Day_047_HW-超參參數調整.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [作業重點]\n",
    "了解如何使用 Sklearn 中的 hyper-parameter search 找出最佳的超參數"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 作業\n",
    "請使用不同的資料集，並使用 hyper-parameter search 的方式，看能不能找出最佳的超參數組合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, metrics\n",
    "from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 讀取資料集\n",
    "diabetes = datasets.load_breast_cancer()\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target,\n",
    "                                                    test_size=0.25, random_state=42)\n",
    "\n",
    "# 建立模型\n",
    "clf = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.958041958041958\n"
     ]
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    2.2s finished\n"
     ]
    }
   ],
   "source": [
    "# 設定要訓練的超參數組合\n",
    "n_estimators = [50, 100, 150]\n",
    "max_depth = [1, 3, 5]\n",
    "param_grid = dict(n_estimators=n_estimators, max_depth=max_depth)\n",
    "\n",
    "## 建立搜尋物件，放入模型及參數組合字典 (n_jobs=-1 會使用全部 cpu 平行運算)\n",
    "grid_search = GridSearchCV(clf, param_grid, scoring=\"accuracy\", n_jobs=-1, verbose=1)\n",
    "\n",
    "# 開始搜尋最佳參數\n",
    "grid_result = grid_search.fit(x_train, y_train)\n",
    "\n",
    "# 預設會跑 3-fold cross-validadtion，總共 9 種參數組合，總共要 train 27 次模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy: 0.974178 using {'max_depth': 1, 'n_estimators': 150}\n"
     ]
    }
   ],
   "source": [
    "# 印出最佳結果與最佳參數\n",
    "print(\"Best Accuracy: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 1, 'n_estimators': 150}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用最佳參數重新建立模型\n",
    "clf_bestparam = GradientBoostingClassifier(max_depth=grid_result.best_params_['max_depth'],\n",
    "                                           n_estimators=grid_result.best_params_['n_estimators'])\n",
    "\n",
    "# 訓練模型\n",
    "clf_bestparam.fit(x_train, y_train)\n",
    "\n",
    "# 預測測試集\n",
    "y_pred = clf_bestparam.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "# 調整參數後約可降至 0.05 的 MSE\n",
    "print(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2019/10/21"
   ]
  }
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