test(losses): unit tests for TauBinaryCrossentropy

tests/test_losses.py

@@ -15,6 +15,7 @@
     MultiMargin,
     TauCategoricalCrossentropy,
     TauSparseCategoricalCrossentropy,
+    TauBinaryCrossentropy,
     CategoricalHinge,
 )
 from deel.lip.utils import process_labels_for_multi_gpu
@@ -234,6 +235,28 @@ def test_tau_sparse_catcrossent(self):
         )
         check_serialization(n_class, tau_sparse_catcrossent_loss)
 
+    def test_tau_binary_crossent(self):
+        loss = TauBinaryCrossentropy(2.0)
+        y_true = binary_tf_data([1.0, 1.0, 1.0, 0.0, 0.0, 0.0])
+        y_pred = binary_tf_data([0.5, 1.5, -0.5, -0.5, -1.5, 0.5])
+
+        # Assert that loss value is equal to expected value
+        expected_loss_val = 0.279185
+        loss_val = loss(y_true, y_pred).numpy()
+        np.testing.assert_allclose(loss_val, expected_loss_val, rtol=1e-6)
+
+        # Assert that loss value is the same when y_true is of type int32
+        loss_val_2 = loss(tf.cast(y_true, dtype=tf.int32), y_pred).numpy()
+        np.testing.assert_allclose(loss_val_2, expected_loss_val, rtol=1e-6)
+
+        # Assert that loss value is the same when y_true is [-1, 1] instead of [0, 1]
+        y_true2 = tf.where(y_true == 1.0, 1.0, -1.0)
+        loss_val_3 = loss(y_true2, y_pred).numpy()
+        np.testing.assert_allclose(loss_val_3, expected_loss_val, rtol=1e-6)
+
+        # Assert that loss object is correctly serialized
+        check_serialization(1, loss)
+
     def test_no_reduction_binary_losses(self):
         """
         Assert binary losses without reduction. Three losses are tested on hardcoded
@@ -246,12 +269,14 @@ def test_no_reduction_binary_losses(self):
             KR(reduction="none"),
             HingeMargin(0.7 * 2.0, reduction="none"),
             HKR(alpha=2.5, min_margin=2.0, reduction="none"),
+            TauBinaryCrossentropy(tau=0.5, reduction="none"),
         )
 
         expected_loss_values = (
             np.array([1.0, 2.2, -0.2, 1.4, 2.6, 0.8, -0.4, 1.8]),
             np.array([0.2, 0, 0.8, 0, 0, 0.3, 0.9, 0]),
             np.array([0.25, -2.2, 2.95, -0.65, -2.6, 0.7, 3.4, -1.55]),
+            [1.15188, 0.91098, 1.43692, 1.06676, 0.84011, 1.19628, 1.48879, 0.98650],
         )
 
         for loss, expected_loss_val in zip(losses, expected_loss_values):
@@ -323,7 +348,7 @@ def test_no_reduction_multiclass_losses(self):
                     0.114224,
                     0.076357,
                 ]
-            )
+            ),
         )
 
         for loss, expected_loss_val in zip(losses, expected_loss_values):
@@ -357,9 +382,10 @@ def test_minibatches_binary_losses(self):
             KR(multi_gpu=True, reduction=reduction),
             HingeMargin(0.7 * 2.0, reduction=reduction),
             HKR(alpha=2.5, min_margin=2.0, multi_gpu=True, reduction=reduction),
+            TauBinaryCrossentropy(tau=1.5, reduction=reduction),
         )
 
-        expected_loss_values = (9.2, 2.2, 0.3)
+        expected_loss_values = (9.2, 2.2, 0.3, 2.19262)
 
         # Losses are tested on full batch
         for loss, expected_loss_val in zip(losses, expected_loss_values):