🚀 CHIẾN LƯỢC TOÀN DIỆN ĐẠT >95% ACCURACY
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Loaded data: X=(706, 24, 16, 16), y=(706,)
Labels unique: [-1  0  1  2  3  4  5  6]
After cleanup: X=(652, 24, 16, 16), y=(652,) (removed 54 bad samples)
Remapped labels: [0 1 2 3 4 5 6]
  Class 0: 65 samples
  Class 1: 52 samples
  Class 2: 48 samples
  Class 3: 72 samples
  Class 4: 108 samples
  Class 5: 219 samples
  Class 6: 88 samples

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STRATEGY 5: Flat pixel features + XGBoost (sanity check)
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  Flat features: (652, 6144)
  ✅ Flat XGBoost acc: 0.7328

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STRATEGY 1: Lightweight CNN (no upsampling)
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Device: cuda
  Epoch 1/300 Loss=1.8379 Acc=0.0763 🌟
  Epoch 2/300 Loss=1.5825 Acc=0.2824 🌟
  Epoch 3/300 Loss=1.4412 Acc=0.5649 🌟
  Epoch 4/300 Loss=1.3274 Acc=0.6336 🌟
  Epoch 5/300 Loss=1.2893 Acc=0.6870 🌟
  Epoch 8/300 Loss=1.2140 Acc=0.7099 🌟
  Epoch 11/300 Loss=1.2224 Acc=0.7252 🌟
  Epoch 14/300 Loss=1.1087 Acc=0.7328 🌟
  Epoch 16/300 Loss=1.1081 Acc=0.7710 🌟
  Epoch 18/300 Loss=1.1847 Acc=0.7939 🌟
  Epoch 20/300 Loss=1.0316 Acc=0.7786 (patience=2)
  Epoch 24/300 Loss=1.0465 Acc=0.8092 🌟
  Epoch 26/300 Loss=0.9583 Acc=0.8397 🌟
  Epoch 40/300 Loss=0.9361 Acc=0.8626 🌟
  Epoch 60/300 Loss=0.9807 Acc=0.8092 (patience=20)
  Epoch 80/300 Loss=0.9459 Acc=0.8015 (patience=40)
  Epoch 100/300 Loss=0.8443 Acc=0.7939 (patience=60)
  Early stop at epoch 100
  ✅ LightCNN best acc: 0.8626

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STRATEGY 2: Hybrid CNN embeddings + XGBoost
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  CNN embeddings: (652, 256)
Extracted 316 rich features per sample
  Combined features: (652, 572)
  ✅ Hybrid XGBoost acc: 0.8244

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STRATEGY 3: Rich Features + Stacking Ensemble
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Extracted 316 rich features per sample
  XGBoost: 0.7939
  LightGBM: 0.7786
  ExtraTrees: 0.7863
  RandomForest: 0.7710
  GBM: 0.7710
[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
Parameters: { "use_label_encoder" } are not used.
[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
Parameters: { "use_label_encoder" } are not used.


[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
Parameters: { "use_label_encoder" } are not used.
[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
Parameters: { "use_label_encoder" } are not used.


[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782: 
Parameters: { "use_label_encoder" } are not used.

[06:56:17] WARNING: /__w/xgboost/xgboost/src/common/error_msg.cc:62: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: cuda:0, while the input data is on: cpu.
Potential solutions:
- Use a data structure that matches the device ordinal in the booster.
- Set the device for booster before call to inplace_predict.

This warning will only be shown once.

  Stacking Ensemble: 0.7710
  Voting Ensemble: 0.7786
  ✅ Best ensemble: XGBoost = 0.7939
Extracted 316 rich features per sample

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STRATEGY 4: 5-Fold Stratified Cross-Validation
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  Fold 1: 0.7939
  Fold 2: 0.8244
  Fold 3: 0.7769
  Fold 4: 0.8154
  Fold 5: 0.7615
  ✅ CV Mean: 0.7944 ± 0.0234

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📊 TỔNG KẾT KẾT QUẢ
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  📈 LightCNN: 0.8626
  📈 Hybrid CNN+XGBoost: 0.8244
  📈 CV Mean (XGBoost rich): 0.7944
  📈 Ensemble XGBoost: 0.7939
  📈 Ensemble ExtraTrees: 0.7863
  📈 Ensemble LightGBM: 0.7786
  📈 Ensemble Voting: 0.7786
  📈 Ensemble RandomForest: 0.7710
  📈 Ensemble GBM: 0.7710
  📈 Ensemble Stacking: 0.7710
  📈 Flat XGBoost (baseline): 0.7328

🏆 BEST: LightCNN = 0.8626

✅ Kết quả đã được lưu vào model_train/ultimate_results.json
⚠️ Chưa đạt 95%. Best = 0.8626. Cần thêm dữ liệu hoặc feature engineering.