fix

downloads-generation/models_class1_pan/GENERATE.sh

@@ -114,7 +114,7 @@ do
     cp "$MODELS_DIR/train_data.csv.bz2" "models.${kind}/train_data.csv.bz2"
 
     # We are now calibrating all alleles.
-    # Previously had argument:  --allele $ALLELE_LIST \
+    # Previously had argument:  --allele $ALLELE_LIST
     time mhcflurry-calibrate-percentile-ranks \
         --models-dir models.${kind} \
         --match-amino-acid-distribution-data "$MODELS_DIR/train_data.csv.bz2" \

mhcflurry/class1_presentation_predictor.py

@@ -217,6 +217,7 @@ class Class1PresentationPredictor(object):
                 model.intercept_ = row.intercept
                 model.coef_ = numpy.expand_dims(
                     row[self.model_inputs].values, axis=0)
+                model.classes_ = numpy.array([0, 1])
         else:
             model = self._models_cache[name]
         return model

mhcflurry/select_pan_allele_models_command.py

@@ -258,10 +258,7 @@ def run(argv=sys.argv[1:]):
         pprint(result)
         fold_num = result['fold_num']
         (all_models_for_fold, _) = folds_to_predictors[fold_num]
-        models = [
-            all_models_for_fold[i]
-            for i in result['selected_indices']
-        ]
+        models = result['selected_models']
         summary_df = result['summary'].copy()
         summary_df.index = summary_df.index.map(
             lambda idx: all_models_for_fold[idx])
@@ -271,7 +268,6 @@ def run(argv=sys.argv[1:]):
             len(models), fold_num, result['selected_indices']))
         models_by_fold[fold_num] = models
         for model in models:
-            model.clear_allele_representations()
             result_predictor.add_pan_allele_model(model)
 
     summary_df = pandas.concat(summary_dfs, ignore_index=False)
@@ -363,6 +359,9 @@ def model_select(
             break
 
     assert selected
+    selected_models = [models[i] for i in selected]
+    for model in selected_models:
+        model.clear_allele_representations()
 
     summary_df = pandas.Series(individual_model_scores)[
         numpy.arange(len(models))
@@ -372,6 +371,7 @@ def model_select(
     return {
         'fold_num': fold_num,
         'selected_indices': selected,
+        'selected_models': selected_models,
         'summary': summary_df,  # indexed by model index
     }