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thêm chức năng train trên odc predict trên planetary

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Victor Phan
2026-03-04 23:03:04 +07:00
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{
"cells": [
{
"cell_type": "markdown",
"id": "b1dab7f7",
"metadata": {},
"source": [
"# 🌍 Decision Tree Land Classification - Planetary Computer\n",
"\n",
"## 📌 Notebook này có thể chạy trên:\n",
"- ✅ **Local machine** (không cần ODC database)\n",
"- ✅ **Server ODC/JupyterHub** (có 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",
"## 🔄 Workflow:\n",
"1. Load data từ Planetary Computer (STAC)\n",
"2. Preprocessing (cloud mask, NDVI, resampling)\n",
"3. Train Decision Tree model\n",
"4. Evaluate & save model\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df9820b8",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"%matplotlib inline\n",
"\n",
"import sys\n",
"import os\n",
"sys.path.insert(0, '/media/x79/2A7D-FAA0/remote-sensing')\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",
"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",
"# Dask for parallel processing\n",
"from dask.distributed import Client, LocalCluster\n",
"\n",
"print(\"✅ All modules loaded successfully!\")\n",
"print(\"📡 Data source: Microsoft Planetary Computer\")\n",
"print(\"💻 Environment: Local or Remote compatible\")"
]
},
{
"cell_type": "markdown",
"id": "53397b2f",
"metadata": {},
"source": [
"## 🚀 Step 1: Initialize Dask Cluster\n",
"\n",
"Khởi tạo Dask local cluster để xử lý song song"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c4d6779",
"metadata": {},
"outputs": [],
"source": [
"# Khởi tạo Dask LocalCluster\n",
"print(\"🚀 Initializing Dask LocalCluster...\")\n",
"\n",
"cluster = LocalCluster(\n",
" n_workers=4,\n",
" threads_per_worker=1,\n",
" memory_limit='4GB'\n",
")\n",
"client = Client(cluster)\n",
"\n",
"print(f\"✅ Dask cluster ready!\")\n",
"print(f\" Workers: {len(cluster.workers)}\")\n",
"print(f\" Dashboard: {client.dashboard_link}\")\n",
"print(\"=\" * 70)"
]
},
{
"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": null,
"id": "15a5291c",
"metadata": {},
"outputs": [],
"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": null,
"id": "611cd1c2",
"metadata": {},
"outputs": [],
"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": [],
"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": [
"# 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",
"# TODO: Load training data from shapefile/GeoJSON\n",
"# Bạn cần cung cấp đường dẫn đến file training data\n",
"train_path = \"/media/x79/2A7D-FAA0/remote-sensing/train/train_data.geojson\" # Thay đổi path này\n",
"\n",
"print(f\"\\n📂 Loading training data from: {train_path}\")\n",
"print(\"⚠ Note: Bạn cần cập nhật train_path với đường dẫn thực tế\")"
]
},
{
"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": [
"# Placeholder for feature extraction\n",
"# Bạn cần implement hàm extract features từ train points\n",
"\n",
"def extract_features(train_data, data_s2, data_s1):\n",
" \"\"\"\n",
" Extract features from S2 and S1 data at training point locations\n",
" \"\"\"\n",
" X_list = []\n",
" y_list = []\n",
" \n",
" for idx, point in train_data.iterrows():\n",
" try:\n",
" lon, lat = point.geometry.x, point.geometry.y\n",
" \n",
" # Extract S2 data\n",
" s2_values = data_s2.sel(x=lon, y=lat, method='nearest').to_array().values.flatten()\n",
" \n",
" # Extract S1 data\n",
" s1_values = data_s1.sel(x=lon, y=lat, method='nearest').to_array().values.flatten()\n",
" \n",
" # Combine features\n",
" features = np.concatenate([s2_values, s1_values])\n",
" \n",
" # Skip if contains NaN\n",
" if not np.isnan(features).any():\n",
" X_list.append(features)\n",
" y_list.append(point['label_id'])\n",
" \n",
" except Exception as e:\n",
" continue\n",
" \n",
" return np.array(X_list), np.array(y_list)\n",
"\n",
"# TODO: Uncomment when training data is available\n",
"# X, y = extract_features(train_data, data_sen2_monthly, data_sen1_monthly)\n",
"# print(f\"✅ Extracted {len(X)} training samples\")\n",
"# print(f\" Features: {X.shape[1]}\")\n",
"# print(f\" Classes: {sorted(set(y.tolist()))}\")\n",
"\n",
"print(\"⚠ Feature extraction step - waiting for training data\")"
]
},
{
"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": [
"# TODO: Uncomment when features are extracted\n",
"\n",
"# # Split train/val/test\n",
"# X_temp, X_test, y_temp, y_test = train_test_split(\n",
"# X, y, test_size=0.2, random_state=42, stratify=y\n",
"# )\n",
"\n",
"# X_train, X_val, y_train, y_val = train_test_split(\n",
"# X_temp, y_temp, test_size=0.125, random_state=42, stratify=y_temp\n",
"# )\n",
"\n",
"# # Combine train + val for final 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\"✅ Data split:\")\n",
"# print(f\" Train: {len(X_train)} samples\")\n",
"# print(f\" Val: {len(X_val)} samples\")\n",
"# print(f\" Test: {len(X_test)} samples\")\n",
"# print(f\" Total: {len(X)} samples\")\n",
"\n",
"print(\"⚠ Data split step - waiting for features\")"
]
},
{
"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",
"\n",
"# TODO: Uncomment when data is ready\n",
"\n",
"# # Train Decision Tree\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",
"# print(\"🚀 Training Decision Tree...\")\n",
"# model.fit(X_fit, y_fit)\n",
"\n",
"# val_acc = model.score(X_val, y_val)\n",
"# print(f\"✅ Training complete!\")\n",
"# print(f\" Tree depth: {model.get_depth()}\")\n",
"# print(f\" Leaves: {model.get_n_leaves()}\")\n",
"# print(f\" Val accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n",
"\n",
"print(\"⚠ Training step - waiting for data\")"
]
},
{
"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",
"\n",
"# TODO: Uncomment when model is trained\n",
"\n",
"# DEPTH_RANGE = list(range(1, 51))\n",
"# THRESHOLD = 0.001\n",
"\n",
"# train_accs = []\n",
"# val_accs = []\n",
"\n",
"# print(\"🔍 Analyzing convergence for max_depth...\")\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",
"# 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\"✅ Best max_depth: {best_depth} (val_acc: {best_val_acc:.4f})\")\n",
"\n",
"# # Plot\n",
"# fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n",
"# ax.plot(DEPTH_RANGE, train_accs, 'b-o', markersize=3, label='Train')\n",
"# ax.plot(DEPTH_RANGE, val_accs, 'g-o', markersize=3, label='Val')\n",
"# ax.axvline(x=best_depth, color='red', linestyle='--', label=f'Best={best_depth}')\n",
"# ax.set_xlabel('max_depth')\n",
"# ax.set_ylabel('Accuracy')\n",
"# ax.set_title('Train/Val Accuracy vs max_depth')\n",
"# ax.legend()\n",
"# ax.grid(True, alpha=0.3)\n",
"# plt.tight_layout()\n",
"# plt.show()\n",
"\n",
"print(\"⚠ Hyperparameter analysis - waiting for model\")"
]
},
{
"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": [
"# TODO: Uncomment when model is trained\n",
"\n",
"# # Predictions\n",
"# y_pred = model.predict(X_test)\n",
"# acc = accuracy_score(y_test, y_pred)\n",
"\n",
"# print(f\"📊 Test Accuracy: {acc:.4f} ({acc*100:.2f}%)\\n\")\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=(9, 7))\n",
"# sns.heatmap(cm, annot=True, fmt='d', cmap='Greens',\n",
"# xticklabels=class_names, yticklabels=class_names)\n",
"# plt.xlabel('Predicted')\n",
"# plt.ylabel('Actual')\n",
"# plt.title('Confusion Matrix — Decision Tree')\n",
"# plt.tight_layout()\n",
"# plt.show()\n",
"\n",
"print(\"⚠ Evaluation step - waiting for model\")"
]
},
{
"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": [
"# TODO: Uncomment when model is trained\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",
"# # Save metadata\n",
"# info = {\n",
"# \"model_type\": \"DecisionTree\",\n",
"# \"data_source\": \"Microsoft Planetary Computer\",\n",
"# \"max_depth\": model.get_depth(),\n",
"# \"n_leaves\": 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",
"# \"test_samples\": int(len(X_test)),\n",
"# \"bbox\": bbox,\n",
"# \"date_range\": date_range,\n",
"# \"saved_at\": datetime.now().isoformat(),\n",
"# }\n",
"\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(json.dumps(info, indent=2, ensure_ascii=False))\n",
"\n",
"print(\"⚠ Save model step - waiting for trained model\")"
]
},
{
"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 cluster\n",
"try:\n",
" client.close()\n",
" cluster.close()\n",
" print(\"✅ Dask cluster closed.\")\n",
"except Exception as e:\n",
" print(f\"⚠ Error closing cluster: {e}\")"
]
},
{
"cell_type": "markdown",
"id": "046bf0f9",
"metadata": {},
"source": [
"---\n",
"\n",
"## ✅ Summary\n",
"\n",
"### Notebook này:\n",
"- ✅ Load dữ liệu từ **Microsoft Planetary Computer**\n",
"- ✅ Không cần **ODC Database**\n",
"- ✅ Có thể chạy trên **local machine** hoặc **server ODC**\n",
"- ✅ Tương thích 100% với dữ liệu ODC\n",
"\n",
"### Workflow train/predict:\n",
"1. **Train trên server ODC**: Chạy notebook này với training data đầy đủ\n",
"2. **Save model**: Export `.joblib` file\n",
"3. **Predict trên local**: Load model và sử dụng cùng `load_data_no_odc.py` để load dữ liệu mới\n",
"\n",
"### Next steps:\n",
"1. Cập nhật `train_path` với đường dẫn training data thực tế\n",
"2. Uncomment các TODO cells\n",
"3. Run notebook để train model\n",
"4. Test prediction trên local machine\n",
"\n",
"---"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}