Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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Merged
TomFinley merged 2 commits into from
Jun 13, 2018
Merged

Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor #338

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Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…
yaeldMS Jun 8, 2018
2ff2d77
Address PR comments.
yaeldMS Jun 11, 2018
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18 changes: 6 additions & 12 deletions src/Microsoft.ML.Data/Prediction/Calibrator.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
/// <param name="trainer">The trainer used to train the predictor.</param>
/// <param name="predictor">The predictor that needs calibration.</param>
/// <param name="data">The examples to used for calibrator training.</param>
/// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
/// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
/// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
/// <returns>The original predictor, if no calibration is needed,
/// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
return predictor;

return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
}

/// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
/// <param name="maxRows">The maximum rows to use for calibrator training.</param>
/// <param name="predictor">The predictor that needs calibration.</param>
/// <param name="data">The examples to used for calibrator training.</param>
/// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
/// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
/// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
/// <returns>The original predictor, if no calibration is needed,
/// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
int maxRows, IPredictor predictor, RoleMappedData data)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
}
}
var cali = caliTrainer.FinishTraining(ch);
return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
}

public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
{
Contracts.Assert(predictor != null);
if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
if (needValueMapper)
if (predictor is IValueMapper)
return new CalibratedPredictor(env, predictor, cali);
return new SchemaBindableCalibratedPredictor(env, predictor, cali);
}
Expand Down
2 changes: 1 addition & 1 deletion src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
else
calibrator = Args.Calibrator.CreateInstance(Host);
var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
trainer, predictor, td, true);
trainer, predictor, td);
predictor = res as TScalarPredictor;
Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
}
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
}
}
}

[Fact]
public void TestOvaMacroWithUncalibratedLearner()
{
var dataPath = GetDataPath(@"iris.txt");
using (var env = new TlcEnvironment(42))
{
// Specify subgraph for OVA
var subGraph = env.CreateExperiment();
var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
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(0.71, acc, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
}
}
}
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

from

Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
Expand Down

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

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, 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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Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

from

Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, 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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Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

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Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, 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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Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

from

Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, 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
Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

from

Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, 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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Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

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Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, 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); }
})();
})();

Skip to content
Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor#338

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 Merged

TomFinley
merged 2 commits into

from

Jun 13, 2018
 Merged


Create CalibratedPredictor instead of SchemaBindableCalibratedPredictor
#338
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8cdafd0

Create CalibratedPredictor instead of SchemaBindableCalibratedPredict…


yaeldMS Jun 8, 2018
2ff2d77

Address PR comments.


yaeldMS Jun 11, 2018

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18 changes: 6 additions & 12 deletions
src/Microsoft.ML.Data/Prediction/Calibrator.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat
 /// <param name="trainer">The trainer used to train the predictor.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator,
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand All@@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema))
 return predictor;

 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper);
 return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data);
 }

 /// <summary>
Expand All@@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel
 /// <param name="maxRows">The maximum rows to use for calibrator training.</param>
 /// <param name="predictor">The predictor that needs calibration.</param>
 /// <param name="data">The examples to used for calibrator training.</param>
 /// <param name="needValueMapper">Indicates whether the predictor returned needs to be an <see cref="IValueMapper"/>.
 /// This parameter is needed for OVA that uses the predictors as <see cref="IValueMapper"/>s. If it is false,
 /// The predictor returned is an an <see cref="ISchemaBindableMapper"/>.</param>
 /// <returns>The original predictor, if no calibration is needed, 
 /// or a metapredictor that wraps the original predictor and the newly trained calibrator.</returns>
 public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer,
 int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false)
 int maxRows, IPredictor predictor, RoleMappedData data)
 {
 Contracts.CheckValue(env, nameof(env));
 env.CheckValue(ch, nameof(ch));
Expand DownExpand Up@@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal
 }
 }
 var cali = caliTrainer.FinishTraining(ch);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali, needValueMapper);
 return CreateCalibratedPredictor(env, (IPredictorProducing<Float>)predictor, cali);
 }

 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali, bool needValueMapper = false)
 public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing<Float> predictor, ICalibrator cali)
 {
 Contracts.Assert(predictor != null);
 if (cali == null)
Expand All@@ -853,7 +847,7 @@ public static IPredictorProducing<Float> CreateCalibratedPredictor(IHostEnvironm
 var predWithFeatureScores = predictor as IPredictorWithFeatureWeights<Float>;
 if (predWithFeatureScores != null && predictor is IParameterMixer<Float> && cali is IParameterMixer)
 return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali);
 if (needValueMapper)
 if (predictor is IValueMapper)
 return new CalibratedPredictor(env, predictor, cali);
 return new SchemaBindableCalibratedPredictor(env, predictor, cali);
 }
Expand Down

2 changes: 1 addition & 1 deletion
src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe
 else
 calibrator = Args.Calibrator.CreateInstance(Host);
 var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples,
 trainer, predictor, td, true);
 trainer, predictor, td);
 predictor = res as TScalarPredictor;
 Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface");
 }
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@@ -798,5 +798,64 @@ public void TestOvaMacro()
 }
 }
 }

 [Fact]
 public void TestOvaMacroWithUncalibratedLearner()
 {
 var dataPath = GetDataPath(@"iris.txt");
 using (var env = new TlcEnvironment(42))
 {
 // Specify subgraph for OVA
 var subGraph = env.CreateExperiment();
 var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false };
 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(0.71, acc, 2);
 b = cursor.MoveNext();
 Assert.False(b);
 }
 }
 }
 }
}