inspect_model.py

# inspect_model.py
import joblib, pprint, importlib, sys
p = "best_overall_XGBoost.joblib"
print("Python:", sys.version)
try:
    obj = joblib.load(p)
except Exception as e:
    import traceback
    traceback.print_exc()
    raise SystemExit(1)

print("Top-level object type:", type(obj))
# If it's a sklearn pipeline, list steps
try:
    from sklearn.pipeline import Pipeline
    if isinstance(obj, Pipeline):
        print("Detected sklearn Pipeline with steps:")
        for name, step in obj.steps:
            print(" -", name, ":", type(step))
    else:
        # try to detect XGBClassifier inside
        attrs = dir(obj)
        print("Top-level attrs preview:", attrs[:40])
        # print some attr types if present
        for candidate in ("estimator_", "steps", "named_steps", "booster", "get_booster", "classes_"):
            if hasattr(obj, candidate):
                val = getattr(obj, candidate)
                print(f"Has {candidate}: type = {type(val)}")
except Exception:
    pass

# versions
def ver(name):
    try:
        m = importlib.import_module(name)
        return getattr(m, "__version__", "unknown")
    except Exception:
        return "not installed"
for mod in ("xgboost", "sklearn", "numpy", "pandas"):
    print(f"{mod}:", ver(mod))

# If pipeline, inspect nested steps types
try:
    if hasattr(obj, "named_steps"):
        print("named_steps detail:")
        for k,v in obj.named_steps.items():
            print(k, "->", type(v))
except Exception:
    pass

pprint.pprint(obj.__dict__ if hasattr(obj, "__dict__") else {})