chore: avoid checking accuracy when early stop in FHE training

tests/sklearn/test_fhe_training.py

@@ -309,7 +309,7 @@ def test_clear_fit(
     model.predict_proba(x, fhe="simulate")
 
 
-# pylint: disable=too-many-arguments
+# pylint: disable=too-many-arguments, too-many-branches
 def check_encrypted_fit(
     x,
     y,
@@ -318,7 +318,7 @@ def check_encrypted_fit(
     parameters_range,
     max_iter,
     fit_intercept,
-    check_accuracy,
+    check_accuracy=None,
     fhe=None,
     partial_fit=False,
     warm_fit=False,
@@ -414,8 +414,9 @@ def check_encrypted_fit(
             (1, 1)
         ), "When the model is fitted without bias, the bias term should only be made of zeros."
 
-    # Check that we overfit properly a linearly separable dataset
-    check_accuracy(y, y_pred_class)
+    # If relevant, check that we overfit properly a linearly separable dataset
+    if check_accuracy is not None:
+        check_accuracy(y, y_pred_class)
 
     return weights, bias, y_pred_proba, y_pred_class, model.random_number_generator
 
@@ -446,7 +447,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             max_iter,
             fit_intercept,
-            check_accuracy,
+            check_accuracy=check_accuracy,
             fhe="disable",
         )
     )
@@ -460,7 +461,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             max_iter,
             fit_intercept,
-            check_accuracy,
+            check_accuracy=check_accuracy,
             fhe="simulate",
         )
     )
@@ -475,7 +476,8 @@ def test_encrypted_fit_coherence(
     # Define early break parameters, with a very high tolerance
     early_break_kwargs = {"early_stopping": True, "tol": 1e100}
 
-    # We don't have any way to detect early break
+    # We don't have any way to properly test early break, we therefore disable the accuracy check
+    # in order to avoid flaky issues
     check_encrypted_fit(
         x,
         y,
@@ -484,7 +486,7 @@ def test_encrypted_fit_coherence(
         parameters_range,
         max_iter,
         fit_intercept,
-        check_accuracy,
+        check_accuracy=None,
         fhe="simulate",
         init_kwargs=early_break_kwargs,
     )
@@ -498,7 +500,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             max_iter,
             fit_intercept,
-            check_accuracy,
+            check_accuracy=check_accuracy,
             partial_fit=True,
         )
     )
@@ -520,7 +522,7 @@ def test_encrypted_fit_coherence(
         parameters_range,
         max_iter,
         fit_intercept,
-        check_accuracy,
+        check_accuracy=check_accuracy,
         warm_fit=True,
         init_kwargs=warm_fit_init_kwargs,
     )
@@ -543,7 +545,7 @@ def test_encrypted_fit_coherence(
         parameters_range,
         first_iterations,
         fit_intercept,
-        check_accuracy,
+        check_accuracy=check_accuracy,
         fhe="simulate",
     )
 
@@ -566,7 +568,7 @@ def test_encrypted_fit_coherence(
             parameters_range,
             last_iterations,
             fit_intercept,
-            check_accuracy,
+            check_accuracy=check_accuracy,
             fhe="simulate",
             random_number_generator=rng_coef_init,
             fit_kwargs=coef_init_fit_kwargs,