distances (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def distance(a,b):\n",
    "       \n",
    "    #distance=1-np.dot(a,b)/np.sqrt(np.dot(a,a)*np.dot(b,b))\n",
    "    return abs(a-b)\n",
    "\n",
    "def Similarity(Id1, Id2):\n",
    "    a = df.iloc[Id1]\n",
    "    b = df.iloc[Id2]\n",
    "    extra=0\n",
    "    genre=0\n",
    "    if a['artists']==b['artists']:\n",
    "        return 1000;\n",
    "    if abs(a['year']-b['year'])>10:\n",
    "        extra=extra+100\n",
    "    for i in a['genres']:\n",
    "        if i in b['genres']:\n",
    "            genre = genre+1\n",
    "    if genre == 0:\n",
    "        extra = extra+100\n",
    "    \n",
    "    aA = a['acousticness']\n",
    "    aB = b['acousticness']\n",
    "    aDistance = distance(aA, aB)\n",
    "    \n",
    "    bA = a['danceability']\n",
    "    bB = b['danceability']\n",
    "    bDistance = distance(bA, bB)\n",
    "    \n",
    "    cA = a['energy']\n",
    "    cB = b['energy']\n",
    "    cDistance = distance(cA, cB)\n",
    "    \n",
    "    dA = a['instrumentalness']\n",
    "    dB = b['instrumentalness']\n",
    "    dDistance = distance(dA, dB)\n",
    "    \n",
    "    eA = a['key']\n",
    "    eB = b['key']\n",
    "    eDistance = distance(eA, eB)\n",
    "    \n",
    "    fA = a['liveness']\n",
    "    fB = b['liveness']\n",
    "    fDistance = distance(fA, fB)\n",
    "    \n",
    "    gA = a['loudness']\n",
    "    gB = b['loudness']\n",
    "    gDistance = distance(gA, gB)\n",
    "    \n",
    "    hA = a['popularity']\n",
    "    hB = b['popularity']\n",
    "    hDistance = distance(hA, hB)\n",
    "    \n",
    "    iA = a['speechiness']\n",
    "    iB = b['speechiness']\n",
    "    iDistance = distance(iA, iB)\n",
    "    \n",
    "    jA = a['tempo']\n",
    "    jB = b['tempo']\n",
    "    jDistance = distance(jA, jB)\n",
    "    \n",
    "    kA = a['valence']\n",
    "    kB = b['valence']\n",
    "    kDistance = distance(kA, kB)\n",
    "    return extra+aDistance+bDistance+cDistance+dDistance+eDistance+fDistance+gDistance+hDistance+iDistance+jDistance+kDistance\n",
    "    #return (-0.59*aDistance)+(0.22*bDistance)+(0.5*cDistance)+(-0.3*dDistance)+(0.011*eDistance)+(-0.075*fDistance)+(0.47*gDistance)+hDistance+(-0.14*iDistance)+(0.14*jDistance)+(0.0093*kDistance)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('onewayoranothernew.csv')\n",
    "df = df.drop(['Unnamed: 0','playcount','userid'],axis=1)\n",
    "df = df.drop_duplicates()\n",
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>tempo</th>\n",
       "      <th>loudness</th>\n",
       "      <th>energy</th>\n",
       "      <th>id</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>popularity</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>valence</th>\n",
       "      <th>year</th>\n",
       "      <th>artists</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>118538</td>\n",
       "      <td>0.556</td>\n",
       "      <td>Lose You To Love Me Selena Gomez</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>-0.001716</td>\n",
       "      <td>0.343</td>\n",
       "      <td>4l0Mvzj72xxOpRrp6h8nHi</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0436</td>\n",
       "      <td>0.0978</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>Selena Gomez</td>\n",
       "      <td>['dance pop', 'pop', 'post-teen pop']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        acousticness                              name  danceability  \\\n",
       "118538         0.556  Lose You To Love Me Selena Gomez         0.488   \n",
       "\n",
       "           tempo  loudness  energy                      id  instrumentalness  \\\n",
       "118538  0.002063 -0.001716   0.343  4l0Mvzj72xxOpRrp6h8nHi               0.0   \n",
       "\n",
       "        key  liveness  popularity  speechiness  valence    year       artists  \\\n",
       "118538  4.0      0.21        85.0       0.0436   0.0978  2020.0  Selena Gomez   \n",
       "\n",
       "                                       genres  \n",
       "118538  ['dance pop', 'pop', 'post-teen pop']  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['name']=='Lose You To Love Me Selena Gomez']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lose You To Love Me Selena Gomez\n"
     ]
    }
   ],
   "source": [
    "query_index1=  118538 ##np.random.choice(df.shape[0])\n",
    "print(df.iloc[query_index1]['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "simdict = []\n",
    "for i in range(0, df.shape[0]):\n",
    "    simdict.append((df.iloc[i]['name'],abs(Similarity(query_index1,i))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = pd.DataFrame.from_dict(simdict)\n",
    "new.columns = [\"name\",\"score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>87178</td>\n",
       "      <td>Bruises Lewis Capaldi</td>\n",
       "      <td>0.984689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130450</td>\n",
       "      <td>Surrender Natalie Taylor</td>\n",
       "      <td>1.769723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129277</td>\n",
       "      <td>No Time To Die Billie Eilish</td>\n",
       "      <td>1.786006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80344</td>\n",
       "      <td>The Less I Know The Better Tame Impala</td>\n",
       "      <td>1.846898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86952</td>\n",
       "      <td>SLOW DANCING IN THE DARK Joji</td>\n",
       "      <td>2.404250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82698</td>\n",
       "      <td>Photograph Ed Sheeran</td>\n",
       "      <td>2.432453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102337</td>\n",
       "      <td>Level of Concern Twenty One Pilots</td>\n",
       "      <td>2.481449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87177</td>\n",
       "      <td>Before You Go Lewis Capaldi</td>\n",
       "      <td>2.530370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129259</td>\n",
       "      <td>ocean eyes Billie Eilish</td>\n",
       "      <td>2.535599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129301</td>\n",
       "      <td>Falling Harry Styles</td>\n",
       "      <td>2.611920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102612</td>\n",
       "      <td>No Idea Don Toliver</td>\n",
       "      <td>2.828608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          name     score\n",
       "87178                    Bruises Lewis Capaldi  0.984689\n",
       "130450                Surrender Natalie Taylor  1.769723\n",
       "129277            No Time To Die Billie Eilish  1.786006\n",
       "80344   The Less I Know The Better Tame Impala  1.846898\n",
       "86952            SLOW DANCING IN THE DARK Joji  2.404250\n",
       "82698                    Photograph Ed Sheeran  2.432453\n",
       "102337      Level of Concern Twenty One Pilots  2.481449\n",
       "87177              Before You Go Lewis Capaldi  2.530370\n",
       "129259                ocean eyes Billie Eilish  2.535599\n",
       "129301                    Falling Harry Styles  2.611920\n",
       "102612                     No Idea Don Toliver  2.828608"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new=new.sort_values('score')\n",
    "new.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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