{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost simple example\n",
    "\n",
    "source : https://www.datacamp.com/community/tutorials/xgboost-in-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "boston = load_boston()\n",
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 학습/테스트 데이터셋 분리\n",
    "\n",
    "_아래 힌트를 참고하여 다음 셀의 TO DO 를 완성하세요._\n",
    "\n",
    "<details>\n",
    "  <summary>힌트</summary>\n",
    "    \n",
    "  ```python\n",
    "    X_train, X_test, y_train, y_test = train_test_split(data, boston.target, test_size=0.2, random_state=123)\n",
    "  ```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(<TO DO>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost를 이용한 Regression 학습\n",
    "\n",
    "- 파라미터 참조 : https://xgboost.readthedocs.io/en/latest/parameter.html#general-parameters\n",
    "- 주요 파리미터\n",
    "    + objective : determines the loss function to be used like `reg:linear` for regression problems, `reg:logistic` for classification problems with only decision, `binary:logistic` for classification problems with probability.\n",
    "    + colsample_bytree : percentage of features used per tree. High value can lead to overfitting.\n",
    "    + learning_rate : step size shrinkage used to prevent overfitting. Range is [0,1]\n",
    "    + max_depth : determines how deeply each tree is allowed to grow during any boosting round.\n",
    "    + alpha : L1 regularization on leaf weights. A large value leads to more regularization.\n",
    "    + n_estimators : number of trees you want to build."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_아래 힌트를 참고하여 다음 셀의 TO DO 를 완성하세요._\n",
    "\n",
    "<details>\n",
    "  <summary>힌트</summary>\n",
    "    \n",
    "  ```python\n",
    "    model = xg_reg.fit(X_train,y_train)\n",
    "  ```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xg_reg.fit(<TO DO>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.get_booster().get_score(importance_type='weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 예측\n",
    "\n",
    "_아래 힌트를 참고하여 다음 셀의 TO DO 를 완성하세요._\n",
    "\n",
    "<details>\n",
    "  <summary>힌트</summary>\n",
    "    \n",
    "  ```python\n",
    "    preds = xg_reg.predict(X_test)\n",
    "  ```\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = xg_reg.predict(<TO DO>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "print(\"RMSE: %f\" % (rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(preds)\n",
    "plt.plot(y_test)\n",
    "plt.legend(['pred','real'])\n",
    "plt.title('Prediction vs Real price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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