fix

mhcflurry/__init__.py

@@ -4,10 +4,15 @@ Class I MHC ligand prediction package
 
 from .class1_affinity_predictor import Class1AffinityPredictor
 from .class1_neural_network import Class1NeuralNetwork
+from .class1_presentation_predictor import Class1PresentationPredictor
+from .class1_presentation_neural_network import Class1PresentationNeuralNetwork
+
 from .version import __version__
 
 __all__ = [
     "__version__",
     "Class1AffinityPredictor",
     "Class1NeuralNetwork",
+    "Class1PresentationPredictor",
+    "Class1PresentationNeuralNetwork",
 ]

mhcflurry/class1_presentation_neural_network.py

@@ -554,6 +554,26 @@ class Class1PresentationNeuralNetwork(object):
                         "peptide"
                     ][:num_random_negatives] = random_negative_peptides_encoding
 
+            if i == 0:
+                (train_generator, test_generator) = (
+                    batch_generator.get_train_and_test_generators(
+                        x_dict=x_dict_with_random_negatives,
+                        y_list=[encoded_y1, encoded_y2],
+                        epochs=1))
+                pairs = [
+                    ("train", train_generator, batch_generator.num_train_batches),
+                    ("test", test_generator, batch_generator.num_test_batches),
+                ]
+                for (kind, generator, steps) in pairs:
+                    self.assert_allele_representations_hash(
+                        allele_representations_hash)
+                    metrics = self.network.evaluate_generator(
+                        generator=generator,
+                        steps=steps,
+                        workers=0,
+                        use_multiprocessing=False)
+                    for (key, val) in zip(self.network.metrics_names, metrics):
+                        fit_info["pre_fit_%s_%s" % (kind, key)] = val
             (train_generator, test_generator) = (
                 batch_generator.get_train_and_test_generators(
                     x_dict=x_dict_with_random_negatives,
@@ -631,7 +651,7 @@ class Class1PresentationNeuralNetwork(object):
         return {
             'batch_generator': batch_generator,
             'last_x': x_dict_with_random_negatives,
-            'last_y': [encoded_y1, encoded_y2, encoded_y2],
+            'last_y': [encoded_y1, encoded_y2],
             'fit_info': fit_info,
         }
 

mhcflurry/custom_loss.py

@@ -183,10 +183,14 @@ class MSEWithInequalities(Loss):
         diff3 *= K.cast(y_true >= 4.0, "float32")
         diff3 *= K.cast(diff3 > 0.0, "float32")
 
+        denominator = K.maximum(
+            K.sum(K.cast(K.not_equal(y_true, 2.0), "float32"), 0),
+            1.0)
+
         result = (
             K.sum(K.square(diff1)) +
             K.sum(K.square(diff2)) +
-            K.sum(K.square(diff3))) / K.cast(K.shape(y_pred)[0], "float32")
+            K.sum(K.square(diff3))) / denominator
 
         return result
 

mhcflurry/downloads.yml

@@ -30,7 +30,7 @@ releases:
               default: false
 
             - name: models_class1_pan_refined
-              url: https://github.com/openvax/mhcflurry/releases/download/1.4.0/models_class1_pan_refined.20191210.tar.bz2
+              url: https://github.com/openvax/mhcflurry/releases/download/1.4.0/models_class1_pan_refined.20191211.tar.bz2
               default: false
 
             - name: models_class1_pan_variants

test/test_class1_presentation_neural_network.py

@@ -91,7 +91,7 @@ def make_motif(presentation_predictor, allele, peptides, frac=0.01):
 # TESTS
 ###################################################
 
-def test_synthetic_allele_refinement(include_affinities=False):
+def test_synthetic_allele_refinement(include_affinities=True):
     """
     Test that in a synthetic example the model is able to learn that HLA-C*01:02
     prefers P at position 3.