Update README.md

README.md

@@ -9,9 +9,9 @@ pretty_name: AggregatorAdvisor
 size_categories:
 - 10K<n<100K
 dataset_summary: >-
-   AggregatorAdvisor identifies molecules that are known to aggregate or may aggregate in biochemical assays.
-   The approach is based on the chemical similarity to known aggregators, and physical properties.
-   The AggregatorAdvisor dataset contains 12645 compounds from 20 different sources.
+  AggregatorAdvisor identifies molecules that are known to aggregate or may aggregate in biochemical assays.
+  The approach is based on the chemical similarity to known aggregators, and physical properties.
+  The AggregatorAdvisor dataset contains 12645 compounds from 20 different sources.
 citation: >-
   @article 
     {Irwin2015, title = {An Aggregation Advisor for Ligand Discovery},
@@ -60,3 +60,81 @@ dataset_info:
 
 # Aggregator Advisor 
 
+
+## 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 one of the `HematoxLong2023` datasets, e.g.,
+
+    >>> AggregatorAdvisor = datasets.load_dataset("maomlab/AggregatorAdvisor", name = "AggregatorAdvisor")
+    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
+
+    >>> AggregatorAdvisor
+    HematoxLong2023
+    DatasetDict({
+      test: Dataset({
+         features: ['new SMILES', 'label'],
+         num_rows: 594
+      })
+      train: Dataset({
+          features: ['new SMILES', 'label'],
+          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/HematoxLong2023', name = 'HematoxLong2023')
+    
+    split_featurised_dataset = featurise_dataset(
+      split_dataset,
+      column = "new SMILES",
+      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']}})
+    
+    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"])        
+
+## Citation