Yn patch 1 (#310)

* [Fix] Predict input without target_col will cause exception, introduced in the last update.
[Fix] When smiles is None, using atoms as SMILES for splitting.
[Update] fix typo and version 0.1.2.post2

* [Update] Safer way to handle predictions with missing predict_cols

unimol_tools/setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name="unimol_tools",
-    version="0.1.2.post1",
+    version="0.1.2.post2",
     description=("unimol_tools is a Python package for property prediciton with Uni-Mol in molecule, materials and protein."),
     long_description=open('README.md').read(),
     long_description_content_type='text/markdown',

unimol_tools/unimol_tools/data/datareader.py

@@ -83,17 +83,18 @@ def read_data(self, data=None, is_train=True, **params):
             elif isinstance(target_cols, list):
                 pass
             else:
-                for col in target_cols:
-                    if col not in data.columns:
-                        data[target_cols] = -1.0
-                        break
-
+                raise ValueError('Unknown target_cols type: {}'.format(type(target_cols)))
+
             if is_train:
                 if anomaly_clean:
                     data = self.anomaly_clean(data, task, target_cols)  
                 if task == 'multiclass':
                     multiclass_cnt = int(data[target_cols].max() + 1)
-
+            else:
+                for col in target_cols:
+                    if col not in data.columns or data[col].isnull().any():
+                        data[col] = -1.0
+
             targets = data[target_cols].values.tolist()
             num_classes = len(target_cols)
 

unimol_tools/unimol_tools/data/split.py

@@ -67,6 +67,9 @@ def split(self, smiles, target=None, group=None, scaffolds=None, **params):
         if self.n_splits == 1:
             logger.warning('Only one fold is used for training, no splitting is performed.')
             return [(np.arange(len(smiles)), ())]
+        if smiles is None and 'atoms' in params:
+            smiles = params['atoms']
+            logger.warning('Atoms are used as SMILES for splitting.')
         if self.method in ['random']:
             self.skf = self.splitter.split(smiles)
         elif self.method in ['scaffold']:

unimol_tools/unimol_tools/predict.py

@@ -47,7 +47,7 @@ def predict(self, data, save_path=None, metrics='none'):
 
             - classification: auc, auprc, log_loss, acc, f1_score, mcc, precision, recall, cohen_kappa. 
 
-            - regression: mse, pearsonr, spearmanr, mse, r2.
+            - regression: mae, pearsonr, spearmanr, mse, r2.
 
             - multiclass: log_loss, acc.