def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
Activation = ui.cbActivation.currentData()
LossNorm = ui.cbLossNorm.currentData()
LossType = ui.cbType.currentData()
try:
Layers = strRange(ui.txtLayers.text(), Unique=False)
if Layers is None:
raise Exception('')
except:
msgBox.setText("Layers is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Alpha = np.float32(ui.txtAlpha.text())
if Alpha < 0:
raise Exception
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Iter = 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
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 = io.loadmat(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
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 = 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"] = "Tensorflow.Session.Deep.RSA"
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 Deep RSA ...")
# RSA Method
OutData['Method'] = dict()
OutData['Method']['Layers'] = ui.txtLayers.text()
OutData['Method']['Alpha'] = Alpha
OutData['Method']['Activation'] = Activation
OutData['Method']['LossNorm'] = LossNorm
OutData['Method']['LearningRate'] = LearningRate
OutData['Method']['NumIter'] = Iter
OutData['Method']['BatchSize'] = BatchSize
OutData['Method']['ReportStep'] = ReportStep
OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()
rsa = DeepRSA(layers=Layers, n_iter=Iter, learning_rate=LearningRate,loss_norm=LossNorm,activation=Activation,\
batch_size=BatchSize,report_step=ReportStep,verbose=ui.cbVerbose.isChecked(),\
CPU=ui.cbDevice.currentData(), alpha=Alpha, loss_type=LossType)
Betas, Weights, Biases, loss_vec, MSE, Performance = rsa.fit(
data_vals=X, design_vals=Design)
OutData["LossVec"] = loss_vec
OutData["MSE"] = MSE
OutData["Performance"] = Performance
print("MSE: %f" % (MSE))
if ui.cbBeta.isChecked():
OutData["Betas"] = Betas
OutData["Weights"] = Weights
OutData["Biases"] = Biases
# 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()
print("Correlation: min: {:3.10f}, max: {:3.10f}, mean: {:3.10f}, std: {:3.10f}".format(corClass.min(), \
corClass.max(), corClass.mean(), corClass.std()))
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()
print("Covariance: min: {:3.10f}, max: {:3.10f}, mean: {:3.10f}, std: {:3.10f}".format(covClass.min(), \
covClass.max(), covClass.mean(), covClass.std()))
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(Corr, vmin=-0.1, vmax=1)
plt.xlim([0, LNum])
plt.ylim([0, LNum])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title(
'DeepRSA: Correlation\nTask: %s\nSub: %d, Counter: %d, Run: %d'
% (TaskIDTitle, SubID, ConID, RunID))
plt.show()
if ui.cbCov.isChecked():
fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(Cov)
plt.xlim([0, LNum])
plt.ylim([0, LNum])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title(
'DeepRSA: Covariance\nTask: %s\nSub: %d, Counter: %d, Run: %d'
% (TaskIDTitle, SubID, ConID, RunID))
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):
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
# 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"] = "RSA"
# 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 = io.loadmat(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
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 = 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 = dict()
OutData["FoldInfo"] = FoldInfo
OutData["ModelAnalysis"] = "Numpy.Group.RSA"
print("Number of all levels is: " + str(len(UniqFold)))
Cov = None
Corr = None
AMSE = list()
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)
BetaLi = np.linalg.lstsq(RegLi, XLi)[0][1:, :]
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()
OutData["MSE"] = np.mean(AMSE)
OutData["MSE_std"] = np.std(AMSE)
print("Average MSE: %f" % (OutData["MSE"]))
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(Corr, vmin=-0.1, vmax=1)
plt.xlim([0, np.shape(Corr)[0]])
plt.ylim([0, np.shape(Corr)[0]])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title('Correlation of Categories\nLevel: ' + FoldStr)
plt.show()
if ui.cbCov.isChecked():
fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(Cov)
plt.xlim([0, np.shape(Cov)[0]])
plt.ylim([0, np.shape(Cov)[0]])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title('Covariance of Categories\nLevel: ' + FoldStr)
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()
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()
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
# 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 = io.loadmat(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
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 = 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"] = "Numpy.Session.RSA"
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 RSA ...")
# RSA Method
Reg = np.insert(Design, 0, 1, axis=1)
Betas = np.linalg.lstsq(Reg, X)[0][1:, :]
print("Calculating MSE ...")
MSE = mean_squared_error(X, np.matmul(Design, Betas))
print("MSE: %f" % (MSE))
OutData["MSE"] = MSE
if ui.cbBeta.isChecked():
OutData["Betas"] = Betas
# 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()
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(Corr, vmin=-0.1, vmax=1)
plt.xlim([0, LNum])
plt.ylim([0, LNum])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title(
'Correlation of Categories\nTask: %s\nSub: %d, Counter: %d, Run: %d'
% (TaskIDTitle, SubID, ConID, RunID))
plt.show()
if ui.cbCov.isChecked():
fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(Cov)
plt.xlim([0, LNum])
plt.ylim([0, LNum])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title(
'Covariance of Categories\nTask: %s\nSub: %d, Counter: %d, Run: %d'
% (TaskIDTitle, SubID, ConID, RunID))
plt.show()
print("DONE.")
msgBox.setText("Representational Similarity Analysis (RSA) 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("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 = io.loadmat(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(con[1][0])
labels = np.array(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 = 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 ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
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()
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):
msgBox = QMessageBox()
tStart = time.time()
Activation = ui.cbActivation.currentData()
LossNorm = ui.cbLossNorm.currentData()
try:
Layers = strRange(ui.txtLayers.text(), Unique=False)
if Layers is None:
raise Exception('')
except:
msgBox.setText("Layers is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
KIter = np.int32(ui.txtKIter.text())
except:
msgBox.setText("Number of iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
RIter = np.int32(ui.txtRIter.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
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 = io.loadmat(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
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 = 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"] = "Tensorflow.Group.Single-Deep-Kernel.RSA"
print("Number of all levels is: " + str(len(UniqFold)))
# RSA Method
OutData['Method'] = dict()
OutData['Method']['Layers'] = ui.txtLayers.text()
OutData['Method']['Activation'] = Activation
OutData['Method']['LossNorm'] = LossNorm
OutData['Method']['LearningRate'] = LearningRate
OutData['Method']['KernelIter'] = KIter
OutData['Method']['RSAIter'] = RIter
OutData['Method']['BatchSize'] = BatchSize
OutData['Method']['ReportStep'] = ReportStep
OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()
TData = list()
TReg = list()
print("Reshaping Data ...")
for foldID, fold in enumerate(GUFold):
print("Reshaping 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))
TData.append(XLi)
TReg.append(RegLi)
print("Running Deep Group RSA ...")
rsa = DeepGroupRSA(layers=Layers, kernel_iter = KIter, rsa_iter = RIter, learning_rate=LearningRate,
loss_norm=LossNorm, activation=Activation, \
batch_size=BatchSize, report_step=ReportStep, verbose=ui.cbVerbose.isChecked(), \
NCat=np.shape(Design)[1], NVoxel=np.shape(X)[1], CPU=ui.cbDevice.currentData())
Betas, Eps, Weights, Bias, MSE, loss_mat = rsa.fit(data_vals=TData,
design_vals=TReg)
OutData["Weight"] = Weights
OutData["Bias"] = Bias
OutData["Perfromance"] = MSE
OutData["Perfromance_Average"] = rsa.AMSE
OutData["Perfromance_std"] = np.std(rsa.AMSE)
OutData["LossMat"] = loss_mat
print("Average Performance: %f" % (OutData["Perfromance"]))
print("Calculating cov & corr ... ")
AvgCov = None
AvgCorr = None
for beta_id, beta in enumerate(Betas):
OutData["BetaL" + str(beta_id)] = beta
if ui.cbCov.isChecked():
co = np.cov(beta)
OutData["Cov" + str(beta_id)] = co
AvgCov = co if AvgCov is None else AvgCov + co
if ui.cbCorr.isChecked():
cr = np.corrcoef(beta)
OutData["Corr" + str(beta_id)] = cr
AvgCorr = cr if AvgCorr is None else AvgCorr + cr
for eps_id, ep in enumerate(Eps):
OutData["EpsL" + str(eps_id)] = ep
if ui.cbCov.isChecked():
AvgCov = AvgCov / len(TData)
covClass = SimilarityMatrixBetweenClass(AvgCov)
OutData["Covariance"] = AvgCov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_mean"] = covClass.mean()
OutData["Covariance_std"] = covClass.std()
if ui.cbCorr.isChecked():
AvgCorr = AvgCorr / len(TData)
corClass = SimilarityMatrixBetweenClass(AvgCorr)
OutData["Correlation"] = AvgCorr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_mean"] = corClass.mean()
OutData["Correlation_std"] = corClass.std()
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(AvgCorr, vmin=-0.1, vmax=1)
plt.xlim([0, np.shape(AvgCorr)[0]])
plt.ylim([0, np.shape(AvgCorr)[0]])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title('Deep Group RSA: Correlation\nLevel: ' + FoldStr)
plt.show()
if ui.cbCov.isChecked():
fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
plt.pcolor(AvgCov)
plt.xlim([0, np.shape(AvgCov)[0]])
plt.ylim([0, np.shape(AvgCov)[0]])
plt.colorbar()
ax = plt.gca()
ax.set_aspect(1)
plt.title('Deep Group RSA: Covariance\nLevel: ' + FoldStr)
plt.show()
print("DONE.")
msgBox.setText(
"Group Level Single-Deep-Kernel Representational Similarity 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()
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 = io.loadmat(InFile)
OutData["imgShape"] = 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(con[1][0])
labels = np.array(labels)
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 = 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])
Yi.append(label_binarize(L[Index], np.unique(L)))
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)
# OutData["Covariance"] = Cov
# OutData["Covariance_min"] = np.min(Cov)
# OutData["Covariance_max"] = np.max(Cov)
# OutData["Covariance_std"] = np.std(Cov)
# OutData["Covariance_mean"] = np.mean(Cov)
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)
# OutData["Correlation"] = Corr
# OutData["Correlation_min"] = np.min(Corr)
# OutData["Correlation_max"] = np.max(Corr)
# OutData["Correlation_std"] = np.std(Corr)
# OutData["Correlation_mean"] = np.mean(Corr)
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 ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
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()
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 = io.loadmat(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(con[1][0])
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]
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 = 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"] = "SK.Session.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 RSA ...")
# RSA Method
Reg = np.insert(Design, 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(Reg, X)
Betas = np.transpose(model.coef_)[1:, :]
print("Calculating MSE ...")
MSE = mean_squared_error(X, np.matmul(Design, Betas))
print("MSE: %f" % (MSE))
OutData["MSE"] = MSE
# 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
if ui.cbBeta.isChecked():
OutData["Betas"] = Betas
# 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()
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
io.savemat(OutFile, mdict=OutData, do_compression=True)
print("Output is saved.")
FontSize = ui.txtFontSize.value()
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('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=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('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,
leaf_rotation=ui.txtXRotation.value())
plt.show()
print("DONE.")
msgBox.setText("Representational Similarity Analysis (RSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()