Add expensive_test_pretrain_optimizable.py

mhcflurry/train_pan_allele_models_command.py

@@ -290,7 +290,6 @@ def main(args):
     GLOBAL_DATA["train_data"] = df
     GLOBAL_DATA["folds_df"] = folds_df
     GLOBAL_DATA["allele_encoding"] = allele_encoding
-    GLOBAL_DATA["args"] = args
 
     if not os.path.exists(args.out_models_dir):
         print("Attempting to create directory: %s" % args.out_models_dir)
@@ -444,7 +443,6 @@ def train_model(
     df = constant_data["train_data"]
     folds_df = constant_data["folds_df"]
     allele_encoding = constant_data["allele_encoding"]
-    args = constant_data["args"]
 
     if predictor is None:
         predictor = Class1AffinityPredictor(

test/expensive_test_pretrain_optimizable.py

+# Expensive test - not run by default.
+
+from mhcflurry import train_pan_allele_models_command
+from mhcflurry.downloads import get_path
+from mhcflurry.allele_encoding import AlleleEncoding
+
+import pandas
+import numpy
+
+PRETRAIN_DATA_PATH = get_path(
+    "random_peptide_predictions", "predictions.csv.bz2")
+
+FULL_TRAIN_DF = pandas.read_csv(
+        get_path(
+            "data_curated",
+            "curated_training_data.no_mass_spec.csv.bz2"))
+TRAIN_DF = FULL_TRAIN_DF.loc[
+    (FULL_TRAIN_DF.peptide.str.len() >= 8) &
+    (FULL_TRAIN_DF.peptide.str.len() <= 15)
+]
+ALLELE_SEQUENCES = pandas.read_csv(
+    get_path("allele_sequences", "allele_sequences.csv"),
+    index_col=0).sequence
+ALLELE_SEQUENCES = ALLELE_SEQUENCES.loc[
+    ALLELE_SEQUENCES.index.isin(TRAIN_DF.allele)
+]
+TRAIN_DF = TRAIN_DF.loc[
+    TRAIN_DF.allele.isin(ALLELE_SEQUENCES.index)
+]
+FOLDS_DF = pandas.DataFrame(index=TRAIN_DF.index)
+FOLDS_DF["fold_0"] = True
+
+HYPERPARAMTERS = {
+    'activation': 'tanh', 'allele_dense_layer_sizes': [],
+    'batch_normalization': False,
+    'dense_layer_l1_regularization': 9.999999999999999e-11,
+    'dense_layer_l2_regularization': 0.0, 'dropout_probability': 0.5,
+    'early_stopping': True, 'init': 'glorot_uniform',
+    'layer_sizes': [1024, 512], 'learning_rate': None,
+    'locally_connected_layers': [], 'loss': 'custom:mse_with_inequalities',
+    'max_epochs': 1, 'min_delta': 0.0, 'minibatch_size': 128,
+    'optimizer': 'rmsprop', 'output_activation': 'sigmoid', 'patience': 20,
+    'peptide_allele_merge_activation': '',
+    'peptide_allele_merge_method': 'concatenate',
+    'peptide_amino_acid_encoding': 'BLOSUM62', 'peptide_dense_layer_sizes': [],
+    'peptide_encoding': {'alignment_method': 'left_pad_centered_right_pad',
+                         'max_length': 15, 'vector_encoding_name': 'BLOSUM62'},
+    'random_negative_affinity_max': 50000.0,
+    'random_negative_affinity_min': 20000.0, 'random_negative_constant': 25,
+    'random_negative_distribution_smoothing': 0.0,
+    'random_negative_match_distribution': True, 'random_negative_rate': 0.2,
+    'train_data': {'pretrain': True,
+                   'pretrain_max_epochs': 1,
+                   'pretrain_peptides_per_epoch': 1024,
+                   'pretrain_steps_per_epoch': 16},
+    'validation_split': 0.1,
+}
+
+
+def test_optimizable():
+    predictor = train_pan_allele_models_command.train_model(
+        work_item_num=0,
+        num_work_items=1,
+        architecture_num=0,
+        num_architectures=1,
+        fold_num=0,
+        num_folds=1,
+        replicate_num=0,
+        num_replicates=1,
+        hyperparameters=HYPERPARAMTERS,
+        pretrain_data_filename=PRETRAIN_DATA_PATH,
+        verbose=1,
+        progress_print_interval=5.0,
+        predictor=None,
+        save_to=None,
+        constant_data={
+            'train_data': TRAIN_DF,
+            'folds_df': FOLDS_DF,
+            'allele_encoding': AlleleEncoding(
+                alleles=ALLELE_SEQUENCES.index.values,
+                allele_to_sequence=ALLELE_SEQUENCES.to_dict()),
+        },
+    )
+    (network,) = predictor.neural_networks
+    pretrain_val_loss = network.fit_info[-1]['training_info']["val_loss"][-1]
+    print(predictor)
+    print(pretrain_val_loss)
+    numpy.testing.assert_array_less(pretrain_val_loss, 0.1)
+
+
+if __name__ == "__main__":
+    test_optimizable()

test/test_train_pan_allele_models_command.py

@@ -95,7 +95,7 @@ HYPERPARAMETERS_LIST = [
         "pretrain": True,
         'pretrain_peptides_per_epoch': 128,
         'pretrain_max_epochs': 2,
-        'pretrain_max_val_loss': 0.1,
+        'pretrain_max_val_loss': 0.2,
     },
     'validation_split': 0.1,
 },