Day_038_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間\n",
    "試著使用 sklearn datasets 的其他資料集 (wine, boston, ...)，來訓練自己的線性迴歸模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HINT: 注意 label 的型態，確定資料集的目標是分類還是回歸，在使用正確的模型訓練！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\philip_chang\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 讀取乳癌資料集\n",
    "breast_cancer = datasets.load_breast_cancer()\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, test_size=0.1, random_state=4)\n",
    "\n",
    "logreg = linear_model.LogisticRegression()\n",
    "\n",
    "# 訓練模型\n",
    "logreg.fit(x_train, y_train)\n",
    "\n",
    "# 預測測試集\n",
    "y_pred = logreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [-0.64959885]\n",
      "Mean squared error: 0.12\n"
     ]
    }
   ],
   "source": [
    "# 可以看回歸模型的參數值\n",
    "print('Coefficients: ', regr.coef_)\n",
    "\n",
    "# 預測值與實際值的差距，使用 MSE\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8771929824561403\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape:  (506, 1)\n"
     ]
    }
   ],
   "source": [
    "# 讀取波士頓房價資料集\n",
    "boston = datasets.load_boston()\n",
    "# 為方便視覺化，我們只使用資料集中的 1 個 feature (column)\n",
    "X = boston.data[:, np.newaxis, 2]\n",
    "print(\"Data shape: \", X.shape) # 可以看見有 442 筆資料與我們取出的其中一個 feature\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, boston.target, test_size=0.1, random_state=4)\n",
    "\n",
    "# 建立一個線性回歸模型\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# 將訓練資料丟進去模型訓練\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "# 將測試資料丟進模型得到預測結果\n",
    "y_pred = regr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [-0.64959885]\n",
      "Mean squared error: 63.06\n"
     ]
    }
   ],
   "source": [
    "# 可以看回歸模型的參數值\n",
    "print('Coefficients: ', regr.coef_)\n",
    "\n",
    "# 預測值與實際值的差距，使用 MSE\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 畫出回歸模型與實際資料的分佈\n",
    "plt.scatter(x_test, y_test,  color='black')\n",
    "plt.plot(x_test, y_pred, color='blue', linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}