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)]
scheme = colorbrewer.colorSchemes["qualitative"]["Dark2"]
if N > len(scheme):
scheme = colorpalette.DefaultRGBColors
self.colors = colorpalette.ColorPaletteGenerator(N, scheme)
self.classifier_names = names
self.selected_classifiers = list(range(N))
for i in range(N):
item = self.classifiers_list_box.item(i)
item.setIcon(colorpalette.ColorPixmap(self.colors[i]))
self.target_cb.addItems(results.data.domain.class_var.values)
def set_sampled_data(self, dataset):
if dataset is not None:
domain = dataset.domain
cvars = list(filter(is_continuous, domain.variables))
dvars = list(filter(is_discrete, domain.variables))
self.x_var_model[:] = cvars
self.y_var_model[:] = cvars
self.z_var_model[:] = dvars
nvars = len(cvars)
self.x_var_index = min(max(0, self.x_var_index), nvars - 1)
self.y_var_index = min(max(0, self.y_var_index), nvars - 1)
self.z_var_index = min(max(0, self.z_var_index), len(cvars) - 1)
if is_discrete(domain.class_var):
self.z_var_index = dvars.index(domain.class_var)
else:
self.z_var_index = len(dvars) - 1
self.openContext(dataset)
if 0 <= self.z_var_index < len(self.z_var_model):
self.z_values = self.z_var_model[self.z_var_index].values
k = len(self.z_values)
self.selected_z_values = range(k)
self.colors = colorpalette.ColorPaletteGenerator(k)
for i in range(k):
item = self.z_values_view.item(i)
item.setIcon(colorpalette.ColorPixmap(self.colors[i]))
self.labelDataInput.setText(
'Data set: %s'
% (getattr(self.dataset, "name", "untitled"),)
)
self.setup_plot()
else:
self.labelDataInput.setText('No data on input')
self.send("Sampled data", None)
def set_sampled_data(self, dataset):
if dataset is None:
return
domain = dataset.domain
cvars = [var for var in domain.variables if var.is_continuous]
dvars = [var for var in domain.variables if var.is_discrete]
self.x_var_model[:] = cvars
self.y_var_model[:] = cvars
self.z_var_model[:] = dvars
nvars = len(cvars)
self.x_var_index = min(max(0, self.x_var_index), nvars - 1)
self.y_var_index = min(max(0, self.y_var_index), nvars - 1)
self.z_var_index = min(max(0, self.z_var_index), len(dvars) - 1)
if domain.has_discrete_class:
self.z_var_index = dvars.index(domain.class_var)
else:
self.z_var_index = len(dvars) - 1
self.openContext(dataset)
if 0 <= self.z_var_index < len(self.z_var_model):
self.z_values = self.z_var_model[self.z_var_index].values
k = len(self.z_values)
self.selected_z_values = range(k)
self.colors = colorpalette.ColorPaletteGenerator(k)
for i in range(k):
item = self.z_values_view.item(i)
item.setIcon(colorpalette.ColorPixmap(self.colors[i]))
if not cvars:
self.error(1, "Data contains no continuous features")
else:
self.error(1)
self.setup_plot()
def set_agent_color(self, agent_index):
item = self.list_box.item(agent_index)
if item:
item.setIcon(colorpalette.ColorPixmap(self.colors[agent_index]))
