Polynomial Logistic Regression weird convergence issue? Bug?
Hi!
I'm wondering if anyone could share some insight into this problem. I'm trying to run an evolutionary search of parameters for the mentioned operator, but it won't converge.
I've been trying to find out why, but ran into some trouble pinning it down. Take the sonar data example proposed by the logistic regression operator:
2) Then I changed max iterations to 1 and started going up. 291 max iterations runs fine, again, almost instantly. 292 max iterations, one minute going already... won't converge.
I'm not a doctor in machine learning algorithms but this looks like a bug to me ;D...
Any ideas of how to cope with this while it gets fixed?
Thanks! I hope the bug report helps.
I'm wondering if anyone could share some insight into this problem. I'm trying to run an evolutionary search of parameters for the mentioned operator, but it won't converge.
I've been trying to find out why, but ran into some trouble pinning it down. Take the sonar data example proposed by the logistic regression operator:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>1) I simply changed to polynomial and set degree at 12. This runs instantly on my PC, but when changing degree to 13, it won't converge not even past 5 minutes.
<process version="5.3.015">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Sonar" width="90" x="380" y="120">
<parameter key="repository_entry" value="//Samples/data/Sonar"/>
</operator>
<operator activated="true" class="split_validation" compatibility="5.3.015" expanded="true" height="112" name="Validation" width="90" x="514" y="120">
<process expanded="true">
<operator activated="true" class="logistic_regression" compatibility="5.3.015" expanded="true" height="94" name="Logistic Regression" width="90" x="112" y="30">
<parameter key="kernel_type" value="polynomial"/>
<parameter key="kernel_degree" value="12.0"/>
</operator>
<connect from_port="training" to_op="Logistic Regression" to_port="training set"/>
<connect from_op="Logistic Regression" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="5.3.015" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_op="Sonar" from_port="output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" to_port="result 1"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="90"/>
<portSpacing port="sink_result 2" spacing="18"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>
2) Then I changed max iterations to 1 and started going up. 291 max iterations runs fine, again, almost instantly. 292 max iterations, one minute going already... won't converge.
I'm not a doctor in machine learning algorithms but this looks like a bug to me ;D...
Any ideas of how to cope with this while it gets fixed?
Thanks! I hope the bug report helps.
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mafern76
New Altair Community Member
OPPS. with same scenario I tried anova and epach with 1000000 degree and it runs instantly.
Thanks Marco..
An update on anova and epach, on a real-case scenario (1000/1000 cases, 17 predictors, evo search) anova also does get stuck immediately. Epach runs but seems to be the slowest, by far, of all the log. reg. kernel types.
This anova runs slowly but at least it runs:
An update on anova and epach, on a real-case scenario (1000/1000 cases, 17 predictors, evo search) anova also does get stuck immediately. Epach runs but seems to be the slowest, by far, of all the log. reg. kernel types.
This anova runs slowly but at least it runs:
<parameter key="LR_ANOVA_MOD.kernel_gamma" value="[0.00001;1]"/>RM gets a little bit unresponsive though. Maybe something overloads something else, somewhere, somehow?.. some combinations of parameters seem to be growing the problem exponentially, sometimes to a point of no return.
<parameter key="LR_ANOVA_MOD.kernel_degree" value="[1;2]"/>
<parameter key="LR_ANOVA_MOD.C" value="[0;10]"/>
<parameter key="LR_ANOVA_MOD.convergence_epsilon" value="[0.1;0.5]"/>
Maybe this short run log can help: as you can see on a plot, time between validations varies a lot.
Oh and by the way, I think this is the first time I've seen this... C takes values beyond the '10' limit, maybe it's related, or maybe just another bug, I don't know.
Oh and by the way, I think this is the first time I've seen this... C takes values beyond the '10' limit, maybe it's related, or maybe just another bug, I don't know.
time memo c gamma degree conv_epsilon perfo perfo_dev
38150.0 1.309247264E9 5.708774309130852 0.4815469360780778 1.6894985156297109 0.19529838321505755 0.6884993281209857 0.009104510275475342
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41967.0 9.52729576E8 4.045221107690375 0.5418650318132358 1.15506380864328 0.34045601477565923 0.7129182553289294 0.007991655337273243
45275.0 1.003653544E9 5.582406057073806 0.7529332875079319 1.5722415400323753 0.3292090558828451 0.6910714863323157 0.02577555337930816
48630.0 1.061641624E9 1.9208786040692127 0.09031466602051914 1.3506065039940034 0.21694383601777456 0.7229121687906822 0.01876950586447982
49876.0 1.107410392E9 7.064506657257637 0.1708202860122061 1.7701408475473643 0.3574992319873054 0.7139310553653035 0.01432962989892049
51702.0 1.149064168E9 9.283586508053055 0.6414603799727803 1.12793541677748 0.18930458951378593 0.7143786177070958 0.008817034286516803
70348.0 1.135039096E9 3.932816792972239 0.6092641243325083 1.1693069734344674 0.4941098025556281 0.7049589509669353 0.008141281022841543
74978.0 1.23385896E9 2.426932483403231 0.31428682470647146 1.0870086081876171 0.20659435998579867 0.7204462063977267 0.016030465001562005
79201.0 1.30768132E9 6.418460635092642 0.5810773923204395 1.166485633857378 0.23746993715202766 0.7101477140228466 0.013473914170796316
85176.0 1.393157744E9 1.184470914832041 0.10107715281597253 1.7012496415722138 0.3995335283412699 0.7202398762791491 0.00782635395314254
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97045.0 1.312284944E9 9.51802920569946 0.8976145120135676 1.1733254479345103 0.37414430487644434 0.7013951787827462 0.022094308373348394
102833.0 1.388376928E9 1.7050302288006303 0.23896274248266555 1.8167816553198142 0.20543990440885818 0.714600868873405 0.002085741291580042
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121557.0 1.67546256E9 5.3924435300248375 0.2425121107196942 1.197427457321235 0.3500783570894842 0.7130529587327702 0.009580636347955454
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142974.0 1.597683736E9 5.218303991020648 0.8676367182533121 1.972198391057401 0.4303996662608066 0.6721368425662538 0.004328388452869105
147476.0 1.651625624E9 8.036174047387362 0.9129158386603619 1.4308164824476644 0.30448794779088084 0.6984339989051289 0.013353620591657922
147730.0 1.675805136E9 8.921163654249808 0.9656971378021245 1.8116543957805584 0.22724217242201505 0.6636951907581815 0.020795025400285255
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174154.0 1.826358744E9 3.2951994655398753 0.5892307812642472 1.54209476170346 0.29614317152020597 0.6981369710146392 0.03402833291333147
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254907.0 1.971291784E9 9.96497724600189 0.05612611640414672 1.0834359529437907 0.3775511447043972 0.7187511593159757 0.011292863665187895
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295929.0 2.155508528E9 342.67737363115833 0.5404634943325216 2.1836355075233818 0.05884254737441337 0.6791311415351625 0.023479206163966097
303978.0 2.19254576E9 2.4624364654236097 0.8174025041672938 1.1502518288632528 0.48315178280150217 0.7202227582278297 0.013892972909008264
309788.0 2.273454056E9 2.8176101971941323 0.4575477587989642 1.2706111274897673 0.48756478978463835 0.7160086260938613 0.00948390904823626
312984.0 2.332199376E9 0.7456447992776349 0.2293319245498495 1.946072463049354 0.24808400140905118 0.7108681841828165 0.025138593987299195
314633.0 2.395150192E9 0.6975301272663237 0.7233412664505098 1.7690119167781742 0.48888819197647293 0.6970509782588327 0.013302220806821701
321418.0 1.827417896E9 1.3983933694768902 0.9852447269108896 1.373261970300323 0.4809482219053224 0.7072075042080977 0.005279657152730951
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339001.0 2.142573664E9 9.807825867576703 0.4997269304832418 1.536639681141258 0.3935068022132546 0.6876287645110363 0.014920285261340066
344528.0 2.226778E9 9.60266936279813 0.20148109582311394 1.7203381737135124 0.14434597509853841 0.7045443422239384 0.0169090032230886
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352430.0 2.081428968E9 9.69625966309308 0.7915715293919935 1.358117345181303 0.3021715550538516 0.7016607875790409 0.008716466946462657
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