mirror of
https://git.victorphan.net/basketballcantho/CSIROBoeingPhase5-Vietnam.git
synced 2026-08-06 22:13:11 +07:00
Victor Phan
406 lines
12 KiB
Plaintext
406 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "912ed572-1658-406b-976c-cd6de2d4e89e",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"%matplotlib inline\n",
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"\n",
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"import importlib\n",
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"import new_import_ODC \n",
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"\n",
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"importlib.reload(new_import_ODC)\n",
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"\n",
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"from new_import_ODC import *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d824dc4f-994b-4d1c-8d24-ce6674da141c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Cấu hình Daskgateway\n",
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"cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n",
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"# Khai báo 1 Datacube là dc\n",
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"dc = datacube.Datacube()\n",
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"\n",
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"# Cấu hình truy cập dịch vụ S3\n",
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"configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
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"\n",
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"client"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fbed4c80-bbf8-4ea8-aa45-2460b2ba04c7",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## cấu hình thời gian lấy ảnh và tọa độ\n",
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"date_range = (\"2022-09-01\", \"2023-10-01\")\n",
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"longtitude_range = (105.5, 106.4)\n",
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"latitude_range = (9.2, 10.0)\n",
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"\n",
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"coordinates = (longtitude_range, latitude_range)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6b90c49b-0665-4478-a23b-d111ef88eb79",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## truy vấn ảnh vệ tinh sen2\n",
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"data = load_data(dc, date_range, longtitude_range, latitude_range)\n",
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"notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
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"display(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2f6938b6-82e2-4916-bc1d-719c169e25e4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n",
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"result = mask_clean(data)\n",
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"progress(result)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "435f9f78-a9a4-4226-86ca-d4bec42d454e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Tiến hành tính toán NDVI\n",
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"ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n",
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"ndvi = ds1[\"NDVI\"]\n",
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"display(ndvi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "84992d28-8e3f-468e-be08-ded511f2c662",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## Hiển thị ảnh NDVI chưa điền các giá trị mây (chưa fill nan)\n",
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"plt.imshow(ndvi.isel(time=6))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "72318b60-532a-4f08-a5f7-94762d08a42c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa\n",
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"time_split = [\n",
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" slice(\"2022-09-01\", \"2023-01-01\"),\n",
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" slice(\"2023-01-01\", \"2023-05-01\"),\n",
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" slice(\"2023-05-01\", \"2023-07-01\"),\n",
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" slice(\"2023-07-01\", \"2023-10-01\"),\n",
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"]\n",
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"\n",
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"# Điền mây ở các vị trí mang giá trị nan (fill nan)\n",
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"fill_nan_ndvi = fill_nan(ndvi, time_split)\n",
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"\n",
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"# In kết quả ảnh NDVI đã điền mây (đã fill nan)\n",
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"plt.imshow(fill_nan_ndvi.isel(time=6))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"## tính ndvi theo tháng\n",
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"average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n",
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"progress(average_ndvi)\n",
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"\n",
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"# compute average_ndvi\n",
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"average_ndvi = average_ndvi.compute()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "187cc640-aef9-476b-91fc-b63f4d3ff2e3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"#Load dữ liệu ảnh Sentinel 1\n",
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"dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n",
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"average_vv = calculate_average(dsvv, time_pattern='1M')\n",
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"average_vh = calculate_average(dsvh, time_pattern='1M')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n",
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"train_path = \"train/ST_training data_updated_1130points_new.shp\" # đường dẫn shp file train\n",
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"\n",
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"## load dữ liệu điểm huấn luyện mô hình (train file)\n",
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"train = load_train_data(train_path)\n",
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"train.head()\n",
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"\n",
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"# cấu hình nhãn dữ liệu \n",
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"label_mapping = {\n",
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" \"Lua tom\": \"0\",\n",
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" \"Lua\": \"1\",\n",
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" \"CHN\": \"2\",\n",
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" \"CLN\": \"3\",\n",
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" \"TS\": \"4\",\n",
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" \"Song\": \"5\",\n",
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" \"Dat xay dung\": \"6\",\n",
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" \"Rung\": \"7\",\n",
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"}\n",
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"\n",
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"# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI\n",
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"datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n",
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"\n",
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"# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test\n",
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"X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n",
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" train, label_mapping, datasets\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2e955884-d4af-422d-a8e6-d436199540e0",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Import XGBoost\n",
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"import xgboost as xgb\n",
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"from sklearn.metrics import accuracy_score\n",
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"import numpy as np\n",
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"\n",
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"# Convert to numpy arrays\n",
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"X_train_np = np.asarray(X_train, dtype=np.float32)\n",
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"X_val_np = np.asarray(X_val, dtype=np.float32)\n",
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"y_train_np = np.asarray(y_train, dtype=np.int32)\n",
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"y_val_np = np.asarray(y_val, dtype=np.int32)\n",
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"\n",
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"print(\"🚀 Training XGBoost model...\")\n",
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"print(f\" Train samples: {len(X_train_np)}\")\n",
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"print(f\" Val samples: {len(X_val_np)}\")\n",
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"print(f\" Features: {X_train_np.shape[1]}\")\n",
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"print(f\" Classes: 8\\n\")\n",
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"\n",
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"# XGBoost parameters\n",
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"params = {\n",
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" 'objective': 'multi:softmax', # Multi-class classification\n",
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" 'num_class': 8, # 8 land use classes\n",
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" 'max_depth': 6, # Maximum tree depth\n",
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" 'learning_rate': 0.1, # Learning rate\n",
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" 'n_estimators': 200, # Number of trees\n",
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" 'subsample': 0.8, # Subsample ratio\n",
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" 'colsample_bytree': 0.8, # Feature sampling ratio\n",
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" 'random_state': 42,\n",
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" 'n_jobs': -1, # Use all CPU cores\n",
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" 'eval_metric': 'mlogloss' # Multi-class log loss\n",
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"}\n",
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"\n",
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"# Train XGBoost model\n",
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"model = xgb.XGBClassifier(**params)\n",
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"\n",
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"model.fit(\n",
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" X_train_np, y_train_np,\n",
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" eval_set=[(X_train_np, y_train_np), (X_val_np, y_val_np)],\n",
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" verbose=True\n",
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")\n",
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"\n",
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"# Validation accuracy\n",
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"y_val_pred = model.predict(X_val_np)\n",
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"val_accuracy = accuracy_score(y_val_np, y_val_pred)\n",
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"print(f\"\\n✅ Training completed!\")\n",
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"print(f\" Validation Accuracy: {val_accuracy:.4f} ({val_accuracy*100:.2f}%)\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Evaluate on test set\n",
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"X_test_np = np.asarray(X_test, dtype=np.float32)\n",
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"y_test_np = np.asarray(y_test, dtype=np.int32)\n",
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"\n",
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"print(\"📊 Evaluating XGBoost model on test set...\\n\")\n",
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"\n",
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"# Predictions\n",
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"y_pred_test = model.predict(X_test_np)\n",
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"\n",
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"# Metrics\n",
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"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
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"\n",
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"test_accuracy = accuracy_score(y_test_np, y_pred_test)\n",
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"precision = precision_score(y_test_np, y_pred_test, average='weighted', zero_division=0)\n",
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"recall = recall_score(y_test_np, y_pred_test, average='weighted', zero_division=0)\n",
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"f1 = f1_score(y_test_np, y_pred_test, average='weighted', zero_division=0)\n",
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"\n",
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"print(f\"📈 Test Results:\")\n",
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"print(f\" Accuracy: {test_accuracy:.4f} ({test_accuracy*100:.2f}%)\")\n",
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"print(f\" Precision: {precision:.4f}\")\n",
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"print(f\" Recall: {recall:.4f}\")\n",
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"print(f\" F1-Score: {f1:.4f}\\n\")\n",
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"\n",
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"# Confusion Matrix\n",
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"from sklearn.metrics import ConfusionMatrixDisplay\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Create figure first\n",
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"fig, ax = plt.subplots(figsize=(10, 8))\n",
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"\n",
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"class_names = list(label_mapping.keys())\n",
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"cm = confusion_matrix(y_test_np, y_pred_test)\n",
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"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
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"disp.plot(cmap='Blues', ax=ax)\n",
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"plt.xticks(rotation=45, ha='right')\n",
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"plt.title('XGBoost Confusion Matrix')\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Lưu mô hình huấn luyện\n",
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"import json\n",
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"import joblib\n",
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"\n",
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"# Save XGBoost model\n",
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"model_path = \"model_xgboost.joblib\"\n",
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"joblib.dump(model, model_path)\n",
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"print(f\"✅ Model saved to {model_path}\")\n",
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"\n",
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"# Save model info\n",
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"info = {\n",
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" \"model_type\": \"XGBoost\",\n",
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" \"num_classes\": 8,\n",
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" \"classes\": list(label_mapping.keys()),\n",
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" \"num_features\": X_train_np.shape[1],\n",
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" \"params\": params,\n",
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" \"accuracy\": float(test_accuracy),\n",
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" \"precision\": float(precision),\n",
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" \"recall\": float(recall),\n",
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" \"f1_score\": float(f1),\n",
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"}\n",
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"\n",
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"with open(\"model_xgboost_info.json\", \"w\") as f:\n",
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" json.dump(info, f, indent=2)\n",
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"\n",
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"print(f\"✅ Model info saved to model_xgboost_info.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "33dd516d-9824-499e-96b9-5cd9224c194c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# đóng client, cluster\n",
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"client.close()\n",
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"cluster.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ee0ecd6a-733b-4655-8864-bc037a539ce2",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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