class ProcessConnection(QWidget, Ui_Process):
def __init__(self, parent=None):
self.training_data_container = DataContainer()
self.testing_data_container = DataContainer()
self.fae = FeatureAnalysisPipelines()
self.logger = eclog(os.path.split(__file__)[-1]).GetLogger()
self.__process_normalizer_list = []
self.__process_dimension_reduction_list = []
self.__process_feature_selector_list = []
self.__process_feature_number_list = []
self.__process_classifier_list = []
super(ProcessConnection, self).__init__(parent)
self.setupUi(self)
self.buttonLoadTrainingData.clicked.connect(self.LoadTrainingData)
self.buttonLoadTestingData.clicked.connect(self.LoadTestingData)
self.checkNormalizeUnit.clicked.connect(self.UpdatePipelineText)
self.checkNormalizeZeroCenter.clicked.connect(self.UpdatePipelineText)
self.checkNormalizeUnitWithZeroCenter.clicked.connect(self.UpdatePipelineText)
self.checkNormalizationAll.clicked.connect(self.SelectAllNormalization)
self.checkPCA.clicked.connect(self.UpdatePipelineText)
self.checkRemoveSimilarFeatures.clicked.connect(self.UpdatePipelineText)
self.checkPreprocessAll.clicked.connect(self.SelectAllPreprocess)
self.spinBoxMinFeatureNumber.valueChanged.connect(self.MinFeatureNumberChange)
self.spinBoxMaxFeatureNumber.valueChanged.connect(self.MaxFeatureNumberChange)
self.checkANOVA.clicked.connect(self.UpdatePipelineText)
self.checkRFE.clicked.connect(self.UpdatePipelineText)
self.checkRelief.clicked.connect(self.UpdatePipelineText)
self.checkFeatureSelectorAll.clicked.connect(self.SelectAllFeatureSelector)
self.checkSVM.clicked.connect(self.UpdatePipelineText)
self.checkLDA.clicked.connect(self.UpdatePipelineText)
self.checkAE.clicked.connect(self.UpdatePipelineText)
self.checkRF.clicked.connect(self.UpdatePipelineText)
self.checkLogisticRegression.clicked.connect(self.UpdatePipelineText)
self.checkLRLasso.clicked.connect(self.UpdatePipelineText)
self.checkAdaboost.clicked.connect(self.UpdatePipelineText)
self.checkDecisionTree.clicked.connect(self.UpdatePipelineText)
self.checkNaiveBayes.clicked.connect(self.UpdatePipelineText)
self.checkGaussianProcess.clicked.connect(self.UpdatePipelineText)
self.checkClassifierAll.clicked.connect(self.SelectAllClassifier)
self.radio5folder.clicked.connect(self.UpdatePipelineText)
self.radio10Folder.clicked.connect(self.UpdatePipelineText)
self.buttonRun.clicked.connect(self.Run)
self.UpdatePipelineText()
self.SetStateButtonBeforeLoading(False)
def LoadTrainingData(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self, 'Open CSV file', directory=r'C:\MyCode\FAE\Example', filter="csv files (*.csv)")
try:
self.training_data_container.Load(file_name)
self.SetStateButtonBeforeLoading(True)
self.lineEditTrainingData.setText(file_name)
self.UpdateDataDescription()
self.logger.info('Open CSV file ' + file_name + ' succeed.')
except OSError as reason:
self.logger.log('Open SCV file Error, The reason is ' + str(reason))
print('出错啦！' + str(reason))
except ValueError:
self.logger.error('Open SCV file ' + file_name + ' Failed. because of value error.')
QMessageBox.information(self, 'Error',
'The selected training data mismatch.')
def LoadTestingData(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self, 'Open CSV file', filter="csv files (*.csv)")
try:
self.testing_data_container.Load(file_name)
self.lineEditTestingData.setText(file_name)
self.UpdateDataDescription()
self.logger.info('Loading testing data ' + file_name + ' succeed.' )
except OSError as reason:
self.logger.log('Open SCV file Error, The reason is ' + str(reason))
print('出错啦！' + str(reason))
except ValueError:
self.logger.error('Open SCV file ' + file_name + ' Failed. because of value error.')
QMessageBox.information(self, 'Error',
'The selected testing data mismatch.')
def GenerateVerboseTest(self, normalizer_name, dimension_reduction_name, feature_selector_name, classifier_name, feature_num,
current_num, total_num):
text = "Current:\n"
text += "{:s} / ".format(normalizer_name)
for temp in self.__process_normalizer_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "{:s} / ".format(dimension_reduction_name)
for temp in self.__process_dimension_reduction_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "{:s} / ".format(feature_selector_name)
for temp in self.__process_feature_selector_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "Feature Number: {:d} / [{:d}-{:d}]\n".format(feature_num, self.spinBoxMinFeatureNumber.value(), self.spinBoxMaxFeatureNumber.value())
text += "{:s} / ".format(classifier_name)
for temp in self.__process_classifier_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "Total process: {:d} / {:d}".format(current_num, total_num)
return text
def SetStateAllButtonWhenRunning(self, state):
self.buttonLoadTrainingData.setEnabled(state)
self.buttonLoadTestingData.setEnabled(state)
self.SetStateButtonBeforeLoading(state)
def SetStateButtonBeforeLoading(self, state):
self.buttonRun.setEnabled(state)
self.checkNormalizeUnit.setEnabled(state)
self.checkNormalizeZeroCenter.setEnabled(state)
self.checkNormalizeUnitWithZeroCenter.setEnabled(state)
self.checkNormalizationAll.setEnabled(state)
self.checkPCA.setEnabled(state)
self.checkRemoveSimilarFeatures.setEnabled(state)
self.checkPreprocessAll.setEnabled(state)
self.checkANOVA.setEnabled(state)
self.checkRFE.setEnabled(state)
self.checkRelief.setEnabled(state)
self.checkFeatureSelectorAll.setEnabled(state)
self.spinBoxMinFeatureNumber.setEnabled(state)
self.spinBoxMaxFeatureNumber.setEnabled(state)
self.checkSVM.setEnabled(state)
self.checkAE.setEnabled(state)
self.checkLDA.setEnabled(state)
self.checkRF.setEnabled(state)
self.checkLogisticRegression.setEnabled(state)
self.checkLRLasso.setEnabled(state)
self.checkAdaboost.setEnabled(state)
self.checkDecisionTree.setEnabled(state)
self.checkNaiveBayes.setEnabled(state)
self.checkGaussianProcess.setEnabled(state)
self.checkClassifierAll.setEnabled(state)
self.checkHyperParameters.setEnabled(state)
self.radio5folder.setEnabled(state)
self.radio10Folder.setEnabled(state)
def Run(self):
if self.training_data_container.IsEmpty():
QMessageBox.about(self, '', 'Training data is empty.')
self.logger.info('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:
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))
else:
return
self.textEditVerbose.setText(store_folder)
if self.MakePipelines():
thread = CVRun()
thread.moveToThread(QThread())
thread.SetProcessConnectionAndStore_folder(self, store_folder)
thread.signal.connect(self.textEditVerbose.setPlainText)
thread.start()
self.SetStateAllButtonWhenRunning(False)
hidden_file_path = os.path.join(store_folder, '.FAEresult4129074093819729087')
with open(hidden_file_path, 'wb') as file:
pass
file_hidden = os.popen('attrib +h '+ hidden_file_path)
file_hidden.close()
else:
QMessageBox.about(self, 'Pipeline Error', 'Pipeline must include Classifier and CV method')
self.logger.error('Make pipeline failed. Pipeline must include Classfier and CV method.')
def MinFeatureNumberChange(self):
if self.spinBoxMinFeatureNumber.value() > self.spinBoxMaxFeatureNumber.value():
self.spinBoxMinFeatureNumber.setValue(self.spinBoxMaxFeatureNumber.value())
self.UpdatePipelineText()
def MaxFeatureNumberChange(self):
if self.spinBoxMaxFeatureNumber.value() < self.spinBoxMinFeatureNumber.value():
self.spinBoxMaxFeatureNumber.setValue(self.spinBoxMinFeatureNumber.value())
self.UpdatePipelineText()
def MakePipelines(self):
self.__process_normalizer_list = []
if self.checkNormalizeUnit.isChecked():
self.__process_normalizer_list.append(NormalizerUnit())
if self.checkNormalizeZeroCenter.isChecked():
self.__process_normalizer_list.append(NormalizerZeroCenter())
if self.checkNormalizeUnitWithZeroCenter.isChecked():
self.__process_normalizer_list.append(NormalizerZeroCenterAndUnit())
if (not self.checkNormalizeUnit.isChecked()) and (not self.checkNormalizeZeroCenter.isChecked()) and \
(not self.checkNormalizeUnitWithZeroCenter.isChecked()):
self.__process_normalizer_list.append(NormalizerNone())
self.__process_dimension_reduction_list = []
if self.checkPCA.isChecked():
self.__process_dimension_reduction_list.append(DimensionReductionByPCA())
if self.checkRemoveSimilarFeatures.isChecked():
self.__process_dimension_reduction_list.append(DimensionReductionByPCC())
self.__process_feature_selector_list = []
if self.checkANOVA.isChecked():
self.__process_feature_selector_list.append(FeatureSelectPipeline([FeatureSelectByANOVA()]))
if self.checkRFE.isChecked():
self.__process_feature_selector_list.append(FeatureSelectPipeline([FeatureSelectByRFE()]))
if self.checkRelief.isChecked():
self.__process_feature_selector_list.append(FeatureSelectPipeline([FeatureSelectByRelief()]))
self.__process_feature_number_list = np.arange(self.spinBoxMinFeatureNumber.value(), self.spinBoxMaxFeatureNumber.value() + 1).tolist()
self.__process_classifier_list = []
if self.checkSVM.isChecked():
self.__process_classifier_list.append(SVM())
if self.checkLDA.isChecked():
self.__process_classifier_list.append(LDA())
if self.checkAE.isChecked():
self.__process_classifier_list.append(AE())
if self.checkRF.isChecked():
self.__process_classifier_list.append(RandomForest())
if self.checkLogisticRegression.isChecked():
self.__process_classifier_list.append(LR())
if self.checkLRLasso.isChecked():
self.__process_classifier_list.append(LRLasso())
if self.checkAdaboost.isChecked():
self.__process_classifier_list.append(AdaBoost())
if self.checkDecisionTree.isChecked():
self.__process_classifier_list.append(DecisionTree())
if self.checkGaussianProcess.isChecked():
self.__process_classifier_list.append(GaussianProcess())
if self.checkNaiveBayes.isChecked():
self.__process_classifier_list.append(NaiveBayes())
if len(self.__process_classifier_list) == 0:
self.logger.error('Process classifier list length is zero.')
return False
if self.radio5folder.isChecked():
cv = CrossValidation5Folder()
elif self.radio10Folder.isChecked():
cv = CrossValidation10Folder()
else:
return False
self.fae.SetNormalizerList(self.__process_normalizer_list)
self.fae.SetDimensionReductionList(self.__process_dimension_reduction_list)
self.fae.SetFeatureSelectorList(self.__process_feature_selector_list)
self.fae.SetFeatureNumberList(self.__process_feature_number_list)
self.fae.SetClassifierList(self.__process_classifier_list)
self.fae.SetCrossValition(cv)
self.fae.GenerateMetircDict()
return True
def UpdateDataDescription(self):
show_text = ""
if self.training_data_container.GetArray().size > 0:
show_text += "The number of training cases: {:d}\n".format(len(self.training_data_container.GetCaseName()))
show_text += "The number of training features: {:d}\n".format(len(self.training_data_container.GetFeatureName()))
if len(np.unique(self.training_data_container.GetLabel())) == 2:
positive_number = len(
np.where(self.training_data_container.GetLabel() == np.max(self.training_data_container.GetLabel()))[0])
negative_number = len(self.training_data_container.GetLabel()) - positive_number
assert (positive_number + negative_number == len(self.training_data_container.GetLabel()))
show_text += "The number of training positive samples: {:d}\n".format(positive_number)
show_text += "The number of training negative samples: {:d}\n".format(negative_number)
show_text += '\n'
if self.testing_data_container.GetArray().size > 0:
show_text += "The number of testing cases: {:d}\n".format(len(self.testing_data_container.GetCaseName()))
show_text += "The number of testing features: {:d}\n".format(
len(self.testing_data_container.GetFeatureName()))
if len(np.unique(self.testing_data_container.GetLabel())) == 2:
positive_number = len(
np.where(
self.testing_data_container.GetLabel() == np.max(self.testing_data_container.GetLabel()))[0])
negative_number = len(self.testing_data_container.GetLabel()) - positive_number
assert (positive_number + negative_number == len(self.testing_data_container.GetLabel()))
show_text += "The number of testing positive samples: {:d}\n".format(positive_number)
show_text += "The number of testing negative samples: {:d}\n".format(negative_number)
self.textEditDescription.setText(show_text)
def UpdatePipelineText(self):
self.listOnePipeline.clear()
normalization_text = 'Normalization:\n'
normalizer_num = 0
if self.checkNormalizeUnit.isChecked():
normalization_text += "Normalize unit\n"
normalizer_num += 1
if self.checkNormalizeZeroCenter.isChecked():
normalization_text += "Normalize zero center\n"
normalizer_num += 1
if self.checkNormalizeUnitWithZeroCenter.isChecked():
normalization_text += "Normalize unit with zero center\n"
normalizer_num += 1
if normalizer_num == 0:
normalizer_num = 1
self.listOnePipeline.addItem(normalization_text)
preprocess_test = 'Preprocess:\n'
dimension_reduction_num = 0
if self.checkPCA.isChecked():
preprocess_test += "PCA\n"
dimension_reduction_num += 1
if self.checkRemoveSimilarFeatures.isChecked():
preprocess_test += "Remove Similary Features\n"
dimension_reduction_num += 1
if dimension_reduction_num == 0:
dimension_reduction_num = 1
self.listOnePipeline.addItem(preprocess_test)
feature_selection_text = "Feature Selection:\n"
if self.spinBoxMinFeatureNumber.value() == self.spinBoxMaxFeatureNumber.value():
feature_selection_text += "Feature Number: " + str(self.spinBoxMinFeatureNumber.value()) + "\n"
else:
feature_selection_text += "Feature Number range: {:d}-{:d}\n".format(self.spinBoxMinFeatureNumber.value(),
self.spinBoxMaxFeatureNumber.value())
feature_num = self.spinBoxMaxFeatureNumber.value() - self.spinBoxMinFeatureNumber.value() + 1
feature_selector_num = 0
if self.checkANOVA.isChecked():
feature_selection_text += "ANOVA\n"
feature_selector_num += 1
if self.checkRFE.isChecked():
feature_selection_text += "RFE\n"
feature_selector_num += 1
if self.checkRelief.isChecked():
feature_selection_text += "Relief\n"
feature_selector_num += 1
if feature_selector_num == 0:
feature_selection_text += "None\n"
feature_selector_num = 1
self.listOnePipeline.addItem(feature_selection_text)
classifier_text = 'Classifier:\n'
classifier_num = 0
if self.checkSVM.isChecked():
classifier_text += "SVM\n"
classifier_num += 1
if self.checkLDA.isChecked():
classifier_text += "LDA\n"
classifier_num += 1
if self.checkAE.isChecked():
classifier_text += "AE\n"
classifier_num += 1
if self.checkRF.isChecked():
classifier_text += "RF\n"
classifier_num += 1
if self.checkLogisticRegression.isChecked():
classifier_text += "Logistic Regression\n"
classifier_num += 1
if self.checkLRLasso.isChecked():
classifier_text += "Logistic Regression with Lasso\n"
classifier_num += 1
if self.checkAdaboost.isChecked():
classifier_text += "Adaboost\n"
classifier_num += 1
if self.checkDecisionTree.isChecked():
classifier_text += "Decision Tree\n"
classifier_num += 1
if self.checkGaussianProcess.isChecked():
classifier_text += "Gaussian Process\n"
classifier_num += 1
if self.checkNaiveBayes.isChecked():
classifier_text += "Naive Bayes\n"
classifier_num += 1
if classifier_num == 0:
classifier_num = 1
self.listOnePipeline.addItem(classifier_text)
cv_method = "Cross Validation:\n"
if self.radio5folder.isChecked():
cv_method += "5-Folder\n"
elif self.radio10Folder.isChecked():
cv_method += "10-folder\n"
self.listOnePipeline.addItem(cv_method)
self.listOnePipeline.addItem("Total number of pipelines is:\n{:d}"
.format(normalizer_num * dimension_reduction_num * feature_selector_num * feature_num * classifier_num))
def SelectAllNormalization(self):
if self.checkNormalizationAll.isChecked():
self.checkNormalizeZeroCenter.setChecked(True)
self.checkNormalizeUnitWithZeroCenter.setChecked(True)
self.checkNormalizeUnit.setChecked(True)
else:
self.checkNormalizeZeroCenter.setChecked(False)
self.checkNormalizeUnitWithZeroCenter.setChecked(False)
self.checkNormalizeUnit.setChecked(False)
self.UpdatePipelineText()
def SelectAllPreprocess(self):
if self.checkPreprocessAll.isChecked():
self.checkPCA.setChecked(True)
self.checkRemoveSimilarFeatures.setChecked(True)
else:
self.checkPCA.setChecked(False)
self.checkRemoveSimilarFeatures.setChecked(False)
self.UpdatePipelineText()
def SelectAllFeatureSelector(self):
if self.checkFeatureSelectorAll.isChecked():
self.checkANOVA.setChecked(True)
self.checkRFE.setChecked(True)
self.checkRelief.setChecked(True)
else:
self.checkANOVA.setChecked(False)
self.checkRFE.setChecked(False)
self.checkRelief.setChecked(False)
self.UpdatePipelineText()
def SelectAllClassifier(self):
if self.checkClassifierAll.isChecked():
self.checkSVM.setChecked(True)
self.checkAE.setChecked(True)
self.checkLDA.setChecked(True)
self.checkRF.setChecked(True)
self.checkLogisticRegression.setChecked(True)
self.checkLRLasso.setChecked(True)
self.checkAdaboost.setChecked(True)
self.checkDecisionTree.setChecked(True)
self.checkGaussianProcess.setChecked(True)
self.checkNaiveBayes.setChecked(True)
else:
self.checkSVM.setChecked(False)
self.checkAE.setChecked(False)
self.checkLDA.setChecked(False)
self.checkRF.setChecked(False)
self.checkLogisticRegression.setChecked(False)
self.checkLRLasso.setChecked(False)
self.checkAdaboost.setChecked(False)
self.checkDecisionTree.setChecked(False)
self.checkGaussianProcess.setChecked(False)
self.checkNaiveBayes.setChecked(False)
self.UpdatePipelineText()
class ProcessConnection(QWidget, Ui_Process):
def __init__(self, parent=None):
self.__training_data_container = DataContainer()
self.__testing_data_container = DataContainer()
self.__fae = FeatureAnalysisPipelines()
self.__process_normalizer_list = []
self.__process_dimension_reduction_list = []
self.__process_feature_selector_list = []
self.__process_feature_number_list = []
self.__process_classifier_list = []
super(ProcessConnection, self).__init__(parent)
self.setupUi(self)
self.buttonLoadTrainingData.clicked.connect(self.LoadTrainingData)
self.buttonLoadTestingData.clicked.connect(self.LoadTestingData)
self.checkNormalizeUnit.clicked.connect(self.UpdatePipelineText)
self.checkNormalizeZeroCenter.clicked.connect(self.UpdatePipelineText)
self.checkNormalizeUnitWithZeroCenter.clicked.connect(
self.UpdatePipelineText)
self.checkPCA.clicked.connect(self.UpdatePipelineText)
self.checkRemoveSimilarFeatures.clicked.connect(
self.UpdatePipelineText)
self.spinBoxMinFeatureNumber.valueChanged.connect(
self.MinFeatureNumberChange)
self.spinBoxMaxFeatureNumber.valueChanged.connect(
self.MaxFeatureNumberChange)
self.checkANOVA.clicked.connect(self.UpdatePipelineText)
self.checkRFE.clicked.connect(self.UpdatePipelineText)
self.checkRelief.clicked.connect(self.UpdatePipelineText)
self.checkSVM.clicked.connect(self.UpdatePipelineText)
self.checkLDA.clicked.connect(self.UpdatePipelineText)
self.checkAE.clicked.connect(self.UpdatePipelineText)
self.checkRF.clicked.connect(self.UpdatePipelineText)
self.buttonRun.clicked.connect(self.Run)
self.UpdatePipelineText()
def LoadTrainingData(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self,
'Open SCV file',
directory=r'C:\MyCode\FAE\Example',
filter="csv files (*.csv)")
try:
self.__training_data_container.Load(file_name)
except:
print('Loading Training Data Error')
self.lineEditTrainingData.setText(file_name)
self.UpdateDataDescription()
def LoadTestingData(self):
dlg = QFileDialog()
file_name, _ = dlg.getOpenFileName(self,
'Open SCV file',
filter="csv files (*.csv)")
try:
self.__testing_data_container.Load(file_name)
except:
print('Loading Testing Data Error')
self.lineEditTestingData.setText(file_name)
self.UpdateDataDescription()
def SetVerboseTest(self, normalizer_name, dimension_reduction_name,
feature_selector_name, classifier_name, feature_num,
current_num, total_num):
text = "Current:\n"
text += "{:s} / ".format(normalizer_name)
for temp in self.__process_normalizer_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "{:s} / ".format(dimension_reduction_name)
for temp in self.__process_dimension_reduction_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "{:s} / ".format(feature_selector_name)
for temp in self.__process_feature_selector_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "Feature Number: {:d} / [{:d}-{:d}]\n".format(
feature_num, self.spinBoxMinFeatureNumber.value(),
self.spinBoxMaxFeatureNumber.value())
text += "{:s} / ".format(classifier_name)
for temp in self.__process_classifier_list:
text += (temp.GetName() + ", ")
text += '\n'
text += "Total process: {:d} / {:d}".format(current_num, total_num)
self.textEditVerbose.setPlainText(text)
def Run(self):
if self.__training_data_container.IsEmpty():
QMessageBox.about(self, '', '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:
QMessageBox.about(self, 'The folder is not empty',
'The folder is not empty')
return
self.textEditVerbose.setText(store_folder)
if self.MakePipelines():
for current_normalizer_name, current_dimension_reductor_name, \
current_feature_selector_name, curreent_feature_num, \
current_classifier_name, num, total_num\
in self.__fae.Run(self.__training_data_container, self.__testing_data_container, store_folder):
self.SetVerboseTest(current_normalizer_name,
current_dimension_reductor_name,
current_feature_selector_name,
current_classifier_name,
curreent_feature_num, num, total_num)
QApplication.processEvents()
text = self.textEditVerbose.toPlainText()
self.textEditVerbose.setPlainText(text + "\n DONE!")
with open(store_folder + '\\.FAEresult4129074093819729087',
'wb') as file:
pass
file_hidden = os.popen('attrib +h ' + store_folder +
'\\.FAEresult4129074093819729087')
file_hidden.close()
else:
QMessageBox.about(
self, 'Pipeline Error',
'Pipeline must include Classifier and CV method')
def MinFeatureNumberChange(self):
if self.spinBoxMinFeatureNumber.value(
) > self.spinBoxMaxFeatureNumber.value():
self.spinBoxMinFeatureNumber.setValue(
self.spinBoxMaxFeatureNumber.value())
self.UpdatePipelineText()
def MaxFeatureNumberChange(self):
if self.spinBoxMaxFeatureNumber.value(
) < self.spinBoxMinFeatureNumber.value():
self.spinBoxMaxFeatureNumber.setValue(
self.spinBoxMinFeatureNumber.value())
self.UpdatePipelineText()
def MakePipelines(self):
self.__process_normalizer_list = []
if self.checkNormalizeUnit.isChecked():
self.__process_normalizer_list.append(NormalizerUnit())
if self.checkNormalizeZeroCenter.isChecked():
self.__process_normalizer_list.append(NormalizerZeroCenter())
if self.checkNormalizeUnitWithZeroCenter.isChecked():
self.__process_normalizer_list.append(
NormalizerZeroCenterAndUnit())
if (not self.checkNormalizeUnit.isChecked()) and (not self.checkNormalizeZeroCenter.isChecked()) and \
(not self.checkNormalizeUnitWithZeroCenter.isChecked()):
self.__process_normalizer_list.append(NormalizerNone())
self.__process_dimension_reduction_list = []
if self.checkPCA.isChecked():
self.__process_dimension_reduction_list.append(
DimensionReductionByPCA())
if self.checkRemoveSimilarFeatures.isChecked():
self.__process_dimension_reduction_list.append(
DimensionReductionByCos())
self.__process_feature_selector_list = []
if self.checkANOVA.isChecked():
self.__process_feature_selector_list.append(
FeatureSelectPipeline([FeatureSelectByANOVA()]))
if self.checkRFE.isChecked():
self.__process_feature_selector_list.append(
FeatureSelectPipeline([FeatureSelectByRFE()]))
if self.checkRelief.isChecked():
self.__process_feature_selector_list.append(
FeatureSelectPipeline([FeatureSelectByRelief()]))
self.__process_feature_number_list = np.arange(
self.spinBoxMinFeatureNumber.value(),
self.spinBoxMaxFeatureNumber.value() + 1).tolist()
self.__process_classifier_list = []
if self.checkSVM.isChecked():
self.__process_classifier_list.append(SVM())
if self.checkLDA.isChecked():
self.__process_classifier_list.append(LDA())
if self.checkAE.isChecked():
self.__process_classifier_list.append(AE())
if self.checkRF.isChecked():
self.__process_classifier_list.append(RandomForest())
if len(self.__process_classifier_list) == 0:
return False
cv = CrossValidation()
if self.radioLeaveOneOut.isChecked():
cv.SetCV('LOO')
elif self.radio5folder.isChecked():
cv.SetCV('5-folder')
elif self.radio10Folder.isChecked():
cv.SetCV('10-folder')
else:
return False
self.__fae.SetNormalizerList(self.__process_normalizer_list)
self.__fae.SetDimensionReductionList(
self.__process_dimension_reduction_list)
self.__fae.SetFeatureSelectorList(self.__process_feature_selector_list)
self.__fae.SetFeatureNumberList(self.__process_feature_number_list)
self.__fae.SetClassifierList(self.__process_classifier_list)
self.__fae.SetCrossValition(cv)
self.__fae.GenerateMetircDict()
return True
def UpdateDataDescription(self):
show_text = ""
if self.__training_data_container.GetArray().size > 0:
show_text += "The number of training cases: {:d}\n".format(
len(self.__training_data_container.GetCaseName()))
show_text += "The number of training features: {:d}\n".format(
len(self.__training_data_container.GetFeatureName()))
if len(np.unique(self.__training_data_container.GetLabel())) == 2:
positive_number = len(
np.where(self.__training_data_container.GetLabel(
) == np.max(self.__training_data_container.GetLabel()))[0])
negative_number = len(self.__training_data_container.GetLabel(
)) - positive_number
assert (positive_number + negative_number == len(
self.__training_data_container.GetLabel()))
show_text += "The number of training positive samples: {:d}\n".format(
positive_number)
show_text += "The number of training negative samples: {:d}\n".format(
negative_number)
show_text += '\n'
if self.__testing_data_container.GetArray().size > 0:
show_text += "The number of testing cases: {:d}\n".format(
len(self.__testing_data_container.GetCaseName()))
show_text += "The number of testing features: {:d}\n".format(
len(self.__testing_data_container.GetFeatureName()))
if len(np.unique(self.__testing_data_container.GetLabel())) == 2:
positive_number = len(
np.where(self.__testing_data_container.GetLabel() == np.
max(self.__testing_data_container.GetLabel()))[0])
negative_number = len(
self.__testing_data_container.GetLabel()) - positive_number
assert (positive_number + negative_number == len(
self.__testing_data_container.GetLabel()))
show_text += "The number of testing positive samples: {:d}\n".format(
positive_number)
show_text += "The number of testing negative samples: {:d}\n".format(
negative_number)
self.textEditDescription.setText(show_text)
def UpdatePipelineText(self):
self.listOnePipeline.clear()
normalization_text = 'Normalization:\n'
normalizer_num = 0
if self.checkNormalizeUnit.isChecked():
normalization_text += "Normalize unit\n"
normalizer_num += 1
if self.checkNormalizeZeroCenter.isChecked():
normalization_text += "Normalize zero center\n"
normalizer_num += 1
if self.checkNormalizeUnitWithZeroCenter.isChecked():
normalization_text += "Normalize unit with zero center\n"
normalizer_num += 1
if normalizer_num == 0:
normalizer_num = 1
self.listOnePipeline.addItem(normalization_text)
preprocess_test = 'Preprocess:\n'
if self.checkPCA.isChecked():
preprocess_test += "PCA\n"
if self.checkRemoveSimilarFeatures.isChecked():
preprocess_test += "Remove Similary Features\n"
self.listOnePipeline.addItem(preprocess_test)
feature_selection_text = "Feature Selection:\n"
if self.spinBoxMinFeatureNumber.value(
) == self.spinBoxMaxFeatureNumber.value():
feature_selection_text += "Feature Number: " + str(
self.spinBoxMinFeatureNumber.value()) + "\n"
else:
feature_selection_text += "Feature Number range: {:d}-{:d}\n".format(
self.spinBoxMinFeatureNumber.value(),
self.spinBoxMaxFeatureNumber.value())
feature_num = self.spinBoxMaxFeatureNumber.value(
) - self.spinBoxMinFeatureNumber.value() + 1
feature_selector_num = 0
if self.checkANOVA.isChecked():
feature_selection_text += "ANOVA\n"
feature_selector_num += 1
if self.checkRFE.isChecked():
feature_selection_text += "RFE\n"
feature_selector_num += 1
if self.checkRelief.isChecked():
feature_selection_text += "Relief\n"
feature_selector_num += 1
if feature_selector_num == 0:
feature_selector_num = 1
self.listOnePipeline.addItem(feature_selection_text)
classifier_test = 'Classifier:\n'
classifier_num = 0
if self.checkSVM.isChecked():
classifier_test += "SVM\n"
classifier_num += 1
if self.checkLDA.isChecked():
classifier_test += "LDA\n"
classifier_num += 1
if self.checkAE.isChecked():
classifier_test += "AE\n"
classifier_num += 1
if self.checkRF.isChecked():
classifier_test += "RF\n"
classifier_num += 1
if classifier_num == 0:
classifier_num = 1
self.listOnePipeline.addItem(classifier_test)
self.listOnePipeline.addItem(
"Total number of pipelines is:\n{:d}".format(
normalizer_num * feature_selector_num * feature_num *
classifier_num))