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)