removing_duplicates_2.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "### Removing Duplicates Stage 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some products whose titles are not adjacent but very similar.\n",
    "\n",
    "Examples:\n",
    "\n",
    "Titles-1\n",
    "86261.  UltraClub Women's Classic Wrinkle-Free Long Sleeve Oxford Shirt, Pink, XX-Large\n",
    "115042. UltraClub Ladies Classic Wrinkle-Free Long-Sleeve Oxford Light Blue XXL\n",
    "\n",
    "TItles-2\n",
    "75004.  EVALY Women's Cool University Of UTAH 3/4 Sleeve Raglan Tee\n",
    "109225. EVALY Women's Unique University Of UTAH 3/4 Sleeve Raglan Tees\n",
    "120832. EVALY Women's New University Of UTAH 3/4-Sleeve Raglan Tshirt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_pickle('pickels/17k_apperal_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-a849a63e279c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'title'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# This takes significant amount of time...\n",
    "# O(n^2) time.\n",
    "# Takes about an hour to run on a decent computer\n",
    "\n",
    "indices=[]\n",
    "for i,row in data.iterrows():\n",
    "        indices.append(i)\n",
    "        \n",
    "stage2_dedupe_asins=[]\n",
    "\n",
    "while len(indices)!=0:\n",
    "    i=indices.pop()\n",
    "    stage2_dedupe_asins.append(data['asin'].loc[i])\n",
    "    a=data['title'].loc[i].split()\n",
    "    \n",
    "    for j in indices:\n",
    "        b=data['title'].loc[j].split()\n",
    "        length=max(len(a),len(b))\n",
    "        \n",
    "        count=0\n",
    "        \n",
    "        for k in itertools.zip_longest(a,b):\n",
    "            if (k[0]==k[1]):\n",
    "                count+=1\n",
    "        \n",
    "        if (length-count) <3:\n",
    "            indices.remove(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.loc[data['asin'].isin(stage2_dedupe_asins)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Number of data points after stage 2 of dedupe : ', data.shape[0])\n",
    "#from 17k apparels we reduced to 16k apparels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.pickle('pickels/16k_apperal_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_pickle('pickels/16k_apperal_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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