How to use the confounds.base.Residualize function in confounds
To help you get started, we’ve selected a few confounds examples, based on popular ways it is used in public projects.
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raamana / neuropredict / neuropredict / algorithms.py View on Github 
def get_deconfounder(xfm_name, grid_search_level=None):
"""Returns a valid sklearn transformer for deconfounding."""
xfm_name = xfm_name.lower()
if xfm_name in ('residualize', 'regressout',
'residualize_linear', 'regressout_linear'):
from confounds.base import Residualize
xfm = Residualize()
param_list_values = []
# elif name in ('residualize_ridge', 'residualize_kernelridge'):
# from confounds.base import Residualize
# xfm = Residualize(model='KernelRidge')
# param_list_values = [('param_1', range_param1),
# ('criterion_2', criteria),
# ]
# elif name in ('residualize_gpr', 'residualize_gaussianprocessregression'):
# from confounds.base import Residualize
# xfm = Residualize(model='GPR')
# param_list_values = [('param_1', range_param1),
# ('criterion_2', criteria),
# ]
elif xfm_name in ('augment', 'pad'):
from confounds.base import Augment
xfm = Augment()confounds
Conquering confounds and covariates in machine learning
