SVM 非线性决策边界 引入多项式特征.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取非线性数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_moons(noise = 0.15, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZHlV2/nZqZmk2ac1KVjnXGMz6qbrzt2Xq7241B2fvWHFGqjFIOkTSrZIeyX4e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\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": [
    "### 使用管道训练带有多项式特征的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSVMwithMultiFeatures(degree,C=1.0):\n",
    "    return Pipeline([\n",
    "        (\"poly\",PolynomialFeatures(degree=degree)),\n",
    "        (\"standard_scaler\",StandardScaler()),\n",
    "        (\"svmClassifier\",LinearSVC(C=C))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_svc = getSVMwithMultiFeatures(degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('poly', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('standard_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svmClassifier', LinearSVC(C=1.0, 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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_svc.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可视化函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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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FhJL1slhdGXnj+qD5s23lWKWWuIhbIA8cJ6MV78valzlu/2UofqzmkoPbWb3vHkamDzI1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\nQUREAAUEERGJKCCIiAiggCAiIpH/D1Qfvfd+AIk0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lin_svc,[-2,2.5,-1.0,1.5])\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": [
    "## SVM核函数简单使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSVMwithKernel(degree,C=1.0):\n",
    "    return Pipeline([\n",
    "        (\"standard_scaler\",StandardScaler()),\n",
    "        (\"svmClassifier\",SVC(kernel=\"poly\",degree=degree,C=C))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel_svc = getSVMwithKernel(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('standard_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svmClassifier', SVC(C=2, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_svc.fit(X,y)"
   ]
  },
  {
   "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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Y2f3B8282sz0Azrlp4FbgfuAI8FXn3OEYb78zSdky5GO5VKZ4VKb4fCyXyhRP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\nl6S7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(kernel_svc,[-2,2.5,-1.0,1.5])\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}