MRG Common Private Loss Module with tempita (scikit-learn#20567)

Author: lorentzenchr

.gitignore

@@ -76,6 +76,7 @@ _configtest.o.d
 .mypy_cache/
 
 # files generated from a template
+sklearn/_loss/_loss.pyx
 sklearn/utils/_seq_dataset.pyx
 sklearn/utils/_seq_dataset.pxd
 sklearn/utils/_weight_vector.pyx

setup.cfg

@@ -69,6 +69,7 @@ allow_redefinition = True
 [check-manifest]
 # ignore files missing in VCS
 ignore =
+    sklearn/_loss/_loss.pyx
     sklearn/linear_model/_sag_fast.pyx
     sklearn/utils/_seq_dataset.pyx
     sklearn/utils/_seq_dataset.pxd

sklearn/_loss/__init__.py

@@ -0,0 +1,27 @@
+"""
+The :mod:`sklearn._loss` module includes loss function classes suitable for
+fitting classification and regression tasks.
+"""
+
+from .loss import (
+    HalfSquaredError,
+    AbsoluteError,
+    PinballLoss,
+    HalfPoissonLoss,
+    HalfGammaLoss,
+    HalfTweedieLoss,
+    HalfBinomialLoss,
+    HalfMultinomialLoss,
+)
+
+
+__all__ = [
+    "HalfSquaredError",
+    "AbsoluteError",
+    "PinballLoss",
+    "HalfPoissonLoss",
+    "HalfGammaLoss",
+    "HalfTweedieLoss",
+    "HalfBinomialLoss",
+    "HalfMultinomialLoss",
+]

sklearn/_loss/_loss.pxd

@@ -0,0 +1,75 @@
+# cython: language_level=3
+
+import numpy as np
+cimport numpy as np
+
+np.import_array()
+
+
+# Fused types for y_true, y_pred, raw_prediction
+ctypedef fused Y_DTYPE_C:
+    np.npy_float64
+    np.npy_float32
+
+
+# Fused types for gradient and hessian
+ctypedef fused G_DTYPE_C:
+    np.npy_float64
+    np.npy_float32
+
+
+# Struct to return 2 doubles
+ctypedef struct double_pair:
+   double val1
+   double val2
+
+
+# C base class for loss functions
+cdef class CyLossFunction:
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyHalfSquaredError(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyAbsoluteError(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyPinballLoss(CyLossFunction):
+    cdef readonly double quantile  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyHalfPoissonLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyHalfGammaLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyHalfTweedieLoss(CyLossFunction):
+    cdef readonly double power  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil
+
+
+cdef class CyHalfBinomialLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) nogil