FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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shauheen merged 10 commits into from
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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available. #122

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Merge branch 'master' of https://github.com/dotnet/machinelearning in…
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53 changes: 53 additions & 0 deletions ...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
Confusion table
||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
positive || 227 | 14 | 0.9419
negative || 13 | 445 | 0.9716
||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.
4 changes: 4 additions & 0 deletions ...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt
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Original file line numberDiff line numberDiff line change
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BinaryClassificationGamTrainer
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
0.991198 0.961373 0.945833 0.941909 0.969499 0.971616 0.145652 84.32696 0.943867 0.971819 BinaryClassificationGamTrainer %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1

700 changes: 700 additions & 0 deletions ...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt
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49 changes: 49 additions & 0 deletions ...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt
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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
Confusion table
||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
positive || 227 | 14 | 0.9419
negative || 13 | 445 | 0.9716
||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.
4 changes: 4 additions & 0 deletions ...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /dt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
0.991198 0.961373 0.945833 0.941909 0.969499 0.971616 0.145652 84.32696 0.943867 0.971819 + BinaryClassificationGamTrainer %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1 /dt:+

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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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shauheen
merged 10 commits into

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May 12, 2018
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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
#122
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Merge branch 'master' of https://github.com/dotnet/machinelearning in…


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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


700 changes: 700 additions & 0 deletions
...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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49 changes: 49 additions & 0 deletions
...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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 Merged

shauheen
merged 10 commits into

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May 12, 2018
 Merged


FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
#122
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Instantiate feature map for disk transpose and make Generalized Addit…


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0da57bd

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119f50f

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afee8ef

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f7a6553

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codemzs May 11, 2018
0763122

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codemzs May 11, 2018
d768f3b

PR comments.


codemzs May 11, 2018
3b248cc

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


codemzs May 11, 2018

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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


700 changes: 700 additions & 0 deletions
...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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49 changes: 49 additions & 0 deletions
...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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, '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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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
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Merge branch 'master' of https://github.com/dotnet/machinelearning in…


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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


700 changes: 700 additions & 0 deletions
...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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Large diffs are not rendered by default.


49 changes: 49 additions & 0 deletions
...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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shauheen
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May 12, 2018
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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
#122
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codemzs May 11, 2018
0763122

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codemzs May 11, 2018
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Merge branch 'master' of https://github.com/dotnet/machinelearning in…


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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


700 changes: 700 additions & 0 deletions
...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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Open in desktop


Large diffs are not rendered by default.


49 changes: 49 additions & 0 deletions
...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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

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May 12, 2018
 Merged


FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
#122
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119f50f

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codemzs May 11, 2018
afee8ef

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codemzs May 11, 2018
f7a6553

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f8e9aa3

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codemzs May 11, 2018
0763122

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codemzs May 11, 2018
d768f3b

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codemzs May 11, 2018
3b248cc

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


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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

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Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


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...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

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Original file line numberDiff line numberDiff line change
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maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

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Original file line numberDiff line numberDiff line change
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BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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May 12, 2018
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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
#122
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Instantiate feature map for disk transpose and make Generalized Addit…


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


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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


700 changes: 700 additions & 0 deletions
...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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49 changes: 49 additions & 0 deletions
...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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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FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.#122

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 Merged

shauheen
merged 10 commits into

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May 12, 2018
 Merged


FastTree: Instantiate feature map for disk transpose and make Generalized Additive Models predictor resilient when feature map is not available.
#122
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0eec9de

Instantiate feature map for disk transpose and make Generalized Addit…


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d81d42c

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0da57bd

misc.


codemzs May 11, 2018
119f50f

misc.


codemzs May 11, 2018
afee8ef

misc.


codemzs May 11, 2018
f7a6553

misc.


codemzs May 11, 2018
f8e9aa3

misc.


codemzs May 11, 2018
0763122

misc.


codemzs May 11, 2018
d768f3b

PR comments.


codemzs May 11, 2018
3b248cc

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


codemzs May 11, 2018

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53 changes: 53 additions & 0 deletions
...ryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,53 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise
Warning: Skipped 16 instances with missing features during training
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree data preparation' started.
[1] 'FastTree data preparation' finished in %Time%.
[2] 'FastTree in-memory bins initialization' started.
[2] 'FastTree in-memory bins initialization' finished in %Time%.
[3] 'FastTree feature conversion' started.
[3] 'FastTree feature conversion' finished in %Time%.
[4] 'GAM training' started.
[4] 'GAM training' finished in %Time%.
[5] 'Saving model' started.
[5] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...aryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1 


700 changes: 700 additions & 0 deletions
...BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt

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Large diffs are not rendered by default.


49 changes: 49 additions & 0 deletions
...ionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,49 @@
maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
Not adding a normalizer.
Making per-feature arrays
Changing data from row-wise to column-wise on disk
Warning: 16 of 699 examples will be skipped due to missing feature values
Processed 683 instances
Binning and forming Feature objects
Starting to train ...
Training calibrator.
TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
Confusion table
 ||======================
PREDICTED || positive | negative | Recall
TRUTH ||======================
 positive || 227 | 14 | 0.9419
 negative || 13 | 445 | 0.9716
 ||======================
Precision || 0.9458 | 0.9695 |
OVERALL 0/1 ACCURACY: 0.961373
LOG LOSS/instance: 0.145652
Test-set entropy (prior Log-Loss/instance): 0.929318
LOG-LOSS REDUCTION (RIG): 84.326961
AUC: 0.991198

OVERALL RESULTS
---------------------------------------
AUC: 0.991198 (0.0000)
Accuracy: 0.961373 (0.0000)
Positive precision: 0.945833 (0.0000)
Positive recall: 0.941909 (0.0000)
Negative precision: 0.969499 (0.0000)
Negative recall: 0.971616 (0.0000)
Log-loss: 0.145652 (0.0000)
Log-loss reduction: 84.326961 (0.0000)
F1 Score: 0.943867 (0.0000)
AUPRC: 0.971819 (0.0000)

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

--- Progress log ---
[1] 'FastTree disk-based bins initialization' started.
[1] 'FastTree disk-based bins initialization' finished in %Time%.
[2] 'GAM training' started.
[2] 'GAM training' finished in %Time%.
[3] 'Saving model' started.
[3] 'Saving model' finished in %Time%.

4 changes: 4 additions & 0 deletions
...tionGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,4 @@
BinaryClassificationGamTrainer
AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	

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