From dda393a3ac947b98ce784090995f548d7e9de744 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 10 Sep 2018 15:09:16 -0700 Subject: [PATCH 01/18] work in progress on prior and random estimators --- .../Standard/Simple/SimpleTrainers.cs | 39 +++++++++++-------- 1 file changed, 22 insertions(+), 17 deletions(-) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index abfff554c9..2b2d7dfc76 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -13,6 +13,7 @@ using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Training; using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Core.Data; [assembly: LoadableClass(RandomTrainer.Summary, typeof(RandomTrainer), typeof(RandomTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, @@ -38,7 +39,8 @@ namespace Microsoft.ML.Runtime.Learners /// /// A trainer that trains a predictor that returns random values /// - public sealed class RandomTrainer : TrainerBase + + public sealed class RandomTrainer : TrainerEstimatorBase, RandomPredictor> { internal const string LoadNameValue = "RandomPredictor"; internal const string UserNameValue = "Random Predictor"; @@ -46,12 +48,6 @@ public sealed class RandomTrainer : TrainerBase public class Arguments { - // Some sample arguments - [Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate", ShortName = "lr")] - public Float LearningRate = (Float)1.0; - - [Argument(ArgumentType.AtMostOnce, HelpText = "Some bool arg", ShortName = "boolarg")] - public bool BooleanArg = false; } public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; @@ -59,17 +55,21 @@ public class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - public RandomTrainer(IHostEnvironment env, Arguments args) - : base(env, LoadNameValue) + protected override SchemaShape.Column[] OutputColumns => throw new NotImplementedException(); + + public RandomTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) + : base(host, feature, label, weight) { - Host.CheckValue(args, nameof(args)); } - public override RandomPredictor Train(TrainContext context) + protected override RandomPredictor TrainModelCore(TrainContext trainContext) { - Host.CheckValue(context, nameof(context)); + Host.CheckValue(trainContext, nameof(trainContext)); return new RandomPredictor(Host, Host.Rand.Next()); } + + protected override BinaryPredictionTransformer MakeTransformer(RandomPredictor model, ISchema trainSchema) + => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); } /// @@ -196,7 +196,7 @@ private void MapDist(ref VBuffer src, ref Float score, ref Float prob) } // Learns the prior distribution for 0/1 class labels and just outputs that. - public sealed class PriorTrainer : TrainerBase + public sealed class PriorTrainer : TrainerEstimatorBase, PriorPredictor> { internal const string LoadNameValue = "PriorPredictor"; internal const string UserNameValue = "Prior Predictor"; @@ -210,13 +210,14 @@ public sealed class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - public PriorTrainer(IHostEnvironment env, Arguments args) - : base(env, LoadNameValue) + protected override SchemaShape.Column[] OutputColumns { get; } + + public PriorTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) + : base(host, feature, label, weight) { - Host.CheckValue(args, nameof(args)); } - public override PriorPredictor Train(TrainContext context) + protected override PriorPredictor TrainModelCore(TrainContext context) { Contracts.CheckValue(context, nameof(context)); var data = context.TrainingSet; @@ -258,6 +259,10 @@ public override PriorPredictor Train(TrainContext context) Float prob = prob = pos + neg > 0 ? (Float)(pos / (pos + neg)) : Float.NaN; return new PriorPredictor(Host, prob); } + + protected override BinaryPredictionTransformer MakeTransformer(PriorPredictor model, ISchema trainSchema) + => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + } public sealed class PriorPredictor : From 602a581beccd26f7964746420ab506f3933ce629 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Tue, 11 Sep 2018 15:18:13 -0700 Subject: [PATCH 02/18] working on conversion of random and prior estimators, fixed constructors --- .../Standard/Simple/SimpleTrainers.cs | 62 ++++++++++++++++--- 1 file changed, 53 insertions(+), 9 deletions(-) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index 2b2d7dfc76..afa4b59cf7 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -14,6 +14,7 @@ using Microsoft.ML.Runtime.Training; using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Core.Data; +using System.Linq; [assembly: LoadableClass(RandomTrainer.Summary, typeof(RandomTrainer), typeof(RandomTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, @@ -40,13 +41,14 @@ namespace Microsoft.ML.Runtime.Learners /// A trainer that trains a predictor that returns random values /// - public sealed class RandomTrainer : TrainerEstimatorBase, RandomPredictor> + public sealed class RandomTrainer : TrainerBase, + ITrainerEstimator, RandomPredictor> { internal const string LoadNameValue = "RandomPredictor"; internal const string UserNameValue = "Random Predictor"; internal const string Summary = "A toy predictor that returns a random value."; - public class Arguments + public sealed class Arguments { } @@ -55,21 +57,41 @@ public class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - protected override SchemaShape.Column[] OutputColumns => throw new NotImplementedException(); + public RandomTrainer(IHostEnvironment env, Arguments args) + : base(env, LoadNameValue) + { + Host.CheckValue(args, nameof(args)); + } - public RandomTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) - : base(host, feature, label, weight) + public BinaryPredictionTransformer Fit(IDataView input) { + var cachedTrain = Info.WantCaching ? new CacheDataView(Host, input, prefetch: null) : input; + + RoleMappedData trainRoles = new RoleMappedData(cachedTrain); + var pred = Train(new TrainContext(trainRoles)); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, trainRoles.Schema.Feature.Name); } - protected override RandomPredictor TrainModelCore(TrainContext trainContext) + public override RandomPredictor Train(TrainContext context) { - Host.CheckValue(trainContext, nameof(trainContext)); + Host.CheckValue(context, nameof(context)); return new RandomPredictor(Host, Host.Rand.Next()); } - protected override BinaryPredictionTransformer MakeTransformer(RandomPredictor model, ISchema trainSchema) - => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + /// + /// Create a new SchemaShape with all columns in inputScema and an additional + /// Score column which is the output of the Estimator. + /// + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + Host.CheckValue(inputSchema, nameof(inputSchema)); + + var outColumns = inputSchema.Columns.ToDictionary(x => x.Name); + var newColumn = new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, isKey: false); + outColumns[DefaultColumnNames.Score] = newColumn; + + return new SchemaShape(outColumns.Values); + } } /// @@ -212,9 +234,22 @@ public sealed class Arguments protected override SchemaShape.Column[] OutputColumns { get; } + public PriorTrainer(IHostEnvironment env, Arguments args) + : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), MakeFeatureColumn(DefaultColumnNames.Score), MakeFeatureColumn(DefaultColumnNames.Label), null) + { + OutputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, isKey: false) + }; + } + public PriorTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) : base(host, feature, label, weight) { + OutputColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, isKey: false) + }; } protected override PriorPredictor TrainModelCore(TrainContext context) @@ -263,6 +298,15 @@ protected override PriorPredictor TrainModelCore(TrainContext context) protected override BinaryPredictionTransformer MakeTransformer(PriorPredictor model, ISchema trainSchema) => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); + private static SchemaShape.Column MakeFeatureColumn(string featureColumn) + { + return new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + } + + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + { + return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); + } } public sealed class PriorPredictor : From 6adb80b8f977f6045ebd96503bb57dd92d227578 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Tue, 11 Sep 2018 18:00:25 -0700 Subject: [PATCH 03/18] got rid of Float, changed random generation function, fixed syntax errors --- .../Standard/Simple/SimpleTrainers.cs | 94 +++++++++---------- 1 file changed, 42 insertions(+), 52 deletions(-) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index afa4b59cf7..dcf984d869 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -2,8 +2,6 @@ // 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; @@ -69,7 +67,7 @@ public BinaryPredictionTransformer Fit(IDataView input) RoleMappedData trainRoles = new RoleMappedData(cachedTrain); var pred = Train(new TrainContext(trainRoles)); - return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, trainRoles.Schema.Feature.Name); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, null); } public override RandomPredictor Train(TrainContext context) @@ -78,10 +76,6 @@ public override RandomPredictor Train(TrainContext context) return new RandomPredictor(Host, Host.Rand.Next()); } - /// - /// Create a new SchemaShape with all columns in inputScema and an additional - /// Score column which is the output of the Estimator. - /// public SchemaShape GetOutputSchema(SchemaShape inputSchema) { Host.CheckValue(inputSchema, nameof(inputSchema)); @@ -99,8 +93,8 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema) /// uniform random probability and classification assignment. /// public sealed class RandomPredictor : - PredictorBase, - IDistPredictorProducing, + PredictorBase, + IDistPredictorProducing, IValueMapperDist, ICanSaveModel { @@ -118,7 +112,7 @@ private static VersionInfo GetVersionInfo() // Keep all the serializable state here. private readonly int _seed; private readonly object _instanceLock; - private readonly Random _random; + private readonly TauswortheHybrid _random; public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; public ColumnType InputType { get; } @@ -131,7 +125,7 @@ public RandomPredictor(IHostEnvironment env, int seed) _seed = seed; _instanceLock = new object(); - _random = new Random(_seed); + _random = RandomUtils.Create(_seed); InputType = new VectorType(NumberType.Float); } @@ -148,7 +142,7 @@ private RandomPredictor(IHostEnvironment env, ModelLoadContext ctx) _seed = ctx.Reader.ReadInt32(); _instanceLock = new object(); - _random = new Random(_seed); + _random = RandomUtils.Create(_seed); } public static RandomPredictor Create(IHostEnvironment env, ModelLoadContext ctx) @@ -176,24 +170,24 @@ protected override void SaveCore(ModelSaveContext ctx) public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); - ValueMapper, Float> del = Map; + ValueMapper, float> del = Map; return (ValueMapper)(Delegate)del; } public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); - Contracts.Check(typeof(TDist) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); + Contracts.Check(typeof(TDist) == typeof(float)); - ValueMapper, Float, Float> del = MapDist; + ValueMapper, float, float> del = MapDist; return (ValueMapper)(Delegate)del; } - private Float PredictCore() + private float PredictCore() { // Predict can be called from different threads. // Ensure your implementation is thread-safe @@ -205,12 +199,12 @@ private Float PredictCore() } } - private void Map(ref VBuffer src, ref Float dst) + private void Map(ref VBuffer src, ref float dst) { dst = PredictCore(); } - private void MapDist(ref VBuffer src, ref Float score, ref Float prob) + private void MapDist(ref VBuffer src, ref float score, ref float prob) { score = PredictCore(); prob = (score + 1) / 2; @@ -270,16 +264,16 @@ protected override PriorPredictor TrainModelCore(TrainContext context) using (var cursor = data.Data.GetRowCursor(c => c == col || c == colWeight)) { var getLab = cursor.GetLabelFloatGetter(data); - var getWeight = colWeight >= 0 ? cursor.GetGetter(colWeight) : null; - Float lab = default(Float); - Float weight = 1; + var getWeight = colWeight >= 0 ? cursor.GetGetter(colWeight) : null; + float lab = default(float); + float weight = 1; while (cursor.MoveNext()) { getLab(ref lab); if (getWeight != null) { getWeight(ref weight); - if (!(0 < weight && weight < Float.PositiveInfinity)) + if (!(0 < weight && weight < float.PositiveInfinity)) continue; } @@ -291,27 +285,23 @@ protected override PriorPredictor TrainModelCore(TrainContext context) } } - Float prob = prob = pos + neg > 0 ? (Float)(pos / (pos + neg)) : Float.NaN; + float prob = prob = pos + neg > 0 ? (float)(pos / (pos + neg)) : float.NaN; return new PriorPredictor(Host, prob); } protected override BinaryPredictionTransformer MakeTransformer(PriorPredictor model, ISchema trainSchema) => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); - private static SchemaShape.Column MakeFeatureColumn(string featureColumn) - { - return new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); - } + private static SchemaShape.Column MakeFeatureColumn(string featureColumn) => + new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); - private static SchemaShape.Column MakeLabelColumn(string labelColumn) - { - return new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); - } + private static SchemaShape.Column MakeLabelColumn(string labelColumn) => + new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); } public sealed class PriorPredictor : - PredictorBase, - IDistPredictorProducing, + PredictorBase, + IDistPredictorProducing, IValueMapperDist, ICanSaveModel { @@ -326,13 +316,13 @@ private static VersionInfo GetVersionInfo() loaderSignature: LoaderSignature); } - private readonly Float _prob; - private readonly Float _raw; + private readonly float _prob; + private readonly float _raw; - public PriorPredictor(IHostEnvironment env, Float prob) + public PriorPredictor(IHostEnvironment env, float prob) : base(env, LoaderSignature) { - Host.Check(!Float.IsNaN(prob)); + Host.Check(!float.IsNaN(prob)); _prob = prob; _raw = 2 * _prob - 1; // This could be other functions -- logodds for instance @@ -347,7 +337,7 @@ private PriorPredictor(IHostEnvironment env, ModelLoadContext ctx) // Float: _prob _prob = ctx.Reader.ReadFloat(); - Host.CheckDecode(!Float.IsNaN(_prob)); + Host.CheckDecode(!float.IsNaN(_prob)); _raw = 2 * _prob - 1; @@ -370,7 +360,7 @@ protected override void SaveCore(ModelSaveContext ctx) // *** Binary format *** // Float: _prob - Contracts.Assert(!Float.IsNaN(_prob)); + Contracts.Assert(!float.IsNaN(_prob)); ctx.Writer.Write(_prob); } @@ -382,29 +372,29 @@ public override PredictionKind PredictionKind public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); - ValueMapper, Float> del = Map; + ValueMapper, float> del = Map; return (ValueMapper)(Delegate)del; } public ValueMapper GetMapper() { - Contracts.Check(typeof(TIn) == typeof(VBuffer)); - Contracts.Check(typeof(TOut) == typeof(Float)); - Contracts.Check(typeof(TDist) == typeof(Float)); + Contracts.Check(typeof(TIn) == typeof(VBuffer)); + Contracts.Check(typeof(TOut) == typeof(float)); + Contracts.Check(typeof(TDist) == typeof(float)); - ValueMapper, Float, Float> del = MapDist; + ValueMapper, float, float> del = MapDist; return (ValueMapper)(Delegate)del; } - private void Map(ref VBuffer src, ref Float dst) + private void Map(ref VBuffer src, ref float dst) { dst = _raw; } - private void MapDist(ref VBuffer src, ref Float score, ref Float prob) + private void MapDist(ref VBuffer src, ref float score, ref float prob) { score = _raw; prob = _prob; From a04ba3f3b66b6d1cef6e1d23e860a6e970dde07d Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Tue, 11 Sep 2018 18:03:41 -0700 Subject: [PATCH 04/18] working on tests, still not passing random --- .../BinaryPrior-CV-breast-cancer-out.txt | 52 ++ .../BinaryPrior-CV-breast-cancer-rp.txt | 4 + .../BinaryPrior-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...inaryPrior-TrainTest-breast-cancer-out.txt | 36 + ...BinaryPrior-TrainTest-breast-cancer-rp.txt | 4 + ...Prior-TrainTest-breast-cancer-test-out.txt | 19 + .../BinaryPrior-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../BinaryRandom-CV-breast-cancer-out.txt | 52 ++ .../BinaryRandom-CV-breast-cancer-rp.txt | 4 + .../BinaryRandom-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...naryRandom-TrainTest-breast-cancer-out.txt | 36 + ...inaryRandom-TrainTest-breast-cancer-rp.txt | 4 + ...andom-TrainTest-breast-cancer-test-out.txt | 18 + .../BinaryRandom-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../TestPredictors.cs | 26 +- test/Microsoft.ML.TestFramework/Learners.cs | 14 + 16 files changed, 3068 insertions(+), 1 deletion(-) create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt create mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt create mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..38585c7ede --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 134 | 0.0000 + negative || 0 | 228 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6298 | +OVERALL 0/1 ACCURACY: 0.629834 +LOG LOSS/instance: 0.959786 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -0.945203 +AUC: 0.500000 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 107 | 0.0000 + negative || 0 | 230 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6825 | +OVERALL 0/1 ACCURACY: 0.682493 +LOG LOSS/instance: 0.910421 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -0.972725 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.656163 (0.0263) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.656163 (0.0263) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.935104 (0.0247) +Log-loss reduction: -0.958964 (0.0138) +F1 Score: NaN (NaN) +AUPRC: 0.418968 (0.0212) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..83b563173d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.656163 0 0 0.656163 1 0.935104 -0.958964 NaN 0.418968 PriorPredictor %Data% %Output% 99 0 0 maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt new file mode 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+674 0 -0.259668529 0.370165735 0.6669558491577029 0 +675 0 -0.259668529 0.370165735 0.6669558491577029 0 +676 0 -0.259668529 0.370165735 0.6669558491577029 0 +677 0 -0.259668529 0.370165735 0.6669558491577029 0 +684 0 -0.259668529 0.370165735 0.6669558491577029 0 +686 0 -0.259668529 0.370165735 0.6669558491577029 0 +687 0 -0.259668529 0.370165735 0.6669558491577029 0 +690 0 -0.259668529 0.370165735 0.6669558491577029 0 +695 0 -0.259668529 0.370165735 0.6669558491577029 0 diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..8e3bf3b17b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 241 | 0.0000 + negative || 0 | 458 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6552 | +OVERALL 0/1 ACCURACY: 0.655222 +LOG LOSS/instance: 0.929318 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.655222 (0.0000) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.655222 (0.0000) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.929318 (0.0000) +Log-loss reduction: 0.000000 (0.0000) +F1 Score: NaN (0.0000) +AUPRC: 0.415719 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..60265c1608 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.655222 0 0 0.655222 1 0.929318 0 NaN 0.415719 PriorPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt new file mode 100644 index 0000000000..07cf437dfe --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt @@ -0,0 +1,19 @@ +maml.exe Test dout=%Output% data=%Data% in=%Output% seed=1 +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +***** Unexpected failure. Please refer to https://aka.ms/MLNetIssue to file an issue with details ***** +***** Error log has been saved to '%Temp%\%ErrorLog%', please refer to https://aka.ms/MLNetIssue to file an issue with details ***** +===== Begin detailed dump ===== +(1) Unexpected exception: Could not find file '%Output% 'System.IO.FileNotFoundException' + at System.IO.FileStream.ValidateFileHandle(SafeFileHandle fileHandle) + at System.IO.FileStream.CreateFileOpenHandle(FileMode mode, FileShare share, FileOptions options) + at System.IO.FileStream..ctor(String path, FileMode mode, FileAccess access, FileShare share, Int32 bufferSize, FileOptions options) + at System.IO.FileStream..ctor(String path, FileMode mode, FileAccess access) + at Microsoft.ML.Runtime.SimpleFileHandle.OpenReadStream() in E:\artidoro-machinelearning\src\Microsoft.ML.Core\Data\IFileHandle.cs:line 197 + at Microsoft.ML.Runtime.Data.DataCommand.ImplBase`1.LoadModelObjects(IChannel ch, Nullable`1 wantPredictor, IPredictor& predictor, Boolean wantTrainSchema, RoleMappedSchema& trainSchema, IDataLoader& pipe) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\DataCommand.cs:line 260 + at Microsoft.ML.Runtime.Data.TestCommand.RunCore(IChannel ch) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 90 + at Microsoft.ML.Runtime.Data.TestCommand.Run() in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 77 + at Microsoft.ML.Runtime.Tools.Maml.MainCore(TlcEnvironment env, String args, Boolean alwaysPrintStacktrace) in E:\artidoro-machinelearning\src\Microsoft.ML.Maml\MAML.cs:line 136 +====== End detailed dump ===== diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5bd2f17a17 --- /dev/null +++ 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0.34477827 1.5362592468820475 0 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..8c63f6ed72 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 63 | 71 | 0.4701 + negative || 111 | 117 | 0.5132 + ||====================== +Precision || 0.3621 | 0.6223 | +OVERALL 0/1 ACCURACY: 0.497238 +LOG LOSS/instance: 1.455174 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -53.047423 +AUC: 0.479314 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 53 | 54 | 0.4953 + negative || 105 | 125 | 0.5435 + ||====================== +Precision || 0.3354 | 0.6983 | +OVERALL 0/1 ACCURACY: 0.528190 +LOG LOSS/instance: 1.306253 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -44.873587 +AUC: 0.529581 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.504448 (0.0251) +Accuracy: 0.512714 (0.0155) +Positive precision: 0.348756 (0.0133) +Positive recall: 0.482738 (0.0126) +Negative precision: 0.660332 (0.0380) +Negative recall: 0.528318 (0.0152) +Log-loss: 1.380713 (0.0745) +Log-loss reduction: -48.960505 (4.0869) +F1 Score: 0.404545 (0.0045) +AUPRC: 0.353016 (0.0056) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..cbdf459531 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.504448 0.512714 0.348756 0.482738 0.660332 0.528318 1.380713 -48.96051 0.404545 0.353016 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt new file mode 100644 index 0000000000..757f9d767b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.836182952 0.081908524 3.6098425920581336 0 +6 0 -0.553042054 0.223478973 0.36490310361014566 0 +8 0 -0.255037 0.3724815 0.6722700916434613 0 +9 0 -0.142923057 0.428538471 0.8072717171007312 0 +10 0 0.186874211 0.5934371 1.298449437118931 1 +11 0 -0.611070633 0.194464684 0.31198025359636589 0 +18 1 0.3969466 0.698473334 0.51772305611667846 1 +20 1 -0.157519519 0.42124024 1.2472848338337834 0 +21 1 -0.239025056 0.380487472 1.3940791435035362 0 +25 1 -0.560221851 0.219889075 2.1851521708397161 0 +28 0 -0.87005347 0.0649732649 0.096920478552667694 0 +31 0 -0.878868 0.0605660081 0.090136298746040081 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+684 0 0.0817723349 0.540886164 1.1230761837607575 1 +686 0 -0.9001114 0.04994431 0.073916013716783621 0 +687 0 -0.921699047 0.0391504765 0.057617583383256891 0 +690 0 -0.8664752 0.06676239 0.099683641498594974 0 +695 0 0.4262053 0.713102639 1.8013933958218449 1 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..88c2c43ad8 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 126 | 115 | 0.5228 + negative || 236 | 222 | 0.4847 + ||====================== +Precision || 0.3481 | 0.6588 | +OVERALL 0/1 ACCURACY: 0.497854 +LOG LOSS/instance: 1.389820 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): -49.552696 +AUC: 0.513345 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.513345 (0.0000) +Accuracy: 0.497854 (0.0000) +Positive precision: 0.348066 (0.0000) +Positive recall: 0.522822 (0.0000) +Negative precision: 0.658754 (0.0000) +Negative recall: 0.484716 (0.0000) +Log-loss: 1.389820 (0.0000) +Log-loss reduction: -49.552696 (0.0000) +F1 Score: 0.417910 (0.0000) +AUPRC: 0.362332 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..5f8aad4b8d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.513345 0.497854 0.348066 0.522822 0.658754 0.484716 1.38982 -49.5527 0.41791 0.362332 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt new file mode 100644 index 0000000000..93de9a0098 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt @@ -0,0 +1,18 @@ +maml.exe Test dout=%Output% data=%Data% in=%Output% seed=1 +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +***** Unexpected failure. Please refer to https://aka.ms/MLNetIssue to file an issue with details ***** +***** Error log has been saved to '%Temp%\%ErrorLog%', please refer to https://aka.ms/MLNetIssue to file an issue with details ***** +===== Begin detailed dump ===== +(1) Unexpected exception: Object reference not set to an instance of an object., 'System.NullReferenceException' + at Microsoft.ML.Runtime.Data.SchemaBindableBinaryPredictorWrapper.CheckValid(IValueMapperDist& distMapper) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Scorers\SchemaBindablePredictorWrapper.cs:line 435 + at Microsoft.ML.Runtime.Data.SchemaBindableBinaryPredictorWrapper..ctor(IPredictor predictor) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Scorers\SchemaBindablePredictorWrapper.cs:line 363 + at Microsoft.ML.Runtime.Data.ScoreUtils.GetSchemaBindableMapper(IHostEnvironment env, IPredictor predictor, IComponentFactory`2 mapperFactory, ICommandLineComponentFactory scorerFactorySettings) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\ScoreCommand.cs:line 384 + at Microsoft.ML.Runtime.Data.ScoreUtils.GetScorerComponentAndMapper(IPredictor predictor, IComponentFactory`4 scorerFactory, RoleMappedSchema schema, IHostEnvironment env, IComponentFactory`2 mapperFactory, ISchemaBoundMapper& mapper) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\ScoreCommand.cs:line 284 + at Microsoft.ML.Runtime.Data.ScoreUtils.GetScorer(IComponentFactory`4 scorer, IPredictor predictor, IDataView input, String featureColName, String groupColName, IEnumerable`1 customColumns, IHostEnvironment env, RoleMappedSchema trainSchema, IComponentFactory`2 mapperFactory) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\ScoreCommand.cs:line 267 + at Microsoft.ML.Runtime.Data.TestCommand.RunCore(IChannel ch) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 112 + at Microsoft.ML.Runtime.Data.TestCommand.Run() in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 77 + at Microsoft.ML.Runtime.Tools.Maml.MainCore(TlcEnvironment env, String args, Boolean alwaysPrintStacktrace) in E:\artidoro-machinelearning\src\Microsoft.ML.Maml\MAML.cs:line 136 +====== End detailed dump ===== diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..bba88e92e3 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -0.613342345 0.193328828 0.30994739493272483 0 +1 0 0.148138508 0.574069262 1.2313092452342478 1 +2 0 -0.248801082 0.375599444 0.6794562727151785 0 +3 0 0.241135135 0.62056756 1.3980850667869362 1 +4 0 0.347133458 0.6735667 1.6151398540715076 1 +5 1 -0.861693263 0.06915337 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1.2168321875033714 0 +649 0 -0.0622140355 0.468893 0.91292552761118928 0 +650 0 -0.3621838 0.3189081 0.55407861027327798 0 +651 0 0.192015186 0.5960076 1.3075998902835098 1 +652 0 -0.3010049 0.349497557 0.6203736191129432 0 +653 0 0.3816384 0.6908192 1.6934773815908499 1 +654 0 -0.6835118 0.1582441 0.24852617219469147 0 +655 0 -0.133314684 0.433342665 0.81945151345120237 0 +656 0 0.492181182 0.7460906 1.9776142373862315 1 +657 0 0.138982028 0.569491 1.2158847931734473 1 +658 1 0.6542544 0.8271272 0.27381885132167755 1 +659 0 -0.846707761 0.07664612 0.11504441992703694 0 +660 0 0.272741467 0.6363707 1.4594597165764982 1 +661 0 0.294931978 0.647466 1.5041657064611611 1 +662 0 0.8652251 0.932612538 3.8913760060883198 1 +663 0 0.06636449 0.533182263 1.0990687179556897 1 +664 0 0.4483161 0.724158049 1.8580862093507253 1 +665 0 0.637850046 0.818925 2.465340903464865 1 +666 0 -0.4915028 0.254248619 0.42323335086166092 0 +667 0 -0.6818888 0.159055591 0.2499176606095283 0 +668 1 0.123747319 0.5618737 0.83168228795208354 1 +669 1 -0.8303837 0.08480814 3.5596534296246487 0 +670 1 0.8635505 0.9317752 0.10194614347617545 1 +671 0 -0.67463243 0.162683785 0.25615553099615729 0 +672 0 0.8572092 0.9286046 3.8080251221110686 1 +673 0 0.9502559 0.975127935 5.3293299214324978 1 +674 0 -0.428619385 0.2856903 0.48537839766862734 0 +675 0 -0.754474163 0.122762918 0.18896129768954514 0 +676 0 -0.6039053 0.19804734 0.31841101915249831 0 +677 0 -0.508016944 0.245991528 0.40734736132131927 0 +678 0 -0.8077447 0.09612766 0.14580906796639401 0 +679 0 -0.920906067 0.0395469666 0.058213027659129224 0 +680 1 0.3929262 0.6964631 0.52188116018652919 1 +681 1 0.9712561 0.985628 0.020884840839147011 1 +682 0 0.70263654 0.85131824 2.7497004253766635 1 +683 0 0.150784656 0.5753923 1.2357975808978203 1 +684 0 0.0443927534 0.522196352 1.0655102276224764 1 +685 0 0.507432461 0.75371623 2.021606539231755 1 +686 0 -0.7381253 0.130937338 0.20246789129160239 0 +687 0 -0.958693862 0.020653069 0.030108073728722415 0 +688 0 -0.579613745 0.210193127 0.34042817323767549 0 +689 0 0.6728682 0.8364341 2.6120563148391662 1 +690 0 -0.566135645 0.216932178 0.35279082838764941 0 +691 1 0.290807843 0.6454039 0.63172575093037175 1 +692 0 0.2772021 0.638601065 1.4683358388611474 1 +693 0 -0.9107473 0.0446263552 0.065863016067760044 0 +694 0 -0.133344933 0.433327526 0.81941296898428195 0 +695 0 0.3865932 0.6932966 1.7050839882086819 1 +696 1 -0.814075351 0.0929623246 3.4272100444961997 0 +697 1 -0.238204867 0.380897582 1.3925249670215807 0 +698 1 0.636992931 0.818496466 0.28895190781852803 1 diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index bde0cc20fc..d22283b6c6 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -91,12 +91,36 @@ public IList GetDatasetsForClassificationWeightingPredictorsTest() [TestCategory("Binary")] public void BinaryClassifierPerceptronTest() { - var binaryPredictors = new[] { TestLearners.perceptron }; + var binaryPredictors = new[] { TestLearners.perceptron}; var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest(); RunAllTests(binaryPredictors, binaryClassificationDatasets); Done(); } + [Fact] + [TestCategory("Binary")] + [TestCategory("SimpleLearners")] + public void BinaryPriorTest() + { + var predictors = new[] { + TestLearners.binaryPrior}; + var datasets = GetDatasetsForBinaryClassifierBaseTest(); + RunAllTests(predictors, datasets); + Done(); + } + + [Fact] + [TestCategory("Binary")] + [TestCategory("SimpleLearners")] + public void BinaryRandomTest() + { + var predictors = new[] { + TestLearners.binaryRandom}; + var datasets = GetDatasetsForBinaryClassifierBaseTest(); + RunAllTests(predictors, datasets); + Done(); + } + /// ///A test for binary classifiers /// diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index 6f7a0c428f..530feccfbf 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -42,6 +42,20 @@ static TestLearnersBase() Contracts.Check(ok, "Missing assemblies!"); } + // New. + public static PredictorAndArgs binaryPrior = new PredictorAndArgs + { + Trainer = new SubComponent("PriorPredictor"), + Tag = "BinaryPrior" + }; + + // New. + public static PredictorAndArgs binaryRandom = new PredictorAndArgs + { + Trainer = new SubComponent("RandomPredictor"), + Tag = "BinaryRandom" + }; + // New. public static PredictorAndArgs binarySdca = new PredictorAndArgs { From e083f1bc75ec73af72b4525bd9913237ec1a7aa8 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Wed, 12 Sep 2018 13:52:17 -0700 Subject: [PATCH 05/18] working on basic tests for random and prior estimators, random produces different outputs --- .../Scorers/PredictionTransformer.cs | 9 +- .../Standard/Simple/SimpleTrainers.cs | 14 +- ...Prior-TrainTest-breast-cancer-test-out.txt | 19 - .../BinaryRandom-CV-breast-cancer-out.txt | 48 +- .../BinaryRandom-CV-breast-cancer-rp.txt | 2 +- .../BinaryRandom-CV-breast-cancer.txt | 1398 ++++++++--------- ...naryRandom-TrainTest-breast-cancer-out.txt | 34 +- ...inaryRandom-TrainTest-breast-cancer-rp.txt | 2 +- ...andom-TrainTest-breast-cancer-test-out.txt | 18 - .../BinaryRandom-TrainTest-breast-cancer.txt | 1398 ++++++++--------- .../TestPredictors.cs | 2 +- 11 files changed, 1455 insertions(+), 1489 deletions(-) delete mode 100644 test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt delete mode 100644 test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt diff --git a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs index f04febd055..8243436328 100644 --- a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs +++ b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs @@ -36,17 +36,18 @@ public abstract class PredictionTransformerBase : IPredictionTransformer public TModel Model { get; } - public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn) + public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn = null) { Contracts.CheckValue(host, nameof(host)); + Contracts.CheckValueOrNull(featureColumn); Host = host; Host.CheckValue(trainSchema, nameof(trainSchema)); Model = model; FeatureColumn = featureColumn; - if (!trainSchema.TryGetColumnIndex(featureColumn, out int col)) + if (!trainSchema.TryGetColumnIndex(featureColumn, out int col) && (featureColumn != null)) throw Host.ExceptSchemaMismatch(nameof(featureColumn), RoleMappedSchema.ColumnRole.Feature.Value, featureColumn); - FeatureColumnType = trainSchema.GetColumnType(col); + FeatureColumnType = (featureColumn != null) ? trainSchema.GetColumnType(col) : null; TrainSchema = trainSchema; BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); @@ -133,7 +134,7 @@ public sealed class BinaryPredictionTransformer : PredictionTransformerB public readonly string ThresholdColumn; public readonly float Threshold; - public BinaryPredictionTransformer(IHostEnvironment env, TModel model, ISchema inputSchema, string featureColumn, + public BinaryPredictionTransformer(IHostEnvironment env, TModel model, ISchema inputSchema, string featureColumn = null, float threshold = 0f, string thresholdColumn = DefaultColumnNames.Score) : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(BinaryPredictionTransformer)), model, inputSchema, featureColumn) { diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index dcf984d869..cafe02060f 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -67,7 +67,7 @@ public BinaryPredictionTransformer Fit(IDataView input) RoleMappedData trainRoles = new RoleMappedData(cachedTrain); var pred = Train(new TrainContext(trainRoles)); - return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, null); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, featureColumn: null); } public override RandomPredictor Train(TrainContext context) @@ -112,7 +112,7 @@ private static VersionInfo GetVersionInfo() // Keep all the serializable state here. private readonly int _seed; private readonly object _instanceLock; - private readonly TauswortheHybrid _random; + private readonly IRandom _random; public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; public ColumnType InputType { get; } @@ -143,6 +143,8 @@ private RandomPredictor(IHostEnvironment env, ModelLoadContext ctx) _instanceLock = new object(); _random = RandomUtils.Create(_seed); + + InputType = new VectorType(NumberType.Float); } public static RandomPredictor Create(IHostEnvironment env, ModelLoadContext ctx) @@ -292,11 +294,11 @@ protected override PriorPredictor TrainModelCore(TrainContext context) protected override BinaryPredictionTransformer MakeTransformer(PriorPredictor model, ISchema trainSchema) => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); - private static SchemaShape.Column MakeFeatureColumn(string featureColumn) => - new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + private static SchemaShape.Column MakeFeatureColumn(string featureColumn) + => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); - private static SchemaShape.Column MakeLabelColumn(string labelColumn) => - new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); } public sealed class PriorPredictor : diff --git a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt deleted file mode 100644 index 07cf437dfe..0000000000 --- a/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-test-out.txt +++ /dev/null @@ -1,19 +0,0 @@ -maml.exe Test dout=%Output% data=%Data% in=%Output% seed=1 -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - -***** Unexpected failure. Please refer to https://aka.ms/MLNetIssue to file an issue with details ***** -***** Error log has been saved to '%Temp%\%ErrorLog%', please refer to https://aka.ms/MLNetIssue to file an issue with details ***** -===== Begin detailed dump ===== -(1) Unexpected exception: Could not find file '%Output% 'System.IO.FileNotFoundException' - at System.IO.FileStream.ValidateFileHandle(SafeFileHandle fileHandle) - at System.IO.FileStream.CreateFileOpenHandle(FileMode mode, FileShare share, FileOptions options) - at System.IO.FileStream..ctor(String path, FileMode mode, FileAccess access, FileShare share, Int32 bufferSize, FileOptions options) - at System.IO.FileStream..ctor(String path, FileMode mode, FileAccess access) - at Microsoft.ML.Runtime.SimpleFileHandle.OpenReadStream() in E:\artidoro-machinelearning\src\Microsoft.ML.Core\Data\IFileHandle.cs:line 197 - at Microsoft.ML.Runtime.Data.DataCommand.ImplBase`1.LoadModelObjects(IChannel ch, Nullable`1 wantPredictor, IPredictor& predictor, Boolean wantTrainSchema, RoleMappedSchema& trainSchema, IDataLoader& pipe) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\DataCommand.cs:line 260 - at Microsoft.ML.Runtime.Data.TestCommand.RunCore(IChannel ch) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 90 - at Microsoft.ML.Runtime.Data.TestCommand.Run() in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 77 - at Microsoft.ML.Runtime.Tools.Maml.MainCore(TlcEnvironment env, String args, Boolean alwaysPrintStacktrace) in E:\artidoro-machinelearning\src\Microsoft.ML.Maml\MAML.cs:line 136 -====== End detailed dump ===== diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt index 8c63f6ed72..67a66d8f68 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt @@ -8,42 +8,42 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 63 | 71 | 0.4701 - negative || 111 | 117 | 0.5132 + positive || 65 | 69 | 0.4851 + negative || 118 | 110 | 0.4825 ||====================== -Precision || 0.3621 | 0.6223 | -OVERALL 0/1 ACCURACY: 0.497238 -LOG LOSS/instance: 1.455174 +Precision || 0.3552 | 0.6145 | +OVERALL 0/1 ACCURACY: 0.483425 +LOG LOSS/instance: 1.486294 Test-set entropy (prior Log-Loss/instance): 0.950799 -LOG-LOSS REDUCTION (RIG): -53.047423 -AUC: 0.479314 +LOG-LOSS REDUCTION (RIG): -56.320456 +AUC: 0.486940 TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 53 | 54 | 0.4953 - negative || 105 | 125 | 0.5435 + positive || 50 | 57 | 0.4673 + negative || 113 | 117 | 0.5087 ||====================== -Precision || 0.3354 | 0.6983 | -OVERALL 0/1 ACCURACY: 0.528190 -LOG LOSS/instance: 1.306253 +Precision || 0.3067 | 0.6724 | +OVERALL 0/1 ACCURACY: 0.495549 +LOG LOSS/instance: 1.451172 Test-set entropy (prior Log-Loss/instance): 0.901650 -LOG-LOSS REDUCTION (RIG): -44.873587 -AUC: 0.529581 +LOG-LOSS REDUCTION (RIG): -60.946260 +AUC: 0.484884 OVERALL RESULTS --------------------------------------- -AUC: 0.504448 (0.0251) -Accuracy: 0.512714 (0.0155) -Positive precision: 0.348756 (0.0133) -Positive recall: 0.482738 (0.0126) -Negative precision: 0.660332 (0.0380) -Negative recall: 0.528318 (0.0152) -Log-loss: 1.380713 (0.0745) -Log-loss reduction: -48.960505 (4.0869) -F1 Score: 0.404545 (0.0045) -AUPRC: 0.353016 (0.0056) +AUC: 0.485912 (0.0010) +Accuracy: 0.489487 (0.0061) +Positive precision: 0.330970 (0.0242) +Positive recall: 0.476182 (0.0089) +Negative precision: 0.643469 (0.0289) +Negative recall: 0.495576 (0.0131) +Log-loss: 1.468733 (0.0176) +Log-loss reduction: -58.633358 (2.3129) +F1 Score: 0.390233 (0.0199) +AUPRC: 0.353840 (0.0310) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt index cbdf459531..8ef5388376 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ RandomPredictor AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.504448 0.512714 0.348756 0.482738 0.660332 0.528318 1.380713 -48.96051 0.404545 0.353016 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 +0.485912 0.489487 0.33097 0.476182 0.643469 0.495576 1.468733 -58.63336 0.390233 0.35384 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt index 757f9d767b..204c869799 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -5 1 -0.836182952 0.081908524 3.6098425920581336 0 -6 0 -0.553042054 0.223478973 0.36490310361014566 0 -8 0 -0.255037 0.3724815 0.6722700916434613 0 -9 0 -0.142923057 0.428538471 0.8072717171007312 0 -10 0 0.186874211 0.5934371 1.298449437118931 1 -11 0 -0.611070633 0.194464684 0.31198025359636589 0 -18 1 0.3969466 0.698473334 0.51772305611667846 1 -20 1 -0.157519519 0.42124024 1.2472848338337834 0 -21 1 -0.239025056 0.380487472 1.3940791435035362 0 -25 1 -0.560221851 0.219889075 2.1851521708397161 0 -28 0 -0.87005347 0.0649732649 0.096920478552667694 0 -31 0 -0.878868 0.0605660081 0.090136298746040081 0 -32 1 -0.9104404 0.0447798073 4.4810078689499493 0 -35 0 0.815087438 0.9075437 3.4350848547115711 1 -37 0 -0.00243839948 0.4987808 0.99648637699262221 0 -40 0 -0.923868537 0.03806573 0.055989780710852768 0 -41 1 0.962026834 0.9810134 0.027655226680414809 1 -44 1 0.207525685 0.603762865 0.72794606945665563 1 -45 0 0.7107666 0.8553833 2.7896937038596925 1 -46 1 -0.47639364 0.26180318 1.9334454738741136 0 -48 0 -0.9836463 0.008176863 0.011845214598844984 0 -50 1 0.8351463 0.917573154 0.12410491277200536 1 -51 1 -0.194503143 0.402748436 1.3120491093341085 0 -52 1 -0.4063241 0.296837956 1.7522525183657618 0 -54 1 0.2599388 0.6299694 0.66664630040203232 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-0.857091665 0.07145417 0.10695497214527057 0 +676 0 0.240653351 0.6203267 1.3971695396383086 1 +677 0 0.5307253 0.7653626 2.0914952301373884 1 +684 0 -0.6496133 0.17519334 0.27787211219700647 0 +686 0 -0.0608141124 0.469592929 0.91482808604224608 0 +687 0 -0.885016859 0.05749157 0.085422573759257131 0 +690 0 0.488756 0.744378 1.967915922926132 1 +695 0 0.433480084 0.71674 1.8198012680900195 1 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt index 88c2c43ad8..d8f02e69ff 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt @@ -6,28 +6,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 126 | 115 | 0.5228 - negative || 236 | 222 | 0.4847 + positive || 118 | 123 | 0.4896 + negative || 226 | 232 | 0.5066 ||====================== -Precision || 0.3481 | 0.6588 | -OVERALL 0/1 ACCURACY: 0.497854 -LOG LOSS/instance: 1.389820 +Precision || 0.3430 | 0.6535 | +OVERALL 0/1 ACCURACY: 0.500715 +LOG LOSS/instance: 1.394054 Test-set entropy (prior Log-Loss/instance): 0.929318 -LOG-LOSS REDUCTION (RIG): -49.552696 -AUC: 0.513345 +LOG-LOSS REDUCTION (RIG): -50.008349 +AUC: 0.515356 OVERALL RESULTS --------------------------------------- -AUC: 0.513345 (0.0000) -Accuracy: 0.497854 (0.0000) -Positive precision: 0.348066 (0.0000) -Positive recall: 0.522822 (0.0000) -Negative precision: 0.658754 (0.0000) -Negative recall: 0.484716 (0.0000) -Log-loss: 1.389820 (0.0000) -Log-loss reduction: -49.552696 (0.0000) -F1 Score: 0.417910 (0.0000) -AUPRC: 0.362332 (0.0000) +AUC: 0.515356 (0.0000) +Accuracy: 0.500715 (0.0000) +Positive precision: 0.343023 (0.0000) +Positive recall: 0.489627 (0.0000) +Negative precision: 0.653521 (0.0000) +Negative recall: 0.506550 (0.0000) +Log-loss: 1.394054 (0.0000) +Log-loss reduction: -50.008349 (0.0000) +F1 Score: 0.403419 (0.0000) +AUPRC: 0.356419 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt index 5f8aad4b8d..08ff48730b 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ RandomPredictor AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.513345 0.497854 0.348066 0.522822 0.658754 0.484716 1.38982 -49.5527 0.41791 0.362332 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 +0.515356 0.500715 0.343023 0.489627 0.653521 0.50655 1.394054 -50.00835 0.403419 0.356419 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt deleted file mode 100644 index 93de9a0098..0000000000 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-test-out.txt +++ /dev/null @@ -1,18 +0,0 @@ -maml.exe Test dout=%Output% data=%Data% in=%Output% seed=1 -Physical memory usage(MB): %Number% -Virtual memory usage(MB): %Number% -%DateTime% Time elapsed(s): %Number% - -***** Unexpected failure. Please refer to https://aka.ms/MLNetIssue to file an issue with details ***** -***** Error log has been saved to '%Temp%\%ErrorLog%', please refer to https://aka.ms/MLNetIssue to file an issue with details ***** -===== Begin detailed dump ===== -(1) Unexpected exception: Object reference not set to an instance of an object., 'System.NullReferenceException' - at Microsoft.ML.Runtime.Data.SchemaBindableBinaryPredictorWrapper.CheckValid(IValueMapperDist& distMapper) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Scorers\SchemaBindablePredictorWrapper.cs:line 435 - at Microsoft.ML.Runtime.Data.SchemaBindableBinaryPredictorWrapper..ctor(IPredictor predictor) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Scorers\SchemaBindablePredictorWrapper.cs:line 363 - at Microsoft.ML.Runtime.Data.ScoreUtils.GetSchemaBindableMapper(IHostEnvironment env, IPredictor predictor, IComponentFactory`2 mapperFactory, ICommandLineComponentFactory scorerFactorySettings) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\ScoreCommand.cs:line 384 - at Microsoft.ML.Runtime.Data.ScoreUtils.GetScorerComponentAndMapper(IPredictor predictor, IComponentFactory`4 scorerFactory, RoleMappedSchema schema, IHostEnvironment env, IComponentFactory`2 mapperFactory, ISchemaBoundMapper& mapper) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\ScoreCommand.cs:line 284 - at Microsoft.ML.Runtime.Data.ScoreUtils.GetScorer(IComponentFactory`4 scorer, IPredictor predictor, IDataView input, String featureColName, String groupColName, IEnumerable`1 customColumns, IHostEnvironment env, RoleMappedSchema trainSchema, IComponentFactory`2 mapperFactory) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\ScoreCommand.cs:line 267 - at Microsoft.ML.Runtime.Data.TestCommand.RunCore(IChannel ch) in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 112 - at Microsoft.ML.Runtime.Data.TestCommand.Run() in E:\artidoro-machinelearning\src\Microsoft.ML.Data\Commands\TestCommand.cs:line 77 - at Microsoft.ML.Runtime.Tools.Maml.MainCore(TlcEnvironment env, String args, Boolean alwaysPrintStacktrace) in E:\artidoro-machinelearning\src\Microsoft.ML.Maml\MAML.cs:line 136 -====== End detailed dump ===== diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt index bba88e92e3..ad6ccbf56a 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt @@ -1,700 +1,700 @@ Instance Label Score Probability Log-loss Assigned -0 0 -0.613342345 0.193328828 0.30994739493272483 0 -1 0 0.148138508 0.574069262 1.2313092452342478 1 -2 0 -0.248801082 0.375599444 0.6794562727151785 0 -3 0 0.241135135 0.62056756 1.3980850667869362 1 -4 0 0.347133458 0.6735667 1.6151398540715076 1 -5 1 -0.861693263 0.06915337 3.854056662751681 0 -6 0 0.39533636 0.6976682 1.7257953367858518 1 -7 0 -0.157458678 0.421270669 0.78903932942500332 0 -8 0 -0.9216203 0.0391898453 0.057676696108677636 0 -9 0 0.26033628 0.63016814 1.4350585803168889 1 -10 0 -0.6839861 0.158006936 0.24811974642198742 0 -11 0 0.197631568 0.5988158 1.3176633016010613 1 -12 1 -0.3744956 0.3127522 1.6769081208826981 0 -13 0 -0.6739732 0.1630134 0.25672356687488079 0 -14 1 -0.5148157 0.242592156 2.0433951923224614 0 -15 1 0.0283120759 0.514156044 0.95972181900731202 1 -16 0 0.4729032 0.7364516 1.9238603027456254 1 -17 0 0.277854 0.638927 1.4696374827457801 1 -18 1 0.852824748 0.926412344 0.1102736182458002 1 -19 0 0.881787837 0.9408939 4.0805488781011299 1 -20 1 -0.7401544 0.1299228 2.9442733848857352 0 -21 1 -0.648095 0.1759525 2.506742130306399 0 -22 0 -0.4031876 0.298406184 0.5112920626990245 0 -23 1 -0.38042438 0.3097878 1.690647719696996 0 -24 0 -0.946494639 0.02675268 0.039121628813095874 0 -25 1 0.0351833478 0.517591655 0.9501137368695256 1 -26 0 0.21517399 0.607587 1.349555184392685 1 -27 0 -0.02384207 0.488078952 0.96600676943699393 0 -28 0 -0.5891134 0.2054433 0.33177790716298344 0 -29 0 -0.6047616 0.1976192 0.31764101002346184 0 -30 0 0.292405158 0.646202564 1.4990045031309507 1 -31 0 0.629873157 0.8149366 2.433908323984797 1 -32 1 -0.288390249 0.355804861 1.4908418757271582 0 -33 0 0.358722121 0.679361045 1.6409783847688044 1 -34 0 0.7081976 0.8540988 2.776936314583295 1 -35 0 0.0192397125 0.509619832 1.0280274603992299 1 -36 1 0.7131237 0.85656184 0.22337068928372147 1 -37 0 0.6594609 0.829730451 2.5541076492381576 1 -38 1 0.8332931 0.91664654 0.12556255850893855 1 -39 1 0.8490842 0.924542069 0.1131891268159938 1 -40 0 -0.0352335572 0.482383221 0.95004371166012669 0 -41 1 -0.879842639 0.06007868 4.057003061925081 0 -42 1 -0.415058285 0.292470872 1.7736351431259196 0 -43 1 0.1253157 0.562657833 0.82967024639841302 1 -44 1 0.2653032 0.632651567 0.660516940098407 1 -45 0 -0.310512066 0.344743967 0.60986936337613085 0 -46 1 0.6010777 0.800538838 0.32095669851618819 1 -47 0 0.5718145 0.785907269 2.2236922783408604 1 -48 0 0.577492952 0.7887465 2.2429526883339181 1 -49 1 -0.6787344 0.160632789 2.6381616821437932 0 -50 1 0.0629689246 0.5314845 0.91190051830067576 1 -51 1 -0.00188733684 0.499056339 1.0027254019930614 0 -52 1 0.04217349 0.521086752 0.94040451727881802 1 -53 1 0.126719356 0.5633597 0.82787178770965097 1 -54 1 0.727730453 0.863865256 0.2111217931627849 1 -55 1 -0.28238523 0.358807385 1.4787185107985712 0 -56 1 0.700039148 0.8500196 0.23443203101805776 1 -57 1 0.508394837 0.7541974 0.40698588215626286 1 -58 1 -0.2145164 0.3927418 1.3483469432483139 0 -59 1 -0.09290318 0.4535484 1.1406715749742293 0 -60 1 0.402648479 0.7013242 0.5118465338205237 1 -61 0 -0.392738223 0.3036309 0.52207588331039556 0 -62 1 0.07424177 0.5371209 0.89668129257772999 1 -63 1 -0.9388294 0.030585289 5.0310182815587847 0 -64 0 0.2891471 0.644573569 1.4923771258866165 1 -65 1 0.9951801 0.997590065 0.0034809974454016305 1 -66 0 -0.456132352 0.271933824 0.45785850751004253 0 -67 1 -0.5442803 0.227859855 2.1337813279393378 0 -68 1 -0.1135536 0.4432232 1.1738946686839882 0 -69 0 0.8865556 0.943277836 4.1399436121557409 1 -70 0 0.796706855 0.898353457 3.2983669513431959 1 -71 1 0.225878119 0.61293906 0.70618445097041682 1 -72 0 0.06687694 0.533438444 1.0998606590860733 1 -73 1 0.214801 0.6074005 0.7192800535734295 1 -74 1 -0.7158216 0.142089188 2.8151313141949768 0 -75 0 -0.842458546 0.07877073 0.11836783979487402 0 -76 0 -0.13209939 0.4339503 0.82099937821818791 0 -77 0 0.7013263 0.8506632 2.7433582288249951 1 -78 0 -0.9141612 0.0429193974 0.0632876653267955 0 -79 0 -0.00426493445 0.497867525 0.99386006068225607 0 -80 0 -0.280963123 0.359518439 0.64277105691597902 0 -81 0 0.113275751 0.5566379 1.1734425908525463 1 -82 0 -0.6425235 0.178738236 0.28408596445825157 0 -83 0 0.722992837 0.861496449 2.8520051248118636 1 -84 1 0.344321549 0.672160745 0.57312180563014781 1 -85 1 0.267255157 0.6336276 0.65829293092856689 1 -86 1 0.0264300536 0.513215 0.96236474227638991 1 -87 1 -0.928419948 0.0357900262 4.8042985903865931 0 -88 0 0.791865766 0.8959329 3.2644142287426408 1 -89 0 0.6318364 0.8159182 2.4415811552761477 1 -90 0 -0.964071155 0.0179644227 0.026152803279675844 0 -91 0 0.00194968423 0.500974834 1.0028155218138906 1 -92 0 -0.917596936 0.0412015319 0.060700490914523939 0 -93 0 -0.475487381 0.262256324 0.43880844664817709 0 -94 0 0.0142983422 0.50714916 1.0207770097859505 1 -95 0 0.212430924 0.6062155 1.3445216850575987 1 -96 0 -0.8246796 0.08766019 0.13235682978797475 0 -97 0 0.5071996 0.753599763 2.0209244507095341 1 -98 1 0.5971811 0.798590541 0.32447211119311936 1 -99 1 0.8244435 0.9122218 0.13254346401832651 1 -100 1 -0.312200248 0.343899876 1.5399394997216072 0 -101 1 0.0736417845 0.5368209 0.89748728479325768 1 -102 0 0.0449970774 0.522498548 1.0664229748373997 1 -103 1 -0.477057 0.2614715 1.9352743366250382 0 -104 1 0.300872177 0.6504361 0.62052075649392713 1 -105 1 -0.6165477 0.191726148 2.3828809858271662 0 -106 1 0.615846157 0.8079231 0.30771015277158531 1 -107 1 -0.5454393 0.227280349 2.1374551458413849 0 -108 0 -0.65297085 0.173514575 0.27493871654129159 0 -109 1 -0.1312559 0.434372067 1.2029967621062843 0 -110 0 0.0947412 0.5473706 1.1435978389411035 1 -111 1 0.753986955 0.8769935 0.18936198224896039 1 -112 1 0.9122673 0.956133664 0.064715779597892154 1 -113 1 0.6032267 0.801613331 0.31902159411574715 1 -114 0 0.6673219 0.83366096 2.5878012857356203 1 -115 0 -0.737694561 0.131152719 0.20282548134661399 0 -116 0 0.439932346 0.7199662 1.8363269861122233 1 -117 1 -0.466519356 0.266740322 1.90649216682853 0 -118 0 0.103018954 0.5515095 1.156850667376325 1 -119 0 0.553656757 0.7768284 2.163774701399535 1 -120 0 -0.588228 0.205886 0.33258197635851633 0 -121 0 -0.238517344 0.380741328 0.69138592800463339 0 -122 1 -0.7097389 0.145130545 2.7845769067064072 0 -123 1 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0.470286936 0.7351435 1.9167170854696685 1 +689 0 0.514514 0.757257 2.0424983108904891 1 +690 0 0.0508895628 0.5254448 1.0753522014778767 1 +691 1 0.332611352 0.666305661 0.5857439435106393 1 +692 0 -0.6633178 0.1683411 0.26593615845958479 0 +693 0 -0.169785172 0.415107429 0.77375643011941242 0 +694 0 0.116221189 0.5581106 1.1782427534636981 1 +695 0 -0.07057267 0.464713663 0.90161726569033396 0 +696 1 -0.288662553 0.355668724 1.491393981087219 0 +697 1 -0.352839351 0.323580325 1.6278042077409844 0 +698 1 -0.661094 0.169453 2.561042958373942 0 diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index d22283b6c6..1fd4692528 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -91,7 +91,7 @@ public IList GetDatasetsForClassificationWeightingPredictorsTest() [TestCategory("Binary")] public void BinaryClassifierPerceptronTest() { - var binaryPredictors = new[] { TestLearners.perceptron}; + var binaryPredictors = new[] { TestLearners.perceptron }; var binaryClassificationDatasets = GetDatasetsForBinaryClassifierBaseTest(); RunAllTests(binaryPredictors, binaryClassificationDatasets); Done(); From f07205fc9afb89487b7cc52fdf4229794cdcb670 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Wed, 12 Sep 2018 17:09:29 -0700 Subject: [PATCH 06/18] fixed random predictor test --- .../BinaryRandom-CV-breast-cancer-out.txt | 36 +- .../BinaryRandom-CV-breast-cancer-rp.txt | 2 +- .../BinaryRandom-CV-breast-cancer.txt | 1140 +++++++------- ...naryRandom-TrainTest-breast-cancer-out.txt | 36 +- ...inaryRandom-TrainTest-breast-cancer-rp.txt | 2 +- .../BinaryRandom-TrainTest-breast-cancer.txt | 1388 ++++++++--------- .../TestPredictors.cs | 2 +- 7 files changed, 1303 insertions(+), 1303 deletions(-) diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt index 67a66d8f68..acd9af8ba2 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 +maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 Not adding a normalizer. Not training a calibrator because it is not needed. Not adding a normalizer. @@ -8,15 +8,15 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 65 | 69 | 0.4851 - negative || 118 | 110 | 0.4825 + positive || 67 | 67 | 0.5000 + negative || 116 | 112 | 0.4912 ||====================== -Precision || 0.3552 | 0.6145 | -OVERALL 0/1 ACCURACY: 0.483425 -LOG LOSS/instance: 1.486294 +Precision || 0.3661 | 0.6257 | +OVERALL 0/1 ACCURACY: 0.494475 +LOG LOSS/instance: 1.411189 Test-set entropy (prior Log-Loss/instance): 0.950799 -LOG-LOSS REDUCTION (RIG): -56.320456 -AUC: 0.486940 +LOG-LOSS REDUCTION (RIG): -48.421356 +AUC: 0.521242 TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) Confusion table ||====================== @@ -34,16 +34,16 @@ AUC: 0.484884 OVERALL RESULTS --------------------------------------- -AUC: 0.485912 (0.0010) -Accuracy: 0.489487 (0.0061) -Positive precision: 0.330970 (0.0242) -Positive recall: 0.476182 (0.0089) -Negative precision: 0.643469 (0.0289) -Negative recall: 0.495576 (0.0131) -Log-loss: 1.468733 (0.0176) -Log-loss reduction: -58.633358 (2.3129) -F1 Score: 0.390233 (0.0199) -AUPRC: 0.353840 (0.0310) +AUC: 0.503063 (0.0182) +Accuracy: 0.495012 (0.0005) +Positive precision: 0.336434 (0.0297) +Positive recall: 0.483645 (0.0164) +Negative precision: 0.649056 (0.0234) +Negative recall: 0.499962 (0.0087) +Log-loss: 1.431181 (0.0200) +Log-loss reduction: -54.683808 (6.2625) +F1 Score: 0.396542 (0.0262) +AUPRC: 0.348911 (0.0260) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt index 8ef5388376..2692f38450 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt @@ -1,4 +1,4 @@ RandomPredictor AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.485912 0.489487 0.33097 0.476182 0.643469 0.495576 1.468733 -58.63336 0.390233 0.35384 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 +0.503063 0.495012 0.336434 0.483645 0.649056 0.499962 1.431181 -54.68381 0.396542 0.348911 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt index 204c869799..c0fc1e43b3 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer.txt @@ -1,366 +1,366 @@ Instance Label Score Probability Log-loss Assigned 5 1 -0.5901826 0.2049087 2.2869468638557469 0 -6 0 -0.285091162 0.357454419 0.63812929512728334 0 -8 0 0.5222254 0.7611127 2.0655978766972183 1 -9 0 -0.595426738 0.202286631 0.32605763818474393 0 -10 0 -0.1367939 0.431603044 0.81502926720166025 0 -11 0 -0.37980178 0.310099125 0.53553900442695013 0 -18 1 -0.626139164 0.186930418 2.4194267459859939 0 -20 1 -0.3332724 0.3333638 1.584830639049839 0 -21 1 0.702328861 0.85116446 0.23249018158271892 1 -25 1 -0.4284833 0.285758346 1.8071324581537556 0 -28 0 0.391822457 0.6959112 1.7174355498982012 1 -31 0 0.1598605 0.579930246 1.2512991822994244 1 -32 1 0.62689 0.813445 0.29788334118707566 1 -35 0 0.998642445 0.9993212 10.524773222257734 1 -37 0 -0.9739329 0.0130335391 0.018927034980782253 0 -40 0 -0.5116809 0.244159549 0.4038463651084534 0 -41 1 0.438331246 0.7191656 0.47560403460438272 1 -44 1 -0.3910213 0.304489344 1.7155363531603987 0 -45 0 0.256788552 0.628394246 1.4281552544780489 1 -46 1 -0.623663247 0.188168377 2.4099039037727445 0 -48 0 -0.639939 0.1800305 0.28635783844932267 0 -50 1 0.192843 0.5964215 0.74559587752517731 1 -51 1 -0.8645337 0.06773314 3.8839943369997916 0 -52 1 -0.27173835 0.364130825 1.4574712203231155 0 -54 1 -0.7341293 0.132935345 2.9112033520034757 0 -56 1 0.519055 0.7595275 0.39682588551660397 1 -60 1 -0.160590634 0.419704676 1.252553559386316 0 -63 1 0.592348337 0.796174169 0.32884403011015545 1 -64 0 0.656457067 0.828228533 2.5414376871828037 1 -66 0 -0.6536715 0.173164248 0.27432732411507782 0 -68 1 -0.668209732 0.165895134 2.591656522958889 0 -69 0 0.8820364 0.9410182 4.0835869199279955 1 -70 0 0.5505148 0.7752574 2.153654536673578 1 -71 1 -0.11782375 0.44108814 1.180861125156812 0 -72 0 -0.5535273 0.223236352 0.36445241005902107 0 -73 1 -0.7498584 0.12507081 2.9991829698379195 0 -74 1 0.0869861245 0.543493032 0.87966655477076794 1 -76 0 0.6123167 0.806158364 2.3670496074159444 1 -77 0 0.439607233 0.719803631 1.8354898361231453 1 -79 0 -0.468785 0.2656075 0.44537678147516724 0 -82 0 -0.728337646 0.135831177 0.21061491215826003 0 -88 0 0.228939816 0.6144699 1.3750845398767648 1 -90 0 0.8813716 0.9406858 4.0754788686726222 1 -91 0 -0.988616049 0.00569197536 0.0082352443192839509 0 -92 0 0.3313811 0.665690541 1.5807439206871343 1 -93 0 -0.6637348 0.1681326 0.26557451986265235 0 -95 0 -0.440364152 0.279817939 0.47356643058104186 0 -96 0 -0.10804186 0.445979059 0.85198758562134524 0 -97 0 0.6045983 0.802299142 2.3386089618211154 1 -98 1 -0.32443288 0.337783575 1.5658289170100705 0 -99 1 -0.76969 0.115155011 3.1183508979386159 0 -100 1 0.6178309 0.808915436 0.30593920317609846 1 -102 0 0.307381541 0.653690755 1.5298671944796727 1 -104 1 -0.6670872 0.1664564 2.5867837421887012 0 -105 1 -0.2530738 0.3734631 1.4209624125036753 0 -106 1 0.6684611 0.834230542 0.26148196289370562 1 -108 0 -0.260704964 0.3696475 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+663 0 0.984719753 0.9923599 7.0321883505936338 1 +664 0 0.3795143 0.689757168 1.6885302182311182 1 +666 0 0.620766044 0.810383 2.3988399469863921 1 +667 0 0.1347426 0.5673713 1.2087987493418666 1 +669 1 -0.325250626 0.3373747 1.5675763608245354 0 +671 0 -0.9225833 0.03870836 0.056953906147263457 0 +672 0 -0.6811158 0.1594421 0.2505808898604916 0 +673 0 0.9864531 0.9932265 7.2058887889230965 1 +674 0 0.104816936 0.552408457 1.1597453166206351 1 +675 0 -0.891022146 0.0544889271 0.080833741221085581 0 +676 0 -0.2472622 0.3763689 0.68123524227875154 0 +677 0 0.744889557 0.872444749 2.9708058026812942 1 +684 0 0.4463557 0.72317785 1.8529687109403419 1 +686 0 0.00173153635 0.500865757 1.0025002132072394 1 +687 0 0.5185336 0.7592668 2.0544929367895226 1 +690 0 -0.600714564 0.199642718 0.32128392721654297 0 +695 0 -0.196187392 0.4019063 0.74155660155077929 0 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt index d8f02e69ff..9d0392fe3b 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt @@ -1,4 +1,4 @@ -maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 +maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 Not adding a normalizer. Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) @@ -6,28 +6,28 @@ Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== - positive || 118 | 123 | 0.4896 - negative || 226 | 232 | 0.5066 + positive || 113 | 128 | 0.4689 + negative || 231 | 227 | 0.4956 ||====================== -Precision || 0.3430 | 0.6535 | -OVERALL 0/1 ACCURACY: 0.500715 -LOG LOSS/instance: 1.394054 +Precision || 0.3285 | 0.6394 | +OVERALL 0/1 ACCURACY: 0.486409 +LOG LOSS/instance: 1.479534 Test-set entropy (prior Log-Loss/instance): 0.929318 -LOG-LOSS REDUCTION (RIG): -50.008349 -AUC: 0.515356 +LOG-LOSS REDUCTION (RIG): -59.206457 +AUC: 0.471290 OVERALL RESULTS --------------------------------------- -AUC: 0.515356 (0.0000) -Accuracy: 0.500715 (0.0000) -Positive precision: 0.343023 (0.0000) -Positive recall: 0.489627 (0.0000) -Negative precision: 0.653521 (0.0000) -Negative recall: 0.506550 (0.0000) -Log-loss: 1.394054 (0.0000) -Log-loss reduction: -50.008349 (0.0000) -F1 Score: 0.403419 (0.0000) -AUPRC: 0.356419 (0.0000) +AUC: 0.471290 (0.0000) +Accuracy: 0.486409 (0.0000) +Positive precision: 0.328488 (0.0000) +Positive recall: 0.468880 (0.0000) +Negative precision: 0.639437 (0.0000) +Negative recall: 0.495633 (0.0000) +Log-loss: 1.479534 (0.0000) +Log-loss reduction: -59.206457 (0.0000) +F1 Score: 0.386325 (0.0000) +AUPRC: 0.314886 (0.0000) --------------------------------------- Physical memory usage(MB): %Number% diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt index 08ff48730b..4af687d0c8 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt @@ -1,4 +1,4 @@ RandomPredictor AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings -0.515356 0.500715 0.343023 0.489627 0.653521 0.50655 1.394054 -50.00835 0.403419 0.356419 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 +0.47129 0.486409 0.328488 0.46888 0.639437 0.495633 1.479534 -59.20646 0.386325 0.314886 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 diff --git a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt index ad6ccbf56a..1980e349e8 100644 --- a/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt +++ b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt @@ -3,698 +3,698 @@ Instance Label Score Probability Log-loss Assigned 1 0 -0.958645165 0.0206774175 0.030143942420402223 0 2 0 -0.981372237 0.009313881 0.013500057520067015 0 3 0 0.912489057 0.9562445 4.514392749742357 1 -4 0 -0.6345405 0.182729751 0.29111487746180126 0 -5 1 -0.237136573 0.3814317 1.3905033513940148 0 -6 0 0.0738204047 0.5369102 1.1106360403417648 1 -7 0 0.388376921 0.6941885 1.7092853197494136 1 -8 0 -0.646579266 0.176710367 0.28052803569655838 0 -9 0 -0.829417 0.0852915049 0.12861604506535063 0 -10 0 0.9220019 0.9610009 4.6804160746674386 1 -11 0 0.334635615 0.6673178 1.5877834505816288 1 -12 1 0.784778655 0.8923893 0.16425488358836296 1 -13 0 0.671536863 0.835768461 2.6061968882249538 1 -14 1 -0.3191869 0.340406537 1.5546693525530475 0 -15 1 -0.849415362 0.07529232 3.7313534938039616 0 -16 0 -0.395185083 0.302407444 0.51954344868249802 0 -17 0 0.06845791 0.534229 1.1023072178628652 1 -18 1 -0.23096092 0.384519547 1.3788711547850896 0 -19 0 0.6589089 0.8294544 2.5517707464206554 1 -20 1 0.09746922 0.5487346 0.86581953431877756 1 -21 1 -0.8997721 0.0501139462 4.3186440431832303 0 -22 0 -0.7276503 0.136174858 0.21118878683487971 0 -23 1 0.5336449 0.766822457 0.38303550621144328 1 -24 0 -0.184236 0.407882 0.75604339559905387 0 -25 1 -0.253162116 0.373418927 1.4211330407164073 0 -26 0 0.7215679 0.860783935 2.8446023884037555 1 -27 0 0.842033446 0.9210167 3.6623084177427598 1 -28 0 -0.671350062 0.164324969 0.2589860645273892 0 -29 0 -0.410026759 0.2949866 0.50427742774886108 0 -30 0 -0.8271305 0.08643475 0.13042032283105803 0 -31 0 -0.0175376926 0.491231143 0.97491773383954083 0 -32 1 0.425943255 0.7129716 0.48808342827888446 1 -33 0 0.445176572 0.7225883 1.8498994631053431 1 -34 0 -0.8599285 0.0700357556 0.10475284691180868 0 -35 0 0.311703533 0.6558518 1.5388980525442173 1 -36 1 -0.528569937 0.235715032 2.0848843322360957 0 -37 0 -0.3306018 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0.925638735 0.11147885657877547 1 -58 1 -0.244329125 0.377835453 1.4041700177562528 0 -59 1 0.2569574 0.6284787 0.6700642308102277 1 -60 1 -0.8142813 0.09285936 3.4288088894546149 0 -61 0 0.909005463 0.9545027 4.4580753132662156 1 -62 1 0.892031133 0.9460156 0.080064126281785067 1 -63 1 0.3594627 0.679731369 0.55696339139996032 1 -64 0 0.9971796 0.9985898 9.4698985241832379 1 -65 1 -0.6535362 0.1732319 2.5292234756963312 0 -66 0 0.962940156 0.9814701 5.7540017195272934 1 -67 1 -0.123970158 0.438014925 1.190948067131393 0 -68 1 -0.0308891423 0.484555423 1.0452664049147795 0 -69 0 -0.27790612 0.36104694 0.64621814606252592 0 -70 0 -0.171487227 0.4142564 0.77165879400519666 0 -71 1 0.3333133 0.6666566 0.58498425669053056 1 -72 0 -0.997940838 0.00102958083 0.0014861363399312237 0 -73 1 -0.6589332 0.170533389 2.551873861697568 0 -74 1 0.9078653 0.953932643 0.068040693574692432 1 -75 0 -0.0784378946 0.460781038 0.89105686361218117 0 -76 0 0.8149418 0.907470942 3.4339496792001247 1 -77 0 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+607 0 0.4331158 0.7165579 1.8188741371577828 1 +608 1 0.042038206 0.5210191 0.9405918306005282 1 +609 0 0.469369382 0.7346847 1.9142202518674523 1 +610 1 -0.4409975 0.279501259 1.839073311566193 0 +611 1 0.576089442 0.7880447 0.34365064568846004 1 +612 1 -0.979093552 0.0104532242 6.5799081953976843 0 +613 0 0.8082966 0.90414834 3.383052775845699 1 +614 0 -0.381685466 0.309157252 0.53357073875790595 0 +615 0 -0.127211764 0.436394125 0.82724144515031472 0 +616 0 0.5924155 0.7962078 2.2948291585469072 1 +617 0 -0.6395387 0.180230647 0.28671003949751844 0 +618 0 0.3540915 0.677045763 1.6305983450311048 1 +619 0 0.6115931 0.805796564 2.3643593658252904 1 +620 0 0.6400163 0.820008159 2.4739965814401228 1 +621 0 0.7968601 0.898430049 3.2994544502632865 1 +622 0 0.675995231 0.8379976 2.6259130452612465 1 +623 0 -0.7748064 0.11259681 0.17233835535887324 0 +624 0 -0.0532539561 0.473373026 0.92514667537194428 0 +625 0 -0.5945378 0.2027311 0.32686170647294815 0 +626 1 0.8289326 0.914466262 0.12899815043528098 1 +627 0 0.104072481 0.5520362 1.1585460253031501 1 +628 0 -0.9865577 0.006721139 0.0097292865309182072 0 +629 0 -0.463386655 0.268306673 0.45068899242407306 0 +630 0 -0.267027766 0.366486132 0.6585518940425763 0 +631 0 -0.3718826 0.314058721 0.54384301734651552 0 +632 0 -0.245580524 0.377209723 0.6831816736355687 0 +633 1 0.0679595545 0.5339798 0.90514299940335918 1 +634 0 0.09977744 0.54988873 1.1516464073052537 1 +635 0 0.95841974 0.9792099 5.5879594854483496 1 +636 1 0.737865 0.8689325 0.20268400818392096 1 +637 0 0.537909746 0.7689549 2.1137534336440491 1 +638 0 0.546947956 0.773474 2.1422513069005187 1 +639 0 0.155516088 0.5777581 1.2438582577750641 1 +640 0 0.797553837 0.8987769 3.3043893726547267 1 +641 0 0.165859073 0.582929552 1.2616370014547258 1 +642 0 -0.602790952 0.198604524 0.31941372957385827 0 +643 0 0.26200825 0.6310041 1.438323289389781 1 +644 0 0.47849384 0.7392469 1.9392437171910466 1 +645 0 0.8082667 0.9041333 3.3828267169922572 1 +646 0 -0.444251865 0.277874053 0.46967761241254513 0 +647 0 -0.6682002 0.1658999 0.26170756807246698 0 +648 1 0.324374646 0.662187338 0.59468867062038799 1 +649 0 0.614939749 0.807469845 2.3768436681748701 1 +650 0 -0.74318254 0.12840873 0.19827634867923982 0 +651 0 0.4591076 0.7295538 1.8865865681200769 1 +652 0 -0.124314286 0.437842846 0.83095459482826206 0 +653 0 -0.476671636 0.261664182 0.43765094683609368 0 +654 0 0.279933721 0.639966846 1.4737983282978608 1 +655 0 0.446524471 0.72326225 1.8534086406314256 1 +656 0 0.07063705 0.5353185 1.1056858666658542 1 +657 0 0.8181843 0.9090922 3.4594519127328351 1 +658 1 -0.163035512 0.418482244 1.2567616836178841 0 +659 0 0.7172431 0.858621538 2.8223657390864321 1 +660 0 0.9027938 0.951396942 4.3628091059314427 1 +661 0 0.452043056 0.7260215 1.8678655590413153 1 +662 0 -0.399452657 0.300273657 0.51513728792308566 0 +663 0 0.31736517 0.6586826 1.5508140671025619 1 +664 0 0.3395698 0.6697849 1.5985220173076775 1 +665 0 -0.9288423 0.0355788469 0.052264800342187279 0 +666 0 0.348564833 0.674282432 1.6183065576637607 1 +667 0 -0.959403038 0.020298481 0.029585817731858574 0 +668 1 -0.0620544665 0.468972772 1.092423929762264 0 +669 1 -0.1093397 0.445330143 1.167052828382644 0 +670 1 0.7400145 0.8700073 0.20090062751552754 1 +671 0 0.257556081 0.62877804 1.4296460394438404 1 +672 0 -0.0252051521 0.487397432 0.96408738964953067 0 +673 0 0.5220516 0.7610258 2.0650731416111654 1 +674 0 -0.150225192 0.424887419 0.79808369619187902 0 +675 0 -0.8285416 0.08572921 0.12930656935508233 0 +676 0 -0.128591537 0.435704231 0.82547656303306038 0 +677 0 -0.772794247 0.113602877 0.17397489553374348 0 +678 0 -0.328698128 0.335650921 0.58998659669085396 0 +679 0 -0.920071542 0.03996423 0.058839932936285387 0 +680 1 0.8711379 0.9355689 0.096084145903302609 1 +681 1 0.431411982 0.715706 0.48256103915508791 1 +682 0 -0.7243137 0.137843162 0.21397775564200355 0 +683 0 0.3713024 0.6856512 1.669561761695042 1 +684 0 -0.7297887 0.13510564 0.20940416465001679 0 +685 0 0.7174773 0.858738661 2.8235614161513505 1 +686 0 -0.994656265 0.00267186761 0.0038598489514670123 0 +687 0 -0.99033767 0.004831165 0.0069867886829129309 0 +688 0 0.4759514 0.7379757 1.9322275734663419 1 +689 0 -0.1828268 0.4085866 0.75776114229334535 0 +690 0 0.870642662 0.935321331 3.950566199919979 1 +691 1 -0.213670343 0.3931648 1.3467938821453134 0 +692 0 -0.6592799 0.170360059 0.26944274322608602 0 +693 0 0.396216482 0.698108256 1.7278967910194019 1 +694 0 0.366007477 0.6830037 1.6574622011524394 1 +695 0 0.410838276 0.7054191 1.7632643162514681 1 +696 1 -0.232817248 0.383591384 1.382357780264464 0 +697 1 0.855639338 0.927819669 0.10808366406515908 1 +698 1 -0.126234785 0.436882615 1.1946823972611171 0 diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 1fd4692528..74a5a26ae8 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -117,7 +117,7 @@ public void BinaryRandomTest() var predictors = new[] { TestLearners.binaryRandom}; var datasets = GetDatasetsForBinaryClassifierBaseTest(); - RunAllTests(predictors, datasets); + RunAllTests(predictors, datasets, extraSettings: new[] { "n=1" }); Done(); } From e3e5d908a9554f0fc5e85d8be01924c05747b75b Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Fri, 14 Sep 2018 11:54:47 -0700 Subject: [PATCH 07/18] working on testestimatorcore --- .../Scorers/PredictionTransformer.cs | 33 ++++++--- .../Scorers/SchemaBindablePredictorWrapper.cs | 48 ++++++------ .../Training/TrainerEstimatorBase.cs | 12 +++ .../Standard/Simple/SimpleTrainers.cs | 54 +++++++++----- .../TestEstimators.cs | 74 +++++++++++++++++++ 5 files changed, 171 insertions(+), 50 deletions(-) create mode 100644 test/Microsoft.ML.Predictor.Tests/TestEstimators.cs diff --git a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs index 8243436328..a356a82625 100644 --- a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs +++ b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs @@ -36,7 +36,7 @@ public abstract class PredictionTransformerBase : IPredictionTransformer public TModel Model { get; } - public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn = null) + public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn) { Contracts.CheckValue(host, nameof(host)); Contracts.CheckValueOrNull(featureColumn); @@ -45,9 +45,12 @@ public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, Model = model; FeatureColumn = featureColumn; - if (!trainSchema.TryGetColumnIndex(featureColumn, out int col) && (featureColumn != null)) + if (featureColumn == null) + FeatureColumnType = null; + else if (!trainSchema.TryGetColumnIndex(featureColumn, out int col)) throw Host.ExceptSchemaMismatch(nameof(featureColumn), RoleMappedSchema.ColumnRole.Feature.Value, featureColumn); - FeatureColumnType = (featureColumn != null) ? trainSchema.GetColumnType(col) : null; + else + FeatureColumnType = trainSchema.GetColumnType(col); TrainSchema = trainSchema; BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); @@ -76,10 +79,13 @@ internal PredictionTransformerBase(IHost host, ModelLoadContext ctx) var loader = new BinaryLoader(host, new BinaryLoader.Arguments(), ms); TrainSchema = loader.Schema; - FeatureColumn = ctx.LoadString(); - if (!TrainSchema.TryGetColumnIndex(FeatureColumn, out int col)) + FeatureColumn = ctx.LoadStringOrNull(); + if (FeatureColumn == null) + FeatureColumnType = null; + else if (!TrainSchema.TryGetColumnIndex(FeatureColumn, out int col)) throw Host.ExceptSchemaMismatch(nameof(FeatureColumn), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn); - FeatureColumnType = TrainSchema.GetColumnType(col); + else + FeatureColumnType = TrainSchema.GetColumnType(col); BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); } @@ -88,10 +94,13 @@ public ISchema GetOutputSchema(ISchema inputSchema) { Host.CheckValue(inputSchema, nameof(inputSchema)); - if (!inputSchema.TryGetColumnIndex(FeatureColumn, out int col)) - throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), null); - if (!inputSchema.GetColumnType(col).Equals(FeatureColumnType)) - throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), inputSchema.GetColumnType(col).ToString()); + if(FeatureColumn != null) + { + if (!inputSchema.TryGetColumnIndex(FeatureColumn, out int col)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), null); + if (!inputSchema.GetColumnType(col).Equals(FeatureColumnType)) + throw Host.ExceptSchemaMismatch(nameof(inputSchema), RoleMappedSchema.ColumnRole.Feature.Value, FeatureColumn, FeatureColumnType.ToString(), inputSchema.GetColumnType(col).ToString()); + } return Transform(new EmptyDataView(Host, inputSchema)).Schema; } @@ -122,7 +131,7 @@ protected virtual void SaveCore(ModelSaveContext ctx) } }); - ctx.SaveString(FeatureColumn); + ctx.SaveStringOrNull(FeatureColumn); } } @@ -134,7 +143,7 @@ public sealed class BinaryPredictionTransformer : PredictionTransformerB public readonly string ThresholdColumn; public readonly float Threshold; - public BinaryPredictionTransformer(IHostEnvironment env, TModel model, ISchema inputSchema, string featureColumn = null, + public BinaryPredictionTransformer(IHostEnvironment env, TModel model, ISchema inputSchema, string featureColumn, float threshold = 0f, string thresholdColumn = DefaultColumnNames.Score) : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(BinaryPredictionTransformer)), model, inputSchema, featureColumn) { diff --git a/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs b/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs index 1e07f587f7..2a017474ed 100644 --- a/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs +++ b/src/Microsoft.ML.Data/Scorers/SchemaBindablePredictorWrapper.cs @@ -108,21 +108,22 @@ public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema) using (var ch = env.Register("SchemaBindableWrapper").Start("Bind")) { ch.CheckValue(schema, nameof(schema)); - ch.CheckParam(schema.Feature != null, nameof(schema), "Need a features column"); - // Ensure that the feature column type is compatible with the needed input type. - var type = schema.Feature.Type; - var typeIn = ValueMapper != null ? ValueMapper.InputType : new VectorType(NumberType.Float); - if (type != typeIn) + if (schema.Feature != null) { - if (!type.ItemType.Equals(typeIn.ItemType)) - throw ch.Except("Incompatible features column type item type: '{0}' vs '{1}'", type.ItemType, typeIn.ItemType); - if (type.IsVector != typeIn.IsVector) - throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); - // typeIn can legally have unknown size. - if (type.VectorSize != typeIn.VectorSize && typeIn.VectorSize > 0) - throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); + // Ensure that the feature column type is compatible with the needed input type. + var type = schema.Feature.Type; + var typeIn = ValueMapper != null ? ValueMapper.InputType : new VectorType(NumberType.Float); + if (type != typeIn) + { + if (!type.ItemType.Equals(typeIn.ItemType)) + throw ch.Except("Incompatible features column type item type: '{0}' vs '{1}'", type.ItemType, typeIn.ItemType); + if (type.IsVector != typeIn.IsVector) + throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); + // typeIn can legally have unknown size. + if (type.VectorSize != typeIn.VectorSize && typeIn.VectorSize > 0) + throw ch.Except("Incompatible features column type: '{0}' vs '{1}'", type, typeIn); + } } - var mapper = BindCore(ch, schema); ch.Done(); return mapper; @@ -463,15 +464,18 @@ public CalibratedRowMapper(RoleMappedSchema schema, SchemaBindableBinaryPredicto Contracts.AssertValue(parent); Contracts.Assert(parent._distMapper != null); Contracts.AssertValue(schema); - Contracts.AssertValue(schema.Feature); + Contracts.AssertValueOrNull(schema.Feature); _parent = parent; _inputSchema = schema; _outputSchema = new BinaryClassifierSchema(); - var typeSrc = _inputSchema.Feature.Type; - Contracts.Check(typeSrc.IsKnownSizeVector && typeSrc.ItemType == NumberType.Float, - "Invalid feature column type"); + if (schema.Feature != null) + { + var typeSrc = _inputSchema.Feature.Type; + Contracts.Check(typeSrc.IsKnownSizeVector && typeSrc.ItemType == NumberType.Float, + "Invalid feature column type"); + } } public RoleMappedSchema InputSchema { get { return _inputSchema; } } @@ -484,7 +488,7 @@ public Func GetDependencies(Func predicate) { for (int i = 0; i < OutputSchema.ColumnCount; i++) { - if (predicate(i)) + if (predicate(i) && _inputSchema.Feature != null) return col => col == _inputSchema.Feature.Index; } return col => false; @@ -492,7 +496,7 @@ public Func GetDependencies(Func predicate) public IEnumerable> GetInputColumnRoles() { - yield return RoleMappedSchema.ColumnRole.Feature.Bind(_inputSchema.Feature.Name); + yield return RoleMappedSchema.ColumnRole.Feature.Bind(_inputSchema.Feature != null ? _inputSchema.Feature.Name : null); } private Delegate[] CreateGetters(IRow input, bool[] active) @@ -504,7 +508,7 @@ private Delegate[] CreateGetters(IRow input, bool[] active) if (active[0] || active[1]) { // Put all captured locals at this scope. - var featureGetter = input.GetGetter>(_inputSchema.Feature.Index); + var featureGetter = _inputSchema.Feature!= null ? input.GetGetter>(_inputSchema.Feature.Index) : null; Float prob = 0; Float score = 0; long cachedPosition = -1; @@ -543,7 +547,9 @@ private static void EnsureCachedResultValueMapper(ValueMapper, Fl Contracts.AssertValue(mapper); if (cachedPosition != input.Position) { - featureGetter(ref features); + if (featureGetter != null) + featureGetter(ref features); + mapper(ref features, ref score, ref prob); cachedPosition = input.Position; } diff --git a/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs b/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs index 01140c4efd..fcc9996ef5 100644 --- a/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs +++ b/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs @@ -2,6 +2,7 @@ // 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.Collections.Generic; using System.Linq; using Microsoft.ML.Core.Data; using Microsoft.ML.Runtime.Data; @@ -61,6 +62,17 @@ public TrainerEstimatorBase(IHost host, SchemaShape.Column feature, SchemaShape. WeightColumn = weight; } + protected static IEnumerable MetadataForScoreColumn(bool isNormalized = false) + { + var cols = new List(); + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnSetId, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true)); + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreValueKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); + if (isNormalized) + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.IsNormalized, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false)); + return cols; + } + public TTransformer Fit(IDataView input) => TrainTransformer(input); public SchemaShape GetOutputSchema(SchemaShape inputSchema) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index cafe02060f..5ffcf9376f 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -2,17 +2,17 @@ // 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.Core.Data; using Microsoft.ML.Runtime; -using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Learners; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Training; -using Microsoft.ML.Runtime.Internal.Internallearn; -using Microsoft.ML.Core.Data; +using System; using System.Linq; +using System.Collections.Generic; [assembly: LoadableClass(RandomTrainer.Summary, typeof(RandomTrainer), typeof(RandomTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, @@ -76,13 +76,31 @@ public override RandomPredictor Train(TrainContext context) return new RandomPredictor(Host, Host.Rand.Next()); } + internal static IEnumerable MetadataForScoreColumn(bool isNormalized = false) + { + var cols = new List(); + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnSetId, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true)); + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreValueKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); + if (isNormalized) + cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.IsNormalized, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false)); + return cols; + } + public SchemaShape GetOutputSchema(SchemaShape inputSchema) { Host.CheckValue(inputSchema, nameof(inputSchema)); var outColumns = inputSchema.Columns.ToDictionary(x => x.Name); - var newColumn = new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, isKey: false); - outColumns[DefaultColumnNames.Score] = newColumn; + + var newColumns = new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataForScoreColumn())) + }; + foreach (SchemaShape.Column column in newColumns) + outColumns[column.Name] = column; return new SchemaShape(outColumns.Values); } @@ -228,24 +246,16 @@ public sealed class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - protected override SchemaShape.Column[] OutputColumns { get; } + protected override SchemaShape.Column[] OutputColumns => MakeOutputColumns(); public PriorTrainer(IHostEnvironment env, Arguments args) - : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), MakeFeatureColumn(DefaultColumnNames.Score), MakeFeatureColumn(DefaultColumnNames.Label), null) + : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null) { - OutputColumns = new[] - { - new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, isKey: false) - }; } public PriorTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) : base(host, feature, label, weight) { - OutputColumns = new[] - { - new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, isKey: false) - }; } protected override PriorPredictor TrainModelCore(TrainContext context) @@ -298,7 +308,17 @@ private static SchemaShape.Column MakeFeatureColumn(string featureColumn) => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); private static SchemaShape.Column MakeLabelColumn(string labelColumn) - => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false); + => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + + private static SchemaShape.Column[] MakeOutputColumns() + { + return new[] + { + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataForScoreColumn())) + }; + } } public sealed class PriorPredictor : diff --git a/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs b/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs new file mode 100644 index 0000000000..7d35ce8006 --- /dev/null +++ b/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs @@ -0,0 +1,74 @@ +// 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; +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.RunTests; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests +{ + public class EstimatorTests: TestDataPipeBase + { + private IDataView GetBreastCancerDataviewWithTextColumns() + { + var dataPath = GetDataPath("breast-cancer.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); + return ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + HasHeader = true, + Column = new[] + { + new TextLoader.Column("Label", type: null, 0), + new TextLoader.Column("F1", DataKind.Text, 1), + new TextLoader.Column("F2", DataKind.I4, 2), + new TextLoader.Column("Rest", type: null, new [] { new TextLoader.Range(3, 9) }) + } + }, + + InputFile = inputFile + }).Data; + } + + public EstimatorTests(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void TestEstimatorRandom() + { + using (var env = new TlcEnvironment()) + { + var dataView = GetBreastCancerDataviewWithTextColumns(); + //dataView = env.CreateTransform("Term{col=F1}", dataView); + //var result = FeatureCombiner.PrepareFeatures(env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; + + var pipe = new RandomTrainer(env, new RandomTrainer.Arguments()); + TestEstimatorCore(pipe, dataView); + } + Done(); + } + + [Fact] + public void TestEstimatorPrior() + { + using (var env = new TlcEnvironment()) + { + var dataView = GetBreastCancerDataviewWithTextColumns(); + dataView = env.CreateTransform("Term{col=F1}", dataView); + var result = FeatureCombiner.PrepareFeatures(env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; + + var pipe = new PriorTrainer(env, new PriorTrainer.Arguments()); + TestEstimatorCore(pipe, result, invalidInput: dataView); + } + Done(); + } + } +} From f05fe556cdbcfdc7f5f2a7a25b34b26d97f5ca24 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Fri, 14 Sep 2018 17:25:08 -0700 Subject: [PATCH 08/18] fixed tests --- .../Training/TrainerEstimatorBase.cs | 11 -------- .../Standard/Simple/SimpleTrainers.cs | 27 +++++-------------- .../TestEstimators.cs | 17 +++++------- 3 files changed, 14 insertions(+), 41 deletions(-) diff --git a/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs b/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs index 784019bfba..02c8c60667 100644 --- a/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs +++ b/src/Microsoft.ML.Data/Training/TrainerEstimatorBase.cs @@ -63,17 +63,6 @@ public TrainerEstimatorBase(IHost host, SchemaShape.Column feature, SchemaShape. WeightColumn = weight; } - protected static IEnumerable MetadataForScoreColumn(bool isNormalized = false) - { - var cols = new List(); - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnSetId, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true)); - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreValueKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); - if (isNormalized) - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.IsNormalized, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false)); - return cols; - } - public TTransformer Fit(IDataView input) => TrainTransformer(input); public SchemaShape GetOutputSchema(SchemaShape inputSchema) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index 5ffcf9376f..f958c3c585 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -76,17 +76,6 @@ public override RandomPredictor Train(TrainContext context) return new RandomPredictor(Host, Host.Rand.Next()); } - internal static IEnumerable MetadataForScoreColumn(bool isNormalized = false) - { - var cols = new List(); - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnSetId, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true)); - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreColumnKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.ScoreValueKind, SchemaShape.Column.VectorKind.Scalar, TextType.Instance, false)); - if (isNormalized) - cols.Add(new SchemaShape.Column(MetadataUtils.Kinds.IsNormalized, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false)); - return cols; - } - public SchemaShape GetOutputSchema(SchemaShape inputSchema) { Host.CheckValue(inputSchema, nameof(inputSchema)); @@ -95,9 +84,9 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema) var newColumns = new[] { - new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), - new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), - new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataForScoreColumn())) + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) }; foreach (SchemaShape.Column column in newColumns) outColumns[column.Name] = column; @@ -246,8 +235,6 @@ public sealed class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - protected override SchemaShape.Column[] OutputColumns => MakeOutputColumns(); - public PriorTrainer(IHostEnvironment env, Arguments args) : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null) { @@ -310,13 +297,13 @@ private static SchemaShape.Column MakeFeatureColumn(string featureColumn) private static SchemaShape.Column MakeLabelColumn(string labelColumn) => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); - private static SchemaShape.Column[] MakeOutputColumns() + protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) { return new[] { - new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), - new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false), - new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataForScoreColumn())) + new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), + new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), + new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) }; } } diff --git a/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs b/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs index 7d35ce8006..517e2deebb 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs @@ -13,7 +13,7 @@ namespace Microsoft.ML.Tests { - public class EstimatorTests: TestDataPipeBase + public class SimpleEstimatorTests : TestDataPipeBase { private IDataView GetBreastCancerDataviewWithTextColumns() { @@ -37,22 +37,19 @@ private IDataView GetBreastCancerDataviewWithTextColumns() }).Data; } - public EstimatorTests(ITestOutputHelper output) : base(output) + public SimpleEstimatorTests(ITestOutputHelper output) : base(output) { } [Fact] public void TestEstimatorRandom() { - using (var env = new TlcEnvironment()) - { - var dataView = GetBreastCancerDataviewWithTextColumns(); - //dataView = env.CreateTransform("Term{col=F1}", dataView); - //var result = FeatureCombiner.PrepareFeatures(env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; + var dataView = GetBreastCancerDataviewWithTextColumns(); + var pipe = new RandomTrainer(Env, new RandomTrainer.Arguments()); - var pipe = new RandomTrainer(env, new RandomTrainer.Arguments()); - TestEstimatorCore(pipe, dataView); - } + // Test only that the schema propagation works. + // REVIEW: the save/load is not preserving the full state of the random predictor. This is unfortunate, but we don't care too much at this point. + TestEstimatorCore(pipe, new EmptyDataView(Env, dataView.Schema)); Done(); } From 0221dfb0db4441c184a15309e7b135fe25fce31f Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Fri, 14 Sep 2018 17:45:07 -0700 Subject: [PATCH 09/18] added output files for release tests --- .../BinaryPrior-CV-breast-cancer-out.txt | 52 ++ .../BinaryPrior-CV-breast-cancer-rp.txt | 4 + .../BinaryPrior-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...inaryPrior-TrainTest-breast-cancer-out.txt | 36 + ...BinaryPrior-TrainTest-breast-cancer-rp.txt | 4 + .../BinaryPrior-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../BinaryRandom-CV-breast-cancer-out.txt | 52 ++ .../BinaryRandom-CV-breast-cancer-rp.txt | 4 + .../BinaryRandom-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...naryRandom-TrainTest-breast-cancer-out.txt | 36 + ...inaryRandom-TrainTest-breast-cancer-rp.txt | 4 + .../BinaryRandom-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ 12 files changed, 2992 insertions(+) create mode 100644 test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt create mode 100644 test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt create mode 100644 test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt create mode 100644 test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt create mode 100644 test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt create mode 100644 test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..38585c7ede --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 134 | 0.0000 + negative || 0 | 228 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6298 | +OVERALL 0/1 ACCURACY: 0.629834 +LOG LOSS/instance: 0.959786 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -0.945203 +AUC: 0.500000 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 107 | 0.0000 + negative || 0 | 230 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6825 | +OVERALL 0/1 ACCURACY: 0.682493 +LOG LOSS/instance: 0.910421 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -0.972725 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.656163 (0.0263) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.656163 (0.0263) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.935104 (0.0247) +Log-loss reduction: -0.958964 (0.0138) +F1 Score: NaN (NaN) +AUPRC: 0.418968 (0.0212) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..83b563173d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.656163 0 0 0.656163 1 0.935104 -0.958964 NaN 0.418968 PriorPredictor %Data% %Output% 99 0 0 maml.exe CV tr=PriorPredictor threads=- dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt new file mode 100644 index 0000000000..56f9146f1d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.364985168 0.317507416 1.6551378056300845 0 +6 0 -0.364985168 0.317507416 0.55111472519016635 0 +8 0 -0.364985168 0.317507416 0.55111472519016635 0 +9 0 -0.364985168 0.317507416 0.55111472519016635 0 +10 0 -0.364985168 0.317507416 0.55111472519016635 0 +11 0 -0.364985168 0.317507416 0.55111472519016635 0 +18 1 -0.364985168 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a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..8e3bf3b17b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 0 | 241 | 0.0000 + negative || 0 | 458 | 1.0000 + ||====================== +Precision || 0.0000 | 0.6552 | +OVERALL 0/1 ACCURACY: 0.655222 +LOG LOSS/instance: 0.929318 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.500000 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.500000 (0.0000) +Accuracy: 0.655222 (0.0000) +Positive precision: 0.000000 (0.0000) +Positive recall: 0.000000 (0.0000) +Negative precision: 0.655222 (0.0000) +Negative recall: 1.000000 (0.0000) +Log-loss: 0.929318 (0.0000) +Log-loss reduction: 0.000000 (0.0000) +F1 Score: NaN (0.0000) +AUPRC: 0.415719 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..60265c1608 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +PriorPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5 0.655222 0 0 0.655222 1 0.929318 0 NaN 0.415719 PriorPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=PriorPredictor dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5bd2f17a17 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -0.310443461 0.34477827 0.60994489004156338 0 +1 0 -0.310443461 0.34477827 0.60994489004156338 0 +2 0 -0.310443461 0.34477827 0.60994489004156338 0 +3 0 -0.310443461 0.34477827 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b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt @@ -0,0 +1,52 @@ +maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 67 | 67 | 0.5000 + negative || 116 | 112 | 0.4912 + ||====================== +Precision || 0.3661 | 0.6257 | +OVERALL 0/1 ACCURACY: 0.494475 +LOG LOSS/instance: 1.411189 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): -48.421356 +AUC: 0.521242 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 50 | 57 | 0.4673 + negative || 113 | 117 | 0.5087 + ||====================== +Precision || 0.3067 | 0.6724 | +OVERALL 0/1 ACCURACY: 0.495549 +LOG LOSS/instance: 1.451172 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): -60.946260 +AUC: 0.484884 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.503063 (0.0182) +Accuracy: 0.495012 (0.0005) +Positive precision: 0.336434 (0.0297) +Positive recall: 0.483645 (0.0164) +Negative precision: 0.649056 (0.0234) +Negative recall: 0.499962 (0.0087) +Log-loss: 1.431181 (0.0200) +Log-loss reduction: -54.683808 (6.2625) +F1 Score: 0.396542 (0.0262) +AUPRC: 0.348911 (0.0260) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..2692f38450 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.503063 0.495012 0.336434 0.483645 0.649056 0.499962 1.431181 -54.68381 0.396542 0.348911 RandomPredictor %Data% %Output% 99 0 0 maml.exe CV tr=RandomPredictor threads=- dout=%Output% data=%Data% seed=1 n=1 + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt new file mode 100644 index 0000000000..c0fc1e43b3 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer.txt @@ -0,0 +1,700 @@ 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mode 100644 index 0000000000..9d0392fe3b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt @@ -0,0 +1,36 @@ +maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 113 | 128 | 0.4689 + negative || 231 | 227 | 0.4956 + ||====================== +Precision || 0.3285 | 0.6394 | +OVERALL 0/1 ACCURACY: 0.486409 +LOG LOSS/instance: 1.479534 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): -59.206457 +AUC: 0.471290 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.471290 (0.0000) +Accuracy: 0.486409 (0.0000) +Positive precision: 0.328488 (0.0000) +Positive recall: 0.468880 (0.0000) +Negative precision: 0.639437 (0.0000) +Negative recall: 0.495633 (0.0000) +Log-loss: 1.479534 (0.0000) +Log-loss reduction: -59.206457 (0.0000) +F1 Score: 0.386325 (0.0000) +AUPRC: 0.314886 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..4af687d0c8 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +RandomPredictor +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.47129 0.486409 0.328488 0.46888 0.639437 0.495633 1.479534 -59.20646 0.386325 0.314886 RandomPredictor %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=RandomPredictor dout=%Output% data=%Data% out=%Output% seed=1 n=1 + diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..1980e349e8 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 0.6896762 0.844838142 2.6881541417898371 1 +1 0 -0.958645165 0.0206774175 0.030143942420402223 0 +2 0 -0.981372237 0.009313881 0.013500057520067015 0 +3 0 0.912489057 0.9562445 4.514392749742357 1 +4 0 0.9971796 0.9985898 9.4698985241832379 1 +5 1 -0.6345405 0.182729751 2.4522165515584184 0 +6 0 -0.6535362 0.1732319 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0 -0.959403038 0.020298481 0.029585817731858574 0 +668 1 -0.0620544665 0.468972772 1.092423929762264 0 +669 1 -0.1093397 0.445330143 1.167052828382644 0 +670 1 0.7400145 0.8700073 0.20090062751552754 1 +671 0 0.257556081 0.62877804 1.4296460394438404 1 +672 0 -0.0252051521 0.487397432 0.96408738964953067 0 +673 0 0.5220516 0.7610258 2.0650731416111654 1 +674 0 -0.150225192 0.424887419 0.79808369619187902 0 +675 0 -0.8285416 0.08572921 0.12930656935508233 0 +676 0 -0.128591537 0.435704231 0.82547656303306038 0 +677 0 -0.772794247 0.113602877 0.17397489553374348 0 +678 0 -0.328698128 0.335650921 0.58998659669085396 0 +679 0 -0.920071542 0.03996423 0.058839932936285387 0 +680 1 0.8711379 0.9355689 0.096084145903302609 1 +681 1 0.431411982 0.715706 0.48256103915508791 1 +682 0 -0.7243137 0.137843162 0.21397775564200355 0 +683 0 0.3713024 0.6856512 1.669561761695042 1 +684 0 -0.7297887 0.13510564 0.20940416465001679 0 +685 0 0.7174773 0.858738661 2.8235614161513505 1 +686 0 -0.994656265 0.00267186761 0.0038598489514670123 0 +687 0 -0.99033767 0.004831165 0.0069867886829129309 0 +688 0 0.4759514 0.7379757 1.9322275734663419 1 +689 0 -0.1828268 0.4085866 0.75776114229334535 0 +690 0 0.870642662 0.935321331 3.950566199919979 1 +691 1 -0.213670343 0.3931648 1.3467938821453134 0 +692 0 -0.6592799 0.170360059 0.26944274322608602 0 +693 0 0.396216482 0.698108256 1.7278967910194019 1 +694 0 0.366007477 0.6830037 1.6574622011524394 1 +695 0 0.410838276 0.7054191 1.7632643162514681 1 +696 1 -0.232817248 0.383591384 1.382357780264464 0 +697 1 0.855639338 0.927819669 0.10808366406515908 1 +698 1 -0.126234785 0.436882615 1.1946823972611171 0 From 3dcb7057e952e84ec90a0969814cab62b030af99 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Fri, 14 Sep 2018 17:51:53 -0700 Subject: [PATCH 10/18] change test file name --- .../{TestEstimators.cs => TestTrainerEstimators.cs} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename test/Microsoft.ML.Predictor.Tests/{TestEstimators.cs => TestTrainerEstimators.cs} (100%) diff --git a/test/Microsoft.ML.Predictor.Tests/TestEstimators.cs b/test/Microsoft.ML.Predictor.Tests/TestTrainerEstimators.cs similarity index 100% rename from test/Microsoft.ML.Predictor.Tests/TestEstimators.cs rename to test/Microsoft.ML.Predictor.Tests/TestTrainerEstimators.cs From df28a3564f5a97e34bd6e98455d3bebfa8bde704 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 13:14:26 -0700 Subject: [PATCH 11/18] fixed review comments and added constructors --- .../Standard/Simple/SimpleTrainers.cs | 21 +++++++++++++++++-- .../TrainerEstimators/PriorRandomTests.cs} | 11 ++++++++-- 2 files changed, 28 insertions(+), 4 deletions(-) rename test/{Microsoft.ML.Predictor.Tests/TestTrainerEstimators.cs => Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs} (79%) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index f958c3c585..349bb3996c 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -55,7 +55,16 @@ public sealed class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - public RandomTrainer(IHostEnvironment env, Arguments args) + /// + /// Initializes RandomTrainer object. + /// + /// + public RandomTrainer(IHostEnvironment env) + : base(env, LoadNameValue) + { + } + + private RandomTrainer(IHostEnvironment env, Arguments args) : base(env, LoadNameValue) { Host.CheckValue(args, nameof(args)); @@ -235,11 +244,19 @@ public sealed class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - public PriorTrainer(IHostEnvironment env, Arguments args) + private PriorTrainer(IHostEnvironment env, Arguments args) : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null) { + Host.CheckValue(args, nameof(args)); } + /// + /// Initializes PriorTrainer object. + /// + /// + /// + /// + /// Optional weight parameter. public PriorTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) : base(host, feature, label, weight) { diff --git a/test/Microsoft.ML.Predictor.Tests/TestTrainerEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs similarity index 79% rename from test/Microsoft.ML.Predictor.Tests/TestTrainerEstimators.cs rename to test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs index 517e2deebb..b75b23e59a 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestTrainerEstimators.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -2,6 +2,7 @@ // 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.Api; using Microsoft.ML.Runtime.Data; @@ -37,6 +38,12 @@ private IDataView GetBreastCancerDataviewWithTextColumns() }).Data; } + private static SchemaShape.Column MakeFeatureColumn(string featureColumn) + => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); + + private static SchemaShape.Column MakeLabelColumn(string labelColumn) + => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + public SimpleEstimatorTests(ITestOutputHelper output) : base(output) { } @@ -45,7 +52,7 @@ public SimpleEstimatorTests(ITestOutputHelper output) : base(output) public void TestEstimatorRandom() { var dataView = GetBreastCancerDataviewWithTextColumns(); - var pipe = new RandomTrainer(Env, new RandomTrainer.Arguments()); + var pipe = new RandomTrainer(Env); // Test only that the schema propagation works. // REVIEW: the save/load is not preserving the full state of the random predictor. This is unfortunate, but we don't care too much at this point. @@ -62,7 +69,7 @@ public void TestEstimatorPrior() dataView = env.CreateTransform("Term{col=F1}", dataView); var result = FeatureCombiner.PrepareFeatures(env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; - var pipe = new PriorTrainer(env, new PriorTrainer.Arguments()); + var pipe = new PriorTrainer(Contracts.CheckRef(env, nameof(env)).Register("PriorPredictor"), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null); TestEstimatorCore(pipe, result, invalidInput: dataView); } Done(); From f9dc9d1cd4bba5351dc13b0d83db130b0977318a Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 13:16:25 -0700 Subject: [PATCH 12/18] no need to create new env --- .../TrainerEstimators/PriorRandomTests.cs | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs index b75b23e59a..50748a886a 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -63,15 +63,12 @@ public void TestEstimatorRandom() [Fact] public void TestEstimatorPrior() { - using (var env = new TlcEnvironment()) - { - var dataView = GetBreastCancerDataviewWithTextColumns(); - dataView = env.CreateTransform("Term{col=F1}", dataView); - var result = FeatureCombiner.PrepareFeatures(env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; + var dataView = GetBreastCancerDataviewWithTextColumns(); + dataView = Env.CreateTransform("Term{col=F1}", dataView); + var result = FeatureCombiner.PrepareFeatures(Env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; - var pipe = new PriorTrainer(Contracts.CheckRef(env, nameof(env)).Register("PriorPredictor"), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null); - TestEstimatorCore(pipe, result, invalidInput: dataView); - } + var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null); + TestEstimatorCore(pipe, result, invalidInput: dataView); Done(); } } From 14c47c0df9a2e873e991741dcaa6fcbc9b5e1faa Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 13:26:10 -0700 Subject: [PATCH 13/18] unused reference --- .../Standard/Simple/SimpleTrainers.cs | 1 - 1 file changed, 1 deletion(-) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index 349bb3996c..4c692d4c99 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -12,7 +12,6 @@ using Microsoft.ML.Runtime.Training; using System; using System.Linq; -using System.Collections.Generic; [assembly: LoadableClass(RandomTrainer.Summary, typeof(RandomTrainer), typeof(RandomTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, From 77392ee3e8ed91742c0c94c982225ccb344e69c0 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 15:07:37 -0700 Subject: [PATCH 14/18] removed dependency on feature column for prior estimator --- .../Data/RoleMappedSchema.cs | 2 +- .../Standard/Simple/SimpleTrainers.cs | 56 +++++++++++++------ .../TrainerEstimators/PriorRandomTests.cs | 11 +--- 3 files changed, 42 insertions(+), 27 deletions(-) diff --git a/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs b/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs index 2e35be86b7..2517f90fe8 100644 --- a/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs +++ b/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs @@ -534,7 +534,7 @@ public RoleMappedData(IDataView data, IEnumerableWhether to consider the column names specified "optional" or not. If false then any non-empty /// values for the column names that does not appear in 's schema will result in an exception being thrown, /// but if true such values will be ignored - public RoleMappedData(IDataView data, string label, string feature, + public RoleMappedData(IDataView data, string label, string feature = null, string group = null, string weight = null, string name = null, IEnumerable> custom = null, bool opt = false) : this(Contracts.CheckRef(data, nameof(data)), diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index 4c692d4c99..782f63ce70 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -57,7 +57,6 @@ public sealed class Arguments /// /// Initializes RandomTrainer object. /// - /// public RandomTrainer(IHostEnvironment env) : base(env, LoadNameValue) { @@ -228,8 +227,11 @@ private void MapDist(ref VBuffer src, ref float score, ref float prob) } } - // Learns the prior distribution for 0/1 class labels and just outputs that. - public sealed class PriorTrainer : TrainerEstimatorBase, PriorPredictor> + /// + /// Learns the prior distribution for 0/1 class labels and just outputs that. + /// + public sealed class PriorTrainer : TrainerBase, + ITrainerEstimator, PriorPredictor> { internal const string LoadNameValue = "PriorPredictor"; internal const string UserNameValue = "Prior Predictor"; @@ -238,13 +240,16 @@ public sealed class Arguments { } + private readonly String _labelColumnName; + private readonly String _weightColumnName; + public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; - private PriorTrainer(IHostEnvironment env, Arguments args) - : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null) + public PriorTrainer(IHostEnvironment env, Arguments args) + : base(env, LoadNameValue) { Host.CheckValue(args, nameof(args)); } @@ -252,16 +257,26 @@ private PriorTrainer(IHostEnvironment env, Arguments args) /// /// Initializes PriorTrainer object. /// - /// - /// - /// - /// Optional weight parameter. - public PriorTrainer(IHost host, SchemaShape.Column feature, SchemaShape.Column label, SchemaShape.Column weight) - : base(host, feature, label, weight) + public PriorTrainer(IHost host, SchemaShape.Column label, SchemaShape.Column weight) + : base(host, LoadNameValue) { + Contracts.CheckValue(label, nameof(label)); + Contracts.CheckValueOrNull(weight); + + _labelColumnName = label.Name.ToString(); + _weightColumnName = weight != null ? weight.Name.ToString() : null; + } + + public BinaryPredictionTransformer Fit(IDataView input) + { + var cachedTrain = Info.WantCaching ? new CacheDataView(Host, input, prefetch: null) : input; + + RoleMappedData trainRoles = new RoleMappedData(cachedTrain, label: _labelColumnName, weight: _weightColumnName); + var pred = Train(new TrainContext(trainRoles)); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, featureColumn: null); } - protected override PriorPredictor TrainModelCore(TrainContext context) + public override PriorPredictor Train(TrainContext context) { Contracts.CheckValue(context, nameof(context)); var data = context.TrainingSet; @@ -280,7 +295,7 @@ protected override PriorPredictor TrainModelCore(TrainContext context) { var getLab = cursor.GetLabelFloatGetter(data); var getWeight = colWeight >= 0 ? cursor.GetGetter(colWeight) : null; - float lab = default(float); + float lab = default; float weight = 1; while (cursor.MoveNext()) { @@ -304,23 +319,28 @@ protected override PriorPredictor TrainModelCore(TrainContext context) return new PriorPredictor(Host, prob); } - protected override BinaryPredictionTransformer MakeTransformer(PriorPredictor model, ISchema trainSchema) - => new BinaryPredictionTransformer(Host, model, trainSchema, FeatureColumn.Name); - private static SchemaShape.Column MakeFeatureColumn(string featureColumn) => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); private static SchemaShape.Column MakeLabelColumn(string labelColumn) => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); - protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape inputSchema) + public SchemaShape GetOutputSchema(SchemaShape inputSchema) { - return new[] + Host.CheckValue(inputSchema, nameof(inputSchema)); + + var outColumns = inputSchema.Columns.ToDictionary(x => x.Name); + + var newColumns = new[] { new SchemaShape.Column(DefaultColumnNames.Score, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())), new SchemaShape.Column(DefaultColumnNames.Probability, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata(true))), new SchemaShape.Column(DefaultColumnNames.PredictedLabel, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false, new SchemaShape(MetadataUtils.GetTrainerOutputMetadata())) }; + foreach (SchemaShape.Column column in newColumns) + outColumns[column.Name] = column; + + return new SchemaShape(outColumns.Values); } } diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs index 50748a886a..a463c610e7 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -38,9 +38,6 @@ private IDataView GetBreastCancerDataviewWithTextColumns() }).Data; } - private static SchemaShape.Column MakeFeatureColumn(string featureColumn) - => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false); - private static SchemaShape.Column MakeLabelColumn(string labelColumn) => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); @@ -64,11 +61,9 @@ public void TestEstimatorRandom() public void TestEstimatorPrior() { var dataView = GetBreastCancerDataviewWithTextColumns(); - dataView = Env.CreateTransform("Term{col=F1}", dataView); - var result = FeatureCombiner.PrepareFeatures(Env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; - - var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), MakeFeatureColumn(DefaultColumnNames.Features), MakeLabelColumn(DefaultColumnNames.Label), null); - TestEstimatorCore(pipe, result, invalidInput: dataView); + + var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), MakeLabelColumn(DefaultColumnNames.Label), null); + TestEstimatorCore(pipe, dataView); Done(); } } From 886d51443f93bb2f9d0cd0a2356106a8fe419b5e Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 15:14:45 -0700 Subject: [PATCH 15/18] fixed review comments --- src/Microsoft.ML.Core/Data/RoleMappedSchema.cs | 2 +- .../Standard/Simple/SimpleTrainers.cs | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs b/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs index 2517f90fe8..2e35be86b7 100644 --- a/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs +++ b/src/Microsoft.ML.Core/Data/RoleMappedSchema.cs @@ -534,7 +534,7 @@ public RoleMappedData(IDataView data, IEnumerableWhether to consider the column names specified "optional" or not. If false then any non-empty /// values for the column names that does not appear in 's schema will result in an exception being thrown, /// but if true such values will be ignored - public RoleMappedData(IDataView data, string label, string feature = null, + public RoleMappedData(IDataView data, string label, string feature, string group = null, string weight = null, string name = null, IEnumerable> custom = null, bool opt = false) : this(Contracts.CheckRef(data, nameof(data)), diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index 782f63ce70..e3c7110bad 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -257,21 +257,21 @@ public PriorTrainer(IHostEnvironment env, Arguments args) /// /// Initializes PriorTrainer object. /// - public PriorTrainer(IHost host, SchemaShape.Column label, SchemaShape.Column weight) + public PriorTrainer(IHost host, String labelColumn, String weightColunn = null) : base(host, LoadNameValue) { - Contracts.CheckValue(label, nameof(label)); - Contracts.CheckValueOrNull(weight); + Contracts.CheckValue(labelColumn, nameof(labelColumn)); + Contracts.CheckValueOrNull(weightColunn); - _labelColumnName = label.Name.ToString(); - _weightColumnName = weight != null ? weight.Name.ToString() : null; + _labelColumnName = labelColumn; + _weightColumnName = weightColunn != null ? weightColunn : null; } public BinaryPredictionTransformer Fit(IDataView input) { var cachedTrain = Info.WantCaching ? new CacheDataView(Host, input, prefetch: null) : input; - RoleMappedData trainRoles = new RoleMappedData(cachedTrain, label: _labelColumnName, weight: _weightColumnName); + RoleMappedData trainRoles = new RoleMappedData(cachedTrain, feature: null, label: _labelColumnName, weight: _weightColumnName); var pred = Train(new TrainContext(trainRoles)); return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, featureColumn: null); } From 005e53fab43a1d177d025e8e618fbc8f816e97c0 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 15:16:49 -0700 Subject: [PATCH 16/18] fixed bug --- test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs index a463c610e7..1886264a24 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -62,7 +62,7 @@ public void TestEstimatorPrior() { var dataView = GetBreastCancerDataviewWithTextColumns(); - var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), MakeLabelColumn(DefaultColumnNames.Label), null); + var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), "Label"); TestEstimatorCore(pipe, dataView); Done(); } From c661496d24e676e51a07cd8b877d8c5a9bc5a6b9 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 15:38:45 -0700 Subject: [PATCH 17/18] keeping constructors public and fix review comments --- .../Standard/Simple/SimpleTrainers.cs | 2 +- test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs | 4 ---- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index e3c7110bad..e823d1fcd1 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -62,7 +62,7 @@ public RandomTrainer(IHostEnvironment env) { } - private RandomTrainer(IHostEnvironment env, Arguments args) + public RandomTrainer(IHostEnvironment env, Arguments args) : base(env, LoadNameValue) { Host.CheckValue(args, nameof(args)); diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs index 1886264a24..fca82edd87 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -41,10 +41,6 @@ private IDataView GetBreastCancerDataviewWithTextColumns() private static SchemaShape.Column MakeLabelColumn(string labelColumn) => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); - public SimpleEstimatorTests(ITestOutputHelper output) : base(output) - { - } - [Fact] public void TestEstimatorRandom() { From 2042edd41748d77211d7830d8a379006afe05551 Mon Sep 17 00:00:00 2001 From: Artidoro Pagnoni Date: Mon, 17 Sep 2018 16:00:39 -0700 Subject: [PATCH 18/18] retriggering build --- .../Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs index fca82edd87..b510ab3bb0 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -38,8 +38,9 @@ private IDataView GetBreastCancerDataviewWithTextColumns() }).Data; } - private static SchemaShape.Column MakeLabelColumn(string labelColumn) - => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false); + public SimpleEstimatorTests(ITestOutputHelper output) : base(output) + { + } [Fact] public void TestEstimatorRandom()