def UpdateSheet(self):
self.tableClinicalStatistic.clear()
self.tableClinicalStatistic.setSortingEnabled(False)
if self.comboSheet.currentText() == TRAIN:
df = self.sheet_dict[TRAIN]
elif self.comboSheet.currentText() == CV_VAL:
df = self.sheet_dict[CV_VAL]
elif self.comboSheet.currentText() == TEST:
df = self.sheet_dict[TEST]
else:
mylog.error('Wrong key in the result table, or may click clear')
return
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)
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 ShowOneResult(self):
if not self.tableClinicalStatistic.selectedIndexes():
return None
index = self.tableClinicalStatistic.selectedIndexes()[0]
row = index.row()
one_item = self.tableClinicalStatistic.item(row, 0)
pipeline_name = str(one_item.text())
normalizer, dr, fs, fn, fitter = pipeline_name.split('_')
fitter_folder = os.path.join(self._root_folder, normalizer, dr,
fs + '_' + fn, fitter)
self._current_fitter.Load(fitter_folder)
self.textEditModelDescription.setText(self._current_fitter.Summary())
try:
if CheckTextInCombo(pipeline_name, self.comboSurvivalModel):
self.comboSurvivalModel.setCurrentText(pipeline_name)
self.UpdateSurvivalCurve()
part_name = self._FullName2PartName(pipeline_name)
if CheckTextInCombo(part_name, self.comboCindexModel):
self.comboCindexModel.setCurrentText(part_name)
self.UpdateFeature()
if CheckTextInCombo(pipeline_name, self.comboModelContribution):
self.comboModelContribution.setCurrentText(pipeline_name)
self.UpdateFeature()
except Exception as e:
mylog.error(e)
def SaveFigure(self):
dlg = QFileDialog()
dlg.setFileMode(QFileDialog.DirectoryOnly)
dlg.setOption(QFileDialog.ShowDirsOnly)
if dlg.exec_():
store_folder = dlg.selectedFiles()[0]
try:
self.canvasSurvival.getFigure().savefig(os.path.join(
store_folder, 'SurvivalCurve.eps'),
dpi=1200)
self.canvasSurvival.getFigure().savefig(os.path.join(
store_folder, 'SurvivalCurve.jpg'),
dpi=300)
except Exception as e:
mylog.error('Saving Survival Curve {}'.format(e.__str__()))
QMessageBox.about(
self, 'Save Figure Failed',
'There is no SurvivalCurve figure.\n' + e.__str__())
try:
self.canvasFeature.getFigure().savefig(os.path.join(
store_folder, 'Feature Contribution.eps'),
dpi=1200)
self.canvasFeature.getFigure().savefig(os.path.join(
store_folder, 'Feature Contribution.jpg'),
dpi=300)
except Exception as e:
mylog.error('Saving Feature Contribution {}'.format(
e.__str__()))
QMessageBox.about(
self, 'Save Figure Failed',
'There is no Feature Contribution figure.\n' + e.__str__())
def LoadReducer(self, store_folder):
with open(os.path.join(store_folder, '{}.csv'.format(TRANSFORM_TYPE_SUB_FEATURES)),
'r', newline='') as file:
reader = csv.reader(file)
for row in reader:
if row[0] == TRANSFORM_TYPE:
assert(row [1] == TRANSFORM_TYPE_SUB)
elif row[0] == TRANSFORM_TYPE_SUB_FEATURES:
self.info = row[1:]
else:
mylog.error('The first work of the dimension reducer file is not correct.')
raise ValueError
def Transform(self, dc: DataContainer, store_folder=None, store_key=None):
for sub_feature in self.sub_features:
if sub_feature not in dc.feature_name:
mylog.error('DataContainer does not have the sub feature')
raise KeyError
new_dc = FeatureSelector().SelectByName(dc, self.sub_features)
if store_folder is not None and store_key is not None:
new_dc.Save(os.path.join(store_folder, '{}_reduced_features.csv'.format(store_key)))
return new_dc
def LoadResult(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
try:
if self.sae.LoadResult(self._root_folder):
self.lineEditResultPath.setText(self._root_folder)
self.SetResultDescription()
self.SetResultTable()
self.InitialUi()
self.buttonSaveFigure.setEnabled(True)
self.UpdateFeature()
self.buttonClearResult.setEnabled(True)
self.buttonLoadResult.setEnabled(False)
# self.buttonGenerateDescription.setEnabled(True)
self.radioSurvivalSplitDataset.setEnabled(True)
self.radioSurvivalSplitFeature.setEnabled(True)
self.radioContribution.setEnabled(True)
self.radioVariance.setEnabled(True)
self.checkSurvivalKM.setEnabled(True)
self.radioSurvivalSplitDataset.setChecked(True)
self.radioContribution.setChecked(True)
self.UpdateFeature()
self.UpdateSurvival()
self._is_loading = True
else:
QMessageBox().about(
self, "Load Failed",
"The results were built by SAE with the previous version and can not be "
"loaded.")
except Exception as ex:
QMessageBox.about(self, "Load Error", ex.__str__())
mylog.error('Load Error, The reason is ' +
traceback.format_exception(ex))
self.ClearAll()
raise ex
def MakePipelines(self):
self.__normalizers = []
if self.checkNormalizeNone.isChecked():
self.__normalizers.append(NormalizerNone)
if self.checkNormalizeMinMax.isChecked():
self.__normalizers.append(NormalizerMinMax)
if self.checkNormalizeZscore.isChecked():
self.__normalizers.append(NormalizerZscore)
if self.checkNormalizeMean.isChecked():
self.__normalizers.append(NormalizerMean)
self.__dimension_reducers = []
if self.checkDimensionReduceNone.isChecked():
self.__dimension_reducers.append(DimensionReducerNone())
if self.checkDimensionReducePCC.isChecked():
self.__dimension_reducers.append(DimensionReducerPcc())
self.__feature_selectors = []
if self.checkFeatureSelectorNone.isChecked():
self.__feature_selectors.append(FeatureSelectorAll)
if self.checkFeatureSelectorCluster.isChecked():
self.__feature_selectors.append(FeatureSelectorCluster)
self.__feature_numbers = np.arange(self.spinBoxMinFeatureNumber.value(),
self.spinBoxMaxFeatureNumber.value() + 1).tolist()
self.__fitters = []
if self.checkCoxPH.isChecked():
self.__fitters.append(CoxPH())
# if self.checkAalen.isChecked():
# self.__fitters.append(AalenAdditive())
if len(self.__fitters) == 0:
mylog.error('Process classifier list length is zero.')
return False
cv = CrossValidation(k=int(self.spinCvFold.value()))
self.pipeline_manager.SetCV(cv)
self.pipeline_manager.SetNormalizers(self.__normalizers)
self.pipeline_manager.SetReducers(self.__dimension_reducers)
self.pipeline_manager.SetFeatureNumbers(self.__feature_numbers)
self.pipeline_manager.SetFeatureSelectors(self.__feature_selectors)
self.pipeline_manager.SetFitters(self.__fitters)
return True
def Run(self):
if self.train_dc.IsEmpty():
QMessageBox.about(self, '', 'Training data is empty.')
mylog.warning('Training data is empty.')
return
dlg = QFileDialog()
dlg.setFileMode(QFileDialog.DirectoryOnly)
dlg.setOption(QFileDialog.ShowDirsOnly)
if dlg.exec_():
store_folder = dlg.selectedFiles()[0]
if len(os.listdir(store_folder)) > 0:
reply = QMessageBox.question(self, 'Continue?',
'The folder is not empty, if you click Yes, '
'the data would be clear in this folder',
QMessageBox.Yes, QMessageBox.No)
if reply == QMessageBox.Yes:
try:
for file in os.listdir(store_folder):
if os.path.isdir(os.path.join(store_folder, file)):
shutil.rmtree(os.path.join(store_folder, file))
else:
os.remove(os.path.join(store_folder, file))
except PermissionError:
mylog.error('Permission Error: {}'.format(store_folder))
QMessageBox().about(self, 'Warning', 'Is there any opened files?')
return
except OSError:
mylog.error('Permission Error: {}'.format(store_folder))
QMessageBox().about(self, 'Warning', 'Is there any opened files?')
return
else:
return
if self.MakePipelines():
self.thread.moveToThread(QThread())
self.thread.SetProcessForm(self, store_folder)
self.thread.signal.connect(self.textEditVerbose.setPlainText)
self.thread.start()
self.SetLoadState(False)
self.SetRunState(False)
else:
mylog.error('Make pipeline failed. Pipeline must include Fitter and CV method.')
QMessageBox.about(self, 'Pipeline Error', 'Pipeline must include Fitter and CV method')
def run(self):
self._process_form.pipeline_manager.SaveInfo(self.store_folder)
try:
for total, num, group in self._process_form.pipeline_manager.RunCV(
self._process_form.train_dc,
self.store_folder):
text = "Cross Validation:\nGroup {}: {} / {}".format(int(group), num, total)
self.signal.emit(text)
except Exception as e:
print(traceback.format_exc())
mylog.error('Thread RunCV Failed: ', e.__str__())
try:
for total, num in self._process_form.pipeline_manager.EstimateCV(
self.store_folder,
self._process_form.train_dc.event_name,
self._process_form.train_dc.duration_name):
text = "Cross Validation:\nDone.\n\n" \
"Merging CV Results:\n{} / {}".format(num, total)
self.signal.emit(text)
except Exception as e:
mylog.error('Thread MergeCV Result Failed: ', e.__str__())
try:
for total, num in self._process_form.pipeline_manager.RunWithoutCV(self._process_form.train_dc,
self._process_form.test_dc,
self.store_folder):
text = "Corss Validation Done.\n\nCV Result Merge Done\n\n" \
"Model Developing:\n{} / {}".format(num, total)
self.signal.emit(text)
except Exception:
mylog.error("Thread RunWithoutCV Failed: ", e.__str__())
text = "Model Developing:\nDone.\n\nCross Validation:\nDone.\n\nMerging CV Results:\nDone.\n"
self.signal.emit(text)
self._process_form.SetLoadState(True)
self._process_form.SetRunState(True)
def LoadData(self):
if self.lineEditTrainingData.text():
try:
self.train_dc.Load(self.lineEditTrainingData.text(),
self.event_name, self.duration_name)
mylog.info('Open CSV file ' + self.lineEditTrainingData.text() + ' succeed.')
self.spinBoxMaxFeatureNumber.setValue(len(self.train_dc.feature_name))
except OSError as reason:
message = 'Open SCV file Error, The reason is ' + str(reason)
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
except ValueError:
message = 'Open SCV file ' + self.lineEditTrainingData.text() + ' Failed. because of value error.'
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
except Exception as e:
message = 'Unexpected Error: {}'.format(e.__str__())
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
if self.lineEditTestingData.text():
try:
self.test_dc.Load(self.lineEditTestingData.text(),
self.event_name, self.duration_name)
mylog.info('Open CSV file ' + self.lineEditTestingData.text() + ' succeed.')
except OSError as reason:
message = 'Open SCV file Error, The reason is ' + str(reason)
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
except ValueError:
message = 'Open SCV file ' + self.lineEditTrainingData.text() + ' Failed. because of value error.'
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
except KeyError:
message = 'The event key {} and duration key {} do not exist in testing data set.'.format(
self.comboEvent.currentText(), self.comboDuration.currentText()
)
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
except Exception as e:
message = 'Unexpected Error: {}'.format(e.__str__())
mylog.error(message)
QMessageBox.information(self, 'Error', message)
return
else:
self.test_dc = DataContainer()
self.SetRunState(True)
self.spinCvFold.setMaximum(len(self.train_dc.case_name))
self.spinCvFold.setValue(5)
self.UpdateDataDescription()
