def compute_components(n_components,
batch_size,
learning_rate,
smoothing_fwhm,
positive,
reduction,
alpha,
method,
n_epochs,
verbose,
n_jobs,
_run):
artifact_dir = join(_run.observers[0].basedir, 'artifacts')
if not os.path.exists(artifact_dir):
os.makedirs(artifact_dir)
raw_res_dir = join(modl_get_output_dir(), 'unmasked', 'hcp')
masker, data = get_raw_rest_data(raw_res_dir)
train_imgs, test_imgs = train_test_split(data, train_size=1000, test_size=1, random_state=0)
train_imgs = train_imgs['filename'].values
test_imgs = test_imgs['filename'].values
cb = rfMRIDictionaryScorer(test_imgs, info=_run.info)
dict_fact = fMRIDictFact(method=method,
mask=masker,
verbose=verbose,
n_epochs=n_epochs,
smoothing_fwhm=smoothing_fwhm,
n_jobs=n_jobs,
random_state=1,
n_components=n_components,
positive=positive,
learning_rate=learning_rate,
batch_size=batch_size,
reduction=reduction,
alpha=alpha,
callback=cb,
)
dict_fact.fit(train_imgs)
dict_fact.components_img_.to_filename(join(artifact_dir,
'components.nii.gz'))
fig = plt.figure()
display_maps(fig, dict_fact.components_img_)
plt.savefig(join(artifact_dir, 'components.png'))
fig, ax = plt.subplots(1, 1)
ax.plot(cb.time, cb.score, marker='o')
plt.savefig(join(artifact_dir, 'score.png'))
def compute_components(n_components, batch_size, learning_rate, positive,
reduction, alpha, method, n_epochs, verbose,
smoothing_fwhm, n_jobs, raw_dir, output_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
info = {}
masker, data = get_raw_rest_data(raw_dir)
train_imgs, test_imgs = train_test_split(data,
train_size=None,
test_size=1,
random_state=0)
train_imgs = train_imgs['filename'].values
test_imgs = test_imgs['filename'].values
cb = rfMRIDictionaryScorer(test_imgs, info=info, artifact_dir=output_dir)
dict_fact = fMRIDictFact(
method=method,
mask=masker,
smoothing_fwhm=smoothing_fwhm,
verbose=verbose,
n_epochs=n_epochs,
n_jobs=n_jobs,
random_state=1,
n_components=n_components,
positive=positive,
learning_rate=learning_rate,
batch_size=batch_size,
reduction=reduction,
alpha=alpha,
callback=cb,
)
dict_fact.fit(train_imgs)
dict_fact.components_img_.to_filename(join(output_dir,
'components.nii.gz'))
n_epochs = 2
verbose = 15
n_jobs = 2
smoothing_fwhm = 6
dict_init = fetch_atlas_smith_2009().rsn20
dataset = fetch_adhd(n_subjects=40)
data = dataset.rest.values
train_data, test_data = train_test_split(data, test_size=1, random_state=0)
train_imgs, train_confounds = zip(*train_data)
test_imgs, test_confounds = zip(*test_data)
mask = dataset.mask
memory = Memory(cachedir=get_cache_dirs()[0], verbose=2)
cb = rfMRIDictionaryScorer(test_imgs, test_confounds=test_confounds)
dict_fact = fMRIDictFact(
smoothing_fwhm=smoothing_fwhm,
method=method,
step_size=step_size,
mask=mask,
memory=memory,
memory_level=2,
verbose=verbose,
n_epochs=n_epochs,
n_jobs=n_jobs,
random_state=1,
n_components=n_components,
dict_init=dict_init,
positive=True,
learning_rate=learning_rate,
def compute_components(n_components, batch_size, learning_rate, method,
reduction, alpha, step_size, n_jobs, n_epochs, verbose,
source, _run):
basedir = join(_run.observers[0].basedir, str(_run._id))
artifact_dir = join(basedir, 'artifacts')
if not os.path.exists(artifact_dir):
os.makedirs(artifact_dir)
if source == 'hcp':
# Hack to recover data from TSP
train_size = None
smoothing_fwhm = 3
test_size = 2
data_dir = get_data_dirs()[0]
mask = fetch_hcp_mask()
masker = MultiRawMasker(mask_img=mask,
smoothing_fwhm=smoothing_fwhm,
detrend=True,
standardize=True)
mapping = json.load(
open(join(data_dir, 'HCP_unmasked/mapping.json'), 'r'))
data = sorted(list(mapping.values()))
data = list(map(lambda x: join(data_dir, x), data))
data = pd.DataFrame(data, columns=['filename'])
else:
smoothing_fwhm = 6
train_size = 4
test_size = 4
raw_res_dir = join(get_output_dir(), 'unmasked', source)
try:
masker, data = get_raw_rest_data(raw_res_dir)
except ValueError: # On local machine:
raw_res_dir = join(get_output_dir(), 'unmask', source)
masker, data = get_raw_rest_data(raw_res_dir)
train_imgs, test_imgs = train_test_split(data,
test_size=test_size,
random_state=0,
train_size=train_size)
train_imgs = train_imgs['filename'].values
test_imgs = test_imgs['filename'].values
cb = rfMRIDictionaryScorer(test_imgs, info=_run.info)
dict_fact = fMRIDictFact(
method=method,
mask=masker,
verbose=verbose,
n_epochs=n_epochs,
n_jobs=n_jobs,
random_state=1,
n_components=n_components,
smoothing_fwhm=smoothing_fwhm,
learning_rate=learning_rate,
batch_size=batch_size,
reduction=reduction,
step_size=step_size,
alpha=alpha,
callback=cb,
)
dict_fact.fit(train_imgs)
dict_fact.components_img_.to_filename(
join(artifact_dir, 'components.nii.gz'))
fig = plt.figure()
display_maps(fig, dict_fact.components_img_)
plt.savefig(join(artifact_dir, 'components.png'))
fig, ax = plt.subplots(1, 1)
ax.plot(cb.cpu_time, cb.score, marker='o')
_run.info['time'] = cb.cpu_time
_run.info['score'] = cb.score
_run.info['iter'] = cb.iter
plt.savefig(join(artifact_dir, 'score.png'))