A3_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np      # To use np.arrays\n",
    "import pandas as pd     # To use dataframes\n",
    "from pandas.plotting import autocorrelation_plot as auto_corr\n",
    "\n",
    "# To plot\n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline    \n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "#For date-time\n",
    "import math\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "\n",
    "# Another imports if needs\n",
    "import itertools\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.tsa.api as smt\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose as season\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression \n",
    "from sklearn import preprocessing\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forecasting(X, y):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(np.array(X), np.array(Y), test_size = 0.3, random_state=42)\n",
    "    model = RandomForestRegressor()\n",
    "    model.fit(X_train,y_train)\n",
    "\n",
    "    # Get the mean absolute error on the validation data\n",
    "    predicted_prices = model.predict(X_test)\n",
    "    MAE = mean_absolute_error(y_test , predicted_prices)\n",
    "    print('Random forest validation MAE = ', MAE)\n",
    "    return model, predicted_prices, X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_store = pd.read_csv('stores.csv') #store data\n",
    "df_train = pd.read_csv('train.csv') # train set\n",
    "df_features = pd.read_csv('features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday_x</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>IsHoliday_y</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>2010-02-05</td>\n",
       "      <td>24924.50</td>\n",
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       "      <td>151315</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>13740.12</td>\n",
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       "      <td>4</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>39954.04</td>\n",
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       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>32229.38</td>\n",
       "      <td>False</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday_x  Temperature  \\\n",
       "0      1     1  2010-02-05      24924.50        False        42.31   \n",
       "1      1     2  2010-02-05      50605.27        False        42.31   \n",
       "2      1     3  2010-02-05      13740.12        False        42.31   \n",
       "3      1     4  2010-02-05      39954.04        False        42.31   \n",
       "4      1     5  2010-02-05      32229.38        False        42.31   \n",
       "\n",
       "   Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
       "0       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "1       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "2       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "3       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "4       2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "          CPI  Unemployment  IsHoliday_y Type    Size  \n",
       "0  211.096358         8.106        False    A  151315  \n",
       "1  211.096358         8.106        False    A  151315  \n",
       "2  211.096358         8.106        False    A  151315  \n",
       "3  211.096358         8.106        False    A  151315  \n",
       "4  211.096358         8.106        False    A  151315  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_train.merge(df_features, on=['Store', 'Date'], how='inner').merge(df_store, on=['Store'], how='inner')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
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       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday_x</th>\n",
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       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
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       "      <th>IsHoliday_y</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
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       "  </thead>\n",
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       "      <td>2010-02-05</td>\n",
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       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>32229.38</td>\n",
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       "      <td>2.572</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>145</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "      <td>2</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>47928.89</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "      <td>3</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>11523.47</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>36826.95</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>26468.27</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>False</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "  </tbody>\n",
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       "<p>150 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store  Dept        Date  Weekly_Sales  IsHoliday_x  Temperature  \\\n",
       "0        1     1  2010-02-05      24924.50        False        42.31   \n",
       "1        1     2  2010-02-05      50605.27        False        42.31   \n",
       "2        1     3  2010-02-05      13740.12        False        42.31   \n",
       "3        1     4  2010-02-05      39954.04        False        42.31   \n",
       "4        1     5  2010-02-05      32229.38        False        42.31   \n",
       "..     ...   ...         ...           ...          ...          ...   \n",
       "145      1     1  2010-02-19      41595.55        False        39.93   \n",
       "146      1     2  2010-02-19      47928.89        False        39.93   \n",
       "147      1     3  2010-02-19      11523.47        False        39.93   \n",
       "148      1     4  2010-02-19      36826.95        False        39.93   \n",
       "149      1     5  2010-02-19      26468.27        False        39.93   \n",
       "\n",
       "     Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
       "0         2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "1         2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "2         2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "3         2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "4         2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "..          ...        ...        ...        ...        ...        ...   \n",
       "145       2.514        NaN        NaN        NaN        NaN        NaN   \n",
       "146       2.514        NaN        NaN        NaN        NaN        NaN   \n",
       "147       2.514        NaN        NaN        NaN        NaN        NaN   \n",
       "148       2.514        NaN        NaN        NaN        NaN        NaN   \n",
       "149       2.514        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            CPI  Unemployment  IsHoliday_y Type    Size  \n",
       "0    211.096358         8.106        False    A  151315  \n",
       "1    211.096358         8.106        False    A  151315  \n",
       "2    211.096358         8.106        False    A  151315  \n",
       "3    211.096358         8.106        False    A  151315  \n",
       "4    211.096358         8.106        False    A  151315  \n",
       "..          ...           ...          ...  ...     ...  \n",
       "145  211.289143         8.106        False    A  151315  \n",
       "146  211.289143         8.106        False    A  151315  \n",
       "147  211.289143         8.106        False    A  151315  \n",
       "148  211.289143         8.106        False    A  151315  \n",
       "149  211.289143         8.106        False    A  151315  \n",
       "\n",
       "[150 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(421570, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['IsHoliday_y'], axis=1,inplace=True)\n",
    "df.rename(columns={'IsHoliday_x':'IsHoliday'},inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(420212, 16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing rows with incorrect (i.e. negative) sales values\n",
    "df = df.loc[df['Weekly_Sales'] > 0]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
       "0      1     1  2010-02-05      24924.50      False        42.31       2.572   \n",
       "1      1     2  2010-02-05      50605.27      False        42.31       2.572   \n",
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       "3      1     4  2010-02-05      39954.04      False        42.31       2.572   \n",
       "4      1     5  2010-02-05      32229.38      False        42.31       2.572   \n",
       "\n",
       "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
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       "4        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "\n",
       "   Unemployment Type    Size  \n",
       "0         8.106    A  151315  \n",
       "1         8.106    A  151315  \n",
       "2         8.106    A  151315  \n",
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    "df = df.fillna(0)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
       "0      1     1  2010-02-05      24924.50      False        42.31       2.572   \n",
       "1      1     2  2010-02-05      50605.27      False        42.31       2.572   \n",
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       "\n",
       "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
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       "3        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "4        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "\n",
       "   Unemployment  Type    Size  \n",
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       "1         8.106     1  151315  \n",
       "2         8.106     1  151315  \n",
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     "metadata": {},
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    "df_encoded = df.copy()\n",
    "type_group = {'A':1, 'B': 2, 'C': 3}\n",
    "df_encoded['Type'] = df_encoded['Type'].replace(type_group)\n",
    "df_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Store  Dept        Date  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  \\\n",
       "0      1     1  2010-02-05      24924.50          0        42.31       2.572   \n",
       "1      1     2  2010-02-05      50605.27          0        42.31       2.572   \n",
       "2      1     3  2010-02-05      13740.12          0        42.31       2.572   \n",
       "3      1     4  2010-02-05      39954.04          0        42.31       2.572   \n",
       "4      1     5  2010-02-05      32229.38          0        42.31       2.572   \n",
       "\n",
       "   MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  \\\n",
       "0        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "1        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "2        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "3        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "4        0.0        0.0        0.0        0.0        0.0  211.096358   \n",
       "\n",
       "   Unemployment  Type    Size  \n",
       "0         8.106     1  151315  \n",
       "1         8.106     1  151315  \n",
       "2         8.106     1  151315  \n",
       "3         8.106     1  151315  \n",
       "4         8.106     1  151315  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded['IsHoliday'] = df_encoded['IsHoliday'].astype(bool).astype(int)\n",
    "df_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encoded.drop(['Date'], axis=1,inplace=True)\n",
    "df_encoded.head(5)\n",
    "\n",
    "feature_cols = [c for c in df_encoded.columns.to_list() if c not in [\"Weekly_Sales\"]]\n",
    "X = df_encoded[feature_cols]\n",
    "Y = df_encoded['Weekly_Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>MarkDown1</th>\n",
       "      <th>MarkDown2</th>\n",
       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>13740.12</td>\n",
       "      <td>0</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>32229.38</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Store  Dept  Weekly_Sales  IsHoliday  Temperature  Fuel_Price  MarkDown1  \\\n",
       "0      1     1      24924.50          0        42.31       2.572        0.0   \n",
       "1      1     2      50605.27          0        42.31       2.572        0.0   \n",
       "2      1     3      13740.12          0        42.31       2.572        0.0   \n",
       "3      1     4      39954.04          0        42.31       2.572        0.0   \n",
       "4      1     5      32229.38          0        42.31       2.572        0.0   \n",
       "\n",
       "   MarkDown2  MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  Type  \\\n",
       "0        0.0        0.0        0.0        0.0  211.096358         8.106     1   \n",
       "1        0.0        0.0        0.0        0.0  211.096358         8.106     1   \n",
       "2        0.0        0.0        0.0        0.0  211.096358         8.106     1   \n",
       "3        0.0        0.0        0.0        0.0  211.096358         8.106     1   \n",
       "4        0.0        0.0        0.0        0.0  211.096358         8.106     1   \n",
       "\n",
       "     Size  \n",
       "0  151315  \n",
       "1  151315  \n",
       "2  151315  \n",
       "3  151315  \n",
       "4  151315  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest validation MAE =  1862.0664125364892\n"
     ]
    }
   ],
   "source": [
    "model, predicted_test, X_train, X_test, y_train, y_test = forecasting(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(suppress=True, formatter={'float_kind':'{:0.2f}'.format})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.00 37.00 0.00 65.68 2.81 0.00 0.00 0.00 0.00 0.00 211.99 7.09 1.00\n",
      " 202505.00] 4170.564900000003 4524.05\n",
      "[10.00 3.00 0.00 54.34 2.96 0.00 0.00 0.00 0.00 0.00 126.44 9.77 2.00\n",
      " 126512.00] 26451.506800000006 30175.46\n",
      "[31.00 54.00 0.00 87.01 2.65 0.00 0.00 0.00 0.00 0.00 211.00 8.20 1.00\n",
      " 203750.00] 117.21 453.0\n",
      "[17.00 74.00 0.00 21.07 3.28 3356.86 0.03 38.78 1286.21 2618.88 129.90\n",
      " 6.62 2.00 93188.00] 12055.1253 19695.72\n",
      "[7.00 46.00 0.00 17.30 3.05 0.00 0.00 0.00 0.00 0.00 191.92 8.82 2.00\n",
      " 70713.00] 14116.015099999991 16062.3\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(X_test[i], predicted_test[i], y_test[i])"
   ]
  }
 ],
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