Adventour_end_to_end_modelling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GyHpquEWAyA8"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBZmKwvOBo_s"
      },
      "source": [
        "### Import necessary ibrary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uw-2w1jxHiBA"
      },
      "outputs": [],
      "source": [
        "# import necessary library\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BdJU7r5BwIA"
      },
      "source": [
        "### Connect colab to drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oCH86G1Rupa",
        "outputId": "e998ac44-87fa-4223-dd21-0a4ff2658b0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
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          "height": 0
        },
        "id": "HTPq8c1hI6GV",
        "outputId": "2ed9e04a-36ff-40dc-ec1e-c53852152531"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mountain_id     Daerah             Nama_Gunung  Ketinggian   \\\n",
              "0          101  Jawa barat          Gunung Cermai        3,078   \n",
              "1          102  Jawa barat          Gunung Puntang       2,223   \n",
              "2          103  Jawa barat      Gunung Papandayan        2,666   \n",
              "3          104  Jawa barat  Gunung Gede Pangrango        2,958   \n",
              "4          105  Jawa barat        Gunung Pangrango       3,019   \n",
              "\n",
              "                               Lokasi            Trek  \\\n",
              "0  Kuningan, Majalengka, dan Cirebon.    Tanah Kering   \n",
              "1                         Kab. Bandung   Tanah kering   \n",
              "2                          Kab. Garut.      bebatuan    \n",
              "3                                Bogor  Tangga Buatan   \n",
              "4                              Cibodas   Akar Pohon     \n",
              "\n",
              "                                          Jalur  Simaksi   Level  \n",
              "0  Jalur Apuy, Linggarjati dan Jalur Palutungan  Rp30.000      1  \n",
              "1                                             -  Rp40.000      1  \n",
              "2                                             -  Rp60.000      1  \n",
              "3                Jalur Cibodas dan Jalur Putri   Rp75.000      1  \n",
              "4                                 Jalur Cibodas  Rp75.000      2  "
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              "      <th></th>\n",
              "      <th>mountain_id</th>\n",
              "      <th>Daerah</th>\n",
              "      <th>Nama_Gunung</th>\n",
              "      <th>Ketinggian</th>\n",
              "      <th>Lokasi</th>\n",
              "      <th>Trek</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101</td>\n",
              "      <td>Jawa barat</td>\n",
              "      <td>Gunung Cermai</td>\n",
              "      <td>3,078</td>\n",
              "      <td>Kuningan, Majalengka, dan Cirebon.</td>\n",
              "      <td>Tanah Kering</td>\n",
              "      <td>Jalur Apuy, Linggarjati dan Jalur Palutungan</td>\n",
              "      <td>Rp30.000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>102</td>\n",
              "      <td>Jawa barat</td>\n",
              "      <td>Gunung Puntang</td>\n",
              "      <td>2,223</td>\n",
              "      <td>Kab. Bandung</td>\n",
              "      <td>Tanah kering</td>\n",
              "      <td>-</td>\n",
              "      <td>Rp40.000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>103</td>\n",
              "      <td>Jawa barat</td>\n",
              "      <td>Gunung Papandayan</td>\n",
              "      <td>2,666</td>\n",
              "      <td>Kab. Garut.</td>\n",
              "      <td>bebatuan</td>\n",
              "      <td>-</td>\n",
              "      <td>Rp60.000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>104</td>\n",
              "      <td>Jawa barat</td>\n",
              "      <td>Gunung Gede Pangrango</td>\n",
              "      <td>2,958</td>\n",
              "      <td>Bogor</td>\n",
              "      <td>Tangga Buatan</td>\n",
              "      <td>Jalur Cibodas dan Jalur Putri</td>\n",
              "      <td>Rp75.000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>105</td>\n",
              "      <td>Jawa barat</td>\n",
              "      <td>Gunung Pangrango</td>\n",
              "      <td>3,019</td>\n",
              "      <td>Cibodas</td>\n",
              "      <td>Akar Pohon</td>\n",
              "      <td>Jalur Cibodas</td>\n",
              "      <td>Rp75.000</td>\n",
              "      <td>2</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Read dataset into Pandas Dataframe\n",
        "\n",
        "df_mount = pd.read_csv('/content/drive/My Drive/dataset/datagunung_lvl.csv',delimiter=',')\n",
        "df_mount = df_mount.rename(columns={\"Id\": \"mountain_id\"})\n",
        "df_mount.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siydG1HsJiY-",
        "outputId": "956b5cc0-26f9-477d-8c88-48d670bce607"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# check how many data in df_mount dataframe\n",
        "df_mount.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72JsEbUaDAtY",
        "outputId": "00fb98b2-f883-4e88-b7a6-77b387eb9512"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17 entries, 0 to 16\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   mountain_id   17 non-null     int64 \n",
            " 1   Daerah        17 non-null     object\n",
            " 2   Nama_Gunung   17 non-null     object\n",
            " 3   Ketinggian    17 non-null     object\n",
            " 4   Lokasi        17 non-null     object\n",
            " 5   Trek          17 non-null     object\n",
            " 6   Jalur         17 non-null     object\n",
            " 7   Simaksi       17 non-null     object\n",
            " 8   Level         17 non-null     int64 \n",
            "dtypes: int64(2), object(7)\n",
            "memory usage: 1.3+ KB\n"
          ]
        }
      ],
      "source": [
        "# Load Information from df_mount dataframe\n",
        "df_mount.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        "colab": {
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          "data": {
            "text/plain": [
              "   Id                 Nama                         Email      Password  \\\n",
              "0   1  Harcourt Leppington    hleppington0@reference.com        Zjvy1t   \n",
              "1   2         Cristi Weeds     cweeds1@blogtalkradio.com     GRjASvqhv   \n",
              "2   3        Thorny Gidden  tgidden2@acquirethisname.com  hTBgDny2C2p7   \n",
              "3   4     Thibaut Spurdens       tspurdens3@omniture.com     aUz4mmfRq   \n",
              "4   5      Cristionna Gaul            cgaul4@storify.com       5dU6hdn   \n",
              "\n",
              "     Domisili Jenis Kelamin  Umur  Pengalaman  \n",
              "0  Yogyakarta     Perempuan    24          12  \n",
              "1     Cilacap     Laki-laki    18           5  \n",
              "2    Semarang     Perempuan    48           9  \n",
              "3     Cilacap     Perempuan    54          10  \n",
              "4      Malang     Laki-laki    61          18  "
            ],
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              "      <th>0</th>\n",
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              "      <td>Harcourt Leppington</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>aUz4mmfRq</td>\n",
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              "      <td>54</td>\n",
              "      <td>10</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Cristionna Gaul</td>\n",
              "      <td>cgaul4@storify.com</td>\n",
              "      <td>5dU6hdn</td>\n",
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              "      <td>Laki-laki</td>\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Read dataset into Pandas Dataframe\n",
        "\n",
        "df_user = pd.read_csv('/content/drive/My Drive/dataset/MOCK_DATA.csv',delimiter=',')\n",
        "df_user.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HerorR1VCtDj",
        "outputId": "dc9aeb45-1a09-456d-ae5b-04552403c29a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# check how many data in df_user dataframe\n",
        "df_user.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3ygGWXI1QCE",
        "outputId": "a261d61e-7740-46cc-9fb2-e1075ac54e33"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Id             1000 non-null   int64 \n",
            " 1   Nama           1000 non-null   object\n",
            " 2   Email          1000 non-null   object\n",
            " 3   Password       1000 non-null   object\n",
            " 4   Domisili       1000 non-null   object\n",
            " 5   Jenis Kelamin  1000 non-null   object\n",
            " 6   Umur           1000 non-null   int64 \n",
            " 7   Pengalaman     1000 non-null   int64 \n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 62.6+ KB\n"
          ]
        }
      ],
      "source": [
        "# Load Information from df_user dataframe\n",
        "df_user.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "B5jrtZ9eJNma",
        "outputId": "0442854a-d5ae-49eb-dccc-495ead94d051"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user_id  mountain_id  rating\n",
              "0      284           13       2\n",
              "1       33           11       5\n",
              "2      167            8       1\n",
              "3      623            5       5\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Read dataset into Pandas Dataframe\n",
        "\n",
        "df_rating = pd.read_csv('/content/drive/My Drive/dataset/rating_data.csv',delimiter=',')\n",
        "df_rating.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBXwUG-ZDk4Z",
        "outputId": "db6a016e-673c-4500-8fc7-024d4c3f80c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# check how many data in df_rating dataframe\n",
        "df_rating.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVR6Au4YDqsE",
        "outputId": "2892eb41-d987-4378-b451-cedd1b6a8ba0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype\n",
            "---  ------       --------------  -----\n",
            " 0   user_id      5000 non-null   int64\n",
            " 1   mountain_id  5000 non-null   int64\n",
            " 2   rating       5000 non-null   int64\n",
            "dtypes: int64(3)\n",
            "memory usage: 117.3 KB\n"
          ]
        }
      ],
      "source": [
        "# Load Information from df_rating dataframe\n",
        "df_rating.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "w6yANkREDgJO"
      },
      "outputs": [],
      "source": [
        "# modify mountain_id in df_rating dataframe\n",
        "df_rating['mountain_id'] = df_rating['mountain_id']+100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8Ygg-xqezc8o",
        "outputId": "41be35fd-9dbf-42c4-e865-047658dad484"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id                 Nama                         Email      Password  \\\n",
              "0   1  Harcourt Leppington    hleppington0@reference.com        Zjvy1t   \n",
              "1   2         Cristi Weeds     cweeds1@blogtalkradio.com     GRjASvqhv   \n",
              "2   3        Thorny Gidden  tgidden2@acquirethisname.com  hTBgDny2C2p7   \n",
              "3   4     Thibaut Spurdens       tspurdens3@omniture.com     aUz4mmfRq   \n",
              "4   5      Cristionna Gaul            cgaul4@storify.com       5dU6hdn   \n",
              "\n",
              "     Domisili Jenis Kelamin  Umur  Pengalaman  level  \n",
              "0  Yogyakarta     Perempuan    24          12      3  \n",
              "1     Cilacap     Laki-laki    18           5      1  \n",
              "2    Semarang     Perempuan    48           9      2  \n",
              "3     Cilacap     Perempuan    54          10      2  \n",
              "4      Malang     Laki-laki    61          18      3  "
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# add level of users\n",
        "def level_stats(value):\n",
        "    if value <= 5:\n",
        "        return 1\n",
        "    if 5 < value <= 10:\n",
        "        return 2\n",
        "    elif value> 10:\n",
        "        return 3\n",
        "\n",
        "df_user['level'] = df_user['Pengalaman'].map(level_stats)\n",
        "df_user.head()\n"
      ]
    },
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              "   user_id  mountain_id  rating\n",
              "0        1          117       4\n",
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      ],
      "source": [
        "# sort data base on user_id\n",
        "df_rating = df_rating.sort_values(['user_id']).reset_index(drop=True)\n",
        "df_rating.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ApNDvzMpDe0t"
      },
      "outputs": [],
      "source": [
        "# Add list mount_name to df_rating dataset\n",
        "\n",
        "mountain_id = df_rating['mountain_id']\n",
        "# List of conditions\n",
        "conditions = [\n",
        "    (mountain_id == 101),\n",
        "    (mountain_id == 102),\n",
        "    (mountain_id == 103),\n",
        "    (mountain_id == 104),\n",
        "    (mountain_id == 105),\n",
        "    (mountain_id == 106),\n",
        "    (mountain_id == 107),\n",
        "    (mountain_id == 108),\n",
        "    (mountain_id == 109),\n",
        "    (mountain_id == 110),\n",
        "    (mountain_id == 111),\n",
        "    (mountain_id == 112),\n",
        "    (mountain_id == 113),\n",
        "    (mountain_id == 114),\n",
        "    (mountain_id == 115),\n",
        "    (mountain_id == 116),\n",
        "    (mountain_id == 117)]\n",
        "# List of values to return\n",
        "choices  = [\n",
        "    \"Gunung Cermai\",\n",
        "    \"Gunung Puntang\",\n",
        "    \"Gunung Papandayan\",\n",
        "    \"Gunung Gede\",\n",
        "    \"Gunung Pangrango\",\n",
        "    \"Gunung Cikuray\",\n",
        "    \"Gunung Prau\",\n",
        "    \"Gunung Merbabu\",\n",
        "    \"Gunung Sindoro\",\n",
        "    \"Gunung Slamet\",\n",
        "    \"Gunung Sumbing\",\n",
        "    \"Gunung Arjuno\",\n",
        "    \"Gunung Argopuro\",\n",
        "    \"Gunung Lawu\",\n",
        "    \"Gunung Semeru\",\n",
        "    \"Gunung Raung\",\n",
        "    \"Gunung Panderman\",\n",
        "\n",
        "]\n",
        "# create a new column in the DF based on the conditions\n",
        "df_rating.insert(2, 'mount_name', np.select(conditions, choices, \"ERROR\"))"
      ]
    },
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              "   user_id  mountain_id         mount_name  rating  level\n",
              "0        1          117   Gunung Panderman       4      3\n",
              "1        1          109     Gunung Sindoro       1      3\n",
              "2        1          112      Gunung Arjuno       2      3\n",
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              "4        2          104        Gunung Gede       5      1\n",
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              "6        2          112      Gunung Arjuno       1      1\n",
              "7        2          109     Gunung Sindoro       4      1\n",
              "8        2          108     Gunung Merbabu       5      1\n",
              "9        2          112      Gunung Arjuno       4      1"
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      ],
      "source": [
        "# add level of users\n",
        "\n",
        "user_id = df_rating['user_id']\n",
        "level = df_user['level']\n",
        "\n",
        "#print(level.loc[0])\n",
        "def level_stats(value):\n",
        "  for a in user_id:\n",
        "    if value == a:\n",
        "      data = df_user.loc[df_user['Id'] == value].reset_index()\n",
        "      data = data['level']\n",
        "      return (data.iloc[0])\n",
        "\n",
        "\n",
        "df_rating['level'] = df_rating['user_id'].map(level_stats)\n",
        "df_rating.head(10)"
      ]
    },
    {
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        "id": "f9coDqhOP9CG",
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        {
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          "data": {
            "text/plain": [
              "    user_id  mountain_id         mount_name  rating  level     gender\n",
              "0         1          117   Gunung Panderman       4      3  Perempuan\n",
              "1         1          109     Gunung Sindoro       1      3  Perempuan\n",
              "2         1          112      Gunung Arjuno       2      3  Perempuan\n",
              "3         1          113    Gunung Argopuro       1      3  Perempuan\n",
              "4         2          104        Gunung Gede       5      1  Laki-laki\n",
              "5         2          103  Gunung Papandayan       2      1  Laki-laki\n",
              "6         2          112      Gunung Arjuno       1      1  Laki-laki\n",
              "7         2          109     Gunung Sindoro       4      1  Laki-laki\n",
              "8         2          108     Gunung Merbabu       5      1  Laki-laki\n",
              "9         2          112      Gunung Arjuno       4      1  Laki-laki\n",
              "10        3          107        Gunung Prau       2      2  Perempuan\n",
              "11        3          105   Gunung Pangrango       2      2  Perempuan\n",
              "12        3          114        Gunung Lawu       4      2  Perempuan\n",
              "13        3          105   Gunung Pangrango       5      2  Perempuan\n",
              "14        3          104        Gunung Gede       2      2  Perempuan"
            ],
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>117</td>\n",
              "      <td>Gunung Panderman</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>Gunung Sindoro</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>112</td>\n",
              "      <td>Gunung Arjuno</td>\n",
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              "      <td>3</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>113</td>\n",
              "      <td>Gunung Argopuro</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>104</td>\n",
              "      <td>Gunung Gede</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>Laki-laki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>103</td>\n",
              "      <td>Gunung Papandayan</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Laki-laki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>Gunung Arjuno</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Laki-laki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>109</td>\n",
              "      <td>Gunung Sindoro</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Laki-laki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>108</td>\n",
              "      <td>Gunung Merbabu</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>Laki-laki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>Gunung Arjuno</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Laki-laki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3</td>\n",
              "      <td>107</td>\n",
              "      <td>Gunung Prau</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>3</td>\n",
              "      <td>105</td>\n",
              "      <td>Gunung Pangrango</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3</td>\n",
              "      <td>114</td>\n",
              "      <td>Gunung Lawu</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3</td>\n",
              "      <td>105</td>\n",
              "      <td>Gunung Pangrango</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>3</td>\n",
              "      <td>104</td>\n",
              "      <td>Gunung Gede</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Perempuan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b63f554-8a38-43a9-969a-9f8487e2659a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-6b63f554-8a38-43a9-969a-9f8487e2659a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6b63f554-8a38-43a9-969a-9f8487e2659a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# add gender column to df_rating dataframe\n",
        "\n",
        "user_id = df_rating['user_id']\n",
        "level = df_user['level']\n",
        "\n",
        "#print(level.loc[0])\n",
        "def level_stats(value):\n",
        "  for a in user_id:\n",
        "    if value == a:\n",
        "      data = df_user.loc[df_user['Id'] == value].reset_index()\n",
        "      data = data['Jenis Kelamin']\n",
        "      return (data.iloc[0])\n",
        "\n",
        "\n",
        "df_rating['gender'] = df_rating['user_id'].map(level_stats)\n",
        "df_rating.head(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dWp5nHGTQYGr",
        "outputId": "67c2f330-e0f1-4810-8bf4-ff65d7ea496c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    user_id  mountain_id         mount_name  rating  level     gender  age\n",
              "0         1          117   Gunung Panderman       4      3  Perempuan   24\n",
              "1         1          109     Gunung Sindoro       1      3  Perempuan   24\n",
              "2         1          112      Gunung Arjuno       2      3  Perempuan   24\n",
              "3         1          113    Gunung Argopuro       1      3  Perempuan   24\n",
              "4         2          104        Gunung Gede       5      1  Laki-laki   18\n",
              "5         2          103  Gunung Papandayan       2      1  Laki-laki   18\n",
              "6         2          112      Gunung Arjuno       1      1  Laki-laki   18\n",
              "7         2          109     Gunung Sindoro       4      1  Laki-laki   18\n",
              "8         2          108     Gunung Merbabu       5      1  Laki-laki   18\n",
              "9         2          112      Gunung Arjuno       4      1  Laki-laki   18\n",
              "10        3          107        Gunung Prau       2      2  Perempuan   48\n",
              "11        3          105   Gunung Pangrango       2      2  Perempuan   48\n",
              "12        3          114        Gunung Lawu       4      2  Perempuan   48\n",
              "13        3          105   Gunung Pangrango       5      2  Perempuan   48\n",
              "14        3          104        Gunung Gede       2      2  Perempuan   48"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-414f1cb4-5ee4-4d80-ba1b-a05264785aae\">\n",
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              "          const element = document.querySelector('#df-414f1cb4-5ee4-4d80-ba1b-a05264785aae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# add user's age\n",
        "user_id = df_rating['user_id']\n",
        "level = df_user['level']\n",
        "\n",
        "#print(level.loc[0])\n",
        "def level_stats(value):\n",
        "  for a in user_id:\n",
        "    if value == a:\n",
        "      data = df_user.loc[df_user['Id'] == value].reset_index()\n",
        "      data = data['Umur']\n",
        "      return (data.iloc[0])\n",
        "\n",
        "\n",
        "df_rating['age'] = df_rating['user_id'].map(level_stats)\n",
        "df_rating.head(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKc50eK5Iwvo",
        "outputId": "2cc4ea2e-2694-4ef9-d1d5-cc25c8ea4ed6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5000, 7)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 7 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   user_id      5000 non-null   int64 \n",
            " 1   mountain_id  5000 non-null   int64 \n",
            " 2   mount_name   5000 non-null   object\n",
            " 3   rating       5000 non-null   int64 \n",
            " 4   level        5000 non-null   int64 \n",
            " 5   gender       5000 non-null   object\n",
            " 6   age          5000 non-null   int64 \n",
            "dtypes: int64(5), object(2)\n",
            "memory usage: 273.6+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# Get to know shape and information from df_rating dataframe\n",
        "\n",
        "print(df_rating.shape)\n",
        "print(df_rating.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "o1AHQavFJ0VN",
        "outputId": "59553d5d-8fac-42de-c17c-9198d228564a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    user_id  mountain_id     mount_name  rating  level     gender  age\n",
              "0       571          101  Gunung Cermai       2      3  Laki-laki   61\n",
              "1       463          101  Gunung Cermai       4      2  Perempuan   21\n",
              "2       972          101  Gunung Cermai       3      1  Laki-laki   59\n",
              "3       776          101  Gunung Cermai       5      3  Laki-laki   51\n",
              "4       863          101  Gunung Cermai       2      3  Laki-laki   49\n",
              "5       609          101  Gunung Cermai       4      1  Laki-laki   68\n",
              "6       972          101  Gunung Cermai       1      1  Laki-laki   59\n",
              "7       610          101  Gunung Cermai       5      2  Laki-laki   59\n",
              "8       610          101  Gunung Cermai       3      2  Laki-laki   59\n",
              "9       366          101  Gunung Cermai       5      2  Laki-laki   58\n",
              "10      215          101  Gunung Cermai       2      3  Perempuan   24\n",
              "11      365          101  Gunung Cermai       4      1  Perempuan   54\n",
              "12      265          101  Gunung Cermai       5      3  Perempuan   18\n",
              "13      606          101  Gunung Cermai       4      3  Laki-laki   28\n",
              "14      864          101  Gunung Cermai       2      1  Perempuan   56"
            ],
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              "\n",
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              "      <div>\n",
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              "      <td>59</td>\n",
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              "    <tr>\n",
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              "      <td>610</td>\n",
              "      <td>101</td>\n",
              "      <td>Gunung Cermai</td>\n",
              "      <td>3</td>\n",
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              "      <td>Laki-laki</td>\n",
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              "      <th>9</th>\n",
              "      <td>366</td>\n",
              "      <td>101</td>\n",
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              "      <th>10</th>\n",
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              "      <td>101</td>\n",
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              "      <td>2</td>\n",
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              "      <td>Perempuan</td>\n",
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              "    </tr>\n",
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              "      <th>11</th>\n",
              "      <td>365</td>\n",
              "      <td>101</td>\n",
              "      <td>Gunung Cermai</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "      <td>54</td>\n",
              "    </tr>\n",
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              "      <th>12</th>\n",
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              "      <td>101</td>\n",
              "      <td>Gunung Cermai</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Perempuan</td>\n",
              "      <td>18</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
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              "      <td>101</td>\n",
              "      <td>Gunung Cermai</td>\n",
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              "      <td>Laki-laki</td>\n",
              "      <td>28</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>864</td>\n",
              "      <td>101</td>\n",
              "      <td>Gunung Cermai</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Perempuan</td>\n",
              "      <td>56</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-866a9f2e-8c0f-47a6-8dee-16b817ab368b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-866a9f2e-8c0f-47a6-8dee-16b817ab368b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-866a9f2e-8c0f-47a6-8dee-16b817ab368b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# sort df_rating base on mountain_id column\n",
        "df_rating = df_rating.sort_values(['mountain_id']).reset_index(drop=True)\n",
        "df_rating.head(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "I3hItgpfBRZR",
        "outputId": "92d02205-3c69-49d9-b456-b8ea4a4255ce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user_id  mountain_id     mount_name  rating     gender\n",
              "0      571          101  Gunung Cermai       2  Laki-laki\n",
              "1      463          101  Gunung Cermai       4  Perempuan\n",
              "2      972          101  Gunung Cermai       3  Laki-laki\n",
              "3      776          101  Gunung Cermai       5  Laki-laki\n",
              "4      863          101  Gunung Cermai       2  Laki-laki"
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# drop level and age columns\n",
        "\n",
        "data_rating = df_rating.drop(['level','age'],axis=1)\n",
        "data_rating.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dFUXMZpkLjlp",
        "outputId": "9b7552b1-c07c-4351-9453-d076c4bfe585"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-8090b665774f>:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  avg_mount_rate = data_rating.groupby(['mountain_id']).mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               rating\n",
              "mountain_id          \n",
              "117          3.190000\n",
              "114          3.151724\n",
              "103          3.104418\n",
              "107          3.048544\n",
              "101          3.045752"
            ],
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              "      <th>rating</th>\n",
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              "    <tr>\n",
              "      <th>mountain_id</th>\n",
              "      <th></th>\n",
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              "      <th>117</th>\n",
              "      <td>3.190000</td>\n",
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              "      <th>114</th>\n",
              "      <td>3.151724</td>\n",
              "    </tr>\n",
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              "      <td>3.104418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>3.048544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>3.045752</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Check the average rating of each mountain\n",
        "\n",
        "avg_mount_rate = data_rating.groupby(['mountain_id']).mean()\n",
        "avg_mount_rate = avg_mount_rate.drop(['user_id'],axis=1)\n",
        "avg_mount_rate = avg_mount_rate.sort_values(['rating'], ascending=False)\n",
        "avg_mount_rate.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FZmo_jAavltt",
        "outputId": "a76c9013-acc0-4d46-8bf2-b8c0bfb52491"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         mount_name  rating  level  gender  age\n",
              "user_id                                        \n",
              "1                 4       4      4       4    4\n",
              "2                 6       6      6       6    6\n",
              "3                 7       7      7       7    7\n",
              "4                 4       4      4       4    4\n",
              "5                 4       4      4       4    4"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>mount_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>level</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8f690ee7-2809-4373-bfc3-ec850cc0c199 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8f690ee7-2809-4373-bfc3-ec850cc0c199');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# check how many each user rated mount\n",
        "\n",
        "count_user = df_rating.groupby(['user_id']).count()\n",
        "count_user = count_user.drop(['mountain_id'],axis=1)\n",
        "count_user = count_user.sort_values(['user_id'], ascending=True)\n",
        "count_user.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "-j6vyA6HSKx1"
      },
      "outputs": [],
      "source": [
        "# import tensorflow\n",
        "\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "w0O_wF5VSXmo"
      },
      "outputs": [],
      "source": [
        "## Convert Pandas Daframe to Tensorflow dataset\n",
        "\n",
        "tf_rating = tf.data.Dataset.from_tensor_slices(dict(df_rating))\n",
        "tf_mount = tf.data.Dataset.from_tensor_slices(dict(df_mount))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12j5oz1vS1kE",
        "outputId": "67243337-156c-4756-b4ab-b6021b2c8ae7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=101>, 'Daerah ': <tf.Tensor: shape=(), dtype=string, numpy=b'Jawa barat'>, 'Nama_Gunung ': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Cermai '>, 'Ketinggian ': <tf.Tensor: shape=(), dtype=string, numpy=b'3,078'>, 'Lokasi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Kuningan, Majalengka, dan Cirebon. '>, 'Trek': <tf.Tensor: shape=(), dtype=string, numpy=b'Tanah Kering'>, 'Jalur': <tf.Tensor: shape=(), dtype=string, numpy=b'Jalur Apuy, Linggarjati dan Jalur Palutungan'>, 'Simaksi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Rp30.000'>, 'Level': <tf.Tensor: shape=(), dtype=int64, numpy=1>}\n",
            "{'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=102>, 'Daerah ': <tf.Tensor: shape=(), dtype=string, numpy=b'Jawa barat'>, 'Nama_Gunung ': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Puntang'>, 'Ketinggian ': <tf.Tensor: shape=(), dtype=string, numpy=b'2,223'>, 'Lokasi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Kab. Bandung'>, 'Trek': <tf.Tensor: shape=(), dtype=string, numpy=b'Tanah kering'>, 'Jalur': <tf.Tensor: shape=(), dtype=string, numpy=b'-'>, 'Simaksi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Rp40.000'>, 'Level': <tf.Tensor: shape=(), dtype=int64, numpy=1>}\n",
            "{'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=103>, 'Daerah ': <tf.Tensor: shape=(), dtype=string, numpy=b'Jawa barat'>, 'Nama_Gunung ': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Papandayan '>, 'Ketinggian ': <tf.Tensor: shape=(), dtype=string, numpy=b'2,666'>, 'Lokasi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Kab. Garut.'>, 'Trek': <tf.Tensor: shape=(), dtype=string, numpy=b'bebatuan '>, 'Jalur': <tf.Tensor: shape=(), dtype=string, numpy=b'-'>, 'Simaksi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Rp60.000'>, 'Level': <tf.Tensor: shape=(), dtype=int64, numpy=1>}\n",
            "{'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=104>, 'Daerah ': <tf.Tensor: shape=(), dtype=string, numpy=b'Jawa barat'>, 'Nama_Gunung ': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Gede Pangrango '>, 'Ketinggian ': <tf.Tensor: shape=(), dtype=string, numpy=b'2,958'>, 'Lokasi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Bogor'>, 'Trek': <tf.Tensor: shape=(), dtype=string, numpy=b'Tangga Buatan'>, 'Jalur': <tf.Tensor: shape=(), dtype=string, numpy=b'Jalur Cibodas dan Jalur Putri '>, 'Simaksi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Rp75.000'>, 'Level': <tf.Tensor: shape=(), dtype=int64, numpy=1>}\n",
            "{'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=105>, 'Daerah ': <tf.Tensor: shape=(), dtype=string, numpy=b'Jawa barat'>, 'Nama_Gunung ': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Pangrango'>, 'Ketinggian ': <tf.Tensor: shape=(), dtype=string, numpy=b'3,019'>, 'Lokasi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Cibodas'>, 'Trek': <tf.Tensor: shape=(), dtype=string, numpy=b'Akar Pohon  '>, 'Jalur': <tf.Tensor: shape=(), dtype=string, numpy=b'Jalur Cibodas'>, 'Simaksi ': <tf.Tensor: shape=(), dtype=string, numpy=b'Rp75.000'>, 'Level': <tf.Tensor: shape=(), dtype=int64, numpy=2>}\n"
          ]
        }
      ],
      "source": [
        "# get the first rows of the mount dataset\n",
        "for m in tf_mount.take(5):\n",
        "  print(m)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBJlCaiqVNJP",
        "outputId": "11235a1f-fc55-445c-ad21-2d60468aa99c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'user_id': <tf.Tensor: shape=(), dtype=int64, numpy=571>, 'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=101>, 'mount_name': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Cermai'>, 'rating': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'level': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'gender': <tf.Tensor: shape=(), dtype=string, numpy=b'Laki-laki'>, 'age': <tf.Tensor: shape=(), dtype=int64, numpy=61>}\n",
            "{'user_id': <tf.Tensor: shape=(), dtype=int64, numpy=463>, 'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=101>, 'mount_name': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Cermai'>, 'rating': <tf.Tensor: shape=(), dtype=int64, numpy=4>, 'level': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'gender': <tf.Tensor: shape=(), dtype=string, numpy=b'Perempuan'>, 'age': <tf.Tensor: shape=(), dtype=int64, numpy=21>}\n",
            "{'user_id': <tf.Tensor: shape=(), dtype=int64, numpy=972>, 'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=101>, 'mount_name': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Cermai'>, 'rating': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'level': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'gender': <tf.Tensor: shape=(), dtype=string, numpy=b'Laki-laki'>, 'age': <tf.Tensor: shape=(), dtype=int64, numpy=59>}\n",
            "{'user_id': <tf.Tensor: shape=(), dtype=int64, numpy=776>, 'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=101>, 'mount_name': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Cermai'>, 'rating': <tf.Tensor: shape=(), dtype=int64, numpy=5>, 'level': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'gender': <tf.Tensor: shape=(), dtype=string, numpy=b'Laki-laki'>, 'age': <tf.Tensor: shape=(), dtype=int64, numpy=51>}\n",
            "{'user_id': <tf.Tensor: shape=(), dtype=int64, numpy=863>, 'mountain_id': <tf.Tensor: shape=(), dtype=int64, numpy=101>, 'mount_name': <tf.Tensor: shape=(), dtype=string, numpy=b'Gunung Cermai'>, 'rating': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'level': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'gender': <tf.Tensor: shape=(), dtype=string, numpy=b'Laki-laki'>, 'age': <tf.Tensor: shape=(), dtype=int64, numpy=49>}\n"
          ]
        }
      ],
      "source": [
        "# get the first rows of the ratings dataset\n",
        "for m in tf_rating.take(5):\n",
        "  print(m)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB9PZmMVHh5f"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "NDBtID9uS67I"
      },
      "outputs": [],
      "source": [
        "## feature selection\n",
        "# we use column user_id, mount_name, level, gender, and user_id from rating dataset\n",
        "# from tf_mount dataset, we use mountain_id, mount_name and level column\n",
        "\n",
        "ratings = tf_rating.map(lambda x: {\n",
        "    \"user_id\": x[\"user_id\"],\n",
        "    \"mount_name\": x[\"mount_name\"],\n",
        "    \"level\": x[\"level\"],\n",
        "    \"gender\":x[\"gender\"],\n",
        "    \"age\": x[\"age\"]\n",
        "\n",
        "})\n",
        "# gunung = tf_mount.map(lambda x: x[\"Nama_Gunung \"])\n",
        "\n",
        "mount =  tf_mount.map(lambda x: {\n",
        "    \"mountain_id\": x[\"mountain_id\"],\n",
        "    \"mount_name\": x[\"Nama_Gunung \"],\n",
        "    \"level\": x[\"Level\"]\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "WKmH596VQlyn"
      },
      "outputs": [],
      "source": [
        "# separate dataset to the training and testing\n",
        "\n",
        "\n",
        "## Split dataset randomly (80% for train and 20% for test)\n",
        "trainset_size = 0.8 * ratings.__len__().numpy()\n",
        "\n",
        "# set the global seed to get same the same sequence of random numbers every time run the code:\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Shuffle the elements of the dataset randomly.\n",
        "ratings_dataset_shuffled = ratings.shuffle(\n",
        "    # the new dataset will be sampled from a buffer window of first `buffer_size`\n",
        "    # elements of the dataset\n",
        "    buffer_size=100_000,\n",
        "    # set the random seed that will be used to create the distribution.\n",
        "    seed=42,\n",
        "    # `list(dataset.as_numpy_iterator()` yields different result for each call\n",
        "    # Because reshuffle_each_iteration defaults to True.\n",
        "    reshuffle_each_iteration=False\n",
        ")\n",
        "\n",
        "ratings_trainset = ratings_dataset_shuffled.take(trainset_size)\n",
        "ratings_testset = ratings_dataset_shuffled.skip(trainset_size)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fm5RaRxxGt4",
        "outputId": "d72611be-9760-42c1-c946-ec6d813afe13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'age': 50,\n",
            " 'gender': b'Perempuan',\n",
            " 'level': 2,\n",
            " 'mount_name': b'Gunung Papandayan',\n",
            " 'user_id': 524}\n"
          ]
        }
      ],
      "source": [
        "# Check ratings_trainset dataset\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "for rating in ratings_trainset.take(1).as_numpy_iterator():\n",
        "  pprint(rating)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jf3UUjeKL1q",
        "outputId": "fe145081-461e-455b-f3a6-7f92fcdab2d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'age': 62,\n",
            " 'gender': b'Laki-laki',\n",
            " 'level': 3,\n",
            " 'mount_name': b'Gunung Lawu',\n",
            " 'user_id': 618}\n"
          ]
        }
      ],
      "source": [
        "# Check ratings_testset dataset\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "for rating in ratings_testset.take(1).as_numpy_iterator():\n",
        "  pprint(rating)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "c2zOlQLfXBCw"
      },
      "outputs": [],
      "source": [
        "# Make unique value for each column\n",
        "\n",
        "unique_gender = np.unique(np.concatenate(list(ratings.map(lambda x: x[\"gender\"]).batch(100))))\n",
        "unique_level = np.unique(np.concatenate(list(ratings.map(lambda x: x[\"level\"]).batch(100))))\n",
        "unique_age = np.unique(np.concatenate(list(ratings.map(lambda x: x[\"age\"]).batch(100))))\n",
        "\n",
        "\n",
        "#unique_mount_names = np.unique(np.concatenate(list(mount.batch(1000))))\n",
        "unique_mount_names = np.unique(np.concatenate(list(mount.map(lambda x: x[\"mount_name\"]).batch(1000))))\n",
        "unique_mount_lvl = np.unique(np.concatenate(list(mount.map(lambda x: x[\"level\"]).batch(1000))))\n",
        "unique_user_ids = np.unique(np.concatenate(list(ratings.batch(1000).map(\n",
        "    lambda x: x[\"user_id\"]))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G46tcFS7gRM7",
        "outputId": "fef10fbd-9287-457e-957e-f3b1d8b3eed3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[b'Gunung Argupuro' b'Gunung Arjuno' b'Gunung Cermai ' b'Gunung Cikuray '\n",
            " b'Gunung Gede Pangrango ' b'Gunung Lawu' b'Gunung Merbabu'\n",
            " b'Gunung Panderman' b'Gunung Pangrango' b'Gunung Papandayan '\n",
            " b'Gunung Prau ' b'Gunung Puntang' b'Gunung Raung' b'Gunung Semeru'\n",
            " b'Gunung Sindoro' b'Gunung Slamet' b'Gunung Sumbing']\n"
          ]
        }
      ],
      "source": [
        "print(unique_mount_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCQDlSwMfpEK",
        "outputId": "69a4082a-d82d-4562-cb90-4a49055b745e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17\n"
          ]
        }
      ],
      "source": [
        "print(len(unique_mount_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "8ciFev-sTWqQ"
      },
      "outputs": [],
      "source": [
        "# user_ids_vocabulary = tf.keras.layers.IntegerLookup(mask_token=None)\n",
        "# user_ids_vocabulary.adapt(ratings_trainset.map(lambda x: x[\"user_id\"]))\n",
        "\n",
        "\n",
        "# gunung_name_vocabulary = tf.keras.layers.StringLookup(mask_token=None)\n",
        "# gunung_name_vocabulary.adapt(gunung)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_Rf_rcUNE3Z"
      },
      "source": [
        "# Modelling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftVvX7tJTjev",
        "outputId": "266c7423-78df-459f-af75-862df78c11f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/96.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.2/96.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q tensorflow-recommenders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "2OgObcYDTmWY"
      },
      "outputs": [],
      "source": [
        "\n",
        "import tensorflow_recommenders as tfrs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "3I8b9X5JXdHh"
      },
      "outputs": [],
      "source": [
        "# Query Model\n",
        "\n",
        "\n",
        "class UserModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self): #use_levels):\n",
        "    super().__init__()\n",
        "\n",
        "   # self._use_levels = use_levels\n",
        "\n",
        "    self.user_embedding = tf.keras.Sequential([\n",
        "        tf.keras.layers.IntegerLookup(\n",
        "            vocabulary=unique_user_ids, mask_token=None),\n",
        "        tf.keras.layers.Embedding(len(unique_user_ids) + 1, 16),\n",
        "    ])\n",
        "\n",
        "    self.gender_embedding = tf.keras.Sequential([\n",
        "          tf.keras.layers.StringLookup(\n",
        "              vocabulary=unique_gender, mask_token=None),\n",
        "          tf.keras.layers.Embedding(len(unique_gender) + 1, 16),\n",
        "      ])\n",
        "\n",
        "    self.age_embedding = tf.keras.Sequential([\n",
        "          tf.keras.layers.IntegerLookup(\n",
        "              vocabulary=unique_age, mask_token=None),\n",
        "          tf.keras.layers.Embedding(len(unique_age) + 1, 16),\n",
        "      ])\n",
        "\n",
        "    self.level_embedding = tf.keras.Sequential([\n",
        "          tf.keras.layers.IntegerLookup(\n",
        "              vocabulary=unique_level, mask_token=None),\n",
        "          tf.keras.layers.Embedding(len(unique_level) + 1, 16)\n",
        "          ])\n",
        "\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return tf.concat([\n",
        "        self.user_embedding(inputs[\"user_id\"]),\n",
        "        self.gender_embedding(inputs[\"gender\"]),\n",
        "        self.age_embedding(inputs[\"age\"]),\n",
        "        self.level_embedding(inputs[\"level\"]),\n",
        "    ], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "momKid1nYjtH"
      },
      "outputs": [],
      "source": [
        "# Candidate Model\n",
        "\n",
        "class MountModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "\n",
        "    max_tokens = 10_000\n",
        "\n",
        "\n",
        "    self.title_embedding = tf.keras.Sequential([\n",
        "    tf.keras.layers.StringLookup(\n",
        "          vocabulary=unique_mount_names, mask_token=None),\n",
        "    tf.keras.layers.Embedding(len(unique_mount_names) + 1, 16)\n",
        "])\n",
        "\n",
        "    # apply tokenization (splitting into constituent words or word-pieces)\n",
        "    self.title_vectorizer = tf.keras.layers.TextVectorization(\n",
        "        max_tokens=max_tokens)\n",
        "\n",
        "    self.title_text_embedding = tf.keras.Sequential([\n",
        "      self.title_vectorizer,\n",
        "      tf.keras.layers.Embedding(max_tokens, 16, mask_zero=True),\n",
        "      tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    ])\n",
        "\n",
        "    self.title_vectorizer.adapt(unique_mount_names)\n",
        "\n",
        "    self.lvl_embedding = tf.keras.Sequential([\n",
        "        tf.keras.layers.IntegerLookup(\n",
        "            vocabulary=unique_mount_lvl, mask_token=None),\n",
        "        tf.keras.layers.Embedding(len(unique_mount_lvl) + 1, 16),\n",
        "    ])\n",
        "\n",
        "  def call(self, inputs):\n",
        "    return tf.concat([\n",
        "        self.title_embedding(inputs['mount_name']),\n",
        "        self.title_text_embedding(inputs['mount_name']),\n",
        "        self.lvl_embedding(inputs['level'])\n",
        "    ], axis=1)\n",
        "\n",
        "\n",
        "      # def call(self, titles):\n",
        "    #   len_titles = tf.shape(titles)[0]\n",
        "    #   # return avg_layer(self.title_text_embedding(titles))\n",
        "    #   return tf.concat([\n",
        "    #       self.title_embedding(tf.reshape(titles,[len_titles,5,1])),\n",
        "    #       self.title_text_embedding(tf.reshape(titles,[len_titles,5,1])),\n",
        "    #   ], axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "hOc3zFMKZPL0"
      },
      "outputs": [],
      "source": [
        "# Combined Model\n",
        "\n",
        "class MountLensModel(tfrs.models.Model):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.query_model = tf.keras.Sequential([\n",
        "      UserModel(),\n",
        "      tf.keras.layers.Dense(16)\n",
        "    ])\n",
        "    self.candidate_model = tf.keras.Sequential([\n",
        "      MountModel(),\n",
        "      tf.keras.layers.Dense(16)\n",
        "    ])\n",
        "    self.task = tfrs.tasks.Retrieval(\n",
        "        metrics=tfrs.metrics.FactorizedTopK(\n",
        "            candidates=mount.batch(100).map(self.candidate_model),\n",
        "        ),\n",
        "    )\n",
        "    self.dropout_layer = tf.keras.layers.Dropout(0.35)\n",
        "    self.dense = tf.keras.layers.Dense(16, activation='relu', kernel_regularizer=tf.keras.regularizers.l1(0.00001))\n",
        "\n",
        "  def compute_loss(self, features, training=False):\n",
        "    # We only pass the user id and timestamp features into the query model. This\n",
        "    # is to ensure that the training inputs would have the same keys as the\n",
        "    # query inputs. Otherwise the discrepancy in input structure would cause an\n",
        "    # error when loading the query model after saving it.\n",
        "    query_embeddings = self.dropout_layer(self.query_model({\n",
        "        \"user_id\": features[\"user_id\"],\n",
        "        \"gender\": features[\"gender\"],\n",
        "        \"age\": features[\"age\"],\n",
        "        \"level\": features[\"level\"]\n",
        "    }))\n",
        "    query_embeddings = self.dense(query_embeddings)\n",
        "\n",
        "\n",
        "   # mount_embeddings = self.dropout_layer(self.candidate_model(features[\"mount_name\"]))\n",
        "    mount_embeddings = self.dropout_layer(self.candidate_model({\n",
        "        \"mount_name\": features[\"mount_name\"],\n",
        "        \"level\": features[\"level\"],\n",
        "        }))\n",
        "    mount_embeddings = self.dense(mount_embeddings)\n",
        "\n",
        "    return self.task(query_embeddings, mount_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "IChYhsJoaWUX"
      },
      "outputs": [],
      "source": [
        "cached_train = ratings_trainset.shuffle(100_000).batch(156)\n",
        "cached_test = ratings_testset.batch(80).cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PdLO8MvcbVU",
        "outputId": "deaa6896-28df-4c39-d9c5-b3545565a2bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'mountain_id': <tf.Tensor 'args_2:0' shape=(None,) dtype=int64>, 'mount_name': <tf.Tensor 'args_1:0' shape=(None,) dtype=string>, 'level': <tf.Tensor 'args_0:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'age': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'mount_name': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 'level': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'age': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'mount_name': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 'level': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26/26 [==============================] - ETA: 0s - factorized_top_k/top_1_categorical_accuracy: 0.5598 - factorized_top_k/top_5_categorical_accuracy: 0.6532 - factorized_top_k/top_10_categorical_accuracy: 0.7965 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 773.8711 - regularization_loss: 5.5243e-04 - total_loss: 773.8717"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'age': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'mount_name': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=string>, 'level': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r26/26 [==============================] - 9s 218ms/step - factorized_top_k/top_1_categorical_accuracy: 0.5598 - factorized_top_k/top_5_categorical_accuracy: 0.6532 - factorized_top_k/top_10_categorical_accuracy: 0.7965 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 762.1485 - regularization_loss: 5.5255e-04 - total_loss: 762.1491 - val_factorized_top_k/top_1_categorical_accuracy: 0.7170 - val_factorized_top_k/top_5_categorical_accuracy: 0.8620 - val_factorized_top_k/top_10_categorical_accuracy: 0.9500 - val_factorized_top_k/top_50_categorical_accuracy: 1.0000 - val_factorized_top_k/top_100_categorical_accuracy: 1.0000 - val_loss: 146.2507 - val_regularization_loss: 5.5626e-04 - val_total_loss: 146.2512\n",
            "Epoch 2/5\n",
            "26/26 [==============================] - 4s 146ms/step - factorized_top_k/top_1_categorical_accuracy: 0.6658 - factorized_top_k/top_5_categorical_accuracy: 0.7442 - factorized_top_k/top_10_categorical_accuracy: 0.8450 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 742.7565 - regularization_loss: 5.6405e-04 - total_loss: 742.7570 - val_factorized_top_k/top_1_categorical_accuracy: 0.7000 - val_factorized_top_k/top_5_categorical_accuracy: 0.7380 - val_factorized_top_k/top_10_categorical_accuracy: 0.8350 - val_factorized_top_k/top_50_categorical_accuracy: 1.0000 - val_factorized_top_k/top_100_categorical_accuracy: 1.0000 - val_loss: 136.0646 - val_regularization_loss: 5.7376e-04 - val_total_loss: 136.0651\n",
            "Epoch 3/5\n",
            "26/26 [==============================] - 6s 219ms/step - factorized_top_k/top_1_categorical_accuracy: 0.9507 - factorized_top_k/top_5_categorical_accuracy: 0.9710 - factorized_top_k/top_10_categorical_accuracy: 0.9822 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 711.8068 - regularization_loss: 5.8325e-04 - total_loss: 711.8074 - val_factorized_top_k/top_1_categorical_accuracy: 1.0000 - val_factorized_top_k/top_5_categorical_accuracy: 1.0000 - val_factorized_top_k/top_10_categorical_accuracy: 1.0000 - val_factorized_top_k/top_50_categorical_accuracy: 1.0000 - val_factorized_top_k/top_100_categorical_accuracy: 1.0000 - val_loss: 128.0221 - val_regularization_loss: 5.9168e-04 - val_total_loss: 128.0227\n",
            "Epoch 4/5\n",
            "26/26 [==============================] - 4s 146ms/step - factorized_top_k/top_1_categorical_accuracy: 1.0000 - factorized_top_k/top_5_categorical_accuracy: 1.0000 - factorized_top_k/top_10_categorical_accuracy: 1.0000 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 671.1226 - regularization_loss: 5.9993e-04 - total_loss: 671.1233 - val_factorized_top_k/top_1_categorical_accuracy: 1.0000 - val_factorized_top_k/top_5_categorical_accuracy: 1.0000 - val_factorized_top_k/top_10_categorical_accuracy: 1.0000 - val_factorized_top_k/top_50_categorical_accuracy: 1.0000 - val_factorized_top_k/top_100_categorical_accuracy: 1.0000 - val_loss: 120.0155 - val_regularization_loss: 6.0705e-04 - val_total_loss: 120.0161\n",
            "Epoch 5/5\n",
            "26/26 [==============================] - 6s 223ms/step - factorized_top_k/top_1_categorical_accuracy: 1.0000 - factorized_top_k/top_5_categorical_accuracy: 1.0000 - factorized_top_k/top_10_categorical_accuracy: 1.0000 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 661.7086 - regularization_loss: 6.1012e-04 - total_loss: 661.7092 - val_factorized_top_k/top_1_categorical_accuracy: 1.0000 - val_factorized_top_k/top_5_categorical_accuracy: 1.0000 - val_factorized_top_k/top_10_categorical_accuracy: 1.0000 - val_factorized_top_k/top_50_categorical_accuracy: 1.0000 - val_factorized_top_k/top_100_categorical_accuracy: 1.0000 - val_loss: 119.8845 - val_regularization_loss: 6.1211e-04 - val_total_loss: 119.8851\n",
            "26/26 [==============================] - 3s 91ms/step - factorized_top_k/top_1_categorical_accuracy: 1.0000 - factorized_top_k/top_5_categorical_accuracy: 1.0000 - factorized_top_k/top_10_categorical_accuracy: 1.0000 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 661.4541 - regularization_loss: 6.1211e-04 - total_loss: 661.4547\n",
            "13/13 [==============================] - 1s 92ms/step - factorized_top_k/top_1_categorical_accuracy: 1.0000 - factorized_top_k/top_5_categorical_accuracy: 1.0000 - factorized_top_k/top_10_categorical_accuracy: 1.0000 - factorized_top_k/top_50_categorical_accuracy: 1.0000 - factorized_top_k/top_100_categorical_accuracy: 1.0000 - loss: 271.2715 - regularization_loss: 6.1211e-04 - total_loss: 271.2721\n",
            "Top-5 accuracy (train): 1.00.\n",
            "Top-5 accuracy (test): 1.00.\n"
          ]
        }
      ],
      "source": [
        "model = MountLensModel()\n",
        "#model.compile(optimizer=tf.keras.optimizers.Adagrad(0.1))\n",
        "model.compile(optimizer='adam')\n",
        "\n",
        "history = model.fit(cached_train, validation_data=cached_test,\n",
        "    validation_freq=1, epochs=5)\n",
        "\n",
        "train_accuracy = model.evaluate(\n",
        "    cached_train, return_dict=True)[\"factorized_top_k/top_5_categorical_accuracy\"]\n",
        "test_accuracy = model.evaluate(\n",
        "    cached_test, return_dict=True)[\"factorized_top_k/top_5_categorical_accuracy\"]\n",
        "\n",
        "print(f\"Top-5 accuracy (train): {train_accuracy:.2f}.\")\n",
        "print(f\"Top-5 accuracy (test): {test_accuracy:.2f}.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "bvUaC_mskAzk",
        "outputId": "b59f1dce-d7a7-45a3-888f-44813d1c0630"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot changes in model loss during training\n",
        "plt.plot(history.history[\"loss\"])\n",
        "plt.plot(history.history[\"val_loss\"])\n",
        "plt.title(\"Model losses during training\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "W9tJixKbwyq7",
        "outputId": "d15d0ed0-e1b3-4ddc-c451-6ff549380b4b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Plot changes in model accuracy during training\n",
        "plt.plot(history.history[\"factorized_top_k/top_5_categorical_accuracy\"])\n",
        "plt.plot(history.history[\"val_factorized_top_k/top_5_categorical_accuracy\"])\n",
        "plt.title(\"Model accuracies during training\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cbdc_cr538rm",
        "outputId": "620dbce3-8558-47cf-adfb-130545038efa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<_TakeDataset element_spec={'user_id': TensorSpec(shape=(None,), dtype=tf.int64, name=None), 'mount_name': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'level': TensorSpec(shape=(None,), dtype=tf.int64, name=None), 'gender': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'age': TensorSpec(shape=(None,), dtype=tf.int64, name=None)}>\n"
          ]
        }
      ],
      "source": [
        "print(cached_test.take(1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJ0MfWgcQJ_L"
      },
      "source": [
        "# Give the recommend mounts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QE3sob3_mQ_8",
        "outputId": "9eb2faec-7f59-45d9-d2e3-8d4f274c5193"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'mountain_id': <tf.Tensor 'args_2:0' shape=(None,) dtype=int64>, 'mount_name': <tf.Tensor 'args_1:0' shape=(None,) dtype=string>, 'level': <tf.Tensor 'args_0:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow_recommenders.layers.factorized_top_k.BruteForce at 0x7f246793e170>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# Create a model that takes in raw query features, and\n",
        "index = tfrs.layers.factorized_top_k.BruteForce(model.query_model)\n",
        "# recommends movies out of the entire movies dataset.\n",
        "tes = mount.batch(1)\n",
        "# index.index_from_dataset(\n",
        "#   tf.data.Dataset.zip((tes.map(lambda x: x[\"mount_name\"]),tes.map(model.candidate_model)))\n",
        "# )\n",
        "index.index_from_dataset(\n",
        "  tf.data.Dataset.zip((tes.map(lambda x: x[\"mountain_id\"]),tes.map(model.candidate_model)))\n",
        ")\n",
        "# index.index_from_dataset(\n",
        "#     tes.map(lambda title: (title, model.candidate_model(title))))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muOZx0NOgYKw",
        "outputId": "a7533deb-2c69-4c21-a64a-6eb8365bb97d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([100])>, 'gender': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Perempuan'], dtype=object)>, 'age': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([12])>, 'level': <tf.Tensor: shape=(1,), dtype=int64, numpy=array([3])>}. Consider rewriting this model with the Functional API.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations for user 100: [112 111 116 115 113 117 114 102 108 105]\n"
          ]
        }
      ],
      "source": [
        "#Get recommendations.\n",
        "query = {\"user_id\": np.array([100]),\"gender\": np.array([\"Perempuan\"]),\"age\": np.array([12]),\"level\": np.array([3])}\n",
        "_, titles = index(query)\n",
        "print(f\"Recommendations for user 100: {titles[0, :]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6PhPQAbQVRX"
      },
      "source": [
        "# Saving model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gopED0USQHyi",
        "outputId": "32060dd5-6685-412c-d816-462c785e7f1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'inputs:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'inputs_1:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'inputs_2:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'inputs_3:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'inputs:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'inputs_1:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'inputs_2:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'inputs_3:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
            "WARNING:tensorflow:Model's `__init__()` arguments contain non-serializable objects. Please implement a `get_config()` method in the subclassed Model for proper saving and loading. Defaulting to empty config.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'queries_user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'queries_gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'queries_age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'queries_level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'queries_user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'queries_gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'queries_age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'queries_level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'inputs_user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'inputs_gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'inputs_age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'inputs_level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor. Received: inputs={'user_id': <tf.Tensor 'inputs_user_id:0' shape=(None,) dtype=int64>, 'gender': <tf.Tensor 'inputs_gender:0' shape=(None,) dtype=string>, 'age': <tf.Tensor 'inputs_age:0' shape=(None,) dtype=int64>, 'level': <tf.Tensor 'inputs_level:0' shape=(None,) dtype=int64>}. Consider rewriting this model with the Functional API.\n",
            "WARNING:absl:Found untraced functions such as query_with_exclusions while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "model_save_name = 'model_id'\n",
        "path = F\"/content/drive/My Drive/{model_save_name}\"\n",
        "tf.saved_model.save(index, path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "oYqOoaXpQobF"
      },
      "outputs": [],
      "source": [
        "# Give recommendation from saved model\n",
        "\n",
        "loaded = tf.saved_model.load(\"/content/drive/My Drive/model_id\")\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdgXqj4zs3_K",
        "outputId": "c81e4bce-ea8e-4020-e57d-060239e55d9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommendations: [105 114 117]\n"
          ]
        }
      ],
      "source": [
        "# Pass a user id in, get top predicted movie titles back.\n",
        "query = {\"user_id\": np.array([80]),\"gender\": np.array([\"Laki-laki\"]),\"age\": np.array([11]),\"level\": np.array([2])}\n",
        "\n",
        "scores, titles = loaded(query)\n",
        "\n",
        "print(f\"Recommendations: {titles[0][:3]}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cqp50XvwsIfU"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "GyHpquEWAyA8",
        "YB9PZmMVHh5f"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}