Music_recommendation_system.ipynb

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    "colab": {
      "name": "Music recommendation system.ipynb",
      "provenance": [],
      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lL2HzZVpnc-W"
      },
      "source": [
        "#Loading the dataset\n",
        "songs_metadata_file = 'https://static.turi.com/datasets/millionsong/song_data.csv'\n",
        "#Userid-songid-listen__count\n",
        "triplets_file = 'https://static.turi.com/datasets/millionsong/10000.txt'\n",
        "\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSuMnhh5cRMb"
      },
      "source": [
        "#Importing the dependencies\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import time\n",
        "from sklearn.externals import joblib\n",
        "import Recommenders as Recommenders\n",
        "import Evaluation as Evaluation\n"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNLX5Etk4TKo"
      },
      "source": [
        "song_df1 = pd.read_table(triplets_file, header = None)\n",
        "song_df1.columns = ['user_id', 'song_id', 'listen_count']\n",
        "\n",
        "#reading song metadata\n",
        "song_df2 = pd.read_csv(songs_metadata_file)\n",
        "\n",
        "#Merging the two dataframes to create the input for our sys\n",
        "song_df = pd.merge(song_df1, song_df2.drop_duplicates(['song_id']),\n",
        "                   on = 'song_id', how = 'left')"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "6M-8PZgX9_L2",
        "outputId": "558e9ba7-0c7f-434b-fca1-08bdd602fd8e"
      },
      "source": [
        "#Exploring the data\n",
        "song_df.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>song_id</th>\n",
              "      <th>listen_count</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOAKIMP12A8C130995</td>\n",
              "      <td>1</td>\n",
              "      <td>The Cove</td>\n",
              "      <td>Thicker Than Water</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>SOBBMDR12A8C13253B</td>\n",
              "      <td>2</td>\n",
              "      <td>Entre Dos Aguas</td>\n",
              "      <td>Flamenco Para Niños</td>\n",
              "      <td>Paco De Lucia</td>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "      <td>Stronger</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Kanye West</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "      <td>Constellations</td>\n",
              "      <td>In Between Dreams</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "      <td>Learn To Fly</td>\n",
              "      <td>There Is Nothing Left To Lose</td>\n",
              "      <td>Foo Fighters</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
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              "                                    user_id  ...  year\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...     0\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  1976\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2007\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  2005\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  1999\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 50
        }
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      "cell_type": "code",
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        },
        "id": "hUsmbhjb_8bP",
        "outputId": "83bd8add-d2b9-4207-c91e-dac0ce9b5e47"
      },
      "source": [
        "len(song_df)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNKlo8OSiWTx"
      },
      "source": [
        "#Creating a subset to ensure faster training\n",
        "song_df = song_df.head(10000)\n",
        "\n",
        "#merge song title and artist name columns\n",
        "song_df['song'] = song_df['title'].map(str) + \"-\" + song_df['artist_name']"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 419
        },
        "id": "3IrImq3QjGiB",
        "outputId": "43d26c08-80bc-4660-9f94-62b637de1733"
      },
      "source": [
        "#Showing the most popular songs in the dataset\n",
        "song_grouped = song_df.groupby(['song']).agg({'listen_count' : 'count'}).reset_index()\n",
        "grouped_sum = song_grouped['listen_count'].sum()\n",
        "song_grouped['percentage'] = song_grouped['listen_count'].div(grouped_sum) * 100\n",
        "song_grouped.sort_values(['listen_count', 'song'], ascending = [0,1])\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song</th>\n",
              "      <th>listen_count</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3660</th>\n",
              "      <td>Sehr kosmisch-Harmonia</td>\n",
              "      <td>45</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4678</th>\n",
              "      <td>Undo-Björk</td>\n",
              "      <td>32</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>You're The One-Dwight Yoakam</td>\n",
              "      <td>32</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1071</th>\n",
              "      <td>Dog Days Are Over (Radio Edit)-Florence + The ...</td>\n",
              "      <td>28</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3655</th>\n",
              "      <td>Secrets-OneRepublic</td>\n",
              "      <td>28</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5139</th>\n",
              "      <td>high fives-Four Tet</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5140</th>\n",
              "      <td>in white rooms-Booka Shade</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5143</th>\n",
              "      <td>paranoid android-Christopher O'Riley</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5149</th>\n",
              "      <td>¿Lo Ves? [Piano Y Voz]-Alejandro Sanz</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5150</th>\n",
              "      <td>Época-Gotan Project</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5151 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   song  ...  percentage\n",
              "3660                             Sehr kosmisch-Harmonia  ...        0.45\n",
              "4678                                         Undo-Björk  ...        0.32\n",
              "5105                       You're The One-Dwight Yoakam  ...        0.32\n",
              "1071  Dog Days Are Over (Radio Edit)-Florence + The ...  ...        0.28\n",
              "3655                                Secrets-OneRepublic  ...        0.28\n",
              "...                                                 ...  ...         ...\n",
              "5139                                high fives-Four Tet  ...        0.01\n",
              "5140                         in white rooms-Booka Shade  ...        0.01\n",
              "5143               paranoid android-Christopher O'Riley  ...        0.01\n",
              "5149              ¿Lo Ves? [Piano Y Voz]-Alejandro Sanz  ...        0.01\n",
              "5150                                Época-Gotan Project  ...        0.01\n",
              "\n",
              "[5151 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIdlF2rYoEDr",
        "outputId": "35ae7127-c9e6-4b4f-bdd2-efbad047cec3"
      },
      "source": [
        "#No of unique users and songs in the dataset\n",
        "users = song_df['user_id'].unique()\n",
        "songs = song_df['song'].unique()\n",
        "print('Unique users', len(users))\n",
        "print('Unique songs', len(songs))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unique users 365\n",
            "Unique songs 5151\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdumMytbp2J8",
        "outputId": "05257149-0890-4a73-f79c-d49e0809de31"
      },
      "source": [
        "#Building the song recommender\n",
        "train_data, test_data = train_test_split(song_df, test_size = 0.20, random_state=0)\n",
        "print(train_data.head(5))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                       user_id  ...                                 song\n",
            "7389  94d5bdc37683950e90c56c9b32721edb5d347600  ...          Half Of My Heart-John Mayer\n",
            "9275  1012ecfd277b96487ed8357d02fa8326b13696a5  ...  The Beautiful People-Marilyn Manson\n",
            "2995  15415fa2745b344bce958967c346f2a89f792f63  ...       Sanctify Yourself-Simple Minds\n",
            "5316  ffadf9297a99945c0513cd87939d91d8b602936b  ...       Heart Cooks Brain-Modest Mouse\n",
            "356   5a905f000fc1ff3df7ca807d57edb608863db05d  ...                Rorol-Octopus Project\n",
            "\n",
            "[5 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVkKS81wq3yc"
      },
      "source": [
        "#Popularity based song recommender\n",
        "\n",
        "pm = Recommenders.popularity_recommender_py()\n",
        "pm.create(train_data, 'user_id', 'song')"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "n19gxQCKrceL",
        "outputId": "9f3e0485-899f-4bd6-d58b-8c30de18dc6c"
      },
      "source": [
        "#Using pm to make predictions\n",
        "user_id = users[5]\n",
        "pm.recommend(user_id)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>song</th>\n",
              "      <th>score</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3194</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Sehr kosmisch-Harmonia</td>\n",
              "      <td>37</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4083</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Undo-Björk</td>\n",
              "      <td>27</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>931</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Dog Days Are Over (Radio Edit)-Florence + The ...</td>\n",
              "      <td>24</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4443</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>You're The One-Dwight Yoakam</td>\n",
              "      <td>24</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3034</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Revelry-Kings Of Leon</td>\n",
              "      <td>21</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3189</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Secrets-OneRepublic</td>\n",
              "      <td>21</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4112</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Use Somebody-Kings Of Leon</td>\n",
              "      <td>21</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1207</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Fireflies-Charttraxx Karaoke</td>\n",
              "      <td>20</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1577</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Hey_ Soul Sister-Train</td>\n",
              "      <td>19</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1626</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Horn Concerto No. 4 in E flat K495: II. Romanc...</td>\n",
              "      <td>19</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       user_id  ...  Rank\n",
              "3194  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   1.0\n",
              "4083  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   2.0\n",
              "931   4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   3.0\n",
              "4443  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   4.0\n",
              "3034  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   5.0\n",
              "3189  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   6.0\n",
              "4112  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   7.0\n",
              "1207  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   8.0\n",
              "1577  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   9.0\n",
              "1626  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...  10.0\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uesgGgltriBT"
      },
      "source": [
        "#Personalised song recommender\n",
        "#Creating an instance of the personalized recommender class\n",
        "is_model = Recommenders.item_similarity_recommender_py() #Item similarity model\n",
        "is_model.create(train_data, 'user_id', 'song')"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "id": "rTiR6WdOsWzR",
        "outputId": "0a3411bc-0fb9-4c92-b057-073deb54e94f"
      },
      "source": [
        "#Print the songs for the following user in training data\n",
        "user_id = users[5]\n",
        "user_items = is_model.get_user_items(user_id)\n",
        "\n",
        "print(\"------------------------------------------------------------------------------------\")\n",
        "print(\"Training data songs for the user userid: %s:\" % user_id)\n",
        "print(\"------------------------------------------------------------------------------------\")\n",
        "\n",
        "for user_item in user_items:\n",
        "    print(user_item)\n",
        "\n",
        "print(\"----------------------------------------------------------------------\")\n",
        "print(\"Recommendation process going on:\")\n",
        "print(\"----------------------------------------------------------------------\")\n",
        "\n",
        "#Recommend songs for the user using personalized model\n",
        "is_model.recommend(user_id)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------------------\n",
            "Training data songs for the user userid: 4bd88bfb25263a75bbdd467e74018f4ae570e5df:\n",
            "------------------------------------------------------------------------------------\n",
            "Just Lose It-Eminem\n",
            "Without Me-Eminem\n",
            "16 Candles-The Crests\n",
            "Speechless-Lady GaGa\n",
            "Push It-Salt-N-Pepa\n",
            "Ghosts 'n' Stuff (Original Instrumental Mix)-Deadmau5\n",
            "Say My Name-Destiny's Child\n",
            "My Dad's Gone Crazy-Eminem / Hailie Jade\n",
            "The Real Slim Shady-Eminem\n",
            "Somebody To Love-Justin Bieber\n",
            "Forgive Me-Leona Lewis\n",
            "Missing You-John Waite\n",
            "Ya Nada Queda-Kudai\n",
            "----------------------------------------------------------------------\n",
            "Recommendation process going on:\n",
            "----------------------------------------------------------------------\n",
            "No. of unique songs for the user: 13\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :2097\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>song</th>\n",
              "      <th>score</th>\n",
              "      <th>rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Superman-Eminem / Dina Rae</td>\n",
              "      <td>0.088692</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Mockingbird-Eminem</td>\n",
              "      <td>0.067663</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>I'm Back-Eminem</td>\n",
              "      <td>0.065385</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>U Smile-Justin Bieber</td>\n",
              "      <td>0.064525</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Here Without You-3 Doors Down</td>\n",
              "      <td>0.062293</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Hellbound-J-Black &amp; Masta Ace</td>\n",
              "      <td>0.055769</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>The Seed (2.0)-The Roots / Cody Chestnutt</td>\n",
              "      <td>0.052564</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>I'm The One Who Understands (Edit Version)-War</td>\n",
              "      <td>0.052564</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Falling-Iration</td>\n",
              "      <td>0.052564</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4bd88bfb25263a75bbdd467e74018f4ae570e5df</td>\n",
              "      <td>Armed And Ready (2009 Digital Remaster)-The Mi...</td>\n",
              "      <td>0.052564</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    user_id  ... rank\n",
              "0  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    1\n",
              "1  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    2\n",
              "2  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    3\n",
              "3  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    4\n",
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              "5  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    6\n",
              "6  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    7\n",
              "7  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    8\n",
              "8  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...    9\n",
              "9  4bd88bfb25263a75bbdd467e74018f4ae570e5df  ...   10\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 903
        },
        "id": "ARHUrzgaslO7",
        "outputId": "bf0ddfc9-42e6-483c-9272-982cc88aef87"
      },
      "source": [
        "#Print the songs for the following user in training data. Note the difference in\n",
        "#songs recommended for both users\n",
        "user_id = users[17]\n",
        "user_items = is_model.get_user_items(user_id)\n",
        "\n",
        "print(\"------------------------------------------------------------------------------------\")\n",
        "print(\"Training data songs for the user userid: %s:\" % user_id)\n",
        "print(\"------------------------------------------------------------------------------------\")\n",
        "\n",
        "for user_item in user_items:\n",
        "    print(user_item)\n",
        "\n",
        "print(\"----------------------------------------------------------------------\")\n",
        "print(\"Recommendation process going on:\")\n",
        "print(\"----------------------------------------------------------------------\")\n",
        "\n",
        "#Recommend songs for the user using personalized model\n",
        "is_model.recommend(user_id)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------------------------------------------------------------------------\n",
            "Training data songs for the user userid: 169f9f4c68b62d1887c7c0ac99d10a79cfca5daf:\n",
            "------------------------------------------------------------------------------------\n",
            "Secrets-OneRepublic\n",
            "Uprising-Muse\n",
            "A-Punk (Album)-Vampire Weekend\n",
            "Time To Pretend-MGMT\n",
            "What You Know-Two Door Cinema Club\n",
            "Times Like These-Jack Johnson\n",
            "Vanilla Twilight-Owl City\n",
            "The Funeral (Album Version)-Band Of Horses\n",
            "Oxford Comma (Album)-Vampire Weekend\n",
            "Electric Feel-MGMT\n",
            "Gold Guns Girls-Metric\n",
            "Teach Me How To Dougie-California Swag District\n",
            "Bleed It Out [Live At Milton Keynes]-Linkin Park\n",
            "Do You Remember-Jack Johnson\n",
            "Tim McGraw-Taylor Swift\n",
            "Breakeven-The Script\n",
            "Home-Edward Sharpe & The Magnetic Zeros\n",
            "If It Means A Lot To You-A Day To Remember\n",
            "Head Full Of Doubt/Road Full Of Promise-The Avett Brothers\n",
            "Pursuit Of Happiness (nightmare)-Kid Cudi / MGMT / Ratatat\n",
            "Ain't No Rest For The Wicked (Original Version)-Cage The Elephant\n",
            "Float On-Modest Mouse\n",
            "Bad Company-Five Finger Death Punch\n",
            "----------------------------------------------------------------------\n",
            "Recommendation process going on:\n",
            "----------------------------------------------------------------------\n",
            "No. of unique songs for the user: 23\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :6773\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>song</th>\n",
              "      <th>score</th>\n",
              "      <th>rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>Here Without You-3 Doors Down</td>\n",
              "      <td>0.178093</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>Heads Will Roll-Yeah Yeah Yeahs</td>\n",
              "      <td>0.171382</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>Heartbreak Warfare-John Mayer</td>\n",
              "      <td>0.170225</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>One Less Lonely Girl-Justin Bieber</td>\n",
              "      <td>0.167876</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>U Smile-Justin Bieber</td>\n",
              "      <td>0.167533</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>Love Me-Justin Bieber</td>\n",
              "      <td>0.166941</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>One Time-Justin Bieber</td>\n",
              "      <td>0.166941</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>Stuck In The Moment-Justin Bieber</td>\n",
              "      <td>0.166941</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>Half Of My Heart-John Mayer</td>\n",
              "      <td>0.150179</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>169f9f4c68b62d1887c7c0ac99d10a79cfca5daf</td>\n",
              "      <td>The Saltwater Room-Owl City</td>\n",
              "      <td>0.143430</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    user_id  ... rank\n",
              "0  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    1\n",
              "1  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    2\n",
              "2  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    3\n",
              "3  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    4\n",
              "4  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    5\n",
              "5  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    6\n",
              "6  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    7\n",
              "7  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    8\n",
              "8  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...    9\n",
              "9  169f9f4c68b62d1887c7c0ac99d10a79cfca5daf  ...   10\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "zG_O98CGuE77",
        "outputId": "bb4072ed-1a87-4f15-dc87-7b156ba32d7a"
      },
      "source": [
        "#Finding songs similar to a particular song\n",
        "\n",
        "is_model.get_similar_items(['Half Of My Heart-John Mayer'])"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :377\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>song</th>\n",
              "      <th>score</th>\n",
              "      <th>rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>The Funeral (Album Version)-Band Of Horses</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>Love Me-Justin Bieber</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>Perfectly Lonely-John Mayer</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>One Time-Justin Bieber</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>Stuck In The Moment-Justin Bieber</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td></td>\n",
              "      <td>Why Can't This Be Love (Remastered Version)-Va...</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td></td>\n",
              "      <td>Highway 20 Ride (Album)-Zac Brown Band</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td></td>\n",
              "      <td>Here Without You-3 Doors Down</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td></td>\n",
              "      <td>One Less Lonely Girl-Justin Bieber</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td></td>\n",
              "      <td>My First Kiss (Feat. Ke$ha) [Album Version]-3OH!3</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  user_id                                               song     score rank\n",
              "0                 The Funeral (Album Version)-Band Of Horses  0.500000    1\n",
              "1                                      Love Me-Justin Bieber  0.400000    2\n",
              "2                                Perfectly Lonely-John Mayer  0.400000    3\n",
              "3                                     One Time-Justin Bieber  0.400000    4\n",
              "4                          Stuck In The Moment-Justin Bieber  0.400000    5\n",
              "5          Why Can't This Be Love (Remastered Version)-Va...  0.400000    6\n",
              "6                     Highway 20 Ride (Album)-Zac Brown Band  0.400000    7\n",
              "7                              Here Without You-3 Doors Down  0.375000    8\n",
              "8                         One Less Lonely Girl-Justin Bieber  0.333333    9\n",
              "9          My First Kiss (Feat. Ke$ha) [Album Version]-3OH!3  0.333333   10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBrSaBuKuTjh",
        "outputId": "432675c2-cf12-4d45-dd1c-23147e666027"
      },
      "source": [
        "#Using precision and recall to calculate ecaluation measures\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "#Define what percentage of users to use for precision recall calculation\n",
        "user_sample = 0.05\n",
        "\n",
        "#Instantiate the precision_recall_calculator class\n",
        "pr = Evaluation.precision_recall_calculator(test_data, train_data, pm, is_model)\n",
        "\n",
        "#Call method to calculate precision and recall values\n",
        "(pm_avg_precision_list, pm_avg_recall_list, ism_avg_precision_list, ism_avg_recall_list) = pr.calculate_measures(user_sample)\n",
        "\n",
        "end = time.time()\n",
        "print(end - start)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of user_test_and_training:319\n",
            "Length of user sample:15\n",
            "Getting recommendations for user:c5d42aa00afa6889de703c49f40ecb26b1c401c4\n",
            "No. of unique songs for the user: 36\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :2994\n",
            "Getting recommendations for user:3f152d355d53865a2ca27ac5ceeffb7ebaea0a26\n",
            "No. of unique songs for the user: 53\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :5331\n",
            "Getting recommendations for user:8dcb524ff75e0ad0f0a80aaccadf7bbaa3b89a41\n",
            "No. of unique songs for the user: 67\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :12668\n",
            "Getting recommendations for user:ca80fbb6d0deb3cae53763099e2cae7306f005ec\n",
            "No. of unique songs for the user: 33\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :2250\n",
            "Getting recommendations for user:66166f4f57bb897f1f8e2aae6f53f776088f43d8\n",
            "No. of unique songs for the user: 7\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :195\n",
            "Getting recommendations for user:9d84d1bcfde6464c02561fbb5dcb294678778b25\n",
            "No. of unique songs for the user: 11\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :984\n",
            "Getting recommendations for user:e6fbcdd689ac0e85992536933f1f86503310447f\n",
            "No. of unique songs for the user: 17\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :1175\n",
            "Getting recommendations for user:c1fc436b58e28b3e3f1b43a4e955baa19d8a69ba\n",
            "No. of unique songs for the user: 13\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :1440\n",
            "Getting recommendations for user:e507a8acd03bbd1f6c2088bd70a0ab2ffb277cd7\n",
            "No. of unique songs for the user: 5\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :215\n",
            "Getting recommendations for user:8ee90038724c4957eb4df16f3e9c6ed2b570a3ec\n",
            "No. of unique songs for the user: 20\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :752\n",
            "Getting recommendations for user:e6b4dbf8e48346fbfd233fa96cdf5daee25ae77c\n",
            "No. of unique songs for the user: 10\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :845\n",
            "Getting recommendations for user:f28f980d8842ebfaa31e8fb3939aace4a43a18ec\n",
            "No. of unique songs for the user: 20\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :1406\n",
            "Getting recommendations for user:0dd93f61fe69f292ac336715ef607214efb3dbaa\n",
            "No. of unique songs for the user: 39\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :11680\n",
            "Getting recommendations for user:78e0065cacc15d6329be91b77045f12ab18cbea5\n",
            "No. of unique songs for the user: 9\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :215\n",
            "Getting recommendations for user:d775b2f1e62284a3ff407b91fe53d08525f6f086\n",
            "No. of unique songs for the user: 12\n",
            "no. of unique songs in the training set: 4483\n",
            "Non zero values in cooccurence_matrix :579\n",
            "70.39818668365479\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uSYMp9muvn-"
      },
      "source": [
        "#Function to plot precision recall curve\n",
        "import pylab as pl\n",
        "\n",
        "#Method to generate precision and recall curve\n",
        "def plot_precision_recall(m1_precision_list, m1_recall_list, m1_label, m2_precision_list, m2_recall_list, m2_label):\n",
        "    pl.clf()    \n",
        "    pl.plot(m1_recall_list, m1_precision_list, label=m1_label)\n",
        "    pl.plot(m2_recall_list, m2_precision_list, label=m2_label)\n",
        "    pl.xlabel('Recall')\n",
        "    pl.ylabel('Precision')\n",
        "    pl.ylim([0.0, 0.20])\n",
        "    pl.xlim([0.0, 0.20])\n",
        "    pl.title('Precision-Recall curve')\n",
        "    #pl.legend(loc=\"upper right\")\n",
        "    pl.legend(loc=9, bbox_to_anchor=(0.5, -0.2))\n",
        "    pl.show()\n",
        "\n"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "ZOMeIx86wjao",
        "outputId": "e6f90f24-173b-4df0-d028-7715718d9514"
      },
      "source": [
        "print(\"Plotting precision recall curves.\")\n",
        "\n",
        "plot_precision_recall(pm_avg_precision_list, pm_avg_recall_list, \"popularity_model\",\n",
        "                      ism_avg_precision_list, ism_avg_recall_list, \"item_similarity_model\")"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Plotting precision recall curves.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "J2Qc9L8mwrH-",
        "outputId": "010d769e-2c76-4e21-ce8d-ce09233046a8"
      },
      "source": [
        "#Generating precision and recall on a larger subset of data\n",
        "#Read the persisted files \n",
        "pm_avg_precision_list = joblib.load('pm_avg_precision_list_3.pkl')\n",
        "pm_avg_recall_list = joblib.load('pm_avg_recall_list_3.pkl')\n",
        "ism_avg_precision_list = joblib.load('ism_avg_precision_list_3.pkl')\n",
        "ism_avg_recall_list = joblib.load('ism_avg_recall_list_3.pkl')\n",
        "\n",
        "print(\"Plotting precision recall curves.\")\n",
        "plot_precision_recall(pm_avg_precision_list, pm_avg_recall_list, \"popularity_model\",\n",
        "                      ism_avg_precision_list, ism_avg_recall_list, \"item_similarity_model\")\n"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Plotting precision recall curves.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "GecZnxmVzVwJ",
        "outputId": "ddc211b1-e457-4a5a-f800-6516740f4bac"
      },
      "source": [
        "print(\"Plotting precision recall curves for a larger subset of data (100,000 rows) (user sample = 0.005).\")\n",
        "\n",
        "pm_avg_precision_list = joblib.load('pm_avg_precision_list_2.pkl')\n",
        "pm_avg_recall_list = joblib.load('pm_avg_recall_list_2.pkl')\n",
        "ism_avg_precision_list = joblib.load('ism_avg_precision_list_2.pkl')\n",
        "ism_avg_recall_list = joblib.load('ism_avg_recall_list_2.pkl')\n",
        "\n",
        "print(\"Plotting precision recall curves.\")\n",
        "plot_precision_recall(pm_avg_precision_list, pm_avg_recall_list, \"popularity_model\",\n",
        "                      ism_avg_precision_list, ism_avg_recall_list, \"item_similarity_model\")"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Plotting precision recall curves for a larger subset of data (100,000 rows) (user sample = 0.005).\n",
            "Plotting precision recall curves.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h20nKcHC01j3"
      },
      "source": [
        "#Popularity model simply recommends top 10 songs to every user based on\n",
        "#listen_count whereas the is_model gives personalilzed recommendation to\n",
        "#every user and hence, we can see in the above graphs that it's performance\n",
        "#is better than the popularity model"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7KvqI9g3nYk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}