Day_040_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [作業重點]\n",
    "使用 Sklearn 中的 Lasso, Ridge 模型，來訓練各種資料集，務必了解送進去模型訓練的**資料型態**為何，也請了解模型中各項參數的意義。\n",
    "\n",
    "機器學習的模型非常多種，但要訓練的資料多半有固定的格式，確保你了解訓練資料的格式為何，這樣在應用新模型時，就能夠最快的上手開始訓練！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間\n",
    "試著使用 sklearn datasets 的其他資料集 (boston, ...)，來訓練自己的線性迴歸模型，並加上適當的正則化來觀察訓練情形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = datasets.load_boston()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "0.05\n",
      "0.1\n",
      "0.2\n",
      "0.4\n",
      "0.5\n",
      "0.7\n",
      "0.8\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "alpha_list = [0.01, 0.05, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8, 1]\n",
    "x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size = 0.2, random_state = 0)\n",
    "\n",
    "coef_list = []\n",
    "mse_list = []\n",
    "r2_list = []\n",
    "for value in alpha_list:\n",
    "    ridge = linear_model.Ridge(alpha = value)\n",
    "    ridge.fit(x_train, y_train)\n",
    "    y_pred = ridge.predict(x_test)\n",
    "    coef_list.append(ridge.coef_)\n",
    "    mse_list.append(mean_squared_error(y_test, y_pred))\n",
    "    r2_list.append(r2_score(y_test, y_pred))\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RtM12q/RZ7qSOiIiiBERERBQlICIioigBERERRQmIiIgoavIy1zgGbdq+l/VbBtg3NMz83h7WrlzCmmULOl1WRByDEhBzyKbte1m3cTfDh0YA2Ds0zLqNuwESEhHxHDnENIes3zLwTDgcNnxohPVbBjpUUUQcyxIQc8i+oeEJtUfE3JaAmEPm9/ZMqD0i5rYExByyduUSerq7ntXW093F2pVLOlRRRBzLcpJ6Djl8IjpXMUVEOxIQc8yaZQsSCBHRlhxiioiIogREREQUJSAiIqKoySlH50l6UNJOSQ9J+nDdfmPdtkvSbZJOHqf/uZK+VvfdLWleU7VGRMRzNbkHcRC40PZ5wFJglaTzgWtsn2f7XOB7wNVjO0o6Hvgs8Du2Xwm8HjjUYK0RETFGY1cxuZrL9ED9trt+2faTAJIE9AClOU/fDOyyvbPe1o+aqjMiIsoaPQchqUvSDmA/cJftB+r2m4AfAGcBNxS6nglY0hZJ35D0oXG2f6Wkfkn9g4ODDY0iImJuajQgbI/YXgosBJZLOrtuvwKYDzwCXFboejzwOuCd9a9vlfTGwvY32G7ZbvX19TU1jIiIOWlarmKyPQRsBVaNahsBbgEuLXR5HPgH2z+0/RTwBeBV01BqRETUmryKqU9Sb73cA6wABiQtrtsEXAQ8Wui+BThX0kn1CetfBx5uqtaIiHiuJh+1cRpws6QuqiC6FbgDuEfSKYCAncBVAJJWAy3b19r+iaQ/Bb5OdRL7C7bvaLDWiIgYQ9XFRjNfq9Vyf39/p8uIiJhRJG2z3Sp9ljupIyKiKAERERFFCYiIiCjKfBAdsGn73kzaExHHvATENNu0fS/rNu5m+NAIAHuHhlm3cTdAQiIijik5xDTN1m8ZeCYcDhs+NML6LQMdqigioiwBMc32DQ1PqD0iolMSENNsfm/PhNojIjolATHN1q5cQk9317Paerq7WLtySYcqiogoy0nqaXb4RHSuYoqIY10CogPWLFuQQIiIY14OMUVERFECIiIiihIQERFRlICIiIiiJmeUmyfpQUk7JT0k6cN1+4112y5Jt0k6udB3kaRhSTvq1yebqjMiIsqavIrpIHCh7QOSuoF7Jd0JXGP7SYB61rirgY8W+n/L9tIG65uUPHAvIma7xgLC1VR1B+q33fXLo8JBQA/VlKIzSh64FxFzQaPnICR1SdoB7Afusv1A3X4T8APgLOCGcbq/VNJ2Sf8g6dfG2f6Vkvol9Q8ODjYxhKI8cC8i5oJGA8L2SH2YaCGwXNLZdfsVwHzgEeCyQtcngDNsLwPeD/yNpFMK299gu2W71dfX19g4xsoD9yJiLpiWq5hsDwFbgVWj2kaAW4BLC+sftP2jenkb8C3gzOmotR154F5EzAVNXsXUJ6m3Xu4BVgADkhbXbQIuAh4dp29Xvfwy4OXAt5uqdaLywL2ImAuavIrpNODm+gf9ccCtwB3APfXhIgE7gasAJK0GWravBS4APiLpaWAE+B3bP26w1gnJA/ciYi5QdbHRzNdqtdzf39/pMiIiZhRJ22y3Sp/lTuqIiChKQERERFHmg6jlzuiIiGdLQJA7oyMiSnKIidwZHRFRkoAgd0ZHRJQkIMid0RERJQkIcmd0RERJTlKTO6MjIkoSELU1yxYkECIiRskhpoiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFs2bKUUmDwHefZ/dTgR9OYTkzwVwbc8Y7+821MU/VeH/Vdl/pg1kTEJMhqX+8OVlnq7k25ox39ptrY56O8eYQU0REFCUgIiKiKAFR2dDpAjpgro0545395tqYGx9vzkFERERR9iAiIqIoAREREUVzKiAkrZI0IGmPpN8vfH6ipFvqzx+QtGj6q5w6bYz3/ZIelrRL0pck/Won6pxKRxvzqPV+Q5IlzejLItsZr6R/U/85PyTpb6a7xqnWxt/rMyR9RdL2+u/2WzpR51SQ9GlJ+yV9c5zPJelj9e/FLkmvmtICbM+JF9AFfAt4GXACsBN4xZh1/j3wyXr57cAtna674fG+ATipXr5qJo+33THX670QuBu4H2h1uu6G/4xfDmwH/ln9/sWdrnsaxrwBuKpefgXwWKfrnsR4LwBeBXxznM/fAtwJCDgfeGAqv38u7UEsB/bY/rbtXwCfBy4es87FwM318m3AGyVpGmucSkcdr+2v2H6qfns/sHCaa5xq7fwZA/wh8MfAz6ezuAa0M973Ap+w/RMA2/unucap1s6YDZxSL78I2DeN9U0p23cDPz7CKhcDf+XK/UCvpNOm6vvnUkAsAL4/6v3jdVtxHdtPAz8FfmVaqpt67Yx3tN+i+p/ITHbUMUtaBpxu+/bpLKwh7fwZnwmcKek+SfdLWjVt1TWjnTH/F+Bdkh4HvgD87vSU1hET/Xc+IcdP1YZmgNKewNhrfNtZZ6ZoeyyS3gW0gF9vtKLmHXHMko4D/jtw+XQV1LB2/oyPpzrM9HqqPcR7JJ1te6jh2prSzpjfAXzG9n+T9Brgf9Zj/mXz5U27Rn9mzaU9iMeB00e9X8hzdz2fWUfS8VS7p0favTuWtTNeJK0A/hOw2vbBaaqtKUcb8wuBs4Gtkh6jOma7eQafqG737/T/sn3I9neAAarAmKnaGfNvAbcC2P4aMI/qwXazUVv/zp+vuRQQXwdeLumlkk6gOgm9ecw6m4H31Mu/AXzZ9ZmgGeio460Pt3yKKhxm+rFpOMqYbf/U9qm2F9leRHXeZbXt/s6UO2nt/J3eRHUxApJOpTrk9O1prXJqtTPm7wFvBJD0L6gCYnBaq5w+m4HfrK9mOh/4qe0npmrjc+YQk+2nJV0NbKG6EuLTth+S9BGg3/Zm4Eaq3dE9VHsOb+9cxZPT5njXAycDf1ufi/+e7dUdK3qS2hzzrNHmeLcAb5b0MDACrLX9o85VPTltjvkDwF9IuobqcMvlM/U/epI+R3V48NT6nMp1QDeA7U9SnWN5C7AHeAq4Ykq/f4b+vkVERMPm0iGmiIiYgAREREQUJSAiIqIoAREREUUJiIiIKEpARIwi6bH6foFJrTOF9Swa70meEU1LQERERFECIuYkSZskbavnSLiy8PkiSY9Kurl+zv5tkk4atcrvSvqGpN2Szqr7LJf01Xoegq9KWlLY7i2j5yeQ9BlJl9bfd0+9zW9I+teFvpdL+vio97dLen29/GZJX6v7/q2kkyf3OxSRgIi569/ZfjXVQwrfJ6n01N4lwAbb5wJPUs0XctgPbb8K+B/AB+u2R4ELbC8DrgX+a2GbnwcuA6gfFfFGqrth9wNvqrd5GfCxdgdSH+76z8CKun8/8P52+0eMZ848aiNijPdJemu9fDrVA+zGPoLi+7bvq5c/C7wP+JP6/cb6123AJfXyi4CbJb2c6hEP3YXvvRP4mKQTgVXA3baHJb0I+LikpVSPxDhzAmM5n2pinPvqR6acAHxtAv0jihIQMefUh2VWAK+x/ZSkrVQPdBtr7HNoRr8//OTbEf7p39EfAl+x/VZV09Vufc4G7Z/X37eSak/hc/VH1wD/DziPas++NJnR0zx7r/9wzQLusv2OQp+I5y2HmGIuehHwkzoczqL6H3jJGfV8AlDNMXBvG9vdWy9ffoT1Pk/1ULVfo3ro3OG+T9RzFryb6kF0Yz0GLJV0nKTTqWZXg+qptK+VtBhA0kmSJrIHElGUgIi56O+B4yXtovpf//3jrPcI8J56vX9Odb7hSP4YuF7SfZR/wB/2f6jmGv5iPW0mwJ/X33U/1eGlnxX63Qd8B9hNdajrGwC2B6kC6XN1rfcDZx2l1oijytNcIwrqQ0S32z67w6VEdEz2ICIioih7EBERUZQ9iIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKL/D53+vdxKG1tkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x = alpha_list, y = mse_list)\n",
    "plt.xlabel('alpha value'); plt.ylabel('mse')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "plt.scatter(x = alpha_list, y = r2_list)\n",
    "plt.xlabel('alpha value'); plt.ylabel('r2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以發現隨著alpha值增加解釋能力下降，而且誤差值越大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n",
      "0.05\n",
      "0.1\n",
      "0.2\n",
      "0.4\n",
      "0.5\n",
      "0.7\n",
      "0.8\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "alpha_list = [0.01, 0.05, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8, 1]\n",
    "x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size = 0.2, random_state = 0)\n",
    "\n",
    "coef_list = []\n",
    "mse_list = []\n",
    "r2_list = []\n",
    "for value in alpha_list:\n",
    "    lasso = linear_model.Lasso(alpha = value)\n",
    "    lasso.fit(x_train, y_train)\n",
    "    y_pred = lasso.predict(x_test)\n",
    "    coef_list.append(ridge.coef_)\n",
    "    mse_list.append(mean_squared_error(y_test, y_pred))\n",
    "    r2_list.append(r2_score(y_test, y_pred))\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "plt.scatter(x = alpha_list, y = mse_list)\n",
    "plt.xlabel('alpha value'); plt.ylabel('mse')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "plt.scatter(x = alpha_list, y = r2_list)\n",
    "plt.xlabel('alpha value'); plt.ylabel('r2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso在正規化上有比較嚴謹的要求，解釋力下降得比較快"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}