From 91ca1a0cdad62b6df2fca93289e4bb1400ad9a99 Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Sun, 4 Aug 2019 14:13:50 +0300 Subject: [PATCH 01/11] LDSVM trainer, and tests --- .../LdSvm/LdSvmModelParameters.cs | 282 +++++++ .../LdSvm/LdSvmTrainer.cs | 489 ++++++++++++ .../StandardTrainersCatalog.cs | 27 + .../LdSvm/LDSVM-def-CV-breast-cancer-out.txt | 56 ++ .../LdSvm/LDSVM-def-CV-breast-cancer-rp.txt | 4 + .../LdSvm/LDSVM-def-CV-breast-cancer.txt | 700 ++++++++++++++++++ .../LDSVM-def-TrainTest-breast-cancer-out.txt | 38 + .../LDSVM-def-TrainTest-breast-cancer-rp.txt | 4 + .../LDSVM-def-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../LdSvm/LDSVM-nob-CV-breast-cancer-out.txt | 56 ++ .../LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt | 4 + .../LdSvm/LDSVM-nob-CV-breast-cancer.txt | 700 ++++++++++++++++++ .../LDSVM-nob-TrainTest-breast-cancer-out.txt | 38 + .../LDSVM-nob-TrainTest-breast-cancer-rp.txt | 4 + .../LDSVM-nob-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../TestPredictors.cs | 34 +- test/Microsoft.ML.TestFramework/Learners.cs | 14 - .../TrainerEstimators/TrainerEstimators.cs | 26 + 18 files changed, 3831 insertions(+), 45 deletions(-) create mode 100644 src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs create mode 100644 src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs new file mode 100644 index 0000000000..fdb9f1cadf --- /dev/null +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs @@ -0,0 +1,282 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Linq; +using Microsoft.ML; +using Microsoft.ML.Data; +using Microsoft.ML.Internal.Utilities; +using Microsoft.ML.Numeric; +using Microsoft.ML.Runtime; +using Microsoft.ML.Trainers; + +[assembly: LoadableClass(typeof(LdSvmModelParameters), null, typeof(SignatureLoadModel), "LDSVM binary predictor", LdSvmModelParameters.LoaderSignature)] + +namespace Microsoft.ML.Trainers +{ + public sealed class LdSvmModelParameters : ModelParametersBase, + IValueMapper, + ICanSaveModel + { + public const string LoaderSignature = "LDSVMBinaryPredictor"; + + /// + /// Version information to be saved in binary format + /// + /// + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "LDSVM BC", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature, + loaderAssemblyName: typeof(LdSvmModelParameters).Assembly.FullName); + } + + // Classifier Parameters + private readonly int _numLeaf; + private readonly float _sigma; + private readonly VBuffer[] _w; + private readonly VBuffer[] _thetaPrime; + private readonly VBuffer[] _theta; + private readonly float[] _biasW; + private readonly float[] _biasTheta; + private readonly float[] _biasThetaPrime; + + /// + /// Constructor. w, thetaPrime, theta must be dense s. + /// Note that this takes over ownership of all such vectors. + /// + internal LdSvmModelParameters(IHostEnvironment env, VBuffer[] w, VBuffer[] thetaPrime, VBuffer[] theta, + float sigma, float[] biasW, float[] biasTheta, float[] biasThetaPrime, int treeDepth) + : base(env, LoaderSignature) + { + // _numLeaf is 32-bit signed integer. + Host.Assert(treeDepth > 0 && treeDepth < 31); + int numLeaf = 1 << treeDepth; + + Host.Assert(w.Length == numLeaf * 2 - 1); + Host.Assert(w.All(v => v.IsDense)); + Host.Assert(w.All(v => v.Length == w[0].Length)); + Host.Assert(thetaPrime.Length == numLeaf * 2 - 1); + Host.Assert(thetaPrime.All(v => v.IsDense)); + Host.Assert(thetaPrime.All(v => v.Length == thetaPrime[0].Length)); + Host.Assert(theta.Length == numLeaf - 1); + Host.Assert(theta.All(v => v.IsDense)); + Host.Assert(theta.All(v => v.Length == theta[0].Length)); + Host.Assert(biasW.Length == numLeaf * 2 - 1); + Host.Assert(biasTheta.Length == numLeaf - 1); + Host.Assert(biasThetaPrime.Length == numLeaf * 2 - 1); + Host.Assert((w[0].Length > 0) && (w[0].Length == thetaPrime[0].Length) && (w[0].Length == theta[0].Length)); + + _numLeaf = numLeaf; + _sigma = sigma; + _w = w; + _thetaPrime = thetaPrime; + _theta = theta; + _biasW = biasW; + _biasTheta = biasTheta; + _biasThetaPrime = biasThetaPrime; + + InputType = new VectorDataViewType(NumberDataViewType.Single, _w[0].Length); + + AssertValid(); + } + + private LdSvmModelParameters(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + // *** Binary format *** + // int: _numLeaf + // int: numFeatures + // float: _sigma + // (_numLeaf * 2 - 1) times: a vector in _w + // float[numFeatures] + // (_numLeaf * 2 - 1) times: a vector in _thetaPrime + // float[numFeatures] + // (_numLeaf - 1) times: a vector in _theta + // float[numFeatures] + // float[_numLeaf * 2 - 1]: _biasW + // float[_numLeaf - 1]: _biasTheta + // float[_numLeaf * 2 - 1]: _biasThetaPrime + + _numLeaf = ctx.Reader.ReadInt32(); + Host.CheckDecode(_numLeaf > 1 && (_numLeaf & (_numLeaf - 1)) == 0); + int numFeatures = ctx.Reader.ReadInt32(); + Host.CheckDecode(numFeatures > 0); + + _sigma = ctx.Reader.ReadFloat(); + + _w = LoadVBufferArray(ctx, _numLeaf * 2 - 1, numFeatures); + _thetaPrime = LoadVBufferArray(ctx, _numLeaf * 2 - 1, numFeatures); + _theta = LoadVBufferArray(ctx, _numLeaf - 1, numFeatures); + _biasW = ctx.Reader.ReadFloatArray(_numLeaf * 2 - 1); + _biasTheta = ctx.Reader.ReadFloatArray(_numLeaf - 1); + _biasThetaPrime = ctx.Reader.ReadFloatArray(_numLeaf * 2 - 1); + WarnOnOldNormalizer(ctx, GetType(), Host); + + InputType = new VectorDataViewType(NumberDataViewType.Single, numFeatures); + + AssertValid(); + } + + private void AssertValid() + { + Host.Assert(_numLeaf > 1 && (_numLeaf & (_numLeaf - 1)) == 0); // Check if _numLeaf is power of 2 + Host.Assert(_w.Length == _numLeaf * 2 - 1); + Host.Assert(_w.All(v => v.IsDense)); + Host.Assert(_w.All(v => v.Length == _w[0].Length)); + Host.Assert(_thetaPrime.Length == _numLeaf * 2 - 1); + Host.Assert(_thetaPrime.All(v => v.IsDense)); + Host.Assert(_thetaPrime.All(v => v.Length == _thetaPrime[0].Length)); + Host.Assert(_theta.Length == _numLeaf - 1); + Host.Assert(_theta.All(v => v.IsDense)); + Host.Assert(_theta.All(v => v.Length == _theta[0].Length)); + Host.Assert(_biasW.Length == _numLeaf * 2 - 1); + Host.Assert(_biasTheta.Length == _numLeaf - 1); + Host.Assert(_biasThetaPrime.Length == _numLeaf * 2 - 1); + Host.Assert((_w[0].Length > 0) && (_w[0].Length == _thetaPrime[0].Length) && (_w[0].Length == _theta[0].Length)); // numFeatures + Host.Assert(InputType != null && InputType.GetVectorSize() == _w[0].Length); + } + + /// + /// Create method to instantiate a predictor. + /// + private static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new LdSvmModelParameters(env, ctx); + } + + private protected override PredictionKind PredictionKind { get { return PredictionKind.BinaryClassification; } } + + /// + /// Save the predictor in binary format. + /// + private protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: _numLeaf + // int: numFeatures + // float: _sigma + // (_numLeaf * 2 - 1) times: a vector in _w + // float[numFeatures] + // (_numLeaf * 2 - 1) times: a vector in _thetaPrime + // float[numFeatures] + // (_numLeaf - 1) times: a vector in _theta + // float[numFeatures] + // float[_numLeaf * 2 - 1]: _biasW + // float[_numLeaf - 1]: _biasTheta + // float[_numLeaf * 2 - 1]: _biasThetaPrime + + int numFeatures = _w[0].Length; + + ctx.Writer.Write(_numLeaf); + ctx.Writer.Write(numFeatures); + ctx.Writer.Write(_sigma); + + Host.Assert(_w.Length == _numLeaf * 2 - 1); + SaveVBufferArray(ctx, _w); + Host.Assert(_thetaPrime.Length == _numLeaf * 2 - 1); + SaveVBufferArray(ctx, _thetaPrime); + Host.Assert(_theta.Length == _numLeaf - 1); + SaveVBufferArray(ctx, _theta); + + Host.Assert(_biasW.Length == _numLeaf * 2 - 1); + ctx.Writer.WriteSinglesNoCount(_biasW.AsSpan()); + Host.Assert(_biasTheta.Length == _numLeaf - 1); + ctx.Writer.WriteSinglesNoCount(_biasTheta.AsSpan()); + Host.Assert(_biasThetaPrime.Length == _numLeaf * 2 - 1); + ctx.Writer.WriteSinglesNoCount(_biasThetaPrime.AsSpan()); + } + + /// + /// Save an array of in binary format. The vectors must be dense. + /// + /// The context where we will save the vectors. + /// An array of vectors. + private void SaveVBufferArray(ModelSaveContext ctx, VBuffer[] data) + { + if (data.Length == 0) + return; + + int vectorLength = data[0].Length; + for (int i = 0; i < data.Length; i++) + { + var vector = data[i]; + Host.Assert(vector.IsDense); + Host.Assert(vector.Length == vectorLength); + ctx.Writer.WriteSinglesNoCount(vector.GetValues()); + } + } + + /// + /// Load an array of from binary format. + /// + /// The context from which to read the vectors. + /// The length of the array of vectors. + /// The length of each vector. + /// An array of vectors. + private VBuffer[] LoadVBufferArray(ModelLoadContext ctx, int length, int vectorLength) + { + Host.Assert(length >= 0); + Host.Assert(vectorLength >= 0); + + VBuffer[] result = new VBuffer[length]; + + for (int i = 0; i < length; i++) + { + result[i] = new VBuffer(vectorLength, ctx.Reader.ReadFloatArray(vectorLength)); + Host.Assert(result[i].IsDense); + Host.Assert(result[i].Length == vectorLength); + } + return result; + } + + /// + /// Compute Margin. + /// + private float Margin(in VBuffer src) + { + Double score = 0; + Double childIndicator; + int current = 0; + while (current < _numLeaf - 1) + { + score += Math.Tanh(_sigma * (VectorUtils.DotProduct(in _thetaPrime[current], in src) + _biasThetaPrime[current])) * + (VectorUtils.DotProduct(in _w[current], in src) + _biasW[current]); + childIndicator = VectorUtils.DotProduct(in _theta[current], in src) + _biasTheta[current]; + current = (childIndicator > 0) ? 2 * current + 1 : 2 * current + 2; + } + score += Math.Tanh(_sigma * (VectorUtils.DotProduct(in _thetaPrime[current], in src) + _biasThetaPrime[current])) * + (VectorUtils.DotProduct(in _w[current], in src) + _biasW[current]); + return (float)score; + } + + public DataViewType InputType { get; } + + public DataViewType OutputType => NumberDataViewType.Single; + + ValueMapper IValueMapper.GetMapper() + { + Host.Check(typeof(TIn) == typeof(VBuffer)); + Host.Check(typeof(TOut) == typeof(float)); + + ValueMapper, float> del = + (in VBuffer src, ref float dst) => + { + Host.Check(src.Length == InputType.GetVectorSize()); + dst = Margin(in src); + }; + return (ValueMapper)(Delegate)del; + } + } +} diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs new file mode 100644 index 0000000000..4368acf1bd --- /dev/null +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs @@ -0,0 +1,489 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML; +using Microsoft.ML.Calibrators; +using Microsoft.ML.CommandLine; +using Microsoft.ML.Data; +using Microsoft.ML.EntryPoints; +using Microsoft.ML.Internal.Internallearn; +using Microsoft.ML.Internal.Utilities; +using Microsoft.ML.Numeric; +using Microsoft.ML.Runtime; +using Microsoft.ML.Trainers; + +[assembly: LoadableClass(LdSvmTrainer.Summary, typeof(LdSvmTrainer), typeof(LdSvmTrainer.Options), + new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, + LdSvmTrainer.UserNameValue, + LdSvmTrainer.LoadNameValue + )] + +[assembly: LoadableClass(typeof(void), typeof(LdSvmTrainer), null, typeof(SignatureEntryPointModule), LdSvmTrainer.LoadNameValue)] + +namespace Microsoft.ML.Trainers +{ + /// + /// Non-Linear SVM that implements Local Deep SVM based on paper : + /// C. Jose, P. Goyal, P. Aggrwal, and M. Varma, Local deep + /// kernel learning for efficient non-linear svm prediction, in ICML, 2013. + /// http://research.microsoft.com/en-us/um/people/manik/code/LDKL/download.html + /// + public sealed class LdSvmTrainer : TrainerEstimatorBase, LdSvmModelParameters> + { + internal const string LoadNameValue = "LDSVM"; + internal const string UserNameValue = "Local Deep SVM (LDSVM)"; + internal const string Summary = "LD-SVM learns a binary, non-linear SVM classifier with a kernel that is specifically designed to reduce prediction time. " + + "LD-SVM learns decision boundaries that are locally linear."; + + public sealed class Options : TrainerInputBaseWithWeight + { + /// + /// Depth of LDSVM Tree + /// + [Argument(ArgumentType.AtMostOnce, HelpText = "Depth of Local Deep SVM tree", ShortName = "depth", SortOrder = 50)] + [TGUI(SuggestedSweeps = "1,3,5,7")] + [TlcModule.SweepableDiscreteParam("TreeDepth", new object[] { 1, 3, 5, 7 })] + public int TreeDepth = Defaults.TreeDepth; + + /// + /// Regularizer for classifier parameter W + /// + [Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for classifier parameter W", ShortName = "lw", SortOrder = 50)] + [TGUI(SuggestedSweeps = "0.1,0.01,0.001")] + [TlcModule.SweepableDiscreteParam("LambdaW", new object[] { 0.1f, 0.01f, 0.001f })] + public float LambdaW = Defaults.LambdaW; + + /// + /// Regularizer for kernel parameter Theta + /// + [Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for kernel parameter Theta", ShortName = "lt", SortOrder = 50)] + [TGUI(SuggestedSweeps = "0.1,0.01,0.001")] + [TlcModule.SweepableDiscreteParam("LambdaTheta", new object[] { 0.1f, 0.01f, 0.001f })] + public float LambdaTheta = Defaults.LambdaTheta; + + /// + /// Regularizer for kernel parameter ThetaPrime + /// + [Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for kernel parameter Thetaprime", ShortName = "lp", SortOrder = 50)] + [TGUI(SuggestedSweeps = "0.1,0.01,0.001")] + [TlcModule.SweepableDiscreteParam("LambdaThetaprime", new object[] { 0.1f, 0.01f, 0.001f })] + public float LambdaThetaprime = Defaults.LambdaThetaprime; + + /// + /// Parameter for sigmoid sharpness + /// + [Argument(ArgumentType.AtMostOnce, HelpText = "Parameter for sigmoid sharpness", ShortName = "s", SortOrder = 50)] + [TGUI(SuggestedSweeps = "1.0,0.1,0.01")] + [TlcModule.SweepableDiscreteParam("Sigma", new object[] { 1.0f, 0.1f, 0.01f })] + public float Sigma = Defaults.Sigma; + + /// + /// Indicates if we should use Bias or not in our model. + /// + [Argument(ArgumentType.AtMostOnce, HelpText = "No bias", ShortName = "noBias")] + [TlcModule.SweepableDiscreteParam("NoBias", null, isBool: true)] + public bool NoBias = Defaults.NoBias; + + /// + /// Number of iterations + /// + [Argument(ArgumentType.AtMostOnce, + HelpText = "Number of iterations", ShortName = "iter,NumIterations", SortOrder = 50)] + [TGUI(SuggestedSweeps = "10000,15000")] + [TlcModule.SweepableDiscreteParam("NumIterations", new object[] { 10000, 15000 })] + public int NumberOfIterations = Defaults.NumberOfIterations; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] + internal ICalibratorTrainerFactory Calibrator = new PlattCalibratorTrainerFactory(); + + [Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] + public int MaxCalibrationExamples = 1000000; + + internal class Defaults + { + public const int NumberOfIterations = 15000; + public const bool NoBias = false; + public const float Sigma = 1.0f; + public const float LambdaThetaprime = 0.01f; + public const float LambdaTheta = 0.01f; + public const float LambdaW = 0.1f; + public const int TreeDepth = 3; + } + } + + private Options _options; + + internal LdSvmTrainer(IHostEnvironment env, Options options) + : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), + TrainerUtils.MakeR4VecFeature(options.FeatureColumnName), + TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName), + TrainerUtils.MakeR4ScalarWeightColumn(options.ExampleWeightColumnName)) + { + Host.CheckValue(options, nameof(options)); + CheckOptions(Host, options); + _options = options; + } + + // REVIEW: This does not need caching, but only because it's very + // badly written and the first thing is it just grabs all instances. If this + // ever changes, do review this return value to make sure it is still + // appropriate. + private static TrainerInfo _info = new TrainerInfo(calibration: true, caching: false); + public override TrainerInfo Info => _info; + + private protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + + private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) + { + return new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberDataViewType.Single, false, new SchemaShape(AnnotationUtils.GetTrainerOutputAnnotation())), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BooleanDataViewType.Instance, false, new SchemaShape(AnnotationUtils.GetTrainerOutputAnnotation())) + }; + } + + private protected override LdSvmModelParameters TrainModelCore(TrainContext trainContext) + { + Host.CheckValue(trainContext, nameof(trainContext)); + using (var ch = Host.Start("Training")) + { + trainContext.TrainingSet.CheckFeatureFloatVector(out var numFeatures); + trainContext.TrainingSet.CheckBinaryLabel(); + + var numLeaf = 1 << _options.TreeDepth; + return TrainCore(ch, trainContext.TrainingSet, numLeaf, numFeatures); + } + } + + /// + /// Compute gradient w.r.t theta for an instance X + /// + private void ComputeGradTheta(in VBuffer feat, float[] gradTheta, int numLeaf, float gamma, + VBuffer[] theta, float[] biasTheta, float[] pathWt, float[] localWt, VBuffer[] w, float[] biasW) + { + Array.Clear(gradTheta, 0, numLeaf - 1); + int numNodes = 2 * numLeaf - 1; + float[] tanhThetaTx = new float[numLeaf - 1]; + for (int i = 0; i < numLeaf - 1; i++) + tanhThetaTx[i] = (float)Math.Tanh(gamma * (VectorUtils.DotProduct(in feat, in theta[i]) + biasTheta[i])); + for (int i = 0; i < numNodes; i++) + { + int current = i; + float tempGrad = pathWt[i] * localWt[i] * (VectorUtils.DotProduct(in feat, in w[i]) + biasW[i]); + while (current > 0) + { + int parent = (current - 1) / 2; + gradTheta[parent] += tempGrad * (current % 2 == 1 ? (1 - tanhThetaTx[parent]) : (-1 - tanhThetaTx[parent])); + current = parent; + } + } + } + + /// + /// Adaptively update gamma for indicator function approximation. + /// + private void UpdateGamma(int iter, int numLeaf, ref float gamma, VBuffer[] data, VBuffer[] theta, float[] biasTheta) + { + if (numLeaf == 1) + gamma = 1.0f; + else + { + float tempSum = 0; + for (int idx = 0; idx < 100; idx++) + { + int thetaIdx = Host.Rand.Next(numLeaf - 1); + int instIdx = Host.Rand.Next(data.Length); + tempSum += Math.Abs(VectorUtils.DotProduct(in data[instIdx], in theta[thetaIdx]) + biasTheta[thetaIdx]); + } + tempSum /= 100.0f; + gamma = 0.1f / tempSum; + gamma *= (float)Math.Pow(2.0, iter / (_options.NumberOfIterations / 10.0)); + } + } + + /// + /// Main LDSVM training routine. + /// + private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, int numLeaf, int numFeatures) + { + int t = 1; + int numNodes = 2 * numLeaf - 1; + + var w = new VBuffer[numNodes]; + var thetaPrime = new VBuffer[numNodes]; + var theta = new VBuffer[numLeaf - 1]; + var biasW = new float[numNodes]; + var biasTheta = new float[numLeaf - 1]; + var biasThetaPrime = new float[numNodes]; + + var tempW = new VBuffer[numNodes]; + var tempThetaPrime = new VBuffer[numNodes]; + var tempTheta = new VBuffer[numLeaf - 1]; + var tempBiasW = new float[numNodes]; + var tempBiasTheta = new float[numLeaf - 1]; + var tempBiasThetaPrime = new float[numNodes]; + + InitClassifierParam(numLeaf, numFeatures, tempW, w, theta, thetaPrime, biasW, + biasTheta, biasThetaPrime, tempThetaPrime, tempTheta, tempBiasW, tempBiasTheta, tempBiasThetaPrime); + + var gamma = 0.01f; + VBuffer[] data = GetData(ch, trainingData, out var labels); + var pathWt = new float[numNodes]; + var localWt = new float[numNodes]; + var gradTheta = new float[numLeaf - 1]; + var wDotX = new float[numNodes]; + + int[] indices = Utils.GetIdentityPermutation(data.Length); + // Number of samples processed in each iteration + int sampleSize = Math.Max(1, (int)Math.Sqrt(indices.Length)); + for (int iter = 1; iter <= _options.NumberOfIterations; iter++) + { + // Update gamma adaptively + if (iter % 100 == 1) + UpdateGamma(iter, numLeaf, ref gamma, data, theta, biasTheta); + + // Select random subset of data - the first sampleSize indices will be + // our subset. + // REVIEW: Shuffling and streaming needed here. + for (int k = 0; k < sampleSize; k++) + { + int randIdx = k + Host.Rand.Next(indices.Length - k); + Utils.Swap(ref indices[k], ref indices[randIdx]); + } + t++; + + // Update learning rate + float etaTW = (float)1.0 / (_options.LambdaW * (float)Math.Sqrt(t)); + float etaTTheta = (float)1.0 / (_options.LambdaTheta * (float)Math.Sqrt(t)); + float etaTThetaPrime = (float)1.0 / (_options.LambdaThetaprime * (float)Math.Sqrt(t)); + float coef = (t - 1) / (float)t; + + // Update classifier parameters + for (int i = 0; i < tempW.Length; ++i) + VectorUtils.ScaleBy(ref tempW[i], coef); + for (int i = 0; i < tempTheta.Length; ++i) + VectorUtils.ScaleBy(ref tempTheta[i], coef); + for (int i = 0; i < tempThetaPrime.Length; ++i) + VectorUtils.ScaleBy(ref tempThetaPrime[i], coef); + + for (int i = 0; i < numNodes; i++) + { + tempBiasW[i] *= coef; + tempBiasThetaPrime[i] *= coef; + } + for (int i = 0; i < numLeaf - 1; i++) + tempBiasTheta[i] *= coef; + + for (int workingId = 0; workingId < sampleSize; workingId++) + { + int index = indices[workingId]; + float trueLabel = labels[index]; + + // Compute path weight + for (int i = 0; i < numNodes; i++) + pathWt[i] = 1; + float tanhDist; + for (int i = 0; i < numLeaf - 1; i++) + { + tanhDist = (float)Math.Tanh(gamma * (VectorUtils.DotProduct(in data[index], in theta[i]) + biasTheta[i])); + pathWt[2 * i + 1] = pathWt[i] * (1 + tanhDist) / (float)2.0; + pathWt[2 * i + 2] = pathWt[i] * (1 - tanhDist) / (float)2.0; + } + + // Compute local weight + for (int l = 0; l < numNodes; l++) + localWt[l] = (float)Math.Tanh(_options.Sigma * (VectorUtils.DotProduct(in data[index], in thetaPrime[l]) + biasThetaPrime[l])); + + // Make prediction + float yPredicted = 0; + for (int l = 0; l < numNodes; l++) + { + wDotX[l] = VectorUtils.DotProduct(in data[index], in w[l]) + biasW[l]; + yPredicted += pathWt[l] * localWt[l] * wDotX[l]; + } + float loss = 1 - trueLabel * yPredicted; + + // If wrong prediction update classifier parameters + if (loss > 0) + { + // Compute gradient w.r.t current instance + ComputeGradTheta(in data[index], gradTheta, numLeaf, gamma, theta, biasTheta, pathWt, localWt, w, biasW); + + // Check if bias is used ot not + int biasUpdateMult = _options.NoBias ? 0 : 1; + + // Update W + for (int l = 0; l < numNodes; l++) + { + float tempGradW = trueLabel * etaTW / sampleSize * pathWt[l] * localWt[l]; + VectorUtils.AddMult(in data[index], tempGradW, ref tempW[l]); + tempBiasW[l] += biasUpdateMult * tempGradW; + } + + // Update ThetaPrime + for (int l = 0; l < numNodes; l++) + { + float tempGradThetaPrime = (1 - localWt[l] * localWt[l]) * trueLabel * etaTThetaPrime / sampleSize * pathWt[l] * wDotX[l]; + VectorUtils.AddMult(in data[index], tempGradThetaPrime, ref tempThetaPrime[l]); + tempBiasThetaPrime[l] += biasUpdateMult * tempGradThetaPrime; + } + + // Update Theta + for (int m = 0; m < numLeaf - 1; m++) + { + float tempGradTheta = trueLabel * etaTTheta / sampleSize * gradTheta[m]; + VectorUtils.AddMult(in data[index], tempGradTheta, ref tempTheta[m]); + tempBiasTheta[m] += biasUpdateMult * tempGradTheta; + } + } + } + + // Copy solution + for (int i = 0; i < numNodes; i++) + { + tempW[i].CopyTo(ref w[i]); + biasW[i] = tempBiasW[i]; + + tempThetaPrime[i].CopyTo(ref thetaPrime[i]); + biasThetaPrime[i] = tempBiasThetaPrime[i]; + } + for (int i = 0; i < numLeaf - 1; i++) + { + tempTheta[i].CopyTo(ref theta[i]); + biasTheta[i] = tempBiasTheta[i]; + } + } + return new LdSvmModelParameters(Host, w, thetaPrime, theta, _options.Sigma, biasW, biasTheta, + biasThetaPrime, _options.TreeDepth); + } + + /// + /// Inititlize classifier parameters + /// + private void InitClassifierParam(int numLeaf, int numFeatures, VBuffer[] tempW, VBuffer[] w, + VBuffer[] theta, VBuffer[] thetaPrime, float[] biasW, float[] biasTheta, + float[] biasThetaPrime, VBuffer[] tempThetaPrime, VBuffer[] tempTheta, + float[] tempBiasW, float[] tempBiasTheta, float[] tempBiasThetaPrime) + { + int count = 2 * numLeaf - 1; + int half = numLeaf - 1; + + Host.Assert(Utils.Size(tempW) == count); + Host.Assert(Utils.Size(w) == count); + Host.Assert(Utils.Size(theta) == half); + Host.Assert(Utils.Size(thetaPrime) == count); + Host.Assert(Utils.Size(biasW) == count); + Host.Assert(Utils.Size(biasTheta) == half); + Host.Assert(Utils.Size(biasThetaPrime) == count); + Host.Assert(Utils.Size(tempThetaPrime) == count); + Host.Assert(Utils.Size(tempTheta) == half); + Host.Assert(Utils.Size(tempBiasW) == count); + Host.Assert(Utils.Size(tempBiasTheta) == half); + Host.Assert(Utils.Size(tempBiasThetaPrime) == count); + + for (int i = 0; i < count; i++) + { + VBufferEditor thetaInit = default; + if (i < numLeaf - 1) + thetaInit = VBufferEditor.Create(ref theta[i], numFeatures); + var wInit = VBufferEditor.Create(ref w[i], numFeatures); + var thetaPrimeInit = VBufferEditor.Create(ref thetaPrime[i], numFeatures); + for (int j = 0; j < numFeatures; j++) + { + wInit.Values[j] = 2 * Host.Rand.NextSingle() - 1; + thetaPrimeInit.Values[j] = 2 * Host.Rand.NextSingle() - 1; + if (i < numLeaf - 1) + thetaInit.Values[j] = 2 * Host.Rand.NextSingle() - 1; + } + + w[i] = wInit.Commit(); + w[i].CopyTo(ref tempW[i]); + thetaPrime[i] = thetaPrimeInit.Commit(); + thetaPrime[i].CopyTo(ref tempThetaPrime[i]); + + if (!_options.NoBias) + { + float bW = 2 * Host.Rand.NextSingle() - 1; + biasW[i] = bW; + tempBiasW[i] = bW; + float bTP = 2 * Host.Rand.NextSingle() - 1; + biasThetaPrime[i] = bTP; + tempBiasThetaPrime[i] = bTP; + } + + if (i >= half) + continue; + + theta[i] = thetaInit.Commit(); + theta[i].CopyTo(ref tempTheta[i]); + + if (!_options.NoBias) + { + float bT = 2 * Host.Rand.NextSingle() - 1; + biasTheta[i] = bT; + tempBiasTheta[i] = bT; + } + } + } + + /// + /// Initialization of model. + /// + private static void CheckOptions(IExceptionContext ectx, Options options) + { + ectx.AssertValue(options); + + ectx.CheckUserArg(options.TreeDepth >= 0, nameof(options.TreeDepth), "Tree depth can not be negative."); + ectx.CheckUserArg(options.TreeDepth <= 24, nameof(options.TreeDepth), "Try running with a tree of smaller depth first and cross validate over other parameters."); + ectx.CheckUserArg(options.LambdaW > 0, nameof(options.LambdaW), "Regularizer for W must be positive and non-zero."); + ectx.CheckUserArg(options.LambdaTheta > 0, nameof(options.LambdaTheta), "Regularizer for Theta must be positive and non-zero."); + ectx.CheckUserArg(options.LambdaThetaprime > 0, nameof(options.LambdaThetaprime), "Regularizer for Thetaprime must be positive and non-zero."); + } + + private VBuffer[] GetData(IChannel ch, RoleMappedData trainingData, out IList labels) + { + // Load the features and labels into our own arrays. Note we toss out rows that have bad + // label or feature values. + long numBad; + using (var cursor = new FloatLabelCursor(trainingData, CursOpt.Label | CursOpt.Features)) + { + var data = new List>(); + labels = new List(); + while (cursor.MoveNext()) + { + var features = default(VBuffer); + cursor.Features.CopyTo(ref features); + data.Add(features); + labels.Add(cursor.Label > 0 ? 1 : -1); + } + ch.Assert(data.Count == cursor.KeptRowCount); + numBad = cursor.SkippedRowCount; + ch.Check(data.Count > 0, NoTrainingInstancesMessage); + + if (numBad > 0) + ch.Warning("Skipped {0} rows with missing feature/label values", numBad); + return data.ToArray(); + } + } + + private protected override BinaryPredictionTransformer MakeTransformer(LdSvmModelParameters model, DataViewSchema trainSchema) + => new BinaryPredictionTransformer(Host, model, trainSchema, _options.FeatureColumnName); + + [TlcModule.EntryPoint(Name = "Trainers.LocalDeepSvmBinaryClassifier", Desc = Summary, UserName = LdSvmTrainer.UserNameValue, ShortName = LoadNameValue)] + internal static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, Options input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainLDSVM"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return TrainerEntryPointsUtils.Train(host, input, + () => new LdSvmTrainer(host, input), + () => TrainerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumnName), + calibrator: input.Calibrator, maxCalibrationExamples: input.MaxCalibrationExamples); + } + } +} diff --git a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs index 3127f61116..77934dc549 100644 --- a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs +++ b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs @@ -873,5 +873,32 @@ public static PriorTrainer Prior(this BinaryClassificationCatalog.BinaryClassifi Contracts.CheckValue(catalog, nameof(catalog)); return new PriorTrainer(CatalogUtils.GetEnvironment(catalog), labelColumnName, exampleWeightColumnName); } + + /// + /// + /// + /// + /// + /// + public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, LdSvmTrainer.Options options) + => new LdSvmTrainer(catalog.GetEnvironment(), options); + + public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, + string labelColumnName = DefaultColumnNames.Label, + string featureColumnName = DefaultColumnNames.Features, + string exampleWeightColumnName = null, + int numberOfIterations = LdSvmTrainer.Options.Defaults.NumberOfIterations, + int treeDepth = LdSvmTrainer.Options.Defaults.TreeDepth, + bool noBias = LdSvmTrainer.Options.Defaults.NoBias) + { + Contracts.CheckValue(catalog, nameof(catalog)); + var options = new LdSvmTrainer.Options() + { + NumberOfIterations = numberOfIterations, + TreeDepth = treeDepth, + NoBias = noBias + }; + return new LdSvmTrainer(catalog.GetEnvironment(), options); + } } } diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..72f9392749 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt @@ -0,0 +1,56 @@ +maml.exe CV tr=LDSVM{iter=1000} threads=- dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +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 || 131 | 3 | 0.9776 + negative || 9 | 211 | 0.9591 + ||====================== +Precision || 0.9357 | 0.9860 | +OVERALL 0/1 ACCURACY: 0.966102 +LOG LOSS/instance: 0.127230 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.867053 +AUC: 0.994573 +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 || 99 | 6 | 0.9429 + negative || 4 | 220 | 0.9821 + ||====================== +Precision || 0.9612 | 0.9735 | +OVERALL 0/1 ACCURACY: 0.969605 +LOG LOSS/instance: 0.180232 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.800508 +AUC: 0.984439 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.989506 (0.0051) +Accuracy: 0.967853 (0.0018) +Positive precision: 0.948440 (0.0127) +Positive recall: 0.960235 (0.0174) +Negative precision: 0.979716 (0.0063) +Negative recall: 0.970617 (0.0115) +Log-loss: 0.153731 (0.0265) +Log-loss reduction: 0.833781 (0.0333) +F1 Score: 0.954064 (0.0021) +AUPRC: 0.986694 (0.0033) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..633e1d2f9b --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LDSVM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.989506 0.967853 0.94844 0.960235 0.979716 0.970617 0.153731 0.833781 0.954064 0.986694 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt new file mode 100644 index 0000000000..dbd892c2f3 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 3.87089658 0.9997447 0.00036834786597783083 1 +6 0 -0.442875654 0.20916079 0.33854369259820988 0 +8 0 -1.8717339 0.0108707342 0.01576902092083874 0 +9 0 -1.94650865 0.009219195 0.013362175896124175 0 +10 0 -1.95956779 0.00895741 0.012981036741083977 0 +11 0 -2.008946 0.008032486 0.011635220173986917 0 +18 1 2.300914 0.991656661 0.012087388754342216 1 +20 1 2.28608131 0.991379 0.01249136292362499 1 +21 1 2.61776853 0.9958615 0.0059830247174293295 1 +25 1 0.559777558 0.7113602 0.49134780305914622 1 +28 0 -2.008946 0.008032486 0.011635220173986917 0 +31 0 -1.9545244 0.009057625 0.013126930515922263 0 +32 1 2.75256538 0.996931 0.0044344154150017428 1 +35 0 -2.008946 0.008032486 0.011635220173986917 0 +37 0 -1.01232767 0.0692904741 0.10359712137837948 0 +40 0 ? ? ? 0 +41 1 1.30429935 0.928200364 0.10749183144119918 1 +44 1 3.01085234 0.9982707 0.0024970260130386225 1 +45 0 -1.97296607 0.00869648252 0.012601245219913204 0 +46 1 1.51603508 0.953942358 0.068026000173882703 1 +48 0 -1.84630322 0.01149661 0.016682181319322233 0 +50 1 1.02722669 0.8746354 0.19324635629531742 1 +51 1 -0.01959964 0.4042607 1.306642153244324 0 +52 1 1.714713 0.9699092 0.044078415254491798 1 +54 1 2.6763165 0.9963653 0.0052533038875894741 1 +56 1 1.69709659 0.9687434 0.045813542588316672 1 +60 1 0.9083496 0.842632949 0.24702376439224341 1 +63 1 0.391953558 0.629111946 0.66861133846008858 1 +64 0 -2.01334238 0.007954879 0.011522354569203365 0 +66 0 -1.89687657 0.0102851018 0.01491509922504613 0 +68 1 3.49144816 0.99940604 0.00085715814207834107 1 +69 0 -1.96029186 0.008943113 0.012960223286032432 0 +70 0 -1.67731035 0.016659949 0.024237690435589952 0 +71 1 2.99234319 0.9981981 0.0026019486998781335 1 +72 0 -0.893330038 0.08844742 0.13360222179755574 0 +73 1 2.587241 0.995571733 0.0064028267325765449 1 +74 1 0.983624935 0.8636002 0.21156452667971334 1 +76 0 -1.81019115 0.0124469893 0.018069902443258078 0 +77 0 -1.81268167 0.0123790251 0.01797061842111378 0 +79 0 -2.014863 0.007928209 0.011483570822696796 0 +82 0 -1.79172945 0.0129624158 0.018823074556416731 0 +88 0 -1.89687657 0.0102851018 0.01491509922504613 0 +90 0 -1.96793783 0.008793511 0.012742463300599671 0 +91 0 -2.0349524 0.00758409547 0.010983238730785416 0 +92 0 -1.89687657 0.0102851018 0.01491509922504613 0 +93 0 -2.01334238 0.007954879 0.011522354569203365 0 +95 0 -1.96793783 0.008793511 0.012742463300599671 0 +96 0 -2.04112387 0.00748139 0.010833941655161322 0 +97 0 -1.84883642 0.0114327008 0.016588910489907162 0 +98 1 3.02338338 0.9983182 0.0024283738364491037 1 +99 1 3.28097486 0.999051452 0.0013691153916687629 1 +100 1 1.97365057 0.98286587 0.0249335469793623 1 +102 0 -1.78309751 0.0132105807 0.01918584822229475 0 +104 1 4.183325 0.9998726 0.00018377516688790579 1 +105 1 1.02462447 0.8739988 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b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt @@ -0,0 +1,38 @@ +maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 16 rows with missing feature/label values +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: 0.110363 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.881839 +AUC: 0.996240 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996240 (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: 0.110363 (0.0000) +Log-loss reduction: 0.881839 (0.0000) +F1 Score: 0.962656 (0.0000) +AUPRC: 0.992024 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..5c4106bf05 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LDSVM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.99624 0.973646 0.954733 0.970711 0.984091 0.975225 0.110363 0.881839 0.962656 0.992024 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt 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b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt @@ -0,0 +1,56 @@ +maml.exe CV tr=LDSVM{iter=1000 noBias=+} threads=- dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +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 || 119 | 15 | 0.8881 + negative || 28 | 192 | 0.8727 + ||====================== +Precision || 0.8095 | 0.9275 | +OVERALL 0/1 ACCURACY: 0.878531 +LOG LOSS/instance: 0.453154 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.526484 +AUC: 0.923338 +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 || 105 | 0 | 1.0000 + negative || 100 | 124 | 0.5536 + ||====================== +Precision || 0.5122 | 1.0000 | +OVERALL 0/1 ACCURACY: 0.696049 +LOG LOSS/instance: 0.114308 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.873477 +AUC: 0.997024 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.960181 (0.0368) +Accuracy: 0.787290 (0.0912) +Positive precision: 0.660859 (0.1487) +Positive recall: 0.944030 (0.0560) +Negative precision: 0.963768 (0.0362) +Negative recall: 0.713149 (0.1596) +Log-loss: 0.283731 (0.1694) +Log-loss reduction: 0.699981 (0.1735) +F1 Score: 0.762197 (0.0848) +AUPRC: 0.952368 (0.0411) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..151cf25d30 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LDSVM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /noBias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.960181 0.78729 0.660859 0.94403 0.963768 0.713149 0.283731 0.699981 0.762197 0.952368 + 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000 noBias=+} threads=- dout=%Output% data=%Data% seed=1 /noBias:+;/iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt new file mode 100644 index 0000000000..42ee8c76c5 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 3.90834475 0.993382335 0.0095790032904714915 1 +6 0 2.76807952 0.956745744 4.5310140878590426 1 +8 0 -1.0917176 0.0327487849 0.048037458801135045 0 +9 0 -0.700886548 0.0612697564 0.091217454731480227 0 +10 0 -0.225876167 0.126582116 0.19525602283184829 0 +11 0 -0.5846785 0.07350296 0.1101417243988199 0 +18 1 2.45982862 0.929482937 0.10549971401874002 1 +20 1 1.51792252 0.7304611 0.45312060844641111 1 +21 1 1.446458 0.706189334 0.50187306270966781 1 +25 1 2.53360128 0.937183738 0.093596174472086111 1 +28 0 -0.5846785 0.07350296 0.1101417243988199 0 +31 0 -0.742950141 0.05733054 0.085176107559313485 0 +32 1 -0.70969677 0.06042428 4.0487278262709028 0 +35 0 -0.5846785 0.07350296 0.1101417243988199 0 +37 0 -1.6407162 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b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..6df3140fce --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -0,0 +1,38 @@ +maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 noBias=+} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 16 rows with missing feature/label values +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 || 24 | 420 | 0.9459 + ||====================== +Precision || 0.9040 | 0.9700 | +OVERALL 0/1 ACCURACY: 0.945827 +LOG LOSS/instance: 0.288451 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.691167 +AUC: 0.966876 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.966876 (0.0000) +Accuracy: 0.945827 (0.0000) +Positive precision: 0.904000 (0.0000) +Positive recall: 0.945607 (0.0000) +Negative precision: 0.969977 (0.0000) +Negative recall: 0.945946 (0.0000) +Log-loss: 0.288451 (0.0000) +Log-loss reduction: 0.691167 (0.0000) +F1 Score: 0.924335 (0.0000) +AUPRC: 0.955880 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..3b8484a8a7 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LDSVM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /noBias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.966876 0.945827 0.904 0.945607 0.969977 0.945946 0.288451 0.691167 0.924335 0.95588 + 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 noBias=+} dout=%Output% data=%Data% out=%Output% seed=1 /noBias:+;/iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..9f90e317ff --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1.62977672 0.0105228247 0.015261666521846514 0 +1 0 1.4701674 0.874539435 2.9946941290313571 1 +2 0 -1.1010586 0.0311435722 0.045645202436404499 0 +3 0 2.87714934 0.9924993 7.058757485223742 1 +4 0 -1.46685541 0.0147335446 0.021414155120391815 0 +5 1 3.59101725 0.998305559 0.0024466348242297023 1 +6 0 1.49068952 0.879174948 3.0490084761020446 1 +7 0 -1.150382 0.02817619 0.041233315992808446 0 +8 0 -1.459084 0.0149714295 0.021762524687166988 0 +9 0 -0.9014765 0.04653196 0.068743514857381643 0 +10 0 -0.690697253 0.0705022961 0.10547679355318071 0 +11 0 -1.01742482 0.03687889 0.054210872155484369 0 +12 1 0.6150113 0.5380885 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0.0028806597208092134 1 +671 0 -0.970632851 0.040518 0.059672354434259896 0 +672 0 -1.23895729 0.02352228 0.034340968172283291 0 +673 0 -0.6864443 0.0710876 0.10638554167449506 0 +674 0 -0.879428148 0.0486218072 0.071909138294425995 0 +675 0 -0.73092 0.06518322 0.097244467638423704 0 +676 0 -0.800936162 0.0568061136 0.084373727764603418 0 +677 0 -1.18527269 0.02624482 0.038368995876215568 0 +678 0 -0.6657946 0.07399373 0.1109061290687056 0 +679 0 -0.8025955 0.0566204041 0.084089697377095665 0 +680 1 3.61986446 0.9984046 0.0023034817674392892 1 +681 1 4.70686865 0.9998356 0.00023718357133142257 1 +682 0 -1.41636515 0.0163481776 0.02378035141589235 0 +683 0 -0.6657946 0.07399373 0.1109061290687056 0 +684 0 -0.6657946 0.07399373 0.1109061290687056 0 +685 0 -0.6657946 0.07399373 0.1109061290687056 0 +686 0 -0.6657946 0.07399373 0.1109061290687056 0 +687 0 -0.788898 0.0581706576 0.086462424842380342 0 +688 0 -1.06251276 0.033671 0.049413635491721029 0 +689 0 -1.831625 0.006923316 0.01002296999597155 0 +690 0 -0.661308646 0.07463935 0.11191234406131501 0 +691 1 3.43799663 0.99766773 0.0033686844426583057 1 +692 0 -0.9349546 0.04352141 0.064195422106459635 0 +693 0 -1.31664014 0.0200647358 0.029241648803359555 0 +694 0 -1.0958 0.0314772427 0.046142146785432492 0 +695 0 -0.8025955 0.0566204041 0.084089697377095665 0 +696 1 2.293165 0.9749981 0.036528663028712265 1 +697 1 2.090678 0.9623072 0.055430549725910688 1 +698 1 2.15748262 0.9670617 0.048320158453941157 1 diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 908436292e..4366f0d2ea 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -11,7 +11,7 @@ namespace Microsoft.ML.RunTests { using System.Linq; using System.Runtime.InteropServices; - using Microsoft.ML; + using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.EntryPoints; using Microsoft.ML.Internal.Utilities; @@ -2107,7 +2107,7 @@ public sealed partial class TestPredictors /// ///A test for binary classifiers /// - [Fact(Skip = "Need CoreTLC specific baseline update")] + [Fact] [TestCategory("Binary")] [TestCategory("LDSVM")] public void BinaryClassifierLDSvmTest() @@ -2121,7 +2121,7 @@ public void BinaryClassifierLDSvmTest() /// ///A test for binary classifiers /// - [Fact(Skip = "Need CoreTLC specific baseline update")] + [Fact] [TestCategory("Binary")] [TestCategory("LDSVM")] public void BinaryClassifierLDSvmNoBiasTest() @@ -2132,34 +2132,6 @@ public void BinaryClassifierLDSvmNoBiasTest() Done(); } - /// - ///A test for binary classifiers - /// - [Fact(Skip = "Need CoreTLC specific baseline update")] - [TestCategory("Binary")] - [TestCategory("LDSVM")] - public void BinaryClassifierLDSvmNoNormTest() - { - var binaryPredictors = new[] { TestLearners.LDSvmNoNorm }; - var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest(); - RunAllTests(binaryPredictors, binaryClassificationDatasets); - Done(); - } - - /// - ///A test for binary classifiers - /// - [Fact(Skip = "Need CoreTLC specific baseline update")] - [TestCategory("Binary")] - [TestCategory("LDSVM")] - public void BinaryClassifierLDSvmNoCalibTest() - { - var binaryPredictors = new[] { TestLearners.LDSvmNoCalib }; - var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest(); - RunAllTests(binaryPredictors, binaryClassificationDatasets); - Done(); - } - /// /// A test for field-aware factorization machine. /// diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index 876710da6d..f8e4a4ee7c 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -692,20 +692,6 @@ public static PredictorAndArgs DssmDefault(int qryFeaturesCount, int docFeatures Tag = "LDSVM-nob" }; - public static PredictorAndArgs LDSvmNoNorm = new PredictorAndArgs - { - Trainer = new SubComponent("LDSVM", "iter=1000"), - MamlArgs = new[] { "norm=no" }, - Tag = "LDSVM-non" - }; - - public static PredictorAndArgs LDSvmNoCalib = new PredictorAndArgs - { - Trainer = new SubComponent("LDSVM", "iter=1000"), - MamlArgs = new[] { "cali={}" }, - Tag = "LDSVM-noc" - }; - public static PredictorAndArgs KMeansDefault = new PredictorAndArgs { Trainer = new SubComponent("KM", "nt=1"), diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs index ac4f2a6de3..2af773f6ba 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs @@ -157,6 +157,32 @@ public void TestEstimatorMulticlassNaiveBayesTrainer() Done(); } + [Fact] + public void TestEstimatorLdSvmTrainer() + { + var trainers = new[] { ML.BinaryClassification.Trainers.LdSvm(new LdSvmTrainer.Options() { LambdaTheta = 0.02f, NumberOfIterations = 100 }), + ML.BinaryClassification.Trainers.LdSvm(numberOfIterations: 100) }; + + foreach (var trainer in trainers) + { + (IEstimator pipe, IDataView dataView) = GetBinaryClassificationPipeline(); + var pipeWithTrainer = pipe.AppendCacheCheckpoint(Env).Append(trainer); + TestEstimatorCore(pipeWithTrainer, dataView); + + var transformedDataView = pipe.Fit(dataView).Transform(dataView); + var model = trainer.Fit(transformedDataView); + TestEstimatorCore(pipe, dataView); + + var result = model.Transform(transformedDataView); + var metrics = ML.BinaryClassification.EvaluateNonCalibrated(result); + + Assert.InRange(metrics.Accuracy, 0.7, 1); + Assert.InRange(metrics.AreaUnderRocCurve, 0.9, 1); + } + + Done(); + } + private (IEstimator, IDataView) GetBinaryClassificationPipeline() { var data = new TextLoader(Env, From d23f9247d17086ea858231c68ec6c0962dcf8626 Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Mon, 16 Sep 2019 18:22:10 +0300 Subject: [PATCH 02/11] Add streaming support, address code review comments. --- .../LdSvm/LdSvmModelParameters.cs | 2 +- .../LdSvm/LdSvmTrainer.cs | 134 +- .../StandardTrainersCatalog.cs | 21 +- .../LdSvm/LDSVM-def-CV-breast-cancer-out.txt | 48 +- .../LdSvm/LDSVM-def-CV-breast-cancer-rp.txt | 2 +- .../LdSvm/LDSVM-def-CV-breast-cancer.txt | 1366 ++++++++--------- .../LDSVM-def-TrainTest-breast-cancer-out.txt | 14 +- .../LDSVM-def-TrainTest-breast-cancer-rp.txt | 2 +- .../LDSVM-def-TrainTest-breast-cancer.txt | 1366 ++++++++--------- .../LdSvm/LDSVM-nob-CV-breast-cancer-out.txt | 48 +- .../LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt | 2 +- .../LdSvm/LDSVM-nob-CV-breast-cancer.txt | 1366 ++++++++--------- .../LDSVM-nob-TrainTest-breast-cancer-out.txt | 34 +- .../LDSVM-nob-TrainTest-breast-cancer-rp.txt | 2 +- .../LDSVM-nob-TrainTest-breast-cancer.txt | 1366 ++++++++--------- 15 files changed, 2904 insertions(+), 2869 deletions(-) diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs index fdb9f1cadf..896490c11d 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs @@ -19,7 +19,7 @@ public sealed class LdSvmModelParameters : ModelParametersBase, IValueMapper, ICanSaveModel { - public const string LoaderSignature = "LDSVMBinaryPredictor"; + internal const string LoaderSignature = "LDSVMBinaryPredictor"; /// /// Version information to be saved in binary format diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs index 4368acf1bd..730d44ebd2 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs @@ -54,7 +54,7 @@ public sealed class Options : TrainerInputBaseWithWeight [Argument(ArgumentType.AtMostOnce, HelpText = "Regularizer for classifier parameter W", ShortName = "lw", SortOrder = 50)] [TGUI(SuggestedSweeps = "0.1,0.01,0.001")] [TlcModule.SweepableDiscreteParam("LambdaW", new object[] { 0.1f, 0.01f, 0.001f })] - public float LambdaW = Defaults.LambdaW; + public float LambdaW = Defaults.LambdaW; /// /// Regularizer for kernel parameter Theta @@ -127,11 +127,7 @@ internal LdSvmTrainer(IHostEnvironment env, Options options) _options = options; } - // REVIEW: This does not need caching, but only because it's very - // badly written and the first thing is it just grabs all instances. If this - // ever changes, do review this return value to make sure it is still - // appropriate. - private static TrainerInfo _info = new TrainerInfo(calibration: true, caching: false); + private static readonly TrainerInfo _info = new TrainerInfo(calibration: true, caching: true); public override TrainerInfo Info => _info; private protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; @@ -185,18 +181,18 @@ private void ComputeGradTheta(in VBuffer feat, float[] gradTheta, int num /// /// Adaptively update gamma for indicator function approximation. /// - private void UpdateGamma(int iter, int numLeaf, ref float gamma, VBuffer[] data, VBuffer[] theta, float[] biasTheta) + private void UpdateGamma(int iter, int numLeaf, ref float gamma, Data data, VBuffer[] theta, float[] biasTheta) { if (numLeaf == 1) gamma = 1.0f; else { float tempSum = 0; - for (int idx = 0; idx < 100; idx++) + var sample = data.SampleWithReplacement(Host.Rand, 100); + foreach (var s in sample) { int thetaIdx = Host.Rand.Next(numLeaf - 1); - int instIdx = Host.Rand.Next(data.Length); - tempSum += Math.Abs(VectorUtils.DotProduct(in data[instIdx], in theta[thetaIdx]) + biasTheta[thetaIdx]); + tempSum += Math.Abs(VectorUtils.DotProduct(in s, in theta[thetaIdx]) + biasTheta[thetaIdx]); } tempSum /= 100.0f; gamma = 0.1f / tempSum; @@ -209,7 +205,6 @@ private void UpdateGamma(int iter, int numLeaf, ref float gamma, VBuffer[ /// private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, int numLeaf, int numFeatures) { - int t = 1; int numNodes = 2 * numLeaf - 1; var w = new VBuffer[numNodes]; @@ -230,36 +225,25 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, biasTheta, biasThetaPrime, tempThetaPrime, tempTheta, tempBiasW, tempBiasTheta, tempBiasThetaPrime); var gamma = 0.01f; - VBuffer[] data = GetData(ch, trainingData, out var labels); + var data = new Data(ch, trainingData); var pathWt = new float[numNodes]; var localWt = new float[numNodes]; var gradTheta = new float[numLeaf - 1]; var wDotX = new float[numNodes]; - int[] indices = Utils.GetIdentityPermutation(data.Length); // Number of samples processed in each iteration - int sampleSize = Math.Max(1, (int)Math.Sqrt(indices.Length)); + int sampleSize = Math.Max(1, (int)Math.Sqrt(data.Length)); for (int iter = 1; iter <= _options.NumberOfIterations; iter++) { // Update gamma adaptively if (iter % 100 == 1) UpdateGamma(iter, numLeaf, ref gamma, data, theta, biasTheta); - // Select random subset of data - the first sampleSize indices will be - // our subset. - // REVIEW: Shuffling and streaming needed here. - for (int k = 0; k < sampleSize; k++) - { - int randIdx = k + Host.Rand.Next(indices.Length - k); - Utils.Swap(ref indices[k], ref indices[randIdx]); - } - t++; - // Update learning rate - float etaTW = (float)1.0 / (_options.LambdaW * (float)Math.Sqrt(t)); - float etaTTheta = (float)1.0 / (_options.LambdaTheta * (float)Math.Sqrt(t)); - float etaTThetaPrime = (float)1.0 / (_options.LambdaThetaprime * (float)Math.Sqrt(t)); - float coef = (t - 1) / (float)t; + float etaTW = (float)1.0 / (_options.LambdaW * (float)Math.Sqrt(iter + 1)); + float etaTTheta = (float)1.0 / (_options.LambdaTheta * (float)Math.Sqrt(iter + 1)); + float etaTThetaPrime = (float)1.0 / (_options.LambdaThetaprime * (float)Math.Sqrt(iter + 1)); + float coef = iter / (float)(iter + 1); // Update classifier parameters for (int i = 0; i < tempW.Length; ++i) @@ -277,10 +261,11 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, for (int i = 0; i < numLeaf - 1; i++) tempBiasTheta[i] *= coef; - for (int workingId = 0; workingId < sampleSize; workingId++) + var sample = data.SampleWithoutReplacement(Host.Rand); + foreach (var s in sample) { - int index = indices[workingId]; - float trueLabel = labels[index]; + float trueLabel = s.Label; + var features = s.Features; // Compute path weight for (int i = 0; i < numNodes; i++) @@ -288,20 +273,20 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, float tanhDist; for (int i = 0; i < numLeaf - 1; i++) { - tanhDist = (float)Math.Tanh(gamma * (VectorUtils.DotProduct(in data[index], in theta[i]) + biasTheta[i])); + tanhDist = (float)Math.Tanh(gamma * (VectorUtils.DotProduct(in features, in theta[i]) + biasTheta[i])); pathWt[2 * i + 1] = pathWt[i] * (1 + tanhDist) / (float)2.0; pathWt[2 * i + 2] = pathWt[i] * (1 - tanhDist) / (float)2.0; } // Compute local weight for (int l = 0; l < numNodes; l++) - localWt[l] = (float)Math.Tanh(_options.Sigma * (VectorUtils.DotProduct(in data[index], in thetaPrime[l]) + biasThetaPrime[l])); + localWt[l] = (float)Math.Tanh(_options.Sigma * (VectorUtils.DotProduct(in features, in thetaPrime[l]) + biasThetaPrime[l])); // Make prediction float yPredicted = 0; for (int l = 0; l < numNodes; l++) { - wDotX[l] = VectorUtils.DotProduct(in data[index], in w[l]) + biasW[l]; + wDotX[l] = VectorUtils.DotProduct(in features, in w[l]) + biasW[l]; yPredicted += pathWt[l] * localWt[l] * wDotX[l]; } float loss = 1 - trueLabel * yPredicted; @@ -310,7 +295,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, if (loss > 0) { // Compute gradient w.r.t current instance - ComputeGradTheta(in data[index], gradTheta, numLeaf, gamma, theta, biasTheta, pathWt, localWt, w, biasW); + ComputeGradTheta(in features, gradTheta, numLeaf, gamma, theta, biasTheta, pathWt, localWt, w, biasW); // Check if bias is used ot not int biasUpdateMult = _options.NoBias ? 0 : 1; @@ -319,7 +304,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, for (int l = 0; l < numNodes; l++) { float tempGradW = trueLabel * etaTW / sampleSize * pathWt[l] * localWt[l]; - VectorUtils.AddMult(in data[index], tempGradW, ref tempW[l]); + VectorUtils.AddMult(in features, tempGradW, ref tempW[l]); tempBiasW[l] += biasUpdateMult * tempGradW; } @@ -327,7 +312,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, for (int l = 0; l < numNodes; l++) { float tempGradThetaPrime = (1 - localWt[l] * localWt[l]) * trueLabel * etaTThetaPrime / sampleSize * pathWt[l] * wDotX[l]; - VectorUtils.AddMult(in data[index], tempGradThetaPrime, ref tempThetaPrime[l]); + VectorUtils.AddMult(in features, tempGradThetaPrime, ref tempThetaPrime[l]); tempBiasThetaPrime[l] += biasUpdateMult * tempGradThetaPrime; } @@ -335,7 +320,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, for (int m = 0; m < numLeaf - 1; m++) { float tempGradTheta = trueLabel * etaTTheta / sampleSize * gradTheta[m]; - VectorUtils.AddMult(in data[index], tempGradTheta, ref tempTheta[m]); + VectorUtils.AddMult(in features, tempGradTheta, ref tempTheta[m]); tempBiasTheta[m] += biasUpdateMult * tempGradTheta; } } @@ -443,29 +428,66 @@ private static void CheckOptions(IExceptionContext ectx, Options options) ectx.CheckUserArg(options.LambdaThetaprime > 0, nameof(options.LambdaThetaprime), "Regularizer for Thetaprime must be positive and non-zero."); } - private VBuffer[] GetData(IChannel ch, RoleMappedData trainingData, out IList labels) + internal struct LabelFeatures { - // Load the features and labels into our own arrays. Note we toss out rows that have bad - // label or feature values. - long numBad; - using (var cursor = new FloatLabelCursor(trainingData, CursOpt.Label | CursOpt.Features)) + public float Label; + public VBuffer Features; + } + + private sealed class Data + { + private readonly IChannel _ch; + private readonly RoleMappedData _data; + + public int Length { get; } + + public Data(IChannel ch, RoleMappedData data) { - var data = new List>(); - labels = new List(); - while (cursor.MoveNext()) + Contracts.AssertValue(ch); + _ch = ch; + _ch.AssertValue(data); + + _data = data; + using (var cursor = new FloatLabelCursor(_data, CursOpt.Label | CursOpt.Features)) { - var features = default(VBuffer); - cursor.Features.CopyTo(ref features); - data.Add(features); - labels.Add(cursor.Label > 0 ? 1 : -1); + while (cursor.MoveNext()) + Length++; + _ch.Check(cursor.KeptRowCount > 0, NoTrainingInstancesMessage); + if (cursor.SkippedRowCount > 0) + _ch.Warning("Skipped {0} rows with missing feature/label values", cursor.SkippedRowCount); } - ch.Assert(data.Count == cursor.KeptRowCount); - numBad = cursor.SkippedRowCount; - ch.Check(data.Count > 0, NoTrainingInstancesMessage); + } - if (numBad > 0) - ch.Warning("Skipped {0} rows with missing feature/label values", numBad); - return data.ToArray(); + public IEnumerable> SampleWithReplacement(Random rand, int size) + { + using (var cursor = new FeatureFloatVectorCursor(_data)) + { + ValueGetter> getter = + (ref VBuffer dst) => cursor.Features.CopyTo(ref dst); + var reservoir = new ReservoirSamplerWithReplacement>(rand, size, getter); + while (cursor.MoveNext()) + reservoir.Sample(); + reservoir.Lock(); + return reservoir.GetSample(); + } + } + public IEnumerable SampleWithoutReplacement(Random rand) + { + var size = Math.Max(1, (int)Math.Sqrt(Length)); + using (var cursor = new FloatLabelCursor(_data, CursOpt.Label | CursOpt.Features)) + { + ValueGetter getter = + (ref LabelFeatures dst) => + { + cursor.Features.CopyTo(ref dst.Features); + dst.Label = cursor.Label > 0 ? 1 : -1; + }; + var reservoir = new ReservoirSamplerWithReplacement(rand, size, getter); + while (cursor.MoveNext()) + reservoir.Sample(); + reservoir.Lock(); + return reservoir.GetSample(); + } } } diff --git a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs index 77934dc549..e106ec3997 100644 --- a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs +++ b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs @@ -875,14 +875,24 @@ public static PriorTrainer Prior(this BinaryClassificationCatalog.BinaryClassifi } /// - /// + /// Create with advanced options, which predicts a target using a Local Deep SVM model model. /// - /// - /// - /// + /// The . + /// Trainer options. public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, LdSvmTrainer.Options options) => new LdSvmTrainer(catalog.GetEnvironment(), options); + /// + /// Create , which predicts a target using a Local Deep SVM model model. + /// + /// The . + /// The name of the label column. + /// The name of the feature column. The column data must be a known-sized vector of . + /// The name of the example weight column (optional). + /// The number of iterations. + /// The depth of a Local Deep SVM tree. + /// Indicates if the model should have a bias term. + /// public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = DefaultColumnNames.Label, string featureColumnName = DefaultColumnNames.Features, @@ -894,6 +904,9 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi Contracts.CheckValue(catalog, nameof(catalog)); var options = new LdSvmTrainer.Options() { + LabelColumnName = labelColumnName, + FeatureColumnName = featureColumnName, + ExampleWeightColumnName = exampleWeightColumnName, NumberOfIterations = numberOfIterations, TreeDepth = treeDepth, NoBias = noBias diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt index 72f9392749..666bf7f357 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 131 | 3 | 0.9776 - negative || 9 | 211 | 0.9591 + positive || 129 | 5 | 0.9627 + negative || 8 | 212 | 0.9636 ||====================== -Precision || 0.9357 | 0.9860 | -OVERALL 0/1 ACCURACY: 0.966102 -LOG LOSS/instance: 0.127230 +Precision || 0.9416 | 0.9770 | +OVERALL 0/1 ACCURACY: 0.963277 +LOG LOSS/instance: 0.169342 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.867053 -AUC: 0.994573 +LOG-LOSS REDUCTION (RIG): 0.823049 +AUC: 0.994437 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 || 99 | 6 | 0.9429 - negative || 4 | 220 | 0.9821 + positive || 104 | 1 | 0.9905 + negative || 5 | 219 | 0.9777 ||====================== -Precision || 0.9612 | 0.9735 | -OVERALL 0/1 ACCURACY: 0.969605 -LOG LOSS/instance: 0.180232 +Precision || 0.9541 | 0.9955 | +OVERALL 0/1 ACCURACY: 0.981763 +LOG LOSS/instance: 0.108202 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.800508 -AUC: 0.984439 +LOG-LOSS REDUCTION (RIG): 0.880235 +AUC: 0.997491 OVERALL RESULTS --------------------------------------- -AUC: 0.989506 (0.0051) -Accuracy: 0.967853 (0.0018) -Positive precision: 0.948440 (0.0127) -Positive recall: 0.960235 (0.0174) -Negative precision: 0.979716 (0.0063) -Negative recall: 0.970617 (0.0115) -Log-loss: 0.153731 (0.0265) -Log-loss reduction: 0.833781 (0.0333) -F1 Score: 0.954064 (0.0021) -AUPRC: 0.986694 (0.0033) +AUC: 0.995964 (0.0015) +Accuracy: 0.972520 (0.0092) +Positive precision: 0.947867 (0.0063) +Positive recall: 0.976581 (0.0139) +Negative precision: 0.986207 (0.0092) +Negative recall: 0.970657 (0.0070) +Log-loss: 0.138772 (0.0306) +Log-loss reduction: 0.851642 (0.0286) +F1 Score: 0.961996 (0.0100) +AUPRC: 0.992216 (0.0023) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt index 633e1d2f9b..92bd9cc9da 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LDSVM AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.989506 0.967853 0.94844 0.960235 0.979716 0.970617 0.153731 0.833781 0.954064 0.986694 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 +0.995964 0.97252 0.947867 0.976581 0.986207 0.970657 0.138772 0.851642 0.961996 0.992216 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt index dbd892c2f3..91bdf7ebc7 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.87089658 0.9997447 0.00036834786597783083 1 -6 0 -0.442875654 0.20916079 0.33854369259820988 0 -8 0 -1.8717339 0.0108707342 0.01576902092083874 0 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-1.88868582 0.00719481474 0.010417444434803989 0 +669 1 2.82913446 0.9857903 0.020647291682614661 1 +671 0 -1.78676069 0.00875676 0.012688972195079272 0 +672 0 -1.964747 0.006212499 0.0089906978455400344 0 +673 0 -1.45146334 0.0166647676 0.024244760093986625 0 +674 0 -2.029431 0.005482802 0.0079317756000028047 0 +675 0 -1.59660184 0.01262144 0.018324776229203945 0 +676 0 -1.99471509 0.00586314872 0.0084836303488544269 0 +677 0 -1.72590733 0.009845015 0.014273732241679649 0 +684 0 -2.032666 0.005448636 0.0078822135394269327 0 +686 0 -2.032666 0.005448636 0.0078822135394269327 0 +687 0 -1.80890894 0.008391011 0.012156744660702504 0 +690 0 -2.00441122 0.00575435348 0.0083257549357458344 0 +695 0 -1.98306417 0.00599658536 0.0086772870849988783 0 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt index 3282aaa4f1..484fd63752 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt @@ -13,23 +13,23 @@ TRUTH ||====================== ||====================== Precision || 0.9547 | 0.9841 | OVERALL 0/1 ACCURACY: 0.973646 -LOG LOSS/instance: 0.110363 +LOG LOSS/instance: 0.134558 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.881839 -AUC: 0.996240 +LOG-LOSS REDUCTION (RIG): 0.855934 +AUC: 0.996485 OVERALL RESULTS --------------------------------------- -AUC: 0.996240 (0.0000) +AUC: 0.996485 (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: 0.110363 (0.0000) -Log-loss reduction: 0.881839 (0.0000) +Log-loss: 0.134558 (0.0000) +Log-loss reduction: 0.855934 (0.0000) F1 Score: 0.962656 (0.0000) -AUPRC: 0.992024 (0.0000) +AUPRC: 0.992617 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt index 5c4106bf05..d332ac223f 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LDSVM AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.99624 0.973646 0.954733 0.970711 0.984091 0.975225 0.110363 0.881839 0.962656 0.992024 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 +0.996485 0.973646 0.954733 0.970711 0.984091 0.975225 0.134558 0.855934 0.962656 0.992617 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt index 8399e4c3d1..046317f735 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.73320436 0.00764186 0.011067214378798498 0 -1 0 1.1527946 0.9317318 3.8726429012572749 1 -2 0 -1.72800326 0.00774476957 0.01121683282310078 0 -3 0 1.01507175 0.9052223 3.399308488702649 1 -4 0 -1.73152852 0.007674869 0.01111520430223639 0 -5 1 2.11784887 0.9940291 0.0086400010419292062 1 -6 0 -1.14098847 0.03450573 0.050660395725731375 0 -7 0 -1.75006878 0.00731742149 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0.01830479725487124 0 +691 1 1.358949 0.903636 0.14618638141966131 1 +692 0 -1.27303088 0.0135630108 0.019701196327824737 0 +693 0 -1.20933521 0.015846353 0.023044526857663634 0 +694 0 -1.24865592 0.0143957166 0.020919568941462942 0 +695 0 -1.29774833 0.0127671408 0.018537680664482338 0 +696 1 1.93012857 0.974773943 0.036860407834190985 1 +697 1 1.48212874 0.9271473 0.10912948557501463 1 +698 1 1.66573274 0.9525223 0.070175259770179868 1 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt index 5c484cbd4f..6d925192d0 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 119 | 15 | 0.8881 - negative || 28 | 192 | 0.8727 + positive || 108 | 26 | 0.8060 + negative || 18 | 202 | 0.9182 ||====================== -Precision || 0.8095 | 0.9275 | -OVERALL 0/1 ACCURACY: 0.878531 -LOG LOSS/instance: 0.453154 +Precision || 0.8571 | 0.8860 | +OVERALL 0/1 ACCURACY: 0.875706 +LOG LOSS/instance: 0.521307 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.526484 -AUC: 0.923338 +LOG-LOSS REDUCTION (RIG): 0.455269 +AUC: 0.896065 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 || 105 | 0 | 1.0000 - negative || 100 | 124 | 0.5536 + positive || 100 | 5 | 0.9524 + negative || 10 | 214 | 0.9554 ||====================== -Precision || 0.5122 | 1.0000 | -OVERALL 0/1 ACCURACY: 0.696049 -LOG LOSS/instance: 0.114308 +Precision || 0.9091 | 0.9772 | +OVERALL 0/1 ACCURACY: 0.954407 +LOG LOSS/instance: 0.330099 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.873477 -AUC: 0.997024 +LOG-LOSS REDUCTION (RIG): 0.634625 +AUC: 0.957440 OVERALL RESULTS --------------------------------------- -AUC: 0.960181 (0.0368) -Accuracy: 0.787290 (0.0912) -Positive precision: 0.660859 (0.1487) -Positive recall: 0.944030 (0.0560) -Negative precision: 0.963768 (0.0362) -Negative recall: 0.713149 (0.1596) -Log-loss: 0.283731 (0.1694) -Log-loss reduction: 0.699981 (0.1735) -F1 Score: 0.762197 (0.0848) -AUPRC: 0.952368 (0.0411) +AUC: 0.926753 (0.0307) +Accuracy: 0.915057 (0.0394) +Positive precision: 0.883117 (0.0260) +Positive recall: 0.879176 (0.0732) +Negative precision: 0.931567 (0.0456) +Negative recall: 0.936769 (0.0186) +Log-loss: 0.425703 (0.0956) +Log-loss reduction: 0.544947 (0.0897) +F1 Score: 0.880501 (0.0497) +AUPRC: 0.924610 (0.0357) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt index 151cf25d30..d7544b63af 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LDSVM AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /noBias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.960181 0.78729 0.660859 0.94403 0.963768 0.713149 0.283731 0.699981 0.762197 0.952368 + 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000 noBias=+} threads=- dout=%Output% data=%Data% seed=1 /noBias:+;/iter:1000 +0.926753 0.915057 0.883117 0.879176 0.931567 0.936769 0.425703 0.544947 0.880501 0.92461 + 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000 noBias=+} threads=- dout=%Output% data=%Data% seed=1 /noBias:+;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt index 42ee8c76c5..d18960b311 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.90834475 0.993382335 0.0095790032904714915 1 -6 0 2.76807952 0.956745744 4.5310140878590426 1 -8 0 -1.0917176 0.0327487849 0.048037458801135045 0 -9 0 -0.700886548 0.0612697564 0.091217454731480227 0 -10 0 -0.225876167 0.126582116 0.19525602283184829 0 -11 0 -0.5846785 0.07350296 0.1101417243988199 0 -18 1 2.45982862 0.929482937 0.10549971401874002 1 -20 1 1.51792252 0.7304611 0.45312060844641111 1 -21 1 1.446458 0.706189334 0.50187306270966781 1 -25 1 2.53360128 0.937183738 0.093596174472086111 1 -28 0 -0.5846785 0.07350296 0.1101417243988199 0 -31 0 -0.742950141 0.05733054 0.085176107559313485 0 -32 1 -0.70969677 0.06042428 4.0487278262709028 0 -35 0 -0.5846785 0.07350296 0.1101417243988199 0 -37 0 -1.6407162 0.0132873673 0.019298115314740055 0 +5 1 3.41612816 0.9884243 0.016797614052768715 1 +6 0 2.00301147 0.904062152 3.3817561098671698 1 +8 0 -1.44672835 0.0415951051 0.061292818379837619 0 +9 0 -0.963073254 0.08448279 0.12734108714568246 0 +10 0 -0.4652979 0.167065129 0.26372440182100232 0 +11 0 -0.9015775 0.09220304 0.13955844325755537 0 +18 1 1.80902731 0.874422967 0.19359679981548028 1 +20 1 0.5211005 0.482979774 1.0499653196470817 1 +21 1 0.7079735 0.5556077 0.84786156255745371 1 +25 1 2.06026888 0.911533237 0.13363283383444213 1 +28 0 -0.9015775 0.09220304 0.13955844325755537 0 +31 0 -1.08978367 0.07039949 0.10531723927270542 0 +32 1 -1.47411072 0.0399255045 4.6465455529539224 0 +35 0 -0.9015775 0.09220304 0.13955844325755537 0 +37 0 -2.03222513 0.0171160474 0.024907004824247615 0 40 0 ? ? ? 0 -41 1 -0.7542473 0.0563137867 4.1503680258200824 0 -44 1 0.533718646 0.341665536 1.5493433645655683 1 -45 0 -0.5364089 0.0792177841 0.1190681255466262 0 -46 1 5.214454 0.999257565 0.0010715057607442913 1 -48 0 -1.05790114 0.03459622 0.050795618769079967 0 -50 1 -0.1268045 0.146147177 2.7745061364984056 0 -51 1 1.31565273 0.6586485 0.60241936519824901 1 -52 1 1.43291676 0.7014489 0.51159005064743179 1 -54 1 -0.582287252 0.0737769 3.7606870044306087 0 -56 1 4.7331953 0.99833554 0.0024033084229980566 1 -60 1 2.03695416 0.8662974 0.20706566782173 1 -63 1 0.491040349 0.3257318 1.6182434616586383 1 -64 0 -0.371573746 0.101907894 0.15506468329159076 0 -66 0 -1.17056382 0.0288042668 0.042166011577590715 0 -68 1 1.41942286 0.6966817 0.52142847185717245 1 -69 0 -0.3045989 0.11267262 0.17246160811499353 0 -70 0 -1.006816 0.0375788361 0.055259726748072585 0 -71 1 -1.15321183 0.0296307486 5.0767611165285924 0 -72 0 -0.07127779 0.158171728 0.24840213329135691 0 -73 1 2.87856865 0.9638059 0.053185441387443182 1 -74 1 -0.02862797 0.16794391 2.5739486155529203 0 -76 0 -0.02894354 0.167869866 0.26511893052600455 0 -77 0 -0.5632322 0.0759936646 0.11402535139252641 0 -79 0 -0.8873909 0.0455444641 0.067250104895742735 0 -82 0 -1.078275 0.0334714763 0.049115785717713252 0 -88 0 -1.17056382 0.0288042668 0.042166011577590715 0 -90 0 -0.5303377 0.07996469 0.12023886337686118 0 -91 0 -0.585692644 0.07338705 0.10996124846154352 0 -92 0 -1.17056382 0.0288042668 0.042166011577590715 0 -93 0 -0.371573746 0.101907894 0.15506468329159076 0 -95 0 -0.5303377 0.07996469 0.12023886337686118 0 -96 0 -0.371952236 0.101849735 0.15497125933370592 0 -97 0 -1.38549018 0.0202539079 0.029520181490608522 0 -98 1 -0.113266572 0.149007142 2.7465466172889159 0 -99 1 3.25259042 0.980355442 0.028623181558234703 1 -100 1 -0.6740661 0.06391212 3.9677665689274062 0 -102 0 -1.05826044 0.0345760733 0.050765512439146218 0 -104 1 -1.3251766 0.0223646145 5.4826382970004337 0 -105 1 -1.09090567 0.0327920057 4.9305120444460586 0 -106 1 4.50645971 0.9975661 0.0035156498835047516 1 -108 0 0.074981004 0.193680242 0.31057602012247831 1 -109 1 3.15301824 0.9768626 0.033772425933191901 1 -111 1 1.81683648 0.817418 0.29085411886947365 1 -112 1 1.48204839 0.718436062 0.47706832603397092 1 -113 1 0.45216462 0.3115591 1.6824221888356503 1 -115 0 1.27702463 0.643917 1.4897146320117072 1 -117 1 2.60778141 0.944130242 0.082942203160019393 1 -120 0 -0.355670273 0.104378387 0.15903875192152875 0 -121 0 -0.494086027 0.08455974 0.12746235135075037 0 -122 1 1.717028 0.791061461 0.33813830664362376 1 -123 1 1.15886855 0.597240031 0.74361722772130756 1 -125 0 -0.371573746 0.101907894 0.15506468329159076 0 -128 1 2.69889045 0.951673746 0.071461022973629579 1 -129 0 -2.61997771 0.002593606 0.0037466432927018138 0 -131 0 -0.742950141 0.05733054 0.085176107559313485 0 -132 1 4.615232 0.997971654 0.0029292565827698487 1 -133 0 -0.5756487 0.07454235 0.111761124318733 0 -137 0 -0.6409206 0.06732428 0.10055253528444953 0 -138 0 -0.899713933 0.0446532667 0.065903655277849194 0 -141 0 -0.426785022 0.0937296152 0.14198655389030862 0 -144 0 -0.5846785 0.07350296 0.1101417243988199 0 +41 1 -1.22863686 0.05747918 4.1208166994413586 0 +44 1 0.474028617 0.46467787 1.1056971550242318 1 +45 0 -0.8510741 0.09901131 0.15041909781911461 0 +46 1 5.09431553 0.9991459 0.001232696184539676 1 +48 0 -1.44285345 0.0418367 0.061656541118507255 0 +50 1 -0.341340035 0.195723951 2.3531077846209714 0 +51 1 1.06120026 0.6844449 0.54699368116570202 1 +52 1 0.9600261 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0.013721983620686327 0 +672 0 -1.94521964 0.00269996678 0.0039004966556092932 0 +673 0 -0.6065816 0.06538578 0.097557109048839172 0 +674 0 -1.36584055 0.0109401681 0.015870297217020487 0 +675 0 -1.107731 0.0202869419 0.029568825530869165 0 +676 0 -1.36917138 0.0108529581 0.015743093734533777 0 +677 0 -1.37519419 0.0106970072 0.015515653484404901 0 +684 0 -0.997518539 0.0263511 0.038526467657834004 0 +686 0 -0.997518539 0.0263511 0.038526467657834004 0 +687 0 -1.20995545 0.0158967134 0.023118353203277164 0 +690 0 -0.9698287 0.0281330664 0.041169298891143097 0 +695 0 -1.104035 0.0204661675 0.029832771699006215 0 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt index 6df3140fce..e448a979c8 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 226 | 13 | 0.9456 - negative || 24 | 420 | 0.9459 + positive || 213 | 26 | 0.8912 + negative || 8 | 436 | 0.9820 ||====================== -Precision || 0.9040 | 0.9700 | -OVERALL 0/1 ACCURACY: 0.945827 -LOG LOSS/instance: 0.288451 +Precision || 0.9638 | 0.9437 | +OVERALL 0/1 ACCURACY: 0.950220 +LOG LOSS/instance: 0.241702 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.691167 -AUC: 0.966876 +LOG-LOSS REDUCTION (RIG): 0.741219 +AUC: 0.974179 OVERALL RESULTS --------------------------------------- -AUC: 0.966876 (0.0000) -Accuracy: 0.945827 (0.0000) -Positive precision: 0.904000 (0.0000) -Positive recall: 0.945607 (0.0000) -Negative precision: 0.969977 (0.0000) -Negative recall: 0.945946 (0.0000) -Log-loss: 0.288451 (0.0000) -Log-loss reduction: 0.691167 (0.0000) -F1 Score: 0.924335 (0.0000) -AUPRC: 0.955880 (0.0000) +AUC: 0.974179 (0.0000) +Accuracy: 0.950220 (0.0000) +Positive precision: 0.963801 (0.0000) +Positive recall: 0.891213 (0.0000) +Negative precision: 0.943723 (0.0000) +Negative recall: 0.981982 (0.0000) +Log-loss: 0.241702 (0.0000) +Log-loss reduction: 0.741219 (0.0000) +F1 Score: 0.926087 (0.0000) +AUPRC: 0.968491 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt index 3b8484a8a7..449e7d176c 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LDSVM AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /noBias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.966876 0.945827 0.904 0.945607 0.969977 0.945946 0.288451 0.691167 0.924335 0.95588 + 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 noBias=+} dout=%Output% data=%Data% out=%Output% seed=1 /noBias:+;/iter:1000 +0.974179 0.95022 0.963801 0.891213 0.943723 0.981982 0.241702 0.741219 0.926087 0.968491 + 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 noBias=+} dout=%Output% data=%Data% out=%Output% seed=1 /noBias:+;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt index 9f90e317ff..eb77b591f0 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.62977672 0.0105228247 0.015261666521846514 0 -1 0 1.4701674 0.874539435 2.9946941290313571 1 -2 0 -1.1010586 0.0311435722 0.045645202436404499 0 -3 0 2.87714934 0.9924993 7.058757485223742 1 -4 0 -1.46685541 0.0147335446 0.021414155120391815 0 -5 1 3.59101725 0.998305559 0.0024466348242297023 1 -6 0 1.49068952 0.879174948 3.0490084761020446 1 -7 0 -1.150382 0.02817619 0.041233315992808446 0 -8 0 -1.459084 0.0149714295 0.021762524687166988 0 -9 0 -0.9014765 0.04653196 0.068743514857381643 0 -10 0 -0.690697253 0.0705022961 0.10547679355318071 0 -11 0 -1.01742482 0.03687889 0.054210872155484369 0 -12 1 0.6150113 0.5380885 0.8940846196302511 1 -13 0 -0.5864183 0.08620808 0.13006241331063334 0 -14 1 2.04975986 0.9590763 0.0602825224628167 1 -15 1 -0.9046689 0.04623654 4.4348227134866924 0 -16 0 -1.27958059 0.0216472652 0.031573387352065531 0 -17 0 -1.50421357 0.013640984 0.019815238969015801 0 -18 1 2.78780031 0.9909715 0.01308452007911385 1 -19 0 -1.75076044 0.008189024 0.011862904270047395 0 -20 1 1.302372 0.8307096 0.26758390865273213 1 -21 1 1.47114491 0.874763668 0.19303479412375851 1 -22 0 -1.15088189 0.02814757 0.041190830241808626 0 +0 0 -2.05067277 0.004818092 0.0069678369315782467 0 +1 0 0.448933244 0.830629 2.5617411442702527 1 +2 0 -1.50646365 0.0213776454 0.031175856409611683 0 +3 0 1.40901577 0.985912263 6.1494163487970059 1 +4 0 -1.98359311 0.005795711 0.0083857680726871133 0 +5 1 2.245398 0.998591661 0.0020332351152866487 1 +6 0 0.201730967 0.7121122 1.7964213842381598 1 +7 0 -1.48738074 0.0225113872 0.032848199031000655 0 +8 0 -1.74930215 0.01102891 0.015999746808623677 0 +9 0 -1.39703393 0.02872553 0.042049052727238002 0 +10 0 -1.1431849 0.0563595742 0.083690869555279684 0 +11 0 -1.34861791 0.032711383 0.047981673383003576 0 +12 1 -0.266166329 0.403765738 1.308409600512231 0 +13 0 -1.03961921 0.07369616 0.11044259875178986 0 +14 1 1.25805247 0.9787578 0.030976197147596852 1 +15 1 -1.05823636 0.0702538341 3.8312792266894777 0 +16 0 -1.66340721 0.0139486128 0.020265261610933002 0 +17 0 -1.88762021 0.00754643371 0.010928490117893923 0 +18 1 1.38500226 0.9849584 0.021865286637118751 1 +19 0 -2.21434546 0.00306784688 0.0044327704862290023 0 +20 1 0.483564258 0.843694031 0.24520819980129582 1 +21 1 1.047329 0.9625603 0.055051179023420355 1 +22 0 -1.50677311 0.0213597286 0.031149443546424762 0 23 1 ? ? ? 0 -24 0 -1.09939063 0.031249037 0.045802255500442486 0 -25 1 1.82644939 0.9362598 0.095019170801931382 1 -26 0 -0.398037016 0.122740552 0.18892451420855935 0 -27 0 -1.403551 0.0167849157 0.024421045453161445 0 -28 0 -1.01742482 0.03687889 0.054210872155484369 0 -29 0 -0.5009352 0.101378456 0.15421444433430814 0 -30 0 -0.739873052 0.06405115 0.095498408784013555 0 -31 0 -1.23895729 0.02352228 0.034340968172283291 0 -32 1 0.288148522 0.370243162 1.4334550045046908 1 -33 0 -1.23374367 0.0237741172 0.03471309233940393 0 -34 0 -1.05580735 0.0341304354 0.05009972079912775 0 -35 0 -1.01742482 0.03687889 0.054210872155484369 0 -36 1 1.91100967 0.946037054 0.080031403220775926 1 -37 0 -1.75114179 0.008182547 0.011853482305294379 0 -38 1 3.18700266 0.9960633 0.0056906777027673563 1 -39 1 1.16365612 0.785912335 0.34755969974597395 1 +24 0 -1.39549279 0.0288448185 0.042226251630381545 0 +25 1 0.6853803 0.9041959 0.14529271144477401 1 +26 0 -1.03658748 0.07427123 0.11133853878997595 0 +27 0 -1.819399 0.00910106 0.013190168163384874 0 +28 0 -1.34861791 0.032711383 0.047981673383003576 0 +29 0 -0.914736331 0.101061143 0.15370510336942747 0 +30 0 -1.25765777 0.0416890755 0.061434279829404324 0 +31 0 -1.560827 0.018445529 0.026859763214252534 0 +32 1 0.169259056 0.6933356 0.5283742709520991 1 +33 0 -1.6178304 0.0157950073 0.022969259931274518 0 +34 0 -1.43008208 0.02627981 0.03842083969747119 0 +35 0 -1.34861791 0.032711383 0.047981673383003576 0 +36 1 1.28157806 0.9800706 0.029042429762764617 1 +37 0 -2.59408617 0.00107421342 0.0015505953556180032 0 +38 1 1.7107656 0.99384135 0.0089125265229150919 1 +39 1 0.454036266 0.8326074 0.26429173398331346 1 40 0 ? ? ? 0 -41 1 -0.04485377 0.226559386 2.1420388363875063 0 -42 1 2.85241485 0.992104053 0.01143665412198123 1 -43 1 -2.06590366 0.00425226055 7.8775542861203851 0 -44 1 1.36376691 0.8480139 0.23784021999457852 1 -45 0 -1.04273343 0.0350436829 0.051464460832077688 0 -46 1 3.791055 0.9988843 0.0016104841888625752 1 -47 0 -0.879428148 0.0486218072 0.071909138294425995 0 -48 0 -1.46685541 0.0147335446 0.021414155120391815 0 -49 1 3.41708183 0.99756366 0.0035191841341646979 1 -50 1 0.8947072 0.6765138 0.56380875029957334 1 -51 1 1.0740279 0.7526787 0.40989396424455005 1 -52 1 1.518044 0.8851233 0.17604963415415856 1 -53 1 -0.6353586 0.07847672 3.6715914537218493 0 -54 1 0.04001243 0.2591685 1.9480376832863808 1 -55 1 1.83790088 0.937674642 0.092840677864733795 1 -56 1 4.086723 0.999398649 0.00086782744300117594 1 -57 1 -2.629247 0.0013123753 9.5736039365203744 0 -58 1 0.152370408 0.306776553 1.7047398714895301 1 -59 1 0.302410364 0.377226561 1.4064968321350246 1 -60 1 1.51772821 0.885056138 0.17615912839077924 1 -61 0 -0.6437752 0.07721273 0.11592999068982598 0 -62 1 2.246617 0.9725112 0.040213270884723228 1 -63 1 0.3170501 0.384448349 1.3791383093411176 1 -64 0 -0.879428148 0.0486218072 0.071909138294425995 0 -65 1 -1.71275079 0.008860795 6.8183481680451052 0 -66 0 -1.50421357 0.013640984 0.019815238969015801 0 -67 1 1.54007339 0.889726937 0.16856546369148745 1 -68 1 1.55023611 0.891795754 0.16521476344983133 1 -69 0 -0.9402488 0.0430626757 0.063503658103502975 0 -70 0 -1.17285013 0.0269172378 0.039365581191928137 0 -71 1 -0.473756582 0.106677108 3.2286774821833641 0 -72 0 -0.6761522 0.07252263 0.10861601810759337 0 -73 1 2.42399621 0.980870247 0.027865790811029929 1 -74 1 0.812297344 0.637698352 0.64905394127091121 1 -75 0 -0.771095335 0.0602450669 0.089643511363014849 0 -76 0 -0.6080809 0.082704246 0.12454113317977131 0 -77 0 -0.568907857 0.0891379938 0.13469558968839096 0 -78 0 -0.806618631 0.0561725 0.083404889436938065 0 -79 0 -1.441644 0.0155191319 0.022564925098350581 0 -80 0 -1.197976 0.0255741179 0.037375641155698698 0 -81 0 -1.263673 0.022363428 0.032629839581371986 0 -82 0 -1.21645844 0.0246280115 0.035975554384126342 0 -83 0 -2.043171 0.00445845537 0.0064465730323026108 0 -84 1 1.87212324 0.941729963 0.086614662588777799 1 -85 1 0.8799051 0.669700146 0.57841281291827151 1 -86 1 1.720396 0.921666265 0.11768364972403332 1 -87 1 3.13008046 0.9955676 0.0064087865378989169 1 -88 0 -1.50421357 0.013640984 0.019815238969015801 0 -89 0 -1.44290519 0.01547887 0.022505924856831439 0 -90 0 -1.09939063 0.031249037 0.045802255500442486 0 -91 0 -0.9248565 0.04440936 0.06553537331834558 0 -92 0 -1.50421357 0.013640984 0.019815238969015801 0 -93 0 -0.879428148 0.0486218072 0.071909138294425995 0 -94 0 -1.23895729 0.02352228 0.034340968172283291 0 -95 0 -1.09939063 0.031249037 0.045802255500442486 0 -96 0 -0.797980964 0.05713828 0.084881892567744033 0 -97 0 -1.62977672 0.0105228247 0.015261666521846514 0 -98 1 0.5542057 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-1.1829437 0.050783135 0.07519036135908308 0 +676 0 -1.143511 0.0563115664 0.083617474144780393 0 +677 0 -1.604116 0.0163962711 0.023850890549125994 0 +678 0 -0.9921977 0.0831760541 0.12528336946938828 0 +679 0 -1.14954662 0.05543011 0.082270548203495941 0 +680 1 2.65842485 0.99955076 0.00064826224341388826 1 +681 1 2.63706946 0.9995234 0.00068775054169162102 1 +682 0 -1.93783259 0.006573408 0.0095147294673608527 0 +683 0 -0.9921977 0.0831760541 0.12528336946938828 0 +684 0 -0.9921977 0.0831760541 0.12528336946938828 0 +685 0 -0.9921977 0.0831760541 0.12528336946938828 0 +686 0 -0.9921977 0.0831760541 0.12528336946938828 0 +687 0 -1.327789 0.03458611 0.050780509855592577 0 +688 0 -1.454422 0.0246093236 0.035947912920366948 0 +689 0 -2.08108616 0.004430719 0.006406379534755707 0 +690 0 -1.11087084 0.061310973 0.091280800199887144 0 +691 1 1.65862143 0.9928916 0.010291862258072463 1 +692 0 -1.30324566 0.0369282179 0.05428476215539646 0 +693 0 -1.660392 0.0140638994 0.020433947617290234 0 +694 0 -1.36513245 0.0312951319 0.045870903108322696 0 +695 0 -1.14954662 0.05543011 0.082270548203495941 0 +696 1 1.473592 0.9881915 0.017137469801137669 1 +697 1 1.16730058 0.9728547 0.0397037852117939 1 +698 1 1.16385174 0.972601354 0.040079494690591556 1 From 367650a258987822cd1f21812a1ce46005b0d01e Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Thu, 19 Sep 2019 15:29:02 +0300 Subject: [PATCH 03/11] Fix baselines, address code review comment. --- .../LdSvm/LdSvmTrainer.cs | 12 +- .../StandardTrainersCatalog.cs | 6 +- .../Common/EntryPoints/core_ep-list.tsv | 1 + .../Common/EntryPoints/core_manifest.json | 273 ++++++++++++++++++ .../LdSvm/LDSVM-nob-CV-breast-cancer-out.txt | 2 +- .../LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt | 4 +- .../LDSVM-nob-TrainTest-breast-cancer-out.txt | 2 +- .../LDSVM-nob-TrainTest-breast-cancer-rp.txt | 4 +- test/Microsoft.ML.TestFramework/Learners.cs | 2 +- 9 files changed, 290 insertions(+), 16 deletions(-) diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs index 730d44ebd2..c1215e446b 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs @@ -83,9 +83,9 @@ public sealed class Options : TrainerInputBaseWithWeight /// /// Indicates if we should use Bias or not in our model. /// - [Argument(ArgumentType.AtMostOnce, HelpText = "No bias", ShortName = "noBias")] + [Argument(ArgumentType.AtMostOnce, HelpText = "No bias", ShortName = "bias")] [TlcModule.SweepableDiscreteParam("NoBias", null, isBool: true)] - public bool NoBias = Defaults.NoBias; + public bool UseBias = Defaults.UseBias; /// /// Number of iterations @@ -105,7 +105,7 @@ public sealed class Options : TrainerInputBaseWithWeight internal class Defaults { public const int NumberOfIterations = 15000; - public const bool NoBias = false; + public const bool UseBias = true; public const float Sigma = 1.0f; public const float LambdaThetaprime = 0.01f; public const float LambdaTheta = 0.01f; @@ -298,7 +298,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, ComputeGradTheta(in features, gradTheta, numLeaf, gamma, theta, biasTheta, pathWt, localWt, w, biasW); // Check if bias is used ot not - int biasUpdateMult = _options.NoBias ? 0 : 1; + int biasUpdateMult = _options.UseBias ? 1 : 0; // Update W for (int l = 0; l < numNodes; l++) @@ -389,7 +389,7 @@ private void InitClassifierParam(int numLeaf, int numFeatures, VBuffer[] thetaPrime[i] = thetaPrimeInit.Commit(); thetaPrime[i].CopyTo(ref tempThetaPrime[i]); - if (!_options.NoBias) + if (_options.UseBias) { float bW = 2 * Host.Rand.NextSingle() - 1; biasW[i] = bW; @@ -405,7 +405,7 @@ private void InitClassifierParam(int numLeaf, int numFeatures, VBuffer[] theta[i] = thetaInit.Commit(); theta[i].CopyTo(ref tempTheta[i]); - if (!_options.NoBias) + if (_options.UseBias) { float bT = 2 * Host.Rand.NextSingle() - 1; biasTheta[i] = bT; diff --git a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs index e106ec3997..6b7cbbf823 100644 --- a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs +++ b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs @@ -891,7 +891,7 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi /// The name of the example weight column (optional). /// The number of iterations. /// The depth of a Local Deep SVM tree. - /// Indicates if the model should have a bias term. + /// Indicates if the model should have a bias term. /// public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = DefaultColumnNames.Label, @@ -899,7 +899,7 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi string exampleWeightColumnName = null, int numberOfIterations = LdSvmTrainer.Options.Defaults.NumberOfIterations, int treeDepth = LdSvmTrainer.Options.Defaults.TreeDepth, - bool noBias = LdSvmTrainer.Options.Defaults.NoBias) + bool useBias = LdSvmTrainer.Options.Defaults.UseBias) { Contracts.CheckValue(catalog, nameof(catalog)); var options = new LdSvmTrainer.Options() @@ -909,7 +909,7 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi ExampleWeightColumnName = exampleWeightColumnName, NumberOfIterations = numberOfIterations, TreeDepth = treeDepth, - NoBias = noBias + UseBias = useBias }; return new LdSvmTrainer(catalog.GetEnvironment(), options); } diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv index bcd7dc5c44..4819c15e65 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv +++ b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv @@ -59,6 +59,7 @@ Trainers.LightGbmClassifier Train a LightGBM multi class model. Microsoft.ML.Tra Trainers.LightGbmRanker Train a LightGBM ranking model. Microsoft.ML.Trainers.LightGbm.LightGbm TrainRanking Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+RankingOutput Trainers.LightGbmRegressor LightGBM Regression Microsoft.ML.Trainers.LightGbm.LightGbm TrainRegression Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput Trainers.LinearSvmBinaryClassifier Train a linear SVM. Microsoft.ML.Trainers.LinearSvmTrainer TrainLinearSvm Microsoft.ML.Trainers.LinearSvmTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput +Trainers.LocalDeepSvmBinaryClassifier LD-SVM learns a binary, non-linear SVM classifier with a kernel that is specifically designed to reduce prediction time. LD-SVM learns decision boundaries that are locally linear. Microsoft.ML.Trainers.LdSvmTrainer TrainBinary Microsoft.ML.Trainers.LdSvmTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.LogisticRegressionBinaryClassifier Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function. Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer TrainBinary Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.LogisticRegressionClassifier Maximum entropy classification is a method in statistics used to predict the probabilities of parallel events. The model predicts the probabilities of parallel events by fitting data to a softmax function. Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer TrainMulticlass Microsoft.ML.Trainers.LbfgsMaximumEntropyMulticlassTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.NaiveBayesClassifier Train a MulticlassNaiveBayesTrainer. Microsoft.ML.Trainers.NaiveBayesMulticlassTrainer TrainMulticlassNaiveBayesTrainer Microsoft.ML.Trainers.NaiveBayesMulticlassTrainer+Options Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index b2587d6cd6..25b21a5e9f 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -13506,6 +13506,279 @@ "ITrainerOutput" ] }, + { + "Name": "Trainers.LocalDeepSvmBinaryClassifier", + "Desc": "LD-SVM learns a binary, non-linear SVM classifier with a kernel that is specifically designed to reduce prediction time. LD-SVM learns decision boundaries that are locally linear.", + "FriendlyName": "Local Deep SVM (LDSVM)", + "ShortName": "LDSVM", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumnName", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumnName", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "ExampleWeightColumnName", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "None" + ] + }, + "Desc": "Whether trainer should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "TreeDepth", + "Type": "Int", + "Desc": "Depth of Local Deep SVM tree", + "Aliases": [ + "depth" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 3, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 3, + 5, + 7 + ] + } + }, + { + "Name": "LambdaW", + "Type": "Float", + "Desc": "Regularizer for classifier parameter W", + "Aliases": [ + "lw" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "LambdaTheta", + "Type": "Float", + "Desc": "Regularizer for kernel parameter Theta", + "Aliases": [ + "lt" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "LambdaThetaprime", + "Type": "Float", + "Desc": "Regularizer for kernel parameter Thetaprime", + "Aliases": [ + "lp" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "Sigma", + "Type": "Float", + "Desc": "Parameter for sigmoid sharpness", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1.0, + 0.1, + 0.01 + ] + } + }, + { + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter", + "NumIterations" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 15000, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10000, + 15000 + ] + } + }, + { + "Name": "NoBias", + "Type": "Bool", + "Desc": "No bias", + "Aliases": [ + "noBias" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, { "Name": "Trainers.LogisticRegressionBinaryClassifier", "Desc": "Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.", diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt index 6d925192d0..68f55f9a1d 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe CV tr=LDSVM{iter=1000 noBias=+} threads=- dout=%Output% data=%Data% seed=1 +maml.exe CV tr=LDSVM{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt index d7544b63af..3e4dda5c38 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LDSVM -AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /noBias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.926753 0.915057 0.883117 0.879176 0.931567 0.936769 0.425703 0.544947 0.880501 0.92461 + 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000 noBias=+} threads=- dout=%Output% data=%Data% seed=1 /noBias:+;/iter:1000 +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.926753 0.915057 0.883117 0.879176 0.931567 0.936769 0.425703 0.544947 0.880501 0.92461 - 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt index e448a979c8..2204c00c13 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 noBias=+} dout=%Output% data=%Data% out=%Output% seed=1 +maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 rows with missing feature/label values Training calibrator. diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt index 449e7d176c..bce1a403fc 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LDSVM -AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /noBias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.974179 0.95022 0.963801 0.891213 0.943723 0.981982 0.241702 0.741219 0.926087 0.968491 + 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 noBias=+} dout=%Output% data=%Data% out=%Output% seed=1 /noBias:+;/iter:1000 +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.974179 0.95022 0.963801 0.891213 0.943723 0.981982 0.241702 0.741219 0.926087 0.968491 - 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index f8e4a4ee7c..017097cdb8 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -688,7 +688,7 @@ public static PredictorAndArgs DssmDefault(int qryFeaturesCount, int docFeatures public static PredictorAndArgs LDSVMNoBias = new PredictorAndArgs { - Trainer = new SubComponent("LDSVM", "iter=1000 noBias=+"), + Trainer = new SubComponent("LDSVM", "iter=1000 bias=-"), Tag = "LDSVM-nob" }; From 2fe693d595b4644596d68a312591b1f0c6e6b10a Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Tue, 24 Sep 2019 16:34:49 +0300 Subject: [PATCH 04/11] Fix baseline --- test/BaselineOutput/Common/EntryPoints/core_manifest.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 25b21a5e9f..2b059f56c6 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -13720,16 +13720,16 @@ } }, { - "Name": "NoBias", + "Name": "UseBias", "Type": "Bool", "Desc": "No bias", "Aliases": [ - "noBias" + "bias" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false, + "Default": true, "SweepRange": { "RangeType": "Discrete", "Values": [ From a300b9503a4ed5b62b6fb5188fce226c27222eb1 Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Wed, 25 Sep 2019 16:27:19 +0300 Subject: [PATCH 05/11] Fix baselines for Linux --- test/Microsoft.ML.TestFramework/Learners.cs | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index 017097cdb8..ab19eb7ce8 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -682,13 +682,13 @@ public static PredictorAndArgs DssmDefault(int qryFeaturesCount, int docFeatures public static PredictorAndArgs LDSVMDefault = new PredictorAndArgs { - Trainer = new SubComponent("LDSVM", "iter=1000"), + Trainer = new SubComponent("LdSvm", "iter=1000"), Tag = "LDSVM-def" }; public static PredictorAndArgs LDSVMNoBias = new PredictorAndArgs { - Trainer = new SubComponent("LDSVM", "iter=1000 bias=-"), + Trainer = new SubComponent("LdSvm", "iter=1000 bias=-"), Tag = "LDSVM-nob" }; From 12faca7708f9b77a063ace357166a8e989f869b0 Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Thu, 26 Sep 2019 14:15:30 +0300 Subject: [PATCH 06/11] baselines --- .../Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt | 2 +- .../Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt | 4 ++-- .../Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt | 2 +- .../Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt | 4 ++-- .../Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt | 2 +- .../Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt | 4 ++-- .../Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt | 2 +- .../Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt | 4 ++-- 8 files changed, 12 insertions(+), 12 deletions(-) diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt index 666bf7f357..3e9f8af200 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe CV tr=LDSVM{iter=1000} threads=- dout=%Output% data=%Data% seed=1 +maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt index 92bd9cc9da..69a75a469a 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ -LDSVM +LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.995964 0.97252 0.947867 0.976581 0.986207 0.970657 0.138772 0.851642 0.961996 0.992216 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 +0.995964 0.97252 0.947867 0.976581 0.986207 0.970657 0.138772 0.851642 0.961996 0.992216 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt index 484fd63752..8c624d4e5d 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 +maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 rows with missing feature/label values Training calibrator. diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt index d332ac223f..98e46d7356 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ -LDSVM +LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.996485 0.973646 0.954733 0.970711 0.984091 0.975225 0.134558 0.855934 0.962656 0.992617 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 +0.996485 0.973646 0.954733 0.970711 0.984091 0.975225 0.134558 0.855934 0.962656 0.992617 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt index 68f55f9a1d..3aa54d6fb8 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe CV tr=LDSVM{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 +maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt index 3e4dda5c38..51c5b74fd2 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ -LDSVM +LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.926753 0.915057 0.883117 0.879176 0.931567 0.936769 0.425703 0.544947 0.880501 0.92461 - 1000 LDSVM %Data% %Output% 99 0 0 maml.exe CV tr=LDSVM{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 +0.926753 0.915057 0.883117 0.879176 0.931567 0.936769 0.425703 0.544947 0.880501 0.92461 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt index 2204c00c13..cb7c0ac1d9 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 +maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 rows with missing feature/label values Training calibrator. diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt index bce1a403fc..4d1fef994d 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ -LDSVM +LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.974179 0.95022 0.963801 0.891213 0.943723 0.981982 0.241702 0.741219 0.926087 0.968491 - 1000 LDSVM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LDSVM{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 +0.974179 0.95022 0.963801 0.891213 0.943723 0.981982 0.241702 0.741219 0.926087 0.968491 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 From 83a58dff04b83a098225206daa95f63978cf73ad Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Thu, 26 Sep 2019 16:28:57 +0300 Subject: [PATCH 07/11] baselines --- test/Microsoft.ML.Predictor.Tests/TestPredictors.cs | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 4366f0d2ea..723933bcee 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -2107,7 +2107,7 @@ public sealed partial class TestPredictors /// ///A test for binary classifiers /// - [Fact] + [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.0 output differs from Baseline")] [TestCategory("Binary")] [TestCategory("LDSVM")] public void BinaryClassifierLDSvmTest() @@ -2121,7 +2121,7 @@ public void BinaryClassifierLDSvmTest() /// ///A test for binary classifiers /// - [Fact] + [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.0 output differs from Baseline")] [TestCategory("Binary")] [TestCategory("LDSVM")] public void BinaryClassifierLDSvmNoBiasTest() From af1e78f771d8edf510e9230e19063f322f4ac5f5 Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Fri, 27 Dec 2019 16:30:56 +0200 Subject: [PATCH 08/11] add sample --- .../Trainers/BinaryClassification/LdSvm.cs | 143 ++++++++++++++++ .../Trainers/BinaryClassification/LdSvm.tt | 39 +++++ .../BinaryClassification/LdSvmWithOptions.cs | 152 ++++++++++++++++++ .../BinaryClassification/LdSvmWithOptions.tt | 45 ++++++ .../Microsoft.ML.Samples.csproj | 18 +++ .../LdSvm/LdSvmTrainer.cs | 2 +- 6 files changed, 398 insertions(+), 1 deletion(-) create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.cs create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.tt create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.cs create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.tt diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.cs new file mode 100644 index 0000000000..f2091e99f9 --- /dev/null +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.cs @@ -0,0 +1,143 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML; +using Microsoft.ML.Data; + +namespace Samples.Dynamic.Trainers.BinaryClassification +{ + public static class LdSvm + { + public static void Example() + { + // Create a new context for ML.NET operations. It can be used for + // exception tracking and logging, as a catalog of available operations + // and as the source of randomness. Setting the seed to a fixed number + // in this example to make outputs deterministic. + var mlContext = new MLContext(seed: 0); + + // Create a list of training data points. + var dataPoints = GenerateRandomDataPoints(1000); + + // Convert the list of data points to an IDataView object, which is + // consumable by ML.NET API. + var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints); + + // Define the trainer. + var pipeline = mlContext.BinaryClassification.Trainers + .LdSvm(); + + // Train the model. + var model = pipeline.Fit(trainingData); + + // Create testing data. Use different random seed to make it different + // from training data. + var testData = mlContext.Data + .LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123)); + + // Run the model on test data set. + var transformedTestData = model.Transform(testData); + + // Convert IDataView object to a list. + var predictions = mlContext.Data + .CreateEnumerable(transformedTestData, + reuseRowObject: false).ToList(); + + // Print 5 predictions. + foreach (var p in predictions.Take(5)) + Console.WriteLine($"Label: {p.Label}, " + + $"Prediction: {p.PredictedLabel}"); + + // Expected output: + // Label: True, Prediction: True + // Label: False, Prediction: True + // Label: True, Prediction: True + // Label: True, Prediction: True + // Label: False, Prediction: False + + // Evaluate the overall metrics. + var metrics = mlContext.BinaryClassification + .EvaluateNonCalibrated(transformedTestData); + + PrintMetrics(metrics); + + // Expected output: + // Accuracy: 0.82 + // AUC: 0.85 + // F1 Score: 0.81 + // Negative Precision: 0.82 + // Negative Recall: 0.82 + // Positive Precision: 0.81 + // Positive Recall: 0.81 + + // TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0)) + // Confusion table + // ||====================== + // PREDICTED || positive | negative | Recall + // TRUTH ||====================== + // positive || 192 | 46 | 0.8067 + // negative || 46 | 216 | 0.8244 + // ||====================== + // Precision || 0.8067 | 0.8244 | + } + + private static IEnumerable GenerateRandomDataPoints(int count, + int seed=0) + + { + var random = new Random(seed); + float randomFloat() => (float)random.NextDouble(); + for (int i = 0; i < count; i++) + { + var label = randomFloat() > 0.5f; + yield return new DataPoint + { + Label = label, + // Create random features that are correlated with the label. + // For data points with false label, the feature values are + // slightly increased by adding a constant. + Features = Enumerable.Repeat(label, 50) + .Select(x => x ? randomFloat() : randomFloat() + + 0.1f).ToArray() + + }; + } + } + + // Example with label and 50 feature values. A data set is a collection of + // such examples. + private class DataPoint + { + public bool Label { get; set; } + [VectorType(50)] + public float[] Features { get; set; } + } + + // Class used to capture predictions. + private class Prediction + { + // Original label. + public bool Label { get; set; } + // Predicted label from the trainer. + public bool PredictedLabel { get; set; } + } + + // Pretty-print BinaryClassificationMetrics objects. + private static void PrintMetrics(BinaryClassificationMetrics metrics) + { + Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}"); + Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}"); + Console.WriteLine($"F1 Score: {metrics.F1Score:F2}"); + Console.WriteLine($"Negative Precision: " + + $"{metrics.NegativePrecision:F2}"); + + Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}"); + Console.WriteLine($"Positive Precision: " + + $"{metrics.PositivePrecision:F2}"); + + Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n"); + Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable()); + } + } +} + diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.tt new file mode 100644 index 0000000000..e7f0e2df42 --- /dev/null +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvm.tt @@ -0,0 +1,39 @@ +<#@ include file="BinaryClassification.ttinclude"#> +<#+ +string ClassName = "LdSvm"; +string Trainer = "LdSvm"; +string TrainerOptions = null; +bool IsCalibrated = false; +bool CacheData = false; + +string LabelThreshold = "0.5f"; +string DataSepValue = "0.1f"; +string OptionsInclude = ""; +string Comments = ""; + +string ExpectedOutputPerInstance = @"// Expected output: + // Label: True, Prediction: True + // Label: False, Prediction: True + // Label: True, Prediction: True + // Label: True, Prediction: True + // Label: False, Prediction: False"; + +string ExpectedOutput = @"// Expected output: + // Accuracy: 0.82 + // AUC: 0.85 + // F1 Score: 0.81 + // Negative Precision: 0.82 + // Negative Recall: 0.82 + // Positive Precision: 0.81 + // Positive Recall: 0.81 + + // TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0)) + // Confusion table + // ||====================== + // PREDICTED || positive | negative | Recall + // TRUTH ||====================== + // positive || 192 | 46 | 0.8067 + // negative || 46 | 216 | 0.8244 + // ||====================== + // Precision || 0.8067 | 0.8244 |"; +#> \ No newline at end of file diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.cs new file mode 100644 index 0000000000..83234021d8 --- /dev/null +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.cs @@ -0,0 +1,152 @@ +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML; +using Microsoft.ML.Data; +using Microsoft.ML.Trainers; + +namespace Samples.Dynamic.Trainers.BinaryClassification +{ + public static class LdSvmWithOptions + { + public static void Example() + { + // Create a new context for ML.NET operations. It can be used for + // exception tracking and logging, as a catalog of available operations + // and as the source of randomness. Setting the seed to a fixed number + // in this example to make outputs deterministic. + var mlContext = new MLContext(seed: 0); + + // Create a list of training data points. + var dataPoints = GenerateRandomDataPoints(1000); + + // Convert the list of data points to an IDataView object, which is + // consumable by ML.NET API. + var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints); + + // Define trainer options. + var options = new LdSvmTrainer.Options + { + TreeDepth = 5, + NumberOfIterations = 10000, + Sigma = 0.1f, + }; + + // Define the trainer. + var pipeline = mlContext.BinaryClassification.Trainers + .LdSvm(options); + + // Train the model. + var model = pipeline.Fit(trainingData); + + // Create testing data. Use different random seed to make it different + // from training data. + var testData = mlContext.Data + .LoadFromEnumerable(GenerateRandomDataPoints(500, seed:123)); + + // Run the model on test data set. + var transformedTestData = model.Transform(testData); + + // Convert IDataView object to a list. + var predictions = mlContext.Data + .CreateEnumerable(transformedTestData, + reuseRowObject: false).ToList(); + + // Print 5 predictions. + foreach (var p in predictions.Take(5)) + Console.WriteLine($"Label: {p.Label}, " + + $"Prediction: {p.PredictedLabel}"); + + // Expected output: + // Label: True, Prediction: True + // Label: False, Prediction: True + // Label: True, Prediction: True + // Label: True, Prediction: True + // Label: False, Prediction: False + + // Evaluate the overall metrics. + var metrics = mlContext.BinaryClassification + .EvaluateNonCalibrated(transformedTestData); + + PrintMetrics(metrics); + + // Expected output: + // Accuracy: 0.80 + // AUC: 0.89 + // F1 Score: 0.79 + // Negative Precision: 0.81 + // Negative Recall: 0.81 + // Positive Precision: 0.79 + // Positive Recall: 0.79 + + // TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0)) + // Confusion table + // ||====================== + // PREDICTED || positive | negative | Recall + // TRUTH ||====================== + // positive || 189 | 49 | 0.7941 + // negative || 50 | 212 | 0.8092 + // ||====================== + // Precision || 0.7908 | 0.8123 | + } + + private static IEnumerable GenerateRandomDataPoints(int count, + int seed=0) + + { + var random = new Random(seed); + float randomFloat() => (float)random.NextDouble(); + for (int i = 0; i < count; i++) + { + var label = randomFloat() > 0.5f; + yield return new DataPoint + { + Label = label, + // Create random features that are correlated with the label. + // For data points with false label, the feature values are + // slightly increased by adding a constant. + Features = Enumerable.Repeat(label, 50) + .Select(x => x ? randomFloat() : randomFloat() + + 0.1f).ToArray() + + }; + } + } + + // Example with label and 50 feature values. A data set is a collection of + // such examples. + private class DataPoint + { + public bool Label { get; set; } + [VectorType(50)] + public float[] Features { get; set; } + } + + // Class used to capture predictions. + private class Prediction + { + // Original label. + public bool Label { get; set; } + // Predicted label from the trainer. + public bool PredictedLabel { get; set; } + } + + // Pretty-print BinaryClassificationMetrics objects. + private static void PrintMetrics(BinaryClassificationMetrics metrics) + { + Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}"); + Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}"); + Console.WriteLine($"F1 Score: {metrics.F1Score:F2}"); + Console.WriteLine($"Negative Precision: " + + $"{metrics.NegativePrecision:F2}"); + + Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}"); + Console.WriteLine($"Positive Precision: " + + $"{metrics.PositivePrecision:F2}"); + + Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n"); + Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable()); + } + } +} + diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.tt b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.tt new file mode 100644 index 0000000000..debc262e5c --- /dev/null +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LdSvmWithOptions.tt @@ -0,0 +1,45 @@ +<#@ include file="BinaryClassification.ttinclude"#> +<#+ +string ClassName="LdSvmWithOptions"; +string Trainer = "LdSvm"; +bool IsCalibrated = false; + +string LabelThreshold = "0.5f"; +string DataSepValue = "0.1f"; +string OptionsInclude = "using Microsoft.ML.Trainers;"; +string Comments= ""; +bool CacheData = false; + +string TrainerOptions = @"LdSvmTrainer.Options + { + TreeDepth = 5, + NumberOfIterations = 10000, + Sigma = 0.1f, + }"; + +string ExpectedOutputPerInstance= @"// Expected output: + // Label: True, Prediction: True + // Label: False, Prediction: True + // Label: True, Prediction: True + // Label: True, Prediction: True + // Label: False, Prediction: False"; + +string ExpectedOutput = @"// Expected output: + // Accuracy: 0.80 + // AUC: 0.89 + // F1 Score: 0.79 + // Negative Precision: 0.81 + // Negative Recall: 0.81 + // Positive Precision: 0.79 + // Positive Recall: 0.79 + + // TEST POSITIVE RATIO: 0.4760 (238.0/(238.0+262.0)) + // Confusion table + // ||====================== + // PREDICTED || positive | negative | Recall + // TRUTH ||====================== + // positive || 189 | 49 | 0.7941 + // negative || 50 | 212 | 0.8092 + // ||====================== + // Precision || 0.7908 | 0.8123 |"; +#> diff --git a/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj b/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj index da47c54d21..812114e7a5 100644 --- a/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj +++ b/docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj @@ -140,6 +140,14 @@ TextTemplatingFileGenerator LbfgsLogisticRegression.cs + + TextTemplatingFileGenerator + LdSvm.cs + + + TextTemplatingFileGenerator + LdSvmWithOptions.cs + TextTemplatingFileGenerator LightGbm.cs @@ -555,6 +563,16 @@ True LbfgsLogisticRegressionWithOptions.tt + + True + True + LdSvm.tt + + + True + True + LdSvmWithOptions.tt + True True diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs index c1215e446b..2b76be63d7 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs @@ -100,7 +100,7 @@ public sealed class Options : TrainerInputBaseWithWeight internal ICalibratorTrainerFactory Calibrator = new PlattCalibratorTrainerFactory(); [Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] - public int MaxCalibrationExamples = 1000000; + internal int MaxCalibrationExamples = 1000000; internal class Defaults { From 9e242e6c66f391d76bebf04ccd91a5ccf9a8bc8e Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Mon, 20 Jan 2020 16:11:17 +0200 Subject: [PATCH 09/11] Add option for caching/streaming --- .../LdSvm/LdSvmModelParameters.cs | 4 +- .../LdSvm/LdSvmTrainer.cs | 207 ++- .../StandardTrainersCatalog.cs | 7 +- .../LdSvm/LDSVM-def-CV-breast-cancer-out.txt | 48 +- .../LdSvm/LDSVM-def-CV-breast-cancer-rp.txt | 2 +- .../LdSvm/LDSVM-def-CV-breast-cancer.txt | 1366 ++++++++--------- .../LDSVM-def-TrainTest-breast-cancer-out.txt | 14 +- .../LDSVM-def-TrainTest-breast-cancer-rp.txt | 2 +- .../LDSVM-def-TrainTest-breast-cancer.txt | 1366 ++++++++--------- .../LdSvm/LDSVM-nob-CV-breast-cancer-out.txt | 48 +- .../LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt | 2 +- .../LdSvm/LDSVM-nob-CV-breast-cancer.txt | 1366 ++++++++--------- .../LDSVM-nob-TrainTest-breast-cancer-out.txt | 34 +- .../LDSVM-nob-TrainTest-breast-cancer-rp.txt | 2 +- .../LDSVM-nob-TrainTest-breast-cancer.txt | 1366 ++++++++--------- .../TrainerEstimators/TrainerEstimators.cs | 7 +- 16 files changed, 2990 insertions(+), 2851 deletions(-) diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs index 896490c11d..7a5fb0ec98 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmModelParameters.cs @@ -246,8 +246,8 @@ private VBuffer[] LoadVBufferArray(ModelLoadContext ctx, int length, int /// private float Margin(in VBuffer src) { - Double score = 0; - Double childIndicator; + double score = 0; + double childIndicator; int current = 0; while (current < _numLeaf - 1) { diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs index 2b76be63d7..12d625e821 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs @@ -102,6 +102,9 @@ public sealed class Options : TrainerInputBaseWithWeight [Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)] internal int MaxCalibrationExamples = 1000000; + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to cache the data before the first iteration")] + public bool Cache = Defaults.Cache; + internal class Defaults { public const int NumberOfIterations = 15000; @@ -111,10 +114,13 @@ internal class Defaults public const float LambdaTheta = 0.01f; public const float LambdaW = 0.1f; public const int TreeDepth = 3; + public const bool Cache = true; } } - private Options _options; + private const int NumberOfSamplesForGammaUpdate = 100; + + private readonly Options _options; internal LdSvmTrainer(IHostEnvironment env, Options options) : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), @@ -127,7 +133,7 @@ internal LdSvmTrainer(IHostEnvironment env, Options options) _options = options; } - private static readonly TrainerInfo _info = new TrainerInfo(calibration: true, caching: true); + private static readonly TrainerInfo _info = new TrainerInfo(calibration: true, caching: false); public override TrainerInfo Info => _info; private protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; @@ -188,13 +194,15 @@ private void UpdateGamma(int iter, int numLeaf, ref float gamma, Data data, VBuf else { float tempSum = 0; - var sample = data.SampleWithReplacement(Host.Rand, 100); + var sample = data.SampleForGammaUpdate(Host.Rand); + int sampleSize = 0; foreach (var s in sample) { int thetaIdx = Host.Rand.Next(numLeaf - 1); tempSum += Math.Abs(VectorUtils.DotProduct(in s, in theta[thetaIdx]) + biasTheta[thetaIdx]); + sampleSize++; } - tempSum /= 100.0f; + tempSum /= sampleSize; gamma = 0.1f / tempSum; gamma *= (float)Math.Pow(2.0, iter / (_options.NumberOfIterations / 10.0)); } @@ -225,7 +233,9 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, biasTheta, biasThetaPrime, tempThetaPrime, tempTheta, tempBiasW, tempBiasTheta, tempBiasThetaPrime); var gamma = 0.01f; - var data = new Data(ch, trainingData); + Data data = _options.Cache ? + (Data)new CachedData(ch, trainingData) : + new StreamingData(ch, trainingData); var pathWt = new float[numNodes]; var localWt = new float[numNodes]; var gradTheta = new float[numLeaf - 1]; @@ -261,7 +271,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, for (int i = 0; i < numLeaf - 1; i++) tempBiasTheta[i] *= coef; - var sample = data.SampleWithoutReplacement(Host.Rand); + var sample = data.SampleExamples(Host.Rand); foreach (var s in sample) { float trueLabel = s.Label; @@ -270,10 +280,9 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, // Compute path weight for (int i = 0; i < numNodes; i++) pathWt[i] = 1; - float tanhDist; for (int i = 0; i < numLeaf - 1; i++) { - tanhDist = (float)Math.Tanh(gamma * (VectorUtils.DotProduct(in features, in theta[i]) + biasTheta[i])); + var tanhDist = (float)Math.Tanh(gamma * (VectorUtils.DotProduct(in features, in theta[i]) + biasTheta[i])); pathWt[2 * i + 1] = pathWt[i] * (1 + tanhDist) / (float)2.0; pathWt[2 * i + 2] = pathWt[i] * (1 - tanhDist) / (float)2.0; } @@ -341,6 +350,7 @@ private LdSvmModelParameters TrainCore(IChannel ch, RoleMappedData trainingData, biasTheta[i] = tempBiasTheta[i]; } } + return new LdSvmModelParameters(Host, w, thetaPrime, theta, _options.Sigma, biasW, biasTheta, biasThetaPrime, _options.TreeDepth); } @@ -434,59 +444,182 @@ internal struct LabelFeatures public VBuffer Features; } - private sealed class Data + private abstract class Data + { + protected readonly IChannel Ch; + //protected readonly int Size; + + public abstract long Length { get; } + + protected Data(IChannel ch) + { + Ch = ch; + } + + public abstract IEnumerable> SampleForGammaUpdate(Random rand); + public abstract IEnumerable SampleExamples(Random rand); + } + + private sealed class CachedData : Data + { + private readonly LabelFeatures[] _examples; + private readonly int[] _indices; + + public override long Length => _examples.Length; + + public CachedData(IChannel ch, RoleMappedData data) + : base(ch) + { + var examples = new List(); + using (var cursor = new FloatLabelCursor(data, CursOpt.Label | CursOpt.Features)) + { + while (cursor.MoveNext()) + { + var example = new LabelFeatures(); + cursor.Features.CopyTo(ref example.Features); + example.Label = cursor.Label > 0 ? 1 : -1; + examples.Add(example); + } + Ch.Check(cursor.KeptRowCount > 0, NoTrainingInstancesMessage); + if (cursor.SkippedRowCount > 0) + Ch.Warning("Skipped {0} rows with missing feature/label values", cursor.SkippedRowCount); + } + _examples = examples.ToArray(); + _indices = Utils.GetIdentityPermutation((int)Length); + } + + public override IEnumerable SampleExamples(Random rand) + { + var sampleSize = Math.Max(1, (int)Math.Sqrt(Length)); + var length = (int)Length; + // Select random subset of data - the first sampleSize indices will be + // our subset. + for (int k = 0; k < sampleSize; k++) + { + int randIdx = k + rand.Next(length - k); + Utils.Swap(ref _indices[k], ref _indices[randIdx]); + } + + for (int k = 0; k < sampleSize; k++) + { + yield return _examples[_indices[k]]; + } + } + + public override IEnumerable> SampleForGammaUpdate(Random rand) + { + int length = (int)Length; + for (int i = 0; i < NumberOfSamplesForGammaUpdate; i++) + { + int index = rand.Next(length); + yield return _examples[index].Features; + } + } + } + + private sealed class StreamingData : Data { - private readonly IChannel _ch; private readonly RoleMappedData _data; + private readonly int[] _indices; + private readonly int[] _indices2; - public int Length { get; } + public override long Length { get; } - public Data(IChannel ch, RoleMappedData data) + public StreamingData(IChannel ch, RoleMappedData data) + : base(ch) { - Contracts.AssertValue(ch); - _ch = ch; - _ch.AssertValue(data); + Ch.AssertValue(data); _data = data; - using (var cursor = new FloatLabelCursor(_data, CursOpt.Label | CursOpt.Features)) + + using (var cursor = _data.Data.GetRowCursor()) { while (cursor.MoveNext()) Length++; - _ch.Check(cursor.KeptRowCount > 0, NoTrainingInstancesMessage); - if (cursor.SkippedRowCount > 0) - _ch.Warning("Skipped {0} rows with missing feature/label values", cursor.SkippedRowCount); } + _indices = Utils.GetIdentityPermutation((int)Length); + _indices2 = new int[NumberOfSamplesForGammaUpdate]; } - public IEnumerable> SampleWithReplacement(Random rand, int size) + public override IEnumerable> SampleForGammaUpdate(Random rand) { - using (var cursor = new FeatureFloatVectorCursor(_data)) + int length = (int)Length; + for (int i = 0; i < NumberOfSamplesForGammaUpdate; i++) + { + _indices2[i] = rand.Next(length); + } + Array.Sort(_indices2); + + using (var cursor = _data.Data.GetRowCursor(_data.Data.Schema[_data.Schema.Feature.Value.Name]/*, labelCol*/)) { - ValueGetter> getter = - (ref VBuffer dst) => cursor.Features.CopyTo(ref dst); - var reservoir = new ReservoirSamplerWithReplacement>(rand, size, getter); + var getter = cursor.GetGetter>(_data.Data.Schema[_data.Schema.Feature.Value.Name]); + var features = default(VBuffer); + int iIndex = 0; while (cursor.MoveNext()) - reservoir.Sample(); - reservoir.Lock(); - return reservoir.GetSample(); + { + if (cursor.Position == _indices2[iIndex]) + { + iIndex++; + getter(ref features); + var noNaNs = FloatUtils.IsFinite(features.GetValues()); + if (noNaNs) + yield return features; + while (iIndex < NumberOfSamplesForGammaUpdate && cursor.Position == _indices2[iIndex]) + { + iIndex++; + if (noNaNs) + yield return features; + } + if (iIndex == NumberOfSamplesForGammaUpdate) + break; + } + } } } - public IEnumerable SampleWithoutReplacement(Random rand) + + public override IEnumerable SampleExamples(Random rand) { - var size = Math.Max(1, (int)Math.Sqrt(Length)); - using (var cursor = new FloatLabelCursor(_data, CursOpt.Label | CursOpt.Features)) + var sampleSize = Math.Max(1, (int)Math.Sqrt(Length)); + var length = (int)Length; + // Select random subset of data - the first sampleSize indices will be + // our subset. + for (int k = 0; k < sampleSize; k++) + { + int randIdx = k + rand.Next(length - k); + Utils.Swap(ref _indices[k], ref _indices[randIdx]); + } + + Array.Sort(_indices, 0, sampleSize); + + var featureCol = _data.Data.Schema[_data.Schema.Feature.Value.Name]; + var labelCol = _data.Data.Schema[_data.Schema.Label.Value.Name]; + using (var cursor = _data.Data.GetRowCursor(featureCol, labelCol)) { + var featureGetter = cursor.GetGetter>(featureCol); + var labelGetter = RowCursorUtils.GetLabelGetter(cursor, labelCol.Index); ValueGetter getter = (ref LabelFeatures dst) => { - cursor.Features.CopyTo(ref dst.Features); - dst.Label = cursor.Label > 0 ? 1 : -1; + featureGetter(ref dst.Features); + var label = default(float); + labelGetter(ref label); + dst.Label = label > 0 ? 1 : -1; }; - var reservoir = new ReservoirSamplerWithReplacement(rand, size, getter); + + int iIndex = 0; while (cursor.MoveNext()) - reservoir.Sample(); - reservoir.Lock(); - return reservoir.GetSample(); + { + if (cursor.Position == _indices[iIndex]) + { + var example = new LabelFeatures(); + getter(ref example); + iIndex++; + if (FloatUtils.IsFinite(example.Features.GetValues())) + yield return example; + if (iIndex == sampleSize) + break; + } + } } } } @@ -494,7 +627,7 @@ public IEnumerable SampleWithoutReplacement(Random rand) private protected override BinaryPredictionTransformer MakeTransformer(LdSvmModelParameters model, DataViewSchema trainSchema) => new BinaryPredictionTransformer(Host, model, trainSchema, _options.FeatureColumnName); - [TlcModule.EntryPoint(Name = "Trainers.LocalDeepSvmBinaryClassifier", Desc = Summary, UserName = LdSvmTrainer.UserNameValue, ShortName = LoadNameValue)] + [TlcModule.EntryPoint(Name = "Trainers.LocalDeepSvmBinaryClassifier", Desc = Summary, UserName = UserNameValue, ShortName = LoadNameValue)] internal static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, Options input) { Contracts.CheckValue(env, nameof(env)); diff --git a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs index 6b7cbbf823..093f1d0074 100644 --- a/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs +++ b/src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs @@ -892,6 +892,7 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi /// The number of iterations. /// The depth of a Local Deep SVM tree. /// Indicates if the model should have a bias term. + /// Indicates whether we should iterate over the data using a cache. /// public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, string labelColumnName = DefaultColumnNames.Label, @@ -899,7 +900,8 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi string exampleWeightColumnName = null, int numberOfIterations = LdSvmTrainer.Options.Defaults.NumberOfIterations, int treeDepth = LdSvmTrainer.Options.Defaults.TreeDepth, - bool useBias = LdSvmTrainer.Options.Defaults.UseBias) + bool useBias = LdSvmTrainer.Options.Defaults.UseBias, + bool useCachedData = LdSvmTrainer.Options.Defaults.Cache) { Contracts.CheckValue(catalog, nameof(catalog)); var options = new LdSvmTrainer.Options() @@ -909,7 +911,8 @@ public static LdSvmTrainer LdSvm(this BinaryClassificationCatalog.BinaryClassifi ExampleWeightColumnName = exampleWeightColumnName, NumberOfIterations = numberOfIterations, TreeDepth = treeDepth, - UseBias = useBias + UseBias = useBias, + Cache = useCachedData }; return new LdSvmTrainer(catalog.GetEnvironment(), options); } diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt index 3e9f8af200..c78b75fd30 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 129 | 5 | 0.9627 - negative || 8 | 212 | 0.9636 + positive || 132 | 2 | 0.9851 + negative || 9 | 211 | 0.9591 ||====================== -Precision || 0.9416 | 0.9770 | -OVERALL 0/1 ACCURACY: 0.963277 -LOG LOSS/instance: 0.169342 +Precision || 0.9362 | 0.9906 | +OVERALL 0/1 ACCURACY: 0.968927 +LOG LOSS/instance: 0.154673 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.823049 -AUC: 0.994437 +LOG-LOSS REDUCTION (RIG): 0.838377 +AUC: 0.993555 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 || 104 | 1 | 0.9905 - negative || 5 | 219 | 0.9777 + positive || 96 | 9 | 0.9143 + negative || 4 | 220 | 0.9821 ||====================== -Precision || 0.9541 | 0.9955 | -OVERALL 0/1 ACCURACY: 0.981763 -LOG LOSS/instance: 0.108202 +Precision || 0.9600 | 0.9607 | +OVERALL 0/1 ACCURACY: 0.960486 +LOG LOSS/instance: 0.242519 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.880235 -AUC: 0.997491 +LOG-LOSS REDUCTION (RIG): 0.731564 +AUC: 0.978189 OVERALL RESULTS --------------------------------------- -AUC: 0.995964 (0.0015) -Accuracy: 0.972520 (0.0092) -Positive precision: 0.947867 (0.0063) -Positive recall: 0.976581 (0.0139) -Negative precision: 0.986207 (0.0092) -Negative recall: 0.970657 (0.0070) -Log-loss: 0.138772 (0.0306) -Log-loss reduction: 0.851642 (0.0286) -F1 Score: 0.961996 (0.0100) -AUPRC: 0.992216 (0.0023) +AUC: 0.985872 (0.0077) +Accuracy: 0.964706 (0.0042) +Positive precision: 0.948085 (0.0119) +Positive recall: 0.949680 (0.0354) +Negative precision: 0.975655 (0.0150) +Negative recall: 0.970617 (0.0115) +Log-loss: 0.198596 (0.0439) +Log-loss reduction: 0.784970 (0.0534) +F1 Score: 0.948293 (0.0117) +AUPRC: 0.982760 (0.0058) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt index 69a75a469a..3d0cfbd32f 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.995964 0.97252 0.947867 0.976581 0.986207 0.970657 0.138772 0.851642 0.961996 0.992216 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 +0.985872 0.964706 0.948085 0.94968 0.975655 0.970617 0.198596 0.78497 0.948293 0.98276 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt index 91bdf7ebc7..eab8595ff4 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.71565676 0.9994225 0.0008334106265114067 1 -6 0 -0.791025 0.080762364 0.12149022792826762 0 -8 0 -1.25896239 0.0305089448 0.044700507155864856 0 -9 0 -1.38335681 0.0233920421 0.034148561115367619 0 -10 0 -1.37105525 0.02401665 0.035071559440580165 0 -11 0 -1.41405082 0.0219019074 0.031948935832550936 0 -18 1 2.348152 0.9885216 0.016655639662023197 1 -20 1 2.12984 0.981598258 0.026795406145621904 1 -21 1 2.45671821 0.9909325 0.013141271895541365 1 -25 1 -0.534199536 0.1337126 2.9027926249889937 0 -28 0 -1.41405082 0.0219019074 0.031948935832550936 0 -31 0 -1.38587177 0.0232663117 0.033962837701420445 0 -32 1 2.57749152 0.993028641 0.010092766592031333 1 -35 0 -1.41405082 0.0219019074 0.031948935832550936 0 -37 0 -1.12074852 0.0408754423 0.060209910436864064 0 +5 1 2.2156775 0.997534633 0.003561164798113537 1 +6 0 -0.911945939 0.0226912573 0.033113697278273707 0 +8 0 -1.14737034 0.0110095935 0.015971568396088372 0 +9 0 -1.23973382 0.008274109 0.011986674365363201 0 +10 0 -1.187979 0.009711217 0.014078797115366068 0 +11 0 -1.21978474 0.00880121 0.012753668191187785 0 +18 1 1.49708533 0.9772281 0.033232738794008475 1 +20 1 1.51304531 0.978311062 0.031634840293273223 1 +21 1 1.74465024 0.9893575 0.015436209766917479 1 +25 1 0.8598778 0.8544031 0.22701124958291558 1 +28 0 -1.21978474 0.00880121 0.012753668191187785 0 +31 0 -1.21522188 0.008926372 0.012935854083571594 0 +32 1 1.84213245 0.992128253 0.01140146421199654 1 +35 0 -1.21978474 0.00880121 0.012753668191187785 0 +37 0 -1.15875232 0.0106292767 0.015416885950538131 0 40 0 ? ? ? 0 -41 1 1.40501475 0.9157823 0.12692345646293324 1 -44 1 2.987932 0.9971555 0.0041096117193789384 1 -45 0 -1.37293565 0.02392013 0.034928890377298467 0 -46 1 1.79317474 0.9622413 0.055529384757224708 1 -48 0 -1.33363008 0.0260185543 0.03803380565588186 0 -50 1 1.36321557 0.908433855 0.13854662296358206 1 -51 1 -0.8197643 0.0762029961 3.7140084684630521 0 -52 1 1.80269814 0.9629932 0.054402477227371071 1 -54 1 2.350358 0.9885764 0.01657561123663184 1 -56 1 2.14331341 0.982124746 0.026021812930855533 1 -60 1 1.20727229 0.875718951 0.19146016196837917 1 -63 1 0.8110667 0.7470937 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-1.126352 0.01348801 0.019591508788153328 0 +672 0 -1.16417992 0.0119843781 0.017394241882785575 0 +673 0 -1.14258194 0.0128213409 0.018616888398042625 0 +674 0 -1.15316188 0.0124043757 0.018007650497104549 0 +675 0 -1.14773512 0.01261655 0.018317630740794042 0 +676 0 -1.14571786 0.0126963295 0.018434204191161176 0 +677 0 -1.17693579 0.0115156593 0.016709983505179046 0 +684 0 -1.14917362 0.0125599606 0.018234949222921695 0 +686 0 -1.14917362 0.0125599606 0.018234949222921695 0 +687 0 -1.18189633 0.0113383159 0.016451173678755544 0 +690 0 -1.09990573 0.0146482782 0.021289307719958551 0 +695 0 -1.15591455 0.0122981044 0.017852415999630816 0 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt index 8c624d4e5d..1889ae15a9 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt @@ -13,23 +13,23 @@ TRUTH ||====================== ||====================== Precision || 0.9547 | 0.9841 | OVERALL 0/1 ACCURACY: 0.973646 -LOG LOSS/instance: 0.134558 +LOG LOSS/instance: 0.111359 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.855934 -AUC: 0.996485 +LOG-LOSS REDUCTION (RIG): 0.880773 +AUC: 0.996127 OVERALL RESULTS --------------------------------------- -AUC: 0.996485 (0.0000) +AUC: 0.996127 (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: 0.134558 (0.0000) -Log-loss reduction: 0.855934 (0.0000) +Log-loss: 0.111359 (0.0000) +Log-loss reduction: 0.880773 (0.0000) F1 Score: 0.962656 (0.0000) -AUPRC: 0.992617 (0.0000) +AUPRC: 0.992120 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt index 98e46d7356..6bf0580535 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.996485 0.973646 0.954733 0.970711 0.984091 0.975225 0.134558 0.855934 0.962656 0.992617 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 +0.996127 0.973646 0.954733 0.970711 0.984091 0.975225 0.111359 0.880773 0.962656 0.99212 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt index 046317f735..5372d61ed6 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -1.16173422 0.0177958626 0.025905195230747628 0 -1 0 1.32534826 0.8961344 3.2672098520960038 1 -2 0 -1.20223069 0.016123388 0.023450696358868185 0 -3 0 1.08325648 0.825607836 2.5195928802768077 1 -4 0 -1.1702559 0.0174303278 0.025368385097309185 0 -5 1 2.90026712 0.997669756 0.003365753905759369 1 -6 0 -0.9797738 0.02765974 0.040466837555981454 0 -7 0 -1.2510047 0.0143133309 0.02079898047262117 0 -8 0 -1.11929226 0.0197314341 0.028751033755950586 0 -9 0 -1.2300154 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0.0077157741858594232 0 +686 0 -2.13968182 0.005333891 0.0077157741858594232 0 +687 0 -1.88697374 0.009315451 0.013502342797023538 0 +688 0 -1.94154692 0.008260281 0.011966558072548136 0 +689 0 -1.7013818 0.0140045062 0.02034704163615253 0 +690 0 -2.09519982 0.00588485 0.0085151235723658954 0 +691 1 1.72635138 0.966538668 0.049100644341601586 1 +692 0 -2.02336884 0.00689637847 0.009983836887529891 0 +693 0 -1.905468 0.00894369651 0.012961073334627225 0 +694 0 -1.91169918 0.00882178452 0.012783615199816776 0 +695 0 -2.08880234 0.005968612 0.0086366873893912732 0 +696 1 2.56445742 0.994653165 0.0077345479985959113 1 +697 1 1.61531138 0.957570732 0.062549038223462514 1 +698 1 1.95258951 0.9794898 0.029897621061773853 1 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt index 3aa54d6fb8..da9594d461 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt @@ -11,43 +11,43 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 108 | 26 | 0.8060 - negative || 18 | 202 | 0.9182 + positive || 122 | 12 | 0.9104 + negative || 13 | 207 | 0.9409 ||====================== -Precision || 0.8571 | 0.8860 | -OVERALL 0/1 ACCURACY: 0.875706 -LOG LOSS/instance: 0.521307 +Precision || 0.9037 | 0.9452 | +OVERALL 0/1 ACCURACY: 0.929379 +LOG LOSS/instance: 0.296235 Test-set entropy (prior Log-Loss/instance): 0.956998 -LOG-LOSS REDUCTION (RIG): 0.455269 -AUC: 0.896065 +LOG-LOSS REDUCTION (RIG): 0.690454 +AUC: 0.976323 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 || 100 | 5 | 0.9524 - negative || 10 | 214 | 0.9554 + positive || 105 | 0 | 1.0000 + negative || 71 | 153 | 0.6830 ||====================== -Precision || 0.9091 | 0.9772 | -OVERALL 0/1 ACCURACY: 0.954407 -LOG LOSS/instance: 0.330099 +Precision || 0.5966 | 1.0000 | +OVERALL 0/1 ACCURACY: 0.784195 +LOG LOSS/instance: 0.125131 Test-set entropy (prior Log-Loss/instance): 0.903454 -LOG-LOSS REDUCTION (RIG): 0.634625 -AUC: 0.957440 +LOG-LOSS REDUCTION (RIG): 0.861497 +AUC: 0.996173 OVERALL RESULTS --------------------------------------- -AUC: 0.926753 (0.0307) -Accuracy: 0.915057 (0.0394) -Positive precision: 0.883117 (0.0260) -Positive recall: 0.879176 (0.0732) -Negative precision: 0.931567 (0.0456) -Negative recall: 0.936769 (0.0186) -Log-loss: 0.425703 (0.0956) -Log-loss reduction: 0.544947 (0.0897) -F1 Score: 0.880501 (0.0497) -AUPRC: 0.924610 (0.0357) +AUC: 0.986248 (0.0099) +Accuracy: 0.856787 (0.0726) +Positive precision: 0.750147 (0.1536) +Positive recall: 0.955224 (0.0448) +Negative precision: 0.972603 (0.0274) +Negative recall: 0.811972 (0.1289) +Log-loss: 0.210683 (0.0856) +Log-loss reduction: 0.775976 (0.0855) +F1 Score: 0.827197 (0.0799) +AUPRC: 0.974238 (0.0176) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt index 51c5b74fd2..16335f15ac 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.926753 0.915057 0.883117 0.879176 0.931567 0.936769 0.425703 0.544947 0.880501 0.92461 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 +0.986248 0.856787 0.750147 0.955224 0.972603 0.811972 0.210683 0.775976 0.827197 0.974238 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt index d18960b311..1892101ed2 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 3.41612816 0.9884243 0.016797614052768715 1 -6 0 2.00301147 0.904062152 3.3817561098671698 1 -8 0 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-0.426068157 0.00200938736 0.0029018496281247642 0 +669 1 2.93621635 0.985921562 0.020455222140025046 1 +671 0 0.08327055 0.009719549 0.014090935014663877 1 +672 0 -0.5169841 0.00151523785 0.0021876839777941236 0 +673 0 0.5364872 0.0386301577 0.056836547350606968 1 +674 0 -0.467838824 0.00176502729 0.0025486459997644214 0 +675 0 0.245766252 0.0160087664 0.023282632221243875 1 +676 0 -0.369622558 0.00239406456 0.0034580461182113303 0 +677 0 -0.02076494 0.00705174264 0.010209554161734765 0 +684 0 -0.360438764 0.0024632588 0.0035581153401573321 0 +686 0 -0.360438764 0.0024632588 0.0035581153401573321 0 +687 0 -0.05869791 0.006271946 0.0090770001465010457 0 +690 0 -0.22539714 0.00374411582 0.0054117547784680335 0 +695 0 -0.2963965 0.00300451647 0.0043411257811459729 0 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt index cb7c0ac1d9..a4b1ebf737 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -8,28 +8,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 213 | 26 | 0.8912 - negative || 8 | 436 | 0.9820 + positive || 229 | 10 | 0.9582 + negative || 15 | 429 | 0.9662 ||====================== -Precision || 0.9638 | 0.9437 | -OVERALL 0/1 ACCURACY: 0.950220 -LOG LOSS/instance: 0.241702 +Precision || 0.9385 | 0.9772 | +OVERALL 0/1 ACCURACY: 0.963397 +LOG LOSS/instance: 0.169853 Test-set entropy (prior Log-Loss/instance): 0.934003 -LOG-LOSS REDUCTION (RIG): 0.741219 -AUC: 0.974179 +LOG-LOSS REDUCTION (RIG): 0.818145 +AUC: 0.985836 OVERALL RESULTS --------------------------------------- -AUC: 0.974179 (0.0000) -Accuracy: 0.950220 (0.0000) -Positive precision: 0.963801 (0.0000) -Positive recall: 0.891213 (0.0000) -Negative precision: 0.943723 (0.0000) -Negative recall: 0.981982 (0.0000) -Log-loss: 0.241702 (0.0000) -Log-loss reduction: 0.741219 (0.0000) -F1 Score: 0.926087 (0.0000) -AUPRC: 0.968491 (0.0000) +AUC: 0.985836 (0.0000) +Accuracy: 0.963397 (0.0000) +Positive precision: 0.938525 (0.0000) +Positive recall: 0.958159 (0.0000) +Negative precision: 0.977221 (0.0000) +Negative recall: 0.966216 (0.0000) +Log-loss: 0.169853 (0.0000) +Log-loss reduction: 0.818145 (0.0000) +F1 Score: 0.948240 (0.0000) +AUPRC: 0.979961 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt index 4d1fef994d..572012f620 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ LdSvm AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.974179 0.95022 0.963801 0.891213 0.943723 0.981982 0.241702 0.741219 0.926087 0.968491 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 +0.985836 0.963397 0.938525 0.958159 0.977221 0.966216 0.169853 0.818145 0.94824 0.979961 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 diff --git a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt index eb77b591f0..6c6cb05ad7 100644 --- a/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/Common/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -2.05067277 0.004818092 0.0069678369315782467 0 -1 0 0.448933244 0.830629 2.5617411442702527 1 -2 0 -1.50646365 0.0213776454 0.031175856409611683 0 -3 0 1.40901577 0.985912263 6.1494163487970059 1 -4 0 -1.98359311 0.005795711 0.0083857680726871133 0 -5 1 2.245398 0.998591661 0.0020332351152866487 1 -6 0 0.201730967 0.7121122 1.7964213842381598 1 -7 0 -1.48738074 0.0225113872 0.032848199031000655 0 -8 0 -1.74930215 0.01102891 0.015999746808623677 0 -9 0 -1.39703393 0.02872553 0.042049052727238002 0 -10 0 -1.1431849 0.0563595742 0.083690869555279684 0 -11 0 -1.34861791 0.032711383 0.047981673383003576 0 -12 1 -0.266166329 0.403765738 1.308409600512231 0 -13 0 -1.03961921 0.07369616 0.11044259875178986 0 -14 1 1.25805247 0.9787578 0.030976197147596852 1 -15 1 -1.05823636 0.0702538341 3.8312792266894777 0 -16 0 -1.66340721 0.0139486128 0.020265261610933002 0 -17 0 -1.88762021 0.00754643371 0.010928490117893923 0 -18 1 1.38500226 0.9849584 0.021865286637118751 1 -19 0 -2.21434546 0.00306784688 0.0044327704862290023 0 -20 1 0.483564258 0.843694031 0.24520819980129582 1 -21 1 1.047329 0.9625603 0.055051179023420355 1 -22 0 -1.50677311 0.0213597286 0.031149443546424762 0 +0 0 -1.34523559 0.0108650913 0.015760790513476976 0 +1 0 1.49407411 0.902731836 3.3618885011824067 1 +2 0 -1.133536 0.0178314857 0.025957520590757389 0 +3 0 2.345435 0.9859183 6.1500329829898224 1 +4 0 -1.42622459 0.008982031 0.013016878019192124 0 +5 1 3.70533013 0.999434054 0.00081671876617773131 1 +6 0 0.736459434 0.6058071 1.3430263969308538 1 +7 0 -1.24728644 0.0136699779 0.019857647483037793 0 +8 0 -1.44022369 0.008691035 0.012593317497228066 0 +9 0 -0.984802961 0.0251905639 0.036807878511661667 0 +10 0 -0.9783698 0.02556825 0.037366954250139035 0 +11 0 -1.16825747 0.016444061 0.023920987789862715 0 +12 1 0.407378256 0.41305083 1.2756087631166826 1 +13 0 -0.918146968 0.0293816924 0.043024022729914492 0 +14 1 2.56169367 0.9915239 0.012280515576689426 1 +15 1 -0.206473827 0.140834853 2.8279236863060184 0 +16 0 -1.22450244 0.0144186364 0.02095311864989343 0 +17 0 -1.34737659 0.0108106136 0.015681334654750991 0 +18 1 2.660923 0.993290663 0.0097121451399610446 1 +19 0 -1.33138049 0.0112242606 0.016284749106563891 0 +20 1 1.75258744 0.944880068 0.08179687226911124 1 +21 1 2.246268 0.982247531 0.02584145798218308 1 +22 0 -1.20437741 0.0151134338 0.021970522437852913 0 23 1 ? ? ? 0 -24 0 -1.39549279 0.0288448185 0.042226251630381545 0 -25 1 0.6853803 0.9041959 0.14529271144477401 1 -26 0 -1.03658748 0.07427123 0.11133853878997595 0 -27 0 -1.819399 0.00910106 0.013190168163384874 0 -28 0 -1.34861791 0.032711383 0.047981673383003576 0 -29 0 -0.914736331 0.101061143 0.15370510336942747 0 -30 0 -1.25765777 0.0416890755 0.061434279829404324 0 -31 0 -1.560827 0.018445529 0.026859763214252534 0 -32 1 0.169259056 0.6933356 0.5283742709520991 1 -33 0 -1.6178304 0.0157950073 0.022969259931274518 0 -34 0 -1.43008208 0.02627981 0.03842083969747119 0 -35 0 -1.34861791 0.032711383 0.047981673383003576 0 -36 1 1.28157806 0.9800706 0.029042429762764617 1 -37 0 -2.59408617 0.00107421342 0.0015505953556180032 0 -38 1 1.7107656 0.99384135 0.0089125265229150919 1 -39 1 0.454036266 0.8326074 0.26429173398331346 1 +24 0 -1.26554847 0.013097696 0.019020819243027041 0 +25 1 1.4272902 0.8878972 0.17153545334841103 1 +26 0 -0.7275266 0.04542882 0.067075314995188123 0 +27 0 -1.23058152 0.01421502 0.020655096138705281 0 +28 0 -1.16825747 0.016444061 0.023920987789862715 0 +29 0 -0.8973669 0.0308214165 0.045165569853283967 0 +30 0 -0.932605565 0.0284186415 0.041593285349571969 0 +31 0 -1.30940664 0.0118181054 0.01715147175223403 0 +32 1 1.5631634 0.916210651 0.12624875989727399 1 +33 0 -1.368497 0.010287472 0.014918554263361989 0 +34 0 -1.13599432 0.0177295823 0.025807843647681798 0 +35 0 -1.16825747 0.016444061 0.023920987789862715 0 +36 1 2.56436753 0.9915771 0.012203157679230926 1 +37 0 -1.15071893 0.0171310771 0.024929065837536443 0 +38 1 2.65691042 0.9932269 0.0098047803329497623 1 +39 1 1.19292676 0.8195301 0.2871311049475303 1 40 0 ? ? ? 0 -41 1 -0.584315956 0.219144791 2.1900437056057305 0 -42 1 1.60723042 0.9918136 0.011859086707395305 1 -43 1 -1.49208629 0.0222264826 5.4915765317418295 0 -44 1 1.01516867 0.9592159 0.060072552690525458 1 -45 0 -1.33344686 0.0340668522 0.050004751218798285 0 -46 1 2.03758025 0.997499 0.0036127156211692045 1 -47 0 -1.187926 0.0501222834 0.07418629618970396 0 -48 0 -1.98359311 0.005795711 0.0083857680726871133 0 -49 1 1.88293779 0.996167541 0.0055396923592444152 1 -50 1 0.08791265 0.6434883 0.63601420456920355 1 -51 1 0.0500853248 0.6191176 0.69171458170096367 1 -52 1 0.8796393 0.9417274 0.086618589013731054 1 -53 1 -0.267266661 0.403032541 1.3110317667029283 0 -54 1 -0.23179841 0.4268672 1.2281408269806007 0 -55 1 0.8853425 0.9425879 0.085300914796779592 1 -56 1 2.14826965 0.9981579 0.0026600126454325167 1 -57 1 -2.77751923 0.0006467127 10.594587383755581 0 -58 1 -0.273287475 0.39902842 1.3254365900948162 0 -59 1 -0.465654731 0.280472159 1.8340705237311181 0 -60 1 0.7051518 0.9088341 0.13791112861259597 1 -61 0 -1.04202878 0.07324203 0.10973547941233293 0 -62 1 1.110082 0.9683424 0.046410794039183342 1 -63 1 -0.149260953 0.483475059 1.0484866266833213 0 -64 0 -1.187926 0.0501222834 0.07418629618970396 0 -65 1 -2.45377922 0.00158335431 9.3028001542711927 0 -66 0 -1.88762021 0.00754643371 0.010928490117893923 0 -67 1 0.8923603 0.9436304 0.08370620179093631 1 -68 1 1.28902006 0.9804691 0.028455919823523728 1 -69 0 -1.33136463 0.03425707 0.050288882906993598 0 -70 0 -1.59450591 0.0168309454 0.024488587496206273 0 -71 1 -0.105343319 0.5138641 0.96054123003917757 0 -72 0 -1.26722908 0.0406431332 0.059860518268417172 0 -73 1 1.46219575 0.9878176 0.017683442589238964 1 -74 1 0.04191252 0.6137675 0.70423582911000748 1 -75 0 -1.22430241 0.04553879 0.067241529037932776 0 -76 0 -1.00789487 0.07992131 0.12017084564841535 0 -77 0 -1.24933112 0.0426199 0.062836274587727989 0 -78 0 -1.27564859 0.0397438779 0.058508838289968494 0 -79 0 -1.60002315 0.0165800266 0.024120438084393913 0 -80 0 -1.81036782 0.009329347 0.013522579523211615 0 -81 0 -1.711612 0.0122271655 0.017748802309320518 0 -82 0 -1.72909486 0.0116561437 0.016915035396113196 0 -83 0 -2.448737 0.00160557823 0.0023182212951019902 0 -84 1 1.49736965 0.98893553 0.016051621742242887 1 -85 1 0.911142051 0.946333468 0.079579446418562877 1 -86 1 0.665838957 0.899405539 0.1529563259406195 1 -87 1 1.88755143 0.996216 0.005469514156908069 1 -88 0 -1.88762021 0.00754643371 0.010928490117893923 0 -89 0 -1.65667427 0.0142073445 0.020643862423956764 0 -90 0 -1.39549279 0.0288448185 0.042226251630381545 0 -91 0 -1.35471714 0.0321812555 0.047191213631587049 0 -92 0 -1.88762021 0.00754643371 0.010928490117893923 0 -93 0 -1.187926 0.0501222834 0.07418629618970396 0 -94 0 -1.560827 0.018445529 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0 +683 0 -0.9620985 0.0265483018 0.038818699401177374 0 +684 0 -0.9620985 0.0265483018 0.038818699401177374 0 +685 0 -0.9620985 0.0265483018 0.038818699401177374 0 +686 0 -0.9620985 0.0265483018 0.038818699401177374 0 +687 0 -1.00408876 0.0240905937 0.035180866279680485 0 +688 0 -1.103018 0.0191454068 0.027888814908375512 0 +689 0 -1.7415607 0.004269537 0.0061728268829501862 0 +690 0 -1.10741115 0.0189505741 0.02760227270938008 0 +691 1 2.76253629 0.9947209 0.0076363400762171447 1 +692 0 -1.07358742 0.0205022264 0.029885881678958708 0 +693 0 -1.30096614 0.0120543735 0.017496452421665955 0 +694 0 -1.17001545 0.01637671 0.023822199119170957 0 +695 0 -1.02731121 0.0228281561 0.033315800349128705 0 +696 1 2.66787577 0.993399739 0.0095537267718683403 1 +697 1 2.13402629 0.976953149 0.033638717498429102 1 +698 1 2.23973656 0.9819752 0.026241508712247105 1 diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs index 2af773f6ba..5feb5eca2e 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/TrainerEstimators.cs @@ -160,8 +160,11 @@ public void TestEstimatorMulticlassNaiveBayesTrainer() [Fact] public void TestEstimatorLdSvmTrainer() { - var trainers = new[] { ML.BinaryClassification.Trainers.LdSvm(new LdSvmTrainer.Options() { LambdaTheta = 0.02f, NumberOfIterations = 100 }), - ML.BinaryClassification.Trainers.LdSvm(numberOfIterations: 100) }; + var trainers = new[] { + ML.BinaryClassification.Trainers.LdSvm(new LdSvmTrainer.Options() { LambdaTheta = 0.02f, NumberOfIterations = 100 }), + ML.BinaryClassification.Trainers.LdSvm(numberOfIterations: 100), + ML.BinaryClassification.Trainers.LdSvm(numberOfIterations: 100, useCachedData: false) + }; foreach (var trainer in trainers) { From 0c7ba1a0162615161f7446257d9d36e047843a1b Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Tue, 21 Jan 2020 11:20:46 +0200 Subject: [PATCH 10/11] Fix entry point catalog test --- .../BaselineOutput/Common/EntryPoints/core_manifest.json | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 2b059f56c6..88b22dd643 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -13760,6 +13760,15 @@ "SortOrder": 150.0, "IsNullable": false, "Default": 1000000 + }, + { + "Name": "Cache", + "Type": "Bool", + "Desc": "Whether to cache the data before the first iteration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true } ], "Outputs": [ From 20022b1354620e3cba643043efe138858fd1bf21 Mon Sep 17 00:00:00 2001 From: Yael Dekel Date: Wed, 5 Feb 2020 13:09:22 +0200 Subject: [PATCH 11/11] Delete commented code. --- src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs index 12d625e821..70e44e8de2 100644 --- a/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs +++ b/src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs @@ -447,7 +447,6 @@ internal struct LabelFeatures private abstract class Data { protected readonly IChannel Ch; - //protected readonly int Size; public abstract long Length { get; } @@ -550,7 +549,7 @@ public override IEnumerable> SampleForGammaUpdate(Random rand) } Array.Sort(_indices2); - using (var cursor = _data.Data.GetRowCursor(_data.Data.Schema[_data.Schema.Feature.Value.Name]/*, labelCol*/)) + using (var cursor = _data.Data.GetRowCursor(_data.Data.Schema[_data.Schema.Feature.Value.Name])) { var getter = cursor.GetGetter>(_data.Data.Schema[_data.Schema.Feature.Value.Name]); var features = default(VBuffer);