include classifier, regressor and lss for Tangos

Author: AnFreTh

mambular/models/__init__.py

@@ -28,8 +28,12 @@
 from .autoint import AutoIntClassifier, AutoIntLSS, AutoIntRegressor
 from .trompt import TromptClassifier, TromptLSS, TromptRegressor
 from .enode import ENODEClassifier, ENODELSS, ENODERegressor
+from .tangos import TangosClassifier, TangosLSS, TangosRegressor
 
 __all__ = [
+    "TangosClassifier",
+    "TangosLSS",
+    "TangosRegressor",
     "ENODEClassifier",
     "ENODELSS",
     "ENODERegressor",

mambular/models/tangos.py

@@ -0,0 +1,66 @@
+from ..base_models.tangos import Tangos
+from ..configs.tangos_config import DefaultTangosConfig
+from ..utils.docstring_generator import generate_docstring
+from .utils.sklearn_base_classifier import SklearnBaseClassifier
+from .utils.sklearn_base_lss import SklearnBaseLSS
+from .utils.sklearn_base_regressor import SklearnBaseRegressor
+
+
+class TangosRegressor(SklearnBaseRegressor):
+    __doc__ = generate_docstring(
+        DefaultTangosConfig,
+        model_description="""
+        Tangos regressor. This class extends the SklearnBaseRegressor class and uses the Tangos model
+        with the default Tangos configuration.
+        """,
+        examples="""
+        >>> from mambular.models import TangosRegressor
+        >>> model = TangosRegressor(d_model=64, n_layers=8)
+        >>> model.fit(X_train, y_train)
+        >>> preds = model.predict(X_test)
+        >>> model.evaluate(X_test, y_test)
+        """,
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(model=Tangos, config=DefaultTangosConfig, **kwargs)
+
+
+class TangosClassifier(SklearnBaseClassifier):
+    __doc__ = generate_docstring(
+        DefaultTangosConfig,
+        model_description="""
+        Tangos classifier This class extends the SklearnBaseClassifier class and uses the Tangos model
+        with the default Tangos configuration.
+        """,
+        examples="""
+        >>> from mambular.models import TangosClassifier
+        >>> model = TangosClassifier(d_model=64, n_layers=8)
+        >>> model.fit(X_train, y_train)
+        >>> preds = model.predict(X_test)
+        >>> model.evaluate(X_test, y_test)
+        """,
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(model=Tangos, config=DefaultTangosConfig, **kwargs)
+
+
+class TangosLSS(SklearnBaseLSS):
+    __doc__ = generate_docstring(
+        DefaultTangosConfig,
+        model_description="""
+        Tangos for distributional regression. This class extends the SklearnBaseLSS class and uses the Tangos model
+        with the default Tangos configuration.
+        """,
+        examples="""
+        >>> from mambular.models import TangosLSS
+        >>> model = TangosLSS(d_model=64, n_layers=8)
+        >>> model.fit(X_train, y_train, family='normal')
+        >>> preds = model.predict(X_test)
+        >>> model.evaluate(X_test, y_test)
+        """,
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(model=Tangos, config=DefaultTangosConfig, **kwargs)