8000 MNT add __reduce__ to loss objects by adrinjalali · Pull Request #30356 · scikit-learn/scikit-learn · GitHub
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MNT add __reduce__ to loss objects #30356

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Nov 29, 2024
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MNT add __reduce__ to loss objects #30356

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8 changes: 8 additions & 0 deletions sklearn/_loss/_loss.pyx.tp
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Original file line number Diff line number Diff line change
Expand Up @@ -818,6 +818,9 @@ cdef inline double_pair cgrad_hess_exponential(
cdef class CyLossFunction:
"""Base class for convex loss functions."""

def __reduce__(self):
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Is it needed actually? It seems to me like CyLossFunction is only used as a base class and you are not not really meant to create an instance of CyLossFunction.

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this is needed since not all losses have a parameter, which means they won't have our custom __reduce__ generated for them. Note the {{if param is not None}} around the __reduce__ implementation bellow.

return (self.__class__, ())

cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil:
"""Compute the loss for a single sample.

Expand Down Expand Up @@ -1013,6 +1016,11 @@ cdef class {{name}}(CyLossFunction):
self.{{param}} = {{param}}
{{endif}}

{{if param is not None}}
def __reduce__(self):
return (self.__class__, (self.{{param}},))
{{endif}}

cdef inline double cy_loss(self, double y_true, double raw_prediction) noexcept nogil:
return {{closs}}(y_true, raw_prediction{{with_param}})

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