sandbox-RL-deeprank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from importlib import reload "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deeprank.dataset import DataLoader, PairGenerator, ListGenerator\n",
    "from deeprank import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7faf7004caf0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 1234\n",
    "torch.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[./data/letor/r5w/word_dict.txt]\n",
      "\tWord dict size: 193367\n",
      "[./data/letor/r5w/qid_query.txt]\n",
      "\tData size: 1692\n",
      "[./data/letor/r5w/docid_doc.txt]\n",
      "\tData size: 65323\n",
      "[./data/letor/r5w/embed_wiki-pdc_d50_norm]\n",
      "\tEmbedding size: 109282\n",
      "[./data/letor/r5w/embed.idf]\n",
      "\tEmbedding size: 193367\n",
      "Generate numpy embed: (193368, 50)\n",
      "Generate numpy embed: (193368, 1)\n"
     ]
    }
   ],
   "source": [
    "loader = DataLoader('./config/letor07_mp_fold1.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "letor_config = json.loads(open('./config/letor07_mp_fold1.model').read())\n",
    "#device = torch.device(\"cuda\")\n",
    "#device = torch.device(\"cpu\")\n",
    "select_device = torch.device(\"cpu\")\n",
    "rank_device = torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[./data/letor/r5w/relation.train.fold1.txt]\n",
      "\tInstance size: 47828\n",
      "Pair Instance Count: 325439\n"
     ]
    }
   ],
   "source": [
    "Letor07Path = letor_config['data_dir']\n",
    "\n",
    "letor_config['fill_word'] = loader._PAD_\n",
    "letor_config['embedding'] = loader.embedding\n",
    "letor_config['feat_size'] = loader.feat_size\n",
    "letor_config['vocab_size'] = loader.embedding.shape[0]\n",
    "letor_config['embed_dim'] = loader.embedding.shape[1]\n",
    "letor_config['pad_value'] = loader._PAD_\n",
    "\n",
    "pair_gen = PairGenerator(rel_file=Letor07Path + '/relation.train.fold%d.txt'%(letor_config['fold']), \n",
    "                         config=letor_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deeprank import select_module\n",
    "from deeprank import rank_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nletor_config['max_match'] = 20\\nletor_config['win_size'] = 5\\nselect_net = select_module.QueryCentricNet(config=letor_config, out_device=rank_device)\\nselect_net = select_net.to(select_device)\\nselect_net.train()\\n\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "letor_config['max_match'] = 20\n",
    "letor_config['win_size'] = 5\n",
    "select_net = select_module.QueryCentricNet(config=letor_config, out_device=rank_device)\n",
    "select_net = select_net.to(select_device)\n",
    "select_net.train()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "letor_config['q_limit'] = 20\n",
    "letor_config['d_limit'] = 2000\n",
    "\n",
    "letor_config['max_match'] = 5\n",
    "letor_config['win_size'] = 5\n",
    "\n",
    "letor_config['q_rep_dim'] = 10\n",
    "letor_config['d_rep_dim'] = 10\n",
    "letor_config['q_rep_kernel'] = 3\n",
    "letor_config['d_rep_kernel'] = letor_config['win_size']*2+1\n",
    "\n",
    "letor_config['finetune_embed'] = False\n",
    "letor_config['lr'] = 0.0001\n",
    "select_net = select_module.GroupPointerNet(config=letor_config, out_device=rank_device)\n",
    "select_net = select_net.to(select_device)\n",
    "select_net.embedding.weight.data.copy_(torch.from_numpy(loader.embedding))\n",
    "select_net.train()\n",
    "select_optimizer = optim.RMSprop(select_net.parameters(), lr=letor_config['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "letor_config[\"dim_q\"] = 1\n",
    "letor_config[\"dim_d\"] = 1\n",
    "letor_config[\"dim_weight\"] = 1\n",
    "letor_config[\"c_reduce\"] = [1, 1]\n",
    "letor_config[\"k_reduce\"] = [1, 50]\n",
    "letor_config[\"s_reduce\"] = 1\n",
    "letor_config[\"p_reduce\"] = [0, 0]\n",
    "\n",
    "letor_config[\"c_en_conv_out\"] = 4\n",
    "letor_config[\"k_en_conv\"] = 3\n",
    "letor_config[\"s_en_conv\"] = 1\n",
    "letor_config[\"p_en_conv\"] = 1\n",
    "\n",
    "letor_config[\"en_pool_out\"] = [1, 1]\n",
    "letor_config[\"en_leaky\"] = 0.2\n",
    "\n",
    "letor_config[\"dim_gru_hidden\"] = 3\n",
    "\n",
    "letor_config['lr'] = 0.005\n",
    "letor_config['finetune_embed'] = False\n",
    "\n",
    "rank_net = rank_module.DeepRankNet(config=letor_config)\n",
    "rank_net = rank_net.to(rank_device)\n",
    "rank_net.embedding.weight.data.copy_(torch.from_numpy(loader.embedding))\n",
    "rank_net.qw_embedding.weight.data.copy_(torch.from_numpy(loader.idf_embedding))\n",
    "rank_net.train()\n",
    "rank_optimizer = optim.Adam(rank_net.parameters(), lr=letor_config['lr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_device(*variables, device):\n",
    "    return (torch.from_numpy(variable).to(device) for variable in variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_device_raw(*variables, device):\n",
    "    return (variable.to(device) for variable in variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_text(x):\n",
    "    print(' '.join([loader.word_dict[w.item()] for w in x]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vdgif regulation regulation action vac final home regulation regulation action vac final vac game turkey vac game turkey open season county area monday october hunting days following thanksgiving monday closest december five hunting days following vac game turkey open season spring season bearded turkey vac game turkey continuous closed season county city area vac game turkey tag turkey obtain official game check card licensee vac new game turkey validate tag check turkey licensee vac game turkey tag turkey obtain official game tag person exempt license requirement vac new game turkey check turkey person exempt license requirement holding license authorization number summary vac open season county area monday october hunting days following thanksgiving monday closest december five hunting days following amendment increase fall turkey hunting season six hunting days county charles city gloucester james city king george lancaster middlesex new kent northumberland richmond westmoreland york camp peary mathew county previous closed vac open season spring season bearded turkey amendment add youth hunting opportunity special youth spring gobbler season saturday april allow hunting turkey half hour sunrise sunset days spring season vac continuous closed season county city area amendment remove mathew county county closed fall turkey hunting vac tag turkey obtain official game check card licensee amendment make section effective june section vac govern tag turkey commence july vac validate tag check turkey licensee amendment establish new validate system tag turkey notch appropriate turkey tag detach tag bear deer turkey license ii establish telecheck system report harvest spring turkey describe procedure properly check spring turkey telecheck system iii specify check cards written documentation attach harvest turkey iv mistaken validate tag delay effective date new section july vac tag turkey obtain official game tag person exempt license requirement amendment make section effective june section vac govern tag turkey commence july vac check turkey person exempt license requirement holding license authorization number amendment establish provisions person exempt hunting license holding license authorization number issue telephone report harvest spring turkey department telecheck system ii describe proper process turkey carcass telecheck check station iii prohibit destruction turkey sex identification prior check license exempt hunter iv delay effective date new section july vac seq game turkey vac open season county area monday october hunting days following thanksgiving monday closest december five hunting days following shall lawful hunt turkey monday october consecutive hunting days following thanksgiving monday closest december five hunting days following county charles city gloucester james city king george lancaster mathew middlesex new kent northumberland richmond westmoreland york camp peary vac open season spring season bearded turkey provided section shall lawful hunt bearded turkey second saturday april consecutive hunting days following dates inclusive hour sunrise noon prevailing time hunting days hour sunrise sunset hunting days spring season turkey hunter years age hunt saturday april half hour sunrise noon prevailing time compliance applicable license requirement accompany directly supervise adult valid virginia hunting license person adult exempt purchase hunting license adult hunter accompany youth hunter assist calling shall carry discharge weapon bearded turkey hunt calling shall unlawful use dogs organized drive purpose hunting e shall unlawful use possession shot large number fine shot hunting turkey shotgun vac continuous closed season county city area shall continuous closed turkey season special spring season bearded turkey provided vac county accomack arlington mathew northampton city chesapeake hampton newport news suffolk virginia beach vac tag turkey obtain official game check card licensee section effective june detach game tag license shall unlawful person detach game tag license hunt turkey prior killing turkey tag detached tag shall subject confiscate representative department immediate tag carcass person killing turkey shall remove carcass place kill detach special license hunting turkey appropriate tag shall attach tag carcass kill place kill shall define location animal reduce possession presentation tag carcass check obtain official game check card killing turkey tag provided licensee shall vehicle transport carcass conclusion legal hunting hour occur unnecessary delay present tag carcass authorize check station appropriate representative department county adjoin county turkey kill time tag attach carcass shall exchange official game check card shall secure attach carcass remain attach carcass process e destruction identity turkey prior tag tag check forfeiture untagg turkey shall unlawful person destroy identity sex turkey kill tag checked require section turkey tag require section found possession person shall forfeit commonwealth disposed provided law vac validate tag check turkey licensee section effective july person killing turkey shall remove carcass place kill validate appropriate tag special license hunting turkey notch designate area completely remove notch adjacent tag left margin license place kill shall define location animal reduce possession shall unlawful person validate notch turkey tag special license hunting bear deer turkey prior killing turkey turkey tag mistaken validate notch prior killing turkey immediately void licensee writing ink word void line provided adjacent notch license tag killing turkey validate notch license tag provided licensee shall vehicle transport carcass conclusion legal hunting hour occur unnecessary delay present carcass validate notch license tag authorize check station appropriate representative department county adjoin county turkey kill report spring kill provided vac department toll free telecheck system time person check report carcass given game check card telecheck confirmation number successful hunter shall immediately record game check card number telecheck confirmation number ink line provided adjacent license tag validate notch field checked big game check station game check card kept possession carcass carcass process carcass left unattended game check card secure attach carcass spring season kill report use telecheck check card require long hunter kill animal possession carcass telecheck report spring carcass left unattended transfer possession individual written documentation successful hunter full name date animal kill telecheck confirmation number create kept possession carcass carcass process carcass left unattended written documentation secure attach carcass shall unlawful person destroy identity sex turkey kill license tag validate notch checked require section turkey found possession person validate notch license tag documentation turkey checked big game check station telecheck require section shall forfeit commonwealth disposed provided law vac tag turkey obtain official game tag person exempt license requirement holding license authorization number section effective june killing turkey person exempt license requirement describe code virginia issue complimentary license prescribed holder permanent license issue pursuant e holder virginia license authorization number issue telephone electronic media agent pursuant shall vehicle transport carcass conclusion legal hunting hour come unnecessary delay present carcass authorize check station appropriate representative department county adjoin county turkey kill time person shall given official game check card furnish department shall secure attach carcass remain attach carcass process vac check turkey person exempt license requirement holding license authorization number section effective july killing turkey person exempt license requirement describe code virginia issue complimentary license prescribed holder permanent license issue pursuant e holder virginia license authorization number issue telephone electronic media agent pursuant shall vehicle transport carcass conclusion legal hunting hour come unnecessary delay present carcass authorize check station appropriate representative department county adjoin county turkey kill report ir spring kill provided vac department toll free telecheck system time person check report carcass shall given game check card furnish epartment telecheck confirmation number checked big game check station game check card kept possession carcass carcass process carcass left unattended game check card secure attach carcass spring season ill report use telecheck report system check card require long hunter kill animal possession carcass telecheck reporte spring ca left unattended transfer possession individual written documentation successful hunter full name date animal kill telecheck confirmation number create kept possession carcass carcass process carcass left unattended written documentation secure attach carcass successful hunter shall immediately create written documentation successful hunter full name date animal kill telecheck confirmation number written documentation kept possession carcass carcass process telecheck report carcass transfer possession individual written documentation transfer carcass individual kept possession carcass carcass process carcass left unattended written documentation secure attach carcass shall unlawful person destroy identity sex turkey kill turkey checked require section return regulation action index vac vdgif please view privacy policy contact dgifweb dgif state va comment question www dgif state va regulation final html $ [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "virginia turkey season $$ [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n",
      "0 carcass left unattended written documentation secure attach carcass successful hunter shall\n",
      "1 pursuant e holder virginia license authorization number issue telephone electronic media\n",
      "2 hunt saturday april half hour sunrise noon prevailing time compliance applicable\n",
      "3 carcass given game check card telecheck confirmation number successful hunter shall\n",
      "4 identity sex turkey kill license tag validate notch checked require section\n"
     ]
    }
   ],
   "source": [
    "X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F = \\\n",
    "        pair_gen.get_batch(data1=loader.query_data, data2=loader.doc_data)\n",
    "X1, X1_len, X2, X2_len, Y, F = \\\n",
    "        to_device(X1, X1_len, X2, X2_len, Y, F, device=select_device)\n",
    "\n",
    "show_text(X2[0])\n",
    "\n",
    "X1, X2_new, X1_len, X2_len_new, X2_pos = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "\n",
    "show_text(X1[0])\n",
    "for i in range(5):\n",
    "    print(i, end=' ')\n",
    "    show_text(X2_new[0][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overseas employment information teacher print friendly version overseas employment information teacher following list organization information compile help foreign service family member interested teaching career overseas addition contact organization mention handout wish contact directly school located city country assign overseas school assist department state america sponsor overseas school assist department state office overseas school opr os nonprofit non denominational independent school establish cooperative basis america citizen living overseas school received grant assistance technical support opr os private institution school hire staff hire foreign service spouse opr os update publish annual directory school information organization involved recruit teacher administrator international school world opr os information university offering teacher certificate professional development program overseas location office overseas school department state washington dc tel fax e mail state gov web site http www state gov os foreign affair spouse teacher training fast train george mason university arlington campus offer following fast train program alternative licensure program prepare teacher international assignment program design bachelor degree teaching credential interested teaching overseas completion six education course taken period successful completion national teacher exam participant receive provisional virginia teaching certificate pk level foreign service spouse found certificate useful obtain teaching position develop nation master degree multilingual multicultural education emphasis teaching second language master program multilingual multicultural education emphasis teaching english second language specifically design licensed teacher educator working abroad plan overseas teach information contact dr jack levy fast train program graduate school education george mason university fairfax va tel fax email gmu edu web site http gse gmu edu program department defense dependent school dodd department defense recruit teacher school military bases overseas contact dodd arrival post possible application overseas position considered united states dodd personnel center recruit unit north fairfax drive arlington va tel ext fax main web site http www edu web site personnel center http www edu fulbright teacher exchange program fulbright program offer teacher opportunity switch position teacher overseas applicant current teaching citizen years full time teaching experience fulbright teacher exchange program maryland avenue sw room washington dc tel fax email fulbright grad usda gov web site http exchange state gov education fulbright peace corps peace corps use volunteer teacher elementary secondary normal vocational school university teaching experience require application made united states recruit office peace corps wilson blvd suite arlington va tel fax web site http www peacecorp gov indexf cfm institute international education library institute international education post list overseas teaching position information available phone fax mail picked person new york institute international education united nation plaza new york ny tel fax web site http www iie org international school service iss iss publish directory maintain placement service overseas school international school service roszel road princeton nj tel fax web site http www iss edu national association independent school nai contact nai teaching opportunity overseas independent school member base group national association independent school street nw suite washington dc tel fax web site http www iie org tutor special education option foreign service spouse individual group tutor student enroll non america school small high school offer full range course tutor attend class school hour post education allowance supplement instruction allowance pay program supplementary instruction allowance use set advanced placement course family pool allowance pay tutor teach course family children special education post school offer special education program require service teacher tutor carry comprehensive program special educational allowance available pay tutor increase employment option spouse overseas consider acquire necessary training teach tutor children learning disability lab school washington offer training program learning disabled ld tutor annual program start fall lab school washington reservoir road nw washington dc tel fax web site http www labschool org resources chronicle higher education weekly newspaper large classified section advertising teaching position overseas publish chronicle higher education street nw suite washington dc tel email help com web site http chronicle com international educator newsletter list employment opportunity america international school publish international educator institute po box west bridgewater ma tel fax email tie capecod net web site www com international school directory publish annual europe council international school available purchase http www eci org publication publication htm information provided family liaison office contact family liaison office return education youth back homepage site manage bureau public affair department state external links internet site construe endorse view contained www state gov dghr flo rsrc pub htm $ [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] 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[PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "employment overseas $$ [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n",
      "0 city country assign overseas school assist department state america sponsor overseas\n",
      "1 friendly version overseas employment information teacher following list organization information compile\n",
      "2 school university teaching experience require application made united states recruit office\n",
      "3 edu program department defense dependent school dodd department defense recruit teacher\n",
      "4 fall lab school washington reservoir road nw washington dc tel fax\n"
     ]
    }
   ],
   "source": [
    "X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F = \\\n",
    "        pair_gen.get_batch(data1=loader.query_data, data2=loader.doc_data)\n",
    "X1, X1_len, X2, X2_len, Y, F = \\\n",
    "        to_device(X1, X1_len, X2, X2_len, Y, F, device=select_device)\n",
    "\n",
    "show_text(X2[0])\n",
    "\n",
    "X1, X2_new, X1_len, X2_len_new, X2_pos = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "\n",
    "show_text(X1[0])\n",
    "for i in range(5):\n",
    "    print(i, end=' ')\n",
    "    show_text(X2_new[0][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([15])\n",
      "200\n",
      "200\n",
      "tensor([  7.,   1.,  72.,  50., 117.,  37., 137.,  23.,  70.,  38., 124., 118.,\n",
      "        137., 130.,  23.], device='cuda:0')\n",
      "tensor([40., 17.,  5., 39., 31., 27.,  9., 70., 40., 60., 27., 75., 48., 77.,\n",
      "        36.], device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "print(X2_pos[0].shape)\n",
    "print(len(X2_pos))\n",
    "print(len(X2))\n",
    "print(X2_pos[0])\n",
    "print(X2_pos[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X1 = X1[:1]\n",
    "# X1_len = X1_len[:1]\n",
    "# X2 = X2[:1]\n",
    "# X2_len = X2_len[:1]\n",
    "# X1_id = X1_id[:1]\n",
    "# X2_id = X2_id[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show_text(X2[0])\n",
    "# X1, X2_new, X1_len, X2_len_new = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "# show_text(X1[0])\n",
    "# for i in range(5):\n",
    "#     print(i, end=' ')\n",
    "#     show_text(X2_new[0][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([20, 1])\n",
      "torch.Size([2000])\n"
     ]
    }
   ],
   "source": [
    "print(X2_pos[i][:,None].shape)\n",
    "print(X2[i].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "rank loss: 0.8442378044128418\n",
      "select loss: -69.44200897216797\n",
      "rank loss: 0.8088563084602356\n",
      "select loss: -85.60549926757812\n",
      "rank loss: 0.9127975106239319\n",
      "select loss: -79.61895751953125\n",
      "rank loss: 0.645484983921051\n",
      "select loss: -114.34009552001953\n",
      "rank loss: 0.9042335152626038\n",
      "select loss: -80.81549072265625\n",
      "rank loss: 0.7666140794754028\n",
      "select loss: -151.45555114746094\n",
      "rank loss: 0.6412215828895569\n",
      "select loss: -131.70028686523438\n",
      "rank loss: 0.7767679691314697\n",
      "select loss: -83.80870819091797\n",
      "rank loss: 0.8134841918945312\n",
      "select loss: -70.04034423828125\n",
      "rank loss: 0.7386025190353394\n",
      "select loss: -126.91075897216797\n",
      "rank loss: 0.7262380719184875\n",
      "select loss: -116.73443603515625\n",
      "rank loss: 0.7222622036933899\n",
      "select loss: -92.78812408447266\n",
      "rank loss: 0.8247576951980591\n",
      "select loss: -55.074649810791016\n",
      "rank loss: 0.8189899921417236\n",
      "select loss: -120.32659149169922\n",
      "rank loss: 0.7387807965278625\n",
      "select loss: -125.11555480957031\n",
      "rank loss: 0.79836505651474\n",
      "select loss: -89.19682312011719\n",
      "rank loss: 0.9185656309127808\n",
      "select loss: -44.897918701171875\n",
      "rank loss: 0.9041879177093506\n",
      "select loss: -46.09493637084961\n",
      "rank loss: 0.8115279674530029\n",
      "select loss: -110.14952087402344\n",
      "rank loss: 0.7376706004142761\n",
      "select loss: -117.9314956665039\n",
      "rank loss: 0.6430310606956482\n",
      "select loss: -126.9104995727539\n",
      "rank loss: 0.9034228324890137\n",
      "select loss: -53.87717819213867\n",
      "rank loss: 0.7977203130722046\n",
      "select loss: -104.16277313232422\n",
      "rank loss: 0.8655745387077332\n",
      "select loss: -74.82989501953125\n",
      "rank loss: 0.9177729487419128\n",
      "select loss: -70.04027557373047\n",
      "rank loss: 0.8434641361236572\n",
      "select loss: -64.65274810791016\n",
      "rank loss: 0.8154888153076172\n",
      "select loss: -118.53054809570312\n",
      "rank loss: 0.7360569834709167\n",
      "select loss: -112.54276275634766\n",
      "rank loss: 0.684194803237915\n",
      "select loss: -148.46092224121094\n",
      "rank loss: 0.9295551776885986\n",
      "select loss: -67.6451644897461\n",
      "rank loss: 0.9059615731239319\n",
      "select loss: -73.6324234008789\n",
      "rank loss: 0.7896173596382141\n",
      "select loss: -64.65280151367188\n",
      "rank loss: 0.8127787113189697\n",
      "select loss: -110.14917755126953\n",
      "rank loss: 0.7180639505386353\n",
      "select loss: -129.9045867919922\n",
      "rank loss: 0.7393569946289062\n",
      "select loss: -91.5904769897461\n",
      "rank loss: 0.8779252171516418\n",
      "select loss: -74.8291015625\n",
      "rank loss: 0.8767220973968506\n",
      "select loss: -92.78894805908203\n",
      "rank loss: 0.8335110545158386\n",
      "select loss: -77.22461700439453\n",
      "rank loss: 0.8164446353912354\n",
      "select loss: -44.299434661865234\n",
      "rank loss: 0.808557391166687\n",
      "select loss: -105.95892333984375\n",
      "rank loss: 0.7803648114204407\n",
      "select loss: -107.75459289550781\n",
      "rank loss: 0.8190893530845642\n",
      "select loss: -58.666290283203125\n",
      "rank loss: 0.8012383580207825\n",
      "select loss: -76.62542724609375\n",
      "rank loss: 0.8297333121299744\n",
      "select loss: -96.3808364868164\n",
      "rank loss: 0.846717894077301\n",
      "select loss: -82.01347351074219\n",
      "rank loss: 0.796245813369751\n",
      "select loss: -114.93822479248047\n",
      "rank loss: 0.6745870113372803\n",
      "select loss: -105.95829772949219\n",
      "rank loss: 0.813469648361206\n",
      "select loss: -131.69956970214844\n",
      "rank loss: 0.775388777256012\n",
      "select loss: -79.0202407836914\n",
      "rank loss: 0.6964038610458374\n",
      "select loss: -140.08172607421875\n",
      "rank loss: 0.6709896922111511\n",
      "select loss: -121.52374267578125\n",
      "rank loss: 0.8154292106628418\n",
      "select loss: -68.84295654296875\n",
      "rank loss: 0.828205406665802\n",
      "select loss: -92.78910064697266\n",
      "rank loss: 0.8763160705566406\n",
      "select loss: -43.10245132446289\n",
      "rank loss: 0.8460870385169983\n",
      "select loss: -56.27216720581055\n",
      "rank loss: 1.0098342895507812\n",
      "select loss: -61.060943603515625\n",
      "rank loss: 0.7784695029258728\n",
      "select loss: -55.67306137084961\n",
      "rank loss: 0.8094311356544495\n",
      "select loss: -73.0342025756836\n",
      "rank loss: 0.7625141739845276\n",
      "select loss: -110.14952850341797\n",
      "rank loss: 0.7880483865737915\n",
      "select loss: -89.19723510742188\n",
      "rank loss: 0.7453616857528687\n",
      "select loss: -101.76854705810547\n",
      "rank loss: 0.7086552381515503\n",
      "select loss: -116.13571166992188\n",
      "rank loss: 0.8416290283203125\n",
      "select loss: -85.60511016845703\n",
      "rank loss: 0.809723973274231\n",
      "select loss: -69.4421157836914\n",
      "rank loss: 0.753241240978241\n",
      "select loss: -116.1355972290039\n",
      "rank loss: 0.8470271229743958\n",
      "select loss: -76.02606964111328\n",
      "rank loss: 0.9152376055717468\n",
      "select loss: -77.2241439819336\n",
      "rank loss: 0.6978883147239685\n",
      "select loss: -122.12237548828125\n",
      "rank loss: 0.9428797960281372\n",
      "select loss: -65.85022735595703\n",
      "rank loss: 0.8841556906700134\n",
      "select loss: -66.44889831542969\n",
      "rank loss: 0.7067204117774963\n",
      "select loss: -105.95903015136719\n",
      "rank loss: 0.6971861720085144\n",
      "select loss: -157.44039916992188\n",
      "rank loss: 0.6132460832595825\n",
      "select loss: -139.48165893554688\n",
      "rank loss: 0.7575796246528625\n",
      "select loss: -141.27691650390625\n",
      "rank loss: 0.6988586187362671\n",
      "select loss: -130.50262451171875\n",
      "rank loss: 0.7585976719856262\n",
      "select loss: -56.87071228027344\n",
      "rank loss: 0.7691135406494141\n",
      "select loss: -75.4281997680664\n",
      "rank loss: 0.9311650395393372\n",
      "select loss: -81.41474151611328\n",
      "rank loss: 0.9127925038337708\n",
      "select loss: -88.00010681152344\n",
      "rank loss: 0.9522081613540649\n",
      "select loss: -53.877464294433594\n",
      "Time Cost: 5020.199316263199 s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "rank_loss_list = []\n",
    "select_loss_list = []\n",
    "\n",
    "start_t = time.time()\n",
    "for i in range(5000):\n",
    "    # One Step Forward\n",
    "    X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F = \\\n",
    "        pair_gen.get_batch(data1=loader.query_data, data2=loader.doc_data)\n",
    "    X1, X1_len, X2, X2_len, Y, F = \\\n",
    "        to_device(X1, X1_len, X2, X2_len, Y, F, device=select_device)\n",
    "    X1, X2, X1_len, X2_len, X2_pos = select_net(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "    X2, X2_len = utils.data_adaptor(X2, X2_len, select_net, rank_net, letor_config)\n",
    "    output = rank_net(X1, X2, X1_len, X2_len, X2_pos)\n",
    "    reward = rank_net.pair_reward(output, mode=0)\n",
    "    \n",
    "    # Update Rank Net\n",
    "    rank_loss = rank_net.pair_loss(output, Y)\n",
    "    print('rank loss:', rank_loss.item())\n",
    "    rank_loss_list.append(rank_loss.item())\n",
    "    if i % 1 == 0:\n",
    "        rank_optimizer.zero_grad()\n",
    "        rank_loss.backward()\n",
    "        rank_optimizer.step()\n",
    "    \n",
    "    # Update Select Net\n",
    "    select_loss = select_net.loss(reward)\n",
    "    print('select loss:', select_loss.item())\n",
    "    select_loss_list.append(select_loss.item())\n",
    "    select_optimizer.zero_grad()\n",
    "    select_loss.backward()\n",
    "    select_optimizer.step()\n",
    "    \n",
    "end_t = time.time()\n",
    "print('Time Cost: %s s' % (end_t-start_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(select_loss_list)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(rank_loss_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "PicklingError",
     "evalue": "Can't pickle <class 'deeprank.select_module.group_pointer_net.GroupPointerNet'>: it's not the same object as deeprank.select_module.group_pointer_net.GroupPointerNet",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPicklingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-aca37c6977c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselect_net\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"qcentric.model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrank_net\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"deeprank.model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/nips/venv/lib/python3.6/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \"\"\"\n\u001b[0;32m--> 219\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_with_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\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    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/nips/venv/lib/python3.6/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_with_file_like\u001b[0;34m(f, mode, body)\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnew_fd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/nips/venv/lib/python3.6/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \"\"\"\n\u001b[0;32m--> 219\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_with_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\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    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/nips/venv/lib/python3.6/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, f, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m    290\u001b[0m     \u001b[0mpickler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\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[1;32m    291\u001b[0m     \u001b[0mpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpersistent_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpersistent_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 292\u001b[0;31m     \u001b[0mpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0mserialized_storage_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mserialized_storages\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPicklingError\u001b[0m: Can't pickle <class 'deeprank.select_module.group_pointer_net.GroupPointerNet'>: it's not the same object as deeprank.select_module.group_pointer_net.GroupPointerNet"
     ]
    }
   ],
   "source": [
    "torch.save(select_net, \"qcentric.model\")\n",
    "torch.save(rank_net, \"deeprank.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "select_net_e = torch.load(f='qcentric.model')\n",
    "rank_net_e = torch.load(f='deeprank.model')\n",
    "\n",
    "list_gen = ListGenerator(rel_file=Letor07Path+'/relation.test.fold%d.txt'%(letor_config['fold']),\n",
    "                         config=letor_config)\n",
    "map_v = 0.0\n",
    "map_c = 0.0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for X1, X1_len, X1_id, X2, X2_len, X2_id, Y, F in \\\n",
    "        list_gen.get_batch(data1=loader.query_data, data2=loader.doc_data):\n",
    "        #print(X1.shape, X2.shape, Y.shape)\n",
    "        X1, X1_len, X2, X2_len, Y, F = to_device(X1, X1_len, X2, X2_len, Y, F, device=select_device)\n",
    "        X1, X2, X1_len, X2_len, X2_pos = select_net_e(X1, X2, X1_len, X2_len, X1_id, X2_id)\n",
    "        X2, X2_len = utils.data_adaptor(X2, X2_len, select_net, rank_net, letor_config)\n",
    "        #print(X1.shape, X2.shape, Y.shape)\n",
    "        pred = rank_net_e(X1, X2, X1_len, X2_len, X2_pos)\n",
    "        map_o = utils.eval_MAP(pred.tolist(), Y.tolist())\n",
    "        #print(pred.shape, Y.shape)\n",
    "        map_v += map_o\n",
    "        map_c += 1.0\n",
    "    map_v /= map_c\n",
    "\n",
    "print('[Test]', map_v)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Reward v0: 0.4359386539000405\n",
    "Reward v1: 0.42572616969349864\n",
    "Reward v2: 0.4245778777643799"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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