Beatles_NLP.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Beatles NLP",
      "provenance": [],
      "collapsed_sections": [
        "BBEDqV7mUWjp",
        "UuzP7_loFMno",
        "NN2KpasqFtPX"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqVlMdfeUQub",
        "colab_type": "text"
      },
      "source": [
        "# Data Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2-ZaiLkb2XL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4SOA-N6STN2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drive.mount('./gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAFcL3I1SUdF",
        "colab_type": "code",
        "outputId": "eec1fdad-56c0-4fc9-fd48-9d7086140494",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gdrive\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDFBFPprSe4O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAb5ExXPSj71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.chdir('./gdrive/My Drive/Google Colaboratory/Colab Notebooks/Data Science   Machine Learning/Beatles NLP/Data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dal-AF92S1Sx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import string\n",
        "import nltk\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aveOrrqN1hdS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tqdm --upgrade"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyuBsL0P05wl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm, tqdm_notebook"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2sWiiY408zD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# using tqdm to get a progress bar on long-loading processes\n",
        "tqdm.pandas(tqdm_notebook)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFcA3ChNS3hG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# reading in the big Beatles data Excel file into a Pandas dataframe. \n",
        "# The Beatles data will continue everything we need, including song names, album names, songwriters, lyrics, and more\n",
        "\n",
        "data = pd.read_excel('beatles_data.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBEDqV7mUWjp",
        "colab_type": "text"
      },
      "source": [
        "# Raw Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kQcB1WFS6dl",
        "colab_type": "code",
        "outputId": "561203ef-04ee-482a-b8b0-7be77b819d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "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</th>\n",
              "      <th>Album Debut</th>\n",
              "      <th>Songwriter(s)</th>\n",
              "      <th>Lead Vocal(s)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Lyrics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>\"12-Bar Original\"</td>\n",
              "      <td>Anthology 2</td>\n",
              "      <td>John Lennon\\n Paul McCartney\\n George Harrison...</td>\n",
              "      <td>Instrumental</td>\n",
              "      <td>1965</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>\"Across the Universe\"</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Words are flowing out like endless rain into a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>\"All I've Got to Do\"</td>\n",
              "      <td>UK: With the Beatles\\n US: Meet the Beatles!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1963</td>\n",
              "      <td>Whenever I want you around yeh \\nAll I gotta d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>\"All My Loving\"</td>\n",
              "      <td>UK: With the Beatles\\n US: Meet the Beatles!</td>\n",
              "      <td>McCartney</td>\n",
              "      <td>McCartney</td>\n",
              "      <td>1963</td>\n",
              "      <td>Close your eyes and I'll kiss you\\nTomorrow I'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>\"All Things Must Pass\"</td>\n",
              "      <td>Anthology 3</td>\n",
              "      <td>Harrison</td>\n",
              "      <td>Harrison</td>\n",
              "      <td>1969</td>\n",
              "      <td>Sunrise doesn't last all morning, \\nA cloudbur...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Song  ...                                             Lyrics\n",
              "0       \"12-Bar Original\"  ...                                                NaN\n",
              "1   \"Across the Universe\"  ...  Words are flowing out like endless rain into a...\n",
              "2    \"All I've Got to Do\"  ...  Whenever I want you around yeh \\nAll I gotta d...\n",
              "3         \"All My Loving\"  ...  Close your eyes and I'll kiss you\\nTomorrow I'...\n",
              "4  \"All Things Must Pass\"  ...  Sunrise doesn't last all morning, \\nA cloudbur...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQhn4IovS-EQ",
        "colab_type": "code",
        "outputId": "39af4921-95c9-49c4-a6cf-6194bfdaeace",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "list(data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Song', 'Album Debut', 'Songwriter(s)', 'Lead Vocal(s)', 'Year', 'Lyrics']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMD6F7qtu_fE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We will remove any songs that have something for lyrics (aka non-instrumental songs)\n",
        "data = data[data['Lyrics'].notnull()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9B3MSQ1TBWM",
        "colab_type": "code",
        "outputId": "df43f362-a716-4ed0-921f-abe82a317389",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "data['Songwriter(s)']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1                         Lennon\n",
              "2                         Lennon\n",
              "3                      McCartney\n",
              "4                       Harrison\n",
              "5      McCartney\\n (with Lennon)\n",
              "                 ...            \n",
              "213                    McCartney\n",
              "214           Lennon\\n McCartney\n",
              "215                    McCartney\n",
              "216                       Lennon\n",
              "217                       Lennon\n",
              "Name: Songwriter(s), Length: 213, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0F_MlgET2kb",
        "colab_type": "code",
        "outputId": "df06812d-c821-487c-c28c-f377296fbf52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data[data['Songwriter(s)'] == 'Lennon']"
      ],
      "execution_count": 0,
      "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</th>\n",
              "      <th>Album Debut</th>\n",
              "      <th>Songwriter(s)</th>\n",
              "      <th>Lead Vocal(s)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Lyrics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>\"Across the Universe\"</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Words are flowing out like endless rain into a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>\"All I've Got to Do\"</td>\n",
              "      <td>UK: With the Beatles\\n US: Meet the Beatles!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1963</td>\n",
              "      <td>Whenever I want you around yeh \\nAll I gotta d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>\"All You Need Is Love\"</td>\n",
              "      <td>Magical Mystery Tour</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1967</td>\n",
              "      <td>Love, love, love\\nLove, love, love\\nLove, love...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>\"Bad to Me\"</td>\n",
              "      <td>The Beatles Bootleg Recordings 1963</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1963</td>\n",
              "      <td>If you ever leave me, I'll be sad and blue\\nDo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>\"The Ballad of John and Yoko\"</td>\n",
              "      <td>UK: 1967–1970\\n US: Hey Jude</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1969</td>\n",
              "      <td>Standing in the dock at Southampton\\nTrying to...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>\"Because\"</td>\n",
              "      <td>Abbey Road</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n McCartney\\n Harrison[26]</td>\n",
              "      <td>1969</td>\n",
              "      <td>Because the world is round it turns me on\\nBec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>\"Child of Nature\"</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>On the road to Rishikesh\\nI was dreaming more ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>\"Come Together\"</td>\n",
              "      <td>Abbey Road</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1969</td>\n",
              "      <td>Here come old flat top\\nHe come grooving up sl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>\"The Continuing Story of Bungalow Bill\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Hey, Bungalow Bill\\nWhat did you kill\\nBungalo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>\"Cry Baby Cry”</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1968</td>\n",
              "      <td>Cry baby cry\\nMake your mother sigh\\nShe's old...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>\"Dear Prudence\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Dear Prudence, won't you come out to play\\nDea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>\"Dig a Pony\"</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1969</td>\n",
              "      <td>I dig a pony\\nWell, you can celebrate anything...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>\"Do You Want to Know a Secret?\"</td>\n",
              "      <td>UK: Please Please Me\\n US: The Early Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Harrison</td>\n",
              "      <td>1963</td>\n",
              "      <td>You'll never know how much I really love you\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>\"Don't Let Me Down\"</td>\n",
              "      <td>UK: 1967–1970\\n US: Hey Jude</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1969</td>\n",
              "      <td>Don't let me down, don't let me down\\nDon't le...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>\"Everybody's Got Something to Hide Except Me a...</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Come on come on, come on come on\\nCome on its ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>\"Girl\"</td>\n",
              "      <td>Rubber Soul</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1965</td>\n",
              "      <td>Is there anybody going to listen to my story\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>\"Glass Onion\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>I told you about strawberry fields\\nYou know t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>\"Good Morning Good Morning\"</td>\n",
              "      <td>Sgt. Pepper's Lonely Hearts Club Band</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1967</td>\n",
              "      <td>Nothing to do to save his life call his wife i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>\"Good Night\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1968</td>\n",
              "      <td>Now it's time to say good night\\nGood night sl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>\"Happiness Is a Warm Gun\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>She's not a girl who misses much\\nDo do do do ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>\"Hello Little Girl\"</td>\n",
              "      <td>Anthology 1</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1962</td>\n",
              "      <td>Hello little girl\\nHello little girl\\nHello li...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>\"Hey Bulldog\"</td>\n",
              "      <td>Yellow Submarine</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1968</td>\n",
              "      <td>Sheepdog, standing in the rain\\nBullfrog, doin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>\"I Am the Walrus\"</td>\n",
              "      <td>Magical Mystery Tour</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1967</td>\n",
              "      <td>I am he as you are he as you are me\\nAnd we ar...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>\"I Call Your Name\"</td>\n",
              "      <td>UK: \"Long Tall Sally\" EP\\n US: The Beatles' Se...</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>I call your name but you're not there\\nWas I t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>\"I Don't Want to Spoil the Party\"</td>\n",
              "      <td>UK: Beatles for Sale\\n US: Beatles VI</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon, with McCartney</td>\n",
              "      <td>1964</td>\n",
              "      <td>I don't want to spoil the party so I'll go,\\nI...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>\"I Feel Fine\"</td>\n",
              "      <td>UK: A Collection of Beatles Oldies\\n US: Beatl...</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>Baby's good to me, you know\\nShe's happy as ca...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>\"I Should Have Known Better\"</td>\n",
              "      <td>UK: A Hard Day's Night</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>I should have known better with a girl like yo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>\"I Want You (She's So Heavy)\"</td>\n",
              "      <td>Abbey Road</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1969</td>\n",
              "      <td>I want you\\nI want you so bad\\nI want you\\nI w...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>\"If I Fell\"</td>\n",
              "      <td>UK: A Hard Day's Night\\n US: Something New</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1964</td>\n",
              "      <td>If I fell in love with you\\nWould you promise ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>\"I'll Be Back\"</td>\n",
              "      <td>UK: A Hard Day's Night\\n US: Beatles '65</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon and McCartney)</td>\n",
              "      <td>1964</td>\n",
              "      <td>You know if you break my heart I'll go\\nBut I'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>\"I'll Cry Instead\"</td>\n",
              "      <td>UK: A Hard Day's Night\\n US: Something New</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>I've got every reason on earth to be mad\\n'Cau...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>\"I'm a Loser\"</td>\n",
              "      <td>UK: Beatles for Sale\\n US: Beatles '65</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>I'm a loser\\nI'm a loser\\nAnd I'm not what I a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>\"I'm In Love\"</td>\n",
              "      <td>The Beatles Bootleg Recordings 1963</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1963</td>\n",
              "      <td>I've got something to tell you, I'm in love,\\n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>\"I'm Only Sleeping\"</td>\n",
              "      <td>UK: Revolver\\n US: Yesterday and Today</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1966</td>\n",
              "      <td>When I wake up early in the morning\\nLift my h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>\"I'm So Tired\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>I'm so tired, I haven't slept a wink\\nI'm so t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>\"It's Only Love\"</td>\n",
              "      <td>UK: Help!\\n US: Rubber Soul</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1965</td>\n",
              "      <td>I get high when I see you go by, (my oh my)\\nW...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>\"Julia\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Half of what I say is meaningless\\nBut I say i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>\"Mean Mr. Mustard\"</td>\n",
              "      <td>Abbey Road</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1969</td>\n",
              "      <td>Mean Mister Mustard sleeps in the park\\nShaves...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>\"Not a Second Time\"</td>\n",
              "      <td>UK: With the Beatles\\n US: Meet the Beatles!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1963</td>\n",
              "      <td>You know you made me cry,\\nI see no use in won...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>\"One After 909\"</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1969</td>\n",
              "      <td>My baby said she's trav'ling on the one after ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>\"Please Please Me\"</td>\n",
              "      <td>UK: Please Please Me\\n US: The Early Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon and McCartney</td>\n",
              "      <td>1962</td>\n",
              "      <td>Last night I said these words to my girl\\nI kn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>\"Polythene Pam\"</td>\n",
              "      <td>Abbey Road</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1969</td>\n",
              "      <td>Well, you should see Polythene Pam\\nShe's so g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>\"Rain\"</td>\n",
              "      <td>UK: Rarities\\n US: Hey Jude</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1966</td>\n",
              "      <td>If the rain comes \\nThey run and hide their he...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>\"Real Love\"</td>\n",
              "      <td>Anthology 2</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1980</td>\n",
              "      <td>All my little plans and schemes\\nLost like som...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>\"Revolution\"</td>\n",
              "      <td>UK: 1967-1970\\n US: Hey Jude</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>You say you want a revolution\\nWell, you know\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>\"Sexy Sadie\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Sexy Sadie, what have you done\\nYou made a foo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>\"She Said She Said\"</td>\n",
              "      <td>Revolver</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1966</td>\n",
              "      <td>She said\\nI know what it's like to be dead\\nI ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>\"Strawberry Fields Forever\"</td>\n",
              "      <td>Magical Mystery Tour</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1966</td>\n",
              "      <td>Let me take you down\\nCause I'm going to Straw...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>\"Sun King\"</td>\n",
              "      <td>Abbey Road</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney and Harrison)</td>\n",
              "      <td>1969</td>\n",
              "      <td>Aaaaahhhhhhhhhh...\\nHere comes the sun king\\nH...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>\"Tell Me Why\"</td>\n",
              "      <td>UK: A Hard Day's Night\\n US: Something New</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>Tell me why you cried, and why you lied to me\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>\"This Boy\"</td>\n",
              "      <td>UK: Rarities\\n US: Meet the Beatles!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney and Harrison)</td>\n",
              "      <td>1963</td>\n",
              "      <td>That boy\\nTook my love away\\nHe'll regret it s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>\"Ticket to Ride\"</td>\n",
              "      <td>Help!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon\\n (with McCartney)</td>\n",
              "      <td>1965</td>\n",
              "      <td>I think I'm gonna be sad,\\nI think it's today,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>\"Tomorrow Never Knows\"</td>\n",
              "      <td>Revolver</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1966</td>\n",
              "      <td>Turn off your mind, relax and float down strea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>\"What's The New Mary Jane\"</td>\n",
              "      <td>Anthology 3</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>She looks as an African Queen\\nShe eating twel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>\"When I Get Home\"</td>\n",
              "      <td>UK: A Hard Day's Night\\n US: Something New</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>Whoa, ah, woah, ah\\nI got a whole lot of thing...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>\"Yer Blues\"</td>\n",
              "      <td>The Beatles</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Yes, I'm lonely\\nWant to die\\nYes, I'm lonely\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>\"Yes It Is\"</td>\n",
              "      <td>UK: Rarities\\n US: Beatles VI</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon, McCartney and Harrison</td>\n",
              "      <td>1965</td>\n",
              "      <td>If you wear red tonight\\nRemember what I said ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>\"You Can't Do That\"</td>\n",
              "      <td>UK: A Hard Day's Night\\n US: The Beatles Secon...</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1964</td>\n",
              "      <td>I got something to say that might cause you pa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>\"You're Going to Lose That Girl\"</td>\n",
              "      <td>Help!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1965</td>\n",
              "      <td>You're going to lose that girl\\nYou're going t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>\"You've Got to Hide Your Love Away\"</td>\n",
              "      <td>Help!</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1965</td>\n",
              "      <td>Here I stand head in hand\\nTurn my face to the...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Song  ...                                             Lyrics\n",
              "1                                \"Across the Universe\"  ...  Words are flowing out like endless rain into a...\n",
              "2                                 \"All I've Got to Do\"  ...  Whenever I want you around yeh \\nAll I gotta d...\n",
              "6                               \"All You Need Is Love\"  ...  Love, love, love\\nLove, love, love\\nLove, love...\n",
              "15                                         \"Bad to Me\"  ...  If you ever leave me, I'll be sad and blue\\nDo...\n",
              "16                       \"The Ballad of John and Yoko\"  ...  Standing in the dock at Southampton\\nTrying to...\n",
              "17                                           \"Because\"  ...  Because the world is round it turns me on\\nBec...\n",
              "26                                   \"Child of Nature\"  ...  On the road to Rishikesh\\nI was dreaming more ...\n",
              "30                                     \"Come Together\"  ...  Here come old flat top\\nHe come grooving up sl...\n",
              "31             \"The Continuing Story of Bungalow Bill\"  ...  Hey, Bungalow Bill\\nWhat did you kill\\nBungalo...\n",
              "32                                      \"Cry Baby Cry”  ...  Cry baby cry\\nMake your mother sigh\\nShe's old...\n",
              "36                                     \"Dear Prudence\"  ...  Dear Prudence, won't you come out to play\\nDea...\n",
              "37                                        \"Dig a Pony\"  ...  I dig a pony\\nWell, you can celebrate anything...\n",
              "39                     \"Do You Want to Know a Secret?\"  ...  You'll never know how much I really love you\\n...\n",
              "42                                 \"Don't Let Me Down\"  ...  Don't let me down, don't let me down\\nDon't le...\n",
              "48   \"Everybody's Got Something to Hide Except Me a...  ...  Come on come on, come on come on\\nCome on its ...\n",
              "59                                              \"Girl\"  ...  Is there anybody going to listen to my story\\n...\n",
              "60                                       \"Glass Onion\"  ...  I told you about strawberry fields\\nYou know t...\n",
              "63                         \"Good Morning Good Morning\"  ...  Nothing to do to save his life call his wife i...\n",
              "64                                        \"Good Night\"  ...  Now it's time to say good night\\nGood night sl...\n",
              "66                           \"Happiness Is a Warm Gun\"  ...  She's not a girl who misses much\\nDo do do do ...\n",
              "70                                 \"Hello Little Girl\"  ...  Hello little girl\\nHello little girl\\nHello li...\n",
              "76                                       \"Hey Bulldog\"  ...  Sheepdog, standing in the rain\\nBullfrog, doin...\n",
              "80                                   \"I Am the Walrus\"  ...  I am he as you are he as you are me\\nAnd we ar...\n",
              "81                                  \"I Call Your Name\"  ...  I call your name but you're not there\\nWas I t...\n",
              "82                   \"I Don't Want to Spoil the Party\"  ...  I don't want to spoil the party so I'll go,\\nI...\n",
              "83                                       \"I Feel Fine\"  ...  Baby's good to me, you know\\nShe's happy as ca...\n",
              "88                        \"I Should Have Known Better\"  ...  I should have known better with a girl like yo...\n",
              "92                       \"I Want You (She's So Heavy)\"  ...  I want you\\nI want you so bad\\nI want you\\nI w...\n",
              "94                                         \"If I Fell\"  ...  If I fell in love with you\\nWould you promise ...\n",
              "97                                      \"I'll Be Back\"  ...  You know if you break my heart I'll go\\nBut I'...\n",
              "99                                  \"I'll Cry Instead\"  ...  I've got every reason on earth to be mad\\n'Cau...\n",
              "102                                      \"I'm a Loser\"  ...  I'm a loser\\nI'm a loser\\nAnd I'm not what I a...\n",
              "105                                      \"I'm In Love\"  ...  I've got something to tell you, I'm in love,\\n...\n",
              "107                                \"I'm Only Sleeping\"  ...  When I wake up early in the morning\\nLift my h...\n",
              "108                                     \"I'm So Tired\"  ...  I'm so tired, I haven't slept a wink\\nI'm so t...\n",
              "114                                   \"It's Only Love\"  ...  I get high when I see you go by, (my oh my)\\nW...\n",
              "119                                            \"Julia\"  ...  Half of what I say is meaningless\\nBut I say i...\n",
              "135                                 \"Mean Mr. Mustard\"  ...  Mean Mister Mustard sleeps in the park\\nShaves...\n",
              "142                                \"Not a Second Time\"  ...  You know you made me cry,\\nI see no use in won...\n",
              "148                                    \"One After 909\"  ...  My baby said she's trav'ling on the one after ...\n",
              "154                                 \"Please Please Me\"  ...  Last night I said these words to my girl\\nI kn...\n",
              "155                                    \"Polythene Pam\"  ...  Well, you should see Polythene Pam\\nShe's so g...\n",
              "157                                             \"Rain\"  ...  If the rain comes \\nThey run and hide their he...\n",
              "158                                        \"Real Love\"  ...  All my little plans and schemes\\nLost like som...\n",
              "159                                       \"Revolution\"  ...  You say you want a revolution\\nWell, you know\\...\n",
              "163                                       \"Sexy Sadie\"  ...  Sexy Sadie, what have you done\\nYou made a foo...\n",
              "168                                \"She Said She Said\"  ...  She said\\nI know what it's like to be dead\\nI ...\n",
              "173                        \"Strawberry Fields Forever\"  ...  Let me take you down\\nCause I'm going to Straw...\n",
              "174                                         \"Sun King\"  ...  Aaaaahhhhhhhhhh...\\nHere comes the sun king\\nH...\n",
              "179                                      \"Tell Me Why\"  ...  Tell me why you cried, and why you lied to me\\...\n",
              "185                                         \"This Boy\"  ...  That boy\\nTook my love away\\nHe'll regret it s...\n",
              "186                                   \"Ticket to Ride\"  ...  I think I'm gonna be sad,\\nI think it's today,...\n",
              "187                             \"Tomorrow Never Knows\"  ...  Turn off your mind, relax and float down strea...\n",
              "194                         \"What's The New Mary Jane\"  ...  She looks as an African Queen\\nShe eating twel...\n",
              "195                                  \"When I Get Home\"  ...  Whoa, ah, woah, ah\\nI got a whole lot of thing...\n",
              "205                                        \"Yer Blues\"  ...  Yes, I'm lonely\\nWant to die\\nYes, I'm lonely\\...\n",
              "206                                        \"Yes It Is\"  ...  If you wear red tonight\\nRemember what I said ...\n",
              "208                                \"You Can't Do That\"  ...  I got something to say that might cause you pa...\n",
              "216                   \"You're Going to Lose That Girl\"  ...  You're going to lose that girl\\nYou're going t...\n",
              "217                \"You've Got to Hide Your Love Away\"  ...  Here I stand head in hand\\nTurn my face to the...\n",
              "\n",
              "[60 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UuzP7_loFMno",
        "colab_type": "text"
      },
      "source": [
        "# Pre-processing the Data\n",
        "\n",
        "We will break down the data into individual lyric lines."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5rDi59lF9U9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import spacy\n",
        "import en_core_web_sm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Qm0UZnrGGMg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocessing_songs(df):\n",
        "\n",
        "  df['Lyric Line'] = df['Lyrics'].apply(lambda x: x.split('\\n'))\n",
        "  df = df.set_index(['Song', 'Album Debut', 'Songwriter(s)', 'Lead Vocal(s)', 'Year'])['Lyric Line'].apply(pd.Series).stack().reset_index().drop('level_5',1)\n",
        "  df.rename(columns={0:'Lyric Line'}, inplace=True)\n",
        "\n",
        "  print('Removing quotes from song...')\n",
        "  df['Song'] = df['Song'].apply(lambda x: remove_song_quotes(x))\n",
        "\n",
        "  print('Cleaning songwriter(s) column...')\n",
        "  df['Songwriter(s)'] = df['Songwriter(s)'].apply(lambda x: condense_band_members(x))\n",
        "  \n",
        "  print('Cleaning lead vocals(s) column...')\n",
        "  df['Lead Vocal(s)'] = df['Lead Vocal(s)'].apply(lambda x: condense_band_members(x))\n",
        "\n",
        "  print('Counting the number of distinct lines in the lyrics.')\n",
        "  df['Number Lines'] = df['Lyric Line'].apply(lambda x: number_lines(x))\n",
        "\n",
        "  print('Counting the number of words in each song.')\n",
        "  df['Number Words'] = df['Lyric Line'].apply(lambda x: number_words(x))\n",
        "\n",
        "  print('Calculating the average number of words in each line.')\n",
        "  df['Average Words Per Line'] = df.apply(lambda row: words_per_line(row['Number Lines'], row['Number Words']), axis=1)\n",
        "\n",
        "  print('Counting the number of apostrophes in each song.')\n",
        "  df['Number Apostrophes'] = df['Lyric Line'].apply(lambda x: apostrophe_count(x))\n",
        "\n",
        "  print('Calculating the average number of words in each line.')\n",
        "  df['Average Words Per Apostrophe'] = df.apply(lambda row: words_per_apostrophe(row['Number Words'], row['Number Apostrophes']), axis=1)\n",
        "\n",
        "  print('Cleaning lyrics...')\n",
        "  df['Cleaned Lyrics'] = clean_text(df, 'Lyric Line')\n",
        "\n",
        "  return df\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOc4sDWusXL1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pre_processing_songs_nlp(df):\n",
        "  print('Extracting NLP features...')\n",
        "  df['NLP Features'] = df['Cleaned Lyrics'].progress_apply(lambda x: nlp_pipeline(x))\n",
        "\n",
        "  df = df.dropna()\n",
        "\n",
        "  df['Tokens'] = df['NLP Features'].apply(lambda x: x['tokens'])\n",
        "\n",
        "  df['Lemmas'] = df['NLP Features'].apply(lambda x: x['lemmas'])\n",
        "\n",
        "  df['Lemmas Text'] = df['Lemmas'].apply(lambda x: ' '.join(x))\n",
        "\n",
        "  df['Album Debut'] = df['Album Debut'].str.replace(r\"\\n\", \" \")\n",
        "  \n",
        "  # Replace two spaces with one space\n",
        "  df['Album Debut'] = df['Album Debut'].str.replace(r\"  \", \" \")\n",
        "\n",
        "  df = df.reset_index(drop=True)\n",
        "\n",
        "  return df\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVikUA2Syg17",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def nlp_features(df):\n",
        "  # Create dataframe of syntactic dependency relation counts 'dep'\n",
        "    df_dep = df['NLP Features'].progress_apply(lambda x: nlp_dict_to_df(x, 'dep_counter'))\n",
        "    df_dep = df_dep.fillna(value = 0)\n",
        "\n",
        "    # Create dataframe of coarse-grained parts-of-speech counts 'pos'\n",
        "    df_pos = df['NLP Features'].progress_apply(lambda x: nlp_dict_to_df(x, 'pos_counter'))\n",
        "    df_pos = df_pos.fillna(value = 0)\n",
        "\n",
        "    # Create dataframe of stop word counts 'stop'\n",
        "    df_stop = df['NLP Features'].progress_apply(lambda x: nlp_dict_to_df(x, 'stop_counter'))\n",
        "    df_stop = df_stop.fillna(value = 0)\n",
        "\n",
        "    # Create dataframe of fine-grained parts-of-speech counts 'tag'\n",
        "    df_tag = df['NLP Features'].progress_apply(lambda x: nlp_dict_to_df(x, 'tag_counter'))\n",
        "    df_tag = df_tag.fillna(value = 0)\n",
        "\n",
        "    # Combine all NLP dataframes\n",
        "    df_spacy = pd.concat([df_stop, df_pos, df_tag, df_dep], axis=1)\n",
        "\n",
        "    df_spacy = df_spacy.reset_index(drop=True)\n",
        "\n",
        "    return df_spacy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDmkbGneKDNa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_song_quotes(song_row):\n",
        "  # Removes any quotations around songs\n",
        "  try:\n",
        "    song_row = re.sub(r'\"', '', song_row)\n",
        "  except:\n",
        "    pass\n",
        "\n",
        "  return song_row"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzET49vnKTF3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def condense_band_members(row):\n",
        "  # Create new pandas df column for either the individual songwriter or group songwriters\n",
        "  if row =='Harrison':\n",
        "    return row\n",
        "  elif row == 'Lennon':\n",
        "    return row\n",
        "  elif row == 'McCartney':\n",
        "    return row\n",
        "  elif row == 'Starr':\n",
        "    return row\n",
        "  elif row == 'Instrumental':\n",
        "    return row\n",
        "  else:\n",
        "    return 'Multiple Beatles'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MB5BOXs0Kj9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def number_lines(lyrics_row):\n",
        "  # Counts the number of distinct lines in the lyrics\n",
        "  try:\n",
        "    number_lines = lyrics_row.count('\\n') + 1\n",
        "    return number_lines\n",
        "  except:\n",
        "    return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4l5WNswRKuao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def number_words(lyrics_row):\n",
        "  # Counts the number of words in each song\n",
        "  try:\n",
        "    list_of_words = lyrics_row.split()\n",
        "    number_words = len(list_of_words)\n",
        "    return number_words\n",
        "  except:\n",
        "    return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1jIFtUyK5r5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def words_per_line(number_lines_row, number_words_row):\n",
        "    # Calculates the average words per line\n",
        "    \n",
        "    if number_words_row == 0:\n",
        "        # Don't want to return np.nan, return 0\n",
        "        return 0\n",
        "    else:\n",
        "        average_words_per_line = number_words_row / number_lines_row\n",
        "        return average_words_per_line"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39F9G2rpL82g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def apostrophe_count(lyrics_row):\n",
        "    # Counts the number of apostrophes\n",
        "    \n",
        "    try:\n",
        "        number_apostrophes = lyrics_row.count(\"'\")\n",
        "        return number_apostrophes\n",
        "    except:\n",
        "        return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0o3MWNLvMBu7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def words_per_apostrophe(number_words_row, number_apostrophes_row):\n",
        "    # Calculates the average number of words used per apostrophe used\n",
        "    \n",
        "    if number_words_row == 0 or number_apostrophes_row == 0:\n",
        "        # Don't want to return np.nan, return 0\n",
        "        return 0\n",
        "    else:\n",
        "        average_words_per_apostrophe = number_words_row / number_apostrophes_row\n",
        "        return average_words_per_apostrophe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJN-ZaBUMKXS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clean_text(df, df_column):\n",
        "    \"\"\"\n",
        "    Removing line breaks, special characters.\n",
        "    Setting all letters to lowercase -- EXCEPT for pronoun I, kept capitalized.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Replacing line breaks with one space\n",
        "    df['Cleaned Lyrics'] = df[df_column].str.replace(r\"\\n\", \" \")\n",
        "    \n",
        "    # Removing special characters\n",
        "    df['Cleaned Lyrics'] = df['Cleaned Lyrics'].str.replace(r\"[.,?!();:]\", \"\")\n",
        "    \n",
        "    # Replace two spaces with one space\n",
        "    df['Cleaned Lyrics'] = df['Cleaned Lyrics'].str.replace(r\"  \", \" \")\n",
        "    \n",
        "    # Lower-case all words\n",
        "    df['Cleaned Lyrics'] = df['Cleaned Lyrics'].str.lower()\n",
        "    \n",
        "    # Upper-case the pronouns I -- this helps with Spacy Lemmatization\n",
        "    df['Cleaned Lyrics'] = df['Cleaned Lyrics'].str.replace(r\" i i \", \" I I \")\n",
        "    df['Cleaned Lyrics'] = df['Cleaned Lyrics'].str.replace(r\" i \", \" I \")\n",
        "    df['Cleaned Lyrics'] = df['Cleaned Lyrics'].str.replace(r\" i'\", \" I'\")\n",
        "    \n",
        "    return df['Cleaned Lyrics']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVryNrl97v7y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_song_quotes(song_row):\n",
        "    \"\"\"\n",
        "    Removing any quotation marks around songs.\n",
        "    \"\"\"      \n",
        "    \n",
        "    # Check if the song names have quotes around them (mine did)\n",
        "    try:\n",
        "        song_row = re.sub(r'\"', '', song_row)\n",
        "    except:\n",
        "        pass\n",
        "    \n",
        "    return song_row"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzD5m-qXMYgn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def nlp_pipeline(text_document):\n",
        "    \"\"\"\n",
        "    Takes in a string and runs it through Spacy's NLP pipeline consisting of a \n",
        "    Tokenizer, Tagger, Dependency Parser, Entity Recognizer, Text Categorizer.\n",
        "    NLP features are then extracted from each Token's Spacy attributes.\n",
        "    NLP features are then aggregated for the entire text_document and returned in a dictionary.\n",
        "    \n",
        "    Inputs:\n",
        "        text_document (str): Text data. In this case it is Beatles lyrics.\n",
        "    \n",
        "    Output:\n",
        "        dict: Aggregated NLP features.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Create Spacy NLP pipeline\n",
        "    nlp = en_core_web_sm.load() \n",
        "    \n",
        "    # Check if text is not a string, if so return np.nan\n",
        "    if type(text_document) != str:\n",
        "        return np.nan\n",
        "    \n",
        "    # Run Spacy NLP pipeline on text_document, creates DOC object filled with tokens\n",
        "    doc = nlp(text_document)\n",
        "\n",
        "    # Tokenization\n",
        "    tokens = [tok.orth_ for tok in doc]\n",
        "\n",
        "    # Lemmatization\n",
        "    lemmas = [tok.lemma_ for tok in doc]\n",
        "\n",
        "    # Create counter dictionaries to collect counts of NLP features\n",
        "    pos_counter = {}   # Coarse-grained part-of-speech\n",
        "    tag_counter = {}   # Fine-grained part-of-speech.\n",
        "    stop_counter = {}   # Stop word or not\n",
        "    dep_counter = {}   # Syntactic dependency relation\n",
        "\n",
        "    # Loop through each Token object contained in Doc object\n",
        "    for token in doc:\n",
        "\n",
        "        # Add token 'POS' to dictionary\n",
        "        pos = \"pos_\" + token.pos_\n",
        "        if pos in pos_counter.keys():\n",
        "            pos_counter[pos] += 1\n",
        "        else:\n",
        "            pos_counter[pos] = 1\n",
        "\n",
        "        # Add token 'TAG' to dictionary\n",
        "        tag = \"tag_\" + token.tag_\n",
        "        if tag in tag_counter.keys():\n",
        "            tag_counter[tag] += 1\n",
        "        else:\n",
        "            tag_counter[tag] = 1\n",
        "\n",
        "        # Add token 'STOP' to dictionary\n",
        "        stop = \"stop_\" + str(token.is_stop)\n",
        "        if stop in stop_counter.keys():\n",
        "            stop_counter[stop] += 1\n",
        "        else:\n",
        "            stop_counter[stop] = 1\n",
        "\n",
        "        # Add token 'DEP' to dictionary\n",
        "        dep = \"dep_\" + token.dep_\n",
        "        if dep in dep_counter.keys():\n",
        "            dep_counter[dep] += 1\n",
        "        else:\n",
        "            dep_counter[dep] = 1\n",
        "\n",
        "    # Combine NLP features into one dictionary\n",
        "    nlp_dictionary = {'pos_counter' : pos_counter,\n",
        "                      'tag_counter' : tag_counter,\n",
        "                      'stop_counter' : stop_counter,\n",
        "                      'dep_counter' : dep_counter,\n",
        "                      'tokens' : tokens,\n",
        "                      'lemmas' : lemmas}\n",
        "    \n",
        "    return nlp_dictionary"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykrQbRDVNm5J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def nlp_dict_to_df(nlp_features, feature):\n",
        "    \"\"\"\n",
        "    Take in nlp_features dictionary, outputs a pd.Series object \n",
        "    containing the NLP features.\n",
        "    \n",
        "    Inputs:\n",
        "        nlp_features (dict): Aggregated NLP features.\n",
        "    \n",
        "    Outputs:\n",
        "        nlp_series (pd.Series): NLP features for each song obtained using Spacy.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Dep dictionary\n",
        "    nlp_dict = nlp_features[feature]\n",
        "\n",
        "    # Total number of entries in dep dictionary\n",
        "    nlp_total = np.sum(list(nlp_dict.values()))\n",
        "\n",
        "    # Calculate fraction of total for each NLP feature (Normalization)\n",
        "    nlp_dict_fractions = {k: v / nlp_total for k, v in nlp_dict.items()}\n",
        "\n",
        "    # Turn into a pandas Series to return\n",
        "    nlp_series = pd.Series(nlp_dict_fractions)\n",
        "    \n",
        "    return nlp_series"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpHxvdAcNpbj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# we have a new dataframe with cleaned lyrics\n",
        "new_df = preprocessing_songs(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKk8OF6qOAsy",
        "colab_type": "code",
        "outputId": "fcb666ab-7fb4-4b56-f376-fc388382d44f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        }
      },
      "source": [
        "new_df.head()"
      ],
      "execution_count": 0,
      "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</th>\n",
              "      <th>Album Debut</th>\n",
              "      <th>Songwriter(s)</th>\n",
              "      <th>Lead Vocal(s)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Lyric Line</th>\n",
              "      <th>Number Lines</th>\n",
              "      <th>Number Words</th>\n",
              "      <th>Average Words Per Line</th>\n",
              "      <th>Number Apostrophes</th>\n",
              "      <th>Average Words Per Apostrophe</th>\n",
              "      <th>Cleaned Lyrics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Words are flowing out like endless rain into a...</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>words are flowing out like endless rain into a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>They slither while they pass, they slip away a...</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>they slither while they pass they slip away ac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Pools of sorrow, waves of joy are drifting thr...</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>pools of sorrow waves of joy are drifting thro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Possessing and caressing me.</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>possessing and caressing me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Jai Guru Deva Om</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>jai guru deva om</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Song  ...                                     Cleaned Lyrics\n",
              "0  Across the Universe  ...  words are flowing out like endless rain into a...\n",
              "1  Across the Universe  ...  they slither while they pass they slip away ac...\n",
              "2  Across the Universe  ...  pools of sorrow waves of joy are drifting thro...\n",
              "3  Across the Universe  ...                        possessing and caressing me\n",
              "4  Across the Universe  ...                                   jai guru deva om\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uvh6ekfujHu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# we will add all of the NLP features to the dataframe\n",
        "new_df = pre_processing_songs_nlp(new_df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fmRb3TLu9ZR",
        "colab_type": "code",
        "outputId": "f930faaf-11cb-41d3-dd44-4c0be710843c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "spacy_df = nlp_features(new_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5664/5664 [00:02<00:00, 2174.94it/s]\n",
            "100%|██████████| 5664/5664 [00:02<00:00, 2303.82it/s]\n",
            "100%|██████████| 5664/5664 [00:02<00:00, 2196.54it/s]\n",
            "100%|██████████| 5664/5664 [00:02<00:00, 2300.75it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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              "4  Across the Universe  ...                                   jai guru deva om\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 104 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   stop_False  stop_True  pos_NOUN  ...  dep_agent  dep_csubj  dep_\n",
              "0    0.636364   0.363636  0.363636  ...        0.0        0.0   0.0\n",
              "1    0.454545   0.545455  0.090909  ...        0.0        0.0   0.0\n",
              "2    0.583333   0.416667  0.416667  ...        0.0        0.0   0.0\n",
              "3    0.500000   0.500000  0.000000  ...        0.0        0.0   0.0\n",
              "4    1.000000   0.000000  1.000000  ...        0.0        0.0   0.0\n",
              "\n",
              "[5 rows x 104 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpcJ2YG0Ea4T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_df.to_csv('data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K66xAAyGEls_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spacy_df.to_csv('spacy.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NN2KpasqFtPX",
        "colab_type": "text"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nk-L3i_xx7Wq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = new_df\n",
        "spacy = spacy_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QzBriS5B5J7e",
        "colab_type": "code",
        "outputId": "e09b825d-ddff-46ac-ef07-1dc95b39b0c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Checking for class imbalance\n",
        "data['Songwriter(s)'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Multiple Beatles    1927\n",
              "Lennon              1629\n",
              "McCartney           1488\n",
              "Harrison             612\n",
              "Starr                  8\n",
              "Name: Songwriter(s), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_a97i_bH5n7m",
        "colab_type": "code",
        "outputId": "ea529550-417b-4d77-c9c2-40c77adbd087",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "data[data['Songwriter(s)'] == 'Starr']"
      ],
      "execution_count": 0,
      "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</th>\n",
              "      <th>Album Debut</th>\n",
              "      <th>Songwriter(s)</th>\n",
              "      <th>Lead Vocal(s)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Lyric Line</th>\n",
              "      <th>Number Lines</th>\n",
              "      <th>Number Words</th>\n",
              "      <th>Average Words Per Line</th>\n",
              "      <th>Number Apostrophes</th>\n",
              "      <th>Average Words Per Apostrophe</th>\n",
              "      <th>Cleaned Lyrics</th>\n",
              "      <th>NLP Features</th>\n",
              "      <th>Tokens</th>\n",
              "      <th>Lemmas</th>\n",
              "      <th>Lemmas Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4495</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>Taking a trip on an ocean liner</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>taking a trip on an ocean liner</td>\n",
              "      <td>{'pos_counter': {'pos_VERB': 1, 'pos_DET': 2, ...</td>\n",
              "      <td>[taking, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>[take, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>take a trip on an ocean liner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4496</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>I'm gonna get to Carolina</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>i'm gonna get to carolina</td>\n",
              "      <td>{'pos_counter': {'pos_PRON': 1, 'pos_VERB': 3,...</td>\n",
              "      <td>[i, 'm, gon, na, get, to, carolina]</td>\n",
              "      <td>[-PRON-, be, go, to, get, to, carolina]</td>\n",
              "      <td>-PRON- be go to get to carolina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4497</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>Taking a trip on an ocean liner</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>taking a trip on an ocean liner</td>\n",
              "      <td>{'pos_counter': {'pos_VERB': 1, 'pos_DET': 2, ...</td>\n",
              "      <td>[taking, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>[take, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>take a trip on an ocean liner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4498</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>I'm gonna get to Carolina</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>i'm gonna get to carolina</td>\n",
              "      <td>{'pos_counter': {'pos_PRON': 1, 'pos_VERB': 3,...</td>\n",
              "      <td>[i, 'm, gon, na, get, to, carolina]</td>\n",
              "      <td>[-PRON-, be, go, to, get, to, carolina]</td>\n",
              "      <td>-PRON- be go to get to carolina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4499</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>Taking a trip on an ocean liner</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>taking a trip on an ocean liner</td>\n",
              "      <td>{'pos_counter': {'pos_VERB': 1, 'pos_DET': 2, ...</td>\n",
              "      <td>[taking, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>[take, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>take a trip on an ocean liner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4500</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>I'm gonna get to Carolina</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>i'm gonna get to carolina</td>\n",
              "      <td>{'pos_counter': {'pos_PRON': 1, 'pos_VERB': 3,...</td>\n",
              "      <td>[i, 'm, gon, na, get, to, carolina]</td>\n",
              "      <td>[-PRON-, be, go, to, get, to, carolina]</td>\n",
              "      <td>-PRON- be go to get to carolina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4501</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>Taking a trip on an ocean liner</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>taking a trip on an ocean liner</td>\n",
              "      <td>{'pos_counter': {'pos_VERB': 1, 'pos_DET': 2, ...</td>\n",
              "      <td>[taking, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>[take, a, trip, on, an, ocean, liner]</td>\n",
              "      <td>take a trip on an ocean liner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4502</th>\n",
              "      <td>Taking a Trip to Carolina</td>\n",
              "      <td>Let It Be... Naked - Fly on the Wall bonus disc</td>\n",
              "      <td>Starr</td>\n",
              "      <td>Starr</td>\n",
              "      <td>1969</td>\n",
              "      <td>I'm gonna get to Carolina</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>i'm gonna get to carolina</td>\n",
              "      <td>{'pos_counter': {'pos_PRON': 1, 'pos_VERB': 3,...</td>\n",
              "      <td>[i, 'm, gon, na, get, to, carolina]</td>\n",
              "      <td>[-PRON-, be, go, to, get, to, carolina]</td>\n",
              "      <td>-PRON- be go to get to carolina</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Song  ...                      Lemmas Text\n",
              "4495  Taking a Trip to Carolina  ...    take a trip on an ocean liner\n",
              "4496  Taking a Trip to Carolina  ...  -PRON- be go to get to carolina\n",
              "4497  Taking a Trip to Carolina  ...    take a trip on an ocean liner\n",
              "4498  Taking a Trip to Carolina  ...  -PRON- be go to get to carolina\n",
              "4499  Taking a Trip to Carolina  ...    take a trip on an ocean liner\n",
              "4500  Taking a Trip to Carolina  ...  -PRON- be go to get to carolina\n",
              "4501  Taking a Trip to Carolina  ...    take a trip on an ocean liner\n",
              "4502  Taking a Trip to Carolina  ...  -PRON- be go to get to carolina\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjhzz87S5whH",
        "colab_type": "text"
      },
      "source": [
        "We're going to drop Ringo from the list. Sorry Ringo, poor lad."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlQagHkE54qM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.drop([4495, 4496, 4497, 4498, 4499, 4500])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBFb8R1l9q9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spacy = spacy.drop([4495, 4496, 4497, 4498, 4499, 4500])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXQizhn59-3j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.drop([4501, 4502])\n",
        "spacy = spacy.drop([4501, 4502])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KpfjSSH6NkT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.reset_index(drop=True)\n",
        "spacy = spacy.reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXNLdfzFrgzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.drop(\"Unnamed: 0\", axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luJfusBurMmG",
        "colab_type": "code",
        "outputId": "d1235f67-1e56-4107-e830-26bb3c9cc88a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
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      },
      "source": [
        "data.head()"
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      "execution_count": 0,
      "outputs": [
        {
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              "                  Song  ...                                        Lemmas Text\n",
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              "1  Across the Universe  ...  -PRON- slither while -PRON- pass -PRON- slip a...\n",
              "2  Across the Universe  ...  pool of sorrow wave of joy be drift through -P...\n",
              "3  Across the Universe  ...                          possess and caress -PRON-\n",
              "4  Across the Universe  ...                                   jai guru deva om\n",
              "\n",
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      "metadata": {
        "id": "hVEyzIC5r_yK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "spacy = spacy.drop(\"Unnamed: 0\", axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ivyd9aYzrPSR",
        "colab_type": "code",
        "outputId": "fb44e146-a065-4d4a-828e-50dd0837878d",
        "colab": {
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              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 104 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   stop_False  stop_True  pos_NOUN  ...  dep_agent  dep_csubj  dep_\n",
              "0    0.636364   0.363636  0.363636  ...        0.0        0.0   0.0\n",
              "1    0.454545   0.545455  0.090909  ...        0.0        0.0   0.0\n",
              "2    0.583333   0.416667  0.416667  ...        0.0        0.0   0.0\n",
              "3    0.500000   0.500000  0.000000  ...        0.0        0.0   0.0\n",
              "4    1.000000   0.000000  1.000000  ...        0.0        0.0   0.0\n",
              "\n",
              "[5 rows x 104 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkuONKfR6SDI",
        "colab_type": "code",
        "outputId": "94abd7fd-d5a7-4aa6-b7b0-21e5577fadfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "data['Songwriter(s)'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Multiple Beatles    1927\n",
              "Lennon              1629\n",
              "McCartney           1488\n",
              "Harrison             612\n",
              "Name: Songwriter(s), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBG_cxFYsYik",
        "colab_type": "code",
        "outputId": "f3117c14-a71d-4da9-fd48-aab7ea502f63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(data.shape)\n",
        "print(spacy.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5656, 16)\n",
            "(5656, 104)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c--cyips6Vaj",
        "colab_type": "text"
      },
      "source": [
        "### Bag of Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z66dSp5B6a7Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7Jsz1Af6dq3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xif18um9v-mX",
        "colab_type": "code",
        "outputId": "1b026cc5-b5a2-44ba-d1af-2f9f7350609a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "data['Lemmas Text']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       word be flow out like endless rain into a pape...\n",
              "1       -PRON- slither while -PRON- pass -PRON- slip a...\n",
              "2       pool of sorrow wave of joy be drift through -P...\n",
              "3                               possess and caress -PRON-\n",
              "4                                        jai guru deva om\n",
              "                              ...                        \n",
              "5651                             \" love will find a way \"\n",
              "5652                        gather round all -PRON- clown\n",
              "5653                           let -PRON- hear -PRON- say\n",
              "5654         hey -PRON- have get to hide -PRON- love away\n",
              "5655         hey -PRON- have get to hide -PRON- love away\n",
              "Name: Lemmas Text, Length: 5656, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkLyxYrS6rqu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define X and y\n",
        "y = data['Songwriter(s)']\n",
        "X = data['Lemmas Text']\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state = 444)\n",
        "\n",
        "# Vocabulary of lemmas, use with CountVectorizer\n",
        "vocabulary = set(itertools.chain.from_iterable(data['Lemmas']))\n",
        "\n",
        "# Count vectorizer and the index-to-word mapping\n",
        "count_vectorizer = CountVectorizer(vocabulary=vocabulary)\n",
        "\n",
        "# Create bag-of-word embeddings using vectorizer\n",
        "X_train_counts = count_vectorizer.fit_transform(X_train)\n",
        "X_test_counts = count_vectorizer.transform(X_test)\n",
        "\n",
        "# Convert to a pandas dataframe\n",
        "X_train_counts = pd.DataFrame(X_train_counts.todense())\n",
        "X_test_counts = pd.DataFrame(X_test_counts.todense())\n",
        "\n",
        "# Create a mapping from index-to-word for the bag of words\n",
        "index_to_word = {v:k for k,v in count_vectorizer.vocabulary_.items()}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCL22PE6wTQy",
        "colab_type": "code",
        "outputId": "75dbef4f-7499-49d9-f956-fa0da21b855e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "X_train_counts"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1844</th>\n",
              "      <th>1845</th>\n",
              "      <th>1846</th>\n",
              "      <th>1847</th>\n",
              "      <th>1848</th>\n",
              "      <th>1849</th>\n",
              "      <th>1850</th>\n",
              "      <th>1851</th>\n",
              "      <th>1852</th>\n",
              "      <th>1853</th>\n",
              "      <th>1854</th>\n",
              "      <th>1855</th>\n",
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              "      <th>1857</th>\n",
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              "      <th>1863</th>\n",
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              "      <th>1865</th>\n",
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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>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>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>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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>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",
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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>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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4519</th>\n",
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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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4520</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "<p>4524 rows × 1870 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      0     1     2     3     4     5     ...  1864  1865  1866  1867  1868  1869\n",
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              "...    ...   ...   ...   ...   ...   ...  ...   ...   ...   ...   ...   ...   ...\n",
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              "\n",
              "[4524 rows x 1870 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FW0cXEKq7u2X",
        "colab_type": "text"
      },
      "source": [
        "### Latent Semantic Analysis of BoW Embedding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maDU-s-w6r1G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "from sklearn.decomposition import PCA, TruncatedSVD\n",
        "from sklearn.preprocessing import LabelEncoder\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3ge6fBm6sJm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "le = LabelEncoder()\n",
        "le.fit(y_train)\n",
        "y_train_encoded = le.transform(y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jv4nhyuK6sQm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_LSA(test_data, test_labels, plot=True):\n",
        "    \"\"\"\n",
        "    This function first uses SK-Learn's truncated SVD (LSA) class to \n",
        "    transform the high dimensionality (number of columns) of the BoW \n",
        "    embedding down to 2 dimensions. Then the two dimensions are used\n",
        "    to plot each song, colored by the song writer (class).\n",
        "    \n",
        "    Inputs:\n",
        "        test_data (pd.DataFrame): BoW embeddings.\n",
        "        test_labels (pd.Series): In this case the songwriter of each\n",
        "        Beatles' song.\n",
        "        plot (boolean): Whether or not to plot. Defaults to True.\n",
        "    \n",
        "    Outputs:\n",
        "        None.\n",
        "    \"\"\"\n",
        "    \n",
        "    lsa = TruncatedSVD(n_components=2)\n",
        "    lsa.fit(test_data)\n",
        "    lsa_scores = lsa.transform(test_data)\n",
        "    color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}\n",
        "    color_column = [color_mapper[label] for label in test_labels]\n",
        "    colors = ['blue','green','purple', 'red']\n",
        "    if plot:\n",
        "        plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))\n",
        "        blue_patch = mpatches.Patch(color='blue', label='Harrison')\n",
        "        green_patch = mpatches.Patch(color='green', label='Lennon')\n",
        "        purple_patch = mpatches.Patch(color='purple', label='McCartney')\n",
        "        orange_patch = mpatches.Patch(color='red', label='Multiple Beatles')\n",
        "        plt.legend(handles=[blue_patch, green_patch, purple_patch, orange_patch], prop={'size': 18})\n",
        "        plt.xlabel('Principal Component One')\n",
        "        plt.ylabel('Principal Component Two')\n",
        "        plt.rcParams[\"xtick.labelsize\"] = 20\n",
        "        plt.rcParams[\"ytick.labelsize\"] = 20\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLVPvXC_6sYG",
        "colab_type": "code",
        "outputId": "211f1f3b-8a63-42c8-8b5e-3aa47f204173",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        }
      },
      "source": [
        "# Run LSA on BoW embeddings and plot it\n",
        "fig = plt.figure(figsize=(12, 12))\n",
        "plot_LSA(X_train_counts, y_train_encoded)\n",
        "plt.title('LSA: BoW', fontsize = 25)\n",
        "plt.rcParams[\"xtick.labelsize\"] = 18\n",
        "plt.rcParams[\"ytick.labelsize\"] = 18\n",
        "plt.rcParams[\"axes.labelsize\"] = 20\n",
        "plt.xlim(-0.2, 3.2)\n",
        "plt.ylim(-0.04, 0.06)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mi7Ytqr6QXysHi4iIiBwJdQGSOhuSOoRobzS5xbnElboYMGsZjGjJt3visIHQmyQLAX/A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gBEROSSZWTAY49Bo0bQqRMcPerqRCIikh8VACIicsl+/BFWrIAKFeD332HJElcnEhGR/KgAEBGR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\nBgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLOxM60S8nXJ",
        "colab_type": "text"
      },
      "source": [
        "### Latent Semantic Analysis of BoW Embedding + TFIDF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhXSYx256sgm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = data['Songwriter(s)']\n",
        "X = data['Lemmas Text']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 444)\n",
        "\n",
        "vocabulary = set(itertools.chain.from_iterable(data['Lemmas']))\n",
        "\n",
        "tfidf_vectorizer = TfidfVectorizer(vocabulary=vocabulary)\n",
        "\n",
        "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
        "\n",
        "X_train_tfidf = pd.DataFrame(X_train_tfidf.todense())\n",
        "X_test_tfidf = pd.DataFrame(X_test_tfidf.todense())\n",
        "\n",
        "index_to_word = {v:k for k, v in count_vectorizer.vocabulary_.items()}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "all6-ZMT6sou",
        "colab_type": "code",
        "outputId": "9f611dc3-8580-47c7-987e-29d706c0df09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        }
      },
      "source": [
        "fig = plt.figure(figsize=(12,12))\n",
        "plot_LSA(X_train_tfidf, y_train_encoded)\n",
        "plt.title('LSA: BoW + TFIDF Transformation', fontsize= 25)\n",
        "plt.rcParams[\"xtick.labelsize\"] = 18\n",
        "plt.rcParams[\"ytick.labelsize\"] = 18\n",
        "plt.rcParams[\"axes.labelsize\"] = 20\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rli1bMnPmTCZNmuSbEyQ+p+43IiIiIj72xhtvcMkll9CsWTMSEhIyl9TUVK67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\nr0m9WDtxLU5/J3ub7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kv2Qe3bw1tvyRzg/v3hzTeLfL78WPDIAtw2Ny6bi0XDF+UcYCqC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Os4zvkxrOnp0KXL1uVJvfZk6i8hQpGrkLaK9HSf3+d8hu4+lOzCbNJT0rlx6o2s\ny17HNQdfg5lx0cSLyA/n+2PjOOvNs7j05UtZ9okvAXr9tNdpu29bWuy2tRc/Z9FvZG5Jpn5qEXkF\nIZZ9tpR9gOyCbEZ9MYoVm1Ywot8I9mxVYtRwtOceMpp2bsrw94ezds5aWvZqSf2mWwcEb87fzPDx\nw8lcl8mq/FXUK6hHanoq2YXZDNhlAH3aRm+ztvu25cSnTyzzvM3Tm2MYYefbqll6M8B/IQTlGzoQ\nSgrRtGvTnW9YQat/WO0H9xqsnb025scXEQk61dSLxEFeKoTCkJUK9Qph4+qlsT9JaipMnOjrzG+4\noXLHWLNm28ebNlU9rgQIh+GUU/yXDkcc4aeQLzZ87+GcufeZNE9vzok9T2Rk/5FkZ/tZU0aPakZo\ncwea1W/G6GNH8+LJL7JXa9/73qFRh23O0Ty9Oatmrdpm2ew3Zm/zOH3X9uR1zmJBaphV6YU0PaUj\nnR/oTOM7GnP3Z3fzwS8fcPrrp1NQtO1Fp7Z9LmHC4TA563N47qjneO0vr7HgvQWEC8Ns/s1f7XZT\n3ibyCvOwtUb9wvqEQ2EytmRw/9H389QJT/1+ddcdycvKY83sNRTlb53z/8BdDuSKg66gaf2mHNLx\nEO496l7Gj4e+fX2VTMl2rW4dDuyAK3K4Qke7vtGmJxIRqdtUfhMDKr+R0r5raTQo8j31axrAHqeO\nIOPeGlhbm5/vu7VXrPCDPmfNgh49quXUq1b5zxDduvmxklUxerQfa2nmhxgcdhi8807Z2199tZ+A\nxQy6d4f334++Xe//9ObH1T/SpkEbFl62kMnnT2bWU7P8SoOj7j2Ktn3a0vnQzgAsy1zG4CeOIG1m\nc1akZLElvDt5nd8ECxMmTLcm3XDm+O7C78hI3b7M6bWfX2P4+OEUFBVw6TOXkvFLxu/nSklPoSiv\niLZ92nLW52dx0yc3MeaDMaRmpTJ02lD6HtCXYY8MK/M55xbmsi57HW0btmXy55P5dPinpBWm0W2v\nbpzy2ikkp0X/4rZ/f/86mflvQb7+uux2jadwYZgZj8/AFTn6Xtg36rz1IiK1ncpvRKrZ3mscC9vU\nZ73Lpef/Da+ZCT343v5ff632077/vr9OU1GRr/i59daqHe+rr6BePf8ZJS8Pevfe8fazZ/uZeerX\nh4ULt069Wdr3F32/zeMTnjyBdv3aseiDRSz/cjkfXP0BAINuH8SAKwcQshCb8h3zGm6C1C0wYx/I\nLSKl19u+970oh5H9R0ZN6AEum3QZ+UX5JFkSeevzyCCynYPCnEJCqSFWzljJsk+Xcevht3Lx7hcz\n78V51OtZj33O3H6wabGlmUsZ+spQ1uesZ582+5D9ajb7rtiXPJfHwi0LWfX9Ktr3iz5eoVMn374A\ne+yx43aNp1ByiP0v2j9xAcRI2IW554t7eHf+uxzT/RiuHHBlmSVoQVCYW8g7F7/D8mnL6XlSTw6/\n5fBqucCXiGwvuH9JRGq4rr/l0GeVo9HjzyQ6lBpnzBh/36gRPP+8T8ar4rTToE0bP5X8oYfCzTfv\nePsRI/z9pk3w979X7JuC/S/cn8F3DWbLqi1YsoGDCW9M4Or3r2bKoikcWHAdFGTAwsHwzcXUW78v\naclpnLPvOcz5+xyuOOiKMo+dkZqBc44iV8RXh2ydcjKpfhIYuCIHBg3b+5lrWrdpzSGXH0K/v/fb\nbtBrSePnjGflppVk5mYyacEkssJZJBckk1SYRDgrTEF22eVADz8Mw4b5C9g+/HD52ymqzZur/mJX\nwuotq5m3bt7vYwUS6dMlnzJmxhhWb1nN498+zseLP050SFUyb+I8Fn24iFBKiJ/G/cSvX1d/J4GI\neOqpF5Fq17s3TJ/ue9W7dPElM1Vx3HGwe/J81j87kX0PTCMpdC5Q9kGPPBK++AJyc8u4euxONNql\nESkNUsjfnE92g2xePPxFUn5O4dWfX+Xm/7ub9wZ+yebNfmKiN/9xA0cfXb4xD6/++VX++Mof2ZK/\nhSvvuJJDbz+U9QvX0+nwTrx+6uusnbOWg644iOa7Nq9QvJ0adyIzL5OcwhwMY/4e8xkwaQChpBCN\nGjaiXuN6Ze7bsiXcfnuFThfd0UfDe+/5T1C33AL/+lcMDrpzny75lPPeOo+icBFDdhvCfUffVy3n\nLUtekb8kcEpSCnlFeb8/DqqkVD/1a7gwjMP9/lhEqp9q6mNANfUiFZOf76coX7cOhg/3veylOef4\nbeZvhJJDtNqrFbaj7vTsbD/7z6ZNgTN92gAAIABJREFUvpbmyivhb3+Lvq1z8PTT8PbbrD3qYL48\nrBs9WuxGj+YVG0uwZvYavrj7CzZ03sDtTW+nUb1GrMtZx9/2/xv/6H8Vn38Oe+8NjRtX6LBxEXZh\n+o7py6INi2ie3pwkkrj5y5tZO3Mte566J4fdcNiO2zdi3rp53P353TSr34xrDr6GpvXLOctNfr6v\njyqWlgY5OZV8NhVz1viz+GzpZzSs15CNuRuZdeEsGtUr3xWA46EwXMjISSN5f+H7DO46mPuPvr9c\nA5trqnBhmKk3TWXJp0vo9ade7P/3/cv1XhKRylFNvYjUKKmpcM45O97m09s/ZdYzszCMAy49gH4X\n9yt748xM2LLF199s3Oin+izLJ5/AtdeS1SCZ49pMZP3GliSlN+DloS+zT5uy69JLa7l7S0546gSK\nwkV8+OaHTFs2jWb1mzF0j6EkJfkyoArJzIRnn/XJ7+mn+1GpMRKyEI8PeZyz3jyLnMIcbh10K3++\n5M8VOoZzjjPeOIO12Wv9+IDCHEYfO7p8Oycl+a8twpHyl7S0Cj6DyuvdujdTl0xlfc562jVsR4OU\nBtV27miSQ8mM/r9ytlsAhJJDDLqtBl3SV6QOU1IvIjXST6/8REp6Cq7I8cNLP+w4qW/TBk44wV/t\ntWFDOPvssre96SZYu5a59WBjsqOxpbGmMIfPl31eZlKflQWLF/uZckrn2kmhJJ4+8WmWZy2nRXoL\n0lMqmYxfcAF8+aX/+ccf4aHYDq7u064Psy6cRdiFSQ5V/E+/w7E+Zz0ZKRnkFOawctPKcu23eTPM\nm5dE7/sfJPm6a/yHlokTK3z+yhpxwAia12/Oyi0rGbbXsN+vFFwjLF7sB6r36VOtH3REpHZSUi8i\nNVKHAzrwywe/YBjdj+m+443N4J574IorfL1LWb3c+fmwaBE0bMiua7NoVBhiQ2qYtFAa/dtvfwVW\n8LN9/uEPMHPJAsINltH63PP57V/zt9kmZCE6Nu5Ymae51Y8/+tgLC/0FxeIgZKFKz7Syadkmjp19\nLK80fYVmLZvxz4P/udN95s6Ffv18dVTLlhfz87KLadKkUqevtORQMsP3GV69Jy2PadPgzDN9Odju\nu/uLACQH519ybmYu3z7+Lc45+pzXh7Qm+lAikmjB+QsiInXKsaOPZc74OYSSQ+z2h912voMZtG27\n421SU/3E69On08Q1ZWLvv/P5IZ3p2aIne7SMPl/jRx/BD4uXE07dCJvas2r+Lhz25GF8fPbWWUs+\nWfIJb8x+gwG7DODkPU4ud02xc4756+fTqF4j2pxzDjz6qF9x/vnl2h/gjdlvMHLySBrVa8Rrf36N\nXZvvWu59K2Ly5ZPpNqMbVyZfSbNOzeh7WdSSzm08+KBP6FNSYO1aP46ieOahWFiwAK6/3r/0t90G\nXbvG7thx9+abUFAATZv6SyEvXRqoJzDpkkks/ngxAKtmreKPL/wxsQGJiJJ6EamZktOS2fPUPWN/\n4KeegilToGlTWh90EH/cSQK+666Q73Iguzmk5ELThcxZt3WQ58INCznvrfMoDBfy1ry3aJbejEFd\nyldjfM2Ua3jt59cIhUI8esKjHDHkXZ8Bd+pUrv3D4TDDXh9GQbiA5VnLOWncSfz4tx/LtW9F5W7M\nJaleEsmhZAoyy54Cs6Ti65gVFPjEu2fP2MZ08cUwfz5YYQE/7P1XuqZPgVGj/Ojrmq5/f3j1VVi/\nHlq12vkH0hpm7Zy1pGakgsHauWsTHY6IoHnqRaSuqV8fjj8eBgwo1wT1c+o9C384B/o9Aif/BZou\nYXjv4WTlZgGwYtMKnHM0qdeEIlfE8qzl5QojtzCXcT+No2G9hjjneGLmE75ov5wJPUCYMIXhQpIt\nGTMjKy+r3PtW1KDbBvnpCg0G3zG4XPtcconvmd9jD7jjDj+VaCxlZvovXx749WSO2/wKrF4Nf/2r\nHyxd051wAowd68d4vPmmf18GSL8R/SjKK6Iot4h+I3Yw3kVEqo166kVEduCbX7+Bzp/6W8SDXz/I\nM98/w4IRC+jbri+9WvXix1U/0r5he47pfky5jpualErHRh1ZkrkEM2Pv1ntXOLbkUDIX9r2Q/0z/\nDymhlLjOwd6hfwcu/O5CgHKXF4VCcF8cp4W/6y7fW79H0Y+EKNq64ttvYVANn5HFDAYO3G5xfmE+\nY2eOpXFaY4btNaz64yqn3qf3pssRXcBBw3YNEx2OiKB56mNC89SL1F4bczfS4u4WFDmfNIYweqyD\nQhzPfNWWg96fQ1FGA1ZuXkmrBq1ITSr7yq6lrfxtPi///ArNmnXgL3v9pdLzlecX5hMKhco9q81j\n0x/jni/uYc/We/Lq0FdJSqq+GWGK/+XEaipz54BTT8FeecUvSE3105sGaNBpSfv8dx9+XvMzDsep\nvU7luT8+l+iQRKQG2dE89Sq/ERHZgSZpTdh87Wb+96f/ce3B19Iw38hOcqQWQbfZq+Dmm0kKJdGh\nUYcKJfSMGkXbTnsy8pib+evrv1TpAkSpyakkT/0Ebr7ZTzmzA7NWzuKity9iwYYFjJ8znqGvDq30\neStqxgw/e+Oee8LkybE5phnYuHHwv//Btdf6mpyAJvQAP635iSRLImQh3l3wbqLDEZEAUVIvIrIT\naclpDN1jKLcfcTt3Nh7Kxd/AuPEptM4J+Z7hyrj9dn9vBvfeW7UAX3sNjj3WH7NPH1iypMxNpy2b\nhmPrN7Q/rorPwNpo/vUvP289wNVXx/jgQ4f65x/w+d47NOpAQbjAXwW43c5nGBIRKaakXkSkAi68\n4iWubHkie25K8wn0jTdW7kANG/orrBYVQYMqXuX09df9cVJS/Fz8kycz6rNRDHttGL9t/m2bTc/Y\n5wzqJ/tBmYZxxUFXVO3cO1NYCOvWgXM0buxnwsnNhUaN4nvaoJpx3gwu7X8p1x96PRP/Un0X6RKR\n4FNNfQyopl4kurVz1/LeP94jXBBm8N2DabN3m0SHVHN8/z2cfrpPxp98Eg44oPLHeuklP+uLc+TV\nS+awGzvxVbYvw0myJHKvzSW5uCTlqafYfOM1PL1viIOvf5x9+h4XgydThlWrfA/6ihVw2GEsv/Fx\nrrshidxcP+nL7rv78QBLMpfQrWk3QqES/UyZmf4TQIsW8YtPRCRgdlRTr6Q+BpTUi0T34vEvsmb2\nGkKhEBltMjjr07MSHVIgnfTySbyz4B1apbfis7M/o1OTKNNeTpjAio8mcGrTD/nULdpm1XvD3uPI\n7kf6K0E1a+a/IQiH/TcNX30Vv8D/+18/l2WLFrBpE7z8Muy33++r566dywFjD2BLwRY6NOrADxf9\nQEZqBkyaBJde6j/w/POfcO658YtRRCRANFBWRBIiXBjGQgYhCBeFEx1OTH3+ub9C6m+/7Xzbqpi6\neCoT508kRIiVm1cycvLI6Bv+4Q98dNbhLG8M9UL1tll1YIcD/Q+5uT5RTkrytfxZ8ZvXHvAXVEpK\n8ucx267X/Z4v7mFz/mZSLIVfs37lxR9e9Cvuu8/Ph9mgwfbjDfLz4ayz4KCDYKLKU6RycjfmMvny\nybw+7HV++y7Ov8Qi1STwSb2ZDTezmWaWY2arzGysmbUs577tzewaM/vYzFaa2RYz+8nMRplZ83jH\nLlLbHXn3kTRs25D0Fukc80D55m8PgkmTfLXLDTf4awhlZ8fvXPWT62MYYRfGFaWQt7Fpmdv2adeH\nesn1aNewHRkpGXRv2p33hr1HRlqG36BZMzjzTF/nnpYGDz0Uv8ABhgzxPe2HHw6PPQYdO26zulOT\nTpgZha4QgO7NuvsV3bpBTo7/MNC587bHvOACeP55+Ppr+NOf4v+pSmqlT27/hNmvz2b518sZ/9fx\nuLCqFiT4gjvvF2BmI4H7gI+BS4EOwOXAgWbWzzm3ZSeHGALcBLwNjAI2Af2Ay4BTzWx/55z+Y4hU\nUuverTnrk4CU3ITDfkBn8+a+l3gHpk3zmzdrBhs2wPLl0KNHfMI6oMMBXNDnAh6f8j7JSw7m55cf\n4Jk0/6GitJ4tejLh1An8sPoH+rXvR4dGHbbf6PHH4T//8c9xJ8+zykIhOP98f4vi2oOvZeGGhUxb\nNo3hvYczqEvkglF33QUdOvhPSxdfvO1Oc+b4yelTU33N/aJF0CY4YzWy12UTSg6R1jjYs/QEXfaa\nbEJJIVLSU8jfnE+4MOyvmCwSYIGtqTezFsAS4CfgQOf8lWHMbAgwAbjOOffvnRyjF7CudOJuZucC\njwP3Oud2OjWEaupFAi47G047DX780Wfn48btcHqWr7+G4cN9Yt+1K0yYUPmZLctr110hIwPy8qB9\ne3j//fier8Z680045RRfRtSjB/zwQ9QPJ+vWwZ13+pf2yiu37/BPhG+f+JbP7vgMSzKOffBYuh/T\nPdEh1Vmrf1rN+OHjyc3M5ZBrD2Hfs/dNdEgi5bKjmvog99SfCKQDo4sTegDn3FtmthA4HdhhUu+c\n+6mMVePwSf2eMYpVRGqyTz6Bn3+GJk1g/nz48EM48cQyN+/XD959F5Ytg75945/Qw9YxrWbQv3/8\nz1ddspZnkbM+h9a9W5dvhxNOgIULfQ/9AQeU+W3DVVfBlCl+9dy58MEHMQy6kqbdO42UBikU5Rcx\n7d5pMU3qnXNYrC7TWwe06tWK8745j3BRmKQU9dBL7RDkpH7/yP20KOu+BE4zswzn3OZKHLv4O+tV\nlYpMRIKluHyjeOBoOco5unTxt+oydqyfjj493ee1VVJUBD17wi+/+Pnyf/7Zd/9Xs+mPTWfSJZNw\nYUfnwztzxntnlG/Hdu38bQd+/dUPG0hNrTll9407NWbdnHU452javeyxERURLgzz7iXvsmDSAnY5\naBeGPD6ElPqVvzpxXWIhIymkhF5qjyAPlC3+i/5rlHW/AlZim4q6OXL/TFkbmNn5ZjbdzKavWbOm\nkqcRkRphn33gnnv8gM477qiRXeEZGb7kZ+hQf42pMk2c6KeAfOONsre55x5YsMDXpmdlRS/QrwZT\n75zKluQt5NbPZfFHi8ndmBuzY193nf9WIycHrr8+ZoetkhOeOIE9T9uT/c7dj8F3Di57w82b/WDm\nclj62VIWvr+QtCZpLJ+2nAXvLohRtCISNAnvqTezJviBqeX1kHNuPb70BiAvyjbF/xnSo6zbWTz/\nAP4EjHHOfVjWds65McAY8DX1FT2PiNQwJ5wQgy7wBJs6FU4+2ffEP/ecL/Y/+ujtt6u37ZSXO/6U\nEAfOUbhkER91/JBdN/gRxvlN8knNiF0d0yGHwHff+aaoXz9mh62Shu0acsS/j9jxRqef7sd0pKT4\ni4rt5D2ZmpGKw1GYW/j7YxGpmxKe1ANNgIpcZ/15YD1QPIlcPSCn1DbF0wpUaKK5yADZUfjZcC7e\nyeYiIjXLhx/63vd69fx87lOmRE/qR4yAZ57xV7Vt0QJeeKH6YnQOLr+cJVNfZ8r/rSCzfib1N6ez\neuBqRiWPiumpqmOsQ0xt3OgT+uRkP7PPFVfsNKlv26ctA64awJw35tDliC50Hdy1moIVkZom4Um9\nc24xvlSmolZE7tsDpb9vbA+4EtvslJmdje95fw842TlXUImYREQS55RT/MWaCgp8YnjqqdG3S0qC\nmTOrN7Zia9bAhAl0atyUjPByvuo/DZKSOG+/8xITT02SluZft8JC/+Gn+c4vl2Jm9DmvD33O61MN\nAYpITRbkmvpvIvcHRlnXH5hb3kGykYR+LPABcKJzLlpJj4jUAu8d04OZrY0pu6VSuGVnl7JIrN69\nfV14o0Z+Ksud6tULZs3yif2338J++8U9xgpr1AgaNyZ5QyazJrRnRP2B3HfUfTx87MOJjizx0tL8\nNyi77OJfu1dfTXREIhIgQZ6nviV+nvofgIOizFN/vXPuthLbd8TX2P9SshfezM4EngA+AoY450qX\n8uyU5qkXCYZvbvk7vW9+lCTnv8r7eNcUBs/NT3RYUY0YAQ+XyHPbtfMzuiSCc/Dll3785mGHxaCs\nZd48ePppP3n82Wf73mkREdmpWjlPvXNujZldD9wDfGBmL+HLbv4BzAEeKLXLs8BhQBdgMYCZ/QGf\n0Gfh56Y/udQ8v5udc+Pj+DREpBqtf/8NDCgIQUoYmmfW3Cq7Tz7Z9nEiJ9n673/hvvt8cn/44f6i\ntFlZcNttforNa6/dmugvX+7vO0S5mO3vevSAf+/wMiIJ4cKOz+76jHkT59F1UFcG3jwQC2nudxEJ\nhiCX3+Ccuxc4C2gGPARcBLwCHFbO0pv98G3QBF9P/1ypW+kPBiISYHvf9xKbUiHJQWEI1vbpmeiQ\nyvTSS9s+vjhOQ/dnrZxFs7uakXZbGtdNuS7qNhMn+slYGjXyY2+dg/33h/vvh9tv3zoW9+qr/RV2\nu3aFf/4zPvHG07IvljHziZnkZeXxw4s/8Mv7vyQ6JBGRcgt0Ug/gnHvaObe3cy7NOdfKOXe2c251\nlO0GOucsMjC3eNlNkWVl3TpX53MRkfhqs/9hFHwylQ8PbsdPt1zCkW/PTnRIZdpjD1i92k8h/+GH\nvqc8Ho594Vg25G4gryiPOz67g/U567fb5uij/WQ6mZlw6KG+zn/xYl81EwptHXP7yCN+ndm2pUNB\nES4MA/zeO1/8OLCc89cruPlmP7+niNRqgS2/ERGpjDb7H8YxHyeoOL2CWrb0pefxlFu49YJPDsfm\n/M00q99sm21GjIC99vI19Ucd5Zftvbcfk+scHHmkX9a0Kaxc6X9u1Sq+ccdDp0M7scef9mD+2/Pp\ncXwPuh3VrXpOfPnl0KQJ3HBDbI87aZKfFjMchldegY8+CuYLIyLloqReRKQOu//o+znnrXMIuzCH\ndjyUjo07breNma+lL+mLL2DMGGjQAM44wy+bNAnOi8xM+cQTcQ48DixkDL5jMIPv2MHVXmMtJWXr\n1WPvu8/PVR8rCxb4Y7doAZs2+ZHWSupFaq3Azn5Tk2j2GxEJuqKiIpKSkhIdRt1jpQbixvJ/8qJF\n8Mc/wpYt/quWl14K4BW5RKSkWjn7jYiIxI4S+lqoSxc/jdLKlX70sqYOFanVAj9QVkREJLBGj/a9\n9aGQn2Yo1ho29FOIKqEXqfWU1IuIJEi4MMzGxRspzC1MdChVEnZhpiycwpSFUwi7OM0YEw7D22/D\ns8/Chg3xOUciXHyxf25FRXDccYmORkQCTB/dRUQSoCCngFdPfZW1s9eS3iKdU944hYzWGYkOq1Ku\n//B6xv00DoBT9zyV2wbdtpM9KuE///EDSYuK4IUX/Kjc0vXoIiJ1mHrqRUQSYMX0Faz9eS2pDVPZ\nvHIzv0wO7oWOJv8ymfSUdNJT0pm0YFJ8TvLFF76EpHlzmD8fcnLicx4RkYBSUi8ikgCN2jfCQkbu\nxlwsyWjcqXG1x/DFsi+4+v2reX3261RlJrSjux1NdkE22QXZHN3t6BhGWMIpp/iZYbKyYNAgqF8/\nPucREQkoTWkZA5rSUkQqY+nnS5k7YS67HLgLPU/sWa3nXrRhEce8cAyFRYVg8PiQxxnUZVCljhV2\nYT5c9CEAg7oMImRx6i+aNw/Wr4e+fTXwU0TqJE1pKSJSA3Uc0JGOA7a/2FN1WLFpBc45mqQ1YX3O\nepZmLq30sUIWYnDXarhgU48e8T+HiEhAqfxGRKQO6tOuD7u32J2s/CzaZLThmO7HJDokERGpAvXU\ni4jUQWnJabx2ymssz1pOm4w2pCWnJTqk6E47DWbP9jPfDKpceZCISF2gpF5EpI5KDiXTuUnnRIdR\ntgMOgK+/9j8fcYS/MmqbNomNSUSkhlL5jYiI1Ezff7/t40lxmi5TRKQWUFIvIhIwv05fyZrZaxMd\nRvyVLLcxg6FDExeLiEgNp6ReRCRAHvzjVAYemMfBe2/i7Zu+TnQ48fX223DTTXD88b6uPiOYV9wV\nEakOmqc+BjRPvYhUl11TF9MgKZe8cAqdG63n3TX7JzokERGpJjuap1499SIiAdK98Vo2FjQgpyiV\n3TtuSXQ4IiJSQ2j2GxGRAHlmaifGXPoTDTLggrH9Eh2OiIjUEErqRUQCpFWvlvzrg4GJDkNERGoY\nld+IiEjtU1gIU6fCihWJjkREpFooqRcRkdolPx969ICjjoJu3WDy5ERHJCISd0rqRUSkdpkyBZYv\nh+RkKCiAe+5JdEQiInGnpF5ERGqXHj0gFPI99maw556JjkhEJO6U1IuISO3SrRu89BL07w8XXQSj\nRiU6IhGRuNPsNyIiUvucdJK/iYjUEeqpFxEREREJOCX1IiIiFZBflM/or0Zz+eTL+XnNz4kOR0QE\nUPmNiIhIhTw2/THum3YfZsaURVOYds400lPSEx2WiNRx6qkXERGpgIUbFmJmNElrwub8zWzK25To\nkERElNSLiIhUxLn7nUvD1IZk5WVx8u4n06pBq0SHJCKi8hsREZGK6NWqF9POnUZWXhatG7TGzBId\nkoiIknoREQHnHJMWTGLl5pUc3+N49T7vRHpKuuroRaRGUVIvIiI8+92z3PLxLRS5Ip6a9RQf/fUj\nkkP6FyEiEhSqqRcREb5Y/gUhC9G8fnNWblrJhpwNiQ5JREQqQEm9iIhwyh6nELIQWflZ9Gvfj+bp\nzRMdkoiIVIC+WxUREQZ1HcS7p7/Lmi1r2LftvoRMfT4iIkGipF5ERADo2rQrXZt2TXQY8ttvkJ8P\nHTsmOhIRCRB1xYiIiNQU48fDoYfCoEEwenSioxGRAFFSLyIiUlOMHg3JydCgATz6aKKjEZEAUVIv\nIiJSU/TqBdnZkJkJPXsmOhoRCRDV1IuIiJSQcmsKheFCujbuyi+X/VK9J7/zTth1V8jJgXPOqd5z\ni0igqadeREQkIvmWZArDhQAszFzIsP8Nq94A0tNhxAi46ipormlFg+bLz75kZN+RjDxgJDOnz0x0\nOFLHKKkXERGJKHJF2zyeMH9CgiKRIHr5vJcJLQ4R+iXEM+c9k+hwpI5RUi8iIhLRJr3NNo/vP+r+\nBEUigZQPLsnhQg7yEx2M1DVK6kVERCJWXrmSo7scTXpSOqOPHs25fc9NdEgSIEfdcxQuzeEaOIbc\nOyTR4UgdY865RMcQeH379nXTp09PdBgiIhJj5044l3E/jmP3lrvzxVlfkJys+SVEJHHMbIZzrm+0\ndeqpFxERieLpWU/zxMwn2FywmW9WfMPxLx+f6JBERMqkpF5ERCSKT5Z8ss3juWvnJiiSiskpyGFp\n5lLCLpzoUESkGimpFxERieLfR/wbw35/fOPAGxMYTfksy1zGoU8fyhHPHMGw14dRUFSQ6JBEpJqo\nOFBERCSKNhltWH/1ep6e9TTHdj+W3VrsluiQdurNuW+yestqWtRvwYwVM/hh9Q/s13a/RIclItVA\nPfUiIiJlaJLWhMv6XxaIhB6gc5POJFsyG3I3kBRKom1G20SHVKNszt3MgCcH0Ps/vQNTTiVSXuqp\nFxERqSWO2/U4NuVt4tuV3/LnXn+mbUMl9SV1fLAjG3I3ANDr0V4U3lCY4IhEYkdJvYiISC1hZpy2\n12mcttdpiQ6lRtqYu/H3n0tfPVgk6FR+IyIiInVCt6bdfv+5fnL9BEYiEnvqqRcREZE6Yf4l87ny\nvStZk72G/x7/30SHIxJTSupFRESkzhh11KhEhyASFyq/EREREREJOCX1IiIiIiIBp6ReRERERCTg\nlNSLiIiIiAScknoRERERkYBTUi8iIiIiEnBK6kVEREREAk5JvYiIiIhIwCmpFxEREREJOCX1IiIi\nIiIBp6ReRERERCTglNSLiIiIiAScknoRERERkYBTUi8iIiIiEnBK6kVEREREAk5JvYiIiIhIwCmp\nFxEREREJOCX1IiIiIiIBp6ReRERERCTglNSLiIiIiAScknoRERERkYBTUi8iIiIiEnBK6kVERERE\nAk5JvYiIiIhIwCmpFxEREREJOCX1IiIiIiIBp6ReREREaqT8onwun3w5+z++P3d9dhfOuUSHJFJj\nKakXERGRGunteW8zfs548grzGPvtWKavmJ7okERqLCX1IiIiUiM5SvTMW6nHIrINJfUiIiJSIx3f\n43iG9BhCSiiFs/Y5i/3b7Z/okERqrOREByAiIiISTWpSKg8e+2CiwxAJBPXUi4iIiIgEnJJ6ERER\nEZGAC3xSb2bDzWymmeWY2SozG2tmLSt5rJCZTTMzZ2YTYx2riIiIiEg8BDqpN7ORwDNAJnAp8Bhw\nKjDVzBpU4pB/A/aMXYQiIiIiIvEX2KTezFoAtwHfAEc458Y4524ATgP2wCf5FTleB+DfwI2xjlVE\nREREJJ4Cm9QDJwLpwGjnXFHxQufcW8BC4PQKHu+RyH4aZi8iIiIigRLkpL54stppUdZ9CfQ0s4zy\nHMjMhgJDgAtLfkAQEREREQmCICf17SL3v0ZZ9ytgJbYpk5k1Bh4CHnPOfRm78EREREREqkfCLz5l\nZk2Ayyqwy0POufX40huAvCjb5Ebu06OsK+1u/IebayoQA2Z2PnA+QMeOHSuyq4iIiIhITCU8qQea\nULHBqc8D64HsyON6QE6pbdIi99nsgJkdApwHnOGc21iBGHDOjQHGAPTt29dVZF8RERERkVhKeFLv\nnFuML5WpqBWR+/bAglLr2gOuxDZleRj4DvjKzLqXWpceWbbRObe2EvGJiIiIiFSLINfUfxO5PzDK\nuv7AXOfc5p0coxOwDzC/1A3g8MjPN1U5UhERERGROEp4T30VvIkf4Hqxmb1YPGuNmQ0BugLXl9zY\nzDria+x/cc4VRBYPB1KjHPt/wAzgTrb/FkBEREREpEYJbFLvnFtjZtcD9wAfmNlL+LKbfwBzgAdK\n7fIscBjQBVgcOcaEaMc2M4DfnHOvxiV4EREREZEYCmxSD+Ccu9fM1gEj8b32WcArwD/LUXojIiIi\nIlIrBDqpB3DOPQ08XY7tBlbgmJUZuCsiIiIikhBBHigrIiIiIiIoqRcRERERCTwl9SIiIiIiAaek\nXkREREQk4JTUi4iIiIgEnJIUq2MBAAAUHklEQVR6EREREZGAU1IvIiIiIhJwSupFRERERAJOSb2I\niIiISMApqRcRERERCTgl9SIiIiIiAaekXkREREQk4Mw5l+gYAs/M1gBLEhhCC2BtAs9f26l940dt\nGz9q2/hS+8aP2ja+1L7xUx1t28k51zLaCiX1tYCZTXfO9U10HLWV2jd+1Lbxo7aNL7Vv/Kht40vt\nGz+JbluV34iIiIiIBJySehERERGRgFNSXzuMSXQAtZzaN37UtvGjto0vtW/8qG3jS+0bPwltW9XU\ni4iIiIgEnHrqRUREREQCTkm9iIiIiEjAKakXEREREQk4JfU1kJmFzGykmc0xs1wzW2Zm95pZg+rY\nvzaLQdteY2b/M7OFZubMbHGcQw6UqrSvmfUws1vM7EszW2Nmm8xslpldp/duldt2NzN7wcxmm1mm\nmWVHjnOfmbWtjvhrulj+3TSz9BJ/Ix6OR7xBEoO/u66M2+Z4xx4EsXjvmlkzM7vHzBZEjrHGzD4y\ns0PiGXtNV8W/uzft4L3rzKwg1vEmx/qAEhP3A5cAbwD3ArtHHu9rZoOdc+E471+bVbVt/g2sB74F\nmsQz0ICqSvueDfwdmAC8ABQAhwO3AX82s/7OuZx4Bl/DVaVtOwBtI/suBwqBvYDzgVPNbB/n3Op4\nBh8Asfy7eQsQ9YqPdVQs2vZTtp9ZJOZJUUBVqX3NrBMwFcgAngDmAY2B3kD7+IUdCFVp29eBBVGW\n9wauBN6KcazgnNOtBt2AXkAYeK3U8hGAA/4Sz/1r8y0WbQN0LfHzj8DiRD+vmnKLwXu3L9A4yvLb\nIvtfnOjnGNS23cFx/xTZ/6pEP8fa0r7AfvgPTZdH9n040c8v6G0b2e7pRD+XmniLUft+CiwD2ib6\n+dSkWxz/7j4W2f+4WMes8pua5zTAgAdKLX8cyAZOj/P+tVmV28Y5tzAOcdUWVWpf59x051xmlFXj\nIvd7VjnC4IrX7/WSyH3TSu5fW8Skfc0sKbLPJHwvncTwvWtmqWaWEcPYaoMqta+ZHQocDNztnFtp\nZilmlh6XSIMn5n93I2U7p+K/MZ1U1QBLU1Jf8+yP/2T4dcmFzrlcYFZkfTz3r83UNvEVr/btELlf\nVfnQAi8mbWtmaWbWwsw6mNlR+B4jgHdiGWwAxeq9OxLoCVwc0+iCLVZtOxSfSG0ys9VmNtrMGsc0\n0mCqavv+X+R+qZm9BeQAW8xsnpnV5U5AiM//tD8BjfDfPBVVOcJSlNTXPO2Atc65vCjrfgVamFlq\nHPevzdQ28RXz9o30fF6PL2d4seohBlas2vZcYA3+q/bJ+HEhpzvnPo1ZpMFU5fY1sy7AzcAtzrnF\nsQ8xsGLx3v0auAmf2P8V+BD/welT9dxXuX13i9w/DjTDt+/ZQD7wnJmdFctgAyYeOcM5+NKbJ6sa\nXDQaKFvzpAPR3kAAuSW2yY/T/rWZ2ia+4tG+DwAHAtc65+ZWIbagi1Xbjgfm4AfE7Qv8AWgRiwAD\nLhbt+19gIXBfDOOqDarcts65A0otetbMvgduBy6N3NdVVW3fhpH7TcDhzrl8ADMbj38//9vMnnF1\nc4KNmP5PM7Pd8KVOU5xzi6oe3vbUU1/zZAP1yliXVmKbeO1fm6lt4ium7Wtmt+J748Y45+6oYmxB\nF5O2dc4td8594Jwb75y7Ed8rd7eZXROjOIOqSu0bKVM4ErjIOacZWbYVr7+7o/DJ1HGVCaoWqWr7\nFs8o9lJxQg/gnNuAn4msDVt78+uaWL93z4ncj610RDuhpL7mWYH/SifaG6k9/qugHX0qrOr+tZna\nJr5i1r5mdhPwL+Ap4MKYRRhccXnvOue+B2YCf6tifEFX6faN7HMfflzCb2bW3cy6A50imzSOLKur\nU+DG671bUHzsKsYXdFVt3+WR+9+irFsZua+rA+lj+T8tGRgOrMNPjxkXSuprnm/wr0u/kgvNLA3Y\nB5ge5/1rM7VNfMWkfSMJ/Y3AM8C5LjIHWB0Xz/dufXwtbV1Wlfatj5+T/jhgfonb1Mj60yOPz41p\nxMERl/duZP8O1O0B9FD19i0eBNohyrriZXX1GhaxfO8OAVoDz5dRox8TSuprnnH4QRSXlVp+Hr52\n64XiBWbWzcx6Vnb/OqiqbSs7VuX2NbMb8An9c8DZdbSOM5oqta2ZtYl2UDM7HD9V6JcxjTZ4qtK+\nW/AzWpS+FX/7MSnyeEJcIq/5qvrebV7GcW/FjwuM/QV8gqWqf3fH4+vpTy856Nj8laZPBOY556Jd\nQKkuiGXOUFx680RMIyzF1AlW85jZaHwt8Rv4r3SLr2D2OTCoONExs8VAJ+ecVWb/uigGbXsGW79W\nHwGk4q8yB7DEOfdcvJ9DTVaV9jWzvwMPA0vxM96Ufp+ucs69H+/nUFNVsW3fwF9R9kP83PRpQB/8\nfMnZwEDn3KxqezI1UFX/NkQ5XmdgEfCIc65OT3FZxffu/UB/4CP834YM/DSMhwNf4Qd31uUrTcfi\n/9r5+Oltf8LPypIKXIT/m3G8c+696nkmNU8s/i6YWTv8e3dGlEHfsRXrq1npVvUbkAT8A5iLH3n9\nK75mM6PUdov9S1i5/eviLQZtOxX/yT3abWqin1+ib1VpX+DpHbRtnW/fKrbtn4GJ+Kksc/GD4+YA\no4GOiX5uNeFW1b8NUY7XGV1RtsptC5yAn37118h7dwt+jvBrgbREP7eacIvFexf4I/4buy34nvv3\ngAGJfm6JvsWoba+N/C04L97xqqdeRERERCTgVFMvIiIiIhJwSupFRERERAJOSb2IiIiISMApqRcR\nERERCTgl9SIiIiIiAaekXkREREQk4JTUi4iIiIgEnJJ6Eal1zGyqmVXrRTjM7CYzc2Y2sDrPGzn3\n4sgVDUVEpI5SUi8iCRdJhkveisxsrZl9aGZ/SXR8tZWZ7WJmd5rZDDPbYGYFZrbazD4ws0vNrHGi\nYxQws4GR34ubqnCMHmb2iJnNMbPNZrbFzOaa2aNmtlsMwxWRBNEVZUUk4Ur0qt8cuU8BeuIvEZ8E\n3O+cu7wCx+sIpDvn5sQ00B2fswXQAljqnMuurvNGzr0YwDnXuQL7nAs8DNQDvgO+ADYAzYGDgV7A\nOudcixiHKxUU+fbnI+Bm59xNldj/Evyl7UPAx8AM/GXr+wADgTBwuXPuodhELCKJkJzoAEREipVO\nWMzsCOB94DIze8g5t7icx1ka++h2es61wNrqPm9lmNkw4HF8En+yc+7tKNsMAB6p7tgktsxsOPAg\nsB44yTn3San1hwDjgQfNbINz7rkEhCkiMaDyGxGpsZxzU4A5gAH7A5hZ50gpwtORkoJxkZKRcHE9\ne7Sa+pIlDGa2j5m9bWYbzSzbzD42s4OixWBmSWZ2oZl9bmaZZpZjZgvMbKyZ7Vpiu6g19ZFlU82s\nnZk9F4k1J1Lysl1pkZmlmtnFZvaOmS0xszwzWx8piTm2ik2KmTUEintkT42W0AM45z4HDoiy/xFm\nNikSU56ZzYuU8GxXqlP8OphZipndYGa/mFlupOzjvBLbXWhmP0TaZbmZ3WxmoVLHKvm69zSz8ZEY\ntpjZZ2Z2VBnPt56Z/TNy/GwzyzKzT83sz1G2LXmOzmb2svkysFwzm25mx++gXU8zs48i76lcM5tt\nZv8ys3pRti1+T7QwszFmtjLSlj+Z2Vmltn0a30sPcKNtW6Y2sKx4Ivs2BB6IPPxL6YQewDn3KTAs\n8vCByD7F+1f2dybZzP5mZl9G2jvbzGZG3tfKO0TiRD31IlLTWeS+dK1gN+ArYB7wAlAfyCrH8foC\nVwHTgLFAR+BkYIqZ7eOcm/v7ic1SgYnAkcAy4MXIOToDJwGfAfPLcc6m+PKWjcBTQBPgz8ALZtbe\nOTeqxLbN8D2rX+C/pVgDtAWGAO+Y2XnOubHlOGdZhkbO8aVz7r0dbeicyyv52MwuAP4DbAH+B6zG\nl29cDQwxswHOuY1RDvUy/gPCO0BBJIYxZlYA9Ab+im/nKcAfgBuAbOCuKMfqgn/tfgAew7fNKcC7\nZvYX59y4EvGmApOBw/AfDh8B0iPnHxd5va+Nco5OwNfAQuA5fHudArxpZoOdcx+V3NjMngTOApYD\nr+Ff5/7ArcARZnakc66w1DmaAJ8D+cCr+DKoPwFPmlnYOfdMZLvxkfu/4ktnppY4xuIosZc0FP/e\n+9o5N7msjZxzk8zsG/wH56H492hJFfmdSQHeAo4G5uJ/Z3KBw4HR+PfBGTuJW0Qqwzmnm2666ZbQ\nGz5hd1GWD8bX+4aBTpFlnYu3B/5dxvGmlj4ePvks3u/MUusuiCx/tNTyf0eWTwDqlVpXD2hZ4vFN\nkW0HRntuwCtAqMTyLviSiHyga6njdojynBoDP0b2qV9q3WJgcTnb+olIPLdV8DXqBOThP9T0LLXu\n0cgxx0R7HYBvgCYllneNPO8NwCKgfYl1TfBlTGuA5BLLS77uo0qdpy/+w8IGoFGJ5ddEtn+n1LFa\nRdrMAQeVcY4bS53j6OJjlVp+ZmT561Fel+L3xKVlvCfGAkkllu8BFAI/l/HevamCr1nxa317Oba9\nvTimKv7OFD/n0aWeW1KJeE6oyPPQTTfdynfT12AiUmNEvua/ycxuN7NXgUn4nvoHnHNLSm2+iq0D\nayvic+fc06WWPYlPpvqViCUJ+BuQA1zoSvVaO+fynHNrynnOIuBq51y4xP6L8GUwKZTouYwcd3np\nAzjnMiNxNiVSilRJbSP3251jJ04HUoGH3fYDkK8DNgFnRCs3Af7pSvTgO+cW4r/laALc6pz7tcS6\njfie3hZA+yjHygRuKbnAOTcd/21NE/w3KMXOxieRl7sSPeXOudX4XnSAc6OcYwlwW6lzTAaWUuI9\nEnEp/r1ztnMup9S6W4F1bC1vKSk7EldRiXP8jO+9393MMqLsU1HFr/WycmxbvE27KOvK+zsTAkYA\nvwEjSz23IuAf+NcjWnuISBWp/EZEapIbI/cOX8LwKfCEc+75KNt+VzrRLqfppRc45wrMbBU+YS7W\nE987/pVzbkUlzlPS0kgSX9pU/HPet+RCM+sFXAkcik/M0krtFy3Zjbf9Ivcfll7hnNtgZjPx8fbE\nz6ZT0nZtDhS36Ywo64qT/A74BLukb51zm6LsMxVforIv8EykNrw78GuUDyEln8e+UdbNKpmQlrAM\nOLD4gZmlA3vjv1m4zMyi7EIesHuU5fOdc9HKxYqT66bA5mgHTIDy/s70wJcqzQf+VUZ75BC9PUSk\nipTUi0iN4ZyLmgWU4bdKniZazTf4XsekEo+bRO5/jbJtRa0qY3nxc/h9kKmZ9ccnnMn4GvMJ+JKX\nMLAPfprPaL3h5bUycl/RDwbFMa4sY33x8ialV0S+ZSituOd8R+tSoqwrb1tWOl52/B4p+Q13U/w3\nSS3Z+oG0vHZ0Dtj2vVhZxW2ySzm2Ld4m2gfY8v7ONI/c78qO2yMW30KISCkqvxGRoIr3RTaKE5lY\n9Iq3LmN5m8h9ycT2X/hBv0c55451zl3mnLvB+ek+v4pBLJ9F7o+o4H7FMbYpY33bUtvFS3nbsjri\nLd53pnPOdnSrwjmqovi1HlyObYu3+bwK5ytujzd20h5dqnAOESmDknoRkejm4BP73mYWrc64Ijqa\nWecoywdG7meWWNYdWO+cmxpl+8OqGAf4mVbWAwea2Q6TvVL18cUxDoyyXRP8twi5wOwYxLgj+5Wc\ndrGE4rhmAkRKdH4B2luJqUdLODxy/21lA3HObQZ+AnqZWbPKHqccikuBKtp7/yr+PdzPzI4sa6PI\nun7498WrlYrQK/6d6R+ZBUdEqpGSehGRKCI11Y/ie83/W3oAqPn55FuW83BJwF0l5+g2sy7AJfgS\nhpJjBhYDzcysd6nznYOfgaVKIsnuJZGH48ws6jEjZUDTSix6Hj/DzAgz615q81uBRsDzlRznUBGN\n8VNe/s7M+uIHX2YCb5RY9SS+PGZUZOBz8fYtgOtLbFMV9+EHED8Z+XCzDTNramb7bb9bhayL3Hes\nyE6Rmv1/RB6+aP6CYqXjOwg/7ST4wa3RxiuU93yF+Flv2gIPmVn9KOdra2Z7VPYcIlI21dSLiJTt\nZvy82kOAeWY2ET/Lyy7AUfjBrE+X4zjfR44zw8zeY+s89U2Aq5xzv5TY9gF88v6Zmb2CT1T7Agfj\ne1GHVvVJOedeiCRcDwOTzGwWfl78Dfi66APZOgC0eJ/FZnYZfq73byOxrcF/e3Agvpf26qrGVg6f\nAOea2QH4UpHieepDwAWlBp/eAxyLH4fwnZm9g5+n/k/4aS3vds59RhU45540sz74mZJ+MbPiWXKa\n4actPRQ/7/uFVTjNXPzYjlMjc/svwZefPRdlVqho8TUB7gY+NbOp+MHJDuiD/8YiDFzmnHu2CjEW\nuxX/3rkQf+2CDyOxt8LX2g/Az5b0cwzOJSIlKKkXESmDcy7fzI7BJyjD8bOrGH4w4RtsrVnemQ34\n5PJu/EWKGuGTmnuccy+W3ND5CwENwdfWn4Ivvfgan3x1JQZJfeQ8YyMJ6MX4i2sNAxrgyyd+BEZS\nqhfbOfeomS0ArsBffCgdP1vLKPw1A8oaUBlLi/Cvx52R+3r4EppbXKkLLEVevyOBy4G/4KdbLMTP\nznOZc+6lWATknPu7mb0biWcw/sPaenxyP4ptv4mpzPGLzOwk/HP+E9AQ/z78jO1nB4q2/32RDzSX\nAoPwF8YCP63pY8CDZcwQVJlYC8zsRPwUqGcCx+MHxq7Bv3bX46cfFZEYM+fiPdZMRKTuMjMHfOyc\nG5joWIIsMiZhEfCMc+7MhAYjIlIDqaZeRERERCTglNSLiIiIiAScknoRERERkYBTTb2IiIiISMCp\np15EREREJOCU1IuIiIiIBJySehERERGRgFNSLyIiIiIScErqRUREREQC7v8B6rOUL3F3jfgAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEPD3P_I9nwt",
        "colab_type": "text"
      },
      "source": [
        "### Latent Semantic Analysis: Spacy NLP Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7t53pJl9t0y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = data['Songwriter(s)']\n",
        "X = spacy\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit(y)\n",
        "y_encoded = le.transform(y)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45CNkLet944K",
        "colab_type": "code",
        "outputId": "dfb5d61e-1e02-474a-eea5-5e081ba46d10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        }
      },
      "source": [
        "fig = plt.figure(figsize=(12, 12))\n",
        "plot_LSA(X, y_encoded)\n",
        "plt.title('LSA: Spacy NLP Features', fontsize = 25)\n",
        "plt.rcParams[\"xtick.labelsize\"] = 18\n",
        "plt.rcParams[\"ytick.labelsize\"] = 18\n",
        "plt.rcParams[\"axes.labelsize\"] = 20\n",
        "plt.xlim(0.5, 1.2)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tQC7cuODoMNIEeAcQNTEqz/kjIiLo0KEDc+bMYeLEiXaMTIjCJz0DQgghMlXX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q4+VO5UGbvFju9RXzyDPHE35YxM5MnEiTBnDigKEz/+kNqdapNuS2dQnUHy+tKl0K8fOBzQsmWR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zCXmDEGGTGcMWBwGHCYHZhsJpL2J1G7f23uFY89dsNwOiAAOHqIaX23YvI1Ue2LapzwOEGH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\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b0GwdOiQCC6KjoXfvUnqfuJ/Ost2nH1UsFzlbWAkFlV69FPz9yz6jkSNh61a4DyMmCgHh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\nEZE0pWBARERERCRNKRgQEREREUlT/w+khzKrnhWgFAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8I-KBzv3-dT9",
        "colab_type": "text"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClhLbDtp0MRX",
        "colab_type": "text"
      },
      "source": [
        "We will use two machine learning algorithms to create models that will predict the songwriters of the various songs using only the lyrics as the independent variable. Then we'll use the get_most_important_features() function to pinpoint the most important words for each songwriter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJ8KoOEi-k5U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import make_scorer, accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyvLc_jQ-zGt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = data['Songwriter(s)']\n",
        "X = data['Cleaned Lyrics']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 444)\n",
        "\n",
        "vocabulary = set(itertools.chain.from_iterable(data['Cleaned Lyrics']))\n",
        "\n",
        "count_vectorizer = CountVectorizer(vocabulary=vocabulary)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltiew59c_CoL",
        "colab_type": "text"
      },
      "source": [
        "### Logistic Regression Grid Search Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_4lYUOo2K3m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DbA7Wbh7qqf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip3 install pactools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YaewaBKS2LMs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from pactools.grid_search import GridSearchCVProgressBar"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4E5wvn732LW2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq0ZywHf2ogo",
        "colab_type": "text"
      },
      "source": [
        "### We want to optimize to find the best estimator parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_LPfihg_F_x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "# Pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('vect', CountVectorizer()),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('clf', LogisticRegression()),])\n",
        "\n",
        "\n",
        "# pipeline uses intermediate steps of CountVectorizer() and TfidfTransformer() before finally applying log regression\n",
        "\n",
        "# This pipeline will be used as the estimator in the GridSearchCV\n",
        "\n",
        "\n",
        "# Parameter Grid\n",
        "param_grid = {\n",
        "        'vect__max_df': (0.05, 0.075, 0.1, 0.15, 0.20, 0.25, 0.5, 0.75, 1.0),\n",
        "        'vect__ngram_range': ((1, 1), (1, 2), (1, 3)),\n",
        "        'vect__stop_words' : (None, 'english'),\n",
        "        'tfidf__use_idf': (True, False),\n",
        "        'tfidf__norm': ('l1', 'l2'),\n",
        "        'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
        "\n",
        "\n",
        "# Create the GridSearchCV object\n",
        "#grid = GridSearchCV(pipeline, cv=10, n_jobs=-1, param_grid=param_grid, scoring=make_scorer(accuracy_score))\n",
        "grid = GridSearchCVProgressBar(pipeline, cv=2, n_jobs=-1, param_grid=param_grid, scoring=make_scorer(accuracy_score))\n",
        "\n",
        "\n",
        "# Run the grid search \n",
        "grid.fit(X_train, y_train)\n",
        "\n",
        "# Predicts usings the best parameters of the grid search\n",
        "y_pred = grid.predict(X_test)\n",
        "\n",
        "# Accuracy Score\n",
        "print('Accuracy Score:')\n",
        "print(accuracy_score(y_test, y_pred))\n",
        "print()\n",
        "\n",
        "# Classification Report\n",
        "print(classification_report(y_test, y_pred, digits=3))\n",
        "print()\n",
        "\n",
        "# Confusion Matrix\n",
        "print('Confusion Matrix:')\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print()\n",
        "\n",
        "# Best parameters from the grid search\n",
        "print('Best parameters set found on development set:')\n",
        "print(grid.best_params_)\n",
        "print()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRVSN4_NBdLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " def get_most_important_features(vectorizer, model, n=5):\n",
        "    \"\"\"\n",
        "    This function is used to find the most important ngrams (words) \n",
        "    for classifying each class (songwriter).\n",
        "    \n",
        "    Args:\n",
        "        vectorizer (sklearn.feature_extraction.text.CountVectorizer):\n",
        "        The vectorizer used to create the Bag of Words space matrix.\n",
        "        model (sklearn.linear_model.logistic.LogisticRegression):\n",
        "        The LR model trained on the dataset.\n",
        "        n (int): Number of top words to return.\n",
        "    \n",
        "    Returns:\n",
        "        important_words (dict): Dicitionary containing the most\n",
        "        critical words used to predict each songwriter class.\n",
        "    \"\"\"\n",
        "    \n",
        "    # Create a mapping dictionary from words to index\n",
        "    index_to_word = {v:k for k,v in vectorizer.vocabulary_.items()}\n",
        "    \n",
        "    # loop for each class\n",
        "    important_words = {}\n",
        "    for class_index in range(model.coef_.shape[0]):\n",
        "        word_importances = [(el, index_to_word[i]) for i,el in enumerate(model.coef_[class_index])]\n",
        "        sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True)\n",
        "        tops = sorted(sorted_coeff[:n], key = lambda x : x[0])\n",
        "        bottom = sorted_coeff[-n:]\n",
        "        important_words[class_index] = {'tops':tops, 'bottom':bottom}\n",
        "    \n",
        "    return important_words\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENtqpQrW3-_t",
        "colab_type": "text"
      },
      "source": [
        "Running Logistic Regression and finding the most important words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3uUy8yz38ZP",
        "colab_type": "code",
        "outputId": "deafeab0-6025-49e0-8f97-70a0fd0e2a0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Vectorize\n",
        "vectorizer = CountVectorizer(max_df = 0.1, ngram_range = (1, 2))\n",
        "X_train_counts = vectorizer.fit_transform(X_train).todense()\n",
        "X_test_counts = vectorizer.transform(X_test).todense()\n",
        "\n",
        "# Tfidf transformation\n",
        "tfidf = TfidfTransformer(norm = 'l2', use_idf = False)\n",
        "X_train_counts_tfidf = tfidf.fit_transform(X_train_counts).todense()\n",
        "X_test_counts_tfidf = tfidf.fit_transform(X_test_counts).todense()\n",
        "\n",
        "clf = LogisticRegression(C = 10)\n",
        "clf.fit(X_train_counts_tfidf, y_train)\n",
        "\n",
        "# Create predictions, using trained model\n",
        "y_pred = clf.predict(X_test_counts_tfidf)\n",
        "\n",
        "# Find the most important words used for classification\n",
        "importance = get_most_important_features(vectorizer, clf, 10)\n",
        "\n",
        "# Re-name the dictionary keys\n",
        "importance['Harrison'] = importance.pop(0)\n",
        "importance['Lennon'] = importance.pop(1)\n",
        "importance['McCartney'] = importance.pop(2)\n",
        "importance['Multiple Beatles'] = importance.pop(3)\n",
        "\n",
        "# Print the most important words\n",
        "importance"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Harrison': {'bottom': [(-2.045166065273923, 'cry'),\n",
              "   (-2.0516036163996256, 'his'),\n",
              "   (-2.074373830608004, 'down'),\n",
              "   (-2.12649886774832, 'to make'),\n",
              "   (-2.245646233845527, 'to be'),\n",
              "   (-2.334401001365231, 'man'),\n",
              "   (-2.603412111344155, 'yeah'),\n",
              "   (-2.7788114268239608, 'better'),\n",
              "   (-2.8376165191451923, 'but it'),\n",
              "   (-3.245071662756538, 'he')],\n",
              "  'tops': [(3.20668293232807, 'where you'),\n",
              "   (3.2813107471829204, 'baby in'),\n",
              "   (3.4526598680364975, 'it all'),\n",
              "   (3.5652237899352883, 'really'),\n",
              "   (3.654003225292855, 'too much'),\n",
              "   (3.9902224848198466, 'got time'),\n",
              "   (4.20846845653345, 'without'),\n",
              "   (4.357034252936614, 'need you'),\n",
              "   (4.579932179604316, 'to do'),\n",
              "   (5.1964079560854515, 'me mine')]},\n",
              " 'Lennon': {'bottom': [(-2.272389847767595, 'where'),\n",
              "   (-2.2956971912605235, 'oh no'),\n",
              "   (-2.3282633105894437, 'love you'),\n",
              "   (-2.4086693661514875, 'here it'),\n",
              "   (-2.4600020848978077, 'didn'),\n",
              "   (-2.5905460013495665, 'know that'),\n",
              "   (-2.614796732779806, 'my love'),\n",
              "   (-2.838097648584486, 'without'),\n",
              "   (-3.008428029862003, 'what you'),\n",
              "   (-3.335549019342312, 'and she')],\n",
              "  'tops': [(2.8503490406813254, 'oh now'),\n",
              "   (2.9526071165781835, 'loser'),\n",
              "   (3.0119376940263276, 'be alone'),\n",
              "   (3.0762549486335216, 'you can'),\n",
              "   (3.1254279080702903, 'nothing'),\n",
              "   (3.1874394844050453, 'good night'),\n",
              "   (3.205521442965275, 'well you'),\n",
              "   (3.461479553947609, 'got to'),\n",
              "   (3.769361331832148, 'but it'),\n",
              "   (3.8967021106142603, 'get home')]},\n",
              " 'McCartney': {'bottom': [(-2.1511610155645258, 'what'),\n",
              "   (-2.241241980400747, 'their'),\n",
              "   (-2.282678302634811, 'call'),\n",
              "   (-2.4375112322430796, 'it true'),\n",
              "   (-2.4586062152526638, 'me down'),\n",
              "   (-2.5498405491478606, 'little girl'),\n",
              "   (-2.6796571304298133, 'more'),\n",
              "   (-2.7250424463530645, 'feel'),\n",
              "   (-2.9858729016244396, 'think'),\n",
              "   (-3.029644036108259, 'everything')],\n",
              "  'tops': [(3.261969533610674, 'hello'),\n",
              "   (3.359352795739651, 'want it'),\n",
              "   (3.36567816522238, 'get back'),\n",
              "   (3.3855530347876437, 'night before'),\n",
              "   (3.4596632406431764, 'see yeah'),\n",
              "   (3.5048001858749482, 'buy'),\n",
              "   (3.5254689500809624, 'all my'),\n",
              "   (3.6436609732156264, 'me back'),\n",
              "   (3.7248591982508557, 'fast'),\n",
              "   (4.011602595399383, 'and in')]},\n",
              " 'Multiple Beatles': {'bottom': [(-2.380702445114838, 'can you'),\n",
              "   (-2.4179787857734545, 'all you'),\n",
              "   (-2.518133878680189, 'for'),\n",
              "   (-2.5239313857912764, 'don'),\n",
              "   (-2.662133994180265, 'me mine'),\n",
              "   (-2.6709051555118153, 'around'),\n",
              "   (-2.68177981063666, 'really'),\n",
              "   (-2.8291554307864586, 'here'),\n",
              "   (-2.892275500844382, 'still'),\n",
              "   (-3.235839403441347, 'to do')],\n",
              "  'tops': [(3.272339742398448, 'be your'),\n",
              "   (3.3451528635041816, 'me do'),\n",
              "   (3.356760928346278, 'anything'),\n",
              "   (3.437673322366979, 'feeling'),\n",
              "   (3.458280892290301, 'rocky'),\n",
              "   (3.4692471969734586, 'know my'),\n",
              "   (3.488929776316816, 'trouble'),\n",
              "   (3.514775621809661, 'loves you'),\n",
              "   (3.547161861254181, 'dance'),\n",
              "   (3.581689596284666, 'better')]}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmLsSuqmBnzX",
        "colab_type": "text"
      },
      "source": [
        "### Multinomial Naive Bayes Grid Search Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFG_xyo62mPf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlfg2QIoBqom",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "# Pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('vect', CountVectorizer()),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('clf', MultinomialNB())])\n",
        "\n",
        "# Parameter Grid\n",
        "param_grid = {\n",
        "        'vect__max_df': (0.05, 0.075, 0.1, 0.15, 0.20, 0.25, 0.5, 0.75, 1.0),\n",
        "        'vect__ngram_range': ((1, 1), (1, 2), (1, 3)),\n",
        "        'vect__stop_words' : (None, 'english'),\n",
        "        'tfidf__use_idf': (True, False),\n",
        "        'tfidf__norm': ('l1', 'l2'),\n",
        "        'clf__alpha': (0, 0.001, 0.01, 0.1, 0.2, 0.25, 0.5, 0.75, 1.0)}\n",
        "\n",
        "# Create the GridSearchCV object\n",
        "grid = GridSearchCV(pipeline, cv=10, n_jobs=-1, param_grid=param_grid, scoring=make_scorer(accuracy_score))\n",
        "\n",
        "# Run the grid search \n",
        "grid.fit(X_train, y_train)\n",
        "\n",
        "# Predict usings the best parameters of the grid search\n",
        "y_pred = grid.predict(X_test)\n",
        "\n",
        "# Accuracy Score\n",
        "print('Accuracy Score:')\n",
        "print(accuracy_score(y_test, y_pred))\n",
        "print()\n",
        "\n",
        "# Classification Report\n",
        "print(classification_report(y_test, y_pred, digits=3))\n",
        "print()\n",
        "\n",
        "# Confusion Matrix\n",
        "print('Confusion Matrix:')\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print()\n",
        "\n",
        "# Best parameters from the grid search\n",
        "print('Best parameters set found on development set:')\n",
        "print(grid.best_params_)\n",
        "print()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbCNL1bN2dl2",
        "colab_type": "code",
        "outputId": "56e310b0-c497-4cb0-9ba5-46b92d36d8a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Vectorize\n",
        "vectorizer = CountVectorizer(max_df = 0.1, ngram_range = (1, 2))\n",
        "X_train_counts = vectorizer.fit_transform(X_train).todense()\n",
        "X_test_counts = vectorizer.transform(X_test).todense()\n",
        "\n",
        "# Tfidf transformation\n",
        "tfidf = TfidfTransformer(norm = 'l2', use_idf = False)\n",
        "X_train_counts_tfidf = tfidf.fit_transform(X_train_counts).todense()\n",
        "X_test_counts_tfidf = tfidf.fit_transform(X_test_counts).todense()\n",
        "\n",
        "nb = MultinomialNB()\n",
        "nb.fit(X_train_counts_tfidf, y_train)\n",
        "\n",
        "# Create predictions, using trained model\n",
        "y_pred = nb.predict(X_test_counts_tfidf)\n",
        "\n",
        "# Find the most important words used for classification\n",
        "importance = get_most_important_features(vectorizer, nb, 10)\n",
        "\n",
        "# Re-name the dictionary keys\n",
        "importance['Harrison'] = importance.pop(0)\n",
        "importance['Lennon'] = importance.pop(1)\n",
        "importance['McCartney'] = importance.pop(2)\n",
        "importance['Multiple Beatles'] = importance.pop(3)\n",
        "\n",
        "# Print the most important words\n",
        "importance"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Harrison': {'bottom': [(-9.377282492934983, 'your trust'),\n",
              "   (-9.377282492934983, 'your voice'),\n",
              "   (-9.377282492934983, 'yours sincerely'),\n",
              "   (-9.377282492934983, 'yours yet'),\n",
              "   (-9.377282492934983, 'yourself home'),\n",
              "   (-9.377282492934983, 'yourself in'),\n",
              "   (-9.377282492934983, 'yourself to'),\n",
              "   (-9.377282492934983, 'zapped'),\n",
              "   (-9.377282492934983, 'zapped in'),\n",
              "   (-9.377282492934983, 'zoo what')],\n",
              "  'tops': [(-7.106033702676536, 'not'),\n",
              "   (-7.099118495814187, 'll'),\n",
              "   (-7.0610869856031915, 'that'),\n",
              "   (-6.910337502354965, 'if'),\n",
              "   (-6.870088757600826, 'be'),\n",
              "   (-6.857382826987659, 'know'),\n",
              "   (-6.831224065606969, 'love'),\n",
              "   (-6.545435529591376, 'don'),\n",
              "   (-6.285568876248815, 'all'),\n",
              "   (-6.179713804302713, 'it')]},\n",
              " 'Lennon': {'bottom': [(-9.557661329838217, 'your voice'),\n",
              "   (-9.557661329838217, 'your window'),\n",
              "   (-9.557661329838217, 'yours sincerely'),\n",
              "   (-9.557661329838217, 'yourself'),\n",
              "   (-9.557661329838217, 'yourself home'),\n",
              "   (-9.557661329838217, 'yourself in'),\n",
              "   (-9.557661329838217, 'yourself no'),\n",
              "   (-9.557661329838217, 'yourself to'),\n",
              "   (-9.557661329838217, 'zoo'),\n",
              "   (-9.557661329838217, 'zoo what')],\n",
              "  'tops': [(-6.234386539162086, 'in'),\n",
              "   (-6.226543065041415, 'all'),\n",
              "   (-6.226129185516754, 'that'),\n",
              "   (-6.2054850694945145, 'know'),\n",
              "   (-6.19591051876331, 'my'),\n",
              "   (-6.188792385169995, 'can'),\n",
              "   (-6.122542730040965, 'is'),\n",
              "   (-6.01312671508577, 'love'),\n",
              "   (-5.973525540057158, 'she'),\n",
              "   (-5.684910114649124, 'it')]},\n",
              " 'McCartney': {'bottom': [(-9.53302596278112, 'your vest'),\n",
              "   (-9.53302596278112, 'your way'),\n",
              "   (-9.53302596278112, 'your window'),\n",
              "   (-9.53302596278112, 'yours yet'),\n",
              "   (-9.53302596278112, 'yourself home'),\n",
              "   (-9.53302596278112, 'yourself no'),\n",
              "   (-9.53302596278112, 'zapped'),\n",
              "   (-9.53302596278112, 'zapped in'),\n",
              "   (-9.53302596278112, 'zoo'),\n",
              "   (-9.53302596278112, 'zoo what')],\n",
              "  'tops': [(-6.488101822072984, 'your'),\n",
              "   (-6.48290034705385, 'down'),\n",
              "   (-6.471984767206914, 'don'),\n",
              "   (-6.446834475852382, 'my'),\n",
              "   (-6.444107708474709, 'love'),\n",
              "   (-6.4276420486337775, 'of'),\n",
              "   (-6.372149255906219, 'she'),\n",
              "   (-6.340993470408326, 'be'),\n",
              "   (-6.0683605112704395, 'in'),\n",
              "   (-5.89485844637437, 'it')]},\n",
              " 'Multiple Beatles': {'bottom': [(-9.598712792303138, 'your taste'),\n",
              "   (-9.598712792303138, 'your teddy'),\n",
              "   (-9.598712792303138, 'your vest'),\n",
              "   (-9.598712792303138, 'your voice'),\n",
              "   (-9.598712792303138, 'yours'),\n",
              "   (-9.598712792303138, 'yours sincerely'),\n",
              "   (-9.598712792303138, 'yours yet'),\n",
              "   (-9.598712792303138, 'yourself no'),\n",
              "   (-9.598712792303138, 'zapped'),\n",
              "   (-9.598712792303138, 'zapped in')],\n",
              "  'tops': [(-6.170681412914195, 'yeah'),\n",
              "   (-6.118398153346851, 'your'),\n",
              "   (-6.016575553239925, 'all'),\n",
              "   (-6.012901653314865, 'that'),\n",
              "   (-5.998315932927394, 'know'),\n",
              "   (-5.9692317567912685, 'be'),\n",
              "   (-5.964634958768828, 'in'),\n",
              "   (-5.9403216686676235, 'my'),\n",
              "   (-5.881664089813732, 'it'),\n",
              "   (-5.6590783376237415, 'love')]}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIBNbHTzE2_E",
        "colab_type": "text"
      },
      "source": [
        "# Lyric Generation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gURiXNfuP3G3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnYuii5pnnai",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os, pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pDGWLfgnROh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.chdir('./gdrive/My Drive/Google Colaboratory/Colab Notebooks/Data Science   Machine Learning/Beatles NLP/Data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrnKEzyinomp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv('data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEluV1_xn2jg",
        "colab_type": "code",
        "outputId": "c96c071e-751e-4edc-eee8-2b032e0a7844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Song</th>\n",
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              "      <th>Songwriter(s)</th>\n",
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              "      <th>Lyric Line</th>\n",
              "      <th>Number Lines</th>\n",
              "      <th>Number Words</th>\n",
              "      <th>Average Words Per Line</th>\n",
              "      <th>Number Apostrophes</th>\n",
              "      <th>Average Words Per Apostrophe</th>\n",
              "      <th>Cleaned Lyrics</th>\n",
              "      <th>NLP Features</th>\n",
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              "      <th>Lemmas Text</th>\n",
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              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>They slither while they pass, they slip away a...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>Pools of sorrow, waves of joy are drifting thr...</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>pools of sorrow waves of joy are drifting thro...</td>\n",
              "      <td>{'pos_counter': {'pos_NOUN': 5, 'pos_ADP': 3, ...</td>\n",
              "      <td>['pools', 'of', 'sorrow', 'waves', 'of', 'joy'...</td>\n",
              "      <td>['pool', 'of', 'sorrow', 'wave', 'of', 'joy', ...</td>\n",
              "      <td>pool of sorrow wave of joy be drift through -P...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
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              "      <td>possessing and caressing me</td>\n",
              "      <td>{'pos_counter': {'pos_VERB': 2, 'pos_CCONJ': 1...</td>\n",
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              "      <td>['possess', 'and', 'caress', '-PRON-']</td>\n",
              "      <td>possess and caress -PRON-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>Lennon</td>\n",
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              "      <td>['jai', 'guru', 'deva', 'om']</td>\n",
              "      <td>jai guru deva om</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                                        Lemmas Text\n",
              "0           0  ...  word be flow out like endless rain into a pape...\n",
              "1           1  ...  -PRON- slither while -PRON- pass -PRON- slip a...\n",
              "2           2  ...  pool of sorrow wave of joy be drift through -P...\n",
              "3           3  ...                          possess and caress -PRON-\n",
              "4           4  ...                                   jai guru deva om\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtI2h19Ftpy-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.rename(columns={'Unnamed: 0':'Line ID'}, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5gNXqv9tZxK",
        "colab_type": "code",
        "outputId": "2726f0c6-d57f-4f3c-b405-9ade74c969a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "list(data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Line ID',\n",
              " 'Song',\n",
              " 'Album Debut',\n",
              " 'Songwriter(s)',\n",
              " 'Lead Vocal(s)',\n",
              " 'Year',\n",
              " 'Lyric Line',\n",
              " 'Number Lines',\n",
              " 'Number Words',\n",
              " 'Average Words Per Line',\n",
              " 'Number Apostrophes',\n",
              " 'Average Words Per Apostrophe',\n",
              " 'Cleaned Lyrics',\n",
              " 'NLP Features',\n",
              " 'Tokens',\n",
              " 'Lemmas',\n",
              " 'Lemmas Text']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP3o0fR6urA9",
        "colab_type": "code",
        "outputId": "2d89e6b4-7968-4bbd-e060-f6ed76d6267a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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            ],
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              "   Line ID  ...                                        Lemmas Text\n",
              "0        0  ...  word be flow out like endless rain into a pape...\n",
              "1        1  ...  -PRON- slither while -PRON- pass -PRON- slip a...\n",
              "2        2  ...  pool of sorrow wave of joy be drift through -P...\n",
              "3        3  ...                          possess and caress -PRON-\n",
              "4        4  ...                                   jai guru deva om\n",
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              "[5 rows x 17 columns]"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "K9SMvIaVn3f4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lyrics_data = data[['Line ID', 'Song', 'Album Debut', 'Songwriter(s)', 'Year', 'Cleaned Lyrics']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HStd-qcluWUe",
        "colab_type": "code",
        "outputId": "10e7bec7-5d72-4436-f611-a4e3fa487359",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "lyrics_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>words are flowing out like endless rain into a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>they slither while they pass they slip away ac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>pools of sorrow waves of joy are drifting thro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>possessing and caressing me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>jai guru deva om</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Line ID  ...                                     Cleaned Lyrics\n",
              "0        0  ...  words are flowing out like endless rain into a...\n",
              "1        1  ...  they slither while they pass they slip away ac...\n",
              "2        2  ...  pools of sorrow waves of joy are drifting thro...\n",
              "3        3  ...                        possessing and caressing me\n",
              "4        4  ...                                   jai guru deva om\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xE3_5I4Cu_lZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lyrics_data = lyrics_data.set_index('Line ID')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpXtmy3tvFpD",
        "colab_type": "code",
        "outputId": "d69c3cf0-7da1-4bae-bc98-927fe6be2355",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "lyrics_data.head()"
      ],
      "execution_count": 0,
      "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</th>\n",
              "      <th>Album Debut</th>\n",
              "      <th>Songwriter(s)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Cleaned Lyrics</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Line ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>words are flowing out like endless rain into a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>they slither while they pass they slip away ac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>pools of sorrow waves of joy are drifting thro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>possessing and caressing me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Across the Universe</td>\n",
              "      <td>Let It Be</td>\n",
              "      <td>Lennon</td>\n",
              "      <td>1968</td>\n",
              "      <td>jai guru deva om</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Song  ...                                     Cleaned Lyrics\n",
              "Line ID                       ...                                                   \n",
              "0        Across the Universe  ...  words are flowing out like endless rain into a...\n",
              "1        Across the Universe  ...  they slither while they pass they slip away ac...\n",
              "2        Across the Universe  ...  pools of sorrow waves of joy are drifting thro...\n",
              "3        Across the Universe  ...                        possessing and caressing me\n",
              "4        Across the Universe  ...                                   jai guru deva om\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCyauAf3warG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We will read in all of the cleaned lyrics into a single text file for training\n",
        "with open('lyricsText.txt', 'w', encoding='utf-8') as filehandle:\n",
        "  for listitem in lyrics_data['Cleaned Lyrics']:\n",
        "    filehandle.write('%s\\n' % listitem)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gQwIZzMxywp",
        "colab_type": "text"
      },
      "source": [
        "## Using textgenrnn to generate lyrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RN43leffxz1d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q textgenrnn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDwAehhgx1nM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from textgenrnn import textgenrnn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIj0NlQGx6Mv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_cfg = {\n",
        "    'rnn_size': 128,\n",
        "    'rnn_layers': 4,\n",
        "    'rnn_bidirectional': True,\n",
        "    'max_length': 40,\n",
        "    'max_words': 10000,\n",
        "    'dim_embeddings': 100,\n",
        "    'word_level': False,\n",
        "}\n",
        "\n",
        "train_cfg = {\n",
        "    'line_delimited': True,\n",
        "    'num_epochs': 10, \n",
        "    'gen_epochs': 2,\n",
        "    'batch_size': 1024,\n",
        "    'train_size': 0.8,\n",
        "    'dropout': 0.0,\n",
        "    'max_gen_length': 300,\n",
        "    'validation': True,\n",
        "    'is_csv': False\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wy5xvABx8Yf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text_file = 'lyricsText.txt'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lqw1UV44QY0-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_name = '500nds_12Lrs_100epchs_Model'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwXosnFAyIdu",
        "colab_type": "code",
        "outputId": "991a8f27-609f-4bdf-e0d2-612cee9b9fb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_name = '500nds_12Lrs_100epchs_Model'\n",
        "textgen = textgenrnn(name=model_name)\n",
        "\n",
        "train_function = textgen.train_from_file if train_cfg['line_delimited'] else textgen.train_from_largetext_file\n",
        "\n",
        "train_function(\n",
        "    file_path=text_file,\n",
        "    new_model=True,\n",
        "    num_epochs=train_cfg['num_epochs'],\n",
        "    gen_epochs=train_cfg['gen_epochs'],\n",
        "    batch_size=train_cfg['batch_size'],\n",
        "    train_size=train_cfg['train_size'],\n",
        "    dropout=train_cfg['dropout'],\n",
        "    max_gen_length=train_cfg['max_gen_length'],\n",
        "    validation=train_cfg['validation'],\n",
        "    is_csv=train_cfg['is_csv'],\n",
        "    rnn_layers=model_cfg['rnn_layers'],\n",
        "    rnn_size=model_cfg['rnn_size'],\n",
        "    rnn_bidirectional=model_cfg['rnn_bidirectional'],\n",
        "    max_length=model_cfg['max_length'],\n",
        "    dim_embeddings=model_cfg['dim_embeddings'],\n",
        "    word_level=model_cfg['word_level'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "5,663 texts collected.\n",
            "Training new model w/ 4-layer, 128-cell Bidirectional LSTMs\n",
            "Training on 134,882 character sequences.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Epoch 1/10\n",
            "131/131 [==============================] - 1124s 9s/step - loss: 2.7811 - val_loss: 2.1586\n",
            "Epoch 2/10\n",
            "131/131 [==============================] - 1108s 8s/step - loss: 2.0265 - val_loss: 1.9074\n",
            "####################\n",
            "Temperature: 0.2\n",
            "####################\n",
            "i could the coud you cand the goon\n",
            "\n",
            "i love the with the love you cand the could to cour\n",
            "\n",
            "i whe whe with a could the she with tour the soud I cone the love and the love the soud\n",
            "\n",
            "####################\n",
            "Temperature: 0.5\n",
            "####################\n",
            "i coud you me with a loood the cous I will the the the shen you coul to tour\n",
            "\n",
            "wan I cand to me love the lowe I'll the criee\n",
            "\n",
            "when don't the with you cone\n",
            "\n",
            "####################\n",
            "Temperature: 1.0\n",
            "####################\n",
            "dacl I'ly homendo gove thete aneaybine can by choum it yis coud\n",
            "\n",
            "oh fouy coont trus\n",
            "\n",
            "she your fell the love githor eplirine\n",
            "\n",
            "Epoch 3/10\n",
            "131/131 [==============================] - 1105s 8s/step - loss: 1.7636 - val_loss: 1.6658\n",
            "Epoch 4/10\n",
            "131/131 [==============================] - 1104s 8s/step - loss: 1.5593 - val_loss: 1.5089\n",
            "####################\n",
            "Temperature: 0.2\n",
            "####################\n",
            "i don't go me me\n",
            "\n",
            "i'm gonna know you know you know\n",
            "\n",
            "i know you've got to be make\n",
            "\n",
            "####################\n",
            "Temperature: 0.5\n",
            "####################\n",
            "baby nah nah nah nah nah nah\n",
            "\n",
            "i'm grad me\n",
            "\n",
            "you're goet to show you know\n",
            "\n",
            "####################\n",
            "Temperature: 1.0\n",
            "####################\n",
            "and need guiltad arone\n",
            "\n",
            "she should dane\n",
            "\n",
            "hele sleep it's liel me\n",
            "\n",
            "Epoch 5/10\n",
            "131/131 [==============================] - 1103s 8s/step - loss: 1.4092 - val_loss: 1.3872\n",
            "Epoch 6/10\n",
            "131/131 [==============================] - 1103s 8s/step - loss: 1.2847 - val_loss: 1.3122\n",
            "####################\n",
            "Temperature: 0.2\n",
            "####################\n",
            "i want to be the life on the man\n",
            "\n",
            "i want to be the monely want\n",
            "\n",
            "i want to be the money the way\n",
            "\n",
            "####################\n",
            "Temperature: 0.5\n",
            "####################\n",
            "and I'll all together now\n",
            "\n",
            "i know you'll be like me a will you\n",
            "\n",
            "when I'm going to me\n",
            "\n",
            "####################\n",
            "Temperature: 1.0\n",
            "####################\n",
            "shece I go a carem on\n",
            "\n",
            "so child ways don't apparty can so\n",
            "\n",
            "mo the pain to but me nobody\n",
            "\n",
            "Epoch 7/10\n",
            "131/131 [==============================] - 1099s 8s/step - loss: 1.1877 - val_loss: 1.2614\n",
            "Epoch 8/10\n",
            "131/131 [==============================] - 1099s 8s/step - loss: 1.1100 - val_loss: 1.2192\n",
            "####################\n",
            "Temperature: 0.2\n",
            "####################\n",
            "i saw a shame what a shame mary jane baby jud\n",
            "\n",
            "i saw a show and she would say so should\n",
            "\n",
            "i want to be your man and she said\n",
            "\n",
            "####################\n",
            "Temperature: 0.5\n",
            "####################\n",
            "the calling and name and the party\n",
            "\n",
            "i saw a walk and I sad a show\n",
            "\n",
            "i need to see you and me mine\n",
            "\n",
            "####################\n",
            "Temperature: 1.0\n",
            "####################\n",
            "is well you see you\n",
            "\n",
            "there will getting better is understand\n",
            "\n",
            "but as belight up an mind after my my hand\n",
            "\n",
            "Epoch 9/10\n",
            "131/131 [==============================] - 1102s 8s/step - loss: 1.0410 - val_loss: 1.1814\n",
            "Epoch 10/10\n",
            "131/131 [==============================] - 1105s 8s/step - loss: 0.9878 - val_loss: 1.1679\n",
            "####################\n",
            "Temperature: 0.2\n",
            "####################\n",
            "it's going to lose that you see\n",
            "\n",
            "and I will sing the word of the same\n",
            "\n",
            "and I will send you and I'll be and I'll be there\n",
            "\n",
            "####################\n",
            "Temperature: 0.5\n",
            "####################\n",
            "can't you see you thangering love boy\n",
            "\n",
            "with in the evening\n",
            "\n",
            "i thought a should can't be long\n",
            "\n",
            "####################\n",
            "Temperature: 1.0\n",
            "####################\n",
            "oh oh ah ah oh\n",
            "\n",
            "don't let me didn't let me down\n",
            "\n",
            "look she's meeting home\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rxQAYo-yLMg",
        "colab_type": "code",
        "outputId": "2c496232-6a51-4723-bdc2-bf720b362bda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "print(textgen.model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input (InputLayer)              (None, 40)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding (Embedding)           (None, 40, 100)      4100        input[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "rnn_1 (Bidirectional)           (None, 40, 256)      234496      embedding[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "rnn_2 (Bidirectional)           (None, 40, 256)      394240      rnn_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "rnn_3 (Bidirectional)           (None, 40, 256)      394240      rnn_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "rnn_4 (Bidirectional)           (None, 40, 256)      394240      rnn_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "rnn_concat (Concatenate)        (None, 40, 1124)     0           embedding[0][0]                  \n",
            "                                                                 rnn_1[0][0]                      \n",
            "                                                                 rnn_2[0][0]                      \n",
            "                                                                 rnn_3[0][0]                      \n",
            "                                                                 rnn_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "attention (AttentionWeightedAve (None, 1124)         1124        rnn_concat[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "output (Dense)                  (None, 41)           46125       attention[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 1,468,565\n",
            "Trainable params: 1,468,565\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2EwQQBiRPXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights_path = '{}_weights.hdf5'.format(model_name)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXz4HM7DTod3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_path = '500nds_12Lrs_100epchs_Model_vocab (1).json'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FNRfGAQSfxl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "config_path = '500nds_12Lrs_100epchs_Model_config.json'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNEpYn-_y8qF",
        "colab_type": "code",
        "outputId": "8ad06310-ede5-4c51-e3bf-d1c2e42ce94f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "textgen = textgenrnn(weights_path=weights_path,\n",
        "                       vocab_path=vocab_path,\n",
        "                       config_path=config_path)\n",
        "\n",
        "generated_characters = 300\n",
        "\n",
        "text = textgen.generate_samples(300)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "####################\n",
            "Temperature: 0.2\n",
            "####################\n",
            "i want you\n",
            "\n",
            "i want to be your man I want to be your man\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "you know you know my name\n",
            "\n",
            "i want you\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want to be your man and kissing the same of the bight the sun\n",
            "\n",
            "i can't be and me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "you know I nearly had a pain at the party\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will sing the time of the same\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i said someone who's got to be your man\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I'm true\n",
            "\n",
            "i want to be your man I'm lonely\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "when I saw you should see you make me the mind\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you\n",
            "\n",
            "it's all too much the number\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so hold you man\n",
            "\n",
            "when I was you see you and me\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I know what you know my name\n",
            "\n",
            "i want you\n",
            "\n",
            "i can't be and me alone\n",
            "\n",
            "it's true another man\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I don't know that you see\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you\n",
            "\n",
            "the word the word there's no need\n",
            "\n",
            "i want you so hold you head you can do\n",
            "\n",
            "i want to be your man I can't see\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold me too much oh\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "the word the word of the start\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hard that you see\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't believe you\n",
            "\n",
            "i want you so hold you man\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so many many jane had a pain at the party\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "when I saw you should see the end\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i'm gonna cread you there met me so mind\n",
            "\n",
            "i'm tried to make you mean to me\n",
            "\n",
            "i think you with another day\n",
            "\n",
            "when I was a shame what a shame mary jane had a pain at the party\n",
            "\n",
            "you know I nearly had a pain at the party\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so hard the word there with a lot of the band\n",
            "\n",
            "i can't be long to be long listen play\n",
            "\n",
            "when I was you see you see you mean to me\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I want to be your man \n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you you should see you know\n",
            "\n",
            "it's all too much you yeah\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "i want you so hard the changes the same\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "i want you so hard the way be baby cry\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so hold me there\n",
            "\n",
            "i think you should see you see\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want you so hard that I was a show\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "when I saw you should see you want to be your man\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and the word the sun she was a shame\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "all the world there's no need you\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "it's all too much oh yeah\n",
            "\n",
            "the world that she said so\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you you see you so make me\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i'm gonna get to be there with you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't be you now the number\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't be and I feel and I'll love you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I'm really down\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I'm really down\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold you oh\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold your mind\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will send the time at all the time\n",
            "\n",
            "i want to be your man why\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "the word the word there's no need\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold me too much to me\n",
            "\n",
            "i want you so hold me that I'm so selly down\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "the word the word she said a love me when you believe been to your man\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I can do\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "and I want to be your man be man\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "when I was you see you there metter sing\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so see you to me\n",
            "\n",
            "i want you you should see you see\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "the world the word the sun\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "when I was you see you believe me be long\n",
            "\n",
            "i can't be baby beep beep me the wall\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i'm down I'm really down\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "i want you so hold you should\n",
            "\n",
            "and I want to be your man I'm really down\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "the word the word there's no need\n",
            "\n",
            "when I say he hard and feeling here\n",
            "\n",
            "i can't be no no no no no no no\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't be there with a was a rich oh\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "come on the beginning armary there\n",
            "\n",
            "i want you so hard that you see\n",
            "\n",
            "the world will spread the sun\n",
            "\n",
            "i want to be your man \n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "it's all too much you\n",
            "\n",
            "i want to be your mother should\n",
            "\n",
            "i want you so hard that you know\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't be and mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want you so many many many jane had a pain at the party\n",
            "\n",
            "i want you so hard me\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will see the word with a lot\n",
            "\n",
            "i want you so hard that I was a shame mary jane what a shame mary jane had a pain at the party\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so cry\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "and I will see the word there's no need\n",
            "\n",
            "i want to be your man I'm true\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i can't be long long long to be bad you belong\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will send the world that you see it a pain of mind\n",
            "\n",
            "i want you so hold your mind\n",
            "\n",
            "i want you you should mean to me\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "you know I nearly hand you have and never been a long that line\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you you should see you mean to me\n",
            "\n",
            "it's all too much yeah\n",
            "\n",
            "she said so how me and I'll go\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I can't stay\n",
            "\n",
            "and I will send the world I'm the world\n",
            "\n",
            "when I was you see you love me down\n",
            "\n",
            "and I want to be your man I'm down\n",
            "\n",
            "and I would see you to make you\n",
            "\n",
            "and I can see your money more\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so hold your mind\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will say the word there's no need\n",
            "\n",
            "i want you\n",
            "\n",
            "she was a shame what a shame mary jane had a pain at the party\n",
            "\n",
            "when I saw you see you know my name\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I want to be your man\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't go that you once belonged\n",
            "\n",
            "she was a way the word of the band\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "the love that's all the time to say you see\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hold me too\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will send you to me\n",
            "\n",
            "i want to be your man I can't do that\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "she said so should can be me\n",
            "\n",
            "i think you should see you see\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "i want you so hard that I was a pain to me\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "and I will send the time at all the time\n",
            "\n",
            "i don't know what you know my name\n",
            "\n",
            "i want you so hold you\n",
            "\n",
            "i want you so hard that head\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want you so hard the word the same on the party\n",
            "\n",
            "the word the sun she was be baby just can't be mine\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i want to be your man I me long to you\n",
            "\n",
            "####################\n",
            "Temperature: 0.5\n",
            "####################\n",
            "i want you\n",
            "\n",
            "you can do that I saw you and that she said\n",
            "\n",
            "you know you know my name\n",
            "\n",
            "the bagin love will you go\n",
            "\n",
            "i want to be your studant so much in the night\n",
            "\n",
            "you can talk to much you\n",
            "\n",
            "come on when I have comes and the children\n",
            "\n",
            "and I need the end\n",
            "\n",
            "i know for the night before\n",
            "\n",
            "but I'll go the end\n",
            "\n",
            "he no one little girl\n",
            "\n",
            "we didn't need me the night before\n",
            "\n",
            "when you go arout the only window you had to go\n",
            "\n",
            "i want you you should see you be bad\n",
            "\n",
            "it could see you know I nearly buy\n",
            "\n",
            "you know what you see it a partaction\n",
            "\n",
            "i'm gonna get that you can be me\n",
            "\n",
            "i want to be your man to make it a pain at a song on the dark\n",
            "\n",
            "the word I want to be your man\n",
            "\n",
            "i saw you should see me the one to me\n",
            "\n",
            "i never been a long to be there\n",
            "\n",
            "i can't talk you away yeah yeah yeah\n",
            "\n",
            "i said and now I need you\n",
            "\n",
            "so cry many man she had had easy\n",
            "\n",
            "when I do you can see that you see\n",
            "\n",
            "and then I'm all you see\n",
            "\n",
            "tell me on love loved to be your life\n",
            "\n",
            "i said\n",
            "\n",
            "you know I'm down for your mind\n",
            "\n",
            "can't you hold me and I have's gonna be a party what you see her too\n",
            "\n",
            "love her\n",
            "\n",
            "i said she said sunshine\n",
            "\n",
            "i'm be my long to be long listen better\n",
            "\n",
            "beep me I'm down the said\n",
            "\n",
            "i would do that you've been me\n",
            "\n",
            "i could never got to see you with yeah\n",
            "\n",
            "and I'll be your man \n",
            "\n",
            "we're gonna stays where do a way be wrong\n",
            "\n",
            "mother sump and when you know\n",
            "\n",
            "see a book and I'll love you\n",
            "\n",
            "when I get to be your man I me mine\n",
            "\n",
            "i want you\n",
            "\n",
            "all the wind I want the road\n",
            "\n",
            "i said some how what it the same\n",
            "\n",
            "it's all too hold me and I'm helalize\n",
            "\n",
            "i'm gonna do you so much yeah\n",
            "\n",
            "i'm be long you no but you'll be mine\n",
            "\n",
            "i'm right all you near you soul\n",
            "\n",
            "hello hello\n",
            "\n",
            "i want to be your man \n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't be good on my love that's belonged\n",
            "\n",
            "i'll get you near me the same with a was a star\n",
            "\n",
            "don't worry\n",
            "\n",
            "when I saw the man oh the same\n",
            "\n",
            "and I will want to be no oh\n",
            "\n",
            "i want to be your man come\n",
            "\n",
            "want to be where you see you need\n",
            "\n",
            "don't say here me so sad her belong\n",
            "\n",
            "she said so hold me there before to me\n",
            "\n",
            "i want you to day\n",
            "\n",
            "i want you so hard she's gonna be too much \n",
            "\n",
            "i don't know what you see me me\n",
            "\n",
            "will you in the one on the one and in the sun\n",
            "\n",
            "i want to be your man I got to be your man\n",
            "\n",
            "i want to be your man I me mine\n",
            "\n",
            "and you know you know I think you that you know\n",
            "\n",
            "now the sun sun now there is way\n",
            "\n",
            "tell me when I am the way of mind\n",
            "\n",
            "but I'm gonna be sad you see\n",
            "\n",
            "it's all too much that like of yeah\n",
            "\n",
            "she takes a father should want\n",
            "\n",
            "you know I'm not there words of you\n",
            "\n",
            "make a way be wants to cold you\n",
            "\n",
            "baby you're so making and still the way\n",
            "\n",
            "and she world the sun I feel again\n",
            "\n",
            "there are no one love is all you need\n",
            "\n",
            "i'm gonna get that I say going to me\n",
            "\n",
            "i want to be the polled you will right\n",
            "\n",
            "i want you\n",
            "\n",
            "there's no need me to the long\n",
            "\n",
            "so on I saw was to see you can't be mine\n",
            "\n",
            "if I want you so hard to me\n",
            "\n",
            "it's all the lonely penet to me\n",
            "\n",
            "look at the old with you\n",
            "\n",
            "i want you\n",
            "\n",
            "you know that I want to be your man\n",
            "\n",
            "i said a shord what I know how may\n",
            "\n",
            "i'm looking through you where you coust to see the love is a place\n",
            "\n",
            "and I will love you you can talk to me\n",
            "\n",
            "treat me so long that have head\n",
            "\n",
            "come on the bottom love girl window\n",
            "\n",
            "when I tell you you near you don't know my nind hey by should\n",
            "\n",
            "i can't be there one on the place in the party\n",
            "\n",
            "when I wake there's on his nowhere\n",
            "\n",
            "come on the bottom things are show\n",
            "\n",
            "and the play back words of things is the sky with and everything I will do you old you can send that something I say be with a lot\n",
            "\n",
            "i want to hold your hand\n",
            "\n",
            "we'll never got a ticket to be a paperback writer\n",
            "\n",
            "i want you\n",
            "\n",
            "she was a little girl\n",
            "\n",
            "i think you what you know my name\n",
            "\n",
            "love you near me the man and there's and I feel the world\n",
            "\n",
            "the world of the sun\n",
            "\n",
            "i want you you so many touch\n",
            "\n",
            "i'm coming oo so hold me there\n",
            "\n",
            "i'm gonna be an our out her them\n",
            "\n",
            "i want you you\n",
            "\n",
            "and I will see you you can't be bad you\n",
            "\n",
            "she keeps something away\n",
            "\n",
            "let it be let it be bad out to be your mone\n",
            "\n",
            "i want you\n",
            "\n",
            "she said where you should can sleep tonight\n",
            "\n",
            "i'm down I'm so so\n",
            "\n",
            "i'm gonna get to see you more\n",
            "\n",
            "i want you so comport me so man\n",
            "\n",
            "there broken't let me down\n",
            "\n",
            "come on let's nice him time\n",
            "\n",
            "and the weater has back the start\n",
            "\n",
            "i said there's no one me what a plann\n",
            "\n",
            "i'm gonna carry that you know\n",
            "\n",
            "i can't go out you and me and I want to be your man \n",
            "\n",
            "to carry to show you have to can all me\n",
            "\n",
            "all the lonely better girl\n",
            "\n",
            "and nothing to see you party change at his pulias\n",
            "\n",
            "i want to be your man to be head\n",
            "\n",
            "i can't believe you\n",
            "\n",
            "just come on baby true\n",
            "\n",
            "cause I hey have gone\n",
            "\n",
            "help me was it is a with you\n",
            "\n",
            "the way where are of the billed in the same\n",
            "\n",
            "she world say she's a lot\n",
            "\n",
            "sexy not a little girl from my friends\n",
            "\n",
            "and I sing a little girl\n",
            "\n",
            "say the love is here the begins\n",
            "\n",
            "me the man what a shame mary jane wanded there with you\n",
            "\n",
            "i want you\n",
            "\n",
            "the word the way will seem me\n",
            "\n",
            "yeah yeah yeah yeah yeah yeah\n",
            "\n",
            "here such a love that show what I near yeah\n",
            "\n",
            "i want you worry\n",
            "\n",
            "i want to be your man to show\n",
            "\n",
            "i don't know why hey love me shere it cry\n",
            "\n",
            "the way thing will go\n",
            "\n",
            "it's only only skenter wearing with a long to be me\n",
            "\n",
            "you know I want you\n",
            "\n",
            "all my number there's have a good from the long out of the end\n",
            "\n",
            "and I want to be the one what you lied you\n",
            "\n",
            "and I will I can see you that you know you know my milly feeling\n",
            "\n",
            "there's a got a fool on the party\n",
            "\n",
            "the world is the sky weres and so his nowhere\n",
            "\n",
            "when I can't hide nowhere and post to the way\n",
            "\n",
            "we do it is it's real\n",
            "\n",
            "i want to hold your mind\n",
            "\n",
            "you know you know my name\n",
            "\n",
            "it's so a love with her it's sleeping\n",
            "\n",
            "and I lost you you know you want me\n",
            "\n",
            "i want you\n",
            "\n",
            "when I was it the sone in the same\n",
            "\n",
            "she's the tearing all the world there are going to see\n",
            "\n",
            "i think you hear such oh\n",
            "\n",
            "i can't a chair that have across of mind\n",
            "\n",
            "they way be won't be sad feeling\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "i can't be and mine\n",
            "\n",
            "all the word there's a lonely\n",
            "\n",
            "now I need to me\n",
            "\n",
            "i want you so bad\n",
            "\n",
            "you know what you say see\n",
            "\n",
            "i want you you can be your mother should\n",
            "\n",
            "yeah yeah yeah yeah yeah\n",
            "\n",
            "and I will send the only time that have can comping out the band\n",
            "\n",
            "well you can be and me my loving of when I say goodbye oh oh oh oh\n",
            "\n",
            "and she said wead too\n",
            "\n",
            "when I was you start the one\n",
            "\n",
            "i want to be your eyes can't you door\n",
            "\n",
            "she outs and heart a really one that with you\n",
            "\n",
            "it's been a long to carolle\n",
            "\n",
            "i got to be the sman I'm mad me ware\n",
            "\n",
            "i can't get it be time to me\n",
            "\n",
            "that is way there's no need\n",
            "\n",
            "and I love you\n",
            "\n",
            "good day so hold me in the fool\n",
            "\n",
            "and I get the same and still wings that you do\n",
            "\n",
            "because I'm the old you have got to have me a show\n",
            "\n",
            "the tange at all the world that you do\n",
            "\n",
            "i want you to stay that you see\n",
            "\n",
            "the calles you say that mean a just before that\n",
            "\n",
            "i want you so see her too\n",
            "\n",
            "now I'm so the door of things of mind\n",
            "\n",
            "the have what do disamet\n",
            "\n",
            "so man I need no no oh head on the end\n",
            "\n",
            "but broken the sun and I think you\n",
            "\n",
            "all together now\n",
            "\n",
            "you can't be the feet and long to you\n",
            "\n",
            "did I take you and the way and I'm gonna be long or gain on\n",
            "\n",
            "i want you so once paperback\n",
            "\n",
            "the world that I still you\n",
            "\n",
            "she said hello changile girl like a parter\n",
            "\n",
            "so the sun going for your door\n",
            "\n",
            "i can't believe me the same long to be make her me\n",
            "\n",
            "yes I'm so leave it all too much me\n",
            "\n",
            "the sun people better here\n",
            "\n",
            "now I need to see the only girl to me\n",
            "\n",
            "and I loved you\n",
            "\n",
            "that you see it and I'll try too\n",
            "\n",
            "hey gonna be a word of long\n",
            "\n",
            "well you near me the word is that there\n",
            "\n",
            "everybody can say him tance and I don't know\n",
            "\n",
            "i want you\n",
            "\n",
            "i know what I just be long to be long\n",
            "\n",
            "and the things I look the place\n",
            "\n",
            "i know I want you\n",
            "\n",
            "i got no be a show of your money so\n",
            "\n",
            "i don't know that she can she say hello\n",
            "\n",
            "when you say you say the end I'm so the dark\n",
            "\n",
            "why will you still the love and free\n",
            "\n",
            "in the morning more me mad\n",
            "\n",
            "i don't know that it's going to make you\n",
            "\n",
            "i can't be long long little girl\n",
            "\n",
            "if you know my name ha no need that hello\n",
            "\n",
            "it's so and on the time of time\n",
            "\n",
            "i can't be mine\n",
            "\n",
            "i think around for you\n",
            "\n",
            "you don't be long nothing you know that you do\n",
            "\n",
            "dear much a want to be your man before to me\n",
            "\n",
            "i'm going to lose you\n",
            "\n",
            "she does it ain't you have me too\n",
            "\n",
            "i want to be your man I love you\n",
            "\n",
            "i want to be your mother baby just can't be mine\n",
            "\n",
            "there's nothing you know my name\n",
            "\n",
            "but I know I nearly say hello\n",
            "\n",
            "the very and so mind you know\n",
            "\n",
            "that she do do you no should\n",
            "\n",
            "when I can't so boy\n",
            "\n",
            "i want you\n",
            "\n",
            "when you know I don't know my name\n",
            "\n",
            "i'm down I'm true to misey to mean a drink\n",
            "\n",
            "i think you want me\n",
            "\n",
            "and he said morning morning\n",
            "\n",
            "i'll be an the word of too much oh yeah\n",
            "\n",
            "that good and so\n",
            "\n",
            "all the lonely time is the sky\n",
            "\n",
            "the word she was a millow good\n",
            "\n",
            "the words and what it is you do\n",
            "\n",
            "all together now\n",
            "\n",
            "i think what you do\n",
            "\n",
            "i want to be your man me away\n",
            "\n",
            "the world of mind the word that she's happy just her help her\n",
            "\n",
            "we do it is so criend rings of love\n",
            "\n",
            "i want to see you that I can be hand\n",
            "\n",
            "i could love for your night and I'd sad you belong\n",
            "\n",
            "i love you yeah yeah yeah yeah\n",
            "\n",
            "and I will wear well the said\n",
            "\n",
            "the word is wondering\n",
            "\n",
            "i said she would do\n",
            "\n",
            "and I can see your money touce of mind\n",
            "\n",
            "we can be what I should gonna go ou\n",
            "\n",
            "she the coustion down the end\n",
            "\n",
            "i'm so home of soo much\n",
            "\n",
            "when you don't worry\n",
            "\n",
            "and I will see you love me do\n",
            "\n",
            "i feel you need you and me you're mine\n",
            "\n",
            "when I saw you can dead the could\n",
            "\n",
            "i think you know my name\n",
            "\n",
            "i can't come home to say\n",
            "\n",
            "all together now\n",
            "\n",
            "someday someone\n",
            "\n",
            "and I loved you\n",
            "\n",
            "and the word is a with on more to me\n",
            "\n",
            "it's my home\n",
            "\n",
            "don't be long with you on the number\n",
            "\n",
            "the calles in a lot to kiss you\n",
            "\n",
            "but you know what you know I heard before out of her back ago to me\n",
            "\n",
            "i'm gonna be the beginning\n",
            "\n",
            "i want to be your man I think you should\n",
            "\n",
            "you may see your mother should\n",
            "\n",
            "i want you\n",
            "\n",
            "you say all the windo that you see you\n",
            "\n",
            "you know I need you\n",
            "\n",
            "and I'm trying to make you\n",
            "\n",
            "and I will do it's only playents\n",
            "\n",
            "all together now\n",
            "\n",
            "he said so hard in the life of the start\n",
            "\n",
            "you know that you know my name\n",
            "\n",
            "what oh she would leave me in the light\n",
            "\n",
            "you'll be my mind\n",
            "\n",
            "for the baginning of the mystery ain't be silver right from the blistle help me\n",
            "\n",
            "you let me down you to let me know\n",
            "\n",
            "i'm gonna get to show that I'm round\n",
            "\n",
            "here got a little happened along\n",
            "\n",
            "i want you\n",
            "\n",
            "make you there mean me the word\n",
            "\n",
            "but the more will go\n",
            "\n",
            "oh yeah yeah yeah\n",
            "\n",
            "now time is here it won't be mine\n",
            "\n",
            "it's a loser\n",
            "\n",
            "the sun my walls here\n",
            "\n",
            "i feel you need to me\n",
            "\n",
            "if you can see that I want me\n",
            "\n",
            "####################\n",
            "Temperature: 1.0\n",
            "####################\n",
            "everyone plamis her eyes\n",
            "\n",
            "here can he can aperivad\n",
            "\n",
            "tell me away sad you can\n",
            "\n",
            "love me love new me the mack\n",
            "\n",
            "kissona without out someone\n",
            "\n",
            "just near right I know how man\n",
            "\n",
            "in you know my will\n",
            "\n",
            "well I'm stone the tartce play\n",
            "\n",
            "well you inter's in driverstile wind me\n",
            "\n",
            "'cause I'm love a woad a skilam\n",
            "\n",
            "the sun we're on a lover hole\n",
            "\n",
            "well you burn of you sunty face out\n",
            "\n",
            "i said it criend from shine\n",
            "\n",
            "we're looks to much the beginning\n",
            "\n",
            "she would daday new there with the night before and been the sun\n",
            "\n",
            "she love you toof\n",
            "\n",
            "christer writced time thationing\n",
            "\n",
            "i only hour them its and longer\n",
            "\n",
            "now no namr named everything you know\n",
            "\n",
            "come on the bird of good her\n",
            "\n",
            "but go on colourther knyings me tires came \n",
            "\n",
            "because you know where yeah till I close are on I be\n",
            "\n",
            "i can't sergline\n",
            "\n",
            "oncs her life sun you leave me and mine\n",
            "\n",
            "ain't you know that I'm telling\n",
            "\n",
            "with a sooo on anyops\n",
            "\n",
            "take your head back \n",
            "\n",
            "a look at the phone and knaw you silly stops I'm borwen\n",
            "\n",
            "where I fell the wrong to what you can no\n",
            "\n",
            "but you know I will the only \n",
            "\n",
            "and now I feel a way\n",
            "\n",
            "she's got to die\n",
            "\n",
            "she world papper as she said\n",
            "\n",
            "love of all the little gight\n",
            "\n",
            "and it's true peop you know\n",
            "\n",
            "in the welt my boywen\n",
            "\n",
            "far had their is nowhere my window\n",
            "\n",
            "with you\n",
            "\n",
            "i know if I want hey wrong\n",
            "\n",
            "and you'll trupted to me\n",
            "\n",
            "here gone only feelon\n",
            "\n",
            "i said around down you know she won't be long\n",
            "\n",
            "where change I wright dose hear you\n",
            "\n",
            "honey my mine\n",
            "\n",
            "there going down\n",
            "\n",
            "but you know I nearly died\n",
            "\n",
            "they wetermed nor like you\n",
            "\n",
            "don't hidw the time without guy\n",
            "\n",
            "i want you you near yeah yeah yeah\n",
            "\n",
            "and then bant tonight\n",
            "\n",
            "oh when I saw you didn't know without you\n",
            "\n",
            "then you're too much what I see a\n",
            "\n",
            "hello looked you make \n",
            "\n",
            "it's only in everything I'll ckild \n",
            "\n",
            "can't you see now tire the apaparazine \n",
            "\n",
            "in the love everyone frirty\n",
            "\n",
            "help you trive to me\n",
            "\n",
            "wonders do it apppear dayis has go words and hurther \n",
            "\n",
            "and I found time the oughtle no come \n",
            "\n",
            "so let you need is toptee another disappenebt never giving on up the show\n",
            "\n",
            "come on let't papperback man\n",
            "\n",
            "looking and I'll never make you\n",
            "\n",
            "remember she feel\n",
            "\n",
            "when I get you like tomorrow me\n",
            "\n",
            "beby cry beep me writer to a par\n",
            "\n",
            "all at the old on my mind of the sun\n",
            "\n",
            "i'm gonna get a fut home\n",
            "\n",
            "good make it all you need\n",
            "\n",
            "since's you're reding nowhere to make you and I love you\n",
            "\n",
            "so know I singing wo man't wind and fine\n",
            "\n",
            "all your neah you\n",
            "\n",
            "befper child she's be my one\n",
            "\n",
            "then I'm going take from my right me\n",
            "\n",
            "imside in the end\n",
            "\n",
            "it looking in her\n",
            "\n",
            "good us shoulder I'm in silver\n",
            "\n",
            "you you say you mather bast broke\n",
            "\n",
            "always fine me thinking of you\n",
            "\n",
            "oh yesterday to so hurmas\n",
            "\n",
            "a baby than the other\n",
            "\n",
            "see he take you much and todays more to play\n",
            "\n",
            "the his he walkad gone\n",
            "\n",
            "i don't let you know I won't all you need\n",
            "\n",
            "you'd gonna change goo gloou\n",
            "\n",
            "when I really his were\n",
            "\n",
            "it's herees born to be out cool\n",
            "\n",
            "come on uncuyfoother had a fretend\n",
            "\n",
            "come on the blits are love me me\n",
            "\n",
            "her came it's giving walks low\n",
            "\n",
            "celloo hil in I love you\n",
            "\n",
            "in a mil out halt look\n",
            "\n",
            "lace it's coming on the carme\n",
            "\n",
            "don't give my will be dance ach don't lonely\n",
            "\n",
            "oh oh i h ane have evened belong\n",
            "\n",
            "so see you told me that I'm senthiee\n",
            "\n",
            "it only times at armout tomorrown\n",
            "\n",
            "look at home\n",
            "\n",
            "it's all the hatchere\n",
            "\n",
            "but she knows prease whoa I want to be in \n",
            "\n",
            "everybody I'm going to show out it's all only girl that boy\n",
            "\n",
            "come together pack boy\n",
            "\n",
            "server all together tone of mysteand\n",
            "\n",
            "so baby that my finger people are I'm long\n",
            "\n",
            "you burner the sun song you\n",
            "\n",
            "honey didn't see you\n",
            "\n",
            "and nothing I sad it come love morning\n",
            "\n",
            "maded that some love and I'm a roa\n",
            "\n",
            "when I feel you dyes a chance that you go\n",
            "\n",
            "sitting roarshing someone or more\n",
            "\n",
            "have another sudt her as good on bad good love\n",
            "\n",
            "home for the little child\n",
            "\n",
            "and you just finting so the walrus\n",
            "\n",
            "when I so I feel good more\n",
            "\n",
            "may's a wes blue\n",
            "\n",
            "yes it way what weagh I'm kissing and ip\n",
            "\n",
            "help me that is when you go\n",
            "\n",
            "man't you realize it's real\n",
            "\n",
            "we'd have a pain to give is in a man summer\n",
            "\n",
            "don't let me real\n",
            "\n",
            "i need \" bitdaged darling\n",
            "\n",
            "i'd inthing yight time to make me I'll be there\n",
            "\n",
            "i need to me\n",
            "\n",
            "take you out your near sin'm on minders\n",
            "\n",
            "told me why you know my naw\n",
            "\n",
            "who she said sun here had a said\n",
            "\n",
            "but is the eggingen appen when you're insted and love girl that I said\n",
            "\n",
            "you better better better them turns\n",
            "\n",
            "martmaliss appartion\n",
            "\n",
            "don't worret home to\n",
            "\n",
            "did ky were baby with I belong\n",
            "\n",
            "c'mon gives you\n",
            "\n",
            "baby in and do\n",
            "\n",
            "when the love any loned's bill spillacper\n",
            "\n",
            "i want you\n",
            "\n",
            "oh oh ano you silver eyes\n",
            "\n",
            "there's nothing some\n",
            "\n",
            "i am a boy had goesboot bag\n",
            "\n",
            "ah ah ah oh\n",
            "\n",
            "love thate's no near like a love\n",
            "\n",
            "and lears where you dranks meettion\n",
            "\n",
            "i don't need now\n",
            "\n",
            "all I gotta do day\n",
            "\n",
            "waiting down to give every just\n",
            "\n",
            "there day with as the egg insterance\"\n",
            "\n",
            "one the little girl by mine\n",
            "\n",
            "come on his love is what you see the oce\n",
            "\n",
            "in mistereasplicter bloked to bill\n",
            "\n",
            "just natten goodbye oncy love with me\n",
            "\n",
            "elach you've got to say\n",
            "\n",
            "couldn't have anyold I'm heard in really stops in her\n",
            "\n",
            "she does at mind I'm gone\n",
            "\n",
            "she one is madh a dranger before to hide and me\n",
            "\n",
            "she will go it be\n",
            "\n",
            "you told me un without of yestalnzaling\n",
            "\n",
            "i've amilan guy you'ted hand before\n",
            "\n",
            "cry brring look at the evening one your flirtantion I've bean\n",
            "\n",
            "to please me coming baby \n",
            "\n",
            "look at home\n",
            "\n",
            "she's got nothing you can good\n",
            "\n",
            "where you have me seem armand\n",
            "\n",
            "paperback little child\n",
            "\n",
            "is who it show home about me in more\n",
            "\n",
            "i sad all weight\n",
            "\n",
            "oh my tump every don't care the man\n",
            "\n",
            "come on letting he's your bot our provy\n",
            "\n",
            "get back me love day sexy have ahearts in the ruin\n",
            "\n",
            "i know you want you know how man\n",
            "\n",
            "oh ah no no\n",
            "\n",
            "but you know my name hate's halmie\n",
            "\n",
            "now about it here such yeah like a with owhere\n",
            "\n",
            "that's agoing there without alone\n",
            "\n",
            "could me\n",
            "\n",
            "if you'll never do\n",
            "\n",
            "came it on simpore away\n",
            "\n",
            "hey break it won't you hear you mum\n",
            "\n",
            "if there's anyo good read\n",
            "\n",
            "oh my changing the whaces she dog deads \n",
            "\n",
            "the newser I said hey'd kad to give or your say hurry with just bag little girl\n",
            "\n",
            "everybody sunshine\n",
            "\n",
            "once bring of little should\n",
            "\n",
            "you know you see her hands inving a millex\n",
            "\n",
            "love me earned alone\n",
            "\n",
            "now I've minemed seeting all it tight\n",
            "\n",
            "where you treat me see it be\n",
            "\n",
            "love and friend out\n",
            "\n",
            "if you look wonate everything you belong\n",
            "\n",
            "sed inside you it's riverhin'\n",
            "\n",
            "so is yeah can't see a remelatly hearts for easy \n",
            "\n",
            "torking out\n",
            "\n",
            "my love without that before the touce blue\n",
            "\n",
            "yes you know I've missteen con't much \n",
            "\n",
            "mn the many with go\n",
            "\n",
            "oh no doe what's a rumb me had bom\n",
            "\n",
            "let it be\n",
            "\n",
            "anyctead is a just again out you\n",
            "\n",
            "have that it kissoni-borncoom gonna come on\n",
            "\n",
            "and I'm hof love is love is you see run\n",
            "\n",
            "ah now I needed your birthday\n",
            "\n",
            "you know you no ooh man ooh moo\n",
            "\n",
            "that's shave wile gonely one\n",
            "\n",
            "back where you're be cromning\n",
            "\n",
            "remember where you'd be but little girl\n",
            "\n",
            "it tell me alone good a\n",
            "\n",
            "before me poot better make\n",
            "\n",
            "martha will be narmown\n",
            "\n",
            "come on his way a cride\n",
            "\n",
            "and she was someone\n",
            "\n",
            "if I think when I was alone bring \n",
            "\n",
            "oh pleasons in the sky name\n",
            "\n",
            "come reople asteantil alrights\n",
            "\n",
            "i say seed you stand after the party\n",
            "\n",
            "made is procters to wrong\n",
            "\n",
            "\"mouls befora\n",
            "\n",
            "you'll be gonna wanger hord inside\n",
            "\n",
            "it the egg on\n",
            "\n",
            "i can't tell my smore but the gues\n",
            "\n",
            "that's that evening now na leave\n",
            "\n",
            "send you didn't nead me in the ga\n",
            "\n",
            "she longer me ow\n",
            "\n",
            "back bydible after my proviess in the sgound of loww\n",
            "\n",
            "little better to you to my feet of more\n",
            "\n",
            "see the things the wild floe pain his out\n",
            "\n",
            "but but if baby\n",
            "\n",
            "well you did it a fa-la bla-la held do\n",
            "\n",
            "hold me along how her dead\n",
            "\n",
            "good just not tell you down\n",
            "\n",
            "write that is in freen't alway\n",
            "\n",
            "spease in the light\n",
            "\n",
            "i me tall you sit compoad\n",
            "\n",
            "someone the sun \n",
            "\n",
            "do\n",
            "\n",
            "that you know homeone no now\n",
            "\n",
            "she becked me mad to me\n",
            "\n",
            "christmas is so my would ret\n",
            "\n",
            "you know you no\n",
            "\n",
            "i know you i had aloight to real\n",
            "\n",
            "three I'm down I'm roadiy\n",
            "\n",
            "boy you're gonna-moom\n",
            "\n",
            "say you know my name nah on\n",
            "\n",
            "it's so things to get good\n",
            "\n",
            "i can't heep you were do\n",
            "\n",
            "but it's all the word porgines\n",
            "\n",
            "man polyong in the stree\n",
            "\n",
            "perase not kind the bibtwas\n",
            "\n",
            "i dig no can kndeen' night you make me hold\n",
            "\n",
            "i'm just a show\n",
            "\n",
            "as I new she changed your dosaine\n",
            "\n",
            "we baby cry\n",
            "\n",
            "seing me more day's girl\n",
            "\n",
            "all around hearter leave me man\n",
            "\n",
            "say to heaven mean to the blinks a face\n",
            "\n",
            "good nudibng so thing but time\n",
            "\n",
            "oh you feeling the cold\n",
            "\n",
            "so the stag\n",
            "\n",
            "how dreamers lough to wrong\n",
            "\n",
            "here someone we lose it love\n",
            "\n",
            "all together nows\n",
            "\n",
            "you say\n",
            "\n",
            "was it is will down you\n",
            "\n",
            "teddy's rolly fadling I'm twice good must to heee a said doing\n",
            "\n",
            "please let chile sail love\n",
            "\n",
            "a the mun and with in \n",
            "\n",
            "here magicat feel be a pain't bed\n",
            "\n",
            "i want you \n",
            "\n",
            "it baby head\n",
            "\n",
            "did I want up \n",
            "\n",
            "someday you don't need\n",
            "\n",
            "and a right come our your caccoo\n",
            "\n",
            "if please don't have some now\n",
            "\n",
            "i got a sain and happy pie\n",
            "\n",
            "you shout to much eon't real me so my\n",
            "\n",
            "i sady looks the cill like you\n",
            "\n",
            "cry\n",
            "\n",
            "you know my name\n",
            "\n",
            "if you lifetime plesed\n",
            "\n",
            "can't you go our in the man I want to be in my had\n",
            "\n",
            "to be I've sween would low to be\n",
            "\n",
            "he i'm an telling the one in my win\n",
            "\n",
            "i'm down I'm silver blings in my eyes\n",
            "\n",
            "it's always to gonna go\n",
            "\n",
            "it's been to with on sounly away\n",
            "\n",
            "ristucion fastio of you\n",
            "\n",
            "you'll be in the nunging must plans\n",
            "\n",
            "somelises homebod that prease don't badak\n",
            "\n",
            "we eave it is my window girl poart\n",
            "\n",
            "and it's making bast singing like it looking that playing closed\n",
            "\n",
            "it's true you to smile\n",
            "\n",
            "i'm just come oor to me\n",
            "\n",
            "i couldn't be night and I've been is blue\n",
            "\n",
            "\"werepartand she was born\n",
            "\n",
            "when I wask when I get you one love\n",
            "\n",
            "everywhere away\n",
            "\n",
            "sa-caling words on scrooution\n",
            "\n",
            "they heavy needs you and I'm going to chack to be the hilleges\n",
            "\n",
            "i think you love me a comfoas away\n",
            "\n",
            "tell me nowwhe could never babe get nob letter\n",
            "\n",
            "ob look yight been here's nothing to holk this back girl\n",
            "\n",
            "all you need man somehel's maken in the dinkes of you\n",
            "\n",
            "eight till me that you know me\n",
            "\n",
            "feel up out on the party\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}