Source code for deeptab.models.modern_nca

from ..base_models.modern_nca import ModernNCA
from ..configs.modernnca_config import DefaultModernNCAConfig
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


[docs]class ModernNCARegressor(SklearnBaseRegressor): __doc__ = generate_docstring( DefaultModernNCAConfig, model_description=""" Multi-Layer Perceptron regressor. This class extends the SklearnBaseRegressor class and uses the ModernNCA model with the default ModernNCA configuration. """, examples=""" >>> from deeptab.models import ModernNCARegressor >>> model = ModernNCARegressor(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=ModernNCA, config=DefaultModernNCAConfig, **kwargs)
[docs]class ModernNCAClassifier(SklearnBaseClassifier): __doc__ = generate_docstring( DefaultModernNCAConfig, model_description=""" Multi-Layer Perceptron classifier This class extends the SklearnBaseClassifier class and uses the ModernNCA model with the default ModernNCA configuration. """, examples=""" >>> from deeptab.models import ModernNCAClassifier >>> model = ModernNCAClassifier(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=ModernNCA, config=DefaultModernNCAConfig, **kwargs)
[docs]class ModernNCALSS(SklearnBaseLSS): __doc__ = generate_docstring( DefaultModernNCAConfig, model_description=""" Multi-Layer Perceptron for distributional regression. This class extends the SklearnBaseLSS class and uses the ModernNCA model with the default ModernNCA configuration. """, examples=""" >>> from deeptab.models import ModernNCALSS >>> model = ModernNCALSS(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=ModernNCA, config=DefaultModernNCAConfig, **kwargs)