): """Apply defined parameters to active subplot.""" self.grid(grid_on=False)
[docs] def plot_decision_regions(self, clf, plot_background=True, levels=None, grid_limits=None, n_grid_points=30, cmap=None): """Plot decision boundaries and regions for the given classifier. Parameters ---------- clf : CClassifier Classifier which decision function should be plotted. plot_background : bool, optional Specifies whether to color the decision regions. Default True. in the background using a colorbar. levels : list or None, optional List of levels to be plotted. If None, CArray.arange(0.5, clf.n_classes) will be plotted. grid_limits : list of tuple List with a tuple of min/max limits for each axis. If None, [(0, 1), (0, 1)] limits will be used. n_grid_points : int, optional Number of grid points. Default 30. cmap : str or list or `` or None, optional Colormap to use. Could be a list of colors. If None and the number of dataset classes is `<= 6`, colors will be chosen from ['blue', 'red', 'lightgreen', 'black', 'gray', 'cyan']. Otherwise the 'jet' colormap will be used. """ if not isinstance(clf, CClassifier): raise TypeError("'clf' must be an instance of `CClassifier`.") if cmap is None: if clf.n_classes <= 6: colors = ['blue', 'red', 'lightgreen', 'black', 'gray', 'cyan'] cmap = colors[:clf.n_classes] else: cmap = 'jet' if levels is None: levels = CArray.arange(0.5, clf.n_classes).tolist() self.plot_fun(func=clf.predict, multipoint=True, colorbar=False, n_colors=clf.n_classes, cmap=cmap, levels=levels, plot_background=plot_background, grid_limits=grid_limits, n_grid_points=n_grid_points, alpha=0.5) self.apply_params_clf()