update train file nam

01.train_ODC.ipynb

@@ -854,8 +854,8 @@
       "TRAINING DATA SETUP\n",
       "======================================================================\n",
       "\n",
-      "[1] Loading training data: train/ST_training data_updated_1130points_new.shp\n",
+      "[1] Loading training data: train/ST_training_data_updated_1130points_new.shp\n",
-      "    ❌ Error: train/ST_training data_updated_1130points_new.shp: No such file or directory\n",
+      "    ❌ Error: train/ST_training_data_updated_1130points_new.shp: No such file or directory\n",
       "\n",
       "[2] Label mapping:\n",
       "    0: Lua tom\n",
@@ -878,7 +878,7 @@
     "print(\"=\"*70)\n",
     "\n",
     "# Load training points\n",
-    "train_path = \"train/ST_training data_updated_1130points_new.shp\"\n",
+    "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n",
     "print(f\"\\n[1] Loading training data: {train_path}\")\n",
     "\n",
     "try:\n",

01.train_ODC.py

@@ -33,7 +33,7 @@ CACHE_DIR  = "dataset_cache"
 CACHE_FILE = f"{CACHE_DIR}/sentinel2_timeseries_40scenes.nc"
 
 # --- Training data ---
-TRAIN_PATH = "train/ST_training data_updated_1130points_new.shp"
+TRAIN_PATH = "train/ST_training_data_updated_1130points_new.shp"
 
 # --- Features sử dụng để train ---
 AVAILABLE_FEATURES = [

01.train_ODC_CNN.ipynb

@@ -2854,7 +2854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
    "metadata": {
     "tags": []
@@ -2862,7 +2862,7 @@
    "outputs": [],
    "source": [
     "## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n",
-    "train_path = \"train/ST_training data_updated_1130points_new.shp\"  # đường dẫn shp file train\n",
+    "train_path = \"train/ST_training_data_updated_1130points_new.shp\"  # đường dẫn shp file train\n",
     "\n",
     "## load dữ liệu điểm huấn luyện mô hình (train file)\n",
     "train = load_train_data(train_path)\n",

01.train_ODC_MobileNet.ipynb

@@ -428,55 +428,6 @@
     "plt.tight_layout()\n",
     "plt.show()\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "28f87d0e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from datetime import datetime\n",
-    "\n",
-    "# ── Lưu mô hình PyTorch ────────────────────────────────────────────────────────\n",
-    "model_path = \"model_mobilenet_land_use.pth\"\n",
-    "torch.save(model.state_dict(), model_path)\n",
-    "print(f\"✅ Model saved → {model_path}\")\n",
-    "\n",
-    "# ── Lưu thông tin mô hình ──────────────────────────────────────────────────────\n",
-    "info = {\n",
-    "    \"model_type\": \"MobileNetV3_LR-ASPP\",\n",
-    "    \"n_features\": n_features,\n",
-    "    \"n_classes\": n_classes,\n",
-    "    \"learning_rate\": LEARNING_RATE,\n",
-    "    \"batch_size\": BATCH_SIZE,\n",
-    "    \"max_epochs\": N_EPOCHS,\n",
-    "    \"early_stopping_patience\": PATIENCE,\n",
-    "    \"optimizer\": \"Adam\",\n",
-    "    \"scheduler\": \"ReduceLROnPlateau(factor=0.5, patience=5)\",\n",
-    "    \"class_weight\": \"balanced\",\n",
-    "    \"label_mapping\": label_mapping,\n",
-    "    \"test_accuracy\": float(acc),\n",
-    "    \"train_samples\": len(X_train_np),\n",
-    "    \"val_samples\": len(X_val_np),\n",
-    "    \"test_samples\": len(X_test_np),\n",
-    "    \"saved_at\": datetime.now().isoformat(),\n",
-    "}\n",
-    "info_path = \"model_mobilenet_land_use_info.json\"\n",
-    "with open(info_path, \"w\") as f:\n",
-    "    json.dump(info, f, indent=2, ensure_ascii=False)\n",
-    "print(f\"✅ Info saved  → {info_path}\")\n",
-    "print(json.dumps(info, indent=2, ensure_ascii=False))\n",
-    "\n",
-    "# ── Đóng kết nối Dask ──────────────────────────────────────────────────────────\n",
-    "try:\n",
-    "    client.close()\n",
-    "    cluster.close()\n",
-    "    print(\"✅ Dask cluster closed.\")\n",
-    "except Exception:\n",
-    "    pass\n"
-   ]
   }
  ],
  "metadata": {

01.train_ODC_RandomForest.ipynb

@@ -103,7 +103,7 @@
    "outputs": [],
    "source": [
     "## Chuẩn bị dữ liệu train\n",
-    "train_path = \"train/ST_training data_updated_1130points_new.shp\"\n",
+    "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n",
     "train = load_train_data(train_path)\n",
     "train.head()\n",
     "\n",

01.train_ODC_SwinUNet.ipynb

@@ -140,7 +140,7 @@
    "outputs": [],
    "source": [
     "## ── Chuẩn bị dữ liệu train ───────────────────────────────────────────────────\n",
-    "train_path = \"train/ST_training data_updated_1130points_new.shp\"\n",
+    "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n",
     "\n",
     "train = load_train_data(train_path)\n",
     "train.head()\n",

01.train_ODC_XGBoost.ipynb

@@ -2854,7 +2854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
    "metadata": {
     "tags": []
@@ -2862,7 +2862,7 @@
    "outputs": [],
    "source": [
     "## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n",
-    "train_path = \"train/ST_training data_updated_1130points_new.shp\"  # đường dẫn shp file train\n",
+    "train_path = \"train/ST_training_data_updated_1130points_new.shp\"  # đường dẫn shp file train\n",
     "\n",
     "## load dữ liệu điểm huấn luyện mô hình (train file)\n",
     "train = load_train_data(train_path)\n",

01.train_ODC_local_with_Mic_supplyer.ipynb

@@ -1727,7 +1727,7 @@
       "FEATURE EXTRACTION\n",
       "======================================================================\n",
       "\n",
-      "[1] Loading training data from: train/ST_training data_updated_1130points_new.shp\n",
+      "[1] Loading training data from: train/ST_training_data_updated_1130points_new.shp\n",
       "✅ Loaded 1130 training points\n",
       "   Available columns: ['No', 'X', 'Y', 'LU2022', 'Hientrang', 'HT_code', 'geometry']\n",
       "   Using label column: 'HT_code'\n",
@@ -1769,7 +1769,7 @@
     "# Load training shapefile\n",
     "import geopandas as gpd\n",
     "\n",
-    "train_path = 'train/ST_training data_updated_1130points_new.shp'\n",
+    "train_path = 'train/ST_training_data_updated_1130points_new.shp'\n",
     "print(f\"\\n[1] Loading training data from: {train_path}\")\n",
     "train_gdf = gpd.read_file(train_path)\n",
     "\n",

TRAINING_UPDATE_SUMMARY.md

@@ -44,7 +44,7 @@ DEFAULT_LABEL_MAPPING = {
 {
   "files": [
     {
-      "filename": "ST_training data_updated_1130points_new.shp",
+      "filename": "ST_training_data_updated_1130points_new.shp",
       "path": "train/...",
       "size_mb": 0.15,
       "point_count": 1130,
@@ -110,7 +110,7 @@ DEFAULT_LABEL_MAPPING = {
 
 Features:
 - Dropdown chọn shapefile từ thư mục `/train`
-- Tự động load default: `ST_training data_updated_1130points_new.shp`
+- Tự động load default: `ST_training_data_updated_1130points_new.shp`
 - Hiển thị thông tin: số điểm, label column, số lớp, bbox
 
 #### ✅ Thêm phần hiển thị thông tin Shapefile:
@@ -149,7 +149,7 @@ Features:
 
 #### ✅ Cập nhật form submission:
 - Thêm `training_shapefile` vào config
-- Default: `train/ST_training data_updated_1130points_new.shp`
+- Default: `train/ST_training_data_updated_1130points_new.shp`
 
 #### ✅ Event listeners:
 ```javascript
@@ -252,7 +252,7 @@ curl http://localhost:8000/api/training/files
 curl http://localhost:8000/api/training/labels
 
 # Get shapefile labels
-curl "http://localhost:8000/api/training/shapefile/ST_training data_updated_1130points_new.shp/labels"
+curl "http://localhost:8000/api/training/shapefile/ST_training_data_updated_1130points_new.shp/labels"
 ```
 
 ### Check Browser Console:
@@ -265,8 +265,8 @@ curl "http://localhost:8000/api/training/shapefile/ST_training data_updated_1130
 ## 📝 Notes
 
 1. **Training shapefile path format**:
-   - Frontend select value: `ST_training data_updated_1130points_new.shp`
+   - Frontend select value: `ST_training_data_updated_1130points_new.shp`
-   - Backend receives: `train/ST_training data_updated_1130points_new.shp`
+   - Backend receives: `train/ST_training_data_updated_1130points_new.shp`
    - Auto-prepend `train/` prefix in form submission
 
 2. **Label mapping**:

api_server.py

@@ -3807,7 +3807,7 @@ async def change_detection_predict_workflow(
         
         # --- STEP 6: COMPARE WITH GROUND TRUTH ---
         print("[CHANGE DETECTION] Comparing with ground truth...")
-        gt_shapefile = "train/ST_training data_updated_1130points_new.shp"
+        gt_shapefile = "train/ST_training_data_updated_1130points_new.shp"
         gt_raster = rasterize_ground_truth(gt_shapefile, (height, width), bbox, class_column="class")
         
         # Calculate changes
@@ -3912,7 +3912,7 @@ async def change_detection_workflow(request: ChangeDetectionWorkflowRequest):
             pred_transform = pred_ds.transform
         
         # Rasterize ground truth training data
-        gt_shapefile = "train/ST_training data_updated_1130points_new.shp"
+        gt_shapefile = "train/ST_training_data_updated_1130points_new.shp"
         gt_raster = rasterize_ground_truth(gt_shapefile, pred_arr.shape, bbox, class_column="class")
         
         # Calculate change detection
@@ -4700,7 +4700,7 @@ async def predict_with_ndvi(config: PredictionWithNDVIConfig, background_tasks:
         change_map = None
         try:
             # Use training shapefile as ground truth
-            gt_shapefile = "train/ST_training data_updated_1130points_new.shp"
+            gt_shapefile = "train/ST_training_data_updated_1130points_new.shp"
             gt_raster = rasterize_ground_truth(gt_shapefile, (height, width), bbox, class_column="class")
             # Compare prediction and ground truth
             mask_valid = (gt_raster >= 0) & (prediction_raster >= 0)

document/giai_thich_quy_trinh_phan_loai_dat.md

@@ -135,7 +135,7 @@ File .tif (mỗi pixel = 1 mã loại đất)
 - **Tổng cộng**: ~39 features cho mỗi pixel
 
 ### Labels (Nhãn):
-- Được lấy từ shapefile training: `train/ST_training data_updated_1130points_new.shp`
+- Được lấy từ shapefile training: `train/ST_training_data_updated_1130points_new.shp`
 - 1130 điểm mẫu đã được gắn nhãn thủ công bởi chuyên gia
 
 ---

test_training_api.py

@@ -45,7 +45,7 @@ def test_training_files():
         print(f"❌ Error: {response.status_code}")
     print()
 
-def test_shapefile_labels(filename="ST_training data_updated_1130points_new.shp"):
+def test_shapefile_labels(filename="ST_training_data_updated_1130points_new.shp"):
     """Test /api/training/shapefile/{filename}/labels endpoint"""
     print("=" * 70)
     print(f"TEST 3: Getting labels from shapefile: {filename}")

train_module.py

@@ -359,7 +359,7 @@ def train_model(
     max_scenes=12,
     cloud_cover=30,
     resolution=20,
-    training_shapefile='train/ST_training data_updated_1130points_new.shp',
+    training_shapefile='train/ST_training_data_updated_1130points_new.shp',
     model_type='xgboost',
     n_estimators=100,
     max_depth=20,

training_interface.html

@@ -804,7 +804,7 @@
                     });
                     
                     // Select default shapefile
-                    const defaultFile = 'ST_training data_updated_1130points_new.shp';
+                    const defaultFile = 'ST_training_data_updated_1130points_new.shp';
                     const defaultOption = Array.from(select.options).find(opt => opt.value === defaultFile);
                     if (defaultOption) {
                         select.value = defaultFile;
@@ -1074,7 +1074,7 @@
             
             // Get selected training shapefile
             const selectedShapefile = document.getElementById('trainingShapefile').value;
-            const trainingShapefile = selectedShapefile || 'train/ST_training data_updated_1130points_new.shp';
+            const trainingShapefile = selectedShapefile || 'train/ST_training_data_updated_1130points_new.shp';
             
             const config = {
                 min_lon: parseFloat(document.getElementById('minLon').value),