11-SVM/04-SVM-in-scikit-learn/04-SVM-in-scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn中的SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "X = X[y<2,:2]\n",
    "y = y[y<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4e3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1], color='red')\n",
    "plt.scatter(X[y==1,0], X[y==1,1], color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "standardScaler = StandardScaler()\n",
    "standardScaler.fit(X)\n",
    "X_standard = standardScaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1000000000.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "svc = LinearSVC(C=1e9)\n",
    "svc.fit(X_standard, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d42ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.01, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc2 = LinearSVC(C=0.01)\n",
    "svc2.fit(X_standard, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11283c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(svc2, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.03243305, -2.49295041]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.9536471])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_svc_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)\n",
    "    \n",
    "    w = model.coef_[0]\n",
    "    b = model.intercept_[0]\n",
    "    \n",
    "    # w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    plot_x = np.linspace(axis[0], axis[1], 200)\n",
    "    up_y = -w[0]/w[1] * plot_x - b/w[1] + 1/w[1]\n",
    "    down_y = -w[0]/w[1] * plot_x - b/w[1] - 1/w[1]\n",
    "    \n",
    "    up_index = (up_y >= axis[2]) & (up_y <= axis[3])\n",
    "    down_index = (down_y >= axis[2]) & (down_y <= axis[3])\n",
    "    plt.plot(plot_x[up_index], up_y[up_index], color='black')\n",
    "    plt.plot(plot_x[down_index], down_y[down_index], color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JH1Bhnl3ZcUdl0I3Yf+oUzSMj8c2Vi4WhoRQvUMDwWCtN/acF41g87D2qv9SC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\nQggPI41dCCE8jDR2IYTwMNLYhRDCw/w/aEBeM5sGJrYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11496f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_decision_boundary(svc, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x2gz6WHDkiD0jK4ubRowg6fBhBrRpIzXGgPHbqZfCIs1rD+Jajh85yNPj5vPN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\ngl0IIXxGgl0IIXxGgl0IIXzm/wM1BljvqJvNxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114917c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_decision_boundary(svc2, axis=[-3, 3, -3, 3])\n",
    "plt.scatter(X_standard[y==0,0], X_standard[y==0,1])\n",
    "plt.scatter(X_standard[y==1,0], X_standard[y==1,1])\n",
    "plt.show()"
   ]
  }
 ],
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