04_week/project/nlp_lyrics_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f0324ecd",
   "metadata": {},
   "source": [
    "# NLP Application: Song Lyrics Classification\n",
    "\n",
    "*Natural Language Processing (NLP)*\n",
    "\n",
    "## Goal\n",
    "\n",
    "Classify a song lyric to an artist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2cc79975",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e986b59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99e7309c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.metrics import plot_confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e468af",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4f6a243",
   "metadata": {},
   "outputs": [],
   "source": [
    "json_files = [\n",
    "    \"data/lyrics_stevie_wonder.json\"\n",
    "    ,\"data/lyrics_jamiroquai.json\"\n",
    "    #,\"data/lyrics_zero_7.json\"\n",
    "    #,\"data/lyrics_ray_charles.json\"\n",
    "    #,\"data/lyrics_james_brown.json\"\n",
    "    #,\"data/lyrics_creedence_clearwater_revival.json\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3bdf5da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "lyrics = []\n",
    "for json_file in json_files:\n",
    "    with open(json_file, encoding='ascii') as file:\n",
    "        lyrics.extend(json.load(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c5efb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(lyrics[0]['song_title'], '', lyrics[0]['lyrics'][:300]+'...', sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fceacf9b",
   "metadata": {},
   "outputs": [
    {
     "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>artist_found</th>\n",
       "      <th>song_title</th>\n",
       "      <th>lyrics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stevie Wonder</td>\n",
       "      <td>What Christmas Means to Me</td>\n",
       "      <td>Oh yeah\\nCandles burnin' low\\nLots of mistleto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stevie Wonder</td>\n",
       "      <td>The Christmas Song</td>\n",
       "      <td>Chestnuts roasting on an open fire\\nJack Frost...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stevie Wonder</td>\n",
       "      <td>Someday At Christmas</td>\n",
       "      <td>Someday at Christmas men won't be boys\\nPlayin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stevie Wonder</td>\n",
       "      <td>We Can Work It Out</td>\n",
       "      <td>Try to see it my way,\\nDo I have to keep on ta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stevie Wonder</td>\n",
       "      <td>Faith</td>\n",
       "      <td>See the girl with the diamonds and the shoes? ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    artist_found                  song_title  \\\n",
       "0  Stevie Wonder  What Christmas Means to Me   \n",
       "1  Stevie Wonder          The Christmas Song   \n",
       "2  Stevie Wonder        Someday At Christmas   \n",
       "3  Stevie Wonder          We Can Work It Out   \n",
       "4  Stevie Wonder                       Faith   \n",
       "\n",
       "                                              lyrics  \n",
       "0  Oh yeah\\nCandles burnin' low\\nLots of mistleto...  \n",
       "1  Chestnuts roasting on an open fire\\nJack Frost...  \n",
       "2  Someday at Christmas men won't be boys\\nPlayin...  \n",
       "3  Try to see it my way,\\nDo I have to keep on ta...  \n",
       "4  See the girl with the diamonds and the shoes? ...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data=lyrics)\n",
    "df.drop(['artist_searched'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e20c72",
   "metadata": {},
   "source": [
    "## Transform: Term Frequency-Inverse Document Frequency (TF-IDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8be0129",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = df['lyrics']\n",
    "\n",
    "# ignore words with numbers, thanks to https://stackoverflow.com/a/29375664\n",
    "token_pattern = r\"(?u)\\b[^\\d\\W]{2,}\\b\"\n",
    "\n",
    "vectorizer = TfidfVectorizer(token_pattern=token_pattern)\n",
    "X = vectorizer.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a9251f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aa',\n",
       " 'aaah',\n",
       " 'aah',\n",
       " 'aaow',\n",
       " 'aaw',\n",
       " 'abaout',\n",
       " 'abbiam',\n",
       " 'abc',\n",
       " 'ability',\n",
       " 'able',\n",
       " 'about',\n",
       " 'above',\n",
       " 'absurdity',\n",
       " 'abuse',\n",
       " 'abused']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names()[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b65d2c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0].todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d2e3449",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bow = pd.DataFrame(data=X.todense(), columns=vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76e35df1",
   "metadata": {},
   "outputs": [
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       "    aa  aaah  aah  aaow  aaw  abaout  abbiam  abc  ability  able  ...  \\\n",
       "0  0.0   0.0  0.0   0.0  0.0     0.0     0.0  0.0      0.0   0.0  ...   \n",
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       "\n",
       "       your  yours  yourself  yourselves  yuletide  zero  zillion  zimbabwe  \\\n",
       "0  0.018872    0.0       0.0         0.0  0.000000   0.0      0.0       0.0   \n",
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       "4  0.000000    0.0       0.0         0.0  0.000000   0.0      0.0       0.0   \n",
       "\n",
       "   zone  zones  \n",
       "0   0.0    0.0  \n",
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       "2   0.0    0.0  \n",
       "3   0.0    0.0  \n",
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       "\n",
       "[5 rows x 5107 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f91faf3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.todense(), vmax=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ce38dbda",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(df['artist_found'].unique()) == 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2afcf486",
   "metadata": {},
   "source": [
    "## Train/Test split\n",
    "\n",
    "Define X and y:\n",
    "\n",
    "* X : Training data\n",
    "* y : Target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fff7f10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X\n",
    "y = df['artist_found']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c6d33c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((463, 5107), (347, 5107), (116, 5107))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "X.shape, X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef538713",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ffbdd7",
   "metadata": {},
   "source": [
    "### Logistic Regression\n",
    "\n",
    "#### Train and evaluate\n",
    "\n",
    "Class balancing using `class_weight='balanced'` was especially important here. Otherwise log reg predictet mostly one class for all!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad86b05e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight='balanced', max_iter=1000)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_logreg = LogisticRegression(max_iter=1000, class_weight='balanced')\n",
    "m_logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0dbb0d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9957492d00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = m_logreg.predict(X_test)\n",
    "plot_confusion_matrix(m_logreg, X_test, y_test, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "165ff9a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "   Jamiroquai       0.60      0.54      0.57        28\n",
      "Stevie Wonder       0.86      0.89      0.87        88\n",
      "\n",
      "     accuracy                           0.80       116\n",
      "    macro avg       0.73      0.71      0.72       116\n",
      " weighted avg       0.80      0.80      0.80       116\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970f1148",
   "metadata": {},
   "source": [
    "#### Phrase classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "66f9af18",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_phrase_stevie = \"Just don't you feel too bad\" # Stevie Wonder (Don't You Worry 'bout a Thing)\n",
    "v_phrase_stevie = vectorizer.transform([test_phrase_stevie])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f368bb77",
   "metadata": {},
   "outputs": [
    {
     "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>bad</th>\n",
       "      <th>don</th>\n",
       "      <th>feel</th>\n",
       "      <th>just</th>\n",
       "      <th>too</th>\n",
       "      <th>you</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.618667</td>\n",
       "      <td>0.322437</td>\n",
       "      <td>0.435079</td>\n",
       "      <td>0.307195</td>\n",
       "      <td>0.433551</td>\n",
       "      <td>0.204098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        bad       don      feel      just       too       you\n",
       "0  0.618667  0.322437  0.435079  0.307195  0.433551  0.204098"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_phrase = pd.DataFrame(data=v_phrase_stevie.todense(), columns=vectorizer.get_feature_names())\n",
    "df_phrase.loc[:, df_phrase.gt(0).all()] # show only columns with values greater than"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a38af1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Stevie Wonder'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_logreg.predict(v_phrase_stevie)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a641f58",
   "metadata": {},
   "source": [
    "### Naive Bayes Classifier\n",
    "\n",
    "Note that this is without any class balancing techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5799103c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=MultinomialNB(),\n",
       "             param_grid={'alpha': array([1.00000000e-05, 2.04179592e-02, 4.08259184e-02, 6.12338776e-02,\n",
       "       8.16418367e-02, 1.02049796e-01, 1.22457755e-01, 1.42865714e-01,\n",
       "       1.63273673e-01, 1.83681633e-01, 2.04089592e-01, 2.24497551e-01,\n",
       "       2.44905510e-01, 2.65313469e-01, 2.85721429e-01, 3.06129388e-01,\n",
       "       3.26537347e-01, 3.46945306e-01, 3.67353265e-01...\n",
       "       5.71432857e-01, 5.91840816e-01, 6.12248776e-01, 6.32656735e-01,\n",
       "       6.53064694e-01, 6.73472653e-01, 6.93880612e-01, 7.14288571e-01,\n",
       "       7.34696531e-01, 7.55104490e-01, 7.75512449e-01, 7.95920408e-01,\n",
       "       8.16328367e-01, 8.36736327e-01, 8.57144286e-01, 8.77552245e-01,\n",
       "       8.97960204e-01, 9.18368163e-01, 9.38776122e-01, 9.59184082e-01,\n",
       "       9.79592041e-01, 1.00000000e+00])},\n",
       "             scoring='f1_weighted')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = dict(alpha=np.linspace(0.00001,1,50))\n",
    "m_bayes = GridSearchCV(estimator=MultinomialNB(), param_grid=param_grid, scoring='f1_weighted')\n",
    "m_bayes.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "34e10c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.020417959183673468}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_bayes.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b65b2d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8240018476083023, 0.059522303802718744)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_bayes.cv_results_['mean_test_score'][m_bayes.best_index_], m_bayes.cv_results_['std_test_score'][m_bayes.best_index_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "59de260a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f9953169d90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = m_bayes.predict(X_test)\n",
    "plot_confusion_matrix(m_bayes, X_test, y_test, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e99eb3f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "   Jamiroquai       0.76      0.68      0.72        28\n",
      "Stevie Wonder       0.90      0.93      0.92        88\n",
      "\n",
      "     accuracy                           0.87       116\n",
      "    macro avg       0.83      0.81      0.82       116\n",
      " weighted avg       0.87      0.87      0.87       116\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6974d9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Stevie Wonder', array([[0.25573919, 0.74426081]]))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_bayes.predict(v_phrase_stevie)[0], m_bayes.predict_proba(v_phrase_stevie)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}