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LOFOImportance accepts groups params for GroupKFold cv #57

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merged 3 commits into from
Jan 16, 2024
Merged

LOFOImportance accepts groups params for GroupKFold cv #57

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1b8f298
Add groups option
kingychiu Nov 9, 2023
d26bb54
Add groups option
kingychiu Nov 9, 2023
beff338
Update lofo_importance.py
kingychiu Jan 15, 2024
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8 changes: 6 additions & 2 deletions lofo/lofo_importance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,12 @@ class LOFOImportance:
Same as cv in sklearn API
n_jobs: int, optional
Number of jobs for parallel computation
cv_groups: array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).
"""

def __init__(self, dataset, scoring, model=None, fit_params=None, cv=4, n_jobs=None):
def __init__(self, dataset, scoring, model=None, fit_params=None, cv=4, n_jobs=None, cv_groups=None):

self.fit_params = fit_params if fit_params else dict()
if model is None:
Expand All @@ -38,6 +41,7 @@ def __init__(self, dataset, scoring, model=None, fit_params=None, cv=4, n_jobs=N
self.dataset = dataset
self.scoring = scoring
self.cv = cv
self.cv_groups = cv_groups
self.n_jobs = n_jobs
if self.n_jobs is not None and self.n_jobs > 1:
warning_str = ("Warning: If your model is multithreaded, please initialise the number"
Expand All @@ -50,7 +54,7 @@ def _get_cv_score(self, feature_to_remove):

with warnings.catch_warnings():
warnings.simplefilter("ignore")
cv_results = cross_validate(self.model, X, y, cv=self.cv, scoring=self.scoring, fit_params=fit_params)
cv_results = cross_validate(self.model, X, y, cv=self.cv, scoring=self.scoring, fit_params=fit_params, groups=self.cv_groups)
return cv_results['test_score']

def _get_cv_score_parallel(self, feature, result_queue):
Expand Down