{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from scipy.sparse.construct import hstack\n",
    "\n",
    "import xgboost as xgb\n",
    "#import lightgbm as lgb\n",
    "from sklearn.metrics import log_loss\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from xgboost import plot_tree\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from contextlib import contextmanager\n",
    "@contextmanager\n",
    "def timer(name):\n",
    "    start_time = time.time()\n",
    "    yield\n",
    "    print(f'[{name} done in {time.time() - start_time:.2f} s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([17, 18, 19, 20, 21, 22, 23, 24])\n",
      "dict_keys([25, 24])\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('data/processed_train.csv')\n",
    "test = pd.read_csv('data/processed_test.csv')\n",
    "print(Counter(train['day']).keys())\n",
    "print(Counter(test['day']).keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort data according to day and time\n",
    "train = train.sort_values(by = ['day','time']).reset_index().iloc[:, 1:]\n",
    "# convert hour to time slot\n",
    "# is_midnight: 0, is_morning: 1, is_afternoon: 2, is_night: 3\n",
    "def f(x):\n",
    "    if x <= 7:\n",
    "        return 0\n",
    "    elif x > 7 and x <= 13:\n",
    "        return 1\n",
    "    elif x > 13 and x <= 19:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3\n",
    "train['hour'] = train['hour'].apply(lambda x: f(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete some features\n",
    "# check importance of features\n",
    "exclude_features = ['instance_id','context_id', 'context_timestamp', 'is_trade','datetime', 'day', 'time']\n",
    "df_train = train[(train['day'] >= 17) & (train['day'] <= 22)]\n",
    "df_val = train[(train['day'] >= 23) & (train['day'] <= 24)]\n",
    "y_train = df_train['is_trade']\n",
    "x_train = df_train.drop(exclude_features, axis = 1)\n",
    "y_val = df_val['is_trade']\n",
    "x_val = df_val.drop(exclude_features, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.to_csv('data/df_train.csv',index=None)\n",
    "df_val.to_csv('data/df_val.csv', index = None)\n",
    "y_train.to_csv('data/y_train.csv', index = None)\n",
    "x_train.to_csv('data/x_train.csv', index =None)\n",
    "y_val.to_csv('data/y_val.csv', index = None)\n",
    "x_val.to_csv('data/x_val.csv', index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_result(model, dtrain_x, dval_x, dtrain_y, dval_y ):\n",
    "    y_pred_dtrain = model.predict_proba(dtrain_x)\n",
    "    y_pred_val = model.predict_proba(dval_x)\n",
    "    xgb_train_logloss = log_loss(dtrain_y, y_pred_dtrain)\n",
    "    xgb_val_logloss = log_loss(dval_y, y_pred_val)\n",
    "    plt.rcParams['figure.figsize'] = 12, 6\n",
    "    fscore = pd.Series(model.get_booster().get_fscore()).sort_values(ascending = False)\n",
    "    fscore.plot(kind = 'bar',title = 'Feature Importance')\n",
    "    plt.ylabel('Feature Importance Score')\n",
    "    print(\"Logloss Score(Train): %f\"%xgb_train_logloss)\n",
    "    print(\"Logloss Score(Val): %f\"%xgb_val_logloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost1 = xgb.XGBClassifier(nthread = 25, learning_rate = 0.05, n_estimator = 500,\n",
    "                           max_depth = 8, subsample = 0.7, colsample_bytree = 0.5, \n",
    "                             min_child_weight = 10, seed = 123, objective = 'binary:logistic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.648111\tvalidation_1-logloss:0.647771\n",
      "Multiple eval metrics have been passed: 'validation_1-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-logloss hasn't improved in 200 rounds.\n",
      "[99]\tvalidation_0-logloss:0.084535\tvalidation_1-logloss:0.07839\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.5, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
       "       max_depth=8, min_child_weight=10, missing=None, n_estimator=500,\n",
       "       n_estimators=100, n_jobs=1, nthread=25, objective='binary:logistic',\n",
       "       random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=123, silent=True, subsample=0.7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logloss Score(Train): 0.084535\n",
      "Logloss Score(Val): 0.078390\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_result(xgboost1, x_train, x_val, y_train, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = {'n_estimator':99}\n",
    "xgboost1.set_params(**t)\n",
    "xgboost1.save_model('model/xgboost_78_start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------- -------------\n",
      "logloss:  0.078354\n",
      "Best so far!\n",
      "max_depth:  11\n",
      "min_child_weight:  2\n",
      "Minimal logloss 0.078354\n",
      "[Tune max_depth and min_child_weight:  done in 420.37 s]\n"
     ]
    }
   ],
   "source": [
    "# Tune max_depth and min_child_weight\n",
    "# 'max_depth':range(3,10,2),range(10,15,1)\n",
    "# 'min_child_weight':range(1,15,2), range(1,8,1)\n",
    "# 9,1,0.78386; 10,2,0.78381\n",
    "# best max_depth = 11, min_child_weight = 2, best_logloss = 0.078354\n",
    "grid1 = {\n",
    "    'max_depth':range(11,12,1),\n",
    "    'min_child_weight':range(2,3,1)\n",
    "}\n",
    "best_logloss = 0.07839\n",
    "with timer('Tune max_depth and min_child_weight: '):\n",
    "    for g in ParameterGrid(grid1):\n",
    "        xgboost1.set_params(**g)\n",
    "        print('----------- -------------')\n",
    "        xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = False)\n",
    "        print('logloss: ',xgboost1.get_booster().best_score)\n",
    "        if xgboost1.get_booster().best_score < best_logloss:\n",
    "            best_logloss = xgboost1.get_booster().best_score\n",
    "            print('Best so far!')\n",
    "            print('max_depth: ', xgboost1.get_params()['max_depth'])\n",
    "            print('min_child_weight: ', xgboost1.get_params()['min_child_weight'])\n",
    "            print('Minimal logloss', best_logloss)          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------- -------------\n",
      "logloss:  0.078354\n",
      "----------- -------------\n",
      "logloss:  0.078444\n",
      "----------- -------------\n",
      "logloss:  0.078417\n",
      "----------- -------------\n",
      "logloss:  0.078416\n",
      "----------- -------------\n",
      "logloss:  0.078446\n",
      "[Tune gamme:  done in 2143.38 s]\n"
     ]
    }
   ],
   "source": [
    "# Tune gamma\n",
    "# best gamma = 0\n",
    "t = {'n_estimator':99,'max_depth':11, 'min_child_weight': 2}\n",
    "xgboost1.set_params(**t)\n",
    "\n",
    "grid2 ={\n",
    "    'gamma': [i/10.0 for i in range(0,5)]\n",
    "}\n",
    "best_logloss = 0.078354\n",
    "with timer('Tune gamme: '):\n",
    "    for g in ParameterGrid(grid2):\n",
    "        xgboost1.set_params(**g)\n",
    "        print('----------- -------------')\n",
    "        xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = False)\n",
    "        print('logloss: ',xgboost1.get_booster().best_score)\n",
    "        if xgboost1.get_booster().best_score < best_logloss:\n",
    "            best_logloss = xgboost1.get_booster().best_score\n",
    "            print('Best so far!')\n",
    "            print('gamma: ', xgboost1.get_params()['gamma'])\n",
    "            print('Minimal logloss', best_logloss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------- -------------\n",
      "logloss:  0.078208\n",
      "Best so far!\n",
      "subsample:  0.9\n",
      "colsample_bytree:  0.8\n",
      "Minimal logloss 0.078208\n",
      "[Tune subsample and colsample_bybree:  done in 584.08 s]\n"
     ]
    }
   ],
   "source": [
    "# Tune subsample and colsample_bytree\n",
    "t = {'n_estimator':99,'max_depth':11, 'min_child_weight': 2,'gamma':0}\n",
    "xgboost1.set_params(**t)\n",
    "\n",
    "# 'subsample': [i/10.0 for i in range(6,10)],\n",
    "#  'colsample_bytree':[i/10.0 for i in range(6,10)]\n",
    "# 0.9; 0.81,0.82,0.83,0.84,0.85\n",
    "# best subsample: 0.9, colsample_bytree: 0.8, best_logloss = 0.078208\n",
    "grid3 ={\n",
    "    'subsample': [0.9],\n",
    "    'colsample_bytree':[0.8]\n",
    "}\n",
    "best_logloss = 0.078354\n",
    "with timer('Tune subsample and colsample_bybree: '):\n",
    "    for g in ParameterGrid(grid3):\n",
    "        xgboost1.set_params(**g)\n",
    "        print('----------- -------------')\n",
    "        xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = False)\n",
    "        print('logloss: ',xgboost1.get_booster().best_score)\n",
    "        if xgboost1.get_booster().best_score < best_logloss:\n",
    "            best_logloss = xgboost1.get_booster().best_score\n",
    "            print('Best so far!')\n",
    "            print('subsample: ', xgboost1.get_params()['subsample'])\n",
    "            print('colsample_bytree: ', xgboost1.get_params()['colsample_bytree'])\n",
    "            print('Minimal logloss', best_logloss)          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------- -------------\n",
      "logloss:  0.078182\n",
      "[Tune reg_alpha:  done in 580.77 s]\n"
     ]
    }
   ],
   "source": [
    "# Tuning Regularization Parameters\n",
    "t = {'n_estimator':99,'max_depth':11, 'min_child_weight': 2,'gamma':0, 'subsample':0.9, 'colsample_bytree': 0.8}\n",
    "xgboost1.set_params(**t)\n",
    "# Tune reg_alpha\n",
    "# 'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "# 'reg_alpha':[0.01,0.02]\n",
    "# best reg_alpha = 0.005\n",
    "grid4 ={\n",
    "    'reg_alpha':[0.005]\n",
    "}\n",
    "best_logloss = 0.078182\n",
    "with timer('Tune reg_alpha: '):\n",
    "    for g in ParameterGrid(grid4):\n",
    "        xgboost1.set_params(**g)\n",
    "        print('----------- -------------')\n",
    "        xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = False)\n",
    "        print('logloss: ',xgboost1.get_booster().best_score)\n",
    "        if xgboost1.get_booster().best_score < best_logloss:\n",
    "            best_logloss = xgboost1.get_booster().best_score\n",
    "            print('Best so far!')\n",
    "            print('reg_alphs: ', xgboost1.get_params()['reg_alpha'])\n",
    "            print('Minimal logloss', best_logloss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------- -------------\n",
      "logloss:  0.078182\n",
      "[Tune reg_lambda:  done in 562.42 s]\n"
     ]
    }
   ],
   "source": [
    "# Tuning Regularization Parameters\n",
    "t = {'n_estimator':99,'max_depth':11, 'min_child_weight': 2,'gamma':0, 'subsample':0.9, 'colsample_bytree': 0.8,'reg_alpha':0.005}\n",
    "xgboost1.set_params(**t)\n",
    "# Tune reg_lambda\n",
    "# 'reg_lambda':[1e-5, 1e-2, 0.1, 1, 100]\n",
    "# 'reg_lambda':[0.01,0.02]\n",
    "# 'reg_lambda':[0.05, 0.06, 0.07,0.88,0.89, 0.91, 0.92, 0.94, 0.96, 0.98,1.02,1.04, 1.06, 1.5]\n",
    "# best reg_lambda = 1\n",
    "grid5 ={\n",
    "    'reg_lambda':[1]\n",
    "}\n",
    "best_logloss = 0.078182\n",
    "with timer('Tune reg_lambda: '):\n",
    "    for g in ParameterGrid(grid5):\n",
    "        xgboost1.set_params(**g)\n",
    "        print('----------- -------------')\n",
    "        xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = False)\n",
    "        print('logloss: ',xgboost1.get_booster().best_score)\n",
    "        if xgboost1.get_booster().best_score < best_logloss:\n",
    "            best_logloss = xgboost1.get_booster().best_score\n",
    "            print('Best so far!')\n",
    "            print('reg_lambda: ', xgboost1.get_params()['reg_lambda'])\n",
    "            print('Minimal logloss', best_logloss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------- -------------\n",
      "logloss:  0.077445\n",
      "Best so far!\n",
      "scale_pos_weight:  0.63\n",
      "Minimal logloss 0.077445\n",
      "----------- -------------\n",
      "logloss:  0.077539\n",
      "----------- -------------\n",
      "logloss:  0.077447\n",
      "----------- -------------\n",
      "logloss:  0.077509\n",
      "[Tune reg_lambda:  done in 2130.78 s]\n"
     ]
    }
   ],
   "source": [
    "# Tuning scale_pos_weight\n",
    "t = {'n_estimator':99,'max_depth':11, 'min_child_weight': 2,'gamma':0, \n",
    "     'subsample':0.9, 'colsample_bytree': 0.8,'reg_alpha':0.005, 'reg_lambda':1}\n",
    "xgboost1.set_params(**t)\n",
    "# Tune scale_pos_weight\n",
    "# 'scale_pos_weight':[0,0.1,0.11,0.12,0.2,0.5,0.7,0.8,0.9]\n",
    "# 'scale_pos_weight':[0.55,0.6, 0.65,0.67,0.72]\n",
    "# best logloss = 0.077521\n",
    "# best scale_pos_weight: 0.63\n",
    "\n",
    "grid6 ={\n",
    "    'scale_pos_weight':[0.63,0.64,0.65,0.66]\n",
    "}\n",
    "best_logloss = 0.0.077445\n",
    "with timer('Tune scale_pos_weight: '):\n",
    "    for g in ParameterGrid(grid6):\n",
    "        xgboost1.set_params(**g)\n",
    "        print('----------- -------------')\n",
    "        xgboost1.fit(x_train,y_train, eval_set=[(x_train, y_train), (x_val, y_val)], early_stopping_rounds=200, eval_metric='logloss', verbose = False)\n",
    "        print('logloss: ',xgboost1.get_booster().best_score)\n",
    "        if xgboost1.get_booster().best_score < best_logloss:\n",
    "            best_logloss = xgboost1.get_booster().best_score\n",
    "            print('Best so far!')\n",
    "            print('scale_pos_weight: ', xgboost1.get_params()['scale_pos_weight'])\n",
    "            print('Minimal logloss', best_logloss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = {'n_estimator':99,'max_depth':11, 'min_child_weight': 2,'gamma':0, \n",
    "     'subsample':0.9, 'colsample_bytree': 0.8,'reg_alpha':0.005, \n",
    "     'reg_lambda':1,'scale_pos_weight':0.63}\n",
    "xgboost1.set_params(**t)\n",
    "xgboost1.save_model('model/xgboost_78_final')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logloss Score(Train): 0.072778\n",
      "Logloss Score(Val): 0.077509\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_result(xgboost1, x_train, x_val, y_train, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see a significant boost in performance and the effect of parameter tuning is clearer.\n",
    "\n",
    "As we come to the end, I would like to share 2 key thoughts:\n",
    "\n",
    "1. It is difficult to get a very big leap in performance by just using parameter tuning or slightly better models. The max score for GBM was 0.8487 while XGBoost gave 0.8494. This is a decent improvement but not something very substantial.   \n",
    "\n",
    "2. A significant jump can be obtained by other methods like feature engineering, creating ensemble of models, stacking, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#xgboost1.get_booster().best_iteration\n",
    "#xgboost1.get_booster().best_score\n",
    "#xgboost1.get_params()"
   ]
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}