(7)exercise-forecasting-with-machine-learning.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "eac7f5ae",
   "metadata": {},
   "source": [
    "**This notebook is an exercise in the [Time Series](https://www.kaggle.com/learn/time-series) course.  You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/forecasting-with-machine-learning).**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4369592",
   "metadata": {},
   "source": [
    "# Introduction #\n",
    "\n",
    "Run this cell to set everything up!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f8728b6",
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    {
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     "text": [
      "/opt/conda/lib/python3.10/site-packages/learntools/time_series/checking_utils.py:16: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
      "  store_sales = pd.read_csv(\n"
     ]
    }
   ],
   "source": [
    "# Setup feedback system\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.time_series.ex6 import *\n",
    "\n",
    "# Setup notebook\n",
    "from pathlib import Path\n",
    "import ipywidgets as widgets\n",
    "from learntools.time_series.style import *  # plot style settings\n",
    "from learntools.time_series.utils import (create_multistep_example,\n",
    "                                          load_multistep_data,\n",
    "                                          make_lags,\n",
    "                                          make_multistep_target,\n",
    "                                          plot_multistep)\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.multioutput import RegressorChain\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "\n",
    "comp_dir = Path('../input/store-sales-time-series-forecasting')\n",
    "\n",
    "store_sales = pd.read_csv(\n",
    "    comp_dir / 'train.csv',\n",
    "    usecols=['store_nbr', 'family', 'date', 'sales', 'onpromotion'],\n",
    "    dtype={\n",
    "        'store_nbr': 'category',\n",
    "        'family': 'category',\n",
    "        'sales': 'float32',\n",
    "        'onpromotion': 'uint32',\n",
    "    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    ")\n",
    "store_sales['date'] = store_sales.date.dt.to_period('D')\n",
    "store_sales = store_sales.set_index(['store_nbr', 'family', 'date']).sort_index()\n",
    "\n",
    "family_sales = (\n",
    "    store_sales\n",
    "    .groupby(['family', 'date'])\n",
    "    .mean()\n",
    "    .unstack('family')\n",
    "    .loc['2017']\n",
    ")\n",
    "\n",
    "test = pd.read_csv(\n",
    "    comp_dir / 'test.csv',\n",
    "    dtype={\n",
    "        'store_nbr': 'category',\n",
    "        'family': 'category',\n",
    "        'onpromotion': 'uint32',\n",
    "    },\n",
    "    parse_dates=['date'],\n",
    "    infer_datetime_format=True,\n",
    ")\n",
    "test['date'] = test.date.dt.to_period('D')\n",
    "test = test.set_index(['store_nbr', 'family', 'date']).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb3167e",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------\n",
    "\n",
    "Consider the following three forecasting tasks:\n",
    "\n",
    "a. 3-step forecast using 4 lag features with a 2-step lead time<br>\n",
    "b. 1-step forecast using 3 lag features with a 1-step lead time<br>\n",
    "c. 3-step forecast using 4 lag features with a 1-step lead time<br>\n",
    "\n",
    "Run the next cell to see three datasets, each representing one of the tasks above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9210b04f",
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       "model_id": "cd6cc118344547d3aacb8678b9a5b79e",
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      "text/plain": [
       "Tab(children=(Output(), Output(), Output()), _titles={'0': 'Dataset 1', '1': 'Dataset 2', '2': 'Dataset 3'})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets = load_multistep_data()\n",
    "\n",
    "data_tabs = widgets.Tab([widgets.Output() for _ in enumerate(datasets)])\n",
    "for i, df in enumerate(datasets):\n",
    "    data_tabs.set_title(i, f'Dataset {i+1}')\n",
    "    with data_tabs.children[i]:\n",
    "        display(df)\n",
    "\n",
    "display(data_tabs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e8d0c1",
   "metadata": {},
   "source": [
    "# 1) Match description to dataset\n",
    "\n",
    "Can you match each task to the appropriate dataset?"
   ]
  },
  {
   "cell_type": "code",
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR CODE HERE: Match the task to the dataset. Answer 1, 2, or 3.\n",
    "task_a = 2\n",
    "task_b = 1\n",
    "task_c = 3\n",
    "\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2c14e10",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_1.hint()\n",
    "#q_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57261672",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------\n",
    "\n",
    "Look at the time indexes of the training and test sets. From this information, can you identify the forecasting task for *Store Sales*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "de476be2",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data \n",
      "-------------\n",
      "                                      sales  onpromotion\n",
      "store_nbr family     date                              \n",
      "1         AUTOMOTIVE 2013-01-01   0.000000            0\n",
      "                     2013-01-02   2.000000            0\n",
      "                     2013-01-03   3.000000            0\n",
      "                     2013-01-04   3.000000            0\n",
      "                     2013-01-05   5.000000            0\n",
      "...                                    ...          ...\n",
      "9         SEAFOOD    2017-08-11  23.830999            0\n",
      "                     2017-08-12  16.859001            4\n",
      "                     2017-08-13  20.000000            0\n",
      "                     2017-08-14  17.000000            0\n",
      "                     2017-08-15  16.000000            0\n",
      "\n",
      "[3000888 rows x 2 columns]\n",
      "\n",
      "\n",
      "Test Data \n",
      "---------\n",
      "                                       id  onpromotion\n",
      "store_nbr family     date                            \n",
      "1         AUTOMOTIVE 2017-08-16  3000888            0\n",
      "                     2017-08-17  3002670            0\n",
      "                     2017-08-18  3004452            0\n",
      "                     2017-08-19  3006234            0\n",
      "                     2017-08-20  3008016            0\n",
      "...                                  ...          ...\n",
      "9         SEAFOOD    2017-08-27  3022271            0\n",
      "                     2017-08-28  3024053            0\n",
      "                     2017-08-29  3025835            0\n",
      "                     2017-08-30  3027617            0\n",
      "                     2017-08-31  3029399            0\n",
      "\n",
      "[28512 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Data\", \"\\n\" + \"-\" * 13 + \"\\n\", store_sales)\n",
    "print(\"\\n\")\n",
    "print(\"Test Data\", \"\\n\" + \"-\" * 9 + \"\\n\", test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4229dc2",
   "metadata": {},
   "source": [
    "# 2) Identify the forecasting task for *Store Sales* competition\n",
    "\n",
    "Try to identify the *forecast origin* and the *forecast horizon*. How many steps are within the forecast horizon? What is the lead time for the forecast?\n",
    "\n",
    "Run this cell after you've thought about your answer."
   ]
  },
  {
   "cell_type": "code",
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n",
       "The training set ends on `2017-08-15`, which gives us the forecast origin. The test set comprises the dates `2017-08-16` to `2017-08-31`, and this gives us the forecast horizon. There is one step between the origin and horizon, so we have a lead time of one day.\n",
       "\n",
       "Put another way, we need a 16-step forecast with a 1-step lead time. We can use lags starting with lag 1, and we make the entire 16-step forecast using features from `2017-08-15`.\n"
      ],
      "text/plain": [
       "Correct: \n",
       "\n",
       "The training set ends on `2017-08-15`, which gives us the forecast origin. The test set comprises the dates `2017-08-16` to `2017-08-31`, and this gives us the forecast horizon. There is one step between the origin and horizon, so we have a lead time of one day.\n",
       "\n",
       "Put another way, we need a 16-step forecast with a 1-step lead time. We can use lags starting with lag 1, and we make the entire 16-step forecast using features from `2017-08-15`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the solution (Run this cell to receive credit!)\n",
    "q_2.check()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0339b19",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------\n",
    "\n",
    "In the tutorial we saw how to create a multistep dataset for a single time series. Fortunately, we can use exactly the same procedure for datasets of multiple series.\n",
    "\n",
    "# 3) Create multistep dataset for *Store Sales*\n",
    "\n",
    "Create targets suitable for the *Store Sales* forecasting task. Use 4 days of lag features. Drop any missing values from both targets and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = family_sales.loc[:, 'sales']\n",
    "\n",
    "X = make_lags(y, lags=4).dropna()\n",
    "\n",
    "y = make_multistep_target(y, steps=16).dropna()\n",
    "\n",
    "y, X = y.align(X, join='inner', axis=0)\n",
    "\n",
    "# Check your answer\n",
    "q_3.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d945a453",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2023-09-20T10:32:20.224473Z"
    }
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_3.hint()\n",
    "#q_3.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae502878",
   "metadata": {},
   "source": [
    "-------------------------------------------------------------------------------\n",
    "\n",
    "In the tutorial, we saw how to forecast with the MultiOutput and Direct strategies on the *Flu Trends* series. Now, you'll apply the DirRec strategy to the multiple time series of *Store Sales*.\n",
    "\n",
    "Make sure you've successfully completed the previous exercise and then run this cell to prepare the data for XGBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>y_step_1</th>\n",
       "      <th>y_step_2</th>\n",
       "      <th>y_step_3</th>\n",
       "      <th>y_step_4</th>\n",
       "      <th>y_step_5</th>\n",
       "      <th>y_step_6</th>\n",
       "      <th>y_step_7</th>\n",
       "      <th>y_step_8</th>\n",
       "      <th>y_step_9</th>\n",
       "      <th>y_step_10</th>\n",
       "      <th>y_step_11</th>\n",
       "      <th>y_step_12</th>\n",
       "      <th>y_step_13</th>\n",
       "      <th>y_step_14</th>\n",
       "      <th>y_step_15</th>\n",
       "      <th>y_step_16</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>family</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2017-01-05</th>\n",
       "      <th>AUTOMOTIVE</th>\n",
       "      <td>6.333333</td>\n",
       "      <td>6.018518</td>\n",
       "      <td>10.259259</td>\n",
       "      <td>9.388889</td>\n",
       "      <td>5.944445</td>\n",
       "      <td>4.777778</td>\n",
       "      <td>6.314815</td>\n",
       "      <td>5.388889</td>\n",
       "      <td>5.240741</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>10.259259</td>\n",
       "      <td>6.407407</td>\n",
       "      <td>5.685185</td>\n",
       "      <td>5.703704</td>\n",
       "      <td>4.777778</td>\n",
       "      <td>5.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BABY CARE</th>\n",
       "      <td>0.351852</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.240741</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.240741</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.425926</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.129630</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEAUTY</th>\n",
       "      <td>5.925926</td>\n",
       "      <td>6.518518</td>\n",
       "      <td>10.037037</td>\n",
       "      <td>11.611111</td>\n",
       "      <td>5.648148</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>5.277778</td>\n",
       "      <td>4.370370</td>\n",
       "      <td>4.703704</td>\n",
       "      <td>7.777778</td>\n",
       "      <td>9.037037</td>\n",
       "      <td>5.648148</td>\n",
       "      <td>5.351852</td>\n",
       "      <td>4.740741</td>\n",
       "      <td>3.981482</td>\n",
       "      <td>4.592593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BEVERAGES</th>\n",
       "      <td>3258.796387</td>\n",
       "      <td>3507.277832</td>\n",
       "      <td>4848.518555</td>\n",
       "      <td>5503.647949</td>\n",
       "      <td>3448.203613</td>\n",
       "      <td>3171.740723</td>\n",
       "      <td>3046.870361</td>\n",
       "      <td>2693.722168</td>\n",
       "      <td>3226.037109</td>\n",
       "      <td>4667.296387</td>\n",
       "      <td>5580.611328</td>\n",
       "      <td>3700.370361</td>\n",
       "      <td>3409.796387</td>\n",
       "      <td>3263.462891</td>\n",
       "      <td>2676.573975</td>\n",
       "      <td>3003.555664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOOKS</th>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.537037</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.518519</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.574074</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.351852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2017-07-31</th>\n",
       "      <th>POULTRY</th>\n",
       "      <td>364.955658</td>\n",
       "      <td>403.601349</td>\n",
       "      <td>377.313995</td>\n",
       "      <td>316.436096</td>\n",
       "      <td>533.497009</td>\n",
       "      <td>416.454041</td>\n",
       "      <td>464.596558</td>\n",
       "      <td>344.051758</td>\n",
       "      <td>313.780884</td>\n",
       "      <td>305.270233</td>\n",
       "      <td>278.819885</td>\n",
       "      <td>468.857391</td>\n",
       "      <td>354.342773</td>\n",
       "      <td>379.801208</td>\n",
       "      <td>344.398285</td>\n",
       "      <td>325.679840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREPARED FOODS</th>\n",
       "      <td>84.698647</td>\n",
       "      <td>87.836800</td>\n",
       "      <td>88.735970</td>\n",
       "      <td>77.172997</td>\n",
       "      <td>91.886765</td>\n",
       "      <td>100.384964</td>\n",
       "      <td>102.248146</td>\n",
       "      <td>86.627441</td>\n",
       "      <td>77.344131</td>\n",
       "      <td>84.796539</td>\n",
       "      <td>78.791443</td>\n",
       "      <td>96.286926</td>\n",
       "      <td>84.693810</td>\n",
       "      <td>91.509422</td>\n",
       "      <td>86.062500</td>\n",
       "      <td>85.954132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRODUCE</th>\n",
       "      <td>2257.140625</td>\n",
       "      <td>2609.180176</td>\n",
       "      <td>3122.895752</td>\n",
       "      <td>1792.220947</td>\n",
       "      <td>2079.319336</td>\n",
       "      <td>2418.970215</td>\n",
       "      <td>2675.105957</td>\n",
       "      <td>2111.133301</td>\n",
       "      <td>2168.535400</td>\n",
       "      <td>2663.076416</td>\n",
       "      <td>1670.264893</td>\n",
       "      <td>2198.854492</td>\n",
       "      <td>2070.154541</td>\n",
       "      <td>2331.922363</td>\n",
       "      <td>2134.399902</td>\n",
       "      <td>2316.832764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCHOOL AND OFFICE SUPPLIES</th>\n",
       "      <td>30.111111</td>\n",
       "      <td>49.333332</td>\n",
       "      <td>57.481480</td>\n",
       "      <td>51.907406</td>\n",
       "      <td>63.222221</td>\n",
       "      <td>85.203705</td>\n",
       "      <td>100.277779</td>\n",
       "      <td>64.407410</td>\n",
       "      <td>59.759258</td>\n",
       "      <td>53.740742</td>\n",
       "      <td>42.962963</td>\n",
       "      <td>65.240738</td>\n",
       "      <td>67.481483</td>\n",
       "      <td>68.851852</td>\n",
       "      <td>52.333332</td>\n",
       "      <td>46.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEAFOOD</th>\n",
       "      <td>20.488333</td>\n",
       "      <td>20.346851</td>\n",
       "      <td>20.801037</td>\n",
       "      <td>17.116297</td>\n",
       "      <td>25.553965</td>\n",
       "      <td>24.209518</td>\n",
       "      <td>23.512852</td>\n",
       "      <td>18.419851</td>\n",
       "      <td>18.481131</td>\n",
       "      <td>18.181425</td>\n",
       "      <td>13.284463</td>\n",
       "      <td>23.566963</td>\n",
       "      <td>19.037594</td>\n",
       "      <td>20.704575</td>\n",
       "      <td>17.975555</td>\n",
       "      <td>17.966240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6864 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          y_step_1     y_step_2     y_step_3  \\\n",
       "date       family                                                              \n",
       "2017-01-05 AUTOMOTIVE                     6.333333     6.018518    10.259259   \n",
       "           BABY CARE                      0.351852     0.277778     0.259259   \n",
       "           BEAUTY                         5.925926     6.518518    10.037037   \n",
       "           BEVERAGES                   3258.796387  3507.277832  4848.518555   \n",
       "           BOOKS                          0.407407     0.537037     0.481481   \n",
       "...                                            ...          ...          ...   \n",
       "2017-07-31 POULTRY                      364.955658   403.601349   377.313995   \n",
       "           PREPARED FOODS                84.698647    87.836800    88.735970   \n",
       "           PRODUCE                     2257.140625  2609.180176  3122.895752   \n",
       "           SCHOOL AND OFFICE SUPPLIES    30.111111    49.333332    57.481480   \n",
       "           SEAFOOD                       20.488333    20.346851    20.801037   \n",
       "\n",
       "                                          y_step_4     y_step_5     y_step_6  \\\n",
       "date       family                                                              \n",
       "2017-01-05 AUTOMOTIVE                     9.388889     5.944445     4.777778   \n",
       "           BABY CARE                      0.240741     0.444444     0.240741   \n",
       "           BEAUTY                        11.611111     5.648148     6.500000   \n",
       "           BEVERAGES                   5503.647949  3448.203613  3171.740723   \n",
       "           BOOKS                          0.722222     0.500000     0.518519   \n",
       "...                                            ...          ...          ...   \n",
       "2017-07-31 POULTRY                      316.436096   533.497009   416.454041   \n",
       "           PREPARED FOODS                77.172997    91.886765   100.384964   \n",
       "           PRODUCE                     1792.220947  2079.319336  2418.970215   \n",
       "           SCHOOL AND OFFICE SUPPLIES    51.907406    63.222221    85.203705   \n",
       "           SEAFOOD                       17.116297    25.553965    24.209518   \n",
       "\n",
       "                                          y_step_7     y_step_8     y_step_9  \\\n",
       "date       family                                                              \n",
       "2017-01-05 AUTOMOTIVE                     6.314815     5.388889     5.240741   \n",
       "           BABY CARE                      0.277778     0.296296     0.296296   \n",
       "           BEAUTY                         5.277778     4.370370     4.703704   \n",
       "           BEVERAGES                   3046.870361  2693.722168  3226.037109   \n",
       "           BOOKS                          0.481481     0.388889     0.444444   \n",
       "...                                            ...          ...          ...   \n",
       "2017-07-31 POULTRY                      464.596558   344.051758   313.780884   \n",
       "           PREPARED FOODS               102.248146    86.627441    77.344131   \n",
       "           PRODUCE                     2675.105957  2111.133301  2168.535400   \n",
       "           SCHOOL AND OFFICE SUPPLIES   100.277779    64.407410    59.759258   \n",
       "           SEAFOOD                       23.512852    18.419851    18.481131   \n",
       "\n",
       "                                         y_step_10    y_step_11    y_step_12  \\\n",
       "date       family                                                              \n",
       "2017-01-05 AUTOMOTIVE                     8.500000    10.259259     6.407407   \n",
       "           BABY CARE                      0.388889     0.425926     0.314815   \n",
       "           BEAUTY                         7.777778     9.037037     5.648148   \n",
       "           BEVERAGES                   4667.296387  5580.611328  3700.370361   \n",
       "           BOOKS                          0.574074     0.555556     0.388889   \n",
       "...                                            ...          ...          ...   \n",
       "2017-07-31 POULTRY                      305.270233   278.819885   468.857391   \n",
       "           PREPARED FOODS                84.796539    78.791443    96.286926   \n",
       "           PRODUCE                     2663.076416  1670.264893  2198.854492   \n",
       "           SCHOOL AND OFFICE SUPPLIES    53.740742    42.962963    65.240738   \n",
       "           SEAFOOD                       18.181425    13.284463    23.566963   \n",
       "\n",
       "                                         y_step_13    y_step_14    y_step_15  \\\n",
       "date       family                                                              \n",
       "2017-01-05 AUTOMOTIVE                     5.685185     5.703704     4.777778   \n",
       "           BABY CARE                      0.166667     0.222222     0.129630   \n",
       "           BEAUTY                         5.351852     4.740741     3.981482   \n",
       "           BEVERAGES                   3409.796387  3263.462891  2676.573975   \n",
       "           BOOKS                          0.500000     0.407407     0.277778   \n",
       "...                                            ...          ...          ...   \n",
       "2017-07-31 POULTRY                      354.342773   379.801208   344.398285   \n",
       "           PREPARED FOODS                84.693810    91.509422    86.062500   \n",
       "           PRODUCE                     2070.154541  2331.922363  2134.399902   \n",
       "           SCHOOL AND OFFICE SUPPLIES    67.481483    68.851852    52.333332   \n",
       "           SEAFOOD                       19.037594    20.704575    17.975555   \n",
       "\n",
       "                                         y_step_16  \n",
       "date       family                                   \n",
       "2017-01-05 AUTOMOTIVE                     5.148148  \n",
       "           BABY CARE                      0.166667  \n",
       "           BEAUTY                         4.592593  \n",
       "           BEVERAGES                   3003.555664  \n",
       "           BOOKS                          0.351852  \n",
       "...                                            ...  \n",
       "2017-07-31 POULTRY                      325.679840  \n",
       "           PREPARED FOODS                85.954132  \n",
       "           PRODUCE                     2316.832764  \n",
       "           SCHOOL AND OFFICE SUPPLIES    46.851852  \n",
       "           SEAFOOD                       17.966240  \n",
       "\n",
       "[6864 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "X = (X\n",
    "    .stack('family')  # wide to long\n",
    "    .reset_index('family')  # convert index to column\n",
    "    .assign(family=lambda x: le.fit_transform(x.family))  # label encode\n",
    ")\n",
    "y = y.stack('family')  # wide to long\n",
    "\n",
    "display(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406665f9",
   "metadata": {},
   "source": [
    "# 4) Forecast with the DirRec strategy\n",
    "\n",
    "Instatiate a model that applies the DirRec strategy to XGBoost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6da32b51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-20T10:32:56.768633Z",
     "iopub.status.busy": "2023-09-20T10:32:56.768159Z",
     "iopub.status.idle": "2023-09-20T10:32:56.779587Z",
     "shell.execute_reply": "2023-09-20T10:32:56.778319Z",
     "shell.execute_reply.started": "2023-09-20T10:32:56.768598Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4_Q4\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.multioutput import RegressorChain\n",
    "\n",
    "# YOUR CODE HERE\n",
    "model = RegressorChain(base_estimator=XGBRegressor())\n",
    "\n",
    "# Check your answer\n",
    "q_4.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4dce488a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-20T10:33:00.805555Z",
     "iopub.status.busy": "2023-09-20T10:33:00.804173Z",
     "iopub.status.idle": "2023-09-20T10:33:00.810014Z",
     "shell.execute_reply": "2023-09-20T10:33:00.808914Z",
     "shell.execute_reply.started": "2023-09-20T10:33:00.805504Z"
    }
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_4.hint()\n",
    "#q_4.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464ec5db",
   "metadata": {},
   "source": [
    "Run this cell if you'd like to train this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f4bbffe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-20T10:33:05.703762Z",
     "iopub.status.busy": "2023-09-20T10:33:05.703258Z",
     "iopub.status.idle": "2023-09-20T10:33:22.474187Z",
     "shell.execute_reply": "2023-09-20T10:33:22.473184Z",
     "shell.execute_reply.started": "2023-09-20T10:33:05.703721Z"
    }
   },
   "outputs": [],
   "source": [
    "model.fit(X, y)\n",
    "\n",
    "y_pred = pd.DataFrame(\n",
    "    model.predict(X),\n",
    "    index=y.index,\n",
    "    columns=y.columns,\n",
    ").clip(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77d5a836",
   "metadata": {},
   "source": [
    "And use this code to see a sample of the 16-step predictions this model makes on the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31e92f31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-09-20T10:33:22.476715Z",
     "iopub.status.busy": "2023-09-20T10:33:22.475838Z",
     "iopub.status.idle": "2023-09-20T10:33:23.124885Z",
     "shell.execute_reply": "2023-09-20T10:33:23.123800Z",
     "shell.execute_reply.started": "2023-09-20T10:33:22.476679Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FAMILY = 'BEAUTY'\n",
    "START = '2017-04-01'\n",
    "EVERY = 16\n",
    "\n",
    "y_pred_ = y_pred.xs(FAMILY, level='family', axis=0).loc[START:]\n",
    "y_ = family_sales.loc[START:, 'sales'].loc[:, FAMILY]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(11, 4))\n",
    "ax = y_.plot(**plot_params, ax=ax, alpha=0.5)\n",
    "ax = plot_multistep(y_pred_, ax=ax, every=EVERY)\n",
    "_ = ax.legend([FAMILY, FAMILY + ' Forecast'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d6f4b84",
   "metadata": {},
   "source": [
    "# Next Steps #\n",
    "\n",
    "Congratulations! You've completed Kaggle's *Time Series* course. If you haven't already, join our companion competition: [Store Sales - Time Series Forecasting](https://www.kaggle.com/c/29781) and apply the skills you've learned.\n",
    "\n",
    "For inspiration, check out Kaggle's previous forecasting competitions. Studying winning competition solutions is a great way to upgrade your skills.\n",
    "\n",
    "- [**Corporación Favorita**](https://www.kaggle.com/c/favorita-grocery-sales-forecasting): the competition *Store Sales* is derived from.\n",
    "- [**Rossmann Store Sales**](https://www.kaggle.com/c/rossmann-store-sales)\n",
    "- [**Wikipedia Web Traffic**](https://www.kaggle.com/c/web-traffic-time-series-forecasting/)\n",
    "- [**Walmart Store Sales**](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting)\n",
    "- [**Walmart Sales in Stormy Weather**](https://www.kaggle.com/c/walmart-recruiting-sales-in-stormy-weather)\n",
    "- [**M5 Forecasting - Accuracy**](https://www.kaggle.com/c/m5-forecasting-accuracy)\n",
    "\n",
    "# References #\n",
    "\n",
    "Here are some great resources you might like to consult for more on time series and forecasting. They all played a part in shaping this course:\n",
    "\n",
    "- *Learnings from Kaggle's forecasting competitions*, an article by Casper Solheim Bojer and Jens Peder Meldgaard.\n",
    "- *Forecasting: Principles and Practice*, a book by Rob J Hyndmann and George Athanasopoulos.\n",
    "- *Practical Time Series Forecasting with R*, a book by Galit Shmueli and Kenneth C. Lichtendahl Jr.\n",
    "- *Time Series Analysis and Its Applications*, a book by Robert H. Shumway and David S. Stoffer.\n",
    "- *Machine learning strategies for time series forecasting*, an article by Gianluca Bontempi, Souhaib Ben Taieb, and Yann-Aël Le Borgne.\n",
    "- *On the use of cross-validation for time series predictor evaluation*, an article by Christoph Bergmeir and José M. Benítez.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d185bf4",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/time-series/discussion) to chat with other learners.*"
   ]
  }
 ],
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