remote-sensing/01.train_DecisionTree_PlanetaryComputer.ipynb

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    "# 🌍 Decision Tree Land Classification - Planetary Computer\n",
    "\n",
    "## 📌 Notebook này chạy trên:\n",
    "- ✅ **Server ODC/JupyterHub** với Dask Gateway\n",
    "- ✅ Load dữ liệu từ **Microsoft Planetary Computer** (không cần ODC database)\n",
    "\n",
    "## 🎯 Nguồn dữ liệu:\n",
    "**Microsoft Planetary Computer STAC API**\n",
    "- Sentinel-2 L2A (optical)\n",
    "- Sentinel-1 RTC (SAR)\n",
    "\n",
    "## 🚀 Infrastructure:\n",
    "- **Dask Gateway**: Adaptive scaling (1-10 workers)\n",
    "- **Datacube**: Initialized nhưng không dùng để load data\n",
    "- **S3 Access**: Configured với requester_pays\n",
    "\n",
    "## 🔄 Workflow:\n",
    "1. Load data từ Planetary Computer (STAC API)\n",
    "2. Preprocessing (cloud mask, NDVI, resampling)\n",
    "3. Train Decision Tree model\n",
    "4. Evaluate & save model\n",
    "\n",
    "---\n"
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      "application/vnd.holoviews_load.v0+json": ""
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     "text": [
      "======================================================================\n",
      "📦 Data Loading Module (No ODC Database Required)\n",
      "======================================================================\n",
      "\n",
      "💡 Usage:\n",
      "   from load_data_no_odc import load_and_process_s2, load_and_process_s1\n",
      "\n",
      "   bbox = (lon_min, lat_min, lon_max, lat_max)\n",
      "   date_range = ('2022-09-01', '2023-10-01')\n",
      "\n",
      "   data_s2 = load_and_process_s2(bbox, date_range)\n",
      "   data_s1 = load_and_process_s1(bbox, date_range)\n",
      "\n",
      "   # Load training data\n",
      "   train = load_train_data('train/data.shp', label_mapping)\n",
      "   X, y = extract_features_at_points(train, data_s2, data_s1)\n",
      "   X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y)\n",
      "======================================================================\n",
      "\n",
      "======================================================================\n",
      "📦 Data Loading Module (No ODC Database Required)\n",
      "======================================================================\n",
      "\n",
      "💡 Usage:\n",
      "   from load_data_no_odc import load_and_process_s2, load_and_process_s1\n",
      "\n",
      "   bbox = (lon_min, lat_min, lon_max, lat_max)\n",
      "   date_range = ('2022-09-01', '2023-10-01')\n",
      "\n",
      "   data_s2 = load_and_process_s2(bbox, date_range)\n",
      "   data_s1 = load_and_process_s1(bbox, date_range)\n",
      "\n",
      "   # Load training data\n",
      "   train = load_train_data('train/data.shp', label_mapping)\n",
      "   X, y = extract_features_at_points(train, data_s2, data_s1)\n",
      "   X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y)\n",
      "======================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
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      "application/vnd.holoviews_load.v0+json": ""
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     "text": [
      "✅ All modules loaded successfully!\n",
      "📡 Data source: Microsoft Planetary Computer\n",
      "🚀 Infrastructure: Dask Gateway + ODC\n",
      "CPU times: user 19.2 s, sys: 3.35 s, total: 22.5 s\n",
      "Wall time: 13.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "import os\n",
    "sys.path.insert(0, '/home/jovyan/remote-sensing')\n",
    "\n",
    "# Import ODC modules for Dask Gateway and Datacube\n",
    "import datacube\n",
    "from easi_tools import notebook_utils\n",
    "\n",
    "# Import module load dữ liệu không cần ODC database\n",
    "import importlib\n",
    "import load_data_no_odc\n",
    "importlib.reload(load_data_no_odc)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import importlib\n",
    "import new_import_ODC  \n",
    "\n",
    "importlib.reload(new_import_ODC)\n",
    "\n",
    "from new_import_ODC import *\n",
    "\n",
    "\n",
    "from load_data_no_odc import (\n",
    "    load_and_process_s2,\n",
    "    load_and_process_s1,\n",
    "    load_sentinel2_stac,\n",
    "    load_sentinel1_stac,\n",
    "    mask_clean_s2,\n",
    "    calculate_ndvi,\n",
    "    fill_nan_temporal,\n",
    "    resample_monthly\n",
    ")\n",
    "\n",
    "# Standard imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# ML imports\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "import joblib\n",
    "import json\n",
    "from datetime import datetime\n",
    "\n",
    "print(\"✅ All modules loaded successfully!\")\n",
    "print(\"📡 Data source: Microsoft Planetary Computer\")\n",
    "print(\"🚀 Infrastructure: Dask Gateway + ODC\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53397b2f",
   "metadata": {},
   "source": [
    "## 🚀 Step 1: Initialize Dask Gateway + Datacube\n",
    "\n",
    "Khởi tạo Dask Gateway với adaptive scaling và cấu hình S3 access\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c4d6779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚀 Step 1: Dask Gateway + Datacube Initialization\n",
      "======================================================================\n",
      "An existing cluster was found. Connecting to: easihub.c5ddeea2a5f442fdb346e0bfe686a138\n",
      "\n",
      "✅ Dask Gateway + Datacube + S3 ready!\n",
      "   Dask dashboard: https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.c5ddeea2a5f442fdb346e0bfe686a138/status\n",
      "   Workers: Adaptive scaling (1-10)\n",
      "======================================================================\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"🚀 Step 1: Dask Gateway + Datacube Initialization\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "\n",
    "# Cấu hình Dask Gateway\n",
    "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n",
    "\n",
    "# Khai báo Datacube\n",
    "dc = datacube.Datacube()\n",
    "\n",
    "# Cấu hình truy cập dịch vụ S3\n",
    "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
    "\n",
    "print(f\"\\n✅ Dask Gateway + Datacube + S3 ready!\")\n",
    "print(f\"   Dask dashboard: {client.dashboard_link}\")\n",
    "print(f\"   Workers: Adaptive scaling (1-10)\")\n",
    "print(\"=\" * 70)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bc5035",
   "metadata": {},
   "source": [
    "## 📍 Step 2: Define Area of Interest (AOI)\n",
    "\n",
    "Định nghĩa vùng nghiên cứu và khoảng thời gian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15a5291c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📍 Area of Interest:\n",
      "   Bbox: (105.5, 9.2, 106.4, 10.0)\n",
      "   Lon range: 105.5 to 106.4\n",
      "   Lat range: 9.2 to 10.0\n",
      "   Date range: 2022-09-01 to 2023-10-01\n",
      "======================================================================\n"
     ]
    }
   ],
   "source": [
    "# Cấu hình vùng và thời gian\n",
    "date_range = (\"2022-09-01\", \"2023-10-01\")\n",
    "\n",
    "# Bounding box: (lon_min, lat_min, lon_max, lat_max)\n",
    "bbox = (105.5, 9.2, 106.4, 10.0)  # Khu vực Mekong Delta\n",
    "\n",
    "print(\"📍 Area of Interest:\")\n",
    "print(f\"   Bbox: {bbox}\")\n",
    "print(f\"   Lon range: {bbox[0]} to {bbox[2]}\")\n",
    "print(f\"   Lat range: {bbox[1]} to {bbox[3]}\")\n",
    "print(f\"   Date range: {date_range[0]} to {date_range[1]}\")\n",
    "print(\"=\" * 70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422e498e",
   "metadata": {},
   "source": [
    "## 📥 Step 3: Load Sentinel-2 Data\n",
    "\n",
    "Load dữ liệu Sentinel-2 L2A từ Planetary Computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "611cd1c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📥 Loading Sentinel-2 data from Planetary Computer...\n",
      "----------------------------------------------------------------------\n",
      "🔍 Searching Sentinel-2 data...\n",
      "   Bbox: (105.5, 9.2, 106.4, 10.0)\n",
      "   Date: 2022-09-01 to 2023-10-01\n",
      "   Bands: ['red', 'nir', 'blue', 'green', 'nir08', 'swir16', 'swir22', 'SCL']\n",
      "✅ Found 777 Sentinel-2 scenes\n",
      "📥 Loading data...\n",
      "❌ Error loading data: No such band/alias: nir08\n",
      "❌ Failed to load Sentinel-2 data\n",
      "CPU times: user 1.01 s, sys: 20.1 ms, total: 1.03 s\n",
      "Wall time: 7.16 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/home/jovyan/remote-sensing/load_data_no_odc.py\", line 71, in load_sentinel2_stac\n",
      "    data = odc.stac.load(\n",
      "           ^^^^^^^^^^^^^^\n",
      "  File \"/env/lib/python3.12/site-packages/odc/stac/_stac_load.py\", line 358, in load\n",
      "    gbox = output_geobox(\n",
      "           ^^^^^^^^^^^^^^\n",
      "  File \"/env/lib/python3.12/site-packages/odc/stac/_mdtools.py\", line 971, in output_geobox\n",
      "    rr = _auto_load_params(items, bands)\n",
      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/env/lib/python3.12/site-packages/odc/stac/_mdtools.py\", line 784, in _auto_load_params\n",
      "    geoboxes = [gbox for gbox in map(_extract_gbox, items) if gbox is not None]\n",
      "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/env/lib/python3.12/site-packages/odc/stac/_mdtools.py\", line 781, in _extract_gbox\n",
      "    gbx = item.geoboxes(bands)\n",
      "          ^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/env/lib/python3.12/site-packages/odc/stac/model.py\", line 244, in geoboxes\n",
      "    b = self.bands.get(self.collection.band_key(name), None)\n",
      "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/env/lib/python3.12/site-packages/odc/stac/model.py\", line 144, in band_key\n",
      "    raise ValueError(f\"No such band/alias: {band}\")\n",
      "ValueError: No such band/alias: nir08\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print(\"📥 Loading Sentinel-2 data from Planetary Computer...\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "# Load và xử lý Sentinel-2: cloud mask + NDVI + monthly resampling\n",
    "data_sen2_monthly = load_and_process_s2(\n",
    "    bbox=bbox,\n",
    "    date_range=date_range,\n",
    "    apply_cloud_mask=True,\n",
    "    calculate_indices=True\n",
    ")\n",
    "\n",
    "if data_sen2_monthly is not None:\n",
    "    print(f\"\\n✅ Sentinel-2 monthly data loaded!\")\n",
    "    print(f\"   Dimensions: {dict(data_sen2_monthly.dims)}\")\n",
    "    print(f\"   Variables: {list(data_sen2_monthly.data_vars)}\")\n",
    "    print(f\"   Time steps: {len(data_sen2_monthly.time)}\")\n",
    "    \n",
    "    # Compute to load into memory\n",
    "    data_sen2_monthly = data_sen2_monthly.compute()\n",
    "    print(f\"   ✓ Data computed and loaded into memory\")\n",
    "else:\n",
    "    print(\"❌ Failed to load Sentinel-2 data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ef9736",
   "metadata": {},
   "source": [
    "## 📥 Step 4: Load Sentinel-1 Data\n",
    "\n",
    "Load dữ liệu Sentinel-1 RTC (SAR) từ Planetary Computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bda3dc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📥 Loading Sentinel-1 data from Planetary Computer...\n",
      "----------------------------------------------------------------------\n",
      "🔍 Searching Sentinel-1 data...\n",
      "   Bbox: (105.5, 9.2, 106.4, 10.0)\n",
      "   Date: 2022-09-01 to 2023-10-01\n",
      "   Bands: ['vv', 'vh']\n",
      "✅ Found 149 Sentinel-1 scenes\n",
      "📥 Loading data...\n",
      "✅ Loaded Sentinel-1 data: FrozenMappingWarningOnValuesAccess({'y': 8874, 'x': 9902, 'time': 64})\n",
      "   Shape: {'y': 8874, 'x': 9902, 'time': 64}\n",
      "   Bands: ['VV', 'VH']\n",
      "📅 Resampling to monthly using mean...\n",
      "✅ Resampled to monthly: FrozenMappingWarningOnValuesAccess({'time': 13, 'y': 8874, 'x': 9902})\n",
      "\n",
      "✅ Sentinel-1 monthly data loaded!\n",
      "   Dimensions: {'time': 13, 'y': 8874, 'x': 9902}\n",
      "   Variables: ['VV', 'VH']\n",
      "   Time steps: 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/remote-sensing/load_data_no_odc.py:158: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  print(f\"   Shape: {dict(data.dims)}\")\n",
      "<timed exec>:12: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print(\"📥 Loading Sentinel-1 data from Planetary Computer...\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "# Load và xử lý Sentinel-1: monthly resampling\n",
    "data_sen1_monthly = load_and_process_s1(\n",
    "    bbox=bbox,\n",
    "    date_range=date_range\n",
    ")\n",
    "\n",
    "if data_sen1_monthly is not None:\n",
    "    print(f\"\\n✅ Sentinel-1 monthly data loaded!\")\n",
    "    print(f\"   Dimensions: {dict(data_sen1_monthly.dims)}\")\n",
    "    print(f\"   Variables: {list(data_sen1_monthly.data_vars)}\")\n",
    "    print(f\"   Time steps: {len(data_sen1_monthly.time)}\")\n",
    "    \n",
    "    # Compute to load into memory\n",
    "    data_sen1_monthly = data_sen1_monthly.compute()\n",
    "    print(f\"   ✓ Data computed and loaded into memory\")\n",
    "else:\n",
    "    print(\"❌ Failed to load Sentinel-1 data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de80d48d",
   "metadata": {},
   "source": [
    "## 🎯 Step 5: Load Training Data\n",
    "\n",
    "Load dữ liệu mẫu huấn luyện (training samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70925d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from load_data_no_odc import load_train_data\n",
    "\n",
    "# Ánh xạ nhãn lớp đất\n",
    "label_mapping = {\n",
    "    \"Lua tom\": 0,\n",
    "    \"Lua\": 1,\n",
    "    \"CHN\": 2,\n",
    "    \"CLN\": 3,\n",
    "    \"TS\": 4,\n",
    "    \"Song\": 5,\n",
    "    \"Dat xay dung\": 6,\n",
    "    \"Rung\": 7,\n",
    "}\n",
    "\n",
    "print(\"🎯 Label mapping:\")\n",
    "for label, idx in label_mapping.items():\n",
    "    print(f\"   {idx}: {label}\")\n",
    "\n",
    "# Load training data from AWS shapefile\n",
    "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n",
    "\n",
    "print(f\"\\n📂 Loading training data from: {train_path}\")\n",
    "train_data = load_train_data(train_path, label_mapping)\n",
    "\n",
    "if train_data is not None:\n",
    "    print(f\"\\n✅ Training data loaded successfully!\")\n",
    "    print(f\"   Total points: {len(train_data)}\")\n",
    "    print(f\"   CRS: {train_data.crs}\")\n",
    "else:\n",
    "    print(\"❌ Failed to load training data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fed9a8f1",
   "metadata": {},
   "source": [
    "## 🔧 Step 6: Extract Features\n",
    "\n",
    "Trích xuất features từ Sentinel-1 và Sentinel-2 tại các điểm mẫu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62c41cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "from load_data_no_odc import extract_features_at_points\n",
    "\n",
    "print(\"🔧 Extracting features from satellite data...\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "if train_data is not None and \\\n",
    "   data_sen2_monthly is not None and \\\n",
    "   data_sen1_monthly is not None:\n",
    "    \n",
    "    # Extract features at training point locations\n",
    "    X, y = extract_features_at_points(\n",
    "        train_data, \n",
    "        data_sen2_monthly, \n",
    "        data_sen1_monthly\n",
    "    )\n",
    "    \n",
    "    print(f\"\\n✅ Feature extraction complete!\")\n",
    "    print(f\"   Total samples: {len(X)}\")\n",
    "    print(f\"   Feature dimension: {X.shape[1]}\")\n",
    "    print(f\"   Classes: {sorted(set(y.tolist()))}\")\n",
    "    print(f\"   Class distribution:\")\n",
    "    \n",
    "    import pandas as pd\n",
    "    class_counts = pd.Series(y).value_counts().sort_index()\n",
    "    for class_id, count in class_counts.items():\n",
    "        class_name = [k for k, v in label_mapping.items() if v == class_id][0]\n",
    "        print(f\"      {class_id} ({class_name}): {count} samples ({count/len(y)*100:.1f}%)\")\n",
    "    \n",
    "else:\n",
    "    print(\"❌ Missing data: Please check training data or satellite data\")\n",
    "    print(f\"   train_data: {'✓' if train_data is not None else '✗'}\")\n",
    "    print(f\"   data_sen2_monthly: {'✓' if data_sen2_monthly is not None else '✗'}\")\n",
    "    print(f\"   data_sen1_monthly: {'✓' if data_sen1_monthly is not None else '✗'}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cd0ca2",
   "metadata": {},
   "source": [
    "## 📊 Step 7: Split Data\n",
    "\n",
    "Chia dữ liệu thành train/val/test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523e1249",
   "metadata": {},
   "outputs": [],
   "source": [
    "from load_data_no_odc import split_train_data\n",
    "\n",
    "if 'X' in locals() and 'y' in locals():\n",
    "    # Split data into train/val/test sets\n",
    "    X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n",
    "        X, y, \n",
    "        test_size=0.2, \n",
    "        val_size=0.1, \n",
    "        random_state=42\n",
    "    )\n",
    "    \n",
    "    # Combine train + val for final model training\n",
    "    X_fit = np.concatenate([X_train, X_val], axis=0)\n",
    "    y_fit = np.concatenate([y_train, y_val], axis=0)\n",
    "    \n",
    "    print(f\"\\n✅ Final training set:\")\n",
    "    print(f\"   X_fit shape: {X_fit.shape}\")\n",
    "    print(f\"   y_fit shape: {y_fit.shape}\")\n",
    "    print(f\"\\n   X_test shape: {X_test.shape}\")\n",
    "    print(f\"   y_test shape: {y_test.shape}\")\n",
    "    \n",
    "else:\n",
    "    print(\"❌ Features not extracted yet. Please run Step 6 first.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a469500c",
   "metadata": {},
   "source": [
    "## 🌲 Step 8: Train Decision Tree Model\n",
    "\n",
    "Huấn luyện mô hình Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "445c2d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "if 'X_fit' in locals() and 'y_fit' in locals():\n",
    "    \n",
    "    print(\"🚀 Training Decision Tree model...\")\n",
    "    print(\"-\" * 70)\n",
    "    \n",
    "    # Train Decision Tree with tuned hyperparameters\n",
    "    model = DecisionTreeClassifier(\n",
    "        max_depth=30,\n",
    "        min_samples_leaf=2,\n",
    "        min_samples_split=5,\n",
    "        class_weight=\"balanced\",\n",
    "        random_state=42,\n",
    "    )\n",
    "    \n",
    "    model.fit(X_fit, y_fit)\n",
    "    \n",
    "    # Evaluate on validation set\n",
    "    val_acc = model.score(X_val, y_val)\n",
    "    \n",
    "    print(f\"\\n✅ Training complete!\")\n",
    "    print(f\"   Model: Decision Tree\")\n",
    "    print(f\"   Tree depth: {model.get_depth()}\")\n",
    "    print(f\"   Number of leaves: {model.get_n_leaves()}\")\n",
    "    print(f\"   Training samples: {len(X_fit)}\")\n",
    "    print(f\"   Validation accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n",
    "    \n",
    "else:\n",
    "    print(\"❌ Data not ready. Please run Step 7 first.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1e681e",
   "metadata": {},
   "source": [
    "## 📈 Step 9: Hyperparameter Analysis\n",
    "\n",
    "Phân tích độ sâu tối ưu (max_depth) cho Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13274e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if 'X_fit' in locals() and 'X_val' in locals():\n",
    "    \n",
    "    DEPTH_RANGE = list(range(1, 51))\n",
    "    \n",
    "    train_accs = []\n",
    "    val_accs = []\n",
    "    \n",
    "    print(\"🔍 Analyzing optimal max_depth for Decision Tree...\")\n",
    "    print(\"-\" * 70)\n",
    "    print(\"Testing depths from 1 to 50...\")\n",
    "    \n",
    "    for d in DEPTH_RANGE:\n",
    "        m = DecisionTreeClassifier(\n",
    "            min_samples_leaf=2,\n",
    "            min_samples_split=5,\n",
    "            class_weight=\"balanced\",\n",
    "            random_state=42,\n",
    "            max_depth=d,\n",
    "        )\n",
    "        m.fit(X_fit, y_fit)\n",
    "        train_accs.append(m.score(X_fit, y_fit))\n",
    "        val_accs.append(m.score(X_val, y_val))\n",
    "        \n",
    "        if d % 10 == 0:\n",
    "            print(f\"  Depth {d:2d}: train={train_accs[-1]:.4f}, val={val_accs[-1]:.4f}\")\n",
    "    \n",
    "    train_accs = np.array(train_accs)\n",
    "    val_accs = np.array(val_accs)\n",
    "    \n",
    "    best_depth = DEPTH_RANGE[np.argmax(val_accs)]\n",
    "    best_val_acc = np.max(val_accs)\n",
    "    \n",
    "    print(f\"\\n✅ Optimal max_depth: {best_depth}\")\n",
    "    print(f\"   Best validation accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)\")\n",
    "    \n",
    "    # Plot convergence analysis\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n",
    "    ax.plot(DEPTH_RANGE, train_accs, 'b-o', markersize=3, label='Train Accuracy')\n",
    "    ax.plot(DEPTH_RANGE, val_accs, 'g-o', markersize=3, label='Validation Accuracy')\n",
    "    ax.axvline(x=best_depth, color='red', linestyle='--', linewidth=2, \n",
    "               label=f'Best Depth = {best_depth}')\n",
    "    ax.set_xlabel('max_depth', fontsize=12)\n",
    "    ax.set_ylabel('Accuracy', fontsize=12)\n",
    "    ax.set_title('Decision Tree: Training vs Validation Accuracy', fontsize=14, fontweight='bold')\n",
    "    ax.legend(fontsize=10)\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "else:\n",
    "    print(\"❌ Data not ready. Please run Step 7 first.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b551cdb4",
   "metadata": {},
   "source": [
    "## 📊 Step 10: Evaluate Model\n",
    "\n",
    "Đánh giá mô hình trên tập test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc3d6b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "if 'model' in locals() and 'X_test' in locals():\n",
    "    \n",
    "    print(\"📊 Evaluating model on test set...\")\n",
    "    print(\"-\" * 70)\n",
    "    \n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_test)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    \n",
    "    print(f\"\\n🎯 Test Accuracy: {acc:.4f} ({acc*100:.2f}%)\\n\")\n",
    "    print(\"📋 Classification Report:\")\n",
    "    print(classification_report(y_test, y_pred, digits=4))\n",
    "    \n",
    "    # Confusion Matrix\n",
    "    class_names = list(label_mapping.keys())\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    \n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Greens',\n",
    "                xticklabels=class_names, yticklabels=class_names,\n",
    "                cbar_kws={'label': 'Count'})\n",
    "    plt.xlabel('Predicted Label', fontsize=12)\n",
    "    plt.ylabel('True Label', fontsize=12)\n",
    "    plt.title('Confusion Matrix — Decision Tree (Test Set)', fontsize=14, fontweight='bold')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "else:\n",
    "    print(\"❌ Model not trained yet. Please run Step 8 first.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d226fc",
   "metadata": {},
   "source": [
    "## 💾 Step 11: Save Model\n",
    "\n",
    "Lưu mô hình và metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "044eefc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import json\n",
    "from datetime import datetime\n",
    "\n",
    "if 'model' in locals() and 'acc' in locals():\n",
    "    \n",
    "    print(\"💾 Saving model and metadata...\")\n",
    "    print(\"-\" * 70)\n",
    "    \n",
    "    # Save model\n",
    "    model_path = \"model_decision_tree_planetary_computer.joblib\"\n",
    "    joblib.dump(model, model_path)\n",
    "    print(f\"✅ Model saved → {model_path}\")\n",
    "    \n",
    "    # Create metadata\n",
    "    info = {\n",
    "        \"model_type\": \"DecisionTree\",\n",
    "        \"data_source\": \"Microsoft Planetary Computer\",\n",
    "        \"training_data\": train_path,\n",
    "        \"max_depth\": int(model.get_depth()),\n",
    "        \"n_leaves\": int(model.get_n_leaves()),\n",
    "        \"n_features\": int(X_fit.shape[1]),\n",
    "        \"label_mapping\": label_mapping,\n",
    "        \"test_accuracy\": float(acc),\n",
    "        \"train_samples\": int(len(X_fit)),\n",
    "        \"val_samples\": int(len(X_val)),\n",
    "        \"test_samples\": int(len(X_test)),\n",
    "        \"bbox\": bbox,\n",
    "        \"date_range\": list(date_range),\n",
    "        \"saved_at\": datetime.now().isoformat(),\n",
    "        \"hyperparameters\": {\n",
    "            \"max_depth\": 30,\n",
    "            \"min_samples_leaf\": 2,\n",
    "            \"min_samples_split\": 5,\n",
    "            \"class_weight\": \"balanced\",\n",
    "            \"random_state\": 42\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    # Save metadata\n",
    "    info_path = \"model_decision_tree_planetary_computer_info.json\"\n",
    "    with open(info_path, \"w\") as f:\n",
    "        json.dump(info, f, indent=2, ensure_ascii=False)\n",
    "    \n",
    "    print(f\"✅ Metadata saved → {info_path}\")\n",
    "    print(\"\\n📋 Model Info:\")\n",
    "    print(json.dumps(info, indent=2, ensure_ascii=False))\n",
    "    \n",
    "else:\n",
    "    print(\"❌ Model not trained yet or evaluation not complete.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c95152",
   "metadata": {},
   "source": [
    "## 🧹 Step 12: Cleanup\n",
    "\n",
    "Đóng Dask cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20445068",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Close Dask Gateway cluster\n",
    "try:\n",
    "    client.close()\n",
    "    cluster.close()\n",
    "    print(\"✅ Dask Gateway cluster closed.\")\n",
    "except Exception as e:\n",
    "    print(f\"⚠ Error closing cluster: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046bf0f9",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## ✅ Summary\n",
    "\n",
    "### Notebook này:\n",
    "- ✅ Chạy trên **Server ODC/JupyterHub** với **Dask Gateway**\n",
    "- ✅ Load dữ liệu từ **Microsoft Planetary Computer** qua STAC API\n",
    "- ✅ Không cần **ODC Database** để load data (dùng Planetary Computer)\n",
    "- ✅ Sử dụng infrastructure của ODC (Dask Gateway, S3 access)\n",
    "- ✅ Tương thích 100% với dữ liệu ODC\n",
    "\n",
    "### Ưu điểm của approach này:\n",
    "1. **Scalability**: Dask Gateway adaptive scaling (1-10 workers)\n",
    "2. **Public data**: Planetary Computer không cần VPN/private network\n",
    "3. **Consistent**: Cùng infrastructure với ODC training pipeline\n",
    "4. **Flexible**: Có thể train trên Planetary Computer, predict trên ODC hoặc ngược lại\n",
    "\n",
    "### Workflow train/predict:\n",
    "1. **Train trên server ODC**: Chạy notebook này với Dask Gateway\n",
    "2. **Save model**: Export `.joblib` file  \n",
    "3. **Predict qua API**: api_server.py tự động dùng Planetary Computer\n",
    "\n",
    "### Architecture:\n",
    "```\n",
    "┌─────────────────────────────────────┐\n",
    "│   TRAINING (ODC Infrastructure)     │\n",
    "│   ✅ Dask Gateway (1-10 workers)   │\n",
    "│   ✅ Planetary Computer STAC API    │\n",
    "│   ✅ S3 access configured           │\n",
    "│   → Model: .joblib                  │\n",
    "└─────────────────────────────────────┘\n",
    "                 ↓\n",
    "┌─────────────────────────────────────┐\n",
    "│   PREDICTION (API Server)           │\n",
    "│   ✅ Planetary Computer (public)   │\n",
    "│   ✅ Same preprocessing pipeline    │\n",
    "│   → GeoTIFF output                  │\n",
    "└─────────────────────────────────────┘\n",
    "```\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "983478ae-8c91-456d-b4e4-aa5c92286e72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "135c5a1d-f97a-423a-9dc4-46bda170c86e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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