2021-06-04_final_submision_split.ipynb

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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "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 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": 30,
   "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": 31,
   "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": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_ratio = 0.1\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, df[col_label], test_size = test_ratio, random_state = 999)\n",
    "X_train, y_train = data_df.drop(col_drop_x + col_drop, axis = 1).fillna(0), data_df[col_label]\n",
    "X_val, y_val = val_df.drop(col_drop_x + col_drop, axis = 1).fillna(0), val_df[col_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train: (980527, 32)\ny_train: 980527\n"
     ]
    }
   ],
   "source": [
    "solver = Solver.Solver(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training rf...\n",
      "Time elapsed for training rf: 30.2\n",
      "Training bag (tree-based)...\n",
      "Time elapsed for training bag (tree-based): 28.14\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 0.030194 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 5020\n",
      "[LightGBM] [Info] Number of data points in the train set: 784421, 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 -2.080999\n",
      "[LightGBM] [Info] Start training from score -2.076881\n",
      "[LightGBM] [Info] Start training from score -3.145077\n",
      "[LightGBM] [Info] Start training from score -2.122769\n",
      "[LightGBM] [Info] Start training from score -1.821799\n",
      "[LightGBM] [Info] Start training from score -1.932976\n",
      "[LightGBM] [Info] Start training from score -1.838161\n",
      "[LightGBM] [Info] Start training from score -2.107433\n",
      "[1]\tvalid_0's multi_logloss: 1.65739\n",
      "[2]\tvalid_0's multi_logloss: 1.44895\n",
      "[3]\tvalid_0's multi_logloss: 1.29575\n",
      "[4]\tvalid_0's multi_logloss: 1.17797\n",
      "[5]\tvalid_0's multi_logloss: 1.08372\n",
      "[6]\tvalid_0's multi_logloss: 1.00426\n",
      "[7]\tvalid_0's multi_logloss: 0.939691\n",
      "[8]\tvalid_0's multi_logloss: 0.882964\n",
      "[9]\tvalid_0's multi_logloss: 0.8349\n",
      "[10]\tvalid_0's multi_logloss: 0.791688\n",
      "[11]\tvalid_0's multi_logloss: 0.755908\n",
      "[12]\tvalid_0's multi_logloss: 0.723341\n",
      "[13]\tvalid_0's multi_logloss: 0.694881\n",
      "[14]\tvalid_0's multi_logloss: 0.66861\n",
      "[15]\tvalid_0's multi_logloss: 0.647121\n",
      "[16]\tvalid_0's multi_logloss: 0.626311\n",
      "[17]\tvalid_0's multi_logloss: 0.60834\n",
      "[18]\tvalid_0's multi_logloss: 0.591094\n",
      "[19]\tvalid_0's multi_logloss: 0.576089\n",
      "[20]\tvalid_0's multi_logloss: 0.561972\n",
      "[21]\tvalid_0's multi_logloss: 0.547925\n",
      "[22]\tvalid_0's multi_logloss: 0.536186\n",
      "[23]\tvalid_0's multi_logloss: 0.524342\n",
      "[24]\tvalid_0's multi_logloss: 0.513613\n",
      "[25]\tvalid_0's multi_logloss: 0.503716\n",
      "[26]\tvalid_0's multi_logloss: 0.493632\n",
      "[27]\tvalid_0's multi_logloss: 0.484361\n",
      "[28]\tvalid_0's multi_logloss: 0.47367\n",
      "[29]\tvalid_0's multi_logloss: 0.463111\n",
      "[30]\tvalid_0's multi_logloss: 0.4536\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[31]\tvalid_0's multi_logloss: 0.445607\n",
      "[32]\tvalid_0's multi_logloss: 0.436884\n",
      "[33]\tvalid_0's multi_logloss: 0.428273\n",
      "[34]\tvalid_0's multi_logloss: 0.419573\n",
      "[35]\tvalid_0's multi_logloss: 0.41096\n",
      "[36]\tvalid_0's multi_logloss: 0.405577\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[37]\tvalid_0's multi_logloss: 0.399959\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[38]\tvalid_0's multi_logloss: 0.394214\n",
      "[39]\tvalid_0's multi_logloss: 0.388021\n",
      "[40]\tvalid_0's multi_logloss: 0.38211\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[41]\tvalid_0's multi_logloss: 0.376642\n",
      "[42]\tvalid_0's multi_logloss: 0.371129\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[43]\tvalid_0's multi_logloss: 0.3666\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",
      "[44]\tvalid_0's multi_logloss: 0.36226\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[45]\tvalid_0's multi_logloss: 0.357509\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[46]\tvalid_0's multi_logloss: 0.353692\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",
      "[47]\tvalid_0's multi_logloss: 0.34958\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[48]\tvalid_0's multi_logloss: 0.345892\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[49]\tvalid_0's multi_logloss: 0.340354\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[50]\tvalid_0's multi_logloss: 0.335804\n",
      "[51]\tvalid_0's multi_logloss: 0.330488\n",
      "[52]\tvalid_0's multi_logloss: 0.326645\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[53]\tvalid_0's multi_logloss: 0.322985\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[54]\tvalid_0's multi_logloss: 0.318291\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",
      "[55]\tvalid_0's multi_logloss: 0.315043\n",
      "[56]\tvalid_0's multi_logloss: 0.311401\n",
      "[57]\tvalid_0's multi_logloss: 0.307844\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[58]\tvalid_0's multi_logloss: 0.304329\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[59]\tvalid_0's multi_logloss: 0.301653\n",
      "[60]\tvalid_0's multi_logloss: 0.297834\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",
      "[61]\tvalid_0's multi_logloss: 0.294887\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[62]\tvalid_0's multi_logloss: 0.291282\n",
      "[63]\tvalid_0's multi_logloss: 0.28864\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[64]\tvalid_0's multi_logloss: 0.286148\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[65]\tvalid_0's multi_logloss: 0.282721\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: 0.279352\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[67]\tvalid_0's multi_logloss: 0.276207\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[68]\tvalid_0's multi_logloss: 0.273648\n",
      "[69]\tvalid_0's multi_logloss: 0.27087\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[70]\tvalid_0's multi_logloss: 0.268239\n",
      "[71]\tvalid_0's multi_logloss: 0.265711\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[72]\tvalid_0's multi_logloss: 0.263751\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",
      "[73]\tvalid_0's multi_logloss: 0.261682\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[74]\tvalid_0's multi_logloss: 0.259262\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[75]\tvalid_0's multi_logloss: 0.256807\n",
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      "[76]\tvalid_0's multi_logloss: 0.254853\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[77]\tvalid_0's multi_logloss: 0.253156\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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      "[78]\tvalid_0's multi_logloss: 0.251591\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[79]\tvalid_0's multi_logloss: 0.249203\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",
      "[80]\tvalid_0's multi_logloss: 0.247275\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",
      "[81]\tvalid_0's multi_logloss: 0.24588\n",
      "[82]\tvalid_0's multi_logloss: 0.244122\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[83]\tvalid_0's multi_logloss: 0.24181\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: 0.239984\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[85]\tvalid_0's multi_logloss: 0.237902\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",
      "[86]\tvalid_0's multi_logloss: 0.236267\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: 0.234821\n",
      "[88]\tvalid_0's multi_logloss: 0.232946\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",
      "[89]\tvalid_0's multi_logloss: 0.230925\n",
      "[90]\tvalid_0's multi_logloss: 0.229507\n",
      "[91]\tvalid_0's multi_logloss: 0.22817\n",
      "[92]\tvalid_0's multi_logloss: 0.226229\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[93]\tvalid_0's multi_logloss: 0.224667\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[94]\tvalid_0's multi_logloss: 0.223106\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[95]\tvalid_0's multi_logloss: 0.22176\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: 0.220194\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[97]\tvalid_0's multi_logloss: 0.218958\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",
      "[98]\tvalid_0's multi_logloss: 0.217592\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[99]\tvalid_0's multi_logloss: 0.216261\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",
      "[100]\tvalid_0's multi_logloss: 0.214583\n",
      "Time elapsed for training LightGBM: 43.08\n"
     ]
    }
   ],
   "source": [
    "solver.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the size of the trained model on hard disc\n",
    "with open('model/model_rf.pickle','wb') as p:\n",
    "    pickle.dump(solver.model_rf, p)\n",
    "with open('model/model_bag.pickle','wb') as p:\n",
    "    pickle.dump(solver.model_bag, p)\n",
    "with open('model/model_lgb.pickle','wb') as p:\n",
    "    pickle.dump(solver.model_lgb, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[118665    889     20    285     87    205     71   2079]\n",
      " [  1006 115172    106    424    122    280   1039   4617]\n",
      " [    81    472  40281   1309     14    104      0     15]\n",
      " [   160    934    191 115504    436     88     90      0]\n",
      " [   242    454     71   4460 151626    905    937      0]\n",
      " [  1161    406     32   1797   9504 127601   1125     17]\n",
      " [  2520    370     24    284   7208   2584 142673    597]\n",
      " [   540     58      0      2   2747    508    726 114602]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Still       0.95      0.97      0.96    122301\n",
      "     Walking       0.97      0.94      0.95    122766\n",
      "         Run       0.99      0.95      0.97     42276\n",
      "        Bike       0.93      0.98      0.96    117403\n",
      "         Car       0.88      0.96      0.92    158695\n",
      "         Bus       0.96      0.90      0.93    141643\n",
      "       Train       0.97      0.91      0.94    156260\n",
      "      Subway       0.94      0.96      0.95    119183\n",
      "\n",
      "    accuracy                           0.94    980527\n",
      "   macro avg       0.95      0.95      0.95    980527\n",
      "weighted avg       0.95      0.94      0.94    980527\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": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[24842   363     0    28     7    43   403  4150]\n",
      " [ 3238 19460     3    41   262  1522   633   990]\n",
      " [    0  1540   202  1032     0     0     0     0]\n",
      " [  145   605   221  9815     0     0     0  1245]\n",
      " [  314   440     0   549 10446  4921  3720    83]\n",
      " [  745    24     0    46     0  4852  1429  2082]\n",
      " [ 3148    45     0    51  4191   312 13247   814]\n",
      " [  592     0     0     0   202   129  4542 16244]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Still       0.75      0.83      0.79     29836\n",
      "     Walking       0.87      0.74      0.80     26149\n",
      "         Run       0.47      0.07      0.13      2774\n",
      "        Bike       0.85      0.82      0.83     12031\n",
      "         Car       0.69      0.51      0.59     20473\n",
      "         Bus       0.41      0.53      0.46      9178\n",
      "       Train       0.55      0.61      0.58     21808\n",
      "      Subway       0.63      0.75      0.69     21709\n",
      "\n",
      "    accuracy                           0.69    143958\n",
      "   macro avg       0.65      0.61      0.61    143958\n",
      "weighted avg       0.70      0.69      0.69    143958\n",
      "\n",
      "Time used: 7.41\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# for validate dataset\n",
    "timer = TimeKeeper.TimeKeeper()\n",
    "solver.predict_raw(X_val, y_val)\n",
    "solver.predict_mean()\n",
    "helper.evaluate(y_val, solver.pred_mean)\n",
    "print(\"Time used: {}\".format(timer.get_update_time()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model/model_for_form.pickle','wb') as p:\n",
    "    pickle.dump(solver, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}