(KDT)_사용자_기반_협업_필터링_추천.ipynb

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  "metadata": {
    "colab": {
      "name": "(KDT) 사용자 기반 협업 필터링 추천",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/learn-programmers/programmers_kdt_II/blob/main/(KDT)_%EC%82%AC%EC%9A%A9%EC%9E%90_%EA%B8%B0%EB%B0%98_%ED%98%91%EC%97%85_%ED%95%84%ED%84%B0%EB%A7%81_%EC%B6%94%EC%B2%9C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqYkO_7p2CWm"
      },
      "source": [
        "# 무비렌즈 데이터를 가지고 사용자 협업 필터링을 구현해보자"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "roEuxynyeM0l"
      },
      "source": [
        "surprise 모듈을 설치"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ep7L0e2ldgY",
        "outputId": "b3192e77-6919-40ec-d1f4-e771748bd8a2"
      },
      "source": [
        "!pip install surprise"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: surprise in /usr/local/lib/python3.7/dist-packages (0.1)\n",
            "Requirement already satisfied: scikit-surprise in /usr/local/lib/python3.7/dist-packages (from surprise) (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.11.2 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.19.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.0.1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.4.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from scikit-surprise->surprise) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZQfxxifzG6k"
      },
      "source": [
        "# 데이터 로딩 및 분석"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4jdjZN254pO"
      },
      "source": [
        "from surprise import Dataset\n",
        "from surprise import Reader\n",
        "\n",
        "from collections import defaultdict\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9m_UFiBnx6m"
      },
      "source": [
        "movies = pd.read_csv(\"https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/movies.csv\")\n",
        "ratings = pd.read_csv(\"https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/ratings.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "kym07ydsoou8",
        "outputId": "ca722412-1612-4f4d-c9ca-6bb600f79984"
      },
      "source": [
        "movies.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...                                       genres\n",
              "0        1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1        2  ...                   Adventure|Children|Fantasy\n",
              "2        3  ...                               Comedy|Romance\n",
              "3        4  ...                         Comedy|Drama|Romance\n",
              "4        5  ...                                       Comedy\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 195
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        "id": "K82Avgs3pHAe",
        "outputId": "d7bffb58-6d9a-4083-9c8d-a2dfb4a7804a"
      },
      "source": [
        "ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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",
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              "      <td>31</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1260759144</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>3.0</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>2.0</td>\n",
              "      <td>1260759185</td>\n",
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              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1172</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759205</td>\n",
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              "   userId  movieId  rating   timestamp\n",
              "0       1       31     2.5  1260759144\n",
              "1       1     1029     3.0  1260759179\n",
              "2       1     1061     3.0  1260759182\n",
              "3       1     1129     2.0  1260759185\n",
              "4       1     1172     4.0  1260759205"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQqs1GQIpI2y"
      },
      "source": [
        "movie_ratings = pd.merge(movies, ratings, left_on='movieId', right_on='movieId')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
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        "id": "iXrDNF1UpWgF",
        "outputId": "9113a19b-fc63-450f-f06d-787e8f87a6a1"
      },
      "source": [
        "movie_ratings.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>userId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>851866703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>9</td>\n",
              "      <td>4.0</td>\n",
              "      <td>938629179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>13</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1331380058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>15</td>\n",
              "      <td>2.0</td>\n",
              "      <td>997938310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>19</td>\n",
              "      <td>3.0</td>\n",
              "      <td>855190091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId             title  ... rating   timestamp\n",
              "0        1  Toy Story (1995)  ...    3.0   851866703\n",
              "1        1  Toy Story (1995)  ...    4.0   938629179\n",
              "2        1  Toy Story (1995)  ...    5.0  1331380058\n",
              "3        1  Toy Story (1995)  ...    2.0   997938310\n",
              "4        1  Toy Story (1995)  ...    3.0   855190091\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udf_2XkOpZ2L",
        "outputId": "c8467f36-72cb-46f6-be41-5669f1acf3f2"
      },
      "source": [
        "movies.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9125, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90izVbp0pcO1",
        "outputId": "573cb08f-2020-4ed0-9bfc-b8c999e4b877"
      },
      "source": [
        "ratings.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100004, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8j8KQfwpeL4",
        "outputId": "a40a89ea-914d-43e2-9922-cdd66b27fa5f"
      },
      "source": [
        "movie_ratings.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100004, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvYbdqFuqC3s"
      },
      "source": [
        "데이터 분석\n",
        "\n",
        "*   영화별 평점수: min, max, median, mean\n",
        "*   가장 평점이 많은 영화 10개는?\n",
        "*   영화별 평점 평균: min, max, median, mean\n",
        "*   가장 평점이 높은 영화 10개는? (100개 이상 평점이 있는 영화중)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KvdXXgEpkYv",
        "outputId": "57fda143-3352-4fcc-cc69-ead032e49aa1"
      },
      "source": [
        "movie_ratings[\"movieId\"].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9066"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ogu009trrT5r"
      },
      "source": [
        "movie_rating_summary = movie_ratings[[\"movieId\", \"rating\"]].groupby([\"movieId\"]).agg([\"count\", \"median\", \"mean\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "6dHMObt1rg6-",
        "outputId": "6226b251-defa-4866-b420-5d9b8593f40d"
      },
      "source": [
        "movie_rating_summary.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>median</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>247</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.872470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>107</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.401869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>59</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.161017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>56</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.267857</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        rating                 \n",
              "         count median      mean\n",
              "movieId                        \n",
              "1          247    4.0  3.872470\n",
              "2          107    3.0  3.401869\n",
              "3           59    3.0  3.161017\n",
              "4           13    3.0  2.384615\n",
              "5           56    3.0  3.267857"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "kB7TU_der9in",
        "outputId": "dd0a48ad-04fc-4acb-8c25-a19ea1f1fe2b"
      },
      "source": [
        "movie_rating_summary.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['count', 'median', 'mean'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3jXMRhKtIn4"
      },
      "source": [
        "movie_rating_summary.columns = [\"count\", \"median\", \"mean\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "GC70Vr6osj-4",
        "outputId": "926ff2f8-d24a-4d15-d5c0-fff0735bb898"
      },
      "source": [
        "movie_rating_summary.sort_values(\"count\", ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>480</th>\n",
              "      <td>274</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.706204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2571</th>\n",
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              "      <td>4.5</td>\n",
              "      <td>4.183398</td>\n",
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              "      <th>1</th>\n",
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              "      <td>4.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>244</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.303279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>589</th>\n",
              "      <td>237</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.006329</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count  median      mean\n",
              "movieId                         \n",
              "356        341     4.0  4.054252\n",
              "296        324     4.5  4.256173\n",
              "318        311     5.0  4.487138\n",
              "593        304     4.0  4.138158\n",
              "260        291     4.5  4.221649\n",
              "480        274     4.0  3.706204\n",
              "2571       259     4.5  4.183398\n",
              "1          247     4.0  3.872470\n",
              "527        244     4.5  4.303279\n",
              "589        237     4.0  4.006329"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "N3Unz16vswiQ",
        "outputId": "2b8fdf6c-6665-4139-944a-97be72f1395c"
      },
      "source": [
        "movie_rating_summary.sort_values(\"mean\", ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>163949</th>\n",
              "      <td>1</td>\n",
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              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>52617</th>\n",
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              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>53887</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54251</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54328</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55555</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56869</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57038</th>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count  median  mean\n",
              "movieId                     \n",
              "163949       1     5.0   5.0\n",
              "71180        1     5.0   5.0\n",
              "51471        1     5.0   5.0\n",
              "52617        1     5.0   5.0\n",
              "53887        1     5.0   5.0\n",
              "54251        1     5.0   5.0\n",
              "54328        1     5.0   5.0\n",
              "55555        1     5.0   5.0\n",
              "56869        1     5.0   5.0\n",
              "57038        1     5.0   5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "hGtlvkOltTug",
        "outputId": "2cd58a68-af9c-45e8-e7cb-c08e20e238fa"
      },
      "source": [
        "movie_rating_summary[movie_rating_summary[\"count\"] > 100].sort_values(\"mean\", ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>200</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.487500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>311</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.487138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1221</th>\n",
              "      <td>135</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.385185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>201</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.370647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>244</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.303279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1193</th>\n",
              "      <td>144</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.256944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>608</th>\n",
              "      <td>224</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.256696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>324</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.256173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2858</th>\n",
              "      <td>220</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.236364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58559</th>\n",
              "      <td>121</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.235537</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count  median      mean\n",
              "movieId                         \n",
              "858        200    5.00  4.487500\n",
              "318        311    5.00  4.487138\n",
              "1221       135    5.00  4.385185\n",
              "50         201    4.50  4.370647\n",
              "527        244    4.50  4.303279\n",
              "1193       144    4.50  4.256944\n",
              "608        224    4.50  4.256696\n",
              "296        324    4.50  4.256173\n",
              "2858       220    4.25  4.236364\n",
              "58559      121    4.50  4.235537"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "n9jnr3U0ta9T",
        "outputId": "89e18bf2-a697-4bb5-9ad8-86503116e72f"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(16,8))\n",
        "sns.distplot(a=movie_rating_summary['mean'], kde=True, color='r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdfa0942050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "7KtT0h1ot-tY",
        "outputId": "0a34c489-498b-42a8-93be-170acf339779"
      },
      "source": [
        "sns.distplot(a=movie_rating_summary['count'], color='g')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdf9e7e3c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qgqvhe7ju28o",
        "outputId": "6198ec8b-53ac-44d7-999c-5effa7d47e6f"
      },
      "source": [
        "movie_ratings[\"userId\"].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "671"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThSh00GOu4LN"
      },
      "source": [
        "user_rating_summary = movie_ratings[[\"userId\", \"rating\"]].groupby([\"userId\"]).agg([\"count\", \"median\", \"mean\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "3cl7PSuwvCUh",
        "outputId": "0f6de17f-0e15-41ca-8bce-2d230911dafc"
      },
      "source": [
        "user_rating_summary.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>76</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.486842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.568627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>204</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.348039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.910000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       rating                 \n",
              "        count median      mean\n",
              "userId                        \n",
              "1          20    2.5  2.550000\n",
              "2          76    3.0  3.486842\n",
              "3          51    3.5  3.568627\n",
              "4         204    5.0  4.348039\n",
              "5         100    4.0  3.910000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0K7Ya-aYvH-q"
      },
      "source": [
        "user_rating_summary.columns = [\"count\", \"median\", \"mean\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "NT660eCjvLMU",
        "outputId": "9a956f1b-7f26-4135-e00b-abc93428047e"
      },
      "source": [
        "user_rating_summary.sort_values(\"count\", ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>2391</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.366792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>1868</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.552463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>1735</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.894236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1700</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.621765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>1610</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.374224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>1340</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.189179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>468</th>\n",
              "      <td>1291</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.965918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <td>1063</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.366416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>1019</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.006379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1011</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.765084</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count  median      mean\n",
              "userId                         \n",
              "547      2391     3.5  3.366792\n",
              "564      1868     4.0  3.552463\n",
              "624      1735     3.0  2.894236\n",
              "15       1700     3.0  2.621765\n",
              "73       1610     3.5  3.374224\n",
              "452      1340     3.0  3.189179\n",
              "468      1291     3.0  2.965918\n",
              "380      1063     3.5  3.366416\n",
              "311      1019     3.0  3.006379\n",
              "30       1011     4.0  3.765084"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "fHL_mm8yvQQT",
        "outputId": "599c6cbc-b242-4aba-8fe7-22a49c1aa382"
      },
      "source": [
        "user_rating_summary.sort_values(\"median\", ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>94</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.473404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>62</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.411290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>622</th>\n",
              "      <td>31</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.725806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>33</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.272727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>38</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>559</th>\n",
              "      <td>129</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.418605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>76</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.315789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>25</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>399</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.466165</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count  median      mean\n",
              "userId                         \n",
              "230        94     5.0  4.473404\n",
              "432        62     5.0  4.411290\n",
              "622        31     5.0  4.725806\n",
              "499        33     5.0  4.272727\n",
              "145        38     5.0  4.500000\n",
              "559       129     5.0  4.418605\n",
              "473        76     5.0  4.315789\n",
              "28         50     5.0  4.280000\n",
              "556        25     5.0  4.400000\n",
              "242       399     5.0  4.466165"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "GBvnP12BvTnP",
        "outputId": "3399cc38-ced7-4ea4-8767-ee0e82a11dd9"
      },
      "source": [
        "user_rating_summary.sort_values(\"median\", ascending=True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>median</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>46</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.804348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>49</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.459184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>21</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>140</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.985714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429</th>\n",
              "      <td>27</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.240741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315</th>\n",
              "      <td>55</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>20</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.525000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>20</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>55</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.518182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>32</td>\n",
              "      <td>2.50</td>\n",
              "      <td>2.953125</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count  median      mean\n",
              "userId                         \n",
              "207        46    0.50  1.804348\n",
              "581        49    0.50  1.459184\n",
              "579        21    0.50  1.333333\n",
              "609       140    1.00  1.985714\n",
              "429        27    2.00  2.240741\n",
              "315        55    2.00  2.454545\n",
              "325        20    2.00  2.525000\n",
              "35         20    2.25  2.400000\n",
              "79         55    2.50  2.518182\n",
              "210        32    2.50  2.953125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "mmEgkNDUvdHk",
        "outputId": "c200e72b-dd8d-48ec-e2e8-18f17e073229"
      },
      "source": [
        "sns.distplot(a=user_rating_summary['mean'], kde=True, color='r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdf9e360110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "S-6SA_rxvjJn",
        "outputId": "98151a80-4ee8-415a-97ec-682081c02d77"
      },
      "source": [
        "sns.distplot(a=user_rating_summary['count'], kde=True, color='r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdf9e16bf90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLs-VTevvlrO"
      },
      "source": [
        "def getMovieName(movie_ratings, movieID):\n",
        "    return movie_ratings[movie_ratings[\"movieId\"] == movieID][[\"title\", \"genres\"]].values[0]\n",
        "\n",
        "def getMovieID(movie_ratings, movieName):\n",
        "    return movie_ratings[movie_ratings[\"title\"] == movieName][[\"movieId\", \"genres\"]].values[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHeH7DsyyxCa"
      },
      "source": [
        "## 영화 데이터를 surprise 모듈을 통해 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vARUVa6rzmiG",
        "outputId": "0b4fe267-1509-4cf3-f909-f57b3322f5ab"
      },
      "source": [
        "!wget \"https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/ratings.csv\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-26 06:56:27--  https://grepp-reco-test.s3.ap-northeast-2.amazonaws.com/movielens/ratings.csv\n",
            "Resolving grepp-reco-test.s3.ap-northeast-2.amazonaws.com (grepp-reco-test.s3.ap-northeast-2.amazonaws.com)... 52.219.60.123\n",
            "Connecting to grepp-reco-test.s3.ap-northeast-2.amazonaws.com (grepp-reco-test.s3.ap-northeast-2.amazonaws.com)|52.219.60.123|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2438266 (2.3M) [text/csv]\n",
            "Saving to: ‘ratings.csv’\n",
            "\n",
            "ratings.csv         100%[===================>]   2.33M  1.78MB/s    in 1.3s    \n",
            "\n",
            "2021-02-26 06:56:29 (1.78 MB/s) - ‘ratings.csv’ saved [2438266/2438266]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgtyCtMaw8Eq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "outputId": "feec97fc-4da2-4633-dbcf-c31a5a89d28e"
      },
      "source": [
        "reader = Reader(line_format='user item rating timestamp', sep=',', skip_lines=1)\n",
        "data = Dataset.load_from_file(\"ratings.csv\", reader=reader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-fd14d86a31bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'user item rating timestamp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_lines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_from_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ratings.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'Reader' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xn5gBOTgzWAR"
      },
      "source": [
        "from surprise import KNNBasic\n",
        "import heapq\n",
        "from collections import defaultdict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpkLzRulzza3"
      },
      "source": [
        "trainSet = data.build_full_trainset()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SulaH4Qz42-"
      },
      "source": [
        "sim_options = {\n",
        "    'name': 'cosine',\n",
        "    'user_based': True\n",
        "}\n",
        "\n",
        "model = KNNBasic(sim_options=sim_options)\n",
        "model.fit(trainSet)\n",
        "simsMatrix = model.compute_similarities()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYnKIcfx0BNh"
      },
      "source": [
        "testUser = '85'\n",
        "k = 10\n",
        "\n",
        "# 주어진 사용자와 가장 흡사한 사용자 N을 찾는다\n",
        "# 먼저 이를 Surprise 내부 ID로 변환\n",
        "testUserInnerID = trainSet.to_inner_uid(testUser)\n",
        "print(testUserInnerID)\n",
        "# 이 사용자에 해당하는 레코드를 읽어온다\n",
        "similarityRow = simsMatrix[testUserInnerID]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXT62k1C0MXA"
      },
      "source": [
        "# users에 모든 사용자들을 일련번호와 유사도를 갖는 튜플의 형태로 저장\n",
        "# 이 때 본인은 제외\n",
        "users = []\n",
        "for innerID, score in enumerate(similarityRow):\n",
        "    if (innerID != testUserInnerID):\n",
        "        users.append( (innerID, score) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfKtrD4_0NVI"
      },
      "source": [
        "# 이제 users 리트스에서 유사도 값을 기준으로 가장 큰 k개를 찾는다\n",
        "kNeighbors = heapq.nlargest(k, users, key=lambda t: t[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZ1PfUxeI7zk"
      },
      "source": [
        "kNeighbors"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFfjdVUM0T0p"
      },
      "source": [
        "# 이제 유사 사용자들을 하나씩 보면서 그들이 평가한 아이템들별로 원 사용자와 유사 사용자간의 유사도를 가중치로 준 평점을 누적한다\n",
        "\n",
        "# candidates에는 아이템별로 점수를 누적한다. 유사사용자(u')의 평점 * 사용자(u)와 유사 사용자(u')의 유사도\n",
        "candidates = defaultdict(float)\n",
        "\n",
        "# 이 K명의 최고 유사 사용자를 한명씩 루프를 돌면서 살펴본다\n",
        "for similarUser in kNeighbors:\n",
        "    # similarUser는 앞서 enumerate로 만든 그 포맷임 - (내부ID, 유사도값)\n",
        "    innerID = similarUser[0]\n",
        "    userSimilarityScore = similarUser[1]\n",
        "\n",
        "    # innerID에 해당하는 사용자의 아이템과 평점 정보를 읽어온다.\n",
        "    # theirRatings는 (아이템ID, 평점)의 리스트임\n",
        "    theirRatings = trainSet.ur[innerID]\n",
        "    # innerID가 평가한 모든 아이템 리스트를 하나씩 보면서 \n",
        "    # 아이템ID별로 평점 정보를 합산하되 사용자와의 유사도값을 가중치로 준다\n",
        "    for rating in theirRatings:\n",
        "        candidates[rating[0]] += (rating[1]) * userSimilarityScore"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LB0Z8koI0WWR"
      },
      "source": [
        "# 사용자가 이미 평가한 아이템들을 제거할 사전을 만든다\n",
        "watched = {}\n",
        "for itemID, rating in trainSet.ur[testUserInnerID]:\n",
        "    watched[itemID] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lj-Tmcat0YlM"
      },
      "source": [
        "# 앞서 candidates에서 합산된 스코어를 기준으로 내림차순으로 소팅한 후\n",
        "# 사용자(u)가 아직 못본 아이템인 경우 추천한다\n",
        "pos = 0\n",
        "for itemID, ratingSum in sorted(candidates.items(), key=lambda k: k[1], reverse=True):\n",
        "    if not itemID in watched:\n",
        "        movieID = trainSet.to_raw_iid(itemID)\n",
        "        print(movieID, getMovieName(movie_ratings, int(movieID)), ratingSum)\n",
        "        pos += 1\n",
        "        if (pos > 10):\n",
        "            break"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKAMNwFq8w8t"
      },
      "source": [
        "def recommendForUser(userID):\n",
        "    testUserInnerID = trainSet.to_inner_uid(userID)\n",
        "    similarityRow = simsMatrix[testUserInnerID]\n",
        "\n",
        "    users = []\n",
        "    for innerID, score in enumerate(similarityRow):\n",
        "        if (innerID != testUserInnerID):\n",
        "            users.append( (innerID, score) )\n",
        "\n",
        "    kNeighbors = heapq.nlargest(k, users, key=lambda t: t[1])\n",
        "\n",
        "    candidates = defaultdict(float)\n",
        "    for similarUser in kNeighbors:\n",
        "        innerID = similarUser[0]\n",
        "        userSimilarityScore = similarUser[1]\n",
        "        theirRatings = trainSet.ur[innerID]\n",
        "        for rating in theirRatings:\n",
        "            candidates[rating[0]] += (rating[1]) * userSimilarityScore\n",
        "\n",
        "    watched = {}\n",
        "    for itemID, rating in trainSet.ur[testUserInnerID]:\n",
        "        watched[itemID] = 1\n",
        "\n",
        "    pos = 0\n",
        "    for itemID, ratingSum in sorted(candidates.items(), key=lambda k: k[1], reverse=True):\n",
        "        if not itemID in watched:\n",
        "            movieID = trainSet.to_raw_iid(itemID)\n",
        "            print(movieID, getMovieName(movie_ratings, int(movieID)), ratingSum)\n",
        "            pos += 1\n",
        "            if (pos > 10):\n",
        "                break"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35XfhXKpXWlD"
      },
      "source": [
        "recommendForUser('85')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qn_PflKmXY63"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}