def run_gat(name, decoder="ridge"):
"""
Function to run Generalization Across Time (GAT).
Parameters
----------
name: str
Name (pseudonym) of individual subject.
decoder: str
Specify type of classifier -'ridge' for Ridge Regression (default),'lin-svm' for linear SVM
'svm' for nonlinear (RBF) SVM and 'log_reg' for Logistic Regression
"""
# load high cloze epochs
epochs = get_epochs(name)['song', 'voice']
# specify whether to use a linear or nonlinear SVM if SVM is used
lin = '' # if not svm it doesn't matter, both log_reg and ridge are linear
if "svm" in decoder:
decoder, lin = decoder.split("-")
# build classifier pipeline #
# pick a machine learning algorithm to use (ridge/SVM/logistic regression)
decoder_dict = {
"ridge":
RidgeClassifier(class_weight='balanced', random_state=42,
solver="sag"),
"svm":
SVC(class_weight='balanced',
kernel=("rbf" if "non" in lin else "linear"),
random_state=42),
"log_reg":
LogisticRegression(class_weight='balanced', random_state=42)
}
clf = make_pipeline(StandardScaler(), decoder_dict[decoder])
gen_clf = GeneralizingEstimator(clf, scoring="roc_auc", n_jobs=4)
scores = cross_val_multiscore(gen_clf,
epochs.get_data(),
epochs.events[:, -1],
cv=5,
n_jobs=4).mean(0)
data = epochs.get_data()
labels = epochs.events[:, -1]
cv = StratifiedKFold(n_splits=5, random_state=42)
# calculate prediction confidence scores
preds = np.empty((len(labels), 225, 225))
for train, test in cv.split(data, labels):
gen_clf.fit(data[train], labels[train])
d = gen_clf.decision_function(data[test])
preds[test] = d
return scores, preds # return subject scores and prediction confidence
def run_gat(subj, decoder="ridge", n_jobs=2):
"""
Function to run Generalization Across Time (GAT).
Parameters
----------
subj: int
decoder: str
Specify type of classifier -'ridge' for Ridge Regression (default),
'lin-svm' for linear SVM 'svm' for nonlinear (RBF) SVM and 'log_reg'
for Logistic Regression
n_jobs: int
The number of jobs to run in parallel.
"""
# load cue A and cue B epochs
epochs = get_epochs(subj)['Correct A', 'Correct B']
# specify whether to use a linear or nonlinear SVM if SVM is used
lin = '' # if not svm it doesn't matter, both log_reg and ridge are linear
if "svm" in decoder:
decoder, lin = decoder.split("-")
# build classifier pipeline #
# pick a machine learning algorithm to use (ridge/SVM/logistic regression)
decoder_dict = {
"ridge":
RidgeClassifier(class_weight='balanced', random_state=42,
solver="sag"),
"svm":
SVC(class_weight='balanced',
kernel=("rbf" if "non" in lin else "linear"),
random_state=42),
"log_reg":
LogisticRegression(class_weight='balanced', random_state=42)
}
# get data and targets
data = epochs.get_data()
labels = epochs.events[:, -1]
# create classifier pipeline
clf = make_pipeline(StandardScaler(), decoder_dict[decoder])
gen_clf = GeneralizingEstimator(clf, scoring="roc_auc", n_jobs=n_jobs)
# compute cross validated performance scores
scores = cross_val_multiscore(gen_clf, data, labels, cv=5,
n_jobs=n_jobs).mean(0)
# calculate prediction confidence scores
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
preds = np.empty((len(labels), data.shape[2], data.shape[2]))
for train, test in cv.split(data, labels):
gen_clf.fit(data[train], labels[train])
d = gen_clf.decision_function(data[test])
preds[test] = d
# compute topographical patterns
dat = Vectorizer().fit_transform(data)
clf.fit(dat, labels)
dat = dat - dat.mean(0, keepdims=True)
# look for the type of classifier and get the weights
if decoder == 'ridge':
filt_ = clf.named_steps.ridgeclassifier.coef_.copy()
elif decoder == 'svm':
filt_ = clf.named_steps.svc.coef_.copy()
elif decoder == 'log_reg':
filt_ = clf.named_steps.logisticregression.coef_.copy()
# Compute patterns using Haufe's trick: A = Cov_X . W . Precision_Y
# cf.Haufe, et al., 2014, NeuroImage,
# doi:10.1016/j.neuroimage.2013.10.067)
inv_y = 1.
patt_ = np.cov(dat.T).dot(filt_.T.dot(inv_y)).T
# store the patterns accordingly
if decoder == 'ridge':
clf.named_steps.ridgeclassifier.patterns_ = patt_
elif decoder == 'svm':
clf.named_steps.svc.patterns_ = patt_
elif decoder == 'log_reg':
clf.named_steps.logisticregression.patterns_ = patt_
# back transform using steps in pipeline
patterns = get_coef(clf, 'patterns_', inverse_transform=True)
# return subject scores, prediction confidence and topographical patterns
return scores, preds, patterns