DemandForecasting/NeuralNetworks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from scipy.stats import describe\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "\n",
    "pd.options.display.max_columns = 12\n",
    "pd.options.display.max_rows = 24\n",
    "\n",
    "# disable warnings in Anaconda\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "# plots inisde jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(style='darkgrid', palette='muted')\n",
    "color_scheme = {\n",
    "    'red': '#F1637A',\n",
    "    'green': '#6ABB3E',\n",
    "    'blue': '#3D8DEA',\n",
    "    'black': '#000000'\n",
    "}\n",
    "\n",
    "# use svg for all plots within inline backend\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "# increase default plot size\n",
    "rcParams['figure.figsize'] = 8, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we do anything, we want to have both sets in a format with 500 sales columns and 1 row for each day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.index = pd.to_datetime(df_train['date'])\n",
    "df_train.drop('date', axis=1, inplace=True)\n",
    "df_test.index = pd.to_datetime(df_test['date'])\n",
    "df_test.drop('date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store</th>\n",
       "      <th>item</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store  item  sales\n",
       "date                          \n",
       "2013-01-01      1     1     13\n",
       "2013-01-02      1     1     11\n",
       "2013-01-03      1     1     14\n",
       "2013-01-04      1     1     13\n",
       "2013-01-05      1     1     10"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import product, starmap\n",
    "\n",
    "\n",
    "def storeitems():\n",
    "    return product(range(1,51), range(1,11))\n",
    "\n",
    "\n",
    "def storeitems_column_names():\n",
    "    return list(starmap(lambda i,s: f'item_{i}_store_{s}_sales', storeitems()))\n",
    "\n",
    "\n",
    "def sales_by_storeitem(df):\n",
    "    ret = pd.DataFrame(index=df.index.unique())\n",
    "    for i, s in storeitems():\n",
    "        ret[f'item_{i}_store_{s}_sales'] = df[(df['item'] == i) & (df['store'] == s)]['sales'].values\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = sales_by_storeitem(df_train)\n",
    "# for test set, we just fill y values with zeros (they won't be used anyway)\n",
    "df_test['sales'] = np.zeros(df_test.shape[0])\n",
    "df_test = sales_by_storeitem(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1826 entries, 2013-01-01 to 2017-12-31\n",
      "Columns: 500 entries, item_1_store_1_sales to item_50_store_10_sales\n",
      "dtypes: int64(500)\n",
      "memory usage: 7.0 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 90 entries, 2018-01-01 to 2018-03-31\n",
      "Columns: 500 entries, item_1_store_1_sales to item_50_store_10_sales\n",
      "dtypes: float64(500)\n",
      "memory usage: 352.3 KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We combine data to prepare it for the model, and later split back into train and test set, not to repeat same transformations twice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make sure all column names are the same and in the same order\n",
    "col_names = list(zip(df_test.columns, df_train.columns))\n",
    "for cn in col_names:\n",
    "    assert cn[0] == cn[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1916 entries, 2013-01-01 to 2018-03-31\n",
      "Columns: 501 entries, item_1_store_1_sales to is_test\n",
      "dtypes: bool(1), float64(500)\n",
      "memory usage: 7.3 MB\n"
     ]
    }
   ],
   "source": [
    "df_test['is_test'] = np.repeat(True, df_test.shape[0])\n",
    "df_train['is_test'] = np.repeat(False, df_train.shape[0])\n",
    "df_total = pd.concat([df_train, df_test])\n",
    "df_total.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekday_df = pd.get_dummies(df_total.index.weekday, prefix='weekday')\n",
    "weekday_df.index = df_total.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "month_df = pd.get_dummies(df_total.index.month, prefix='month')\n",
    "month_df.index =  df_total.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_total = pd.concat([weekday_df, month_df, df_total], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "assert df_total.isnull().any().any() == False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to append sales from previous day to each row, which we will then use as input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shift_series(series, days):\n",
    "    return series.transform(lambda x: x.shift(days))\n",
    "\n",
    "\n",
    "def shift_series_in_df(df, series_names=[], days_delta=90):\n",
    "    ret = pd.DataFrame(index=df.index.copy())\n",
    "    str_sgn = 'future' if np.sign(days_delta) < 0 else 'past'\n",
    "    for sn in series_names:\n",
    "        ret[f'{sn}_{str_sgn}_{np.abs(days_delta)}'] = shift_series(df[sn], days_delta)\n",
    "    return ret\n",
    "\n",
    "    \n",
    "def stack_shifted_sales(df, days_deltas=[1, 90, 360]):\n",
    "    names = storeitems_column_names()\n",
    "    dfs = [df.copy()]\n",
    "    for delta in days_deltas:\n",
    "        shifted = shift_series_in_df(df, series_names=names, days_delta=delta)\n",
    "        dfs.append(shifted)\n",
    "    return pd.concat(dfs, axis=1, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_total = stack_shifted_sales(df_total, days_deltas=[1])\n",
    "df_total.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1915 entries, 2013-01-02 to 2018-03-31\n",
      "Columns: 1020 entries, weekday_0 to item_50_store_10_sales_past_1\n",
      "dtypes: bool(1), float64(1000), uint8(19)\n",
      "memory usage: 14.7 MB\n"
     ]
    }
   ],
   "source": [
    "df_total.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to make sure that stacked and not-stacked sales columns appar in the same order. We do this by sorting the names (as strings), which works fine because we only need 1 past day for the network (see regression models and their rolling mechanism if you are interested in rolling more than 1 past days of sales as input)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_cols = [col for col in df_total.columns if '_sales' in col and '_sales_' not in col]\n",
    "stacked_sales_cols = [col for col in df_total.columns if '_sales_' in col]\n",
    "other_cols = [col for col in df_total.columns if col not in set(sales_cols) and col not in set(stacked_sales_cols)]\n",
    "\n",
    "sales_cols = sorted(sales_cols)\n",
    "stacked_sales_cols = sorted(stacked_sales_cols)\n",
    "\n",
    "new_cols = other_cols + stacked_sales_cols + sales_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_total = df_total.reindex(columns=new_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "assert df_total.isnull().any().any() == False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "cols_to_scale = [col for col in df_total.columns if 'weekday' not in col and 'month' not in col]\n",
    "scaled_cols = scaler.fit_transform(df_total[cols_to_scale])\n",
    "df_total[cols_to_scale] = scaled_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = df_total[df_total['is_test'] == False].drop('is_test', axis=1)\n",
    "df_test = df_total[df_total['is_test'] == True].drop('is_test', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_cols_stacked = [col for col in df_train.columns if '_past_' in col]\n",
    "X_cols_caldata = [col for col in df_train.columns if 'weekday_' in col or 'month_' in col or 'year' in col]\n",
    "X_cols = X_cols_stacked + X_cols_caldata\n",
    "\n",
    "X = df_train[X_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_colset = set(X_cols)\n",
    "y_cols = [col for col in df_train.columns if col not in X_colset]\n",
    "\n",
    "y = df_train[y_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1825 entries, 2013-01-02 to 2017-12-31\n",
      "Columns: 519 entries, item_10_store_10_sales_past_1 to month_12\n",
      "dtypes: float64(500), uint8(19)\n",
      "memory usage: 7.0 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1825 entries, 2013-01-02 to 2017-12-31\n",
      "Columns: 500 entries, item_10_store_10_sales to item_9_store_9_sales\n",
      "dtypes: float64(500)\n",
      "memory usage: 7.0 MB\n"
     ]
    }
   ],
   "source": [
    "y.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To achieve similar error values as on Kaggle, we will use only 1st quarter of 2017 for validation (test set contains 1st quarter of 2018 and the data is highly seasonal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_valid, y_valid = X_valid.head(90), y_valid.head(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Keras, we need to make further transformations on input values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_vals = X_train.values.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
    "X_valid_vals = X_valid.values.reshape((X_valid.shape[0], 1, X_valid.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import LSTM, Dense, Conv1D, Input, Dropout, AvgPool1D, Reshape, Concatenate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple LSTM model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basic_model = Sequential()\n",
    "basic_model.add(LSTM(500, input_shape=(X_train_vals.shape[1], X_train_vals.shape[2])))\n",
    "basic_model.add(Dense(500))\n",
    "basic_model.compile(loss='mean_absolute_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More complex model combining LSTM with convolutional layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = Input(shape=(X_train_vals.shape[1], X_train_vals.shape[2]))\n",
    "# top pipeline\n",
    "top_lstm = LSTM(500)(inputs)\n",
    "top_dense = Dense(500, activation='relu')(top_lstm)\n",
    "top_dropout = Dropout(500)(top_dense)\n",
    "# bottom pipeline\n",
    "bottom_dense = Dense(500)(inputs)\n",
    "bottom_conv1 = Conv1D(\n",
    "    500, \n",
    "    kernel_size=1,\n",
    "    input_shape=(X_train_vals.shape[1], X_train_vals.shape[2])\n",
    ")(bottom_dense)\n",
    "bottom_conv2 = Conv1D(\n",
    "    1000,\n",
    "    kernel_size=50,\n",
    "    padding='same',\n",
    "    activation='relu'\n",
    ")(bottom_conv1)\n",
    "bottom_conv3 = Conv1D(\n",
    "    500,\n",
    "    kernel_size=10,\n",
    "    padding='same',\n",
    "    activation='relu'\n",
    ")(bottom_conv2)\n",
    "bottom_pooling = AvgPool1D(\n",
    "    pool_size=60, \n",
    "    padding='same'\n",
    ")(bottom_conv3)\n",
    "bottom_reshape = Reshape(\n",
    "    target_shape=[500]\n",
    ")(bottom_conv3)\n",
    "# concat output from both pipelines\n",
    "final_concat = Concatenate()([top_dropout, bottom_reshape])\n",
    "final_dense = Dense(500)(final_concat)\n",
    "# compile and return\n",
    "complex_model = Model(inputs=inputs, outputs=final_dense)\n",
    "complex_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mape'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can fit the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1460 samples, validate on 90 samples\n",
      "Epoch 1/60\n",
      " - 3s - loss: 0.1447 - val_loss: 0.0954\n",
      "Epoch 2/60\n",
      " - 3s - loss: 0.0942 - val_loss: 0.0732\n",
      "Epoch 3/60\n",
      " - 3s - loss: 0.0849 - val_loss: 0.0688\n",
      "Epoch 4/60\n",
      " - 3s - loss: 0.0737 - val_loss: 0.0625\n",
      "Epoch 5/60\n",
      " - 3s - loss: 0.0672 - val_loss: 0.0660\n",
      "Epoch 6/60\n",
      " - 3s - loss: 0.0647 - val_loss: 0.0599\n",
      "Epoch 7/60\n",
      " - 3s - loss: 0.0666 - val_loss: 0.0616\n",
      "Epoch 8/60\n",
      " - 3s - loss: 0.0701 - val_loss: 0.0729\n",
      "Epoch 9/60\n",
      " - 3s - loss: 0.0935 - val_loss: 0.0932\n",
      "Epoch 10/60\n",
      " - 3s - loss: 0.0931 - val_loss: 0.0724\n",
      "Epoch 11/60\n",
      " - 3s - loss: 0.0773 - val_loss: 0.0704\n",
      "Epoch 12/60\n",
      " - 3s - loss: 0.0762 - val_loss: 0.0592\n",
      "Epoch 13/60\n",
      " - 3s - loss: 0.0669 - val_loss: 0.0827\n",
      "Epoch 14/60\n",
      " - 3s - loss: 0.0802 - val_loss: 0.0608\n",
      "Epoch 15/60\n",
      " - 3s - loss: 0.0640 - val_loss: 0.0574\n",
      "Epoch 16/60\n",
      " - 3s - loss: 0.0606 - val_loss: 0.0577\n",
      "Epoch 17/60\n",
      " - 3s - loss: 0.0655 - val_loss: 0.0587\n",
      "Epoch 18/60\n",
      " - 3s - loss: 0.0679 - val_loss: 0.0671\n",
      "Epoch 19/60\n",
      " - 3s - loss: 0.0760 - val_loss: 0.0565\n",
      "Epoch 20/60\n",
      " - 3s - loss: 0.0618 - val_loss: 0.0566\n",
      "Epoch 21/60\n",
      " - 3s - loss: 0.0623 - val_loss: 0.0628\n",
      "Epoch 22/60\n",
      " - 3s - loss: 0.0641 - val_loss: 0.0591\n",
      "Epoch 23/60\n",
      " - 3s - loss: 0.0592 - val_loss: 0.0564\n",
      "Epoch 24/60\n",
      " - 3s - loss: 0.0604 - val_loss: 0.0561\n",
      "Epoch 25/60\n",
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      "Epoch 26/60\n",
      " - 3s - loss: 0.0624 - val_loss: 0.0654\n",
      "Epoch 27/60\n",
      " - 3s - loss: 0.0650 - val_loss: 0.0583\n",
      "Epoch 28/60\n",
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      "Epoch 29/60\n",
      " - 3s - loss: 0.0611 - val_loss: 0.0563\n",
      "Epoch 30/60\n",
      " - 3s - loss: 0.0645 - val_loss: 0.0578\n",
      "Epoch 31/60\n",
      " - 3s - loss: 0.0640 - val_loss: 0.0650\n",
      "Epoch 32/60\n",
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      "Epoch 33/60\n",
      " - 3s - loss: 0.0592 - val_loss: 0.0582\n",
      "Epoch 34/60\n",
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      "Epoch 35/60\n",
      " - 3s - loss: 0.0643 - val_loss: 0.0610\n",
      "Epoch 36/60\n",
      " - 3s - loss: 0.0636 - val_loss: 0.0631\n",
      "Epoch 37/60\n",
      " - 3s - loss: 0.0674 - val_loss: 0.0626\n",
      "Epoch 38/60\n",
      " - 3s - loss: 0.0683 - val_loss: 0.0562\n",
      "Epoch 39/60\n",
      " - 3s - loss: 0.0705 - val_loss: 0.0623\n",
      "Epoch 40/60\n",
      " - 3s - loss: 0.0703 - val_loss: 0.0575\n",
      "Epoch 41/60\n",
      " - 3s - loss: 0.0640 - val_loss: 0.0567\n",
      "Epoch 42/60\n",
      " - 3s - loss: 0.0621 - val_loss: 0.0566\n",
      "Epoch 43/60\n",
      " - 3s - loss: 0.0614 - val_loss: 0.0563\n",
      "Epoch 44/60\n",
      " - 3s - loss: 0.0611 - val_loss: 0.0561\n",
      "Epoch 45/60\n",
      " - 3s - loss: 0.0610 - val_loss: 0.0560\n",
      "Epoch 46/60\n",
      " - 3s - loss: 0.0608 - val_loss: 0.0559\n",
      "Epoch 47/60\n",
      " - 3s - loss: 0.0610 - val_loss: 0.0559\n",
      "Epoch 48/60\n",
      " - 3s - loss: 0.0609 - val_loss: 0.0558\n",
      "Epoch 49/60\n",
      " - 3s - loss: 0.0612 - val_loss: 0.0557\n",
      "Epoch 50/60\n",
      " - 3s - loss: 0.0605 - val_loss: 0.0558\n",
      "Epoch 51/60\n",
      " - 3s - loss: 0.0611 - val_loss: 0.0558\n",
      "Epoch 52/60\n",
      " - 3s - loss: 0.0610 - val_loss: 0.0556\n",
      "Epoch 53/60\n",
      " - 3s - loss: 0.0608 - val_loss: 0.0558\n",
      "Epoch 54/60\n",
      " - 3s - loss: 0.0609 - val_loss: 0.0559\n",
      "Epoch 55/60\n",
      " - 3s - loss: 0.0616 - val_loss: 0.0557\n",
      "Epoch 56/60\n",
      " - 4s - loss: 0.0627 - val_loss: 0.0558\n",
      "Epoch 57/60\n",
      " - 3s - loss: 0.0607 - val_loss: 0.0558\n",
      "Epoch 58/60\n",
      " - 3s - loss: 0.0614 - val_loss: 0.0557\n",
      "Epoch 59/60\n",
      " - 3s - loss: 0.0608 - val_loss: 0.0558\n",
      "Epoch 60/60\n",
      " - 3s - loss: 0.0622 - val_loss: 0.0558\n"
     ]
    }
   ],
   "source": [
    "basic_history = basic_model.fit(\n",
    "    X_train_vals, \n",
    "    y_train.values, \n",
    "    epochs=60, \n",
    "    batch_size=30,\n",
    "    validation_data=(X_valid_vals, y_valid.values),\n",
    "    verbose=2,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    plt.plot(history.history['loss'], label='train')\n",
    "    plt.plot(history.history['val_loss'], label='test')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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  {
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   "execution_count": 38,
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      "Train on 1460 samples, validate on 90 samples\n",
      "Epoch 1/20\n",
      " - 188s - loss: 0.0555 - mean_absolute_percentage_error: 289.9472 - val_loss: 0.0215 - val_mean_absolute_percentage_error: 33.2537\n",
      "Epoch 2/20\n",
      " - 183s - loss: 0.0141 - mean_absolute_percentage_error: 297.0889 - val_loss: 0.0096 - val_mean_absolute_percentage_error: 21.3084\n",
      "Epoch 3/20\n",
      " - 188s - loss: 0.0101 - mean_absolute_percentage_error: 317.2420 - val_loss: 0.0077 - val_mean_absolute_percentage_error: 18.8264\n",
      "Epoch 4/20\n",
      " - 191s - loss: 0.0087 - mean_absolute_percentage_error: 322.0520 - val_loss: 0.0064 - val_mean_absolute_percentage_error: 15.5867\n",
      "Epoch 5/20\n",
      " - 191s - loss: 0.0094 - mean_absolute_percentage_error: 416.5274 - val_loss: 0.0100 - val_mean_absolute_percentage_error: 22.6484\n",
      "Epoch 6/20\n",
      " - 180s - loss: 0.0091 - mean_absolute_percentage_error: 276.5517 - val_loss: 0.0089 - val_mean_absolute_percentage_error: 21.5800\n",
      "Epoch 7/20\n",
      " - 181s - loss: 0.0084 - mean_absolute_percentage_error: 317.8493 - val_loss: 0.0061 - val_mean_absolute_percentage_error: 16.9132\n",
      "Epoch 8/20\n",
      " - 178s - loss: 0.0078 - mean_absolute_percentage_error: 301.3948 - val_loss: 0.0057 - val_mean_absolute_percentage_error: 15.9136\n",
      "Epoch 9/20\n",
      " - 178s - loss: 0.0075 - mean_absolute_percentage_error: 306.1259 - val_loss: 0.0055 - val_mean_absolute_percentage_error: 15.6722\n",
      "Epoch 10/20\n",
      " - 178s - loss: 0.0074 - mean_absolute_percentage_error: 303.0013 - val_loss: 0.0052 - val_mean_absolute_percentage_error: 14.8604\n",
      "Epoch 11/20\n",
      " - 178s - loss: 0.0068 - mean_absolute_percentage_error: 305.6572 - val_loss: 0.0055 - val_mean_absolute_percentage_error: 15.4054\n",
      "Epoch 12/20\n",
      " - 178s - loss: 0.0068 - mean_absolute_percentage_error: 307.1261 - val_loss: 0.0058 - val_mean_absolute_percentage_error: 16.0763\n",
      "Epoch 13/20\n",
      " - 178s - loss: 0.0069 - mean_absolute_percentage_error: 294.9059 - val_loss: 0.0052 - val_mean_absolute_percentage_error: 14.6737\n",
      "Epoch 14/20\n",
      " - 178s - loss: 0.0066 - mean_absolute_percentage_error: 312.0921 - val_loss: 0.0051 - val_mean_absolute_percentage_error: 14.2217\n",
      "Epoch 15/20\n",
      " - 186s - loss: 0.0062 - mean_absolute_percentage_error: 321.5720 - val_loss: 0.0052 - val_mean_absolute_percentage_error: 14.6195\n",
      "Epoch 16/20\n",
      " - 189s - loss: 0.0062 - mean_absolute_percentage_error: 335.6709 - val_loss: 0.0051 - val_mean_absolute_percentage_error: 14.4125\n",
      "Epoch 17/20\n",
      " - 178s - loss: 0.0062 - mean_absolute_percentage_error: 353.9061 - val_loss: 0.0057 - val_mean_absolute_percentage_error: 15.8617\n",
      "Epoch 18/20\n",
      " - 180s - loss: 0.0067 - mean_absolute_percentage_error: 294.9052 - val_loss: 0.0056 - val_mean_absolute_percentage_error: 15.7448\n",
      "Epoch 19/20\n",
      " - 180s - loss: 0.0070 - mean_absolute_percentage_error: 303.3001 - val_loss: 0.0051 - val_mean_absolute_percentage_error: 14.1322\n",
      "Epoch 20/20\n",
      " - 183s - loss: 0.0062 - mean_absolute_percentage_error: 306.7812 - val_loss: 0.0052 - val_mean_absolute_percentage_error: 14.7392\n"
     ]
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   "source": [
    "complex_history = complex_model.fit(\n",
    "    X_train_vals, \n",
    "    y_train.values, \n",
    "    epochs=20, \n",
    "    batch_size=70,\n",
    "    validation_data=(X_valid_vals, y_valid.values),\n",
    "    verbose=2,\n",
    "    shuffle=False\n",
    ")"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
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   ],
   "source": [
    "plot_history(complex_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating model predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_eval(model, X_test, y_test):\n",
    "    \"\"\"\n",
    "    Evaluate (step-by-step) model predictions from X_test and return predictions and real values in comparable format.\n",
    "    \"\"\"\n",
    "    # prepare data\n",
    "    sales_x_cols = [col for col in X_test.columns if 'sales' in col]\n",
    "    sales_x_idxs = [X_test.columns.get_loc(col) for col in sales_x_cols]\n",
    "    sales_y_cols = [col for col in y_test.columns if 'sales' in col]\n",
    "    sales_y_idxs = [y_test.columns.get_loc(col) for col in sales_y_cols]\n",
    "    n_samples = y_test.shape[0]\n",
    "    y_pred = np.zeros(y_test.shape)\n",
    "    # iterate\n",
    "    x_next = X_test.iloc[0].values\n",
    "    for i in range(0, n_samples):\n",
    "        x_arr = np.array([x_next])\n",
    "        x_arr = x_arr.reshape(x_arr.shape[0], 1, x_arr.shape[1])\n",
    "        y_pred[i] = model.predict(x_arr)[0] # input for prediction must be 2d, output is immediately extracted from 2d to 1d\n",
    "        try:\n",
    "            x_next = X_test.iloc[i+1].values\n",
    "            x_next[sales_x_idxs] = y_pred[i][sales_y_idxs]\n",
    "        except IndexError:\n",
    "            pass  # this happens on last iteration, and x_next does not matter anymore\n",
    "    return y_pred, y_test.values\n",
    "\n",
    "\n",
    "def unscale(y_arr, scaler, template_df, toint=False):\n",
    "    \"\"\"\n",
    "    Unscale array y_arr of model predictions, based on a scaler fitted \n",
    "    to template_df.\n",
    "    \"\"\"\n",
    "    tmp = template_df.copy()\n",
    "    tmp[y_cols] = pd.DataFrame(y_arr, index=tmp.index)\n",
    "    tmp[cols_to_scale] = scaler.inverse_transform(tmp[cols_to_scale])\n",
    "    if toint:\n",
    "        return tmp[y_cols].astype(int)\n",
    "    return tmp[y_cols]\n",
    "\n",
    "\n",
    "def vector_smape(y_pred, y_real):\n",
    "    nom = np.abs(y_pred-y_real)\n",
    "    denom = (np.abs(y_pred) + np.abs(y_real)) / 2\n",
    "    results = nom / denom\n",
    "    return 100*np.mean(results)  # in percent, same as at kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_basic, y_real = model_eval(basic_model, X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_complex = model_eval(complex_model, X_valid, y_valid)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this is just for unscaling\n",
    "template_df = pd.concat([X_valid, y_valid], axis=1)\n",
    "template_df['is_test'] = np.repeat(True, template_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basic_pred = unscale(y_pred_basic, scaler, template_df, toint=True)\n",
    "complex_pred = unscale(y_pred_complex, scaler, template_df, toint=True)\n",
    "real = unscale(y_real, scaler, template_df, toint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basic_smapes = [vector_smape(basic_pred[col], real[col]) for col in basic_pred.columns]\n",
    "complex_smapes = [vector_smape(complex_pred[col], real[col]) for col in complex_pred.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
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     "output_type": "display_data"
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   ],
   "source": [
    "sns.distplot(basic_smapes, label='Basic model')\n",
    "sns.distplot(complex_smapes, label='Complex model')\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig('smape_basic_vs_complex.svg')\n",
    "plt.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on training parameters, basic model can sometimes achieve results comparable to the complex one, however after uploading to Kaggle complex model achieved better (and more consistent) results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=500, minmax=(8.1567758716429157, 30.451669284570077), mean=13.676887158992596, variance=14.385318973989973, skewness=0.9774275202167098, kurtosis=0.7664863452792408)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe(basic_smapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=500, minmax=(10.092775029103246, 29.562752522523422), mean=15.973179356236203, variance=10.851904013402756, skewness=0.9281911545227496, kurtosis=0.7337651742860074)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe(complex_smapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing model prediction\n",
    "We will plot predictions of 2 models for a sample store and item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store, item = 1,1\n",
    "plot_lengths = [7, 30, 90]\n",
    "rolling_mean_windows = [1, 1, 2]  # to make plots more readable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "storeitem_col = f'item_{item}_store_{store}_sales'\n",
    "\n",
    "for pl, mw in zip(plot_lengths, rolling_mean_windows):\n",
    "    plt.plot(basic_pred[storeitem_col].rolling(mw).mean().values[:pl],\n",
    "             color_scheme['blue'],\n",
    "             lw=2,\n",
    "             label='Basic model prediction')\n",
    "    plt.plot(complex_pred[storeitem_col].rolling(mw).mean().values[:pl],\n",
    "             color_scheme['green'],\n",
    "             lw=2, \n",
    "             label='Complex model prediction')\n",
    "    plt.plot(real[storeitem_col].rolling(mw).mean().values[:pl],\n",
    "             color_scheme['black'],\n",
    "             lw=2, \n",
    "             label='Real values')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.savefig(f'plot_prediction_{pl}_{mw}.svg')\n",
    "    plt.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'neuralNetwork.sav'\n",
    "filename1 = 'basicmodel.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "import pickle"
   ]
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  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(basic_model, open(filename1, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(complex_pred).to_csv(\"file.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(real).to_csv(\"file1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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