test_sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager\n",
    "import sklearn\n",
    "from sklearn import svm\n",
    "from sklearn import cross_validation as cv\n",
    "import sklearn.preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma=1, kernel='rbf',\n",
       "      max_iter=-1, nu=0.001, random_state=None, shrinking=True, tol=0.001,\n",
       "      verbose=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.OneClassSVM(nu=10**(-3), kernel=\"rbf\", gamma=1)\n",
    "data = np.zeros((1000,1))\n",
    "data[:,0] = 2*(-0.5 + np.random.rand(1000))\n",
    "clf.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "pJ9PdnWif+AoihP0HcANwJvb2Pd5wJ+AF4GHgQ93eez99t/hse8KvFLzWpqdfhddG38nfnI+h/n8\n",
       "5fOXz1/DG78XYjUzMzPrsip9CtLMzMwsC07AzMzMzLrMCZiZmZlZlzkBMzMzM+syJ2BmZmZmXeYE\n",
       "zMzMzKzLnICZmZmZdZkTMDMzM7Mu+/8wniIl5vtHCgAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x938ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(-2,2,1000)\n",
    "y = list(map(lambda x :clf.predict(x),x))\n",
    "#fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(1,2)\n",
    "f.set_size_inches(10, 4)\n",
    "ax1.plot(x,y)\n",
    "y = list(map(lambda x : clf.decision_function(x)[0][0],x))\n",
    "ax2.plot(x,y)\n",
    "ax2.plot(x,np.zeros((np.shape(x))),'r')\n",
    "f.suptitle(\"Prédiction de [-2;2], ensemble d'apprentissage [-1;1] avec scikit-learn\")\n",
    "f.savefig(\"predict_sklearn.pdf\",format = 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x993550>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "SLd/FwAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa26828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = list(map(lambda x : clf.decision_function(x)[0][0],x))\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Rythme cardiaque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "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": 9,
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma=0.0095, kernel='rbf',\n",
       "      max_iter=-1, nu=0.03, random_state=None, shrinking=True, tol=0.001,\n",
       "      verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_osvm = svm.OneClassSVM(nu=0.03,gamma = 0.95*10**(-2))\n",
    "card_osvm.fit(data_card_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test données entrainement :  0.825641025641\n",
      "Test données validation :  0.775510204082\n",
      "Test données ok:  0.815573770492\n",
      "Test données dead:  0.28640776699\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": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'savefig'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-9d1c350c174c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest_plt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict_sklearn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'savefig'"
     ]
    }
   ],
   "source": [
    "test_plt.savefig(predict_sklearn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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