fix

mhcflurry/class1_neural_network.py

@@ -719,6 +719,8 @@ class Class1NeuralNetwork(object):
                 ]),
             }
             adjusted_inequalities_with_random_negatives = None
+        assert numpy.isnan(y_dict_with_random_negatives['output']).sum() == 0, (
+            y_dict_with_random_negatives)
         if sample_weights is not None:
             sample_weights_with_random_negatives = numpy.concatenate([
                 numpy.ones(int(num_random_negative.sum())),

mhcflurry/custom_loss.py

@@ -82,11 +82,11 @@ class MSEWithInequalities(Loss):
     def encode_y(y, inequalities=None):
         y = array(y, dtype="float32")
         if isnan(y).any():
-            raise ValueError("y contains NaN: %s" % str(y))
+            raise ValueError("y contains NaN", y)
         if (y > 1.0).any():
-            raise ValueError("y contains values > 1.0")
+            raise ValueError("y contains values > 1.0", y)
         if (y < 0.0).any():
-            raise ValueError("y contains values < 0.0")
+            raise ValueError("y contains values < 0.0", y)
 
         if inequalities is None:
             encoded = y
@@ -141,11 +141,11 @@ class MSEWithInequalitiesAndMultipleOutputs(Loss):
     def encode_y(y, inequalities=None, output_indices=None):
         y = array(y, dtype="float32")
         if isnan(y).any():
-            raise ValueError("y contains NaN: %s" % str(y))
+            raise ValueError("y contains NaN", y)
         if (y > 1.0).any():
-            raise ValueError("y contains values > 1.0")
+            raise ValueError("y contains values > 1.0", y)
         if (y < 0.0).any():
-            raise ValueError("y contains values < 0.0")
+            raise ValueError("y contains values < 0.0", y)
 
         encoded = MSEWithInequalities.encode_y(
             y, inequalities=inequalities)