tag:blogger.com,1999:blog-34983319254950161072024-03-19T01:18:32.588-07:00ai @ wvuModeling Intelligence Lab ("the MILL")Unknownnoreply@blogger.comBlogger459125tag:blogger.com,1999:blog-3498331925495016107.post-69814730161285315902014-11-20T08:54:00.002-08:002014-11-20T08:54:39.600-08:00New Param Ranges<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg2-H7Ftdrhq-6mV_0lLHdrEVHHsqfwuCvNhnEXUsV-ODzUxo9cp-fFRVeyreCf5Z1mXv0UWQjs-7NvNFG9CmYsDZQfbeZPCz2hK6cjQRX0wjmzRHXKrd3vpzQ133Mw0lE23Df4QA07iLs/s1600/0_summary.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg2-H7Ftdrhq-6mV_0lLHdrEVHHsqfwuCvNhnEXUsV-ODzUxo9cp-fFRVeyreCf5Z1mXv0UWQjs-7NvNFG9CmYsDZQfbeZPCz2hK6cjQRX0wjmzRHXKrd3vpzQ133Mw0lE23Df4QA07iLs/s1600/0_summary.png" height="640" width="568" /></a></div>
Table of Rank Sums Across All Datasets (Random Forest)<br />
66 : Default Cur -> Cur<br />
41 : Tuned Prev -> Cur<br />
28 : Tuned Cur -> Cur<br />
0 : Default Prev -> Cur<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxAAyBBPRKXQS2JxMSe49xGwwdfbWJ8Xg-V3yTPHMeKEqybJ6xIQOg0WCbPikiAUpE69_PQZmwInU3ty7WEnHhOzZLKeeXGsRMDzilcTLP5NDnWXdf6oY7cbFbgTtl0f7UPn20irfK00U/s1600/0_summary.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhxAAyBBPRKXQS2JxMSe49xGwwdfbWJ8Xg-V3yTPHMeKEqybJ6xIQOg0WCbPikiAUpE69_PQZmwInU3ty7WEnHhOzZLKeeXGsRMDzilcTLP5NDnWXdf6oY7cbFbgTtl0f7UPn20irfK00U/s1600/0_summary.png" height="640" width="568" /></a><br /><br />Table of Rank Sums Across All Datasets (Logistic Regression)<br />66 : Default Cur -> Cur<br />41 : Tuned Prev -> Cur<br />28 : Tuned Cur -> Cur<br /> 0 : Default Prev -> Cur</div>
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As it turns out, I had a flawed assumption about Bernouli NB. I thought that the binarize parameter was a percentage threshold, where instead it is a value threshold.<br /><br />In other words, I was under the impression a binarize of 0.3 meant that the bottom 30% of values got translated to 'low' and 70% got translated as 'high'. This is not the case. A binarize of 0.3 means that all values below 0.3 become 'low' and all values greater than 0.3 become 'high'. </div>
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Below are the results generated from Bernouli NB without the binarize parameter being fixed. For the next set of results, I intend to pre-process the inputs by normalizing 0-100 and then providing a binarize param of 0-100.<br /><br /></div>
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<br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhv8snxpNczErmb9aDGDBFJrlFSEhBze1xNW97RPS20Qz2F1KxxitZJ6tyXBlobn5u6yXO6Oo5YLDbeiIPLHLIijieVyo6VRuv2bc6kaoUwfKVljPALUf56AI7STQk0tOoRqOOELzhcsKU/s1600/0_summary.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhv8snxpNczErmb9aDGDBFJrlFSEhBze1xNW97RPS20Qz2F1KxxitZJ6tyXBlobn5u6yXO6Oo5YLDbeiIPLHLIijieVyo6VRuv2bc6kaoUwfKVljPALUf56AI7STQk0tOoRqOOELzhcsKU/s1600/0_summary.png" height="640" width="568" /></a></div>
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Table of Rank Sums Across All Datasets (Bernoulli NB)</div>
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41 : Tuned Cur -> Cur</div>
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28 : Tuned Prev -> Cur</div>
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20 : Default Cur -> Cur</div>
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6 : Default Prev -> Cur</div>
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Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-65047427225638267662014-10-08T08:01:00.001-07:002014-10-08T08:22:06.628-07:00XOMO PAPER v2.0<div dir="ltr" style="text-align: left;" trbidi="on">
<pre><span style="font-size: 5pt;">Techniques -effort -months -defects -risks #
Base Line xomoal500 m 3 4 1 63 #
CT0*_xomoal500 m 2 4 0 63 #
CT0_xomoal500 m <span style="background-color: lime;">1 4 0 </span> 23 #
CT1*_xomoal500 m 2 3 3 24 #
CT1_xomoal500 m 1 3 3 23 #
NSGA xomoal500 m 7 44 1 0 #
Base Line xomoal500 q 1 0 6 11 #
CT0*_xomoal500 q 2 0 6 15 #
CT0_xomoal500 q <span style="background-color: lime;">3 1 </span> <span style="background-color: lime;">3 5 </span> #
CT1*_xomoal500 q 0 0 6 1 #
CT1_xomoal500 q 0 0 6 1 #
NSGA xomoal500 q 8 22 1 2 #
Base Line xomoal500 w 22 4 39 87 #
CT0*_xomoal500 w 36 4 100 100 #
CT0_xomoal500 w 100 <span style="background-color: lime;"> 4 </span> 62 49 #
CT1*_xomoal500 w 42 3 65 44 #
CT1_xomoal500 w <span style="background-color: red;">40 </span> 3 65 <span style="background-color: red;"> 42</span> #
NSGA xomoal500 w 94 100 24 33 #
100 6809.1 65.27 229326.0 25.7 #
0 544.09 0.0 3752.55 1.69 # </span></pre>
<pre><span style="font-size: 5pt;"> </span></pre>
<pre><span style="font-size: 5pt;"> </span></pre>
<pre><span style="font-size: 5pt;"> </span></pre>
<pre><span style="font-size: 5pt;">Techniques -effort -months -defects -risks #
Base Line xomofl500 m 10 4 3 84 #
CT0*_xomofl500 m 10 4 3 85 #
CT0_xomofl500 m <span style="background-color: lime;">0 3 0 </span> 17 #
CT1*_xomofl500 m 3 3 4 3 #
CT1_xomofl500 m 4 3 6 <span style="background-color: red;"> 4 </span> #
NSGA xomofl500 m 6 41 2 4 #
Base Line xomofl500 q 8 0 6 5 #
CT0*_xomofl500 q 10 0 8 6 #
CT0_xomofl500 q <span style="background-color: lime;">6 0 4 0 </span> #
CT1*_xomofl500 q 3 0 8 0 #
CT1_xomofl500 q 4 0 9 0 #
NSGA xomofl500 q 8 21 3 7 #
Base Line xomofl500 w 53 4 37 98 #
CT0*_xomofl500 w 100 4 60 100 #
CT0_xomofl500 w <span style="background-color: lime;">71 3 </span> 41 <span style="background-color: lime;"> 27 </span> #
CT1*_xomofl500 w 56 3 81 21 #
CT1_xomofl500 w <span style="background-color: red;">64 </span> 3 100 22 #
NSGA xomofl500 w 96 100 34 46 #
100 7557.7 68.97 184550.15 21.4 #
0 560.08 0.0 2178.01 0.93 # </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: 5pt;">Techniques -effort -months -defects -risks #
Base Line xomogr500 m 6 4 2 64 #
CT0*_xomogr500 m 6 4 2 63 #
CT0_xomogr500 m <span style="background-color: lime;">0 2 0 </span> 11 #
CT1*_xomogr500 m 6 3 8 18 #
CT1_xomogr500 m 6 3 7 18 #
NSGA xomogr500 m 11 41 2 1 #
Base Line xomogr500 q 4 0 7 11 #
CT0*_xomogr500 q 5 0 8 12 #
CT0_xomogr500 q <span style="background-color: lime;">0 0 2 0 </span> #
CT1*_xomogr500 q 3 0 9 6 #
CT1_xomogr500 q 3 0 9 7 #
NSGA xomogr500 q 13 22 3 5 #
Base Line xomogr500 w 23 4 34 86 #
CT0*_xomogr500 w 36 4 100 100 #
CT0_xomogr500 w <span style="background-color: lime;">18 2 27 23 </span> #
CT1*_xomogr500 w 37 3 73 42 #
CT1_xomogr500 w 39 3 80 43 #
NSGA xomogr500 w 100 100 34 41 #
100 6319.6 67.75 182979.6 23.6 #
0 248.43 0.0 1640.18 1.3 # </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: 5pt;">Techniques -effort -months -defects -risks #
Base Line xomoo2500 m 6 4 31 78 #
CT0*_xomoo2500 m 5 4 16 42 #
CT0_xomoo2500 m <span style="background-color: lime;">2 2 5 </span> 14 #
CT1*_xomoo2500 m 8 3 2 25 #
CT1_xomoo2500 m 7 2 2 20 #
NSGA xomoo2500 m 14 42 5 5 #
Base Line xomoo2500 q 1 0 14 17 #
CT0*_xomoo2500 q 1 0 8 9 #
CT0_xomoo2500 q <span style="background-color: lime;">0 0 1 2</span> #
CT1*_xomoo2500 q 2 0 0 1 #
CT1_xomoo2500 q 1 0 0 0 #
NSGA xomoo2500 q 15 21 8 9 #
Base Line xomoo2500 w 12 4 65 100 #
CT0*_xomoo2500 w 16 4 47 57 #
CT0_xomoo2500 w <span style="background-color: lime;">9 2 17 21</span> #
CT1*_xomoo2500 w 24 3 15 33 #
CT1_xomoo2500 w 19 2 13 26 #
NSGA xomoo2500 w 100 100 100 51 #
100 6163.92 67.25 65267.84 18.53 #
0 111.2 0.0 1703.33 0.71 # </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: xx-small;"> </span></pre>
<pre><span style="font-size: 5pt;"> Techniques -effort -months -defects -risks #
Base Line xomoos500 m 4 4 0 88 #
CT0*_xomoos500 m 3 4 0 88 #
CT0_xomoos500 m <span style="background-color: lime;">6 3 4 </span> 36 #
CT1*_xomoos500 m 4 2 2 25 #
CT1_xomoos500 m 4 2 2 <span style="background-color: red;"> 26 </span> #
NSGA xomoos500 m 15 43 8 8 #
Base Line xomoos500 q 0 0 1 4 #
CT0*_xomoos500 q 0 0 1 3 #
CT0_xomoos500 q <span style="background-color: lime;">2 0 4 4 </span> #
CT1*_xomoos500 q 0 0 2 1 #
CT1_xomoos500 q 0 0 2 0 #
NSGA xomoos500 q 16 21 11 13 #
Base Line xomoos500 w 8 4 6 98 #
CT0*_xomoos500 w 14 4 11 100 #
CT0_xomoos500 w <span style="background-color: lime;">26 3 29 47</span> #
CT1*_xomoos500 w 13 2 16 32 #
CT1_xomoos500 w <span style="background-color: red;">13 </span> 2 <span style="background-color: red;"> 17</span> <span style="background-color: red;"> 31 </span> #
NSGA xomoos500 w 100 100 100 73 #
100 5905.91 65.63 60206.72 13.9 #
0 119.84 0.0 197.22 0.72 #
</span></pre>
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NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-78169014409037258602014-10-03T09:18:00.001-07:002014-10-03T09:18:17.148-07:00XOMO and POM PAPERXOMO<br />
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<span style="font-size: x-small;">Base Line xomofl500 m 13 12 10 6 #<br /> CT0*_xomofl500 m 12 11 9 7 #<br /> CT0_xomofl500 m 0 1 0 0 #<br /> CT1*_xomofl500 m 3 7 1 4 #<br /> CT1_xomofl500 m 11 7 4 3 #<br /> NSGA xomofl500 m 0 48 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomofl500 q 11 0 10 14 #<br /> CT0*_xomofl500 q 12 0 11 13 #<br /> CT0_xomofl500 q 0 0 0 1 #<br /> CT1*_xomofl500 q 3 0 1 8 #<br /> CT1_xomofl500 q 7 0 4 9 #<br /> NSGA xomofl500 q 1 21 0 13 #<br />-------------------------------------------------------------------------------------<br />Base Line xomofl500 w 49 12 47 41 #<br /> CT0*_xomofl500 w 100 11 100 60 #<br /> CT0_xomofl500 w 8 1 8 6 #<br /> CT1*_xomofl500 w 21 7 15 33 #<br /> CT1_xomofl500 w 51 8 45 33 #<br /> NSGA xomofl500 w 6 100 3 100 #<br />-------------------------------------------------------------------------------------<br /> Techniques -effort -months -defects -risks #<br /> 100 6845.6 33.3 66229.2 4.0 #<br /> 0 81.3 0.0 259.29 0.0 #</span><br />
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<span style="font-size: x-small;">Base Line xomogr500 m 18 10 8 14 #<br /> CT0*_xomogr500 m 17 10 7 14 #<br /> CT0_xomogr500 m 17 10 8 14 #<br /> CT1*_xomogr500 m 8 5 4 2 #<br /> CT1_xomogr500 m 17 10 8 13 #<br /> NSGA xomogr500 m 0 42 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomogr500 q 17 0 11 20 #<br /> CT0*_xomogr500 q 19 0 11 20 #<br /> CT0_xomogr500 q 19 0 11 19 #<br /> CT1*_xomogr500 q 9 0 5 5 #<br /> CT1_xomogr500 q 17 0 11 19 #<br /> NSGA xomogr500 q 1 19 0 7 #<br />-------------------------------------------------------------------------------------<br />Base Line xomogr500 w 65 10 40 62 #<br /> CT0*_xomogr500 w 100 10 100 100 #<br /> CT0_xomogr500 w 99 10 91 93 #<br /> CT1*_xomogr500 w 58 5 37 42 #<br /> CT1_xomogr500 w 64 10 39 60 #<br /> NSGA xomogr500 w 22 100 1 92 #<br />-------------------------------------------------------------------------------------<br /> Techniques -effort -months -defects -risks #<br /> 100 3145.8 37.0 68144.5 4.3 #<br /> 0 61.16 0.0 174.68 0.54 #</span><br />
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<span style="font-size: x-small;">Base Line xomoos500 m 28 10 21 46 #<br /> CT0*_xomoos500 m 27 10 19 33 #<br /> CT0_xomoos500 m 33 10 27 31 #<br /> CT1*_xomoos500 m 13 5 12 26 #<br /> CT1_xomoos500 m 13 4 11 22 #<br /> NSGA xomoos500 m 0 39 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoos500 q 9 0 10 33 #<br /> CT0*_xomoos500 q 10 1 11 42 #<br /> CT0_xomoos500 q 10 1 13 49 #<br /> CT1*_xomoos500 q 3 0 6 6 #<br /> CT1_xomoos500 q 3 0 6 5 #<br /> NSGA xomoos500 q 1 10 0 12 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoos500 w 52 11 45 100 #<br /> CT0*_xomoos500 w 93 10 65 99 #<br /> CT0_xomoos500 w 100 10 100 92 #<br /> CT1*_xomoos500 w 32 5 39 27 #<br /> CT1_xomoos500 w 33 5 35 26 #<br /> NSGA xomoos500 w 9 100 10 93 #<br />-------------------------------------------------------------------------------------<br /> Techniques -effort -months -defects -risks #<br /> 100 3479.32 29.4 26699.79 5.95 #<br /> 0 67.25 0.1 191.75 0.67 #</span><br />
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<span style="font-size: x-small;">Base Line xomoo2500 m 57 16 31 7 #<br /> CT0*_xomoo2500 m 27 8 16 7 #<br /> CT0_xomoo2500 m 6 3 2 2 #<br /> CT1*_xomoo2500 m 20 7 15 1 #<br /> CT1_xomoo2500 m 21 6 15 2 #<br /> NSGA xomoo2500 m 2 59 1 23 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoo2500 q 16 2 11 13 #<br /> CT0*_xomoo2500 q 8 1 10 8 #<br /> CT0_xomoo2500 q 0 1 0 3 #<br /> CT1*_xomoo2500 q 3 0 6 0 #<br /> CT1_xomoo2500 q 4 0 5 0 #<br /> NSGA xomoo2500 q 2 45 2 34 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoo2500 w 100 18 66 16 #<br /> CT0*_xomoo2500 w 78 9 100 8 #<br /> CT0_xomoo2500 w 18 3 21 3 #<br /> CT1*_xomoo2500 w 41 7 48 1 #<br /> CT1_xomoo2500 w 46 6 49 2 #<br /> NSGA xomoo2500 w 20 100 18 100 #</span><br />
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<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">Base Line xomoal500 m 12 11 6 10 #<br /> CT0*_xomoal500 m 12 12 6 9 #<br /> CT0_xomoal500 m 11 11 6 9 #<br /> CT1*_xomoal500 m 6 5 4 0 #<br /> CT1_xomoal500 m 12 10 5 13 #<br /> NSGA xomoal500 m 0 39 0 1 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoal500 q 12 2 9 14 #<br /> CT0*_xomoal500 q 14 5 10 15 #<br /> CT0_xomoal500 q 14 5 10 17 #<br /> CT1*_xomoal500 q 7 0 5 4 #<br /> CT1_xomoal500 q 14 1 8 16 #<br /> NSGA xomoal500 q 0 21 0 21 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoal500 w 52 16 40 59 #<br /> CT0*_xomoal500 w 99 16 100 100 #<br /> CT0_xomoal500 w 83 16 90 91 #<br /> CT1*_xomoal500 w 52 8 62 49 #<br /> CT1_xomoal500 w 100 16 73 96 #<br /> NSGA xomoal500 w 3 100 2 56 #<br />-------------------------------------------------------------------------------------<br /> Techniques -effort -months -defects -risks #<br /> 100 6069.8 27.8 91852.0 9.7 #<br /> 0 47.89 0.52 164.88 1.1 #</span><br />
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<span style="font-size: x-small;">POM</span><br />
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<span style="font-size: x-small;">Base Line pom3A500 m 25 88 14 #<br /> CT0*_pom3A500 m 22 84 14 #<br /> CT0_pom3A500 m 22 84 14 #<br /> CT1*_pom3A500 m 14 49 4 #<br /> CT1_pom3A500 m 10 35 0 #<br /> NSGA pom3A500 m 1 85 16 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3A500 q 16 8 30 #<br /> CT0*_pom3A500 q 21 5 42 #<br /> CT0_pom3A500 q 21 5 41 #<br /> CT1*_pom3A500 q 8 1 12 #<br /> CT1_pom3A500 q 4 0 4 #<br /> NSGA pom3A500 q 0 15 42 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3A500 w 60 100 71 #<br /> CT0*_pom3A500 w 100 96 86 #<br /> CT0_pom3A500 w 99 96 84 #<br /> CT1*_pom3A500 w 41 56 48 #<br /> CT1_pom3A500 w 26 40 32 #<br /> NSGA pom3A500 w 4 96 100 #<br />-------------------------------------------------------------------------------------<br /> Techniques -cost +completion -idle #<br /> 100 2694.4 1.04 0.8 #<br /> 0 37.6 0.05 0.11 #</span><br />
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<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">Base Line pom3B500 m 24 87 22 #<br /> CT0*_pom3B500 m 21 83 20 #<br /> CT0_pom3B500 m 21 83 20 #<br /> CT1*_pom3B500 m 13 39 0 #<br /> CT1_pom3B500 m 10 38 3 #<br /> NSGA pom3B500 m 3 85 3 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 q 19 9 28 #<br /> CT0*_pom3B500 q 21 5 23 #<br /> CT0_pom3B500 q 21 5 23 #<br /> CT1*_pom3B500 q 9 0 8 #<br /> CT1_pom3B500 q 9 1 2 #<br /> NSGA pom3B500 q 0 5 23 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 w 72 100 72 #<br /> CT0*_pom3B500 w 100 96 100 #<br /> CT0_pom3B500 w 97 96 97 #<br /> CT1*_pom3B500 w 45 45 35 #<br /> CT1_pom3B500 w 47 45 38 #<br /> NSGA pom3B500 w 9 96 100 #<br />-------------------------------------------------------------------------------------<br /> Techniques -cost +completion -idle #<br /> 100 31762.3 1.04 0.8 #<br /> 0 494.8 0.05 0.15 #</span><br />
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<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">Base Line pom3B500 m 24 87 22 #<br /> CT0*_pom3B500 m 21 83 20 #<br /> CT0_pom3B500 m 21 83 20 #<br /> CT1*_pom3B500 m 13 39 0 #<br /> CT1_pom3B500 m 10 38 3 #<br /> NSGA pom3B500 m 3 85 3 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 q 19 9 28 #<br /> CT0*_pom3B500 q 21 5 23 #<br /> CT0_pom3B500 q 21 5 23 #<br /> CT1*_pom3B500 q 9 0 8 #<br /> CT1_pom3B500 q 9 1 2 #<br /> NSGA pom3B500 q 0 5 23 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 w 72 100 72 #<br /> CT0*_pom3B500 w 100 96 100 #<br /> CT0_pom3B500 w 97 96 97 #<br /> CT1*_pom3B500 w 45 45 35 #<br /> CT1_pom3B500 w 47 45 38 #<br /> NSGA pom3B500 w 9 96 100 #<br />-------------------------------------------------------------------------------------<br /> Techniques -cost +completion -idle #<br /> 100 31762.3 1.04 0.8 #<br /> 0 494.8 0.05 0.15 #<br /></span><br />
<span style="font-size: x-small;"><br /></span>NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-63820507979341244612014-09-25T14:23:00.001-07:002014-10-30T08:59:39.631-07:00New Results Format<h2>
<span style="font-size: x-large;">
10/30/14 - Rank Sums, NSGAII-style Selection</span></h2>
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<h3>
<span style="font-size: large;"><b>Random Forest</b></span></h3>
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<div>
<b>Table of Rank Sums Across All Data-sets</b></div>
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60 : Default Cur -> Cur</div>
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35 : Tuned Prev -> Cur</div>
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28 : Tuned Cur -> Cur</div>
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0 : Default Prev -> Cur</div>
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<br /></div>
<div>
<b>Overall Rankings</b></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEif082MG6FT_BIvRvOe0x1IYBGBwQL5RrX7k6z5W0OQr32mD5HVth-0hPvkKaDUb7Rcxwu1mBoWCgB_-Y1vqE9POnhI6sEAPy8miHLW_PJFZGi2R0yaaYNbZHveHUWQFR5iBOlDKhLvjNo/s1600/0_summary.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEif082MG6FT_BIvRvOe0x1IYBGBwQL5RrX7k6z5W0OQr32mD5HVth-0hPvkKaDUb7Rcxwu1mBoWCgB_-Y1vqE9POnhI6sEAPy8miHLW_PJFZGi2R0yaaYNbZHveHUWQFR5iBOlDKhLvjNo/s1600/0_summary.png" height="134" width="640" /></a></div>
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</h2>
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<b>Params (387 permutations)</b></div>
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<div>
<span style="font-size: x-small;">('bootstrap', ['values', True])</span></div>
<div>
<span style="font-size: x-small;">('min_samples_leaf', ['values', 1])</span></div>
<div>
<span style="font-size: x-small;">('n_estimators', ['values', 8, 16, 32])</span></div>
<div>
<span style="font-size: x-small;">('min_samples_split', ['values', 2])</span></div>
<div>
<span style="font-size: x-small;">('criterion', ['values', 'gini'])</span></div>
<div>
<span style="font-size: x-small;">('max_features', ['values', 2, 4, 8, 16])</span></div>
<div>
<span style="font-size: x-small;">('max_depth', ['values', 2, 4, 6, 8, 10, 12, 14, 16, 18])</span></div>
</div>
<div>
<br /></div>
<div>
<div>
<span style="font-size: large;"><b>Bernoulli Bayes</b></span></div>
<div>
<div>
<b><br /></b></div>
<div>
<b>Table of Rank Sums Across All Data-sets</b></div>
<div>
29 : Tuned Cur -> Cur</div>
<div>
28 : Tuned Prev -> Cur</div>
<div>
21 : Default Cur -> Cur</div>
<div>
9 : Default Prev -> Cur</div>
</div>
<div>
<br /></div>
<div>
<b>Overall Rankings</b></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgAyyQS_JvFd2TstHbfTj_8rmvkaoUF7SDHRlU0oU-ZBoHxcI0Ajs6EAymcQ36prUIbfYb4-2hmn2giyNs9WOC7eaxWYb6zPAW1MwfeIHG0wlPm1bf9AWmIZWcJA1326_jWi7xQ_dsg8Mg/s1600/0_summary.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgAyyQS_JvFd2TstHbfTj_8rmvkaoUF7SDHRlU0oU-ZBoHxcI0Ajs6EAymcQ36prUIbfYb4-2hmn2giyNs9WOC7eaxWYb6zPAW1MwfeIHG0wlPm1bf9AWmIZWcJA1326_jWi7xQ_dsg8Mg/s1600/0_summary.png" height="136" width="640" /></a></div>
<h2>
</h2>
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
<div>
<b>Params (50 permutations)</b></div>
<div>
<div>
<span style="font-size: x-small;">('binarize', ['values', 0.0, 0.2, 0.4, 0.6, 0.8])</span></div>
<div>
<span style="font-size: x-small;">('alpha', ['values', 0.0, 0.2, 0.4, 0.6, 0.8])</span></div>
<div>
<span style="font-size: x-small;">('fit_prior', ['values', True, False])</span></div>
</div>
</div>
<div>
<br /></div>
<div>
<div>
<span style="font-size: large;"><b>Logistic Regression</b></span></div>
<div>
<div>
<b><br /></b></div>
<div>
<b>Table of Rank Sums Across All Data-sets</b></div>
<div>
31 : Default Cur -> Cur</div>
<div>
27 : Tuned Cur -> Cur</div>
<div>
24 : Tuned Prev -> Cur</div>
<div>
5 : Default Prev -> Cur</div>
</div>
<div>
<br /></div>
<div>
<b>Overall Rankings</b></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiU9W6SMfHgD8ytAT7ktC5PFpInxj0uU2ZXmYO6CQMJQK6tNoqw86V-SdJKPNhgsTNxHj0d6zo6TnADJu7qOQXu2Z5Y1VXEtj53H6eAyAfzYBfn6KPvHIQQOoUuogEPspMtiiQZ40a8b6Y/s1600/0_summary.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiU9W6SMfHgD8ytAT7ktC5PFpInxj0uU2ZXmYO6CQMJQK6tNoqw86V-SdJKPNhgsTNxHj0d6zo6TnADJu7qOQXu2Z5Y1VXEtj53H6eAyAfzYBfn6KPvHIQQOoUuogEPspMtiiQZ40a8b6Y/s1600/0_summary.png" height="136" width="640" /></a></div>
<h2>
</h2>
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
<div>
<b>Params (36 permutations)</b></div>
<div>
<div>
<span style="font-size: x-small;">('penalty', ['values', 'l1', 'l2'])</span></div>
<div>
<span style="font-size: x-small;">('C', ['values', 0.5, 1, 2])</span></div>
<div>
<span style="font-size: x-small;">('class_weight', ['values', None])</span></div>
<div>
<span style="font-size: x-small;">('intercept_scaling', ['values', 0.5, 1, 2])</span></div>
<div>
<span style="font-size: x-small;">('fit_intercept', ['values', True, False])</span></div>
</div>
</div>
<div>
<br /></div>
<div>
<br /></div>
<h2>
10/16/14 - Summary results, 3 frontiers, and F-measure rankings.<br /><br /><span style="font-size: small;"><span style="font-weight: normal;">The rig used for these results is different (simplified) in the following ways:</span></span><br />
<span style="font-size: small;"><span style="font-weight: normal;">-RF only</span></span><br />
<span style="font-size: small;"><span style="font-weight: normal;">-One parameter (maxDepth) swept from 0 to 16</span></span><br />
<span style="font-size: small;"><span style="font-weight: normal;">-All other parameters fixed</span></span><br />
<span style="font-size: small;"><span style="font-weight: normal;">-Three ND frontiers are returned from tuning instead of one.</span></span><br />
<span style="font-size: small;"><span style="font-weight: normal;">-Due to multiple frontiers, pD/pF AUC is out. F-measure is used as ranking field instead.</span></span><br />
<span style="font-size: small;"><span style="font-weight: normal;"><br /></span></span>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMmR-ihNWosJT02GP8wkCFNhda_cMGMbX2FFrx1gUx0QeC4VdHVJOLCLFa-hGbXudQEpbOgSyKLdj2mUmk-FzIH09eQNGBCdOWvFCZUUqDFvfiH177NxAjJPmhnHf71vNqiZ_JmHBDJSI/s1600/figure_1_RF.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMmR-ihNWosJT02GP8wkCFNhda_cMGMbX2FFrx1gUx0QeC4VdHVJOLCLFa-hGbXudQEpbOgSyKLdj2mUmk-FzIH09eQNGBCdOWvFCZUUqDFvfiH177NxAjJPmhnHf71vNqiZ_JmHBDJSI/s1600/figure_1_RF.png" height="476" width="640" /></a></div>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfwCBWfvLmvb7JjVXyXVGQIsM6_jiqZiwpWV8QvbGrYWMIyWoei10QUzowsRe0zPlMUrUVSHr23RzJiWWNkW6QMi9_YBjBEYSBrdKaqZJO4o96Um-UMQ6UlhdX4P7-txpVzm_MX6dDbFg/s1600/figure_2_RF.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgfwCBWfvLmvb7JjVXyXVGQIsM6_jiqZiwpWV8QvbGrYWMIyWoei10QUzowsRe0zPlMUrUVSHr23RzJiWWNkW6QMi9_YBjBEYSBrdKaqZJO4o96Um-UMQ6UlhdX4P7-txpVzm_MX6dDbFg/s1600/figure_2_RF.png" height="134" width="640" /></a></div>
<span style="font-size: small;"><span style="font-weight: normal;"><br /></span></span></h2>
<h2>
</h2>
<h2>
New Results Format</h2>
Link to the results: (I think landscape is easiest to see)<br />
<a href="https://www.dropbox.com/sh/iowdac5ki9hyyw4/AAAzRAdOf0pI571udSY0qf-ya?dl=0">https://www.dropbox.com/sh/iowdac5ki9hyyw4/AAAzRAdOf0pI571udSY0qf-ya?dl=0</a><br />
<br />
In this format, the top item is a comparison of the prev and current version dataset stats. These are the usual suspects plus "overlapping instances" where the software module name is the same in both versions and "identical instances" where the software module's metrics are unchanged from one version to the next.<br />
<br />
The rest of the chart shows the results of parameter tuning on both the previous and current versions. There is a table for each learner which lists all of its explored parameter values and the frequency with which they were selected by the grid search in both the previous and current versions. For example, a parameter value of "False" appearing in 90% of the selected combinations in the previous version and 43% of the current version selections would be represented as "False: (90/43)".<br />
<br />
It also shows the pD/pF performance of each learner's non-dominated turnings applied in-set and out of set. in this case we have four combinations:<br />
<br />
<ul>
<li><span style="color: #c27ba0;">tune on prev -> apply in-version</span></li>
<li><span style="color: #6aa84f;">tune on prev -> apply out-of-version (current)</span></li>
<li><span style="color: blue;">tune on current -> apply in-version</span></li>
<li><span style="color: red;">tune on current -> apply out-of-version (prev)</span></li>
</ul>
<div>
In this case, the major effect that we see is that the green sticks with the blue and the red sticks with the purple. This scenario arises when one dataset is more difficult to preform well on than the other. Beyond that, the performance in-version and out-of version seem pretty comparable. There are occasional exceptions, but not a real trend towards in-version or out-of-version doing better.</div>
Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-9727414895111081132014-09-23T14:19:00.001-07:002014-09-23T14:19:25.756-07:001000 bootstrapXOMOflight<br />
<br />
<span style="font-size: x-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomofl500 m 14 50 8 6 #<br /> CT0_xomofl500 m 11 44 4 7 #<br /> CT1_xomofl500 m 7 24 4 4 #<br /> NSGA xomofl500 m 0 21 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomofl500 q 13 8 10 14 #<br /> CT0_xomofl500 q 12 7 6 13 #<br /> CT1_xomofl500 q 8 0 5 11 #<br /> NSGA xomofl500 q 1 2 0 13 #<br />-------------------------------------------------------------------------------------<br />Base Line xomofl500 w 50 79 38 43 #<br /> CT0_xomofl500 w 100 100 100 60 #<br /> CT1_xomofl500 w 49 52 47 36 #<br /> NSGA xomofl500 w 6 55 3 100 #<br />-------------------------------------------------------------------------------------<br /> 100 6505.94 56.0 72279.91 4.0 #<br /> 0 81.3 5.79 259.29 0.0 #</span><br />
<br />
XOMOground<br />
<br />
<span style="font-size: x-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomogr500 m 11 48 5 14 #<br /> CT0_xomogr500 m 9 43 3 14 #<br /> CT1_xomogr500 m 5 21 2 5 #<br /> NSGA xomogr500 m 0 23 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomogr500 q 10 8 6 20 #<br /> CT0_xomogr500 q 10 8 5 20 #<br /> CT1_xomogr500 q 5 0 3 4 #<br /> NSGA xomogr500 q 1 5 0 7 #<br />-------------------------------------------------------------------------------------<br />Base Line xomogr500 w 39 74 24 62 #<br /> CT0_xomogr500 w 100 100 100 100 #<br /> CT1_xomogr500 w 34 44 24 47 #<br /> NSGA xomogr500 w 13 69 1 92 #<br />-------------------------------------------------------------------------------------<br /> 100 5260.07 51.62 115072.54 4.29 #<br /> 0 61.16 4.5 174.68 0.54 #</span><br />
<br />
XOMO OSP<br />
<br />
<span style="font-size: x-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomoos500 m 29 65 17 46 #<br /> CT0_xomoos500 m 26 63 17 8 #<br /> CT1_xomoos500 m 12 26 7 16 #<br /> NSGA xomoos500 m 0 28 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoos500 q 9 6 8 33 #<br /> CT0_xomoos500 q 8 4 11 11 #<br /> CT1_xomoos500 q 3 0 4 13 #<br /> NSGA xomoos500 q 1 4 0 12 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoos500 w 54 82 37 100 #<br /> CT0_xomoos500 w 100 100 100 32 #<br /> CT1_xomoos500 w 31 38 26 23 #<br /> NSGA xomoos500 w 9 77 8 93 #<br />-------------------------------------------------------------------------------------<br /> 100 3360.27 37.54 32578.89 5.95 #<br /> 0 67.25 1.64 191.75 0.67 #</span><br />
<br />
XOMO OSP2<br />
<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomoo2500 m 57 81 35 4 #<br /> CT0_xomoo2500 m 27 41 17 0 #<br /> CT1_xomoo2500 m 40 56 34 9 #<br /> NSGA xomoo2500 m 0 38 0 21 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoo2500 q 15 5 11 11 #<br /> CT0_xomoo2500 q 7 0 9 7 #<br /> CT1_xomoo2500 q 11 2 13 9 #<br /> NSGA xomoo2500 q 0 27 1 32 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoo2500 w 100 100 75 14 #<br /> CT0_xomoo2500 w 74 62 93 7 #<br /> CT1_xomoo2500 w 94 80 100 10 #<br /> NSGA xomoo2500 w 19 70 19 100 #<br />-------------------------------------------------------------------------------------<br /> 100 1185.47 29.91 3115.06 7.0 #<br /> 0 59.1 2.31 165.2 0.2 #</span><br />
<br />
XOMO AL<br />
<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomoal500 m 10 33 7 12 #<br /> CT0_xomoal500 m 7 28 3 12 #<br /> CT1_xomoal500 m 3 9 5 0 #<br /> NSGA xomoal500 m 0 10 0 5 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoal500 q 10 10 10 16 #<br /> CT0_xomoal500 q 8 9 5 17 #<br /> CT1_xomoal500 q 4 0 7 3 #<br /> NSGA xomoal500 q 0 2 0 22 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoal500 w 41 67 45 56 #<br /> CT0_xomoal500 w 100 100 100 100 #<br /> CT1_xomoal500 w 31 35 84 29 #<br /> NSGA xomoal500 w 3 34 2 53 #<br />-------------------------------------------------------------------------------------<br /> 100 7691.63 72.55 82741.7 10.49 #<br /> 0 47.89 4.77 164.88 0.74 #</span><br />
<br />
POM3A<br />
<span style="font-size: x-small;"> Techniques -cost +completion -idle #<br />Base Line pom3A500 m 42 89 29 #<br /> CT0_pom3A500 m 35 69 46 #<br /> CT1_pom3A500 m 19 41 14 #<br /> NSGA pom3A500 m 4 86 28 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3A500 q 28 13 39 #<br /> CT0_pom3A500 q 0 0 0 #<br /> CT1_pom3A500 q 12 5 20 #<br /> NSGA pom3A500 q 2 19 50 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3A500 w 100 100 76 #<br /> CT0_pom3A500 w 81 87 49 #<br /> CT1_pom3A500 w 49 47 43 #<br /> NSGA pom3A500 w 9 96 100 #<br />-------------------------------------------------------------------------------------<br /> 100 1672.42 1.04 0.8 #<br /> 0 0.0 0.0 0.0 #</span><br />
<br />
POM3B<br />
<span style="font-size: x-small;"> Techniques -cost +completion -idle #<br />Base Line pom3B500 m 34 87 35 #<br /> CT0_pom3B500 m 39 80 6 #<br /> CT1_pom3B500 m 32 83 34 #<br /> NSGA pom3B500 m 6 85 20 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 q 28 13 41 #<br /> CT0_pom3B500 q 0 0 0 #<br /> CT1_pom3B500 q 27 12 39 #<br /> NSGA pom3B500 q 2 9 37 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 w 100 100 77 #<br /> CT0_pom3B500 w 50 78 27 #<br /> CT1_pom3B500 w 97 96 75 #<br /> NSGA pom3B500 w 14 96 100 #<br />-------------------------------------------------------------------------------------<br /> 100 22987.74 1.04 0.8 #<br /> 0 117.64 0.01 0.01 #</span><br />
<span style="font-size: x-small;"></span><br />
<span style="font-size: x-small;"><br /></span><br />
POM3C<br />
<span style="font-size: x-small;"> Techniques -cost +completion -idle #<br />Base Line pom3B500 m 34 87 35 #<br /> CT0_pom3B500 m 39 80 6 #<br /> CT1_pom3B500 m 32 83 34 #<br /> NSGA pom3B500 m 6 85 20 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 q 28 13 41 #<br /> CT0_pom3B500 q 0 0 0 #<br /> CT1_pom3B500 q 27 12 39 #<br /> NSGA pom3B500 q 2 9 37 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 w 100 100 77 #<br /> CT0_pom3B500 w 50 78 27 #<br /> CT1_pom3B500 w 97 96 75 #<br /> NSGA pom3B500 w 14 96 100 #<br />-------------------------------------------------------------------------------------<br /> 100 22987.74 1.04 0.8 #<br /> 0 117.64 0.01 0.01 #</span>NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-51391878497621629102014-09-23T14:06:00.000-07:002014-09-23T21:24:00.234-07:00250 bootstrap results<div dir="ltr" style="text-align: left;" trbidi="on">
<h2>
<b><span style="font-family: Verdana, sans-serif;">250</span></b></h2>
<span style="font-family: Verdana, sans-serif;"><br /></span>
<span style="font-family: Verdana, sans-serif; font-size: xx-small;">FLIGHT</span><br />
<span style="font-family: Verdana, sans-serif;"><span style="font-size: xx-small;"><br /></span>
<span style="font-size: xx-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomofl500 m 27 67 18 6 #<br /> CT0_xomofl500 m 2 7 1 0 #<br /> CT1_xomofl500 m 14 37 8 4 #<br /> NSGA xomofl500 m 0 33 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomofl500 q 26 18 25 14 #<br /> CT0_xomofl500 q 2 0 2 2 #<br /> CT1_xomofl500 q 15 9 11 10 #<br /> NSGA xomofl500 q 2 12 1 13 #<br />-------------------------------------------------------------------------------------<br />Base Line xomofl500 w 97 100 90 43 #<br /> CT0_xomofl500 w 24 17 30 10 #<br /> CT1_xomofl500 w 100 70 100 37 #<br /> NSGA xomofl500 w 11 72 7 100 #<br />-------------------------------------------------------------------------------------<br /> 100 3408.55 45.35 30775.62 4.0 #<br /> 0 81.3 1.68 259.29 0.0 #</span></span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;">Ground:</span><br />
<span style="font-family: Verdana, sans-serif;"><span style="font-size: xx-small;"><br /></span>
<span style="font-size: xx-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomogr500 m 28 66 20 19 #<br /> CT0_xomogr500 m 7 16 4 0 #<br /> CT1_xomogr500 m 13 29 7 7 #<br /> NSGA xomogr500 m 0 34 0 4 #<br />-------------------------------------------------------------------------------------<br />Base Line xomogr500 q 27 15 27 25 #<br /> CT0_xomogr500 q 7 0 6 2 #<br /> CT1_xomogr500 q 14 3 9 6 #<br /> NSGA xomogr500 q 1 11 1 12 #<br />-------------------------------------------------------------------------------------<br />Base Line xomogr500 w 100 100 100 69 #<br /> CT0_xomogr500 w 75 41 97 31 #<br /> CT1_xomogr500 w 89 57 93 44 #<br /> NSGA xomogr500 w 34 94 3 100 #<br />-------------------------------------------------------------------------------------<br /> 100 2065.56 39.28 27430.38 4.0 #<br /> 0 61.16 2.97 174.68 0.38 #</span></span><br />
<span style="font-family: Verdana, sans-serif;"><span style="font-size: xx-small;"><br /></span>
<span style="font-size: xx-small;">OSp</span></span><br />
<span style="font-family: Verdana, sans-serif;"><span style="font-size: xx-small;"><br /></span>
<span style="font-size: xx-small;"> Techniques -effort -months -defects -risks #<br />Base Line xomoos500 m 35 65 16 46 #<br /> CT0_xomoos500 m 33 62 14 33 #<br /> CT1_xomoos500 m 16 27 9 20 #<br /> NSGA xomoos500 m 0 28 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoos500 q 11 6 8 33 #<br /> CT0_xomoos500 q 12 6 11 42 #<br /> CT1_xomoos500 q 4 0 4 14 #<br /> NSGA xomoos500 q 1 4 0 12 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoos500 w 65 82 35 100 #<br /> CT0_xomoos500 w 100 100 100 99 #<br /> CT1_xomoos500 w 40 39 27 26 #<br /> NSGA xomoos500 w 11 77 7 93 #<br />-------------------------------------------------------------------------------------<br /> 100 2798.94 37.57 34928.22 5.95 #<br /> 0 67.25 1.66 191.75 0.67 #</span></span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
osp2</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
<br />
Techniques -effort -months -defects -risks #<br />Base Line xomoo2500 m 58 82 48 7 #<br /> CT0_xomoo2500 m 6 11 3 0 #<br /> CT1_xomoo2500 m 25 35 29 5 #<br /> NSGA xomoo2500 m 2 41 3 23 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoo2500 q 16 10 17 13 #<br /> CT0_xomoo2500 q 0 0 0 3 #<br /> CT1_xomoo2500 q 6 3 12 4 #<br /> NSGA xomoo2500 q 2 31 3 34 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoo2500 w 100 100 100 16 #<br /> CT0_xomoo2500 w 19 17 25 3 #<br /> CT1_xomoo2500 w 55 46 88 5 #<br /> NSGA xomoo2500 w 20 72 27 100 #<br />-------------------------------------------------------------------------------------<br /> 100 1185.47 29.91 2385.2 7.0 #<br /> 0 39.1 0.65 103.31 0.0 #</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
ALL</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
Techniques -effort -months -defects -risks #<br />Base Line xomoal500 m 24 52 11 29 #<br /> CT0_xomoal500 m 0 3 0 0 #<br /> CT1_xomoal500 m 11 25 6 11 #<br /> NSGA xomoal500 m 0 21 0 17 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoal500 q 23 21 15 35 #<br /> CT0_xomoal500 q 1 0 1 1 #<br /> CT1_xomoal500 q 12 10 8 19 #<br /> NSGA xomoal500 q 0 11 1 46 #<br />-------------------------------------------------------------------------------------<br />Base Line xomoal500 w 100 100 69 100 #<br /> CT0_xomoal500 w 19 11 10 12 #<br /> CT1_xomoal500 w 97 67 100 79 #<br /> NSGA xomoal500 w 7 54 3 96 #<br />-------------------------------------------------------------------------------------<br /> 100 3151.95 50.35 54140.25 6.17 #<br /> 0 47.89 1.07 164.88 0.17 #</span><br />
<span style="font-family: Verdana, sans-serif;"><span style="font-size: xx-small;"><br /></span>
<span style="font-size: xx-small;">POM3A</span></span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
Techniques -cost +completion -idle #<br />Base Line pom3A500 m 24 89 15 #<br /> CT0_pom3A500 m 20 85 13 #<br /> CT1_pom3A500 m 7 42 0 #<br /> NSGA pom3A500 m 1 85 13 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3A500 q 15 6 27 #<br /> CT0_pom3A500 q 19 3 25 #<br /> CT1_pom3A500 q 4 0 12 #<br /> NSGA pom3A500 q 0 13 40 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3A500 w 58 100 72 #<br /> CT0_pom3A500 w 100 96 84 #<br /> CT1_pom3A500 w 22 48 40 #<br /> NSGA pom3A500 w 4 96 100 #<br />-------------------------------------------------------------------------------------<br /> 100 2873.72 1.04 0.8 #<br /> 0 37.6 0.07 0.13 #</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
POM3B</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
Techniques -cost +completion -idle #<br />Base Line pom3B500 m 33 87 34 #<br /> CT0_pom3B500 m 1 9 0 #<br /> CT1_pom3B500 m 16 50 16 #<br /> NSGA pom3B500 m 4 85 18 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 q 26 13 39 #<br /> CT0_pom3B500 q 1 0 1 #<br /> CT1_pom3B500 q 15 5 26 #<br /> NSGA pom3B500 q 0 9 35 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3B500 w 100 100 77 #<br /> CT0_pom3B500 w 13 10 8 #<br /> CT1_pom3B500 w 70 56 52 #<br /> NSGA pom3B500 w 12 96 100 #<br />-------------------------------------------------------------------------------------<br /> 100 22987.74 1.04 0.8 #<br /> 0 494.8 0.01 0.03 #</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
POM3C</span><br />
<span style="font-family: Verdana, sans-serif; font-size: xx-small;"><br />
Techniques -cost +completion -idle #<br />Base Line pom3C500 m 33 90 32 #<br /> CT0_pom3C500 m 30 86 32 #<br /> CT1_pom3C500 m 19 70 28 #<br /> NSGA pom3C500 m 8 87 37 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3C500 q 16 1 28 #<br /> CT0_pom3C500 q 20 11 33 #<br /> CT1_pom3C500 q 9 3 25 #<br /> NSGA pom3C500 q 0 0 0 #<br />-------------------------------------------------------------------------------------<br />Base Line pom3C500 w 74 100 88 #<br /> CT0_pom3C500 w 100 96 100 #<br /> CT1_pom3C500 w 55 78 80 #<br /> NSGA pom3C500 w 16 96 83 #<br />-------------------------------------------------------------------------------------<br /> 100 2613.81 1.04 0.7 #<br /> 0 47.7 0.1 0.1 #</span><br />
<span style="font-size: x-small;"><br /></span>
</div>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-22050500692496289772014-07-13T22:28:00.002-07:002014-07-14T10:32:10.883-07:00Results all models<br />
Flight<br />
<br />
<pre> <span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 23 67 5 0 #
2 Bef disc m 3 14 4 2 #
3 Aft disc m 9 30 6 2 #
4 T9:j/j_ m 0 33 0 22 #
-------------------------------------------------------------------------------------
1 T0 q 21 16 8 1 #
2 Bef disc q 3 0 5 3 #
3 Aft disc q 9 6 8 6 #
4 T9:j/j_ q 3 21 1 38 #
-------------------------------------------------------------------------------------
1 T0 w 79 100 36 17 #
2 Bef disc w 29 28 46 8 #
3 Aft disc w 100 66 100 14 #
4 T9:j/j_ w 24 98 19 100 #
-------------------------------------------------------------------------------------
100 3500.22 42.38 50660.52 8.6 #
0 117.21 2.51 381.83 0.0 # </span></pre>
<pre> </pre>
<pre>Ground</pre>
<pre></pre>
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 5 43 3 12 #
2 Bef disc m 0 12 3 6 #
3 Aft disc m 9 49 3 0 #
4 T9:j/jground m 0 32 0 10 #
-------------------------------------------------------------------------------------
1 T0 q 4 8 5 17 #
2 Bef disc q 0 0 5 7 #
3 Aft disc q 10 12 5 9 #
4 T9:j/jground q 5 35 1 16 #
-------------------------------------------------------------------------------------
1 T0 w 31 69 34 48 #
2 Bef disc w 16 24 37 22 #
3 Aft disc w 100 100 100 49 #
4 T9:j/jground w 40 78 34 100 #
-------------------------------------------------------------------------------------
100 6628.14 55.18 76485.1 4.8 #
0 254.49 2.97 806.33 0.05 #</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;">Osp</span></span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;"> </span></span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;"> <span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 9 36 5 36 #
2 Bef disc m 0 8 1 12 #
3 Aft disc m 8 44 3 0 #
4 T9:j/josp m 1 29 0 11 #
-------------------------------------------------------------------------------------
1 T0 q 2 2 3 32 #
2 Bef disc q 0 0 1 5 #
3 Aft disc q 9 12 4 3 #
4 T9:j/josp q 1 18 1 29 #
-------------------------------------------------------------------------------------
1 T0 w 17 43 14 65 #
2 Bef disc w 7 17 15 32 #
3 Aft disc w 100 100 100 29 #
4 T9:j/josp w 31 64 5 100 #
-------------------------------------------------------------------------------------
100 9665.91 63.82 103492.82 7.5 #
0 142.78 2.31 722.41 0.27 #</span></span></span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;"><span style="font-size: x-small;"> </span></span></span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;"><span style="font-size: x-small;"><span style="font-size: small;">Osp2</span></span></span></span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;"><span style="font-size: x-small;"><span style="font-size: small;"> </span></span></span></span></pre>
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 9 65 1 10 #
2 Bef disc m 9 61 1 0 #
3 Aft disc m 7 42 3 3 #
4 T9:j/josp2 m 0 39 0 13 #
-------------------------------------------------------------------------------------
1 T0 q 1 0 0 12 #
2 Bef disc q 2 4 0 20 #
3 Aft disc q 8 8 5 5 #
4 T9:j/josp2 q 3 17 2 31 #
-------------------------------------------------------------------------------------
1 T0 w 17 79 5 20 #
2 Bef disc w 21 85 8 20 #
3 Aft disc w 100 100 100 26 #
4 T9:j/josp2 w 18 93 12 100 #
-------------------------------------------------------------------------------------
100 5937.27 35.8 59880.52 5.1 #
0 130.08 2.84 637.4 0.0 #
</span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;">All</span></span></pre>
<pre><span style="font-size: x-small;"><span style="font-size: small;"> </span></span></pre>
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 13 47 4 20 #
2 Bef disc m 11 32 10 7 #
3 Aft disc m 6 24 3 0 #
4 T9:j/jall m 0 34 0 12 #
-------------------------------------------------------------------------------------
1 T0 q 14 12 7 37 #
2 Bef disc q 11 3 13 10 #
3 Aft disc q 7 0 4 5 #
4 T9:j/jall q 5 18 2 36 #
-------------------------------------------------------------------------------------
1 T0 w 80 100 52 100 #
2 Bef disc w 88 64 100 25 #
3 Aft disc w 100 55 69 20 #
4 T9:j/jall w 22 79 10 84 #
-------------------------------------------------------------------------------------
100 3597.84 49.22 55387.16 5.67 #
0 262.02 5.17 1028.96 0.11 #
</span></pre>
<pre><span style="font-size: x-small;">Flight tree</span></pre>
<pre><span style="font-size: x-small;"> 58 flex <= 27.5 samples = 941
59 |- docu <= 6.5 samples = 483
60 |-|- ['__7'] # samples = 31 # branch_id = 0
61 |-|- team <= 27.0 samples = 452
62 |-|-|- ['__2'] # samples = 440 # branch_id = 1
63 |-|-|- ['__6'] # samples = 12 # branch_id = 2
64 |- ruse <= 35.5 samples = 458
65 |-|- docu <= 38.5 samples = 50
66 |-|-|- team <= 21.5 samples = 30
67 |-|-|-|- ['__4'] # samples = 14 # branch_id = 3
68 |-|-|-|- ['__1'] # samples = 16 # branch_id = 4
69 |-|-|- ['__2'] # samples = 20 # branch_id = 5
70 |-|- ['__1'] # samples = 408 # branch_id = 6</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Ground tree</span></pre>
<pre><span style="font-size: x-small;"> pr <= 12.5 samples = 918
63 |- resl <= 17.5 samples = 191
64 |-|- ['__7'] # samples = 28 # branch_id = 0
65 |-|- ['__2'] # samples = 163 # branch_id = 1
66 |- rely <= 19.5 samples = 727
67 |-|- pcon <= 24.5 samples = 44
68 |-|-|- ['__5'] # samples = 28 # branch_id = 2
69 |-|-|- ['__4'] # samples = 16 # branch_id = 3
70 |-|- resl <= 30.0 samples = 683
71 |-|-|- ['__1'] # samples = 651 # branch_id = 4
72 |-|-|- ['__1'] # samples = 32 # branch_id = 5
</span></pre>
<pre><span style="font-size: x-small;">Osp Tree</span></pre>
<pre><span style="font-size: x-small;"> etat <= 5.5 samples = 939
49 |- ['__2'] # samples = 47 # branch_id = 0
50 |- time <= 1.5 samples = 892
51 |-|- ['__4'] # samples = 12 # branch_id = 1
52 |-|- ['__1'] # samples = 880 # branch_id = 2
</span></pre>
<pre><span style="font-size: x-small;">Osp2 Tree</span></pre>
<pre><span style="font-size: x-small;"> 69 etat <= 22.5 samples = 932
70 |- ltex <= 33.5 samples = 369
71 |-|- ruse <= 11.0 samples = 52
72 |-|-|- sced <= 20.5 samples = 28
73 |-|-|-|- ['__4'] # samples = 11 # branch_id = 0
74 |-|-|-|- ['__7'] # samples = 17 # branch_id = 1
75 |-|-|- ['__5'] # samples = 24 # branch_id = 2
76 |-|- ltex <= 40.5 samples = 317
77 |-|-|- pr <= 12.5 samples = 47
78 |-|-|-|- ['__2'] # samples = 35 # branch_id = 3
79 |-|-|- ['__2'] # samples = 270 # branch_id = 4
80 |- aa <= 6.5 samples = 563
81 |-|- ['__1'] # samples = 146 # branch_id = 5
82 |-|- prec <= 22.0 samples = 417
83 |-|-|- ['__3'] # samples = 43 # branch_id = 6
84 |-|-|- ['__3'] # samples = 374 # branch_id = 7
</span></pre>
<pre><span style="font-size: x-small;">All Tree</span></pre>
<pre><span style="font-size: x-small;">75 pr <= 25.5 samples = 824
76 |- site <= 20.5 samples = 283
77 |-|- site <= 9.5 samples = 101
78 |-|-|- site <= 7.5 samples = 69
79 |-|-|-|- ['__3'] # samples = 14 # branch_id = 0
80 |-|-|-|- ['__9'] # samples = 55 # branch_id = 1
81 |-|-|- ruse <= 16.5 samples = 32
82 |-|-|-|- ['__5'] # samples = 15 # branch_id = 2
83 |-|-|-|- ['__7'] # samples = 17 # branch_id = 3
84 |-|- flex <= 26.5 samples = 182
85 |-|-|- ['__2'] # samples = 150 # branch_id = 4
86 |-|-|- team <= 48.0 samples = 32
87 |-|-|-|- ['__8'] # samples = 17 # branch_id = 5
88 |-|-|-|- ['__2'] # samples = 15 # branch_id = 6
89 |- prec <= 35.0 samples = 541
90 |-|- flex <= 22.0 samples = 289
91 |-|-|- docu <= 24.5 samples = 47
92 |-|-|-|- ['__6'] # samples = 14 # branch_id = 7
93 |-|-|-|- ['__5'] # samples = 33 # branch_id = 8
94 |-|-|- team <= 37.5 samples = 242
95 |-|-|-|- aexp <= 23.5 samples = 26
96 |-|-|-|-|- ['__8'] # samples = 11 # branch_id = 9
97 |-|-|-|-|- ['__1'] # samples = 15 # branch_id = 10
98 |-|-|-|- ['__1'] # samples = 216 # branch_id = 11
99 |-|- aexp <= 10.5 samples = 252
100 |-|-|- ['__1'] # samples = 14 # branch_id = 12
101 |-|-|- ['__3'] # samples = 238 # branch_id = 13
</span></pre>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-44370973196901254532014-07-13T21:20:00.000-07:002014-07-14T10:28:25.689-07:00TEAK on dtree learning results<br />
TEAK works:<br />
<br />
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 29 66 10 0 #
2 Bef disc m 9 17 9 2 #
3 Aft disc m 7 17 11 2 #
4 T9:j/j_ m 0 32 0 22 #
-------------------------------------------------------------------------------------
1 T0 q 28 16 16 2 #
2 Bef disc q 9 0 12 3 #
3 Aft disc q 7 0 14 3 #
4 T9:j/j_ q 3 20 3 38 #
-------------------------------------------------------------------------------------
1 T0 w 100 100 71 17 #
2 Bef disc w 56 33 89 8 #
3 Aft disc w 54 34 100 8 #
4 T9:j/j_ w 30 97 38 100 #
-------------------------------------------------------------------------------------
100 2856.84 42.5 25882.79 8.6 #
0 117.21 3.05 381.83 0.0 #</span></pre>
<pre> </pre>
<pre>But trees are still big:</pre>
<pre> </pre>
<pre>Bef disc:</pre>
<pre> </pre>
<pre><span style="font-size: xx-small;">111 $docu <= 3.5 samples = 500
112 |- $rely <= 3.5 samples = 386
113 |-|- $pcap <= 3.5 samples = 195
114 |-|-|- $flex <= 1.5 samples = 102
115 |-|-|-|- $aexp <= 2.5 samples = 31
116 |-|-|-|-|- ['__3', '__5'] # samples = 13 # branch_id = 0
117 |-|-|-|-|- ['__2'] # samples = 18 # branch_id = 1
118 |-|-|-|- $pcon <= 1.5 samples = 71
119 |-|-|-|-|- ['__11'] # samples = 15 # branch_id = 2
120 |-|-|-|-|- $team <= 3.5 samples = 56
121 |-|-|-|-|-|- $kloc <= 161.0 samples = 42
122 |-|-|-|-|-|-|- ['__7'] # samples = 16 # branch_id = 3
123 |-|-|-|-|-|-|- $aexp <= 1.5 samples = 26
124 |-|-|-|-|-|-|-|- ['__3'] # samples = 12 # branch_id = 4
125 |-|-|-|-|-|-|-|- ['__3'] # samples = 14 # branch_id = 5
126 |-|-|-|-|-|- ['__2'] # samples = 14 # branch_id = 6
127 |-|-|- $ruse <= 3.5 samples = 93
128 |-|-|-|- $team <= 3.5 samples = 40
129 |-|-|-|-|- $cplx <= 3.5 samples = 22
130 |-|-|-|-|-|- ['__7'] # samples = 11 # branch_id = 7
131 |-|-|-|-|-|- ['__9'] # samples = 11 # branch_id = 8
132 |-|-|-|-|- ['__3', '__8', '__18'] # samples = 18 # branch_id = 9
133 |-|-|-|- $resl <= 3.5 samples = 53
134 |-|-|-|-|- $resl <= 1.5 samples = 39
135 |-|-|-|-|-|- ['__12', '__13'] # samples = 16 # branch_id = 10
136 |-|-|-|-|-|- $prec <= 2.5 samples = 23
137 |-|-|-|-|-|-|- ['__11'] # samples = 11 # branch_id = 11
138 |-|-|-|-|-|-|- ['__14'] # samples = 12 # branch_id = 12
139 |-|-|-|-|- ['__5', '__10'] # samples = 14 # branch_id = 13
140 |-|- $team <= 3.5 samples = 191
141 |-|-|- $flex <= 1.5 samples = 141
142 |-|-|-|- $kloc <= 262.0 samples = 30
143 |-|-|-|-|- ['__12', '__14'] # samples = 16 # branch_id = 14
144 |-|-|-|-|- ['__1', '__5', '__10', '__12', '__13'] # samples = 14 # branch_id = 15
145 |-|-|-|- $site <= 4.5 samples = 111
146 |-|-|-|-|- $flex <= 2.5 samples = 89
147 |-|-|-|-|-|- $ruse <= 3.5 samples = 27
148 |-|-|-|-|-|-|- ['__4', '__16'] # samples = 12 # branch_id = 16
149 |-|-|-|-|-|-|- ['__12'] # samples = 15 # branch_id = 17
150 |-|-|-|-|-|- $docu <= 2.5 samples = 62
151 |-|-|-|-|-|-|- $pr <= 3.5 samples = 47
152 |-|-|-|-|-|-|-|- $flex <= 3.5 samples = 25
153 |-|-|-|-|-|-|-|-|- ['__13'] # samples = 11 # branch_id = 18
154 |-|-|-|-|-|-|-|-|- ['__13'] # samples = 14 # branch_id = 19
155 |-|-|-|-|-|-|-|- $pr <= 4.5 samples = 22
156 |-|-|-|-|-|-|-|-|- ['__15'] # samples = 11 # branch_id = 20
157 |-|-|-|-|-|-|-|-|- ['__16'] # samples = 11 # branch_id = 21
158 |-|-|-|-|-|-|- ['__12'] # samples = 15 # branch_id = 22
159 |-|-|-|-|- $pcap <= 3.5 samples = 22
160 |-|-|-|-|-|- ['__12'] # samples = 11 # branch_id = 23
161 |-|-|-|-|-|- ['__14'] # samples = 11 # branch_id = 24
162 |-|-|- $site <= 2.5 samples = 50
163 |-|-|-|- ['__1'] # samples = 19 # branch_id = 25
164 |-|-|-|- $ruse <= 2.5 samples = 31
165 |-|-|-|-|- ['__17'] # samples = 14 # branch_id = 26
166 |-|-|-|-|- ['__12'] # samples = 17 # branch_id = 27
167 |- $rely <= 3.5 samples = 114
168 |-|- $pcon <= 2.5 samples = 58
169 |-|-|- $acap <= 3.5 samples = 33
170 |-|-|-|- ['__1', '__2', '__3'] # samples = 17 # branch_id = 28
171 |-|-|-|- ['__10'] # samples = 16 # branch_id = 29
172 |-|-|- $pvol <= 2.5 samples = 25
173 |-|-|-|- ['__1', '__2', '__8'] # samples = 13 # branch_id = 30
174 |-|-|-|- ['__2'] # samples = 12 # branch_id = 31
175 |-|- $pcap <= 3.5 samples = 56
176 |-|-|- $plex <= 1.5 samples = 29
177 |-|-|-|- ['__19'] # samples = 12 # branch_id = 32
178 |-|-|-|- ['__18'] # samples = 17 # branch_id = 33
179 |-|-|- $pcon <= 1.5 samples = 27
180 |-|-|-|- ['__17'] # samples = 11 # branch_id = 34
181 |-|-|-|- ['__17'] # samples = 16 # branch_id = 35</span></pre>
<pre><span style="font-size: xx-small;"></span></pre>
<pre><span style="font-size: xx-small;"></span></pre>
<pre><span style="font-size: xx-small;"></span></pre>
<pre><span style="font-size: xx-small;"></span></pre>
<pre><span style="font-size: xx-small;">
</span>After disc:</pre>
<pre> </pre>
<pre>44 rows after pruned 304
45 $pcap <= 3.5 samples = 304
46 |- $ltex <= 2.5 samples = 166
47 |-|- $plex <= 1.5 samples = 104
48 |-|-|- $site <= 2.5 samples = 36
49 |-|-|-|- ['__2', '__3'] # samples = 18 # branch_id = 0
50 |-|-|-|- ['__5'] # samples = 18 # branch_id = 1
51 |-|-|- $flex <= 2.5 samples = 68
52 |-|-|-|- $etat <= 2.5 samples = 31
53 |-|-|-|-|- ['__6'] # samples = 17 # branch_id = 2
54 |-|-|-|-|- ['__4'] # samples = 14 # branch_id = 3
55 |-|-|-|- $prec <= 2.5 samples = 37
56 |-|-|-|-|- ['__6'] # samples = 20 # branch_id = 4
57 |-|-|-|-|- ['__2'] # samples = 17 # branch_id = 5
58 |-|- $etat <= 3.5 samples = 62
59 |-|-|- $aexp <= 2.5 samples = 35
60 |-|-|-|- ['__5'] # samples = 21 # branch_id = 6
61 |-|-|-|- ['__3'] # samples = 14 # branch_id = 7
62 |-|-|- $kloc <= 232.5 samples = 27
63 |-|-|-|- ['__3'] # samples = 14 # branch_id = 8
64 |-|-|-|- ['__2'] # samples = 13 # branch_id = 9
65 |- $acap <= 3.5 samples = 138
66 |-|- $etat <= 1.5 samples = 64
67 |-|-|- ['__3', '__9'] # samples = 11 # branch_id = 10
68 |-|-|- $ruse <= 4.5 samples = 53
69 |-|-|-|- $pvol <= 3.5 samples = 37
70 |-|-|-|-|- $team <= 3.5 samples = 22
71 |-|-|-|-|-|- ['__7'] # samples = 11 # branch_id = 11
72 |-|-|-|-|-|- ['__7'] # samples = 11 # branch_id = 12
73 |-|-|-|-|- ['__6'] # samples = 15 # branch_id = 13
74 |-|-|-|- ['__5'] # samples = 16 # branch_id = 14
75 |-|- $ltex <= 2.5 samples = 74
76 |-|-|- $aexp <= 3.5 samples = 50
77 |-|-|-|- $cplx <= 3.5 samples = 38
78 |-|-|-|-|- ['__8'] # samples = 13 # branch_id = 15
79 |-|-|-|-|- $rely <= 3.5 samples = 25
80 |-|-|-|-|-|- ['__7'] # samples = 11 # branch_id = 16
81 |-|-|-|-|-|- ['__8'] # samples = 14 # branch_id = 17
82 |-|-|-|- ['__6'] # samples = 12 # branch_id = 18
83 |-|-|- $rely <= 3.5 samples = 24
84 |-|-|-|- ['__6'] # samples = 11 # branch_id = 19
85 |-|-|-|- ['__8'] # samples = 13 # branch_id = 20
</pre>
<pre><span style="font-size: small;">Tree is </span> almost halved with improvement in performance, but trees are still big enough for business users.</pre>
<pre><span style="font-size: x-large;">
</span></pre>
<pre><span style="font-size: x-large;">TEAK with everything:</span></pre>
<pre><span style="font-size: x-large;"> <span style="font-size: small;">Infogain, Dtree prune, distance prune, discretization</span> </span></pre>
<pre></pre>
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
1 T0 m 20 66 6 0 #
2 Bef disc m 3 15 7 1 #
3 Aft disc m 12 39 6 0 #
4 T9:j/j_ m 0 32 0 22 #
-------------------------------------------------------------------------------------
1 T0 q 19 16 9 0 #
2 Bef disc q 3 0 8 3 #
3 Aft disc q 13 9 9 4 #
4 T9:j/j_ q 2 21 1 38 #
-------------------------------------------------------------------------------------
1 T0 w 73 100 37 15 #
2 Bef disc w 28 30 57 9 #
3 Aft disc w 100 78 100 18 #
4 T9:j/j_ w 21 96 21 100 #
-------------------------------------------------------------------------------------
100 3954.71 42.89 46036.21 8.6 #
0 117.21 2.71 381.83 0.01 #</span></pre>
<pre> </pre>
<pre>Tree size:</pre>
<pre><span style="font-size: xx-small;">106 kloc <= 3.5 samples = 407
107 |- resl <= 7.5 samples = 234
108 |-|- cplx <= 12.0 samples = 77
109 |-|-|- ['__7'] # samples = 40 # branch_id = 0
110 |-|-|- ['__8'] # samples = 37 # branch_id = 1
111 |-|- flex <= 3.5 samples = 157
112 |-|-|- ['__7'] # samples = 86 # branch_id = 2
113 |-|-|- team <= 9.0 samples = 71
114 |-|-|-|- ['__7'] # samples = 26 # branch_id = 3
115 |-|-|-|- ['__6'] # samples = 45 # branch_id = 4
116 |- prec <= 1.5 samples = 173
117 |-|- site <= 3.5 samples = 45
118 |-|-|- ['__3'] # samples = 15 # branch_id = 5
119 |-|-|- ['__1'] # samples = 30 # branch_id = 6
120 |-|- etat <= 4.5 samples = 128
121 |-|-|- kloc <= 24.0 samples = 94
122 |-|-|-|- ['__3'] # samples = 19 # branch_id = 7
123 |-|-|-|- kloc <= 90.5 samples = 75
124 |-|-|-|-|- ['__4'] # samples = 21 # branch_id = 8
125 |-|-|-|-|- ruse <= 2.5 samples = 54
126 |-|-|-|-|-|- ['__3'] # samples = 15 # branch_id = 9
127 |-|-|-|-|-|- kloc <= 299.0 samples = 39
128 |-|-|-|-|-|-|- ['__5'] # samples = 13 # branch_id = 10
129 |-|-|-|-|-|-|- pvol <= 2.5 samples = 26
130 |-|-|-|-|-|-|-|- ['__4'] # samples = 15 # branch_id = 11
131 |-|-|-|-|-|-|-|- ['__2'] # samples = 11 # branch_id = 12
132 |-|-|- ['__2'] # samples = 34 # branch_id = 13</span></pre>
<pre><span style="font-size: xx-small;"></span></pre>
<pre><span style="font-size: xx-small;">
</span><span style="font-size: x-large;">Best result so far:</span></pre>
<pre><span style="font-size: x-large;"> </span></pre>
<pre><span style="font-size: x-large;"><span style="font-size: small;">Cluster size: 44,66</span></span></pre>
<pre><span style="font-size: x-large;"><span style="font-size: small;">Dtree pruned, Distance pruned, 50% infogained, discretized.</span></span></pre>
<pre><span style="font-size: x-large;"><span style="font-size: small;"> </span></span></pre>
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
1 T0 m 23 67 5 0 #
2 Bef disc m 3 14 4 2 #
3 Aft disc m 9 30 6 2 #
4 T9:j/j_ m 0 33 0 22 #
-------------------------------------------------------------------------------------
1 T0 q 21 16 8 1 #
2 Bef disc q 3 0 5 3 #
3 Aft disc q 9 6 8 6 #
4 T9:j/j_ q 3 21 1 38 #
-------------------------------------------------------------------------------------
1 T0 w 79 100 36 17 #
2 Bef disc w 29 28 46 8 #
3 Aft disc w 100 66 100 14 #
4 T9:j/j_ w 24 98 19 100 #
-------------------------------------------------------------------------------------
100 3500.22 42.38 50660.52 8.6 #
0 117.21 2.51 381.83 0.0 #</span></pre>
<pre><span style="font-size: x-large;"><span style="font-size: small;"> </span></span></pre>
<pre><span style="font-size: x-large;"><span style="font-size: small;">Tree size:</span></span></pre>
<pre><span style="font-size: x-large;"><span style="font-size: small;">58 flex <= 27.5 samples = 941
59 |- docu <= 6.5 samples = 483
60 |-|- ['__7'] # samples = 31 # branch_id = 0
61 |-|- team <= 27.0 samples = 452
62 |-|-|- ['__2'] # samples = 440 # branch_id = 1
63 |-|-|- ['__6'] # samples = 12 # branch_id = 2
64 |- ruse <= 35.5 samples = 458
65 |-|- docu <= 38.5 samples = 50
66 |-|-|- team <= 21.5 samples = 30
67 |-|-|-|- ['__4'] # samples = 14 # branch_id = 3
68 |-|-|-|- ['__1'] # samples = 16 # branch_id = 4
69 |-|-|- ['__2'] # samples = 20 # branch_id = 5
70 |-|- ['__1'] # samples = 408 # branch_id = 6
71 500
</span> </span> </pre>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-19022981215300565842014-07-13T20:18:00.003-07:002014-07-13T20:18:52.129-07:00Results 10 repeats, loo, scrott knott, <div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">### repeats = 10 </span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">coc81</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coc2000 * 33 35</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coconut * 37 41</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 coco2000s3 ** 41 43</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 coconuts3 ** 44 31</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 coco2000s5 ** 52 49</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 coconuts5 ** 52 53</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 cart *** 78 135</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 loc(3) **** 80 237</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 5 k=3nn **** 80 352</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 5 k=5nn **** 85 368</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 5 k=1nn **** 92 164</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 6 (c=1)n **** 95 594</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 6 (c=1)n-noloc **** 97 657</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 6 (c=2)n ****** 125 588</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 6 (c=2)n-noloc ******* 140 692</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 7 guess ****************************** 616 1529</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">726.86 45 45</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">xyz14</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">..........=</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coc2000 ** 42 34</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 k=3nn ** 44 77</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 coconut ** 49 30</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 loc(3) ** 49 96</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 k=5nn ** 51 86</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 (c=2)n ** 52 48</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 (c=1)n-noloc ** 53 51</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 (c=2)n-noloc ** 56 46</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 (c=1)n ** 58 42</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 cart ** 58 48</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 k=1nn ** 59 25</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 guess *** 66 74</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">107.2 25 70</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">newCIIdata</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">..........=</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coc2000 * 39 97</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 k=1nn ** 49 73</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coconut ** 54 50</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 loc(3) ** 57 43</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 cart *** 72 69</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 k=3nn **** 90 97</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 k=5nn **** 90 161</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 (c=2)n-noloc ************* 279 717</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 (c=2)n ************** 293 989</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 (c=1)n ****************** 374 1632</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 (c=1)n-noloc ******************* 391 1044</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 guess ******************************* 631 1248</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">127.99 23 93</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">nasa93</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">... </span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coconuts3 * 34 45</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coco2000s3 * 35 45</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coconut * 36 37</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coc2000 * 38 38</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 0 coconuts5 * 39 40</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 cart ** 41 65</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 1 coco2000s5 ** 51 30</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 2 k=1nn ** 56 75</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 k=5nn *** 63 62</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 k=3nn *** 64 70</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 3 loc(3) *** 75 101</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 (c=1)n **** 91 549</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 (c=1)n-noloc **** 100 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 4 (c=2)n-noloc **** 100 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 5 (c=2)n ******* 144 574</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 5 guess ******* 149 785</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1250.34 48 141</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">real<span class="Apple-tab-span" style="white-space: pre;"> </span>55m35.975s</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">user<span class="Apple-tab-span" style="white-space: pre;"> </span>53m35.948s</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">sys<span class="Apple-tab-span" style="white-space: pre;"> </span>0m3.708s</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">"</span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-20515354258553323362014-07-09T11:45:00.000-07:002014-07-09T11:45:25.475-07:00New results dtree learning<br />
<br />
<h2>
Results</h2>
The samples generated are equal to original samples we started with.<br />
sample work flow:<br />
<br />
1. 500 samples<br />
2. cluster into C1.C2....Cn<br />
3. build Dtrees. form branches B1,B2,...Bn. Find best and worst between clusters in B1..Bn.<br />
4. CS1,CS2,...CSn between worse clusters to become better clusters.<br />
5. Regenerate samples using CS1,CS2...CSn. =Total 500. Maintaining the ratio.<br />
6. Generate result tables.<br />
<br />
<h2>
Techniques:</h2>
<br />
1. Distance pruning: Prune clusters that are less than 0.3 (normalized distance) each other into one big cluster.<br />
<br />
2. Dtree pruning: Prune leaves of decision trees -remove leaves with multiple majority classes - remove subtrees with same majority class<br />
<br />
3. Discretize: Discretize continous data into discrete values and generate trees. Reduces trees a lot! Affects performance.<br />
<br />
4. Infogain: Prune columns with infogain. <br />
<h2>
Distance pruning works:</h2>
Techniques: Distance pruning and DTree pruning<br />
<br />
<pre>Techniques -effort -months -defects -risks #
1 T0 m 31 67 6 0 #
2 Bef prune m 11 20 8 2 #
3 Aft prune m 8 20 11 2 #
4 T9:j/j_ m 0 31 0 22 #
-------------------------------------------------------------------------------------
1 T0 q 26 13 9 2 #
2 Bef prune q 10 0 10 3 #
3 Aft prune q 8 0 14 4 #
4 T9:j/j_ q 3 19 2 38 #
-------------------------------------------------------------------------------------
1 T0 w 100 100 42 16 #
2 Bef prune w 67 38 63 9 #
3 Aft prune w 58 39 100 9 #
4 T9:j/j_ w 31 98 25 100 #
-------------------------------------------------------------------------------------
100 2761.71 42.2 39498.09 8.6 #
0 117.21 3.48 381.83 0.0 #</pre>
<pre> </pre>
<pre> </pre>
<i><u><b><span style="font-size: x-small;">but tree is big:</span></b></u></i><br />
<br />
<span style="font-size: x-small;">MAIN_TREE:</span><br />
<br />
<span style="font-size: x-small;"><span style="font-size: xx-small;">173 $rely <= 3.5 samples = 500<br /> 174 |- $ltex <= 2.5 samples = 253<br /> 175 |-|- $cplx <= 3.5 samples = 167<br /> 176 |-|-|- $site <= 2.5 samples = 54<br /> 177 |-|-|-|- $kloc <= 249.0 samples = 24<br /> 178 |-|-|-|-|- ['__2'] # samples = 13 # branch_id = 0<br /> 179 |-|-|-|-|- ['__4', '__9'] # samples = 11 # branch_id = 1<br /> 180 |-|-|-|- $pr <= 2.5 samples = 30<br /> 181 |-|-|-|-|- ['__7'] # samples = 11 # branch_id = 2<br /> 182 |-|-|-|-|- ['__5'] # samples = 19 # branch_id = 3<br /> 183 |-|-|- $ruse <= 3.5 samples = 113<br /> 184 |-|-|-|- $pcon <= 2.5 samples = 52<br /> 185 |-|-|-|-|- $pcap <= 3.5 samples = 25<br /> 186 |-|-|-|-|-|- ['__6'] # samples = 13 # branch_id = 4<br /> 187 |-|-|-|-|-|- ['__13'] # samples = 12 # branch_id = 5<br /> 188 |-|-|-|-|- $flex <= 2.5 samples = 27<br /> 189 |-|-|-|-|-|- ['__7'] # samples = 13 # branch_id = 6<br /> 190 |-|-|-|-|-|- ['__8', '__13'] # samples = 14 # branch_id = 7<br /> 191 |-|-|-|- $pcap <= 3.5 samples = 61<br /> 192 |-|-|-|-|- $kloc <= 152.0 samples = 29<br /> 193 |-|-|-|-|-|- ['__15'] # samples = 11 # branch_id = 8<br /> 194 |-|-|-|-|-|- ['__13'] # samples = 18 # branch_id = 9<br /> 195 |-|-|-|-|- $aexp <= 2.5 samples = 32<br /> 196 |-|-|-|-|-|- ['__13', '__15'] # samples = 19 # branch_id = 10<br /> 197 |-|-|-|-|-|- ['__12'] # samples = 13 # branch_id = 11<br /> 198 |-|- $pvol <= 2.5 samples = 86<br /> 199 |-|-|- $resl <= 2.5 samples = 28<br /> 200 |-|-|-|- ['__4'] # samples = 15 # branch_id = 12<br /> 201 |-|-|-|- ['__2', '__7'] # samples = 13 # branch_id = 13<br /> 202 |-|-|- $acap <= 3.5 samples = 58<br /> 203 |-|-|-|- $resl <= 2.5 samples = 23<br /> 204 |-|-|-|-|- ['__2'] # samples = 12 # branch_id = 14<br /> 205 |-|-|-|-|- ['__5'] # samples = 11 # branch_id = 15<br /> 206 |-|-|-|- $team <= 2.5 samples = 35<br /> 207 |-|-|-|-|- ['__1', '__11'] # samples = 15 # branch_id = 16<br /> 208 |-|-|-|-|- ['__7'] # samples = 20 # branch_id = 17<br /> 209 |- $pr <= 4.5 samples = 247<br /> 210 |-|- $plex <= 2.5 samples = 193<br /> 211 |-|-|- $flex <= 2.5 samples = 129<br /> 212 |-|-|-|- $pvol <= 2.5 samples = 63<br /> 213 |-|-|-|-|- $kloc <= 164.5 samples = 22<br /> 214 |-|-|-|-|-|- ['__13'] # samples = 11 # branch_id = 18<br /> 215 |-|-|-|-|-|- ['__7'] # samples = 11 # branch_id = 19<br /> 216 |-|-|-|-|- $pr <= 3.5 samples = 41<br /> 217 |-|-|-|-|-|- $pcap <= 3.5 samples = 29<br /> 218 |-|-|-|-|-|-|- ['__11'] # samples = 13 # branch_id = 20<br /> 219 |-|-|-|-|-|-|- ['__14'] # samples = 16 # branch_id = 21<br /> 220 |-|-|-|-|-|- ['__10'] # samples = 12 # branch_id = 22<br /> 221 |-|-|-|- $site <= 1.5 samples = 66<br /> 222 |-|-|-|-|- ['__15'] # samples = 17 # branch_id = 23<br /> 223 |-|-|-|-|- $ruse <= 3.5 samples = 49<br /> 224 |-|-|-|-|-|- ['__15'] # samples = 20 # branch_id = 24<br /> 225 |-|-|-|-|-|- $pvol <= 3.5 samples = 29<br /> 226 |-|-|-|-|-|-|- ['__12'] # samples = 16 # branch_id = 25<br /> 227 |-|-|-|-|-|-|- ['__6', '__16'] # samples = 13 # branch_id = 26<br /> 228 |-|-|- $kloc <= 295.5 samples = 64<br /> 229 |-|-|-|- $team <= 1.5 samples = 47<br /> 230 |-|-|-|-|- ['__13', '__15'] # samples = 17 # branch_id = 27<br /> 231 |-|-|-|-|- $etat <= 2.5 samples = 30<br /> 232 |-|-|-|-|-|- ['__13'] # samples = 13 # branch_id = 28<br /> 233 |-|-|-|-|-|- ['__7'] # samples = 17 # branch_id = 29<br /> 234 |-|-|-|- ['__7'] # samples = 17 # branch_id = 30<br /> 235 |-|- $cplx <= 3.5 samples = 54<br /> 236 |-|-|- ['__9'] # samples = 21 # branch_id = 31<br /> 237 |-|-|- $team <= 2.5 samples = 33<br /> 238 |-|-|-|- ['__15'] # samples = 15 # branch_id = 32<br /> 239 |-|-|-|- ['__12', '__13'] # samples = 18 # branch_id = 33</span></span><br />
<span style="font-size: x-small;"><span style="font-size: xx-small;"></span></span><br />
<span style="font-size: x-small;"><span style="font-size: xx-small;"></span></span><br />
<span style="font-size: x-small;"><span style="font-size: xx-small;"><span style="font-size: small;">To dist prune tree:</span></span></span><br />
<span style="font-size: x-small;"><span style="font-size: xx-small;"><span style="font-size: small;"><span style="font-size: xx-small;"> 94 $pr <= 1.5 samples = 500<br /> 95 |- $prec <= 1.5 samples = 98<br /> 96 |-|- $pcon <= 3.5 samples = 22<br /> 97 |-|-|- ['__2'] # samples = 11 # branch_id = 0<br /> 98 |-|- $flex <= 1.5 samples = 76<br /> 99 |-|-|- $kloc <= 256.0 samples = 24<br /> 100 |-|-|-|- ['__9'] # samples = 11 # branch_id = 1<br /> 101 |-|-|-|- ['__5'] # samples = 13 # branch_id = 2<br /> 102 |-|-|- $team <= 1.5 samples = 52<br /> 103 |-|-|-|- ['__11'] # samples = 13 # branch_id = 3<br /> 104 |-|-|-|- $pcon <= 2.5 samples = 39<br /> 105 |-|-|-|-|- ['__13'] # samples = 20 # branch_id = 4<br /> 106 |-|-|-|-|- ['__9'] # samples = 19 # branch_id = 5<br /> 107 |- $prec <= 1.5 samples = 402<br /> 108 |-|- $etat <= 1.5 samples = 103<br /> 109 |-|-|- ['__13'] # samples = 13 # branch_id = 6<br /> 110 |-|-|- $resl <= 3.5 samples = 90<br /> 111 |-|-|-|- $ltex <= 2.5 samples = 66<br /> 112 |-|-|-|-|- $site <= 3.5 samples = 38<br /> 113 |-|-|-|-|-|- $rely <= 3.5 samples = 25<br /> 114 |-|-|-|-|-|-|- ['__6'] # samples = 12 # branch_id = 7<br /> 115 |-|-|-|-|-|- ['__7'] # samples = 13 # branch_id = 8<br /> 116 |-|-|-|-|- $acap <= 3.5 samples = 28<br /> 117 |-|-|-|-|-|- ['__13'] # samples = 12 # branch_id = 9<br /> 118 |-|-|-|-|-|- ['__4'] # samples = 16 # branch_id = 10<br /> 119 |-|-|-|- $site <= 2.5 samples = 24<br /> 120 |-|-|-|-|- ['__9'] # samples = 12 # branch_id = 11<br /> 121 |-|- $acap <= 3.5 samples = 299<br /> 122 |-|-|- $rely <= 3.5 samples = 157<br /> 123 |-|-|-|- $ruse <= 3.5 samples = 82<br /> 124 |-|-|-|-|- $prec <= 2.5 samples = 47<br /> 125 |-|-|-|-|-|- $pcap <= 3.5 samples = 31<br /> 126 |-|-|-|-|-|-|- ['__7'] # samples = 16 # branch_id = 12<br /> 127 |-|-|-|-|-|-|- ['__1'] # samples = 15 # branch_id = 13<br /> 128 |-|-|-|-|- $docu <= 2.5 samples = 35<br /> 129 |-|-|-|-|-|- ['__13'] # samples = 21 # branch_id = 14<br /> 130 |-|-|-|-|-|- ['__1'] # samples = 14 # branch_id = 15<br /> 131 |-|-|-|- $team <= 2.5 samples = 75<br /> 132 |-|-|-|-|- $docu <= 2.5 samples = 44<br /> 133 |-|-|-|-|-|- ['__1'] # samples = 27 # branch_id = 16<br /> 134 |-|-|-|-|-|- ['__1'] # samples = 17 # branch_id = 17<br /> 135 |-|-|-|-|- ['__1'] # samples = 31 # branch_id = 18<br /> 136 |-|-|- $flex <= 2.5 samples = 142<br /> 137 |-|-|-|- $pcon <= 1.5 samples = 70<br /> 138 |-|-|-|-|- $prec <= 2.5 samples = 53<br /> 139 |-|-|-|-|-|- $kloc <= 204.0 samples = 35<br /> 140 |-|-|-|-|-|-|- ['__7'] # samples = 15 # branch_id = 19<br /> 141 |-|-|-|-|-|-|- ['__1'] # samples = 20 # branch_id = 20<br /> 142 |-|-|-|- $site <= 2.5 samples = 72<br /> 143 |-|-|-|-|- $pcon <= 2.5 samples = 33<br /> 144 |-|-|-|-|-|- ['__6'] # samples = 15 # branch_id = 21<br /> 145 |-|-|-|-|-|- ['__7'] # samples = 18 # branch_id = 22<br /> 146 |-|-|-|-|- $pvol <= 3.5 samples = 39<br /> 147 |-|-|-|-|-|- $pcap <= 3.5 samples = 25<br /> 148 |-|-|-|-|-|-|- ['__1'] # samples = 14 # branch_id = 23<br /> 149 |-|-|-|-|-|-|- ['__11'] # samples = 11 # branch_id = 24<br /> 150 |-|-|-|-|-|- ['__3'] # samples = 14 # branch_id = 25</span></span></span></span><br />
<br />
<h2>
<span style="font-size: x-small;"><span style="font-size: xx-small;"><span style="font-size: small;"><span style="font-size: xx-small;"> </span> </span></span></span></h2>
<h2>
<span style="font-size: x-small;"> </span> Infogain Discretized Dtree and Dist pruned doesnt work.</h2>
Tree:<br />
<br />
<pre> flex <= 27.5 samples = 500
|- docu <= 6.5 samples = 261
|-|- ['__1'] # samples = 31 # branch_id = 0
|-|- resl <= 33.5 samples = 230
|-|-|- ['__2'] # samples = 200 # branch_id = 1
|-|-|- ['__2'] # samples = 30 # branch_id = 2
|- pr <= 18.0 samples = 239
|-|- ['__6'] # samples = 16 # branch_id = 3
|-|- ruse <= 32.0 samples = 223
|-|-|- ['__4'] # samples = 203 # branch_id = 4</pre>
<pre> </pre>
<pre> </pre>
<pre>Performance:</pre>
<pre> </pre>
<pre> Techniques -effort -months -defects -risks #
1 T0 m 31 68 16 0 #
2 Bef disc m 18 43 17 2 #
3 Aft disc m 29 61 39 8 #
4 T9:j/j_ m 0 33 0 22 #
-------------------------------------------------------------------------------------
1 T0 q 29 16 24 0 #
2 Bef disc q 15 8 26 3 #
3 Aft disc q 3 0 10 9 #
4 T9:j/j_ q 3 22 4 38 #
-------------------------------------------------------------------------------------
1 T0 w 100 100 97 16 #
2 Bef disc w 63 65 100 15 #
3 Aft disc w 74 77 62 13 #
4 T9:j/j_ w 32 99 58 100 #
-------------------------------------------------------------------------------------
100 2702.14 41.94 17094.69 8.6 #
0 117.21 2.44 381.83 0.0 # </pre>
<br />
<br />
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-18270106984107844462014-07-06T19:38:00.001-07:002014-07-06T19:38:11.618-07:00Results with discretization, smart cluster by pruning. Pruning columns and Discretization of values --> disc<br />
<br />
<br />
<span style="white-space: pre-wrap;"> <span style="font-size: x-small;">Techniques -effort -months -defects -risks #</span></span><br />
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;"> 1 T0 m 27 65 10 0 #
2 Bef disc m 7 15 9 1 #
3 Aft disc m 15 32 5 2 #
4 T9:j/j_ m 0 31 0 22 #
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1 T0 q 26 15 17 0 #
2 Bef disc q 7 0 12 2 #
3 Aft disc q 15 5 8 3 #
4 T9:j/j_ q 3 20 3 38 #
-------------------------------------------------------------------------------------
1 T0 w 96 100 75 16 #
2 Bef disc w 47 31 91 5 #
3 Aft disc w 100 58 100 12 #
4 T9:j/j_ w 28 96 41 100 #
-------------------------------------------------------------------------------------
100 3096.1 43.2 24082.25 8.6 #
0 117.21 2.99 381.83 0.0 #</span></pre>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-3178014841396405952014-06-13T07:36:00.003-07:002014-09-18T09:04:02.483-07:00Thesis Planning<h2>
9/18/14- Possible problems with "defaults only"</h2>
<div>
Here is the NB+RF result I was waiting on. Looks like we now have a convincing argument that tuning works, right?</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8zLMEB27Ll7mRYsbHbmTXxLCCo5CTE_lqaFpsoyyGxMjicyFBqI7G1Xlltg4awohBy0nlFdXYzJz0vh-qGmWZm62JoBnQFQ0AvPO2RekdjYIp23JY97v_HUHnsp3QwcXrC5Mgu9plz8c/s1600/figure_3.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg8zLMEB27Ll7mRYsbHbmTXxLCCo5CTE_lqaFpsoyyGxMjicyFBqI7G1Xlltg4awohBy0nlFdXYzJz0vh-qGmWZm62JoBnQFQ0AvPO2RekdjYIp23JY97v_HUHnsp3QwcXrC5Mgu9plz8c/s1600/figure_3.png" height="134" width="640" /></a></div>
<br />
Let's confirm by checking RF only. Not as nice of a picture:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaDNGI8puclWO4Sz-5ADlPEfLLR6BPgTGjhW_BjwjrXMk928K_jUcdiJ5nqY2aBuOi9oQzHODBdlCxDTnrkmSXmNNa4RKuUg_v1LaLnqaJy6Xxb09fQhBUOX50cVNAQd_LVzB9RLwEn7s/s1600/figure_3_RF.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaDNGI8puclWO4Sz-5ADlPEfLLR6BPgTGjhW_BjwjrXMk928K_jUcdiJ5nqY2aBuOi9oQzHODBdlCxDTnrkmSXmNNa4RKuUg_v1LaLnqaJy6Xxb09fQhBUOX50cVNAQd_LVzB9RLwEn7s/s1600/figure_3_RF.png" height="134" width="640" /></a></div>
What about NB only?<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwmLFpdfrOZ58NbWO00a3J9FBMUZ0Ldyww5ZUnnKqI3J0QH08OtXFpXylYEvER9J-DVXlcjp8hu_Cl49ve01zmBxDsaAHPDdImiLcY3La-o5b4kqLdG7BYw_MyzHP-HWjphP_IVPlfxNc/s1600/Bayes1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwmLFpdfrOZ58NbWO00a3J9FBMUZ0Ldyww5ZUnnKqI3J0QH08OtXFpXylYEvER9J-DVXlcjp8hu_Cl49ve01zmBxDsaAHPDdImiLcY3La-o5b4kqLdG7BYw_MyzHP-HWjphP_IVPlfxNc/s1600/Bayes1.png" height="136" width="640" /></a></div>
Now we can see that the low performance in the defaults only case merely reflects that RF is a better choice of learner than NB. (not a big surprise) The "defaults only" case works just as well as out-of-set tuning.</div>
<h2>
</h2>
<h2>
8/15/14-- Bayes Test Confirms Combination Suspicions, Proposed Experiments</h2>
<div>
So here's the old results from the combination of three Baysean classifiers (stupid combination method):</div>
<div>
<div>
<span style="font-family: 'Courier New', Courier, monospace; font-size: x-small;">Rranking Param: pD,pF AUC</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.76 defaults only -- current xval</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.75 best on current -- current xval</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.74 best on prev -- current xval</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.63 defaults only -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.63 best on current -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.62 best on current -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.61 best on prev -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.61 defaults only -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.60 best on prev -- prev to current xval</span></div>
</div>
<div>
<br /></div>
<div>
And here are the new results from the same three classifiers, same params, new linear score-weighted combination method:</div>
<div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Rranking Param: pD,pF AUC</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.83 best on current -- current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.83 best on prev -- current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.83 defaults only -- current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.67 best on current -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.67 defaults only -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.67 best on prev -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.67 best on prev -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.65 best on current -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.63 defaults only -- prev to current xval</span></div>
</div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span></div>
<div>
<span style="font-family: inherit;">Clearly this shows that my old combination method is inferior and gives us a hint that choice of combination method may have a </span>sizable<span style="font-family: inherit;"> effect on performance.</span></div>
<div>
<span style="font-family: inherit;"><br /></span></div>
<div>
<span style="font-family: inherit;">As per our discussion the other day about using the RQs to guide the experiments, here's my current plan:</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOf7FoDk-fxGgxAiyFP1jWh9FuKFhHTSJDu9DRu8mA7fRGWRQx1v9iYXjbBMo7bhvV_ye6oKiDH8eTLA6a2EzVkLbnEUhWLkAHyAJTfdQlgKiOnNugnyllM13o7WRqJlFzFxVOFg3G4V4/s1600/Screenshot+2014-08-15+14.07.55.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOf7FoDk-fxGgxAiyFP1jWh9FuKFhHTSJDu9DRu8mA7fRGWRQx1v9iYXjbBMo7bhvV_ye6oKiDH8eTLA6a2EzVkLbnEUhWLkAHyAJTfdQlgKiOnNugnyllM13o7WRqJlFzFxVOFg3G4V4/s1600/Screenshot+2014-08-15+14.07.55.png" height="250" width="640" /></a></div>
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<span style="font-family: inherit;"><br /></span></div>
<h2>
</h2>
<h2>
8/10/14 -- All Learners Results, Flowcharts, Suggested Changes</h2>
<div>
To start off, here's the results from the same experiment as before, but with all learners:<br />
(Gaussian NB, Bernoulli Bayes, Multionomial Bayes, Random Forest, Logistic Regression)</div>
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<div>
<br /></div>
<div>
<span style="font-family: 'Courier New', Courier, monospace;">Scott-Knott Rank: pD,pF AUC</span> </div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.84 best on current -- current xval</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.84 best on prev -- current xval</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">1: 0.81 defaults only -- current xval</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.71 best on current -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.70 best on prev -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.70 best on current -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.69 best on prev -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.64 defaults only -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">0: 0.64 defaults only -- prev to current xval</span></div>
</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGpurzLV3t92svMAOn-sIFhQ9oEv6RdxYr6D5OiRujUPl7Ua7-8mRVETARkATwyKqmDK2tKukVI5IoJKqtpHTMjrRpd7eaIa6GWL_IQpjdYPzN0AH3Q4MFEYMYeLNb76KXNwwkTlfkLCk/s1600/figure_1_all.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgGpurzLV3t92svMAOn-sIFhQ9oEv6RdxYr6D5OiRujUPl7Ua7-8mRVETARkATwyKqmDK2tKukVI5IoJKqtpHTMjrRpd7eaIa6GWL_IQpjdYPzN0AH3Q4MFEYMYeLNb76KXNwwkTlfkLCk/s1600/figure_1_all.png" height="140" width="200" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIIftL850AIshnoKMgBkHXGbIQjFmMswducXQUjUeK78XAxbNRdOApG2IsCA9Uhm9Ii7mH7q0xYZvQxdBdgFB0SAdTzsTLVbJPw3Tf5OPBpRj7Fx-kwWrgae9WZ4v3TOPNL_0b39xh_Ak/s1600/figure_2_all.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIIftL850AIshnoKMgBkHXGbIQjFmMswducXQUjUeK78XAxbNRdOApG2IsCA9Uhm9Ii7mH7q0xYZvQxdBdgFB0SAdTzsTLVbJPw3Tf5OPBpRj7Fx-kwWrgae9WZ4v3TOPNL_0b39xh_Ak/s1600/figure_2_all.png" height="140" width="200" /></a></div>
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<div>
As you can see, the pD and pF here are inferior to the results that we saw for RF or LR, but slightly better than what we saw for NB. This is contrary to what we would expect to see. After reading Thomas et al. on classier combination, I don't think my combination method is smart enough. I'm doing the equivalent of an unweighted voting by combining the results from multiple learners post-hock. Whereas I think the strategy best suited to this is score-based weighted voting from Thomas et al.</div>
<div>
<br /></div>
<div>
I've also realized that the process I'm doing is going to be very difficult to explain in a way that makes sense, so I've started on a couple of flowcharts. I know these doesn't entirely conform to the actual definition of the various symbols (using "document" instead of "data" for datasets, etc.), but are these flowcharts, with a few captions sufficient to convey what's being done in the experiment? Suggestions?</div>
<div>
<br /></div>
<div>
<b>Experiment Overview: </b>Shows how data flows through the two-step experiment for each combination of tuning method and evaluation method. The tuning step and evaluation step will be shown in more detail in the next chart.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgstKg5q6aeXUPitG_ze_sAECWCdY-d1mW5a-ALYy_V_DjOrtkWSLx_OsGavN42XV5vvCQB7BfT53iXHupQTL7o1O4oTj16blnK0FybFVyxwj0QVXEXqqLRYSJIucAHIMxYh09JawsSp0o/s1600/Experimenta+setup+-+Dataset+Individual+Runs+-+New+Page.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgstKg5q6aeXUPitG_ze_sAECWCdY-d1mW5a-ALYy_V_DjOrtkWSLx_OsGavN42XV5vvCQB7BfT53iXHupQTL7o1O4oTj16blnK0FybFVyxwj0QVXEXqqLRYSJIucAHIMxYh09JawsSp0o/s1600/Experimenta+setup+-+Dataset+Individual+Runs+-+New+Page.png" height="640" width="560" /></a></div>
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<b>Tuning Step:</b> This step can be in one of three modes: defaults only, best on prev, best on current. This step accepts learner objects, a previous and a current dataset, and the tuning method and passes the datasets and a list of "tuned" learners objects with their default params overridden with optimized params to the next step.</div>
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Note: Here is where the proposed change to reflect the score-based method discussed in Thomas et al.</div>
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Rather than returning a list of non-dominated learners, this section should return a single ensemble learner object with has all the non-dominated learners as constituents. The ensemble learner will also carry two weights for each constituent learner based on its precision and negative predictive value from the tuning study. The precision weight will be applied to each positive classification from a learner, and the negative predictive value weight will be applied to its negative classifications. After weighting has been applied, the ensemble learner will determine a consensus though voting and report only on the consensus classifications. For the "Defaults Only" case, the ensemble learner will contain one of each type of learner with weights of 1 for each learner with the exception of Baysean learners which will receive a weight of 1/3 because three different Baysean learners are used as opposed to one of each other scheme.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg2oQT8aYhCybidxUERNYQ-2XHmlt61l86ikPzFcjorbw4MB0DyD1PIllDPOcdlUGW9nDsDrpr4JFfHv2rnMJNY1jd4nYwT6YXr2vrYixHFlSGDEPMcRAoZeXXNU7SwQJMAlJHtycgzQ4E/s1600/Param+Tuning+Overview+-+Param+Tuning+Overview.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg2oQT8aYhCybidxUERNYQ-2XHmlt61l86ikPzFcjorbw4MB0DyD1PIllDPOcdlUGW9nDsDrpr4JFfHv2rnMJNY1jd4nYwT6YXr2vrYixHFlSGDEPMcRAoZeXXNU7SwQJMAlJHtycgzQ4E/s1600/Param+Tuning+Overview+-+Param+Tuning+Overview.png" height="640" width="484" /></a></div>
<div>
<br /></div>
<div>
<b>Evaluation Step:</b> This step evaluates the "tuned" learners which are passed from the previous step in one of three ways: prev to current xval, current xval, or prev to current full set. This step generates a result which constitutes a single point on each of the pD, pF plots.<br />
(I know this is probably a little small in the blog, but opening the image in a new tab should get you the full resolution.)</div>
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<img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOrbjrfFihix_p4Ar-FaHSbP6hsyqHQNGHZD68zMntz_Lw2SZzzINnXaaXq3bU2FmS5Rctus1smUrhe1D2pmRD-FbaQgcN7fED29fJ7V1u6_lJi6FEPUFFnEKysxzCjG8isbCpEBsKVDo/s1600/Evaluation+Overview+-+New+Page.png" height="640" width="380" /></div>
<br />
<br />
<b>Result Structure:</b> The "flow" part of "flowchart" doesn't really apply here, but this is the structure of the results generated after the experiment is finished. For Each combination of tuning method and evaluation method, there is a list of individual results, one for each dataset.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnpNjfzKgjFMq2qbxbGdkpr_CN1ZZOE65c2Pho_wyEznDyvAgXHeFU4Zu_wVDkmwozG757J-0G_6IyQUphXMDyeK3Y7v3VItTkt5OQFr07CzdCS9XNTQRiv5NgKnjbsfeA3bXNxnrWxWQ/s1600/Results+Layout+-+New+Page.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnpNjfzKgjFMq2qbxbGdkpr_CN1ZZOE65c2Pho_wyEznDyvAgXHeFU4Zu_wVDkmwozG757J-0G_6IyQUphXMDyeK3Y7v3VItTkt5OQFr07CzdCS9XNTQRiv5NgKnjbsfeA3bXNxnrWxWQ/s1600/Results+Layout+-+New+Page.png" height="594" width="640" /></a></div>
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<br /></div>
<h2>
8/04/14 -- Logistic Regression</h2>
<div>
Same story, different learner.</div>
<div>
<br /></div>
<div>
<div>
<span style="font-family: 'Courier New', Courier, monospace;">Scott-Knott Rank: pD,pF AUC</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">1: 0.86 defaults only -- current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">1: 0.85 best on prev -- current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">1: 0.85 best on current -- current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">0: 0.73 best on prev -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">0: 0.72 best on current -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">0: 0.72 defaults only -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">0: 0.72 best on prev -- prev to current full</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">0: 0.71 defaults only -- prev to current xval</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace;">0: 0.71 best on current -- prev to current xval</span></div>
</div>
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<span style="font-family: Courier New, Courier, monospace;"><br /></span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFRpyL-p1FHo7bH_VvgyNOjFPIWNNc0lFtFm8ghfJBrvuTbqKg9N6STbBOzwsWZynXtV8BkRN48cEaHcbAz7RX7_jHaQJyHKayXXYm81zTDwc7dE8BCJW0Q-jflMIb4jLaidjXt9lDYzI/s1600/LR+Fig1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiFRpyL-p1FHo7bH_VvgyNOjFPIWNNc0lFtFm8ghfJBrvuTbqKg9N6STbBOzwsWZynXtV8BkRN48cEaHcbAz7RX7_jHaQJyHKayXXYm81zTDwc7dE8BCJW0Q-jflMIb4jLaidjXt9lDYzI/s1600/LR+Fig1.png" height="344" width="640" /></a></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-Kn-Pih4pgu_SqZ3aVbEkjQpj5ZC8fZbTX0zPaIWjY0Gld9O6y7z5_ypKUUKq41RgTlxeowWp8vCKs-vyz1C0PYPHQ_np4wqXn0lrbpOvPrS_RagxUgpbq3DWnn12I8CIqdVdAFfDvwk/s1600/LR+FIg2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-Kn-Pih4pgu_SqZ3aVbEkjQpj5ZC8fZbTX0zPaIWjY0Gld9O6y7z5_ypKUUKq41RgTlxeowWp8vCKs-vyz1C0PYPHQ_np4wqXn0lrbpOvPrS_RagxUgpbq3DWnn12I8CIqdVdAFfDvwk/s1600/LR+FIg2.png" height="344" width="640" /></a></div>
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<h2>
</h2>
<h2>
8/02/14 -- Random Forests</h2>
<div>
Doing the same thing as before, but with RF instead of NB.</div>
<div>
<br /></div>
<div>
Step 1: Choose one tuning strategy from:</div>
<div>
<br />
<ul>
<li>Defaults Only- no tuning occurs, only the default parameters are used</li>
<li>Best on Prev- parameters are tuned for best performance on previous version's data</li>
<li>Best on Current-parameters are tuned by peeking at this version's data (must know current version's class)</li>
</ul>
<div>
Step 2: Choose one evaluation method from:</div>
<br />
<div>
<br />
<ul>
<li>Current Xval- current version split into test/train groups for 5x5 cross-validation</li>
<li>Prev to Current Xval- like above, but training on previous version and testing on the current</li>
<li>Prev to Current Full- Entire previous set is used for training, entire current set used for testing</li>
</ul>
</div>
<br />
<div>
Scikit-Learn's Random Forrest was used as the sole learner and parameter tuning was conducted within the following parameter space:</div>
<div>
<br /></div>
<br />
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<!--StartFragment--><span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> params={</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">'n_estimators':['values', 3, 5, 10], </span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">'criterion':['values', "gini", "entropy"],</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'max_features':['values', 'sqrt', 'log2', None],</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'max_depth':['values', None, 4, 8],</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'min_samples_split':['values', 4, 8],</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'min_samples_leaf':['values', 2, 4],</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'bootstrap':['values', True, False],</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">}</span><br />
<br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">default_params={</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">'n_estimators':10, </span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'criterion':'gini',</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'max_features':"sqrt",</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'max_depth':None,</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'min_samples_split':2,</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'min_samples_leaf':1,</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 'bootstrap':True,</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">}</span><!--EndFragment--><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<br />
<div>
All nine combinations of tuning strategy and evaluation method were tried on every non-0th-version dataset from the usual group. (Ant, Camel, Ivy, Jedit, Log4J, LUcene, Synapse, Velocity, Xalan, Xerces)</div>
<div>
<br /></div>
<div>
datasets*usable versions=26<br />
Runtime~= 22hrs</div>
<div>
<br />
<span style="font-family: Courier New, Courier, monospace;">Scott-Knott Rank: pD,pF AUC</span><br />
<span style="font-family: Courier New, Courier, monospace;">1: 0.99 <span style="background-color: lime;">defaults only</span> -- <span style="background-color: red;">current xval</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">1: 0.99 <span style="background-color: cyan;">best on current</span> -- <span style="background-color: red;">current xval</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">1: 0.98 <span style="background-color: magenta;">best on prev</span> -- <span style="background-color: red;">current xval</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">0: 0.74 <span style="background-color: lime;">defaults only</span> -- <span style="background-color: yellow;">prev to current full</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">0: 0.73 <span style="background-color: cyan;">best on current</span> -- <span style="background-color: yellow;">prev to current full</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">0: 0.73 <span style="background-color: magenta;">best on prev</span> -- <span style="background-color: yellow;">prev to current full</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">0: 0.72 <span style="background-color: magenta;">best on prev</span> -- <span style="background-color: orange;">prev to current xval</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">0: 0.72 <span style="background-color: lime;">defaults only</span> -- <span style="background-color: orange;">prev to current xval</span></span><br />
<span style="font-family: Courier New, Courier, monospace;">0: 0.70 <span style="background-color: cyan;">best on current</span> -- <span style="background-color: orange;">prev to current xval</span></span><br />
<br /></div>
<div>
Pd/Pf plots with each dot representing an individual dataset version:<br />
Note: colors above != colors below<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgT8ZvxkSNEyl9Xak8bAhhbn27QvHE30PXekMK3SLBrswCai9aTjsw84KKUAxe57z6H6vyeg30WbuilnjECg7CrHKIKkKIFf06KzwTHnUjQV0ZirrDzCuA6_cROnxvrRSgHXWAAuCg2AYk/s1600/Figure+1+RF.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgT8ZvxkSNEyl9Xak8bAhhbn27QvHE30PXekMK3SLBrswCai9aTjsw84KKUAxe57z6H6vyeg30WbuilnjECg7CrHKIKkKIFf06KzwTHnUjQV0ZirrDzCuA6_cROnxvrRSgHXWAAuCg2AYk/s1600/Figure+1+RF.png" height="344" width="640" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEimf9XfGwqZVQajRv-HB-mx7Kq20n3SyckjgQCowgi5GU-9Pcoha_oqDtdQBgSX09Fsfs5V9Jw33qkM9flwbqd2bcI_6H_-7CtcEqwuM9bTkTZwHhvgSFS8WGmUPrarRWKbl9l9j-2Zm-c/s1600/figure+2+RF.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEimf9XfGwqZVQajRv-HB-mx7Kq20n3SyckjgQCowgi5GU-9Pcoha_oqDtdQBgSX09Fsfs5V9Jw33qkM9flwbqd2bcI_6H_-7CtcEqwuM9bTkTZwHhvgSFS8WGmUPrarRWKbl9l9j-2Zm-c/s1600/figure+2+RF.png" height="344" width="640" /></a></div>
Looks like we're seeing a little bit more pronounced version of the same effect we saw with naive Bayes. Stay tuned for logistic regression.</div>
</div>
</div>
<h2>
</h2>
<h2>
Update 7/28/14</h2>
<div>
Three styles of parameter tuning and three styles of test->train setup were compared. They are defined as follows:</div>
<div>
<br /></div>
<div>
Defaults Only- no tuning occurs, only the default parameters are used</div>
<div>
Best on Prev- parameters are tuned for best performance on previous version's data</div>
<div>
Best on Current-parameters are tuned by peeking at this version's data (must know current version's class)</div>
<div>
<br /></div>
<div>
Current Xval- current version split into test/train groups for 5x5 cross-validation</div>
<div>
Prev to Current Xval- like above, but training on previous version and testing on the current</div>
<div>
Prev to Current Full- Entire previous set is used for training, entire current set used for testing</div>
<div>
<br /></div>
<div>
All nine combinations were tried on every non-0th-version dataset from the usual group. (Ant, Camel, Ivy, Jedit, Log4J, LUcene, Synapse, Velocity, Xalan, Xerces)</div>
<div>
<br /></div>
<div>
datasets*usable versions=26</div>
<div>
<br /></div>
<div>
Pd/Pf plots with each dot representing an individual dataset version:</div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0zoGGk-kyfwfJxnG_TUwRBsISV4PWq2CJYS6O5-hYWcqefXKfzTkVCnlfJpn5b4PwONrGlB0ezMZSUoNOAadMW59kPza4PszvjWeuALqw_kBNe4YkHy4D07J6Vq5p7KKDqJshpvcI3Tw/s1600/figure_1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0zoGGk-kyfwfJxnG_TUwRBsISV4PWq2CJYS6O5-hYWcqefXKfzTkVCnlfJpn5b4PwONrGlB0ezMZSUoNOAadMW59kPza4PszvjWeuALqw_kBNe4YkHy4D07J6Vq5p7KKDqJshpvcI3Tw/s1600/figure_1.png" height="454" width="640" /></a></div>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhD9OYaP1bYiWwwClVMFlXHW8lM2YiHt1MqnYxE7VRn3FcmawfIpfWauG0Zs8l_k9A8Du0LvEnoZI0pRiQEZm3HrAhRj5H22ur9qHmpgfxyCWhcvUcDSB_3Vafh8mLzY3F9t9ZzolR7LsE/s1600/figure_2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhD9OYaP1bYiWwwClVMFlXHW8lM2YiHt1MqnYxE7VRn3FcmawfIpfWauG0Zs8l_k9A8Du0LvEnoZI0pRiQEZm3HrAhRj5H22ur9qHmpgfxyCWhcvUcDSB_3Vafh8mLzY3F9t9ZzolR7LsE/s1600/figure_2.png" height="454" width="640" /></a></div>
<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
<div>
and if we rank all 9 treatments by pd/pf AUC using Scott Knott:</div>
<div>
<br /></div>
<div>
<div>
1: 0.76 defaults only -- current xval</div>
<div>
1: 0.75 best on current -- current xval</div>
<div>
1: 0.74 best on prev -- current xval</div>
<div>
0: 0.63 defaults only -- prev to current full</div>
<div>
0: 0.63 best on current -- prev to current full</div>
<div>
0: 0.62 best on current -- prev to current xval</div>
<div>
0: 0.61 best on prev -- prev to current full</div>
<div>
0: 0.61 defaults only -- prev to current xval</div>
<div>
0: 0.60 best on prev -- prev to current xval</div>
</div>
<div>
<br /></div>
<div>
We find , unsurprisingly, that results look better when both the train and test data come from the same dataset. Other than that, nothing else appears to matter too much.</div>
<div>
<br /></div>
<h2>
</h2>
<h2>
Update 6/16/14</h2>
Outline of how I see these topics being presented in referance to the Sheppard, Bowes, Hall and Hall et al. results. (to make sure we're on the same page)<br />
<br />
<ul>
<li>Parameter Tuning Style</li>
<ul>
<li>parameter tuning practices fall into the reasercher group "basket" of concepts and prior knowledge mentioned by Sheppard, Bowes, and Hall</li>
<li>Perhaps parameter tuning can explain some of the variance in literature</li>
</ul>
<li>Train -> Test Style</li>
<ul>
<li>Much consideration and discussion of error in results focuses on sound experimental design. One design element often suspect is the style of segregating training and testing data.</li>
<li>This was not examined in Sheppard, Bowes, Hall or Hall et al. but if this truly has a large effect on results, perhaps it could explain some of the variance.</li>
</ul>
<li>History Inclusion</li>
<ul>
<li>This doesn't really fit. Perhaps we should drop it?</li>
</ul>
<li>Learn by Cluster</li>
<ul>
<li>What if some authors are only using a subset of data that preforms the best?</li>
<li>Comparing the performance of clusters should tell us if this is even worth considering</li>
<li>(Spoiler alert: It's probably not)</li>
</ul>
<li>Other Things I think may be appropriate:</li>
<ul>
<li>Since ~70% of the studies in meta-studies used NASA or Eclipse datasets, I probably should pick those up as well and make them the primary focus</li>
<li>I should probably also include the Matthews Correlation Coefficient for comparison even if we keep pD, pF and pD/pF-AUC as our primary means of comparison. </li>
<li>I should probably also include more learners eventually.</li>
</ul>
</ul>
<br />
<div>
<h2>
</h2>
<h2>
Original Post:</h2>
Things with which to experiment:</div>
<ul>
<li>Parameter Tuning Style</li>
<ul>
<li>Default parameters only</li>
<li>Global best tuning</li>
<li>best parameters from x-val on previous versions</li>
<li>best parameters from x-val on current versions</li>
</ul>
<li>Train->Test Style</li>
<ul>
<li>current->current (standard x-val or leave-one-out)</li>
<li>prev->current full</li>
<li>prev->current with subsampling</li>
</ul>
<li>History Included</li>
<ul>
<li>No historical deltas</li>
<li>Historical deltas to previous version</li>
<li>Historical deltas to all (or n) previous versions</li>
</ul>
<li>Learn by Cluster</li>
<ul>
<li>No</li>
<li>Yes</li>
</ul>
</ul>
Questions:<br />
<div>
<ul>
<li>Shepperd, Hall, Bowes results</li>
<ul>
<li>I've seen authorship in Shepperd's ppt, but not in the Hall, Bowes paper</li>
<li>Is there a paper to go with the embarrassing result?</li>
</ul>
<li>Previous->Current :: Train->Test</li>
<ul>
<li>The same as incremental learning or not quite? </li>
<li>More papers?</li>
</ul>
</ul>
</div>
Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-80798257292410735792014-06-11T07:04:00.001-07:002014-06-11T07:04:51.250-07:00PLAN C: Prune trees<u style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;"><b>1a) delete data from any leaf containing things from > 1 cluster.</b></u><br />
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">Did this one . Check </span><a href="http://unbox.org/things/var/nave/lpj/out/10_June_2014/dats/pruned_tree.dat" style="background-color: white; color: #1155cc; font-family: arial, sans-serif; font-size: 13px;" target="_blank">http://unbox.org/things/var/<wbr></wbr>nave/lpj/out/10_June_2014/<wbr></wbr>dats/pruned_tree.dat</a><br />
<br style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;" />
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">for pruned tree and actual tree. </span><br />
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">To check if the code is working or not see: </span><a href="http://unbox.org/things/var/nave/lpj/out/10_June_2014/dats/short_example.dat" style="background-color: white; color: #1155cc; font-family: arial, sans-serif; font-size: 13px;" target="_blank">http://unbox.org/things/var/<wbr></wbr>nave/lpj/out/10_June_2014/<wbr></wbr>dats/short_example.dat</a><br />
<br style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;" />
<u style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;"><b>1b) descend the trees generated from CART looking for sub-trees that<br />whose items with cluster ID have HIGHER entropy than the parents, then<br />delete all items in those sub-trees-of-confusion<br /><br />1c) for all sub-trees built by CART, compute the entropy of the leaf<br />items in that tree. sort those entropies to find "too much confusion"<br />e.g. half way down that list. delete all sub-trees with MORE than "too<br />much confusion"<br /><br />then after step1, rebuild the trees using the reduced data sets.</b></u><br />
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
<div>
<br /></div>
<div>
Apparently scikit-learn dtrees do not maintain samples in their trees. meaning. at the leafs or at the nodes there is no sample data. It runs through the sample data and gathers values(results) but not store samples themselves. So at a leaf, I can only find stats of samples, like<br />
[ 7. 8. 3. 0. 0. 0. 0. 0. 3. 0. 5. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]<img src="https://blogger.googleusercontent.com/img/proxy/AVvXsEhYG8IhW9ZVN6gwclq3mblO8Z61XCbaRT7hOBAZZUMxuOQ1mg5vsh4lwNNqUkW-AjicFosKaO9VUE8rHa7SwhLuGPmgC2UAnc0ZP4_XDz4IpHk4IkBs-5qRPkJHFjFs2VrmN2JHmMPv4Ve3y2nf-udD5Pr1nA=s0-d-e1-ft" /><br />
<br />
indicating there are 7 samples of first cluster, 8 of second and so on.<br />
<br />
So I cannot actually go back to dataset remove those rows and rebuild the tree.</div>
<div>
Instead I can calculate entropies using above array at each leaf/node and prune ones using 1b and 1c.</div>
<div>
<br /></div>
<div>
PS: One more problem, those arrays of values are not maintained at nodes! there are only available at leaves, so I have to traverse down entire branch beneath a node and add those arrays together to get that above array at that node.<br />
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NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-1126803924419431862014-06-02T22:06:00.002-07:002014-06-02T22:42:44.802-07:00results Contrast set learner<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-size: x-small;">55555 rounding</span><br />
<span style="font-size: x-small;">Before rounding:</span><br />
<span style="font-size: x-small;"><br /></span>
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 29 67 14 2 #
T3 C25 N100 m 2 10 3 0 #
T9:j/j_ m 0 35 0 21 #
-------------------------------------------------------------------------------------
T0 q 26 16 20 6 #
T3 C25 N100 q 3 0 5 1 #
T9:j/j_ q 3 23 4 38 #
-------------------------------------------------------------------------------------
T0 w 100 100 100 19 #
T3 C25 N100 w 32 21 49 4 #
T9:j/j_ w 32 100 56 100 #
-------------------------------------------------------------------------------------
100 2687.38 41.4 17612.38 8.6 #
0 117.21 1.93 381.83 0.05 #
</span></pre>
<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">After rounding:</span><br />
<span style="font-size: x-small;">Dataset rounded to integers:</span><br />
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 29 67 14 0 #
T3 C25 N100 m 3 10 10 1 #
T9:j/j_ m 0 35 0 22 #
-------------------------------------------------------------------------------------
T0 q 28 17 24 2 #
T3 C25 N100 q 3 0 13 2 #
T9:j/j_ q 3 23 4 38 #
-------------------------------------------------------------------------------------
T0 w 100 99 100 15 #
T3 C25 N100 w 26 19 78 4 #
T9:j/j_ w 32 100 59 100 #
-------------------------------------------------------------------------------------
100 2695.0 41.4 16771.11 8.6 #
0 117.21 1.85 381.83 0.0 # </span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">7777 Trees of all</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">http://unbox.org/things/var/nave/lpj/out/02_June_2014/dats/tree*</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">88888 Results for all models after rounding (makes sense):</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">flight:</span></pre>
<pre></pre>
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 27 67 12 4 #
T3 C25 N100 m 0 10 0 0 #
T9:j/jall m 4 46 1 16 #
-------------------------------------------------------------------------------------
T0 q 24 16 18 10 #
T3 C25 N100 q 0 0 2 1 #
T9:j/jall q 10 29 6 43 #
-------------------------------------------------------------------------------------
T0 w 100 100 100 35 #
T3 C25 N100 w 31 21 47 7 #
T9:j/jall w 32 97 35 100 #
-------------------------------------------------------------------------------------
100 2687.38 41.35 17612.38 4.8 #
0 172.58 1.92 892.87 0.04 #
</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">ground:</span></pre>
<pre><span style="font-size: x-small;">
Techniques -effort -months -defects -risks #
T0 m 15 55 8 10 #
T3 C25 N100 m 2 12 6 0 #
T9:j/jground m 4 42 0 9 #
-------------------------------------------------------------------------------------
T0 q 12 13 13 24 #
T3 C25 N100 q 0 0 6 0 #
T9:j/jground q 15 47 2 15 #
-------------------------------------------------------------------------------------
T0 w 68 84 100 54 #
T3 C25 N100 w 26 21 44 4 #
T9:j/jground w 100 100 98 100 #
-------------------------------------------------------------------------------------
100 2819.5 43.9 27175.92 4.8 #
0 155.25 1.55 806.33 0.12 # </span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">osp:</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 30 54 38 37 #
T3 C25 N100 m 0 8 2 0 #
T9:j/josp m 4 47 0 11 #
-------------------------------------------------------------------------------------
T0 q 9 5 21 29 #
T3 C25 N100 q 0 0 4 0 #
T9:j/josp q 4 30 10 30 #
-------------------------------------------------------------------------------------
T0 w 54 67 100 64 #
T3 C25 N100 w 13 16 55 10 #
T9:j/josp w 100 100 41 100 #
-------------------------------------------------------------------------------------
100 3063.6 41.7 14504.17 7.5 #
0 89.69 1.47 722.41 0.26 #
</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">osp2:</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 49 67 11 5 #
T3 C25 N100 m 1 12 1 0 #
T9:j/josp2 m 2 44 0 9 #
-------------------------------------------------------------------------------------
T0 q 9 5 2 7 #
T3 C25 N100 q 0 0 4 2 #
T9:j/josp2 q 16 21 18 28 #
-------------------------------------------------------------------------------------
T0 w 90 82 34 15 #
T3 C25 N100 w 44 23 93 17 #
T9:j/josp2 w 100 100 100 100 #
-------------------------------------------------------------------------------------
100 1198.0 33.4 7610.0 5.1 #
0 112.45 1.57 637.4 0.21 #
</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<span style="font-size: x-small;">all:</span><br />
<span style="font-size: x-small;"><br /></span>
<pre><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 20 54 11 21 #
T3 C25 N100 m 0 12 5 0 #
T9:j/jall m 2 42 0 14 #
-------------------------------------------------------------------------------------
T0 q 20 18 19 35 #
T3 C25 N100 q 0 0 7 2 #
T9:j/jall q 8 26 4 40 #
-------------------------------------------------------------------------------------
T0 w 100 100 100 100 #
T3 C25 N100 w 42 24 64 10 #
T9:j/jall w 31 89 27 94 #
-------------------------------------------------------------------------------------
100 2645.2 44.82 22300.72 5.08 #
0 225.33 2.24 1028.96 0.12 #</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">99999 Cohen <=> Bootstrap</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Bootstrap:</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 27 67 12 4 #
T3 C25 N100 m 0 10 0 0 #
T9:j/jall m 4 46 1 16 #
-------------------------------------------------------------------------------------
T0 q 24 16 18 10 #
T3 C25 N100 q 0 0 2 1 #
T9:j/jall q 10 29 6 43 #
-------------------------------------------------------------------------------------
T0 w 100 100 100 35 #
T3 C25 N100 w 31 21 47 7 #
T9:j/jall w 32 97 35 100 #
-------------------------------------------------------------------------------------
100 2687.38 41.35 17612.38 4.8 #
0 172.58 1.92 892.87 0.04 #</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Cohen:</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 27 67 11 4 #
T3 C25 N100 m 0 10 0 0 #
T9:j/jall m 3 46 0 16 #
-------------------------------------------------------------------------------------
T0 q 24 16 18 10 #
T3 C25 N100 q 1 0 2 1 #
T9:j/jall q 9 28 5 43 #
-------------------------------------------------------------------------------------
T0 w 100 100 100 35 #
T3 C25 N100 w 27 21 51 8 #
T9:j/jall w 32 97 34 100 #
-------------------------------------------------------------------------------------
100 2687.38 41.35 17612.38 4.8 #
0 185.14 2.08 989.83 0.04 #
</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Runtimes:</span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Bootstrap:</span></pre>
<pre><span style="font-size: x-small;">real 1m51.292s
user 1m44.647s
sys 0m6.395s </span></pre>
<pre><span style="font-size: x-small;"> </span></pre>
<pre><span style="font-size: x-small;">Cohen:</span></pre>
<pre><span style="font-size: x-small;">real 0m56.032s
user 0m52.087s
sys 0m3.737s
</span></pre>
<pre></pre>
<h2>
<i><u><b><span style="font-size: x-small;">Summary</span></b></u></i></h2>
<span style="font-size: x-small;">-555--Rounding dataset into integers makes more sense as it would be similar to the daataset we receive. Results indicate minor changes.</span><br />
<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">-777--Trees as indicated are very big. Working on axe to get it down.</span><br />
<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">-888--Results for other models are similar to flight. a little better.</span><br />
<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;">-999--Swapping cohens to bootstrap gave little change to output. but runtime is reduced by half.</span></div>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-77288329276816555242014-05-28T08:32:00.003-07:002014-06-13T07:41:50.515-07:00Cluster-based Optimization<h3 style="clear: both; text-align: left;">
Update: Hedges Comparison</h3>
<div>
<!--StartFragment--><span style="font-family: Courier New, Courier, monospace; font-size: x-small;">ant-1.4</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[32, 31, 32, 30]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[54, 48, 44, 32]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 50 | 51 | 0.03 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">ant-1.5</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[46, 44, 45, 43]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[90, 96, 59, 48]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 51 | 52 | 0.29 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">ant-1.6</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[38, 36, 37, 36, 38, 36, 37, 35]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[48, 39, 41, 36, 50, 50, 57, 30]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 49 | 50 | 0.22 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">ant-1.7</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[43, 45, 44, 44, 43, 44, 45, 43]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[114, 210, 76, 68, 59, 88, 77, 53]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 50 | 50 | 0.04 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">camel-1.2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[42, 43, 41, 44, 43, 42, 43, 41]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[97, 75, 65, 89, 59, 63, 117, 43]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 51 | 50 | 0.37 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">camel-1.4</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[77, 76, 75, 77, 77, 75, 76, 75]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[106, 119, 127, 119, 109, 100, 137, 55]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 55 | 51 | 0.91 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">camel-1.6</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[112, 106, 105, 114, 110, 108, 109, 108]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[137, 114, 138, 134, 129, 107, 153, 53]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 49 | 50 | 0.51 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">ivy-1.4</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[29, 27, 28, 27]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[82, 58, 67, 34]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 42 | 52 | 1.68 | 2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">ivy-2.0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[61, 60, 61, 59]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[116, 76, 114, 46]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 49 | 50 | 0.10 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">jedit-4.0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[35, 34, 35, 33, 35, 33, 34, 33]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[42, 45, 35, 37, 38, 40, 52, 17]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 57 | 55 | 0.22 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">jedit-4.1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[40, 38, 39, 38, 39, 37, 38, 37]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[41, 36, 34, 43, 38, 48, 56, 16]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 55 | 52 | 0.39 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">jedit-4.2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[40, 39, 40, 38, 40, 38, 39, 38]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[46, 46, 36, 48, 48, 59, 63, 21]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 53 | 52 | 0.10 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">jedit-4.3</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[47, 46, 45, 46, 47, 45, 46, 45]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[106, 56, 48, 59, 51, 56, 102, 14]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 50 | 49 | 0.04 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">log4j-1.1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[35, 33, 34, 33]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[27, 21, 35, 26]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 62 | 57 | 0.35 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">log4j-1.2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[28, 27, 28, 26]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[65, 45, 64, 31]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 64 | 60 | 0.20 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">lucene-2.2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[50, 48, 49, 48]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[71, 66, 79, 31]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 51 | 54 | 0.80 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">lucene-2.4</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[32, 31, 62, 62, 60]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[50, 50, 86, 102, 52]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 53 | 51 | 0.44 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">synapse-1.1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[40, 39, 40, 38]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[70, 46, 61, 45]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 54 | 52 | 0.25 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">synapse-1.2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[58, 54, 56, 54]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[76, 49, 77, 54]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 55 | 52 | 0.79 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">velocity-1.5</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[50, 49, 49, 48]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[74, 45, 80, 15]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 53 | 52 | 0.39 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">velocity-1.6</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[55, 53, 54, 52]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[76, 48, 89, 16]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 52 | 49 | 1.15 | 2</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">xalan-2.5</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[93, 89, 91, 89, 91, 90, 91, 89]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[124, 103, 89, 86, 92, 105, 129, 75]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 50 | 50 | 0.03 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">xalan-2.6</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[102, 100, 101, 99, 101, 100, 101, 99]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[115, 94, 87, 128, 119, 106, 149, 87]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 47 | 50 | 0.36 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">xalan-2.7</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[113, 111, 112, 111, 111, 109, 110, 108]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[130, 117, 106, 102, 132, 129, 112, 81]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 73 | 63 | 0.64 | 1</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">xerces-1.3</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[58, 54, 60, 50, 56, 54, 55, 53]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[75, 60, 73, 31, 54, 64, 62, 34]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 49 | 50 | 0.16 | 0</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span>
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">xerces-1.4</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[59, 55, 61, 52, 58, 56, 57, 55]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">[104, 77, 96, 48, 70, 84, 84, 25]</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;">Cluster pD,pF mean AUC | Straw Cluster mean AUC | Hedges | Level</span><br />
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> 65 | 57 | 0.58 | 1</span><!--EndFragment--></div>
<h3 style="clear: both; text-align: left;">
<br /></h3>
<h3 style="clear: both; text-align: left;">
Explanation:</h3>
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<h3 style="clear: both; text-align: left;">
Results:</h3>
<div>
This doesn't always help us, (sometimes it hurts) but for some clusters, it REALLY works.</div>
<div>
<br /></div>
<div>
Key: (too verbose for each graph)</div>
<div>
Optimized Results for each cluster == colored Solid line with Stars</div>
<div>
Default Param Results for each cluster == colored Dashed line with Circles</div>
<div>
No clustering, just optimize N-1, test N == Double-dashed Black line with Diamonds</div>
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<div>
<h3>
The AUCs:</h3>
</div>
<div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">ant-1.4 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 50/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 45/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 49/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 57/56</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">ant-1.5 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 40/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 50/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 55/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 57/55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">ant-1.6 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 65</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 49/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 49/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 57/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 46/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 50/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 49/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 57/58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">ant-1.7 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 66</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 53/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 50/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 44/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 53/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 54/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 49/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 46/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 56/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">camel-1.2 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 49/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 53/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 55/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 54/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">camel-1.4 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 60</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 60/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 53/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 50/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 63/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 53/56</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 52/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 55/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 50/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">camel-1.6 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 51/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 46/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 47/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 51/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 47/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 54/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 47/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">ivy-1.4 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 46/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 33/61</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 42/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 39/43</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">ivy-2.0 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 47/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 41/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 49/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 56/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">jedit-4.0 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 65</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 47/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 50/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 49/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 52/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 67/65</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 69/58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 56/59</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 69/62</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">jedit-4.1 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 68</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 48/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 52/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 52/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 45/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 42/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 66/57</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 57/60</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 68/61</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">jedit-4.2 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 68</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 60/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 47/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 47/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 50/59</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 54/55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 61/58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 52/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 56/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">jedit-4.3 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 61</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 49/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 48/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 47/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 45/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 40/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 51/45</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 58/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 60/57</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">log4j-1.1 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 75</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 39/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 52/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 73/65</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 73/69</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">log4j-1.2 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 57</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 47/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 54/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 58/56</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 94/80</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">lucene-2.2 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 57</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 49/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 54/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 56/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 55/56</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">lucene-2.4 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 51/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 43/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 54/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 58/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 52/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">synapse-1.1 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 49/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 44/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 48/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 64/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">synapse-1.2 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 62</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 58/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 51/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 49/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 54/55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">velocity-1.5 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 57/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 57/55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 59/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">velocity-1.6 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 44/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 54/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 49/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 53/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">xalan-2.5 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 51/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 52/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 48/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 52/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 47/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 59/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">xalan-2.6 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 56</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 51/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 53/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 50/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 56/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 47/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 47/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 47/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 40/47</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">xalan-2.7 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 72</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 53/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 63/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 58/40</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 79/59</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 81/64</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 69/70</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 88/79</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 98/89</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">xerces-1.3 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 39/46</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 61/49</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 46/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 33/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 49/55</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 46/48</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 54/53</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 52/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">xerces-1.4 pD, pF AUCs (optimized/default)</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">No Clustering: 63</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 0 50/50</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 1 54/51</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 2 60/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 3 56/52</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 4 67/54</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 5 73/58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 6 68/58</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">Cluster 7 97/83</span></div>
<div>
<span style="font-family: Courier New, Courier, monospace; font-size: xx-small;">===============================================</span></div>
</div>
Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-20976195327316017452014-05-27T18:53:00.000-07:002014-05-27T18:53:32.089-07:00Results technique tables j*Just with last gen values:<br />
<br />
<pre style="background-color: white; white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 29 67 14 2 #
T3 C25 N100 m 2 10 3 0 #
T9:jlast m 0 35 0 22 #
------------------------------<wbr></wbr>------------------------------<wbr></wbr>-------------------------
T0 q 26 16 20 6 #
T3 C25 N100 q 2 0 5 1 #
T9:jlast q 3 23 4 38 #
------------------------------<wbr></wbr>------------------------------<wbr></wbr>-------------------------
T0 w 100 100 100 20 #
T3 C25 N100 w 31 21 47 4 #
T9:jlast w 32 100 56 100 #
------------------------------<wbr></wbr>------------------------------<wbr></wbr>-------------------------
100 2687.38 41.4 17612.38 8.6 #
0 117.21 1.92 381.83 0.03 #</span></pre>
<pre style="background-color: white; white-space: pre-wrap; word-wrap: break-word;"></pre>
<pre style="background-color: white; white-space: pre-wrap; word-wrap: break-word;">Different Techniques:</pre>
<pre style="background-color: white; white-space: pre-wrap; word-wrap: break-word;"></pre>
<pre style="background-color: white; white-space: pre-wrap; word-wrap: break-word;"><pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 22 44 10 2 #
T3 C25 N100 m 4 6 3 0 #
T9:j/j_ m 2 23 1 16 #
T9:j/j_l20 m 6 30 4 4 #
T9:j/j_l6 m 3 33 2 9 #
T9:j/j_m0.01 m 3 26 2 5 #
T9:j/j_m0.5 m 7 26 5 8 #
T9:j/j_m0.7 m 3 25 1 2 #
T9:j/j_p300 m 5 45 2 7 #
T9:j/j_p500 m 0 16 0 4 #
-------------------------------------------------------------------------------------
T0 q 20 11 15 4 #
T3 C25 N100 q 4 0 4 1 #
T9:j/j_ q 5 15 3 28 #
T9:j/j_l20 q 20 20 7 14 #
T9:j/j_l6 q 6 22 6 16 #
T9:j/j_m0.01 q 7 9 3 14 #
T9:j/j_m0.5 q 11 13 6 11 #
T9:j/j_m0.7 q 7 21 2 7 #
T9:j/j_p300 q 10 30 4 30 #
T9:j/j_p500 q 0 8 1 9 #
-------------------------------------------------------------------------------------
T0 w 71 65 68 15 #
T3 C25 N100 w 22 13 32 3 #
T9:j/j_ w 24 65 38 74 #
T9:j/j_l20 w 52 59 26 54 #
T9:j/j_l6 w 31 74 29 66 #
T9:j/j_m0.01 w 26 58 14 79 #
T9:j/j_m0.5 w 67 66 100 55 #
T9:j/j_m0.7 w 100 69 34 37 #
T9:j/j_p300 w 43 100 23 100 #
T9:j/j_p500 w 6 33 6 60 #
-------------------------------------------------------------------------------------
100 3798.0 62.54 25832.5 11.55 #
0 28.96 1.89 163.65 0.04 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">m = mutation rate</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">p = population size</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">l = lives of bstop/generations</span></pre>
</pre>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-49562817072265309682014-05-19T21:42:00.002-07:002014-05-19T21:42:46.850-07:00Different Techniques Contrast Set Learner<h2>
Flight</h2>
<span style="font-size: x-small;"><br /></span>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 14 59 5 10 #
T3 C100 N100 m 11 44 9 5 #
T3 C100 N50 m 12 53 10 2 #
T3 C100 N75 m 10 47 6 10 #
T3 C25 N100 m 0 9 0 3 #
T3 C25 N50 m 4 23 3 2 #
T3 C25 N75 m 0 9 0 2 #
T3 C50 N100 m 2 19 3 2 #
T3 C50 N50 m 5 38 4 0 #
T3 C50 N75 m 6 30 4 6 #
-------------------------------------------------------------------------------------
T0 q 12 15 8 26 #
T3 C100 N100 q 9 10 11 17 #
T3 C100 N50 q 12 15 14 17 #
T3 C100 N75 q 10 13 9 23 #
T3 C25 N100 q 0 0 1 5 #
T3 C25 N50 q 4 6 5 10 #
T3 C25 N75 q 0 0 1 6 #
T3 C50 N100 q 2 3 4 9 #
T3 C50 N50 q 5 8 5 11 #
T3 C50 N75 q 6 7 6 14 #
-------------------------------------------------------------------------------------
T0 w 50 88 45 89 #
T3 C100 N100 w 62 75 99 76 #
T3 C100 N50 w 100 100 97 55 #
T3 C100 N75 w 61 80 100 100 #
T3 C25 N100 w 18 19 21 20 #
T3 C25 N50 w 30 42 51 35 #
T3 C25 N75 w 16 19 28 23 #
T3 C50 N100 w 24 34 50 33 #
T3 C50 N50 w 39 65 100 38 #
T3 C50 N75 w 41 52 74 61 #
-------------------------------------------------------------------------------------
100 2214.8 25.1 28044.4 1.2 #
0 464.6 4.8 2429.9 0.1 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<h2>
<span style="font-size: large;">Ground:</span></h2>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 17 64 6 32 #
T3 C100 N100 m 11 34 3 3 #
T3 C100 N50 m 18 60 9 7 #
T3 C100 N75 m 18 47 5 5 #
T3 C25 N100 m 0 11 0 0 #
T3 C25 N50 m 4 20 2 0 #
T3 C25 N75 m 2 15 2 1 #
T3 C50 N100 m 5 23 2 0 #
T3 C50 N50 m 11 44 5 3 #
T3 C50 N75 m 7 28 2 3 #
-------------------------------------------------------------------------------------
T0 q 12 14 9 43 #
T3 C100 N100 q 11 7 5 8 #
T3 C100 N50 q 17 14 12 15 #
T3 C100 N75 q 17 11 6 10 #
T3 C25 N100 q 0 0 1 3 #
T3 C25 N50 q 4 3 3 5 #
T3 C25 N75 q 2 1 2 4 #
T3 C50 N100 q 5 4 3 6 #
T3 C50 N50 q 13 11 8 10 #
T3 C50 N75 q 7 5 3 7 #
-------------------------------------------------------------------------------------
T0 w 82 100 63 100 #
T3 C100 N100 w 82 60 47 39 #
T3 C100 N50 w 85 99 100 70 #
T3 C100 N75 w 100 80 86 51 #
T3 C25 N100 w 26 22 12 14 #
T3 C25 N50 w 39 35 31 11 #
T3 C25 N75 w 35 28 24 20 #
T3 C50 N100 w 54 44 32 22 #
T3 C50 N50 w 77 78 58 39 #
T3 C50 N75 w 56 48 31 32 #
-------------------------------------------------------------------------------------
100 1512.1 19.5 13341.2 1.0 #
0 340.6 3.9 1505.5 0.1 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<h2>
<span style="font-size: large;">Osp</span></h2>
<div>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 54 82 22 49 #
T3 C100 N100 m 11 30 10 10 #
T3 C100 N50 m 12 40 7 24 #
T3 C100 N75 m 16 58 8 25 #
T3 C25 N100 m 0 11 0 2 #
T3 C25 N50 m 7 26 5 16 #
T3 C25 N75 m 7 19 4 8 #
T3 C50 N100 m 8 25 4 8 #
T3 C50 N50 m 12 42 7 27 #
T3 C50 N75 m 7 29 5 14 #
-------------------------------------------------------------------------------------
T0 q 16 7 11 35 #
T3 C100 N100 q 8 5 12 4 #
T3 C100 N50 q 10 9 10 19 #
T3 C100 N75 q 16 15 11 13 #
T3 C25 N100 q 0 0 1 0 #
T3 C25 N50 q 6 5 7 12 #
T3 C25 N75 q 5 3 6 2 #
T3 C50 N100 q 7 4 6 3 #
T3 C50 N50 q 13 11 9 19 #
T3 C50 N75 q 6 5 7 5 #
-------------------------------------------------------------------------------------
T0 w 93 100 56 100 #
T3 C100 N100 w 63 51 100 27 #
T3 C100 N50 w 62 66 81 62 #
T3 C100 N75 w 100 97 89 59 #
T3 C25 N100 w 21 21 24 10 #
T3 C25 N50 w 48 46 65 42 #
T3 C25 N75 w 48 35 61 20 #
T3 C50 N100 w 57 44 55 22 #
T3 C50 N50 w 73 71 82 65 #
T3 C50 N75 w 53 49 65 34 #
-------------------------------------------------------------------------------------
100 976.0 14.9 15980.1 2.1 #
0 199.0 2.7 2022.7 0.5 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<h2>
<span style="font-size: large;">Osp2:</span></h2>
</div>
<div>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 50 82 8 16 #
T3 C100 N100 m 9 41 5 8 #
T3 C100 N50 m 15 60 6 17 #
T3 C100 N75 m 18 52 6 15 #
T3 C25 N100 m 1 16 0 1 #
T3 C25 N50 m 7 36 4 7 #
T3 C25 N75 m 3 22 0 0 #
T3 C50 N100 m 4 24 1 10 #
T3 C50 N50 m 12 47 5 11 #
T3 C50 N75 m 9 35 3 9 #
-------------------------------------------------------------------------------------
T0 q 9 7 2 34 #
T3 C100 N100 q 8 7 8 20 #
T3 C100 N50 q 13 12 9 45 #
T3 C100 N75 q 16 11 8 29 #
T3 C25 N100 q 0 0 1 5 #
T3 C25 N50 q 6 6 6 21 #
T3 C25 N75 q 2 2 1 9 #
T3 C50 N100 q 3 2 2 9 #
T3 C50 N50 q 11 9 7 30 #
T3 C50 N75 q 8 6 4 19 #
-------------------------------------------------------------------------------------
T0 w 88 100 21 54 #
T3 C100 N100 w 60 67 80 71 #
T3 C100 N50 w 82 100 100 100 #
T3 C100 N75 w 100 88 88 79 #
T3 C25 N100 w 30 29 28 28 #
T3 C25 N50 w 59 60 55 59 #
T3 C25 N75 w 39 39 28 34 #
T3 C50 N100 w 39 39 36 57 #
T3 C50 N50 w 74 78 72 76 #
T3 C50 N75 w 70 61 56 58 #
-------------------------------------------------------------------------------------
100 862.2 18.1 7153.8 1.2 #
0 172.9 3.2 825.3 0.3 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<h2>
<span style="font-size: large;">All:</span></h2>
</div>
<div>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 13 37 7 27 #
T3 C100 N100 m 7 26 3 0 #
T3 C100 N50 m 14 46 7 3 #
T3 C100 N75 m 10 35 6 1 #
T3 C25 N100 m 0 7 0 0 #
T3 C25 N50 m 5 18 4 2 #
T3 C25 N75 m 2 12 1 1 #
T3 C50 N100 m 4 17 1 0 #
T3 C50 N50 m 8 26 5 3 #
T3 C50 N75 m 6 24 4 1 #
-------------------------------------------------------------------------------------
T0 q 12 13 10 35 #
T3 C100 N100 q 8 7 5 5 #
T3 C100 N50 q 17 17 10 8 #
T3 C100 N75 q 10 11 8 7 #
T3 C25 N100 q 0 0 1 2 #
T3 C25 N50 q 4 3 5 4 #
T3 C25 N75 q 2 1 2 3 #
T3 C50 N100 q 4 4 3 3 #
T3 C50 N50 q 8 7 7 7 #
T3 C50 N75 q 6 6 7 4 #
-------------------------------------------------------------------------------------
T0 w 65 70 65 100 #
T3 C100 N100 w 66 56 50 20 #
T3 C100 N50 w 100 100 84 31 #
T3 C100 N75 w 96 83 100 28 #
T3 C25 N100 w 17 17 15 8 #
T3 C25 N50 w 43 38 48 12 #
T3 C25 N75 w 27 26 26 12 #
T3 C50 N100 w 51 37 29 15 #
T3 C50 N50 w 70 57 54 21 #
T3 C50 N75 w 71 53 51 20 #
-------------------------------------------------------------------------------------
100 3002.5 35.6 20668.2 1.2 #
0 400.5 6.2 2341.5 0.1 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
</div>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-87252116261139082242014-05-08T11:04:00.002-07:002014-05-08T11:04:43.353-07:00Pantry and Survey Correlations<h2>
Initial post 5/08: we have some charts, but outlier removal is needed.</h2>
<div>
Items A:</div>
<div>
Trans Fat</div>
<div>
Sodium</div>
<div>
Sugars</div>
<div>
Calories</div>
<div>
Saturated Fat</div>
<div>
Total Fat</div>
<div>
Cholesterol</div>
<div>
Total Carbohydrate</div>
<div>
Protein</div>
<div>
Dietary Fiber</div>
<div>
<br /></div>
<div>
<br /></div>
<div>
Items B: </div>
<div>
<div>
cholesterol_mg_usda</div>
<div>
protein_g_usda</div>
<div>
total_fat_g_usda</div>
<div>
total_sugars_g_usda</div>
<div>
alcohol_g_usda</div>
<div>
total_monounsaturated_fatty_acid</div>
<div>
total_saturated_fatty_acids_g_us</div>
<div>
energy_kcal_usda</div>
<div>
dietary_fiber_g_usda</div>
<div>
total_polyunsaturated_fatty_acid</div>
<div>
carbohydrate_g_usda</div>
</div>
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Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-16711759997998436072014-05-08T09:47:00.000-07:002014-05-08T10:18:41.018-07:00JPL prelim results for may<br />
<h2>
Val0</h2>
<div>
How?</div>
<div>
<br /></div>
<div>
ONLY on new data:</div>
<div>
<br /></div>
<div>
1. Do a LOO experiment. At each instance gives, left out row and rest rows.</div>
<div>
2. Cluster rest rows.</div>
<div>
3. Find nearest cluster to left out row with nearest attribute distance of centroids to left out row.</div>
<div>
4. Estimate = Mean or median of LogicalEKLOC/ Logical Delivered,</div>
<div>
Actual = Actual LogicalEKLOC/Logical Delivered.</div>
<div>
5. Calculate MRE.</div>
<div>
<br /></div>
<div class="separator" style="clear: both; text-align: center;">
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<br />
<div class="separator" style="clear: both; text-align: center;">
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<h2>
Val1</h2>
<h2>
Val2</h2>
<div>
How?</div>
<div>
<br /></div>
<div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
0) cluster the dense columns </div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
1) take the "left out" example, described in terms of:</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
a) the new columns ("new" means "not traditional cocomo")</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
b) its "class" e.g. "flight systems"<br />
c) act effort </div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
2) find the "left out" example's cluster and extract the LOC in that cluster<br />
- note: until step 5 we will ignore the actual effort</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
3) go to darren's rig and generate effort predictions (constrained by (2) and (1b))</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
4) show the 50 to 70th percentile range of (3)</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
5) mark on that range the actual effort</div>
<div style="background-color: white;">
<div style="color: #222222; font-family: arial, sans-serif; font-size: 13px;">
6) dance and sing if the the actual effort (from 1c) is in the range of (5)</div>
<div style="color: #222222; font-family: arial, sans-serif; font-size: 13px;">
<br /></div>
<div style="color: #222222; font-family: arial, sans-serif; font-size: 13px;">
Estimate = Mean/Median of generated effort from darren's rig</div>
<div style="color: #222222; font-family: arial, sans-serif; font-size: 13px;">
Actual = actual effort of project</div>
<div style="color: #222222; font-family: arial, sans-serif; font-size: 13px;">
<br />
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<br /></div>
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<br /></div>
<div class="separator" style="clear: both; color: #222222; font-family: arial, sans-serif; font-size: 13px; text-align: left;">
The 50 - 70 range sheet:</div>
<div class="separator" style="clear: both; color: #222222; font-family: arial, sans-serif; font-size: 13px; text-align: left;">
<br /></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="color: #222222; font-family: arial, sans-serif; font-size: x-small;">http://unbox.org/things/var/nave/lpj/out/master/Effort_check_50_70.xlsx</span></div>
<div class="separator" style="clear: both; text-align: left;">
<span style="color: #222222; font-family: arial, sans-serif; font-size: x-small;"><br /></span></div>
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<span style="color: #222222; font-family: arial, sans-serif; font-size: x-small;"><br /></span></div>
<div style="color: #222222; font-family: arial, sans-serif; font-size: 13px;">
<br /></div>
</div>
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NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-82612711722085228152014-05-05T15:25:00.002-07:002014-05-06T10:55:24.214-07:00Magic Learner-JPL and others<h2>
FOR JPL</h2>
<div class="separator" style="clear: both; text-align: center;">
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<br />
<b>Study 1:</b><br />
<br />
<ol>
<li>Predict LogicalDelivered, excluding EKLOC, inheritance.</li>
<li>Predict LogicalEKLOC, excluding delivered, and including inheritance.</li>
</ol>
<div>
<b>Study 2:</b></div>
<div>
<ol>
<li>Boring COCOMO using 1.1</li>
<li>Boring COCOMO using 1.2</li>
</ol>
</div>
<br />
Boring COCOMO ready and set to go.<br />
<br />
<h3>
Effort Predictions(val2):</h3>
<div>
<br /></div>
<div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
so, we need a LOO experiment where we:</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
0) cluster the dense columns </div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
1) take the "left out" example, described in terms of:</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
a) the new columns ("new" means "not traditional cocomo")</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
b) its "class" e.g. "flight systems"<br />
c) act effort </div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
2) find the "left out" example's cluster and extract the LOC in that cluster<br />
- note: until step 5 we will ignore the actual effort</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
3) go to darren's rig and generate effort predictions (constrained by (2) and (1b))</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
4) show the 50 to 70th percentile range of (3)</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
5) mark on that range the actual effort</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
6) dance and sing if the the actual effort (from 1c) is in the range of (5</div>
</div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
<br /></div>
<div style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">
<br /></div>
<div>
</div>
http://unbox.org/things/var/nave/lpj/out/master/effort_check_with_cocomo_50_70.xlsx<br />
<br />
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<br />
<h3>
Graphs learnt on whole *old+new* with specific conditions(val0):</h3>
<div>
<br /></div>
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<h2>
OTHER THAN JPL</h2>
<h3>
Results for tables showing different techniques:</h3>
http://unbox.org/things/var/nave/lpj/out/master/*preptab*.dat<br />
<br />
<h3>
Contrast sets sorted:</h3>
<h2>
<span style="font-size: small; font-weight: normal;">http://unbox.org/things/var/nave/lpj/out/master/*contrast_sets_sorted.dat</span></h2>
<h3>
Trees for different models:</h3>
http://unbox.org/things/var/nave/lpj/out/master/*dtree*.dat<br />
<h3>
Other graphs:</h3>
<br />
MRE with and without learning 20 records around cluster centroid:<br />
<br />
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<br />NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-43535217632246538412014-04-28T15:39:00.003-07:002014-04-28T15:40:39.924-07:00Results of contrast set learning techniques <br />
<h2>
Results of contrast set learning techniques </h2>
<div>
What was done using different techniques:</div>
<div>
<br /></div>
<div>
1. Cluster jplflight(-->C1) with xy_proj.py -->C2</div>
<div>
2. Build decision trees using xy_dt.py</div>
<div>
3. Use diff.py to get decisions(contrast sets) to be made for worse cluster to be better cluster.</div>
<div>
4. Using the contrast sets generate 500 samples with gen.py. (used xomo)-->C3</div>
<div>
5. Compare initial clusters to newly generated data.</div>
<div>
6. Represent results as in fig 9 of http://menzies.us/pdf/12gense.pdf .<br />
<br />
Techniques:<br />
T0: asIs<br />
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">T2 =C1+C3 </span><br />
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;">T3 = C2+C3</span><br />
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;"><br /></span>
<span style="background-color: white; color: #222222; font-family: arial, sans-serif; font-size: 13px;"><br /></span>
Flight data<br />
<pre style="white-space: pre-wrap; word-wrap: break-word;"></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"> T<span style="font-size: x-small;">echniques -effort -months -defects -risks #
T0 m 43 74 14 9 #
T2 m 0 3 0 1 #
T3 m 0 4 0 0 #
T0 q 32 17 21 26 #
T2 q 0 0 1 2 #
T3 q 0 0 1 2 #
T0 w 100 100 100 100 #
T2 w 2 7 25 15 #
T3 w 2 7 21 13 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 4340.4 0.2 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">Ground data</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"> <span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 43 74 15 10 #
T2 m 0 3 0 1 #
T3 m 0 3 0 0 #
T0 q 32 17 21 27 #
T2 q 0 0 1 3 #
T3 q 0 0 1 3 #
T0 w 100 100 100 100 #
T2 w 1 7 18 17 #
T3 w 1 6 16 15 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 4340.4 0.2 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">Osp data</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;"> Techniques -effort -months -defects -risks #
T0 m 43 74 12 0 #
T2 m 0 4 0 12 #
T3 m 0 4 0 12 #
T0 q 32 18 19 19 #
T2 q 0 0 1 9 #
T3 q 0 0 0 8 #
T0 w 100 100 100 100 #
T2 w 1 6 24 33 #
T3 w 1 6 20 33 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 6021.0 0.2 #</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">Osp2 data</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: Times; white-space: normal;">
</span></pre>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 43 74 14 3 #
T2 m 0 4 0 0 #
T3 m 0 4 0 0 #
T0 q 32 18 21 22 #
T2 q 0 0 0 2 #
T3 q 0 0 0 2 #
T0 w 100 100 100 100 #
T2 w 1 6 14 14 #
T3 w 1 6 11 14 #
100 30166.1 88.6 27118.6 1.8 #
0 9598.8 16.4 6021.0 0.2 #</span></pre>
</div>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-66355167491884362222014-04-22T06:24:00.000-07:002014-05-06T11:46:12.199-07:00Learning from Version Deltas: A side-quest wrapped up... mostly<h2 style="clear: both; text-align: left;">
Update 5/6: Business case with test on i+1</h2>
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<div>
<br /></div>
<h2 style="clear: both; text-align: left;">
Update 4/24: No tuning, all tunings, and top tunings</h2>
<div>
To complicate things a little more, let's add another variable!</div>
<div>
<br /></div>
<div>
<ul>
<li>Curiously, I was unable to replicate the previous results without parameter tuning</li>
<ul>
<li>Using paramaterless Gaussian Bayes only, there is little difference between HI</li>
</ul>
<li>I repeated using parameter tuning, but calculating stats based on ALL results rather than only top-ranked results</li>
<ul>
<li>These are using 30 random train/test splits, but...</li>
<li>These are only using 2 out of 3 learners to save param tune time</li>
<ul>
<li>3-learner results can come later, but from what I've seen, 2vs3 doesn't matter</li>
</ul>
</ul>
<li>Results with <b>top-ranked</b> parameters > results with <b>all</b> parameters > results with <b>no</b> parameters</li>
</ul>
</div>
<h3>
Results with No Parameters:</h3>
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<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"><br /></span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> Label , A12, U, p, meanA, meanB</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 0 , 0.000, 0, 0.500000, 0.771908, 0.680063</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 1 , 0.000, 0, 0.500000, 0.771908, 0.758529</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 2 , 0.000, 0, 0.500000, 0.771908, 0.758749</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 3 , 1.000, 0, 0.500000, 0.771908, 0.790568</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 0 , 0.600, 11, 0.417266, 0.574222, 0.547370</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 1 , 0.560, 9, 0.265435, 0.574222, 0.595885</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 2 , 0.320, 9, 0.265435, 0.574222, 0.582948</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 2 > HI 0 , 0.281, 85, 0.282867, 0.601895, 0.561865</span></div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 2 > HI 1 , 0.862, 90, 0.365195, 0.601895, 0.595265</span></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<div class="p1">
<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 1 > HI 0 , 0.023, 216, 0.145822, 0.596752, 0.544316</span></div>
<div class="p1">
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<h3>
Results with All Parameters:</h3>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> Label , A12, U, p, meanA, meanB</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 0 , 0.000, 0, 0.500000, 0.730214, 0.677388</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 1 , 1.000, 0, 0.500000, 0.730214, 0.745145</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 2 , 1.000, 0, 0.500000, 0.730214, 0.754882</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 3 , 1.000, 0, 0.500000, 0.730214, 0.750597</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 0 , 0.200, 8, 0.201698, 0.589507, 0.527802</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 1 , 0.360, 11, 0.417266, 0.589507, 0.619794</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 2 , 0.400, 12, 0.500000, 0.589507, 0.609074</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 2 > HI 0 , 0.281, 74, 0.140122, 0.623904, 0.552588</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 2 > HI 1 , 0.699, 97, 0.490836, 0.623904, 0.618135</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 1 > HI 0 , 0.234, 188, 0.047494, 0.618359, 0.550335</span></div>
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<h3>
Results with Top-Ranked Parameters:</h3>
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<span style="font-family: 'Courier New', Courier, monospace; font-size: x-small; text-align: start;"> Label , A12, U, p, meanA, meanB</span><br />
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 0 , 0.000, 0, 0.500000, 0.909430, 0.766430</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 1 , 0.000, 0, 0.500000, 0.909430, 0.869284</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 2 , 0.000, 0, 0.500000, 0.909430, 0.857104</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 4 > HI 3 , 1.000, 0, 0.500000, 0.909430, 0.911470</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 0 , 0.160, 6, 0.105038, 0.905671, 0.787974</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 1 , 0.160, 5, 0.071836, 0.905671, 0.808840</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 3 > HI 2 , 0.160, 7, 0.148135, 0.905671, 0.824976</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 2 > HI 0 , 0.066, 64, 0.061872, 0.831493, 0.760456</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 2 > HI 1 , 0.071, 85, 0.282867, 0.831493, 0.801677</span></div>
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<span style="font-family: Courier New, Courier, monospace; font-size: x-small;"> HI 1 > HI 0 , 0.119, 192, 0.056850, 0.788835, 0.734083</span></div>
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<h2 style="clear: both; text-align: left;">
Original Post</h2>
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OK, to start off with, HI = History Index = number of past deltas included</div>
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<ul>
<li>ant 1.7 with HI=3 would include deltas from ant 1.6, and 1.5, and ant 1.4 </li>
<li>ant 1.7 with HI=0 would included no deltas (just the original set)</li>
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The results below come from comparing only the top-ranked param tuning results on each delta</div>
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<br />Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-21086369666354762532014-04-01T05:53:00.002-07:002014-04-01T05:53:35.347-07:00JPL results in new format<span style="font-size: x-small;"><br /></span>
<span style="font-size: x-small;"><br /></span>
<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-size: x-small;">Techniques -effort -months -defects -risks #
T0 m 35.0 73.0 11.0 9.0 #
T1 m 8.0 55.0 3.0 0.0 #
T2 m 2.0 28.0 0.0 2.0 #
T3 m 2.0 28.0 1.0 2.0 #
T0 q 19.0 9.0 17.0 28.0 #
T1 q 1.0 5.0 7.0 30.0 #
T2 q 0.0 0.0 2.0 16.0 #
T3 q 0.0 0.0 3.0 16.0 #
T0 w 100.0 100.0 76.0 100.0 #
T1 w 49.0 83.0 100.0 47.0 #
T2 w 50.0 47.0 35.0 34.0 #
T3 w 48.0 47.0 42.0 34.0 #</span></pre>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-32867111793715930592014-03-25T06:42:00.000-07:002014-05-13T09:54:07.766-07:00Parameter Tuning<h2 style="clear: both; text-align: left;">
Update 5/13 B: Looks about the same with heaven rankings</h2>
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Closeness definition: (ranges from 0: bad to 1: good)<br />
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Graphs; Beware: they alternate Heaven+Hell, pD+pF</div>
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<h2 style="clear: both; text-align: left;">
</h2>
<h2 style="clear: both; text-align: left;">
Update 5/13: Looking good against default parameters</h2>
<div>
The learners:</div>
<div>
<table style="width: 100%;">
<tbody align="center">
<tr>
<td>Learner</td><td># of Parameters</td><td># of Param Permutations</td>
</tr>
<tr>
<td>SKL Gaussian Bayes</td><td>0</td><td>1</td>
</tr>
<tr>
<td>SKL Multinomial Bayes</td><td>2</td><td>14</td>
</tr>
<tr>
<td>SKL Bernoulli Bayes</td><td>3</td><td>98</td>
</tr>
<tr>
<td>Total</td><td></td><td>113</td>
</tr>
</tbody></table>
<br />
<br />
"Unoptimized" learners are the following default parameters:<br />
<br />
<ul>
<li><span style="text-align: -webkit-center;">SKL Gaussian Bayes: {}</span></li>
<li><span style="text-align: -webkit-center;">SKL Multinomial Bayes </span>{'alpha': 1.0, 'fit_prior': True}</li>
<li><span style="text-align: -webkit-center;">SKL Bernoulli Bayes </span>{'binarize': 0.5, 'alpha': 1.0, 'fit_prior': True}</li>
</ul>
<div>
<br /></div>
<div>
Getting away from the whole "top ranked" reporting, I'm now reporting top 10 mean g.</div>
<div>
<ul>
<li>This still isn't the best, considering it's a summary statistic</li>
<li>How do you feel about non-dominated sort on pD, 1-pF?</li>
</ul>
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<h2 style="clear: both; text-align: left;">
</h2>
<h2 style="clear: both; text-align: left;">
Update 4/08: Interfaced with JMOO, nothing too fancy yet.</h2>
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<h2 style="clear: both; text-align: left;">
</h2>
<h2 style="clear: both; text-align: left;">
Update: More Evals, New Sets, Corrected Stats</h2>
<div>
These results generated with the following Xval/permutation params: (this took about 1.5hrs)</div>
<div>
<!--StartFragment--><span style="font-size: x-small;">default_cross_val_folds=4</span><br />
<span style="font-size: x-small;"> default_cross_val_repeats=4</span><br />
<span style="font-size: x-small;"> default_param_permutations=5</span><br />
<span style="font-size: x-small;"> default_percent_test=1.0/default_cross_val_folds</span><br />
<span style="font-size: x-small;"><br /></span>
Stats generated with:<br />
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<!--StartFragment--><span style="font-size: x-small;">self.metrics={</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;">'accuracy':metrics.accuracy_score(self.actual,self.predicted),</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'precision':metrics.precision_score(self.actual,self.predicted),</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'recall':metrics.recall_score(self.actual,self.predicted),</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'confusion matrix':metrics.confusion_matrix(self.actual,self.predicted),</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'Jaccard Score':metrics.jaccard_similarity_score(self.actual,self.predicted),</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'ROC-AUC':metrics.accuracy_score(self.actual,self.predicted),</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'class precision':metrics.precision_score(self.actual,self.predicted, average=None)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> }</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> cm=self.metrics['confusion matrix']</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> d=cm[1][1] #######from here out, it's basically your ABCD code, but I had to flip the CM</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> b=cm[1][0]</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> c=cm[0][1]</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> a=cm[0][0]</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> pd = 1.0*d / (b+d)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> pf = 1.0*c / (a+c)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> pn = 1.0*(b+d) / (a+c)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> prec = 1.0*d / (c+d)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> g = 2.0*(1-pf)*pd / (1-pf+pd)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> f = 2.0*prec*pd/(prec+pd)</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> assert(sum(cm[1])==sum(self.actual))</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> self.metrics.update({</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;">'pD':pd,</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'pF':pf,</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'g':g,</span></div>
<div style="-qt-block-indent: 0; -qt-user-state: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;">
<span style="font-size: x-small;"> 'f':f,</span></div>
<br />
<span style="font-size: x-small;">'pN':pn</span><br />
<span style="font-size: x-small;">})</span><!--EndFragment--><br />
<span style="font-size: x-small;"><br /></span>
Tabular results:<br />
<a href="http://unbox.org/things/var/ben/consolidated/pres/results_summary_3-25-14.txt">http://unbox.org/things/var/ben/consolidated/pres/results_summary_3-25-14.txt</a><br />
<a href="http://unbox.org/things/var/ben/consolidated/pres/full_tuning_results_3-25-14.txt">http://unbox.org/things/var/ben/consolidated/pres/full_tuning_results_3-25-14.txt</a><br />
<span style="font-size: x-small;"><br /></span>
Note about the charts: I switched to 1-pF so that heaven, hell are always (1,1) and (0,0).<br />
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Results from train on N, test on N+1</h2>
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<a href="http://unbox.org/things/var/ben/consolidated/">http://unbox.org/things/var/ben/consolidated/</a>Anonymoushttp://www.blogger.com/profile/01914726903570443772noreply@blogger.com0tag:blogger.com,1999:blog-3498331925495016107.post-37616775148235204762014-03-14T12:40:00.001-07:002014-03-14T12:40:14.088-07:00Lit Review Skelton<br />
Skelton for lit review:<br />
<ol>
<li>Overview(10)</li>
<li>Parametric(18)</li>
<ol>
<li>...</li>
<li>...</li>
<li>...</li>
</ol>
<li>Non-Parametric(4)</li>
<ol>
<li>CBR</li>
<li>...</li>
</ol>
<li>Calibration(5)</li>
<li>Evaluation(1)</li>
<li>Validation(1)</li>
</ol>
<div>
Few Overviews covers calibration and validations as well.<br />
<br />
Overview has papers with evolution, justification, etc.</div>
<div>
<br />
Focus to be laid on:</div>
<div>
<ul>
<li>Justify existing numbers</li>
<li>Evolution of models</li>
<li>How risks are calculated--variances or not?</li>
<li>Questions on cost, time, reliability.</li>
</ul>
</div>
NaveenLhttp://www.blogger.com/profile/14079863374429336972noreply@blogger.com0