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window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", "application/vnd.holoviews_load.v0+json": "" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… EASI tools loaded successfully (with Gateway support)\n" ] }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, 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handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", "application/vnd.holoviews_load.v0+json": "" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "πŸ“¦ ODC Module with Cognito Authentication Loaded\n", "======================================================================\n", "\n", "πŸ’‘ Quick Start:\n", " 1. setup_cognito_auth('train_files/crediential.txt')\n", " 2. 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"application/vnd.holoviews_load.v0+json": "" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "πŸ“¦ ODC Module with Cognito Authentication Loaded\n", "======================================================================\n", "\n", "πŸ’‘ Quick Start:\n", " 1. setup_cognito_auth('train_files/crediential.txt')\n", " 2. Use datacube normally with authenticated S3 access\n", "\n", "πŸ“š Functions:\n", " - setup_cognito_auth() : Setup Cognito authentication\n", " - get_cognito_auth() : Get authenticator instance\n", " - print_auth_status() : Show auth status\n", " - auto_setup() : Auto-setup if credentials exist\n", "======================================================================\n", "\n", "βœ… All modules loaded successfully\n", "βœ… Module loaded with Cognito authentication support\n", "CPU times: user 4.9 s, sys: 182 ms, total: 5.08 s\n", "Wall time: 4.12 s\n" ] } ], "source": [ "%%time\n", "%matplotlib inline\n", "\n", "import importlib\n", "import sys\n", "sys.path.insert(0, '/media/x79/2A7D-FAA0/remote-sensing')\n", "\n", "# Import ODC module with Cognito authentication\n", "\n", "\n", "from new_import_ODC_cognito import *\n", "\n", "import new_import_ODC_cognito \n", "importlib.reload(new_import_ODC_cognito)\n", "\n", "\n", "print(\"βœ… All modules loaded successfully\")\n", "print(\"βœ… Module loaded with Cognito authentication support\")" ] }, { "cell_type": "markdown", "id": "6abb2be1", "metadata": {}, "source": [ "# πŸ” Cognito Authentication for ODC\n", "\n", "## Overview / Tα»•ng quan\n", "\n", "Notebook nΓ y sα»­ dα»₯ng **AWS Cognito authentication** để truy cαΊ­p S3 vΓ  Open Data Cube (ODC).\n", "\n", "### Luα»“ng xΓ‘c thα»±c:\n", "```\n", "Cognito Tokens β†’ AWS Credentials β†’ S3/ODC Access\n", "```\n", "\n", "### Lợi Γ­ch:\n", "- βœ… **BαΊ£o mαΊ­t cao hΖ‘n**: Identity-based authentication\n", "- βœ… **ThΓ΄ng tin user**: Username, email, groups\n", "- βœ… **Token auto-expire**: TΔƒng cường bαΊ£o mαΊ­t\n", "- βœ… **QuαΊ£n lΓ½ quyền tα»‘t**: Group-based permissions\n", "\n", "### Credentials file:\n", "```\n", "/media/x79/2A7D-FAA0/remote-sensing/train_files/crediential.txt\n", "```\n", "\n", "---\n", "\n", "**πŸ“š Docs**: `COGNITO_GUIDE.md`, `S3_ACCESS_GUIDE.md`" ] }, { "cell_type": "code", "execution_count": 2, "id": "b794d005", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ” Step 1: Cognito Authentication Setup\n", "----------------------------------------------------------------------\n", "πŸ” Setting up Cognito authentication...\n", "βœ“ AWS credentials loaded from file\n", "βœ“ Cognito tokens loaded successfully\n", "\n", "πŸ“‹ Token Information:\n", "\n", "=== Cognito Token Information ===\n", "\n", "User Information:\n", " Username: hienm2523001\n", " Name: Hien Phan\n", " Email: hienm2523001@gstudent.ctu.edu.vn\n", " Groups: default-group, allocation:R-19244:CSIRO and Vietnam partners\n", " Token expires: 2026-03-05 04:52:38\n", " Time remaining: 6h 54m\n", "\n", "=== Getting AWS Credentials from Cognito ===\n", "⚠ No Identity Pool ID provided\n", "⚠ Using existing AWS credentials (already exchanged from Cognito)...\n", "βœ“ Using AWS credentials loaded from file\n", "βœ“ AWS credentials set in environment\n", "\n", "🌐 Configuring datacube S3 access...\n", "\n", "βœ… Cognito authentication setup complete!\n", "βœ… Ready to use datacube with S3 access\n", "\n", "\n", "βœ… Authentication successful!\n", " Ready to access S3 buckets with authenticated credentials\n", "\n", "======================================================================\n", "πŸš€ Step 2: Dask + Datacube Initialization\n", "----------------------------------------------------------------------\n" ] }, { "ename": "ValueError", "evalue": "No dask-gateway address provided or found in configuration", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[2], line 21\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m70\u001b[39m)\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Khởi tαΊ‘o Dask + Datacube\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m cluster, client \u001b[38;5;241m=\u001b[39m \u001b[43mnotebook_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minitialize_dask\u001b[49m\u001b[43m(\u001b[49m\u001b[43muse_gateway\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Khai bΓ‘o Datacube\u001b[39;00m\n\u001b[1;32m 24\u001b[0m dc \u001b[38;5;241m=\u001b[39m datacube\u001b[38;5;241m.\u001b[39mDatacube()\n", "File \u001b[0;32m/media/x79/2A7D-FAA0/remote-sensing/easi_tools/notebook_utils.py:123\u001b[0m, in \u001b[0;36minitialize_dask\u001b[0;34m(use_gateway, workers, wait, local_port, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# Dask gateway\u001b[39;00m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_gateway:\n\u001b[0;32m--> 123\u001b[0m gateway \u001b[38;5;241m=\u001b[39m \u001b[43mGateway\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 124\u001b[0m clusters \u001b[38;5;241m=\u001b[39m gateway\u001b[38;5;241m.\u001b[39mlist_clusters()\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m clusters:\n", "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/dask_gateway/client.py:282\u001b[0m, in \u001b[0;36mGateway.__init__\u001b[0;34m(self, address, proxy_address, public_address, auth, asynchronous, loop)\u001b[0m\n\u001b[1;32m 280\u001b[0m address \u001b[38;5;241m=\u001b[39m format_template(dask\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgateway.address\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m address \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 282\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 283\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo dask-gateway address provided or found in configuration\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 284\u001b[0m )\n\u001b[1;32m 285\u001b[0m address \u001b[38;5;241m=\u001b[39m address\u001b[38;5;241m.\u001b[39mrstrip(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m public_address \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[0;31mValueError\u001b[0m: No dask-gateway address provided or found in configuration" ] } ], "source": [ "# ══════════════════════════════════════════════════════════════════════════════\n", "# SETUP COGNITO AUTHENTICATION\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "print(\"πŸ” Step 1: Cognito Authentication Setup\")\n", "print(\"-\" * 70)\n", "\n", "# Setup Cognito authentication for S3 access\n", "auth = setup_cognito_auth('/media/x79/2A7D-FAA0/remote-sensing/train_files/crediential.txt')\n", "\n", "if auth:\n", " print(\"\\nβœ… Authentication successful!\")\n", " print(\" Ready to access S3 buckets with authenticated credentials\")\n", "else:\n", " print(\"\\n⚠ Authentication failed - falling back to unsigned access\")\n", "\n", "print(\"\\n\" + \"=\" * 70)\n", "print(\"πŸš€ Step 2: Dask + Datacube Initialization\")\n", "print(\"-\" * 70)\n", "\n", "# Khởi tαΊ‘o Dask + Datacube\n", "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", "\n", "# Khai bΓ‘o Datacube\n", "dc = datacube.Datacube()\n", "\n", "print(\"\\nβœ… Dask + Datacube + S3 (Cognito authenticated) ready!\")\n", "print(f\" Dask dashboard: {client.dashboard_link}\")\n", "print(\"=\" * 70)" ] }, { "cell_type": "code", "execution_count": null, "id": "5bc42a3c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Most common native CRS: EPSG:32648\n", "No datasets require offset correction\n", "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n", "βœ… Sentinel-2 raw: FrozenMappingWarningOnValuesAccess({'time': 151, 'y': 8874, 'x': 9902})\n" ] } ], "source": [ "# CαΊ₯u hΓ¬nh vΓΉng vΓ  thời gian\n", "date_range = (\"2022-09-01\", \"2023-10-01\")\n", "longtitude_range = (105.5, 106.4)\n", "latitude_range = (9.2, 10.0)\n", "\n", "# TαΊ£i dα»― liệu Sentinel-2\n", "data_sen2 = load_data(\n", " dc=dc,\n", " date_range=date_range,\n", " longtitude_range=longtitude_range,\n", " latitude_range=latitude_range,\n", ")\n", "print(f\"βœ… Sentinel-2 raw: {data_sen2.dims}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0ef51e7d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bitsvaluesdescription
qa[0, 1, 2, 3, 4, 5, 6, 7]{'0': 'no data', '1': 'saturated or defective'...Sen2Cor Scene Classification
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" ], "text/plain": [ " bits \\\n", "qa [0, 1, 2, 3, 4, 5, 6, 7] \n", "\n", " values \\\n", "qa {'0': 'no data', '1': 'saturated or defective'... \n", "\n", " description \n", "qa Sen2Cor Scene Classification " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'0': 'no data',\n", " '1': 'saturated or defective',\n", " '2': 'dark area pixels',\n", " '3': 'cloud shadows',\n", " '4': 'vegetation',\n", " '5': 'bare soils',\n", " '6': 'water',\n", " '7': 'unclassified',\n", " '8': 'cloud medium probability',\n", " '9': 'cloud high probability',\n", " '10': 'thin cirrus',\n", " '11': 'snow or ice'}" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "ValueError", "evalue": "No `satellite_mission` was provided. Please specify either 'ls' or 's2' to ensure the \nfunction calculates indices using the correct spectral bands.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Tiền xα»­ lΓ½ Sentinel-2: cloud mask + NDVI + resampling\u001b[39;00m\n\u001b[1;32m 2\u001b[0m data_clean \u001b[38;5;241m=\u001b[39m mask_clean(data_sen2)\n\u001b[0;32m----> 3\u001b[0m data_ndvi \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_clean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNDVI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m data_fill \u001b[38;5;241m=\u001b[39m fill_nan(data_ndvi)\n\u001b[1;32m 5\u001b[0m data_sen2_monthly \u001b[38;5;241m=\u001b[39m data_fill\u001b[38;5;241m.\u001b[39mresample(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1MS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mcompute()\n", "File \u001b[0;32m~/remote-sensing/deafrica_tools/bandindices.py:374\u001b[0m, in \u001b[0;36mcalculate_indices\u001b[0;34m(ds, index, collection, satellite_mission, custom_varname, normalise, drop, deep_copy)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;66;03m# Rename bands to a consistent format if depending on what satellite mission\u001b[39;00m\n\u001b[1;32m 369\u001b[0m \u001b[38;5;66;03m# is specified in `satellite_mission`. This allows the same index calculations\u001b[39;00m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;66;03m# to be applied to all satellite missions. If no satellite mission was provided,\u001b[39;00m\n\u001b[1;32m 371\u001b[0m \u001b[38;5;66;03m# raise an exception.\u001b[39;00m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m satellite_mission \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 374\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 375\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo `satellite_mission` was provided. Please specify \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meither \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mls\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms2\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to ensure the \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mfunction \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalculates indices using the correct spectral \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbands.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 379\u001b[0m )\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m satellite_mission \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mls\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 382\u001b[0m sr_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", "\u001b[0;31mValueError\u001b[0m: No `satellite_mission` was provided. Please specify either 'ls' or 's2' to ensure the \nfunction calculates indices using the correct spectral bands." ] } ], "source": [ "# Tiền xα»­ lΓ½ Sentinel-2: cloud mask + NDVI + resampling\n", "data_clean = mask_clean(data_sen2)\n", "data_ndvi = calculate_indices(data_clean, index=\"NDVI\", satellite_mission=\"s2\")\n", "data_fill = fill_nan(data_ndvi)\n", "data_sen2_monthly = data_fill.resample(time=\"1MS\").mean().compute()\n", "print(f\"βœ… S2 monthly shape: {data_sen2_monthly.dims}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "0da4f86d", "metadata": {}, "outputs": [], "source": [ "# TαΊ£i Sentinel-1 (SAR VV/VH)\n", "data_sen1 = load_data_sen1(\n", " dc=dc,\n", " date_range=date_range,\n", " longtitude_range=longtitude_range,\n", " latitude_range=latitude_range,\n", ")\n", "data_sen1_monthly = calculate_average(data_sen1, [\"VV\", \"VH\"], resample=\"1MS\").compute()\n", "print(f\"βœ… S1 monthly shape: {data_sen1_monthly.dims}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "412b3716", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# Ánh xαΊ‘ nhΓ£n lα»›p Δ‘αΊ₯t\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", "# TαΊ£i vΓ  ghΓ©p dα»― liệu train tα»« S1 + S2\n", "train_data = load_train_data(label_mapping=label_mapping)\n", "X, y = get_data_sen1_and_sen2(train_data, data_sen2_monthly, data_sen1_monthly)\n", "\n", "# Chia tαΊ­p train / val / test\n", "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y, test_size=0.2, val_size=0.1)\n", "\n", "X_train_np = np.array(X_train, dtype=np.float32)\n", "X_val_np = np.array(X_val, dtype=np.float32)\n", "X_test_np = np.array(X_test, dtype=np.float32)\n", "y_train_np = np.array(y_train, dtype=np.int64)\n", "y_val_np = np.array(y_val, dtype=np.int64)\n", "y_test_np = np.array(y_test, dtype=np.int64)\n", "\n", "# Gα»™p train + val cho sklearn\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\"βœ… X_fit: {X_fit.shape} | X_test: {X_test_np.shape}\")\n", "print(f\" Classes: {sorted(set(y_fit.tolist()))}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "84a8a1d0", "metadata": {}, "outputs": [], "source": [ "%%time\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# ── XΓ’y dα»±ng vΓ  train mΓ΄ hΓ¬nh 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(\"πŸš€ Training Decision Tree...\")\n", "model.fit(X_fit, y_fit)\n", "\n", "val_acc = model.score(X_val_np, y_val_np)\n", "print(f\"βœ… Training hoΓ n tαΊ₯t! Depth: {model.get_depth()} \"\n", " f\"Leaves: {model.get_n_leaves()} Val accuracy: {val_acc:.4f}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "8248d748", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# PHΓ‚N TÍCH ĐIα»‚M HỘI TỀ β€” Decision Tree\n", "# PhΖ°Ζ‘ng phΓ‘p: quΓ©t max_depth tα»« 1β†’50 vΓ  theo dΓ΅i train/val accuracy\n", "# Điểm hα»™i tα»₯ = Δ‘α»™ sΓ’u tαΊ‘i Δ‘Γ³ val_acc Δ‘αΊ‘t cα»±c Δ‘αΊ‘i rα»“i bαΊ―t Δ‘αΊ§u giαΊ£m (overfitting)\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "DEPTH_RANGE = list(range(1, 51))\n", "THRESHOLD = 0.001 # cαΊ£i thiện val_acc < 0.1% β†’ coi lΓ  hα»™i tα»₯\n", "\n", "train_accs_d, val_accs_d = [], []\n", "print(\"πŸ” PhΓ’n tΓ­ch hα»™i tα»₯ theo max_depth ...\")\n", "for d in DEPTH_RANGE:\n", " m = DecisionTreeClassifier(\n", " min_samples_leaf=2, min_samples_split=5,\n", " class_weight=\"balanced\", random_state=42, max_depth=d,\n", " )\n", " m.fit(X_fit, y_fit)\n", " train_accs_d.append(m.score(X_fit, y_fit))\n", " val_accs_d.append( m.score(X_val_np, y_val_np))\n", "\n", "train_accs_d = np.array(train_accs_d)\n", "val_accs_d = np.array(val_accs_d)\n", "improvements = np.diff(val_accs_d)\n", "\n", "# ── TΓ¬m Δ‘iểm hα»™i tα»₯ ───────────────────────────────────────────────────────────\n", "best_depth = DEPTH_RANGE[int(np.argmax(val_accs_d))]\n", "best_val_acc = float(np.max(val_accs_d))\n", "\n", "# Điểm hα»™i tα»₯ sα»›m: lαΊ§n Δ‘αΊ§u cαΊ£i thiện < threshold\n", "conv_depth = None\n", "for i, imp in enumerate(improvements):\n", " if abs(imp) < THRESHOLD:\n", " conv_depth = DEPTH_RANGE[i + 1]\n", " break\n", "\n", "# Điểm overfit: val_acc bαΊ―t Δ‘αΊ§u giαΊ£m so vα»›i peak\n", "overfit_depth = None\n", "peak_idx = int(np.argmax(val_accs_d))\n", "for i in range(peak_idx + 1, len(val_accs_d)):\n", " if val_accs_d[i] < best_val_acc - 0.005: # giαΊ£m > 0.5%\n", " overfit_depth = DEPTH_RANGE[i]\n", " break\n", "\n", "# KhoαΊ£ng cΓ‘ch train-val (generalization gap)\n", "gap = train_accs_d - val_accs_d\n", "\n", "# ── VαΊ½ Δ‘α»“ thα»‹ ─────────────────────────────────────────────────────────────────\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", "\n", "# --- TrΓ‘i: accuracy curves ---\n", "axes[0].plot(DEPTH_RANGE, train_accs_d, \"b-o\", markersize=3, label=\"Train\")\n", "axes[0].plot(DEPTH_RANGE, val_accs_d, \"g-o\", markersize=3, label=\"Val\")\n", "axes[0].axvline(x=best_depth, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", " label=f\"Best depth={best_depth} ({best_val_acc*100:.2f}%)\")\n", "if conv_depth:\n", " axes[0].axvline(x=conv_depth, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", " label=f\"Hα»™i tα»₯ depth={conv_depth}\")\n", "if overfit_depth:\n", " axes[0].axvline(x=overfit_depth, color=\"purple\", linestyle=\"-.\", linewidth=1.5,\n", " label=f\"Overfit depth={overfit_depth}\")\n", "axes[0].set_xlabel(\"max_depth\")\n", "axes[0].set_ylabel(\"Accuracy\")\n", "axes[0].set_title(\"Train / Val Accuracy vs max_depth\")\n", "axes[0].legend(fontsize=8)\n", "axes[0].grid(True, alpha=0.3)\n", "\n", "# --- Giα»―a: marginal improvement ---\n", "axes[1].bar(DEPTH_RANGE[1:], improvements * 100,\n", " color=[\"green\" if v > 0 else \"red\" for v in improvements], alpha=0.7)\n", "axes[1].axhline(y=0, color=\"black\", linewidth=0.8)\n", "axes[1].axhline(y=THRESHOLD * 100, color=\"orange\", linestyle=\"--\",\n", " label=f\"Threshold={THRESHOLD*100:.2f}%\")\n", "if conv_depth:\n", " axes[1].axvline(x=conv_depth, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", " label=f\"Hα»™i tα»₯ depth={conv_depth}\")\n", "axes[1].set_xlabel(\"max_depth\")\n", "axes[1].set_ylabel(\"Ξ”Val Accuracy (%)\")\n", "axes[1].set_title(\"Marginal Val Improvement per Depth Step\")\n", "axes[1].legend(fontsize=8)\n", "axes[1].grid(True, alpha=0.3)\n", "\n", "# --- PhαΊ£i: generalization gap ---\n", "axes[2].fill_between(DEPTH_RANGE, gap * 100, alpha=0.5, color=\"tomato\", label=\"Gap = Train βˆ’ Val\")\n", "axes[2].plot(DEPTH_RANGE, gap * 100, \"r-o\", markersize=3)\n", "if best_depth:\n", " axes[2].axvline(x=best_depth, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", " label=f\"Best depth={best_depth}\")\n", "axes[2].set_xlabel(\"max_depth\")\n", "axes[2].set_ylabel(\"Gap (%)\")\n", "axes[2].set_title(\"Generalization Gap (Overfitting Risk)\")\n", "axes[2].legend(fontsize=8)\n", "axes[2].grid(True, alpha=0.3)\n", "\n", "plt.suptitle(\"Decision Tree β€” Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# ── Tα»•ng kαΊΏt ──────────────────────────────────────────────────────────────────\n", "print(f\"\\n{'═'*58}\")\n", "print(f\" Độ sΓ’u TỐI Ζ―U (best val acc) : max_depth = {best_depth} ({best_val_acc*100:.4f}%)\")\n", "if conv_depth:\n", " print(f\" Điểm HỘI TỀ (Ξ”acc < {THRESHOLD*100:.1f}%) : max_depth = {conv_depth}\")\n", "if overfit_depth:\n", " print(f\" Điểm OVERFIT bαΊ―t Δ‘αΊ§u : max_depth β‰₯ {overfit_depth}\")\n", " print(f\" β†’ NΓͺn dΓΉng max_depth ≀ {best_depth} để trΓ‘nh overfit\")\n", "print(f\"{'═'*58}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5660e2ec", "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", "# ── ĐÑnh giΓ‘ trΓͺn tαΊ­p 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=\"Greens\",\n", " xticklabels=class_names, yticklabels=class_names)\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix β€” Decision Tree\")\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e416c2aa", "metadata": {}, "outputs": [], "source": [ "import joblib, json\n", "from datetime import datetime\n", "\n", "# ── LΖ°u mΓ΄ hΓ¬nh ────────────────────────────────────────────────────────────────\n", "model_path = \"model_decision_tree_land_use.joblib\"\n", "joblib.dump(model, model_path)\n", "print(f\"βœ… Model saved β†’ {model_path}\")\n", "\n", "# ── LΖ°u thΓ΄ng tin mΓ΄ hΓ¬nh ──────────────────────────────────────────────────────\n", "info = {\n", " \"model_type\": \"DecisionTree\",\n", " \"max_depth\": model.get_depth(),\n", " \"n_leaves\": model.get_n_leaves(),\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_decision_tree_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\"βœ… Info saved β†’ {info_path}\")\n", "print(json.dumps(info, indent=2, ensure_ascii=False))\n", "\n", "# ── Đóng kαΊΏt nα»‘i Dask ──────────────────────────────────────────────────────────\n", "try:\n", " client.close()\n", " cluster.close()\n", " print(\"βœ… Dask cluster closed.\")\n", "except Exception:\n", " pass\n" ] } ], "metadata": { "kernelspec": { "display_name": "env_01", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 5 }