models/cat + rob base.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ce32f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from catboost import  Pool\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d53168b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfff = pd.read_csv('train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18d0299e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_object = dfff['object']\n",
    "y_test_relevant = dfff['is_relevant']\n",
    "y_test_positive = dfff['is_positive']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89ad77b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_embed_object_test = pd.read_csv('testing_loader_is_obj.csv')\n",
    "df_embed_object_test = df_embed_object_test.drop(['Unnamed: 0'], axis=1)\n",
    "\n",
    "df_embed_is_relevant_test = pd.read_csv('testing_loader_is_relevant.csv')\n",
    "df_embed_is_relevant_test = df_embed_is_relevant_test.drop(['Unnamed: 0'], axis=1)\n",
    "\n",
    "df_embed_is_positive_test = pd.read_csv('testing_loader_is_positive.csv')\n",
    "df_embed_is_positive_test = df_embed_is_positive_test.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7dd042ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_embed_object = pd.read_csv('training_loader_is_obj.csv')\n",
    "df_embed_object = df_embed_object.drop(['Unnamed: 0'], axis=1)\n",
    "\n",
    "df_embed_is_relevant = pd.read_csv('training_loader_is_obj.csv')\n",
    "df_embed_is_relevant = df_embed_is_relevant.drop(['Unnamed: 0'], axis=1)\n",
    "\n",
    "df_embed_is_positive = pd.read_csv('training_loader_is_obj.csv')\n",
    "df_embed_is_positive = df_embed_is_positive.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80420859",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"itog_data.csv\")\n",
    "objectt = data['object']\n",
    "\n",
    "data = pd.read_csv(\"itog_data.csv\")\n",
    "relevant = data['is_relevant']\n",
    "\n",
    "data = pd.read_csv(\"itog_data.csv\")\n",
    "positive = data['is_positive']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d77ad859",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import compute_class_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fada59f3",
   "metadata": {},
   "source": [
    "# object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab2ca0a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights_ob = compute_class_weight(class_weight = 'balanced', classes = np.unique(objectt),y = objectt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef4d5ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.05115907, 1.01701469, 0.93861527])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights_ob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "46857b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_objectt = CatBoostClassifier(iterations=3000,\n",
    "                                    learning_rate=0.01,\n",
    "                                    depth=2,\n",
    "                                    eval_metric='MultiClass',\n",
    "                                    random_seed=0,\n",
    "                                    class_weights= class_weights_ob\n",
    "                                    # early_stopping_rounds=50\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "77cba860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cce0465dda94426a36b860053d82b8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "1872:\tlearn: 0.4466020\ttest: 0.6974174\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.5s\n",
      "1873:\tlearn: 0.4464815\ttest: 0.6974809\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.49s\n",
      "1874:\tlearn: 0.4464201\ttest: 0.6974964\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.48s\n",
      "1875:\tlearn: 0.4463249\ttest: 0.6976672\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.47s\n",
      "1876:\tlearn: 0.4461834\ttest: 0.6976267\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.46s\n",
      "1877:\tlearn: 0.4460893\ttest: 0.6974473\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.46s\n",
      "1878:\tlearn: 0.4459877\ttest: 0.6973898\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.45s\n",
      "1879:\tlearn: 0.4458144\ttest: 0.6973360\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.44s\n",
      "1880:\tlearn: 0.4456524\ttest: 0.6974141\tbest: 0.6970487 (1845)\ttotal: 15.8s\tremaining: 9.43s\n",
      "1881:\tlearn: 0.4455032\ttest: 0.6971954\tbest: 0.6970487 (1845)\ttotal: 15.9s\tremaining: 9.42s\n",
      "1882:\tlearn: 0.4453533\ttest: 0.6972482\tbest: 0.6970487 (1845)\ttotal: 15.9s\tremaining: 9.41s\n",
      "1883:\tlearn: 0.4451983\ttest: 0.6971034\tbest: 0.6970487 (1845)\ttotal: 15.9s\tremaining: 9.4s\n",
      "1884:\tlearn: 0.4450652\ttest: 0.6969429\tbest: 0.6969429 (1884)\ttotal: 15.9s\tremaining: 9.39s\n",
      "1885:\tlearn: 0.4449169\ttest: 0.6968535\tbest: 0.6968535 (1885)\ttotal: 15.9s\tremaining: 9.38s\n",
      "1886:\tlearn: 0.4447358\ttest: 0.6969692\tbest: 0.6968535 (1885)\ttotal: 15.9s\tremaining: 9.38s\n",
      "1887:\tlearn: 0.4446206\ttest: 0.6969913\tbest: 0.6968535 (1885)\ttotal: 15.9s\tremaining: 9.37s\n",
      "1888:\tlearn: 0.4444471\ttest: 0.6970371\tbest: 0.6968535 (1885)\ttotal: 15.9s\tremaining: 9.36s\n",
      "1889:\tlearn: 0.4443403\ttest: 0.6969372\tbest: 0.6968535 (1885)\ttotal: 15.9s\tremaining: 9.36s\n",
      "1890:\tlearn: 0.4442501\ttest: 0.6968149\tbest: 0.6968149 (1890)\ttotal: 15.9s\tremaining: 9.35s\n",
      "1891:\tlearn: 0.4441262\ttest: 0.6967767\tbest: 0.6967767 (1891)\ttotal: 15.9s\tremaining: 9.34s\n",
      "1892:\tlearn: 0.4440484\ttest: 0.6966691\tbest: 0.6966691 (1892)\ttotal: 16s\tremaining: 9.33s\n",
      "1893:\tlearn: 0.4439639\ttest: 0.6965856\tbest: 0.6965856 (1893)\ttotal: 16s\tremaining: 9.32s\n",
      "1894:\tlearn: 0.4438414\ttest: 0.6965047\tbest: 0.6965047 (1894)\ttotal: 16s\tremaining: 9.31s\n",
      "1895:\tlearn: 0.4436882\ttest: 0.6963560\tbest: 0.6963560 (1895)\ttotal: 16s\tremaining: 9.3s\n",
      "1896:\tlearn: 0.4435242\ttest: 0.6961186\tbest: 0.6961186 (1896)\ttotal: 16s\tremaining: 9.29s\n",
      "1897:\tlearn: 0.4433909\ttest: 0.6959744\tbest: 0.6959744 (1897)\ttotal: 16s\tremaining: 9.29s\n",
      "1898:\tlearn: 0.4432118\ttest: 0.6959627\tbest: 0.6959627 (1898)\ttotal: 16s\tremaining: 9.28s\n",
      "1899:\tlearn: 0.4430240\ttest: 0.6959642\tbest: 0.6959627 (1898)\ttotal: 16s\tremaining: 9.27s\n",
      "1900:\tlearn: 0.4429022\ttest: 0.6958868\tbest: 0.6958868 (1900)\ttotal: 16s\tremaining: 9.26s\n",
      "1901:\tlearn: 0.4427524\ttest: 0.6959097\tbest: 0.6958868 (1900)\ttotal: 16s\tremaining: 9.25s\n",
      "1902:\tlearn: 0.4425387\ttest: 0.6958230\tbest: 0.6958230 (1902)\ttotal: 16s\tremaining: 9.24s\n",
      "1903:\tlearn: 0.4423966\ttest: 0.6959047\tbest: 0.6958230 (1902)\ttotal: 16s\tremaining: 9.23s\n",
      "1904:\tlearn: 0.4422814\ttest: 0.6960480\tbest: 0.6958230 (1902)\ttotal: 16s\tremaining: 9.22s\n",
      "1905:\tlearn: 0.4421747\ttest: 0.6960959\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.21s\n",
      "1906:\tlearn: 0.4420635\ttest: 0.6960950\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.21s\n",
      "1907:\tlearn: 0.4419601\ttest: 0.6960735\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.2s\n",
      "1908:\tlearn: 0.4418017\ttest: 0.6959952\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.19s\n",
      "1909:\tlearn: 0.4417019\ttest: 0.6960911\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.18s\n",
      "1910:\tlearn: 0.4415652\ttest: 0.6961277\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.17s\n",
      "1911:\tlearn: 0.4414520\ttest: 0.6960774\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.16s\n",
      "1912:\tlearn: 0.4412181\ttest: 0.6959011\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.15s\n",
      "1913:\tlearn: 0.4410197\ttest: 0.6958300\tbest: 0.6958230 (1902)\ttotal: 16.1s\tremaining: 9.14s\n",
      "1914:\tlearn: 0.4408634\ttest: 0.6955783\tbest: 0.6955783 (1914)\ttotal: 16.1s\tremaining: 9.13s\n",
      "1915:\tlearn: 0.4407272\ttest: 0.6956314\tbest: 0.6955783 (1914)\ttotal: 16.1s\tremaining: 9.13s\n",
      "1916:\tlearn: 0.4405894\ttest: 0.6955169\tbest: 0.6955169 (1916)\ttotal: 16.1s\tremaining: 9.12s\n",
      "1917:\tlearn: 0.4404610\ttest: 0.6954577\tbest: 0.6954577 (1917)\ttotal: 16.1s\tremaining: 9.11s\n",
      "1918:\tlearn: 0.4403196\ttest: 0.6953960\tbest: 0.6953960 (1918)\ttotal: 16.2s\tremaining: 9.1s\n",
      "1919:\tlearn: 0.4401849\ttest: 0.6950537\tbest: 0.6950537 (1919)\ttotal: 16.2s\tremaining: 9.09s\n",
      "1920:\tlearn: 0.4401019\ttest: 0.6949668\tbest: 0.6949668 (1920)\ttotal: 16.2s\tremaining: 9.08s\n",
      "1921:\tlearn: 0.4399090\ttest: 0.6949965\tbest: 0.6949668 (1920)\ttotal: 16.2s\tremaining: 9.07s\n",
      "1922:\tlearn: 0.4397894\ttest: 0.6950259\tbest: 0.6949668 (1920)\ttotal: 16.2s\tremaining: 9.06s\n",
      "1923:\tlearn: 0.4396594\ttest: 0.6950661\tbest: 0.6949668 (1920)\ttotal: 16.2s\tremaining: 9.05s\n",
      "1924:\tlearn: 0.4394936\ttest: 0.6949558\tbest: 0.6949558 (1924)\ttotal: 16.2s\tremaining: 9.05s\n",
      "1925:\tlearn: 0.4393440\ttest: 0.6950772\tbest: 0.6949558 (1924)\ttotal: 16.2s\tremaining: 9.04s\n",
      "1926:\tlearn: 0.4392007\ttest: 0.6951682\tbest: 0.6949558 (1924)\ttotal: 16.2s\tremaining: 9.03s\n",
      "1927:\tlearn: 0.4390374\ttest: 0.6950946\tbest: 0.6949558 (1924)\ttotal: 16.2s\tremaining: 9.02s\n",
      "1928:\tlearn: 0.4389148\ttest: 0.6949318\tbest: 0.6949318 (1928)\ttotal: 16.2s\tremaining: 9.01s\n",
      "1929:\tlearn: 0.4387892\ttest: 0.6949476\tbest: 0.6949318 (1928)\ttotal: 16.2s\tremaining: 9s\n",
      "1930:\tlearn: 0.4387224\ttest: 0.6948898\tbest: 0.6948898 (1930)\ttotal: 16.2s\tremaining: 8.99s\n",
      "1931:\tlearn: 0.4385312\ttest: 0.6948385\tbest: 0.6948385 (1931)\ttotal: 16.3s\tremaining: 8.98s\n",
      "1932:\tlearn: 0.4383934\ttest: 0.6947775\tbest: 0.6947775 (1932)\ttotal: 16.3s\tremaining: 8.97s\n",
      "1933:\tlearn: 0.4382363\ttest: 0.6946895\tbest: 0.6946895 (1933)\ttotal: 16.3s\tremaining: 8.97s\n",
      "1934:\tlearn: 0.4381229\ttest: 0.6945978\tbest: 0.6945978 (1934)\ttotal: 16.3s\tremaining: 8.96s\n",
      "1935:\tlearn: 0.4380007\ttest: 0.6943885\tbest: 0.6943885 (1935)\ttotal: 16.3s\tremaining: 8.95s\n",
      "1936:\tlearn: 0.4378514\ttest: 0.6944448\tbest: 0.6943885 (1935)\ttotal: 16.3s\tremaining: 8.94s\n",
      "1937:\tlearn: 0.4377168\ttest: 0.6943019\tbest: 0.6943019 (1937)\ttotal: 16.3s\tremaining: 8.93s\n",
      "1938:\tlearn: 0.4375689\ttest: 0.6941881\tbest: 0.6941881 (1938)\ttotal: 16.3s\tremaining: 8.92s\n",
      "1939:\tlearn: 0.4374026\ttest: 0.6939786\tbest: 0.6939786 (1939)\ttotal: 16.3s\tremaining: 8.91s\n",
      "1940:\tlearn: 0.4372367\ttest: 0.6939228\tbest: 0.6939228 (1940)\ttotal: 16.3s\tremaining: 8.9s\n",
      "1941:\tlearn: 0.4371458\ttest: 0.6938863\tbest: 0.6938863 (1941)\ttotal: 16.3s\tremaining: 8.89s\n",
      "1942:\tlearn: 0.4370283\ttest: 0.6938821\tbest: 0.6938821 (1942)\ttotal: 16.3s\tremaining: 8.89s\n",
      "1943:\tlearn: 0.4368444\ttest: 0.6940291\tbest: 0.6938821 (1942)\ttotal: 16.3s\tremaining: 8.88s\n",
      "1944:\tlearn: 0.4367000\ttest: 0.6937026\tbest: 0.6937026 (1944)\ttotal: 16.4s\tremaining: 8.87s\n",
      "1945:\tlearn: 0.4365637\ttest: 0.6936758\tbest: 0.6936758 (1945)\ttotal: 16.4s\tremaining: 8.86s\n",
      "1946:\tlearn: 0.4364831\ttest: 0.6937745\tbest: 0.6936758 (1945)\ttotal: 16.4s\tremaining: 8.85s\n",
      "1947:\tlearn: 0.4364025\ttest: 0.6937707\tbest: 0.6936758 (1945)\ttotal: 16.4s\tremaining: 8.84s\n",
      "1948:\tlearn: 0.4362320\ttest: 0.6936221\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.83s\n",
      "1949:\tlearn: 0.4361042\ttest: 0.6936731\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.82s\n",
      "1950:\tlearn: 0.4359288\ttest: 0.6936864\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.82s\n",
      "1951:\tlearn: 0.4357914\ttest: 0.6936782\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.81s\n",
      "1952:\tlearn: 0.4357421\ttest: 0.6936445\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.8s\n",
      "1953:\tlearn: 0.4356397\ttest: 0.6937487\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.79s\n",
      "1954:\tlearn: 0.4355753\ttest: 0.6937975\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.78s\n",
      "1955:\tlearn: 0.4354955\ttest: 0.6936767\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.77s\n",
      "1956:\tlearn: 0.4354095\ttest: 0.6936405\tbest: 0.6936221 (1948)\ttotal: 16.4s\tremaining: 8.76s\n",
      "1957:\tlearn: 0.4353012\ttest: 0.6935351\tbest: 0.6935351 (1957)\ttotal: 16.4s\tremaining: 8.75s\n",
      "1958:\tlearn: 0.4351505\ttest: 0.6933819\tbest: 0.6933819 (1958)\ttotal: 16.5s\tremaining: 8.74s\n",
      "1959:\tlearn: 0.4350272\ttest: 0.6933016\tbest: 0.6933016 (1959)\ttotal: 16.5s\tremaining: 8.74s\n",
      "1960:\tlearn: 0.4348824\ttest: 0.6931252\tbest: 0.6931252 (1960)\ttotal: 16.5s\tremaining: 8.73s\n",
      "1961:\tlearn: 0.4347228\ttest: 0.6930753\tbest: 0.6930753 (1961)\ttotal: 16.5s\tremaining: 8.72s\n",
      "1962:\tlearn: 0.4345462\ttest: 0.6932634\tbest: 0.6930753 (1961)\ttotal: 16.5s\tremaining: 8.71s\n",
      "1963:\tlearn: 0.4344051\ttest: 0.6932151\tbest: 0.6930753 (1961)\ttotal: 16.5s\tremaining: 8.7s\n",
      "1964:\tlearn: 0.4343136\ttest: 0.6931668\tbest: 0.6930753 (1961)\ttotal: 16.5s\tremaining: 8.69s\n",
      "1965:\tlearn: 0.4342065\ttest: 0.6931011\tbest: 0.6930753 (1961)\ttotal: 16.5s\tremaining: 8.68s\n",
      "1966:\tlearn: 0.4340872\ttest: 0.6927170\tbest: 0.6927170 (1966)\ttotal: 16.5s\tremaining: 8.68s\n",
      "1967:\tlearn: 0.4339398\ttest: 0.6927569\tbest: 0.6927170 (1966)\ttotal: 16.5s\tremaining: 8.67s\n",
      "1968:\tlearn: 0.4337448\ttest: 0.6926480\tbest: 0.6926480 (1968)\ttotal: 16.5s\tremaining: 8.66s\n",
      "1969:\tlearn: 0.4336333\ttest: 0.6925416\tbest: 0.6925416 (1969)\ttotal: 16.5s\tremaining: 8.65s\n",
      "1970:\tlearn: 0.4335106\ttest: 0.6924667\tbest: 0.6924667 (1970)\ttotal: 16.5s\tremaining: 8.64s\n",
      "1971:\tlearn: 0.4334163\ttest: 0.6923977\tbest: 0.6923977 (1971)\ttotal: 16.6s\tremaining: 8.63s\n",
      "1972:\tlearn: 0.4333041\ttest: 0.6921607\tbest: 0.6921607 (1972)\ttotal: 16.6s\tremaining: 8.62s\n",
      "1973:\tlearn: 0.4330864\ttest: 0.6919180\tbest: 0.6919180 (1973)\ttotal: 16.6s\tremaining: 8.61s\n",
      "1974:\tlearn: 0.4329435\ttest: 0.6918912\tbest: 0.6918912 (1974)\ttotal: 16.6s\tremaining: 8.61s\n",
      "1975:\tlearn: 0.4328717\ttest: 0.6919393\tbest: 0.6918912 (1974)\ttotal: 16.6s\tremaining: 8.6s\n",
      "1976:\tlearn: 0.4327176\ttest: 0.6917394\tbest: 0.6917394 (1976)\ttotal: 16.6s\tremaining: 8.59s\n",
      "1977:\tlearn: 0.4325555\ttest: 0.6913698\tbest: 0.6913698 (1977)\ttotal: 16.6s\tremaining: 8.58s\n",
      "1978:\tlearn: 0.4323944\ttest: 0.6913149\tbest: 0.6913149 (1978)\ttotal: 16.6s\tremaining: 8.57s\n",
      "1979:\tlearn: 0.4322672\ttest: 0.6913070\tbest: 0.6913070 (1979)\ttotal: 16.6s\tremaining: 8.56s\n",
      "1980:\tlearn: 0.4321701\ttest: 0.6911498\tbest: 0.6911498 (1980)\ttotal: 16.6s\tremaining: 8.55s\n",
      "1981:\tlearn: 0.4320095\ttest: 0.6910145\tbest: 0.6910145 (1981)\ttotal: 16.6s\tremaining: 8.54s\n",
      "1982:\tlearn: 0.4318518\ttest: 0.6908045\tbest: 0.6908045 (1982)\ttotal: 16.6s\tremaining: 8.54s\n",
      "1983:\tlearn: 0.4316704\ttest: 0.6907088\tbest: 0.6907088 (1983)\ttotal: 16.7s\tremaining: 8.53s\n",
      "1984:\tlearn: 0.4315499\ttest: 0.6906676\tbest: 0.6906676 (1984)\ttotal: 16.7s\tremaining: 8.52s\n",
      "1985:\tlearn: 0.4314134\ttest: 0.6906439\tbest: 0.6906439 (1985)\ttotal: 16.7s\tremaining: 8.51s\n",
      "1986:\tlearn: 0.4313027\ttest: 0.6904844\tbest: 0.6904844 (1986)\ttotal: 16.7s\tremaining: 8.5s\n",
      "1987:\tlearn: 0.4311531\ttest: 0.6902838\tbest: 0.6902838 (1987)\ttotal: 16.7s\tremaining: 8.49s\n",
      "1988:\tlearn: 0.4310087\ttest: 0.6901197\tbest: 0.6901197 (1988)\ttotal: 16.7s\tremaining: 8.48s\n",
      "1989:\tlearn: 0.4308589\ttest: 0.6900513\tbest: 0.6900513 (1989)\ttotal: 16.7s\tremaining: 8.47s\n",
      "1990:\tlearn: 0.4307042\ttest: 0.6899858\tbest: 0.6899858 (1990)\ttotal: 16.7s\tremaining: 8.47s\n",
      "1991:\tlearn: 0.4305573\ttest: 0.6899271\tbest: 0.6899271 (1991)\ttotal: 16.7s\tremaining: 8.46s\n",
      "1992:\tlearn: 0.4304550\ttest: 0.6899658\tbest: 0.6899271 (1991)\ttotal: 16.7s\tremaining: 8.45s\n",
      "1993:\tlearn: 0.4303419\ttest: 0.6899600\tbest: 0.6899271 (1991)\ttotal: 16.7s\tremaining: 8.44s\n",
      "1994:\tlearn: 0.4302254\ttest: 0.6899507\tbest: 0.6899271 (1991)\ttotal: 16.7s\tremaining: 8.43s\n",
      "1995:\tlearn: 0.4300623\ttest: 0.6898507\tbest: 0.6898507 (1995)\ttotal: 16.7s\tremaining: 8.42s\n",
      "1996:\tlearn: 0.4299630\ttest: 0.6899454\tbest: 0.6898507 (1995)\ttotal: 16.8s\tremaining: 8.41s\n",
      "1997:\tlearn: 0.4298639\ttest: 0.6898450\tbest: 0.6898450 (1997)\ttotal: 16.8s\tremaining: 8.4s\n",
      "1998:\tlearn: 0.4297794\ttest: 0.6896917\tbest: 0.6896917 (1998)\ttotal: 16.8s\tremaining: 8.4s\n",
      "1999:\tlearn: 0.4296824\ttest: 0.6896825\tbest: 0.6896825 (1999)\ttotal: 16.8s\tremaining: 8.39s\n",
      "2000:\tlearn: 0.4295629\ttest: 0.6897344\tbest: 0.6896825 (1999)\ttotal: 16.8s\tremaining: 8.38s\n",
      "2001:\tlearn: 0.4294392\ttest: 0.6897524\tbest: 0.6896825 (1999)\ttotal: 16.8s\tremaining: 8.37s\n",
      "2002:\tlearn: 0.4292988\ttest: 0.6897435\tbest: 0.6896825 (1999)\ttotal: 16.8s\tremaining: 8.36s\n",
      "2003:\tlearn: 0.4292053\ttest: 0.6897153\tbest: 0.6896825 (1999)\ttotal: 16.8s\tremaining: 8.35s\n",
      "2004:\tlearn: 0.4291101\ttest: 0.6897011\tbest: 0.6896825 (1999)\ttotal: 16.8s\tremaining: 8.34s\n",
      "2005:\tlearn: 0.4289505\ttest: 0.6894030\tbest: 0.6894030 (2005)\ttotal: 16.8s\tremaining: 8.33s\n",
      "2006:\tlearn: 0.4287502\ttest: 0.6892976\tbest: 0.6892976 (2006)\ttotal: 16.8s\tremaining: 8.33s\n",
      "2007:\tlearn: 0.4286517\ttest: 0.6893195\tbest: 0.6892976 (2006)\ttotal: 16.8s\tremaining: 8.32s\n",
      "2008:\tlearn: 0.4284961\ttest: 0.6893377\tbest: 0.6892976 (2006)\ttotal: 16.8s\tremaining: 8.31s\n",
      "2009:\tlearn: 0.4284074\ttest: 0.6893722\tbest: 0.6892976 (2006)\ttotal: 16.9s\tremaining: 8.3s\n",
      "2010:\tlearn: 0.4282468\ttest: 0.6892382\tbest: 0.6892382 (2010)\ttotal: 16.9s\tremaining: 8.29s\n",
      "2011:\tlearn: 0.4281103\ttest: 0.6891649\tbest: 0.6891649 (2011)\ttotal: 16.9s\tremaining: 8.28s\n",
      "2012:\tlearn: 0.4279550\ttest: 0.6891814\tbest: 0.6891649 (2011)\ttotal: 16.9s\tremaining: 8.27s\n",
      "2013:\tlearn: 0.4278877\ttest: 0.6890803\tbest: 0.6890803 (2013)\ttotal: 16.9s\tremaining: 8.26s\n",
      "2014:\tlearn: 0.4277621\ttest: 0.6890158\tbest: 0.6890158 (2014)\ttotal: 16.9s\tremaining: 8.26s\n",
      "2015:\tlearn: 0.4275685\ttest: 0.6887337\tbest: 0.6887337 (2015)\ttotal: 16.9s\tremaining: 8.25s\n",
      "2016:\tlearn: 0.4274261\ttest: 0.6887848\tbest: 0.6887337 (2015)\ttotal: 16.9s\tremaining: 8.26s\n",
      "2017:\tlearn: 0.4272716\ttest: 0.6886419\tbest: 0.6886419 (2017)\ttotal: 17s\tremaining: 8.25s\n",
      "2018:\tlearn: 0.4271466\ttest: 0.6884890\tbest: 0.6884890 (2018)\ttotal: 17s\tremaining: 8.24s\n",
      "2019:\tlearn: 0.4269906\ttest: 0.6882791\tbest: 0.6882791 (2019)\ttotal: 17s\tremaining: 8.23s\n",
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      "2990:\tlearn: 0.3335823\ttest: 0.6525682\tbest: 0.6523590 (2988)\ttotal: 25.1s\tremaining: 75.6ms\n",
      "2991:\tlearn: 0.3334660\ttest: 0.6524533\tbest: 0.6523590 (2988)\ttotal: 25.1s\tremaining: 67.2ms\n",
      "2992:\tlearn: 0.3333748\ttest: 0.6525484\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 58.8ms\n",
      "2993:\tlearn: 0.3332873\ttest: 0.6525539\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 50.4ms\n",
      "2994:\tlearn: 0.3331637\ttest: 0.6526439\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 42ms\n",
      "2995:\tlearn: 0.3330822\ttest: 0.6527178\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 33.6ms\n",
      "2996:\tlearn: 0.3329673\ttest: 0.6525540\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 25.2ms\n",
      "2997:\tlearn: 0.3328751\ttest: 0.6524692\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 16.8ms\n",
      "2998:\tlearn: 0.3328183\ttest: 0.6523874\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 8.4ms\n",
      "2999:\tlearn: 0.3327701\ttest: 0.6524313\tbest: 0.6523590 (2988)\ttotal: 25.2s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.6523589906\n",
      "bestIteration = 2988\n",
      "\n",
      "Shrink model to first 2989 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x14d0c8210>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pool_ob = Pool(df_embed_object, objectt)\n",
    "val_pool_ob = Pool(df_embed_object_test, y_test_object)\n",
    "\n",
    "\n",
    "model_objectt.fit(train_pool_ob, eval_set= val_pool_ob, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "4d6bc69e",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_preds_оb = model_objectt.predict(df_embed_object_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6be6bd03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.7475728155339806\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_val_ob = f1_score(y_test_object, np.array(val_preds_оb).ravel(), average='micro')  \n",
    "# Или можно использовать 'macro' или 'weighted' в зависимости от вашего выбора\n",
    "print(\"F1 Score:\", f1_val_ob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd7b28aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "53ca7b4e",
   "metadata": {},
   "source": [
    "# relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cbea1292",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_weights_rel = compute_class_weight(class_weight = 'balanced', classes = np.unique(relevant),y = relevant)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0769f04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_relevant = CatBoostClassifier(iterations=500,\n",
    "                                    learning_rate=0.00001,\n",
    "                                    # loss_function = 'Logloss',\n",
    "                                    depth=2,\n",
    "                                    eval_metric='F1',\n",
    "                                    random_seed=42,\n",
    "\n",
    "                                    # class_weights=[3, 1]\n",
    "                                    class_weights =class_weights_rel\n",
    "                                    # early_stopping_rounds=50\n",
    "                                    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "55ea1d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7b0a5d23f184f7784cf650e1b49eab0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.7794179\ttest: 0.8857473\tbest: 0.8857473 (0)\ttotal: 21.8ms\tremaining: 10.9s\n",
      "1:\tlearn: 0.8165997\ttest: 0.8857473\tbest: 0.8857473 (0)\ttotal: 26.8ms\tremaining: 6.68s\n",
      "2:\tlearn: 0.8479779\ttest: 0.8802372\tbest: 0.8857473 (0)\ttotal: 31.8ms\tremaining: 5.27s\n",
      "3:\tlearn: 0.8495627\ttest: 0.8802372\tbest: 0.8857473 (0)\ttotal: 36.9ms\tremaining: 4.58s\n",
      "4:\tlearn: 0.8479400\ttest: 0.8857473\tbest: 0.8857473 (0)\ttotal: 41.9ms\tremaining: 4.15s\n",
      "5:\tlearn: 0.8625040\ttest: 0.8857473\tbest: 0.8857473 (0)\ttotal: 47.6ms\tremaining: 3.92s\n",
      "6:\tlearn: 0.8600292\ttest: 0.8857473\tbest: 0.8857473 (0)\ttotal: 52.4ms\tremaining: 3.69s\n",
      "7:\tlearn: 0.8564571\ttest: 0.8857473\tbest: 0.8857473 (0)\ttotal: 57.9ms\tremaining: 3.56s\n",
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      "492:\tlearn: 0.8984828\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.6s\tremaining: 36.9ms\n",
      "493:\tlearn: 0.8994336\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.6s\tremaining: 31.6ms\n",
      "494:\tlearn: 0.8987811\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.6s\tremaining: 26.3ms\n",
      "495:\tlearn: 0.8987811\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.61s\tremaining: 21ms\n",
      "496:\tlearn: 0.8987811\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.61s\tremaining: 15.8ms\n",
      "497:\tlearn: 0.8978304\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.62s\tremaining: 10.5ms\n",
      "498:\tlearn: 0.8978304\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.62s\tremaining: 5.25ms\n",
      "499:\tlearn: 0.8978304\ttest: 0.8936009\tbest: 0.8936009 (65)\ttotal: 2.63s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.8936009061\n",
      "bestIteration = 65\n",
      "\n",
      "Shrink model to first 66 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x1695d73d0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pool_rel = Pool(df_embed_is_relevant, relevant)\n",
    "val_pool_rel= Pool(df_embed_is_relevant_test, y_test_relevant)\n",
    "\n",
    "\n",
    "model_relevant.fit(train_pool_rel, eval_set=val_pool_rel, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "83a825eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_preds_rel = model_relevant.predict(df_embed_is_relevant_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "be7b5e6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.5401785714285714\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_val_rel = f1_score(y_test_relevant, np.array(val_preds_rel).ravel(), average='macro')\n",
    "print(\"F1 Score:\", f1_val_rel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "bf889544",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_rel = model_relevant.predict(df_embed_is_relevant_test)\n",
    "relevant_df_pred = pd.DataFrame({'is_relevant': predictions_rel})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "812d995c",
   "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>is_relevant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     is_relevant\n",
       "0              1\n",
       "1              1\n",
       "2              1\n",
       "3              1\n",
       "4              1\n",
       "..           ...\n",
       "98             1\n",
       "99             1\n",
       "100            1\n",
       "101            1\n",
       "102            1\n",
       "\n",
       "[103 rows x 1 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relevant_df_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2e8519",
   "metadata": {},
   "source": [
    "# positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "bad2f3e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.25957854, 0.82912989])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights_pos = compute_class_weight(class_weight = 'balanced', classes = np.unique(positive),y = positive)\n",
    "class_weights_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "99db3a9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_positive = CatBoostClassifier(iterations=50,\n",
    "                                    learning_rate=0.0001,\n",
    "                                    depth=4,\n",
    "                                    eval_metric='F1',\n",
    "                                    random_seed=42,\n",
    "\n",
    "                                    class_weights=[3, 5]\n",
    "                                    #  class_weights =class_weights_pos\n",
    "                                    # early_stopping_rounds=50\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "8182d382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c42793f86544a27b98355523f53c481",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.8845774\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 29.7ms\tremaining: 1.45s\n",
      "1:\tlearn: 0.8909189\ttest: 0.8510638\tbest: 0.9584665 (0)\ttotal: 55.1ms\tremaining: 1.32s\n",
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      "40:\tlearn: 0.8963317\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 617ms\tremaining: 135ms\n",
      "41:\tlearn: 0.8951896\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 628ms\tremaining: 120ms\n",
      "42:\tlearn: 0.8966177\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 641ms\tremaining: 104ms\n",
      "43:\tlearn: 0.8949045\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 655ms\tremaining: 89.4ms\n",
      "44:\tlearn: 0.8980505\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 669ms\tremaining: 74.3ms\n",
      "45:\tlearn: 0.8969039\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 681ms\tremaining: 59.2ms\n",
      "46:\tlearn: 0.8966177\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 695ms\tremaining: 44.3ms\n",
      "47:\tlearn: 0.8971903\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 707ms\tremaining: 29.5ms\n",
      "48:\tlearn: 0.8977636\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 720ms\tremaining: 14.7ms\n",
      "49:\tlearn: 0.8971903\ttest: 0.9584665\tbest: 0.9584665 (0)\ttotal: 733ms\tremaining: 0us\n",
      "\n",
      "bestTest = 0.9584664537\n",
      "bestIteration = 0\n",
      "\n",
      "Shrink model to first 1 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x16a52be90>"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pool_pos = Pool(df_embed_is_relevant, relevant)\n",
    "val_pool_pos= Pool(df_embed_is_relevant_test, y_test_relevant)\n",
    "\n",
    "\n",
    "model_positive.fit(train_pool_pos, eval_set=val_pool_pos, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "a7c84b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_preds_pos = model_positive.predict(df_embed_is_positive_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "28804bf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.5283882783882784\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_val_pos = f1_score(y_test_positive, np.array(val_preds_pos).ravel(), average='macro')\n",
    "print(\"F1 Score:\", f1_val_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd2e141e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "val_preds_pos = best_model.predict(df_embed_is_positive_test)\n",
    "from sklearn.metrics import f1_score\n",
    "f1_val_pos = f1_score(y_test_positive, np.array(val_preds_pos).ravel(), average='macro')\n",
    "print(\"F1 Score:\", f1_val_pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "582e1466",
   "metadata": {},
   "source": [
    "СУПЕР ВАЖНАЯ ФУНКЦИЯ \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f86ba93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(y_true, y_pred):\n",
    "    precision = precision_score(y_true, y_pred, average='macro')\n",
    "    recall = recall_score(y_true, y_pred, average='macro')\n",
    "    f1 = f1_score(y_true, y_pred, average='macro')\n",
    "    return precision, recall, f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "fa9e164a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8623417721518987, 0.9138554216867469, 0.8843995510662177)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model(y_test_positive, val_preds_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "467da454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.45173160173160176, 0.4047008547008547, 0.4122282608695652)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model(y_test_relevant, val_preds_rel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "caf55f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7856118056978888, 0.7733500417710945, 0.7692637305313362)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_model(y_test_object, val_preds_оb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e224b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_pos = model_positive.predict(X_test_positive)\n",
    "\n",
    "# Создание датафрейма с одной колонкой \"object\"\n",
    "positive_df_pred = pd.DataFrame({'is_relevant': predictions_pos})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3cee0eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\n",
       "       1., 1., 1., 1., 0., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 0., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 0.,\n",
       "       1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1.,\n",
       "       0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1.,\n",
       "       1., 0., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\n",
       "       1., 0., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1.])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(val_preds_pos).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6482d63e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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