Resultados Surveys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import pickle\n",
    "\n",
    "font = {'family' : 'sans-serif',\n",
    "        #'weight' : 'bold',\n",
    "        'size'   : 14}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "def getResultsAvg(fileName):\n",
    "    results_aux = []\n",
    "    with open(fileName) as f:\n",
    "        for line in f:\n",
    "            if \"Schilke_OrionSurvey\" in line:\n",
    "                tokens = line.split(',')\n",
    "                results_aux.append((tokens[0],float(tokens[1]),float(tokens[2])))\n",
    "    precision_list = [x[1] for x in results_aux]\n",
    "    recall_list = [x[2] for x in results_aux]\n",
    "    \n",
    "    return (results_aux[0][0],sum(precision_list)/float(len(precision_list)), sum(recall_list)/float(len(recall_list)))\n",
    "\n",
    "def plotPrecisionRecall(results_avg, title=\"\"):\n",
    "    print(results_avg)\n",
    "    x = np.arange(len(results_avg))\n",
    "    mBarPrecision = plt.bar(x, [i[1] for i in results_avg], width,align='center')\n",
    "    mBarRecall = plt.bar(x+width, [i[2] for i in results_avg], width,align='center')\n",
    "    plt.ylabel('Score')\n",
    "    plt.xlabel('Window')\n",
    "    plt.title(title)\n",
    "    plt.xticks(x, [i+1 for i in range(len(results_avg))])\n",
    "    plt.ylim(0, 1)\n",
    "    plt.legend((mBarPrecision[0], mBarRecall[0]), ('Precision', 'Recall'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Schilke_OrionSurvey.1-30.model_hot_cores_full_500it.output', 0.33, 0.13999999999999999), ('Schilke_OrionSurvey.2-30.model_hot_cores_full_500it.output', 0.3533333333333334, 0.15), ('Schilke_OrionSurvey.3-30.model_hot_cores_full_500it.output', 0.06000000000000001, 0.1), ('Schilke_OrionSurvey.4-30.model_hot_cores_full_500it.output', 0.16999999999999998, 0.1), ('Schilke_OrionSurvey.5-30.model_hot_cores_full_500it.output', 0.02, 0.01111111111111111)]\n",
      "[('Schilke_OrionSurvey.1-100.model_hot_cores_full_500it.output', 0.32333333333333336, 0.11538461538461538), ('Schilke_OrionSurvey.2-100.model_hot_cores_full_500it.output', 0.0, 0.0), ('Schilke_OrionSurvey.3-100.model_hot_cores_full_500it.output', 0.06000000000000001, 0.025), ('Schilke_OrionSurvey.4-100.model_hot_cores_full_500it.output', 0.02, 0.008333333333333333), ('Schilke_OrionSurvey.5-100.model_hot_cores_full_500it.output', 0.04, 0.02)]\n",
      "[('Schilke_OrionSurvey.1-200.model_hot_cores_full_500it.output', 0.08, 0.023529411764705882), ('Schilke_OrionSurvey.2-200.model_hot_cores_full_500it.output', 0.085, 0.02222222222222222), ('Schilke_OrionSurvey.3-200.model_hot_cores_full_500it.output', 0.025, 0.00625), ('Schilke_OrionSurvey.4-200.model_hot_cores_full_500it.output', 0.02, 0.005263157894736842), ('Schilke_OrionSurvey.5-200.model_hot_cores_full_500it.output', 0.04, 0.014285714285714285)]\n"
     ]
    },
    {
     "data": {
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LS3VSalV+28scYCZwYkppnZnC8/sd35tSKtXtLhFxFNkbz1kppW+00eYNwJ4ppZvr2rns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\nJEkquP8Pi71ccmObQNAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8295b4e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOPN = 5\n",
    "results_avg_spw30 = []\n",
    "results_avg_spw100 = []\n",
    "results_avg_spw200 = []\n",
    "for i in range(1,5+1):\n",
    "    filename_spw30 = './results_csv_spw30/results.p-r.Schilke_OrionSurvey.'+str(i)+'-30.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    filename_spw100 = './results_csv_spw100/results.p-r.Schilke_OrionSurvey.'+str(i)+'-100.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    filename_spw200 = './results_csv_spw200/results.p-r.Schilke_OrionSurvey.'+str(i)+'-200.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    \n",
    "    results_avg_spw30.append(getResultsAvg(filename_spw30))\n",
    "    results_avg_spw100.append(getResultsAvg(filename_spw100))\n",
    "    results_avg_spw200.append(getResultsAvg(filename_spw200))\n",
    "\n",
    "width = 0.35 # the width of the bars\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "#plt.figure()\n",
    "\n",
    "plt.subplot(131)\n",
    "plotPrecisionRecall(results_avg_spw30, r\"P@\"+str(TOPN)+\" & R@\"+str(TOPN)+\" spw=30\")\n",
    "\n",
    "plt.subplot(132)\n",
    "plotPrecisionRecall(results_avg_spw100, r\"P@\"+str(TOPN)+\" & R@\"+str(TOPN)+\" spw=100\")\n",
    "\n",
    "plt.subplot(133)\n",
    "plotPrecisionRecall(results_avg_spw200, r\"P@\"+str(TOPN)+\" & R@\"+str(TOPN)+\" spw=200\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Schilke_OrionSurvey.1-30.model_hot_cores_full_500it.output', 0.40714285714285714, 0.21000000000000002), ('Schilke_OrionSurvey.2-30.model_hot_cores_full_500it.output', 0.3533333333333334, 0.15), ('Schilke_OrionSurvey.3-30.model_hot_cores_full_500it.output', 0.05333333333333333, 0.1), ('Schilke_OrionSurvey.4-30.model_hot_cores_full_500it.output', 0.16999999999999998, 0.1), ('Schilke_OrionSurvey.5-30.model_hot_cores_full_500it.output', 0.05, 0.03333333333333333)]\n",
      "[('Schilke_OrionSurvey.1-100.model_hot_cores_full_500it.output', 0.3683333333333333, 0.16923076923076924), ('Schilke_OrionSurvey.2-100.model_hot_cores_full_500it.output', 0.014285714285714285, 0.00909090909090909), ('Schilke_OrionSurvey.3-100.model_hot_cores_full_500it.output', 0.1194047619047619, 0.06666666666666667), ('Schilke_OrionSurvey.4-100.model_hot_cores_full_500it.output', 0.03333333333333333, 0.016666666666666666), ('Schilke_OrionSurvey.5-100.model_hot_cores_full_500it.output', 0.03428571428571429, 0.02)]\n",
      "[('Schilke_OrionSurvey.1-200.model_hot_cores_full_500it.output', 0.20845238095238097, 0.08823529411764706), ('Schilke_OrionSurvey.2-200.model_hot_cores_full_500it.output', 0.1045238095238095, 0.03333333333333334), ('Schilke_OrionSurvey.3-200.model_hot_cores_full_500it.output', 0.0869047619047619, 0.03125), ('Schilke_OrionSurvey.4-200.model_hot_cores_full_500it.output', 0.050277777777777775, 0.021052631578947368), ('Schilke_OrionSurvey.5-200.model_hot_cores_full_500it.output', 0.08761904761904762, 0.03571428571428571)]\n"
     ]
    },
    {
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4/fTTmTVrFqtWrWKTTTbhppvW/pKLYcOGAdnpmF155zvfybhx4/je977HuHHj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\nJEmSJEkFZwFHkiRJkiSp4P4/kqaA/xz1FBsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82a0143c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOPN = 10\n",
    "results_avg_spw30 = []\n",
    "results_avg_spw100 = []\n",
    "results_avg_spw200 = []\n",
    "for i in range(1,5+1):\n",
    "    #filename_spw30 = './results_csv_spw30_v2/results.p-r.Schilke_OrionSurvey.'+str(i)+'-30.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    filename_spw30 = './results_csv_spw30/results.p-r.Schilke_OrionSurvey.'+str(i)+'-30.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    filename_spw100 = './results_csv_spw100/results.p-r.Schilke_OrionSurvey.'+str(i)+'-100.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    filename_spw200 = './results_csv_spw200/results.p-r.Schilke_OrionSurvey.'+str(i)+'-200.top'+str(TOPN)+'.hot_cores_full.csv'\n",
    "    \n",
    "    results_avg_spw30.append(getResultsAvg(filename_spw30))\n",
    "    results_avg_spw100.append(getResultsAvg(filename_spw100))\n",
    "    results_avg_spw200.append(getResultsAvg(filename_spw200))\n",
    "\n",
    "width = 0.35 # the width of the bars\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "#plt.figure()\n",
    "\n",
    "plt.subplot(131)\n",
    "plotPrecisionRecall(results_avg_spw30, r\"P@\"+str(TOPN)+\" & R@\"+str(TOPN)+\" spw=30\")\n",
    "\n",
    "plt.subplot(132)\n",
    "plotPrecisionRecall(results_avg_spw100, r\"P@\"+str(TOPN)+\" & R@\"+str(TOPN)+\" spw=100\")\n",
    "\n",
    "plt.subplot(133)\n",
    "plotPrecisionRecall(results_avg_spw200, r\"P@\"+str(TOPN)+\" & R@\"+str(TOPN)+\" spw=200\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
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