Update README.md

README.md

@@ -142,57 +142,6 @@ then load, featurize, split, fit, and evaluate the catboost model
         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