{ "cells": [ { "cell_type": "markdown", "id": "b1dab7f7", "metadata": {}, "source": [ "# 🌍 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. 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newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "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": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = false;\n", " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = true;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() 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url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " const url = js_exports[name];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", " const js_modules = [];\n", " const js_exports = {};\n", " const css_urls = [];\n", " const inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {} // ensure no trailing comma for IE\n", " ];\n", "\n", " function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (let i = 0; i < inline_js.length; i++) {\n", " try {\n", " inline_js[i].call(root, root.Bokeh);\n", " } catch(e) {\n", " if (!reloading) {\n", " throw e;\n", " }\n", " }\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", " var NewBokeh = root.Bokeh;\n", " if (Bokeh.versions === undefined) {\n", " Bokeh.versions = new Map();\n", " }\n", " if (NewBokeh.version !== Bokeh.version) {\n", " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", " }\n", " root.Bokeh = Bokeh;\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > 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console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n 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console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = 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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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "✅ All modules loaded successfully!\n", "📡 Data source: Microsoft Planetary Computer\n", "🚀 Infrastructure: Dask Gateway + ODC\n", "CPU times: user 19.1 s, sys: 3.38 s, total: 22.4 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" ] }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", " if (js_modules == null) js_modules = [];\n", " if (js_exports == null) js_exports = {};\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", "\n", " if (root._bokeh_is_loading > 0) {\n", " // Don't load bokeh if it is still initializing\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", " // There is nothing to load\n", " run_callbacks();\n", " return null;\n", " }\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", " window._bokeh_on_load = on_load\n", "\n", " function on_error(e) {\n", " const src_el = e.srcElement\n", " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", " }\n", "\n", " const skip = [];\n", " if (window.requirejs) {\n", " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", " root._bokeh_is_loading = css_urls.length + 0;\n", " } else {\n", " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", " 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scripts[i]\n", " if (script.src != null) {\n", " existing_scripts.push(script.src)\n", " }\n", " }\n", " for (let i = 0; i < js_urls.length; i++) {\n", " const url = js_urls[i];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " const element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (let i = 0; i < js_modules.length; i++) {\n", " const url = js_modules[i];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " var element = 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" function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (let i = 0; i < inline_js.length; i++) {\n", " try {\n", " inline_js[i].call(root, root.Bokeh);\n", " } catch(e) {\n", " if (!reloading) {\n", " throw e;\n", " }\n", " }\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", " var NewBokeh = root.Bokeh;\n", " if (Bokeh.versions === undefined) {\n", " Bokeh.versions = new Map();\n", " }\n", " if (NewBokeh.version !== Bokeh.version) {\n", " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", " }\n", " root.Bokeh = Bokeh;\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " // If the timeout and bokeh was not successfully loaded we reset\n", " // everything and try loading again\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " root._bokeh_is_loading = 0\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", " if (root.Bokeh) {\n", " root.Bokeh = undefined;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], 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null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length 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console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = 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not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length 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];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", "}\n", 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null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "An existing cluster was found. Connecting to: easihub.1ae64254f06742f4ab6a565436953b3a\n", "\n", "✅ Dask Gateway + Datacube + S3 ready!\n", " Dask dashboard: https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.1ae64254f06742f4ab6a565436953b3a/status\n", " Workers: Adaptive scaling (1-10)\n", "======================================================================\n" ] } ], "source": [ "\n", "print(\"🚀 Step 1: Dask Gateway + Datacube Initialization\")\n", "print(\"=\" * 70)\n", "\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", "# 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": null, "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: ['B02', 'B03', 'B04', 'B08', 'B8A', 'B11', 'B12', 'SCL']\n", "✅ Found 777 Sentinel-2 scenes\n", "📥 Loading data...\n", "✅ Loaded Sentinel-2 data: FrozenMappingWarningOnValuesAccess({'y': 8874, 'x': 9902, 'time': 154})\n", " Shape: {'y': 8874, 'x': 9902, 'time': 154}\n", " Bands: ['B02', 'B03', 'B04', 'B08', 'B8A', 'B11', 'B12', 'SCL']\n", "🧹 Applying cloud mask...\n", "✅ Cloud mask applied\n", "📊 Calculating NDVI using B08 and B04...\n", "✅ NDVI calculated\n", "🔧 Filling NaN values using temporal interpolation...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jovyan/remote-sensing/load_data_no_odc.py:82: 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ NaN values filled\n", "📅 Resampling to monthly using mean...\n", "✅ Resampled to monthly: FrozenMappingWarningOnValuesAccess({'time': 13, 'y': 8874, 'x': 9902})\n", "\n", "✅ Sentinel-2 monthly data loaded!\n", " Dimensions: {'time': 13, 'y': 8874, 'x': 9902}\n", " Variables: ['B02', 'B03', 'B04', 'B08', 'B8A', 'B11', 'B12', 'NDVI']\n", " Time steps: 13\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":14: 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", "/env/lib/python3.12/site-packages/distributed/client.py:3357: UserWarning: Sending large graph of size 25.57 MiB.\n", "This may cause some slowdown.\n", "Consider scattering data ahead of time and using futures.\n", " warnings.warn(\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": [], "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": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }