public void BenchmarkEval()
{
var rand = new Random(Seed);
int numColumns = 100;
var pms = new Parameters();
pms.Objective.Objective = ObjectiveType.Binary;
pms.Dataset.MaxBin = 63;
pms.Learning.LearningRate = 1e-3;
pms.Learning.NumIterations = 1000;
pms.Common.DeviceType = DeviceType.CPU;
var categorical = new Dictionary <int, int>(); // i.e., no cat
var trainData = CreateRandomDenseClassifyData(rand, 2, ref categorical, pms.Dataset.UseMissing, numColumns);
DataDense validData = null;
pms.Dataset.CategoricalFeature = categorical.Keys.ToArray();
using (var datasets = new Datasets(pms.Common, pms.Dataset, trainData, validData))
using (var trainer = new BinaryTrainer(pms.Learning, pms.Objective))
{
var model = trainer.Train(datasets);
output.WriteLine($"MaxNumTrees={model.Managed.MaxNumTrees}");
var timer = System.Diagnostics.Stopwatch.StartNew();
model.Native.GetOutputs(trainData.Features);
var elapsed1 = timer.Elapsed;
output.WriteLine($"EvalNativeMulti={elapsed1.TotalMilliseconds}");
timer.Restart();
foreach (var row in trainData.Features)
{
trainer.Evaluate(Booster.PredictType.Normal, row);
}
var elapsed2 = timer.Elapsed;
output.WriteLine($"EvalNativeSingle={elapsed2.TotalMilliseconds}");
foreach (var maxThreads in new int[] { 1, 2, 4, 8, 16, 32, Environment.ProcessorCount }) //
{
model.Managed.MaxThreads = maxThreads;
timer.Restart();
foreach (var row in trainData.Features)
{
double output = 0;
var input = new VBuffer <float>(row.Length, row);
model.Managed.GetOutput(ref input, ref output);
}
var elapsed3 = timer.Elapsed;
output.WriteLine($"MaxThreads={maxThreads} EvalManaged={elapsed3.TotalMilliseconds}");
}
}
}
//[Fact]
public void BenchmarkBinary()
{
var rand = new Random(Seed);
for (int test = 0; test < 3; ++test)
{
for (int gpu = 0; gpu < 2; gpu++)
{
int numColumns = 50 * (test + 1);
var pms = new Parameters();
pms.Objective.Objective = ObjectiveType.Binary;
pms.Dataset.MaxBin = 63;
pms.Learning.BaggingFraction = 1;
pms.Learning.BaggingFreq = 1;
pms.Learning.LearningRate = 1e-3;
pms.Learning.NumIterations = 10;
pms.Common.DeviceType = (gpu > 0) ? DeviceType.GPU : DeviceType.CPU;
var categorical = new Dictionary <int, int>(); // i.e., no cat
var trainData = CreateRandomDenseClassifyData(rand, 2, ref categorical, pms.Dataset.UseMissing, numColumns);
DataDense validData = null;
pms.Dataset.CategoricalFeature = categorical.Keys.ToArray();
try
{
using (var datasets = new Datasets(pms.Common, pms.Dataset, trainData, validData))
using (var trainer = new BinaryTrainer(pms.Learning, pms.Objective))
{
var timer = System.Diagnostics.Stopwatch.StartNew();
var model = trainer.Train(datasets);
var elapsed = timer.Elapsed;
output.WriteLine($"{pms.Common.DeviceType}: NumRows={trainData.NumRows} NumCols={numColumns} MaxNumTrees={model.Managed.MaxNumTrees} TrainTimeSecs={elapsed.TotalSeconds}");
}
}
catch (Exception e)
{
throw new Exception($"Failed: {Seed} #{test} {pms}", e);
}
}
}
}
public void TrainBinary()
{
var rand = new Random(Seed);
for (int test = 0; test < 5; ++test)
{
int numColumns = rand.Next(1, 10);
var pms = GenerateParameters(rand, ObjectiveType.Binary, numColumns);
Dictionary <int, int> categorical = null;
var trainData = CreateRandomDenseClassifyData(rand, 2, ref categorical, pms.Dataset.UseMissing, numColumns);
var validData = (pms.Learning.EarlyStoppingRound > 0 || rand.Next(2) == 0) ? CreateRandomDenseClassifyData(rand, 2, ref categorical, pms.Dataset.UseMissing, numColumns) : null;
pms.Dataset.CategoricalFeature = categorical.Keys.ToArray();
var learningRateSchedule = (rand.Next(2) == 0) ? (Func <int, double>)null : (iter => pms.Learning.LearningRate * Math.Pow(0.99, iter));
try
{
using (var datasets = (rand.Next(2) == 0) ? new Datasets(pms.Common, pms.Dataset, trainData, validData) :
new Datasets(pms.Common, pms.Dataset, Dense2Sparse(trainData), Dense2Sparse(validData)))
using (var trainer = new BinaryTrainer(pms.Learning, pms.Objective))
{
//trainer.ToCommandLineFiles(datasets);
var model = trainer.Train(datasets, learningRateSchedule);
model.Managed.MaxThreads = rand.Next(1, Environment.ProcessorCount);
// possibly use subset of trees
var numIterations = -1;
if (rand.Next(2) == 0)
{
numIterations = rand.Next(1, model.Managed.MaxNumTrees);
model.Managed.MaxNumTrees = numIterations;
model.Native.MaxNumTrees = numIterations;
}
CalibratedPredictor model2 = null;
using (var ms = new System.IO.MemoryStream())
using (var writer = new System.IO.BinaryWriter(ms))
using (var reader = new System.IO.BinaryReader(ms))
{
PredictorPersist.Save(model.Managed, writer);
ms.Position = 0;
model2 = PredictorPersist.Load <double>(reader) as CalibratedPredictor;
Assert.Equal(ms.Position, ms.Length);
}
BinaryNativePredictor model2native = null;
using (var ms = new System.IO.MemoryStream())
using (var writer = new System.IO.BinaryWriter(ms))
using (var reader = new System.IO.BinaryReader(ms))
{
NativePredictorPersist.Save(model.Native, writer);
ms.Position = 0;
model2native = NativePredictorPersist.Load <double>(reader) as BinaryNativePredictor;
Assert.Equal(ms.Position, ms.Length);
}
var rawscore2s = trainer.Evaluate(Booster.PredictType.RawScore, trainData.Features, numIterations);
Assert.Equal(trainData.Features.Length, rawscore2s.GetLength(0));
Assert.Equal(1, rawscore2s.GetLength(1));
var output3s = trainer.Evaluate(Booster.PredictType.Normal, trainData.Features, numIterations);
Assert.Equal(trainData.Features.Length, output3s.GetLength(0));
Assert.Equal(1, output3s.GetLength(1));
var output3natives = model.Native.GetOutputs(trainData.Features);
Assert.Equal(trainData.Features.Length, output3s.Length);
for (int i = 0; i < trainData.Features.Length; i++)
{
var row = trainData.Features[i];
double output = 0;
var input = new VBuffer <float>(row.Length, row);
model.Managed.GetOutput(ref input, ref output);
Assert.True(output >= 0);
Assert.True(output <= 1);
double output2 = 0;
model2.GetOutput(ref input, ref output2);
Compare(output, output2);
// check raw score against native booster object
var rawscore = 0.0;
(model.Managed as CalibratedPredictor).SubPredictor.GetOutput(ref input, ref rawscore);
var rawscore2 = trainer.Evaluate(Booster.PredictType.RawScore, row, numIterations);
Assert.Single(rawscore2);
Assert.Equal(rawscore2[0], rawscore2s[i, 0]);
var isRf = (pms.Learning.Boosting == BoostingType.RandomForest);
Compare(isRf ? rawscore * model.Managed.MaxNumTrees : rawscore, rawscore2[0]);
var output3 = trainer.Evaluate(Booster.PredictType.Normal, row, numIterations);
Assert.Single(output3);
Assert.Equal(output3[0], output3s[i, 0]);
Assert.Equal(output3[0], output3natives[i]);
Compare(output, output3[0]);
double outputNative = 0;
model.Native.GetOutput(ref input, ref outputNative);
Assert.Equal(outputNative, output3[0]);
model2native.GetOutput(ref input, ref outputNative);
Assert.Equal(outputNative, output3[0]);
//Console.WriteLine(trainer.GetModelString());
//throw new Exception($"Output mismatch {output} vs {output3[0]} (error: {Math.Abs(output - output3[0])}) input: {String.Join(", ", row)}");
}
var normalise = rand.Next(2) == 0;
var getSplits = rand.Next(2) == 0;
var gains = model.Managed.GetFeatureWeights(normalise, getSplits);
var gainsNative = model.Native.GetFeatureWeights(normalise, getSplits);
Assert.Equal(gains.Count, gainsNative.Count);
foreach (var kv in gains)
{
Assert.True(0 <= kv.Key && kv.Key < trainData.NumColumns);
Assert.True(0.0 <= kv.Value);
Compare(kv.Value, gainsNative[kv.Key]);
}
if (!getSplits && !normalise)
{
var totGain1 = gains.Values.Sum();
var totGain2 = Enumerable.Range(0, trainData.NumColumns).SelectMany(i => model.Managed.GetFeatureGains(i)).Sum();
Compare(totGain1, totGain2);
}
}
}
catch (Exception e)
{
throw new Exception($"Failed: {Seed} #{test} {pms}", e);
}
}
}