Update README.md

README.md

@@ -72,4 +72,82 @@ We utilized the raw data uploaded on [Github](https://github.com/LumosBio/MolDat
 3. 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.
+please follow the instructions in the [Preprocessing Script.py](address) file located in our MolData repository.
+
+
+
+## Quickstart Usage
+
+### Load a dataset in python
+Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
+First, from the command line install the `datasets` library
+
+    $ pip install datasets
+
+then, from within python load the datasets library
+
+    >>> import datasets
+   
+and load the `MolData` datasets, e.g.,
+
+    >>> MolData = datasets.load_dataset("maomlab/MolData")
+    Downloading readme: 100%|██████████| 5.23k/5.23k [00:00<00:00, 35.1kkB/s]
+    Downloading data: 100%|██████████| 34.5k//34.5k/ [00:00<00:00, 155kB/s]
+    Downloading data: 100%|██████████| 97.1k/97.1k [00:00<00:00, 587kB/s]
+    Generating test split: 100%|██████████| 594/594 [00:00<00:00, 12705.92 examples/s]
+    Generating train split: 100%|██████████| 1788/1788 [00:00<00:00, 43895.91 examples/s]
+
+and inspecting the loaded dataset
+
+    >>> MolData
+    MolData
+    DatasetDict({
+      test: Dataset({
+         features: ['SMILES', 'Y'],
+         num_rows: 594
+      })
+      train: Dataset({
+          features: ['SMILES', 'Y'],
+          num_rows: 1788
+      })    
+    })
+
+### 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.
+First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
+
+    pip install 'molflux[catboost,rdkit]'
+
+then load, featurize, split, fit, and evaluate the catboost model
+
+    import json
+    from datasets import load_dataset
+    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
+
+Split and evaluate the catboost model
+    
+    split_dataset = load_dataset('maomlab/MolData', name = 'MolData')
+    
+    split_featurised_dataset = featurise_dataset(
+      split_dataset,
+      column = "SMILES",
+      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
+
+    model = load_model_from_dict({
+        "name": "cat_boost_regressor",
+        "config": {
+            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
+            "y_features": ['Y']}})
+    
+    model.train(split_featurised_dataset["train"])
+    preds = model.predict(split_featurised_dataset["test"])
+    
+    regression_suite = load_suite("regression")
+    
+    scores = regression_suite.compute(
+        references=split_featurised_dataset["test"]['Y'],
+        predictions=preds["cat_boost_regressor::Y"])