public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(env => new LinearSvmTrainer(env, LabelColumnName, FeatureColumnName))
};
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(env => new OnlineGradientDescentTrainer(env, LabelColumnName, FeatureColumnName))
};
}
public void TestDeterministicSweeperAsyncCancellation()
{
var random = new Random(42);
var env = new MLContext(42);
var args = new DeterministicSweeperAsync.Arguments();
args.BatchSize = 5;
args.Relaxation = 1;
args.Sweeper = ComponentFactoryUtils.CreateFromFunction(
environ => new KdoSweeper(environ,
new KdoSweeper.Arguments()
{
SweptParameters = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
t => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
t => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 1000, LogBase = true
}))
}
}));
var sweeper = new DeterministicSweeperAsync(env, args);
int sweeps = 20;
var tasks = new List <Task <ParameterSetWithId> >();
int numCompleted = 0;
for (int i = 0; i < sweeps; i++)
{
var task = sweeper.Propose();
if (i < args.BatchSize - args.Relaxation)
{
Assert.True(task.IsCompleted);
sweeper.Update(task.Result.Id, new RunResult(task.Result.ParameterSet, random.NextDouble(), true));
numCompleted++;
}
else
{
tasks.Add(task);
}
}
// Cancel after the first barrier and check if the number of registered actions
// is indeed 2 * batchSize.
sweeper.Cancel();
Task.WaitAll(tasks.ToArray());
foreach (var task in tasks)
{
if (task.Result != null)
{
numCompleted++;
}
}
Assert.Equal(args.BatchSize + args.BatchSize, numCompleted);
}
public void TestSmacSweeper()
{
RunMTAThread(() =>
{
var random = new Random(42);
using (var env = new ConsoleEnvironment(42))
{
int maxInitSweeps = 5;
var args = new SmacSweeper.Arguments()
{
NumberInitialPopulation = 20,
SweptParameters = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 100, LogBase = true
}))
}
};
var sweeper = new SmacSweeper(env, args);
var results = new List <IRunResult>();
var sweeps = sweeper.ProposeSweeps(maxInitSweeps, results);
Assert.Equal(Math.Min(args.NumberInitialPopulation, maxInitSweeps), sweeps.Length);
for (int i = 1; i < 10; i++)
{
foreach (var parameterSet in sweeps)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = float.Parse(parameterValue.ValueText, CultureInfo.InvariantCulture);
Assert.InRange(val, 1, 5);
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.InRange(val, 1, 1000);
}
else
{
Assert.True(false, "Wrong parameter");
}
}
results.Add(new RunResult(parameterSet, random.NextDouble(), true));
}
sweeps = sweeper.ProposeSweeps(5, results);
}
Assert.Equal(5, sweeps.Length);
}
});
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => new MulticlassLogisticRegression(env, new MulticlassLogisticRegression.Arguments()))
};
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => new OnlineGradientDescentTrainer(env, new OnlineGradientDescentTrainer.Arguments()))
};
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => new MulticlassLogisticRegression(env, LabelColumn, FeatureColumn))
};
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => new LinearSvm(env, new LinearSvm.Arguments()))
};
}
public void TestSmacSweeper()
{
var random = new Random(42);
var env = new MLContext(42);
const int maxInitSweeps = 5;
var args = new SmacSweeper.Options()
{
NumberInitialPopulation = 20,
SweptParameters = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new FloatValueGenerator(new FloatParamOptions()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamOptions()
{
Name = "bar", Min = 1, Max = 100, LogBase = true
}))
}
};
var sweeper = new SmacSweeper(env, args);
var results = new List <IRunResult>();
var sweeps = sweeper.ProposeSweeps(maxInitSweeps, results);
Assert.Equal(Math.Min(args.NumberInitialPopulation, maxInitSweeps), sweeps.Length);
for (int i = 1; i < 10; i++)
{
foreach (var parameterSet in sweeps)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = float.Parse(parameterValue.ValueText, CultureInfo.InvariantCulture);
Assert.InRange(val, 1, 5);
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.InRange(val, 1, 1000);
}
else
{
Assert.True(false, "Wrong parameter");
}
}
results.Add(new RunResult(parameterSet, random.NextDouble(), true));
}
sweeps = sweeper.ProposeSweeps(5, results);
}
// Because only unique configurations are considered, the number asked for may exceed the number actually returned.
Assert.True(sweeps.Length <= 5);
}
/// <summary>
/// Determine the default scorer for a schema bound mapper. This looks for text-valued ScoreColumnKind
/// metadata on the first column of the mapper. If that text is found and maps to a scorer loadable class,
/// that component is used. Otherwise, the GenericScorer is used.
/// </summary>
/// <param name="environment">The host environment.</param>.
/// <param name="mapper">The schema bound mapper to get the default scorer.</param>.
/// <param name="suffix">An optional suffix to append to the default column names.</param>
public static TScorerFactory GetScorerComponent(
IHostEnvironment environment,
ISchemaBoundMapper mapper,
string suffix = null)
{
Contracts.CheckValue(environment, nameof(environment));
Contracts.AssertValue(mapper);
ComponentCatalog.LoadableClassInfo info = null;
ReadOnlyMemory <char> scoreKind = default;
if (mapper.OutputSchema.Count > 0 &&
mapper.OutputSchema.TryGetMetadata(TextType.Instance, MetadataUtils.Kinds.ScoreColumnKind, 0, ref scoreKind) &&
!scoreKind.IsEmpty)
{
var loadName = scoreKind.ToString();
info = environment.ComponentCatalog.GetLoadableClassInfo <SignatureDataScorer>(loadName);
if (info == null || !typeof(IDataScorerTransform).IsAssignableFrom(info.Type))
{
info = null;
}
}
Func <IHostEnvironment, IDataView, ISchemaBoundMapper, RoleMappedSchema, IDataScorerTransform> factoryFunc;
if (info == null)
{
factoryFunc = (env, data, innerMapper, trainSchema) =>
new GenericScorer(
env,
new GenericScorer.Arguments()
{
Suffix = suffix
},
data,
innerMapper,
trainSchema);
}
else
{
factoryFunc = (env, data, innerMapper, trainSchema) =>
{
object args = info.CreateArguments();
if (args is ScorerArgumentsBase scorerArgs)
{
scorerArgs.Suffix = suffix;
}
return((IDataScorerTransform)info.CreateInstance(
env,
args,
new object[] { data, innerMapper, trainSchema }));
};
}
return(ComponentFactoryUtils.CreateFromFunction(factoryFunc));
}
public Arguments()
{
// REVIEW: Perhaps we can have a better non-parametetric learner.
BasePredictorType = ComponentFactoryUtils.CreateFromFunction(
env => new Ova(env, new Ova.Arguments()
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new FastTreeBinaryClassificationTrainer(e, DefaultColumnNames.Label, DefaultColumnNames.Features))
}));
}
public Arguments()
{
// REVIEW: Perhaps we can have a better non-parametetric learner.
BasePredictorType = ComponentFactoryUtils.CreateFromFunction(
env => new Ova(env, new Ova.Arguments()
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new AveragedPerceptronTrainer(e, new AveragedPerceptronTrainer.Arguments()))
}));
}
public void TestNelderMeadSweeperWithDefaultFirstBatchSweeper()
{
var random = new Random(42);
var env = new MLContext(42);
var param = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 1000, LogBase = true
}))
};
var args = new NelderMeadSweeper.Arguments();
args.SweptParameters = param;
var sweeper = new NelderMeadSweeper(env, args);
var sweeps = sweeper.ProposeSweeps(5, new List <RunResult>());
Assert.Equal(3, sweeps.Length);
var results = new List <IRunResult>();
for (int i = 1; i < 10; i++)
{
foreach (var parameterSet in sweeps)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = float.Parse(parameterValue.ValueText, CultureInfo.InvariantCulture);
Assert.InRange(val, 1, 5);
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.InRange(val, 1, 1000);
}
else
{
Assert.True(false, "Wrong parameter");
}
}
results.Add(new RunResult(parameterSet, random.NextDouble(), true));
}
sweeps = sweeper.ProposeSweeps(5, results);
}
Assert.True(sweeps == null || sweeps.Length <= 5);
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => {
var trainerEstimator = new LinearSvmTrainer(env);
return(TrainerUtils.MapTrainerEstimatorToTrainer <LinearSvmTrainer,
LinearBinaryModelParameters, LinearBinaryModelParameters>(env, trainerEstimator));
})
};
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => {
var trainerEstimator = new OnlineGradientDescentTrainer(env);
return(TrainerUtils.MapTrainerEstimatorToTrainer <OnlineGradientDescentTrainer,
LinearRegressionModelParameters, LinearRegressionModelParameters>(env, trainerEstimator));
})
};
}
public Arguments()
{
BasePredictors = new[]
{
// Note that this illustrates a fundamental problem with the mixture of `ITrainer` and `ITrainerEstimator`
// present in this class. The options to the estimator have no way of being communicated to the `ITrainer`
// implementation, so there is a fundamental disconnect if someone chooses to ever use the *estimator* with
// non-default column names. Unfortunately no method of resolving this temporary strikes me as being any
// less laborious than the proper fix, which is that this "meta" component should itself be a trainer
// estimator, as opposed to a regular trainer.
ComponentFactoryUtils.CreateFromFunction(env => new LbfgsMaximumEntropyMulticlassTrainer(env, LabelColumnName, FeatureColumnName))
};
}
public void TestRandomSweeper()
{
using (var env = new ConsoleEnvironment(42))
{
var args = new SweeperBase.ArgumentsBase()
{
SweptParameters = new[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "foo", Min = 10, Max = 20
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 100, Max = 200
}))
}
};
var sweeper = new UniformRandomSweeper(env, args);
var initialList = sweeper.ProposeSweeps(5, new List <RunResult>());
Assert.Equal(5, initialList.Length);
foreach (var parameterSet in initialList)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = long.Parse(parameterValue.ValueText);
Assert.InRange(val, 10, 20);
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.InRange(val, 100, 200);
}
else
{
Assert.True(false, "Wrong parameter");
}
}
}
}
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => {
// Note that this illustrates a fundamnetal problem with the mixture of `ITrainer` and `ITrainerEstimator`
// present in this class. The options to the estimator have no way of being communicated to the `ITrainer`
// implementation, so there is a fundamnetal disconnect if someone chooses to ever use the *estimator* with
// non-default column names. Unfortuantely no method of resolving this temporary strikes me as being any
// less laborious than the proper fix, which is that this "meta" component should itself be a trainer
// estimator, as opposed to a regular trainer.
var trainerEstimator = new MulticlassLogisticRegression(env, LabelColumn, FeatureColumn);
return(TrainerUtils.MapTrainerEstimatorToTrainer <MulticlassLogisticRegression,
MulticlassLogisticRegressionModelParameters, MulticlassLogisticRegressionModelParameters>(env, trainerEstimator));
})
};
}
public void Metacomponents()
{
using (var env = new LocalEnvironment())
{
var loader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(GetDataPath(TestDatasets.irisData.trainFilename)));
var term = TermTransform.Create(env, loader, "Label");
var concat = new ConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth").Transform(term);
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new AveragedPerceptronTrainer(env, new AveragedPerceptronTrainer.Arguments()))
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new TrainContext(trainRoles));
}
}
public void Metacomponents()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
e => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments()))
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new TrainContext(trainRoles));
}
}
void Metacomponents()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = ComponentFactoryUtils.CreateFromFunction(
(e) => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments()))
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var keyToValue = new KeyToValueTransform(env, scorer, "PredictedLabel");
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(keyToValue);
var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisData>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
private void RunCore(IChannel ch, string cmd)
{
Host.AssertValue(ch);
IPredictor inputPredictor = null;
if (Args.ContinueTrain && !TrainUtils.TryLoadPredictor(ch, Host, Args.InputModelFile, out inputPredictor))
{
ch.Warning("No input model file specified or model file did not contain a predictor. The model state cannot be initialized.");
}
ch.Trace("Constructing data pipeline");
IDataLoader loader = CreateRawLoader();
// If the per-instance results are requested and there is no name column, add a GenerateNumberTransform.
var preXf = Args.PreTransform;
if (!string.IsNullOrEmpty(Args.OutputDataFile))
{
string name = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema, nameof(Args.NameColumn), Args.NameColumn, DefaultColumnNames.Name);
if (name == null)
{
preXf = preXf.Concat(
new[]
{
new KeyValuePair <string, IComponentFactory <IDataView, IDataTransform> >(
"", ComponentFactoryUtils.CreateFromFunction <IDataView, IDataTransform>(
(env, input) =>
{
var args = new GenerateNumberTransform.Arguments();
args.Column = new[] { new GenerateNumberTransform.Column()
{
Name = DefaultColumnNames.Name
}, };
args.UseCounter = true;
return(new GenerateNumberTransform(env, args, input));
}))
}).ToArray();
}
}
loader = CompositeDataLoader.Create(Host, loader, preXf);
ch.Trace("Binding label and features columns");
IDataView pipe = loader;
var stratificationColumn = GetSplitColumn(ch, loader, ref pipe);
var scorer = Args.Scorer;
var evaluator = Args.Evaluator;
Func <IDataView> validDataCreator = null;
if (Args.ValidationFile != null)
{
validDataCreator =
() =>
{
// Fork the command.
var impl = new CrossValidationCommand(this);
return(impl.CreateRawLoader(dataFile: Args.ValidationFile));
};
}
FoldHelper fold = new FoldHelper(Host, RegistrationName, pipe, stratificationColumn,
Args, CreateRoleMappedData, ApplyAllTransformsToData, scorer, evaluator,
validDataCreator, ApplyAllTransformsToData, inputPredictor, cmd, loader, !string.IsNullOrEmpty(Args.OutputDataFile));
var tasks = fold.GetCrossValidationTasks();
var eval = evaluator?.CreateComponent(Host) ??
EvaluateUtils.GetEvaluator(Host, tasks[0].Result.ScoreSchema);
// Print confusion matrix and fold results for each fold.
for (int i = 0; i < tasks.Length; i++)
{
var dict = tasks[i].Result.Metrics;
MetricWriter.PrintWarnings(ch, dict);
eval.PrintFoldResults(ch, dict);
}
// Print the overall results.
if (!TryGetOverallMetrics(tasks.Select(t => t.Result.Metrics).ToArray(), out var overallList))
{
throw ch.Except("No overall metrics found");
}
var overall = eval.GetOverallResults(overallList.ToArray());
MetricWriter.PrintOverallMetrics(Host, ch, Args.SummaryFilename, overall, Args.NumFolds);
eval.PrintAdditionalMetrics(ch, tasks.Select(t => t.Result.Metrics).ToArray());
Dictionary <string, IDataView>[] metricValues = tasks.Select(t => t.Result.Metrics).ToArray();
SendTelemetryMetric(metricValues);
// Save the per-instance results.
if (!string.IsNullOrWhiteSpace(Args.OutputDataFile))
{
var perInstance = EvaluateUtils.ConcatenatePerInstanceDataViews(Host, eval, Args.CollateMetrics,
Args.OutputExampleFoldIndex, tasks.Select(t => t.Result.PerInstanceResults).ToArray(), out var variableSizeVectorColumnNames);
if (variableSizeVectorColumnNames.Length > 0)
{
ch.Warning("Detected columns of variable length: {0}. Consider setting collateMetrics- for meaningful per-Folds results.",
string.Join(", ", variableSizeVectorColumnNames));
}
if (Args.CollateMetrics)
{
ch.Assert(perInstance.Length == 1);
MetricWriter.SavePerInstance(Host, ch, Args.OutputDataFile, perInstance[0]);
}
else
{
int i = 0;
foreach (var idv in perInstance)
{
MetricWriter.SavePerInstance(Host, ch, ConstructPerFoldName(Args.OutputDataFile, i), idv);
i++;
}
}
}
}
// Factory method for SignatureDataTransform.
private static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register("Tree Featurizer Transform");
host.CheckValue(args, nameof(args));
host.CheckValue(input, nameof(input));
host.CheckUserArg(!string.IsNullOrWhiteSpace(args.TrainedModelFile) || args.Trainer != null, nameof(args.TrainedModelFile),
"Please specify either a trainer or an input model file.");
host.CheckUserArg(!string.IsNullOrEmpty(args.FeatureColumn), nameof(args.FeatureColumn), "Transform needs an input features column");
IDataTransform xf;
using (var ch = host.Start("Create Tree Ensemble Scorer"))
{
var scorerArgs = new TreeEnsembleFeaturizerBindableMapper.Arguments()
{
Suffix = args.Suffix
};
if (!string.IsNullOrWhiteSpace(args.TrainedModelFile))
{
if (args.Trainer != null)
{
ch.Warning("Both an input model and a trainer were specified. Using the model file.");
}
ch.Trace("Loading model");
IPredictor predictor;
using (Stream strm = new FileStream(args.TrainedModelFile, FileMode.Open, FileAccess.Read))
using (var rep = RepositoryReader.Open(strm, ch))
ModelLoadContext.LoadModel <IPredictor, SignatureLoadModel>(host, out predictor, rep, ModelFileUtils.DirPredictor);
ch.Trace("Creating scorer");
var data = TrainAndScoreTransformer.CreateDataFromArgs(ch, input, args);
Contracts.Assert(data.Schema.Feature.HasValue);
// Make sure that the given predictor has the correct number of input features.
if (predictor is CalibratedPredictorBase)
{
predictor = ((CalibratedPredictorBase)predictor).SubPredictor;
}
// Predictor should be a TreeEnsembleModelParameters, which implements IValueMapper, so this should
// be non-null.
var vm = predictor as IValueMapper;
ch.CheckUserArg(vm != null, nameof(args.TrainedModelFile), "Predictor in model file does not have compatible type");
if (vm.InputType.VectorSize != data.Schema.Feature.Value.Type.VectorSize)
{
throw ch.ExceptUserArg(nameof(args.TrainedModelFile),
"Predictor in model file expects {0} features, but data has {1} features",
vm.InputType.VectorSize, data.Schema.Feature.Value.Type.VectorSize);
}
ISchemaBindableMapper bindable = new TreeEnsembleFeaturizerBindableMapper(env, scorerArgs, predictor);
var bound = bindable.Bind(env, data.Schema);
xf = new GenericScorer(env, scorerArgs, input, bound, data.Schema);
}
else
{
ch.AssertValue(args.Trainer);
ch.Trace("Creating TrainAndScoreTransform");
var trainScoreArgs = new TrainAndScoreTransformer.Arguments();
args.CopyTo(trainScoreArgs);
trainScoreArgs.Trainer = args.Trainer;
trainScoreArgs.Scorer = ComponentFactoryUtils.CreateFromFunction <IDataView, ISchemaBoundMapper, RoleMappedSchema, IDataScorerTransform>(
(e, data, mapper, trainSchema) => Create(e, scorerArgs, data, mapper, trainSchema));
var mapperFactory = ComponentFactoryUtils.CreateFromFunction <IPredictor, ISchemaBindableMapper>(
(e, predictor) => new TreeEnsembleFeaturizerBindableMapper(e, scorerArgs, predictor));
var labelInput = AppendLabelTransform(host, ch, input, trainScoreArgs.LabelColumn, args.LabelPermutationSeed);
var scoreXf = TrainAndScoreTransformer.Create(host, trainScoreArgs, labelInput, mapperFactory);
if (input == labelInput)
{
return(scoreXf);
}
return((IDataTransform)ApplyTransformUtils.ApplyAllTransformsToData(host, scoreXf, input, labelInput));
}
}
return(xf);
}
public ParameterMixingCalibratedPredictor TrainKMeansAndLR()
{
using (var env = new ConsoleEnvironment(seed: 1))
{
// Pipeline
var loader = TextLoader.ReadFile(env,
new TextLoader.Arguments()
{
HasHeader = true,
Separator = ",",
Column = new[] {
new TextLoader.Column("Label", DataKind.R4, 14),
new TextLoader.Column("CatFeatures", DataKind.TX,
new [] {
new TextLoader.Range()
{
Min = 1, Max = 1
},
new TextLoader.Range()
{
Min = 3, Max = 3
},
new TextLoader.Range()
{
Min = 5, Max = 9
},
new TextLoader.Range()
{
Min = 13, Max = 13
}
}),
new TextLoader.Column("NumFeatures", DataKind.R4,
new [] {
new TextLoader.Range()
{
Min = 0, Max = 0
},
new TextLoader.Range()
{
Min = 2, Max = 2
},
new TextLoader.Range()
{
Min = 4, Max = 4
},
new TextLoader.Range()
{
Min = 10, Max = 12
}
})
}
}, new MultiFileSource(_dataPath));
IDataView trans = new CategoricalEstimator(env, "CatFeatures").Fit(loader).Transform(loader);
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "NumFeatures");
trans = new ConcatTransform(env, "Features", "NumFeatures", "CatFeatures").Transform(trans);
trans = TrainAndScoreTransform.Create(env, new TrainAndScoreTransform.Arguments
{
Trainer = ComponentFactoryUtils.CreateFromFunction(host =>
new KMeansPlusPlusTrainer(host, "Features", advancedSettings: s =>
{
s.K = 100;
})),
FeatureColumn = "Features"
}, trans);
trans = new ConcatTransform(env, "Features", "Features", "Score").Transform(trans);
// Train
var trainer = new LogisticRegression(env, "Features", "Label", advancedSettings: args => { args.EnforceNonNegativity = true; args.OptTol = 1e-3f; });
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
return(trainer.Train(trainRoles));
}
}
public void TestSimpleSweeperAsync()
{
var random = new Random(42);
var env = new MLContext(42);
const int sweeps = 100;
var sweeper = new SimpleAsyncSweeper(env, new SweeperBase.ArgumentsBase
{
SweptParameters = new IComponentFactory <IValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 1000, LogBase = true
}))
}
});
var paramSets = new List <ParameterSet>();
for (int i = 0; i < sweeps; i++)
{
var task = sweeper.Propose();
Assert.True(task.IsCompleted);
paramSets.Add(task.Result.ParameterSet);
var result = new RunResult(task.Result.ParameterSet, random.NextDouble(), true);
sweeper.Update(task.Result.Id, result);
}
Assert.Equal(sweeps, paramSets.Count);
CheckAsyncSweeperResult(paramSets);
// Test consumption without ever calling Update.
var gridArgs = new RandomGridSweeper.Arguments();
gridArgs.SweptParameters = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 100, LogBase = true
}))
};
var gridSweeper = new SimpleAsyncSweeper(env, gridArgs);
paramSets.Clear();
for (int i = 0; i < sweeps; i++)
{
var task = gridSweeper.Propose();
Assert.True(task.IsCompleted);
paramSets.Add(task.Result.ParameterSet);
}
Assert.Equal(sweeps, paramSets.Count);
CheckAsyncSweeperResult(paramSets);
}
public Arguments()
{
BasePredictorType = ComponentFactoryUtils.CreateFromFunction(
env => new FastTreeRegressionTrainer(env, DefaultColumnNames.Label, DefaultColumnNames.Features));
}
public void TestDeterministicSweeperAsync()
{
var random = new Random(42);
var env = new MLContext(42);
var args = new DeterministicSweeperAsync.Arguments();
args.BatchSize = 5;
args.Relaxation = args.BatchSize - 1;
args.Sweeper = ComponentFactoryUtils.CreateFromFunction(
environ => new SmacSweeper(environ,
new SmacSweeper.Arguments()
{
SweptParameters = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
t => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
t => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 1000, LogBase = true
}))
}
}));
var sweeper = new DeterministicSweeperAsync(env, args);
// Test single-threaded consumption.
int sweeps = 10;
var paramSets = new List <ParameterSet>();
for (int i = 0; i < sweeps; i++)
{
var task = sweeper.Propose();
Assert.True(task.IsCompleted);
paramSets.Add(task.Result.ParameterSet);
var result = new RunResult(task.Result.ParameterSet, random.NextDouble(), true);
sweeper.Update(task.Result.Id, result);
}
Assert.Equal(sweeps, paramSets.Count);
CheckAsyncSweeperResult(paramSets);
// Create two batches and test if the 2nd batch is executed after the synchronization barrier is reached.
object mlock = new object();
var tasks = new Task <ParameterSetWithId> [sweeps];
args.Relaxation = args.Relaxation - 1;
sweeper = new DeterministicSweeperAsync(env, args);
paramSets.Clear();
var results = new List <KeyValuePair <int, IRunResult> >();
for (int i = 0; i < args.BatchSize; i++)
{
var task = sweeper.Propose();
Assert.True(task.IsCompleted);
tasks[i] = task;
if (task.Result == null)
{
continue;
}
results.Add(new KeyValuePair <int, IRunResult>(task.Result.Id, new RunResult(task.Result.ParameterSet, 0.42, true)));
}
// Register consumers for the 2nd batch. Those consumers will await until at least one run
// in the previous batch has been posted to the sweeper.
for (int i = args.BatchSize; i < 2 * args.BatchSize; i++)
{
var task = sweeper.Propose();
Assert.False(task.IsCompleted);
tasks[i] = task;
}
// Call update to unblock the 2nd batch.
foreach (var run in results)
{
sweeper.Update(run.Key, run.Value);
}
Task.WaitAll(tasks);
tasks.All(t => t.IsCompleted);
}
public void TestDeterministicSweeperAsyncParallel()
{
var random = new Random(42);
var env = new MLContext(42);
const int batchSize = 5;
const int sweeps = 20;
var paramSets = new List <ParameterSet>();
var args = new DeterministicSweeperAsync.Arguments();
args.BatchSize = batchSize;
args.Relaxation = batchSize - 2;
args.Sweeper = ComponentFactoryUtils.CreateFromFunction(
environ => new SmacSweeper(environ,
new SmacSweeper.Arguments()
{
SweptParameters = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
t => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
t => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 1000, LogBase = true
}))
}
}));
var sweeper = new DeterministicSweeperAsync(env, args);
var mlock = new object();
var options = new ParallelOptions();
options.MaxDegreeOfParallelism = 4;
// Sleep randomly to simulate doing work.
int[] sleeps = new int[sweeps];
for (int i = 0; i < sleeps.Length; i++)
{
sleeps[i] = random.Next(10, 100);
}
var r = Parallel.For(0, sweeps, options, (int i) =>
{
var task = sweeper.Propose();
task.Wait();
Assert.Equal(TaskStatus.RanToCompletion, task.Status);
var paramWithId = task.Result;
if (paramWithId == null)
{
return;
}
Thread.Sleep(sleeps[i]);
var result = new RunResult(paramWithId.ParameterSet, 0.42, true);
sweeper.Update(paramWithId.Id, result);
lock (mlock)
paramSets.Add(paramWithId.ParameterSet);
});
Assert.True(paramSets.Count <= sweeps);
CheckAsyncSweeperResult(paramSets);
}
public async Task TestNelderMeadSweeperAsync()
{
var random = new Random(42);
var env = new MLContext(42);
const int batchSize = 5;
const int sweeps = 40;
var paramSets = new List <ParameterSet>();
var args = new DeterministicSweeperAsync.Arguments();
args.BatchSize = batchSize;
args.Relaxation = 0;
args.Sweeper = ComponentFactoryUtils.CreateFromFunction(
environ =>
{
var param = new IComponentFactory <INumericValueGenerator>[] {
ComponentFactoryUtils.CreateFromFunction(
innerEnviron => new FloatValueGenerator(new FloatParamArguments()
{
Name = "foo", Min = 1, Max = 5
})),
ComponentFactoryUtils.CreateFromFunction(
innerEnviron => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 1, Max = 1000, LogBase = true
}))
};
var nelderMeadSweeperArgs = new NelderMeadSweeper.Arguments()
{
SweptParameters = param,
FirstBatchSweeper = ComponentFactoryUtils.CreateFromFunction <IValueGenerator[], ISweeper>(
(firstBatchSweeperEnviron, firstBatchSweeperArgs) =>
new RandomGridSweeper(environ, new RandomGridSweeper.Arguments()
{
SweptParameters = param
}))
};
return(new NelderMeadSweeper(environ, nelderMeadSweeperArgs));
}
);
var sweeper = new DeterministicSweeperAsync(env, args);
var mlock = new object();
double[] metrics = new double[sweeps];
for (int i = 0; i < metrics.Length; i++)
{
metrics[i] = random.NextDouble();
}
for (int i = 0; i < sweeps; i++)
{
var paramWithId = await sweeper.Propose();
if (paramWithId == null)
{
return;
}
var result = new RunResult(paramWithId.ParameterSet, metrics[i], true);
sweeper.Update(paramWithId.Id, result);
lock (mlock)
paramSets.Add(paramWithId.ParameterSet);
}
Assert.True(paramSets.Count <= sweeps);
CheckAsyncSweeperResult(paramSets);
}
public void TestRandomGridSweeper()
{
var env = new MLContext(42);
var args = new RandomGridSweeper.Arguments()
{
SweptParameters = new[] {
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "foo", Min = 10, Max = 20, NumSteps = 3
})),
ComponentFactoryUtils.CreateFromFunction(
environ => new LongValueGenerator(new LongParamArguments()
{
Name = "bar", Min = 100, Max = 10000, LogBase = true, StepSize = 10
}))
}
};
var sweeper = new RandomGridSweeper(env, args);
var initialList = sweeper.ProposeSweeps(5, new List <RunResult>());
Assert.Equal(5, initialList.Length);
var gridPoint = new bool[3][] {
new bool[3],
new bool[3],
new bool[3]
};
int i = 0;
int j = 0;
foreach (var parameterSet in initialList)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = long.Parse(parameterValue.ValueText);
Assert.True(val == 10 || val == 15 || val == 20);
i = (val == 10) ? 0 : (val == 15) ? 1 : 2;
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.True(val == 100 || val == 1000 || val == 10000);
j = (val == 100) ? 0 : (val == 1000) ? 1 : 2;
}
else
{
Assert.True(false, "Wrong parameter");
}
}
Assert.False(gridPoint[i][j]);
gridPoint[i][j] = true;
}
var nextList = sweeper.ProposeSweeps(5, initialList.Select(p => new RunResult(p)));
Assert.Equal(4, nextList.Length);
foreach (var parameterSet in nextList)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = long.Parse(parameterValue.ValueText);
Assert.True(val == 10 || val == 15 || val == 20);
i = (val == 10) ? 0 : (val == 15) ? 1 : 2;
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.True(val == 100 || val == 1000 || val == 10000);
j = (val == 100) ? 0 : (val == 1000) ? 1 : 2;
}
else
{
Assert.True(false, "Wrong parameter");
}
}
Assert.False(gridPoint[i][j]);
gridPoint[i][j] = true;
}
gridPoint = new bool[3][] {
new bool[3],
new bool[3],
new bool[3]
};
var lastList = sweeper.ProposeSweeps(10, null);
Assert.Equal(9, lastList.Length);
foreach (var parameterSet in lastList)
{
foreach (var parameterValue in parameterSet)
{
if (parameterValue.Name == "foo")
{
var val = long.Parse(parameterValue.ValueText);
Assert.True(val == 10 || val == 15 || val == 20);
i = (val == 10) ? 0 : (val == 15) ? 1 : 2;
}
else if (parameterValue.Name == "bar")
{
var val = long.Parse(parameterValue.ValueText);
Assert.True(val == 100 || val == 1000 || val == 10000);
j = (val == 100) ? 0 : (val == 1000) ? 1 : 2;
}
else
{
Assert.True(false, "Wrong parameter");
}
}
Assert.False(gridPoint[i][j]);
gridPoint[i][j] = true;
}
Assert.True(gridPoint.All(bArray => bArray.All(b => b)));
}
