diff --git a/metric_learn/base_metric.py b/metric_learn/base_metric.py
index 721d7ba0..21506011 100644
--- a/metric_learn/base_metric.py
+++ b/metric_learn/base_metric.py
@@ -602,7 +602,7 @@ def predict(self, triplets):
     prediction : `numpy.ndarray` of floats, shape=(n_constraints,)
       Predictions of the ordering of pairs, for each triplet.
     """
-    return np.sign(self.decision_function(triplets))
+    return 2 * (self.decision_function(triplets) > 0) - 1
 
   def decision_function(self, triplets):
     """Predicts differences between sample distances in input triplets.
diff --git a/test/test_triplets_classifiers.py b/test/test_triplets_classifiers.py
index 0f0bf7df..f2d5c015 100644
--- a/test/test_triplets_classifiers.py
+++ b/test/test_triplets_classifiers.py
@@ -6,6 +6,7 @@
 from metric_learn.sklearn_shims import set_random_state
 from sklearn import clone
 import numpy as np
+from numpy.testing import assert_array_equal
 
 
 @pytest.mark.parametrize('with_preprocessor', [True, False])
@@ -26,6 +27,49 @@ def test_predict_only_one_or_minus_one(estimator, build_dataset,
   assert len(not_valid) == 0
 
 
+@pytest.mark.parametrize('estimator, build_dataset', triplets_learners,
+                         ids=ids_triplets_learners)
+def test_no_zero_prediction(estimator, build_dataset):
+  """
+  Test that all predicted values are not zero, even when the
+  distance d(x,y) and d(x,z) is the same for a triplet of the
+  form (x, y, z). i.e border cases.
+  """
+  triplets, _, _, X = build_dataset(with_preprocessor=False)
+  # Force 3 dimentions only, to use cross product and get easy orthogonal vec.
+  triplets = np.array([[t[0][:3], t[1][:3], t[2][:3]] for t in triplets])
+  X = X[:, :3]
+  # Dummy fit
+  estimator = clone(estimator)
+  set_random_state(estimator)
+  estimator.fit(triplets)
+  # We force the transformation to be identity, to force euclidean distance
+  estimator.components_ = np.eye(X.shape[1])
+
+  # Get two orthogonal vectors in respect to X[1]
+  k = X[1] / np.linalg.norm(X[1])  # Normalize first vector
+  x = X[2] - X[2].dot(k) * k  # Get random orthogonal vector
+  x /= np.linalg.norm(x)  # Normalize
+  y = np.cross(k, x)  # Get orthogonal vector to x
+  # Assert these orthogonal vectors are different
+  with pytest.raises(AssertionError):
+    assert_array_equal(X[1], x)
+  with pytest.raises(AssertionError):
+    assert_array_equal(X[1], y)
+  # Assert the distance is the same for both
+  assert estimator.get_metric()(X[1], x) == estimator.get_metric()(X[1], y)
+
+  # Form the three scenarios where predict() gives 0 with numpy.sign
+  triplets_test = np.array(  # Critical examples
+    [[X[0], X[2], X[2]],
+     [X[1], X[1], X[1]],
+     [X[1], x, y]])
+  # Predict
+  predictions = estimator.predict(triplets_test)
+  # Check there are no zero values
+  assert np.sum(predictions == 0) == 0
+
+
 @pytest.mark.parametrize('with_preprocessor', [True, False])
 @pytest.mark.parametrize('estimator, build_dataset', triplets_learners,
                          ids=ids_triplets_learners)