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fix skip unnecessary check of gram_matrix in coordinate_descent #22008

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fix skip unnecessary check of gram_matrix in coordinate_descent #22008

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3 changes: 2 additions & 1 deletion sklearn/linear_model/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -792,6 +792,7 @@ def _pre_fit(
fit_intercept,
copy,
check_input=True,
check_gram=True,
sample_weight=None,
):
"""Aux function used at beginning of fit in linear models
Expand Down Expand Up @@ -846,7 +847,7 @@ def _pre_fit(
# recompute Gram
precompute = "auto"
Xy = None
elif check_input:
elif check_gram:
# If we're going to use the user's precomputed gram matrix, we
# do a quick check to make sure its not totally bogus.
_check_precomputed_gram_matrix(X, precompute, X_offset, X_scale)
Expand Down
3 changes: 3 additions & 0 deletions sklearn/linear_model/_coordinate_descent.py
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Expand Up @@ -504,6 +504,7 @@ def enet_path(
max_iter = params.pop("max_iter", 1000)
random_state = params.pop("random_state", None)
selection = params.pop("selection", "cyclic")
check_gram = params.pop("check_gram", True)

if len(params) > 0:
raise ValueError("Unexpected parameters in params", params.keys())
Expand Down Expand Up @@ -564,6 +565,7 @@ def enet_path(
fit_intercept=False,
copy=False,
check_input=check_input,
check_gram=check_gram,
)
if alphas is None:
# No need to normalize of fit_intercept: it has been done
Expand Down Expand Up @@ -1392,6 +1394,7 @@ def _path_residuals(
path_params["precompute"] = precompute
path_params["copy_X"] = False
path_params["alphas"] = alphas
path_params["check_gram"] = False

if "l1_ratio" in path_params:
path_params["l1_ratio"] = l1_ratio
Expand Down