def run_fast_ica(samples, params):
print("Beginning Fast ICA")
if not params['NUM_COMPONENTS'] == None:
params['NUM_COMPONENTS'] = nChans
samples = np.rot90(samples)
print(samples.shape)
ica = FastICA(n_components=params['NUM_COMPONENTS'],
max_iter=params['MAX_ITER'],
tol=params['TOLERANCE'])
print("ica.get_params(): ", ica.get_params())
print("type(ica): ", type(ica))
S_ = ica.fit_transform(samples) # Reconstruct signals
print("S_", S_.shape)
A_ = ica.mixing_ # Get estimated mixing matrix
print("A_", A_.shape)
combinedSignals = np.dot(S_, A_.T)
print("Combined signals shape: ", combinedSignals.shape)
print("ica.mean_ ", ica.mean_.shape)
combinedSignalsICAmean = combinedSignals + ica.mean_
print("combined + mean: ", combinedSignalsICAmean)
return np.rot90(S_)
class ICA(object):
"""
Wrapper for sklearn package. Performs fast ICA (Independent Component Analysis)
ICA has 4 methods:
- fit(waveforms)
update class instance with ICA fit
- fit_transform()
do what fit() does, but additionally return the projection onto ICA space
- inverse_transform(A)
inverses the decomposition, returns waveforms for an input A, using Z
- get_params()
returns metadata used for fits.
"""
def __init__(self, num_components=10,
catalog_name='unknown',
whiten=True,
fun = 'logcosh',
fun_args = None,
max_iter = 600,
tol = .00001,
w_init = None,
random_state = None,
algorithm = 'parallel'):
self._decomposition = 'Fast ICA'
self._num_components = num_components
self._catalog_name = catalog_name
self._whiten = whiten
self._fun = fun
self._fun_args = fun_args
self._max_iter = max_iter
self._tol = tol
self._w_init = w_init
self._random_state = random_state
self._algorithm = algorithm
self._ICA = FastICA(n_components=self._num_components,
whiten = self._whiten,
fun = self._fun,
fun_args = self._fun_args,
max_iter = self._max_iter,
tol = self._tol,
w_init = self._w_init,
random_state = self._random_state,
algorithm = self._algorithm)
def fit(self,waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._ICA.fit(self._waveforms)
def fit_transform(self,waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._A = self._ICA.fit_transform(self._waveforms)
return self._A
def inverse_transform(self,A):
# convert basis back to waveforms using fit
new_waveforms = self._ICA.inverse_transform(A)
return new_waveforms
def get_params(self):
# TODO know what catalog was used! (include waveform metadata)
params = self._ICA.get_params()
params['num_components'] = params.pop('n_components')
params['Decompositon'] = self._decomposition
return params
def get_basis(self):
""" Return the ICA basis vectors (Z^\dagger)"""
return self._ICA.get_mixing_matrix()
def btnConvert_click(self):
msgBox = QMessageBox()
Alg = ui.cbAlg.currentText()
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter < 1:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.rbScale.isChecked() == True and ui.rbALScale.isChecked(
) == False:
msgBox.setText(
"Subject Level Normalization is just available for Subject Level Analysis!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = io.loadmat(InFile)
OutData = dict()
OutData["imgShape"] = InData["imgShape"]
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked() and (not ui.rbScale.isChecked()):
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(X)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = Subject
except:
print("Cannot load Subject ID")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = InData[ui.txtLabel.currentText()]
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = InData[ui.txtTask.currentText()]
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = InData[ui.txtRun.currentText()]
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = InData[
ui.txtCounter.currentText()]
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = InData[ui.txtScan.currentText()]
Models = dict()
Models["Name"] = "FastICA"
if ui.rbALScale.isChecked():
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
X_Sub.append(
preprocessing.scale(
X[np.where(Subject == subj)[1], :]))
print("Data in subject level is scaled, X_" + str(subj) +
"~N(0,1).")
else:
X_Sub.append(X[np.where(Subject == subj)[1], :])
print("Subject ", subj, " is extracted from data.")
print("Running FICA in subject level ...")
X_Sub_PCA = list()
lenPCA = len(X_Sub)
for xsubindx, xsub in enumerate(X_Sub):
model = FastICA(n_components=NumFea,
algorithm=Alg,
max_iter=MaxIter,
tol=Tol)
X_Sub_PCA.append(model.fit_transform(xsub))
Models["Model" + str(xsubindx + 1)] = str(
model.get_params(deep=True))
print("FICA: ", xsubindx + 1, " of ", lenPCA, " is done.")
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub_PCA):
X_new = np.concatenate(
(X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA,
" is done.")
OutData[ui.txtOData.text()] = X_new
else:
print("Running FastICA ...")
model = FastICA(n_components=NumFea,
algorithm=Alg,
max_iter=MaxIter,
tol=Tol)
OutData[ui.txtOData.text()] = model.fit_transform(X)
Models["Model"] = str(model.get_params(deep=True))
OutData["ModelParameter"] = Models
print("Saving ...")
io.savemat(ui.txtOutFile.text(), mdict=OutData)
print("DONE.")
msgBox.setText("FastICA is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
class ICA(object):
"""
Wrapper for sklearn package. Performs fast ICA (Independent Component Analysis)
ICA has 4 methods:
- fit(waveforms)
update class instance with ICA fit
- fit_transform()
do what fit() does, but additionally return the projection onto ICA space
- inverse_transform(A)
inverses the decomposition, returns waveforms for an input A, using Z
- get_params()
returns metadata used for fits.
"""
def __init__(self,
num_components=10,
catalog_name='unknown',
whiten=True,
fun='logcosh',
fun_args=None,
max_iter=600,
tol=.00001,
w_init=None,
random_state=None,
algorithm='parallel'):
self._decomposition = 'Fast ICA'
self._num_components = num_components
self._catalog_name = catalog_name
self._whiten = whiten
self._fun = fun
self._fun_args = fun_args
self._max_iter = max_iter
self._tol = tol
self._w_init = w_init
self._random_state = random_state
self._algorithm = algorithm
self._ICA = FastICA(n_components=self._num_components,
whiten=self._whiten,
fun=self._fun,
fun_args=self._fun_args,
max_iter=self._max_iter,
tol=self._tol,
w_init=self._w_init,
random_state=self._random_state,
algorithm=self._algorithm)
def fit(self, waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._ICA.fit(self._waveforms)
def fit_transform(self, waveforms):
# TODO make sure there are more columns than rows (transpose if not)
# normalize waveforms
self._waveforms = waveforms
self._A = self._ICA.fit_transform(self._waveforms)
return self._A
def inverse_transform(self, A):
# convert basis back to waveforms using fit
new_waveforms = self._ICA.inverse_transform(A)
return new_waveforms
def get_params(self):
# TODO know what catalog was used! (include waveform metadata)
params = self._ICA.get_params()
params['num_components'] = params.pop('n_components')
params['Decompositon'] = self._decomposition
return params
def get_basis(self):
""" Return the ICA basis vectors (Z^\dagger)"""
return self._ICA.get_mixing_matrix()