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galvanic/1609071531-vary-knowledge.yaml

Created September 12, 2016 13:18
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1609071531-vary-knowledge.yaml
default_parameters:
experiment: offline training
dataset_filename: trec2007-1607252257
label_type:
ham_label: -1
spam_label: 1
classifier:
type: null
training_parameters:
gradient_descent_method: mini-batch
batch_size: 10
max_epochs: 100
initial_weights: null
convergence_look_back: 5
divergence_threshold: 1.
testing_parameters: {}
attack:
type: null
parameters: {}
parameter_ranges:
- name: classifier
key: [classifier, type]
values:
- adaline
- logistic regression
- naive bayes
- name: attack
key: [attack, type]
values:
- dictionary
- ham
- name: '% poisoned'
key: [attack, parameters, percentage_samples_poisoned]
values:
- .0
- .2
- .5
- name: 'attacker knowledge'
key: [attack, parameters, percentage_features_poisoned]
values:
- .20
- .50
- .90
- .95
- .98
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1609071531.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>div.container { width:100% !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<style>div.cell { width:65% !important; margin: auto; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<style>div.output_png { background-color: white; position: relative; width: 400% !important; left: -35%; overflow-x: visible !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<style>div.output_png img { max-width: 1900px !important; margin: auto !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from IPython.core.display import display, HTML\n",
"display(HTML(\"<style>div.container { width:100% !important; }</style>\"))\n",
"display(HTML(\"<style>div.cell { width:65% !important; margin: auto; }</style>\"))\n",
"display(HTML(\"<style>div.output_png { background-color: white; position: relative; width: 400% !important; left: -35%; overflow-x: visible !important; }</style>\"))\n",
"display(HTML(\"<style>div.output_png img { max-width: 1900px !important; margin: auto !important; }</style>\"))\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 1\n",
"\n",
"import sys\n",
"import os.path\n",
"absolute_path_code_dir = os.path.join('/', 'home', 'justine', 'Dropbox', 'imperial', 'computing', 'thesis', 'code')\n",
"sys.path.insert(0, absolute_path_code_dir)\n",
"\n",
"from helpers.i_o import join_repetitions\n",
"from analyse.visualise_offline import visualise"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>AUC</th>\n",
" <th>FN</th>\n",
" <th>FNR</th>\n",
" <th>FP</th>\n",
" <th>FPR</th>\n",
" <th>TN</th>\n",
" <th>TP</th>\n",
" <th>error_test</th>\n",
" <th>error_train</th>\n",
" </tr>\n",
" <tr>\n",
" <th>classifier</th>\n",
" <th>attack</th>\n",
" <th>% poisoned</th>\n",
" <th>attacker knowledge</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">adaline</th>\n",
" <th rowspan=\"15\" valign=\"top\">dictionary</th>\n",
" <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
" <th>0.20</th>\n",
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" <td>191.0</td>\n",
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" <td>1066.0</td>\n",
" <td>0.042124</td>\n",
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" <td>24865.0</td>\n",
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" <th>0.90</th>\n",
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" <td>182.0</td>\n",
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" <td>223.0</td>\n",
" <td>0.004442</td>\n",
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" <td>11540.0</td>\n",
" <td>24879.0</td>\n",
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" <td>0.003916</td>\n",
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" <td>0.043434</td>\n",
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" <th rowspan=\"5\" valign=\"top\">0.2</th>\n",
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" <td>0.0</td>\n",
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" <td>0.867767</td>\n",
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" <tr>\n",
" <th>0.50</th>\n",
" <td>0.499247</td>\n",
" <td>25142.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12567.0</td>\n",
" <td>0.0</td>\n",
" <td>0.833369</td>\n",
" <td>0.865699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.498136</td>\n",
" <td>25079.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12630.0</td>\n",
" <td>0.0</td>\n",
" <td>0.832533</td>\n",
" <td>0.866614</td>\n",
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" <tr>\n",
" <th>0.95</th>\n",
" <td>0.499066</td>\n",
" <td>25079.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12630.0</td>\n",
" <td>0.0</td>\n",
" <td>0.832533</td>\n",
" <td>0.866335</td>\n",
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" <tr>\n",
" <th>0.98</th>\n",
" <td>0.501965</td>\n",
" <td>25011.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12698.0</td>\n",
" <td>0.0</td>\n",
" <td>0.831632</td>\n",
" <td>0.867091</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.5</th>\n",
" <th>0.20</th>\n",
" <td>0.501712</td>\n",
" <td>25136.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12573.0</td>\n",
" <td>0.0</td>\n",
" <td>0.833289</td>\n",
" <td>0.914558</td>\n",
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" <tr>\n",
" <th>0.50</th>\n",
" <td>0.500321</td>\n",
" <td>25080.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12629.0</td>\n",
" <td>0.0</td>\n",
" <td>0.832547</td>\n",
" <td>0.917224</td>\n",
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" <tr>\n",
" <th>0.90</th>\n",
" <td>0.500776</td>\n",
" <td>25136.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12573.0</td>\n",
" <td>0.0</td>\n",
" <td>0.833289</td>\n",
" <td>0.916295</td>\n",
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" <tr>\n",
" <th>0.95</th>\n",
" <td>0.501889</td>\n",
" <td>25178.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12531.0</td>\n",
" <td>0.0</td>\n",
" <td>0.833846</td>\n",
" <td>0.916256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.496853</td>\n",
" <td>25055.0</td>\n",
" <td>0.500000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12654.0</td>\n",
" <td>0.0</td>\n",
" <td>0.832215</td>\n",
" <td>0.917011</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"15\" valign=\"top\">ham</th>\n",
" <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
" <th>0.20</th>\n",
" <td>0.725973</td>\n",
" <td>201.0</td>\n",
" <td>0.004020</td>\n",
" <td>1065.0</td>\n",
" <td>0.041893</td>\n",
" <td>11646.0</td>\n",
" <td>24797.0</td>\n",
" <td>0.516786</td>\n",
" <td>0.517396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.725535</td>\n",
" <td>189.0</td>\n",
" <td>0.003771</td>\n",
" <td>1096.0</td>\n",
" <td>0.043324</td>\n",
" <td>11553.0</td>\n",
" <td>24871.0</td>\n",
" <td>0.517038</td>\n",
" <td>0.517210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.727004</td>\n",
" <td>216.0</td>\n",
" <td>0.004287</td>\n",
" <td>1082.0</td>\n",
" <td>0.043225</td>\n",
" <td>11434.0</td>\n",
" <td>24977.0</td>\n",
" <td>0.517211</td>\n",
" <td>0.516879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.728099</td>\n",
" <td>216.0</td>\n",
" <td>0.004295</td>\n",
" <td>1033.0</td>\n",
" <td>0.041116</td>\n",
" <td>11529.0</td>\n",
" <td>24931.0</td>\n",
" <td>0.516561</td>\n",
" <td>0.517330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.725933</td>\n",
" <td>221.0</td>\n",
" <td>0.004385</td>\n",
" <td>1068.0</td>\n",
" <td>0.042693</td>\n",
" <td>11440.0</td>\n",
" <td>24980.0</td>\n",
" <td>0.517091</td>\n",
" <td>0.516852</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.2</th>\n",
" <th>0.20</th>\n",
" <td>0.696786</td>\n",
" <td>267.0</td>\n",
" <td>0.005286</td>\n",
" <td>2600.0</td>\n",
" <td>0.104376</td>\n",
" <td>9855.0</td>\n",
" <td>24987.0</td>\n",
" <td>0.538015</td>\n",
" <td>0.531252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.695858</td>\n",
" <td>119.0</td>\n",
" <td>0.002378</td>\n",
" <td>2643.0</td>\n",
" <td>0.104145</td>\n",
" <td>10046.0</td>\n",
" <td>24901.0</td>\n",
" <td>0.536623</td>\n",
" <td>0.528573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.691495</td>\n",
" <td>293.0</td>\n",
" <td>0.005859</td>\n",
" <td>2798.0</td>\n",
" <td>0.110114</td>\n",
" <td>9907.0</td>\n",
" <td>24711.0</td>\n",
" <td>0.540985</td>\n",
" <td>0.533095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.698240</td>\n",
" <td>306.0</td>\n",
" <td>0.006092</td>\n",
" <td>2603.0</td>\n",
" <td>0.103326</td>\n",
" <td>9993.0</td>\n",
" <td>24807.0</td>\n",
" <td>0.538572</td>\n",
" <td>0.529793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.690520</td>\n",
" <td>248.0</td>\n",
" <td>0.004947</td>\n",
" <td>3102.0</td>\n",
" <td>0.122667</td>\n",
" <td>9542.0</td>\n",
" <td>24817.0</td>\n",
" <td>0.544419</td>\n",
" <td>0.534036</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.5</th>\n",
" <th>0.20</th>\n",
" <td>0.691024</td>\n",
" <td>112.0</td>\n",
" <td>0.002243</td>\n",
" <td>2851.0</td>\n",
" <td>0.111848</td>\n",
" <td>9894.0</td>\n",
" <td>24852.0</td>\n",
" <td>0.539288</td>\n",
" <td>0.519173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.668628</td>\n",
" <td>277.0</td>\n",
" <td>0.005523</td>\n",
" <td>3873.0</td>\n",
" <td>0.153277</td>\n",
" <td>8761.0</td>\n",
" <td>24798.0</td>\n",
" <td>0.555027</td>\n",
" <td>0.526412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.662987</td>\n",
" <td>398.0</td>\n",
" <td>0.007920</td>\n",
" <td>4204.0</td>\n",
" <td>0.167051</td>\n",
" <td>8379.0</td>\n",
" <td>24728.0</td>\n",
" <td>0.561020</td>\n",
" <td>0.531716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.673692</td>\n",
" <td>398.0</td>\n",
" <td>0.007946</td>\n",
" <td>3721.0</td>\n",
" <td>0.146901</td>\n",
" <td>8944.0</td>\n",
" <td>24646.0</td>\n",
" <td>0.554616</td>\n",
" <td>0.527950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.655739</td>\n",
" <td>266.0</td>\n",
" <td>0.005320</td>\n",
" <td>4593.0</td>\n",
" <td>0.180713</td>\n",
" <td>8115.0</td>\n",
" <td>24735.0</td>\n",
" <td>0.564428</td>\n",
" <td>0.531756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">naive bayes</th>\n",
" <th rowspan=\"15\" valign=\"top\">dictionary</th>\n",
" <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
" <th>0.20</th>\n",
" <td>0.916107</td>\n",
" <td>3058.0</td>\n",
" <td>0.147944</td>\n",
" <td>346.0</td>\n",
" <td>0.019842</td>\n",
" <td>12176.0</td>\n",
" <td>22129.0</td>\n",
" <td>0.105214</td>\n",
" <td>0.103381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.902886</td>\n",
" <td>3975.0</td>\n",
" <td>0.182055</td>\n",
" <td>179.0</td>\n",
" <td>0.012173</td>\n",
" <td>12556.0</td>\n",
" <td>20999.0</td>\n",
" <td>0.124944</td>\n",
" <td>0.125630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.913592</td>\n",
" <td>4018.0</td>\n",
" <td>0.159169</td>\n",
" <td>162.0</td>\n",
" <td>0.013648</td>\n",
" <td>12457.0</td>\n",
" <td>21072.0</td>\n",
" <td>0.110531</td>\n",
" <td>0.106523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.918469</td>\n",
" <td>3210.0</td>\n",
" <td>0.144721</td>\n",
" <td>274.0</td>\n",
" <td>0.018341</td>\n",
" <td>12336.0</td>\n",
" <td>21889.0</td>\n",
" <td>0.102403</td>\n",
" <td>0.101604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.911886</td>\n",
" <td>3898.0</td>\n",
" <td>0.162419</td>\n",
" <td>198.0</td>\n",
" <td>0.013810</td>\n",
" <td>12380.0</td>\n",
" <td>21233.0</td>\n",
" <td>0.112745</td>\n",
" <td>0.112437</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.2</th>\n",
" <th>0.20</th>\n",
" <td>0.610513</td>\n",
" <td>17023.0</td>\n",
" <td>0.775661</td>\n",
" <td>80.0</td>\n",
" <td>0.003313</td>\n",
" <td>12447.0</td>\n",
" <td>8159.0</td>\n",
" <td>0.518842</td>\n",
" <td>0.421440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.605454</td>\n",
" <td>17905.0</td>\n",
" <td>0.719805</td>\n",
" <td>844.0</td>\n",
" <td>0.069288</td>\n",
" <td>11802.0</td>\n",
" <td>7158.0</td>\n",
" <td>0.501459</td>\n",
" <td>0.403660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.530613</td>\n",
" <td>20337.0</td>\n",
" <td>0.879187</td>\n",
" <td>1503.0</td>\n",
" <td>0.059586</td>\n",
" <td>11109.0</td>\n",
" <td>4760.0</td>\n",
" <td>0.604803</td>\n",
" <td>0.481398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.540461</td>\n",
" <td>21412.0</td>\n",
" <td>0.820020</td>\n",
" <td>829.0</td>\n",
" <td>0.099057</td>\n",
" <td>11725.0</td>\n",
" <td>3743.0</td>\n",
" <td>0.580007</td>\n",
" <td>0.460316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.535808</td>\n",
" <td>19997.0</td>\n",
" <td>0.791507</td>\n",
" <td>1724.0</td>\n",
" <td>0.136877</td>\n",
" <td>10853.0</td>\n",
" <td>5135.0</td>\n",
" <td>0.572980</td>\n",
" <td>0.454044</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.5</th>\n",
" <th>0.20</th>\n",
" <td>0.583120</td>\n",
" <td>20209.0</td>\n",
" <td>0.793653</td>\n",
" <td>481.0</td>\n",
" <td>0.040107</td>\n",
" <td>12112.0</td>\n",
" <td>4907.0</td>\n",
" <td>0.540866</td>\n",
" <td>0.270631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.531636</td>\n",
" <td>23435.0</td>\n",
" <td>0.936728</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>12642.0</td>\n",
" <td>1632.0</td>\n",
" <td>0.621258</td>\n",
" <td>0.312861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.505839</td>\n",
" <td>23179.0</td>\n",
" <td>0.962692</td>\n",
" <td>649.0</td>\n",
" <td>0.025630</td>\n",
" <td>12012.0</td>\n",
" <td>1869.0</td>\n",
" <td>0.649633</td>\n",
" <td>0.325510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.508239</td>\n",
" <td>25084.0</td>\n",
" <td>0.963870</td>\n",
" <td>0.0</td>\n",
" <td>0.019653</td>\n",
" <td>12625.0</td>\n",
" <td>0.0</td>\n",
" <td>0.647816</td>\n",
" <td>0.324344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.512900</td>\n",
" <td>23148.0</td>\n",
" <td>0.924017</td>\n",
" <td>687.0</td>\n",
" <td>0.050183</td>\n",
" <td>11929.0</td>\n",
" <td>1945.0</td>\n",
" <td>0.631467</td>\n",
" <td>0.315487</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"15\" valign=\"top\">ham</th>\n",
" <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
" <th>0.20</th>\n",
" <td>0.922996</td>\n",
" <td>3514.0</td>\n",
" <td>0.129219</td>\n",
" <td>260.0</td>\n",
" <td>0.024789</td>\n",
" <td>12505.0</td>\n",
" <td>21430.0</td>\n",
" <td>0.094023</td>\n",
" <td>0.093397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.909844</td>\n",
" <td>4292.0</td>\n",
" <td>0.167088</td>\n",
" <td>156.0</td>\n",
" <td>0.013224</td>\n",
" <td>12443.0</td>\n",
" <td>20818.0</td>\n",
" <td>0.115569</td>\n",
" <td>0.115937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.918608</td>\n",
" <td>4117.0</td>\n",
" <td>0.144164</td>\n",
" <td>159.0</td>\n",
" <td>0.018621</td>\n",
" <td>12427.0</td>\n",
" <td>21006.0</td>\n",
" <td>0.102031</td>\n",
" <td>0.103010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.909389</td>\n",
" <td>4326.0</td>\n",
" <td>0.167699</td>\n",
" <td>176.0</td>\n",
" <td>0.013522</td>\n",
" <td>12463.0</td>\n",
" <td>20744.0</td>\n",
" <td>0.115994</td>\n",
" <td>0.113524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.867476</td>\n",
" <td>8181.0</td>\n",
" <td>0.256823</td>\n",
" <td>47.0</td>\n",
" <td>0.008226</td>\n",
" <td>12613.0</td>\n",
" <td>16868.0</td>\n",
" <td>0.173593</td>\n",
" <td>0.172739</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.2</th>\n",
" <th>0.20</th>\n",
" <td>0.894211</td>\n",
" <td>4914.0</td>\n",
" <td>0.191410</td>\n",
" <td>246.0</td>\n",
" <td>0.020169</td>\n",
" <td>12467.0</td>\n",
" <td>20082.0</td>\n",
" <td>0.133867</td>\n",
" <td>0.105940</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.871814</td>\n",
" <td>6820.0</td>\n",
" <td>0.237965</td>\n",
" <td>192.0</td>\n",
" <td>0.018406</td>\n",
" <td>12377.0</td>\n",
" <td>18320.0</td>\n",
" <td>0.164748</td>\n",
" <td>0.130270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.879673</td>\n",
" <td>5305.0</td>\n",
" <td>0.218841</td>\n",
" <td>271.0</td>\n",
" <td>0.021812</td>\n",
" <td>12355.0</td>\n",
" <td>19778.0</td>\n",
" <td>0.152603</td>\n",
" <td>0.121374</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.871538</td>\n",
" <td>5169.0</td>\n",
" <td>0.237972</td>\n",
" <td>299.0</td>\n",
" <td>0.018952</td>\n",
" <td>12318.0</td>\n",
" <td>19923.0</td>\n",
" <td>0.164748</td>\n",
" <td>0.134089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.868471</td>\n",
" <td>6882.0</td>\n",
" <td>0.243693</td>\n",
" <td>155.0</td>\n",
" <td>0.019366</td>\n",
" <td>12507.0</td>\n",
" <td>18165.0</td>\n",
" <td>0.168687</td>\n",
" <td>0.135521</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">0.5</th>\n",
" <th>0.20</th>\n",
" <td>0.811746</td>\n",
" <td>8905.0</td>\n",
" <td>0.358583</td>\n",
" <td>221.0</td>\n",
" <td>0.017924</td>\n",
" <td>12441.0</td>\n",
" <td>16142.0</td>\n",
" <td>0.244915</td>\n",
" <td>0.121175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>0.810832</td>\n",
" <td>9374.0</td>\n",
" <td>0.350105</td>\n",
" <td>248.0</td>\n",
" <td>0.028231</td>\n",
" <td>12357.0</td>\n",
" <td>15730.0</td>\n",
" <td>0.241958</td>\n",
" <td>0.122302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.90</th>\n",
" <td>0.802617</td>\n",
" <td>8779.0</td>\n",
" <td>0.371270</td>\n",
" <td>335.0</td>\n",
" <td>0.023496</td>\n",
" <td>12244.0</td>\n",
" <td>16351.0</td>\n",
" <td>0.255284</td>\n",
" <td>0.126518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.95</th>\n",
" <td>0.833649</td>\n",
" <td>4407.0</td>\n",
" <td>0.277834</td>\n",
" <td>1149.0</td>\n",
" <td>0.054868</td>\n",
" <td>11456.0</td>\n",
" <td>20697.0</td>\n",
" <td>0.202843</td>\n",
" <td>0.104177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.98</th>\n",
" <td>0.789400</td>\n",
" <td>10336.0</td>\n",
" <td>0.401920</td>\n",
" <td>248.0</td>\n",
" <td>0.019280</td>\n",
" <td>12390.0</td>\n",
" <td>14735.0</td>\n",
" <td>0.273502</td>\n",
" <td>0.136622</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" AUC FN \\\n",
"classifier attack % poisoned attacker knowledge \n",
"adaline dictionary 0.0 0.20 0.726775 202.0 \n",
" 0.50 0.723329 191.0 \n",
" 0.90 0.728414 182.0 \n",
" 0.95 0.726159 223.0 \n",
" 0.98 0.727338 196.0 \n",
" 0.2 0.20 0.501838 24994.0 \n",
" 0.50 0.499247 25142.0 \n",
" 0.90 0.498136 25079.0 \n",
" 0.95 0.499066 25079.0 \n",
" 0.98 0.501965 25011.0 \n",
" 0.5 0.20 0.501712 25136.0 \n",
" 0.50 0.500321 25080.0 \n",
" 0.90 0.500776 25136.0 \n",
" 0.95 0.501889 25178.0 \n",
" 0.98 0.496853 25055.0 \n",
" ham 0.0 0.20 0.725973 201.0 \n",
" 0.50 0.725535 189.0 \n",
" 0.90 0.727004 216.0 \n",
" 0.95 0.728099 216.0 \n",
" 0.98 0.725933 221.0 \n",
" 0.2 0.20 0.696786 267.0 \n",
" 0.50 0.695858 119.0 \n",
" 0.90 0.691495 293.0 \n",
" 0.95 0.698240 306.0 \n",
" 0.98 0.690520 248.0 \n",
" 0.5 0.20 0.691024 112.0 \n",
" 0.50 0.668628 277.0 \n",
" 0.90 0.662987 398.0 \n",
" 0.95 0.673692 398.0 \n",
" 0.98 0.655739 266.0 \n",
"... ... ... \n",
"naive bayes dictionary 0.0 0.20 0.916107 3058.0 \n",
" 0.50 0.902886 3975.0 \n",
" 0.90 0.913592 4018.0 \n",
" 0.95 0.918469 3210.0 \n",
" 0.98 0.911886 3898.0 \n",
" 0.2 0.20 0.610513 17023.0 \n",
" 0.50 0.605454 17905.0 \n",
" 0.90 0.530613 20337.0 \n",
" 0.95 0.540461 21412.0 \n",
" 0.98 0.535808 19997.0 \n",
" 0.5 0.20 0.583120 20209.0 \n",
" 0.50 0.531636 23435.0 \n",
" 0.90 0.505839 23179.0 \n",
" 0.95 0.508239 25084.0 \n",
" 0.98 0.512900 23148.0 \n",
" ham 0.0 0.20 0.922996 3514.0 \n",
" 0.50 0.909844 4292.0 \n",
" 0.90 0.918608 4117.0 \n",
" 0.95 0.909389 4326.0 \n",
" 0.98 0.867476 8181.0 \n",
" 0.2 0.20 0.894211 4914.0 \n",
" 0.50 0.871814 6820.0 \n",
" 0.90 0.879673 5305.0 \n",
" 0.95 0.871538 5169.0 \n",
" 0.98 0.868471 6882.0 \n",
" 0.5 0.20 0.811746 8905.0 \n",
" 0.50 0.810832 9374.0 \n",
" 0.90 0.802617 8779.0 \n",
" 0.95 0.833649 4407.0 \n",
" 0.98 0.789400 10336.0 \n",
"\n",
" FNR FP \\\n",
"classifier attack % poisoned attacker knowledge \n",
"adaline dictionary 0.0 0.20 0.004036 1118.0 \n",
" 0.50 0.003811 1066.0 \n",
" 0.90 0.003624 1074.0 \n",
" 0.95 0.004442 1067.0 \n",
" 0.98 0.003916 1102.0 \n",
" 0.2 0.20 0.500000 0.0 \n",
" 0.50 0.500000 0.0 \n",
" 0.90 0.500000 0.0 \n",
" 0.95 0.500000 0.0 \n",
" 0.98 0.500000 0.0 \n",
" 0.5 0.20 0.500000 0.0 \n",
" 0.50 0.500000 0.0 \n",
" 0.90 0.500000 0.0 \n",
" 0.95 0.500000 0.0 \n",
" 0.98 0.500000 0.0 \n",
" ham 0.0 0.20 0.004020 1065.0 \n",
" 0.50 0.003771 1096.0 \n",
" 0.90 0.004287 1082.0 \n",
" 0.95 0.004295 1033.0 \n",
" 0.98 0.004385 1068.0 \n",
" 0.2 0.20 0.005286 2600.0 \n",
" 0.50 0.002378 2643.0 \n",
" 0.90 0.005859 2798.0 \n",
" 0.95 0.006092 2603.0 \n",
" 0.98 0.004947 3102.0 \n",
" 0.5 0.20 0.002243 2851.0 \n",
" 0.50 0.005523 3873.0 \n",
" 0.90 0.007920 4204.0 \n",
" 0.95 0.007946 3721.0 \n",
" 0.98 0.005320 4593.0 \n",
"... ... ... \n",
"naive bayes dictionary 0.0 0.20 0.147944 346.0 \n",
" 0.50 0.182055 179.0 \n",
" 0.90 0.159169 162.0 \n",
" 0.95 0.144721 274.0 \n",
" 0.98 0.162419 198.0 \n",
" 0.2 0.20 0.775661 80.0 \n",
" 0.50 0.719805 844.0 \n",
" 0.90 0.879187 1503.0 \n",
" 0.95 0.820020 829.0 \n",
" 0.98 0.791507 1724.0 \n",
" 0.5 0.20 0.793653 481.0 \n",
" 0.50 0.936728 0.0 \n",
" 0.90 0.962692 649.0 \n",
" 0.95 0.963870 0.0 \n",
" 0.98 0.924017 687.0 \n",
" ham 0.0 0.20 0.129219 260.0 \n",
" 0.50 0.167088 156.0 \n",
" 0.90 0.144164 159.0 \n",
" 0.95 0.167699 176.0 \n",
" 0.98 0.256823 47.0 \n",
" 0.2 0.20 0.191410 246.0 \n",
" 0.50 0.237965 192.0 \n",
" 0.90 0.218841 271.0 \n",
" 0.95 0.237972 299.0 \n",
" 0.98 0.243693 155.0 \n",
" 0.5 0.20 0.358583 221.0 \n",
" 0.50 0.350105 248.0 \n",
" 0.90 0.371270 335.0 \n",
" 0.95 0.277834 1149.0 \n",
" 0.98 0.401920 248.0 \n",
"\n",
" FPR TN \\\n",
"classifier attack % poisoned attacker knowledge \n",
"adaline dictionary 0.0 0.20 0.044071 11566.0 \n",
" 0.50 0.042124 11587.0 \n",
" 0.90 0.042626 11524.0 \n",
" 0.95 0.042318 11540.0 \n",
" 0.98 0.043434 11584.0 \n",
" 0.2 0.20 0.000000 12715.0 \n",
" 0.50 0.000000 12567.0 \n",
" 0.90 0.000000 12630.0 \n",
" 0.95 0.000000 12630.0 \n",
" 0.98 0.000000 12698.0 \n",
" 0.5 0.20 0.000000 12573.0 \n",
" 0.50 0.000000 12629.0 \n",
" 0.90 0.000000 12573.0 \n",
" 0.95 0.000000 12531.0 \n",
" 0.98 0.000000 12654.0 \n",
" ham 0.0 0.20 0.041893 11646.0 \n",
" 0.50 0.043324 11553.0 \n",
" 0.90 0.043225 11434.0 \n",
" 0.95 0.041116 11529.0 \n",
" 0.98 0.042693 11440.0 \n",
" 0.2 0.20 0.104376 9855.0 \n",
" 0.50 0.104145 10046.0 \n",
" 0.90 0.110114 9907.0 \n",
" 0.95 0.103326 9993.0 \n",
" 0.98 0.122667 9542.0 \n",
" 0.5 0.20 0.111848 9894.0 \n",
" 0.50 0.153277 8761.0 \n",
" 0.90 0.167051 8379.0 \n",
" 0.95 0.146901 8944.0 \n",
" 0.98 0.180713 8115.0 \n",
"... ... ... \n",
"naive bayes dictionary 0.0 0.20 0.019842 12176.0 \n",
" 0.50 0.012173 12556.0 \n",
" 0.90 0.013648 12457.0 \n",
" 0.95 0.018341 12336.0 \n",
" 0.98 0.013810 12380.0 \n",
" 0.2 0.20 0.003313 12447.0 \n",
" 0.50 0.069288 11802.0 \n",
" 0.90 0.059586 11109.0 \n",
" 0.95 0.099057 11725.0 \n",
" 0.98 0.136877 10853.0 \n",
" 0.5 0.20 0.040107 12112.0 \n",
" 0.50 0.000000 12642.0 \n",
" 0.90 0.025630 12012.0 \n",
" 0.95 0.019653 12625.0 \n",
" 0.98 0.050183 11929.0 \n",
" ham 0.0 0.20 0.024789 12505.0 \n",
" 0.50 0.013224 12443.0 \n",
" 0.90 0.018621 12427.0 \n",
" 0.95 0.013522 12463.0 \n",
" 0.98 0.008226 12613.0 \n",
" 0.2 0.20 0.020169 12467.0 \n",
" 0.50 0.018406 12377.0 \n",
" 0.90 0.021812 12355.0 \n",
" 0.95 0.018952 12318.0 \n",
" 0.98 0.019366 12507.0 \n",
" 0.5 0.20 0.017924 12441.0 \n",
" 0.50 0.028231 12357.0 \n",
" 0.90 0.023496 12244.0 \n",
" 0.95 0.054868 11456.0 \n",
" 0.98 0.019280 12390.0 \n",
"\n",
" TP error_test \\\n",
"classifier attack % poisoned attacker knowledge \n",
"adaline dictionary 0.0 0.20 24823.0 0.517502 \n",
" 0.50 24865.0 0.516667 \n",
" 0.90 24929.0 0.516654 \n",
" 0.95 24879.0 0.517105 \n",
" 0.98 24827.0 0.517211 \n",
" 0.2 0.20 0.0 0.831406 \n",
" 0.50 0.0 0.833369 \n",
" 0.90 0.0 0.832533 \n",
" 0.95 0.0 0.832533 \n",
" 0.98 0.0 0.831632 \n",
" 0.5 0.20 0.0 0.833289 \n",
" 0.50 0.0 0.832547 \n",
" 0.90 0.0 0.833289 \n",
" 0.95 0.0 0.833846 \n",
" 0.98 0.0 0.832215 \n",
" ham 0.0 0.20 24797.0 0.516786 \n",
" 0.50 24871.0 0.517038 \n",
" 0.90 24977.0 0.517211 \n",
" 0.95 24931.0 0.516561 \n",
" 0.98 24980.0 0.517091 \n",
" 0.2 0.20 24987.0 0.538015 \n",
" 0.50 24901.0 0.536623 \n",
" 0.90 24711.0 0.540985 \n",
" 0.95 24807.0 0.538572 \n",
" 0.98 24817.0 0.544419 \n",
" 0.5 0.20 24852.0 0.539288 \n",
" 0.50 24798.0 0.555027 \n",
" 0.90 24728.0 0.561020 \n",
" 0.95 24646.0 0.554616 \n",
" 0.98 24735.0 0.564428 \n",
"... ... ... \n",
"naive bayes dictionary 0.0 0.20 22129.0 0.105214 \n",
" 0.50 20999.0 0.124944 \n",
" 0.90 21072.0 0.110531 \n",
" 0.95 21889.0 0.102403 \n",
" 0.98 21233.0 0.112745 \n",
" 0.2 0.20 8159.0 0.518842 \n",
" 0.50 7158.0 0.501459 \n",
" 0.90 4760.0 0.604803 \n",
" 0.95 3743.0 0.580007 \n",
" 0.98 5135.0 0.572980 \n",
" 0.5 0.20 4907.0 0.540866 \n",
" 0.50 1632.0 0.621258 \n",
" 0.90 1869.0 0.649633 \n",
" 0.95 0.0 0.647816 \n",
" 0.98 1945.0 0.631467 \n",
" ham 0.0 0.20 21430.0 0.094023 \n",
" 0.50 20818.0 0.115569 \n",
" 0.90 21006.0 0.102031 \n",
" 0.95 20744.0 0.115994 \n",
" 0.98 16868.0 0.173593 \n",
" 0.2 0.20 20082.0 0.133867 \n",
" 0.50 18320.0 0.164748 \n",
" 0.90 19778.0 0.152603 \n",
" 0.95 19923.0 0.164748 \n",
" 0.98 18165.0 0.168687 \n",
" 0.5 0.20 16142.0 0.244915 \n",
" 0.50 15730.0 0.241958 \n",
" 0.90 16351.0 0.255284 \n",
" 0.95 20697.0 0.202843 \n",
" 0.98 14735.0 0.273502 \n",
"\n",
" error_train \n",
"classifier attack % poisoned attacker knowledge \n",
"adaline dictionary 0.0 0.20 0.516972 \n",
" 0.50 0.517025 \n",
" 0.90 0.517383 \n",
" 0.95 0.517038 \n",
" 0.98 0.516786 \n",
" 0.2 0.20 0.867767 \n",
" 0.50 0.865699 \n",
" 0.90 0.866614 \n",
" 0.95 0.866335 \n",
" 0.98 0.867091 \n",
" 0.5 0.20 0.914558 \n",
" 0.50 0.917224 \n",
" 0.90 0.916295 \n",
" 0.95 0.916256 \n",
" 0.98 0.917011 \n",
" ham 0.0 0.20 0.517396 \n",
" 0.50 0.517210 \n",
" 0.90 0.516879 \n",
" 0.95 0.517330 \n",
" 0.98 0.516852 \n",
" 0.2 0.20 0.531252 \n",
" 0.50 0.528573 \n",
" 0.90 0.533095 \n",
" 0.95 0.529793 \n",
" 0.98 0.534036 \n",
" 0.5 0.20 0.519173 \n",
" 0.50 0.526412 \n",
" 0.90 0.531716 \n",
" 0.95 0.527950 \n",
" 0.98 0.531756 \n",
"... ... \n",
"naive bayes dictionary 0.0 0.20 0.103381 \n",
" 0.50 0.125630 \n",
" 0.90 0.106523 \n",
" 0.95 0.101604 \n",
" 0.98 0.112437 \n",
" 0.2 0.20 0.421440 \n",
" 0.50 0.403660 \n",
" 0.90 0.481398 \n",
" 0.95 0.460316 \n",
" 0.98 0.454044 \n",
" 0.5 0.20 0.270631 \n",
" 0.50 0.312861 \n",
" 0.90 0.325510 \n",
" 0.95 0.324344 \n",
" 0.98 0.315487 \n",
" ham 0.0 0.20 0.093397 \n",
" 0.50 0.115937 \n",
" 0.90 0.103010 \n",
" 0.95 0.113524 \n",
" 0.98 0.172739 \n",
" 0.2 0.20 0.105940 \n",
" 0.50 0.130270 \n",
" 0.90 0.121374 \n",
" 0.95 0.134089 \n",
" 0.98 0.135521 \n",
" 0.5 0.20 0.121175 \n",
" 0.50 0.122302 \n",
" 0.90 0.126518 \n",
" 0.95 0.104177 \n",
" 0.98 0.136622 \n",
"\n",
"[90 rows x 9 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BATCH_IDs = ['1609071531', '1609072309']\n",
"DATASET_DIR = os.path.join('.')\n",
"\n",
"DF = join_repetitions(os.path.join(DATASET_DIR, '%s-df.dat' % batch_id) for batch_id in BATCH_IDs)\n",
"DF = DF.mean(level=DF.index.names[:-1]) ## assuming repetition is at the end\n",
"\n",
"display(DF)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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l9GA+34XGkoi4KCIeiohHIuLPB3j+8sLVT+4pfL2zEu1UJqV0fErpp30ea0opjSuEV12P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hlzUrmMOalcjWKuu8mJXdSSVs8DSk9cSZIkSZIkSdBkOGFmDgKPRsQhJbVHkiRJkiRJGqfhnViF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\nSZKkjvf/A2X54Mk9/bkMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dee119cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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NKX1qqAvtpw6/uUpFYIjVk9cfjWXNLc2sWreK2TNm9xiB1cFfqKXi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\njuuxWLBmSboiV2IRjDNR+UERKxn5B8gslOmEh2O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TKzfYOI64FjgFtpXE4YQGbmL3R/eGPGkdONVdLsRQSZGfM9jl5i/pHam+u8YcxJ7RlzUrGMOalY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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dede51518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = DF[['AUC', 'FNR', 'FPR', 'error_test', 'error_train']]\n",
"visualise(df.xs(0.2, axis=0, level='% poisoned'), dimension_rows='classifier', dimension_color='attack')\n",
"visualise(df.xs(0.5, axis=0, level='% poisoned'), dimension_rows='classifier', dimension_color='attack')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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29MCC+jcubFomPPro4FBraNB1330wf37j1ly77gpz5kzMm5PaiCHWcF5oSI15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MzMzMzMzMys5rmJZWZmZmZmZmZVJWlK0fAgSV/rwrp+LmlClbbrWknfqsa6OogzUtKrFb6navvZ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\nzMzMzKzm/T8OKgyCPvfK0wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dee456b70>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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9WW2fk2qzz0nNVavPNTqINQn4C3AccA9wLfCalNLNFcvMBkoppRQRHwO2ppTO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\n3v8DL8JbxUjK3YQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7dee33e390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = DF[['AUC', 'FNR', 'FPR', 'error_test', 'error_train']]\n",
"visualise(df.xs(0.2, axis=0, level='% poisoned'), dimension_rows='attack', dimension_color='classifier')\n",
"visualise(df.xs(0.5, axis=0, level='% poisoned'), dimension_rows='attack', dimension_color='classifier')"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
Raw
1609072309-vary-knowledge.yaml
default_parameters:
experiment: offline training
dataset_filename: trec2007-1607252257
label_type:
ham_label: -1
spam_label: 1
classifier:
type: null
training_parameters:
gradient_descent_method: mini-batch
batch_size: 10
max_epochs: 100
initial_weights: null
convergence_look_back: 5
divergence_threshold: 1.
testing_parameters: {}
attack:
type: null
parameters: {}
parameter_ranges:
- name: classifier
key: [classifier, type]
values:
- adaline
- logistic regression
- naive bayes
- name: attack
key: [attack, type]
values:
- dictionary
- ham
- name: '% poisoned'
key: [attack, parameters, percentage_samples_poisoned]
values:
- .0
- .2
- .5
- name: 'attacker knowledge'
key: [attack, parameters, percentage_features_poisoned]
values:
- .20
- .50
- .90
- .95
- .98
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