makeSimpleRegressionModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68ff8704",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Ridge, Lasso, LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "import json\n",
    "from src.metric_participants import ComputeMetrics\n",
    "from pandasql import sqldf\n",
    "from typing import Tuple, Dict, List\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "import sys\n",
    "from datetime import datetime\n",
    "from itertools import product\n",
    "\n",
    "makeQuery = lambda q: sqldf(q, globals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0614713",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./data/data_files/train_validation_split_5.json', 'r') as f: \n",
    "    cvSplit = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da601f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "referenceDate = datetime.strptime('2020-07', '%Y-%m')\n",
    "salesOffset = 100\n",
    "salesTrain = pd.read_csv('./data/data_files/sales_train_splitted.csv', index_col=0)\n",
    "\n",
    "regions = ['region_{}'.format(i) for i in range(201)]\n",
    "months = list([datetime.strptime(m, '%Y-%m') for m in set(salesTrain.month.values.tolist())])\n",
    "months.sort()\n",
    "months = ['{}-{}'.format(d.year, '0'+str(d.month) if d.month < 10 else d.month) for d in months]\n",
    "brands = ['brand_1', 'brand_2']\n",
    "\n",
    "def sortDf(df: pd.DataFrame, isTrain: bool) -> pd.DataFrame: \n",
    "    tempRegions = regions[:151] if isTrain else regions[151:]\n",
    "    df.region = pd.Categorical(df.region, tempRegions)\n",
    "    df.month = pd.Categorical(df.month, months)\n",
    "    df.sort_values(['region', 'month'], inplace=True)\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dcdecdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>region</th>\n",
       "      <th>brand</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>2020-02</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <td>2020-03</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2715</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3620</th>\n",
       "      <td>2020-05</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4525</th>\n",
       "      <td>2020-06</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5430</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6335</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>612.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7240</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1653.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8145</th>\n",
       "      <td>2020-10</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1898.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9050</th>\n",
       "      <td>2020-11</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>2939.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9955</th>\n",
       "      <td>2020-12</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>4440.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10860</th>\n",
       "      <td>2021-01</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>3888.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11765</th>\n",
       "      <td>2021-02</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>2664.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12670</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>5603.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13575</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>2572.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14480</th>\n",
       "      <td>2021-05</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>4807.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15385</th>\n",
       "      <td>2021-06</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>3674.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16290</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>4225.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17195</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>2927.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         month    region    brand    sales\n",
       "0      2020-01  region_0  brand_1     0.00\n",
       "905    2020-02  region_0  brand_1     0.00\n",
       "1810   2020-03  region_0  brand_1     0.00\n",
       "2715   2020-04  region_0  brand_1     0.00\n",
       "3620   2020-05  region_0  brand_1     0.00\n",
       "4525   2020-06  region_0  brand_1     0.00\n",
       "5430   2020-07  region_0  brand_1     0.00\n",
       "6335   2020-08  region_0  brand_1   612.44\n",
       "7240   2020-09  region_0  brand_1  1653.59\n",
       "8145   2020-10  region_0  brand_1  1898.56\n",
       "9050   2020-11  region_0  brand_1  2939.71\n",
       "9955   2020-12  region_0  brand_1  4440.19\n",
       "10860  2021-01  region_0  brand_1  3888.99\n",
       "11765  2021-02  region_0  brand_1  2664.11\n",
       "12670  2021-03  region_0  brand_1  5603.83\n",
       "13575  2021-04  region_0  brand_1  2572.25\n",
       "14480  2021-05  region_0  brand_1  4807.65\n",
       "15385  2021-06  region_0  brand_1  3674.64\n",
       "16290  2021-07  region_0  brand_1  4225.84\n",
       "17195  2021-08  region_0  brand_1  2927.80"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salesTrain[(salesTrain['region'] == 'region_0') * (salesTrain['brand'] == 'brand_1')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f4347d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split by brand and transform time data to columns\n",
    "def prepareRegionYTrue(salesTrain): \n",
    "    transformedData = {}\n",
    "    for brand in brands: \n",
    "        brandData = {}\n",
    "        for month in months: \n",
    "            query = \"\"\"\n",
    "            SELECT region, sales\n",
    "            FROM salesTrain\n",
    "            WHERE brand = '{brand}'\n",
    "            AND month = '{month}'\n",
    "            \"\"\".format(brand=brand, month=month)\n",
    "            df = makeQuery(query)\n",
    "            df.set_index('region', drop=True, inplace=True)\n",
    "            brandData[month] = df['sales'].to_dict()\n",
    "        df = pd.DataFrame.from_dict(brandData, orient='index').sort_index(ascending=True).T\n",
    "        transformedData[brand] = pd.DataFrame(np.where((df == 0), sys.float_info.epsilon, df.values), index=df.index, columns=df.columns)\n",
    "    return transformedData\n",
    "\n",
    "transformedData = prepareRegionYTrue(salesTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3ce69eb",
   "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>2020-01</th>\n",
       "      <th>2020-02</th>\n",
       "      <th>2020-03</th>\n",
       "      <th>2020-04</th>\n",
       "      <th>2020-05</th>\n",
       "      <th>2020-06</th>\n",
       "      <th>2020-07</th>\n",
       "      <th>2020-08</th>\n",
       "      <th>2020-09</th>\n",
       "      <th>2020-10</th>\n",
       "      <th>2020-11</th>\n",
       "      <th>2020-12</th>\n",
       "      <th>2021-01</th>\n",
       "      <th>2021-02</th>\n",
       "      <th>2021-03</th>\n",
       "      <th>2021-04</th>\n",
       "      <th>2021-05</th>\n",
       "      <th>2021-06</th>\n",
       "      <th>2021-07</th>\n",
       "      <th>2021-08</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>region_0</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>6.124400e+02</td>\n",
       "      <td>1.653590e+03</td>\n",
       "      <td>1898.56</td>\n",
       "      <td>2939.71</td>\n",
       "      <td>4440.19</td>\n",
       "      <td>3888.99</td>\n",
       "      <td>2664.11</td>\n",
       "      <td>5603.83</td>\n",
       "      <td>2572.25</td>\n",
       "      <td>4807.65</td>\n",
       "      <td>3674.64</td>\n",
       "      <td>4225.84</td>\n",
       "      <td>2927.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_1</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>7.655500e+02</td>\n",
       "      <td>1286.12</td>\n",
       "      <td>1653.59</td>\n",
       "      <td>1408.61</td>\n",
       "      <td>2021.05</td>\n",
       "      <td>1500.48</td>\n",
       "      <td>1837.32</td>\n",
       "      <td>1377.99</td>\n",
       "      <td>2021.05</td>\n",
       "      <td>2572.25</td>\n",
       "      <td>2204.78</td>\n",
       "      <td>3009.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_2</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>91.87</td>\n",
       "      <td>367.46</td>\n",
       "      <td>183.73</td>\n",
       "      <td>704.31</td>\n",
       "      <td>826.79</td>\n",
       "      <td>275.60</td>\n",
       "      <td>734.93</td>\n",
       "      <td>1561.72</td>\n",
       "      <td>1561.72</td>\n",
       "      <td>1469.86</td>\n",
       "      <td>2071.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_3</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.756000e+02</td>\n",
       "      <td>643.06</td>\n",
       "      <td>2021.05</td>\n",
       "      <td>275.60</td>\n",
       "      <td>2786.60</td>\n",
       "      <td>1224.88</td>\n",
       "      <td>2021.05</td>\n",
       "      <td>1010.53</td>\n",
       "      <td>2572.25</td>\n",
       "      <td>3858.37</td>\n",
       "      <td>2939.71</td>\n",
       "      <td>2374.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_4</th>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>2.220446e-16</td>\n",
       "      <td>9.187000e+01</td>\n",
       "      <td>1.500480e+03</td>\n",
       "      <td>459.33</td>\n",
       "      <td>1102.39</td>\n",
       "      <td>2939.71</td>\n",
       "      <td>1194.26</td>\n",
       "      <td>826.79</td>\n",
       "      <td>2572.25</td>\n",
       "      <td>2112.92</td>\n",
       "      <td>2112.92</td>\n",
       "      <td>1653.59</td>\n",
       "      <td>2847.85</td>\n",
       "      <td>2651.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               2020-01       2020-02       2020-03       2020-04  \\\n",
       "region_0  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_1  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_2  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_3  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_4  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "\n",
       "               2020-05       2020-06       2020-07       2020-08  \\\n",
       "region_0  2.220446e-16  2.220446e-16  2.220446e-16  6.124400e+02   \n",
       "region_1  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_2  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_3  2.220446e-16  2.220446e-16  2.220446e-16  2.220446e-16   \n",
       "region_4  2.220446e-16  2.220446e-16  2.220446e-16  9.187000e+01   \n",
       "\n",
       "               2020-09  2020-10  2020-11  2020-12  2021-01  2021-02  2021-03  \\\n",
       "region_0  1.653590e+03  1898.56  2939.71  4440.19  3888.99  2664.11  5603.83   \n",
       "region_1  7.655500e+02  1286.12  1653.59  1408.61  2021.05  1500.48  1837.32   \n",
       "region_2  2.220446e-16    91.87   367.46   183.73   704.31   826.79   275.60   \n",
       "region_3  2.756000e+02   643.06  2021.05   275.60  2786.60  1224.88  2021.05   \n",
       "region_4  1.500480e+03   459.33  1102.39  2939.71  1194.26   826.79  2572.25   \n",
       "\n",
       "          2021-04  2021-05  2021-06  2021-07  2021-08  \n",
       "region_0  2572.25  4807.65  3674.64  4225.84  2927.80  \n",
       "region_1  1377.99  2021.05  2572.25  2204.78  3009.96  \n",
       "region_2   734.93  1561.72  1561.72  1469.86  2071.56  \n",
       "region_3  1010.53  2572.25  3858.37  2939.71  2374.68  \n",
       "region_4  2112.92  2112.92  1653.59  2847.85  2651.60  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformedData['brand_1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be271611",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def func(x, a, b): \n",
    "#     return a * np.log10(x) + b\n",
    "#def func(x, L ,x0, k, b):\n",
    "    # code for fitting this sigmoid curve was found here: https://stackoverflow.com/questions/55725139/fit-sigmoid-function-s-shape-curve-to-data-using-python\n",
    "#    y = L / (1 + np.exp(-k*(x-x0)))+b\n",
    "#    return (y)\n",
    "#def func(x, a, b, c, d, e): \n",
    "#    return a*x**4 + b*x**3 + c*x**2 + d*x + e\n",
    "def func(x, a, b, c, d): \n",
    "    return a*x**3 + b*x**2 + c*x + d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4fa87678",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit a linear model on each region -> parameters are target values for prediction model\n",
    "def fitSalesCurves(transformedData): \n",
    "    linearModels = {}\n",
    "    failedModels = []\n",
    "    initialGuesses = []\n",
    "    for brand in brands: \n",
    "        # df = transformedData[brand].applymap(lambda x: np.log10(x) if x > 0 else 0)\n",
    "        df = transformedData[brand]\n",
    "        regionParameters = {}\n",
    "        x = np.arange(6, df.shape[1]) # start at july\n",
    "        for i, row in df.iterrows(): \n",
    "            # k, d = np.polyfit(x, row.values[6:], deg=1)\n",
    "            # regionParameters[i] = {'k': k, 'd': d}\n",
    "            #make initial guess\n",
    "            y = row.values[6: ]\n",
    "            #initialGuess = [max(y), np.median(x),1,min(y)] \n",
    "            #initialGuesses.append(initialGuess)\n",
    "            #bounds = (np.array(initialGuess)/2, np.array(initialGuess)*2)\n",
    "            try: \n",
    "                # fittedParams = curve_fit(func, x, y, initialGuess, method='dogbox', maxfev=10000, bounds=bounds)[0]\n",
    "                fittedParams = curve_fit(func, x, y, method='dogbox', maxfev=10000)[0]\n",
    "            except RuntimeError as e: \n",
    "                print(i, e)\n",
    "                temp = pd.DataFrame.from_dict(regionParameters, orient='index')\n",
    "                # fittedParams = initialGuess\n",
    "                fittedParams = temp.mean(axis=0).values\n",
    "                failedModels.append(i)\n",
    "            regionParameters[i] = {i: p for i, p in enumerate(fittedParams)}\n",
    "        linearModels[brand] = pd.DataFrame.from_dict(regionParameters, orient='index')\n",
    "    return linearModels\n",
    "\n",
    "linearModels = fitSalesCurves(transformedData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1b84029",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>region_0</th>\n",
       "      <td>0.450511</td>\n",
       "      <td>-72.608002</td>\n",
       "      <td>1847.618261</td>\n",
       "      <td>-8731.240008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_1</th>\n",
       "      <td>4.685378</td>\n",
       "      <td>-183.550181</td>\n",
       "      <td>2441.372340</td>\n",
       "      <td>-9313.974366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_2</th>\n",
       "      <td>0.364704</td>\n",
       "      <td>-2.646535</td>\n",
       "      <td>37.753383</td>\n",
       "      <td>-246.405451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_3</th>\n",
       "      <td>-0.635400</td>\n",
       "      <td>16.931775</td>\n",
       "      <td>129.444849</td>\n",
       "      <td>-1338.909974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_4</th>\n",
       "      <td>2.995615</td>\n",
       "      <td>-122.374156</td>\n",
       "      <td>1746.867477</td>\n",
       "      <td>-6826.425228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_146</th>\n",
       "      <td>1.549092</td>\n",
       "      <td>-64.825945</td>\n",
       "      <td>929.515552</td>\n",
       "      <td>-3434.735421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_147</th>\n",
       "      <td>5.171481</td>\n",
       "      <td>-212.512539</td>\n",
       "      <td>2873.622761</td>\n",
       "      <td>-11220.856163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_148</th>\n",
       "      <td>10.474054</td>\n",
       "      <td>-512.118510</td>\n",
       "      <td>8232.348038</td>\n",
       "      <td>-34355.976860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_149</th>\n",
       "      <td>4.143983</td>\n",
       "      <td>-143.966760</td>\n",
       "      <td>1727.065242</td>\n",
       "      <td>-6082.071647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_150</th>\n",
       "      <td>-10.160681</td>\n",
       "      <td>364.328191</td>\n",
       "      <td>-3451.232380</td>\n",
       "      <td>10171.423559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0           1            2             3\n",
       "region_0     0.450511  -72.608002  1847.618261  -8731.240008\n",
       "region_1     4.685378 -183.550181  2441.372340  -9313.974366\n",
       "region_2     0.364704   -2.646535    37.753383   -246.405451\n",
       "region_3    -0.635400   16.931775   129.444849  -1338.909974\n",
       "region_4     2.995615 -122.374156  1746.867477  -6826.425228\n",
       "...               ...         ...          ...           ...\n",
       "region_146   1.549092  -64.825945   929.515552  -3434.735421\n",
       "region_147   5.171481 -212.512539  2873.622761 -11220.856163\n",
       "region_148  10.474054 -512.118510  8232.348038 -34355.976860\n",
       "region_149   4.143983 -143.966760  1727.065242  -6082.071647\n",
       "region_150 -10.160681  364.328191 -3451.232380  10171.423559\n",
       "\n",
       "[151 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearModels['brand_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2d88fd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictSales(x, *params): \n",
    "    predictedSales = func(x, *params)\n",
    "    return np.where(predictedSales < salesOffset, 0, predictedSales)  # consider these values 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55df7b69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#modelType = LinearRegression\n",
    "modelType = RandomForestRegressor\n",
    "modelParams = {\n",
    "    'n_estimators': 200, \n",
    "    'max_depth': 40\n",
    "    # 'max_depth': 10\n",
    "    # 'criterion': 'absolute_error'\n",
    "}\n",
    "# modelParams = {}\n",
    "regionTrainFeatures = {brand: pd.read_csv('./data/data_files/region_features/training_{}.csv'.format(brand), index_col=0) for brand in brands}\n",
    "monthsToPredict = np.arange(6, len(months))\n",
    "monthColumns = months[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df806a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainEstimationCrossValidation(): \n",
    "    models, scalers = {}, {}\n",
    "    for fold, splits in cvSplit.items():\n",
    "        trainingSplit, testSplit = splits['training'], splits['test']\n",
    "        trainingSplit = ['region_{}'.format(i) for i in trainingSplit]\n",
    "        testSplit = ['region_{}'.format(i) for i in testSplit]\n",
    "        print('CV fold:', fold)\n",
    "\n",
    "        brandModels, brandScalers = {}, {}\n",
    "        for brand in brands: \n",
    "            print(brand)\n",
    "\n",
    "            # define models and parameters, yTrue and make cross-validation\n",
    "            modelyTrue = linearModels[brand]\n",
    "            currentFeatures = regionTrainFeatures[brand]\n",
    "\n",
    "            xTrain, xTest = currentFeatures.loc[trainingSplit].values, currentFeatures.loc[testSplit].values\n",
    "            yTrain, yTest = modelyTrue.loc[trainingSplit].values, modelyTrue.loc[testSplit].values \n",
    "\n",
    "            # scale x data\n",
    "            scaler = StandardScaler()\n",
    "            xTrainScaled = scaler.fit_transform(xTrain)\n",
    "            xTestScaled = scaler.transform(xTest)\n",
    "\n",
    "            # train model\n",
    "            model = modelType(**modelParams)\n",
    "            model.fit(xTrainScaled, yTrain)\n",
    "\n",
    "            brandModels[brand] = model\n",
    "            brandScalers[brand] = scaler\n",
    "\n",
    "        models[fold] = brandModels\n",
    "        scalers[fold] = brandScalers\n",
    "    return models, scalers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5236598a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV fold: 0\n",
      "brand_1\n",
      "brand_2\n",
      "CV fold: 1\n",
      "brand_1\n",
      "brand_2\n",
      "CV fold: 2\n",
      "brand_1\n",
      "brand_2\n",
      "CV fold: 3\n",
      "brand_1\n",
      "brand_2\n",
      "CV fold: 4\n",
      "brand_1\n",
      "brand_2\n"
     ]
    }
   ],
   "source": [
    "estimationCvModels, estimationCvScalers = trainEstimationCrossValidation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef10cb1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictDataWithCvModels(features, models, scalers): \n",
    "    # evaluate cv. with each model, predict training data to get ci bounds. calculate score for each run and average\n",
    "    brandPredictions = {}\n",
    "    for brand in brands: \n",
    "        foldPredictions = {}\n",
    "        for fold in models.keys(): \n",
    "            currentFeatures = features[brand]\n",
    "            scaler = scalers[str(fold)][brand]\n",
    "            xTrainScaled = scaler.transform(currentFeatures.values)\n",
    "            model = models[str(fold)][brand]\n",
    "            parameters = model.predict(xTrainScaled)\n",
    "            \n",
    "            predictedSales = []\n",
    "            for params in parameters: \n",
    "                salesForecasts = predictSales(monthsToPredict, *params)\n",
    "                predictedSales.append(salesForecasts)\n",
    "            predictedSales = pd.DataFrame(predictedSales, index=currentFeatures.index)\n",
    "            foldPredictions[int(fold)] = predictedSales\n",
    "        brandPredictions[brand] = foldPredictions\n",
    "    return brandPredictions\n",
    "\n",
    "\n",
    "def getCiFromCv(predictions: Dict[int, pd.DataFrame], correctionFactor: float = 1) -> Tuple[pd.DataFrame, pd.DataFrame]: \n",
    "    \"\"\"\n",
    "    Calculate ci bounds, such that 80% of the yTrue points fall within the margin of yPred +- bounds. \n",
    "    CI is calculated for each data point\n",
    "    \"\"\"\n",
    "    index = predictions[0].index.values\n",
    "    stackedPredictions = np.stack([preds.values for preds in predictions.values()], axis=0)  # 3D -> fold, region, month\n",
    "    # mean = stackedPredictions.mean(axis=0)  # average over folds\n",
    "    lowerBound = np.percentile(stackedPredictions, 10, axis=0)\n",
    "    upperBound = np.percentile(stackedPredictions, 90, axis=0)\n",
    "    return (pd.DataFrame(lowerBound*correctionFactor, index=index), \n",
    "            pd.DataFrame(upperBound*correctionFactor, index=index))\n",
    "\n",
    "\n",
    "def getCiBoundsFromCvData(cvPredictions): \n",
    "    cvCiBounds = {brand: getCiFromCv(cvPredictions[brand]) for brand in brands}\n",
    "    for brand, bounds in cvCiBounds.items(): \n",
    "        lower, upper = bounds\n",
    "        lower.rename(columns={c: monthColumns[c] for c in lower.columns.values}, inplace=True)\n",
    "        upper.rename(columns={c: monthColumns[c] for c in upper.columns.values}, inplace=True)\n",
    "    return cvCiBounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "071109f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSubmissionDataFromSales(predictions: Dict[str, pd.DataFrame], \n",
    "                               isTrain: bool = False\n",
    "                              ) -> pd.DataFrame: \n",
    "    \"\"\"\n",
    "    Takse a dict for predictions and bounds. In both cases the keys are the brands. \n",
    "    The values for predictions are dataframes containing the predictions per region (index) and month (column). \n",
    "    The values for the bounds are tuples of two dataframes of lower and upper bounds with same index and \n",
    "    columns as the predictions df. \n",
    "    Transforms the data into a dataframe with columns, month, region, brand, sales, lower, upper, as required\n",
    "    by the submission platform.\n",
    "    \"\"\"\n",
    "    submissionData = []\n",
    "    tempRegions = regions[:151] if isTrain else regions[151:]\n",
    "    for brand in brands: \n",
    "        preds = predictions[brand]\n",
    "        # lower, upper = bounds[brand]\n",
    "        for region in tempRegions: \n",
    "            for month in months[6:]: \n",
    "                row = [month, region, brand, preds.at[region, month]]\n",
    "                submissionData.append(row)\n",
    "    submissionData = pd.DataFrame(submissionData, columns=['month', 'region', 'brand', 'sales'])\n",
    "    # submissionData.sort_values(by=['month', 'region', 'brand'], ascending=True, inplace=True)\n",
    "    # submissionData.reset_index(drop=True, inplace=True)\n",
    "    sortDf(submissionData, isTrain)\n",
    "    return submissionData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d09fa0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainEstimatedSales = predictDataWithCvModels(regionTrainFeatures, estimationCvModels, estimationCvScalers)\n",
    "ciBoundsEstimationTrain = getCiBoundsFromCvData(trainEstimatedSales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac13d36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterBrands(x): \n",
    "    return x in ['brand_1', 'brand_2']\n",
    "\n",
    "def filterMonths(x): \n",
    "    d = datetime.strptime(x, '%Y-%m')\n",
    "    return d >= referenceDate\n",
    "\n",
    "def scoreEstimationCv(cvPredictions, isTrain):\n",
    "    accScores, confScores = [], []\n",
    "    submissions = {}\n",
    "    # groundTruth = salesTrain[salesTrain['brand'].apply(filterBrands)][salesTrain['month'].apply(filterMonths)]\n",
    "    for i in range(len(cvSplit.keys())): \n",
    "        foldData = {}\n",
    "        for brand in brands: \n",
    "            df = cvPredictions[brand][i]\n",
    "            df.rename(columns={c: monthColumns[c] for c in df.columns.values}, inplace=True)\n",
    "            foldData[brand] = df\n",
    "\n",
    "        submissionData = getSubmissionDataFromSales(foldData, isTrain)\n",
    "        submissionData.rename(columns={'sales': 'sales_estimated'}, inplace=True)\n",
    "        submissions[i] = submissionData\n",
    "        \n",
    "        \n",
    "        #score = ComputeMetrics(submissionData, \n",
    "        #                       salesTrain, \n",
    "        #                       groundTruth)\n",
    "        #accScores.append(score[0])\n",
    "        #confScores.append(score[1])\n",
    "        \n",
    "    # print(np.mean(accScores), np.std(accScores), np.mean(confScores), np.std(confScores))\n",
    "    return submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b21d996",
   "metadata": {},
   "outputs": [],
   "source": [
    "submissionsTrainEstimation = scoreEstimationCv(trainEstimatedSales, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9373622a",
   "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>month</th>\n",
       "      <th>region</th>\n",
       "      <th>brand</th>\n",
       "      <th>sales_estimated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>789.254505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>311.923054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>region_0</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1622.975798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4223</th>\n",
       "      <td>2021-06</td>\n",
       "      <td>region_150</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>2344.838010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4224</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>region_150</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>6362.414548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4225</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>region_150</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>2236.341750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>region_150</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>6483.293659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4227</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>region_150</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>2039.333662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4228 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        month      region    brand  sales_estimated\n",
       "0     2020-07    region_0  brand_1         0.000000\n",
       "1     2020-07    region_0  brand_2         0.000000\n",
       "2     2020-08    region_0  brand_1       789.254505\n",
       "3     2020-08    region_0  brand_2       311.923054\n",
       "4     2020-09    region_0  brand_1      1622.975798\n",
       "...       ...         ...      ...              ...\n",
       "4223  2021-06  region_150  brand_2      2344.838010\n",
       "4224  2021-07  region_150  brand_1      6362.414548\n",
       "4225  2021-07  region_150  brand_2      2236.341750\n",
       "4226  2021-08  region_150  brand_1      6483.293659\n",
       "4227  2021-08  region_150  brand_2      2039.333662\n",
       "\n",
       "[4228 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissionsTrainEstimation[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e7c0286f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotRandomEstimateRegions(dfTrue, dfPred):\n",
    "    xTicks = dfTrue.columns.values\n",
    "    fig, ax = plt.subplots(nrows=4, ncols=4, figsize=(8,8), sharex=True)\n",
    "    for i in range(4):\n",
    "        for j in range(4): \n",
    "            # yPred = trainingPredictions[brand]\n",
    "            randomRegion = np.random.randint(0, 151, size=1)[0]\n",
    "            yPred = dfPred.iloc[randomRegion, :].values\n",
    "            ax[i, j].plot(dfTrue.columns.values, dfTrue.iloc[randomRegion, :].values)\n",
    "            ax[i, j].plot(dfTrue.columns.values, np.where(yPred < 0, np.zeros(yPred.shape), yPred))\n",
    "\n",
    "    plt.setp(ax, \n",
    "             xticks=[xTicks[i] for i in range(len(xTicks)) if i%5 == 0], \n",
    "            )\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9c8b652f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for brand, data in trainEstimatedSales.items(): \n",
    "    df = trainEstimatedSales['brand_2'][0]\n",
    "    dfTrue = transformedData['brand_1']\n",
    "    columns = [c for c in dfTrue.columns if filterMonths(c)]\n",
    "    plotRandomEstimateRegions(dfTrue[columns], df)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b00f208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for brand, data in trainEstimatedSales.items(): \n",
    "    df = trainEstimatedSales['brand_2'][0]\n",
    "    dfTrue = transformedData['brand_2']\n",
    "    columns = [c for c in dfTrue.columns if filterMonths(c)]\n",
    "    plotRandomEstimateRegions(dfTrue[columns], df)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6dcd34f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50, 26), (50, 19))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load test data\n",
    "# salesTest = pd.read_csv('./data/data_files/sales_test_splitted.csv', index_col=0)\n",
    "testRegionFeatures = {brand: pd.read_csv('./data/data_files/region_features/test_{}.csv'.format(brand), index_col=0) for brand in brands}\n",
    "testRegionFeatures['brand_1'].shape, testRegionFeatures['brand_2'].shape"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e4729db8",
   "metadata": {},
   "source": [
    "# predict entire test data with all models\n",
    "predictions = {}\n",
    "for brand in brands: \n",
    "    brandPredictions = {}\n",
    "    for fold in range(len(cvSplit)): \n",
    "        model, scaler = models[str(fold)][brand], scalers[str(fold)][brand]\n",
    "        currentFeatures = testFeatures[brand]\n",
    "        xScaled = scaler.transform(currentFeatures.values)\n",
    "        parameters = model.predict(xScaled)\n",
    "        predictedSales = []\n",
    "        for params in parameters: \n",
    "            salesForecasts = predictSales(monthsToPredict, *params)\n",
    "            predictedSales.append(salesForecasts)\n",
    "        predictedSales = pd.DataFrame(predictedSales, index=currentFeatures.index)\n",
    "        brandPredictions[fold] = predictedSales\n",
    "    predictions[brand] = brandPredictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7538e15b",
   "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>month</th>\n",
       "      <th>region</th>\n",
       "      <th>brand</th>\n",
       "      <th>sales_estimated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_151</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_151</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>142.000273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_151</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>175.467538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_151</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>225.395690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>region_151</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>416.018686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>2021-06</td>\n",
       "      <td>region_200</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1446.317148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>region_200</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>3774.537843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>2021-07</td>\n",
       "      <td>region_200</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1390.852933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>region_200</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>3812.746317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>2021-08</td>\n",
       "      <td>region_200</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1269.166456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1400 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        month      region    brand  sales_estimated\n",
       "0     2020-07  region_151  brand_1         0.000000\n",
       "1     2020-07  region_151  brand_2       142.000273\n",
       "2     2020-08  region_151  brand_1       175.467538\n",
       "3     2020-08  region_151  brand_2       225.395690\n",
       "4     2020-09  region_151  brand_1       416.018686\n",
       "...       ...         ...      ...              ...\n",
       "1395  2021-06  region_200  brand_2      1446.317148\n",
       "1396  2021-07  region_200  brand_1      3774.537843\n",
       "1397  2021-07  region_200  brand_2      1390.852933\n",
       "1398  2021-08  region_200  brand_1      3812.746317\n",
       "1399  2021-08  region_200  brand_2      1269.166456\n",
       "\n",
       "[1400 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testEstimatedCvSales = predictDataWithCvModels(testRegionFeatures, estimationCvModels, estimationCvScalers)\n",
    "submissionsTestEstimation = scoreEstimationCv(testEstimatedCvSales, False)\n",
    "submissionsTestEstimation[0]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "dcb06d17",
   "metadata": {},
   "source": [
    "testCiBounds = {brand: getCiFromCv(predictions[brand]) for brand in brands}"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a144dd7e",
   "metadata": {},
   "source": [
    "# train model on entire trainings data and predict test data\n",
    "testEstimates = {}\n",
    "models, scalers = {}, {}\n",
    "for brand in brands: \n",
    "    modelyTrue = linearModels[brand]\n",
    "    currentFeatures = features[brand]\n",
    "    \n",
    "    scaler = StandardScaler()\n",
    "    xTrainScaled = scaler.fit_transform(currentFeatures.values)\n",
    "    xTestScaled = scaler.transform(testFeatures[brand].values)\n",
    "\n",
    "    model = modelType(**modelParams)\n",
    "    model.fit(xTrainScaled, modelyTrue)\n",
    "\n",
    "    parameters = model.predict(xTestScaled)\n",
    "    predictedSales = []\n",
    "    for params in parameters: \n",
    "        salesForecasts = predictSales(monthsToPredict, *params)\n",
    "        predictedSales.append(salesForecasts)\n",
    "    predictedSales = pd.DataFrame(predictedSales, index=testFeatures[brand].index)\n",
    "    testEstimates[brand] = predictedSales\n",
    "\n",
    "    models[brand] = model\n",
    "    scalers[brand] = scaler\n",
    "    "
   ]
  },
  {
   "cell_type": "raw",
   "id": "61795d10",
   "metadata": {},
   "source": [
    "# predict training set\n",
    "trainingEstimates = {}\n",
    "for brand in brands: \n",
    "    currentFeatures = features[brand]\n",
    "    scaler = scalers[brand]\n",
    "    xTrainScaled = scaler.transform(currentFeatures.values)\n",
    "    model = models[brand]\n",
    "    \n",
    "    parameters = model.predict(xTrainScaled)\n",
    "    predictedSales = []\n",
    "    for params in parameters: \n",
    "        salesForecasts = predictSales(monthsToPredict, *params)\n",
    "        predictedSales.append(salesForecasts)\n",
    "    predictedSales = pd.DataFrame(predictedSales, index=currentFeatures.index)\n",
    "    predictedSales.rename(columns={c: monthColumns[c] for c in predictedSales.columns.values}, inplace=True)\n",
    "    trainingEstimates[brand] = predictedSales\n",
    "    \n",
    "# evaluate training set\n",
    "#estimatedSubmissionData = getSubmissionDataFromSales(trainingEstimates, testCiBounds)\n",
    "#groundTruth = salesTrain[salesTrain['brand'].apply(filterBrands)][salesTrain['month'].apply(filterMonths)]\n",
    "#score = ComputeMetrics(submissionData, \n",
    "#                       salesTrain, \n",
    "#                       groundTruth)\n",
    "#score"
   ]
  },
  {
   "cell_type": "raw",
   "id": "df999ba9",
   "metadata": {},
   "source": [
    "# get submission data for training data\n",
    "estimatedSubmissionData = []\n",
    "regions = trainingEstimates[brands[0]].index.values\n",
    "months = trainingEstimates[brands[0]].columns.values\n",
    "for brand in brands: \n",
    "    preds = trainingEstimates[brand]\n",
    "    for region in regions: \n",
    "        for month in months: \n",
    "            row = [month, region, brand, preds.at[region, month]]\n",
    "            estimatedSubmissionData.append(row)\n",
    "estimatedSubmissionData = pd.DataFrame(estimatedSubmissionData, columns=['month', 'region', 'brand', 'sales'])\n",
    "# submissionData.sort_values(by=['month', 'region', 'brand'], ascending=True, inplace=True)\n",
    "# submissionData.reset_index(drop=True, inplace=True)\n",
    "estimatedSubmissionData.head()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4a104356",
   "metadata": {},
   "source": [
    "for brand, df in testEstimates.items(): \n",
    "    df.rename(columns={c: monthColumns[c] for c in df.columns.values}, inplace=True)\n",
    "    \n",
    "for brand, bounds in testCiBounds.items(): \n",
    "    lower, upper = bounds\n",
    "    lower.rename(columns={c: monthColumns[c] for c in lower.columns.values}, inplace=True)\n",
    "    upper.rename(columns={c: monthColumns[c] for c in upper.columns.values}, inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4d9c68a4",
   "metadata": {},
   "source": [
    "testSubmission = getSubmissionDataFromSales(testEstimates, testCiBounds)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e9f4f70f",
   "metadata": {},
   "source": [
    "submissionData.shape"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b6d1669b",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "submissionData.head(n=25)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c1dc82e6",
   "metadata": {},
   "source": [
    "submissionData.to_csv('./data/data_files/submissions/sigmoid_ridge_default_regression.csv', \n",
    "                     index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c75b391",
   "metadata": {},
   "source": [
    "### Time-based prediction\n",
    "The idea is to use the previous predictions as a baseline and now train an additional model on top, which predicts a correction on top of the base model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d0a4663",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>month</th>\n",
       "      <th>delta_interactions_1_f2f</th>\n",
       "      <th>delta_distinct_interactions_1_f2f</th>\n",
       "      <th>delta_interactions_1_other</th>\n",
       "      <th>delta_distinct_interactions_1_other</th>\n",
       "      <th>delta_interactions_1_phone</th>\n",
       "      <th>delta_distinct_interactions_1_phone</th>\n",
       "      <th>delta_interactions_1_video</th>\n",
       "      <th>delta_distinct_interactions_1_video</th>\n",
       "      <th>...</th>\n",
       "      <th>delta_distinct_interactions_2_video</th>\n",
       "      <th>delta_nr_contacts_1</th>\n",
       "      <th>delta_hcp_contacted_1</th>\n",
       "      <th>delta_mails_opened_1</th>\n",
       "      <th>delta_mails_clicked_1</th>\n",
       "      <th>delta_nr_contacts_2</th>\n",
       "      <th>delta_hcp_contacted_2</th>\n",
       "      <th>delta_mails_opened_2</th>\n",
       "      <th>delta_mails_clicked_2</th>\n",
       "      <th>market_potential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>501181.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>478167.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>463424.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529006.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>522498.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>543796.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2021-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>505188.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2021-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>517642.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2021-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>614682.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>609680.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     region    month  delta_interactions_1_f2f  \\\n",
       "0  region_0  2020-07                       0.0   \n",
       "1  region_0  2020-08                      13.0   \n",
       "2  region_0  2020-09                       6.0   \n",
       "3  region_0  2020-10                     -15.0   \n",
       "4  region_0  2020-11                      -4.0   \n",
       "5  region_0  2020-12                       0.0   \n",
       "6  region_0  2021-01                       0.0   \n",
       "7  region_0  2021-02                       0.0   \n",
       "8  region_0  2021-03                       0.0   \n",
       "9  region_0  2021-04                       0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_f2f  delta_interactions_1_other  \\\n",
       "0                                0.0                         0.0   \n",
       "1                               10.0                         0.0   \n",
       "2                                0.0                         0.0   \n",
       "3                               -6.0                         0.0   \n",
       "4                               -4.0                         0.0   \n",
       "5                                0.0                         0.0   \n",
       "6                                0.0                         0.0   \n",
       "7                                0.0                         0.0   \n",
       "8                                0.0                         0.0   \n",
       "9                                0.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_other  delta_interactions_1_phone  \\\n",
       "0                                  0.0                         0.0   \n",
       "1                                  0.0                         2.0   \n",
       "2                                  0.0                         1.0   \n",
       "3                                  0.0                        -3.0   \n",
       "4                                  0.0                         2.0   \n",
       "5                                  0.0                         0.0   \n",
       "6                                  0.0                        -2.0   \n",
       "7                                  0.0                         0.0   \n",
       "8                                  0.0                         0.0   \n",
       "9                                  0.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_phone  delta_interactions_1_video  \\\n",
       "0                                  0.0                         0.0   \n",
       "1                                  2.0                         0.0   \n",
       "2                                  1.0                         0.0   \n",
       "3                                 -3.0                         0.0   \n",
       "4                                  2.0                         0.0   \n",
       "5                                  0.0                         0.0   \n",
       "6                                 -2.0                         0.0   \n",
       "7                                  0.0                         1.0   \n",
       "8                                  0.0                        -1.0   \n",
       "9                                  0.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_video  ...  \\\n",
       "0                                  0.0  ...   \n",
       "1                                  0.0  ...   \n",
       "2                                  0.0  ...   \n",
       "3                                  0.0  ...   \n",
       "4                                  0.0  ...   \n",
       "5                                  0.0  ...   \n",
       "6                                  0.0  ...   \n",
       "7                                  1.0  ...   \n",
       "8                                 -1.0  ...   \n",
       "9                                  0.0  ...   \n",
       "\n",
       "   delta_distinct_interactions_2_video  delta_nr_contacts_1  \\\n",
       "0                                  0.0                  0.0   \n",
       "1                                  0.0                  0.0   \n",
       "2                                  0.0                  0.0   \n",
       "3                                  0.0                  0.0   \n",
       "4                                  0.0                  0.0   \n",
       "5                                  0.0                  0.0   \n",
       "6                                  0.0                  0.0   \n",
       "7                                  0.0                  0.0   \n",
       "8                                  0.0                  0.0   \n",
       "9                                  0.0                  0.0   \n",
       "\n",
       "   delta_hcp_contacted_1  delta_mails_opened_1  delta_mails_clicked_1  \\\n",
       "0                    0.0                   0.0                    0.0   \n",
       "1                    0.0                   0.0                    0.0   \n",
       "2                    0.0                   0.0                    0.0   \n",
       "3                    0.0                   0.0                    0.0   \n",
       "4                    0.0                   0.0                    0.0   \n",
       "5                    0.0                   0.0                    0.0   \n",
       "6                    0.0                   0.0                    0.0   \n",
       "7                    0.0                   0.0                    0.0   \n",
       "8                    0.0                   0.0                    0.0   \n",
       "9                    0.0                   0.0                    0.0   \n",
       "\n",
       "   delta_nr_contacts_2  delta_hcp_contacted_2  delta_mails_opened_2  \\\n",
       "0                  0.0                    0.0                   0.0   \n",
       "1                  0.0                    0.0                   0.0   \n",
       "2                  0.0                    0.0                   0.0   \n",
       "3                  0.0                    0.0                   0.0   \n",
       "4                  0.0                    0.0                   0.0   \n",
       "5                  0.0                    0.0                   0.0   \n",
       "6                  0.0                    0.0                   0.0   \n",
       "7                  0.0                    0.0                   0.0   \n",
       "8                  1.0                    1.0                   1.0   \n",
       "9                 -1.0                   -1.0                  -1.0   \n",
       "\n",
       "   delta_mails_clicked_2  market_potential  \n",
       "0                    0.0         501181.82  \n",
       "1                    0.0         478167.37  \n",
       "2                    0.0         463424.44  \n",
       "3                    0.0         529006.82  \n",
       "4                    0.0         522498.60  \n",
       "5                    0.0         543796.76  \n",
       "6                    0.0         505188.62  \n",
       "7                    0.0         517642.40  \n",
       "8                    1.0         614682.66  \n",
       "9                   -1.0         609680.12  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare time data\n",
    "\n",
    "def loadTimeData(isTrain): \n",
    "    timeData = {}\n",
    "    for brand in brands: \n",
    "        df = pd.read_csv('./data/data_files/time_features/time_features_{}_{}.csv'.format(brand, 'train' if isTrain else 'test'), index_col=0)\n",
    "        df = df[df['month'].apply(lambda x: datetime.strptime(x, '%Y-%m') >= referenceDate)]\n",
    "        sortDf(df, isTrain)\n",
    "        timeData[brand] = df\n",
    "    return timeData\n",
    "        \n",
    "timeTrain = loadTimeData(True)\n",
    "timeTrain['brand_1'].head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a57290b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mergeEstimationTimeData(estimationCvSubmissions, timeData, isTrain): \n",
    "    mergedData = {}\n",
    "    for fold, submission in estimationCvSubmissions.items(): \n",
    "        if 'lower' in submission.columns.values: \n",
    "            submission.drop('lower', axis=1, inplace=True)\n",
    "        if 'upper' in submission.columns.values: \n",
    "            submission.drop('upper', axis=1, inplace=True)\n",
    "\n",
    "        sortDf(submission, isTrain)\n",
    "        \n",
    "        foldMerged = {}\n",
    "        for brand in brands: \n",
    "            merged = pd.concat([submission[submission['brand'] == brand].reset_index(drop=True), \n",
    "                                timeData[brand].drop(['month', 'region'], axis=1)], \n",
    "                               axis=1)\n",
    "            merged.drop('brand', axis=1, inplace=True)\n",
    "            foldMerged[brand] = merged\n",
    "        mergedData[fold] = foldMerged\n",
    "        \n",
    "    return mergedData"
   ]
  },
  {
   "cell_type": "raw",
   "id": "617b4a2f",
   "metadata": {},
   "source": [
    "for fold, submission in submissions.items(): \n",
    "    submission.region = pd.Categorical(submission.region, regions)\n",
    "    submission.month = pd.Categorical(submission.month, months)\n",
    "    submission.sort_values(['region', 'month'], inplace=True)\n",
    "    submission.drop(['lower', 'upper'], axis=1, inplace=True)\n",
    "    submission.rename(columns={'sales': 'sales_estimates'}, inplace=True)\n",
    "    submission.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a1107c93",
   "metadata": {},
   "source": [
    "trainingsData = {}\n",
    "for brand in brands: \n",
    "    temp = {}\n",
    "    for fold, submission in submissions.items(): \n",
    "        df = pd.concat([submission[submission['brand'] == brand].reset_index(drop=True), \n",
    "                      timeData[brand].drop(['month', 'region'], axis=1)], axis=1)\n",
    "        df.drop('brand', axis=1, inplace=True)\n",
    "        print(df.shape)\n",
    "        temp[fold] = df\n",
    "    trainingsData[brand] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7c01e75e",
   "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>month</th>\n",
       "      <th>region</th>\n",
       "      <th>sales_estimated</th>\n",
       "      <th>delta_interactions_1_f2f</th>\n",
       "      <th>delta_distinct_interactions_1_f2f</th>\n",
       "      <th>delta_interactions_1_other</th>\n",
       "      <th>delta_distinct_interactions_1_other</th>\n",
       "      <th>delta_interactions_1_phone</th>\n",
       "      <th>delta_distinct_interactions_1_phone</th>\n",
       "      <th>delta_interactions_1_video</th>\n",
       "      <th>...</th>\n",
       "      <th>delta_distinct_interactions_2_video</th>\n",
       "      <th>delta_nr_contacts_1</th>\n",
       "      <th>delta_hcp_contacted_1</th>\n",
       "      <th>delta_mails_opened_1</th>\n",
       "      <th>delta_mails_clicked_1</th>\n",
       "      <th>delta_nr_contacts_2</th>\n",
       "      <th>delta_hcp_contacted_2</th>\n",
       "      <th>delta_mails_opened_2</th>\n",
       "      <th>delta_mails_clicked_2</th>\n",
       "      <th>market_potential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>501181.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_0</td>\n",
       "      <td>789.254505</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>478167.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>region_0</td>\n",
       "      <td>1622.975798</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>463424.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-10</td>\n",
       "      <td>region_0</td>\n",
       "      <td>2339.443621</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529006.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11</td>\n",
       "      <td>region_0</td>\n",
       "      <td>2942.564373</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>522498.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month    region  sales_estimated  delta_interactions_1_f2f  \\\n",
       "0  2020-07  region_0         0.000000                       0.0   \n",
       "1  2020-08  region_0       789.254505                      13.0   \n",
       "2  2020-09  region_0      1622.975798                       6.0   \n",
       "3  2020-10  region_0      2339.443621                     -15.0   \n",
       "4  2020-11  region_0      2942.564373                      -4.0   \n",
       "\n",
       "   delta_distinct_interactions_1_f2f  delta_interactions_1_other  \\\n",
       "0                                0.0                         0.0   \n",
       "1                               10.0                         0.0   \n",
       "2                                0.0                         0.0   \n",
       "3                               -6.0                         0.0   \n",
       "4                               -4.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_other  delta_interactions_1_phone  \\\n",
       "0                                  0.0                         0.0   \n",
       "1                                  0.0                         2.0   \n",
       "2                                  0.0                         1.0   \n",
       "3                                  0.0                        -3.0   \n",
       "4                                  0.0                         2.0   \n",
       "\n",
       "   delta_distinct_interactions_1_phone  delta_interactions_1_video  ...  \\\n",
       "0                                  0.0                         0.0  ...   \n",
       "1                                  2.0                         0.0  ...   \n",
       "2                                  1.0                         0.0  ...   \n",
       "3                                 -3.0                         0.0  ...   \n",
       "4                                  2.0                         0.0  ...   \n",
       "\n",
       "   delta_distinct_interactions_2_video  delta_nr_contacts_1  \\\n",
       "0                                  0.0                  0.0   \n",
       "1                                  0.0                  0.0   \n",
       "2                                  0.0                  0.0   \n",
       "3                                  0.0                  0.0   \n",
       "4                                  0.0                  0.0   \n",
       "\n",
       "   delta_hcp_contacted_1  delta_mails_opened_1  delta_mails_clicked_1  \\\n",
       "0                    0.0                   0.0                    0.0   \n",
       "1                    0.0                   0.0                    0.0   \n",
       "2                    0.0                   0.0                    0.0   \n",
       "3                    0.0                   0.0                    0.0   \n",
       "4                    0.0                   0.0                    0.0   \n",
       "\n",
       "   delta_nr_contacts_2  delta_hcp_contacted_2  delta_mails_opened_2  \\\n",
       "0                  0.0                    0.0                   0.0   \n",
       "1                  0.0                    0.0                   0.0   \n",
       "2                  0.0                    0.0                   0.0   \n",
       "3                  0.0                    0.0                   0.0   \n",
       "4                  0.0                    0.0                   0.0   \n",
       "\n",
       "   delta_mails_clicked_2  market_potential  \n",
       "0                    0.0         501181.82  \n",
       "1                    0.0         478167.37  \n",
       "2                    0.0         463424.44  \n",
       "3                    0.0         529006.82  \n",
       "4                    0.0         522498.60  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainMergedEstimationTimeData = mergeEstimationTimeData(submissionsTrainEstimation, timeTrain, True)\n",
    "trainMergedEstimationTimeData[0]['brand_1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dadc801d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissionsTestEstimation.keys()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2651ea72",
   "metadata": {},
   "source": [
    "# get yTrue\n",
    "sales = {}\n",
    "for brand in brands: \n",
    "    df = pd.read_csv('./data/data_files/time_features/sales_delta_{}_train.csv'.format(brand), index_col=0)\n",
    "    df = df[df['month'].apply(lambda x: datetime.strptime(x, '%Y-%m') >= referenceDate)]\n",
    "    df.region = pd.Categorical(df.region, regions)\n",
    "    df.month = pd.Categorical(df.month, months)\n",
    "    df.sort_values(['region', 'month'], inplace=True)\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    print(df.shape)\n",
    "    sales[brand] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f61c1c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadTimeYTrueForBrands():\n",
    "    loaded = {}\n",
    "    for brand in brands: \n",
    "        df = pd.read_csv('./data/data_files/time_features/sales_delta_{}_train.csv'.format(brand), index_col=0)\n",
    "        df = df[df['month'].apply(lambda x: datetime.strptime(x, '%Y-%m') >= referenceDate)]\n",
    "        sortDf(df, True)\n",
    "        loaded[brand] = df\n",
    "    return loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "10bf21d6",
   "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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>sales</th>\n",
       "      <th>delta_sales</th>\n",
       "      <th>rel_delta_sales</th>\n",
       "      <th>prevSales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>612.44</td>\n",
       "      <td>612.44</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>1653.59</td>\n",
       "      <td>1041.15</td>\n",
       "      <td>1.700003</td>\n",
       "      <td>612.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>1898.56</td>\n",
       "      <td>244.97</td>\n",
       "      <td>0.148144</td>\n",
       "      <td>1653.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>2939.71</td>\n",
       "      <td>1041.15</td>\n",
       "      <td>0.548389</td>\n",
       "      <td>1898.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>6828.71</td>\n",
       "      <td>1286.13</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>5542.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>6491.86</td>\n",
       "      <td>-336.85</td>\n",
       "      <td>-0.049328</td>\n",
       "      <td>6828.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>5726.31</td>\n",
       "      <td>-765.55</td>\n",
       "      <td>-0.117925</td>\n",
       "      <td>6491.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>8543.54</td>\n",
       "      <td>2817.23</td>\n",
       "      <td>0.491980</td>\n",
       "      <td>5726.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>5634.64</td>\n",
       "      <td>-2908.90</td>\n",
       "      <td>-0.340479</td>\n",
       "      <td>8543.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2114 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          region    month    sales  delta_sales  rel_delta_sales  prevSales\n",
       "0       region_0  2020-07     0.00         0.00         0.000000       0.00\n",
       "1       region_0  2020-08   612.44       612.44       100.000000       0.00\n",
       "2       region_0  2020-09  1653.59      1041.15         1.700003     612.44\n",
       "3       region_0  2020-10  1898.56       244.97         0.148144    1653.59\n",
       "4       region_0  2020-11  2939.71      1041.15         0.548389    1898.56\n",
       "...          ...      ...      ...          ...              ...        ...\n",
       "2109  region_150  2021-04  6828.71      1286.13         0.232045    5542.58\n",
       "2110  region_150  2021-05  6491.86      -336.85        -0.049328    6828.71\n",
       "2111  region_150  2021-06  5726.31      -765.55        -0.117925    6491.86\n",
       "2112  region_150  2021-07  8543.54      2817.23         0.491980    5726.31\n",
       "2113  region_150  2021-08  5634.64     -2908.90        -0.340479    8543.54\n",
       "\n",
       "[2114 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeYTrue = loadTimeYTrueForBrands()\n",
    "timeYTrue['brand_1']"
   ]
  },
  {
   "cell_type": "raw",
   "id": "19ad2424",
   "metadata": {},
   "source": [
    "correctionModels = {}\n",
    "for brand in brands: \n",
    "    brandModels = {}\n",
    "    currentSales = sales[brand]\n",
    "    \n",
    "    for fold, df in trainingsData[brand].items(): \n",
    "        trainingSplit, testSplit = cvSplit[str(fold)]['training'], cvSplit[str(fold)]['test']\n",
    "        trainingSplit = ['region_{}'.format(i) for i in trainingSplit]\n",
    "        testSplit = ['region_{}'.format(i) for i in testSplit]\n",
    "        print('CV fold:', fold)\n",
    "        \n",
    "        currentFeatures = trainingsData[brand][fold]\n",
    "        \n",
    "        yTrain = currentSales[currentSales['region'].apply(lambda x: x in trainingSplit)]['sales'].values\n",
    "        yTest = currentSales[currentSales['region'].apply(lambda x: x in testSplit)]['sales'].values\n",
    "        xTrain = currentFeatures[currentFeatures['region'].apply(lambda x: x in trainingSplit)].values[:, 2:]\n",
    "        xTest = currentFeatures[currentFeatures['region'].apply(lambda x: x in testSplit)].values[:, 2:]\n",
    "\n",
    "        \n",
    "        # scale x data --> skip here since we use RF\n",
    "        #scaler = StandardScaler()\n",
    "        #xTrainScaled = scaler.fit_transform(xTrain)\n",
    "        #xTestScaled = scaler.transform(xTest)\n",
    "\n",
    "        # train model\n",
    "        model = modelType(**modelParams)\n",
    "        model.fit(xTrain, yTrain)\n",
    "        \n",
    "        brandModels[fold] = model\n",
    "        # brandScalers[brand] = scaler\n",
    "    \n",
    "    correctionModels[brand] = brandModels\n",
    "    # scalers[fold] = brandScalers\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f328f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainCvCorrectionModels(xTrain, yTrain, modelType, modelParams): \n",
    "    correctionCvModels = {}\n",
    "    for fold, data in xTrain.items(): \n",
    "        \n",
    "        foldModels = {}\n",
    "        for brand in brands: \n",
    "            split = cvSplit[str(4-fold)]\n",
    "            trainingSplit, testSplit = split['training'], split['test']\n",
    "            trainingSplit = ['region_{}'.format(i) for i in trainingSplit]\n",
    "            # testSplit = ['region_{}'.format(i) for i in testSplit]\n",
    "            \n",
    "            print('CV fold:', fold)\n",
    "            \n",
    "            currentY = yTrain[brand]\n",
    "            currentX = xTrain[fold][brand]\n",
    "            \n",
    "            y = currentY[currentY['region'].apply(lambda x: x in trainingSplit)]['sales'].values\n",
    "            x = currentX[currentX['region'].apply(lambda x: x in trainingSplit)]\n",
    "            \n",
    "            model = modelType(**modelParams)\n",
    "            model.fit(x.values[:, 2:], y)\n",
    "            \n",
    "            foldModels[brand] = model\n",
    "        \n",
    "        correctionCvModels[fold] = foldModels#\n",
    "    \n",
    "    return correctionCvModels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e35b1fd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV fold: 0\n",
      "CV fold: 0\n",
      "CV fold: 1\n",
      "CV fold: 1\n",
      "CV fold: 2\n",
      "CV fold: 2\n",
      "CV fold: 3\n",
      "CV fold: 3\n",
      "CV fold: 4\n",
      "CV fold: 4\n"
     ]
    }
   ],
   "source": [
    "#modelType = RandomForestRegressor\n",
    "#modelParams = {\n",
    "#    'n_estimators': 100\n",
    "#}\n",
    "correctionCvModels = trainCvCorrectionModels(trainMergedEstimationTimeData, timeYTrue, modelType, modelParams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a2906584",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictCorrectionCvModels(x, models): \n",
    "    predictions = {}\n",
    "    for fold, data in x.items(): \n",
    "        \n",
    "        brandPredictions = {}\n",
    "        for brand in brands: \n",
    "            model = models[fold][brand]\n",
    "            \n",
    "            currentFeatures = x[fold][brand]\n",
    "            yPred = model.predict(currentFeatures.values[:, 2:])\n",
    "            yPred = pd.DataFrame(yPred, columns=['sales'], index=currentFeatures.index)\n",
    "            yPred = pd.concat([currentFeatures[['region', 'month']], yPred], axis=1)\n",
    "            \n",
    "            brandPredictions[brand] = yPred\n",
    "            \n",
    "        predictions[fold] = brandPredictions\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8995f07a",
   "metadata": {},
   "source": [
    "correctedPredictions = {}\n",
    "for brand in brands: \n",
    "    foldPredictions = {}\n",
    "    for fold, df in trainingsData[brand].items(): \n",
    "        xTrain = trainingsData[brand][fold]\n",
    "        \n",
    "        \n",
    "        model = correctionModels[brand][fold]\n",
    "        correctedSales = model.predict(xTrain.values[:, 2:])\n",
    "        correctedSales = pd.DataFrame(correctedSales, columns=['sales'], index=xTrain.index)\n",
    "        correctedSales = pd.concat([xTrain[['region', 'month']], correctedSales], axis=1)\n",
    "        foldPredictions[fold] = correctedSales\n",
    "    \n",
    "    correctedPredictions[brand] = foldPredictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc78a1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "correctedTrainPredictions = predictCorrectionCvModels(trainMergedEstimationTimeData, correctionCvModels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fb3a9fb3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>134.73720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>1088.53755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>2407.36760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>2856.64005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>6625.88965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>6425.71795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>5871.73110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>7808.34865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>6136.76285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2114 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          region    month       sales\n",
       "0       region_0  2020-07     0.00000\n",
       "1       region_0  2020-08   134.73720\n",
       "2       region_0  2020-09  1088.53755\n",
       "3       region_0  2020-10  2407.36760\n",
       "4       region_0  2020-11  2856.64005\n",
       "...          ...      ...         ...\n",
       "2109  region_150  2021-04  6625.88965\n",
       "2110  region_150  2021-05  6425.71795\n",
       "2111  region_150  2021-06  5871.73110\n",
       "2112  region_150  2021-07  7808.34865\n",
       "2113  region_150  2021-08  6136.76285\n",
       "\n",
       "[2114 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correctedTrainPredictions[0]['brand_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c7d7f217",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCiBoundsForCorrectionModels(correctionCvPredictions): \n",
    "    ciBounds = {}\n",
    "    for brand in brands: \n",
    "        stacked = []\n",
    "        for fold, data in correctionCvPredictions.items(): \n",
    "            df = data[brand]\n",
    "            stacked.append(df['sales'].values.reshape(-1, 1))\n",
    "        stacked = np.concatenate(stacked, axis=1)\n",
    "        \n",
    "        lower = pd.DataFrame(np.percentile(stacked, 10, axis=1), columns=['lower'])\n",
    "        upper = pd.DataFrame(np.percentile(stacked, 90, axis=1), columns=['upper'])\n",
    "        \n",
    "        ciBounds[brand] = pd.concat([lower, upper], axis=1)\n",
    "    return ciBounds"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d924cbc2",
   "metadata": {},
   "source": [
    "ciBounds = {}\n",
    "for brand in brands: \n",
    "    stacked = np.concatenate([preds['sales'].values.reshape(-1, 1) for preds in correctedPredictions[brand].values()], axis=1)\n",
    "    lowerBound = np.percentile(stacked, 20, axis=1)\n",
    "    upperBound = np.percentile(stacked, 80, axis=1)\n",
    "    ciBounds[brand] = pd.DataFrame(np.concatenate([lowerBound.reshape(-1, 1), upperBound.reshape(-1, 1)], axis=1),\n",
    "                                  columns=['lower', 'upper'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "218c0f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>lower</th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>263.41064</td>\n",
       "      <td>511.20372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1112.26577</td>\n",
       "      <td>1549.54611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963.96993</td>\n",
       "      <td>2301.71290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2635.41642</td>\n",
       "      <td>2985.49755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>6696.94447</td>\n",
       "      <td>6981.64151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>6510.94559</td>\n",
       "      <td>6730.69399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>6006.00914</td>\n",
       "      <td>6606.31002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>7311.15660</td>\n",
       "      <td>8077.43978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>6030.42928</td>\n",
       "      <td>6510.69170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           lower       upper\n",
       "0        0.00000     0.00000\n",
       "1      263.41064   511.20372\n",
       "2     1112.26577  1549.54611\n",
       "3     1963.96993  2301.71290\n",
       "4     2635.41642  2985.49755\n",
       "...          ...         ...\n",
       "2109  6696.94447  6981.64151\n",
       "2110  6510.94559  6730.69399\n",
       "2111  6006.00914  6606.31002\n",
       "2112  7311.15660  8077.43978\n",
       "2113  6030.42928  6510.69170\n",
       "\n",
       "[2114 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correctedTrainCiBounds = getCiBoundsForCorrectionModels(correctedTrainPredictions)\n",
    "correctedTrainCiBounds['brand_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0f29ecc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSubmissionDataFromPredictionsAndCi(correctedCvPredictions, correctedCiBounds): \n",
    "    cvSubmissions = {}\n",
    "    for fold, data in correctedCvPredictions.items(): \n",
    "        merged = []\n",
    "        for brand in brands: \n",
    "            df = correctedCvPredictions[fold][brand]\n",
    "            df['brand'] = brand\n",
    "            bounds = correctedCiBounds[brand]\n",
    "            df = pd.concat([df, bounds], axis=1)\n",
    "            \n",
    "            merged.append(df)\n",
    "        \n",
    "        submission = pd.concat(merged, axis=0)\n",
    "        cvSubmissions[fold] = submission\n",
    "            \n",
    "    return cvSubmissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "adc947bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "correctedTrainCvSubmissions = getSubmissionDataFromPredictionsAndCi(correctedTrainPredictions, correctedTrainCiBounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "60e96af4",
   "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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>sales</th>\n",
       "      <th>brand</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>134.73720</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>263.41064</td>\n",
       "      <td>511.20372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>1088.53755</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1112.26577</td>\n",
       "      <td>1549.54611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>2407.36760</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1963.96993</td>\n",
       "      <td>2301.71290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>2856.64005</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>2635.41642</td>\n",
       "      <td>2985.49755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>1254.91510</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1172.64695</td>\n",
       "      <td>1635.69922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>1442.60070</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1034.03453</td>\n",
       "      <td>2733.08646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>2114.78500</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1701.87387</td>\n",
       "      <td>2174.15236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>1117.12745</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1154.85853</td>\n",
       "      <td>1372.73299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>region_150</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>995.43745</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>696.68579</td>\n",
       "      <td>1251.88189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4228 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          region    month       sales    brand       lower       upper\n",
       "0       region_0  2020-07     0.00000  brand_1     0.00000     0.00000\n",
       "1       region_0  2020-08   134.73720  brand_1   263.41064   511.20372\n",
       "2       region_0  2020-09  1088.53755  brand_1  1112.26577  1549.54611\n",
       "3       region_0  2020-10  2407.36760  brand_1  1963.96993  2301.71290\n",
       "4       region_0  2020-11  2856.64005  brand_1  2635.41642  2985.49755\n",
       "...          ...      ...         ...      ...         ...         ...\n",
       "2109  region_150  2021-04  1254.91510  brand_2  1172.64695  1635.69922\n",
       "2110  region_150  2021-05  1442.60070  brand_2  1034.03453  2733.08646\n",
       "2111  region_150  2021-06  2114.78500  brand_2  1701.87387  2174.15236\n",
       "2112  region_150  2021-07  1117.12745  brand_2  1154.85853  1372.73299\n",
       "2113  region_150  2021-08   995.43745  brand_2   696.68579  1251.88189\n",
       "\n",
       "[4228 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correctedTrainCvSubmissions[0]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "49606f22",
   "metadata": {},
   "source": [
    "\n",
    "accScores, confScores = [], []\n",
    "finalSubmissions = {}\n",
    "for i in range(len(brandModels.keys())): \n",
    "    foldData = []\n",
    "    for brand in brands: \n",
    "        df = correctedPredictions[brand][i]\n",
    "        df = pd.concat([df, ciBounds[brand]], axis=1)\n",
    "        df['brand'] = brand\n",
    "        print(df.shape)\n",
    "        foldData.append(df)\n",
    "    \n",
    "    submissionData = pd.concat(foldData, axis=0)\n",
    "    submissionData.region = pd.Categorical(submissionData.region, regions)\n",
    "    submissionData.month = pd.Categorical(submissionData.month, months)\n",
    "    submissionData.sort_values(['region', 'month'], inplace=True)\n",
    "    submissionData.reset_index(drop=True, inplace=True)\n",
    "    # submissionData = getSubmissionDataFromSales(foldData, cvCiBounds)\n",
    "    \n",
    "    finalSubmissions[i] = submissionData\n",
    "    groundTruth = salesTrain[salesTrain['brand'].apply(filterBrands)][salesTrain['month'].apply(filterMonths)]\n",
    "    score = ComputeMetrics(submissionData, \n",
    "                           salesTrain, \n",
    "                           groundTruth)\n",
    "    accScores.append(score[0])\n",
    "    confScores.append(score[1])\n",
    "np.mean(accScores), np.std(accScores), np.mean(confScores), np.std(confScores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "36897080",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scoreCorrectCv(submissions): \n",
    "    accScores, confScores = [], []\n",
    "    \n",
    "    groundTruth = salesTrain[salesTrain['brand'].apply(filterBrands)].copy()\n",
    "    groundTruth = groundTruth[groundTruth['month'].apply(filterMonths)]\n",
    "    \n",
    "    for fold, data in submissions.items(): \n",
    "        score = ComputeMetrics(data, salesTrain, groundTruth)\n",
    "        accScores.append(score[0])\n",
    "        confScores.append(score[1])\n",
    "        \n",
    "    print('Average accuracy score', np.mean(accScores))\n",
    "    print('Average confidence score', np.mean(confScores))\n",
    "    return accScores, confScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ff8684e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy score 14.212877291046121\n",
      "Average confidence score 56.43958583611659\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([13.145432159676705,\n",
       "  13.999646525026435,\n",
       "  16.125487594387742,\n",
       "  14.216648531474807,\n",
       "  13.577171644664915],\n",
       " [56.439585836116585,\n",
       "  56.439585836116585,\n",
       "  56.439585836116585,\n",
       "  56.439585836116585,\n",
       "  56.439585836116585])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accScores, confScores = scoreCorrectCv(correctedTrainCvSubmissions)\n",
    "accScores, confScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "166fa5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAverageSubmission(submissions): \n",
    "    sales = []\n",
    "    for fold, data in submissions.items(): \n",
    "        sales.append(data['sales'].values.reshape(-1, 1))\n",
    "    \n",
    "    sales = np.concatenate(sales, axis=1)\n",
    "    meanSales = np.mean(sales, axis=1)\n",
    "    finalSubmission = submissions[0].copy()\n",
    "    finalSubmission.drop('sales', axis=1, inplace=True)\n",
    "    finalSubmission['sales'] = meanSales\n",
    "    return finalSubmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4d87995",
   "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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>brand</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>263.41064</td>\n",
       "      <td>511.20372</td>\n",
       "      <td>412.35593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1112.26577</td>\n",
       "      <td>1549.54611</td>\n",
       "      <td>1294.77935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>1963.96993</td>\n",
       "      <td>2301.71290</td>\n",
       "      <td>2113.86889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_0</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>2635.41642</td>\n",
       "      <td>2985.49755</td>\n",
       "      <td>2770.60387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     region    month    brand       lower       upper       sales\n",
       "0  region_0  2020-07  brand_1     0.00000     0.00000     0.00000\n",
       "1  region_0  2020-08  brand_1   263.41064   511.20372   412.35593\n",
       "2  region_0  2020-09  brand_1  1112.26577  1549.54611  1294.77935\n",
       "3  region_0  2020-10  brand_1  1963.96993  2301.71290  2113.86889\n",
       "4  region_0  2020-11  brand_1  2635.41642  2985.49755  2770.60387"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTrainSubmission = getAverageSubmission(correctedTrainCvSubmissions)\n",
    "finalTrainSubmission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "18389dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy score 12.718662114548014\n",
      "Average confidence score 56.439585836116585\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([12.718662114548014], [56.439585836116585])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accScores, confScores = scoreCorrectCv({0: finalTrainSubmission})\n",
    "accScores, confScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aa6e344f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotRandomRegions(submissionData: pd.DataFrame, \n",
    "                      brand: str, \n",
    "                      isTrain: bool = False, \n",
    "                      groundTruth: pd.DataFrame = None\n",
    "                     ): \n",
    "    fig, ax = plt.subplots(nrows=4, ncols=4, figsize=(8, 8), sharex=True)\n",
    "    for i in range(4): \n",
    "        for j in range(4): \n",
    "            if isTrain: \n",
    "                randomRegion = 'region_{}'.format(np.random.randint(151))\n",
    "            else: \n",
    "                randomRegion = 'region_{}'.format(np.random.randint(151, 201, 1)[0])\n",
    "            df = submissionData[submissionData['brand'] == brand]\n",
    "            df = df[df['region'] == randomRegion]\n",
    "            sales, lower, upper = df['sales'].values, df['lower'].values, df['upper'].values\n",
    "            ax[i, j].plot(df['month'].values, sales, 'r--')\n",
    "            ax[i, j].plot(df['month'].values, lower, 'b--')\n",
    "            ax[i, j].plot(df['month'].values, upper, 'b--')\n",
    "            \n",
    "            if groundTruth is not None: \n",
    "                df = groundTruth[groundTruth['brand'] == brand]\n",
    "                df = df[df['region'] == randomRegion]\n",
    "                ax[i, j].plot(df['month'].values, df['sales'].values, 'g--')\n",
    "            \n",
    "    plt.setp(ax, \n",
    "             xticks=[df['month'].values[i] for i in range(df.shape[0]) if i%5 == 0], \n",
    "            )\n",
    "    \n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7e8e73cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "groundTruth = salesTrain[salesTrain['brand'].apply(filterBrands)].copy()\n",
    "groundTruth = groundTruth[groundTruth['month'].apply(filterMonths)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "61ff9b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRandomRegions(finalTrainSubmission, 'brand_1', True, groundTruth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5f5827e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRandomRegions(finalTrainSubmission, 'brand_2', True, groundTruth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f2a1a54b",
   "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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>delta_interactions_1_f2f</th>\n",
       "      <th>delta_distinct_interactions_1_f2f</th>\n",
       "      <th>delta_interactions_1_other</th>\n",
       "      <th>delta_distinct_interactions_1_other</th>\n",
       "      <th>delta_interactions_1_phone</th>\n",
       "      <th>delta_distinct_interactions_1_phone</th>\n",
       "      <th>delta_interactions_1_video</th>\n",
       "      <th>delta_distinct_interactions_1_video</th>\n",
       "      <th>...</th>\n",
       "      <th>delta_distinct_interactions_2_video</th>\n",
       "      <th>delta_nr_contacts_1</th>\n",
       "      <th>delta_hcp_contacted_1</th>\n",
       "      <th>delta_mails_opened_1</th>\n",
       "      <th>delta_mails_clicked_1</th>\n",
       "      <th>delta_nr_contacts_2</th>\n",
       "      <th>delta_hcp_contacted_2</th>\n",
       "      <th>delta_mails_opened_2</th>\n",
       "      <th>delta_mails_clicked_2</th>\n",
       "      <th>market_potential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212777.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166766.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196588.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221102.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188318.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226453.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2021-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190923.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2021-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>193173.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2021-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226620.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>235207.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       region    month  delta_interactions_1_f2f  \\\n",
       "0  region_151  2020-07                       0.0   \n",
       "1  region_151  2020-08                       4.0   \n",
       "2  region_151  2020-09                       5.0   \n",
       "3  region_151  2020-10                      -3.0   \n",
       "4  region_151  2020-11                      -6.0   \n",
       "5  region_151  2020-12                       0.0   \n",
       "6  region_151  2021-01                       0.0   \n",
       "7  region_151  2021-02                       0.0   \n",
       "8  region_151  2021-03                       0.0   \n",
       "9  region_151  2021-04                       0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_f2f  delta_interactions_1_other  \\\n",
       "0                                0.0                         0.0   \n",
       "1                                4.0                         0.0   \n",
       "2                                1.0                         0.0   \n",
       "3                               -1.0                         0.0   \n",
       "4                               -4.0                         0.0   \n",
       "5                                0.0                         0.0   \n",
       "6                                0.0                         0.0   \n",
       "7                                0.0                         0.0   \n",
       "8                                0.0                         0.0   \n",
       "9                                0.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_other  delta_interactions_1_phone  \\\n",
       "0                                  0.0                         0.0   \n",
       "1                                  0.0                         0.0   \n",
       "2                                  0.0                         1.0   \n",
       "3                                  0.0                         2.0   \n",
       "4                                  0.0                        -1.0   \n",
       "5                                  0.0                        -2.0   \n",
       "6                                  0.0                         0.0   \n",
       "7                                  0.0                         0.0   \n",
       "8                                  0.0                         0.0   \n",
       "9                                  0.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_phone  delta_interactions_1_video  \\\n",
       "0                                  0.0                         0.0   \n",
       "1                                  0.0                         0.0   \n",
       "2                                  1.0                         0.0   \n",
       "3                                  2.0                         0.0   \n",
       "4                                 -2.0                         0.0   \n",
       "5                                 -1.0                         0.0   \n",
       "6                                  0.0                         0.0   \n",
       "7                                  0.0                         0.0   \n",
       "8                                  0.0                         0.0   \n",
       "9                                  0.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_video  ...  \\\n",
       "0                                  0.0  ...   \n",
       "1                                  0.0  ...   \n",
       "2                                  0.0  ...   \n",
       "3                                  0.0  ...   \n",
       "4                                  0.0  ...   \n",
       "5                                  0.0  ...   \n",
       "6                                  0.0  ...   \n",
       "7                                  0.0  ...   \n",
       "8                                  0.0  ...   \n",
       "9                                  0.0  ...   \n",
       "\n",
       "   delta_distinct_interactions_2_video  delta_nr_contacts_1  \\\n",
       "0                                  0.0                  0.0   \n",
       "1                                  0.0                  3.0   \n",
       "2                                  0.0                 -3.0   \n",
       "3                                  0.0                  0.0   \n",
       "4                                  0.0                  0.0   \n",
       "5                                  0.0                  0.0   \n",
       "6                                  0.0                  0.0   \n",
       "7                                  0.0                  0.0   \n",
       "8                                  0.0                  0.0   \n",
       "9                                  0.0                  0.0   \n",
       "\n",
       "   delta_hcp_contacted_1  delta_mails_opened_1  delta_mails_clicked_1  \\\n",
       "0                    0.0                   0.0                    0.0   \n",
       "1                    1.0                   3.0                    3.0   \n",
       "2                   -1.0                  -3.0                   -3.0   \n",
       "3                    0.0                   0.0                    0.0   \n",
       "4                    0.0                   0.0                    0.0   \n",
       "5                    0.0                   0.0                    0.0   \n",
       "6                    0.0                   0.0                    0.0   \n",
       "7                    0.0                   0.0                    0.0   \n",
       "8                    0.0                   0.0                    0.0   \n",
       "9                    0.0                   0.0                    0.0   \n",
       "\n",
       "   delta_nr_contacts_2  delta_hcp_contacted_2  delta_mails_opened_2  \\\n",
       "0                  0.0                    0.0                   0.0   \n",
       "1                  0.0                    0.0                   0.0   \n",
       "2                  0.0                    0.0                   0.0   \n",
       "3                  0.0                    0.0                   0.0   \n",
       "4                  0.0                    0.0                   0.0   \n",
       "5                  0.0                    0.0                   0.0   \n",
       "6                  0.0                    0.0                   0.0   \n",
       "7                  0.0                    0.0                   0.0   \n",
       "8                  0.0                    0.0                   0.0   \n",
       "9                  0.0                    0.0                   0.0   \n",
       "\n",
       "   delta_mails_clicked_2  market_potential  \n",
       "0                    0.0         212777.53  \n",
       "1                    0.0         166766.21  \n",
       "2                    0.0         196588.63  \n",
       "3                    0.0         221102.67  \n",
       "4                    0.0         188318.94  \n",
       "5                    0.0         226453.77  \n",
       "6                    0.0         190923.73  \n",
       "7                    0.0         193173.29  \n",
       "8                    0.0         226620.01  \n",
       "9                    0.0         235207.48  \n",
       "\n",
       "[10 rows x 27 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict test data\n",
    "timeTest = loadTimeData(False)\n",
    "timeTest['brand_1'].head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "162af75f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>region</th>\n",
       "      <th>sales_estimated</th>\n",
       "      <th>delta_interactions_1_f2f</th>\n",
       "      <th>delta_distinct_interactions_1_f2f</th>\n",
       "      <th>delta_interactions_1_other</th>\n",
       "      <th>delta_distinct_interactions_1_other</th>\n",
       "      <th>delta_interactions_1_phone</th>\n",
       "      <th>delta_distinct_interactions_1_phone</th>\n",
       "      <th>delta_interactions_1_video</th>\n",
       "      <th>...</th>\n",
       "      <th>delta_distinct_interactions_2_video</th>\n",
       "      <th>delta_nr_contacts_1</th>\n",
       "      <th>delta_hcp_contacted_1</th>\n",
       "      <th>delta_mails_opened_1</th>\n",
       "      <th>delta_mails_clicked_1</th>\n",
       "      <th>delta_nr_contacts_2</th>\n",
       "      <th>delta_hcp_contacted_2</th>\n",
       "      <th>delta_mails_opened_2</th>\n",
       "      <th>delta_mails_clicked_2</th>\n",
       "      <th>market_potential</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07</td>\n",
       "      <td>region_151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>212777.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-08</td>\n",
       "      <td>region_151</td>\n",
       "      <td>175.467538</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166766.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>region_151</td>\n",
       "      <td>416.018686</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196588.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-10</td>\n",
       "      <td>region_151</td>\n",
       "      <td>682.006605</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221102.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11</td>\n",
       "      <td>region_151</td>\n",
       "      <td>963.573224</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month      region  sales_estimated  delta_interactions_1_f2f  \\\n",
       "0  2020-07  region_151         0.000000                       0.0   \n",
       "1  2020-08  region_151       175.467538                       4.0   \n",
       "2  2020-09  region_151       416.018686                       5.0   \n",
       "3  2020-10  region_151       682.006605                      -3.0   \n",
       "4  2020-11  region_151       963.573224                      -6.0   \n",
       "\n",
       "   delta_distinct_interactions_1_f2f  delta_interactions_1_other  \\\n",
       "0                                0.0                         0.0   \n",
       "1                                4.0                         0.0   \n",
       "2                                1.0                         0.0   \n",
       "3                               -1.0                         0.0   \n",
       "4                               -4.0                         0.0   \n",
       "\n",
       "   delta_distinct_interactions_1_other  delta_interactions_1_phone  \\\n",
       "0                                  0.0                         0.0   \n",
       "1                                  0.0                         0.0   \n",
       "2                                  0.0                         1.0   \n",
       "3                                  0.0                         2.0   \n",
       "4                                  0.0                        -1.0   \n",
       "\n",
       "   delta_distinct_interactions_1_phone  delta_interactions_1_video  ...  \\\n",
       "0                                  0.0                         0.0  ...   \n",
       "1                                  0.0                         0.0  ...   \n",
       "2                                  1.0                         0.0  ...   \n",
       "3                                  2.0                         0.0  ...   \n",
       "4                                 -2.0                         0.0  ...   \n",
       "\n",
       "   delta_distinct_interactions_2_video  delta_nr_contacts_1  \\\n",
       "0                                  0.0                  0.0   \n",
       "1                                  0.0                  3.0   \n",
       "2                                  0.0                 -3.0   \n",
       "3                                  0.0                  0.0   \n",
       "4                                  0.0                  0.0   \n",
       "\n",
       "   delta_hcp_contacted_1  delta_mails_opened_1  delta_mails_clicked_1  \\\n",
       "0                    0.0                   0.0                    0.0   \n",
       "1                    1.0                   3.0                    3.0   \n",
       "2                   -1.0                  -3.0                   -3.0   \n",
       "3                    0.0                   0.0                    0.0   \n",
       "4                    0.0                   0.0                    0.0   \n",
       "\n",
       "   delta_nr_contacts_2  delta_hcp_contacted_2  delta_mails_opened_2  \\\n",
       "0                  0.0                    0.0                   0.0   \n",
       "1                  0.0                    0.0                   0.0   \n",
       "2                  0.0                    0.0                   0.0   \n",
       "3                  0.0                    0.0                   0.0   \n",
       "4                  0.0                    0.0                   0.0   \n",
       "\n",
       "   delta_mails_clicked_2  market_potential  \n",
       "0                    0.0         212777.53  \n",
       "1                    0.0         166766.21  \n",
       "2                    0.0         196588.63  \n",
       "3                    0.0         221102.67  \n",
       "4                    0.0         188318.94  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testMergedEstimationTimeData = mergeEstimationTimeData(submissionsTestEstimation, timeTest, False)\n",
    "testMergedEstimationTimeData[0]['brand_1'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0f7fabdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>9.18670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>428.24870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>617.80070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>522.10545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>2047.41480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>3553.01500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>3841.54665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>3473.79255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>3109.85445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>700 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         region    month       sales\n",
       "0    region_151  2020-07     0.00000\n",
       "1    region_151  2020-08     9.18670\n",
       "2    region_151  2020-09   428.24870\n",
       "3    region_151  2020-10   617.80070\n",
       "4    region_151  2020-11   522.10545\n",
       "..          ...      ...         ...\n",
       "695  region_200  2021-04  2047.41480\n",
       "696  region_200  2021-05  3553.01500\n",
       "697  region_200  2021-06  3841.54665\n",
       "698  region_200  2021-07  3473.79255\n",
       "699  region_200  2021-08  3109.85445\n",
       "\n",
       "[700 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correctedTestPredictions = predictCorrectionCvModels(testMergedEstimationTimeData, correctionCvModels)\n",
    "correctedTestPredictions[0]['brand_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7ec28512",
   "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>lower</th>\n",
       "      <th>upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.30726</td>\n",
       "      <td>14.88256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>167.96215</td>\n",
       "      <td>457.73816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>476.05019</td>\n",
       "      <td>797.81537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>618.10725</td>\n",
       "      <td>1232.37868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2438.37562</td>\n",
       "      <td>3668.59259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>2017.04611</td>\n",
       "      <td>4377.37742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>2665.99407</td>\n",
       "      <td>4113.65112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2542.63210</td>\n",
       "      <td>5060.51335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>2947.80993</td>\n",
       "      <td>4249.05988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>700 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          lower       upper\n",
       "0       0.00000     0.00000\n",
       "1       3.30726    14.88256\n",
       "2     167.96215   457.73816\n",
       "3     476.05019   797.81537\n",
       "4     618.10725  1232.37868\n",
       "..          ...         ...\n",
       "695  2438.37562  3668.59259\n",
       "696  2017.04611  4377.37742\n",
       "697  2665.99407  4113.65112\n",
       "698  2542.63210  5060.51335\n",
       "699  2947.80993  4249.05988\n",
       "\n",
       "[700 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correctedTestCiBounds = getCiBoundsForCorrectionModels(correctedTestPredictions)\n",
    "correctedTestCiBounds['brand_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9d63c31d",
   "metadata": {},
   "outputs": [],
   "source": [
    "correctedCvSubmissions = getSubmissionDataFromPredictionsAndCi(correctedTestPredictions, correctedTestCiBounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7907531e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRandomRegions(correctedCvSubmissions[0], 'brand_1', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "079404e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRandomRegions(correctedCvSubmissions[0], 'brand_2', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9547e00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalSubmission = getAverageSubmission(correctedCvSubmissions) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bfa9b011",
   "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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>brand</th>\n",
       "      <th>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-07</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-08</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>3.30726</td>\n",
       "      <td>14.88256</td>\n",
       "      <td>8.91115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-09</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>167.96215</td>\n",
       "      <td>457.73816</td>\n",
       "      <td>317.67321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-10</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>476.05019</td>\n",
       "      <td>797.81537</td>\n",
       "      <td>653.74393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>region_151</td>\n",
       "      <td>2020-11</td>\n",
       "      <td>brand_1</td>\n",
       "      <td>618.10725</td>\n",
       "      <td>1232.37868</td>\n",
       "      <td>916.06800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-04</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>899.31119</td>\n",
       "      <td>1761.00322</td>\n",
       "      <td>1260.85321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-05</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>839.10519</td>\n",
       "      <td>1935.37897</td>\n",
       "      <td>1326.75065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-06</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>682.39860</td>\n",
       "      <td>1853.41340</td>\n",
       "      <td>1193.80497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-07</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>903.25321</td>\n",
       "      <td>1880.34762</td>\n",
       "      <td>1361.56093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>region_200</td>\n",
       "      <td>2021-08</td>\n",
       "      <td>brand_2</td>\n",
       "      <td>1025.74608</td>\n",
       "      <td>1851.32296</td>\n",
       "      <td>1408.48861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         region    month    brand       lower       upper       sales\n",
       "0    region_151  2020-07  brand_1     0.00000     0.00000     0.00000\n",
       "1    region_151  2020-08  brand_1     3.30726    14.88256     8.91115\n",
       "2    region_151  2020-09  brand_1   167.96215   457.73816   317.67321\n",
       "3    region_151  2020-10  brand_1   476.05019   797.81537   653.74393\n",
       "4    region_151  2020-11  brand_1   618.10725  1232.37868   916.06800\n",
       "..          ...      ...      ...         ...         ...         ...\n",
       "695  region_200  2021-04  brand_2   899.31119  1761.00322  1260.85321\n",
       "696  region_200  2021-05  brand_2   839.10519  1935.37897  1326.75065\n",
       "697  region_200  2021-06  brand_2   682.39860  1853.41340  1193.80497\n",
       "698  region_200  2021-07  brand_2   903.25321  1880.34762  1361.56093\n",
       "699  region_200  2021-08  brand_2  1025.74608  1851.32296  1408.48861\n",
       "\n",
       "[1400 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalSubmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "26ef58b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRandomRegions(finalSubmission, 'brand_1', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9401506b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRandomRegions(finalSubmission, 'brand_2', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2db822d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalSubmission.to_csv('./data/data_files/submissions/correction_cv_model_2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0f252398",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gridSearch(modelType, parameters):\n",
    "    timeData = loadTimeData(True)\n",
    "    timeYTrue = loadTimeYTrueForBrands()\n",
    "    \n",
    "    scores = {}\n",
    "    for i, modelParams in enumerate(parameters): \n",
    "        print(json.dumps(modelParams))\n",
    "        \n",
    "        # estimation model\n",
    "        estimationCvModels, estimationCvScalers = trainEstimationCrossValidation()\n",
    "        trainEstimatedSales = predictDataWithCvModels(regionTrainFeatures, estimationCvModels, estimationCvScalers)\n",
    "        ciBoundsEstimationTrain = getCiBoundsFromCvData(trainEstimatedSales)\n",
    "        submissionsTrainEstimation = scoreEstimationCv(trainEstimatedSales, True)\n",
    "        \n",
    "        # correctionModel\n",
    "        trainMergedEstimationTimeData = mergeEstimationTimeData(submissionsTrainEstimation, timeTrain, True)\n",
    "        correctionCvModels = trainCvCorrectionModels(trainMergedEstimationTimeData, timeYTrue, modelType, modelParams)\n",
    "        correctedTrainPredictions = predictCorrectionCvModels(trainMergedEstimationTimeData, correctionCvModels)\n",
    "        correctedTrainCiBounds = getCiBoundsForCorrectionModels(correctedTrainPredictions)\n",
    "        correctedTrainCvSubmissions = getSubmissionDataFromPredictionsAndCi(correctedTrainPredictions, correctedTrainCiBounds)\n",
    "        accScores, confScores =  scoreCorrectCv(correctedTrainCvSubmissions)\n",
    "        scores[i] = {\n",
    "            'accuracy': np.mean(accScores), \n",
    "            'confidence': np.mean(confScores), \n",
    "            **modelParams\n",
    "        }\n",
    "        \n",
    "        print('\\n')\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "72e12b10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'max_depth': 20, 'max_features': 'auto', 'n_estimators': 50},\n",
       " {'max_depth': 20, 'max_features': 'auto', 'n_estimators': 100},\n",
       " {'max_depth': 20, 'max_features': 'auto', 'n_estimators': 200},\n",
       " {'max_depth': 20, 'max_features': 'sqrt', 'n_estimators': 50},\n",
       " {'max_depth': 20, 'max_features': 'sqrt', 'n_estimators': 100},\n",
       " {'max_depth': 20, 'max_features': 'sqrt', 'n_estimators': 200},\n",
       " {'max_depth': 30, 'max_features': 'auto', 'n_estimators': 50},\n",
       " {'max_depth': 30, 'max_features': 'auto', 'n_estimators': 100},\n",
       " {'max_depth': 30, 'max_features': 'auto', 'n_estimators': 200},\n",
       " {'max_depth': 30, 'max_features': 'sqrt', 'n_estimators': 50},\n",
       " {'max_depth': 30, 'max_features': 'sqrt', 'n_estimators': 100},\n",
       " {'max_depth': 30, 'max_features': 'sqrt', 'n_estimators': 200},\n",
       " {'max_depth': 40, 'max_features': 'auto', 'n_estimators': 50},\n",
       " {'max_depth': 40, 'max_features': 'auto', 'n_estimators': 100},\n",
       " {'max_depth': 40, 'max_features': 'auto', 'n_estimators': 200},\n",
       " {'max_depth': 40, 'max_features': 'sqrt', 'n_estimators': 50},\n",
       " {'max_depth': 40, 'max_features': 'sqrt', 'n_estimators': 100},\n",
       " {'max_depth': 40, 'max_features': 'sqrt', 'n_estimators': 200}]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parametersToTest = {\n",
    "    'n_estimators': [50, 100, 200], \n",
    "    'max_features': ['auto', 'sqrt'], \n",
    "    'max_depth': [20, 30, 40]\n",
    "}\n",
    "keys = sorted(parametersToTest.keys())\n",
    "combinations = product(*(parametersToTest[k] for k in keys))\n",
    "combinedParameters = [{keys[k]: values[k] for k in range(len(keys))} for values in combinations]  # all parameters\n",
    "combinedParameters"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b9200ba2",
   "metadata": {},
   "source": [
    "evaluatedScores = gridSearch(RandomForestRegressor, combinedParameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3a5697c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'evaluatedScores' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_15091/2833782738.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevaluatedScores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluatedScores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'index'\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      2\u001b[0m \u001b[0mevaluatedScores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mevaluatedScores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'evaluatedScores' is not defined"
     ]
    }
   ],
   "source": [
    "evaluatedScores = pd.DataFrame.from_dict(evaluatedScores, orient='index')\n",
    "evaluatedScores.sort_values('accuracy', ascending=True, inplace=True)\n",
    "evaluatedScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cab4210",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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