Update README.md

README.md

@@ -2,7 +2,7 @@
 version: 1.0.0
 license: cc-by-sa-4.0
 task_categories:
-- tabular-regression
+- tabular-classification
 language:
 - en
 pretty_name: MolData
@@ -42,6 +42,8 @@ dataset_info:
     dtype: string
   - name: 'Y'
     dtype: int64
+    description: >-
+        Binary classification (0/1)   
   splits:
   - name: train
     num_bytes: 12634275804
@@ -76,8 +78,9 @@ This is a mirror of the [Official Github repo](https://github.com/LumosBio/MolDa
 
 We utilized the raw data uploaded on [Github](https://github.com/LumosBio/MolData) and performed several preprocessing:
 1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
-2. Rename the columns
-3. Split the dataset (train, test, validation)
+2. Formatting (from wide form to long form)
+3. Rename the columns
+4. Split the dataset (train, test, validation)
 
 If you would like to try these processes with the original dataset, 
 please follow the instructions in the [Preprocessing Script.py](address) file located in our MolData repository.
@@ -145,9 +148,9 @@ Split and evaluate the catboost model
       split_dataset,
       column = "SMILES",
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
-
+    
     model = load_model_from_dict({
-        "name": "cat_boost_regressor",
+        "name": "cat_boost_classifier",
         "config": {
             "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
             "y_features": ['Y']}})
@@ -155,11 +158,11 @@ Split and evaluate the catboost model
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
     
-    regression_suite = load_suite("regression")
+    classification_suite = load_suite("classification")
     
-    scores = regression_suite.compute(
+    scores = classification_suite.compute(
         references=split_featurised_dataset["test"]['Y'],
-        predictions=preds["cat_boost_regressor::Y"])  
+        predictions=preds["cat_boost_classifier::Y"])
 
 
 ### Citation