15.4 Train a music recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "import sqlite3\n",
    "from operator import itemgetter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WordSplitter(object):\n",
    "    def __init__(self, filename):\n",
    "        self.filename = filename\n",
    " \n",
    "    def __iter__(self):\n",
    "        with open(self.filename) as fin:\n",
    "            for line in fin:\n",
    "                yield line.split()\n",
    "\n",
    "model = gensim.models.Word2Vec(model_input, min_count=4)"
   ]
  },
  {
   "cell_type": "code",
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    {
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-ea3cc35022fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'zoo/15/songs.word2vec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/proj/notebooks/venv3/lib/python3.5/site-packages/gensim/models/word2vec.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1561\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'syn0norm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'table'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cum_table'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1563\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWord2Vec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1565\u001b[0m     \u001b[0msave\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaveLoad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/proj/notebooks/venv3/lib/python3.5/site-packages/gensim/utils.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fname_or_handle, separately, sep_limit, ignore, pickle_protocol)\u001b[0m\n\u001b[1;32m    490\u001b[0m         \"\"\"\n\u001b[1;32m    491\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m             \u001b[0m_pickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname_or_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"saved %s object\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# `fname_or_handle` does not have write attribute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.save(open('zoo/15/songs.word2vec', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2ZqGzZWWZXEyPxJy6N9QhG The eye of the tiger - version éditée Olivier Bernet - Arnaud Aguergaray - Stéphane Garin - Stéphane Goueytes - Maitane Sebastian - Chiara Mastroianni 39\n",
      "4rr0ol3zvLiEBmep7HaHtx The Eye Of The Tiger Survivor 37\n",
      "0R85QWa6KRzB8p44XXE7ky The Eye of the Tiger Gloria Gaynor 29\n",
      "3GxdO4rTwVfRvLRIZFXJVu The Eye of the Tiger Gloria Gaynor 19\n",
      "1W602jfZkdAsbabmJEYfFi The Eye of the Tiger Gloria Gaynor 5\n",
      "6g197iis9V2HP7gvc5ZpGy I Got the Eye of the Tiger Circus Roar 5\n",
      "00VQxzTLqwqBBE0BuCVeer The Eye Of The Tiger Gloria Gaynor 5\n",
      "28FwycRDU81YOiGgIcxcPq The Eye of the Tiger Gloria Gaynor 5\n",
      "62UagxK6LuPbqUmlygGjcU It's the Eye of the Tiger Be Cult 4\n",
      "6lUHKc9qrIHvkknXIrBq6d The Eye Of The Tiger Survivor 4\n"
     ]
    }
   ],
   "source": [
    "conn = sqlite3.connect('data/songs.db')\n",
    "def find_song(song_name, limit=10):\n",
    "    c = conn.cursor()\n",
    "    c.execute(\"SELECT * FROM songs WHERE UPPER(name) LIKE '%\" + song_name + \"%'\")\n",
    "    res = sorted((x + (model.wv.vocab[x[0]].count,) \n",
    "                  for x in c.fetchall() if x[0] in model.wv.vocab),\n",
    "                 key=itemgetter(-1), reverse=True)\n",
    "    return [*res][:limit]\n",
    "\n",
    "for t in find_song('the eye of the tiger'):\n",
    "    print(*t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6lQMrYnRqv3ILwvpmHWXjT Girls Just Wanna Have Fun Cyndi Lauper 0.9735351204872131\n",
      "3f2UsY38rm3QOkxnzyyAMb Enola Gay - 2003 Remastered Version Orchestral Manoeuvres In The Dark 0.9719518423080444\n",
      "6nzQPViPJEjysZBybcIVKt You're My Heart, You're My Soul Modern Talking 0.9589041471481323\n",
      "1qHuRij9fsG8duSmeupC27 Gold - 2003 Remastered Version Spandau Ballet 0.9566971659660339\n",
      "6r8rLHn2H5KEss31OexCz8 Dolce Vita Ryan Paris 0.9553133249282837\n",
      "2nt2B4wZsOP2VsxyzpjOwQ Karma Chameleon - 2002 Remastered Version Culture Club 0.9531201720237732\n",
      "2u0j4TA6K7zNMAstX48oXK Bette Davis Eyes Kim Carnes 0.9499865770339966\n",
      "7p9Tsoczgmvd50wwnz9kai Walking On Sunshine Katrina & The Waves 0.9481900930404663\n",
      "0RY4siAV10AgtLPIn37sBC Maneater Daryl Hall & John Oates 0.9481032490730286\n",
      "62sd69ls8HdPUlfD75axcN Don't You Want Me - 2002 Remastered Version The Human League 0.9471924901008606\n"
     ]
    },
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     "text": [
      "/home/douwe/proj/notebooks/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "def suggest_songs(song_id):\n",
    "    c = conn.cursor()\n",
    "    similar = dict(model.most_similar([song_id]))\n",
    "    song_ids = ', '.join((\"'%s'\" % x) for x in similar.keys())\n",
    "    c.execute(\"SELECT * FROM songs WHERE id in (%s)\" % song_ids)\n",
    "    res = sorted((rec + (similar[rec[0]],) for rec in c.fetchall()), \n",
    "                 key=itemgetter(-1),\n",
    "                 reverse=True)\n",
    "    return [*res]\n",
    "\n",
    "\n",
    "for t in suggest_songs('4rr0ol3zvLiEBmep7HaHtx'):\n",
    "    print(*t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
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     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = model.wv.vocab['2yqVlvXgZ51ynGqwfYADx4']\n",
    "x.count"
   ]
  }
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