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))