nodes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices = pd.read_csv('vertices.csv')\n",
    "edges = pd.read_csv('edges.csv')\n",
    "ids = pd.read_csv('ids.csv')\n",
    "okved_pairs = pd.read_csv('okved_pairs.csv')\n",
    "new_vertices = pd.read_csv('new_vertices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = np.load('embs.pickle', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "              0         1         2         3         4         5         6    \\\n",
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       "\n",
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       "...           ...       ...       ...  ...       ...       ...       ...   \n",
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       "\n",
       "              121       122       123       124       125       126       127  \n",
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       "...           ...       ...       ...       ...       ...       ...       ...  \n",
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       "\n",
       "[1514713 rows x 128 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>[1370219, 938974, 119234, 1440742, 973344, 111...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1514712</td>\n",
       "      <td>1534749</td>\n",
       "      <td>[692041, 840706, 206964]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1514713 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id_1                                         connection\n",
       "0              1                                  [1031361, 597119]\n",
       "1              2                                    [831794, 38851]\n",
       "2              3                                           [332774]\n",
       "3              4                   [35912, 281811, 1296937, 524354]\n",
       "4              5                  [36359, 1119909, 1168944, 857006]\n",
       "...          ...                                                ...\n",
       "1514708  1534745                         [293059, 1154911, 1382703]\n",
       "1514709  1534746                                          [1478295]\n",
       "1514710  1534747                                   [417399, 532322]\n",
       "1514711  1534748  [1370219, 938974, 119234, 1440742, 973344, 111...\n",
       "1514712  1534749                           [692041, 840706, 206964]\n",
       "\n",
       "[1514713 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_connections = pd.read_csv('all_connections')\n",
    "all_connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "q =all_connections[all_connections.id_1==ids.id[0]].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "524354"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids.id[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix.index = matrix.index.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([58, 15, 93, ..., 70, 10, 70], dtype=int32)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = joblib.load('kmeans.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15, 32, 22]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_classes(i):\n",
    "    q =all_connections[all_connections.id_1==ids.id[i]].values[0]\n",
    "    arr = eval(q[1])\n",
    "    types = pd.DataFrame({'index':matrix[matrix.index.isin(arr)].index, 'type':kmeans.predict(matrix[matrix.index.isin(arr)])})\n",
    "    return(list(types.groupby('type').agg({'index':'count'}).sort_values('index', ascending=False).index[:3]))\n",
    "get_classes(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['kmeans.pkl']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(kmeans, 'kmeans.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2 = joblib.load('kmeans.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=100, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "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>id</th>\n",
       "      <th>count_connections</th>\n",
       "      <th>sum_value</th>\n",
       "      <th>sum_transactions</th>\n",
       "      <th>main_okved</th>\n",
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       "      <th>first_okved</th>\n",
       "      <th>second_okved</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.000000</td>\n",
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       "      <td>77</td>\n",
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       "      <td>75</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.788663e+05</td>\n",
       "      <td>207.791701</td>\n",
       "      <td>25.11</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.361190e+06</td>\n",
       "      <td>604.608179</td>\n",
       "      <td>45.31</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.578424e+06</td>\n",
       "      <td>535.494874</td>\n",
       "      <td>56.10</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>1534744</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.142074e+06</td>\n",
       "      <td>162.995900</td>\n",
       "      <td>63.99</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1534745</td>\n",
       "      <td>1534746</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.035241e+05</td>\n",
       "      <td>207.791701</td>\n",
       "      <td>47.19</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1534746</td>\n",
       "      <td>1534747</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.906844e+05</td>\n",
       "      <td>81.997950</td>\n",
       "      <td>41.20</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <td>1534747</td>\n",
       "      <td>1534748</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.597791e+06</td>\n",
       "      <td>618.781450</td>\n",
       "      <td>74.20</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1534748</td>\n",
       "      <td>1534749</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.215793e+06</td>\n",
       "      <td>484.777078</td>\n",
       "      <td>68.20</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1534749 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count_connections     sum_value  sum_transactions  \\\n",
       "0              1                2.0  7.478158e+05          2.000000   \n",
       "1              2                2.0  9.293199e+05        187.749487   \n",
       "2              3                1.0  4.788663e+05        207.791701   \n",
       "3              4                4.0  1.361190e+06        604.608179   \n",
       "4              5                4.0  1.578424e+06        535.494874   \n",
       "...          ...                ...           ...               ...   \n",
       "1534744  1534745                3.0  1.142074e+06        162.995900   \n",
       "1534745  1534746                1.0  5.035241e+05        207.791701   \n",
       "1534746  1534747                2.0  7.906844e+05         81.997950   \n",
       "1534747  1534748               12.0  4.597791e+06        618.781450   \n",
       "1534748  1534749                3.0  1.215793e+06        484.777078   \n",
       "\n",
       "         main_okved  region_code  company_type  first_okved  second_okved  \n",
       "0             46.75           77             3           46            75  \n",
       "1             41.20           78             3           41            20  \n",
       "2             25.11           50             3           25            11  \n",
       "3             45.31           89             3           45            31  \n",
       "4             56.10           50             3           56            10  \n",
       "...             ...          ...           ...          ...           ...  \n",
       "1534744       63.99           77             2           63            99  \n",
       "1534745       47.19           66             2           47            19  \n",
       "1534746       41.20           77             2           41            20  \n",
       "1534747       74.20           33             2           74            20  \n",
       "1534748       68.20           66             2           68            20  \n",
       "\n",
       "[1534749 rows x 9 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = pd.DataFrame({'id':matrix.index,'class':clf2.predict(matrix)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_vertices = new_vertices.merge(classes, on='id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20036"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_vertices['class'].isna().astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_vertices = new_vertices.fillna(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_vertices.to_csv('new_vertices.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_vertices = pd.read_csv('new_vertices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.215793e+06</td>\n",
       "      <td>484.777078</td>\n",
       "      <td>68.20</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>20</td>\n",
       "      <td>15.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1534749 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count_connections     sum_value  sum_transactions  \\\n",
       "0              1                2.0  7.478158e+05          2.000000   \n",
       "1              2                2.0  9.293199e+05        187.749487   \n",
       "2              3                1.0  4.788663e+05        207.791701   \n",
       "3              4                4.0  1.361190e+06        604.608179   \n",
       "4              5                4.0  1.578424e+06        535.494874   \n",
       "...          ...                ...           ...               ...   \n",
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       "1534745  1534746                1.0  5.035241e+05        207.791701   \n",
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       "1534747  1534748               12.0  4.597791e+06        618.781450   \n",
       "1534748  1534749                3.0  1.215793e+06        484.777078   \n",
       "\n",
       "         main_okved  region_code  company_type  first_okved  second_okved  \\\n",
       "0             46.75           77             3           46            75   \n",
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       "...             ...          ...           ...          ...           ...   \n",
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       "1534746       41.20           77             2           41            20   \n",
       "1534747       74.20           33             2           74            20   \n",
       "1534748       68.20           66             2           68            20   \n",
       "\n",
       "         class  \n",
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       "4          3.0  \n",
       "...        ...  \n",
       "1534744   95.0  \n",
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       "1534747   62.0  \n",
       "1534748   15.0  \n",
       "\n",
       "[1534749 rows x 10 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1514713"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(kmeans.predict(matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 386676,  599978, 1162317,  649655,  372829,  432666,  854151,\n",
       "             345404, 1399095,  989680,\n",
       "            ...\n",
       "             190479,  925010, 1455093,  618560,  271323,  952075,  158004,\n",
       "            1523835,  688591,  537368],\n",
       "           dtype='int64', length=1514713)"
      ]
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     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = pd.DataFrame({'id':matrix.index, 'class':kmeans.predict(matrix) })\n",
    "matrix.index = matrix.index.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = new_vertices.drop(columns=['class']).merge(classes, on='id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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       "              id  count_connections     sum_value  sum_transactions  \\\n",
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       "\n",
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     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "t"
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  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.to_csv('new_vertices.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "              id  count_connections     sum_value  sum_transactions  \\\n",
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       "1534696  1534697               18.0  8.169551e+06       2980.104468   \n",
       "1534720  1534721                1.0  3.859837e+05          1.000000   \n",
       "1534745  1534746                1.0  5.035241e+05        207.791701   \n",
       "1534748  1534749                3.0  1.215793e+06        484.777078   \n",
       "\n",
       "         main_okved  region_code  company_type  first_okved  second_okved  \\\n",
       "27            43.99           66             3           43            99   \n",
       "40            68.20           66             3           68            20   \n",
       "98            41.20           66             3           41            20   \n",
       "130           16.10           66             3           16            10   \n",
       "139           73.11           66             3           73            11   \n",
       "...             ...          ...           ...          ...           ...   \n",
       "1534646       45.20           66             2           45            20   \n",
       "1534696       68.20           73             2           68            20   \n",
       "1534720       16.10           66             2           16            10   \n",
       "1534745       47.19           66             2           47            19   \n",
       "1534748       68.20           66             2           68            20   \n",
       "\n",
       "         class  \n",
       "27        15.0  \n",
       "40        32.0  \n",
       "98        32.0  \n",
       "130       32.0  \n",
       "139       15.0  \n",
       "...        ...  \n",
       "1534646   15.0  \n",
       "1534696   15.0  \n",
       "1534720   32.0  \n",
       "1534745   32.0  \n",
       "1534748   15.0  \n",
       "\n",
       "[77348 rows x 10 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[t['class'].isin(get_classes(1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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