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()