Assignment_04_GB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# data 시각화\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Korean pont\n",
    "matplotlib.rc('font',family = 'NanumGothicCoding')\n",
    "\n",
    "# train, test 데이터 분할 함수\n",
    "from sklearn.model_selection  import train_test_split\n",
    "# Grid Search\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "# from sklearn.tree import export_graphviz\n",
    "# import graphviz\n",
    "\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>CEMENT</th>\n",
       "      <th>BLAST_FURNACE_SLAG</th>\n",
       "      <th>FLY_ASH</th>\n",
       "      <th>WATER</th>\n",
       "      <th>SUPERPLASTICIZER</th>\n",
       "      <th>COARSE_AGGREGATE</th>\n",
       "      <th>FINE_AGGREGATE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CONCRETE_COMPRESSIVE_STRENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CEMENT  BLAST_FURNACE_SLAG  FLY_ASH  WATER  SUPERPLASTICIZER  \\\n",
       "0   540.0                 0.0      0.0  162.0               2.5   \n",
       "1   540.0                 0.0      0.0  162.0               2.5   \n",
       "2   332.5               142.5      0.0  228.0               0.0   \n",
       "3   332.5               142.5      0.0  228.0               0.0   \n",
       "4   198.6               132.4      0.0  192.0               0.0   \n",
       "\n",
       "   COARSE_AGGREGATE  FINE_AGGREGATE  AGE  CONCRETE_COMPRESSIVE_STRENGTH  \n",
       "0            1040.0           676.0   28                          79.99  \n",
       "1            1055.0           676.0   28                          61.89  \n",
       "2             932.0           594.0  270                          40.27  \n",
       "3             932.0           594.0  365                          41.05  \n",
       "4             978.4           825.5  360                          44.30  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"./data/Concrete.csv\", encoding = '949')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step01 - 목표변수(Y)제거 후 따로 저장, 설명변수와 목표변수 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw_y = df_raw['CONCRETE_COMPRESSIVE_STRENGTH']\n",
    "df_raw_x = df_raw.drop('CONCRETE_COMPRESSIVE_STRENGTH', axis = 1, inplace = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 분할\n",
    "> - at first Devide (train 0.7 test 0.3)\n",
    "- and then, dived train data to (train 0.4, validation 0.3)\n",
    "- only use validation data instead of test to check cost\n",
    "- finally use test date at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data X size : (412, 8)\n",
      "train data Y size : (412,)\n",
      "test data X size : (618, 8)\n",
      "test data Y size : (618,)\n"
     ]
    }
   ],
   "source": [
    "df_train_x, df_test_x, df_train_y, df_test_y = train_test_split(df_raw_x, df_raw_y, test_size = 0.6, random_state = 1234)\n",
    "### 데이터 분할\n",
    "\n",
    "\n",
    "print(\"train data X size : {}\".format(df_train_x.shape))\n",
    "print(\"train data Y size : {}\".format(df_train_y.shape))\n",
    "print(\"test data X size : {}\".format(df_test_x.shape))\n",
    "print(\"test data Y size : {}\".format(df_test_y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data X size : (309, 8)\n",
      "test data Y size : (309,)\n",
      "validation data X size : (309, 8)\n",
      "validation data Y size : (309,)\n"
     ]
    }
   ],
   "source": [
    "df_test_x, df_validation_x, df_test_y, df_validation_y = train_test_split(df_test_x, df_test_y, test_size = 0.5, random_state = 1234)\n",
    "print(\"test data X size : {}\".format(df_test_x.shape))\n",
    "print(\"test data Y size : {}\".format(df_test_y.shape))\n",
    "print(\"validation data X size : {}\".format(df_validation_x.shape))\n",
    "print(\"validation data Y size : {}\".format(df_validation_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 모델 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training set : 0.949\n",
      "Score on validation set : 0.900\n"
     ]
    }
   ],
   "source": [
    "gb_uncustomized.fit(df_train_x, df_train_y)\n",
    "\n",
    "print(\"Score on training set : {:.3f}\".format(gb_uncustomized.score(df_train_x, df_train_y)))\n",
    "print(\"Score on validation set : {:.3f}\".format(gb_uncustomized.score(df_validation_x, df_validation_y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> test data의 설명력이 높은편 : `과대적합은 아닌 듯해 보임`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, n_iter_no_change=None, presort='auto',\n",
       "             random_state=1234, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_uncustomized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 트리 수 변경에 다른 모델 성능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>n_estimators</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>validationScore</th>\n",
       "      <th>gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>0.944</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>120</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>130</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>140</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_estimators  TrainScore  validationScore    gap\n",
       "0             10       0.635            0.617  0.019\n",
       "1             20       0.800            0.780  0.020\n",
       "2             30       0.863            0.844  0.019\n",
       "3             40       0.893            0.866  0.027\n",
       "4             50       0.911            0.876  0.035\n",
       "5             60       0.922            0.884  0.039\n",
       "6             70       0.931            0.890  0.042\n",
       "7             80       0.938            0.893  0.045\n",
       "8             90       0.944            0.898  0.046\n",
       "9            100       0.949            0.900  0.049\n",
       "10           110       0.953            0.901  0.052\n",
       "11           120       0.957            0.902  0.054\n",
       "12           130       0.960            0.903  0.057\n",
       "13           140       0.962            0.905  0.057"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = []; validation_score = []\n",
    "\n",
    "para_n_tree = [n_tree * 10 for n_tree in range(1,15)]\n",
    "\n",
    "for v_n_estimators in para_n_tree:\n",
    "    gb = GradientBoostingRegressor(n_estimators = v_n_estimators, random_state = 1234)\n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    validation_score.append(gb.score(df_validation_x, df_validation_y))\n",
    "\n",
    "df_score_n = pd.DataFrame()\n",
    "df_score_n['n_estimators'] = para_n_tree\n",
    "df_score_n['TrainScore'] = train_score\n",
    "df_score_n['validationScore'] = validation_score\n",
    "df_score_n['gap'] = (np.array(train_score) - np.array(validation_score))\n",
    "\n",
    "df_score_n.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe4d0cbcf8>"
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     "execution_count": 60,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_n_tree, train_score, linestyle = '-', label = \"Train Score\")\n",
    "plt.plot(para_n_tree, validation_score, linestyle = '--', label = 'validation Score')\n",
    "plt.ylabel('score'); plt.xlabel('n_estimators')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - Leaf Node 자료 수 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = []; validation_score = []\n",
    "para_leaf = [n_leaf * 1 for n_leaf in range(1,21)]\n",
    "\n",
    "for v_min_samples_leaf in para_leaf :\n",
    "    rf = GradientBoostingRegressor(random_state = 1234, n_estimators = 70,\n",
    "                               min_samples_leaf = v_min_samples_leaf)\n",
    "    rf.fit(df_train_x, df_train_y)\n",
    "    train_score.append(rf.score(df_train_x, df_train_y))\n",
    "    validation_score.append(rf.score(df_validation_x, df_validation_y))\n",
    "\n",
    "df_score_leaf = pd.DataFrame()\n",
    "df_score_leaf['MinSamplesLeaf'] = para_leaf\n",
    "df_score_leaf['TrainScore'] = train_score\n",
    "df_score_leaf['validationScore'] = validation_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe4cedaf28>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_leaf, train_score, linestyle = '-', label = \"Train Score\")\n",
    "plt.plot(para_leaf, validation_score, linestyle = '--', label = 'validation Score')\n",
    "plt.ylabel('score'); plt.xlabel('min samples leaf')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 분리노드(Splite)의 최소 자료 수 변경에 따른 모델 성능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>MinSamplesSplit</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>validationScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>32</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>36</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>0.918</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MinSamplesSplit  TrainScore  validationScore\n",
       "0                 2       0.926            0.890\n",
       "1                 4       0.926            0.890\n",
       "2                 6       0.926            0.890\n",
       "3                 8       0.926            0.890\n",
       "4                10       0.926            0.890\n",
       "5                12       0.926            0.890\n",
       "6                14       0.924            0.892\n",
       "7                16       0.923            0.889\n",
       "8                18       0.924            0.889\n",
       "9                20       0.922            0.890\n",
       "10               22       0.920            0.889\n",
       "11               24       0.922            0.889\n",
       "12               26       0.917            0.886\n",
       "13               28       0.920            0.888\n",
       "14               30       0.922            0.886\n",
       "15               32       0.921            0.889\n",
       "16               34       0.918            0.892\n",
       "17               36       0.920            0.890\n",
       "18               38       0.918            0.888\n",
       "19               40       0.917            0.889"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = []; validation_score = []\n",
    "\n",
    "for v_min_samples_split in para_split :\n",
    "    gb = GradientBoostingRegressor(random_state = 1234, n_estimators = 70, min_samples_leaf = 6,\n",
    "                                   min_samples_split = v_min_samples_split)\n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    validation_score.append(gb.score(df_validation_x, df_validation_y))\n",
    "\n",
    "df_score_split = pd.DataFrame()\n",
    "df_score_split['MinSamplesSplit'] = para_split\n",
    "df_score_split['TrainScore'] = train_score\n",
    "df_score_split['validationScore'] = validation_score\n",
    "\n",
    "df_score_split.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe4ce440f0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_split, train_score, linestyle = '-', label = \"Train Score\")\n",
    "plt.plot(para_split, validation_score, linestyle = '--', label = 'validation Score')\n",
    "plt.ylabel('score'); plt.xlabel('min samples leaf')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> min_split 는 28이 적당"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Depth</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>validationScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Depth  TrainScore  validationScore\n",
       "0      1       0.758            0.766\n",
       "1      2       0.883            0.877\n",
       "2      3       0.920            0.888\n",
       "3      4       0.951            0.895\n",
       "4      5       0.967            0.908\n",
       "5      6       0.974            0.913\n",
       "6      7       0.978            0.913\n",
       "7      8       0.981            0.911\n",
       "8      9       0.982            0.912\n",
       "9     10       0.983            0.913"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = []; validation_score = []\n",
    "para_depth = [depth for depth in range(1,11)]\n",
    "\n",
    "for v_max_depth in para_depth :\n",
    "    gb = GradientBoostingRegressor(random_state = 1234, n_estimators = 70, min_samples_leaf = 6,\n",
    "                               min_samples_split = 28, max_depth = v_max_depth)\n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    validation_score.append(gb.score(df_validation_x, df_validation_y))\n",
    "\n",
    "\n",
    "df_score_depth = pd.DataFrame()\n",
    "df_score_depth['Depth'] = para_depth\n",
    "df_score_depth['TrainScore'] = train_score\n",
    "df_score_depth['validationScore'] = validation_score\n",
    "\n",
    "df_score_depth.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe4cdbe2e8>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_depth, train_score, linestyle = '-', label = \"Train Score\")\n",
    "plt.plot(para_depth, validation_score, linestyle = '--', label = 'Test Score')\n",
    "plt.ylabel('score'); plt.xlabel('min samples depth')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> depth = 5가 적당한듯 보여진다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Rate 을 0.1씩 증가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>lr</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>validationScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.989</td>\n",
       "      <td>0.889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.991</td>\n",
       "      <td>0.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    lr  TrainScore  validationScore\n",
       "0  0.1       0.967            0.908\n",
       "1  0.2       0.978            0.908\n",
       "2  0.3       0.985            0.904\n",
       "3  0.4       0.987            0.897\n",
       "4  0.5       0.989            0.905\n",
       "5  0.6       0.989            0.889\n",
       "6  0.7       0.991            0.864\n",
       "7  0.8       0.991            0.841\n",
       "8  0.9       0.992            0.869"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = []; validation_score = []\n",
    "para_lr = [lr * 0.1 for lr in range(1,10)]\n",
    "\n",
    "for v_learning_rate in para_lr :\n",
    "    gb = GradientBoostingRegressor(random_state = 1234, n_estimators = 70, min_samples_leaf = 6,\n",
    "                               min_samples_split = 28, max_depth = 5, learning_rate=v_learning_rate)\n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    validation_score.append(gb.score(df_validation_x, df_validation_y))\n",
    "\n",
    "# 결과 저장\n",
    "df_score_lr = pd.DataFrame()\n",
    "df_score_lr['lr'] = para_lr\n",
    "df_score_lr['TrainScore'] = train_score\n",
    "df_score_lr['validationScore'] = validation_score\n",
    "\n",
    "df_score_lr.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe4cd9f390>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(para_lr, train_score, linestyle = '-', label = \"Train Score\")\n",
    "plt.plot(para_lr, validation_score, linestyle = '--', label = 'validation Score')\n",
    "plt.ylabel('score'); plt.xlabel('learning_rate')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> learning rate 는 `0.1` 이 적합하다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training set : 0.948\n",
      "Score on validation set : 0.896\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=4, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=11,\n",
       "             min_samples_split=22, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=70, n_iter_no_change=None, presort='auto',\n",
       "             random_state=1234, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_final= GradientBoostingRegressor(random_state = 1234, n_estimators = 70, min_samples_leaf = 11,\n",
    "                                    min_samples_split = 22, max_depth = 4, learning_rate=0.1)\n",
    "gb_final.fit(df_train_x, df_train_y)\n",
    "\n",
    "#train, test 데이터 설명력\n",
    "print(\"Score on training set : {:.3f}\".format(gb_final.score(df_train_x, df_train_y)))\n",
    "print(\"Score on validation set : {:.3f}\".format(gb_final.score(df_validation_x, df_validation_y)))\n",
    "\n",
    "gb_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AGE</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CEMENT</td>\n",
       "      <td>0.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WATER</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BLAST_FURNACE_SLAG</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SUPERPLASTICIZER</td>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FINE_AGGREGATE</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>COARSE_AGGREGATE</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FLY_ASH</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Feature  Importance\n",
       "7                 AGE       0.339\n",
       "0              CEMENT       0.285\n",
       "3               WATER       0.167\n",
       "1  BLAST_FURNACE_SLAG       0.074\n",
       "4    SUPERPLASTICIZER       0.070\n",
       "6      FINE_AGGREGATE       0.042\n",
       "5    COARSE_AGGREGATE       0.016\n",
       "2             FLY_ASH       0.007"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_feature_names = df_train_x.columns\n",
    "\n",
    "df_importance = pd.DataFrame()\n",
    "df_importance['Feature'] = v_feature_names\n",
    "df_importance['Importance'] = gb_final.feature_importances_\n",
    "\n",
    "df_importance.sort_values('Importance', ascending = False, inplace = True)\n",
    "df_importance.round(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '변수')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_importance.sort_values('Importance', ascending = True, inplace = True)\n",
    "coordinates = range(len(df_importance))\n",
    "plt.barh(y = coordinates, width = df_importance['Importance'])\n",
    "plt.yticks(coordinates, df_importance['Feature'])\n",
    "plt.xlabel('변수 중요도')\n",
    "plt.ylabel('변수')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 변수 중요도를 확인 할 수 있다. AGE, CEMENT, WATER가 중요한 설명변수이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best estimator model : \n",
      "GradientBoostingRegressor(alpha=0.9, criterion='mse', init=None,\n",
      "             learning_rate=0.2, loss='ls', max_depth=6,\n",
      "             max_features='log2', max_leaf_nodes=None,\n",
      "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "             min_samples_leaf=20, min_samples_split=2,\n",
      "             min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "             n_iter_no_change=None, presort='auto', random_state=1234,\n",
      "             subsample=1.0, tol=0.0001, validation_fraction=0.1, verbose=0,\n",
      "             warm_start=False)\n",
      "\n",
      "best parameter : \n",
      "{'criterion': 'mse', 'learning_rate': 0.2, 'max_depth': 6, 'max_features': 'log2', 'min_samples_leaf': 20}\n",
      "\n",
      "best score : \n",
      "0.855\n"
     ]
    }
   ],
   "source": [
    "estimator = GradientBoostingRegressor(random_state = 1234)\n",
    "\n",
    "param_grid = {'criterion':['mse','friedman_mse','mae'],'max_features':['auto','sqrt','log2'], 'learning_rate':para_lr,\n",
    "             'max_depth':para_depth,'min_samples_leaf':para_leaf}\n",
    "\n",
    "\n",
    "grid_gb = GridSearchCV(estimator, param_grid, scoring='r2', n_jobs=-1)\n",
    "grid_gb.fit(df_train_x, df_train_y)\n",
    "\n",
    "print(\"best estimator model : \\n{}\".format(grid_gb.best_estimator_))\n",
    "print(\"\\nbest parameter : \\n{}\".format(grid_gb.best_params_))\n",
    "print(\"\\nbest score : \\n{}\".format(grid_gb.best_score_.round(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse:25.887\n",
      "mae:3.912\n",
      "Score on training set : 0.948\n",
      "Score on test set : 0.905\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "y_pred = gb_final.predict(df_test_x)\n",
    "\n",
    "print('mse:{:.3f}'.format(mean_squared_error(df_test_y, y_pred)))\n",
    "print('mae:{:.3f}'.format(mean_absolute_error(df_test_y, y_pred)))\n",
    "print(\"Score on training set : {:.3f}\".format(gb_final.score(df_train_x, df_train_y)))\n",
    "print(\"Score on test set : {:.3f}\".format(gb_final.score(df_test_x, df_test_y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> GridSearchCV 했을 때 `mse`, `best score=0.855` 로 확인됐다\n",
    "하지만 직접 최적의 파리미터를 찾았을 땐 `mae`, `best score=0.905`으로 더 높게 나왔다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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