add caculate accuracy

2026-08-05 05:43:10 +07:00 · 2024-08-30 00:52:15 +00:00
parent b15ec91267
commit 1125a1e2de
9 changed files with 216 additions and 161 deletions
@@ -3279,6 +3279,17 @@
    "progress(average_ndvi)"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6be55b09-44d9-4957-a2d9-00f1c58d0aee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute average_ndvi\n",
+    "average_ndvi = average_ndvi.compute()"
+   ]
+  },
  {
   "cell_type": "code",
   "execution_count": 10,
@@ -3394,73 +3405,19 @@
    }
   ],
   "source": [
-    "# compute average_ndvi\n",
-    "average_ndvi = average_ndvi.compute()\n",
-    "\n",
-    "## 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",
+    "## cấu hình dữ liệu vh vv file\n",
    "name_vh = \"vh-0922_0923-full_ST.tif\"\n",
    "name_vv = \"vv-0922_0923-full_ST.tif\"\n",
    "\n",
-    "## load dữ liệu điểm train\n",
-    "train = load_train_data(train_path)\n",
-    "train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "b32cfb3f-e267-4995-853d-8e0e26f5501f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 527 µs, sys: 111 µs, total: 638 µs\n",
-      "Wall time: 375 µs\n"
-     ]
-    }
-   ],
-   "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": 12,
-   "id": "9838697a-2260-45ef-a810-533428a783e9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n",
-      "Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n",
-      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n",
-      "Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n",
-      "Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n",
-      "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n"
-     ]
-    }
-   ],
-   "source": [
+    "    !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",
+    "    \n",
    "# load dữ liệu sen1\n",
-    "dsvh, dsvv = load_sen1(name_vh, name_vv)\n",
-    "\n",
-    "# xây dựng tập dataset\n",
-    "datasets = get_data_sen1_and_sen2(train, average_ndvi, dsvh, dsvv)"
+    "dsvh, dsvv = load_sen1(name_vh, name_vv)"
   ]
  },
  {
@@ -3472,6 +3429,11 @@
   },
   "outputs": [],
   "source": [
+    "## load dữ liệu điểm train\n",
+    "train_path = \"train/ST_training data_updated_1130points.shp\"  # đường dẫn shp file train\n",
+    "train = load_train_data(train_path)\n",
+    "train.head()\n",
+    "\n",
    "# cấu hình nhãn dữ liệu\n",
    "label_mapping = {\n",
    "    \"Lua tom\": \"0\",\n",
@@ -3484,6 +3446,9 @@
    "    \"Rung\": \"7\"\n",
    "}\n",
    "\n",
+    "# xây dựng tập dataset\n",
+    "datasets = get_data_sen1_and_sen2(train, average_ndvi, dsvh, dsvv)\n",
+    "\n",
    "# chia tập dữ liệu train, val, test\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)"
   ]
@@ -3530,26 +3495,8 @@
    "# kiểm tra độ chính xác với tập 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} %\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done!\n"
-     ]
-    }
-   ],
-   "source": [
+    "print(f\"Accuracy for test data {round(test_accuracy, 2)*100} %\")\n",
+    "\n",
    "# Lưu mô hình huấn luyện\n",
    "save_model(\"model.joblib\", grid_search)"
   ]
@@ -0,0 +1 @@
+UTF-8
@@ -0,0 +1 @@
+PROJCS["WGS_1984_UTM_Zone_48N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",105.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
@@ -0,0 +1,44 @@
+<!DOCTYPE qgis PUBLIC 'http://mrcc.com/qgis.dtd' 'SYSTEM'>
+<qgis version="3.30.0-'s-Hertogenbosch">
+  <identifier></identifier>
+  <parentidentifier></parentidentifier>
+  <language></language>
+  <type>dataset</type>
+  <title></title>
+  <abstract></abstract>
+  <contact>
+    <name></name>
+    <organization></organization>
+    <position></position>
+    <voice></voice>
+    <fax></fax>
+    <email></email>
+    <role></role>
+  </contact>
+  <links/>
+  <dates/>
+  <fees></fees>
+  <encoding></encoding>
+  <crs>
+    <spatialrefsys nativeFormat="Wkt">
+      <wkt>PROJCRS["WGS 84 / UTM zone 48N",BASEGEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]],CONVERSION["UTM zone 48N",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",105,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",0.9996,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",500000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["(E)",east,ORDER[1],LENGTHUNIT["metre",1]],AXIS["(N)",north,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Engineering survey, topographic mapping."],AREA["Between 102°E and 108°E, northern hemisphere between equator and 84°N, onshore and offshore. Cambodia. China. Indonesia. Laos. Malaysia - West Malaysia. Mongolia. Russian Federation. Singapore. Thailand. Vietnam."],BBOX[0,102,84,108]],ID["EPSG",32648]]</wkt>
+      <proj4>+proj=utm +zone=48 +datum=WGS84 +units=m +no_defs</proj4>
+      <srsid>3132</srsid>
+      <srid>32648</srid>
+      <authid>EPSG:32648</authid>
+      <description>WGS 84 / UTM zone 48N</description>
+      <projectionacronym>utm</projectionacronym>
+      <ellipsoidacronym>EPSG:7030</ellipsoidacronym>
+      <geographicflag>false</geographicflag>
+    </spatialrefsys>
+  </crs>
+  <extent>
+    <spatial miny="0" minz="0" maxz="0" crs="EPSG:32648" minx="0" maxy="0" dimensions="2" maxx="0"/>
+    <temporal>
+      <period>
+        <start></start>
+        <end></end>
+      </period>
+    </temporal>
+  </extent>
+</qgis>
@@ -317,4 +317,38 @@ def save_result(result, HT_MAP):
        # cbar.ax.set_yticklabels(labels)
        # plt.title(f'{HT_MAP[k]["name"]}')
        # plt.axis('off')
-        # plt.show()
+        # plt.show()
+        
+
+def accuracy_test(test, data_array):
+    # cấu hình nhãn dữ liệu
+    label_mapping = {
+        "Lua tom": "0",
+        "Lua": "1",
+        "CHN": "2",
+        "CLN": "3",
+        "TS": "4",
+        "Song": "5",
+        "Dat xay dung": "6",
+        "Rung": "7"
+    }
+
+    chk = []
+    pred = []
+    dd = []
+    for idx, point in test.iterrows():
+        label = point.LULC
+        predict = data_array.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
+        pred.append(label_mapping[label])
+        dd.append(str(predict))
+        chk.append(predict == int(label_mapping[label]))
+    test["code"] = pred
+    test["dd"] = dd
+    test["check"] = chk
+    path = "ThuanHoa/TestAccuracy"
+    if not os.path.exists(path):
+        os.mkdir(path)
+    test.to_file(f"{path}/result.shp")
+    
+    percentage_true = np.mean(chk) * 100
+    print(f"độ chính xác: {percentage_true:.2f}%")
				`@@ -0,0 +1 @@`
				`PROJCS["WGS_1984_UTM_Zone_48N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",105.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]`