def btnSave_click(self):
global data
msgBox = QMessageBox()
if len(ui.txtInFile.text()):
do_compress = False
if can_do_compression(ui.txtInFile.text()):
reply = QMessageBox.question(None, 'Data Compress',
"Do you like to compress data?",
QMessageBox.Yes, QMessageBox.No)
do_compress = True if reply == QMessageBox.Yes else False
mainIO_save(data, ui.txtInFile.text(), do_compression=do_compress)
ui.btnSave.setEnabled(False)
print("Data is saved in: ", ui.txtInFile.text())
msgBox.setText("Data is saved")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnSaveAs_click(self):
global data
msgBox = QMessageBox()
if not len(ui.txtInFile.text()):
return False
ofile = SaveFile("Save MatLab data file ...",['easyX files (*.ezx)', 'MatLab files (*.mat)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(ofile):
if len(ui.txtInFile.text()):
do_compress = False
if can_do_compression(ofile):
reply = QMessageBox.question(
None, 'Data Compress', "Do you like to compress data?",
QMessageBox.Yes, QMessageBox.No)
do_compress = True if reply == QMessageBox.Yes else False
mainIO_save(data, ofile, do_compression=do_compress)
ui.btnSave.setEnabled(False)
ui.txtInFile.setText(ofile)
print("Data is saved in: ", ui.txtInFile.text())
msgBox.setText("Data is saved")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
Method = ui.cbMethod.currentData()
LossType = ui.cbLossType.currentData()
Optim = ui.cbOptim.currentData()
try:
Epoch = np.int32(ui.txtIter.text())
except:
msgBox.setText("Number of iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
BatchSize = np.int32(ui.txtBatch.text())
except:
msgBox.setText("Number of batch is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ReportStep = np.int32(ui.txtReportStep.text())
except:
msgBox.setText("Number of Report Step is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
LearningRate = np.float32(ui.txtRate.text())
except:
msgBox.setText("Learning rate is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
LassoAlpha = np.float32(ui.txtLParam.text())
except:
msgBox.setText("Number of Lasso Parameter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ElasticLambda1 = np.float32(ui.txtEL1.text())
except:
msgBox.setText("Number of Elastic Lambda 1 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ElasticAlpha = np.float32(ui.txtEL2.text())
except:
msgBox.setText("Number of Elastic Lambda 2 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
RidgeReg = np.float32(ui.txtRRP.text())
except:
msgBox.setText("Number of Ridge Regression Parameter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
msgBox.setText("At least, you must select one metric!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading ...")
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if not len(ui.txtDesign.currentText()):
msgBox.setText("Please enter Input Design variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Design = InData[ui.txtDesign.currentText()]
except:
msgBox.setText("Design value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
FontSize = ui.txtFontSize.value()
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
except:
msgBox.setText("Task variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TaskTitleUnique = np.unique(TaskTitle)
Task = np.zeros(np.shape(TaskTitle))
for ttinx, tt in enumerate(TaskTitle):
for ttlinx, ttl in enumerate(TaskTitleUnique):
if tt[0] == ttl:
Task[ttinx] = ttlinx + 1
break
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sub = InData[ui.txtSubject.currentText()][0]
except:
msgBox.setText("Subject variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()][0]
except:
msgBox.setText("Run variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Con = InData[ui.txtCounter.currentText()][0]
except:
msgBox.setText("Counter variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
Design = np.delete(Design, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
Task = np.delete(Task, labelIndx, axis=0)
Sub = np.delete(Sub, labelIndx, axis=0)
Run = np.delete(Run, labelIndx, axis=0)
Con = np.delete(Con, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
try:
Unit = np.int32(ui.txtUnit.text())
except:
msgBox.setText("Unit for the test set must be a number!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Unit < 1:
msgBox.setText("Unit for the test set must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Calculating Levels ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = [Sub]
FoldStr = "Subject"
else:
GroupFold = np.concatenate(([Sub], [Run]))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Task])) if GroupFold is not None else [Task]
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Con])) if GroupFold is not None else [Con]
FoldStr = FoldStr + "+Counter"
if FoldStr == "":
FoldStr = "Whole-Data"
GUFold = [1]
ListFold = [1]
UniqFold = [1]
GroupFold = [1]
UnitFold = np.ones((1, np.shape(X)[0]))
else:
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i)
for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText(
"Unit must be smaller than all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold), Unit):
msgBox.setText(
"Unit must be divorceable to all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
GUFold = np.unique(UnitFold)
FoldInfo = dict()
FoldInfo["Unit"] = Unit
FoldInfo["Group"] = GroupFold
FoldInfo["Order"] = FoldStr
FoldInfo["List"] = ListFold
FoldInfo["Unique"] = UniqFold
FoldInfo["Folds"] = UnitFold
OutData["FoldInfo"] = FoldInfo
OutData[
"ModelAnalysis"] = "PyTorch.Group.Gradient.RSA." + ui.cbMethod.currentText(
)
print("Number of all levels is: " + str(len(UniqFold)))
Cov = None
Corr = None
AMSE = list()
APer = list()
# RSA Method
OutData['Method'] = dict()
OutData['Method']['Method'] = Method
OutData['Method']['LossType'] = LossType
OutData['Method']['Optimization'] = Optim
OutData['Method']['LearningRate'] = LearningRate
OutData['Method']['Epoch'] = Epoch
OutData['Method']['BatchSize'] = BatchSize
OutData['Method']['ReportStep'] = ReportStep
OutData['Method']['RidgeAlpha'] = RidgeReg
OutData['Method']['ElaticLambda1'] = ElasticLambda1
OutData['Method']['ElaticAlpha'] = ElasticAlpha
OutData['Method']['LassoAlpha'] = LassoAlpha
OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()
Beta = None
for foldID, fold in enumerate(GUFold):
print("Analyzing level " + str(foldID + 1), " of ",
str(len(UniqFold)), " ...")
Index = np.where(UnitFold == fold)
# Whole-Data
if FoldStr == "Whole-Data" and np.shape(Index)[0]:
Index = [Index[1]]
XLi = X[Index]
RegLi = Design[Index]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
XLi = preprocessing.scale(XLi)
print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
print("Running Gradient RSA ...")
rsa = GrRSA(method=Method, loss_type=LossType, optim=Optim, learning_rate=LearningRate, epoch=Epoch, \
batch_size=BatchSize, report_step=ReportStep, ridge_param=RidgeReg,
elstnet_l1_ratio=ElasticLambda1, \
elstnet_alpha=ElasticAlpha, lasso_alpha=LassoAlpha, verbose=ui.cbVerbose.isChecked(), \
gpu_enable=ui.cbDevice.currentData(), normalization=False)
BetaLi, EpsLi, loss_vec, MSE, Performance, _ = rsa.fit(
data_vals=XLi, design_vals=RegLi)
OutData["LossVec"] = loss_vec
print("Calculating MSE for level %d ..." % (foldID + 1))
print("MSE%d: %f" % (foldID + 1, MSE))
print("Perfromance%d: %f" % (foldID + 1, Performance))
OutData["MSE" + str(foldID)] = MSE
OutData["Performance" + str(foldID)] = MSE
AMSE.append(MSE)
APer.append(Performance)
Beta = BetaLi if Beta is None else Beta + BetaLi
if ui.cbBeta.isChecked():
OutData["BetaL" + str(foldID + 1)] = BetaLi
OutData["EpsL" + str(foldID + 1)] = EpsLi
# Calculate Results
if ui.cbCorr.isChecked():
print("Calculating Correlation for level %d ..." %
(foldID + 1))
CorrLi = np.corrcoef(BetaLi)
OutData["Corr" + str(foldID + 1)] = CorrLi
if Corr is None:
Corr = CorrLi.copy()
else:
if ui.rbAvg.isChecked():
Corr = np.add(Corr, CorrLi)
elif ui.rbMin.isChecked():
Corr = np.minimum(Corr, CorrLi)
else:
Corr = np.maximum(Corr, CorrLi)
if ui.cbCov.isChecked():
print("Calculating Covariance for level %d ..." % (foldID + 1))
CovLi = np.cov(BetaLi)
OutData["Cov" + str(foldID + 1)] = CovLi
if Cov is None:
Cov = CovLi.copy()
else:
if ui.rbAvg.isChecked():
Cov = np.add(Cov, CovLi)
elif ui.rbMin.isChecked():
Cov = np.minimum(Cov, CovLi)
else:
Cov = np.maximum(Cov, CovLi)
if ui.cbCov.isChecked():
if ui.rbAvg.isChecked():
Cov = Cov / len(UniqFold)
covClass = SimilarityMatrixBetweenClass(Cov)
OutData["Covariance"] = Cov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_std"] = covClass.std()
OutData["Covariance_mean"] = covClass.mean()
if ui.cbCorr.isChecked():
if ui.rbAvg.isChecked():
Corr = Corr / len(UniqFold)
corClass = SimilarityMatrixBetweenClass(Corr)
OutData["Correlation"] = Corr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_std"] = corClass.std()
OutData["Correlation_mean"] = corClass.mean()
OutData["MSE"] = np.mean(AMSE)
OutData["MSE_std"] = np.std(AMSE)
OutData["Performance"] = np.mean(APer)
OutData["Performance_std"] = np.std(APer)
# Calculating Distance Matrix
dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))
for i in range(np.shape(Beta)[0]):
for j in range(i + 1, np.shape(Beta)[0]):
dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
dis[j, i] = dis[i, j]
OutData["DistanceMatrix"] = dis
Z = linkage(dis)
OutData["Linkage"] = Z
print("Average MSE: %f" % (OutData["MSE"]))
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
mainIO_save(OutData, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
NumData = np.shape(Corr)[0]
fig1 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=45)
ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCorr.text()):
plt.title(ui.txtTitleCorr.text())
else:
plt.title('Group MP Gradient RSA: Correlation\nLevel: ' +
FoldStr)
plt.show()
if ui.cbCov.isChecked():
NumData = np.shape(Cov)[0]
fig2 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=45)
ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCov.text()):
plt.title(ui.txtTitleCov.text())
else:
plt.title('Group MP Gradient RSA: Covariance\nLevel: ' +
FoldStr)
plt.show()
fig3 = plt.figure(figsize=(25, 10), )
if len(ui.txtTitleDen.text()):
plt.title(ui.txtTitleDen.text())
else:
plt.title(
'Group MP Gradient RSA: Similarity Analysis\nLevel: ' +
FoldStr)
dn = dendrogram(Z,
labels=labels,
leaf_font_size=FontSize,
color_threshold=1)
plt.show()
print("DONE.")
msgBox.setText(
"Group Representational Similarity Analysis (RSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
try:
Threshold = np.float64(ui.txtThreshold.text())
except:
msgBox.setText("Standard deviation threshold value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
net_model = createModel(ui.tbModel)
except Exception as e:
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if net_model is None:
msgBox.setText("Please create a network model!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.rbScale.isChecked() == True and ui.rbALScale.isChecked(
) == False:
msgBox.setText(
"Subject Level Normalization is just available for Subject Level Analysis!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked() and (not ui.rbScale.isChecked()):
X = X - X.mean()
X = X / X.std()
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = reshape_1Dvector(Subject)
except:
print("Cannot load Subject ID")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = reshape_1Dvector(
InData[ui.txtLabel.currentText()])
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = reshape_1Dvector(
InData[ui.txtTask.currentText()])
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = reshape_1Dvector(
InData[ui.txtRun.currentText()])
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = reshape_1Dvector(
InData[ui.txtCounter.currentText()])
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = reshape_1Dvector(
InData[ui.txtScan.currentText()])
if ui.rbALScale.isChecked():
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
extractXsub = X[np.where(Subject == subj)[1], :]
extractXsub = extractXsub - extractXsub.mean()
extractXsub = extractXsub / extractXsub.std()
X_Sub.append(extractXsub)
print("Data in subject level is scaled, X_" + str(subj) +
"~N(0,1).")
else:
X_Sub.append(X[np.where(Subject == subj)[1], :])
print("Subject ", subj, " is extracted from data.")
print("Running CNN in subject level ...")
X_Sub_PCA = list()
lenPCA = len(X_Sub)
for xsubindx, xsub in enumerate(X_Sub):
net = CNN(net_model)
if ui.rbMat.isChecked():
xsub_tran = net.tonumpy(
net.vectorize(
net(torch.Tensor(np.expand_dims(xsub, axis=1)))))
else:
xsub_tran = net.tonumpy(
net(torch.Tensor(np.expand_dims(xsub, axis=1))))
X_Sub_PCA.append(xsub_tran)
print("CNN: ", xsubindx + 1, " of ", lenPCA,
" is done. Shape: " + str(np.shape(xsub_tran)))
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub_PCA):
X_new = np.concatenate(
(X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA,
" is done.")
if not ui.rbMat.isChecked():
X_new = X_new[:, 0, :, :, :]
# Apply Thresholding
print("Data shape before thresholding: ", np.shape(X_new))
X_new_th = X_new
if ui.rbMat.isChecked() and ui.cbThreshold.isChecked():
index = np.where(np.std(X_new, axis=0) >= Threshold)
X_new_index = X_new[:, index]
print("Standard deviation thresholding ...")
X_new_th = None
for xx_new in X_new_index:
X_new_th = xx_new if X_new_th is None else np.concatenate(
(X_new_th, xx_new), axis=0)
# Apply Normalization
if ui.rbMat.isChecked() and ui.cbOutNormal.isChecked():
X_new_th = X_new_th - np.mean(X_new_th)
X_new_th = X_new_th / np.std(X_new_th)
print("Output data is normalized!")
OutData[ui.txtOData.text()] = X_new_th
else:
print("Running CNN ...")
net = CNN(net_model)
if ui.rbMat.isChecked():
X_new = net.tonumpy(
net.vectorize(net(torch.Tensor(np.expand_dims(X,
axis=1)))))
else:
X_new = net.tonumpy(
net(torch.Tensor(np.expand_dims(X, axis=1))))
print("CNN is done. Shape: " + str(np.shape(X_new)))
# Apply Thresholding
print("Data shape before thresholding: ", np.shape(X_new))
X_new_th = X_new
if ui.rbMat.isChecked() and ui.cbThreshold.isChecked():
index = np.where(np.std(X_new, axis=0) >= Threshold)
X_new_index = X_new[:, index]
print("Standard deviation thresholding ...")
X_new_th = None
for xx_new in X_new_index:
X_new_th = xx_new if X_new_th is None else np.concatenate(
(X_new_th, xx_new), axis=0)
# Apply Normalization
if ui.rbMat.isChecked() and ui.cbOutNormal.isChecked():
X_new_th = X_new_th - np.mean(X_new_th)
X_new_th = X_new_th / np.std(X_new_th)
print("Output data is normalized!")
OutData[ui.txtOData.text()] = X_new_th
print("Saving ...")
print("Final data shape: ", np.shape(OutData[ui.txtOData.text()]))
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("CNN is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Class filter is wrong!")
msgBox.setText("Class filter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
Fold = list()
accuracy = list()
precision = list()
average_precision = list()
f1score = list()
recall = list()
accuracyTr = list()
precisionTr = list()
average_precisionTr = list()
f1scoreTr = list()
recallTr = list()
InFileList = list()
OutData = dict()
OutData["ModelAnalysis"] = "Gaussian Naive Bayes"
for fold in range(FoldFrom, FoldTo + 1):
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
else:
OutModel = OutModel.replace("$FOLD$", str(fold))
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold))
InFileList.append(InFile)
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrX = InData[ui.txtITrData.currentText()]
TeX = InData[ui.txtITeData.currentText()]
TrL = InData[ui.txtITrLabel.currentText()][0]
TeL = InData[ui.txtITeLabel.currentText()][0]
# Ref Filter Train
if ui.cbFilterTrID.isChecked():
try:
# Create content structure for training
TrFilterContent = ui.txtFilterTrContent.text()
TrFilterContent = TrFilterContent.replace(" ", "")
if not len(TrFilterContent):
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TrFilterContent = TrFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TrFilterContent = np.int32(TrFilterContent)
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
if np.shape(TrX)[0] != np.shape(TrF)[0] or np.shape(
TrL)[0] != np.shape(TrF)[0]:
print(
"Shape of reference for training must be the same as data and label"
)
msgBox.setText(
"Shape of reference for training must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for training is wrong!")
msgBox.setText("Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove training set
try:
print(
"Removing training set based on reference. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
for content in TrFilterContent:
contIndex = np.where(TrF == content)[0]
TrL = np.delete(TrL, contIndex, axis=0)
TrX = np.delete(TrX, contIndex, axis=0)
TrF = np.delete(TrF, contIndex, axis=0)
print(" Content", content,
"is removed from training set. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
except Exception as e:
print("Cannot filter the training set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the training set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Create content structure for testing
TeFilterContent = ui.txtFilterTeContent.text()
TeFilterContent = TeFilterContent.replace(" ", "")
if not len(TeFilterContent):
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TeFilterContent = TeFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TeFilterContent = np.int32(TeFilterContent)
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
if np.shape(TeX)[0] != np.shape(TeF)[0] or np.shape(
TeL)[0] != np.shape(TeF)[0]:
print(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setText(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for testing is wrong!")
msgBox.setText("Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove testing set
try:
print(
"Removing testing set based on reference. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
for content in TeFilterContent:
contIndex = np.where(TeF == content)[0]
TeL = np.delete(TeL, contIndex, axis=0)
TeX = np.delete(TeX, contIndex, axis=0)
TeF = np.delete(TeF, contIndex, axis=0)
print(" Content", content,
"is removed from testing set. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
except Exception as e:
print("Cannot filter the testing set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the testing set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(TrL == fil)[0]
TrL = np.delete(TrL, labelIndx, axis=0)
TrX = np.delete(TrX, labelIndx, axis=0)
# Remove Testing Set
labelIndx = np.where(TeL == fil)[0]
TeL = np.delete(TeL, labelIndx, axis=0)
TeX = np.delete(TeX, labelIndx, axis=0)
print("Class ID = " + str(fil) +
" is removed from data.")
if ui.cbScale.isChecked():
TrX = preprocessing.scale(TrX)
TeX = preprocessing.scale(TeX)
print(
"Whole of data is scaled Train~N(0,1) and Test~N(0,1)."
)
except:
print("Cannot load data or label")
return
# FoldID
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
currFID = InData[ui.txtFoldID.currentText()][0][0]
Fold.append(currFID)
except:
print("Cannot load Fold ID!")
return
try:
clf = GaussianNB()
print("FoldID = " + str(currFID) + " is training ...")
clf.fit(TrX, TrL)
if OutModel is not None:
joblib.dump(clf, OutModel)
print("FoldID = " + str(currFID) + " Model is saved: " +
OutModel)
print("FoldID = " + str(currFID) + " is testing ...")
PeL = clf.predict(TeX)
PrL = clf.predict(TrX)
OutData["fold" + str(currFID) +
"_confusion_matrix"] = confusion_matrix(
TeL, PeL, np.unique(TeL))
OutData["fold" + str(currFID) +
"_classification_report"] = classification_report(
TeL, PeL)
print(OutData["fold" + str(currFID) +
"_classification_report"])
except Exception as e:
print(e)
msgBox = QMessageBox()
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if ui.cbAverage.isChecked():
acc = accuracy_score(TeL, PeL)
accTr = accuracy_score(TrL, PrL)
accuracy.append(acc)
accuracyTr.append(accTr)
print(
"FoldID = {:d}, Average Train {:5.2f} Test {:5.2f}"
.format(currFID, accTr * 100, acc * 100))
if ui.cbPrecision.isChecked():
pre = precision_score(TeL,
PeL,
average=ui.cbPrecisionAvg.currentData())
preTr = precision_score(
TrL, PrL, average=ui.cbPrecisionAvg.currentData())
precision.append(pre)
precisionTr.append(preTr)
print(
"FoldID = {:d}, Precision Train {:5.2f} Test {:5.2f}"
.format(currFID, preTr * 100, pre * 100))
if ui.cbAPrecision.isChecked():
prA = average_precision_score(
TeL, PeL, average=ui.cbAPrecisionAvg.currentData())
prATr = average_precision_score(
TrL, PrL, average=ui.cbAPrecisionAvg.currentData())
average_precision.append(prA)
average_precisionTr.append(prATr)
print(
"FoldID = {:d}, Average Precision: Train {:5.2f} Test {:5.2f}"
.format(currFID, prATr * 100, prA * 100))
if ui.cbRecall.isChecked():
rec = recall_score(TeL,
PeL,
average=ui.cbRecallAvg.currentData())
recTr = recall_score(TrL,
PrL,
average=ui.cbRecallAvg.currentData())
recall.append(rec)
recallTr.append(recTr)
print(
"FoldID = {:d}, Recall: Train {:5.2f} Test {:5.2f}"
.format(currFID, recTr * 100, rec * 100))
if ui.cbF1.isChecked():
f1 = f1_score(TeL, PeL, average=ui.cbF1Avg.currentData())
f1Tr = f1_score(TrL, PrL, average=ui.cbF1Avg.currentData())
f1score.append(f1)
f1scoreTr.append(f1Tr)
print(
"FoldID = {:d}, F1: Train {:5.2f} Test {:5.2f}"
.format(currFID, f1Tr * 100, f1 * 100))
print("FoldID = " + str(currFID) + " is analyzed!")
if ui.cbAverage.isChecked():
OutData["FoldAccuracy"] = accuracy
MeanAcc = np.mean(accuracy)
OutData["MeanTestAccuracy"] = MeanAcc
STDAcc = np.std(accuracy)
OutData["StdTestAccuracy"] = STDAcc
MeanAccTr = np.mean(accuracyTr)
OutData["MeanTrainAccuracy"] = MeanAccTr
STDAccTr = np.std(accuracyTr)
OutData["StdTrainAccuracy"] = STDAccTr
print(
"Accuracy: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAccTr * 100, STDAccTr, MeanAcc * 100, STDAcc))
if ui.cbPrecision.isChecked():
OutData["ModePrecision"] = ui.cbPrecisionAvg.currentText()
OutData["FoldPrecision"] = precision
MeanPre = np.mean(precision)
OutData["MeanTrainPrecision"] = MeanPre
STDPre = np.std(precision)
OutData["StdTrainPrecision"] = STDPre
MeanPreTr = np.mean(precisionTr)
OutData["MeanTestPrecision"] = MeanPreTr
STDPreTr = np.std(precisionTr)
OutData["StdTestPrecision"] = STDPreTr
print(
"Precision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanPreTr * 100, STDPreTr, MeanPre * 100, STDPre))
if ui.cbAPrecision.isChecked():
OutData["ModeAveragePrecision"] = ui.cbAPrecisionAvg.currentText()
OutData["FoldAveragePrecision"] = average_precision
MeanAPre = np.mean(average_precision)
OutData["MeanTrainAveragePrecision"] = MeanAPre
STDAPre = np.std(average_precision)
OutData["StdTestAveragePrecision"] = STDAPre
MeanAPreTr = np.mean(average_precisionTr)
OutData["MeanTrainAveragePrecision"] = MeanAPreTr
STDAPreTr = np.std(average_precisionTr)
OutData["StdTrainAveragePrecision"] = STDAPreTr
print(
"AveragePrecision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAPreTr * 100, STDAPreTr, MeanAPre * 100, STDAPre))
if ui.cbRecall.isChecked():
OutData["ModeRecall"] = ui.cbRecallAvg.currentText()
OutData["FoldRecall"] = recall
MeanRec = np.mean(recall)
OutData["MeanTestRecall"] = MeanRec
STDRec = np.std(recall)
OutData["StdTestRecall"] = STDRec
MeanRecTr = np.mean(recallTr)
OutData["MeanTrainRecall"] = MeanRecTr
STDRecTr = np.std(recallTr)
OutData["StdTrainRecall"] = STDRecTr
print(
"Recall: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanRecTr * 100, STDRecTr, MeanRec * 100, STDRec))
if ui.cbF1.isChecked():
OutData["ModeF1"] = ui.cbF1Avg.currentText()
OutData["FoldF1"] = f1score
MeanF1 = np.mean(f1score)
OutData["MeanTestF1"] = MeanF1
STDF1 = np.std(f1score)
OutData["StdTestF1"] = STDF1
MeanF1Tr = np.mean(f1scoreTr)
OutData["MeanTrainF1"] = MeanF1Tr
STDF1Tr = np.std(f1scoreTr)
OutData["StdTrainF1"] = STDF1Tr
print(
"F1: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanF1Tr * 100, STDF1Tr, MeanF1 * 100, STDF1))
OutData["InputFiles"] = InFileList
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Gaussian Naive Bayes is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
totalTime = 0
Alg = ui.cbAlg.currentText()
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter < 1:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo+1):
tic = time.time()
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTe)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText("Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText("Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText("Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText("Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText("Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText("Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText("Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText("Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText("Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText("Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText("Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText("Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText("Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText("Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText("Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText("Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText("Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText("Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText("Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText("Please enter Train Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText("Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText("Please enter Train Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText("Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText("Please enter Train Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText("Please enter Test Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
try:
model = FastICA(n_components=NumFea, algorithm=Alg, max_iter=MaxIter, tol=Tol)
print("Running ICA Functional Alignment on Training Data ...")
OutData[ui.txtOTrData.text()] = model.fit_transform(XTr)
print("Running ICA Functional Alignment on Testing Data ...")
OutData[ui.txtOTeData.text()] = model.fit_transform(XTe)
except Exception as e:
print(str(e))
HAParam = dict()
HAParam["Method"] = "FastICA"
HAParam["NumFea"] = NumFea
HAParam["Algorithm"]= Alg
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Runtime: ", totalTime)
print("ICA Functional Alignment is done.")
msgBox.setText("ICA Functional Alignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# CType
CType = ui.cbCType.currentData()
# IParams
IParams = ui.cbIParams.currentData()
# Warm Start
WStart = ui.cbWarmStart.isChecked()
# NCluster
try:
NComponent = np.int32(ui.txtNComponent.text())
except:
msgBox.setText("Number of Component is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# NInit
try:
NInit = np.int32(ui.txtNInit.text())
except:
msgBox.setText("Number of init is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIter
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Max Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Regularization
try:
Regularization = np.float(ui.txtRegularization.text())
except:
msgBox.setText("Regularization is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Verbose
try:
Verbose = np.int(ui.txtVerbose.text())
except:
msgBox.setText("Verbose is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# IVerbose
try:
IVerbose = np.int(ui.txtIVerbose.text())
except:
msgBox.setText("Verbose Interval is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
OutData["ModelAnalysis"] = "Gaussian Mixture"
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
L = np.delete(L, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
if ui.cbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled to N(0,1).")
except:
print("Cannot load data or label")
return
try:
cls = GaussianMixture(n_components=NComponent,covariance_type=CType,tol=Tol,reg_covar=Regularization,\
max_iter=MaxIter,n_init=NInit,init_params=IParams,\
warm_start=WStart,verbose=Verbose,verbose_interval=IVerbose)
print("Run Clustering ...")
cls.fit(X)
PeL = cls.predict(X)
except Exception as e:
print(e)
msgBox = QMessageBox()
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
OutData["predict"] = PeL
if OutModel is not None:
joblib.dump(cls, OutModel)
print("Model is saved: " + OutModel)
if ui.cbAverage.isChecked():
acc = accuracy_score(L, PeL)
OutData["Accuracy"] = acc
print("Average {:5.2f}".format(acc *
100))
if ui.cbNMI.isChecked():
NMI = normalized_mutual_info_score(L, PeL)
OutData["NMI"] = NMI
print("Normalized Mutual Information (NMI) {:7.6f}".format(NMI))
if ui.cbRIA.isChecked():
RIA = adjusted_rand_score(L, PeL)
OutData["RIA"] = RIA
print("Rand Index Adjusted (RIA) {:7.6f}".format(RIA))
if ui.cbAMI.isChecked():
AMI = adjusted_mutual_info_score(L, PeL)
OutData["AMI"] = AMI
print("Adjusted Mutual Information (AMI) {:7.6f}".format(AMI))
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Gaussian Mixture Clustering is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
try:
Threshold = np.float(ui.txtThre.text())
assert Threshold <= 1
assert Threshold >= 0
except:
msgBox.setText("Threshold is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
EdgeThreshold = np.float(ui.txtEdgeThre.text())
assert EdgeThreshold <= 1
assert EdgeThreshold >= 0
except:
msgBox.setText("Edge threshold is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
MetricCode = ui.txtMetric.toPlainText()
allvars = dict(locals(), **globals())
exec(MetricCode, allvars, allvars)
Metric = allvars['metric']
except Exception as e:
msgBox.setText("Metric is wrong!\n" + str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
IntegrationCode = ui.txtIntegration.toPlainText()
allvars = dict(locals(), **globals())
exec(IntegrationCode, allvars, allvars)
Integration = allvars['integration']
except Exception as e:
msgBox.setText("Integration is wrong!\n" + str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
AtlasPath = ui.txtAtlasPath.text()
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# Region Filter
try:
RegionFilter = ui.txtRegions.text()
if not len(RegionFilter):
RegionFilter = []
else:
RegionFilter = RegionFilter.replace("\'", " ").replace(
",", " ").replace("[", "").replace("]", "").split()
RegionFilter = np.int32(RegionFilter)
except:
print("Region filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# AtlasFile
AtlasFile = ui.txtAtlasFile.text()
if not len(AtlasFile):
msgBox.setText("Please enter atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(AtlasFile):
msgBox.setText("Atlas file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
AtlasHDR = nb.load(AtlasFile)
AtlasImg = np.asanyarray(AtlasHDR.dataobj)
AtlasReg = np.unique(AtlasImg)
if not 0 in RegionFilter:
AtlasReg = AtlasReg[np.where(AtlasReg != 0)[0]]
print("Region 0 is considered as rest mode!")
AtlasShape = np.shape(AtlasImg)
except:
msgBox.setText("Cannot load atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
print("Loading ...")
InData = mainIO_load(InFile)
imgShape = InData["imgShape"][0]
except:
print("Cannot load data!")
return
try:
assert imgShape[0] == AtlasShape[0]
assert imgShape[1] == AtlasShape[1]
assert imgShape[2] == AtlasShape[2]
except:
msgBox.setText("Input file (" +
str(imgShape).replace("]", "").replace("[", "") +
") and Atlas file " +
str(AtlasShape).replace(",", "") +
" must have the same shape!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
try:
if not len(ui.txtCond.currentText()):
Cond = 0
else:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
labels = np.array(labels)
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
try:
if len(ui.txtCoord.currentText()):
Coord = np.transpose(InData[ui.txtCoord.currentText()])
else:
Coord = None
except:
msgBox.setText("Coordinate variable is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
Net, ThrNet, ActiveRegions, A, ACoord, listL = ClassicNetworkAnalysis(
X=X,
L=L,
Coord=Coord,
Integration=Integration,
Metric=Metric,
AtlasImg=AtlasImg,
affine=AtlasHDR.affine,
KeepRegions=RegionFilter,
AtlasPath=AtlasPath,
Threshold=Threshold)
Out = {}
Out["Networks"] = Net
Out["ThresholdNetworks"] = ThrNet
Out["ActiveRegions"] = ActiveRegions
Out["Atlas"] = A
Out["Atlas_parcellation"] = ACoord
Out["Atlas_affine"] = AtlasHDR.affine
Out["condition"] = Cond
Out["labels"] = listL
Out["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (Out["RunTime"]))
print("Saving results ...")
mainIO_save(Out, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
for nnIndex, (nn, tnn) in enumerate(zip(Net, ThrNet)):
try:
Title = f"Label: {reshape_condition_cell(Cond[nnIndex][1])}"
except:
Title = f"Label: {nnIndex}"
PlotConnectome(tnn, ACoord, Title, EdgeThreshold)
fig = plt.figure()
plt.imshow(np.transpose(nn))
plt.title(Title)
plt.show()
print("DONE.")
msgBox.setText("Network analysis is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
msgBox.setText("At least, you must select one metric!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
Method = ui.cbMethod.currentData()
NuregMethod = ui.cbNuregMethod.currentData()
Tau2Prior = ui.cbTau2Prior.currentData()
SNRPrior = ui.cbSNRPrior.currentData()
GPS = ui.cbGBS.isChecked()
GPI = ui.cbGPI.isChecked()
BaselineSingle = ui.cbBaselineSingle.isChecked()
AutoNuisance = ui.cbAutoNuisance.isChecked()
NuregZscore = ui.cbNuregZscore.isChecked()
try:
miter = np.int(ui.txtMaxIter.text())
assert miter >= 1, None
except:
msgBox.setText("Max Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
iiter = np.int(ui.txtInitIter.text())
assert iiter >= 1, None
except:
msgBox.setText("Init Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Speed = np.int(ui.txtAnnealSpeed.text())
assert Speed >= 1, None
except:
msgBox.setText("Anneal speed is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
rank = np.int(ui.txtRank.text())
assert rank >= 0, None
if rank == 0:
rank = None
except:
msgBox.setText("Rank is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
NumReg = np.int(ui.txtNumReg.text())
assert NumReg >= 0, None
if NumReg == 0:
NumReg = None
except:
msgBox.setText("Number of Reg is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
SNRBin = np.int(ui.txtSNRBins.text())
assert SNRBin > 0, None
except:
msgBox.setText("SNR bin is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
rhoBin = np.int(ui.txtRhoBins.text())
assert rhoBin > 0, None
except:
msgBox.setText("Rho bin is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
eta = np.float(ui.txtEta.text())
except:
msgBox.setText("Eta is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
LogSRange = np.float(ui.txtLogSRange.text())
except:
msgBox.setText("LogS range is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
SpaceSmoothRange = np.float(ui.txtSpaceSmoothRange.text())
if SpaceSmoothRange == 0:
SpaceSmoothRange = None
except:
msgBox.setText("Space Smooth Range is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
IntenSmoothRange = np.float(ui.txtIntenSmoothRange.text())
if IntenSmoothRange == 0:
IntenSmoothRange = None
except:
msgBox.setText("Inten Smooth Range is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Tau = np.float(ui.txtTauRange.text())
except:
msgBox.setText("Eta is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
CodeText = ui.txtEvents.toPlainText()
allvars = dict(locals(), **globals())
exec(CodeText, allvars, allvars)
Optimizer = allvars['optimizer']
MinimizeOptions = allvars['minimize_options']
except Exception as e:
msgBox.setText("Optimizer is wrong!\n" + str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace("[", "").replace("]","").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading ...")
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if not len(ui.txtDesign.currentText()):
msgBox.setText("Please enter Input Design variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Design = InData[ui.txtDesign.currentText()]
except:
msgBox.setText("Design value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
FontSize = ui.txtFontSize.value()
try:
X = InData[ui.txtData.currentText()]
Intensity = np.mean(X, axis=0)
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
except:
msgBox.setText("Task variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TaskTitleUnique = np.unique(TaskTitle)
Task = np.zeros(np.shape(TaskTitle))
for ttinx, tt in enumerate(TaskTitle):
for ttlinx, ttl in enumerate(TaskTitleUnique):
if tt[0] == ttl:
Task[ttinx] = ttlinx + 1
break
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sub = InData[ui.txtSubject.currentText()][0]
except:
msgBox.setText("Subject variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()][0]
except:
msgBox.setText("Run variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Con = InData[ui.txtCounter.currentText()][0]
except:
msgBox.setText("Counter variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
if len(ui.txtCoord.currentText()):
Coord = np.transpose(InData[ui.txtCoord.currentText()])
else:
Coord = None
except:
msgBox.setText("Coordinate variable is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
Design = np.delete(Design, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
Task = np.delete(Task, labelIndx, axis=0)
Sub = np.delete(Sub, labelIndx, axis=0)
Run = np.delete(Run, labelIndx, axis=0)
Con = np.delete(Con, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
try:
Unit = np.int32(ui.txtUnit.text())
except:
msgBox.setText("Unit for the test set must be a number!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Unit < 1:
msgBox.setText("Unit for the test set must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Calculating Levels ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = [Sub]
FoldStr = "Subject"
else:
GroupFold = np.concatenate(([Sub],[Run]))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate((GroupFold,[Task])) if GroupFold is not None else [Task]
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate((GroupFold,[Con])) if GroupFold is not None else [Con]
FoldStr = FoldStr + "+Counter"
if FoldStr == "":
FoldStr = "Whole-Data"
GUFold = [1]
ListFold = [1]
UniqFold = [1]
GroupFold = [1]
UnitFold = np.ones((1, np.shape(X)[0]))
else:
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i) for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText("Unit must be smaller than all possible levels! Number of all levels is: " + str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold),Unit):
msgBox.setText("Unit must be divorceable to all possible levels! Number of all levels is: " + str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
GUFold = np.unique(UnitFold)
FoldInfo = dict()
FoldInfo["Unit"] = Unit
FoldInfo["Group"] = GroupFold
FoldInfo["Order"] = FoldStr
FoldInfo["List"] = ListFold
FoldInfo["Unique"] = UniqFold
FoldInfo["Folds"] = UnitFold
OutData["FoldInfo"] = FoldInfo
OutData["ModelAnalysis"] = "SK.Group.RSA." + ui.cbMethod.currentText()
print("Number of all levels is: " + str(len(UniqFold)))
Cov = None
Corr = None
AMSE = list()
Beta = None
for foldID, fold in enumerate(GUFold):
print("Analyzing level " + str(foldID + 1)," of ", str(len(UniqFold)) , " ...")
Index = np.where(UnitFold == fold)
# Whole-Data
if FoldStr == "Whole-Data" and np.shape(Index)[0]:
Index = [Index[1]]
XLi = X[Index]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
XLi = preprocessing.scale(XLi)
print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
#RegLi = np.insert(Design[Index], 0, 1, axis=1)
RegLi = Design[Index]
try:
if Method == "brsa":
model = BRSA(n_iter=miter, rank=rank, auto_nuisance=AutoNuisance,
n_nureg=NumReg, nureg_zscore=NuregZscore, nureg_method=NuregMethod,
baseline_single=BaselineSingle, GP_space=GPS, GP_inten=GPI,
space_smooth_range=SpaceSmoothRange, inten_smooth_range=IntenSmoothRange,
tau_range=Tau,
tau2_prior=Tau2Prior, eta=eta, init_iter=iiter, anneal_speed=Speed, tol=tol,
optimizer=Optimizer, minimize_options=MinimizeOptions)
model.fit(XLi, RegLi, coords=Coord, inten=Intensity)
else:
model = GBRSA(n_iter=miter, rank=rank, auto_nuisance=AutoNuisance,
n_nureg=NumReg, nureg_zscore=NuregZscore, nureg_method=NuregMethod,
baseline_single=BaselineSingle, tol=tol, anneal_speed=Speed,
SNR_prior=SNRPrior, logS_range=LogSRange, SNR_bins=SNRBin, rho_bins=rhoBin,
optimizer=Optimizer, minimize_options=MinimizeOptions)
model.fit(XLi, RegLi)
except Exception as e:
msgBox.setText(str(e))
print(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
BetaLi = model.beta_
Beta = BetaLi if Beta is None else Beta + BetaLi
print("Calculating MSE for level %d ..." % (foldID + 1))
MSE = mean_squared_error(XLi, np.matmul(RegLi, BetaLi))
print("MSE%d: %f" % (foldID + 1, MSE))
OutData["MSE" + str(foldID)] = MSE
AMSE.append(MSE)
if ui.cbBeta.isChecked():
OutData["BetaL" + str(foldID + 1)] = BetaLi
# Calculate Results
if ui.cbCorr.isChecked():
print("Calculating Correlation for level %d ..." % (foldID + 1))
CorrLi = np.corrcoef(BetaLi)
OutData["Corr" + str(foldID + 1)] = CorrLi
if Corr is None:
Corr = CorrLi.copy()
else:
if ui.rbAvg.isChecked():
Corr = np.add(Corr, CorrLi)
elif ui.rbMin.isChecked():
Corr = np.minimum(Corr, CorrLi)
else:
Corr = np.maximum(Corr, CorrLi)
if ui.cbCov.isChecked():
print("Calculating Covariance for level %d ..." % (foldID + 1))
CovLi = np.cov(BetaLi)
OutData["Cov" + str(foldID + 1)] = CovLi
if Cov is None:
Cov = CovLi.copy()
else:
if ui.rbAvg.isChecked():
Cov = np.add(Cov, CovLi)
elif ui.rbMin.isChecked():
Cov = np.minimum(Cov, CovLi)
else:
Cov = np.maximum(Cov, CovLi)
CoEff = len(UniqFold) - 1 if len(UniqFold) > 2 else 1
if ui.cbCov.isChecked():
if ui.rbAvg.isChecked():
Cov = Cov / CoEff
covClass = SimilarityMatrixBetweenClass(Cov)
OutData["Covariance"] = Cov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_std"] = covClass.std()
OutData["Covariance_mean"] = covClass.mean()
if ui.cbCorr.isChecked():
if ui.rbAvg.isChecked():
Corr = Corr / CoEff
corClass = SimilarityMatrixBetweenClass(Corr)
OutData["Correlation"] = Corr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_std"] = corClass.std()
OutData["Correlation_mean"] = corClass.mean()
# Calculating Distance Matrix
dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))
for i in range(np.shape(Beta)[0]):
for j in range(i + 1, np.shape(Beta)[0]):
dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
dis[j, i] = dis[i, j]
OutData["DistanceMatrix"] = dis
Z = linkage(dis)
OutData["Linkage"] = Z
OutData["MSE"] = np.mean(AMSE)
print("Average MSE: %f" % (OutData["MSE"]))
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
mainIO_save(OutFile, OutData)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
NumData = np.shape(Corr)[0]
fig1 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCorr.text()):
plt.title(ui.txtTitleCorr.text())
else:
plt.title('Group RSA: Correlation\nLevel: ' + FoldStr)
plt.show()
if ui.cbCov.isChecked():
NumData = np.shape(Cov)[0]
fig2 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCov.text()):
plt.title(ui.txtTitleCov.text())
else:
plt.title('Group RSA: Covariance\nLevel: ' + FoldStr)
plt.show()
fig3 = plt.figure(figsize=(25, 10), )
if len(ui.txtTitleDen.text()):
plt.title(ui.txtTitleDen.text())
else:
plt.title('Group MP Gradient RSA: Similarity Analysis\nLevel: ' + FoldStr)
dn = dendrogram(Z, labels=labels, leaf_font_size=FontSize, color_threshold=1, leaf_rotation=ui.txtXRotation.value())
plt.show()
print("DONE.")
msgBox.setText("Group Representational Similarity Analysis (RSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sigma = np.int32(ui.txtSigma.text())
except:
msgBox.setText("Sigma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Sigma < 1:
msgBox.setText("Sigma must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
try:
X = InData[ui.txtData.currentText()]
except:
print("Cannot load data")
return
try:
DM = np.transpose(InData[ui.txtDM.currentText()])
except:
print("Cannot load design")
return
# Gaussian Kernel
G = np.arange(-2 * Sigma, 2 * Sigma + 1, 1)
G = np.exp(-1 * G**2 / 2 * Sigma * Sigma)
G = G / np.sum(G)
LocalMaximum = list()
for dmx in DM:
dmx = dmx - np.min(dmx)
dmx = dmx / np.max(dmx)
LocalMaximum.append(
argrelextrema(np.convolve(dmx, G)[0:len(dmx)], np.greater))
#Check Conflict
AllLabels = []
AllIndex = []
for lblinx, lbl in enumerate(LocalMaximum):
lbldata = LocalMaximum[lblinx]
if lblinx == 0:
AllIndex = lbldata[0]
AllLabels = np.ones(np.shape(lbldata)[1])
else:
Indexs = list()
for dat in lbldata:
if len(np.where(AllIndex == dat)[0]) == 0:
Indexs.append(dat)
AllIndex = np.concatenate((AllIndex, Indexs[0]))
AllLabels = np.concatenate(
(AllLabels, (lblinx + 1) * np.ones(np.shape(Indexs)[1])))
# Label
OutData[ui.txtOLabel.text()] = reshape_1Dvector(np.int32(AllLabels))
# Data
OutData[ui.txtOData.text()] = X[AllIndex, :]
# DM
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()][AllIndex, :]
# Subject
if ui.cbSubject.isChecked():
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOSubject.text()] = reshape_1Dvector(
InData[ui.txtSubject.currentText()][0][AllIndex])
except:
print("Cannot load Subject ID")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = reshape_1Dvector(
InData[ui.txtTask.currentText()][0][AllIndex])
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = reshape_1Dvector(
InData[ui.txtRun.currentText()][0][AllIndex])
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = reshape_1Dvector(
InData[ui.txtCounter.currentText()][0][AllIndex])
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = label_binarize(
AllLabels, np.unique(AllLabels))
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = reshape_1Dvector(
InData[ui.txtScan.currentText()][0][AllIndex])
Models = dict()
Models["Name"] = "MRPA"
Models["Sigma"] = Sigma
OutData["ModelParameter"] = Models
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
print("Number of all instances :", np.shape(X)[0])
print("Number of selected instances: ", len(AllLabels))
msgBox.setText("MRPA is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
InFFile = ui.txtInFFile.text()
if not len(InFFile):
msgBox.setText("Please select the first input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
elif not os.path.isfile(InFFile):
msgBox.setText("The first input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InSFile = ui.txtInSFile.text()
if not len(InSFile):
msgBox.setText("Please select the second input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
elif not os.path.isfile(InSFile):
msgBox.setText("The second input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please select the output file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not ui.cbCondUnion.isChecked():
if not len(ui.txtFCondPre.text()):
msgBox.setText("Please enter the condition prefix!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InFData = mainIO_load(InFFile)
InSData = mainIO_load(InSFile)
OutData = dict()
# Data
if not len(ui.txtFData.currentText()):
msgBox.setText("Please enter First Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSData.currentText()):
msgBox.setText("Please enter Second Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOData.text()):
msgBox.setText("Please enter Out Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
InFX = InFData[ui.txtFData.currentText()]
except:
print("Cannot load data from the first file!")
return
try:
InSX = InSData[ui.txtSData.currentText()]
except:
print("Cannot load data from the second file!")
return
if np.shape(InFX)[1] != np.shape(InSX)[1]:
print("Both data files must have the same size!")
return
# fMRI Size
InterfMRISize = InFData["imgShape"][0] - InSData["imgShape"][0]
if (np.max(InterfMRISize) != 0) or (np.min(InterfMRISize) != 0):
print(
"Both data files must extract from the same size fMRI images!")
return
OutData["imgShape"] = reshape_1Dvector(InFData["imgShape"])
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtFCol.currentText()):
msgBox.setText("Please enter First Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSCol.currentText()):
msgBox.setText(
"Please enter Second Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCoI.text()):
msgBox.setText("Please enter Out Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
InFCol = np.array(InFData[ui.txtFCol.currentText()])
except:
print("Cannot load coordinate in the first file!")
return
try:
InSCol = np.array(InSData[ui.txtSCol.currentText()])
except:
print("Cannot load coordinate in the second file!")
return
SubCol = InFCol - InSCol
if (np.max(SubCol) != 0) or (np.min(SubCol) != 0):
msgBox.setText("Coordinates are not matched!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCoI.text()] = InFData[ui.txtFCol.currentText()]
OutData[ui.txtOData.text()] = np.concatenate((InFX, InSX))
# Label
if not len(ui.txtFLabel.currentText()):
msgBox.setText("Please enter First Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSLabel.currentText()):
msgBox.setText("Please enter Second Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOLabel.text()):
msgBox.setText("Please enter Out Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
InFL = InFData[ui.txtFLabel.currentText()]
InSL = InSData[ui.txtSLabel.currentText()]
InFLUniq = np.unique(InFL)
InSLUniq = np.unique(InSL)
if not ui.cbRelabeling.isChecked():
OutData[ui.txtOLabel.text()] = reshape_1Dvector(
np.concatenate((InFL, InSL), 1)[0])
InSLUniqNew = InSLUniq.copy()
else:
InSLUniqNew = InSLUniq + np.max(InFLUniq) + 1
InSLNew = InSL.copy()
for lblinx, lbl in enumerate(InSLUniq):
InSLNew[np.where(InSL == lbl)] = InSLUniqNew[lblinx]
OutData[ui.txtOLabel.text()] = reshape_1Dvector(
np.concatenate((InFL, InSLNew), 1)[0])
except:
print("Cannot combine Labels!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtOmLabel.text()):
msgBox.setText("Please enter Out Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = label_binarize(
OutData[ui.txtOLabel.text()],
np.unique(OutData[ui.txtOLabel.text()]))
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtFCond.currentText()):
msgBox.setText("Please enter First Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSCond.currentText()):
msgBox.setText("Please enter Second Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Out Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbCondUnion.isChecked():
OutData[ui.txtOCond.text()] = np.concatenate(
(InFData[ui.txtFCond.currentText()],
InSData[ui.txtSCond.currentText()]))
else:
NewCond = Conditions()
InFCond = InFData[ui.txtFCond.currentText()]
for cond in InFCond:
NewCond.add_cond(
str.replace(cond[0][0], ui.txtFCondPre.text(),
ui.txtOCondPre.text()), cond[1][0])
InSCond = InSData[ui.txtSCond.currentText()]
for cond in InSCond:
condID = np.int32(
str.replace(cond[0][0],
ui.txtSCondPre.text() + "_", ""))
condIDNew = InSLUniqNew[np.where(InSLUniq == condID)][0]
NewCond.add_cond(
ui.txtOCondPre.text() + "_" + str(condIDNew),
cond[1][0])
OutData[ui.txtOCond.text()] = np.array(NewCond.get_cond(),
dtype=object)
# Subject
if ui.cbSubject.isChecked():
if not len(ui.txtFSubject.currentText()):
msgBox.setText("Please enter First Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSSubject.currentText()):
msgBox.setText("Please enter Second Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOSubject.text()):
msgBox.setText("Please enter Out Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOSubject.text()] = reshape_1Dvector(
np.concatenate((InFData[ui.txtFSubject.currentText()],
InSData[ui.txtSSubject.currentText()]),
1)[0])
except:
print("Cannot combine Subject IDs!")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtFTask.currentText()):
msgBox.setText("Please enter First Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSTask.currentText()):
msgBox.setText("Please enter Second Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTask.text()):
msgBox.setText("Please enter Out Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTask.text()] = reshape_1Dvector(
np.concatenate((InFData[ui.txtFTask.currentText()],
InSData[ui.txtSTask.currentText()]), 1)[0])
except:
print("Cannot combine Task IDs!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtFRun.currentText()):
msgBox.setText("Please enter First Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSRun.currentText()):
msgBox.setText("Please enter Second Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtORun.text()):
msgBox.setText("Please enter Out Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtORun.text()] = reshape_1Dvector(
np.concatenate((InFData[ui.txtFRun.currentText()],
InSData[ui.txtSRun.currentText()]), 1)[0])
except:
print("Cannot combine Run IDs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtFCounter.currentText()):
msgBox.setText("Please enter First Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSCounter.currentText()):
msgBox.setText("Please enter Second Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCounter.text()):
msgBox.setText("Please enter Out Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCounter.text()] = reshape_1Dvector(
np.concatenate((InFData[ui.txtFCounter.currentText()],
InSData[ui.txtSCounter.currentText()]),
1)[0])
except:
print("Cannot combine Counter IDs!")
return
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtFScan.currentText()):
msgBox.setText("Please enter First NScan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbNScan.isChecked():
if not len(ui.txtSScan.currentText()):
msgBox.setText("Please enter Second NScan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbNScan.isChecked():
if not len(ui.txtOScan.text()):
msgBox.setText("Please enter Out NScan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOScan.text()] = reshape_1Dvector(
np.concatenate((InFData[ui.txtFScan.currentText()],
InSData[ui.txtSScan.currentText()]), 1)[0])
except:
print("Cannot combine Counter IDs!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtFDM.currentText()):
msgBox.setText(
"Please enter First Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSDM.currentText()):
msgBox.setText(
"Please enter Second Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtODM.text()):
msgBox.setText("Please enter Out Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtODM.text()] = np.concatenate(
(InFData[ui.txtFDM.currentText()],
InSData[ui.txtSDM.currentText()]))
except:
print("Cannot combine Design Matrices!")
return
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("Datasets are combined.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
msgBox.setText("At least, you must select one metric!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Method
gpu = ui.cbGPU.currentData()
Verbose = ui.cbVerbose.isChecked()
try:
gamma = np.float(ui.txtGamma.text())
except:
msgBox.setText("Gamma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Iter = np.int(ui.txtIter.text())
except:
msgBox.setText("Max Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
NumFea = ui.txtNumFea.value()
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea <= 0:
NumFea = None
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSharedSpace.text()):
msgBox.setText("Please enter Shared Space variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtSharedVoxelSpace.text()):
msgBox.setText("Please enter Shared Voxel Space variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtViewSpaces.text()):
msgBox.setText("Please enter View Space variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTransformMats.text()):
msgBox.setText("Please enter Transform Matrices variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading ...")
InData = mainIO_load(InFile)
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coordinate
if not len(ui.txtCoordinate.currentText()):
msgBox.setText("Please enter Input Coordinate variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Coordinate = InData[ui.txtCoordinate.currentText()]
OutData["coordinate"] = Coordinate
except:
msgBox.setText("Coordinate value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Cond = InData[ui.txtCond.currentText()]
OutData["condition"] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
OutData["labels"] = labels
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
FontSize = ui.txtFontSize.value()
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
except:
msgBox.setText("Task variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TaskTitleUnique = np.unique(TaskTitle)
Task = np.zeros(np.shape(TaskTitle))
for ttinx, tt in enumerate(TaskTitle):
for ttlinx, ttl in enumerate(TaskTitleUnique):
if tt[0] == ttl:
Task[ttinx] = ttlinx + 1
break
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sub = InData[ui.txtSubject.currentText()][0]
except:
msgBox.setText("Subject variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()][0]
except:
msgBox.setText("Run variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Con = InData[ui.txtCounter.currentText()][0]
except:
msgBox.setText("Counter variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
Task = np.delete(Task, labelIndx, axis=0)
Sub = np.delete(Sub, labelIndx, axis=0)
Run = np.delete(Run, labelIndx, axis=0)
Con = np.delete(Con, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
try:
Unit = np.int32(ui.txtUnit.text())
except:
msgBox.setText("Unit for the test set must be a number!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Unit < 1:
msgBox.setText("Unit for the test set must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Calculating Levels ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = [Sub]
FoldStr = "Subject"
else:
GroupFold = np.concatenate(([Sub], [Run]))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Task])) if GroupFold is not None else [Task]
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Con])) if GroupFold is not None else [Con]
FoldStr = FoldStr + "+Counter"
if FoldStr == "":
FoldStr = "Whole-Data"
GUFold = [1]
ListFold = [1]
UniqFold = [1]
GroupFold = [1]
UnitFold = np.ones((1, np.shape(X)[0]))
else:
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i)
for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText(
"Unit must be smaller than all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold), Unit):
msgBox.setText(
"Unit must be divorceable to all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
GUFold = np.unique(UnitFold)
FoldInfo = dict()
FoldInfo["Unit"] = Unit
FoldInfo["Group"] = GroupFold
FoldInfo["Order"] = FoldStr
FoldInfo["List"] = ListFold
FoldInfo["Unique"] = UniqFold
FoldInfo["Folds"] = UnitFold
OutData["FoldInfo"] = FoldInfo
OutData["ModelAnalysis"] = "SSA"
print("Number of all levels is: " + str(len(UniqFold)))
Xi = list()
Yi = list()
for foldID, fold in enumerate(GUFold):
print("Extracting view " + str(foldID + 1), " of ",
str(len(UniqFold)), " ...")
Index = np.where(UnitFold == fold)
# Whole-Data
if FoldStr == "Whole-Data" and np.shape(Index)[0]:
Index = [Index[1]]
Xi.append(X[Index])
yyi = label_binarize(L[Index], np.unique(L))
# Adopt it for binary labels (just two class SSA comparison)
if np.shape(yyi)[1] == 1:
yyi_inv = yyi.copy()
yyi_inv[np.where(yyi_inv == 0)] = 2
yyi_inv -= 1
yyi = np.concatenate((yyi, yyi_inv), axis=1)
Yi.append(yyi)
try:
ssa = SSA(gamma=gamma, gpu=gpu)
Beta = ssa.run(X=Xi,
Y=Yi,
Dim=NumFea,
verbose=Verbose,
Iteration=Iter,
ShowError=ui.cbError.isChecked())
if ui.cbError.isChecked():
if ssa.LostVec is not None:
OutData["LossVec"] = ssa.LostVec
OutData["Error"] = ssa.Loss
except Exception as e:
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData["AlgorithmRuntime"] = ssa.Runtime
OutData[ui.txtSharedSpace.text()] = Beta
OutData[ui.txtSharedVoxelSpace.text()] = ssa.getSharedVoxelSpace()
OutData[ui.txtTransformMats.text()] = ssa.getTransformMats()
if ui.cbViewSpace.isChecked():
for viewID, view in enumerate(ssa.getSubjectSpace()):
OutData[ui.txtViewSpaces.text() + "_View" +
str(viewID + 1)] = np.transpose(view)
else:
OutData[ui.txtViewSpaces.text()] = ssa.getSubjectSpace()
print("Calculating Distance Matrix ...")
dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))
for i in range(np.shape(Beta)[0]):
for j in range(i + 1, np.shape(Beta)[0]):
dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
dis[j, i] = dis[i, j]
# dis = dis - np.min(dis)
# dis = dis / np.max(dis)
OutData["DistanceMatrix"] = dis
print("Applying linkage ...")
Z = linkage(dis,
method=ui.cbLMethod.currentData(),
metric=ui.cbLMetric.currentData(),
optimal_ordering=ui.cbLOrder.isChecked())
OutData["Linkage"] = Z
if ui.cbCov.isChecked():
Cov = np.cov(Beta)
covClass = SimilarityMatrixBetweenClass(Cov)
OutData["Covariance"] = Cov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_std"] = covClass.std()
OutData["Covariance_mean"] = covClass.mean()
if ui.cbCorr.isChecked():
Corr = np.corrcoef(Beta)
corClass = SimilarityMatrixBetweenClass(Corr)
OutData["Correlation"] = Corr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_std"] = corClass.std()
OutData["Correlation_mean"] = corClass.mean()
OutData["Runtime"] = time.time() - tStart
print("Runtime: ", OutData["Runtime"])
print("Algorithm Runtime: ", OutData["AlgorithmRuntime"])
print("Saving results ...")
mainIO_save(OutData, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
drawrsa = DrawRSA()
if ui.cbCorr.isChecked():
drawrsa.ShowFigure(Corr, labels, ui.txtTitleCorr.text(),
FontSize, ui.txtXRotation.value(),
ui.txtYRotation.value())
if ui.cbCov.isChecked():
drawrsa.ShowFigure(Cov, labels, ui.txtTitleCov.text(),
FontSize, ui.txtXRotation.value(),
ui.txtYRotation.value())
drawrsa.ShowDend(Z, labels, ui.txtTitleDen.text(), FontSize,
ui.txtXRotation.value())
print("DONE.")
msgBox.setText("Shared Similarity Analysis (SSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold in range(FoldFrom, FoldTo + 1):
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Solver
Solver = ui.cbSolver.currentData()
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!\n" + InFile)
print(InFile + " - not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText(
"Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
YTr = InData[ui.txtITrLabel.currentText()][0]
YTe = InData[ui.txtITeLabel.currentText()][0]
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(YTr)
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(YTe)
except:
print("Cannot load labels!")
# Subject
if ui.cbSubject.isChecked():
if not len(ui.txtITrSubject.currentText()):
msgBox.setText(
"Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText(
"Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText(
"Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText(
"Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(
InData[ui.txtITrSubject.currentText()])
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(
InData[ui.txtITeSubject.currentText()])
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText(
"Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText(
"Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText(
"Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText(
"Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(
InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(
InData[ui.txtITeTask.currentText()])
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText(
"Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText(
"Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText(
"Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText(
"Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(
InData[ui.txtITrRun.currentText()])
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(
InData[ui.txtITeRun.currentText()])
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText(
"Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText(
"Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText(
"Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText(
"Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(
InData[ui.txtITrCounter.currentText()])
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(
InData[ui.txtITeCounter.currentText()])
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText(
"Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText(
"Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText(
"Please enter Train Output Matrix Label variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText(
"Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[
ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[
ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText(
"Please enter Train Input Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText(
"Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText(
"Please enter Train Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText(
"Please enter Test Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[
ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[
ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[
ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(
InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[
ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(
InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(
InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
print("Running LDA:")
model = LDA(n_components=NumFea, solver=Solver, tol=Tol)
print("Training ...")
XTr_New = model.fit_transform(XTr, YTr)
OutData[ui.txtOTrData.text()] = XTr_New
print("Testing ...")
XTe_New = model.transform(XTe)
OutData[ui.txtOTeData.text()] = XTe_New
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold) + " is DONE: " + OutFile)
print("LDA is done.")
msgBox.setText("LDA is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter output file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOData.text()):
msgBox.setText("Please enter output Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOLabel.text()):
msgBox.setText("Please enter output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOSubject.text()):
msgBox.setText("Please enter output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtORun.text()):
msgBox.setText("Please enter output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCounter.text()):
msgBox.setText("Please enter output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter input Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTask.text()):
msgBox.setText("Please enter output Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# mLabel
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter input mLabel variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOmLabel.text()):
msgBox.setText("Please enter output mLabel variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter input Coordinate variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter output Coordinate variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText(
"Please enter input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtODM.text()):
msgBox.setText(
"Please enter output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter input Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter output Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# NScan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText(
"Please enter input Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOScan.text()):
msgBox.setText(
"Please enter output Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Creating label filters
try:
ClassListString = str.split(str.strip(ui.txtFLabels.toPlainText()),
"\n")
ClassList = list()
if len(ClassListString):
for classstr in ClassListString:
if len(classstr):
ClassList.append(int(classstr))
print("Label filter is ", ClassList)
except:
print("Cannot load label filter")
msgBox.setText("Cannot load label filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Creating Fold filter
try:
FoldString = str.split(str.strip(ui.txtFFold.toPlainText()), "\n")
FoldFilterList = list()
if len(FoldString):
for foldstr in FoldString:
if len(foldstr):
FoldFilterList.append(int(foldstr))
print("Fold filter is ", FoldFilterList)
except:
print("Cannot load fold filter")
msgBox.setText("Cannot load fold filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
try:
X = InData[ui.txtData.currentText()]
except:
print("Cannot load data")
return
# Label
try:
Label = InData[ui.txtLabel.currentText()][0]
except:
print("Cannot load Labels!")
return
# Subject
try:
Subject = InData[ui.txtSubject.currentText()]
except:
print("Cannot load Subject ID")
return
# Run
try:
Run = InData[ui.txtRun.currentText()]
except:
print("Cannot load Run ID")
return
# Counter
try:
Counter = InData[ui.txtCounter.currentText()]
except:
print("Cannot load Counter ID")
return
# Task
try:
Task = np.asarray(InData[ui.txtTask.currentText()])
TaskIndex = Task.copy()
for tasindx, tas in enumerate(np.unique(Task)):
TaskIndex[Task == tas] = tasindx + 1
except:
print("Cannot load Task ID")
return
# mLabel
mLabel = None
try:
if ui.cbmLabel.isChecked():
mLabel = InData[ui.txtmLabel.currentText()]
except:
print("Cannot load mLabel ID")
return
# Coordinate
try:
if ui.cbCol.isChecked():
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
except:
print("Cannot load Coordinate ID")
return
# Design Matrix
DM = None
try:
if ui.cbDM.isChecked():
DM = InData[ui.txtDM.currentText()]
except:
print("Cannot load Design Matrix ID")
return
# Condition
try:
if ui.cbCond.isChecked():
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
except:
print("Cannot load Condition ID")
return
# NScan
Scan = None
try:
if ui.cbNScan.isChecked():
Scan = InData[ui.txtScan.currentText()]
except:
print("Cannot load NScan ID")
return
# Apply filter
print("Filtering labels from data ...")
for lbl in ClassList:
# Remove Training Set
labelIndx = np.where(Label == lbl)[0]
X = np.delete(X, labelIndx, axis=0)
Label = np.delete(Label, labelIndx)
Subject = np.delete(Subject, labelIndx, axis=1)
Run = np.delete(Run, labelIndx, axis=1)
Task = np.delete(Task, labelIndx, axis=1)
Counter = np.delete(Counter, labelIndx, axis=1)
mLabel = np.delete(mLabel, labelIndx,
axis=0) if mLabel is not None else None
DM = np.delete(DM, labelIndx, axis=0) if DM is not None else None
Scan = np.delete(Scan, labelIndx,
axis=1) if Scan is not None else None
print("Label %d is filtered!" % (lbl))
Unit = 1
print("Calculating Alignment Level ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = Subject
FoldStr = "Subject"
else:
GroupFold = np.concatenate((Subject, Run))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate(
(GroupFold, TaskIndex)) if GroupFold is not None else TaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate(
(GroupFold, Counter)) if GroupFold is not None else Counter
FoldStr = FoldStr + "+Counter"
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i) for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText(
"Unit for the test set must be smaller than all possible folds! Number of all folds is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold), Unit):
msgBox.setText(
"Unit for the test set must be divorceable to all possible folds! Number of all folds is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
GUFold = np.unique(UnitFold)
print("Number of data in this level is " + str(len(UniqFold)))
# Filtering folds
print("Applying fold filter ...")
for foldfilter in FoldFilterList:
IndexFold = np.where(UnitFold != foldfilter)
X = X[IndexFold, :][0]
Label = Label[IndexFold]
Subject = Subject[:, IndexFold][0]
Run = Run[:, IndexFold][0]
Task = Task[:, IndexFold][0]
Counter = Counter[:, IndexFold][0]
mLabel = mLabel[IndexFold, :][0] if mLabel is not None else None
DM = DM[IndexFold, :][0] if DM is not None else None
Scan = Scan[:, IndexFold][0] if Scan is not None else None
UnitFold = UnitFold[IndexFold]
print("Fold %d is filtered!" % (foldfilter))
# Reconstruct Labels
print("Reforming labels based on folds ...")
LabelCounter = dict()
for lbl in np.unique(Label):
LabelCounter[lbl] = None
for foldID, fold in enumerate(GUFold):
foldLevelLabel = Label[np.where(UnitFold == fold)]
foldLevelUnique, foldLevelCounter = np.unique(foldLevelLabel,
return_counts=True)
for flu, flc in zip(foldLevelUnique, foldLevelCounter):
LabelCounter[flu] = np.min(
(LabelCounter[flu],
flc)) if LabelCounter[flu] is not None else flc
if ui.cbBalence.isChecked():
MinAllLabelCounter = None
for lbl in np.unique(Label):
MinAllLabelCounter = LabelCounter[
lbl] if MinAllLabelCounter is None else np.min(
(MinAllLabelCounter, LabelCounter[lbl]))
for lbl in np.unique(Label):
LabelCounter[lbl] = MinAllLabelCounter
# Report Label Threshold
for lbl in np.unique(Label):
print("Class ID: {:6d}, \t Selected Timepoints: {:10d}".format(
lbl, LabelCounter[lbl]))
# Make free space memory
del InData
XOut = None
LabelOut = None
SubjectOut = None
RunOut = None
TaskOut = None
CounterOut = None
mLabelOut = None
DMOut = None
ScanOut = None
for foldID, fold in enumerate(GUFold):
IndexFold = np.where(UnitFold == fold)
SX = X[IndexFold, :][0]
SLabel = Label[IndexFold]
SSubject = Subject[:, IndexFold][0][0]
SRun = Run[:, IndexFold][0][0]
STask = Task[:, IndexFold][0][0]
SCounter = Counter[:, IndexFold][0][0]
if ui.cbmLabel.isChecked():
SmLabel = mLabel[
IndexFold, :][0] if mLabel is not None else None
if ui.cbDM.isChecked():
SDM = DM[IndexFold, :][0] if DM is not None else None
if ui.cbNScan.isChecked():
SScan = Scan[:, IndexFold][0][0] if Scan is not None else None
for lbl in np.unique(Label):
IndexLabel = np.where(SLabel == lbl)
IndexLabel = IndexLabel[0][:LabelCounter[lbl]]
XOut = SX[IndexLabel, :] if XOut is None else np.concatenate(
(XOut, SX[IndexLabel, :]), axis=0)
LabelOut = SLabel[
IndexLabel] if LabelOut is None else np.concatenate(
(LabelOut, SLabel[IndexLabel]), axis=0)
SubjectOut = SSubject[
IndexLabel] if SubjectOut is None else np.concatenate(
(SubjectOut, SSubject[IndexLabel]), axis=0)
RunOut = SRun[IndexLabel] if RunOut is None else np.concatenate(
(RunOut, SRun[IndexLabel]), axis=0)
TaskOut = STask[
IndexLabel] if TaskOut is None else np.concatenate(
(TaskOut, STask[IndexLabel]), axis=0)
CounterOut = SCounter[
IndexLabel] if CounterOut is None else np.concatenate(
(CounterOut, SCounter[IndexLabel]), axis=0)
if ui.cbmLabel.isChecked():
mLabelOut = SmLabel[
IndexLabel, :] if mLabelOut is None else np.concatenate(
(mLabelOut, SmLabel[IndexLabel, :]), axis=0)
if ui.cbDM.isChecked():
DMOut = SDM[
IndexLabel, :] if DMOut is None else np.concatenate(
(DMOut, SDM[IndexLabel, :]), axis=0)
if ui.cbNScan.isChecked():
ScanOut = SScan[
IndexLabel] if ScanOut is None else np.concatenate(
(ScanOut, SScan[IndexLabel]), axis=0)
print("Data belong to fold {:6} and label {:6d} is collected.".
format(fold, lbl))
OutData[ui.txtOData.text()] = XOut
OutData[ui.txtOTask.text()] = reshape_1Dvector(TaskOut)
OutData[ui.txtOLabel.text()] = reshape_1Dvector(LabelOut)
OutData[ui.txtOSubject.text()] = reshape_1Dvector(SubjectOut)
OutData[ui.txtORun.text()] = reshape_1Dvector(RunOut)
OutData[ui.txtOCounter.text()] = reshape_1Dvector(CounterOut)
if ui.cbmLabel.isChecked():
OutData[ui.txtOmLabel.text()] = mLabelOut
if ui.cbDM.isChecked():
OutData[ui.txtODM.text()] = DMOut
if ui.cbNScan.isChecked():
OutData[ui.txtOScan.text()] = reshape_1Dvector(ScanOut)
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Temporal Alignment is done.")
msgBox.setText("Temporal Alignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
totalTime = 0
# Batch
try:
Batch = np.int32(ui.txtBatch.text())
except:
msgBox.setText("Size of batch is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Batch == 0:
Batch = None
# Kernel
Kernel = ui.cbKernel.currentText()
# Method
Method = ui.cbMethod.currentText()
# Gamma
try:
Gamma = np.float(ui.txtGamma.text())
except:
msgBox.setText("Gamma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Degree
try:
Degree = np.int32(ui.txtDegree.text())
except:
msgBox.setText("Degree is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coef0
try:
Coef0 = np.float(ui.txtCoef0.text())
except:
msgBox.setText("Coef0 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Alpha
try:
Alpha = np.int32(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter <= 0:
MaxIter = None
# Number of Job
try:
NJob = np.int32(ui.txtJobs.text())
except:
msgBox.setText("The number of parallel jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NJob < -1 or NJob == 0:
msgBox.setText(
"The number of parallel jobs must be -1 or greater than 0!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo + 1):
tic = time.time()
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 0:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTe)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText(
"Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(
InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(
InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText(
"Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText(
"Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText(
"Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText(
"Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText(
"Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText(
"Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText(
"Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText(
"Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText(
"Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText(
"Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText(
"Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText(
"Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText(
"Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText(
"Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText(
"Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText(
"Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(
TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(
TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText(
"Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText(
"Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText(
"Please enter Train Output Matrix Label variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText(
"Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[
ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[
ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText(
"Please enter Train Input Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText(
"Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText(
"Please enter Train Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText(
"Please enter Test Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[
ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[
ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[
ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(
InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[
ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(
InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(
InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
if NumFea == 0:
NumFea = np.min(np.shape(XTr))
print("Number of features are automatically selected as ",
NumFea)
try:
if Method == "PCA":
model = PCA(n_components=NumFea, copy=False, tol=Tol)
elif Method == "Kernel PCA":
model = KernelPCA(n_components=NumFea,kernel=Kernel,gamma=Gamma,degree=Degree,\
coef0=Coef0, alpha=Alpha, tol=Tol, max_iter=MaxIter, n_jobs=NJob,copy_X=False)
else:
model = IncrementalPCA(n_components=NumFea,
copy=False,
batch_size=Batch)
print("Running PCA Functional Alignment on Training Data ...")
OutData[ui.txtOTrData.text()] = model.fit_transform(XTr)
print("Running PCA Functional Alignment on Testing Data ...")
OutData[ui.txtOTeData.text()] = model.fit_transform(XTe)
except Exception as e:
print(str(e))
HAParam = dict()
HAParam["Method"] = Method
HAParam["NumFea"] = NumFea
HAParam["Kernel"] = Kernel
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Runtime: ", totalTime)
print("PCA Functional Alignment is done.")
msgBox.setText("PCA Functional Alignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# Subject
if ui.cbSubject.isChecked():
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Train Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTSubject.currentText()):
msgBox.setText("Please enter Test Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTTask.currentText()):
msgBox.setText("Please enter Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Train Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTRun.currentText()):
msgBox.setText("Please enter Test Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Train Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTCounter.currentText()):
msgBox.setText("Please enter Test Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTLabel.currentText()):
msgBox.setText("Please enter Test Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText(
"Please enter Train Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTmLabel.currentText()):
msgBox.setText("Please enter Test Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTData.currentText()):
msgBox.setText("Please enter Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText(
"Please enter Train Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTDM.currentText()):
msgBox.setText(
"Please enter Test Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtNScan.currentText()):
msgBox.setText(
"Please enter Train Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtTNScan.currentText()):
msgBox.setText(
"Please enter Test Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter Fold ID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter Fold Info variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ClassID = np.int32(ui.txtClassID.text())
except:
msgBox.setText("Class ID is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
if ui.cbFoldID.isChecked():
OutData[ui.txtFoldID.currentText()] = reshape_1Dvector(
InData[ui.txtFoldID.currentText()])
if ui.cbFoldInfo.isChecked():
OutData[ui.txtFoldInfo.currentText()] = InData[
ui.txtFoldInfo.currentText()]
# Condition
if ui.cbCond.isChecked():
try:
Cond = InData[ui.txtCond.currentText()]
New_Condition = Conditions()
for condition in Cond:
if reshape_condition_cell(
condition[0]) != ui.txtClassName.currentText():
New_Condition.add_cond(reshape_condition_cell(condition[0]),\
reshape_condition_cell(condition[1]))
OutData[ui.txtCond.currentText()] = np.array(
New_Condition.get_cond(), dtype=object)
except Exception as e:
print(e)
print("Cannot load Condition ID!")
return
# Label
try:
Y = InData[ui.txtLabel.currentText()]
YT = InData[ui.txtTLabel.currentText()]
except:
print("Cannot load class labels!")
return
if not len(np.where(Y == ClassID)[0]):
msgBox.setText("Train Data: There is no label with this Class ID!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(np.where(YT == ClassID)[0]):
msgBox.setText("Test Data: There is no label with this Class ID!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
NoneZeroArea = np.where(Y != ClassID)
New_Y = Y[NoneZeroArea]
OutData[ui.txtLabel.currentText()] = reshape_1Dvector(New_Y)
NoneZeroAreaT = np.where(YT != ClassID)
New_YT = YT[NoneZeroAreaT]
OutData[ui.txtTLabel.currentText()] = reshape_1Dvector(New_YT)
# Data
try:
X = InData[ui.txtData.currentText()]
XT = InData[ui.txtTData.currentText()]
except:
print("Cannot load data")
return
OutData[ui.txtData.currentText()] = X[NoneZeroArea[1]]
OutData[ui.txtTData.currentText()] = XT[NoneZeroAreaT[1]]
# Subject
if ui.cbSubject.isChecked():
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtSubject.currentText()] = reshape_1Dvector(
Subject[NoneZeroArea])
SubjectT = InData[ui.txtTSubject.currentText()]
OutData[ui.txtTSubject.currentText()] = reshape_1Dvector(
SubjectT[NoneZeroAreaT])
except:
print("Cannot load Subject ID!")
return
# Task
if ui.cbTask.isChecked():
try:
Task = InData[ui.txtTask.currentText()]
OutData[ui.txtTask.currentText()] = reshape_1Dvector(
np.array(Task[NoneZeroArea], dtype=object))
TaskT = InData[ui.txtTTask.currentText()]
OutData[ui.txtTTask.currentText()] = reshape_1Dvector(
np.array(TaskT[NoneZeroAreaT], dtype=object))
except:
print("Cannot load Task ID!")
return
# Run
if ui.cbRun.isChecked():
try:
Run = InData[ui.txtRun.currentText()]
OutData[ui.txtRun.currentText()] = reshape_1Dvector(
Run[NoneZeroArea])
RunT = InData[ui.txtTRun.currentText()]
OutData[ui.txtTRun.currentText()] = reshape_1Dvector(
RunT[NoneZeroAreaT])
except:
print("Cannot load Run ID!")
return
# Counter
if ui.cbCounter.isChecked():
try:
Counter = InData[ui.txtCounter.currentText()]
OutData[ui.txtCounter.currentText()] = reshape_1Dvector(
Counter[NoneZeroArea])
CounterT = InData[ui.txtTCounter.currentText()]
OutData[ui.txtTCounter.currentText()] = reshape_1Dvector(
CounterT[NoneZeroAreaT])
except:
print("Cannot load Counter ID!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
try:
OutData[ui.txtmLabel.currentText()] = label_binarize(
New_Y, np.unique(New_Y))
OutData[ui.txtTmLabel.currentText()] = label_binarize(
New_YT, np.unique(New_YT))
except:
print("Cannot load Matrix Label ID!")
return
# Design
if ui.cbDM.isChecked():
try:
DM = InData[ui.txtDM.currentText()]
OutData[ui.txtDM.currentText()] = DM[NoneZeroArea[1]]
DMT = InData[ui.txtTDM.currentText()]
OutData[ui.txtTDM.currentText()] = DMT[NoneZeroAreaT[1]]
except:
print("Cannot load Design Matrix ID!")
return
# Coordinate
if ui.cbCol.isChecked():
try:
OutData[ui.txtCol.currentText()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load Coordinate ID!")
return
# NScan
if ui.cbNScan.isChecked():
try:
NScan = InData[ui.txtNScan.currentText()]
OutData[ui.txtNScan.currentText()] = reshape_1Dvector(
NScan[NoneZeroArea])
NScanT = InData[ui.txtTNScan.currentText()]
OutData[ui.txtTNScan.currentText()] = reshape_1Dvector(
NScanT[NoneZeroAreaT])
except:
print("Cannot load NScan ID!")
return
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("Train: Number of selected instances: ",
np.shape(NoneZeroArea)[1])
print("Train: Number of all instances: ", np.shape(Y)[1])
print("Test: Number of selected instances: ",
np.shape(NoneZeroAreaT)[1])
print("Test: Number of all instances: ", np.shape(YT)[1])
print("DONE.")
msgBox.setText("Rest scans are removed.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
print("Loading...")
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Atlas
InAtlas = ui.txtAtlas.text()
if not len(InAtlas):
msgBox.setText("Please enter atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InAtlas):
msgBox.setText("Atlas file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
AtlasHdr = nb.load(InAtlas)
AtlasImg = np.asanyarray(AtlasHdr.dataobj)
AtlasShape = np.shape(AtlasImg)
if np.shape(AtlasShape)[0] != 3:
msgBox.setText("Atlas must be 3D image")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("Cannot load atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Class Filter is wrong!")
msgBox.setText("Class filter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
MappingVectorRegion = None
CodeText = ui.txtEvents.toPlainText()
eCodeText = ui.txtEvents2.toPlainText()
Fold = list()
accuracy = list()
precision = list()
average_precision = list()
f1score = list()
recall = list()
accuracyTr = list()
precisionTr = list()
average_precisionTr = list()
f1scoreTr = list()
recallTr = list()
InFileList = list()
OutData = dict()
OutData["ModelAnalysis"] = "Atlas-based ensemble"
for fold in range(FoldFrom, FoldTo + 1):
print(f"Analyzing Fold: {fold}...")
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText(f"FOLD {fold}: Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InFile = InFile.replace("$FOLD$", str(fold))
InFileList.append(InFile)
if not os.path.isfile(InFile):
msgBox.setText(f"FOLD {fold}: Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Load InData
try:
InData = mainIO_load(InFile)
except:
print(f"FOLD {fold}: Cannot load file: {InFile}")
return False
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrX = InData[ui.txtITrData.currentText()]
except:
print(f"FOLD {fold}: Cannot load train data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TeX = InData[ui.txtITeData.currentText()]
except:
print(f"FOLD {fold}: Cannot load test data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrL = InData[ui.txtITrLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load label")
return
try:
TeL = InData[ui.txtITeLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load test label")
return
# Ref Filter Train
if ui.cbFilterTrID.isChecked():
try:
# Create content structure for training
TrFilterContent = ui.txtFilterTrContent.text()
TrFilterContent = TrFilterContent.replace(" ", "")
if not len(TrFilterContent):
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TrFilterContent = TrFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TrFilterContent = np.int32(TrFilterContent)
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
if np.shape(TrX)[0] != np.shape(TrF)[0] or np.shape(
TrL)[0] != np.shape(TrF)[0]:
print(
"Shape of reference for training must be the same as data and label"
)
msgBox.setText(
"Shape of reference for training must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for training is wrong!")
msgBox.setText("Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove training set
try:
print(
"Removing training set based on reference. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
for content in TrFilterContent:
contIndex = np.where(TrF == content)[0]
TrL = np.delete(TrL, contIndex, axis=0)
TrX = np.delete(TrX, contIndex, axis=0)
TrF = np.delete(TrF, contIndex, axis=0)
print(" Content", content,
"is removed from training set. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
except Exception as e:
print("Cannot filter the training set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the training set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Create content structure for testing
TeFilterContent = ui.txtFilterTeContent.text()
TeFilterContent = TeFilterContent.replace(" ", "")
if not len(TeFilterContent):
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TeFilterContent = TeFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TeFilterContent = np.int32(TeFilterContent)
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
if np.shape(TeX)[0] != np.shape(TeF)[0] or np.shape(
TeL)[0] != np.shape(TeF)[0]:
print(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setText(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for testing is wrong!")
msgBox.setText("Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove testing set
try:
print(
"Removing testing set based on reference. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
for content in TeFilterContent:
contIndex = np.where(TeF == content)[0]
TeL = np.delete(TeL, contIndex, axis=0)
TeX = np.delete(TeX, contIndex, axis=0)
TeF = np.delete(TeF, contIndex, axis=0)
print(" Content", content,
"is removed from testing set. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
except Exception as e:
print("Cannot filter the testing set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the testing set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(TrL == fil)[0]
TrL = np.delete(TrL, labelIndx, axis=0)
TrX = np.delete(TrX, labelIndx, axis=0)
# Remove Testing Set
labelIndx = np.where(TeL == fil)[0]
TeL = np.delete(TeL, labelIndx, axis=0)
TeX = np.delete(TeX, labelIndx, axis=0)
print("Class ID = " + str(fil) +
" is removed from data.")
if ui.cbScale.isChecked():
TrX = preprocessing.scale(TrX)
TeX = preprocessing.scale(TeX)
print(
"Whole of data is scaled Train~N(0,1) and Test~N(0,1)."
)
except:
print("Cannot load data or label")
return
# FoldID
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
currFID = InData[ui.txtFoldID.currentText()][0][0]
Fold.append(currFID)
except:
print("Cannot load Fold ID!")
return
# Creating Mapping Dictionary for Atlas
if MappingVectorRegion is None:
# Coordinate
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Coord = np.transpose(InData[ui.txtCol.currentText()])
except:
print("Cannot load coordinate")
return
# imgShape
if not len(ui.txtImg.currentText()):
msgBox.setText("Please enter Image Shape variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
DataShape = tuple(InData[ui.txtImg.currentText()][0])
except:
print("Cannot load data image shape")
return
# Check shape
if AtlasShape[0] != DataShape[0] or AtlasShape[1] != DataShape[
1] or AtlasShape[2] != DataShape[2]:
print(
f'Atlas and data must have the same shape.\nAtlas: {AtlasShape} vs. Data: {DataShape}'
)
msgBox.setText(
f'Atlas and data must have the same shape.\nAtlas: {AtlasShape} vs. Data: {DataShape}'
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Mapping 2D vector space to 3D region area...")
MappingVectorRegion = dict()
for cooIdx, coo in enumerate(Coord):
reg = AtlasImg[coo[0], coo[1], coo[2]]
if reg == 0:
print(f'{tuple(coo)} is belong to black area.')
else:
try:
MappingVectorRegion[str(reg)].append(cooIdx)
except:
MappingVectorRegion[str(reg)] = list()
MappingVectorRegion[str(reg)].append(cooIdx)
print(
f'{tuple(coo)}:{cooIdx} is belong to region {reg}')
print(f"FOLD {fold}: analyzing regions...")
baseTrX = list()
baseTeX = list()
for reg in sorted(MappingVectorRegion.keys()):
RegIndex = MappingVectorRegion[reg]
SubXtr = TrX[:, RegIndex]
SubXte = TeX[:, RegIndex]
try:
allvars = dict(locals(), **globals())
exec(CodeText, allvars, allvars)
model = allvars['model']
model.fit(SubXtr, TrL)
baseTrX.append(model.predict(SubXtr))
TeP = model.predict(SubXte)
baseTeX.append(TeP)
bacc = accuracy_score(TeL, TeP)
except Exception as e:
print(f'Cannot generate model\n{e}')
msgBox.setText(f'Cannot generate model\n{e}')
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print(f"FOLD {fold}: Linear accuracy for {reg} is {bacc}")
baseTrX = np.transpose(baseTrX)
baseTeX = np.transpose(baseTeX)
try:
print(f"FOLD {fold}: Creating ensemble model ...")
allvars = dict(locals(), **globals())
exec(eCodeText, allvars, allvars)
emodel = allvars['emodel']
emodel.fit(baseTrX, TrL)
PrL = emodel.predict(baseTrX)
PeL = emodel.predict(baseTeX)
except Exception as e:
print(f"Cannot generate the final model\n{e}")
msgBox.setText(f"Cannot generate the final model\n{e}")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbAverage.isChecked():
acc = accuracy_score(TeL, PeL)
accTr = accuracy_score(TrL, PrL)
accuracy.append(acc)
accuracyTr.append(accTr)
print(
"FoldID = {:d}, Average Train {:5.2f} Test {:5.2f}"
.format(currFID, accTr * 100, acc * 100))
if ui.cbPrecision.isChecked():
pre = precision_score(TeL,
PeL,
average=ui.cbPrecisionAvg.currentData())
preTr = precision_score(
TrL, PrL, average=ui.cbPrecisionAvg.currentData())
precision.append(pre)
precisionTr.append(preTr)
print(
"FoldID = {:d}, Precision Train {:5.2f} Test {:5.2f}"
.format(currFID, preTr * 100, pre * 100))
if ui.cbAPrecision.isChecked():
prA = average_precision_score(
TeL, PeL, average=ui.cbAPrecisionAvg.currentData())
prATr = average_precision_score(
TrL, PrL, average=ui.cbAPrecisionAvg.currentData())
average_precision.append(prA)
average_precisionTr.append(prATr)
print(
"FoldID = {:d}, Average Precision: Train {:5.2f} Test {:5.2f}"
.format(currFID, prATr * 100, prA * 100))
if ui.cbRecall.isChecked():
rec = recall_score(TeL,
PeL,
average=ui.cbRecallAvg.currentData())
recTr = recall_score(TrL,
PrL,
average=ui.cbRecallAvg.currentData())
recall.append(rec)
recallTr.append(recTr)
print(
"FoldID = {:d}, Recall: Train {:5.2f} Test {:5.2f}"
.format(currFID, recTr * 100, rec * 100))
if ui.cbF1.isChecked():
f1 = f1_score(TeL, PeL, average=ui.cbF1Avg.currentData())
f1Tr = f1_score(TrL, PrL, average=ui.cbF1Avg.currentData())
f1score.append(f1)
f1scoreTr.append(f1Tr)
print(
"FoldID = {:d}, F1: Train {:5.2f} Test {:5.2f}"
.format(currFID, f1Tr * 100, f1 * 100))
print("FoldID = " + str(currFID) + " is analyzed!")
if ui.cbAverage.isChecked():
OutData["FoldAccuracy"] = accuracy
MeanAcc = np.mean(accuracy)
OutData["MeanTestAccuracy"] = MeanAcc
STDAcc = np.std(accuracy)
OutData["StdTestAccuracy"] = STDAcc
MeanAccTr = np.mean(accuracyTr)
OutData["MeanTrainAccuracy"] = MeanAccTr
STDAccTr = np.std(accuracyTr)
OutData["StdTrainAccuracy"] = STDAccTr
print(
"Accuracy: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAccTr * 100, STDAccTr, MeanAcc * 100, STDAcc))
if ui.cbPrecision.isChecked():
OutData["ModePrecision"] = ui.cbPrecisionAvg.currentText()
OutData["FoldPrecision"] = precision
MeanPre = np.mean(precision)
OutData["MeanTrainPrecision"] = MeanPre
STDPre = np.std(precision)
OutData["StdTrainPrecision"] = STDPre
MeanPreTr = np.mean(precisionTr)
OutData["MeanTestPrecision"] = MeanPreTr
STDPreTr = np.std(precisionTr)
OutData["StdTestPrecision"] = STDPreTr
print(
"Precision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanPreTr * 100, STDPreTr, MeanPre * 100, STDPre))
if ui.cbAPrecision.isChecked():
OutData["ModeAveragePrecision"] = ui.cbAPrecisionAvg.currentText()
OutData["FoldAveragePrecision"] = average_precision
MeanAPre = np.mean(average_precision)
OutData["MeanTrainAveragePrecision"] = MeanAPre
STDAPre = np.std(average_precision)
OutData["StdTestAveragePrecision"] = STDAPre
MeanAPreTr = np.mean(average_precisionTr)
OutData["MeanTrainAveragePrecision"] = MeanAPreTr
STDAPreTr = np.std(average_precisionTr)
OutData["StdTrainAveragePrecision"] = STDAPreTr
print(
"AveragePrecision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAPreTr * 100, STDAPreTr, MeanAPre * 100, STDAPre))
if ui.cbRecall.isChecked():
OutData["ModeRecall"] = ui.cbRecallAvg.currentText()
OutData["FoldRecall"] = recall
MeanRec = np.mean(recall)
OutData["MeanTestRecall"] = MeanRec
STDRec = np.std(recall)
OutData["StdTestRecall"] = STDRec
MeanRecTr = np.mean(recallTr)
OutData["MeanTrainRecall"] = MeanRecTr
STDRecTr = np.std(recallTr)
OutData["StdTrainRecall"] = STDRecTr
print(
"Recall: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanRecTr * 100, STDRecTr, MeanRec * 100, STDRec))
if ui.cbF1.isChecked():
OutData["ModeF1"] = ui.cbF1Avg.currentText()
OutData["FoldF1"] = f1score
MeanF1 = np.mean(f1score)
OutData["MeanTestF1"] = MeanF1
STDF1 = np.std(f1score)
OutData["StdTestF1"] = STDF1
MeanF1Tr = np.mean(f1scoreTr)
OutData["MeanTrainF1"] = MeanF1Tr
STDF1Tr = np.std(f1scoreTr)
OutData["StdTrainF1"] = STDF1Tr
print(
"F1: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanF1Tr * 100, STDF1Tr, MeanF1 * 100, STDF1))
OutData["InputFiles"] = InFileList
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("Atlas-based ensemble analysis is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
print("Loading...")
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Atlas
InAtlas = ui.txtAtlas.text()
if not len(InAtlas):
msgBox.setText("Please enter atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InAtlas):
msgBox.setText("Atlas file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
AtlasHdr = nb.load(InAtlas)
AtlasImg = np.asanyarray(AtlasHdr.dataobj)
#AtlasImg = AtlasHdr.get_data()
AtlasShape = np.shape(AtlasImg)
if np.shape(AtlasShape)[0] != 3:
msgBox.setText("Atlas must be 3D image")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("Cannot load atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
MappingVectorRegion = None
CodeText = ui.txtEvents.toPlainText()
for fold in range(FoldFrom, FoldTo + 1):
print(f"Analyzing Fold: {fold}...")
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText(f"FOLD {fold}: Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InFile = InFile.replace("$FOLD$", str(fold))
if not os.path.isfile(InFile):
msgBox.setText(f"FOLD {fold}: Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Load InData
try:
InData = mainIO_load(InFile)
except:
print(f"FOLD {fold}: Cannot load file: {InFile}")
# Train data
if not len(ui.txtData.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter train data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Xtr = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked():
Xtr = preprocessing.scale(Xtr)
print(
f"FOLD {fold}: Whole of train data is scaled X~N(0,1)."
)
except:
print(f"FOLD {fold}: Cannot load train data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Test data
if not len(ui.txtTeData.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter test data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Xte = InData[ui.txtTeData.currentText()]
if ui.cbScale.isChecked():
Xte = preprocessing.scale(Xte)
print(
f"FOLD {fold}: Whole of test data is scaled X~N(0,1).")
except:
print(f"FOLD {fold}: Cannot load test data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Train Label
if not len(ui.txtLabel.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Ytr = InData[ui.txtLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load label")
return
# Test Label
if not len(ui.txtTeLabel.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter test Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Yte = InData[ui.txtTeLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load test label")
return
if MappingVectorRegion is None:
# Coordinate
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Coord = np.transpose(InData[ui.txtCol.currentText()])
except:
print("Cannot load coordinate")
return
# imgShape
if not len(ui.txtImg.currentText()):
msgBox.setText("Please enter Image Shape variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
DataShape = tuple(InData[ui.txtImg.currentText()][0])
except:
print("Cannot load data image shape")
return
# Check shape
if AtlasShape[0] != DataShape[0] or AtlasShape[1] != DataShape[
1] or AtlasShape[2] != DataShape[2]:
print(
f'Atlas and data must have the same shape.\nAtlas: {AtlasShape} vs. Data: {DataShape}'
)
msgBox.setText(
f'Atlas and data must have the same shape.\nAtlas: {AtlasShape} vs. Data: {DataShape}'
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Mapping 2D vector space to 3D region area...")
MappingVectorRegion = dict()
for cooIdx, coo in enumerate(Coord):
reg = AtlasImg[coo[0], coo[1], coo[2]]
if reg == 0:
print(f'{tuple(coo)} is belong to black area.')
else:
try:
MappingVectorRegion[str(reg)].append(cooIdx)
except:
MappingVectorRegion[str(reg)] = list()
MappingVectorRegion[str(reg)].append(cooIdx)
print(
f'{tuple(coo)}:{cooIdx} is belong to region {reg}')
print(f"FOLD {fold}: analyzing regions...")
for reg in sorted(MappingVectorRegion.keys()):
RegIndex = MappingVectorRegion[reg]
SubXtr = Xtr[:, RegIndex]
SubXte = Xte[:, RegIndex]
try:
allvars = dict(locals(), **globals())
exec(CodeText, allvars, allvars)
model = allvars['model']
model.fit(SubXtr, Ytr)
Pte = model.predict(SubXte)
acc = accuracy_score(Yte, Pte)
clr = classification_report(Yte, Pte)
cfm = confusion_matrix(Yte, Pte)
pre = precision_score(Yte, Pte)
f1 = f1_score(Yte, Pte)
rca = recall_score(Yte, Pte)
except Exception as e:
print(f'Cannot generate model\n{e}')
msgBox.setText(f'Cannot generate model\n{e}')
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[f"Region_{reg}_accuracy_score"].append(acc)
OutData[f"Region_{reg}_count"] += 1
OutData[f"Region_{reg}_classification_report"].append(clr)
OutData[f"Region_{reg}_precision_score"].append(pre)
OutData[f"Region_{reg}_f1_score"].append(f1)
OutData[f"Region_{reg}_recall_score"].append(rca)
OutData[f"Region_{reg}_confusion_matrix"].append(cfm)
except:
OutData[f"Region_{reg}_count"] = 1
OutData[f"Region_{reg}_accuracy_score"] = list()
OutData[f"Region_{reg}_accuracy_score"].append(acc)
OutData[f"Region_{reg}_classification_report"] = list()
OutData[f"Region_{reg}_classification_report"].append(clr)
OutData[f"Region_{reg}_precision_score"] = list()
OutData[f"Region_{reg}_precision_score"].append(pre)
OutData[f"Region_{reg}_f1_score"] = list()
OutData[f"Region_{reg}_f1_score"].append(f1)
OutData[f"Region_{reg}_recall_score"] = list()
OutData[f"Region_{reg}_recall_score"].append(rca)
OutData[f"Region_{reg}_confusion_matrix"] = list()
OutData[f"Region_{reg}_confusion_matrix"].append(cfm)
print(f"FOLD {fold}: Linear accuracy for {reg} is {acc}")
for reg in sorted(MappingVectorRegion.keys()):
macc = np.mean(OutData[f"Region_{reg}_accuracy_score"])
mpre = np.mean(OutData[f"Region_{reg}_precision_score"])
mf1 = np.mean(OutData[f"Region_{reg}_f1_score"])
mrca = np.mean(OutData[f"Region_{reg}_recall_score"])
mcfm = None
for ecmf in OutData[f"Region_{reg}_confusion_matrix"]:
mcfm = ecmf if mcfm is None else mcfm + ecmf
mcfm = mcfm / OutData[f"Region_{reg}_count"]
OutData[f"Region_{reg}_mean_accuracy"] = macc
OutData[f"Region_{reg}_mean_precision"] = mpre
OutData[f"Region_{reg}_mean_recall"] = mrca
OutData[f"Region_{reg}_mean_f1"] = mf1
OutData[f"Region_{reg}_mean_confusion_matrix"] = mcfm
print(f"Mean accuracy of region {reg}: {macc}")
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("Wise area analysis: Atlas is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
SVD = ui.cbSVD.currentText()
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter < 1:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# PIter
try:
PIter = np.int32(ui.txtPIter.text())
except:
msgBox.setText("Power iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if PIter < 1:
msgBox.setText("Power iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.rbScale.isChecked() == True and ui.rbALScale.isChecked(
) == False:
msgBox.setText(
"Subject Level Normalization is just available for Subject Level Analysis!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked() and (not ui.rbScale.isChecked()):
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(X)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = reshape_1Dvector(Subject)
except:
print("Cannot load Subject ID")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = reshape_1Dvector(
InData[ui.txtLabel.currentText()])
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = reshape_1Dvector(
InData[ui.txtTask.currentText()])
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = reshape_1Dvector(
InData[ui.txtRun.currentText()])
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = reshape_1Dvector(
InData[ui.txtCounter.currentText()])
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = reshape_1Dvector(
InData[ui.txtScan.currentText()])
Models = dict()
Models["Name"] = "FactorAnalysis"
if ui.rbALScale.isChecked():
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
X_Sub.append(
preprocessing.scale(
X[np.where(Subject == subj)[1], :]))
print("Data in subject level is scaled, X_" + str(subj) +
"~N(0,1).")
else:
X_Sub.append(X[np.where(Subject == subj)[1], :])
print("Subject ", subj, " is extracted from data.")
print("Running Factor Analysis in subject level ...")
X_Sub_PCA = list()
lenPCA = len(X_Sub)
for xsubindx, xsub in enumerate(X_Sub):
model = FactorAnalysis(n_components=NumFea,
tol=Tol,
max_iter=MaxIter,
svd_method=SVD,
iterated_power=PIter)
X_Sub_PCA.append(model.fit_transform(xsub))
Models["Model" + str(xsubindx + 1)] = str(
model.get_params(deep=True))
print("Factor Analysis: ", xsubindx + 1, " of ", lenPCA,
" is done.")
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub_PCA):
X_new = np.concatenate(
(X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA,
" is done.")
OutData[ui.txtOData.text()] = X_new
else:
print("Running Factor Analysis ...")
model = FactorAnalysis(n_components=NumFea,
tol=Tol,
max_iter=MaxIter,
svd_method=SVD,
iterated_power=PIter)
OutData[ui.txtOData.text()] = model.fit_transform(X)
Models["Model"] = str(model.get_params(deep=True))
OutData["ModelParameter"] = Models
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("Factor Analysis is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# ComputeLabels
ComputeLabels = ui.cbComputeLabels.isChecked()
# Copy
Copy = ui.cbCopy.isChecked()
# NCluster
try:
NCluster = np.int32(ui.txtNCluster.text())
except:
msgBox.setText("Number of Cluster is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Threshold
try:
Threshold = np.float(ui.txtThreshold.text())
except:
msgBox.setText("Threshold is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# BranchingFactor
try:
BranchingFactor = np.int(ui.txtBranchingFactor.text())
except:
msgBox.setText("Branching Factor is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace("[", "").replace("]","").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
OutData["ModelAnalysis"] = "Birch"
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
L = np.delete(L, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
if ui.cbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled to N(0,1).")
except:
print("Cannot load data or label")
return
try:
cls = Birch(threshold=Threshold,branching_factor=BranchingFactor,n_clusters=NCluster,\
compute_labels=ComputeLabels,copy=Copy)
print("Run Clustering ...")
PeL = cls.fit_predict(X)
except Exception as e:
print(e)
msgBox = QMessageBox()
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
OutData["predict"] = PeL
if OutModel is not None:
joblib.dump(cls, OutModel)
print("Model is saved: " + OutModel)
if ui.cbAverage.isChecked():
acc = accuracy_score(L, PeL)
OutData["Accuracy"] = acc
print("Average {:5.2f}".format(acc * 100))
if ui.cbNMI.isChecked():
NMI = normalized_mutual_info_score(L, PeL)
OutData["NMI"] = NMI
print("Normalized Mutual Information (NMI) {:7.6f}".format(NMI))
if ui.cbRIA.isChecked():
RIA = adjusted_rand_score(L, PeL)
OutData["RIA"] = RIA
print("Rand Index Adjusted (RIA) {:7.6f}".format(RIA))
if ui.cbAMI.isChecked():
AMI = adjusted_mutual_info_score(L, PeL)
OutData["AMI"] = AMI
print("Adjusted Mutual Information (AMI) {:7.6f}".format(AMI))
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Birch Clustering is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
msgBox.setText("At least, you must select one metric!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Method
method = ui.cbMethod.currentData()
# Solver
solver = ui.cbSolver.currentText()
# Selection
selection = ui.cbSelection.currentText()
# Fit
fit = ui.cbFit.isChecked()
# normalize
normalize = ui.cbNormalize.isChecked()
try:
alpha = np.float(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
iter = np.int(ui.txtMaxIter.text())
except:
msgBox.setText("Max Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
l1 = np.float(ui.txtL1.text())
except:
msgBox.setText("L1 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
njob = np.float(ui.txtJobs.text())
except:
msgBox.setText("Number of jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading ...")
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if not len(ui.txtDesign.currentText()):
msgBox.setText("Please enter Input Design variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Design = InData[ui.txtDesign.currentText()]
except:
msgBox.setText("Design value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
FontSize = ui.txtFontSize.value()
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
except:
msgBox.setText("Task variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TaskTitleUnique = np.unique(TaskTitle)
Task = np.zeros(np.shape(TaskTitle))
for ttinx, tt in enumerate(TaskTitle):
for ttlinx, ttl in enumerate(TaskTitleUnique):
if tt[0] == ttl:
Task[ttinx] = ttlinx + 1
break
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sub = InData[ui.txtSubject.currentText()][0]
except:
msgBox.setText("Subject variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()][0]
except:
msgBox.setText("Run variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Con = InData[ui.txtCounter.currentText()][0]
except:
msgBox.setText("Counter variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
Design = np.delete(Design, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
Task = np.delete(Task, labelIndx, axis=0)
Sub = np.delete(Sub, labelIndx, axis=0)
Run = np.delete(Run, labelIndx, axis=0)
Con = np.delete(Con, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
try:
Unit = np.int32(ui.txtUnit.text())
except:
msgBox.setText("Unit for the test set must be a number!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Unit < 1:
msgBox.setText("Unit for the test set must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Calculating Levels ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = [Sub]
FoldStr = "Subject"
else:
GroupFold = np.concatenate(([Sub], [Run]))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Task])) if GroupFold is not None else [Task]
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Con])) if GroupFold is not None else [Con]
FoldStr = FoldStr + "+Counter"
if FoldStr == "":
FoldStr = "Whole-Data"
GUFold = [1]
ListFold = [1]
UniqFold = [1]
GroupFold = [1]
UnitFold = np.ones((1, np.shape(X)[0]))
else:
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i)
for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText(
"Unit must be smaller than all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold), Unit):
msgBox.setText(
"Unit must be divorceable to all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
GUFold = np.unique(UnitFold)
FoldInfo = dict()
FoldInfo["Unit"] = Unit
FoldInfo["Group"] = GroupFold
FoldInfo["Order"] = FoldStr
FoldInfo["List"] = ListFold
FoldInfo["Unique"] = UniqFold
FoldInfo["Folds"] = UnitFold
OutData["FoldInfo"] = FoldInfo
OutData["ModelAnalysis"] = "SK.Group.RSA." + ui.cbMethod.currentText()
print("Number of all levels is: " + str(len(UniqFold)))
Cov = None
Corr = None
AMSE = list()
Beta = None
for foldID, fold in enumerate(GUFold):
print("Analyzing level " + str(foldID + 1), " of ",
str(len(UniqFold)), " ...")
Index = np.where(UnitFold == fold)
# Whole-Data
if FoldStr == "Whole-Data" and np.shape(Index)[0]:
Index = [Index[1]]
XLi = X[Index]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
XLi = preprocessing.scale(XLi)
print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
RegLi = np.insert(Design[Index], 0, 1, axis=1)
if method == "ols":
model = linmdl.LinearRegression(fit_intercept=fit,
normalize=normalize,
n_jobs=njob)
elif method == "ridge":
model = linmdl.Ridge(alpha=alpha,
fit_intercept=fit,
normalize=normalize,
max_iter=iter,
tol=tol,
solver=solver)
elif method == "lasso":
model = linmdl.Lasso(alpha=alpha,
fit_intercept=fit,
normalize=normalize,
max_iter=iter,
tol=tol,
selection=selection)
elif method == "elast":
model = linmdl.ElasticNet(alpha=alpha, l1_ratio=l1, fit_intercept=fit, normalize=normalize, \
max_iter=iter, tol=tol, selection=selection)
model.fit(RegLi, XLi)
BetaLi = np.transpose(model.coef_)[1:, :]
Beta = BetaLi if Beta is None else Beta + BetaLi
print("Calculating MSE for level %d ..." % (foldID + 1))
MSE = mean_squared_error(XLi, np.matmul(Design[Index], BetaLi))
print("MSE%d: %f" % (foldID + 1, MSE))
OutData["MSE" + str(foldID)] = MSE
AMSE.append(MSE)
if ui.cbBeta.isChecked():
OutData["BetaL" + str(foldID + 1)] = BetaLi
# Calculate Results
if ui.cbCorr.isChecked():
print("Calculating Correlation for level %d ..." %
(foldID + 1))
CorrLi = np.corrcoef(BetaLi)
OutData["Corr" + str(foldID + 1)] = CorrLi
if Corr is None:
Corr = CorrLi.copy()
else:
if ui.rbAvg.isChecked():
Corr = np.add(Corr, CorrLi)
elif ui.rbMin.isChecked():
Corr = np.minimum(Corr, CorrLi)
else:
Corr = np.maximum(Corr, CorrLi)
if ui.cbCov.isChecked():
print("Calculating Covariance for level %d ..." % (foldID + 1))
CovLi = np.cov(BetaLi)
OutData["Cov" + str(foldID + 1)] = CovLi
if Cov is None:
Cov = CovLi.copy()
else:
if ui.rbAvg.isChecked():
Cov = np.add(Cov, CovLi)
elif ui.rbMin.isChecked():
Cov = np.minimum(Cov, CovLi)
else:
Cov = np.maximum(Cov, CovLi)
CoEff = len(UniqFold) - 1 if len(UniqFold) > 2 else 1
if ui.cbCov.isChecked():
if ui.rbAvg.isChecked():
Cov = Cov / CoEff
covClass = SimilarityMatrixBetweenClass(Cov)
OutData["Covariance"] = Cov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_std"] = covClass.std()
OutData["Covariance_mean"] = covClass.mean()
if ui.cbCorr.isChecked():
if ui.rbAvg.isChecked():
Corr = Corr / CoEff
corClass = SimilarityMatrixBetweenClass(Corr)
OutData["Correlation"] = Corr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_std"] = corClass.std()
OutData["Correlation_mean"] = corClass.mean()
# Calculating Distance Matrix
dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))
for i in range(np.shape(Beta)[0]):
for j in range(i + 1, np.shape(Beta)[0]):
dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
dis[j, i] = dis[i, j]
OutData["DistanceMatrix"] = dis
Z = linkage(dis)
OutData["Linkage"] = Z
OutData["MSE"] = np.mean(AMSE)
print("Average MSE: %f" % (OutData["MSE"]))
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
mainIO_save(OutData, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
NumData = np.shape(Corr)[0]
fig1 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCorr.text()):
plt.title(ui.txtTitleCorr.text())
else:
plt.title('Group RSA: Correlation\nLevel: ' + FoldStr)
plt.show()
if ui.cbCov.isChecked():
NumData = np.shape(Cov)[0]
fig2 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCov.text()):
plt.title(ui.txtTitleCov.text())
else:
plt.title('Group RSA: Covariance\nLevel: ' + FoldStr)
plt.show()
fig3 = plt.figure(figsize=(25, 10), )
if len(ui.txtTitleDen.text()):
plt.title(ui.txtTitleDen.text())
else:
plt.title(
'Group MP Gradient RSA: Similarity Analysis\nLevel: ' +
FoldStr)
dn = dendrogram(Z,
labels=labels,
leaf_font_size=FontSize,
color_threshold=1,
leaf_rotation=ui.txtXRotation.value())
plt.show()
print("DONE.")
msgBox.setText(
"Group Representational Similarity Analysis (RSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
print("Loading...")
# Job
NumJob = ui.txtJob.value()
if NumJob < 1:
NumJob = getCPUCore()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ResData = dict()
Coord = None
CodeText = ui.txtEvents.toPlainText()
for fold in range(FoldFrom, FoldTo + 1):
print(f"Analyzing Fold: {fold}...")
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText(f"FOLD {fold}: Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InFile = InFile.replace("$FOLD$", str(fold))
if not os.path.isfile(InFile):
msgBox.setText(f"FOLD {fold}: Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Load InData
InData = None
try:
InData = mainIO_load(InFile)
except:
print(f"FOLD {fold}: Cannot load file: {InFile}")
# Train data
if not len(ui.txtData.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter train data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Xtr = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked():
Xtr = preprocessing.scale(Xtr)
print(
f"FOLD {fold}: Whole of train data is scaled X~N(0,1)."
)
except:
print(f"FOLD {fold}: Cannot load train data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Test data
if not len(ui.txtTeData.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter test data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Xte = InData[ui.txtTeData.currentText()]
if ui.cbScale.isChecked():
Xte = preprocessing.scale(Xte)
print(
f"FOLD {fold}: Whole of test data is scaled X~N(0,1).")
except:
print(f"FOLD {fold}: Cannot load test data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Train Label
if not len(ui.txtLabel.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Ytr = InData[ui.txtLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load label")
return
# Test Label
if not len(ui.txtTeLabel.currentText()):
msgBox.setText(
f"FOLD {fold}: Please enter test Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Yte = InData[ui.txtTeLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load test label")
return
# Coordinate
if Coord is None:
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Coord = np.transpose(InData[ui.txtCol.currentText()])
CoordSize = np.shape(Coord)[0]
except:
print("Cannot load coordinate")
return
print(f"FOLD {fold}: analyzing regions...")
startIndex = 0
stepIndex = int(CoordSize / NumJob) + 1
ThreadList = list()
ThreadID = 1
while startIndex < CoordSize:
try:
allvars = dict(locals(), **globals())
exec(CodeText, allvars, allvars)
model = allvars['model']
except Exception as e:
print(f'Cannot generate model\n{e}')
msgBox.setText(f'Cannot generate model\n{e}')
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if startIndex + stepIndex < CoordSize:
endIndex = startIndex + stepIndex
else:
endIndex = CoordSize
TrainX = Xtr[:, startIndex:endIndex]
TestX = Xte[:, startIndex:endIndex]
T = MultiThreadingVectorClassification(TrX=TrainX,
Try=Ytr,
TeX=TestX,
Tey=Yte,
startIndex=startIndex,
model=model,
processID=ThreadID)
print(
"Thread {:5d} is generated for voxels in range from {:15d} to {:15d}. # voxels: {:15d}. # cores: {:5d}. Step size: {:10d}"
.format(ThreadID, startIndex, endIndex, CoordSize, NumJob,
stepIndex))
ThreadList.append(T)
ThreadID += 1
startIndex += stepIndex
print(f"FOLD {fold}: running threads...")
for tt in ThreadList:
if not tt.is_alive():
tt.start()
# Waiting the process will be finish
for tt in ThreadList:
tt.join()
print(f"FOLD {fold}: finalizing results...")
for ttIdx, tt in enumerate(ThreadList):
for res in tt.Results:
try:
ResData[res[0]] += res[1]
except:
ResData[res[0]] = res[1]
print('FOLD {:5d} and thread {:5d} is done.'.format(
fold, ttIdx + 1))
OutData = dict()
NumFold = FoldTo - FoldFrom + 1
FinalResult = list()
FinalAccuracy = list()
for key in ResData.keys():
coo = Coord[key, :]
acc = ResData[key] / NumFold
FinalResult.append([[coo], [[acc]]])
FinalAccuracy.append(acc)
print("Saving ...")
mainIO_save({"accuracy": reshape_1Dvector(FinalAccuracy),\
"results": reshape_1Dvector(FinalResult),\
ui.txtCol.currentText():Coord}, \
ui.txtOutFile.text())
# io.savemat(ui.txtOutFile.text(), mdict={"accuracy": FinalAccuracy,
# "results": FinalResult,
# ui.txtCol.currentText():Coord})
print("DONE.")
msgBox.setText("Wised voxel analysis is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
totalTime = 0
msgBox = QMessageBox()
Model = ui.cbMethod.currentText()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo+1):
tic = time.time()
# Regularization
try:
NIter = np.int32(ui.txtIter.text())
except:
msgBox.setText("Number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Gamma = np.float(ui.txtGamma.text())
except:
msgBox.setText("Gamma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# AR Rank
try:
Rank = np.int32(ui.txtRank.text())
except:
msgBox.setText("Rank value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# AR Rank
try:
Rho = np.float(ui.txtRho.text())
except:
msgBox.setText("Rho value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 0:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText("Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText("Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText("Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText("Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText("Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText("Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText("Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText("Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText("Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText("Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText("Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText("Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText("Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText("Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText("Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText("Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText("Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText("Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText("Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText("Please enter Train Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText("Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText("Please enter Train Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText("Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText("Please enter Train Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText("Please enter Test Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
# Train Analysis Level
print("Calculating Analysis Level for Training Set ...")
TrGroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TrGroupFold = TrSubject
FoldStr = "Subject"
else:
TrGroupFold = np.concatenate((TrSubject,TrRun))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
TrGroupFold = np.concatenate((TrGroupFold,TrTaskIndex)) if TrGroupFold is not None else TrTaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
TrGroupFold = np.concatenate((TrGroupFold,TrCounter)) if TrGroupFold is not None else TrCounter
FoldStr = FoldStr + "+Counter"
TrGroupFold = np.transpose(TrGroupFold)
TrUniqFold = np.array(list(set(tuple(i) for i in TrGroupFold.tolist())))
TrFoldIDs = np.arange(len(TrUniqFold)) + 1
TrListFold = list()
for gfold in TrGroupFold:
for ufoldindx, ufold in enumerate(TrUniqFold):
if (ufold == gfold).all():
currentID = TrFoldIDs[ufoldindx]
break
TrListFold.append(currentID)
TrListFold = np.int32(TrListFold)
TrListFoldUniq = np.unique(TrListFold)
# Test Analysis Level
print("Calculating Analysis Level for Testing Set ...")
TeGroupFold = None
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TeGroupFold = TeSubject
else:
TeGroupFold = np.concatenate((TeSubject,TeRun))
if ui.cbFTask.isChecked():
TeGroupFold = np.concatenate((TeGroupFold,TeTaskIndex)) if TeGroupFold is not None else TeTaskIndex
if ui.cbFCounter.isChecked():
TeGroupFold = np.concatenate((TeGroupFold,TeCounter)) if TeGroupFold is not None else TeCounter
TeGroupFold = np.transpose(TeGroupFold)
TeUniqFold = np.array(list(set(tuple(i) for i in TeGroupFold.tolist())))
TeFoldIDs = np.arange(len(TeUniqFold)) + 1
TeListFold = list()
for gfold in TeGroupFold:
for ufoldindx, ufold in enumerate(TeUniqFold):
if (ufold == gfold).all():
currentID = TeFoldIDs[ufoldindx]
break
TeListFold.append(currentID)
TeListFold = np.int32(TeListFold)
TeListFoldUniq = np.unique(TeListFold)
# Train Partition
print("Partitioning Training Data ...")
TrX = list()
TrShape = None
for foldindx, fold in enumerate(TrListFoldUniq):
dat = XTr[np.where(TrListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) + " is scaled X~N(0,1).")
AR_MAT = AR(np.shape(dat)[0], rho=Rho, rank=Rank)
TrX.append(np.dot(np.transpose(dat), AR_MAT))
if TrShape is None:
TrShape = np.shape(dat)
else:
if not(TrShape == np.shape(dat)):
print("ERROR: Train, Reshape problem for Fold " + str(foldindx + 1) + ", Shape: " + str(np.shape(dat)))
return
print("Train: View " + str(foldindx + 1) + " is extracted. Shape: " + str(np.shape(dat)))
print("Training Shape (sub x voxel x time): " + str(np.shape(TrX)))
# Test Partition
print("Partitioning Testing Data ...")
TeX = list()
TeShape = None
for foldindx, fold in enumerate(TeListFoldUniq):
dat = XTe[np.where(TeListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) + " is scaled X~N(0,1).")
AR_MAT = AR(np.shape(dat)[0], rho=Rho, rank=Rank)
TeX.append(np.dot(np.transpose(dat), AR_MAT))
if TeShape is None:
TeShape = np.shape(dat)
else:
if not(TeShape == np.shape(dat)):
print("Test: Reshape problem for Fold " + str(foldindx + 1))
return
print("Test: View " + str(foldindx + 1) + " is extracted.")
print("Testing Shape (sub x voxel x time): " + str(np.shape(TeX)))
if NumFea == 0:
NumFea = np.min(np.shape(TrX)[1:3])
print("Number of features are automatically selected as ", NumFea)
MessageString = ""
SharedR = None
try:
if Model == "Probabilistic SRM":
model = SRM(n_iter=NIter,features=NumFea)
MessageString = "Probabilistic "
elif Model == "Deterministic SRM":
model = DetSRM(n_iter=NIter,features=NumFea)
MessageString = "Deterministic "
else:
model = RSRM(n_iter=NIter, features=NumFea, gamma=Gamma)
SharedR = True
MessageString = "Robust "
print("Running Hyperalignment on Training Data ...")
model.fit(TrX)
if SharedR == True:
S = model.r_
Spec_train = list()
for spec in model.s_:
Spec_train.append(np.transpose(spec))
else:
S = model.s_
WTr = list()
for wtri in model.w_ :
WTr.append(wtri)
# Train Dot Product
print("Producting Training Data ...")
TrHX = None
for mapping, viewi in zip(WTr, TrX):
TrHX = np.concatenate((TrHX, np.transpose(np.dot(np.transpose(mapping),viewi)))) if TrHX is not None else np.transpose(np.dot(np.transpose(mapping),viewi))
OutData[ui.txtOTrData.text()] = TrHX
print("Running Hyperalignment on Testing Data ...")
TeHX = None
WTe = list()
if SharedR:
Specific_test = np.zeros(np.shape(TeX))
for ijk in range(NIter):
# Considering Specific_Test is fixed and Updating Wi
WTe = list()
for vid, view in enumerate(TeX):
product = np.dot(view - Specific_test[vid], np.transpose(S))
U, _, V = lg.svd(product, full_matrices=False, check_finite=False)
WTe.append(np.dot(U, V))
# Considering Wi is fixed and Updating Specific_test
Specific_test = list()
for vid, (view, Wtest) in enumerate(zip(TeX, WTe)):
NewSpec = view - np.dot(Wtest, S)
posIndex = NewSpec > Gamma
negIndex = NewSpec < -Gamma
NewSpec[posIndex] -= Gamma
NewSpec[negIndex] += Gamma
NewSpec[np.logical_and(~posIndex, ~negIndex)] = .0
Specific_test.append(NewSpec)
# Generating the concatenate results
Spec_test = list()
for (Wtest, spec) in zip(WTe, Specific_test):
TeHX = np.concatenate((TeHX, np.transpose(np.dot(np.transpose(Wtest), view - spec)))) if TeHX is not None else np.transpose(np.dot(np.transpose(Wtest),view - spec))
Spec_test.append(np.transpose(spec))
else:
for vid, view in enumerate(TeX):
product = np.dot(view, np.transpose(S))
U, _, V = lg.svd(product, full_matrices=False, check_finite=False)
Wtest = np.dot(U,V)
WTe.append(Wtest)
TeHX = np.concatenate((TeHX, np.transpose(np.dot(np.transpose(Wtest),view)))) if TeHX is not None else np.transpose(np.dot(np.transpose(Wtest),view))
OutData[ui.txtOTeData.text()] = TeHX
except Exception as e:
print(e)
HAParam = dict()
if ui.cbSaveShare.isChecked():
HAParam["Share"] = S
if SharedR:
HAParam["Specific_train"] = Spec_train
HAParam["Specific_test"] = Spec_test
if ui.cbSaveMap.isChecked():
HAParam["WTrain"] = WTr
HAParam["WTest"] = WTe
HAParam["Model"] = Model
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Runtime: ", totalTime)
print(MessageString + "Autoregressive Shared Response Model is done.")
msgBox.setText(MessageString + "Autoregressive Shared Response Model is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
global data
data = None
msgBox = QMessageBox()
if ui.vwSele.rowCount() == 0:
print("Please select some voxel first")
msgBox.setText("Please select some voxel first")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading...")
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
OutData[ui.txtOLabel.text()] = reshape_1Dvector(
InData[ui.txtLabel.currentText()])
except:
print("Cannot load Label ID")
return
# Subject
if ui.cbSubject.isChecked():
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOSubject.text()] = reshape_1Dvector(
InData[ui.txtSubject.currentText()])
except:
print("Cannot load Subject ID")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTask.text()] = reshape_1Dvector(
InData[ui.txtTask.currentText()])
except:
print("Cannot load Task ID")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtORun.text()] = reshape_1Dvector(
InData[ui.txtRun.currentText()])
except:
print("Cannot load Run ID")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCounter.text()] = reshape_1Dvector(
InData[ui.txtCounter.currentText()])
except:
print("Cannot load Counter ID")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOmLabel.text()] = InData[
ui.txtmLabel.currentText()]
except:
print("Cannot load Matrix Label ID")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
except:
print("Cannot load Design ID")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
except:
print("Cannot load Condition ID")
return
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOScan.text()] = reshape_1Dvector(
InData[ui.txtScan.currentText()])
except:
print("Cannot load NumScan ID")
return
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
except:
print("Cannot load data")
return
# Coordinate
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Coord = InData[ui.txtCol.currentText()]
except:
print("Cannot load data")
return
# Make dict of Coord indexes
dCoord = {}
for cooInd, coo in enumerate(np.transpose(Coord)):
dCoord[tuple(coo)] = cooInd
# Find selected indexes
VoxelID = list()
NewCoord = list()
for rowID in range(ui.vwSele.rowCount()):
itemString = str(ui.vwSele.item(rowID, 0).text()).split(";")
item = (int(itemString[0]), int(itemString[1]), int(itemString[2]))
try:
VoxelID.append(dCoord[item])
NewCoord.append(item)
except:
print(f"WARNING: Cannot find selected coordinate: {item}")
OutData[ui.txtOCol.text()] = np.transpose(NewCoord)
OutData[ui.txtOData.text()] = X[:, VoxelID]
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("Data with new ROI is created.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
totalTime = 0
msgBox = QMessageBox()
TrFoldErr = list()
TeFoldErr = list()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo + 1):
tic = time.time()
# Regularization
try:
Regularization = np.float(ui.txtRegularization.text())
except:
msgBox.setText("Regularization value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 0:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea == 0:
NumFea = None
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText(
"Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(
InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(
InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText(
"Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText(
"Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText(
"Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText(
"Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText(
"Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText(
"Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText(
"Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText(
"Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText(
"Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText(
"Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText(
"Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText(
"Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText(
"Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText(
"Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText(
"Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText(
"Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(
TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(
TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText(
"Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText(
"Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText(
"Please enter Train Output Matrix Label variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText(
"Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[
ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[
ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText(
"Please enter Train Input Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText(
"Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText(
"Please enter Train Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText(
"Please enter Test Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[
ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[
ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[
ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(
InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[
ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(
InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(
InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
# Train Analysis Level
print("Calculating Analysis Level for Training Set ...")
TrGroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TrGroupFold = TrSubject
FoldStr = "Subject"
else:
TrGroupFold = np.concatenate((TrSubject, TrRun))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrTaskIndex)) if TrGroupFold is not None else TrTaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrCounter)) if TrGroupFold is not None else TrCounter
FoldStr = FoldStr + "+Counter"
TrGroupFold = np.transpose(TrGroupFold)
TrUniqFold = np.array(
list(set(tuple(i) for i in TrGroupFold.tolist())))
TrFoldIDs = np.arange(len(TrUniqFold)) + 1
TrListFold = list()
for gfold in TrGroupFold:
for ufoldindx, ufold in enumerate(TrUniqFold):
if (ufold == gfold).all():
currentID = TrFoldIDs[ufoldindx]
break
TrListFold.append(currentID)
TrListFold = np.int32(TrListFold)
TrListFoldUniq = np.unique(TrListFold)
# Test Analysis Level
print("Calculating Analysis Level for Testing Set ...")
TeGroupFold = None
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TeGroupFold = TeSubject
else:
TeGroupFold = np.concatenate((TeSubject, TeRun))
if ui.cbFTask.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeTaskIndex)) if TeGroupFold is not None else TeTaskIndex
if ui.cbFCounter.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeCounter)) if TeGroupFold is not None else TeCounter
TeGroupFold = np.transpose(TeGroupFold)
TeUniqFold = np.array(
list(set(tuple(i) for i in TeGroupFold.tolist())))
TeFoldIDs = np.arange(len(TeUniqFold)) + 1
TeListFold = list()
for gfold in TeGroupFold:
for ufoldindx, ufold in enumerate(TeUniqFold):
if (ufold == gfold).all():
currentID = TeFoldIDs[ufoldindx]
break
TeListFold.append(currentID)
TeListFold = np.int32(TeListFold)
TeListFoldUniq = np.unique(TeListFold)
# Train Partition
print("Partitioning Training Data ...")
TrX = list()
TrShape = None
for foldindx, fold in enumerate(TrListFoldUniq):
dat = XTr[np.where(TrListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
TrX.append(dat)
if TrShape is None:
TrShape = np.shape(dat)
else:
if not (TrShape == np.shape(dat)):
print("ERROR: Train, Reshape problem for Fold " +
str(foldindx + 1) + ", Shape: " +
str(np.shape(dat)))
return
print("Train: View " + str(foldindx + 1) +
" is extracted. Shape: " + str(np.shape(dat)))
print("Training Shape: " + str(np.shape(TrX)))
# Test Partition
print("Partitioning Testing Data ...")
TeX = list()
TeShape = None
for foldindx, fold in enumerate(TeListFoldUniq):
dat = XTe[np.where(TeListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
TeX.append(dat)
if TeShape is None:
TeShape = np.shape(dat)
else:
if not (TeShape == np.shape(dat)):
print("Test: Reshape problem for Fold " +
str(foldindx + 1))
return
print("Test: View " + str(foldindx + 1) + " is extracted.")
print("Testing Shape: " + str(np.shape(TeX)))
model = RHA(regularization=Regularization, Dim=NumFea)
print("Running Hyperalignment on Training Data ...")
model.fit(TrX)
G = model.get_G()
TrU = model.get_U()
print("Running Hyperalignment on Testing Data ...")
model.project(TeX)
TeU = model.get_U_tilde()
# Train Dot Product
print("Producting Training Data ...")
TrHX = None
TrErr = None
for foldindx, fold in enumerate(TrListFoldUniq):
mapping = np.dot(TrX[foldindx], TrU[foldindx])
TrErr = TrErr + (G -
mapping) if TrErr is not None else G - mapping
TrHX = np.concatenate(
(TrHX, mapping)) if TrHX is not None else mapping
OutData[ui.txtOTrData.text()] = TrHX
foldindx = foldindx + 1
TrErr = TrErr / foldindx
print("Train: alignment error ", np.linalg.norm(TrErr))
TrFoldErr.append(np.linalg.norm(TrErr))
# Train Dot Product
print("Producting Testing Data ...")
TeHX = None
TeErr = None
for foldindx, fold in enumerate(TeListFoldUniq):
mapping = np.dot(TeX[foldindx], TeU[foldindx])
TeErr = TeErr + (G -
mapping) if TeErr is not None else G - mapping
TeHX = np.concatenate(
(TeHX, mapping)) if TeHX is not None else mapping
OutData[ui.txtOTeData.text()] = TeHX
foldindx = foldindx + 1
TeErr = TeErr / foldindx
print("Test: alignment error ", np.linalg.norm(TeErr))
TeFoldErr.append(np.linalg.norm(TeErr))
HAParam = dict()
HAParam["Share"] = G
HAParam["Train"] = TrU
HAParam["Test"] = TeU
HAParam["Level"] = FoldStr
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Training -> Alignment Error: mean " + str(np.mean(TrFoldErr)) +
" std " + str(np.std(TrFoldErr)))
print("Testing -> Alignment Error: mean " + str(np.mean(TeFoldErr)) +
" std " + str(np.std(TeFoldErr)))
print("Runtime: ", totalTime)
print("Regularized Hyperalignment is done.")
msgBox.setText("Regularized Hyperalignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
Method = ui.cbMethod.currentData()
LossType = ui.cbLossType.currentData()
Optim = ui.cbOptim.currentData()
try:
Epoch = np.int32(ui.txtIter.text())
except:
msgBox.setText("Number of iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
BatchSize = np.int32(ui.txtBatch.text())
except:
msgBox.setText("Number of batch is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ReportStep = np.int32(ui.txtReportStep.text())
except:
msgBox.setText("Number of Report Step is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
LearningRate = np.float32(ui.txtRate.text())
except:
msgBox.setText("Number of Report Step is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
LassoAlpha = np.float32(ui.txtLParam.text())
except:
msgBox.setText("Number of Lasso Parameter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ElasticLambda1 = np.float32(ui.txtEL1.text())
except:
msgBox.setText("Number of Elastic Lambda 1 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ElasticAlpha = np.float32(ui.txtEL2.text())
except:
msgBox.setText("Number of Elastic Lambda 2 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
RidgeReg = np.float32(ui.txtRRP.text())
except:
msgBox.setText("Number of Ridge Regression Parameter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
msgBox.setText("At least, you must select one metric!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace("[", "").replace("]","").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading ...")
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if not len(ui.txtDesign.currentText()):
msgBox.setText("Please enter Input Design variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Design = InData[ui.txtDesign.currentText()]
except:
msgBox.setText("Design value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
FontSize = ui.txtFontSize.value()
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
except:
print("Cannot load data or label")
return
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Task Val
if not len(ui.txtTaskVal.currentText()):
msgBox.setText("Please enter Task value!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskIDTitle = ui.txtTaskVal.currentText()
except:
msgBox.setText("Task value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
except:
msgBox.setText("Task variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TaskTitleUnique = np.unique(TaskTitle)
Task = np.zeros(np.shape(TaskTitle))
for ttinx, tt in enumerate(TaskTitle):
for ttlinx, ttl in enumerate(TaskTitleUnique):
if tt[0] == ttl:
Task[ttinx] = ttlinx + 1
break
for ttlinx, ttl in enumerate(TaskTitleUnique):
if TaskIDTitle == ttl:
TaskID = ttlinx + 1
break
OutData["Task"] = TaskIDTitle
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Subject Val
if not len(ui.txtSubjectVal.currentText()):
msgBox.setText("Please enter Subject value!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
SubID = np.int32(ui.txtSubjectVal.currentText())
except:
msgBox.setText("Subject value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sub = InData[ui.txtSubject.currentText()][0]
except:
msgBox.setText("Subject variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData["SubjectID"] = SubID
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run Val
if not len(ui.txtRunVal.currentText()):
msgBox.setText("Please enter Run value!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
RunID = np.int32(ui.txtRunVal.currentText())
except:
msgBox.setText("Run value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()][0]
except:
msgBox.setText("Run variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData["RunID"] = RunID
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter Val
if not len(ui.txtCounterVal.currentText()):
msgBox.setText("Please enter Counter value!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
ConID = np.int32(ui.txtCounterVal.currentText())
except:
msgBox.setText("Counter value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Con = InData[ui.txtCounter.currentText()][0]
except:
msgBox.setText("Counter variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData["CounterID"] = ConID
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
Design = np.delete(Design, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
Task = np.delete(Task, labelIndx, axis=0)
Sub = np.delete(Sub, labelIndx, axis=0)
Run = np.delete(Run, labelIndx, axis=0)
Con = np.delete(Con, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
# Select Task
TaskIndex = np.where(Task == TaskID)
Design = Design[TaskIndex,:][0]
X = X[TaskIndex,:][0]
L = L[TaskIndex]
Sub = Sub[TaskIndex]
Run = Run[TaskIndex]
Con = Con[TaskIndex]
# Select Subject
SubIndex = np.where(Sub == SubID)
Design = Design[SubIndex,:][0]
X = X[SubIndex,:][0]
L = L[SubIndex]
Run = Run[SubIndex]
Con = Con[SubIndex]
# Select Counter
ConIndex = np.where(Con == ConID)
Design = Design[ConIndex,:][0]
X = X[ConIndex,:][0]
L = L[ConIndex]
Run = Run[ConIndex]
# Select Run
RunIndex = np.where(Run == RunID)
Design = Design[RunIndex,:][0]
X = X[RunIndex,:][0]
L = L[RunIndex] # This will only use in supervised methods
LUnique = np.unique(L)
LNum = np.shape(LUnique)[0]
OutData["Label"] = LUnique
OutData["ModelAnalysis"] = "PyTorch.Session.Gradient.RSA." + ui.cbMethod.currentText()
if np.shape(X)[0] == 0:
msgBox.setText("The selected data is empty!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbScale.isChecked():
X = preprocessing.scale(X)
print("Data is scaled to N(0,1).")
print("Running Gradient RSA ...")
# RSA Method
OutData['Method'] = dict()
OutData['Method']['Method'] = Method
OutData['Method']['LossType'] = LossType
OutData['Method']['Optimization'] = Optim
OutData['Method']['LearningRate'] = LearningRate
OutData['Method']['Epoch'] = Epoch
OutData['Method']['BatchSize'] = BatchSize
OutData['Method']['ReportStep'] = ReportStep
OutData['Method']['RidgeAlpha'] = RidgeReg
OutData['Method']['ElaticLambda1'] = ElasticLambda1
OutData['Method']['ElaticAlpha'] = ElasticAlpha
OutData['Method']['LassoAlpha'] = LassoAlpha
OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()
rsa = GrRSA(method=Method,loss_type=LossType, optim=Optim, learning_rate=LearningRate, epoch=Epoch, \
batch_size=BatchSize, report_step=ReportStep, ridge_param=RidgeReg, elstnet_l1_ratio=ElasticLambda1,\
elstnet_alpha=ElasticAlpha, lasso_alpha=LassoAlpha, verbose=ui.cbVerbose.isChecked(),\
gpu_enable=ui.cbDevice.currentData(),normalization=False)
Betas, Eps, loss_vec, MSE, Performacne, _ = rsa.fit(data_vals=X, design_vals=Design)
OutData["LossVec"] = loss_vec
OutData["MSE"] = MSE
OutData["Performance"] = Performacne
print("MSE: ", OutData["MSE"])
if ui.cbBeta.isChecked():
OutData["Betas"] = Betas
OutData["Eps"] = Eps
# Calculate Results
if ui.cbCorr.isChecked():
print("Calculating Correlation ...")
Corr = np.corrcoef(Betas)
corClass = SimilarityMatrixBetweenClass(Corr)
OutData["Correlation"] = Corr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_std"] = corClass.std()
OutData["Correlation_mean"] = corClass.mean()
if ui.cbCov.isChecked():
print("Calculating Covariance ...")
Cov = np.cov(Betas)
covClass = SimilarityMatrixBetweenClass(Cov)
OutData["Covariance"] = Cov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_std"] = covClass.std()
OutData["Covariance_mean"] = covClass.mean()
# Calculating Distance Matrix
dis = np.zeros((np.shape(Betas)[0], np.shape(Betas)[0]))
for i in range(np.shape(Betas)[0]):
for j in range(i + 1, np.shape(Betas)[0]):
dis[i, j] = 1 - np.dot(Betas[i, :], Betas[j, :].T)
dis[j, i] = dis[i, j]
OutData["DistanceMatrix"] = dis
Z = linkage(dis)
OutData["Linkage"] = Z
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
mainIO_save(OutData, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
NumData = np.shape(Corr)[0]
fig1 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=45)
ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCorr.text()):
plt.title(ui.txtTitleCorr.text())
else:
plt.title('Correlation (' + ui.cbMethod.currentText() + \
')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
plt.show()
if ui.cbCov.isChecked():
NumData = np.shape(Cov)[0]
fig2 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=45)
ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCov.text()):
plt.title(ui.txtTitleCov.text())
else:
plt.title('Covariance (' + ui.cbMethod.currentText() + \
')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
plt.show()
fig3 = plt.figure(figsize=(25, 10), )
if len(ui.txtTitleDen.text()):
plt.title(ui.txtTitleDen.text())
else:
plt.title('Similarity Analysis (' + ui.cbMethod.currentText() + \
')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
dn = dendrogram(Z, labels=labels, leaf_font_size=FontSize, color_threshold=1)
plt.show()
print("DONE.")
msgBox.setText("Gradient Representational Similarity Analysis (RSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
tme = time.time()
msgBox = QMessageBox()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
# Shuffle
Shuffle = ui.cbShuffle.isChecked()
# FitIntercept
FitIntercept = ui.cbFitIntercept.isChecked()
# WarmStart
WarmStart = ui.cbWarmStart.isChecked()
# NesterovsMomentum
AverageParm = ui.cbAverageParm.isChecked()
# Loss
Loss = ui.cbLoss.currentText()
# Penalty
Penalty = ui.cbPenalty.currentText()
# Learning Rate
LearningRate = ui.cbLearningRate.currentText()
# Alpha
try:
Alpha = np.float(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# L1Rate
try:
L1Rate = np.float(ui.txtL1Rate.text())
except:
msgBox.setText("L1 Rate is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Eta0
try:
Eta0 = np.double(ui.txtEta0.text())
except:
msgBox.setText("Eta 0 Rate is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Powert
try:
Powert = np.double(ui.txtPowert.text())
except:
msgBox.setText("Power_t is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTol.text())
if Tol == 0:
Tol = None
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
if MaxIter <= 0:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Epsilon
try:
Epsilon = np.float(ui.txtEpsilon.text())
except:
msgBox.setText("Epsilon is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Epoach
try:
NIterNoChange = np.int32(ui.txtEpochs.text())
if NIterNoChange <= 0:
msgBox.setText("N Iteration No Change must be positive!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("N Iteration No Change is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Verbose
try:
Verbose = np.int32(ui.txtVerbose.text())
except:
msgBox.setText("Verbose is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# NJob
try:
NJobs = np.int32(ui.txtNJobs.text())
if NJobs <= 0:
NJobs = -1
except:
msgBox.setText("n_jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Class filter is wrong!")
msgBox.setText("Class filter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
Fold = list()
accuracy = list()
precision = list()
average_precision = list()
f1score = list()
recall = list()
accuracyTr = list()
precisionTr = list()
average_precisionTr = list()
f1scoreTr = list()
recallTr = list()
InFileList = list()
OutData = dict()
OutData[
"ModelAnalysis"] = "Stochastic Gradient Descent based Classifier"
for fold in range(FoldFrom, FoldTo + 1):
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
else:
OutModel = OutModel.replace("$FOLD$", str(fold))
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold))
InFileList.append(InFile)
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrX = InData[ui.txtITrData.currentText()]
TeX = InData[ui.txtITeData.currentText()]
TrL = InData[ui.txtITrLabel.currentText()][0]
TeL = InData[ui.txtITeLabel.currentText()][0]
# Ref Filter Train
if ui.cbFilterTrID.isChecked():
try:
# Create content structure for training
TrFilterContent = ui.txtFilterTrContent.text()
TrFilterContent = TrFilterContent.replace(" ", "")
if not len(TrFilterContent):
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TrFilterContent = TrFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TrFilterContent = np.int32(TrFilterContent)
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
if np.shape(TrX)[0] != np.shape(TrF)[0] or np.shape(
TrL)[0] != np.shape(TrF)[0]:
print(
"Shape of reference for training must be the same as data and label"
)
msgBox.setText(
"Shape of reference for training must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for training is wrong!")
msgBox.setText("Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove training set
try:
print(
"Removing training set based on reference. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
for content in TrFilterContent:
contIndex = np.where(TrF == content)[0]
TrL = np.delete(TrL, contIndex, axis=0)
TrX = np.delete(TrX, contIndex, axis=0)
TrF = np.delete(TrF, contIndex, axis=0)
print(" Content", content,
"is removed from training set. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
except Exception as e:
print("Cannot filter the training set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the training set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Create content structure for testing
TeFilterContent = ui.txtFilterTeContent.text()
TeFilterContent = TeFilterContent.replace(" ", "")
if not len(TeFilterContent):
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TeFilterContent = TeFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TeFilterContent = np.int32(TeFilterContent)
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
if np.shape(TeX)[0] != np.shape(TeF)[0] or np.shape(
TeL)[0] != np.shape(TeF)[0]:
print(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setText(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for testing is wrong!")
msgBox.setText("Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove testing set
try:
print(
"Removing testing set based on reference. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
for content in TeFilterContent:
contIndex = np.where(TeF == content)[0]
TeL = np.delete(TeL, contIndex, axis=0)
TeX = np.delete(TeX, contIndex, axis=0)
TeF = np.delete(TeF, contIndex, axis=0)
print(" Content", content,
"is removed from testing set. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
except Exception as e:
print("Cannot filter the testing set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the testing set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(TrL == fil)[0]
TrL = np.delete(TrL, labelIndx, axis=0)
TrX = np.delete(TrX, labelIndx, axis=0)
# Remove Testing Set
labelIndx = np.where(TeL == fil)[0]
TeL = np.delete(TeL, labelIndx, axis=0)
TeX = np.delete(TeX, labelIndx, axis=0)
print("Class ID = " + str(fil) +
" is removed from data.")
if ui.cbScale.isChecked():
TrX = preprocessing.scale(TrX)
TeX = preprocessing.scale(TeX)
print(
"Whole of data is scaled Train~N(0,1) and Test~N(0,1)."
)
except:
print("Cannot load data or label")
return
# FoldID
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
currFID = InData[ui.txtFoldID.currentText()][0][0]
Fold.append(currFID)
except:
print("Cannot load Fold ID!")
return
try:
clf = SGDClassifier(loss=Loss,penalty=Penalty,alpha=Alpha,l1_ratio=L1Rate,fit_intercept=FitIntercept,\
max_iter=MaxIter,tol=Tol,shuffle=Shuffle,verbose=Verbose,epsilon=Epsilon,\
n_jobs=NJobs,learning_rate=LearningRate,eta0=Eta0,power_t=Powert,\
warm_start=WarmStart,average=AverageParm,n_iter_no_change=NIterNoChange)
print("FoldID = " + str(currFID) + " is training ...")
clf.fit(TrX, TrL)
if OutModel is not None:
joblib.dump(clf, OutModel)
print("FoldID = " + str(currFID) + " Model is saved: " +
OutModel)
print("FoldID = " + str(currFID) + " is testing ...")
PeL = clf.predict(TeX)
PrL = clf.predict(TrX)
OutData["fold" + str(currFID) +
"_confusion_matrix"] = confusion_matrix(
TeL, PeL, np.unique(TeL))
OutData["fold" + str(currFID) +
"_classification_report"] = classification_report(
TeL, PeL)
print(OutData["fold" + str(currFID) +
"_classification_report"])
except Exception as e:
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbAverage.isChecked():
acc = accuracy_score(TeL, PeL)
accTr = accuracy_score(TrL, PrL)
accuracy.append(acc)
accuracyTr.append(accTr)
print(
"FoldID = {:d}, Average Train {:5.2f} Test {:5.2f}"
.format(currFID, accTr * 100, acc * 100))
if ui.cbPrecision.isChecked():
pre = precision_score(TeL,
PeL,
average=ui.cbPrecisionAvg.currentData())
preTr = precision_score(
TrL, PrL, average=ui.cbPrecisionAvg.currentData())
precision.append(pre)
precisionTr.append(preTr)
print(
"FoldID = {:d}, Precision Train {:5.2f} Test {:5.2f}"
.format(currFID, preTr * 100, pre * 100))
if ui.cbAPrecision.isChecked():
prA = average_precision_score(
TeL, PeL, average=ui.cbAPrecisionAvg.currentData())
prATr = average_precision_score(
TrL, PrL, average=ui.cbAPrecisionAvg.currentData())
average_precision.append(prA)
average_precisionTr.append(prATr)
print(
"FoldID = {:d}, Average Precision: Train {:5.2f} Test {:5.2f}"
.format(currFID, prATr * 100, prA * 100))
if ui.cbRecall.isChecked():
rec = recall_score(TeL,
PeL,
average=ui.cbRecallAvg.currentData())
recTr = recall_score(TrL,
PrL,
average=ui.cbRecallAvg.currentData())
recall.append(rec)
recallTr.append(recTr)
print(
"FoldID = {:d}, Recall: Train {:5.2f} Test {:5.2f}"
.format(currFID, recTr * 100, rec * 100))
if ui.cbF1.isChecked():
f1 = f1_score(TeL, PeL, average=ui.cbF1Avg.currentData())
f1Tr = f1_score(TrL, PrL, average=ui.cbF1Avg.currentData())
f1score.append(f1)
f1scoreTr.append(f1Tr)
print(
"FoldID = {:d}, F1: Train {:5.2f} Test {:5.2f}"
.format(currFID, f1Tr * 100, f1 * 100))
print("FoldID = " + str(currFID) + " is analyzed!")
if ui.cbAverage.isChecked():
OutData["FoldAccuracy"] = accuracy
MeanAcc = np.mean(accuracy)
OutData["MeanTestAccuracy"] = MeanAcc
STDAcc = np.std(accuracy)
OutData["StdTestAccuracy"] = STDAcc
MeanAccTr = np.mean(accuracyTr)
OutData["MeanTrainAccuracy"] = MeanAccTr
STDAccTr = np.std(accuracyTr)
OutData["StdTrainAccuracy"] = STDAccTr
print(
"Accuracy: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAccTr * 100, STDAccTr, MeanAcc * 100, STDAcc))
if ui.cbPrecision.isChecked():
OutData["ModePrecision"] = ui.cbPrecisionAvg.currentText()
OutData["FoldPrecision"] = precision
MeanPre = np.mean(precision)
OutData["MeanTrainPrecision"] = MeanPre
STDPre = np.std(precision)
OutData["StdTrainPrecision"] = STDPre
MeanPreTr = np.mean(precisionTr)
OutData["MeanTestPrecision"] = MeanPreTr
STDPreTr = np.std(precisionTr)
OutData["StdTestPrecision"] = STDPreTr
print(
"Precision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanPreTr * 100, STDPreTr, MeanPre * 100, STDPre))
if ui.cbAPrecision.isChecked():
OutData["ModeAveragePrecision"] = ui.cbAPrecisionAvg.currentText()
OutData["FoldAveragePrecision"] = average_precision
MeanAPre = np.mean(average_precision)
OutData["MeanTrainAveragePrecision"] = MeanAPre
STDAPre = np.std(average_precision)
OutData["StdTestAveragePrecision"] = STDAPre
MeanAPreTr = np.mean(average_precisionTr)
OutData["MeanTrainAveragePrecision"] = MeanAPreTr
STDAPreTr = np.std(average_precisionTr)
OutData["StdTrainAveragePrecision"] = STDAPreTr
print(
"AveragePrecision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAPreTr * 100, STDAPreTr, MeanAPre * 100, STDAPre))
if ui.cbRecall.isChecked():
OutData["ModeRecall"] = ui.cbRecallAvg.currentText()
OutData["FoldRecall"] = recall
MeanRec = np.mean(recall)
OutData["MeanTestRecall"] = MeanRec
STDRec = np.std(recall)
OutData["StdTestRecall"] = STDRec
MeanRecTr = np.mean(recallTr)
OutData["MeanTrainRecall"] = MeanRecTr
STDRecTr = np.std(recallTr)
OutData["StdTrainRecall"] = STDRecTr
print(
"Recall: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanRecTr * 100, STDRecTr, MeanRec * 100, STDRec))
if ui.cbF1.isChecked():
OutData["ModeF1"] = ui.cbF1Avg.currentText()
OutData["FoldF1"] = f1score
MeanF1 = np.mean(f1score)
OutData["MeanTestF1"] = MeanF1
STDF1 = np.std(f1score)
OutData["StdTestF1"] = STDF1
MeanF1Tr = np.mean(f1scoreTr)
OutData["MeanTrainF1"] = MeanF1Tr
STDF1Tr = np.std(f1scoreTr)
OutData["StdTrainF1"] = STDF1Tr
print(
"F1: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanF1Tr * 100, STDF1Tr, MeanF1 * 100, STDF1))
OutData["InputFiles"] = InFileList
OutData["Runtime"] = time.time() - tme
print("Runtime: ", OutData["Runtime"])
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Stochastic Gradient Descent based Classifier is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
totalTime = 0
msgBox = QMessageBox()
# Batch
try:
Batch = np.int32(ui.txtBatch.text())
except:
msgBox.setText("Size of batch is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Batch == 0:
Batch = None
# Kernel
Kernel = ui.cbKernel.currentText()
# Method
Method = ui.cbMethod.currentText()
# Gamma
try:
Gamma = np.float(ui.txtGamma.text())
except:
msgBox.setText("Gamma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Degree
try:
Degree = np.int32(ui.txtDegree.text())
except:
msgBox.setText("Degree is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coef0
try:
Coef0 = np.float(ui.txtCoef0.text())
except:
msgBox.setText("Coef0 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Alpha
try:
Alpha = np.int32(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# HAIte
try:
Iter = np.int32(ui.txtIter.text())
except:
msgBox.setText("Number of HA Iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Iter <= 0:
msgBox.setText("Number of HA Iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter <= 0:
MaxIter = None
# Number of Job
try:
NJob = np.int32(ui.txtJobs.text())
except:
msgBox.setText("The number of parallel jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NJob < -1 or NJob == 0:
msgBox.setText(
"The number of parallel jobs must be -1 or greater than 0!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrFoldErr = list()
TeFoldErr = list()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo + 1):
tic = time.time()
# Regularization
try:
Regularization = np.float(ui.txtRegularization.text())
except:
msgBox.setText("Regularization value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Regularization <= 0 or Regularization >= 1:
msgBox.setText("HA Regularization must be between 0 and 1")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText(
"Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(
InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(
InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText(
"Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText(
"Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText(
"Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText(
"Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText(
"Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText(
"Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText(
"Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText(
"Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText(
"Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText(
"Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText(
"Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText(
"Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText(
"Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText(
"Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText(
"Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText(
"Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(
TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(
TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText(
"Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText(
"Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText(
"Please enter Train Output Matrix Label variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText(
"Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[
ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[
ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText(
"Please enter Train Input Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText(
"Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText(
"Please enter Train Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText(
"Please enter Test Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[
ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[
ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[
ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(
InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[
ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(
InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(
InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
# Train Analysis Level
print("Calculating Analysis Level for Training Set ...")
TrGroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TrGroupFold = TrSubject
FoldStr = "Subject"
else:
TrGroupFold = np.concatenate((TrSubject, TrRun))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrTaskIndex)) if TrGroupFold is not None else TrTaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrCounter)) if TrGroupFold is not None else TrCounter
FoldStr = FoldStr + "+Counter"
TrGroupFold = np.transpose(TrGroupFold)
TrUniqFold = np.array(
list(set(tuple(i) for i in TrGroupFold.tolist())))
TrFoldIDs = np.arange(len(TrUniqFold)) + 1
TrListFold = list()
for gfold in TrGroupFold:
for ufoldindx, ufold in enumerate(TrUniqFold):
if (ufold == gfold).all():
currentID = TrFoldIDs[ufoldindx]
break
TrListFold.append(currentID)
TrListFold = np.int32(TrListFold)
TrListFoldUniq = np.unique(TrListFold)
# Test Analysis Level
print("Calculating Analysis Level for Testing Set ...")
TeGroupFold = None
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TeGroupFold = TeSubject
else:
TeGroupFold = np.concatenate((TeSubject, TeRun))
if ui.cbFTask.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeTaskIndex)) if TeGroupFold is not None else TeTaskIndex
if ui.cbFCounter.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeCounter)) if TeGroupFold is not None else TeCounter
TeGroupFold = np.transpose(TeGroupFold)
TeUniqFold = np.array(
list(set(tuple(i) for i in TeGroupFold.tolist())))
TeFoldIDs = np.arange(len(TeUniqFold)) + 1
TeListFold = list()
for gfold in TeGroupFold:
for ufoldindx, ufold in enumerate(TeUniqFold):
if (ufold == gfold).all():
currentID = TeFoldIDs[ufoldindx]
break
TeListFold.append(currentID)
TeListFold = np.int32(TeListFold)
TeListFoldUniq = np.unique(TeListFold)
# Train Partition
print("Partitioning Training Data ...")
TrX = list()
TrShape = None
if Method == "PCA":
svdmodel = PCA(n_components=NumFea, copy=False, tol=Tol)
elif Method == "Kernel PCA":
svdmodel = KernelPCA(n_components=NumFea,kernel=Kernel,gamma=Gamma,degree=Degree,\
coef0=Coef0, alpha=Alpha, tol=Tol, max_iter=MaxIter, n_jobs=NJob,copy_X=False)
else:
svdmodel = IncrementalPCA(n_components=NumFea,
copy=False,
batch_size=Batch)
for foldindx, fold in enumerate(TrListFoldUniq):
dat = XTr[np.where(TrListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
dat = svdmodel.fit_transform(dat)
TrX.append(dat)
if TrShape is None:
TrShape = np.shape(dat)
else:
if not (TrShape == np.shape(dat)):
print("ERROR: Train, Reshape problem for Fold " +
str(foldindx + 1) + ", Shape: " +
str(np.shape(dat)))
return
print("Train: View " + str(foldindx + 1) +
" is extracted. Shape: " + str(np.shape(dat)))
print("Training Shape: " + str(np.shape(TrX)))
# Test Partition
print("Partitioning Testing Data ...")
TeX = list()
TeShape = None
for foldindx, fold in enumerate(TeListFoldUniq):
dat = XTe[np.where(TeListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
dat = svdmodel.fit_transform(dat)
TeX.append(dat)
if TeShape is None:
TeShape = np.shape(dat)
else:
if not (TeShape == np.shape(dat)):
print("Test: Reshape problem for Fold " +
str(foldindx + 1))
return
print("Test: View " + str(foldindx + 1) + " is extracted.")
print("Testing Shape: " + str(np.shape(TeX)))
#model = RHA(regularization=Regularization,Dim=NumFea)
model = rHA(alpha=Regularization)
print("Running Hyperalignment on Training Data ...")
model.train(TrX, iter=Iter)
G = model.G
TrU = model.TrainQt
TrErr = model.TrainError
OutData[ui.txtOTrData.text()] = model.TrainXp
print("Running Hyperalignment on Testing Data ...")
model.test(TeX)
TeU = model.TestQt
TeErr = model.TestError
OutData[ui.txtOTeData.text()] = model.TestXp
HAParam = dict()
HAParam["Method"] = Method
HAParam["Kernel"] = Kernel
HAParam["Share"] = G
HAParam["Train"] = TrU
HAParam["TrainError"] = TrErr
HAParam["Test"] = TeU
HAParam["TestError"] = TeErr
HAParam["Level"] = FoldStr
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Training -> Alignment Error: mean " + str(np.mean(TrFoldErr)) +
" std " + str(np.std(TrFoldErr)))
print("Testing -> Alignment Error: mean " + str(np.mean(TeFoldErr)) +
" std " + str(np.std(TeFoldErr)))
print("Runtime: ", totalTime)
print("Kernel/SVD Hyperalignment is done.")
msgBox.setText("Kernel/SVD Hyperalignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = reshape_1Dvector(Subject)
except:
print("Cannot load Subject ID")
return
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if not ui.rbScale.isChecked():
X_new = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
else:
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
X_Sub.append(preprocessing.scale(X[np.where(Subject == subj)[1], :]))
print("Data in subject level is scaled, X_" + str(subj) + "~N(0,1).")
lenPCA = len(X_Sub)
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub):
X_new = np.concatenate((X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA, " is done.")
OutData[ui.txtOData.text()] = X_new
except:
print("Cannot load data")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = reshape_1Dvector(InData[ui.txtLabel.currentText()])
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = reshape_1Dvector(InData[ui.txtTask.currentText()])
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = reshape_1Dvector(InData[ui.txtRun.currentText()])
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = reshape_1Dvector(InData[ui.txtCounter.currentText()])
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = reshape_1Dvector(InData[ui.txtScan.currentText()])
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
#io.savemat(ui.txtOutFile.text(), mdict=OutData)
print("DONE.")
msgBox.setText("Normalization is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
if not ui.cbFSubject.isChecked():
if not ui.cbFTask.isChecked():
if not ui.cbFCounter.isChecked():
msgBox.setText("You must at least select one Fold Level!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Subject Condition
if ui.cbFSubject.isChecked():
if not ui.cbSubject.isChecked():
msgBox.setText(
"For using task as a fold level, you have to enable task filed in input file!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run Condition
if ui.rbFRun.isChecked():
if not ui.cbRun.isChecked():
msgBox.setText(
"For using run as a fold level, you have to enable run filed in input file!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Task Condition
if not ui.cbTask.isChecked():
if ui.cbFTask.isChecked():
msgBox.setText(
"For using task as a fold level, you have to enable task filed in input file!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not ui.cbCounter.isChecked():
if ui.cbFCounter.isChecked():
msgBox.setText(
"For using counter as a fold level, you have to enable counter filed in input file!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutDIR
OutDIR = ui.txtOutDIR.text()
if not len(OutDIR):
msgBox.setText("Please enter out directory!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isdir(OutDIR):
msgBox.setText("Output directory not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Out Files
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
InData = mainIO_load(InFile)
except:
print("Cannot load data file!")
return
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
except:
print("Cannot load data")
return
# Subject
if ui.cbSubject.isChecked():
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
except:
print("Cannot load Subject ID")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Task = np.asarray(InData[ui.txtTask.currentText()])
TaskIndex = Task.copy()
for tasindx, tas in enumerate(np.unique(Task)):
TaskIndex[Task == tas] = tasindx + 1
except:
print("Cannot load Task ID")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()]
except:
print("Cannot load Run ID")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Counter = InData[ui.txtCounter.currentText()]
except:
print("Cannot load Counter ID")
return
try:
Unit = np.int32(ui.txtUnit.text())
except:
msgBox.setText("Unit for the test set must be a number!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Unit < 1:
msgBox.setText("Unit for the test set must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Calculating Folds ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = Subject
FoldStr = "Subject"
else:
GroupFold = np.concatenate((Subject, Run))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate(
(GroupFold, TaskIndex)) if GroupFold is not None else TaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate(
(GroupFold, Counter)) if GroupFold is not None else Counter
FoldStr = FoldStr + "+Counter"
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i) for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText(
"Unit for the test set must be smaller than all possible folds! Number of all folds is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold), Unit):
msgBox.setText(
"Unit for the test set must be divorceable to all possible folds! Number of all folds is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
FoldInfo = dict()
FoldInfo["Unit"] = Unit
FoldInfo["Group"] = GroupFold
FoldInfo["Order"] = FoldStr
FoldInfo["List"] = ListFold
FoldInfo["Unique"] = UniqFold
FoldInfo["Folds"] = UnitFold
GUFold = np.unique(UnitFold)
print("Number of all folds is: " + str(len(UniqFold)))
for foldID, fold in enumerate(GUFold):
print("Saving Fold " + str(foldID + 1), " of ", str(len(UniqFold)),
" ...")
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
OutData["FoldInfo"] = FoldInfo
OutData["FoldID"] = [[fold]]
TestIndex = np.where(UnitFold == fold)
TrainIndex = np.where(UnitFold != fold)
OutData[ui.txtTrain.text() + ui.txtOData.text()] = X[TrainIndex]
OutData[ui.txtTest.text() + ui.txtOData.text()] = X[TestIndex]
# Subject
if ui.cbSubject.isChecked():
OutData[ui.txtTrain.text() +
ui.txtOSubject.text()] = reshape_1Dvector(
Subject[0, TrainIndex])
OutData[ui.txtTest.text() +
ui.txtOSubject.text()] = reshape_1Dvector(
Subject[0, TestIndex])
# Task
if ui.cbTask.isChecked():
OutData[ui.txtTrain.text() +
ui.txtOTask.text()] = reshape_1Dvector(
Task[0, TrainIndex])
OutData[ui.txtTest.text() +
ui.txtOTask.text()] = reshape_1Dvector(Task[0,
TestIndex])
# Run
if ui.cbRun.isChecked():
OutData[ui.txtTrain.text() +
ui.txtORun.text()] = reshape_1Dvector(Run[0,
TrainIndex])
OutData[ui.txtTest.text() +
ui.txtORun.text()] = reshape_1Dvector(Run[0,
TestIndex])
# Counter
if ui.cbCounter.isChecked():
OutData[ui.txtTrain.text() +
ui.txtOCounter.text()] = reshape_1Dvector(
Counter[0, TrainIndex])
OutData[ui.txtTest.text() +
ui.txtOCounter.text()] = reshape_1Dvector(
Counter[0, TestIndex])
# Label
Label = InData[ui.txtLabel.currentText()]
TrainLabel = Label[0, TrainIndex]
TestLabel = Label[0, TestIndex]
OutData[ui.txtTrain.text() +
ui.txtOLabel.text()] = reshape_1Dvector(TrainLabel)
OutData[ui.txtTest.text() +
ui.txtOLabel.text()] = reshape_1Dvector(TestLabel)
# m Label
if ui.cbmLabel.isChecked():
OutData[ui.txtTrain.text() +
ui.txtOmLabel.text()] = label_binarize(
TrainLabel[0], np.unique(TrainLabel))
OutData[ui.txtTest.text() +
ui.txtOmLabel.text()] = label_binarize(
TestLabel[0], np.unique(TestLabel))
# DM
if ui.cbDM.isChecked():
DM = InData[ui.txtDM.currentText()]
OutData[ui.txtTrain.text() + ui.txtODM.text()] = DM[TrainIndex]
OutData[ui.txtTest.text() + ui.txtODM.text()] = DM[TestIndex]
# NScan
if ui.cbNScan.isChecked():
NScan = InData[ui.txtScan.currentText()]
OutData[ui.txtTrain.text() +
ui.txtOScan.text()] = reshape_1Dvector(
NScan[0, TrainIndex])
OutData[ui.txtTest.text() +
ui.txtOScan.text()] = reshape_1Dvector(
NScan[0, TestIndex])
# Coordination
if ui.cbCol.isChecked():
OutData[ui.txtCol.currentText()] = InData[ui.txtOCol.text()]
# Condition
if ui.cbCond.isChecked():
OutData[ui.txtCond.currentText()] = InData[ui.txtOCond.text()]
OutFileUpdate = str.replace(OutFile, "$FOLD$", str(foldID + 1))
mainIO_save(OutData, OutDIR + OutFileUpdate)
#io.savemat(OutDIR + OutFileUpdate, mdict=OutData)
print("Cross validation is done.")
msgBox.setText("Cross validation is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
