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Script to replicate the main analyses.
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import os | ||
from joblib import Parallel, delayed | ||
import numpy as np | ||
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from IDM_pred.cv import nested_cv_ridge, compute_ss0 | ||
from IDM_pred.io import get_connectivity_PCs, subject_sets, get_measure_info | ||
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def prediction_pipeline_region(task, align, info, parcel, y_name, y, mask, families, ss0, overwrite=False): | ||
out_fn = f'predictions/{y_name}_{task}_{align}_{info}/parcel{parcel:03d}.npz' | ||
if os.path.exists(out_fn) and not overwrite: | ||
return | ||
os.makedirs(os.path.dirname(out_fn), exist_ok=True) | ||
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X = get_connectivity_PCs(task, align, info, parcel, mask=mask) | ||
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yhat = np.zeros_like(y) | ||
clf_info = np.zeros((len(families), 3)) | ||
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for i, fam in enumerate(families): | ||
yhat[fam], *clf_info[i] = nested_cv_ridge(X, y, fam) | ||
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r2 = 1 - np.sum((y - yhat)**2) / ss0 | ||
np.savez(out_fn, yhat=yhat, clf_info=clf_info, r2=r2) | ||
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def prediction_pipeline(y_name, task, align, info, overwrite=False, n_jobs=1): | ||
""" | ||
Parameters | ||
---------- | ||
y_name : str | ||
Name of the target variable, e.g, `"g"` or `"PMAT24_A_CR"`. | ||
task : {'TASK', 'REST'} | ||
The type of fMRI data used to derive the connectivity profiles. | ||
align : {'ROICHT', 'ROICHR', 'MSMAll'} | ||
The alignment method applied to the fMRI data. `ROICHT` and `ROICHR` mean ROI Connectivity Hyperalignment based on task and resting data, respectively. `MSMAll` means MSM-aligned data (i.e., no hyperalignment). | ||
info : {'fine', 'coarse', 'all'} | ||
The spatial scale of information used. Options are `fine` (residual fine-grained connectivity profiles), `coarse` (coarse-grained connectivity profiles), and `all` (full fine-grained connectivity profiles). | ||
overwrite: boolean | ||
Whether to recompute the predictions if the result file already exists. | ||
n_jobs : int | ||
The `n_jobs` parameter for joblib's parallel computing. | ||
""" | ||
y, mask, families, sub_df = get_measure_info(y_name, subject_sets['s888']) | ||
ss0 = compute_ss0(y, families) | ||
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jobs = [] | ||
for parcel in range(360): | ||
jobs.append(delayed(prediction_pipeline_region)( | ||
task, align, info, parcel, y_name, y, mask, families, ss0, overwrite=overwrite | ||
)) | ||
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if jobs: | ||
with Parallel(n_jobs=n_jobs, verbose=10, batch_size=1) as parallel: | ||
parallel(jobs) | ||
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if __name__ == '__main__': | ||
y_name = 'g' | ||
for task in ['TASK', 'REST']: | ||
for align in ['ROICHT', 'ROICHR', 'MSMAll']: | ||
for info in ['fine', 'coarse', 'all']: | ||
prediction_pipeline(y_name, task, align, info, overwrite=False, n_jobs=32) |