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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 = '3.7.3'.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 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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 && 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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 = '3.7.3'.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 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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; 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 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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": [ "CPU times: user 5.95 s, sys: 295 ms, total: 6.25 s\n", "Wall time: 3.83 s\n" ] } ], "source": [ "%%time \n", "%matplotlib inline\n", "from new_import_ODC import *" ] }, { "cell_type": "code", "execution_count": 2, "id": "bfdefd7d-90d2-43bf-aa58-ebb4caef0f85", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:41.952804Z", "iopub.status.busy": "2026-07-15T08:37:41.952227Z", "iopub.status.idle": "2026-07-15T08:37:42.192682Z", "shell.execute_reply": "2026-07-15T08:37:42.192079Z" }, "tags": [] }, "outputs": [ { "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)", "File \u001b[0;32m:2\u001b[0m\n", "File \u001b[0;32m~/CSIROBoeingPhase4-Vietnam/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": [ "%%time\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 = None\n", "\n", "# Cấu hình truy cập dịch vụ S3\n", "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", "\n", "client" ] }, { "cell_type": "markdown", "id": "657547be-5136-439b-b194-e5944c905138", "metadata": {}, "source": [ "LOAD VH, VV" ] }, { "cell_type": "code", "execution_count": 3, "id": "a0a8a450-3725-48d8-837e-2f19c2e6db59", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:42.194339Z", "iopub.status.busy": "2026-07-15T08:37:42.194178Z", "iopub.status.idle": "2026-07-15T08:37:42.196730Z", "shell.execute_reply": "2026-07-15T08:37:42.196204Z" }, "tags": [] }, "outputs": [], "source": [ "## cấu hình thời gian lấy ảnh và tọa độ\n", "date_range = ('2022-09-01', '2022-10-01')\n", "longtitude_range = (105.86, 105.94)\n", "latitude_range = (9.65, 9.69) " ] }, { "cell_type": "code", "execution_count": 4, "id": "05c2a58f-d0e5-4b20-9320-de05897cbdf7", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:42.198046Z", "iopub.status.busy": "2026-07-15T08:37:42.197907Z", "iopub.status.idle": "2026-07-15T08:37:42.263613Z", "shell.execute_reply": "2026-07-15T08:37:42.263029Z" }, "tags": [] }, "outputs": [], "source": [ "## cấu hình dữ liệu train và vh vv file\n", "train_path = \"train/ST_training_data_updated_1130points.shp\" # đường dẫn shp file train\n", "name_vh = \"vh-0922_0923-full_ST.tif\"\n", "name_vv = \"vv-0922_0923-full_ST.tif\"\n", "\n", "\n", "train = load_train_data(train_path)" ] }, { "cell_type": "code", "execution_count": 5, "id": "33471ca1-6e5a-403e-b9af-2e7b45c1d892", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:42.265662Z", "iopub.status.busy": "2026-07-15T08:37:42.265332Z", "iopub.status.idle": "2026-07-15T08:37:42.267937Z", "shell.execute_reply": "2026-07-15T08:37:42.267325Z" }, "tags": [] }, "outputs": [], "source": [ "# %%time\n", "# ## tải về dữ liệu sen1\n", "# import os\n", "# if not os.path.exists(name_vh):\n", "# !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif vh-0922_0923-full_ST.tif\n", "# if not os.path.exists(name_vv):\n", "# !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif" ] }, { "cell_type": "code", "execution_count": 5, "id": "6ebfc48a-2bed-4909-b495-6bbfd3f3cc6f", "metadata": { "tags": [] }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "id": "8471dbbf-c3c6-4f03-94b0-11795b8d2062", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:42.269386Z", "iopub.status.busy": "2026-07-15T08:37:42.269204Z", "iopub.status.idle": "2026-07-15T08:37:42.272474Z", "shell.execute_reply": "2026-07-15T08:37:42.271675Z" }, "tags": [] }, "outputs": [], "source": [ "# ds = dc.load(\n", "# product=\"sentinel1_grd_gamma0_20m\",\n", "# x=(105.5, 106.4),\n", "# y=(9.2, 10.0),\n", "# time=(\"2022-09-01\", \"2022-10-01\"),\n", "# measurements=[\"vv\", \"vh\"],\n", "# output_crs=\"EPSG:32648\",\n", "# resolution=(-10,10),\n", "# dask_chunks={\"x\":2048, \"y\":2048},\n", "# skip_broken_datasets=True,\n", "# group_by=\"solar_day\"\n", "# )\n", "# notebook_utils.heading(notebook_utils.xarray_object_size(ds))\n", "# ds" ] }, { "cell_type": "code", "execution_count": 7, "id": "3b51729a-3cdc-4ec5-957d-6861c0338dd8", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:42.274085Z", "iopub.status.busy": "2026-07-15T08:37:42.273926Z", "iopub.status.idle": "2026-07-15T08:37:42.276046Z", "shell.execute_reply": "2026-07-15T08:37:42.275590Z" }, "tags": [] }, "outputs": [], "source": [ "# vv_data = ds.vv\n", "# vv_data\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "8e5fd090-d355-45bd-ba1b-b95d0584dd8c", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:42.277353Z", "iopub.status.busy": "2026-07-15T08:37:42.277201Z", "iopub.status.idle": "2026-07-15T08:37:49.631445Z", "shell.execute_reply": "2026-07-15T08:37:49.630800Z" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/rasterio/warp.py:387: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. 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       "    scl          (time, y, x) float32 1kB dask.array<chunksize=(1, 6, 10), meta=np.ndarray>
" ], "text/plain": [ " Size: 4kB\n", "Dimensions: (y: 6, x: 10, time: 6)\n", "Coordinates:\n", " * y (y) float64 48B 1.072e+06 1.07e+06 ... 1.068e+06 1.066e+06\n", " * x (x) float64 80B 5.945e+05 5.955e+05 ... 6.025e+05 6.035e+05\n", " spatial_ref int32 4B 32648\n", " * time (time) datetime64[ns] 48B 2022-09-02T03:15:31.024000 ... 202...\n", "Data variables:\n", " red (time, y, x) float32 1kB dask.array\n", " nir (time, y, x) float32 1kB dask.array\n", " scl (time, y, x) float32 1kB dask.array" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = load_data(None, date_range, longtitude_range, latitude_range)\n", "notebook_utils.heading(notebook_utils.xarray_object_size(data))\n", "display(data)" ] }, { "cell_type": "code", "execution_count": 10, "id": "0c1ac39c-7a78-425d-afbc-b04a7988f012", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:37:51.716952Z", "iopub.status.busy": "2026-07-15T08:37:51.716786Z", "iopub.status.idle": "2026-07-15T08:38:00.451888Z", "shell.execute_reply": "2026-07-15T08:38:00.451199Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 680 ms, sys: 81.4 ms, total: 762 ms\n", "Wall time: 8.73 s\n" ] } ], "source": [ "%%time\n", "# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n", "result = mask_clean(data)\n", "# progress(result)" ] }, { "cell_type": "markdown", "id": "cd57a554-a804-4d0f-ac69-136e34d089db", "metadata": {}, "source": [ "CALCULATING THE MEAN VALUE AND FILL TO NAN POINT" ] }, { "cell_type": "code", "execution_count": 11, "id": "973ac9b2-14fd-45e4-934c-b201ac51ec9e", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.453708Z", "iopub.status.busy": "2026-07-15T08:38:00.453535Z", "iopub.status.idle": "2026-07-15T08:38:00.474852Z", "shell.execute_reply": "2026-07-15T08:38:00.474179Z" }, "tags": [] }, "outputs": [], "source": [ "ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')\n", "ndvi = ds1[\"NDVI\"]\n", "average_ndvi = ndvi.resample(time='1M').mean().persist() ## tính mean cho từng tháng -> time = 12\n", "# progress(average_ndvi)" ] }, { "cell_type": "code", "execution_count": 12, "id": "5af8393a-e667-4c93-ae32-ca69ee9d9da7", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.476536Z", "iopub.status.busy": "2026-07-15T08:38:00.476365Z", "iopub.status.idle": "2026-07-15T08:38:00.479889Z", "shell.execute_reply": "2026-07-15T08:38:00.479306Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(1, 6, 10)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dsvh.shape" ] }, { "cell_type": "code", "execution_count": 13, "id": "5b81db73-d533-483e-bab5-44d2c3eff64c", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.481357Z", "iopub.status.busy": "2026-07-15T08:38:00.481213Z", "iopub.status.idle": "2026-07-15T08:38:00.484838Z", "shell.execute_reply": "2026-07-15T08:38:00.484119Z" }, "tags": [] }, "outputs": [], "source": [ "average_ndvi = average_ndvi.compute()\n", "average_ndvi = average_ndvi[:, :dsvh.shape[1], :dsvh.shape[2]]" ] }, { "cell_type": "code", "execution_count": 14, "id": "504a37be-62cc-4121-b0bf-46639cb82fe6", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.486732Z", "iopub.status.busy": "2026-07-15T08:38:00.486546Z", "iopub.status.idle": "2026-07-15T08:38:00.492223Z", "shell.execute_reply": "2026-07-15T08:38:00.491642Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.06 ms, sys: 59 μs, total: 1.12 ms\n", "Wall time: 1.09 ms\n" ] } ], "source": [ "%%time\n", "filled_ds = average_ndvi.bfill(dim='time')\n", "filled_ds = filled_ds.ffill(dim='time')\n" ] }, { "cell_type": "markdown", "id": "2b6caa44-5793-4bac-ab67-d1e75d19aab3", "metadata": {}, "source": [ "FIND NAN POINT AFTER FILLING AND FILLING AGAIN WITH LINEARREGRESSION ALGORITHM" ] }, { "cell_type": "code", "execution_count": 15, "id": "0c47e46d-2c74-4b96-91b8-fcd5e46e7699", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.493859Z", "iopub.status.busy": "2026-07-15T08:38:00.493715Z", "iopub.status.idle": "2026-07-15T08:38:00.497379Z", "shell.execute_reply": "2026-07-15T08:38:00.496493Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of NaNs: 30\n" ] } ], "source": [ "nan_mask = filled_ds.isnull()\n", "\n", "# Print the NaN mask\n", "# print(nan_mask)\n", "\n", "# Count the number of NaNs\n", "num_nans = nan_mask.sum()\n", "print(f'Number of NaNs: {num_nans.values}')" ] }, { "cell_type": "code", "execution_count": 16, "id": "ca51b1a5-2499-457b-bb99-65a950092270", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.499105Z", "iopub.status.busy": "2026-07-15T08:38:00.498936Z", "iopub.status.idle": "2026-07-15T08:38:00.502527Z", "shell.execute_reply": "2026-07-15T08:38:00.502022Z" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "mask = ~np.isnan(filled_ds)\n", "X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)\n", "y_train = filled_ds.values[mask]" ] }, { "cell_type": "code", "execution_count": 17, "id": "c78559a3-a503-4dac-a21d-63b414f932a2", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.503833Z", "iopub.status.busy": "2026-07-15T08:38:00.503695Z", "iopub.status.idle": "2026-07-15T08:38:00.571145Z", "shell.execute_reply": "2026-07-15T08:38:00.570557Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "LinearRegression()" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = LinearRegression()\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 18, "id": "6a987dee-50b3-46d0-8fcd-d8fefe44ecd8", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.573061Z", "iopub.status.busy": "2026-07-15T08:38:00.572844Z", "iopub.status.idle": "2026-07-15T08:38:00.576089Z", "shell.execute_reply": "2026-07-15T08:38:00.575525Z" }, "tags": [] }, "outputs": [], "source": [ "X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)\n", "filled_ds.values[~mask] = model.predict(X_pred)" ] }, { "cell_type": "markdown", "id": "146aa63d-217f-4c36-be36-e84a1ff3bf52", "metadata": {}, "source": [ "MATCH LABEL TO DATASET" ] }, { "cell_type": "code", "execution_count": 19, "id": "62024ea2-5b65-4aad-b1ea-66adb843f136", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:00.577560Z", "iopub.status.busy": "2026-07-15T08:38:00.577409Z", "iopub.status.idle": "2026-07-15T08:38:01.882407Z", "shell.execute_reply": "2026-07-15T08:38:01.881856Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.31 s, sys: 3.62 ms, total: 1.31 s\n", "Wall time: 1.3 s\n" ] } ], "source": [ "%%time\n", "\n", "# Takes 1 minute to complete.\n", "loaded_datasets = {}\n", "for idx, point in train.iterrows():\n", " key = f\"point_{idx + 1}\"\n", " try:\n", " ndvi_data = filled_ds.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n", " vh_data = dsvh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n", " vv_data = dsvv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n", " loaded_datasets[key] = {\n", " \"data\": np.concatenate((ndvi_data, vh_data, vv_data)),\n", " \"label\": point.HT_code\n", " }\n", " except Exception as e:\n", " # loaded_datasets[key] = None\n", " print(e)" ] }, { "cell_type": "code", "execution_count": 20, "id": "9d80765f-c25c-465e-8c99-582276477561", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:01.883983Z", "iopub.status.busy": "2026-07-15T08:38:01.883827Z", "iopub.status.idle": "2026-07-15T08:38:01.887162Z", "shell.execute_reply": "2026-07-15T08:38:01.886711Z" }, "tags": [] }, "outputs": [], "source": [ "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", "label_encoder = LabelEncoder()\n", "\n", "# Fit and transform the labels\n", "labels = train.Hientrang.values\n", "numeric_labels = label_encoder.fit_transform([label_mapping[label] for label in labels])" ] }, { "cell_type": "code", "execution_count": 21, "id": "dc42088b-3aae-457f-bed8-206692121a37", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:01.888548Z", "iopub.status.busy": "2026-07-15T08:38:01.888404Z", "iopub.status.idle": "2026-07-15T08:38:01.891669Z", "shell.execute_reply": "2026-07-15T08:38:01.891178Z" }, "tags": [] }, "outputs": [], "source": [ "X = []\n", "x_new = []\n", "lb_new = []\n", "for k, v in loaded_datasets.items():\n", " X.append(v)\n", "for i in range(len(X)):\n", " if X[i] is not None:\n", " x_new.append(X[i][\"data\"])\n", " lb_new.append(numeric_labels[i])" ] }, { "cell_type": "markdown", "id": "6abe9a1b-55a6-48b0-a4ee-a1b2c7f209cf", "metadata": {}, "source": [ "BUILDING DATASETS " ] }, { "cell_type": "code", "execution_count": 22, "id": "e4e234b9-88e0-4f72-bf61-02cd6c6a6cfe", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:01.893018Z", "iopub.status.busy": "2026-07-15T08:38:01.892877Z", "iopub.status.idle": "2026-07-15T08:38:01.896290Z", "shell.execute_reply": "2026-07-15T08:38:01.895825Z" }, "tags": [] }, "outputs": [], "source": [ "X_train, X_temp, y_train, y_temp= train_test_split(x_new, lb_new, test_size=0.4, random_state=42)\n", "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)" ] }, { "cell_type": "markdown", "id": "717baa23-76b6-45e0-8e7b-42d7a79fe773", "metadata": {}, "source": [ "TRAIN MODEL" ] }, { "cell_type": "code", "execution_count": 23, "id": "131df37a-b1c0-4c16-9086-0ba0104fd803", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:01.897567Z", "iopub.status.busy": "2026-07-15T08:38:01.897435Z", "iopub.status.idle": "2026-07-15T08:38:18.586807Z", "shell.execute_reply": "2026-07-15T08:38:18.586226Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'classifier': RandomForestClassifier(n_jobs=-1, random_state=42), 'classifier__criterion': 'gini', 'classifier__max_depth': 10, 'classifier__n_estimators': 700}\n", "Accuracy: 38.0 %\n", "CPU times: user 4.94 s, sys: 597 ms, total: 5.54 s\n", "Wall time: 16.7 s\n" ] } ], "source": [ "%%time\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Define the models\n", "rf_model = RandomForestClassifier(random_state=42, n_jobs=-1)\n", "knn_model = KNeighborsClassifier()\n", "nb_model = GaussianNB()\n", "svm_model = SVC()\n", "\n", "# Create a pipeline\n", "pipeline = Pipeline([\n", " ('scaler', StandardScaler()), # Apply scaling\n", " ('classifier', rf_model) # Placeholder, will be set by param_grid\n", "])\n", "\n", "# Define the parameter grid for each classifier\n", "param_grid = [\n", " # RandomForest\n", " {\n", " 'classifier': [rf_model],\n", " 'classifier__n_estimators': [100, 300, 500, 700],\n", " 'classifier__max_depth': [6, 8, 10, 15],\n", " 'classifier__criterion': ['gini', 'entropy'],\n", " },\n", " # KNeighborsClassifier\n", " {\n", " 'classifier': [knn_model],\n", " 'classifier__n_neighbors': [3, 5, 7, 9],\n", " 'classifier__weights': ['uniform', 'distance'],\n", " 'classifier__metric': ['euclidean', 'manhattan']\n", " },\n", " # Naive Bayes (GaussianNB doesn't have hyperparameters to tune here)\n", " {\n", " 'classifier': [nb_model],\n", " },\n", " # SVM\n", " {\n", " 'classifier': [svm_model],\n", " 'classifier__C': [0.1, 1, 10, 100],\n", " 'classifier__kernel': ['linear', 'rbf'],\n", " 'classifier__gamma': ['scale', 'auto']\n", " }\n", "]\n", "\n", "# Use GridSearchCV to find the best classifier and hyperparameters\n", "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n", "grid_search.fit(X_train, y_train)\n", "\n", "# Print out the best parameters and classifier\n", "best_params = grid_search.best_params_\n", "print(\"Best Parameters:\", best_params)\n", "\n", "# Make predictions on the validation set\n", "y_pred = grid_search.predict(X_val)\n", "\n", "# Evaluate the results\n", "accuracy = accuracy_score(y_val, y_pred)\n", "print(f\"Accuracy: {round(accuracy, 2)*100} %\")\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "bd2d4a3d-061d-4238-8fd9-ab4aaf062a2e", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:18.588478Z", "iopub.status.busy": "2026-07-15T08:38:18.588243Z", "iopub.status.idle": "2026-07-15T08:38:18.761584Z", "shell.execute_reply": "2026-07-15T08:38:18.760828Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy for test data 27.0 %\n" ] } ], "source": [ "## check accuracy score\n", "\n", "y_pred_test = grid_search.predict(X_test)\n", "test_accuracy = accuracy_score(y_test, y_pred_test)\n", "print(f\"Accuracy for test data {round(test_accuracy, 2)*100} %\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "c5cd8314-c444-4bd4-a608-41e8a925ac51", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:18.763406Z", "iopub.status.busy": "2026-07-15T08:38:18.763117Z", "iopub.status.idle": "2026-07-15T08:38:18.956602Z", "shell.execute_reply": "2026-07-15T08:38:18.956021Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "['model_train/model_new2.joblib']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir_save_model = \"model_train\"\n", "if not os.path.exists(dir_save_model):\n", " os.mkdir(dir_save_model)\n", "joblib.dump(grid_search, os.path.join(dir_save_model, \"model_new2.joblib\"))" ] }, { "cell_type": "code", "execution_count": 26, "id": "f11593ca-dcb4-4018-8142-2e933812878e", "metadata": { "execution": { "iopub.execute_input": "2026-07-15T08:38:18.958220Z", "iopub.status.busy": "2026-07-15T08:38:18.958062Z", "iopub.status.idle": "2026-07-15T08:38:18.960154Z", "shell.execute_reply": "2026-07-15T08:38:18.959685Z" }, "tags": [] }, "outputs": [], "source": [ "# # client.close()\n", "# # cluster.close()" ] } ], "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": "3.10.18" } }, "nbformat": 4, "nbformat_minor": 5 }