look_on_validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tight-solution",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%config IPCompleter.use_jedi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "solved-cooler",
   "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>query</th>\n",
       "      <th>first_category</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>redirect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>колготки</td>\n",
       "      <td>колготки и носки</td>\n",
       "      <td>3245756.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>киберпонедельник</td>\n",
       "      <td>3672618.0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>бады</td>\n",
       "      <td>БАДы</td>\n",
       "      <td>3390215.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>бад</td>\n",
       "      <td>БАДы</td>\n",
       "      <td>3390215.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>для машины</td>\n",
       "      <td>для посудомоечной машины</td>\n",
       "      <td>3245766.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        query            first_category     cat_id redirect\n",
       "0    колготки          колготки и носки  3245756.0        1\n",
       "1          40          киберпонедельник  3672618.0        -\n",
       "2        бады                      БАДы  3390215.0        1\n",
       "3         бад                      БАДы  3390215.0        1\n",
       "4  для машины  для посудомоечной машины  3245766.0        1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data/420_validation.csv\", index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "subjective-process",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '-', '0'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['redirect'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "residential-worse",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'0': 0, '1': 1}\n",
    "data = data[data['redirect'] != '-']\n",
    "data['redirect'] = data['redirect'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "enabling-empty",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['redirect'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "southern-narrative",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['cat_id'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "prerequisite-military",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query</th>\n",
       "      <th>first_category</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>redirect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>basi</td>\n",
       "      <td>Armand Basi, Angel Schlesser</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>viktor</td>\n",
       "      <td>Viktor &amp; Rolf, Atelier Cologne, MAISON MARGIELA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>для женщин</td>\n",
       "      <td>для женщин</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>rolf</td>\n",
       "      <td>Viktor &amp; Rolf, Atelier Cologne, MAISON MARGIELA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>rabanne</td>\n",
       "      <td>Carolina Herrera, Paco Rabanne</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>calvin klein calvin klein</td>\n",
       "      <td>Hugo Boss, Gucci, Calvin Klein</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>sebastian</td>\n",
       "      <td>Sebastian и Seb man</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         query  \\\n",
       "5                         basi   \n",
       "33                      viktor   \n",
       "36                  для женщин   \n",
       "55                        rolf   \n",
       "79                     rabanne   \n",
       "139  calvin klein calvin klein   \n",
       "145                  sebastian   \n",
       "\n",
       "                                      first_category  cat_id  redirect  \n",
       "5                       Armand Basi, Angel Schlesser     NaN         1  \n",
       "33   Viktor & Rolf, Atelier Cologne, MAISON MARGIELA     NaN         1  \n",
       "36                                        для женщин     NaN         1  \n",
       "55   Viktor & Rolf, Atelier Cologne, MAISON MARGIELA     NaN         1  \n",
       "79                    Carolina Herrera, Paco Rabanne     NaN         1  \n",
       "139                   Hugo Boss, Gucci, Calvin Klein     NaN         1  \n",
       "145                              Sebastian и Seb man     NaN         1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['cat_id'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "boring-sewing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query</th>\n",
       "      <th>first_category</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>redirect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>колготки</td>\n",
       "      <td>колготки и носки</td>\n",
       "      <td>3245756.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>бады</td>\n",
       "      <td>БАДы</td>\n",
       "      <td>3390215.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>бад</td>\n",
       "      <td>БАДы</td>\n",
       "      <td>3390215.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>для машины</td>\n",
       "      <td>для посудомоечной машины</td>\n",
       "      <td>3245766.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>basi</td>\n",
       "      <td>Armand Basi, Angel Schlesser</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>колготки 20 den</td>\n",
       "      <td>колготки и носки</td>\n",
       "      <td>3245756.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>маска для лица mixit</td>\n",
       "      <td>маски</td>\n",
       "      <td>873249.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>маска для лица aha</td>\n",
       "      <td>маски</td>\n",
       "      <td>873249.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>тушь для ресниц кабаре</td>\n",
       "      <td>тушь для ресниц</td>\n",
       "      <td>873823.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>essence для бровей</td>\n",
       "      <td>тени для бровей</td>\n",
       "      <td>873843.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>243 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      query                first_category     cat_id  redirect\n",
       "0                  колготки              колготки и носки  3245756.0         1\n",
       "2                      бады                          БАДы  3390215.0         1\n",
       "3                       бад                          БАДы  3390215.0         1\n",
       "4                для машины      для посудомоечной машины  3245766.0         1\n",
       "5                      basi  Armand Basi, Angel Schlesser        NaN         1\n",
       "..                      ...                           ...        ...       ...\n",
       "243         колготки 20 den              колготки и носки  3245756.0         0\n",
       "244    маска для лица mixit                         маски   873249.0         0\n",
       "245      маска для лица aha                         маски   873249.0         0\n",
       "246  тушь для ресниц кабаре               тушь для ресниц   873823.0         0\n",
       "247      essence для бровей               тени для бровей   873843.0         0\n",
       "\n",
       "[243 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "moved-singing",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['query', 'category_name', 'category_id', 'is_redirect']\n",
    "data = data[~data['category_id'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "southeast-skating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "query            0\n",
       "category_name    0\n",
       "category_id      0\n",
       "is_redirect      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "behavioral-dressing",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"validation_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "overhead-bidder",
   "metadata": {},
   "source": [
    "### Проверка tfidf-бейзлайна"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "national-avatar",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pipeline.training import load_pipeline, score_pipeline\n",
    "%config IPCompleter.use_jedi = False\n",
    "\n",
    "pipe = load_pipeline(\"./models/tfidf_baseline1\")\n",
    "data = pd.read_csv(\"validation_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "statutory-single",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.731845238095238"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = score_pipeline(pipe, data, data['is_redirect'])\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "controlling-retail",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5540540540540541"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "pred = pipe.predict(data)\n",
    "res = f1_score(pred, data['is_redirect'])\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "subjective-cyprus",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 184, 1: 52})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "running-siemens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "%matplotlib inline\n",
    "\n",
    "p = plot_roc_curve(pipe, data, data['is_redirect'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fleet-person",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "comptech",
   "language": "python",
   "name": "comptech"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}