2021-06-04_final_submision_combine.ipynb

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   "metadata": {
    "interpreter": {
     "hash": "910010b9128fec0bda95a110deaa2f7baca3ef79608cce04836aad2a922a4fb5"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "from utils import helper, Solver, TimeKeeper\n",
    "from utils import Solver\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.read_csv('data/mid_res/20210526_data_df.csv')\n",
    "val_df = pd.read_csv('data/mid_res/20210526_val_df.csv')\n",
    "df = pd.concat([data_df, val_df], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_drop_x = ['time', 'label']\n",
    "col_label = 'label'\n",
    "col_drop = ['latitude',\n",
    " 'longitude',\n",
    " 'altitude',\n",
    " 'time_dlt',\n",
    " 'valid_dlt',\n",
    " 'east',\n",
    " 'north',\n",
    " 'east_dlt',\n",
    " 'north_dlt',\n",
    " 'east_speed',\n",
    " 'north_speed',\n",
    " 'cells_ctype_mode',\n",
    " 'speed_dif',\n",
    " 'speed_dlt',\n",
    " 'speed'\n",
    " ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/mid_res/test.pickle', 'rb') as f:  \n",
    "    test = pickle.load(f)\n",
    "\n",
    "X_train, y_train = df.drop(col_drop_x + col_drop, axis = 1).fillna(0), df[col_label]\n",
    "X_test = test.df.drop(['time'] + col_drop, axis = 1).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train: (1124485, 32)\ny_train: 1124485\n"
     ]
    }
   ],
   "source": [
    "solver = Solver.Solver(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training rf...\n",
      "Time elapsed for training rf: 30.23\n",
      "Training bag (tree-based)...\n",
      "Time elapsed for training bag (tree-based): 38.15\n",
      "Training LightGBM...\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021087 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 4948\n",
      "[LightGBM] [Info] Number of data points in the train set: 899588, number of used features: 32\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Start training from score -2.000468\n",
      "[LightGBM] [Info] Start training from score -2.021512\n",
      "[LightGBM] [Info] Start training from score -3.216919\n",
      "[LightGBM] [Info] Start training from score -2.163032\n",
      "[LightGBM] [Info] Start training from score -1.836515\n",
      "[LightGBM] [Info] Start training from score -2.010071\n",
      "[LightGBM] [Info] Start training from score -1.841753\n",
      "[LightGBM] [Info] Start training from score -2.076817\n",
      "[1]\tvalid_0's multi_logloss: 1.67418\n",
      "[2]\tvalid_0's multi_logloss: 1.47068\n",
      "[3]\tvalid_0's multi_logloss: 1.32108\n",
      "[4]\tvalid_0's multi_logloss: 1.20569\n",
      "[5]\tvalid_0's multi_logloss: 1.11278\n",
      "[6]\tvalid_0's multi_logloss: 1.03445\n",
      "[7]\tvalid_0's multi_logloss: 0.969471\n",
      "[8]\tvalid_0's multi_logloss: 0.915272\n",
      "[9]\tvalid_0's multi_logloss: 0.867055\n",
      "[10]\tvalid_0's multi_logloss: 0.825022\n",
      "[11]\tvalid_0's multi_logloss: 0.787319\n",
      "[12]\tvalid_0's multi_logloss: 0.754072\n",
      "[13]\tvalid_0's multi_logloss: 0.724891\n",
      "[14]\tvalid_0's multi_logloss: 0.698399\n",
      "[15]\tvalid_0's multi_logloss: 0.67506\n",
      "[16]\tvalid_0's multi_logloss: 0.654761\n",
      "[17]\tvalid_0's multi_logloss: 0.63591\n",
      "[18]\tvalid_0's multi_logloss: 0.619659\n",
      "[19]\tvalid_0's multi_logloss: 0.603091\n",
      "[20]\tvalid_0's multi_logloss: 0.587528\n",
      "[21]\tvalid_0's multi_logloss: 0.573622\n",
      "[22]\tvalid_0's multi_logloss: 0.559838\n",
      "[23]\tvalid_0's multi_logloss: 0.548792\n",
      "[24]\tvalid_0's multi_logloss: 0.538418\n",
      "[25]\tvalid_0's multi_logloss: 0.527638\n",
      "[26]\tvalid_0's multi_logloss: 0.517185\n",
      "[27]\tvalid_0's multi_logloss: 0.507031\n",
      "[28]\tvalid_0's multi_logloss: 0.498005\n",
      "[29]\tvalid_0's multi_logloss: 0.487954\n",
      "[30]\tvalid_0's multi_logloss: 0.478015\n",
      "[31]\tvalid_0's multi_logloss: 0.471291\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[32]\tvalid_0's multi_logloss: 0.465001\n",
      "[33]\tvalid_0's multi_logloss: 0.455666\n",
      "[34]\tvalid_0's multi_logloss: 0.448134\n",
      "[35]\tvalid_0's multi_logloss: 0.44267\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[36]\tvalid_0's multi_logloss: 0.436002\n",
      "[37]\tvalid_0's multi_logloss: 0.430048\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[38]\tvalid_0's multi_logloss: 0.423709\n",
      "[39]\tvalid_0's multi_logloss: 0.417502\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[40]\tvalid_0's multi_logloss: 0.411452\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[41]\tvalid_0's multi_logloss: 0.406612\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[42]\tvalid_0's multi_logloss: 0.401438\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[43]\tvalid_0's multi_logloss: 0.397382\n",
      "[44]\tvalid_0's multi_logloss: 0.391615\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[45]\tvalid_0's multi_logloss: 0.385939\n",
      "[46]\tvalid_0's multi_logloss: 0.380759\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[47]\tvalid_0's multi_logloss: 0.375746\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[48]\tvalid_0's multi_logloss: 0.37127\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[49]\tvalid_0's multi_logloss: 0.366372\n",
      "[50]\tvalid_0's multi_logloss: 0.361892\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[51]\tvalid_0's multi_logloss: 0.35791\n",
      "[52]\tvalid_0's multi_logloss: 0.354275\n",
      "[53]\tvalid_0's multi_logloss: 0.350345\n",
      "[54]\tvalid_0's multi_logloss: 0.345992\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[55]\tvalid_0's multi_logloss: 0.342296\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[56]\tvalid_0's multi_logloss: 0.338661\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[57]\tvalid_0's multi_logloss: 0.335364\n",
      "[58]\tvalid_0's multi_logloss: 0.331796\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[59]\tvalid_0's multi_logloss: 0.328955\n",
      "[60]\tvalid_0's multi_logloss: 0.325779\n",
      "[61]\tvalid_0's multi_logloss: 0.322388\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[62]\tvalid_0's multi_logloss: 0.319558\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[63]\tvalid_0's multi_logloss: 0.315525\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[64]\tvalid_0's multi_logloss: 0.312784\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[65]\tvalid_0's multi_logloss: 0.309208\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[66]\tvalid_0's multi_logloss: 0.306991\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[67]\tvalid_0's multi_logloss: 0.304436\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[68]\tvalid_0's multi_logloss: 0.301818\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[69]\tvalid_0's multi_logloss: 0.298281\n",
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      "[70]\tvalid_0's multi_logloss: 0.296167\n",
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      "[71]\tvalid_0's multi_logloss: 0.29366\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[72]\tvalid_0's multi_logloss: 0.291983\n",
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      "[73]\tvalid_0's multi_logloss: 0.288928\n",
      "[74]\tvalid_0's multi_logloss: 0.287038\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[75]\tvalid_0's multi_logloss: 0.284072\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[76]\tvalid_0's multi_logloss: 0.281942\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[77]\tvalid_0's multi_logloss: 0.279456\n",
      "[78]\tvalid_0's multi_logloss: 0.27691\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[79]\tvalid_0's multi_logloss: 0.27436\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[80]\tvalid_0's multi_logloss: 0.272336\n",
      "[81]\tvalid_0's multi_logloss: 0.270379\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[82]\tvalid_0's multi_logloss: 0.267559\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[83]\tvalid_0's multi_logloss: 0.26557\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[84]\tvalid_0's multi_logloss: 0.263997\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[85]\tvalid_0's multi_logloss: 0.262151\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[86]\tvalid_0's multi_logloss: 0.260022\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[87]\tvalid_0's multi_logloss: 0.258636\n",
      "[88]\tvalid_0's multi_logloss: 0.256839\n",
      "[89]\tvalid_0's multi_logloss: 0.254921\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[90]\tvalid_0's multi_logloss: 0.25324\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[91]\tvalid_0's multi_logloss: 0.251875\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[92]\tvalid_0's multi_logloss: 0.249844\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[93]\tvalid_0's multi_logloss: 0.24812\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[94]\tvalid_0's multi_logloss: 0.246625\n",
      "[95]\tvalid_0's multi_logloss: 0.2455\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[96]\tvalid_0's multi_logloss: 0.243726\n",
      "[97]\tvalid_0's multi_logloss: 0.242137\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[98]\tvalid_0's multi_logloss: 0.240449\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[99]\tvalid_0's multi_logloss: 0.239297\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[100]\tvalid_0's multi_logloss: 0.238086\n",
      "Time elapsed for training LightGBM: 45.1\n"
     ]
    }
   ],
   "source": [
    "solver.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[147390   1229     21    399    133    190    496   2279]\n",
      " [  2350 138492    113    507    248    468   1566   5171]\n",
      " [    79   1366  41459   2043     10     79      0     14]\n",
      " [   287   1230    194 125718    489    185     86   1245]\n",
      " [   419    846     64   4208 171374   1024   1110    123]\n",
      " [  1414    490     35   1546   9943 134933   2456      4]\n",
      " [  3224    544     29    374   8057   3483 161698    659]\n",
      " [   561     83      0      6   2952    401   2091 134798]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Still       0.95      0.97      0.96    152137\n",
      "     Walking       0.96      0.93      0.94    148915\n",
      "         Run       0.99      0.92      0.95     45050\n",
      "        Bike       0.93      0.97      0.95    129434\n",
      "         Car       0.89      0.96      0.92    179168\n",
      "         Bus       0.96      0.89      0.93    150821\n",
      "       Train       0.95      0.91      0.93    178068\n",
      "      Subway       0.93      0.96      0.95    140892\n",
      "\n",
      "    accuracy                           0.94   1124485\n",
      "   macro avg       0.95      0.94      0.94   1124485\n",
      "weighted avg       0.94      0.94      0.94   1124485\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# for training dataset\n",
    "solver.predict_raw(X_train, y_train)\n",
    "solver.predict_mean()\n",
    "helper.evaluate(y_train, solver.pred_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Time used: 47.03\n"
     ]
    }
   ],
   "source": [
    "# for validate dataset\n",
    "timer = TimeKeeper.TimeKeeper()\n",
    "solver.predict_raw(X_test)\n",
    "solver.predict_mean()\n",
    "print(\"Time used: {}\".format(timer.get_update_time()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe137f73940>]"
      ]
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x576 with 1 Axes>",
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196.401039 \nL 247.282922 196.401039 \nL 247.394781 83.445195 \nL 247.449199 83.445195 \nL 247.491524 139.923117 \nL 247.559546 83.445195 \nL 247.692567 83.445195 \nL 247.804426 365.834805 \nL 250.47392 365.834805 \nL 250.505664 422.312727 \nL 250.507176 139.923117 \nL 250.578221 139.923117 \nL 250.673452 139.923117 \nL 250.785311 196.401039 \nL 250.81252 196.401039 \nL 250.924379 139.923117 \nL 251.105771 139.923117 \nL 251.219142 365.834805 \nL 251.408092 365.834805 \nL 251.519951 422.312727 \nL 251.586462 422.312727 \nL 251.698321 26.967273 \nL 252.123082 26.967273 \nL 252.23494 83.445195 \nL 252.727724 83.445195 \nL 252.839582 26.967273 \nL 252.842606 26.967273 \nL 252.954464 196.401039 \nL 253.410969 196.401039 \nL 253.527363 422.312727 \nL 253.540967 422.312727 \nL 253.652826 196.401039 \nL 254.781995 196.401039 \nL 254.893854 83.445195 \nL 254.898388 83.445195 \nL 255.010247 196.401039 \nL 255.256639 196.401039 \nL 255.368498 252.878961 \nL 255.463729 252.878961 \nL 255.575587 196.401039 \nL 260.715045 196.401039 \nL 260.826903 252.878961 \nL 261.062714 252.878961 \nL 261.174572 196.401039 \nL 262.798036 196.401039 \nL 262.909895 252.878961 \nL 263.039893 252.878961 \nL 263.151752 196.401039 \nL 263.242448 196.401039 \nL 263.354307 252.878961 \nL 263.425352 252.878961 \nL 263.467677 83.445195 \nL 263.469189 422.312727 \nL 263.534188 422.312727 \nL 263.644535 422.312727 \nL 263.756394 83.445195 \nL 264.912772 83.445195 \nL 265.02463 26.967273 \nL 265.515902 26.967273 \nL 265.627761 83.445195 \nL 267.476454 83.445195 \nL 267.541453 26.967273 \nL 267.586801 83.445195 \nL 268.23528 83.445195 \nL 268.347138 26.967273 \nL 268.451439 26.967273 \nL 268.563298 83.445195 \nL 269.483865 83.445195 \nL 269.539795 139.923117 \nL 269.594212 83.445195 \nL 269.709094 83.445195 \nL 269.827 365.834805 \nL 270.144437 365.834805 \nL 270.256295 252.878961 \nL 271.167793 252.878961 \nL 271.279652 365.834805 \nL 271.346163 365.834805 \nL 271.459533 252.878961 \nL 272.608353 252.878961 \nL 272.638585 309.356883 \nL 272.7187 252.878961 \nL 274.472162 252.878961 \nL 274.561347 309.356883 \nL 274.582509 252.878961 \nL 275.484937 252.878961 \nL 275.596796 365.834805 \nL 276.159113 365.834805 \nL 276.270972 422.312727 \nL 276.281553 422.312727 \nL 276.393412 139.923117 \nL 276.446318 139.923117 \nL 276.558177 83.445195 \nL 277.496883 83.445195 \nL 277.608742 139.923117 \nL 277.935249 139.923117 \nL 277.995713 83.445195 \nL 277.997225 196.401039 \nL 278.044084 196.401039 \nL 280.192075 196.401039 \nL 280.303934 83.445195 \nL 280.316027 83.445195 \nL 280.427886 196.401039 \nL 280.514047 196.401039 \nL 280.544279 83.445195 \nL 280.624394 196.401039 \nL 280.769508 196.401039 \nL 280.881367 365.834805 \nL 281.011365 365.834805 \nL 281.123224 26.967273 \nL 281.694611 26.967273 \nL 281.715773 422.312727 \nL 281.804958 26.967273 \nL 282.728548 26.967273 \nL 282.840407 196.401039 \nL 282.849477 196.401039 \nL 282.961336 252.878961 \nL 283.01273 252.878961 \nL 283.124589 196.401039 \nL 283.130635 196.401039 \nL 283.242494 252.878961 \nL 283.325632 252.878961 \nL 283.437491 196.401039 \nL 283.825974 196.401039 \nL 283.937832 252.878961 \nL 283.962018 252.878961 \nL 284.073877 422.312727 \nL 284.438174 422.312727 \nL 284.550033 139.923117 \nL 284.610497 139.923117 \nL 284.722355 422.312727 \nL 284.823633 422.312727 \nL 284.935492 139.923117 \nL 285.198511 139.923117 \nL 285.31037 422.312727 \nL 285.674667 422.312727 \nL 285.786525 139.923117 \nL 286.253611 139.923117 \nL 286.36547 422.312727 \nL 286.418376 422.312727 \nL 286.530235 196.401039 \nL 286.696512 196.401039 \nL 286.766045 83.445195 \nL 286.80837 139.923117 \nL 287.075925 139.923117 \nL 287.187783 196.401039 \nL 287.225573 196.401039 \nL 287.326851 139.923117 \nL 287.335921 196.401039 \nL 287.600451 196.401039 \nL 287.71231 252.878961 \nL 287.809053 252.878961 \nL 287.920912 365.834805 \nL 290.968307 365.834805 \nL 291.080166 83.445195 \nL 291.273652 83.445195 \nL 291.319 139.923117 \nL 291.320511 26.967273 \nL 291.382487 26.967273 \nL 291.527601 26.967273 \nL 291.63946 83.445195 \nL 291.778528 83.445195 \nL 291.890386 139.923117 \nL 291.949339 139.923117 \nL 292.061198 83.445195 \nL 293.060369 83.445195 \nL 293.172228 196.401039 \nL 293.592454 196.401039 \nL 293.705824 26.967273 \nL 293.915937 26.967273 \nL 294.027796 83.445195 \nL 295.306614 83.445195 \nL 295.367078 139.923117 \nL 295.416961 83.445195 \nL 296.219623 83.445195 \nL 296.331482 139.923117 \nL 296.337528 139.923117 \nL 296.449387 83.445195 \nL 296.626245 83.445195 \nL 296.738104 139.923117 \nL 297.097866 139.923117 \nL 297.209725 83.445195 \nL 297.25205 83.445195 \nL 297.369955 365.834805 \nL 297.397164 365.834805 \nL 297.509022 26.967273 \nL 299.313879 26.967273 \nL 299.425738 83.445195 \nL 299.842941 83.445195 \nL 299.954799 196.401039 \nL 300.043984 196.401039 \nL 300.155843 83.445195 \nL 300.322119 83.445195 \nL 300.433978 196.401039 \nL 300.529209 196.401039 \nL 300.641068 26.967273 \nL 301.034085 26.967273 \nL 301.145944 196.401039 \nL 301.393847 196.401039 \nL 301.508729 365.834805 \nL 301.826166 365.834805 \nL 301.938025 83.445195 \nL 302.551737 83.445195 \nL 302.663595 196.401039 \nL 302.690804 196.401039 \nL 302.802663 26.967273 \nL 306.707139 26.967273 \nL 306.818998 83.445195 \nL 307.110737 83.445195 \nL 307.222596 422.312727 \nL 307.278526 422.312727 \nL 307.348059 83.445195 \nL 307.388873 365.834805 \nL 307.50829 365.834805 \nL 307.617125 26.967273 \nL 307.620148 83.445195 \nL 308.339672 83.445195 \nL 308.451531 365.834805 \nL 309.214892 365.834805 \nL 309.32675 422.312727 \nL 310.227667 422.312727 \nL 310.339526 365.834805 \nL 310.859518 365.834805 \nL 310.971377 26.967273 \nL 311.83148 26.967273 \nL 311.943339 422.312727 \nL 311.94485 422.312727 \nL 312.056709 26.967273 \nL 316.428271 26.967273 \nL 316.54013 83.445195 \nL 318.396381 83.445195 \nL 318.508239 26.967273 \nL 318.515797 26.967273 \nL 318.627656 83.445195 \nL 321.19285 83.445195 \nL 321.304709 26.967273 \nL 322.119464 26.967273 \nL 322.231323 365.834805 \nL 322.680269 365.834805 \nL 322.792128 422.312727 \nL 323.024915 422.312727 \nL 323.136774 139.923117 \nL 324.479079 139.923117 \nL 324.590938 365.834805 \nL 324.639309 365.834805 \nL 324.751168 252.878961 \nL 328.75541 252.878961 \nL 328.867269 365.834805 \nL 329.267844 365.834805 \nL 329.379703 26.967273 \nL 331.98571 26.967273 \nL 332.097568 365.834805 \nL 332.652328 365.834805 \nL 332.747559 83.445195 \nL 332.764186 139.923117 \nL 333.489757 139.923117 \nL 333.601615 365.834805 \nL 334.083817 365.834805 \nL 334.195676 83.445195 \nL 334.239513 83.445195 \nL 334.262187 196.401039 \nL 334.351372 139.923117 \nL 334.520671 139.923117 \nL 334.63253 365.834805 \nL 335.209963 365.834805 \nL 335.321822 252.878961 \nL 336.611221 252.878961 \nL 336.706452 139.923117 \nL 336.707964 365.834805 \nL 336.720057 365.834805 \nL 336.800172 365.834805 \nL 336.91203 139.923117 \nL 336.955867 139.923117 \nL 337.067726 252.878961 \nL 337.140283 252.878961 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348.33523 196.401039 \nL 348.353369 83.445195 \nL 348.445577 196.401039 \nL 348.490925 196.401039 \nL 348.604295 83.445195 \nL 351.031933 83.445195 \nL 351.143792 196.401039 \nL 351.896571 196.401039 \nL 352.00843 365.834805 \nL 352.100638 365.834805 \nL 352.212497 26.967273 \nL 352.723419 26.967273 \nL 352.835278 365.834805 \nL 352.984927 365.834805 \nL 353.096785 26.967273 \nL 353.190505 26.967273 \nL 353.302364 365.834805 \nL 354.108049 365.834805 \nL 354.219908 26.967273 \nL 359.569478 26.967273 \nL 359.681337 422.312727 \nL 361.55875 422.312727 \nL 361.616191 83.445195 \nL 361.669098 422.312727 \nL 361.675144 422.312727 \nL 361.758282 365.834805 \nL 361.785491 422.312727 \nL 362.090835 422.312727 \nL 362.202694 83.445195 \nL 362.511062 83.445195 \nL 362.62292 26.967273 \nL 364.146618 26.967273 \nL 364.258477 83.445195 \nL 364.7044 83.445195 \nL 364.816259 26.967273 \nL 364.885793 26.967273 \nL 364.997652 365.834805 \nL 365.351367 365.834805 \nL 365.463226 26.967273 \nL 366.176704 26.967273 \nL 366.288562 422.312727 \nL 366.386817 422.312727 \nL 366.498676 26.967273 \nL 366.950645 26.967273 \nL 367.062504 196.401039 \nL 368.238533 196.401039 \nL 368.351903 26.967273 \nL 370.099319 26.967273 \nL 370.211178 83.445195 \nL 370.404663 83.445195 \nL 370.516522 196.401039 \nL 370.604195 196.401039 \nL 370.716054 83.445195 \nL 370.726635 83.445195 \nL 370.838494 196.401039 \nL 370.874772 196.401039 \nL 370.941283 252.878961 \nL 370.985119 196.401039 \nL 371.431043 196.401039 \nL 371.542902 83.445195 \nL 371.760573 83.445195 \nL 371.872431 26.967273 \nL 374.865409 26.967273 \nL 374.977268 365.834805 \nL 375.034709 365.834805 \nL 375.146568 26.967273 \nL 375.275054 26.967273 \nL 375.386913 83.445195 \nL 380.091028 83.445195 \nL 380.202887 139.923117 \nL 380.332885 139.923117 \nL 380.444744 365.834805 \nL 381.30787 365.834805 \nL 381.419729 26.967273 \nL 382.204252 26.967273 \nL 382.316111 139.923117 \nL 383.641788 139.923117 \nL 383.753647 422.312727 \nL 383.814111 422.312727 \nL 383.92597 139.923117 \nL 384.423288 139.923117 \nL 384.535147 365.834805 \nL 385.307577 365.834805 \nL 385.342344 422.312727 \nL 385.343855 83.445195 \nL 385.410366 83.445195 \nL 385.511644 83.445195 \nL 385.608386 139.923117 \nL 385.621991 83.445195 \nL 385.751989 83.445195 \nL 385.863848 139.923117 \nL 385.918265 139.923117 \nL 386.030124 83.445195 \nL 386.067914 83.445195 \nL 386.179773 139.923117 \nL 387.246966 139.923117 \nL 387.358825 365.834805 \nL 388.277881 365.834805 \nL 388.38974 422.312727 \nL 390.333664 422.312727 \nL 390.445522 365.834805 \nL 391.999452 365.834805 \nL 392.111311 422.312727 \nL 392.300262 422.312727 \nL 392.41212 83.445195 \nL 392.416655 83.445195 \nL 392.528514 139.923117 \nL 392.776417 139.923117 \nL 392.888276 83.445195 \nL 393.036413 83.445195 \nL 393.148272 139.923117 \nL 393.449082 139.923117 \nL 393.542801 83.445195 \nL 393.559429 139.923117 \nL 395.447423 139.923117 \nL 395.559282 365.834805 \nL 395.716489 365.834805 \nL 395.828348 139.923117 \nL 395.922067 139.923117 \nL 396.033926 83.445195 \nL 397.784365 83.445195 \nL 397.896224 365.834805 \nL 398.042849 365.834805 \nL 398.154708 83.445195 \nL 399.12667 83.445195 \nL 399.238529 139.923117 \nL 399.743405 139.923117 \nL 399.855264 365.834805 \nL 400.134911 365.834805 \nL 400.246769 83.445195 \nL 400.298164 83.445195 \nL 400.410023 365.834805 \nL 401.465123 365.834805 \nL 401.576982 422.312727 \nL 401.833955 422.312727 \nL 401.945813 26.967273 \nL 402.502084 26.967273 \nL 402.613943 139.923117 \nL 402.695569 139.923117 \nL 402.807428 26.967273 \nL 402.920799 26.967273 \nL 403.032657 139.923117 \nL 403.803576 139.923117 \nL 403.915435 252.878961 \nL 403.968341 252.878961 \nL 404.0802 139.923117 \nL 404.130083 139.923117 \nL 404.241941 252.878961 \nL 404.281243 252.878961 \nL 404.282755 309.356883 \nL 404.39159 252.878961 \nL 407.183525 252.878961 \nL 407.295384 365.834805 \nL 407.384568 365.834805 \nL 407.496427 252.878961 \nL 410.049528 252.878961 \nL 410.161387 422.312727 \nL 410.924747 422.312727 \nL 411.036606 365.834805 \nL 411.125791 365.834805 \nL 411.237649 252.878961 \nL 413.911679 252.878961 \nL 414.023538 309.356883 \nL 414.177721 309.356883 \nL 414.28958 252.878961 \nL 414.333417 252.878961 \nL 414.445275 309.356883 \nL 414.480042 309.356883 \nL 414.591901 252.878961 \nL 415.686303 252.878961 \nL 415.798162 365.834805 \nL 418.383006 365.834805 \nL 418.494865 422.312727 \nL 420.209025 422.312727 \nL 420.320884 365.834805 \nL 421.557377 365.834805 \nL 421.670747 139.923117 \nL 422.606431 139.923117 \nL 422.698639 83.445195 \nL 422.716778 139.923117 \nL 422.780265 139.923117 \nL 422.892124 365.834805 \nL 423.369791 365.834805 \nL 423.48165 196.401039 \nL 424.180012 196.401039 \nL 424.29187 83.445195 \nL 424.515588 83.445195 \nL 424.627447 196.401039 \nL 425.380226 196.401039 \nL 425.492085 365.834805 \nL 425.960682 365.834805 \nL 426.072541 83.445195 \nL 426.792065 83.445195 \nL 426.903924 139.923117 \nL 427.011248 139.923117 \nL 427.027876 83.445195 \nL 427.121595 139.923117 \nL 427.349847 139.923117 \nL 427.378568 422.312727 \nL 427.460195 252.878961 \nL 427.552402 252.878961 \nL 427.590193 139.923117 \nL 427.664261 196.401039 \nL 428.034604 196.401039 \nL 428.146463 83.445195 \nL 428.31274 83.445195 \nL 428.424599 196.401039 \nL 428.621107 196.401039 \nL 428.732966 83.445195 \nL 428.74657 83.445195 \nL 428.804011 139.923117 \nL 428.856918 83.445195 \nL 429.378421 83.445195 \nL 429.476676 139.923117 \nL 429.488768 83.445195 \nL 430.241548 83.445195 \nL 430.298989 139.923117 \nL 430.351895 83.445195 \nL 431.258858 83.445195 \nL 431.370717 26.967273 \nL 431.645829 26.967273 \nL 431.757688 83.445195 \nL 434.489158 83.445195 \nL 434.502762 26.967273 \nL 434.504274 196.401039 \nL 434.599505 83.445195 \nL 434.706829 83.445195 \nL 434.818688 365.834805 \nL 435.069614 365.834805 \nL 435.181473 26.967273 \nL 436.730868 26.967273 \nL 436.842727 83.445195 \nL 437.465508 83.445195 \nL 437.577367 196.401039 \nL 437.899339 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196.401039 \nL 518.127775 139.923117 \nL 518.239634 196.401039 \nL 518.505677 196.401039 \nL 518.62207 422.312727 \nL 518.882066 422.312727 \nL 518.996948 252.878961 \nL 519.033227 252.878961 \nL 519.146597 139.923117 \nL 521.052731 139.923117 \nL 521.16459 26.967273 \nL 521.241682 26.967273 \nL 521.353541 139.923117 \nL 521.427609 139.923117 \nL 521.539468 26.967273 \nL 521.56063 26.967273 \nL 521.672489 139.923117 \nL 522.5205 139.923117 \nL 522.632358 365.834805 \nL 522.984562 365.834805 \nL 523.096421 422.312727 \nL 523.131188 422.312727 \nL 523.243047 365.834805 \nL 523.344324 365.834805 \nL 523.456183 422.312727 \nL 523.947455 422.312727 \nL 524.059314 365.834805 \nL 524.382797 365.834805 \nL 524.431168 83.445195 \nL 524.43268 422.312727 \nL 524.493144 422.312727 \nL 524.880115 422.312727 \nL 524.991974 83.445195 \nL 525.146158 83.445195 \nL 525.258016 422.312727 \nL 526.139282 422.312727 \nL 526.251141 139.923117 \nL 526.43858 139.923117 \nL 526.550439 365.834805 \nL 528.175414 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365.834805 \nL 565.961005 365.834805 \nL 566.072864 422.312727 \nL 566.701691 422.312727 \nL 566.81355 365.834805 \nL 570.518494 365.834805 \nL 570.630353 83.445195 \nL 571.425457 83.445195 \nL 571.537316 139.923117 \nL 573.110896 139.923117 \nL 573.222755 83.445195 \nL 574.120649 83.445195 \nL 574.232507 365.834805 \nL 574.61192 365.834805 \nL 574.723779 422.312727 \nL 575.107727 422.312727 \nL 575.219585 139.923117 \nL 576.37294 139.923117 \nL 576.484799 365.834805 \nL 576.945838 365.834805 \nL 577.057697 139.923117 \nL 577.405366 139.923117 \nL 577.517225 252.878961 \nL 577.994892 252.878961 \nL 578.01152 365.834805 \nL 578.105239 252.878961 \nL 578.465001 252.878961 \nL 578.528489 139.923117 \nL 578.575349 252.878961 \nL 579.659169 252.878961 \nL 579.771028 365.834805 \nL 581.269029 365.834805 \nL 581.380888 139.923117 \nL 581.98553 139.923117 \nL 582.097388 83.445195 \nL 582.216805 83.445195 \nL 582.328664 139.923117 \nL 582.491917 139.923117 \nL 582.603776 365.834805 \nL 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize = [20, 8])\n",
    "plt.plot(solver.pred_mean, alpha = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model/model_for_submission.pickle','wb') as p:\n",
    "    pickle.dump(solver, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}