[Notebook4]XGBoostModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBOOST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why XGBoost?\n",
    "https://towardsdatascience.com/a-beginners-guide-to-xgboost-87f5d4c30ed7\n",
    "\n",
    "https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d\n",
    "\n",
    "TLDR: Its fast and powerful :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://miro.medium.com/max/1920/1*U72CpSTnJ-XTjCisJqCqLg.jpeg\"\n",
    "     alt=\"Why XGBoost\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "\n",
    "# The output of multiple commands in a cell will be output at once.\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We start by importing the new data set\n",
    "home_folder = '/Users/anthonymiyoro/Documents/code/DataTho/'\n",
    "\n",
    "#Load dataset\n",
    "train_modified = pd.read_csv(home_folder + 'train_modified.csv', encoding = \"ISO-8859-1\")\n",
    "test_bundas = pd.read_csv(home_folder + 'test_modified.csv', encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Max_Price</th>\n",
       "      <th>Store_Establishment_Year</th>\n",
       "      <th>Item_Store_Sales</th>\n",
       "      <th>tracker</th>\n",
       "      <th>FatContent_LF</th>\n",
       "      <th>FatContent_Low Fat</th>\n",
       "      <th>FatContent_Regular</th>\n",
       "      <th>...</th>\n",
       "      <th>Store_Type_Supermarket Type1</th>\n",
       "      <th>Store_Type_Supermarket Type2</th>\n",
       "      <th>Store_Type_Supermarket Type3</th>\n",
       "      <th>Store_ID_OUT013</th>\n",
       "      <th>Store_ID_OUT018</th>\n",
       "      <th>Store_ID_OUT019</th>\n",
       "      <th>Store_ID_OUT027</th>\n",
       "      <th>Store_ID_OUT035</th>\n",
       "      <th>Store_ID_OUT046</th>\n",
       "      <th>Store_ID_OUT049</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9.300</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1999</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.920</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>2009</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.500</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1999</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>8.930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1987</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>10.395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>2009</td>\n",
       "      <td>556.6088</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Weight  Visibility  Max_Price  Store_Establishment_Year  \\\n",
       "0           1   9.300    0.016047   249.8092                      1999   \n",
       "1           2   5.920    0.019278    48.2692                      2009   \n",
       "2           3  17.500    0.016760   141.6180                      1999   \n",
       "3           5   8.930    0.000000    53.8614                      1987   \n",
       "4           6  10.395    0.000000    51.4008                      2009   \n",
       "\n",
       "   Item_Store_Sales  tracker  FatContent_LF  FatContent_Low Fat  \\\n",
       "0         3735.1380        1              0                   1   \n",
       "1          443.4228        2              0                   0   \n",
       "2         2097.2700        3              0                   1   \n",
       "3          994.7052        5              0                   1   \n",
       "4          556.6088        6              0                   0   \n",
       "\n",
       "   FatContent_Regular  ...  Store_Type_Supermarket Type1  \\\n",
       "0                   0  ...                             1   \n",
       "1                   1  ...                             0   \n",
       "2                   0  ...                             1   \n",
       "3                   0  ...                             1   \n",
       "4                   1  ...                             0   \n",
       "\n",
       "   Store_Type_Supermarket Type2  Store_Type_Supermarket Type3  \\\n",
       "0                             0                             0   \n",
       "1                             1                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             1                             0   \n",
       "\n",
       "   Store_ID_OUT013  Store_ID_OUT018  Store_ID_OUT019  Store_ID_OUT027  \\\n",
       "0                0                0                0                0   \n",
       "1                0                1                0                0   \n",
       "2                0                0                0                0   \n",
       "3                1                0                0                0   \n",
       "4                0                1                0                0   \n",
       "\n",
       "   Store_ID_OUT035  Store_ID_OUT046  Store_ID_OUT049  \n",
       "0                0                0                1  \n",
       "1                0                0                0  \n",
       "2                0                0                1  \n",
       "3                0                0                0  \n",
       "4                0                0                0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_modified.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data between train and test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then remove the Item_ID feature as it does not correlate to Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_modified = train_modified.drop('Item_ID', axis=1)\n",
    "test_bundas = test_bundas.drop('Item_ID', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our scikit learn package requires that the explanatory variables are stored seperately from the outcome variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Item_Store_Sales'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-739715ce2806>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mrf_testX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_bundas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Item_Store_Sales'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3938\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3939\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3940\u001b[0;31m                                            errors=errors)\n\u001b[0m\u001b[1;32m   3941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3942\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3778\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3779\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3780\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3810\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   4963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4964\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 4965\u001b[0;31m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[1;32m   4966\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4967\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Item_Store_Sales'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# Data\n",
    "rf_trainX = train_modified.drop('Item_Store_Sales', axis=1)\n",
    "\n",
    "# Label\n",
    "rf_trainY = train_modified['Item_Store_Sales']\n",
    "\n",
    "# Data\n",
    "rf_testX = test_bundas.drop('Item_Store_Sales', axis=1)\n",
    "\n",
    "# Label\n",
    "rf_testY = test_bundas['Item_Store_Sales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will convert the dataset into an optimized data structure called Dmatrix that XGBoost supports and gives it acclaimed performance and efficiency gains.... It will be used later on in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/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",
      "/usr/local/lib/python3.7/site-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    }
   ],
   "source": [
    "data_dmatrix = xgb.DMatrix(data=rf_trainX,label=rf_trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4887"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1222"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "4887"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1222"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(rf_trainX, rf_trainY, test_size=0.2)\n",
    "\n",
    "len(X_train)\n",
    "len(X_test)\n",
    "len(y_train)\n",
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Visibility</th>\n",
       "      <th>Max_Price</th>\n",
       "      <th>Store_Establishment_Year</th>\n",
       "      <th>tracker</th>\n",
       "      <th>FatContent_LF</th>\n",
       "      <th>FatContent_Low Fat</th>\n",
       "      <th>FatContent_Regular</th>\n",
       "      <th>FatContent_low fat</th>\n",
       "      <th>...</th>\n",
       "      <th>Store_Type_Supermarket Type1</th>\n",
       "      <th>Store_Type_Supermarket Type2</th>\n",
       "      <th>Store_Type_Supermarket Type3</th>\n",
       "      <th>Store_ID_OUT013</th>\n",
       "      <th>Store_ID_OUT018</th>\n",
       "      <th>Store_ID_OUT019</th>\n",
       "      <th>Store_ID_OUT027</th>\n",
       "      <th>Store_ID_OUT035</th>\n",
       "      <th>Store_ID_OUT046</th>\n",
       "      <th>Store_ID_OUT049</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3802</th>\n",
       "      <td>6932</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.037232</td>\n",
       "      <td>183.1634</td>\n",
       "      <td>1997</td>\n",
       "      <td>6932</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>3479</td>\n",
       "      <td>21.25</td>\n",
       "      <td>0.019423</td>\n",
       "      <td>120.8756</td>\n",
       "      <td>1997</td>\n",
       "      <td>3479</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2870</th>\n",
       "      <td>5228</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.063513</td>\n",
       "      <td>149.9708</td>\n",
       "      <td>1987</td>\n",
       "      <td>5228</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Weight  Visibility  Max_Price  Store_Establishment_Year  \\\n",
       "3802        6932   15.70    0.037232   183.1634                      1997   \n",
       "1903        3479   21.25    0.019423   120.8756                      1997   \n",
       "2870        5228   13.30    0.063513   149.9708                      1987   \n",
       "\n",
       "      tracker  FatContent_LF  FatContent_Low Fat  FatContent_Regular  \\\n",
       "3802     6932              0                   1                   0   \n",
       "1903     3479              0                   1                   0   \n",
       "2870     5228              0                   1                   0   \n",
       "\n",
       "      FatContent_low fat  ...  Store_Type_Supermarket Type1  \\\n",
       "3802                   0  ...                             1   \n",
       "1903                   0  ...                             1   \n",
       "2870                   0  ...                             1   \n",
       "\n",
       "      Store_Type_Supermarket Type2  Store_Type_Supermarket Type3  \\\n",
       "3802                             0                             0   \n",
       "1903                             0                             0   \n",
       "2870                             0                             0   \n",
       "\n",
       "      Store_ID_OUT013  Store_ID_OUT018  Store_ID_OUT019  Store_ID_OUT027  \\\n",
       "3802                0                0                0                0   \n",
       "1903                0                0                0                0   \n",
       "2870                1                0                0                0   \n",
       "\n",
       "      Store_ID_OUT035  Store_ID_OUT046  Store_ID_OUT049  \n",
       "3802                0                1                0  \n",
       "1903                0                1                0  \n",
       "2870                0                0                0  \n",
       "\n",
       "[3 rows x 44 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order for XGBoost to be able to use our data, we’ll need to transform it into a specific format that XGBoost can handle. \n",
    "\n",
    "That format is called DMatrix. We first transform our dataframe to a numoy array after which we can convert it to a DMatrix format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.93200000e+03, 1.57000000e+01, 3.72321100e-02, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
       "       [3.47900000e+03, 2.12500000e+01, 1.94232320e-02, ...,\n",
       "        0.00000000e+00, 1.00000000e+00, 0.00000000e+00],\n",
       "       [5.22800000e+03, 1.33000000e+01, 6.35132720e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [9.93000000e+02, 1.97000000e+01, 1.80213610e-02, ...,\n",
       "        1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.76300000e+03, 1.21000000e+01, 1.68529090e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.60400000e+03, 7.78500000e+00, 1.39436695e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[7.21300000e+03, 1.03000000e+01, 7.01490880e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.00000000e+00],\n",
       "       [1.08810000e+04, 1.93500000e+01, 5.77623010e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [4.27500000e+03, 1.78500000e+01, 8.78249670e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [4.22700000e+03, 1.81000000e+01, 2.23809520e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [6.33200000e+03, 5.44000000e+00, 2.54587160e-02, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [3.21000000e+02, 9.80000000e+00, 1.27599399e-01, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([5634.6654, 3150.5656, 2106.5912, ..., 1857.582 , 2876.256 ,\n",
       "       2404.2038])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([3436.1938, 2145.2076, 1755.7146, ...,  476.047 , 3124.5994,\n",
       "        120.5098])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.to_numpy()\n",
    "X_test.to_numpy()\n",
    "y_train.to_numpy()\n",
    "y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "D_train = xgb.DMatrix(X_train, label=y_train)\n",
    "D_test = xgb.DMatrix(X_test, label=y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define the parameters of our XGBoost model as shown below:\n",
    "\n",
    "We use eta to prevent overfitting in our ensemble model as shown below: https://www.datacamp.com/community/tutorials/xgboost-in-python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We now train the model as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = XGBRegressor(n_estimators=1000, learning_rate=0.05)"
   ]
  },
  {
   "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reg:squarederror replaces reg:linear\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(alpha=10, base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.3, gamma=0,\n",
       "             importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "             max_depth=5, min_child_weight=1, missing=None, n_estimators=10,\n",
       "             n_jobs=1, nthread=None, objective='reg:squarederror',\n",
       "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "             seed=None, silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_reg.fit(X_train,y_train)\n",
    "\n",
    "preds = xg_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1801.915228\n",
      "R2 Score: 0.020703\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "print(\"RMSE: %f\" % (rmse))\n",
    "\n",
    "r2_score = r2_score(y_test, preds)\n",
    "print(\"R2 Score: %f\" % (r2_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-fold Cross Validation using XGBoost\n",
    "\n",
    "Fine tune the model using below:\n",
    "https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:11:21] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[17:11:21] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[17:11:21] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "params = {\"objective\":\"reg:linear\",'colsample_bytree': 0.3,'learning_rate': 0.1,\n",
    "                'max_depth': 5, 'alpha': 10}\n",
    "\n",
    "cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,\n",
    "                    num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train-rmse-mean</th>\n",
       "      <th>train-rmse-std</th>\n",
       "      <th>test-rmse-mean</th>\n",
       "      <th>test-rmse-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2697.902751</td>\n",
       "      <td>14.698176</td>\n",
       "      <td>2698.028239</td>\n",
       "      <td>31.649928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2519.046305</td>\n",
       "      <td>14.111945</td>\n",
       "      <td>2521.082845</td>\n",
       "      <td>32.476915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2355.684815</td>\n",
       "      <td>9.604629</td>\n",
       "      <td>2359.451660</td>\n",
       "      <td>54.140254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2223.191569</td>\n",
       "      <td>9.713364</td>\n",
       "      <td>2229.652425</td>\n",
       "      <td>53.950996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2091.813476</td>\n",
       "      <td>19.336102</td>\n",
       "      <td>2098.800985</td>\n",
       "      <td>45.483756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
       "0      2697.902751       14.698176     2698.028239      31.649928\n",
       "1      2519.046305       14.111945     2521.082845      32.476915\n",
       "2      2355.684815        9.604629     2359.451660      54.140254\n",
       "3      2223.191569        9.713364     2229.652425      53.950996\n",
       "4      2091.813476       19.336102     2098.800985      45.483756"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49    1158.395386\n",
      "Name: test-rmse-mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((cv_results[\"test-rmse-mean\"]).tail(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:13:03] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "xg_reg = xgb.train(params=params, dtrain=data_dmatrix, num_boost_round=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125ea67f0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(xg_reg)\n",
    "plt.rcParams['figure.figsize'] = [30,10]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2 = r2_score(y_test, preds)\n",
    "print(\"R2 Score: %f\" % (r2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = xg_reg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "print(\"RMSE: %f\" % (rmse))"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}