formatting

examples/oom_training.py

@@ -227,8 +227,7 @@ def __init__(
         # mock the target scaler used for reporting some human-readable metrics
         self.target_scaler = SimpleNamespace(n_features_in_=1, inverse_transform=lambda i: np.array(i))
 
-    def setup(self, stage=None):
-        ...  # skip feature scaling and dataset splitting
+    def setup(self, stage=None): ...  # skip feature scaling and dataset splitting
 
     def _init_dataloader(self, shuffle, idxs):
         return TorchDataloader(

fastprop/cli/base.py

@@ -32,7 +32,9 @@ def main():
     train_subparser.add_argument("-tc", "--target-columns", nargs="+", help="column name(s) for target(s)")
     train_subparser.add_argument("-sc", "--smiles-column", help="column name for SMILES")
     train_subparser.add_argument("-ds", "--descriptor-set", help="descriptors to calculate (one of all, optimized, or debug)")
-    train_subparser.add_argument("-s", "--standardize", action="store_true", default=False, help="call rdMolStandardize.Cleanup function on molecules")
+    train_subparser.add_argument(
+        "-s", "--standardize", action="store_true", default=False, help="call rdMolStandardize.Cleanup function on molecules"
+    )
     train_subparser.add_argument("-ec", "--enable-cache", type=bool, help="allow saving and loading of cached descriptors")
     train_subparser.add_argument("-p", "--precomputed", help="precomputed descriptors from fastprop or mordred")
 

fastprop/data.py

@@ -1,4 +1,3 @@
-
 from typing import List, Literal, Optional, Tuple
 
 import numpy as np
@@ -76,7 +75,7 @@ def split(
                 val_idxs,
             ) = train_test_split_molecules(smiles, test_size=val_size, **split_kwargs)
             test_idxs = np.array([])
-        
+
     return train_idxs, val_idxs, test_idxs