{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b429d931",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/mlzoo/multi-step-regression-tutorials/main/Raotbl6.csv\n",
    "# !pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd78d743",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "\n",
    "df = pd.read_csv('Raotbl6.csv')\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.index = df['date']\n",
    "df.drop('date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf306d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make future targets\n",
    "for i in range(12):\n",
    "    df['rgnp_{}'.format(i)] = df['rgnp'].shift(-i-1)\n",
    "\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58042d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = [item for item in df.columns if 'rgnp_' in item]\n",
    "\n",
    "X_train = df.drop(targets, axis=1)[: int(len(df) * 0.8)]\n",
    "y_train = df[targets][: int(len(df) * 0.8)]\n",
    "\n",
    "X_test = df.drop(targets, axis=1)[int(len(df) * 0.8) :]\n",
    "y_test = df[targets][int(len(df) * 0.8) :]\n",
    "\n",
    "model = MultiOutputRegressor(lgb.LGBMRegressor(objective='regression')).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b0617b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame(model.predict(X_test), columns=targets)\n",
    "pred.index = y_test.index\n",
    "(y_test - pred).abs().mean()"
   ]
  }
 ],
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