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tab 3/4 notebook
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{
"cells": [
{
"cell_type": "code",
report work
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"execution_count": 1,
tab 3/4 notebook
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"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\n",
done fig v report
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"import os\n",
tab 3/4 notebook
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"from IPython.display import display, Markdown"
]
},
{
"cell_type": "code",
report work
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"execution_count": 2,
tab 3/4 notebook
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"metadata": {},
"outputs": [],
"source": [
done fig v report
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"DIR = os.environ[\"HOME\"] + \"/Git/bachelorThesis/table_iii/\""
tab 3/4 notebook
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]
},
{
"cell_type": "code",
report work
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"execution_count": 3,
tab 3/4 notebook
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"metadata": {},
"outputs": [],
"source": [
"NAMES = {-1: \"No termination\", 0: \"SUBMIT\", 1: \"QUEUE\", 2: \"ENABLE\", \n",
" 3: \"SCHEDULE\", 4: \"EVICT\", 5: \"FAIL\", 6: \"FINISH\",\n",
" 7: \"KILL\", 8: \"LOST\", 9: \"UPDATE_PENDING\", 10: \"UPDATE_RUNNING\"}\n",
"\n",
"def rename(df, new, old):\n",
" df.rename(columns={old: new}, inplace=True)"
]
},
{
"cell_type": "code",
done fig v report
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"execution_count": 9,
tab 3/4 notebook
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"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"# Table III"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
report
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"name": "stdout",
"output_type": "stream",
"text": [
"\\tableIII{A}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 103.228 (719) & 73.694 & 0.769 & 0.000 & 28.766 \\\\\n",
" FAIL & 11.819 (26) & 0.288 & 11.062 & 0.002 & 0.468 \\\\\n",
" FINISH & 2.185 (1) & 0.019 & 0.004 & 2.153 & 0.008 \\\\\n",
" KILL & 5.963 (11) & 2.350 & 0.214 & 0.003 & 3.396 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{B}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 83.018 (394) & 64.817 & 0.240 & 0.000 & 17.962 \\\\\n",
" FAIL & 20.851 (62) & 0.518 & 19.657 & 0.001 & 0.675 \\\\\n",
" FINISH & 2.995 (4) & 0.020 & 0.021 & 2.943 & 0.012 \\\\\n",
" KILL & 9.173 (12) & 3.351 & 0.276 & 0.004 & 5.541 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{C}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 98.437 (444) & 73.716 & 1.813 & 0.000 & 22.908 \\\\\n",
" FAIL & 52.010 (30) & 0.773 & 48.446 & 2.035 & 0.756 \\\\\n",
" FINISH & 2.507 (2) & 0.018 & 0.013 & 2.471 & 0.006 \\\\\n",
" KILL & 5.452 (6) & 1.533 & 0.116 & 0.004 & 3.799 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{D}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 76.759 (366) & 62.001 & 0.700 & 0.000 & 14.058 \\\\\n",
" FAIL & 62.314 (62) & 0.496 & 58.968 & 0.810 & 2.040 \\\\\n",
" FINISH & 3.877 (2) & 0.059 & 0.019 & 3.789 & 0.010 \\\\\n",
" KILL & 6.795 (6) & 1.960 & 0.151 & 0.002 & 4.682 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{E}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 17.678 (72) & 11.781 & 0.106 & 0.000 & 5.791 \\\\\n",
" FAIL & 112.384 (28) & 0.458 & 111.471 & 0.000 & 0.456 \\\\\n",
" FINISH & 2.029 (2) & 0.014 & 0.008 & 1.999 & 0.008 \\\\\n",
" KILL & 13.505 (64) & 1.288 & 0.057 & 0.000 & 12.160 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{F}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 70.146 (114) & 23.974 & 0.192 & 0.000 & 45.980 \\\\\n",
" FAIL & 41.087 (54) & 0.279 & 39.257 & 0.000 & 1.550 \\\\\n",
" FINISH & 3.129 (4) & 0.019 & 0.004 & 3.008 & 0.098 \\\\\n",
" KILL & 10.288 (38) & 0.384 & 0.098 & 0.001 & 9.804 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{G}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 136.032 (490) & 77.429 & 0.303 & 0.000 & 58.299 \\\\\n",
" FAIL & 8.948 (8) & 0.016 & 8.593 & 0.000 & 0.339 \\\\\n",
" FINISH & 14.176 (2) & 0.015 & 0.002 & 14.154 & 0.005 \\\\\n",
" KILL & 32.320 (164) & 6.909 & 0.135 & 0.000 & 25.276 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{H}{\n",
table iii wip
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"\\begin{tabular}{llrrrr}\n",
report
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"\\toprule\n",
done fig v report
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" EVICT & 14.734 (40) & 6.733 & 0.837 & 0.000 & 7.165 \\\\\n",
" FAIL & 41.067 (120) & 0.600 & 37.600 & 0.000 & 2.867 \\\\\n",
" FINISH & 3.681 (2) & 0.024 & 0.014 & 3.633 & 0.011 \\\\\n",
" KILL & 17.976 (98) & 0.633 & 0.170 & 0.000 & 17.173 \\\\\n",
report done table III
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n",
"\\tableIII{ALL}{\n",
"\\begin{tabular}{llrrrr}\n",
"\\toprule\n",
done fig v report
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" EVICT & 78.710 (342) & 52.242 & 0.673 & 0.000 & 25.795 \\\\\n",
" FAIL & 24.962 (26) & 0.290 & 23.635 & 0.348 & 0.691 \\\\\n",
" FINISH & 2.962 (2) & 0.022 & 0.012 & 2.915 & 0.013 \\\\\n",
" KILL & 8.763 (16) & 1.876 & 0.143 & 0.003 & 6.741 \\\\\n",
report
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"\\bottomrule\n",
"\\end{tabular}\n",
"}\n"
]
tab 3/4 notebook
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}
],
"source": [
"display(Markdown(\"# Table III\"))\n",
report done table III
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"for cluster in list(\"abcdefgh\") + [\"all\"]:\n",
tab 3/4 notebook
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" df = pd.read_csv(glob.glob(DIR + \"/table-iii-\" + cluster + \".csv/part-00000-*\")[0])\n",
table iii wip
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" df = df[df[\"task_term\"].isin(range(4,8))]\n",
" df = df.sort_values(\"task_term\")\n",
report done table III
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" df[\"mean\"] = df[\"mean\"].round(3).apply(lambda x: \"%.03f\" % x) + \" (\" + df[\"%95\"].apply(lambda x: \"%d\" % x) + \")\"\n",
table iii wip
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" rename(df, \"# Evts. mean (95-th percentile)\", \"mean\")\n",
" del df[\"%95\"]\n",
tab 3/4 notebook
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" \n",
table iii wip
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" for i in [4,5,6,7]:\n",
tab 3/4 notebook
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" df.loc[df.task_term == i, \"task_term\"] = NAMES[i]\n",
report done table III
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" df[\"avg_count_%d\" % i] = df[\"avg_count_%d\" % i].round(3)\n",
table iii wip
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" rename(df, \"mean # \" + NAMES[i] + \" evts.\", \"avg_count_\" + str(i))\n",
" for i in [0,1,2,3,8,9,10]:\n",
tab 3/4 notebook
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" del df[\"avg_count_\" + str(i)]\n",
" rename(df, \"Task termination\", \"task_term\")\n",
report
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" print((\"\\\\tableIII{\" + cluster.upper() + \"}{\"))\n",
report done table III
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" print(df.to_latex(index=False, header=False), end=\"}\\n\")\n"
tab 3/4 notebook
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]
},
{
"cell_type": "code",
done fig v report
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"execution_count": 10,
tab 3/4 notebook
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"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"# Table IV"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
report
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"name": "stdout",
"output_type": "stream",
"text": [
"\\tableIV{A}{\n",
done fig v report
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" EVICT & 0.000 (0) & NaN & NaN & NaN & NaN \\\\\n",
" FAIL & 90.793 (499) & 0.695 & 0.684 & 0.086 & 1.850 \\\\\n",
" FINISH & 1.187 (1) & 0.005 & 0.001 & 1.073 & 0.024 \\\\\n",
" KILL & 16.533 (10) & 1.045 & 0.074 & 0.461 & 1.189 \\\\\n",
report
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"}\n",
"\\tableIV{B}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 74.368 (374) & 2.003 & 1.994 & 0.267 & 4.944 \\\\\n",
" FINISH & 6.304 (10) & 0.022 & 0.008 & 2.349 & 0.013 \\\\\n",
" KILL & 69.853 (234) & 1.696 & 0.158 & 0.614 & 3.009 \\\\\n",
report
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"}\n",
"\\tableIV{C}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.001 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 41.982 (200) & 3.484 & 0.998 & 0.376 & 3.998 \\\\\n",
" FINISH & 1.991 (1) & 0.022 & 0.017 & 1.565 & 0.017 \\\\\n",
" KILL & 110.681 (652) & 0.627 & 0.059 & 0.656 & 2.267 \\\\\n",
report
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"}\n",
"\\tableIV{D}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 43.356 (250) & 6.112 & 0.949 & 0.531 & 6.498 \\\\\n",
" FINISH & 2.109 (2) & 0.268 & 0.013 & 1.723 & 0.019 \\\\\n",
" KILL & 89.648 (283) & 1.013 & 0.054 & 0.283 & 3.256 \\\\\n",
report
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"}\n",
"\\tableIV{E}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 23.081 (25) & 0.247 & 0.666 & 0.717 & 1.588 \\\\\n",
" FINISH & 7.776 (2) & 0.019 & 0.029 & 1.934 & 0.021 \\\\\n",
" KILL & 88.790 (309) & 0.706 & 0.029 & 0.461 & 7.572 \\\\\n",
report
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"}\n",
"\\tableIV{F}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 17.161 (8) & 0.621 & 0.546 & 0.426 & 7.559 \\\\\n",
" FINISH & 2.941 (2) & 0.015 & 0.051 & 1.670 & 0.162 \\\\\n",
" KILL & 103.889 (361) & 0.183 & 0.064 & 0.417 & 5.824 \\\\\n",
report
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"}\n",
"\\tableIV{G}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 51.835 (250) & 0.556 & 3.335 & 0.608 & 20.352 \\\\\n",
" FINISH & 8.519 (36) & 0.002 & 0.630 & 1.760 & 0.005 \\\\\n",
" KILL & 37.055 (100) & 5.687 & 0.065 & 0.080 & 19.166 \\\\\n",
report
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"}\n",
"\\tableIV{H}{\n",
done fig v report
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" EVICT & 1.000 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 20.504 (1) & 0.114 & 2.300 & 0.981 & 12.833 \\\\\n",
" FINISH & 4.278 (14) & 0.005 & 0.153 & 1.778 & 0.014 \\\\\n",
" KILL & 11.023 (3) & 0.235 & 0.103 & 0.288 & 11.337 \\\\\n",
table iv mostly done
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"}\n",
"\\tableIV{ALL}{\n",
done fig v report
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" EVICT & 0.989 (1) & 1.000 & 0.000 & 0.000 & 0.000 \\\\\n",
" FAIL & 43.126 (200) & 0.114 & 2.300 & 0.981 & 12.833 \\\\\n",
" FINISH & 3.074 (2) & 0.005 & 0.153 & 1.778 & 0.014 \\\\\n",
" KILL & 53.919 (178) & 0.235 & 0.103 & 0.288 & 11.337 \\\\\n",
report
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"}\n"
]
}
],
"source": [
"display(Markdown(\"# Table IV\"))\n",
table iv mostly done
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"for cluster in list(\"abcdefgh\") + [\"all\"]:\n",
report
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" df = pd.read_csv(glob.glob(DIR + \"/table-iv-evts-\" + cluster + \".csv/part-00000-*\")[0], header=None,\n",
" names=[\"term\"] + [str(i) for i in range(0,11)])\n",
" df2 = pd.read_csv(glob.glob(DIR + \"/table-iv-tasks-\" + cluster + \".csv/part-00000-*\")[0], header=None,\n",
table iv mostly done
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" names=[\"term\", \"mean\", \"%95\"])\n",
report
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" df[\"term\"] = df[\"term\"].astype(int)\n",
" df2[\"term\"] = df2[\"term\"].astype(int)\n",
" df.sort_values(by=\"term\", inplace=True)\n",
" df2.sort_values(by=\"term\", inplace=True)\n",
" \n",
" df = df2.merge(df, on=\"term\", how=\"outer\")\n",
table iv mostly done
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" df = df[df[\"term\"].isin(range(4,8))]\n",
" df.loc[df[\"mean\"] == -1, \"mean\"] = 0\n",
" df.loc[df[\"%95\"] == -1, \"%95\"] = 0\n",
" df[\"mean\"] = df[\"mean\"].round(3).apply(lambda x: \"%.03f\" % x) + \" (\" + df[\"%95\"].apply(lambda x: \"%d\" % x) + \")\"\n",
" rename(df, \"# Tasks. mean (95-th p)\", \"mean\")\n",
" del df[\"%95\"]\n",
" \n",
report
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"\n",
" rename(df, \"# Evts. mean\", \"mean\")\n",
" rename(df, \"# Evts. 95% p.tile\", \"%95\")\n",
" \n",
table iv mostly done
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" for i in [4,5,6,7]:\n",
report
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" df.loc[df.term == i, \"term\"] = NAMES[i]\n",
table iv mostly done
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" df[str(i)] = df[str(i)].round(3)\n",
report
2021-05-16 10:22:27 +00:00
" rename(df, \"# \" + NAMES[i] + \" Evts. mean\", str(i))\n",
table iv mostly done
2021-05-24 10:44:49 +00:00
" for i in [0,1,2,3,8,9,10]:\n",
report
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" del df[str(i)]\n",
" rename(df, \"Job termination\", \"term\")\n",
" print((\"\\\\tableIV{\" + cluster.upper() + \"}{\"))\n",
table iv mostly done
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" s = df.to_latex(index=False,header=False)\n",
" s = s.split(\"\\\\toprule\\n\")[1]\n",
" s = s.split(\"\\\\bottomrule\")[0]\n",
" print(s, end=\"}\\n\")\n"
report
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]
},
{
"cell_type": "code",
report work
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"execution_count": 5,
report
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"metadata": {},
"outputs": [],
"source": [
"max_count = 50"
]
},
{
"cell_type": "code",
report work
2021-06-08 15:30:33 +00:00
"execution_count": 14,
report
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"metadata": {},
"outputs": [
report work
2021-06-08 15:30:33 +00:00
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cluster a\n",
" t4 t5 t7\n",
"x \n",
"0 47.362837 46.554701 97.884036\n",
"1 16.332856 5.089623 0.888921\n",
"2 6.524053 0.105539 0.129768\n",
"3 3.214002 0.030579 0.043725\n",
"4 1.922403 0.026895 0.050441\n",
"5 1.338548 0.041612 0.028431\n",
"6 1.018056 0.005256 0.013225\n",
"7 0.722958 0.007037 0.006126\n",
"8 0.558332 0.000000 0.002636\n",
"9 0.370550 0.000000 0.001270\n",
"10 0.292962 0.000000 0.000875\n",
"11 0.224133 0.000000 0.000000\n",
"12 0.178119 0.000000 0.005838\n",
"13 0.149303 0.000000 0.001727\n",
"14 0.123257 0.000000 0.000000\n",
"15 0.139037 0.000000 0.000000\n",
"16 0.167366 0.000000 0.000000\n",
"17 0.203744 0.000000 0.000000\n",
"18 0.201785 0.000000 0.000000\n",
"19 0.272658 0.000000 0.000000\n",
"20 0.301028 0.000000 0.000000\n",
"21 0.307559 0.000000 0.000000\n",
"22 0.365285 0.000000 0.000000\n",
"23 0.348940 0.000000 0.000000\n",
"24 0.403881 0.000000 0.000000\n",
"25 0.433774 0.000000 0.000000\n",
"26 0.583566 0.000000 0.000000\n",
"27 0.603533 0.000000 0.000000\n",
"28 0.508347 0.000000 0.000000\n",
"29 0.469310 0.000000 0.000000\n",
"30 0.612706 0.000000 0.000000\n",
"31 0.501672 0.000000 0.000000\n",
"32 0.409605 0.000000 0.000000\n",
"33 0.374111 0.000000 0.000000\n",
"34 0.398718 0.000000 0.000000\n",
"35 0.467942 0.000000 0.000000\n",
"36 0.431704 0.000000 0.000000\n",
"37 0.412002 0.000000 0.000000\n",
"38 0.468911 0.000000 0.000000\n",
"39 0.264317 0.000000 0.000000\n",
"40 0.276155 0.000000 0.000000\n",
"41 0.288184 0.000000 0.000000\n",
"42 0.263883 0.000000 0.000000\n",
"43 0.290839 0.000000 0.000000\n",
"44 0.234163 0.000000 0.000000\n",
"45 0.285677 0.000000 0.000000\n",
"46 0.299973 0.000000 0.000000\n",
"47 0.200602 0.000000 0.000000\n",
"48 0.133968 0.000000 0.000000\n",
"49 0.231624 0.000000 0.000000\n",
"50 0.246427 0.000000 0.000000\n",
"Cluster b\n",
" t4 t5 t7\n",
"x \n",
"0 36.320530 34.309040 98.618346\n",
"1 26.594129 10.444409 1.171874\n",
"2 5.573492 27.567062 0.299673\n",
"3 19.855097 6.627517 6.087220\n",
"4 3.256016 1.578588 0.144134\n",
"5 14.672686 2.670623 3.251599\n",
"6 1.812073 0.068354 0.118964\n",
"7 7.969639 8.620690 0.127632\n",
"8 0.903039 0.143705 0.072289\n",
"9 6.896552 0.000000 0.000000\n",
"10 0.439832 0.283714 0.045550\n",
"11 6.060606 0.175747 0.000000\n",
"12 0.238994 0.303344 0.028309\n",
"13 8.695652 5.882353 0.000000\n",
"14 0.131841 0.136813 0.027090\n",
"15 11.363636 11.428571 0.000000\n",
"16 0.086630 0.170514 0.017097\n",
"17 19.354839 3.333333 0.000000\n",
"18 0.048183 0.229606 0.019054\n",
"19 12.500000 4.000000 0.000000\n",
"20 0.052340 0.177276 0.021857\n",
"21 25.000000 6.060606 0.000000\n",
"22 0.040403 0.088594 0.027238\n",
"23 5.555556 5.555556 0.000000\n",
"24 0.026294 0.095572 0.023853\n",
"25 20.000000 6.250000 0.000000\n",
"26 0.023747 0.206186 0.021003\n",
"27 14.285714 0.000000 0.000000\n",
"28 0.052672 0.078818 0.007426\n",
"29 0.000000 0.000000 0.000000\n",
"30 0.068830 0.098309 0.012387\n",
"31 0.000000 0.000000 0.000000\n",
"32 0.050386 0.137127 0.009484\n",
"33 7.692308 0.000000 0.000000\n",
"34 0.068846 0.000000 0.007023\n",
"35 0.000000 0.000000 0.000000\n",
"36 0.080070 0.053262 0.008368\n",
"37 66.666667 0.000000 0.000000\n",
"38 0.091681 0.000000 0.014958\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.097831 0.000000 0.003045\n",
"41 0.000000 0.000000 0.000000\n",
"42 0.058265 0.000000 0.002896\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.061367 0.025355 0.005606\n",
"45 9.090909 0.000000 0.000000\n",
"46 0.048591 0.050942 0.003122\n",
"47 0.000000 0.000000 0.000000\n",
"48 0.045963 0.192123 0.000000\n",
"49 0.000000 0.000000 0.000000\n",
"50 0.078819 0.136333 0.000000\n",
"Cluster c\n",
" t4 t5 t7\n",
"x \n",
"0 34.700920 34.244187 99.449071\n",
"1 32.133579 2.658985 5.362580\n",
"2 11.507749 13.749614 0.127211\n",
"3 31.860347 12.113402 1.082349\n",
"4 9.508958 2.728234 0.031753\n",
"5 29.913754 12.466368 1.580905\n",
"6 6.906893 3.103991 0.024434\n",
"7 31.560593 3.180862 3.205128\n",
"8 4.878342 1.776444 0.017919\n",
"9 32.667618 19.892473 0.186567\n",
"10 3.235434 2.721451 0.010076\n",
"11 31.178707 10.545455 0.234192\n",
"12 2.155569 5.704472 0.005586\n",
"13 36.528497 3.596288 0.000000\n",
"14 1.490700 5.219576 0.009520\n",
"15 32.363636 22.033898 2.448980\n",
"16 1.053054 8.133270 0.023441\n",
"17 36.199095 10.000000 4.750000\n",
"18 0.778531 11.835851 0.011607\n",
"19 38.636364 2.040816 0.495050\n",
"20 0.579882 13.535099 0.007809\n",
"21 42.675159 3.125000 0.000000\n",
"22 0.476852 19.568463 0.002137\n",
"23 39.436620 15.789474 0.529101\n",
"24 0.348962 18.518519 0.002317\n",
"25 41.964286 11.111111 0.877193\n",
"26 0.283916 15.388631 0.006934\n",
"27 41.758242 3.846154 0.000000\n",
"28 0.298426 15.383536 0.001477\n",
"29 42.222222 6.250000 0.000000\n",
"30 0.239024 21.026035 0.000000\n",
"31 40.000000 27.272727 0.000000\n",
"32 0.253931 18.458524 0.003032\n",
"33 41.818182 50.000000 1.562500\n",
"34 0.223272 15.286715 0.002772\n",
"35 50.000000 50.000000 2.525253\n",
"36 0.201950 12.893482 0.006265\n",
"37 38.461538 20.000000 0.680272\n",
"38 0.194737 12.593703 0.003507\n",
"39 47.368421 0.000000 0.000000\n",
"40 0.217931 9.067358 0.000000\n",
"41 50.847458 66.666667 0.000000\n",
"42 0.215771 3.875328 0.000000\n",
"43 45.652174 11.111111 0.000000\n",
"44 0.213527 1.301953 0.000000\n",
"45 38.636364 14.285714 0.000000\n",
"46 0.274613 0.265803 0.002078\n",
"47 33.333333 11.111111 0.000000\n",
"48 0.149810 0.102102 0.000000\n",
"49 36.111111 0.000000 0.000000\n",
"50 0.200553 0.045777 0.000000\n",
"Cluster d\n",
" t4 t5 t7\n",
"x \n",
"0 15.998574 15.744658 98.467699\n",
"1 39.241581 4.457610 0.407357\n",
"2 9.002476 6.888459 0.101781\n",
"3 46.903866 5.417925 0.599774\n",
"4 7.426166 4.110437 0.021777\n",
"5 46.852425 11.572700 0.386001\n",
"6 4.128459 5.157894 0.033838\n",
"7 48.850575 2.659574 0.145843\n",
"8 2.171374 3.593708 0.018238\n",
"9 45.427729 5.194805 0.065746\n",
"10 1.210312 9.673319 0.008470\n",
"11 32.017544 1.066790 0.000000\n",
"12 0.782879 7.090615 0.006522\n",
"13 28.089888 8.391608 0.000000\n",
"14 0.586503 2.948347 0.010075\n",
"15 29.133858 5.228758 0.000000\n",
"16 0.503178 3.976640 0.006712\n",
"17 26.923077 5.050505 0.000000\n",
"18 0.413075 6.716740 0.004206\n",
"19 15.957447 0.000000 0.000000\n",
"20 0.365594 4.436353 0.004179\n",
"21 18.571429 11.428571 0.000000\n",
"22 0.327552 1.143489 0.001154\n",
"23 14.814815 10.000000 0.000000\n",
"24 0.228291 0.517282 0.001413\n",
"25 14.035088 8.928571 0.000000\n",
"26 0.197619 0.297279 0.000000\n",
"27 14.893617 0.000000 0.000000\n",
"28 0.142954 0.040409 0.000000\n",
"29 11.764706 0.000000 0.000000\n",
"30 0.107823 0.070602 0.000000\n",
"31 21.428571 0.000000 0.000000\n",
"32 0.101385 0.016483 0.000000\n",
"33 17.948718 0.000000 0.000000\n",
"34 0.055946 0.020825 0.000000\n",
"35 8.888889 0.000000 0.000000\n",
"36 0.036581 0.000000 0.000000\n",
"37 15.384615 0.000000 0.000000\n",
"38 0.024701 0.000000 0.000000\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.026115 0.000000 0.000000\n",
"41 5.882353 0.000000 0.000000\n",
"42 0.023865 0.000000 0.000000\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.051188 0.000000 0.000000\n",
"45 2.380952 33.333333 0.000000\n",
"46 0.013461 0.000000 0.000000\n",
"47 3.125000 0.000000 0.000000\n",
"48 0.014197 0.000000 0.000000\n",
"49 5.555556 0.000000 0.000000\n",
"50 0.029987 0.000000 0.000000\n",
"Cluster e\n",
" t4 t5 t7\n",
"x \n",
"0 29.060181 27.568113 98.405342\n",
"1 42.092142 1.143201 0.331637\n",
"2 4.545281 0.862833 0.143521\n",
"3 42.342799 14.806867 1.183835\n",
"4 1.779696 1.917233 0.016046\n",
"5 39.553753 19.806763 0.792988\n",
"6 0.955921 4.733897 0.025259\n",
"7 17.818182 10.052910 0.288600\n",
"8 0.618527 7.872036 0.010712\n",
"9 8.474576 5.084746 0.184332\n",
"10 0.387358 26.811898 0.005487\n",
"11 2.816901 24.000000 0.080645\n",
"12 0.262131 21.145678 0.005748\n",
"13 3.092784 7.317073 0.000000\n",
"14 0.174203 6.922733 0.001833\n",
"15 0.000000 8.333333 0.000000\n",
"16 0.094376 9.696811 0.001886\n",
"17 0.990099 14.285714 0.000000\n",
"18 0.054997 13.310156 0.003360\n",
"19 0.000000 5.263158 0.000000\n",
"20 0.046538 8.521303 0.001230\n",
"21 0.000000 0.000000 0.000000\n",
"22 0.017076 3.672408 0.001389\n",
"23 0.000000 3.846154 0.000000\n",
"24 0.024240 9.278351 0.000000\n",
"25 0.000000 0.000000 0.000000\n",
"26 0.015338 2.069297 0.000000\n",
"27 0.000000 0.000000 0.000000\n",
"28 0.004213 1.397516 0.002578\n",
"29 0.000000 0.000000 0.000000\n",
"30 0.002302 0.338409 0.000000\n",
"31 0.000000 0.000000 0.000000\n",
"32 0.007490 0.000000 0.000971\n",
"33 0.000000 0.000000 0.000000\n",
"34 0.000000 0.139082 0.000000\n",
"35 0.000000 0.000000 0.000000\n",
"36 0.002919 0.115607 0.000000\n",
"37 0.000000 0.000000 0.000000\n",
"38 0.000000 0.000000 0.000000\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.000000 0.089127 0.000000\n",
"41 0.000000 0.000000 0.000000\n",
"42 0.000000 0.000000 0.000000\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.000000 0.000000 0.001411\n",
"45 0.000000 0.000000 0.373134\n",
"46 0.000000 0.000000 0.000000\n",
"47 0.000000 0.000000 0.000000\n",
"48 0.000000 0.000000 0.000000\n",
"49 0.000000 0.000000 0.000000\n",
"50 0.000000 0.281690 0.003120\n",
"Cluster f\n",
" t4 t5 t7\n",
"x \n",
"0 23.221217 22.949616 98.182529\n",
"1 28.964663 7.406842 0.602731\n",
"2 8.778786 2.814571 0.920639\n",
"3 5.952381 6.164384 1.419332\n",
"4 4.603357 0.210631 0.816905\n",
"5 2.777778 7.352941 0.488906\n",
"6 2.541858 0.163299 1.200103\n",
"7 3.546099 6.976744 0.353982\n",
"8 0.971659 0.056633 0.895532\n",
"9 1.739130 12.280702 0.068446\n",
"10 0.383972 0.064602 2.059272\n",
"11 1.111111 0.961538 0.160256\n",
"12 0.180200 0.129596 1.568797\n",
"13 1.388889 9.090909 0.000000\n",
"14 0.112934 0.143170 0.622607\n",
"15 0.000000 5.882353 0.000000\n",
"16 0.057648 0.406147 0.322456\n",
"17 0.000000 7.692308 0.000000\n",
"18 0.037331 0.178838 0.115619\n",
"19 0.000000 0.000000 0.143678\n",
"20 0.028396 0.100806 0.039895\n",
"21 3.448276 0.000000 0.000000\n",
"22 0.054437 0.074221 0.002923\n",
"23 2.173913 0.000000 0.000000\n",
"24 0.047396 0.154799 0.002537\n",
"25 0.000000 0.000000 0.000000\n",
"26 0.080215 0.074322 0.000803\n",
"27 3.846154 0.000000 0.000000\n",
"28 0.055064 0.097229 0.000000\n",
"29 0.000000 0.000000 0.000000\n",
"30 0.044152 0.000000 0.000000\n",
"31 0.000000 0.000000 0.000000\n",
"32 0.039355 0.245851 0.000000\n",
"33 0.000000 0.000000 0.000000\n",
"34 0.032819 0.247934 0.000000\n",
"35 0.000000 0.000000 0.000000\n",
"36 0.006266 0.401070 0.000000\n",
"37 0.000000 0.000000 0.000000\n",
"38 0.000000 0.318979 0.000000\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.000000 0.907029 0.000000\n",
"41 0.000000 0.000000 0.000000\n",
"42 0.000000 0.271739 0.000000\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.000000 0.000000 0.000000\n",
"45 0.000000 0.000000 0.000000\n",
"46 0.000000 0.000000 0.000000\n",
"47 0.000000 0.000000 0.000000\n",
"48 0.000000 0.420168 0.000000\n",
"49 0.000000 0.000000 0.000000\n",
"50 0.000000 0.500000 0.000000\n",
"Cluster g\n",
" t4 t5 t7\n",
"x \n",
"0 27.144219 23.955918 85.297571\n",
"1 37.458417 3.664392 0.336521\n",
"2 3.962138 1.156336 0.117015\n",
"3 39.669421 0.384044 6.434540\n",
"4 1.217580 0.389784 0.030781\n",
"5 43.333333 7.589286 1.724138\n",
"6 0.400887 0.111507 0.043164\n",
"7 24.637681 15.517241 0.663350\n",
"8 0.129372 0.001327 0.031519\n",
"9 3.125000 10.000000 0.569260\n",
"10 0.041683 0.166972 0.009957\n",
"11 0.000000 59.459459 0.000000\n",
"12 0.013887 0.282415 0.000674\n",
"13 3.846154 40.625000 0.000000\n",
"14 0.005518 0.024504 0.000000\n",
"15 0.000000 14.285714 0.000000\n",
"16 0.001399 0.090975 0.000000\n",
"17 4.761905 0.000000 0.000000\n",
"18 0.002394 0.026469 0.000000\n",
"19 0.000000 0.000000 0.000000\n",
"20 0.000000 0.081766 0.000000\n",
"21 10.526316 0.000000 0.000000\n",
"22 0.000000 0.000000 0.000000\n",
"23 0.000000 0.000000 0.000000\n",
"24 0.000000 0.000000 0.000000\n",
"25 0.000000 12.500000 0.000000\n",
"26 0.000000 0.000000 0.000000\n",
"27 0.000000 0.000000 0.000000\n",
"28 0.000000 0.000000 0.000000\n",
"29 0.000000 0.000000 0.000000\n",
"30 0.000000 0.000000 0.000000\n",
"31 3.448276 0.000000 0.000000\n",
"32 0.000000 0.000000 0.000000\n",
"33 0.000000 0.000000 0.000000\n",
"34 0.000000 0.000000 0.000000\n",
"35 0.000000 0.000000 0.000000\n",
"36 0.000000 0.000000 0.000000\n",
"37 0.000000 0.000000 0.000000\n",
"38 0.000000 0.000000 0.000000\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.000000 0.000000 0.000000\n",
"41 0.000000 0.000000 0.000000\n",
"42 0.000000 0.000000 0.000000\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.000000 0.000000 0.000000\n",
"45 0.000000 0.000000 0.000000\n",
"46 0.000000 0.000000 0.000000\n",
"47 0.000000 0.000000 0.000000\n",
"48 0.000000 0.000000 0.000000\n",
"49 0.000000 0.000000 0.000000\n",
"50 0.000000 0.000000 0.000000\n",
"Cluster h\n",
" t4 t5 t7\n",
"x \n",
"0 19.700148 18.546258 97.156117\n",
"1 15.942300 1.793886 0.020084\n",
"2 1.935968 14.486904 0.203083\n",
"3 1.666667 2.083333 0.443047\n",
"4 0.345462 2.401347 0.044810\n",
"5 0.000000 1.298701 0.167317\n",
"6 0.240148 0.446334 0.039865\n",
"7 0.000000 0.884956 0.000000\n",
"8 0.302454 0.041514 0.018718\n",
"9 0.000000 0.000000 0.000000\n",
"10 0.438648 0.009005 0.006319\n",
"11 0.000000 1.558074 0.121951\n",
"12 0.510128 0.059876 0.005273\n",
"13 0.000000 0.000000 0.000000\n",
"14 0.550489 0.088731 0.001569\n",
"15 0.000000 0.000000 0.300300\n",
"16 0.591507 0.009006 0.000884\n",
"17 0.000000 0.000000 0.000000\n",
"18 0.685781 0.000000 0.000472\n",
"19 0.000000 0.000000 0.416667\n",
"20 0.780825 0.000000 0.000000\n",
"21 0.000000 0.000000 0.000000\n",
"22 0.767510 0.000000 0.000000\n",
"23 0.000000 0.000000 0.000000\n",
"24 0.870496 0.000000 0.000000\n",
"25 0.000000 0.000000 0.000000\n",
"26 0.767858 0.000000 0.000000\n",
"27 0.000000 0.000000 0.751880\n",
"28 0.689920 0.000000 0.000000\n",
"29 0.000000 0.000000 0.000000\n",
"30 0.665122 0.000000 0.000000\n",
"31 0.000000 0.000000 0.000000\n",
"32 0.689776 0.000000 0.000000\n",
"33 0.000000 0.000000 0.000000\n",
"34 0.938967 0.000000 0.000000\n",
"35 0.000000 0.000000 0.000000\n",
"36 0.745282 0.000000 0.000000\n",
"37 0.000000 0.000000 0.000000\n",
"38 0.847693 0.000000 0.000000\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.768979 0.000000 0.000000\n",
"41 0.000000 0.000000 0.000000\n",
"42 0.866218 0.000000 0.001087\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.956106 0.000000 0.000000\n",
"45 0.000000 0.000000 0.000000\n",
"46 0.927086 0.000000 0.000000\n",
"47 0.000000 0.000000 0.000000\n",
"48 1.061634 0.000000 0.000000\n",
"49 0.000000 0.000000 0.000000\n",
"50 1.080351 0.000000 0.000000\n",
"Cluster all\n",
" t4 t5 t7\n",
"x \n",
"0 19.700148 18.546258 97.156117\n",
"1 15.942300 1.793886 0.020084\n",
"2 1.935968 14.486904 0.203083\n",
"3 1.666667 2.083333 0.443047\n",
"4 0.345462 2.401347 0.044810\n",
"5 0.000000 1.298701 0.167317\n",
"6 0.240148 0.446334 0.039865\n",
"7 0.000000 0.884956 0.000000\n",
"8 0.302454 0.041514 0.018718\n",
"9 0.000000 0.000000 0.000000\n",
"10 0.438648 0.009005 0.006319\n",
"11 0.000000 1.558074 0.121951\n",
"12 0.510128 0.059876 0.005273\n",
"13 0.000000 0.000000 0.000000\n",
"14 0.550489 0.088731 0.001569\n",
"15 0.000000 0.000000 0.300300\n",
"16 0.591507 0.009006 0.000884\n",
"17 0.000000 0.000000 0.000000\n",
"18 0.685781 0.000000 0.000472\n",
"19 0.000000 0.000000 0.416667\n",
"20 0.780825 0.000000 0.000000\n",
"21 0.000000 0.000000 0.000000\n",
"22 0.767510 0.000000 0.000000\n",
"23 0.000000 0.000000 0.000000\n",
"24 0.870496 0.000000 0.000000\n",
"25 0.000000 0.000000 0.000000\n",
"26 0.767858 0.000000 0.000000\n",
"27 0.000000 0.000000 0.751880\n",
"28 0.689920 0.000000 0.000000\n",
"29 0.000000 0.000000 0.000000\n",
"30 0.665122 0.000000 0.000000\n",
"31 0.000000 0.000000 0.000000\n",
"32 0.689776 0.000000 0.000000\n",
"33 0.000000 0.000000 0.000000\n",
"34 0.938967 0.000000 0.000000\n",
"35 0.000000 0.000000 0.000000\n",
"36 0.745282 0.000000 0.000000\n",
"37 0.000000 0.000000 0.000000\n",
"38 0.847693 0.000000 0.000000\n",
"39 0.000000 0.000000 0.000000\n",
"40 0.768979 0.000000 0.000000\n",
"41 0.000000 0.000000 0.000000\n",
"42 0.866218 0.000000 0.001087\n",
"43 0.000000 0.000000 0.000000\n",
"44 0.956106 0.000000 0.000000\n",
"45 0.000000 0.000000 0.000000\n",
"46 0.927086 0.000000 0.000000\n",
"47 0.000000 0.000000 0.000000\n",
"48 1.061634 0.000000 0.000000\n",
"49 0.000000 0.000000 0.000000\n",
"50 1.080351 0.000000 0.000000\n"
]
},
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
{
report done table III
2021-05-24 10:06:24 +00:00
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
report done table III
2021-05-24 10:06:24 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
report done table III
2021-05-24 10:06:24 +00:00
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
},
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"text/plain": [
done fig v report
2021-05-24 12:41:16 +00:00
"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
]
},
report
2021-05-16 10:22:27 +00:00
"metadata": {
"needs_background": "light"
},
tab 3/4 notebook
2021-04-28 12:59:45 +00:00
"output_type": "display_data"
},
{
"data": {
done fig v report
2021-05-24 12:41:16 +00:00
"image/png": "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
tab 3/4 notebook
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"text/plain": [
done fig v report
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"<Figure size 360x288 with 1 Axes>"
tab 3/4 notebook
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]
},
report
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"metadata": {
"needs_background": "light"
},
tab 3/4 notebook
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"output_type": "display_data"
}
],
"source": [
report done table III
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"def figure_5_plot(df, cluster):\n",
report
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" dft = {}\n",
done fig v report
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" _, ax = plt.subplots(figsize=(5,4))\n",
" colors = plt.cm.Spectral([0.9, 0.3, 0.8, 0.1])\n",
report work
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" \n",
" pds = {}\n",
"\n",
report done table III
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" for i in [4,5,7]:\n",
report
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" dft[i] = df[[\"count_\" + str(i), \"succ\", \"non\"]].copy()\n",
" dft[i] = dft[i].groupby(\"count_\" + str(i)).sum().reset_index()\n",
" \n",
" over = dft[i][dft[i][\"count_\" + str(i)] > max_count].sum()\n",
" if over[\"succ\"] == 0 and over[\"non\"] == 0:\n",
" percover = 0\n",
" else:\n",
" percover = over[\"succ\"] / (over[\"succ\"] + over[\"non\"])\n",
" \n",
" dft[i][\"perc\"] = dft[i][\"succ\"] / (dft[i][\"succ\"] + dft[i][\"non\"])\n",
" dfi = dft[i]\n",
report done table III
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" dft[i].loc[dfi[\"succ\"].eq(0) & dfi[\"non\"].eq(0), [\"perc\"]] = 0\n",
report
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" \n",
" dft[i] = dft[i].drop(dft[i][dft[i][\"count_\" + str(i)] > max_count].index)\n",
" #dft[i][\"count_\" + str(i)] = dft[i][\"count_\" + str(i)].astype(str)\n",
" dft[i] = dft[i].append({\"count_\" + str(i): max_count + 1, \"perc\": percover}, ignore_index=True)\n",
tab 3/4 notebook
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"\n",
report
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" del dft[i][\"succ\"]\n",
" del dft[i][\"non\"]\n",
done fig v report
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" plt.xticks([0,5,10,15,20,25,30,35,40,45,50])\n",
report
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" \n",
" ys = []\n",
" for j in range(0, max_count + 2):\n",
" a = dft[i][dft[i][\"count_\" + str(i)] == j]\n",
report done table III
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" ys.append(0 if a.empty else a[\"perc\"].squeeze() * 100)\n",
report work
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" \n",
" pds[\"t\" + str(i)] = ys[:-1]\n",
" \n",
done fig v report
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" plt.plot([x for x in range(0,51)], ys[:-1], color=colors[i-4])\n",
report work
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" \n",
" if cluster == \"all\":\n",
" plt.title(\"2019 data\")\n",
" elif cluster == \"2011\":\n",
" plt.title(\"2011 data\")\n",
" else:\n",
" plt.title(\"Cluster \" + cluster.upper())\n",
" \n",
" print(\"Cluster \" + cluster)\n",
" \n",
" print(pd.DataFrame({\"x\": [x for x in range(0,51)], \"t4\": pds[\"t4\"], \"t5\": pds[\"t5\"], \"t7\": pds[\"t7\"]}).set_index(\"x\"))\n",
" \n",
report done table III
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" lgd = plt.legend([\"EVICT\", \"FAIL\", \"KILL\"])\n",
done fig v report
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" plt.xlabel(\"Event count\")\n",
" plt.xlim([-2,52])\n",
" plt.ylim([-5,105])\n",
" plt.ylabel(\"Prob. of success [%]\")\n",
report done table III
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" plt.savefig('../report/figures/figure_5/figure-5-%s.pgf' % cluster, \n",
" bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
"\n",
"dftot = None\n",
"for cluster in \"abcdefgh\":\n",
" df = pd.read_csv(glob.glob(DIR + \"fig-5-\" + cluster + \".csv/part-00000-*\")[0], \n",
" names=[\"count_4\", \"count_5\", \"count_7\", \"count_8\", \"succ\", \"non\"])\n",
" figure_5_plot(df, cluster)\n",
" if dftot is None:\n",
" dftot = df\n",
" else:\n",
" dftot = dftot.append(df)\n",
" \n",
"dftot = dftot.groupby([\"count_4\", \"count_5\", \"count_7\", \"count_8\"]).sum().reset_index()\n",
"figure_5_plot(df, \"all\")"
tab 3/4 notebook
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]
},
done fig v report
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{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 360x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"EVICT2011 = [70,41,35,30,26,23,22,21,20,19,18,17,16,15,\n",
" 14,13,12,11,10,9,8,7,6,5,4,3,2,1,0,0]\n",
"KILL2011 = [70, 1,34,18,17,15,15,16,14,14,12, 8, 0, 0,\n",
" 0, 0, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0]\n",
"FAIL2011 = [70,17, 5, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0,0,0,0,0,0,0,0,0,0,0,0]\n",
"\n",
"dft = {}\n",
"_, ax = plt.subplots(figsize=(5,4))\n",
"colors = plt.cm.Spectral([0.9, 0.3, 0.8, 0.1])\n",
"for i,S in [(4,EVICT2011),(5,FAIL2011),(7,KILL2011)]:\n",
" plt.xticks([0,5,10,15,20,25,30,35,40,45,50])\n",
" plt.plot([x for x in range(0,len(S))], S, color=colors[i-4])\n",
" plt.title(\"2011 data\")\n",
"lgd = plt.legend([\"EVICT\", \"FAIL\", \"KILL\"])\n",
"plt.xlabel(\"Event count\")\n",
"plt.xlim([-2,52])\n",
"plt.ylim([-3,103])\n",
"plt.ylabel(\"Prob. of success [%]\")\n",
"plt.savefig('../report/figures/figure_5/figure-5-2011.pgf', \n",
" bbox_extra_artists=(lgd,), bbox_inches='tight')\n"
]
},
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{
"cell_type": "code",
"execution_count": null,
report
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"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
done fig v report
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
done fig v report
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"version": "3.8.3"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}