Substituted Wine Quality dataset with machine generated dataset#937

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Merged
shauheen merged 1 commit into from
Sep 18, 2018
Merged

Substituted Wine Quality dataset with machine generated dataset #937

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6 changes: 1 addition & 5 deletions build.proj
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
Targets="Pack" />
</Target>

<ItemGroup>
<TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

<ItemGroup>
<TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down
34 changes: 17 additions & 17 deletions ...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%) Iteration: 50 Training-l2: 0.189697165394939
[2] (%Time%) Iteration: 50 Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%) Iteration: 50 Training-l2: 0.204982247876212
[4] (%Time%) Iteration: 50 Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.
2 changes: 1 addition & 1 deletion ...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
0.520928 0.462387 0.679983 0.462387 0.410287 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1
27.13977 1420.996 37.69597 1420.996 0.921913 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1

501 changes: 501 additions & 0 deletions test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt
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Large diffs are not rendered by default.

4,899 changes: 0 additions & 4,899 deletions test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt
View file
Open in desktop

This file was deleted.

22 changes: 11 additions & 11 deletions ...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%) Iteration: 50 Training-l2: 0.272273893168108
[2] (%Time%) Iteration: 50 Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.
4 changes: 4 additions & 0 deletions ...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
3.472291 26.06443 5.105333 26.06443 0.998571 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1

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Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

from

Sep 18, 2018
 Merged


Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

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Open in desktop


Large diffs are not rendered by default.


4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

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This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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Skip to content
Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

from

Sep 18, 2018
 Merged


Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

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4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

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This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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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Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

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Sep 18, 2018
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Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

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Large diffs are not rendered by default.


4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

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Open in desktop


This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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" + '

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Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

from

Sep 18, 2018
 Merged


Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

View file


Open in desktop


Large diffs are not rendered by default.


4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

View file


Open in desktop


This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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

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Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

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Sep 18, 2018
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Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

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Large diffs are not rendered by default.


4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

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This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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, 'i');
if (__m === '*' || __re.test(location.href)) {
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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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

from

Sep 18, 2018
 Merged


Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

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Large diffs are not rendered by default.


4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

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This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

View file


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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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, '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");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Substituted Wine Quality dataset with machine generated dataset#937

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 Merged

shauheen
merged 1 commit into

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Sep 18, 2018
 Merged


Substituted Wine Quality dataset with machine generated dataset
#937
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6 changes: 1 addition & 5 deletions
build.proj

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Original file line numberDiff line numberDiff line change
Expand Up@@ -75,11 +75,7 @@
 Targets="Pack" />
 </Target>

 <ItemGroup>
 <TestFile Include="$(MSBuildThisFileDirectory)/test/data/external/winequality-white.csv"
 Url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/winequality-white.csv" />

 <ItemGroup> 
 <TestFile Condition="'$(IncludeBenchmarkData)' == 'true'" Include="$(MSBuildThisFileDirectory)/test/data/external/WikiDetoxAnnotated160kRows.tsv"
 Url="https://aka.ms/tlc-resources/benchmarks/WikiDetoxAnnotated160kRows.tsv"
 DestinationFile="$(MSBuildThisFileDirectory)test/data/external/WikiDetoxAnnotated160kRows.tsv" />
Expand Down

34 changes: 17 additions & 17 deletions
...bug/LightGBMR/LightGBMReg-CV-wine-out.txt → ...Reg-CV-generatedRegressionDataset-out.txt

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Original file line numberDiff line numberDiff line change
Expand Up@@ -7,24 +7,24 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.524348
L2(avg): 0.466735
RMS(avg): 0.683180
Loss-fn(avg): 0.466735
R Squared: 0.400415
L1(avg): 0.517508
L2(avg): 0.458039
RMS(avg): 0.676786
Loss-fn(avg): 0.458039
R Squared: 0.420159
L1(avg): 27.477977
L2(avg): 1,428.594095
RMS(avg): 37.796747
Loss-fn(avg): 1,428.594094
R Squared: 0.920504
L1(avg): 26.801569
L2(avg): 1,413.398603
RMS(avg): 37.595194
Loss-fn(avg): 1,413.398596
R Squared: 0.923322

OVERALL RESULTS
---------------------------------------
L1(avg): 0.520928 (0.0034)
L2(avg): 0.462387 (0.0043)
RMS(avg): 0.679983 (0.0032)
Loss-fn(avg): 0.462387 (0.0043)
R Squared: 0.410287 (0.0099)
L1(avg): 27.139773 (0.3382)
L2(avg): 1,420.996349 (7.5977)
RMS(avg): 37.695971 (0.1008)
Loss-fn(avg): 1,420.996345 (7.5977)
R Squared: 0.921913 (0.0014)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -35,10 +35,10 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.189697165394939
[2] (%Time%)	Iteration: 50	Training-l2: 37.107605006517
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Loading data for LightGBM #2' started.
[3] 'Loading data for LightGBM #2' finished in %Time%.
[4] 'Training with LightGBM #2' started.
[4] (%Time%)	Iteration: 50	Training-l2: 0.204982247876212
[4] (%Time%)	Iteration: 50	Training-l2: 27.7037679135951
[4] 'Training with LightGBM #2' finished in %Time%.

2 changes: 1 addition & 1 deletion
...ebug/LightGBMR/LightGBMReg-CV-wine-rp.txt → ...MReg-CV-generatedRegressionDataset-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -1,4 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.520928	0.462387	0.679983	0.462387	0.410287	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	
27.13977	1420.996	37.69597	1420.996	0.921913	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data% %Output%	99	0	0	maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	


501 changes: 501 additions & 0 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-generatedRegressionDataset.txt

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4,899 changes: 0 additions & 4,899 deletions
test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt

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This file was deleted.


22 changes: 11 additions & 11 deletions
...htGBMR/LightGBMReg-TrainTest-wine-out.txt → ...inTest-generatedRegressionDataset-out.txt

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Original file line numberDiff line numberDiff line change
Expand Up@@ -3,19 +3,19 @@ Not adding a normalizer.
Auto-tuning parameters: UseCat = False
LightGBM objective=regression
Not training a calibrator because it is not needed.
L1(avg): 0.402080
L2(avg): 0.272274
RMS(avg): 0.521799
Loss-fn(avg): 0.272274
R Squared: 0.652798
L1(avg): 3.472291
L2(avg): 26.064428
RMS(avg): 5.105333
Loss-fn(avg): 26.064428
R Squared: 0.998571

OVERALL RESULTS
---------------------------------------
L1(avg): 0.402080 (0.0000)
L2(avg): 0.272274 (0.0000)
RMS(avg): 0.521799 (0.0000)
Loss-fn(avg): 0.272274 (0.0000)
R Squared: 0.652798 (0.0000)
L1(avg): 3.472291 (0.0000)
L2(avg): 26.064428 (0.0000)
RMS(avg): 5.105333 (0.0000)
Loss-fn(avg): 26.064428 (0.0000)
R Squared: 0.998571 (0.0000)

---------------------------------------
Physical memory usage(MB): %Number%
Expand All@@ -26,7 +26,7 @@ Virtual memory usage(MB): %Number%
[1] 'Loading data for LightGBM' started.
[1] 'Loading data for LightGBM' finished in %Time%.
[2] 'Training with LightGBM' started.
[2] (%Time%)	Iteration: 50	Training-l2: 0.272273893168108
[2] (%Time%)	Iteration: 50	Training-l2: 26.0644295080124
[2] 'Training with LightGBM' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...elineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-generatedRegressionDataset-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
LightGBMR
L1(avg)	L2(avg)	RMS(avg)	Loss-fn(avg)	R Squared	/iter	/lr	/nl	/mil	/booster	/v	/nt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
3.472291	26.06443	5.105333	26.06443	0.998571	50	0.2	20	10	gbdt{l2=0.2 l1=0.2}	+	1	LightGBMR	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1	/iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1	

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