118 lines
4.4 KiB
Plaintext
118 lines
4.4 KiB
Plaintext
🚀 CHIẾN LƯỢC TOÀN DIỆN ĐẠT >95% ACCURACY
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============================================================
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Loaded data: X=(706, 24, 16, 16), y=(706,)
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Labels unique: [-1 0 1 2 3 4 5 6]
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After cleanup: X=(652, 24, 16, 16), y=(652,) (removed 54 bad samples)
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Remapped labels: [0 1 2 3 4 5 6]
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Class 0: 65 samples
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Class 1: 52 samples
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Class 2: 48 samples
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Class 3: 72 samples
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Class 4: 108 samples
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Class 5: 219 samples
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Class 6: 88 samples
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============================================================
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STRATEGY 5: Flat pixel features + XGBoost (sanity check)
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============================================================
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Flat features: (652, 6144)
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✅ Flat XGBoost acc: 0.7328
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STRATEGY 1: Lightweight CNN (no upsampling)
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============================================================
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Device: cuda
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Epoch 1/300 Loss=1.8379 Acc=0.0763 🌟
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Epoch 2/300 Loss=1.5825 Acc=0.2824 🌟
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Epoch 3/300 Loss=1.4412 Acc=0.5649 🌟
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Epoch 4/300 Loss=1.3274 Acc=0.6336 🌟
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Epoch 5/300 Loss=1.2893 Acc=0.6870 🌟
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Epoch 8/300 Loss=1.2140 Acc=0.7099 🌟
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Epoch 11/300 Loss=1.2224 Acc=0.7252 🌟
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Epoch 14/300 Loss=1.1087 Acc=0.7328 🌟
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Epoch 16/300 Loss=1.1081 Acc=0.7710 🌟
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Epoch 18/300 Loss=1.1847 Acc=0.7939 🌟
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Epoch 20/300 Loss=1.0316 Acc=0.7786 (patience=2)
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Epoch 24/300 Loss=1.0465 Acc=0.8092 🌟
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Epoch 26/300 Loss=0.9583 Acc=0.8397 🌟
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Epoch 40/300 Loss=0.9361 Acc=0.8626 🌟
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Epoch 60/300 Loss=0.9807 Acc=0.8092 (patience=20)
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Epoch 80/300 Loss=0.9459 Acc=0.8015 (patience=40)
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Epoch 100/300 Loss=0.8443 Acc=0.7939 (patience=60)
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Early stop at epoch 100
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✅ LightCNN best acc: 0.8626
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============================================================
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STRATEGY 2: Hybrid CNN embeddings + XGBoost
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============================================================
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CNN embeddings: (652, 256)
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Extracted 316 rich features per sample
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Combined features: (652, 572)
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✅ Hybrid XGBoost acc: 0.8244
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============================================================
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STRATEGY 3: Rich Features + Stacking Ensemble
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============================================================
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Extracted 316 rich features per sample
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XGBoost: 0.7939
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LightGBM: 0.7786
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ExtraTrees: 0.7863
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RandomForest: 0.7710
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GBM: 0.7710
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[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782:
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Parameters: { "use_label_encoder" } are not used.
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[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782:
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Parameters: { "use_label_encoder" } are not used.
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[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782:
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Parameters: { "use_label_encoder" } are not used.
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[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782:
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Parameters: { "use_label_encoder" } are not used.
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[06:55:44] WARNING: /__w/xgboost/xgboost/src/learner.cc:782:
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Parameters: { "use_label_encoder" } are not used.
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[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.
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Potential solutions:
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- Use a data structure that matches the device ordinal in the booster.
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- Set the device for booster before call to inplace_predict.
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This warning will only be shown once.
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Stacking Ensemble: 0.7710
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Voting Ensemble: 0.7786
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✅ Best ensemble: XGBoost = 0.7939
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Extracted 316 rich features per sample
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============================================================
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STRATEGY 4: 5-Fold Stratified Cross-Validation
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============================================================
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Fold 1: 0.7939
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Fold 2: 0.8244
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Fold 3: 0.7769
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Fold 4: 0.8154
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Fold 5: 0.7615
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✅ CV Mean: 0.7944 ± 0.0234
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============================================================
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📊 TỔNG KẾT KẾT QUẢ
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============================================================
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📈 LightCNN: 0.8626
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📈 Hybrid CNN+XGBoost: 0.8244
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📈 CV Mean (XGBoost rich): 0.7944
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📈 Ensemble XGBoost: 0.7939
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📈 Ensemble ExtraTrees: 0.7863
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📈 Ensemble LightGBM: 0.7786
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📈 Ensemble Voting: 0.7786
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📈 Ensemble RandomForest: 0.7710
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📈 Ensemble GBM: 0.7710
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📈 Ensemble Stacking: 0.7710
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📈 Flat XGBoost (baseline): 0.7328
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🏆 BEST: LightCNN = 0.8626
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✅ Kết quả đã được lưu vào model_train/ultimate_results.json
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⚠️ Chưa đạt 95%. Best = 0.8626. Cần thêm dữ liệu hoặc feature engineering.
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