implement Class1AffinityPredictor.merge() method

mhcflurry/class1_affinity_prediction/class1_affinity_predictor.py

@@ -96,10 +96,67 @@ def __init__(
                 columns=["model_name", "allele", "config_json", "model"])
         self.manifest_df = manifest_df
 
-        if allele_to_percent_rank_transform is None:
+        if not allele_to_percent_rank_transform:
             allele_to_percent_rank_transform = {}
         self.allele_to_percent_rank_transform = allele_to_percent_rank_transform
 
+    @property
+    def neural_networks(self):
+        """
+        List of the neural networks in the ensemble.
+
+        Returns
+        -------
+        list of Class1NeuralNetwork
+        """
+        result = []
+        for models in self.allele_to_allele_specific_models.values():
+            result.extend(models)
+        result.extend(self.class1_pan_allele_models)
+        return result
+
+    @classmethod
+    def merge(cls, predictors):
+        """
+        Merge the ensembles of two or more Class1AffinityPredictor instances.
+
+        Note: the resulting merged predictor will NOT have calibrated percentile
+        ranks. Call calibrate_percentile_ranks() on it if these are needed.
+
+        Parameters
+        ----------
+        predictors : sequence of Class1AffinityPredictor
+
+        Returns
+        -------
+        Class1AffinityPredictor
+
+        """
+        assert len(predictors) > 0
+        if len(predictors) == 1:
+            return predictors[0]
+
+        allele_to_allele_specific_models = collections.defaultdict(list)
+        class1_pan_allele_models = []
+        allele_to_pseudosequence = predictors[0].allele_to_pseudosequence
+
+        for predictor in predictors:
+            for (allele, networks) in (
+                    predictor.allele_to_allele_specific_models.items()):
+                allele_to_allele_specific_models[allele].extend(networks)
+            class1_pan_allele_models.extend(
+                predictor.class1_pan_allele_models)
+
+        return Class1AffinityPredictor(
+            allele_to_allele_specific_models=allele_to_allele_specific_models,
+            class1_pan_allele_models=class1_pan_allele_models,
+            allele_to_pseudosequence=allele_to_pseudosequence
+        )
+
+    @property
+    def num_networks(self):
+        return self.manifest_df.shape[0]
+
 
     @property
     def supported_alleles(self):