def UpdatePlot(self):
if self.comboPlotX.count() == 0:
return
x_ticks = []
x_label = ''
selected_index = -1
if self.comboPlotX.currentText() == 'Normaliaztion':
selected_index = 0
x_ticks = [
instance.GetName()
for instance in self._fae.GetNormalizerList()
]
x_label = 'Normalization Method'
elif self.comboPlotX.currentText() == 'Dimension Reduction':
selected_index = 1
x_ticks = [
instance.GetName()
for instance in self._fae.GetDimensionReductionList()
]
x_label = 'Dimension Reduction Method'
elif self.comboPlotX.currentText() == 'Feature Selector':
selected_index = 2
x_ticks = [
instance.GetName()
for instance in self._fae.GetFeatureSelectorList()
]
x_label = 'Feature Selecotr Method'
elif self.comboPlotX.currentText() == 'Classifier':
selected_index = 4
x_ticks = [
instance.GetName()
for instance in self._fae.GetClassifierList()
]
x_label = 'Classifier Method'
elif self.comboPlotX.currentText() == 'Feature Number':
selected_index = 3
x_ticks = list(map(int, self._fae.GetFeatureNumberList()))
x_label = 'Feature Number'
max_axis_list = [0, 1, 2, 3, 4]
max_axis_list.remove(selected_index)
max_axis = tuple(max_axis_list)
index = self._UpdatePlotButtons(selected_index)
show_data = []
show_data_std = []
name_list = []
if self.comboPlotY.currentText() == 'AUC':
if self.checkPlotCVTrain.isChecked():
temp = deepcopy(self._fae.GetAUCMetric()['train'])
auc_std = deepcopy(self._fae.GetAUCstdMetric()['train'])
if self.checkPlotMaximum.isChecked():
show_data.append(np.max(temp, axis=max_axis).tolist())
else:
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append('CV Train')
if self.checkPlotCVValidation.isChecked():
temp = deepcopy(self._fae.GetAUCMetric()['val'])
auc_std = deepcopy(self._fae.GetAUCstdMetric()['val'])
if self.checkPlotMaximum.isChecked():
show_data.append(np.max(temp, axis=max_axis).tolist())
else:
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append('CV Validation')
if self.checkPlotTrain.isChecked():
temp = deepcopy(self._fae.GetAUCMetric()['all_train'])
auc_std = deepcopy(self._fae.GetAUCstdMetric()['all_train'])
if self.checkPlotMaximum.isChecked():
show_data.append(np.max(temp, axis=max_axis).tolist())
else:
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append('Train')
if self.checkPlotTest.isChecked():
temp = deepcopy(self._fae.GetAUCMetric()['test'])
auc_std = deepcopy(self._fae.GetAUCstdMetric()['test'])
if temp.size > 0:
if self.checkPlotMaximum.isChecked():
show_data.append(np.max(temp, axis=max_axis).tolist())
else:
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append('Test')
if len(show_data) > 0:
if selected_index == 3:
DrawCurve(x_ticks,
show_data,
show_data_std,
xlabel=x_label,
ylabel=self.comboPlotY.currentText(),
name_list=name_list,
is_show=False,
fig=self.canvasPlot.getFigure())
else:
DrawBar(x_ticks,
show_data,
ylabel=self.comboPlotY.currentText(),
name_list=name_list,
is_show=False,
fig=self.canvasPlot.getFigure())
self.canvasPlot.draw()
def UpdatePlot(self):
if (not self.__is_ui_ready) or self.__is_clear:
return
if self.comboPlotX.count() == 0:
return
x_ticks = []
x_label = ''
selected_index = -1
if self.comboPlotX.currentText() == 'Normaliaztion':
selected_index = 0
x_ticks = [
instance.GetName() for instance in self._fae.normalizer_list
]
x_label = 'Normalization Method'
elif self.comboPlotX.currentText() == 'Dimension Reduction':
selected_index = 1
x_ticks = [
instance.GetName()
for instance in self._fae.dimension_reduction_list
]
x_label = 'Dimension Reduction Method'
elif self.comboPlotX.currentText() == 'Feature Selector':
selected_index = 2
x_ticks = [
instance.GetName()
for instance in self._fae.feature_selector_list
]
x_label = 'Feature Selecotr Method'
elif self.comboPlotX.currentText() == 'Classifier':
selected_index = 4
x_ticks = [
instance.GetName() for instance in self._fae.classifier_list
]
x_label = 'Classifier Method'
elif self.comboPlotX.currentText() == 'Feature Number':
selected_index = 3
x_ticks = list(map(int, self._fae.feature_selector_num_list))
x_label = 'Feature Number'
max_axis_list = [0, 1, 2, 3, 4]
max_axis_list.remove(selected_index)
max_axis = tuple(max_axis_list)
index = self._UpdatePlotButtons(selected_index)
show_data = []
show_data_std = []
name_list = []
if self.comboPlotY.currentText() == 'AUC':
if self.checkPlotCVTrain.isChecked():
temp = deepcopy(self._fae.GetAuc()[CV_TRAIN])
auc_std = deepcopy(self._fae.GetAucStd()[CV_TRAIN])
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append(CV_TRAIN)
if self.checkPlotCVValidation.isChecked():
temp = deepcopy(self._fae.GetAuc()[CV_VAL])
auc_std = deepcopy(self._fae.GetAucStd()[CV_VAL])
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append(CV_VAL)
if self.checkPlotTrain.isChecked():
temp = deepcopy(self._fae.GetAuc()[TRAIN])
auc_std = deepcopy(self._fae.GetAucStd()[TRAIN])
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append(TRAIN)
if self.checkPlotTest.isChecked():
temp = deepcopy(self._fae.GetAuc()[TEST])
auc_std = deepcopy(self._fae.GetAucStd()[TEST])
if temp.size > 0:
show_data.append(temp[tuple(index)].tolist())
show_data_std.append(auc_std[tuple(index)].tolist())
name_list.append(TEST)
if len(show_data) > 0:
if selected_index == 3:
DrawCurve(x_ticks,
show_data,
show_data_std,
xlabel=x_label,
ylabel=self.comboPlotY.currentText(),
name_list=name_list,
is_show=False,
one_se=self.checkPlotOneSE.isChecked(),
fig=self.canvasPlot.getFigure())
else:
DrawBar(x_ticks,
show_data,
ylabel=self.comboPlotY.currentText(),
name_list=name_list,
is_show=False,
fig=self.canvasPlot.getFigure())
self.canvasPlot.draw()