chore: fix pcc

conftest.py

@@ -13,6 +13,7 @@
 from concrete.fhe.compilation import Circuit, Configuration
 from concrete.fhe.mlir.utils import MAXIMUM_TLU_BIT_WIDTH
 from sklearn.datasets import make_classification, make_regression
+from sklearn.metrics import accuracy_score
 
 from concrete.ml.common.utils import (
     SUPPORTED_FLOAT_TYPES,
@@ -420,7 +421,7 @@ def check_accuracy():
     """Fixture to check the accuracy."""
 
     def check_accuracy_impl(expected, actual, threshold=0.9):
-        accuracy = numpy.mean(expected == actual)
+        accuracy = accuracy_score(expected, actual)
         assert accuracy >= threshold, f"Accuracy of {accuracy} is not high enough ({threshold})."
 
     return check_accuracy_impl

src/concrete/ml/sklearn/base.py

@@ -1783,13 +1783,17 @@ def predict_proba(self, X: Data, fhe: Union[FheMode, str] = FheMode.DISABLE) ->
         y_proba = self.post_processing(y_logits)
         return y_proba
 
-    # In scikit-learn, the argmax is done on the scores directly, not the probabilities
+    # In scikit-learn, the argmax is done on the logits directly, not the probabilities
     def predict(self, X: Data, fhe: Union[FheMode, str] = FheMode.DISABLE) -> numpy.ndarray:
         # Compute the predicted scores
-        y_proba = self.decision_function(X, fhe=fhe)
+        y_logits = self.decision_function(X, fhe=fhe)
 
         # Retrieve the class with the highest score
-        y_preds = numpy.argmax(y_proba, axis=1)
+        # If there is a single dimension, only compare the scores to 0
+        if y_logits.ndim == 1 or y_logits.shape[1] == 1:
+            y_preds = (y_logits > 0).astype(int)
+        else:
+            y_preds = numpy.argmax(y_logits, axis=1)
 
         return self.classes_[y_preds]