Enables Calibrators Tests for Linear Svm#233

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Enables Calibrators Tests for Linear Svm #233

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58 changes: 58 additions & 0 deletions ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt
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maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))
Confusion table
||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
positive || 128 | 6 | 0.9552
negative || 7 | 213 | 0.9682
||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))
Confusion table
||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
positive || 97 | 8 | 0.9238
negative || 2 | 222 | 0.9911
||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%

4 changes: 4 additions & 0 deletions ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt
View file
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /lambda /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
0.995862 0.966441 0.963973 0.939517 0.96891 0.979627 Infinity -Infinity 0.951327 0.991949 0.03 100 LinearSVM %Data% %Output% 99 0 0 maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1 /lambda:0.03;/iter:100

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})();
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Enables Calibrators Tests for Linear Svm#233

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Enables Calibrators Tests for Linear Svm
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58 changes: 58 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Enables Calibrators Tests for Linear Svm#233

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TomFinley
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May 24, 2018
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Enables Calibrators Tests for Linear Svm
#233
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58 changes: 58 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
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var __m = "*";
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Enables Calibrators Tests for Linear Svm#233

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 Merged

TomFinley
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May 24, 2018
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Enables Calibrators Tests for Linear Svm
#233
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58 changes: 58 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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var __m = "youtube.com";
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Enables Calibrators Tests for Linear Svm#233

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Enables Calibrators Tests for Linear Svm
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ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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Enables Calibrators Tests for Linear Svm#233

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Enables Calibrators Tests for Linear Svm
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58 changes: 58 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Original file line numberDiff line numberDiff line change
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LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Enables Calibrators Tests for Linear Svm#233

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 Merged

TomFinley
merged 6 commits into

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May 24, 2018
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Enables Calibrators Tests for Linear Svm
#233
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4096cff

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80cbed1

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58 changes: 58 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
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})();

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Enables Calibrators Tests for Linear Svm#233

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 Merged

TomFinley
merged 6 commits into

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May 24, 2018
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Enables Calibrators Tests for Linear Svm
#233
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4096cff

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80cbed1

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Anipik May 24, 2018
f8f6567

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d5959ad

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58 changes: 58 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,58 @@
maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
Training calibrator.
PAV calibrator: piecewise function approximation has 6 components.
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 128 | 6 | 0.9552
 negative || 7 | 213 | 0.9682
 ||======================
Precision || 0.9481 | 0.9726 |
OVERALL 0/1 ACCURACY: 0.963277
LOG LOSS/instance: Infinity
Test-set entropy (prior Log-Loss/instance): 0.956998
LOG-LOSS REDUCTION (RIG): -Infinity
AUC: 0.994233
Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 97 | 8 | 0.9238
 negative || 2 | 222 | 0.9911
 ||======================
Precision || 0.9798 | 0.9652 |
OVERALL 0/1 ACCURACY: 0.969605
LOG LOSS/instance: 0.220291
Test-set entropy (prior Log-Loss/instance): 0.903454
LOG-LOSS REDUCTION (RIG): 75.616820
AUC: 0.997491

OVERALL RESULTS
---------------------------------------
AUC: 0.995862 (0.0016)
Accuracy: 0.966441 (0.0032)
Positive precision: 0.963973 (0.0158)
Positive recall: 0.939517 (0.0157)
Negative precision: 0.968910 (0.0037)
Negative recall: 0.979627 (0.0114)
Log-loss: Infinity (NaN)
Log-loss reduction: -Infinity (NaN)
F1 Score: 0.951327 (0.0003)
AUPRC: 0.991949 (0.0025)

---------------------------------------
Physical memory usage(MB): %Number%
Virtual memory usage(MB): %Number%
%DateTime% Time elapsed(s): %Number%


4 changes: 4 additions & 0 deletions
ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LinearSVM
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data% %Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	

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