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[ENH] Add CNeuro handling #1

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[ENH] Add CNeuro handling #1

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66c4de3
Allow running cneuromod on beluga
emdupre Mar 4, 2021
821b0b2
specify data_dir to avoid re-fetching
emdupre Mar 4, 2021
f07f2ab
Add pickling of intermediates
emdupre Mar 5, 2021
d7cfeb5
correct pickling of splits
emdupre Mar 12, 2021
7f3f837
Correct and update cneuro handling to smooth derivs
emdupre Apr 20, 2021
8a08427
Merge branch 'eliz-tests' of https://github.com/neurodatascience/fmra…
emdupre Apr 20, 2021
286a989
Remove extra pickling from results checks
emdupre Apr 20, 2021
8e5d8d7
Remove print statements
emdupre Apr 20, 2021
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4 changes: 2 additions & 2 deletions fmralignbench/conf.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1,2 @@
ROOT_FOLDER = '/storage/store2/work/tbazeill/neuroimage'
N_JOBS = 15
ROOT_FOLDER = '/home/emdupre/scratch/cneuro'
N_JOBS = 5
44 changes: 39 additions & 5 deletions fmralignbench/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
import time
import numpy as np
import pandas as pd
import os
from os.path import join as opj
from collections import namedtuple

Expand All @@ -23,16 +22,17 @@
from fmralignbench.fastsrm import FastSRM
from fmralignbench.conf import ROOT_FOLDER, N_JOBS

mask_gm = os.path.join(ROOT_FOLDER, 'masks', 'gm_mask_3mm.nii.gz')
mask_gm = os.path.join(
ROOT_FOLDER,
'tpl-MNI152NLin2009cAsym_res-3mm_label-GM_desc-thr02_probseg.nii.gz')
mask_audio_3mm = os.path.join(
ROOT_FOLDER, 'masks', 'audio_mask_resampled_3mm.nii.gz')
language_mask_3mm = os.path.join(
ROOT_FOLDER, 'masks', 'left_language_mask_3mm.nii.gz')


WHOLEBRAIN_DATASETS = [{"decoding_task": "ibc_rsvp", "alignment_data_label": "53_tasks",
"roi_code": "fullbrain", "mask": mask_gm},
{"decoding_task": "ibc_tonotopy_cond", "alignment_data_label": "53_tasks",
WHOLEBRAIN_DATASETS = [{"decoding_task": "cneuromod",
"alignment_data_label": None,
"roi_code": "fullbrain", "mask": mask_gm}]

ROI_DATASETS = [{"decoding_task": "ibc_rsvp", "alignment_data_label": "53_tasks",
Expand Down Expand Up @@ -170,6 +170,7 @@ def align_one_target(sources_train, sources_test, target_train, target_test, met
source_align = PairwiseAlignment(
alignment_method=pairwise_method, clustering=clustering,
n_pieces=n_pieces, mask=masker, n_jobs=n_jobs)

source_align.fit(source_train, target_train)
aligned_sources_test.append(
source_align.transform(source_test))
Expand Down Expand Up @@ -295,6 +296,7 @@ def try_methods_decoding(method, subjects, train, test, pairwise_method,
fit_timings = []
overhead_timings = []
i = 0

for (train_align, train_decode, train_decode_labels,
LO_align, LO_decode, LO_decode_labels) in zip(
train.alignment, train.x, train.y,
Expand Down Expand Up @@ -464,6 +466,15 @@ def experiments_variables(task, surface="", root_dir='/'):
mask_cache = opj(root_dir, 'ibc_tonotopy_cond', 'mask_cache')
# audio_mask_3mm = opj(root_dir,"ibc/audio_mask_resampled_3mm.nii.gz")

elif task == "cneuromod":
subjects = ["sub-01", "sub-02", "sub-03", "sub-04", "sub-05", "sub-06"]
mask = opj(
root_dir,
"tpl-MNI152NLin2009cAsym_res-3mm_label-GM_desc-thr02_probseg.nii.gz")
task_dir = opj(root_dir, "cneuro_wm_5mm", "3mm")
out_dir = opj(root_dir, "cneuro_wm_5mm", "decoding_fmralignbench")
mask_cache = opj(root_dir, "cneuro_wm_5mm", "mask_cache")

else:
err_msg = ("Unrecognized decoding task. Please provide a " +
"recognized decoding task and try again.")
Expand Down Expand Up @@ -590,6 +601,29 @@ def fetch_align_decode_data(task, subjects, data_dir,
LOs_decode_labels.append([decoding_conditions[lo]])
LOs_align.append([paths_align[lo]])

elif task == "cneuromod":
life = opj(
root_dir, 'alignment_data_5mm',
'{}_task-life_run-1_space-MNI152NLin2009cAsym_desc-fwhm5_bold.nii.gz')
paths_align = np.asarray([life.format(sub)
for sub in subjects])
n_subj = np.arange(len(subjects))
leave_outs = n_subj # use LOO

for lo in leave_outs:
# training alignment data
train_align.append(
paths_align[np.isin(n_subj, (lo), invert=True)])
# training decoding data splits, labels
train_decode.append(
decoding_subjects[np.isin(n_subj, (lo), invert=True)])
train_decode_labels.append(
decoding_conditions[np.isin(n_subj, (lo), invert=True)])
# testing decoding data splits, labels
LOs_align.append([paths_align[lo]])
LOs_decode.append([decoding_subjects[lo]])
LOs_decode_labels.append([decoding_conditions[lo]])

train = DataSplit(
x=train_decode, y=train_decode_labels, alignment=train_align)
test = DataSplit(x=LOs_decode, y=LOs_decode_labels, alignment=LOs_align)
Expand Down