def test_plot(self):
""" Compare the classifiers graphically"""
ds = CDLRandom(n_features=2, n_redundant=0, n_informative=2,
n_clusters_per_class=1, random_state=0).load()
ds.X = CNormalizerMinMax().fit_transform(ds.X)
fig = self._test_plot(self.ridges[0], ds)
fig.savefig(fm.join(fm.abspath(__file__), 'figs',
'test_c_classifier_ridge.pdf'))
def test_draw(self):
""" Compare the classifiers graphically"""
self.logger.info("Testing classifiers graphically")
# generate 2D synthetic data
dataset = CDLRandom(n_features=2,
n_redundant=1,
n_informative=1,
n_clusters_per_class=1).load()
dataset.X = CNormalizerMinMax().fit_transform(dataset.X)
self.sgds[0].fit(dataset.X, dataset.Y)
svm = CClassifierSVM()
svm.fit(dataset.X, dataset.Y)
fig = CFigure(width=10, markersize=8)
fig.subplot(2, 1, 1)
# Plot dataset points
fig.sp.plot_ds(dataset)
# Plot objective function
fig.sp.plot_fun(svm.decision_function,
grid_limits=dataset.get_bounds(),
y=1)
fig.sp.title('SVM')
fig.subplot(2, 1, 2)
# Plot dataset points
fig.sp.plot_ds(dataset)
# Plot objective function
fig.sp.plot_fun(self.sgds[0].decision_function,
grid_limits=dataset.get_bounds(),
y=1)
fig.sp.title('SGD Classifier')
fig.savefig(
fm.join(fm.abspath(__file__), 'figs',
'test_c_classifier_sgd1.pdf'))
