Precision-Recall-Curve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose you have a detection algorithm to detect an existence of certain event exists in an item in a stream of\n",
    "items. The algorithm can be tuned with a threshold value th = [0 1] - the lower the threshold, the more\n",
    "prone is the algorithm to choose 'event detected'. If the threshold is set to 0, all the items are classifies\n",
    "as 'even detected'. On the other hand, if the threshold is set to 1, all the items are left undetected. The\n",
    "stream of likelihood-values of a certain event within a stream of measurements is given in **PRdata.mat**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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AcB0pJjWsXLmyqalp5syZbOTtypUr48ePP3DgQH/tFQrFunXrsrKyNBpNXV1d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\nAFxAIAEAABcQSAAAwAUEEgAAcOH/AY9CMZk04WTGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clear all; close all;\n",
    "load PRdata.mat\n",
    "\n",
    "TrueIdxs = zeros(size(L));\n",
    "TrueIdxs(TrueInds) = 1;\n",
    "th = 0:0.001:1;\n",
    "PRE = zeros(size(th));\n",
    "REC = zeros(size(th));\n",
    "\n",
    "for i=1:length(th)\n",
    "    eventdetected = L > th(i);\n",
    "    PRE(i)= sum(eventdetected & TrueIdxs)/sum(eventdetected);\n",
    "    REC(i) = sum(eventdetected & TrueIdxs)/sum(TrueIdxs);\n",
    "end\n",
    "\n",
    "PRE(isnan(PRE)) = 0;\n",
    "REC(isnan(REC)) = 0;\n",
    "figure('Name','Precision-Recall Curve','NumberTitle','off');\n",
    "plot(REC,PRE,'g'); xlabel('Recall'), ylabel('Precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Matlab",
   "language": "matlab",
   "name": "matlab"
  },
  "language_info": {
   "codemirror_mode": "octave",
   "file_extension": ".m",
   "help_links": [
    {
     "text": "MetaKernel Magics",
     "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md"
    }
   ],
   "mimetype": "text/x-octave",
   "name": "matlab",
   "version": "0.15.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}