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ShakoHo/network.predictor.preconnect-min-confidence.ipynb

Created December 8, 2017 02:26
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network.predictor.preconnect-min-confidence.ipynb
{
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
},
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os \n",
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"from IPython.display import display, display_markdown\n",
"\n",
"#\n",
"# Hide the plotbox if case miss any one data source\n",
"#\n",
"HIDE_MISSING_CASE = False\n",
"\n",
"DATA_LIST = 'time_list'\n",
"DATA_NAME = 'run_time'\n",
"DELETE_KEY = \"video-recording-fps\"\n",
"\n",
"#\n",
"# Input data source\n",
"#\n",
"input_json_fp = os.getenv(\"input_json_fp\")\n",
"#input_json_fp = \"output/javascript.options.baselinejit.threshold.json\"\n",
"with open(input_json_fp) as read_fh:\n",
" data_dict = json.load(read_fh)\n",
" for item in data_dict:\n",
" if DELETE_KEY in data_dict[item]:\n",
" data_dict[item].pop(DELETE_KEY)\n",
"\n",
"\n",
"#\n",
"# Definde layout and figure size\n",
"#\n",
"layout_row = 1\n",
"layout_column = 1\n",
"fig_size_w = 10\n",
"fig_size_h = 5\n",
"matplotlib.rcParams.update({'figure.max_open_warning': 0})\n",
"\n",
"#\n",
"# generate merged data\n",
"#\n",
"casename_list = list(set([casename for lable, data in data_dict.items() for casename in data.keys()]))\n",
"\n",
"case_result = []\n",
"for casename in casename_list:\n",
" result = {\n",
" \"casename\": casename,\n",
" \"result\": {}\n",
" }\n",
" for lable, data in data_dict.items():\n",
" result[\"result\"][lable] = {}\n",
" \n",
" for lable, data in data_dict.items():\n",
" if data.get(casename):\n",
" result[\"result\"][lable][DATA_LIST] = data.get(casename).get(DATA_LIST)\n",
" else:\n",
" result[\"result\"][lable][DATA_LIST] = []\n",
" case_result.append(result)\n",
"\n",
"nightly_better_list = []\n",
"for case in sorted(case_result):\n",
" casename = case.get('casename')\n",
" result = case.get('result')\n",
" d = pd.DataFrame(result)\n",
" \n",
" # drop empty 'DATA_LIST'\n",
" for c in d:\n",
" if (d[c][DATA_LIST] == []) :\n",
" d.drop(c, axis=1, inplace=True)\n",
"\n",
" # Retrive the value of 'DATA_NAME' from each run\n",
" value = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME] for c in d]).T\n",
" value.columns = d.columns\n",
" \n",
" # Plot input latency boxplot\n",
" value_min = min(value.min())\n",
" value_max = max(value.max())\n",
" value_median = pd.DataFrame([pd.DataFrame(d[c][DATA_LIST])[DATA_NAME].median() for c in d]).T\n",
" value_median.columns = d.columns\n",
" \n",
" # When HIDE_MISSING_CASE is enabled, skip the case if the case data less then input data amount.\n",
" if HIDE_MISSING_CASE and len(INPUT_SOURCE) > len(value.T):\n",
" # display summary\n",
" display_markdown('### {}\\nskip draw plot box'.format(casename), raw=True)\n",
" display(value.describe())\n",
" else:\n",
" \n",
" fig, ax = plt.subplots()\n",
" ax.plot(list(range(1,len(list(value_median.median()))+1)), list(value_median.median()), \"r:\")\n",
" \n",
" value.plot.box(layout=(layout_row, layout_column),\n",
" sharey=True, sharex=True, figsize=(fig_size_w, fig_size_h),\n",
" ylim=(0, value_max*1.1), ax=ax)\n",
" plt.title(casename)\n",
" if \"Nightly\" in value_median and \"ESR52\" in value_median:\n",
" if value_median['Nightly'][0] < value_median['ESR52'][0]:\n",
" nightly_better_list.append(casename)\n",
" # display summary\n",
" display(casename)\n",
" display(value.describe().T)"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
"text": [
"u'test_firefox_gsheet_ail_clicktab_0'"
]
},
{
"html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>6.0</td>\n",
" <td>135.185185</td>\n",
" <td>89.281540</td>\n",
" <td>55.555556</td>\n",
" <td>69.444444</td>\n",
" <td>111.111111</td>\n",
" <td>169.444444</td>\n",
" <td>288.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>10.0</td>\n",
" <td>667.777778</td>\n",
" <td>300.591552</td>\n",
" <td>122.222222</td>\n",
" <td>525.000000</td>\n",
" <td>666.666667</td>\n",
" <td>891.666667</td>\n",
" <td>1144.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10.0</td>\n",
" <td>432.222222</td>\n",
" <td>385.379225</td>\n",
" <td>33.333333</td>\n",
" <td>66.666667</td>\n",
" <td>350.000000</td>\n",
" <td>716.666667</td>\n",
" <td>1055.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2.0</td>\n",
" <td>427.777778</td>\n",
" <td>133.564614</td>\n",
" <td>333.333333</td>\n",
" <td>380.555556</td>\n",
" <td>427.777778</td>\n",
" <td>475.000000</td>\n",
" <td>522.222222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>10.0</td>\n",
" <td>156.666667</td>\n",
" <td>110.175070</td>\n",
" <td>11.111111</td>\n",
" <td>83.333333</td>\n",
" <td>138.888889</td>\n",
" <td>194.444444</td>\n",
" <td>388.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>10.0</td>\n",
" <td>175.000000</td>\n",
" <td>137.018267</td>\n",
" <td>5.555556</td>\n",
" <td>63.888889</td>\n",
" <td>172.222222</td>\n",
" <td>241.666667</td>\n",
" <td>433.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>10.0</td>\n",
" <td>273.888889</td>\n",
" <td>257.561238</td>\n",
" <td>5.555556</td>\n",
" <td>147.222222</td>\n",
" <td>205.555556</td>\n",
" <td>350.000000</td>\n",
" <td>922.222222</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "display_data",
"text": [
" count mean std min 25% 50% \\\n",
"10 6.0 135.185185 89.281540 55.555556 69.444444 111.111111 \n",
"100 10.0 667.777778 300.591552 122.222222 525.000000 666.666667 \n",
"20 10.0 432.222222 385.379225 33.333333 66.666667 350.000000 \n",
"30 2.0 427.777778 133.564614 333.333333 380.555556 427.777778 \n",
"50 10.0 156.666667 110.175070 11.111111 83.333333 138.888889 \n",
"70 10.0 175.000000 137.018267 5.555556 63.888889 172.222222 \n",
"90 10.0 273.888889 257.561238 5.555556 147.222222 205.555556 \n",
"\n",
" 75% max \n",
"10 169.444444 288.888889 \n",
"100 891.666667 1144.444444 \n",
"20 716.666667 1055.555556 \n",
"30 475.000000 522.222222 \n",
"50 194.444444 388.888889 \n",
"70 241.666667 433.333333 \n",
"90 350.000000 922.222222 "
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# show plot\n",
"plt.show()"
],
"language": "python",
"metadata": {
"scrolled": false
},
"outputs": [
{
"output_type": "display_data",
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aaWkpffr0qbCuT58+lJaWJhRR5pSAiYiISE7r3r07M2fOrLBu5syZdO/ePaGIMqcETERE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\n",
"text": [
"<matplotlib.figure.Figure at 0x104b65cd0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Case list which Nightly peform better than ESR52"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nightly_better_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
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