{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f6b4fb29",
   "metadata": {
    "id": "f6b4fb29"
   },
   "source": [
    "# Land Cover Validation with LUCAS dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c43ba2",
   "metadata": {
    "id": "61c43ba2"
   },
   "source": [
    "This is an example of a land cover product validation using LUCAS points. The process is using the class `Validator` to perform the main validation steps. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11e86ef-51ce-4cd7-9746-b1f4275dc15b",
   "metadata": {},
   "source": [
    "## Install\n",
    "\n",
    "### Note for Google Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a3aca7e-c155-4981-9b9a-fe5154c1251a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone https://gitlab.com/geoharmonizer_inea/st_lucas/st_lucas-python-package.git\n",
    "!(cd st_lucas-python-package/; git pull)\n",
    "import sys\n",
    "sys.path.insert(0, './st_lucas-python-package/docs/notebooks/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e92dcd-ad5e-4763-8f29-fdce33d62865",
   "metadata": {},
   "source": [
    "### Install requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5b6d920",
   "metadata": {
    "id": "c5b6d920"
   },
   "outputs": [],
   "source": [
    "!pip3 install geopandas pyyaml==6.0 ipyleaflet \n",
    "print(\"INSTALLATION COMPLETED\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GkWH_uKh8sl8",
   "metadata": {
    "id": "GkWH_uKh8sl8"
   },
   "source": [
    "Now **we have to restart runtime**: `Runtime -> Restart runtime` (on Google Colab) or ``Kernel -> Restart`` (on JupyterLab)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2tJuyMGs89M6",
   "metadata": {
    "id": "2tJuyMGs89M6"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import yaml \n",
    "\n",
    "from osgeo import gdal\n",
    "from osgeo import gdalconst\n",
    "import geopandas as gpd\n",
    "import numpy \n",
    "import urllib\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "from validator import Validator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd684782",
   "metadata": {
    "id": "bd684782"
   },
   "source": [
    "## Land Cover validation \n",
    "\n",
    "### Configure validation \n",
    "\n",
    "Check contents of the `config.yaml` file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "69bef016",
   "metadata": {
    "id": "69bef016"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "project:\n",
      "  name: 'Geoharmonizer Land Cover validation'\n",
      "  abbrev: 'cz_lc_18'\n",
      "  run_id: '20210907'\n",
      "\n",
      "# land cover & reference definitions \n",
      "input:\n",
      "  # raster map \n",
      "  path: ./sample_land_cover\n",
      "  in_ras: cz_land_cover_osm_2018.tif\n",
      "  ndv: 0\n",
      "  legend: legend.yaml\n",
      "  # vector reference \n",
      "  in_vec: cz_lucas_points_l1_2018.shp\n",
      "  ref_att: 'label_l1'\n",
      "\n",
      "# validation report settings\n",
      "report:\n",
      "  path: ./sample_land_cover\n",
      "  dir_name: 'lc_2018_validation'\n",
      "\n",
      "# validation points for GIS exploration \n",
      "validation_points: \n",
      "    file_name: 'validation_points'\n",
      "    ogr_format: 'ESRI Shapefile'\n",
      "    epsg: 3035 \n",
      "\n",
      "    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# configuration with sample data \n",
    "config_file = \"sample_land_cover/config.yaml\" # On Google Colab: \"./st_lucas-python-package/docs/notebooks/sample_land_cover/config.yaml\"\n",
    "\n",
    "with open(config_file, 'r') as file: \n",
    "    file_contents = file.read()\n",
    "    \n",
    "print(file_contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2910653b",
   "metadata": {
    "id": "2910653b"
   },
   "source": [
    "### Initialize the validator "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874f9348",
   "metadata": {
    "id": "874f9348"
   },
   "source": [
    "Initilize the validator by passing the config file or a Python dictionary with the same structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbd60738",
   "metadata": {
    "id": "fbd60738"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Validation project initialized!\n",
      "Inputs: \n",
      "cz_land_cover_osm_2018.tif\n",
      "cz_lucas_points_l1_2018.shp\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validation = Validator(config_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c46e609",
   "metadata": {
    "id": "2c46e609"
   },
   "source": [
    "### Check validity of the inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f217242",
   "metadata": {
    "id": "8f217242"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation data ready: True\n"
     ]
    }
   ],
   "source": [
    "# Check if you can read the geodata \n",
    "\n",
    "inputs_valid = validation.check_inputs()\n",
    "print('Validation data ready: {}'.format(inputs_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d20976e6",
   "metadata": {
    "id": "d20976e6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_id</th>\n",
       "      <th>survey_dat</th>\n",
       "      <th>gps_altitu</th>\n",
       "      <th>gps_lat</th>\n",
       "      <th>gps_long</th>\n",
       "      <th>nuts0</th>\n",
       "      <th>obs_dist</th>\n",
       "      <th>obs_type</th>\n",
       "      <th>lc1</th>\n",
       "      <th>lc1_perc</th>\n",
       "      <th>label_l1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48262936</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>309</td>\n",
       "      <td>49.321800</td>\n",
       "      <td>16.956880</td>\n",
       "      <td>CZ</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>C33</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (4826005.575 2935998.024)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47162980</td>\n",
       "      <td>2018-08-02</td>\n",
       "      <td>409</td>\n",
       "      <td>49.799533</td>\n",
       "      <td>15.492019</td>\n",
       "      <td>CZ</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>B75</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>POINT (4716021.636 2979995.779)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46183058</td>\n",
       "      <td>2018-07-19</td>\n",
       "      <td>242</td>\n",
       "      <td>50.557290</td>\n",
       "      <td>14.193572</td>\n",
       "      <td>CZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>B13</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POINT (4617999.865 3057999.905)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45262962</td>\n",
       "      <td>2018-07-17</td>\n",
       "      <td>545</td>\n",
       "      <td>49.734924</td>\n",
       "      <td>12.844097</td>\n",
       "      <td>CZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>B35</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POINT (4526000.963 2961999.910)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47463004</td>\n",
       "      <td>2018-07-24</td>\n",
       "      <td>251</td>\n",
       "      <td>49.993666</td>\n",
       "      <td>15.933803</td>\n",
       "      <td>CZ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>B11</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>POINT (4745999.810 3004000.403)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   point_id  survey_dat  gps_altitu    gps_lat   gps_long nuts0  obs_dist  \\\n",
       "0  48262936  2018-05-10         309  49.321800  16.956880    CZ       6.0   \n",
       "1  47162980  2018-08-02         409  49.799533  15.492019    CZ      22.0   \n",
       "2  46183058  2018-07-19         242  50.557290  14.193572    CZ       0.0   \n",
       "3  45262962  2018-07-17         545  49.734924  12.844097    CZ       0.0   \n",
       "4  47463004  2018-07-24         251  49.993666  15.933803    CZ       0.0   \n",
       "\n",
       "   obs_type  lc1  lc1_perc  label_l1                         geometry  \n",
       "0         1  C33         5       NaN  POINT (4826005.575 2935998.024)  \n",
       "1         1  B75         4       3.0  POINT (4716021.636 2979995.779)  \n",
       "2         1  B13         3       2.0  POINT (4617999.865 3057999.905)  \n",
       "3         1  B35         4       2.0  POINT (4526000.963 2961999.910)  \n",
       "4         1  B11         4       2.0  POINT (4745999.810 3004000.403)  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check contents of the raster and vector geodata \n",
    "\n",
    "with open(config_file) as file:\n",
    "    cfg = yaml.load(file, Loader=yaml.FullLoader)\n",
    "    \n",
    "# Vector data\n",
    "vector_fn = os.path.join(cfg['input']['path'], cfg['input']['in_vec'])\n",
    "gdf = gpd.read_file(vector_fn)\n",
    "\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e06365c-9050-4a85-9505-dce1e7334796",
   "metadata": {
    "id": "8e06365c-9050-4a85-9505-dce1e7334796"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "legend:\n",
      "    1: Artificial\n",
      "    2: Cropland\n",
      "    3: Perenial\n",
      "    4: Forest\n",
      "    5: Shrubland\n",
      "    6: Grassland\n",
      "    7: Barren\n",
      "    8: Wetlands\n",
      "    9: Water\n",
      "    10: Glaciers\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the legend\n",
    "\n",
    "legend_file = \"sample_land_cover/legend.yaml\" # On Google Colab: \"./st_lucas-python-package/docs/notebooks/sample_land_cover/legend.yaml\"\n",
    "\n",
    "with open(legend_file, \"r\") as file: \n",
    "    legend = file.read()\n",
    "print(legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c62233d",
   "metadata": {
    "id": "9c62233d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='label_l1'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# view distribution of the classes \n",
    "\n",
    "attribute = cfg['input']['ref_att']\n",
    "gdf[attribute].value_counts().plot.pie(figsize=(7, 7), autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bce4e9f-3e28-496b-94d2-a6f3882afef4",
   "metadata": {
    "id": "0bce4e9f-3e28-496b-94d2-a6f3882afef4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17328/1624841052.py:6: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  center = gdf_4326.dissolve().centroid\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56d6e61ab4d244eaab016c48c349f140",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[49.74326124744614, 15.425656062445968], controls=(ZoomControl(options=['position', 'zoom_in_text',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show a map of the data\n",
    "\n",
    "from ipyleaflet import Map, GeoData, basemaps, LayersControl\n",
    "\n",
    "gdf_4326 = gdf.to_crs(\"EPSG:4326\")\n",
    "center = gdf_4326.dissolve().centroid\n",
    "\n",
    "m = Map(center=(float(center.y), float(center.x)), zoom = 7, basemap= basemaps.OpenStreetMap.Mapnik)\n",
    "\n",
    "lucas_gd = GeoData(geo_dataframe = gdf_4326,\n",
    "                   style={'color': 'black', 'fillColor': '#3366cc', 'opacity':0.05, 'weight':1.9, 'dashArray':'2', 'fillOpacity':0.6},\n",
    "                   point_style={'radius': 2, 'color': 'red', 'fillOpacity': 0.8, 'fillColor': 'blue', 'weight': 3},\n",
    "                   name='LUCAS points')\n",
    "\n",
    "m.add_layer(lucas_gd)\n",
    "m.add_control(LayersControl())\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce5be84",
   "metadata": {
    "id": "8ce5be84"
   },
   "source": [
    "### Run land cover map and reference overlay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d6a0d78",
   "metadata": {
    "id": "2d6a0d78"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Processed: 100% | 4930 reference points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.overlay()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2072e116",
   "metadata": {
    "id": "2072e116"
   },
   "source": [
    "### Report the validation results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ccac8c8",
   "metadata": {
    "id": "9ccac8c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation indicators: \n",
      "---\n",
      "Overall map accuracy is 84.16 %\n",
      "No. passed: 3426\n",
      "No. failed: 645\n",
      "No. points used in validation: 4071\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# short report\n",
    "validation.short_report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f22c7b7c",
   "metadata": {
    "id": "f22c7b7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Machine learning validation indicators (per class): \n",
      "---\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.44      0.84      0.58       185\n",
      "           2       0.86      0.97      0.91      1801\n",
      "           3       0.62      0.13      0.22        61\n",
      "           4       0.93      0.90      0.92       989\n",
      "           5       0.15      0.18      0.16        17\n",
      "           6       0.91      0.63      0.75       952\n",
      "           7       0.62      0.11      0.19        46\n",
      "           8       0.25      0.27      0.26        11\n",
      "           9       0.30      0.33      0.32         9\n",
      "\n",
      "    accuracy                           0.84      4071\n",
      "   macro avg       0.56      0.49      0.48      4071\n",
      "weighted avg       0.86      0.84      0.84      4071\n",
      "\n",
      "Classical LC validation indicators: \n",
      "---\n",
      "overall_accuracy :  0.8416\n",
      "producers_accuracy :  0.8583\n",
      "users_accuracy :  0.8416\n",
      "kappa :  0.7670\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# full report\n",
    "validation.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d84f06e2",
   "metadata": {
    "id": "d84f06e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving validation report:\n",
      "---\n",
      "cz_lc_18_validation_report.txt\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the validation report to a text file for a future use\n",
    "validation.save_report()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97c79d8",
   "metadata": {
    "id": "a97c79d8"
   },
   "source": [
    "### Plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d27da8b",
   "metadata": {
    "id": "6d27da8b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.show_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2167482-35a5-4eb6-9f0b-22c436285fcb",
   "metadata": {
    "id": "e2167482-35a5-4eb6-9f0b-22c436285fcb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "legend:\n",
      "    1: Artificial\n",
      "    2: Cropland\n",
      "    3: Perenial\n",
      "    4: Forest\n",
      "    5: Shrubland\n",
      "    6: Grassland\n",
      "    7: Barren\n",
      "    8: Wetlands\n",
      "    9: Water\n",
      "    10: Glaciers\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the legend again\n",
    "\n",
    "print(legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45f3ac1c",
   "metadata": {
    "id": "45f3ac1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save the confusion matrix\n",
    "\n",
    "validation.save_normalized_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a6993cc9",
   "metadata": {
    "id": "a6993cc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can also save the plots into the validation directory for later use \n",
    "validation.save_confusion_matrix()\n",
    "validation.save_normalized_confusion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131c2d95",
   "metadata": {
    "id": "131c2d95"
   },
   "source": [
    "### Save the validation overlay geodata to a vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1a5113",
   "metadata": {
    "id": "5e1a5113"
   },
   "source": [
    " You can "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b5e15a2",
   "metadata": {
    "id": "2b5e15a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving validation data:\n",
      "---\n",
      "Vector data: validation_points.shp created from overlay data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.save_vec()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca28629-1471-484d-851f-22db3ee241d0",
   "metadata": {
    "id": "7ca28629-1471-484d-851f-22db3ee241d0"
   },
   "source": [
    "### Classes aggregation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05d440e6-9c24-4a7a-8742-beaed6f81903",
   "metadata": {
    "id": "05d440e6-9c24-4a7a-8742-beaed6f81903"
   },
   "outputs": [],
   "source": [
    "config_aggregation = {\n",
    "    'project':\n",
    "      {'name': 'Geoharmonizer Land Cover validation', \n",
    "      'abbrev': 'cz_lc_18', \n",
    "      'run_id': '20210907'\n",
    "      }, \n",
    "    'input':\n",
    "      {'path': './sample_land_cover', \n",
    "      'in_ras': 'cz_land_cover_osm_2018.tif', \n",
    "      'ndv': 0, \n",
    "      'legend': 'legend.yaml', \n",
    "      'in_vec': 'cz_lucas_points_l1_2018.shp', \n",
    "      'ref_att': 'label_l1'\n",
    "      }, \n",
    "    'report':\n",
    "      {'path': './sample_land_cover', \n",
    "      'dir_name': 'lc_2018_validation_aggregation'\n",
    "      }, \n",
    "    'validation_points': \n",
    "      {'file_name': 'validation_points', \n",
    "       'ogr_format': 'ESRI Shapefile',\n",
    "       'epsg': 3035 \n",
    "      }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "737f549a-eb33-4d13-ad07-e6441d2437f1",
   "metadata": {
    "id": "737f549a-eb33-4d13-ad07-e6441d2437f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Validation project initialized!\n",
      "Inputs: \n",
      "cz_land_cover_osm_2018.tif\n",
      "cz_lucas_points_l1_2018.shp\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validation_lc_aggregated = Validator(config_aggregation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "90e21ce6-b581-4c7d-8e0c-78c42e8d8f50",
   "metadata": {
    "id": "90e21ce6-b581-4c7d-8e0c-78c42e8d8f50"
   },
   "outputs": [],
   "source": [
    "# 2: agriculture (arable land & grassland)\n",
    "aggregartion = {\n",
    "    2: [2, 6]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "65e5cf8a-33c7-4016-b87b-de68ca903cad",
   "metadata": {
    "id": "65e5cf8a-33c7-4016-b87b-de68ca903cad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Processed: 100% | 4930 reference points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_lc_aggregated.overlay(aggregartion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "799cba14-708e-46e3-912e-37aec3d49c6a",
   "metadata": {
    "id": "799cba14-708e-46e3-912e-37aec3d49c6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Machine learning validation indicators (per class): \n",
      "---\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.65      0.90      0.76       174\n",
      "           2       1.00      1.00      1.00      3113\n",
      "           3       1.00      0.18      0.31        44\n",
      "           4       0.97      0.95      0.96       945\n",
      "           5       0.21      0.25      0.23        12\n",
      "           7       0.71      0.25      0.37        20\n",
      "           8       0.30      0.43      0.35         7\n",
      "           9       0.33      0.38      0.35         8\n",
      "\n",
      "    accuracy                           0.97      4323\n",
      "   macro avg       0.65      0.54      0.54      4323\n",
      "weighted avg       0.97      0.97      0.97      4323\n",
      "\n",
      "Classical LC validation indicators: \n",
      "---\n",
      "overall_accuracy :  0.9683\n",
      "producers_accuracy :  0.9735\n",
      "users_accuracy :  0.9683\n",
      "kappa :  0.9267\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_lc_aggregated.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "738b3fe6-53fa-4ecd-9028-46e5e6c1ad91",
   "metadata": {
    "id": "738b3fe6-53fa-4ecd-9028-46e5e6c1ad91"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_lc_aggregated.show_confusion_matrix()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "02_land_cover_validation.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}