Song_recommendations_with_genre.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Song recommendations with genre.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyODgWTPFqdmeTw8jAhyUUzb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sonichandni/Song-Recommendations/blob/master/Song_recommendations_with_genre.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jX0NqcbRmFlH",
        "colab_type": "code",
        "outputId": "ec32c094-9e21-45a0-90da-0f639d0233e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPief-NzmaMR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "from gensim.models import Word2Vec \n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings;\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npKOsaSomkAd",
        "colab_type": "code",
        "outputId": "10e62962-032e-4fd6-e9c7-deb7a683feec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "df = pd.read_csv('songs.csv',  encoding = \"ISO-8859-1\")\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "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>Song-Name</th>\n",
              "      <th>Singer/Artists</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Album/Movie</th>\n",
              "      <th>User-Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aankh Marey</td>\n",
              "      <td>Kumar Sanu, Mika Singh, Neha Kakkar</td>\n",
              "      <td>BollywoodDance</td>\n",
              "      <td>Simmba</td>\n",
              "      <td>8.8/10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Coca Cola</td>\n",
              "      <td>Neha Kakkar, Tony Kakkar</td>\n",
              "      <td>BollywoodDanceRomantic</td>\n",
              "      <td>Luka Chuppi</td>\n",
              "      <td>9.0/10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apna Time Aayega</td>\n",
              "      <td>Ranveer Singh</td>\n",
              "      <td>BollywoodDance</td>\n",
              "      <td>Gully Boy</td>\n",
              "      <td>9.7/10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mungda</td>\n",
              "      <td>Jyotica Tangri, Shaan, Subhro Ganguly</td>\n",
              "      <td>BollywoodDance</td>\n",
              "      <td>Total Dhamaal</td>\n",
              "      <td>9.1/10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Tere Bin</td>\n",
              "      <td>Asees Kaur, Rahat Fateh Ali Khan, Tanishk Bagchi</td>\n",
              "      <td>BollywoodRomantic</td>\n",
              "      <td>Simmba</td>\n",
              "      <td>9.2/10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Song-Name  ... User-Rating\n",
              "1       Aankh Marey  ...      8.8/10\n",
              "2         Coca Cola  ...      9.0/10\n",
              "3  Apna Time Aayega  ...      9.7/10\n",
              "4            Mungda  ...      9.1/10\n",
              "5          Tere Bin  ...      9.2/10\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJ18kjpnnAFP",
        "colab_type": "code",
        "outputId": "7a9c4628-e2fc-4c04-ba99-4f04e0640e7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2420, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dcGkCcsnFoc",
        "colab_type": "code",
        "outputId": "edfb6848-6b30-41a5-ebac-83972c279175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Song-Name          0\n",
              "Singer/Artists    10\n",
              "Genre              0\n",
              "Album/Movie        3\n",
              "User-Rating        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywb3ZcednIb5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['SongCode'] = [\"SB\" + str(i) for i in range(df.shape[0])]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sM5v7kKSopQJ",
        "colab_type": "code",
        "outputId": "32dfdce1-f9d2-475d-9364-826a9e5b0a94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "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>Song-Name</th>\n",
              "      <th>Singer/Artists</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Album/Movie</th>\n",
              "      <th>User-Rating</th>\n",
              "      <th>SongCode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aankh Marey</td>\n",
              "      <td>Kumar Sanu, Mika Singh, Neha Kakkar</td>\n",
              "      <td>BollywoodDance</td>\n",
              "      <td>Simmba</td>\n",
              "      <td>8.8/10</td>\n",
              "      <td>SB0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Coca Cola</td>\n",
              "      <td>Neha Kakkar, Tony Kakkar</td>\n",
              "      <td>BollywoodDanceRomantic</td>\n",
              "      <td>Luka Chuppi</td>\n",
              "      <td>9.0/10</td>\n",
              "      <td>SB1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apna Time Aayega</td>\n",
              "      <td>Ranveer Singh</td>\n",
              "      <td>BollywoodDance</td>\n",
              "      <td>Gully Boy</td>\n",
              "      <td>9.7/10</td>\n",
              "      <td>SB2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mungda</td>\n",
              "      <td>Jyotica Tangri, Shaan, Subhro Ganguly</td>\n",
              "      <td>BollywoodDance</td>\n",
              "      <td>Total Dhamaal</td>\n",
              "      <td>9.1/10</td>\n",
              "      <td>SB3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Tere Bin</td>\n",
              "      <td>Asees Kaur, Rahat Fateh Ali Khan, Tanishk Bagchi</td>\n",
              "      <td>BollywoodRomantic</td>\n",
              "      <td>Simmba</td>\n",
              "      <td>9.2/10</td>\n",
              "      <td>SB4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Song-Name  ... SongCode\n",
              "1       Aankh Marey  ...      SB0\n",
              "2         Coca Cola  ...      SB1\n",
              "3  Apna Time Aayega  ...      SB2\n",
              "4            Mungda  ...      SB3\n",
              "5          Tere Bin  ...      SB4\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BiITqiZpowYP",
        "colab_type": "code",
        "outputId": "02243114-ba5b-4a0d-963e-8088646816c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "songs = df[\"Genre\"].unique().tolist()\n",
        "len(songs)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJjxrE71o4tC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# shuffle song ID's\n",
        "random.shuffle(songs)\n",
        "\n",
        "# extract 90% of song ID's\n",
        "song_train = [songs[i] for i in range(round(0.9*len(songs)))]\n",
        "\n",
        "# split data into train and validation set\n",
        "train_df = df[df['Genre'].isin(song_train)]\n",
        "validation_df = df[~df['Genre'].isin(song_train)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gc7zCxkhpPyv",
        "colab_type": "code",
        "outputId": "add77783-6640-468f-94f2-a839ddd3a25d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# list to capture the genre\n",
        "like_train = []\n",
        "\n",
        "# populate the list with the product codes\n",
        "for i in tqdm(song_train):\n",
        "    temp = train_df[train_df[\"Genre\"] == i][\"SongCode\"].tolist()\n",
        "    like_train.append(temp)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 19/19 [00:00<00:00, 750.14it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RibMuIdWpg-3",
        "colab_type": "code",
        "outputId": "7bfad801-ac21-481c-dc1d-89680ea3de43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# list to capture the genre\n",
        "like_val = []\n",
        "\n",
        "# populate the list with the song codes\n",
        "for i in tqdm(validation_df['Genre'].unique()):\n",
        "    temp = validation_df[validation_df[\"Genre\"] == i][\"SongCode\"].tolist()\n",
        "    like_val.append(temp)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 2/2 [00:00<00:00, 455.80it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAIFd-lWpjjJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train word2vec model\n",
        "model = Word2Vec(window = 10, sg = 1, hs = 0,\n",
        "                 negative = 10, # for negative sampling\n",
        "                 alpha=0.03, min_alpha=0.0007,\n",
        "                 seed = 14, min_count=1)\n",
        "\n",
        "model.build_vocab(like_train, progress_per=200)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHG2xC4_sBrR",
        "colab_type": "code",
        "outputId": "c04fc3db-ab46-416c-f67f-5fe161ce3aa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.train(like_train, total_examples = model.corpus_count, \n",
        "            epochs=1, report_delay=1)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2393, 2393)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqn3PmvAU8F_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.init_sims(replace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jhId_ZJU-QJ",
        "colab_type": "code",
        "outputId": "a3737a33-14d8-4bb3-d444-b9092f5b2063",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(model)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Word2Vec(vocab=2393, size=100, alpha=0.03)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lKwHzaHU_1L",
        "colab_type": "code",
        "outputId": "aa147283-c116-4aa9-d292-49d17bb3376f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# extract all vectors\n",
        "X = model[model.wv.vocab]\n",
        "\n",
        "X.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2393, 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7m8PujPVBk-",
        "colab_type": "code",
        "outputId": "4d6ff348-5c65-4030-df03-14f2c554335a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "import umap\n",
        "\n",
        "cluster_embedding = umap.UMAP(n_neighbors=30, min_dist=0.0,\n",
        "                              n_components=2, random_state=42).fit_transform(X)\n",
        "\n",
        "plt.figure(figsize=(10,9))\n",
        "plt.scatter(cluster_embedding[:, 0], cluster_embedding[:, 1], s=3, cmap='Spectral')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4178160e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAIICAYAAAClygDiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1JNFSpIkqad/3Ecx8YIyIkcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UW-yalAFVEac",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sng = train_df[[\"SongCode\", \"Song-Name\"]]\n",
        "# sng\n",
        "# remove duplicates\n",
        "sng.drop_duplicates(inplace=True, subset='SongCode', keep=\"last\")\n",
        "\n",
        "# # create song-ID and Song-Name dictionary\n",
        "sng_dict = sng.groupby('SongCode')['Song-Name'].apply(list).to_dict()\n",
        "# sng_dict"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHzbvGH2VGgB",
        "colab_type": "code",
        "outputId": "5485ae3a-b62c-4f2c-d053-3bf778e15006",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# test the dictionary\n",
        "sng_dict['SB1057']"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Chandni O Meri Chandni']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueW0v5_NVIJy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def similar_songs(v, n = 6):\n",
        "    \n",
        "    # extract most similar song for the input vector\n",
        "    ms = model.similar_by_vector(v, topn= n+1)[1:]\n",
        "    \n",
        "    # extract name and similarity score of the similar song\n",
        "    new_ms = []\n",
        "    for j in ms:\n",
        "        pair = (sng_dict[j[0]][0], j[1])\n",
        "        new_ms.append(pair)\n",
        "        \n",
        "    return new_ms    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztjWr42SVK4b",
        "colab_type": "code",
        "outputId": "72b0c909-6c21-412f-c743-a285f9090dc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "similar_songs(model['SB1057'])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Mere Mehboob Mein Kya Nahin', 0.3300647437572479),\n",
              " ('It\\x92s Magic', 0.31959035992622375),\n",
              " ('Dua', 0.29243916273117065),\n",
              " ('Tum Saath Ho Jab Apne', 0.2893286943435669),\n",
              " ('Zor Ka Jhatka Haye (Remix)', 0.26737716794013977),\n",
              " ('Piya O Re Piya', 0.26233115792274475)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    }
  ]
}