Migrate all ODC models and prediction pipeline to Microsoft Planetary Computer
This commit is contained in:
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-46
@@ -1132,7 +1132,7 @@
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"source": [
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"%%time\n",
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"%matplotlib inline\n",
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"from new_import import *"
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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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@@ -1216,7 +1216,7 @@
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"%%time\n",
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"# Dask gateway\n",
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"cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\n",
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"dc = datacube.Datacube()\n",
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"dc = None\n",
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"\n",
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"# Configure s3 access\n",
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"configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
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@@ -1233,11 +1233,11 @@
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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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"## c\u1ea5u h\u00ecnh th\u1eddi gian l\u1ea5y \u1ea3nh v\u00e0 t\u1ecda \u0111\u1ed9\n",
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"# date_range = ('2022-09-01', '2022-10-01')\n",
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"# longtitude_range = (105.86575, 105.94120)\n",
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"# latitude_range = (9.65070, 9.69850)\n",
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"date_range = ('2022-09-01', '2023-10-01')\n",
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"date_range = ('2022-09-01', '2022-10-01')\n",
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"longtitude_range = (105.5, 106.4)\n",
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"latitude_range = (9.2, 10.0) "
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]
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@@ -1396,7 +1396,7 @@
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"\n",
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".xr-section-summary-in + label:before {\n",
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" display: inline-block;\n",
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" content: '►';\n",
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" content: '\u25ba';\n",
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" font-size: 11px;\n",
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" width: 15px;\n",
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" text-align: center;\n",
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@@ -1407,7 +1407,7 @@
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label:before {\n",
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" content: '▼';\n",
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" content: '\u25bc';\n",
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label > span {\n",
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@@ -2424,8 +2424,8 @@
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}
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],
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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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"## truy v\u1ea5n \u1ea3nh v\u1ec7 tinh sen2\n",
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"data = load_data(None, 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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@@ -2440,10 +2440,10 @@
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"outputs": [],
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"source": [
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"# Specify the start and end times \n",
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"min_date = '2022-09-01' # Thời gian bắt đầu lấy data cho quá trình train\n",
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"max_date = '2023-10-01' # Thời gian kết thúc lấy data cho quá trình train\n",
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"min_date = '2022-09-01' # Th\u1eddi gian b\u1eaft \u0111\u1ea7u l\u1ea5y data cho qu\u00e1 tr\u00ecnh train\n",
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"max_date = '2022-10-01' # Th\u1eddi gian k\u1ebft th\u00fac l\u1ea5y data cho qu\u00e1 tr\u00ecnh train\n",
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"# Just do 1 month for testing\n",
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"# max_date = '2022-10-01' # Thời gian kết thúc lấy data cho quá trình train\n",
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"# max_date = '2022-10-01' # Th\u1eddi gian k\u1ebft th\u00fac l\u1ea5y data cho qu\u00e1 tr\u00ecnh train\n",
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"\n",
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"# Specify a spatail region to search using latitude/longitude cooridinates\n",
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"min_longitude, max_longitude = (105.5, 106.4)\n",
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@@ -2657,7 +2657,7 @@
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"\n",
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".xr-section-summary-in + label:before {\n",
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" display: inline-block;\n",
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" content: '►';\n",
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" content: '\u25ba';\n",
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" font-size: 11px;\n",
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" width: 15px;\n",
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" text-align: center;\n",
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@@ -2668,7 +2668,7 @@
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label:before {\n",
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" content: '▼';\n",
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" content: '\u25bc';\n",
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label > span {\n",
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@@ -3423,23 +3423,23 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 1.57 s, sys: 177 µs, total: 1.57 s\n",
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"CPU times: user 1.57 s, sys: 177 \u00b5s, total: 1.57 s\n",
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"Wall time: 1.66 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# %%time\n",
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"# The replacement \"dc.load()\" function for this product\n",
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"data = load_s2l2a_with_offset(\n",
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" dc,\n",
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" query | load_params # Combine the two dicts that contain our search and load parameters\n",
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")\n",
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"\n",
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"# data = load_s2l2a_with_offset(\n",
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"# dc,\n",
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"# query | load_params # Combine the two dicts that contain our search and load parameters\n",
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"# )\n",
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"# \n",
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"# This line prints the total size of the dataset hat was loaded\n",
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"notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
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"\n",
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"display(data)"
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"# notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
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"# \n",
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"# display(data)"
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]
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},
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{
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@@ -3546,9 +3546,9 @@
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],
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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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"# # Ti\u1ebfn h\u00e0nh lo\u1ea1i b\u1ecf c\u00e1c v\u1ecb tr\u00ed b\u1ecb m\u00e2y \u1ea3nh h\u01b0\u1edfng\n",
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"# result = mask_clean(data)\n",
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"# progress(result)"
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"# # progress(result)"
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]
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},
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{
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@@ -3684,7 +3684,7 @@
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"\n",
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".xr-section-summary-in + label:before {\n",
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" display: inline-block;\n",
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" content: '►';\n",
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" content: '\u25ba';\n",
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" font-size: 11px;\n",
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" width: 15px;\n",
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" text-align: center;\n",
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@@ -3695,7 +3695,7 @@
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label:before {\n",
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" content: '▼';\n",
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" content: '\u25bc';\n",
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"}\n",
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"\n",
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".xr-section-summary-in:checked + label > span {\n",
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@@ -4174,7 +4174,7 @@
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}
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],
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"source": [
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"# Tiến hành tính toán NDVI\n",
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"# Ti\u1ebfn h\u00e0nh t\u00ednh to\u00e1n 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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@@ -4213,9 +4213,9 @@
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],
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"source": [
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"%%time\n",
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"## tính ndvi theo tháng\n",
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"## t\u00ednh ndvi theo th\u00e1ng\n",
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"average_ndvi = ndvi.resample(time='1M').mean().persist()\n",
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"progress(average_ndvi)"
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"# progress(average_ndvi)"
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]
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},
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{
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@@ -4253,12 +4253,12 @@
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}
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],
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"source": [
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"# cấu hình vh vv file\n",
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"# c\u1ea5u h\u00ecnh vh vv file\n",
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"# name_vh = \"ThuanHoa/ThuanHoa_VH.tif\"\n",
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"# name_vv = \"ThuanHoa/ThuanHoa_VV.tif\"\n",
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"\n",
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"# load dữ liệu sen1\n",
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"# dsvh, dsvv = load_sen1(name_vh, name_vv)\n",
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"# load d\u1eef li\u1ec7u sen1\n",
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"bbox = [longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1]]\ntime_range = f'{date_range[0]}/{date_range[1]}'\n# dsvh, dsvv = load_sen1(bbox, time_range)\n",
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"\n",
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"name_vh = \"vh-0922_0923-full_ST.tif\"\n",
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"name_vv = \"vv-0922_0923-full_ST.tif\"\n",
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@@ -4268,7 +4268,7 @@
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"if not os.path.exists(name_vv):\n",
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" !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif\n",
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" \n",
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"dsvh, dsvv = load_sen1(name_vh, name_vv)"
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"bbox = [longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1]]\ntime_range = f'{date_range[0]}/{date_range[1]}'\ndsvh, dsvv = load_sen1(bbox, time_range)"
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]
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},
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{
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@@ -4326,7 +4326,7 @@
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{
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"data": {
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"text/html": [
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"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
|
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"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"\u25b8\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"\u25be\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
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||||
],
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"text/plain": [
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"LinearRegression()"
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@@ -4397,7 +4397,7 @@
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}
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],
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"source": [
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"plt.imshow(average_ndvi_filled.isel(time=6))"
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"plt.imshow(average_ndvi_filled.isel(time=0))"
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]
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},
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{
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@@ -4430,7 +4430,7 @@
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}
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],
|
||||
"source": [
|
||||
"plt.imshow(average_ndvi.isel(time=6))"
|
||||
"plt.imshow(average_ndvi.isel(time=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -4440,7 +4440,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_path = \"train/ST_training data_updated_1130points.shp\""
|
||||
"train_path = \"train/ST_training_data_updated_1130points.shp\""
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -4476,7 +4476,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# cấu hình nhãn dữ liệu\n",
|
||||
"# c\u1ea5u h\u00ecnh nh\u00e3n d\u1eef li\u1ec7u\n",
|
||||
"label_mapping = {\n",
|
||||
" \"Lua tom\": \"0\",\n",
|
||||
" \"Lua\": \"1\",\n",
|
||||
@@ -4488,7 +4488,7 @@
|
||||
" \"Rung\": \"7\"\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"# chia tập dữ liệu train, val, test\n",
|
||||
"# chia t\u1eadp d\u1eef li\u1ec7u train, val, test\n",
|
||||
"X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)"
|
||||
]
|
||||
},
|
||||
@@ -4510,7 +4510,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Huấn luyện mô hình\n",
|
||||
"# Hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh\n",
|
||||
"grid_search = train_with_rf(X_train, X_val, y_train, y_val)"
|
||||
]
|
||||
},
|
||||
@@ -4531,7 +4531,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# kiểm tra độ chính xác với tập test\n",
|
||||
"# ki\u1ec3m tra \u0111\u1ed9 ch\u00ednh x\u00e1c v\u1edbi t\u1eadp test\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} %\")"
|
||||
@@ -4554,7 +4554,7 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Lưu mô hình huấn luyện\n",
|
||||
"# L\u01b0u m\u00f4 h\u00ecnh hu\u1ea5n luy\u1ec7n\n",
|
||||
"save_model(\"model_new.joblib\", grid_search)"
|
||||
]
|
||||
},
|
||||
@@ -4567,7 +4567,7 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# đóng client, cluster\n",
|
||||
"# \u0111\u00f3ng client, cluster\n",
|
||||
"client.close()\n",
|
||||
"cluster.close()"
|
||||
]
|
||||
@@ -4602,4 +4602,4 @@
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user