GridSearch 교차검증.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sklearn.model_selection.GridSearchCV\n",
    "* class sklearn.model_selection.GridSearchCV(estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# refit=True: 제일 좋은걸 저장\n",
    "# 격자 전체를 crossvalidation을 통해서 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "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=121)\n",
    "\n",
    "dtree = DecisionTreeClassifier(random_state=12)\n",
    "parameters = {'max_depth': [1,2,3], 'min_samples_split':[2,3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(max_depth=3, random_state=12)\n",
      "{'max_depth': 3, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "grid_dtree = GridSearchCV(dtree, param_grid=parameters, cv = 3)\n",
    "grid_dtree.fit(X_train, y_train)\n",
    "\n",
    "print(grid_dtree.best_estimator_)\n",
    "print(grid_dtree.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002334</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>4.711456e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 1, 'min_samples_split': 2}</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001998</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>4.704732e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 1, 'min_samples_split': 3}</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.110223e-16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002664</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>4.709208e-04</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 2, 'min_samples_split': 2}</td>\n",
       "      <td>0.925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>3.118048e-02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>5.947204e-07</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 2, 'min_samples_split': 3}</td>\n",
       "      <td>0.925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>3.118048e-02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004331</td>\n",
       "      <td>0.001248</td>\n",
       "      <td>0.002332</td>\n",
       "      <td>1.245578e-03</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 3, 'min_samples_split': 2}</td>\n",
       "      <td>0.975</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>2.041241e-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.003663</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>4.707522e-04</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 3, 'min_samples_split': 3}</td>\n",
       "      <td>0.975</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>2.041241e-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.002334      0.000471         0.001332    4.711456e-04   \n",
       "1       0.001998      0.000816         0.000665    4.704732e-04   \n",
       "2       0.002664      0.000942         0.000333    4.709208e-04   \n",
       "3       0.001997      0.000817         0.001001    5.947204e-07   \n",
       "4       0.004331      0.001248         0.002332    1.245578e-03   \n",
       "5       0.003663      0.001697         0.000666    4.707522e-04   \n",
       "\n",
       "  param_max_depth param_min_samples_split  \\\n",
       "0               1                       2   \n",
       "1               1                       3   \n",
       "2               2                       2   \n",
       "3               2                       3   \n",
       "4               3                       2   \n",
       "5               3                       3   \n",
       "\n",
       "                                     params  split0_test_score  \\\n",
       "0  {'max_depth': 1, 'min_samples_split': 2}              0.700   \n",
       "1  {'max_depth': 1, 'min_samples_split': 3}              0.700   \n",
       "2  {'max_depth': 2, 'min_samples_split': 2}              0.925   \n",
       "3  {'max_depth': 2, 'min_samples_split': 3}              0.925   \n",
       "4  {'max_depth': 3, 'min_samples_split': 2}              0.975   \n",
       "5  {'max_depth': 3, 'min_samples_split': 3}              0.975   \n",
       "\n",
       "   split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "0                0.7               0.70         0.700000    1.110223e-16   \n",
       "1                0.7               0.70         0.700000    1.110223e-16   \n",
       "2                1.0               0.95         0.958333    3.118048e-02   \n",
       "3                1.0               0.95         0.958333    3.118048e-02   \n",
       "4                1.0               0.95         0.975000    2.041241e-02   \n",
       "5                1.0               0.95         0.975000    2.041241e-02   \n",
       "\n",
       "   rank_test_score  \n",
       "0                5  \n",
       "1                5  \n",
       "2                3  \n",
       "3                3  \n",
       "4                1  \n",
       "5                1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(grid_dtree.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.1, 3.5, 1.4, 0.2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "X = np.array([5.2, 3.3, 1.7, 0.3]).reshape(1,-1)\n",
    "grid_dtree.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = grid_dtree.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 0, 0, 1, 1, 1, 1, 2, 1, 1, 1, 0, 0, 2, 1, 0, 2, 0, 2, 2,\n",
       "       1, 1, 1, 1, 0, 0, 2, 2])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "pred = dtree.predict(X_test)\n",
    "print(accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 0, 0, 1, 1, 1, 1, 2, 2, 1, 1, 0, 0, 2, 1, 0, 2, 0, 2, 2,\n",
       "       1, 1, 1, 1, 0, 0, 2, 2])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  }
 ],
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