used existing split rather than MolFlux splitting

README.md

@@ -143,27 +143,20 @@ First, from the command line, install `MolFlux` library with `catboost` and `rdk
 then load, featurize, split, fit, and evaluate the a catboost model
 
     import json
-    from molflux.datasets import load_dataset, featurise_dataset, split_dataset
+    from datasets import load_set
+    from molflux.datasets import featurise_dataset
     from molflux.features import load_from_dicts as load_representations_from_dicts
     from molflux.splits import load_from_dict as load_split_from_dict
     from molflux.modelzoo import load_from_dict as load_model_from_dict
     from molflux.metrics import load_suite
     
-    dataset = load_dataset('maomlab/B3DB', config_name = 'B3DB_classification')
+    split_dataset = load_dataset('maomlab/B3DB', name = 'B3DB_classification')
     
-    featurised_dataset = featurise_dataset(
-      dataset,
+    split_featurised_dataset = featurise_dataset(
+      split_dataset,
       column = "SMILES",
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
-    
-    shuffle_strategy = load_split_from_dict({
-        "name": "shuffle_split",
-        "presets": {
-            "train_fraction": 0.8,
-            "validation_fraction": 0.0,
-            "test_fraction": 0.2}})
-    split_featurised_dataset = next(split_dataset(featurised_dataset, shuffle_strategy))
-            
+
     model = load_model_from_dict({
         "name": "cat_boost_classifier",
         "config": {