Feature/684 openstef 40 support hyperparameter tuning

packages/openstef-models/src/openstef_models/models/forecasting/gblinear_forecaster.py

@@ -11,7 +11,7 @@
 to predict values outside the range of the training data.
 """
 
-from typing import ClassVar, Literal, override
+from typing import Annotated, ClassVar, Literal, override
 
 import numpy as np
 import pandas as pd
@@ -22,7 +22,6 @@
 from openstef_core.datasets.mixins import LeadTime
 from openstef_core.datasets.validated_datasets import ForecastDataset, ForecastInputDataset
 from openstef_core.exceptions import InputValidationError, MissingExtraError, NotFittedError
-from openstef_core.mixins.predictor import HyperParams
 from openstef_core.utils.pandas import normalize_to_unit_sum
 from openstef_models.explainability.mixins import ContributionsMixin, ExplainableForecaster
 from openstef_models.models.forecasting.forecaster import Forecaster
@@ -32,26 +31,27 @@
     get_objective_function,
     xgb_prepare_target_for_objective,
 )
+from openstef_models.utils.tuning import CategoricalRange, FloatRange, IntRange, TunableHyperParams
 
 try:
     import xgboost as xgb
 except ImportError as e:
     raise MissingExtraError("xgboost", "openstef-models") from e
 
 
-class GBLinearHyperParams(HyperParams):
+class GBLinearHyperParams(TunableHyperParams):
     """Hyperparameter configuration for GBLinear forecaster."""
 
     # Learning Parameters
-    n_steps: int = Field(
+    n_steps: Annotated[int, IntRange(50, 1000)] = Field(
         default=500,
         description="Number for steps (boosting rounds) to train the GBLinear model.",
     )
-    updater: str = Field(
+    updater: Annotated[str, CategoricalRange(("shotgun", "coord_descent"))] = Field(
         default="shotgun",
         description="The updater to use for the GBLinear booster.",
     )
-    learning_rate: float = Field(
+    learning_rate: Annotated[float, FloatRange(0.01, 0.5, log=True)] = Field(
         default=0.15,
         description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require more boosting "
         "rounds.",
@@ -68,15 +68,15 @@ class GBLinearHyperParams(HyperParams):
     )
 
     # Regularization
-    reg_alpha: float = Field(
+    reg_alpha: Annotated[float, FloatRange(1e-8, 1.0, log=True)] = Field(
         default=0.0001, description="L1 regularization on weights. Higher values increase regularization. Range: [0,∞]"
     )
-    reg_lambda: float = Field(
+    reg_lambda: Annotated[float, FloatRange(1e-8, 1.0, log=True)] = Field(
         default=0.1, description="L2 regularization on weights. Higher values increase regularization. Range: [0,∞]"
     )
 
     # Feature selection
-    feature_selector: str = Field(
+    feature_selector: Annotated[str, CategoricalRange(("cyclic", "shuffle", "random", "greedy", "thrifty"))] = Field(
         default="shuffle",
         description="Feature selection method.",
     )

packages/openstef-models/src/openstef_models/models/forecasting/xgboost_forecaster.py

@@ -9,7 +9,7 @@
 comprehensive hyperparameter control for production forecasting workflows.
 """
 
-from typing import ClassVar, Literal, override
+from typing import Annotated, ClassVar, Literal, override
 
 import numpy as np
 import pandas as pd
@@ -18,7 +18,6 @@
 
 from openstef_core.datasets import ForecastDataset, ForecastInputDataset, TimeSeriesDataset
 from openstef_core.exceptions import MissingExtraError, NotFittedError
-from openstef_core.mixins import HyperParams
 from openstef_core.utils.pandas import normalize_to_unit_sum
 from openstef_models.explainability.mixins import ContributionsMixin, ExplainableForecaster
 from openstef_models.models.forecasting.forecaster import Forecaster
@@ -28,14 +27,15 @@
     get_objective_function,
     xgb_prepare_target_for_objective,
 )
+from openstef_models.utils.tuning import CategoricalRange, FloatRange, IntRange, TunableHyperParams
 
 try:
     import xgboost as xgb
 except ImportError as e:
     raise MissingExtraError("xgboost", "openstef-models") from e
 
 
-class XGBoostHyperParams(HyperParams):
+class XGBoostHyperParams(TunableHyperParams):
     """XGBoost hyperparameters for gradient boosting tree models.
 
     Configures tree-specific parameters for XGBoost gbtree booster. Provides
@@ -65,28 +65,28 @@ class XGBoostHyperParams(HyperParams):
     """
 
     # Core Tree Boosting Parameters
-    n_estimators: int = Field(
+    n_estimators: Annotated[int, IntRange(50, 500)] = Field(
         default=100,
         description="Number of boosting rounds/trees to fit. Higher values may improve performance but "
         "increase training time and risk overfitting.",
     )
-    learning_rate: float = Field(
+    learning_rate: Annotated[float, FloatRange(0.01, 0.5, log=True)] = Field(
         default=0.3,
         alias="eta",
         description="Step size shrinkage used to prevent overfitting. Range: [0,1]. Lower values require "
         "more boosting rounds.",
     )
-    max_depth: int = Field(
+    max_depth: Annotated[int, IntRange(1, 15)] = Field(
         default=6,
         description="Maximum depth of trees. Higher values capture more complex patterns but risk "
         "overfitting. Range: [1,∞]",
     )
-    min_child_weight: float = Field(
+    min_child_weight: Annotated[float, FloatRange(1.0, 10.0)] = Field(
         default=1,
         description="Minimum sum of instance weight (hessian) needed in a child. Higher values prevent "
         "overfitting. Range: [0,∞]",
     )
-    gamma: float = Field(
+    gamma: Annotated[float, FloatRange(0.0, 5.0)] = Field(
         default=0,
         alias="min_split_loss",
         description="Minimum loss reduction required to make a split. Higher values make algorithm more "
@@ -103,10 +103,10 @@ class XGBoostHyperParams(HyperParams):
     )
 
     # Regularization
-    reg_alpha: float = Field(
+    reg_alpha: Annotated[float, FloatRange(1e-8, 10.0, log=True)] = Field(
         default=0, description="L1 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]"
     )
-    reg_lambda: float = Field(
+    reg_lambda: Annotated[float, FloatRange(1e-8, 10.0, log=True)] = Field(
         default=1, description="L2 regularization on leaf weights. Higher values increase regularization. Range: [0,∞]"
     )
     max_delta_step: float = Field(
@@ -119,7 +119,7 @@ class XGBoostHyperParams(HyperParams):
     max_leaves: int = Field(
         default=0, description="Maximum number of leaves. 0 means no limit. Only relevant when grow_policy='lossguide'."
     )
-    grow_policy: Literal["depthwise", "lossguide"] = Field(
+    grow_policy: Annotated[Literal["depthwise", "lossguide"], CategoricalRange(("depthwise", "lossguide"))] = Field(
         default="depthwise",
         description="Controls how new nodes are added. 'depthwise' grows level by level, 'lossguide' adds leaves "
         "with highest loss reduction.",
@@ -136,11 +136,11 @@ class XGBoostHyperParams(HyperParams):
     )
 
     # Subsampling Parameters
-    subsample: float = Field(
+    subsample: Annotated[float, FloatRange(0.5, 1.0)] = Field(
         default=1.0,
         description="Fraction of training samples used for each tree. Lower values prevent overfitting. Range: (0,1]",
     )
-    colsample_bytree: float = Field(
+    colsample_bytree: Annotated[float, FloatRange(0.5, 1.0)] = Field(
         default=1.0, description="Fraction of features used when constructing each tree. Range: (0,1]"
     )
     colsample_bylevel: float = Field(
@@ -151,7 +151,10 @@ class XGBoostHyperParams(HyperParams):
     )
 
     # Tree Construction Method
-    tree_method: Literal["auto", "exact", "hist", "approx", "gpu_hist"] = Field(
+    tree_method: Annotated[
+        Literal["auto", "exact", "hist", "approx", "gpu_hist"],
+        CategoricalRange(("auto", "hist", "approx")),
+    ] = Field(
         default="auto",
         description="Tree construction algorithm. 'hist' is fastest for large datasets, 'exact' for small "
         "datasets, 'approx' is deprecated.",

packages/openstef-models/src/openstef_models/presets/__init__.py

@@ -7,9 +7,18 @@
 Provides configurations and utilities for setting up forecasting workflows.
 """
 
-from .forecasting_workflow import ForecastingWorkflowConfig, create_forecasting_workflow
+from .forecasting_workflow import (
+    ForecastingWorkflowConfig,
+    TuningResult,
+    create_forecasting_workflow,
+    fit_with_tuning,
+    tune,
+)
 
 __all__ = [
     "ForecastingWorkflowConfig",
+    "TuningResult",
     "create_forecasting_workflow",
+    "fit_with_tuning",
+    "tune",
 ]