From 0d78e2c91fab00eabca798fbfa84e977d336ed76 Mon Sep 17 00:00:00 2001
From: Paul Andrey <paul.andrey@inria.fr>
Date: Fri, 16 Feb 2024 17:37:03 +0100
Subject: [PATCH] Quit using a scikit-learn deprecated attribute.
---
declearn/model/sklearn/_sgd.py | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/declearn/model/sklearn/_sgd.py b/declearn/model/sklearn/_sgd.py
index 160a6d39..17263da5 100644
--- a/declearn/model/sklearn/_sgd.py
+++ b/declearn/model/sklearn/_sgd.py
@@ -489,12 +489,13 @@ class SklearnSGDModel(Model):
) -> Callable[[np.ndarray, np.ndarray], np.ndarray]:
"""Return a function to compute point-wise loss for a given batch."""
# fmt: off
- # Gather / instantiate a loss function from the wrapped model's specs.
- if hasattr(self._model, "loss_function_"):
- loss_smp = self._model.loss_function_.py_loss
- else:
- loss_cls, *args = self._model.loss_functions[self._model.loss]
- loss_smp = loss_cls(*args).py_loss
+ # Instantiate a loss function from the wrapped model's specs.
+ loss_cls, *args = self._model.loss_functions[self._model.loss]
+ if self._model.loss in (
+ "huber", "epsilon_insensitive", "squared_epsilon_insensitive"
+ ):
+ args = (self._model.epsilon,)
+ loss_smp = loss_cls(*args).py_loss
# Wrap it to support batched inputs.
def loss_1d(y_true: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
return np.array([loss_smp(*smp) for smp in zip(y_pred, y_true)])
--
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