song_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os.path\n",
    "from collections import OrderedDict\n",
    "\n",
    "from models.Song import ComparableSong\n",
    "from utils.utils import Utils\n",
    "\n",
    "utils = Utils()\n",
    "\n",
    "if not (os.path.exists(os.path.join(\"data\", \"output\", \"songs.out\")) \n",
    "        and os.path.exists(os.path.join(\"data\", \"output\", \"duplicates.out\"))):\n",
    "    \n",
    "    # Generate files\n",
    "    import main\n",
    "    main.main()\n",
    "songs = pickle.load(open(os.path.join(\"data\", \"output\", \"songs.out\"), \"rb\"))\n",
    "\n",
    "duplicates = pickle.load(open(os.path.join(\"data\", \"output\", \"duplicates.out\"), \"rb\"))\n",
    "server_ids = utils.get_server_ids()\n",
    "\n",
    "\n",
    "class Server:\n",
    "    def __init__(self, uid):\n",
    "        self.uid = uid\n",
    "        self.index_times = 0\n",
    "        self.first_place = 0\n",
    "    \n",
    "    @property\n",
    "    def relative_score(self):\n",
    "        if self.index_times:\n",
    "            return self.first_place/self.index_times\n",
    "        return 0\n",
    "    \n",
    "    def add_first_place(self):\n",
    "        self.first_place += 1\n",
    "        \n",
    "    def add_index(self):\n",
    "        self.index_times += 1\n",
    "\n",
    "servers = {uid: Server(uid) for uid in server_ids}\n",
    "\n",
    "# Make scores\n",
    "\n",
    "scoring_table = []\n",
    "for duplicate_songs in duplicates:\n",
    "    temp = OrderedDict()\n",
    "    all_servers = []  # for easier searching\n",
    "    for comparable_song in duplicate_songs:\n",
    "        temp.setdefault(comparable_song.song.bit_rate, []).append(comparable_song.song.server)\n",
    "        all_servers.append(comparable_song.song.server)\n",
    "    temp[\"all\"] = all_servers\n",
    "    scoring_table.append(temp)\n",
    "\n",
    "for sample in scoring_table:\n",
    "    sample = sample  # type: OrderedDict\n",
    "    keys = list(sample.keys())\n",
    "    for server_uid in sample[keys[0]]:\n",
    "        servers[server_uid].add_first_place()\n",
    "    for server_uid in sample[\"all\"]:\n",
    "        servers[server_uid].add_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NvyaSQsuXL2fHHXfkjDPOYPHixfTt25fx48fTqlUrLr/8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\nDGV5yOi8pAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4961e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.set_xticks(np.arange(len(servers)) + width/2)\n",
    "ax.set_xticklabels(list(server_dict[s] for s in server_ids))\n",
    "\n",
    "ax.set_ylabel('Najbolj')\n",
    "ax.set_title('Točkovanje glede število najbolj kvalitetnih zmag')\n",
    "\n",
    "for rect in ax.bar(range(len(servers)), [servers[k].first_place for k in servers], color=\"blue\", width=width):\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n",
    "                '{0}'.format(height),\n",
    "                ha=\"center\", va=\"bottom\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zgornji graf prikazuje, kolikokrat je posamezni strežnik imel skladbo najboljše kvalitete. Vidimo, da največkrat najboljšo kvaliteto glasbe zagotovi Bolgarski server klav.ma3x, ni pa presenetljivo, da je server upload (glasba, ki jo naložijo uporabniki sami) prav tako uvrščen visoko, saj je v osnovi namenjen ravno temu, da se dopolni zbirka z bolj kvalitetno glasbo. Kratek komantar si zaslužita še oba izključno slovenska strežnika, narodnjak.si in zlatizvoki.com, kjer očitno način določanja podobnik skladb ni bil dovolj dober, ali pa je podobne skladbe vseeno po klaviteti prekosil kateri izmed drugi strežnikov (zanimivo, večina strežnikov, za katere bi po poimenovanju sicer trdili, da večinoma zastopajo glasbo držav bivše jugoslavije je zelo dobro založena tudi s slovensko narodno-zabavno glasbo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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55BH22GOPLevq6+sZM2YMAwYMoLm5mYggImhubu7y2CVJkiR1HxZrJEmSJO2S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\neXSY0t8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1018e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "server_dict = utils.get_server_dict()\n",
    "server_ids = utils.get_server_ids()\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (len(servers)*1.5, 6)\n",
    "\n",
    "width = 0.5\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.set_xticks(np.arange(len(servers)) + width/2)\n",
    "ax.set_xticklabels(list(server_dict[s] for s in server_ids))\n",
    "\n",
    "ax.set_ylabel('najbolj/ponovitve')\n",
    "ax.set_title('Točkovanje glede na razmerje najbolj kvalitetne skladbe')\n",
    "\n",
    "ind = 0\n",
    "for rect in ax.bar(range(len(servers)), [servers[k].relative_score for k in servers], color=\"blue\", width=width):\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n",
    "                '{:.0%}'.format(height),\n",
    "                ha=\"center\", va=\"bottom\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}