12. grocery matching/grocery matching.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Анализ данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import catboost as cb\n",
    "import faiss\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import phik\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base = pd.read_csv('data/base.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x2000 with 75 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_columns = df_base.shape[1]\n",
    "num_rows = 15\n",
    "num_cols = 5 \n",
    "\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, 20))\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    if i < num_columns:\n",
    "        df_base.iloc[:, i].hist(bins=100, ax=ax)\n",
    "        ax.set_title(f'Столбец {i}')\n",
    "    else:\n",
    "        ax.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"data/train.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 73)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "<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",
       "      <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>9</th>\n",
       "      <th>...</th>\n",
       "      <th>63</th>\n",
       "      <th>64</th>\n",
       "      <th>65</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-query</th>\n",
       "      <td>-53.882748</td>\n",
       "      <td>17.971436</td>\n",
       "      <td>-42.117104</td>\n",
       "      <td>-183.936680</td>\n",
       "      <td>187.517490</td>\n",
       "      <td>-87.144930</td>\n",
       "      <td>-347.360606</td>\n",
       "      <td>38.307602</td>\n",
       "      <td>109.085560</td>\n",
       "      <td>30.413513</td>\n",
       "      <td>...</td>\n",
       "      <td>70.107360</td>\n",
       "      <td>-155.802570</td>\n",
       "      <td>-101.965943</td>\n",
       "      <td>65.903790</td>\n",
       "      <td>34.457500</td>\n",
       "      <td>62.642094</td>\n",
       "      <td>134.763600</td>\n",
       "      <td>-415.750254</td>\n",
       "      <td>-25.958572</td>\n",
       "      <td>675816-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1-query</th>\n",
       "      <td>-87.776370</td>\n",
       "      <td>6.806268</td>\n",
       "      <td>-32.054546</td>\n",
       "      <td>-177.260390</td>\n",
       "      <td>120.803330</td>\n",
       "      <td>-83.810590</td>\n",
       "      <td>-94.572749</td>\n",
       "      <td>-78.433090</td>\n",
       "      <td>124.915900</td>\n",
       "      <td>140.331070</td>\n",
       "      <td>...</td>\n",
       "      <td>4.669178</td>\n",
       "      <td>-151.697710</td>\n",
       "      <td>-1.638704</td>\n",
       "      <td>68.170876</td>\n",
       "      <td>25.096191</td>\n",
       "      <td>89.974976</td>\n",
       "      <td>130.589630</td>\n",
       "      <td>-1035.092211</td>\n",
       "      <td>-51.276833</td>\n",
       "      <td>366656-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-query</th>\n",
       "      <td>-49.979565</td>\n",
       "      <td>3.841486</td>\n",
       "      <td>-116.118590</td>\n",
       "      <td>-180.401980</td>\n",
       "      <td>190.128430</td>\n",
       "      <td>-50.837620</td>\n",
       "      <td>26.943937</td>\n",
       "      <td>-30.447489</td>\n",
       "      <td>125.771164</td>\n",
       "      <td>211.607820</td>\n",
       "      <td>...</td>\n",
       "      <td>78.039764</td>\n",
       "      <td>-169.146200</td>\n",
       "      <td>82.144186</td>\n",
       "      <td>66.008220</td>\n",
       "      <td>18.400496</td>\n",
       "      <td>212.409730</td>\n",
       "      <td>121.931470</td>\n",
       "      <td>-1074.464888</td>\n",
       "      <td>-22.547178</td>\n",
       "      <td>1447819-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-query</th>\n",
       "      <td>-47.810562</td>\n",
       "      <td>9.086598</td>\n",
       "      <td>-115.401695</td>\n",
       "      <td>-121.011360</td>\n",
       "      <td>94.652840</td>\n",
       "      <td>-109.255410</td>\n",
       "      <td>-775.150134</td>\n",
       "      <td>79.186520</td>\n",
       "      <td>124.003100</td>\n",
       "      <td>242.650650</td>\n",
       "      <td>...</td>\n",
       "      <td>44.515266</td>\n",
       "      <td>-145.416750</td>\n",
       "      <td>93.990981</td>\n",
       "      <td>64.131350</td>\n",
       "      <td>106.061920</td>\n",
       "      <td>83.178760</td>\n",
       "      <td>118.277725</td>\n",
       "      <td>-1074.464888</td>\n",
       "      <td>-19.902788</td>\n",
       "      <td>1472602-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-query</th>\n",
       "      <td>-79.632126</td>\n",
       "      <td>14.442886</td>\n",
       "      <td>-58.903397</td>\n",
       "      <td>-147.052540</td>\n",
       "      <td>57.127068</td>\n",
       "      <td>-16.239529</td>\n",
       "      <td>-321.317964</td>\n",
       "      <td>45.984676</td>\n",
       "      <td>125.941284</td>\n",
       "      <td>103.392670</td>\n",
       "      <td>...</td>\n",
       "      <td>45.028910</td>\n",
       "      <td>-196.092070</td>\n",
       "      <td>-117.626337</td>\n",
       "      <td>66.926220</td>\n",
       "      <td>42.456170</td>\n",
       "      <td>77.621765</td>\n",
       "      <td>92.479930</td>\n",
       "      <td>-1074.464888</td>\n",
       "      <td>-21.149351</td>\n",
       "      <td>717819-base</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>99995-query</th>\n",
       "      <td>-68.180530</td>\n",
       "      <td>11.657515</td>\n",
       "      <td>-63.179720</td>\n",
       "      <td>-188.726530</td>\n",
       "      <td>131.113100</td>\n",
       "      <td>-65.125340</td>\n",
       "      <td>-514.276885</td>\n",
       "      <td>95.990750</td>\n",
       "      <td>118.761185</td>\n",
       "      <td>-5.635696</td>\n",
       "      <td>...</td>\n",
       "      <td>38.573555</td>\n",
       "      <td>-133.087130</td>\n",
       "      <td>14.040794</td>\n",
       "      <td>66.477340</td>\n",
       "      <td>-36.148730</td>\n",
       "      <td>61.689774</td>\n",
       "      <td>107.037950</td>\n",
       "      <td>-1074.464888</td>\n",
       "      <td>-35.128250</td>\n",
       "      <td>811868-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996-query</th>\n",
       "      <td>-66.540890</td>\n",
       "      <td>2.201112</td>\n",
       "      <td>-70.292114</td>\n",
       "      <td>-146.167850</td>\n",
       "      <td>77.817140</td>\n",
       "      <td>-47.525654</td>\n",
       "      <td>-118.994688</td>\n",
       "      <td>-25.183374</td>\n",
       "      <td>131.499820</td>\n",
       "      <td>183.260880</td>\n",
       "      <td>...</td>\n",
       "      <td>33.324960</td>\n",
       "      <td>-110.576416</td>\n",
       "      <td>-84.448525</td>\n",
       "      <td>65.818100</td>\n",
       "      <td>-30.268772</td>\n",
       "      <td>133.784000</td>\n",
       "      <td>154.495860</td>\n",
       "      <td>-1144.590904</td>\n",
       "      <td>-108.663510</td>\n",
       "      <td>1460772-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997-query</th>\n",
       "      <td>-53.948240</td>\n",
       "      <td>4.726887</td>\n",
       "      <td>-29.278950</td>\n",
       "      <td>-147.182830</td>\n",
       "      <td>264.090670</td>\n",
       "      <td>-101.135630</td>\n",
       "      <td>-427.544045</td>\n",
       "      <td>68.057560</td>\n",
       "      <td>118.084310</td>\n",
       "      <td>192.449250</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.146965</td>\n",
       "      <td>-184.663830</td>\n",
       "      <td>22.971385</td>\n",
       "      <td>67.347800</td>\n",
       "      <td>110.437100</td>\n",
       "      <td>35.785603</td>\n",
       "      <td>111.616710</td>\n",
       "      <td>-714.897734</td>\n",
       "      <td>-4.270569</td>\n",
       "      <td>2366140-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998-query</th>\n",
       "      <td>-77.321770</td>\n",
       "      <td>10.120371</td>\n",
       "      <td>-63.995037</td>\n",
       "      <td>-115.817314</td>\n",
       "      <td>140.170430</td>\n",
       "      <td>-94.855590</td>\n",
       "      <td>-676.686800</td>\n",
       "      <td>125.189285</td>\n",
       "      <td>117.982080</td>\n",
       "      <td>197.338410</td>\n",
       "      <td>...</td>\n",
       "      <td>57.748695</td>\n",
       "      <td>-186.625340</td>\n",
       "      <td>-93.810520</td>\n",
       "      <td>66.186200</td>\n",
       "      <td>-39.842730</td>\n",
       "      <td>56.899708</td>\n",
       "      <td>110.702940</td>\n",
       "      <td>-1074.464888</td>\n",
       "      <td>-79.494965</td>\n",
       "      <td>9252-base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999-query</th>\n",
       "      <td>-89.048460</td>\n",
       "      <td>10.938594</td>\n",
       "      <td>-53.206207</td>\n",
       "      <td>-144.188580</td>\n",
       "      <td>143.445330</td>\n",
       "      <td>-86.684190</td>\n",
       "      <td>-79.986290</td>\n",
       "      <td>77.225540</td>\n",
       "      <td>117.949430</td>\n",
       "      <td>73.297440</td>\n",
       "      <td>...</td>\n",
       "      <td>5.184549</td>\n",
       "      <td>-134.788450</td>\n",
       "      <td>-78.863722</td>\n",
       "      <td>66.884630</td>\n",
       "      <td>45.619125</td>\n",
       "      <td>129.991240</td>\n",
       "      <td>100.656710</td>\n",
       "      <td>-43.558286</td>\n",
       "      <td>-80.152910</td>\n",
       "      <td>2769109-base</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0          1           2           3           4  \\\n",
       "Id                                                                      \n",
       "0-query     -53.882748  17.971436  -42.117104 -183.936680  187.517490   \n",
       "1-query     -87.776370   6.806268  -32.054546 -177.260390  120.803330   \n",
       "2-query     -49.979565   3.841486 -116.118590 -180.401980  190.128430   \n",
       "3-query     -47.810562   9.086598 -115.401695 -121.011360   94.652840   \n",
       "4-query     -79.632126  14.442886  -58.903397 -147.052540   57.127068   \n",
       "...                ...        ...         ...         ...         ...   \n",
       "99995-query -68.180530  11.657515  -63.179720 -188.726530  131.113100   \n",
       "99996-query -66.540890   2.201112  -70.292114 -146.167850   77.817140   \n",
       "99997-query -53.948240   4.726887  -29.278950 -147.182830  264.090670   \n",
       "99998-query -77.321770  10.120371  -63.995037 -115.817314  140.170430   \n",
       "99999-query -89.048460  10.938594  -53.206207 -144.188580  143.445330   \n",
       "\n",
       "                      5           6           7           8           9  ...  \\\n",
       "Id                                                                       ...   \n",
       "0-query      -87.144930 -347.360606   38.307602  109.085560   30.413513  ...   \n",
       "1-query      -83.810590  -94.572749  -78.433090  124.915900  140.331070  ...   \n",
       "2-query      -50.837620   26.943937  -30.447489  125.771164  211.607820  ...   \n",
       "3-query     -109.255410 -775.150134   79.186520  124.003100  242.650650  ...   \n",
       "4-query      -16.239529 -321.317964   45.984676  125.941284  103.392670  ...   \n",
       "...                 ...         ...         ...         ...         ...  ...   \n",
       "99995-query  -65.125340 -514.276885   95.990750  118.761185   -5.635696  ...   \n",
       "99996-query  -47.525654 -118.994688  -25.183374  131.499820  183.260880  ...   \n",
       "99997-query -101.135630 -427.544045   68.057560  118.084310  192.449250  ...   \n",
       "99998-query  -94.855590 -676.686800  125.189285  117.982080  197.338410  ...   \n",
       "99999-query  -86.684190  -79.986290   77.225540  117.949430   73.297440  ...   \n",
       "\n",
       "                    63          64          65         66          67  \\\n",
       "Id                                                                      \n",
       "0-query      70.107360 -155.802570 -101.965943  65.903790   34.457500   \n",
       "1-query       4.669178 -151.697710   -1.638704  68.170876   25.096191   \n",
       "2-query      78.039764 -169.146200   82.144186  66.008220   18.400496   \n",
       "3-query      44.515266 -145.416750   93.990981  64.131350  106.061920   \n",
       "4-query      45.028910 -196.092070 -117.626337  66.926220   42.456170   \n",
       "...                ...         ...         ...        ...         ...   \n",
       "99995-query  38.573555 -133.087130   14.040794  66.477340  -36.148730   \n",
       "99996-query  33.324960 -110.576416  -84.448525  65.818100  -30.268772   \n",
       "99997-query -14.146965 -184.663830   22.971385  67.347800  110.437100   \n",
       "99998-query  57.748695 -186.625340  -93.810520  66.186200  -39.842730   \n",
       "99999-query   5.184549 -134.788450  -78.863722  66.884630   45.619125   \n",
       "\n",
       "                     68          69           70          71        Target  \n",
       "Id                                                                          \n",
       "0-query       62.642094  134.763600  -415.750254  -25.958572   675816-base  \n",
       "1-query       89.974976  130.589630 -1035.092211  -51.276833   366656-base  \n",
       "2-query      212.409730  121.931470 -1074.464888  -22.547178  1447819-base  \n",
       "3-query       83.178760  118.277725 -1074.464888  -19.902788  1472602-base  \n",
       "4-query       77.621765   92.479930 -1074.464888  -21.149351   717819-base  \n",
       "...                 ...         ...          ...         ...           ...  \n",
       "99995-query   61.689774  107.037950 -1074.464888  -35.128250   811868-base  \n",
       "99996-query  133.784000  154.495860 -1144.590904 -108.663510  1460772-base  \n",
       "99997-query   35.785603  111.616710  -714.897734   -4.270569  2366140-base  \n",
       "99998-query   56.899708  110.702940 -1074.464888  -79.494965     9252-base  \n",
       "99999-query  129.991240  100.656710   -43.558286  -80.152910  2769109-base  \n",
       "\n",
       "[100000 rows x 73 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_visualisation = df_train.copy().drop(['Target'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 75 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_columns = df_train_visualisation.shape[1]\n",
    "\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(20, 20))\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    if i < num_columns:\n",
    "        df_train_visualisation.iloc[:, i].hist(bins=100, ax=ax)\n",
    "        ax.set_title(f'Столбец {i}')\n",
    "    else:\n",
    "        ax.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_train_visualisation\n",
    "del num_columns\n",
    "del num_rows\n",
    "del num_cols\n",
    "del fig\n",
    "del axes\n",
    "del ax\n",
    "del i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Обработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [df_base, df_train]:\n",
    "    for column in ['6', '21', '25', '33', '44', '59', '65', '70']:\n",
    "        df.drop(column, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df\n",
    "del column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = df_train[\"Target\"]\n",
    "df_train.drop(\"Target\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_index = {k: v for k, v in enumerate(df_base.index.to_list())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_index = {k: v for k, v in enumerate(df_train.index.to_list())}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "columns_base = df_base.columns\n",
    "df_base = scaler.fit_transform(df_base)\n",
    "df_base = pd.DataFrame(df_base, columns=columns_base)\n",
    "\n",
    "columns_train = df_train.columns\n",
    "df_train = scaler.transform(df_train)\n",
    "df_train = pd.DataFrame(df_train, columns=columns_train)\n",
    "\n",
    "df_base['id'] = base_index\n",
    "df_train['id'] = train_index\n",
    "\n",
    "df_base.set_index('id', inplace=True)\n",
    "df_train.set_index('id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del columns_base\n",
    "del columns_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Работа с Faiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dims = df_base.shape[1] # количество признаков\n",
    "n_cells = 500 # количество ячеек в индексе\n",
    "\n",
    "quantizer = faiss.IndexFlatL2(dims)\n",
    "idx_l2 = faiss.IndexIVFFlat(quantizer, dims, n_cells)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dims\n",
    "del n_cells\n",
    "del quantizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_l2.train(np.ascontiguousarray(df_base).astype('float32'))\n",
    "\n",
    "idx_l2.add(np.ascontiguousarray(df_base).astype('float32'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_l2.nprobe = 100 # количество ячеек, которые будут просмотрены при поиске`\n",
    "r, idx = idx_l2.search(np.ascontiguousarray(df_train.values).astype('float32'), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.908 %\n"
     ]
    }
   ],
   "source": [
    "acc = 0\n",
    "for target, el in zip(targets.values.tolist(), idx.tolist()):\n",
    "    acc += int(target in [base_index[r] for r in el])\n",
    "\n",
    "print(100 * acc / len(idx), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "del acc\n",
    "del target\n",
    "del el"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "r, idx = idx_l2.search(np.ascontiguousarray(df_train.values).astype('float32'), k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "del idx_l2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "70.87% является лучшим показателем Faiss. Для ранджирования будем использовать большее количество значений k у отдадим их в другие модели. Если модель покажет значение меньше 70.87%, то будем использовать Faiss. Если больше, то заказчик примет решение о эфективности зятрат времени / качества."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ранжирование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.array([[base_index[_] for _ in el] for el in idx], dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_targets = targets.copy()\n",
    "targets = np.array([[1 if x == target else 0 for x in el] for el, target in zip(idx.tolist(), targets.values.tolist())])\n",
    "targets = targets.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_targets = np.repeat(cb_targets, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "candidate_features = df_base.loc[idx.flatten()].values\n",
    "object_features = df_train.values\n",
    "\n",
    "reshaped_dist = r.reshape(-1,1)\n",
    "\n",
    "repeated_object = np.repeat(object_features, k, axis=0)\n",
    "\n",
    "cb_features = np.hstack((reshaped_dist, candidate_features, repeated_object))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "del candidate_features\n",
    "del object_features\n",
    "del reshaped_dist\n",
    "del repeated_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cb_features, targets, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "del cb_features\n",
    "del targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.373986\n",
      "0:\tlearn: 0.1648680\ttotal: 398ms\tremaining: 6m 37s\n",
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      "995:\tlearn: 0.0301966\ttotal: 3m 51s\tremaining: 929ms\n",
      "996:\tlearn: 0.0301844\ttotal: 3m 51s\tremaining: 697ms\n",
      "997:\tlearn: 0.0301758\ttotal: 3m 51s\tremaining: 465ms\n",
      "998:\tlearn: 0.0301695\ttotal: 3m 52s\tremaining: 232ms\n",
      "999:\tlearn: 0.0301628\ttotal: 3m 52s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "cb_model = cb.CatBoostClassifier(verbose=True, random_state=42)\n",
    "cb_fitted = cb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "del X_train\n",
    "del y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_prediction = cb_fitted.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.984426\n"
     ]
    }
   ],
   "source": [
    "print(cb_fitted.score(cb_prediction, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "proba = cb_fitted.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "del X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_valid_preds = (np.vstack(proba[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_max_indices(arr: np.array, k: int, n: int) -> np.array:\n",
    "    max_indices = np.empty((0, n), dtype=int)\n",
    "    for i in range(0, len(arr), k):\n",
    "        sub_arr = arr[i:i+k]\n",
    "        max_values = np.argpartition(sub_arr, -n)[-n:]\n",
    "        max_indices = np.vstack((max_indices, max_values))\n",
    "    return max_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_candidates = find_max_indices(cb_valid_preds.flatten(), k, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.vstack(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "candidates_idx = idx[:, 0][best_candidates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.49 %\n"
     ]
    }
   ],
   "source": [
    "acc = 0\n",
    "for target, el in zip(cb_targets.tolist(), candidates_idx.tolist()):\n",
    "    acc += int(target in el)\n",
    "\n",
    "print(100 * acc / len(candidates_idx), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выводы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Работа с сэмплом базовых данных без предобработки в колличестве 50 000 строк: **13.8%** точность на тестовой выборке\n",
    "2. Кластеры 50, вся база: **8.681%, 14с.** \n",
    "3. Кластеры 50, вся база, n_porbe 10: **13.787%, 140с.**\n",
    "4. Кластеры 50, вся база, n_porbe 10, нормализация: **14.374%, 154с.**\n",
    "5. Вся база, нормализация: **14.396%, 655с.**\n",
    "6. Кластеры 500, вся база, n_probe 100, нормализация: **14.391%, 93с.**\n",
    "7. Кластеры 1700, вся база, n_probe 300, нормализация: **14.392%, 131с.**\n",
    "8. Кластеры 500, вся база, n_probe 100, нормализация, удаление признайов без распределения: **17.044%, 148с.**\n",
    "9. Кластеры 500, вся база, n_probe 100, нормализация, удаление признайов без распределения, удаление признаков с разбросанными показателями: **30.646%, 139с.**\n",
    "10. Кластеры 500, вся база, n_probe 100, нормализация, удаление всех признаков без нормального распределения: **69.661%, 120с.**\n",
    "11. Кластеры 500, вся база, n_probe 100, удаление всех признаков без нормального распределения: **69.601%, 120с.**\n",
    "12. Кластеры 1, вся база, удаление всех признаков без нормального распределения: **69.729%, 570с.**\n",
    "13. Кластеры 500, вся база, n_probe 100, StandartScaler, удаление всех признаков без нормального распределения: **70.91%, 128с.**\n",
    "14. Кластеры 500, вся база, n_probe 100, нормализация, StandartScaler, удаление всех признаков без нормального распределения: **70.432%, 125с.**\n",
    "15. Кластеры 500, вся база, n_probe 100, MinMax, StandartScaler, удаление всех признаков без нормального распределения: **70.659%, 123с.**\n",
    "16. Кластеры 500, вся база, n_probe 100, MinMax, удаление всех признаков без нормального распределения: **70.659%, 125с.**\n",
    "17. Кластеры 500, вся база, n_probe 100, StandartScaler: **65.393%, 147с.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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