Create inspect_model.py

inspect_model.py

@@ -0,0 +1,51 @@
+# 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 {})