From c89fbd4dd8c4eee6795e6b0b6393d9950cdbe58c Mon Sep 17 00:00:00 2001 From: Pete Luferenko Date: Fri, 31 Aug 2018 15:14:42 -0700 Subject: [PATCH 1/6] Converted normalizers to be estimators. --- .../Transforms/NormalizeColumn.cs | 369 +++++++------- .../Transforms/NormalizeColumnDbl.cs | 78 ++- .../Transforms/NormalizeColumnSng.cs | 78 ++- .../Transforms/NormalizeTransform.cs | 482 ------------------ .../Transforms/NormalizeUtils.cs | 176 +++++++ .../Transforms/Normalizer.cs | 430 ++++++++++++++++ .../SingleDebug/Normalizer/normalized.tsv | 176 +++++++ .../SingleRelease/Normalizer/normalized.tsv | 176 +++++++ .../Transformers/NormalizerTests.cs | 77 +++ 9 files changed, 1289 insertions(+), 753 deletions(-) delete mode 100644 src/Microsoft.ML.Data/Transforms/NormalizeTransform.cs create mode 100644 src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs create mode 100644 src/Microsoft.ML.Data/Transforms/Normalizer.cs create mode 100644 test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv create mode 100644 test/BaselineOutput/SingleRelease/Normalizer/normalized.tsv create mode 100644 test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs index a01b584a97..60030214f0 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs @@ -13,22 +13,21 @@ using Microsoft.ML.Runtime.Model.Pfa; using Microsoft.ML.Runtime.Internal.Internallearn; using Newtonsoft.Json.Linq; +using Microsoft.ML.Runtime.EntryPoints; +using System.Linq; -[assembly: LoadableClass(NormalizeTransform.MinMaxNormalizerSummary, typeof(NormalizeTransform), typeof(NormalizeTransform.MinMaxArguments), typeof(SignatureDataTransform), +[assembly: LoadableClass(NormalizeTransform.MinMaxNormalizerSummary, typeof(IDataTransform), typeof(NormalizeTransform), typeof(NormalizeTransform.MinMaxArguments), typeof(SignatureDataTransform), NormalizeTransform.MinMaxNormalizerUserName, "MinMaxNormalizer", NormalizeTransform.MinMaxNormalizerShortName)] -[assembly: LoadableClass(NormalizeTransform.MeanVarNormalizerSummary, typeof(NormalizeTransform), typeof(NormalizeTransform.MeanVarArguments), typeof(SignatureDataTransform), +[assembly: LoadableClass(NormalizeTransform.MeanVarNormalizerSummary, typeof(IDataTransform), typeof(NormalizeTransform), typeof(NormalizeTransform.MeanVarArguments), typeof(SignatureDataTransform), NormalizeTransform.MeanVarNormalizerUserName, "MeanVarNormalizer", NormalizeTransform.MeanVarNormalizerShortName, "ZScoreNormalizer", "ZScore", "GaussianNormalizer", "Gaussian")] -[assembly: LoadableClass(NormalizeTransform.LogMeanVarNormalizerSummary, typeof(NormalizeTransform), typeof(NormalizeTransform.LogMeanVarArguments), typeof(SignatureDataTransform), +[assembly: LoadableClass(NormalizeTransform.LogMeanVarNormalizerSummary, typeof(IDataTransform), typeof(NormalizeTransform), typeof(NormalizeTransform.LogMeanVarArguments), typeof(SignatureDataTransform), NormalizeTransform.LogMeanVarNormalizerUserName, "LogMeanVarNormalizer", NormalizeTransform.LogMeanVarNormalizerShortName, "LogNormalNormalizer", "LogNormal")] -[assembly: LoadableClass(NormalizeTransform.BinNormalizerSummary, typeof(NormalizeTransform), typeof(NormalizeTransform.BinArguments), typeof(SignatureDataTransform), +[assembly: LoadableClass(NormalizeTransform.BinNormalizerSummary, typeof(IDataTransform), typeof(NormalizeTransform), typeof(NormalizeTransform.BinArguments), typeof(SignatureDataTransform), NormalizeTransform.BinNormalizerUserName, "BinNormalizer", NormalizeTransform.BinNormalizerShortName)] -[assembly: LoadableClass(NormalizeTransform.SupervisedBinNormalizerSummary, typeof(NormalizeTransform), typeof(NormalizeTransform.SupervisedBinArguments), typeof(SignatureDataTransform), - NormalizeTransform.SupervisedBinNormalizerUserName, "SupervisedBinNormalizer", NormalizeTransform.SupervisedBinNormalizerShortName)] - [assembly: LoadableClass(typeof(NormalizeTransform.AffineColumnFunction), null, typeof(SignatureLoadColumnFunction), "Affine Normalizer", AffineNormSerializationUtils.LoaderSignature)] @@ -170,6 +169,26 @@ public sealed class MeanVarArguments : AffineArgumentsBase public bool UseCdf = Defaults.MeanVarCdf; } + public abstract class ArgumentsBase : TransformInputBase + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Max number of examples used to train the normalizer", ShortName = "maxtrain")] + public long MaxTrainingExamples = 1000000000; + + public abstract OneToOneColumn[] GetColumns(); + + public string TestType(ColumnType type) + { + if (type.ItemType != NumberType.R4 && type.ItemType != NumberType.R8) + return "Expected R4 or R8 item type"; + + // We require vectors to be of known size. + if (type.IsVector && !type.IsKnownSizeVector) + return "Expected known size vector"; + + return null; + } + } + public sealed class LogMeanVarArguments : ArgumentsBase { [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use CDF as the output", ShortName = "cdf")] @@ -234,190 +253,120 @@ public sealed class SupervisedBinArguments : BinArgumentsBase /// Input . This is the output from previous transform or loader. /// Name of the output column. /// Name of the column to be transformed. If this is null '' will be used. - public static NormalizeTransform CreateMinMaxNormalizer(IHostEnvironment env, IDataView input, string name, string source = null) - { - var args = new MinMaxArguments() - { - Column = new[] { new AffineColumn(){ - Source = source ?? name, - Name = name - } - } - }; - return Create(env, args, input); - } - - /// - /// Public create method corresponding to SignatureDataTransform. - /// - public static NormalizeTransform Create(IHostEnvironment env, MinMaxArguments args, IDataView input) + public static IDataTransform CreateMinMaxNormalizer(IHostEnvironment env, IDataView input, string name, string source = null) { Contracts.CheckValue(env, nameof(env)); - var h = env.Register("Normalize(MinMax)"); - NormalizeTransform func; - using (var ch = h.Start("Training")) - { - func = Create(h, args, input, MinMaxUtils.CreateBuilder); - ch.Done(); - } - return func; - } - /// - /// A helper method to create MeanVarNormalizer transform for public facing API. - /// - /// Host Environment. - /// Input . This is the output from previous transform or loader. - /// Name of the output column. - /// Name of the column to be transformed. If this is null '' will be used. - /// /// Whether to use CDF as the output. - public static NormalizeTransform CreateMeanVarNormalizer(IHostEnvironment env, - IDataView input, - string name, - string source = null, - bool useCdf = Defaults.MeanVarCdf) - { - var args = new MeanVarArguments() - { - Column = new[] { new AffineColumn(){ - Source = source ?? name, - Name = name - } - }, - UseCdf = useCdf - }; - return Create(env, args, input); + var normalizer = new Normalizer(env, new Normalizer.MinMaxColumn(source ?? name, name)); + return normalizer.Fit(input).MakeDataTransform(input); } /// - /// Public create method corresponding to SignatureDataTransform. + /// Potentially apply a min-max normalizer to the data's feature column, keeping all existing role + /// mappings except for the feature role mapping. /// - public static NormalizeTransform Create(IHostEnvironment env, MeanVarArguments args, IDataView input) + /// The host environment to use to potentially instantiate the transform + /// The role-mapped data that is potentially going to be modified by this method. + /// The trainer to query as to whether it wants normalization. If the + /// 's is true + /// True if the normalizer was applied and was modified + public static bool CreateIfNeeded(IHostEnvironment env, ref RoleMappedData data, ITrainer trainer) { Contracts.CheckValue(env, nameof(env)); - var h = env.Register("Normalize(MeanVar)"); - NormalizeTransform func; - using (var ch = h.Start("Training")) - { - func = Create(h, args, input, MeanVarUtils.CreateBuilder); - ch.Done(); - } - return func; + env.CheckValue(data, nameof(data)); + env.CheckValue(trainer, nameof(trainer)); + + // If the trainer does not need normalization, or if the features either don't exist + // or are not normalized, return false. + if (!trainer.Info.NeedNormalization || data.Schema.FeaturesAreNormalized() != false) + return false; + var featInfo = data.Schema.Feature; + env.AssertValue(featInfo); // Should be defined, if FeaturesAreNormalized returned a definite value. + + var view = CreateMinMaxNormalizer(env, data.Data, name: featInfo.Name); + data = new RoleMappedData(view, data.Schema.GetColumnRoleNames()); + return true; } /// - /// A helper method to create LogMeanVarNormalizer transform for public facing API. + /// Public create method corresponding to SignatureDataTransform. /// - /// Host Environment. - /// Input . This is the output from previous transform or loader. - /// Name of the output column. - /// Name of the column to be transformed. If this is null '' will be used. - /// /// Whether to use CDF as the output. - public static NormalizeTransform CreateLogMeanVarNormalizer(IHostEnvironment env, - IDataView input, - string name, - string source = null, - bool useCdf = Defaults.LogMeanVarCdf) + public static IDataTransform Create(IHostEnvironment env, MinMaxArguments args, IDataView input) { - var args = new LogMeanVarArguments() - { - Column = new[] { new LogNormalColumn(){ - Source = source ?? name, - Name = name - } - }, - UseCdf = useCdf - }; - return Create(env, args, input); + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(args, nameof(args)); + env.CheckValue(args.Column, nameof(args.Column)); + + var columns = args.Column + .Select(col => new Normalizer.MinMaxColumn( + col.Source ?? col.Name, + col.Name, + col.MaxTrainingExamples ?? args.MaxTrainingExamples, + col.FixZero ?? args.FixZero)) + .ToArray(); + var normalizer = new Normalizer(env, columns); + return normalizer.Fit(input).MakeDataTransform(input); } /// /// Public create method corresponding to SignatureDataTransform. /// - public static NormalizeTransform Create(IHostEnvironment env, LogMeanVarArguments args, IDataView input) + public static IDataTransform Create(IHostEnvironment env, MeanVarArguments args, IDataView input) { Contracts.CheckValue(env, nameof(env)); - var h = env.Register("Normalize(LogMeanVar)"); - NormalizeTransform func; - using (var ch = h.Start("Training")) - { - func = Create(h, args, input, LogMeanVarUtils.CreateBuilder); - ch.Done(); - } - return func; - } - - public static NormalizeTransform CreateBinningNormalizer(IHostEnvironment env, - IDataView input, - string name, - string source = null, - int numBins = Defaults.NumBins) - { - var args = new BinArguments() - { - Column = new[] { new BinColumn(){ - Source = source ?? name, - Name = name - } - }, - NumBins = numBins - }; - return Create(env, args, input); + env.CheckValue(args, nameof(args)); + env.CheckValue(args.Column, nameof(args.Column)); + + var columns = args.Column + .Select(col => new Normalizer.MeanVarColumn( + col.Source ?? col.Name, + col.Name, + col.MaxTrainingExamples ?? args.MaxTrainingExamples, + col.FixZero ?? args.FixZero)) + .ToArray(); + var normalizer = new Normalizer(env, columns); + return normalizer.Fit(input).MakeDataTransform(input); } /// /// Public create method corresponding to SignatureDataTransform. /// - public static NormalizeTransform Create(IHostEnvironment env, BinArguments args, IDataView input) + public static IDataTransform Create(IHostEnvironment env, LogMeanVarArguments args, IDataView input) { Contracts.CheckValue(env, nameof(env)); - var h = env.Register("Normalize(Bin)"); - NormalizeTransform func; - using (var ch = h.Start("Training")) - { - func = Create(h, args, input, BinUtils.CreateBuilder); - ch.Done(); - } - return func; - } - - public static NormalizeTransform CreateSupervisedBinningNormalizer(IHostEnvironment env, - IDataView input, - string labelColumn, - string name, - string source = null, - int numBins = Defaults.NumBins, - int minBinSize = Defaults.MinBinSize) - { - var args = new SupervisedBinArguments() - { - Column = new[] { new BinColumn(){ - Source = source ?? name, - Name = name - } - }, - LabelColumn = labelColumn, - NumBins = numBins, - MinBinSize = minBinSize - }; - return Create(env, args, input); + env.CheckValue(args, nameof(args)); + env.CheckValue(args.Column, nameof(args.Column)); + + var columns = args.Column + .Select(col => new Normalizer.LogMeanVarColumn( + col.Source ?? col.Name, + col.Name, + col.MaxTrainingExamples ?? args.MaxTrainingExamples, + args.UseCdf)) + .ToArray(); + var normalizer = new Normalizer(env, columns); + return normalizer.Fit(input).MakeDataTransform(input); } /// /// Public create method corresponding to SignatureDataTransform. /// - public static NormalizeTransform Create(IHostEnvironment env, SupervisedBinArguments args, IDataView input) + public static IDataTransform Create(IHostEnvironment env, BinArguments args, IDataView input) { Contracts.CheckValue(env, nameof(env)); - var h = env.Register("Normalize(SupervisedBin)"); - NormalizeTransform func; - using (var ch = h.Start("Training")) - { - var labelColumnId = SupervisedBinUtils.GetLabelColumnId(ch, input.Schema, args.LabelColumn); - func = Create(h, args, input, SupervisedBinUtils.CreateBuilder, labelColumnId); - ch.Done(); - } - return func; + env.CheckValue(args, nameof(args)); + env.CheckValue(args.Column, nameof(args.Column)); + + var columns = args.Column + .Select(col => new Normalizer.BinningColumn( + col.Source ?? col.Name, + col.Name, + col.MaxTrainingExamples ?? args.MaxTrainingExamples, + col.FixZero ?? args.FixZero, + col.NumBins ?? args.NumBins)) + .ToArray(); + var normalizer = new Normalizer(env, columns); + return normalizer.Fit(input).MakeDataTransform(input); } public abstract partial class AffineColumnFunction : IColumnFunction @@ -960,7 +909,7 @@ protected override bool AcceptColumnValue() protected abstract bool AcceptColumnValue(ref VBuffer buffer); } - private static partial class MinMaxUtils + internal static partial class MinMaxUtils { public static IColumnFunctionBuilder CreateBuilder(MinMaxArguments args, IHost host, int icol, int srcIndex, ColumnType srcType, IRowCursor cursor) @@ -968,25 +917,35 @@ public static IColumnFunctionBuilder CreateBuilder(MinMaxArguments args, IHost h Contracts.AssertValue(host); host.AssertValue(args); + return CreateBuilder(new Normalizer.MinMaxColumn( + args.Column[icol].Source ?? args.Column[icol].Name, + args.Column[icol].Name, + args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples, + args.Column[icol].FixZero ?? args.FixZero), host, srcIndex, srcType, cursor); + } + + public static IColumnFunctionBuilder CreateBuilder(Normalizer.MinMaxColumn column, IHost host, + int srcIndex, ColumnType srcType, IRowCursor cursor) + { if (srcType.IsNumber) { if (srcType == NumberType.R4) - return Sng.MinMaxOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Sng.MinMaxOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); if (srcType == NumberType.R8) - return Dbl.MinMaxOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Dbl.MinMaxOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); } - if (srcType.IsVector && srcType.ItemType.IsNumber) + if (srcType.IsKnownSizeVector && srcType.ItemType.IsNumber) { if (srcType.ItemType == NumberType.R4) - return Sng.MinMaxVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Sng.MinMaxVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); if (srcType.ItemType == NumberType.R8) - return Dbl.MinMaxVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Dbl.MinMaxVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); } - throw host.ExceptUserArg(nameof(args.Column), "Wrong column type for column {0}. Expected: R4, R8, Vec or Vec. Got: {1}.", args.Column[icol].Source, srcType.ToString()); + throw host.ExceptParam(nameof(srcType), "Wrong column type for input column. Expected: R4, R8, Vec or Vec. Got: {0}.", srcType.ToString()); } } - private static partial class MeanVarUtils + internal static partial class MeanVarUtils { public static IColumnFunctionBuilder CreateBuilder(MeanVarArguments args, IHost host, int icol, int srcIndex, ColumnType srcType, IRowCursor cursor) @@ -994,25 +953,39 @@ public static IColumnFunctionBuilder CreateBuilder(MeanVarArguments args, IHost Contracts.AssertValue(host); host.AssertValue(args); + return CreateBuilder(new Normalizer.MeanVarColumn( + args.Column[icol].Source ?? args.Column[icol].Name, + args.Column[icol].Name, + args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples, + args.Column[icol].FixZero ?? args.FixZero, + args.UseCdf), host, srcIndex, srcType, cursor); + } + + public static IColumnFunctionBuilder CreateBuilder(Normalizer.MeanVarColumn column, IHost host, + int srcIndex, ColumnType srcType, IRowCursor cursor) + { + Contracts.AssertValue(host); + if (srcType.IsNumber) { if (srcType == NumberType.R4) - return Sng.MeanVarOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Sng.MeanVarOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); if (srcType == NumberType.R8) - return Dbl.MeanVarOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Dbl.MeanVarOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); } - if (srcType.IsVector && srcType.ItemType.IsNumber) + if (srcType.IsKnownSizeVector && srcType.ItemType.IsNumber) { if (srcType.ItemType == NumberType.R4) - return Sng.MeanVarVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Sng.MeanVarVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); if (srcType.ItemType == NumberType.R8) - return Dbl.MeanVarVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Dbl.MeanVarVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); } - throw host.ExceptUserArg(nameof(args.Column), "Wrong column type for column {0}. Expected: R4, R8, Vec or Vec. Got: {1}.", args.Column[icol].Source, srcType.ToString()); + throw host.ExceptParam(nameof(srcType), "Wrong column type for input column. Expected: R4, R8, Vec or Vec. Got: {0}.", srcType.ToString()); } + } - private static partial class LogMeanVarUtils + internal static partial class LogMeanVarUtils { public static IColumnFunctionBuilder CreateBuilder(LogMeanVarArguments args, IHost host, int icol, int srcIndex, ColumnType srcType, IRowCursor cursor) @@ -1020,25 +993,38 @@ public static IColumnFunctionBuilder CreateBuilder(LogMeanVarArguments args, IHo Contracts.AssertValue(host); host.AssertValue(args); + return CreateBuilder(new Normalizer.LogMeanVarColumn( + args.Column[icol].Source ?? args.Column[icol].Name, + args.Column[icol].Name, + args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples, + args.UseCdf), host, srcIndex, srcType, cursor); + } + + public static IColumnFunctionBuilder CreateBuilder(Normalizer.LogMeanVarColumn column, IHost host, + int srcIndex, ColumnType srcType, IRowCursor cursor) + { + Contracts.AssertValue(host); + host.AssertValue(column); + if (srcType.IsNumber) { if (srcType == NumberType.R4) - return Sng.MeanVarOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Sng.MeanVarOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); if (srcType == NumberType.R8) - return Dbl.MeanVarOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Dbl.MeanVarOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); } - if (srcType.IsVector && srcType.ItemType.IsNumber) + if (srcType.IsKnownSizeVector && srcType.ItemType.IsNumber) { if (srcType.ItemType == NumberType.R4) - return Sng.MeanVarVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Sng.MeanVarVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); if (srcType.ItemType == NumberType.R8) - return Dbl.MeanVarVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Dbl.MeanVarVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); } - throw host.ExceptUserArg(nameof(args.Column), "Wrong column type for column {0}. Expected: R4, R8, Vec or Vec. Got: {1}.", args.Column[icol].Source, srcType.ToString()); + throw host.ExceptUserArg(nameof(column), "Wrong column type for column {0}. Expected: R4, R8, Vec or Vec. Got: {1}.", column.Input, srcType.ToString()); } } - private static partial class BinUtils + internal static partial class BinUtils { public static IColumnFunctionBuilder CreateBuilder(BinArguments args, IHost host, int icol, int srcIndex, ColumnType srcType, IRowCursor cursor) @@ -1046,25 +1032,38 @@ public static IColumnFunctionBuilder CreateBuilder(BinArguments args, IHost host Contracts.AssertValue(host); host.AssertValue(args); + return CreateBuilder(new Normalizer.BinningColumn( + args.Column[icol].Source ?? args.Column[icol].Name, + args.Column[icol].Name, + args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples, + args.Column[icol].FixZero ?? args.FixZero, + args.Column[icol].NumBins ?? args.NumBins), host, srcIndex, srcType, cursor); + } + + public static IColumnFunctionBuilder CreateBuilder(Normalizer.BinningColumn column, IHost host, + int srcIndex, ColumnType srcType, IRowCursor cursor) + { + Contracts.AssertValue(host); + if (srcType.IsNumber) { if (srcType == NumberType.R4) - return Sng.BinOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Sng.BinOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); if (srcType == NumberType.R8) - return Dbl.BinOneColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter(srcIndex)); + return Dbl.BinOneColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter(srcIndex)); } - if (srcType.IsVector && srcType.ItemType.IsNumber) + if (srcType.IsKnownSizeVector && srcType.ItemType.IsNumber) { if (srcType.ItemType == NumberType.R4) - return Sng.BinVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Sng.BinVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); if (srcType.ItemType == NumberType.R8) - return Dbl.BinVecColumnFunctionBuilder.Create(args, host, icol, srcType, cursor.GetGetter>(srcIndex)); + return Dbl.BinVecColumnFunctionBuilder.Create(column, host, srcType, cursor.GetGetter>(srcIndex)); } - throw host.ExceptUserArg(nameof(args.Column), "Wrong column type for column {0}. Expected: R4, R8, Vec or Vec. Got: {1}.", args.Column[icol].Source, srcType.ToString()); + throw host.ExceptParam(nameof(column), "Wrong column type for column {0}. Expected: R4, R8, Vec or Vec. Got: {1}.", column.Input, srcType.ToString()); } } - private static class SupervisedBinUtils + internal static class SupervisedBinUtils { public static IColumnFunctionBuilder CreateBuilder(SupervisedBinArguments args, IHost host, int icol, int srcIndex, ColumnType srcType, IRowCursor cursor) diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs index 6cad82c127..11a23e52ef 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs @@ -1255,7 +1255,7 @@ private void GetResult(ref VBuffer input, ref VBuffer value, Buf } } - private static partial class MinMaxUtils + internal static partial class MinMaxUtils { public static void ComputeScaleAndOffset(bool fixZero, TFloat max, TFloat min, out TFloat scale, out TFloat offset) { @@ -1312,7 +1312,7 @@ private static void ComputeScaleAndOffsetFixZero(TFloat max, TFloat min, out TFl } } - private static partial class MeanVarUtils + internal static partial class MeanVarUtils { public static void ComputeScaleAndOffset(Double mean, Double stddev, out TFloat scale, out TFloat offset) { @@ -1364,7 +1364,7 @@ public static TFloat Cdf(TFloat input, TFloat mean, TFloat stddev) } } - private static partial class BinUtils + internal static partial class BinUtils { public static TFloat GetValue(ref TFloat input, TFloat[] binUpperBounds, TFloat den, TFloat offset) { @@ -1422,13 +1422,11 @@ private MinMaxOneColumnFunctionBuilder(IHost host, long lim, bool fix, ValueGett { } - public static IColumnFunctionBuilder Create(MinMaxArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MinMaxColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - return new MinMaxOneColumnFunctionBuilder(host, lim, fix, getter); + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + return new MinMaxOneColumnFunctionBuilder(host, column.MaxTrainingExamples, column.FixZero, getter); } public override IColumnFunction CreateColumnFunction() @@ -1474,14 +1472,12 @@ private MinMaxVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, { } - public static IColumnFunctionBuilder Create(MinMaxArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MinMaxColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); var cv = srcType.ValueCount; - return new MinMaxVecColumnFunctionBuilder(host, cv, lim, fix, getter); + return new MinMaxVecColumnFunctionBuilder(host, cv, column.MaxTrainingExamples, column.FixZero, getter); } public override IColumnFunction CreateColumnFunction() @@ -1538,21 +1534,19 @@ private MeanVarOneColumnFunctionBuilder(IHost host, long lim, bool fix, ValueGet _buffer = new VBuffer(1, new TFloat[1]); } - public static IColumnFunctionBuilder Create(MeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MeanVarColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - return new MeanVarOneColumnFunctionBuilder(host, lim, fix, getter, false, args.UseCdf); + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + return new MeanVarOneColumnFunctionBuilder(host, column.MaxTrainingExamples, column.FixZero, getter, false, column.UseCdf); } - public static IColumnFunctionBuilder Create(LogMeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.LogMeanVarColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - return new MeanVarOneColumnFunctionBuilder(host, lim, false, getter, true, args.UseCdf); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + return new MeanVarOneColumnFunctionBuilder(host, lim, false, getter, true, column.UseCdf); } protected override bool ProcessValue(ref TFloat origVal) @@ -1614,23 +1608,21 @@ private MeanVarVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, _useCdf = useCdf; } - public static IColumnFunctionBuilder Create(MeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MeanVarColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); var cv = srcType.ValueCount; - return new MeanVarVecColumnFunctionBuilder(host, cv, lim, fix, getter, false, args.UseCdf); + return new MeanVarVecColumnFunctionBuilder(host, cv, column.MaxTrainingExamples, column.FixZero, getter, false, column.UseCdf); } - public static IColumnFunctionBuilder Create(LogMeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.LogMeanVarColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); var cv = srcType.ValueCount; - return new MeanVarVecColumnFunctionBuilder(host, cv, lim, false, getter, true, args.UseCdf); + return new MeanVarVecColumnFunctionBuilder(host, cv, lim, false, getter, true, column.UseCdf); } protected override bool ProcessValue(ref VBuffer buffer) @@ -1732,14 +1724,14 @@ private BinOneColumnFunctionBuilder(IHost host, long lim, bool fix, int numBins, _values = new List(); } - public static IColumnFunctionBuilder Create(BinArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.BinningColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - var numBins = args.Column[icol].NumBins ?? args.NumBins; - host.CheckUserArg(numBins > 1, nameof(args.NumBins), "Must be greater than 1"); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + bool fix = column.FixZero; + var numBins = column.NumBins; + host.CheckUserArg(numBins > 1, nameof(column.NumBins), "Must be greater than 1"); return new BinOneColumnFunctionBuilder(host, lim, fix, numBins, getter); } @@ -1781,14 +1773,14 @@ private BinVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, int } } - public static IColumnFunctionBuilder Create(BinArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.BinningColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - var numBins = args.Column[icol].NumBins ?? args.NumBins; - host.CheckUserArg(numBins > 1, nameof(args.NumBins), "Must be greater than 1"); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + bool fix = column.FixZero; + var numBins = column.NumBins; + host.CheckUserArg(numBins > 1, nameof(column.NumBins), "numBins must be greater than 1"); var cv = srcType.ValueCount; return new BinVecColumnFunctionBuilder(host, cv, lim, fix, numBins, getter); } diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs index af94f31454..5001b3122b 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs @@ -1257,7 +1257,7 @@ private void GetResult(ref VBuffer input, ref VBuffer value, Buf } } - private static partial class MinMaxUtils + internal static partial class MinMaxUtils { public static void ComputeScaleAndOffset(bool fixZero, TFloat max, TFloat min, out TFloat scale, out TFloat offset) { @@ -1314,7 +1314,7 @@ private static void ComputeScaleAndOffsetFixZero(TFloat max, TFloat min, out TFl } } - private static partial class MeanVarUtils + internal static partial class MeanVarUtils { public static void ComputeScaleAndOffset(Double mean, Double stddev, out TFloat scale, out TFloat offset) { @@ -1366,7 +1366,7 @@ public static TFloat Cdf(TFloat input, TFloat mean, TFloat stddev) } } - private static partial class BinUtils + internal static partial class BinUtils { public static TFloat GetValue(ref TFloat input, TFloat[] binUpperBounds, TFloat den, TFloat offset) { @@ -1424,13 +1424,11 @@ private MinMaxOneColumnFunctionBuilder(IHost host, long lim, bool fix, ValueGett { } - public static IColumnFunctionBuilder Create(MinMaxArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MinMaxColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(ColumnBase.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - return new MinMaxOneColumnFunctionBuilder(host, lim, fix, getter); + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + return new MinMaxOneColumnFunctionBuilder(host, column.MaxTrainingExamples, column.FixZero, getter); } public override IColumnFunction CreateColumnFunction() @@ -1476,14 +1474,12 @@ private MinMaxVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, { } - public static IColumnFunctionBuilder Create(MinMaxArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MinMaxColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); var cv = srcType.ValueCount; - return new MinMaxVecColumnFunctionBuilder(host, cv, lim, fix, getter); + return new MinMaxVecColumnFunctionBuilder(host, cv, column.MaxTrainingExamples, column.FixZero, getter); } public override IColumnFunction CreateColumnFunction() @@ -1540,21 +1536,19 @@ private MeanVarOneColumnFunctionBuilder(IHost host, long lim, bool fix, ValueGet _buffer = new VBuffer(1, new TFloat[1]); } - public static IColumnFunctionBuilder Create(MeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MeanVarColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - return new MeanVarOneColumnFunctionBuilder(host, lim, fix, getter, false, args.UseCdf); + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + return new MeanVarOneColumnFunctionBuilder(host, column.MaxTrainingExamples, column.FixZero, getter, false, column.UseCdf); } - public static IColumnFunctionBuilder Create(LogMeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.LogMeanVarColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - return new MeanVarOneColumnFunctionBuilder(host, lim, false, getter, true, args.UseCdf); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + return new MeanVarOneColumnFunctionBuilder(host, lim, false, getter, true, column.UseCdf); } protected override bool ProcessValue(ref TFloat origVal) @@ -1616,23 +1610,21 @@ private MeanVarVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, _useCdf = useCdf; } - public static IColumnFunctionBuilder Create(MeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.MeanVarColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; + host.CheckUserArg(column.MaxTrainingExamples > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); var cv = srcType.ValueCount; - return new MeanVarVecColumnFunctionBuilder(host, cv, lim, fix, getter, false, args.UseCdf); + return new MeanVarVecColumnFunctionBuilder(host, cv, column.MaxTrainingExamples, column.FixZero, getter, false, column.UseCdf); } - public static IColumnFunctionBuilder Create(LogMeanVarArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.LogMeanVarColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); + var lim =column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); var cv = srcType.ValueCount; - return new MeanVarVecColumnFunctionBuilder(host, cv, lim, false, getter, true, args.UseCdf); + return new MeanVarVecColumnFunctionBuilder(host, cv, lim, false, getter, true, column.UseCdf); } protected override bool ProcessValue(ref VBuffer buffer) @@ -1734,14 +1726,14 @@ private BinOneColumnFunctionBuilder(IHost host, long lim, bool fix, int numBins, _values = new List(); } - public static IColumnFunctionBuilder Create(BinArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.BinningColumn column, IHost host, ColumnType srcType, ValueGetter getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - var numBins = args.Column[icol].NumBins ?? args.NumBins; - host.CheckUserArg(numBins > 1, nameof(args.NumBins), "Must be greater than 1"); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + bool fix = column.FixZero; + var numBins = column.NumBins; + host.CheckUserArg(numBins > 1, nameof(column.NumBins), "Must be greater than 1"); return new BinOneColumnFunctionBuilder(host, lim, fix, numBins, getter); } @@ -1783,14 +1775,14 @@ private BinVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, int } } - public static IColumnFunctionBuilder Create(BinArguments args, IHost host, int icol, ColumnType srcType, + public static IColumnFunctionBuilder Create(Normalizer.BinningColumn column, IHost host, ColumnType srcType, ValueGetter> getter) { - var lim = args.Column[icol].MaxTrainingExamples ?? args.MaxTrainingExamples; - host.CheckUserArg(lim > 1, nameof(args.MaxTrainingExamples), "Must be greater than 1"); - bool fix = args.Column[icol].FixZero ?? args.FixZero; - var numBins = args.Column[icol].NumBins ?? args.NumBins; - host.CheckUserArg(numBins > 1, nameof(args.NumBins), "numBins must be greater than 1"); + var lim = column.MaxTrainingExamples; + host.CheckUserArg(lim > 1, nameof(column.MaxTrainingExamples), "Must be greater than 1"); + bool fix = column.FixZero; + var numBins = column.NumBins; + host.CheckUserArg(numBins > 1, nameof(column.NumBins), "numBins must be greater than 1"); var cv = srcType.ValueCount; return new BinVecColumnFunctionBuilder(host, cv, lim, fix, numBins, getter); } diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeTransform.cs b/src/Microsoft.ML.Data/Transforms/NormalizeTransform.cs deleted file mode 100644 index bf9d77ed49..0000000000 --- a/src/Microsoft.ML.Data/Transforms/NormalizeTransform.cs +++ /dev/null @@ -1,482 +0,0 @@ -// 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 Float = System.Single; - -using System; -using Microsoft.ML.Runtime; -using Microsoft.ML.Runtime.CommandLine; -using Microsoft.ML.Runtime.Data; -using Microsoft.ML.Runtime.EntryPoints; -using Microsoft.ML.Runtime.Internal.Utilities; -using Microsoft.ML.Runtime.Model; -using Microsoft.ML.Runtime.Model.Onnx; -using Microsoft.ML.Runtime.Model.Pfa; -using Newtonsoft.Json.Linq; -using System.Collections.Generic; - -[assembly: LoadableClass(typeof(NormalizeTransform), null, typeof(SignatureLoadDataTransform), - "Normalizer", NormalizeTransform.LoaderSignature, NormalizeTransform.LoaderSignatureOld)] - -[assembly: LoadableClass(typeof(void), typeof(Normalize), null, typeof(SignatureEntryPointModule), "Normalize")] - -namespace Microsoft.ML.Runtime.Data -{ - /// - /// Signature for a repository based loader of a IColumnFunction - /// - public delegate void SignatureLoadColumnFunction(ModelLoadContext ctx, IHost host, ColumnType typeSrc); - - public interface IColumnFunctionBuilder - { - /// - /// Trains on the current value. - /// - /// True if it can use more values for training. - bool ProcessValue(); - - /// - /// Finishes training and returns a column function. - /// - IColumnFunction CreateColumnFunction(); - } - - /// - /// Interface to define an aggregate function over values - /// - public interface IColumnAggregator - { - /// - /// Updates the aggregate function with a value - /// - void ProcessValue(ref T val); - - /// - /// Finishes the aggregation - /// - void Finish(); - } - - public interface IColumnFunction : ICanSaveModel - { - Delegate GetGetter(IRow input, int icol); - - void AttachMetadata(MetadataDispatcher.Builder bldr, ColumnType typeSrc); - - JToken PfaInfo(BoundPfaContext ctx, JToken srcToken); - - bool CanSaveOnnx { get; } - - bool OnnxInfo(OnnxContext ctx, OnnxNode nodeProtoWrapper, int featureCount); - } - - public sealed partial class NormalizeTransform : OneToOneTransformBase - { - public abstract class ArgumentsBase : TransformInputBase - { - [Argument(ArgumentType.AtMostOnce, HelpText = "Max number of examples used to train the normalizer", ShortName = "maxtrain")] - public long MaxTrainingExamples = 1000000000; - - public abstract OneToOneColumn[] GetColumns(); - - public string TestType(ColumnType type) - { - if (type.ItemType != NumberType.R4 && type.ItemType != NumberType.R8) - return "Expected R4 or R8 item type"; - - // We require vectors to be of known size. - if (type.IsVector && !type.IsKnownSizeVector) - return "Expected known size vector"; - - return null; - } - } - - public override bool CanSavePfa => true; - public override bool CanSaveOnnx => true; - public const string LoaderSignature = "NormalizeTransform"; - internal const string LoaderSignatureOld = "NormalizeFunction"; - private static VersionInfo GetVersionInfo() - { - return new VersionInfo( - modelSignature: "NORMFUNC", - // verWrittenCur: 0x00010001, // Initial - // verWrittenCur: 0x00010002, // Changed to OneToOneColumn - verWrittenCur: 0x00010003, // Support generic column functions - verReadableCur: 0x00010003, - verWeCanReadBack: 0x00010003, - loaderSignature: LoaderSignature, - loaderSignatureAlt: LoaderSignatureOld); - } - - private readonly IColumnFunction[] _functions; - - private static NormalizeTransform Create(IHost host, TArgs args, IDataView input, - Func fnCreate, - params int[] extraTrainColumnIds) where TArgs : ArgumentsBase - { - Contracts.AssertValue(host, "host"); - host.CheckValue(args, nameof(args)); - host.CheckValue(input, nameof(input)); - - // Curry the args and host in a lambda to that is passed to the ctor. - Func fn = - (int iinfo, int colSrc, ColumnType typeSrc, IRowCursor curs) => - fnCreate(args, host, iinfo, colSrc, typeSrc, curs); - - return new NormalizeTransform(host, args, input, fn, extraTrainColumnIds); - } - - private NormalizeTransform(IHost host, ArgumentsBase args, IDataView input, - Func fnCreate, - params int[] extraTrainColumnIds) - : base(host, host.CheckRef(args, nameof(args)).GetColumns(), input, args.TestType) - { - Host.AssertNonEmpty(Infos); - Host.Assert(Utils.Size(Infos) == Utils.Size(args.GetColumns())); - - bool[] activeInput = new bool[Source.Schema.ColumnCount]; - if (Utils.Size(extraTrainColumnIds) > 0) - { - foreach (var colId in extraTrainColumnIds) - { - Host.Assert(0 <= colId && colId < activeInput.Length); - activeInput[colId] = true; - } - } - - foreach (var info in Infos) - activeInput[info.Source] = true; - - var functionBuilders = new IColumnFunctionBuilder[Infos.Length]; - var needMoreData = new bool[Infos.Length]; - - // Go through the input data and pass it to the column function builders. - using (var pch = Host.StartProgressChannel("Normalize")) - { - long numRows = 0; - - pch.SetHeader(new ProgressHeader("examples"), e => e.SetProgress(0, numRows)); - using (var cursor = Source.GetRowCursor(col => activeInput[col])) - { - for (int i = 0; i < Infos.Length; i++) - { - needMoreData[i] = true; - var info = Infos[i]; - functionBuilders[i] = fnCreate(i, info.Source, info.TypeSrc, cursor); - } - - while (cursor.MoveNext()) - { - // If the row has bad values, the good values are still being used for training. - // The comparisons in the code below are arranged so that NaNs in the input are not recorded. - // REVIEW: Should infinities and/or NaNs be filtered before the normalization? Should we not record infinities for min/max? - // Currently, infinities are recorded and will result in zero scale which in turn will result in NaN output for infinity input. - bool any = false; - for (int i = 0; i < Infos.Length; i++) - { - if (!needMoreData[i]) - continue; - var info = Infos[i]; - Host.Assert(!info.TypeSrc.IsVector || info.TypeSrc.IsVector && info.TypeSrc.IsKnownSizeVector); - Host.Assert(functionBuilders[i] != null); - any |= needMoreData[i] = functionBuilders[i].ProcessValue(); - } - numRows++; - - if (!any) - break; - } - } - - pch.Checkpoint(numRows); - - _functions = new IColumnFunction[Infos.Length]; - for (int i = 0; i < Infos.Length; i++) - _functions[i] = functionBuilders[i].CreateColumnFunction(); - } - SetMetadata(); - } - - /// - /// Potentially apply a min-max normalizer to the data's feature column, keeping all existing role - /// mappings except for the feature role mapping. - /// - /// The host environment to use to potentially instantiate the transform - /// The role-mapped data that is potentially going to be modified by this method. - /// The trainer to query as to whether it wants normalization. If the - /// 's is true - /// True if the normalizer was applied and was modified - public static bool CreateIfNeeded(IHostEnvironment env, ref RoleMappedData data, ITrainer trainer) - { - Contracts.CheckValue(env, nameof(env)); - env.CheckValue(data, nameof(data)); - env.CheckValue(trainer, nameof(trainer)); - - // If the trainer does not need normalization, or if the features either don't exist - // or are not normalized, return false. - if (!trainer.Info.NeedNormalization || data.Schema.FeaturesAreNormalized() != false) - return false; - var featInfo = data.Schema.Feature; - env.AssertValue(featInfo); // Should be defined, if FeaturesAreNormalized returned a definite value. - - var view = CreateMinMaxNormalizer(env, data.Data, name: featInfo.Name); - data = new RoleMappedData(view, data.Schema.GetColumnRoleNames()); - return true; - } - - private NormalizeTransform(IHost host, ModelLoadContext ctx, IDataView input) - : base(host, ctx, input, null) - { - Host.AssertValue(ctx); - - // *** Binary format *** - // - // - Host.AssertNonEmpty(Infos); - - // Individual normalization models. - _functions = new IColumnFunction[Infos.Length]; - for (int iinfo = 0; iinfo < Infos.Length; iinfo++) - { - var typeSrc = Infos[iinfo].TypeSrc; - // REVIEW: this check (was even an assert) here is too late. Apparently, no-one tests compatibility - // of the types at deserialization (aka re-application), which is a bug. - if (typeSrc.ValueCount == 0) - throw Host.Except("Column '{0}' is a vector of variable size, which is not supported for normalizers", Infos[iinfo].Name); - var dir = string.Format("Normalizer_{0:000}", iinfo); - ctx.LoadModel(Host, out _functions[iinfo], dir, Host, typeSrc); - } - SetMetadata(); - } - - public static NormalizeTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) - { - Contracts.CheckValue(env, nameof(env)); - var h = env.Register("Normalize"); - - h.CheckValue(ctx, nameof(ctx)); - ctx.CheckAtModel(GetVersionInfo()); - h.CheckValue(input, nameof(input)); - - return h.Apply("Loading Model", - ch => - { - // *** Binary format *** - // int: sizeof(Float) - // - int cbFloat = ctx.Reader.ReadInt32(); - ch.CheckDecode(cbFloat == sizeof(Float)); - return new NormalizeTransform(h, ctx, input); - }); - } - - public override void Save(ModelSaveContext ctx) - { - Host.CheckValue(ctx, nameof(ctx)); - ctx.CheckAtModel(); - ctx.SetVersionInfo(GetVersionInfo()); - - // *** Binary format *** - // int: sizeof(Float) - // - ctx.Writer.Write(sizeof(Float)); - SaveBase(ctx); - - // Individual normalization models. - Host.Assert(_functions.Length == Infos.Length); - for (int iinfo = 0; iinfo < _functions.Length; iinfo++) - { - Host.Assert(Infos[iinfo].TypeSrc.ValueCount > 0); - var dir = string.Format("Normalizer_{0:000}", iinfo); - Host.Assert(_functions[iinfo] != null); - ctx.SaveSubModel(dir, _functions[iinfo].Save); - } - } - - protected override JToken SaveAsPfaCore(BoundPfaContext ctx, int iinfo, ColInfo info, JToken srcToken) - { - Contracts.AssertValue(ctx); - Contracts.Assert(0 <= iinfo && iinfo < Infos.Length); - Contracts.Assert(Infos[iinfo] == info); - Contracts.AssertValue(srcToken); - Contracts.Assert(CanSavePfa); - return _functions[iinfo].PfaInfo(ctx, srcToken); - } - - protected override bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName) - { - Contracts.AssertValue(ctx); - Contracts.Assert(0 <= iinfo && iinfo < Infos.Length); - Contracts.Assert(Infos[iinfo] == info); - Contracts.Assert(CanSaveOnnx); - - if (info.TypeSrc.ValueCount == 0) - return false; - - if (_functions[iinfo].CanSaveOnnx) - { - string opType = "Scaler"; - var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType)); - _functions[iinfo].OnnxInfo(ctx, node, info.TypeSrc.ValueCount); - return true; - } - - return false; - } - - protected override ColumnType GetColumnTypeCore(int iinfo) - { - Host.Assert(0 <= iinfo & iinfo < Infos.Length); - return Infos[iinfo].TypeSrc; - } - - private void SetMetadata() - { - var md = Metadata; - for (int iinfo = 0; iinfo < Infos.Length; iinfo++) - { - using (var bldr = md.BuildMetadata(iinfo, Source.Schema, Infos[iinfo].Source, - MetadataUtils.Kinds.SlotNames)) - { - bldr.AddPrimitive(MetadataUtils.Kinds.IsNormalized, BoolType.Instance, DvBool.True); - _functions[iinfo].AttachMetadata(bldr, Infos[iinfo].TypeSrc); - } - } - md.Seal(); - } - - protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) - { - Host.AssertValueOrNull(ch); - Host.AssertValue(input); - Host.Assert(0 <= iinfo && iinfo < Infos.Length); - disposer = null; - - return _functions[iinfo].GetGetter(input, Infos[iinfo].Source); - } - } - - public static class NormalizeUtils - { - /// - /// Returns whether the feature column in the schema is indicated to be normalized. If the features column is not - /// specified on the schema, then this will return null. - /// - /// The role-mapped schema to query - /// Returns null if does not have - /// defined, and otherwise returns a Boolean value as returned from - /// on that feature column - /// - public static bool? FeaturesAreNormalized(this RoleMappedSchema schema) - { - // REVIEW: The role mapped data has the ability to have multiple columns fill the role of features, which is - // useful in some trainers that are nonetheless parameteric and can therefore benefit from normalization. - Contracts.CheckValue(schema, nameof(schema)); - var featInfo = schema.Feature; - return featInfo == null ? default(bool?) : schema.Schema.IsNormalized(featInfo.Index); - } - } - - /// - /// This contains entry-point definitions related to . - /// - public static class Normalize - { - [TlcModule.EntryPoint(Name = "Transforms.MinMaxNormalizer", Desc = NormalizeTransform.MinMaxNormalizerSummary, UserName = NormalizeTransform.MinMaxNormalizerUserName, ShortName = NormalizeTransform.MinMaxNormalizerShortName)] - public static CommonOutputs.TransformOutput MinMax(IHostEnvironment env, NormalizeTransform.MinMaxArguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("MinMax"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - var xf = NormalizeTransform.Create(host, input, input.Data); - return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; - } - - [TlcModule.EntryPoint(Name = "Transforms.MeanVarianceNormalizer", Desc = NormalizeTransform.MeanVarNormalizerSummary, UserName = NormalizeTransform.MeanVarNormalizerUserName, ShortName = NormalizeTransform.MeanVarNormalizerShortName)] - public static CommonOutputs.TransformOutput MeanVar(IHostEnvironment env, NormalizeTransform.MeanVarArguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("MeanVar"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - var xf = NormalizeTransform.Create(host, input, input.Data); - return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; - } - - [TlcModule.EntryPoint(Name = "Transforms.LogMeanVarianceNormalizer", Desc = NormalizeTransform.LogMeanVarNormalizerSummary, UserName = NormalizeTransform.LogMeanVarNormalizerUserName, ShortName = NormalizeTransform.LogMeanVarNormalizerShortName)] - public static CommonOutputs.TransformOutput LogMeanVar(IHostEnvironment env, NormalizeTransform.LogMeanVarArguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("LogMeanVar"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - var xf = NormalizeTransform.Create(host, input, input.Data); - return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; - } - - [TlcModule.EntryPoint(Name = "Transforms.BinNormalizer", Desc = NormalizeTransform.BinNormalizerSummary, UserName = NormalizeTransform.BinNormalizerUserName, ShortName = NormalizeTransform.BinNormalizerShortName)] - public static CommonOutputs.TransformOutput Bin(IHostEnvironment env, NormalizeTransform.BinArguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("Bin"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - var xf = NormalizeTransform.Create(host, input, input.Data); - return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; - } - - [TlcModule.EntryPoint(Name = "Transforms.SupervisedBinNormalizer", Desc = NormalizeTransform.SupervisedBinNormalizerSummary, UserName = NormalizeTransform.SupervisedBinNormalizerUserName, ShortName = NormalizeTransform.SupervisedBinNormalizerShortName)] - public static CommonOutputs.TransformOutput SupervisedBin(IHostEnvironment env, NormalizeTransform.SupervisedBinArguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("SupervisedBin"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - var xf = NormalizeTransform.Create(host, input, input.Data); - return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; - } - - [TlcModule.EntryPoint(Name = "Transforms.ConditionalNormalizer", Desc = "Normalize the columns only if needed", UserName = "Normalize If Needed")] - public static CommonOutputs.MacroOutput IfNeeded( - IHostEnvironment env, - NormalizeTransform.MinMaxArguments input, - EntryPointNode node) - { - var schema = input.Data.Schema; - DvBool isNormalized = DvBool.False; - var columnsToNormalize = new List(); - foreach (var column in input.Column) - { - if (!schema.TryGetColumnIndex(column.Source, out int col)) - throw env.ExceptUserArg(nameof(input.Column), $"Column '{column.Source}' does not exist."); - if (!schema.IsNormalized(col)) - columnsToNormalize.Add(column); - } - - var entryPoints = new List(); - if (columnsToNormalize.Count == 0) - { - var entryPointNode = EntryPointNode.Create(env, "Transforms.NoOperation", new NopTransform.NopInput(), - node.Catalog, node.Context, node.InputBindingMap, node.InputMap, node.OutputMap); - entryPoints.Add(entryPointNode); - } - else - { - input.Column = columnsToNormalize.ToArray(); - var entryPointNode = EntryPointNode.Create(env, "Transforms.MinMaxNormalizer", input, - node.Catalog, node.Context, node.InputBindingMap, node.InputMap, node.OutputMap); - entryPoints.Add(entryPointNode); - } - - return new CommonOutputs.MacroOutput() { Nodes = entryPoints }; - } - } -} diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs new file mode 100644 index 0000000000..7a34cd3a24 --- /dev/null +++ b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs @@ -0,0 +1,176 @@ +// 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 Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Model.Onnx; +using Microsoft.ML.Runtime.Model.Pfa; +using Newtonsoft.Json.Linq; +using System.Collections.Generic; + +[assembly: LoadableClass(typeof(void), typeof(Normalize), null, typeof(SignatureEntryPointModule), "Normalize")] + +namespace Microsoft.ML.Runtime.Data +{ + /// + /// Signature for a repository based loader of a IColumnFunction + /// + public delegate void SignatureLoadColumnFunction(ModelLoadContext ctx, IHost host, ColumnType typeSrc); + + public interface IColumnFunctionBuilder + { + /// + /// Trains on the current value. + /// + /// True if it can use more values for training. + bool ProcessValue(); + + /// + /// Finishes training and returns a column function. + /// + IColumnFunction CreateColumnFunction(); + } + + /// + /// Interface to define an aggregate function over values + /// + public interface IColumnAggregator + { + /// + /// Updates the aggregate function with a value + /// + void ProcessValue(ref T val); + + /// + /// Finishes the aggregation + /// + void Finish(); + } + + public interface IColumnFunction : ICanSaveModel + { + Delegate GetGetter(IRow input, int icol); + + void AttachMetadata(MetadataDispatcher.Builder bldr, ColumnType typeSrc); + + JToken PfaInfo(BoundPfaContext ctx, JToken srcToken); + + bool CanSaveOnnx { get; } + + bool OnnxInfo(OnnxContext ctx, OnnxNode nodeProtoWrapper, int featureCount); + } + + public static class NormalizeUtils + { + /// + /// Returns whether the feature column in the schema is indicated to be normalized. If the features column is not + /// specified on the schema, then this will return null. + /// + /// The role-mapped schema to query + /// Returns null if does not have + /// defined, and otherwise returns a Boolean value as returned from + /// on that feature column + /// + public static bool? FeaturesAreNormalized(this RoleMappedSchema schema) + { + // REVIEW: The role mapped data has the ability to have multiple columns fill the role of features, which is + // useful in some trainers that are nonetheless parameteric and can therefore benefit from normalization. + Contracts.CheckValue(schema, nameof(schema)); + var featInfo = schema.Feature; + return featInfo == null ? default(bool?) : schema.Schema.IsNormalized(featInfo.Index); + } + } + + /// + /// This contains entry-point definitions related to . + /// + public static class Normalize + { + [TlcModule.EntryPoint(Name = "Transforms.MinMaxNormalizer", Desc = NormalizeTransform.MinMaxNormalizerSummary, UserName = NormalizeTransform.MinMaxNormalizerUserName, ShortName = NormalizeTransform.MinMaxNormalizerShortName)] + public static CommonOutputs.TransformOutput MinMax(IHostEnvironment env, NormalizeTransform.MinMaxArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("MinMax"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + var xf = NormalizeTransform.Create(host, input, input.Data); + return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; + } + + [TlcModule.EntryPoint(Name = "Transforms.MeanVarianceNormalizer", Desc = NormalizeTransform.MeanVarNormalizerSummary, UserName = NormalizeTransform.MeanVarNormalizerUserName, ShortName = NormalizeTransform.MeanVarNormalizerShortName)] + public static CommonOutputs.TransformOutput MeanVar(IHostEnvironment env, NormalizeTransform.MeanVarArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("MeanVar"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + var xf = NormalizeTransform.Create(host, input, input.Data); + return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; + } + + [TlcModule.EntryPoint(Name = "Transforms.LogMeanVarianceNormalizer", Desc = NormalizeTransform.LogMeanVarNormalizerSummary, UserName = NormalizeTransform.LogMeanVarNormalizerUserName, ShortName = NormalizeTransform.LogMeanVarNormalizerShortName)] + public static CommonOutputs.TransformOutput LogMeanVar(IHostEnvironment env, NormalizeTransform.LogMeanVarArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("LogMeanVar"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + var xf = NormalizeTransform.Create(host, input, input.Data); + return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; + } + + [TlcModule.EntryPoint(Name = "Transforms.BinNormalizer", Desc = NormalizeTransform.BinNormalizerSummary, UserName = NormalizeTransform.BinNormalizerUserName, ShortName = NormalizeTransform.BinNormalizerShortName)] + public static CommonOutputs.TransformOutput Bin(IHostEnvironment env, NormalizeTransform.BinArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("Bin"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + var xf = NormalizeTransform.Create(host, input, input.Data); + return new CommonOutputs.TransformOutput { Model = new TransformModel(env, xf, input.Data), OutputData = xf }; + } + + [TlcModule.EntryPoint(Name = "Transforms.ConditionalNormalizer", Desc = "Normalize the columns only if needed", UserName = "Normalize If Needed")] + public static CommonOutputs.MacroOutput IfNeeded( + IHostEnvironment env, + NormalizeTransform.MinMaxArguments input, + EntryPointNode node) + { + var schema = input.Data.Schema; + DvBool isNormalized = DvBool.False; + var columnsToNormalize = new List(); + foreach (var column in input.Column) + { + if (!schema.TryGetColumnIndex(column.Source, out int col)) + throw env.ExceptUserArg(nameof(input.Column), $"Column '{column.Source}' does not exist."); + if (!schema.IsNormalized(col)) + columnsToNormalize.Add(column); + } + + var entryPoints = new List(); + if (columnsToNormalize.Count == 0) + { + var entryPointNode = EntryPointNode.Create(env, "Transforms.NoOperation", new NopTransform.NopInput(), + node.Catalog, node.Context, node.InputBindingMap, node.InputMap, node.OutputMap); + entryPoints.Add(entryPointNode); + } + else + { + input.Column = columnsToNormalize.ToArray(); + var entryPointNode = EntryPointNode.Create(env, "Transforms.MinMaxNormalizer", input, + node.Catalog, node.Context, node.InputBindingMap, node.InputMap, node.OutputMap); + entryPoints.Add(entryPointNode); + } + + return new CommonOutputs.MacroOutput() { Nodes = entryPoints }; + } + } +} diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs new file mode 100644 index 0000000000..dc5176b0f9 --- /dev/null +++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs @@ -0,0 +1,430 @@ +// 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 Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Model; +using System; +using System.Collections.Generic; +using System.Linq; + +[assembly: LoadableClass(typeof(NormalizerTransformer), null, typeof(SignatureLoadModel), + "", NormalizerTransformer.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Data +{ + public sealed class Normalizer : IEstimator + { + private static class Defaults + { + public const bool FixZero = true; + public const bool MeanVarCdf = false; + public const bool LogMeanVarCdf = true; + public const int NumBins = 1024; + public const int MinBinSize = 10; + public const long MaxTrainingExamples = 1000000000; + } + + public abstract class ColumnBase + { + public readonly string Input; + public readonly string Output; + public readonly long MaxTrainingExamples; + + protected ColumnBase(string input, string output, long maxTrainingExamples) + { + Contracts.CheckNonEmpty(input, nameof(input)); + Contracts.CheckNonEmpty(output, nameof(output)); + Contracts.CheckParam(maxTrainingExamples > 1, nameof(maxTrainingExamples), "Must be greater than 1"); + + Input = input; + Output = output; + MaxTrainingExamples = maxTrainingExamples; + } + + internal abstract IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, ColumnType srcType, IRowCursor cursor); + } + + public abstract class FixZeroColumnBase : ColumnBase + { + public readonly bool FixZero; + + protected FixZeroColumnBase(string input, string output, long maxTrainingExamples, bool fixZero) + : base(input, output, maxTrainingExamples) + { + FixZero = fixZero; + } + } + + public sealed class MinMaxColumn : FixZeroColumnBase + { + public MinMaxColumn(string input, string output = null, long maxTrainingExamples = Defaults.MaxTrainingExamples, bool fixZero = Defaults.FixZero) + : base(input, output ?? input, maxTrainingExamples, fixZero) + { + } + + internal override IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, ColumnType srcType, IRowCursor cursor) + => NormalizeTransform.MinMaxUtils.CreateBuilder(this, host, srcIndex, srcType, cursor); + } + + public sealed class MeanVarColumn : FixZeroColumnBase + { + public readonly bool UseCdf; + + public MeanVarColumn(string input, string output = null, + long maxTrainingExamples = Defaults.MaxTrainingExamples, bool fixZero = Defaults.FixZero, bool useCdf = Defaults.MeanVarCdf) + : base(input, output ?? input, maxTrainingExamples, fixZero) + { + UseCdf = useCdf; + } + + internal override IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, ColumnType srcType, IRowCursor cursor) + => NormalizeTransform.MeanVarUtils.CreateBuilder(this, host, srcIndex, srcType, cursor); + } + + public sealed class LogMeanVarColumn : ColumnBase + { + public readonly bool UseCdf; + + public LogMeanVarColumn(string input, string output = null, + long maxTrainingExamples = Defaults.MaxTrainingExamples, bool useCdf = Defaults.LogMeanVarCdf) + : base(input, output ?? input, maxTrainingExamples) + { + UseCdf = useCdf; + } + + internal override IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, ColumnType srcType, IRowCursor cursor) + => NormalizeTransform.LogMeanVarUtils.CreateBuilder(this, host, srcIndex, srcType, cursor); + } + + public sealed class BinningColumn : FixZeroColumnBase + { + public readonly int NumBins; + + public BinningColumn(string input, string output = null, + long maxTrainingExamples = Defaults.MaxTrainingExamples, bool fixZero = true, int numBins = Defaults.NumBins) + : base(input, output ?? input, maxTrainingExamples, fixZero) + { + NumBins = numBins; + } + + internal override IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, ColumnType srcType, IRowCursor cursor) + => NormalizeTransform.BinUtils.CreateBuilder(this, host, srcIndex, srcType, cursor); + } + + private readonly IHost _host; + private readonly ColumnBase[] _columns; + + public Normalizer(IHostEnvironment env, params ColumnBase[] columns) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(Normalizer)); + _host.CheckValue(columns, nameof(columns)); + + _columns = columns.ToArray(); + } + + public NormalizerTransformer Fit(IDataView input) + { + _host.CheckValue(input, nameof(input)); + return NormalizerTransformer.Train(_host, input, _columns); + } + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + _host.CheckValue(inputSchema, nameof(inputSchema)); + var result = inputSchema.Columns.ToDictionary(x => x.Name); + + foreach (var colInfo in _columns) + { + var col = inputSchema.FindColumn(colInfo.Input); + + if (col == null) + throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", colInfo.Input); + if (col.Kind == SchemaShape.Column.VectorKind.VariableVector) + throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", colInfo.Input, "fixed-size vector or scalar", col.GetTypeString()); + + if (!col.ItemType.Equals(NumberType.R4) && !col.ItemType.Equals(NumberType.R8)) + throw _host.ExceptSchemaMismatch(nameof(inputSchema), "input", colInfo.Input, "vector or scalar of R4 or R8", col.GetTypeString()); + + var newMetadataKinds = new List { MetadataUtils.Kinds.IsNormalized }; + if (col.MetadataKinds.Contains(MetadataUtils.Kinds.SlotNames)) + newMetadataKinds.Add(MetadataUtils.Kinds.SlotNames); + result[colInfo.Output] = new SchemaShape.Column(colInfo.Output, col.Kind, col.ItemType, col.IsKey, newMetadataKinds.ToArray()); + } + + return new SchemaShape(result.Values); + } + } + + public sealed partial class NormalizerTransformer : OneToOneTransformerBase + { + public const string LoaderSignature = "Normalizer"; + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "NORMALZR", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + private class ColumnInfo + { + public readonly string Input; + public readonly string Output; + public readonly ColumnType InputType; + public readonly IColumnFunction ColumnFunction; + + public ColumnInfo(string input, string output, ColumnType inputType, IColumnFunction columnFunction) + { + Input = input; + Output = output; + InputType = inputType; + ColumnFunction = columnFunction; + } + + internal static ColumnType LoadType(ModelLoadContext ctx) + { + Contracts.AssertValue(ctx); + // *** Binary format *** + // - bool: is vector + // - int: vector size + // - byte: ItemKind of input column (only R4 and R8 are valid) + bool isVector = ctx.Reader.ReadBoolean(); + int vectorSize = ctx.Reader.ReadInt32(); + Contracts.CheckDecode(vectorSize >= 0); + Contracts.CheckDecode(vectorSize > 0 || !isVector); + + DataKind itemKind = (DataKind)ctx.Reader.ReadByte(); + Contracts.CheckDecode(itemKind == DataKind.R4 || itemKind == DataKind.R8); + + var itemType = PrimitiveType.FromKind(itemKind); + return isVector ? (ColumnType)(new VectorType(itemType, vectorSize)) : itemType; + } + + internal static void SaveType(ModelSaveContext ctx, ColumnType type) + { + Contracts.AssertValue(ctx); + // *** Binary format *** + // - bool: is vector + // - int: vector size + // - byte: ItemKind of input column (only R4 and R8 are valid) + ctx.Writer.Write(type.IsVector); + + Contracts.Assert(!type.IsVector || type.VectorSize > 0); + ctx.Writer.Write(type.VectorSize); + + var itemKind = type.ItemType.RawKind; + Contracts.Assert(itemKind == DataKind.R4 || itemKind == DataKind.R8); + ctx.Writer.Write((byte)itemKind); + } + } + + private readonly ColumnInfo[] _columns; + + public (string input, string output)[] Columns => ColumnPairs; + + private NormalizerTransformer(IHostEnvironment env, ColumnInfo[] columns) + : base(env.Register(nameof(NormalizerTransformer)), columns.Select(x => (x.Input, x.Output)).ToArray()) + { + _columns = columns; + } + + public static NormalizerTransformer Train(IHostEnvironment env, IDataView data, Normalizer.ColumnBase[] columns) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(data, nameof(data)); + env.CheckValue(columns, nameof(columns)); + + bool[] activeInput = new bool[data.Schema.ColumnCount]; + + var srcCols = new int[columns.Length]; + var srcTypes = new ColumnType[columns.Length]; + for (int i = 0; i < columns.Length; i++) + { + var info = columns[i]; + bool success = data.Schema.TryGetColumnIndex(info.Input, out srcCols[i]); + if (!success) + throw env.ExceptSchemaMismatch(nameof(data), "input", info.Input); + srcTypes[i] = data.Schema.GetColumnType(srcCols[i]); + activeInput[srcCols[i]] = true; + } + + var functionBuilders = new IColumnFunctionBuilder[columns.Length]; + var needMoreData = new bool[columns.Length]; + + // Go through the input data and pass it to the column function builders. + using (var pch = env.StartProgressChannel("Normalize")) + { + long numRows = 0; + + pch.SetHeader(new ProgressHeader("examples"), e => e.SetProgress(0, numRows)); + using (var cursor = data.GetRowCursor(col => activeInput[col])) + { + for (int i = 0; i < columns.Length; i++) + { + needMoreData[i] = true; + var info = columns[i]; + var host = env.Register($"Column_{i:000}"); + + functionBuilders[i] = info.MakeBuilder(host, srcCols[i], srcTypes[i], cursor); + } + + while (cursor.MoveNext()) + { + // If the row has bad values, the good values are still being used for training. + // The comparisons in the code below are arranged so that NaNs in the input are not recorded. + // REVIEW: Should infinities and/or NaNs be filtered before the normalization? Should we not record infinities for min/max? + // Currently, infinities are recorded and will result in zero scale which in turn will result in NaN output for infinity input. + bool any = false; + for (int i = 0; i < columns.Length; i++) + { + if (!needMoreData[i]) + continue; + var info = columns[i]; + env.Assert(!srcTypes[i].IsVector || srcTypes[i].IsVector && srcTypes[i].IsKnownSizeVector); + env.Assert(functionBuilders[i] != null); + any |= needMoreData[i] = functionBuilders[i].ProcessValue(); + } + numRows++; + + if (!any) + break; + } + } + + pch.Checkpoint(numRows); + + var result = new ColumnInfo[columns.Length]; + for (int i = 0; i < columns.Length; i++) + { + var func = functionBuilders[i].CreateColumnFunction(); + result[i] = new ColumnInfo(columns[i].Input, columns[i].Output, srcTypes[i], func); + } + + return new NormalizerTransformer(env, result); + } + } + + private NormalizerTransformer(IHost host, ModelLoadContext ctx) + : base(host, ctx) + { + // *** Binary format *** + // + // for each added column: + // - source type + // - separate model for column function + + _columns = new ColumnInfo[ColumnPairs.Length]; + for (int iinfo = 0; iinfo < ColumnPairs.Length; iinfo++) + { + var dir = string.Format("Normalizer_{0:000}", iinfo); + var typeSrc = ColumnInfo.LoadType(ctx); + ctx.LoadModel(Host, out var function, dir, Host, typeSrc); + _columns[iinfo] = new ColumnInfo(ColumnPairs[iinfo].input, ColumnPairs[iinfo].output, typeSrc, function); + } + } + + public static NormalizerTransformer Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new NormalizerTransformer(env.Register(nameof(NormalizerTransformer)), ctx); + } + + // Factory method for SignatureRowMapper. + public static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, ISchema inputSchema) + => Create(env, ctx).MakeRowMapper(inputSchema); + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // + // for each added column: + // - source type + // - separate model for column function + base.SaveColumns(ctx); + + // Individual normalization models. + for (int iinfo = 0; iinfo < _columns.Length; iinfo++) + { + ColumnInfo.SaveType(ctx, _columns[iinfo].InputType); + var dir = string.Format("Normalizer_{0:000}", iinfo); + ctx.SaveSubModel(dir, _columns[iinfo].ColumnFunction.Save); + } + } + + protected override void CheckInputColumn(ISchema inputSchema, int col, int srcCol) + { + const string expectedType = "scalar or known-size vector of R4"; + + var colType = inputSchema.GetColumnType(srcCol); + if (colType.IsVector && !colType.IsKnownSizeVector) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", ColumnPairs[col].input, expectedType, "variable-size vector"); + if (!colType.ItemType.Equals(NumberType.R4) && !colType.ItemType.Equals(NumberType.R8)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", ColumnPairs[col].input, expectedType, colType.ToString()); + } + + // Temporary: enables SignatureDataTransform factory methods. + public new IDataTransform MakeDataTransform(IDataView input) + => base.MakeDataTransform(input); + + protected override IRowMapper MakeRowMapper(ISchema schema) + => new Mapper(this, schema); + + private sealed class Mapper : MapperBase + { + private NormalizerTransformer _parent; + + public Mapper(NormalizerTransformer parent, ISchema schema) + : base(parent.Host.Register(nameof(Mapper)), parent, schema) + { + _parent = parent; + } + + public override RowMapperColumnInfo[] GetOutputColumns() + { + var result = new RowMapperColumnInfo[_parent._columns.Length]; + for (int i = 0; i < _parent.Columns.Length; i++) + result[i] = new RowMapperColumnInfo(_parent._columns[i].Output, _parent._columns[i].InputType, MakeMetadata(i)); + return result; + } + + private ColumnMetadataInfo MakeMetadata(int iinfo) + { + var colInfo = _parent._columns[iinfo]; + var result = new ColumnMetadataInfo(colInfo.Output); + result.Add(MetadataUtils.Kinds.IsNormalized, new MetadataInfo(BoolType.Instance, IsNormalizedGetter)); + if (InputSchema.HasSlotNames(ColMapNewToOld[iinfo], colInfo.InputType.VectorSize)) + { + MetadataUtils.MetadataGetter> getter = (int col, ref VBuffer slotNames) => + InputSchema.GetMetadata(MetadataUtils.Kinds.SlotNames, ColMapNewToOld[iinfo], ref slotNames); + var metaType = InputSchema.GetMetadataTypeOrNull(MetadataUtils.Kinds.SlotNames, ColMapNewToOld[iinfo]); + Contracts.AssertValue(metaType); + result.Add(MetadataUtils.Kinds.SlotNames, new MetadataInfo>(metaType, getter)); + } + return result; + } + + private void IsNormalizedGetter(int col, ref DvBool dst) + { + dst = true; + } + + protected override Delegate MakeGetter(IRow input, int iinfo, out Action disposer) + { + disposer = null; + return _parent._columns[iinfo].ColumnFunction.GetGetter(input, ColMapNewToOld[iinfo]); + } + } + } +} diff --git a/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv b/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv new file mode 100644 index 0000000000..bd11b4d05c --- /dev/null +++ b/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv @@ -0,0 +1,176 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=float1:R4:0 +#@ col=float1:R4:1 +#@ col=float4:R4:2-5 +#@ col=float4:R4:6-9 +#@ col=float4:R4:10-13 +#@ col=double1:R8:14 +#@ col=double1:R8:15 +#@ col=double4:R8:16-19 +#@ col=double4:R8:20-23 +#@ col=int1:I4:24 +#@ col=float1bin:R4:25 +#@ col=float4bin:R4:26-29 +#@ col=double1bin:R8:30 +#@ col=double4bin:R8:31-34 +#@ col=float1mv:R4:35 +#@ col=float4mv:R4:36-39 +#@ col=double1mv:R8:40 +#@ col=double4mv:R8:41-44 +#@ col=float1lmv:R4:45 +#@ col=float4lmv:R4:46-49 +#@ col=double1lmv:R8:50 +#@ col=double4lmv:R8:51-54 +#@ } +float1 float1 5.1 3.5 1.4 0.2 5.1 3.5 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0.76000000000000001 2 0.441176474 0.441176474 0.272727281 0.642857134 0.714285731 0.44117647058823528 0.44117647058823528 0.27272727272727271 0.6428571428571429 0.7142857142857143 0.981996 0.981996 0.876212358 1.22536623 1.33400178 0.98199589949448451 0.98199589949448451 0.87621235531294217 1.2253663297299173 1.3340017403117959 0.504585147 0.504585147 0.214467555 0.777955949 0.794432342 0.50458515122771275 0.50458515122771275 0.21446754872116464 0.77795595083214475 0.7944323164067586 +6.8 0.860759556 6.8 3.2 5.9 2.3 6.8 3.2 5.9 2.3 0.860759556 0.7272727 0.855072439 0.92 6.7999999999999998 0.86075949367088589 6.7999999999999998 3.2000000000000002 5.9000000000000004 2.2999999999999998 0.86075949367088589 0.72727272727272729 0.85507246376811608 0.91999999999999993 2 0.7352941 0.7352941 0.5 0.8333333 0.9047619 0.73529411764705888 0.73529411764705888 0.5 0.83333333333333337 0.90476190476190477 1.15130568 1.15130568 1.038474 1.4175806 1.6148442 1.1513055373383612 1.1513055373383612 1.0384739025931167 1.4175806559620614 1.6148442119563844 0.873058 0.873058 0.6578964 0.8447406 0.845170259 0.87305788341059976 0.87305788341059976 0.65789648182451921 0.8447405985468005 0.84517026625737923 +6.7 0.848101258 6.7 3.3 5.7 2.5 6.7 3.3 5.7 2.5 0.848101258 0.74999994 0.8260869 1 6.7000000000000002 0.84810126582278467 6.7000000000000002 3.2999999999999998 5.7000000000000002 2.5 0.84810126582278467 0.74999999999999989 0.82608695652173914 1 2 0.7058824 0.7058824 0.545454562 0.785714269 1 0.70588235294117652 0.70588235294117652 0.54545454545454541 0.7857142857142857 1 1.13437462 1.13437462 1.07092619 1.369527 1.75526547 1.1343745735539736 1.1343745735539736 1.0709262120491514 1.3695270744040255 1.7552654477786789 0.84984225 0.84984225 0.733567655 0.8302718 0.8644717 0.84984228863096656 0.84984228863096656 0.73356785053506501 0.83027178393794032 0.86447170475057145 +6.7 0.848101258 6.7 3 5.2 2.3 6.7 3 5.2 2.3 0.848101258 0.6818181 0.7536231 0.92 6.7000000000000002 0.84810126582278467 6.7000000000000002 3 5.2000000000000002 2.2999999999999998 0.84810126582278467 0.68181818181818177 0.75362318840579712 0.91999999999999993 2 0.7058824 0.7058824 0.4090909 0.6666667 0.9047619 0.70588235294117652 0.70588235294117652 0.40909090909090912 0.66666666666666663 0.90476190476190477 1.13437462 1.13437462 0.9735693 1.24939311 1.6148442 1.1343745735539736 1.1343745735539736 0.97356928368104678 1.2493931205089355 1.6148442119563844 0.84984225 0.84984225 0.480745673 0.7877 0.845170259 0.84984228863096656 0.84984228863096656 0.4807456731235793 0.78769997391633806 0.84517026625737923 +6.3 0.797468364 6.3 2.5 5 1.9 6.3 2.5 5 1.9 0.797468364 0.5681818 0.7246376 0.76 6.2999999999999998 0.79746835443037956 6.2999999999999998 2.5 5 1.8999999999999999 0.79746835443037956 0.56818181818181812 0.72463768115942029 0.76000000000000001 2 0.5882353 0.5882353 0.181818187 0.619047642 0.714285731 0.58823529411764708 0.58823529411764708 0.18181818181818182 0.61904761904761907 0.7142857142857143 1.06665075 1.06665075 0.8113077 1.20133948 1.33400178 1.0666507184164229 1.0666507184164229 0.81130773640087239 1.2013395389508994 1.3340017403117959 0.72531265 0.72531265 0.09116498 0.7677612 0.794432342 0.72531248018388961 0.72531248018388961 0.091164973250557446 0.76776110588492996 0.7944323164067586 +6.5 0.822784841 6.5 3 5.2 2 6.5 3 5.2 2 0.822784841 0.6818181 0.7536231 0.8 6.5 0.82278481012658211 6.5 3 5.2000000000000002 2 0.82278481012658211 0.68181818181818177 0.75362318840579712 0.80000000000000004 2 0.647058845 0.647058845 0.4090909 0.6666667 0.7619048 0.6470588235294118 0.6470588235294118 0.40909090909090912 0.66666666666666663 0.76190476190476186 1.10051274 1.10051274 0.9735693 1.24939311 1.40421236 1.1005126459851982 1.1005126459851982 0.97356928368104678 1.2493931205089355 1.4042123582229431 0.7940583 0.7940583 0.480745673 0.7877 0.808938 0.79405825408863862 0.79405825408863862 0.4807456731235793 0.78769997391633806 0.80893802463851161 +6.2 0.7848101 6.2 3.4 5.4 2.3 6.2 3.4 5.4 2.3 0.7848101 0.772727251 0.7826087 0.92 6.2000000000000002 0.78481012658227833 6.2000000000000002 3.3999999999999999 5.4000000000000004 2.2999999999999998 0.78481012658227833 0.77272727272727271 0.78260869565217395 0.91999999999999993 2 0.5588235 0.5588235 0.590909064 0.714285731 0.9047619 0.55882352941176472 0.55882352941176472 0.59090909090909094 0.7142857142857143 0.90476190476190477 1.04971981 1.04971981 1.10337853 1.29744673 1.6148442 1.0497197546320352 1.0497197546320352 1.1033785215051863 1.2974467020669715 1.6148442119563844 0.6861942 0.6861942 0.797875941 0.805906951 0.845170259 0.6861941068147408 0.6861941068147408 0.79787580879856601 0.80590691835886541 0.84517026625737923 +5.9 0.7468355 5.9 3 5.1 1.8 5.9 3 5.1 1.8 0.7468355 0.6818181 0.7391304 0.719999969 5.9000000000000004 0.74683544303797467 5.9000000000000004 3 5.0999999999999996 1.8 0.74683544303797467 0.68181818181818177 0.73913043478260865 0.72000000000000008 2 0.470588237 0.470588237 0.4090909 0.642857134 0.6666667 0.47058823529411764 0.47058823529411764 0.40909090909090912 0.6428571428571429 0.66666666666666663 0.998926938 0.998926938 0.9735693 1.22536623 1.26379108 0.99892686327887226 0.99892686327887226 0.97356928368104678 1.2253663297299173 1.2637911224006488 0.5528621 0.5528621 0.480745673 0.777955949 0.778455853 0.55286190159866166 0.55286190159866166 0.4807456731235793 0.77795595083214475 0.77845583371698512 diff --git a/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs b/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs new file mode 100644 index 0000000000..086179b28d --- /dev/null +++ b/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs @@ -0,0 +1,77 @@ +// 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 Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.RunTests; +using System.IO; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.Transformers +{ + public sealed class NormalizerTests : TestDataPipeBase + { + public NormalizerTests(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void NormalizerWorkout() + { + string dataPath = GetDataPath("iris.txt"); + + var loader = new TextLoader(Env, new TextLoader.Arguments + { + Column = new[]{ + new TextLoader.Column("float1", DataKind.R4, 1), + new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), + new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), + new TextLoader.Column("double1", DataKind.R8, 1), + new TextLoader.Column("double4", DataKind.R8, new[]{new TextLoader.Range(1, 4) }), + new TextLoader.Column("int1", DataKind.I4, 0), + new TextLoader.Column("float0", DataKind.R4, new[]{ new TextLoader.Range { Min = 1, VariableEnd = true } }), + }, + HasHeader = true + }, new MultiFileSource(dataPath)); + + var est = new Normalizer(Env, + new Normalizer.MinMaxColumn("float1"), + new Normalizer.MinMaxColumn("float4"), + new Normalizer.MinMaxColumn("double1"), + new Normalizer.MinMaxColumn("double4"), + new Normalizer.BinningColumn("float1", "float1bin"), + new Normalizer.BinningColumn("float4", "float4bin"), + new Normalizer.BinningColumn("double1", "double1bin"), + new Normalizer.BinningColumn("double4", "double4bin"), + new Normalizer.MeanVarColumn("float1", "float1mv"), + new Normalizer.MeanVarColumn("float4", "float4mv"), + new Normalizer.MeanVarColumn("double1", "double1mv"), + new Normalizer.MeanVarColumn("double4", "double4mv"), + new Normalizer.LogMeanVarColumn("float1", "float1lmv"), + new Normalizer.LogMeanVarColumn("float4", "float4lmv"), + new Normalizer.LogMeanVarColumn("double1", "double1lmv"), + new Normalizer.LogMeanVarColumn("double4", "double4lmv")); + + var data = loader.Read(new MultiFileSource(dataPath)); + + var badData1 = new CopyColumnsTransform(Env, ("int1", "float1")).Transform(data); + var badData2 = new CopyColumnsTransform(Env, ("float0", "float4")).Transform(data); + + TestEstimatorCore(est, data, null, badData1); + TestEstimatorCore(est, data, null, badData2); + + var outputPath = GetOutputPath("Normalizer", "normalized.tsv"); + using (var ch = Env.Start("save")) + { + var saver = new TextSaver(Env, new TextSaver.Arguments { Silent = true }); + using (var fs = File.Create(outputPath)) + DataSaverUtils.SaveDataView(ch, saver, new DropColumnsTransform(Env, est.Fit(data).Transform(data), "float0"), fs, keepHidden: true); + } + + CheckEquality("Normalizer", "normalized.tsv"); + Done(); + } + } +} From b9efe0f9ac758833e9f3aaec1967bed0e16c5a48 Mon Sep 17 00:00:00 2001 From: Pete Luferenko Date: Fri, 31 Aug 2018 16:42:27 -0700 Subject: [PATCH 2/6] Added convenience constructors --- .../Transforms/Normalizer.cs | 38 +++++++++++++++++++ .../Transformers/NormalizerTests.cs | 33 +++++++++++++++- 2 files changed, 70 insertions(+), 1 deletion(-) diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs index dc5176b0f9..75d82feef1 100644 --- a/src/Microsoft.ML.Data/Transforms/Normalizer.cs +++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs @@ -27,6 +27,14 @@ private static class Defaults public const long MaxTrainingExamples = 1000000000; } + public enum NormalizerMode + { + MinMax = 0, + MeanVariance = 1, + LogMeanVariance = 2, + Binning = 3 + } + public abstract class ColumnBase { public readonly string Input; @@ -45,6 +53,23 @@ protected ColumnBase(string input, string output, long maxTrainingExamples) } internal abstract IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, ColumnType srcType, IRowCursor cursor); + + internal static ColumnBase Create(string input, string output, NormalizerMode mode) + { + switch (mode) + { + case NormalizerMode.MinMax: + return new MinMaxColumn(input, output); + case NormalizerMode.MeanVariance: + return new MeanVarColumn(input, output); + case NormalizerMode.LogMeanVariance: + return new LogMeanVarColumn(input, output); + case NormalizerMode.Binning: + return new BinningColumn(input, output); + default: + throw Contracts.ExceptParam(nameof(mode), "Unknown normalizer mode"); + } + } } public abstract class FixZeroColumnBase : ColumnBase @@ -117,6 +142,19 @@ internal override IColumnFunctionBuilder MakeBuilder(IHost host, int srcIndex, C private readonly IHost _host; private readonly ColumnBase[] _columns; + public Normalizer(IHostEnvironment env, string columnName, NormalizerMode mode = NormalizerMode.MinMax) + : this(env, mode, (columnName, columnName)) + { + } + + public Normalizer(IHostEnvironment env, NormalizerMode mode, params (string inputColumn, string outputColumn)[] columns) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(nameof(Normalizer)); + _host.CheckValue(columns, nameof(columns)); + _columns = columns.Select(x => ColumnBase.Create(x.inputColumn, x.outputColumn, mode)).ToArray(); + } + public Normalizer(IHostEnvironment env, params ColumnBase[] columns) { Contracts.CheckValue(env, nameof(env)); diff --git a/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs b/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs index 086179b28d..65a100eb63 100644 --- a/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs +++ b/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs @@ -24,7 +24,7 @@ public void NormalizerWorkout() var loader = new TextLoader(Env, new TextLoader.Arguments { - Column = new[]{ + Column = new[] { new TextLoader.Column("float1", DataKind.R4, 1), new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), @@ -71,6 +71,37 @@ public void NormalizerWorkout() } CheckEquality("Normalizer", "normalized.tsv"); + + Done(); + } + + [Fact] + public void SimpleConstructors() + { + string dataPath = GetDataPath("iris.txt"); + + var loader = new TextLoader(Env, new TextLoader.Arguments + { + Column = new[] { + new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), + } + }); + + var data = loader.Read(new MultiFileSource(dataPath)); + + var est1 = new Normalizer(Env, "float4"); + var est2 = new Normalizer(Env, Normalizer.NormalizerMode.MinMax, ("float4", "float4")); + var est3 = new Normalizer(Env, new Normalizer.MinMaxColumn("float4")); + + var data1 = est1.Fit(data).Transform(data); + var data2 = est2.Fit(data).Transform(data); + var data3 = est3.Fit(data).Transform(data); + + CheckSameSchemas(data1.Schema, data2.Schema); + CheckSameSchemas(data1.Schema, data3.Schema); + CheckSameValues(data1, data2); + CheckSameValues(data1, data3); + Done(); } } From 2591a47f614bef72b2edcdfa4d9ed34cdaf1f51c Mon Sep 17 00:00:00 2001 From: Pete Luferenko Date: Fri, 31 Aug 2018 16:49:31 -0700 Subject: [PATCH 3/6] Test fix --- .../SingleDebug/Normalizer/normalized.tsv | 333 +++++++++--------- .../SingleRelease/Normalizer/normalized.tsv | 333 +++++++++--------- .../Transformers/NormalizerTests.cs | 1 - 3 files changed, 332 insertions(+), 335 deletions(-) diff --git a/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv b/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv index bd11b4d05c..b231f8e23d 100644 --- a/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv +++ b/test/BaselineOutput/SingleDebug/Normalizer/normalized.tsv @@ -5,172 +5,171 @@ #@ col=float1:R4:1 #@ col=float4:R4:2-5 #@ col=float4:R4:6-9 -#@ col=float4:R4:10-13 -#@ col=double1:R8:14 -#@ col=double1:R8:15 +#@ col=double1:R8:10 +#@ col=double1:R8:11 +#@ col=double4:R8:12-15 #@ col=double4:R8:16-19 -#@ col=double4:R8:20-23 -#@ col=int1:I4:24 -#@ col=float1bin:R4:25 -#@ col=float4bin:R4:26-29 -#@ col=double1bin:R8:30 -#@ col=double4bin:R8:31-34 -#@ col=float1mv:R4:35 -#@ col=float4mv:R4:36-39 -#@ col=double1mv:R8:40 -#@ col=double4mv:R8:41-44 -#@ col=float1lmv:R4:45 -#@ col=float4lmv:R4:46-49 -#@ col=double1lmv:R8:50 -#@ col=double4lmv:R8:51-54 +#@ col=int1:I4:20 +#@ col=float1bin:R4:21 +#@ col=float4bin:R4:22-25 +#@ col=double1bin:R8:26 +#@ col=double4bin:R8:27-30 +#@ col=float1mv:R4:31 +#@ col=float4mv:R4:32-35 +#@ col=double1mv:R8:36 +#@ col=double4mv:R8:37-40 +#@ col=float1lmv:R4:41 +#@ col=float4lmv:R4:42-45 +#@ col=double1lmv:R8:46 +#@ col=double4lmv:R8:47-50 #@ } -float1 float1 5.1 3.5 1.4 0.2 5.1 3.5 1.4 0.2 5.1 3.5 1.4 0.2 double1 double1 5.1 3.5 1.4 0.2 5.1 3.5 1.4 0.2 int1 float1bin 5.1 3.5 1.4 0.2 double1bin 5.1 3.5 1.4 0.2 float1mv 5.1 3.5 1.4 0.2 double1mv 5.1 3.5 1.4 0.2 float1lmv 5.1 3.5 1.4 0.2 double1lmv 5.1 3.5 1.4 0.2 -4.9 0.6202532 4.9 3 1.4 0.2 4.9 3 1.4 0.2 0.6202532 0.6818181 0.202898547 0.0800000057 4.9000000000000004 0.620253164556962 4.9000000000000004 3 1.3999999999999999 0.20000000000000001 0.620253164556962 0.68181818181818177 0.20289855072463767 0.080000000000000016 0 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-5,172 +5,171 @@ #@ col=float1:R4:1 #@ col=float4:R4:2-5 #@ col=float4:R4:6-9 -#@ col=float4:R4:10-13 -#@ col=double1:R8:14 -#@ col=double1:R8:15 +#@ col=double1:R8:10 +#@ col=double1:R8:11 +#@ col=double4:R8:12-15 #@ col=double4:R8:16-19 -#@ col=double4:R8:20-23 -#@ col=int1:I4:24 -#@ col=float1bin:R4:25 -#@ col=float4bin:R4:26-29 -#@ col=double1bin:R8:30 -#@ col=double4bin:R8:31-34 -#@ col=float1mv:R4:35 -#@ col=float4mv:R4:36-39 -#@ col=double1mv:R8:40 -#@ col=double4mv:R8:41-44 -#@ col=float1lmv:R4:45 -#@ col=float4lmv:R4:46-49 -#@ col=double1lmv:R8:50 -#@ col=double4lmv:R8:51-54 +#@ col=int1:I4:20 +#@ col=float1bin:R4:21 +#@ col=float4bin:R4:22-25 +#@ col=double1bin:R8:26 +#@ col=double4bin:R8:27-30 +#@ col=float1mv:R4:31 +#@ col=float4mv:R4:32-35 +#@ col=double1mv:R8:36 +#@ col=double4mv:R8:37-40 +#@ col=float1lmv:R4:41 +#@ col=float4lmv:R4:42-45 +#@ col=double1lmv:R8:46 +#@ col=double4lmv:R8:47-50 #@ } -float1 float1 5.1 3.5 1.4 0.2 5.1 3.5 1.4 0.2 5.1 3.5 1.4 0.2 double1 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0.75362318840579712 0.91999999999999993 2 0.7058824 0.7058824 0.4090909 0.6666667 0.9047619 0.70588235294117652 0.70588235294117652 0.40909090909090912 0.66666666666666663 0.90476190476190477 1.13437462 1.13437462 0.9735693 1.24939311 1.6148442 1.1343745735539736 1.1343745735539736 0.97356928368104678 1.2493931205089355 1.6148442119563844 0.84984225 0.84984225 0.480745673 0.7877 0.845170259 0.84984228863096656 0.84984228863096656 0.4807456731235793 0.78769997391633806 0.84517026625737923 +6.3 0.797468364 6.3 2.5 5 1.9 0.797468364 0.5681818 0.7246376 0.76 6.2999999999999998 0.79746835443037956 6.2999999999999998 2.5 5 1.8999999999999999 0.79746835443037956 0.56818181818181812 0.72463768115942029 0.76000000000000001 2 0.5882353 0.5882353 0.181818187 0.619047642 0.714285731 0.58823529411764708 0.58823529411764708 0.18181818181818182 0.61904761904761907 0.7142857142857143 1.06665075 1.06665075 0.8113077 1.20133948 1.33400178 1.0666507184164229 1.0666507184164229 0.81130773640087239 1.2013395389508994 1.3340017403117959 0.72531265 0.72531265 0.09116498 0.7677612 0.794432342 0.72531248018388961 0.72531248018388961 0.091164973250557446 0.76776110588492996 0.7944323164067586 +6.5 0.822784841 6.5 3 5.2 2 0.822784841 0.6818181 0.7536231 0.8 6.5 0.82278481012658211 6.5 3 5.2000000000000002 2 0.82278481012658211 0.68181818181818177 0.75362318840579712 0.80000000000000004 2 0.647058845 0.647058845 0.4090909 0.6666667 0.7619048 0.6470588235294118 0.6470588235294118 0.40909090909090912 0.66666666666666663 0.76190476190476186 1.10051274 1.10051274 0.9735693 1.24939311 1.40421236 1.1005126459851982 1.1005126459851982 0.97356928368104678 1.2493931205089355 1.4042123582229431 0.7940583 0.7940583 0.480745673 0.7877 0.808938 0.79405825408863862 0.79405825408863862 0.4807456731235793 0.78769997391633806 0.80893802463851161 +6.2 0.7848101 6.2 3.4 5.4 2.3 0.7848101 0.772727251 0.7826087 0.92 6.2000000000000002 0.78481012658227833 6.2000000000000002 3.3999999999999999 5.4000000000000004 2.2999999999999998 0.78481012658227833 0.77272727272727271 0.78260869565217395 0.91999999999999993 2 0.5588235 0.5588235 0.590909064 0.714285731 0.9047619 0.55882352941176472 0.55882352941176472 0.59090909090909094 0.7142857142857143 0.90476190476190477 1.04971981 1.04971981 1.10337853 1.29744673 1.6148442 1.0497197546320352 1.0497197546320352 1.1033785215051863 1.2974467020669715 1.6148442119563844 0.6861942 0.6861942 0.797875941 0.805906951 0.845170259 0.6861941068147408 0.6861941068147408 0.79787580879856601 0.80590691835886541 0.84517026625737923 +5.9 0.7468355 5.9 3 5.1 1.8 0.7468355 0.6818181 0.7391304 0.719999969 5.9000000000000004 0.74683544303797467 5.9000000000000004 3 5.0999999999999996 1.8 0.74683544303797467 0.68181818181818177 0.73913043478260865 0.72000000000000008 2 0.470588237 0.470588237 0.4090909 0.642857134 0.6666667 0.47058823529411764 0.47058823529411764 0.40909090909090912 0.6428571428571429 0.66666666666666663 0.998926938 0.998926938 0.9735693 1.22536623 1.26379108 0.99892686327887226 0.99892686327887226 0.97356928368104678 1.2253663297299173 1.2637911224006488 0.5528621 0.5528621 0.480745673 0.777955949 0.778455853 0.55286190159866166 0.55286190159866166 0.4807456731235793 0.77795595083214475 0.77845583371698512 diff --git a/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs b/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs index 65a100eb63..eefae183e2 100644 --- a/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs +++ b/test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs @@ -27,7 +27,6 @@ public void NormalizerWorkout() Column = new[] { new TextLoader.Column("float1", DataKind.R4, 1), new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), - new TextLoader.Column("float4", DataKind.R4, new[]{new TextLoader.Range(1, 4) }), new TextLoader.Column("double1", DataKind.R8, 1), new TextLoader.Column("double4", DataKind.R8, new[]{new TextLoader.Range(1, 4) }), new TextLoader.Column("int1", DataKind.I4, 0), From 85bee228c55037fcb55822ccdd0c1d44f105c4eb Mon Sep 17 00:00:00 2001 From: Pete Luferenko Date: Fri, 31 Aug 2018 17:40:29 -0700 Subject: [PATCH 4/6] Fixed tests --- src/Microsoft.ML.Data/Transforms/Normalizer.cs | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs index 75d82feef1..b5de9f3cbc 100644 --- a/src/Microsoft.ML.Data/Transforms/Normalizer.cs +++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs @@ -13,6 +13,9 @@ [assembly: LoadableClass(typeof(NormalizerTransformer), null, typeof(SignatureLoadModel), "", NormalizerTransformer.LoaderSignature)] +[assembly: LoadableClass(typeof(IRowMapper), typeof(NormalizerTransformer), null, typeof(SignatureLoadRowMapper), + "", NormalizerTransformer.LoaderSignature)] + namespace Microsoft.ML.Runtime.Data { public sealed class Normalizer : IEstimator @@ -375,7 +378,7 @@ public static NormalizerTransformer Create(IHostEnvironment env, ModelLoadContex return new NormalizerTransformer(env.Register(nameof(NormalizerTransformer)), ctx); } - // Factory method for SignatureRowMapper. + // Factory method for SignatureLoadRowMapper. public static IRowMapper Create(IHostEnvironment env, ModelLoadContext ctx, ISchema inputSchema) => Create(env, ctx).MakeRowMapper(inputSchema); From 25f8ad118491181e0fc1b1ab0cd30a9fbdc4a113 Mon Sep 17 00:00:00 2001 From: Pete Luferenko Date: Tue, 4 Sep 2018 11:02:05 -0700 Subject: [PATCH 5/6] Fixed PR comments and hopefully tests --- .../Transforms/NormalizeColumn.cs | 12 +-- .../Transforms/NormalizeUtils.cs | 6 +- .../Transforms/Normalizer.cs | 95 ++++++++++++++++++- 3 files changed, 101 insertions(+), 12 deletions(-) diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs index 60030214f0..22d3e35965 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs @@ -2,19 +2,19 @@ // 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 System.Text; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Model.Onnx; using Microsoft.ML.Runtime.Model.Pfa; -using Microsoft.ML.Runtime.Internal.Internallearn; using Newtonsoft.Json.Linq; -using Microsoft.ML.Runtime.EntryPoints; +using System; +using System.Collections.Generic; using System.Linq; +using System.Text; [assembly: LoadableClass(NormalizeTransform.MinMaxNormalizerSummary, typeof(IDataTransform), typeof(NormalizeTransform), typeof(NormalizeTransform.MinMaxArguments), typeof(SignatureDataTransform), NormalizeTransform.MinMaxNormalizerUserName, "MinMaxNormalizer", NormalizeTransform.MinMaxNormalizerShortName)] @@ -247,7 +247,7 @@ public sealed class SupervisedBinArguments : BinArgumentsBase public const string SupervisedBinNormalizerShortName = "SupBin"; /// - /// A helper method to create MinMaxNormalizer transform for public facing API. + /// A helper method to create a MinMax normalizer. /// /// Host Environment. /// Input . This is the output from previous transform or loader. diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs index 7a34cd3a24..63c49cfe67 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs @@ -21,7 +21,7 @@ namespace Microsoft.ML.Runtime.Data /// public delegate void SignatureLoadColumnFunction(ModelLoadContext ctx, IHost host, ColumnType typeSrc); - public interface IColumnFunctionBuilder + internal interface IColumnFunctionBuilder { /// /// Trains on the current value. @@ -38,7 +38,7 @@ public interface IColumnFunctionBuilder /// /// Interface to define an aggregate function over values /// - public interface IColumnAggregator + internal interface IColumnAggregator { /// /// Updates the aggregate function with a value @@ -51,7 +51,7 @@ public interface IColumnAggregator void Finish(); } - public interface IColumnFunction : ICanSaveModel + internal interface IColumnFunction : ICanSaveModel { Delegate GetGetter(IRow input, int icol); diff --git a/src/Microsoft.ML.Data/Transforms/Normalizer.cs b/src/Microsoft.ML.Data/Transforms/Normalizer.cs index b5de9f3cbc..5d888cd433 100644 --- a/src/Microsoft.ML.Data/Transforms/Normalizer.cs +++ b/src/Microsoft.ML.Data/Transforms/Normalizer.cs @@ -6,6 +6,9 @@ using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Model.Onnx; +using Microsoft.ML.Runtime.Model.Pfa; +using Newtonsoft.Json.Linq; using System; using System.Collections.Generic; using System.Linq; @@ -44,7 +47,7 @@ public abstract class ColumnBase public readonly string Output; public readonly long MaxTrainingExamples; - protected ColumnBase(string input, string output, long maxTrainingExamples) + private protected ColumnBase(string input, string output, long maxTrainingExamples) { Contracts.CheckNonEmpty(input, nameof(input)); Contracts.CheckNonEmpty(output, nameof(output)); @@ -79,7 +82,7 @@ public abstract class FixZeroColumnBase : ColumnBase { public readonly bool FixZero; - protected FixZeroColumnBase(string input, string output, long maxTrainingExamples, bool fixZero) + private protected FixZeroColumnBase(string input, string output, long maxTrainingExamples, bool fixZero) : base(input, output, maxTrainingExamples) { FixZero = fixZero; @@ -422,10 +425,13 @@ protected override void CheckInputColumn(ISchema inputSchema, int col, int srcCo protected override IRowMapper MakeRowMapper(ISchema schema) => new Mapper(this, schema); - private sealed class Mapper : MapperBase + private sealed class Mapper : MapperBase, ISaveAsOnnx, ISaveAsPfa { private NormalizerTransformer _parent; + public bool CanSaveOnnx => true; + public bool CanSavePfa => true; + public Mapper(NormalizerTransformer parent, ISchema schema) : base(parent.Host.Register(nameof(Mapper)), parent, schema) { @@ -466,6 +472,89 @@ protected override Delegate MakeGetter(IRow input, int iinfo, out Action dispose disposer = null; return _parent._columns[iinfo].ColumnFunction.GetGetter(input, ColMapNewToOld[iinfo]); } + + public void SaveAsOnnx(OnnxContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + + for (int iinfo = 0; iinfo < _parent._columns.Length; ++iinfo) + { + var info = _parent._columns[iinfo]; + string sourceColumnName = info.Input; + if (!ctx.ContainsColumn(sourceColumnName)) + { + ctx.RemoveColumn(info.Output, false); + continue; + } + + if (!SaveAsOnnxCore(ctx, iinfo, info, ctx.GetVariableName(sourceColumnName), + ctx.AddIntermediateVariable(info.InputType, info.Output))) + { + ctx.RemoveColumn(info.Output, true); + } + } + } + + public void SaveAsPfa(BoundPfaContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + + var toHide = new List(); + var toDeclare = new List>(); + + for (int iinfo = 0; iinfo < _parent._columns.Length; ++iinfo) + { + var info = _parent._columns[iinfo]; + var srcName = info.Input; + string srcToken = ctx.TokenOrNullForName(srcName); + if (srcToken == null) + { + toHide.Add(info.Output); + continue; + } + var result = SaveAsPfaCore(ctx, iinfo, info, srcToken); + if (result == null) + { + toHide.Add(info.Output); + continue; + } + toDeclare.Add(new KeyValuePair(info.Output, result)); + } + ctx.Hide(toHide.ToArray()); + ctx.DeclareVar(toDeclare.ToArray()); + } + + private JToken SaveAsPfaCore(BoundPfaContext ctx, int iinfo, ColumnInfo info, JToken srcToken) + { + Contracts.AssertValue(ctx); + Contracts.Assert(0 <= iinfo && iinfo < _parent._columns.Length); + Contracts.Assert(_parent._columns[iinfo] == info); + Contracts.AssertValue(srcToken); + Contracts.Assert(CanSavePfa); + return info.ColumnFunction.PfaInfo(ctx, srcToken); + } + + private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColumnInfo info, string srcVariableName, string dstVariableName) + { + Contracts.AssertValue(ctx); + Contracts.Assert(0 <= iinfo && iinfo < _parent._columns.Length); + Contracts.Assert(_parent._columns[iinfo] == info); + Contracts.Assert(CanSaveOnnx); + + if (info.InputType.ValueCount == 0) + return false; + + if (info.ColumnFunction.CanSaveOnnx) + { + string opType = "Scaler"; + var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType)); + info.ColumnFunction.OnnxInfo(ctx, node, info.InputType.ValueCount); + return true; + } + + return false; + } + } } } From a1a8f213984ca88a8f41fb5adce8115e006e4224 Mon Sep 17 00:00:00 2001 From: Pete Luferenko Date: Tue, 4 Sep 2018 16:02:42 -0700 Subject: [PATCH 6/6] Make it compile again, fixed the auto-API by removing SupervisedBin --- .../Transforms/NormalizeColumn.cs | 6 +- .../Transforms/NormalizeColumnDbl.cs | 6 +- .../Transforms/NormalizeColumnSng.cs | 6 +- .../Transforms/NormalizeUtils.cs | 2 +- src/Microsoft.ML/CSharpApi.cs | 142 --------------- .../Common/EntryPoints/core_ep-list.tsv | 1 - .../Common/EntryPoints/core_manifest.json | 170 ------------------ 7 files changed, 10 insertions(+), 323 deletions(-) diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs index 22d3e35965..ed05da32bb 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumn.cs @@ -369,7 +369,7 @@ public static IDataTransform Create(IHostEnvironment env, BinArguments args, IDa return normalizer.Fit(input).MakeDataTransform(input); } - public abstract partial class AffineColumnFunction : IColumnFunction + internal abstract partial class AffineColumnFunction : IColumnFunction { protected readonly IHost Host; @@ -477,7 +477,7 @@ private void OffsetMetadataGetter(int col, ref VBuffer dst) } } - public abstract partial class CdfColumnFunction : IColumnFunction + internal abstract partial class CdfColumnFunction : IColumnFunction { protected readonly IHost Host; @@ -603,7 +603,7 @@ public static VersionInfo GetVersionInfo() } } - public abstract partial class BinColumnFunction : IColumnFunction + internal abstract partial class BinColumnFunction : IColumnFunction { protected readonly IHost Host; diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs index 11a23e52ef..9c052a18d3 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumnDbl.cs @@ -520,7 +520,7 @@ private void Update(int j, TFloat origVal) public sealed partial class NormalizeTransform { - public abstract partial class AffineColumnFunction + internal abstract partial class AffineColumnFunction { public static IColumnFunction Create(IHost host, TFloat scale, TFloat offset) { @@ -846,7 +846,7 @@ private static void FillValues(ref VBuffer input, BufferBuilder } } - public abstract partial class CdfColumnFunction + internal abstract partial class CdfColumnFunction { public static IColumnFunction Create(IHost host, TFloat mean, TFloat stddev, bool useLog) { @@ -1021,7 +1021,7 @@ private static void FillValues(ref VBuffer input, BufferBuilder } } - public abstract partial class BinColumnFunction + internal abstract partial class BinColumnFunction { public static IColumnFunction Create(IHost host, TFloat[] binUpperBounds, bool fixZero) { diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs b/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs index 5001b3122b..c728b2ff73 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeColumnSng.cs @@ -520,7 +520,7 @@ private void Update(int j, TFloat origVal) public sealed partial class NormalizeTransform { - public abstract partial class AffineColumnFunction + internal abstract partial class AffineColumnFunction { public static IColumnFunction Create(IHost host, TFloat scale, TFloat offset) { @@ -848,7 +848,7 @@ private static void FillValues(ref VBuffer input, BufferBuilder } } - public abstract partial class CdfColumnFunction + internal abstract partial class CdfColumnFunction { public static IColumnFunction Create(IHost host, TFloat mean, TFloat stddev, bool useLog) { @@ -1023,7 +1023,7 @@ private static void FillValues(ref VBuffer input, BufferBuilder } } - public abstract partial class BinColumnFunction + internal abstract partial class BinColumnFunction { public static IColumnFunction Create(IHost host, TFloat[] binUpperBounds, bool fixZero) { diff --git a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs index 63c49cfe67..8c7246acc2 100644 --- a/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs +++ b/src/Microsoft.ML.Data/Transforms/NormalizeUtils.cs @@ -38,7 +38,7 @@ internal interface IColumnFunctionBuilder /// /// Interface to define an aggregate function over values /// - internal interface IColumnAggregator + public interface IColumnAggregator { /// /// Updates the aggregate function with a value diff --git a/src/Microsoft.ML/CSharpApi.cs b/src/Microsoft.ML/CSharpApi.cs index 66718150d1..ea6393f3e2 100644 --- a/src/Microsoft.ML/CSharpApi.cs +++ b/src/Microsoft.ML/CSharpApi.cs @@ -1522,18 +1522,6 @@ public void Add(Microsoft.ML.Transforms.SentimentAnalyzer input, Microsoft.ML.Tr _jsonNodes.Add(Serialize("Transforms.SentimentAnalyzer", input, output)); } - public Microsoft.ML.Transforms.SupervisedBinNormalizer.Output Add(Microsoft.ML.Transforms.SupervisedBinNormalizer input) - { - var output = new Microsoft.ML.Transforms.SupervisedBinNormalizer.Output(); - Add(input, output); - return output; - } - - public void Add(Microsoft.ML.Transforms.SupervisedBinNormalizer input, Microsoft.ML.Transforms.SupervisedBinNormalizer.Output output) - { - _jsonNodes.Add(Serialize("Transforms.SupervisedBinNormalizer", input, output)); - } - public Microsoft.ML.Transforms.TensorFlowScorer.Output Add(Microsoft.ML.Transforms.TensorFlowScorer input) { var output = new Microsoft.ML.Transforms.TensorFlowScorer.Output(); @@ -15786,136 +15774,6 @@ public SentimentAnalyzerPipelineStep(Output output) } } - namespace Transforms - { - - /// - /// Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins. - /// - public sealed partial class SupervisedBinNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { - - public SupervisedBinNormalizer() - { - } - - public SupervisedBinNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public SupervisedBinNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (var inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); - } - } - } - - public void AddColumn(string inputColumn) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); - Column = list.ToArray(); - } - - public void AddColumn(string outputColumn, string inputColumn) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); - Column = list.ToArray(); - } - - - /// - /// Label column for supervised binning - /// - public string LabelColumn { get; set; } - - /// - /// Minimum number of examples per bin - /// - public int MinBinSize { get; set; } = 10; - - /// - /// New column definition(s) (optional form: name:src) - /// - public NormalizeTransformBinColumn[] Column { get; set; } - - /// - /// Max number of bins, power of 2 recommended - /// - public int NumBins { get; set; } = 1024; - - /// - /// Whether to map zero to zero, preserving sparsity - /// - public bool FixZero { get; set; } = true; - - /// - /// Max number of examples used to train the normalizer - /// - public long MaxTrainingExamples { get; set; } = 1000000000; - - /// - /// Input dataset - /// - public Var Data { get; set; } = new Var(); - - - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); - - } - public Var GetInputData() => Data; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(SupervisedBinNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } - - Data = dataStep.Data; - } - Output output = experiment.Add(this); - return new SupervisedBinNormalizerPipelineStep(output); - } - - private class SupervisedBinNormalizerPipelineStep : ILearningPipelineDataStep - { - public SupervisedBinNormalizerPipelineStep(Output output) - { - Data = output.OutputData; - Model = output.Model; - } - - public Var Data { get; } - public Var Model { get; } - } - } - } - namespace Transforms { diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv index 14cab2b984..c69bf1171f 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv +++ b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv @@ -123,7 +123,6 @@ Transforms.ScoreColumnSelector Selects only the last score columns and the extra Transforms.Scorer Turn the predictor model into a transform model Microsoft.ML.Runtime.EntryPoints.ScoreModel MakeScoringTransform Microsoft.ML.Runtime.EntryPoints.ScoreModel+ModelInput Microsoft.ML.Runtime.EntryPoints.ScoreModel+Output Transforms.Segregator Un-groups vector columns into sequences of rows, inverse of Group transform Microsoft.ML.Runtime.Data.GroupingOperations Ungroup Microsoft.ML.Runtime.Data.UngroupTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.SentimentAnalyzer Uses a pretrained sentiment model to score input strings Microsoft.ML.Runtime.Transforms.TextAnalytics AnalyzeSentiment Microsoft.ML.Runtime.TextAnalytics.SentimentAnalyzingTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput -Transforms.SupervisedBinNormalizer Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins. Microsoft.ML.Runtime.Data.Normalize SupervisedBin Microsoft.ML.Runtime.Data.NormalizeTransform+SupervisedBinArguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.TensorFlowScorer Transforms the data using the TensorFlow model. Microsoft.ML.Transforms.TensorFlowTransform TensorFlowScorer Microsoft.ML.Transforms.TensorFlowTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.TextFeaturizer A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) ngrams in a given tokenized text. Microsoft.ML.Runtime.Transforms.TextAnalytics TextTransform Microsoft.ML.Runtime.Data.TextTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.TextToKeyConverter Converts input values (words, numbers, etc.) to index in a dictionary. Microsoft.ML.Runtime.Data.Categorical TextToKey Microsoft.ML.Runtime.Data.TermTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 99db39b419..f41aa09fd6 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -21709,176 +21709,6 @@ "ITransformOutput" ] }, - { - "Name": "Transforms.SupervisedBinNormalizer", - "Desc": "Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins.", - "FriendlyName": "Supervised Binning Normalizer", - "ShortName": "SupBin", - "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins, power of 2 recommended", - "Aliases": [ - "bins" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", - "Aliases": [ - "col" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Label column for supervised binning", - "Aliases": [ - "label", - "lab" - ], - "Required": true, - "SortOrder": 150.0, - "IsNullable": false - }, - { - "Name": "MinBinSize", - "Type": "Int", - "Desc": "Minimum number of examples per bin", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 10 - }, - { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins, power of 2 recommended", - "Aliases": [ - "bins" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1024 - }, - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1000000000 - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, { "Name": "Transforms.TensorFlowScorer", "Desc": "Transforms the data using the TensorFlow model.",