Update README.md

README.md

@@ -50,9 +50,9 @@ dataset_info:
   features:
   - name: ID
     dtype: string
-  - name: Smiles
+  - name: SMILES
     dtype: string
-  - name: 'Y'
+  - name: Y
     dtype:
       class_label:
         names:
@@ -72,9 +72,9 @@ dataset_info:
   features:
   - name: ID
     dtype: string
-  - name: Smiles
+  - name: SMILES
     dtype: string
-  - name: 'Y'
+  - name: Y
     dtype:
       class_label:
         names:
@@ -92,7 +92,7 @@ dataset_info:
     num_examples: 2482
 - config_name: Marketed_Drug
   features:
-  - name: Smiles
+  - name: SMILES
     dtype: string
   - name: Class
     dtype:
@@ -159,15 +159,15 @@ and inspecting the loaded dataset
     HLM
     DatasetDict({
       test: Dataset({
-         features: ['ChEMBL ID (source)', 'IUPAC Names', 'Smiles', 'Class', 'Dataset'],
+         features: ['ID','SMILES', 'Y'],
          num_rows: 1131
       })
       train: Dataset({
-          features: ['ChEMBL ID (source)', 'IUPAC Names', 'Smiles', 'Class', 'Dataset'],,
+          features: ['ID','SMILES', 'Y'],
           num_rows: 4771
       }) 
       external: Dataset({
-          features: ['ChEMBL ID (source)', 'IUPAC Names', 'Smiles', 'Class', 'Dataset'],
+          features: ['ID','SMILES', 'Y'],
           num_rows: 111          
       })
     })
@@ -192,13 +192,13 @@ then load, featurize, split, fit, and evaluate the a catboost model
     
     split_featurised_dataset = featurise_dataset(
       split_dataset,
-      column = "Smiles",
+      column = "SMILES",
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
 
     model = load_model_from_dict({
         "name": "cat_boost_classifier",
         "config": {
-            "x_features": ['Smiles::morgan', 'Smiles::maccs_rdkit'],
+            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
             "y_features": ['Class'],
         }})