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+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+
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+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+
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+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+![Description]()
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+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+
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+## TOAR-classifier v2: A data-driven classification tool for global air quality stations
+
diff --git a/.ipynb_checkpoints/TOAR-classifier_v2-checkpoint.ipynb b/.ipynb_checkpoints/TOAR-classifier_v2-checkpoint.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "31473ebc",
+ "metadata": {},
+ "source": [
+ "## TOAR database station classifications\n",
+ "### Import libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7fab0f12-d316-4be1-9825-e4e3e8265eee",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#!pip install fancyimpute"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "8666a040",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import requests\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.colors import ListedColormap\n",
+ "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
+ "from catboost import CatBoostClassifier, CatBoostRegressor\n",
+ "from lightgbm import LGBMClassifier, LGBMRegressor\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
+ "from sklearn.cluster import KMeans\n",
+ "from imblearn.combine import SMOTETomek, SMOTEENN\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "from sklearn.metrics import *\n",
+ "from scipy import stats\n",
+ "from scipy.special import inv_boxcox\n",
+ "from collections import Counter\n",
+ "import warnings\n",
+ "from sklearn.utils import shuffle\n",
+ "from fancyimpute import IterativeImputer\n",
+ "import os\n",
+ "\n",
+ "warnings.filterwarnings('ignore')\n",
+ "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+ "sns.set_style(\"whitegrid\")\n",
+ "font_title = {\"family\": \"serif\",\n",
+ " \"color\": \"#0b5394\", \n",
+ " \"weight\": \"bold\", \n",
+ " \"size\": 14}\n",
+ "# Graphing…\n",
+ "plt.style.use('fivethirtyeight')\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " 'xtick.labelsize':12,\n",
+ " 'ytick.labelsize':12,\n",
+ " 'axes.labelsize': 12, \n",
+ " 'legend.fontsize': 12, \n",
+ " 'axes.titlesize': 12, \n",
+ " 'axes.titleweight':'bold',\n",
+ " 'axes.titleweight':'bold'\n",
+ " })"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19a46989-c115-4be9-a251-7580673cb9db",
+ "metadata": {},
+ "source": [
+ "#### Some labelled stations for testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a3d18ced-8c4d-472d-b31c-9329a8d29843",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "test_cord_urban = ['GB0682A', 'GB1072A', 'GB1095A', 'GB0960A', 'GB1035A', 'DEBE092', 'DEHH026', 'DENW376', 'DEBW118', 'FR04118', 'ES1422A',\n",
+ " 'IT1016A', '36-081-0124', '13-121-0001', '06-037-4002','openaq_225445', 'jp23105510']\n",
+ "\n",
+ "test_cord_suburban = ['GB1092A', 'GB0885A', 'FR21031', '06-037-0019', 'jp20421030']\n",
+ "\n",
+ "test_cord_rural = ['GB1055R', 'GB0013R', 'GB0006R', 'IE0031R', 'NO0015R', 'FI00363', 'DENI051', 'DENW192', 'DERP015', 'FR23068','FR19020', \n",
+ " 'ES1616A', 'IT1942A', 'MT0001R', '37-105-0002', '08-123-0013','06-111-0005', 'openaq_230819', 'openaq_226125', \n",
+ " 'jp21601010']\n",
+ "\n",
+ "test_cord = {'urban': test_cord_urban, 'suburban': test_cord_suburban, 'rural': test_cord_rural}\n",
+ "categories = ['urban', 'suburban', 'rural']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b41b440a-7b02-498a-88e5-a61efe624492",
+ "metadata": {},
+ "source": [
+ "#### Some useful functions for data preparation and preprocessing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "7510e0d0-4c69-43ee-96f9-d4d391895eac",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def get_station_codes(n:int = 1000):\n",
+ " \"return the first n station codes in the TOAR databse\"\n",
+ " stations_codes = requests.get(f'https://toar-data.fz-juelich.de/api/v2/search/?limit={n}&fields=codes').json()\n",
+ " return [station_code['codes'][0] for station_code in stations_codes]\n",
+ "\n",
+ "def get_station_cord(station_code):\n",
+ " data_point = requests.get(f'https://toar-data.fz-juelich.de/api/v2/stationmeta/{station_code}').json()\n",
+ " cord = {}\n",
+ " cord['lat'] = data_point['coordinates']['lat']\n",
+ " cord['lon'] = data_point['coordinates']['lng']\n",
+ " cord['type_of_area'] = data_point['type_of_area']\n",
+ " cord['alt'] = data_point['coordinates']['alt']\n",
+ " return cord\n",
+ "\n",
+ "def get_test_data_from_station_code(station_codes):\n",
+ " type_of_area_truth = []\n",
+ " type_of_area_given = []\n",
+ " area_code = []\n",
+ " lat = []\n",
+ " lon = []\n",
+ " alt = []\n",
+ " for area_type, st_code in station_codes.items():\n",
+ " for code in st_code:\n",
+ " data_point_cord = get_station_cord(code)\n",
+ " lat.append(data_point_cord['lat'])\n",
+ " lon.append(data_point_cord['lon'])\n",
+ " alt.append(data_point_cord['alt'])\n",
+ " type_of_area_truth.append(data_point_cord['type_of_area'])\n",
+ " type_of_area_given.append(area_type)\n",
+ " area_code.append(code)\n",
+ " df = pd.DataFrame({'lat':lat, 'lon':lon, \n",
+ " 'area_code': area_code,\n",
+ " 'type_of_are_toar': type_of_area_truth, \n",
+ " 'type_of_area_gmap': type_of_area_given})\n",
+ " df = df[~(df['type_of_are_toar']=='unknown')]\n",
+ " return df\n",
+ "# #download and save the hand-labeeled(label using google map) as csv. \n",
+ "# test_data = get_test_data_from_station_code(test_cord)\n",
+ "# test_data.to_csv('hand_labeled_test_data.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "40251495-25f5-44ec-9d82-1650efb88d5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This function downloads the dataset from the TAOR-II database. \n",
+ "# It can be used to refresh the dataset and update the model whenever TAOR-II is updated.\n",
+ "# Note: The dataset has already been downloaded and saved as a CSV file in data folder.\n",
+ "def get_N_first_station_data(data_points=None, N:int=1000, n_sample:int=24):\n",
+ " type_of_area = []\n",
+ " area_code = []\n",
+ " lat = []\n",
+ " lon = []\n",
+ " alt = []\n",
+ " mean_topography_srtm_alt_90m_year1994 = []\n",
+ " mean_topography_srtm_alt_1km_year1994 = []\n",
+ " max_topography_srtm_relative_alt_5km_year1994 = []\n",
+ " min_topography_srtm_relative_alt_5km_year1994 = []\n",
+ " stddev_topography_srtm_relative_alt_5km_year1994 =[]\n",
+ " climatic_zone_year2016 = []\n",
+ " \n",
+ " distance_to_major_road_year2020 = []\n",
+ " mean_stable_nightlights_1km_year2013 = []\n",
+ " mean_stable_nightlights_5km_year2013 = []\n",
+ " max_stable_nightlights_25km_year2013 = []\n",
+ " \n",
+ " max_stable_nightlights_25km_year1992 = []\n",
+ " mean_population_density_250m_year2015 = []\n",
+ " mean_population_density_5km_year2015 = []\n",
+ " max_population_density_25km_year2015 = []\n",
+ " mean_population_density_250m_year1990 = []\n",
+ " mean_population_density_5km_year1990 = []\n",
+ " max_population_density_25km_year1990 = []\n",
+ " mean_nox_emissions_10km_year2015 = []\n",
+ " mean_nox_emissions_10km_year2000 = []\n",
+ " \n",
+ " if data_points is None:\n",
+ " data_points= [requests.get(f'https://toar-data.fz-juelich.de/api/v2/stationmeta/?limit={N}&offset={i*N}').json() for i in range(n_sample)]\n",
+ " data_points = [datapoint for datapoints_sublist in data_points for datapoint in datapoints_sublist] \n",
+ " for data_point in data_points:\n",
+ " type_of_area.append(data_point['type_of_area'])\n",
+ " area_code.append(data_point['codes'][0])\n",
+ " lat.append(data_point['coordinates']['lat'])\n",
+ " lon.append(data_point['coordinates']['lng'])\n",
+ " alt.append(data_point['coordinates']['alt'])\n",
+ " distance_to_major_road_year2020.append(data_point['globalmeta']['distance_to_major_road_year2020'])\n",
+ " mean_stable_nightlights_1km_year2013.append(data_point['globalmeta']['mean_stable_nightlights_1km_year2013'])\n",
+ " mean_stable_nightlights_5km_year2013.append(data_point['globalmeta']['mean_stable_nightlights_5km_year2013'])\n",
+ " max_stable_nightlights_25km_year2013.append(data_point['globalmeta']['max_stable_nightlights_25km_year2013'])\n",
+ " max_stable_nightlights_25km_year1992.append(data_point['globalmeta']['max_stable_nightlights_25km_year1992'])\n",
+ " mean_population_density_250m_year2015.append(data_point['globalmeta']['mean_population_density_250m_year2015'])\n",
+ " mean_population_density_5km_year2015.append(data_point['globalmeta']['mean_population_density_5km_year2015'])\n",
+ " max_population_density_25km_year2015.append(data_point['globalmeta']['max_population_density_25km_year2015'])\n",
+ " mean_population_density_250m_year1990.append(data_point['globalmeta']['mean_population_density_250m_year1990'])\n",
+ " mean_population_density_5km_year1990.append(data_point['globalmeta']['mean_population_density_5km_year1990'])\n",
+ " max_population_density_25km_year1990.append(data_point['globalmeta']['max_population_density_25km_year1990'])\n",
+ " mean_nox_emissions_10km_year2015.append(data_point['globalmeta']['mean_nox_emissions_10km_year2015'])\n",
+ " mean_nox_emissions_10km_year2000.append(data_point['globalmeta']['mean_nox_emissions_10km_year2000'])\n",
+ " mean_topography_srtm_alt_90m_year1994.append(data_point['globalmeta']['mean_topography_srtm_alt_90m_year1994'])\n",
+ " mean_topography_srtm_alt_1km_year1994.append(data_point['globalmeta']['mean_topography_srtm_alt_1km_year1994'])\n",
+ " max_topography_srtm_relative_alt_5km_year1994.append(data_point['globalmeta']['max_topography_srtm_relative_alt_5km_year1994'])\n",
+ " min_topography_srtm_relative_alt_5km_year1994.append(data_point['globalmeta']['min_topography_srtm_relative_alt_5km_year1994'])\n",
+ " stddev_topography_srtm_relative_alt_5km_year1994.append(data_point['globalmeta']['stddev_topography_srtm_relative_alt_5km_year1994'])\n",
+ " climatic_zone_year2016.append(data_point['globalmeta']['climatic_zone_year2016'])\n",
+ " \n",
+ " dataset = pd.DataFrame({'lat': lat,\n",
+ " 'lon': lon,\n",
+ " 'altitude': alt,\n",
+ " 'mean_topography_srtm_alt_90m_year1994': mean_topography_srtm_alt_90m_year1994,\n",
+ " 'mean_topography_srtm_alt_1km_year1994': mean_topography_srtm_alt_1km_year1994,\n",
+ " 'max_topography_srtm_relative_alt_5km_year1994': max_topography_srtm_relative_alt_5km_year1994,\n",
+ " 'min_topography_srtm_relative_alt_5km_year1994': min_topography_srtm_relative_alt_5km_year1994,\n",
+ " 'stddev_topography_srtm_relative_alt_5km_year1994': stddev_topography_srtm_relative_alt_5km_year1994,\n",
+ " 'climatic_zone_year2016': climatic_zone_year2016,\n",
+ " 'distance_to_major_road_year2020': distance_to_major_road_year2020,\n",
+ " 'mean_stable_nightlights_1km_year2013': mean_stable_nightlights_1km_year2013,\n",
+ " 'mean_stable_nightlights_5km_year2013': mean_stable_nightlights_5km_year2013,\n",
+ " 'max_stable_nightlights_25km_year2013': max_stable_nightlights_25km_year2013,\n",
+ " 'max_stable_nightlights_25km_year1992': max_stable_nightlights_25km_year1992,\n",
+ " 'mean_population_density_250m_year2015': mean_population_density_250m_year2015,\n",
+ " 'mean_population_density_5km_year2015': mean_population_density_5km_year2015,\n",
+ " 'max_population_density_25km_year2015': max_population_density_25km_year2015,\n",
+ " 'mean_population_density_250m_year1990': mean_population_density_250m_year1990,\n",
+ " 'mean_population_density_5km_year1990': mean_population_density_5km_year1990,\n",
+ " 'max_population_density_25km_year1990': max_population_density_25km_year1990,\n",
+ " 'mean_nox_emissions_10km_year2015': mean_nox_emissions_10km_year2015,\n",
+ " 'mean_nox_emissions_10km_year2000': mean_nox_emissions_10km_year2000,\n",
+ " 'mean_nox_emissions_10km_year2015': mean_nox_emissions_10km_year2015,\n",
+ " 'area_code': area_code,\n",
+ " 'type_of_area': type_of_area\n",
+ " })\n",
+ " return dataset\n",
+ "# # Download the dataset and save as csv file.\n",
+ "# df = get_N_first_station_data()\n",
+ "# df.to_csv('data/stationglobalmetadata.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "id": "d564a27a-c42c-4685-a8ff-1bafb3067c25",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def parse_code(series):\n",
+ " parsed_code = series.str.partition(\" \")[0] \n",
+ " return pd.to_numeric(parsed_code)\n",
+ "\n",
+ "def groupe_landcover_cat(df):\n",
+ " landcover = df['dominant_landcover_year2012'].unique()\n",
+ " dominant_landcover = {}\n",
+ " for key in landcover:\n",
+ " dominant_landcover[key] = (key//10)*10\n",
+ " df['dominant_landcover_year2012'] = df['dominant_landcover_year2012'].replace(dominant_landcover)\n",
+ " return df\n",
+ "\n",
+ "def encode_categorical_variables(df):\n",
+ " df = groupe_landcover_cat(df)\n",
+ " df['climatic_zone_year2016'] = df['climatic_zone_year2016'].astype('category')\n",
+ " df['dominant_landcover_year2012'] = df['dominant_landcover_year2012'].astype('category')\n",
+ " df = pd.get_dummies(df, columns=['climatic_zone_year2016', 'dominant_landcover_year2012'], dtype=float)\n",
+ " return df\n",
+ "\n",
+ "def data_preprocessing(data_frame=None, path_to_data=None, display_duplicate:bool=False, display_inconsistent_example:bool=False):\n",
+ " if data_frame is not None:\n",
+ " dataset = data_frame.copy()\n",
+ " if data_frame is None:\n",
+ " if path_to_data is not None:\n",
+ " file_path = os.path.abspath(os.path.join(\"data\", \"stationglobalmetadata.csv\"))\n",
+ " dataset = pd.read_csv(file_path)\n",
+ " else:\n",
+ " raise NotImplementedError(\"Unknown dataset\")\n",
+ " if 'lat' in list(dataset.columns) and 'lon' in list(dataset.columns):\n",
+ " dataset.set_index(['lat', 'lon'], inplace=True)\n",
+ " dataset.replace(-999.0, np.nan, inplace=True)\n",
+ " dataset.replace(-9999.0, np.nan, inplace=True)\n",
+ " dataset.replace(-1.0, np.nan, inplace=True)\n",
+ " dataset['altitude'].replace(9999.0, np.nan, inplace=True)\n",
+ " init_shape = dataset.shape\n",
+ " print(\"initial data size: \", init_shape)\n",
+ " dataset['climatic_zone_year2016'] = parse_code(dataset['climatic_zone_year2016'])\n",
+ " #drop row if all these variables are missing (NaN)\n",
+ " dataset = dataset[~dataset.index.duplicated(keep='first')]\n",
+ " \n",
+ " nan_subset=[\n",
+ " 'mean_topography_srtm_alt_90m_year1994', \n",
+ " 'mean_topography_srtm_alt_1km_year1994', \n",
+ " 'max_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'stddev_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'mean_nox_emissions_10km_year2000', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'climatic_zone_year2016', \n",
+ " 'mean_stable_nightlights_1km_year2013'\n",
+ " ]\n",
+ " \n",
+ " if display_inconsistent_example:\n",
+ " display(dataset[dataset['mean_population_density_5km_year2015']<0])\n",
+ " dataset.dropna(subset=nan_subset, how='any', inplace=True) \n",
+ " dataset = dataset[dataset['mean_nox_emissions_10km_year2000']>=0]\n",
+ " dataset = dataset[dataset['max_population_density_25km_year2015']>=0]\n",
+ " dataset = dataset[dataset['mean_population_density_5km_year2015']>=0]\n",
+ " \n",
+ " duplicate = [\n",
+ " 'altitude', \n",
+ " 'mean_topography_srtm_alt_90m_year1994', \n",
+ " 'mean_topography_srtm_alt_1km_year1994', \n",
+ " 'max_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994',\n",
+ " 'stddev_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'climatic_zone_year2016', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'mean_stable_nightlights_1km_year2013', \n",
+ " 'mean_stable_nightlights_5km_year2013', \n",
+ " 'max_stable_nightlights_25km_year2013', \n",
+ " 'max_stable_nightlights_25km_year1992', \n",
+ " 'mean_population_density_250m_year2015', \n",
+ " 'mean_population_density_5km_year2015', \n",
+ " 'max_population_density_25km_year2015',\n",
+ " 'mean_population_density_250m_year1990', \n",
+ " 'mean_population_density_5km_year1990', \n",
+ " 'max_population_density_25km_year1990', \n",
+ " 'mean_nox_emissions_10km_year2015',\n",
+ " 'mean_nox_emissions_10km_year2000'\n",
+ " ]\n",
+ " dataset_duplicate = dataset[dataset.duplicated(keep=False, subset=duplicate)]\n",
+ " \n",
+ " if display_duplicate:\n",
+ " print('Duplicate data points')\n",
+ " display(dataset_duplicate)\n",
+ " \n",
+ " dataset.drop_duplicates(keep=False, subset=duplicate, inplace=True)\n",
+ " # Fill the missing data\n",
+ " dataset['altitude'] = dataset['altitude'].fillna(dataset['mean_topography_srtm_alt_90m_year1994'])\n",
+ " dataset = iterative_imputer(dataset, subset_to_impute=list(dataset.columns)[:-2])\n",
+ " final_shape = dataset.shape\n",
+ " print('New shape: ', final_shape)\n",
+ " print('Total points drop: ', init_shape[0] - final_shape[0])\n",
+ " eps_nox = sorted(dataset['mean_nox_emissions_10km_year2000'].values)[1]\n",
+ " eps_nox/=100\n",
+ " dataset['mean_nox_emissions_10km_year2000'].replace(0.0, eps_nox, inplace=True)\n",
+ " return dataset\n",
+ "\n",
+ "def iterative_imputer(df_, subset_to_impute, estimator=None):\n",
+ " df=df_.copy()\n",
+ " df_to_impute = df[subset_to_impute]\n",
+ " if estimator is None:\n",
+ " imputer = IterativeImputer()\n",
+ " elif estimator=='rf':\n",
+ " imputer = IterativeImputer(estimator=RandomForestRegressor(n_estimators=100))\n",
+ " elif estimator=='lgbm':\n",
+ " imputer = IterativeImputer(estimator=LGBMRegressor(verbose=0))\n",
+ " elif estimator=='cboost':\n",
+ " imputer = IterativeImputer(CatBoostRegressor(verbose=0))\n",
+ " else:\n",
+ " raise NotImplementedError(\"Unknow estimator\")\n",
+ " df_impute = pd.DataFrame(imputer.fit_transform(df_to_impute), columns=subset_to_impute, index=df.index)\n",
+ " df[subset_to_impute] = df_impute\n",
+ " return df\n",
+ "\n",
+ "def feature_engineering_selection(data, selected_columns:list=None, scaling:str=None, encode_cat:bool=False, handle_outliers:bool=False, \n",
+ " columns_maxvalues:dict=None):\n",
+ " df = data.copy()\n",
+ " if handle_outliers:\n",
+ " if columns_maxvalues is None:\n",
+ " columns_maxvalues = {'altitude':4000, 'mean_nox_emissions_10km_year2015':3.4038e-9, 'distance_to_major_road_year2020': 15000, \n",
+ " 'mean_population_density_5km_year2015': 46000, 'max_topography_srtm_relative_alt_5km_year1994': 2000}\n",
+ " for col, value in columns_maxvalues.items():\n",
+ " df[col] = df[col].where(df[col]<=value, value)\n",
+ " if selected_columns is None:\n",
+ " selected_colunms = [\n",
+ " 'altitude', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'mean_topography_srtm_alt_1km_year1994', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'mean_stable_nightlights_1km_year2013', \n",
+ " 'mean_stable_nightlights_5km_year2013',\n",
+ " 'max_stable_nightlights_25km_year2013', \n",
+ " 'mean_population_density_250m_year2015', \n",
+ " 'mean_population_density_5km_year2015',\n",
+ " 'max_population_density_25km_year2015', \n",
+ " 'mean_nox_emissions_10km_year2015'\n",
+ " ]\n",
+ " df_selected = df[selected_columns]\n",
+ " if encode_cat:\n",
+ " if 'climatic_zone_year2016' in list(df_selected.columns):\n",
+ " df_selected['climatic_zone_year2016'] = df_selected['climatic_zone_year2016'].astype('category')\n",
+ " df_selected = pd.get_dummies(df_selected, columns=['climatic_zone_year2016'], dtype=float)\n",
+ " if 'dominant_landcover_year2012' in list(df_selected.columns):\n",
+ " df_selected['dominant_landcover_year2012'] = df_selected['dominant_landcover_year2012'].astype('category')\n",
+ " df_selected = pd.get_dummies(df_selected, columns=['dominant_landcover_year2012'], dtype=float)\n",
+ " if scaling is not None:\n",
+ " scaling = scaling.lower()\n",
+ " if scaling=='minmax':\n",
+ " scaler = MinMaxScaler()\n",
+ " elif scaling=='standard':\n",
+ " scaler = StandardScaler()\n",
+ " elif scaling=='robust':\n",
+ " scaler = RobustScaler()\n",
+ " else:\n",
+ " raise(ValueError('Invalide scaler'))\n",
+ " numerical_cols = df_selected.select_dtypes(include=['int', 'float']).columns\n",
+ " df_selected[numerical_cols] = scaler.fit_transform(df_selected[numerical_cols])\n",
+ " if 'type_of_area' in list(df_selected.columns):\n",
+ " df_selected = df_selected[[col for col in df_selected.columns if col != 'type_of_area'] + ['type_of_area']]\n",
+ " return df_selected"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "id": "997bc298-b007-4fa6-b638-0c96abb67f5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_correlation(data, fig_name=' '):\n",
+ " df = data.select_dtypes(include=['int64', 'float64'])\n",
+ " corr = df.corr()\n",
+ " mask = np.zeros_like(corr)\n",
+ " mask[np.triu_indices_from(mask)] = True\n",
+ " fig, ax = plt.subplots(figsize=(22,14))\n",
+ " ax = sns.heatmap(corr, annot=True, fmt=\".3f\", annot_kws={'size':9}, mask=mask, center=0, cmap=\"coolwarm\")\n",
+ " plt.title(\"Linear correlation heatmap\")\n",
+ " plt.show()\n",
+ " fig_path = os.path.abspath(os.path.join(\"figures\", fig_name + 'correlation.jpg'))\n",
+ " plt.savefig(fig_path, dpi=400, bbox_inches='tight')\n",
+ " return\n",
+ "\n",
+ "def boxplot(df):\n",
+ " import plotly.express as px\n",
+ " for col in list(df.columns):\n",
+ " fig = px.box(df, y=col, width=400, height=400)\n",
+ " fig.show()\n",
+ " return\n",
+ "\n",
+ "def elbow(df, save:bool=False):\n",
+ " 'Determine the number of clusters for kmeans using elbow method'\n",
+ " cluster_iniertia = []\n",
+ " for i in range(1, 14):\n",
+ " kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 500, n_init = 50, random_state = 42)\n",
+ " kmeans.fit(df)\n",
+ " cluster_iniertia.append(kmeans.inertia_)\n",
+ " plt.plot(range(1, 13), cluster_iniertia)\n",
+ " plt.title('The Elbow Method')\n",
+ " plt.xlabel('Number of clusters')\n",
+ " plt.ylabel('cluster_iniertia')\n",
+ " if save:\n",
+ " fig_path = os.path.abspath(os.path.join(\"figures\", \"elbow_num_clusters.jpg\"))\n",
+ " plt.savefig(fig_path, dpi=400, bbox_inches='tight')\n",
+ " plt.show()\n",
+ " return\n",
+ "\n",
+ "def cf_matrix(y_true, y_pred, fig_name='_'):\n",
+ " '''This function will make a pretty plot of an sklearn Confusion Matrix using a Seaborn heatmap visualization'''\n",
+ " print(\"Accuracy: {:.2f}%\".format(accuracy_score(y_true, y_pred)*100))\n",
+ " cf_matrix = confusion_matrix(y_true, y_pred)\n",
+ " class_labels = np.array(['rural', 'suburban', 'urban'])\n",
+ " fig = plt.figure(figsize=(11, 9))\n",
+ " sns.set(font_scale=1.4) # Adjust font scale if necessary\n",
+ " sns.heatmap(cf_matrix, annot=True, cmap='Blues', fmt='g', xticklabels=class_labels, yticklabels=class_labels)\n",
+ " plt.xlabel('Predicted classes')\n",
+ " plt.ylabel('True classes')\n",
+ " plt.title('Confusion Matrix')\n",
+ " fig_path = os.path.abspath(os.path.join(\"figures\", fig_name + '.jpg'))\n",
+ " plt.savefig(fig_name, dpi=400, bbox_inches='tight')\n",
+ " plt.show()\n",
+ " return "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a9455ea-4c86-4622-9276-1c063e4230db",
+ "metadata": {},
+ "source": [
+ "### ============== Unsupervized approach: Kmeans clustering =========================\n",
+ "**Use only metadata in unservized learning algorithm to find clusters corresponding to different station**\n",
+ "\n",
+ "#### Evaluation\n",
+ "**Adjusted Rand Index(ARI)**: Measures the similarity between the true clusters of the samples and the predicted one. It range from -1.0 to 1.0\n",
+ "where a score close to 1 indicates strong agreement between the true labels and the clustering algorithm's labels, a score around 0 indicates random labeling, and a negative score indicates disagreement\n",
+ "\n",
+ "**Normalized Mutual Information (NMI)**: Measures the mutual information between the true clusters of the samples and the clusters assigned by Kmeans, normalized by the average entropy of the two label sets. It range from 0 to 1, where a score close to 1 indicates strong agreement between the true clusters and the Kmeans clusters. A score of 0 indicates no mutual information between the two cluters set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "d58e8d30-7b75-4e89-8827-10cf1d420efc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# y_boxcox_nox_2015, lmb_func_nox_2015 = stats.boxcox(dataset['mean_nox_emissions_10km_year2015'].values)\n",
+ "# y_boxcox_nox_2000, lmb_func_nox_2000 = stats.boxcox(dataset['mean_nox_emissions_10km_year2000'].values)\n",
+ "# #stats.boxcox(Y_train)\n",
+ "# dataset['mean_nox_emissions_10km_year2015'] = y_boxcox_nox_2015\n",
+ "# dataset['mean_nox_emissions_10km_year2000'] = y_boxcox_nox_2000"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "id": "31a37a57-8cb9-4ab7-9029-c91758d93698",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "initial data size: (23969, 22)\n",
+ "New shape: (22378, 22)\n",
+ "Total points drop: 1591\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th>altitude</th>\n",
+ " <th>mean_topography_srtm_alt_90m_year1994</th>\n",
+ " <th>mean_topography_srtm_alt_1km_year1994</th>\n",
+ " <th>max_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>min_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>stddev_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>climatic_zone_year2016</th>\n",
+ " <th>distance_to_major_road_year2020</th>\n",
+ " <th>mean_stable_nightlights_1km_year2013</th>\n",
+ " <th>mean_stable_nightlights_5km_year2013</th>\n",
+ " <th>...</th>\n",
+ " <th>mean_population_density_250m_year2015</th>\n",
+ " <th>mean_population_density_5km_year2015</th>\n",
+ " <th>max_population_density_25km_year2015</th>\n",
+ " <th>mean_population_density_250m_year1990</th>\n",
+ " <th>mean_population_density_5km_year1990</th>\n",
+ " <th>max_population_density_25km_year1990</th>\n",
+ " <th>mean_nox_emissions_10km_year2015</th>\n",
+ " <th>mean_nox_emissions_10km_year2000</th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
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+ " <td>140.0</td>\n",
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+ " <td>148.964264</td>\n",
+ " <td>101.446434</td>\n",
+ " <td>CPT134S00</td>\n",
+ " <td>rural</td>\n",
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+ " <tr>\n",
+ " <th>46.547500</th>\n",
+ " <th>7.985000</th>\n",
+ " <td>3578.0</td>\n",
+ " <td>3466.0</td>\n",
+ " <td>3354.269450</td>\n",
+ " <td>603.0</td>\n",
+ " <td>-2341.0</td>\n",
+ " <td>-2873.715416</td>\n",
+ " <td>7.0</td>\n",
+ " <td>6197.879799</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2.082707</td>\n",
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+ " <td>0.000000</td>\n",
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+ " <td>3845.294377</td>\n",
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+ " <td>1.979643</td>\n",
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+ " <td>72.823387</td>\n",
+ " <td>114.493874</td>\n",
+ " <td>CH0001G</td>\n",
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+ " <th>36.280000</th>\n",
+ " <th>100.900000</th>\n",
+ " <td>3810.0</td>\n",
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+ " <td>192.781525</td>\n",
+ " <td>107.847565</td>\n",
+ " <td>WLG</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
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+ " <td>rural</td>\n",
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+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows Ă— 22 columns</p>\n",
+ "</div>"
+ ],
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+ "lat lon \n",
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+ "\n",
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+ " 46.547500 7.985000 72.823387 \n",
+ " 36.280000 100.900000 192.781525 \n",
+ "-40.683119 144.689939 0.084431 \n",
+ "-14.247000 -170.564000 34.131149 \n",
+ "\n",
+ " mean_nox_emissions_10km_year2000 area_code \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 101.446434 CPT134S00 \n",
+ " 46.547500 7.985000 114.493874 CH0001G \n",
+ " 36.280000 100.900000 107.847565 WLG \n",
+ "-40.683119 144.689939 0.137280 CGO540S00 \n",
+ "-14.247000 -170.564000 28.190823 SMO514S00 \n",
+ "\n",
+ " type_of_area \n",
+ "lat lon \n",
+ "-34.350000 18.480000 rural \n",
+ " 46.547500 7.985000 unknown \n",
+ " 36.280000 100.900000 rural \n",
+ "-40.683119 144.689939 rural \n",
+ "-14.247000 -170.564000 rural \n",
+ "\n",
+ "[5 rows x 22 columns]"
+ ]
+ },
+ "execution_count": 114,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset = data_preprocessing(path_to_data='stationglobalmetadata.csv')\n",
+ "dataset.head(5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "id": "779730ee-dce3-4121-890a-1296ebb4f717",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>-34.350000</th>\n",
+ " <th>46.547500</th>\n",
+ " <th>36.280000</th>\n",
+ " <th>-40.683119</th>\n",
+ " <th>-14.247000</th>\n",
+ " <th>35.692200</th>\n",
+ " <th>-54.848400</th>\n",
+ " <th>-26.230594</th>\n",
+ " <th>-25.722592</th>\n",
+ " <th>19.325150</th>\n",
+ " <th>...</th>\n",
+ " <th>47.766666</th>\n",
+ " <th>49.964994</th>\n",
+ " <th>32.750000</th>\n",
+ " <th>59.317200</th>\n",
+ " <th>35.233400</th>\n",
+ " <th>35.310600</th>\n",
+ " <th>40.768800</th>\n",
+ " <th>31.684200</th>\n",
+ " <th>38.609769</th>\n",
+ " <th>41.614685</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lon</th>\n",
+ " <th>18.480000</th>\n",
+ " <th>7.985000</th>\n",
+ " <th>100.900000</th>\n",
+ " <th>144.689939</th>\n",
+ " <th>-170.564000</th>\n",
+ " <th>139.768900</th>\n",
+ " <th>-68.310700</th>\n",
+ " <th>28.020442</th>\n",
+ " <th>28.420414</th>\n",
+ " <th>-99.204100</th>\n",
+ " <th>...</th>\n",
+ " <th>16.766666</th>\n",
+ " <th>8.565859</th>\n",
+ " <th>128.680000</th>\n",
+ " <th>18.048900</th>\n",
+ " <th>129.010200</th>\n",
+ " <th>128.987200</th>\n",
+ " <th>114.903200</th>\n",
+ " <th>120.288000</th>\n",
+ " <th>-86.082006</th>\n",
+ " <th>-87.124560</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>altitude</th>\n",
+ " <td>230.0</td>\n",
+ " <td>3578.0</td>\n",
+ " <td>3810.0</td>\n",
+ " <td>94.0</td>\n",
+ " <td>42.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>18.0</td>\n",
+ " <td>1773.0</td>\n",
+ " <td>1754.0</td>\n",
+ " <td>2326.0</td>\n",
+ " <td>...</td>\n",
+ " <td>117.0</td>\n",
+ " <td>99.0</td>\n",
+ " <td>500.0</td>\n",
+ " <td>24.0</td>\n",
+ " <td>12.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>728.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>226.1</td>\n",
+ " <td>192.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_topography_srtm_alt_90m_year1994</th>\n",
+ " <td>112.0</td>\n",
+ " <td>3466.0</td>\n",
+ " <td>3730.0</td>\n",
+ " <td>53.0</td>\n",
+ " <td>61.0</td>\n",
+ " <td>21.0</td>\n",
+ " <td>3.0</td>\n",
+ " <td>1696.0</td>\n",
+ " <td>1333.0</td>\n",
+ " <td>2349.0</td>\n",
+ " <td>...</td>\n",
+ " <td>113.0</td>\n",
+ " <td>110.0</td>\n",
+ " <td>73.0</td>\n",
+ " <td>43.0</td>\n",
+ " <td>12.0</td>\n",
+ " <td>4.0</td>\n",
+ " <td>734.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>225.0</td>\n",
+ " <td>196.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_topography_srtm_alt_1km_year1994</th>\n",
+ " <td>84.784127</td>\n",
+ " <td>3354.26945</td>\n",
+ " <td>3697.235165</td>\n",
+ " <td>54.430556</td>\n",
+ " <td>31.087379</td>\n",
+ " <td>20.059341</td>\n",
+ " <td>20.369295</td>\n",
+ " <td>1699.634568</td>\n",
+ " <td>1332.74938</td>\n",
+ " <td>2345.320413</td>\n",
+ " <td>...</td>\n",
+ " <td>113.502773</td>\n",
+ " <td>104.123894</td>\n",
+ " <td>65.820513</td>\n",
+ " <td>25.151899</td>\n",
+ " <td>21.232662</td>\n",
+ " <td>34.868009</td>\n",
+ " <td>734.194969</td>\n",
+ " <td>5.645688</td>\n",
+ " <td>238.473233</td>\n",
+ " <td>195.474012</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <td>140.0</td>\n",
+ " <td>603.0</td>\n",
+ " <td>84.0</td>\n",
+ " <td>57.0</td>\n",
+ " <td>253.0</td>\n",
+ " <td>53.0</td>\n",
+ " <td>270.0</td>\n",
+ " <td>102.0</td>\n",
+ " <td>225.0</td>\n",
+ " <td>350.0</td>\n",
+ " <td>...</td>\n",
+ " <td>10.0</td>\n",
+ " <td>28.0</td>\n",
+ " <td>315.0</td>\n",
+ " <td>36.0</td>\n",
+ " <td>611.0</td>\n",
+ " <td>497.0</td>\n",
+ " <td>398.0</td>\n",
+ " <td>33.0</td>\n",
+ " <td>73.0</td>\n",
+ " <td>28.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <td>-112.0</td>\n",
+ " <td>-2341.0</td>\n",
+ " <td>-752.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>-62.0</td>\n",
+ " <td>-65.0</td>\n",
+ " <td>-101.0</td>\n",
+ " <td>...</td>\n",
+ " <td>-8.0</td>\n",
+ " <td>-23.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>-59.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>-23.0</td>\n",
+ " <td>-26.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>stddev_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <td>-68.713116</td>\n",
+ " <td>-2873.715416</td>\n",
+ " <td>-3560.781547</td>\n",
+ " <td>-25.259737</td>\n",
+ " <td>7.074067</td>\n",
+ " <td>-9.352232</td>\n",
+ " <td>31.423392</td>\n",
+ " <td>-1668.003299</td>\n",
+ " <td>-1281.138149</td>\n",
+ " <td>-2262.805486</td>\n",
+ " <td>...</td>\n",
+ " <td>-110.112831</td>\n",
+ " <td>-102.475289</td>\n",
+ " <td>-0.688234</td>\n",
+ " <td>-28.694404</td>\n",
+ " <td>149.07072</td>\n",
+ " <td>106.34765</td>\n",
+ " <td>-654.514377</td>\n",
+ " <td>-4.160356</td>\n",
+ " <td>-211.419405</td>\n",
+ " <td>-187.203666</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>climatic_zone_year2016</th>\n",
+ " <td>6.0</td>\n",
+ " <td>7.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>2.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>...</td>\n",
+ " <td>6.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>5.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>distance_to_major_road_year2020</th>\n",
+ " <td>24.235018</td>\n",
+ " <td>6197.879799</td>\n",
+ " <td>3595.14835</td>\n",
+ " <td>5230.684142</td>\n",
+ " <td>511.406318</td>\n",
+ " <td>34.213688</td>\n",
+ " <td>1043.890433</td>\n",
+ " <td>96.586932</td>\n",
+ " <td>85.449787</td>\n",
+ " <td>238.581076</td>\n",
+ " <td>...</td>\n",
+ " <td>197.163512</td>\n",
+ " <td>338.208048</td>\n",
+ " <td>505.19027</td>\n",
+ " <td>0.008772</td>\n",
+ " <td>37.155775</td>\n",
+ " <td>23.089269</td>\n",
+ " <td>34.57441</td>\n",
+ " <td>95.008468</td>\n",
+ " <td>634.926265</td>\n",
+ " <td>106.808314</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_stable_nightlights_1km_year2013</th>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>8.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>14.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>56.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>...</td>\n",
+ " <td>8.0</td>\n",
+ " <td>31.0</td>\n",
+ " <td>6.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>61.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>62.0</td>\n",
+ " <td>43.0</td>\n",
+ " <td>59.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_stable_nightlights_5km_year2013</th>\n",
+ " <td>0.0</td>\n",
+ " <td>2.082707</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4.041237</td>\n",
+ " <td>63.0</td>\n",
+ " <td>18.677019</td>\n",
+ " <td>62.951456</td>\n",
+ " <td>43.861386</td>\n",
+ " <td>62.979381</td>\n",
+ " <td>...</td>\n",
+ " <td>5.402878</td>\n",
+ " <td>33.524476</td>\n",
+ " <td>2.198198</td>\n",
+ " <td>63.0</td>\n",
+ " <td>61.486957</td>\n",
+ " <td>52.678261</td>\n",
+ " <td>54.017094</td>\n",
+ " <td>60.288288</td>\n",
+ " <td>21.913043</td>\n",
+ " <td>54.239669</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_stable_nightlights_25km_year2013</th>\n",
+ " <td>50.0</td>\n",
+ " <td>52.0</td>\n",
+ " <td>54.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>47.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>62.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>...</td>\n",
+ " <td>57.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>41.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>54.0</td>\n",
+ " <td>63.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_stable_nightlights_25km_year1992</th>\n",
+ " <td>40.0</td>\n",
+ " <td>42.0</td>\n",
+ " <td>23.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>32.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>61.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>...</td>\n",
+ " <td>41.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>32.0</td>\n",
+ " <td>63.0</td>\n",
+ " <td>61.0</td>\n",
+ " <td>61.0</td>\n",
+ " <td>62.0</td>\n",
+ " <td>59.0</td>\n",
+ " <td>53.0</td>\n",
+ " <td>63.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_population_density_250m_year2015</th>\n",
+ " <td>1.339095</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4910.184053</td>\n",
+ " <td>0.0</td>\n",
+ " <td>683.74621</td>\n",
+ " <td>23970.364846</td>\n",
+ " <td>2455.663829</td>\n",
+ " <td>...</td>\n",
+ " <td>86.317243</td>\n",
+ " <td>991.754621</td>\n",
+ " <td>20.728297</td>\n",
+ " <td>92283.564398</td>\n",
+ " <td>278516.909439</td>\n",
+ " <td>27525.825639</td>\n",
+ " <td>78374.101361</td>\n",
+ " <td>53906.583742</td>\n",
+ " <td>402.390048</td>\n",
+ " <td>1728.186332</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_population_density_5km_year2015</th>\n",
+ " <td>0.652963</td>\n",
+ " <td>1.64468</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.39804</td>\n",
+ " <td>259.638586</td>\n",
+ " <td>13827.48708</td>\n",
+ " <td>1230.179145</td>\n",
+ " <td>3772.176769</td>\n",
+ " <td>3844.87098</td>\n",
+ " <td>9133.435742</td>\n",
+ " <td>...</td>\n",
+ " <td>31.180866</td>\n",
+ " <td>494.029262</td>\n",
+ " <td>32.934671</td>\n",
+ " <td>6697.156876</td>\n",
+ " <td>4361.633324</td>\n",
+ " <td>1192.047095</td>\n",
+ " <td>3462.646015</td>\n",
+ " <td>2220.836806</td>\n",
+ " <td>102.342802</td>\n",
+ " <td>244.667348</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_population_density_25km_year2015</th>\n",
+ " <td>5356.886333</td>\n",
+ " <td>3845.294377</td>\n",
+ " <td>4960.909885</td>\n",
+ " <td>230.173008</td>\n",
+ " <td>2648.221272</td>\n",
+ " <td>22738.63592</td>\n",
+ " <td>13383.638228</td>\n",
+ " <td>59504.144856</td>\n",
+ " <td>27545.669643</td>\n",
+ " <td>37332.160881</td>\n",
+ " <td>...</td>\n",
+ " <td>5965.60968</td>\n",
+ " <td>6787.482117</td>\n",
+ " <td>2050.823651</td>\n",
+ " <td>23707.527229</td>\n",
+ " <td>30320.882859</td>\n",
+ " <td>30349.793048</td>\n",
+ " <td>43562.550513</td>\n",
+ " <td>90470.580623</td>\n",
+ " <td>971.682298</td>\n",
+ " <td>2829.102999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_population_density_250m_year1990</th>\n",
+ " <td>297.652617</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>4062.595509</td>\n",
+ " <td>0.0</td>\n",
+ " <td>295.306869</td>\n",
+ " <td>10017.486937</td>\n",
+ " <td>1740.817741</td>\n",
+ " <td>...</td>\n",
+ " <td>3.767978</td>\n",
+ " <td>747.184087</td>\n",
+ " <td>18.049066</td>\n",
+ " <td>60448.397686</td>\n",
+ " <td>260331.747506</td>\n",
+ " <td>21824.192189</td>\n",
+ " <td>74094.474606</td>\n",
+ " <td>72672.567509</td>\n",
+ " <td>472.818732</td>\n",
+ " <td>1225.107014</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_population_density_5km_year1990</th>\n",
+ " <td>-1.744407</td>\n",
+ " <td>1.979643</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.343217</td>\n",
+ " <td>122.390694</td>\n",
+ " <td>10911.150329</td>\n",
+ " <td>661.777481</td>\n",
+ " <td>1628.943119</td>\n",
+ " <td>1701.044226</td>\n",
+ " <td>8097.352945</td>\n",
+ " <td>...</td>\n",
+ " <td>36.922714</td>\n",
+ " <td>452.74777</td>\n",
+ " <td>37.191118</td>\n",
+ " <td>4467.052098</td>\n",
+ " <td>3816.279499</td>\n",
+ " <td>556.038768</td>\n",
+ " <td>3156.993358</td>\n",
+ " <td>1236.589719</td>\n",
+ " <td>92.550635</td>\n",
+ " <td>196.181747</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_population_density_25km_year1990</th>\n",
+ " <td>2700.758359</td>\n",
+ " <td>3047.447108</td>\n",
+ " <td>5311.749164</td>\n",
+ " <td>260.081728</td>\n",
+ " <td>4216.752589</td>\n",
+ " <td>17796.726371</td>\n",
+ " <td>7554.773868</td>\n",
+ " <td>25683.872557</td>\n",
+ " <td>12208.812374</td>\n",
+ " <td>40316.710058</td>\n",
+ " <td>...</td>\n",
+ " <td>4922.215553</td>\n",
+ " <td>7162.790053</td>\n",
+ " <td>2733.137455</td>\n",
+ " <td>16324.082429</td>\n",
+ " <td>30455.515258</td>\n",
+ " <td>30484.553816</td>\n",
+ " <td>39192.710089</td>\n",
+ " <td>61069.93486</td>\n",
+ " <td>887.06403</td>\n",
+ " <td>2336.722565</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_nox_emissions_10km_year2015</th>\n",
+ " <td>148.964264</td>\n",
+ " <td>72.823387</td>\n",
+ " <td>192.781525</td>\n",
+ " <td>0.084431</td>\n",
+ " <td>34.131149</td>\n",
+ " <td>132864.84375</td>\n",
+ " <td>632.638062</td>\n",
+ " <td>14969.367188</td>\n",
+ " <td>1623.259521</td>\n",
+ " <td>18833.537109</td>\n",
+ " <td>...</td>\n",
+ " <td>158.663528</td>\n",
+ " <td>2697.373291</td>\n",
+ " <td>194.632599</td>\n",
+ " <td>27311.535156</td>\n",
+ " <td>27003.199219</td>\n",
+ " <td>5525.850098</td>\n",
+ " <td>75536.96875</td>\n",
+ " <td>42011.578125</td>\n",
+ " <td>748.71698</td>\n",
+ " <td>2249.27417</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean_nox_emissions_10km_year2000</th>\n",
+ " <td>101.446434</td>\n",
+ " <td>114.493874</td>\n",
+ " <td>107.847565</td>\n",
+ " <td>0.13728</td>\n",
+ " <td>28.190823</td>\n",
+ " <td>253501.109375</td>\n",
+ " <td>622.21759</td>\n",
+ " <td>18234.947266</td>\n",
+ " <td>1864.907349</td>\n",
+ " <td>17115.994141</td>\n",
+ " <td>...</td>\n",
+ " <td>192.829391</td>\n",
+ " <td>3962.57959</td>\n",
+ " <td>369.921936</td>\n",
+ " <td>40483.15625</td>\n",
+ " <td>34869.03125</td>\n",
+ " <td>6456.148438</td>\n",
+ " <td>37354.527344</td>\n",
+ " <td>21093.828125</td>\n",
+ " <td>1135.177734</td>\n",
+ " <td>2862.125488</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>area_code</th>\n",
+ " <td>CPT134S00</td>\n",
+ " <td>CH0001G</td>\n",
+ " <td>WLG</td>\n",
+ " <td>CGO540S00</td>\n",
+ " <td>SMO514S00</td>\n",
+ " <td>jp13101010</td>\n",
+ " <td>AR0002G</td>\n",
+ " <td>RSA024</td>\n",
+ " <td>RSA005</td>\n",
+ " <td>MX_PED</td>\n",
+ " <td>...</td>\n",
+ " <td>AT0002R</td>\n",
+ " <td>DEHE160</td>\n",
+ " <td>JP0004C</td>\n",
+ " <td>SE0003A</td>\n",
+ " <td>KOR221183</td>\n",
+ " <td>KOR238363</td>\n",
+ " <td>1060A</td>\n",
+ " <td>1195A</td>\n",
+ " <td>18-175-0001</td>\n",
+ " <td>18-127-0903</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>type_of_area</th>\n",
+ " <td>rural</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>urban</td>\n",
+ " <td>rural</td>\n",
+ " <td>urban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>urban</td>\n",
+ " <td>...</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>rural</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>unknown</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>22 rows Ă— 22378 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "lat -34.350000 46.547500 \\\n",
+ "lon 18.480000 7.985000 \n",
+ "altitude 230.0 3578.0 \n",
+ "mean_topography_srtm_alt_90m_year1994 112.0 3466.0 \n",
+ "mean_topography_srtm_alt_1km_year1994 84.784127 3354.26945 \n",
+ "max_topography_srtm_relative_alt_5km_year1994 140.0 603.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 -112.0 -2341.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 -68.713116 -2873.715416 \n",
+ "climatic_zone_year2016 6.0 7.0 \n",
+ "distance_to_major_road_year2020 24.235018 6197.879799 \n",
+ "mean_stable_nightlights_1km_year2013 0.0 0.0 \n",
+ "mean_stable_nightlights_5km_year2013 0.0 2.082707 \n",
+ "max_stable_nightlights_25km_year2013 50.0 52.0 \n",
+ "max_stable_nightlights_25km_year1992 40.0 42.0 \n",
+ "mean_population_density_250m_year2015 1.339095 0.0 \n",
+ "mean_population_density_5km_year2015 0.652963 1.64468 \n",
+ "max_population_density_25km_year2015 5356.886333 3845.294377 \n",
+ "mean_population_density_250m_year1990 297.652617 0.0 \n",
+ "mean_population_density_5km_year1990 -1.744407 1.979643 \n",
+ "max_population_density_25km_year1990 2700.758359 3047.447108 \n",
+ "mean_nox_emissions_10km_year2015 148.964264 72.823387 \n",
+ "mean_nox_emissions_10km_year2000 101.446434 114.493874 \n",
+ "area_code CPT134S00 CH0001G \n",
+ "type_of_area rural unknown \n",
+ "\n",
+ "lat 36.280000 -40.683119 \\\n",
+ "lon 100.900000 144.689939 \n",
+ "altitude 3810.0 94.0 \n",
+ "mean_topography_srtm_alt_90m_year1994 3730.0 53.0 \n",
+ "mean_topography_srtm_alt_1km_year1994 3697.235165 54.430556 \n",
+ "max_topography_srtm_relative_alt_5km_year1994 84.0 57.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 -752.0 0.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 -3560.781547 -25.259737 \n",
+ "climatic_zone_year2016 8.0 5.0 \n",
+ "distance_to_major_road_year2020 3595.14835 5230.684142 \n",
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+ "mean_stable_nightlights_5km_year2013 0.0 0.0 \n",
+ "max_stable_nightlights_25km_year2013 54.0 0.0 \n",
+ "max_stable_nightlights_25km_year1992 23.0 0.0 \n",
+ "mean_population_density_250m_year2015 0.0 0.0 \n",
+ "mean_population_density_5km_year2015 0.0 0.39804 \n",
+ "max_population_density_25km_year2015 4960.909885 230.173008 \n",
+ "mean_population_density_250m_year1990 0.0 0.0 \n",
+ "mean_population_density_5km_year1990 0.0 0.343217 \n",
+ "max_population_density_25km_year1990 5311.749164 260.081728 \n",
+ "mean_nox_emissions_10km_year2015 192.781525 0.084431 \n",
+ "mean_nox_emissions_10km_year2000 107.847565 0.13728 \n",
+ "area_code WLG CGO540S00 \n",
+ "type_of_area rural rural \n",
+ "\n",
+ "lat -14.247000 35.692200 \\\n",
+ "lon -170.564000 139.768900 \n",
+ "altitude 42.0 2.0 \n",
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+ "max_topography_srtm_relative_alt_5km_year1994 253.0 53.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 0.0 0.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 7.074067 -9.352232 \n",
+ "climatic_zone_year2016 2.0 5.0 \n",
+ "distance_to_major_road_year2020 511.406318 34.213688 \n",
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+ "mean_stable_nightlights_5km_year2013 4.041237 63.0 \n",
+ "max_stable_nightlights_25km_year2013 47.0 63.0 \n",
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+ "mean_population_density_250m_year2015 0.0 4910.184053 \n",
+ "mean_population_density_5km_year2015 259.638586 13827.48708 \n",
+ "max_population_density_25km_year2015 2648.221272 22738.63592 \n",
+ "mean_population_density_250m_year1990 0.0 4062.595509 \n",
+ "mean_population_density_5km_year1990 122.390694 10911.150329 \n",
+ "max_population_density_25km_year1990 4216.752589 17796.726371 \n",
+ "mean_nox_emissions_10km_year2015 34.131149 132864.84375 \n",
+ "mean_nox_emissions_10km_year2000 28.190823 253501.109375 \n",
+ "area_code SMO514S00 jp13101010 \n",
+ "type_of_area rural urban \n",
+ "\n",
+ "lat -54.848400 -26.230594 \\\n",
+ "lon -68.310700 28.020442 \n",
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+ "min_topography_srtm_relative_alt_5km_year1994 0.0 -62.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 31.423392 -1668.003299 \n",
+ "climatic_zone_year2016 8.0 6.0 \n",
+ "distance_to_major_road_year2020 1043.890433 96.586932 \n",
+ "mean_stable_nightlights_1km_year2013 14.0 63.0 \n",
+ "mean_stable_nightlights_5km_year2013 18.677019 62.951456 \n",
+ "max_stable_nightlights_25km_year2013 62.0 63.0 \n",
+ "max_stable_nightlights_25km_year1992 61.0 63.0 \n",
+ "mean_population_density_250m_year2015 0.0 683.74621 \n",
+ "mean_population_density_5km_year2015 1230.179145 3772.176769 \n",
+ "max_population_density_25km_year2015 13383.638228 59504.144856 \n",
+ "mean_population_density_250m_year1990 0.0 295.306869 \n",
+ "mean_population_density_5km_year1990 661.777481 1628.943119 \n",
+ "max_population_density_25km_year1990 7554.773868 25683.872557 \n",
+ "mean_nox_emissions_10km_year2015 632.638062 14969.367188 \n",
+ "mean_nox_emissions_10km_year2000 622.21759 18234.947266 \n",
+ "area_code AR0002G RSA024 \n",
+ "type_of_area rural urban \n",
+ "\n",
+ "lat -25.722592 19.325150 \\\n",
+ "lon 28.420414 -99.204100 \n",
+ "altitude 1754.0 2326.0 \n",
+ "mean_topography_srtm_alt_90m_year1994 1333.0 2349.0 \n",
+ "mean_topography_srtm_alt_1km_year1994 1332.74938 2345.320413 \n",
+ "max_topography_srtm_relative_alt_5km_year1994 225.0 350.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 -65.0 -101.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 -1281.138149 -2262.805486 \n",
+ "climatic_zone_year2016 6.0 6.0 \n",
+ "distance_to_major_road_year2020 85.449787 238.581076 \n",
+ "mean_stable_nightlights_1km_year2013 56.0 63.0 \n",
+ "mean_stable_nightlights_5km_year2013 43.861386 62.979381 \n",
+ "max_stable_nightlights_25km_year2013 63.0 63.0 \n",
+ "max_stable_nightlights_25km_year1992 63.0 63.0 \n",
+ "mean_population_density_250m_year2015 23970.364846 2455.663829 \n",
+ "mean_population_density_5km_year2015 3844.87098 9133.435742 \n",
+ "max_population_density_25km_year2015 27545.669643 37332.160881 \n",
+ "mean_population_density_250m_year1990 10017.486937 1740.817741 \n",
+ "mean_population_density_5km_year1990 1701.044226 8097.352945 \n",
+ "max_population_density_25km_year1990 12208.812374 40316.710058 \n",
+ "mean_nox_emissions_10km_year2015 1623.259521 18833.537109 \n",
+ "mean_nox_emissions_10km_year2000 1864.907349 17115.994141 \n",
+ "area_code RSA005 MX_PED \n",
+ "type_of_area suburban urban \n",
+ "\n",
+ "lat ... 47.766666 \\\n",
+ "lon ... 16.766666 \n",
+ "altitude ... 117.0 \n",
+ "mean_topography_srtm_alt_90m_year1994 ... 113.0 \n",
+ "mean_topography_srtm_alt_1km_year1994 ... 113.502773 \n",
+ "max_topography_srtm_relative_alt_5km_year1994 ... 10.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 ... -8.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 ... -110.112831 \n",
+ "climatic_zone_year2016 ... 6.0 \n",
+ "distance_to_major_road_year2020 ... 197.163512 \n",
+ "mean_stable_nightlights_1km_year2013 ... 8.0 \n",
+ "mean_stable_nightlights_5km_year2013 ... 5.402878 \n",
+ "max_stable_nightlights_25km_year2013 ... 57.0 \n",
+ "max_stable_nightlights_25km_year1992 ... 41.0 \n",
+ "mean_population_density_250m_year2015 ... 86.317243 \n",
+ "mean_population_density_5km_year2015 ... 31.180866 \n",
+ "max_population_density_25km_year2015 ... 5965.60968 \n",
+ "mean_population_density_250m_year1990 ... 3.767978 \n",
+ "mean_population_density_5km_year1990 ... 36.922714 \n",
+ "max_population_density_25km_year1990 ... 4922.215553 \n",
+ "mean_nox_emissions_10km_year2015 ... 158.663528 \n",
+ "mean_nox_emissions_10km_year2000 ... 192.829391 \n",
+ "area_code ... AT0002R \n",
+ "type_of_area ... unknown \n",
+ "\n",
+ "lat 49.964994 32.750000 \\\n",
+ "lon 8.565859 128.680000 \n",
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+ "max_stable_nightlights_25km_year2013 63.0 41.0 \n",
+ "max_stable_nightlights_25km_year1992 63.0 32.0 \n",
+ "mean_population_density_250m_year2015 991.754621 20.728297 \n",
+ "mean_population_density_5km_year2015 494.029262 32.934671 \n",
+ "max_population_density_25km_year2015 6787.482117 2050.823651 \n",
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+ "mean_population_density_5km_year1990 452.74777 37.191118 \n",
+ "max_population_density_25km_year1990 7162.790053 2733.137455 \n",
+ "mean_nox_emissions_10km_year2015 2697.373291 194.632599 \n",
+ "mean_nox_emissions_10km_year2000 3962.57959 369.921936 \n",
+ "area_code DEHE160 JP0004C \n",
+ "type_of_area urban rural \n",
+ "\n",
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+ "climatic_zone_year2016 8.0 5.0 \n",
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+ "mean_stable_nightlights_1km_year2013 63.0 63.0 \n",
+ "mean_stable_nightlights_5km_year2013 63.0 61.486957 \n",
+ "max_stable_nightlights_25km_year2013 63.0 63.0 \n",
+ "max_stable_nightlights_25km_year1992 63.0 61.0 \n",
+ "mean_population_density_250m_year2015 92283.564398 278516.909439 \n",
+ "mean_population_density_5km_year2015 6697.156876 4361.633324 \n",
+ "max_population_density_25km_year2015 23707.527229 30320.882859 \n",
+ "mean_population_density_250m_year1990 60448.397686 260331.747506 \n",
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+ "mean_nox_emissions_10km_year2000 40483.15625 34869.03125 \n",
+ "area_code SE0003A KOR221183 \n",
+ "type_of_area unknown urban \n",
+ "\n",
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+ "climatic_zone_year2016 5.0 8.0 \n",
+ "distance_to_major_road_year2020 23.089269 34.57441 \n",
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+ "mean_stable_nightlights_5km_year2013 52.678261 54.017094 \n",
+ "max_stable_nightlights_25km_year2013 63.0 63.0 \n",
+ "max_stable_nightlights_25km_year1992 61.0 62.0 \n",
+ "mean_population_density_250m_year2015 27525.825639 78374.101361 \n",
+ "mean_population_density_5km_year2015 1192.047095 3462.646015 \n",
+ "max_population_density_25km_year2015 30349.793048 43562.550513 \n",
+ "mean_population_density_250m_year1990 21824.192189 74094.474606 \n",
+ "mean_population_density_5km_year1990 556.038768 3156.993358 \n",
+ "max_population_density_25km_year1990 30484.553816 39192.710089 \n",
+ "mean_nox_emissions_10km_year2015 5525.850098 75536.96875 \n",
+ "mean_nox_emissions_10km_year2000 6456.148438 37354.527344 \n",
+ "area_code KOR238363 1060A \n",
+ "type_of_area urban unknown \n",
+ "\n",
+ "lat 31.684200 38.609769 \\\n",
+ "lon 120.288000 -86.082006 \n",
+ "altitude 7.0 226.1 \n",
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+ "mean_topography_srtm_alt_1km_year1994 5.645688 238.473233 \n",
+ "max_topography_srtm_relative_alt_5km_year1994 33.0 73.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 0.0 -23.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 -4.160356 -211.419405 \n",
+ "climatic_zone_year2016 5.0 5.0 \n",
+ "distance_to_major_road_year2020 95.008468 634.926265 \n",
+ "mean_stable_nightlights_1km_year2013 62.0 43.0 \n",
+ "mean_stable_nightlights_5km_year2013 60.288288 21.913043 \n",
+ "max_stable_nightlights_25km_year2013 63.0 54.0 \n",
+ "max_stable_nightlights_25km_year1992 59.0 53.0 \n",
+ "mean_population_density_250m_year2015 53906.583742 402.390048 \n",
+ "mean_population_density_5km_year2015 2220.836806 102.342802 \n",
+ "max_population_density_25km_year2015 90470.580623 971.682298 \n",
+ "mean_population_density_250m_year1990 72672.567509 472.818732 \n",
+ "mean_population_density_5km_year1990 1236.589719 92.550635 \n",
+ "max_population_density_25km_year1990 61069.93486 887.06403 \n",
+ "mean_nox_emissions_10km_year2015 42011.578125 748.71698 \n",
+ "mean_nox_emissions_10km_year2000 21093.828125 1135.177734 \n",
+ "area_code 1195A 18-175-0001 \n",
+ "type_of_area unknown suburban \n",
+ "\n",
+ "lat 41.614685 \n",
+ "lon -87.124560 \n",
+ "altitude 192.0 \n",
+ "mean_topography_srtm_alt_90m_year1994 196.0 \n",
+ "mean_topography_srtm_alt_1km_year1994 195.474012 \n",
+ "max_topography_srtm_relative_alt_5km_year1994 28.0 \n",
+ "min_topography_srtm_relative_alt_5km_year1994 -26.0 \n",
+ "stddev_topography_srtm_relative_alt_5km_year1994 -187.203666 \n",
+ "climatic_zone_year2016 6.0 \n",
+ "distance_to_major_road_year2020 106.808314 \n",
+ "mean_stable_nightlights_1km_year2013 59.0 \n",
+ "mean_stable_nightlights_5km_year2013 54.239669 \n",
+ "max_stable_nightlights_25km_year2013 63.0 \n",
+ "max_stable_nightlights_25km_year1992 63.0 \n",
+ "mean_population_density_250m_year2015 1728.186332 \n",
+ "mean_population_density_5km_year2015 244.667348 \n",
+ "max_population_density_25km_year2015 2829.102999 \n",
+ "mean_population_density_250m_year1990 1225.107014 \n",
+ "mean_population_density_5km_year1990 196.181747 \n",
+ "max_population_density_25km_year1990 2336.722565 \n",
+ "mean_nox_emissions_10km_year2015 2249.27417 \n",
+ "mean_nox_emissions_10km_year2000 2862.125488 \n",
+ "area_code 18-127-0903 \n",
+ "type_of_area suburban \n",
+ "\n",
+ "[22 rows x 22378 columns]"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "id": "9d0305e1-6e18-49a1-ac9f-5386c0913de8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(data=dataset, x='type_of_area')\n",
+ "plt.title('Value Counts of type_of_area')\n",
+ "plt.xlabel('Values')\n",
+ "plt.ylabel('Counts')\n",
+ "fig_path = os.path.abspath(os.path.join(\"figures\", \"value_counts_type_of_area.jpg\"))\n",
+ "plt.savefig(fig_path, dpi=400, bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e2b7e153-7a99-434d-bdeb-856a59f53f7e",
+ "metadata": {},
+ "source": [
+ "#### functions for training, testing, evaluate clustering algorithm and visualized clusters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "5aa52c01-bae2-40f5-a03e-eb1d9d7588de",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def train_clustering_model(df, num_clusters:int=3, use_pca:bool=False, model='kmeans'):\n",
+ " train_model={'pca':None}\n",
+ " if use_pca:\n",
+ " pca_ = PCA(n_components=0.99, random_state=42)\n",
+ " print(f\"Original shape: {df.shape}\")\n",
+ " pca = pca_.fit(df)\n",
+ " df_pca = pca.transform(df)\n",
+ " print(f\"Shape after PCA: {df_pca.shape}\")\n",
+ " df=df_pca\n",
+ " train_model['pca']=pca\n",
+ " model=model.lower()\n",
+ " if model=='kmeans':\n",
+ " clustering_model= KMeans(n_clusters=num_clusters, n_init=300, random_state=42)\n",
+ " else:\n",
+ " raise ValueError('invalid model, Implement another method if needed')\n",
+ " clustering_model.fit(df)\n",
+ " train_model['model']=clustering_model\n",
+ " return train_model\n",
+ "\n",
+ "def kmeans_predict(df, kmeans):\n",
+ " kmean=kmeans['kmean']\n",
+ " pca = kmeans['pca']\n",
+ " if pca is not None:\n",
+ " df = pca.transform(df)\n",
+ " y_pred = kmean.predict(df)\n",
+ " return y_pred\n",
+ "\n",
+ "def visualized(df, labels_pred, labels_truth=None, centroid=None, num_clusters=3):\n",
+ " print(\"Visualization...\")\n",
+ " cluster_labels = ['cluster_' + str(i) for i in range(num_clusters)]\n",
+ " tsne = TSNE(n_components=2,perplexity=50,n_iter=2000, init='pca', learning_rate=200, random_state=42)\n",
+ " df_tsne = tsne.fit_transform(df)\n",
+ " fig = plt.figure(figsize=(15,6))\n",
+ " ax1 = fig.add_subplot(121)\n",
+ " scatter = ax1.scatter(df_tsne[:,0], df_tsne[:,1], c=labels_pred, cmap='Set1')\n",
+ " ax1.legend(handles=scatter.legend_elements()[0], labels=cluster_labels, loc=\"best\", title=\"Clusters\")\n",
+ " ax1.set_title(f'2D visualization of different Kmeans clusters: {num_clusters} clusters.')\n",
+ " if labels_truth is not None:\n",
+ " ax2 = fig.add_subplot(122)\n",
+ " scatter = ax2.scatter(df_tsne[:,0],df_tsne[:,1], c=labels_truth, cmap='Set1')\n",
+ " ax2.legend(handles=scatter.legend_elements()[0], labels=cluster_labels, loc=\"best\", title=\"Clusters\")\n",
+ " ax2.set_title(f'2D visualization of truth clusters')\n",
+ " plt.subplots_adjust(wspace=0.3)\n",
+ " plt.show()\n",
+ " return\n",
+ "\n",
+ "def ari_nmi_clustering(y_pred, y_truth):\n",
+ " ari = adjusted_mutual_info_score(y_pred, y_truth)\n",
+ " nmi = normalized_mutual_info_score(y_pred, y_truth)\n",
+ " return ari, nmi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "18d71fc1-7a34-4ddc-a646-661b235b91f0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "number of data point: (22378, 22)\n",
+ "number of known station: (12408, 22)\n",
+ "number of unknown station: (9970, 22)\n",
+ "test shape: (1000, 22)\n"
+ ]
+ }
+ ],
+ "source": [
+ "labelled_data = dataset[~(dataset['type_of_area']=='unknown')]\n",
+ "unlabelled_data = dataset[(dataset['type_of_area']=='unknown')]\n",
+ "df_train, df_test = train_test_split(labelled_data, test_size=1000, shuffle=False, random_state=42)\n",
+ "df_test_idx = list(df_test.index)\n",
+ "df_train_idx = list(df_train.index)\n",
+ "un_labeled_idx = list(unlabelled_data.index)\n",
+ "print('number of data point: ', dataset.shape)\n",
+ "print(\"number of known station: \", labelled_data.shape)\n",
+ "print(\"number of unknown station: \", unlabelled_data.shape)\n",
+ "print('test shape: ', df_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "ee9ab82e-e32a-4b70-bf34-c189dabbec4d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "test_data_path = os.path.abspath(os.path.join(\"data\", \"hand_labeled_test_data.csv\"))\n",
+ "test_data = pd.read_csv(test_data_path)\n",
+ "test_data.set_index(['lat', 'lon'], inplace=True)\n",
+ "test_data = test_data[~(test_data['type_of_are_toar']=='unknown')]\n",
+ "test_indeces = list(test_data.index)\n",
+ "test_indeces = [idx for idx in test_indeces if idx in list(dataset.index)]\n",
+ "test_data = test_data.loc[test_indeces]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54d9c49a-b81b-4382-8567-6bd1c6871a7e",
+ "metadata": {},
+ "source": [
+ "#### Prepare train and test data and train clustering algorithm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "id": "bfed2136-c5af-4612-9820-e94ccde98518",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# we first selecte the most recent infos and analyse the correlation between variable\n",
+ "selected_colunms_0 = [\n",
+ " 'altitude', \n",
+ " 'mean_topography_srtm_alt_90m_year1994', \n",
+ " 'mean_topography_srtm_alt_1km_year1994', \n",
+ " 'max_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994',\n",
+ " 'stddev_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'climatic_zone_year2016', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'mean_stable_nightlights_1km_year2013', \n",
+ " 'mean_stable_nightlights_5km_year2013', \n",
+ " 'max_stable_nightlights_25km_year2013', \n",
+ " 'max_stable_nightlights_25km_year1992', \n",
+ " 'mean_population_density_250m_year2015', \n",
+ " 'mean_population_density_5km_year2015', \n",
+ " 'max_population_density_25km_year2015',\n",
+ " 'mean_population_density_250m_year1990', \n",
+ " 'mean_population_density_5km_year1990', \n",
+ " 'max_population_density_25km_year1990', \n",
+ " 'mean_nox_emissions_10km_year2015', \n",
+ " 'mean_nox_emissions_10km_year2000',\n",
+ "]\n",
+ "dataset_0 = feature_engineering_selection(dataset, selected_columns=selected_colunms_0, scaling='minmax')\n",
+ "df_train_0 = dataset_0[~(dataset_0.index.isin(df_test_idx))]\n",
+ "df_test_0 = dataset_0[(dataset_0.index.isin(df_test_idx))]\n",
+ "test_data_0 = dataset_0.loc[test_indeces]\n",
+ "#display(dataset_0.head())\n",
+ "#plot_correlation(df_train_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "47ab7414-d151-4384-a7f1-fe9e6f5de4b3",
+ "metadata": {},
+ "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></th>\n",
+ " <th>altitude</th>\n",
+ " <th>mean_topography_srtm_alt_90m_year1994</th>\n",
+ " <th>mean_topography_srtm_alt_1km_year1994</th>\n",
+ " <th>max_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>min_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>stddev_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>climatic_zone_year2016</th>\n",
+ " <th>distance_to_major_road_year2020</th>\n",
+ " <th>mean_stable_nightlights_1km_year2013</th>\n",
+ " <th>mean_stable_nightlights_5km_year2013</th>\n",
+ " <th>max_stable_nightlights_25km_year2013</th>\n",
+ " <th>max_stable_nightlights_25km_year1992</th>\n",
+ " <th>mean_population_density_250m_year2015</th>\n",
+ " <th>mean_population_density_5km_year2015</th>\n",
+ " <th>max_population_density_25km_year2015</th>\n",
+ " <th>mean_population_density_250m_year1990</th>\n",
+ " <th>mean_population_density_5km_year1990</th>\n",
+ " <th>max_population_density_25km_year1990</th>\n",
+ " <th>mean_nox_emissions_10km_year2015</th>\n",
+ " <th>mean_nox_emissions_10km_year2000</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>-34.350000</th>\n",
+ " <th>18.480000</th>\n",
+ " <td>0.081592</td>\n",
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+ " <td>0.087507</td>\n",
+ " <td>0.052006</td>\n",
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+ " <td>7.704299e-05</td>\n",
+ " <td>9.664181e-05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46.547500</th>\n",
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+ " <td>0.013209</td>\n",
+ " <td>3.766361e-05</td>\n",
+ " <td>1.090713e-04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>9.970488e-05</td>\n",
+ " <td>1.027398e-04</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
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+ " </tr>\n",
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+ " </tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " <td>1.412528e-02</td>\n",
+ " <td>3.856583e-02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " <tr>\n",
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+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>21378 rows Ă— 20 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " altitude mean_topography_srtm_alt_90m_year1994 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.081592 0.096158 \n",
+ " 46.547500 7.985000 0.521482 0.737335 \n",
+ " 36.280000 100.900000 0.551964 0.787803 \n",
+ "-40.683119 144.689939 0.063724 0.084879 \n",
+ "-14.247000 -170.564000 0.056891 0.086408 \n",
+ "... ... ... \n",
+ "-34.353480 18.489680 0.081592 0.091761 \n",
+ " 47.766666 16.766666 0.066745 0.096349 \n",
+ " 59.317200 18.048900 0.054526 0.082967 \n",
+ " 40.768800 114.903200 0.147024 0.215064 \n",
+ " 31.684200 120.288000 0.052293 0.076085 \n",
+ "\n",
+ " mean_topography_srtm_alt_1km_year1994 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.087507 \n",
+ " 46.547500 7.985000 0.721275 \n",
+ " 36.280000 100.900000 0.787757 \n",
+ "-40.683119 144.689939 0.081624 \n",
+ "-14.247000 -170.564000 0.077099 \n",
+ "... ... \n",
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+ " 59.317200 18.048900 0.075948 \n",
+ " 40.768800 114.903200 0.213391 \n",
+ " 31.684200 120.288000 0.072167 \n",
+ "\n",
+ " max_topography_srtm_relative_alt_5km_year1994 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.052006 \n",
+ " 46.547500 7.985000 0.223997 \n",
+ " 36.280000 100.900000 0.031204 \n",
+ "-40.683119 144.689939 0.021174 \n",
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+ "... ... \n",
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+ " 59.317200 18.048900 0.013373 \n",
+ " 40.768800 114.903200 0.147845 \n",
+ " 31.684200 120.288000 0.012259 \n",
+ "\n",
+ " min_topography_srtm_relative_alt_5km_year1994 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.952157 \n",
+ " 46.547500 7.985000 0.000000 \n",
+ " 36.280000 100.900000 0.678770 \n",
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+ "-14.247000 -170.564000 1.000000 \n",
+ "... ... \n",
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+ " 40.768800 114.903200 0.974797 \n",
+ " 31.684200 120.288000 1.000000 \n",
+ "\n",
+ " stddev_topography_srtm_relative_alt_5km_year1994 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.799935 \n",
+ " 46.547500 7.985000 0.323021 \n",
+ " 36.280000 100.900000 0.206204 \n",
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+ "-14.247000 -170.564000 0.812821 \n",
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+ " 47.766666 16.766666 0.792897 \n",
+ " 59.317200 18.048900 0.806740 \n",
+ " 40.768800 114.903200 0.700336 \n",
+ " 31.684200 120.288000 0.810911 \n",
+ "\n",
+ " climatic_zone_year2016 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.500000 \n",
+ " 46.547500 7.985000 0.583333 \n",
+ " 36.280000 100.900000 0.666667 \n",
+ "-40.683119 144.689939 0.416667 \n",
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+ " 31.684200 120.288000 0.416667 \n",
+ "\n",
+ " distance_to_major_road_year2020 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 9.811367e-04 \n",
+ " 46.547500 7.985000 2.509335e-01 \n",
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+ "-40.683119 144.689939 2.117746e-01 \n",
+ "-14.247000 -170.564000 2.070524e-02 \n",
+ "... ... \n",
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+ " 40.768800 114.903200 1.399748e-03 \n",
+ " 31.684200 120.288000 3.846541e-03 \n",
+ "\n",
+ " mean_stable_nightlights_1km_year2013 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.000000 \n",
+ " 46.547500 7.985000 0.000000 \n",
+ " 36.280000 100.900000 0.000000 \n",
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+ " 31.684200 120.288000 0.984127 \n",
+ "\n",
+ " mean_stable_nightlights_5km_year2013 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.000000 \n",
+ " 46.547500 7.985000 0.033059 \n",
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+ " 40.768800 114.903200 0.857414 \n",
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+ "\n",
+ " max_stable_nightlights_25km_year2013 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.793651 \n",
+ " 46.547500 7.985000 0.825397 \n",
+ " 36.280000 100.900000 0.857143 \n",
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+ " 59.317200 18.048900 1.000000 \n",
+ " 40.768800 114.903200 1.000000 \n",
+ " 31.684200 120.288000 1.000000 \n",
+ "\n",
+ " max_stable_nightlights_25km_year1992 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.634921 \n",
+ " 46.547500 7.985000 0.666667 \n",
+ " 36.280000 100.900000 0.365079 \n",
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+ " 40.768800 114.903200 0.984127 \n",
+ " 31.684200 120.288000 0.936508 \n",
+ "\n",
+ " mean_population_density_250m_year2015 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.000005 \n",
+ " 46.547500 7.985000 0.000000 \n",
+ " 36.280000 100.900000 0.000000 \n",
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+ " 40.768800 114.903200 0.281398 \n",
+ " 31.684200 120.288000 0.193549 \n",
+ "\n",
+ " mean_population_density_5km_year2015 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.000012 \n",
+ " 46.547500 7.985000 0.000030 \n",
+ " 36.280000 100.900000 0.000000 \n",
+ "-40.683119 144.689939 0.000007 \n",
+ "-14.247000 -170.564000 0.004726 \n",
+ "... ... \n",
+ "-34.353480 18.489680 0.000012 \n",
+ " 47.766666 16.766666 0.000568 \n",
+ " 59.317200 18.048900 0.121900 \n",
+ " 40.768800 114.903200 0.063026 \n",
+ " 31.684200 120.288000 0.040423 \n",
+ "\n",
+ " max_population_density_25km_year2015 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.018819 \n",
+ " 46.547500 7.985000 0.013509 \n",
+ " 36.280000 100.900000 0.017428 \n",
+ "-40.683119 144.689939 0.000809 \n",
+ "-14.247000 -170.564000 0.009303 \n",
+ "... ... \n",
+ "-34.353480 18.489680 0.015572 \n",
+ " 47.766666 16.766666 0.020957 \n",
+ " 59.317200 18.048900 0.083285 \n",
+ " 40.768800 114.903200 0.153036 \n",
+ " 31.684200 120.288000 0.317825 \n",
+ "\n",
+ " mean_population_density_250m_year1990 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.001143 \n",
+ " 46.547500 7.985000 0.000000 \n",
+ " 36.280000 100.900000 0.000000 \n",
+ "-40.683119 144.689939 0.000000 \n",
+ "-14.247000 -170.564000 0.000000 \n",
+ "... ... \n",
+ "-34.353480 18.489680 0.000002 \n",
+ " 47.766666 16.766666 0.000014 \n",
+ " 59.317200 18.048900 0.232198 \n",
+ " 40.768800 114.903200 0.284616 \n",
+ " 31.684200 120.288000 0.279154 \n",
+ "\n",
+ " mean_population_density_5km_year1990 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.000000 \n",
+ " 46.547500 7.985000 0.000071 \n",
+ " 36.280000 100.900000 0.000033 \n",
+ "-40.683119 144.689939 0.000040 \n",
+ "-14.247000 -170.564000 0.002356 \n",
+ "... ... \n",
+ "-34.353480 18.489680 0.000039 \n",
+ " 47.766666 16.766666 0.000734 \n",
+ " 59.317200 18.048900 0.084799 \n",
+ " 40.768800 114.903200 0.059940 \n",
+ " 31.684200 120.288000 0.023498 \n",
+ "\n",
+ " max_population_density_25km_year1990 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 0.011706 \n",
+ " 46.547500 7.985000 0.013209 \n",
+ " 36.280000 100.900000 0.023024 \n",
+ "-40.683119 144.689939 0.001127 \n",
+ "-14.247000 -170.564000 0.018277 \n",
+ "... ... \n",
+ "-34.353480 18.489680 0.009673 \n",
+ " 47.766666 16.766666 0.021335 \n",
+ " 59.317200 18.048900 0.070756 \n",
+ " 40.768800 114.903200 0.169880 \n",
+ " 31.684200 120.288000 0.264706 \n",
+ "\n",
+ " mean_nox_emissions_10km_year2015 \\\n",
+ "lat lon \n",
+ "-34.350000 18.480000 7.704299e-05 \n",
+ " 46.547500 7.985000 3.766361e-05 \n",
+ " 36.280000 100.900000 9.970488e-05 \n",
+ "-40.683119 144.689939 4.366696e-08 \n",
+ "-14.247000 -170.564000 1.765233e-05 \n",
+ "... ... \n",
+ "-34.353480 18.489680 7.704299e-05 \n",
+ " 47.766666 16.766666 8.205936e-05 \n",
+ " 59.317200 18.048900 1.412528e-02 \n",
+ " 40.768800 114.903200 3.906705e-02 \n",
+ " 31.684200 120.288000 2.172801e-02 \n",
+ "\n",
+ " mean_nox_emissions_10km_year2000 \n",
+ "lat lon \n",
+ "-34.350000 18.480000 9.664181e-05 \n",
+ " 46.547500 7.985000 1.090713e-04 \n",
+ " 36.280000 100.900000 1.027398e-04 \n",
+ "-40.683119 144.689939 1.307782e-07 \n",
+ "-14.247000 -170.564000 2.685567e-05 \n",
+ "... ... \n",
+ "-34.353480 18.489680 9.664181e-05 \n",
+ " 47.766666 16.766666 1.836968e-04 \n",
+ " 59.317200 18.048900 3.856583e-02 \n",
+ " 40.768800 114.903200 3.558537e-02 \n",
+ " 31.684200 120.288000 2.009480e-02 \n",
+ "\n",
+ "[21378 rows x 20 columns]"
+ ]
+ },
+ "execution_count": 80,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train_0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "a9d5427e-5485-4a75-a967-5cee7d2618ea",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "clustering_model_kmeans = train_clustering_model(df_train_0, model='kmeans')['model']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba1f605a-f8f0-45dd-a09d-9d5a9b4de17d",
+ "metadata": {},
+ "source": [
+ "## Testing 1:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "c8e396aa-46f5-4f77-80d3-9432af0a6c60",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 1, 1, 1, 1,\n",
+ " 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "y_pred_0 = clustering_model_kmeans.predict(test_data_0)\n",
+ "display(y_pred_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c4292fc-dece-4785-afb3-18a77fc059ae",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "d416394f-4792-43a5-8595-b56d290e43df",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 90.91%\n",
+ "ARI Score: 0.71\n",
+ "NMI Score: 0.73\n",
+ "\n",
+ "Accuracy w.r.t given labels: 87.88%\n",
+ "ARI Score w.r.t given labels: 0.66\n",
+ "NMI Score w.r.t given labels: 0.69\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_data['category_toar']=test_data['type_of_area_gmap'].map({'urban':0, 'suburban':2, 'rural':1})\n",
+ "test_data['category_pred']=y_pred_0\n",
+ "test_data['type_of_area_pred'] = test_data['category_pred'].map({0:'urban', 2:'suburban', 1:'rural'})\n",
+ "test_data = test_data[['area_code',\t'type_of_are_toar', 'type_of_area_gmap', 'type_of_area_pred', 'category_toar', 'category_pred']]\n",
+ "accuracy_0 = accuracy_score(test_data['category_pred'].values, test_data['category_toar'].values)\n",
+ "accuracy_1 = accuracy_score(test_data['type_of_are_toar'].values, test_data['type_of_area_gmap'].values)\n",
+ "ari_0, nmi_0 = ari_nmi_clustering(test_data['category_pred'].values, test_data['category_toar'].values)\n",
+ "ari_1, nmi_1 = ari_nmi_clustering(test_data['type_of_are_toar'].values, test_data['type_of_area_gmap'].values)\n",
+ "test_data = test_data[['area_code', 'type_of_are_toar', 'type_of_area_gmap', 'type_of_area_pred', 'category_pred']]\n",
+ "print(\"Accuracy: {:.2f}%\".format(accuracy_0*100))\n",
+ "print(\"ARI Score: {:.2f}\".format(ari_0))\n",
+ "print(\"NMI Score: {:.2f}\".format(nmi_0))\n",
+ "print()\n",
+ "print(\"Accuracy w.r.t given labels: {:.2f}%\".format(accuracy_1*100))\n",
+ "print(\"ARI Score w.r.t given labels: {:.2f}\".format(ari_1))\n",
+ "print(\"NMI Score w.r.t given labels: {:.2f}\".format(nmi_1))\n",
+ "#test_data['type_of_area_pred'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "9cc1c033-e9fd-4c49-a001-fe11eae49255",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pca = PCA(n_components=2)\n",
+ "test_data_0_pca = pca.fit_transform(test_data_0)\n",
+ "# Plot the clusters\n",
+ "scatter = plt.scatter(test_data_0_pca[:, 0], test_data_0_pca[:, 1], c=y_pred_0, s=50, cmap=ListedColormap(['darkblue', 'purple', 'darkorange']))\n",
+ "# Plot centroids\n",
+ "centers = clustering_model_kmeans.cluster_centers_\n",
+ "centers_pca = pca.transform(centers)\n",
+ "plt.scatter(centers_pca[:, 0], centers_pca[:, 1], c='red', s=150, alpha=0.5)\n",
+ "plt.xlabel('PCA Feature 1')\n",
+ "plt.ylabel('PCA Feature 2')\n",
+ "plt.title('K-means Clustering')\n",
+ "plt.legend(handles=scatter.legend_elements()[0], labels=['rural', 'urban', 'suburban'], loc=\"best\", title=\"Clusters\")\n",
+ "plt.ylim((-1, 1))\n",
+ "plt.xlim((-1, 1))\n",
+ "plt.savefig('figures/kmeans_clusters_0.jpg', dpi=400, pad_inches=0.2, bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "bad6a85f-b1fe-47d4-933f-99257d9afca6",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 64.80%\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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Dhw7q0qVLgc/j6+urOXPmqGbNmmbjmZmZFufu2LGjJkyYUKg4Hd1bb72l4sWLm41dunRJ2dnZqlq1qkXHRV6ufZTYVampqTpz5oxNdyO/1tq1azV//nzT68DAQL399tuSpOrVq+v11183HcvIyNCIESPy/YUVAAC3E5VsALjFunfvri5dumjlypWKiorSvn37lJycrIyMDBUvXlwVK1ZUw4YN1b59e4sKaqlSpfTVV19p3bp1+v777/Xnn3+aNukqVaqU6tatqy5duuihhx7K897Z26lixYpavHixJk+erI0bN+rChQsqV66c2rVrp4EDB+r333/XggUL8l0/duxYRUdHa8eOHYqNjdW5c+d0/vx5FStWTGXLllXt2rXVuXNnderUqdAbjVWqVEnfffedfvrpJ61atUr79u1TSkqK6dwNGjTQE088oebNmxf1y+BwQkJC9O2332ry5MmKjo5WWlqaAgMD9dBDD2nw4MH65ZdfbnqOF198Ud7e3lq8eLFiY2NNz+q+VU6ePKn//Oc/ptcGg0Hjxo0zuyXhhRde0G+//Wa6nz4mJkYffvihRo8efUtjAwDgZgzG65+xAgAAAAAArEK7OAAAAAAANkKSDQAAAACAjZBkAwAAAABgIyTZAAAAAADYCEk2AAAAAAA2QpINAAAAAICNkGQDAAAAAGAjrvYOwNHN3RZv7xAA2EHbe8rZOwQAdlDG193eIQCwA283g71DKBKvhkPtHYKFSzun2jsEu6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIAzM1A7dSR8NwAAAAAAsBGSbAAAAAAAbIR2cQAAAABwZgbn3h39TkMlGwAAAAAAGyHJBgAAAADARmgXBwAAAABnxu7iDoXvBgAAAAAANkKSDQAAAACAjdAuDgAAAADOjN3FHQqVbAAAAAAAbIRKNgAAAAA4MzY+cyh8NwAAAAAAsBGSbAAAAAAAbIR2cQAAAABwZmx85lCoZAMAAAAAYCMk2QAAAAAA2Ajt4gAAAADgzNhd3KHw3QAAAAAAwEZIsgEAAAAAsBHaxQEAAADAmbG7uEOhkg0AAAAAgI2QZAMAAAAAYCO0iwMAAACAM2N3cYfCdwMAAAAAABshyQYAAAAAwEZoFwcAAAAAZ8bu4g6FSjYAAAAAADZCkg0AAAAAgI3QLg4AAAAAzozdxR0K3w0AAAAAAGyESjYAAAAAODM2PnMoVLIBAAAAALARkmwAAAAAAGyEdnEAAAAAcGZsfOZQ+G4AAAAAAGAjJNkAAAAAANgI7eIAAAAA4MxoF3cofDcAAAAAALARkmwAAAAAAGyEdnEAAAAAcGYuBntHgGtQyQYAAAAAwEZIsgEAAAAAsBHaxQEAAADAmbG7uEPhuwEAAAAAgI2QZAMAAAAAYCO0iwMAAACAMzOwu7gjoZINAAAAAICNkGQDAAAAAGAjtIsDAAAAgDNjd3GHwncDAAAAAAAboZINAAAAAM6Mjc8cCpVsAAAAAABshCQbAAAAAAAboV0cAAAAAJwZG585FL4bAAAAAADYCEk2AAAAAAA2Qrs4AAAAADgzdhd3KFSyAQAAAACwEZJsAAAAAABshHZxAAAAAHBm7C7uUPhuAAAAAABgIyTZAAAAAADYCO3iAAAAAODM2F3coVDJBgAAAADARkiyAQAAAACwEdrFAQAAAMCZsbu4Q+G7AQAAAACAjVDJBgAAAABndodvfGY0GtW7d29t3rxZkvTTTz+pevXqFvPi4+M1efJkRUdHKzU1VYGBgerUqZMGDRokb2/vfM8fGRmpiIgIxcTESJJCQkLUu3dvdezY0ap4qWQDAAAAABzWokWLtHnzZhlu8MuE/fv36/HHH9f333+vcuXKqX379srJydHMmTPVo0cPXbx4Mc91kyZN0quvvqq9e/eqadOmatq0qf78808NHz5cU6dOtSpekmwAAAAAgENKSkrSRx99pFatWqlChQp5zsnJydHIkSOVlpamkSNHaunSpZo0aZJWrVqltm3bKiYmRh999JHFuu3btys8PFx+fn5avny5pk+frunTp2v58uXy8/PTlClTtHv37kLHTJINAAAAAM7M4OJ4Hzby9ttvKzc3V++++26+c9atW6e4uDgFBwerX79+pnF3d3eNGTNGrq6uWrJkiVJSUszWzZ49W5I0cOBAs/bz6tWra8CAAZKkWbNmFTpmkmwAAAAAgMNZvny51q9fr1deeUUVK1bMd966deskSR07drRoKS9XrpwaNWqk7OxsrV+/3jSelZWlqKgoSVJYWJjFOTt37ixJ2rBhg7KysgoVN0k2AAAAAMChnDlzRuPHj1doaKh69ux5w7kHDx6UJIWGhuZ5vE6dOpJk2thMkuLi4pSZmamSJUvm2YZeoUIF+fv7KyMjQ0eOHClU7CTZAAAAAODM7N0afgvaxceMGaOLFy9q7NixKlas2A3nnjhxQpIUGBiY5/GAgABJUkJCQoHXXHvs2nUFwSO8AAAAAAA21b59+xseX7t2bb7Hfv75Z61evVr9+/dXSEjITa+VlpYmSfLy8srzuI+Pj9m8gqyRZHrs17XrCoJKNgAAAADAIZw7d07vvfeeKleurCFDhhRqbX6P+DIajfmO3eixYHmtKwgq2QAAAADgzG6QKNrLjSrVNzJ+/HidOXNGX331lTw9PQu0xtvbW6mpqUpPT8/z+KVLlyT9U9G+9vP81khSRkaGxbqCIMkGAAAAADiEtWvXysPDQ9OmTdO0adPMjp0+fVqS9MYbb8jLy0vPP/+8OnXqpAoVKig1NVVJSUl5tpcnJSVJktkGZ0FBQWbH8pLXuoIgyQYAAAAAOIzMzExt2bIl3+N//vmnpH/u+65Vq5YOHDigffv26cEHH7SYv3//fkkyS8CrVasmDw8PpaSkKDExUeXLlzdbk5iYqJSUFHl6eqpatWqFip97sgEAAADAmdl7J3Eb7i6+bds2xcTE5Plxtfr8008/KSYmRi+++KIkqW3btpKk1atXW5zv9OnT2r59u1xdXdW6dWvTuIeHh5o3by5JWrVqlcW6yMhISVLLli3l7u5eqPdAkg0AAAAAcFrt2rVT1apVFRMTo1mzZpnGs7Oz9fbbbys7O1tPPvmkSpUqZbauT58+kqTw8HDFxsaaxmNjYzV9+nRJUt++fQsdD+3iAAAAAACn5erqqk8++UQ9e/bUxx9/rFWrVqly5cravXu3EhISFBwcrH/9618W65o0aaIBAwZoxowZ6tatm6myvXHjRmVmZmrw4MFq2LBh4eMp8jsCAAAAANiPA+4ufruFhoZq+fLlmjJliqKjoxUTE6PAwED17dtXgwcPzneH8BEjRigkJEQRERHavHmzJKl27drq3bu3wsLCrIrFYLT24V93ibnb4u0dAgA7aHtPOXuHAMAOyvgW7r47AHcGbzfnTlK9us60dwgWLi3vb+8Q7IZ7sgEAAAAAsBHaxQEAAADAmRVhN2/YHt8NAAAAAABshEo2AAAAADgzNj5zKFSyAQAAAACwEZJsAAAAAABshHZxAAAAAHBiBtrFHQqVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGO3ijoVKNgAAAAAANkKSDQAAAACAjdAuDgAAAADOjG5xh+LwSfby5cuLtL5r1642iQMAAAAAgJtx+CR71KhRVt3IbzQaZTAYSLIBAAAAALeNwyfZQ4YMYbc8AAAAAMgH+ZJjcfgke9iwYfYOAQAAAACAAmF3cQAAAAAAbMThK9kAAAAAgPzRLu5YnDbJzszM1KZNm3TkyBFdvHhRRqPRYo7BYNCQIUPsEB0AAAAA4G7klEn2Tz/9pPfee0/nz583jV3dTfz61yTZAAAAAO5kVLIdi9Ml2du3b9frr78uHx8f9e/fX1u3btXOnTv13nvv6ciRI1qzZo3i4+PVs2dP1a5d297hAgAAAADuIk638dkXX3who9GoOXPm6LXXXlOVKlUkSc8884zeeOMN/fjjj+revbu+++471a9f387RAgAAAADuJk5Xyd69e7fq1q2runXr5nnc3d1db7/9ttavX6/PP/9cn3zyyW2OEPZ08VyyjuzdocTDh5QYF6OTR2OVnZmhEmUCNOSz+XmuObp/l+aPfb1A56/XuqO6DPiX2di2n1coMe6gTh6N1cVzycpIuyg3d3eVDKyoGg3vV+NO3eTl41vk9wag6A7u+1Mrly3Wnh1bdfbsGXl6eKp02XIKrd9QT/boqYqVq5rmJiUmaOfWzTp0cJ8OHdyvw38fUnZ2tuo1bKxPps2x35sAUCBnzpzW5uho7d+3V/v37dXBgweUcemSyleooJ9+Xlegc2zaGKVlS77T7l07lZx8Vj7Fi6t8+fK6r1ETvdinr8qUKXuL3wVQMLSLOxanS7JTU1PVrFkz02s3NzdJUnp6ury9vU1j9913nzZv3myXGGE/+6N/1Zqvwwu1xsPbRxWD6+R7PDsrUyeP/C1Jec77Zd7nMubmytXdQ74lS6tEmXK6eC5FSYcPKenwIe1Y94OefeN/Klf5nsK9GQA29cW0z7To6zkyGo3yK+Gvqvfcq8yMDJ1KStQPcX+rTr2GZkn20oVfa9nivH85B8DxrY78SR9/ON6qtZcvX9Z7b7+lH75fIUkqU6asgmuG6OLFCzocF6cD+/er/UMPk2QDyJPTJdklS5ZUWlqa2WtJSkhIUI0aNUzjmZmZunjx4m2PD/bl4eWtqnUaqvw9wQqsFqwLKWe1Zt60G64JrFpDvd75LN/jO9b+oFVzJsnNw1O17n/Q4njbZ/qoSu0GCqxaQwaXf+7ASDr8l1ZMG6+zJ45pxefj1O/D2Va/LwBFEzF7mhbO+0JlywXo1VFvq8n9LU2/9c/NzVXMgb3y9fUzW1PCv6SaNm+lGjVrK7hWbe3bs0uLv/7SHuEDsIKPT3E1vf8B1a4Tqtq16+j0qVP66MNxBVo75p3/6ofvV+jeGjX01tvvqUHD+0zHLl++rJ07timoYsVbFToAJ+d0SXbFihV14sQJ0+vQ0FAZjUb98MMPeu211yRJp0+f1pYtW1SRv/zuOvUfDFP9B8NMrw9s/r3I59yzfpUkKaRpK3l4eVscv7/LM3muC6xWQ10G/Etz3xmm08eP6MyJYypToXKR4wFQOHF/H9I3X82Wp5eXJkydrYqVqpgdd3FxUa069SzWPf9Sf7PXCceO3tI4AdhW1yeeVNcnnjS9/uXnVQVat+H39Vq5YrnKliunWV9GyN+/pNlxV1dXNWl6v01jBYqMbnGH4nRJdosWLfT5558rPj5elSpVUuvWrVW2bFnNnDlTR48eVWBgoFavXq309HQ98sgj9g4XTu708aM6EXtQklSvTadCry8T9M8/5rMzM2wWF4CCW7JwnnJyLqvr489aJNgAcL2IL7+QJL34cl+LBBsACsLpkuxHH31UaWlpOnv2rCpVqiQPDw9NmjRJw4YN06pV//yGsk2bNurbt68dI8WdYM/vV/5MlQyooMohlpWumzl2YI8kyd3TW6XLV7JpbABuzmg0Kvr3XyVJzVu31fH4o4pcsUSHY/+SJFWsXFUPduik2nV5GgUA6XxqqrZv2ypJatu+gw7s36fvly/TkcNxcnNzU/Uawery6GOqfm+Nm5wJwN3M6ZLsKlWq6I033jAba9SokdauXatt27YpNTVV99xzD8/IRpHl5uRob9RaSVLdVg8XeNfG3NwcpZ1L0d+7Nuu3RV9IBoPaPddf7p5etzJcAHlIiD+qCxfOS5Li/v5Lo14dqOysLNPxrZuitGzxfD3y+FMa9q+3VKxYMXuFCsAB7Nv7p4xGo4oXL67VkT9pyqRPlZubazq+4ff1ivjyC/UfOFgDBg+1Y6SAOXYXdyxOl2SvXbtWrq6uatOmjdm4l5eXWrVqZaeocCeK3b1FaeeSZTC4qF7rh286/5d507R11VKzsUo166rlEy+oWmijWxUmgBs4e+a06fMZkz9S+aBKGvb6f1SnXkNdOJ+qH5Yt1vwvZ+rHFd+pZOnS6t1viB2jBWBvp0+fkiRlZGTos08/Vt369fXGqP8qOKSmTp86pTmzZ2nJt4s0fdpUBZYvr8e7PXmTMwK4G7ncfIpjGTp0qCIiIuwdBu4Cu9evliRVDW0ov9Llbjrfv2x5VQyuowrVQ+RT4so9XIlxMfpzwy/KTE+7yWoAt8Kl9HSz12M/+VwNGzeTu7u7Spcpq979hujRJ65sXvjdN3N14fx5e4QJwEGk///fGZcvX5a/v78+nz5bderWlZubuyoEVdR/33lPrVpfKfRMmzpZOTk59gwXgINyukp2yZIl5e/vb+8wcIdLO39Of+/cJEmqX8ANz5p06qYmnbqZXicd/ks/R0zV3j/W6EzCMb04ZopcXGhFBW4ndw8P0+et2j6k8kGWT514+vkX9f2ShcrIyNCuHVvU6sEOtzNEAA7Ew8PT9PkTTz0tX19fizm9XuqjDb+v16mTJ3Uo5qBq1a5zO0ME8kS7uGNxukp2gwYNtH//fnuHgTvcvqg1ys25LE8fXwU3bmHVOQKr1dAz/xonL98SSjp8SAeif7NtkABuys+vhOnzKtWq5zknoHwFeXpd2TMhKeH4bYkLgGMqUeKfvzPuqX5vnnOuHT9+PP6WxwTA+Thdkj106FAdP35c06ZNs3couIPt+f1nSVKd5u3k6uZu9Xk8vH1Mu5InHj5kk9gAFFzFKlVNv913c3PLd56b65VjOddscATg7lO12j2mz93d8/7/v4fHP+O5OfydAcCS07WLHzp0SI8//rimTJmiyMhItWvXTuXLl5enp2ee87t27Xp7A4TTSzx8SKeOxUkqeKv4jRhzr9yvlcs/3oHbztPTS9Vr1NTfhw7qRD5V6osXzpt2IC9bLuB2hgfAwVSpWlUlS5VSSnKy4uOP5Tkn/tg/4wEBgbcrNOCGaBd3LE6XZI8aNUoGg0FGo1F//fWX/vrrrzz/UBmNRhkMBpJsFNqe/9/wrFzlexRYrWjPwUy/kKqjB3ZLkgKr5N12BuDWavdwZ/196KB+X/ez+g15VT7Fze+x/GnFEklSsWKuqn9fE3uECMBBFCtWTA937KRFC77RyhXL1PulPhaP9lu25DtJkq+fn+rUDbVHmAAcnNMl2UOGDOE3NbhlLmdnaX/0OklSvQJUsfesX63s7EyFNGll2lH8qhOxB7VqzmfKTE+TX+lyCmnW+pbEDODGHn3yGS1bPF+nT53URx+8rX/9d4wp0d6+JVrzv5olSer0aDeVKXvzJwkAuLO93G+AVq5YriOHD+vDcR9o5L9HyeP/N1H86YeVWvrdt5KkF1/uK7ci3FIG4M5lMBqNRnsH4cjmbmNDC2dy/uwpffGfgabXOZcvKysjXQaDizx9ipvGazdvq469h1ms37/pNy2f8oGKubpp2NSF8vYtYTHnWr8vmas/ls6TJPmVLiufEqVMcaSlpkiSSpQJUPfXP1C5StWK/P5w+7S9h2TrTvJ3zAH9e3h/XTifKk9PT1WuVl0Xz5/XiYQrf8c3bNxM7380RR7X3Hq0d/dOvfPGK6bXWZkZysjIULFirvIp/s/fJ0+/8KKeeeHl2/dmcEuV8SVpulMkJSbq2e7/PPUjOztbaWlpcnFxkZ+fn2m8Y+dHNOo/o83WRv2xQa+/OkwZGRkq7uurqlWr6fSpUzp5MkmS1PmRR/X++A/l4uJ02xshH95uzl3EK91rgb1DsHA24ll7h2A3TlfJBm4kNzdXly5aPufWaDQfz7p0Kc/1V1vF7214/00TbEmq07y9XFyKKf7gn0o+maAzCUeVm5Mjz+K+qhbaSDUaNVf9Nh3l5pH3ngEAbo97a9bSFwuWa2HEF9oU9bsOx/4lN1c31a7bQA+FPaqwR7upmKv5/xJzci7rfOo5i3NdP56ZkXGLowdgjdzcHJ07dy6P8Vyz8fS0NIs5LVq20uKl3+vLL2YpemOUYg4ekKeXl5o2u19PPPW0OoZ1voWRA3B2VLJvgko2cHeikg3cnahkA3cnp69k93bASvZcKtlOY+rUqQWeazAYNGTIkFsYDQAAAAAA/3DKJPvq7uJ5ubop2tXdxUmyAQAAAAC3i9Ml2ePHj89zPDc3V4mJifrjjz+0a9cu9ezZU7Vr177N0QEAAADA7cXTlxyL0yXZ3bp1u+HxoUOHaurUqZozZ4569Ohxm6ICAAAAAEC6I587MGTIEJUqVUqTJk2ydygAAAAAgLuI01WyC8JgMKhWrVrasmWLvUMBAAAAgFuKdnHHckdWsiUpJSVFGTy7FAAAAABwG92RSfZPP/2k7du3695777V3KAAAAACAu4jTtYu/+eab+R5LS0vT4cOH9ffff0uS+vXrd7vCAgAAAAC7oF3csThdkr1s2bKbzgkKCtKrr76qTp063YaIAAAAAAC4wumS7IiIiHyPubm5qVy5cgoKCrqNEQEAAAAAcIXTJdlNmza1dwgAAAAA4DjoFncoTrfxWbdu3TR8+HB7hwEAAAAAgAWnS7Lj4uLk6up0BXgAAAAAwF3A6bLVSpUq6cKFC/YOAwAAAAAcAruLOxanq2R36dJFW7duVWJior1DAQAAAADAjNMl2X379tX999+vnj17KjIyUllZWfYOCQAAAADsxmAwONzH3czp2sU7duwoo9GoxMREjRgxQpJUunRpeXh4WMw1GAxas2bN7Q4RAAAAAHCXcrokOyEhwWLszJkzdogEAAAAAABzTpdkHzx40N4hAAAAAIDDuNvbsx2N092TDQAAAACAoyLJBgAAAADARpyuXRwAAAAA8A/axR0LlWwAAAAAAGyEJBsAAAAAABuhXRwAAAAAnBnd4g6FJBsAAAAA4FAWLVqk6OhoxcTE6OzZs0pLS1OJEiVUt25d9ejRQ23btrVYU7NmzZues0GDBnkei4yMVEREhGJiYiRJISEh6t27tzp27Fjo2EmyAQAAAAAO5csvv1R8fLyCg4N13333ydPTU/Hx8frtt9/022+/6eWXX9Ybb7xhsc7b2zvfxLhUqVJ5jk+aNEnh4eFyd3dXixYtJElRUVEaPny4hg0bpqFDhxYqdpJsAAAAAHBid+Lu4uPHj1dwcLB8fHzMxrdt26Z+/fppzpw56tSpk+rXr292vGTJkvrf//5X4Ots375d4eHh8vPz08KFC1W9enVJUmxsrHr06KEpU6aoVatWFte5ETY+AwAAAAA4lIYNG1ok2JLUuHFjhYWFSZKio6OLfJ3Zs2dLkgYOHGhKsCWpevXqGjBggCRp1qxZhTonSTYAAAAAwGm4ul5pyHZ3dy/SebKyshQVFSVJpsT9Wp07d5YkbdiwQVlZWQWPr0hRAQAAAADs6k5sF8/PgQMHFBkZqWLFiqlVq1YWx9PT0zV9+nSdOHFC7u7uqlGjhtq3b68yZcpYzI2Li1NmZqZKliypChUqWByvUKGC/P39de7cOR05ckTBwcEFipEkGwAAAADgkJYsWaKtW7cqOztbCQkJ2rVrl1xdXfXuu++qRo0aFvNTUlI0ceJEs7GxY8dqxIgRevHFF83GT5w4IUkKDAzM9/qBgYE6d+6cEhISSLIBAAAAAPbRvn37Gx5fu3Ztgc6zY8cOLVu2zPTay8tLb731lp588kmLuY8//rgeeeQRhYSEyM/PT8eOHdPixYu1YMECjR8/Xp6enurRo4dpflpamumc+fH29jabWxDckw0AAAAATsxgMDjch62MHTtWMTEx2rlzp5YvX67OnTvrv//9rwYNGqTMzEyzuRMmTFCbNm0UEBAgLy8v1axZU6NHj9bo0aMlSZ9++qnZvdVGo9H09cvP1TmFQSUbAAAAAGBTBa1UF5S3t7dq1aqlcePGycXFRd9++63mzJmjQYMG3XTtM888o8mTJys5OVm7du1S06ZNJcm0e3l6enq+azMyMszmFgSVbAAAAABwZgYH/LiFHn/8cUkFT+RdXFxUpUoVSdKpU6dM40FBQZKkpKSkfNdePZbXxmj5Xq/AMwEAAAAAsLNSpUpJkpKTkwu8JjU1VZL5/dfVqlWTh4eHUlJSlJiYaLEmMTFRKSkp8vT0VLVq1Qp8LZJsAAAAAIDT2LRpkySZqtM3c+jQIcXFxUmSQkNDTeMeHh5q3ry5JGnVqlUW6yIjIyVJLVu2LNQzuUmyAQAAAMCJ2XuTM1tvfPbnn39q2bJlZpuUXfXrr79q0qRJkqTu3bubxpctW6Z9+/ZZzN+3b5+GDx8uSerYsaMCAgLMjvfp00eSFB4ertjYWNN4bGyspk+fLknq27dvoeJn4zMAAAAAgMM4efKkRo0apbFjxyo0NFSlS5fWhQsXdPjwYR07dkyS9PLLL6tz586mNb/88otGjRqlqlWrqkaNGnJzc9PRo0d14MAB5ebmqk6dOhozZozFtZo0aaIBAwZoxowZ6tatm6myvXHjRmVmZmrw4MFq2LBhoeInyQYAAAAAOIy6detqyJAh2rp1qw4fPqzt27fLxcVF5cqV02OPPaZnnnlGjRs3NlvTtWtXeXl5af/+/dqyZYvS0tJUvHhxNW7cWGFhYXrqqafybfkeMWKEQkJCFBERoc2bN0uSateurd69eyssLKzQ8RuM1jz46y4yd1u8vUMAYAdt7yln7xAA2EEZ34LfcwfgzuHtdou3w77Fqgxfae8QLByd/Ki9Q7Ab7skGAAAAAMBGSLIBAAAAALAR7skGAAAAACdW1N28YVtUsgEAAAAAsBGSbAAAAAAAbIR2cQAAAABwYrSLOxYq2QAAAAAA2AhJNgAAAAAANkK7OAAAAAA4M7rFHQqVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGLuLOxYq2QAAAAAA2AiVbAAAAABwYlSyHQuVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGN3ijoVKNgAAAAAANkKSDQAAAACAjdAuDgAAAABOjN3FHQuVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGN3ijoVKNgAAAAAANkKSDQAAAACAjdAuDgAAAABOjN3FHQuVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGN3ijoVKNgAAAAAANkIlGwAAAACcmIsLpWxHQiUbAAAAAAAbIckGAAAAAMBGaBcHAAAAACfGxmeOhUo2AAAAAAA2QpINAAAAAICN0C4OAAAAAE7MQL+4Q6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATo1vcsVDJBgAAAADARkiyAQAAAACwEdrFAQAAAMCJsbu4Y6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATo13csVDJBgAAAADARkiyAQAAAACwEdrFAQAAAMCJ0S3uWKhkAwAAAABgI1SyAQAAAMCJsfGZY6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATo1vcsVDJBgAAAADARkiyAQAAAACwEdrFAQAAAMCJsbu4Y6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATo1vcsVDJBgAAAADARkiyAQAAAACwEdrFAQAAAMCJsbu4Y6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATo1vcsVDJBgAAAADARqhkAwAAAIATuxM3Plu0aJGio6MVExOjs2fPKi0tTSVKlFDdunXVo0cPtW3bNs918fHxmjx5sqKjo5WamqrAwEB16tRJgwYNkre3d77Xi4yMVEREhGJiYiRJISEh6t27tzp27Fjo2A1Go9FY6FV3kbnb4u0dAgA7aHtPOXuHAMAOyvi62zsEAHbg7ebcSWqz8evtHYKFzW+2KdL6Tp06KT4+XsHBwQoICJCnp6fi4+O1d+9eSdLLL7+sN954w2zN/v379cILLygtLU116tRR5cqVtWfPHiUkJKhmzZr65ptvVLx4cYtrTZo0SeHh4XJ3d1eLFi0kSVFRUcrKytKwYcM0dOjQQsVOJRsAAAAA4FDGjx+v4OBg+fj4mI1v27ZN/fr105w5c9SpUyfVr19fkpSTk6ORI0cqLS1NI0eOVP/+/SVJWVlZGj58uH799Vd99NFHeu+998zOt337doWHh8vPz08LFy5U9erVJUmxsbHq0aOHpkyZolatWpmuUxDckw0AAAAATsxgcLyPomrYsKFFgi1JjRs3VlhYmCQpOjraNL5u3TrFxcUpODhY/fr1M427u7trzJgxcnV11ZIlS5SSkmJ2vtmzZ0uSBg4caEqwJal69eoaMGCAJGnWrFmFip1K9k0806CSvUMAYAeZ2bn2DgGAHZxMzbR3CADsoFoZT3uHgEJwdb2Sxrq7/3OLz7p16yRJHTt2tLhHvVy5cmrUqJE2b96s9evXq2vXrpKuVLmjoqIkyZS4X6tz58766KOPtGHDBmVlZZld70aoZAMAAAAAnMKBAwcUGRmpYsWKqVWrVqbxgwcPSpJCQ0PzXFenTh1JMm1sJklxcXHKzMxUyZIlVaFCBYs1FSpUkL+/vzIyMnTkyJECx0glGwAAAACc2J24u/hVS5Ys0datW5Wdna2EhATt2rVLrq6uevfdd1WjRg3TvBMnTkiSAgMD8zxPQECAJCkhIaHAa64eO3funBISEhQcHFygmEmyAQAAAAA21b59+xseX7t2bYHOs2PHDi1btsz02svLS2+99ZaefPJJs3lpaWmm43m5en/31XkFWSPJ9Niva9fdDO3iAAAAAACHNHbsWMXExGjnzp1avny5OnfurP/+978aNGiQMjMt99HIr6qf15Orr47dqBPAmideU8kGAAAAACfmiN3iBa1UF5S3t7dq1aqlcePGycXFRd9++63mzJmjQYMGmY6npqYqPT09z/WXLl2SJLMdy69+nt8aScrIyLBYdzNUsgEAAAAATuPxxx+XZJ7IX924LCkpKc81V8ev3eAsKCjohmvyW3czJNkAAAAAAKdRqlQpSVJycrJprFatWpKkffv25blm//79kqSQkBDTWLVq1eTh4aGUlBQlJiZarElMTFRKSoo8PT1VrVq1AsdHkg0AAAAATsxgMDjcx620adMmSVKVKlVMY23btpUkrV692mL+6dOntX37drm6uqp169amcQ8PDzVv3lyStGrVKot1kZGRkqSWLVsW+BnZEkk2AAAAAMCB/Pnnn1q2bJmysrIsjv3666+aNGmSJKl79+6m8Xbt2qlq1aqKiYnRrFmzTOPZ2dl6++23lZ2drSeffNJUBb+qT58+kqTw8HDFxsaaxmNjYzV9+nRJUt++fQsVv8FozXZpd5GMy/aOAIA9ZGbn2jsEAHaQnGb5DzoAd75qZTztHUKRtPhog71DsBD1r1ZWr12zZo2GDBkiX19fhYaGqnTp0rpw4YIOHz6sY8eOSZJefvllvfHGG2br9u7dq549eyo9PV2hoaGqXLmydu/ebXrG9TfffCNfX1+L63366aeaMWOGWWV748aNyszM1ODBg/XKK68UKn6S7JsgyQbuTiTZwN2JJBu4Ozl7kt3yY8dLsv943fok++TJk1q0aJG2bt2qY8eOKTk5WS4uLipXrpwaNGigZ555Ro0bN85z7dGjRzVlyhRFR0crNTVVgYGB6tixowYPHnzDHcJ/+uknRUREKCYmRpJUs2ZN9e7dW2FhYYWOnyT7JkiygbsTSTZwdyLJBu5OJNm2V5Qk29lxTzYAAAAAADbiau8AAAAAAADWu9W7eaNwqGQDAAAAAGAjVLIBAAAAwIlRyXYsVLIBAAAAALARkmwAAAAAAGyEdnEAAAAAcGJ0izsWKtkAAAAAANgISTYAAAAAADZCuzgAAAAAODF2F3csVLIBAAAAALARkmwAAAAAAGyEdnEAAAAAcGJ0izsWKtkAAAAAANgISTYAAAAAADZCuzgAAAAAODF2F3csVLIBAAAAALARkmwAAAAAAGyEdnEAAAAAcGJ0izsWKtkAAAAAANgIlWwAAAAAcGIulLIdCpVsAAAAAABshCQbAAAAAAAboV0cAAAAAJwY3eKOhUo2AAAAAAA2QpINAAAAAICN0C4OAAAAAE7MQL+4Q6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATc6Fb3KFQyQYAAAAAwEZIsgEAAAAAsBHaxQEAAADAibG7uGOhkg0AAAAAgI2QZAMAAAAAYCO0iwMAAACAE6Nb3LFQyQYAAAAAwEZIsgEAAAAAsBHaxQEAAADAiRlEv7gjoZINAAAAAICNFKmSnZubKxcXyzz90KFDWrJkiU6dOqV69erp+eefl7u7e1EuBQAAAADIgwuFbIdidSX7q6++Up06dfTFF1+YjW/dulXdu3dXRESEIiMjNWHCBPXu3VuXL18ucrAAAAAAADgyq5Ps6OhoSdIjjzxiNv7RRx8pMzNT9erVU69evVS6dGnt2rVLixcvLlqkAAAAAAA4OKvbxePi4lSyZEkFBgaaxuLj47Vnzx5VrFhR33zzjYoVK6bHHntMTz31lCIjI/Xcc8/ZJGgAAAAAwBUGHpTtUKyuZJ89e9YswZautIpLUlhYmIoVKyZJCg0NVVBQkA4dOlSEMAEAAAAAcHxWJ9nZ2dnKyckxG9u9e7cMBoOaNm1qNl66dGmlpaVZeykAAAAAAJyC1e3iZcuW1fHjx5WVlWXaOTwqKkouLi5q2LCh2dz09HT5+voWLVIAAAAAgAW6xR2L1ZXsRo0aKT09XVOnTtXFixc1f/58HT9+XPXq1VPx4sVN87Kzs3X06FGVLVvWJgEDAAAAAOCorK5k9+nTR6tWrdKsWbM0a9YsSVduuH/ppZfM5m3evFnZ2dmqX79+0SIFAAAAAMDBWV3JDgkJUXh4uGrWrCk3NzdVrlxZY8aM0cMPP2w279tvv5Uk3X///UWLFAAAAABgwcVgcLiPu5nBaDQab+UFLl68KKPRKB8fH7m4WJ3T203GZXtHAMAeMrNz7R0CADtITsuydwgA7KBaGU97h1AkT3yx3d4hWFjap5G9Q7Abq9vFC+ra+7MBAAAAALiT2STJzs7O1r59+5SUlKSMjAx17drVFqcFAAAAANzEXd6d7XCKlGTn5uZq2rRpioiI0IULF0zj1ybZ//3vfxUdHa0vv/xSlStXLsrlAAAAAABwaFbfJJ2bm6uhQ4fq888/14ULF1ShQgV5e3tbzGvdurUSEhK0Zs2aIgUKAAAAAICjszrJXrJkidatW6fKlStryZIlWrt2rWrWrGkxr1WrVipWrJg2bNhQpEABAAAAAJYMBoPDfdzNrE6yly1bJoPBoIkTJ6p27dr5zvPy8lLFihWVkJBg7aUAAAAAAHAKVt+T/ddff6l8+fI3TLCv8vf316FDh6y9FAAAAAAgH3d54djhWF3JzszMlJ+fX4Hmpqen3/UtAwAAAACAO5/VSXa5cuV07NgxGY3GG867ePGiDh8+rKCgIGsvBQAAAACAU7A6yW7SpIkuXbqkb7/99obzZs2apZycHDVv3tzaSwEAAAAA8uFiMDjcx93M6iT7pZdekouLi8aPH69vv/1WWVlZZscvXryoiRMnaubMmXJ3d9cLL7xQ5GABAAAAAHBkBuPN+r1vYP78+frggw8kSR4eHjIYDMrIyFCtWrX0119/6fLly5KksWPH6oknnrBNxJIyMjL0559/6vTp0xbJ/bW6du1a9GtdLvIpADihzOxce4cAwA6S0/L/dwWAO1e1Mp72DqFInpm7094hWFjUu6G9Q7Abq3cXl6Tnn39eFStW1IQJExQbG2sa379/vySpSpUqevPNN/Xggw8WKchrhYeHa9asWbp06VK+c4xGowwGg02SbAAAAABwZHd3c7bjKVKSLUlt2rRRmzZt9Pfff+vgwYNKTU2Vj4+PgoODC/R4r8KYN2+ePvvsM7m4uKh169a655575OPjY9NrAAAAAABgrSIn2Vfde++9uvfee211ujwtWLBAbm5uioiIUMOGd2/7AQAAAADAMdksyc5LZmamPDw8bHa+48ePq3HjxiTYAAAAAPD/DHfQbt7Z2dnavHmzfvvtN+3YsUMJCQlKT09XYGCgWrZsqb59++b5eOiaNWve8LyLFi1SgwYN8jwWGRmpiIgIxcTESJJCQkLUu3dvdezY0ar3YHWSHR8fr82bN6tGjRqqX7++aTw3N1efffaZFi5cqPPnz+vee+/Ve++9p/vuu8/aS5n4+fnJy8uryOcBAAAAADierVu3qk+fPpKkoKAgNW7cWJK0Z88effPNN/r+++81e/bsPAuv3t7e+SbGpUqVynN80qRJCg8Pl7u7u1q0aCFJioqK0vDhwzVs2DANHTq00O/B6iR7wYIF+vLLLzVp0iSzJHv27NmaMWOG6fVff/2lfv36aeXKlapQoYK1l5MktWrVSlFRUcrKypK7u3uRzgUAAAAAcCwGg0GdO3fWSy+9pHr16pnGMzMz9e6772rp0qUaOXKkVq9eLTc3N7O1JUuW1P/+978CX2v79u0KDw+Xn5+fFi5cqOrVq0uSYmNj1aNHD02ZMkWtWrUyy3cLwurnZG/btk2urq5q27ataSwnJ0dfffWVDAaD3nrrLS1fvlydOnVSWlqavvzyS2svZfLaa68pNzdX77//vrKzs4t8PgAAAABwdi4Gx/uw1gMPPKCJEyeaJdjSlUdGv/vuu/L19VVCQoJ27iz6Y8tmz54tSRo4cKApwZak6tWra8CAAZKkWbNmFfq8Vleyk5KSVK5cObOK8q5du5ScnKzmzZurZ8+ekqT3339f69atU1RUlLWXMlm8eLFat26t7777TlFRUbr//vtVvnz5PO9BMBgMGjJkSJGvCQAAAACwPw8PD1WtWlV//vmnTp06VaRzZWVlmXLUsLAwi+OdO3fWRx99pA0bNhS6k9rqJDslJUW1atUyG9u1a5cMBoPZc7F9fX1VpUoVHT9+3NpLmUydOlUGg0FGo1EnTpzQ0qVL851Lkg0AAAAAd46cnBwlJCRIksqUKWNxPD09XdOnT9eJEyfk7u6uGjVqqH379nnOjYuLU2ZmpkqWLJnnbc0VKlSQv7+/zp07pyNHjig4OLjAcVqdZLu5uSk1NdVsbMeOHZJkujn9Ki8vL+Xk5Fh7KZPx48cX+RwAAAAAcCe5k3YXv5EVK1YoOTlZpUuXznNj7ZSUFE2cONFsbOzYsRoxYoRefPFFs/ETJ05IkgIDA/O9XmBgoM6dO6eEhITbk2RXqVJFMTExSkxMVPny5XXx4kVt3LhRPj4+FhXuM2fOqHTp0tZeyqRbt25FPgcAAAAA4NZq3779DY+vXbu2UOc7fvy4PvzwQ0lX9uq6vn378ccf1yOPPKKQkBD5+fnp2LFjWrx4sRYsWKDx48fL09NTPXr0MM1PS0uTpBs+vcrb29tsbkFZvfFZ+/btlZubq0GDBmnevHkaMmSIMjIy1KFDB7PfpJw9e1YnTpzI81lmAAAAAADcyIULFzR48GCdO3dOYWFh6t69u8WcCRMmqE2bNgoICJCXl5dq1qyp0aNHa/To0ZKkTz/9VFlZWab5RqNR0o27AK7OKSyrK9kvvfSSVq9erYMHD2rcuHEyGo3y9/e3uA/6559/liQ1bdrU2ksBAAAAAPLhiN3iha1U5ycjI0ODBg1STEyM7r//fk2YMKFQ65955hlNnjxZycnJ2rVrlykv9fHxkXTlPu4bXfvauQVldZLt4+OjRYsW6bvvvlNsbKwqVKigJ5980uKm8sTERLVv314dOnSw9lIWTp48qbVr1+rIkSO6ePFinr9hMBgMGjdunM2uCQAAAAC4fbKysjRs2DBt3bpVDRs21LRp0wq1y7ckubi4qEqVKkpOTjbbkfxqp3VSUlK+a68ey2tjtBuxOsmWrvSo9+rV64ZzRowYUZRLWJgzZ44+/fRTs43Uri/1G41GkmwAAAAAd4U7ceOznJwcvf766/r9999Vq1YtzZw5s9AV5auubth97f3X1apVk4eHh1JSUkz7jF0rMTFRKSkp8vT0VLVq1Qp1PavvybaHX3/9VRMmTFDZsmU1ZswYtWjRQtKVh4iPHj1aDRo0kNFoVJ8+fTR37lw7RwsAAAAAKCyj0ai33npLq1evVvXq1TVnzhz5+flZda5Dhw4pLi5OkhQaGmoa9/DwUPPmzSVJq1atslgXGRkpSWrZsmXhq+dWRaor/enx8fFKTk62OBYVFaWhQ4fq6aef1gcffJDnHGvMmzdPrq6umjt3rrp3766yZctKuvLGn3/+eS1YsEBDhw7V3Llz5enpaZNrAgAAAABun3HjxmnZsmWqVKmSvvzyS5UqVeqG85ctW6Z9+/ZZjO/bt0/Dhw+XJHXs2FEBAQFmx/v06SNJCg8PV2xsrGk8NjZW06dPlyT17du30PFb3S4+d+5cTZo0SaNHj9Zzzz1nGl+zZo2GDx8uo9Eoo9GoP//8U1FRUVq6dOkNt0cviH379qlBgwaqXLlyvnOGDBmiFStWKDw8XOHh4UW6HgAAAAA4Opc7qFt8zZo1ioiIkHTlXujrn3t9VYcOHUz7fv3yyy8aNWqUqlatqho1asjNzU1Hjx7VgQMHlJubqzp16mjMmDEW52jSpIkGDBigGTNmqFu3bqbK9saNG5WZmanBgwerYcOGhX4PVifZmzZtkouLi8LCwszGJ06cqNzcXD388MNq1KiRFi1apMOHD2vevHnq37+/tZeTdGXnt2t/+3C1bH/x4kUVL15c0pX7EerWrauNGzcW6VoAAAAAgNvr/Pnzps83b96c77ygoCBTkt21a1d5eXlp//792rJli9LS0lS8eHE1btxYYWFheuqpp/Jt+R4xYoRCQkIUERFhul7t2rXVu3dvi1y3oKxOso8ePapSpUqpZMmSprG///5bsbGxqlGjhiZPnixJatu2rTp27Kg1a9YUOckuU6aMzp07Z/Zako4dO6batWubxs+fP6/MzMwiXQsAAAAAcHs98cQTeuKJJwq15uGHH9bDDz9s9TU7d+6szp07W73+elbfk52SkmLR075t2zZJV/rdr6pcubIqV66sw4cPW3sps3MdP37c9Lp+/foyGo1auHChaSw2NlabN29WlSpVinw9AAAAAHB0BoPB4T7uZlZXsnNzcy2qxbt375bBYFCTJk3Mxv39/ZWQkGDtpUxatmypTz75RIcOHVJwcLCaN2+uKlWq6Ntvv9X+/ftVvnx5RUdH6/Lly3rmmWeKfD0AAAAAAArD6iQ7MDBQ8fHxSktLk4+Pj3Jzc7Vhwwa5urqqQYMGZnPPnz8vf3//IoYqPfbYY/L29jY9F9vV1VXTpk3TK6+8or1792rv3r1ycXHRs88+q2effbbI1wMAAAAAoDCsTrKbNWumb7/9Vu+//75eeuklrVy5UmfOnFGLFi3k4eFhmnf1UV/BwcFFDjYgIEDPP/+82Vj16tX1ww8/KC4uTqmpqapSpcpNt3gHAAAAgDvF3d2c7XisTrL79++vyMhIrVixQitWrJAkubi4aMCAAWbzNmzYoMuXL+u+++4rWqQ3cc8999zS8wMAAAAAcDNWJ9kVK1bUvHnzNGXKFMXFxalChQp6+eWX1bRpU7N5P/zwg3x9fdWiRYsiB3utrKws7du3T0lJSTIYDAoICFCdOnXy3ZodAAAAAIBbzWC8eoOzk7h06ZImT56sxYsXKz093eyYt7e3nn76aQ0fPlxeXl42uV7GZZucBoCTyczOtXcIAOwgOS3L3iEAsINqZTztHUKR9F20194hWJj9TKi9Q7AbqyvZ9pCWlqZevXpp//79MhqNuueeexQUFCSj0agTJ04oLi5OX331lbZs2aJ58+bJ29vb3iEDAAAAAO4iTpVkT5s2Tfv27VODBg00evRo1alTx+z4/v37NWbMGO3evVvTpk3T66+/bqdIAQAAAAB3oyK3i2dmZurXX3/V/v37de7cOWVnZ+d9IYNB48aNK8ql1K5dO6WlpWnt2rUqXrx4nnMuXLigDh06yMfHR+vWrSvS9STaxYG7Fe3iwN2JdnHg7uTs7eL9Fjteu/isp2kXt8r69ev15ptvKiUlxTR2NWc3GAxmY7ZIss+cOaMHH3ww3wRbknx9fdWsWTP99ttvRboWAAAAAACFZXWS/ddff2nYsGHKyclRly5dtG3bNiUlJWnw4ME6d+6cdu7cqQMHDsjT01PPPfecTe6PLlu2rHJycm46LycnR2XKlCny9QAAAAAAKAyrk+wvvvhC2dnZGj16tJ577jk999xzSkpK0vDhw01zNm7cqJEjR2rTpk1asGBBkYN9+OGHtXjxYp09e1alS5fOc86ZM2e0adMmPf3000W+HgAAAAA4umu7iGF/LtYu3LJli7y9vdW9e/d85zRv3lwTJ07U/v37NXPmTGsvZTJ8+HBVr15dvXr10oYNGyyO//HHH3rppZdUvXp1s2QfAAAAAIDbwepK9pkzZ1SlShW5ublJkooVKyZJysrKkru7u2ne/fffr4oVK2rVqlUaNmxYoa7Rq1cvi7FixYopNjZW/fv3l5+fn4KCgiRJCQkJOn/+vCSpfv36GjhwoObOnWvVewMAAAAAZ0Eh27FYnWR7eXmZEmxJ8vHxkSSdPHlSlSpVMpvr5+enw4cPF/oaW7ZsyfeY0WhUamqqUlNTLY7t2rWLlglYOHP6tDZt2qj9e/dq3769OnjwgDIuXVKFCkGK/CX/negPHNivDet/0+ZN0YqN/VsXzp+Xl7e3qle/Vx0e6qjuz/SQp6dz70gJ3MmOHjmsPzas16aNf+jvv2KUei5VHp4eqly5qlq1eVBPP/uC/PxK5Ln24oULWjA/QhvWr1P8saPKyMxUCb8Sqlmrtro81lUPdex8m98NAGvF7P9TPyxbrD07tyn57Bl5eHiqTNlyqlOvobo984IqVq5qNj/t4gUtWzxfm/74TSfijykzM1O+fn66t2YtPRT2mNp06GSfNwLA4VmdZJcrV06nT582va5WrZrWr1+vrVu3miXZ58+f1+HDh+XiUvjO9LVr11obHmBhVeSP+ujD8YVas3lTtPr3edH0ukJQkCqUr6CkpCTt2rlDu3bu0JLvFmnGrC8VEBho44gBFNXx+GPq3vWfRLhs2XKqEVxTZ8+c1oH9e3Vg/14t/W6RJk+brXtrBJutTTger4F9e+tkUqIMBoMCA8urkr+/TpxIUHTUBkVHbdBv69bog/99wi92AQc3J/wzfTv/SxmNRvmV8FfVavcqMzNDp5ISdSTub9Wu18AsyU5MOK5/D+uj0yeTZDAYVDagvCqUKKGTiSe0bVOUtm2K0sbf12nUex/y8w/AgtVJdmhoqL7//nudP39efn5+at26tb788kt9/PHHKlu2rBo1aqRTp05p7NixysjIUPPmzQt9jaut4IAt+BQvrmb3P6DadUJVu04dnT51ShP+d+PHyhmNRlUICtJzz/dSp86dVbZsOdOx335dp9H/GaXDcXH69+uvae7XRd/cD4BtGY1GlSpVWt17PKdOnR9VUMV/fgm8e+cOvf2ffykx8YT+9dpQLVr6g9ntTuPff0cnkxJVvkKQPvzkM4XUqiPpyhMsli/9VhPGjdEvqyP1QIvW6vJY19v91gAU0LwvwrX46zkqUy5Ar/z7bTW+v4UpMc7NzdWhA3tV3NfPbM3kCe/r9MkkBZSvoP+O/VQ1ataSdOXnf9X3SzT1k3Fav3a1Gt/fUg91fuy2vyfgei78ssehWL3xWdu2bZWTk6P169dLkh544AE1b95cycnJ6t+/vxo1aqSwsDBt2LBBxYoV0+DBg4scbLdu3djQDFbr9sRTmvnFV3p1xOt6uGOYypULuOma0Lr19P2Pq9Wz94tmCbYkPdi2nd58a7QkadfOHToUc/CWxA3AeuUCArXsx5/Vp/9gswRbkuo3vE/vj/9Y0pWq9aaNf5iOpaenaeuWTZKkV19/w5RgS1f2Bnmyew891DFMkvTH77/e6rcBwEpxfx/Swrmz5enlpQ8nz1KTB1qaVZ5dXFwUUqeeWRX7Unq6dm3fLEnqP+x1U4ItXfn5f6Tb02rdvqMkaXPU+tvzRgA4lSIl2StXrjSrUE+dOlVPP/20vLy8ZDQaZTQaFRISohkzZqhRo0ZFDjYuLk6urlYX34FCK168uNneA9dr2aq16fO4uLjbERKAQvDw8JCXl3e+x+s1aKjixX0lSYcPx5rGszKzZDQaJUmVK1fJc23l//9H+eXLl20ULQBbW7ZonnJyLivssScVVCnvn+XrZWVlmn7+gypVznNOxf8/Fz//APJidcbq5uamGjVqmI15e3trzJgxeuedd5ScnCwvLy8VL168yEFeValSJV24cMFm5wOKKjMr0/Q5m58BzicnJ8f0j2RPTy/TuH/JkgoILK+TSYnauWO7qt8bbLF2587tkqTQuvVvT7AACsVoNCp6w2+SpOat2ykh/qgiv1+qI3F/SZIqVqqq1h06qnao+c9wCf+SKhsQqNMnk7R3905VvaeGrrd39w5JUkiderf0PQAFRbe4Y7G6kn0jxYoVU9myZW2aYEtSly5dtHXrViUmJtr0vIC1fvphpSTJ1dVV9Rs0sG8wAArtt3VrlJFxSZLUsKF5x9WwV0fKxcVFUyd9rO8WfaPTp04pMzNTsX//pfdGv6ntWzfr3hrBevrZ5+0ROoCbSIg/qosXrjze9fDfhzSw11P67puvTBuXLf92vkYM6KXPJoxRTk6O2do+g1+Vi4uLvvh8olYuXaSzp08pKzNTR+L+1scfjNbuHVtVtXoNPf7Us/Z4awAcnFP1Xvft21e7du1Sz549NXLkSLVv395skxrgdjoeH6+Z06dJkp586mmVLFnKzhEBKIzU1HP67NMPJUktWrZWcEgts+MPd3pEvr5+mjN7hiaMf18Txr9vOubh6am+/Qfr+d4vy9vb57bGDaBgzp755yk4M6d8rPJBlTRk5JuqXbehLpxP1U/Lv9U3X81U5IolKlWqjHr2/Wf/oAc7hKl4cT8tmDtLn38yTp9/8s9GqR4ennr+pQF68tne8vLO/3YUAHevAiXZy5cvt8nFunbtWqT1HTt2lNFoVGJiokaMGCFJKl26tDw8PCzmGgwGrVmzpkjXA/Jz8eJFDR86SBcvXlSVqlX16ojX7R0SgEK4nJ2tN//1mpISE1WyZCn95+3385yXcDxeKclnJV15/Fep0qWVeOKEzp9P1arIHxRSu45aP9judoYOoIAyLqWbvR7z0VSVD6ooSSpdpqx69h2s86nntHLpIn23YK66Pv2CfP3+2WU88cRxnUtJNs33L1VaJxNP6OKF81r380+6t2ZtPdDqwdv2foAb4VFyjqVASfaoUaNs8o0rapKdkJBgMXbmzJkinRMorEuXLmnY4AGK/fsvlS1bVlOnzZS3D5UswFnk5OTo7bf+rW1bNsnHx0efTJ6msuXKWcz7dMI4LfxmnoIqVtIXEQtUt14DSVfu8/xx5QqNf/9t/XvEMH00capatWl7m98FgJtx9/hnr5SWbR8yJdjXeur5F7Vy6SJlZmRo944tavlgB0nS9EkTtPzb+SpfoaImzpinWqFX7r02Go1aE7lSkyeM0fv/eU1vj5+o+1s+eFveDwDnUaAku0mTJrc6jgI5eJBHJMG+MjMz9erwIdqxfZtKlS6tWXPmqnKVgu1WCsD+cnNz9f47b2nNz6vk5eWtT6dMz3Pjsti/D2nRgq8lSe+8P96UYEtXqgVdHuuqpMQEzQyfqqmffUKSDTiga6vSlavek+ecgMAK8vTyUsalS0o6caWYcyTuL6347htJ0sj/vm9KsKUrP/8PdX5Mp5JOaN4X4ZoT/hlJNgALBUqy582bd6vjABxedlaWRrwyVJs2RqlkqVKaNWeuqt1T3d5hASggo9Go8e+/o59+WCFPTy99OjlcDe9rnOfcnTu2y2g0ysvLW/Ub3JfnnAeat9LM8Kk6HBertLQ0+dDRAjiUipWrymAwyGg0ys0t/z18XF3dJF1Sbu6Vzc/27t4po9EoTy8v1anXMM81je9voXlfhOvYkTilp6XR0Qa7uyW7WcNqfD+AAsjOztbrI17RHxt+l7+/v2bO/lL33mv5SA8AjmvCuDFasew7eXh66uPPPlejJk3znZuWdlHSjR+JYpTR9Hl2VpbN4gRgG56eXrqnRk1JUmJCfJ5zLl44b9qBvEzZAElS+tWff+X/F8DV52hLUnY2P/8AzJFkAzdx+fJl/fv11/Tbr+uuJNhfzFVwzRB7hwWgED7+cKyWfLtQHh4e+njiVDVt9sAN51epUk2SlJ6erl3//zzs60VH/SFJKlmylEr4+9s0XgC20fahzpKkDb/+orSLFyyOR36/VJJUrJir6t935fbIipWrSpIuXUrXvt078zzvts0bJV15prZfCX8bRw3A2VmdZMfGxmr8+PFavXr1DeetWrVK48eP1+HDh629lEmtWrUK/FG7du0iXw/Izc3VW6P+rXVrflGJEv6aMfsr1QwhwQacyeSJH2nxgq/l4eGhjyZ+rmYPtLjpmvsfaKHSZcpIksa8/R/9uWeX6ZjRaNQP3y/XV1/MkCQ98lhXdnUFHFSXJ55WmXIBunA+VZ+Mfdss0d6xJVoL5s6SJHXs0lWly17ZAPG+pg+oZOkrP/+fjB2tA3v3mNYYjUb98tP3WhQxW5L0UOfH+fmHQzAYDA73cTczGK/tdymE//3vf5o7d65mzpypVq1a5Ttvw4YN6tevn/r06aN//etfVgcqSe3a5f2YFKPRqNOnT+vy5cuSpAoVKkiS1q1bV6TrSVLG5SKfAg4iKTFRzzzV1fQ6OztbaWlpcnFxkd81m6N06txFb741WpIU+eMPGvXvkZKkwMDyCixfPt/z9xswUC1btbk1weO2y8zOtXcIsIE9u3eqb+/nJEmlSpVWpcr5b1T4aNcn9FjXJ02vt2/dotdfHay0tDRJUrlyASpVurROJCTo/PlUSVKjxk01ccp0eXp53cJ3gdspOY3W3zvN34cO6M1XBujC+VR5eHqqctXqunjhvKmFvEHjZnpvwmR5XLMb+e4dW/XeG68oPf3Kz3+ZsuXkX6q0kk4kmNrL6zVsrDEfT5WnJz//d4JqZTxvPsmBDV/ueBtET+569xamCrTxWV42btwoT0/PGybYktSyZUt5eXlp48aN1l7K5EZJc05OjjZu3KgPPvhAdevW1ccff1zk6+HOkpObo3PnzlmM5+bmmo2n//8/qCUp65r7LJOSEpWUlJjv+c+eOWuTOAHYTnZWtunz5OSzSk7O/+e0yXUt5I2aNNXCJSu18Jt52hwdpYTjx3U2+ax8fX3VpNn96hjWRY882lXFihW7ZfEDKLp7g2tp5vxlWjxvjjZv/F1H4v6Sm6ubatdtoPaduqhTl24q5mr+T+L69zXRjK+Xavm387V9S7SSThxXSnKyivv6qkHjZmr7UJg6hD3Gzz8chsvdXTh2OFZXsps1a6Zy5cpp5cqVN5376KOP6vTp09q0aZM1lyqUuLg4devWTa+99ppefPHFIp+PSjZwd6KSDdydqGQDdydnr2S/usLxKtmTHr97K9lW35OdkZEhNze3As11c3NTenq6tZcqlHvuuUf16tXT4sWLb8v1AAAAAAC4yup28bJly+rw4cPKysqSu3v+zx7MysrS4cOHVeb/N5C5HUqWLKndu3fftusBAAAAgL3QLu5YrK5kN27cWBkZGZo7d+4N50VEROjSpUtq1KiRtZcqlLS0NO3cudNsIysAAAAAAG4HqyvZL7zwglasWKFJkyYpJydHvXv3ltc1u6teTcAnT54sg8Ggnj17FjnYEydO5HssLS1Nhw8f1uzZs3XmzBk98cQTRb4eAAAAAACFYfXGZ5I0depUTZ06VQaDQZ6enqpZs6Z8fX114cIFxcTEKCMjQ0ajUUOHDtXQoUOLHGxISMhNn7lmNBpVo0YNzZ07V6VKlSryNdn4DLg7sfEZcHdi4zPg7uTsG5+NXBlj7xAsfPJoTXuHYDdWV7IlaejQoQoMDNSkSZN05swZ7dq1y+x42bJl9dprr9msqtykSZN8j7m7u6tcuXJq1qyZOnfufMP7xAEAAAAAuBWKVMm+Kjs7Wzt27NBff/2lixcvqnjx4goODlbDhg0LvAO5o6KSDdydqGQDdycq2cDdiUq27VHJLiI3Nzc1a9ZMzZo1s8XpAAAAAAAFxO7ijsUmSba9nDp1SklJSTIYDAoICFC5cuXsHRIAAAAA4C7mdEm20WjU/PnzFRERofj4eLNjlStXVs+ePfX888/fdIM0AAAAAABszamS7OzsbA0ZMkQbNmyQ0WiUl5eXypcvL6PRqKSkJB09elRjx47Vhg0b9Pnnn8vV1aneHgAAAAAUGvVFx+JUWeicOXP0+++/q0qVKho5cqQefvhhs+O//PKLPv74Y/3++++aM2eO+vfvb6dIAQAAAAB3Ixd7B1AYy5Ytk7e3tyIiIiwSbEl66KGHFBERIU9PTy1dutQOEQIAAAAA7mZOVck+ceKEWrRooYCAgHznBAQE6IEHHtAff/xxGyMDAAAAAPtwoV/coThVJdvf31/u7u43nefm5qYSJUrchogAAAAAAPiHUyXZDz74oLZs2aL09PR856SlpWnLli1q06bNbYwMAAAAAAAbJdl79uzRzJkzNWbMGP3nP/8xO3bq1CmdOHHCFpfRq6++Kh8fHw0cOFCxsbEWx2NjYzV48GD5+PhoxIgRNrkmAAAAADgyFwf8uJsZjEaj0drFJ0+e1L/+9S9t3bpV0pVnWBsMBh04cMA056233tLSpUu1YMECNWjQoFDnf/PNNy3Gzp07p19//VUuLi4KCQlRUFCQDAaDjh8/roMHD8poNKpt27by9/fXuHHjrH1rJhmXi3wKAE4oMzvX3iEAsIPktCx7hwDADqqV8bR3CEXyn58O2TsEC+M6B9s7BLuxeuOzixcvqlevXjp69KgCAgLUvHlzRUdH6+TJk2bzunXrpiVLlmjt2rWFTrKXLVuW77Hc3Fzt379f+/fvtzi2bt06GQwGmyTZAAAAAODI2PfMsVidZM+ZM0dHjx5VmzZt9Omnn8rHx0fPPfecRZJ93333ydPT01TtLoyIiAhrwwMAAAAA4LazOsn+5Zdf5OrqqnHjxsnHxyffeS4uLqpUqZJF8l0QTZs2tTY8AAAAAABuO6uT7Pj4eFWpUkWlS5e+6VwfHx8dOXLE2ksBAAAAAPLBc7Idi9Ubv7m4uCg3t2AbAyUnJ8vb29vaSwEAAAAA4BSsrmRXrFhRhw8f1sWLF1W8ePF858XHx+vYsWNq2LChtZcy6dWrV4HnGgwGzZ07t8jXBAAAAACgoKxOstu0aaNDhw5pypQpeT5qS7rySK9x48bJYDCoXbt2Vgd51ZYtW246x2AwmB4lBgAAAAB3OlIfx2J1kv3SSy9p8eLFioiI0NmzZ/XMM8/o8uUrD5W+ePGiduzYoZkzZ2rbtm0KCAjQs88+W+Rg165dm+d4bm6uEhMTtWHDBkVEROjFF1/U008/XeTrAQAAAABQGAaj0Wi0dvH27ds1ePBgpaam5lk5NhqN8vf316xZs1S3bt0iBVpQ69ev16BBgxQeHq42bdoU+XwZl20QFACnk5ldsD0nANxZktOy7B0CADuoVsbT3iEUydur/7J3CBbGdKxh7xDsxuqNzySpUaNG+v7779WjRw+VLFlSRqPR9OHr66unnnpKy5Ytu20JtnSljT0kJETTp0+/bdcEAAAAAHtxMTjex93M6nbxqwICAvTuu+/q3Xff1alTp3T+/Hl5e3srMDBQLi5FyuGtVqlSJf3+++92uTYAAAAA4O5V5CT7WuXKlVO5cuVseUqrxMbGqlixYvYOAwAAAABwl7Fpkm1vKSkpmjJlimJjY9WyZUt7hwMAAAAAt5wL24s7FKuT7OXLlxd6TdeuXa29nCSpffv2+R5LS0tTamqqjEajvLy8NGLEiCJdCwAAAACAwrI6yR41alShn0Vd1CQ7ISEh32Ourq4qX768mjVrpv79+6tatWpFuhYAAAAAAIVldZLdpEmTfI9dunRJR48e1YULF+Tm5qYGDRpYexkzBw8etMl5AAAAAOBOQbe4Y7E6yZ43b95N53z//fcaP368qlSpog8++MDaSxVYSkqK/Pz82PQMAAAAAJxQdna2Nm/erN9++007duxQQkKC0tPTFRgYqJYtW6pv374KCgrKc218fLwmT56s6OhopaamKjAwUJ06ddKgQYPk7e2d7zUjIyMVERGhmJgYSVJISIh69+6tjh07WvUeDEaj0WjVygKKjo7Wyy+/rA8++EBPPvlkkc61d+9e/fHHH3rooYdUvXp10/ivv/6q0aNH6+zZs/Lx8dGIESP03HPPFTV0SVLGZZucBoCTyczOtXcIAOwgOS3L3iEAsINqZTztHUKRjF37t71DsPBW+3utWrdx40a99NJLkqSgoCDVqlVLkrRnzx6dOnVKxYsX1+zZs9WwYUOzdfv379cLL7ygtLQ01alTR5UrV9aePXuUkJCgmjVr6ptvvlHx4sUtrjdp0iSFh4fL3d1dLVq0kCRFRUUpKytLw4YN09ChQwv9Hm55ki1J7dq1U6lSpfTdd98V6TxvvvmmVq5cqfXr16t06dKSpKSkJHXs2FGZmZny8vLSpUuXZDAY9M0331h84a1Bkg3cnUiygbsTSTZwdyLJtj1rk+zo6GgtXrxYL730kurVq2caz8zM1LvvvqulS5cqKChIq1evlpubmyQpJydHXbp0UVxcnEaOHKn+/ftLkrKysjR8+HD9+uuv6tGjh9577z2za23fvl3PPfec/Pz8tHDhQlMhNzY2Vj169ND58+e1ePFi1a9fv1DvwcWqd15I/v7+io2NLfJ5du/erZo1a5oSbElaunSpMjMzNXjwYO3cuVNfffWVDAaDIiIiinw9AAAAAMDt88ADD2jixIlmCbYkeXh46N1335Wvr68SEhK0c+dO07F169YpLi5OwcHB6tevn2nc3d1dY8aMkaurq5YsWaKUlBSzc86ePVuSNHDgQLNO6erVq2vAgAGSpFmzZhX6PdzyJDsjI0NHjhyRi0vRL3XmzBlVqFDBbGzjxo3y8PAwfTHvv/9+1a9fX/v27Svy9QAAAADA0Rkc8L9bwcPDQ1WrVpUknTp1yjS+bt06SVLHjh0tnoBVrlw5NWrUSNnZ2Vq/fr1pPCsrS1FRUZKksLAwi2t17txZkrRhwwZlZRWuy+mWJtnJyckaNWqU0tPTVadOnSKfLzMzU66u/+zVlpWVpT179qhevXry8vIyjVeoUMHsiw4AAAAAcG45OTmmxzqXKVPGNH71KVShoaF5rruai17d2EyS4uLilJmZqZIlS1oUcqUrOaW/v7+paFwYVu8u3qtXr3yPGY1GnT17VsePH1d2draKFSumQYMGWXspk8DAQMXFxZleb926VVlZWWratKnZvKysLHl6Ovd9FQAAAACAf6xYsULJyckqXbq07rvvPtP4iRMnJF3JF/MSEBAgSaYEvSBrrh47d+6cEhISFBwcXOA4rU6yt2zZUqB5QUFBevPNN/XAAw9YeymT++67T8uXL9fcuXPVrFkzffbZZzIYDGrTpo3ZvL/++sv0hQQAAACAO5mLAz4nu3379jc8vnbt2kKd7/jx4/rwww8lSa+99prc3d1Nx9LS0iTJrLv5Wj4+PmbzCrJGkumxX9euKwirk+zx48fne8xgMMjLy0tVqlRRzZo1LfrirdWvXz9FRkbqf//7n6QrFfMWLVqY3RR/7NgxHTlyRE8//bRNrgkAAAAAsJ8LFy5o8ODBOnfunMLCwtS9e/c85+WXd+b1QK2rYzfKVa19EJfVSXa3bt2sXWq1e+65RwsWLNDcuXOVkpKiOnXqqG/fvmZzoqKiFBISonbt2t32+AAAAAAAha9U5ycjI0ODBg1STEyM7r//fk2YMMFijre3t1JTU5Wenp7nOS5duiTpn4r2tZ/nt+bqta9fVxBFqmQbDAaNGDHCrFR/q9WqVctUyc7Ls88+q2efffa2xQMAAAAA9uSI7eK2kJWVpWHDhmnr1q1q2LChpk2blmfuWaFCBaWmpiopKUkhISEWx5OSkkzzrgoKCjI7lpe81hWE1buLf/3114qKirqtCTYAAAAA4M6Xk5Oj119/Xb///rtq1aqlmTNn5ltRrlWrliTl+xjn/fv3S5JZAl6tWjV5eHgoJSVFiYmJFmsSExOVkpIiT09PVatWrVCxW51kly5dmgQbAAAAAGBTRqNRb731llavXq3q1atrzpw58vPzy3d+27ZtJUmrV6+2OHb69Glt375drq6uat26tWncw8NDzZs3lyStWrXKYl1kZKQkqWXLloXOe61Osps2barY2NhC77QGAAAAALAdg8HgcB9FMW7cOC1btkyVKlXSl19+qVKlSt1wfrt27VS1alXFxMRo1qxZpvHs7Gy9/fbbys7O1pNPPmlxnj59+kiSwsPDFRsbaxqPjY3V9OnTJcliD7CCMBit3DItNjZWTz75pMLCwjRu3Dib7SDuaDIu2zsCAPaQmZ1r7xAA2EFyWpa9QwBgB9XKeNo7hCL56Lc4e4dg4V8P3mPVujVr1mjIkCGSpGbNmuV7P3SHDh3UoUMH0+u9e/eqZ8+eSk9PV2hoqCpXrqzdu3ebnnH9zTffyNfX1+I8n376qWbMmGFW2d64caMyMzM1ePBgvfLKK4V+D1ZvfJacnKyBAwdqypQp2rdvn7p27arq1aubniWWlyZNmlh7OQAAAADAHe78+fOmzzdv3pzvvKCgILMkOzQ0VMuXL9eUKVMUHR2tmJgYBQYGqm/fvho8eHC+93OPGDFCISEhioiIMF2vdu3a6t27t8LCwqx6DwWuZC9fvlylS5dWq1atJF25abww1WuDwWC64dyZUMkG7k5UsoG7E5Vs4O7k7JXsT9Y7XiV7ZBvrKtl3ggJXskeNGqVGjRqZkuzCbmMOAAAAAMCdzup28XXr1tkyDgAAAAAAnJ7VSTYAAAAAwP7u0D2onZbVj/ACAAAAAADmqGQDAAAAgBNzoZTtUAqVZJ89e1bLly+3+mJdu3a1ei0AAAAAAI6uUEn20aNH9eabb1p1IYPBQJINAAAAALijFSrJLuAjtW2+FgAAAACQNxe6xR1KoZLsRo0aaf78+bcqFgAAAAAAnBq7iwMAAAAAYCPsLg4AAAAATozNxR0LlWwAAAAAAGyEJBsAAAAAABuhXRwAAAAAnJiL6Bd3JAVOsg8ePHgr4wAAAAAAwOnRLg4AAAAAgI3QLg4AAAAATozdxR0LlWwAAAAAAGyEJBsAAAAAABuhXRwAAAAAnJgL7eIOhUo2AAAAAAA2QiUbAAAAAJyYCzufORQq2QAAAAAA2AhJNgAAAAAANkK7OAAAAAA4MbrFHQuVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGLuLOxYq2QAAAAAA2AhJNgAAAAAANkK7OAAAAAA4MbrFHQuVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGJVTx8L3AwAAAAAAGyHJBgAAAADARmgXBwAAAAAnZmB7cYdCJRsAAAAAABshyQYAAAAAwEZoFwcAAAAAJ0azuGOhkg0AAAAAgI1QyQYAAAAAJ+bCxmcOhUo2AAAAAAA2QpINAAAAAICN0C4OAAAAAE6MZnHHQiUbAAAAAAAbIckGAAAAAMBGaBcHAAAAACfG5uKOhUo2AAAAAAA2QpINAAAAAICN0C4OAAAAAE7MQL+4Q6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATo3LqWPh+AAAAAABgIyTZAAAAAADYCO3iAAAAAODE2F3csVDJBgAAAADARkiyAQAAAACwEdrFAQAAAMCJ0SzuWKhkAwAAAABgI1SyAQAAAMCJsfGZY6GSDQAAAACAjVDJvolz6dn2DgGAHWw9mmzvEADYwdO93rd3CADs4NLOqfYOAXcQkmwAAAAAcGK0JzsWvh8AAAAAANgISTYAAAAAADZCuzgAAAAAOLE7bXfxffv2aePGjfrzzz+1d+9eJSQkSJLWrl2rihUr5rmmZs2aNzznokWL1KBBgzyPRUZGKiIiQjExMZKkkJAQ9e7dWx07drQqfpJsAAAAAIDD+Pzzz7V27dpCr/P29s43MS5VqlSe45MmTVJ4eLjc3d3VokULSVJUVJSGDx+uYcOGaejQoYWOgyQbAAAAAOAwGjRooODgYIWGhqpu3bp64okndObMmZuuK1mypP73v/8V+Drbt29XeHi4/Pz8tHDhQlWvXl2SFBsbqx49emjKlClq1aqV6tevX6j4uScbAAAAAJyYwQE/iqJ///569dVX1aFDBwUEBBTxbPmbPXu2JGngwIGmBFuSqlevrgEDBkiSZs2aVejzkmQDAAAAAO4qWVlZioqKkiSFhYVZHO/cubMkacOGDcrKyirUuWkXBwAAAAA4vfT0dE2fPl0nTpyQu7u7atSoofbt26tMmTIWc+Pi4pSZmamSJUuqQoUKFscrVKggf39/nTt3TkeOHFFwcHCB4yDJBgAAAAAndodtLm61lJQUTZw40Wxs7NixGjFihF588UWz8RMnTkiSAgMD8z1fYGCgzp07p4SEBJJsAAAAAID9tG/f/obHrdk9/EYef/xxPfLIIwoJCZGfn5+OHTumxYsXa8GCBRo/frw8PT3Vo0cP0/y0tDRJkpeXV77n9Pb2NptbUNyTDQAAAABwahMmTFCbNm0UEBAgLy8v1axZU6NHj9bo0aMlSZ9++qnZvdVGo1HSjZ8xfnVOYVHJBgAAAAAn5lLk/bxtz9aVams988wzmjx5spKTk7Vr1y41bdpUkuTj4yPpyn3c+cnIyDCbW1BUsgEAAAAAdyQXFxdVqVJFknTq1CnTeFBQkCQpKSkp37VXj+W1MdoNr1nYIAEAAAAAjsNgcLwPR5KamirJ/P7ratWqycPDQykpKUpMTLRYk5iYqJSUFHl6eqpatWqFuh5JNgAAAADgjnTo0CHFxcVJkkJDQ03jHh4eat68uSRp1apVFusiIyMlSS1btpS7u3uhrkmSDQAAAABwWsuWLdO+ffssxvft26fhw4dLkjp27KiAgACz43369JEkhYeHKzY21jQeGxur6dOnS5L69u1b6HjY+AwAAAAAnJjBATc+K4rffvtN06ZNM72+2u49dOhQU1W5TZs2GjJkiCTpl19+0ahRo1S1alXVqFFDbm5uOnr0qA4cOKDc3FzVqVNHY8aMsbhOkyZNNGDAAM2YMUPdunUzVbY3btyozMxMDR48WA0bNix0/CTZAAAAAACHkZycrN27d1uMHzhwwPT5PffcY/q8a9eu8vLy0v79+7VlyxalpaWpePHiaty4scLCwvTUU0/l2/I9YsQIhYSEKCIiQps3b5Yk1a5dW71791ZYWJhV8RuM1j786y6RdD7b3iEAsIOtR5PtHQIAO3i61/v2DgGAHVzaOdXeIRTJj3tP3XzSbfZIaDl7h2A3VLIBAAAAwIk52m7edzs2PgMAAAAAwEZIsgEAAAAAsBHaxQEAAADAibncYbuLOzsq2QAAAAAA2AhJNgAAAAAANkK7OAAAAAA4MXYXdyxUsgEAAAAAsBGSbAAAAAAAbIR2cQAAAABwYrSLOxYq2QAAAAAA2AhJNgAAAAAANkK7OAAAAAA4MYPoF3ckVLIBAAAAALARKtkAAAAA4MRcKGQ7FCrZAAAAAADYCEk2AAAAAAA2Qrs4AAAAADgxNj5zLFSyAQAAAACwEZJsAAAAAABshHZxAAAAAHBiBrrFHQqVbAAAAAAAbIQkGwAAAAAAG6FdHAAAAACcGLuLOxYq2QAAAAAA2AhJNgAAAAAANkK7OAAAAAA4MRe6xR0KlWwAAAAAAGyEJBsAAAAAABuhXRwAAAAAnBi7izsWKtkAAAAAANgIlWwAAAAAcGIGCtkOhUo2AAAAAAA2QpINAAAAAICN0C4OAAAAAE6MbnHHQiUbAAAAAAAbIckGAAAAAMBGaBcHAAAAACfmwvbiDoVKNgAAAAAANkKSDQAAAACAjdAuDgAAAABOjGZxx0IlGwAAAAAAGyHJBgAAAADARmgXBwAAAABnRr+4Q6GSDQAAAACAjZBkAwAAAABgI7SLAwAAAIATM9Av7lCoZAMAAAAAYCMk2QAAAAAA2Ajt4gAAAADgxAx0izsUKtkAAAAAANgIlWwAAAAAcGIUsh0LlWwAAAAAAGyEJBsAAAAAABuhXRwAAAAAnBn94g6FSjYAAAAAADZCkg0AAAAAgI3QLg4AAAAATsxAv7hDoZINAAAAAICNkGQDAAAAAGAjtIsDAAAAgBMz0C3uUKhkAwAAAABgIyTZAAAAAADYCO3iAAAAAODE6BZ3LFSyAQAAAACwEZJsAAAAAABshHZxAAAAAHBm9Is7FJJsAAAAAIDD2LdvnzZu3Kg///xTe/fuVUJCgiRp7dq1qlixYr7r4uPjNXnyZEVHRys1NVWBgYHq1KmTBg0aJG9v73zXRUZGKiIiQjExMZKkkJAQ9e7dWx07drQqfpJsAAAAAHBihjuslP35559r7dq1hVqzf/9+vfDCC0pLS1OdOnXUuHFj7dmzRzNnztT69ev1zTffqHjx4hbrJk2apPDwcLm7u6tFixaSpKioKA0fPlzDhg3T0KFDCx0/STYAAAAAwGE0aNBAwcHBCg0NVd26dfXEE0/ozJkz+c7PycnRyJEjlZaWppEjR6p///6SpKysLA0fPly//vqrPvroI7333ntm67Zv367w8HD5+flp4cKFql69uiQpNjZWPXr00JQpU9SqVSvVr1+/UPGz8RkAAAAAwGH0799fr776qjp06KCAgICbzl+3bp3i4uIUHBysfv36mcbd3d01ZswYubq6asmSJUpJSTFbN3v2bEnSwIEDTQm2JFWvXl0DBgyQJM2aNavQ8ZNkAwAAAIATMxgc7+N2WrdunSSpY8eOMlx38XLlyqlRo0bKzs7W+vXrTeNZWVmKioqSJIWFhVmcs3PnzpKkDRs2KCsrq1DxkGQDAAAAAJzWwYMHJUmhoaF5Hq9Tp44kmTY2k6S4uDhlZmaqZMmSqlChgsWaChUqyN/fXxkZGTpy5Eih4iHJBgAAAAA4rRMnTkiSAgMD8zx+teX86i7lBVlz7bFr1xWE0218dvjwYc2aNUubN2/W6dOnlZ2dnec8g8Gg/fv33+boAAAAAOD2csS9xdu3b3/D44XdPfxG0tLSJEleXl55Hvfx8TGbV5A1kkyP/bp2XUE4VZK9d+9e9ezZUxkZGTIajSpRooTKlClj77AAAAAAAHZ2/f3YVxmNxnzH8luT37qCcKoke+LEibp06ZJ69Oih4cOHq1SpUvYOCQAAAABwHVtWqm/G29tbqampSk9Pz/P4pUuXJP1T0b728/zWSFJGRobFuoJwqnuyd+3apRo1aujdd98lwQYAAAAA6Uq/uKN93EZXNy5LSkrK8/jV8Ws3OAsKCrrhmvzWFYRTJdlGo9Hs+WUAAAAAgLtbrVq1JEn79u3L8/jVvbpCQkJMY9WqVZOHh4dSUlKUmJhosSYxMVEpKSny9PRUtWrVChWPUyXZISEhOnbsmL3DAAAAAAA4iLZt20qSVq9ebXHs9OnT2r59u1xdXdW6dWvTuIeHh5o3by5JWrVqlcW6yMhISVLLli3l7u5eqHicKskeMGCADhw4YPYQcQAAAAC4mxkc8L/bqV27dqpatapiYmI0a9Ys03h2drbefvttZWdn68knn7S45bhPnz6SpPDwcMXGxprGY2NjNX36dElS3759Cx2PwWjtlml2cOLECS1cuFARERHq3bu3WrVqpcDAQLm45P27gsL2zucl6XzejwgDcGfbejTZ3iEAsIOne71v7xAA2MGlnVPtHUKR7Im/aO8QLNSrVNzqtb/99pumTZtmer1//35lZ2erVq1apqpymzZtNGTIENOcq0+iSk9PV2hoqCpXrqzdu3crISFBwcHB+uabb+Tr62txrU8//VQzZswwq2xv3LhRmZmZGjx4sF555ZVCx+9Uu4u3a9dOBoNBRqNRM2fO1MyZM/Ody3OyAQAAAMD5JCcna/fu3RbjBw4cMH1+zz33mB0LDQ3V8uXLNWXKFEVHRysmJkaBgYHq27evBg8enO8O4SNGjFBISIgiIiK0efNmSVLt2rXVu3dvhYWFWRW/U1Wye/bsWaj58+bNK/I1qWQDdycq2cDdiUo2cHdy9kr2n8cdr5Jdt6L1lWxn51SVbFskzQAAAAAA3CpOtfEZAAAAAACOzKkq2QAAAAAAc7d3L2/cjNMm2enp6Tp27JguXryo/G4rb9KkyW2OCgAAAABwN3O6JPvIkSMaO3asNm7cqNzc3BvOvXb3OQAAAAC4I1HKdihOlWQnJCTomWeeUWpqqho2bKizZ88qPj5enTt31tGjR3Xw4EHl5OSoffv2eT4DDQAAAACAW8mpkuwZM2YoNTVV77//vrp3764333xT8fHx+uSTTyRJf//9t0aNGqW4uDgtXrzYztHCGZxKSlTvHl2VnpYmSVq4YrXKVwgyHd+5fYteHfhygc7VqUtXvfnOB7ckTgA3dj7lrP76c7uOxx5UfGyMEg7/pezMDJUsG6i3wgv3/4P4vw9qyn8GKTc3R5L08Xe/5zs3NfmMfl+5SDG7t+jsyUTl5lyWj5+/Kt9bSw88/LhqNmhapPcFoPAqBvhr+3dvya+4lySpZue3dSzx5o9lvK92Za2fO1KursUkSV4Nh1rMeeHRZpo1pmCPlH0//EeNmxlZiMgB3CmcKsmOiopS1apV1b179zyP33vvvZoxY4Yeeughff7553rjjTduc4RwNh+Ne8+UYOfFp7iv6tZvmO/xjIwM/RVz5baEuvUb2Do8AAW0K2qtvv+q6M84zbl8WYun/c+UYN/Isb8PaNb7I3Up7aJcXIqpZLlAeXh6K/nUCe3dskF7t2zQg48/qy49BxU5LgAFN3X0s6YEu6BcXV00/Z3nTQl2fk4lX9DGnbH5Hvf381bt6uUlSdG74woVA1AUBvrFHYpTJdmnTp1S27ZtTa+LFbvyF2FWVpbc3d0lSaVLl1bTpk31yy+/kGTjhn76fpm2RP+h1m076Pdf1+Q5J7hmLU2dnf/z2b9fulifjB8jT08vtXso7FaFCuAmPL18VKNuI1WsXlMV76mp8ylntOLLKYU+z5olEUo8Fqe6zVrr/9q78/CYzv6P45+JLE1iCRVblDQqscQSIpZSxBKqpWiKKqrKo1S19XhKW2v3lnqqqraqXa3Vlqhd7BG7okjsIbEmdllmfn/kl3lMs4hkyAzv13X1ujj3ue/zPdOZM75zf8999kdkPoNtMpk0d+ynunXjurx8fPXae8PkWfIpSVJyUqJWL5qp1Quna/1vc1WxRl2Vq1w9p6cG4D50aV1HIc9W1pI1e/RSk+rZ7jfozRaq4ut1z34rNx/Uys0HM20f2a+1KpUrqZNnL2ldxOH7iBzAo8SunpPt7u5usZJ42n3X58+ft9jPxcVFFy5ceKixwb5cunhBP/z3G5Uo5aU3/pW+HCy7wn7/VZLUsEkzubm7Wys8APcpqEkr/WvYGLV6rbeq1WusgkU873uMsyeitfbXWSrrW1n1WrTNct+4Myd14expSdIrvf9jTrAlydHJWS069lBZ38qSpIM7Nt93LADuX4miBfXVgHY6EXNRI8cvzXY///KlNPCN5orYd1wT5mX+49q9ODgY1PmF1FtEZv0RkeNxANg/u0qyS5QoodjYWPPfy5UrJ0mKiPjfhSwpKUl79+5V0aJFH3p8sB/ffvmJrl+7qn8PHqonXO+vpCzNiWPROnRgvyTp+Rez/gc5ANtmTEnR/PFfSpJe7j1QBkPWX49Jd26b/1y0ZOkM90nbnpKSbKUoAWRl7IcdVLigm97+9BfduJWYrT4ODgZNGP6aJKnPyDkyGjN+LGx2NKtXUaWKechoNGrm7yTZeLgMBtv773FmV0l2YGCgDh8+rGvXrkmSgoOD5ezsrC+++EJz5szR2rVr1a9fP8XFxalu3bp5HC1s1eoVYdoUvlbNWr6gWnWezfE4y/9IncX2Kv2UqtUItFZ4APLAut/m6syxw2rc5lWVLONzz/2LeZWRk8sTkqRjh/ala09OStKpo6klpWkz2gAenFda1NSLjatpztLtWrPt72z3e//1ZqpZqYxGT1ulg9HnchVD19Z1JEnhkUd18uylXI0FwL7ZVZLdokULlS9fXrt27ZIkFSlSRP/5z39048YNffLJJ+rbt6/Wr1+v4sWL67333svjaGGL4q9c1thRX6iQR2G9/X7O79lPTk7WyuWppWgtXnhJhsf95zrAjp2POalVC6bJs1QZNX25a7b6uLi6qXno65Kk+eO/1K4NK3Ut4YoS79zW6ai/Nf2bj3Xh7GmVr1JT1eoFP8DoARQtnF+j/vOyLly5pv+MXpTtfr7exfVRr5Y6fDxWX05ekasYChd0U6uGVSRJM37flquxANg/u1r4LDAwUIsWWV48O3furKpVq2rlypVKSEiQj4+P2rVrp4IFC+ZRlLBl//36MyXEX9HHI7+Uh0fhHI8TsWWTLl+6KAcHB7Vo1dqKEQJ4mIxGo+b98KVSkpMU2nugHJ2cs9238UuvqlCRolr/+zzNGWv5+D63AoXUutvberZlOzk42NXv2YDdGfNBqDwLF1D3D6fpUnzmTwy5m8Fg0MThneXslE99P5mrxKTc3dbR8flacnF2UsK1W1qyZk+uxgJyguke22JXSXZmqlSpoipVquR1GLBxG9ev0brVKxRU51k1a/lCrsZKKxWvGVRHxUqUtEZ4APLApmULdfLIAdVp1lo+lardV19jSoouxZ3TjWvxMjg4yOPJYnJ1z69LsTG6eS1BO8L/VJnyFeVdge8n4EF5sVFVvRxSUys3H9Qvy3dku9/brzZSnWo+mrxwkzZn8Uiu7Ory/6XiC1bs1O07SbkeD4B9eySSbOBerl1N0LdffiJXV1e9P3horsaKv3JZWzeFS2LBM8CeXYyN0fJfpqhgkaJq9Vrv++4/fdQQHYjcpNI+fur58WiVeMpbUuqztjcsW6BlM3/UhJHv6e1PflDpcn5Wjh6ARwFXffdhB12/eUf9Pvsl2/2eLl1Uw/q+oLPn4/Xxd0tyHUcVXy8FVEx9wsCM37bmejwA9s8uk+y///5bs2bNUmRkpOLi4mQwGFSsWDEFBQXp1VdfVcWKFfM6RNiYH8eO1uVLF/X2ex+oZCmvXI21cvlSJScnq0DBgqrfqImVIgTwsC2aOEpJd26rc/8hcnXPf199D+7cqgORm+SQL5+6DBihJ4uXMrflc3RU4zaddP7MSUWuC9Ofv0zRmx99Y+3wgcfe5++1VUnPQhr4zUKdOnc52/3GfdxR7q4u6v7hdF29fvveHe6hW5vUWeyD0ecU+dfJXI8H5Aj14jbF7pLsSZMm6bvvvpPRaLR4ZvbJkyd18uRJLV68WP3791evXr3yMErYmsMHD0iSZk2brDnTp1i0pRiN5j//q1tH5XNw0AttQ9Wjd8bPz/5z6RJJUtOQVnJ2zv79mwBsy+now5KkRZNGa9Gk0RZtycn/K/cc8eZLkqSQDj1Up9mLkqRjB/dIkjxLPmWRYN/NLyBIkevCdCoq+ysdA8i+GpXKSJIG9gjRgO7NLdry5ftfxrFp9kClpJg0dfFmffLjMnO/sR911NiPOlr0c3bKZ/7z8VWfS5JGjl+qn3/dkmEMTo759ErL1CeMzPyNBc8ApLKrJHv58uX69ttv5eLiotDQULVu3VpeXl4ymUw6e/asfv/9dy1cuFBjxoxR2bJlFRISktchw8bEX8n6l+6E+CuSpJs3rmfYfvjQAUUfPSJJer41peLAo+BafNbXhbT2xDu3zNvu3Lp574H//4fg5KTsPa8XQM4UK1Igy3bPwqntBd2fsNheomjWi+Smtbu7Zv6DequGVeRZuICSklI0Z9n27IQL4DFgV0n2tGnTlC9fPk2ZMkW1atWyaCtatKiqVq2qkJAQdevWTT///DNJNsx+mpP5Iz3OnY1Rxzap75VffluRZTn58j+WSJLKlfeVb4VKVo0RwMP16YywTNui/tqtCcP7S5JGLdyQrt2zVOr9lxfOndaluLMZzmb/vSf1H9zFvMpaI1wA/1Cn45eZtpUpWUSHw0ZKkvyeH2pRTl7yuf9k2q9BzfJaOSX1s+8akHFF293SFjz7c/MBnb98LVtxAw+CgXpxm2JXzxU5cuSIatasmS7BvlutWrUUGBiow4cPP8TI8DhITEzUmpWp/yhnwTPg8VatbmM5ObvImJKimaOHKe7MCXNbSnKy1v02VzvWLZck1WrcMo+iBPAglShaUM3rpa4DxIJnAO5mVzPZzs7O8vT0vOd+np6e3CsLq9sUvlZXExLk5OSkprl8BBgA64q/GKdvB75p/nvK/99THX/pvIZ2f9G8PaB+E7Xt8W6uj1foSU+90meQfhn3uc4cO6xR778uj6LF5OpWQJfiYszl5AH1m6pu8za5Ph4A2/NqqyA5OuZT3KWrWr7xQF6Hg8ecgYlsm2JXSXZAQIAOHMj6ImYymXTgwAFVr1794QSFx0ZaqXi9Bo3k4VE4b4MBYMFoNOrmtYR0203/2J6te6mzKaB+E5XyLqeNyxYq+sAexV+M09XLF+WWv6B8KlZTreCWqlqnkdWOB8C2pJWKz10WqZQU4z32BvA4MZjuXqLbxh08eFAdO3ZU586dNWDAADk6Wv5GkJKSotGjR2vWrFmaO3euKleunOtjxl5NuvdOAB45kSez/zgYAI+OV7p+ktchAMgDt3aPy+sQcuVwrPV+RLYWvxJueR1CnrHpmewlS5ak29a2bVtNmzZNf/75p0JCQuTl5SWDwaCYmBitWLFC586dU4cOHXT06FGrJNkAAAAAYMuoFrctNj2TXaFCBRkyuMHg7pDT2v95GgaDQYcOHcp1DMxkA48nZrKBxxMz2cDjyd5nso/Y4Ey2LzPZtqlv374ZJtkAAAAAANgim06y+/Xrl9chAAAAAIBtY17SptjVc7JnzJihBQsW5HUYAAAAAABkyK6S7C+//FJr1qzJ6zAAAAAAAMiQTZeL/1ORIkXk5vb43kAPAAAAAP9koF7cptjVTHadOnW0Z88epaSk5HUoAAAAAACkY1dJdv/+/XX9+nWNGDFCt27dyutwAAAAAACwYFfl4r/99psaNGigBQsWaNWqVapXr55KlSolFxeXdPsaDAb17ds3D6IEAAAAgIeHpx7bFrtKsseNGyeDwSCTyaQrV65o2bJlme5Lkg0AAAAAeNjsKsn+4osv8joEAAAAAAAyZVdJdtu2bfM6BAAAAACwKVSL2xa7WvgMAAAAAABbZlcz2YMHD872vgaDQZ9//vkDjAYAAAAAAEt2lWT/+uuvWbYb/n9ZPZPJRJINAAAA4PFAvbhNsaske8aMGRluNxqNOnfunDZu3Kjly5erR48eatiw4UOODgAAAADwuLOrJDsoKCjL9rZt26pevXoaPny4mjVr9pCiAgAAAIC8Y2Aq26Y8cgufvfzyyypbtqy+//77vA4FAAAAAPCYeeSSbEl65plntHfv3rwOAwAAAADwmLGrcvHsiomJUUpKSl6HAQAAAAAPnIFqcZvySM1kp6SkaPLkyfrrr79UuXLlvA4HAAAAAPCYsauZ7K5du2baduPGDZ05c0ZXr16Vo6Oj3n777YcYGQAAAAAAdpZkb9++Pct2R0dH1apVS/369VOtWrUeUlQAAAAAkHeoFrctdpVkr1mzJtM2JycnFS5cWE5OTg8xIgAAAAAA/seukmwvL6+8DgEAAAAAgEzZVZINAAAAAPgH6sVtyiO1ujgAAAAAAHmJJBsAAAAAACuhXBwAAAAA7JiBenGbwkw2AAAAAABWQpINAAAAAICVUC4OAAAAAHbMQLW4TWEmGwAAAAAAKyHJBgAAAADASigXBwAAAAA7RrW4bWEmGwAAAAAAK2EmGwAAAADsGAuf2RZmsgEAAAAAsBKSbAAAAAAArIRycQAAAACwa9SL2xJmsgEAAAAAsBKSbAAAAAAArIRycQAAAACwY4/i6uKDBg3Sr7/+mml7hw4dNHLkyHTbT58+rbFjx2rr1q1KSEhQiRIl1KJFC7311ltyc3N7kCGbkWQDAAAAAGxS/fr15enpmW57QEBAum0HDx7Ua6+9phs3bqhy5coKDAzUvn37NGnSJIWHh2vOnDnKnz//A4+ZJBsAAAAAYJN69eql2rVr33O/lJQUDRgwQDdu3NCAAQPUq1cvSVJiYqLeeecdrVu3Tt98841GjBjxoEPmnmwAAAAAsGcGG/zvYVu7dq2OHTsmX19f9ezZ07zd2dlZI0eOlKOjoxYtWqQrV6488FhIsgEAAAAAdm3t2rWSpJCQEBn+cZN6sWLFVLNmTSUlJSk8PPyBx0K5OAAAAADAJq1atUqrVq1SYmKiSpYsqWeffVZVq1ZNt9/ff/8tSfL3989wnMqVKysiIkKHDx9+oPFKJNkAAAAAYNcexdXF08ycOdPi7//973/VsGFDff311/Lw8DBvP3v2rCSpRIkSGY5TvHhxSVJMTMyDCfQuJNkAAAAAAKtq0qRJlu1r1qzJsr1ChQoaNmyY6tSpo5IlS+ry5cvavn27vv32W4WHh6t3796aM2eOHBxS74C+ceOGJMnV1TXD8dzd3S32e5BIsgEAAAAANuX111+3+LuXl5fatm2revXqqXXr1tq9e7f+/PNPPf/88xb7/fN+7DQmk+lBhZoOSTYAAAAA2DFDnqznnbV7zVTnVPHixdWuXTtNnTpVGzduNCfZbm5uSkhI0M2bNzPsd+vWLUn/m9F+kFhdHAAAAABgN7y9vSVJ58+fN28rVaqUJCk2NjbDPmnb0/Z7kEiyAQAAAMCe5fVDsR/yg7ITEhIkWd5/XbFiRUnSgQMHMuxz8OBBSan3ej9oJNkAAAAAALtgMpm0cuVKSZaP62rcuLEkacWKFen6XLhwQTt37pSjo6Oee+65Bx4jSTYAAAAAwGYcPHhQf/zxhxITEy22X79+XR9//LH2798vNzc3tW/f3twWHBwsb29vHT58WJMnTzZvT0pK0tChQ5WUlKT27durSJEiDzx+g+lhLrNmh2KvJuV1CADyQOTJy3kdAoA88ErXT/I6BAB54NbucXkdQq7E2WDOUrygU477rl69Wn379lWhQoXk7++vwoUL6+LFizp06JASEhLk5uZmfl723f766y916dJFN2/elL+/v8qUKaO9e/cqJiZGvr6+mjNnjgoUKJDbU7snVhcHAAAAANgMPz8/denSRfv379eRI0cUHx8vJycneXl56aWXXlLXrl1VunTpdP38/f21ZMkSff/999q6dasOHz6sEiVK6M0331SfPn0eysriEjPZ98RMNvB4YiYbeDwxkw08npjJtr7czGTbO2ayAQAAAMCOGWzvMdmPNRY+AwAAAADASkiyAQAAAACwEsrFAQAAAMCOGUS9uC1hJhsAAAAAACshyQYAAAAAwEooFwcAAAAAe0a1uE1hJhsAAAAAACshyQYAAAAAwEooFwcAAAAAO0a1uG1hJhsAAAAAACshyQYAAAAAwEooFwcAAAAAO2agXtymMJMNAAAAAICVMJMNAAAAAHbMwNJnNoWZbAAAAAAArIQkGwAAAAAAK6FcHAAAAADsGAuf2RZmsgEAAAAAsBKSbAAAAAAArIQkGwAAAAAAKyHJBgAAAADASkiyAQAAAACwElYXBwAAAAA7xuritoWZbAAAAAAArIQkGwAAAAAAK6FcHAAAAADsmEHUi9sSZrIBAAAAALASkmwAAAAAAKyEcnEAAAAAsGOsLm5bmMkGAAAAAMBKmMkGAAAAADvGRLZtYSYbAAAAAAArIckGAAAAAMBKKBcHAAAAAHtGvbhNYSYbAAAAAAArIckGAAAAAMBKKBcHAAAAADtmoF7cpjCTDQAAAACAlZBkAwAAAABgJZSLAwAAAIAdM1AtblOYyQYAAAAAwEpIsgEAAAAAsBLKxQEAAADAjlEtbluYyQYAAAAAwEpIsgEAAAAAsBLKxQEAAADAnlEvblOYyQYAAAAAwEpIsgEAAAAAsBLKxQEAAADAjhmoF7cpzGQDAAAAAGAlzGQDAAAAgB0zMJFtU5jJBgAAAADASkiyAQAAAACwEoPJZDLldRAAAAAAADwKmMkGAAAAAMBKSLIBAAAAALASkmwAAAAAAKyEJBsAAAAAACshyQYAAAAAwEpIsgEAAAAAsBKSbAAAAAAArIQkGwAAAAAAKyHJBgAAAADASkiyAQAAAACwEpJsAAAAAACshCQbAAAAAAArIckGAAAAAMBKSLKBh+j777+Xn5+fFi9enNehAHYpIiJCfn5+GjRoUF6HIkk6c+aM/Pz81KVLl7wOBUA28D0M4GEgyQYAAAAAwEpIsgEAAAAAsBKSbAAAAAAArIQkG3bNz89PwcHBSkpK0oQJE9SqVStVrVpVbdq0kXTv+zcXL14sPz8/ff/99xbb775n68CBA+rTp4/q1q2rChUqaPXq1ZKkEydOaNy4cerYsaPq168vf39/1atXT2+99ZZ27NjxYE8csGHR0dEaPHiwmjdvrqpVq6pWrVpq2bKlBg8erP3795v3S/v8ZiQ79zpfvnxZQ4cOVYMGDVSlShWFhIRowoQJSkxMTLdvly5d5OfnpzNnzmQ4VnBwsPz8/NJtv9c15m43b97UV199peDgYFWpUkXBwcH6+uuvdf369XT7xsXFafLkyeratasaNWokf39/1a5dW927d9fatWszjPHu61VcXJwGDx6sZ599VlWqVFHLli01Y8aMTF8rwN7c6xqQ0ff73Z+RU6dO6d///rfq16+vihUratq0aenGOHDggHr37q3atWurevXqeuWVVxQWFpbh8SIjI/Xpp5+qTZs2ql27tvz9/RUcHKwhQ4Zkel25+7oTHh6uV199VQEBAapRo4Z69OhhcT0E8GhxzOsAgNwyGo16++23tXXrVtWqVUvly5dXUlKSVcbeuXOnhg4dKi8vL9WtW1dXrlyRo2Pqx2bevHmaOnWqnnnmGVWsWFHu7u46c+aM1q5dq/Xr12vUqFFq1aqVVeIA7MXBgwfVqVMn3b59W76+vmrcuLGSk5N17tw5/fbbb3rqqadUpUqVXB8nPj5eoaGhun79umrXrq3ExERFRERozJgx2rFjhyZOnKh8+fJZ4Yyyd41JSkpSt27dFBUVpTp16qhy5cqKiIjQTz/9pG3btmnmzJlyd3c3779q1SqNGjVKZcuWlY+PjwICAhQXF6eIiAht2bJFAwcO1JtvvplhPGfPnlX79u0lSVWqVNGtW7e0c+dOffbZZ7p27Zr69u1rlfMG7NWJEyfUvn17ubu7KzAwULdu3ZKrq6vFPnv27NGwYcNUqlQpPfvss7pw4YJ27Nih9957T6dOnVLv3r0t9v/iiy905MgR+fn5KTAwUAaDQUePHtX8+fO1cuVKzZ07Vz4+PhnGM2/ePE2ePFkVKlRQgwYNFB0drU2bNmnnzp1atGiRypUr98BeCwB5gyQbdu/cuXNycHBQWFiYSpcubdWxFy5cqH79+qlv374yGAwWbc2aNVOnTp1UpkwZi+379u3TG2+8oREjRig4ODjdFzvwKJsxY4Zu376tQYMGqXv37hZtFy5cUHx8vFWOs27dOtWqVUsTJkxQ/vz5JUmxsbHq2rWrNm7cqNmzZ6tr165WOVZ2rjG7d++Wj4+PVqxYoWLFikmSrl69qp49e2rPnj0aO3asBg8ebN4/MDBQv/32mypUqGAxzokTJ/T6669rzJgxatWqlUqWLJnuWIsXL1b79u01bNgwubi4SEr9QfC1117TlClT1L17d7m5uVnl3AF7tHTpUoWGhmrYsGFycnLKcJ958+bp9ddf1wcffCAHh9TCzoiICPXs2VPfffednnvuOVWqVMm8/zvvvKOAgAAVKlTIvM1kMmn+/PkaOnSoPvvsM/30008ZHmv69On68ccf1bhxY3O/4cOH65dfftGUKVP0xRdfWOvUAdgIysXxSBgwYIDVE2xJKleunPr06ZMuwZakGjVqpEuwJalq1arq3LmzEhISFBERYfWYAFt2+fJlSVKdOnXStXl6eqp8+fJWOY7BYNCwYcPMCbYklShRQgMGDJAkq5dOZ+caM2jQIHOCLUkFCxbUkCFDJEnz58/X7du3zW0VKlRIl2BLkre3t/r06aPk5ORMy8ZLlSql4cOHmxNsSapZs6YaNGigmzdvUoKKx56Hh4cGDx6caYIt/e96kZZgS1Lt2rUVGhoqo9GoWbNmWezfqFEjiwRbSr0OdejQQQEBAdqyZUuGt4ZIUteuXc0Jdlq//v37SxL/TgAeUcxk45HQtGnTBzJukyZNLL6A/+n27dvasGGD/vrrL125csVcQnry5ElJqbNSwOOkcuXKCg8P19ChQ9W/f38FBQXJ2dnZ6sepUKFChgl78+bN5erqqtOnTysuLk7Fixe3yvHudY0pVKiQGjZsmG67v7+/fHx8dOzYMR04cEA1a9Y0tyUlJWnLli3au3evLl68qKSkJJlMJl24cEGSdPz48QyPVadOnQxfUx8fH4WHh5v7A4+rZ5991uL2jIyEhIRk+Dlq3bq1Zs2aleHaKpcuXdLatWsVHR2ta9euKSUlxbzdaDTq1KlTFrPfaZ577rl024oUKSIPDw+dP38+u6cFwI6QZMPuPfnkkxYzOtaUUalmmp07d+rdd9/N8gsys1+1gUfVm2++qb1792rz5s3q0aOHnJ2dVbVqVdWrV0/t2rXL8jN1P7y8vDLcbjAYVLJkSR07dkyxsbFWSbKzc40pVapUpm1eXl46duyY4uLizNuio6PVp0+fLH+Iu3HjRobbS5QokeH2tKQio4XfgMdJdq4zmV1D0rbHxsZabJ89e7a++uor3blzJ9MxM/vOzywed3d3q91CA8C2kGTD7j3xxBM57ms0GnM09s2bN9WvXz9dunRJvXr10gsvvCAvLy+5ubnJwcFBv/zyi4YNGyaTyZTj2AB75O7urqlTp2rv3r1av369IiMjtXfvXvNiZGPGjFGTJk3uOc69PjsZ3cLxz75Z7fNPWV0LsnONyU48d+vfv79OnDihl19+WZ06dVLZsmXl7u4uBwcHbdy4UW+++Wamr0FW1TXA4+JBfWYzun7s27dPn3zyidzc3DRkyBDVqVNHnp6e5uO8//77WrZsWaaf2fu5FgF4NJBk45GWdj9WZjNC586dy9G4O3bs0KVLlxQSEmK+B/RuaeXiwOOqWrVqqlatmqTUz9/kyZP1448/aujQoeYk28nJKdPP5tmzZ7McPyYmJsPtJpPJPAN19yx2VteC5OTkXJdYZxaP9L9zSYsnOjpaR48eVeXKlfXZZ5+l2//UqVO5igV4FDyo7+80mX1m//l5laSVK1fKZDLp/fffV2hoaLo+fOcD+Cd+DscjLW0Roszubdy8eXOOxk1ISJCUcdlmYmKiVq1alaNxgUeRu7u73n33Xbm6uurixYvmxdGKFSum+Ph489/vtmnTpizHPHTokKKiotJtX716tW7duqWnnnrK4h/JWV0Ltm7dquTk5Ps6p39KSEjQhg0b0m0/ePCgjh07Jjc3N1WuXNm8r5R5CenSpUtzFQvwKChcuLCcnJx0+vTpDB/Lea9rxL2sWLEiw3H/+OMPSalPAEiT1Xd+VFSU/v7771zFAuDRQ5KNR1rp0qXl5eWlo0ePatmyZebtRqNR48aN0+7du3M0btqzMFesWGFxT3ZiYqI++eQTnT59OneBA3Zqzpw5Gc7qbNu2Tbdu3ZK7u7sKFCggKXUlX0kaO3asRZlleHi4pk+fnuVxTCaTRowYYXEPZFxcnEaNGiVJ6tKli8X+aceaMmWKRZ9jx47p008/vZ9TzNRXX31lMSN+7do1jRw5UpIUGhpqLi319vaWg4ODtm7davFDQdp1adeuXVaJB7Bnzs7OCggI0NWrV/Xzzz9btC1cuFBhYWG5Gv/cuXP69ttvLcrOIyMjtWDBAjk4OKhz587m7Wnf+fPnz7dY8+DSpUsaNGhQrn+kA/DooVwcj7x3331XAwcO1IABAzRnzhwVLlxYhw4d0uXLl9WlSxfNnDnzvsesXLmyGjZsqPDwcLVo0UJBQUFycXHRrl27dO3atRyPC9i7efPmacSIEfL29lb58uXl4uKimJgY7d27V1LqvYtpZaA9e/bUn3/+qblz5yoyMlLlypXT6dOndejQIfXs2VOTJk3K9DiNGzfWkSNH1LRpUwUFBSkpKUnbtm3TzZs31aBBA7322msW+7dq1UpTp07V/v371aJFCwUEBOjKlSvat2+fmjVrpqSkpCxLvu+levXqSklJUUhIiOrUqSNHR0dFREQoPj5elSpVMj+uR0pdVbhjx46aM2eOXnrpJdWuXVsFCxbU/v37dfbsWb3xxhuaOnVqjmMBHhX9+vVT9+7dNXr0aK1YsUJeXl6KiorSyZMn1b1791x9Tjp06KCZM2dq7dq18vf31/nz57Vjxw4ZjUa999575soTSWrXrp2mTZum8PBwNWvWTNWqVdOdO3e0fft2FS9eXE2bNtXq1autccoAHhHMZOOR17p1a40ZM0aVKlXSvn37FBERIT8/Py1YsCDDR21k17hx4/TOO++oRIkS2rJliyIjI1WzZk0tWrQoV+MC9qx///565ZVX5OzsrMjISK1atUoXL15Us2bNNHv2bIvk18fHR7NmzVL9+vV17tw5bdiwQS4uLho/frw6dOiQ5XE8PDw0f/58BQcHa9euXdq4caOKFSumd999V+PHj1e+fPks9nd2dta0adPUrl07GY1GrVu3ThcvXlT//v31zTff5Pq8nZ2dNX36dIWGhurQoUNau3at3Nzc9MYbb2jmzJnpHic0ZMgQDR06VD4+Ptq5c6e2bNmicuXKafbs2WrUqFGu4wEeBUFBQZoyZYpq1qyp6Ohobdq0ScWKFdOMGTNy/TmpXr265s6dqzJlymjDhg3at2+f/P39NWbMGPXu3dti30KFCmnhwoVq37698uXLp3Xr1uno0aMKDQ3V/PnzzdU5AJDGYGL5YwAAAAAArIKZbAAAAAAArIQkGwAAAAAAKyHJBgAAAADASkiyAQAAAACwEpJsAAAAAACshCQbAAAAAAArIckGAAAAAMBKSLIBAAAAALASkmwAQI4tXrxYfn5+6tKlS7q24OBg+fn5KSIiIg8is66HdS4RERHy8/NTcHDwAz0OAAB4cBzzOgAAQKouXbpo+/btFtscHBxUoEAB+fj4qEmTJurcubPc3NzyKMK8s3jxYsXExKhp06aqWLFiXocDAACQKZJsALAxJUuWVMmSJSVJycnJOn36tHbv3q3du3dr4cKFmjFjhooXL57HUd7bU089JWdnZ7m6uuZ6rF9//VXbt2+Xl5cXSTYAALBpJNkAYGPat2+vfv36WWxbsWKFBg0apBMnTmj48OH68ccf8yi67Js+fXpehwAAAPDQcU82ANiBkJAQvfXWW5Kk9evXKz4+Pm8DAgAAQIaYyQYAO1G3bl1JktFo1KlTp+Th4aGIiAh17dpVXl5eWrt2rZYsWaJ58+YpKipKV69e1ZIlS8zl1YmJiZo/f77CwsIUFRWlmzdvqlixYqpbt6569eqlsmXLZnjcGzduaOLEiQoLC1NsbKw8PDxUv359vfPOO1nGGxwcrJiYGM2YMUO1a9dO1378+HFNnz5d27ZtU2xsrBwcHFSiRAkFBgaqffv2qlatmvn80gwePFiDBw82/z0oKEgzZ860GPfw4cOaNm2atm/frvPnz8vFxUXly5dXu3bt1K5dO+XLly/DeCMiIjRx4kTt27dPKSkp8vHxUadOnfTyyy9neZ73cuvWLc2bN08rV640v+6enp565pln1Lx58yxjutvx48e1atUqbdy4UWfOnNHFixfl4uKiZ555Rq1atVLHjh3l5OSUrp/RaNSSJUv066+/6siRI7p+/boKFCggT09PVa1aVS+++KLq1Klj0Wf79u2aOXOm9uzZoytXruiJJ57Qk08+KT8/PzVs2FDt27dPd5ycvL8SExM1d+5chYWFKTo6Wrdv31ahQoVUrFgx1ahRQ+3atVPlypXv49UGACDvkWQDgJ0wmUxZtn/66aeaOXOmPD09VbZsWZ07d87cdv78ef3rX//SwYMHZTAYVLx4cZUsWVInT57UwoULFRYWpvHjx5sT+TQJCQnq0qWLDh8+LEny8fGRi4uL/vjjD61bt06vvvpqjs5lwYIFGjFihJKSkuTk5CQfHx9J0pkzZzRv3jxdvHhR48ePV4ECBVSjRg1zcujt7a0iRYqYx/H19bUYd/bs2frss8+UkpIiNzc3Pf3007p69ap27dqlXbt2ac2aNRo3bpwcHR3TxTNkyBCZTCYVLFhQPj4+iouL00cffaQjR47k6Bwl6eTJk+rVq5dOnDghSfLy8tJTTz2luLg4bdy4URs2bFBISIgKFix4z7HGjBmjFStWyM3NTZ6envLz89Ply5fN9+uvXr1aU6ZMSZdof/jhh/r1118lScWLF1fp0qV1/fp1nTlzRkeOHNGdO3cskuxFixbpo48+Mr8W5cqVk8lkUmxsrFasWKE9e/akS7Jz8v5KSUlRz549tW3bNvNr4+3trfj4eEVHR+vgwYPKnz8/STYAwP6YAAA24bXXXjP5+vqaxo4dm2H7xIkTTb6+vqYKFSqY4uPjTSaTybRt2zaTr6+vqWLFiqYqVaqYli5dat4/JSXFdOfOHVNKSoqpQ4cOJl9fX1O3bt1Mx44dM+9z584d06hRo0y+vr6moKAg0+XLly2O+e9//9vk6+treu6550wHDhwwbz937pwpNDTUVLlyZZOvr6/ptddeSxdv48aNTb6+vqZt27ZZbN+8ebOpQoUKJl9fX9Onn35qunr1qkV7ZGSkaf78+Rm+NosWLcr09Vu/fr3Jz8/PVLVqVdOcOXNMycnJ5rb9+/ebmjdvnuHrGxUVZT6Pzz//3HTnzh2TyWQyGY1G0y+//GKqWLGiqVKlShmeS1Zu3bplCgkJMfn6+pratGljOnTokEV7bGysafz48aYbN26Yt6X9/2zcuHG68VatWmXau3evyWg0pov/lVdeMfn6+pomT55s0Xbo0CGTr6+vqUaNGuliNxqNpu3bt5uWLVtm3pacnGwKCgoy+fr6mmbMmGFKSkpKd6zp06dbbMvp+2vVqlXm99Y/X5ukpCTT+vXrTeHh4eleBwAAbB33ZAOAHVixYoV5sbNGjRqpUKFCFu0pKSl6++231apVK/M2BwcHOTs7688//9Tu3btVtmxZ/fDDD3r66afN+zg7O2vAgAFq3Lix4uPjtWDBAnNbTEyMli5dKkkaMWKEKlWqZG4rUaKExowZc8/Z9YyMGjVKRqNR7du310cffaQCBQpYtAcGBio0NPS+xjSZTPr6669lMpk0ePBgderUyaIE29/fX6NHj5bBYND06dOVmJhobvvpp5+UlJQkf39/DR48WM7OzpIkg8GgDh06qH379kpOTr7v81ywYIGOHz+uJ598UlOnTlWFChUs2osXL6633nor249ka9q0qapWrSqDwWCxvVy5cvr6668lpT7q7G7R0dGSpDp16qQr2TcYDKpVq5aef/5587bLly8rPj5eBQsWVJcuXdLN+JcrV86ifF9Sjt9fx44dk5S63sA/XxtHR0c1bNhQzz333L1fGAAAbAzl4gBgYxYtWqQtW7ZI+t8jvK5cuSJJ8vb21vDhwzPsl9F9slJqEiRJL774otzd3TPcJyQkROvWrdO2bdvUq1cvSdLGjRtlNBpVpkwZNWrUKF0fLy8vNW3a1Dx+dpw+fVoHDhyQJPXu3Tvb/e4lKipKUVFRcnFxUdu2bTPcx9/fX6VKlVJMTIz++usv1ahRQ5K0YcMGSUqXPKbp1q2b5s+ff98xrVixQpLUoUMHixL33Lh06ZKWLVumffv26dKlS7pz547FDx3Hjh3T7du39cQTT0iS+VFwe/bs0YkTJ+Tt7Z3l+EWKFJGLi4uuXr2qNWvWqEmTJveMKafvr7TYNm/erAsXLsjT0/OexwIAwB6QZAOAjTl37pz5fmoHBwflz59fAQEBatKkiTp37pzhzGfhwoX15JNPZjhe2v3Uy5YtMyfv/3Tt2jVJUmxsrHlb2kxjuXLlMo21fPny95VkHz16VJJUtGhRlSlTJtv97uXvv/+WlDo7+/rrr2e6X9qq7Gnnee3aNV24cEGS9Mwzz2TY5+mnn5ajo+N9z2annWtAQMB99cvMn3/+qcGDB+vmzZuZ7mMymZSQkGBOsqtXr67AwEDt2LFDLVu2VM2aNVWrVi1Vq1ZNgYGByp8/v0X/fPnyqXv37powYYL69OmjZ555RvXq1VPVqlUVFBSU4fPZc/r+atq0qby9vRUVFaXGjRurdu3aqlmzpgICAlSjRg25uLjc3wsEAICNIMkGABvz9ttvp3tO9r1kVXKckJAgKXV16uPHj2c5zu3bt81/vnHjhqTUhDgzmSX2mbl+/bokpSsRz62rV69KSo1/165d99w/7TzTzlHK/Dzz5csnDw8PXbx48b5iSjvX7Cxqdi9nzpzRwIEDlZiYqJYtW6pLly7y8fFRgQIF5OjoKKPRaF5FPikpydzPwcFBEydO1KRJk7RkyRJFRkYqMjJSkuTi4qIXXnhBAwcOVOHChc193n33XZUqVUqzZ8/W4cOHFRUVJSn1B4y6devqgw8+sCjvzun7y9XVVbNnz9b48eMVFhamTZs2adOmTZIkd3d3dejQQe+8845cXV1z89IBAPDQkWQDwCPOzc1NV65c0Q8//KCmTZtmu19a6W9WyeWlS5fuK5a0mdO0mU1rSfuRoWLFilqyZEm2+91d3nzx4sUMZ2pTUlJy9Fzy/PnzKz4+3vwDQG6EhYUpMTFR1apV07fffisHB8slVbKKL3/+/Hr//ff1/vvv6+TJk9q5c6c2b96sVatWadGiRTpx4oRmzZplHjPtXvQOHTro8uXL2rlzpyIiIhQWFqYtW7aoW7du+v33382vVU7fX1LqDxtDhw7VkCFDFBUVpR07dmjDhg0KDw/X1KlTdf78eY0ePfr+XiwAAPIYC58BwCMu7TFX2ZnhvVvaY7XSysYzklYSnV1+fn6SUhPa06dP31ffrKSdY9rzwbMr7XnRaX0zcvz48RwtfJYW0+7du++77z+dOXNGklSjRo10CbaU/f+3ZcuWVbt27TR69GjNnz9fBoNBO3fuNJd8/1ORIkXUrFkzffzxx1q5cqW8vLwUHx+v5cuXm/fJ6fvrbgaDQeXLl1enTp30448/6vvvv5ckLV261Co/UgAA8DCRZAPAI65ly5aSpIULF+r8+fPZ7le/fn05ODjo5MmTCg8PT9d+9uxZrVmz5r5i8fLyMj/3eOLEidnul1YyfHe58d0qVaokb29vJSUlafLkyfcVU4MGDSRJs2bNyrB9xowZ9zVempCQEEnS/PnzzQvX5VTaPdYZ/f8zmUyaOnXqfY9ZoUIFc9l+dt4X+fPnN5eJ371/Tt9fWalVq5b5z9YaEwCAh4UkGwAecS+88IKqV6+uhIQEdevWTTt27Ei3T3R0tL777jutXbvWvK106dLmxzsNHz7cvLiYJMXFxen999/PUTwDBw6Ug4ODFixYoC+//NJ873KanTt3WjzqSZJ5kbTt27dn+Ngwg8GgQYMGyWAwaPLkyfr222/TlaTfvHlTK1eu1EcffWSx/Y033pCTk5P27dunr776yvx4L5PJpAULFmjhwoXpHmWVHS+//LJ8fHx08eJF9ejRw+L1k1KTxwkTJmS5kFmaoKAgSakrlt/9w8b169f14Ycf6q+//sqw32+//abvvvvO/CivNElJSZoyZYquXr0qR0dHc/IcFRWlDz/8UDt27JDRaLTos3nzZm3dulWSVKVKFfP2nL6/fv75Z02ePFkxMTEW+966dcs8k12oUCGrLpAHAMDDwD3ZAPCIy5cvn8aPH6++fftq9+7d6ty5s4oWLapSpUopOTlZZ8+eNd/T+8UXX1j0/fjjj3XkyBEdOXJEbdq0Ubly5eTs7KyjR48qf/786tmzp8aPH39f8dStW1cjR47U8OHD9fPPP2vWrFkqV66cTCaTzpw5oxs3bqhJkyYWz8pu3bq1Zs+ereXLl2v37t3y8vJSvnz5VKFCBXPS3LhxY3366acaMWKEJk6cqKlTp+rpp5+Wq6urEhISdPr0aaWkpMjLy8sinvLly+vjjz/W8OHDNXXqVC1atEhlypRRbGysLly4oG7dumn16tXpksF7eeKJJzRhwgT17NlTBw4cUJs2bVS6dGkVLlxYcXFxunDhgkwmk1599dV7jhUcHKygoCBt375dffr0kZeXlzw8PHTs2DHduXNHX3zxhT744IN0/a5cuaLx48dr/Pjx8vDwkJeXl/l1TivDHjhwoPn+6qSkJC1atEiLFi2Sq6urypQpI2dnZ8XFxZlnlJs2baoWLVqYj5HT99fZs2c1Y8YMjRo1Sp6enipevLiSkpJ0+vRp3bx5U46OjhoxYoT5ueUAANgLkmwAeAw8+eSTmjVrlpYtW6alS5fqwIEDOnTokAoUKKCSJUuqSZMmatKkierXr2/Rr3Dhwpo7d64mTpyosLAwnTp1Sh4eHnrxxRf1zjvvaNu2bTmKJzQ0VDVq1NC0adO0bds2HT9+XM7OzipZsqQCAwPTPfO7atWq+uGHH/Tzzz/r77//1u7du9PNtEqps8dBQUGaOXOmtmzZojNnzshoNMrT01O1atVS/fr11axZs3T9OnbsqLJly2rSpEnau3evoqOj5ePjo/79+ys0NFSrV6/O0XmWLVtWS5Ys0Zw5c7Ry5UpFR0fr/Pnz8vT0VMOGDdW8efNMny19NwcHB02aNMm8EndcXJxu3bql2rVrq0ePHgoKCsowyQ4JCZHRaFRERISioqJ0/PhxJSUlqWjRoqpfv746d+6swMBA8/7e3t767LPPtHXrVh08eFCxsbG6ceOGChQooHr16qlNmzZq3bq1DAaDxXFy8v7q1KmTihQpooiICJ06dUpRUVEyGo0qUaKEAgMD9frrr5vv4QcAwJ4YTBnV3QEAAAAAgPvGPdkAAAAAAFgJSTYAAAAAAFZCkg0AAAAAgJWQZAMAAAAAYCUk2QAAAAAAWAlJNgAAAAAAVkKSDQAAAACAlZBkAwAAAABgJSTZAAAAAABYCUk2AAAAAABWQpINAAAAAICVkGQDAAAAAGAlJNkAAAAAAFgJSTYAAAAAAFbyfyE7USBA1YiGAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1100x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 64.80%\n",
+ "ARI Score : 0.24\n",
+ "NMI Score: 0.24\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_test_result = df_test[['area_code', 'type_of_area']]\n",
+ "df_test_result['category_toar']=df_test_result['type_of_area'].map({'urban':0, 'suburban':2, 'rural':1})\n",
+ "y_pred_0 = clustering_model_kmeans.predict(df_test_0)\n",
+ "df_test_result['category_pred'] = y_pred_0\n",
+ "df_test_result['type_of_area_pred'] = df_test_result['category_pred'].map({0:'urban', 2:'suburban', 1:'rural'})\n",
+ "accuracy_0 = accuracy_score(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred'].values)\n",
+ "ari, nmi = ari_nmi_clustering(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred'].values)\n",
+ "df_test_result = df_test_result[['area_code', 'type_of_area', 'type_of_area_pred']]\n",
+ "cf_matrix(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred'].values, fig_name='confision_matrix_kmeans_v2')\n",
+ "print(\"Accuracy: {:.2f}%\".format(accuracy_0*100))\n",
+ "print(\"ARI Score : {:.2f}\".format(ari))\n",
+ "print(\"NMI Score: {:.2f}\".format(nmi))\n",
+ "#display(df_test_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "d0f5a5c9-dba4-4e57-819b-17bc18e1bc69",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pca = PCA(n_components=2)\n",
+ "df_test_0_pca = pca.fit_transform(df_test_0)\n",
+ "# Plot the clusters\n",
+ "y_pred_0=clustering_model_kmeans.predict(df_test_0)\n",
+ "scatter = plt.scatter(df_test_0_pca[:, 0], df_test_0_pca[:, 1], c=y_pred_0, s=50, cmap=ListedColormap(['darkblue', 'purple', 'darkorange']))\n",
+ "# Plot centroids\n",
+ "centers = clustering_model_kmeans.cluster_centers_\n",
+ "centers_pca = pca.transform(centers)\n",
+ "plt.scatter(centers_pca[:, 0], centers_pca[:, 1], c='red', s=150, alpha=0.5)\n",
+ "plt.xlabel('PCA Feature 1')\n",
+ "plt.ylabel('PCA Feature 2')\n",
+ "plt.title('K-means Clustering')\n",
+ "plt.legend(handles=scatter.legend_elements()[0], labels=['rural', 'urban', 'suburban'], loc=\"best\", title=\"Clusters\")\n",
+ "plt.ylim((-1.5, 1.5))\n",
+ "plt.xlim((-.7, 1.7))\n",
+ "plt.savefig('figures/kmeans_clusters_1.jpg', dpi=400, pad_inches=0.2, bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "c7d0e67a-beab-432e-9b13-eb5cb834add8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "accuraccy for separate test, random forest\n",
+ "accuracy of predicting urban 69.63\n",
+ "accuracy of predicting rural 69.2\n",
+ "accuracy of predicting suburan 25.93\n"
+ ]
+ }
+ ],
+ "source": [
+ "urban_pred = df_test_result[df_test_result['type_of_area']=='urban']\n",
+ "suburban_pred = df_test_result[df_test_result['type_of_area']=='suburban']\n",
+ "rural_pred = df_test_result[df_test_result['type_of_area']=='rural']\n",
+ "print()\n",
+ "print(\"accuraccy for separate test, random forest\")\n",
+ "print('accuracy of predicting urban', round(100*accuracy_score(urban_pred['type_of_area'].values, urban_pred['type_of_area_pred'].values),2))\n",
+ "print('accuracy of predicting rural', round(100*accuracy_score(rural_pred['type_of_area'].values, rural_pred['type_of_area_pred'].values), 2))\n",
+ "print('accuracy of predicting suburan', round(100*accuracy_score(suburban_pred['type_of_area'].values, suburban_pred['type_of_area_pred'].values), 2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e6d9c85b-7a0d-49d4-977c-44d99792bcfd",
+ "metadata": {},
+ "source": [
+ "### ========================= Supervized learning approach ======================= #"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "f0506280-ce8b-492e-8807-9e873bd033cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_no2 = pd.read_csv('data/no2/p75.csv', comment=\"#\")\n",
+ "df_no2.set_index(['lat', 'lon'], inplace=True)\n",
+ "df_no2 = df_no2.loc[~df_no2.index.duplicated(keep=False)]\n",
+ "df_no2 = df_no2.rename(columns={'value': 'no2'})\n",
+ "\n",
+ "df_nox = pd.read_csv('data/nox/p75.csv', comment=\"#\")\n",
+ "df_nox.set_index(['lat', 'lon'], inplace=True)\n",
+ "df_nox = df_nox.loc[~df_nox.index.duplicated(keep=False)]\n",
+ "df_nox = df_nox.rename(columns={'value': 'nox'})\n",
+ "df_nox = df_nox.dropna()\n",
+ "\n",
+ "df_pm = pd.read_csv('data/pm2p5/p75.csv', comment=\"#\")\n",
+ "df_pm.set_index(['lat', 'lon'], inplace=True)\n",
+ "df_pm = df_pm.loc[~df_pm.index.duplicated(keep=False)]\n",
+ "df_pm = df_pm.rename(columns={'value': 'pm2p5'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "c3003969-5442-4992-8839-c4483e9fd62d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def voting_clf(clfs:dict, X_test):\n",
+ " y_pred_voting = []\n",
+ " y_pred_rf = clfs['rf_clf'].predict(X_test)\n",
+ " y_pred_cboost = clfs['cboost'].predict(X_test)\n",
+ " y_pred_lgbm = clfs['lgbm'].predict(X_test)\n",
+ " for pred in zip(y_pred_rf, y_pred_cboost, y_pred_lgbm):\n",
+ " my_pred = (pred[0], pred[1][0], pred[2])\n",
+ " counter = Counter(my_pred)\n",
+ " y_pred_voting.append(counter.most_common(1)[0][0])\n",
+ " return np.asarray(y_pred_voting, dtype=object)\n",
+ "\n",
+ "\n",
+ "def threshold_prob_clf(y_proba:np.ndarray):\n",
+ " y_argmax = np.argmax(y_proba,axis=1)\n",
+ " y_max = np.max(y_prob,axis=1)\n",
+ " y_max = y_max >= 0.5\n",
+ " y_argmax[~y_max] = 1\n",
+ " classes_ = np.array(['rural', 'suburban', 'urban'], dtype=object)\n",
+ " y_pred = classes_[y_argmax]\n",
+ " return y_pred\n",
+ "\n",
+ "def threshold_clf(y_proba:np.ndarray):\n",
+ " y_pred = []\n",
+ " for proba in list(y_proba):\n",
+ " p_max = max(proba)\n",
+ " if p_max == proba[1]:\n",
+ " y_pred.append('suburban')\n",
+ " elif p_max == proba[0] and p_max >= 0.5:\n",
+ " y_pred.append('rural')\n",
+ " elif p_max == proba[2] and p_max >= 0.5:\n",
+ " y_pred.append('urban')\n",
+ " else:\n",
+ " y_pred.append('suburban')\n",
+ " return np.asarray(y_pred, dtype=object)\n",
+ "\n",
+ "def prepare_data_for_stat_eval(df_clean, df_spice, know_station_only:bool=False, n_sample:int=1000):\n",
+ " df = pd.merge(df_clean, df_spice, left_index=True, right_index=True)\n",
+ " print(df['type_of_area'].value_counts())\n",
+ " print('total data points: ', df.shape)\n",
+ " if know_station_only:\n",
+ " df = df[~((df['type_of_area']=='unknown'))]\n",
+ " if n_sample is not None:\n",
+ " n_indiv_sample = int(n_sample/3)\n",
+ " n_rural = min(len(df[df['type_of_area']=='rural']), n_indiv_sample) \n",
+ " n_suburban = min(len(df[df['type_of_area']=='suburban']), n_indiv_sample) \n",
+ " n_urban = n_sample - n_rural - n_suburban\n",
+ " print(n_urban, n_suburban, n_rural)\n",
+ " df = pd.concat([\n",
+ " df[df['type_of_area'] == 'urban'].sample(n=n_urban, random_state=42),\n",
+ " df[df['type_of_area'] == 'suburban'].sample(n=n_suburban, random_state=42),\n",
+ " df[df['type_of_area'] == 'rural'].sample(n = n_rural, random_state=42)\n",
+ " ])\n",
+ " return df\n",
+ "\n",
+ "def stat_evaluation(df_spice, clf, spice:str):\n",
+ " y_pred = clf.predict(df_spice[list(df_spice.columns)[:-2]])\n",
+ " df_spice['type_of_area_pred']=y_pred\n",
+ " print('accuracy: ', accuracy_score(df_spice['type_of_area'].values, df_spice['type_of_area_pred'].values))\n",
+ " df_urban = df_spice[df_spice['type_of_area_pred']=='urban']\n",
+ " df_suburban = df_spice[df_spice['type_of_area_pred']=='suburban']\n",
+ " df_rural = df_spice[df_spice['type_of_area_pred']=='rural']\n",
+ " data_clf = [df_urban[spice].values, df_suburban[spice].values, df_rural[spice].values]\n",
+ " # Creating the box plot\n",
+ " plt.figure(figsize=(9, 8))\n",
+ " plt.boxplot(data_clf, patch_artist=True, notch=False, vert=True, showmeans=False, widths=0.15, positions=[0.25, 0.75, 1.25])\n",
+ " # Adding titles and labels\n",
+ " plt.title(f'Statistic evaluion {spice}')\n",
+ " plt.xlabel('Station location')\n",
+ " plt.ylabel(f'p75 of {spice}')\n",
+ " plt.xticks([0.25, 0.75, 1.25], ['urban', 'suburban', 'rural'])\n",
+ " plt.savefig('figures/box_' + spice + '.jpg', dpi=400, bbox_inches='tight')\n",
+ " # Display the plot\n",
+ " plt.show()\n",
+ " return\n",
+ "\n",
+ "def print_prediction_accuracies(df_test_result, model_pred):\n",
+ " urban_pred = df_test_result[df_test_result['type_of_area']=='urban']\n",
+ " suburban_pred = df_test_result[df_test_result['type_of_area']=='suburban']\n",
+ " rural_pred = df_test_result[df_test_result['type_of_area']=='rural']\n",
+ " for model_name, pred in model_pred.items():\n",
+ " acc = accuracy_score(df_test_result['type_of_area'].values, df_test_result[pred].values)\n",
+ " acc_urban = accuracy_score(urban_pred['type_of_area'].values, urban_pred[pred].values)\n",
+ " acc_suburban = accuracy_score(suburban_pred['type_of_area'].values, suburban_pred[pred].values)\n",
+ " acc_rural = accuracy_score(rural_pred['type_of_area'].values, rural_pred[pred].values)\n",
+ " print(f'{model_name}')\n",
+ " print('global accuracy: ', acc*100)\n",
+ " print('accuracy for predicting urban: ', round(acc_urban*100, 2))\n",
+ " print('accuracy for predicting suburban: ', round(acc_suburban*100, 2))\n",
+ " print('accuracy for predicting rural: ', round(acc_rural*100, 2))\n",
+ " print()\n",
+ " return\n",
+ "\n",
+ "def get_feature_importance(trained_clf, model_name:str):\n",
+ " if model_name=='cboost':\n",
+ " importance = trained_clf.get_feature_importance()\n",
+ " elif model_name in ['rf', 'lgbm']:\n",
+ " importance = trained_clf.feature_importances_\n",
+ " else:\n",
+ " raise ValueError('unknown classifier')\n",
+ " return importance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "2909f641-7ba5-48aa-af36-13b8f9d36951",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def train_with_important_feature(clf:dict, X_train, Y_train, X_test=None, Y_test=None, init_num_feature=10):\n",
+ " # first train\n",
+ " model = clf['model']\n",
+ " model_name = clf['model_name']\n",
+ " model.fit(X_train, Y_train)\n",
+ " importance = get_feature_importance(model, model_name)\n",
+ " feature_rank = pd.DataFrame({'feature': list(X_train.columns), 'importance': importance})\n",
+ " feature_rank = feature_rank.sort_values('importance', ascending=False)\n",
+ " features = feature_rank['feature'].to_list()\n",
+ " num_feature=len(features)\n",
+ " Y_pred = model.predict(X_test)\n",
+ " accuracy = accuracy_score(Y_test, Y_pred)\n",
+ " imp_feature = {'trained_model': model, 'features': features, 'accuracy': accuracy}\n",
+ " print(accuracy)\n",
+ " for k in range(num_feature-init_num_feature):\n",
+ " select_features = features[:init_num_feature+k]\n",
+ " X_train_selected = X_train[select_features]\n",
+ " X_test_selected = X_test[select_features]\n",
+ " model.fit(X_train_selected, Y_train)\n",
+ " Y_pred = model.predict(X_test_selected)\n",
+ " acc = accuracy_score(Y_test, Y_pred)\n",
+ " if acc > imp_feature['accuracy']:\n",
+ " imp_feature['features']= select_features\n",
+ " imp_feature['accuracy']=acc\n",
+ " imp_feature['model'] = model\n",
+ " print(imp_feature['accuracy'])\n",
+ " return imp_feature"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9975351a-f029-42f1-a03a-7e3da0de1ba4",
+ "metadata": {},
+ "source": [
+ "#### Prepare data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "d69bb783-8849-43da-be63-54773dbc7733",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "initial data size: (23969, 22)\n",
+ "New shape: (22378, 22)\n",
+ "Total points drop: 1591\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = data_preprocessing(path_to_data='stationglobalmetadata_update.csv')\n",
+ "dataset_origin = dataset.copy()\n",
+ "data = dataset_origin.sample(frac=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "1148cc1f-f9ea-4888-8362-c54ed142317a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# y_boxcox_nox_2015, lmb_func_nox_2015 = stats.boxcox(data['mean_nox_emissions_10km_year2015'].values)\n",
+ "# y_boxcox_nox_2000, lmb_func_nox_2000 = stats.boxcox(data['mean_nox_emissions_10km_year2000'].values)\n",
+ "# #stats.boxcox(Y_train)\n",
+ "# data['mean_nox_emissions_10km_year2015'] = y_boxcox_nox_2015\n",
+ "# data['mean_nox_emissions_10km_year2000'] = y_boxcox_nox_2000"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "98b25a8a-1764-453b-a2ed-a02264e74890",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# df_train = labelled_data[~(labelled_data.index.isin(df_pm_test_idx))]\n",
+ "# df_test = labelled_data[(labelled_data.index.isin(df_pm_test_idx))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "293de0f8-8b16-4375-bc48-d9d43f41cea4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "number of data point: (22378, 22)\n",
+ "number of known station: (12408, 22)\n",
+ "number of unlabelled station: (9970, 22)\n",
+ "training shape: (11408, 22)\n",
+ "test shape: (1000, 22)\n"
+ ]
+ }
+ ],
+ "source": [
+ "labelled_data = data[~(data['type_of_area']=='unknown')]\n",
+ "unlabelled_data = data[(data['type_of_area']=='unknown')]\n",
+ "print('number of data point: ', data.shape)\n",
+ "print(\"number of known station: \", labelled_data.shape)\n",
+ "print('number of unlabelled station: ', unlabelled_data.shape)\n",
+ "df_train, df_test = train_test_split(labelled_data, test_size=1000, shuffle=False, random_state=42)\n",
+ "print('training shape: ', df_train.shape)\n",
+ "print('test shape: ', df_test.shape)\n",
+ "df_test_idx = list(df_test.index)\n",
+ "df_train_idx = list(df_train.index)\n",
+ "labeled_idx = list(labelled_data.index)\n",
+ "unlabeled_idx = list(unlabelled_data.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "id": "4df7f2e2-e5ab-473b-8745-185032e2a9f6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "test_data = pd.read_csv('data/hand_labeled_test_data.csv')\n",
+ "test_data.set_index(['lat', 'lon'], inplace=True)\n",
+ "test_indeces = list(test_data.index)\n",
+ "test_indeces = [idx for idx in test_indeces if idx in list(labelled_data.index)]\n",
+ "test_data = test_data.loc[test_indeces]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "id": "d528dc81-b8b5-4ea1-8ef8-3ea04f4c2271",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "training shape: 11408\n",
+ "unkwon station: 9970\n",
+ "test shape: 1000\n"
+ ]
+ }
+ ],
+ "source": [
+ "# we first selecte the most recent infos and analyse the correlation between variable\n",
+ "selected_colunms_0 = [\n",
+ " 'area_code',\n",
+ " 'altitude',\n",
+ " 'mean_topography_srtm_alt_90m_year1994', \n",
+ " 'mean_topography_srtm_alt_1km_year1994', \n",
+ " 'max_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994',\n",
+ " 'stddev_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'climatic_zone_year2016', \n",
+ " 'mean_stable_nightlights_5km_year2013',\n",
+ " 'max_stable_nightlights_25km_year1992', \n",
+ " 'mean_population_density_250m_year2015', \n",
+ " 'mean_population_density_5km_year2015', \n",
+ " 'max_population_density_25km_year2015', \n",
+ " 'mean_population_density_250m_year1990', \n",
+ " 'mean_population_density_5km_year1990', \n",
+ " 'max_population_density_25km_year1990', \n",
+ " 'mean_nox_emissions_10km_year2015', \n",
+ " 'mean_nox_emissions_10km_year2000', \n",
+ " 'type_of_area'\n",
+ "]\n",
+ "dataset_0 = feature_engineering_selection(data, selected_columns=selected_colunms_0, scaling='robust', encode_cat=True)\n",
+ "#plot_correlation(dataset_0[list(dataset_0.columns)[:-1]])\n",
+ "# print(len(df_train_idx))\n",
+ "# print(len(df_test_idx))\n",
+ "# print(len(unlabeled_idx))\n",
+ "df_train_0 = dataset_0.loc[df_train_idx]#[(dataset_0.index.isin(df_train_idx))]\n",
+ "df_test_0 = dataset_0.loc[df_test_idx]#[(dataset_0.index.isin(df_test_idx))]\n",
+ "print('training shape: ', df_train_0.shape[0])\n",
+ "print('unkwon station: ', len(unlabeled_idx))\n",
+ "print('test shape: ', df_test_0.shape[0])\n",
+ "df_unlabeled_0 = dataset_0.loc[unlabeled_idx]#[(dataset_0.index.isin(unlabeled_idx))]\n",
+ "test_data_0 = dataset_0.loc[test_indeces]\n",
+ "#display(df_train_0.head(2))\n",
+ "X_train_0 = df_train_0.drop(columns=['area_code', 'type_of_area'])\n",
+ "Y_train_0 = df_train_0[['type_of_area']]\n",
+ "X_test_0 = df_test_0.drop(columns=['area_code', 'type_of_area'])\n",
+ "Y_test_0 = df_test_0[['type_of_area']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "id": "d9776c21-9ea6-489f-b2cd-678da3010d1f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "cl_weights = {'urban': 0.275, 'suburban':0.45, 'rural':0.275}\n",
+ "\n",
+ "param_grid = {\n",
+ " 'n_estimators': [100, 200, 250, 300, 400, 500],\n",
+ " 'max_features': ['auto', 'sqrt', 'log2'],\n",
+ " 'max_depth' : [4,5,6,7,8],\n",
+ " 'criterion' :['gini', 'entropy']\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "id": "38eb85e7-1fc2-474a-b415-4e220a3bac68",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "rf_clf = RandomForestClassifier(n_estimators=500, criterion = 'entropy', random_state = 42, class_weight=cl_weights)\n",
+ "\n",
+ "cboost_clf = CatBoostClassifier(n_estimators=500, learning_rate= 0.03986794927756705, depth= 9, \n",
+ " l2_leaf_reg= 6, random_strength= 3.4439060846939396, min_data_in_leaf= 49, \n",
+ " bootstrap_type = 'MVS', verbose=False)\n",
+ "\n",
+ "lgbm_clf = LGBMClassifier(n_estimators=500, verbose=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "c5c5659c-171c-4ad4-8f2a-d7b4e422f20d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "techniques = [\n",
+ " (SMOTE(random_state=42), \"SMOTE\"),\n",
+ " (SMOTETomek(random_state=42), \"SMOTE+Tomek\"),\n",
+ " (SMOTEENN(random_state=42), \"SMOTE+ENN\")\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "id": "061a82c0-3944-414b-9c61-595bf9437afb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "resampler_smot = SMOTETomek(random_state=42) # \"SMOTE\"\n",
+ "resampler_smotetomek = SMOTETomek(random_state=42) # \"SMOTE+Tomek\"\n",
+ "resampler_smoteenn = SMOTEENN(random_state=42) # \"SMOTE+ENN\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "id": "a5f15c86-2ab0-42ac-a9a2-05c92a6a0ed3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train_0_smot, Y_train_0_smot = resampler_smot.fit_resample(X_train_0, Y_train_0)\n",
+ "X_train_0_smotetomek, Y_train_0_smotetomek = resampler_smotetomek.fit_resample(X_train_0, Y_train_0)\n",
+ "X_train_0_smoteenn, Y_train_0_smoteenn = resampler_smoteenn.fit_resample(X_train_0, Y_train_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "id": "22c7764c-2b29-4922-bc9b-0a8ddcc50445",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Create a count plot using Seaborn\n",
+ "sns.countplot(data=Y_train_0_smotetomek, x='type_of_area')\n",
+ "plt.title('Value Counts of type_of_area SMOT')\n",
+ "plt.xlabel('Values')\n",
+ "plt.ylabel('Counts')\n",
+ "plt.savefig('value_counts_smot_type_of_area.jpg', dpi=400, bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "id": "b01d0d23-cf4c-43d6-8072-e204ddd8f8fe",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {\n",
+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
+ " /* Specific color for light theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+ " --sklearn-color-icon: #878787;\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 pre {\n",
+ " padding: 0;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 input.sk-hidden--visually {\n",
+ " border: 0;\n",
+ " clip: rect(1px 1px 1px 1px);\n",
+ " clip: rect(1px, 1px, 1px, 1px);\n",
+ " height: 1px;\n",
+ " margin: -1px;\n",
+ " overflow: hidden;\n",
+ " padding: 0;\n",
+ " position: absolute;\n",
+ " width: 1px;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+ " border: 1px dashed var(--sklearn-color-line);\n",
+ " margin: 0 0.4em 0.5em 0.4em;\n",
+ " box-sizing: border-box;\n",
+ " padding-bottom: 0.4em;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-container {\n",
+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+ " but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+ " so we also need the `!important` here to be able to override the\n",
+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
+ " background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+ " background-size: 2px 100%;\n",
+ " background-repeat: no-repeat;\n",
+ " background-position: center center;\n",
+ "}\n",
+ "\n",
+ "/* Parallel-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item::after {\n",
+ " content: \"\";\n",
+ " width: 100%;\n",
+ " border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+ " flex-grow: 1;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel {\n",
+ " display: flex;\n",
+ " align-items: stretch;\n",
+ " justify-content: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+ " align-self: flex-end;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+ " align-self: flex-start;\n",
+ " width: 50%;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+ " width: 0;\n",
+ "}\n",
+ "\n",
+ "/* Serial-specific style estimator block */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-serial {\n",
+ " display: flex;\n",
+ " flex-direction: column;\n",
+ " align-items: center;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " padding-right: 1em;\n",
+ " padding-left: 1em;\n",
+ "}\n",
+ "\n",
+ "\n",
+ "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+ "clickable and can be expanded/collapsed.\n",
+ "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+ "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+ "*/\n",
+ "\n",
+ "/* Pipeline and ColumnTransformer style (default) */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable {\n",
+ " /* Default theme specific background. It is overwritten whether we have a\n",
+ " specific estimator or a Pipeline/ColumnTransformer */\n",
+ " background-color: var(--sklearn-color-background);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable label */\n",
+ "#sk-container-id-1 label.sk-toggleable__label {\n",
+ " cursor: pointer;\n",
+ " display: block;\n",
+ " width: 100%;\n",
+ " margin-bottom: 0;\n",
+ " padding: 0.5em;\n",
+ " box-sizing: border-box;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+ " /* Arrow on the left of the label */\n",
+ " content: \"â–¸\";\n",
+ " float: left;\n",
+ " margin-right: 0.25em;\n",
+ " color: var(--sklearn-color-icon);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+ " color: var(--sklearn-color-text);\n",
+ "}\n",
+ "\n",
+ "/* Toggleable content - dropdown */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content {\n",
+ " max-height: 0;\n",
+ " max-width: 0;\n",
+ " overflow: hidden;\n",
+ " text-align: left;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+ " /* Expand drop-down */\n",
+ " max-height: 200px;\n",
+ " max-width: 100%;\n",
+ " overflow: auto;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+ " content: \"â–ľ\";\n",
+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific style */\n",
+ "\n",
+ "/* Colorize estimator box */\n",
+ "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+ "#sk-container-id-1 div.sk-label label {\n",
+ " /* The background is the default theme color */\n",
+ " color: var(--sklearn-color-text-on-default-background);\n",
+ "}\n",
+ "\n",
+ "/* On hover, darken the color of the background */\n",
+ "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Label box, darken color on hover, fitted */\n",
+ "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Estimator label */\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label label {\n",
+ " font-family: monospace;\n",
+ " font-weight: bold;\n",
+ " display: inline-block;\n",
+ " line-height: 1.2em;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-label-container {\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ "/* Estimator-specific */\n",
+ "#sk-container-id-1 div.sk-estimator {\n",
+ " font-family: monospace;\n",
+ " border: 1px dotted var(--sklearn-color-border-box);\n",
+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
+ " margin-bottom: 0.5em;\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-1 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
+ "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+ "\n",
+ "/* Common style for \"i\" and \"?\" */\n",
+ "\n",
+ ".sk-estimator-doc-link,\n",
+ "a:link.sk-estimator-doc-link,\n",
+ "a:visited.sk-estimator-doc-link {\n",
+ " float: right;\n",
+ " font-size: smaller;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1em;\n",
+ " height: 1em;\n",
+ " width: 1em;\n",
+ " text-decoration: none !important;\n",
+ " margin-left: 1ex;\n",
+ " /* unfitted */\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-1 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(n_estimators=500, verbose=0)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\"> LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(n_estimators=500, verbose=0)</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "LGBMClassifier(n_estimators=500, verbose=0)"
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rf_clf.fit(X_train_0, Y_train_0)\n",
+ "cboost_clf.fit(X_train_0, Y_train_0)\n",
+ "lgbm_clf.fit(X_train_0, Y_train_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "127d1265-fbfe-4678-852b-56324a3300c2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# rf_clf.fit(X_train_0_smot, Y_train_0_smot)\n",
+ "# cboost_clf.fit(X_train_0_smot, Y_train_0_smot)\n",
+ "# lgbm_clf.fit(X_train_0_smot, Y_train_0_smot)\n",
+ "\n",
+ "# rf_clf.fit(X_train_0_smotetomek, Y_train_0_smotetomek)\n",
+ "# cboost_clf.fit(X_train_0_smotetomek, Y_train_0_smotetomek)\n",
+ "# lgbm_clf.fit(X_train_0_smotetomek, Y_train_0_smotetomek)\n",
+ "\n",
+ "# rf_clf.fit(X_train_0_smoteenn, Y_train_0_smoteenn)\n",
+ "# cboost_clf.fit(X_train_0_smoteenn, Y_train_0_smoteenn)\n",
+ "# lgbm_clf.fit(X_train_0_smoteenn, Y_train_0_smoteenn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "cf5330cd-0e27-4e4a-b20e-ba52f35bffd9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#print('training lgbm')\n",
+ "#selected_feature_lgbm = train_with_important_feature({'model_name': 'lgbm', 'model': lgbm_clf}, X_train_0, Y_train_0, X_test_0, Y_test_0, init_num_feature=15)\n",
+ "#print('training rf')\n",
+ "#selected_feature_rf = train_with_important_feature({'model_name': 'rf', 'model': rf_clf}, X_train_0, Y_train_0, X_test_0, Y_test_0, init_num_feature=15)\n",
+ "#print('training cboost')\n",
+ "#selected_feature_cboost = train_with_important_feature({'model_name': 'cboost', 'model': cboost_clf}, X_train_0, Y_train_0, X_test_0, Y_test_0, init_num_feature=15)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "2152f09f-b5a8-44ee-976c-f61551bdbff2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# lgbm_features = selected_feature_lgbm['features']\n",
+ "# lgbm_clf = selected_feature_lgbm['model']\n",
+ "\n",
+ "# rf_features = selected_feature_rf['features']\n",
+ "# rf_clf = selected_feature_rf['model']\n",
+ "\n",
+ "# cboost_features = selected_feature_cboost['features']\n",
+ "# cboost_clf = selected_feature_cboost['model']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "c0e8e8d6-1da3-4712-bea5-47cbe7955868",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['rural', 'suburban', 'urban'], dtype=object)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_pred_prob_rf=rf_clf.predict_proba(test_data_0.drop(columns=['area_code', 'type_of_area']))\n",
+ "y_pred_prob_cboost=cboost_clf.predict_proba(test_data_0.drop(columns=['area_code', 'type_of_area']))\n",
+ "y_pred_prob_lgbm=lgbm_clf.predict_proba(test_data_0.drop(columns=['area_code', 'type_of_area']))\n",
+ "y_pred_prob_voting = (y_pred_prob_rf + y_pred_prob_cboost + y_pred_prob_lgbm)/3\n",
+ "rf_clf.classes_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "018ae7db-b5a1-4cf3-924c-dd4b15ea83b1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy w.r.t truth labels : 100.00%\n",
+ "Accuracy w.r.t given labels: 87.88%\n",
+ "Accuracy w.r.t voting labels: 100.00%\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_pred_0 = rf_clf.predict(test_data_0.drop(columns=['area_code', 'type_of_area']))\n",
+ "test_data['type_of_area_pred_rf'] = y_pred_0\n",
+ "test_data['type_of_area_pred_thr_voting'] = threshold_clf(y_pred_prob_voting)\n",
+ "test_data['type_of_area_voting'] = test_data[['type_of_area_gmap', 'type_of_area_pred_rf', 'type_of_area_pred_thr_voting']].apply(lambda row: Counter(row).most_common(1)[0][0], axis=1)\n",
+ "Acc_0 = accuracy_score(test_data['type_of_are_toar'].values, test_data['type_of_area_pred_rf'].values)\n",
+ "Acc_1 = accuracy_score(test_data['type_of_area_gmap'].values, test_data['type_of_area_pred_rf'].values)\n",
+ "Acc_2 = accuracy_score(test_data['type_of_are_toar'].values, test_data['type_of_area_pred_thr_voting'].values)\n",
+ "print(\"Accuracy w.r.t truth labels : {:.2f}%\".format(Acc_0*100))\n",
+ "print(\"Accuracy w.r.t given labels: {:.2f}%\".format(Acc_1*100))\n",
+ "print(\"Accuracy w.r.t voting labels: {:.2f}%\".format(Acc_2*100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "ad3cb942-5943-44be-b426-410d48da77dc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "y_pred_prob_rf=rf_clf.predict_proba(X_test_0)\n",
+ "y_pred_prob_cboost=cboost_clf.predict_proba(X_test_0)\n",
+ "y_pred_prob_lgbm=lgbm_clf.predict_proba(X_test_0)\n",
+ "y_pred_prob_voting = (y_pred_prob_rf + y_pred_prob_cboost + y_pred_prob_lgbm)/3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "ce4f1c5c-4919-451c-8f7c-77c1ab9cb6a6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "y_pred_0_rf = rf_clf.predict(X_test_0)\n",
+ "y_pred_0_cboost = cboost_clf.predict(X_test_0)\n",
+ "y_pred_0_lgbm = lgbm_clf.predict(X_test_0)\n",
+ "df_test_result = df_test[['area_code', 'type_of_area']]\n",
+ "df_test_result['type_of_area_pred_rf'] = y_pred_0_rf\n",
+ "df_test_result['type_of_area_pred_cboost'] = y_pred_0_cboost.reshape(len(y_pred_0_rf), )\n",
+ "df_test_result['type_of_area_pred_lgbm'] = y_pred_0_lgbm\n",
+ "df_test_result['type_of_area_pred_voting'] = df_test_result[['type_of_area_pred_rf', 'type_of_area_pred_cboost', 'type_of_area_pred_lgbm']].apply(lambda row: Counter(row).most_common(1)[0][0], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "ec66508b-5dfc-487a-b606-217715a32a58",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Random Forest\n",
+ "global accuracy: 76.4\n",
+ "accuracy for predicting urban: 82.78\n",
+ "accuracy for predicting suburban: 56.15\n",
+ "accuracy for predicting rural: 84.67\n",
+ "\n",
+ "Lgbm\n",
+ "global accuracy: 76.0\n",
+ "accuracy for predicting urban: 81.68\n",
+ "accuracy for predicting suburban: 58.08\n",
+ "accuracy for predicting rural: 83.28\n",
+ "\n",
+ "CatBoost\n",
+ "global accuracy: 75.5\n",
+ "accuracy for predicting urban: 82.34\n",
+ "accuracy for predicting suburban: 55.0\n",
+ "accuracy for predicting rural: 83.28\n",
+ "\n",
+ "Voting\n",
+ "global accuracy: 76.1\n",
+ "accuracy for predicting urban: 82.34\n",
+ "accuracy for predicting suburban: 56.54\n",
+ "accuracy for predicting rural: 83.97\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_pred = {'Random Forest':'type_of_area_pred_rf', 'Lgbm': 'type_of_area_pred_lgbm', 'CatBoost': 'type_of_area_pred_cboost', 'Voting': 'type_of_area_pred_voting'}\n",
+ "print_prediction_accuracies(df_test_result, model_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "7a1674f6-6264-4790-bbe1-19aeb4c3dd03",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "y_pred_0_rf = threshold_clf(rf_clf.predict_proba(X_test_0))\n",
+ "y_pred_0_cboost = threshold_clf(cboost_clf.predict_proba(X_test_0))\n",
+ "y_pred_0_lgbm = threshold_clf(lgbm_clf.predict_proba(X_test_0))\n",
+ "df_test_result = df_test[['area_code', 'type_of_area']]\n",
+ "df_test_result['type_of_area_pred_rf'] = y_pred_0_rf\n",
+ "df_test_result['type_of_area_pred_cboost'] = y_pred_0_cboost.reshape(len(y_pred_0_rf), )\n",
+ "df_test_result['type_of_area_pred_lgbm'] = y_pred_0_lgbm\n",
+ "df_test_result['type_of_area_pred_voting'] = threshold_clf(y_pred_prob_voting)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "919f6939-b60e-4d39-aecb-7f23e819b2d5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Random Forest\n",
+ "global accuracy: 75.5\n",
+ "accuracy for predicting urban: 79.91\n",
+ "accuracy for predicting suburban: 63.46\n",
+ "accuracy for predicting rural: 79.44\n",
+ "\n",
+ "Lgbm\n",
+ "global accuracy: 76.5\n",
+ "accuracy for predicting urban: 81.46\n",
+ "accuracy for predicting suburban: 60.38\n",
+ "accuracy for predicting rural: 83.28\n",
+ "\n",
+ "CatBoost\n",
+ "global accuracy: 73.9\n",
+ "accuracy for predicting urban: 79.25\n",
+ "accuracy for predicting suburban: 59.62\n",
+ "accuracy for predicting rural: 78.4\n",
+ "\n",
+ "Voting\n",
+ "global accuracy: 75.7\n",
+ "accuracy for predicting urban: 80.79\n",
+ "accuracy for predicting suburban: 60.38\n",
+ "accuracy for predicting rural: 81.53\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_pred = {'Random Forest':'type_of_area_pred_rf', 'Lgbm': 'type_of_area_pred_lgbm', 'CatBoost': 'type_of_area_pred_cboost', 'Voting': 'type_of_area_pred_voting'}\n",
+ "print_prediction_accuracies(df_test_result, model_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "77459eb9-7b9b-44f8-9ce4-34f9e0c8701a",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 75.60%\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1100x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "type_of_area\n",
+ "urban 459\n",
+ "suburban 280\n",
+ "rural 261\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cf_matrix(\n",
+ " df_test_result['type_of_area'].values,\n",
+ " df_test_result['type_of_area_pred_rf'].values,\n",
+ " fig_name='confusion_matrix_rf')\n",
+ "df_test_result['type_of_area'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "id": "a591362c-65de-4cfc-8713-ea28f6374493",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 75.30%\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1100x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "type_of_area\n",
+ "urban 439\n",
+ "rural 281\n",
+ "suburban 280\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cf_matrix(\n",
+ " df_test_result['type_of_area'].values,\n",
+ " df_test_result['type_of_area_pred_voting'].values,\n",
+ " fig_name='confusion_matrix_voting')\n",
+ "df_test_result['type_of_area'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "id": "828ea821-0a68-4632-ac10-47ea40aad630",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "type_of_area\n",
+ "urban 772\n",
+ "suburban 368\n",
+ "rural 301\n",
+ "unknown 207\n",
+ "Name: count, dtype: int64\n",
+ "total data points: (1648, 31)\n",
+ "366 333 301\n",
+ "accuracy: 0.983756345177665\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_test_nox = prepare_data_for_stat_eval(dataset_0, df_nox, know_station_only=True)\n",
+ "stat_evaluation(df_test_nox[df_test_nox['nox']<=100], rf_clf, 'nox')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "id": "cff93371-9d4c-4408-9199-9e3acf57440e",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "type_of_area\n",
+ "urban 768\n",
+ "unknown 378\n",
+ "suburban 294\n",
+ "rural 240\n",
+ "Name: count, dtype: int64\n",
+ "total data points: (1680, 31)\n",
+ "466 294 240\n",
+ "accuracy: 0.9838383838383838\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 900x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_test_pm = prepare_data_for_stat_eval(dataset_0, df_pm, know_station_only=True)\n",
+ "stat_evaluation(df_test_pm[df_test_pm['pm2p5']<=70], rf_clf, 'pm2p5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "id": "acb9c7e8-163a-4ce6-8432-134cc4fbea13",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1200x1400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "importance = rf_clf.feature_importances_\n",
+ "feature_rank = pd.DataFrame({'feature': list(X_train_0.columns), 'importance': importance})\n",
+ "feature_rank = feature_rank.sort_values('importance', ascending=False)\n",
+ "features = feature_rank['feature'].to_list()\n",
+ "important_features = features[:]\n",
+ "plt.figure(figsize=(12, 14))\n",
+ "sns.barplot(y='feature', x='importance', data=feature_rank)\n",
+ "plt.title(f\"feature importance for rf_clf\", size=10)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "03e95f14-9f4e-4ae7-bd24-9a52a5c73c83",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df_test_misclass = df_test_result[~(df_test_result['type_of_area']==df_test_result['type_of_area_pred_rf'])]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "1de863d1-1849-4d3c-afce-1cfb211d447a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#df_test_misclass[(df_test_misclass['type_of_area']=='urban')&(df_test_misclass['type_of_area_pred_rf']=='rural')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "2b0a9c45-7479-440e-a1dc-dc59061a02d8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#df_test_misclass[(df_test_misclass['type_of_area']=='urban')&(df_test_misclass['type_of_area_pred_rf']=='suburban')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "dbfaee0e-f78b-4d5e-a26c-ad0e478d9dd7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#df_test_misclass[(df_test_misclass['type_of_area']=='suburban')&(df_test_misclass['type_of_area_pred_rf']=='rural')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "id": "ab20cb39-9d15-48a4-8fdb-7d3f18495044",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "number of data point: (22352, 22)\n",
+ "number of known station: (12455, 22)\n",
+ "number of unlabelled station: (9897, 22)\n",
+ "training shape: (11455, 22)\n",
+ "test shape: (1000, 22)\n"
+ ]
+ }
+ ],
+ "source": [
+ "labelled_data = data[~(data['type_of_area']=='unknown')]\n",
+ "unlabelled_data = data[(data['type_of_area']=='unknown')]\n",
+ "print('number of data point: ', data.shape)\n",
+ "print(\"number of known station: \", labelled_data.shape)\n",
+ "print('number of unlabelled station: ', unlabelled_data.shape)\n",
+ "df_train, df_test = train_test_split(labelled_data, test_size=1000, shuffle=False, random_state=42)\n",
+ "print('training shape: ', df_train.shape)\n",
+ "print('test shape: ', df_test.shape)\n",
+ "df_test_idx = list(df_test.index)\n",
+ "df_train_idx = list(df_train.index)\n",
+ "labeled_idx = list(labelled_data.index)\n",
+ "unlabeled_idx = list(unlabelled_data.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "e08c059d-d44f-41f9-b9fe-be925a3d034b",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "11455\n",
+ "1000\n",
+ "training shape (11455, 24)\n",
+ "test shape: (1000, 24)\n"
+ ]
+ },
+ {
+ "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></th>\n",
+ " <th>mean_topography_srtm_alt_90m_year1994</th>\n",
+ " <th>max_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>min_topography_srtm_relative_alt_5km_year1994</th>\n",
+ " <th>distance_to_major_road_year2020</th>\n",
+ " <th>mean_stable_nightlights_5km_year2013</th>\n",
+ " <th>max_stable_nightlights_25km_year1992</th>\n",
+ " <th>mean_population_density_250m_year2015</th>\n",
+ " <th>mean_population_density_5km_year2015</th>\n",
+ " <th>max_population_density_25km_year2015</th>\n",
+ " <th>mean_nox_emissions_10km_year2015</th>\n",
+ " <th>...</th>\n",
+ " <th>climatic_zone_year2016_4.0</th>\n",
+ " <th>climatic_zone_year2016_5.0</th>\n",
+ " <th>climatic_zone_year2016_6.0</th>\n",
+ " <th>climatic_zone_year2016_7.0</th>\n",
+ " <th>climatic_zone_year2016_8.0</th>\n",
+ " <th>climatic_zone_year2016_9.0</th>\n",
+ " <th>climatic_zone_year2016_10.0</th>\n",
+ " <th>climatic_zone_year2016_11.0</th>\n",
+ " <th>climatic_zone_year2016_12.0</th>\n",
+ " <th>type_of_area</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>39.515338</th>\n",
+ " <th>-84.187159</th>\n",
+ " <td>0.453855</td>\n",
+ " <td>-0.155556</td>\n",
+ " <td>-0.632653</td>\n",
+ " <td>-0.318906</td>\n",
+ " <td>-0.433221</td>\n",
+ " <td>0.0</td>\n",
+ " <td>-0.367367</td>\n",
+ " <td>-0.312672</td>\n",
+ " <td>-0.083297</td>\n",
+ " <td>0.356377</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42.854800</th>\n",
+ " <th>-2.680700</th>\n",
+ " <td>1.243214</td>\n",
+ " <td>0.408889</td>\n",
+ " <td>0.061224</td>\n",
+ " <td>-0.241582</td>\n",
+ " <td>0.314559</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.567690</td>\n",
+ " <td>0.806182</td>\n",
+ " <td>0.302764</td>\n",
+ " <td>-0.417194</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2 rows Ă— 24 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " mean_topography_srtm_alt_90m_year1994 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.453855 \n",
+ "42.854800 -2.680700 1.243214 \n",
+ "\n",
+ " max_topography_srtm_relative_alt_5km_year1994 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.155556 \n",
+ "42.854800 -2.680700 0.408889 \n",
+ "\n",
+ " min_topography_srtm_relative_alt_5km_year1994 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.632653 \n",
+ "42.854800 -2.680700 0.061224 \n",
+ "\n",
+ " distance_to_major_road_year2020 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.318906 \n",
+ "42.854800 -2.680700 -0.241582 \n",
+ "\n",
+ " mean_stable_nightlights_5km_year2013 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.433221 \n",
+ "42.854800 -2.680700 0.314559 \n",
+ "\n",
+ " max_stable_nightlights_25km_year1992 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 \n",
+ "42.854800 -2.680700 0.0 \n",
+ "\n",
+ " mean_population_density_250m_year2015 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.367367 \n",
+ "42.854800 -2.680700 1.567690 \n",
+ "\n",
+ " mean_population_density_5km_year2015 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.312672 \n",
+ "42.854800 -2.680700 0.806182 \n",
+ "\n",
+ " max_population_density_25km_year2015 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 -0.083297 \n",
+ "42.854800 -2.680700 0.302764 \n",
+ "\n",
+ " mean_nox_emissions_10km_year2015 ... \\\n",
+ "lat lon ... \n",
+ "39.515338 -84.187159 0.356377 ... \n",
+ "42.854800 -2.680700 -0.417194 ... \n",
+ "\n",
+ " climatic_zone_year2016_4.0 climatic_zone_year2016_5.0 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 1.0 \n",
+ "42.854800 -2.680700 0.0 0.0 \n",
+ "\n",
+ " climatic_zone_year2016_6.0 climatic_zone_year2016_7.0 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 0.0 \n",
+ "42.854800 -2.680700 1.0 0.0 \n",
+ "\n",
+ " climatic_zone_year2016_8.0 climatic_zone_year2016_9.0 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 0.0 \n",
+ "42.854800 -2.680700 0.0 0.0 \n",
+ "\n",
+ " climatic_zone_year2016_10.0 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 \n",
+ "42.854800 -2.680700 0.0 \n",
+ "\n",
+ " climatic_zone_year2016_11.0 \\\n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 \n",
+ "42.854800 -2.680700 0.0 \n",
+ "\n",
+ " climatic_zone_year2016_12.0 type_of_area \n",
+ "lat lon \n",
+ "39.515338 -84.187159 0.0 suburban \n",
+ "42.854800 -2.680700 0.0 urban \n",
+ "\n",
+ "[2 rows x 24 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# we first selecte the most recent infos and analyse the correlation between variable\n",
+ "selected_colunms_1 = ['mean_topography_srtm_alt_90m_year1994', \n",
+ " 'max_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'stddev_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'climatic_zone_year2016', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'mean_stable_nightlights_1km_year2013',\n",
+ " 'mean_stable_nightlights_5km_year2013', \n",
+ " 'mean_population_density_250m_year2015',\n",
+ " 'mean_population_density_5km_year2015', \n",
+ " 'max_population_density_25km_year2015',\n",
+ " 'mean_nox_emissions_10km_year2015', \n",
+ " 'type_of_area']\n",
+ "colunms_1 = [\n",
+ " 'mean_topography_srtm_alt_90m_year1994', \n",
+ " 'max_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'min_topography_srtm_relative_alt_5km_year1994', \n",
+ " 'distance_to_major_road_year2020', \n",
+ " 'climatic_zone_year2016',\n",
+ " 'mean_stable_nightlights_5km_year2013', \n",
+ " 'max_stable_nightlights_25km_year1992', \n",
+ " 'mean_population_density_250m_year2015', \n",
+ " 'mean_population_density_5km_year2015', \n",
+ " 'max_population_density_25km_year2015', \n",
+ " 'mean_nox_emissions_10km_year2015', \n",
+ " 'type_of_area'\n",
+ "]\n",
+ "\n",
+ "dataset_1 = feature_engineering_selection(data, selected_columns=colunms_1, scaling='robust', encode_cat=True)\n",
+ "#plot_correlation(dataset_1[list(dataset_1.columns)[:-1]])\n",
+ "print(len(df_train_idx))\n",
+ "print(len(df_test_idx))\n",
+ "df_train_1 = dataset_1.loc[df_train_idx] #[(dataset_1.index.isin(df_train_idx))]\n",
+ "df_test_1 = dataset_1.loc[df_test_idx] #[(dataset_1.index.isin(df_test_idx))]\n",
+ "print('training shape', df_train_1.shape)\n",
+ "print('test shape: ', df_test_1.shape)\n",
+ "df_unlabeled_1 = dataset_1.loc[unlabeled_idx] #[(dataset_0.index.isin(unlabeled_idx))]\n",
+ "test_data_1 = dataset_1.loc[test_indeces]\n",
+ "#display(df_train_1.head(2))\n",
+ "X_train_1 = df_train_1.drop(columns=['type_of_area'])\n",
+ "Y_train_1 = df_train_1[['type_of_area']]\n",
+ "X_test_1 = df_test_1.drop(columns=['type_of_area'])\n",
+ "Y_test_1 = df_test_1[['type_of_area']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "654fa1e9-a44e-42a7-8d2b-234748616e99",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "rf_clf = RandomForestClassifier(n_estimators=500, criterion = 'entropy', random_state = 42, class_weight=cl_weights)\n",
+ "cboost_clf = CatBoostClassifier(n_estimators=500, verbose=False)\n",
+ "lgbm_clf = LGBMClassifier(n_estimators=500 , verbose=0)\n",
+ "#v_clf = VotingClassifier(estimators=[('rf_clf', rf_clf), ('cboost_clf', cboost_clf), ('lgbm', lbm_clf)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "0a8ee605-2088-47bb-853d-1672d1ca0f3a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–ľ\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(n_estimators=500, verbose=0)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(n_estimators=500, verbose=0)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "LGBMClassifier(n_estimators=500, verbose=0)"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rf_clf.fit(X_train_1, Y_train_1)\n",
+ "cboost_clf.fit(X_train_1, Y_train_1)\n",
+ "lgbm_clf.fit(X_train_1, Y_train_1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 563,
+ "id": "1e7dd518-9acd-4dad-b4be-df5be1ccbfc1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy w.r.t truth labels : 100.00%\n",
+ "Accuracy w.r.t given labels: 87.88%\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_pred_1 = rf_clf.predict(test_data_1.drop(columns=['type_of_area']))\n",
+ "test_data['type_of_area_pred_rf'] = y_pred_1\n",
+ "Acc_0 = accuracy_score(test_data['type_of_are_truth'].values, test_data['type_of_area_pred_rf'].values)\n",
+ "Acc_1 = accuracy_score(test_data['type_of_area_given'].values, test_data['type_of_area_pred_rf'].values)\n",
+ "print(\"Accuracy w.r.t truth labels : {:.2f}%\".format(Acc_0*100))\n",
+ "print(\"Accuracy w.r.t given labels: {:.2f}%\".format(Acc_1*100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 564,
+ "id": "36bd1934-9672-4e5d-a948-97129187fa3d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "y_pred_1_rf = rf_clf.predict(X_test_1)\n",
+ "y_pred_1_cboost = cboost_clf.predict(X_test_1)\n",
+ "y_pred_1_lbm = lgbm_clf.predict(X_test_1)\n",
+ "y_pred_1_voting = voting_clf({'rf_clf': rf_clf, 'cboost': cboost_clf, 'lgbm': lgbm_clf}, X_test_1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 565,
+ "id": "3064fa25-2393-409b-93b4-50e3c398fd33",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "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></th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " <th>type_of_area_pred_rf</th>\n",
+ " <th>type_of_area_pred_cboost</th>\n",
+ " <th>type_of_area_pred_lbm</th>\n",
+ " <th>type_of_area_pred_voting</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>47.286109</th>\n",
+ " <th>-2.032286</th>\n",
+ " <td>FR23149</td>\n",
+ " <td>suburban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45.757731</th>\n",
+ " <th>4.854217</th>\n",
+ " <td>FR20062</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46.775002</th>\n",
+ " <th>23.596678</th>\n",
+ " <td>RO0074A</td>\n",
+ " <td>urban</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>56.047271</th>\n",
+ " <th>12.691271</th>\n",
+ " <td>SE0053A</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48.273290</th>\n",
+ " <th>14.314790</th>\n",
+ " <td>AT4S416</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_code type_of_area type_of_area_pred_rf \\\n",
+ "lat lon \n",
+ "47.286109 -2.032286 FR23149 suburban urban \n",
+ "45.757731 4.854217 FR20062 urban urban \n",
+ "46.775002 23.596678 RO0074A urban rural \n",
+ "56.047271 12.691271 SE0053A urban urban \n",
+ "48.273290 14.314790 AT4S416 urban urban \n",
+ "\n",
+ " type_of_area_pred_cboost type_of_area_pred_lbm \\\n",
+ "lat lon \n",
+ "47.286109 -2.032286 urban urban \n",
+ "45.757731 4.854217 urban urban \n",
+ "46.775002 23.596678 rural rural \n",
+ "56.047271 12.691271 urban urban \n",
+ "48.273290 14.314790 urban urban \n",
+ "\n",
+ " type_of_area_pred_voting \n",
+ "lat lon \n",
+ "47.286109 -2.032286 urban \n",
+ "45.757731 4.854217 urban \n",
+ "46.775002 23.596678 rural \n",
+ "56.047271 12.691271 urban \n",
+ "48.273290 14.314790 urban "
+ ]
+ },
+ "execution_count": 565,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_test_result = df_test[['area_code', 'type_of_area']]\n",
+ "df_test_result['type_of_area_pred_rf'] = y_pred_1_rf\n",
+ "df_test_result['type_of_area_pred_cboost'] = y_pred_1_cboost.reshape(len(y_pred_1_rf), )\n",
+ "df_test_result['type_of_area_pred_lbm'] = y_pred_1_lbm\n",
+ "df_test_result['type_of_area_pred_voting'] = y_pred_1_voting\n",
+ "df_test_result.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f04ef1b3-7b9c-4a0f-9b07-39860cb16d79",
+ "metadata": {},
+ "source": [
+ "#### ====================== Predict unknown stations ==========================="
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "87ae3276-fa5d-46bc-838c-012ce050efdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_unlabelled_0 = df_unlabeled_0.drop(columns=['type_of_area'])\n",
+ "Y_unlabelled_0 = df_train_0[['type_of_area']]\n",
+ "df_unlabeled_idxcode = dataset.loc[unlabeled_idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "59465861-c97f-4bc3-aa6c-3f23760bb5fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Y_unlabelled_pred_rf = rf_clf.predict(X_unlabelled_0)\n",
+ "Y_unlabelled_pred_cboost = cboost_clf.predict(X_unlabelled_0)\n",
+ "Y_unlabelled_pred_lbm = lgbm_clf.predict(X_unlabelled_0)\n",
+ "Y_unlabelled_pred_voting = voting_clf({'rf_clf': rf_clf, 'cboost': cboost_clf, 'lgbm': lgbm_clf}, X_unlabelled_0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "061ecfa4-9aa5-43b0-9b3c-00dbaf5b2f02",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_unknow_stations_pred = df_unlabeled_idxcode[['area_code', 'type_of_area']]\n",
+ "df_unknow_stations_pred['type_of_area_pred_rf'] = Y_unlabelled_pred_rf\n",
+ "df_unknow_stations_pred['type_of_area_pred_cboost'] = Y_unlabelled_pred_cboost.reshape(len(Y_unlabelled_pred_voting), )\n",
+ "df_unknow_stations_pred['type_of_area_pred_lbm'] = Y_unlabelled_pred_lbm\n",
+ "df_unknow_stations_pred['type_of_area_pred_voting'] = Y_unlabelled_pred_voting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "b287c152-7e46-49a1-8d05-b6501de4caae",
+ "metadata": {
+ "scrolled": true
+ },
+ "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></th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " <th>type_of_area_pred_rf</th>\n",
+ " <th>type_of_area_pred_cboost</th>\n",
+ " <th>type_of_area_pred_lbm</th>\n",
+ " <th>type_of_area_pred_voting</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>37.972200</th>\n",
+ " <th>-122.518900</th>\n",
+ " <td>openaq_2002</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14.352220</th>\n",
+ " <th>100.565325</th>\n",
+ " <td>openaq_225676</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34.668600</th>\n",
+ " <th>112.443300</th>\n",
+ " <td>3022A</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41.322472</th>\n",
+ " <th>-95.937992</th>\n",
+ " <td>openaq_1075</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45.294130</th>\n",
+ " <th>-73.348570</th>\n",
+ " <td>055301</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_code type_of_area type_of_area_pred_rf \\\n",
+ "lat lon \n",
+ "37.972200 -122.518900 openaq_2002 unknown urban \n",
+ "14.352220 100.565325 openaq_225676 unknown urban \n",
+ "34.668600 112.443300 3022A unknown urban \n",
+ "41.322472 -95.937992 openaq_1075 unknown urban \n",
+ "45.294130 -73.348570 055301 unknown rural \n",
+ "\n",
+ " type_of_area_pred_cboost type_of_area_pred_lbm \\\n",
+ "lat lon \n",
+ "37.972200 -122.518900 urban urban \n",
+ "14.352220 100.565325 urban urban \n",
+ "34.668600 112.443300 urban urban \n",
+ "41.322472 -95.937992 suburban suburban \n",
+ "45.294130 -73.348570 rural suburban \n",
+ "\n",
+ " type_of_area_pred_voting \n",
+ "lat lon \n",
+ "37.972200 -122.518900 urban \n",
+ "14.352220 100.565325 urban \n",
+ "34.668600 112.443300 urban \n",
+ "41.322472 -95.937992 suburban \n",
+ "45.294130 -73.348570 rural "
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_unknow_stations_pred.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de59189a-9f8e-4ab4-9c81-0f7fc38bb890",
+ "metadata": {},
+ "source": [
+ "#### Here we make prediction using aforementioned threshold probability mathod"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "8e684bbd-2ee8-4204-8c61-df7766b5945d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Y_unlabelled_pred_prob_rf=rf_clf.predict_proba(X_unlabelled_0)\n",
+ "Y_unlabelled_pred_prob_cboost=cboost_clf.predict_proba(X_unlabelled_0)\n",
+ "Y_unlabelled_pred_prob_lgbm=lgbm_clf.predict_proba(X_unlabelled_0)\n",
+ "Y_unlabelled_pred_prob_voting = (Y_unlabelled_pred_prob_rf + Y_unlabelled_pred_prob_cboost + Y_unlabelled_pred_prob_lgbm)/3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "25e7d6d1-df4f-4159-9dba-b3e1f879a338",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Y_unlabelled_0_rf = threshold_clf(rf_clf.predict_proba(X_unlabelled_0))\n",
+ "Y_unlabelled_0_cboost = threshold_clf(cboost_clf.predict_proba(X_unlabelled_0))\n",
+ "Y_unlabelled_0_lgbm = threshold_clf(lgbm_clf.predict_proba(X_unlabelled_0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "b97fc810-065e-412f-83ff-50f42d362ffc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_unknow_stations_pred_prob = df_unlabeled_idxcode[['area_code', 'type_of_area']]\n",
+ "df_unknow_stations_pred_prob['type_of_area_pred_rf'] = Y_unlabelled_0_rf\n",
+ "df_unknow_stations_pred_prob['type_of_area_pred_cboost'] = Y_unlabelled_0_cboost.reshape(len(Y_unlabelled_0_cboost), )\n",
+ "df_unknow_stations_pred_prob['type_of_area_pred_lgbm'] = Y_unlabelled_0_lgbm\n",
+ "df_unknow_stations_pred_prob['type_of_area_pred_voting'] = threshold_clf(Y_unlabelled_pred_prob_voting)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "2a5cf428-be70-4c86-89cd-aa3f8194042e",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "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></th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " <th>type_of_area_pred_rf</th>\n",
+ " <th>type_of_area_pred_cboost</th>\n",
+ " <th>type_of_area_pred_lgbm</th>\n",
+ " <th>type_of_area_pred_voting</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>37.972200</th>\n",
+ " <th>-122.518900</th>\n",
+ " <td>openaq_2002</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14.352220</th>\n",
+ " <th>100.565325</th>\n",
+ " <td>openaq_225676</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34.668600</th>\n",
+ " <th>112.443300</th>\n",
+ " <td>3022A</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41.322472</th>\n",
+ " <th>-95.937992</th>\n",
+ " <td>openaq_1075</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45.294130</th>\n",
+ " <th>-73.348570</th>\n",
+ " <td>055301</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47.349140</th>\n",
+ " <th>11.693101</th>\n",
+ " <td>openaq_2767</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28.632775</th>\n",
+ " <th>-106.038867</th>\n",
+ " <td>openaq_8095</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41.266400</th>\n",
+ " <th>123.799200</th>\n",
+ " <td>openaq_8931</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41.704444</th>\n",
+ " <th>140.505556</th>\n",
+ " <td>jp01334020</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32.213611</th>\n",
+ " <th>130.399444</th>\n",
+ " <td>jp43205160</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>9970 rows Ă— 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_code type_of_area type_of_area_pred_rf \\\n",
+ "lat lon \n",
+ "37.972200 -122.518900 openaq_2002 unknown urban \n",
+ "14.352220 100.565325 openaq_225676 unknown urban \n",
+ "34.668600 112.443300 3022A unknown urban \n",
+ "41.322472 -95.937992 openaq_1075 unknown urban \n",
+ "45.294130 -73.348570 055301 unknown rural \n",
+ "... ... ... ... \n",
+ "47.349140 11.693101 openaq_2767 unknown suburban \n",
+ "28.632775 -106.038867 openaq_8095 unknown urban \n",
+ "41.266400 123.799200 openaq_8931 unknown urban \n",
+ "41.704444 140.505556 jp01334020 unknown rural \n",
+ "32.213611 130.399444 jp43205160 unknown urban \n",
+ "\n",
+ " type_of_area_pred_cboost type_of_area_pred_lgbm \\\n",
+ "lat lon \n",
+ "37.972200 -122.518900 urban urban \n",
+ "14.352220 100.565325 urban urban \n",
+ "34.668600 112.443300 urban urban \n",
+ "41.322472 -95.937992 suburban suburban \n",
+ "45.294130 -73.348570 rural suburban \n",
+ "... ... ... \n",
+ "47.349140 11.693101 suburban suburban \n",
+ "28.632775 -106.038867 urban urban \n",
+ "41.266400 123.799200 urban urban \n",
+ "41.704444 140.505556 rural rural \n",
+ "32.213611 130.399444 urban urban \n",
+ "\n",
+ " type_of_area_pred_voting \n",
+ "lat lon \n",
+ "37.972200 -122.518900 urban \n",
+ "14.352220 100.565325 urban \n",
+ "34.668600 112.443300 urban \n",
+ "41.322472 -95.937992 suburban \n",
+ "45.294130 -73.348570 rural \n",
+ "... ... \n",
+ "47.349140 11.693101 suburban \n",
+ "28.632775 -106.038867 urban \n",
+ "41.266400 123.799200 urban \n",
+ "41.704444 140.505556 rural \n",
+ "32.213611 130.399444 urban \n",
+ "\n",
+ "[9970 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_unknow_stations_pred_prob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "d8c1bb79-ac4e-411e-a2ea-3752ab759592",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " <th>type_of_area_pred_rf</th>\n",
+ " <th>type_of_area_pred_cboost</th>\n",
+ " <th>type_of_area_pred_lgbm</th>\n",
+ " <th>type_of_area_pred_voting</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>37.972200</td>\n",
+ " <td>-122.518900</td>\n",
+ " <td>openaq_2002</td>\n",
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+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>14.352220</td>\n",
+ " <td>100.565325</td>\n",
+ " <td>openaq_225676</td>\n",
+ " <td>unknown</td>\n",
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+ " <th>2</th>\n",
+ " <td>34.668600</td>\n",
+ " <td>112.443300</td>\n",
+ " <td>3022A</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>41.322472</td>\n",
+ " <td>-95.937992</td>\n",
+ " <td>openaq_1075</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
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+ " <tr>\n",
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+ " <td>45.294130</td>\n",
+ " <td>-73.348570</td>\n",
+ " <td>055301</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9965</th>\n",
+ " <td>47.349140</td>\n",
+ " <td>11.693101</td>\n",
+ " <td>openaq_2767</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9966</th>\n",
+ " <td>28.632775</td>\n",
+ " <td>-106.038867</td>\n",
+ " <td>openaq_8095</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9967</th>\n",
+ " <td>41.266400</td>\n",
+ " <td>123.799200</td>\n",
+ " <td>openaq_8931</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9968</th>\n",
+ " <td>41.704444</td>\n",
+ " <td>140.505556</td>\n",
+ " <td>jp01334020</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9969</th>\n",
+ " <td>32.213611</td>\n",
+ " <td>130.399444</td>\n",
+ " <td>jp43205160</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>9970 rows Ă— 8 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " lat lon area_code type_of_area type_of_area_pred_rf \\\n",
+ "0 37.972200 -122.518900 openaq_2002 unknown urban \n",
+ "1 14.352220 100.565325 openaq_225676 unknown urban \n",
+ "2 34.668600 112.443300 3022A unknown urban \n",
+ "3 41.322472 -95.937992 openaq_1075 unknown urban \n",
+ "4 45.294130 -73.348570 055301 unknown rural \n",
+ "... ... ... ... ... ... \n",
+ "9965 47.349140 11.693101 openaq_2767 unknown suburban \n",
+ "9966 28.632775 -106.038867 openaq_8095 unknown urban \n",
+ "9967 41.266400 123.799200 openaq_8931 unknown urban \n",
+ "9968 41.704444 140.505556 jp01334020 unknown rural \n",
+ "9969 32.213611 130.399444 jp43205160 unknown urban \n",
+ "\n",
+ " type_of_area_pred_cboost type_of_area_pred_lgbm type_of_area_pred_voting \n",
+ "0 urban urban urban \n",
+ "1 urban urban urban \n",
+ "2 urban urban urban \n",
+ "3 suburban suburban suburban \n",
+ "4 rural suburban rural \n",
+ "... ... ... ... \n",
+ "9965 suburban suburban suburban \n",
+ "9966 urban urban urban \n",
+ "9967 urban urban urban \n",
+ "9968 rural rural rural \n",
+ "9969 urban urban urban \n",
+ "\n",
+ "[9970 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_pred = df_unknow_stations_pred_prob.copy()\n",
+ "df_pred.reset_index(drop=False, inplace=True)\n",
+ "df_pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "6c4724cc-011c-4915-a917-13e7d4550503",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = dataset_0.drop(columns=['area_code', 'type_of_area'])\n",
+ "Y = df_train_0[['type_of_area']]\n",
+ "\n",
+ "Y_pred_prob_rf=rf_clf.predict_proba(X)\n",
+ "Y_pred_prob_cboost=cboost_clf.predict_proba(X)\n",
+ "Y_pred_prob_lgbm=lgbm_clf.predict_proba(X)\n",
+ "Y_pred_prob_voting = (Y_pred_prob_rf + Y_pred_prob_cboost + Y_pred_prob_lgbm)/3\n",
+ "\n",
+ "Y_rf = threshold_clf(rf_clf.predict_proba(X))\n",
+ "Y_cboost = threshold_clf(cboost_clf.predict_proba(X))\n",
+ "Y_lgbm = threshold_clf(lgbm_clf.predict_proba(X))\n",
+ "\n",
+ "df_pred_prob = dataset_0[['area_code', 'type_of_area']]\n",
+ "df_pred_prob['type_of_area_pred_rf'] = Y_rf\n",
+ "df_pred_prob['type_of_area_pred_cboost'] = Y_cboost.reshape(len(Y_cboost), )\n",
+ "df_pred_prob['type_of_area_pred_lgbm'] = Y_lgbm\n",
+ "df_pred_prob['type_of_area_pred_voting'] = threshold_clf(Y_pred_prob_voting)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "05d8f878-7462-4b7a-948b-77a308113a47",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " }\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></th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " <th>type_of_area_pred_rf</th>\n",
+ " <th>type_of_area_pred_cboost</th>\n",
+ " <th>type_of_area_pred_lgbm</th>\n",
+ " <th>type_of_area_pred_voting</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>50.095683</th>\n",
+ " <th>4.594763</th>\n",
+ " <td>BETN100</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <th>139.343333</th>\n",
+ " <td>jp11329010</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>urban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46.151899</th>\n",
+ " <th>-86.918479</th>\n",
+ " <td>26-041-0912</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33.404050</th>\n",
+ " <th>-84.745728</th>\n",
+ " <td>13-077-0002</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36.190833</th>\n",
+ " <th>136.143056</th>\n",
+ " <td>jp18361010</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>27.389080</th>\n",
+ " <th>-80.311035</th>\n",
+ " <td>12-111-0013</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42.869800</th>\n",
+ " <th>-109.870800</th>\n",
+ " <td>56-035-0101</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36.715000</th>\n",
+ " <th>140.711389</th>\n",
+ " <td>jp08214010</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30.579700</th>\n",
+ " <th>105.751900</th>\n",
+ " <td>2527A</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35.645000</th>\n",
+ " <th>-88.906389</th>\n",
+ " <td>47-113-1001</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>22378 rows Ă— 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_code type_of_area type_of_area_pred_rf \\\n",
+ "lat lon \n",
+ "50.095683 4.594763 BETN100 unknown rural \n",
+ "35.893333 139.343333 jp11329010 unknown suburban \n",
+ "46.151899 -86.918479 26-041-0912 rural rural \n",
+ "33.404050 -84.745728 13-077-0002 suburban suburban \n",
+ "36.190833 136.143056 jp18361010 unknown suburban \n",
+ "... ... ... ... \n",
+ "27.389080 -80.311035 12-111-0013 suburban suburban \n",
+ "42.869800 -109.870800 56-035-0101 suburban rural \n",
+ "36.715000 140.711389 jp08214010 unknown urban \n",
+ "30.579700 105.751900 2527A unknown suburban \n",
+ "35.645000 -88.906389 47-113-1001 rural rural \n",
+ "\n",
+ " type_of_area_pred_cboost type_of_area_pred_lgbm \\\n",
+ "lat lon \n",
+ "50.095683 4.594763 rural rural \n",
+ "35.893333 139.343333 urban suburban \n",
+ "46.151899 -86.918479 rural rural \n",
+ "33.404050 -84.745728 suburban suburban \n",
+ "36.190833 136.143056 suburban suburban \n",
+ "... ... ... \n",
+ "27.389080 -80.311035 suburban suburban \n",
+ "42.869800 -109.870800 rural rural \n",
+ "36.715000 140.711389 urban urban \n",
+ "30.579700 105.751900 rural rural \n",
+ "35.645000 -88.906389 rural rural \n",
+ "\n",
+ " type_of_area_pred_voting \n",
+ "lat lon \n",
+ "50.095683 4.594763 rural \n",
+ "35.893333 139.343333 urban \n",
+ "46.151899 -86.918479 rural \n",
+ "33.404050 -84.745728 suburban \n",
+ "36.190833 136.143056 suburban \n",
+ "... ... \n",
+ "27.389080 -80.311035 suburban \n",
+ "42.869800 -109.870800 rural \n",
+ "36.715000 140.711389 urban \n",
+ "30.579700 105.751900 rural \n",
+ "35.645000 -88.906389 rural \n",
+ "\n",
+ "[22378 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_pred_prob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "699a3344-11e7-414f-8df2-0a1228faf395",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=df_pred_prob[df_pred_prob['type_of_area']!='unknown']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "81ae4e39-d58e-4305-a637-4100b07f05cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Acc = accuracy_score(df['type_of_area'].values, df['type_of_area_pred_rf'].values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "e6f69cb5-efe1-4efe-b70a-fa9658611595",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "global accuracy w.r.t toar repport station: 98.025%\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"global accuracy w.r.t toar repport station: {:.3f}%\".format(Acc*100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "fbdb9e87-863d-4231-9801-6fe326ca2462",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " vertical-align: middle;\n",
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+ " .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></th>\n",
+ " <th>area_code</th>\n",
+ " <th>type_of_area</th>\n",
+ " <th>type_of_area_pred_rf</th>\n",
+ " <th>type_of_area_pred_cboost</th>\n",
+ " <th>type_of_area_pred_lgbm</th>\n",
+ " <th>type_of_area_pred_voting</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>lat</th>\n",
+ " <th>lon</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>50.095683</th>\n",
+ " <th>4.594763</th>\n",
+ " <td>BETN100</td>\n",
+ " <td>unknown</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35.893333</th>\n",
+ " <th>139.343333</th>\n",
+ " <td>jp11329010</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>urban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46.151899</th>\n",
+ " <th>-86.918479</th>\n",
+ " <td>26-041-0912</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33.404050</th>\n",
+ " <th>-84.745728</th>\n",
+ " <td>13-077-0002</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36.190833</th>\n",
+ " <th>136.143056</th>\n",
+ " <td>jp18361010</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27.389080</th>\n",
+ " <th>-80.311035</th>\n",
+ " <td>12-111-0013</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " <td>suburban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42.869800</th>\n",
+ " <th>-109.870800</th>\n",
+ " <td>56-035-0101</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36.715000</th>\n",
+ " <th>140.711389</th>\n",
+ " <td>jp08214010</td>\n",
+ " <td>unknown</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " <td>urban</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30.579700</th>\n",
+ " <th>105.751900</th>\n",
+ " <td>2527A</td>\n",
+ " <td>unknown</td>\n",
+ " <td>suburban</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35.645000</th>\n",
+ " <th>-88.906389</th>\n",
+ " <td>47-113-1001</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " <td>rural</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>22378 rows Ă— 6 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " area_code type_of_area type_of_area_pred_rf \\\n",
+ "lat lon \n",
+ "50.095683 4.594763 BETN100 unknown rural \n",
+ "35.893333 139.343333 jp11329010 unknown suburban \n",
+ "46.151899 -86.918479 26-041-0912 rural rural \n",
+ "33.404050 -84.745728 13-077-0002 suburban suburban \n",
+ "36.190833 136.143056 jp18361010 unknown suburban \n",
+ "... ... ... ... \n",
+ "27.389080 -80.311035 12-111-0013 suburban suburban \n",
+ "42.869800 -109.870800 56-035-0101 suburban rural \n",
+ "36.715000 140.711389 jp08214010 unknown urban \n",
+ "30.579700 105.751900 2527A unknown suburban \n",
+ "35.645000 -88.906389 47-113-1001 rural rural \n",
+ "\n",
+ " type_of_area_pred_cboost type_of_area_pred_lgbm \\\n",
+ "lat lon \n",
+ "50.095683 4.594763 rural rural \n",
+ "35.893333 139.343333 urban suburban \n",
+ "46.151899 -86.918479 rural rural \n",
+ "33.404050 -84.745728 suburban suburban \n",
+ "36.190833 136.143056 suburban suburban \n",
+ "... ... ... \n",
+ "27.389080 -80.311035 suburban suburban \n",
+ "42.869800 -109.870800 rural rural \n",
+ "36.715000 140.711389 urban urban \n",
+ "30.579700 105.751900 rural rural \n",
+ "35.645000 -88.906389 rural rural \n",
+ "\n",
+ " type_of_area_pred_voting \n",
+ "lat lon \n",
+ "50.095683 4.594763 rural \n",
+ "35.893333 139.343333 urban \n",
+ "46.151899 -86.918479 rural \n",
+ "33.404050 -84.745728 suburban \n",
+ "36.190833 136.143056 suburban \n",
+ "... ... \n",
+ "27.389080 -80.311035 suburban \n",
+ "42.869800 -109.870800 rural \n",
+ "36.715000 140.711389 urban \n",
+ "30.579700 105.751900 rural \n",
+ "35.645000 -88.906389 rural \n",
+ "\n",
+ "[22378 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_pred_prob"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "a37ab34e-ca0e-459e-86dd-0beb84726a35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_pred_prob.to_csv('ml_station_prediction.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f41f284-3517-419d-9f07-7ccd0c4755f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_pred.to_csv('unknow_station_predict.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "7509447d-d7a5-456b-8a1f-ebdb89d446c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "closer_analysis_idx= [(59.123314, 11.391136), (34.985670, -84.375193), (34.710100, 128.587500), (37.360500, 128.125600),\n",
+ " (41.285533, -93.583983), (44.785616, -69.885058), (35.102638, -85.162194), (45.542222, 9.516389),\n",
+ " (39.720451, -94.872693), (48.691113, 21.286388), (35.259502, -120.644720), (37.049115, -122.019962),\n",
+ " (41.895812, -87.607683), (41.193587, 1.236703), (34.131714, -109.282309), (33.061150, -112.052040),\n",
+ " (49.076550, 8.406660), (36.186210, -5.380810), (45.647310, 13.854970), (43.045269, -70.713958),\n",
+ " (44.013056, 12.420000), (43.186600, -8.471600), (36.587027, -89.546742), (40.077780, -6.147220),\n",
+ " (35.498711, -83.310242)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "bfe2b0e9-502c-4a3f-b215-099f4d24ddd9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_closer_analysis = dataset.loc[closer_analysis_idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "7b635215-74bb-4aa7-8fac-06e262778d9e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(34.98567, -84.375193),\n",
+ " (48.691113, 21.286388),\n",
+ " (35.259502, -120.64472),\n",
+ " (41.193587, 1.236703),\n",
+ " (36.18621, -5.38081),\n",
+ " (43.1866, -8.4716),\n",
+ " (36.587027, -89.546742),\n",
+ " (40.07778, -6.14722)]"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[idx for idx in closer_analysis_idx if idx not in list(dataset_0.index)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b5746f40-7d0f-457c-8a15-814ac51a1f31",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_pred_0 = rf_clf.predict(test_data_0.drop(columns=['type_of_area']))\n",
+ "test_data['type_of_area_pred_rf'] = y_pred_0\n",
+ "test_data['type_of_area_pred_thr_voting'] = threshold_clf(y_pred_prob_voting)\n",
+ "Acc_0 = accuracy_score(test_data['type_of_are_truth'].values, test_data['type_of_area_pred_rf'].values)\n",
+ "Acc_1 = accuracy_score(test_data['type_of_area_given'].values, test_data['type_of_area_pred_rf'].values)\n",
+ "Acc_2 = accuracy_score(test_data['type_of_are_truth'].values, test_data['type_of_area_pred_thr_voting'].values)\n",
+ "print(\"Accuracy w.r.t truth labels : {:.2f}%\".format(Acc_0*100))\n",
+ "print(\"Accuracy w.r.t given labels: {:.2f}%\".format(Acc_1*100))\n",
+ "print(\"Accuracy w.r.t voting labels: {:.2f}%\".format(Acc_2*100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 566,
+ "id": "9919af14-b7f5-408a-8440-47f4fb400887",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Random forest accuracy: 0.356\n",
+ "catboost accuracy: 0.355\n",
+ "lgbm accuracy: 0.352\n",
+ "voting accuracy: 0.355\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Random forest accuracy: ', accuracy_score(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred_rf'].values))\n",
+ "print('catboost accuracy: ', accuracy_score(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred_lbm'].values))\n",
+ "print('lgbm accuracy: ', accuracy_score(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred_cboost'].values))\n",
+ "print('voting accuracy: ', accuracy_score(df_test_result['type_of_area'].values, df_test_result['type_of_area_pred_voting'].values))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 567,
+ "id": "5e9ba739-eb95-4431-8101-82f468761454",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "accuracy of predicting urban rf: 0.4889867841409692\n",
+ "accuracy of predicting rural rf: 0.2753623188405797\n",
+ "accuracy of predicting suburan rf: 0.21481481481481482\n",
+ "\n",
+ "accuracy of predicting urban lgbm: 0.486784140969163\n",
+ "accuracy of predicting rural lgbm: 0.26811594202898553\n",
+ "accuracy of predicting suburan lgbm: 0.2222222222222222\n",
+ "\n",
+ "accuracy of predicting urban cboost: 0.473568281938326\n",
+ "accuracy of predicting rural cboost: 0.26811594202898553\n",
+ "accuracy of predicting suburan cboost: 0.23333333333333334\n",
+ "\n",
+ "accuracy of predicting urban cboost: 0.4801762114537445\n",
+ "accuracy of predicting rural cboost: 0.2717391304347826\n",
+ "accuracy of predicting suburan voting: 0.22962962962962963\n"
+ ]
+ }
+ ],
+ "source": [
+ "urban_pred = df_test_result[df_test_result['type_of_area']=='urban']\n",
+ "suburban_pred = df_test_result[df_test_result['type_of_area']=='suburban']\n",
+ "rural_pred = df_test_result[df_test_result['type_of_area']=='rural']\n",
+ "print('accuracy of predicting urban rf: ', accuracy_score(urban_pred['type_of_area'].values, urban_pred['type_of_area_pred_rf'].values))\n",
+ "print('accuracy of predicting rural rf: ', accuracy_score(rural_pred['type_of_area'].values, rural_pred['type_of_area_pred_rf'].values))\n",
+ "print('accuracy of predicting suburan rf: ', accuracy_score(suburban_pred['type_of_area'].values, suburban_pred['type_of_area_pred_rf'].values))\n",
+ "print()\n",
+ "print('accuracy of predicting urban lgbm: ', accuracy_score(urban_pred['type_of_area'].values, urban_pred['type_of_area_pred_lbm'].values))\n",
+ "print('accuracy of predicting rural lgbm: ', accuracy_score(rural_pred['type_of_area'].values, rural_pred['type_of_area_pred_lbm'].values))\n",
+ "print('accuracy of predicting suburan lgbm: ', accuracy_score(suburban_pred['type_of_area'].values, suburban_pred['type_of_area_pred_lbm'].values))\n",
+ "print()\n",
+ "print('accuracy of predicting urban cboost: ', accuracy_score(urban_pred['type_of_area'].values, urban_pred['type_of_area_pred_cboost'].values))\n",
+ "print('accuracy of predicting rural cboost: ', accuracy_score(rural_pred['type_of_area'].values, rural_pred['type_of_area_pred_cboost'].values))\n",
+ "print('accuracy of predicting suburan cboost: ', accuracy_score(suburban_pred['type_of_area'].values, suburban_pred['type_of_area_pred_cboost'].values))\n",
+ "print()\n",
+ "print('accuracy of predicting urban cboost: ', accuracy_score(urban_pred['type_of_area'].values, urban_pred['type_of_area_pred_voting'].values))\n",
+ "print('accuracy of predicting rural cboost: ', accuracy_score(rural_pred['type_of_area'].values, rural_pred['type_of_area_pred_voting'].values))\n",
+ "print('accuracy of predicting suburan voting: ', accuracy_score(suburban_pred['type_of_area'].values, suburban_pred['type_of_area_pred_voting'].values))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "24c64365-3941-431e-8dae-acc5154e7763",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 76.60%\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1100x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "type_of_area\n",
+ "urban 452\n",
+ "rural 296\n",
+ "suburban 252\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cf_matrix_roc_auc(\n",
+ " df_test_result['type_of_area'].values,\n",
+ " df_test_result['type_of_area_pred_rf'].values,\n",
+ " fig_name='confusion_matrix_rf')\n",
+ "df_test_result['type_of_area'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "f1c86c9d-e547-44fe-862b-a7896732cd1a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy: 75.90%\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1100x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "cf_matrix_roc_auc(\n",
+ " df_test_result['type_of_area'].values,\n",
+ " df_test_result['type_of_area_pred_voting'].values,\n",
+ " fig_name='confusion_matrix_voting')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "9484855b-d8df-4afb-b5b8-c43c09ad85d7",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x1400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "importance = rf_clf.feature_importances_\n",
+ "feature_rank = pd.DataFrame({'feature': list(X_train_1.columns), 'importance': importance})\n",
+ "feature_rank = feature_rank.sort_values('importance', ascending=False)\n",
+ "features = feature_rank['feature'].to_list()\n",
+ "important_features = features[:]\n",
+ "plt.figure(figsize=(12, 14))\n",
+ "sns.barplot(y='feature', x='importance', data=feature_rank)\n",
+ "plt.title(f\"feature importance for rf_clf\", size=10)\n",
+ "plt.savefig('features_importance.jpg', dpi=300, bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7f50ad6f-3996-4d54-8c45-66ea7b9ec195",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "lat_train = list(df_train.index.get_level_values('lat'))\n",
+ "lon_train = list(df_train.index.get_level_values('lon'))\n",
+ "\n",
+ "lat_test = list(df_test.index.get_level_values('lat'))\n",
+ "lon_test = list(df_test.index.get_level_values('lon'))\n",
+ "# lat_pred = list(df_unlabeled_1.index.get_level_values('lat'))\n",
+ "# lon_pred = list(df_unlabeled_1.index.get_level_values('lon'))\n",
+ "lat_no2 = list(df_no2.index.get_level_values('lat'))\n",
+ "lon_no2 = list(df_no2.index.get_level_values('lon'))\n",
+ "\n",
+ "lat_pm = list(df_pm_test.index.get_level_values('lat'))\n",
+ "lon_pm = list(df_pm_test.index.get_level_values('lon'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7df1c7b5-c46a-41d1-954a-3016a9a9b3e4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Create a figure and axes with Plate Carree projection\n",
+ "import cartopy.crs as ccrs\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+ "ax.stock_img()\n",
+ "#ax.set_axis_on()\n",
+ "# Add coastlines and borders\n",
+ "ax.coastlines()\n",
+ "ax.add_feature(cartopy.feature.BORDERS)\n",
+ "# Plot earthquake locations with magnitude as size and color\n",
+ "#ax.scatter(lon_no2, lat_no2, s=10, color = 'blue', alpha=0.7, label='no2')\n",
+ "ax.scatter(lon_pm, lat_pm, s=10, color = 'orange', alpha=0.7, label='pm2p5')\n",
+ "# ax.scatter(lon_train, lat_train, s=10, color = 'blue', alpha=0.7, label='train')\n",
+ "# ax.scatter(lon_test, lat_test, s=10, color = 'red', alpha=0.7, label='test')\n",
+ "# ax.scatter(lon_pred, lat_pred, s=10, color = 'orange', alpha=0.7, label='predict')\n",
+ "# ax.scatter(lon_train, lat_train, s=10, color = 'blue', alpha=0.7)\n",
+ "# ax.scatter(lon_test, lat_test, s=10, color = 'red', alpha=0.7)\n",
+ "plt.xlabel('Longitude')\n",
+ "plt.ylabel('Latitude')\n",
+ "plt.title('Distribution of train, test, and predict station locations',fontsize=12)\n",
+ "plt.legend(loc='best')\n",
+ "plt.savefig('data_split.jpg', dpi=300, bbox_inches='tight')\n",
+ "plt.show()\n",
+ "# Add color bar legend"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "790b96e4-ea99-4e3e-ba77-f64c1072d2e0",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### ============================== END ================================"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 660,
+ "id": "d07c551b-4777-4487-9685-3fc0018c2d28",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df_unlabeled_pred_correct = df_unlabeled_pred[(df_unlabeled_pred['type_of_area_pred_rf'] == df_unlabeled_pred['type_of_area_pred_lgbm']) & (df_unlabeled_pred['type_of_area_pred_rf']==df_unlabeled_pred['type_of_area_pred_cboost'])]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 568,
+ "id": "32bd2608-955d-4277-9acb-d4e5d626e511",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "unlabeled_pred_urban_idx = list(df_unlabeled_pred_correct[df_unlabeled_pred_correct['type_of_area_pred_rf']=='urban'].index)\n",
+ "unlabeled_pred_suburban_idx = list(df_unlabeled_pred_correct[df_unlabeled_pred_correct['type_of_area_pred_rf']=='suburban'].index)\n",
+ "unlabeled_pred_rural_idx = list(df_unlabeled_pred_correct[df_unlabeled_pred_correct['type_of_area_pred_rf']=='rural'].index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "3ff21da4-cc3a-4e63-8397-6fc035643df3",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original shape: (22383, 55)\n",
+ "Shape after PCA: (22383, 26)\n",
+ "KL divergence before PCA: 0.7851837873458862\n",
+ "KL diversgence after PCA: 0.7359404563903809\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1500x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1500x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_tsne_6cls, labels_tsne_6cls, df_pca_tsne_6cls, labels_pca_tsne_6cls = TSNE_Kmean(df_without_stats__encode_scaled, num_clusters=6)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:base] *",
+ "language": "python",
+ "name": "conda-base-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 9b87bb81c8c25db0768f3946fa984d0793b91379..2f0e72a71a12e6c75eef50d1a79e9d1cb4e9f4d6 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,2 @@
## TOAR-classifier v2: A data-driven classification tool for global air quality stations
-<img src="fiures/kmeans_clusters_1.jpg" alt="Figure Description" width="500"/>
+
diff --git a/TOAR-classifier_v2.ipynb b/TOAR-classifier_v2.ipynb
index 1822e3c0995932e11f6e0f64e80e972b26dd5d3d..fe15b9a37b8b60547ad21adcfaa1b0662470f3a6 100755
--- a/TOAR-classifier_v2.ipynb
+++ b/TOAR-classifier_v2.ipynb
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "8666a040",
"metadata": {
"tags": []
@@ -49,10 +49,7 @@
"from sklearn.manifold import TSNE\n",
"from sklearn.metrics import *\n",
"from scipy import stats\n",
- "from scipy.special import inv_boxcox\n",
"from collections import Counter\n",
- "import cartopy.crs as ccrs\n",
- "import plotly.express as px\n",
"import warnings\n",
"from sklearn.utils import shuffle\n",
"from fancyimpute import IterativeImputer\n",
@@ -458,6 +455,7 @@
" return\n",
"\n",
"def boxplot(df):\n",
+ " import plotly.express as px\n",
" for col in list(df.columns):\n",
" fig = px.box(df, y=col, width=400, height=400)\n",
" fig.show()\n",
@@ -514,13 +512,14 @@
},
{
"cell_type": "code",
- "execution_count": 107,
+ "execution_count": 2,
"id": "d58e8d30-7b75-4e89-8827-10cf1d420efc",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
+ "#from scipy.special import inv_boxcox\n",
"# y_boxcox_nox_2015, lmb_func_nox_2015 = stats.boxcox(dataset['mean_nox_emissions_10km_year2015'].values)\n",
"# y_boxcox_nox_2000, lmb_func_nox_2000 = stats.boxcox(dataset['mean_nox_emissions_10km_year2000'].values)\n",
"# #stats.boxcox(Y_train)\n",
@@ -3096,13 +3095,14 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": 3,
"id": "1148cc1f-f9ea-4888-8362-c54ed142317a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
+ "#from scipy.special import inv_boxcox\n",
"# y_boxcox_nox_2015, lmb_func_nox_2015 = stats.boxcox(data['mean_nox_emissions_10km_year2015'].values)\n",
"# y_boxcox_nox_2000, lmb_func_nox_2000 = stats.boxcox(data['mean_nox_emissions_10km_year2000'].values)\n",
"# #stats.boxcox(Y_train)\n",
@@ -6325,6 +6325,7 @@
"outputs": [],
"source": [
"# Create a figure and axes with Plate Carree projection\n",
+ "import cartopy.crs as ccrs\n",
"plt.figure(figsize=(10, 6))\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.stock_img()\n",
diff --git a/figures/toar_classifier_v2.png b/figures/toar_classifier_v2.png
new file mode 100644
index 0000000000000000000000000000000000000000..a390fa7d5132667767d4ea47291aae962be0e421
Binary files /dev/null and b/figures/toar_classifier_v2.png differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391