diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs index cef3eb35d5..5416542bc0 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs @@ -22,10 +22,10 @@ public abstract class ArgumentsBase [TGUI(Label = "Validation Dataset Proportion")] public Single ValidationDatasetProportion = 0.3f; - internal abstract IComponentFactory>> GetPredictorFactory(); + internal abstract IComponentFactory>, IPredictorProducing>> GetPredictorFactory(); } - private protected readonly IComponentFactory>> BasePredictorType; + private protected readonly IComponentFactory>, IPredictorProducing>> BasePredictorType; private protected readonly IHost Host; private protected IPredictorProducing Meta; @@ -140,54 +140,75 @@ public void Train(List> models, RoleMappedData data, maps[i] = m.GetMapper, TOutput>(); } - // REVIEW: Should implement this better.... - var labels = new Single[100]; - var features = new VBuffer[100]; - int count = 0; - // REVIEW: Should this include bad values or filter them? - using (var cursor = new FloatLabelCursor(data, CursOpt.AllFeatures | CursOpt.AllLabels)) + var view = CreateDataView(host, ch, data, maps, models); + var trainer = BasePredictorType.CreateComponent(host); + if (trainer.Info.NeedNormalization) + ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this."); + Meta = trainer.Fit(view).Model; + CheckMeta(); + } + } + + private IDataView CreateDataView(IHostEnvironment env, IChannel ch, RoleMappedData data, ValueMapper, + TOutput>[] maps, List> models) + { + switch (data.Schema.Label.Value.Type.GetRawKind()) + { + case InternalDataKind.BL: + return CreateDataView(env, ch, data, maps, models, x => x > 0); + case InternalDataKind.R4: + return CreateDataView(env, ch, data, maps, models, x => x); + case InternalDataKind.U4: + ch.Check(data.Schema.Label.Value.Type is KeyDataViewType); + return CreateDataView(env, ch, data, maps, models, x => float.IsNaN(x) ? 0 : (uint)(x + 1)); + default: + throw ch.Except("Unsupported label type"); + } + } + + private IDataView CreateDataView(IHostEnvironment env, IChannel ch, RoleMappedData data, ValueMapper, TOutput>[] maps, + List> models, Func labelConvert) + { + // REVIEW: Should implement this better.... + var labels = new T[100]; + var features = new VBuffer[100]; + int count = 0; + // REVIEW: Should this include bad values or filter them? + using (var cursor = new FloatLabelCursor(data, CursOpt.AllFeatures | CursOpt.AllLabels)) + { + TOutput[] predictions = new TOutput[maps.Length]; + var vBuffers = new VBuffer[maps.Length]; + while (cursor.MoveNext()) { - TOutput[] predictions = new TOutput[maps.Length]; - var vBuffers = new VBuffer[maps.Length]; - while (cursor.MoveNext()) + Parallel.For(0, maps.Length, i => { - Parallel.For(0, maps.Length, i => + var model = models[i]; + if (model.SelectedFeatures != null) { - var model = models[i]; - if (model.SelectedFeatures != null) - { - EnsembleUtils.SelectFeatures(in cursor.Features, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]); - maps[i](in vBuffers[i], ref predictions[i]); - } - else - maps[i](in cursor.Features, ref predictions[i]); - }); - - Utils.EnsureSize(ref labels, count + 1); - Utils.EnsureSize(ref features, count + 1); - labels[count] = cursor.Label; - FillFeatureBuffer(predictions, ref features[count]); - count++; - } + EnsembleUtils.SelectFeatures(in cursor.Features, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]); + maps[i](in vBuffers[i], ref predictions[i]); + } + else + maps[i](in cursor.Features, ref predictions[i]); + }); + + Utils.EnsureSize(ref labels, count + 1); + Utils.EnsureSize(ref features, count + 1); + labels[count] = labelConvert(cursor.Label); + FillFeatureBuffer(predictions, ref features[count]); + count++; } + } - ch.Info("The number of instances used for stacking trainer is {0}", count); - - var bldr = new ArrayDataViewBuilder(host); - Array.Resize(ref labels, count); - Array.Resize(ref features, count); - bldr.AddColumn(DefaultColumnNames.Label, NumberDataViewType.Single, labels); - bldr.AddColumn(DefaultColumnNames.Features, NumberDataViewType.Single, features); + ch.Info("The number of instances used for stacking trainer is {0}", count); - var view = bldr.GetDataView(); - var rmd = new RoleMappedData(view, DefaultColumnNames.Label, DefaultColumnNames.Features); + var bldr = new ArrayDataViewBuilder(env); + Array.Resize(ref labels, count); + Array.Resize(ref features, count); + bldr.AddColumn(DefaultColumnNames.Label, data.Schema.Label.Value.Type as PrimitiveDataViewType, labels); + bldr.AddColumn(DefaultColumnNames.Features, NumberDataViewType.Single, features); - var trainer = BasePredictorType.CreateComponent(host); - if (trainer.Info.NeedNormalization) - ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this."); - Meta = trainer.Train(rmd); - CheckMeta(); - } + return bldr.GetDataView(); } } } diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs index dcf2b47805..a74923185c 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs @@ -19,7 +19,8 @@ namespace Microsoft.ML.Trainers.Ensemble { - using TVectorPredictor = IPredictorProducing>; + using TVectorTrainer = ITrainerEstimator>>, IPredictorProducing>>; + internal sealed class MultiStacking : BaseStacking>, IMulticlassOutputCombiner { public const string LoadName = "MultiStacking"; @@ -44,9 +45,9 @@ public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerF [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureMulticlassClassifierTrainer))] [TGUI(Label = "Base predictor")] - public IComponentFactory> BasePredictorType; + public IComponentFactory BasePredictorType; - internal override IComponentFactory> GetPredictorFactory() => BasePredictorType; + internal override IComponentFactory GetPredictorFactory() => BasePredictorType; public IMulticlassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiStacking(env, this); } diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs index c42b488d7e..fb77f28795 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs @@ -17,7 +17,7 @@ namespace Microsoft.ML.Trainers.Ensemble { - using TScalarPredictor = IPredictorProducing; + using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; internal sealed class RegressionStacking : BaseScalarStacking, IRegressionOutputCombiner { @@ -43,9 +43,9 @@ public sealed class Arguments : ArgumentsBase, ISupportRegressionOutputCombinerF [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureRegressorTrainer))] [TGUI(Label = "Base predictor")] - public IComponentFactory> BasePredictorType; + public IComponentFactory BasePredictorType; - internal override IComponentFactory> GetPredictorFactory() => BasePredictorType; + internal override IComponentFactory GetPredictorFactory() => BasePredictorType; public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new RegressionStacking(env, this); } diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs index 686aed36f4..6602841bbc 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs @@ -15,7 +15,8 @@ namespace Microsoft.ML.Trainers.Ensemble { - using TScalarPredictor = IPredictorProducing; + using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; + internal sealed class Stacking : BaseScalarStacking, IBinaryOutputCombiner { public const string UserName = "Stacking"; @@ -41,9 +42,9 @@ public sealed class Arguments : ArgumentsBase, ISupportBinaryOutputCombinerFacto [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))] [TGUI(Label = "Base predictor")] - public IComponentFactory> BasePredictorType; + public IComponentFactory BasePredictorType; - internal override IComponentFactory> GetPredictorFactory() => BasePredictorType; + internal override IComponentFactory GetPredictorFactory() => BasePredictorType; public IBinaryOutputCombiner CreateComponent(IHostEnvironment env) => new Stacking(env, this); } diff --git a/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs index 92c3b2e21b..f1b4e9daa7 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs @@ -23,10 +23,12 @@ namespace Microsoft.ML.Trainers.Ensemble { using TDistPredictor = IDistPredictorProducing; using TScalarPredictor = IPredictorProducing; + using TScalarTrainer = ITrainerEstimator>, IPredictorProducing>; + /// /// A generic ensemble trainer for binary classification. /// - internal sealed class EnsembleTrainer : EnsembleTrainerBase, IModelCombiner { @@ -47,20 +49,15 @@ public sealed class Arguments : ArgumentsBase // REVIEW: If we make this public again it should be an *estimator* of this type of predictor, rather than the (deprecated) ITrainer. [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))] - public IComponentFactory>[] BasePredictors; + public IComponentFactory[] BasePredictors; - internal override IComponentFactory>[] GetPredictorFactories() => BasePredictors; + internal override IComponentFactory[] GetPredictorFactories() => BasePredictors; public Arguments() { BasePredictors = new[] { - ComponentFactoryUtils.CreateFromFunction( - env => { - var trainerEstimator = new LinearSvmTrainer(env); - return TrainerUtils.MapTrainerEstimatorToTrainer(env, trainerEstimator); - }) + ComponentFactoryUtils.CreateFromFunction(env => new LinearSvmTrainer(env, LabelColumnName, FeatureColumnName)) }; } } @@ -83,7 +80,7 @@ private EnsembleTrainer(IHostEnvironment env, Arguments args, PredictionKind pre private protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; - private protected override TScalarPredictor CreatePredictor(List> models) + private protected override IPredictor CreatePredictor(List> models) { if (models.All(m => m.Predictor is TDistPredictor)) return new EnsembleDistributionModelParameters(Host, PredictionKind, CreateModels(models), Combiner); diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs index 3ab77000bc..ae44bff5e4 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs @@ -17,8 +17,7 @@ namespace Microsoft.ML.Trainers.Ensemble { using Stopwatch = System.Diagnostics.Stopwatch; - internal abstract class EnsembleTrainerBase : ITrainer - where TPredictor : class, IPredictorProducing + internal abstract class EnsembleTrainerBase : ITrainer where TSelector : class, ISubModelSelector where TCombiner : class, IOutputCombiner { @@ -51,7 +50,7 @@ public abstract class ArgumentsBase : TrainerInputBaseWithLabel [TGUI(Label = "Show Sub-Model Metrics")] public bool ShowMetrics; - internal abstract IComponentFactory>>[] GetPredictorFactories(); + internal abstract IComponentFactory>, IPredictorProducing>>[] GetPredictorFactories(); #pragma warning restore CS0649 } @@ -62,7 +61,7 @@ public abstract class ArgumentsBase : TrainerInputBaseWithLabel private protected readonly IHost Host; /// Ensemble members - private protected readonly ITrainer>[] Trainers; + private protected readonly ITrainerEstimator>, IPredictorProducing>[] Trainers; private readonly ISubsetSelector _subsetSelector; private protected ISubModelSelector SubModelSelector; @@ -95,7 +94,7 @@ private protected EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, _subsetSelector = Args.SamplingType.CreateComponent(Host); - Trainers = new ITrainer>[NumModels]; + Trainers = new ITrainerEstimator>, IPredictorProducing>[NumModels]; for (int i = 0; i < Trainers.Length; i++) Trainers[i] = predictorFactories[i % predictorFactories.Length].CreateComponent(Host); // We infer normalization and calibration preferences from the trainers. However, even if the internal trainers @@ -106,7 +105,7 @@ private protected EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, } } - TPredictor ITrainer.Train(TrainContext context) + IPredictor ITrainer.Train(TrainContext context) { Host.CheckValue(context, nameof(context)); @@ -117,9 +116,9 @@ TPredictor ITrainer.Train(TrainContext context) } IPredictor ITrainer.Train(TrainContext context) - => ((ITrainer)this).Train(context); + => ((ITrainer)this).Train(context); - private TPredictor TrainCore(IChannel ch, RoleMappedData data) + private IPredictor TrainCore(IChannel ch, RoleMappedData data) { Host.AssertValue(ch); ch.AssertValue(data); @@ -155,8 +154,9 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data) { if (EnsureMinimumFeaturesSelected(subset)) { + // REVIEW: How to pass the role mappings to the trainer? var model = new FeatureSubsetModel( - Trainers[(int)index].Train(subset.Data), + Trainers[(int)index].Fit(subset.Data.Data).Model, subset.SelectedFeatures, null); SubModelSelector.CalculateMetrics(model, _subsetSelector, subset, batch, needMetrics); @@ -190,7 +190,7 @@ private TPredictor TrainCore(IChannel ch, RoleMappedData data) return CreatePredictor(models); } - private protected abstract TPredictor CreatePredictor(List> models); + private protected abstract IPredictor CreatePredictor(List> models); private bool EnsureMinimumFeaturesSelected(Subset subset) { diff --git a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs index 20427668b8..edc4ca53cd 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs @@ -24,11 +24,13 @@ namespace Microsoft.ML.Trainers.Ensemble { using TVectorPredictor = IPredictorProducing>; + using TVectorTrainer = ITrainerEstimator>>, IPredictorProducing>>; + /// /// A generic ensemble classifier for multi-class classification /// internal sealed class MulticlassDataPartitionEnsembleTrainer : - EnsembleTrainerBase, EnsembleMulticlassModelParameters, + EnsembleTrainerBase, IMulticlassSubModelSelector, IMulticlassOutputCombiner>, IModelCombiner { @@ -48,26 +50,21 @@ public sealed class Arguments : ArgumentsBase // REVIEW: If we make this public again it should be an *estimator* of this type of predictor, rather than the (deprecated) ITrainer. [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureMulticlassClassifierTrainer))] - internal IComponentFactory>[] BasePredictors; + internal IComponentFactory[] BasePredictors; - internal override IComponentFactory>[] GetPredictorFactories() => BasePredictors; + internal override IComponentFactory[] GetPredictorFactories() => BasePredictors; public Arguments() { BasePredictors = new[] { - ComponentFactoryUtils.CreateFromFunction( - env => { - // Note that this illustrates a fundamnetal problem with the mixture of `ITrainer` and `ITrainerEstimator` - // present in this class. The options to the estimator have no way of being communicated to the `ITrainer` - // implementation, so there is a fundamnetal disconnect if someone chooses to ever use the *estimator* with - // non-default column names. Unfortuantely no method of resolving this temporary strikes me as being any - // less laborious than the proper fix, which is that this "meta" component should itself be a trainer - // estimator, as opposed to a regular trainer. - var trainerEstimator = new LbfgsMaximumEntropyMulticlassTrainer(env, LabelColumnName, FeatureColumnName); - return TrainerUtils.MapTrainerEstimatorToTrainer(env, trainerEstimator); - }) + // Note that this illustrates a fundamnetal problem with the mixture of `ITrainer` and `ITrainerEstimator` + // present in this class. The options to the estimator have no way of being communicated to the `ITrainer` + // implementation, so there is a fundamnetal disconnect if someone chooses to ever use the *estimator* with + // non-default column names. Unfortuantely no method of resolving this temporary strikes me as being any + // less laborious than the proper fix, which is that this "meta" component should itself be a trainer + // estimator, as opposed to a regular trainer. + ComponentFactoryUtils.CreateFromFunction(env => new LbfgsMaximumEntropyMulticlassTrainer(env, LabelColumnName, FeatureColumnName)) }; } } @@ -90,7 +87,7 @@ private MulticlassDataPartitionEnsembleTrainer(IHostEnvironment env, Arguments a private protected override PredictionKind PredictionKind => PredictionKind.MulticlassClassification; - private protected override EnsembleMulticlassModelParameters CreatePredictor(List>> models) + private protected override IPredictor CreatePredictor(List>> models) { return new EnsembleMulticlassModelParameters(Host, CreateModels(models), Combiner as IMulticlassOutputCombiner); } diff --git a/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs index ab17f5f28e..490cdd463d 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs @@ -23,7 +23,9 @@ namespace Microsoft.ML.Trainers.Ensemble { using TScalarPredictor = IPredictorProducing; - internal sealed class RegressionEnsembleTrainer : EnsembleTrainerBase>, IPredictorProducing>; + + internal sealed class RegressionEnsembleTrainer : EnsembleTrainerBase, IModelCombiner { @@ -42,20 +44,15 @@ public sealed class Arguments : ArgumentsBase // REVIEW: If we make this public again it should be an *estimator* of this type of predictor, rather than the (deprecated) ITrainer. [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureRegressorTrainer))] - public IComponentFactory>[] BasePredictors; + public IComponentFactory[] BasePredictors; - internal override IComponentFactory>[] GetPredictorFactories() => BasePredictors; + internal override IComponentFactory[] GetPredictorFactories() => BasePredictors; public Arguments() { BasePredictors = new[] { - ComponentFactoryUtils.CreateFromFunction( - env => { - var trainerEstimator = new OnlineGradientDescentTrainer(env); - return TrainerUtils.MapTrainerEstimatorToTrainer(env, trainerEstimator); - }) + ComponentFactoryUtils.CreateFromFunction(env => new OnlineGradientDescentTrainer(env, LabelColumnName, FeatureColumnName)) }; } } @@ -78,7 +75,7 @@ private RegressionEnsembleTrainer(IHostEnvironment env, Arguments args, Predicti private protected override PredictionKind PredictionKind => PredictionKind.Regression; - private protected override TScalarPredictor CreatePredictor(List> models) + private protected override IPredictor CreatePredictor(List> models) { return new EnsembleModelParameters(Host, PredictionKind, CreateModels(models), Combiner); } diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..ad0a480605 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-out.txt @@ -0,0 +1,98 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 232 | 7 | 0.9707 + negative || 11 | 433 | 0.9752 + ||====================== +Precision || 0.9547 | 0.9841 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.117326 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.874384 +AUC: 0.995995 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995995 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.954733 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984091 (0.0000) +Negative recall: 0.975225 (0.0000) +Log-loss: 0.117326 (0.0000) +Log-loss reduction: 0.874384 (0.0000) +F1 Score: 0.962656 (0.0000) +AUPRC: 0.991908 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..853ec2e2eb --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995995 0.973646 0.954733 0.970711 0.984091 0.975225 0.117326 0.874384 0.962656 0.991908 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 AllInstanceSelector WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20;/st:AllInstanceSelector + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..6b46036c9f --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.72854328 0.0153081212 0.022255735289143007 0 +1 0 2.20268083 0.917555869 3.6004393879063104 1 +2 0 -2.865943 0.0127788186 0.018554746224242847 0 +3 0 2.35591817 0.931748569 3.8729968952719278 1 +4 0 -2.55102944 0.019316094 0.028139892924164366 0 +5 1 7.342854 0.999905 0.00013707668465523314 1 +6 0 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0.0083569136151318106 0 +693 0 -3.21145988 0.008100514 0.011734162611424938 0 +694 0 -3.08965349 0.00951504 0.013793025632202006 0 +695 0 -3.72542262 0.004099309 0.0059262077288994046 0 +696 1 4.598934 0.9963301 0.0053043110546258845 1 +697 1 2.802182 0.9611624 0.05714790005987188 1 +698 1 3.39275622 0.98194325 0.026288446863364429 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..6137deb3e9 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.116559 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.875205 +AUC: 0.995920 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995920 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.116559 (0.0000) +Log-loss reduction: 0.875205 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.991714 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..9b3a12e295 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.99592 0.973646 0.958506 0.966527 0.9819 0.977477 0.116559 0.875205 0.9625 0.991714 Average Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Average;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..ec1f5268a8 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.6282382 0.01783696 0.0259655617646397 0 +1 0 1.96705842 0.9275491 3.7868530516332033 1 +2 0 -2.85387278 0.0129900519 0.018863469141483385 0 +3 0 1.93591881 0.924505353 3.7274818364991082 1 +4 0 -2.598872 0.0185861979 0.027066533968906802 0 +5 1 6.95403957 0.99993664 9.1411674874021938E-05 1 +6 0 -1.23903906 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+695 0 -3.662728 0.00413203659 0.005973618813146799 0 +696 1 4.03151941 0.995913267 0.0059079896641200755 1 +697 1 2.39331079 0.9592212 0.060064574083243262 1 +698 1 3.007902 0.982622743 0.025290464456233838 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..cf6af2aa28 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-out.txt @@ -0,0 +1,50 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 3 learners for the batch 1 +Beginning training model 1 of 3 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 3 finished in %Time% +Beginning training model 2 of 3 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 3 finished in %Time% +Beginning training model 3 of 3 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 3 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 234 | 5 | 0.9791 + negative || 11 | 433 | 0.9752 + ||====================== +Precision || 0.9551 | 0.9886 | +OVERALL 0/1 ACCURACY: 0.976574 +LOG LOSS/instance: 0.120266 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.871236 +AUC: 0.995797 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995797 (0.0000) +Accuracy: 0.976574 (0.0000) +Positive precision: 0.955102 (0.0000) +Positive recall: 0.979079 (0.0000) +Negative precision: 0.988584 (0.0000) +Negative recall: 0.975225 (0.0000) +Log-loss: 0.120266 (0.0000) +Log-loss reduction: 0.871236 (0.0000) +F1 Score: 0.966942 (0.0000) +AUPRC: 0.991415 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..9e65852a06 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995797 0.976574 0.955102 0.979079 0.988584 0.975225 0.120266 0.871236 0.966942 0.991415 AvgPer 3 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:AvgPer;/nm:3 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..cd6e2281db --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.64225721 0.01699872 0.024734798891773106 0 +1 0 2.53032255 0.9175873 3.6009891670480099 1 +2 0 -2.85259032 0.0131193595 0.019052488192279435 0 +3 0 3.18263674 0.961787164 4.7097988651720648 1 +4 0 -2.602043 0.01785964 0.025998876067635845 0 +5 1 8.328943 0.9999362 9.2013652418763235E-05 1 +6 0 -0.8214984 0.1441913 0.22463974108554102 0 +7 0 -3.162962 0.008937426 0.012951945167532284 0 +8 0 -3.40084314 0.00665333029 0.0096308007078662172 0 +9 0 -2.78199434 0.01431271 0.020798071268316794 0 +10 0 -3.76753688 0.00421679672 0.0060964144371177219 0 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ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 20 of 20 finished in %Time% +List of models and the metrics after sorted +| AUC(Sorted) || Name of Model | +| 0.996916752312436 |LinearBinaryModelParameters +| 0.996659815005139 |LinearBinaryModelParameters +| 0.996659815005139 |LinearBinaryModelParameters +| 0.996659815005139 |LinearBinaryModelParameters +| 0.99653134635149 |LinearBinaryModelParameters +| 0.996402877697842 |LinearBinaryModelParameters +| 0.996402877697842 |LinearBinaryModelParameters +| 0.996274409044193 |LinearBinaryModelParameters +| 0.996274409044193 |LinearBinaryModelParameters +| 0.996145940390545 |LinearBinaryModelParameters +| 0.996145940390545 |LinearBinaryModelParameters +| 0.996145940390545 |LinearBinaryModelParameters +| 0.995889003083248 |LinearBinaryModelParameters +| 0.995760534429599 |LinearBinaryModelParameters +| 0.995632065775951 |LinearBinaryModelParameters +| 0.995503597122302 |LinearBinaryModelParameters +| 0.995246659815005 |LinearBinaryModelParameters +| 0.994732785200411 |LinearBinaryModelParameters +| 0.994218910585817 |LinearBinaryModelParameters +| 0.994026207605344 |LinearBinaryModelParameters +Warning: 10 of 20 trainings failed. +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 229 | 10 | 0.9582 + negative || 9 | 435 | 0.9797 + ||====================== +Precision || 0.9622 | 0.9775 | +OVERALL 0/1 ACCURACY: 0.972182 +LOG LOSS/instance: 0.117306 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.874405 +AUC: 0.996042 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996042 (0.0000) +Accuracy: 0.972182 (0.0000) +Positive precision: 0.962185 (0.0000) +Positive recall: 0.958159 (0.0000) +Negative precision: 0.977528 (0.0000) +Negative recall: 0.979730 (0.0000) +Log-loss: 0.117306 (0.0000) +Log-loss reduction: 0.874405 (0.0000) +F1 Score: 0.960168 (0.0000) +AUPRC: 0.991960 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..c15ac44dee --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /pt /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996042 0.972182 0.962185 0.958159 0.977528 0.97973 0.117306 0.874405 0.960168 0.99196 BestPerformanceSelector Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /pt:BestPerformanceSelector;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..481ee4c0a6 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.66742277 0.0173032172 0.025181762183421101 0 +1 0 1.92188334 0.935199559 3.9478525524547492 1 +2 0 -2.84293485 0.013440121 0.01952147722305005 0 +3 0 1.9052124 0.933706939 3.9149983255128973 1 +4 0 -2.64747477 0.0178061072 0.025920242852290207 0 +5 1 6.54004669 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0.6550276 0.6937055 0.52760477366555414 1 +26 0 -3.15779281 0.008524471 0.012350929666763313 0 +27 0 -2.76338673 0.0150725525 0.021910639322057369 0 +28 0 -3.5356257 0.00492454553 0.0071221685657312048 0 +29 0 -3.44263363 0.005637582 0.0081563241231015181 0 +30 0 -3.333743 0.006603924 0.0095590470040104909 0 +31 0 -3.413193 0.005884048 0.0085139598725839372 0 +32 1 3.61568475 0.994208336 0.0083798953714551323 1 +33 0 -3.26127243 0.00733653875 0.010623405411459631 0 +34 0 -3.044885 0.0100388862 0.014556238440078305 0 +35 0 -3.5356257 0.00492454553 0.0071221685657312048 0 +36 1 4.2966814 0.9978519 0.0031023746642231979 1 +37 0 -1.85091662 0.0548986979 0.08145911980851514 0 +38 1 2.82893562 0.9819327 0.026303947297194059 1 +39 1 0.750898838 0.7226509 0.46862924892958807 1 +40 0 ? ? ? 0 +41 1 1.0369091 0.7983079 0.3249828147671498 1 +42 1 4.974605 0.999201536 0.001152399931555637 1 +43 1 0.142294168 0.516984344 0.95180750458980801 1 +44 1 4.135688 0.9972835 0.0039243873362431953 1 +45 0 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transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 129 | 5 | 0.9627 + negative || 7 | 213 | 0.9682 + ||====================== +Precision || 0.9485 | 0.9771 | +OVERALL 0/1 ACCURACY: 0.966102 +LOG LOSS/instance: 0.143167 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.850400 +AUC: 0.993996 +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 103 | 2 | 0.9810 + negative || 4 | 220 | 0.9821 + ||====================== +Precision || 0.9626 | 0.9910 | +OVERALL 0/1 ACCURACY: 0.981763 +LOG LOSS/instance: 0.115437 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.872227 +AUC: 0.997832 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995914 (0.0019) +Accuracy: 0.973932 (0.0078) +Positive precision: 0.955573 (0.0070) +Positive recall: 0.971819 (0.0091) +Negative precision: 0.984028 (0.0070) +Negative recall: 0.975162 (0.0070) +Log-loss: 0.129302 (0.0139) +Log-loss reduction: 0.861313 (0.0109) +F1 Score: 0.963627 (0.0081) +AUPRC: 0.992159 (0.0031) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..a1a6ed89f7 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995914 0.973932 0.955573 0.971819 0.984028 0.975162 0.129302 0.861313 0.963627 0.992159 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Output% 99 0 0 maml.exe CV tr=WeightedEnsemble{nm=20 tp=-} threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer.txt new file mode 100644 index 0000000000..6c8ed143a6 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 7.057208 0.999964833 5.0735774115656486E-05 1 +6 0 -0.569736 0.326803476 0.57090036663055121 0 +8 0 -3.265584 0.009919913 0.014382865566317179 0 +9 0 -2.8438797 0.0180531759 0.026283195253444185 0 +10 0 -3.42607021 0.007889865 0.011427810517824316 0 +11 0 -3.27301645 0.009815384 0.014230559406451833 0 +18 1 4.12974262 0.9976289 0.0034247967532404057 1 +20 1 3.61101723 0.995010138 0.0072168704068302217 1 +21 1 3.70798635 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-3.05244 0.00658178562 0.0095268959776332787 0 +695 0 -3.111528 0.006108972 0.0088404137863029985 0 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..981dca228a --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.115894 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.875917 +AUC: 0.995976 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995976 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.115894 (0.0000) +Log-loss reduction: 0.875917 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.991794 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..d908097f1c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995976 0.973646 0.958506 0.966527 0.9819 0.977477 0.115894 0.875917 0.9625 0.991794 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..05522d073a --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.6328392 0.0173636749 0.02527052278685812 0 +1 0 2.11736512 0.9386242 4.0261863408227248 1 +2 0 -2.931671 0.0114151845 0.016563347762008285 0 +3 0 2.00851679 0.9290655 3.8173692357034033 1 +4 0 -2.64747477 0.017011689 0.024753833758538169 0 +5 1 6.8502965 0.9999226 0.00011170705633068974 1 +6 0 -1.16596484 0.124842755 0.1923858373893238 0 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0.008477148383655193 0 +693 0 -3.22007346 0.007600192 0.01100663874759376 0 +694 0 -3.02062726 0.0100708874 0.014602875304934926 0 +695 0 -3.6535368 0.00411452027 0.0059482434828329106 0 +696 1 3.89277768 0.9948068 0.0075116878874550473 1 +697 1 2.35095572 0.9552115 0.066107858757860072 1 +698 1 2.92370939 0.9796768 0.029622243979937089 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..174f4eabce --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-out.txt @@ -0,0 +1,119 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 20 of 20 finished in %Time% +Warning: 10 of 20 trainings failed. +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 236 | 3 | 0.9874 + negative || 14 | 430 | 0.9685 + ||====================== +Precision || 0.9440 | 0.9931 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.114893 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.876989 +AUC: 0.996127 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996127 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.944000 (0.0000) +Positive recall: 0.987448 (0.0000) +Negative precision: 0.993072 (0.0000) +Negative recall: 0.968468 (0.0000) +Log-loss: 0.114893 (0.0000) +Log-loss reduction: 0.876989 (0.0000) +F1 Score: 0.965235 (0.0000) +AUPRC: 0.992160 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..a080a632bf --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /pt /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996127 0.97511 0.944 0.987448 0.993072 0.968468 0.114893 0.876989 0.965235 0.99216 BestDiverseSelector Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /pt:BestDiverseSelector;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..4e982f3a7a --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.04737854 0.0177238826 0.025799472331098337 0 +1 0 2.47291875 0.9068278 3.4239567458634292 1 +2 0 -2.23222542 0.0137591986 0.019988155613655037 0 +3 0 2.197465 0.869003952 2.9324048019558933 1 +4 0 -1.9936024 0.0190748721 0.02778507238959756 0 +5 1 7.7100296 0.9999298 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-2.569418 0.00865066051 0.012534559642738887 0 +690 0 -3.06616545 0.004352222 0.0062926328026999918 0 +691 1 3.44010854 0.9739527 0.038076369600267737 1 +692 0 -2.85225725 0.00585241336 0.0084680512460020736 0 +693 0 -2.55013871 0.008883801 0.012873885632171393 0 +694 0 -2.51790786 0.009287548 0.013461710163383293 0 +695 0 -3.09389186 0.0041881837 0.0060549603597100897 0 +696 1 5.02741051 0.9970715 0.004231124243362026 1 +697 1 3.06741762 0.9570085 0.063396384931027977 1 +698 1 3.59143019 0.9787938 0.030923132124325001 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..4efbdc50df --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 232 | 7 | 0.9707 + negative || 11 | 433 | 0.9752 + ||====================== +Precision || 0.9547 | 0.9841 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.119488 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.872069 +AUC: 0.995872 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995872 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.954733 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984091 (0.0000) +Negative recall: 0.975225 (0.0000) +Log-loss: 0.119488 (0.0000) +Log-loss reduction: 0.872069 (0.0000) +F1 Score: 0.962656 (0.0000) +AUPRC: 0.991591 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..35884c4374 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995872 0.973646 0.954733 0.970711 0.984091 0.975225 0.119488 0.872069 0.962656 0.991591 svm,ap 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:svm,ap;/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..a480b459d5 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.64253736 0.018180633 0.026470469789084766 0 +1 0 2.413429 0.931832552 3.8747732152900811 1 +2 0 -2.90884018 0.0129081747 0.018743795610062966 0 +3 0 2.35082054 0.9264507 3.7651451550773323 1 +4 0 -2.645568 0.0181101058 0.026366840155240968 0 +5 1 7.87455273 0.999941647 8.4187963929142817E-05 1 +6 0 -0.963278532 0.1423926 0.22161074984231105 0 +7 0 -3.258512 0.008214119 0.011899407344248809 0 +8 0 -3.394763 0.006884138 0.0099660552425570367 0 +9 0 -2.84032249 0.0140996873 0.020486316132759173 0 +10 0 -3.75712681 0.00429946231 0.006216185673502031 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0.023625633638672462 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..1aebbbfe11 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-out.txt @@ -0,0 +1,94 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 233 | 8 | 0.9668 + negative || 12 | 446 | 0.9738 + ||====================== +Precision || 0.9510 | 0.9824 | +OVERALL 0/1 ACCURACY: 0.971388 +LOG LOSS/instance: 0.130701 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 0.859358 +AUC: 0.994927 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.994927 (0.0000) +Accuracy: 0.971388 (0.0000) +Positive precision: 0.951020 (0.0000) +Positive recall: 0.966805 (0.0000) +Negative precision: 0.982379 (0.0000) +Negative recall: 0.973799 (0.0000) +Log-loss: 0.130701 (0.0000) +Log-loss reduction: 0.859358 (0.0000) +F1 Score: 0.958848 (0.0000) +AUPRC: 0.989450 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..2774ce294c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.994927 0.971388 0.95102 0.966805 0.982379 0.973799 0.130701 0.859358 0.958848 0.98945 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 AllInstanceSelector{fs=RandomFeatureSelector} WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20;/st:AllInstanceSelector{fs=RandomFeatureSelector} + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..c3c65d1a01 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.35020351 0.02142645 0.031247807073335927 0 +1 0 2.20269275 0.9221814 3.6837416735536537 1 +2 0 -2.66031551 0.0140613765 0.020430255849727907 0 +3 0 1.976532 0.8965738 3.2733261468421255 1 +4 0 -2.42708087 0.0193079133 0.028127858200114327 0 +5 1 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0.049182764441884248 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..1708f624e7 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-out.txt @@ -0,0 +1,58 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter) +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter) +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 1 instances with missing features during training (over 1 iterations; 1 inst/iter) +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter) +Trainer 5 of 5 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 235 | 4 | 0.9833 + negative || 13 | 431 | 0.9707 + ||====================== +Precision || 0.9476 | 0.9908 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.126392 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.864677 +AUC: 0.995448 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995448 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.947581 (0.0000) +Positive recall: 0.983264 (0.0000) +Negative precision: 0.990805 (0.0000) +Negative recall: 0.970721 (0.0000) +Log-loss: 0.126392 (0.0000) +Log-loss reduction: 0.864677 (0.0000) +F1 Score: 0.965092 (0.0000) +AUPRC: 0.990701 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..41f59ab412 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995448 0.97511 0.947581 0.983264 0.990805 0.970721 0.126392 0.864677 0.965092 0.990701 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 5 RandomPartitionSelector WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:5;/st:RandomPartitionSelector + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..9b8991888c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1.88945413 0.0146355424 0.021270660765179913 0 +1 0 2.388715 0.9462658 4.218016030721552 1 +2 0 -1.95107377 0.0132358046 0.019222726322552175 0 +3 0 2.181253 0.925892234 3.7542314515478239 1 +4 0 -1.67818642 0.0206327718 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0 +690 0 -2.3103478 0.00734832324 0.01064053259217026 0 +691 1 3.3837285 0.9891714 0.015707588087033722 1 +692 0 -2.25917244 0.007992394 0.011576913076588022 0 +693 0 -2.10952473 0.0102147507 0.014812552839430559 0 +694 0 -1.9774822 0.0126771368 0.018406159184860077 0 +695 0 -2.4000175 0.006341602 0.0091781309117178744 0 +696 1 4.448575 0.9981234 0.0027098943698025662 1 +697 1 2.12820244 0.9196401 0.12085868275888806 1 +698 1 2.66754818 0.965436757 0.050746340022452187 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..1d42306786 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-out.txt @@ -0,0 +1,61 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 5 finished in %Time% +The number of instances used for stacking trainer is 213 +Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 226 | 13 | 0.9456 + negative || 9 | 435 | 0.9797 + ||====================== +Precision || 0.9617 | 0.9710 | +OVERALL 0/1 ACCURACY: 0.967789 +LOG LOSS/instance: 0.117470 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.874229 +AUC: 0.995929 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995929 (0.0000) +Accuracy: 0.967789 (0.0000) +Positive precision: 0.961702 (0.0000) +Positive recall: 0.945607 (0.0000) +Negative precision: 0.970982 (0.0000) +Negative recall: 0.979730 (0.0000) +Log-loss: 0.117470 (0.0000) +Log-loss reduction: 0.874229 (0.0000) +F1 Score: 0.953586 (0.0000) +AUPRC: 0.991703 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..7f7342f834 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995929 0.967789 0.961702 0.945607 0.970982 0.97973 0.11747 0.874229 0.953586 0.991703 Stacking{bp=ap} Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 5 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Stacking{bp=ap};/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..e0c19d881d --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -37.39005 0.0169453844 0.024656524298402895 0 +1 0 21.7282028 0.926378369 3.7637264849928962 1 +2 0 -39.4836464 0.0134643717 0.019556940662900459 0 +3 0 22.48525 0.9319332 3.8769054163038046 1 +4 0 -36.54084 0.018597357 0.027082938140064027 0 +5 1 85.79065 0.9999373 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0.008777407740639679 0 +693 0 -43.4978 0.008647323 0.012529702115805152 0 +694 0 -42.5315933 0.009621691 0.013948377448766133 0 +695 0 -49.5061264 0.004443391 0.0064247429041880541 0 +696 1 48.3968163 0.9959561 0.0058459095264315449 1 +697 1 28.2233028 0.9629129 0.054522763791826356 1 +698 1 36.0325165 0.9841342 0.023073038992678114 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..bc323f4a00 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Voting tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.129466 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.861385 +AUC: 0.992339 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.992339 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.129466 (0.0000) +Log-loss reduction: 0.861385 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.963675 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..1396d347ab --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.992339 0.973646 0.958506 0.966527 0.9819 0.977477 0.129466 0.861385 0.9625 0.963675 Voting Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Voting tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Voting;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..61b4a9aff0 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1 0.0124691306 0.018102248502573935 0 +1 0 0.9 0.969497263 5.0349175091769718 1 +2 0 -1 0.0124691306 0.018102248502573935 0 +3 0 1 0.9795901 5.6145883434887764 1 +4 0 -1 0.0124691306 0.018102248502573935 0 +5 1 1 0.9795901 0.029749874765915209 1 +6 0 -0.9 0.0187102128 0.027248849528611944 0 +7 0 -1 0.0124691306 0.018102248502573935 0 +8 0 -1 0.0124691306 0.018102248502573935 0 +9 0 -1 0.0124691306 0.018102248502573935 0 +10 0 -1 0.0124691306 0.018102248502573935 0 +11 0 -1 0.0124691306 0.018102248502573935 0 +12 1 -0.2 0.254505575 1.974230837373812 0 +13 0 -1 0.0124691306 0.018102248502573935 0 +14 1 1 0.9795901 0.029749874765915209 1 +15 1 0.4 0.801897347 0.31851052964248283 1 +16 0 -1 0.0124691306 0.018102248502573935 0 +17 0 -1 0.0124691306 0.018102248502573935 0 +18 1 1 0.9795901 0.029749874765915209 1 +19 0 -1 0.0124691306 0.018102248502573935 0 +20 1 0.9 0.969497263 0.044691268798002393 1 +21 1 1 0.9795901 0.029749874765915209 1 +22 0 -1 0.0124691306 0.018102248502573935 0 +23 1 ? ? ? 0 +24 0 -1 0.0124691306 0.018102248502573935 0 +25 1 0.4 0.801897347 0.31851052964248283 1 +26 0 -1 0.0124691306 0.018102248502573935 0 +27 0 -1 0.0124691306 0.018102248502573935 0 +28 0 -1 0.0124691306 0.018102248502573935 0 +29 0 -1 0.0124691306 0.018102248502573935 0 +30 0 -1 0.0124691306 0.018102248502573935 0 +31 0 -1 0.0124691306 0.018102248502573935 0 +32 1 1 0.9795901 0.029749874765915209 1 +33 0 -1 0.0124691306 0.018102248502573935 0 +34 0 -1 0.0124691306 0.018102248502573935 0 +35 0 -1 0.0124691306 0.018102248502573935 0 +36 1 1 0.9795901 0.029749874765915209 1 +37 0 -1 0.0124691306 0.018102248502573935 0 +38 1 0.9 0.969497263 0.044691268798002393 1 +39 1 0.8 0.954644561 0.066964414934699193 1 +40 0 ? ? ? 0 +41 1 0.8 0.954644561 0.066964414934699193 1 +42 1 1 0.9795901 0.029749874765915209 1 +43 1 0.3 0.7283054 0.45738455403071537 1 +44 1 1 0.9795901 0.029749874765915209 1 +45 0 -1 0.0124691306 0.018102248502573935 0 +46 1 1 0.9795901 0.029749874765915209 1 +47 0 -1 0.0124691306 0.018102248502573935 0 +48 0 -1 0.0124691306 0.018102248502573935 0 +49 1 1 0.9795901 0.029749874765915209 1 +50 1 0.7 0.9330589 0.099959912702360154 1 +51 1 -0.2 0.254505575 1.974230837373812 0 +52 1 1 0.9795901 0.029749874765915209 1 +53 1 0.9 0.969497263 0.044691268798002393 1 +54 1 1 0.9795901 0.029749874765915209 1 +55 1 1 0.9795901 0.029749874765915209 1 +56 1 1 0.9795901 0.029749874765915209 1 +57 1 0.1 0.540345848 0.88804499702609785 1 +58 1 0.7 0.9330589 0.099959912702360154 1 +59 1 0.4 0.801897347 0.31851052964248283 1 +60 1 0.6 0.9022521 0.14839753396284014 1 +61 0 -1 0.0124691306 0.018102248502573935 0 +62 1 1 0.9795901 0.029749874765915209 1 +63 1 0.1 0.540345848 0.88804499702609785 1 +64 0 -1 0.0124691306 0.018102248502573935 0 +65 1 0.8 0.954644561 0.066964414934699193 1 +66 0 -1 0.0124691306 0.018102248502573935 0 +67 1 1 0.9795901 0.029749874765915209 1 +68 1 1 0.9795901 0.029749874765915209 1 +69 0 -1 0.0124691306 0.018102248502573935 0 +70 0 -1 0.0124691306 0.018102248502573935 0 +71 1 0.9 0.969497263 0.044691268798002393 1 +72 0 -1 0.0124691306 0.018102248502573935 0 +73 1 1 0.9795901 0.029749874765915209 1 +74 1 0.8 0.954644561 0.066964414934699193 1 +75 0 -1 0.0124691306 0.018102248502573935 0 +76 0 -1 0.0124691306 0.018102248502573935 0 +77 0 -1 0.0124691306 0.018102248502573935 0 +78 0 -1 0.0124691306 0.018102248502573935 0 +79 0 -1 0.0124691306 0.018102248502573935 0 +80 0 -1 0.0124691306 0.018102248502573935 0 +81 0 -1 0.0124691306 0.018102248502573935 0 +82 0 -1 0.0124691306 0.018102248502573935 0 +83 0 -1 0.0124691306 0.018102248502573935 0 +84 1 1 0.9795901 0.029749874765915209 1 +85 1 1 0.9795901 0.029749874765915209 1 +86 1 0.8 0.954644561 0.066964414934699193 1 +87 1 1 0.9795901 0.029749874765915209 1 +88 0 -1 0.0124691306 0.018102248502573935 0 +89 0 -1 0.0124691306 0.018102248502573935 0 +90 0 -1 0.0124691306 0.018102248502573935 0 +91 0 -1 0.0124691306 0.018102248502573935 0 +92 0 -1 0.0124691306 0.018102248502573935 0 +93 0 -1 0.0124691306 0.018102248502573935 0 +94 0 -1 0.0124691306 0.018102248502573935 0 +95 0 -1 0.0124691306 0.018102248502573935 0 +96 0 -1 0.0124691306 0.018102248502573935 0 +97 0 -1 0.0124691306 0.018102248502573935 0 +98 1 1 0.9795901 0.029749874765915209 1 +99 1 1 0.9795901 0.029749874765915209 1 +100 1 1 0.9795901 0.029749874765915209 1 +101 1 -0.3 0.184390724 2.4391620161320371 0 +102 0 -1 0.0124691306 0.018102248502573935 0 +103 1 0.1 0.540345848 0.88804499702609785 1 +104 1 1 0.9795901 0.029749874765915209 1 +105 1 0.4 0.801897347 0.31851052964248283 1 +106 1 1 0.9795901 0.029749874765915209 1 +107 1 1 0.9795901 0.029749874765915209 1 +108 0 -1 0.0124691306 0.018102248502573935 0 +109 1 1 0.9795901 0.029749874765915209 1 +110 0 -1 0.0124691306 0.018102248502573935 0 +111 1 0.9 0.969497263 0.044691268798002393 1 +112 1 1 0.9795901 0.029749874765915209 1 +113 1 1 0.9795901 0.029749874765915209 1 +114 0 -1 0.0124691306 0.018102248502573935 0 +115 0 -1 0.0124691306 0.018102248502573935 0 +116 0 -0.6 0.0616101734 0.091740722231051938 0 +117 1 1 0.9795901 0.029749874765915209 1 +118 0 -1 0.0124691306 0.018102248502573935 0 +119 0 -1 0.0124691306 0.018102248502573935 0 +120 0 -1 0.0124691306 0.018102248502573935 0 +121 0 -1 0.0124691306 0.018102248502573935 0 +122 1 1 0.9795901 0.029749874765915209 1 +123 1 0.9 0.969497263 0.044691268798002393 1 +124 1 1 0.9795901 0.029749874765915209 1 +125 0 -1 0.0124691306 0.018102248502573935 0 +126 1 1 0.9795901 0.029749874765915209 1 +127 0 -1 0.0124691306 0.018102248502573935 0 +128 1 1 0.9795901 0.029749874765915209 1 +129 0 -1 0.0124691306 0.018102248502573935 0 +130 0 -1 0.0124691306 0.018102248502573935 0 +131 0 -1 0.0124691306 0.018102248502573935 0 +132 1 1 0.9795901 0.029749874765915209 1 +133 0 -1 0.0124691306 0.018102248502573935 0 +134 0 -1 0.0124691306 0.018102248502573935 0 +135 0 -1 0.0124691306 0.018102248502573935 0 +136 0 -1 0.0124691306 0.018102248502573935 0 +137 0 -1 0.0124691306 0.018102248502573935 0 +138 0 -1 0.0124691306 0.018102248502573935 0 +139 0 ? ? ? 0 +140 0 -1 0.0124691306 0.018102248502573935 0 +141 0 -1 0.0124691306 0.018102248502573935 0 +142 1 0.9 0.969497263 0.044691268798002393 1 +143 0 -1 0.0124691306 0.018102248502573935 0 +144 0 -1 0.0124691306 0.018102248502573935 0 +145 0 ? ? ? 0 +146 1 0.5 0.859403968 0.21859165685519127 1 +147 0 -1 0.0124691306 0.018102248502573935 0 +148 0 -0.5 0.09020085 0.13638000634685848 0 +149 1 1 0.9795901 0.029749874765915209 1 +150 0 -1 0.0124691306 0.018102248502573935 0 +151 1 0.9 0.969497263 0.044691268798002393 1 +152 1 1 0.9795901 0.029749874765915209 1 +153 0 -1 0.0124691306 0.018102248502573935 0 +154 0 -1 0.0124691306 0.018102248502573935 0 +155 1 0.9 0.969497263 0.044691268798002393 1 +156 0 -1 0.0124691306 0.018102248502573935 0 +157 0 -1 0.0124691306 0.018102248502573935 0 +158 0 ? ? ? 0 +159 1 1 0.9795901 0.029749874765915209 1 +160 1 1 0.9795901 0.029749874765915209 1 +161 0 -1 0.0124691306 0.018102248502573935 0 +162 0 -1 0.0124691306 0.018102248502573935 0 +163 0 -1 0.0124691306 0.018102248502573935 0 +164 0 ? ? ? 0 +165 0 -1 0.0124691306 0.018102248502573935 0 +166 1 1 0.9795901 0.029749874765915209 1 +167 1 0.9 0.969497263 0.044691268798002393 1 +168 0 -1 0.0124691306 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0.0124691306 0.018102248502573935 0 +685 0 -1 0.0124691306 0.018102248502573935 0 +686 0 -1 0.0124691306 0.018102248502573935 0 +687 0 -1 0.0124691306 0.018102248502573935 0 +688 0 -1 0.0124691306 0.018102248502573935 0 +689 0 -1 0.0124691306 0.018102248502573935 0 +690 0 -1 0.0124691306 0.018102248502573935 0 +691 1 1 0.9795901 0.029749874765915209 1 +692 0 -1 0.0124691306 0.018102248502573935 0 +693 0 -1 0.0124691306 0.018102248502573935 0 +694 0 -1 0.0124691306 0.018102248502573935 0 +695 0 -1 0.0124691306 0.018102248502573935 0 +696 1 1 0.9795901 0.029749874765915209 1 +697 1 0.9 0.969497263 0.044691268798002393 1 +698 1 1 0.9795901 0.029749874765915209 1 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-out.txt new file mode 100644 index 0000000000..5e299a5d5c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-out.txt @@ -0,0 +1,61 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 5 of 5 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 45 | 5 | 0.9000 + 2 || 0 | 3 | 47 | 0.9400 + ||======================== +Precision ||1.0000 |0.9375 |0.9038 | +Accuracy(micro-avg): 0.946667 +Accuracy(macro-avg): 0.946667 +Log-loss: 0.433374 +Log-loss reduction: 0.605526 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.946667 (0.0000) +Accuracy(macro-avg): 0.946667 (0.0000) +Log-loss: 0.433374 (0.0000) +Log-loss reduction: 0.605526 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..aee52944d9 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.946667 0.946667 0.433374 0.605526 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt new file mode 100644 index 0000000000..e0958b0827 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.19964178652788431 0.8190241 0.169545561 0.0114304228 0 1 2 +1 0 0 0.22951878783682567 0.794916034 0.193235487 0.0118485028 0 1 2 +2 0 0 0.2084796113210233 0.8118176 0.177210674 0.0109718461 0 1 2 +3 0 0 0.23157376368569668 0.7932842 0.194256365 0.0124595007 0 1 2 +4 0 0 0.19400474503683612 0.823654 0.165016592 0.0113294413 0 1 2 +5 0 0 0.25624980316652674 0.7739486 0.204800844 0.0212505851 0 1 2 +6 0 0 0.23077132670987072 0.793921 0.19157438 0.0145046478 0 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0.49710850703430304 0.608287 0.355297565 0.0364154875 2 1 0 +138 2 2 0.59703036598034886 0.5504438 0.396652877 0.05290335 2 1 0 +139 2 2 0.35515806685565859 0.7010626 0.278541237 0.0203961879 2 1 0 +140 2 2 0.23235270716193504 0.7926665 0.198152646 0.0091809025 2 1 0 +141 2 2 0.29924923237144857 0.7413746 0.242910311 0.0157151017 2 1 0 +142 2 2 0.51434815688955982 0.5978902 0.3675669 0.03454289 2 1 0 +143 2 2 0.24025451662084005 0.7864277 0.203824714 0.009747634 2 1 0 +144 2 2 0.19277744540305095 0.8246655 0.168277055 0.00705749542 2 1 0 +145 2 2 0.29643526389506997 0.743463755 0.242001578 0.0145347184 2 1 0 +146 2 2 0.53942056698930985 0.583086 0.381982148 0.0349318273 2 1 0 +147 2 2 0.42786767900756778 0.651897669 0.320094556 0.028007796 2 1 0 +148 2 2 0.26539295353718467 0.766904533 0.219215557 0.0138799325 2 1 0 +149 2 2 0.5556033513335692 0.573726 0.381408066 0.0448659733 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-out.txt new file mode 100644 index 0000000000..1446808e2b --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-out.txt @@ -0,0 +1,151 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 20 of 20 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 45 | 5 | 0.9000 + 2 || 0 | 3 | 47 | 0.9400 + ||======================== +Precision ||1.0000 |0.9375 |0.9038 | +Accuracy(micro-avg): 0.946667 +Accuracy(macro-avg): 0.946667 +Log-loss: 0.434962 +Log-loss reduction: 0.604081 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.946667 (0.0000) +Accuracy(macro-avg): 0.946667 (0.0000) +Log-loss: 0.434962 (0.0000) +Log-loss reduction: 0.604081 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..c071d09bbd --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.946667 0.946667 0.434962 0.604081 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt new file mode 100644 index 0000000000..65fd773a6c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.19138086602936072 0.825818 0.163233042 0.0112717841 0 1 2 +1 0 0 0.21081139603990082 0.8099268 0.177834481 0.0116552487 0 1 2 +2 0 0 0.19393455236298313 0.8237118 0.165597647 0.0107214814 0 1 2 +3 0 0 0.21423806664589068 0.8071562 0.180384234 0.0122111794 0 1 2 +4 0 0 0.18771144144498667 0.828853846 0.160079092 0.0111219641 0 1 2 +5 0 0 0.25527505392676209 0.7747034 0.202383041 0.0211011246 0 1 2 +6 0 0 0.22015310238339808 0.80239594 0.1834884 0.0140191894 0 1 2 +7 0 0 0.20277266873254787 0.8164638 0.17210038 0.0120522631 0 1 2 +8 0 0 0.21475895892869667 0.8067359 0.180851191 0.011660195 0 1 2 +9 0 0 0.18917001968647357 0.8276458 0.16313988 0.009780681 0 1 2 +10 0 0 0.1923006008088228 0.8250588 0.162721261 0.0118958335 0 1 2 +11 0 0 0.21070785656836516 0.8100107 0.177851424 0.0127292387 0 1 2 +12 0 0 0.18598646367878061 0.830284834 0.160191745 0.00923373 0 1 2 +13 0 0 0.16375354954827126 0.8489512 0.1434604 0.00762989931 0 1 2 +14 0 0 0.1596759482253457 0.85242 0.136936992 0.009566072 0 1 2 +15 0 0 0.20845163822409238 0.8118403 0.169246256 0.0179626718 0 1 2 +16 0 0 0.21744498529134865 0.804571867 0.177335054 0.0164115764 0 1 2 +17 0 0 0.21691780548735229 0.804996133 0.181036428 0.0141520379 0 1 2 +18 0 0 0.23189900760647247 0.7930262 0.188312337 0.01704574 0 1 2 +19 0 0 0.21253030374720744 0.8085358 0.175618231 0.0147444345 0 1 2 +20 0 0 0.22016261069522264 0.8023883 0.184409738 0.0138513763 0 1 2 +21 0 0 0.24602820967441841 0.781900167 0.198100865 0.0185912009 0 1 2 +22 0 0 0.15894935190694653 0.853039563 0.137772977 0.008559331 0 1 2 +23 0 0 0.32356854476605679 0.72356236 0.2498953 0.026824316 0 1 2 +24 0 0 0.23726801509885151 0.788779855 0.196431652 0.01535066 0 1 2 +25 0 0 0.22838375008659054 0.7958188 0.190765321 0.0132612176 0 1 2 +26 0 0 0.27040443187140795 0.7630708 0.2173248 0.0200844 0 1 2 +27 0 0 0.19932606476395923 0.8192827 0.169289112 0.0120774228 0 1 2 +28 0 0 0.1951161099660853 0.8227391 0.166436434 0.0114225345 0 1 2 +29 0 0 0.21846443459392462 0.803752065 0.183887184 0.012927331 0 1 2 +30 0 0 0.22307894196650208 0.8000517 0.187162071 0.0130768539 0 1 2 +31 0 0 0.26075994599575425 0.770465851 0.2110865 0.0192779079 0 1 2 +32 0 0 0.1544188472754566 0.85691303 0.133014292 0.00901475549 0 1 2 +33 0 0 0.16400149087641372 0.848740757 0.1394019 0.01065959 0 1 2 +34 0 0 0.18917001968647357 0.8276458 0.16313988 0.009780681 0 1 2 +35 0 0 0.1867339144240599 0.829664469 0.160415888 0.0101680746 0 1 2 +36 0 0 0.18437912639336113 0.831620455 0.158098668 0.010674349 0 1 2 +37 0 0 0.18917001968647357 0.8276458 0.16313988 0.009780681 0 1 2 +38 0 0 0.20168448813751255 0.8173528 0.171194747 0.0108815078 0 1 2 +39 0 0 0.20291445140827119 0.8163481 0.172192335 0.0121164536 0 1 2 +40 0 0 0.20830326907859717 0.811960757 0.174680948 0.01324258 0 1 2 +41 0 0 0.27346553912976834 0.760738552 0.2233804 0.0145715475 0 1 2 +42 0 0 0.19325957932886312 0.824268 0.164984852 0.0106696784 0 1 2 +43 0 0 0.33929133391732746 0.7122749 0.2542582 0.0308294818 0 1 2 +44 0 0 0.28214916847960514 0.7541612 0.219979763 0.0237289015 0 1 2 +45 0 0 0.2386627953540944 0.787680447 0.19647029 0.0145918755 0 1 2 +46 0 0 0.19526536108078277 0.822616339 0.164269522 0.0124966912 0 1 2 +47 0 0 0.20167019513296086 0.817364454 0.171325445 0.011365016 0 1 2 +48 0 0 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0.388658673 0.0463798642 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-out.txt new file mode 100644 index 0000000000..a6c93db8f3 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-out.txt @@ -0,0 +1,71 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 10563. +Auto-tuning parameters: L2 = 2.667015E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 958. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 8928. +Auto-tuning parameters: L2 = 2.666837E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 874. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 9201. +Auto-tuning parameters: L2 = 2.667378E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 754. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 10344. +Auto-tuning parameters: L2 = 2.66688E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 976. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 9315. +Auto-tuning parameters: L2 = 2.66746E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 1058. +Trainer 5 of 5 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 48 | 2 | 0.9600 + 2 || 0 | 1 | 49 | 0.9800 + ||======================== +Precision ||1.0000 |0.9796 |0.9608 | +Accuracy(micro-avg): 0.980000 +Accuracy(macro-avg): 0.980000 +Log-loss: 0.061647 +Log-loss reduction: 0.943887 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.980000 (0.0000) +Accuracy(macro-avg): 0.980000 (0.0000) +Log-loss: 0.061647 (0.0000) +Log-loss reduction: 0.943887 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..0992b489d5 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.98 0.98 0.061647 0.943887 MultiAverage SDCAMC{nt=1} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:SDCAMC{nt=1};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt new file mode 100644 index 0000000000..ba4318c575 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.0021632449196932351 0.9978391 0.002160947 6.04765346E-15 0 1 2 +1 0 0 0.0079895884500299184 0.992042243 0.007957775 9.68857156E-14 0 1 2 +2 0 0 0.0034284286585717972 0.996577442 0.003422501 2.36189361E-14 0 1 2 +3 0 0 0.005962289210830086 0.99405545 0.005944559 1.11923078E-13 0 1 2 +4 0 0 0.0015346853281102198 0.9984665 0.00153351948 3.82847542E-15 0 1 2 +5 0 0 0.0035573859226362497 0.996448934 0.00355116441 5.743688E-14 0 1 2 +6 0 0 0.0034875220381507346 0.996518552 0.00348154618 6.53355747E-14 0 1 2 +7 0 0 0.00316841143066658 0.9968366 0.00316347065 1.73878618E-14 0 1 2 +8 0 0 0.0080876483364587699 0.991944969 0.008055024 2.330561E-13 0 1 2 +9 0 0 0.0044032577016354458 0.9956064 0.004393641 2.00896513E-14 0 1 2 +10 0 0 0.0017300303383470188 0.998271465 0.00172853586 2.84920941E-15 0 1 2 +11 0 0 0.0033182659118411637 0.996687233 0.00331283966 3.18519252E-14 0 1 2 +12 0 0 0.0047188101464650705 0.9952923 0.00470773876 2.27861671E-14 0 1 2 +13 0 0 0.0022097785731952629 0.997792661 0.00220742333 7.561946E-15 0 1 2 +14 0 0 0.00061662130811644331 0.999383569 0.000616459 1.30595712E-16 0 1 2 +15 0 0 0.00080075129044645252 0.999199569 0.0008004363 1.77890027E-15 0 1 2 +16 0 0 0.0019398060233437617 0.9980621 0.00193793536 9.545111E-15 0 1 2 +17 0 0 0.0035064829009267837 0.996499658 0.00350032561 2.81692919E-14 0 1 2 +18 0 0 0.0035088754643611184 0.9964973 0.003502793 1.68672178E-14 0 1 2 +19 0 0 0.0017625716470783322 0.998239 0.00176108012 9.636584E-15 0 1 2 +20 0 0 0.0055482836599041047 0.9944671 0.00553299161 3.46845457E-14 0 1 2 +21 0 0 0.0038022471215707378 0.996205 0.00379505358 7.55707549E-14 0 1 2 +22 0 0 0.00065187000241988691 0.999348342 0.0006516743 7.705539E-16 0 1 2 +23 0 0 0.025671561453119392 0.974655151 0.0253448915 6.881716E-12 0 1 2 +24 0 0 0.0054063646497523711 0.9946082 0.00539183 1.33688159E-13 0 1 2 +25 0 0 0.011664505897147102 0.988403261 0.0115968315 2.35799282E-13 0 1 2 +26 0 0 0.0096848780603521201 0.990361869 0.009638118 5.94649438E-13 0 1 2 +27 0 0 0.0026899363724517031 0.9973137 0.00268630125 9.070273E-15 0 1 2 +28 0 0 0.0030583968972770301 0.9969463 0.00305377436 9.592416E-15 0 1 2 +29 0 0 0.0055600911742843106 0.994455338 0.005544726 9.881408E-14 0 1 2 +30 0 0 0.0078139218642764643 0.9922165 0.00778349629 1.575795E-13 0 1 2 +31 0 0 0.010642996060550395 0.98941344 0.0105865467 2.91241E-13 0 1 2 +32 0 0 0.00030426839186627181 0.9996958 0.0003043345 8.896724E-17 0 1 2 +33 0 0 0.00038851893370880301 0.999611557 0.000388382963 1.3963084E-16 0 1 2 +34 0 0 0.0044032577016354458 0.9956064 0.004393641 2.00896513E-14 0 1 2 +35 0 0 0.0034938622235497398 0.996512234 0.00348785683 1.21640614E-14 0 1 2 +36 0 0 0.0023991612209818066 0.9976037 0.00239626318 3.001639E-15 0 1 2 +37 0 0 0.0044032577016354458 0.9956064 0.004393641 2.00896513E-14 0 1 2 +38 0 0 0.0051597923110851384 0.9948535 0.00514655141 8.41418542E-14 0 1 2 +39 0 0 0.0033633582075250873 0.9966423 0.00335767167 1.631569E-14 0 1 2 +40 0 0 0.0028259114314285834 0.9971781 0.002821923 1.88525885E-14 0 1 2 +41 0 0 0.063285225115162325 0.9386757 0.0613243356 1.506976E-11 0 1 2 +42 0 0 0.002919819727088034 0.997084439 0.00291556516 2.894814E-14 0 1 2 +43 0 0 0.019364377670876787 0.9808219 0.0191781074 7.75738848E-12 0 1 2 +44 0 0 0.0053491951991256319 0.9946651 0.00533493049 2.87338268E-13 0 1 2 +45 0 0 0.012085757567287364 0.987987 0.0120130256 4.707762E-13 0 1 2 +46 0 0 0.0012669837540169471 0.9987338 0.00126628845 3.24697375E-15 0 1 2 +47 0 0 0.0038111620857659971 0.9961961 0.003803882 4.06568869E-14 0 1 2 +48 0 0 0.001623397800359935 0.9983779 0.001622087 3.02381826E-15 0 1 2 +49 0 0 0.0035964472251083443 0.99641 0.00359009276 1.83884127E-14 0 1 2 +50 1 1 0.0074352983065214123 0.9925923 0.00625397358 0.00115381461 1 2 0 +51 1 1 0.019926841553419904 0.9802704 0.0183767434 0.00135288027 1 2 0 +52 1 1 0.047162589813160904 0.9539323 0.0456770733 0.00039064986 1 2 0 +53 1 1 0.017346598963771529 0.982803 0.0161944963 0.00100264873 1 2 0 +54 1 1 0.061816310629283465 0.940055549 0.05961532 0.0003291377 1 2 0 +55 1 1 0.020161695297233093 0.9800402 0.0183108151 0.00164904189 1 2 0 +56 1 1 0.088502278834805548 0.915301 0.08389361 0.0008053317 1 2 0 +57 1 1 0.025073464908208595 0.975238264 0.0246359184 0.000125828636 1 0 2 +58 1 1 0.0068805350198553328 0.9931431 0.005733231 0.001123723 1 2 0 +59 1 1 0.020703796500439534 0.979509056 0.0177517869 0.00273919129 1 2 0 +60 1 1 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1.64982072E-08 2 1 0 +142 2 2 0.018773745222579363 0.9814014 0.01859814 4.897308E-07 2 1 0 +143 2 2 0.00021412280668525778 0.9997859 0.000214120941 5.683524E-10 2 1 0 +144 2 2 5.9427596625705856E-05 0.9999406 5.93945733E-05 1.04773613E-10 2 1 0 +145 2 2 0.0011606392732310788 0.998840034 0.00115994 5.18025045E-09 2 1 0 +146 2 2 0.027995750014108291 0.9723925 0.0276071317 3.96819644E-07 2 1 0 +147 2 2 0.020005038927645435 0.980193734 0.0198057871 4.547713E-07 2 1 0 +148 2 2 0.00086076339787730691 0.9991396 0.0008603865 1.10151221E-08 2 1 0 +149 2 2 0.10889557677394049 0.896824062 0.103166319 9.609822E-06 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-out.txt new file mode 100644 index 0000000000..99e556e559 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-out.txt @@ -0,0 +1,67 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 5 of 5 finished in %Time% +The number of instances used for stacking trainer is 43 +Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. +Beginning optimization +num vars: 48 +improvement criterion: Mean Improvement +L1 regularization selected 26 of 48 weights. +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 35 | 15 | 0.7000 + 2 || 0 | 0 | 50 | 1.0000 + ||======================== +Precision ||1.0000 |1.0000 |0.7692 | +Accuracy(micro-avg): 0.900000 +Accuracy(macro-avg): 0.900000 +Log-loss: 0.431088 +Log-loss reduction: 0.607607 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.900000 (0.0000) +Accuracy(macro-avg): 0.900000 (0.0000) +Log-loss: 0.431088 (0.0000) +Log-loss reduction: 0.607607 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..4613357145 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.9 0.9 0.431088 0.607607 MultiStacking{bp=mlr{t-}} mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiStacking{bp=mlr{t-}};/bp:mlr{t-};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt new file mode 100644 index 0000000000..f835cbe99a --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.18415280900555114 0.8318087 0.122028828 0.04616249 0 1 2 +1 0 0 0.20102714840054076 0.8178902 0.133012265 0.0490975529 0 1 2 +2 0 0 0.18796467611284964 0.828644 0.124647491 0.04670851 0 1 2 +3 0 0 0.20338126432029827 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0.64271581882801387 0.525862336 0.386756778 0.08738086 2 1 0 +107 2 2 0.31022976744503572 0.733278453 0.216761291 0.04996032 2 1 0 +108 2 2 0.37690521018236767 0.6859811 0.257114917 0.0569039136 2 1 0 +109 2 2 0.15634514606062988 0.855263948 0.11390055 0.03083543 2 1 0 +110 2 2 0.33718827317238759 0.713774443 0.231632337 0.05459329 2 1 0 +111 2 2 0.37944264976304698 0.684242666 0.257651418 0.05810583 2 1 0 +112 2 2 0.27324249863884714 0.760908246 0.193030283 0.04606139 2 1 0 +113 2 2 0.37884472862275104 0.6846519 0.257622838 0.05772521 2 1 0 +114 2 2 0.22750424400264527 0.796519041 0.163256958 0.0402238965 2 1 0 +115 2 2 0.22895193226059671 0.795366764 0.16392526 0.04070787 2 1 0 +116 2 2 0.39065734627057058 0.67661196 0.263285518 0.0601025522 2 1 0 +117 2 2 0.17291209607975502 0.841211557 0.1255971 0.0331913531 2 1 0 +118 2 2 0.16959072963129782 0.8440102 0.123667806 0.0323220268 2 1 0 +119 2 2 0.71281825117767161 0.490260571 0.418660969 0.09107839 2 1 0 +120 2 2 0.20727468753483022 0.812796354 0.149459183 0.0377443731 2 1 0 +121 2 2 0.37806864376202554 0.685183465 0.256260544 0.05855613 2 1 0 +122 2 2 0.22676757566096883 0.797106 0.163146824 0.0397470556 2 1 0 +123 2 2 0.48465511775599091 0.6159096 0.313702852 0.07038761 2 1 0 +124 2 2 0.2517602171038133 0.77743113 0.178920835 0.0436479524 2 1 0 +125 2 2 0.32715654603030991 0.720970869 0.226308525 0.0527204871 2 1 0 +126 2 2 0.4951267110043599 0.6094937 0.3182633 0.07224296 2 1 0 +127 2 2 0.47111250874852117 0.624307334 0.3053932 0.07029938 2 1 0 +128 2 2 0.27314999099426979 0.760978639 0.193284348 0.04573701 2 1 0 +129 2 2 0.45285059997385629 0.6358131 0.297234565 0.06695242 2 1 0 +130 2 2 0.29503916938347752 0.7445024 0.207360968 0.04813649 2 1 0 +131 2 2 0.22044277504974089 0.802163541 0.158022642 0.0398138724 2 1 0 +132 2 2 0.24528051555040742 0.782485 0.17519024 0.04232483 2 1 0 +133 2 2 0.65295816831312881 0.520503759 0.3896718 0.08982452 2 1 0 +134 2 2 0.64944637633309854 0.5223349 0.391364247 0.0863008052 2 1 0 +135 2 2 0.19184347996657866 0.825436056 0.139052927 0.0355111249 2 1 0 +136 2 2 0.19107437944816186 0.826071143 0.138234288 0.03569463 2 1 0 +137 2 2 0.38702141493447267 0.679076552 0.260932773 0.05999077 2 1 0 +138 2 2 0.48647798676067955 0.6147879 0.312924117 0.07228809 2 1 0 +139 2 2 0.27737520965534401 0.7577701 0.195463046 0.0467668772 2 1 0 +140 2 2 0.19539260437810943 0.8225117 0.141371578 0.0361168236 2 1 0 +141 2 2 0.24146746346253872 0.78547436 0.172211587 0.04231404 2 1 0 +142 2 2 0.40604648196440402 0.6662792 0.272454619 0.0612662435 2 1 0 +143 2 2 0.19849826883938582 0.8199612 0.143505931 0.0365328938 2 1 0 +144 2 2 0.17281651629066277 0.841291964 0.125598609 0.0331094638 2 1 0 +145 2 2 0.23844745859411229 0.7878501 0.170364022 0.0417857766 2 1 0 +146 2 2 0.4245241018940587 0.654081 0.283138216 0.06278075 2 1 0 +147 2 2 0.33463382486357185 0.7156001 0.23068 0.05371997 2 1 0 +148 2 2 0.219788289204249 0.8026887 0.157672763 0.0396385565 2 1 0 +149 2 2 0.44450485886110414 0.641141653 0.29202044 0.06683803 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-out.txt new file mode 100644 index 0000000000..94766eed5c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-out.txt @@ -0,0 +1,61 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 5 of 5 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 44 | 6 | 0.8800 + 2 || 0 | 2 | 48 | 0.9600 + ||======================== +Precision ||1.0000 |0.9565 |0.8889 | +Accuracy(micro-avg): 0.946667 +Accuracy(macro-avg): 0.946667 +Log-loss: 0.511576 +Log-loss reduction: 0.534344 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.946667 (0.0000) +Accuracy(macro-avg): 0.946667 (0.0000) +Log-loss: 0.511576 (0.0000) +Log-loss reduction: 0.534344 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..704a2be9d5 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.946667 0.946667 0.511576 0.534344 MultiVoting mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiVoting;/bp:mlr{t-};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt new file mode 100644 index 0000000000..780cf4a618 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0 1 0 0 0 1 2 +1 0 0 0 1 0 0 0 1 2 +2 0 0 0 1 0 0 0 1 2 +3 0 0 0 1 0 0 0 1 2 +4 0 0 0 1 0 0 0 1 2 +5 0 0 0 1 0 0 0 1 2 +6 0 0 0 1 0 0 0 1 2 +7 0 0 0 1 0 0 0 1 2 +8 0 0 0 1 0 0 0 1 2 +9 0 0 0 1 0 0 0 1 2 +10 0 0 0 1 0 0 0 1 2 +11 0 0 0 1 0 0 0 1 2 +12 0 0 0 1 0 0 0 1 2 +13 0 0 0 1 0 0 0 1 2 +14 0 0 0 1 0 0 0 1 2 +15 0 0 0 1 0 0 0 1 2 +16 0 0 0 1 0 0 0 1 2 +17 0 0 0 1 0 0 0 1 2 +18 0 0 0 1 0 0 0 1 2 +19 0 0 0 1 0 0 0 1 2 +20 0 0 0 1 0 0 0 1 2 +21 0 0 0 1 0 0 0 1 2 +22 0 0 0 1 0 0 0 1 2 +23 0 0 0 1 0 0 0 1 2 +24 0 0 0 1 0 0 0 1 2 +25 0 0 0 1 0 0 0 1 2 +26 0 0 0 1 0 0 0 1 2 +27 0 0 0 1 0 0 0 1 2 +28 0 0 0 1 0 0 0 1 2 +29 0 0 0 1 0 0 0 1 2 +30 0 0 0 1 0 0 0 1 2 +31 0 0 0 1 0 0 0 1 2 +32 0 0 0 1 0 0 0 1 2 +33 0 0 0 1 0 0 0 1 2 +34 0 0 0 1 0 0 0 1 2 +35 0 0 0 1 0 0 0 1 2 +36 0 0 0 1 0 0 0 1 2 +37 0 0 0 1 0 0 0 1 2 +38 0 0 0 1 0 0 0 1 2 +39 0 0 0 1 0 0 0 1 2 +40 0 0 0 1 0 0 0 1 2 +41 0 0 0 1 0 0 0 1 2 +42 0 0 0 1 0 0 0 1 2 +43 0 0 0 1 0 0 0 1 2 +44 0 0 0 1 0 0 0 1 2 +45 0 0 0 1 0 0 0 1 2 +46 0 0 0 1 0 0 0 1 2 +47 0 0 0 1 0 0 0 1 2 +48 0 0 0 1 0 0 0 1 2 +49 0 0 0 1 0 0 0 1 2 +50 1 1 0 1 0 0 1 0 2 +51 1 1 0 1 0 0 1 0 2 +52 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +53 1 1 0 1 0 0 1 0 2 +54 1 1 0 1 0 0 1 0 2 +55 1 1 0 1 0 0 1 0 2 +56 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +57 1 1 0 1 0 0 1 0 2 +58 1 1 0 1 0 0 1 0 2 +59 1 1 0 1 0 0 1 0 2 +60 1 1 0 1 0 0 1 0 2 +61 1 1 0 1 0 0 1 0 2 +62 1 1 0 1 0 0 1 0 2 +63 1 1 0 1 0 0 1 0 2 +64 1 1 0 1 0 0 1 0 2 +65 1 1 0 1 0 0 1 0 2 +66 1 1 0 1 0 0 1 0 2 +67 1 1 0 1 0 0 1 0 2 +68 1 1 0 1 0 0 1 0 2 +69 1 1 0 1 0 0 1 0 2 +70 1 2 34.538776391283193 1 0 0 2 0 1 +71 1 1 0 1 0 0 1 0 2 +72 1 1 0.22314353641304868 0.8 0.2 0 1 2 0 +73 1 1 0 1 0 0 1 0 2 +74 1 1 0 1 0 0 1 0 2 +75 1 1 0 1 0 0 1 0 2 +76 1 1 0 1 0 0 1 0 2 +77 1 2 34.538776391283193 1 0 0 2 0 1 +78 1 1 0 1 0 0 1 0 2 +79 1 1 0 1 0 0 1 0 2 +80 1 1 0 1 0 0 1 0 2 +81 1 1 0 1 0 0 1 0 2 +82 1 1 0 1 0 0 1 0 2 +83 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +84 1 1 0 1 0 0 1 0 2 +85 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +86 1 1 0.22314353641304868 0.8 0.2 0 1 2 0 +87 1 1 0 1 0 0 1 0 2 +88 1 1 0 1 0 0 1 0 2 +89 1 1 0 1 0 0 1 0 2 +90 1 1 0 1 0 0 1 0 2 +91 1 1 0 1 0 0 1 0 2 +92 1 1 0 1 0 0 1 0 2 +93 1 1 0 1 0 0 1 0 2 +94 1 1 0 1 0 0 1 0 2 +95 1 1 0 1 0 0 1 0 2 +96 1 1 0 1 0 0 1 0 2 +97 1 1 0 1 0 0 1 0 2 +98 1 1 0 1 0 0 1 0 2 +99 1 1 0 1 0 0 1 0 2 +100 2 2 0 1 0 0 2 0 1 +101 2 2 0 1 0 0 2 0 1 +102 2 2 0 1 0 0 2 0 1 +103 2 2 0 1 0 0 2 0 1 +104 2 2 0 1 0 0 2 0 1 +105 2 2 0 1 0 0 2 0 1 +106 2 2 0.51082558402956157 0.6 0.4 0 2 1 0 +107 2 2 0 1 0 0 2 0 1 +108 2 2 0 1 0 0 2 0 1 +109 2 2 0 1 0 0 2 0 1 +110 2 2 0 1 0 0 2 0 1 +111 2 2 0 1 0 0 2 0 1 +112 2 2 0 1 0 0 2 0 1 +113 2 2 0 1 0 0 2 0 1 +114 2 2 0 1 0 0 2 0 1 +115 2 2 0 1 0 0 2 0 1 +116 2 2 0 1 0 0 2 0 1 +117 2 2 0 1 0 0 2 0 1 +118 2 2 0 1 0 0 2 0 1 +119 2 1 1.6094378975329393 0.8 0.2 0 1 2 0 +120 2 2 0 1 0 0 2 0 1 +121 2 2 0 1 0 0 2 0 1 +122 2 2 0 1 0 0 2 0 1 +123 2 2 0 1 0 0 2 0 1 +124 2 2 0 1 0 0 2 0 1 +125 2 2 0 1 0 0 2 0 1 +126 2 2 0 1 0 0 2 0 1 +127 2 2 0 1 0 0 2 0 1 +128 2 2 0 1 0 0 2 0 1 +129 2 2 0 1 0 0 2 0 1 +130 2 2 0 1 0 0 2 0 1 +131 2 2 0 1 0 0 2 0 1 +132 2 2 0 1 0 0 2 0 1 +133 2 2 0.51082558402956157 0.6 0.4 0 2 1 0 +134 2 1 0.91629071697299402 0.6 0.4 0 1 2 0 +135 2 2 0 1 0 0 2 0 1 +136 2 2 0 1 0 0 2 0 1 +137 2 2 0 1 0 0 2 0 1 +138 2 2 0 1 0 0 2 0 1 +139 2 2 0 1 0 0 2 0 1 +140 2 2 0 1 0 0 2 0 1 +141 2 2 0 1 0 0 2 0 1 +142 2 2 0 1 0 0 2 0 1 +143 2 2 0 1 0 0 2 0 1 +144 2 2 0 1 0 0 2 0 1 +145 2 2 0 1 0 0 2 0 1 +146 2 2 0 1 0 0 2 0 1 +147 2 2 0 1 0 0 2 0 1 +148 2 2 0 1 0 0 2 0 1 +149 2 2 0 1 0 0 2 0 1 diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 134e35716c..1657c34c96 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -2181,6 +2181,135 @@ public void MulticlassNaiveBayes() RunOneAllTests(TestLearners.MulticlassNaiveBayesClassifier, TestDatasets.breastCancerPipe); Done(); } + + [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")] + public void EnsemblesMultiClassBootstrapSelectorTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-"), "WE-Bootstrap"); + Run_TrainTest(pa, TestDatasets.iris, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesDefaultTest() + { + // This one does CV as well as TrainTest. + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 tp=-"), "WE-Default"); + RunOneAllTests(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesBaseLearnerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "bp=AvgPer nm=3 tp=-"), "WE-AvgPer"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesHeterogeneousTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "bp=svm bp=ap nm=20 tp=-"), "WE-Hetero"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesVotingCombinerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 oc=Voting tp=-"), "WE-Voting"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesStackingCombinerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=5 oc=Stacking{bp=ap} tp=-"), "WE-StackingAP"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesAveragerCombinerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 oc=Average tp=-"), "WE-Average"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesBestPerformanceSelectorTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 pt=BestPerformanceSelector tp=-"), "WE-BestPerf"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 4, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesBestDiverseSelectorTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 pt=BestDiverseSelector tp=-"), "WE-Diverse"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesRandomPartitionInstanceSelectorTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=5 st=RandomPartitionSelector tp=-"), "WE-RandomPartition"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 4, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesAllDataSetSelectorTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 st=AllInstanceSelector tp=-"), "WE-All"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesRandomSubSpaceSelectorTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsemble", "nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-"), "WE-RandomFeature"); + Run_TrainTest(pa, TestDatasets.breastCancer, new[] { "loader=Text{col=Label:BL:0 col=Features:R4:1-9}" }, digitsOfPrecision: 5, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")] + public void EnsemblesMultiAveragerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=5 oc=MultiAverage tp=-"), "WE-Average"); + Run_TrainTest(pa, TestDatasets.iris, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")] + public void EnsemblesMultiVotingCombinerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=5 oc=MultiVoting tp=-"), "WE-Voting"); + Run_TrainTest(pa, TestDatasets.iris, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")] + public void EnsemblesMultiStackCombinerTest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-"), "WE-Stacking"); + Run_TrainTest(pa, TestDatasets.iris, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } + + [Fact] + public void EnsemblesMultiAveragerSDCATest() + { + var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-"), "WE-SDCA-Average"); + Run_TrainTest(pa, TestDatasets.iris, digitsOfPrecision: 6, parseOption: NumberParseOption.UseSingle); + Done(); + } } #if OLD_TESTS // REVIEW: We should have some tests that verify we can't deserialize old models.