6.varing_parameter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. evaluate with varing parameter (appendix)\n",
    "not refactored\n",
    "## 6. 1. load RD-Gen from directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.dag_rd_gen import RDG_DAG\n",
    "\n",
    "dags: list[RDG_DAG] = RDG_DAG.load_from_rd_gen_dir('./sample_input')\n",
    "# print([d.filepath for d in dags])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 2. evaluate with varing parameter\n",
    "parameters are handed to src.simulate.evaluate_varing_parameter function as arguments\n",
    "\n",
    "the return value is a dict type:\n",
    "\n",
    "  evaluate_varing_parameter(samples, first_param=[...], second_param=[...], ...)\n",
    "\n",
    "  -> results[first][second] = [...(result when first and second for all samples)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 / 20 : 100% - {'method': 'proposed', 'core_num': 2}\n",
      "20 / 20 : 100% - {'method': 'proposed', 'core_num': 4}\n",
      "20 / 20 : 100% - {'method': 'proposed', 'core_num': 6}\n",
      "20 / 20 : 100% - {'method': 'existing', 'core_num': 2}\n",
      "20 / 20 : 100% - {'method': 'existing', 'core_num': 4}\n",
      "20 / 20 : 100% - {'method': 'existing', 'core_num': 6}\n",
      "{'proposed': {2: [155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242], 4: [155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242], 6: [155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242]}, 'existing': {2: [155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242], 4: [155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242], 6: [155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242]}}\n"
     ]
    }
   ],
   "source": [
    "from src.simulate import evaluate_varing_parameter\n",
    "\n",
    "results = evaluate_varing_parameter(dags, method=['proposed', 'existing'], core_num=[2, 4, 6])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 3. visualize\n",
    "ex) case of boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import boxplot, show\n",
    "\n",
    "for method in ['proposed', 'existing']:\n",
    "    array = []\n",
    "    for core in [2, 4, 6]:\n",
    "        array.append(results[method][core])\n",
    "    plot = boxplot(array, labels=[2, 4, 6])\n",
    "    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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}