restore n_jobs=-2 and outer_bags=14 parameters

interpretml · Jan 11, 2025 · fbe7cf8 · fbe7cf8
1 parent 4fe5db6
commit fbe7cf8
Showing 1 changed file with 12 additions and 12 deletions.
diff --git a/python/interpret-core/interpret/glassbox/_ebm/_ebm.py b/python/interpret-core/interpret/glassbox/_ebm/_ebm.py
@@ -2739,7 +2739,7 @@ class ExplainableBoostingClassifier(ClassifierMixin, EBMModel):
             - Integer (1 <= validation_size): Count of samples to put in the validation sets
             - Percentage (validation_size < 1.0): Percentage of the data to put in the validation sets
             - 0: Turns off early stopping. Outer bags have no utility. Error bounds will be eliminated
-    outer_bags : int, default=16
+    outer_bags : int, default=14
         Number of outer bags. Outer bags are used to generate error bounds and help with smoothing the graphs.
     inner_bags : int, default=0
         Number of inner bags. 0 turns off inner bagging.
@@ -2838,7 +2838,7 @@ class ExplainableBoostingClassifier(ClassifierMixin, EBMModel):
             - -1: The partial response of the corresponding feature should be monotonically decreasing with respect to the target.
     objective : str, default="log_loss"
         The objective to optimize.
-    n_jobs : int, default=-1
+    n_jobs : int, default=-2
         Number of jobs to run in parallel. Negative integers are interpreted as following joblib's formula
         (n_cpus + 1 + n_jobs), just like scikit-learn. Eg: -2 means using all threads except 1.
     random_state : int or None, default=42
@@ -2957,7 +2957,7 @@ def __init__(
         exclude: Optional[Sequence[Union[int, str, Sequence[Union[int, str]]]]] = None,
         # Ensemble
         validation_size: Optional[Union[int, float]] = 0.15,
-        outer_bags: int = 16,
+        outer_bags: int = 14,
         inner_bags: Optional[int] = 0,
         # Boosting
         learning_rate: float = 0.015,
@@ -2982,7 +2982,7 @@ def __init__(
         monotone_constraints: Optional[Sequence[int]] = None,
         objective: str = "log_loss",
         # Overall
-        n_jobs: Optional[int] = -1,
+        n_jobs: Optional[int] = -2,
         random_state: Optional[int] = 42,
     ):
         super().__init__(
@@ -3126,7 +3126,7 @@ class ExplainableBoostingRegressor(RegressorMixin, EBMModel):
             - Integer (1 <= validation_size): Count of samples to put in the validation sets
             - Percentage (validation_size < 1.0): Percentage of the data to put in the validation sets
             - 0: Turns off early stopping. Outer bags have no utility. Error bounds will be eliminated
-    outer_bags : int, default=16
+    outer_bags : int, default=14
         Number of outer bags. Outer bags are used to generate error bounds and help with smoothing the graphs.
     inner_bags : int, default=0
         Number of inner bags. 0 turns off inner bagging.
@@ -3227,7 +3227,7 @@ class ExplainableBoostingRegressor(RegressorMixin, EBMModel):
         The objective to optimize. Options include: "rmse",
         "poisson_deviance", "tweedie_deviance:variance_power=1.5", "gamma_deviance",
         "pseudo_huber:delta=1.0", "rmse_log" (rmse with a log link function)
-    n_jobs : int, default=-1
+    n_jobs : int, default=-2
         Number of jobs to run in parallel. Negative integers are interpreted as following joblib's formula
         (n_cpus + 1 + n_jobs), just like scikit-learn. Eg: -2 means using all threads except 1.
     random_state : int or None, default=42
@@ -3344,7 +3344,7 @@ def __init__(
         exclude: Optional[Sequence[Union[int, str, Sequence[Union[int, str]]]]] = None,
         # Ensemble
         validation_size: Optional[Union[int, float]] = 0.15,
-        outer_bags: int = 16,
+        outer_bags: int = 14,
         inner_bags: Optional[int] = 0,
         # Boosting
         learning_rate: float = 0.04,
@@ -3369,7 +3369,7 @@ def __init__(
         monotone_constraints: Optional[Sequence[int]] = None,
         objective: str = "rmse",
         # Overall
-        n_jobs: Optional[int] = -1,
+        n_jobs: Optional[int] = -2,
         random_state: Optional[int] = 42,
     ):
         super().__init__(
@@ -3474,7 +3474,7 @@ class DPExplainableBoostingClassifier(ClassifierMixin, EBMModel):
         Total number of boosting rounds with n_terms boosting steps per round.
     max_leaves : int, default=3
         Maximum number of leaves allowed in each tree.
-    n_jobs : int, default=-1
+    n_jobs : int, default=-2
         Number of jobs to run in parallel. Negative integers are interpreted as following joblib's formula
         (n_cpus + 1 + n_jobs), just like scikit-learn. Eg: -2 means using all threads except 1.
     random_state : int or None, default=None
@@ -3598,7 +3598,7 @@ def __init__(
         # Trees
         max_leaves: int = 3,
         # Overall
-        n_jobs: Optional[int] = -1,
+        n_jobs: Optional[int] = -2,
         random_state: Optional[int] = None,
         # Differential Privacy
         epsilon: float = 1.0,
@@ -3745,7 +3745,7 @@ class DPExplainableBoostingRegressor(RegressorMixin, EBMModel):
         Total number of boosting rounds with n_terms boosting steps per round.
     max_leaves : int, default=3
         Maximum number of leaves allowed in each tree.
-    n_jobs : int, default=-1
+    n_jobs : int, default=-2
         Number of jobs to run in parallel. Negative integers are interpreted as following joblib's formula
         (n_cpus + 1 + n_jobs), just like scikit-learn. Eg: -2 means using all threads except 1.
     random_state : int or None, default=None
@@ -3878,7 +3878,7 @@ def __init__(
         # Trees
         max_leaves: int = 3,
         # Overall
-        n_jobs: Optional[int] = -1,
+        n_jobs: Optional[int] = -2,
         random_state: Optional[int] = None,
         # Differential Privacy
         epsilon: float = 1.0,