Copy_of_CS539_Final_Project_Team_ART.ipynb

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      "provenance": [],
      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import metrics\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qPBoYfAQBiuQ",
        "outputId": "cf39fd0c-c81f-440b-fe2e-0782a2dc1789"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTmi0nKJYr9W",
        "outputId": "1ae8c3f1-1d83-4d60-8016-9a8ba254336c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num GPUs Available:  1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Some data preprocessing"
      ],
      "metadata": {
        "id": "HTE6L99RLSUE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "0ADy6D62Ax4y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "f990ad02-3407-4f21-c963-c54cc244f32a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            track_name    artist(s)_name  artist_count  \\\n",
              "0  Seven (feat. Latto) (Explicit Ver.)  Latto, Jung Kook             2   \n",
              "1                                 LALA       Myke Towers             1   \n",
              "2                              vampire    Olivia Rodrigo             1   \n",
              "3                         Cruel Summer      Taylor Swift             1   \n",
              "4                       WHERE SHE GOES         Bad Bunny             1   \n",
              "\n",
              "   released_year  released_month  released_day  in_spotify_playlists  \\\n",
              "0           2023               7            14                   553   \n",
              "1           2023               3            23                  1474   \n",
              "2           2023               6            30                  1397   \n",
              "3           2019               8            23                  7858   \n",
              "4           2023               5            18                  3133   \n",
              "\n",
              "   in_spotify_charts    streams  in_apple_playlists  ...  bpm key   mode  \\\n",
              "0                147  141381703                  43  ...  125   B  Major   \n",
              "1                 48  133716286                  48  ...   92  C#  Major   \n",
              "2                113  140003974                  94  ...  138   F  Major   \n",
              "3                100  800840817                 116  ...  170   A  Major   \n",
              "4                 50  303236322                  84  ...  144   A  Minor   \n",
              "\n",
              "  danceability_%  valence_% energy_% acousticness_%  instrumentalness_%  \\\n",
              "0             80         89       83             31                   0   \n",
              "1             71         61       74              7                   0   \n",
              "2             51         32       53             17                   0   \n",
              "3             55         58       72             11                   0   \n",
              "4             65         23       80             14                  63   \n",
              "\n",
              "   liveness_%  speechiness_%  \n",
              "0           8              4  \n",
              "1          10              4  \n",
              "2          31              6  \n",
              "3          11             15  \n",
              "4          11              6  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>track_name</th>\n",
              "      <th>artist(s)_name</th>\n",
              "      <th>artist_count</th>\n",
              "      <th>released_year</th>\n",
              "      <th>released_month</th>\n",
              "      <th>released_day</th>\n",
              "      <th>in_spotify_playlists</th>\n",
              "      <th>in_spotify_charts</th>\n",
              "      <th>streams</th>\n",
              "      <th>in_apple_playlists</th>\n",
              "      <th>...</th>\n",
              "      <th>bpm</th>\n",
              "      <th>key</th>\n",
              "      <th>mode</th>\n",
              "      <th>danceability_%</th>\n",
              "      <th>valence_%</th>\n",
              "      <th>energy_%</th>\n",
              "      <th>acousticness_%</th>\n",
              "      <th>instrumentalness_%</th>\n",
              "      <th>liveness_%</th>\n",
              "      <th>speechiness_%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Seven (feat. Latto) (Explicit Ver.)</td>\n",
              "      <td>Latto, Jung Kook</td>\n",
              "      <td>2</td>\n",
              "      <td>2023</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>553</td>\n",
              "      <td>147</td>\n",
              "      <td>141381703</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>125</td>\n",
              "      <td>B</td>\n",
              "      <td>Major</td>\n",
              "      <td>80</td>\n",
              "      <td>89</td>\n",
              "      <td>83</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LALA</td>\n",
              "      <td>Myke Towers</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>3</td>\n",
              "      <td>23</td>\n",
              "      <td>1474</td>\n",
              "      <td>48</td>\n",
              "      <td>133716286</td>\n",
              "      <td>48</td>\n",
              "      <td>...</td>\n",
              "      <td>92</td>\n",
              "      <td>C#</td>\n",
              "      <td>Major</td>\n",
              "      <td>71</td>\n",
              "      <td>61</td>\n",
              "      <td>74</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vampire</td>\n",
              "      <td>Olivia Rodrigo</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "      <td>1397</td>\n",
              "      <td>113</td>\n",
              "      <td>140003974</td>\n",
              "      <td>94</td>\n",
              "      <td>...</td>\n",
              "      <td>138</td>\n",
              "      <td>F</td>\n",
              "      <td>Major</td>\n",
              "      <td>51</td>\n",
              "      <td>32</td>\n",
              "      <td>53</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cruel Summer</td>\n",
              "      <td>Taylor Swift</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>8</td>\n",
              "      <td>23</td>\n",
              "      <td>7858</td>\n",
              "      <td>100</td>\n",
              "      <td>800840817</td>\n",
              "      <td>116</td>\n",
              "      <td>...</td>\n",
              "      <td>170</td>\n",
              "      <td>A</td>\n",
              "      <td>Major</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>72</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WHERE SHE GOES</td>\n",
              "      <td>Bad Bunny</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>3133</td>\n",
              "      <td>50</td>\n",
              "      <td>303236322</td>\n",
              "      <td>84</td>\n",
              "      <td>...</td>\n",
              "      <td>144</td>\n",
              "      <td>A</td>\n",
              "      <td>Minor</td>\n",
              "      <td>65</td>\n",
              "      <td>23</td>\n",
              "      <td>80</td>\n",
              "      <td>14</td>\n",
              "      <td>63</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
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              "<p>5 rows × 24 columns</p>\n",
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              "\n",
              "  @keyframes spin {\n",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Reading in the df\n",
        "df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/539_final_project/spotify-2023.csv', encoding='cp1252')\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# raw dataset info\n",
        "df.info()"
      ],
      "metadata": {
        "id": "Yzp76itI5-0A",
        "outputId": "e74acf4d-4eaa-48e1-eb7e-9571a88cc2f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 953 entries, 0 to 952\n",
            "Data columns (total 24 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   track_name            953 non-null    object\n",
            " 1   artist(s)_name        953 non-null    object\n",
            " 2   artist_count          953 non-null    int64 \n",
            " 3   released_year         953 non-null    int64 \n",
            " 4   released_month        953 non-null    int64 \n",
            " 5   released_day          953 non-null    int64 \n",
            " 6   in_spotify_playlists  953 non-null    int64 \n",
            " 7   in_spotify_charts     953 non-null    int64 \n",
            " 8   streams               953 non-null    object\n",
            " 9   in_apple_playlists    953 non-null    int64 \n",
            " 10  in_apple_charts       953 non-null    int64 \n",
            " 11  in_deezer_playlists   953 non-null    object\n",
            " 12  in_deezer_charts      953 non-null    int64 \n",
            " 13  in_shazam_charts      903 non-null    object\n",
            " 14  bpm                   953 non-null    int64 \n",
            " 15  key                   858 non-null    object\n",
            " 16  mode                  953 non-null    object\n",
            " 17  danceability_%        953 non-null    int64 \n",
            " 18  valence_%             953 non-null    int64 \n",
            " 19  energy_%              953 non-null    int64 \n",
            " 20  acousticness_%        953 non-null    int64 \n",
            " 21  instrumentalness_%    953 non-null    int64 \n",
            " 22  liveness_%            953 non-null    int64 \n",
            " 23  speechiness_%         953 non-null    int64 \n",
            "dtypes: int64(17), object(7)\n",
            "memory usage: 178.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UtWICagEJjL",
        "outputId": "bef490a3-42cf-4f08-c7fb-533925f6f5f6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['track_name', 'artist(s)_name', 'artist_count', 'released_year',\n",
              "       'released_month', 'released_day', 'in_spotify_playlists',\n",
              "       'in_spotify_charts', 'streams', 'in_apple_playlists', 'in_apple_charts',\n",
              "       'in_deezer_playlists', 'in_deezer_charts', 'in_shazam_charts', 'bpm',\n",
              "       'key', 'mode', 'danceability_%', 'valence_%', 'energy_%',\n",
              "       'acousticness_%', 'instrumentalness_%', 'liveness_%', 'speechiness_%'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can see from this that we're missing values in cols 'in_shazam_charts' and 'key'\n",
        "print(df.isnull().sum())\n",
        "print(df['streams'].isna().sum())\n",
        "\n",
        "df = df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MB9UnMb2Gjxc",
        "outputId": "6bf0bb81-a0cb-4a79-bab5-4b490d7f8337"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "track_name               0\n",
            "artist(s)_name           0\n",
            "artist_count             0\n",
            "released_year            0\n",
            "released_month           0\n",
            "released_day             0\n",
            "in_spotify_playlists     0\n",
            "in_spotify_charts        0\n",
            "streams                  0\n",
            "in_apple_playlists       0\n",
            "in_apple_charts          0\n",
            "in_deezer_playlists      0\n",
            "in_deezer_charts         0\n",
            "in_shazam_charts        50\n",
            "bpm                      0\n",
            "key                     95\n",
            "mode                     0\n",
            "danceability_%           0\n",
            "valence_%                0\n",
            "energy_%                 0\n",
            "acousticness_%           0\n",
            "instrumentalness_%       0\n",
            "liveness_%               0\n",
            "speechiness_%            0\n",
            "dtype: int64\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['streams'] = pd.to_numeric(df['streams'], errors='coerce')\n",
        "df['streams'].isna().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-10-21T04:06:35.724499Z",
          "iopub.execute_input": "2023-10-21T04:06:35.725094Z",
          "iopub.status.idle": "2023-10-21T04:06:35.741099Z",
          "shell.execute_reply.started": "2023-10-21T04:06:35.725063Z",
          "shell.execute_reply": "2023-10-21T04:06:35.739428Z"
        },
        "trusted": true,
        "id": "XU-2xl8MM3xF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae2ccb76-b09b-4b09-c6d9-854fc88b08fa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-76e74de21b63>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['streams'] = pd.to_numeric(df['streams'], errors='coerce')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove NaN values after converting streams to numeric\n",
        "df.dropna(subset=df.columns.difference(['track_and_artist']), inplace=True)\n",
        "\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4tz0W-rj90D",
        "outputId": "499e917d-c797-466f-a1e8-2a2a3916a0be"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-69a43fc13207>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.dropna(subset=df.columns.difference(['track_and_artist']), inplace=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "track_name              0\n",
              "artist(s)_name          0\n",
              "artist_count            0\n",
              "released_year           0\n",
              "released_month          0\n",
              "released_day            0\n",
              "in_spotify_playlists    0\n",
              "in_spotify_charts       0\n",
              "streams                 0\n",
              "in_apple_playlists      0\n",
              "in_apple_charts         0\n",
              "in_deezer_playlists     0\n",
              "in_deezer_charts        0\n",
              "in_shazam_charts        0\n",
              "bpm                     0\n",
              "key                     0\n",
              "mode                    0\n",
              "danceability_%          0\n",
              "valence_%               0\n",
              "energy_%                0\n",
              "acousticness_%          0\n",
              "instrumentalness_%      0\n",
              "liveness_%              0\n",
              "speechiness_%           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['streams'], bins=30, edgecolor='black', linewidth=1)\n",
        "plt.xlabel('Number of streams')\n",
        "plt.ylabel('Number of tracks')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "-cm0xC_Gvrvx",
        "outputId": "0bfd2a14-2be9-4c84-b118-a073fb6e6a70"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-10-21T04:06:35.745192Z",
          "iopub.execute_input": "2023-10-21T04:06:35.745586Z",
          "iopub.status.idle": "2023-10-21T04:06:35.764001Z",
          "shell.execute_reply.started": "2023-10-21T04:06:35.745554Z",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 816 entries, 0 to 952\n",
            "Data columns (total 24 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   track_name            816 non-null    object \n",
            " 1   artist(s)_name        816 non-null    object \n",
            " 2   artist_count          816 non-null    int64  \n",
            " 3   released_year         816 non-null    int64  \n",
            " 4   released_month        816 non-null    int64  \n",
            " 5   released_day          816 non-null    int64  \n",
            " 6   in_spotify_playlists  816 non-null    int64  \n",
            " 7   in_spotify_charts     816 non-null    int64  \n",
            " 8   streams               816 non-null    float64\n",
            " 9   in_apple_playlists    816 non-null    int64  \n",
            " 10  in_apple_charts       816 non-null    int64  \n",
            " 11  in_deezer_playlists   816 non-null    object \n",
            " 12  in_deezer_charts      816 non-null    int64  \n",
            " 13  in_shazam_charts      816 non-null    object \n",
            " 14  bpm                   816 non-null    int64  \n",
            " 15  key                   816 non-null    object \n",
            " 16  mode                  816 non-null    object \n",
            " 17  danceability_%        816 non-null    int64  \n",
            " 18  valence_%             816 non-null    int64  \n",
            " 19  energy_%              816 non-null    int64  \n",
            " 20  acousticness_%        816 non-null    int64  \n",
            " 21  instrumentalness_%    816 non-null    int64  \n",
            " 22  liveness_%            816 non-null    int64  \n",
            " 23  speechiness_%         816 non-null    int64  \n",
            "dtypes: float64(1), int64(17), object(6)\n",
            "memory usage: 159.4+ KB\n"
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        "## TODO: re-add key, 'mode'\n",
        "df['track_and_artist'] = df['track_name'] + '-' + df['artist(s)_name']\n",
        "voi_col = ['track_and_artist', 'streams', 'danceability_%', 'valence_%', 'energy_%', 'acousticness_%', 'instrumentalness_%', 'liveness_%', 'speechiness_%', 'key', 'mode', 'bpm']\n",
        "df_voi = df[voi_col]\n",
        "df_voi.head()"
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              "                                    track_and_artist      streams  \\\n",
              "0  Seven (feat. Latto) (Explicit Ver.)-Latto, Jun...  141381703.0   \n",
              "1                                   LALA-Myke Towers  133716286.0   \n",
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              "3                          Cruel Summer-Taylor Swift  800840817.0   \n",
              "4                           WHERE SHE GOES-Bad Bunny  303236322.0   \n",
              "\n",
              "   danceability_%  valence_%  energy_%  acousticness_%  instrumentalness_%  \\\n",
              "0              80         89        83              31                   0   \n",
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              "4              65         23        80              14                  63   \n",
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              "   liveness_%  speechiness_% key   mode  bpm  \n",
              "0           8              4   B  Major  125  \n",
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        "df_features = df_voi\n",
        "df_features = pd.get_dummies(df_voi, columns=['key', 'mode'], prefix=['key', 'mode'])\n",
        "df_features.head()"
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              "                                    track_and_artist      streams  \\\n",
              "0  Seven (feat. Latto) (Explicit Ver.)-Latto, Jun...  141381703.0   \n",
              "1                                   LALA-Myke Towers  133716286.0   \n",
              "2                             vampire-Olivia Rodrigo  140003974.0   \n",
              "3                          Cruel Summer-Taylor Swift  800840817.0   \n",
              "4                           WHERE SHE GOES-Bad Bunny  303236322.0   \n",
              "\n",
              "   danceability_%  valence_%  energy_%  acousticness_%  instrumentalness_%  \\\n",
              "0              80         89        83              31                   0   \n",
              "1              71         61        74               7                   0   \n",
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              "3              55         58        72              11                   0   \n",
              "4              65         23        80              14                  63   \n",
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              "   liveness_%  speechiness_%  bpm  ...  key_C#  key_D  key_D#  key_E  key_F  \\\n",
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              "      <th></th>\n",
              "      <th>track_and_artist</th>\n",
              "      <th>streams</th>\n",
              "      <th>danceability_%</th>\n",
              "      <th>valence_%</th>\n",
              "      <th>energy_%</th>\n",
              "      <th>acousticness_%</th>\n",
              "      <th>instrumentalness_%</th>\n",
              "      <th>liveness_%</th>\n",
              "      <th>speechiness_%</th>\n",
              "      <th>bpm</th>\n",
              "      <th>...</th>\n",
              "      <th>key_C#</th>\n",
              "      <th>key_D</th>\n",
              "      <th>key_D#</th>\n",
              "      <th>key_E</th>\n",
              "      <th>key_F</th>\n",
              "      <th>key_F#</th>\n",
              "      <th>key_G</th>\n",
              "      <th>key_G#</th>\n",
              "      <th>mode_Major</th>\n",
              "      <th>mode_Minor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Seven (feat. Latto) (Explicit Ver.)-Latto, Jun...</td>\n",
              "      <td>141381703.0</td>\n",
              "      <td>80</td>\n",
              "      <td>89</td>\n",
              "      <td>83</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>125</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LALA-Myke Towers</td>\n",
              "      <td>133716286.0</td>\n",
              "      <td>71</td>\n",
              "      <td>61</td>\n",
              "      <td>74</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>92</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vampire-Olivia Rodrigo</td>\n",
              "      <td>140003974.0</td>\n",
              "      <td>51</td>\n",
              "      <td>32</td>\n",
              "      <td>53</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>6</td>\n",
              "      <td>138</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cruel Summer-Taylor Swift</td>\n",
              "      <td>800840817.0</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>72</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>170</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WHERE SHE GOES-Bad Bunny</td>\n",
              "      <td>303236322.0</td>\n",
              "      <td>65</td>\n",
              "      <td>23</td>\n",
              "      <td>80</td>\n",
              "      <td>14</td>\n",
              "      <td>63</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>144</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#rename columns of interests\n",
        "re_col = {\n",
        "    'danceability_%': 'danceability',\n",
        "    'valence_%': 'valence',\n",
        "    'energy_%': 'energy',\n",
        "    'acousticness_%': 'acousticness',\n",
        "    'liveness_%': 'liveness',\n",
        "    'speechiness_%': 'speechiness',\n",
        "    'instrumentalness_%': 'instrumentalness'\n",
        "}\n",
        "\n",
        "df_features.rename(columns=re_col, inplace=True)\n",
        "\n",
        "for feat in re_col.values():\n",
        "  df_features[feat] = df_features[feat].div(100)\n",
        "\n",
        "df_features.head()"
      ],
      "metadata": {
        "_kg_hide-input": false,
        "execution": {
          "iopub.status.busy": "2023-10-21T04:06:35.860236Z",
          "iopub.execute_input": "2023-10-21T04:06:35.860705Z",
          "iopub.status.idle": "2023-10-21T04:06:35.888084Z",
          "shell.execute_reply.started": "2023-10-21T04:06:35.860673Z",
          "shell.execute_reply": "2023-10-21T04:06:35.887156Z"
        },
        "trusted": true,
        "id": "3QINiQGWM3xK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "outputId": "b501eb5b-8620-4a70-8b79-7c2e4ad39332"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    track_and_artist      streams  \\\n",
              "0  Seven (feat. Latto) (Explicit Ver.)-Latto, Jun...  141381703.0   \n",
              "1                                   LALA-Myke Towers  133716286.0   \n",
              "2                             vampire-Olivia Rodrigo  140003974.0   \n",
              "3                          Cruel Summer-Taylor Swift  800840817.0   \n",
              "4                           WHERE SHE GOES-Bad Bunny  303236322.0   \n",
              "\n",
              "   danceability  valence  energy  acousticness  instrumentalness  liveness  \\\n",
              "0          0.80     0.89    0.83          0.31              0.00      0.08   \n",
              "1          0.71     0.61    0.74          0.07              0.00      0.10   \n",
              "2          0.51     0.32    0.53          0.17              0.00      0.31   \n",
              "3          0.55     0.58    0.72          0.11              0.00      0.11   \n",
              "4          0.65     0.23    0.80          0.14              0.63      0.11   \n",
              "\n",
              "   speechiness  bpm  ...  key_C#  key_D  key_D#  key_E  key_F  key_F#  key_G  \\\n",
              "0         0.04  125  ...       0      0       0      0      0       0      0   \n",
              "1         0.04   92  ...       1      0       0      0      0       0      0   \n",
              "2         0.06  138  ...       0      0       0      0      1       0      0   \n",
              "3         0.15  170  ...       0      0       0      0      0       0      0   \n",
              "4         0.06  144  ...       0      0       0      0      0       0      0   \n",
              "\n",
              "   key_G#  mode_Major  mode_Minor  \n",
              "0       0           1           0  \n",
              "1       0           1           0  \n",
              "2       0           1           0  \n",
              "3       0           1           0  \n",
              "4       0           0           1  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>vampire-Olivia Rodrigo</td>\n",
              "      <td>140003974.0</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.06</td>\n",
              "      <td>138</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cruel Summer-Taylor Swift</td>\n",
              "      <td>800840817.0</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.15</td>\n",
              "      <td>170</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>WHERE SHE GOES-Bad Bunny</td>\n",
              "      <td>303236322.0</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.06</td>\n",
              "      <td>144</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cd246ef-d4b9-42f4-a7f8-90f4b5392270')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-6cd246ef-d4b9-42f4-a7f8-90f4b5392270 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-6cd246ef-d4b9-42f4-a7f8-90f4b5392270');\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",
              "\n",
              "\n",
              "<div id=\"df-aec02242-b7c7-4f75-9158-f02e6176e3ce\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aec02242-b7c7-4f75-9158-f02e6176e3ce')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aec02242-b7c7-4f75-9158-f02e6176e3ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Preprocessor:\n",
        "  def __init__(self):\n",
        "    self.x_scaler = None\n",
        "    self.max_y = None\n",
        "\n",
        "  def fit_transform(self, df):\n",
        "    \"\"\"Use for training\"\"\"\n",
        "\n",
        "    y = df['streams'].values.reshape(-1, 1)\n",
        "    y = np.log(y)\n",
        "    self.max_y = max(y)\n",
        "    self.min_y = min(y)\n",
        "    y_scaled = (y - self.min_y) / (self.max_y - self.min_y)\n",
        "\n",
        "    X = df.drop(['streams', 'track_and_artist'], axis=1).values\n",
        "\n",
        "    self.x_scaler = StandardScaler()\n",
        "    X_scaled = self.x_scaler.fit_transform(X)\n",
        "\n",
        "    return X_scaled, y_scaled\n",
        "\n",
        "  def transform(self, df):\n",
        "    \"\"\"Use for predictions\"\"\"\n",
        "\n",
        "    if self.x_scaler is None or self.max_y is None:\n",
        "      raise Exception('Call fit_transform first!')\n",
        "\n",
        "    y = df['streams'].values.reshape(-1, 1)\n",
        "    y = np.log(y)\n",
        "    y_scaled = y / self.max_y\n",
        "\n",
        "    X = df.drop(['streams', 'track_and_artist'], axis=1).values\n",
        "    X_scaled = self.x_scaler.transform(X)\n",
        "\n",
        "    return X_scaled, y_scaled\n",
        "\n",
        "  def inverse_transform(self, X_scaled, y_scaled):\n",
        "    if self.x_scaler is None or self.max_y is None:\n",
        "      raise Exception('Call fit_transform first!')\n",
        "\n",
        "    if X_scaled is not None:\n",
        "      X_orig = self.x_scaler.inverse_transform(X_scaled)\n",
        "    else:\n",
        "      X_orig = None\n",
        "\n",
        "\n",
        "    y_orig = np.exp(y_scaled * self.max_y) if y_scaled is not None else None\n",
        "\n",
        "    return X_orig, y_orig"
      ],
      "metadata": {
        "id": "F9PdkxpwcYIs"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize the feature variables using StandardScaler, log y values and scale to [0, 1]\n",
        "preprocessor = Preprocessor()\n",
        "X, y = preprocessor.fit_transform(df_features)\n",
        "\n",
        "# Construct track to index dictionary\n",
        "track2idx = dict()\n",
        "for idx in range(len(df_features)):\n",
        "  track2idx[df_features.iloc[idx]['track_and_artist']] = idx\n",
        "\n",
        "print('Cruel Summer-Taylor Swift idx',  track2idx['Cruel Summer-Taylor Swift'])\n",
        "\n",
        "print(\"X[0]\", X[0])\n",
        "print(\"y[0]\", y[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-h63PEqRQNKn",
        "outputId": "687c6a8d-01ea-44e1-d452-5dd3829f7517"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cruel Summer-Taylor Swift idx 3\n",
            "X[0] [ 0.85768756  1.60242241  1.15748749  0.18327973 -0.19150181 -0.7510259\n",
            " -0.63969891  0.08587151 -0.306323   -0.26883699  3.09796749 -0.40503254\n",
            " -0.32510161 -0.19536617 -0.27917599 -0.34545848 -0.30392347 -0.35428413\n",
            " -0.34099717  0.89961805 -0.89961805]\n",
            "y[0] [0.77066384]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training (70%), validation (15%), and test (15%) sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "print(\"Training set:\", X_train.shape, y_train.shape)\n",
        "print(\"Validation set:\", X_val.shape, y_val.shape)\n",
        "print(\"Test set:\", X_test.shape, y_test.shape)\n",
        "\n",
        "print(\"X_train[0]\", X_train[0])\n",
        "print(\"y_train[0]\", y_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DY_Gb2k2bDl1",
        "outputId": "6ba68fd0-740f-4a3b-9b8a-a3a363f64425"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set: (571, 21) (571, 1)\n",
            "Validation set: (122, 21) (122, 1)\n",
            "Test set: (123, 21) (123, 1)\n",
            "X_train[0] [ 0.380842    1.56006077 -0.45674078  1.16513545 -0.19150181 -0.97255999\n",
            " -0.83545901 -0.8725485  -0.306323   -0.26883699  3.09796749 -0.40503254\n",
            " -0.32510161 -0.19536617 -0.27917599 -0.34545848 -0.30392347 -0.35428413\n",
            " -0.34099717  0.89961805 -0.89961805]\n",
            "y_train[0] [0.80733708]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining our Model"
      ],
      "metadata": {
        "id": "RseyhhveQ-Qk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(n_hidden=3, hidden_dim=16):\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Dropout(0.2, input_shape=(X_train.shape[1],)))\n",
        "  model.add(Dense(hidden_dim, activation='relu', input_shape=(X_train.shape[1],)))\n",
        "\n",
        "  for i in range(n_hidden - 1):\n",
        "    model.add(Dense(hidden_dim, activation='relu'))\n",
        "\n",
        "  model.add(Dense(8, activation='sigmoid'))\n",
        "  model.add(Dense(1))\n",
        "\n",
        "  return model\n",
        "\n",
        "model = build_model(hidden_dim=8)\n",
        "\n",
        "# Optimizer\n",
        "opt = tf.keras.optimizers.Adam(learning_rate=0.0005, clipnorm=1.0) # 0.001 #0.0005\n",
        "\n",
        "# Compile the model\n",
        "# Using Mean Squared Error (MSE) as loss function\n",
        "model.compile(optimizer=opt, loss='huber_loss', metrics=['mean_squared_error'])"
      ],
      "metadata": {
        "id": "C-28PhcdQUVt"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruNm0km4UADG",
        "outputId": "a5acca9e-b4d5-4f90-ccad-63f0584ffc43"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dropout_2 (Dropout)         (None, 21)                0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 8)                 176       \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 8)                 72        \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 8)                 72        \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 8)                 72        \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 401 (1.57 KB)\n",
            "Trainable params: 401 (1.57 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "Epoches = 100\n",
        "class CustomCallback(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        val_loss = logs['val_loss']\n",
        "        val_mse = logs['val_mean_squared_error']\n",
        "        test_loss, test_mse = model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "        print(f' - Validation Loss: {val_loss:.4f} - Validation MSE: {val_mse:.4f} - Test Loss: {test_loss:.4f} - Test MSE: {test_mse:.4f}')\n",
        "\n",
        "callback = CustomCallback()\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_data=(X_val, y_val),\n",
        "    epochs=Epoches,\n",
        "    batch_size=32,\n",
        "    callbacks=[callback]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlFEcMkMRISf",
        "outputId": "dfe54b22-ed97-438b-ed27-65d942c13ac4"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "16/18 [=========================>....] - ETA: 0s - loss: 0.1223 - mean_squared_error: 0.2446 - Validation Loss: 0.1066 - Validation MSE: 0.2140 - Test Loss: 0.1014 - Test MSE: 0.2029\n",
            "18/18 [==============================] - 3s 26ms/step - loss: 0.1206 - mean_squared_error: 0.2413 - val_loss: 0.1066 - val_mean_squared_error: 0.2140\n",
            "Epoch 2/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0925 - mean_squared_error: 0.1850 - Validation Loss: 0.0755 - Validation MSE: 0.1514 - Test Loss: 0.0698 - Test MSE: 0.1397\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0850 - mean_squared_error: 0.1701 - val_loss: 0.0755 - val_mean_squared_error: 0.1514\n",
            "Epoch 3/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0627 - mean_squared_error: 0.1254 - Validation Loss: 0.0533 - Validation MSE: 0.1069 - Test Loss: 0.0478 - Test MSE: 0.0956\n",
            "18/18 [==============================] - 0s 17ms/step - loss: 0.0566 - mean_squared_error: 0.1131 - val_loss: 0.0533 - val_mean_squared_error: 0.1069\n",
            "Epoch 4/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0421 - mean_squared_error: 0.0842 - Validation Loss: 0.0386 - Validation MSE: 0.0773 - Test Loss: 0.0334 - Test MSE: 0.0668\n",
            "18/18 [==============================] - 0s 13ms/step - loss: 0.0404 - mean_squared_error: 0.0807 - val_loss: 0.0386 - val_mean_squared_error: 0.0773\n",
            "Epoch 5/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0311 - mean_squared_error: 0.0623 - Validation Loss: 0.0294 - Validation MSE: 0.0588 - Test Loss: 0.0247 - Test MSE: 0.0494\n",
            "18/18 [==============================] - 0s 13ms/step - loss: 0.0290 - mean_squared_error: 0.0580 - val_loss: 0.0294 - val_mean_squared_error: 0.0588\n",
            "Epoch 6/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0232 - mean_squared_error: 0.0464 - Validation Loss: 0.0237 - Validation MSE: 0.0474 - Test Loss: 0.0195 - Test MSE: 0.0391\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0207 - mean_squared_error: 0.0414 - val_loss: 0.0237 - val_mean_squared_error: 0.0474\n",
            "Epoch 7/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0175 - mean_squared_error: 0.0351 - Validation Loss: 0.0199 - Validation MSE: 0.0398 - Test Loss: 0.0162 - Test MSE: 0.0324\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0175 - mean_squared_error: 0.0351 - val_loss: 0.0199 - val_mean_squared_error: 0.0398\n",
            "Epoch 8/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0157 - mean_squared_error: 0.0313 - Validation Loss: 0.0174 - Validation MSE: 0.0347 - Test Loss: 0.0140 - Test MSE: 0.0279\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0155 - mean_squared_error: 0.0309 - val_loss: 0.0174 - val_mean_squared_error: 0.0347\n",
            "Epoch 9/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0126 - mean_squared_error: 0.0252 - Validation Loss: 0.0155 - Validation MSE: 0.0311 - Test Loss: 0.0123 - Test MSE: 0.0247\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0135 - mean_squared_error: 0.0269 - val_loss: 0.0155 - val_mean_squared_error: 0.0311\n",
            "Epoch 10/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0120 - mean_squared_error: 0.0241 - Validation Loss: 0.0141 - Validation MSE: 0.0282 - Test Loss: 0.0111 - Test MSE: 0.0222\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0116 - mean_squared_error: 0.0232 - val_loss: 0.0141 - val_mean_squared_error: 0.0282\n",
            "Epoch 11/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0096 - mean_squared_error: 0.0191 - Validation Loss: 0.0130 - Validation MSE: 0.0260 - Test Loss: 0.0102 - Test MSE: 0.0204\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0096 - mean_squared_error: 0.0193 - val_loss: 0.0130 - val_mean_squared_error: 0.0260\n",
            "Epoch 12/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0092 - mean_squared_error: 0.0184 - Validation Loss: 0.0121 - Validation MSE: 0.0242 - Test Loss: 0.0094 - Test MSE: 0.0187\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0090 - mean_squared_error: 0.0180 - val_loss: 0.0121 - val_mean_squared_error: 0.0242\n",
            "Epoch 13/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0098 - mean_squared_error: 0.0197 - Validation Loss: 0.0114 - Validation MSE: 0.0227 - Test Loss: 0.0087 - Test MSE: 0.0175\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0090 - mean_squared_error: 0.0180 - val_loss: 0.0114 - val_mean_squared_error: 0.0227\n",
            "Epoch 14/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0075 - mean_squared_error: 0.0150 - Validation Loss: 0.0108 - Validation MSE: 0.0215 - Test Loss: 0.0082 - Test MSE: 0.0165\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0082 - mean_squared_error: 0.0164 - val_loss: 0.0108 - val_mean_squared_error: 0.0215\n",
            "Epoch 15/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0080 - mean_squared_error: 0.0159 - Validation Loss: 0.0102 - Validation MSE: 0.0204 - Test Loss: 0.0078 - Test MSE: 0.0155\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0078 - mean_squared_error: 0.0156 - val_loss: 0.0102 - val_mean_squared_error: 0.0204\n",
            "Epoch 16/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0071 - mean_squared_error: 0.0143 - Validation Loss: 0.0097 - Validation MSE: 0.0194 - Test Loss: 0.0073 - Test MSE: 0.0146\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 0.0068 - mean_squared_error: 0.0136 - val_loss: 0.0097 - val_mean_squared_error: 0.0194\n",
            "Epoch 17/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0063 - mean_squared_error: 0.0127 - Validation Loss: 0.0093 - Validation MSE: 0.0186 - Test Loss: 0.0070 - Test MSE: 0.0139\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0064 - mean_squared_error: 0.0127 - val_loss: 0.0093 - val_mean_squared_error: 0.0186\n",
            "Epoch 18/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0058 - mean_squared_error: 0.0116 - Validation Loss: 0.0089 - Validation MSE: 0.0178 - Test Loss: 0.0067 - Test MSE: 0.0133\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0059 - mean_squared_error: 0.0118 - val_loss: 0.0089 - val_mean_squared_error: 0.0178\n",
            "Epoch 19/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0058 - mean_squared_error: 0.0115 - Validation Loss: 0.0086 - Validation MSE: 0.0172 - Test Loss: 0.0064 - Test MSE: 0.0129\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0056 - mean_squared_error: 0.0112 - val_loss: 0.0086 - val_mean_squared_error: 0.0172\n",
            "Epoch 20/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0051 - mean_squared_error: 0.0102 - Validation Loss: 0.0084 - Validation MSE: 0.0168 - Test Loss: 0.0062 - Test MSE: 0.0125\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 0.0053 - mean_squared_error: 0.0105 - val_loss: 0.0084 - val_mean_squared_error: 0.0168\n",
            "Epoch 21/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0053 - mean_squared_error: 0.0106 - Validation Loss: 0.0081 - Validation MSE: 0.0163 - Test Loss: 0.0060 - Test MSE: 0.0120\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0052 - mean_squared_error: 0.0104 - val_loss: 0.0081 - val_mean_squared_error: 0.0163\n",
            "Epoch 22/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0050 - mean_squared_error: 0.0101 - Validation Loss: 0.0079 - Validation MSE: 0.0158 - Test Loss: 0.0058 - Test MSE: 0.0117\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 0.0050 - mean_squared_error: 0.0099 - val_loss: 0.0079 - val_mean_squared_error: 0.0158\n",
            "Epoch 23/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0049 - mean_squared_error: 0.0098 - Validation Loss: 0.0077 - Validation MSE: 0.0154 - Test Loss: 0.0057 - Test MSE: 0.0113\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0049 - mean_squared_error: 0.0097 - val_loss: 0.0077 - val_mean_squared_error: 0.0154\n",
            "Epoch 24/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0047 - mean_squared_error: 0.0094 - Validation Loss: 0.0076 - Validation MSE: 0.0151 - Test Loss: 0.0056 - Test MSE: 0.0111\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0048 - mean_squared_error: 0.0096 - val_loss: 0.0076 - val_mean_squared_error: 0.0151\n",
            "Epoch 25/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0042 - mean_squared_error: 0.0084 - Validation Loss: 0.0074 - Validation MSE: 0.0148 - Test Loss: 0.0054 - Test MSE: 0.0109\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0043 - mean_squared_error: 0.0086 - val_loss: 0.0074 - val_mean_squared_error: 0.0148\n",
            "Epoch 26/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0041 - mean_squared_error: 0.0083 - Validation Loss: 0.0072 - Validation MSE: 0.0145 - Test Loss: 0.0053 - Test MSE: 0.0106\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0043 - mean_squared_error: 0.0087 - val_loss: 0.0072 - val_mean_squared_error: 0.0145\n",
            "Epoch 27/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0039 - mean_squared_error: 0.0078 - Validation Loss: 0.0071 - Validation MSE: 0.0142 - Test Loss: 0.0052 - Test MSE: 0.0104\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0039 - mean_squared_error: 0.0078 - val_loss: 0.0071 - val_mean_squared_error: 0.0142\n",
            "Epoch 28/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0043 - mean_squared_error: 0.0085 - Validation Loss: 0.0070 - Validation MSE: 0.0139 - Test Loss: 0.0051 - Test MSE: 0.0102\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0041 - mean_squared_error: 0.0081 - val_loss: 0.0070 - val_mean_squared_error: 0.0139\n",
            "Epoch 29/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0038 - mean_squared_error: 0.0077 - Validation Loss: 0.0068 - Validation MSE: 0.0137 - Test Loss: 0.0050 - Test MSE: 0.0100\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0037 - mean_squared_error: 0.0073 - val_loss: 0.0068 - val_mean_squared_error: 0.0137\n",
            "Epoch 30/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0041 - mean_squared_error: 0.0082 - Validation Loss: 0.0067 - Validation MSE: 0.0135 - Test Loss: 0.0049 - Test MSE: 0.0098\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0039 - mean_squared_error: 0.0079 - val_loss: 0.0067 - val_mean_squared_error: 0.0135\n",
            "Epoch 31/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0036 - mean_squared_error: 0.0072 - Validation Loss: 0.0066 - Validation MSE: 0.0133 - Test Loss: 0.0048 - Test MSE: 0.0097\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0038 - mean_squared_error: 0.0076 - val_loss: 0.0066 - val_mean_squared_error: 0.0133\n",
            "Epoch 32/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0036 - mean_squared_error: 0.0071 - Validation Loss: 0.0065 - Validation MSE: 0.0131 - Test Loss: 0.0047 - Test MSE: 0.0094\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0036 - mean_squared_error: 0.0071 - val_loss: 0.0065 - val_mean_squared_error: 0.0131\n",
            "Epoch 33/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0034 - mean_squared_error: 0.0068 - Validation Loss: 0.0065 - Validation MSE: 0.0129 - Test Loss: 0.0046 - Test MSE: 0.0093\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0034 - mean_squared_error: 0.0068 - val_loss: 0.0065 - val_mean_squared_error: 0.0129\n",
            "Epoch 34/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0036 - mean_squared_error: 0.0072 - Validation Loss: 0.0064 - Validation MSE: 0.0128 - Test Loss: 0.0046 - Test MSE: 0.0092\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0034 - mean_squared_error: 0.0067 - val_loss: 0.0064 - val_mean_squared_error: 0.0128\n",
            "Epoch 35/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0034 - mean_squared_error: 0.0068 - Validation Loss: 0.0063 - Validation MSE: 0.0126 - Test Loss: 0.0045 - Test MSE: 0.0091\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0034 - mean_squared_error: 0.0069 - val_loss: 0.0063 - val_mean_squared_error: 0.0126\n",
            "Epoch 36/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0031 - mean_squared_error: 0.0062 - Validation Loss: 0.0063 - Validation MSE: 0.0125 - Test Loss: 0.0045 - Test MSE: 0.0089\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0032 - mean_squared_error: 0.0064 - val_loss: 0.0063 - val_mean_squared_error: 0.0125\n",
            "Epoch 37/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0031 - mean_squared_error: 0.0063 - Validation Loss: 0.0062 - Validation MSE: 0.0124 - Test Loss: 0.0044 - Test MSE: 0.0089\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 0.0032 - mean_squared_error: 0.0063 - val_loss: 0.0062 - val_mean_squared_error: 0.0124\n",
            "Epoch 38/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0032 - mean_squared_error: 0.0064 - Validation Loss: 0.0061 - Validation MSE: 0.0123 - Test Loss: 0.0044 - Test MSE: 0.0088\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0031 - mean_squared_error: 0.0063 - val_loss: 0.0061 - val_mean_squared_error: 0.0123\n",
            "Epoch 39/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0031 - mean_squared_error: 0.0062 - Validation Loss: 0.0061 - Validation MSE: 0.0122 - Test Loss: 0.0044 - Test MSE: 0.0087\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0031 - mean_squared_error: 0.0062 - val_loss: 0.0061 - val_mean_squared_error: 0.0122\n",
            "Epoch 40/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0032 - mean_squared_error: 0.0064 - Validation Loss: 0.0060 - Validation MSE: 0.0121 - Test Loss: 0.0043 - Test MSE: 0.0086\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0032 - mean_squared_error: 0.0063 - val_loss: 0.0060 - val_mean_squared_error: 0.0121\n",
            "Epoch 41/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0029 - mean_squared_error: 0.0058 - Validation Loss: 0.0060 - Validation MSE: 0.0120 - Test Loss: 0.0043 - Test MSE: 0.0085\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0030 - mean_squared_error: 0.0059 - val_loss: 0.0060 - val_mean_squared_error: 0.0120\n",
            "Epoch 42/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0032 - mean_squared_error: 0.0064 - Validation Loss: 0.0060 - Validation MSE: 0.0119 - Test Loss: 0.0042 - Test MSE: 0.0085\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0030 - mean_squared_error: 0.0061 - val_loss: 0.0060 - val_mean_squared_error: 0.0119\n",
            "Epoch 43/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0030 - mean_squared_error: 0.0061 - Validation Loss: 0.0059 - Validation MSE: 0.0118 - Test Loss: 0.0042 - Test MSE: 0.0084\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0030 - mean_squared_error: 0.0060 - val_loss: 0.0059 - val_mean_squared_error: 0.0118\n",
            "Epoch 44/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0059 - Validation MSE: 0.0117 - Test Loss: 0.0042 - Test MSE: 0.0083\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0029 - mean_squared_error: 0.0059 - val_loss: 0.0059 - val_mean_squared_error: 0.0117\n",
            "Epoch 45/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0030 - mean_squared_error: 0.0060 - Validation Loss: 0.0058 - Validation MSE: 0.0116 - Test Loss: 0.0041 - Test MSE: 0.0083\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0029 - mean_squared_error: 0.0058 - val_loss: 0.0058 - val_mean_squared_error: 0.0116\n",
            "Epoch 46/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0029 - mean_squared_error: 0.0057 - Validation Loss: 0.0058 - Validation MSE: 0.0116 - Test Loss: 0.0041 - Test MSE: 0.0082\n",
            "18/18 [==============================] - 0s 25ms/step - loss: 0.0031 - mean_squared_error: 0.0062 - val_loss: 0.0058 - val_mean_squared_error: 0.0116\n",
            "Epoch 47/100\n",
            "11/18 [=================>............] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0058 - Validation MSE: 0.0115 - Test Loss: 0.0041 - Test MSE: 0.0082\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0028 - mean_squared_error: 0.0056 - val_loss: 0.0058 - val_mean_squared_error: 0.0115\n",
            "Epoch 48/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0029 - mean_squared_error: 0.0058 - Validation Loss: 0.0057 - Validation MSE: 0.0115 - Test Loss: 0.0041 - Test MSE: 0.0081\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0029 - mean_squared_error: 0.0057 - val_loss: 0.0057 - val_mean_squared_error: 0.0115\n",
            "Epoch 49/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0029 - mean_squared_error: 0.0058 - Validation Loss: 0.0057 - Validation MSE: 0.0114 - Test Loss: 0.0041 - Test MSE: 0.0081\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0028 - mean_squared_error: 0.0056 - val_loss: 0.0057 - val_mean_squared_error: 0.0114\n",
            "Epoch 50/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0029 - mean_squared_error: 0.0058 - Validation Loss: 0.0057 - Validation MSE: 0.0114 - Test Loss: 0.0040 - Test MSE: 0.0081\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0029 - mean_squared_error: 0.0057 - val_loss: 0.0057 - val_mean_squared_error: 0.0114\n",
            "Epoch 51/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0052 - Validation Loss: 0.0057 - Validation MSE: 0.0114 - Test Loss: 0.0040 - Test MSE: 0.0080\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0028 - mean_squared_error: 0.0056 - val_loss: 0.0057 - val_mean_squared_error: 0.0114\n",
            "Epoch 52/100\n",
            "11/18 [=================>............] - ETA: 0s - loss: 0.0029 - mean_squared_error: 0.0058 - Validation Loss: 0.0057 - Validation MSE: 0.0113 - Test Loss: 0.0040 - Test MSE: 0.0080\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0027 - mean_squared_error: 0.0055 - val_loss: 0.0057 - val_mean_squared_error: 0.0113\n",
            "Epoch 53/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0056 - Validation MSE: 0.0113 - Test Loss: 0.0040 - Test MSE: 0.0080\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0028 - mean_squared_error: 0.0056 - val_loss: 0.0056 - val_mean_squared_error: 0.0113\n",
            "Epoch 54/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0056 - Validation MSE: 0.0113 - Test Loss: 0.0040 - Test MSE: 0.0080\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0027 - mean_squared_error: 0.0055 - val_loss: 0.0056 - val_mean_squared_error: 0.0113\n",
            "Epoch 55/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0056 - Validation MSE: 0.0113 - Test Loss: 0.0040 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0056 - val_mean_squared_error: 0.0113\n",
            "Epoch 56/100\n",
            "11/18 [=================>............] - ETA: 0s - loss: 0.0028 - mean_squared_error: 0.0057 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0040 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0028 - mean_squared_error: 0.0055 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 57/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0040 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0027 - mean_squared_error: 0.0055 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 58/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0040 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 59/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0052 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0039 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 60/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0039 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 61/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0039 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 62/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0039 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 63/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0056 - Validation MSE: 0.0112 - Test Loss: 0.0039 - Test MSE: 0.0079\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0056 - val_mean_squared_error: 0.0112\n",
            "Epoch 64/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0056 - Validation MSE: 0.0111 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0055 - val_loss: 0.0056 - val_mean_squared_error: 0.0111\n",
            "Epoch 65/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0056 - Validation MSE: 0.0111 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0056 - val_mean_squared_error: 0.0111\n",
            "Epoch 66/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0056 - Validation MSE: 0.0111 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0056 - val_mean_squared_error: 0.0111\n",
            "Epoch 67/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0055 - Validation MSE: 0.0111 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0055 - val_mean_squared_error: 0.0111\n",
            "Epoch 68/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 69/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0025 - mean_squared_error: 0.0050 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 70/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 16ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 71/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 72/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0028 - mean_squared_error: 0.0055 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 73/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0028 - mean_squared_error: 0.0056 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 74/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 75/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 76/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0052 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 77/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 78/100\n",
            "10/18 [===============>..............] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 14ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 79/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 80/100\n",
            "18/18 [==============================] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0055 - Validation MSE: 0.0110 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 15ms/step - loss: 0.0027 - mean_squared_error: 0.0054 - val_loss: 0.0055 - val_mean_squared_error: 0.0110\n",
            "Epoch 81/100\n",
            "17/18 [===========================>..] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 18ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 82/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 83/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0025 - mean_squared_error: 0.0051 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 84/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 85/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0054 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 86/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0051 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 87/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0025 - mean_squared_error: 0.0051 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 88/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0052 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 12ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 89/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0027 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 90/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0051 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 91/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0025 - mean_squared_error: 0.0049 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 92/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0024 - mean_squared_error: 0.0049 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0051 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 93/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0052 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 94/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0053 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 95/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0052 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0025 - mean_squared_error: 0.0051 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 96/100\n",
            "14/18 [======================>.......] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0055 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 97/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0026 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 98/100\n",
            "13/18 [====================>.........] - ETA: 0s - loss: 0.0027 - mean_squared_error: 0.0053 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 10ms/step - loss: 0.0026 - mean_squared_error: 0.0052 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 99/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0024 - mean_squared_error: 0.0048 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0025 - mean_squared_error: 0.0051 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n",
            "Epoch 100/100\n",
            "12/18 [===================>..........] - ETA: 0s - loss: 0.0025 - mean_squared_error: 0.0050 - Validation Loss: 0.0055 - Validation MSE: 0.0109 - Test Loss: 0.0039 - Test MSE: 0.0078\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.0025 - mean_squared_error: 0.0050 - val_loss: 0.0055 - val_mean_squared_error: 0.0109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_training(history):\n",
        "  plt.plot(history.history['loss'])\n",
        "  plt.plot(history.history['val_loss'])\n",
        "  plt.title('Model loss')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "plot_training(history)"
      ],
      "metadata": {
        "id": "_wz6EKkeR06V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "34e793e3-5f7a-4456-a4be-3ffa67e21457"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tune Hyperparameters"
      ],
      "metadata": {
        "id": "1EYeogdyr5ey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_and_fit(n_hidden=3, hidden_dim=16, lr=0.0005, batch_size=32, epochs=50):\n",
        "  model = build_model(n_hidden=n_hidden, hidden_dim=hidden_dim)\n",
        "\n",
        "  # Optimizer\n",
        "  opt = tf.keras.optimizers.Adam(learning_rate=lr, clipnorm=1.0)\n",
        "\n",
        "  # Compile the model\n",
        "  # Using Mean Squared Error (MSE) as loss function\n",
        "  model.compile(optimizer=opt, loss='huber_loss', metrics=['mean_squared_error'])\n",
        "\n",
        "\n",
        "  history = model.fit(\n",
        "      X_train, y_train,\n",
        "      validation_data=(X_val, y_val),\n",
        "      epochs=epochs,\n",
        "      batch_size=batch_size\n",
        "  )\n",
        "\n",
        "  plot_training(history)\n",
        "\n",
        "  return history"
      ],
      "metadata": {
        "id": "SFq_8fkBr7YJ"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate model on test set"
      ],
      "metadata": {
        "id": "qgNI9fTpc3ZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yhat_test = model.predict(X_test)\n",
        "print(y_test.shape, yhat_test.shape)\n",
        "\n",
        "# yhat_test_orig = scaler.inverse_transform(yhat_test).flatten()\n",
        "\n",
        "mse = metrics.mean_squared_error(y_test[None, :], yhat_test[None, :]).numpy().ravel()[0]\n",
        "print(f\"MSE on test set = {mse:.4f}\")\n",
        "\n",
        "yhat_all = model.predict(X)\n",
        "_, yhat_all_inv = preprocessor.inverse_transform(None, yhat_all)\n",
        "plt.hist(yhat_all_inv, bins= 30)\n",
        "plt.axvline(x= np.mean(yhat_all_inv), color='r', ls='--')\n",
        "plt.show()\n",
        "\n",
        "print(np.mean(yhat_all_inv))"
      ],
      "metadata": {
        "id": "ui0GSYp-ULoc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "596aecb6-8a7a-4103-ce91-ccb398ea2ab4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n",
            "(123, 1) (123, 1)\n",
            "MSE on test set = 0.0018\n",
            "26/26 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "67826536.1857958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Test on specific songs\n",
        "import random\n",
        "\n",
        "# songs = [\n",
        "#     'Cruel Summer-Taylor Swift',\n",
        "#     'First Class-Jack Harlow',\n",
        "#     'Softcore-The Neighbourhood',\n",
        "#     'Seven (feat. Latto) (Explicit Ver.)-Latto, Jung Kook'\n",
        "# ]\n",
        "\n",
        "songs = [\n",
        "    'Stargirl Interlude-The Weeknd, Lana Del Rey',\n",
        "    'Falling-Harry Styles',\n",
        "    'All of Me-John Legend',\n",
        "    'Blank Space-Taylor Swift'\n",
        "]\n",
        "\n",
        "song_idx = [track2idx[song] for song in songs]\n",
        "\n",
        "def evaluate_on_songs(model, n=4):\n",
        "\n",
        "    for i in range(n):\n",
        "        idx = song_idx[i] #random.randint(0, len(X) - 1)\n",
        "        df_song = df_features.iloc[idx]\n",
        "        song_name = f\"Song {idx}: {df_song['track_and_artist']}\"\n",
        "\n",
        "        X_song = X[idx][None] # input features\n",
        "        y_song = y[idx] # transformed y_true\n",
        "\n",
        "        yhat = model.predict(X_song).flatten() # output in log(streams) / max_y\n",
        "\n",
        "        _, yhat_orig = preprocessor.inverse_transform(None, yhat) # transform yhat back to raw streams number\n",
        "        streams_true = df_song['streams'].item() # get original streams from dataframe\n",
        "        sq_err = np.square(yhat - y_song).item() # squared error using transformed streams\n",
        "\n",
        "        print(f'{song_name}:\\n\\tyhat={yhat.item():.3f} | y_song={y_song.item():.3f}')\n",
        "        print(f'\\tstreams_pred={yhat_orig.item():,.0f} | streams_true={streams_true:,.0f} | sq_err = {sq_err:.3f}')\n",
        "\n",
        "evaluate_on_songs(model)"
      ],
      "metadata": {
        "id": "C-n7IS1kd4GT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f04ecbde-ca58-4b66-90c0-16dad76839ec"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 18ms/step\n",
            "Song 126: Stargirl Interlude-The Weeknd, Lana Del Rey:\n",
            "\tyhat=0.856 | y_song=0.875\n",
            "\tstreams_pred=149,430,947 | streams_true=611,700,552 | sq_err = 0.000\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "Song 632: Falling-Harry Styles:\n",
            "\tyhat=0.847 | y_song=0.911\n",
            "\tstreams_pred=123,819,340 | streams_true=1,023,187,129 | sq_err = 0.004\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "Song 518: All of Me-John Legend:\n",
            "\tyhat=0.831 | y_song=0.962\n",
            "\tstreams_pred=86,252,227 | streams_true=2,086,124,197 | sq_err = 0.017\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "Song 32: Blank Space-Taylor Swift:\n",
            "\tyhat=0.826 | y_song=0.931\n",
            "\tstreams_pred=78,124,369 | streams_true=1,355,959,075 | sq_err = 0.011\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.layers[1].weights"
      ],
      "metadata": {
        "id": "_pFtonGdmb1i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9feecdf1-f28f-4784-b558-0265baff6570"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Variable 'dense_10/kernel:0' shape=(21, 8) dtype=float32, numpy=\n",
              " array([[-0.37894416,  0.00808292,  0.00782294, -0.3624453 ,  0.05251507,\n",
              "          0.40666753, -0.25674966,  0.44834107],\n",
              "        [-0.29898226,  0.19433379, -0.37401375,  0.21485516, -0.33682284,\n",
              "          0.16484231, -0.27121535,  0.11734293],\n",
              "        [-0.318673  , -0.32622632,  0.1040136 ,  0.38768578, -0.16704638,\n",
              "         -0.23588167,  0.16508132,  0.02285749],\n",
              "        [ 0.0939489 ,  0.05626744,  0.33222649,  0.36977628,  0.17695643,\n",
              "         -0.03832205, -0.30910134, -0.33310562],\n",
              "        [ 0.01257311, -0.17742074,  0.09487409,  0.01243522, -0.24153352,\n",
              "         -0.3474803 , -0.3665943 ,  0.11805411],\n",
              "        [ 0.3154732 , -0.10924163,  0.39287764, -0.38703197,  0.33604977,\n",
              "         -0.3947903 , -0.17169908,  0.10167804],\n",
              "        [ 0.22472285, -0.0742264 , -0.22458874,  0.3467505 , -0.19133106,\n",
              "          0.18452433,  0.0813211 ,  0.369418  ],\n",
              "        [ 0.26350862, -0.16186824,  0.04617249, -0.11674906, -0.36421368,\n",
              "         -0.39516434,  0.40156627,  0.30532497],\n",
              "        [-0.44539967, -0.01908229,  0.02143972,  0.15387087, -0.06673523,\n",
              "          0.15650289,  0.2915554 , -0.463858  ],\n",
              "        [ 0.2165471 ,  0.04993382,  0.09756036,  0.15754367, -0.10204123,\n",
              "         -0.05451241,  0.15954779,  0.1622468 ],\n",
              "        [-0.04725342, -0.07772746,  0.14839253, -0.27762306,  0.337711  ,\n",
              "          0.01931226,  0.31293595,  0.04103702],\n",
              "        [ 0.04992401,  0.32960427, -0.34789565, -0.33191538,  0.41583392,\n",
              "         -0.27886793,  0.45299536, -0.37120947],\n",
              "        [-0.41775107, -0.05134193,  0.3176843 , -0.11057193, -0.2839886 ,\n",
              "          0.27282372,  0.23254707,  0.0698405 ],\n",
              "        [ 0.1927876 , -0.17507349,  0.03259749,  0.09267949,  0.21399574,\n",
              "         -0.02892396,  0.15069096, -0.38650367],\n",
              "        [ 0.30536175, -0.14892551, -0.2039189 ,  0.4243411 , -0.03337723,\n",
              "          0.08412495, -0.51259047, -0.05710284],\n",
              "        [-0.01124399,  0.24774286, -0.32067773, -0.51952046,  0.00326551,\n",
              "          0.2858915 , -0.30440894,  0.29029712],\n",
              "        [ 0.29003325,  0.24171434,  0.3286553 , -0.13943088,  0.27540368,\n",
              "          0.30739537,  0.29818746,  0.1429679 ],\n",
              "        [-0.07823874,  0.03088283,  0.2427746 , -0.29617298, -0.27854252,\n",
              "          0.37032396,  0.13106808, -0.1436265 ],\n",
              "        [ 0.02480807,  0.19341202,  0.22493377,  0.30730236,  0.4228138 ,\n",
              "         -0.22459424, -0.0094845 ,  0.27700606],\n",
              "        [-0.06585467, -0.07435829,  0.42694324, -0.28078613,  0.06869423,\n",
              "          0.38391003,  0.29666355,  0.07494044],\n",
              "        [-0.15825394, -0.27447945, -0.3909715 ,  0.25239515, -0.04165561,\n",
              "          0.12903911, -0.39167455, -0.18590279]], dtype=float32)>,\n",
              " <tf.Variable 'dense_10/bias:0' shape=(8,) dtype=float32, numpy=\n",
              " array([-0.00488731, -0.0637605 ,  0.04302945,  0.07711925,  0.02407077,\n",
              "         0.02616314,  0.05967999,  0.07039303], dtype=float32)>]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nfysoabds5Ze"
      },
      "execution_count": 44,
      "outputs": []
    }
  ]
}