diff --git a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs index f04febd055..a356a82625 100644 --- a/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs +++ b/src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs @@ -39,14 +39,18 @@ public abstract class PredictionTransformerBase : IPredictionTransformer public PredictionTransformerBase(IHost host, TModel model, ISchema trainSchema, string featureColumn) { 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 (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); TrainSchema = trainSchema; BindableMapper = ScoreUtils.GetSchemaBindableMapper(Host, model); @@ -75,10 +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); } @@ -87,10 +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; } @@ -121,7 +131,7 @@ protected virtual void SaveCore(ModelSaveContext ctx) } }); - ctx.SaveString(FeatureColumn); + ctx.SaveStringOrNull(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.StandardLearners/Standard/Simple/SimpleTrainers.cs b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs index abfff554c9..e823d1fcd1 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Simple/SimpleTrainers.cs @@ -2,17 +2,16 @@ // 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.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 System; +using System.Linq; [assembly: LoadableClass(RandomTrainer.Summary, typeof(RandomTrainer), typeof(RandomTrainer.Arguments), new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, @@ -38,20 +37,16 @@ namespace Microsoft.ML.Runtime.Learners /// /// A trainer that trains a predictor that returns random values /// - public sealed class RandomTrainer : TrainerBase + + 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 { - // 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 +54,52 @@ public class Arguments private static readonly TrainerInfo _info = new TrainerInfo(normalization: false, caching: false); public override TrainerInfo Info => _info; + /// + /// Initializes RandomTrainer object. + /// + public RandomTrainer(IHostEnvironment env) + : base(env, LoadNameValue) + { + } + public RandomTrainer(IHostEnvironment env, Arguments args) : base(env, LoadNameValue) { Host.CheckValue(args, nameof(args)); } + 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, featureColumn: null); + } + public override RandomPredictor Train(TrainContext context) { Host.CheckValue(context, nameof(context)); return new RandomPredictor(Host, Host.Rand.Next()); } + + public SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + 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); + } } /// @@ -77,8 +107,8 @@ public override RandomPredictor Train(TrainContext context) /// uniform random probability and classification assignment. /// public sealed class RandomPredictor : - PredictorBase, - IDistPredictorProducing, + PredictorBase, + IDistPredictorProducing, IValueMapperDist, ICanSaveModel { @@ -96,7 +126,7 @@ private static VersionInfo GetVersionInfo() // Keep all the serializable state here. private readonly int _seed; private readonly object _instanceLock; - private readonly Random _random; + private readonly IRandom _random; public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; public ColumnType InputType { get; } @@ -109,7 +139,7 @@ public RandomPredictor(IHostEnvironment env, int seed) _seed = seed; _instanceLock = new object(); - _random = new Random(_seed); + _random = RandomUtils.Create(_seed); InputType = new VectorType(NumberType.Float); } @@ -126,7 +156,9 @@ private RandomPredictor(IHostEnvironment env, ModelLoadContext ctx) _seed = ctx.Reader.ReadInt32(); _instanceLock = new object(); - _random = new Random(_seed); + _random = RandomUtils.Create(_seed); + + InputType = new VectorType(NumberType.Float); } public static RandomPredictor Create(IHostEnvironment env, ModelLoadContext ctx) @@ -154,24 +186,24 @@ 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 @@ -183,20 +215,23 @@ 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; } } - // Learns the prior distribution for 0/1 class labels and just outputs that. - public sealed class PriorTrainer : TrainerBase + /// + /// 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"; @@ -205,6 +240,9 @@ 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); @@ -216,6 +254,28 @@ public PriorTrainer(IHostEnvironment env, Arguments args) Host.CheckValue(args, nameof(args)); } + /// + /// Initializes PriorTrainer object. + /// + public PriorTrainer(IHost host, String labelColumn, String weightColunn = null) + : base(host, LoadNameValue) + { + Contracts.CheckValue(labelColumn, nameof(labelColumn)); + Contracts.CheckValueOrNull(weightColunn); + + _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, feature: null, label: _labelColumnName, weight: _weightColumnName); + var pred = Train(new TrainContext(trainRoles)); + return new BinaryPredictionTransformer(Host, pred, cachedTrain.Schema, featureColumn: null); + } + public override PriorPredictor Train(TrainContext context) { Contracts.CheckValue(context, nameof(context)); @@ -234,16 +294,16 @@ public override PriorPredictor Train(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 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; } @@ -255,14 +315,38 @@ public override PriorPredictor Train(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); } + + 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 SchemaShape GetOutputSchema(SchemaShape inputSchema) + { + 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); + } } public sealed class PriorPredictor : - PredictorBase, - IDistPredictorProducing, + PredictorBase, + IDistPredictorProducing, IValueMapperDist, ICanSaveModel { @@ -277,13 +361,13 @@ 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 @@ -298,7 +382,7 @@ 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; @@ -321,7 +405,7 @@ protected override void SaveCore(ModelSaveContext ctx) // *** Binary format *** // Float: _prob - Contracts.Assert(!Float.IsNaN(_prob)); + Contracts.Assert(!float.IsNaN(_prob)); ctx.Writer.Write(_prob); } @@ -333,29 +417,29 @@ 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; 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 100644 index 0000000000..56f9146f1d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.364985168 0.317507416 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||====================== +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.txt b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5bd2f17a17 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability 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b/test/BaselineOutput/SingleDebug/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..acd9af8ba2 --- /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 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/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..2692f38450 --- /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 +0.503063 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 new 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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..9d0392fe3b --- /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 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/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..4af687d0c8 --- /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 +0.47129 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 new file mode 100644 index 0000000000..1980e349e8 --- /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 0.6896762 0.844838142 2.6881541417898371 1 +1 0 -0.958645165 0.0206774175 0.030143942420402223 0 +2 0 -0.981372237 0.009313881 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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 +++ 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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 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+696 1 -0.310443461 0.34477827 1.5362592468820475 0 +697 1 -0.310443461 0.34477827 1.5362592468820475 0 +698 1 -0.310443461 0.34477827 1.5362592468820475 0 diff --git a/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..acd9af8ba2 --- /dev/null +++ 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 @@ +Instance Label Score Probability Log-loss Assigned +5 1 -0.5901826 0.2049087 2.2869468638557469 0 +6 0 0.128673017 0.564336538 1.1987139728117662 1 +8 0 -0.2906418 0.3546791 0.63191136124048231 0 +9 0 -0.285091162 0.357454419 0.63812929512728334 0 +10 0 0.5222254 0.7611127 2.0655978766972183 1 +11 0 0.512239 0.7561195 2.0357536248772812 1 +18 1 -0.595426738 0.202286631 2.3055271196103777 0 +20 1 -0.9194106 0.0402947068 4.6332658533540503 0 +21 1 0.8478753 0.9239377 0.11413255297145798 1 +25 1 -0.1367939 0.431603044 1.2122230541117975 0 +28 0 -0.142563447 0.428718269 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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/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/RandomPredictor/BinaryRandom-TrainTest-breast-cancer-out.txt new file 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 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+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 bde0cc20fc..74a5a26ae8 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -97,6 +97,30 @@ public void BinaryClassifierPerceptronTest() 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, extraSettings: new[] { "n=1" }); + 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 { diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs new file mode 100644 index 0000000000..b510ab3bb0 --- /dev/null +++ b/test/Microsoft.ML.Tests/TrainerEstimators/PriorRandomTests.cs @@ -0,0 +1,67 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Core.Data; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.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 SimpleEstimatorTests : 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 SimpleEstimatorTests(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void TestEstimatorRandom() + { + var dataView = GetBreastCancerDataviewWithTextColumns(); + 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. + TestEstimatorCore(pipe, new EmptyDataView(Env, dataView.Schema)); + Done(); + } + + [Fact] + public void TestEstimatorPrior() + { + var dataView = GetBreastCancerDataviewWithTextColumns(); + + var pipe = new PriorTrainer(Contracts.CheckRef(Env, nameof(Env)).Register("PriorPredictor"), "Label"); + TestEstimatorCore(pipe, dataView); + Done(); + } + } +}