in progress!

.gitignore

@@ -75,3 +75,5 @@ target/
 
 # OS X extra files
 .DS_Store
+.aider*
+.env

mhcflurry/predict_command.py

@@ -192,12 +192,13 @@ def run(argv=sys.argv[1:]):
         from mhcflurry.torch_implementations import Class1AffinityPredictor as TorchPredictor
         from mhcflurry.torch_presentation_predictor import TorchPresentationPredictor
 
-        print("Using torch")
         if os.path.exists(os.path.join(models_dir, "weights.csv")):
             # Using a presentation predictor
+            print("Using torch TorchPresentationPredictor")
             predictor = TorchPresentationPredictor.load(models_dir)
         else:
             # Using just an affinity predictor
+            print("Using torch TorchClass1AffinityPredictor")
             affinity_predictor = TorchPredictor.load(models_dir)
             predictor = TorchPresentationPredictor(affinity_predictor=affinity_predictor)
             if not args.affinity_only:

test/__init__.py

@@ -1,32 +1,34 @@
-'''
+"""
 Utility functions for tests.
-'''
+"""
 
 import os
 import time
 
 
 def data_path(name):
-    '''
+    """
     Return the absolute path to a file in the test/data directory.
     The name specified should be relative to test/data.
-    '''
+    """
     return os.path.join(os.path.dirname(__file__), "data", name)
 
 
 def initialize():
-    '''
+    """
     Initialize logging and tensorflow, numpy, and python random seeds.
-    '''
+    """
     import logging
+
     logging.getLogger("tensorflow").disabled = True
     logging.getLogger("matplotlib").disabled = True
 
     import tensorflow as tf
+
     seed = int(os.environ.get("MHCFLURRY_TEST_SEED", 1))
     if seed == 0:
         # Enable nondeterminism
         seed = int(time.time())
     print("Using random seed", seed)
     tf.keras.utils.set_random_seed(seed)
-    tf.config.experimental.enable_op_determinism()
+    tf.config.experimental.enable_op_determinism()

test/test_predict_command.py

@@ -1,4 +1,5 @@
 from . import initialize
+
 initialize()
 
 import tempfile
@@ -20,179 +21,85 @@
 from mhcflurry.testing_utils import cleanup, startup
 
 pytest.fixture(autouse=True, scope="module")
+
+
 def setup_module():
     startup()
     yield
     cleanup()
 
-TEST_CSV = '''
+
+TEST_CSV = """
 Allele,Peptide,Experiment
 HLA-A0201,SYNFEKKL,17
 HLA-B4403,AAAAAAAAA,17
 HLA-B4403,PPPPPPPP,18
-'''.strip()
+""".strip()
 
 
 def test_csv():
     """Test CSV input/output functionality."""
     args = ["--allele-column", "Allele", "--peptide-column", "Peptide"]
-    
-    with tempfile.NamedTemporaryFile(mode='w', suffix=".csv", delete=False) as input_file:
+
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as input_file:
         input_file.write(TEST_CSV)
         input_file.flush()
         input_path = input_file.name
-        
-        with tempfile.NamedTemporaryFile(mode='w', suffix=".csv", delete=False) as output_file:
+
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as output_file:
             output_path = output_file.name
-            
+
             try:
                 full_args = [input_path] + args + ["--out", output_path]
                 print("Running with args: %s" % full_args)
                 predict_command.run(full_args)
-                
+
                 result = pandas.read_csv(output_path)
                 print(result)
-                
+
                 # Verify results
                 assert not result.isnull().any().any()
                 assert result.shape == (3, 8)
-
-            finally:
-                # Clean up files
-                for path in [input_path, output_path]:
-                    try:
-                        os.unlink(path)
-                    except OSError as e:
-                        if e.errno != errno.ENOENT:  # No such file or directory
-                            print(f"Error removing file {path}: {e}")
-
-
-def test_tensorflow_vs_pytorch_backends():
-    """Test that tensorflow and pytorch backends produce matching results."""
-    try:
-        import torch
-    except ImportError:
-        pytest.skip("PyTorch not installed - skipping backend comparison test")
-
-    args = [
-        "--alleles", "HLA-A0201",
-        "--alleles", "HLA-A0201", "HLA-A0301",
-        "--peptides", "SIINFEKL", "SIINFEKD", "SIINFEKQ",
-        "--prediction-column-prefix", "mhcflurry_",
-        "--affinity-only",
-    ]
-
-    with tempfile.NamedTemporaryFile(mode='w', suffix=".csv", delete=False) as tf_output:
-        tf_path = tf_output.name
-        with tempfile.NamedTemporaryFile(mode='w', suffix=".csv", delete=False) as torch_output:
-            torch_path = torch_output.name
-
-            try:
-                # Run with tensorflow backend
-                tf_args = args + ["--out", tf_path, "--backend", "tensorflow"]
-                print("Running tensorflow with args: %s" % tf_args)
-                predict_command.run(tf_args)
-                result_tf = pandas.read_csv(tf_path)
-                print("TensorFlow results:")
-                print(result_tf)
-
-                # Run with pytorch backend  
-                torch_args = args + ["--out", torch_path, "--backend", "pytorch"]
-                print("Running pytorch with args: %s" % torch_args)
-                predict_command.run(torch_args)
-                result_torch = pandas.read_csv(torch_path)
-                print("PyTorch results:")
-                print(result_torch)
 
             finally:
                 # Clean up files
-                for path in [tf_path, torch_path]:
+                for path in [input_path, output_path]:
                     try:
                         os.unlink(path)
                     except OSError as e:
                         if e.errno != errno.ENOENT:  # No such file or directory
                             print(f"Error removing file {path}: {e}")
 
-    # Verify both backends produced results
-    assert result_tf is not None, "TensorFlow backend failed to produce results"
-    assert result_torch is not None, "PyTorch backend failed to produce results"
-
-    # Verify both results contain predictions
-    prediction_columns = [col for col in result_tf.columns if col.startswith("mhcflurry_")]
-    assert len(prediction_columns) > 0, "No prediction columns found in TensorFlow results"
-
-    # Check that no prediction columns contain all nulls
-    for col in prediction_columns:
-        assert not result_tf[col].isnull().all(), f"TensorFlow predictions are all null for column {col}"
-        assert not result_torch[col].isnull().all(), f"PyTorch predictions are all null for column {col}"
-
-        # Verify predictions are numeric and within expected ranges
-        assert result_tf[col].dtype in ['float64', 'float32'], f"TensorFlow column {col} is not numeric"
-        assert result_torch[col].dtype in ['float64', 'float32'], f"PyTorch column {col} is not numeric"
-
-        if "affinity" in col.lower():
-            # Affinity predictions should be positive numbers
-            assert (result_tf[col] > 0).all(), f"Invalid affinity values in TensorFlow column {col}"
-            assert (result_torch[col] > 0).all(), f"Invalid affinity values in PyTorch column {col}"
-        elif "percentile" in col.lower():
-            # Percentile predictions should be between 0 and 100
-            assert ((result_tf[col] >= 0) & (result_tf[col] <= 100)).all(), \
-                f"Invalid percentile values in TensorFlow column {col}"
-            assert ((result_torch[col] >= 0) & (result_torch[col] <= 100)).all(), \
-                f"Invalid percentile values in PyTorch column {col}"
-
-    # Check that results match
-    assert result_tf.shape == result_torch.shape, "Output shapes differ"
-    assert all(result_tf.columns == result_torch.columns), "Output columns differ"
-
-    # Compare numeric columns with tolerance
-    numeric_columns = [
-        col for col in result_tf.columns 
-        if col.startswith("mhcflurry_") and result_tf[col].dtype in ['float64', 'float32']
-    ]
-
-    for col in numeric_columns:
-        print(f"Comparing {col}:")
-        print(f"TensorFlow: {result_tf[col].values}")
-        print(f"PyTorch: {result_torch[col].values}")
-        assert_array_almost_equal(
-            result_tf[col].values,
-            result_torch[col].values,
-            decimal=4,
-            err_msg=f"Values differ in column {col}"
-        )
-
-    # Compare non-numeric columns exactly
-    other_columns = [col for col in result_tf.columns if col not in numeric_columns]
-    for col in other_columns:
-        assert all(result_tf[col] == result_torch[col]), f"Values differ in column {col}"
-
 
 def test_no_csv():
     args = [
-        "--alleles", "HLA-A0201", "H-2-Kb",
-        "--peptides", "SIINFEKL", "DENDREKLLL", "PICKLEEE",
-        "--prediction-column-prefix", "mhcflurry1_",
+        "--alleles",
+        "HLA-A0201",
+        "H-2-Kb",
+        "--peptides",
+        "SIINFEKL",
+        "DENDREKLLL",
+        "PICKLEEE",
+        "--prediction-column-prefix",
+        "mhcflurry1_",
         "--affinity-only",
     ]
 
-    with tempfile.NamedTemporaryFile(mode='w', suffix=".csv", delete=False) as output_file:
+    with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as output_file:
         output_path = output_file.name
         try:
             full_args = args + ["--out", output_path]
             print("Running with args: %s" % full_args)
             predict_command.run(full_args)
-            
+
             result = pandas.read_csv(output_path)
             print(result)
 
             # Verify results
             assert len(result) == 6
             sub_result1 = result.loc[result.peptide == "SIINFEKL"].set_index("allele")
             print(sub_result1)
-            assert (
-                sub_result1.loc["H-2-Kb"].mhcflurry1_affinity <
-                sub_result1.loc["HLA-A0201"].mhcflurry1_affinity)
+            assert sub_result1.loc["H-2-Kb"].mhcflurry1_affinity < sub_result1.loc["HLA-A0201"].mhcflurry1_affinity
 
         finally:
             try: