Pagerank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_train = pd.read_csv('./input/train.csv').fillna(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Generating a graph of Questions and their neighbors\n",
    "def generate_qid_graph_table(row):\n",
    "\n",
    "    hash_key1 = row[\"qid1\"]\n",
    "    hash_key2 = row[\"qid2\"]\n",
    "        \n",
    "    qid_graph.setdefault(hash_key1, []).append(hash_key2)\n",
    "    qid_graph.setdefault(hash_key2, []).append(hash_key1)\n",
    "\n",
    "qid_graph = {}\n",
    "df_train.apply(generate_qid_graph_table, axis = 1); #You should apply this on df_test too. Avoiding here on the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       " 1000: 1.8589675665928657e-06,\n",
       " ...}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pagerank_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "def pagerank():\n",
    "\n",
    "    MAX_ITER = 20 #Let me know if you find an optimal iteration number!\n",
    "    d = 0.85\n",
    "    \n",
    "    #Initializing -- every node gets a uniform value!\n",
    "    pagerank_dict = {i:1/len(qid_graph) for i in qid_graph}\n",
    "    num_nodes = len(pagerank_dict)\n",
    "    \n",
    "    for iter in range(0, MAX_ITER):\n",
    "        print(iter)\n",
    "        \n",
    "        for node in qid_graph:    \n",
    "            local_pr = 0\n",
    "            \n",
    "            for neighbor in qid_graph[node]:\n",
    "                local_pr += pagerank_dict[neighbor]/len(qid_graph[neighbor])\n",
    "            \n",
    "            pagerank_dict[node] = (1-d)/num_nodes + d*local_pr\n",
    "\n",
    "    return pagerank_dict\n",
    "\n",
    "pagerank_dict = pagerank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_pagerank_value(row):\n",
    "    return pd.Series({\n",
    "        \"pr_q1\": pagerank_dict[row[\"qid1\"]],\n",
    "        \"pr_q2\": pagerank_dict[row[\"qid2\"]],\n",
    "        \"pr_diff\": abs(pagerank_dict[row[\"qid1\"]] - pagerank_dict[row[\"qid2\"]])\n",
    "    })\n",
    "\n",
    "pagerank_feats_train = df_train.apply(get_pagerank_value, axis = 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pagerank_feats_train.to_csv('./datasets/pr_train.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3366657625615588e-06"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pagerank_feats_train['pr_q2'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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