class VisualizationConnection(QWidget, Ui_Visualization): def __init__(self, parent=None): self._root_folder = '' self._fae = FeatureAnalysisPipelines() self.sheet_dict = dict() super(VisualizationConnection, self).__init__(parent) self.setupUi(self) self.buttonLoadResult.clicked.connect(self.LoadAll) self.buttonClearResult.clicked.connect(self.ClearAll) self.buttonSave.clicked.connect(self.Save) self.__plt_roc = self.canvasROC.getFigure().add_subplot(111) self.__plt_plot = self.canvasPlot.getFigure().add_subplot(111) self.__contribution = self.canvasFeature.getFigure().add_subplot(111) # Update Sheet self.comboSheet.currentIndexChanged.connect(self.UpdateSheet) self.checkMaxFeatureNumber.stateChanged.connect(self.UpdateSheet) # Update ROC canvas self.comboNormalizer.currentIndexChanged.connect(self.UpdateROC) self.comboDimensionReduction.currentIndexChanged.connect( self.UpdateROC) self.comboFeatureSelector.currentIndexChanged.connect(self.UpdateROC) self.comboClassifier.currentIndexChanged.connect(self.UpdateROC) self.spinBoxFeatureNumber.valueChanged.connect(self.UpdateROC) self.checkROCCVTrain.stateChanged.connect(self.UpdateROC) self.checkROCCVValidation.stateChanged.connect(self.UpdateROC) self.checkROCTrain.stateChanged.connect(self.UpdateROC) self.checkROCTest.stateChanged.connect(self.UpdateROC) # Update Plot canvas self.comboPlotX.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotY.currentIndexChanged.connect(self.UpdatePlot) self.checkPlotMaximum.stateChanged.connect(self.UpdatePlot) self.comboPlotNormalizer.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotDimensionReduction.currentIndexChanged.connect( self.UpdatePlot) self.comboPlotFeatureSelector.currentIndexChanged.connect( self.UpdatePlot) self.comboPlotClassifier.currentIndexChanged.connect(self.UpdatePlot) self.spinPlotFeatureNumber.valueChanged.connect(self.UpdatePlot) self.checkPlotCVTrain.stateChanged.connect(self.UpdatePlot) self.checkPlotCVValidation.stateChanged.connect(self.UpdatePlot) self.checkPlotTrain.stateChanged.connect(self.UpdatePlot) self.checkPlotTest.stateChanged.connect(self.UpdatePlot) # Update Contribution canvas self.checkContributionShow.stateChanged.connect( self.UpdateContribution) self.radioContributionFeatureSelector.toggled.connect( self.UpdateContribution) self.radioContributionClassifier.toggled.connect( self.UpdateContribution) self.comboContributionFeatureSelector.currentIndexChanged.connect( self.UpdateContribution) self.comboContributionClassifier.currentIndexChanged.connect( self.UpdateContribution) self.spinFeatureSelectorFeatureNumber.valueChanged.connect( self.UpdateContribution) self.spinClassifierFeatureNumber.valueChanged.connect( self.UpdateContribution) def LoadAll(self): dlg = QFileDialog() dlg.setFileMode(QFileDialog.DirectoryOnly) dlg.setOption(QFileDialog.ShowDirsOnly) if dlg.exec_(): self._root_folder = dlg.selectedFiles()[0] if not os.path.exists(self._root_folder): return if not r'.FAEresult4129074093819729087' in os.listdir( self._root_folder): QMessageBox.about(self, 'Load Error', 'This folder is not supported for import') return try: self.lineEditResultPath.setText(self._root_folder) self._fae.LoadAll(self._root_folder) self.SetResultDescription() self.SetResultTable() self.InitialUi() except Exception as ex: QMessageBox.about(self, "Load Error", ex.__str__()) self.logger.log('Load Error, The reason is ' + str(ex)) self.ClearAll() return self.buttonClearResult.setEnabled(True) self.buttonSave.setEnabled(True) self.buttonLoadResult.setEnabled(False) def ClearAll(self): self.buttonLoadResult.setEnabled(True) self.buttonSave.setEnabled(False) self.buttonClearResult.setEnabled(False) self.checkROCCVTrain.setChecked(False) self.checkROCCVValidation.setChecked(False) self.checkROCTrain.setChecked(False) self.checkROCTest.setChecked(False) self.checkPlotCVTrain.setChecked(False) self.checkPlotCVValidation.setChecked(False) self.checkPlotTrain.setChecked(False) self.checkPlotTest.setChecked(False) self.checkPlotMaximum.setChecked(False) self.checkContributionShow.setChecked(False) self.radioContributionFeatureSelector.setChecked(True) self.radioContributionFeatureSelector.setChecked(False) self.checkMaxFeatureNumber.setChecked(False) self.canvasROC.getFigure().clear() self.canvasPlot.getFigure().clear() self.canvasFeature.getFigure().clear() self.__plt_roc = self.canvasROC.getFigure().add_subplot(111) self.__plt_plot = self.canvasPlot.getFigure().add_subplot(111) self.__contribution = self.canvasFeature.getFigure().add_subplot(111) self.canvasROC.draw() self.canvasPlot.draw() self.canvasFeature.draw() self.textEditDescription.clear() self.lineEditResultPath.clear() self.comboSheet.clear() self.comboClassifier.clear() self.comboDimensionReduction.clear() self.comboNormalizer.clear() self.comboFeatureSelector.clear() self.comboPlotClassifier.clear() self.comboPlotDimensionReduction.clear() self.comboPlotFeatureSelector.clear() self.comboPlotNormalizer.clear() self.comboPlotX.clear() self.comboPlotY.clear() self.comboContributionClassifier.clear() self.comboContributionFeatureSelector.clear() self.spinBoxFeatureNumber.setValue(0) self.spinPlotFeatureNumber.setValue(0) self.spinPlotFeatureNumber.setEnabled(False) self.spinFeatureSelectorFeatureNumber.setValue(1) self.spinClassifierFeatureNumber.setValue(1) self.tableClinicalStatistic.clear() self.tableClinicalStatistic.setRowCount(0) self.tableClinicalStatistic.setColumnCount(0) self.tableClinicalStatistic.setHorizontalHeaderLabels(list([])) self.tableClinicalStatistic.setVerticalHeaderLabels(list([])) self._fae = FeatureAnalysisPipelines() self._root_folder = '' self.sheet_dict = dict() def Save(self): dlg = QFileDialog() dlg.setFileMode(QFileDialog.DirectoryOnly) dlg.setOption(QFileDialog.ShowDirsOnly) if dlg.exec_(): store_folder = dlg.selectedFiles()[0] self.canvasROC.getFigure().savefig(os.path.join( store_folder, 'ROC.eps'), dpi=1200) self.canvasROC.getFigure().savefig(os.path.join( store_folder, 'ROC.jpg'), dpi=300) self.canvasPlot.getFigure().savefig(os.path.join( store_folder, 'Compare.eps'), dpi=1200) self.canvasPlot.getFigure().savefig(os.path.join( store_folder, 'Compare.jpg'), dpi=300) self.canvasFeature.getFigure().savefig(os.path.join( store_folder, 'FeatureWeights.eps'), dpi=1200) self.canvasFeature.getFigure().savefig(os.path.join( store_folder, 'FeatureWeights.jpg'), dpi=300) def InitialUi(self): # Update ROC canvers for normalizer in self._fae.GetNormalizerList(): self.comboNormalizer.addItem(normalizer.GetName()) for dimension_reduction in self._fae.GetDimensionReductionList(): self.comboDimensionReduction.addItem(dimension_reduction.GetName()) for classifier in self._fae.GetClassifierList(): self.comboClassifier.addItem(classifier.GetName()) for feature_selector in self._fae.GetFeatureSelectorList(): self.comboFeatureSelector.addItem(feature_selector.GetName()) self.spinBoxFeatureNumber.setMinimum( int(self._fae.GetFeatureNumberList()[0])) self.spinBoxFeatureNumber.setMaximum( int(self._fae.GetFeatureNumberList()[-1])) # Update Plot canvars if len(self._fae.GetNormalizerList()) > 1: self.comboPlotX.addItem('Normaliaztion') if len(self._fae.GetDimensionReductionList()) > 1: self.comboPlotX.addItem('Dimension Reduction') if len(self._fae.GetFeatureSelectorList()) > 1: self.comboPlotX.addItem('Feature Selector') if len(self._fae.GetClassifierList()) > 1: self.comboPlotX.addItem('Classifier') if len(self._fae.GetFeatureNumberList()) > 1: self.comboPlotX.addItem('Feature Number') self.comboPlotY.addItem('AUC') for index in self._fae.GetNormalizerList(): self.comboPlotNormalizer.addItem(index.GetName()) for index in self._fae.GetDimensionReductionList(): self.comboPlotDimensionReduction.addItem(index.GetName()) for index in self._fae.GetFeatureSelectorList(): self.comboPlotFeatureSelector.addItem(index.GetName()) for index in self._fae.GetClassifierList(): self.comboPlotClassifier.addItem(index.GetName()) self.spinPlotFeatureNumber.setMinimum( int(self._fae.GetFeatureNumberList()[0])) self.spinPlotFeatureNumber.setMaximum( int(self._fae.GetFeatureNumberList()[-1])) # Update Contribution canvas # self.spinFeatureSelectorFeatureNumber.setMaximum(int(self._fae.GetFeatureNumberList()[-1])) self.spinClassifierFeatureNumber.setMinimum( int(self._fae.GetFeatureNumberList()[0])) self.spinClassifierFeatureNumber.setMaximum( int(self._fae.GetFeatureNumberList()[-1])) for selector in self._fae.GetFeatureSelectorList(): self.comboContributionFeatureSelector.addItem(selector.GetName()) for classifier in self._fae.GetClassifierList(): specific_name = classifier.GetName() + '_coef.csv' if self._SearchSpecificFile( int(self._fae.GetFeatureNumberList()[0]), specific_name): self.comboContributionClassifier.addItem(classifier.GetName()) def UpdateROC(self): if (self.comboNormalizer.count() == 0) or \ (self.comboDimensionReduction.count() == 0) or \ (self.comboFeatureSelector.count() == 0) or \ (self.comboClassifier.count() == 0) or \ (self.spinBoxFeatureNumber.value() == 0): return case_name = self.comboNormalizer.currentText() + '_' + \ self.comboDimensionReduction.currentText() + '_' + \ self.comboFeatureSelector.currentText() + '_' + \ str(self.spinBoxFeatureNumber.value()) + '_' + \ self.comboClassifier.currentText() case_folder = os.path.join(self._root_folder, case_name) pred_list, label_list, name_list = [], [], [] if self.checkROCCVTrain.isChecked(): train_pred = np.load(os.path.join(case_folder, 'train_predict.npy')) train_label = np.load(os.path.join(case_folder, 'train_label.npy')) pred_list.append(train_pred) label_list.append(train_label) name_list.append('CV Train') if self.checkROCCVValidation.isChecked(): val_pred = np.load(os.path.join(case_folder, 'val_predict.npy')) val_label = np.load(os.path.join(case_folder, 'val_label.npy')) pred_list.append(val_pred) label_list.append(val_label) name_list.append('CV Validation') if self.checkROCTrain.isChecked(): all_train_pred = np.load( os.path.join(case_folder, 'all_train_predict.npy')) all_train_label = np.load( os.path.join(case_folder, 'all_train_label.npy')) pred_list.append(all_train_pred) label_list.append(all_train_label) name_list.append('Train') if self.checkROCTest.isChecked(): if os.path.exists(os.path.join(case_folder, 'test_label.npy')): test_pred = np.load( os.path.join(case_folder, 'test_predict.npy')) test_label = np.load( os.path.join(case_folder, 'test_label.npy')) pred_list.append(test_pred) label_list.append(test_label) name_list.append('Test') if len(pred_list) > 0: DrawROCList(pred_list, label_list, name_list=name_list, is_show=False, fig=self.canvasROC.getFigure()) self.canvasROC.draw() def _UpdatePlotButtons(self, selected_index): index = [0, 0, 0, 0, 0] if self.checkPlotMaximum.isChecked(): self.comboPlotNormalizer.setEnabled(False) self.comboPlotDimensionReduction.setEnabled(False) self.comboPlotFeatureSelector.setEnabled(False) self.comboPlotClassifier.setEnabled(False) self.spinPlotFeatureNumber.setEnabled(False) else: self.comboPlotNormalizer.setEnabled(True) self.comboPlotDimensionReduction.setEnabled(True) self.comboPlotFeatureSelector.setEnabled(True) self.comboPlotClassifier.setEnabled(True) self.spinPlotFeatureNumber.setEnabled(True) index[0] = self.comboPlotNormalizer.currentIndex() index[1] = self.comboPlotDimensionReduction.currentIndex() index[2] = self.comboPlotFeatureSelector.currentIndex() index[4] = self.comboPlotClassifier.currentIndex() index[3] = self.spinPlotFeatureNumber.value() - int( self._fae.GetFeatureNumberList()[0]) if selected_index == 0: self.comboPlotNormalizer.setEnabled(False) index[0] = [ temp for temp in range(len(self._fae.GetNormalizerList())) ] elif selected_index == 1: self.comboPlotDimensionReduction.setEnabled(False) index[1] = [ temp for temp in range( len(self._fae.GetDimensionReductionList())) ] elif selected_index == 2: self.comboPlotFeatureSelector.setEnabled(False) index[2] = [ temp for temp in range(len(self._fae.GetFeatureSelectorList())) ] elif selected_index == 4: self.comboPlotClassifier.setEnabled(False) index[4] = [ temp for temp in range(len(self._fae.GetClassifierList())) ] elif selected_index == 3: self.spinPlotFeatureNumber.setEnabled(False) index[3] = [ temp for temp in range(len(self._fae.GetFeatureNumberList())) ] return index 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 UpdateContribution(self): if not self.checkContributionShow.isChecked(): return if self.radioContributionFeatureSelector.isChecked(): file_name = self.comboContributionFeatureSelector.currentText( ) + '_sort.csv' file_path = self._SearchSpecificFile( int(self._fae.GetFeatureNumberList()[0]), file_name) if file_path: df = pd.read_csv(file_path, index_col=0) feature_name = list(df.index) value = list(np.abs(df.iloc[:, 0])) GeneralFeatureSort( feature_name, value, max_num=self.spinFeatureSelectorFeatureNumber.value(), is_show=False, fig=self.canvasFeature.getFigure()) elif self.radioContributionClassifier.isChecked(): specific_name = self.comboContributionClassifier.currentText( ) + '_coef.csv' feature_selector_name = self.comboContributionFeatureSelector.currentText( ) file_path = self._SearchSpecificFile( self.spinClassifierFeatureNumber.value(), specific_name, feature_selector_name) if file_path: df = pd.read_csv(file_path, index_col=0) feature_name = list(df.index) value = list(np.abs(df.iloc[:, 0])) try: SortRadiomicsFeature(feature_name, value, is_show=False, fig=self.canvasFeature.getFigure()) except: GeneralFeatureSort(feature_name, value, is_show=False, fig=self.canvasFeature.getFigure()) self.canvasFeature.draw() def SetResultDescription(self): text = "Normalizer:\n" for index in self._fae.GetNormalizerList(): text += (index.GetName() + '\n') text += '\n' text += "Dimension Reduction:\n" for index in self._fae.GetDimensionReductionList(): text += (index.GetName() + '\n') text += '\n' text += "Feature Selector:\n" for index in self._fae.GetFeatureSelectorList(): text += (index.GetName() + '\n') text += '\n' text += "Feature Number:\n" text += "{:s} - {:s}\n".format(self._fae.GetFeatureNumberList()[0], self._fae.GetFeatureNumberList()[-1]) text += '\n' text += "Classifier:\n" for index in self._fae.GetClassifierList(): text += (index.GetName() + '\n') text += '\n' self.textEditDescription.setPlainText(text) def UpdateSheet(self): data = np.array([]) df = pd.DataFrame() data = self._fae.GetAUCMetric() std_data = self._fae.GetAUCstdMetric() self.tableClinicalStatistic.clear() self.tableClinicalStatistic.setSortingEnabled(False) if self.comboSheet.currentText() == 'Train': data = self._fae.GetAUCMetric()['train'] std_data = self._fae.GetAUCstdMetric()['train'] df = self.sheet_dict['train'] elif self.comboSheet.currentText() == 'Validation': data = self._fae.GetAUCMetric()['val'] std_data = self._fae.GetAUCstdMetric()['val'] df = self.sheet_dict['val'] elif self.comboSheet.currentText() == 'Test': data = self._fae.GetAUCMetric()['test'] std_data = self._fae.GetAUCstdMetric()['test'] df = self.sheet_dict['test'] elif self.comboSheet.currentText() == 'Test On Val': data = self._fae.GetAUCMetric()['val'] std_data = self._fae.GetAUCstdMetric()['val'] df = self.sheet_dict['test'] else: return if self.checkMaxFeatureNumber.isChecked(): name_list = [] for normalizer, normalizer_index in zip( self._fae.GetNormalizerList(), range(len(self._fae.GetNormalizerList()))): for dimension_reducer, dimension_reducer_index in zip( self._fae.GetDimensionReductionList(), range(len(self._fae.GetDimensionReductionList()))): for feature_selector, feature_selector_index in zip( self._fae.GetFeatureSelectorList(), range(len(self._fae.GetFeatureSelectorList()))): for classifier, classifier_index in zip( self._fae.GetClassifierList(), range(len(self._fae.GetClassifierList()))): sub_auc = data[normalizer_index, dimension_reducer_index, feature_selector_index, :, classifier_index] sub_auc_std = std_data[normalizer_index, dimension_reducer_index, feature_selector_index, :, classifier_index] one_se = max(sub_auc) - sub_auc_std[np.argmax( sub_auc)] for feature_number_index in range( len(self._fae.GetFeatureNumberList())): if data[normalizer_index, dimension_reducer_index, feature_selector_index, feature_number_index, classifier_index] >= one_se: name = normalizer.GetName() + '_' + dimension_reducer.GetName() + '_' + \ feature_selector.GetName() + '_' + str(self._fae.GetFeatureNumberList()[feature_number_index]) + '_' + \ classifier.GetName() name_list.append(name) break df = df.loc[name_list] df.sort_index(inplace=True) self.tableClinicalStatistic.setRowCount(df.shape[0]) self.tableClinicalStatistic.setColumnCount(df.shape[1] + 1) headerlabels = df.columns.tolist() headerlabels.insert(0, 'models name') self.tableClinicalStatistic.setHorizontalHeaderLabels(headerlabels) # self.tableClinicalStatistic.setVerticalHeaderLabels(list(df.index)) for row_index in range(df.shape[0]): for col_index in range(df.shape[1] + 1): if col_index == 0: self.tableClinicalStatistic.setItem( row_index, col_index, QTableWidgetItem(df.index[row_index])) else: self.tableClinicalStatistic.setItem( row_index, col_index, QTableWidgetItem(str(df.iloc[row_index, col_index - 1]))) self.tableClinicalStatistic.setSortingEnabled(True) def SetResultTable(self): self.sheet_dict['train'] = pd.read_csv(os.path.join( self._root_folder, 'train_result.csv'), index_col=0) self.comboSheet.addItem('Train') self.sheet_dict['val'] = pd.read_csv(os.path.join( self._root_folder, 'val_result.csv'), index_col=0) self.comboSheet.addItem('Validation') if os.path.exists(os.path.join(self._root_folder, 'test_result.csv')): self.sheet_dict['test'] = pd.read_csv(os.path.join( self._root_folder, 'test_result.csv'), index_col=0) self.comboSheet.addItem('Test') self.comboSheet.addItem('Test On Val') self.UpdateSheet() def _SearchSpecificFile(self, feature_number, specific_file_name, specific_file_name2=''): for rt, folder, files in os.walk(self._root_folder): for file_name in files: # print(file_name) if specific_file_name2: if (file_name.lower() == specific_file_name.lower()) and \ ('_{:d}_'.format(feature_number) in rt) and \ (specific_file_name2 in rt): return os.path.join(rt, file_name) else: if (file_name.lower() == specific_file_name.lower()) and ( '_{:d}_'.format(feature_number) in rt): return os.path.join(rt, file_name) return ''
class VisualizationConnection(QWidget, Ui_Visualization): def __init__(self, parent=None): self._root_folder = '' self._fae = FeatureAnalysisPipelines() self.sheet_dict = dict() self.logger = eclog(os.path.split(__file__)[-1]).GetLogger() self.__is_ui_ready = False self.__is_clear = False super(VisualizationConnection, self).__init__(parent) self.setupUi(self) self.buttonLoadResult.clicked.connect(self.LoadAll) self.buttonClearResult.clicked.connect(self.ClearAll) self.buttonSave.clicked.connect(self.Save) self.buttonGenerateDescription.clicked.connect(self.GenerateDescription) self.__plt_roc = self.canvasROC.getFigure().add_subplot(111) self.__plt_plot = self.canvasPlot.getFigure().add_subplot(111) self.__contribution = self.canvasFeature.getFigure().add_subplot(111) # Update Sheet self.tableClinicalStatistic.setEditTriggers(QAbstractItemView.NoEditTriggers) self.tableClinicalStatistic.setSelectionBehavior(QAbstractItemView.SelectRows) self.comboSheet.currentIndexChanged.connect(self.UpdateSheet) self.checkMaxFeatureNumber.stateChanged.connect(self.UpdateSheet) self.tableClinicalStatistic.itemSelectionChanged.connect(self.ShowOneResult) # Update ROC canvas self.comboNormalizer.currentIndexChanged.connect(self.UpdateROC) self.comboDimensionReduction.currentIndexChanged.connect(self.UpdateROC) self.comboFeatureSelector.currentIndexChanged.connect(self.UpdateROC) self.comboClassifier.currentIndexChanged.connect(self.UpdateROC) self.spinBoxFeatureNumber.valueChanged.connect(self.UpdateROC) self.checkROCCVTrain.stateChanged.connect(self.UpdateROC) self.checkROCCVValidation.stateChanged.connect(self.UpdateROC) self.checkROCTrain.stateChanged.connect(self.UpdateROC) self.checkROCTest.stateChanged.connect(self.UpdateROC) # Update Plot canvas self.comboPlotX.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotY.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotNormalizer.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotDimensionReduction.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotFeatureSelector.currentIndexChanged.connect(self.UpdatePlot) self.comboPlotClassifier.currentIndexChanged.connect(self.UpdatePlot) self.spinPlotFeatureNumber.valueChanged.connect(self.UpdatePlot) self.checkPlotCVTrain.stateChanged.connect(self.UpdatePlot) self.checkPlotCVValidation.stateChanged.connect(self.UpdatePlot) self.checkPlotTrain.stateChanged.connect(self.UpdatePlot) self.checkPlotOneSE.stateChanged.connect(self.UpdatePlot) self.checkPlotTest.stateChanged.connect(self.UpdatePlot) # # Update Contribution canvas self.radioContributionFeatureSelector.toggled.connect(self.UpdateContribution) self.radioContributionClassifier.toggled.connect(self.UpdateContribution) self.comboContributionNormalizor.currentIndexChanged.connect(self.UpdateContribution) self.comboContributionDimension.currentIndexChanged.connect(self.UpdateContribution) self.comboContributionFeatureSelector.currentIndexChanged.connect(self.UpdateContribution) self.comboContributionClassifier.currentIndexChanged.connect(self.UpdateContribution) self.spinContributeFeatureNumber.valueChanged.connect(self.UpdateContribution) def LoadAll(self): dlg = QFileDialog() dlg.setFileMode(QFileDialog.DirectoryOnly) dlg.setOption(QFileDialog.ShowDirsOnly) if dlg.exec_(): self._root_folder = dlg.selectedFiles()[0] if not os.path.exists(self._root_folder): return if not r'.FAEresult4129074093819729087' in os.listdir(self._root_folder): QMessageBox.about(self, 'Load Error', 'This folder is not supported for import') return try: self.lineEditResultPath.setText(self._root_folder) self._fae.LoadAll(self._root_folder) self.SetResultDescription() self.SetResultTable() self.InitialUi() except Exception as ex: QMessageBox.about(self, "Load Error", ex.__str__()) self.logger.log('Load Error, The reason is ' + str(ex)) self.ClearAll() return self.buttonClearResult.setEnabled(True) self.buttonSave.setEnabled(True) self.buttonLoadResult.setEnabled(False) def ClearAll(self): self.__is_clear = True self.buttonLoadResult.setEnabled(True) self.buttonSave.setEnabled(False) self.buttonClearResult.setEnabled(False) self.checkROCCVTrain.setChecked(False) self.checkROCCVValidation.setChecked(False) self.checkROCTrain.setChecked(False) self.checkROCTest.setChecked(False) self.checkPlotCVTrain.setChecked(False) self.checkPlotCVValidation.setChecked(False) self.checkPlotTrain.setChecked(False) self.checkPlotOneSE.setChecked(False) self.checkPlotTest.setChecked(False) self.radioContributionFeatureSelector.setChecked(False) self.checkMaxFeatureNumber.setChecked(False) self.canvasROC.getFigure().clear() self.canvasPlot.getFigure().clear() self.canvasFeature.getFigure().clear() self.__plt_roc = self.canvasROC.getFigure().add_subplot(111) self.__plt_plot = self.canvasPlot.getFigure().add_subplot(111) self.__contribution = self.canvasFeature.getFigure().add_subplot(111) self.canvasROC.draw() self.canvasPlot.draw() self.canvasFeature.draw() self.textEditDescription.clear() self.lineEditResultPath.clear() self.comboSheet.clear() self.comboClassifier.clear() self.comboDimensionReduction.clear() self.comboNormalizer.clear() self.comboFeatureSelector.clear() self.comboPlotClassifier.clear() self.comboPlotDimensionReduction.clear() self.comboPlotFeatureSelector.clear() self.comboPlotNormalizer.clear() self.comboPlotX.clear() self.comboPlotY.clear() self.comboContributionNormalizor.clear() self.comboContributionDimension.clear() self.comboContributionClassifier.clear() self.comboContributionFeatureSelector.clear() self.spinBoxFeatureNumber.setValue(0) self.spinPlotFeatureNumber.setValue(0) self.spinPlotFeatureNumber.setEnabled(False) self.spinContributeFeatureNumber.setValue(1) self.tableClinicalStatistic.clear() self.tableClinicalStatistic.setRowCount(0) self.tableClinicalStatistic.setColumnCount(0) self.tableClinicalStatistic.setHorizontalHeaderLabels(list([])) self.tableClinicalStatistic.setVerticalHeaderLabels(list([])) self._fae = FeatureAnalysisPipelines() self._root_folder = '' self.sheet_dict = dict() self.__is_ui_ready = False self.__is_clear = False def Save(self): dlg = QFileDialog() dlg.setFileMode(QFileDialog.DirectoryOnly) dlg.setOption(QFileDialog.ShowDirsOnly) if dlg.exec_(): store_folder = dlg.selectedFiles()[0] try: self.canvasROC.getFigure().savefig(os.path.join(store_folder, 'ROC.eps'), dpi=1200) self.canvasROC.getFigure().savefig(os.path.join(store_folder, 'ROC.jpg'), dpi=300) except Exception as e: QMessageBox.about(self, 'Save Figure Failed', 'There is no ROC figure.\n' + e.__str__()) try: self.canvasPlot.getFigure().savefig(os.path.join(store_folder, 'Compare.eps'), dpi=1200) self.canvasPlot.getFigure().savefig(os.path.join(store_folder, 'Compare.jpg'), dpi=300) except Exception as e: QMessageBox.about(self, 'Save Figure Failed', 'There is no AUC comparison figure.\n' + e.__str__()) try: self.canvasFeature.getFigure().savefig(os.path.join(store_folder, 'FeatureWeights.eps'), dpi=1200) self.canvasFeature.getFigure().savefig(os.path.join(store_folder, 'FeatureWeights.jpg'), dpi=300) except Exception as e: QMessageBox.about(self, 'Save Figure Failed', 'There is no Feature Contribution figure.\n' + e.__str__()) def InitialUi(self): # Update ROC canvers for normalizer in self._fae.GetNormalizerList(): self.comboNormalizer.addItem(normalizer.GetName()) for dimension_reduction in self._fae.GetDimensionReductionList(): self.comboDimensionReduction.addItem(dimension_reduction.GetName()) for classifier in self._fae.GetClassifierList(): self.comboClassifier.addItem(classifier.GetName()) for feature_selector in self._fae.GetFeatureSelectorList(): self.comboFeatureSelector.addItem(feature_selector.GetName()) self.spinBoxFeatureNumber.setMinimum(int(self._fae.GetFeatureNumberList()[0])) self.spinBoxFeatureNumber.setMaximum(int(self._fae.GetFeatureNumberList()[-1])) # Update Plot canvars if len(self._fae.GetNormalizerList()) > 1: self.comboPlotX.addItem('Normaliaztion') if len(self._fae.GetDimensionReductionList()) > 1: self.comboPlotX.addItem('Dimension Reduction') if len(self._fae.GetFeatureSelectorList()) > 1: self.comboPlotX.addItem('Feature Selector') if len(self._fae.GetClassifierList()) > 1: self.comboPlotX.addItem('Classifier') if len(self._fae.GetFeatureNumberList()) > 1: self.comboPlotX.addItem('Feature Number') self.comboPlotY.addItem('AUC') for index in self._fae.GetNormalizerList(): self.comboPlotNormalizer.addItem(index.GetName()) for index in self._fae.GetDimensionReductionList(): self.comboPlotDimensionReduction.addItem(index.GetName()) for index in self._fae.GetFeatureSelectorList(): self.comboPlotFeatureSelector.addItem(index.GetName()) for index in self._fae.GetClassifierList(): self.comboPlotClassifier.addItem(index.GetName()) self.spinPlotFeatureNumber.setMinimum(int(self._fae.GetFeatureNumberList()[0])) self.spinPlotFeatureNumber.setMaximum(int(self._fae.GetFeatureNumberList()[-1])) # Update Contribution canvas for index in self._fae.GetNormalizerList(): self.comboContributionNormalizor.addItem(index.GetName()) for index in self._fae.GetDimensionReductionList(): self.comboContributionDimension.addItem(index.GetName()) for selector in self._fae.GetFeatureSelectorList(): self.comboContributionFeatureSelector.addItem(selector.GetName()) for classifier in self._fae.GetClassifierList(): specific_name = classifier.GetName() + '_coef.csv' if self._SearchSpecificFile(int(self._fae.GetFeatureNumberList()[0]), specific_name): self.comboContributionClassifier.addItem(classifier.GetName()) self.spinContributeFeatureNumber.setMinimum(int(self._fae.GetFeatureNumberList()[0])) self.spinContributeFeatureNumber.setMaximum(int(self._fae.GetFeatureNumberList()[-1])) self.__is_ui_ready = True def UpdateROC(self): if not self.__is_ui_ready: return if (self.comboNormalizer.count() == 0) or \ (self.comboDimensionReduction.count() == 0) or \ (self.comboFeatureSelector.count() == 0) or \ (self.comboClassifier.count() == 0) or \ (self.spinBoxFeatureNumber.value() == 0): return case_name = self.comboNormalizer.currentText() + '_' + \ self.comboDimensionReduction.currentText() + '_' + \ self.comboFeatureSelector.currentText() + '_' + \ str(self.spinBoxFeatureNumber.value()) + '_' + \ self.comboClassifier.currentText() case_folder = os.path.join(self._root_folder, case_name) pred_list, label_list, name_list = [], [], [] if self.checkROCCVTrain.isChecked(): train_pred = np.load(os.path.join(case_folder, 'train_predict.npy')) train_label = np.load(os.path.join(case_folder, 'train_label.npy')) pred_list.append(train_pred) label_list.append(train_label) name_list.append('CV Train') if self.checkROCCVValidation.isChecked(): val_pred = np.load(os.path.join(case_folder, 'val_predict.npy')) val_label = np.load(os.path.join(case_folder, 'val_label.npy')) pred_list.append(val_pred) label_list.append(val_label) name_list.append('CV Validation') if self.checkROCTrain.isChecked(): all_train_pred = np.load(os.path.join(case_folder, 'all_train_predict.npy')) all_train_label = np.load(os.path.join(case_folder, 'all_train_label.npy')) pred_list.append(all_train_pred) label_list.append(all_train_label) name_list.append('Train') if self.checkROCTest.isChecked(): if os.path.exists(os.path.join(case_folder, 'test_label.npy')): test_pred = np.load(os.path.join(case_folder, 'test_predict.npy')) test_label = np.load(os.path.join(case_folder, 'test_label.npy')) pred_list.append(test_pred) label_list.append(test_label) name_list.append('Test') if len(pred_list) > 0: DrawROCList(pred_list, label_list, name_list=name_list, is_show=False, fig=self.canvasROC.getFigure()) self.canvasROC.draw() def _UpdatePlotButtons(self, selected_index): index = [0, 0, 0, 0, 0] self.comboPlotNormalizer.setEnabled(True) self.comboPlotDimensionReduction.setEnabled(True) self.comboPlotFeatureSelector.setEnabled(True) self.comboPlotClassifier.setEnabled(True) self.spinPlotFeatureNumber.setEnabled(True) index[0] = self.comboPlotNormalizer.currentIndex() index[1] = self.comboPlotDimensionReduction.currentIndex() index[2] = self.comboPlotFeatureSelector.currentIndex() index[4] = self.comboPlotClassifier.currentIndex() index[3] = self.spinPlotFeatureNumber.value() - int(self._fae.GetFeatureNumberList()[0]) if selected_index == 0: self.comboPlotNormalizer.setEnabled(False) index[0] = [temp for temp in range(len(self._fae.GetNormalizerList()))] elif selected_index == 1: self.comboPlotDimensionReduction.setEnabled(False) index[1] = [temp for temp in range(len(self._fae.GetDimensionReductionList()))] elif selected_index == 2: self.comboPlotFeatureSelector.setEnabled(False) index[2] = [temp for temp in range(len(self._fae.GetFeatureSelectorList()))] elif selected_index == 4: self.comboPlotClassifier.setEnabled(False) index[4] = [temp for temp in range(len(self._fae.GetClassifierList()))] elif selected_index == 3: self.spinPlotFeatureNumber.setEnabled(False) index[3] = [temp for temp in range(len(self._fae.GetFeatureNumberList()))] return index 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.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']) 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']) 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']) 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: 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() def UpdateContribution(self): if (not self.__is_ui_ready) or self.__is_clear: return try: one_result_folder_name = self.comboContributionNormalizor.currentText() + '_' + \ self.comboContributionDimension.currentText() + '_' + \ self.comboContributionFeatureSelector.currentText() + '_' + \ str(self.spinContributeFeatureNumber.value()) + '_' + \ self.comboContributionClassifier.currentText() one_result_folder = os.path.join(self._root_folder, one_result_folder_name) # This is compatible with the previous version if not os.path.exists(one_result_folder): one_result_folder_name = self.comboContributionNormalizor.currentText() + '_Cos_' + \ self.comboContributionFeatureSelector.currentText() + '_' + \ str(self.spinContributeFeatureNumber.value()) + '_' + \ self.comboContributionClassifier.currentText() one_result_folder = os.path.join(self._root_folder, one_result_folder_name) if self.radioContributionFeatureSelector.isChecked(): file_name = self.comboContributionFeatureSelector.currentText() + '_sort.csv' file_path = os.path.join(one_result_folder, file_name) if not os.path.exists(file_path): file_name = self.comboContributionFeatureSelector.currentText().lower() + '_sort.csv' file_path = os.path.join(one_result_folder, file_name) if file_path: df = pd.read_csv(file_path, index_col=0) value = list(df.iloc[:, 0]) sort_by = df.columns.values[0] if sort_by == 'rank': reverse = False elif sort_by == 'F' or sort_by == 'weight': reverse = True else: reverse = False print('Invalid feature selector sort name.') #add positive and negatiove info for coef processed_feature_name = list(df.index) original_value = list(df.iloc[:, 0]) for index in range(len(original_value)): if original_value[index] > 0: processed_feature_name[index] = processed_feature_name[index] + ' P' else: processed_feature_name[index] = processed_feature_name[index] + ' N' GeneralFeatureSort(processed_feature_name, value, max_num=self.spinContributeFeatureNumber.value(), is_show=False, fig=self.canvasFeature.getFigure(), reverse=reverse) elif self.radioContributionClassifier.isChecked(): specific_name = self.comboContributionClassifier.currentText() + '_coef.csv' file_path = os.path.join(one_result_folder, specific_name) if not os.path.exists(file_path): specific_name = self.comboContributionClassifier.currentText().lower() + '_coef.csv' file_path = os.path.join(one_result_folder, specific_name) if file_path: df = pd.read_csv(file_path, index_col=0) feature_name = list(df.index) value = list(np.abs(df.iloc[:, 0])) #add positive and negatiove info for coef processed_feature_name = list(df.index) original_value = list(df.iloc[:, 0]) for index in range(len(original_value)): if original_value[index] > 0: processed_feature_name[index] = processed_feature_name[index] + ' P' else: processed_feature_name[index] = processed_feature_name[index] + ' N' # try: # SortRadiomicsFeature(processed_feature_name, value, is_show=False, fig=self.canvasFeature.getFigure()) # except: GeneralFeatureSort(processed_feature_name, value, is_show=False, fig=self.canvasFeature.getFigure()) self.canvasFeature.draw() except Exception as e: content = 'In Visualization, UpdateContribution failed' self.logger.error('{}{}'.format(content, str(e))) QMessageBox.about(self, content, e.__str__()) def SetResultDescription(self): text = "Normalizer:\n" for index in self._fae.GetNormalizerList(): text += (index.GetName() + '\n') text += '\n' text += "Dimension Reduction:\n" for index in self._fae.GetDimensionReductionList(): text += (index.GetName() + '\n') text += '\n' text += "Feature Selector:\n" for index in self._fae.GetFeatureSelectorList(): text += (index.GetName() + '\n') text += '\n' text += "Feature Number:\n" text += "{:s} - {:s}\n".format(self._fae.GetFeatureNumberList()[0], self._fae.GetFeatureNumberList()[-1]) text += '\n' text += "Classifier:\n" for index in self._fae.GetClassifierList(): text += (index.GetName() + '\n') text += '\n' text += 'Cross Validation: ' + self._fae.GetCrossValidation().GetName() self.textEditDescription.setPlainText(text) def UpdateSheet(self): if self.__is_clear: self.comboSheet.setEnabled(False) return None if self.checkMaxFeatureNumber.isChecked(): self.comboSheet.setEnabled(False) else: self.comboSheet.setEnabled(True) self.tableClinicalStatistic.clear() self.tableClinicalStatistic.setSortingEnabled(False) if self.comboSheet.currentText() == 'Train': df = self.sheet_dict['train'] elif self.comboSheet.currentText() == 'Validation': df = self.sheet_dict['val'] elif self.comboSheet.currentText() == 'Test': df = self.sheet_dict['test'] else: return if self.checkMaxFeatureNumber.isChecked(): self.sheet_dict['test'] = pd.read_csv(os.path.join(self._root_folder, 'test_result.csv'), index_col=0) data = self._fae.GetAUCMetric()['val'] std_data = self._fae.GetAUCstdMetric()['val'] df_val = self.sheet_dict['val'] df_test = self.sheet_dict['test'] name_list = [] for normalizer_index, normalizer in enumerate(self._fae.GetNormalizerList()): for dimension_reducer_index, dimension_reducer in enumerate(self._fae.GetDimensionReductionList()): for feature_selector_index, feature_selector in enumerate(self._fae.GetFeatureSelectorList()): for classifier_index, classifier in enumerate(self._fae.GetClassifierList()): sub_auc = data[normalizer_index, dimension_reducer_index, feature_selector_index, :, classifier_index] sub_auc_std = std_data[normalizer_index, dimension_reducer_index, feature_selector_index, :, classifier_index] one_se = max(sub_auc)-sub_auc_std[np.argmax(sub_auc)] for feature_number_index in range(len(self._fae.GetFeatureNumberList())): if data[normalizer_index, dimension_reducer_index, feature_selector_index, feature_number_index, classifier_index] >= one_se: name = normalizer.GetName() + '_' + dimension_reducer.GetName() + '_' + \ feature_selector.GetName() + '_' + str(self._fae.GetFeatureNumberList()[feature_number_index]) + '_' + \ classifier.GetName() name_list.append(name) break # choose the selected models from all test result df_val = df_val.loc[name_list] max_index = df_val['auc'].idxmax() sub_serise = df_val.loc[max_index] max_array = sub_serise.get_values().reshape(1, -1) max_auc_df = pd.DataFrame(data=max_array, columns=sub_serise.index.tolist(), index=[max_index]) max_auc_95ci = max_auc_df.at[max_index, 'auc 95% CIs'] max_auc_95ci = re.findall(r"\d+\.?\d*", max_auc_95ci) sub_val_df = df_val[(df_val['auc'] >= float(max_auc_95ci[0])) & (df_val['auc'] <= float(max_auc_95ci[1]))] index_by_val = sub_val_df.index.tolist() df = df_test.loc[index_by_val] df.sort_index(inplace=True) self.tableClinicalStatistic.setRowCount(df.shape[0]) self.tableClinicalStatistic.setColumnCount(df.shape[1]+1) headerlabels = df.columns.tolist() headerlabels.insert(0, 'models name') self.tableClinicalStatistic.setHorizontalHeaderLabels(headerlabels) # self.tableClinicalStatistic.setVerticalHeaderLabels(list(df.index)) for row_index in range(df.shape[0]): for col_index in range(df.shape[1]+1): if col_index == 0: self.tableClinicalStatistic.setItem(row_index, col_index, QTableWidgetItem(df.index[row_index])) else: self.tableClinicalStatistic.setItem(row_index, col_index, QTableWidgetItem(str(df.iloc[row_index, col_index-1]))) self.tableClinicalStatistic.setSortingEnabled(True) def SetResultTable(self): self.sheet_dict['train'] = pd.read_csv(os.path.join(self._root_folder, 'train_result.csv'), index_col=0) self.comboSheet.addItem('Train') self.sheet_dict['val'] = pd.read_csv(os.path.join(self._root_folder, 'val_result.csv'), index_col=0) self.comboSheet.addItem('Validation') if os.path.exists(os.path.join(self._root_folder, 'test_result.csv')): self.sheet_dict['test'] = pd.read_csv(os.path.join(self._root_folder, 'test_result.csv'), index_col=0) self.comboSheet.addItem('Test') self.UpdateSheet() def _SearchSpecificFile(self, feature_number, specific_file_name, specific_file_name2=''): for rt, folder, files in os.walk(self._root_folder): for file_name in files: # print(file_name) if specific_file_name2: if (file_name.lower() == specific_file_name.lower()) and \ ('_{:d}_'.format(feature_number) in rt) and \ (specific_file_name2 in rt): return os.path.join(rt, file_name) else: if (file_name.lower() == specific_file_name.lower()) and ('_{:d}_'.format(feature_number) in rt): return os.path.join(rt, file_name) return '' def ShowOneResult(self): try: # for index in self.tableClinicalStatistic.selectedIndexes(): if not self.tableClinicalStatistic.selectedIndexes(): return None index = self.tableClinicalStatistic.selectedIndexes()[0] row = index.row() one_item = self.tableClinicalStatistic.item(row, 0) text = str(one_item.text()) current_normalizer, current_dimension_reducer, current_feature_selector, current_feature_number, current_classifier = \ text.split('_') self.comboNormalizer.setCurrentText(current_normalizer) self.comboDimensionReduction.setCurrentText(current_dimension_reducer) self.comboFeatureSelector.setCurrentText(current_feature_selector) self.comboClassifier.setCurrentText(current_classifier) self.spinBoxFeatureNumber.setValue(int(current_feature_number)) if not (self.checkROCTrain.isChecked() or self.checkROCCVTrain.isChecked() or self.checkROCCVValidation.isChecked() or self.checkROCTrain.isChecked()): self.checkROCCVTrain.setCheckState(True) self.checkROCCVValidation.setCheckState(True) self.UpdateROC() # Update the AUC versus feature number self.comboPlotNormalizer.setCurrentText(current_normalizer) self.comboPlotDimensionReduction.setCurrentText(current_dimension_reducer) self.comboPlotFeatureSelector.setCurrentText(current_feature_selector) self.comboPlotClassifier.setCurrentText(current_classifier) self.comboPlotX.setCurrentText('Feature Number') if not (self.checkPlotTrain.isChecked() or self.checkPlotCVTrain.isChecked() or self.checkPlotCVValidation.isChecked()): self.checkPlotCVValidation.setCheckState(True) self.UpdatePlot() # Update the Contribution self.comboContributionNormalizor.setCurrentText(current_normalizer) self.comboContributionDimension.setCurrentText(current_dimension_reducer) self.comboContributionFeatureSelector.setCurrentText(current_feature_selector) self.comboContributionClassifier.setCurrentText(current_classifier) self.spinContributeFeatureNumber.setValue(int(current_feature_number)) self.UpdateContribution() except Exception as e: content = 'Visualization, ShowOneResult failed: ' self.logger.error('{}{}'.format(content, str(e))) QMessageBox.about(self, content, e.__str__()) def GenerateDescription(self): if (self.comboNormalizer.count() == 0) or \ (self.comboDimensionReduction.count() == 0) or \ (self.comboFeatureSelector.count() == 0) or \ (self.comboClassifier.count() == 0) or \ (self.spinBoxFeatureNumber.value() == 0): return case_name = self.comboNormalizer.currentText() + '_' + \ self.comboDimensionReduction.currentText() + '_' + \ self.comboFeatureSelector.currentText() + '_' + \ str(self.spinBoxFeatureNumber.value()) + '_' + \ self.comboClassifier.currentText() case_folder = os.path.join(self._root_folder, case_name) current_pipeline = OnePipeline() try: current_pipeline.LoadPipeline(os.path.join(case_folder, 'pipeline_info.csv')) except Exception as ex: QMessageBox.about(self, "In Description, Load Pipeline_info Error", ex.__str__()) self.logger.error('Load Pipeline Error, The reason is ' + str(ex)) dlg = QFileDialog() dlg.setFileMode(QFileDialog.DirectoryOnly) dlg.setOption(QFileDialog.ShowDirsOnly) if dlg.exec_(): store_folder = dlg.selectedFiles()[0] roc_path = os.path.join(store_folder, 'ROC.jpg') self.canvasROC.getFigure().savefig(roc_path, dpi=300) report = Description() try: report.Run(current_pipeline, self._root_folder, store_folder) os.system("explorer.exe {:s}".format(os.path.normpath(store_folder))) except Exception as ex: QMessageBox.about(self, 'Description Generate Error: ', ex.__str__()) self.logger.log('Description Generate Error: ' + str(ex))