def compute_score(self, state):
attrs = [self.attrs[i] for i in state]
domain = Domain(attributes=attrs, class_vars=[self.attr_color])
data = self.data.transform(domain)
projector = RadViz()
projection = projector(data)
radviz_xy = projection(data)
y = projector.preprocess(data).Y
return -self._evaluate_projection(radviz_xy, y)
def compute_score(self, state):
attrs = [self.attrs[i] for i in state]
domain = Domain(attributes=attrs, class_vars=[self.attr_color])
data = self.data.transform(domain)
projector = RadViz()
projection = projector(data)
radviz_xy = projection(data)
y = projector.preprocess(data).Y
return -self._evaluate_projection(radviz_xy, y)
def test_discrete_features(self):
table = self.titanic[::10]
projector = RadViz()
self.assertRaises(ValueError, projector, table)
table = table.transform(Domain(table.domain.attributes[1:]))
projector = RadViz()
projection = projector(table)
embedding = projection(table[::10])
self.assertEqual(np.sum(embedding), -17)
def clear(self):
if self.model_selected:
self.model_selected.clear()
if self.model_other:
self.model_other.clear()
super().clear()
self.projector = RadViz()
def test_radviz(self):
table = self.iris
table = table.transform(Domain(table.domain.attributes[2:]))
projector = RadViz()
projection = projector(table)
embedding = projection(table)
self.assertEqual(len(embedding), len(table))
self.assertTrue(np.isnan(embedding.X).any())
np.testing.assert_array_equal(embedding[:100], projection(table[:100]))
def clear(self):
super().clear()
self.projector = RadViz()
