Multimolecule features (#242)

* Allows fingerprinting of single-molecule lists when using a multi-molecule model within limitations. Added fingerprinting and multi-molecule classification tests. Fingerprinting output now differentiates between molecules for multi-molecule input, rather than treating the fingerprints of all molecules for a row as a single set of fingerprints from a single molecule.

* Added multimolecule classification test file

* Fixed column ordering for fingerprint outout

* Allows number-of-molecules mismatch between fingerprinting data and multi-molecule models only when the fingerprinting data has one molecule per entry.

* Fixed accidentally hardcoded value for the number of molecules in a fingerprint output file.

* Update tests/test_integration.py

Co-authored-by: Charles McGill <44245643+cjmcgill@users.noreply.github.com>

* Updated single task fingerprinting test to aggregrate fingerprint values and compare sum rather than comparing against a reference true_fingerprint file

Co-authored-by: Charles McGill <44245643+cjmcgill@users.noreply.github.com>

chemprop/train/molecule_fingerprint.py

@@ -137,13 +137,25 @@ def molecule_fingerprint(args: FingerprintArgs, smiles: List[List[str]] = None)
 
     # Set column names
     fingerprint_columns = []
-    if len(args.checkpoint_paths) == 1:
-        for j in range(total_fp_size):
-            fingerprint_columns.append(f'fp_{j}')
-    else:
-        for j in range(total_fp_size):
-            for i in range(len(args.checkpoint_paths)):
-                fingerprint_columns.append(f'fp_{j}_model_{i}')
+    if args.fingerprint_type == 'MPN':
+        if len(args.checkpoint_paths) == 1:
+            for j in range(total_fp_size//args.number_of_molecules):
+                for k in range(args.number_of_molecules):
+                    fingerprint_columns.append(f'fp_{j}_mol_{k}')
+        else:
+            for j in range(total_fp_size//args.number_of_molecules):
+                for i in range(len(args.checkpoint_paths)):
+                    for k in range(args.number_of_molecules):
+                        fingerprint_columns.append(f'fp_{j}_mol_{k}_model_{i}')
+
+    else: # args == 'last_FNN'
+        if len(args.checkpoint_paths) == 1:
+            for j in range(total_fp_size):
+                fingerprint_columns.append(f'fp_{j}')
+        else:
+            for j in range(total_fp_size):
+                for i in range(len(args.checkpoint_paths)):
+                    fingerprint_columns.append(f'fp_{j}_model_{i}')
 
     # Copy predictions over to full_data
     for full_index, datapoint in enumerate(full_data):

chemprop/utils.py

@@ -16,7 +16,7 @@
 from torch.optim.lr_scheduler import _LRScheduler
 from tqdm import tqdm
 
-from chemprop.args import PredictArgs, TrainArgs
+from chemprop.args import PredictArgs, TrainArgs, FingerprintArgs
 from chemprop.data import StandardScaler, MoleculeDataset, preprocess_smiles_columns, get_task_names
 from chemprop.models import MoleculeModel
 from chemprop.nn_utils import NoamLR
@@ -577,10 +577,11 @@ def update_prediction_args(predict_args: PredictArgs,
                 setattr(predict_args,key,override_defaults.get(key,value))
 
     # Same number of molecules must be used in training as in making predictions
-    if train_args.number_of_molecules != predict_args.number_of_molecules:
+    if train_args.number_of_molecules != predict_args.number_of_molecules \
+            and not (isinstance(predict_args, FingerprintArgs) and predict_args.fingerprint_type == "MPN" and predict_args.mpn_shared and predict_args.number_of_molecules == 1):
         raise ValueError('A different number of molecules was used in training '
-                        f'model than is specified for prediction, {train_args.number_of_molecules} '
-                         'smiles fields must be provided')
+                         'model than is specified for prediction. This is only supported for models with shared MPN networks'
+                         f'and a fingerprint type of MPN. {train_args.number_of_molecules} smiles fields must be provided.')
 
     # If atom-descriptors were used during training, they must be used when predicting and vice-versa
     if train_args.atom_descriptors != predict_args.atom_descriptors: