svm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Muskan Pandey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SVM(object):\n",
    "    def __init__(self,visualization=True):\n",
    "        self.visualization = visualization\n",
    "        self.colors = {1:'r',-1:'b'}\n",
    "        if self.visualization:\n",
    "            self.fig = plt.figure()\n",
    "            self.ax = self.fig.add_subplot(1,1,1)\n",
    "    \n",
    "    def fit(self,data):\n",
    "        #train with data\n",
    "        self.data = data\n",
    "        # { |\\w\\|:{w,b}}\n",
    "        opt_dict = {}\n",
    "        \n",
    "        transforms = [[1,1],[-1,1],[-1,-1],[1,-1]]\n",
    "        \n",
    "        all_data = np.array([])\n",
    "        for yi in self.data:\n",
    "            all_data = np.append(all_data,self.data[yi])\n",
    "                    \n",
    "        self.max_feature_value = max(all_data)         \n",
    "        self.min_feature_value = min(all_data)\n",
    "        all_data = None\n",
    "        \n",
    "        #with smaller steps our margins and db will be more precise\n",
    "        step_sizes = [self.max_feature_value * 0.1,\n",
    "                      self.max_feature_value * 0.01,\n",
    "                      #point of expense\n",
    "                      self.max_feature_value * 0.001,]\n",
    "        \n",
    "        #extremly expensise\n",
    "        b_range_multiple = 5\n",
    "        #we dont need to take as small step as w\n",
    "        b_multiple = 5\n",
    "        \n",
    "        latest_optimum = self.max_feature_value*10\n",
    "        \n",
    "        \"\"\"\n",
    "        objective is to satisfy yi(x.w)+b>=1 for all training dataset such that ||w|| is minimum\n",
    "        for this we will start with random w, and try to satisfy it with making b bigger and bigger\n",
    "        \"\"\"\n",
    "        #making step smaller and smaller to get precise value\n",
    "        for step in step_sizes:\n",
    "            w = np.array([latest_optimum,latest_optimum])\n",
    "            \n",
    "            #we can do this because convex\n",
    "            optimized = False\n",
    "            while not optimized:\n",
    "                for b in np.arange(-1*self.max_feature_value*b_range_multiple,\n",
    "                                   self.max_feature_value*b_range_multiple,\n",
    "                                   step*b_multiple):\n",
    "                    for transformation in transforms:\n",
    "                        w_t = w*transformation\n",
    "                        found_option = True\n",
    "                        \n",
    "                        #weakest link in SVM fundamentally\n",
    "                        #SMO attempts to fix this a bit\n",
    "                        # ti(xi.w+b) >=1\n",
    "                        for i in self.data:\n",
    "                            for xi in self.data[i]:\n",
    "                                yi=i\n",
    "                                if not yi*(np.dot(w_t,xi)+b)>=1:\n",
    "                                    found_option=False\n",
    "                        if found_option:\n",
    "                            \"\"\"\n",
    "                            all points in dataset satisfy y(w.x)+b>=1 for this cuurent w_t, b\n",
    "                            then put w,b in dict with ||w|| as key\n",
    "                            \"\"\"\n",
    "                            opt_dict[np.linalg.norm(w_t)]=[w_t,b]\n",
    "                \n",
    "                #after w[0] or w[1]<0 then values of w starts repeating itself because of transformation\n",
    "                #Think about it, it is easy\n",
    "                #print(w,len(opt_dict)) Try printing to understand\n",
    "                if w[0]<0:\n",
    "                    optimized=True\n",
    "                    print(\"optimized a step\")\n",
    "                else:\n",
    "                    w = w-step\n",
    "                    \n",
    "            # sorting ||w|| to put the smallest ||w|| at poition 0 \n",
    "            norms = sorted([n for n in opt_dict])\n",
    "            #optimal values of w,b\n",
    "            opt_choice = opt_dict[norms[0]]\n",
    "\n",
    "            self.w=opt_choice[0]\n",
    "            self.b=opt_choice[1]\n",
    "            \n",
    "            #start with new latest_optimum (initial values for w)\n",
    "            latest_optimum = opt_choice[0][0]+step*2\n",
    "    \n",
    "    def predict(self,features):\n",
    "        #sign(x.w+b)\n",
    "        classification = np.sign(np.dot(np.array(features),self.w)+self.b)\n",
    "        if classification!=0 and self.visualization:\n",
    "            self.ax.scatter(features[0],features[1],s=200,marker='*',c=self.colors[classification])\n",
    "        return (classification,np.dot(np.array(features),self.w)+self.b)\n",
    "    \n",
    "    def visualize(self):\n",
    "        [[self.ax.scatter(x[0],x[1],s=100,c=self.colors[i]) for x in data_dict[i]] for i in data_dict]\n",
    "        \n",
    "        # hyperplane = x.w+b (actually its a line)\n",
    "        # v = x0.w0+x1.w1+b -> x1 = (v-w[0].x[0]-b)/w1\n",
    "        #psv = 1     psv line ->  x.w+b = 1a small value of b we will increase it later\n",
    "        #nsv = -1    nsv line ->  x.w+b = -1\n",
    "        # dec = 0    db line  ->  x.w+b = 0\n",
    "        def hyperplane(x,w,b,v):\n",
    "            #returns a x2 value on line when given x1\n",
    "            return (-w[0]*x-b+v)/w[1]\n",
    "       \n",
    "        hyp_x_min= self.min_feature_value*0.9\n",
    "        hyp_x_max = self.max_feature_value*1.1\n",
    "        \n",
    "        # (w.x+b)=1\n",
    "        # positive support vector hyperplane\n",
    "        pav1 = hyperplane(hyp_x_min,self.w,self.b,1)\n",
    "        pav2 = hyperplane(hyp_x_max,self.w,self.b,1)\n",
    "        self.ax.plot([hyp_x_min,hyp_x_max],[pav1,pav2],'k')\n",
    "        \n",
    "        # (w.x+b)=-1\n",
    "        # negative support vector hyperplane\n",
    "        nav1 = hyperplane(hyp_x_min,self.w,self.b,-1)\n",
    "        nav2 = hyperplane(hyp_x_max,self.w,self.b,-1)\n",
    "        self.ax.plot([hyp_x_min,hyp_x_max],[nav1,nav2],'k')\n",
    "        \n",
    "        # (w.x+b)=0\n",
    "        # db support vector hyperplane\n",
    "        db1 = hyperplane(hyp_x_min,self.w,self.b,0)\n",
    "        db2 = hyperplane(hyp_x_max,self.w,self.b,0)\n",
    "        self.ax.plot([hyp_x_min,hyp_x_max],[db1,db2],'y--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a basic data\n",
    "data_dict = {-1:np.array([[1,7],[2,8],[3,8]]),1:np.array([[5,1],[6,-1],[7,3]])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimized a step\n",
      "optimized a step\n",
      "optimized a step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "svm = SVM() # Linear Kernel\n",
    "svm.fit(data=data_dict)\n",
    "svm.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, -1.000000000000098)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.predict([3,8])"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}