Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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Merged
TomFinley merged 7 commits into from
May 30, 2018
Merged

Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest #255

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f8cbea8
Tests Enabled & Dataset Moved to correct place
Anipik May 28, 2018
021ec59
Debug Files added for both tests
Anipik May 28, 2018
de2d38d
Release Files Added For both tests
Anipik May 28, 2018
67485a4
Correcting path for adult data set for autoInference class
Anipik May 28, 2018
0c284b9
Removing @ from path
Anipik May 29, 2018
c985767
Moved Files from ZbaseLines to test\BaselineOutput
Anipik May 30, 2018
d387868
Merge branch 'master' of https://github.com/dotnet/machinelearning in…
Anipik May 30, 2018
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56 changes: 56 additions & 0 deletions ...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO: 0.2362 (3846.0/(3846.0+12435.0))
Confusion table
||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
positive || 1,982 | 1,864 | 0.5153
negative || 895 | 11,540 | 0.9280
||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%) 32561 examples Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.
4 changes: 4 additions & 0 deletions ...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /lr /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
0.87196 0.830539 0.688912 0.515341 0.860937 0.928026 0.537244 31.88307 0.589618 0.670582 0.25 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} /lr:0.25;/nl:5;/mil:5;/iter:20

31 changes: 31 additions & 0 deletions ...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt
View file
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Original file line numberDiff line numberDiff line change
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Per-feature gain summary for the boosted tree ensemble:
marital-status.Married-civ-spouse 1
occupation.Exec-managerial 0.399117896915284
occupation.Prof-specialty 0.391227805213273
education.Bachelors 0.334466450228397
education.Masters 0.287494503306969
education.Prof-school 0.204600035294214
education.Doctorate 0.187985462587772
occupation.Sales 0.150856624152445
occupation.Other-service 0.147997280910012
relationship.Own-child 0.137529286784067
marital-status.Never-married 0.131366568817433
education.7th-8th 0.122497278808477
workclass.Self-emp-inc 0.119940186278871
education.HS-grad 0.113890497534289
occupation.Tech-support 0.104801559978143
workclass.Self-emp-not-inc 0.0911869726068162
native-country.Mexico 0.081461599208144
workclass.Federal-gov 0.0785782301236086
sex.Male 0.0783006369313957
relationship.Wife 0.0749144815167656
education.11th 0.0746051436850124
occupation.Handlers-cleaners 0.0601209002480329
native-country.United-States 0.0593295291546631
occupation.Farming-fishing 0.0583149634881263
education.10th 0.0544727793656118
education.9th 0.052272828491541
workclass.Local-gov 0.0519635659099365
occupation.? 0.0479530108171804
occupation.Protective-serv 0.0451534478142547
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Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

from

May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
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Skip to content
Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

from

May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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7 commits


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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
Loading

, 'i');
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");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

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May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

from

May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
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");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

from

May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
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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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Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

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May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
Loading

, 'i');
if (__m === '*' || __re.test(location.href)) {
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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Skip to content
Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest#255

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 Merged

TomFinley
merged 7 commits into

from

May 30, 2018
 Merged


Enables FastTreeBinaryClassificationCategoricalSplitTest and BinaryClassifierTesterThresholdingTest
#255
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f8cbea8

Tests Enabled & Dataset Moved to correct place


Anipik May 28, 2018
021ec59

Debug Files added for both tests


Anipik May 28, 2018
de2d38d

Release Files Added For both tests


Anipik May 28, 2018
67485a4

Correcting path for adult data set for autoInference class


Anipik May 28, 2018
0c284b9

Removing @ from path


Anipik May 29, 2018
c985767

Moved Files from ZbaseLines to test\BaselineOutput


Anipik May 30, 2018
d387868

Merge branch 'master' of https://github.com/dotnet/machinelearning in…


Anipik May 30, 2018

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56 changes: 56 additions & 0 deletions
...t/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,56 @@
maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Processed 32561 instances
Binning and forming Feature objects
Reserved memory for tree learner: 4980 bytes
Starting to train ...
Not training a calibrator because it is not needed.
TEST POSITIVE RATIO:	0.2362 (3846.0/(3846.0+12435.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 1,982 | 1,864 | 0.5153
 negative || 895 | 11,540 | 0.9280
 ||======================
Precision || 0.6889 | 0.8609 |
OVERALL 0/1 ACCURACY: 0.830539
LOG LOSS/instance: 0.537244
Test-set entropy (prior Log-Loss/instance): 0.788708
LOG-LOSS REDUCTION (RIG): 31.883066
AUC: 0.871960

OVERALL RESULTS
---------------------------------------
AUC: 0.871960 (0.0000)
Accuracy: 0.830539 (0.0000)
Positive precision: 0.688912 (0.0000)
Positive recall: 0.515341 (0.0000)
Negative precision: 0.860937 (0.0000)
Negative recall: 0.928026 (0.0000)
Log-loss: 0.537244 (0.0000)
Log-loss reduction: 31.883066 (0.0000)
F1 Score: 0.589618 (0.0000)
AUPRC: 0.670582 (0.0000)

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

--- Progress log ---
[1] 'Building term dictionary' started.
[1] (%Time%)	32561 examples	Total Terms: 100
[1] 'Building term dictionary' finished in %Time%.
[2] 'FastTree data preparation' started.
[2] 'FastTree data preparation' finished in %Time%.
[3] 'FastTree in-memory bins initialization' started.
[3] 'FastTree in-memory bins initialization' finished in %Time%.
[4] 'FastTree feature conversion' started.
[4] 'FastTree feature conversion' finished in %Time%.
[5] 'FastTree training' started.
[5] 'FastTree training' finished in %Time%.
[6] 'Saving model' started.
[6] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...ut/SingleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
FastTreeBinaryClassification
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.87196	0.830539	0.688912	0.515341	0.860937	0.928026	0.537244	31.88307	0.589618	0.670582	0.25	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{sep=, header+ col=Label:14 col=Cat:TX:1,3,5-9,13} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}	/lr:0.25;/nl:5;/mil:5;/iter:20	


31 changes: 31 additions & 0 deletions
...ngleDebug/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,31 @@

Per-feature gain summary for the boosted tree ensemble:
	marital-status.Married-civ-spouse	1
	occupation.Exec-managerial	0.399117896915284
	occupation.Prof-specialty	0.391227805213273
	education.Bachelors	0.334466450228397
	education.Masters	0.287494503306969
	education.Prof-school	0.204600035294214
	education.Doctorate	0.187985462587772
	occupation.Sales	0.150856624152445
	occupation.Other-service	0.147997280910012
	relationship.Own-child	0.137529286784067
	marital-status.Never-married	0.131366568817433
	education.7th-8th	0.122497278808477
	workclass.Self-emp-inc	0.119940186278871
	education.HS-grad	0.113890497534289
	occupation.Tech-support	0.104801559978143
	workclass.Self-emp-not-inc	0.0911869726068162
	native-country.Mexico	0.081461599208144
	workclass.Federal-gov	0.0785782301236086
	sex.Male	0.0783006369313957
	relationship.Wife	0.0749144815167656
	education.11th	0.0746051436850124
	occupation.Handlers-cleaners	0.0601209002480329
	native-country.United-States	0.0593295291546631
	occupation.Farming-fishing	0.0583149634881263
	education.10th	0.0544727793656118
	education.9th	0.052272828491541
	workclass.Local-gov	0.0519635659099365
	occupation.?	0.0479530108171804
	occupation.Protective-serv	0.0451534478142547
Loading