add integration test for nan losses cases

tests/test_integration.py

@@ -1,16 +1,21 @@
+import glob
 import os
 import warnings
 from shutil import rmtree
 from tempfile import mkdtemp
 
 import h5py
+import pandas as pd
+import pytest
+import torch
 
 from deeprank2.dataset import GraphDataset, GridDataset
 from deeprank2.domain import edgestorage as Efeat
 from deeprank2.domain import nodestorage as Nfeat
 from deeprank2.domain import targetstorage as targets
 from deeprank2.neuralnets.cnn.model3d import CnnClassification
 from deeprank2.neuralnets.gnn.ginet import GINet
+from deeprank2.neuralnets.gnn.naive_gnn import NaiveNetwork
 from deeprank2.query import (ProteinProteinInterfaceResidueQuery,
                              QueryCollection)
 from deeprank2.tools.target import compute_ppi_scores
@@ -196,3 +201,77 @@ def test_gnn(): # pylint: disable=too-many-locals
     finally:
         rmtree(hdf5_directory)
         rmtree(output_directory)
+
+@pytest.fixture(scope='session')
+def hdf5_files_for_nan(tmpdir_factory):
+    # For testing cases in which the loss function is nan for the validation and/or for
+    # the training sets. It doesn't matter if the dataset is a GraphDataset or a GridDataset,
+    # since it is a functionality of the trainer module, which does not depend on the dataset type.
+    # The settings and the parameters have been carefully chosen to result in nan losses.
+    pdb_paths = [
+        "tests/data/pdb/3C8P/3C8P.pdb",
+        "tests/data/pdb/1A0Z/1A0Z.pdb",
+        "tests/data/pdb/1ATN/1ATN_1w.pdb"
+        ]
+    chain_id1 = "A"
+    chain_id2 = "B"
+    targets_values = [0, 1, 1]
+    prefix = os.path.join(tmpdir_factory.mktemp("data"), "test-queries-process")
+
+    queries = QueryCollection()
+    for idx, pdb_path in enumerate(pdb_paths):
+        query = ProteinProteinInterfaceResidueQuery(
+            pdb_path,
+            chain_id1,
+            chain_id2,
+            # A very low cutoff distance helps for not making the network to learn
+            distance_cutoff=3,
+            targets = {targets.BINARY: targets_values[idx]}
+        )
+        queries.add(query)
+
+    hdf5_paths = queries.process(prefix = prefix)
+    return hdf5_paths
+
+@pytest.mark.parametrize("validate, best_model", [(True, True), (False, True), (False, False), (True, False)])
+def test_nan_loss_cases(validate, best_model, hdf5_files_for_nan):
+    mols = []
+    for fname in hdf5_files_for_nan:
+        with h5py.File(fname, 'r') as hdf5:
+            for mol in hdf5.keys():
+                mols.append(mol)
+
+    dataset_train = GraphDataset(
+        hdf5_path = hdf5_files_for_nan,
+        subset = mols[1:],
+        target = targets.BINARY,
+        task = targets.CLASSIF
+        )
+    dataset_valid = GraphDataset(
+        hdf5_path = hdf5_files_for_nan,
+        subset = [mols[0]],
+        dataset_train=dataset_train,
+        train=False
+        )
+
+    trainer = Trainer(
+        NaiveNetwork,
+        dataset_train,
+        dataset_valid)
+
+    optimizer = torch.optim.SGD
+    lr = 10000
+    weight_decay = 10000
+
+    trainer.configure_optimizers(optimizer, lr, weight_decay=weight_decay)
+    w_msg = "A model has been saved but the validation and/or the training losses were NaN;" + \
+    "\n\ttry to increase the cutoff distance during the data processing or the number of data points " + \
+    "during the training."
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("ignore", category=pd.errors.PerformanceWarning)
+        trainer.train(
+            nepoch=5, batch_size=1, validate=validate,
+            best_model=best_model, filename='test.pth.tar')
+        assert len(w) == 1
+        assert issubclass(w[-1].category, UserWarning)
+        assert w_msg in str(w[-1].message)