def test(self, views, TrainShare=None, verbose=True):
tic = time.time()
assert len(np.shape(
views)) == 3, "Data shape must be 3D, i.e. sub x time x voxel"
self.TestFeatures = None
NumSub, NumTime, NumVoxel = np.shape(views)
NumFea = self.net_shape[-1]
if NumFea is None:
NumFea = np.min((NumTime, NumVoxel))
if verbose:
print(
"Number of features is automatically assigned, Features: ",
NumFea)
self.net_shape[-1] = NumFea
if TrainShare is not None:
Share = TrainShare
self.Share = TrainShare
elif self.Share is not None:
Share = self.Share
if self.loss_type == 'mse':
criterion = torch.nn.MSELoss()
elif self.loss_type == 'soft':
criterion = torch.nn.MultiLabelSoftMarginLoss()
elif self.loss_type == 'mean':
criterion = torch.mean
elif self.loss_type == 'norm':
criterion = torch.norm
else:
raise Exception(
"Loss function type is wrong! Options: \'mse\', \'soft\', \'mean\', or \'norm\'"
)
self.ha_loss_test_vec = list()
self.ha_loss_test = None
NewViews = list()
G = torch.Tensor(Share)
for s in range(NumSub):
net_shape = np.concatenate(([NumVoxel], self.net_shape))
net = MLP(model=net_shape,
activation=self.activation,
gpu_enable=self.gpu_enable)
if self.optim == "adam":
optimizer = optim.Adam(net.parameters(), lr=self.learning_rate)
elif self.optim == "sgd":
optimizer = optim.SGD(net.parameters(), lr=self.learning_rate)
else:
raise Exception(
"Optimization algorithm is wrong! Options: \'adam\' or \'sgd\'"
)
X = torch.Tensor(views[s])
net.train()
for j in range(self.iteration):
for epoch in range(self.epoch):
perm = torch.randperm(NumTime)
sum_loss = 0
for i in range(0, NumTime, self.batch_size):
x = X[perm[i:i + self.batch_size]]
g = G[perm[i:i + self.batch_size]]
# Send data to GPU
if self.gpu_enable:
x = x.cuda()
g = g.cuda()
optimizer.zero_grad()
fx = net(x)
if self.loss_type == 'mse' or self.loss_type == 'soft':
loss = criterion(g, fx)
else:
loss = criterion(g - fx)
loss.backward()
optimizer.step()
sum_loss += loss.data.cpu().numpy()
if self.epoch_internal_iteration > (i / NumTime):
break
if verbose:
print(
"TEST, UPDATE NETWORK: Iteration {:6d}, Subject {:6d}, Epoch {:6d}, loss error: {}"
.format(j + 1, s + 1, epoch + 1, sum_loss))
if self.gpu_enable:
X = X.cuda()
NewViews.append(net(X).data.cpu().numpy())
ha_model = GPUHA(Dim=NumFea, regularization=self.regularization)
ha_model.test(views=NewViews, G=Share, verbose=verbose)
self.TestFeatures = ha_model.Xtest
self.TestRuntime = time.time() - tic
return self.TestFeatures
if __name__ == "__main__":
data = np.random.rand(10, 10, 10)
dat = np.random.rand(2, 10, 10)
model = RDHA([None], [None],
epoch_internal_iteration=0.5,
batch_size=10,
epoch=1,
norm2_enable=False)
model.train(data)
model.test(dat)
X = model.TrainFeatures
Y = model.TestFeatures
G = model.Share
model1 = GPUHA()
tic = time.time()
model1.train(data)
toc = time.time() - tic
model1.test(dat)
X2 = model1.Xtrain
Y2 = model1.Xtest
G2 = model1.G
print("\nRDHA, trace(G) = ", np.trace(G), " G^TG= ",
np.trace(np.dot(np.transpose(G), G)))
print("RDHA, Shared Space Shape: ", np.shape(G))
print("RDHA, Features Shape: ", np.shape(X))
# print("RDHA, Loss vec: ", model.ha_loss_vec)
print("RDHA, Error: ", model.ha_loss, ", Runtime: ", model.TrainRuntime)
error = 0
def train(self, views, verbose=True):
tic = time.time()
assert len(np.shape(
views)) == 3, "Data shape must be 3D, i.e. sub x time x voxel"
self.Share = None
self.TrainFeatures = None
NumSub, NumTime, NumVoxel = np.shape(views)
NumFea = self.net_shape[-1]
if NumFea is None:
NumFea = np.min((NumTime, NumVoxel))
if verbose:
print(
"Number of features is automatically assigned, Features: ",
NumFea)
self.net_shape[-1] = NumFea
Share = np.random.randn(NumTime, NumFea)
if self.loss_type == 'mse':
criterion = torch.nn.MSELoss()
elif self.loss_type == 'soft':
criterion = torch.nn.MultiLabelSoftMarginLoss()
elif self.loss_type == 'mean':
criterion = torch.mean
elif self.loss_type == 'norm':
criterion = torch.norm
else:
raise Exception(
"Loss function type is wrong! Options: \'mse\', \'soft\', \'mean\', or \'norm\'"
)
self.ha_loss_vec = list()
self.ha_loss = None
for j in range(self.iteration):
NewViews = list()
G = torch.Tensor(Share)
for s in range(NumSub):
net_shape = np.concatenate(([NumVoxel], self.net_shape))
net = MLP(model=net_shape,
activation=self.activation,
gpu_enable=self.gpu_enable)
if self.optim == "adam":
optimizer = optim.Adam(net.parameters(),
lr=self.learning_rate)
elif self.optim == "sgd":
optimizer = optim.SGD(net.parameters(),
lr=self.learning_rate)
else:
raise Exception(
"Optimization algorithm is wrong! Options: \'adam\' or \'sgd\'"
)
X = torch.Tensor(views[s])
net.train()
for epoch in range(self.epoch):
perm = torch.randperm(NumTime)
sum_loss = 0
for i in range(0, NumTime, self.batch_size):
x = X[perm[i:i + self.batch_size]]
g = G[perm[i:i + self.batch_size]]
# Send data to GPU
if self.gpu_enable:
x = x.cuda()
g = g.cuda()
optimizer.zero_grad()
fx = net(x)
if self.loss_type == 'mse' or self.loss_type == 'soft':
loss = criterion(fx, g) / NumTime
else:
loss = criterion(fx - g) / NumTime
if self.norm1_enable or self.norm2_enable:
for weight in net.get_weights():
if self.norm1_enable:
loss += self.alpha * torch.mean(
torch.abs(weight[1]))
if self.norm2_enable:
loss += self.alpha * torch.mean(weight[1]**
2)
loss.backward()
optimizer.step()
sum_loss += loss.data.cpu().numpy()
if self.epoch_internal_iteration > (i / NumTime):
break
if verbose:
print(
"TRAIN, UPDATE NETWORK: Iteration {:5d}, Subject {:6d}, Epoch {:6d}, loss error: {}"
.format(j + 1, s + 1, epoch + 1, sum_loss))
if self.gpu_enable:
X = X.cuda()
NewViews.append(net(X).data.cpu().numpy())
ha_model = GPUHA(Dim=NumFea, regularization=self.regularization)
if NumFea >= NumTime:
ha_model.train(views=NewViews,
verbose=verbose,
gpu=self.gpu_enable)
else:
ha_model.train(views=NewViews, verbose=verbose, gpu=False)
Share = ha_model.G
out_features = ha_model.Xtrain
error = np.mean(ha_model.Etrain)
if error == 0:
assert self.Share is not None, "All extracted features are zero, i.e. number of features is not enough for creating a shared space"
self.TrainRuntime = time.time() - tic
return self.TrainFeatures, self.Share
if self.best_result_enable:
if self.ha_loss is None:
self.Share = Share
self.TrainFeatures = out_features
self.ha_loss = error
if error <= self.ha_loss:
self.Share = Share
self.TrainFeatures = out_features
self.ha_loss = error
else:
self.Share = Share
self.TrainFeatures = out_features
self.ha_loss = error
if verbose:
print("Hyperalignment error: {}".format(error))
self.ha_loss_vec.append(error)
self.TrainRuntime = time.time() - tic
return self.TrainFeatures, self.Share
def btnConvert_click(self):
totalTime = 0
msgBox = QMessageBox()
TrFoldErr = list()
TeFoldErr = list()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo + 1):
tic = time.time()
# Regularization
try:
Regularization = np.float(ui.txtRegularization.text())
except:
msgBox.setText("Regularization value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = io.loadmat(InFile)
OutData = dict()
OutData["imgShape"] = InData["imgShape"]
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 0:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea == 0:
NumFea = None
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText(
"Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = InData[
ui.txtITrLabel.currentText()]
OutData[ui.txtOTeLabel.text()] = InData[
ui.txtITeLabel.currentText()]
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText(
"Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText(
"Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText(
"Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText(
"Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = TrSubject
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = TeSubject
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText(
"Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText(
"Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText(
"Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText(
"Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = InData[ui.txtITrTask.currentText()]
OutData[ui.txtOTrTask.text()] = TrTask
TeTask = InData[ui.txtITeTask.currentText()]
OutData[ui.txtOTeTask.text()] = TeTask
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText(
"Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText(
"Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText(
"Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText(
"Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = TrRun
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = TeRun
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText(
"Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText(
"Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText(
"Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText(
"Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = TrCounter
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = TeCounter
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText(
"Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText(
"Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText(
"Please enter Train Output Matrix Label variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText(
"Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[
ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[
ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText(
"Please enter Train Input Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText(
"Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText(
"Please enter Train Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText(
"Please enter Test Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[
ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[
ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[
ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = InData[
ui.txtFoldID.currentText()]
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[
ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = InData[
ui.txtITrScan.currentText()]
OutData[ui.txtOTeScan.text()] = InData[
ui.txtITeScan.currentText()]
except:
print("Cannot load NScan!")
return
# Train Analysis Level
print("Calculating Analysis Level for Training Set ...")
TrGroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TrGroupFold = TrSubject
FoldStr = "Subject"
else:
TrGroupFold = np.concatenate((TrSubject, TrRun))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrTaskIndex)) if TrGroupFold is not None else TrTaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrCounter)) if TrGroupFold is not None else TrCounter
FoldStr = FoldStr + "+Counter"
TrGroupFold = np.transpose(TrGroupFold)
TrUniqFold = np.array(
list(set(tuple(i) for i in TrGroupFold.tolist())))
TrFoldIDs = np.arange(len(TrUniqFold)) + 1
TrListFold = list()
for gfold in TrGroupFold:
for ufoldindx, ufold in enumerate(TrUniqFold):
if (ufold == gfold).all():
currentID = TrFoldIDs[ufoldindx]
break
TrListFold.append(currentID)
TrListFold = np.int32(TrListFold)
TrListFoldUniq = np.unique(TrListFold)
# Test Analysis Level
print("Calculating Analysis Level for Testing Set ...")
TeGroupFold = None
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TeGroupFold = TeSubject
else:
TeGroupFold = np.concatenate((TeSubject, TeRun))
if ui.cbFTask.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeTaskIndex)) if TeGroupFold is not None else TeTaskIndex
if ui.cbFCounter.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeCounter)) if TeGroupFold is not None else TeCounter
TeGroupFold = np.transpose(TeGroupFold)
TeUniqFold = np.array(
list(set(tuple(i) for i in TeGroupFold.tolist())))
TeFoldIDs = np.arange(len(TeUniqFold)) + 1
TeListFold = list()
for gfold in TeGroupFold:
for ufoldindx, ufold in enumerate(TeUniqFold):
if (ufold == gfold).all():
currentID = TeFoldIDs[ufoldindx]
break
TeListFold.append(currentID)
TeListFold = np.int32(TeListFold)
TeListFoldUniq = np.unique(TeListFold)
# Train Partition
print("Partitioning Training Data ...")
TrX = list()
TrShape = None
for foldindx, fold in enumerate(TrListFoldUniq):
dat = XTr[np.where(TrListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
TrX.append(dat)
if TrShape is None:
TrShape = np.shape(dat)
else:
if not (TrShape == np.shape(dat)):
print("ERROR: Train, Reshape problem for Fold " +
str(foldindx + 1) + ", Shape: " +
str(np.shape(dat)))
return
print("Train: View " + str(foldindx + 1) +
" is extracted. Shape: " + str(np.shape(dat)))
print("Training Shape: " + str(np.shape(TrX)))
# Test Partition
print("Partitioning Testing Data ...")
TeX = list()
TeShape = None
for foldindx, fold in enumerate(TeListFoldUniq):
dat = XTe[np.where(TeListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
TeX.append(dat)
if TeShape is None:
TeShape = np.shape(dat)
else:
if not (TeShape == np.shape(dat)):
print("Test: Reshape problem for Fold " +
str(foldindx + 1))
return
print("Test: View " + str(foldindx + 1) + " is extracted.")
print("Testing Shape: " + str(np.shape(TeX)))
model = GPUHA(Dim=NumFea, regularization=Regularization)
print("Running Hyperalignment on Training Data ...")
MappedXtr, G, _, _, _ = model.train(TrX)
print("Running Hyperalignment on Testing Data ...")
MappedXte, _, _, _ = model.test(TeX)
# Train Dot Product
print("Producting Training Data ...")
TrHX = None
TrErr = None
for foldindx, fold in enumerate(TrListFoldUniq):
TrErr = TrErr + (
G - MappedXtr[foldindx]
) if TrErr is not None else G - MappedXtr[foldindx]
TrHX = np.concatenate(
(TrHX, MappedXtr[foldindx]
)) if TrHX is not None else MappedXtr[foldindx]
OutData[ui.txtOTrData.text()] = TrHX
foldindx = foldindx + 1
TrErr = TrErr / foldindx
print("Train: alignment error ", np.linalg.norm(TrErr))
TrFoldErr.append(np.linalg.norm(TrErr))
# Train Dot Product
print("Producting Testing Data ...")
TeHX = None
TeErr = None
for foldindx, fold in enumerate(TeListFoldUniq):
TeErr = TeErr + (
G - MappedXte[foldindx]
) if TeErr is not None else G - MappedXte[foldindx]
TeHX = np.concatenate(
(TeHX, MappedXte[foldindx]
)) if TeHX is not None else MappedXte[foldindx]
OutData[ui.txtOTeData.text()] = TeHX
foldindx = foldindx + 1
TeErr = TeErr / foldindx
print("Test: alignment error ", np.linalg.norm(TeErr))
TeFoldErr.append(np.linalg.norm(TeErr))
HAParam = dict()
HAParam["Share"] = G
HAParam["Level"] = FoldStr
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
io.savemat(OutFile, mdict=OutData)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Training -> Alignment Error: mean " + str(np.mean(TrFoldErr)) +
" std " + str(np.std(TrFoldErr)))
print("Testing -> Alignment Error: mean " + str(np.mean(TeFoldErr)) +
" std " + str(np.std(TeFoldErr)))
print("Runtime: ", totalTime)
print("GPU Hyperalignment is done.")
msgBox.setText("GPU Hyperalignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()