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Last active May 18, 2018 19:40
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Explore anomaly detection lolbench-analysis
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import json\n",
"from pprint import pprint\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.stats\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('allresults-Mean.json') as f:\n",
" data = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def prp_bench(bench):\n",
" # TODO use 'errors', currently we are throwing away half the data\n",
" names, frame = zip(*[(i, bench[i][u'estimate']) for i in bench])\n",
" df = pd.DataFrame(list(frame), names).T\n",
" # Remove missing data\n",
" df = df[~df.isnull().any(1)]\n",
" # Remove collums that are all zeros\n",
" df = df.loc[:, df.max() > 0]\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def proses(df):\n",
" logdf = df.apply(np.log1p)\n",
" metrics = ['nanoseconds', 'instructions', 'ref-cpu-cycles']\n",
" # kde reqiers more sampls then attibutes. So use a subset for now:\n",
" subset = logdf[metrics]\n",
"\n",
" out = pd.Series(index=subset.index)\n",
" for i in range(10, len(subset.index) - 1):\n",
" kde = scipy.stats.gaussian_kde(subset.iloc[(i - 10):i].T)\n",
" out.iloc[i] = kde.logpdf(subset.iloc[i:(i + 2)].T).max()\n",
" out.loc[out < -1e3] = -1e3 \n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def make_plot(df, name=''):\n",
" coll = ['prob', 'nanoseconds', 'instructions', 'ref-cpu-cycles']\n",
" fig, axes = plt.subplots(nrows=len(coll), ncols=1)\n",
" fig.suptitle(name)\n",
" index = df['prob'].index.values\n",
" change = df[df['prob'] < -20].index.values\n",
" for axis, i in zip(axes, coll):\n",
" axis.set_title(i)\n",
" means = df[i].values\n",
" \n",
" for c in change:\n",
" axis.axvline(c, color='r', alpha=0.3)\n",
" \n",
" axis.set_xlim(index.min(), index.max())\n",
" axis.plot(index, means, 'o')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = prp_bench(data[u'rayon_1_0_0::pythagoras::euclid_parallel_one'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\lib\\site-packages\\scipy\\stats\\kde.py:537: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(self.evaluate(x))\n"
]
},
{
"data": {
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"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>prob</th>\n",
" <th>nanoseconds</th>\n",
" <th>instructions</th>\n",
" <th>ref-cpu-cycles</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>1.386225e+07</td>\n",
" <td>398501.073002</td>\n",
" <td>1.032938e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>1.385367e+07</td>\n",
" <td>396546.322641</td>\n",
" <td>1.033019e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>1.393695e+07</td>\n",
" <td>383546.621297</td>\n",
" <td>1.033155e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>1.379134e+07</td>\n",
" <td>397412.528663</td>\n",
" <td>1.033209e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>1.400414e+07</td>\n",
" <td>392013.113731</td>\n",
" <td>1.032797e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>1.389680e+07</td>\n",
" <td>398349.316578</td>\n",
" <td>1.032834e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>1.394192e+07</td>\n",
" <td>383931.657261</td>\n",
" <td>1.032756e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>1.400772e+07</td>\n",
" <td>388927.416426</td>\n",
" <td>1.033080e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>1.376132e+07</td>\n",
" <td>417287.350849</td>\n",
" <td>1.032761e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>1.456623e+07</td>\n",
" <td>417740.813784</td>\n",
" <td>1.032905e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8.078756</td>\n",
" <td>1.472306e+07</td>\n",
" <td>399149.019966</td>\n",
" <td>1.033107e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>9.415542</td>\n",
" <td>1.449903e+07</td>\n",
" <td>385363.433337</td>\n",
" <td>1.032771e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>10.449593</td>\n",
" <td>1.467705e+07</td>\n",
" <td>417213.174682</td>\n",
" <td>1.033592e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>11.606307</td>\n",
" <td>1.443629e+07</td>\n",
" <td>379732.074698</td>\n",
" <td>1.033175e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>11.686179</td>\n",
" <td>1.470267e+07</td>\n",
" <td>400573.358179</td>\n",
" <td>1.033081e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>11.446679</td>\n",
" <td>1.465322e+07</td>\n",
" <td>406084.955345</td>\n",
" <td>1.033255e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>11.524847</td>\n",
" <td>1.457787e+07</td>\n",
" <td>399411.788898</td>\n",
" <td>1.032796e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>11.736288</td>\n",
" <td>1.439021e+07</td>\n",
" <td>407095.517395</td>\n",
" <td>1.032727e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>11.517025</td>\n",
" <td>1.441017e+07</td>\n",
" <td>386775.445257</td>\n",
" <td>1.033111e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>12.821930</td>\n",
" <td>1.447407e+07</td>\n",
" <td>383243.731606</td>\n",
" <td>1.033036e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>12.054079</td>\n",
" <td>1.443353e+07</td>\n",
" <td>409738.755337</td>\n",
" <td>1.032951e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>12.796496</td>\n",
" <td>1.529657e+07</td>\n",
" <td>408467.910437</td>\n",
" <td>1.032573e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>12.446108</td>\n",
" <td>1.445959e+07</td>\n",
" <td>396839.772459</td>\n",
" <td>1.033043e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>10.102911</td>\n",
" <td>1.442327e+07</td>\n",
" <td>379264.707364</td>\n",
" <td>1.032692e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>11.908257</td>\n",
" <td>1.442940e+07</td>\n",
" <td>425839.145932</td>\n",
" <td>1.032855e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>11.726516</td>\n",
" <td>1.456977e+07</td>\n",
" <td>380033.731158</td>\n",
" <td>1.032657e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-235.232739</td>\n",
" <td>1.450462e+07</td>\n",
" <td>413647.935458</td>\n",
" <td>1.035511e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-74.540250</td>\n",
" <td>1.480505e+07</td>\n",
" <td>383279.552284</td>\n",
" <td>1.048582e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>-77.062053</td>\n",
" <td>1.213903e+07</td>\n",
" <td>384765.926679</td>\n",
" <td>1.033182e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>6.448076</td>\n",
" <td>1.217044e+07</td>\n",
" <td>399414.450654</td>\n",
" <td>1.033381e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>7.510699</td>\n",
" <td>1.197197e+07</td>\n",
" <td>413156.305753</td>\n",
" <td>1.033216e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>7.406869</td>\n",
" <td>1.195349e+07</td>\n",
" <td>388886.342745</td>\n",
" <td>1.033068e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>7.914847</td>\n",
" <td>1.217835e+07</td>\n",
" <td>383056.157065</td>\n",
" <td>1.032769e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>8.146366</td>\n",
" <td>1.198293e+07</td>\n",
" <td>390565.553725</td>\n",
" <td>1.032954e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>7.045777</td>\n",
" <td>1.463413e+07</td>\n",
" <td>382077.420898</td>\n",
" <td>1.033010e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>7.316471</td>\n",
" <td>1.461299e+07</td>\n",
" <td>371862.563389</td>\n",
" <td>1.032724e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>7.452437</td>\n",
" <td>1.511440e+07</td>\n",
" <td>396328.957900</td>\n",
" <td>1.032848e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>7.605326</td>\n",
" <td>1.431939e+07</td>\n",
" <td>385194.618444</td>\n",
" <td>1.032958e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>8.066706</td>\n",
" <td>1.457921e+07</td>\n",
" <td>413630.887363</td>\n",
" <td>1.032839e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>8.411226</td>\n",
" <td>1.428545e+07</td>\n",
" <td>378828.982728</td>\n",
" <td>1.033242e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>NaN</td>\n",
" <td>2.887652e+07</td>\n",
" <td>377608.564773</td>\n",
" <td>1.030664e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" prob nanoseconds instructions ref-cpu-cycles\n",
"0 NaN 1.386225e+07 398501.073002 1.032938e+06\n",
"1 NaN 1.385367e+07 396546.322641 1.033019e+06\n",
"2 NaN 1.393695e+07 383546.621297 1.033155e+06\n",
"3 NaN 1.379134e+07 397412.528663 1.033209e+06\n",
"4 NaN 1.400414e+07 392013.113731 1.032797e+06\n",
"5 NaN 1.389680e+07 398349.316578 1.032834e+06\n",
"6 NaN 1.394192e+07 383931.657261 1.032756e+06\n",
"7 NaN 1.400772e+07 388927.416426 1.033080e+06\n",
"8 NaN 1.376132e+07 417287.350849 1.032761e+06\n",
"9 NaN 1.456623e+07 417740.813784 1.032905e+06\n",
"10 8.078756 1.472306e+07 399149.019966 1.033107e+06\n",
"11 9.415542 1.449903e+07 385363.433337 1.032771e+06\n",
"12 10.449593 1.467705e+07 417213.174682 1.033592e+06\n",
"13 11.606307 1.443629e+07 379732.074698 1.033175e+06\n",
"14 11.686179 1.470267e+07 400573.358179 1.033081e+06\n",
"15 11.446679 1.465322e+07 406084.955345 1.033255e+06\n",
"16 11.524847 1.457787e+07 399411.788898 1.032796e+06\n",
"17 11.736288 1.439021e+07 407095.517395 1.032727e+06\n",
"18 11.517025 1.441017e+07 386775.445257 1.033111e+06\n",
"19 12.821930 1.447407e+07 383243.731606 1.033036e+06\n",
"23 12.054079 1.443353e+07 409738.755337 1.032951e+06\n",
"24 12.796496 1.529657e+07 408467.910437 1.032573e+06\n",
"25 12.446108 1.445959e+07 396839.772459 1.033043e+06\n",
"26 10.102911 1.442327e+07 379264.707364 1.032692e+06\n",
"27 11.908257 1.442940e+07 425839.145932 1.032855e+06\n",
"28 11.726516 1.456977e+07 380033.731158 1.032657e+06\n",
"29 -235.232739 1.450462e+07 413647.935458 1.035511e+06\n",
"30 -74.540250 1.480505e+07 383279.552284 1.048582e+06\n",
"31 -77.062053 1.213903e+07 384765.926679 1.033182e+06\n",
"32 6.448076 1.217044e+07 399414.450654 1.033381e+06\n",
"33 7.510699 1.197197e+07 413156.305753 1.033216e+06\n",
"34 7.406869 1.195349e+07 388886.342745 1.033068e+06\n",
"35 7.914847 1.217835e+07 383056.157065 1.032769e+06\n",
"36 8.146366 1.198293e+07 390565.553725 1.032954e+06\n",
"37 7.045777 1.463413e+07 382077.420898 1.033010e+06\n",
"38 7.316471 1.461299e+07 371862.563389 1.032724e+06\n",
"39 7.452437 1.511440e+07 396328.957900 1.032848e+06\n",
"40 7.605326 1.431939e+07 385194.618444 1.032958e+06\n",
"41 8.066706 1.457921e+07 413630.887363 1.032839e+06\n",
"42 8.411226 1.428545e+07 378828.982728 1.033242e+06\n",
"43 NaN 2.887652e+07 377608.564773 1.030664e+06"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['prob'] = proses(df)\n",
"df[['prob', 'nanoseconds', 'instructions', 'ref-cpu-cycles']]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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bW5rW8I+vCfhGeBBYB/zD6Y6Zqnon3k3/hyKyD89Z3iinAPaJyDhgHp4Z78Oq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\nRLYo1NuhIvKOiCx1st3hwvu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"text/plain": [
"<matplotlib.figure.Figure at 0x57007b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"make_plot(df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>rayon_1_0_0::pythagoras::euclid_parallel_weightless</th>\n",
" <td>-1000.000000</td>\n",
" <td>5.912588</td>\n",
" <td>6.465346</td>\n",
" <td>6.854614</td>\n",
" <td>7.307200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rayon_1_0_0::pythagoras::euclid_parallel_one</th>\n",
" <td>-235.232739</td>\n",
" <td>-77.062053</td>\n",
" <td>-74.540250</td>\n",
" <td>6.448076</td>\n",
" <td>7.045777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>brotli_1_1_3::bench_e2e_decode_q9_5_1024k</th>\n",
" <td>-179.756832</td>\n",
" <td>-26.007971</td>\n",
" <td>8.099810</td>\n",
" <td>8.619415</td>\n",
" <td>8.783121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rayon_1_0_0::map_collect::i_mod_10_to_i::with_linked_list_collect_vec</th>\n",
" <td>-169.923679</td>\n",
" <td>5.149761</td>\n",
" <td>6.132014</td>\n",
" <td>7.430106</td>\n",
" <td>7.648628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>quickcheck_0_6_1::shrink_vec_u8_1_tuple</th>\n",
" <td>-132.827616</td>\n",
" <td>-0.557077</td>\n",
" <td>3.048649</td>\n",
" <td>3.657540</td>\n",
" <td>4.607090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rayon_1_0_0::map_collect::i_to_i::with_linked_list_collect_vec_sized</th>\n",
" <td>-122.009479</td>\n",
" <td>4.571876</td>\n",
" <td>7.194191</td>\n",
" <td>7.208918</td>\n",
" <td>7.639461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rayon_1_0_0::vec_collect::vec_i::with_vec_vec_sized</th>\n",
" <td>-51.412091</td>\n",
" <td>-15.284525</td>\n",
" <td>-1.981681</td>\n",
" <td>6.935573</td>\n",
" <td>7.438691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>snap_0_2_4::rust::uflat10_pb</th>\n",
" <td>-43.194540</td>\n",
" <td>3.713461</td>\n",
" <td>4.729142</td>\n",
" <td>6.100689</td>\n",
" <td>6.224781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diesel_1_1_1::bench_trivial_query_selecting______1_row</th>\n",
" <td>-38.973641</td>\n",
" <td>2.729230</td>\n",
" <td>3.020335</td>\n",
" <td>3.490965</td>\n",
" <td>4.051586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>diesel_1_1_1::bench_medium_complex_query_selecting______1_row_boxed</th>\n",
" <td>-37.139787</td>\n",
" <td>4.913024</td>\n",
" <td>6.882357</td>\n",
" <td>7.058166</td>\n",
" <td>7.122142</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 \\\n",
"rayon_1_0_0::pythagoras::euclid_parallel_weight... -1000.000000 5.912588 \n",
"rayon_1_0_0::pythagoras::euclid_parallel_one -235.232739 -77.062053 \n",
"brotli_1_1_3::bench_e2e_decode_q9_5_1024k -179.756832 -26.007971 \n",
"rayon_1_0_0::map_collect::i_mod_10_to_i::with_l... -169.923679 5.149761 \n",
"quickcheck_0_6_1::shrink_vec_u8_1_tuple -132.827616 -0.557077 \n",
"rayon_1_0_0::map_collect::i_to_i::with_linked_l... -122.009479 4.571876 \n",
"rayon_1_0_0::vec_collect::vec_i::with_vec_vec_s... -51.412091 -15.284525 \n",
"snap_0_2_4::rust::uflat10_pb -43.194540 3.713461 \n",
"diesel_1_1_1::bench_trivial_query_selecting____... -38.973641 2.729230 \n",
"diesel_1_1_1::bench_medium_complex_query_select... -37.139787 4.913024 \n",
"\n",
" 2 3 \\\n",
"rayon_1_0_0::pythagoras::euclid_parallel_weight... 6.465346 6.854614 \n",
"rayon_1_0_0::pythagoras::euclid_parallel_one -74.540250 6.448076 \n",
"brotli_1_1_3::bench_e2e_decode_q9_5_1024k 8.099810 8.619415 \n",
"rayon_1_0_0::map_collect::i_mod_10_to_i::with_l... 6.132014 7.430106 \n",
"quickcheck_0_6_1::shrink_vec_u8_1_tuple 3.048649 3.657540 \n",
"rayon_1_0_0::map_collect::i_to_i::with_linked_l... 7.194191 7.208918 \n",
"rayon_1_0_0::vec_collect::vec_i::with_vec_vec_s... -1.981681 6.935573 \n",
"snap_0_2_4::rust::uflat10_pb 4.729142 6.100689 \n",
"diesel_1_1_1::bench_trivial_query_selecting____... 3.020335 3.490965 \n",
"diesel_1_1_1::bench_medium_complex_query_select... 6.882357 7.058166 \n",
"\n",
" 4 \n",
"rayon_1_0_0::pythagoras::euclid_parallel_weight... 7.307200 \n",
"rayon_1_0_0::pythagoras::euclid_parallel_one 7.045777 \n",
"brotli_1_1_3::bench_e2e_decode_q9_5_1024k 8.783121 \n",
"rayon_1_0_0::map_collect::i_mod_10_to_i::with_l... 7.648628 \n",
"quickcheck_0_6_1::shrink_vec_u8_1_tuple 4.607090 \n",
"rayon_1_0_0::map_collect::i_to_i::with_linked_l... 7.639461 \n",
"rayon_1_0_0::vec_collect::vec_i::with_vec_vec_s... 7.438691 \n",
"snap_0_2_4::rust::uflat10_pb 6.224781 \n",
"diesel_1_1_1::bench_trivial_query_selecting____... 4.051586 \n",
"diesel_1_1_1::bench_medium_complex_query_select... 7.122142 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"out = {}\n",
"for i in list(data):\n",
" df = prp_bench(data[i])\n",
" if len(df) > 20:\n",
" out[i] = proses(df).nsmallest(5).values\n",
"anomaly_sort = pd.DataFrame(out).T.sort_values(0).iloc[:10]\n",
"anomaly_sort"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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bZi2rVemnUgiNVRZNEXe6Z89W4Q1DJko0mxVTtiu9fFSUYcthmA+rbHwQxlmZ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\n/w+O5K4kiAd5DQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5345da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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GHCZ+2LzDyJYr77D+UZIr726lJRnjpdzDXFvU5ZarTguVsG05TJ3G0ZYLTpmo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\nChNTJoZhREq30pJ6uRuFiY2ZGJEwbV4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"text/plain": [
"<matplotlib.figure.Figure at 0xde516a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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4bXExc0F1sp3gzz4b/HufSZtRqOkBQyc+QFO1r4DlUz6TNf6YKXOa7FetKC/j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\nE8dxHCc2rkwcx3Gc2Px/1HaitX4xX90AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xdce4a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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8BrgWwMzWmdnKIknGALeb2VozWwDMBw4Kf/PN7DUzWwfcDoyRJGAUcFdIfwMw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\nSZKqYVGTJFXDoiZJqoZFTZJUjR8i1DXQow6tOwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xe7c4e80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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UxZV1JVjO4bRilGo50lYDWTYmumtjxCmeVBPSkr4DbAFuCUErgb3MbAzwDeBW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\n43QZ24eNWoD2w0ZJrfck09bFDL+Uuk9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"text/plain": [
"<matplotlib.figure.Figure at 0xeb29278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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9whsIvBwIHikiZ+K2/f6+qn6UnqmIXABcANBuj32AlrUa7YWpLvIZx0lqxVsM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\nzSzB0AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xea1d080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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t30PP5+fktbJLugM9iHxU9kHPNk4zXXPPNxemTIzYiDqDPsg/ql26mGewF4Jc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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xef8fcf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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YQ2GzU9q/37Rpzwg5SSS4eyQtlDRP0u0hEBCSTpC0JidC3NU5xzol1FkkaXRO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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xf386940>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cb2bDgPvDMUQ7Yw0Lf2cDl3eJxA5Q22un9zTPh3JJ22U0y3yt5fJYT5Tdwpe0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\notRaV9UpTdaD3XGVdFwrOE+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thHs6tZzvncley6bRjvqpHCoy6UhqlPQHSX+T9LykAyUN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\nV/iO4zh1wv8P3KUSgrqIDA4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10766080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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tDGps4LJxI7aHO7mZsaDVy81xnJqhKpRndxKm4T/Q0tKSdRry+FFNXumXyIwF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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10665dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in anomaly_sort.index:\n",
" df = prp_bench(data[i])\n",
" df['prob'] = proses(df)\n",
" make_plot(df, i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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