def run_subject(sub, maskname="graymatter-bin_mask",
settings=projectSettings,
options=analysisSettings, logger=None):
mask = pu.load_img(paths["root"], "derivatives", sub,
"masks", "{}.nii.gz".format(maskname))
fmri_data, labels, bg_image = load_betas(settings, sub,
t="cope-LSS", center=True,
scale=False, logger=logger)
selector = labels[labels.AB == 1].sort_values(["SubRel", "TrialTag"])
fmri_data = pu.index_img(fmri_data, selector.index)
options["searchlight"]["rdm_metric"] = "correlation"
results = pa.searchlight_rsa(
fmri_data,
model_rdms[(model_rdms.type == "full")].dropna(axis=1).values[:, 2:],
m=mask,
write=False,
logger=logger,
**options["searchlight"])
pu.data_to_img(results.scores_, bg_image).to_filename(
os.path.join(paths["root"], "analysis", sub,
"multivariate", "rsa",
"{}_{}_AB_corr_rsaSearchlight.nii.gz".format(sub,
maskname)))
return results
def select_data(self, phase="AB", equals=True, val=1):
# will be a little more complex
if equals:
self.selector = self.labels[self.labels[phase] == val].sort_values(["SubRel", "TrialTag"])
else:
self.selector = self.labels[self.labels[phase] != val]
self.fmri_data = pu.index_img(self.fmri_data, self.selector.index)
self.labels = self.labels.loc[self.selector.index]
def select_data(self, phase="AB", equals=True, val=1):
if phase.upper() == "CDMATCH":
self.selector = self.labels[(self.labels["CD"] == 1) & (self.labels["Match"] == "1")]
else:
if equals:
self.selector = self.labels[self.labels[phase] == val]
else:
self.selector = self.labels[self.labels[phase] != val]
self.fmri_data = pu.index_img(self.fmri_data, self.selector.index)
self.labels = self.labels.loc[self.selector.index]
def run_subject(sub,
maskname="graymatter-bin_mask",
settings=projectSettings,
options=analysisSettings,
logger=None):
mask = pu.load_img(paths["root"],
"derivatives",
sub,
"masks",
"{}.nii.gz".format(maskname),
logger=logger)
fmri_data, labels, bg_image = load_betas(settings,
sub,
t="cope-LSS",
logger=logger)
selector = labels[(labels.CD == 1) & (labels.Match == "1")]
fmri_data = pu.index_img(fmri_data, selector.index)
options["searchlight"]["estimator"] = Pipeline(steps=[ # (
# "variance_threshold",
# VarianceThreshold()),
("scaling", StandardScaler()),
# ("feature_select", SelectPercentile(f_classif, percentile=20)),
# ("feature_select", SelectKBest(f_classif, k=100)),
("svm", LinearSVC(C=0.05))
# ("plr", LogisticRegression(C=0.05, penalty="l1", tol=0.01))
])
# analysis
# output order: CD2AB, AB2CD
cv = LeaveOneGroupOut()
result = pa.searchlight(fmri_data,
selector.CDMainRel,
m=mask,
cv=cv,
groups=selector['chunks'],
write=False,
logger=logger,
**options['searchlight'])
outpath = os.path.join(paths["root"], "analysis", sub, "multivariate",
"searchlight", "lss",
"{}_CDMatch-MainRel-cvsl.nii.gz".format(sub))
pu.data_to_img(result.scores_, bg_image,
logger=logger).to_filename(outpath)
return result
def main(_):
import getopt
# every possible variable
roi = FLAGS.mask
sub = FLAGS.sub
phase = FLAGS.phase
debug = FLAGS.debug
analysis = FLAGS.analysis
if FLAGS.jobs:
analysisSettings["searchlight"]["n_jobs"] = int(FLAGS.jobs)
if FLAGS.verbosity:
analysisSettings["searchlight"]["verbose"] = int(FLAGS.verbose)
if FLAGS.radius:
analysisSettings["searchlight"]["radius"] = int(FLAGS.radius)
# if not con:
# print "not a valid contrast... exiting"
# sys.exit(1)
if None in [sub, roi]:
print("Argument missing")
sys.exit(2)
logger = pu.setup_logger(os.path.join(paths['root'], 'analysis', sub,
'multivariate', 'searchlight',
"lss"),
fname="{}_{}-Match.log".format(roi, FLAGS.phase))
mask_file = os.path.join(paths["root"], "derivatives", sub, "masks",
"{}.nii.gz".format(roi))
if analysis == "cvsl":
pu.write_to_logger("Running MVPA", logger)
sl = CVSearchlight(sub,
mask_file,
phase=phase,
settings=analysisSettings["searchlight"],
logger=logger,
permutations=FLAGS.permutations)
slargs = {}
elif analysis == "rsa":
pu.write_to_logger("Running RSA", logger)
raw_models_df = pu.load_labels(
os.path.join(paths["code"], "labels/raw_models.csv"))
sl = RSASearchlight(sub,
mask_file,
phase=phase,
settings=analysisSettings["searchlight"],
logger=logger)
if FLAGS.phase == "CD":
sl.fmri_data = pu.index_img(sl.fmri_data, sl.selector.Match == '1')
sl.labels = sl.labels[sl.selector.Match == '1']
sl.selector = sl.labels
modelrdms, modelnames = get_cd_models(raw_models_df, sl.labels)
else:
modelnames = [
"mainrel", "rel", "numchar", "humanratings", "typicality",
"w2vdiff", "concatword", "accuracy", "rstpostprob9",
"rstpostprob79", "bart79thresh", "rstpostprob270",
"bartregressacc", "accregressbart"
]
model_rdms = get_model_rdms(raw_models_df, modelnames)
# modelrdms = model_rdms[(model_rdms.type == "full")].dropna(axis=1).values[:, 2:].astype(np.float64)
# deal with NAs in bart thresh
rdms = model_rdms[model_rdms.type == "full"]
bart = rdms[rdms.name == "rstpostprob79"].iloc[:, 2:].fillna(0)
acc = rdms[rdms.name == "accuracy"].iloc[:, 2:].fillna(0)
bart_regress_acc = sm.OLS(bart.T, acc.T).fit().resid
acc_regress_bart = sm.OLS(acc.T, bart.T).fit().resid
modelrdms = model_rdms[(
model_rdms.type == "full")].iloc[:,
2:].fillna(0).values.astype(
np.float64)
modelrdms = np.concatenate(
[modelrdms,
np.vstack([bart_regress_acc, acc_regress_bart])],
axis=0)
slargs = {"modelrdms": modelrdms}
else:
pu.write_to_logger("wrong analysis specified, exiting...", logger)
sys.exit(2)
if not debug:
_ = sl.run(**slargs)
pu.write_to_logger("Session ended at " + str(datetime.now()),
logger=logger)