def _initialize(self, results):
n = len(results.predicted)
names = getattr(results, "learner_names", None)
if names is None:
names = ["#{}".format(i + 1) for i in range(n)]
self.classifier_names = names
self.colors = colorpalettes.get_default_curve_colors(n)
for i in range(n):
item = self.classifiers_list_box.item(i)
item.setIcon(colorpalettes.ColorIcon(self.colors[i]))
self.selected_classifiers = list(range(n))
self.target_cb.addItems(results.domain.class_var.values)
def _initialize(self, results):
n_models = len(results.predicted)
self.classifier_names = getattr(results, "learner_names", None) \
or [f"#{i}" for i in range(n_models)]
self.selected_classifiers = list(range(n_models))
self.colors = colorpalettes.get_default_curve_colors(n_models)
for i, color in enumerate(self.colors):
item = self.classifiers_list_box.item(i)
item.setIcon(colorpalettes.ColorIcon(color))
class_values = results.data.domain.class_var.values
self.target_cb.addItems(class_values)
if class_values:
self.target_index = 0
def _initialize(self, results):
names = getattr(results, "learner_names", None)
if names is None:
names = ["#{}".format(i + 1)
for i in range(len(results.predicted))]
self.colors = colorpalettes.get_default_curve_colors(len(names))
self.classifier_names = names
self.selected_classifiers = list(range(len(names)))
for i in range(len(names)):
listitem = self.classifiers_list_box.item(i)
listitem.setIcon(colorpalettes.ColorIcon(self.colors[i]))
class_var = results.data.domain.class_var
self.target_cb.addItems(class_var.values)
self.target_index = 0
self._set_target_prior()
