| """Export the trained Random Forest to model.json so a browser can run it (no server needed).""" | |
| import json | |
| import joblib | |
| bundle = joblib.load("model.joblib") | |
| model, classes = bundle["model"], bundle["classes"] | |
| trees = [] | |
| for est in model.estimators_: | |
| t = est.tree_ | |
| values = t.value[:, 0, :] | |
| probs = (values / values.sum(axis=1, keepdims=True)).round(6).tolist() | |
| trees.append({ | |
| "left": t.children_left.tolist(), | |
| "right": t.children_right.tolist(), | |
| "feature": t.feature.tolist(), | |
| "threshold": [float(x) for x in t.threshold], | |
| "probs": probs, | |
| }) | |
| with open("model.json", "w") as f: | |
| json.dump({"classes": classes, "trees": trees}, f, separators=(",", ":")) | |
| print(f"Saved model.json with {len(trees)} trees") | |