6_1_대규모_추천시스템_KNN.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "id": "FovW5-yTyQn2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import random\n",
        "from sklearn.neighbors import NearestNeighbors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVpJK8CPyQn4"
      },
      "source": [
        "### 데이터 분석 및 전처리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 260,
      "metadata": {
        "id": "X6G35frFyQn5",
        "outputId": "1ed62648-5f1a-41d3-95ee-7014c18fc923"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>196</td>\n",
              "      <td>242</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>186</td>\n",
              "      <td>302</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22</td>\n",
              "      <td>377</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>244</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>166</td>\n",
              "      <td>346</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99995</th>\n",
              "      <td>880</td>\n",
              "      <td>476</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>716</td>\n",
              "      <td>204</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>276</td>\n",
              "      <td>1090</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>13</td>\n",
              "      <td>225</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>12</td>\n",
              "      <td>203</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       userID  movieID  rating\n",
              "0         196      242       3\n",
              "1         186      302       3\n",
              "2          22      377       1\n",
              "3         244       51       2\n",
              "4         166      346       1\n",
              "...       ...      ...     ...\n",
              "99995     880      476       3\n",
              "99996     716      204       5\n",
              "99997     276     1090       1\n",
              "99998      13      225       2\n",
              "99999      12      203       3\n",
              "\n",
              "[100000 rows x 3 columns]"
            ]
          },
          "execution_count": 260,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#\n",
        "rating_df = pd.read_csv(r'C:\\Users\\User\\Desktop\\새 폴더\\u.data', sep='\\t', header=None, names = ['userID', 'movieID', 'rating', 'timestemp'])[['userID', 'movieID', 'rating']]\n",
        "rating_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "metadata": {
        "id": "lkvzobzSyQn6"
      },
      "outputs": [],
      "source": [
        "movie_info_df = pd.read_csv(r'C:\\Users\\User\\Desktop\\새 폴더\\u.item', sep='|', encoding='latin')\n",
        "movie_info_df.columns = ['movieID' , 'movie_title' , 'release_date' , 'video_release_date' ,\n",
        "              'IMDb_URL' , 'unknown' , 'Action' , 'Adventure' , 'Animation' ,\n",
        "              'Children' , 'Comedy' , 'Crime' , 'Documentary' , 'Drama' , 'Fantasy' ,\n",
        "              'Film-Noir' , 'Horror' , 'Musical' , 'Mystery' , 'Romance' , 'Sci-Fi' ,\n",
        "              'Thriller' , 'War' , 'Western']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "데이터 준비\n",
        "- sparse matrix (데이터가 너무 크기 때문에)\n",
        "- 일반적으로 유저 아이디와 무비 아이디가 매끄럽게 연속적으로 나열되어 있지 않음(탈퇴, 순서 뒤죽박죽, 아이디에 기호 포함)\n",
        "- id -> index 로 변경 필요 \n",
        "    - 0번부터 시작\n",
        "    - 중간에 비어 있는 게 없어야 함."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 263,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>u_idx</th>\n",
              "      <th>i_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>196</td>\n",
              "      <td>242</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22773</th>\n",
              "      <td>196</td>\n",
              "      <td>257</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10017</th>\n",
              "      <td>196</td>\n",
              "      <td>111</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10981</th>\n",
              "      <td>196</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35197</th>\n",
              "      <td>196</td>\n",
              "      <td>382</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>92090</th>\n",
              "      <td>941</td>\n",
              "      <td>300</td>\n",
              "      <td>4</td>\n",
              "      <td>942</td>\n",
              "      <td>652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99006</th>\n",
              "      <td>941</td>\n",
              "      <td>1007</td>\n",
              "      <td>4</td>\n",
              "      <td>942</td>\n",
              "      <td>672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69939</th>\n",
              "      <td>941</td>\n",
              "      <td>124</td>\n",
              "      <td>5</td>\n",
              "      <td>942</td>\n",
              "      <td>873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74458</th>\n",
              "      <td>941</td>\n",
              "      <td>993</td>\n",
              "      <td>4</td>\n",
              "      <td>942</td>\n",
              "      <td>935</td>\n",
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              "    <tr>\n",
              "      <th>88541</th>\n",
              "      <td>941</td>\n",
              "      <td>358</td>\n",
              "      <td>2</td>\n",
              "      <td>942</td>\n",
              "      <td>1023</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       userID  movieID  rating  u_idx  i_idx\n",
              "0         196      242       3      0      0\n",
              "22773     196      257       2      0     10\n",
              "10017     196      111       4      0     83\n",
              "10981     196       25       4      0     86\n",
              "35197     196      382       4      0     92\n",
              "...       ...      ...     ...    ...    ...\n",
              "92090     941      300       4    942    652\n",
              "99006     941     1007       4    942    672\n",
              "69939     941      124       5    942    873\n",
              "74458     941      993       4    942    935\n",
              "88541     941      358       2    942   1023\n",
              "\n",
              "[100000 rows x 5 columns]"
            ]
          },
          "execution_count": 263,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#apply(복잡한 처리, 룰이 있는 것) map(카테고리 데이터를 치환)\n",
        "user_ids = rating_df['userID'].unique()\n",
        "#mapping table = {id : index, ....}\n",
        "user2idx_dict = {x : i for i, x in enumerate(user_ids)} #enumerate 사용해서 뒤엔 유저id 앞엔 인덱스\n",
        "\n",
        "movie_ids = rating_df['movieID'].unique()\n",
        "#mapping table = {id : index, ....}\n",
        "movie2idx_dict = {x : i for i, x in enumerate(movie_ids)}\n",
        "\n",
        "rating_df['u_idx'] = rating_df['userID'].map(user2idx_dict)\n",
        "rating_df['i_idx'] = rating_df['movieID'].map(movie2idx_dict)\n",
        "\n",
        "rating_df = rating_df.sort_values(['u_idx', 'i_idx']) #정렬\n",
        "rating_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "추천시스템을 위한 데이터의 분포 조사(편향)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 264,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "movieID\n",
              "50      583\n",
              "258     509\n",
              "100     508\n",
              "181     507\n",
              "294     485\n",
              "       ... \n",
              "1576      1\n",
              "1577      1\n",
              "1348      1\n",
              "1579      1\n",
              "1682      1\n",
              "Name: userID, Length: 1682, dtype: int64"
            ]
          },
          "execution_count": 264,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#영화별로 사람들이 얼마나 평가했는지\n",
        "rating_count = rating_df.groupby('movieID').count()['userID'].sort_values(ascending = False)\n",
        "rating_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(rating_count.to_list())\n",
        "plt.xlabel('movie')\n",
        "plt.ylabel('rating counts')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "sparse matrix 만들기\n",
        "- data, row_index, col_index, shape\n",
        "- (row, col)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 265,
      "metadata": {},
      "outputs": [],
      "source": [
        "num_user = rating_df['u_idx'].nunique()\n",
        "num_movie = rating_df['i_idx'].nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "train, val , test data split 해야 함"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 266,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "#어떤 비율로 나눌 것인가 고민 필요(6:2:2, 6:3:1, 7:2:1, 7:1.5:1.5)\n",
        "#CF_기반 추천시스템 9:0.5:0.5\n",
        "train_df, test_df = train_test_split(rating_df, test_size = 0.1, random_state = 1004)\n",
        "val_df, test_df = train_test_split(test_df, test_size = 0.5, random_state = 1004)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 259,
      "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>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>u_idx</th>\n",
              "      <th>i_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48242</th>\n",
              "      <td>186</td>\n",
              "      <td>38</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97859</th>\n",
              "      <td>938</td>\n",
              "      <td>273</td>\n",
              "      <td>5</td>\n",
              "      <td>930</td>\n",
              "      <td>141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48799</th>\n",
              "      <td>567</td>\n",
              "      <td>124</td>\n",
              "      <td>4</td>\n",
              "      <td>565</td>\n",
              "      <td>873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1132</th>\n",
              "      <td>7</td>\n",
              "      <td>662</td>\n",
              "      <td>3</td>\n",
              "      <td>38</td>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66528</th>\n",
              "      <td>554</td>\n",
              "      <td>531</td>\n",
              "      <td>4</td>\n",
              "      <td>551</td>\n",
              "      <td>333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63556</th>\n",
              "      <td>548</td>\n",
              "      <td>79</td>\n",
              "      <td>5</td>\n",
              "      <td>543</td>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60783</th>\n",
              "      <td>836</td>\n",
              "      <td>875</td>\n",
              "      <td>1</td>\n",
              "      <td>827</td>\n",
              "      <td>570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72226</th>\n",
              "      <td>889</td>\n",
              "      <td>29</td>\n",
              "      <td>3</td>\n",
              "      <td>885</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11822</th>\n",
              "      <td>37</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>237</td>\n",
              "      <td>408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421</th>\n",
              "      <td>43</td>\n",
              "      <td>815</td>\n",
              "      <td>4</td>\n",
              "      <td>98</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       userID  movieID  rating  u_idx  i_idx\n",
              "48242     186       38       5      1    543\n",
              "97859     938      273       5    930    141\n",
              "48799     567      124       4    565    873\n",
              "1132        7      662       3     38    264\n",
              "66528     554      531       4    551    333\n",
              "...       ...      ...     ...    ...    ...\n",
              "63556     548       79       5    543    156\n",
              "60783     836      875       1    827    570\n",
              "72226     889       29       3    885     14\n",
              "11822      37       22       5    237    408\n",
              "421        43      815       4     98    124\n",
              "\n",
              "[5000 rows x 5 columns]"
            ]
          },
          "execution_count": 259,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 267,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<943x1682 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 90000 stored elements in COOrdinate format>"
            ]
          },
          "execution_count": 267,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from scipy.sparse import coo_matrix \n",
        "train_sp = coo_matrix((train_df['rating'], \n",
        "                (train_df['u_idx'], train_df['i_idx'])),\n",
        "                shape = (num_user, num_movie))\n",
        "\n",
        "train_sp\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "모델 빌드 및 학습"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 268,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(metric=&#x27;cosine&#x27;, n_neighbors=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(metric=&#x27;cosine&#x27;, n_neighbors=50)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "NearestNeighbors(metric='cosine', n_neighbors=50)"
            ]
          },
          "execution_count": 268,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn = NearestNeighbors(n_neighbors= 50, metric = 'cosine')\n",
        "knn.fit(train_sp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "결과 추론 및 평가"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 269,
      "metadata": {},
      "outputs": [],
      "source": [
        "# target_user를 sp matrux   형태로 선언\n",
        "target_user_idx = 8\n",
        "target_tr_df = train_df[train_df['u_idx'] == target_user_idx]\n",
        "target_sp = coo_matrix((target_tr_df['rating'],\n",
        "                        ([0] * len(target_tr_df), target_tr_df['i_idx'])),\n",
        "                        shape = (1, num_movie))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 270,
      "metadata": {},
      "outputs": [],
      "source": [
        "dist, idx = knn.kneighbors(target_sp, n_neighbors= 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.        , 0.50633107, 0.51369616, 0.51953965, 0.51971038,\n",
              "        0.52378343, 0.52452881, 0.53467548, 0.53579367, 0.53714239,\n",
              "        0.54532484, 0.54685245, 0.54687104, 0.54782692, 0.54790311,\n",
              "        0.55287978, 0.56093259, 0.56241147, 0.56278344, 0.56443476,\n",
              "        0.56463025, 0.56465575, 0.56687907, 0.56700303, 0.56970768,\n",
              "        0.57010652, 0.57040344, 0.57233254, 0.57235286, 0.57259434,\n",
              "        0.57828037, 0.57845564, 0.57847139, 0.57938688, 0.57969024,\n",
              "        0.58044763, 0.58095662, 0.58160852, 0.58166129, 0.58283363,\n",
              "        0.58334616, 0.58402177, 0.58407507, 0.58472429, 0.58506896,\n",
              "        0.58664096, 0.58675889, 0.58743511, 0.58828246, 0.58934206]])"
            ]
          },
          "execution_count": 239,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  8, 114, 534, 907, 375, 129, 311, 556, 504, 658, 333, 406, 421,\n",
              "        885, 467, 453,   9, 325,  68,  52, 118, 339, 743,  88, 531, 739,\n",
              "        102,  10,  23,  83, 915, 814,  22,  58,  80, 340, 662, 842, 442,\n",
              "        348, 867,  85, 193, 542,  65, 584, 446,  45, 831, 110]],\n",
              "      dtype=int64)"
            ]
          },
          "execution_count": 240,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 271,
      "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>movieID</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>113</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1643</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1466</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1512</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>814</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1344</th>\n",
              "      <td>1165</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1345</th>\n",
              "      <td>352</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1346</th>\n",
              "      <td>907</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1347</th>\n",
              "      <td>35</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1348</th>\n",
              "      <td>351</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1349 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieID  pred\n",
              "0         113   5.0\n",
              "1        1643   5.0\n",
              "2        1466   5.0\n",
              "3        1512   5.0\n",
              "4         814   5.0\n",
              "...       ...   ...\n",
              "1344     1165   1.0\n",
              "1345      352   1.0\n",
              "1346      907   1.0\n",
              "1347       35   1.0\n",
              "1348      351   1.0\n",
              "\n",
              "[1349 rows x 2 columns]"
            ]
          },
          "execution_count": 271,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 8번 유저에 대한 이웃들의 영화 평점 예측\n",
        "n_df = train_df[train_df['u_idx'].isin(idx[0][1:])]\n",
        "pred_df  = n_df.groupby('movieID').mean()['rating'].sort_values(ascending =False)\n",
        "pred_df = pred_df.reset_index()\n",
        "pred_df.columns = ['movieID', 'pred']\n",
        "pred_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "모델 평가"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 272,
      "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>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>u_idx</th>\n",
              "      <th>i_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>305</td>\n",
              "      <td>214</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51934</th>\n",
              "      <td>305</td>\n",
              "      <td>89</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97694</th>\n",
              "      <td>305</td>\n",
              "      <td>484</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36389</th>\n",
              "      <td>305</td>\n",
              "      <td>184</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36315</th>\n",
              "      <td>305</td>\n",
              "      <td>686</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79892</th>\n",
              "      <td>305</td>\n",
              "      <td>59</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5341</th>\n",
              "      <td>305</td>\n",
              "      <td>1101</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8092</th>\n",
              "      <td>305</td>\n",
              "      <td>156</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>351</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       userID  movieID  rating  u_idx  i_idx\n",
              "237       305      214       2      8    195\n",
              "51934     305       89       3      8    217\n",
              "97694     305      484       3      8    241\n",
              "36389     305      184       3      8    373\n",
              "36315     305      686       3      8    470\n",
              "79892     305       59       3      8    574\n",
              "5341      305     1101       4      8    677\n",
              "8092      305      156       4      8    351"
            ]
          },
          "execution_count": 272,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "target_te_df = test_df[test_df['u_idx'] == target_user_idx]\n",
        "# target_te_df.sort_values('movieID')\n",
        "target_te_df.sort_values('rating')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "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>movieID</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>184</td>\n",
              "      <td>3.312500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>531</th>\n",
              "      <td>214</td>\n",
              "      <td>3.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>1101</td>\n",
              "      <td>3.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>686</td>\n",
              "      <td>3.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>156</td>\n",
              "      <td>3.925926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>59</td>\n",
              "      <td>4.125000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>484</td>\n",
              "      <td>4.291667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>89</td>\n",
              "      <td>4.372093</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     movieID      pred\n",
              "594      184  3.312500\n",
              "531      214  3.466667\n",
              "456     1101  3.550000\n",
              "415      686  3.642857\n",
              "256      156  3.925926\n",
              "118       59  4.125000\n",
              "60       484  4.291667\n",
              "40        89  4.372093"
            ]
          },
          "execution_count": 273,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# pred_df[pred_df['movieID'].isin(target_te_df['movieID'])].sort_values('movieID')\n",
        "pred_te_df = pred_df[pred_df['movieID'].isin(target_te_df['movieID'])].sort_values('pred')\n",
        "pred_te_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .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>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>89</td>\n",
              "      <td>3</td>\n",
              "      <td>4.372093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>484</td>\n",
              "      <td>3</td>\n",
              "      <td>4.291667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1101</td>\n",
              "      <td>4</td>\n",
              "      <td>3.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>184</td>\n",
              "      <td>3</td>\n",
              "      <td>3.312500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>156</td>\n",
              "      <td>4</td>\n",
              "      <td>3.925926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>686</td>\n",
              "      <td>3</td>\n",
              "      <td>3.642857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>214</td>\n",
              "      <td>2</td>\n",
              "      <td>3.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>59</td>\n",
              "      <td>3</td>\n",
              "      <td>4.125000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieID  rating      pred\n",
              "0       89       3  4.372093\n",
              "1      484       3  4.291667\n",
              "2     1101       4  3.550000\n",
              "3      184       3  3.312500\n",
              "4      156       4  3.925926\n",
              "5      686       3  3.642857\n",
              "6      214       2  3.466667\n",
              "7       59       3  4.125000"
            ]
          },
          "execution_count": 274,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#유저 한 명에 대해서 테스트\n",
        "result_df = pd.merge(target_te_df, pred_te_df, on = 'movieID', how = 'inner')[['movieID', 'rating', 'pred']]\n",
        "result_df "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- 평점은 조금 틀려보임(RMSE)\n",
        "- 추천시스템의 평가는 top N개를 추천할 때 그게 맞느냐? Top1(0/1), Top2(2/2), Top3(2/3), Top4(3/4) 탑n개에 대한 정확한예측 Top@10 \n",
        "- 순서를 잘 맞추냐 nDCG"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "전체 test 데이터에 있는 모든 데이터를 바탕으로 일반화 시켜보기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "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>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [movieID, rating, pred]\n",
              "Index: []"
            ]
          },
          "execution_count": 246,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# total_pred_df = pd.DataFrame(data = None, columns = ['movieID', 'rating', 'pred'])\n",
        "# total_pred_df "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {},
      "outputs": [],
      "source": [
        "# target_user를 sp matrux   형태로 선언\n",
        "\n",
        "total_pred_df = pd.DataFrame(data = None, columns = ['movieID', 'userID', 'rating', 'pred'])\n",
        "for target_user_idx in range(num_user):\n",
        "    target_tr_df = train_df[train_df['u_idx'] == target_user_idx]\n",
        "    target_sp = coo_matrix((target_df['rating'],\n",
        "                            ([0] * len(target_df), target_df['i_idx'])),\n",
        "                            shape = (1, num_movie))\n",
        "\n",
        "    dist, idx = knn.kneighbors(target_sp, n_neighbors= 50)\n",
        "\n",
        "    n_df = rating_df[rating_df['u_idx'].isin(idx[0][1:])]\n",
        "    pred_df  = n_df.groupby('movieID').mean()['rating'].sort_values(ascending =False)\n",
        "    pred_df = pred_df.reset_index()\n",
        "    pred_df.columns = ['movieID', 'pred']\n",
        "    \n",
        "    target_te_df = test_df[test_df['u_idx'] == target_user_idx]\n",
        "    target_te_df.sort_values('rating')\n",
        "    pred_te_df = pred_df[pred_df['movieID'].isin(target_te_df['movieID'])].sort_values('pred')\n",
        "    \n",
        "    result_df = pd.merge(target_te_df, pred_te_df, on = 'movieID', how = 'inner')[['movieID', 'userID', 'rating', 'pred']]\n",
        "    \n",
        "    total_pred_df = pd.concat([total_pred_df, result_df])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "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>movieID</th>\n",
              "      <th>userID</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>173</td>\n",
              "      <td>196</td>\n",
              "      <td>2</td>\n",
              "      <td>4.255814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>251</td>\n",
              "      <td>196</td>\n",
              "      <td>3</td>\n",
              "      <td>4.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>38</td>\n",
              "      <td>186</td>\n",
              "      <td>5</td>\n",
              "      <td>2.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>306</td>\n",
              "      <td>186</td>\n",
              "      <td>4</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>291</td>\n",
              "      <td>186</td>\n",
              "      <td>4</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>98</td>\n",
              "      <td>186</td>\n",
              "      <td>5</td>\n",
              "      <td>4.297872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>880</td>\n",
              "      <td>186</td>\n",
              "      <td>3</td>\n",
              "      <td>3.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1213</td>\n",
              "      <td>186</td>\n",
              "      <td>3</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>829</td>\n",
              "      <td>186</td>\n",
              "      <td>4</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>356</td>\n",
              "      <td>186</td>\n",
              "      <td>5</td>\n",
              "      <td>3.062500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  movieID userID rating      pred\n",
              "0     173    196      2  4.255814\n",
              "1     251    196      3  4.666667\n",
              "0      38    186      5  2.333333\n",
              "1     306    186      4  4.000000\n",
              "2     291    186      4  3.000000\n",
              "3      98    186      5  4.297872\n",
              "4     880    186      3  3.500000\n",
              "5    1213    186      3  2.000000\n",
              "6     829    186      4  2.500000\n",
              "7     356    186      5  3.062500"
            ]
          },
          "execution_count": 277,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "total_pred_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
              },
              "data": [
                {
                  "alignmentgroup": "True",
                  "box": {
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                  "hovertemplate": "rating=%{x}<br>pred=%{y}<extra></extra>",
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                    "color": "#636efa"
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                      "ticks": "",
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                      },
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                      "zerolinewidth": 2
                    },
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                      "automargin": true,
                      "gridcolor": "white",
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                      "ticks": "",
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                "violinmode": "group",
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                    0,
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                  }
                },
                "yaxis": {
                  "anchor": "x",
                  "domain": [
                    0,
                    1
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                  "title": {
                    "text": "pred"
                  }
                }
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "\n",
        "px.violin(total_pred_df, x = 'rating', y = 'pred', box =True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.1065275515939181"
            ]
          },
          "execution_count": 279,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#전체 RMSE 값 \n",
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(total_pred_df['rating'], total_pred_df['pred'], squared = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 280,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 1.9897098473285506\n",
            "2 1.2608806772011012\n",
            "3 0.7772836478282501\n",
            "4 0.8203680679688852\n",
            "5 1.3780686406266023\n"
          ]
        }
      ],
      "source": [
        "for i in range(1, 6):\n",
        "    rmse = mean_squared_error(total_pred_df[total_pred_df['rating'] == i]['rating'],\n",
        "                               total_pred_df[total_pred_df['rating'] == i]['pred'],\n",
        "                                squared = False)\n",
        "    print(i, rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 285,
      "metadata": {},
      "outputs": [],
      "source": [
        "good_df = total_pred_df[total_pred_df['rating'] > 3]\n",
        "bad_df = total_pred_df[total_pred_df['rating'] < 3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 287,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_4872\\2430273948.py:1: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_4872\\2430273948.py:2: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .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>movieID</th>\n",
              "      <th>userID</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
              "      <th>target_bool</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>38</td>\n",
              "      <td>186</td>\n",
              "      <td>5</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>306</td>\n",
              "      <td>186</td>\n",
              "      <td>4</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>291</td>\n",
              "      <td>186</td>\n",
              "      <td>4</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>98</td>\n",
              "      <td>186</td>\n",
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              "      <td>4.297872</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>829</td>\n",
              "      <td>186</td>\n",
              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>926</td>\n",
              "      <td>1</td>\n",
              "      <td>2.142857</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1067</td>\n",
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              "      <td>2</td>\n",
              "      <td>3.500000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>2.375000</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3618 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieID userID rating      pred  target_bool\n",
              "0       38    186      5  2.333333            1\n",
              "1      306    186      4  4.000000            1\n",
              "2      291    186      4  3.000000            1\n",
              "3       98    186      5  4.297872            1\n",
              "6      829    186      4  2.500000            1\n",
              "..     ...    ...    ...       ...          ...\n",
              "1      325    926      1  2.142857            0\n",
              "2     1067    943      2  3.500000            0\n",
              "3      720    943      1  2.375000            0\n",
              "2      535    936      2  2.666667            0\n",
              "4      148    930      1  2.375000            0\n",
              "\n",
              "[3618 rows x 5 columns]"
            ]
          },
          "execution_count": 287,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "good_df['target_bool'] = 1\n",
        "bad_df['target_bool'] = 0\n",
        "cls_df = pd.concat([good_df, bad_df])\n",
        "cls_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import RocCurveDisplay\n",
        "\n",
        "RocCurveDisplay.from_predictions(\n",
        "    cls_df['target_bool'], #real\n",
        "    (cls_df['pred'] - 1)/4, #pred prob\n",
        "    name=f\"knn \",\n",
        "    color=\"darkorange\",\n",
        ")\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='k', label='Random Model', linestyle = '--', alpha = 0.6)\n",
        "\n",
        "plt.axis(\"square\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC AUC\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 296,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "========================================\n",
            "thd = 0.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00       821\n",
            "           1       0.77      1.00      0.87      2797\n",
            "\n",
            "    accuracy                           0.77      3618\n",
            "   macro avg       0.39      0.50      0.44      3618\n",
            "weighted avg       0.60      0.77      0.67      3618\n",
            "\n",
            "========================================\n",
            "thd = 1.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.04      0.07       821\n",
            "           1       0.78      1.00      0.87      2797\n",
            "\n",
            "    accuracy                           0.78      3618\n",
            "   macro avg       0.76      0.52      0.47      3618\n",
            "weighted avg       0.77      0.78      0.69      3618\n",
            "\n",
            "========================================\n",
            "thd = 1.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.08      0.14       821\n",
            "           1       0.79      0.99      0.88      2797\n",
            "\n",
            "    accuracy                           0.78      3618\n",
            "   macro avg       0.76      0.53      0.51      3618\n",
            "weighted avg       0.77      0.78      0.71      3618\n",
            "\n",
            "========================================\n",
            "thd = 2.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.18      0.28       821\n",
            "           1       0.80      0.97      0.88      2797\n",
            "\n",
            "    accuracy                           0.79      3618\n",
            "   macro avg       0.72      0.58      0.58      3618\n",
            "weighted avg       0.77      0.79      0.74      3618\n",
            "\n",
            "========================================\n",
            "thd = 2.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.33      0.42       821\n",
            "           1       0.82      0.93      0.87      2797\n",
            "\n",
            "    accuracy                           0.79      3618\n",
            "   macro avg       0.70      0.63      0.65      3618\n",
            "weighted avg       0.77      0.79      0.77      3618\n",
            "\n",
            "========================================\n",
            "thd = 3.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.47      0.56      0.51       821\n",
            "           1       0.86      0.82      0.84      2797\n",
            "\n",
            "    accuracy                           0.76      3618\n",
            "   macro avg       0.67      0.69      0.67      3618\n",
            "weighted avg       0.77      0.76      0.76      3618\n",
            "\n",
            "========================================\n",
            "thd = 3.5\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.37      0.73      0.49       821\n",
            "           1       0.89      0.63      0.74      2797\n",
            "\n",
            "    accuracy                           0.66      3618\n",
            "   macro avg       0.63      0.68      0.61      3618\n",
            "weighted avg       0.77      0.66      0.68      3618\n",
            "\n",
            "========================================\n",
            "thd = 4.0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.28      0.92      0.43       821\n",
            "           1       0.93      0.31      0.46      2797\n",
            "\n",
            "    accuracy                           0.44      3618\n",
            "   macro avg       0.60      0.61      0.44      3618\n",
            "weighted avg       0.78      0.44      0.45      3618\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\User\\anaconda3\\envs\\py39_test\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "c:\\Users\\User\\anaconda3\\envs\\py39_test\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "c:\\Users\\User\\anaconda3\\envs\\py39_test\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "for thd in np.linspace(0.5, 4, 8):\n",
        "    cls_df['pred_bool'] = cls_df['pred'].apply(lambda x : 1 if x > thd else 0)\n",
        "    print('=' * 40)\n",
        "    print(f'thd = {thd}')\n",
        "    print(classification_report(cls_df['target_bool'],cls_df['pred_bool']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "-------------------------------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fI8ILO-DyQn7"
      },
      "source": [
        "matrix로 만들기 위해 빈공간을 제거하고 index를 새로 부여"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "3yttGqodyQn7",
        "outputId": "cb54fd2c-a04f-492b-ff39-0af7e76c980e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "943 1682\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>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>u_idx</th>\n",
              "      <th>i_idx</th>\n",
              "      <th>new_u_idx</th>\n",
              "      <th>new_i_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>204</td>\n",
              "      <td>302</td>\n",
              "      <td>5</td>\n",
              "      <td>280</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>204</td>\n",
              "      <td>242</td>\n",
              "      <td>5</td>\n",
              "      <td>280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>204</td>\n",
              "      <td>874</td>\n",
              "      <td>3</td>\n",
              "      <td>280</td>\n",
              "      <td>736</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>204</td>\n",
              "      <td>318</td>\n",
              "      <td>5</td>\n",
              "      <td>280</td>\n",
              "      <td>200</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>204</td>\n",
              "      <td>288</td>\n",
              "      <td>3</td>\n",
              "      <td>280</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>73</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99995</th>\n",
              "      <td>934</td>\n",
              "      <td>157</td>\n",
              "      <td>2</td>\n",
              "      <td>927</td>\n",
              "      <td>740</td>\n",
              "      <td>942</td>\n",
              "      <td>1624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>934</td>\n",
              "      <td>96</td>\n",
              "      <td>4</td>\n",
              "      <td>927</td>\n",
              "      <td>102</td>\n",
              "      <td>942</td>\n",
              "      <td>1626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>934</td>\n",
              "      <td>65</td>\n",
              "      <td>4</td>\n",
              "      <td>927</td>\n",
              "      <td>203</td>\n",
              "      <td>942</td>\n",
              "      <td>1646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>934</td>\n",
              "      <td>286</td>\n",
              "      <td>4</td>\n",
              "      <td>927</td>\n",
              "      <td>289</td>\n",
              "      <td>942</td>\n",
              "      <td>1661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>934</td>\n",
              "      <td>605</td>\n",
              "      <td>4</td>\n",
              "      <td>927</td>\n",
              "      <td>763</td>\n",
              "      <td>942</td>\n",
              "      <td>1678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       userID  movieID  rating  u_idx  i_idx  new_u_idx  new_i_idx\n",
              "0         204      302       5    280      1          0         18\n",
              "1         204      242       5    280      0          0         30\n",
              "2         204      874       3    280    736          0         36\n",
              "3         204      318       5    280    200          0         70\n",
              "4         204      288       3    280     60          0         73\n",
              "...       ...      ...     ...    ...    ...        ...        ...\n",
              "99995     934      157       2    927    740        942       1624\n",
              "99996     934       96       4    927    102        942       1626\n",
              "99997     934       65       4    927    203        942       1646\n",
              "99998     934      286       4    927    289        942       1661\n",
              "99999     934      605       4    927    763        942       1678\n",
              "\n",
              "[100000 rows x 7 columns]"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "user_ids = rating_df[\"userID\"].unique().tolist()\n",
        "random.shuffle(user_ids)\n",
        "user2idx_dict = {x: i for i, x in enumerate(user_ids)}\n",
        "idx2user_dict = {i: x for i, x in enumerate(user_ids)}\n",
        "\n",
        "movie_ids = rating_df[\"movieID\"].unique().tolist()\n",
        "random.shuffle(movie_ids)\n",
        "movie2idx_dict = {x: i for i, x in enumerate(movie_ids)}\n",
        "idx2movie_dict = {i: x for i, x in enumerate(movie_ids)}\n",
        "\n",
        "rating_df[\"new_u_idx\"] = rating_df[\"userID\"].map(user2idx_dict)\n",
        "rating_df[\"new_i_idx\"] = rating_df[\"movieID\"].map(movie2idx_dict)\n",
        "\n",
        "num_users = rating_df[\"userID\"].nunique()\n",
        "num_movies = rating_df[\"movieID\"].nunique()\n",
        "print(num_users, num_movies)\n",
        "\n",
        "rating_df = rating_df.sort_values(['new_u_idx', 'new_i_idx'])\n",
        "rating_df = rating_df.reset_index(drop=True)\n",
        "rating_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyHkvCGJyQn7"
      },
      "source": [
        "데이터 분포 조사"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bq4lX5W0yQn8",
        "outputId": "e0f0ab18-bf31-4b67-b0ea-a50c29582f9a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'number of ratings')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "movies_count_df = pd.DataFrame(rating_df.groupby('movieID').size(), columns=['count'])\n",
        "movies_count_df.head()\n",
        "ax = movies_count_df.sort_values('count', ascending=False) \\\n",
        "                    .reset_index(drop=True) \\\n",
        "                    .plot(\n",
        "                        figsize=(8, 6),\n",
        "                        title='Rating Frequency of All Movies',\n",
        "                        fontsize=12\n",
        "                    )\n",
        "ax.set_xlabel(\"movie Id\")\n",
        "ax.set_ylabel(\"number of ratings\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IVyToIEzyQn8",
        "outputId": "14663b35-dec9-49f9-a053-9159d3aa226e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "943 1682\n"
          ]
        }
      ],
      "source": [
        "num_users = rating_df['new_u_idx'].nunique()\n",
        "num_movies = rating_df['new_i_idx'].nunique()\n",
        "print(num_users, num_movies)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o1k4P_P0yQn9"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_df, test_df = train_test_split(rating_df, test_size=0.1, random_state=1004)\n",
        "val_df, test_df = train_test_split(test_df, test_size=0.5, random_state=1004)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jyR4oiwRyQn9",
        "outputId": "63957ec6-5bf5-41b7-850e-0cea0d18b2ce"
      },
      "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>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>new_u_idx</th>\n",
              "      <th>new_i_idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>96120</th>\n",
              "      <td>907</td>\n",
              "      <td>628</td>\n",
              "      <td>5</td>\n",
              "      <td>880158986</td>\n",
              "      <td>915</td>\n",
              "      <td>614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91921</th>\n",
              "      <td>640</td>\n",
              "      <td>689</td>\n",
              "      <td>4</td>\n",
              "      <td>886353852</td>\n",
              "      <td>873</td>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42605</th>\n",
              "      <td>488</td>\n",
              "      <td>207</td>\n",
              "      <td>3</td>\n",
              "      <td>891294942</td>\n",
              "      <td>417</td>\n",
              "      <td>1448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38525</th>\n",
              "      <td>887</td>\n",
              "      <td>143</td>\n",
              "      <td>5</td>\n",
              "      <td>881379781</td>\n",
              "      <td>377</td>\n",
              "      <td>772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88615</th>\n",
              "      <td>110</td>\n",
              "      <td>684</td>\n",
              "      <td>4</td>\n",
              "      <td>886988480</td>\n",
              "      <td>847</td>\n",
              "      <td>1186</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79795</th>\n",
              "      <td>495</td>\n",
              "      <td>179</td>\n",
              "      <td>5</td>\n",
              "      <td>888632470</td>\n",
              "      <td>758</td>\n",
              "      <td>1379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42223</th>\n",
              "      <td>472</td>\n",
              "      <td>218</td>\n",
              "      <td>4</td>\n",
              "      <td>875980120</td>\n",
              "      <td>414</td>\n",
              "      <td>807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13637</th>\n",
              "      <td>263</td>\n",
              "      <td>197</td>\n",
              "      <td>4</td>\n",
              "      <td>891299752</td>\n",
              "      <td>133</td>\n",
              "      <td>511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86047</th>\n",
              "      <td>334</td>\n",
              "      <td>488</td>\n",
              "      <td>5</td>\n",
              "      <td>891546231</td>\n",
              "      <td>816</td>\n",
              "      <td>571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48130</th>\n",
              "      <td>788</td>\n",
              "      <td>159</td>\n",
              "      <td>3</td>\n",
              "      <td>880869135</td>\n",
              "      <td>471</td>\n",
              "      <td>613</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90000 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       userID  movieID  rating  timestamp  new_u_idx  new_i_idx\n",
              "96120     907      628       5  880158986        915        614\n",
              "91921     640      689       4  886353852        873        418\n",
              "42605     488      207       3  891294942        417       1448\n",
              "38525     887      143       5  881379781        377        772\n",
              "88615     110      684       4  886988480        847       1186\n",
              "...       ...      ...     ...        ...        ...        ...\n",
              "79795     495      179       5  888632470        758       1379\n",
              "42223     472      218       4  875980120        414        807\n",
              "13637     263      197       4  891299752        133        511\n",
              "86047     334      488       5  891546231        816        571\n",
              "48130     788      159       3  880869135        471        613\n",
              "\n",
              "[90000 rows x 6 columns]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dofxIhxzyQn-"
      },
      "source": [
        "sparse data matrix 생성"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X-DBYtBtyQn-"
      },
      "outputs": [],
      "source": [
        "from scipy.sparse import coo_matrix\n",
        "rating_sparse_arr = coo_matrix((train_df['rating'].to_list(),\n",
        "                              (train_df['new_u_idx'].to_list(),\n",
        "                              train_df['new_i_idx'].to_list())),\n",
        "                             shape=(num_users, num_movies))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXkxwH3SyQn-"
      },
      "source": [
        "### model 빌드 및 학습"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xr8hSQk7yQn-",
        "outputId": "24d7a3a9-e21a-4c19-f973-e7eaa48f05c8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;, n_neighbors=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;, n_neighbors=50)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "NearestNeighbors(algorithm='brute', metric='cosine', n_neighbors=50)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_model = NearestNeighbors(n_neighbors = 50, metric='cosine', algorithm='brute')\n",
        "knn_model.fit(rating_sparse_arr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HM9Cz8doyQn-"
      },
      "source": [
        "최근접 이웃 추론"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oktI_dhQyQn-",
        "outputId": "db152432-1f6a-4131-f325-7da258daff1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "500 -  0.027\n",
            "374 -  0.414\n",
            "46 -  0.444\n",
            "544 -  0.491\n",
            "623 -  0.504\n",
            "78 -  0.521\n",
            "10 -  0.522\n",
            "794 -  0.524\n",
            "283 -  0.530\n",
            "659 -  0.537\n",
            "427 -  0.541\n",
            "227 -  0.558\n"
          ]
        }
      ],
      "source": [
        "selected_usr_id = 500\n",
        "get_single_user = rating_df[rating_df['new_u_idx'] == selected_usr_id]\n",
        "get_single_user = coo_matrix((get_single_user['rating'].to_list(),\n",
        "                              ([0]*len(get_single_user),\n",
        "                              get_single_user['new_i_idx'].to_list())),\n",
        "                             shape=(1, num_movies))\n",
        "\n",
        "distances, indices = knn_model.kneighbors(get_single_user, n_neighbors=40)\n",
        "cnt = 0\n",
        "for d, i in zip(distances[0], indices[0]):\n",
        "    print(f'{i} -  {d:.3f}')\n",
        "\n",
        "    if cnt > 10:\n",
        "        break\n",
        "    cnt += 1\n",
        "\n",
        "# 그래프 방식의 모델\n",
        "# arr = knn_model.kneighbors_graph(get_single_user)\n",
        "# for a in arr:\n",
        "#     print(a)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOALotDQyQn-"
      },
      "source": [
        "평점 예측"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j2Ci6oOyyQn_",
        "outputId": "afe55692-d2c0-4c78-b8b0-2ac456293dd9"
      },
      "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>movieID</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>607</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>511</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>285</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>275</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1293</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>908</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>341</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>1025</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>1671</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>1678</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>233 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     movieID  pred\n",
              "0        607   5.0\n",
              "1        511   5.0\n",
              "2        285   5.0\n",
              "3        275   5.0\n",
              "4       1293   5.0\n",
              "..       ...   ...\n",
              "228      908   1.0\n",
              "229      341   1.0\n",
              "230     1025   1.0\n",
              "231     1671   1.0\n",
              "232     1678   1.0\n",
              "\n",
              "[233 rows x 2 columns]"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pred_df = rating_df[rating_df['new_u_idx'].isin(indices[0][1:])].groupby('movieID').agg('mean').sort_values('rating', ascending = False)['rating']\n",
        "pred_df = pred_df.rename('pred')\n",
        "pred_df = pred_df.reset_index()\n",
        "pred_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLDun_duyQn_"
      },
      "source": [
        "### 모델 평가하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hPW1k1DXyQn_",
        "outputId": "276f0011-664d-4de8-a3d0-47ff9575a1ab"
      },
      "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>userID</th>\n",
              "      <th>movieID</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>new_u_idx</th>\n",
              "      <th>new_i_idx</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>349</td>\n",
              "      <td>546</td>\n",
              "      <td>3</td>\n",
              "      <td>879466200</td>\n",
              "      <td>942</td>\n",
              "      <td>562</td>\n",
              "      <td>3.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userID  movieID  rating  timestamp  new_u_idx  new_i_idx  pred\n",
              "0     349      546       3  879466200        942        562  3.04"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rating_result_df = pd.merge(single_test_df, pred_df, on = 'movieID', how='inner')\n",
        "rating_result_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXPceszgyQn_",
        "outputId": "05f643f8-77df-4022-9e35-ab2a25d985da"
      },
      "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>new_u_idx</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [new_u_idx, rating, pred]\n",
              "Index: []"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "total_pred_df = pd.DataFrame(data=None, columns=['new_u_idx', 'rating', 'pred'])\n",
        "total_pred_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KpWzDKCayQn_",
        "outputId": "231c22a3-d7c1-485c-ab23-07abd21065c5"
      },
      "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>new_u_idx</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.230769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>940</td>\n",
              "      <td>1</td>\n",
              "      <td>3.736842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>940</td>\n",
              "      <td>2</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>941</td>\n",
              "      <td>2</td>\n",
              "      <td>3.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>941</td>\n",
              "      <td>4</td>\n",
              "      <td>3.655172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>942</td>\n",
              "      <td>3</td>\n",
              "      <td>3.040000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4923 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   new_u_idx rating      pred\n",
              "0          0      3  3.100000\n",
              "1          0      2  2.800000\n",
              "2          0      1  3.230769\n",
              "3          0      2  4.111111\n",
              "4          0      2  2.800000\n",
              "..       ...    ...       ...\n",
              "1        940      1  3.736842\n",
              "2        940      2  4.000000\n",
              "0        941      2  3.384615\n",
              "1        941      4  3.655172\n",
              "0        942      3  3.040000\n",
              "\n",
              "[4923 rows x 3 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for selected_usr_id in range(num_users):\n",
        "    get_single_user = rating_df[rating_df['new_u_idx'] == selected_usr_id]\n",
        "    get_single_user = coo_matrix((get_single_user['rating'].to_list(),\n",
        "                                ([0]*len(get_single_user),\n",
        "                                get_single_user['new_i_idx'].to_list())),\n",
        "                                shape=(1, num_movies))\n",
        "\n",
        "    distances, indices = knn_model.kneighbors(get_single_user, n_neighbors=40)\n",
        "    pred_df = rating_df[rating_df['new_u_idx'].isin(indices[0][1:])].groupby('movieID').agg('mean').sort_values('rating', ascending = False)['rating']\n",
        "    pred_df = pred_df.rename('pred')\n",
        "    pred_df = pred_df.reset_index()\n",
        "\n",
        "    single_test_df = test_df[test_df.new_u_idx == selected_usr_id]\n",
        "    rating_result_df = pd.merge(single_test_df, pred_df, on = 'movieID', how='inner')[['new_u_idx', 'rating', 'pred']]\n",
        "    total_pred_df = pd.concat([total_pred_df, rating_result_df])\n",
        "total_pred_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CpBwBQcGyQn_",
        "outputId": "1e86a443-e4c6-4ca2-b43a-982e72642709"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
              },
              "data": [
                {
                  "alignmentgroup": "True",
                  "box": {
                    "visible": true
                  },
                  "hovertemplate": "rating=%{x}<br>pred=%{y}<extra></extra>",
                  "legendgroup": "",
                  "marker": {
                    "color": "#636efa"
                  },
                  "name": "",
                  "offsetgroup": "",
                  "orientation": "v",
                  "scalegroup": "True",
                  "showlegend": false,
                  "type": "violin",
                  "x": [
                    3,
                    2,
                    1,
                    2,
                    2,
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                    4,
                    4,
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('65abaae2-ac00-452a-a5f6-03dfe712b753');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                });            </script>        </div>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "px.violin(total_pred_df, x='rating', y='pred', box=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PLxMzIalyQoA",
        "outputId": "e3aeb5f2-aea1-4c9d-bf36-f15511a0e08f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0202850684533553"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "total_rmse = mean_squared_error(total_pred_df.rating, total_pred_df.pred, squared=False)\n",
        "total_rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PONI9Hn6yQoA",
        "outputId": "7eb47703-1b53-4157-9eca-c77612e3f933"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8760981511793955"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "high_rmse = mean_squared_error(total_pred_df[total_pred_df.rating >= 4].rating, total_pred_df[total_pred_df.rating >= 4].pred, squared=False)\n",
        "high_rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OJ3r0Z4DyQoA",
        "outputId": "0c469da3-e8b7-4415-b418-be2bb2b831a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.667919175409322"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "low_rmse = mean_squared_error(total_pred_df[total_pred_df.rating <= 2].rating, total_pred_df[total_pred_df.rating <= 2].pred, squared=False)\n",
        "low_rmse"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIoHoKSCyQoA",
        "outputId": "f05a695c-967c-480e-ffb8-aff6c19bfdbf"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>new_u_idx</th>\n",
              "      <th>rating</th>\n",
              "      <th>pred</th>\n",
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              "<p>4923 rows × 5 columns</p>\n",
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            ],
            "text/plain": [
              "   new_u_idx rating      pred  rating_bool  pred_bool\n",
              "0          0      3  3.100000        False       True\n",
              "1          0      2  2.800000        False      False\n",
              "2          0      1  3.230769        False       True\n",
              "3          0      2  4.111111        False       True\n",
              "4          0      2  2.800000        False      False\n",
              "..       ...    ...       ...          ...        ...\n",
              "1        940      1  3.736842        False       True\n",
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      ],
      "source": [
        "total_pred_df['rating_bool'] = total_pred_df['rating'] > 3\n",
        "total_pred_df['pred_bool'] = total_pred_df['pred'] > 3\n",
        "total_pred_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gqwTUtXqyQoA",
        "outputId": "90a66d3e-7c6b-4e33-b209-373cf78c7e30"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import RocCurveDisplay\n",
        "\n",
        "RocCurveDisplay.from_predictions(\n",
        "    total_pred_df['rating_bool'],\n",
        "    (total_pred_df['pred'] - 1)/4,\n",
        "    name=f\"knn \",\n",
        "    color=\"darkorange\",\n",
        ")\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='k', label='Random Model', linestyle = '--', alpha = 0.6)\n",
        "\n",
        "plt.axis(\"square\")\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC AUC\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B2n0ubUKyQoA"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "recom",
      "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": "3.9.17"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "6670e2bb6e7a238574625451da9fb531705e8e370265a1c6b3f7fd97501e6ef3"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}