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Fix Segmentation Fault and ZeroDivisionError in Group Lasso #292

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merged 17 commits into from
Apr 8, 2024
Merged

Fix Segmentation Fault and ZeroDivisionError in Group Lasso #292

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dd40914
fix index & divide by zero
Badr-MOUFAD Mar 29, 2024
cd77bb6
provide kwarg to _preprocess_data
Badr-MOUFAD Mar 29, 2024
afaf973
fit_intercept as bool
Badr-MOUFAD Mar 29, 2024
629cee7
CI trigger
Badr-MOUFAD Mar 29, 2024
028ec44
fix index group
Badr-MOUFAD Mar 30, 2024
b988f1a
debug circle ci
Badr-MOUFAD Mar 30, 2024
169be60
print pwd
Badr-MOUFAD Mar 30, 2024
a87fbf8
print pwd
Badr-MOUFAD Mar 30, 2024
2e89487
content of dir
Badr-MOUFAD Mar 30, 2024
de6133c
MNT - remove requirements files (#285)
Badr-MOUFAD Apr 19, 2023
265d2c9
MNT cosmit readme (#287)
mathurinm Jul 21, 2023
71f8dc8
CI trigger
Badr-MOUFAD Mar 29, 2024
a6de333
debug circle ci
Badr-MOUFAD Mar 30, 2024
69372b8
print pwd
Badr-MOUFAD Mar 30, 2024
32309a1
content of dir
Badr-MOUFAD Mar 30, 2024
c4d6a43
rm commands circle-ci
Badr-MOUFAD Mar 30, 2024
138bd97
Merge branch 'main' of github.com:mathurinm/celer into fix-grp
mathurinm Apr 2, 2024
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2 changes: 1 addition & 1 deletion celer/dropin_sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -646,7 +646,7 @@ def fit(self, X, y):
% (n_samples, y.shape[0]))

X, y, X_offset, y_offset, X_scale = _preprocess_data(
X, y, self.fit_intercept, copy=False)
X, y, fit_intercept=self.fit_intercept, copy=False)

if not self.warm_start or not hasattr(self, "coef_"):
self.coef_ = None
Expand Down
10 changes: 6 additions & 4 deletions celer/group_fast.pyx
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Expand Up @@ -89,10 +89,11 @@ cpdef floating dnorm_grp(

else: # scaling only with features in C
for g_idx in range(ws_size):
g = C[g_idx]

if weights[g] == INFINITY:
continue

g = C[g_idx]
tmp = 0
for k in range(grp_ptr[g], grp_ptr[g + 1]):
j = grp_indices[k]
Expand Down Expand Up @@ -418,8 +419,11 @@ cpdef celer_grp(
&inc) / lc_groups[g]
norm_wg += w[j] ** 2
norm_wg = sqrt(norm_wg)
bst_scal = max(0.,
if norm_wg != 0.:
bst_scal = max(0.,
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I'm perhaps lacking context about the code, namely I ignore the purpose of bst_scal.

I just followed commun sense to handle the case of norm w being zero.

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bst_scal is for BockSofthThresholding scaling : the formula for the BST of wg at level lambda is:
wg * max(0, 1 - lambda / norm(wg)) aka 0 if norm(wg) < lambda, and (1 - lambda/norm(wg)) wg otherwise

I'm curious, how do you end up with a vanishing wg after a gradient step ? This should not happen with probability 1, I'm guessing a 0 group X_g ?

1. - alpha * weights[g] / lc_groups[g] * n_samples / norm_wg)
else:
bst_scal = 0.

for k in range(grp_ptr[g + 1] - grp_ptr[g]):
j = grp_indices[grp_ptr[g] + k]
Expand Down Expand Up @@ -448,5 +452,3 @@ cpdef celer_grp(
'Fitting data with very small alpha causes precision issues.',
ConvergenceWarning)
return np.asarray(w), np.asarray(theta), np.asarray(gaps[:t + 1])


2 changes: 1 addition & 1 deletion celer/tests/test_mtl.py
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Expand Up @@ -18,7 +18,7 @@


@pytest.mark.parametrize("sparse_X, fit_intercept",
itertools.product([0, 1], [0, 1]))
itertools.product([0, 1], [False, True]))
def test_GroupLasso_Lasso_equivalence(sparse_X, fit_intercept):
"""Check that GroupLasso with groups of size 1 gives Lasso."""
n_features = 1000
Expand Down
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