Final project Rev 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import os\n",
    "import gc\n",
    "import json \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = 'E:\\\\indoor-location-navigation\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull out all the buildings actually used in the test set, given current method we don't need the other ones\n",
    "ssubm = pd.read_csv(base_path + 'sample_submission.csv')\n",
    "\n",
    "# only 24 of the total buildings are used in the test set, \n",
    "# this allows us to greatly reduce the intial size of the dataset\n",
    "\n",
    "ssubm_df = ssubm[\"site_path_timestamp\"].apply(lambda x: pd.Series(x.split(\"_\")))\n",
    "used_buildings = sorted(ssubm_df[0].value_counts().index.tolist())\n",
    "\n",
    "# dictionary used to map the floor codes to the values used in the submission file. \n",
    "floor_map = {\"B2\":-2, \"B1\":-1, \"F1\":0, \"F2\": 1, \"F3\":2, \"F4\":3, \"F5\":4, \"F6\":5, \"F7\":6,\"F8\":7, \"F9\":8,\n",
    "             \"1F\":0, \"2F\":1, \"3F\":2, \"4F\":3, \"5F\":4, \"6F\":5, \"7F\":6, \"8F\": 7, \"9F\":8}"
   ]
  },
  {
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   "source": [
    "# get only the wifi bssid that occur over 1000 times(this number can be experimented with)\n",
    "# these will be the only ones used when constructing features\n",
    "bssid = dict()\n",
    "\n",
    "for building in used_buildings:\n",
    "    folders = sorted(glob.glob(os.path.join(base_path,'train\\\\'+building+'\\\\*')))\n",
    "    print(building)\n",
    "    wifi = list()\n",
    "    for folder in folders:\n",
    "        print(folder)\n",
    "        floor = floor_map[folder.split('\\\\')[-1]]\n",
    "        print(floor)\n",
    "        files = glob.glob(os.path.join(folder, \"*.txt\"))\n",
    "        for file in files:\n",
    "            with open(file, encoding=\"utf8\") as f:\n",
    "                txt = f.readlines()\n",
    "                for e, line in enumerate(txt):\n",
    "                    tmp = line.strip().split()\n",
    "                    if tmp[1] == \"TYPE_WIFI\":\n",
    "                        wifi.append(tmp)\n",
    "    df = pd.DataFrame(wifi)\n",
    "    #top_bssid = df[3].value_counts().iloc[:500].index.tolist()\n",
    "    value_counts = df[3].value_counts()\n",
    "    top_bssid = value_counts[value_counts > 1000].index.tolist()\n",
    "    print(len(top_bssid))\n",
    "    bssid[building] = top_bssid\n",
    "    del df\n",
    "    del wifi\n",
    "    gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"bssid_1000.json\", \"w\") as f:\n",
    "    json.dump(bssid, f)\n",
    "\n",
    "with open(\"bssid_1000.json\") as f:\n",
    "    bssid = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5a0546857ecc773753327266\n",
      "5c3c44b80379370013e0fd2b\n",
      "5d27075f03f801723c2e360f\n",
      "5d27096c03f801723c31e5e0\n",
      "5d27097f03f801723c320d97\n",
      "5d27099f03f801723c32511d\n",
      "5d2709a003f801723c3251bf\n",
      "5d2709b303f801723c327472\n",
      "5d2709bb03f801723c32852c\n",
      "5d2709c303f801723c3299ee\n",
      "5d2709d403f801723c32bd39\n",
      "5d2709e003f801723c32d896\n",
      "5da138274db8ce0c98bbd3d2\n",
      "5da1382d4db8ce0c98bbe92e\n",
      "5da138314db8ce0c98bbf3a0\n",
      "5da138364db8ce0c98bc00f1\n",
      "5da1383b4db8ce0c98bc11ab\n",
      "5da138754db8ce0c98bca82f\n",
      "5da138764db8ce0c98bcaa46\n",
      "5da1389e4db8ce0c98bd0547\n",
      "5da138b74db8ce0c98bd4774\n",
      "5da958dd46f8266d0737457b\n",
      "5dbc1d84c1eb61796cf7c010\n",
      "5dc8cea7659e181adb076a3f\n"
     ]
    }
   ],
   "source": [
    "# generate all the training data \n",
    "building_dfs = dict()\n",
    "\n",
    "for building in used_buildings:\n",
    "    folders = sorted(glob.glob(os.path.join(base_path,'train/', building +'/*')))\n",
    "    dfs = list()\n",
    "    index = sorted(bssid[building])\n",
    "    print(building)\n",
    "    for folder in folders:\n",
    "        floor = floor_map[folder.split('\\\\')[-1]]\n",
    "        files = glob.glob(os.path.join(folder, \"*.txt\"))\n",
    "        for file in files:\n",
    "            wifi = list()\n",
    "            waypoint = list()\n",
    "            with open(file, encoding=\"utf8\") as f:\n",
    "                txt = f.readlines()\n",
    "            for line in txt:\n",
    "                line = line.strip().split()\n",
    "                if line[1] == \"TYPE_WAYPOINT\":\n",
    "                    waypoint.append(line)\n",
    "                if line[1] == \"TYPE_WIFI\":\n",
    "                    wifi.append(line)\n",
    "\n",
    "            df = pd.DataFrame(np.array(wifi))    \n",
    "\n",
    "            # generate a feature, and label for each wifi block\n",
    "            for gid, g in df.groupby(0):\n",
    "                dists = list()\n",
    "                for e, k in enumerate(waypoint):\n",
    "                    dist = abs(int(gid) - int(k[0]))\n",
    "                    dists.append(dist)\n",
    "                nearest_wp_index = np.argmin(dists)\n",
    "                \n",
    "                g = g.drop_duplicates(subset=3)\n",
    "                tmp = g.iloc[:,3:5]\n",
    "                feat = tmp.set_index(3).reindex(index).replace(np.nan, -999).T\n",
    "                feat[\"x\"] = float(waypoint[nearest_wp_index][2])\n",
    "                feat[\"y\"] = float(waypoint[nearest_wp_index][3])\n",
    "                feat[\"f\"] = floor\n",
    "                feat[\"path\"] = file.split('/')[-1].split('.')[0] # useful for crossvalidation\n",
    "                dfs.append(feat)\n",
    "                \n",
    "    building_df = pd.concat(dfs)\n",
    "    building_dfs[building] = df\n",
    "    building_df.to_csv(building+\"_1000_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssubm_building_g = ssubm_df.groupby(0) #Grouped by building\n",
    "feature_dict = dict()\n",
    "\n",
    "for gid0, g0 in ssubm_building_g:\n",
    "    index = sorted(bssid[g0.iloc[0,0]])\n",
    "    feats = list()\n",
    "#     print(gid0, 'gid0')\n",
    "#     print(g0, 'g0')\n",
    "#     print()\n",
    "    for gid,g in g0.groupby(1):\n",
    "        # get all wifi time locations, \n",
    "#         print(gid, 'gid')\n",
    "#         print(g, 'g')\n",
    "#         print()\n",
    "        with open(os.path.join(base_path, 'test\\\\' + g.iloc[0,1] + '.txt'), encoding=\"utf8\") as f:\n",
    "            txt = f.readlines()\n",
    "\n",
    "        wifi = list()\n",
    "\n",
    "        for line in txt:\n",
    "            line = line.strip().split()\n",
    "            if line[1] == \"TYPE_WIFI\":\n",
    "                wifi.append(line)\n",
    "\n",
    "        wifi_df = pd.DataFrame(wifi) #All wifi points for certain path\n",
    "        wifi_points = pd.DataFrame(wifi_df.groupby(0).count().index.tolist()) #Timestamps for wifi for certain path\n",
    "        #print(wifi_points)\n",
    "        for timepoint in g.iloc[:,2].tolist():\n",
    "            deltas = (wifi_points.astype(int) - int(timepoint)).abs()\n",
    "            min_delta_idx = deltas.values.argmin()\n",
    "            wifi_block_timestamp = wifi_points.iloc[min_delta_idx].values[0]\n",
    "            \n",
    "            wifi_block = wifi_df[wifi_df[0] == wifi_block_timestamp].drop_duplicates(subset=3)\n",
    "            feat = wifi_block.set_index(3)[4].reindex(index).fillna(-999)\n",
    "\n",
    "            feat['site_path_timestamp'] = g.iloc[0,0] + \"_\" + g.iloc[0,1] + \"_\" + timepoint\n",
    "            feats.append(feat)\n",
    "    feature_df = pd.concat(feats, axis=1).T\n",
    "    feature_df.to_csv(gid0+\"_1000_test.csv\")\n",
    "    feature_dict[gid0] = feature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_path = 'C:\\\\Users\\\\user\\\\Documents\\\\LightHouseLabs\\\\W09\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_files = sorted(glob.glob(os.path.join(feature_path, '*_train.csv')))\n",
    "test_files = sorted(glob.glob(os.path.join(feature_path, '*_test.csv')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tTest-mae:79.96051\n",
      "[1]\tTest-mae:64.18188\n",
      "[2]\tTest-mae:51.55757\n",
      "[3]\tTest-mae:41.47234\n",
      "[4]\tTest-mae:33.38947\n",
      "[5]\tTest-mae:26.98509\n",
      "[6]\tTest-mae:21.85614\n",
      "[7]\tTest-mae:17.78715\n",
      "[8]\tTest-mae:14.53086\n",
      "[9]\tTest-mae:11.99084\n",
      "[10]\tTest-mae:9.98102\n",
      "[11]\tTest-mae:8.41313\n",
      "[12]\tTest-mae:7.20636\n",
      "[13]\tTest-mae:6.27802\n",
      "[14]\tTest-mae:5.56948\n",
      "[15]\tTest-mae:5.03812\n",
      "[16]\tTest-mae:4.62828\n",
      "[17]\tTest-mae:4.31516\n",
      "[18]\tTest-mae:4.08629\n",
      "[19]\tTest-mae:3.92109\n",
      "[20]\tTest-mae:3.79706\n",
      "[21]\tTest-mae:3.70534\n",
      "[22]\tTest-mae:3.63383\n",
      "[23]\tTest-mae:3.58209\n",
      "[24]\tTest-mae:3.54596\n",
      "[25]\tTest-mae:3.51221\n",
      "[26]\tTest-mae:3.48600\n",
      "[27]\tTest-mae:3.46629\n",
      "[28]\tTest-mae:3.45153\n",
      "[29]\tTest-mae:3.43873\n",
      "[30]\tTest-mae:3.42787\n",
      "[31]\tTest-mae:3.42103\n",
      "[32]\tTest-mae:3.41445\n",
      "[33]\tTest-mae:3.40899\n",
      "[34]\tTest-mae:3.40435\n",
      "[35]\tTest-mae:3.40064\n",
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      "[42]\tTest-mae:3.38693\n",
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      "[55]\tTest-mae:3.37983\n",
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      "[60]\tTest-mae:3.37913\n",
      "[61]\tTest-mae:3.37903\n",
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      "[66]\tTest-mae:3.37864\n",
      "[67]\tTest-mae:3.37856\n",
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      "[69]\tTest-mae:3.37848\n",
      "[70]\tTest-mae:3.37845\n",
      "[71]\tTest-mae:3.37842\n",
      "[72]\tTest-mae:3.37839\n",
      "[73]\tTest-mae:3.37837\n",
      "[74]\tTest-mae:3.37835\n",
      "[75]\tTest-mae:3.37832\n",
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      "[79]\tTest-mae:3.37826\n",
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      "[0]\tTest-mae:76.93556\n",
      "[1]\tTest-mae:61.70528\n",
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      "[3]\tTest-mae:39.77667\n",
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      "[35]\tTest-mae:2.41398\n",
      "[36]\tTest-mae:2.41129\n",
      "[37]\tTest-mae:2.40939\n",
      "[38]\tTest-mae:2.40766\n",
      "[39]\tTest-mae:2.40672\n",
      "[40]\tTest-mae:2.40562\n",
      "[41]\tTest-mae:2.40420\n",
      "[42]\tTest-mae:2.40376\n",
      "[43]\tTest-mae:2.40313\n",
      "[44]\tTest-mae:2.40243\n",
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      "[48]\tTest-mae:2.40066\n",
      "[49]\tTest-mae:2.40033\n",
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      "[51]\tTest-mae:2.39996\n",
      "[52]\tTest-mae:2.39970\n",
      "[53]\tTest-mae:2.39947\n",
      "[54]\tTest-mae:2.39931\n",
      "[55]\tTest-mae:2.39920\n",
      "[56]\tTest-mae:2.39899\n",
      "[57]\tTest-mae:2.39888\n",
      "[58]\tTest-mae:2.39879\n",
      "[59]\tTest-mae:2.39871\n",
      "[60]\tTest-mae:2.39867\n",
      "[61]\tTest-mae:2.39866\n",
      "[62]\tTest-mae:2.39858\n",
      "[63]\tTest-mae:2.39850\n",
      "[64]\tTest-mae:2.39846\n",
      "[65]\tTest-mae:2.39842\n",
      "[66]\tTest-mae:2.39838\n",
      "[67]\tTest-mae:2.39844\n",
      "[68]\tTest-mae:2.39842\n",
      "[69]\tTest-mae:2.39841\n",
      "[70]\tTest-mae:2.39838\n",
      "[71]\tTest-mae:2.39837\n",
      "[72]\tTest-mae:2.39834\n",
      "[73]\tTest-mae:2.39835\n",
      "[74]\tTest-mae:2.39835\n",
      "[75]\tTest-mae:2.39831\n",
      "[76]\tTest-mae:2.39830\n",
      "[77]\tTest-mae:2.39829\n",
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      "[79]\tTest-mae:2.39827\n",
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      "[104]\tTest-mae:2.39828\n",
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     "output_type": "stream",
     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
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      "[125]\tTest-mae:3.79571\n",
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      "[127]\tTest-mae:3.79550\n",
      "[128]\tTest-mae:3.79543\n",
      "[129]\tTest-mae:3.79538\n",
      "[130]\tTest-mae:3.79534\n",
      "[131]\tTest-mae:3.79551\n",
      "[132]\tTest-mae:3.79550\n",
      "[133]\tTest-mae:3.79543\n",
      "[134]\tTest-mae:3.79558\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[135]\tTest-mae:3.79567\n",
      "[136]\tTest-mae:3.79568\n",
      "[137]\tTest-mae:3.79569\n",
      "[138]\tTest-mae:3.79560\n",
      "[139]\tTest-mae:3.79564\n",
      "[140]\tTest-mae:3.79562\n",
      "[141]\tTest-mae:3.79556\n",
      "[142]\tTest-mae:3.79561\n",
      "[143]\tTest-mae:3.79560\n",
      "[144]\tTest-mae:3.79564\n",
      "[145]\tTest-mae:3.79565\n",
      "[146]\tTest-mae:3.79572\n",
      "[147]\tTest-mae:3.79570\n",
      "[148]\tTest-mae:3.79578\n",
      "[149]\tTest-mae:3.79577\n",
      "[150]\tTest-mae:3.79572\n",
      "[151]\tTest-mae:3.79565\n",
      "[152]\tTest-mae:3.79562\n",
      "[153]\tTest-mae:3.79561\n",
      "[154]\tTest-mae:3.79568\n",
      "[155]\tTest-mae:3.79569\n",
      "[156]\tTest-mae:3.79573\n",
      "[157]\tTest-mae:3.79573\n",
      "[158]\tTest-mae:3.79574\n",
      "[159]\tTest-mae:3.79572\n",
      "[160]\tTest-mae:3.79579\n",
      "[161]\tTest-mae:3.79582\n",
      "[162]\tTest-mae:3.79583\n",
      "[163]\tTest-mae:3.79583\n",
      "[164]\tTest-mae:3.79580\n",
      "[165]\tTest-mae:3.79573\n",
      "[166]\tTest-mae:3.79583\n",
      "[167]\tTest-mae:3.79583\n",
      "[168]\tTest-mae:3.79584\n",
      "[169]\tTest-mae:3.79586\n",
      "[170]\tTest-mae:3.79585\n",
      "[171]\tTest-mae:3.79586\n",
      "[172]\tTest-mae:3.79583\n",
      "[173]\tTest-mae:3.79582\n",
      "[174]\tTest-mae:3.79583\n",
      "[175]\tTest-mae:3.79582\n",
      "[176]\tTest-mae:3.79581\n",
      "[177]\tTest-mae:3.79581\n",
      "[178]\tTest-mae:3.79586\n",
      "[179]\tTest-mae:3.79582\n",
      "[180]\tTest-mae:3.79584\n",
      "[181]\tTest-mae:3.79582\n",
      "[182]\tTest-mae:3.79578\n",
      "[183]\tTest-mae:3.79584\n",
      "[184]\tTest-mae:3.79584\n",
      "[185]\tTest-mae:3.79587\n",
      "[186]\tTest-mae:3.79589\n",
      "[187]\tTest-mae:3.79592\n",
      "[188]\tTest-mae:3.79595\n",
      "[189]\tTest-mae:3.79594\n",
      "[190]\tTest-mae:3.79598\n",
      "[0]\tTest-merror:0.04446\n",
      "[1]\tTest-merror:0.02606\n",
      "[2]\tTest-merror:0.02172\n",
      "[3]\tTest-merror:0.01507\n",
      "[4]\tTest-merror:0.01380\n",
      "[5]\tTest-merror:0.01329\n",
      "[6]\tTest-merror:0.01329\n",
      "[7]\tTest-merror:0.01329\n",
      "[8]\tTest-merror:0.01277\n",
      "[9]\tTest-merror:0.01303\n",
      "[10]\tTest-merror:0.01252\n",
      "[11]\tTest-merror:0.01252\n",
      "[12]\tTest-merror:0.01201\n",
      "[13]\tTest-merror:0.01201\n",
      "[14]\tTest-merror:0.01175\n",
      "[15]\tTest-merror:0.01175\n",
      "[16]\tTest-merror:0.01175\n",
      "[17]\tTest-merror:0.01175\n",
      "[18]\tTest-merror:0.01175\n",
      "[19]\tTest-merror:0.01150\n",
      "[20]\tTest-merror:0.01073\n",
      "[21]\tTest-merror:0.01099\n",
      "[22]\tTest-merror:0.01073\n",
      "[23]\tTest-merror:0.01047\n",
      "[24]\tTest-merror:0.01047\n",
      "[25]\tTest-merror:0.01047\n",
      "[26]\tTest-merror:0.01047\n",
      "[27]\tTest-merror:0.01047\n",
      "[28]\tTest-merror:0.01047\n",
      "[29]\tTest-merror:0.01047\n",
      "[30]\tTest-merror:0.01047\n",
      "[31]\tTest-merror:0.01047\n",
      "[32]\tTest-merror:0.01047\n",
      "[33]\tTest-merror:0.01047\n",
      "[34]\tTest-merror:0.01047\n",
      "[35]\tTest-merror:0.01047\n",
      "[36]\tTest-merror:0.01047\n",
      "[37]\tTest-merror:0.01047\n",
      "[38]\tTest-merror:0.01073\n",
      "[39]\tTest-merror:0.01073\n",
      "[40]\tTest-merror:0.01047\n",
      "[41]\tTest-merror:0.01047\n",
      "[42]\tTest-merror:0.01047\n",
      "[43]\tTest-merror:0.01047\n",
      "[44]\tTest-merror:0.01047\n",
      "[45]\tTest-merror:0.01047\n",
      "[46]\tTest-merror:0.01047\n",
      "[47]\tTest-merror:0.01073\n",
      "[48]\tTest-merror:0.01099\n",
      "[49]\tTest-merror:0.01073\n",
      "[50]\tTest-merror:0.01099\n",
      "[51]\tTest-merror:0.01099\n",
      "[52]\tTest-merror:0.01099\n",
      "[53]\tTest-merror:0.01099\n",
      "[54]\tTest-merror:0.01099\n",
      "[55]\tTest-merror:0.01099\n",
      "[56]\tTest-merror:0.01099\n",
      "[57]\tTest-merror:0.01099\n",
      "[58]\tTest-merror:0.01099\n",
      "[59]\tTest-merror:0.01099\n",
      "[60]\tTest-merror:0.01099\n",
      "[61]\tTest-merror:0.01099\n",
      "[62]\tTest-merror:0.01099\n",
      "[63]\tTest-merror:0.01099\n",
      "[64]\tTest-merror:0.01099\n",
      "[65]\tTest-merror:0.01099\n",
      "[66]\tTest-merror:0.01099\n",
      "[67]\tTest-merror:0.01099\n",
      "[68]\tTest-merror:0.01099\n",
      "[69]\tTest-merror:0.01099\n",
      "[70]\tTest-merror:0.01099\n",
      "[71]\tTest-merror:0.01099\n",
      "[72]\tTest-merror:0.01099\n",
      "[73]\tTest-merror:0.01099\n",
      "[74]\tTest-merror:0.01099\n",
      "[75]\tTest-merror:0.01099\n",
      "[76]\tTest-merror:0.01099\n",
      "[77]\tTest-merror:0.01099\n",
      "[78]\tTest-merror:0.01099\n",
      "[79]\tTest-merror:0.01099\n",
      "[80]\tTest-merror:0.01099\n",
      "[81]\tTest-merror:0.01099\n",
      "[82]\tTest-merror:0.01099\n"
     ]
    }
   ],
   "source": [
    "predictions = list()\n",
    "\n",
    "for e, file in enumerate(train_files):\n",
    "    data = pd.read_csv(file, index_col=0)\n",
    "    test_data = pd.read_csv(test_files[e], index_col=0)\n",
    "\n",
    "    # simple grid search for tuning using path and groupkfold cv\n",
    "    #for i in [127]:\n",
    "    #    for n in [75]:\n",
    "    #        modely = lgb.LGBMRegressor(\n",
    "    #        n_estimators=n, num_leaves=i, n_jobs=1)\n",
    "    #        gkf = model_selection.GroupKFold()\n",
    "    #        scores = model_selection.cross_val_score(\n",
    "    #            modely, x_train, y_trainy, scoring='neg_mean_squared_error',\n",
    "    #            cv=gkf, groups=data.iloc[:,-1], n_jobs=5)\n",
    "    #        print(i, n, l, scores, scores.mean())\n",
    "    \n",
    "    data = data.sample(frac=1)\n",
    "    x_train = data.iloc[:int(len(data)*0.75),:-4]\n",
    "    y_trainy = data.iloc[:int(len(data)*0.75),-3]\n",
    "    y_trainx = data.iloc[:int(len(data)*0.75),-4]\n",
    "    y_trainf = data.iloc[:int(len(data)*0.75),-2] + 2 #To make floors classes from 0 to 10\n",
    "    \n",
    "    x_test = data.iloc[int(len(data)*0.75):,:-4]\n",
    "    y_testy = data.iloc[int(len(data)*0.75):,-3]\n",
    "    y_testx = data.iloc[int(len(data)*0.75):,-4]\n",
    "    y_testf = data.iloc[int(len(data)*0.75):,-2] + 2 #To make floors classes from 0 to 10\n",
    "    \n",
    "    \n",
    "    dtrainX = xgb.DMatrix(x_train, label=y_trainx)\n",
    "    dtestX = xgb.DMatrix(x_test, label=y_testx)\n",
    "    \n",
    "    dtrainY = xgb.DMatrix(x_train, label=y_trainy)\n",
    "    dtestY = xgb.DMatrix(x_test, label=y_testy)\n",
    "    \n",
    "    dtrainF = xgb.DMatrix(x_train, label=y_trainf)\n",
    "    dtestF = xgb.DMatrix(x_test, label=y_testf)\n",
    "    \n",
    "    paramsXY = {\n",
    "    # Parameters that we are going to tune.\n",
    "    'max_depth':60,\n",
    "    'min_child_weight': 1,\n",
    "    'eta':.2,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 0.5,\n",
    "    # Other parameters\n",
    "    'objective':'reg:squarederror',\n",
    "    'eval_metric':'mae',\n",
    "    'booster':'dart',\n",
    "    'random_state':17,\n",
    "    'verbosity':1,\n",
    "    'n_jobs':-1\n",
    "    }\n",
    "    \n",
    "    paramsF = {\n",
    "    # Parameters that we are going to tune.\n",
    "    'max_depth':60,\n",
    "    'min_child_weight': 1,\n",
    "    'eta':.2,\n",
    "    'subsample': 1,\n",
    "    'colsample_bytree': 0.5,\n",
    "    # Other parameters\n",
    "    'objective':'multi:softmax',\n",
    "    'eval_metric':'merror',\n",
    "    'num_class':11,\n",
    "    'booster':'gbtree',\n",
    "    'random_state':17,\n",
    "    'verbosity':1,\n",
    "    'n_jobs':-1\n",
    "    }\n",
    "    \n",
    "    num_boost_round=999\n",
    "    \n",
    "    xgb_rX = xgb.train(\n",
    "    paramsXY,\n",
    "    dtrainX,\n",
    "    num_boost_round=num_boost_round,\n",
    "    evals=[(dtestX, \"Test\")],\n",
    "    early_stopping_rounds=60\n",
    "    )\n",
    "    \n",
    "    xgb_rY = xgb.train(\n",
    "    paramsXY,\n",
    "    dtrainY,\n",
    "    num_boost_round=num_boost_round,\n",
    "    evals=[(dtestY, \"Test\")],\n",
    "    early_stopping_rounds=60\n",
    "    )\n",
    "    \n",
    "    xgb_cF = xgb.train(\n",
    "    paramsF,\n",
    "    dtrainF,\n",
    "    num_boost_round=num_boost_round,\n",
    "    evals=[(dtestF, \"Test\")],\n",
    "    early_stopping_rounds=60\n",
    "    )\n",
    "    \n",
    "#     modely = lgb.LGBMRegressor(\n",
    "#         n_estimators=125, num_leaves=90)\n",
    "#     modely.fit(x_train, y_trainy)\n",
    "\n",
    "#     modelx = lgb.LGBMRegressor(\n",
    "#         n_estimators=125, num_leaves=90)\n",
    "#     modelx.fit(x_train, y_trainx)\n",
    "\n",
    "#     modelf = lgb.LGBMClassifier(\n",
    "#         n_estimators=125, num_leaves=90)\n",
    "#     modelf.fit(x_train, y_trainf)\n",
    "    \n",
    "    test_predsx = xgb_rX.predict(xgb.DMatrix(test_data.iloc[:,:-1]))\n",
    "    test_predsy = xgb_rY.predict(xgb.DMatrix(test_data.iloc[:,:-1]))\n",
    "    test_predsf = xgb_cF.predict(xgb.DMatrix(test_data.iloc[:,:-1]+2))\n",
    "    \n",
    "    test_preds = pd.DataFrame(np.stack((test_predsf, test_predsx, test_predsy))).T\n",
    "    test_preds.columns = ssubm.columns[1:]\n",
    "    test_preds.index = test_data[\"site_path_timestamp\"]\n",
    "    test_preds[\"floor\"] = test_preds[\"floor\"].astype(int)-2 #Returning floor classes back to original\n",
    "    predictions.append(test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate prediction file \n",
    "all_preds = pd.concat(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = all_preds.reindex(ssubm.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>site_path_timestamp</th>\n",
       "      <th>floor</th>\n",
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       "</table>\n",
       "<p>10133 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     site_path_timestamp  floor           x  \\\n",
       "0      5a0546857ecc773753327266_046cfa46be49fc1083481...      0   95.224716   \n",
       "1      5a0546857ecc773753327266_046cfa46be49fc1083481...      0   88.289108   \n",
       "2      5a0546857ecc773753327266_046cfa46be49fc1083481...      0   89.959503   \n",
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       "4      5a0546857ecc773753327266_046cfa46be49fc1083481...      0   95.809952   \n",
       "...                                                  ...    ...         ...   \n",
       "10128  5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f...      5  214.591034   \n",
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       "10131  5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f...      5  201.129257   \n",
       "10132  5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f...      5  208.284683   \n",
       "\n",
       "                y  \n",
       "0       99.557564  \n",
       "1       93.667480  \n",
       "2      104.976700  \n",
       "3      106.487343  \n",
       "4      110.089241  \n",
       "...           ...  \n",
       "10128   87.356285  \n",
       "10129   84.988022  \n",
       "10130  108.069359  \n",
       "10131  103.072083  \n",
       "10132  104.103966  \n",
       "\n",
       "[10133 rows x 4 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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