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Add alpha to args #612

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8 changes: 7 additions & 1 deletion bayes_opt/bayesian_optimization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,11 @@ class BayesianOptimization:
numpy.random.RandomState. Otherwise the random state provided is used.
When set to None, an unseeded random state is generated.

alpha: float, optional(default=1e-6)
Regularization parameter to prevent numerical issues or to adjust to an
error gaussian distribution with high variance. The higher, the higher
the regularization.

verbose: int, optional(default=2)
The level of verbosity.

Expand All @@ -89,6 +94,7 @@ def __init__(
acquisition_function: AcquisitionFunction | None = None,
constraint: NonlinearConstraint | None = None,
random_state: int | RandomState | None = None,
alpha: float = 1e-6,
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@coderabbitai coderabbitai Bot May 31, 2026

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⚠️ Potential issue | 🟡 Minor | ⚡ Quick win

🧩 Analysis chain

🌐 Web query:

Does scikit-learn GaussianProcessRegressor validate that the alpha parameter is positive?

💡 Result:

No clear positive-only validation is enforced for GaussianProcessRegressor(alpha=...). In the public API docs, alpha is described as a nonnegative “value added to the diagonal” to help ensure a positive definite matrix, but the docs do not explicitly state that scikit-learn validates alpha > 0 at input time [1]. In the current scikit-learn implementation file for GaussianProcessRegressor (sklearn/gaussian_process/_gpr.py), the fitting code shown performs shape/compatibility checks (e.g., alpha must be a scalar or an array with the same number of entries as the training data), but the snippet does not include any explicit check rejecting alpha <= 0 [2]. Therefore, based on the evidence found in the official documentation and the examined implementation, scikit-learn does not explicitly validate that alpha is strictly positive; instead, incorrect (e.g., nonpositive) values may lead to numerical issues later (e.g., failure to produce a positive definite matrix), rather than being rejected by an immediate ValueError [1][2].

Citations:

🏁 Script executed:

#!/bin/bash
set -euo pipefail
FILE="bayes_opt/bayesian_optimization.py"
# Show a small window around the line mentioned in the review comment
nl -ba "$FILE" | sed -n '70,130p'
# Also search for any mention of alpha in the file
rg -n "alpha" "$FILE" || true

Repository: bayesian-optimization/BayesianOptimization

Length of output: 129

🏁 Script executed:

#!/bin/bash
set -euo pipefail
FILE="bayes_opt/bayesian_optimization.py"
nl -ba "$FILE" | sed -n '70,130p'
rg -n "alpha" "$FILE" || true

Repository: bayesian-optimization/BayesianOptimization

Length of output: 129

🏁 Script executed:

#!/bin/bash
set -euo pipefail
FILE="bayes_opt/bayesian_optimization.py"

awk 'NR>=70 && NR<=130 {printf "%d\t%s\n", NR, $0}' "$FILE"
echo "----"
rg -n "alpha" "$FILE" || true

Repository: bayesian-optimization/BayesianOptimization

Length of output: 2876

🏁 Script executed:

#!/bin/bash
set -euo pipefail
FILE="bayes_opt/bayesian_optimization.py"
awk 'NR>=120 && NR<=170 {printf "%d\t%s\n", NR, $0}' "$FILE"

Repository: bayesian-optimization/BayesianOptimization

Length of output: 2137

Validate alpha is strictly positive in BayesianOptimization.__init__

alpha is passed directly to sklearn.gaussian_process.GaussianProcessRegressor(alpha=...), and scikit-learn doesn’t enforce alpha > 0 at input time—nonpositive values may fail later during GP fitting. Add an explicit check to fail fast with a clear ValueError.

🛡️ Suggested validation 
 def __init__(
     self,
     f: Callable[..., float] | None,
     pbounds: BoundsMapping,
     acquisition_function: AcquisitionFunction | None = None,
     constraint: NonlinearConstraint | None = None,
     random_state: int | RandomState | None = None,
     alpha: float = 1e-6,
     verbose: int = 2,
     bounds_transformer: DomainTransformer | None = None,
     allow_duplicate_points: bool = False,
 ):
+    if alpha <= 0:
+        msg = "alpha must be positive"
+        raise ValueError(msg)
     self._random_state = ensure_rng(random_state)
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
alpha: float = 1e-6,
def __init__(
self,
f: Callable[..., float] | None,
pbounds: BoundsMapping,
acquisition_function: AcquisitionFunction | None = None,
constraint: NonlinearConstraint | None = None,
random_state: int | RandomState | None = None,
alpha: float = 1e-6,
verbose: int = 2,
bounds_transformer: DomainTransformer | None = None,
allow_duplicate_points: bool = False,
):
if alpha <= 0:
msg = "alpha must be positive"
raise ValueError(msg)
self._random_state = ensure_rng(random_state)
🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@bayes_opt/bayesian_optimization.py` at line 97, In
BayesianOptimization.__init__, add an explicit validation that the alpha
parameter is strictly positive (alpha > 0) before it’s passed to
sklearn.gaussian_process.GaussianProcessRegressor; if the check fails raise a
ValueError with a clear message (e.g., "alpha must be > 0, got {alpha}") so the
constructor fails fast and avoids later GP fitting errors. Ensure this check
sits at the top of __init__ (next to the alpha parameter handling) and covers
both int/float inputs and rejects non-positive values.

verbose: int = 2,
bounds_transformer: DomainTransformer | None = None,
allow_duplicate_points: bool = False,
Expand Down Expand Up @@ -123,7 +129,7 @@ def __init__(
# Internal GP regressor
self._gp = GaussianProcessRegressor(
kernel=wrap_kernel(Matern(nu=2.5), transform=self._space.kernel_transform),
alpha=1e-6,
alpha=alpha,
normalize_y=True,
n_restarts_optimizer=5,
random_state=self._random_state,
Expand Down
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