Source code for deeptab.models.ndtf

from ..base_models.ndtf import NDTF
from ..configs.ndtf_config import DefaultNDTFConfig
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 NDTFRegressor(SklearnBaseRegressor):
    __doc__ = generate_docstring(
        DefaultNDTFConfig,
        model_description="""
        Neural Decision Forest regressor. This class extends the SklearnBaseRegressor class and uses the NDTF model
        with the default NDTF configuration.
        """,
        examples="""
        >>> from deeptab.models import NDTFRegressor
        >>> model = NDTFRegressor(n_ensembles=12, max_depth=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=NDTF, config=DefaultNDTFConfig, **kwargs)





class NDTFClassifier(SklearnBaseClassifier):
    __doc__ = generate_docstring(
        DefaultNDTFConfig,
        model_description="""
        Neural Decision Forest classifier. This class extends the SklearnBaseClassifier class and uses the NDTF model
        with the default NDTF configuration.
        """,
        examples="""
        >>> from deeptab.models import NDTFClassifier
        >>> model = NDTFClassifier(n_ensembles=12, max_depth=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=NDTF, config=DefaultNDTFConfig, **kwargs)





class NDTFLSS(SklearnBaseLSS):
    __doc__ = generate_docstring(
        DefaultNDTFConfig,
        model_description="""
        Neural Decision Forest for distributional regression. This class extends the SklearnBaseLSS class and uses the NDTF model
        with the default NDTF configuration.
        """,
        examples="""
        >>> from deeptab.models import NDTFLSS
        >>> model = NDTFLSS(n_ensembles=12, max_depth=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=NDTF, config=DefaultNDTFConfig, **kwargs)