Update README.md

README.md

@@ -126,11 +126,12 @@ then load, featurize, split, fit, and evaluate the catboost model
       representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
 
     model = load_model_from_dict({
-        "name": "cat_boost_classifier",
+        "name": "random_forest_regressor",
         "config": {
-            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
-            "y_features": ['Solubility']}})
-    
+            "x_features": ['smiles::morgan', 'smiles::maccs_rdkit'],
+            "y_features": ['log_solubility'],
+        })
+        
     model.train(split_featurised_dataset["train"])
     preds = model.predict(split_featurised_dataset["test"])
     
@@ -140,6 +141,58 @@ then load, featurize, split, fit, and evaluate the catboost model
         references=split_featurised_dataset["test"]['Solubility'],
         predictions=preds["cat_boost_classifier::Solubility"])
 
+
+
+import json
+
+from molflux.modelzoo import load_from_dict as load_model_from_dict
+from molflux.metrics import load_suite
+
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+
+import matplotlib.pyplot as plt
+
+model = load_model_from_dict(
+    {
+        "name": "random_forest_classifier",
+        "config": {
+            "x_features": ['smiles::morgan', 'smiles::maccs_rdkit'],
+            "y_features": ['log_solubility_cls'],
+        }
+    }
+)
+
+model.train(split_featurised_dataset["train"])
+
+preds = model.predict(split_featurised_dataset["test"])
+
+regression_suite = load_suite("classification")
+
+scores = regression_suite.compute(
+    references=split_featurised_dataset["test"]["log_solubility_cls"],
+    predictions=preds["random_forest_classifier::log_solubility_cls"],
+)
+
+print(json.dumps({k: round(v, 2) for k, v in scores.items()}, indent=4))
+
+cm = confusion_matrix(
+    split_featurised_dataset["test"]["log_solubility_cls"],
+    preds["random_forest_classifier::log_solubility_cls"],
+    labels=[0, 1]
+)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[0, 1])
+disp.plot()
+plt.show()
+{
+    "accuracy": 0.83,
+    "balanced_accuracy": 0.83,
+    "precision": 0.83,
+    "recall": 0.85,
+    "f1_score": 0.84,
+    "matthews_corrcoef": 0.66
+}
+        
+
 ## AttentiveSkin
 To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods
 Download: https://github.com/BeeBeeWong/AttentiveSkin/releases/tag/v1.0