hoàn thành chức năng tính ndvi analysys 2 màn hình

inspect_model_odc.py

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+"""
+Inspect model_odc.joblib to see what it actually contains
+"""
+
+import joblib
+from pathlib import Path
+
+model_path = Path("model_train/model_odc.joblib")
+
+if model_path.exists():
+    print("Loading model_odc.joblib...")
+    model_data = joblib.load(model_path)
+    
+    print(f"\nModel type: {type(model_data)}")
+    print(f"Model class: {model_data.__class__.__name__}")
+    
+    # Check if it's a dict
+    if isinstance(model_data, dict):
+        print(f"\nModel is a dict with keys: {model_data.keys()}")
+        model = model_data.get('model')
+    else:
+        model = model_data
+    
+    print(f"\nActual model type: {type(model)}")
+    print(f"Actual model class: {model.__class__.__name__}")
+    
+    # Try to get feature info
+    if hasattr(model, 'n_features_in_'):
+        print(f"\nn_features_in_: {model.n_features_in_}")
+    
+    if hasattr(model, 'feature_names_in_'):
+        print(f"feature_names_in_: {model.feature_names_in_}")
+    
+    # If it's a GridSearchCV
+    if hasattr(model, 'best_estimator_'):
+        print(f"\nThis is a GridSearchCV!")
+        print(f"Best estimator: {model.best_estimator_}")
+        
+        best_est = model.best_estimator_
+        if hasattr(best_est, 'steps'):
+            print(f"\nPipeline steps:")
+            for step_name, step in best_est.steps:
+                print(f"  - {step_name}: {step.__class__.__name__}")
+                if hasattr(step, 'n_features_in_'):
+                    print(f"      n_features_in_: {step.n_features_in_}")
+    
+    # If it's a Pipeline
+    if hasattr(model, 'steps'):
+        print(f"\nThis is a Pipeline!")
+        print(f"Pipeline steps:")
+        for step_name, step in model.steps:
+            print(f"  - {step_name}: {step.__class__.__name__}")
+            if hasattr(step, 'n_features_in_'):
+                print(f"      n_features_in_: {step.n_features_in_}")
+    
+    # Try to get booster for XGBoost
+    try:
+        if hasattr(model, 'get_booster'):
+            print(f"\nXGBoost num_features: {model.get_booster().num_features()}")
+    except:
+        pass
+    
+else:
+    print(f"Model file not found: {model_path}")