clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "78899362",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bdcd94-58c3-41f2-9fc9-589da2dc7b4e",
   "metadata": {},
   "source": [
    "# 1. Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20cf4350-3c6e-42f9-9865-9fd150fe4e1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mykol\\AppData\\Local\\Temp\\ipykernel_18760\\3573810269.py:1: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  votes = pd.read_csv(\"votes.csv\", parse_dates=['time'])\n"
     ]
    }
   ],
   "source": [
    "votes = pd.read_csv(\"votes.csv\", parse_dates=['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f767198c-c84b-4872-8951-9dde623ebe14",
   "metadata": {},
   "outputs": [],
   "source": [
    "term_votes = votes[(votes['time'] > datetime.datetime(2012, 11, 15)) & (votes['time'] < datetime.datetime(2016, 11, 13))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f76060f6-f4a1-4ea7-bd7f-cbbd3f7db460",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voting_id</th>\n",
       "      <th>voter_id</th>\n",
       "      <th>time</th>\n",
       "      <th>group</th>\n",
       "      <th>voter</th>\n",
       "      <th>question</th>\n",
       "      <th>sitting_type</th>\n",
       "      <th>vote</th>\n",
       "      <th>n_eligible_voters</th>\n",
       "      <th>n_voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88168</th>\n",
       "      <td>-24780</td>\n",
       "      <td>53916</td>\n",
       "      <td>2016-11-10 10:10:43</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>Anušauskas Arvydas</td>\n",
       "      <td>dėl pritarimo  frakcijos „Tvarka ir teisinguma...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88169</th>\n",
       "      <td>-24780</td>\n",
       "      <td>7208</td>\n",
       "      <td>2016-11-10 10:10:43</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>Ažubalis Audronius</td>\n",
       "      <td>dėl pritarimo  frakcijos „Tvarka ir teisinguma...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88170</th>\n",
       "      <td>-24780</td>\n",
       "      <td>73568</td>\n",
       "      <td>2016-11-10 10:10:43</td>\n",
       "      <td>MG</td>\n",
       "      <td>Balsys Linas</td>\n",
       "      <td>dėl pritarimo  frakcijos „Tvarka ir teisinguma...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88171</th>\n",
       "      <td>-24780</td>\n",
       "      <td>34360</td>\n",
       "      <td>2016-11-10 10:10:43</td>\n",
       "      <td>TTF</td>\n",
       "      <td>Bartkevičius Kęstutis</td>\n",
       "      <td>dėl pritarimo  frakcijos „Tvarka ir teisinguma...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88172</th>\n",
       "      <td>-24780</td>\n",
       "      <td>38116</td>\n",
       "      <td>2016-11-10 10:10:43</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>Bastys Mindaugas</td>\n",
       "      <td>dėl pritarimo  frakcijos „Tvarka ir teisinguma...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789342</th>\n",
       "      <td>-16826</td>\n",
       "      <td>47836</td>\n",
       "      <td>2013-10-10 10:07:37</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>Žakaris Edvardas</td>\n",
       "      <td>dėl pritarimo pasiūlymui svarstyti 1 straipsni...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789343</th>\n",
       "      <td>-16826</td>\n",
       "      <td>34505</td>\n",
       "      <td>2013-10-10 10:07:37</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>Žeimys Pranas</td>\n",
       "      <td>dėl pritarimo pasiūlymui svarstyti 1 straipsni...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789344</th>\n",
       "      <td>-16826</td>\n",
       "      <td>65703</td>\n",
       "      <td>2013-10-10 10:07:37</td>\n",
       "      <td>TTF</td>\n",
       "      <td>Žemaitaitis Remigijus</td>\n",
       "      <td>dėl pritarimo pasiūlymui svarstyti 1 straipsni...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <th>2789345</th>\n",
       "      <td>-16826</td>\n",
       "      <td>53926</td>\n",
       "      <td>2013-10-10 10:07:37</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>Žilinskas Rokas</td>\n",
       "      <td>dėl pritarimo pasiūlymui svarstyti 1 straipsni...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <th>2789346</th>\n",
       "      <td>-16826</td>\n",
       "      <td>47865</td>\n",
       "      <td>2013-10-10 10:07:37</td>\n",
       "      <td>DPF</td>\n",
       "      <td>Žvikienė Zita</td>\n",
       "      <td>dėl pritarimo pasiūlymui svarstyti 1 straipsni...</td>\n",
       "      <td>rytinis</td>\n",
       "      <td>1.0</td>\n",
       "      <td>141</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>671170 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         voting_id  voter_id                time    group  \\\n",
       "88168       -24780     53916 2016-11-10 10:10:43  TS-LKDF   \n",
       "88169       -24780      7208 2016-11-10 10:10:43  TS-LKDF   \n",
       "88170       -24780     73568 2016-11-10 10:10:43       MG   \n",
       "88171       -24780     34360 2016-11-10 10:10:43      TTF   \n",
       "88172       -24780     38116 2016-11-10 10:10:43    LSDPF   \n",
       "...            ...       ...                 ...      ...   \n",
       "2789342     -16826     47836 2013-10-10 10:07:37    LSDPF   \n",
       "2789343     -16826     34505 2013-10-10 10:07:37  TS-LKDF   \n",
       "2789344     -16826     65703 2013-10-10 10:07:37      TTF   \n",
       "2789345     -16826     53926 2013-10-10 10:07:37  TS-LKDF   \n",
       "2789346     -16826     47865 2013-10-10 10:07:37      DPF   \n",
       "\n",
       "                         voter  \\\n",
       "88168       Anušauskas Arvydas   \n",
       "88169       Ažubalis Audronius   \n",
       "88170             Balsys Linas   \n",
       "88171    Bartkevičius Kęstutis   \n",
       "88172         Bastys Mindaugas   \n",
       "...                        ...   \n",
       "2789342       Žakaris Edvardas   \n",
       "2789343          Žeimys Pranas   \n",
       "2789344  Žemaitaitis Remigijus   \n",
       "2789345        Žilinskas Rokas   \n",
       "2789346          Žvikienė Zita   \n",
       "\n",
       "                                                  question sitting_type  vote  \\\n",
       "88168    dėl pritarimo  frakcijos „Tvarka ir teisinguma...      rytinis  -0.5   \n",
       "88169    dėl pritarimo  frakcijos „Tvarka ir teisinguma...      rytinis  -0.5   \n",
       "88170    dėl pritarimo  frakcijos „Tvarka ir teisinguma...      rytinis  -0.5   \n",
       "88171    dėl pritarimo  frakcijos „Tvarka ir teisinguma...      rytinis   1.0   \n",
       "88172    dėl pritarimo  frakcijos „Tvarka ir teisinguma...      rytinis   1.0   \n",
       "...                                                    ...          ...   ...   \n",
       "2789342  dėl pritarimo pasiūlymui svarstyti 1 straipsni...      rytinis   1.0   \n",
       "2789343  dėl pritarimo pasiūlymui svarstyti 1 straipsni...      rytinis   1.0   \n",
       "2789344  dėl pritarimo pasiūlymui svarstyti 1 straipsni...      rytinis   1.0   \n",
       "2789345  dėl pritarimo pasiūlymui svarstyti 1 straipsni...      rytinis  -0.5   \n",
       "2789346  dėl pritarimo pasiūlymui svarstyti 1 straipsni...      rytinis   1.0   \n",
       "\n",
       "         n_eligible_voters  n_voters  \n",
       "88168                  139        88  \n",
       "88169                  139        88  \n",
       "88170                  139        88  \n",
       "88171                  139        88  \n",
       "88172                  139        88  \n",
       "...                    ...       ...  \n",
       "2789342                141        83  \n",
       "2789343                141        83  \n",
       "2789344                141        83  \n",
       "2789345                141        83  \n",
       "2789346                141        83  \n",
       "\n",
       "[671170 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term_votes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccccec81-5702-4fc8-ac1c-ec4ac9fcad36",
   "metadata": {},
   "source": [
    "1. Let’s take one kadencija for starters (by time) \n",
    "2. Transform from individual votes into a datatype where every row (datapoint) is an MP, and every column (feature) is vote for a particular motion.\n",
    "- Find set of MP_ids (set of row values)\n",
    "- Find set of vote_ids (set of column values)\n",
    "3. If datapoint (MP_id_i, vote_id_j) exists, add its vote value to the (i,j) entry of array, otherwise add 0 (we equate non-participation and abstention, I think this is reasonable but can of course be debated).\n",
    "4. Perform PCA on that\n",
    "5. Pick first N components and try k-means clustering on it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "afa226e6-80af-43fd-8705-d726f8edac65",
   "metadata": {},
   "outputs": [],
   "source": [
    "voter_ids = term_votes['voter_id'].unique()\n",
    "mp_names = np.array([term_votes[term_votes['voter_id'] == voter_id].iloc[0]['voter'] for voter_id in voter_ids])\n",
    "groups = np.array([term_votes[term_votes['voter_id'] == voter_id].iloc[0]['group'] for voter_id in voter_ids])\n",
    "voting_ids = term_votes['voting_id'].unique()\n",
    "\n",
    "\n",
    "x = np.zeros([len(voter_ids), len(voting_ids)])\n",
    "\n",
    "for index in range(len(term_votes)):\n",
    "    vote = term_votes.iloc[index]\n",
    "    i = np.where(voter_ids == vote['voter_id'])[0]\n",
    "    j = np.where(voting_ids == vote['voting_id'])[0]\n",
    "    x[i,j] = vote['vote']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "888065b7-ed7c-46da-ac7e-9447affe0a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"votes_array.csv\", x, delimiter=\",\")\n",
    "np.savetxt(\"voter_ids.csv\", voter_ids, delimiter=\",\")\n",
    "np.savetxt(\"voting_ids.csv\", voting_ids, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "598684d0-8183-43dc-bd94-5c56711dba58",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.loadtxt(\"votes_array.csv\", delimiter=\",\")\n",
    "voter_ids = np.loadtxt(\"voter_ids.csv\", delimiter=\",\")\n",
    "voting_ids = np.loadtxt(\"voting_ids.csv\", delimiter=\",\")\n",
    "\n",
    "voter_ids = term_votes['voter_id'].unique()\n",
    "mp_names = np.array([term_votes[term_votes['voter_id'] == voter_id].iloc[0]['voter'] for voter_id in voter_ids])\n",
    "groups = np.array([term_votes[term_votes['voter_id'] == voter_id].iloc[0]['group'] for voter_id in voter_ids])\n",
    "voting_ids = term_votes['voting_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "56c15b56-5292-4e4f-bb63-33cd25b29432",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 2\n",
    "pca = PCA(n_components = n_components)\n",
    "x_tr = pca.fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13636091-50ad-47d4-849b-0bdca19fa58b",
   "metadata": {},
   "source": [
    "# 2. Checking for existence of clusters - Hopkins test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "31d6f45a-59b7-49b7-939a-251e3f2d1015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Masiulis Eligijus', 'Šimašius Remigijus', 'Kašėta Algis',\n",
       "       'Patackas Algirdas Vaclovas', 'Andriukaitis Vytenis Povilas',\n",
       "       'Auštrevičius Petras', 'Mazuronis Valentinas', 'Uspaskich Viktor',\n",
       "       'Venckienė Neringa', 'Veselka Julius'], dtype='<U29')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp_names[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "57545ef9-3215-43b8-b122-3e0516c4d833",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hopkins_statistic(data, n_samples = 10):\n",
    "\n",
    "    rng = np.random.default_rng()\n",
    "    \n",
    "    # get n_samples of samples from data\n",
    "    ds = rng.choice(data, n_samples, replace = False)\n",
    "\n",
    "    # remove those samples from data\n",
    "    ddata = np.copy(data)\n",
    "    for s in ds:\n",
    "        index = None\n",
    "        for i in range(ddata.shape[0]):\n",
    "            index = i if np.array_equal(ddata[i],s) else index\n",
    "        ddata = np.delete(ddata, (index), axis = 0)\n",
    "    \n",
    "    # get n_samples of random samples\n",
    "    #rs = rng.binomial(n = 1, p = 0.5, size = (n_samples, ddata.shape[1]))\n",
    "    rs = rng.uniform(low = -1, high = 1, size = (n_samples, ddata.shape[1]))\n",
    "\n",
    "    # set up nearest neighbor finding\n",
    "    nn = NearestNeighbors()\n",
    "\n",
    "    # for every point in ds, find minimum distance to the other points in ddata, and sum up those minimum distances\n",
    "    nn.fit(ddata)\n",
    "    sum_ds = np.sum(nn.kneighbors(ds, 1)[0])\n",
    "    print(nn.kneighbors(ds,1)[1])\n",
    "    \n",
    "    # for every point in rs, find minimum distance to points in data, and sum up those minimum distances\n",
    "    nn.fit(data)\n",
    "    sum_rs = np.sum(nn.kneighbors(rs, 1)[0])\n",
    "    print(nn.kneighbors(rs,1)[1])\n",
    "    \n",
    "    H = sum_rs / (sum_rs + sum_ds)\n",
    "    return H\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "f77435c5-978a-49f8-82d6-b94d1520fe34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 29]\n",
      " [140]\n",
      " [140]\n",
      " [129]\n",
      " [ 75]\n",
      " [124]\n",
      " [ 11]\n",
      " [ 33]\n",
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      " [ 91]]\n",
      "[[150]\n",
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      " [150]\n",
      " [150]\n",
      " [150]\n",
      " [150]\n",
      " [150]\n",
      " [150]]\n",
      "0.4930160052122688\n",
      "[[ 9]\n",
      " [90]\n",
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      " [40]\n",
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      "[[81]\n",
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      " [81]\n",
      " [81]]\n",
      "0.42210744978654713\n"
     ]
    }
   ],
   "source": [
    "print(hopkins_statistic(x))\n",
    "print(hopkins_statistic(x_tr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552882f5-f0a6-45f2-be4d-cc2c070ddafc",
   "metadata": {},
   "source": [
    "# 2. Coalition-oposition clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "94a3e47b-ea9e-4640-9ba3-70b686893857",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mykol\\.conda\\envs\\mlenv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\mykol\\.conda\\envs\\mlenv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "n_clusters = 2\n",
    "km = KMeans(n_clusters = n_clusters)\n",
    "\n",
    "#x_train, x_test = train_test_split(x_tr)\n",
    "# i'm not sure if train-test-split is necessary...\n",
    "\n",
    "km.fit(x_tr)\n",
    "\n",
    "y_pred = km.predict(x_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "863a74a1-0906-41d3-ab6f-8145346a19fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fe0e191ff0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_tr0 = x_tr[y_pred == 0]\n",
    "x_tr1 = x_tr[y_pred == 1]\n",
    "\n",
    "plt.scatter(x_tr0[:,0], x_tr0[:,1], color = 'blue', label = 'predicted opposition cluster')\n",
    "plt.scatter(x_tr1[:,0], x_tr1[:,1], color = 'red', label = 'predicted coalition cluster')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "9a33e18a-92af-4702-b6da-b2c67482b740",
   "metadata": {},
   "outputs": [],
   "source": [
    "term_votes['group'].unique()\n",
    "\n",
    "coalition = ['TTF','LSDPF','DPF']\n",
    "\n",
    "coalition_labels = []\n",
    "\n",
    "for voter_id in voter_ids:\n",
    "    party = term_votes[term_votes['voter_id'] == voter_id].iloc[0]['group']\n",
    "    if party in coalition:\n",
    "        coalition_labels.append(1)\n",
    "    else:\n",
    "        coalition_labels.append(0)\n",
    "\n",
    "coalition_labels = np.array(coalition_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "9c55047d-0195-4a74-8a11-49d4ece44584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.846153846153847% of MPs in cluster 0 are in the coalition and 86.15384615384616% are in opposition\n",
      "84.88372093023256% of MPs in cluster 1 are in the coalition and 15.116279069767444% are in opposition\n"
     ]
    }
   ],
   "source": [
    "co0 = sum((1-y_pred) * coalition_labels) / sum(1-y_pred) * 100\n",
    "op0 = 100 - co0\n",
    "co1 = sum(y_pred * coalition_labels) / sum(y_pred) * 100\n",
    "op1 = 100 - co1\n",
    "\n",
    "print(f\"{co0}% of MPs in cluster 0 are in the coalition and {op0}% are in opposition\")\n",
    "print(f\"{co1}% of MPs in cluster 1 are in the coalition and {op1}% are in opposition\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "94bd320b-0d11-4eb4-b701-906176e8d9c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2578\n",
      "51\n",
      "0\n",
      "0\n",
      "dėl Seimo nutarimo priėmimo\n",
      "dėl Lietuvos socialdemokratų partijos frakcijos pasiūlymo išbraukti iš darbotvarkės projektą Nr. XIIP-4237\n",
      "dėl pritarimo Seimo Pirmininkės L. Graužinienės pasiūlymui baigti neeilinę sesiją\n",
      "dėl įstatymo priėmimo\n",
      "dėl 2 straipsnio J. Razmos antros pataisos \n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo dėl slapto balsavimo\n",
      "dėl Seimo nutarimo priėmimo\n",
      "dėl pasiūlymo daryti pertrauką (pagal Seimo statuto 154 straipsnio 3 dalį)\n",
      "dėl pasiūlymo daryti pertrauką pagal Seimo statuto 158 straipsnio 3 dalį\n",
      "dėl pasiūlymo daryti pertrauką pagal Seimo statuto 158 straipsnio 3 dalį\n",
      "dėl pasiūlymo daryti pertrauką pagal Seimo statuto 158 straipsnio 3 dalį\n",
      "dėl pasiūlymo daryti pertrauką iki kito posėdžio pagal Seimo statuto 109 straipsnį\n",
      "dėl pasiūlymo priimti šią rezoliuciją be pataisų\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo išbraukti iš darbotvarkės projektą Nr. XIIP-2841\n",
      "dėl įstatymo priėmimo\n",
      "dėl Seimo nutarimo priėmimo\n",
      "dėl pritarimo patikslintai darbotvarkei\n",
      "dėl Darbo partijos frakcijos pasiūlymo išbraukti iš darbotvarkės projektą Nr. XIIP-1699(2)\n",
      "dėl pritarimo po pateikimo\n",
      "dėl pritarimo po pateikimo\n",
      "dėl pritarimo po svarstymo\n",
      "dėl Seimo nutarimo priėmimo\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo išbraukti iš darbotvarkės Miškų įstatymo 7 ir 11 straipsnių pakeitimo įstatymo projektą Nr. XIIP-1092(2)\n",
      "dėl 4 straipsnio\n",
      "dėl įstatymo priėmimo\n",
      "dėl 1 straipsnio\n",
      "dėl 2 straipsnio\n",
      "dėl įstatymo priėmimo\n",
      "dėl Seimo Pirmininkės pasiūlymo sulaukti Etikos ir procedūrų komisijos išvados dėl projekto Nr. XIIP-1264\n",
      "dėl darbotvarkės patvirtinimo\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo grąžinti šį projektą Vyriausybei tobulinti\n",
      "dėl pritarimo po pateikimo\n",
      "dėl pasiūlymo svarstyti šį projektą ypatingos skubos tvarka\n",
      "dėl 1 straipsnio\n",
      "dėl 2 straipsnio\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo išbraukti iš darbotvarkės projektą Nr. XIIP-371(2)\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo išbraukti iš darbotvarkės projektą Nr. XIIP-399\n",
      "dėl pritarimo po svarstymo\n",
      "dėl pritarimo po pateikimo\n",
      "dėl pasiūlymo svarstyti ypatingos skubos tvarka\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo svarstyti rezoliucijų projektus Nr. XIIP-128, Nr. XIIP-129 ir Nr. XIIP-130 rytinio posėdžio pradžioje\n",
      "dėl Seimo nutarimo priėmimo\n",
      "dėl Tėvynės sąjungos-Lietuvos krikščionių demokratų frakcijos pasiūlymo daryti pertrauką iki kito posėdžio\n",
      "dėl Seimo nutarimo priėmimo\n",
      "dėl pritarimo po svarstymo\n",
      "dėl šio statuto priėmimo\n",
      "dėl P. Auštrevičiaus ir kt. pataisos\n",
      "dėl pritarimo po svarstymo\n",
      "dėl J. Razmos pataisos\n",
      "dėl 3 straipsnio E. Masiulio, V. Gailiaus ir E. Gentvilo pataisos\n",
      "dėl pritarimo A. Butkevičiaus kandidatūrai į Ministras Pirmininko pareigas\n"
     ]
    }
   ],
   "source": [
    "comp = pca.components_[0]\n",
    "print(sum(np.abs(comp) > 1e-2))\n",
    "print(sum(np.abs(comp) > 3e-2))\n",
    "print(sum(np.abs(comp) > 5e-2))\n",
    "print(sum(np.abs(comp) > 7e-2))\n",
    "\n",
    "deciding_votes = voting_ids[np.abs(comp) > 3e-2]\n",
    "\n",
    "for deciding_vote in deciding_votes:\n",
    "    print(term_votes[term_votes['voting_id'] == deciding_vote].iloc[0]['question'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "28b98106-cde1-4df5-9526-abccb7f7cd63",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.DataFrame(data = {\n",
    "    'MP': mp_names,\n",
    "    'group': groups,\n",
    "    'voter_id': voter_id,\n",
    "    'in_coalition': coalition_labels,\n",
    "    'predicted_cluster': y_pred,\n",
    "    'distance to opposition': km.transform(x_tr)[:,0],\n",
    "    'distance to coalition': km.transform(x_tr)[:,1],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eec44733-9a8b-4dba-828d-0fb7e0d4f578",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MP</th>\n",
       "      <th>group</th>\n",
       "      <th>voter_id</th>\n",
       "      <th>in_coalition</th>\n",
       "      <th>predicted_cluster</th>\n",
       "      <th>distance to opposition</th>\n",
       "      <th>distance to coalition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Zingeris Emanuelis</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.663418</td>\n",
       "      <td>41.137024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Glaveckas Kęstutis</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.667845</td>\n",
       "      <td>40.928703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Kašėta Algis</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.087762</td>\n",
       "      <td>42.356272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Kreivys Dainius</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.537606</td>\n",
       "      <td>50.346384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Teišerskytė Dalia</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.019719</td>\n",
       "      <td>46.374554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bilotaitė Agnė</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.172857</td>\n",
       "      <td>50.085913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Saudargas Paulius</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.355002</td>\n",
       "      <td>50.120941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Vareikis Egidijus</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.553777</td>\n",
       "      <td>48.915811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Auštrevičius Petras</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.630007</td>\n",
       "      <td>43.693306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Patackas Algirdas Vaclovas</td>\n",
       "      <td>MG</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.791817</td>\n",
       "      <td>43.484191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Vidžiūnas Arvydas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.083758</td>\n",
       "      <td>52.406586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Jakimavičius Saulius</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.960003</td>\n",
       "      <td>41.732520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Juknevičienė Rasa</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.252636</td>\n",
       "      <td>53.542138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Lydeka Arminas</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.579994</td>\n",
       "      <td>44.807902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Venckienė Neringa</td>\n",
       "      <td>DKF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.753351</td>\n",
       "      <td>42.186430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Mazuronis Valentinas</td>\n",
       "      <td>TTF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.845273</td>\n",
       "      <td>39.791205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Andriukaitis Vytenis Povilas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.881038</td>\n",
       "      <td>40.787753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Gentvilas Eugenijus</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.895666</td>\n",
       "      <td>50.097755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Čmilytė-Nielsen Viktorija</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.896720</td>\n",
       "      <td>44.461213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Veselka Julius</td>\n",
       "      <td>TTF</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.004423</td>\n",
       "      <td>43.437594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               MP    group  voter_id  in_coalition  \\\n",
       "129            Zingeris Emanuelis  TS-LKDF      54.0             0   \n",
       "133            Glaveckas Kęstutis      LSF      54.0             0   \n",
       "143                  Kašėta Algis      LSF      54.0             0   \n",
       "116               Kreivys Dainius  TS-LKDF      54.0             0   \n",
       "77              Teišerskytė Dalia      LSF      54.0             0   \n",
       "6                  Bilotaitė Agnė  TS-LKDF      54.0             0   \n",
       "120             Saudargas Paulius  TS-LKDF      54.0             0   \n",
       "80              Vareikis Egidijus  TS-LKDF      54.0             0   \n",
       "146           Auštrevičius Petras      LSF      54.0             0   \n",
       "144    Patackas Algirdas Vaclovas       MG      54.0             0   \n",
       "108             Vidžiūnas Arvydas  TS-LKDF      54.0             0   \n",
       "128          Jakimavičius Saulius      DPF      54.0             1   \n",
       "109             Juknevičienė Rasa  TS-LKDF      54.0             0   \n",
       "41                 Lydeka Arminas      LSF      54.0             0   \n",
       "149             Venckienė Neringa      DKF      54.0             0   \n",
       "147          Mazuronis Valentinas      TTF      54.0             1   \n",
       "145  Andriukaitis Vytenis Povilas    LSDPF      54.0             1   \n",
       "21            Gentvilas Eugenijus      LSF      54.0             0   \n",
       "10      Čmilytė-Nielsen Viktorija      LSF      54.0             0   \n",
       "150                Veselka Julius      TTF      54.0             1   \n",
       "\n",
       "     predicted_cluster  distance to opposition  distance to coalition  \n",
       "129                  0               20.663418              41.137024  \n",
       "133                  0               24.667845              40.928703  \n",
       "143                  0               26.087762              42.356272  \n",
       "116                  0               26.537606              50.346384  \n",
       "77                   0               27.019719              46.374554  \n",
       "6                    0               27.172857              50.085913  \n",
       "120                  0               27.355002              50.120941  \n",
       "80                   0               27.553777              48.915811  \n",
       "146                  0               27.630007              43.693306  \n",
       "144                  0               27.791817              43.484191  \n",
       "108                  0               29.083758              52.406586  \n",
       "128                  0               29.960003              41.732520  \n",
       "109                  0               30.252636              53.542138  \n",
       "41                   0               30.579994              44.807902  \n",
       "149                  0               30.753351              42.186430  \n",
       "147                  0               30.845273              39.791205  \n",
       "145                  0               30.881038              40.787753  \n",
       "21                   0               30.895666              50.097755  \n",
       "10                   0               30.896720              44.461213  \n",
       "150                  0               31.004423              43.437594  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.sort_values('distance to opposition').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "62a209b7-2c49-4f8d-8dc0-55a0faa7bebc",
   "metadata": {
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    "jupyter": {
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MP</th>\n",
       "      <th>group</th>\n",
       "      <th>voter_id</th>\n",
       "      <th>in_coalition</th>\n",
       "      <th>predicted_cluster</th>\n",
       "      <th>distance to opposition</th>\n",
       "      <th>distance to coalition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Paulauskas Artūras</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42.130287</td>\n",
       "      <td>27.660279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Gedvilas Vydas</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>44.106436</td>\n",
       "      <td>28.002417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Kirkilas Gediminas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.936905</td>\n",
       "      <td>28.312144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Skardžius Artūras</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43.626684</td>\n",
       "      <td>29.394324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bradauskas Bronius</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41.145443</td>\n",
       "      <td>29.771402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Mikolaitis Gintautas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46.795385</td>\n",
       "      <td>30.264978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Fiodorovas Viktoras</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48.343295</td>\n",
       "      <td>30.477927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Vonžutaitė Vitalija</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40.620698</td>\n",
       "      <td>30.539608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Požela Juras</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40.413166</td>\n",
       "      <td>30.668420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Salamakinas Algimantas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.729020</td>\n",
       "      <td>30.718938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Žakaris Edvardas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.058771</td>\n",
       "      <td>30.826693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Mockus Arvydas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49.403070</td>\n",
       "      <td>30.918513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Vėsaitė Birutė</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41.350480</td>\n",
       "      <td>31.247331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Olekas Juozas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.389293</td>\n",
       "      <td>31.324539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Popovienė Raminta</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.558669</td>\n",
       "      <td>31.336670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Pauža Bronius</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51.104158</td>\n",
       "      <td>31.592839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Miškinienė Kristina</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.663058</td>\n",
       "      <td>31.652239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dudėnas Arūnas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.988994</td>\n",
       "      <td>31.664048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Gapšys Vytautas</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.156182</td>\n",
       "      <td>31.789546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Šiaulienė Irena</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.718280</td>\n",
       "      <td>32.059181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         MP  group  voter_id  in_coalition  predicted_cluster  \\\n",
       "125      Paulauskas Artūras    DPF        54             1                  1   \n",
       "101          Gedvilas Vydas    DPF        54             1                  1   \n",
       "34       Kirkilas Gediminas  LSDPF        54             1                  1   \n",
       "67        Skardžius Artūras  LSDPF        54             1                  1   \n",
       "7        Bradauskas Bronius  LSDPF        54             1                  1   \n",
       "46     Mikolaitis Gintautas  LSDPF        54             1                  1   \n",
       "19      Fiodorovas Viktoras    DPF        54             1                  1   \n",
       "115     Vonžutaitė Vitalija    DPF        54             1                  1   \n",
       "140            Požela Juras  LSDPF        54             1                  1   \n",
       "122  Salamakinas Algimantas  LSDPF        54             1                  1   \n",
       "84         Žakaris Edvardas  LSDPF        54             1                  1   \n",
       "49           Mockus Arvydas  LSDPF        54             1                  1   \n",
       "136          Vėsaitė Birutė  LSDPF        54             1                  1   \n",
       "52            Olekas Juozas  LSDPF        54             1                  1   \n",
       "95        Popovienė Raminta  LSDPF        54             1                  1   \n",
       "55            Pauža Bronius  LSDPF        54             1                  1   \n",
       "47      Miškinienė Kristina  LSDPF        54             1                  1   \n",
       "15           Dudėnas Arūnas  LSDPF        54             1                  1   \n",
       "139         Gapšys Vytautas    DPF        54             1                  1   \n",
       "74          Šiaulienė Irena  LSDPF        54             1                  1   \n",
       "\n",
       "     distance to opposition  distance to coalition  \n",
       "125               42.130287              27.660279  \n",
       "101               44.106436              28.002417  \n",
       "34                38.936905              28.312144  \n",
       "67                43.626684              29.394324  \n",
       "7                 41.145443              29.771402  \n",
       "46                46.795385              30.264978  \n",
       "19                48.343295              30.477927  \n",
       "115               40.620698              30.539608  \n",
       "140               40.413166              30.668420  \n",
       "122               45.729020              30.718938  \n",
       "84                56.058771              30.826693  \n",
       "49                49.403070              30.918513  \n",
       "136               41.350480              31.247331  \n",
       "52                38.389293              31.324539  \n",
       "95                56.558669              31.336670  \n",
       "55                51.104158              31.592839  \n",
       "47                52.663058              31.652239  \n",
       "15                56.988994              31.664048  \n",
       "139               45.156182              31.789546  \n",
       "74                58.718280              32.059181  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.sort_values('distance to coalition').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "49065e9c-b903-4e52-96bb-51abe6dd1228",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>MP</th>\n",
       "      <th>group</th>\n",
       "      <th>voter_id</th>\n",
       "      <th>in_coalition</th>\n",
       "      <th>predicted_cluster</th>\n",
       "      <th>distance to opposition</th>\n",
       "      <th>distance to coalition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Jedinskij Zbignev</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55.410083</td>\n",
       "      <td>44.590607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Kravčionok Vanda</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.948798</td>\n",
       "      <td>44.598688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Kvetkovskij Juzef</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.327495</td>\n",
       "      <td>48.880401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Mackevič Michal</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.204865</td>\n",
       "      <td>48.919282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Mazuronis Andrius</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.687074</td>\n",
       "      <td>47.111396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Rozova Irina</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49.248096</td>\n",
       "      <td>39.928656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Talmont Leonard</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>49.785664</td>\n",
       "      <td>43.506983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Tamašunienė Rita</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.354067</td>\n",
       "      <td>44.839720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Varkala Jonas</td>\n",
       "      <td>MG</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>62.816366</td>\n",
       "      <td>60.326991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Vasiliauskas Valdas</td>\n",
       "      <td>MG</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.932947</td>\n",
       "      <td>47.748882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Bucevičius Saulius</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.448151</td>\n",
       "      <td>37.548216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Vaickienė Jolita</td>\n",
       "      <td>LSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46.817388</td>\n",
       "      <td>38.499628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Butkevičius Algirdas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.298191</td>\n",
       "      <td>37.464392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Narkevič Jaroslav</td>\n",
       "      <td>LLRA-KŠSF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44.716431</td>\n",
       "      <td>39.478229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Birutis Šarūnas</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32.546572</td>\n",
       "      <td>35.830595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Jakimavičius Saulius</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.866739</td>\n",
       "      <td>41.675034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Sinkevičius Rimantas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32.684568</td>\n",
       "      <td>36.507735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Ačas Remigijus</td>\n",
       "      <td>TTF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36.988514</td>\n",
       "      <td>45.088613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Andriukaitis Vytenis Povilas</td>\n",
       "      <td>LSDPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.781983</td>\n",
       "      <td>40.724338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Mazuronis Valentinas</td>\n",
       "      <td>TTF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.746632</td>\n",
       "      <td>39.711381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Uspaskich Viktor</td>\n",
       "      <td>DPF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.207823</td>\n",
       "      <td>39.574442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Veselka Julius</td>\n",
       "      <td>TTF</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.938652</td>\n",
       "      <td>43.401142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               MP      group  voter_id  in_coalition  \\\n",
       "28              Jedinskij Zbignev  LLRA-KŠSF        54             0   \n",
       "36               Kravčionok Vanda  LLRA-KŠSF        54             0   \n",
       "39              Kvetkovskij Juzef  LLRA-KŠSF        54             0   \n",
       "42                Mackevič Michal  LLRA-KŠSF        54             0   \n",
       "44              Mazuronis Andrius        LSF        54             0   \n",
       "62                   Rozova Irina  LLRA-KŠSF        54             0   \n",
       "75                Talmont Leonard  LLRA-KŠSF        54             0   \n",
       "76               Tamašunienė Rita  LLRA-KŠSF        54             0   \n",
       "81                  Varkala Jonas         MG        54             0   \n",
       "82            Vasiliauskas Valdas         MG        54             0   \n",
       "89             Bucevičius Saulius        LSF        54             0   \n",
       "107              Vaickienė Jolita        LSF        54             0   \n",
       "113          Butkevičius Algirdas      LSDPF        54             1   \n",
       "118             Narkevič Jaroslav  LLRA-KŠSF        54             0   \n",
       "124               Birutis Šarūnas        DPF        54             1   \n",
       "128          Jakimavičius Saulius        DPF        54             1   \n",
       "134          Sinkevičius Rimantas      LSDPF        54             1   \n",
       "137                Ačas Remigijus        TTF        54             1   \n",
       "145  Andriukaitis Vytenis Povilas      LSDPF        54             1   \n",
       "147          Mazuronis Valentinas        TTF        54             1   \n",
       "148              Uspaskich Viktor        DPF        54             1   \n",
       "150                Veselka Julius        TTF        54             1   \n",
       "\n",
       "     predicted_cluster  distance to opposition  distance to coalition  \n",
       "28                   1               55.410083              44.590607  \n",
       "36                   1               51.948798              44.598688  \n",
       "39                   1               59.327495              48.880401  \n",
       "42                   1               59.204865              48.919282  \n",
       "44                   1               48.687074              47.111396  \n",
       "62                   1               49.248096              39.928656  \n",
       "75                   1               49.785664              43.506983  \n",
       "76                   1               54.354067              44.839720  \n",
       "81                   1               62.816366              60.326991  \n",
       "82                   1               53.932947              47.748882  \n",
       "89                   1               39.448151              37.548216  \n",
       "107                  1               46.817388              38.499628  \n",
       "113                  0               34.298191              37.464392  \n",
       "118                  1               44.716431              39.478229  \n",
       "124                  0               32.546572              35.830595  \n",
       "128                  0               29.866739              41.675034  \n",
       "134                  0               32.684568              36.507735  \n",
       "137                  0               36.988514              45.088613  \n",
       "145                  0               30.781983              40.724338  \n",
       "147                  0               30.746632              39.711381  \n",
       "148                  0               31.207823              39.574442  \n",
       "150                  0               30.938652              43.401142  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[predictions['in_coalition'] != predictions['predicted_cluster']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb7860d-30f9-4e54-b306-73a8d46316bb",
   "metadata": {},
   "source": [
    "# 3. Party-level clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "1b7652d0-becb-4697-a1de-a1a09e316755",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = term_votes['group'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "9d3076f8-8fad-4456-a2b1-b07071236239",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mykol\\.conda\\envs\\mlenv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\mykol\\.conda\\envs\\mlenv\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "n_clusters = 7\n",
    "kmp = KMeans(n_clusters = n_clusters)\n",
    "\n",
    "#x_train, x_test = train_test_split(x_tr)\n",
    "# i'm not sure if train-test-split is necessary...\n",
    "\n",
    "kmp.fit(x_tr)\n",
    "\n",
    "y_pred = kmp.predict(x_tr)\n",
    "\n",
    "predictions['party_cluster'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "072b9b0d-5d02-4ba3-b214-499d4d2e4d4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fe0ffc34f0>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize = (16, 6))\n",
    "for i in range(n_clusters):\n",
    "    ax1.scatter(x_tr[y_pred == i,0], x_tr[y_pred == i, 1], label = str(i))\n",
    "ax1.legend()\n",
    "\n",
    "for group in term_votes['group'].unique():\n",
    "    ax2.scatter(x_tr[predictions['group'] == group,0], x_tr[predictions['group'] == group, 1], label = group)\n",
    "ax2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "70192410-a90e-4797-8088-f5fb7b7950b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MP</th>\n",
       "      <th>group</th>\n",
       "      <th>voter_id</th>\n",
       "      <th>in_coalition</th>\n",
       "      <th>predicted_cluster</th>\n",
       "      <th>distance to opposition</th>\n",
       "      <th>distance to coalition</th>\n",
       "      <th>party_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dagys Rimantas Jonas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>28.391678</td>\n",
       "      <td>56.254975</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Jankauskas Donatas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>25.105608</td>\n",
       "      <td>49.840446</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Jovaiša Sergejus</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>41.637863</td>\n",
       "      <td>61.434628</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Juozapaitis Vytautas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27.899224</td>\n",
       "      <td>53.183357</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Kuzminskas Kazimieras</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>34.352509</td>\n",
       "      <td>57.519892</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Matulas Antanas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>25.839257</td>\n",
       "      <td>48.117740</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Razma Jurgis</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30.792364</td>\n",
       "      <td>54.365269</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Strelčiūnas Algis</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>32.430012</td>\n",
       "      <td>53.253207</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Stundys Valentinas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26.751610</td>\n",
       "      <td>56.018702</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Šedbaras Stasys</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27.789025</td>\n",
       "      <td>52.989613</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Žeimys Pranas</td>\n",
       "      <td>TS-LKDF</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>39.513557</td>\n",
       "      <td>58.629043</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       MP    group  voter_id  in_coalition  predicted_cluster  \\\n",
       "11   Dagys Rimantas Jonas  TS-LKDF        54             0                  5   \n",
       "27     Jankauskas Donatas  TS-LKDF        54             0                  5   \n",
       "30       Jovaiša Sergejus  TS-LKDF        54             0                  5   \n",
       "32   Juozapaitis Vytautas  TS-LKDF        54             0                  5   \n",
       "38  Kuzminskas Kazimieras  TS-LKDF        54             0                  5   \n",
       "43        Matulas Antanas  TS-LKDF        54             0                  5   \n",
       "61           Razma Jurgis  TS-LKDF        54             0                  5   \n",
       "69      Strelčiūnas Algis  TS-LKDF        54             0                  5   \n",
       "70     Stundys Valentinas  TS-LKDF        54             0                  5   \n",
       "73        Šedbaras Stasys  TS-LKDF        54             0                  5   \n",
       "85          Žeimys Pranas  TS-LKDF        54             0                  5   \n",
       "\n",
       "    distance to opposition  distance to coalition  party_cluster  \n",
       "11               28.391678              56.254975              4  \n",
       "27               25.105608              49.840446              4  \n",
       "30               41.637863              61.434628              4  \n",
       "32               27.899224              53.183357              4  \n",
       "38               34.352509              57.519892              4  \n",
       "43               25.839257              48.117740              4  \n",
       "61               30.792364              54.365269              4  \n",
       "69               32.430012              53.253207              4  \n",
       "70               26.751610              56.018702              4  \n",
       "73               27.789025              52.989613              4  \n",
       "85               39.513557              58.629043              4  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[(predictions['group'] == 'TS-LKDF') & (predictions['party_cluster'] == 4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "897a4931-e4fb-4722-8a5f-a6dd90b0c6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03333333333333333\n",
      "0.0\n",
      "0.6\n",
      "0.0\n",
      "0.36666666666666664\n",
      "0.0\n",
      "0.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "group = 'TS-LKDF'\n",
    "for cluster_no in range(n_clusters):\n",
    "    print(len(predictions[(predictions['party_cluster'] == cluster_no) & (predictions['group'] == group)]) / len(predictions[predictions['group'] == group]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "96cfbd27-a0f7-4e10-adb3-c1d779c80f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "clc = np.zeros((n_clusters, len(groups)))\n",
    "for cluster_no in range(n_clusters):\n",
    "    for j, group in zip(range(len(groups)), groups):\n",
    "        clc[cluster_no, j] = len(predictions[(predictions['party_cluster'] == cluster_no) & (predictions['group'] == group)]) / len(predictions[predictions['party_cluster'] == cluster_no])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "1ac3a61f-516b-4726-80b3-9432122a20cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TS-LKDF</th>\n",
       "      <th>MG</th>\n",
       "      <th>TTF</th>\n",
       "      <th>LSDPF</th>\n",
       "      <th>DPF</th>\n",
       "      <th>LSF</th>\n",
       "      <th>LLRA-KŠSF</th>\n",
       "      <th>LLRAF</th>\n",
       "      <th>DKF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.413793</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TS-LKDF        MG       TTF     LSDPF       DPF       LSF  LLRA-KŠSF  \\\n",
       "0  0.000000  0.000000  0.103448  0.586207  0.310345  0.000000   0.000000   \n",
       "1  0.809524  0.142857  0.000000  0.000000  0.000000  0.047619   0.000000   \n",
       "2  0.000000  0.176471  0.176471  0.176471  0.176471  0.235294   0.000000   \n",
       "3  0.000000  0.000000  0.068966  0.413793  0.344828  0.000000   0.172414   \n",
       "4  0.846154  0.153846  0.000000  0.000000  0.000000  0.000000   0.000000   \n",
       "5  0.166667  0.166667  0.000000  0.000000  0.000000  0.666667   0.000000   \n",
       "6  0.000000  0.133333  0.100000  0.300000  0.266667  0.100000   0.100000   \n",
       "\n",
       "   LLRAF       DKF  \n",
       "0    0.0  0.000000  \n",
       "1    0.0  0.000000  \n",
       "2    0.0  0.058824  \n",
       "3    0.0  0.000000  \n",
       "4    0.0  0.000000  \n",
       "5    0.0  0.000000  \n",
       "6    0.0  0.000000  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clc = pd.DataFrame(clc, columns = groups, index = [str(i) for i in range(n_clusters)])\n",
    "\n",
    "clc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06ae015-5e6f-4ef0-bffd-999b85b6755b",
   "metadata": {},
   "source": [
    "Each row represents one cluster, specifically the percentage of the cluster that is represented by a certain group."
   ]
  }
 ],
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