cz_elections_analysis.ipynb

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   "cell_type": "markdown",
   "source": [
    "# Analýza kandidátů do Poslanecké sněmovny napříč volbami\n",
    "## Úvod\n",
    "Tato práce se zabývá analýzou dat o kandidátech do Poslanecké sněmovny od roku 2006 do roku 2021. Zdrojem jsou [data z Českého statistického úřadu](https://volby.cz/opendata/opendata.htm).\n",
    "* Analýza dat se snaží zodpovědět následující otázky:\n",
    "    *  Jaké je věkové rozložení kandidátů napříč volbami?\n",
    "    *  Jak velké zastoupení mají muži a ženy?\n",
    "    *  Jaká ideologie převládá?\n",
    "    *  Jaké obory jsou u kandidátů nejčastější?\n",
    "    *  Jaký vliv má vysokoškolské vzdělání na volbu ideologie kandidátem?\n",
    "\n",
    "Další součástí této práce je natrénování binárního klasifikačního modelu, kterým se na základě informací o kandidátech obsažených v datových zdrojích predikuje, zda kandidát ve volbách získá mandát anebo nikoliv."
   ],
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   "outputs": [],
   "source": [
    "# Installations\n",
    "# !pip install matplotlib\n",
    "# !pip install scikit-learn\n",
    "# !pip install seaborn"
   ],
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  {
   "cell_type": "markdown",
   "source": [
    "## Import knihoven a inicializace potřebných funkcí\n",
    "Ke splnění cílů této práce autoři užívají celkem pět knihoven jazyku Python, které jsou v dalším kroku práce naimportovány. Knihovny pandas a numpy se užívají k manipulaci s daty, matplotlib a seaborn pro vizualizaci a sci-kit learn pro účely strojového učení. Knihovna warnings je použita za účelem skrytí upozornění buněk."
   ],
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   "cell_type": "code",
   "execution_count": 169,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "# filtrace upozornění\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# nastavení standardního rozměru grafu\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 9)"
   ],
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   "source": [
    "Následující buňka zavádí funkci \"obor\". Cílem je povolání kandidátů (uvedené ve zdrojových datech) namapovat na širší/obecnější obory či kategorie (i.e. finance, právo, politika, řemeslo). Kandidáti totiž mohou mít ve sloupci POVOLANI uvedeno cokoliv a proto bylo nutné vytvořit předem definovaný slovník, jehož pomocí se pomocí hodnoty sloupce POVOLANI, každému kandidátovi přiřadí obecnější kategorie, do níž jeho povolání spadá. K tomu autoři vytvořili JSON soubor (viz dict_occupations.json). Ten jako hodnoty vnořených klíčů \"var\" uvádí různé varianty názvu povolání, ke kterým se má mapovat příslušná kategorie. Kategorie byly utvořeny a definovány na základě nejčastějších hodnot ve sloupci POVOLANI. Pokud k dané hodnotě ve sloupci POVOLANÍ neexistuje kategorie, pak se přiřadí kategorie \"Jiné\"."
   ],
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   "cell_type": "code",
   "execution_count": 170,
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['duchod', 'podnik', 'zivnost', 'remeslo', 'soukzam', 'finance', 'student', 'akademik', 'statzam', 'zdravotnictvi', 'politik', 'nezam', 'manager', 'zemedel', 'pravo', 'odbor', 'umeni', 'sport'])"
     },
     "execution_count": 170,
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    }
   ],
   "source": [
    "import json # daná knihovna umožňuje snadný import JSON souborů ve formátu python slovníků\n",
    "\n",
    "with open(\"dict_occupations.json\", \"r\", encoding=\"utf-8\") as file: # otevření JSON souboru\n",
    "    occup_dict = json.load(file)\n",
    "\n",
    "\n",
    "def obor(val: str) -> str:\n",
    "    \"\"\"\n",
    "    Checks to what field does candidate's profession belongs to (such as finance, law, medicine etc.) with the help of keywords from global occup_dict dictionary\n",
    "    :param val: string value containing candidate's profession description\n",
    "    :return: string value with the particular field, returns \"Jiné\" if no matches were found\n",
    "    \"\"\"\n",
    "    global occup_dict\n",
    "    for key in occup_dict.keys():\n",
    "        for i in occup_dict[key][\"var\"]:\n",
    "            if i in val.lower():\n",
    "                return occup_dict[key][\"name\"]\n",
    "    return \"jiné\"\n",
    "\n",
    "\n",
    "occup_dict.keys()"
   ],
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   "cell_type": "markdown",
   "source": [
    "Jelikož jsou použita volební data z parlamentních voleb v různých letech, které mají více či méně standardizovaný tvar, autoři užívají jednu předem definovanou funkci na čištění všech datových souborů."
   ],
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   "source": [
    "def clean_data(data: pd.DataFrame, strany: dict) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Clean and prepare a dataframe containing data from different types of elections for further visualisation and modelling purposes.\n",
    "    The output is:\n",
    "        -[cele_jmeno]: combining name columns into one\n",
    "        -[vek]: candidate's age\n",
    "        -[vs_vzdelani]: Creating a boolean column checking whether a candidate has university degree\n",
    "        -[obor]: field of candidate's profession\n",
    "        -[pohlavi]: candidate's gender (based on their surname's ending)\n",
    "        -[kraj]: region where candidate runs their campaign\n",
    "        -[NSTRANA]: political party that nominated the candidate\n",
    "        -[ideologie]: canidate's ideology (left-wing, center, right-wing)\n",
    "        -[mandat]: whether candidate got their mandate\n",
    "        -[year]: year of elections\n",
    "    :param data: pandas dataframe including elections data\n",
    "    :param strany: dictionary data type with political parties' data\n",
    "    :return: cleaned pandas dataframe\n",
    "    \"\"\"\n",
    "    df = data.copy()\n",
    "\n",
    "    df[\"cele_jmeno\"] = df[\"JMENO\"] + \" \" + df[\"PRIJMENI\"]\n",
    "    df[\"pohlavi\"] = np.where(df.loc[:, \"cele_jmeno\"].str[-1] == \"á\", \"F\", \"M\")\n",
    "    df[\"vs_vzdelani\"] = np.where((df[\"TITULPRED\"].notnull()) | (df[\"TITULZA\"].notnull()), 1, 0)\n",
    "    df[\"vek\"] = df[\"VEK\"]\n",
    "\n",
    "    # seznam krajů dle kódů\n",
    "    kraj_list = [\"Hlavní město Praha\", \"Středočeský kraj\", \"Jihočeský kraj\", \"Plzeňský kraj\", \"Karlovarský kraj\",\n",
    "                \"Ústecký kraj\", \"Liberecký kraj\", \"Královéhradecký kraj\", \"Pardubický kraj\", \"Kraj Vysočina\",\n",
    "                \"Jihomoravský kraj\", \"Olomoucký kraj\", \"Zlínský kraj\", \"Moravskoslezský kraj\"]\n",
    "    kraj_dict = dict(zip(list(range(1, 15)), kraj_list))\n",
    "    df[\"ideologie\"] = df[\"NSTRANA\"].map(strany)\n",
    "    df[\"kraj\"] = df[\"VOLKRAJ\"].map(kraj_dict)\n",
    "    df[\"obor\"] = df[\"POVOLANI\"].apply(lambda row: obor(str(row))) # mapuje slovník oborů na sloupec \"POVOLANI\"\n",
    "    df[\"mandat\"] = df[\"MANDAT\"].apply(lambda row: 1 if str(row) == \"A\" or str(row) == \"1\" else 0) # převádí různé záznamy na jeden tvar (v některých letech je to A/N, někde 1/0)\n",
    "    columns = [\"cele_jmeno\", \"vek\", \"vs_vzdelani\", \"obor\", \"pohlavi\", \"kraj\", \"NSTRANA\", \"ideologie\", \"mandat\", \"year\"]\n",
    "\n",
    "    return df[columns]"
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   "source": [
    "## Načítání dat\n",
    "Popis některých sloupců, které jsou součástí výstupního souboru:\n",
    "   * **VOLKRAJ** - číslo kraje, na základě těchto čísel se navazují názvy krajů\n",
    "   * **JMENO**, **PRIJMENI** - jméno a příjmení, na základě koncovky u příjmení se vytváří sloupec \"pohlavi\"\n",
    "   * **TITULPRED**, **TITULZA** - tituly před a za jménem, při přítomnosti aspoň jedné ze dvou se sloupci \"vs_vzdelani\" přiřazuje hodnota 1, jinak 0\n",
    "   * **VEK** - věk\n",
    "   * **POVOLANI** - povolaní kandidáta, jak bylo uvedeno výše - tuto hodnotu kandidáti vyplňují sami a proto jeho hodnoty nemají žádný standard. Sloupec \"obor\" ve výstupním souboru vzniká mapováním sloupce POVOLANI pomocí slovníku dict_occupations.json.\n",
    "   * **NSTRANA** - strana, která kandidáta navrhla - ze všech sloupců se obsahující strany byl použit tento, neboť obsahuje nejméně chybějících hodnot. Je tomu tak proto, že každého kandidáta musela nějaká strana navrhnout. Na rozdíl od např. KSTRANA, která označuje stranu kandidáta, kde se může vyskytovat kandidát bez strany. Hlavním účelem tohoto sloupce je udržení tzv. **ideologie**, pomocí ní dochází ke třídění kandidátů do úzších skupin dle jednodimenzionálního rozdělení (levicové, středové a pravicové strany).\n",
    "   * **MANDAT** - zda kandidát nakonec získal mandát nebo nikoliv\n",
    "   * **year** - rok voleb - přídan během načítání dat"
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    {
     "data": {
      "text/plain": "   VOLKRAJ  KSTRANA  PORCISLO      JMENO    PRIJMENI  TITULPRED TITULZA  VEK  \\\n0       14        6         1  František     Zwyrtek        NaN     NaN   45   \n1       14        6         2   Vladimír        Mach      RNDr.     NaN   63   \n2       14        6         3     Zdeněk      Stolař      PhDr.     NaN   51   \n3       14        6         4      Alena    Hasáková      PhDr.     NaN   51   \n4       14        6         5      Alois  Čtvrtníček       Ing.    CSc.   60   \n5       14        6         6       Hana  Doležalová       Mgr.     NaN   33   \n6       14        6         7     Václav       Kafka  Doc. Ing.    CSc.   63   \n7       14        6         8     Libuše     Koppová        NaN     NaN   59   \n8       14        6         9  Alexander    Nedbálek      MUDr.     NaN   51   \n9       14        6        10        Jan      Fismol        Bc.     NaN   59   \n\n                 POVOLANI      BYDLISTEN  ...  NSTRANA  PLATNOST  POCHLASU  \\\n0          předseda hnutí    Český Těšín  ...       88         0       120   \n1  středoškolský profesor        Havířov  ...       88         0        93   \n2               psycholog  Frýdek-Místek  ...       88         0       137   \n3  středoškolský profesor    Český Těšín  ...       88         0       124   \n4              podnikatel        Bruntál  ...       88         0        35   \n5                učitelka        Ostrava  ...       88         0        81   \n6      výzkumný pracovník         Orlová  ...       88         0        76   \n7        zdravotní sestra         Třinec  ...       88         0        95   \n8                   lékař        Havířov  ...       88         0        48   \n9           místostarosta   Dolní Lutyně  ...       88         0        56   \n\n   POCPROC  POCPROCVSE  MANDAT  PORADIMAND  PORADINAHR  CPU  year  \n0      0.0        2.86       0           0           0  NaN  2006  \n1      0.0        2.21       0           0           0  NaN  2006  \n2      0.0        3.26       0           0           0  NaN  2006  \n3      0.0        2.95       0           0           0  NaN  2006  \n4      0.0        0.83       0           0           0  NaN  2006  \n5      0.0        1.93       0           0           0  NaN  2006  \n6      0.0        1.81       0           0           0  NaN  2006  \n7      0.0        2.26       0           0           0  NaN  2006  \n8      0.0        1.14       0           0           0  NaN  2006  \n9      0.0        1.33       0           0           0  NaN  2006  \n\n[10 rows x 22 columns]",
      "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>VOLKRAJ</th>\n      <th>KSTRANA</th>\n      <th>PORCISLO</th>\n      <th>JMENO</th>\n      <th>PRIJMENI</th>\n      <th>TITULPRED</th>\n      <th>TITULZA</th>\n      <th>VEK</th>\n      <th>POVOLANI</th>\n      <th>BYDLISTEN</th>\n      <th>...</th>\n      <th>NSTRANA</th>\n      <th>PLATNOST</th>\n      <th>POCHLASU</th>\n      <th>POCPROC</th>\n      <th>POCPROCVSE</th>\n      <th>MANDAT</th>\n      <th>PORADIMAND</th>\n      <th>PORADINAHR</th>\n      <th>CPU</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>14</td>\n      <td>6</td>\n      <td>1</td>\n      <td>František</td>\n      <td>Zwyrtek</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>45</td>\n      <td>předseda hnutí</td>\n      <td>Český Těšín</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>120</td>\n      <td>0.0</td>\n      <td>2.86</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>14</td>\n      <td>6</td>\n      <td>2</td>\n      <td>Vladimír</td>\n      <td>Mach</td>\n      <td>RNDr.</td>\n      <td>NaN</td>\n      <td>63</td>\n      <td>středoškolský profesor</td>\n      <td>Havířov</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>93</td>\n      <td>0.0</td>\n      <td>2.21</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>14</td>\n      <td>6</td>\n      <td>3</td>\n      <td>Zdeněk</td>\n      <td>Stolař</td>\n      <td>PhDr.</td>\n      <td>NaN</td>\n      <td>51</td>\n      <td>psycholog</td>\n      <td>Frýdek-Místek</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>137</td>\n      <td>0.0</td>\n      <td>3.26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>14</td>\n      <td>6</td>\n      <td>4</td>\n      <td>Alena</td>\n      <td>Hasáková</td>\n      <td>PhDr.</td>\n      <td>NaN</td>\n      <td>51</td>\n      <td>středoškolský profesor</td>\n      <td>Český Těšín</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>124</td>\n      <td>0.0</td>\n      <td>2.95</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>14</td>\n      <td>6</td>\n      <td>5</td>\n      <td>Alois</td>\n      <td>Čtvrtníček</td>\n      <td>Ing.</td>\n      <td>CSc.</td>\n      <td>60</td>\n      <td>podnikatel</td>\n      <td>Bruntál</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>35</td>\n      <td>0.0</td>\n      <td>0.83</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>14</td>\n      <td>6</td>\n      <td>6</td>\n      <td>Hana</td>\n      <td>Doležalová</td>\n      <td>Mgr.</td>\n      <td>NaN</td>\n      <td>33</td>\n      <td>učitelka</td>\n      <td>Ostrava</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>81</td>\n      <td>0.0</td>\n      <td>1.93</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>14</td>\n      <td>6</td>\n      <td>7</td>\n      <td>Václav</td>\n      <td>Kafka</td>\n      <td>Doc. Ing.</td>\n      <td>CSc.</td>\n      <td>63</td>\n      <td>výzkumný pracovník</td>\n      <td>Orlová</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>76</td>\n      <td>0.0</td>\n      <td>1.81</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>14</td>\n      <td>6</td>\n      <td>8</td>\n      <td>Libuše</td>\n      <td>Koppová</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>59</td>\n      <td>zdravotní sestra</td>\n      <td>Třinec</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>95</td>\n      <td>0.0</td>\n      <td>2.26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>14</td>\n      <td>6</td>\n      <td>9</td>\n      <td>Alexander</td>\n      <td>Nedbálek</td>\n      <td>MUDr.</td>\n      <td>NaN</td>\n      <td>51</td>\n      <td>lékař</td>\n      <td>Havířov</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>48</td>\n      <td>0.0</td>\n      <td>1.14</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>14</td>\n      <td>6</td>\n      <td>10</td>\n      <td>Jan</td>\n      <td>Fismol</td>\n      <td>Bc.</td>\n      <td>NaN</td>\n      <td>59</td>\n      <td>místostarosta</td>\n      <td>Dolní Lutyně</td>\n      <td>...</td>\n      <td>88</td>\n      <td>0</td>\n      <td>56</td>\n      <td>0.0</td>\n      <td>1.33</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>2006</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 22 columns</p>\n</div>"
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   "source": [
    "# data budou staženy do listu snem_list\n",
    "snem_list = []\n",
    "snem_years = [2006, 2010, 2013, 2017, 2021] # jednotlivé roky voleb\n",
    "for year in snem_years:\n",
    "    temp_df = pd.read_csv(f\"legislative-elections/snem_{year}.csv\",\n",
    "                          sep=\";\", encoding=\"utf-8\")\n",
    "    temp_df[\"year\"] = year\n",
    "    snem_list.append(temp_df)\n",
    "\n",
    "snem_list[0].head(10)"
   ],
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   "cell_type": "markdown",
   "source": [
    "Následující dataframe obsahuje data o stranách, které se zúčastnily voleb do sněmovny v letech 2006 až 2021. Sloupec **Ideologie** je přidán ručně na základě veřejně dostupných informací ohledně každé strany (jednodimenzionální rozdělení na levicové, středové a pravicové strany)."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [
    {
     "data": {
      "text/plain": "   VSTRANA                                          NAZEVCELK ZKRATKAK8  \\\n0        1  Křesťanská a demokratická unie - Československ...   KDU-ČSL   \n1        2                      Česká strana národně sociální      ČSNS   \n2        5                                    Strana zelených        SZ   \n3        7                 Česká strana sociálně demokratická      ČSSD   \n4       11  Sdružení pro republiku-Republikánská strana Če...   SPR-RSČ   \n\n  Ideologie  \n0     Střed  \n1    Levice  \n2    Levice  \n3    Levice  \n4   Pravice  ",
      "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>VSTRANA</th>\n      <th>NAZEVCELK</th>\n      <th>ZKRATKAK8</th>\n      <th>Ideologie</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Křesťanská a demokratická unie - Československ...</td>\n      <td>KDU-ČSL</td>\n      <td>Střed</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Česká strana národně sociální</td>\n      <td>ČSNS</td>\n      <td>Levice</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5</td>\n      <td>Strana zelených</td>\n      <td>SZ</td>\n      <td>Levice</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>7</td>\n      <td>Česká strana sociálně demokratická</td>\n      <td>ČSSD</td>\n      <td>Levice</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11</td>\n      <td>Sdružení pro republiku-Republikánská strana Če...</td>\n      <td>SPR-RSČ</td>\n      <td>Pravice</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strany = pd.read_csv(\"legislative-elections/strany.csv\",\n",
    "                    sep=\";\", encoding=\"utf-8\")\n",
    "strany_dict = dict(zip(strany[\"VSTRANA\"], strany[\"Ideologie\"]))\n",
    "strany.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Sloupec \"pohlavi\" je vytvořen na základě koncovky příjmení kandidáta."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [
    {
     "data": {
      "text/plain": "           cele_jmeno  vek  vs_vzdelani                obor pohlavi  \\\n0   František Zwyrtek   45            0          management       M   \n1       Vladimír Mach   63            1  státní zaměstnanec       M   \n2       Zdeněk Stolař   51            1       zdravotnictví       M   \n3      Alena Hasáková   51            1  státní zaměstnanec       F   \n4    Alois Čtvrtníček   60            1           podnikání       M   \n5     Hana Doležalová   33            1  státní zaměstnanec       F   \n6        Václav Kafka   63            1                věda       M   \n7      Libuše Koppová   59            0       zdravotnictví       F   \n8  Alexander Nedbálek   51            1       zdravotnictví       M   \n9          Jan Fismol   59            1            politika       M   \n\n                   kraj  NSTRANA ideologie  mandat  year  \n0  Moravskoslezský kraj       88     Střed       0  2006  \n1  Moravskoslezský kraj       88     Střed       0  2006  \n2  Moravskoslezský kraj       88     Střed       0  2006  \n3  Moravskoslezský kraj       88     Střed       0  2006  \n4  Moravskoslezský kraj       88     Střed       0  2006  \n5  Moravskoslezský kraj       88     Střed       0  2006  \n6  Moravskoslezský kraj       88     Střed       0  2006  \n7  Moravskoslezský kraj       88     Střed       0  2006  \n8  Moravskoslezský kraj       88     Střed       0  2006  \n9  Moravskoslezský kraj       88     Střed       0  2006  ",
      "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>cele_jmeno</th>\n      <th>vek</th>\n      <th>vs_vzdelani</th>\n      <th>obor</th>\n      <th>pohlavi</th>\n      <th>kraj</th>\n      <th>NSTRANA</th>\n      <th>ideologie</th>\n      <th>mandat</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>František Zwyrtek</td>\n      <td>45</td>\n      <td>0</td>\n      <td>management</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Vladimír Mach</td>\n      <td>63</td>\n      <td>1</td>\n      <td>státní zaměstnanec</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Zdeněk Stolař</td>\n      <td>51</td>\n      <td>1</td>\n      <td>zdravotnictví</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Alena Hasáková</td>\n      <td>51</td>\n      <td>1</td>\n      <td>státní zaměstnanec</td>\n      <td>F</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Alois Čtvrtníček</td>\n      <td>60</td>\n      <td>1</td>\n      <td>podnikání</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Hana Doležalová</td>\n      <td>33</td>\n      <td>1</td>\n      <td>státní zaměstnanec</td>\n      <td>F</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Václav Kafka</td>\n      <td>63</td>\n      <td>1</td>\n      <td>věda</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Libuše Koppová</td>\n      <td>59</td>\n      <td>0</td>\n      <td>zdravotnictví</td>\n      <td>F</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Alexander Nedbálek</td>\n      <td>51</td>\n      <td>1</td>\n      <td>zdravotnictví</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Jan Fismol</td>\n      <td>59</td>\n      <td>1</td>\n      <td>politika</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>88</td>\n      <td>Střed</td>\n      <td>0</td>\n      <td>2006</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Čištění dat pomocí funkce clean_data\n",
    "snem_clean_list = []\n",
    "\n",
    "for i in range(5):\n",
    "    temp_df = clean_data(snem_list[i], strany_dict)\n",
    "    snem_clean_list.append(temp_df)\n",
    "\n",
    "snem_clean_list[0].head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "outputs": [
    {
     "data": {
      "text/plain": "                          cele_jmeno   vek  vs_vzdelani                obor  \\\n28768                  Michal Havlík  42.0            0           podnikání   \n28769  Michaela Hanzlík Skarabellová  51.0            0          management   \n28770                   Jiří Minařík  51.0            0          management   \n28771                Vladimír Herout  62.0            0  státní zaměstnanec   \n28772              František Kolínek  43.0            0     soukromý sektor   \n28773                 Roman Zvoníček  46.0            0     soukromý sektor   \n28774                   Tomáš Kleibl  25.0            0             řemeslo   \n28775                  Alois Nezbeda  77.0            0             řemeslo   \n28776                    Jiří Žváček  58.0            0             řemeslo   \n28777                   Petr Srovnal  61.0            0     soukromý sektor   \n\n      pohlavi                  kraj  NSTRANA ideologie  mandat  year  \n28768       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28769       F  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28770       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28771       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28772       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28773       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28774       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28775       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28776       M  Moravskoslezský kraj     83.0   Pravice       0  2021  \n28777       M  Moravskoslezský kraj     83.0   Pravice       0  2021  ",
      "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>cele_jmeno</th>\n      <th>vek</th>\n      <th>vs_vzdelani</th>\n      <th>obor</th>\n      <th>pohlavi</th>\n      <th>kraj</th>\n      <th>NSTRANA</th>\n      <th>ideologie</th>\n      <th>mandat</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>28768</th>\n      <td>Michal Havlík</td>\n      <td>42.0</td>\n      <td>0</td>\n      <td>podnikání</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28769</th>\n      <td>Michaela Hanzlík Skarabellová</td>\n      <td>51.0</td>\n      <td>0</td>\n      <td>management</td>\n      <td>F</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28770</th>\n      <td>Jiří Minařík</td>\n      <td>51.0</td>\n      <td>0</td>\n      <td>management</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28771</th>\n      <td>Vladimír Herout</td>\n      <td>62.0</td>\n      <td>0</td>\n      <td>státní zaměstnanec</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28772</th>\n      <td>František Kolínek</td>\n      <td>43.0</td>\n      <td>0</td>\n      <td>soukromý sektor</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28773</th>\n      <td>Roman Zvoníček</td>\n      <td>46.0</td>\n      <td>0</td>\n      <td>soukromý sektor</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28774</th>\n      <td>Tomáš Kleibl</td>\n      <td>25.0</td>\n      <td>0</td>\n      <td>řemeslo</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28775</th>\n      <td>Alois Nezbeda</td>\n      <td>77.0</td>\n      <td>0</td>\n      <td>řemeslo</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28776</th>\n      <td>Jiří Žváček</td>\n      <td>58.0</td>\n      <td>0</td>\n      <td>řemeslo</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>28777</th>\n      <td>Petr Srovnal</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>soukromý sektor</td>\n      <td>M</td>\n      <td>Moravskoslezský kraj</td>\n      <td>83.0</td>\n      <td>Pravice</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tato funkce dává v dané buňce veškeré dataframy do jednoho, jelikož pro budoucí účely je nutné mít data ve formátu long\n",
    "snem_long = pd.concat(snem_clean_list, ignore_index=True)\n",
    "snem_long.tail(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Ošetření chybějících hodnot\n",
    "Nyní je nutné zkontrolovat, zda nějaké data nechybí."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Počet chybějících hodnot v každém sloupci:\n",
      "\n",
      "cele_jmeno       0\n",
      "vek             11\n",
      "vs_vzdelani      0\n",
      "obor             0\n",
      "pohlavi          0\n",
      "kraj             0\n",
      "NSTRANA         11\n",
      "ideologie      214\n",
      "mandat           0\n",
      "year             0\n",
      "dtype: int64\n",
      "----------------------------------------\n",
      "Nejčastější ideologie kandidátů:\n",
      "\n",
      "Pravice    14370\n",
      "Levice      7505\n",
      "Střed       6689\n",
      "Name: ideologie, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Počet chybějících hodnot v každém sloupci:\\n\")\n",
    "print(snem_long.isnull().sum())\n",
    "print(\"-\" * 40)\n",
    "print(\"Nejčastější ideologie kandidátů:\\n\")\n",
    "print(snem_long[\"ideologie\"].value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Jak je zřejmé z výstupu nad, chybějící hodnoty jsou ve sloupcích vek, NSTRANA a ideologie.\n",
    "Nejčastější ideologie je pravicová. Tedy lze bez ztráty informace zaměnit NA hodnoty za hodnotu **pravicová**.\n",
    "NA hodnoty ve sloupcích vek a NSTRANA patří neexistujícím kandidátům, tudíž je lze odstranit."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "outputs": [
    {
     "data": {
      "text/plain": "                                              cele_jmeno  vek  vs_vzdelani  \\\n6689   Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n8151   Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n8153   Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n8423   Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n8427   Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n14145  Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n22308  Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n25605  Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n25671  Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n26775  Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n26779  Registrační úřad ponechal pozici volnou Kandid...  NaN            0   \n\n       obor pohlavi            kraj  NSTRANA ideologie  mandat  year  \n6689   jiné       M    Zlínský kraj      NaN       NaN       0  2010  \n8151   jiné       M    Ústecký kraj      NaN       NaN       0  2010  \n8153   jiné       M    Ústecký kraj      NaN       NaN       0  2010  \n8423   jiné       M    Ústecký kraj      NaN       NaN       0  2010  \n8427   jiné       M    Ústecký kraj      NaN       NaN       0  2010  \n14145  jiné       M  Jihočeský kraj      NaN       NaN       0  2013  \n22308  jiné       M    Zlínský kraj      NaN       NaN       0  2017  \n25605  jiné       M    Ústecký kraj      NaN       NaN       0  2021  \n25671  jiné       M    Ústecký kraj      NaN       NaN       0  2021  \n26775  jiné       M   Kraj Vysočina      NaN       NaN       0  2021  \n26779  jiné       M   Kraj Vysočina      NaN       NaN       0  2021  ",
      "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>cele_jmeno</th>\n      <th>vek</th>\n      <th>vs_vzdelani</th>\n      <th>obor</th>\n      <th>pohlavi</th>\n      <th>kraj</th>\n      <th>NSTRANA</th>\n      <th>ideologie</th>\n      <th>mandat</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6689</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Zlínský kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>8151</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Ústecký kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>8153</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Ústecký kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>8423</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Ústecký kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>8427</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Ústecký kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2010</td>\n    </tr>\n    <tr>\n      <th>14145</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Jihočeský kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2013</td>\n    </tr>\n    <tr>\n      <th>22308</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Zlínský kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2017</td>\n    </tr>\n    <tr>\n      <th>25605</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Ústecký kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>25671</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Ústecký kraj</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>26775</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Kraj Vysočina</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n    <tr>\n      <th>26779</th>\n      <td>Registrační úřad ponechal pozici volnou Kandid...</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>jiné</td>\n      <td>M</td>\n      <td>Kraj Vysočina</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2021</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snem_long[snem_long[\"vek\"].isnull()]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stav po ošetření chybějících hodnot:\n",
      "Počet chybějících hodnot v každém sloupci:\n",
      "\n",
      "cele_jmeno     0\n",
      "vek            0\n",
      "vs_vzdelani    0\n",
      "obor           0\n",
      "pohlavi        0\n",
      "kraj           0\n",
      "NSTRANA        0\n",
      "ideologie      0\n",
      "mandat         0\n",
      "year           0\n",
      "dtype: int64\n",
      "----------------------------------------\n",
      "Nejčastější ideologie kandidátů:\n",
      "\n",
      "Pravice    14573\n",
      "Levice      7505\n",
      "Střed       6689\n",
      "Name: ideologie, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Veškerým NA hodnotám ve sloupci ideologie budou přiřazeny \"pravicová\" ideologie, jelikož je nejčastější\n",
    "\n",
    "snem_long = snem_long.dropna(subset=[\"vek\"])\n",
    "snem_long.ideologie = snem_long.ideologie.fillna(\"Pravice\")\n",
    "\n",
    "print(\"Stav po ošetření chybějících hodnot:\")\n",
    "print(\"Počet chybějících hodnot v každém sloupci:\\n\")\n",
    "print(snem_long.isnull().sum())\n",
    "print(\"-\" * 40)\n",
    "print(\"Nejčastější ideologie kandidátů:\\n\")\n",
    "print(snem_long[\"ideologie\"].value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Exploratorní analýza\n",
    "Další částí této práce je exploratorní datová analýza.\n",
    "První řadě je nutné zkontrolovat datové typy jednotlivých sloupců:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28767 entries, 0 to 28777\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   cele_jmeno   28767 non-null  object \n",
      " 1   vek          28767 non-null  float64\n",
      " 2   vs_vzdelani  28767 non-null  int32  \n",
      " 3   obor         28767 non-null  object \n",
      " 4   pohlavi      28767 non-null  object \n",
      " 5   kraj         28767 non-null  object \n",
      " 6   NSTRANA      28767 non-null  float64\n",
      " 7   ideologie    28767 non-null  object \n",
      " 8   mandat       28767 non-null  int64  \n",
      " 9   year         28767 non-null  int64  \n",
      "dtypes: float64(2), int32(1), int64(2), object(5)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "snem_long.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Všechny kategoriální sloupce převedeme na příslušný datový typ, proměnná věk bude integerem.\n",
    "Tímto vzniká situace, kdy je většina proměnných kategoriální, což způsobuje zvýšení počtu dimenzí při trénování modelů."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28767 entries, 0 to 28777\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   cele_jmeno   28767 non-null  object  \n",
      " 1   vek          28767 non-null  int64   \n",
      " 2   vs_vzdelani  28767 non-null  category\n",
      " 3   obor         28767 non-null  category\n",
      " 4   pohlavi      28767 non-null  category\n",
      " 5   kraj         28767 non-null  category\n",
      " 6   NSTRANA      28767 non-null  category\n",
      " 7   ideologie    28767 non-null  category\n",
      " 8   mandat       28767 non-null  category\n",
      " 9   year         28767 non-null  category\n",
      "dtypes: category(8), int64(1), object(1)\n",
      "memory usage: 903.7+ KB\n"
     ]
    }
   ],
   "source": [
    "for col in [\"vs_vzdelani\", \"obor\", \"pohlavi\", \"kraj\", \"NSTRANA\", \"ideologie\", \"mandat\", \"year\"]:\n",
    "    snem_long[col] = snem_long[col].astype(\"category\")\n",
    "\n",
    "snem_long[\"vek\"] = snem_long[\"vek\"].astype(\"int64\")\n",
    "\n",
    "snem_long.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "První vizualizací je rozložení věků kandidátů podle pohlaví v jednotlivých volbách. Každý graf zobrazuje míry centrální tendence."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1124.38x720 with 5 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# knihovna seaborn a distribution plot, samostatný graf pro každý volební rok\n",
    "g = sns.displot(\n",
    "        data=snem_long, x=\"vek\", hue=\"pohlavi\", kind=\"kde\",\n",
    "        fill=True, legend=True, col=\"year\", col_wrap=3, hue_order=[\"M\", \"F\"],\n",
    "        height=5\n",
    "    )\n",
    "g.set_titles(\"Rozložení věků kanidátů do Poslanecké sněmovny\\n podle pohlaví v roce {col_name}\",\n",
    "             size=\"large\")\n",
    "g.set(xlabel=\"Věk kandidáta\", ylabel=\"Hustota\")\n",
    "sns.move_legend(g, \"center right\", bbox_to_anchor=(.75, .45), frameon=True, fontsize=\"medium\",\n",
    "                title=\"Pohlaví\", title_fontsize=\"medium\", labels=[\"Ženy\", \"Muži\"])\n",
    "\n",
    "# svislé přímky pro označení míry centrální tendence\n",
    "years_avgs = {}\n",
    "for i, year in enumerate(snem_years):\n",
    "    years_avgs[i] = [snem_long.query(f\"year == {year}\")[\"vek\"].mean(),\n",
    "                     snem_long.query(f\"year == {year}\")[\"vek\"].median(),\n",
    "                     snem_long.query(f\"year == {year}\")[\"vek\"].mode().iat[0]]\n",
    "\n",
    "# přímky pro jednotlivé grafy rozložení\n",
    "axes = g.axes.flatten()\n",
    "for i, ax in enumerate(axes):\n",
    "    ax.axvline(years_avgs[i][0], ls=\"--\", lw=1.0, label=f\"\"\"Průměr: {years_avgs[i][0]:.2f}\"\"\")\n",
    "    ax.axvline(years_avgs[i][1], color=\"darkred\", lw=1.0, label=f\"\"\"Medián: {years_avgs[i][1]:.2f}\"\"\")\n",
    "    ax.axvline(years_avgs[i][2], ls=\"-.\", color=\"orange\", lw=1.0, label=f\"\"\"Modus: {years_avgs[i][2]:.2f}\"\"\")\n",
    "    ax.legend(loc=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Podle výstupu lze usoudit, že poměr mužů a žen je u většiny věkových skupin 2-2,5 ku 1. Mužů tudíž kandiduje víc.\n",
    "Rozdělení jsou špičatá, ale symetrická kolem 47 let, jak to ukazují průměry a mediány. Lze usoudit, že u voleb do poslanecké sněmovny nedochází k nerovnoměrnému rozdělení věků a žádná věková skupina nepřevládá.\n",
    "\n",
    "Nyní zhodnoťme míry centrální tendence věku pro každou ideologii zvlášť:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "outputs": [
    {
     "data": {
      "text/plain": "             count       mean        std   min   25%   50%   75%   max\nideologie                                                             \nLevice      7505.0  47.353764  12.571288  21.0  38.0  48.0  57.0  90.0\nPravice    14573.0  46.424209  12.958917  21.0  37.0  46.0  56.0  94.0\nStřed       6689.0  46.012408  11.723246  21.0  37.0  46.0  55.0  84.0",
      "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>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>ideologie</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>Levice</th>\n      <td>7505.0</td>\n      <td>47.353764</td>\n      <td>12.571288</td>\n      <td>21.0</td>\n      <td>38.0</td>\n      <td>48.0</td>\n      <td>57.0</td>\n      <td>90.0</td>\n    </tr>\n    <tr>\n      <th>Pravice</th>\n      <td>14573.0</td>\n      <td>46.424209</td>\n      <td>12.958917</td>\n      <td>21.0</td>\n      <td>37.0</td>\n      <td>46.0</td>\n      <td>56.0</td>\n      <td>94.0</td>\n    </tr>\n    <tr>\n      <th>Střed</th>\n      <td>6689.0</td>\n      <td>46.012408</td>\n      <td>11.723246</td>\n      <td>21.0</td>\n      <td>37.0</td>\n      <td>46.0</td>\n      <td>55.0</td>\n      <td>84.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snem_long.groupby(\"ideologie\").vek.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Z tabulky je zřejmé, že je každá skupina zastupena rovnoměrně a příliš se od ostatních neliší.\n",
    "\n",
    "Druhým grafem znázorňuje porovnání ideologií kandidátů podle krajů u voleb v roce 2021 a také jaké zastoupení mají zvolené ideologické kategorie ve sněmovně:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x648 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1080x648 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pořadí tříd a příslušných barev\n",
    "hue_order = [\"Levice\", \"Střed\", \"Pravice\"]\n",
    "hue_palette = [\"Red\", \"Green\", \"Blue\"]\n",
    "\n",
    "# první graf\n",
    "sns.countplot(snem_clean_list[4], y=\"kraj\", hue=\"ideologie\",\n",
    "              hue_order=hue_order, palette=hue_palette)\n",
    "plt.title(\"Ideologie kandidátů do sněmovny podle krajů v roce 2021\")\n",
    "plt.xlabel(\"Počet\")\n",
    "plt.ylabel(\"Kraj\")\n",
    "plt.legend(title=\"Ideologie\")\n",
    "plt.show()\n",
    "\n",
    "# druhý graf\n",
    "sns.countplot(snem_clean_list[4][snem_clean_list[4][\"mandat\"] == 1], y=\"kraj\", hue=\"ideologie\",\n",
    "              hue_order=hue_order, palette=hue_palette)\n",
    "plt.title(\"Ideologie zvolených kandidátů do sněmovny podle krajů v roce 2021\")\n",
    "plt.xlabel(\"Počet\")\n",
    "plt.ylabel(\"Kraj\")\n",
    "plt.legend(title=\"Ideologie\")\n",
    "plt.show();"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Lze vidět, že pravicové ideologie převládají u kandidátů ve všech krajích, ale do sněmovny se nakonec dostávají nejčastěji kandidáti ze středu.\n",
    "\n",
    "Třetí vizualizace představuje zastoupení různých oborů podle VŠ vzdělání."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x648 with 1 Axes>",
      "image/png": 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uWzdTTvKfNWV7Zt5IGff0s4j4E6XlZydKy94m1ZiVqyknRy+uTtz3AQ6OiOspofGLmXl7td/LIuL8qqvfjsAPq+MeQum29niv576fMonDZRFxHWU8T6eL00CmAx+pjrs/8O6qq+iXgHspE0DcQgkMu/V63EmU1p7Tq+efSTkR3q16nf9HCXe/6vN8+1G6HV5bvS9X0Kub6nP0BKUL3I3V11Or9+MDwDERcSNl0pLtMvNvlLB6LSV8XUGZiOZZskw68T7g8Oq9+S5lfN8dffetnESZzOaCPttPpYSDi6vj/onSUnZt9R5sD3xmkHp7+zKly+velG62/wdcExG3UCYN+Z8sY/R2B74fEX+ktJ5sn8/M5kpmTqecAB9etdb9D6Vr6I2UUHgez4wP7DiEcgL9J0rIOL96/3rbnTLW74OUCwIXAb+LiL9QusZ+bB7vX6eumymB8kdRJgn5GrBl1ZWx774zKBNxjM1nJnr5IqVb4md6tQxdXP1u7kP5/biWXhdMhljnD4BlI+Jm4DpK69vSETFloNczyGvtr/7B9Ptzqrqw/g9wetVFdQ+g75hMgF0p4fDPlO7DX83M64byxFW34UMo792Per2/+1e7bE25UHQT5XP6gcyck5l/pgTpXwJ/pbTGHVo95gDK35mbgL8A/6CMTZ4fO1PG5b6nT4vgMpn5E0qPh2uq57iOctHkRdV7sSzwi16P2W4+n1tSTcb09MzX0i6StECqK9f3Axv16ko2ms9/JDAzMz/7HB67CoOswdZ0UWbvvCkzv153LX1Vof5Y4I7MPHSw/SVJ0tDY0idp1FRdGO+iXD3OGp7/u5TWoXNG+7k1sKoF6AFgVUrwkyRJw8SWPkmSJElqMVv6JEmSJKnFDH2SJEmS1GKGPkmSJElqsfF1FzAc5syZ0zN7tmMTJUmSJC2cJkwYdz8wtb/vjVjoi4jnU2boeytl0eTTKAsL3wTskplzIuILwBbV93fPzGsiYrX+9h3ouWbP7mHatCdG6qVIkiRJUqNNnTplXmvjjkz3zoiYQFnM98lq05HAAZn5BsoCzVtFxLrAJsAGlAVKj5vXviNRoyRJkiQtDEZqTN/XgROBe6r76wFXVLcvAd4CbARcmpk9mXknMD4ips5jX0mSJEnSczDs3Tsj4mPAfZn584jYt9o8JjM7g+4eBZYElqAsxEuf7f3tO6Bx48aw1FKLDkf5kiRJktQqIzGmb3ugJyLeAqwDnAE8v9f3pwDTgEeq2323z+ln24Ac0ydJkiQNbvbsWTz00H3MmvVU3aXoORo/fiLPe95Uxo17dpSbOnXKPB4xAqEvMzfu3I6Iy4GdgMMjYtPMvBx4B/Ar4FbgsIj4OrAiMDYz74+I6/vZV5IkSdICeuih+5g8eVEWW2x5xowZU3c5mk89PT08/vgjPPTQfSy77AuG/LjRWrJhT+CkiJgI3AKcm5mzI+Iq4GrK2MJd5rXvKNUoSZIktdqsWU8Z+LrYmDFjWGyxJXjssWnz97ienu5f327mzNk9du+UJEmSBnbvvXew/PIr112GFlB/P8epU6dcB6zf3/4jNXunJEmSpIbbddcdue66Pzxr21FHfZ2f/OSCBTruxRf/hBNOOGZI+/73v//hYx/7MPvvvzdXXFFGdn35ywfxu9/9dkiPf9e7Npvn9wY6zhlnnMKNN/5pSM8xmBkzZvD+928JwJw5c9hjj1054ohDOfTQLwPwxz9eyxe+sO/T+z/yyCMcffQRPPLIwwMe9/777+eb3zyCxx9/bIHqG63unZIkSZIaZsst383PfvZT1lvv1QDMnDmT3/zmKj75yV0GeeTwef7zl2Ovvfblv//9D5ts8sZRe95tt91+RI47duxYDjjgi1x22S9417ve0+8+SyyxBLvttuegx1p22WX59KcH328whj5JkiRpIbXppm/mW986junTpzN58mSuuuoKXvOaDbj00kv417/uYpdddmP27Nlst92HOemkMzjwwM/x+OOPM336dHbc8VO85jWv5dJLL+Hss3/AhAkTeNGLVmKfffZ/+vgPPfQQ++23Jx//+E6ss866fOUrX+See+5m9uzZbL31//DmN7+N888/l4sv/gljx47lz3++nt133/vpx//lLzdx1FGHc8ghh9LTM4evfvVgZs+ezZgxY9htt7146UtXf3rfH/3oHC655CLGjh3Ly1++xjyPM2YMHHbYV5gxYzqTJk1mn332Y7nlln963/3225sPfGBrXvWq9fjrX2/mtNNOZoMNXs9ll10KwN13/4tXv3oDdt99bw4++AAeffRRXvjCFZ9+/G233cpRRx1OT08PN9xwHfvu+4VnvefnnXcWV1zxK5588kmWWmopvvKVr/OLX/yMq6/+DTNmTOfuu//F//zPR9l88y25+OKfcMcdt7Pzzv+7QD9nu3dKkiRJC6lJkyax8cabcuWVpVvlxRf/mK22ei9vfetmXHXV5cyePZvf//5q1l13fe6++188/PDDHHrokRx00JeZPXsWDz88je9851t885sncMIJ32HxxRfnwgvPA+Chhx7kc5/bg1133YP1138NF154HksttRQnnngKRx99PCeddALTpk3joosuZI899uFb3zqVFVd8EbNmzQLgppv+zLHHHslhh32D5ZdfnuOOO4oPfGBrjjvuJHbbbU++9rVDnvVaLr74J08fZ+WVXzzAcY7m/e//fxx77Lf50Ie24cQTj33Wcbbc8t1ccslFAPz0pz9hyy3fw3ve836OPfbb7Lrr7iy33PLsuutnuOCC83jxi1fluONOYqut3vf04w899EvsscdnOfbYb/O6123ImWee/vT35syZw8MPP8xRRx3PSSedzuzZs7nllr8A8Pjjj3HYYUfxta8dyfe+d9qw/YzBlj5JkiRpobbllu/huOOO5lWvWo9HH32U1Vd/GQDrrLMu11xzNRdf/GM+9rEdeMlLVmWrrd7LQQftz6xZs3j/+7fmnnvu5sUvfgmLLroYAGuvvS5/+MPvWGONV/D73/+WZZZZlp6esgz37bffzvrrvwaARRddjFVWeTF33/0vPvvZ/fne907n3/++h3XXfWYekmuu+R1PPPHE0+vR3X777ay99roAvPSlwX//+59nvY799juQH/zge/z730ez5pprzfM4//jHrXz3u6c+Hcb6rne3wQav4/jjj+aRRx6uWh73qp7/nxx22Fc49NAjWWKJJbjrrjt5/es3BGDNNV/B+PHlOHfc8U+OOOJrQFkXccUVV3r62GPHjmXChAkcdND+LLLIIvz3v/99OpyutlpptXz+85fjqaeGdx1FQ58kSZK0EFt11dV48snHOeecH7LFFu96evuWW76HM888nYcfnsZqq72U2267lSeeeJzDDz+a+++/n5133p6TTjqD22//J08++SSLLLIIN9zwR170ohJy3v72d7LZZptz4IH7ctJJp7PKKqvw5z9fzyabvJEnnnic2267jRVWWIGzzvo+Bx54COPGjWOPPXZ9enKV7bffkfvu+y9HHPE1vvjFrzz9+I022oS//z1ZeullnvU6fvzjC9hrr32ZNGnSgMdZaaVV+NCHtmGttdbmjjtu5/rrr3vWccaOHcsb3/gWvv71r/GGN2zKuHHjuPfef3PQQftz0EFfZurU5wPw4he/mJtuupE3vGFT/va3vz4d3lZaaWUOOOBgll9+ef785xt44IH7nz72rbf+nSuvvJyTTjqd6dOn8/GPb/P090ZyGQ1DnyRJkrSQ22KLd3Hccd/kvPMuenrbmmu+grvvvov3vOcDAKy44os49dRv88tf/h9z5szh4x//JEsttRTbb/9JPv3pTzJmzFhWXPFF7LTTrk+Pf3vJS1Zls83ewTe/eSR77LEPhx76JXbe+ePMmDGD7bffgec9b2le8IIV2GOPXVl88Skss8yyrLHGK7j44p8Apavlr371f1x66c/YZZfdOfTQL/GDH3yPWbNmse++n3/Wa1h11dXYZZcdWHTRRZk6deoAx9mNI474Gk899RQzZkxnt9326vf9+OAHt+KHPzwfgCOO+BrTp0/nyCMPZc6cOSy33PLss8/+fOlLX2DnnT/OyiuvwoQJEwDYc899+dKXDnx67OHnPvd57r//vqffw0UWWYSddy6TyCyzzLJPf28kuU6fJEmStJCYn3X65syZw847f5wjjzyGxRZbfIQr0/xwnT5JkiRJC+See+5m++234c1vfpuBrwXs3ilJkiTpWVZY4YWcdtr36y5Dw8SWPkmSJElqMUOfJEmSJLWYoU+SJEmSWszQJ0mSJEkt1tqJXBZfYjKLTJqwQMd4csZMHntk+jBVJEmSJAmG51y9t6Gct//xj9ey7757csYZZ7HccssDcMIJx7Dyyquw+eZbDlstTdTa0LfIpAmst/cZC3SM6w7flscw9EmSJEnDaTjO1Xsb6nn7hAkT+cpXDuaoo45jzJgxw/b8TWf3TkmSJEkLhfXWW58llliCH/3o7GdtP/fcH/LJT27HTjttzznn/LCm6kaOoU+SJEnSQmOvvT7HWWd9n3/96y4Apk+fzmWX/YLjjz+Z4447iauuupw777y9zhKHXWu7d0qSJElSX0suuRSf/vSefPnLX2CttdbmySef4D//uZfddtsZgEcffZS77rqLlVZapd5Ch5EtfZIkSZIWKhtttDEvetHKXHzxRUyYMJFVVnkJxxzzLY499ttsvvk7WXXVl9Zd4rCypU+SJEnSQme33fbkuuv+wOKLL87667+aT33q4zz11Exe/vI1mTp1at3lDStDnyRJkqRR9eSMmVx3+LbDerzBrLvu+qy77vpP319sscU577yLnr7/4Q8PXz1NY+iTJEmSNKoee2S6S6ONIsf0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZizd0qSJEkaVUsvOYFxEycP2/FmPzWdBx8efNkGgDPPPJ2zz/4+Z5/9YyZNmjRsNTSZoU+SJEnSqBo3cTJ3HrzWsB1vpQNvBIYW+i699BLe/Oa3cdlll7L55lsOWw1NZuiTJEmStFD44x+vZYUVVuTd734fBx98IJtvviW77rojL31p8I9/3MYTTzzGIYccyvLLv4Af/OB7XHbZpYwbN461134Vn/rUp+su/zlzTJ8kSZKkhcJFF13Illu+m5VWWoUJEybwl7/cBMDLX74mRx99POuvvwG/+MXPue22W/nlL3/BiSeewoknnsK//nUXv/nNVTVX/9zZ0idJkiSp9R555BGuvvo3PPTQg5x77lk8/vhj/OhHZwGw+uoBwHLLLccDDzzAHXfczpprrsX48SUurb32Ovzzn7ex4YZvqK3+BWHokyRJktR6l156Me9851bssstuAEyfPp0PfOBdLLnkUowZM+ZZ+6688ir88IffY9asWYwbN44bbriet799izrKHhZ275QkSZLUej/5yYVsttnmT9+fPHkym2zyJv71rzvn2nfVVVfjTW96Czvv/HF22OGjvOAFL2DjjTcdxWqH15ienp66a1hgM2fO7pk27YlnbZs6dQrr7X3GAh33usO35b77Hl2gY0iSJElNce+9d7D88ivXXUatSza0QX8/x6lTp1wHrN/f/nbvlCRJkjSqSkBbeEJa3ezeKUmSJEktZuiTJEmSpBYz9EmSJEkLkTbM6bEwey4/P0OfJEmStJAYP34ijz/+iMGvS/X09PD4448wfvzE+XqcE7lIkiRJC4nnPW8qDz10H489Nq3uUvQcjR8/kec9b+r8PWaEapEkSZLUMOPGjWfZZV9QdxkaZXbvlCRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWGz8SB42IccBJQAA9wE7ABOAi4O/Vbidk5lkR8QVgC2AWsHtmXhMRqwGnVY+9CdglM+eMRK2SJEmS1GYj1dK3JUBmbggcAHwZWA84MjM3rf6dFRHrApsAGwBbA8dVjz8SOCAz3wCMAbYaoTolSZIkqdVGpKUvMy+IiIuquysD0yihLyJiK0pr3+7ARsClmdkD3BkR4yNiarXvFdXjLwHeBpw/ErVKkiRJUpuNSOgDyMxZEXE68B7g/cALgZMz87qI2B/4AiUMPtDrYY8CSwJjqiDYe9s8jRs3hqWWWnSYX0ExUseVJEmSpNEwYqEPIDM/GhGfBX4PvD4z766+dT5wDHAhMKXXQ6ZQguCcfrbN0+zZPUyb9sSztk2dOmUee8+fvseVJEmSpKYZKP+MyJi+iPhIROxb3X2CEuJ+FBGvqba9GbgO+A2wWUSMjYiVgLGZeT9wfURsWu37DuCqkahTkiRJktpupFr6fgScGhFXUmbt3B24CzgmImYC9wI7ZuYjEXEVcDUlgO5SPX5P4KSImAjcApw7QnVKkiRJUquN6enpGXyvhps5c3ZPf90719v7jAU67nWHb8t99z26QMeQJEmSpJE2deqU64D1+/uei7NLkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS12PiROGhEjANOAgLoAXYCpgOnVfdvAnbJzDkR8QVgC2AWsHtmXhMRq/W370jUKkmSJEltNlItfVsCZOaGwAHAl4EjgQMy8w3AGGCriFgX2ATYANgaOK56/Fz7jlCdkiRJktRqIxL6MvMCYMfq7srANGA94Ipq2yXAW4CNgEszsycz7wTGR8TUeewrSZIkSZpPI9K9EyAzZ0XE6cB7gPcDb83MnurbjwJLAksAD/R6WGf7mH72nadx48aw1FKLDmf5Txup40qSJEnSaBix0AeQmR+NiM8CvwcW6fWtKZTWv0eq2323z+ln2zzNnt3DtGlPPGvb1KlT5rH3/Ol7XEmSJElqmoHyz4h074yIj0TEvtXdJygh7tqI2LTa9g7gKuA3wGYRMTYiVgLGZub9wPX97CtJkiRJmk8j1dL3I+DUiLgSmADsDtwCnBQRE6vb52bm7Ii4CriaEkB3qR6/Z999R6hOSZIkSWq1MT09PYPv1XAzZ87u6a9753p7n7FAx73u8G25775HF+gYkiRJkjTSpk6dch2wfn/fc3F2SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFhs/3AeMiAnAKcAqwCTgS8BdwEXA36vdTsjMsyLiC8AWwCxg98y8JiJWA04DeoCbgF0yc85w1ylJkiRJC4ORaOnbBnggM98AvB04FlgPODIzN63+nRUR6wKbABsAWwPHVY8/EjigevwYYKsRqFGSJEmSFgrD3tIHnAOcW90eQ2nFWw+IiNiK0tq3O7ARcGlm9gB3RsT4iJha7XtF9fhLgLcB549AnZIkSZLUesMe+jLzMYCImEIJfwdQunmenJnXRcT+wBeAacADvR76KLAkMKYKgr23DWjcuDEstdSiw/Yaehup40qSJEnSaBiJlj4i4kWU1rnjM/P7EbFUZk6rvn0+cAxwITCl18OmUILgnH62DWj27B6mTXviWdumTp0yj73nT9/jSpIkSVLTDJR/hn1MX0QsB1wKfDYzT6k2/zwiXlPdfjNwHfAbYLOIGBsRKwFjM/N+4PqI2LTa9x3AVcNdoyRJkiQtLEaipW8/4HnA5yPi89W2PYBvRMRM4F5gx8x8JCKuAq6mhM9dqn33BE6KiInALTwzPlCSJEmSNJ/G9PT0DL5Xw82cObunv+6d6+19xgId97rDt+W++x5doGNIkiRJ0kibOnXKdcD6/X3PxdklSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWqxIYW+iHhnn/sfHJlyJEmSJEnDafxA36zC3obAhyLi9dXmccC7gLNHuDbNp6WXnMC4iZMX+Dizn5rOgw/PHIaKJEmSJNVtwNAH/AlYBngSyGrbHOAHI1mUnptxEydz58FrLfBxVjrwRsDQJ0mSJLXBgN07M/OuzDwdWBP4LXAf8CtKGJQkSZIkNdxgLX0dnwLeAywNnAa8FNh1hGqSJEmSJA2Toc7euTXwVmBaZh4NbDByJUmSJEmShstQQ99YoKf6BzBjZMqRJEmSJA2noXbv/D5wJbByRFwMXDBiFUmSJEmShs2QQl9mHhsRv6RM6PLXzLxxZMuSJEmSJA2HwdbpWyMzb46IHYBXZub/RsT5EfHjzDx1Ho+ZAJwCrAJMAr4E3EyZAKYHuAnYJTPnRMQXgC2AWcDumXlNRKzW374L/lIlSZIkaeEz2Ji+EyNiY2BnYJ9q2/uADw3wmG2ABzLzDcDbgWOBI4EDqm1jgK0iYl1gE8qkMFsDx1WPn2vf+X5VkiRJkiRg8NA3C1iCsiD7dICq1a1ngMecA3y+uj2mOsZ6wBXVtkuAtwAbAZdmZk9m3gmMj4ip89hXkiRJkvQcDDamb//MvDoi1gF+HRG/A14J/N+8HpCZjwFExBTgXOAA4OuZ2QmKjwJLUsLkA70e2tk+pp99BzRu3BiWWmrRwXZ7TkbquE23sL5uSZIkqW0GDH2ZeXX19UsRcRGwOnB6Zv55oMdFxIuA84HjM/P7EXFYr29PAaYBj1S3+26f08+2Ac2e3cO0aU88a9vUqVPmsff86XvcJhuu1wzd9bolSZKkhd1AWWBI6/RFxIrA/sBBwBcjYpUB9l0OuBT4bGaeUm2+PiI2rW6/A7gK+A2wWUSMjYiVgLGZef889pUkSZIkPQdDXafvJOAEylp9mwLfAd48j333A54HfD4iOmP7dgO+GRETgVuAczNzdkRcBVxNCZ+7VPvuCZzUe9/5ekWSJEmSpKcNNfRNzswfV7cviIjPzGvHzNyNEvL62qSffQ+itB723va3/vaVJEmSJM2/IXXvpMysuRZA56skSZIkqfkGbemLiCWAfYFTIuIFwD3ADiNdmCRJkiRpwQ0Y+iJiV8oYu1nA/2bmz0alKkmSJEnSsBise+eHgQBeR//j9CRJkiRJDTZY6JuemU9VSylMHI2CJEmSJEnDZ6gTuQCMGbEqJEmSJEkjYrCJXNaMiO9TAl/nNgCZ+eERrUySJEmStMAGC30f7HX7xJEsRJIkSZI0/AYMfZl5xWgVIkmSJEkafvMzpk+SJEmS1GUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJabLAlG6R+Lb7EZBaZNGGBjvHkjJk89sj0YapIkiRJUn8MfXpOFpk0gfX2PmOBjnHd4dvyGIY+SZIkaSTZvVOSJEmSWszQJ0mSJEktZuiTJEmSpBYz9EmSJElSixn6JEmSJKnFDH2SJEmS1GKGPkmSJElqMUOfJEmSJLWYoU+SJEmSWszQJ0mSJEktZuiTJEmSpBYz9EmSJElSi42vuwBJ6lh6yQmMmzh5gY4x+6npPPjwzGGqSJIkqfsZ+iQ1xriJk7nz4LUW6BgrHXgjYOiTJEnqsHunJEmSJLWYoU+SJEmSWszunVooLL7EZBaZNGGBj/PkjJk89sj0YahIkiRJGh2GPi0UFpk0gfX2PmOBj3Pd4dvyGIY+SZIkdQ+7d0qSJElSixn6JEmSJKnFDH2SJEmS1GKGPkmSJElqMUOfJEmSJLWYoU+SJEmSWszQJ0mSJEktZuiTJEmSpBYz9EmSJElSixn6JEmSJKnFDH2SJEmS1GKGPkmSJElqMUOfJEmSJLWYoU+SJEmSWszQJ0mSJEktZuiTJEmSpBYz9EmSJElSixn6JEmSJKnFDH2SJEmS1GLjR+rAEbEBcGhmbhoRrwIuAv5effuEzDwrIr4AbAHMAnbPzGsiYjXgNKAHuAnYJTPnjFSdkiRJktRmIxL6ImIf4CPA49Wm9YAjM/OIXvusC2wCbAC8CDgPeDVwJHBAZl4eEScCWwHnj0SdkiRJktR2I9XSdxvwXuC71f31gIiIrSitfbsDGwGXZmYPcGdEjI+IqdW+V1SPuwR4G4Y+SZIkSXpORiT0ZeZ5EbFKr03XACdn5nURsT/wBWAa8ECvfR4FlgTGVEGw97YBjRs3hqWWWnQ4Sp/LSB236UbrdXfj+9uNNS9s/BlJkiQ9Y8TG9PVxfmZO69wGjgEuBKb02mcKJQjO6WfbgGbP7mHatCeetW3q1Cnz2Hv+9D1ukw3Xa4bBX3e3vb+j+d7oueu2z5UkSVJTDHQeNVqzd/48Il5T3X4zcB3wG2CziBgbESsBYzPzfuD6iNi02vcdwFWjVKMkSZIktc5otfTtDBwTETOBe4EdM/ORiLgKuJoSPnep9t0TOCkiJgK3AOeOUo1S6yy95ATGTZy8QMeY/dR0Hnx45jBVJEmSpNE2YqEvM28HXlvd/iOwYT/7HAQc1Gfb3yizekpaQOMmTubOg9daoGOsdOCNgKFPkiSpW7k4uyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUosZ+iRJkiSpxQx9kiRJktRi4+suQJI0OpZecgLjJk5eoGPMfmo6Dz48c5gqkiRJo8HQJ0kLiXETJ3PnwWst0DFWOvBGwNAnSVI3sXunJEmSJLWYoU+SJEmSWszQJ0mSJEkt5pg+SXqOnBhFkiR1A0OfatMzawZTp05Z4ON40qy6ODGKJEnqBoa+ARhKRtaY8ZMW+IQZPGmWJEmSBmLoG4ChRJIkSVK3cyIXSZIkSWoxQ58kSZIktZjdOxti8SUms8ikCXWXIUmSJKllDH0NscikCay39xkLdIzrDt92mKqRJEmS1BZ275QkSZKkFhuxlr6I2AA4NDM3jYjVgNOAHuAmYJfMnBMRXwC2AGYBu2fmNfPad6TqlCRJkqQ2G5GWvojYBzgZmFxtOhI4IDPfAIwBtoqIdYFNgA2ArYHj5rXvSNQoSZIkSQuDkereeRvw3l731wOuqG5fArwF2Ai4NDN7MvNOYHxETJ3HvpIkSZKk52BEundm5nkRsUqvTWMys6e6/SiwJLAE8ECvfTrb+9t3QOPGjWGppRZd4LpHUtPr68t6m/FcTdFtr9l6R1a31StJ0sJutGbv7D0mbwowDXikut13e3/7Dmj27B6mTXviWdumTp0yj73r0be+vqx3wYxmvYM9V5MM1+serddsvSOr2+qVJElDN9D/86M1e+f1EbFpdfsdwFXAb4DNImJsRKwEjM3M++exryRJkiTpORitlr49gZMiYiJwC3BuZs6OiKuAqynhc5d57TtKNUqSJElS64xY6MvM24HXVrf/Rpmps+8+BwEH9dnW777SwmTxJSazyKQJdZchSZKkFhitlj5J82GRSRNYb+8zFvg41x2+7TBUI0mSpG42WmP6JEmSJEk1sKVPkrRQG67u1E/OmMljj0wfhookSRpehj5J0kJtOLtTP4ahT5LUPHbvlCRJkqQWs6VPkqQuYndUSdL8MvRJktRF7I4qSZpfdu+UJEmSpBYz9EmSJElSi9m9U5oPPbNmMHXqlAU6xuynpvPgwzOHqSJJkiRpYIY+aT6MGT+JOw9ea4GOsdKBNwKGPkmSJI0Ou3dKkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrMJRskSY209JITGDdx8gIdw3UxJUky9EmSGmrcxMmuiylJ0jCwe6ckSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWoxQ58kSZIktZihT5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYoY+SZIkSWqx8XUXIEmjbfElJrPIpAl1lyFJkjQqDH2SFjqLTJrAenufscDHue7wbYehGqndhusiy5MzZvLYI9OHoSJJWvgY+iRJ0ogZzossj2Hok6TnwjF9kiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrM0CdJkiRJLWbokyRJkqQWM/RJkiRJUou5Tp+kBTZciy9LkiRp+Bn6JC2w4Vx8WZIkScPL0CdJGla2/EqS1CyGPknSsLLlV5KkZnEiF0mSJElqMUOfJEmSJLWYoU+SJEmSWmxUx/RFxB+BR6q7/wS+BRwNzAIuzcwvRsRY4HhgbWAG8InMvHU065QkSZKkthi10BcRk4Exmblpr203AO8D/gH8NCJeBbwYmJyZr4uI1wJHAFuNVp2SJEmS1Caj2dK3NrBoRFxaPe9BwKTMvA0gIn4OvAV4AfAzgMz8XUSsP4o1SpIkSVKrjOaYvieArwObATsBp1bbOh4FlgSWAB7utX12RLi0hCRJkiQ9B6MZpv4G3JqZPcDfIuJhYOle358CTAMWrW53jM3MWQMdeNy4MSy11KLDXO7wanp9fVnvyLLekWW9I8t6m/Fcw8F6JWnhMJqhb3tgLeBTEbECJdw9HhGrUsb0bQZ8EVgR2BI4uxrTd+NgB549u4dp05541rapU6fMY+969K2vL+tdMNY7sqx3ZA1W73AZrtfdtvd3OOsdjZ+l9UqS+jPQ39vRDH3fAU6LiF8DPZQQOAc4ExhHmb3z9xHxB+CtEfFbYAyw3SjWKEmStNBZeskJjJs4eYGPM/up6Tz48MxhqEjScBq10JeZTwEf7udbr+2z3xzKmD9JkiSNgnETJ3PnwWst8HFWOvBGwNAnNY0TpEiSJKmr2DIpzR9DnyRJkrqKLZPS/BnNJRskSZIkSaPM0CdJkiRJLWbokyRJkqQWM/RJkiRJUos5kYskSVJl8SUms8ikCQt8nCdnzOSxR6YPQ0WStOAMfZIkSZVFJk1gvb3PWODjXHf4tjyGoU9SM9i9U5IkSZJazNAnSZIkSS1m6JMkSZKkFjP0SZIkSVKLGfokSZIkqcWcvVOSGm64ppCXJEkLJ0OfJDXccE4hL0mSFj5275QkSZKkFjP0SZIkSVKLGfokSZIkqcUMfZIkSZLUYk7kIknSMOiZNYOpU6cs0DFmPzWdBx+eOUwVSZJUGPokSRoGY8ZP4s6D11qgY6x04I2AoU+SNLzs3ilJkiRJLWbokyRJkqQWM/RJkiRJUos5pk+SJEmah8WXmMwikyYs0DGenDGTxx6ZPkwVSfPP0CdJkiTNwyKTJrDe3mcs0DGuO3xbHsPQp/oY+iRJkqSWsGVS/TH0SZIkSS1hy6T640QukiRJktRihj5JkiRJajFDnyRJkiS1mKFPkiRJklrMiVwkSZIk1cLZRkeHoU+SJElSLZxtdHQY+iRJkoZZz6wZTJ06ZYGPM/up6Tz48MxhqEjSwszQJ0nSQmg4QomBZN7GjJ/EnQevtcDHWenAGwHfY0kLxtAnSdJCaDhCiYFEkrqDs3dKkiRJUosZ+iRJkiSpxQx9kiRJktRihj5JkiRJajEncpEkSZKkIejWxeQNfZIkSZI0BN26mLzdOyVJkiSpxQx9kiRJktRidu+UJEmSRlDPrBlMnTplgY8z+6npPPjwzGGoSAsbQ58kSZI0gsaMn8SdB6+1wMdZ6cAbAUNft6vjIoChT5IkSZJGSR0XARzTJ0mSJEktZkufJElSlxqONcMktZ+hT5IkNZ4TYfRvONYMg7JumKT2MvRJkqTGcyIMSfPiRaHBGfokSZIkPa3bQpQXhQbXyNAXEWOB44G1gRnAJzLz1nqrkiRJktrPENU+TZ29893A5Mx8HfA54Ih6y5EkSZKk7tTIlj5gI+BnAJn5u4hYv+Z6JEmStICcbVSqx5ienp66a5hLRJwMnJeZl1T37wRekpmz5vGQ+4A7Rqs+SZIkSWqYlYGp/X2jqS19jwC9R4+OHSDwwTxenCRJkiQt7Jo6pu83wOYAEfFa4MZ6y5EkSZKk7tTUlr7zgbdGxG+BMcB2NdcjSZIkSV2pkWP6JEmSJEnDo6ndOyVJkiRJw8DQJ0mSJEktZujrIyLG1F2DJEmSJA0XQ9/cfl53AWqWiFgiIl4ZEYvVXUvbRMSkiFinur1VRDR6xd6IWLHP/a3rqkWSJGmoDH1ze6g6+XxZRKweEavXXVBbRMRqddcwVJ0W34h4P3AFcCawR0QcUGthQxARn+hz/9N11TIEZwKvqm4HcHqNtQzFuRGxTEQsGRHfBz5ad0H96abftf5ExNJ11zC/uqXmiFi/z/1N6qplqCJizYj4QOcCUZNFxPg+95eqqZTnJCJeUHcNbRARm1VfJ/b9V3dtA+nCC7HrD75X/SJiubprgOYu2VCn5wO797rfA7ypnlKGplrLcDtgAmWJixUyc7N6q+rXjsA+EXE15X2FUm9PZr6+vrL6tQ9wKPAZ4LXAz4AvAddWXxsnIj4EvAt4Y0R0PrPjgFcA36ytsIG9MDNPBcjMwyLiV3UXNIhPAxcCSwLfyMxTaq5nXrrpd+1pVQA5DhgXEecAd2Tmd2oua0DdUnNEvAFYA/hMRBxZbR4H7EL5G9FI1UWrDwO/A/aOiLMz8+s1lzWXiFgeWAI4IyI+Qvl9GwucAbymztoGEhGHADsBE4FFgb8Ba9Za1AAiYgng85TP8t+AQzLzwXqr6tdLKT3HkvI3uDN0qAd4SV1FDcGZwE+BGygXYv8f5fevUSJikcx8EtgrIlYBvgd8LzOn1VrYvH2O8rf3BzzzfzIAmTlq76+hr4/MfGNELAOsCvwjM++vu6YhOAE4DHg/ZSH7pl5J2rf62g1d4i6vvs7JzBkR0ZOZPRHxeJ1FDeJnwL+BZYBvVdvmALfVVtHgeiJi9cz8W0SsSjkJbZyIeFuvu5cBbwX+FRFvy8xLayprIN30u9bbIcDGwHnAV4DfAI0LUH10S80PAcsDk4BOa84cygWuJvsQsFFmzqpaHX4LNC70US4O7kY5Uf52tW0OzR8ysiWwIvAN4Ejg+HrLGdQpwJWUcLIJcBrlYmejZOax1dcX113LfOqWC7EHAZ/NzK0j4nmUYHpORPwXOCkzL6+zuH50eomdWGcRhr4+IuIDlJacW4BXRMRBmfm9mssazP2Z+YPqBPSgiLii7oL6k5mzq693RMTzgck1lzRPmfn76uZV1ZWZFSPiROAPNZY1oMx8iBJWL+/z/jb59/wzwFlV14d7KFecm+hDfe5nta0HaFzo6/yuUXotfDszb6mxnPkxJzMfrC6yTI+IR+suaAi6oubMvAm4KSJOysx7ACLiRZl5V82lDWZMZs4CyMyZETGz7oL6k5kXABdExOaZeXHd9cyHf1cXNqdk5q1N734ILJOZnZ4rN1RDMBorIqYA76DX+U5mnlFfRYPqiguxwDG9bi8HrAQsC9wMvD8iPpGZ29RSWT8ys9NgsA5wRnW+NuqafDJYlz2A9TLzseqX9ZeUZuMmmxMRawKLRkQAjR5bEhHHA5tTTvLHUE6cG9nlLDP3i4i3A38EbsnMi+quaTARcRywBd3x/v4+IjYFVgFuy8zH6q1onr6SmX+vu4jn4NfA4dXfslOBs6ouMU11a0R8FVgmIj4H3FF3QUPQbTX/T0RMA5YCtouIn2XmHvWWNKBfR8S5wFXAGygtqU12T/V/XO+T/O1rrGcw/4qI7YHHq8/xUjXXM5hFImL5zLy3uljY1FDScSHl/+LOxZWeAfZtgq64EJuZ/wKIiN8DTwAnAwdm5oxqe1Nb2McD/xcRf6WGFklD39zmdE48M/PRiJhed0FDsAelD/43ge/TzK5Fvb0GeElmzqm7kMFUJ8tLAP8Blo6IbRt+lQ5gA7rn/X0fpdvDeODsqrWkiWMmd2DuMXIANHmMXGaeB5xXTc7wDeAomn1StxPwCUpYfYzyvjddt9X8Pkp31J9l5hoR8cu6CxpIZu4VEVsALwdO6YJWtNOAY3nmJL/pPgm8CDgH+BgNHL/Vx+eB30bEw5T/m5v++za2SS1Og+miC7Ed2/R3Qbah81qQmUcAR0TEqyljlL+dmaM2YaShb27/iIgjKH3GN6bZ46E6Hs7Ms6vb60Xzp5G/lXIV9Im6CxmCbrtKB931/u5Bd0yU05Vj5CJiJWBbynjfP1K6GTXZesDEzNwlIs6ktOpcX3NNg+m2mmdTxvb9p7q/aI21zFNE7Nhn0yOUbvY7Zua3+3tMQ9ybmSfXXcRgImLbfjY/DKxP6SLXVP/NzJdExLJdMufCnyNiA8rEKD0AmflUrRUNoIsuxHasEhFHU855OpOVNXbyxYhYhHLh7aOUer8wms9v6JvbdpQrX2+ljOv7bL3lDMk5EfFOYBZlUpfnAT+st6QBrQTcERG3VvebPKNgV12lq3TT+zu7GybK6abxqH2cR+n2snFmPlJ3MUNwLM8E689TWk02rq2aoem2mi+v/m0TEd+gzNTXRN26dMDtVTff63nmJL9x434pLadQLro9QZkg59WUWcCb3JvlS9Vke6dGxPd7jZVqqk0ok+V0NH32zm65ENvxDcrY9W5pWf8zcC6wc2beOtjOw83QN7ejMnPXzp2IOINypbzJdqM7ppHv6DspRpN11VW6Sje9v7/ulolyoLvGo1YuzMzOTK5ExFczc9+BHlCzmZl5G0Bm/iMiGt9FmS6rOTP3B/aPiKnAPpnZ1IlRvti5HRFLUmbCfDfQ9HHVkygzeEZ1v6mTPe0LUI3p3KKzPSIaV2tvmblltTzGR4BLI+KWzPzEYI+rS2auDVAF1Qczs+m9hbriQmwvd2bm/9VdxHw4vHdPhYj4dK+JiUacoa8SEbtQmrSXjoj3VpvHUFr7GqkLp5HvmAB8gF7rClJaV5uo267SQWnxPZSy5uQ5lCtLjZxcogsnyumK8agR8XHKOLOXR8Tm1eZxlN+5Joe+OyLiK8DVlPf67prrGYquqrkar3MKpbvkUhGxQ2b+ot6q5i0ifkgJeq+nrHv3XuA9tRY1gMzcLiJWB1aj/O29p+aSBvP8iFgqM6dVwWSZugsaggmUcD2O8v9d40TEMpn5QERsTFkGYxylV1Yj1/HspasuxAL/rers3bLeuO7f8ex1lDetNo/6OsqGvkpmHgccFxH7ZeZX6q5niLpqGvmIWCMzb6ZMNnM+sBHlP8TFay1sAJ2rdF3m28ARlK5mVwKnU7prNE4XTpTTLeMlv0e5ELQfpWvOGEpLyX/rLGoItqNMjLI5ZVxRk7sVdXRbzV+irHt3T0S8EPgR0NjQB6yQmd+LiI9nWUe30Vf1I2JXSihdmtLV96XArgM9pmZfpix98CClt9D/1lzPgKqJhyZRJqx7c4O7d14SEZ+g/L51wzqewFwXYv+amT+pu6ZB/LP6unz1taktqY1YR9nQN7fOB4iqC8GpmdnUyQ92yLJgbdPX1en4MKU19bHM/GpEvDQzt4+Iq+ourK+IODYzd42I3/b5Vk9mblhLUUO3SGb+MiIOyMxs+Ay03TZRTleMl6ymrb49Ik4B3p2Z36wmGfk6zZ5kZAZlbNEfq/sbUC5cNE51Bb/jj3RBzZXZnXX6MvPuhv99AJhY9b65OSKWBabUXdAgtqac5F+WmUdHRKNbSjLzvIi4kDKG8t6mdvftZbfMvLHuIobgDuCVdMk6nh0RcS2lJ8C3umEceGZ+sZqdunfPscap1uW7PMo62lMoge89wE2jWYehb24fqX4pJ1OugI3qzDrz6QxKkEqeOVnujDNqXBfEzDygutlTBeopEbEYzWzp68xsN1e3yIg4CLg0M/sGwqaYHhGbAeMi4rVAk0/qum2inG2Bpp8U9XYM3TXJyHnAVMpFgM7fsqYGqJ2rr6sCEyndoF5FWbZh05pqGopHIuJ/eWaG6gdrrmcwhwH/D9gT+DRwSL3lDGos5XPb+T95Ro21zFOv7oebAMfRPd0PXxwRX+eZk/xlM3Otmmvqz4VVC/Wm0V3reG5BGS95WUT8hbKWXGPXxoyI7wCvAxYDFgH+QUN7NlV+QI3d1Q19c3sf8BNK6NsoM++ruZ55yszOejofzMynryb26i/cVF+kDMj/LuUX9Lu1VtO/a6qvP+vnexOAEylX8ZpoR0qLzrLAXjxzctpE3TZRzk+qfydn5t/qLmYIumqSEWD5Jrac9iczPwQQET8Ftqp6XYyjubNhdmxD6XHxZUp31CYvHE5m/igibqL8vf02DR8zSRm+cCWwckRcDFxQbznz1Ol+eAhd0P0wIrbKzAsp3SU/SelS/SvgLbUWNg+Z+b3qZt91PBs76QxAZv4H+HpEnE254PITSlflplqbsk71tyjDGc6tt5xB1dpd3dBXqQaudq7MPUkZkH90RPQOV40SERtRPuyfiYgjq81jKeMHXlFbYYO7ITM7V+9/HBGNO8nLzJ9XX0/v7/sR0dgTj8z8V5Q1rrphWYFumyhnbcpg7CMjYjKl+/eZNdc0kK6aZAT4a0Ss0Ol+2CV6Ly0wnjKBUmNl5sMRcTjP/H2YAjxUY0kD6rYxcpl5bERcRvk/ODPzz3XXNA/d1v2wM8HMvzPz6ojYKTNPi4iP1VnUECxGGcLQaVF/D3D2vHevV5T1Gz9Kafk9hTJmuckeqGYZXSwz74+IwR9Rr1q7qxv6nnFin/tfr6WK+TONMnh1Es+ceMwB9qmroCE6PyK2oMy6dQiwGbBuvSXNn8y8pO4a5iXKMiMbUhba7XSRa+T7220T5VStkOdGxL2UtYEOAJoc+rptkpGNgDsjotPDoiczGzlGo5fvAH+pWqPWBL5Wcz0DirLsyDsokwp0w7IjXTFGLiIO7GfzyyPi3Zl58KgXNLi+3Q+XbXL3w15LUc2oxtNOqIYxLFtjWUNxKeVv77Tqfg8NDn2UC5u7ZOZf6y5kiK6LiL2Ae6qZfhetu6BBHEb5m7YHNXRXN/RVMvMKeHo2wc9SBoNeRJlyuZEy8ybgpoj4dmb+u+565sNRlC4vzwN+Tpn4QMMnMnPVuosYioh4F7ALz4zPWCYzm9pttnNi90HKpB3f7NVi3SgRsX5mXks5Wb65+gelZbVxM/t2ZObqddcwvzLzuIg4h9JC/ffMfKDumgbxGmDVpi870ktXjJHjmXHg76ZMCPcbymLnK9VV0EB6dT9cnjK5U6f74Q61FTU0OwMvo1zAOoTmX8h6ODOb3lrW28HAARGxBvA34JDMbOy432q20SmUHnrvAH5fc0n96jXh4kU8s9boqH92DX1zOwW4hHJydC/lKu4mtVY0DxFxbma+H/hjRDxrIpcmXh2v1i6CMvHMFcCbKFPLv5jyx0XD45qIiMzMugsZgr7jM95abzmDegjYMDMfrruQQbwZuJa5l3Vp5HIuHdXEQ9vRaya2zNys3qoGFhFrUf7feBHw74jYPjObPENqtyw70tEVY+Qy81sAEfG+zPxUtfnMiGjychhQJqvbjtI75L/Acjwzm3ITHd5ryM37aq1kaH4eETvxzIU3mnqxsPIdyu/bmZRz39MoQxoaKSJeRPl/rtNd/VWU4No0vSdchPL/G4zykBZD39yWycxTImKbzPxtRIytu6B5qQIfmfmCwfZtiG/NY1sPJQBqeDwM/CEiHqPBFwEq3TY+4wLg5F5XQT+TmbfXWlE/MvPQ6ms3XWEGOIHS/eX9wI2UWTGb7pvAJzLzTxGxDmUmxCYv69IVy4509Bkj99cumK5/6YhYNTNvizLAaMm6CxpIZl5H6SL3PMrv362UISNNNSkiXkn5+zsHGj/51xso72en8aDJMxJDOQfuLBZ+Q0S8v9ZqBncO8H80+0IFmfni3vcjYhngwcwc1WWqDH39iIiXVV9XpIw7a6Q+k888SxMnn8nMN9Zdw0LiTcDSmdnYz24v3TY+49uUE6MrKdPyf4fSqtZIEbEvpbv6EzT/AgDA/Zn5g4h4W2YeVK1p1HRjMvNPAJl5Q0Q0/feuq5YdiYjXUMbATAbeWE2u9qlBHlan3Snj1pcD/kXpxdBYEfEG4GOUrqjnUGZ8brLVKd3jplJaJmfT7Mm/Fs/MRs4wOg+LRMTymXlv9RkeV3dBg3i013JgjdVriZSNgeOpaYkUQ9/cPg2cCrycMvVrk/9z6Tv5TKN1uqNGxL/ps65gw09Eu83fKF10mj5TI3Tf+IzJmfnj6vYFEfGZWqsZ3NaULpLd0pVvTkSsCSxatZI0earwjtkR8U7gKsoYyqaOOevotmVHTgcOpcEzjPaWmb+mucv59Gd34CRKa/Wotjo8R18AjgT+CixBs5ckgjLvwoco48A7yxI1+ffu88BvI+Jhyvvb9DGeN0XE1pRxqU1+fztLpHyJGpdIMfT1UU2O8joofYUzs7FNxr0mn1mC8ov69MDbOuualy7sjtqtNgRuj4gHqCZAaGqozsy7I2Im5Sp+0wMUwPiIWCszb6zGcjXdPykD3LvFHpQZML9JGct1ysC7N8L2lNmev0YZt9P0k6RuW3bk75l5Wt1FDFVE/JNn98B5JDPXqamcQWVmN4yL6+3zwGsy879VS9RPaPA4ZcrvW9+LAE0ezvJQZr4kIpatlkBo5JwWvaxT/eto6nChRiyRYujrIyL2pkytuxSwXUT8LDP3qLWowZ1CmRilWwberkNZQPzpdeQys9ELBHeTzHxp3TUMVTV9/OaUdYy6Yfr4TwOnRMQLKDXvWHM9g5kI3BgRN/LMVdDGdf3uyMy/AH+p7q5XZy1DlZl3RMTOlPW4Gt9S0oXLjpxXTcXeeyKMJk7U0PGy6usYymf4AzXW0kYPZOZ/oSwkHhGP1F3QIF7Ms/8uNHISsKqb7xr0Wve5mtOi6es+H5GZndkwiYgP1lnMAPoukbJMHUukGPrm9j5K0+vPMnONiPhl3QUNwTKZeUx1uxsG3p4GHEvDB952q6p73ImUJTG+B9zU+49iw7wGeEkXTR9/M7BjZl4fEe/mmYDSVIfWXcD8iIj9KOuMPt0dtamt1B0R8W3KleX/0gUXLrpl2ZFedqF0hZpWcx1Dkpm9u/f+pjrB0/B5NCJ+TrnQvR6lK/hXoEzfX2tl/eusFt65CNDU87OH6KJ1n6su9RsCH4qIzt/bscBWNHAdxF5LpOwEfIKalkgx9M1tNuWD31lzp+kLPcKzB94uT/MH3t6bmSfXXUSLfZMyBfdJlL7il/DMujBN023Tx58J/JQyfmB1yslz41rOImIc5e/AbsD/o5xwjKPU3sSuLx3/j+4agwily85Lu2Q8FJSTu40yc1rdhQzRA53ZaLtBFfI6n4UXUM0wqWFzQa/bjR+33i0XAXqt+9zT8Jb0jj8By1CGL3SWp5oD/LC2igZQTeDS0Xvt3NcyirO5Gvrmdnn1b5uI+AblJKnpDqD8MXmK0p2r6WNKbq+atXsPvG1yn/yuk5m3Vn+87xvtPuPzqaumjwdemJmnAmTmYRHxq7oLmoftgf0oF7CSEvrmUCYbabJuG4MIpZvvFKDR3cwi4hPVxbYXAPuUeXKKhraQdNwfEd/i2RNhfLvekgb01163/0S56KZhkpmn113D/OjCiwCbRsSXM3N23YUMpJpv4/SI+G61aSxlPo6b5/2oWnUmHFqVcp7+B8qago9RZgIfFYa+PjJzf2B/gIi4tuHrv3QsQbmKP5tyctf0n+skSpeHzllHoxeM7kIPRsQngcWqWa2m1VzPQD4K9P4da/psjT0RsXpm/i0iVqWhreqZeRJwUrVQeDdMhtLRewwilIsAjWtJBYiIqyl/u54P/D0i/lF9q6kXLjrd6T9HmTRpWnX/i5QLBE3VuSC0fK1VDN2rM3PXzp2IOIOyTIYWTn0vAvysrkKGaCpwT68JiZr696zjSOAWYGVgXUovvY/WWlE/MvNDABHxU2CrzJxV9cgZ1YalpoeDWnVJ4IMum82qCxeM7jYfp5zE3Q+sX91vlKob8hLAGcBHKBcrxgLfoozza6rPAGdV9d9Nw9fgAn4REefwzMy+jVxMvpeu6cZHWQ6jozOWbxLNXbJh34j4E+Vz+wHgA5n574j4WL1lDSwzvxgRb6GsxfY7yue4cSJiF0qvm6Uj4r3V5jE0t+VBo6DbWiaBd9ZdwHx6dWbuHhG/ysw3RsRldRc0iN6z14+nXDQcNYa+duiK2ax6rc83iTJW8i7ghcB9mblKjaW1zTrAxdU/gIiIuzLzX/WVNJfXUsabBSXodbof/rzOogaTmb+ndMkAICIm1FjOUHTFYvIR8ZHM/C5l5sO+Y+MauUB7Zt4BEBE7AKtn5t4RcSnw3epf09wKbAT8Hdgb+HFEbEPpIdJY1SQdK1LWzp0B7At8qNai+pGZxwHHVRPlnAfMAj5LGWMtdYvZwDfodaGw3nIGNS4i1qMMG5pI6WrfZN8B/hIRN1GWJxrVC52Gvj4iYi/g9My8r+5a5kNXzGbVWZ8vIr4H7JuZd0XECpQ/MBo+X6J0hbqOElCeAiZHxEmZeXitlVUy8wLK4uabZ+bFg+3fFFW32T2ACZSgOgto8hIZ3bKY/FcpQelVlDFy3WRnnmmd3oISsJsY+v6amedGxC6ZeV1EbEuZ9GCRugsbxEaZuXF1Jf/0anmMJnsz5TOwK3Au5f+3N9ZakTR0J9EFFwp7OQM4njKO/TDKReTGyszjqt43q1LWIL1/NJ9/7Gg+WZNFxMrVzceA8yPi3Ih4R0SMqbOuIbqAspDx3cCPKR/65JkZjZrmJZ1F7zPzHspkHho+TwCvrPqQrw3cSVlnp4mL8D4VEW+PiM0j4raIaOT4rV52ofxHeAllhtSmL9kwvrOIfMMXk/97RPyB0mXyHb3+vb3WqoZmdmbOAsjMmTR0rb7M/Hp186zq/i3Au3lmzFxTja8Wke+pxsA0umWSZyZMWiozf0jzJ+6QepucmT/OzGnVxdlG92bJzOOBt1HG1++fmd+puaR+RcSk6uualJ4AJwGfqJaeGDW29D3j08CemXkicGL1g9kf+FZEnAIcnZkP1VrhPHRhn/GbqxmXrqGsZ3VdzfW0zdTMnA5luuiIWDYzn6oWWm2aL1OWPDiOsubO2ZQLGE11TzUOakpmXh4RX6i7oEH8L2Ux+RUoF4WaOrPvWyhdvU8APlVzLfPrwoi4ivL3bF3KhbfGqv6P69y+g4aO4YmI51fDFo6i/B8xFfg9ze8ZMoHS4nBlRLyRMjmR1C3GR8RamXljdaGwkRexIuKlmfn3iHgfZSzteODsatbyL9VcXn8uq7rT17qklqHvGZ8FiIilKFebt6XMbrYb5QrCRZSTUi24HYH3ULrF/aBX9zMNjwsi4teUk9BXU8bu7AzcVG9Z/XqCMtvWrGqdyUb+B9PLw9Wi7D1VV89la65nMCsDG2Rmo1sbqunB76R0j+wqmfmliLiIMj71jMz8U901tcT51SQzu1L+730p8M/R7g71HGwHvJVyQrcVDZxJUBrApykXCl9A6Wq/Y831zMup1dJfe1DmCPgZZWjLtdXXpklgM6h3SS1DX6XTPYeydsb3gK0z887O9yPiVf0+UM/F4pRf0jWB5SLi15n5YM01tUZmHhIRF1ImPjglM2+KiKnAiYM8tA6PUP5Yf7ua/e6/NdczmB0offH3BfaktKQ12VuAL0XEj4GTM/OfdRfUNtXEHb3uxlZdsrhx0/2b8rvWA5xKOWmaExGNGqveV2b+nTJZDpSeC1LXyMzrI2Jzyu/e3xp8bjadssTT7KpHU09m9kTE43UXNg//pEys9pY6l9Qa09PT9Avro6sawzcFmNzZ1pkZU8MjIs6lTDpzFbAJ8ObMfFe9VbVHRKxGmZK9M9nICpn5yXqr6l/Vz33VzLw5Il5BGdjc1CnviYhLM/NtddcxP6oZzbaitEBMzMy31FxSK0TEipQTj85Y2TGU7p1jM/MTtRXWEhHxwcw8OyLmainrwiENUleIiE8Bu1N6Bq0JHJKZ36u1qH5ExCaZeUU1aeGLKZMY/hJ4PDP3rLe6eYuIJShLaq1FWV/wK6MZrG3pm9tplGmtH+aZtZfWrbOgFlomM4+pbt8QEe+vtZr2+T5wPuVzfA+lZbWplgX2i4jnA+cAi1HG7TTVQxGxFVWrA0BmNnLdsF5eQ+lWshxlNkENj9OBGZm5ee+NEXFJTfW0SmaeXX014EmjZwfKRHDTI2JRygX6xoW+zLyi+rpfRLwd+CNwS2aO2vi45+hxyjlap2FpNcpQnFFh6JvbyzJz1bqLaLlFImL5agzX8pQxkxo+j2XmV6uBzttXk0w0SjUGaltKd4cjgM9Tpog+ndL1t6meT7kK2tEDvKmeUgYXETcDf6J07bT1aXgdDTwWEav32rYCZRylJHWj/1CWIgJ4EnigxloGFREvBlanNNKsERFrZOZhNZc1kIspkzs9xDMNS+8drSc39M3tmoiIzGzqcgdtcADwm4h4ivLhb+qMgt2qpwrTUyJiMZrZ0ncH8D/AIpn5y4g4IDMzIqbXXdhAMvON1fjIpo936HhDZj79n3ZETKiWFdAC6kxAFRG/4pkZ7qZTJhaQpG40ltID67fAOsDEiPg+QGY2cUmlC4EfUUJUN5icmZvU9eSGvrk9DPwhIh6jSuGZuULNNbXNEpTWvdmU99jP4fD6ImV21O8C/6CZC0VPAk4G3hERmwHjIuK1lJPmxqpmQf0MZX2+NSKikeMdenl/ROxJ+R3rhsXku0ZE/JMS9jpruc6kjKP9BmVyIknqNmcAS1L+r3gLZYmB62utaGB3ZeZBdRcxH66sznlu6WzoPWnkSPNke25vApbuNZunht/ngddk5n8jYjngJ8ClNdfUGpl5JaWrJDR0zbBOV8OI2BH4OmVs317AznXWNQQ70gXjHXrZhTJZ0gGUMZO711pNu7yMEviOA76VmddUszw3/TMsSfOyA3AQ5f+O/YBPZuZRdRY0iJ9ExNeAmzsbMvOMGusZzHKUtUenVfd7KOtVjwpD39z+Rvmh3F13IS32QGdG1Mz8T0Q8UndBbRIR21KWFJjU2ZaZL6mvonnLzH9VwW/yoDs3Q1eNd6D7FpPvGp1ZZiNi1cy8ptp2fUS8rN7KJOk5m0O5aLx/Zv4wIpo+/GZrSqvZy6v7TV+S4GWZ+fLBdxsZhr65bQjcHhEPUD48du8cfo9GxM8prSTrAYtW0+42ev2lLvJZYEvgrroLGUxEnEH5neuW2XJ7j3d4FTCh4eMdum0x+W40LSIOoczA9nrK+nKS1I0mAIdRuiG+kTLvQpPNyMxu6l3x52ooy/VUATUznxqtJzf09ZGZjncZeRf0um2L6vD7R2beWncRQxRdNlvul3vdPrO2Koau2xaT70b/A+wEvJPSxeigWquRpOduO+CtwHco67vOtU5mw9wREftSlmzohKgmDxfaGNii1/0eYNR6Yrk4ex8RsRZwCrAicC+wfWY2eRCr9CwRcRZlspwbeOaPYCNbUCPiGOBYZ8sdGd24mLwkSUMREaf22dSTmdvXUsx8iIhlgAczc1RDmC19c/sm8InM/FNErEMZpL9hvSVJ8+XiuguYD86WO7K6cTF5SZIGlZnb9b4fES+oq5ahiIiNgeMpM9ifExF3ZOZ3Ruv5DX1zG5OZfwLIzBsiwlk81W3OBF5N6Zs/hrJgdFN11Wy5EbF8Zt5bdx3zoasWk5ckaagi4mDKjMkTgUUpkzGuWWtRA/sSpYvnecBXgN9QutKOCkPf3GZHxDuBqyg/mBk11yPNr/Mpge+FlKtJ9wA/qLWieeuK2XIjYmxmzgHOjYj7KH+kL662NVZmvrHuGiRJGiHvogzH+gZwJKUVrcnmZOaDEdFTLf306Gg+uaFvbttT1g3rrPvR9Olqpb6WzczXRcTJlIk7flF3QQPozJZ7f3W/qd079wcOycyNImINymD3AyLiMuA7mfmPest7tl4Lh3d0Fg6fnplr1FOVJEnD6t+ZOaNalujWiGj6bKO3RsRXgWUi4nPA7aP55Ia+uX06Mz9QdxHSAnii+rpYZj4ZEbUWM5Aumi239yL3dwP/oCw38grg6Ij4S2Z+rpbK+jevhcM/VW9ZkiQNm39FxPbA41WYWqrmegYzlrIw+6+BxykXY0eNoW9ua0TEUpk5re5CpOfoRxHxeeBPEXE15Q9LI1Xr1WxHr/GHmblZvVXNrTPONyLOpgS97wHbZOY91fZrayxvLgMsHN7cKwCSJM2fTwIvAs4BPgZ8qNZqBvdWYHXgA5n574h4z2g+uaFvbmsA91fdzVycXd3oLuBtlIHNTwBNniTlBMpCsO8HbqT5C8GelJn9dZfdaNQrGZq+C4ffU3M9kiQNl18AH83MR4FjIuJnwNtrrmkgtwJ7AT+OiG2oZtUeLYa+PjJz5d73I2LpumqRnqPDKVe/Hqq7kCG4PzN/EBFvy8yDIuKKugsaxH0RcTwwubMhM7fPzOk11jSQByldSToLh7+41mokSRo+KwHnRcR2mXkzMKnuggaTmddFxLbAD4FFRvO5DX19RMQxmfm/1e23AcdSmmKlbvGXzLy87iKGaE5ErAksWnU9bPpFltMofxPuqrmOoerblWSruguSJGmY3EWZgPGciPgMze7ZBHAWQGbeEhHvpoy7HzWGvrk9EhFfAxanjN15R831SPPrwmos3y2dDZm5fY31DGQPypo63wS+zyiuV/Mc3ZuZJ9ddxHyotSuJJEkjaExm3h4RW1KWq2r04uyZeWKv23dQeuGMGkNfH5m5f0QcDqyWmZvWXY/0HHyaMk5uWs11DMW6mfnd6vZ6EbF7ncUMwe3VNMvXUy2JkJmX1lvSwOrsSiJJ0gg6GiAz742ItwNfrrmeRjP0VSLi3zyzrtUYYLmIuAfAiVzUZe7NzLPqLmKIjo+INwPbVwudvws4qt6SBjQJiOoflL8ZTQ59tXYlkSRpBP0yIr4ErABcRLPPH2pn6KtkZqObhKX58GQ1g1Xv1qj96i1pnq4FfkvpftgN62M+mJl71l3EUNXdlUSSpBH0HeASYBPg3ur+JrVW1GCGvj4i4pS+2xo8Hkrqz0/qLmA+9GTmtyPiYUqL2bi6CxqE63hKktQMy2TmKRGxTWb+NiLG1l1Qkxn65tbpFjcGWJfSZCx1jcw8ve4a5sPfADLzrCr4nVtzPYNZA3igWsdzDq7jKUlSbSLiZdXXFWn+7J21GtPT0zP4XguxiLg0M99Wdx2SJEmSioh4BfBt4OXAX4FPZeb19VbVXLb09VGtzdfxAmC5umqR1CwRsRZwCrAiZfzA9v4HI0lSLdYFnkeZrXx54DzgJXUW1GSGvrl9qNft6ZRFHyUJynqCn8jMP0XEOpTZMDestyRJkhZKnwW2pCzSrkEY+vrIzO2q5uI1gL9l5g01lyS1TkRslpk/j4iJfb+XmU/VUdMQjcnMPwFk5g0R4fgBSZLq8Y/MvLXuIrqFoa+PiPhf4MPA74G9IuLszPx6zWVJbfNS4OdAUpaVGFNt76HZXTNmR8Q7gauAjYEZNdcjSdLC6omIuAS4geYvUVU7Q9/cPgy8ITNnRcQEyhpihj5pGGXmsdXXF9ddy3zanvL34KvALcAO9ZYjSdJC6+K6C+gmzt7ZR0T8LjNf2+v+bzPz9XXWJLVVREwB3gFM7mzLzDPqq2hwEfFKYHXgpsz8a931SJIkDcaWvrn9OiLOpXTfegPwm5rrkdrsQuAenhmE3eirUBFxAPB24A/AHlX376PqrUqSJGlghr4+MnOviNiCsubHKZlp07E0csZm5jZ1FzEftgA2zMw5ETEe+DVwVL0lSZIkDczQ10dEvBD4J3ArsE9E3OMMntKI+XNEbMCzB2E3efbO/wCLAo8BE4H76i1HkiRpcIa+uX0fOAjYBTgX+AbwxjoLklpsE8oaOx2NnL0zIq6m1PZ84O8R8SfKsi4P1FqYJEnSEBj65jYHuBLYPzN/GBHOzieNnE9k5h86dyJi0xprGcjWdRcgSZL0XBn65jYBOAy4MiLeSOnCJWkYRcQbKC1ln4mII6vNY4FdgVfUVti8fXSA7x08alVIkiQ9B2PrLqCBtgNuAw4FpjLwyZ6k5+YhYHlgEvCC6t9UYJ86ixrAf6p/r6PUfRuwNLBOjTVJkiQNiev0SapNRKyQmff0uj8hM2fWWdNAIuLSzHxbr/u/yMy31lmTJEnSYOzeKalOW0bEnpS/RWOAmZSFz5tq6YhYNTNvi4gAlqy7IEmSpMHYvVPSqOs1QdIulBk8L6F0rb65tqKGZnfg/Ii4lzLT7y71liNJkjQ4Q5+kOnS6dN6Tmf8GpmTm5TS85Swzf01ZwuVdwGa9Zx6VJElqKkOfpFGXmT+tbj4cEe8GeiLik8Cy9VU1uIj4IPBbYF/gdxGxTc0lSZIkDcrQJ6lOnwDuoISo1SlLNjTZZ4D1MvM9wKuA3WquR5IkaVCGPkl1+mpmXp+Z92TmnsDH6y5oEHMy8zGAzHwUmF5zPZIkSYNy9k5Joy4idgEOoMyG+V7KzJ3Q/Ilc/hERRwBXAhtT1uuTJElqNNfpk1SbiNgvM79Sdx1DFRHjgU8CLwduAb7d5HUFJUmSwNAnqUYR8ULKjJ2zgM8C38zMP9VblSRJUrs4pk9Snb4PLAd8BfgFcFSt1UiSJLWQoU9SneZQxsctlZk/rO5LkiRpGBn6JNVpAnAYcGVEvBGYWHM9kiRJrWPok1Sn7SgzYB4KTAU+Wm85kiRJ7eNELpIkSZLUYrb0SZIkSVKLGfokSZIkqcXG112AJEl1iYhNgbOBm4EeYBHgzMw8ZgGPexBwb2aeWN1fH9gM+Fpmzh6knvWBIzPT2WwlScPC0CdJWtj9MjO3BoiISUBGxHczc9pwPUFmXgtcO4T9LgcuH67nlSQJDH2SJPU2BZgNrBURX61uTwd2AN4NPC8zv1iFwz8BrwT+F9gamAVcmZmf7RwsIsYB3wJeBLwA+HFmHhARpwEzgFWq7R/LzD9W23+YmT8b+ZcqSVpYOKZPkrSwe1NEXB4RvwTOpIS4bwC7ZuYmwPHAkcB3gQ9GxBjgXcBFQAAfBF5f/XtpRLyz17FfBPwuMzcDXgPs1Ot7d1TbjwF2HMkXKElauNnSJ0la2D3dvbMjIk7OzBuqu1dSxuI9FBHXAxsBHwP2BNaihLqZ1eOuAtbsdagHgVdHxBuBR4BJvb53ffX1LmDDYX1FkiT1YkufJElzuyciXlnd3gT4W3X7JGB3YJHM/CvwV2CDiBhftQBu3GtfKOFwWmb+D3AEsGi1H5SJYyRJGnGGPkmS5rYDcGzVcrcb8BmAzLwCeAVwWnX/Rsrsn78BrgFuBy7odZzLgLdHxJXACcDfgRVG4wVIktQxpqfHC42SJEmS1Fa29EmSJElSixn6JEmSJKnFDH2SJEmS1GKGPkmSJElqMUOfJEmSJLWYoU+SJEmSWszQJ0mSJEktZuiTJEmSpBb7/zpPTLY5QgmlAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 9))\n",
    "g = sns.countplot(\n",
    "    data=snem_long, x=\"obor\", hue=\"vs_vzdelani\",\n",
    "    order=snem_long.obor.value_counts().index\n",
    ")\n",
    "plt.title(\"Nejčastější povolání podle vysokoškolského vzdělání v rocích 2006 - 2021\")\n",
    "plt.xlabel(\"Povolání\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Počet\")\n",
    "plt.legend(title=\"Vysokoškolské vzdělání\",\n",
    "           labels=[\"Ne\", \"Ano\"]);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Nejčastěji jdou do voleb lidé ze soukromého sektoru bez vysokoškolského vzdělání, druhou nejčastější skupinou jsou, kteří již v politice působí (např. starostové, hejtmani), kteří většinou za sebou vysokou školu mají, třetí nejčastější skupinu kandidátů zastupují podnikatelé.\n",
    "\n",
    "Následný graf ukazuje, jaký vliv má vzdělání na ideologickou kategorii jíž kandidát reprezentuje:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 789.375x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(\n",
    "    data=snem_long, x=\"vs_vzdelani\", y=\"vek\",\n",
    "    hue=\"ideologie\", hue_order=hue_order, palette=hue_palette,\n",
    "    height = 8, aspect = 1.25\n",
    ")\n",
    "plt.title(\"Ideologie kandidátů do Poslanecké sněmovny podle vysokoškolského vzdělání\")\n",
    "plt.xlabel(\"Vysokoškolské vzdělání\")\n",
    "plt.ylabel(\"Věk kandidáta\");"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Kandidáti bez vysokoškolského vzdělání nejčastěji reprezentují pravicovou ideologii, zatímco kandidáti se vzděláním jsou spíše středo-levicoví. Avšak podíl levicových kandidátů je značný jen u starších věkových skupin."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Využití klasifikačního modelu za účelem predikce zvolení kandidáta\n",
    "Nejdříve je nutné provést tzv. dummifying kategoriálních proměnných, tj. převedou se na vícedimenzionální tvar"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "outputs": [
    {
     "data": {
      "text/plain": "   vek mandat  vs_vzdelani_0  vs_vzdelani_1  obor_důchodce  obor_finance  \\\n0   45      0              1              0              0             0   \n1   63      0              0              1              0             0   \n2   51      0              0              1              0             0   \n3   51      0              0              1              0             0   \n4   60      0              0              1              0             0   \n\n   obor_jiné  obor_management  obor_nezaměstnaný  obor_odborný pracovník  ...  \\\n0          0                1                  0                       0  ...   \n1          0                0                  0                       0  ...   \n2          0                0                  0                       0  ...   \n3          0                0                  0                       0  ...   \n4          0                0                  0                       0  ...   \n\n   kraj_Moravskoslezský kraj  kraj_Olomoucký kraj  kraj_Pardubický kraj  \\\n0                          1                    0                     0   \n1                          1                    0                     0   \n2                          1                    0                     0   \n3                          1                    0                     0   \n4                          1                    0                     0   \n\n   kraj_Plzeňský kraj  kraj_Středočeský kraj  kraj_Zlínský kraj  \\\n0                   0                      0                  0   \n1                   0                      0                  0   \n2                   0                      0                  0   \n3                   0                      0                  0   \n4                   0                      0                  0   \n\n   kraj_Ústecký kraj  ideologie_Levice  ideologie_Pravice  ideologie_Střed  \n0                  0                 0                  0                1  \n1                  0                 0                  0                1  \n2                  0                 0                  0                1  \n3                  0                 0                  0                1  \n4                  0                 0                  0                1  \n\n[5 rows x 42 columns]",
      "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>vek</th>\n      <th>mandat</th>\n      <th>vs_vzdelani_0</th>\n      <th>vs_vzdelani_1</th>\n      <th>obor_důchodce</th>\n      <th>obor_finance</th>\n      <th>obor_jiné</th>\n      <th>obor_management</th>\n      <th>obor_nezaměstnaný</th>\n      <th>obor_odborný pracovník</th>\n      <th>...</th>\n      <th>kraj_Moravskoslezský kraj</th>\n      <th>kraj_Olomoucký kraj</th>\n      <th>kraj_Pardubický kraj</th>\n      <th>kraj_Plzeňský kraj</th>\n      <th>kraj_Středočeský kraj</th>\n      <th>kraj_Zlínský kraj</th>\n      <th>kraj_Ústecký kraj</th>\n      <th>ideologie_Levice</th>\n      <th>ideologie_Pravice</th>\n      <th>ideologie_Střed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>45</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>63</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>51</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>51</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>60</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 42 columns</p>\n</div>"
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummifying kategoriálních proměnných\n",
    "snem_ml = snem_long.drop([\"cele_jmeno\", \"NSTRANA\", \"year\"], axis=1) # tyto proměnné nejsou potřeba, jelikož nemají velký vliv na cílovou proměnnou a mají hodně dimenzí\n",
    "snem_ml = pd.get_dummies(data=snem_ml, columns=[\"vs_vzdelani\", \"obor\", \"pohlavi\", \"kraj\", \"ideologie\"])\n",
    "snem_ml.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Jak je zřejmé z grafu níže, cílová proměnná \"mandat\" je silně nebalancovaná - na mandát dosáhlo jen 1000 kandidátů ze skoro 29 tisíc."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    27767\n",
      "1     1000\n",
      "Name: mandat, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(\n",
    "    data=snem_ml, x=\"mandat\"\n",
    ")\n",
    "print(snem_ml[\"mandat\"].value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC # Support Vector Machine Classifier algorithm\n",
    "from sklearn.ensemble import RandomForestClassifier # Random Forest Classifier\n",
    "from sklearn.preprocessing import StandardScaler # škálování dat\n",
    "from sklearn.decomposition import PCA # Principal Component Analysis"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Metoda undersampling byla zvolena s cílem zabránit nevyváženosti dat, tj. náhodně se zvolí stejný počet kandidátů bez mandátu jako s mandátem."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1000\n",
      "1    1000\n",
      "Name: mandat, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "\n",
    "sn_maj = snem_ml[snem_ml[\"mandat\"] == 0] # rozdělení datasetu na minor/major\n",
    "sn_min = snem_ml[snem_ml[\"mandat\"] == 1]\n",
    "\n",
    "sn_maj_und = resample(sn_maj,\n",
    "                     replace=True,\n",
    "                     n_samples=1000, # vyrovnání se s minoritní třídou\n",
    "                     random_state=42)\n",
    "\n",
    "# sjednocení obou setů - tím se vyváží\n",
    "snem_und = pd.concat([sn_maj_und, sn_min])\n",
    "print(snem_und[\"mandat\"].value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(\n",
    "    data=snem_und, x=\"mandat\"\n",
    ");"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Nyní je cílová proměnná vyvážená.\n",
    "\n",
    "Dalším krokem je rozdělení na testovací a trénovací datasety."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rozměr trénovacího datasetu: 1500\n",
      "Rozměr testovacího datasetu: 500\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# dělení sloupců na prediktory a cílovou proměnnou\n",
    "predictors = snem_ml.columns.to_list()\n",
    "predictors.remove(\"mandat\")\n",
    "target = [\"mandat\"]\n",
    "\n",
    "X = snem_und[predictors]\n",
    "y = snem_und[target]\n",
    "\n",
    "# dělení dat na trénovací a testovací sety\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "print(f\"Rozměr trénovacího datasetu: {len(X_train)}\")\n",
    "print(f\"Rozměr testovacího datasetu: {len(X_test)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Pro snadnou práci s více modely najednou jsou použity pipeliny. Porovnání Support Vector Classifier bez preprocessingu a Random Forest Classifier s PCA - PCA nastaveno s 95% objasněním variability komponentů."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "# vytvoření pipeliny jen s SVC modelem\n",
    "svc_pipeline = Pipeline([(\"svc\", SVC(class_weight=\"balanced\", random_state=42))]) # Support Vector Machine Classifier\n",
    "\n",
    "# pipeline se škalováním (potřebné pro PCA), PCA a samotným Random Forest modelem\n",
    "randf_pipeline = Pipeline([(\"scaler\", StandardScaler()),\n",
    "                         (\"pca\", PCA(n_components=0.95)),\n",
    "                         (\"randf\", RandomForestClassifier(class_weight=\"balanced\", random_state=42))]) # Random Forest Classifier with PCA decomposition\n",
    "\n",
    "# mřížky hyperparametrů\n",
    "svc_grid = {\"svc__kernel\": [\"linear\", \"rbf\"],\n",
    "        \"svc__C\": [0.1, 1, 10, 100]}\n",
    "\n",
    "randf_grid = {\"randf__n_estimators\": [200, 400, 600],\n",
    "              \"randf__max_depth\": [10, 50, 100],\n",
    "              \"randf__min_samples_split\": [2, 5, 10]}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* **GridSearch** hledá následující hyperparametry:\n",
    "    * **SVC**: kernel a C. Prvním parametrem je jádro daného modelu - v našem případě jsou to lineární a rbf. Druhým parametrem je síla regularizace: 0.1, 1, 10 a 100.\n",
    "    * **Random Forest**: n_estimators, max_depth a min_sample_split. Prvním je počet stromů ve forestu. Druhým - maximální hloubka jednotlivých stromů. Třetí parametr reguluje minimální počet vzorků v jednom stromu.\n",
    "\n",
    "Nejlepší hyperparametry jsou vybrány dle metriky ROC AUC (Plocha pod ROC křivkou). V tomto případě obyčejná přesnost může být nepostačující z důvodu nevyváženosti originálního datasetu. Je nutné zajistit, aby True Positive Rate byl větší proto, aby klasifikátor správně přiřazoval pozitivní hodnoty."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC se zpracovává\n",
      "\n",
      "Random Forest se zpracovává\n",
      "\n",
      "Proces se skončil úspěšně\n",
      "\n",
      "Nejlepší parametry SVC modelu jsou {'svc__C': 0.1, 'svc__kernel': 'linear'}\n",
      "\n",
      "Nejlepší parametry Random Forest modelu jsou {'randf__max_depth': 10, 'randf__min_samples_split': 10, 'randf__n_estimators': 600}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "results = {}\n",
    "pipelines = [svc_pipeline, randf_pipeline]\n",
    "grids = [svc_grid, randf_grid]\n",
    "names = [\"SVC\", \"Random Forest\"]\n",
    "\n",
    "# aplikování gridsearch na jednotlivé modely a mřížky\n",
    "for name, pipe, grid in zip(names, pipelines, grids):\n",
    "    print(f\"{name} se zpracovává\\n\")\n",
    "    temp_search = GridSearchCV(pipe, grid,\n",
    "                               cv=3, n_jobs=3, # počet Cross Validations je jen 3 pro lepší výkonnost\n",
    "                               scoring=\"roc_auc\")\n",
    "    temp_search.fit(X_train, y_train.values.ravel())\n",
    "    results[name] = {}\n",
    "    results[name][\"model\"] = temp_search.best_estimator_\n",
    "    results[name][\"params\"] = temp_search.best_params_ # uložení nejlepších parametrů\n",
    "\n",
    "print(\"Proces se skončil úspěšně\\n\")\n",
    "for name in names:\n",
    "    print(f\"\"\"Nejlepší parametry {name} modelu jsou {results[name][\"params\"]}\\n\"\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Výsledky SVC modelu na testovacím datasetu:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Mandat 0       0.80      0.77      0.78       257\n",
      "    Mandat 1       0.76      0.80      0.78       243\n",
      "\n",
      "    accuracy                           0.78       500\n",
      "   macro avg       0.78      0.78      0.78       500\n",
      "weighted avg       0.78      0.78      0.78       500\n",
      "\n",
      "------------------------------------------------------------\n",
      "Výsledky Random Forest modelu na testovacím datasetu:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Mandat 0       0.76      0.76      0.76       257\n",
      "    Mandat 1       0.75      0.75      0.75       243\n",
      "\n",
      "    accuracy                           0.76       500\n",
      "   macro avg       0.76      0.76      0.76       500\n",
      "weighted avg       0.76      0.76      0.76       500\n",
      "\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "class_names = [\"Mandat 0\", \"Mandat 1\"]\n",
    "for name in names:\n",
    "    print(f\"Výsledky {name} modelu na testovacím datasetu:\")\n",
    "    print(classification_report(y_test.values.ravel(),\n",
    "                                results[name][\"model\"].predict(X_test),\n",
    "                                target_names=class_names))\n",
    "    print(\"-\" * 60)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Přesnost obou algoritmů je skoro stejná: 0.78 u SVC a 0.76 u Random Forest modelu. Nyní zobrazme matice záměn každého modelu:"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 504x504 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "_, ax = plt.subplots(figsize=(7, 7))\n",
    "svc_pred = results[\"SVC\"][\"model\"].predict(X_test) # uložení predikcí modelu\n",
    "svc_cm = confusion_matrix(y_test, svc_pred) # vytvoření matici záměn\n",
    "svc_confm = ConfusionMatrixDisplay(confusion_matrix=svc_cm,\n",
    "                                   display_labels=class_names) # vizualizace této matice\n",
    "svc_confm.plot(ax=ax, cmap=plt.cm.Blues);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 504x504 with 2 Axes>",
      "image/png": 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Q+yRJ6iCVHk3ax78DdwI9wO2NbUmSKqPfYJiZ34mIHwNTgIcy84nB75YkqSPUqjGatN9rhhGxN/Bj4D+BayJij8HulCSpM9Ra+GingQyguRR4d2buCJwEfHFwuyRJ0tAaSDBclpm/BcjMXwMrB7dLkqTOUdzCqRWPdhrIcmzPRsQXgZuAfYAlQ9ExSVJnqMAlwwEtx3Zr498AFgO/GswOSZI01Aa6HNv2wBYU1zh3GIJ+SZI6RBVGkw5kObavAPsDY4EtgfnAfoPcL0lSByhWoGl3L8obyACaVwIvB64HXgYsH9QeSZI0xAYSDJ/IzDowNjMXDnaHJEkdpEa1R5P2cWdEfBT4Y0RcBYwZ5D5JkjpIFcqkA1mO7ayI2IqiPPpmivVJJUmqjI3NMzwfqK/n0P7AWYPWo83Aq3ffmUW/+Hy7u9FxRg7Hz60JEw+7qN1d6FhzLjuW3lOubHc3Os6y609vaXtVH016/5D1QpLUkWoMbPDJ5m5j8wy/NpQdkSSpXQYygEaSpA2qeplUkqSNq3XJne4j4oXABcA2wHeAezLTEaWSpOKaYQWC4UCue04HvkqxNulNwCWD2iNJkobYQILhlpn5U6CemYnLsUmSnlOjVmvNo50Gcs1weURMA4ZHxH4YDCVJfXRLmfRE4HhgMvBR4P2D2iNJkobYQJZj+wNw1BD0RZLUYapyC6eBjCZ9jGJZthowCZifmbsPdsckSR2gcdeKTjeQzHD7tc8j4kXAuYPZIUmShtomLSmXmQ8Duw1SXyRJHWhYix7tNJAy6Syev3vF9sD/DGqPJEkdo2uuGQLfAhY1ni8H7hi87kiSNPQGEgw/mpm9g94TSVJH6ooBNMCTEfEhIIE1AJl5w6D2SpLUMSoQCwcUDJ8AXtV4QHH90GAoSaqMDQbDiPhWZh6ZmccPZYckSZ1jqO9aERH7Ahdk5tSIeBVwGbAaWAG8JzP/JyJOAE4CVgGfzswf9Nfuxkaz9pTvtiSp0hqT7lvx6E9EnAFcAYxu7LoEOCUzpwLfAz4eEdsBpwKvB6YB50fEqP7a3liZdEpE/Ov6DmTmWf32WpKk1noQOAL4RmP7qMx8rPF8BMWMh32AmzNzBbAiIuYBewK/2FjDGwuGf6YYNCNJ0ga1agDNggULJvf29vadvjc9M6ev3cjMqyPixX22HwOIiNcBHwTeQJENLu7TxtPAhP5ee2PB8PHM/NqA3oEkqSu18pphT0/Pwszce1N+JiKOBM4GDsvMBRGxBBjX55RxwFP9tbOxYHjnpnRIkqShFBHvohgoMzUzn2zsngucFxGjgVHA7sC9/bW1wWCYmR9tQV8lSRVXY+gnGkbEcOBS4PfA9yIC4OeZ+cmIuBSYTTFI9OzM7Pem9AOZZyhJ0gYN5dSKzPwdsF9jc9IGzpkBzNiUdtu9ULgkSW1nZihJatpQT7ofLAZDSVLzajVqFVic1DKpJKnrmRlKkkqxTCpJ6noVqJJaJpUkycxQktS0YjRp56eGBkNJUilVuGZomVSS1PXMDCVJzatVYwCNwVCS1LQaMKwNC3W3mmVSSVLXMzOUJJVimVSS1PUcTSpJUgWYGUqSmuake0mSqMY1Q8ukkqSuZ2YoSWpezTKpJKnL1bBMKklSJZgZSpJKqUJWZTCUJJVQo1aBOmkVArokSaWYGUqSSun8vNBgKEkqoSor0FgmlSR1PTNDSVIpnZ8XGgwlSSVVoEpqmVSSJDNDSVLzalRinqHBUJLUtBrVKDEaDCVJpVQhM6xCQJckqRQzQ0lSKZ2fFxoMJUklWSaVJKkCzAwlSU1zNKkkSd7PUJKkajAzlCSV0vl5ocFQklRCDRfqliSpEswMJUmlDKtAodRgKElqXs0yqSRJlWBmKEkqpWaZVN3s4v9zPdfN/jUrn13NP/7dAUzd+yWc8MlvUKvV2H3K9nz2jHcybJjFB5WzV2zHuce9gb/9xLd5xS49XPyBg1i9us68Rxc9d877DnsVRx/0cur1Op//3h3855wH2tjj7jLUo0kjYl/ggsycGhG7AjOBOnAv8IHMXBMRnwQOA1YBp2Xm3P7abflvqoiYGhH1iDhqnf33RMTMkm2fHBHnbuT4pIg4ZgPHhkfEdyPikDJ9UGHOnQ8w956HuO6Kj/CDL5/Go/+ziDMvvppz3v8Wrp3xYer1Oj/6+a/b3U11uFPf8VouPfVNjBo5HICPH70/n5l1G28+4ypGbTGcrcaMYtL4LXnvoa9k2kdncfjZ3+HT75va3k5r0ETEGcAVwOjGrouBczLzAIq4/LaIeA3wRmBf4CjgCwNpe7D+bL+/0QkAImIPYOwgvVZfewJvXXdnREwBbgJeOwR96Ao/ufU+XrbrDrzrYzM4+iNfYlrvK7jrvkd4/WteAsDBr3s5N869v829VKd76LGnePd5339u+575f2LiuOL34FZjRkK9zpNLlnHAKV9n1eo1bDNxLMtXrmpXd7vWMGoteQzAg8ARfbb3An7eeH4tcBDQC9yQmfXM/D0wIiJ6+mt4sMqkdwMRERMyczHwLuBKYGeKAx+keENjgYXA24FjgEOBMcAUijR4ZkT0ApcAiyhS3tsabZwP7A1sDdydmccDZwOvjIgTM3N6n/5sBbwP+Pggvd+u8+RTz/DI409y1b+fzMOPPsExp3+ZNfX6c2sUbjVmFEuWLm9zL9Xprrnlv9lpm/HPbc//41N85v0HcvqR+7Hkzyt4ZvlKAFavqXPCW17Fmce+ji9//652dbdrDVWZNDOvjogX933pzKw3nj8NTADGA0/0OWft/gUba3swrxleDRzRKI3uA1wA7BwRwygC2EGN2u71PJ+xTcjMaRHxEuAailrw5cA7MvOBiLgcICLGA4sy8+BGe7+JiBcC5wEnrxMIycy7Gz83oI7XgEZVRhsweeJYdttlW7YaPYKXT9mW0aO24NE/LXruc1u+fAWTxm/p5zgAcy47tt1d2KxtMWI4O24zgTmXHUu8qIff/XERzz67ihduPZZtJ439i8/v8YWLOeUde3HUgbvx50agVOdYsGDB5N7e3jv67Jq+7u/zdazp83wc8BSwpPF83f0bNZjB8JsUgWw+MHvtzkYAXAnMioilwI7AFo3Dv2r8+wjP14S3zcy1V8NvBnYFlgHbRMQsYClF5re2jdLqwMrVrWqtml675y58+aobOfnoA3l84WKeWbaCqa8Nfjr3AXr3einXzvkNB+z1Uj/HAeg95cp2d2GzttM24/nKxw/jTafP4vYvHc8R53yPRxc+zWH778rnT3sTx/3bj/hfxx3Aexrl1G+f+3Y+991fcMu9f2hzzzdfy64/vaXttSoz7OnpWZiZe2/Cj9wVEVMz80bgzcDPgHnAhRHxWYr4MiwzF/bX0KAFw8ycHxFjgVOBTwC7AETEnsDhmblvRIwB7uT5dV7r62nq0YjYPTPvo8ggF1G86Z0y88hGLfjtjTbW4NzJIXHIAXtwy10P8jf/8BnW1Ot85ox3sutOW/NPn5rFs6u+z0tfvB1v+5tXt7ubqpgPXXoDX/n4YaxavYaVq9bwpyeXMu/RRdw7fwE3XHQ09Tr8+M6HDIRDqEZbp1acDsyIiJHAfcB3M3N1RMwGbqWIBx8YSEODPbXiW8C7GyXOXRr75gHPRMTNje3HgB020sZJwNcjYglF7XcRMBf454i4iSKAzm+08SCwR0Sclpmfa/m70V/41KmH/8X2yOHww+mntaUvqq5H/rSEN50+C4Dbfvsoh3zsqueOrS2RXjjrVi6cdWtb+qehlZm/A/ZrPH+AYuTouuecC5y7Ke3W6vX1JWPdbU2duuW9TTdyuOXlZkw87KJ2d6FjzbnsWMvMTVh2/el3UgxALG3Jsmfrv3h4cSua4m92m9yyfm0qJ91LkkqoVWIFGq+vSZK6npmhJKl5FblrhcFQktS0No8mbRnLpJKkrmdmKEkqZVjnJ4YGQ0lSOZZJJUmqADNDSVIpjiaVJHW1GlSgSGqZVJIkM0NJUjnDKlAnNRhKkkrp/FBomVSSJDNDSVJJFUgNDYaSpFKcdC9JUgWYGUqSmlbzFk6SJFXikqFlUkmSzAwlSeVUIDU0GEqSSqg5mlSSpCowM5QkleJoUklS16tALLRMKkmSmaEkqZwKpIYGQ0lS04o73Xd+NLRMKknqemaGkqRSHE0qSep6FYiFBkNJUgk1KhENvWYoSep6ZoaSpFKqMJrUYChJKqUKA2gsk0qSup6ZoSSpaRUZP2MwlCSVVIFoaJlUktT1zAwlSaU4mlSS1PUcTSpJUgWYGUqSSqlAYmgwlCSVVIFoaJlUktT1zAwlSaU4mlSS1NVqNUeTSpJUCWaGkqRShioxjIgtgK8BLwZWAycAq4CZQB24F/hAZq7Z1LbNDCVJ5dRa9OjfocCIzHwd8CngPOBi4JzMPKDRytuaeQsGQ0lSp3gAGBERw4DxwLPAXsDPG8evBQ5qpmHLpJKkEmotG026YMGCyb29vXf02TU9M6f32V5KUSK9H5gMvAV4Q2bWG8efBiY089oGQ0lSKa0aTdrT07MwM/feyCkfBq7PzE9ExE7AT4GRfY6PA55q5rUtk0qSOsUiYHHj+ZPAFsBdETG1se/NwOxmGjYzlCSVMoTTDP8d+GpEzKbICM8C7gBmRMRI4D7gu800bDCUJJUzRNEwM5cC71zPoTeWbdsyqSSp65kZSpKaVkwR7Pz12AyGkqRSXJtUkqQKMDOUJJVSgcTQYChJKmHg64pu1iyTSpK6npmhJKkUR5NKkrqeo0klSaoAM0NJUtMqMn7GYChJKscyqSRJFWBmKEkqqfNTQ4OhJKkUy6SSJFWAmaEkqZQKJIbU6vV6u/uwOVoAPNzuTkjSIHkR0NOKhlauWlN/4plVrWiK7SeMvBPYuyWNbSIzw/VryZdEktQZDIaSpKZ5p3tJkiqyBI2jSSVJXc/MUJJUSgUSQ4OhJKkcJ91LklQBBsMuFRFTI6IeEUets/+eiJhZsu2TI+LcjRyfFBHHrGf/fhFxe0TcHBGfLNMHbX42x+9c49jwiPhuRBxSpg/dq9ay/9rJYNjd7gee+8UUEXsAY4fgdfcE3rqe/V8CjgF6gX0j4tVD0BcNrc3qOxcRU4CbgNcOQR+qq9aiRxt5zbC73Q1EREzIzMXAu4ArgZ0pDnwQOILil9VC4O0UwepQYAwwBbggM2dGRC9wCbAIWAXc1mjjfIoVJbYG7s7M44GzgVdGxImZOb1x3nhgVGY+2Ni+HjgIuGvQPwUNpc3mO9ewFfA+4OOD+q612TMz1NXAERFRA/YBbgGIiGEUv0wOysx9Kf5wWvvX84TMfAvFX9pnNvZdDhydmQcBDzXaGA8sysyDKX457RcRLwTOA366zi+l8cCSPttPAxNa/Wa1WdhcvnNk5t2Zed/gvdXuUIHE0MxQfJPil8p8YPbanZm5JiJWArMiYimwI7BF4/CvGv8+AoxuPN82Mx9oPL8Z2BVYBmwTEbOApRR/ha9tY11LgHF9tscBTzX9rrQ521y+c2qBGo4mVQVk5nyKktSpwH+s3R8RewKHZ+aRwCkU35W1X/n1re7+aETs3ni+9q/5NwM7ZebRwFnAlo021rDOdy8zlwArI2JKI2OYRp9flKqOzeU7p9ZxAI2q4lsUv0Ae6LNvHvBMRNwM/BfwGLDDRto4Cfh6RPyEYkV8gLnALhFxE/BdikxgB+BBYI+IOG2dNk6muH40F7grM28v9a60OdtcvnMS4C2cJEklrFpdrz+9Yk1L2po4ZnjbbuFkZihJ6noGQ0lS13M0qSSpebVqjCY1GEqSSmn3SNBWsEwqSep6ZobqahExFfg28FuKuWxbAldm5mVNtPVvFGtv/gp4a2Z+agPnvR24PTP/OIA2DwGOyszj1unzyZl51AZ+5jhgt8w8c33Hmz1XWp+qTLo3GErFMl1HAUTEKCAj4huZ+VQzjWXmr3h+xZT1+RDFnMp+g6HUCSoQCw2G0jrGAauBVRFxI/AnYBJwGPBF4CUUlxfOycwbI+IdwDnAAmAkcH/fzC0i/hF4PzAc+D7FpPBXUUwW76WYOH4MRVZ6VWZe2lhV5avAM43Hog11dgMLWwPs35iMPh44NzN/GBFvpFijczXFJPSTyn1UUnV4zVCCAyPixoj4KcUKOKdk5tLGsVmNhaDfCyzMzDcAbwO+EBFbABdT3F1jGvDnvo1GxDYUi0ofALwGGAX8nCJrfA/FWppHUtyy6gDg8IgI4DPA/2q87i0b6nQ/C1s/0+jXYcDnI2I4MAM4IjPfCDwKHLfpH5W0HhVYqdvMUOpTJl2PbPy7B3BAROzb2B4BbA88mZlPAETEuoFrF+DezFzW2D6zcd7a46+gWEbsJ43tiRSZ50spMkgoFqBeu/7mX3Zs4wtbz8nMOvCniFgMTG7099uN19+SYsmzeRt439KAOZpUqr6160zdT5ElTqVYDPo7wOPACyKip3HOujeIfRDYrXEdksbd1F/I84tGJ/Ab4K8b7c4E7qEYzLP/Btp8Tj8LW7+2cc52FHduWAj8AXhb47XOA3468I9BqjaDoTQwX6YIbD+nKF0+nJkrgQ8C10fEjymuGT4nMxcAFwA/j4hbgV9m5qONn/86xe2IfgLMiYg7KLLCR4HTgXMa1/z2ZcM2trD1lo2y7/eBkzJzNcXAnR82Mth/Au4t9YlIDbVaax5tfQ8u1C1JatbqNfX6ilWtaWvMyJoLdUuS1C4OoJEkldP542cMhpKk5g31Peoj4hPAWymu0X+RYrrSTIq5uvcCH8jMTb7BomVSSVJHaCxo8Trg9cAbgZ0o5vqek5kHUOSob2umbYOhJKl5LRpJOsDRpNOAXwP/F7gG+AGwF0V2CHAtxWITm8wyqSSpacNqXD96BJNb0dbvf//70QcffPAdfXZNz8zpfbYnUyxU8RbgryimDg1rLDAB8DQwoZnXNhhKkso4pFUN7bzzzmTmxk55Ari/Mcc3I2I5Ral0rXHAU828tmVSSVKnmAMcEhG1iNiBYoH6nzSuJUKxOtTsZhp20r0kqWNExIXAX1Mkc2cBD1EsQj8SuA84obHi0iYxGEqSup5lUklS1zMYSpK6nsFQktT1DIaSpK5nMJQkdT2DoSSp6xkMJUldz2AoSep6/x8Z38HzbSLYjgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(7, 7))\n",
    "randf_pred = results[\"Random Forest\"][\"model\"].predict(X_test)\n",
    "randf_cm = confusion_matrix(y_test, randf_pred)\n",
    "randf_confm = ConfusionMatrixDisplay(confusion_matrix=randf_cm,\n",
    "                                   display_labels=class_names)\n",
    "randf_confm.plot(ax=ax, cmap=plt.cm.Blues);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Jak lze vidět, matice jsou si velice podobné. Nicméně True Positives Rate je větší u SVC modelu, než u Random Forest, což je pro naši klasifikaci lepší (chceme menší počet False Negatives). Přesto je rozdíl víceméně zanedbatelný."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x432 with 2 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, RocCurveDisplay\n",
    "_, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 6))\n",
    "\n",
    "svc_fpr, svc_tpr, _ = roc_curve(y_test, svc_pred) # kalkulace False Positive Rate a True Positive Rate pro nakreslení ROC křivky\n",
    "svc_auc = auc(svc_fpr, svc_tpr) # kalkulace plochy pod křivkou ROC\n",
    "svc_roc = RocCurveDisplay(fpr=svc_fpr, tpr=svc_tpr, roc_auc=svc_auc) # vizualizace samotné křivky\n",
    "svc_roc.plot(ax=ax1, name=\"SVC\")\n",
    "\n",
    "randf_fpr, randf_tpr, _ = roc_curve(y_test, randf_pred)\n",
    "randf_auc = auc(randf_fpr, randf_tpr)\n",
    "randf_roc = RocCurveDisplay(fpr=randf_fpr, tpr=randf_tpr, roc_auc=randf_auc)\n",
    "randf_roc.plot(ax=ax2, name=\"Random Forest\")\n",
    "plt.show();"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plochy pod křivkou u obou modelů jsou si opět podobné. SVC dosáhl 0.78, Random Forest - 0.76.\n",
    "Finálně můžeme tedy usoudit, že na daném datasetu dosahuje SVC lepších výsledků oproti Random Forest klasifikátoru, i když byly pro Random Forest uplatněny preprocessingové kroky."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Závěr\n",
    "* Při zpracování této práce byly zjištěny následující výsledky:\n",
    "    * věkové rozložení kandidátů je symetrické, žádná věková skupina nepřevládá,\n",
    "    * poměr muži:ženy je 2-2,5 ku 1 ve většině věkových skupin,\n",
    "    * pravicová ideologie převládá u kandidátů, ale nakonec největší počet zvolených je ze středu,\n",
    "    * nejčastěji do Poslanecké sněmovny kandidují zaměstnanci ze soukromého sektoru, podnikatelé a stávající politici.\n",
    "    * kandidáti bez vysokoškolského vzdělání mají větší tendenci být pravicovými, než-li kandidáti se vysokoškolským stupněm vzdělání - ti jsou většinou ve středu politického spektra.\n",
    "\n",
    "Pro trénování klasifikačního modelu bylo použito dvou metodik: **Support Vector Machine Classifier** bez preprocessingu a **Random Forest Classifier** s PCA preprocessingem. Originální dataset je velice nevyvážený, proto byl použit **undersampling**. Hyperparametry obou modelů byly zvoleny pomocí **Grid Search** algoritmu metrikou **AUC**. Ve výsledku měly oba modely skoro shodné výsledky - **SVC** model dosáhl o 0,02 větší plochy pod křivkou a **F-1** skóre -- **0.78**."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
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