sm-xgb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from xgboost.sklearn import XGBRegressor\n",
    "import scipy.stats as st\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Obtain Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ld = pd.read_csv('../data/interim/listingplusreviewfinal.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_ld.loc[:, df_ld.columns != 'polarity']\n",
    "y = df_ld['polarity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1053065, 45)\n",
      "(1053065,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "             X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# 2. XGboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor()\n",
    "#base_score=0.5, booster='gbtree', objective='reg:linear', random_state=0, seed=42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, importance_type='gain',\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
       "       min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, objective='reg:linear', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03663996409365067"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26743852471939583"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## GridSearch for best Hyper Parameter (Params from results on HPC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearch on XGboost run on HPC yielded following results -  \n",
    "```\n",
    "BEST Score: -0.071170BEST Estimator: XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bytree=0.9809552080191412, gamma=0.9277541727646688,\n",
    "       importance_type='gain', learning_rate=0.422880206247594,\n",
    "       max_delta_step=0, max_depth=24, min_child_weight=20.316769021830574,\n",
    "       missing=None, n_estimators=58, n_jobs=1, nthread=None,\n",
    "       objective='reg:linear', random_state=0,\n",
    "       reg_alpha=19.294198125487966, reg_lambda=1, scale_pos_weight=1,\n",
    "       seed=None, silent=True, subsample=0.9819023896694455)BEST RMSE: 0.266812\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "                   colsample_bytree=0.9809552080191412, gamma=0.9277541727646688,\n",
    "                   importance_type='gain', learning_rate=0.422880206247594,\n",
    "                   max_delta_step=0, max_depth=24, min_child_weight=20.316769021830574,\n",
    "                   missing=None, n_estimators=58, n_jobs=1, nthread=None,\n",
    "                   objective='reg:linear', random_state=0,\n",
    "                   reg_alpha=19.294198125487966, reg_lambda=1, scale_pos_weight=1,\n",
    "                   seed=None, silent=True, subsample=0.9819023896694455)\n",
    "#reg_cv = GridSearchCV(xgb, {'max_depth': [50,100], 'n_estimators': [100,200], 'random_state':[42],'n_jobs':[3]},verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.9809552080191412, gamma=0.9277541727646688,\n",
       "       importance_type='gain', learning_rate=0.422880206247594,\n",
       "       max_delta_step=0, max_depth=24, min_child_weight=20.316769021830574,\n",
       "       missing=None, n_estimators=58, n_jobs=1, nthread=None,\n",
       "       objective='reg:linear', random_state=0,\n",
       "       reg_alpha=19.294198125487966, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.9819023896694455)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reg_cv.fit(X_train,y_train)\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26672539241230053"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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