Threshold must be a real number

metric_learn/base_metric.py

@@ -569,8 +569,14 @@ def set_threshold(self, threshold):
       The pairs classifier with the new threshold set.
     """
     check_is_fitted(self, 'preprocessor_')
-
-    self.threshold_ = threshold
+    try:
+      self.threshold_ = float(threshold)
+    except TypeError:
+      raise ValueError('Parameter threshold must be a real number. '
+                       'Got {} instead.'.format(type(threshold)))
+    except ValueError:
+      raise ValueError('Parameter threshold must be a real number. '
+                       'Got {} instead.'.format(type(threshold)))
     return self
 
   def calibrate_threshold(self, pairs_valid, y_valid, strategy='accuracy',

test/test_pairs_classifiers.py

@@ -180,6 +180,25 @@ def test_set_threshold():
   assert identity_pairs_classifier.threshold_ == 0.5
 
 
+@pytest.mark.parametrize('value', ["ABC", None, [1, 2, 3], {'key': None},
+                         (1, 2), set(),
+                         np.array([[[0.], [1.]], [[1.], [3.]]])])
+def test_set_wrong_type_threshold(value):
+  """
+  Test that `set_threshold` indeed sets the threshold
+  and cannot accept nothing but float or integers, but
+  being permissive with boolean True=1.0 and False=0.0
+  """
+  model = IdentityPairsClassifier()
+  model.fit(np.array([[[0.], [1.]]]), np.array([1]))
+  msg = ('Parameter threshold must be a real number. '
+         'Got {} instead.'.format(type(value)))
+
+  with pytest.raises(ValueError) as e:  # String
+    model.set_threshold(value)
+  assert str(e.value).startswith(msg)
+
+
 def test_f_beta_1_is_f_1():
   # test that putting beta to 1 indeed finds the best threshold to optimize
   # the f1_score