(For paper)Trip_rnn_model_5_fold.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LeeWonSeok\\Anaconda3\\envs\\for_tensor\\lib\\importlib\\_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 88 from C header, got 96 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import sys\n",
    "from modeling.GRU_recommend_concat import *\n",
    "from modeling.evaluation_trip import *\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_spot = pd.read_excel('data_spot_5_fold.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_id = pd.DataFrame(columns=[\"Spot\",\"new_id\"])\n",
    "new_id[\"Spot\"] = data_spot.Spot.unique()\n",
    "new_id[\"new_id\"] = range(data_spot.Spot.nunique())\n",
    "#data_spot = data_spot.merge(new_id,on=\"Spot\")\n",
    "#data_spot = data_spot.sort_values([\"Session_id\",\"Session_num\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "spot = pd.read_excel('../최종DB관리/Spot_VSS/2019.02.13.최종_통합DB2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_id_spot = new_id.merge(spot,on=\"Spot\",how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class args():\n",
    "    is_training = True\n",
    "    retraining = True\n",
    "    layers = 1\n",
    "    rnn_size = [4000] # , 20, 5 ,50\n",
    "    n_epochs = 15\n",
    "    batch_size = 50\n",
    "    dropout_p_hidden= 0.5\n",
    "    learning_rate = 0.00001\n",
    "    decay = 0.96\n",
    "    decay_steps = 5e3\n",
    "    initializer = 'xavier'\n",
    "    init_as_normal = False\n",
    "    freq_key = 'd'\n",
    "    rating_sort = 'f'\n",
    "    reset_after_session = True\n",
    "    session_key = 'Session_id'\n",
    "    attribute_key =['new_id'] # ,\"City_encoder\",\"Tag_encoder\"  ,\"city_sub_encoder\",\n",
    "    item_key = 'Spot'\n",
    "    time_key = 'Session_num'\n",
    "    grad_cap = 0\n",
    "    attribute = [len(new_id_spot) ] # ,new_id_spot.City_encoder.nunique(),new_id_spot.Tag_encoder.nunique() ,spot.city_sub_encoder.nunique(),spot.City_encoder.nunique()\n",
    "    n_items = len(new_id_spot)\n",
    "    max_ckpt_keep = 100\n",
    "    test_model = 9\n",
    "    sigma=0\n",
    "    checkpoint_dir = 'ID_City_Tag'  # 2번에 ID\n",
    "                                        # 3번에 100,20 하고 10번 사용 할것 ID,city_encoder\n",
    "                                        # 4번에 100,20,10 넣고 학습할 거임 ID,city,Tag\n",
    "                                        # 5번 비상 4\n",
    "                                        #  6번\n",
    "    loss = 'top1'\n",
    "    final_act = 'linear'\n",
    "    hidden_act = 'tanh'\n",
    "    item_table = new_id_spot\n",
    "    \n",
    "    \n",
    "    ## new_id_0 13\n",
    "    ## new_id_1 13\n",
    "    ## new_id_2 12\n",
    "    ## new_id_3 12\n",
    "    ## new_id_4 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "fitting model....\n",
      "Epoch 0\tStep 1\tlr: 0.000010\tloss: 0.990615\n",
      "Epoch: 0 cost: 1.0635191202163696\n",
      "Epoch: 1 cost: 1.0466216802597046\n",
      "Epoch: 2 cost: 1.0281829833984375\n",
      "Epoch: 3 cost: 1.0099824666976929\n",
      "Epoch: 4 cost: 0.9959667325019836\n",
      "Epoch: 5 cost: 0.980468213558197\n",
      "Epoch: 6 cost: 0.9659399390220642\n",
      "Epoch: 7 cost: 0.9542955756187439\n",
      "Epoch: 8 cost: 0.9434660077095032\n",
      "Epoch: 9 cost: 0.9334992170333862\n",
      "Epoch: 10 cost: 0.9234046936035156\n",
      "Epoch: 11 cost: 0.9155591130256653\n",
      "Epoch 12\tStep 5000\tlr: 0.000010\tloss: 0.908697\n",
      "Epoch: 12 cost: 0.9095268249511719\n",
      "Epoch: 13 cost: 0.9011585712432861\n",
      "Epoch: 14 cost: 0.8961999416351318\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\2019.01.04~여행앱개발\\modeling\\evaluation_trip.py:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  test_data.sort_values([session_key,time_key],inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 5, 0.32302839116719245, 0.2306063792499123, 539]\n",
      "[0, 0, 10, 0.3594111461619348, 0.2355830954884583, 693]\n",
      "[0, 0, 15, 0.3743427970557308, 0.23676531670222523, 795]\n",
      "[0, 0, 20, 0.38633017875920084, 0.23743358018892485, 869]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-1\n",
      "[0, 1, 5, 0.3766561514195584, 0.2545776375744831, 543]\n",
      "[0, 1, 10, 0.42607781282860147, 0.2614081251147497, 717]\n",
      "[0, 1, 15, 0.444794952681388, 0.2628909640423836, 833]\n",
      "[0, 1, 20, 0.45467928496319665, 0.26343904462093565, 917]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-2\n",
      "[0, 2, 5, 0.4021030494216614, 0.26658254468980014, 532]\n",
      "[0, 2, 10, 0.4637223974763407, 0.27503196301303573, 726]\n",
      "[0, 2, 15, 0.4889589905362776, 0.2770374373844404, 869]\n",
      "[0, 2, 20, 0.5003154574132492, 0.27768282107520903, 945]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-3\n",
      "[0, 3, 5, 0.416403785488959, 0.27254819488257975, 506]\n",
      "[0, 3, 10, 0.4927444794952681, 0.28298733163086476, 745]\n",
      "[0, 3, 15, 0.519453207150368, 0.28513333844564137, 886]\n",
      "[0, 3, 20, 0.5366982124079915, 0.28612053006440685, 975]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-4\n",
      "[0, 4, 5, 0.4256572029442692, 0.27632316859446204, 482]\n",
      "[0, 4, 10, 0.508937960042061, 0.2876178792582577, 751]\n",
      "[0, 4, 15, 0.5415352260778128, 0.2902108173401548, 899]\n",
      "[0, 4, 20, 0.562565720294427, 0.29140564349132475, 984]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-5\n",
      "[0, 5, 5, 0.4325972660357518, 0.27865404837013663, 444]\n",
      "[0, 5, 10, 0.5217665615141955, 0.29066463038071866, 725]\n",
      "[0, 5, 15, 0.559621451104101, 0.29364285089837155, 891]\n",
      "[0, 5, 20, 0.582124079915878, 0.29492311298014456, 983]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-6\n",
      "[0, 6, 5, 0.4336487907465825, 0.27867858394672285, 404]\n",
      "[0, 6, 10, 0.5276550998948475, 0.29131490661459114, 690]\n",
      "[0, 6, 15, 0.5743427970557308, 0.29503871522798963, 870]\n",
      "[0, 6, 20, 0.5987381703470032, 0.29641008232224736, 971]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-7\n",
      "[0, 7, 5, 0.4330178759200841, 0.2790045566070803, 364]\n",
      "[0, 7, 10, 0.5329127234490011, 0.2925336738270491, 653]\n",
      "[0, 7, 15, 0.5840168243953733, 0.2966081552516883, 840]\n",
      "[0, 7, 20, 0.6063091482649843, 0.29786474084569137, 962]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-8\n",
      "[0, 8, 5, 0.4332281808622503, 0.2801226778829303, 312]\n",
      "[0, 8, 10, 0.5375394321766561, 0.2942461569275448, 600]\n",
      "[0, 8, 15, 0.5856992639327024, 0.2981075461517101, 805]\n",
      "[0, 8, 20, 0.6143007360672976, 0.2997238394674204, 946]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-9\n",
      "[0, 9, 5, 0.434279705573081, 0.28107255520504754, 291]\n",
      "[0, 9, 10, 0.5377497371188223, 0.2950654282042295, 547]\n",
      "[0, 9, 15, 0.58801261829653, 0.2990823086248952, 785]\n",
      "[0, 9, 20, 0.6170347003154574, 0.3007247390396054, 913]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-10\n",
      "[0, 10, 5, 0.432807570977918, 0.28008412197686655, 276]\n",
      "[0, 10, 10, 0.5366982124079915, 0.2941188890557976, 510]\n",
      "[0, 10, 15, 0.5899053627760252, 0.29836101916543556, 741]\n",
      "[0, 10, 20, 0.6214511041009464, 0.3001494060570785, 889]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-11\n",
      "[0, 11, 5, 0.434279705573081, 0.28030494216614094, 265]\n",
      "[0, 11, 10, 0.5375394321766561, 0.29411371488658544, 476]\n",
      "[0, 11, 15, 0.5899053627760252, 0.2982623948081362, 700]\n",
      "[0, 11, 20, 0.6231335436382754, 0.3001447416000453, 865]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-12\n",
      "[0, 12, 5, 0.4355415352260778, 0.28082018927444796, 263]\n",
      "[0, 12, 10, 0.5360672975814932, 0.29429456044598024, 452]\n",
      "[0, 12, 15, 0.5926393270241851, 0.2987485642059775, 666]\n",
      "[0, 12, 20, 0.6229232386961093, 0.30046731787971254, 829]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-13\n",
      "[0, 13, 5, 0.4344900105152471, 0.280347003154574, 259]\n",
      "[0, 13, 10, 0.5337539432176656, 0.2937178909418659, 424]\n",
      "[0, 13, 15, 0.5926393270241851, 0.29836499987919535, 626]\n",
      "[0, 13, 20, 0.625236593059937, 0.30021784917837796, 806]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_0/attribute_model-14\n",
      "[0, 14, 5, 0.4368033648790747, 0.28093235191026983, 262]\n",
      "[0, 14, 10, 0.5316508937960042, 0.29372423347186744, 415]\n",
      "[0, 14, 15, 0.5909568874868559, 0.298434255311227, 587]\n",
      "[0, 14, 20, 0.6246056782334385, 0.30033227278744457, 773]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "fitting model....\n",
      "Epoch 0\tStep 1\tlr: 0.000010\tloss: 0.989502\n",
      "Epoch: 0 cost: 1.0652016401290894\n",
      "Epoch: 1 cost: 1.0463807582855225\n",
      "Epoch: 2 cost: 1.0281425714492798\n",
      "Epoch: 3 cost: 1.01095712184906\n",
      "Epoch: 4 cost: 0.9972618818283081\n",
      "Epoch: 5 cost: 0.9804150462150574\n",
      "Epoch: 6 cost: 0.9674733281135559\n",
      "Epoch: 7 cost: 0.9550256729125977\n",
      "Epoch: 8 cost: 0.9451882243156433\n",
      "Epoch: 9 cost: 0.9345470666885376\n",
      "Epoch: 10 cost: 0.9255092740058899\n",
      "Epoch: 11 cost: 0.9179570078849792\n",
      "Epoch 12\tStep 5000\tlr: 0.000010\tloss: 0.910433\n",
      "Epoch: 12 cost: 0.9092759490013123\n",
      "Epoch: 13 cost: 0.9045661687850952\n",
      "Epoch: 14 cost: 0.8976510167121887\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-0\n",
      "[1, 0, 5, 0.33709508881922673, 0.241765935214211, 548]\n",
      "[1, 0, 10, 0.3638453500522466, 0.24533155860742062, 702]\n",
      "[1, 0, 15, 0.374294670846395, 0.24616717370635857, 812]\n",
      "[1, 0, 20, 0.38892371995820274, 0.24700090872926847, 883]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-1\n",
      "[1, 1, 5, 0.38934169278996866, 0.27107628004179707, 534]\n",
      "[1, 1, 10, 0.43845350052246607, 0.2777923736544426, 728]\n",
      "[1, 1, 15, 0.4537095088819227, 0.2790037258689296, 844]\n",
      "[1, 1, 20, 0.4631138975966562, 0.27953595287548133, 917]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-2\n",
      "[1, 2, 5, 0.4167189132706374, 0.2834796238244514, 552]\n",
      "[1, 2, 10, 0.47836990595611284, 0.2918789703272462, 757]\n",
      "[1, 2, 15, 0.4994775339602926, 0.29356587276649976, 879]\n",
      "[1, 2, 20, 0.5128526645768025, 0.2943211902558302, 945]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-3\n",
      "[1, 3, 5, 0.4367816091954023, 0.2916544757924069, 519]\n",
      "[1, 3, 10, 0.5109717868338558, 0.30178890713373446, 754]\n",
      "[1, 3, 15, 0.5366771159874608, 0.3038564599222905, 882]\n",
      "[1, 3, 20, 0.5494252873563218, 0.30457576104353473, 963]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-4\n",
      "[1, 4, 5, 0.44764890282131664, 0.2975722744688263, 481]\n",
      "[1, 4, 10, 0.529362591431557, 0.3088363934915659, 731]\n",
      "[1, 4, 15, 0.5582027168234065, 0.3111656003348793, 888]\n",
      "[1, 4, 20, 0.5732497387669802, 0.3120185068240621, 971]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-5\n",
      "[1, 5, 5, 0.4528735632183908, 0.29911529083942867, 443]\n",
      "[1, 5, 10, 0.5381400208986415, 0.3108019439053921, 720]\n",
      "[1, 5, 15, 0.5755485893416928, 0.31376574429865967, 890]\n",
      "[1, 5, 20, 0.5926854754440961, 0.3147323981328271, 980]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-6\n",
      "[1, 6, 5, 0.4555903866248694, 0.30103796586555215, 408]\n",
      "[1, 6, 10, 0.5471264367816092, 0.31355625217694183, 683]\n",
      "[1, 6, 15, 0.5847439916405434, 0.3165420368555166, 873]\n",
      "[1, 6, 20, 0.6117032392894461, 0.3180806687888853, 978]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 7, 5, 0.45517241379310347, 0.3010031347962384, 382]\n",
      "[1, 7, 10, 0.54858934169279, 0.31388880595777136, 640]\n",
      "[1, 7, 15, 0.593103448275862, 0.31741953506843795, 843]\n",
      "[1, 7, 20, 0.61901776384535, 0.3188877922461005, 969]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-8\n",
      "[1, 8, 5, 0.4549634273772205, 0.3009265064437479, 352]\n",
      "[1, 8, 10, 0.5517241379310345, 0.31416944485910014, 605]\n",
      "[1, 8, 15, 0.5943573667711599, 0.3175545341689543, 807]\n",
      "[1, 8, 20, 0.6234064785788924, 0.3191904585528237, 955]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-9\n",
      "[1, 9, 5, 0.4541274817136886, 0.2997561825148031, 321]\n",
      "[1, 9, 10, 0.5513061650992686, 0.3129596457182663, 558]\n",
      "[1, 9, 15, 0.5979101358411704, 0.316664084783207, 773]\n",
      "[1, 9, 20, 0.6271682340647858, 0.31833381489454055, 927]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-10\n",
      "[1, 10, 5, 0.45454545454545453, 0.29894810170672237, 306]\n",
      "[1, 10, 10, 0.5510971786833856, 0.3119336219336219, 523]\n",
      "[1, 10, 15, 0.599582027168234, 0.31578337719403543, 726]\n",
      "[1, 10, 20, 0.6288401253918495, 0.31744744321275076, 896]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-11\n",
      "[1, 11, 5, 0.45538140020898643, 0.29968652037617566, 287]\n",
      "[1, 11, 10, 0.5496342737722049, 0.3123141099003169, 494]\n",
      "[1, 11, 15, 0.6010449320794148, 0.3163725592879198, 693]\n",
      "[1, 11, 20, 0.6317659352142111, 0.31810867421822697, 859]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-12\n",
      "[1, 12, 5, 0.4535005224660397, 0.298662486938349, 269]\n",
      "[1, 12, 10, 0.5460815047021944, 0.31123890796304593, 461]\n",
      "[1, 12, 15, 0.6025078369905956, 0.3157043032434881, 658]\n",
      "[1, 12, 20, 0.6317659352142111, 0.3173618128256267, 832]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-13\n",
      "[1, 13, 5, 0.45203761755485894, 0.298052943225357, 265]\n",
      "[1, 13, 10, 0.5464994775339603, 0.3109032857308718, 438]\n",
      "[1, 13, 15, 0.599582027168234, 0.3150800981208505, 626]\n",
      "[1, 13, 20, 0.6344827586206897, 0.3170520120216105, 797]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_1/attribute_model-14\n",
      "[1, 14, 5, 0.45141065830721006, 0.298662486938349, 258]\n",
      "[1, 14, 10, 0.5454545454545454, 0.3114676319848733, 428]\n",
      "[1, 14, 15, 0.5981191222570533, 0.3156019546458417, 597]\n",
      "[1, 14, 20, 0.632183908045977, 0.3175329824787058, 769]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "fitting model....\n",
      "Epoch 0\tStep 1\tlr: 0.000010\tloss: 0.989077\n",
      "Epoch: 0 cost: 1.0648990869522095\n",
      "Epoch: 1 cost: 1.0466217994689941\n",
      "Epoch: 2 cost: 1.0291231870651245\n",
      "Epoch: 3 cost: 1.012655258178711\n",
      "Epoch: 4 cost: 0.9967775940895081\n",
      "Epoch: 5 cost: 0.9827354550361633\n",
      "Epoch: 6 cost: 0.9693134427070618\n",
      "Epoch: 7 cost: 0.9562360644340515\n",
      "Epoch: 8 cost: 0.9480650424957275\n",
      "Epoch: 9 cost: 0.9371852278709412\n",
      "Epoch: 10 cost: 0.9283642768859863\n",
      "Epoch: 11 cost: 0.9210776090621948\n",
      "Epoch 12\tStep 5000\tlr: 0.000010\tloss: 0.914500\n",
      "Epoch: 12 cost: 0.9144995808601379\n",
      "Epoch: 13 cost: 0.9051681756973267\n",
      "Epoch: 14 cost: 0.8997394442558289\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-0\n",
      "[2, 0, 5, 0.35402618008726694, 0.252327118868174, 573]\n",
      "[2, 0, 10, 0.3867512891709639, 0.2569429490690894, 714]\n",
      "[2, 0, 15, 0.40519635065450216, 0.25838567532339846, 808]\n",
      "[2, 0, 20, 0.4159063863546212, 0.25898882779686977, 889]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-1\n",
      "[2, 1, 5, 0.40797302657675527, 0.28221274626470977, 577]\n",
      "[2, 1, 10, 0.45973819912733044, 0.2893191634712856, 745]\n",
      "[2, 1, 15, 0.47798492661642206, 0.29076952456067884, 847]\n",
      "[2, 1, 20, 0.48809996033320113, 0.2913412494196448, 919]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-2\n",
      "[2, 2, 5, 0.4341531138437128, 0.2940103133677112, 580]\n",
      "[2, 2, 10, 0.5007933359777866, 0.30324732563923373, 764]\n",
      "[2, 2, 15, 0.525386751289171, 0.30520186323439, 870]\n",
      "[2, 2, 20, 0.5366917889726299, 0.3058350847652064, 953]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-3\n",
      "[2, 3, 5, 0.4478381594605315, 0.3017750892502974, 550]\n",
      "[2, 3, 10, 0.5281634272114241, 0.31284346410784325, 766]\n",
      "[2, 3, 15, 0.5596985323284411, 0.31533537917413373, 895]\n",
      "[2, 3, 20, 0.5717969059896866, 0.3160253752606703, 971]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-4\n",
      "[2, 4, 5, 0.45656485521618406, 0.30484265503107244, 508]\n",
      "[2, 4, 10, 0.5458151527171757, 0.3169890381116085, 765]\n",
      "[2, 4, 15, 0.5805236017453391, 0.3197520162415046, 891]\n",
      "[2, 4, 20, 0.5985719952399842, 0.32077451855335426, 978]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-5\n",
      "[2, 5, 5, 0.46112653708845697, 0.30653510511701726, 452]\n",
      "[2, 5, 10, 0.5561285204284014, 0.31937494884242207, 738]\n",
      "[2, 5, 15, 0.5934153113843713, 0.3223493907696606, 888]\n",
      "[2, 5, 20, 0.6186037286790956, 0.3237626936044517, 985]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-6\n",
      "[2, 6, 5, 0.4625148750495835, 0.30757305302128773, 416]\n",
      "[2, 6, 10, 0.561483538278461, 0.3209328938503868, 681]\n",
      "[2, 6, 15, 0.60710035700119, 0.32451988599350734, 861]\n",
      "[2, 6, 20, 0.631297104323681, 0.3258773864980413, 977]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-7\n",
      "[2, 7, 5, 0.4625148750495835, 0.3076424699193439, 380]\n",
      "[2, 7, 10, 0.5628718762395875, 0.32127793203755123, 641]\n",
      "[2, 7, 15, 0.6114637048790162, 0.3250942730415162, 845]\n",
      "[2, 7, 20, 0.6380404601348671, 0.32660007374798666, 958]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-8\n",
      "[2, 8, 5, 0.4615232050773503, 0.3065251884172945, 340]\n",
      "[2, 8, 10, 0.5652518841729472, 0.3205459851532836, 600]\n",
      "[2, 8, 15, 0.6122570408568029, 0.3242528759089646, 808]\n",
      "[2, 8, 20, 0.64379214597382, 0.32602835575510614, 933]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-9\n",
      "[2, 9, 5, 0.4613248710829036, 0.3058078804707125, 306]\n",
      "[2, 9, 10, 0.5630702102340341, 0.3195697411583965, 556]\n",
      "[2, 9, 15, 0.6160253867512892, 0.32370783498312244, 770]\n",
      "[2, 9, 20, 0.6483538278460929, 0.3255138435616306, 911]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-10\n",
      "[2, 10, 5, 0.46231654105513686, 0.3076656088853629, 291]\n",
      "[2, 10, 10, 0.5604918683062277, 0.32094044943112776, 518]\n",
      "[2, 10, 15, 0.617215390717969, 0.3253795875771281, 734]\n",
      "[2, 10, 20, 0.650535501785006, 0.32725023550365456, 881]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-11\n",
      "[2, 11, 5, 0.4615232050773503, 0.3078374983472167, 277]\n",
      "[2, 11, 10, 0.5587068623562078, 0.32105110720739444, 494]\n",
      "[2, 11, 15, 0.6156287187623959, 0.3255143485805921, 696]\n",
      "[2, 11, 20, 0.6531138437128123, 0.327625515705535, 849]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-12\n",
      "[2, 12, 5, 0.4615232050773503, 0.30742760809202685, 263]\n",
      "[2, 12, 10, 0.5573185243950813, 0.3204283227240386, 459]\n",
      "[2, 12, 15, 0.6134470448234828, 0.32485009426203376, 666]\n",
      "[2, 12, 20, 0.6545021816739389, 0.32716799916187295, 825]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-13\n",
      "[2, 13, 5, 0.4613248710829036, 0.30702432896998544, 256]\n",
      "[2, 13, 10, 0.5553351844506148, 0.3199108441472583, 437]\n",
      "[2, 13, 15, 0.6142403808012693, 0.3245588505108537, 628]\n",
      "[2, 13, 20, 0.6543038476794922, 0.3268071807392059, 795]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_2/attribute_model-14\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 14, 5, 0.4615232050773503, 0.30731852439508134, 254]\n",
      "[2, 14, 10, 0.5567235224117414, 0.3202498221290367, 422]\n",
      "[2, 14, 15, 0.6142403808012693, 0.32479471859182274, 602]\n",
      "[2, 14, 20, 0.6535105117017057, 0.326990740531779, 767]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "fitting model....\n",
      "Epoch 0\tStep 1\tlr: 0.000010\tloss: 0.990209\n",
      "Epoch: 0 cost: 1.0657081604003906\n",
      "Epoch: 1 cost: 1.0458959341049194\n",
      "Epoch: 2 cost: 1.0296493768692017\n",
      "Epoch: 3 cost: 1.010726809501648\n",
      "Epoch: 4 cost: 0.9941440224647522\n",
      "Epoch: 5 cost: 0.9829103946685791\n",
      "Epoch: 6 cost: 0.9692258834838867\n",
      "Epoch: 7 cost: 0.9566265940666199\n",
      "Epoch: 8 cost: 0.9462468028068542\n",
      "Epoch: 9 cost: 0.9359008073806763\n",
      "Epoch: 10 cost: 0.9267097115516663\n",
      "Epoch: 11 cost: 0.918525218963623\n",
      "Epoch: 12 cost: 0.9107794761657715\n",
      "Epoch 13\tStep 5000\tlr: 0.000010\tloss: 0.896059\n",
      "Epoch: 13 cost: 0.9053168892860413\n",
      "Epoch: 14 cost: 0.8986871242523193\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-0\n",
      "[3, 0, 5, 0.33497536945812806, 0.23812479474548445, 514]\n",
      "[3, 0, 10, 0.3694581280788177, 0.24271960278364219, 682]\n",
      "[3, 0, 15, 0.38600985221674877, 0.24402874356076318, 806]\n",
      "[3, 0, 20, 0.3980295566502463, 0.2447089023756504, 870]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-1\n",
      "[3, 1, 5, 0.39369458128078816, 0.2684564860426928, 522]\n",
      "[3, 1, 10, 0.44275862068965516, 0.2752628039721636, 702]\n",
      "[3, 1, 15, 0.4644334975369458, 0.276944954334117, 829]\n",
      "[3, 1, 20, 0.47665024630541875, 0.2776277600637894, 906]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-2\n",
      "[3, 2, 5, 0.4169458128078818, 0.2815632183908046, 500]\n",
      "[3, 2, 10, 0.48137931034482756, 0.2905073891625616, 723]\n",
      "[3, 2, 15, 0.5081773399014778, 0.2926369738635747, 868]\n",
      "[3, 2, 20, 0.5213793103448275, 0.2933697354497579, 953]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-3\n",
      "[3, 3, 5, 0.4315270935960591, 0.28718883415435137, 475]\n",
      "[3, 3, 10, 0.5069950738916256, 0.2976765970756117, 743]\n",
      "[3, 3, 15, 0.5391133004926109, 0.30022939075155813, 890]\n",
      "[3, 3, 20, 0.5523152709359606, 0.3009605619859321, 968]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-4\n",
      "[3, 4, 5, 0.4403940886699507, 0.28944499178981936, 433]\n",
      "[3, 4, 10, 0.5225615763546798, 0.3007923215263117, 721]\n",
      "[3, 4, 15, 0.5611822660098522, 0.30384556056969836, 874]\n",
      "[3, 4, 20, 0.5791133004926108, 0.30486211819093195, 979]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-5\n",
      "[3, 5, 5, 0.44275862068965516, 0.2913464696223316, 400]\n",
      "[3, 5, 10, 0.532807881773399, 0.30363578074908115, 685]\n",
      "[3, 5, 15, 0.5747783251231527, 0.3069230124008449, 865]\n",
      "[3, 5, 20, 0.5990147783251232, 0.308292702684702, 967]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-6\n",
      "[3, 6, 5, 0.443743842364532, 0.2920623973727422, 359]\n",
      "[3, 6, 10, 0.5385221674876848, 0.30496809758386106, 639]\n",
      "[3, 6, 15, 0.5850246305418719, 0.3086652411627781, 840]\n",
      "[3, 6, 20, 0.6133990147783251, 0.3102828483992602, 953]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-7\n",
      "[3, 7, 5, 0.44236453201970444, 0.2916486042692939, 329]\n",
      "[3, 7, 10, 0.5456157635467981, 0.3055090311986861, 596]\n",
      "[3, 7, 15, 0.5927093596059113, 0.3092166601871034, 811]\n",
      "[3, 7, 20, 0.6214778325123153, 0.31085028278314725, 948]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-8\n",
      "[3, 8, 5, 0.44236453201970444, 0.2913727422003285, 308]\n",
      "[3, 8, 10, 0.5462068965517242, 0.3052377824693095, 551]\n",
      "[3, 8, 15, 0.5958620689655172, 0.3091837576763684, 784]\n",
      "[3, 8, 20, 0.6242364532019704, 0.3107936080290927, 931]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-9\n",
      "[3, 9, 5, 0.4433497536945813, 0.2907651888341544, 286]\n",
      "[3, 9, 10, 0.5475862068965517, 0.3046027836421925, 511]\n",
      "[3, 9, 15, 0.5984236453201971, 0.30862523081734894, 733]\n",
      "[3, 9, 20, 0.6283743842364532, 0.31032215653258366, 906]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-10\n",
      "[3, 10, 5, 0.4451231527093596, 0.2911592775041051, 272]\n",
      "[3, 10, 10, 0.5456157635467981, 0.3045381968879504, 483]\n",
      "[3, 10, 15, 0.5992118226600985, 0.30878800619194713, 695]\n",
      "[3, 10, 20, 0.6307389162561576, 0.3105748558380366, 866]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-11\n",
      "[3, 11, 5, 0.44413793103448274, 0.29126765188834175, 257]\n",
      "[3, 11, 10, 0.5416748768472907, 0.3042549847525216, 469]\n",
      "[3, 11, 15, 0.5964532019704434, 0.30858784401148925, 668]\n",
      "[3, 11, 20, 0.6327093596059113, 0.31065276120643753, 832]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-12\n",
      "[3, 12, 5, 0.44236453201970444, 0.29085714285714304, 252]\n",
      "[3, 12, 10, 0.5416748768472907, 0.30412839158651955, 438]\n",
      "[3, 12, 15, 0.5950738916256157, 0.30833530782299245, 637]\n",
      "[3, 12, 20, 0.6333004926108374, 0.31048975326723177, 800]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-13\n",
      "[3, 13, 5, 0.4417733990147783, 0.2913004926108376, 247]\n",
      "[3, 13, 10, 0.5422660098522167, 0.30465657987332856, 412]\n",
      "[3, 13, 15, 0.5913300492610838, 0.3085182813902026, 611]\n",
      "[3, 13, 20, 0.6319211822660099, 0.3108065985319413, 786]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_3/attribute_model-14\n",
      "[3, 14, 5, 0.4396059113300493, 0.2904039408866994, 241]\n",
      "[3, 14, 10, 0.541871921182266, 0.30401423097974806, 393]\n",
      "[3, 14, 15, 0.5917241379310345, 0.3079038881206369, 582]\n",
      "[3, 14, 20, 0.6299507389162562, 0.3100673257885048, 758]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "fitting model....\n",
      "Epoch 0\tStep 1\tlr: 0.000010\tloss: 0.990279\n",
      "Epoch: 0 cost: 1.0637348890304565\n",
      "Epoch: 1 cost: 1.0454844236373901\n",
      "Epoch: 2 cost: 1.0289242267608643\n",
      "Epoch: 3 cost: 1.012937307357788\n",
      "Epoch: 4 cost: 0.9970621466636658\n",
      "Epoch: 5 cost: 0.9816796183586121\n",
      "Epoch: 6 cost: 0.970406711101532\n",
      "Epoch: 7 cost: 0.9559479355812073\n",
      "Epoch: 8 cost: 0.9472141861915588\n",
      "Epoch: 9 cost: 0.9353868365287781\n",
      "Epoch: 10 cost: 0.9279872179031372\n",
      "Epoch: 11 cost: 0.9194397926330566\n",
      "Epoch 12\tStep 5000\tlr: 0.000010\tloss: 0.909711\n",
      "Epoch: 12 cost: 0.910440981388092\n",
      "Epoch: 13 cost: 0.9036235213279724\n",
      "Epoch: 14 cost: 0.897823691368103\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-0\n",
      "[4, 0, 5, 0.34219200659250104, 0.24686169482213977, 561]\n",
      "[4, 0, 10, 0.37680263700041206, 0.2516751852505869, 741]\n",
      "[4, 0, 15, 0.39101771734651836, 0.252781684163019, 836]\n",
      "[4, 0, 20, 0.40523279769262466, 0.25358995810473856, 886]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-1\n",
      "[4, 1, 5, 0.4002884219200659, 0.2773829144348306, 550]\n",
      "[4, 1, 10, 0.44870210135970334, 0.28406329912819406, 765]\n",
      "[4, 1, 15, 0.47012772970745775, 0.2857511834180561, 874]\n",
      "[4, 1, 20, 0.48022249690976515, 0.28631023829872776, 940]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-2\n",
      "[4, 2, 5, 0.4295426452410383, 0.28975758824337317, 546]\n",
      "[4, 2, 10, 0.4929954676555418, 0.29858911975722857, 776]\n",
      "[4, 2, 15, 0.5150391429748661, 0.30033921350361387, 906]\n",
      "[4, 2, 20, 0.5265760197775031, 0.30098498790066547, 979]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-3\n",
      "[4, 3, 5, 0.44437577255871447, 0.2976445543194615, 530]\n",
      "[4, 3, 10, 0.5197775030902348, 0.30791639688425826, 792]\n",
      "[4, 3, 15, 0.5440873506386485, 0.3098265935410556, 916]\n",
      "[4, 3, 20, 0.5591264936135146, 0.3106949917030586, 998]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 4, 5, 0.450144210960033, 0.3000240351600054, 511]\n",
      "[4, 4, 10, 0.5364647713226205, 0.3117650473181993, 773]\n",
      "[4, 4, 15, 0.5696332921302019, 0.31443131046035877, 911]\n",
      "[4, 4, 20, 0.584672435105068, 0.3152727049446951, 993]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-5\n",
      "[4, 5, 5, 0.4542645241038319, 0.3018541409147097, 473]\n",
      "[4, 5, 10, 0.5444993819530284, 0.31430386328408566, 741]\n",
      "[4, 5, 15, 0.5852904820766378, 0.3175708476388327, 889]\n",
      "[4, 5, 20, 0.6046559538524928, 0.31867291978640894, 998]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-6\n",
      "[4, 6, 5, 0.4542645241038319, 0.30157258618321653, 418]\n",
      "[4, 6, 10, 0.5521219612690564, 0.3149735776744429, 687]\n",
      "[4, 6, 15, 0.5974454058508447, 0.31861559216318186, 877]\n",
      "[4, 6, 20, 0.6223733003708282, 0.32003506094752215, 985]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-7\n",
      "[4, 7, 5, 0.45529460238978164, 0.30318294190358464, 385]\n",
      "[4, 7, 10, 0.5550061804697157, 0.316790848326695, 638]\n",
      "[4, 7, 15, 0.6042439225381129, 0.32071384470951825, 851]\n",
      "[4, 7, 20, 0.6316440049443758, 0.32227094339817597, 961]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-8\n",
      "[4, 8, 5, 0.4565306963329213, 0.3022352698805109, 345]\n",
      "[4, 8, 10, 0.5554182117840956, 0.3156433738170451, 616]\n",
      "[4, 8, 15, 0.6071281417387722, 0.3197206555519288, 809]\n",
      "[4, 8, 20, 0.6407086938607334, 0.3216257679789322, 941]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-9\n",
      "[4, 9, 5, 0.4581788215904409, 0.30253399258343633, 324]\n",
      "[4, 9, 10, 0.554182117840956, 0.3154473319337351, 568]\n",
      "[4, 9, 15, 0.6085702513391018, 0.31972584767702183, 770]\n",
      "[4, 9, 20, 0.6448290070045324, 0.32179175968289436, 921]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-10\n",
      "[4, 10, 5, 0.4579728059332509, 0.30187474248042856, 305]\n",
      "[4, 10, 10, 0.5545941491553358, 0.31490261672585523, 522]\n",
      "[4, 10, 15, 0.6108364235681912, 0.31932384444745393, 728]\n",
      "[4, 10, 20, 0.646477132262052, 0.32136036538231566, 891]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-11\n",
      "[4, 11, 5, 0.4583848372476308, 0.30188847685757453, 285]\n",
      "[4, 11, 10, 0.5531520395550061, 0.31473052825030545, 489]\n",
      "[4, 11, 15, 0.6102183765966214, 0.3192029991937284, 685]\n",
      "[4, 11, 20, 0.6479192418623816, 0.321340481620033, 844]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-12\n",
      "[4, 12, 5, 0.4561186650185414, 0.30178890262326613, 277]\n",
      "[4, 12, 10, 0.5519159456118665, 0.3148348441148194, 468]\n",
      "[4, 12, 15, 0.6096003296250515, 0.3193922905140457, 659]\n",
      "[4, 12, 20, 0.6470951792336218, 0.3215110095346273, 823]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-13\n",
      "[4, 13, 5, 0.45529460238978164, 0.3023073753605273, 258]\n",
      "[4, 13, 10, 0.5523279769262464, 0.3154483129606739, 445]\n",
      "[4, 13, 15, 0.6089822826534816, 0.3198905298565371, 615]\n",
      "[4, 13, 20, 0.6450350226617223, 0.32192917154081124, 788]\n",
      "(50, 4000)\n",
      "----------------\n",
      "(50, 4000)\n",
      "(1201, 1)\n",
      "INFO:tensorflow:Restoring parameters from new_id_4/attribute_model-14\n",
      "[4, 14, 5, 0.453646477132262, 0.3029151215492376, 256]\n",
      "[4, 14, 10, 0.5510918829831067, 0.31611483901347925, 427]\n",
      "[4, 14, 15, 0.6058920477956324, 0.32042075147761195, 599]\n",
      "[4, 14, 20, 0.6456530696332922, 0.3226832261260635, 760]\n"
     ]
    }
   ],
   "source": [
    "arg = args()\n",
    "result = pd.DataFrame(columns=[\"k\",\"epoch\",\"top\",\"recall\",\"mrr\",\"unique_lst\"])\n",
    "for k in range(5):\n",
    "    train = data_spot[data_spot[\"k\"]!=k]\n",
    "    test = data_spot[data_spot[\"k\"]==k]\n",
    "    tf.reset_default_graph()\n",
    "    config = tf.ConfigProto()\n",
    "    config.gpu_options.allow_growth=True\n",
    "    sess = tf.Session(config=config)\n",
    "    arg.is_training = True\n",
    "    arg.checkpoint_dir = \"new_id_\" + str(k)\n",
    "    arg.dropout_p_hidden = 0.5\n",
    "    models = GRU4Rec(sess,arg)\n",
    "    models.fit(train)\n",
    "    # test#\n",
    "    arg.is_training = False\n",
    "    arg.dropout_p_hidden = 1.0\n",
    "    for i in range(0,15):\n",
    "        tf.reset_default_graph()\n",
    "        config = tf.ConfigProto()\n",
    "        config.gpu_options.allow_growth=True\n",
    "        sess = tf.Session(config=config)\n",
    "        arg.test_model = i\n",
    "        models = GRU4Rec(sess,arg)\n",
    "        l_old=evaluate_sessions_batch(models,test, session_key= 'Session_id', item_key='Spot', time_key= 'Session_num',batch_size=50)\n",
    "        #print(l_old[:2])\n",
    "        #print(len(set(np.array(l_old[2].Rec_lst.tolist()).reshape(-1))))\n",
    "        for idx,tt in enumerate([5,10,15,20],1):\n",
    "            mini=[k,i,tt,l_old[0][idx],l_old[1][idx],len(set(np.array(l_old[2].Rec_lst.apply(lambda x:x[:tt]).tolist()).reshape(-1)))]\n",
    "            result_mini = pd.Series(mini,index=[\"k\",\"epoch\",\"top\",\"recall\",\"mrr\",\"unique_lst\"])\n",
    "            result=result.append(result_mini,ignore_index=True)\n",
    "            print(mini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_excel('rnn_model_5_fold_result_ID_City_Tag.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n"
     ]
    }
   ],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth=True\n",
    "sess = tf.Session(config=config)\n",
    "arg = args()\n",
    "models = GRU4Rec(sess,arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting model....\n",
      "Epoch 0\tStep 1\tlr: 0.000010\tloss: 0.990053\n",
      "Epoch: 0 cost: 0.9815821051597595\n",
      "Epoch: 1 cost: 0.963460385799408\n",
      "Epoch: 2 cost: 0.9476490616798401\n",
      "Epoch: 3 cost: 0.9325414896011353\n",
      "Epoch: 4 cost: 0.9179233312606812\n",
      "Epoch: 5 cost: 0.9038062691688538\n",
      "Epoch: 6 cost: 0.888063371181488\n",
      "Epoch: 7 cost: 0.8767971992492676\n",
      "Epoch: 8 cost: 0.8675164580345154\n",
      "Epoch: 9 cost: 0.8582621216773987\n",
      "Epoch: 10 cost: 0.8503099083900452\n",
      "Epoch: 11 cost: 0.8458187580108643\n",
      "Epoch 12\tStep 5000\tlr: 0.000010\tloss: 0.841073\n",
      "Epoch: 12 cost: 0.838900089263916\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-86692735ca30>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\2019.01.04~여행앱개발\\modeling\\model_trip_GRU_make2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, data2)\u001b[0m\n\u001b[0;32m    285\u001b[0m                                  self.state: state}\n\u001b[0;32m    286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 287\u001b[1;33m                     \u001b[0mcost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    288\u001b[0m                     \u001b[0mepoch_cost\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\for_tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    893\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 895\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    896\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\for_tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1126\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1127\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1128\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1129\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1130\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\for_tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1342\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1343\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[1;32m-> 1344\u001b[1;33m                            options, run_metadata)\n\u001b[0m\u001b[0;32m   1345\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1346\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\for_tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1348\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1349\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1350\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1351\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1352\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\for_tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1327\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[0;32m   1328\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1329\u001b[1;33m                                    status, run_metadata)\n\u001b[0m\u001b[0;32m   1330\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1331\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "models.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class args():\n",
    "    is_training = False\n",
    "    retraining = True\n",
    "    layers = 1\n",
    "    rnn_size = [2500,50] # , 20, 5 \n",
    "    n_epochs = 50\n",
    "    batch_size = 50\n",
    "    dropout_p_hidden= 1.0\n",
    "    learning_rate = 0.00001\n",
    "    decay = 0.96\n",
    "    decay_steps = 5e3\n",
    "    initializer = 'xavier'\n",
    "    init_as_normal = False\n",
    "    freq_key = 'd'\n",
    "    rating_sort = 'f'\n",
    "    reset_after_session = True\n",
    "    session_key = 'Session_id'\n",
    "    attribute_key =['ID2','City_encoder'] #   ,'Tag_encoder'   ,\"city_sub_encoder\"\n",
    "    item_key = 'Spot'\n",
    "    time_key = 'Session_num'\n",
    "    grad_cap = 0\n",
    "    attribute = [len(spot),spot.City_encoder.nunique()] # ,spot.Tag_encoder.nunique()  ,spot.city_sub_encoder.nunique()\n",
    "    n_items = len(spot)\n",
    "    max_ckpt_keep = 100\n",
    "    test_model = 5\n",
    "    sigma=0\n",
    "    checkpoint_dir = 'chkpt_app_model7'  # 2번에 ID\n",
    "                                        # 3번에 100,20 하고 10번 사용 할것 ID,city_encoder\n",
    "                                        # 4번에 100,20,10 넣고 학습할 거임 ID,city,Tag\n",
    "                                        # 5번 비상 4\n",
    "                                        #  6번\n",
    "    loss = 'top1'\n",
    "    final_act = 'relu'\n",
    "    hidden_act = 'tanh'\n",
    "    item_table = spot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-5\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "arg = args()\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth=True\n",
    "sess = tf.Session(config=config)\n",
    "arg = args()\n",
    "models = GRU4Rec(sess,arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\2019.01.04~여행앱개발\\modeling\\evaluation_trip.py:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  test_data.sort_values([session_key,time_key],inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0.17149503, 0.34595052, 0.43941637, 0.49164728, 0.52548107]), array([0.17149503, 0.23389723, 0.24654622, 0.25072221, 0.25262217]))\n",
      "671\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-1\n",
      "(array([0.1765701 , 0.35673504, 0.47134701, 0.5220977 , 0.55275957]), array([0.1765701 , 0.24003313, 0.25544009, 0.25953596, 0.26126252]))\n",
      "573\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-2\n",
      "(array([0.17043772, 0.36011842, 0.46732925, 0.51490801, 0.55233665]), array([0.17043772, 0.23774935, 0.25235813, 0.25616513, 0.25828844]))\n",
      "541\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-3\n",
      "(array([0.16874604, 0.36159865, 0.46331148, 0.51892578, 0.55952633]), array([0.16874604, 0.23682244, 0.25051305, 0.25487542, 0.25716557]))\n",
      "500\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-4\n",
      "(array([0.1662085 , 0.35187143, 0.45865934, 0.51596532, 0.55889194]), array([0.1662085 , 0.23306901, 0.24751256, 0.25198468, 0.25440905]))\n",
      "455\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-5\n",
      "(array([0.16536266, 0.35165997, 0.4495665 , 0.51448509, 0.55550856]), array([0.16536266, 0.23207514, 0.24515855, 0.25023734, 0.25257029]))\n",
      "446\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-6\n",
      "(array([0.16515119, 0.34404737, 0.44279975, 0.50729541, 0.55275957]), array([0.16515119, 0.22935434, 0.24255153, 0.24759192, 0.25017559]))\n",
      "418\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-7\n",
      "(array([0.16599704, 0.34320152, 0.43941637, 0.50750687, 0.55487418]), array([0.16599704, 0.22906887, 0.24190565, 0.24723699, 0.249918  ]))\n",
      "411\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-8\n",
      "(array([0.16705435, 0.34383591, 0.43941637, 0.50771833, 0.55212519]), array([0.16705435, 0.22902657, 0.24162756, 0.24697495, 0.24947858]))\n",
      "409\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-9\n",
      "(array([0.16832311, 0.34637344, 0.43962783, 0.5089871 , 0.55085642]), array([0.16832311, 0.23020371, 0.24245671, 0.24787409, 0.25022806]))\n",
      "406\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-10\n",
      "(array([0.16726581, 0.34383591, 0.44449144, 0.50877564, 0.55170226]), array([0.16726581, 0.22912173, 0.24255178, 0.24754718, 0.2499553 ]))\n",
      "422\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-11\n",
      "(array([0.16938042, 0.34616198, 0.44512582, 0.50919856, 0.55381687]), array([0.16938042, 0.23058434, 0.24379168, 0.24881526, 0.25131596]))\n",
      "418\n",
      "(50, 2550)\n",
      "(1333, 2550)\n",
      "----------------\n",
      "(50, 2550)\n",
      "(1333, 1)\n",
      "INFO:tensorflow:Restoring parameters from chkpt_app_model7/attribute_model-12\n",
      "(array([0.1700148 , 0.34806513, 0.44491436, 0.51025587, 0.55297103]), array([0.1700148 , 0.23202932, 0.24499752, 0.25014741, 0.25252939]))\n",
      "420\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,13):\n",
    "    tf.reset_default_graph()\n",
    "    config = tf.ConfigProto()\n",
    "    config.gpu_options.allow_growth=True\n",
    "    sess = tf.Session(config=config)\n",
    "    arg.test_model = i\n",
    "    models = GRU4Rec(sess,arg)\n",
    "    l_old=evaluate_sessions_batch(models,test, session_key= 'Session_id', item_key='Spot', time_key= 'Session_num',batch_size=50)\n",
    "    print(l_old[:2])\n",
    "    print(len(set(np.array(l_old[2].Rec_lst.tolist()).reshape(-1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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