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Merged
merged 11 commits into from
Mar 19, 2019
Merged

Fixing ModelParameter discrepancies #2968

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7b2fffe
fixing model parameter discrepencies
abgoswam Mar 14, 2019
e5fff99
multiclass LR singe that refactoring is happening in a parallel PR
abgoswam Mar 15, 2019
0ca3031
fix merge conflicts
abgoswam Mar 15, 2019
2788023
Merge branch 'master' into abgoswam/modelparameters
abgoswam Mar 18, 2019
50fc607
Merge branch 'master' into abgoswam/modelparameters
abgoswam Mar 18, 2019
64b8969
review comments. Added Multiclass to NaiveBayes
abgoswam Mar 18, 2019
dd3f4b8
Drop Classification from trainer names - v1 (more trainers to follow)
abgoswam Mar 18, 2019
df99292
Merge branch 'master' into abgoswam/modelparameters
abgoswam Mar 18, 2019
6fe26d3
multiclass LR will be handled separately
abgoswam Mar 18, 2019
2f0aba0
Drop Classification from trainer names - v2 (all trainers taken care…
abgoswam Mar 19, 2019
26e2810
fix entrypoint file
abgoswam Mar 19, 2019
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2 changes: 1 addition & 1 deletion docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ private class BinaryOutputRow
private readonly static Action<ContinuousInputRow, BinaryOutputRow> GreaterThanAverage = (input, output)
=> output.AboveAverage = input.MedianHomeValue > 22.6;

public static float[] GetLinearModelWeights(OrdinaryLeastSquaresRegressionModelParameters linearModel)
public static float[] GetLinearModelWeights(OlsModelParameters linearModel)
{
return linearModel.Weights.ToArray();
}
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2 changes: 1 addition & 1 deletion src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs
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Expand Up @@ -66,7 +66,7 @@ public Arguments()
// estimator, as opposed to a regular trainer.
var trainerEstimator = new LogisticRegressionMulticlassClassificationTrainer(env, LabelColumnName, FeatureColumnName);
return TrainerUtils.MapTrainerEstimatorToTrainer<LogisticRegressionMulticlassClassificationTrainer,
MulticlassLogisticRegressionModelParameters, MulticlassLogisticRegressionModelParameters>(env, trainerEstimator);
LogisticRegressionMulticlassModelParameters, LogisticRegressionMulticlassModelParameters>(env, trainerEstimator);
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@wschin wschin Mar 14, 2019 •
edited by abgoswam
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We can NOT swap them. Logistic regression is never a multiclass classification model while multi-class logistic regression is an alternative name of multinomial logistic regression. Can you revert this change? I will handle this in my issue #1100. I am refactorizing it. #Resolved

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One alternative name we can use is SoftmaxRegression (also mentioned in wikipedia link above).

While refactoring, could you fix the name of the trainer estimator for this as well.

I will revert this, and wait for your refactoring PR. Sounds good ?

In reply to: 265806976 [](ancestors = 265806976)

})
};
}
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34 changes: 17 additions & 17 deletions src/Microsoft.ML.FastTree/GamClassification.cs
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Expand Up @@ -21,16 +21,16 @@
GamBinaryClassificationTrainer.LoadNameValue,
GamBinaryClassificationTrainer.ShortName, DocName = "trainer/GAM.md")]

[assembly: LoadableClass(typeof(IPredictorProducing<float>), typeof(BinaryClassificationGamModelParameters), null, typeof(SignatureLoadModel),
[assembly: LoadableClass(typeof(IPredictorProducing<float>), typeof(GamBinaryModelParameters), null, typeof(SignatureLoadModel),
"GAM Binary Class Predictor",
BinaryClassificationGamModelParameters.LoaderSignature)]
GamBinaryModelParameters.LoaderSignature)]

namespace Microsoft.ML.Trainers.FastTree
{
public sealed class GamBinaryClassificationTrainer :
GamTrainerBase<GamBinaryClassificationTrainer.Options,
BinaryPredictionTransformer<CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>>,
CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>>
BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>,
CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>
{
public sealed class Options : OptionsBase
{
Expand Down Expand Up @@ -102,13 +102,13 @@ private static bool[] ConvertTargetsToBool(double[] targets)
Parallel.Invoke(new ParallelOptions { MaxDegreeOfParallelism = BlockingThreadPool.NumThreads }, actions);
return boolArray;
}
private protected override CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator> TrainModelCore(TrainContext context)
private protected override CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator> TrainModelCore(TrainContext context)
{
TrainBase(context);
var predictor = new BinaryClassificationGamModelParameters(Host,
var predictor = new GamBinaryModelParameters(Host,
BinUpperBounds, BinEffects, MeanEffect, InputLength, FeatureMap);
var calibrator = new PlattCalibrator(Host, -1.0 * _sigmoidParameter, 0);
return new ValueMapperCalibratedModelParameters<BinaryClassificationGamModelParameters, PlattCalibrator>(Host, predictor, calibrator);
return new ValueMapperCalibratedModelParameters<GamBinaryModelParameters, PlattCalibrator>(Host, predictor, calibrator);
}

private protected override ObjectiveFunctionBase CreateObjectiveFunction()
Expand Down Expand Up @@ -137,15 +137,15 @@ private protected override void DefinePruningTest()
PruningTest = new TestHistory(validTest, PruningLossIndex);
}

private protected override BinaryPredictionTransformer<CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>>
MakeTransformer(CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator> model, DataViewSchema trainSchema)
=> new BinaryPredictionTransformer<CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>>(Host, model, trainSchema, FeatureColumn.Name);
private protected override BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>
MakeTransformer(CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator> model, DataViewSchema trainSchema)
=> new BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>(Host, model, trainSchema, FeatureColumn.Name);

/// <summary>
/// Trains a <see cref="GamBinaryClassificationTrainer"/> using both training and validation data, returns
/// a <see cref="BinaryPredictionTransformer{CalibratedModelParametersBase}"/>.
/// </summary>
public BinaryPredictionTransformer<CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>> Fit(IDataView trainData, IDataView validationData)
public BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>> Fit(IDataView trainData, IDataView validationData)
=> TrainTransformer(trainData, validationData);

private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema)
Expand All @@ -162,7 +162,7 @@ private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape
/// <summary>
/// The model parameters class for Binary Classification GAMs
/// </summary>
public sealed class BinaryClassificationGamModelParameters : GamModelParametersBase, IPredictorProducing<float>
public sealed class GamBinaryModelParameters : GamModelParametersBase, IPredictorProducing<float>
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@eerhardt eerhardt Mar 18, 2019 •
edited by abgoswam
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BinaryClassification?

Do we need to put Classification in the name? We do everywhere else. #Resolved

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For ModelParameters we do not. Please see the other comment.

In reply to: 266626783 [](ancestors = 266626783)

{
internal const string LoaderSignature = "BinaryClassGamPredictor";
private protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
Expand All @@ -179,11 +179,11 @@ public sealed class BinaryClassificationGamModelParameters : GamModelParametersB
/// <param name="featureToInputMap">A map from the feature shape functions (as described by the binUpperBounds and BinEffects)
/// to the input feature. Used when the number of input features is different than the number of shape functions. Use default if all features have
/// a shape function.</param>
internal BinaryClassificationGamModelParameters(IHostEnvironment env,
internal GamBinaryModelParameters(IHostEnvironment env,
double[][] binUpperBounds, double[][] binEffects, double intercept, int inputLength, int[] featureToInputMap)
: base(env, LoaderSignature, binUpperBounds, binEffects, intercept, inputLength, featureToInputMap) { }

private BinaryClassificationGamModelParameters(IHostEnvironment env, ModelLoadContext ctx)
private GamBinaryModelParameters(IHostEnvironment env, ModelLoadContext ctx)
: base(env, LoaderSignature, ctx) { }

private static VersionInfo GetVersionInfo()
Expand All @@ -196,7 +196,7 @@ private static VersionInfo GetVersionInfo()
verReadableCur: 0x00010002,
verWeCanReadBack: 0x00010001,
loaderSignature: LoaderSignature,
loaderAssemblyName: typeof(BinaryClassificationGamModelParameters).Assembly.FullName);
loaderAssemblyName: typeof(GamBinaryModelParameters).Assembly.FullName);
}

private static IPredictorProducing<float> Create(IHostEnvironment env, ModelLoadContext ctx)
Expand All @@ -205,12 +205,12 @@ private static IPredictorProducing<float> Create(IHostEnvironment env, ModelLoad
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());

var predictor = new BinaryClassificationGamModelParameters(env, ctx);
var predictor = new GamBinaryModelParameters(env, ctx);
ICalibrator calibrator;
ctx.LoadModelOrNull<ICalibrator, SignatureLoadModel>(env, out calibrator, @"Calibrator");
if (calibrator == null)
return predictor;
return new SchemaBindableCalibratedModelParameters<BinaryClassificationGamModelParameters, ICalibrator>(env, predictor, calibrator);
return new SchemaBindableCalibratedModelParameters<GamBinaryModelParameters, ICalibrator>(env, predictor, calibrator);
}

private protected override void SaveCore(ModelSaveContext ctx)
Expand Down
4 changes: 2 additions & 2 deletions src/Microsoft.ML.FastTree/GamModelParameters.cs
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Expand Up @@ -880,12 +880,12 @@ private Context Init(IChannel ch)
// 2. RegressionGamModelParameters
// For (1), the trained model, GamModelParametersBase, is a field we need to extract. For (2),
// we don't need to do anything because RegressionGamModelParameters is derived from GamModelParametersBase.
var calibrated = rawPred as CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>;
var calibrated = rawPred as CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>;
while (calibrated != null)
{
hadCalibrator = true;
rawPred = calibrated.SubModel;
calibrated = rawPred as CalibratedModelParametersBase<BinaryClassificationGamModelParameters, PlattCalibrator>;
calibrated = rawPred as CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>;
}
var pred = rawPred as GamModelParametersBase;
ch.CheckUserArg(pred != null, nameof(ImplOptions.InputModelFile), "Predictor was not a " + nameof(GamModelParametersBase));
Expand Down
28 changes: 14 additions & 14 deletions src/Microsoft.ML.FastTree/GamRegression.cs
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Expand Up @@ -18,13 +18,13 @@
GamRegressionTrainer.LoadNameValue,
GamRegressionTrainer.ShortName, DocName = "trainer/GAM.md")]

[assembly: LoadableClass(typeof(RegressionGamModelParameters), null, typeof(SignatureLoadModel),
[assembly: LoadableClass(typeof(GamRegressionModelParameters), null, typeof(SignatureLoadModel),
"GAM Regression Predictor",
RegressionGamModelParameters.LoaderSignature)]
GamRegressionModelParameters.LoaderSignature)]

namespace Microsoft.ML.Trainers.FastTree
{
public sealed class GamRegressionTrainer : GamTrainerBase<GamRegressionTrainer.Options, RegressionPredictionTransformer<RegressionGamModelParameters>, RegressionGamModelParameters>
public sealed class GamRegressionTrainer : GamTrainerBase<GamRegressionTrainer.Options, RegressionPredictionTransformer<GamRegressionModelParameters>, GamRegressionModelParameters>
{
public partial class Options : OptionsBase
{
Expand Down Expand Up @@ -68,10 +68,10 @@ private protected override void CheckLabel(RoleMappedData data)
data.CheckRegressionLabel();
}

private protected override RegressionGamModelParameters TrainModelCore(TrainContext context)
private protected override GamRegressionModelParameters TrainModelCore(TrainContext context)
{
TrainBase(context);
return new RegressionGamModelParameters(Host, BinUpperBounds, BinEffects, MeanEffect, InputLength, FeatureMap);
return new GamRegressionModelParameters(Host, BinUpperBounds, BinEffects, MeanEffect, InputLength, FeatureMap);
}

private protected override ObjectiveFunctionBase CreateObjectiveFunction()
Expand All @@ -87,14 +87,14 @@ private protected override void DefinePruningTest()
PruningTest = new TestHistory(validTest, PruningLossIndex);
}

private protected override RegressionPredictionTransformer<RegressionGamModelParameters> MakeTransformer(RegressionGamModelParameters model, DataViewSchema trainSchema)
=> new RegressionPredictionTransformer<RegressionGamModelParameters>(Host, model, trainSchema, FeatureColumn.Name);
private protected override RegressionPredictionTransformer<GamRegressionModelParameters> MakeTransformer(GamRegressionModelParameters model, DataViewSchema trainSchema)
=> new RegressionPredictionTransformer<GamRegressionModelParameters>(Host, model, trainSchema, FeatureColumn.Name);

/// <summary>
/// Trains a <see cref="GamRegressionTrainer"/> using both training and validation data, returns
/// a <see cref="RegressionPredictionTransformer{RegressionGamModelParameters}"/>.
/// </summary>
public RegressionPredictionTransformer<RegressionGamModelParameters> Fit(IDataView trainData, IDataView validationData)
public RegressionPredictionTransformer<GamRegressionModelParameters> Fit(IDataView trainData, IDataView validationData)
=> TrainTransformer(trainData, validationData);

private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema)
Expand All @@ -109,7 +109,7 @@ private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape
/// <summary>
/// The model parameters class for Binary Classification GAMs
/// </summary>
public sealed class RegressionGamModelParameters : GamModelParametersBase
public sealed class GamRegressionModelParameters : GamModelParametersBase
{
internal const string LoaderSignature = "RegressionGamPredictor";
private protected override PredictionKind PredictionKind => PredictionKind.Regression;
Expand All @@ -126,11 +126,11 @@ public sealed class RegressionGamModelParameters : GamModelParametersBase
/// <param name="featureToInputMap">A map from the feature shape functions (as described by the binUpperBounds and BinEffects)
/// to the input feature. Used when the number of input features is different than the number of shape functions. Use default if all features have
/// a shape function.</param>
internal RegressionGamModelParameters(IHostEnvironment env,
internal GamRegressionModelParameters(IHostEnvironment env,
double[][] binUpperBounds, double[][] binEffects, double intercept, int inputLength = -1, int[] featureToInputMap = null)
: base(env, LoaderSignature, binUpperBounds, binEffects, intercept, inputLength, featureToInputMap) { }

private RegressionGamModelParameters(IHostEnvironment env, ModelLoadContext ctx)
private GamRegressionModelParameters(IHostEnvironment env, ModelLoadContext ctx)
: base(env, LoaderSignature, ctx) { }

private static VersionInfo GetVersionInfo()
Expand All @@ -143,16 +143,16 @@ private static VersionInfo GetVersionInfo()
verReadableCur: 0x00010002,
verWeCanReadBack: 0x00010001,
loaderSignature: LoaderSignature,
loaderAssemblyName: typeof(RegressionGamModelParameters).Assembly.FullName);
loaderAssemblyName: typeof(GamRegressionModelParameters).Assembly.FullName);
}

private static RegressionGamModelParameters Create(IHostEnvironment env, ModelLoadContext ctx)
private static GamRegressionModelParameters Create(IHostEnvironment env, ModelLoadContext ctx)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());

return new RegressionGamModelParameters(env, ctx);
return new GamRegressionModelParameters(env, ctx);
}

private protected override void SaveCore(ModelSaveContext ctx)
Expand Down
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