04. IBCF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IBCF(Item Based Collaborative Filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 준비하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>beer</th>\n",
       "      <th>user</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47658</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66674</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>4.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48824</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55939</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68916</td>\n",
       "      <td>bluejacket74</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    beer          user  rating\n",
       "0  47658  bluejacket74    4.97\n",
       "1  66674  bluejacket74    4.04\n",
       "2  48824  bluejacket74    3.71\n",
       "3  55939  bluejacket74    4.10\n",
       "4  68916  bluejacket74    4.34"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('my_df.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['user'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "user_enc = LabelEncoder()\n",
    "uid = data['user'].values\n",
    "data['user'] = user_enc.fit_transform(uid.tolist()) #array 인식이 안돼서 list로 빼냄.(userid의 경우만 오류 발생)\n",
    "n_users = data['user'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([190, 101,  37, 301,  48, 119, 186, 323,  18, 171,  53, 296,  85,\n",
       "       293, 130, 262, 201,  98, 131, 118, 283, 327, 177,  97, 266, 318,\n",
       "       213,  50, 153,  38, 157,  33, 252, 164, 208,  75, 315, 281, 185,\n",
       "       121, 255, 326, 224, 238, 199, 267, 217, 256, 196,  79,  63, 173,\n",
       "        71, 114, 308, 161, 146,  56, 163,   3, 314, 269, 222, 100, 155,\n",
       "       182, 319, 166,  28,  47, 247,  67,  24,  62, 332, 138, 309, 126,\n",
       "       249, 188, 212, 102, 270,  54,  49, 104, 200,  57, 329, 144, 242,\n",
       "       274, 307,   4, 257, 250,  70, 209, 264,  35,  15, 127, 116, 234,\n",
       "       311,  76, 304,  73, 278, 261, 288, 174, 302, 122,   8,  81, 205,\n",
       "       179, 312, 152, 292, 136, 202, 260,  21,   9, 282,  41, 134, 135,\n",
       "       221, 271,  59,  14, 154, 223,  94, 253, 147,  80,  29, 306, 330,\n",
       "       277, 187, 273, 106,  13, 294, 139, 246, 218, 178, 245, 298, 317,\n",
       "       113, 142, 141, 272,   2, 310,   0,  61, 192, 280,  16,  23, 112,\n",
       "       244, 299,  64, 239, 220,  32, 284, 145,  22,  84, 148,  74, 225,\n",
       "       124,  12, 243,  58,  89, 259, 287,  60, 105,  99,  77, 233,   5,\n",
       "       276,  19,  69, 128, 248, 110, 129, 159, 211,  25, 297,  90, 151,\n",
       "       170,  82,  51,  10, 291, 169,  92, 176, 123,  43, 235, 321, 180,\n",
       "        78, 132,  11, 203, 300, 275,  42, 149, 263, 322, 168,  44,  31,\n",
       "       103,   1, 279, 210, 158,  86, 295, 206, 226, 289, 331,   7, 198,\n",
       "       165,   6, 140, 125, 197, 204, 137,  93, 268, 181, 240,  36,  40,\n",
       "       133, 324, 219, 290,  45, 254, 258, 228, 117,  95,  55, 285, 230,\n",
       "       241, 156, 108, 189,  91, 115,  65, 316, 214, 286,  17, 215, 229,\n",
       "       150, 236, 195, 237,  68,  87, 191, 328,  46,  34, 107, 227,  52,\n",
       "       167, 231, 305, 184, 175, 325, 111, 160, 162, 193, 120, 320, 194,\n",
       "       251, 303, 265,  30,  26, 216,  66, 109,  39,  96, 172,  88, 207,\n",
       "       313,  20, 143, 183,  27, 232,  83,  72], dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['user'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer      0\n",
       "user      0\n",
       "rating    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- shape (101889,3)\n",
    "- beer&rating: numeric\n",
    "- user: object\n",
    "- NaN: None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### username과 beerID를 기준으로 rating정보만 담은 피벗 테이블 작성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(333, 481)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_matrix = data.pivot_table('rating', index='user', columns='beer') #ItemID를 기준으로 정렬\n",
    "ratings_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>beer</th>\n",
       "      <th>6</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>39</th>\n",
       "      <th>59</th>\n",
       "      <th>61</th>\n",
       "      <th>63</th>\n",
       "      <th>65</th>\n",
       "      <th>...</th>\n",
       "      <th>178857</th>\n",
       "      <th>179482</th>\n",
       "      <th>181572</th>\n",
       "      <th>182256</th>\n",
       "      <th>187317</th>\n",
       "      <th>189272</th>\n",
       "      <th>197183</th>\n",
       "      <th>202078</th>\n",
       "      <th>211516</th>\n",
       "      <th>221289</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</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</th>\n",
       "      <td>3.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>...</td>\n",
       "      <td>3.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.69</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3.48</td>\n",
       "      <td>4.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.25</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.07</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.72</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.95</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.51</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.56</td>\n",
       "      <td>4.28</td>\n",
       "      <td>4.32</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 481 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "beer  6       30      31      33      34      39      59      61      63      \\\n",
       "user                                                                           \n",
       "0       3.69     NaN     NaN     NaN    3.63     NaN     NaN     NaN    4.00   \n",
       "1        NaN     NaN     NaN     NaN    4.50    4.73     NaN     NaN    3.90   \n",
       "2       4.07    3.95    3.16    3.88    4.30     NaN    4.15     NaN    3.46   \n",
       "3        NaN    4.07     NaN    3.88    4.12    3.95    3.41     NaN     NaN   \n",
       "4        NaN    2.51    3.27    3.56    4.28    4.32    4.01     4.0    4.17   \n",
       "\n",
       "beer  65      ...  178857  179482  181572  182256  187317  189272  197183  \\\n",
       "user          ...                                                           \n",
       "0       2.18  ...    3.98     NaN    3.94    3.94    3.69    4.68    3.48   \n",
       "1       2.25  ...     NaN     NaN    4.38     NaN     NaN     NaN     NaN   \n",
       "2       1.72  ...     NaN    4.24     NaN     NaN     NaN     NaN    4.12   \n",
       "3        NaN  ...    3.44     NaN    4.12     NaN     NaN     NaN     NaN   \n",
       "4        NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "beer  202078  211516  221289  \n",
       "user                          \n",
       "0       4.18     NaN     NaN  \n",
       "1       4.25    4.50     NaN  \n",
       "2        NaN     NaN     NaN  \n",
       "3        NaN    3.95     4.0  \n",
       "4        NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 481 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ratings_matrix.shape = (333,481)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 수리연산을 위해 NaN를 0으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>beer</th>\n",
       "      <th>6</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>39</th>\n",
       "      <th>59</th>\n",
       "      <th>61</th>\n",
       "      <th>63</th>\n",
       "      <th>65</th>\n",
       "      <th>...</th>\n",
       "      <th>178857</th>\n",
       "      <th>179482</th>\n",
       "      <th>181572</th>\n",
       "      <th>182256</th>\n",
       "      <th>187317</th>\n",
       "      <th>189272</th>\n",
       "      <th>197183</th>\n",
       "      <th>202078</th>\n",
       "      <th>211516</th>\n",
       "      <th>221289</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</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</th>\n",
       "      <td>3.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>...</td>\n",
       "      <td>3.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.69</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3.48</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.07</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 481 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "beer  6       30      31      33      34      39      59      61      63      \\\n",
       "user                                                                           \n",
       "0       3.69    0.00    0.00    0.00    3.63    0.00    0.00     0.0    4.00   \n",
       "1       0.00    0.00    0.00    0.00    4.50    4.73    0.00     0.0    3.90   \n",
       "2       4.07    3.95    3.16    3.88    4.30    0.00    4.15     0.0    3.46   \n",
       "\n",
       "beer  65      ...  178857  179482  181572  182256  187317  189272  197183  \\\n",
       "user          ...                                                           \n",
       "0       2.18  ...    3.98    0.00    3.94    3.94    3.69    4.68    3.48   \n",
       "1       2.25  ...    0.00    0.00    4.38    0.00    0.00    0.00    0.00   \n",
       "2       1.72  ...    0.00    4.24    0.00    0.00    0.00    0.00    4.12   \n",
       "\n",
       "beer  202078  211516  221289  \n",
       "user                          \n",
       "0       4.18     0.0     0.0  \n",
       "1       4.25     4.5     0.0  \n",
       "2       0.00     0.0     0.0  \n",
       "\n",
       "[3 rows x 481 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# beerID는 나중에\n",
    "# rating_beer = pd.merge(data, beers, on='beer_ID')\n",
    "\n",
    "# columns='beer_ID' 로 변환 & beer_ID 컬럼으로 pivot 수행. \n",
    "#ratings_matrix = rating_beer.pivot_table('rating', index='user', columns='beer_ID')\n",
    "\n",
    "# NaN 값을 모두 0 으로 변환\n",
    "ratings_matrix = ratings_matrix.fillna(0)\n",
    "ratings_matrix.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### userID기준으로 작성된 rating matrix를 beerID기준으로 전치한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>user</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>323</th>\n",
       "      <th>324</th>\n",
       "      <th>325</th>\n",
       "      <th>326</th>\n",
       "      <th>327</th>\n",
       "      <th>328</th>\n",
       "      <th>329</th>\n",
       "      <th>330</th>\n",
       "      <th>331</th>\n",
       "      <th>332</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer</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>6</th>\n",
       "      <td>3.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.95</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.42</td>\n",
       "      <td>...</td>\n",
       "      <td>3.85</td>\n",
       "      <td>4.79</td>\n",
       "      <td>4.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>4.47</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>...</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.10</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 333 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user   0    1     2     3     4    5     6    7    8     9    ...   323   324  \\\n",
       "beer                                                          ...               \n",
       "6     3.69  0.0  4.07  0.00  0.00  0.0  0.00  3.6  0.0  0.00  ...  0.00  0.00   \n",
       "30    0.00  0.0  3.95  4.07  2.51  0.0  3.77  0.0  4.0  4.42  ...  3.85  4.79   \n",
       "31    0.00  0.0  3.16  0.00  3.27  0.0  0.00  0.0  0.0  3.90  ...  4.02  0.00   \n",
       "\n",
       "user   325   326  327   328   329   330   331   332  \n",
       "beer                                                 \n",
       "6     3.95  0.00  0.0  3.61  0.00  0.00  0.00  3.17  \n",
       "30    4.10  0.00  0.0  4.14  4.47  4.38  4.31  4.33  \n",
       "31    0.00  3.11  0.0  0.00  4.10  3.06  0.00  4.23  \n",
       "\n",
       "[3 rows x 333 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_matrix_T = ratings_matrix.transpose() #User의 평점이 아니라 맥주간의 유사도를 측정하는 프로세스\n",
    "ratings_matrix_T.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> index가 beer , columns가 user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Item(beerID)간의 유사도 측정\n",
    "- index가 user로 설정되어있던 ratings matrix를 itemID가 index가 되도록 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>beer</th>\n",
       "      <th>6</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>39</th>\n",
       "      <th>59</th>\n",
       "      <th>61</th>\n",
       "      <th>63</th>\n",
       "      <th>65</th>\n",
       "      <th>...</th>\n",
       "      <th>178857</th>\n",
       "      <th>179482</th>\n",
       "      <th>181572</th>\n",
       "      <th>182256</th>\n",
       "      <th>187317</th>\n",
       "      <th>189272</th>\n",
       "      <th>197183</th>\n",
       "      <th>202078</th>\n",
       "      <th>211516</th>\n",
       "      <th>221289</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer</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>6</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7117</td>\n",
       "      <td>0.6496</td>\n",
       "      <td>0.7137</td>\n",
       "      <td>0.7147</td>\n",
       "      <td>0.6112</td>\n",
       "      <td>0.6738</td>\n",
       "      <td>0.6923</td>\n",
       "      <td>0.7124</td>\n",
       "      <td>0.6083</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4869</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>0.4043</td>\n",
       "      <td>0.4582</td>\n",
       "      <td>0.4639</td>\n",
       "      <td>0.4754</td>\n",
       "      <td>0.4922</td>\n",
       "      <td>0.4128</td>\n",
       "      <td>0.2802</td>\n",
       "      <td>0.4054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.7117</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7582</td>\n",
       "      <td>0.9201</td>\n",
       "      <td>0.9364</td>\n",
       "      <td>0.8062</td>\n",
       "      <td>0.8288</td>\n",
       "      <td>0.7936</td>\n",
       "      <td>0.8541</td>\n",
       "      <td>0.7063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6231</td>\n",
       "      <td>0.5792</td>\n",
       "      <td>0.4821</td>\n",
       "      <td>0.6126</td>\n",
       "      <td>0.5958</td>\n",
       "      <td>0.5943</td>\n",
       "      <td>0.5701</td>\n",
       "      <td>0.5339</td>\n",
       "      <td>0.4125</td>\n",
       "      <td>0.5155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.6496</td>\n",
       "      <td>0.7582</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7624</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.6532</td>\n",
       "      <td>0.6952</td>\n",
       "      <td>0.7148</td>\n",
       "      <td>0.7234</td>\n",
       "      <td>0.6334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4531</td>\n",
       "      <td>0.4725</td>\n",
       "      <td>0.3797</td>\n",
       "      <td>0.4495</td>\n",
       "      <td>0.4679</td>\n",
       "      <td>0.4627</td>\n",
       "      <td>0.4774</td>\n",
       "      <td>0.3830</td>\n",
       "      <td>0.2865</td>\n",
       "      <td>0.3525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.7137</td>\n",
       "      <td>0.9201</td>\n",
       "      <td>0.7624</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.9179</td>\n",
       "      <td>0.7924</td>\n",
       "      <td>0.8332</td>\n",
       "      <td>0.8032</td>\n",
       "      <td>0.8376</td>\n",
       "      <td>0.7051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5939</td>\n",
       "      <td>0.5657</td>\n",
       "      <td>0.4816</td>\n",
       "      <td>0.5787</td>\n",
       "      <td>0.5799</td>\n",
       "      <td>0.5814</td>\n",
       "      <td>0.5504</td>\n",
       "      <td>0.5362</td>\n",
       "      <td>0.3814</td>\n",
       "      <td>0.4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.7147</td>\n",
       "      <td>0.9364</td>\n",
       "      <td>0.7592</td>\n",
       "      <td>0.9179</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.8495</td>\n",
       "      <td>0.8489</td>\n",
       "      <td>0.8123</td>\n",
       "      <td>0.8813</td>\n",
       "      <td>0.7525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6397</td>\n",
       "      <td>0.5919</td>\n",
       "      <td>0.5468</td>\n",
       "      <td>0.6199</td>\n",
       "      <td>0.6196</td>\n",
       "      <td>0.6463</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.5920</td>\n",
       "      <td>0.4511</td>\n",
       "      <td>0.5547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 481 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "beer  6       30      31      33      34      39      59      61      63      \\\n",
       "beer                                                                           \n",
       "6     1.0000  0.7117  0.6496  0.7137  0.7147  0.6112  0.6738  0.6923  0.7124   \n",
       "30    0.7117  1.0000  0.7582  0.9201  0.9364  0.8062  0.8288  0.7936  0.8541   \n",
       "31    0.6496  0.7582  1.0000  0.7624  0.7592  0.6532  0.6952  0.7148  0.7234   \n",
       "33    0.7137  0.9201  0.7624  1.0000  0.9179  0.7924  0.8332  0.8032  0.8376   \n",
       "34    0.7147  0.9364  0.7592  0.9179  1.0000  0.8495  0.8489  0.8123  0.8813   \n",
       "\n",
       "beer  65      ...  178857  179482  181572  182256  187317  189272  197183  \\\n",
       "beer          ...                                                           \n",
       "6     0.6083  ...  0.4869  0.4740  0.4043  0.4582  0.4639  0.4754  0.4922   \n",
       "30    0.7063  ...  0.6231  0.5792  0.4821  0.6126  0.5958  0.5943  0.5701   \n",
       "31    0.6334  ...  0.4531  0.4725  0.3797  0.4495  0.4679  0.4627  0.4774   \n",
       "33    0.7051  ...  0.5939  0.5657  0.4816  0.5787  0.5799  0.5814  0.5504   \n",
       "34    0.7525  ...  0.6397  0.5919  0.5468  0.6199  0.6196  0.6463  0.6040   \n",
       "\n",
       "beer  202078  211516  221289  \n",
       "beer                          \n",
       "6     0.4128  0.2802  0.4054  \n",
       "30    0.5339  0.4125  0.5155  \n",
       "31    0.3830  0.2865  0.3525  \n",
       "33    0.5362  0.3814  0.4999  \n",
       "34    0.5920  0.4511  0.5547  \n",
       "\n",
       "[5 rows x 481 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "# 코사인 유사도 matrix 작성\n",
    "item_sim = cosine_similarity(ratings_matrix_T, ratings_matrix_T) #Item유사도 - corr.matrix처럼 n*n행렬\n",
    "\n",
    "# cosine_similarity() 로 반환된 넘파이 행렬을 맥주ID로 매핑하여 DataFrame으로 변환\n",
    "item_sim_df = pd.DataFrame(data=item_sim, index=ratings_matrix.columns,columns=ratings_matrix.columns).round(4) # shape (481,481) # 소수점 4번째 자리까지\n",
    "item_sim_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">beer_ID간의 코사인 유사도를 계산한 결과값을 담은 행렬"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 유사도가 가장 높은 상위 n가지 추출하는 모듈 만들기\n",
    "- `item`: beerID같은 특정 Item를 나타내는 정보를 입력한다. 위의 경우 유사도 계산을 ItemID(beerID)로 진행했기 때문에 beerID형식에 맞춰 입력한다.\n",
    "- `n`: 상위 몇개까지 추출할 것인지 입력한다.\n",
    "> ※주의: 자기 자신 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>33</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.9201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.9179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1708</th>\n",
       "      <td>0.9032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.8984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>0.8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.8889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>0.8871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>0.8865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>0.8851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          33\n",
       "beer        \n",
       "30    0.9201\n",
       "34    0.9179\n",
       "1708  0.9032\n",
       "88    0.8984\n",
       "412   0.8900\n",
       "131   0.8889\n",
       "1013  0.8871\n",
       "2512  0.8865\n",
       "1904  0.8851"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def corr_top(item,n):\n",
    "    ans = pd.DataFrame(item_sim_df[item].sort_values(ascending = False)[1:n])\n",
    "    return ans\n",
    "\n",
    "corr_top(33,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 33번 맥주와 유사도가 가장 높은 맥주 목록"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 아이템 기반 인접 이웃 협업 필터링으로 개인화된 맥주 추천"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prediction matrix 작성하기 ###\n",
    "##### predict_rating(ratins_arr,item_sim_arr)\n",
    "> user가 item에 대해 평가한 데이터와 item간의 유사도를 계산한 데이터를 array형식으로 입력받아 rating 예측값을 계산하는 함수\n",
    "- ratings_arr: user가 item에 대해 점수를 매긴 matrix를 array형식으로 입력\n",
    "- item_sim_arr: item 유사도를 계산한 matrix를 array형식으로 입력\n",
    "- ratings_pred: user의 scoring정보와 item유사도를 dot product로 예측값을 연산. array형식으로 반환한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_rating(ratings_arr, item_sim_arr ):\n",
    "    ratings_pred = ratings_arr.dot(item_sim_arr)/ np.array([np.abs(item_sim_arr).sum(axis=1)])\n",
    "    return ratings_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>beer</th>\n",
       "      <th>6</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>39</th>\n",
       "      <th>59</th>\n",
       "      <th>61</th>\n",
       "      <th>63</th>\n",
       "      <th>65</th>\n",
       "      <th>...</th>\n",
       "      <th>178857</th>\n",
       "      <th>179482</th>\n",
       "      <th>181572</th>\n",
       "      <th>182256</th>\n",
       "      <th>187317</th>\n",
       "      <th>189272</th>\n",
       "      <th>197183</th>\n",
       "      <th>202078</th>\n",
       "      <th>211516</th>\n",
       "      <th>221289</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</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</th>\n",
       "      <td>2.557521</td>\n",
       "      <td>2.539770</td>\n",
       "      <td>2.542699</td>\n",
       "      <td>2.537633</td>\n",
       "      <td>2.537942</td>\n",
       "      <td>2.517111</td>\n",
       "      <td>2.529003</td>\n",
       "      <td>2.544423</td>\n",
       "      <td>2.548360</td>\n",
       "      <td>2.534147</td>\n",
       "      <td>...</td>\n",
       "      <td>2.562583</td>\n",
       "      <td>2.548413</td>\n",
       "      <td>2.502485</td>\n",
       "      <td>2.563750</td>\n",
       "      <td>2.555198</td>\n",
       "      <td>2.511763</td>\n",
       "      <td>2.552204</td>\n",
       "      <td>2.531040</td>\n",
       "      <td>2.415096</td>\n",
       "      <td>2.489413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.264491</td>\n",
       "      <td>2.302495</td>\n",
       "      <td>2.253402</td>\n",
       "      <td>2.297710</td>\n",
       "      <td>2.327306</td>\n",
       "      <td>2.359716</td>\n",
       "      <td>2.313593</td>\n",
       "      <td>2.282406</td>\n",
       "      <td>2.316119</td>\n",
       "      <td>2.328348</td>\n",
       "      <td>...</td>\n",
       "      <td>2.368528</td>\n",
       "      <td>2.329951</td>\n",
       "      <td>2.443169</td>\n",
       "      <td>2.355068</td>\n",
       "      <td>2.342703</td>\n",
       "      <td>2.406483</td>\n",
       "      <td>2.353883</td>\n",
       "      <td>2.408393</td>\n",
       "      <td>2.511186</td>\n",
       "      <td>2.438862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.563730</td>\n",
       "      <td>2.579625</td>\n",
       "      <td>2.580783</td>\n",
       "      <td>2.575660</td>\n",
       "      <td>2.572463</td>\n",
       "      <td>2.551211</td>\n",
       "      <td>2.568175</td>\n",
       "      <td>2.565421</td>\n",
       "      <td>2.568891</td>\n",
       "      <td>2.549082</td>\n",
       "      <td>...</td>\n",
       "      <td>2.515182</td>\n",
       "      <td>2.522537</td>\n",
       "      <td>2.468213</td>\n",
       "      <td>2.517621</td>\n",
       "      <td>2.499227</td>\n",
       "      <td>2.498136</td>\n",
       "      <td>2.505825</td>\n",
       "      <td>2.491471</td>\n",
       "      <td>2.425870</td>\n",
       "      <td>2.483437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.025298</td>\n",
       "      <td>2.119282</td>\n",
       "      <td>2.035929</td>\n",
       "      <td>2.109260</td>\n",
       "      <td>2.137561</td>\n",
       "      <td>2.167387</td>\n",
       "      <td>2.133007</td>\n",
       "      <td>2.067375</td>\n",
       "      <td>2.096473</td>\n",
       "      <td>2.077914</td>\n",
       "      <td>...</td>\n",
       "      <td>2.202948</td>\n",
       "      <td>2.135681</td>\n",
       "      <td>2.286017</td>\n",
       "      <td>2.165826</td>\n",
       "      <td>2.133756</td>\n",
       "      <td>2.261145</td>\n",
       "      <td>2.142712</td>\n",
       "      <td>2.233455</td>\n",
       "      <td>2.404158</td>\n",
       "      <td>2.324640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.021488</td>\n",
       "      <td>2.059952</td>\n",
       "      <td>2.064185</td>\n",
       "      <td>2.061413</td>\n",
       "      <td>2.054135</td>\n",
       "      <td>2.061562</td>\n",
       "      <td>2.058637</td>\n",
       "      <td>2.055588</td>\n",
       "      <td>2.048894</td>\n",
       "      <td>2.025354</td>\n",
       "      <td>...</td>\n",
       "      <td>1.923869</td>\n",
       "      <td>1.913971</td>\n",
       "      <td>1.912202</td>\n",
       "      <td>1.932263</td>\n",
       "      <td>1.918706</td>\n",
       "      <td>1.926352</td>\n",
       "      <td>1.918871</td>\n",
       "      <td>1.901625</td>\n",
       "      <td>1.875120</td>\n",
       "      <td>1.905469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 481 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "beer    6         30        31        33        34        39        59      \\\n",
       "user                                                                         \n",
       "0     2.557521  2.539770  2.542699  2.537633  2.537942  2.517111  2.529003   \n",
       "1     2.264491  2.302495  2.253402  2.297710  2.327306  2.359716  2.313593   \n",
       "2     2.563730  2.579625  2.580783  2.575660  2.572463  2.551211  2.568175   \n",
       "3     2.025298  2.119282  2.035929  2.109260  2.137561  2.167387  2.133007   \n",
       "4     2.021488  2.059952  2.064185  2.061413  2.054135  2.061562  2.058637   \n",
       "\n",
       "beer    61        63        65      ...    178857    179482    181572  \\\n",
       "user                                ...                                 \n",
       "0     2.544423  2.548360  2.534147  ...  2.562583  2.548413  2.502485   \n",
       "1     2.282406  2.316119  2.328348  ...  2.368528  2.329951  2.443169   \n",
       "2     2.565421  2.568891  2.549082  ...  2.515182  2.522537  2.468213   \n",
       "3     2.067375  2.096473  2.077914  ...  2.202948  2.135681  2.286017   \n",
       "4     2.055588  2.048894  2.025354  ...  1.923869  1.913971  1.912202   \n",
       "\n",
       "beer    182256    187317    189272    197183    202078    211516    221289  \n",
       "user                                                                        \n",
       "0     2.563750  2.555198  2.511763  2.552204  2.531040  2.415096  2.489413  \n",
       "1     2.355068  2.342703  2.406483  2.353883  2.408393  2.511186  2.438862  \n",
       "2     2.517621  2.499227  2.498136  2.505825  2.491471  2.425870  2.483437  \n",
       "3     2.165826  2.133756  2.261145  2.142712  2.233455  2.404158  2.324640  \n",
       "4     1.932263  1.918706  1.926352  1.918871  1.901625  1.875120  1.905469  \n",
       "\n",
       "[5 rows x 481 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_pred = predict_rating(ratings_matrix.values , item_sim_df.values)\n",
    "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index= ratings_matrix.index,columns = ratings_matrix.columns)\n",
    "ratings_pred_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rmse 출력하기 ###\n",
    "##### get_rmse(predm actual)\n",
    "> 평점이 존재하는 항목만 추려서 모델 성능 평가하기\n",
    "- pred: 예측값을 담은 prediction matrix를 array형식으로 입력\n",
    "- actual: 실제값을 array형식으로 입력\n",
    "\n",
    "<참고>\n",
    "- array.nonzero(): array에서 0이 아닌 값들의 row, col을 array형식으로 반환 -> 0이 아닌 값의 위치 찾기\n",
    "- array.flatten(): 2차원의 배열을 1차원으로 한줄서기 시키는 함수\n",
    "    - 'c'는 순서대로 나열\n",
    "    - 'f'는 순서 섞기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# 사용자가 평점을 부여한 맥주에 대해서만 예측 성능 평가 RMSE 를 구함. \n",
    "def get_rmse(pred, actual): ##pred와 actual 모두 array 형태. matrix의 value만 가져오면 됨\n",
    "    # Ignore nonzero terms.\n",
    "    pred = pred[actual.nonzero()].flatten()\n",
    "    actual = actual[actual.nonzero()].flatten()\n",
    "    mse = mean_squared_error(pred, actual)\n",
    "    return mse**.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "아이템 기반 모든 인접 이웃 RMSE:  1.4378451782862234\n"
     ]
    }
   ],
   "source": [
    "# 결과보기\n",
    "print('아이템 기반 모든 인접 이웃 RMSE: ', get_rmse(ratings_pred, ratings_matrix.values ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> RMSE = 1.4378"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예측 평점(선호도)이 높은 top n개 추천하기 ###\n",
    "##### predict_rating_topsim(ratings_arr, item_sim_arr, n)\n",
    "> 특정 user가 가지는 특정 item(beer)에 대한 예측 평점 구하기\n",
    "- ratings_arr: 실제 ratings정보를 담은 matrix를 array형식으로 입력\n",
    "- item_sim_df: 아이템 유사도를 담은 matrix를 array형식으로 입력\n",
    "- n: 몇 개를 뽑을 건지\n",
    "\n",
    "<참고>\n",
    "\n",
    "- np.zeros(shape): 지정한 shape에 맞게 모두 0으로 채워진 array를 생성\n",
    "- argsort(): array의 전체 elements를 오름차순으로 정렬하는 함수. 단, 배열 안의 값은 values대신 index로 채워져있다.\n",
    "    - 정렬된 값들로 반환하기 위해서는 array[array.argsort()]로 인덱스 전환을 해야한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_rating_topsim(ratings_arr, item_sim_arr, n):\n",
    "    # 사용자-아이템 평점 행렬 크기만큼 0으로 채운 예측 행렬 초기화\n",
    "    pred = np.zeros(ratings_arr.shape) # (333, 481)\n",
    "\n",
    "    # 사용자-아이템 평점 행렬의 열 크기만큼 Loop 수행. \n",
    "    for col in range(ratings_arr.shape[1]): ## 481 - beer 개수\n",
    "        top_n_items = [np.argsort(item_sim_arr[:, col])[:-n-1:-1]]  ## 유사도 행렬(item_sim_arr)을 오름차순 정렬한 후, 뒤에서 유사도가 큰 순으로 n개 데이터 행렬의 index 반환\n",
    "        # 개인화된 예측 평점을 계산\n",
    "        for row in range(ratings_arr.shape[0]): ## 333 - user 개수\n",
    "            pred[row, col] = item_sim_arr[col, :][top_n_items].dot(ratings_arr[row, :][top_n_items].T) ## row번째 user가 가지는 col번째 beer에 대한 예측 평점 = pred[row, col]\n",
    "            pred[row, col] /= np.sum(np.abs(item_sim_arr[col, :][top_n_items])) ## 0~5 사이로 범위 맞추기\n",
    "            \n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\genie\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\genie\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "아이템 기반 인접 TOP-20 이웃 RMSE:  0.6286827438667582\n"
     ]
    }
   ],
   "source": [
    "# 결과 확인하기\n",
    "ratings_pred = predict_rating_topsim(ratings_matrix.values , item_sim_df.values, 20)\n",
    "print('아이템 기반 인접 TOP-20 이웃 RMSE: ', get_rmse(ratings_pred, ratings_matrix.values )) # top 20개 예측한 matrix와 실제값을 비교하여 성능 확인하기\n",
    "\n",
    "\n",
    "# 계산된 예측 평점 데이터는 DataFrame으로 재생성\n",
    "ratings_pred_matrix = pd.DataFrame(data=ratings_pred, index= ratings_matrix.index,columns = ratings_matrix.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 아이템 기반 인접 top 20 RMSE: 0.6286"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 특정 user의 예측 평점 상위 10개 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer\n",
       "129      4.55\n",
       "19216    4.54\n",
       "2512     4.53\n",
       "646      4.53\n",
       "224      4.53\n",
       "673      4.52\n",
       "34       4.52\n",
       "1696     4.51\n",
       "3916     4.51\n",
       "131      4.51\n",
       "Name: BEERMILER12, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_rating_id = ratings_pred_matrix.iloc[9, :] #9번째 user의 rating정보\n",
    "user_rating_id[ user_rating_id > 0].sort_values(ascending=False)[:10].round(2) #평점을 준 item만 추려서 평점 순으로 나열 - top 10개 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 평점을 매기지 않은 Item(beerID) 추려내기\n",
    "##### get_unlist_beer(ratings_matrix, userId)\n",
    "> 특정 유저에 대하여 평가 정보가 없는 beer의 리스트를 출력하는 함수\n",
    "- ratings_matrix: 평점 정보가 있는 matrix (DataFrame형태)\n",
    "- userId: 특정 유저의 ID\n",
    "\n",
    "<참고>\n",
    "- tolist(): list로 변환하는 함수\n",
    "- iloc(): index기반 DataFrame에 대한 subset 함수\n",
    "    - 원래 loc기반으로 작성되었으나 userId, beerId 모두 numeric 타입이므로 인덱스 기반의 iloc로 수정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_unlist_beer(ratings_matrix, userId):\n",
    "    # userId로 입력받은 사용자의 모든 beer정보 추출하여 Series로 반환함. \n",
    "    # 반환된 user_rating 은 beerID를 index로 가지는 Series 객체임. \n",
    "    user_rating = ratings_matrix.iloc[userId,:] ##shape (481,) - 모든 beer에 대한 Series(평점을 매긴 것+매기지 않은 것 포함)\n",
    "    \n",
    "    # user_rating이 0보다 크면 기존에 평점을 매긴 beer임. 대상 index를 추출하여 list 객체로 만듬\n",
    "    already_beer = user_rating[ user_rating > 0].index.tolist()\n",
    "    \n",
    "    # 모든 beerID를 list 객체로 만듬. \n",
    "    all_beer_list = ratings_matrix.columns.tolist()\n",
    "    \n",
    "    # list comprehension으로 already_beer 해당하는 beer는 all_beer_list 제외함. \n",
    "    unlist_beer = [beer for beer in all_beer_list if beer not in already_beer]\n",
    "    \n",
    "    return unlist_beer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결과 확인하기\n",
    "unlist_beer = get_unlist_beer(ratings_matrix, 9) # 사용자가 평점을 매기지 않은 beer list 추출 ## type int\n",
    "unlist_str = list(map(str,unlist_beer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 특정 user에게 평점을 매기지 않은 맥주 중 선호도가 높을 것으로 예상되는 제품 n개 추천하기\n",
    "##### recom_beer_by_userid(pred_df, userId, n)\n",
    "> 예측 평점 데이터를 기반으로 특정 user가 평점을 매기지 않은 beer 중에서 높은 평점으로 예상되는 제품을 n개 추천하기\n",
    "- pred_df: 모든 user와 모든 beer에 대한 예측 평점 데이터\n",
    "- userId: target user\n",
    "- n: 추천할 개수\n",
    "\n",
    "<참고>\n",
    "- unlist_beer: target이 평점을 매기지 않은 beer 리스트\n",
    "\n",
    "<**오류 수정**>\n",
    "pd.DataFrame.iloc[]인덱스 문제\n",
    "- user, unlist_beer 모두 int형식이지만 index기반 slicing에 맞지 않은 ID형태\n",
    "    - (481,333)형태의 인덱스는 0~480, 0~333의 범위 안에 있어야함\n",
    "- 특정 user의 (원하는)인덱스를 입력하면 userId를 담은 list에서 해당 위치의 userId를 반환->loc의 입력으로\n",
    "- pred_df의 columns은 각각 beerId를 숫자형식(이름이 아닌)으로 담은 int에 대한 리스트 -> str으로 반환해야함\n",
    "\n",
    "=> 인덱스 기반의 iloc는 적합하지 않고, userId와 beerId를 str형식으로 변환한 후 loc를 통해 원하는 userId와 unlist_beer를 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recomm_beer_by_userid(pred_df, userId, n):\n",
    "    # 예측 평점 DataFrame에서 사용자id index와 unlist_beer로 들어온 맥주명 컬럼을 추출하여 가장 예측 평점이 높은 순으로 정렬함\n",
    "    pred_df.columns = pred_df.columns.astype(str)\n",
    "    unlist_beer = get_unlist_beer(ratings_matrix, userId) ##입력받은 특정 user가 평점을 매기지 않은 beer list 출력\n",
    "    \n",
    "    # loc를 위해 str변환\n",
    "    user = pred_df.index.tolist()[userId]\n",
    "    unlist_beer = list(map(str,unlist_beer))\n",
    "    \n",
    "    # 최종 \n",
    "    recomm_beer = pred_df.loc[userId,unlist_beer].sort_values(ascending=False)[:n].round(2) ## 예상 평점 내림차순으로 top n개를 추출. 평점은 소수점 둘째까지\n",
    "    \n",
    "    return recomm_beer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>pred_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7971</th>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57908</th>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>4.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27804</th>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73764</th>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21950</th>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24905</th>\n",
       "      <td>3.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35738</th>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16909</th>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pred_score\n",
       "beer             \n",
       "7971         4.20\n",
       "57908        4.11\n",
       "656          4.11\n",
       "808          4.09\n",
       "27804        4.08\n",
       "73764        4.00\n",
       "21950        3.97\n",
       "24905        3.96\n",
       "35738        3.95\n",
       "16909        3.95"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과 확인하기\n",
    "recomm_beer = recomm_beer_by_userid(ratings_pred_matrix, 190, n=10)\n",
    "\n",
    "# 평점 데이타를 DataFrame으로 생성. \n",
    "recomm_beer = pd.DataFrame(data=recomm_beer.values,index=recomm_beer.index,columns=['pred_score'])\n",
    "recomm_beer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 190번 user의 예측평점이 가장 높을 것으로 예상되는 상위 10개 beerID와 예상 평점"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surprise 패키지를 이용한 추천 시스템\n",
    "> surprise.KNNWithMean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import surprise\n",
    "from surprise import KNNBasic\n",
    "from surprise import Dataset                                                     \n",
    "from surprise import Reader                                                      \n",
    "from surprise import dump\n",
    "from surprise.accuracy import rmse\n",
    "from surprise.model_selection import cross_validate\n",
    "from surprise.model_selection import KFold\n",
    "\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### surprise 패키지 형식에 맞춰 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = df.reindex(columns=sorted(df.columns))\n",
    "data_s = data[['user','beer','rating']]\n",
    "reader = surprise.reader.Reader()\n",
    "data_sur = surprise.Dataset.load_from_df(data_s,reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> reader를 먼저 설정하고 데이터프레임 형식으로 지정했던 데이터를 패키지 형식으로 불러온다(csv파일을 load하는 방식 아님)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 코사인 유사도로 KNN사용하여 예측 모델 구축하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) 패키지, 모듈 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import accuracy \n",
    "from surprise.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) trainset과 testset을 3:1비율로 나눈다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset, testset = train_test_split(data_sur, test_size=.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) 코사인 유사도 기반으로 KNN 모델 학습하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNWithMeans at 0x2bd8510a470>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_options = {'name': 'cosine'}\n",
    "algo = surprise.KNNWithMeans(sim_options=sim_options) #KNN 사용하기\n",
    "algo.fit(trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4) 예측값 반환하기\n",
    "- predictions: 모든 user의 모든 beer에 대한 예측값을 담은 DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.3390\n"
     ]
    }
   ],
   "source": [
    "predictions = algo.test( testset )\n",
    "accuracy.rmse(predictions) \n",
    "\n",
    "pred_df = pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- RMSE = 0.3390\n",
    "    - 패키지 없이 학습했을 때 RMSE가 1.6이 나온 걸 감안하면 성능이 아주 좋게 나온 편\n",
    ">rmse 측정시 DataFrame형식을 사용하면 안된다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction type : <class 'list'>  size: 25473\n",
      "prediction 결과의 최초 10개 추출\n"
     ]
    },
    {
     "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>uid</th>\n",
       "      <th>iid</th>\n",
       "      <th>r_ui</th>\n",
       "      <th>est</th>\n",
       "      <th>details</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mig100</td>\n",
       "      <td>80197</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.381592</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BEERMILER12</td>\n",
       "      <td>6518</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.019257</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beginner2</td>\n",
       "      <td>152542</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.812198</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IckyT2012</td>\n",
       "      <td>580</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.654541</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>smcolw</td>\n",
       "      <td>3457</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.987483</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>flagmantho</td>\n",
       "      <td>622</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.107149</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kojevergas</td>\n",
       "      <td>248</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.116729</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>drtth</td>\n",
       "      <td>752</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.018209</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad007</td>\n",
       "      <td>4083</td>\n",
       "      <td>4.97</td>\n",
       "      <td>4.321027</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Stinkypuss</td>\n",
       "      <td>412</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.224146</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           uid     iid  r_ui       est  \\\n",
       "0       mig100   80197  4.40  4.381592   \n",
       "1  BEERMILER12    6518  3.88  4.019257   \n",
       "2    Beginner2  152542  3.85  3.812198   \n",
       "3    IckyT2012     580  2.80  2.654541   \n",
       "4       smcolw    3457  4.20  3.987483   \n",
       "5   flagmantho     622  4.13  4.107149   \n",
       "6   kojevergas     248  3.15  3.116729   \n",
       "7        drtth     752  4.36  4.018209   \n",
       "8      Brad007    4083  4.97  4.321027   \n",
       "9   Stinkypuss     412  4.20  4.224146   \n",
       "\n",
       "                                     details  \n",
       "0  {'actual_k': 40, 'was_impossible': False}  \n",
       "1  {'actual_k': 40, 'was_impossible': False}  \n",
       "2  {'actual_k': 40, 'was_impossible': False}  \n",
       "3  {'actual_k': 40, 'was_impossible': False}  \n",
       "4  {'actual_k': 40, 'was_impossible': False}  \n",
       "5  {'actual_k': 40, 'was_impossible': False}  \n",
       "6  {'actual_k': 40, 'was_impossible': False}  \n",
       "7  {'actual_k': 40, 'was_impossible': False}  \n",
       "8  {'actual_k': 40, 'was_impossible': False}  \n",
       "9  {'actual_k': 40, 'was_impossible': False}  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('prediction type :',type(predictions), ' size:',len(predictions)) # 예측값은 list형식의 instance로 반환되며 총 25000개의 length를 가짐\n",
    "print('prediction 결과의 최초 10개 추출')\n",
    "pred_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 특정 user의 예측결과 상위 n개 맥주 추천하기\n",
    "\n",
    "#### pred_user_item(userid,n)\n",
    ">- userid: 특정 user에 대한 ID(object)\n",
    "- n: 상위 몇개까지 뽑을 것인지(numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_user_item(userid,n):\n",
    "    target_u = pred_df[pred_df['uid']==userid].sort_values('est',ascending = False).reset_index(drop=True).drop('details',axis = 1)\n",
    "    \n",
    "    return target_u.head(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>uid</th>\n",
       "      <th>iid</th>\n",
       "      <th>r_ui</th>\n",
       "      <th>est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mig100</td>\n",
       "      <td>16814</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.646248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mig100</td>\n",
       "      <td>47658</td>\n",
       "      <td>4.72</td>\n",
       "      <td>4.639134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mig100</td>\n",
       "      <td>1545</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.603621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mig100</td>\n",
       "      <td>100421</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.514016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mig100</td>\n",
       "      <td>123062</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.498029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mig100</td>\n",
       "      <td>116067</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.488185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mig100</td>\n",
       "      <td>125646</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4.474309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mig100</td>\n",
       "      <td>46849</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.472899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mig100</td>\n",
       "      <td>107406</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.456421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mig100</td>\n",
       "      <td>62761</td>\n",
       "      <td>4.54</td>\n",
       "      <td>4.451872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      uid     iid  r_ui       est\n",
       "0  mig100   16814  4.58  4.646248\n",
       "1  mig100   47658  4.72  4.639134\n",
       "2  mig100    1545  5.00  4.603621\n",
       "3  mig100  100421  4.75  4.514016\n",
       "4  mig100  123062  4.62  4.498029\n",
       "5  mig100  116067  4.58  4.488185\n",
       "6  mig100  125646  4.56  4.474309\n",
       "7  mig100   46849  4.21  4.472899\n",
       "8  mig100  107406  4.63  4.456421\n",
       "9  mig100   62761  4.54  4.451872"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_user_item('mig100',10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> userID = '80197'의 예측 평점이 높을 것으로 예상되는 상위 10개 항목 추천하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 총정리 - 새로운 데이터를 받아서 추천해보는 모듈 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "RMSE: 0.3390\n"
     ]
    }
   ],
   "source": [
    "from surprise import accuracy \n",
    "from surprise.model_selection import train_test_split\n",
    "\n",
    "trainset, testset = train_test_split(data_sur, test_size=.25, random_state=0)\n",
    "\n",
    "sim_options = {'name': 'cosine'}\n",
    "algo = surprise.KNNWithMeans(sim_options=sim_options) #KNN 사용하기\n",
    "algo.fit(trainset)\n",
    "\n",
    "predictions = algo.test( testset )\n",
    "accuracy.rmse(predictions) \n",
    "\n",
    "pred_df = pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction type : <class 'list'>  size: 25473\n",
      "prediction 결과의 최초 10개 추출\n"
     ]
    },
    {
     "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>uid</th>\n",
       "      <th>iid</th>\n",
       "      <th>r_ui</th>\n",
       "      <th>est</th>\n",
       "      <th>details</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mig100</td>\n",
       "      <td>80197</td>\n",
       "      <td>4.40</td>\n",
       "      <td>4.381592</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BEERMILER12</td>\n",
       "      <td>6518</td>\n",
       "      <td>3.88</td>\n",
       "      <td>4.019257</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Beginner2</td>\n",
       "      <td>152542</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.812198</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IckyT2012</td>\n",
       "      <td>580</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.654541</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>smcolw</td>\n",
       "      <td>3457</td>\n",
       "      <td>4.20</td>\n",
       "      <td>3.987483</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>flagmantho</td>\n",
       "      <td>622</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.107149</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kojevergas</td>\n",
       "      <td>248</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.116729</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>drtth</td>\n",
       "      <td>752</td>\n",
       "      <td>4.36</td>\n",
       "      <td>4.018209</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad007</td>\n",
       "      <td>4083</td>\n",
       "      <td>4.97</td>\n",
       "      <td>4.321027</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Stinkypuss</td>\n",
       "      <td>412</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.224146</td>\n",
       "      <td>{'actual_k': 40, 'was_impossible': False}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           uid     iid  r_ui       est  \\\n",
       "0       mig100   80197  4.40  4.381592   \n",
       "1  BEERMILER12    6518  3.88  4.019257   \n",
       "2    Beginner2  152542  3.85  3.812198   \n",
       "3    IckyT2012     580  2.80  2.654541   \n",
       "4       smcolw    3457  4.20  3.987483   \n",
       "5   flagmantho     622  4.13  4.107149   \n",
       "6   kojevergas     248  3.15  3.116729   \n",
       "7        drtth     752  4.36  4.018209   \n",
       "8      Brad007    4083  4.97  4.321027   \n",
       "9   Stinkypuss     412  4.20  4.224146   \n",
       "\n",
       "                                     details  \n",
       "0  {'actual_k': 40, 'was_impossible': False}  \n",
       "1  {'actual_k': 40, 'was_impossible': False}  \n",
       "2  {'actual_k': 40, 'was_impossible': False}  \n",
       "3  {'actual_k': 40, 'was_impossible': False}  \n",
       "4  {'actual_k': 40, 'was_impossible': False}  \n",
       "5  {'actual_k': 40, 'was_impossible': False}  \n",
       "6  {'actual_k': 40, 'was_impossible': False}  \n",
       "7  {'actual_k': 40, 'was_impossible': False}  \n",
       "8  {'actual_k': 40, 'was_impossible': False}  \n",
       "9  {'actual_k': 40, 'was_impossible': False}  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('prediction type :',type(predictions), ' size:',len(predictions)) # 예측값은 list형식의 instance로 반환되며 총 25000개의 length를 가짐\n",
    "print('prediction 결과의 최초 10개 추출')\n",
    "pred_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>uid</th>\n",
       "      <th>iid</th>\n",
       "      <th>r_ui</th>\n",
       "      <th>est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [uid, iid, r_ui, est]\n",
       "Index: []"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_user_item(80197,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> beerID로 입력하면 결과가 나타나지 않음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>uid</th>\n",
       "      <th>iid</th>\n",
       "      <th>r_ui</th>\n",
       "      <th>est</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mig100</td>\n",
       "      <td>16814</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.646248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mig100</td>\n",
       "      <td>47658</td>\n",
       "      <td>4.72</td>\n",
       "      <td>4.639134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mig100</td>\n",
       "      <td>1545</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.603621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mig100</td>\n",
       "      <td>100421</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.514016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mig100</td>\n",
       "      <td>123062</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.498029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mig100</td>\n",
       "      <td>116067</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.488185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mig100</td>\n",
       "      <td>125646</td>\n",
       "      <td>4.56</td>\n",
       "      <td>4.474309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mig100</td>\n",
       "      <td>46849</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.472899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mig100</td>\n",
       "      <td>107406</td>\n",
       "      <td>4.63</td>\n",
       "      <td>4.456421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mig100</td>\n",
       "      <td>62761</td>\n",
       "      <td>4.54</td>\n",
       "      <td>4.451872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      uid     iid  r_ui       est\n",
       "0  mig100   16814  4.58  4.646248\n",
       "1  mig100   47658  4.72  4.639134\n",
       "2  mig100    1545  5.00  4.603621\n",
       "3  mig100  100421  4.75  4.514016\n",
       "4  mig100  123062  4.62  4.498029\n",
       "5  mig100  116067  4.58  4.488185\n",
       "6  mig100  125646  4.56  4.474309\n",
       "7  mig100   46849  4.21  4.472899\n",
       "8  mig100  107406  4.63  4.456421\n",
       "9  mig100   62761  4.54  4.451872"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_user_item('mig100',10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> userID로 입력하면 예측값이 높은 순서대로 10개를 정상 출력한다"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}