private protected abstract TModel TrainCore(IChannel ch, RoleMappedData data, LinearModelParameters predictor, int weightSetCount);
protected override void CheckLabel(RoleMappedData data)
{
Contracts.AssertValue(data);
data.CheckBinaryLabel();
}
private FieldAwareFactorizationMachineModelParameters TrainCore(IChannel ch, IProgressChannel pch, RoleMappedData data,
RoleMappedData validData = null, FieldAwareFactorizationMachineModelParameters predictor = null)
{
Host.AssertValue(ch);
Host.AssertValue(pch);
data.CheckBinaryLabel();
var featureColumns = data.Schema.GetColumns(RoleMappedSchema.ColumnRole.Feature);
int fieldCount = featureColumns.Count;
int totalFeatureCount = 0;
int[] fieldColumnIndexes = new int[fieldCount];
for (int f = 0; f < fieldCount; f++)
{
var col = featureColumns[f];
Host.Assert(!col.IsHidden);
if (!(col.Type is VectorType vectorType) ||
!vectorType.IsKnownSize ||
vectorType.ItemType != NumberType.Float)
{
throw ch.ExceptParam(nameof(data), "Training feature column '{0}' must be a known-size vector of R4, but has type: {1}.", col.Name, col.Type);
}
Host.Assert(vectorType.Size > 0);
fieldColumnIndexes[f] = col.Index;
totalFeatureCount += vectorType.Size;
}
ch.Check(checked (totalFeatureCount * fieldCount * _latentDimAligned) <= Utils.ArrayMaxSize, "Latent dimension or the number of fields too large");
if (predictor != null)
{
ch.Check(predictor.FeatureCount == totalFeatureCount, "Input model's feature count mismatches training feature count");
ch.Check(predictor.LatentDim == _latentDim, "Input model's latent dimension mismatches trainer's");
}
if (validData != null)
{
validData.CheckBinaryLabel();
var validFeatureColumns = data.Schema.GetColumns(RoleMappedSchema.ColumnRole.Feature);
Host.Assert(fieldCount == validFeatureColumns.Count);
for (int f = 0; f < fieldCount; f++)
{
var featCol = featureColumns[f];
var validFeatCol = validFeatureColumns[f];
Host.Assert(featCol.Name == validFeatCol.Name);
Host.Assert(featCol.Type == validFeatCol.Type);
}
}
bool shuffle = _shuffle;
if (shuffle && !data.Data.CanShuffle)
{
ch.Warning("Training data does not support shuffling, so ignoring request to shuffle");
shuffle = false;
}
var rng = shuffle ? Host.Rand : null;
var featureGetters = new ValueGetter <VBuffer <float> > [fieldCount];
var featureBuffer = new VBuffer <float>();
var featureValueBuffer = new float[totalFeatureCount];
var featureIndexBuffer = new int[totalFeatureCount];
var featureFieldBuffer = new int[totalFeatureCount];
var latentSum = new AlignedArray(fieldCount * fieldCount * _latentDimAligned, 16);
var metricNames = new List <string>()
{
"Training-loss"
};
if (validData != null)
{
metricNames.Add("Validation-loss");
}
int iter = 0;
long exampleCount = 0;
long badExampleCount = 0;
long validBadExampleCount = 0;
double loss = 0;
double validLoss = 0;
pch.SetHeader(new ProgressHeader(metricNames.ToArray(), new string[] { "iterations", "examples" }), entry =>
{
entry.SetProgress(0, iter, _numIterations);
entry.SetProgress(1, exampleCount);
});
Func <int, bool> pred = c => fieldColumnIndexes.Contains(c) || c == data.Schema.Label.Value.Index || c == data.Schema.Weight?.Index;
InitializeTrainingState(fieldCount, totalFeatureCount, predictor, out float[] linearWeights,
out AlignedArray latentWeightsAligned, out float[] linearAccSqGrads, out AlignedArray latentAccSqGradsAligned);
// refer to Algorithm 3 in https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
while (iter++ < _numIterations)
{
using (var cursor = data.Data.GetRowCursor(pred, rng))
{
var labelGetter = RowCursorUtils.GetLabelGetter(cursor, data.Schema.Label.Value.Index);
var weightGetter = data.Schema.Weight?.Index is int weightIdx?RowCursorUtils.GetGetterAs <float>(NumberType.R4, cursor, weightIdx) : null;
for (int i = 0; i < fieldCount; i++)
{
featureGetters[i] = cursor.GetGetter <VBuffer <float> >(fieldColumnIndexes[i]);
}
loss = 0;
exampleCount = 0;
badExampleCount = 0;
while (cursor.MoveNext())
{
float label = 0;
float weight = 1;
int count = 0;
float modelResponse = 0;
labelGetter(ref label);
weightGetter?.Invoke(ref weight);
float annihilation = label - label + weight - weight;
if (!FloatUtils.IsFinite(annihilation))
{
badExampleCount++;
continue;
}
if (!FieldAwareFactorizationMachineUtils.LoadOneExampleIntoBuffer(featureGetters, featureBuffer, _norm, ref count,
featureFieldBuffer, featureIndexBuffer, featureValueBuffer))
{
badExampleCount++;
continue;
}
// refer to Algorithm 1 in [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
FieldAwareFactorizationMachineInterface.CalculateIntermediateVariables(fieldCount, _latentDimAligned, count,
featureFieldBuffer, featureIndexBuffer, featureValueBuffer, linearWeights, latentWeightsAligned, latentSum, ref modelResponse);
var slope = CalculateLossSlope(label, modelResponse);
// refer to Algorithm 2 in [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
FieldAwareFactorizationMachineInterface.CalculateGradientAndUpdate(_lambdaLinear, _lambdaLatent, _learningRate, fieldCount, _latentDimAligned, weight, count,
featureFieldBuffer, featureIndexBuffer, featureValueBuffer, latentSum, slope, linearWeights, latentWeightsAligned, linearAccSqGrads, latentAccSqGradsAligned);
loss += weight * CalculateLoss(label, modelResponse);
exampleCount++;
}
loss /= exampleCount;
}
if (_verbose)
{
if (validData == null)
{
pch.Checkpoint(loss, iter, exampleCount);
}
else
{
validLoss = CalculateAvgLoss(ch, validData, _norm, linearWeights, latentWeightsAligned, _latentDimAligned, latentSum,
featureFieldBuffer, featureIndexBuffer, featureValueBuffer, featureBuffer, ref validBadExampleCount);
pch.Checkpoint(loss, validLoss, iter, exampleCount);
}
}
}
if (badExampleCount != 0)
{
ch.Warning($"Skipped {badExampleCount} examples with bad label/weight/features in training set");
}
if (validBadExampleCount != 0)
{
ch.Warning($"Skipped {validBadExampleCount} examples with bad label/weight/features in validation set");
}
return(new FieldAwareFactorizationMachineModelParameters(Host, _norm, fieldCount, totalFeatureCount, _latentDim, linearWeights, latentWeightsAligned));
}
private protected IDataView MapLabelsCore <T>(ColumnType type, InPredicate <T> equalsTarget, RoleMappedData data)
{
Host.AssertValue(type);
Host.Assert(type.RawType == typeof(T));
Host.AssertValue(equalsTarget);
Host.AssertValue(data);
Host.Assert(data.Schema.Label.HasValue);
var lab = data.Schema.Label.Value;
InPredicate <T> isMissing;
if (!Args.ImputeMissingLabelsAsNegative && Conversions.Instance.TryGetIsNAPredicate(type, out isMissing))
{
return(LambdaColumnMapper.Create(Host, "Label mapper", data.Data,
lab.Name, lab.Name, type, NumberType.Float,
(in T src, ref float dst) =>
dst = equalsTarget(in src) ? 1 : (isMissing(in src) ? float.NaN : default(float))));
}
return(LambdaColumnMapper.Create(Host, "Label mapper", data.Data,
lab.Name, lab.Name, type, NumberType.Float,
(in T src, ref float dst) =>
dst = equalsTarget(in src) ? 1 : default(float)));
}
private static void GetPipeline(IHostEnvironment env, InputBase input, out IDataView startingData, out RoleMappedData transformedData)
{
Contracts.AssertValue(env);
env.AssertValue(input);
env.AssertNonEmpty(input.Models);
ISchema inputSchema = null;
startingData = null;
transformedData = null;
byte[][] transformedDataSerialized = null;
string[] transformedDataZipEntryNames = null;
for (int i = 0; i < input.Models.Length; i++)
{
var model = input.Models[i];
var inputData = new EmptyDataView(env, model.TransformModel.InputSchema);
model.PrepareData(env, inputData, out RoleMappedData transformedDataCur, out IPredictor pred);
if (inputSchema == null)
{
env.Assert(i == 0);
inputSchema = model.TransformModel.InputSchema;
startingData = inputData;
transformedData = transformedDataCur;
}
else if (input.ValidatePipelines)
{
using (var ch = env.Start("Validating pipeline"))
{
if (transformedDataSerialized == null)
{
ch.Assert(transformedDataZipEntryNames == null);
SerializeRoleMappedData(env, ch, transformedData, out transformedDataSerialized,
out transformedDataZipEntryNames);
}
CheckSamePipeline(env, ch, transformedDataCur, transformedDataSerialized, transformedDataZipEntryNames);
ch.Done();
}
}
}
}
private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearModelParameters predictor, int weightSetCount)
{
int numFeatures = data.Schema.Feature.Value.Type.GetVectorSize();
var cursorFactory = new FloatLabelCursor.Factory(data, CursOpt.Label | CursOpt.Features);
int numThreads = 1;
ch.CheckUserArg(numThreads > 0, nameof(_options.NumberOfThreads),
"The number of threads must be either null or a positive integer.");
var positiveInstanceWeight = _options.PositiveInstanceWeight;
VBuffer <float> weights = default;
float bias = 0.0f;
if (predictor != null)
{
predictor.GetFeatureWeights(ref weights);
VBufferUtils.Densify(ref weights);
bias = predictor.Bias;
}
else
{
weights = VBufferUtils.CreateDense <float>(numFeatures);
}
var weightsEditor = VBufferEditor.CreateFromBuffer(ref weights);
// Reference: Parasail. SymSGD.
bool tuneLR = _options.LearningRate == null;
var lr = _options.LearningRate ?? 1.0f;
bool tuneNumLocIter = (_options.UpdateFrequency == null);
var numLocIter = _options.UpdateFrequency ?? 1;
var l2Const = _options.L2Regularization;
var piw = _options.PositiveInstanceWeight;
// This is state of the learner that is shared with the native code.
State state = new State();
GCHandle stateGCHandle = default;
try
{
stateGCHandle = GCHandle.Alloc(state, GCHandleType.Pinned);
state.TotalInstancesProcessed = 0;
using (InputDataManager inputDataManager = new InputDataManager(this, cursorFactory, ch))
{
bool shouldInitialize = true;
using (var pch = Host.StartProgressChannel("Preprocessing"))
inputDataManager.LoadAsMuchAsPossible();
int iter = 0;
if (inputDataManager.IsFullyLoaded)
{
ch.Info("Data fully loaded into memory.");
}
using (var pch = Host.StartProgressChannel("Training"))
{
if (inputDataManager.IsFullyLoaded)
{
pch.SetHeader(new ProgressHeader(new[] { "iterations" }),
entry => entry.SetProgress(0, state.PassIteration, _options.NumberOfIterations));
// If fully loaded, call the SymSGDNative and do not come back until learned for all iterations.
Native.LearnAll(inputDataManager, tuneLR, ref lr, l2Const, piw, weightsEditor.Values, ref bias, numFeatures,
_options.NumberOfIterations, numThreads, tuneNumLocIter, ref numLocIter, _options.Tolerance, _options.Shuffle, shouldInitialize,
stateGCHandle, ch.Info);
shouldInitialize = false;
}
else
{
pch.SetHeader(new ProgressHeader(new[] { "iterations" }),
entry => entry.SetProgress(0, iter, _options.NumberOfIterations));
// Since we loaded data in batch sizes, multiple passes over the loaded data is feasible.
int numPassesForABatch = inputDataManager.Count / 10000;
while (iter < _options.NumberOfIterations)
{
// We want to train on the final passes thoroughly (without learning on the same batch multiple times)
// This is for fine tuning the AUC. Experimentally, we found that 1 or 2 passes is enough
int numFinalPassesToTrainThoroughly = 2;
// We also do not want to learn for more passes than what the user asked
int numPassesForThisBatch = Math.Min(numPassesForABatch, _options.NumberOfIterations - iter - numFinalPassesToTrainThoroughly);
// If all of this leaves us with 0 passes, then set numPassesForThisBatch to 1
numPassesForThisBatch = Math.Max(1, numPassesForThisBatch);
state.PassIteration = iter;
Native.LearnAll(inputDataManager, tuneLR, ref lr, l2Const, piw, weightsEditor.Values, ref bias, numFeatures,
numPassesForThisBatch, numThreads, tuneNumLocIter, ref numLocIter, _options.Tolerance, _options.Shuffle, shouldInitialize,
stateGCHandle, ch.Info);
shouldInitialize = false;
// Check if we are done with going through the data
if (inputDataManager.FinishedTheLoad)
{
iter += numPassesForThisBatch;
// Check if more passes are left
if (iter < _options.NumberOfIterations)
{
inputDataManager.RestartLoading(_options.Shuffle, Host);
}
}
// If more passes are left, load as much as possible
if (iter < _options.NumberOfIterations)
{
inputDataManager.LoadAsMuchAsPossible();
}
}
}
// Maps back the dense features that are mislocated
if (numThreads > 1)
{
Native.MapBackWeightVector(weightsEditor.Values, stateGCHandle);
}
Native.DeallocateSequentially(stateGCHandle);
}
}
}
finally
{
if (stateGCHandle.IsAllocated)
{
stateGCHandle.Free();
}
}
return(CreatePredictor(weights, bias));
}
private protected override void CheckLabel(RoleMappedData examples, out int weightSetCount)
{
examples.CheckMulticlassLabel(out weightSetCount);
}
private protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups)
{
Host.AssertValue(ch);
GbmOptions["objective"] = "lambdarank";
ch.CheckValue(groups, nameof(groups));
// Only output one ndcg score.
GbmOptions["eval_at"] = "5";
}
/// <summary>
/// Constructor, given a training set and optional other arguments.
/// </summary>
/// <param name="trainingSet">Will set <see cref="TrainingSet"/> to this value. This must be specified</param>
/// <param name="validationSet">Will set <see cref="ValidationSet"/> to this value if specified</param>
/// <param name="testSet">Will set <see cref="TestSet"/> to this value if specified</param>
/// <param name="initialPredictor">Will set <see cref="InitialPredictor"/> to this value if specified</param>
public TrainContext(RoleMappedData trainingSet, RoleMappedData validationSet = null, RoleMappedData testSet = null, IPredictor initialPredictor = null)
{
Contracts.CheckValue(trainingSet, nameof(trainingSet));
Contracts.CheckValueOrNull(validationSet);
Contracts.CheckValueOrNull(initialPredictor);
// REVIEW: Should there be code here to ensure that the role mappings between the two are compatible?
// That is, all the role mappings are the same and the columns between them have identical types?
TrainingSet = trainingSet;
ValidationSet = validationSet;
TestSet = testSet;
InitialPredictor = initialPredictor;
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // x86 fails with "An attempt was made to load a program with an incorrect format."
void TestOldSavingAndLoading()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var modelFile = "squeezenet/00000001/model.onnx";
var samplevector = GetSampleArrayData();
var dataView = ComponentCreation.CreateDataView(Env,
new TestData[] {
new TestData()
{
data_0 = samplevector
}
});
var inputNames = new[] { "data_0" };
var outputNames = new[] { "softmaxout_1" };
var est = new OnnxScoringEstimator(Env, modelFile, inputNames, outputNames);
var transformer = est.Fit(dataView);
var result = transformer.Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(Env, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(Env, dataView, ms);
loadedView.Schema.TryGetColumnIndex(outputNames[0], out int softMaxOut1);
using (var cursor = loadedView.GetRowCursor(col => col == softMaxOut1))
{
VBuffer <float> softMaxValue = default;
var softMaxGetter = cursor.GetGetter <VBuffer <float> >(softMaxOut1);
float sum = 0f;
int i = 0;
while (cursor.MoveNext())
{
softMaxGetter(ref softMaxValue);
var values = softMaxValue.DenseValues();
foreach (var val in values)
{
sum += val;
if (i == 0)
{
Assert.InRange(val, 0.00004, 0.00005);
}
if (i == 1)
{
Assert.InRange(val, 0.003844, 0.003845);
}
if (i == 999)
{
Assert.InRange(val, 0.0029566, 0.0029567);
}
i++;
}
}
Assert.InRange(sum, 1.0, 1.00001);
}
}
}
private PcaPredictor TrainCore(IChannel ch, RoleMappedData data, int dimension)
{
Host.AssertValue(ch);
ch.AssertValue(data);
if (_rank > dimension)
{
throw ch.Except("Rank ({0}) cannot be larger than the original dimension ({1})", _rank, dimension);
}
int oversampledRank = Math.Min(_rank + _oversampling, dimension);
//exact: (size of the 2 big matrices + other minor allocations) / (2^30)
Double memoryUsageEstimate = 2.0 * dimension * oversampledRank * sizeof(Float) / 1e9;
if (memoryUsageEstimate > 2)
{
ch.Info("Estimate memory usage: {0:G2} GB. If running out of memory, reduce rank and oversampling factor.", memoryUsageEstimate);
}
var y = Zeros(oversampledRank, dimension);
var mean = _center ? VBufferUtils.CreateDense <Float>(dimension) : VBufferUtils.CreateEmpty <Float>(dimension);
var omega = GaussianMatrix(oversampledRank, dimension, _seed);
var cursorFactory = new FeatureFloatVectorCursor.Factory(data, CursOpt.Features | CursOpt.Weight);
long numBad;
Project(Host, cursorFactory, ref mean, omega, y, out numBad);
if (numBad > 0)
{
ch.Warning("Skipped {0} instances with missing features/weights during training", numBad);
}
//Orthonormalize Y in-place using stabilized Gram Schmidt algorithm.
//Ref: https://en.wikipedia.org/wiki/Gram-Schmidt#Algorithm
for (var i = 0; i < oversampledRank; ++i)
{
var v = y[i];
VectorUtils.ScaleBy(ref v, 1 / VectorUtils.Norm(y[i]));
// Make the next vectors in the queue orthogonal to the orthonormalized vectors.
for (var j = i + 1; j < oversampledRank; ++j) //subtract the projection of y[j] on v.
{
VectorUtils.AddMult(ref v, -VectorUtils.DotProduct(ref v, ref y[j]), ref y[j]);
}
}
var q = y; // q in QR decomposition.
var b = omega; // reuse the memory allocated by Omega.
Project(Host, cursorFactory, ref mean, q, b, out numBad);
//Compute B2 = B' * B
var b2 = new Float[oversampledRank * oversampledRank];
for (var i = 0; i < oversampledRank; ++i)
{
for (var j = i; j < oversampledRank; ++j)
{
b2[i * oversampledRank + j] = b2[j * oversampledRank + i] = VectorUtils.DotProduct(ref b[i], ref b[j]);
}
}
Float[] smallEigenvalues;// eigenvectors and eigenvalues of the small matrix B2.
Float[] smallEigenvectors;
EigenUtils.EigenDecomposition(b2, out smallEigenvalues, out smallEigenvectors);
PostProcess(b, smallEigenvalues, smallEigenvectors, dimension, oversampledRank);
return(new PcaPredictor(Host, _rank, b, ref mean));
}
protected override void CheckLabel(RoleMappedData examples)
{
examples.CheckRegressionLabel();
}
private protected override void CheckLabel(RoleMappedData data)
{
Contracts.AssertValue(data);
data.CheckRegressionLabel();
}
protected override void CheckLabel(RoleMappedData examples, out int weightSetCount)
{
examples.CheckRegressionLabel();
weightSetCount = 1;
}
private TPredictor TrainCore(IChannel ch, RoleMappedData data)
{
Host.AssertValue(ch);
ch.AssertValue(data);
// 1. Subset Selection
var stackingTrainer = Combiner as IStackingTrainer <TOutput>;
//REVIEW: Implement stacking for Batch mode.
ch.CheckUserArg(stackingTrainer == null || Args.BatchSize <= 0, nameof(Args.BatchSize), "Stacking works only with Non-batch mode");
var validationDataSetProportion = SubModelSelector.ValidationDatasetProportion;
if (stackingTrainer != null)
{
validationDataSetProportion = Math.Max(validationDataSetProportion, stackingTrainer.ValidationDatasetProportion);
}
var needMetrics = Args.ShowMetrics || Combiner is IWeightedAverager;
var models = new List <FeatureSubsetModel <TOutput> >();
_subsetSelector.Initialize(data, NumModels, Args.BatchSize, validationDataSetProportion);
int batchNumber = 1;
foreach (var batch in _subsetSelector.GetBatches(Host.Rand))
{
// 2. Core train
ch.Info("Training {0} learners for the batch {1}", Trainers.Length, batchNumber++);
var batchModels = new FeatureSubsetModel <TOutput> [Trainers.Length];
Parallel.ForEach(_subsetSelector.GetSubsets(batch, Host.Rand),
new ParallelOptions()
{
MaxDegreeOfParallelism = Args.TrainParallel ? -1 : 1
},
(subset, state, index) =>
{
ch.Info("Beginning training model {0} of {1}", index + 1, Trainers.Length);
Stopwatch sw = Stopwatch.StartNew();
try
{
if (EnsureMinimumFeaturesSelected(subset))
{
var model = new FeatureSubsetModel <TOutput>(
Trainers[(int)index].Train(subset.Data),
subset.SelectedFeatures,
null);
SubModelSelector.CalculateMetrics(model, _subsetSelector, subset, batch, needMetrics);
batchModels[(int)index] = model;
}
}
catch (Exception ex)
{
ch.Assert(batchModels[(int)index] == null);
ch.Warning(ex.Sensitivity(), "Trainer {0} of {1} was not learned properly due to the exception '{2}' and will not be added to models.",
index + 1, Trainers.Length, ex.Message);
}
ch.Info("Trainer {0} of {1} finished in {2}", index + 1, Trainers.Length, sw.Elapsed);
});
var modelsList = batchModels.Where(m => m != null).ToList();
if (Args.ShowMetrics)
{
PrintMetrics(ch, modelsList);
}
modelsList = SubModelSelector.Prune(modelsList).ToList();
if (stackingTrainer != null)
{
stackingTrainer.Train(modelsList, _subsetSelector.GetTestData(null, batch), Host);
}
models.AddRange(modelsList);
int modelSize = Utils.Size(models);
if (modelSize < Utils.Size(Trainers))
{
ch.Warning("{0} of {1} trainings failed.", Utils.Size(Trainers) - modelSize, Utils.Size(Trainers));
}
ch.Check(modelSize > 0, "Ensemble training resulted in no valid models.");
}
return(CreatePredictor(models));
}
private protected override void CheckLabels(RoleMappedData data)
{
data.CheckRegressionLabel();
}
protected override void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels)
{
// Only initialize one time.
if (_numClass < 0)
{
float minLabel = float.MaxValue;
float maxLabel = float.MinValue;
bool hasNaNLabel = false;
foreach (var label in labels)
{
if (float.IsNaN(label))
{
hasNaNLabel = true;
}
else
{
minLabel = Math.Min(minLabel, label);
maxLabel = Math.Max(maxLabel, label);
}
}
ch.CheckParam(minLabel >= 0, nameof(data), "min label cannot be negative");
if (maxLabel >= _maxNumClass)
{
throw ch.ExceptParam(nameof(data), $"max label cannot exceed {_maxNumClass}");
}
if (data.Schema.Label.Type.IsKey)
{
ch.Check(data.Schema.Label.Type.AsKey.Contiguous, "label value should be contiguous");
if (hasNaNLabel)
{
_numClass = data.Schema.Label.Type.AsKey.Count + 1;
}
else
{
_numClass = data.Schema.Label.Type.AsKey.Count;
}
_tlcNumClass = data.Schema.Label.Type.AsKey.Count;
}
else
{
if (hasNaNLabel)
{
_numClass = (int)maxLabel + 2;
}
else
{
_numClass = (int)maxLabel + 1;
}
_tlcNumClass = (int)maxLabel + 1;
}
}
float defaultLabel = _numClass - 1;
for (int i = 0; i < labels.Length; ++i)
{
if (float.IsNaN(labels[i]))
{
labels[i] = defaultLabel;
}
}
}
public Subset(RoleMappedData data, BitArray features = null)
{
Contracts.AssertValue(data);
Data = data;
SelectedFeatures = features;
}
private void CheckLabel(RoleMappedData examples, out int weightSetCount)
{
examples.CheckBinaryLabel();
weightSetCount = 1;
}
public static TOut Train <TArg, TOut>(IHost host, TArg input,
Func <ITrainer> createTrainer,
Func <string> getLabel = null,
Func <string> getWeight = null,
Func <string> getGroup = null,
Func <string> getName = null,
Func <IEnumerable <KeyValuePair <RoleMappedSchema.ColumnRole, string> > > getCustom = null,
ICalibratorTrainerFactory calibrator = null,
int maxCalibrationExamples = 0)
where TArg : LearnerInputBase
where TOut : CommonOutputs.TrainerOutput, new()
{
using (var ch = host.Start("Training"))
{
var schema = input.TrainingData.Schema;
var feature = FindColumn(ch, schema, input.FeatureColumn);
var label = getLabel?.Invoke();
var weight = getWeight?.Invoke();
var group = getGroup?.Invoke();
var name = getName?.Invoke();
var custom = getCustom?.Invoke();
var trainer = createTrainer();
IDataView view = input.TrainingData;
TrainUtils.AddNormalizerIfNeeded(host, ch, trainer, ref view, feature, input.NormalizeFeatures);
ch.Trace("Binding columns");
var roleMappedData = new RoleMappedData(view, label, feature, group, weight, name, custom);
RoleMappedData cachedRoleMappedData = roleMappedData;
Cache.CachingType?cachingType = null;
switch (input.Caching)
{
case CachingOptions.Memory:
{
cachingType = Cache.CachingType.Memory;
break;
}
case CachingOptions.Disk:
{
cachingType = Cache.CachingType.Disk;
break;
}
case CachingOptions.Auto:
{
// REVIEW: we should switch to hybrid caching in future.
if (!(input.TrainingData is BinaryLoader) && trainer.Info.WantCaching)
{
// default to Memory so mml is on par with maml
cachingType = Cache.CachingType.Memory;
}
break;
}
case CachingOptions.None:
break;
default:
throw ch.ExceptParam(nameof(input.Caching), "Unknown option for caching: '{0}'", input.Caching);
}
if (cachingType.HasValue)
{
var cacheView = Cache.CacheData(host, new Cache.CacheInput()
{
Data = roleMappedData.Data,
Caching = cachingType.Value
}).OutputData;
cachedRoleMappedData = new RoleMappedData(cacheView, roleMappedData.Schema.GetColumnRoleNames());
}
var predictor = TrainUtils.Train(host, ch, cachedRoleMappedData, trainer, calibrator, maxCalibrationExamples);
return(new TOut()
{
PredictorModel = new PredictorModelImpl(host, roleMappedData, input.TrainingData, predictor)
});
}
}
private protected abstract TModel TrainCore(IChannel ch, RoleMappedData data, int count);
private protected override void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels)
{
// Only initialize one time.
if (_numClass < 0)
{
float minLabel = float.MaxValue;
float maxLabel = float.MinValue;
bool hasNaNLabel = false;
foreach (var labelColumn in labels)
{
if (float.IsNaN(labelColumn))
{
hasNaNLabel = true;
}
else
{
minLabel = Math.Min(minLabel, labelColumn);
maxLabel = Math.Max(maxLabel, labelColumn);
}
}
ch.CheckParam(minLabel >= 0, nameof(data), "min labelColumn cannot be negative");
if (maxLabel >= _maxNumClass)
{
throw ch.ExceptParam(nameof(data), $"max labelColumn cannot exceed {_maxNumClass}");
}
if (data.Schema.Label.Value.Type is KeyType keyType)
{
if (hasNaNLabel)
{
_numClass = keyType.GetCountAsInt32(Host) + 1;
}
else
{
_numClass = keyType.GetCountAsInt32(Host);
}
_tlcNumClass = keyType.GetCountAsInt32(Host);
}
else
{
if (hasNaNLabel)
{
_numClass = (int)maxLabel + 2;
}
else
{
_numClass = (int)maxLabel + 1;
}
_tlcNumClass = (int)maxLabel + 1;
}
}
float defaultLabel = _numClass - 1;
for (int i = 0; i < labels.Length; ++i)
{
if (float.IsNaN(labels[i]))
{
labels[i] = defaultLabel;
}
}
}
/// <summary>
/// This method takes a <see cref="RoleMappedData"/> as input, saves it as an in-memory <see cref="ZipArchive"/>
/// and returns two arrays indexed by the entries in the zip:
/// 1. An array of byte arrays, containing the byte sequences of each entry.
/// 2. An array of strings, containing the name of each entry.
///
/// This method is used for comparing pipelines. Its outputs can be passed to <see cref="CheckSamePipeline"/>
/// to check if this pipeline is identical to another pipeline.
/// </summary>
public static void SerializeRoleMappedData(IHostEnvironment env, IChannel ch, RoleMappedData data,
out byte[][] dataSerialized, out string[] dataZipEntryNames)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
ch.CheckValue(data, nameof(data));
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(env, ch, ms, null, data);
var zip = new ZipArchive(ms);
var entries = zip.Entries.OrderBy(e => e.FullName).ToArray();
dataSerialized = new byte[Utils.Size(entries)][];
dataZipEntryNames = new string[Utils.Size(entries)];
for (int i = 0; i < Utils.Size(entries); i++)
{
dataZipEntryNames[i] = entries[i].FullName;
dataSerialized[i] = new byte[entries[i].Length];
using (var s = entries[i].Open())
s.Read(dataSerialized[i], 0, (int)entries[i].Length);
}
}
}
private protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups)
{
Host.AssertValue(ch);
ch.Assert(PredictionKind == PredictionKind.MultiClassClassification);
ch.Assert(_numClass > 1);
Options["num_class"] = _numClass;
bool useSoftmax = false;
if (Args.UseSoftmax.HasValue)
{
useSoftmax = Args.UseSoftmax.Value;
}
else
{
if (labels.Length >= _minDataToUseSoftmax)
{
useSoftmax = true;
}
ch.Info("Auto-tuning parameters: " + nameof(Args.UseSoftmax) + " = " + useSoftmax);
}
if (useSoftmax)
{
Options["objective"] = "multiclass";
}
else
{
Options["objective"] = "multiclassova";
}
// Add default metric.
if (!Options.ContainsKey("metric"))
{
Options["metric"] = "multi_error";
}
}
private ISingleFeaturePredictionTransformer <TScalarPredictor> TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls)
{
var view = MapLabels(data, cls);
string trainerLabel = data.Schema.Label.Value.Name;
// REVIEW: In principle we could support validation sets and the like via the train context, but
// this is currently unsupported.
var transformer = trainer.Fit(view);
if (_options.UseProbabilities)
{
var calibratedModel = transformer.Model as TDistPredictor;
// REVIEW: restoring the RoleMappedData, as much as we can.
// not having the weight column on the data passed to the TrainCalibrator should be addressed.
var trainedData = new RoleMappedData(view, label: trainerLabel, feature: transformer.FeatureColumn);
if (calibratedModel == null)
{
calibratedModel = CalibratorUtils.GetCalibratedPredictor(Host, ch, Calibrator, transformer.Model, trainedData, Args.MaxCalibrationExamples) as TDistPredictor;
}
Host.Check(calibratedModel != null, "Calibrated predictor does not implement the expected interface");
return(new BinaryPredictionTransformer <TScalarPredictor>(Host, calibratedModel, trainedData.Data.Schema, transformer.FeatureColumn));
}
return(new BinaryPredictionTransformer <TScalarPredictor>(Host, transformer.Model, view.Schema, transformer.FeatureColumn));
}
protected override void CheckLabel(RoleMappedData examples)
{
examples.CheckMultiClassLabel(out _numClasses);
}
private protected abstract void CheckLabel(RoleMappedData data);
private ISingleFeaturePredictionTransformer <TDistPredictor> TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedData data, int cls1, int cls2)
{
// this should not be necessary when the legacy constructor doesn't exist, and the label column is not an optional parameter on the
// MetaMulticlassTrainer constructor.
string trainerLabel = data.Schema.Label.Value.Name;
var view = MapLabels(data, cls1, cls2);
var transformer = trainer.Fit(view);
// the validations in the calibrator check for the feature column, in the RoleMappedData
var trainedData = new RoleMappedData(view, label: trainerLabel, feature: transformer.FeatureColumn);
var calibratedModel = transformer.Model as TDistPredictor;
if (calibratedModel == null)
{
calibratedModel = CalibratorUtils.GetCalibratedPredictor(Host, ch, Calibrator, transformer.Model, trainedData, Args.MaxCalibrationExamples) as TDistPredictor;
}
return(new BinaryPredictionTransformer <TDistPredictor>(Host, calibratedModel, trainedData.Data.Schema, transformer.FeatureColumn));
}
private MatrixFactorizationModelParameters TrainCore(IChannel ch, RoleMappedData data, RoleMappedData validData = null)
{
_host.AssertValue(ch);
ch.AssertValue(data);
ch.AssertValueOrNull(validData);
ch.CheckParam(data.Schema.Label.HasValue, nameof(data), "Input data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(data, out var matrixColumnIndexColInfo, out var matrixRowIndexColInfo, isDecode: false);
var labelCol = data.Schema.Label.Value;
if (labelCol.Type != NumberDataViewType.Single && labelCol.Type != NumberDataViewType.Double)
{
throw ch.Except("Column '{0}' for label should be floating point, but is instead {1}", labelCol.Name, labelCol.Type);
}
MatrixFactorizationModelParameters predictor;
if (validData != null)
{
ch.CheckValue(validData, nameof(validData));
ch.CheckParam(validData.Schema.Label.HasValue, nameof(validData), "Input validation data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out var validMatrixColumnIndexColInfo, out var validMatrixRowIndexColInfo, isDecode: false);
var validLabelCol = validData.Schema.Label.Value;
if (validLabelCol.Type != NumberDataViewType.Single && validLabelCol.Type != NumberDataViewType.Double)
{
throw ch.Except("Column '{0}' for validation label should be floating point, but is instead {1}", validLabelCol.Name, validLabelCol.Type);
}
if (!matrixColumnIndexColInfo.Type.Equals(validMatrixColumnIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-column types differed, {0} vs. {1}",
matrixColumnIndexColInfo.Type, validMatrixColumnIndexColInfo.Type);
}
if (!matrixRowIndexColInfo.Type.Equals(validMatrixRowIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-row types differed, {0} vs. {1}",
matrixRowIndexColInfo.Type, validMatrixRowIndexColInfo.Type);
}
}
int colCount = matrixColumnIndexColInfo.Type.GetKeyCountAsInt32(_host);
int rowCount = matrixRowIndexColInfo.Type.GetKeyCountAsInt32(_host);
ch.Assert(rowCount > 0);
ch.Assert(colCount > 0);
// Checks for equality on the validation set ensure it is correct here.
using (var cursor = data.Data.GetRowCursor(matrixColumnIndexColInfo, matrixRowIndexColInfo, data.Schema.Label.Value))
{
// LibMF works only over single precision floats, but we want to be able to consume either.
var labGetter = RowCursorUtils.GetGetterAs <float>(NumberDataViewType.Single, cursor, data.Schema.Label.Value.Index);
var matrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, cursor, matrixColumnIndexColInfo.Index);
var matrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, cursor, matrixRowIndexColInfo.Index);
if (validData == null)
{
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.Train(ch, rowCount, colCount, cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter);
predictor = new MatrixFactorizationModelParameters(_host, buffer, (KeyType)matrixColumnIndexColInfo.Type, (KeyType)matrixRowIndexColInfo.Type);
}
}
else
{
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out var validMatrixColumnIndexColInfo, out var validMatrixRowIndexColInfo, isDecode: false);
using (var validCursor = validData.Data.GetRowCursor(matrixColumnIndexColInfo, matrixRowIndexColInfo, data.Schema.Label.Value))
{
ValueGetter <float> validLabelGetter = RowCursorUtils.GetGetterAs <float>(NumberDataViewType.Single, validCursor, validData.Schema.Label.Value.Index);
var validMatrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, validCursor, validMatrixColumnIndexColInfo.Index);
var validMatrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberDataViewType.UInt32, validCursor, validMatrixRowIndexColInfo.Index);
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.TrainWithValidation(ch, rowCount, colCount,
cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter,
validCursor, validLabelGetter, validMatrixRowIndexGetter, validMatrixColumnIndexGetter);
predictor = new MatrixFactorizationModelParameters(_host, buffer, (KeyType)matrixColumnIndexColInfo.Type, (KeyType)matrixRowIndexColInfo.Type);
}
}
}
}
return(predictor);
}
/// <summary>
/// This method ensures that the data meets the requirements of this trainer and its
/// subclasses, injects necessary transforms, and throws if it couldn't meet them.
/// </summary>
/// <param name="ch">The channel</param>
/// <param name="examples">The training examples</param>
/// <param name="weightSetCount">Gets the length of weights and bias array. For binary classification and regression,
/// this is 1. For multi-class classification, this equals the number of classes on the label.</param>
/// <returns>A potentially modified version of <paramref name="examples"/></returns>
private protected RoleMappedData PrepareDataFromTrainingExamples(IChannel ch, RoleMappedData examples, out int weightSetCount)
{
ch.AssertValue(examples);
CheckLabel(examples, out weightSetCount);
examples.CheckFeatureFloatVector();
var idvToShuffle = examples.Data;
IDataView idvToFeedTrain;
if (idvToShuffle.CanShuffle)
{
idvToFeedTrain = idvToShuffle;
}
else
{
var shuffleArgs = new RowShufflingTransformer.Options
{
PoolOnly = false,
ForceShuffle = ShuffleData
};
idvToFeedTrain = new RowShufflingTransformer(Host, shuffleArgs, idvToShuffle);
}
ch.Assert(idvToFeedTrain.CanShuffle);
var roles = examples.Schema.GetColumnRoleNames();
var examplesToFeedTrain = new RoleMappedData(idvToFeedTrain, roles);
ch.Assert(examplesToFeedTrain.Schema.Label.HasValue);
ch.Assert(examplesToFeedTrain.Schema.Feature.HasValue);
if (examples.Schema.Weight.HasValue)
{
ch.Assert(examplesToFeedTrain.Schema.Weight.HasValue);
}
ch.Check(examplesToFeedTrain.Schema.Feature.Value.Type is VectorType vecType && vecType.Size > 0, "Training set has no features, aborting training.");
return(examplesToFeedTrain);
}
