ch07-逻辑回归/04-决策边界.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策边界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=667)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from playML.LogisticRegression import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def x2(x1):\n",
    "    return (-log_reg.coef_[0] * x1 - log_reg.intercept_) / log_reg.coef_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1_plot = np.linspace(4, 8, 1000) # 1个区间中均匀的取值\n",
    "x2_plot = x2(x1_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118cf4da0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d1b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0, 0], X[y==0,1], color='r')\n",
    "plt.scatter(X[y==1, 0], X[y==1,1], color='b')\n",
    "plt.plot(x1_plot, x2_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118d9d2b0>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d1b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test[y_test==0, 0], X_test[y_test==0,1], color='r')\n",
    "plt.scatter(X_test[y_test==1, 0], X_test[y_test==1,1], color='b')\n",
    "plt.plot(x1_plot, x2_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制决策边界的方法\n",
    "\n",
    "- 传1个模型，和范围\n",
    "- 把网格上没1点都表示出来！！用颜色涂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgbook/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ddfd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(log_reg, axis=[4, 7.5, 1.5, 4.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kNN的决策边界\n",
    "\n",
    "- knn参数k越小越复杂，过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgbook/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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HKkJI/WV/j2XVt9G7+4vu/jvgXuBZd/9ll2FHAy80Hx8ExjoHmNlmM9thZjv2j+ezv0pIPDGVXR27CYlOhtaf6vuMGY8Lje7FfM1Uj1WEkPrL/h7Lqm+jN7OjzWwhcBpwlJl1m63vB5Y0Hy9p/n4Gd9/m7mvcfc0xo/nMIkPiiSns6thLSHQytP5U32fMeFxodC/ma6Z6rCKE1F/291hWIUs3nwPOcfcp4HfASJcxdwJnNB9vAO6OU978hMQTU97VMSQ6GVp/qu8zZjwuNLoX8zVTPVYRQuov+3ssq5CLsd8Evm1mfws8Aew2syvd/dK2Md8BPmpmu4CHaDT+woXEE4ve1XE2IdHJ0PpTfZ8x43Gh0b2Yr5nqsYoQUn/Z32NZ9W307r6Xxiy93aUdY14BzopYVzQxo45F2LLsYN9MfOh7HPS5mOudsDHjcbGjeyFC6081hpl3pFNxyPzV/oapVGOHZZPCDpWh0b0iIn4xo4epRjoVnUxX7bdASDV2WDYpbHkQGt0rIuIXM3qYaqRT0cl01b7Rpxo7LJOlG9M4V6HRvSIifjGjh6lGOhWdTFftG32qscMySWE2D+HRvSIifjGjh6lGOhWdTFftG32qscMySG0r4tDoXhERv5jRw1QjnYpOpqv2F2NTjR2mprU8M2P2ntgulaHRvSIifjGjh6lGOhWdTJe+HFx6Smm2LiL6cnCJLIW45CCFbmesuhqK2D657Fs2p0SNXrpK5QLrIIRuZ6y6GlK970DC1f5irNRP6HbGeUu1rlTvO5BwavRSO6HbGect1bpSve9AwqnRS+2Ebmect1TrSvW+AwmnRi+1E7qdcd5SrSvV+w4knC7GCtBlG4MKJ25CtzNWXQ2p3ncg4ZSjr5mlG4eiJGpSjb7FrCt21DHVcybloRy9zNDzZqcIM/VUo28x64oddUz1nEk9qNFXQKxZeqjZom9FNq2Ydc0WdZxLo0/1nEk96GJsBeR9c1Oq0beYdcWOOqZ6zqQe1OhLrKjdI1ONvsWsK3bUMdVzJvWgRl9iRW1TkGr0LWZdsaOOqZ4zqQet0ZdUkTtLphp9i1lX7KhjqudM6kGNPlGzfT1fChuO/WLFx3JvUiHxxNC6Qo61b2wtJzx/O6MTe3lx0XL2ja2dV/1FnDMRUKNPQtfUTIVvWJqLmPHEkGMpDilVojX6gm1ftSmJGXrqYu5mGHIs7Z4oVaJGX5DUvm81dTHjiSHHUhxSqkRLNzmasUSjpZlMxkdWsHhiT9fnB3GsmK8nUjTN6HPQmr1riWbuYsYTQ46lOKRUiWb0AzQ9g9fsfd5ixhNDjqU4pFRJ30ZvZgb8C3Ay8BzwUXef7BjzPuBa4MnmU59y991RKy2R6bX3Ejb4su+wGFp/SNSx7HHIsv9dSjwhM/p1wLC7v8PMfgicAdzSZdw17l7bSEIr917m5ZmUI4WKRGajcyHtQhr9s8BVzce/n2Xc2Wb2N8DTwMe8iI3uC1Dm2XunlHdYDKkt5frzpnMh7fpejHX3x9z9fjP7CPA64LYuw54Avujua4HlwPrOAWa22cx2mNmO/ePj8607CVWLR6YcKVQkMhudC2kXdDHWzD4EfAb4oHvXfVoPAHc0Hz8JLOsc4O7bgG3Q+IapuRRbtCosz8wm5UihIpHZ6FxIu74zejM7Dvg88AF37zUVvwQ418wWAKcAj8QrMQ2tBE1VmzykHSlUJDIbnQtpFzKj30RjOea2RgCH64C3uvulbWOuBr4LXAjc6O6Pxi60CHW7wSnlSKEikdnoXEg7fTl4D1VbfxeR8tOXg0eQ93evxlKXvPT6hy+Ltj+8SJ2o0VPuiGRd8tLrH76MU5+6bvoLu82nOPWp6wDU7EX6qP1eN2VfoqnLdrpve/pb002+xZrPi8jsajmjL+sSTTd1yUtb11Rv7+dF5DW1m9FXqclD71x01fLSbt2/WrHX8yLymlo1+ipuFVyXvPSuN36cznyYN58XkdnVYummarP4dnXJS7cuuCp1I5JdpRt97P3gf/7UPu595HHGJ15mdGQR6045kbe+aXmcg89D6Ha6ZY9h7htbywnP387oxF5eXLScfWNriy4paWX/+5Z4Ktvot6/aFDUu+fOn9nHHg48yOXUIgPGJl7njwcYNwCk0+37KHsMse/150/mSdpVcox9EZPLeRx6fbvItk1OHuPeRx6O/1iCUPYZZ9vrzpvMl7Sozox/0Ovz4xMuZnk9N2WOYZa8/bzpf0q4SjT72Mk03oyOLujb10ZFFg33hSMq+bW3Z68+bzpe0K/3STV53tq475USGh2aeruGhBaw75cRcXn++yh7DLHv9edP5knalndHnHZlsXXBNMXUTouwxzLLXnzedL2lXum2Ky743jdRXzLijopP1VIttitXkpaxixh0VnZSsSrFGv3TjkJq8lFrMuKOik5JV0jP6qn8Zt9RHzLijopOSVbKNPo/IpEheYsYdFZ2UrJJbutEyjVRRzLijopOSVVIz+irvMin1FjPuqOikZJVEoy/zd7aKhArdZTTvY0n1Fb50o2UaEZHBKqzRay1eRCQfhTT634wcrbV4EZGcFL50IyIig6VGLyJScWr0IiIV17fRW8N2M/uJmf27mR0WyTSzRWZ2k5k9ZGbfNjMbTLkiIpJVyIx+HTDs7u8AFgNndBlzPrDH3U8FjgLeG69EieWkvddzwV2r+bubl3HBXas5ae/1RZckIjkIafTPAlc1H/++x5gNwO3Nx3cBp8+zLomstbXt4ok9GD69ta2avUj19W307v6Yu99vZh8BXgfc1mXY0cALzccHgbF4JUoM2tpWpL6CLsaa2YeAzwAfdPepLkP2A0uaj5c0f995jM1mtsPMdrz4f4f9WAZMW9uK1FfIxdjjgM8DH3D38R7D7uS1tfsNwN2dA9x9m7uvcfc1f7j0mLnWK3PUawtbbW0rUn0hM/pNwHLgNjO7x8w+ZWZXdoz5DrDCzHYBB2g0fkmItrYVqa++u1e6+1eBr/YZ8wpwVqyiJD5tbStSX0lsUyz50Na2IvWkO2NFRCpOjV5EpOLU6EVEKk6NXkSk4tToRUQqTo1eRKTi1OhFRCpOjV5EpOLU6EVEKk6NXkSk4tToRUQqTo1eRKTi1OhFRCpOjV5EpOLU6EVEKk6NXkSk4tToRUQqTo1eRKTi1OhFRCpOjV5EpOLU6EVEKk6NXkSk4tToRUQqTo1eRKTi1OhFRCpOjV5EpOLU6EVEKk6NXkSk4oIavZkdYWbfn+Xn7zOzPWZ2T/PXyfFKFBGR+RjuN8DMRoCfAif1GXqNu2+NUpWIiETTd0bv7hPu/jZgT5+hZ5vZ/WZ2g5lZnPJERGS+zN3DBpo97u4n9vjZW4CT3P1mM7sP+Ad3/2HHmM3A5uZvTwEemXPVxTsG2F90EfOg+otV5vrLXDuUv/6T3X006x/qu3QT6ABwR/Pxk8CyzgHuvg3YBmBmO9x9TaTXzp3qL5bqL06Za4dq1D+XPxcrdXMJcK6ZLaD8s3URkUrJ3OjN7I/M7MqOp68GLqBx0fZGd380RnEiIjJ/wUs3rfV5d/8VcGnHz/YB787wutsyjE2R6i+W6i9OmWuHmtYffDFWRETKSXfGiohU3MAbvZl91szu6PGzY8zsv8zsYTP7yqBrmYs+9X/azB5vuyN4Sd719RJyt7KZLTKzm8zsITP7dkr3PwTWn/Qd2WZ2WfPzfauZva7Lz5P9/AfUnvJn/91tdT1tZpu6jEn5sx9Sf6bP/kAbvZm9GfjELEMuBm4GTgXeb2b97r7NVUD9AF9y93c2f72QQ1lZXNNW2+4uPz8f2OPupwJHAe/Nt7y++tUfOiZ3ZnYC8Cfu/i7gVuD4LsOS/PwH1g6Jfvbd/YetuoBdwM4uw5L97AfWDxk++4Oe0V8FfGGWn28Abnf3Q8CPgNMHXE9W/eoHuNDMdprZVXkUlFG/u5U3ALc3H99Feuc/5G7rVO/I/ivgKDP7T+BdwK+6jEn18x9SO6T92cfM/gA40d13dflx6p/9fvVDhs/+wBq9mZ0HPATMFrU8GmjNBA4CY4OqJ6vA+h+gkUBaA3zEzFYOvrJgTwBfdPe1wHJgfZcxyZ5/wuoPGVOUY4Hn3f0vacyI39llTKrnP6T2lD/7Le8F7uzxs1TPfbvZ6s/02R/kjP4sGjODfwPebmYXdhmzH2it7S0hrVuTQ+p/CviJu0/R2AvosDuCC9T3bmXSPv8h9YeMKcpBoPXf6V8CK7qMSfX8h9Se8me/5YPATT1+luq5bzdb/Zk++wNr9O5+XnON6VzgAXe/usuwO4EzmnfUrgfuHlQ9WQXW/3XgndbY4fNNwGN51thHyN3KdwJnNB9vIKHzT1j9Kd+R/QDw583HJ9JomJ1S/fyH1J7yZ5/mUsbpNJZlukn5sx9Sf6bPfm7xSjM7zcwu7Xj6n4AzaVxwuNndH8+rnqx61P9l4CvAPcAV7v7b/CvracbdysBElzuavwOsMLNdNGYIvf6bWISQ+pO9I9vdfwzsN7P/pjE7Hi7L5z+w9pQ/+9D4h+pn7v6ydb+bP+XPPvSvP9NnXzdMiYhUnG6YEhGpODV6EZGKU6MXEak4NXoRkYpToxcRqTg1ehGRilOjFxGpuP8HBzveyFfDUiEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b1b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(knn_clf, axis=[4, 7.5, 1.5, 4.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf_all = KNeighborsClassifier()\n",
    "knn_clf_all.fit(iris.data[:,:2], iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgbook/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b1b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(knn_clf_all, axis=[4, 8, 1.5, 4.5])\n",
    "plt.scatter(iris.data[iris.target==0,0], iris.data[iris.target==0,1])\n",
    "plt.scatter(iris.data[iris.target==1,0], iris.data[iris.target==1,1])\n",
    "plt.scatter(iris.data[iris.target==2,0], iris.data[iris.target==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgbook/.pyenv/versions/3.6.4rc1/lib/python3.6/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b1b278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clf_all = KNeighborsClassifier(n_neighbors=50)\n",
    "knn_clf_all.fit(iris.data[:,:2], iris.target)\n",
    "\n",
    "plot_decision_boundary(knn_clf_all, axis=[4, 8, 1.5, 4.5])\n",
    "plt.scatter(iris.data[iris.target==0,0], iris.data[iris.target==0,1])\n",
    "plt.scatter(iris.data[iris.target==1,0], iris.data[iris.target==1,1])\n",
    "plt.scatter(iris.data[iris.target==2,0], iris.data[iris.target==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/13.png)\n",
    "![](images/14.png)"
   ]
  },
  {
   "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": "3.6.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}