Logistic_Regression_Model.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Logistic Regression Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KritikaIyer17/Song_Genre_Classifier/blob/master/Logistic_Regression_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CyIWIHgOGFsG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_Tg3qyxGx1m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer,TfidfTransformer\n",
        "from sklearn.metrics import accuracy_score\n",
        "from nltk.corpus import stopwords\n",
        "import re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_keH80PBOszC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/data/lyrics1.csv\")\n",
        "df['ranker_genre'] = np.where((df['ranker_genre'] == 'screamo')|(df['ranker_genre'] == 'punk rock')|(df['ranker_genre'] == 'heavy metal'),'alt rock',df['ranker_genre'])\n",
        "df['ranker_genre'] = np.where((df['ranker_genre']== 'rythm and blues'),'pop',df['ranker_genre'])\n",
        "group = ['song', 'year', 'album', 'genre', 'artist', 'ranker_genre']\n",
        "lyrics_by_song = df.sort_values(group).groupby(group).lyric.apply(' '.join).apply(lambda x: x.lower()).reset_index(name='lyric')\n",
        "lyrics_by_song[\"lyric\"] = lyrics_by_song['lyric'].str.replace(r'[^\\w\\s]','')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yIY5b6yIrSR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "REPLACE_BY_SPACE_RE = re.compile('[/(){}\\[\\]\\|@,;|\\n]')\n",
        "BAD_SYMBOLS_RE = re.compile('[^0-9a-z #+_]')\n",
        "STOPWORDS = set(stopwords.words('english'))\n",
        "lyrics = lyrics_by_song[\"lyric\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7HCFHSyImQd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def clean_text(text):\n",
        "    text = text.lower() \n",
        "    text = REPLACE_BY_SPACE_RE.sub(' ', text) \n",
        "    text = BAD_SYMBOLS_RE.sub('', text) \n",
        "    text = ' '.join(word for word in text.split() if word not in STOPWORDS) \n",
        "    return text\n",
        "lyrics = lyrics.apply(clean_text)\n",
        "lyrics_by_song[\"lyric\"]=lyrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUuC7i72JFQm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "63eacf77-c8ef-4579-f73c-0eaeed6b7e42"
      },
      "source": [
        "X = lyrics_by_song.lyric\n",
        "y = lyrics_by_song.ranker_genre\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 42)\n",
        "logreg = Pipeline([('vect', CountVectorizer()),('tfidf', TfidfTransformer()),('clf', LogisticRegression(n_jobs=1, multi_class = \"multinomial\",  C=100000, solver = \"sag\", class_weight=\"balanced\")),])\n",
        "logreg.fit(X_train, y_train)\n",
        "y_pred = logreg.predict(X_test)\n",
        "print(accuracy_score(y_pred, y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0.8125167587279455\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}