Update README.md

README.md

@@ -49,7 +49,7 @@ dataset_info:
     dtype: string
   - name: CAS RN
     dtype: string
-  - name: GHS
+  - name: Y
     dtype:
       class_label:
         names:
@@ -90,7 +90,7 @@ dataset_info:
     dtype: string
   - name: CAS RN
     dtype: string
-  - name: GHS
+  - name: Y
     dtype:
       class_label:
         names:
@@ -155,11 +155,11 @@ and inspecting the loaded dataset
     >>> Corr_Neg
     DatasetDict({
     test: Dataset({
-        features: ['Name', 'Synonym', 'CAS RN', 'GHS', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split'],
+        features: ['Name', 'Synonym', 'CAS RN', 'Y', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split'],
         num_rows: 181
     })
     train: Dataset({
-        features: ['Name', 'Synonym', 'CAS RN', 'GHS', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split'],
+        features: ['Name', 'Synonym', 'CAS RN', 'Y', 'Detailed Page', 'Evidence', 'OECD TG 404', 'Data Source', 'Frequency', 'SMILES', 'SMILES URL', 'SMILES Source', 'Canonical SMILES', 'Split'],
         num_rows: 1755
     })
 })
@@ -192,7 +192,7 @@ then load, featurize, split, fit, and evaluate the catboost model
         "name": "cat_boost_classifier",
         "config": {
             "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
-            "y_features": ['GHS']}})
+            "y_features": ['Y']}})
     
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
@@ -200,8 +200,8 @@ then load, featurize, split, fit, and evaluate the catboost model
     classification_suite = load_suite("classification")
     
     scores = classification_suite.compute(
-        references=split_featurised_dataset["test"]['GHS'],
-        predictions=preds["cat_boost_classifier::GHS"])
+        references=split_featurised_dataset["test"]['Y'],
+        predictions=preds["cat_boost_classifier::Y"])
 
 
 ### Data splits