remote-sensing/train_files/01.train_ODC_DecisionTree.ipynb

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       "  var render_items = [{\"docid\":\"07c259d4-2a9a-4a0e-bd87-21144d3ecf4a\",\"roots\":{\"p1002\":\"fcccf349-5ecc-4ba5-8aa2-38f12a0e6de7\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  async function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t  for (const child of root_el.children) {\n",
       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
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      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
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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        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; 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      "application/vnd.holoviews_load.v0+json": ""
     },
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     "output_type": "display_data"
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     "data": {
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       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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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));",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    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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    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      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.9 s, sys: 3.43 s, total: 22.4 s\n",
      "Wall time: 13.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\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 *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b794d005",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting new cluster\n",
      "✅ Dask + Datacube + S3 sẵn sàng\n"
     ]
    }
   ],
   "source": [
    "# Khởi tạo Dask + Datacube + S3\n",
    "# Cấu hình Daskgateway\n",
    "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n",
    "# Khai báo 1 Datacube là dc\n",
    "dc = datacube.Datacube()\n",
    "configure_s3_access(aws_unsigned=True)\n",
    "print(\"✅ Dask + Datacube + S3 sẵn sàng\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "✅ Sentinel-2 raw: FrozenMappingWarningOnValuesAccess({'time': 151, 'y': 8874, 'x': 9902})\n"
     ]
    }
   ],
   "source": [
    "# Cấu hình vùng và 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",
    "# Tải dữ 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\"✅ Sentinel-2 raw: {data_sen2.dims}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef51e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bits</th>\n",
       "      <th>values</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>qa</th>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7]</td>\n",
       "      <td>{'0': 'no data', '1': 'saturated or defective'...</td>\n",
       "      <td>Sen2Cor Scene Classification</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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 xử lý 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 xử lý 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\"✅ S2 monthly shape: {data_sen2_monthly.dims}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0da4f86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tải 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\"✅ 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 xạ nhãn lớp đất\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",
    "# Tải và ghép dữ liệu train từ 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 tập 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",
    "# Gộp 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\"✅ 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",
    "# ── Xây dựng và train mô hình Decision Tree ─────────────────────────────────\n",
    "model = DecisionTreeClassifier(\n",
    "    max_depth=30,\n",
    "    min_samples_leaf=2,\n",
    "    min_samples_split=5,\n",
    "    class_weight=\"balanced\",\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "print(\"🚀 Training Decision Tree...\")\n",
    "model.fit(X_fit, y_fit)\n",
    "\n",
    "val_acc = model.score(X_val_np, y_val_np)\n",
    "print(f\"✅ Training hoàn tất!  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",
    "# PHÂN TÍCH ĐIỂM HỘI TỤ — Decision Tree\n",
    "# Phương pháp: quét max_depth từ 1→50 và theo dõi train/val accuracy\n",
    "# Điểm hội tụ = độ sâu tại đó val_acc đạt cực đại rồi bắt đầu giảm (overfitting)\n",
    "# ═══════════════════════════════════════════════════════════════════════════════\n",
    "DEPTH_RANGE = list(range(1, 51))\n",
    "THRESHOLD   = 0.001    # cải thiện val_acc < 0.1% → coi là hội tụ\n",
    "\n",
    "train_accs_d, val_accs_d = [], []\n",
    "print(\"🔍 Phân tích hội tụ 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",
    "# ── Tìm điểm hội tụ ───────────────────────────────────────────────────────────\n",
    "best_depth      = DEPTH_RANGE[int(np.argmax(val_accs_d))]\n",
    "best_val_acc    = float(np.max(val_accs_d))\n",
    "\n",
    "# Điểm hội tụ sớm: lần đầu cải 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 bắt đầu giảm so với 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:   # giảm > 0.5%\n",
    "        overfit_depth = DEPTH_RANGE[i]\n",
    "        break\n",
    "\n",
    "# Khoảng cách train-val (generalization gap)\n",
    "gap = train_accs_d - val_accs_d\n",
    "\n",
    "# ── Vẽ đồ thị ─────────────────────────────────────────────────────────────────\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "# --- Trái: 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\"Hội tụ 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",
    "# --- Giữa: 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\"Hội tụ depth={conv_depth}\")\n",
    "axes[1].set_xlabel(\"max_depth\")\n",
    "axes[1].set_ylabel(\"ΔVal 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",
    "# --- Phải: generalization gap ---\n",
    "axes[2].fill_between(DEPTH_RANGE, gap * 100, alpha=0.5, color=\"tomato\", label=\"Gap = Train − 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 — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n",
    "print(f\"\\n{'═'*58}\")\n",
    "print(f\"  Độ sâu TỐI ƯU (best val acc) : max_depth = {best_depth}  ({best_val_acc*100:.4f}%)\")\n",
    "if conv_depth:\n",
    "    print(f\"  Điểm HỘI TỤ (Δacc < {THRESHOLD*100:.1f}%)  : max_depth = {conv_depth}\")\n",
    "if overfit_depth:\n",
    "    print(f\"  Điểm OVERFIT bắt đầu         : max_depth ≥ {overfit_depth}\")\n",
    "    print(f\"  → Nên dùng max_depth ≤ {best_depth} để tránh 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",
    "# ── 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=\"Greens\",\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix — Decision Tree\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e416c2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib, json\n",
    "from datetime import datetime\n",
    "\n",
    "# ── Lưu mô hình ────────────────────────────────────────────────────────────────\n",
    "model_path = \"model_decision_tree_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\": \"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\"✅ 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"
   ]
  }
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