{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "3ab233c4", "metadata": {}, "outputs": [], "source": [ "%%time\n", "%matplotlib inline\n", "\n", "import importlib\n", "import new_import_ODC\n", "\n", "importlib.reload(new_import_ODC)\n", "from new_import_ODC import *\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0af1f969", "metadata": {}, "outputs": [], "source": [ "%%time\n", "# Cấu hình Dask + ODC + S3\n", "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", "dc = datacube.Datacube()\n", "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", "client\n" ] }, { "cell_type": "code", "execution_count": null, "id": "b0809939", "metadata": {}, "outputs": [], "source": [ "## cấu hình thời gian và tọa độ\n", "date_range = (\"2022-09-01\", \"2023-10-01\")\n", "longtitude_range = (105.5, 106.4)\n", "latitude_range = (9.2, 10.0)\n", "coordinates = (longtitude_range, latitude_range)\n", "\n", "## truy vấn ảnh Sentinel-2\n", "data = load_data(dc, date_range, longtitude_range, latitude_range)\n", "notebook_utils.heading(notebook_utils.xarray_object_size(data))\n", "display(data)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "b74030e3", "metadata": {}, "outputs": [], "source": [ "%%time\n", "# Cloud masking + NDVI + fill nan + resample\n", "result = mask_clean(data)\n", "progress(result)\n", "\n", "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n", "ndvi = ds1[\"NDVI\"]\n", "\n", "time_split = [\n", " slice(\"2022-09-01\", \"2023-01-01\"),\n", " slice(\"2023-01-01\", \"2023-05-01\"),\n", " slice(\"2023-05-01\", \"2023-07-01\"),\n", " slice(\"2023-07-01\", \"2023-10-01\"),\n", "]\n", "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", "plt.imshow(fill_nan_ndvi.isel(time=6)); plt.title(\"NDVI (after fill)\"); plt.colorbar(); plt.show()\n", "\n", "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n", "progress(average_ndvi)\n", "average_ndvi = average_ndvi.compute()\n", "print(f\"NDVI monthly: {average_ndvi.shape}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "df61871b", "metadata": {}, "outputs": [], "source": [ "# Load Sentinel-1 (VH, VV)\n", "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", "average_vv = calculate_average(dsvv, time_pattern=\"1M\")\n", "average_vh = calculate_average(dsvh, time_pattern=\"1M\")\n", "print(f\"VV: {average_vv.shape} VH: {average_vh.shape}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d0def6c0", "metadata": {}, "outputs": [], "source": [ "## Chuẩn bị dữ liệu train\n", "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n", "train = load_train_data(train_path)\n", "train.head()\n", "\n", "label_mapping = {\n", " \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n", " \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n", "}\n", "\n", "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n", "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)\n", "\n", "import numpy as np\n", "X_train_np = np.asarray(X_train, dtype=np.float32)\n", "X_val_np = np.asarray(X_val, dtype=np.float32)\n", "X_test_np = np.asarray(X_test, dtype=np.float32)\n", "y_train_np = np.asarray(y_train, dtype=np.int32)\n", "y_val_np = np.asarray(y_val, dtype=np.int32)\n", "y_test_np = np.asarray(y_test, dtype=np.int32)\n", "\n", "# Gộp train + val để tận dụng toàn bộ dữ liệu train\n", "X_fit = np.concatenate([X_train_np, X_val_np], axis=0)\n", "y_fit = np.concatenate([y_train_np, y_val_np], axis=0)\n", "\n", "print(f\"Train (fit): {X_fit.shape} Test: {X_test_np.shape} Classes: {len(label_mapping)}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2dc75f84", "metadata": {}, "outputs": [], "source": [ "%%time\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "# ── Xây dựng và train mô hình Random Forest ───────────────────────────────────\n", "model = RandomForestClassifier(\n", " n_estimators=200,\n", " max_depth=30,\n", " min_samples_leaf=2,\n", " n_jobs=-1,\n", " class_weight=\"balanced\", # xử lý mất cân bằng nhãn\n", " random_state=42,\n", ")\n", "\n", "print(\"🚀 Training Random Forest...\")\n", "print(f\" n_estimators = {model.n_estimators}\")\n", "print(f\" max_depth = {model.max_depth}\")\n", "print(f\" Train samples: {len(X_fit)}\")\n", "\n", "model.fit(X_fit, y_fit)\n", "\n", "val_acc = model.score(X_val_np, y_val_np)\n", "print(f\"\\n✅ Training hoàn tất! Val accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "90588a5d", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# PHÂN TÍCH ĐIỂM HỘI TỤ — Random Forest\n", "# Phương pháp: tăng dần n_estimators (warm_start) và theo dõi val accuracy\n", "# Điểm hội tụ = lần đầu cải thiện val_acc < THRESHOLD trong WINDOW bước liên tiếp\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "N_RANGE = list(range(5, 205, 5)) # 5, 10, 15, ... 200\n", "THRESHOLD = 0.0005 # cải thiện < 0.05% → coi là hội tụ\n", "WINDOW = 3 # cần WINDOW bước liên tiếp dưới threshold\n", "\n", "conv_model = RandomForestClassifier(\n", " max_depth=30, min_samples_leaf=2, n_jobs=-1,\n", " class_weight=\"balanced\", random_state=42,\n", " warm_start=True, # ← cho phép thêm cây mà không retrain lại\n", ")\n", "\n", "train_accs_c, val_accs_c = [], []\n", "print(\"🔍 Phân tích hội tụ (warm_start)...\")\n", "for n in N_RANGE:\n", " conv_model.n_estimators = n\n", " conv_model.fit(X_fit, y_fit)\n", " train_accs_c.append(conv_model.score(X_fit, y_fit))\n", " val_accs_c.append(conv_model.score(X_val_np, y_val_np))\n", "\n", "val_accs_c = np.array(val_accs_c)\n", "train_accs_c = np.array(train_accs_c)\n", "\n", "# ── Tìm điểm hội tụ ───────────────────────────────────────────────────────────\n", "improvements = np.abs(np.diff(val_accs_c))\n", "convergence_idx = None\n", "for i in range(len(improvements) - WINDOW + 1):\n", " if all(improvements[i : i + WINDOW] < THRESHOLD):\n", " convergence_idx = i + 1 # chỉ số của điểm đầu tiên trong cửa sổ\n", " break\n", "\n", "best_idx = int(np.argmax(val_accs_c))\n", "best_n = N_RANGE[best_idx]\n", "conv_n = N_RANGE[convergence_idx] if convergence_idx is not None else None\n", "\n", "# ── Vẽ đồ thị ─────────────────────────────────────────────────────────────────\n", "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", "\n", "# --- Trái: accuracy curve ---\n", "axes[0].plot(N_RANGE, train_accs_c, \"b-o\", markersize=3, label=\"Train\")\n", "axes[0].plot(N_RANGE, val_accs_c, \"g-o\", markersize=3, label=\"Val\")\n", "axes[0].axvline(x=best_n, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", " label=f\"Best val acc n={best_n} ({max(val_accs_c)*100:.2f}%)\")\n", "if conv_n:\n", " axes[0].axvline(x=conv_n, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", " label=f\"Hội tụ n={conv_n} ({val_accs_c[convergence_idx]*100:.2f}%)\")\n", "axes[0].set_xlabel(\"n_estimators\")\n", "axes[0].set_ylabel(\"Accuracy\")\n", "axes[0].set_title(\"Convergence — Val Accuracy vs n_estimators\")\n", "axes[0].legend(fontsize=8)\n", "axes[0].grid(True, alpha=0.3)\n", "\n", "# --- Phải: cải thiện biên (marginal improvement) ---\n", "axes[1].bar(N_RANGE[1:], improvements * 100, color=\"steelblue\", alpha=0.7)\n", "axes[1].axhline(y=THRESHOLD * 100, color=\"red\", linestyle=\"--\",\n", " label=f\"Threshold = {THRESHOLD*100:.3f}%\")\n", "if conv_n:\n", " axes[1].axvline(x=conv_n, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", " label=f\"Hội tụ n={conv_n}\")\n", "axes[1].set_xlabel(\"n_estimators\")\n", "axes[1].set_ylabel(\"ΔVal Accuracy (%)\")\n", "axes[1].set_title(\"Marginal Improvement per Step\")\n", "axes[1].legend(fontsize=8)\n", "axes[1].grid(True, alpha=0.3)\n", "\n", "plt.suptitle(\"Random Forest — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n", "print(f\"\\n{'═'*55}\")\n", "print(f\" Best val accuracy : {max(val_accs_c)*100:.4f}% (n_estimators={best_n})\")\n", "if conv_n:\n", " print(f\" Điểm HỘI TỤ : n_estimators = {conv_n}\")\n", " print(f\" → Có thể dùng n_estimators={conv_n} thay vì 200 để tiết kiệm thời gian\")\n", " saved_pct = (1 - conv_n / 200) * 100\n", " print(f\" → Tiết kiệm ~{saved_pct:.0f}% thời gian train\")\n", "else:\n", " print(\" → Mô hình chưa hội tụ trong phạm vi [5, 200]. Thử tăng n_estimators.\")\n", "print(f\"{'═'*55}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "bc57f244", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n", "y_pred = model.predict(X_test_np)\n", "\n", "acc = accuracy_score(y_test_np, y_pred)\n", "print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n", "print(classification_report(y_test_np, y_pred, digits=4))\n", "\n", "# ── Feature importance ──────────────────────────────────────────────────────────\n", "feat_imp = model.feature_importances_\n", "idx = feat_imp.argsort()[::-1][:20]\n", "plt.figure(figsize=(12, 4))\n", "plt.bar(range(len(idx)), feat_imp[idx])\n", "plt.xticks(range(len(idx)), idx, rotation=45)\n", "plt.title(\"Top-20 Feature Importances\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# ── Confusion matrix ────────────────────────────────────────────────────────────\n", "class_names = list(label_mapping.keys())\n", "cm = confusion_matrix(y_test_np, y_pred)\n", "plt.figure(figsize=(9, 7))\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", " xticklabels=class_names, yticklabels=class_names)\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix — Random Forest\")\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "efc23f2c", "metadata": {}, "outputs": [], "source": [ "import joblib, json, os\n", "from datetime import datetime\n", "\n", "# ── Lưu mô hình ────────────────────────────────────────────────────────────────\n", "model_path = \"model_random_forest_land_use.joblib\"\n", "joblib.dump(model, model_path)\n", "print(f\"✅ Model saved → {model_path}\")\n", "\n", "# ── Lưu thông tin mô hình ──────────────────────────────────────────────────────\n", "info = {\n", " \"model_type\": \"RandomForest\",\n", " \"n_estimators\": model.n_estimators,\n", " \"max_depth\": model.max_depth,\n", " \"min_samples_leaf\": model.min_samples_leaf,\n", " \"class_weight\": \"balanced\",\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_np)),\n", " \"saved_at\": datetime.now().isoformat(),\n", "}\n", "info_path = \"model_random_forest_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": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 }