def _run(epochs, events, analysis):
"""Runs temporal generalization for a given subject and analysis"""
print(subject, analysis['name'])
# subselect the trials (e.g. exclude absent trials) with a
# dataframe query defined in conditions.py
query, condition = analysis['query'], analysis['condition']
sel = range(len(events)) if query is None \
else events.query(query).index
sel = [ii for ii in sel if ~np.isnan(events[condition][sel][ii])]
# The to-be-predicted value, for each trial:
y = np.array(events[condition], dtype=np.float32)
print analysis['name'], np.unique(y[sel]), len(sel)
# Abort if there is no trial
if len(sel) == 0:
return
# Apply analysis
gat = GeneralizationAcrossTime(clf=analysis['clf'],
cv=analysis['cv'],
scorer=analysis['scorer'],
n_jobs=-1)
print(subject, analysis['name'], 'fit')
gat.fit(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'score')
score = gat.score(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'save')
# save space
if analysis['name'] not in ['probe_phase', 'target_circAngle']:
# we'll need the estimator trained on the probe_phase and to generalize
# to the target phase and prove that there is a significant signal.
gat.estimators_ = None
if analysis['name'] not in [
'target_present', 'target_circAngle', 'probe_circAngle'
]:
# We need these individual prediction to control for the correlation
# between target and probe angle.
gat.y_pred_ = None
# Save analysis
save([gat, analysis, sel, events],
'decod',
subject=subject,
analysis=analysis['name'],
overwrite=True,
upload=True)
save([score, epochs.times],
'score',
subject=subject,
analysis=analysis['name'],
overwrite=True,
upload=True)
return
def _decod(subject, analysis):
from mne.decoding import GeneralizationAcrossTime
# if already computed let's just load it from disk
fname_kwargs = dict(subject=subject, analysis=analysis['name'] + '_vhp')
score_fname = paths('score', **fname_kwargs)
if op.exists(score_fname):
return load('score', **fname_kwargs)
epochs = _get_epochs(subject)
events = load('behavior', subject=subject)
# Let's not recompute everything, this is just a control analysis
print(subject, analysis['name'])
epochs._data = epochs.get_data()
epochs.preload = True
epochs.crop(0., .900)
epochs.decimate(2)
query, condition = analysis['query'], analysis['condition']
sel = range(len(events)) if query is None else events.query(query).index
sel = [ii for ii in sel if ~np.isnan(events[condition][sel][ii])]
y = np.array(events[condition], dtype=np.float32)
print analysis['name'], np.unique(y[sel]), len(sel)
if len(sel) == 0:
return
# Apply analysis
gat = GeneralizationAcrossTime(clf=analysis['clf'],
cv=analysis['cv'],
scorer=analysis['scorer'],
n_jobs=-1)
print(subject, analysis['name'], 'fit')
gat.fit(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'score')
score = gat.score(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'save')
# save space
gat.estimators_ = None
gat.y_pred_ = None
# Save analysis
save([score, epochs.times],
'score',
overwrite=True,
upload=True,
**fname_kwargs)
return score, epochs.times
def _run(epochs, events, analysis):
"""Runs temporal generalization for a given subject and analysis"""
print(subject, analysis['name'])
# subselect the trials (e.g. exclude absent trials) with a
# dataframe query defined in conditions.py
query, condition = analysis['query'], analysis['condition']
sel = range(len(events)) if query is None \
else events.query(query).index
sel = [ii for ii in sel if ~np.isnan(events[condition][sel][ii])]
# The to-be-predicted value, for each trial:
y = np.array(events[condition], dtype=np.float32)
print analysis['name'], np.unique(y[sel]), len(sel)
# Abort if there is no trial
if len(sel) == 0:
return
# Apply analysis
gat = GeneralizationAcrossTime(clf=analysis['clf'],
cv=analysis['cv'],
scorer=analysis['scorer'],
n_jobs=-1)
print(subject, analysis['name'], 'fit')
gat.fit(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'score')
score = gat.score(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'save')
# save space
if analysis['name'] not in ['probe_phase', 'target_circAngle']:
# we'll need the estimator trained on the probe_phase and to generalize
# to the target phase and prove that there is a significant signal.
gat.estimators_ = None
if analysis['name'] not in ['target_present', 'target_circAngle',
'probe_circAngle']:
# We need these individual prediction to control for the correlation
# between target and probe angle.
gat.y_pred_ = None
# Save analysis
save([gat, analysis, sel, events], 'decod',
subject=subject, analysis=analysis['name'], overwrite=True,
upload=True)
save([score, epochs.times], 'score',
subject=subject, analysis=analysis['name'], overwrite=True,
upload=True)
return
def _decod(subject, analysis):
from mne.decoding import GeneralizationAcrossTime
# if already computed let's just load it from disk
fname_kwargs = dict(subject=subject, analysis=analysis['name'] + '_vhp')
score_fname = paths('score', **fname_kwargs)
if op.exists(score_fname):
return load('score', **fname_kwargs)
epochs = _get_epochs(subject)
events = load('behavior', subject=subject)
# Let's not recompute everything, this is just a control analysis
print(subject, analysis['name'])
epochs._data = epochs.get_data()
epochs.preload = True
epochs.crop(0., .900)
epochs.decimate(2)
query, condition = analysis['query'], analysis['condition']
sel = range(len(events)) if query is None else events.query(query).index
sel = [ii for ii in sel if ~np.isnan(events[condition][sel][ii])]
y = np.array(events[condition], dtype=np.float32)
print analysis['name'], np.unique(y[sel]), len(sel)
if len(sel) == 0:
return
# Apply analysis
gat = GeneralizationAcrossTime(clf=analysis['clf'],
cv=analysis['cv'],
scorer=analysis['scorer'],
n_jobs=-1)
print(subject, analysis['name'], 'fit')
gat.fit(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'score')
score = gat.score(epochs[sel], y=y[sel])
print(subject, analysis['name'], 'save')
# save space
gat.estimators_ = None
gat.y_pred_ = None
# Save analysis
save([score, epochs.times], 'score', overwrite=True, upload=True,
**fname_kwargs)
return score, epochs.times
