Spaces:
Sleeping
Sleeping
| import requests, json, time | |
| print("Running benchmark on breast_cancer (569 rows, 30 features)...") | |
| t0 = time.time() | |
| with open("webapp/test_upload.csv", "rb") as f: | |
| r = requests.post( | |
| "http://localhost:8000/benchmark", | |
| files={"file": ("test.csv", f, "text/csv")}, | |
| data={"target_col": "target"}, | |
| timeout=300 | |
| ) | |
| elapsed = time.time() - t0 | |
| if r.status_code == 200: | |
| d = r.json() | |
| task = d["task"] | |
| pk = "roc_auc" if task == "classification" else "r2" | |
| print(f"Task: {task} | Time: {elapsed:.1f}s\n") | |
| for model, res in d["results"].items(): | |
| if "error" in res: | |
| err = res["error"] | |
| print(f" {model:15s} ERROR: {err}") | |
| else: | |
| score = res["mean"].get(pk, res["mean"].get("accuracy", 0)) | |
| ft = res["mean"]["fit_time"] | |
| print(f" {model:15s} {pk}={score:.4f} fit_time={ft:.3f}s") | |
| print() | |
| rec = d["recommendation"]["recommendations"] | |
| print("RECOMMENDATION:") | |
| print(f" Best Overall: {rec['best_overall']['model']}") | |
| print(f" Best Accuracy: {rec['best_accuracy']['model']}") | |
| print(f" Fastest: {rec['best_speed']['model']}") | |
| print(f" Most Consistent: {rec['best_consistency']['model']}") | |
| print(f" Production: {rec['production']['model']}") | |
| else: | |
| print("ERROR", r.status_code, r.text[:500]) | |