Polynomial Logistic Regression in scikit learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(666)\n",
    "X = np.random.normal(0, 1, size=(200, 2))\n",
    "y = np.array((X[:,0]**2+X[:,1])<1.5, dtype='int')\n",
    "for _ in range(20):\n",
    "    y[np.random.randint(200)] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mMzZXbgB+/BSw5+tBWaEMBQueYTrEJUdt8tKjbo+HTB84vcKFVtMYI79PozdH2a6btOab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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": [
    "### 分割数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练普通逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81999999999999995"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80000000000000004"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AOMazm3dZEgDl6jFXEXNbrDRtWtEsdCYxniktDiymftIS3TDF6Gt9ZdeJdOzg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\nFe4V2lBPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(log_reg,[-4,4,-4,4])\n",
    "\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": [
    "### 使用多项式特征并引入正则项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PolynomialLogisticRegression(degree,C):\n",
    "    return Pipeline([\n",
    "        (\"poly\",PolynomialFeatures(degree=degree)),\n",
    "        (\"scaler\",StandardScaler()),\n",
    "        (\"log_reg\",LogisticRegression(C=C))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = PolynomialLogisticRegression(2,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly', PolynomialFeatures(degree=2, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('log_reg', LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AOMazm3dZEgDl6jFXEXNbrDRtWtEsdCYxniktDiymftIS3TDF6Gt9ZdeJdOzg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\nFe4V2lBPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(log_reg,[-4,4,-4,4])\n",
    "\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": [
    "### 看看过拟合的情况 引入正则化的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = PolynomialLogisticRegression(20,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly', PolynomialFeatures(degree=20, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('log_reg', LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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rtOgmho3neWWimMSpWBiaHVQdVOy8hM6phHWKpcq985qGfLTaTCts/nqYxlxe\nC+l0+2IzUdMiT13xEWyubMSR6kJUVXCkuhD/rfwHWD7/ikjXJcnAUEzKCTLHNA6CbiAG6VhYG0t2\nG+WWL5/x7DPuht+A6Vqh8xJjU7zcFO8Om78ONMfOHQFvHF4dZXM0alrk5ZcswUFsxPUHrp2Nu98x\ncHQ23Ofs8xwt59ADoApgCZt/pRYKe8qJMsfUxOT2CjDsfU5Qz9QrvAKIazy7dsqSACiUTniKmNdm\npWloRa3QmcT4bH5+aDENkpbohSlGX+4peC6kI4e2GJ+GypK30j+mMe7+uysexOT25n0eJ5cm7fs9\n3QxDMSlnXf8U7lx0Ckt6yxAolvSWceeiU7F/kMxhk8GA57mHVw4Prke5d17ggiSvkNDhwfX45pVf\nbIphl2UOesunZ0MaLy5a4xrnVqirmF4zbhbJVnqe12J6EjKNCHQWTq8FdPeV91rpH9OIkznlts/j\n0K79HhIOCnsHsK5/CrvecgzP/NJR7HrLsbZ4R0ErK8NO7QHC9Vnx26xsFNqp/AIAirmlk7MLQeNm\nqiPGpslMfaUTno3CapudhdkXMH2PXjgLp9cCGoeo1+K3n8OReOmDwk5cCeqZtuLBBn0aAIItArVC\nW+qdi16tz7xu3Ew913/GPbghQOQsExNT+QHD8fmeC2SQBbSVLCa/zpvbhkd993M4Ei99MMZOjASd\nxhN2ak+YOHXYzcogxUWNTcfczz8SekPXj2UTOzCnfLrpeO2IQNNegq3K1FYqWN32eRw4Ei+dRBJ2\nEfktAP8dwOUA3qWqT9owimSboH1WgPCblX7FRWv2b2pKfXRDgJZL9k2MHNrieu9Sb3+gCVOtVKYG\nyWJyCrvWjt3i+rs4/t4P487vPcysmA4iqsd+AMBvAPifFmwhXUSYpwEgeF92rz41JmE1ka9MYe3Y\nRzFyaEvLDbeC9MbpM8T6wxA1i8nJLDJ58ByF11lEEnZVPQgAIl4PtSRJbPeZSYKgi4B7nxpAZ0II\nrRTx1E5ecmwJwrKJHXVpnV5M5Qea8trDLiJBQ1ZB5q66efrbhkcxOhZu6DVJjrZtnorIRhF5UkSe\nfK3ImFw7cPKPj5Z7oZDZvOOdxeBzNzsJ9z41wJzqaax+7mM4m59vfK93n8lw/eGdOPbcAKJeljzm\nlE/7Vvj6ESWLyY04Bnx0KjuLBVz/0oVY9ZMluP6lCzvi8+Mr7CKyR0QOuPz7UJgbqepWVb1aVa9e\n2M+813aQZJ+ZJPASo3xlCgpFWeY0fc1tLF3Y69fiV41b2xun1Nsfqt2BiVazmEzzV902p7cNj4ay\nKQt0qnPkG4pR1dXtMITYJ6lFbDFtAAANK0lEQVQ+M61gY4iEX5ihUJrEY8Nfms2KAQBFD3pQNb6n\n8fpB8FsAnKlJAHD7oxe2dA03Wsliah7ZF66SNut4OUdpDmkyjz3DmPKL05Z3HLThmB9+YYZiYRCH\nB9dj63WHcO8HjuPeDxyvi8d7EUbsvBaAxuuEyemPg7B1CAMb0ucUxEknOUe1RBJ2Efl1ETkC4FcA\nPCoi37RjFrHBbQuK6JN6bzSNecdhW+GacETqTH5+k1x75cl7oQDO5OeHahvgtsBM95lf0HSdVip3\nbROmkrZxQEvW6RTnqJFIwq6qX1XVIVU9T1X/g6q+35ZhJDpJ9ZkJS9hWuF4cHlyPv7vuMB4b/nIg\nL9RdhKf9eCf+XO6dF9qGRi/4seEvY+t1h6xU7iZNN8XaO8U5aoSVpxmnE/KPW2mFW4tX610/3PLk\nX1y0pm4CUyvpjmGqccNW7pL24Xx2Oi1lmMJOYsdvYzRKK1wbQ54bhfXmx1cFquLsZgY25KbbP3cB\nneAcNcLNUxIrQTZGo4QjbMXna7EZGkqSMM2+wtJtsfZOgx47iZWgPUxaDUfEIcJRQ0NpIOyTTCvp\npqxGTS/02EmsxO39xpEumGSmii0vO8yTjK10U5IeKOwkVuLO07YlwrWCOnJoi+tgDi8P1oYg2xRY\nm8NMvOimDJlOgqEYEitRZ4T6Ebb7oxtuYYsVEw95inlt6GIqP4A55dOzrQFa2cB1vgdbm7ZhwklZ\n2VMg56DHTmIlzjxtx0teO3YLAOCx4S+FHlUHhPdYGz3ruaWTVvq92BTYME8yUZ+q6LWnD3rsJHbi\nyNO2keboEFZQ/Zp8+b3fhM1NWxvDTF5ctCZyO2GSDBR20pEkFbYAggt2WEG2HbZqZ5EWM2TSBYW9\nC8iffgp9xV3oqUyimhvA2f7rAXT243NQLztIGp+t8Xu1tCLINvYLWsVGkVY3FS2lHQp7xsmffgpz\nT+2AaAkAkKtMYu6pHVg28Z87+rE6iJcdNFwTVlDdFoKy5FHq7Udf6WQkQU5Le4FW4v33jN+EUdBr\nTwMU9ozTV9w1K+oOoqXUl8fbaEMQJlwTtreLc/2sxp9bjffTa08HFPaM0Tjj9P/2Trqe1z81YWW4\nRRwE8bSDiGucaXxp8azjotV4P732dEBhzxDOGC9n4svRci9eyb0Zg/KzpnPP5udbyyqxja02BHG3\nBmj3wtjO+0V5KuFGavJQ2DOE2xivz5Z+G5/N34+C1ORZ9+ah0NR2MLTladvOMqkV1rP5+ciXi+id\nCXPFvTDaTO8MStafSrIMC5QyhNu4rkeq78EnS3+ISm4ACqCSG8CulfeiUDKHaJLGVhsCm8VRjUVJ\nhdKJWVF3iNpV0os4uljGCYuWkoUee4ZY3FvB0XLzr/SHPe9GcfGls68PD67HyKEtqe1gaNPTtuV1\nxlWUFJROLPvnRmpyRJ15+nkR+bGIPCsiXxWRAVuGkfCEGeOVhlmbJtI4Li6uoiQvahuLqbh/VNOw\nEJtgz/bkiOqx7wZwh6qWReSzAO4A8GfRzSKtEGaMV9pT9tIW342rKMlEY0xdtAIFIDHdLy64kZoM\nkYRdVb9V8/L7ANLzSexS/MZ4DWzIAePT/0+beKYZ96KkOXijdx4KpUnrC6Nb6EcwPWBbtJq6hdgL\nhmTaj80Y+0cA/G/TF0VkI4CNADC0YIHF25Iw8PG4Ndr9hGMK/YhWce8HjsVyz7hgbnv78RV2EdkD\nYLHLlzar6tdnztkMoAxgu+k6qroVwFYAWLV0qbZkLSEJ0s4nnCyM56uFIZn24ivsqrra6+siMgrg\ngwB+TVUp2IRYIO4BJSTbRM2KWYvpzdIbVPWMHZNIXDC3uHNIY2ZQVPj31z6ixti/COA8ALtFBAC+\nr6r/NbJVhJBMbm4zJNMeombFXGbLEEIIIXZgS4EuYWBDc7sBQpKAIZn4obB3CUxzJGmCjka8UNgJ\nIW2Hjka8UNgJIYnAkEx8UNi7AH6ASFphSCYeKOyEkMRgSCYeKOwZhx4RSTt8orQPhZ0QkjgUd7tQ\n2DMOH3UJ6T4o7BmGYRjSSdBrtweFPcPQWyedBsXdDhR2Qkiq4JNmdCjsGYWeD+lU+KQZHQo7ISR1\n0DGJBoU9g/BDQbIAQzKtQ2EnhKQShmRah8KeMeitp5NlEztw8+OrcPujF+Lmx1dh2cSOpE3qCPj3\n3BpRR+MRQnxYNrGjbjD1+VNHsPq5jwFA5kbfkXQQdZj1p0XkWREZE5FvichFtgwj4WFMMp2MHNoy\nK+oO+coURg5tSciizoJee3iihmI+r6rvUNVhAN8AcKcFm0iLMCaZTvqnJkIdJ81Q3MMRSdhV9fWa\nl/MAaDRzSKvQW08vxcJgqOOERCXy5qmIbBGRnwLYAHrsiUFvPb3sW74ZpVyh7lgpV8C+5ZsTsqgz\nodceHF9hF5E9InLA5d+HAEBVN6vqxQC2A7jV4zobReRJEXnytWLR3ndA+Aefcg4PrseeK+7G64Uh\nKASvF4aw54q7uXFKYkNU7URPROQtAB5V1ZV+565aulS/vZneii0o7KSbGB3blrQJiTF/48anVPVq\nv/OiZsW8reblDQB+HOV6JDyMrRNCGokaY//MTFjmWQDXAdhkwSYSAsbWSbfBJ1R/IhUoqepv2jKE\nEEKIHdhSoIOh50K6FYYgvaGwdyj8wybdDEOQ3lDYOxT+YRNCTFDYOxB664QQLyjsHQi9dUKIFxR2\nQgjJGBR2QgjJGBT2DoMpjoQAm1Y8mLQJqYbCTgjpOCa3V5I2IdVQ2AkhHQW9dX8485QQ0jGMjm3D\n5FjSVqQfeuyEkI6gm9v1hoXCTghJPRT1cDAUQwhJLRT01qDH3mHwD510A6Nj2/i3HgF67ISQVEAh\ntweFvQMZHdvGQiXS8Wxa8SDz0WOCwt6hjI5tw8CGHBuCkY6hUciZthgfFPYOZnJ7BaOg907Sh5s3\nTiFvH1aEXUQ+DuDzABap6ms2rkmC48Qm6cGTduIVE6eIJ0tkYReRiwGsAfBydHNIFBwPHqDIk2g4\nZfuMgXcmNjz2vwbwCQBft3AtYolakXdgyIYE3bCkx93ZiKq2/maRGwD8mqpuEpF/B3C1KRQjIhsB\nbJx5uRLAgZZv3D4WAuiE0BLttEcn2AjQTtt0ip3LVbXf7yRfYReRPQAWu3xpM4BPAbhOVU/5CXvD\nNZ9U1av9zksa2mmXTrCzE2wEaKdtsmanbyhGVVcbbnAFgLcC2C8iADAE4GkReZeqvhrSXkIIIZZo\nOcauqs8BuNB5HcZjJ4QQEh9J9YrZmtB9w0I77dIJdnaCjQDttE2m7Iy0eUoIISR9sLsjIYRkDAo7\nIYRkjMSFXUQ+LiIqIguTtsUNEfm0iDwrImMi8i0RuShpmxoRkc+LyI9n7PyqiAwkbZMbIvJbIjIu\nIlURSV1qmYisFZFDIvKCiHwyaXvcEJF/EJFjIpLqOhARuVhEvi0iB2d+55uStskNEekTkR+KyP4Z\nO/8iaZtMiEhORJ4RkW/4nZuosHdIO4LPq+o7VHUYwDcA3Jm0QS7sBrBSVd8B4DCAOxK2x8QBAL8B\n4LtJG9KIiOQA/A2A6wG8HcDviMjbk7XKla8AWJu0EQEoA/gTVb0cwLsB/FFKf56/AHCtql4JYBjA\nWhF5d8I2mdgE4GCQE5P22J12BKndwVXV12tezkMKbVXVb6lqeebl9zFdU5A6VPWgqh5K2g4D7wLw\ngqq+qKpvAHgIwIcStqkJVf0ugBNJ2+GHqh5V1adn/l/EtCANJmtVMzrNz2de5mf+pe4zLiJDAD4A\n4P4g5ycm7DPtCCZUdX9SNgRFRLaIyE8BbEA6PfZaPgJgV9JGdCCDAH5a8/oIUihEnYiILAWwCsAP\nkrXEnZkQxxiAYwB2q2oa7fwCpp3gapCTY+3HHqQdQZz3D4qXnar6dVXdDGCziNwB4FYAf95WA+Fv\n48w5mzH9CLy9nbbVEsTOlCIux1LnuXUaIvImAP8C4I8bnn5Tg6pWAAzP7E19VURWqmpq9jBE5IMA\njqnqUyJyTZD3xCrsndKOwGSnC/8LwKNIQNj9bBSRUQAfxHRTtsQEKcTPMm0cAXBxzeshAK8kZEsm\nEJE8pkV9u6r+a9L2+KGqkyKyF9N7GKkRdgAjAG4QkXUA+gCcLyIPqqqxL3cioRhVfU5VL1TVpaq6\nFNMfql9OY48ZEXlbzcsbAPw4KVtMiMhaAH8G4AZVPZO0PR3KjwC8TUTeKiJzANwI4JGEbepYZNpj\n+3sAB1X17qTtMSEii5wsMhEpAFiNlH3GVfUOVR2a0cobATzuJepA8punncBnROSAiDyL6dBRGtO2\nvgigH8DumbTMv03aIDdE5NdF5AiAXwHwqIh8M2mbHGY2n28F8E1Mb/Q9rKrjyVrVjIj8E4B/A7Bc\nRI6IyB8kbZOBEQC/B+Damb/JsRmPM20sAfDtmc/3jzAdY/dNJ0w7bClACCEZgx47IYRkDAo7IYRk\nDAo7IYRkDAo7IYRkDAo7IYRkDAo7IYRkDAo7IYRkjP8P3BoOzTEqmHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(log_reg,[-4,4,-4,4])\n",
    "\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": [
    "#### 引入正则化（L2正则化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = PolynomialLogisticRegression(20,0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly', PolynomialFeatures(degree=20, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('log_reg', LogisticRegression(C=0.001, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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eNxP3k5u0YXzOZwO1gP7k/sMYe6cy9f76/0+jb3s97kpND1MxGRc0X2m7o2CQBln13j3v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\nwE5ElDMM7EREOfP/Acucvtt4SszmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(log_reg,[-4,4,-4,4])\n",
    "\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": [
    "#### 引入正则化（L1正则化）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PolynomialLogisticRegression(degree,C,penalty=\"l2\"):\n",
    "    return Pipeline([\n",
    "        (\"poly\",PolynomialFeatures(degree=degree)),\n",
    "        (\"scaler\",StandardScaler()),\n",
    "        (\"log_reg\",LogisticRegression(C=C,penalty=penalty))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = PolynomialLogisticRegression(20,0.1,penalty=\"l1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly', PolynomialFeatures(degree=20, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('log_reg', LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83333333333333337"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85999999999999999"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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dIjsXUdOFgT3lgpxj6giyaGpiOzP1Sq8A4prPrj1lSQAUy8c8g5jXYqXp0Ira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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(log_reg,[-4,4,-4,4])\n",
    "\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": [
    "## 寻找最好的超参数最好使用网格搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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