fix

mhcflurry/class1_cleavage_neural_network.py

@@ -98,7 +98,7 @@ class Class1CleavageNeuralNetwork(object):
         convolutional_filters=16,
         convolutional_kernel_size=8,
         convolutional_activation="relu",
-        convolutional_kernel_l1_l2=(0.001, 0.001),
+        convolutional_kernel_l1_l2=[0.001, 0.001],
         dropout_rate=0.5,
         post_convolutional_dense_layer_sizes=[],
     )
@@ -410,22 +410,22 @@ class Class1CleavageNeuralNetwork(object):
             if flank == "n_flank":
                 peptide_input = "peptide_right_pad"
                 concat_order = [flank, peptide_input]
-                cleavage_position = n_flank_length
                 noncleaved_peptide_extractor = lambda x: x[
                     :, (n_flank_length + 1):]
                 flanking_extractor = lambda x: x[
                     :, : n_flank_length
                 ]
+                cleavage_position_extractor = lambda x: x[:, n_flank_length]
             else:
                 assert flank == "c_flank"
                 peptide_input = "peptide_left_pad"
                 concat_order = [peptide_input, flank]
-                cleavage_position = peptide_max_length - 1
                 noncleaved_peptide_extractor = lambda x: x[
                     :, 0 : peptide_max_length - 1]
                 flanking_extractor = lambda x: x[
                     :, peptide_max_length :
                 ]
+                cleavage_position_extractor = lambda x: x[:, peptide_max_length - 1]
 
             if include_flank:
                 current_layer = Concatenate(
@@ -465,7 +465,7 @@ class Class1CleavageNeuralNetwork(object):
 
             # Single output at cleavage position
             single_output_at_cleavage_position = keras.layers.Lambda(
-                lambda x: x[:, cleavage_position])(single_output_result)
+                cleavage_position_extractor)(single_output_result)
             outputs_for_final_dense.append(single_output_at_cleavage_position)
 
             # Max of single-output at non-cleaved (peptide) positions.

test/test_class1_cleavage_predictor.py

@@ -40,6 +40,13 @@ def test_basic():
     })
     df["score"] = predictor.predict(df.peptide, df.n_flank, df.c_flank)
 
+    # Test predictions are deterministic
+    df1b = predictor.predict_to_dataframe(
+        peptides=df.peptide.values,
+        n_flanks=df.n_flank.values,
+        c_flanks=df.c_flank.values)
+    assert_array_equal(df.score.values, df1b.score.values)
+
     # Test saving and loading
     models_dir = tempfile.mkdtemp("_models")
     print(models_dir)