OVA should respect normalization in underlying learner#310

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Merged
Ivanidzo4ka merged 4 commits into from
Jun 5, 2018
Merged

OVA should respect normalization in underlying learner #310

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ad6ea9f
Respect normalization in OVA.
Jun 5, 2018
b4174f7
some cleanup
Jun 5, 2018
634de75
merge with master
Jun 5, 2018
7897dce
fix copypaste issues
Jun 5, 2018
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9 changes: 6 additions & 3 deletions src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
host.CheckValue(input, nameof(input));
EntryPointUtils.CheckInputArgs(host, input);
host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

// Something tells me we should put normalization as part of macro expansion, but since i get
// subgraph instead of learner it's a bit tricky to get learner and decide should we add
// normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
using (var ch = host.Start("CombineOvaModels"))
{
ISchema schema = input.TrainingData.Schema;
ISchema schema = normalizedView.Schema;
var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
input.LabelColumn,
DefaultColumnNames.Label);
var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
input.FeatureColumn, DefaultColumnNames.Features);
var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
input.WeightColumn, DefaultColumnNames.Weight);
var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

return new ModelOperations.PredictorModelOutput
{
Expand Down
59 changes: 59 additions & 0 deletions test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
}
}
}

[Fact]
public void TestOvaMacro()

@zeahmedzeahmedJun 5, 2018•
edited by Ivanidzo4ka
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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved

@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.

In reply to: 193240523 [](ancestors = 193240523)

{
var dataPath = GetDataPath(@"iris.txt");
using (var env = new TlcEnvironment(42))
{
// Specify subgraph for OVA
var subGraph = env.CreateExperiment();
var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
var learnerOutput = subGraph.Add(learnerInput);
// Create pipeline with OVA and multiclass scoring.
var experiment = env.CreateExperiment();
var importInput = new ML.Data.TextLoader(dataPath);
importInput.Arguments.Column = new TextLoaderColumn[]
{
new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
};
var importOutput = experiment.Add(importInput);
var oneVersusAll = new Models.OneVersusAll
{
TrainingData = importOutput.Data,
Nodes = subGraph,
UseProbabilities = true,
};
var ovaOutput = experiment.Add(oneVersusAll);
var scoreInput = new ML.Transforms.DatasetScorer
{
Data = importOutput.Data,
PredictorModel = ovaOutput.PredictorModel
};
var scoreOutput = experiment.Add(scoreInput);
var evalInput = new ML.Models.ClassificationEvaluator
{
Data = scoreOutput.ScoredData
};
var evalOutput = experiment.Add(evalInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();

var data = experiment.GetOutput(evalOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == accCol))
{
var getter = cursor.GetGetter<double>(accCol);
b = cursor.MoveNext();
Assert.True(b);
double acc = 0;
getter(ref acc);
Assert.Equal(0.96, acc, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
}
}
}
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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OVA should respect normalization in underlying learner#310

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 Merged

Ivanidzo4ka
merged 4 commits into

from

Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
Expand Down

59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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OVA should respect normalization in underlying learner#310

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 Merged

Ivanidzo4ka
merged 4 commits into

from

Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
Expand Down

59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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Choose a reason for hiding this comment

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Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}

, '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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OVA should respect normalization in underlying learner#310

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 Merged

Ivanidzo4ka
merged 4 commits into

from

Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
Expand Down

59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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Choose a reason for hiding this comment

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}

, '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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OVA should respect normalization in underlying learner#310

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Ivanidzo4ka
merged 4 commits into

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Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
Expand Down

59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
OVA should respect normalization in underlying learner#310

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 Merged

Ivanidzo4ka
merged 4 commits into

from

Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
Expand Down

59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}

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

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OVA should respect normalization in underlying learner#310

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 Merged

Ivanidzo4ka
merged 4 commits into

from

Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
Expand Down

59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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Choose a reason for hiding this comment

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}

, '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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OVA should respect normalization in underlying learner#310

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 Merged

Ivanidzo4ka
merged 4 commits into

from

Jun 5, 2018
 Merged


OVA should respect normalization in underlying learner
#310
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ad6ea9f

Respect normalization in OVA.

Jun 5, 2018
b4174f7

some cleanup

Jun 5, 2018
634de75

merge with master

Jun 5, 2018
7897dce

fix copypaste issues

Jun 5, 2018

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9 changes: 6 additions & 3 deletions
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -200,18 +200,21 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron
 host.CheckValue(input, nameof(input));
 EntryPointUtils.CheckInputArgs(host, input);
 host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray));

 // Something tells me we should put normalization as part of macro expansion, but since i get 
 // subgraph instead of learner it's a bit tricky to get learner and decide should we add 
 // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel.
 var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData);
 using (var ch = host.Start("CombineOvaModels"))
 {
 ISchema schema = input.TrainingData.Schema;
 ISchema schema = normalizedView.Schema;
 var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn),
 input.LabelColumn,
 DefaultColumnNames.Label);
 var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.FeatureColumn),
 input.FeatureColumn, DefaultColumnNames.Features);
 var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn),
 input.WeightColumn, DefaultColumnNames.Weight);
 var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight);
 var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight);

 return new ModelOperations.PredictorModelOutput
 {
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59 changes: 59 additions & 0 deletions
test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs

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Expand Up@@ -739,5 +739,64 @@ public void TestCrossValidationMacroWithNonDefaultNames()
 }
 }
 }

 [Fact]
 public void TestOvaMacro()
@zeahmedzeahmedJun 5, 2018•

edited by Ivanidzo4ka

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I see that this PR is for fixing normalization issue. However, this test does not test for normalization anywhere. Is this test specifically for normalization or its just the test for "OVA"? #Resolved
@Ivanidzo4kaIvanidzo4kaJun 5, 2018

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Initially I plan just add test for Ova, but metrics were really bad (around 0.6 accuracy) so I start digging and found problem with normalization. So indirectly it tests presence of normalization through accuracy metric.
In reply to: 193240523 [](ancestors = 193240523)
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 };
 var learnerOutput = subGraph.Add(learnerInput);
 // Create pipeline with OVA and multiclass scoring.
 var experiment = env.CreateExperiment();
 var importInput = new ML.Data.TextLoader(dataPath);
 importInput.Arguments.Column = new TextLoaderColumn[]
 {
 new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } },
 new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } }
 };
 var importOutput = experiment.Add(importInput);
 var oneVersusAll = new Models.OneVersusAll
 {
 TrainingData = importOutput.Data,
 Nodes = subGraph,
 UseProbabilities = true,
 };
 var ovaOutput = experiment.Add(oneVersusAll);
 var scoreInput = new ML.Transforms.DatasetScorer
 {
 Data = importOutput.Data,
 PredictorModel = ovaOutput.PredictorModel
 };
 var scoreOutput = experiment.Add(scoreInput);
 var evalInput = new ML.Models.ClassificationEvaluator
 {
 Data = scoreOutput.ScoredData
 };
 var evalOutput = experiment.Add(evalInput);
 experiment.Compile();
 experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
 experiment.Run();

 var data = experiment.GetOutput(evalOutput.OverallMetrics);
 var schema = data.Schema;
 var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol);
 Assert.True(b);
 using (var cursor = data.GetRowCursor(col => col == accCol))
 {
 var getter = cursor.GetGetter<double>(accCol);
 b = cursor.MoveNext();
 Assert.True(b);
 double acc = 0;
 getter(ref acc);
 Assert.Equal(0.96, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}