[ENH]: Add XGBoost and XGBoost with CV Early Stopping

docs/api/models.rst

@@ -19,6 +19,21 @@ Functions
     register_model
     reset_model_register
 
+Julearn custom models
+---------------------
+
+This is a list of models implemented by Julearn that are not simple wrappers
+around existing models in other libraries but rather variants of existing
+models or novel models.
+
+.. autosummary::
+   :nosignatures:
+   :toctree: generated/
+   :template: class.rst
+
+   xgb_cvearlystopping.XGBClassifierCVEarlyStopping
+   xgb_cvearlystopping.XGBRegressorCVEarlyStopping
+
 Dynamic Selection (DESLib)
 ==========================
 

docs/available_pipeline_steps.rst

@@ -235,6 +235,20 @@ Ensemble
      - Y
      - Y
      - Y
+   * - ``xgb``
+     - XGBoost
+     - | :class:`~xgboost.XGBClassifier` and
+       | :class:`~xgboost.XGBRegressor`
+     - Y
+     - Y
+     - Y
+   * - ``xgb_cvearlystopping``
+     - XGBoost with Cross-Validation and Early Stopping
+     - | :class:`~julearn.models.xgb_cvearlystopping.XGBClassifierCVEarlyStopping` and
+       | :class:`~julearn.models.xgb_cvearlystopping.XGBRegressorCVEarlyStopping`
+     - Y
+     - Y
+     - Y
 
 Gaussian Processes
 ~~~~~~~~~~~~~~~~~~

docs/conf.py

@@ -231,6 +231,7 @@
         None,
     ),
     "panel": ("https://panel.holoviz.org/", None),
+    "xgboost": ("https://xgboost.readthedocs.io/en/stable/", None),
 }
 
 # -- sphinx.ext.extlinks configuration ---------------------------------------

julearn/models/available_models.py

@@ -46,6 +46,19 @@
 )
 from sklearn.svm import SVC, SVR
 
+
+try:  # pragma: no cover
+    from xgboost import XGBClassifier, XGBRegressor
+
+    from .xgb_cvearlystopping import (
+        XGBClassifierCVEarlyStopping,
+        XGBRegressorCVEarlyStopping,
+    )
+
+    _has_xgboost = True
+except ImportError:
+    _has_xgboost = False
+
 from ..utils import logger, raise_error, warn_with_log
 from ..utils.logging import DelayedFmtMessage as __
 from ..utils.typing import ModelLike
@@ -137,6 +150,24 @@
     },
 }
 
+if _has_xgboost is True:
+    _available_models["xgb"] = {
+        "regression": XGBRegressor,
+        "classification": XGBClassifier,
+    }
+    _available_models["xgb_cvearlystopping"] = {
+        "regression": XGBRegressorCVEarlyStopping,
+        "classification": XGBClassifierCVEarlyStopping,
+    }
+    logger.info(
+        "XGBoost is available and has been added to the model registry."
+    )
+else:
+    logger.info(
+        "XGBoost is not available and has not been added to the model "
+        "registry. To use XGBoost models, please install the xgboost package."
+    )
+
 _available_models_reset = deepcopy(_available_models)