TF-IDF Search Engine (SKLEARN).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Packages Needed\n",
    "1. Numpy\n",
    "2. Pandas\n",
    "3. Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download dataset from https://www.kaggle.com/mousehead/songlyrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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</th>\n",
       "      <th>song</th>\n",
       "      <th>link</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABBA</td>\n",
       "      <td>Ahe's My Kind Of Girl</td>\n",
       "      <td>/a/abba/ahes+my+kind+of+girl_20598417.html</td>\n",
       "      <td>Look at her face, it's a wonderful face  \\nAnd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABBA</td>\n",
       "      <td>Andante, Andante</td>\n",
       "      <td>/a/abba/andante+andante_20002708.html</td>\n",
       "      <td>Take it easy with me, please  \\nTouch me gentl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBA</td>\n",
       "      <td>As Good As New</td>\n",
       "      <td>/a/abba/as+good+as+new_20003033.html</td>\n",
       "      <td>I'll never know why I had to go  \\nWhy I had t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABBA</td>\n",
       "      <td>Bang</td>\n",
       "      <td>/a/abba/bang_20598415.html</td>\n",
       "      <td>Making somebody happy is a question of give an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABBA</td>\n",
       "      <td>Bang-A-Boomerang</td>\n",
       "      <td>/a/abba/bang+a+boomerang_20002668.html</td>\n",
       "      <td>Making somebody happy is a question of give an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  artist                   song                                        link  \\\n",
       "0   ABBA  Ahe's My Kind Of Girl  /a/abba/ahes+my+kind+of+girl_20598417.html   \n",
       "1   ABBA       Andante, Andante       /a/abba/andante+andante_20002708.html   \n",
       "2   ABBA         As Good As New        /a/abba/as+good+as+new_20003033.html   \n",
       "3   ABBA                   Bang                  /a/abba/bang_20598415.html   \n",
       "4   ABBA       Bang-A-Boomerang      /a/abba/bang+a+boomerang_20002668.html   \n",
       "\n",
       "                                                text  \n",
       "0  Look at her face, it's a wonderful face  \\nAnd...  \n",
       "1  Take it easy with me, please  \\nTouch me gentl...  \n",
       "2  I'll never know why I had to go  \\nWhy I had t...  \n",
       "3  Making somebody happy is a question of give an...  \n",
       "4  Making somebody happy is a question of give an...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('songdata.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF-IDF Search Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get tf-idf matrix using fit_transform function\n",
    "vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.6 s, sys: 220 ms, total: 12.9 s\n",
      "Wall time: 13.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "X = vectorizer.fit_transform(df['text']) # Store tf-idf representations of all docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(57650, 82385)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape) # (Number of songs, Number of unique words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"Take it easy with me, please\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 72 ms, sys: 7.63 ms, total: 79.7 ms\n",
      "Wall time: 79.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "query_vec = vectorizer.transform([query]) # Ip -- (n_docs,x), Op -- (n_docs,n_Feats)\n",
    "results = cosine_similarity(X,query_vec).reshape((-1,)) # Op -- (n_docs,1) -- Cosine Sim with each doc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Guns N' Roses -- It's So Easy\n",
      "Linda Ronstadt -- It's So Easy (To Fall In Love)\n",
      "Kris Kristofferson -- Easy, Come On\n",
      "Lorde -- Easy\n",
      "Kiss -- Easy As It Seems\n",
      "Ne-Yo -- Make It Easy\n",
      "Rolling Stones -- It's Not Easy\n",
      "Frank Zappa -- Easy Meat\n",
      "Billy Joel -- Easy Money\n",
      "Stevie Wonder -- Please, Please, Please\n"
     ]
    }
   ],
   "source": [
    "# Print Top 10 results\n",
    "for i in results.argsort()[-10:][::-1]:\n",
    "    print(df.iloc[i,0],\"--\",df.iloc[i,1])"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}