update pan variants

downloads-generation/models_class1_pan_variants/GENERATE.sh

@@ -66,10 +66,11 @@ then
     python generate_hyperparameters.production.py > hyperparameters.production.yaml
     python generate_hyperparameters.py hyperparameters.production.yaml no_pretrain > hyperparameters.no_pretrain.yaml
     python generate_hyperparameters.py hyperparameters.no_pretrain.yaml single_hidden > hyperparameters.single_hidden_no_pretrain.yaml
+    python generate_hyperparameters.py hyperparameters.compact_peptide.yaml compact_peptide > hyperparameters.compact_peptide.yaml
     python normalize_allele_names.py "$(mhcflurry-downloads path allele_sequences)/class1_pseudosequences.csv"  --out allele_sequences.34mer.csv
 fi
 
-for kind in 34mer_sequence single_hidden_no_pretrain no_pretrain 
+for kind in 34mer_sequence single_hidden_no_pretrain no_pretrain compact_peptide
 do
     CONTINUE_INCOMPLETE_ARGS=""
     if [ "$2" == "continue-incomplete" ] && [ -d "models.unselected.${kind}" ]
@@ -100,7 +101,7 @@ done
 
 echo "Done training. Beginning model selection."
 
-for kind in single_hidden_no_pretrain no_pretrain 34mer_sequence
+for kind in single_hidden_no_pretrain no_pretrain 34mer_sequence compact_peptide
 do
     MODELS_DIR="models.unselected.${kind}"
     mhcflurry-class1-select-pan-allele-models \

downloads-generation/models_class1_pan_variants/generate_hyperparameters.py

@@ -14,7 +14,7 @@ parser.add_argument(
     help="Production (i.e. standard) hyperparameters grid.")
 parser.add_argument(
     "kind",
-    choices=('single_hidden', 'no_pretrain'),
+    choices=('single_hidden', 'no_pretrain', 'compact_peptide'),
     help="Hyperameters variant to output")
 
 args = parser.parse_args(argv[1:])
@@ -38,9 +38,16 @@ def transform_to_no_pretrain(hyperparameters):
     return [result]
 
 
+def transform_to_compact_peptide(hyperparameters):
+    result = deepcopy(hyperparameters)
+    result['peptide_encoding']['alignment_method'] = 'left_pad_right_pad'
+    return [result]
+
+
 TRANSFORMS={
     "single_hidden": transform_to_single_hidden,
     "no_pretrain": transform_to_no_pretrain,
+    "compact_peptide": transform_to_compact_peptide,
 }
 
 transform = TRANSFORMS[args.kind]