Access Control Effector - Investigate and Compare Response Times.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Access Control Effector - Investigate and Compare Response Times\n",
    "This notebook helps to analyze single response-time.csv files to understand the nature of the response times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[1] \"Processing data/probe-experiment-node2/exp-1/1/response-time/response-time.csv\"\n",
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      "[1] \"File data/probe-experiment-node2/exp-1/10000/response-time/response-time.csv is missing\"\n"
     ]
    }
   ],
   "source": [
    "base_path <- \"data/probe-experiment-node2\"\n",
    "\n",
    "library(ggplot2)\n",
    "library(magrittr)\n",
    "library(ggpubr)\n",
    "library(repr)\n",
    "library(reshape2)\n",
    "library(nlme)\n",
    "\n",
    "model_sizes <- c(1,2,3,4,5,6,7,8,9,\n",
    "                 10,11,12,13,14,15,16,17,18,19,\n",
    "                 20,30,40,50,60,70,80,90,95,\n",
    "                 100,110,120,130,140,150,\n",
    "                 200,300,400,\n",
    "                 500,550,560,570,580,590,\n",
    "                 600,\n",
    "                 700,710,720,730,740,750,\n",
    "                 800,\n",
    "                 900,950,960,970,980,990,\n",
    "                 1000,1010,1020,1030,1040,1050,\n",
    "                 2000,3000,4000,5000,6000,7000,8000,9000,\n",
    "                 10000)\n",
    "experiment <- 1\n",
    "\n",
    "values <- c()\n",
    "ids <- c()\n",
    "\n",
    "runtime <- c()\n",
    "\n",
    "i <- 0\n",
    "for (model_size in model_sizes) {\n",
    "    data_path <- sprintf(\"%s/exp-%d/%d/response-time/response-time.csv\", base_path, experiment, model_size)\n",
    "\n",
    "    if (file.exists(data_path)) {\n",
    "        if (file.size(data_path) > 0) {\n",
    "            data <- read.csv(data_path, header=TRUE)\n",
    "\n",
    "            data <- data[ which(data$event.type == 'AfterOperationEvent'), ]\n",
    "\n",
    "            base_time <- data[1,4]\n",
    "\n",
    "            data[,4] <- (data[,4] - base_time)/1000/1000/1000\n",
    "            data[,3] <- data[,3]/1000/1000/1000\n",
    "\n",
    "            p <- ggplot(data, aes(x=time, y=response.time, linetype = task, color = task)) + \n",
    "                scale_color_discrete(name = \"Task\", \n",
    "                    breaks = c(\"org.iobserve.selenium.behavior.tasks.jpetstore.common.LoginJPetStoreTask\",\n",
    "                               \"org.iobserve.selenium.behavior.tasks.jpetstore.account.ChangeAccountInformationTask\",\n",
    "                               \"org.iobserve.selenium.behavior.tasks.jpetstore.account.ViewOrderTask\"), \n",
    "                    labels = c(\"LoginJPetStoreTask\", \"ChangeAccountInformationTask\", \"ViewOrderTask\")) +\n",
    "                geom_point() + \n",
    "                xlab(\"Runtime (s)\") + \n",
    "                ylab(\"Response Time (s)\")\n",
    "\n",
    "            hist <- data$response.time[which(data$response.time < 0.25)]\n",
    "\n",
    "            i <- i + 1\n",
    "            \n",
    "            print(sprintf(\"row %d size %d\", model_size, length(hist)))\n",
    "            \n",
    "            values <- c(values, hist)\n",
    "            ids <- c(ids, rep(model_size, length(hist)))\n",
    "\n",
    "            print(sprintf(\"Processing %s\", data_path))\n",
    "        } else {\n",
    "            print(sprintf(\"File %s is empty.\", data_path))\n",
    "        }\n",
    "    } else {\n",
    "        print(sprintf(\"File %s is missing\", data_path))\n",
    "    }  \n",
    "}\n",
    "\n",
    "histograms <- data.frame(id=ids, value=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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8ccMagjWY3yyGaRlY7aIUiHABIEaRBAgiANAkgQpEEA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data=histograms) + geom_histogram(aes(x=value), binwidth=0.005) + facet_wrap(~id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}