Paper Paper layer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import csv\n",
    "import nltk\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "import re\n",
    "import warnings\n",
    "import random\n",
    "warnings.filterwarnings('ignore')\n",
    "from collections import Counter,defaultdict\n",
    "from nltk import word_tokenize\n",
    "from math import log\n",
    "import string\n",
    "import pickle\n",
    "from math import log\n",
    "\n",
    "import gensim\n",
    "from gensim import corpora\n",
    "from gensim import models\n",
    "from string import digits\n",
    "from collections import defaultdict\n",
    "import ast\n",
    "cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "osname = os.name\n",
    "if osname =='Windows':\n",
    "    sym = \"//\"\n",
    "else:\n",
    "    sym = \"/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_path=cwd+sym+'nips_reviewer_data'+sym+'reviewers.txt'\n",
    "review_df=pd.read_csv(r_path,sep='\\t',header=None)\n",
    "review_df.columns=['sno','name']\n",
    "review_lt=list(review_df.name)\n",
    "review_sno={val:ind for ind,val in enumerate(review_lt)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df=pd.read_pickle('data_df.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#paper_local.to_pickle('paper_net.pkl')\n",
    "paper_net=pd.read_pickle('paper_net.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path1=cwd+sym+'Saved_Items'+sym\n",
    "with open(path1+'paper_vec.pkl','rb') as f:\n",
    "    paper_vec=pickle.load(f)\n",
    "with open(path1+'paper_id.pkl','rb') as f:\n",
    "    paper_id=pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2sn={}\n",
    "for sn,pid in enumerate(paper_id):\n",
    "    id2sn[pid]=sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "paper_vec2={}\n",
    "for pid in paper_id:\n",
    "    samp_df=data_df[data_df['id']==pid]\n",
    "    index_lt=samp_df.index\n",
    "    op=[]\n",
    "    for ind in index_lt:\n",
    "        op.append(paper_vec[ind])\n",
    "    paper_vec2[id2sn[pid]]=np.array((sum(op)/len(op)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(paper_vec2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "paper_auth={}\n",
    "for pid in paper_id:\n",
    "    samp_df=data_df[data_df['id']==pid]\n",
    "    index_lt=samp_df.index\n",
    "    op=[]\n",
    "    for ind in index_lt:\n",
    "        op.append(review_sno[samp_df['author'][ind]])\n",
    "    paper_auth[id2sn[pid]]=op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''path1=cwd+sym+'Saved_Items'+sym\n",
    "with open(path1+'paper_auth.pkl','wb') as f:\n",
    "    pickle.dump(paper_auth,f)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''path1=cwd+sym+'Saved_Items'+sym\n",
    "with open(path1+'paper_vec2.pkl','wb') as f:\n",
    "    pickle.dump(paper_vec2,f)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''path1=cwd+sym+'Saved_Items'+sym\n",
    "with open(path1+'paper_id.pkl','wb') as f:\n",
    "    pickle.dump(paper_id,f)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_list={}\n",
    "for ind,pid in enumerate(paper_id):\n",
    "    samp_df=paper_net[paper_net['src']==pid]\n",
    "    n=len(samp_df)\n",
    "    outlink=[]\n",
    "    for i in range(n):\n",
    "        dest=samp_df['dest'].iloc[i]\n",
    "        if dest in paper_id:\n",
    "            outlink.append(id2sn[dest])\n",
    "    if len(outlink):\n",
    "        adj_list[ind]=outlink\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''with open('adj_list.pkl','wb') as f:\n",
    "    pickle.dump(adj_list,f)'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj_list.pkl','rb') as f:\n",
    "    adj_list=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from igraph import * \n",
    "g=Graph(directed=True)\n",
    "n=len(paper_id)\n",
    "g.add_vertices(n)\n",
    "keys=list(adj_list.keys())\n",
    "edge_lt=[]\n",
    "for key in keys:\n",
    "    for ele in adj_list[key]:\n",
    "        #print((key,ele))\n",
    "        edge_lt.append((key,ele))\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "g.add_edges(edge_lt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc=g.bibcoupling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=len(bc)\n",
    "a=[sum(bc[i])for i in range(m)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cocit=g.cocitation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=len(cocit)\n",
    "b=[sum(cocit[i])for i in range(m)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "g.to_undirected()\n",
    "dist=g.shortest_paths_dijkstra()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cocit_mat=b\n",
    "bc_mat=a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"'with open(path1+'distance.pkl','wb') as f:\\n    pickle.dump(dist,f)\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''path1=cwd+sym+'Saved_Items'+sym\n",
    "with open(path1+'cocit.pkl','wb') as f:\n",
    "    pickle.dump(cocit_mat,f)\n",
    "with open(path1+'bibcoupling.pkl','wb') as f:\n",
    "    pickle.dump(bc_mat,f)\n",
    "with open(path1+'distance.pkl','wb') as f:\n",
    "    pickle.dump(dist,f)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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