bachelorThesis/figure_9/figure_9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = \"/home/claudio/hdd/git/bachelorThesis/figure_9/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categorical_plot(df, key, title, sorted_xs = None):\n",
    "    if sorted_xs == None:\n",
    "        xs = set()\n",
    "        lines = []\n",
    "        for t in [4,5,6,7,8]:\n",
    "            xs = xs.union(np.array(df[df.term == t][key]).reshape(-1))\n",
    "        xs = [str(x) for x in sorted(xs)]\n",
    "    else:\n",
    "        xs = sorted_xs\n",
    "        \n",
    "    plt.figure(figsize=(25,8))\n",
    "    ax = plt.gca()\n",
    "    lines = []\n",
    "    for t in [4,5,6,7,8]:\n",
    "        ys = []\n",
    "        for x in xs:\n",
    "            partsum = df[(df.term != -1) & (df[key] == x)][\"count\"].sum().astype(int)\n",
    "            y = df[(df.term == t) & (df[key] == x)][\"count\"]\n",
    "            ys.append((y.values.astype(int)[0] / partsum if partsum > 0 else y.values.astype(int)[0]) if y.any() else 0)\n",
    "        lines.append(ax.plot(xs,ys, marker='.')[0])\n",
    "\n",
    "    plt.xticks(xs)\n",
    "    plt.legend(lines, [\"EVICT\", \"FAIL\", \"FINISH\", \"KILL\", \"LOST\"],\n",
    "              bbox_to_anchor=(1,1))\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
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      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_9a_bin(col):\n",
    "    # 1 [2,10] [11,50] [51,100] 101+\n",
    "    if col <= 0:\n",
    "        return \"None known\"\n",
    "    elif col == 1:\n",
    "        return \"1\"\n",
    "    elif col <= 10:\n",
    "        return \"2-10\"\n",
    "    elif col <= 50:\n",
    "        return \"11-50\"\n",
    "    elif col <= 100:\n",
    "        return \"51-100\"\n",
    "    else:\n",
    "        return \"101+\"\n",
    "    \n",
    "def to_9c_bin(col):\n",
    "    # 1 [2,10] [11,50] [51,100] 101+\n",
    "    if col <= 0:\n",
    "        return \"Unknown\"\n",
    "    elif col < 0.5:\n",
    "        return \"(0,0.5)\"\n",
    "    elif col < 0.9:\n",
    "        return \"[0.5,0.9)\"\n",
    "    elif col < 1.1:\n",
    "        return \"[0.9,1.1)\"\n",
    "    elif col < 1.5:\n",
    "        return \"[1.1,1.5)\"\n",
    "    else:\n",
    "        return \">=1.5\"\n",
    "\n",
    "for cluster in \"abcdefgh\":\n",
    "    dfa = pd.read_csv(DIR + cluster + \"_term_task_count.csv\") \\\n",
    "        .sort_values([\"term\", \"task_count\"])\n",
    "    dfa[\"task_count\"] = dfa[\"task_count\"].apply(to_9a_bin)\n",
    "    dfa = dfa.groupby([\"term\", \"task_count\"]).sum().reset_index()\n",
    "    \n",
    "    categorical_plot(dfa, \"task_count\", \"# of tasks w.r.t. job rate (cluster \" + \n",
    "                     cluster + \")\", [to_9a_bin(x) for x in [-1,1,10,50,100,101]])\n",
    "    \n",
    "    # column should be called machine_count here, typo in analysis scripts\n",
    "    dfb = pd.read_csv(DIR + cluster + \"_priority_exectime/part-00000\", header=None,\n",
    "                names=[\"term\", \"priority\", \"interval\", \"count\"]).sort_values([\"term\", \"interval\"])\n",
    "    del dfb[\"priority\"]\n",
    "    dfb = dfb.groupby([\"term\", \"interval\"]).sum().reset_index()\n",
    "   \n",
    "    xs = [\"<1\", \"1-2\", \"2-4\", \"4-10\", \"10-60\", \"60-1d\", \">=1d\"]\n",
    "    categorical_plot(dfb, \"interval\", \"Job execution time (in mins) w.r.t. job rate (cluster \" + \n",
    "                     cluster + \")\", xs)\n",
    "      \n",
    "    dfc = pd.read_csv(DIR + cluster + \"_term_machine_locality.csv\") \\\n",
    "        .sort_values([\"term\", \"machine_locality\"])\n",
    "    dfc[\"machine_locality\"] = dfc[\"machine_locality\"].apply(to_9c_bin)\n",
    "    dfc = dfc.groupby([\"term\", \"machine_locality\"]).sum().reset_index()\n",
    "    \n",
    "    categorical_plot(dfc, \"machine_locality\", \"Machine locality w.r.t. job rate (cluster \" + \n",
    "                     cluster + \")\", [to_9c_bin(x) for x in [-1,0.49,0.99,1.09,1.49,1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}