microsoft_algorithm_clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import time\n",
    "import sklearn\n",
    "import sklearn.metrics\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import cross_validation as cv\n",
    "import sklearn.preprocessing\n",
    "%matplotlib inline\n",
    "tol = 10**(-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def kernel_expo(x,y,gamma):\n",
    "    return np.exp(-gamma*(np.linalg.norm(x-y)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def KKTviol(O,rho,mu,l,alpha,ens):\n",
    "    ens2 = np.where(np.logical_or((rho-O[ens])*(1/(mu*l)-alpha[ens])>tol, (O[ens]-rho)*alpha[ens]>tol))\n",
    "    if len(ens2[0]) == 0:\n",
    "        return -1\n",
    "    else :\n",
    "        index = np.random.randint(len(ens2[0]))\n",
    "        return ens[ens2[0][index]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def argmax(O,i,ens):\n",
    "    index = 0\n",
    "    value = 0\n",
    "    ens = list(ens)\n",
    "    Oens = O[ens]\n",
    "    return ens[np.argmax(O[i]-Oens)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def optim(alpha,i,j,K,O,mu,l):\n",
    "    delta = alpha[i]+alpha[j]\n",
    "    Ci = O[i] - alpha[i]*K[i,i] -alpha[j]*K[i,j]\n",
    "    Cj = O[j] - alpha[i]*K[i,j] -alpha[j]*K[j,j]\n",
    "    # Condition droite (inutile dans le cas du noyau gaussien)\n",
    "    if K[i,i]+K[j,j]-2*K[i,j] == 0:\n",
    "        if Cj-Ci-K[i,i]>0:\n",
    "            alphaj = max(0,delta-1/(l*mu))\n",
    "            alphai = delta - alphaj\n",
    "        elif Cj-Ci-K[i,i] <0:\n",
    "            alphaj = min(1/(l*mu),delta)\n",
    "            alphai = delta - alphaj\n",
    "        else :\n",
    "            alphaj = alpha[j]\n",
    "            alphai = alpha[i]\n",
    "    else:\n",
    "        xmin =  (delta*(K[i,i]-K[i,j])+Ci-Cj)/( K[i,i]+K[j,j]-2*K[i,j])\n",
    "        \n",
    "        # condition si le min n'est pas atteint dans 0 1/(l*mu)\n",
    "        # condition sur K inutile si noyau gaussien\n",
    "        if K[i,i]+K[j,j]-2*K[i,j] < 0 or xmin <max(0,delta-1/(mu*l)) or xmin > min(1/(mu*l),delta):\n",
    "            x0 = max(0,delta-1/(l*mu))\n",
    "            fx0  = 1/2*(delta-x0)**2*K[i,i] + (delta-x0)*K[i,j] + 1/2*x0**2*K[j,j] + (delta-x0)*Ci + x0*Cj\n",
    "            xmu = min(1/(l*mu),delta)\n",
    "            fxmu  = 1/2*(delta-xmu)**2*K[i,i] + (delta-xmu)*K[i,j] + 1/2*xmu**2*K[j,j] + (delta-xmu)*Ci + xmu*Cj\n",
    "            if fx0 > fxmu:\n",
    "                alphaj = xmu\n",
    "                alphai = delta - alphaj\n",
    "            else :\n",
    "                alphaj = x0\n",
    "                alphai = delta - alphaj\n",
    "        else :\n",
    "            alphaj = xmin\n",
    "            alphai = delta - alphaj\n",
    "\n",
    "    alphai = delta - alphaj\n",
    "    \n",
    "    if alpha[i]+alpha[j] - (alphai + alphaj)>10**(-8):\n",
    "        print(\"delta : \",alpha[i]+alpha[j], alphai + alphaj)\n",
    "        print(alpha[i],alpha[j],alphai,alphaj)\n",
    "    if alphai<0 or alphaj <0 or alphai >1/(mu*l) or alphaj >1/(mu*l):\n",
    "        print(\"oups\",alphai,alphaj)\n",
    "    return alphai,alphaj\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def SVcalc(alpha,c=1):\n",
    "    SV = list(np.where(((alpha>tol) & (alpha<(c-tol))))[0])\n",
    "    return SV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class osvm:\n",
    "    def __init__(self,mu=0.01,maxiter=1000,maxiterii = 100,eps=0.001,gamma=5):\n",
    "        assert mu>0,\"mu doit être strictement positif\"\n",
    "        assert mu<=1,\"mu doit être inférieur à 1\"\n",
    "        self.mu = mu\n",
    "        self.eps = eps\n",
    "        self.maxiter = maxiter\n",
    "        self.maxiterii = maxiterii\n",
    "        self.gamma = gamma\n",
    "        self.len_sv = []\n",
    "        self.rho_hist = []\n",
    "        self.rho_var = []\n",
    "        self.kkt_hist = []\n",
    "        \n",
    "    def fit(self,data):\n",
    "        print(\"Initialisation\")\n",
    "        self.alpha = np.zeros(np.shape(data)[0])\n",
    "        self.l = np.shape(data)[0] \n",
    "        l = self.l\n",
    "        self.data = data\n",
    "        K = np.array([[kernel_expo(x,y,self.gamma) for x in data] for y in data])\n",
    "        choose = [i for i in range(l)]\n",
    "        self.rho = 0\n",
    "        s = 0\n",
    "        while s <= 1-1/(l*self.mu):\n",
    "            index = np.random.randint(len(choose))\n",
    "            elem = choose[index]\n",
    "            choose.remove(elem)\n",
    "            self.alpha[elem] = 1/(l*self.mu)\n",
    "            s += 1/(l*self.mu)\n",
    "        if s != 1:\n",
    "            index = np.random.randint(len(choose))\n",
    "            elem = choose[index]\n",
    "            choose.remove(elem)\n",
    "            self.alpha[elem] = 1-s\n",
    "            s = 1\n",
    "\n",
    "        O = np.dot(self.alpha,K)\n",
    "        self.rho = max(O[self.alpha>tol])\n",
    "        self.rho_hist.append(self.rho)\n",
    "        self.rho_var.append(np.var(max(O[self.alpha>tol])))\n",
    "\n",
    "        print(\"Apprentissage\")\n",
    "        iter_etape = 0\n",
    "        iteration = 0\n",
    "        while iteration < self.maxiter:\n",
    "            # Etape (i)\n",
    "            enskkt = list(range(l))\n",
    "            np.random.shuffle(enskkt)\n",
    "            index = KKTviol(O,self.rho,self.mu,l,self.alpha,enskkt)\n",
    "            iteration += 1\n",
    "            if index == -1:\n",
    "                print(iteration,\"break index\")\n",
    "                break\n",
    "\n",
    "            if iteration == 1 :\n",
    "                SV = SVcalc(self.alpha)\n",
    "            else:\n",
    "                SV = SVcalc(self.alpha,1/(self.mu*l))\n",
    "            \n",
    "            while index in SV:\n",
    "                    SV.remove(index)\n",
    "            if SV == []:\n",
    "                SV = SVcalc(self.alpha)\n",
    "                while index in SV:\n",
    "                    SV.remove(index)\n",
    "            if SV ==[]:\n",
    "                continue\n",
    "            \n",
    "            j = argmax(O,index,SV)\n",
    "            alpha_index,alpha_j = self.alpha[index],self.alpha[j]\n",
    "            self.alpha[index],self.alpha[j] = optim(self.alpha,index,j,K,O,self.mu,l)\n",
    "            if sum(self.alpha) > 1.001:\n",
    "                print(\"etape i \" ,sum(self.alpha))\n",
    "                time.sleep(0.5)\n",
    "            \n",
    "            # update de rho\n",
    "            O += K[index,:]*(self.alpha[index]-alpha_index) + K[j,:]*(self.alpha[j]-alpha_j)\n",
    "            O_actif = O[ ((self.alpha>tol) & (self.alpha< (1/(l*self.mu)-tol)))]\n",
    "            if len(O_actif)==0:\n",
    "                self.rho = max(O[ (self.alpha>tol )]) \n",
    "            else:\n",
    "                self.rho = np.mean(O_actif)\n",
    "\n",
    "            self.rho_hist.append(self.rho)\n",
    "            self.rho_var.append(np.var(O_actif))\n",
    "            SV = SVcalc(self.alpha,1/(self.mu*l))\n",
    "            \n",
    "            # Etape (ii)\n",
    "            np.random.shuffle(SV)\n",
    "            index = KKTviol(O,self.rho,self.mu,l,self.alpha,SV)\n",
    "            self.kkt_hist.append(len(np.where((self.rho-O[SV])*(1/(self.mu*l)-self.alpha[SV])>tol)[0])+ \n",
    "                                                            len(np.where((O[SV]-self.rho)*self.alpha[SV]>tol)[0]))\n",
    "            iterii = 0\n",
    "            while index !=-1 and len(SV)>=1 and iterii < self.maxiterii:\n",
    "                iterii += 1\n",
    "                while index in SV:\n",
    "                    SV.remove(index)\n",
    "                    \n",
    "                if SV != []:\n",
    "                    iter_etape += 1\n",
    "                    j = argmax(O,index,SV)\n",
    "                    Osv = O[index] - O\n",
    "                    delta = self.alpha[index] + self.alpha[j]\n",
    "                    alpha_index,alpha_j = self.alpha[index],self.alpha[j]\n",
    "                    self.alpha[index] , self.alpha[j] = optim(self.alpha,index,j,K,O,self.mu,l)\n",
    "\n",
    "                    O += K[index,:]*(self.alpha[index]-alpha_index) + K[j,:]*(self.alpha[j]-alpha_j)\n",
    "                    O_actif = O[ ((self.alpha>tol) & (self.alpha< (1/(l*self.mu)-tol)))]\n",
    "                    if len(O_actif) == 0:\n",
    "                        self.rho = max(O[ (self.alpha>tol )]) \n",
    "                    else:\n",
    "                        self.rho = np.mean(O_actif)\n",
    "                    self.rho_hist.append(self.rho)\n",
    "                    self.rho_var.append(np.var(O_actif))\n",
    "                    self.O = O\n",
    "                    self.K = K\n",
    "                    SV = SVcalc(self.alpha,1/(self.mu*l))\n",
    "                else:\n",
    "                    continue\n",
    "                    \n",
    "                \n",
    "                if len(SV) == 1:\n",
    "                    print(\"len sv break\")\n",
    "                    break\n",
    "                np.random.shuffle(SV)   \n",
    "                index = KKTviol(O,self.rho,self.mu,l,self.alpha,SV)\n",
    "                self.kkt_hist.append(len(np.where((self.rho-O[SV])*(1/(self.mu*l)-self.alpha[SV])>tol)[0])+ \n",
    "                                                            len(np.where((O[SV]-self.rho)*self.alpha[SV]>tol)[0]))\n",
    "                if index == -1:\n",
    "                    break\n",
    "                while index in SV:\n",
    "                    SV.remove(index)\n",
    "                \n",
    "            if iteration%100 == 0 :\n",
    "                print(iter_etape,iteration,self.maxiter,self.rho,np.dot(self.alpha,np.dot(self.alpha,K)))\n",
    "                iter_etape = 0 \n",
    "            \n",
    "        # update rho\n",
    "        O_actif = O[ ((self.alpha>tol) & (self.alpha< (1/(l*self.mu)-tol)))]\n",
    "        if len(O_actif) == 0:\n",
    "            print(\"rho out\")\n",
    "            self.rho = max(O[ (self.alpha>tol )]) \n",
    "        else:\n",
    "            self.rho = np.mean(O_actif)\n",
    "    \n",
    "    def predict(self,xi):\n",
    "        K = np.array([[kernel_expo(x,xie,self.gamma) for xie in xi ]for x in self.data])\n",
    "        return np.sign(np.dot(self.alpha,K)-self.rho)\n",
    "    \n",
    "    def decision_function(self,xi):\n",
    "        K = np.array([[kernel_expo(x,xie,self.gamma) for xie in xi ]for x in self.data])\n",
    "        return np.dot(self.alpha,K)-self.rho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apprentissage sur uniforme [-1;1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = 2*(-0.5 + np.random.rand(500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "4733 100 1000 0.483733403117 0.483733403081\n",
      "5000 200 1000 0.483733403117 0.483733403081\n",
      "5000 300 1000 0.483733403117 0.483733403081\n",
      "5000 400 1000 0.483733403117 0.483733403081\n",
      "5000 500 1000 0.483733403117 0.483733403081\n",
      "5000 600 1000 0.483733403117 0.483733403081\n",
      "5000 700 1000 0.483733403117 0.483733403081\n",
      "5000 800 1000 0.483733403117 0.483733403081\n",
      "5000 900 1000 0.483733403117 0.483733403081\n",
      "5000 1000 1000 0.483733403117 0.483733403081\n"
     ]
    }
   ],
   "source": [
    "test = osvm(mu=10**(-3),gamma = 1,maxiter=1000,maxiterii=50)\n",
    "test.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "qjqjql7J5P+hW6rqF4FPs8ljA0iytfuNkiTHARcDd9PA1w6ga708lOTV3ak3AfcwdnwLWEzYwWRn\n",
       "xv3A1UMtYqxzrh8DHgGeAb4JXAGcCHyui2En8H1T17+byUr2fuDiqfM/yuSb837gg1PnjwH+pjt/\n",
       "G3DWgLFdCDzPZMfSXuDO7mtzUiPx/VAX0z7gLuC3u/NNxDc1h5/kOwuqTcTGpCe98n1590qeaCW+\n",
       "bvwfYVLs7gM+yWS3zKjxeROTJDXIBVVJapDJXZIaZHKXpAaZ3CWpQSZ3SWqQyV2SGmRyl6QGmdwl\n",
       "qUH/B8syHR+D9K6yAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbb6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test.kkt_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "g54Y3HnA/Yy+WXEA+K1u+YXAV8fqtnY1C8CtY8vPAj4HPAJ8m7E/JTLrV9+xTezrh6y+b0P1PXcL\n",
       "jB7s/G73+sdZj2mpfhn9UvKnYzV7uh/c7wNXncy5nPVrBeP73W7ZG4Fnuzep73XnbOusxzPUuRtb\n",
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       "C0lSk2EhSWr6P/UY1NlbQPhoAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x552e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test.len_sv)\n",
    "#plt.plot(test.lenOsv,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x55d2518>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "/hbxoshaBnei7AWuXaz9nkSdHwbuB/4X+AZwFXAm8Jmu/m3AU4bmX8fgqv0u4PKh8V9k8AO8F3j3\n",
       "0PjjgY9247cB5y1yf5cCRxjcobUDuKP73py11HsEfr7rZydwF/Bn3fiS722eXl/KTy4MT0V/DM6Z\n",
       "H/25vPvo+8S09Nft//kMPhTvBG5kcHfQRPvzYTFJapgXhiWpYYaAJDXMEJCkhhkCktQwQ0CSGmYI\n",
       "SFLDDAFJapghIEkN+38y9bbpvfCihwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5580f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test.rho_hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.18073172,  0.40974696,  0.40952132])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.alpha[test.alpha>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x5dda0f0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "MAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x55eef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(-2,2,500)\n",
    "y = test.predict(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x5655ac8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "zAb4/yX4aly7RQhDAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbae3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = test.decision_function(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5668b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(-2,2,1000)\n",
    "#fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(1,2)\n",
    "f.set_size_inches(10, 4)\n",
    "ax1.plot(x,test.predict(x))\n",
    "ax2.plot(x,test.decision_function(x))\n",
    "ax2.plot(x,np.zeros((np.shape(x))),'r')\n",
    "f.suptitle(\"Prédiction de [-2;2], ensemble d'apprentissage [-1;1]\")\n",
    "f.savefig(\"predict_microsoft.pdf\",format = 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xtest=np.linspace(-1,1,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "993.0 498.0\n"
     ]
    }
   ],
   "source": [
    "nb = sum(0.5*(1+test.predict(xtest)))\n",
    "nbdata = sum(0.5*(1+test.predict(data)))\n",
    "print(nb,nbdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apprentissage sur  uniforme $[-2,-1.7]^2 \\cup [2,2.3]^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "1 100 2000 0.510824618897 0.499486660769\n",
      "602 200 2000 0.493915754309 0.489155459909\n",
      "1961 300 2000 0.485862295112 0.470441375183\n",
      "0 400 2000 0.485862295112 0.470441375183\n",
      "0 500 2000 0.485862295112 0.470441375183\n",
      "0 600 2000 0.485862295112 0.470441375183\n",
      "0 700 2000 0.485862295112 0.470441375183\n",
      "0 800 2000 0.485862295112 0.470441375183\n",
      "0 900 2000 0.485862295112 0.470441375183\n",
      "0 1000 2000 0.485862295112 0.470441375183\n",
      "0 1100 2000 0.485862295112 0.470441375183\n",
      "0 1200 2000 0.485862295112 0.470441375183\n",
      "0 1300 2000 0.485862295112 0.470441375183\n",
      "0 1400 2000 0.485862295112 0.470441375183\n",
      "0 1500 2000 0.485862295112 0.470441375183\n",
      "0 1600 2000 0.485862295112 0.470441375183\n",
      "0 1700 2000 0.485862295112 0.470441375183\n",
      "0 1800 2000 0.485862295112 0.470441375183\n",
      "0 1900 2000 0.485862295112 0.470441375183\n",
      "0 2000 2000 0.485862295112 0.470441375183\n",
      "rho out\n"
     ]
    }
   ],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))\n",
    "# Generate train data\n",
    "X = 0.3 * np.random.randn(100, 2)\n",
    "X_train = np.r_[X + 2, X - 2]\n",
    "# Generate some regular novel observations\n",
    "X = 0.3 * np.random.randn(20, 2)\n",
    "X_test = np.r_[X + 2, X - 2]\n",
    "# Generate some abnormal novel observations\n",
    "X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))\n",
    "\n",
    "# fit the model\n",
    "clf = osvm(mu=0.07,gamma = 0.1,maxiter=2000,maxiterii=100)\n",
    "clf.fit(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5, 5, 100))\n",
    "y_pred_train = clf.predict(X_train)\n",
    "y_pred_test = clf.predict(X_test)\n",
    "y_pred_outliers = clf.predict(X_outliers)\n",
    "n_error_train = y_pred_train[y_pred_train == -1].size\n",
    "n_error_test = y_pred_test[y_pred_test == -1].size\n",
    "n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size\n",
    "\n",
    "# plot the line, the points, and the nearest vectors to the plane\n",
    "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "TkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5e085c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "f.set_size_inches(8, 5)\n",
    "plt.title(\"Novelty Detection\")\n",
    "plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.Blues_r)\n",
    "a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='red')\n",
    "plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='orange')\n",
    "\n",
    "b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')\n",
    "b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')\n",
    "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')\n",
    "plt.axis('tight')\n",
    "plt.xlim((-5, 5))\n",
    "plt.ylim((-5, 5))\n",
    "plt.legend([a.collections[0], b1, b2, c],\n",
    "           [\"learned frontier\", \"training observations\",\n",
    "            \"new regular observations\", \"new abnormal observations\"],\n",
    "           loc=\"upper left\",\n",
    "           prop=matplotlib.font_manager.FontProperties(size=11))\n",
    "plt.xlabel(\n",
    "    \"error train: %d/200 ; errors novel regular: %d/40 ; \"\n",
    "    \"errors novel abnormal: %d/40\"\n",
    "    % (n_error_train, n_error_test, n_error_outliers))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f.savefig(\"predict_2d_microsoft_nu_faible.pdf\",format = 'pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test sur les données USPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_usps(filename):\n",
    "    with open(filename ,\"r\") as f:\n",
    "        f.readline()\n",
    "        data = [[float(x) for x in l.split()] for l in f if len(l.split())>2]\n",
    "    tmp = np.array(data)\n",
    "    return tmp[:,1:],tmp[:,0].astype(int)\n",
    "datax,y = load_usps(\"usps.txt\")\n",
    "#plt.imshow(datax[1].reshape((16,16)),interpolation=\"nearest\")\n",
    "\n",
    "data_entier = [[] for i in range(10)]\n",
    "for entier in range(10):\n",
    "    data_entier[entier] = datax[ y == entier]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usps_osvm = osvm(mu=0.2,gamma = 1/(0.5*256),maxiter=3000,maxiterii=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "len sv break\n",
      "len sv break\n",
      "2 100 3000 0.328710409869 0.279770371634\n",
      "len sv break\n",
      "len sv break\n",
      "546 200 3000 0.307859370908 0.263736615181\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "755 300 3000 0.310048644522 0.254919893992\n",
      "17739 400 3000 0.308476445326 0.254404424567\n",
      "18792 500 3000 0.303910316158 0.249639354139\n",
      "len sv break\n",
      "21552 600 3000 0.292326132096 0.246104547427\n",
      "15673 700 3000 0.290031596393 0.243201689725\n",
      "23985 800 3000 0.290218076687 0.242880310718\n",
      "24239 900 3000 0.284263966827 0.237859706727\n",
      "25917 1000 3000 0.27959544623 0.234951786364\n",
      "15790 1100 3000 0.27771192939 0.234609341712\n",
      "27900 1200 3000 0.274789236276 0.23417858983\n",
      "28072 1300 3000 0.273157763163 0.23387314746\n",
      "25510 1400 3000 0.269067681449 0.233166985692\n",
      "24007 1500 3000 0.266447061416 0.232473831\n",
      "18972 1600 3000 0.265542940338 0.231270610266\n",
      "29191 1700 3000 0.26222497259 0.230598589567\n",
      "12948 1800 3000 0.260476255555 0.229945387731\n",
      "14293 1900 3000 0.255911734765 0.228791597808\n",
      "19944 2000 3000 0.254634560588 0.227459226888\n",
      "16917 2100 3000 0.251703956281 0.226416093936\n",
      "21510 2200 3000 0.250712425553 0.225308758714\n",
      "29422 2300 3000 0.249546211851 0.22242752355\n",
      "21822 2400 3000 0.248397496672 0.221852787084\n",
      "28854 2500 3000 0.248025753599 0.221646995157\n",
      "29700 2600 3000 0.248208416993 0.221620020962\n",
      "30000 2700 3000 0.2479916767 0.221586158223\n",
      "30000 2800 3000 0.24763331055 0.221496369242\n",
      "30000 2900 3000 0.247696075543 0.221491930908\n",
      "30000 3000 3000 0.247696075543 0.221491930908\n"
     ]
    }
   ],
   "source": [
    "data_train_6,data_test_6=cv.train_test_split(data_entier[6],test_size=0.3)\n",
    "usps_osvm.fit(data_train_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "q3duAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba64e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(1,2)\n",
    "f.set_size_inches(10, 4)\n",
    "ax1.plot(usps_osvm.rho_hist[::10])\n",
    "ax2.plot(np.sqrt(usps_osvm.rho_var[::200]))\n",
    "f.suptitle(\"rho et son écart type\",fontsize=14, fontweight='bold')\n",
    "#f.savefig(\"rho.pdf\",format = 'pdf')\n",
    "#plt.plot(usps_osvm.rho_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction sur l'ensemble test :  0.54\n"
     ]
    }
   ],
   "source": [
    "is_6_a_6_test = sum(0.5*(1+usps_osvm.predict(data_test_6)))\n",
    "print(\"Prediction sur l'ensemble test : \",is_6_a_6_test/len(data_test_6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.536144578313\n"
     ]
    }
   ],
   "source": [
    "is_9_a_6 = sum(0.5*(1+usps_osvm.predict(data_entier[9])))\n",
    "is_6_a_6 = sum(0.5*(1+usps_osvm.predict(data_entier[6])))\n",
    "print(is_9_a_6,is_6_a_6/len(data_entier[6]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Plus précis ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "usps_osvm_precis = osvm(mu=0.000002,gamma = 1/(0.5*256),maxiter=1000,maxiterii=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "9800 100 1000 0.186744699465 0.186751282949\n",
      "10000 200 1000 0.169090700908 0.169075600309\n",
      "10000 300 1000 0.168149699463 0.168149641203\n",
      "10000 400 1000 0.166282466314 0.166282665741\n",
      "10000 500 1000 0.166036140962 0.16603613615\n",
      "10000 600 1000 0.166022389032 0.16602237262\n",
      "10000 700 1000 0.159980558679 0.159958416154\n",
      "10000 800 1000 0.159835234274 0.159835234029\n",
      "10000 900 1000 0.159817379898 0.159817372762\n",
      "10000 1000 1000 0.159817386555 0.159817361865\n"
     ]
    }
   ],
   "source": [
    "usps_osvm_precis.fit(data_train_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction sur l'ensemble test: 0.82\n",
      "Prediction sur la classe 9 0.0 Prediction sur l'ensemble de la classe 6 0.871987951807\n"
     ]
    }
   ],
   "source": [
    "is_6_a_6_test_precis = sum(0.5*(1+usps_osvm.predict(data_test_6)))\n",
    "print(\"Prediction sur l'ensemble test:\",is_6_a_6_test_precis/len(data_test_6))\n",
    "\n",
    "is_9_a_6_precis = sum(0.5*(1+usps_osvm_precis.predict(data_entier[9])))\n",
    "is_6_a_6_precis = sum(0.5*(1+usps_osvm_precis.predict(data_entier[6])))\n",
    "print(\"Prediction sur la classe 9\",is_9_a_6_precis, \"Prediction sur l'ensemble de la classe 6\",is_6_a_6_precis/len(data_entier[6]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Test sur les troubles du rythme cardiaque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def load_cardiac(filename):\n",
    "    with open(filename ,\"r\") as f:\n",
    "        f.readline()\n",
    "        data = [[float(x) for x in l.split()] for l in f]\n",
    "    tmp = np.array(data)\n",
    "    return tmp[:,:-1],tmp[:,-1].astype(int)\n",
    "\n",
    "data_card,y_card = load_cardiac(\"card_clean.txt\")\n",
    "data_card = sklearn.preprocessing.scale(data_card,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_card_ok = data_card[y_card == 1]\n",
    "data_card_dead = data_card[y_card != 1]\n",
    "data_card_train,data_card_test=cv.train_test_split(data_card_ok,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "card_osvm = osvm(mu=0.03,gamma = 0.95*10**(-2),maxiter=1000,maxiterii=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "9835 100 1000 0.0428295610064 0.0428241930494\n",
      "10000 200 1000 0.0330729666116 0.0330632201944\n",
      "10000 300 1000 0.0303577092649 0.0303621204099\n",
      "10000 400 1000 0.030257651186 0.0302576560725\n",
      "10000 500 1000 0.0297699352189 0.0297699241792\n",
      "10000 600 1000 0.029468884948 0.0294688801156\n",
      "10000 700 1000 0.029468884948 0.0294688801156\n",
      "10000 800 1000 0.029468884948 0.0294688801156\n",
      "10000 900 1000 0.029468884948 0.0294688801156\n",
      "10000 1000 1000 0.029468884948 0.0294688801156\n"
     ]
    }
   ],
   "source": [
    "card_osvm.fit(data_card_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test données entrainement :  0.764102564103\n",
      "Test données validation :  0.69387755102\n",
      "Test données ok:  0.75\n",
      "Test données dead:  0.281553398058\n"
     ]
    }
   ],
   "source": [
    "print(\"Test données entrainement : \", sum(0.5*(1+card_osvm.predict(data_card_train)))/len(data_card_train))\n",
    "print(\"Test données validation : \", sum(0.5*(1+card_osvm.predict(data_card_test)))/len(data_card_test))\n",
    "print(\"Test données ok: \", sum(0.5*(1+card_osvm.predict(data_card_ok)))/len(data_card_ok))\n",
    "print(\"Test données dead: \", sum(0.5*(1+card_osvm.predict(data_card_dead)))/len(data_card_dead))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Yahoo\n",
    "http://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.02326715,  0.        ,  0.        ,  0.02009537,  0.00625608,\n",
       "        0.00828821,  0.0073981 ,  0.01432296,  0.        ,  0.01558495,\n",
       "        0.0110989 ,  0.00745983,  0.00130038,  0.        ,  0.00488351,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.00027705,\n",
       "        0.00203914,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.01635565,  0.01529841,  0.00440153,  0.01150863,  0.01187373,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.00485726,  0.        ,  0.        ,  0.01971075,  0.        ,\n",
       "        0.0016465 ,  0.        ,  0.        ,  0.        ,  0.00186744,\n",
       "        0.014914  ,  0.        ,  0.        ,  0.01177404,  0.        ,\n",
       "        0.02197333,  0.        ,  0.01835562,  0.        ,  0.        ,\n",
       "        0.01026658,  0.        ,  0.        ,  0.01067953,  0.0037196 ,\n",
       "        0.00983698,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.01003382,  0.00851804,  0.01421792,  0.00142653,  0.00590009,\n",
       "        0.01850831,  0.01908747,  0.00672811,  0.0024991 ,  0.        ,\n",
       "        0.        ,  0.00290203,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.00095136,  0.        ,\n",
       "        0.        ,  0.00111066,  0.01334151,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.00147696,  0.01501152,\n",
       "        0.00457714,  0.01827837,  0.00353161,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.00177468,  0.        ,  0.00273806,  0.        ,  0.00715989,\n",
       "        0.        ,  0.        ,  0.        ,  0.00599918,  0.00847768,\n",
       "        0.        ,  0.01230334,  0.01436266,  0.        ,  0.01956444,\n",
       "        0.00382904,  0.0115155 ,  0.00453083,  0.00358323,  0.02045957,\n",
       "        0.        ,  0.        ,  0.        ,  0.01528247,  0.0029739 ,\n",
       "        0.        ,  0.00606064,  0.02527364,  0.        ,  0.        ,\n",
       "        0.01349358,  0.00405983,  0.0162031 ,  0.        ,  0.01488598,\n",
       "        0.01230176,  0.01731678,  0.00155887,  0.        ,  0.        ,\n",
       "        0.        ,  0.00660328,  0.        ,  0.        ,  0.01415752,\n",
       "        0.        ,  0.        ,  0.00029946,  0.00626755,  0.01332401,\n",
       "        0.02201008,  0.00934343,  0.        ,  0.        ,  0.02108787,\n",
       "        0.        ,  0.02231068,  0.        ,  0.00970601,  0.00392538,\n",
       "        0.        ,  0.        ,  0.01605609,  0.00714625,  0.00056481,\n",
       "        0.01140872,  0.01560387,  0.00022005,  0.        ,  0.00107322,\n",
       "        0.        ,  0.        ,  0.00652446,  0.01517986,  0.00304872,\n",
       "        0.        ,  0.00570047,  0.        ,  0.00707577,  0.0221371 ,\n",
       "        0.        ,  0.00519515,  0.00317219,  0.00224133,  0.01693532,\n",
       "        0.00863437,  0.00399053,  0.01043422,  0.0013936 ,  0.00210819])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_osvm.alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def load_hack(filename):\n",
    "    with open(filename ,\"r\") as f:\n",
    "        len_f = len(list(f))\n",
    "        f.seek(0)\n",
    "        len_line = len(f.readline().split(','))\n",
    "        data = np.array([[0 for x in range(len_line)] for i in range(len_f)])\n",
    "        dict_convert = [[] for i in range(len_line)]\n",
    "        f.seek(0)\n",
    "        for line in range(len_f):\n",
    "            freadline = f.readline()\n",
    "            spl = freadline .split(',')\n",
    "            for elem in range(len_line):\n",
    "                try:\n",
    "                    data[line,elem] = float(spl[elem])\n",
    "                except:\n",
    "                    if spl[elem] in dict_convert[elem]:\n",
    "                        getnb = dict_convert[elem].index(spl[elem])\n",
    "                    else:\n",
    "                        dict_convert[elem].append(spl[elem])\n",
    "                        getnb = dict_convert[elem].index(spl[elem])\n",
    "                    data[line,elem] = float(getnb)\n",
    "\n",
    "    tmp = np.array(data)\n",
    "    return tmp[:,:-1],tmp[:,-1].astype(int)\n",
    "\n",
    "data_hack,y_hack = load_hack('hack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:420: DataConversionWarning: Data with input dtype int32 was converted to float64 by the scale function.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "data_hack_nm = sklearn.preprocessing.scale(data_hack,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_hack_ras = data_hack_nm[y_hack == 1][:2000]\n",
    "data_hack_wip = data_hack_nm[y_hack != 1][:2000]\n",
    "data_hack_train,data_hack_test=cv.train_test_split(data_hack_ras,test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hack_osvm = osvm(mu=0.005,gamma =10**(-2),maxiter=1000,maxiterii=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "0 100 2000 0.864642706747 0.782694590759\n",
      "0 200 2000 0.864642706747 0.782694590759\n",
      "0 300 2000 0.864642706747 0.782694590759\n",
      "81 400 2000 0.734249360301 0.671176489728\n",
      "18 500 2000 0.734249379198 0.671176487711\n",
      "5425 600 2000 0.734248363744 0.671176294372\n",
      "10000 700 2000 0.733901276508 0.671101796791\n",
      "669 800 2000 0.733753678748 0.67067456251\n",
      "0 900 2000 0.733753678748 0.67067456251\n",
      "0 1000 2000 0.733753678748 0.67067456251\n",
      "0 1100 2000 0.733753678748 0.67067456251\n",
      "0 1200 2000 0.733753678748 0.67067456251\n",
      "0 1300 2000 0.733753678748 0.67067456251\n",
      "0 1400 2000 0.733753678748 0.67067456251\n",
      "0 1500 2000 0.733753678748 0.67067456251\n",
      "0 1600 2000 0.733753678748 0.67067456251\n",
      "0 1700 2000 0.733753678748 0.67067456251\n",
      "0 1800 2000 0.733753678748 0.67067456251\n",
      "0 1900 2000 0.733753678748 0.67067456251\n",
      "0 2000 2000 0.733753678748 0.67067456251\n"
     ]
    }
   ],
   "source": [
    "hack_osvm.fit(data_hack_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test données entrainement :  0.957\n",
      "Test données validation :  0.962\n",
      "Test données ok:  0.9595\n",
      "Test données dead:  0.0405\n"
     ]
    }
   ],
   "source": [
    "print(\"Test données entrainement : \", sum(0.5*(1+hack_osvm.predict(data_hack_train)))/len(data_hack_train))\n",
    "print(\"Test données validation : \", sum(0.5*(1+hack_osvm.predict(data_hack_test)))/len(data_hack_test))\n",
    "print(\"Test données ok: \", sum(0.5*(1+hack_osvm.predict(data_hack_ras)))/len(data_hack_ras))\n",
    "print(\"Test données dead: \", sum(0.5*(1+hack_osvm.predict(data_hack_wip)))/len(data_hack_wip))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Breast cancer Winsconsin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[], [], [], [], [], [], ['?'], [], [], [], []]\n"
     ]
    }
   ],
   "source": [
    "def load_cancer_1(filename):\n",
    "    with open(filename ,\"r\") as f:\n",
    "        len_f = len(list(f))\n",
    "        f.seek(0)\n",
    "        len_line = len(f.readline().split(','))\n",
    "        data = np.array([[0 for x in range(len_line)] for i in range(len_f)])\n",
    "        dict_convert = [[] for i in range(len_line)]\n",
    "        f.seek(0)\n",
    "        for line in range(len_f):\n",
    "            freadline = f.readline()\n",
    "            spl = freadline .split(',')\n",
    "            for elem in range(len_line):\n",
    "                try:\n",
    "                    data[line,elem] = float(spl[elem])\n",
    "                except:\n",
    "                    if spl[elem] in dict_convert[elem]:\n",
    "                        getnb = dict_convert[elem].index(spl[elem])\n",
    "                    else:\n",
    "                        dict_convert[elem].append(spl[elem])\n",
    "                        getnb = dict_convert[elem].index(spl[elem])\n",
    "                    data[line,elem] = float(getnb)\n",
    "    tmp = np.array(data)\n",
    "    return tmp[:,1:-1],tmp[:,-1].astype(int)\n",
    "\n",
    "data_cancer_1,y_cancer_1 = load_cancer_1('breast-cancer-wisconsin.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_cancer_1_ben = data_cancer_1[y_cancer_1 == 2]\n",
    "data_cancer_1_mal = data_cancer_1[y_cancer_1 == 4]\n",
    "data_cancer_1_train,data_cancer_1_test=cv.train_test_split(data_cancer_1_ben,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialisation\n",
      "Apprentissage\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "38 100 1500 0.747995371369 0.612520071225\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "694 200 1500 0.625393538695 0.495566484566\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "15603 300 1500 0.585833324003 0.46130704886\n",
      "len sv break\n",
      "15906 400 1500 0.610567181062 0.459482750829\n",
      "len sv break\n",
      "len sv break\n",
      "9 500 1500 0.537592263821 0.428525517076\n",
      "1951 600 1500 0.534937556373 0.428312455652\n",
      "7663 700 1500 0.532963563242 0.427547255217\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "len sv break\n",
      "9192 800 1500 0.515004975541 0.419180552815\n",
      "20564 900 1500 0.497040527319 0.415089881921\n",
      "6095 1000 1500 0.478794240956 0.404995361842\n",
      "len sv break\n",
      "3523 1100 1500 0.474296150428 0.401857011035\n",
      "0 1200 1500 0.474296150428 0.401857011035\n",
      "0 1300 1500 0.474296150428 0.401857011035\n",
      "0 1400 1500 0.474296150428 0.401857011035\n",
      "0 1500 1500 0.474296150428 0.401857011035\n"
     ]
    }
   ],
   "source": [
    "cancer_1_osvm = osvm(mu=0.05,gamma =10**(-2),maxiter=1500,maxiterii=300)\n",
    "cancer_1_osvm.fit(data_cancer_1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xeb41630>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "Uuf+Pz1A4AcpXc12AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca7d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "f.suptitle(\"Taille de l'ensemble qui ne remplit pas les conditions KKT\")\n",
    "plt.plot(cancer_1_osvm.kkt_hist)\n",
    "#f.savefig(\"kkt_viol.pdf\",format = 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test données entrainement :  0.907103825137\n",
      "Test données validation :  0.934782608696\n",
      "Test données ok:  0.912663755459\n",
      "Test données dead:  0.0290456431535\n"
     ]
    }
   ],
   "source": [
    "print(\"Test données entrainement : \", sum(0.5*(1+cancer_1_osvm.predict(data_cancer_1_train)))/len(data_cancer_1_train))\n",
    "print(\"Test données validation : \", sum(0.5*(1+cancer_1_osvm.predict(data_cancer_1_test)))/len(data_cancer_1_test))\n",
    "print(\"Test données ok: \", sum(0.5*(1+cancer_1_osvm.predict(data_cancer_1_ben)))/len(data_cancer_1_ben))\n",
    "print(\"Test données dead: \", sum(0.5*(1+cancer_1_osvm.predict(data_cancer_1_mal)))/len(data_cancer_1_mal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}