{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T15:52:09.761101700Z",
     "start_time": "2024-03-29T15:51:56.199239Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "with open('actors_usa_embeddings.pkl', 'rb') as f:\n",
    "    dict_usa = pickle.load(f)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:36.800593700Z",
     "start_time": "2024-03-29T18:30:36.753716200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "            0                                                  1\n0         1_1  [-1.5706723928451538, 0.6335924863815308, 1.53...\n1     10009_1  [0.5788402557373047, -0.5220502614974976, -1.7...\n2     10013_1  [-0.5845983028411865, 1.5863220691680908, -1.1...\n3     10013_2  [-0.41548410058021545, 1.5801037549972534, -0....\n4      1003_1  [-0.052914053201675415, -0.35606303811073303, ...\n...       ...                                                ...\n1051  13616_1  [0.6910120248794556, 1.2189278602600098, -1.64...\n1052  13616_2  [0.2329411804676056, 1.3537031412124634, -1.17...\n1053  13616_3  [0.8600691556930542, 1.441170573234558, -1.856...\n1054  13616_4  [1.2998018264770508, 2.4858486652374268, -1.51...\n1055  13631_2  [0.08967465162277222, 1.597952127456665, -1.96...\n\n[1056 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1_1</td>\n      <td>[-1.5706723928451538, 0.6335924863815308, 1.53...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10009_1</td>\n      <td>[0.5788402557373047, -0.5220502614974976, -1.7...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10013_1</td>\n      <td>[-0.5845983028411865, 1.5863220691680908, -1.1...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10013_2</td>\n      <td>[-0.41548410058021545, 1.5801037549972534, -0....</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1003_1</td>\n      <td>[-0.052914053201675415, -0.35606303811073303, ...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1051</th>\n      <td>13616_1</td>\n      <td>[0.6910120248794556, 1.2189278602600098, -1.64...</td>\n    </tr>\n    <tr>\n      <th>1052</th>\n      <td>13616_2</td>\n      <td>[0.2329411804676056, 1.3537031412124634, -1.17...</td>\n    </tr>\n    <tr>\n      <th>1053</th>\n      <td>13616_3</td>\n      <td>[0.8600691556930542, 1.441170573234558, -1.856...</td>\n    </tr>\n    <tr>\n      <th>1054</th>\n      <td>13616_4</td>\n      <td>[1.2998018264770508, 2.4858486652374268, -1.51...</td>\n    </tr>\n    <tr>\n      <th>1055</th>\n      <td>13631_2</td>\n      <td>[0.08967465162277222, 1.597952127456665, -1.96...</td>\n    </tr>\n  </tbody>\n</table>\n<p>1056 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_usa = pd.DataFrame.from_dict(dict_usa.items())\n",
    "data_usa"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:39.592265600Z",
     "start_time": "2024-03-29T18:30:39.569341700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "data_usa.rename(columns={0: \"id\", 1: \"embd\"}, inplace=True, errors=\"ignore\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:43.890527400Z",
     "start_time": "2024-03-29T18:30:43.859276Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "           id                                               embd\n0         1_1  [-1.5706723928451538, 0.6335924863815308, 1.53...\n1     10009_1  [0.5788402557373047, -0.5220502614974976, -1.7...\n2     10013_1  [-0.5845983028411865, 1.5863220691680908, -1.1...\n3     10013_2  [-0.41548410058021545, 1.5801037549972534, -0....\n4      1003_1  [-0.052914053201675415, -0.35606303811073303, ...\n...       ...                                                ...\n1051  13616_1  [0.6910120248794556, 1.2189278602600098, -1.64...\n1052  13616_2  [0.2329411804676056, 1.3537031412124634, -1.17...\n1053  13616_3  [0.8600691556930542, 1.441170573234558, -1.856...\n1054  13616_4  [1.2998018264770508, 2.4858486652374268, -1.51...\n1055  13631_2  [0.08967465162277222, 1.597952127456665, -1.96...\n\n[1056 rows x 2 columns]",
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     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data_usa"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:45.270827700Z",
     "start_time": "2024-03-29T18:30:45.243534900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "           id         0         1         2         3         4         5  \\\n0         1_1 -1.570672  0.633592  1.539401 -0.443073  1.144602 -0.819344   \n1     10009_1  0.578840 -0.522050 -1.722523 -0.849324  0.872231  0.697576   \n2     10013_1 -0.584598  1.586322 -1.155406 -1.400013 -1.503451 -0.089089   \n3     10013_2 -0.415484  1.580104 -0.833656 -0.852378 -1.105216 -0.190370   \n4      1003_1 -0.052914 -0.356063 -0.832296 -0.387270  0.796773 -0.258821   \n...       ...       ...       ...       ...       ...       ...       ...   \n1051  13616_1  0.691012  1.218928 -1.645177 -0.200526  1.032170 -0.010177   \n1052  13616_2  0.232941  1.353703 -1.175303  0.109807  1.326917 -0.320142   \n1053  13616_3  0.860069  1.441171 -1.856320 -0.822068  2.987537 -0.414389   \n1054  13616_4  1.299802  2.485849 -1.519942 -1.161255  1.662391  0.192611   \n1055  13631_2  0.089675  1.597952 -1.962265 -2.278883  0.774878 -1.617782   \n\n             6         7         8  ...       118       119       120  \\\n0     2.068340 -1.029341 -1.676797  ...  1.457394 -0.088586  1.581023   \n1    -1.690455  3.806507  0.681398  ...  0.730348 -1.359728 -1.243532   \n2    -0.447129 -1.302794 -0.083066  ...  0.953941  0.069133  0.304748   \n3    -0.418510 -1.473158  0.411066  ...  1.337585 -0.197534  0.382660   \n4    -0.461007  0.343941  0.571979  ...  0.492704  1.220060  0.774966   \n...        ...       ...       ...  ...       ...       ...       ...   \n1051 -0.753952  0.626138  1.611161  ... -0.331170 -0.605570  1.232265   \n1052 -0.360853  0.236935  1.272749  ... -0.093058 -0.735331  1.791282   \n1053 -0.009051  1.364074  0.779438  ...  0.194024 -1.225692  0.427923   \n1054 -0.043378  1.498528  0.937637  ...  1.762125 -0.648232  0.858571   \n1055 -0.833502  1.292249 -0.641687  ...  0.419192  1.590851 -1.200108   \n\n           121       122       123       124       125       126       127  \n0    -1.279289 -0.174264 -1.437022  0.825406  0.065602  0.862288  0.475075  \n1    -0.721210  0.133973  1.686131 -0.457207 -0.259445  0.367829 -1.191762  \n2     0.323025  1.243084 -0.399308  0.769429 -1.011941 -0.092408  0.252634  \n3     0.953445  0.843356 -0.828000  1.240614 -0.750917  0.343088  0.759789  \n4    -0.848771  0.852844 -0.558431  0.560696 -1.167510  1.353986 -0.561515  \n...        ...       ...       ...       ...       ...       ...       ...  \n1051 -0.853597 -0.000137  1.154459 -1.125317 -1.453662 -0.933203  2.152028  \n1052 -0.932450 -0.078320  0.398722 -2.315437 -1.291452 -0.972061  1.980307  \n1053  0.216618  0.209311  0.205166 -2.612580 -0.337485 -1.237361  0.451241  \n1054 -0.705204 -0.280197  0.615498 -1.899799 -1.392390 -1.029162  1.198919  \n1055  1.717416 -1.234612  0.177451 -0.677651  0.334710  0.589466  0.305988  \n\n[1056 rows x 129 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>...</th>\n      <th>118</th>\n      <th>119</th>\n      <th>120</th>\n      <th>121</th>\n      <th>122</th>\n      <th>123</th>\n      <th>124</th>\n      <th>125</th>\n      <th>126</th>\n      <th>127</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1_1</td>\n      <td>-1.570672</td>\n      <td>0.633592</td>\n      <td>1.539401</td>\n      <td>-0.443073</td>\n      <td>1.144602</td>\n      <td>-0.819344</td>\n      <td>2.068340</td>\n      <td>-1.029341</td>\n      <td>-1.676797</td>\n      <td>...</td>\n      <td>1.457394</td>\n      <td>-0.088586</td>\n      <td>1.581023</td>\n      <td>-1.279289</td>\n      <td>-0.174264</td>\n      <td>-1.437022</td>\n      <td>0.825406</td>\n      <td>0.065602</td>\n      <td>0.862288</td>\n      <td>0.475075</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10009_1</td>\n      <td>0.578840</td>\n      <td>-0.522050</td>\n      <td>-1.722523</td>\n      <td>-0.849324</td>\n      <td>0.872231</td>\n      <td>0.697576</td>\n      <td>-1.690455</td>\n      <td>3.806507</td>\n      <td>0.681398</td>\n      <td>...</td>\n      <td>0.730348</td>\n      <td>-1.359728</td>\n      <td>-1.243532</td>\n      <td>-0.721210</td>\n      <td>0.133973</td>\n      <td>1.686131</td>\n      <td>-0.457207</td>\n      <td>-0.259445</td>\n      <td>0.367829</td>\n      <td>-1.191762</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10013_1</td>\n      <td>-0.584598</td>\n      <td>1.586322</td>\n      <td>-1.155406</td>\n      <td>-1.400013</td>\n      <td>-1.503451</td>\n      <td>-0.089089</td>\n      <td>-0.447129</td>\n      <td>-1.302794</td>\n      <td>-0.083066</td>\n      <td>...</td>\n      <td>0.953941</td>\n      <td>0.069133</td>\n      <td>0.304748</td>\n      <td>0.323025</td>\n      <td>1.243084</td>\n      <td>-0.399308</td>\n      <td>0.769429</td>\n      <td>-1.011941</td>\n      <td>-0.092408</td>\n      <td>0.252634</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10013_2</td>\n      <td>-0.415484</td>\n      <td>1.580104</td>\n      <td>-0.833656</td>\n      <td>-0.852378</td>\n      <td>-1.105216</td>\n      <td>-0.190370</td>\n      <td>-0.418510</td>\n      <td>-1.473158</td>\n      <td>0.411066</td>\n      <td>...</td>\n      <td>1.337585</td>\n      <td>-0.197534</td>\n      <td>0.382660</td>\n      <td>0.953445</td>\n      <td>0.843356</td>\n      <td>-0.828000</td>\n      <td>1.240614</td>\n      <td>-0.750917</td>\n      <td>0.343088</td>\n      <td>0.759789</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1003_1</td>\n      <td>-0.052914</td>\n      <td>-0.356063</td>\n      <td>-0.832296</td>\n      <td>-0.387270</td>\n      <td>0.796773</td>\n      <td>-0.258821</td>\n      <td>-0.461007</td>\n      <td>0.343941</td>\n      <td>0.571979</td>\n      <td>...</td>\n      <td>0.492704</td>\n      <td>1.220060</td>\n      <td>0.774966</td>\n      <td>-0.848771</td>\n      <td>0.852844</td>\n      <td>-0.558431</td>\n      <td>0.560696</td>\n      <td>-1.167510</td>\n      <td>1.353986</td>\n      <td>-0.561515</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1051</th>\n      <td>13616_1</td>\n      <td>0.691012</td>\n      <td>1.218928</td>\n      <td>-1.645177</td>\n      <td>-0.200526</td>\n      <td>1.032170</td>\n      <td>-0.010177</td>\n      <td>-0.753952</td>\n      <td>0.626138</td>\n      <td>1.611161</td>\n      <td>...</td>\n      <td>-0.331170</td>\n      <td>-0.605570</td>\n      <td>1.232265</td>\n      <td>-0.853597</td>\n      <td>-0.000137</td>\n      <td>1.154459</td>\n      <td>-1.125317</td>\n      <td>-1.453662</td>\n      <td>-0.933203</td>\n      <td>2.152028</td>\n    </tr>\n    <tr>\n      <th>1052</th>\n      <td>13616_2</td>\n      <td>0.232941</td>\n      <td>1.353703</td>\n      <td>-1.175303</td>\n      <td>0.109807</td>\n      <td>1.326917</td>\n      <td>-0.320142</td>\n      <td>-0.360853</td>\n      <td>0.236935</td>\n      <td>1.272749</td>\n      <td>...</td>\n      <td>-0.093058</td>\n      <td>-0.735331</td>\n      <td>1.791282</td>\n      <td>-0.932450</td>\n      <td>-0.078320</td>\n      <td>0.398722</td>\n      <td>-2.315437</td>\n      <td>-1.291452</td>\n      <td>-0.972061</td>\n      <td>1.980307</td>\n    </tr>\n    <tr>\n      <th>1053</th>\n      <td>13616_3</td>\n      <td>0.860069</td>\n      <td>1.441171</td>\n      <td>-1.856320</td>\n      <td>-0.822068</td>\n      <td>2.987537</td>\n      <td>-0.414389</td>\n      <td>-0.009051</td>\n      <td>1.364074</td>\n      <td>0.779438</td>\n      <td>...</td>\n      <td>0.194024</td>\n      <td>-1.225692</td>\n      <td>0.427923</td>\n      <td>0.216618</td>\n      <td>0.209311</td>\n      <td>0.205166</td>\n      <td>-2.612580</td>\n      <td>-0.337485</td>\n      <td>-1.237361</td>\n      <td>0.451241</td>\n    </tr>\n    <tr>\n      <th>1054</th>\n      <td>13616_4</td>\n      <td>1.299802</td>\n      <td>2.485849</td>\n      <td>-1.519942</td>\n      <td>-1.161255</td>\n      <td>1.662391</td>\n      <td>0.192611</td>\n      <td>-0.043378</td>\n      <td>1.498528</td>\n      <td>0.937637</td>\n      <td>...</td>\n      <td>1.762125</td>\n      <td>-0.648232</td>\n      <td>0.858571</td>\n      <td>-0.705204</td>\n      <td>-0.280197</td>\n      <td>0.615498</td>\n      <td>-1.899799</td>\n      <td>-1.392390</td>\n      <td>-1.029162</td>\n      <td>1.198919</td>\n    </tr>\n    <tr>\n      <th>1055</th>\n      <td>13631_2</td>\n      <td>0.089675</td>\n      <td>1.597952</td>\n      <td>-1.962265</td>\n      <td>-2.278883</td>\n      <td>0.774878</td>\n      <td>-1.617782</td>\n      <td>-0.833502</td>\n      <td>1.292249</td>\n      <td>-0.641687</td>\n      <td>...</td>\n      <td>0.419192</td>\n      <td>1.590851</td>\n      <td>-1.200108</td>\n      <td>1.717416</td>\n      <td>-1.234612</td>\n      <td>0.177451</td>\n      <td>-0.677651</td>\n      <td>0.334710</td>\n      <td>0.589466</td>\n      <td>0.305988</td>\n    </tr>\n  </tbody>\n</table>\n<p>1056 rows × 129 columns</p>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cols = pd.DataFrame(data_usa['embd'].apply(pd.Series))\n",
    "df_usa = pd.concat([data_usa, new_cols], axis=1)\n",
    "df_usa.drop([\"embd\"], axis=1, inplace=True, errors=\"ignore\")\n",
    "df_usa"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:49.465033800Z",
     "start_time": "2024-03-29T18:30:49.271020400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "df_usa[\"id\"] = df_usa[\"id\"].apply(lambda x: str(x)[:str(x).find(\"_\")])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:52.716203400Z",
     "start_time": "2024-03-29T18:30:52.692504100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "df_usa[\"id\"] = df_usa[\"id\"].astype(int)\n",
    "y, X = df_usa[\"id\"], df_usa.drop([\"id\"], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:30:57.870505500Z",
     "start_time": "2024-03-29T18:30:57.845736100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "id\n13078    4\n10593    4\n10766    4\n13209    4\n1320     4\n        ..\n12114    1\n12143    1\n12151    1\n12163    1\n13631    1\nName: count, Length: 457, dtype: int64"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T18:40:33.247010800Z",
     "start_time": "2024-03-29T18:40:33.123293900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "[]"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_features = [col for col in df_usa.columns if df_usa[col].dtype.name == 'category' or df_usa[col].dtype.name == 'object']\n",
    "cat_features"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T17:08:02.745660400Z",
     "start_time": "2024-03-29T17:08:02.735557900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2f8ef01d3ad3469ba13c90ed7dcc9b43"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.185024\n",
      "0:\tlearn: 6.0784997\ttest: 6.1433807\tbest: 6.1433807 (0)\ttotal: 2.35s\tremaining: 11m 43s\n",
      "1:\tlearn: 5.9910635\ttest: 6.1084452\tbest: 6.1084452 (1)\ttotal: 4.87s\tremaining: 12m 5s\n",
      "2:\tlearn: 5.8984368\ttest: 6.0727539\tbest: 6.0727539 (2)\ttotal: 7.29s\tremaining: 12m 1s\n",
      "3:\tlearn: 5.8057325\ttest: 6.0476334\tbest: 6.0476334 (3)\ttotal: 9.36s\tremaining: 11m 32s\n",
      "4:\tlearn: 5.7158628\ttest: 6.0047780\tbest: 6.0047780 (4)\ttotal: 11.5s\tremaining: 11m 21s\n",
      "5:\tlearn: 5.6240899\ttest: 5.9781342\tbest: 5.9781342 (5)\ttotal: 13.9s\tremaining: 11m 19s\n",
      "6:\tlearn: 5.5320867\ttest: 5.9263351\tbest: 5.9263351 (6)\ttotal: 16.1s\tremaining: 11m 14s\n",
      "7:\tlearn: 5.5121678\ttest: 5.9146790\tbest: 5.9146790 (7)\ttotal: 16.4s\tremaining: 9m 58s\n",
      "8:\tlearn: 5.4277463\ttest: 5.8509918\tbest: 5.8509918 (8)\ttotal: 18.5s\tremaining: 9m 58s\n",
      "9:\tlearn: 5.3415770\ttest: 5.8210129\tbest: 5.8210129 (9)\ttotal: 20.7s\tremaining: 10m\n",
      "10:\tlearn: 5.2492825\ttest: 5.7737183\tbest: 5.7737183 (10)\ttotal: 22s\tremaining: 9m 37s\n",
      "11:\tlearn: 5.1631285\ttest: 5.7284953\tbest: 5.7284953 (11)\ttotal: 23.3s\tremaining: 9m 20s\n",
      "12:\tlearn: 5.0751767\ttest: 5.6733948\tbest: 5.6733948 (12)\ttotal: 24.5s\tremaining: 9m 1s\n",
      "13:\tlearn: 4.9856146\ttest: 5.6309850\tbest: 5.6309850 (13)\ttotal: 25.7s\tremaining: 8m 45s\n",
      "14:\tlearn: 4.8978781\ttest: 5.6149078\tbest: 5.6149078 (14)\ttotal: 26.9s\tremaining: 8m 30s\n",
      "15:\tlearn: 4.8156413\ttest: 5.5612625\tbest: 5.5612625 (15)\ttotal: 28.1s\tremaining: 8m 18s\n",
      "16:\tlearn: 4.7319675\ttest: 5.5175181\tbest: 5.5175181 (16)\ttotal: 29.4s\tremaining: 8m 8s\n",
      "17:\tlearn: 4.6593926\ttest: 5.4840571\tbest: 5.4840571 (17)\ttotal: 30.5s\tremaining: 7m 58s\n",
      "18:\tlearn: 4.5787229\ttest: 5.4402802\tbest: 5.4402802 (18)\ttotal: 31.7s\tremaining: 7m 48s\n",
      "19:\tlearn: 4.4907160\ttest: 5.4152125\tbest: 5.4152125 (19)\ttotal: 32.9s\tremaining: 7m 40s\n",
      "20:\tlearn: 4.4052203\ttest: 5.3817332\tbest: 5.3817332 (20)\ttotal: 34.1s\tremaining: 7m 32s\n",
      "21:\tlearn: 4.3219876\ttest: 5.3373820\tbest: 5.3373820 (21)\ttotal: 35.4s\tremaining: 7m 26s\n",
      "22:\tlearn: 4.2420161\ttest: 5.2880178\tbest: 5.2880178 (22)\ttotal: 36.6s\tremaining: 7m 20s\n",
      "23:\tlearn: 4.1608379\ttest: 5.2396993\tbest: 5.2396993 (23)\ttotal: 37.7s\tremaining: 7m 13s\n",
      "24:\tlearn: 4.0805923\ttest: 5.2040298\tbest: 5.2040298 (24)\ttotal: 38.9s\tremaining: 7m 7s\n",
      "25:\tlearn: 4.0007839\ttest: 5.1598663\tbest: 5.1598663 (25)\ttotal: 40.2s\tremaining: 7m 3s\n",
      "26:\tlearn: 3.9255511\ttest: 5.1133169\tbest: 5.1133169 (26)\ttotal: 41.5s\tremaining: 6m 59s\n",
      "27:\tlearn: 3.8538269\ttest: 5.0867518\tbest: 5.0867518 (27)\ttotal: 42.8s\tremaining: 6m 55s\n",
      "28:\tlearn: 3.7818685\ttest: 5.0266500\tbest: 5.0266500 (28)\ttotal: 43.9s\tremaining: 6m 50s\n",
      "29:\tlearn: 3.7069801\ttest: 5.0114441\tbest: 5.0114441 (29)\ttotal: 45.2s\tremaining: 6m 46s\n",
      "30:\tlearn: 3.6382706\ttest: 4.9597992\tbest: 4.9597992 (30)\ttotal: 46.3s\tremaining: 6m 41s\n",
      "31:\tlearn: 3.5615131\ttest: 4.9208328\tbest: 4.9208328 (31)\ttotal: 47.5s\tremaining: 6m 37s\n",
      "32:\tlearn: 3.4859040\ttest: 4.8779531\tbest: 4.8779531 (32)\ttotal: 48.6s\tremaining: 6m 33s\n",
      "33:\tlearn: 3.4185298\ttest: 4.8498103\tbest: 4.8498103 (33)\ttotal: 49.8s\tremaining: 6m 29s\n",
      "34:\tlearn: 3.3444067\ttest: 4.8153386\tbest: 4.8153386 (34)\ttotal: 51s\tremaining: 6m 26s\n",
      "35:\tlearn: 3.2735936\ttest: 4.7766815\tbest: 4.7766815 (35)\ttotal: 52.3s\tremaining: 6m 23s\n",
      "36:\tlearn: 3.2040863\ttest: 4.7235006\tbest: 4.7235006 (36)\ttotal: 53.7s\tremaining: 6m 21s\n",
      "37:\tlearn: 3.1379644\ttest: 4.7010030\tbest: 4.7010030 (37)\ttotal: 54.9s\tremaining: 6m 18s\n",
      "38:\tlearn: 3.0709210\ttest: 4.6795237\tbest: 4.6795237 (38)\ttotal: 56.1s\tremaining: 6m 15s\n",
      "39:\tlearn: 3.0039365\ttest: 4.6355535\tbest: 4.6355535 (39)\ttotal: 57.3s\tremaining: 6m 12s\n",
      "40:\tlearn: 2.9383490\ttest: 4.6026723\tbest: 4.6026723 (40)\ttotal: 58.6s\tremaining: 6m 10s\n",
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      "42:\tlearn: 2.8080188\ttest: 4.5525731\tbest: 4.5525731 (42)\ttotal: 1m 1s\tremaining: 6m 5s\n",
      "43:\tlearn: 2.7462077\ttest: 4.5130310\tbest: 4.5130310 (43)\ttotal: 1m 2s\tremaining: 6m 3s\n",
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      "45:\tlearn: 2.6241649\ttest: 4.4433533\tbest: 4.4433533 (45)\ttotal: 1m 4s\tremaining: 5m 57s\n",
      "46:\tlearn: 2.5639366\ttest: 4.4178904\tbest: 4.4178904 (46)\ttotal: 1m 6s\tremaining: 5m 55s\n",
      "47:\tlearn: 2.5067896\ttest: 4.3975774\tbest: 4.3975774 (47)\ttotal: 1m 7s\tremaining: 5m 53s\n",
      "48:\tlearn: 2.4538681\ttest: 4.3666336\tbest: 4.3666336 (48)\ttotal: 1m 8s\tremaining: 5m 51s\n",
      "49:\tlearn: 2.3986265\ttest: 4.3355123\tbest: 4.3355123 (49)\ttotal: 1m 10s\tremaining: 5m 49s\n",
      "50:\tlearn: 2.3446018\ttest: 4.3332296\tbest: 4.3332296 (50)\ttotal: 1m 11s\tremaining: 5m 47s\n",
      "51:\tlearn: 2.2861106\ttest: 4.3116740\tbest: 4.3116740 (51)\ttotal: 1m 12s\tremaining: 5m 45s\n",
      "52:\tlearn: 2.2328525\ttest: 4.3072728\tbest: 4.3072728 (52)\ttotal: 1m 13s\tremaining: 5m 43s\n",
      "53:\tlearn: 2.1842162\ttest: 4.2899190\tbest: 4.2899190 (53)\ttotal: 1m 14s\tremaining: 5m 40s\n",
      "54:\tlearn: 2.1396860\ttest: 4.2635773\tbest: 4.2635773 (54)\ttotal: 1m 15s\tremaining: 5m 38s\n",
      "55:\tlearn: 2.0905466\ttest: 4.2227315\tbest: 4.2227315 (55)\ttotal: 1m 17s\tremaining: 5m 36s\n",
      "56:\tlearn: 2.0419869\ttest: 4.1879898\tbest: 4.1879898 (56)\ttotal: 1m 18s\tremaining: 5m 33s\n",
      "57:\tlearn: 1.9987320\ttest: 4.1525574\tbest: 4.1525574 (57)\ttotal: 1m 19s\tremaining: 5m 31s\n",
      "58:\tlearn: 1.9531243\ttest: 4.1342023\tbest: 4.1342023 (58)\ttotal: 1m 20s\tremaining: 5m 30s\n",
      "59:\tlearn: 1.9056501\ttest: 4.1110240\tbest: 4.1110240 (59)\ttotal: 1m 22s\tremaining: 5m 28s\n",
      "60:\tlearn: 1.8657994\ttest: 4.0953285\tbest: 4.0953285 (60)\ttotal: 1m 23s\tremaining: 5m 25s\n",
      "61:\tlearn: 1.8243551\ttest: 4.0813792\tbest: 4.0813792 (61)\ttotal: 1m 24s\tremaining: 5m 23s\n",
      "62:\tlearn: 1.7809334\ttest: 4.0727074\tbest: 4.0727074 (62)\ttotal: 1m 25s\tremaining: 5m 21s\n",
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      "65:\tlearn: 1.6625649\ttest: 4.0219477\tbest: 4.0219477 (65)\ttotal: 1m 29s\tremaining: 5m 16s\n",
      "66:\tlearn: 1.6250331\ttest: 4.0043874\tbest: 4.0043874 (66)\ttotal: 1m 30s\tremaining: 5m 14s\n",
      "67:\tlearn: 1.5874199\ttest: 3.9934575\tbest: 3.9934575 (67)\ttotal: 1m 31s\tremaining: 5m 13s\n",
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      "69:\tlearn: 1.5127327\ttest: 3.9472862\tbest: 3.9472862 (69)\ttotal: 1m 34s\tremaining: 5m 9s\n",
      "70:\tlearn: 1.4777435\ttest: 3.9355334\tbest: 3.9355334 (70)\ttotal: 1m 35s\tremaining: 5m 8s\n",
      "71:\tlearn: 1.4413622\ttest: 3.9174815\tbest: 3.9174815 (71)\ttotal: 1m 36s\tremaining: 5m 6s\n",
      "72:\tlearn: 1.4096054\ttest: 3.8972992\tbest: 3.8972992 (72)\ttotal: 1m 37s\tremaining: 5m 4s\n",
      "73:\tlearn: 1.3761202\ttest: 3.8763499\tbest: 3.8763499 (73)\ttotal: 1m 39s\tremaining: 5m 2s\n",
      "74:\tlearn: 1.3462455\ttest: 3.8373062\tbest: 3.8373062 (74)\ttotal: 1m 40s\tremaining: 5m\n",
      "75:\tlearn: 1.3197731\ttest: 3.8176537\tbest: 3.8176537 (75)\ttotal: 1m 41s\tremaining: 4m 59s\n",
      "76:\tlearn: 1.2882575\ttest: 3.7995501\tbest: 3.7995501 (76)\ttotal: 1m 42s\tremaining: 4m 57s\n",
      "77:\tlearn: 1.2591927\ttest: 3.7776555\tbest: 3.7776555 (77)\ttotal: 1m 43s\tremaining: 4m 55s\n",
      "78:\tlearn: 1.2305810\ttest: 3.7544545\tbest: 3.7544545 (78)\ttotal: 1m 44s\tremaining: 4m 53s\n",
      "79:\tlearn: 1.2066412\ttest: 3.7386561\tbest: 3.7386561 (79)\ttotal: 1m 46s\tremaining: 4m 51s\n",
      "80:\tlearn: 1.1825994\ttest: 3.7253298\tbest: 3.7253298 (80)\ttotal: 1m 47s\tremaining: 4m 49s\n",
      "81:\tlearn: 1.1569464\ttest: 3.6922796\tbest: 3.6922796 (81)\ttotal: 1m 48s\tremaining: 4m 48s\n",
      "82:\tlearn: 1.1338915\ttest: 3.6659922\tbest: 3.6659922 (82)\ttotal: 1m 49s\tremaining: 4m 45s\n",
      "83:\tlearn: 1.1119852\ttest: 3.6367154\tbest: 3.6367154 (83)\ttotal: 1m 50s\tremaining: 4m 43s\n",
      "84:\tlearn: 1.0879619\ttest: 3.6156316\tbest: 3.6156316 (84)\ttotal: 1m 51s\tremaining: 4m 42s\n",
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      "91:\tlearn: 0.9394349\ttest: 3.4906398\tbest: 3.4906398 (91)\ttotal: 1m 59s\tremaining: 4m 30s\n",
      "92:\tlearn: 0.9193775\ttest: 3.4833733\tbest: 3.4833733 (92)\ttotal: 2m\tremaining: 4m 28s\n",
      "93:\tlearn: 0.9007292\ttest: 3.4619919\tbest: 3.4619919 (93)\ttotal: 2m 1s\tremaining: 4m 26s\n",
      "94:\tlearn: 0.8868973\ttest: 3.4545959\tbest: 3.4545959 (94)\ttotal: 2m 2s\tremaining: 4m 24s\n",
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      "297:\tlearn: 0.0900808\ttest: 2.4503985\tbest: 2.4503985 (297)\ttotal: 6m 16s\tremaining: 2.53s\n",
      "298:\tlearn: 0.0895346\ttest: 2.4491837\tbest: 2.4491837 (298)\ttotal: 6m 18s\tremaining: 1.26s\n",
      "299:\tlearn: 0.0889600\ttest: 2.4474767\tbest: 2.4474767 (299)\ttotal: 6m 19s\tremaining: 0us\n",
      "bestTest = 2.447476705\n",
      "bestIteration = 299\n"
     ]
    },
    {
     "data": {
      "text/plain": "<catboost.core.CatBoostClassifier at 0x1c3b6fb2ce0>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_cat = {\n",
    "    \"iterations\":300,\n",
    "    \"depth\":7,\n",
    "    #\"learning_rate\":0.1,\n",
    "    #\"eval_metric\":'F1',\n",
    "    \"loss_function\": \"MultiClass\",\n",
    "    \"random_seed\":1,\n",
    "    \"bootstrap_type\":'Bayesian',\n",
    "    \"bagging_temperature\":1,\n",
    "    \"od_type\":'Iter',\n",
    "    \"od_wait\":20,\n",
    "    \"task_type\":'GPU',\n",
    "}\n",
    "cat_model = CatBoostClassifier(**params_cat)\n",
    "cat_model.fit(\n",
    "    X_train, y_train,\n",
    "    eval_set=(X_test, y_test),\n",
    "    use_best_model=True,\n",
    "    verbose=True,\n",
    "    plot=True\n",
    " )"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T17:14:29.140678900Z",
     "start_time": "2024-03-29T17:08:08.540374300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "cat_model.save_model(\"catboost_blogger.cbm\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-29T17:26:38.910177200Z",
     "start_time": "2024-03-29T17:26:38.604629500Z"
    }
   }
  }
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