OneClassSVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 828,
     "status": "ok",
     "timestamp": 1603095638981,
     "user": {
      "displayName": "Rohith Teja",
      "photoUrl": "https://lh3.googleusercontent.com/-nt8x4joQmgY/AAAAAAAAAAI/AAAAAAAAAvg/AbgIIUozOq0/s64/photo.jpg",
      "userId": "01155222072916958278"
     },
     "user_tz": -120
    },
    "id": "_gnuG_VXzTrK"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine, make_blobs,make_classification\n",
    "import matplotlib.pyplot as plt\n",
    "import cvxopt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import pairwise_distances\n",
    "from sklearn import preprocessing\n",
    "from sklearn.svm import OneClassSVM\n",
    "from scipy import linalg\n",
    "from sklearn.metrics.pairwise import rbf_kernel, polynomial_kernel, laplacian_kernel, sigmoid_kernel, linear_kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "id": "8iRP0txQ3ZI8"
   },
   "outputs": [],
   "source": [
    "#loading wine datasets and blob dataset\n",
    "\n",
    "x_wine = load_wine()['data'][:, [1, 2]]\n",
    "x_wine2 = load_wine()['data'][:, [6, 9]] \n",
    "x_blob, y = make_blobs(n_samples=[10,100], n_features=2, cluster_std=1,centers= [(2,3), (2.5,3.5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to save the visualisations of datasets\n",
    "plt.scatter(x_wine[:,0],x_wine[:,1])\n",
    "plt.savefig(\"original xwine\", bbox_inches='tight',dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_wine2[:,0],x_wine2[:,1])\n",
    "plt.savefig(\"original xwine2\", bbox_inches='tight',dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_blob[:,0],x_blob[:,1])\n",
    "plt.savefig(\"original xblob\", bbox_inches='tight',dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "id": "6VCGppidEq0O"
   },
   "outputs": [],
   "source": [
    "#solver using the python library CVXOPT\n",
    "\n",
    "def qp(P, q, A, b, C):\n",
    "    n = P.shape[0]\n",
    "    P = cvxopt.matrix(P)\n",
    "    q = cvxopt.matrix(q)\n",
    "    A = cvxopt.matrix(A)\n",
    "    b = cvxopt.matrix(b)\n",
    "    G = cvxopt.matrix(np.concatenate(\n",
    "        [np.diag(np.ones(n) * -1), np.diag(np.ones(n))], axis=0))\n",
    "    h = cvxopt.matrix(np.concatenate([np.zeros(n), C * np.ones(n)]))\n",
    "    cvxopt.solvers.options['show_progress']=False\n",
    "    solution = cvxopt.solvers.qp(P, q, G, h, A, b, solver='mosec')\n",
    "    return np.ravel(solution['x'])\n",
    "\n",
    "def ocsvm_solver(K, nu=0.1):\n",
    "    n = len(K)\n",
    "    P = K\n",
    "    q = np.zeros(n)\n",
    "    A = np.matrix(np.ones(n))\n",
    "    b = 1.\n",
    "    C = 1. / (nu * n)\n",
    "    mu = qp(P, q, A, b, C)\n",
    "    idx_support = np.where(np.abs(mu) > 1e-5)[0]\n",
    "    mu_support = mu[idx_support]\n",
    "    return mu_support, idx_support\n",
    "\n",
    "def rho(K, mu_support, idx_support):\n",
    "    index = int(np.argmin(mu_support))\n",
    "    K_support = K[idx_support][:, idx_support]\n",
    "    rho1 = mu_support.dot(K_support[index])\n",
    "    return rho1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to plot\n",
    "\n",
    "def plot(X, mu_support, idx_support, rho,kernel,gamma):\n",
    "    X1, X2 = np.meshgrid(np.linspace(0, 6, 500), np.linspace(0, 6, 500))\n",
    "    na, nb = X1.shape\n",
    "    X_test = np.c_[np.reshape(X1, (na * nb, 1)),\n",
    "                   np.reshape(X2, (na * nb, 1))]\n",
    "\n",
    "    X_support = X[idx_support]\n",
    "    if kernel == \"RBF\":\n",
    "        K = rbf_kernel(X_test, X_support,gamma=gamma)\n",
    "    elif kernel == \"Polynomial\":\n",
    "        K = polynomial_kernel(X_test, X_support,gamma=gamma)\n",
    "    elif kernel == \"Sigmoid\":\n",
    "        K = sigmoid_kernel(X_test, X_support,gamma=gamma)\n",
    "    elif kernel == \"Laplacian\":\n",
    "        K = laplacian_kernel(X_test, X_support,gamma=gamma)\n",
    "    elif kernel == \"Linear\":\n",
    "        K = linear_kernel(X_test, X_support)\n",
    "\n",
    "    decision = K.dot(mu_support) - rho\n",
    "    y_pred = np.sign(decision)\n",
    "\n",
    "    # Plot\n",
    "    plt.scatter(X[:,0], X[:, 1], linewidth=2)\n",
    "    Z = np.reshape(decision, (na, nb))\n",
    "    plt.contour(X1, X2, Z,[0], linewidths=2, zorder=10,colors=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to perform experiments\n",
    "\n",
    "def ocsvm(x,kernel,gamma,nuu=0.1):\n",
    "    if kernel == \"RBF\":\n",
    "        K = rbf_kernel(x,gamma=gamma)\n",
    "    elif kernel == \"Polynomial\":\n",
    "        K = polynomial_kernel(x,gamma=gamma)\n",
    "    elif kernel == \"Sigmoid\":\n",
    "        K = sigmoid_kernel(x,gamma=gamma)\n",
    "    elif kernel == \"Laplacian\":\n",
    "        K = laplacian_kernel(x,gamma=gamma)\n",
    "    elif kernel == \"Linear\":\n",
    "        K = linear_kernel(x)\n",
    "        \n",
    "    mu_support, idx_support = ocsvm_solver(K, nu=nuu)\n",
    "    rho1 = rho(K, mu_support, idx_support)\n",
    "    plot(x, mu_support, idx_support, rho1,kernel,gamma)\n",
    "    plt.savefig(\"blob poly\", bbox_inches='tight',dpi = 200)\n",
    "    \n",
    "#test\n",
    "ocsvm(x_blob,\"Polynomial\",2,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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