feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging

log_ultimate_95.txt

@@ -0,0 +1,117 @@
+🚀 CHIẾN LƯỢC TOÀN DIỆN ĐẠT >95% ACCURACY
+============================================================
+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
+
+============================================================
+STRATEGY 5: Flat pixel features + XGBoost (sanity check)
+============================================================
+  Flat features: (652, 6144)
+  ✅ Flat XGBoost acc: 0.7328
+
+============================================================
+STRATEGY 1: Lightweight CNN (no upsampling)
+============================================================
+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
+
+============================================================
+STRATEGY 2: Hybrid CNN embeddings + XGBoost
+============================================================
+  CNN embeddings: (652, 256)
+Extracted 316 rich features per sample
+  Combined features: (652, 572)
+  ✅ Hybrid XGBoost acc: 0.8244
+
+============================================================
+STRATEGY 3: Rich Features + Stacking Ensemble
+============================================================
+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
+
+============================================================
+STRATEGY 4: 5-Fold Stratified Cross-Validation
+============================================================
+  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
+
+============================================================
+📊 TỔNG KẾT KẾT QUẢ
+============================================================
+  📈 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.