Update README.md

README.md

@@ -45,7 +45,7 @@ dataset_info:
       dtype: string
     - name: "SMILES"
       dtype: string
-    - name: "Solubility"
+    - name: "Y"
       dtype: float64
     - name: "SD"
       dtype: float64
@@ -136,12 +136,12 @@ and inspecting the loaded dataset
     AqSolDB
     DatasetDict({
         test: Dataset({
-            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
+            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Y', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
     ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'],
             num_rows: 2494
         })
         train: Dataset({
-            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
+            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Y', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
     ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'],
             num_rows: 7488
         })
@@ -174,7 +174,7 @@ then load, featurize, split, fit, and evaluate the catboost model
         "name": "cat_boost_regressor",
         "config": {
             "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
-            "y_features": ['Solubility']}})
+            "y_features": ['Y']}})
     
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
@@ -182,8 +182,8 @@ then load, featurize, split, fit, and evaluate the catboost model
     regression_suite = load_suite("regression")
     
     scores = regression_suite.compute(
-        references=split_featurised_dataset["test"]['Solubility'],
-        predictions=preds["cat_boost_regressor::Solubility"])    
+        references=split_featurised_dataset["test"]['Y'],
+        predictions=preds["cat_boost_regressor::Y"])    
 
 
 ## Aqueous Solubility Database