c24_模型泛化与岭回归_正则化.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型正则化 Regularization\n",
    "\n",
    "#### 目标 : 使 $J(\\theta) = MSE(y, \\hat y ; \\theta)$ 尽可能小\n",
    "##### 加入模型正则化\n",
    "#### 新目标 : 使 $J(\\theta) = MSE(y, \\hat y ; \\theta) + \\alpha \\frac{1}{2} \\sum_{i=1}^{n}\\theta_{i}^2$ 除了让MSE最小, 还要保证$\\theta$最小\n",
    "\n",
    "$J(\\theta) = MSE(y, \\hat y ; \\theta) + \\alpha \\frac{1}{2} \\sum_{i=1}^{n}\\theta_{i}^2$\n",
    "\n",
    "上面这个公式也叫 岭回归 Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "x = np.random.uniform(-3.0, 3.0, size=100)\n",
    "X = x.reshape(-1, 1)\n",
    "y = 0.5 * x + 3 + np.random.normal(0, 1, size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc93f14588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "def PolynomialRegression(degree):\n",
    "    return Pipeline([\n",
    "        ('poly', PolynomialFeatures(degree=degree)),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "        ('lin_reg', LinearRegression())\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(666)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167.9401086144252"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "poly10_reg = PolynomialRegression(degree=20)\n",
    "poly10_reg.fit(X_train, y_train)\n",
    "\n",
    "y10_predict = poly10_reg.predict(X_test)\n",
    "mean_squared_error(y_test, y10_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc96cda080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_plot = np.linspace(-3,3,100).reshape(100,1)\n",
    "y_plot = poly10_reg.predict(X_plot)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(X_plot[:, 0], y_plot, color='r')\n",
    "plt.axis([-3,3,0,6])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model(model):\n",
    "    '''封装一个画模型的函数'''\n",
    "    X_plot = np.linspace(-3,3,100).reshape(100,1)\n",
    "    y_plot = model.predict(X_plot)\n",
    "\n",
    "    plt.scatter(x, y)\n",
    "    plt.plot(X_plot[:, 0], y_plot, color='r')\n",
    "    plt.axis([-3,3,0,6])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc9660e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(poly10_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# ridge = Ridge(alpha=1)\n",
    "\n",
    "def RidgeRegression(degree, alpha):\n",
    "    return Pipeline([\n",
    "        ('poly', PolynomialFeatures(degree=degree)),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "        ('ridge_reg', Ridge(alpha=alpha))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3233492754011087"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge1_reg = RidgeRegression(20, 0.0001)\n",
    "ridge1_reg.fit(X_train, y_train)\n",
    "\n",
    "y1_predict = ridge1_reg.predict(X_test)\n",
    "mean_squared_error(y_test, y1_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc9636c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(ridge1_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1888759304218461"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge2_reg = RidgeRegression(20, 1)\n",
    "ridge2_reg.fit(X_train, y_train)\n",
    "\n",
    "y2_predict = ridge2_reg.predict(X_test)\n",
    "mean_squared_error(y_test, y2_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc94d85b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(ridge2_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3196456113086197"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge3_reg = RidgeRegression(20, 100)\n",
    "ridge3_reg.fit(X_train, y_train)\n",
    "\n",
    "y3_predict = ridge3_reg.predict(X_test)\n",
    "mean_squared_error(y_test, y3_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc96510f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(ridge3_reg)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}