Update README.md

README.md

@@ -73,7 +73,7 @@ dataset_info:
       dtype: float64  
     - name: "logBB"       
       dtype: float64 
-    - name: "BBB+/BBB-"            
+    - name: "Y"            
       dtype:
         class_label:
           names:
@@ -114,7 +114,7 @@ dataset_info:
       dtype: float64
     - name: "logBB"
       dtype: float64
-    - name: "BBB+/BBB-"            
+    - name: "Y"            
       dtype:
         class_label:
           names:
@@ -3379,7 +3379,7 @@ dataset_info:
       dtype: string
     - name: "CID"
       dtype: string
-    - name: "logBB"
+    - name: "Y"
       dtype: float64
     - name: "Inchi"
       dtype: string
@@ -3412,7 +3412,7 @@ dataset_info:
       dtype: string
     - name: "CID"
       dtype: string
-    - name: "logBB"
+    - name: "Y"
       dtype: float64
     - name: "Inchi"
       dtype: string
@@ -6705,11 +6705,11 @@ and inspecting the loaded dataset
     B3DB_classification
     DatasetDict({
       test: Dataset({
-         features: ['B3DB_classification_index', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
+         features: ['B3DB_classification_index', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'Y', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
          num_rows: 1951
       })
       train: Dataset({
-          features: ['B3DB_classification_index', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
+          features: ['B3DB_classification_index', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'Y', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
           num_rows: 5856
       })
     })
@@ -6741,7 +6741,7 @@ then load, featurize, split, fit, and evaluate the a catboost model
         "name": "cat_boost_classifier",
         "config": {
             "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
-            "y_features": ['BBB+/BBB-']}})
+            "y_features": ['Y']}})
     
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
@@ -6749,8 +6749,8 @@ then load, featurize, split, fit, and evaluate the a catboost model
     classification_suite = load_suite("classification")
     
     scores = classification_suite.compute(
-        references=split_featurised_dataset["test"]['BBB+/BBB-'],
-        predictions=preds["cat_boost_classifier::BBB+/BBB-"])
+        references=split_featurised_dataset["test"]['Y'],
+        predictions=preds["cat_boost_classifier::Y"])
 
 
 ## About the DB3B