Update README.md

README.md

@@ -76,26 +76,25 @@ then, from within python load the datasets library
 and load one of the `HematoxLong2023` datasets, e.g.,
 
     >>> AggregatorAdvisor = datasets.load_dataset("maomlab/AggregatorAdvisor", name = "AggregatorAdvisor")
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-    Generating train split: 100%|██████████| 1788/1788 [00:00<00:00, 43895.91 examples/s]
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+    Generating test split: 100%|██████████| 2529/2529 [00:00<00:00, 29924.07 examples/s]
+    Generating train split: 100%|██████████| 10116/10116 [00:00<00:00, 95081.99 examples/s]
 
 and inspecting the loaded dataset
 
     >>> AggregatorAdvisor
-    HematoxLong2023
     DatasetDict({
-      test: Dataset({
-         features: ['new SMILES', 'label'],
-         num_rows: 594
-      })
-      train: Dataset({
-          features: ['new SMILES', 'label'],
-          num_rows: 1788
-      })    
+    test: Dataset({
+        features: ['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference'],
+        num_rows: 2529
     })
+    train: Dataset({
+        features: ['new SMILES', 'substance_id', 'aggref_index', 'logP', 'reference'],
+        num_rows: 10116
+    })
+})
 
 ### Use a dataset to train a model
 One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
@@ -115,7 +114,7 @@ then load, featurize, split, fit, and evaluate the catboost model
 
 Split and evaluate the catboost model
     
-    split_dataset = load_dataset('maomlab/HematoxLong2023', name = 'HematoxLong2023')
+    split_dataset = load_dataset('maomlab/AggregatorAdvisor', name = 'AggregatorAdvisor')
     
     split_featurised_dataset = featurise_dataset(
       split_dataset,
@@ -123,18 +122,18 @@ Split and evaluate the catboost model
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
 
     model = load_model_from_dict({
-        "name": "cat_boost_classifier",
-        "config": {
-            "x_features": ['new SMILES::morgan', 'new SMILES::maccs_rdkit'],
-            "y_features": ['Label']}})
-    
+    "name": "cat_boost_regressor",
+    "config": {
+        "x_features": ['new SMILES::morgan', 'SMILES::maccs_rdkit'],
+        "y_features": ['logP']}})
+
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
-    
-    classification_suite = load_suite("classification")
-    
-    scores = classification_suite.compute(
-        references=split_featurised_dataset["test"]['Label'],
-        predictions=preds["cat_boost_classifier::Label"])        
+
+    regression_suite = load_suite("regression")
+
+    scores = regression_suite.compute(
+        references=split_featurised_dataset["test"]['logP'],
+        predictions=preds["cat_boost_regressor::logP"])    
 
 ## Citation