{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] 배깅(bagging, bootstrap aggregating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X, y = iris.data[:, [0,2]], iris.target\n",
    "\n",
    "model1 = DecisionTreeClassifier(max_depth =10, random_state=0).fit(X, y)\n",
    "model2 = BaggingClassifier(DecisionTreeClassifier(max_depth=4), n_estimators=50, random_state=0).fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min, x_max = X[:,0].min() - 1, X[:,0].max() + 1\n",
    "y_min, y_max = X[:,1].min() - 1, X[:,1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "plt.subplot(121)\n",
    "Z1 = model1.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "plt.contourf(xx, yy, Z1, alpha=0.6, cmap=mpl.cm.jet)\n",
    "plt.scatter(X[:,0], X[:,1], c=y, alpha=1, s=50, cmap=mpl.cm.jet, edgecolors=\"k\")\n",
    "plt.title(\"Decision tree\")\n",
    "plt.subplot(122)\n",
    "\n",
    "Z2 = model2.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
    "plt.contourf(xx, yy, Z2, alpha=0.6, cmap=mpl.cm.jet)\n",
    "plt.scatter(X[:,0], X[:,1],c=y,alpha=1,s=50,cmap=mpl.cm.jet,edgecolors=\"k\")\n",
    "plt.title(\"Bagging of decision trees\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] 랜덤 포리스트 (random forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class names : ['setosa' 'versicolor' 'virginica']\n",
      "target : [0:setosa, 1:versicolor, 2:virginical]\n",
      "No. of Data : 150\n",
      "Featrue names : ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "   sepal length  sepal width  petal length  petal width  species\n",
      "0           5.1          3.5           1.4          0.2        0\n",
      "1           4.9          3.0           1.4          0.2        0\n",
      "2           4.7          3.2           1.3          0.2        0\n",
      "3           4.6          3.1           1.5          0.2        0\n",
      "4           5.0          3.6           1.4          0.2        0\n",
      "No. of traing data:  105\n",
      "No. of test data: 45\n",
      "Accuracy : 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "print('Class names :', iris.target_names)\n",
    "print('target : [0:setosa, 1:versicolor, 2:virginical]')\n",
    "print('No. of Data :', len(iris.data))\n",
    "print('Featrue names :', iris.feature_names)\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    'sepal length': iris.data[:,0], 'sepal width': iris.data[:,1], 'petal length': iris.data[:,2],\n",
    "    'petal width':iris.data[:,3], 'species':iris.target\n",
    "})\n",
    "print(data.head()) # 일부 데이터 출력\n",
    "\n",
    "x = data[['sepal length', 'sepal width', 'petal length', 'petal width']] # 입력\n",
    "y = data['species'] # 출력\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3) # 테스트 데이터 30%\n",
    "print('No. of traing data: ', len(x_train))\n",
    "print('No. of test data:', len(y_test))\n",
    "\n",
    "forest = RandomForestClassifier(n_estimators=100) # 모델 생성\n",
    "forest.fit(x_train, y_train)\n",
    "\n",
    "y_pred = forest.predict(x_test) # 추론 (예측)\n",
    "print('Accuracy :', metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] 배깅 회귀 (Bagging Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston # scikit-leanr < 1.2\n",
    "# from sklearn.datasets import fetch_california_housing # replace dataset\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX   \n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0  \\\n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  PRICE  \n",
      "0     15.3  396.90   4.98   24.0  \n",
      "1     17.8  396.90   9.14   21.6  \n",
      "2     17.8  392.83   4.03   34.7  \n",
      "3     18.7  394.63   2.94   33.4  \n",
      "4     18.7  396.90   5.33   36.2  \n",
      "RMSE: 4.594919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "boston = load_boston() # < 1.2\n",
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names\n",
    "data['PRICE'] = boston.target\n",
    "print(data.head())\n",
    "\n",
    "# replace dataset\n",
    "# california = fetch_california_housing()\n",
    "# data = pd.DataFrame(california.data)\n",
    "# data.columns = california.feature_names\n",
    "# data['PRICE'] = california.target\n",
    "# print(data.head())\n",
    "\n",
    "X, y = data.iloc[:,:-1],data.iloc[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "bag = BaggingRegressor(base_estimator = DecisionTreeRegressor( ), n_estimators = 10,\n",
    "max_features=1.0, bootstrap_features=False, random_state=0)\n",
    "bag.fit(X_train,y_train)\n",
    "preds = bag.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "print(\"RMSE: %f\" % (rmse))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] AdaBoost - 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "X = np.linspace(0, 6, 100)[:, np.newaxis]\n",
    "y = np.sin(X).ravel() + np.sin(6*X).ravel() + rng.normal(0, 0.1, X.shape[0])\n",
    "\n",
    "regr_1 = DecisionTreeRegressor(max_depth=4)\n",
    "regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=100, random_state=rng)\n",
    "\n",
    "regr_1.fit(X, y)\n",
    "regr_2.fit(X, y)\n",
    "y_1 = regr_1.predict(X)\n",
    "y_2 = regr_2.predict(X)\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(X, y, c=\"k\", label=\"training samples\")\n",
    "plt.plot(X, y_1, c=\"g\", label=\"n_estimators=1\", linewidth=2)\n",
    "plt.plot(X, y_2, c=\"r\", label=\"n_estimators=100\", linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"AdaBoost Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] Gradient Boosting 기반 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import ensemble\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13) (506,)\n",
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n",
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX   \n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0  \\\n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  MEDV  \n",
      "0     15.3  396.90   4.98  24.0  \n",
      "1     17.8  396.90   9.14  21.6  \n",
      "2     17.8  392.83   4.03  34.7  \n",
      "3     18.7  394.63   2.94  33.4  \n",
      "4     18.7  396.90   5.33  36.2  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "boston = datasets.load_boston() # Boston 집값 데이터, 13개 속성, 마지막 중간값 정보\n",
    "print(boston.data.shape, boston.target.shape)\n",
    "print(boston.feature_names)\n",
    "\n",
    "data = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "data = pd.concat([data, pd.Series(boston.target, name='MEDV')], axis=1)\n",
    "print(data.head())\n",
    "X = data.iloc[:,:-1]\n",
    "y = data.iloc[:,-1]\n",
    "x_training_set, x_test_set, y_training_set, y_test_set = train_test_split(X, y, test_size=0.10, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\ensemble\\_gb.py:294: FutureWarning: The loss 'ls' was deprecated in v1.0 and will be removed in version 1.2. Use 'squared_error' which is equivalent.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 sq:  0.9800347273281852\n",
      "Mean squared error: 5.88\n",
      "Test Variance score: 0.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'n_estimators':500, 'max_depth':4, 'min_samples_split':2, 'learning_rate':0.01, 'loss':'ls'}\n",
    "model = ensemble.GradientBoostingRegressor(**params)\n",
    "model.fit(x_training_set, y_training_set)\n",
    "model_score = model.score(x_training_set, y_training_set)\n",
    "print('R2 sq: ', model_score)\n",
    "\n",
    "y_predicted = model.predict(x_test_set)\n",
    "print('Mean squared error: %.2f'% mean_squared_error(y_test_set, y_predicted))\n",
    "print('Test Variance score: %.2f' % r2_score(y_test_set, y_predicted))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test_set, y_predicted, edgecolors=(0,0,0))\n",
    "ax.plot([y_test_set.min(), y_test_set.max()], [y_test_set.min(), y_test_set.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Actual')\n",
    "ax.set_ylabel('Predicted')\n",
    "ax.set_title('Ground Truth vs Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] Gradient Boosting 기반 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_hastie_10_2\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12000, 10) (12000,)\n",
      "[[ 1.76405235  0.40015721  0.97873798  2.2408932   1.86755799 -0.97727788\n",
      "   0.95008842 -0.15135721 -0.10321885  0.4105985 ]\n",
      " [ 0.14404357  1.45427351  0.76103773  0.12167502  0.44386323  0.33367433\n",
      "   1.49407907 -0.20515826  0.3130677  -0.85409574]\n",
      " [-2.55298982  0.6536186   0.8644362  -0.74216502  2.26975462 -1.45436567\n",
      "   0.04575852 -0.18718385  1.53277921  1.46935877]\n",
      " [ 0.15494743  0.37816252 -0.88778575 -1.98079647 -0.34791215  0.15634897\n",
      "   1.23029068  1.20237985 -0.38732682 -0.30230275]\n",
      " [-1.04855297 -1.42001794 -1.70627019  1.9507754  -0.50965218 -0.4380743\n",
      "  -1.25279536  0.77749036 -1.61389785 -0.21274028]]\n",
      "[ 1. -1.  1. -1.  1.]\n",
      "Accuracy score (training): 0.879\n",
      "Accuracy score (testing): 0.819\n"
     ]
    }
   ],
   "source": [
    "X, y = make_hastie_10_2(random_state=0)\n",
    "X_train, X_test = X[:2000], X[2000:]\n",
    "y_train, y_test = y[:2000], y[2000:]\n",
    "print(X.shape, y.shape)\n",
    "print(X[0:5,:])\n",
    "print(y[0:5])\n",
    "\n",
    "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0)\n",
    "clf.fit(X_train, y_train)\n",
    "print('Accuracy score (training): {0:.3f}'.format(clf.score(X_train, y_train)))\n",
    "print('Accuracy score (testing): {0:.3f}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] XGBoosting 기반 회귀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX   \n",
      "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0  \\\n",
      "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
      "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
      "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
      "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  PRICE  \n",
      "0     15.3  396.90   4.98   24.0  \n",
      "1     17.8  396.90   9.14   21.6  \n",
      "2     17.8  392.83   4.03   34.7  \n",
      "3     18.7  394.63   2.94   33.4  \n",
      "4     18.7  396.90   5.33   36.2  \n",
      "RMSE: 10.423243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "boston = load_boston()\n",
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names\n",
    "data['PRICE'] = boston.target\n",
    "print(data.head())\n",
    "X, y = data.iloc[:,:-1], data.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "xg_reg = xgb.XGBRegressor(objective='reg:squarederror', colsample_bytree=0.3, learning_rate=0.1, max_depth=5, alpha=10, n_estimators=10)\n",
    "xg_reg.fit(X_train, y_train)\n",
    "preds = xg_reg.predict(X_test)\n",
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "print('RMSE: %f' % (rmse))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [실습] LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier, LGBMRegressor\n",
    "from lightgbm import plot_importance, plot_metric, plot_tree\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's multi_logloss: 0.95847\n",
      "[2]\tvalid_0's multi_logloss: 0.832184\n",
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      "[136]\tvalid_0's multi_logloss: 0.374115\n",
      "[137]\tvalid_0's multi_logloss: 0.376748\n",
      "avg fit time: 0.5514350891113281 (+/- 0.3701610138582717)\n",
      "avg fit time: 0.010002517700195312 (+/- 0.009552237668971902)\n",
      "avg fit time: 0.9600000000000002 (+/- 0.04898979485566355)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDC6hZ6Aay7ncbPjBV1GxZ6GckqqZPFJb0juo69J5avzBV6Ocky59phzjXPjGuHOdcOc64d5rzu5l+0T6GXsEFzpg8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AACBhog8AANggPfzww7HffvtFq1atIpfLxa233lph/zHHHBO5XK7C16BBg/L7H3zwwdX2r/p65plnavnR1JwNPvrGjh0bO+ywQ6GXAQAA1LKlS5fG9ttvH5MmTVrrMYMGDYqFCxfmv2666ab8vr59+1bYt3Dhwjj22GOjXbt20bNnz9p4CLWi4NH33nvvxUknnRTt27ePkpKSaN26dey3334xc+bMQi+tWl199dWxyy67RJMmTaJJkyYxYMCAePrppwu9LAAA2GDtvffecf7558dBBx201mNKSkqiZcuW+a8mTZrk99WrV6/CvmbNmsVtt90WRx99dORyudp4CLWioNE3f/782HHHHeP++++PCRMmxEsvvRTTp0+P3XbbLUaMGFHIpVW7Bx98MA4//PB44IEH4oknnojWrVvHXnvtFe+8806hlwYAAMl68MEHo0WLFvH9738/jj/++Pjwww/Xeuztt98eH374YRx99NG1uMKaV9DoO+GEEyKXy8XTTz8dP/7xj2PbbbeN7bbbLkaNGhVPPvlkRES8/fbbccABB0SjRo2icePGceihh8b777+/1tvcdddd45RTTqmw7cADD4yjjjoqf7lt27Zx/vnnx9ChQ6NRo0bRpk2buP322+ODDz7I31f37t3j2WefzV9n2rRpUVpaGvfcc0907tw5GjVqlD9VXBk33HBDnHDCCbHDDjtEp06d4pprrony8vLkzmgCAMB3xV577RXXXnttzJw5My6++OJ46KGHYu+9946VK1eu8fg//OEPMXDgwPje975XyyutWXULdccfffRRTJ8+PS644IJo2LDhavtLS0ujvLw8H2EPPfRQrFixIkaMGBE//elP48EHH1yv+584cWJceOGFMXr06Jg4cWIceeSR0bdv3xg+fHhMmDAhzjjjjBg6dGi88sor+VO7y5Yti0svvTSuu+66KCoqiiOOOCJOO+20uOGGG9b5/pctWxZlZWXRtGnTtR6zfPnyWL58ef7ykiVLIiKipCiLOnWydb5PKqekKKvwX2qGOdcOc655Zlw7zLl2mHPtMOd1V1ZWVqnjVqxYEWVlZfnjDz744CguLo6IiE6dOkXnzp2jU6dOcd9998Xuu+9e4br/+te/4p577okbb7yx0vdXSOuyxoJF39y5cyPLsujUqdNaj5k5c2a89NJLMW/evGjdunVERFx77bWx3XbbxTPPPBO9evWq8v0PHjw4fvnLX0ZExJgxY2Ly5MnRq1evOOSQQyIi4owzzog+ffrE+++/Hy1btoyILwc7ZcqU6NChQ0REnHjiiXHuuedW6f7POOOMaNWqVQwYMGCtx4wfPz7GjRu32vZf9yiPBg3W/K8TVJ/zepYXegkbBXOuHeZc88y4dphz7TDn2mHOlXfXXXdV6rjnnnsuH3kRETNmzFjtmMaNG8dtt90Wn3/+eYXtN998c2y66aZRt27dSt9fIS1btqzSxxYs+rLs2/9lY/bs2dG6det88EVEdOnSJUpLS2P27NnrFX3du3fP/3mLLbaIiIhu3bqttm3RokX56GvQoEE++CIittxyy1i0aNE63/dFF10Uf/zjH+PBBx+M+vXrr/W4s846K0aNGpW/vGTJkmjdunWc/0JRrCius873S+WUFGVxXs/yGP1sUSwvT+cFvN815lw7zLnmmXHtMOfaYc61w5zX3ctjB1bquB133DEGDx4cZWVlMWPGjNhzzz0rROC//vWv+PTTT2PAgAExePDg/PYsy2LkyJExfPjw2H///at9/TVh1bMAK6Ng0dexY8fI5XLx2muvVevtFhUVrRaUazr1+dVv/qqnb65pW3l5+Rqvs+qYysTrV1166aVx0UUXxX333VchPNekpKQkSkpKVtu+vDwXK1b6H0RNW16ei+XmXOPMuXaYc80z49phzrXDnGuHOVfe138PX+Wzzz6LuXPn5i8vWLAgXnnlldh0003jP//5T4wePToOOeSQaNmyZbzxxhvx//7f/4ttttkm9tlnnwq3OXPmzJg3b1784he/WOt9fdesyzoL9kYuTZs2jYEDB8akSZNi6dKlq+1fvHhxdO7cORYsWBALFizIb3/11Vdj8eLF0aVLlzXebvPmzSu8ucrKlSvj5Zdfrv4HUAWXXHJJnHfeeTF9+vSkPvcDAAAK4dlnn40ePXpEjx49IiJi1KhR0aNHjxg3blwUFRXFSy+9FPvvv39su+22ccwxx8SOO+4YjzzyyGonVv7whz9E3759v/GlZxuygp3pi4iYNGlS9OvXL3r37h3nnntudO/ePVasWBEzZsyIyZMnx6uvvhrdunWLIUOGxOWXXx4rVqyIE044Ifr377/WaNp9991j1KhRceedd0aHDh3iN7/5TSxevLh2H9gaXHzxxTFmzJi48cYbo23btvHee+9FRESjRo2iUaNGBV4dAABseHbdddc1PvOurKws7rrrrrjzzjsrdUbsxhtvrInlfWcU9CMb2rdvH88//3zstttuceqpp0bXrl1jzz33jJkzZ8bkyZMjl8vFbbfdFk2aNIkf/ehHMWDAgGjfvn3cfPPNa73N4cOHx7Bhw2Lo0KHRv3//aN++fey22261+KjWbPLkyfHFF1/ET37yk9hyyy3zX5deemmhlwYAACSsoGf6Ir58M5Qrr7wyrrzyyjXu33rrreO2225b6/XHjh0bY8eOzV8uLi6Oq666Kq666qq1Xmf+/Pmrbfv6vxC0bdu2wrajjjqqwmf9RXz5+X+VfU3fmu4TAACgphX0TB8AAAA1S/RVk1WvzVvT1yOPPFLo5QEAABupgj+9MxWzZs1a676tttqq9hYCAADwFaKvmmyzzTaFXgIAAMBqPL0TAAAgYaIPAAAgYaIPAAAgYaIPAAAgYaIPAAAgYaIPAAAgYaIPAAAgYT6nbwP01Fl7RLNmzQq9jGSVlZXFXXfdFS+PHRjFxcWFXk6yzLl2mHPNM+PaYc61w5xrhzlT25zpAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASJjoAwAASFjdQi+AdbfT+Jmxom7DQi8jWSV1srikd0TXsffE8pW5Qi8nWeZcO8y55plx7TDndTf/on0KvQTgO8KZPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgAAgISJPgCAjcjDDz8c++23X7Rq1SpyuVzceuutFfY/8cQTMXjw4GjWrFnkcrmYNWtWhf3z58+PXC63xq8///nPtfdAgErb4KNv7NixscMOOxR6GQAAG4SlS5fG9ttvH5MmTVrj/s8//zz69u0bF1988Rr3t27dOhYuXFjha9y4cdGoUaPYe++9a3LpQBUVPPree++9OOmkk6J9+/ZRUlISrVu3jv322y9mzpxZ6KVVq1tuuSV69uwZpaWl0bBhw9hhhx3iuuuuK/SyAICNzN577x3nn39+HHTQQWvcv9tuu8Wvf/3rGDBgwBr316lTJ1q2bFnh629/+1sceuih0ahRo5pcOlBFdQt55/Pnz49+/fpFaWlpTJgwIbp16xZlZWVxzz33xIgRI+K1114r5PKqVdOmTePss8+OTp06Rb169eKOO+6Io48+Olq0aBEDBw4s9PIAAKrkueeei1mzZq31zCFQeAWNvhNOOCFyuVw8/fTT0bBhw/z27bbbLoYPHx4REW+//XacdNJJMXPmzCgqKopBgwbFFVdcEVtsscUab3PXXXeNHXbYIS6//PL8tgMPPDBKS0tj2rRpERHRtm3bOPbYY+Of//xn3HLLLdGsWbO44oorok+fPnHsscfGzJkzo3379vE///M/0bNnz4iImDZtWpxyyilx8803xymnnBILFiyInXfeOaZOnRpbbrnltz7WXXfdtcLlk08+Of73f/83Hn300bVG3/Lly2P58uX5y0uWLImIiJKiLOrUyb71PqmakqKswn+pGeZcO8y55plx7TDndVdWVlap41asWJE/9qv/XdOf1+Tqq6+OTp06Ra9evSp9nxu7r8+b6rcxzHhdHlvBou+jjz6K6dOnxwUXXFAh+FYpLS2N8vLyOOCAA6JRo0bx0EMPxYoVK2LEiBHx05/+NB588MH1uv+JEyfGhRdeGKNHj46JEyfGkUceGX379o3hw4fHhAkT4owzzoihQ4fGK6+8ErlcLiIili1bFpdeemlcd911UVRUFEcccUScdtppccMNN6zTfWdZFvfff3/MmTNnrc+Xj4gYP358jBs3brXtv+5RHg0arFy3B8w6O69neaGXsFEw59phzjXPjGuHOVfeXXfdVanjnnvuuSguLq6wbcaMGfH+++9HRMSjjz4a77777hqvu3z58rjuuuvi0EMPrfT98X9mzJhR6CUkL+UZL1u2rNLHFiz65s6dG1mWRadOndZ6zMyZM+Oll16KefPmRevWrSMi4tprr43tttsunnnmmejVq1eV73/w4MHxy1/+MiIixowZE5MnT45evXrFIYccEhERZ5xxRvTp0yfef//9aNmyZUR8WdNTpkyJDh06RETEiSeeGOeee26l7/OTTz6JrbbaKpYvXx516tSJq666Kvbcc8+1Hn/WWWfFqFGj8peXLFkSrVu3jvNfKIoVxXXW+TFTOSVFWZzXszxGP1sUy8tzhV5Ossy5dphzzTPj2mHO6+7lsZV7+ciOO+4YgwcPjogvf9eZMWNG7LnnnvHOO+9ERMTOO++81jfNu/7666OsrCwuuOCCaN68ebWse2Pw1Tl/PbipHhvDjFc9C7AyChZ9WfbtT8+YPXt2tG7dOh98ERFdunSJ0tLSmD179npFX/fu3fN/XvVU0W7duq22bdGiRfnoa9CgQT74IiK23HLLWLRoUaXvc9NNN41Zs2bFZ599FjNnzoxRo0ZF+/btV3vq5yolJSVRUlKy2vbl5blYsdJfeDVteXkulptzjTPn2mHONc+Ma4c5V15lf9GtW7fuascWFxfnt331z1/3v//7v7H//vtHq1at1m+xG6lvmi3VI+UZr8vjKlj0dezYMXK5XLW/WUtRUdFqQbmm57t+dUirnr65pm3l5eVrvM6qYyoTr19d2zbbbBMRETvssEPMnj07xo8fv9boAwCobp999lnMnTs3f3nevHkxa9asaNq0aWy55Zbx6aefxqxZs+KDDz6IiIg5c+ZEROTfqXOVuXPnxsMPP+xpnbABKNhHNjRt2jQGDhwYkyZNiqVLl662f/HixdG5c+dYsGBBLFiwIL/91VdfjcWLF0eXLl3WeLvNmzePhQsX5i+vXLkyXn755ep/ANWgvLy8whu1AADUtGeffTZ69OgRPXr0iIiIUaNGRY8ePWLMmDEREfH0009H7969Y5999omIiMMOOyx69OgRU6ZMqXA7//M//xPf+973Yq+99qrdBwCss4J+Tt+kSZNi5cqV0bt37/jrX/8ar7/+esyePTt+97vfRZ8+fWLAgAHRrVu3GDJkSDz//PPx9NNPx9ChQ6N///75d9X8ut133z3uvPPOuPPOO+O1116L448/PhYvXly7D2wNxo8fHzNmzIg333wzZs+eHZdddllcd911ccQRRxR6aQDARmTXXXeNLMtW+1r1Lud77LFHfPHFF6vtHzt2bIXbufDCC+Ptt9+OoqKCf+wz8C0K+pEN7du3j+effz4uuOCCOPXUU2PhwoXRvHnz2HHHHWPy5MmRy+Xitttui5NOOil+9KMfVfjIhrUZPnx4vPjiizF06NCoW7dujBw5MnbbbbdafFRrtnTp0jjhhBPiX//6V2yyySbRqVOnuP766+OnP/1poZcGAAAkLJety4vSKKglS5bEZpttFh1OvTlW1F39Yy6oHiV1srik98r4f0/X8WYBNcica4c51zwzrh3mvO7mX7TPOl+nrKws7rrrrhg8eHCyb37xXWDONW9jmPGqNvjkk0+icePG33is8/EAAAAJE33VpFGjRmv9euSRRwq9PAAAYCNV0Nf0pWTWrFlr3bfVVlvV3kIAAAC+QvRVk1WfvwcAAPBd4umdAAAACRN9AAAACRN9AAAACRN9AAAACRN9AAAACRN9AAAACRN9AAAACfM5fRugp87aI5o1a1boZSSrrKws7rrrrnh57MAoLi4u9HKSZc61w5xrnhnXDnMGqDpn+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABIm+gAAABJWbdG3ePHi6ropAAAAqkmVou/iiy+Om2++OX/50EMPjWbNmsVWW20VL774YrUtDgAAgPVTpeibMmVKtG7dOiIiZsyYETNmzIi777479t577zj99NOrdYEAAABUXd2qXOm9997LR98dd9wRhx56aOy1117Rtm3b2Gmnnap1gQAAAFRdlc70NWnSJBYsWBAREdOnT48BAwZERESWZbFy5crqWx0AAADrpUpn+g4++OD42c9+Fh07dowPP/ww9t5774iIeOGFF2Kbbbap1gUCAABQdVWKvokTJ0bbtm1jwYIFcckll0SjRo0iImLhwoVxwgknVOsCAQAAqLoqRV9xcXGcdtppq20fOXLkei8IAACA6lPlz+m77rrrYuedd45WrVrFW2+9FRERl19+edx2223VtjgAAADWT5Wib/LkyTFq1KjYe++9Y/Hixfk3byktLY3LL7+8OtcHAADAeqhS9F1xxRVx9dVXx9lnnx116tTJb+/Zs2e89NJL1bY4AAAA1k+Vom/evHnRo0eP1baXlJTE0qVL13tRAAAAVI8qRV+7du1i1qxZq22fPn16dO7ceX3XBAAAQDWp0rt3jho1KkaMGBGff/55ZFkWTz/9dNx0000xfvz4uOaaa6p7jQAAAFRRlaLv2GOPjU022SR+/etfx7Jly+JnP/tZtGrVKn7729/GYYcdVt1rBAAAoIrWOfpWrFgRN954YwwcODCGDBkSy5Yti88++yxatGhRE+sDAABgPazza/rq1q0bxx13XHz++ecREdGgQQPBBwAA8B1VpTdy6d27d7zwwgvVvRYAAACqWZVe03fCCSfEqaeeGv/6179ixx13jIYNG1bY371792pZHAAAAOunStG36s1afvWrX+W35XK5yLIscrlcrFy5snpWBwAAwHqpUvTNmzevutcBAABADahS9LVp06a61wEAAEANqFL0XXvttd+4f+jQoVVaDAAAANWrStF38sknV7hcVlYWy5Yti3r16kWDBg1EHwAAwHdElT6y4eOPP67w9dlnn8WcOXNi5513jptuuqm61wgAAEAVVSn61qRjx45x0UUXrXYWEAAAgMKptuiLiKhbt268++671XmTAAAArIcqvabv9ttvr3A5y7JYuHBhXHnlldGvX79qWRgAAADrr0rRd+CBB1a4nMvlonnz5rH77rvHZZddVh3rAgAAoBpUKfrKy8urex0AAADUgCq9pu/cc8+NZcuWrbb9P//5T5x77rnrvSgAAACqR5Wib9y4cfHZZ5+ttn3ZsmUxbty49V4UAAAA1aNK0ZdlWeRyudW2v/jii9G0adP1XhQAAADVY51e09ekSZPI5XKRy+Vi2223rRB+K1eujM8++yyOO+64al8kAAAAVbNO0Xf55ZdHlmUxfPjwGDduXGy22Wb5ffX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mnGVZfPrpp9GqVatvPVb0bUCKir58CeZmm22W7A/vd0njxo3NuRaYc+0w55pnxrXDnGuHOdcOc655qc+4sieCvJELAABAwkQfAABAwkTfBqSkpCTOOeecKCkpKfRSkmbOtcOca4c51zwzrh3mXDvMuXaYc80z44pyWWXe4xMAAIANkjN9AAAACRN9AAAACRN9AAAACRN9AAAACRN9G5BJkyZF27Zto379+rHTTjvF008/XeglbbDGjx8fvXr1ik033TRatGgRBx54YMyZM6fCMZ9//nmMGDEimjVrFo0aNYof//jH8f777xdoxWm46KKLIpfLxSmnnJLfZs7V45133okjjjgimjVrFptsskl069Ytnn322fz+LMtizJgxseWWW8Ymm2wSAwYMiNdff72AK96wrFy5MkaPHh3t2rWLTTbZJDp06BDnnXdefPW90Mx43T388MOx3377RatWrSKXy8Wtt95aYX9lZvrRRx/FkCFDonHjxlFaWhrHHHNMfPbZZ7X4KL77vmnOZWVlccYZZ0S3bt2iYcOG0apVqxg6dGi8++67FW7DnL/dt/08f9Vxxx0XuVwuLr/88grbzfnbVWbOs2fPjv333z8222yzaNiwYfTq1Svefvvt/P6N8XcP0beBuPnmm2PUqFFxzjnnxPPPPx/bb799DBw4MBYtWlTopW2QHnrooRgxYkQ8+eSTMWPGjCgrK4u99torli5dmj9m5MiR8fe//z3+/Oc/x0MPPRTvvvtuHHzwwQVc9YbtmWeeif/+7/+O7t27V9huzuvv448/jn79+kVxcXHcfffd8eqrr8Zll10WTZo0yR9zySWXxO9+97uYMmVKPPXUU9GwYcMYOHBgfP755wVc+Ybj4osvjsmTJ8eVV14Zs2fPjosvvjguueSSuOKKK/LHmPG6W7p0aWy//fYxadKkNe6vzEyHDBkSr7zySsyYMSPuuOOOePjhh+MXv/hFbT2EDcI3zXnZsmXx/PPPx+jRo+P555+PW265JebMmRP7779/hePM+dt928/zKn/729/iySefjFatWq22z5y/3bfN+Y033oidd945OnXqFA8++GD84x//iNGjR0f9+vXzx2yUv3tkbBB69+6djRgxIn955cqVWatWrbLx48cXcFXpWLRoURYR2UMPPZRlWZYtXrw4Ky4uzv785z/nj5k9e3YWEdkTTzxRqGVusD799NOsY8eO2YwZM7L+/ftnJ598cpZl5lxdzjjjjGznnXde6/7y8vKsZcuW2YQJE/LbFi9enJWUlGQ33XRTbSxxg7fPPvtkw4cPr7Dt4IMPzoYMGZJlmRlXh4jI/va3v+UvV2amr776ahYR2TPPPJM/5u67785yuVz2zjvv1NraNyRfn/OaPP3001lEZG+99VaWZeZcFWub87/+9a9sq622yl5++eWsTZs22cSJE/P7zHndrWnOP/3pT7MjjjhirdfZWH/3cKZvA/DFF1/Ec889FwMGDMhvKyoqigEDBsQTTzxRwJWl45NPPomIiKZNm0ZExHPPPRdlZWUVZt6pU6fYeuutzbwKRowYEfvss0+FeUaYc3W5/fbbo2fPnnHIIYdEixYtokePHnH11Vfn98+bNy/ee++9CnPebLPNYqeddjLnSurbt2/MnDkz/vnPf0ZExIsvvhiPPvpo7L333hFhxjWhMjN94oknorS0NHr27Jk/ZsCAAVFUVBRPPfVUra85FZ988knkcrkoLS2NCHOuLuXl5XHkkUfG6aefHtttt91q+815/ZWXl8edd94Z2267bQwcODBatGgRO+20U4WngG6sv3uIvg3Av//971i5cmVsscUWFbZvscUW8d577xVoVekoLy+PU045Jfr16xddu3aNiIj33nsv6tWrl/8LbxUzX3d//OMf4/nnn4/x48evts+cq8ebb74ZkydPjo4dO8Y999wTxx9/fPzqV7+K//3f/42IyM/S/0Oq7swzz4zDDjssOnXqFMXFxdGjR4845ZRTYsiQIRFhxjWhMjN97733okWLFhX2161bN5o2bWruVfT555/HGWecEYcffng0btw4Isy5ulx88cVRt27d+NWvfrXG/ea8/hYtWhSfffZZXHTRRTFo0KC4995746CDDoqDDz44HnrooYjYeH/3qFvoBUChjRgxIl5++eV49NFHC72U5CxYsCBOPvnkmDFjRoXn0lO9ysvLo2fPnnHhhRdGRESPHj3i5ZdfjilTpsSwYcMKvLo0/OlPf4obbrghbrzxxthuu+1i1qxZccopp0SrVq3MmGSUlZXFoYceGlmWxeTJkwu9nKQ899xz8dvf/jaef/75yOVyhV5OssrLyyMi4oADDoiRI0dGRMQOO+wQjz/+eEyZMiX69+9fyOUVlDN9G4DNN9886tSps9q7Cr3//vvRsmXLAq0qDSeeeGLccccd8cADD8T3vve9/PaWLVvGF198EYsXL65wvJmvm+eeey4WLVoUP/jBD6Ju3bpRt27deOihh+J3v/td1K1bN7bYYgtzrgZbbrlldOnSpcK2zp0759+pbNUs/T+k6k4//fT82b5u3brFkUceGSNHjsyfwTbj6leZmbZs2XK1NzRbsWJFfPTRR+a+jlYF31tvvRUzZszIn+WLMOfq8Mgjj8SiRYti6623zv99+NZbb8Wpp54abdu2jQhzrg6bb7551K1b91v/TtwYf/cQfRuAevXqxY477hgzZ87MbysvL4+ZM2dGnz59CriyDVeWZXHiiSfG3/72t7j//vujXbt2FfbvuOOOUVxcXGHmc+bMibffftvM18Eee+wRL730UsyaNSv/1bNnzxgyZEj+z+a8/vr167faR47885//jDZt2kRERLt27aJly5YV5rxkyZJ46qmnzLmSli1bFkVFFf/KrFOnTv5flc24+lVmpn369InFixfHc889lz/m/vvvj/Ly8thpp51qfc0bqlXB9/rrr8d9990XzZo1q7DfnNffkUceGf/4xz8q/H3YqlWrOP300+Oee+6JCHOuDvXq1YtevXp949+JG+3veIV+Jxkq549//GNWUlKSTZs2LXv11VezX/ziF1lpaWn23nvvFXppG6Tjjz8+22yzzbIHH3wwW7hwYf5r2bJl+WOOO+64bOutt87uv//+7Nlnn8369OmT9enTp4CrTsNX370zy8y5Ojz99NNZ3bp1swsuuCB7/fXXsxtuuCFr0KBBdv311+ePueiii7LS0tLstttuy/7xj39kBxxwQNauXbvsP//5TwFXvuEYNmxYttVWW2V33HFHNm/evOyWW27JNt988+z//b//lz/GjNfdp59+mr3wwgvZCy+8kEVE9pvf/CZ74YUX8u8aWZmZDho0KOvRo0f21FNPZY8++mjWsWPH7PDDDy/UQ/pO+qY5f/HFF9n++++ffe9738tmzZpV4e/E5cuX52/DnL/dt/08f93X370zy8y5Mr5tzrfccktWXFyc/f73v89ef/317Iorrsjq1KmTPfLII/nb2Bh/9xB9G5Arrrgi23rrrbN69eplvXv3zp588slCL2mDFRFr/Jo6dWr+mP/85z/ZCSeckDVp0iRr0KBBdtBBB2ULFy4s3KIT8fXoM+fq8fe//z3r2rVrVlJSknXq1Cn7/e9/X2F/eXl5Nnr06GyLLbbISkpKsj322CObM2dOgVa74VmyZEl28sknZ1tvvXVWv379rH379tnZZ59d4ZdiM153DzzwwBr/Xzxs2LAsyyo30w8//DA7/PDDs0aNGmWNGzfOjj766OzTTz8twKP57vqmOc+bN2+tfyc+8MAD+dsw52/3bT/PX7em6DPnb1eZOf/hD3/Ittlmm6x+/frZ9ttvn916660VbmNj/N0jl2VZVrPnEgEAACgUr+kDAABImOgDAABImOgDAABImOgDAABImOgDAABImOgDAABImOgDAABImOgDAABImOgDgO+QXXfdNU455ZRCLwOAhIg+ADYYRx11VORyudW+5s6dWy23P23atCgtLa2W26qqW265Jc4777yCruGbPPjgg5HL5WLx4sWFXgoAlVS30AsAgHUxaNCgmDp1aoVtzZs3L9Bq1q6srCyKi4vX+XpNmzatgdVUj7KyskIvAYAqcKYPgA1KSUlJtGzZssJXnTp1IiLitttuix/84AdRv379aN++fYwbNy5WrFiRv+5vfvOb6NatWzRs2DBat24dJ5xwQnz22WcR8eUZrKOPPjo++eST/BnEsWPHRkRELpeLW2+9tcI6SktLY9q0aRERMX/+/MjlcnHzzTdH//79o379+nHDDTdERMQ111wTnTt3jvr160enTp3iqquu+sbH9/Wnd7Zt2zbOP//8GDp0aDRq1CjatGkTt99+e3zwwQdxwAEHRKNGjaJ79+7x7LPP5q+z6ozlrbfeGh07doz69evHwIEDY8GCBRXua/LkydGhQ4eoV69efP/734/rrruuwv5cLheTJ0+O/fffPxo2bBg///nPY7fddouIiCZNmkQul4ujjjoqIiKmT58eO++8c5SWlkazZs1i3333jTfeeCN/W6tmdMstt8Ruu+0WDRo0iO233z6eeOKJCvf52GOPxa677hoNGjSIJk2axMCBA+Pjjz+OiIjy8vIYP358tGvXLjbZZJPYfvvt4y9/+cs3zhOAiMgAYAMxbNiw7IADDljjvocffjhr3LhxNm3atOyNN97I7r333qxt27bZ2LFj88dMnDgxu//++7N58+ZlM2fOzL7//e9nxx9/fJZlWbZ8+fLs8ssvzxo3bpwtXLgwW7hwYfbpp59mWZZlEZH97W9/q3B/m222WTZ16tQsy7Js3rx5WURkbdu2zf76179mb775Zvbuu+9m119/fbblllvmt/31r3/NmjZtmk2bNm2tj7F///7ZySefnL/cpk2brGnTptmUKVOyf/7zn9nxxx+fNW7cOBs0aFD2pz/9KZszZ0524IEHZp07d87Ky8uzLMuyqVOnZsXFxVnPnj2zxx9/PHv22Wez3r17Z3379s3f7i233JIVFxdnkyZNyubMmZNddtllWZ06dbL7778/f0xEZC1atMj+53/+J3vjjTey+fPnZ3/961+ziMjmzJmTLVy4MFu8eHGWZVn2l7/8JfvrX/+avf7669kLL7yQ7bffflm3bt2ylStXVphRp06dsjvuuCObM2dO9pOf/CRr06ZNVlZWlmVZlr3wwgtZSUlJdvzxx2ezZs3KXn755eyKK67IPvjggyzLsuz888/POnXqlE2fPj174403sqlTp2YlJSXZgw8+uNZ5ApBlog+ADcawYcOyOnXqZA0bNsx//eQnP8myLMv22GOP7MILL6xw/HXXXZdtueWWa729P//5z1mzZs3yl6dOnZptttlmqx1X2ei7/PLLKxzToUOH7MYbb6yw7bzzzsv69Omz1jWtKfqOOOKI/OWFCxdmEZGNHj06v+2JJ57IIiJbuHBh/nFERPbkk0/mj5k9e3YWEdlTTz2VZVmW9e3bN/v5z39e4b4POeSQbPDgwRUe9ymnnFLhmAceeCCLiOzjjz9e62PIsiz74IMPsojIXnrppSzL/m9G11xzTf6YV155JYuIbPbs2VmWZdnhhx+e9evXb4239/nnn2cNGjTIHn/88QrbjznmmOzwww//xrUAbOy8pg+ADcpuu+0WkydPzl9u2LBhRES8+OKL8dhjj8UFF1yQ37dy5cr4/PPPY9myZdGgQYO47777Yvz48fHaa6/FkiVLYsWKFRX2r6+ePXvm/7x06dJ444034phjjomf//zn+e0rVqyIzTbbbJ1ut3v37vk/b7HFFhER0a1bt9W2LVq0KFq2bBkREXXr1o1evXrlj+nUqVOUlpbG7Nmzo3fv3jF79uz4xS9+UeF++vXrF7/97W/X+pi+yeuvvx5jxoyJp556Kv79739HeXl5RES8/fbb0bVr1zU+li233DK/7k6dOsWsWbPikEMOWePtz507N5YtWxZ77rlnhe1ffPFF9OjRo1JrBNhYiT4ANigNGzaMbbbZZrXtn332WYwbNy4OPvjg1fbVr18/5s+fH/vuu28cf/zxccEFF0TTpk3j0UcfjWOOOSa++OKLb4y+XC4XWZZV2LamNzVZFaCr1hMRcfXVV8dOO+1U4bhVr0GsrK++IUwul1vrtlWhVZ2++pi+yX777Rdt2rSJq6++Olq1ahXl5eXRtWvX+OKLLyoc903r3mSTTdZ6+6vmeeedd8ZWW21VYV9JSUml1giwsRJ9ACThBz/4QcyZM2eNQRgR8dxzz0V5eXlcdtllUVT05fuY/elPf6pwTL169WLlypWrXbd58+axcOHC/OXXX389li1b9o3r2WKLLaJVq1bx5ptvxpAhQ9b14ay3FStWxLPPPhu9e/eOiIg5c+bE4sWLo3PnzhER0blz53jsscdi2LBh+es89thj0aVLl2+83Xr16kVEVJjThx9+GHPmzImrr746dtlll4iIePTRR9d5zd27d4+ZM2fGuHHjVtvXpUuXKCkpibfffjv69++/zrcNsDETfQAkYcyYMbHvvvvG1ltvHT/5yU+iqKgoXnzxxXj55Zfj/PPPj2222SbKysriiiuuiP322y8ee+yxmDJlSoXbaNu2bXz22Wcxc+bM2H777aNBgwbRoEGD2H333ePKK6+MPn36xMqVK+OMM86o1McxjBs3Ln71q1/FZpttFoMGDYrly5fHs88+Gx9//HGMGjWqpkYREV+eUTvppJPid7/7XdStWzdOPPHE+OEPf5iPwNNPPz0OPfTQ6NGjRwwYMCD+/ve/xy233BL33XffN95umzZtIpfLxR133BGDBw+OTTbZJJo0aRLNmjWL3//+97HlllvG22+/HWeeeeY6r/mss86Kbt26xQknnBDHHXdc1KtXLx544IE45JBDYvPNN4/TTjstRo4cGeXl5bHzzjvHJ598Eo899lg0bty4QrwCUJGPbAAgCQMHDow77rgj7r333ujVq1f88Ic/jIkTJ0abNm0iImL77beP3/zmN3HxxRdH165d44Ybbojx48dXuI2+ffvGcccdFz/96U+jefPmcckll0RExGWXXRatW7eOXXbZJX72s5/FaaedVqnXAB577LFxzTXXxNSpU6Nbt27Rv3//mDZtWrRr1676B/A1DRo0iDPOOCN+9rOfRb9+/aJRo0Zx88035/cfeOCB8dvf/jYuvfTS2G677eK///u/Y+rUqbHrrrt+4+1utdVWMW7cuDjzzDNjiy22iBNPPDGKiorij3/8Yzz33HPRtWvXGDlyZEyYMGGd17ztttvGvffeGy+++GL07t07+vTpE7fddlvUrfvlv1Gfd955MXr06Bg/fnx07tw5Bg0aFHfeeWetzBNgQ5bLvv4iBQBggzZt2rQ45ZRTYvHixYVeCgDfAc70AQAAJEz0AQAAJMzTOwEAABLmTB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDCRB8AAEDC/j9s+dqWmcApGgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2800x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=123)\n",
    "lgbmc = LGBMClassifier(n_estimators=400)\n",
    "evals = [(X_test, y_test)]\n",
    "lgbmc.fit(X_train, y_train, early_stopping_rounds=100, eval_metric='logloss', eval_set=evals, verbose=True)\n",
    "preds = lgbmc.predict(X_test)\n",
    "\n",
    "cross_val = cross_validate(\n",
    "    estimator=lgbmc,\n",
    "    X=iris.data, y=iris.target,\n",
    "    cv=5\n",
    ")\n",
    "\n",
    "print('avg fit time: {} (+/- {})'.format(cross_val['fit_time'].mean(), cross_val['fit_time'].std()))\n",
    "print('avg fit time: {} (+/- {})'.format(cross_val['score_time'].mean(), cross_val['score_time'].std()))\n",
    "print('avg fit time: {} (+/- {})'.format(cross_val['test_score'].mean(), cross_val['test_score'].std()))\n",
    "\n",
    "plot_metric(lgbmc)\n",
    "plot_importance(lgbmc, figsize=(10,12))\n",
    "plot_tree(lgbmc, figsize=(28,14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}