static void TestCacheTransformSimple(int nt, bool async)
{
using (var host = EnvHelper.NewTestEnvironment(conc: nt == 1 ? 1 : 0))
{
var inputs = new InputOutput[] {
new InputOutput()
{
X = new float[] { 0, 1 }, Y = 1
},
new InputOutput()
{
X = new float[] { 0, 1 }, Y = 0
}
};
var data = host.CreateStreamingDataView(inputs);
using (var cursor = data.GetRowCursor(i => true))
{
var sortedValues = new List <int>();
var sortColumnGetter = cursor.GetGetter <int>(1);
while (cursor.MoveNext())
{
int got = 0;
sortColumnGetter(ref got);
sortedValues.Add((int)got);
}
if (sortedValues.Count != 2)
{
throw new Exception();
}
if (sortedValues[0] != 1)
{
throw new Exception();
}
if (sortedValues[1] != 0)
{
throw new Exception();
}
}
var args = new ExtendedCacheTransform.Arguments {
numTheads = nt, async = async
};
var transformedData = new ExtendedCacheTransform(host, args, data);
var lastTransform = transformedData;
LambdaTransform.CreateMap <InputOutput, InputOutput, EnvHelper.EmptyState>(host, data,
(input, output, state) =>
{
output.X = input.X;
output.Y = input.Y;
}, (EnvHelper.EmptyState state) => { });
using (var cursor = lastTransform.GetRowCursor(i => true))
{
var sortedValues = new List <int>();
var sortColumnGetter = cursor.GetGetter <int>(1);
while (cursor.MoveNext())
{
int got = 0;
sortColumnGetter(ref got);
sortedValues.Add((int)got);
}
if (sortedValues.Count != 2)
{
throw new Exception();
}
}
}
}
private List <Tuple <int, TimeSpan, int, float[]> > _MeasureTime(int conc,
string strategy, string engine, IDataScorerTransform scorer, ITransformer trscorer, int ncall)
{
var testFilename = FileHelper.GetTestFile("wikipedia-detox-250-line-test.tsv");
var times = new List <Tuple <int, TimeSpan, int, float[]> >();
/*using (*/
var env = EnvHelper.NewTestEnvironment(seed: 1, conc: conc);
{
if (engine == "mlnet")
{
var args = new TextLoader.Options()
{
Separators = new[] { '\t' },
HasHeader = true,
Columns = new[] {
new TextLoader.Column("Label", DataKind.Boolean, 0),
new TextLoader.Column("SentimentText", DataKind.String, 1)
}
};
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, args).Load(new MultiFileSource(testFilename));
IDataView cache;
if (strategy.Contains("extcache"))
{
cache = new ExtendedCacheTransform(env, new ExtendedCacheTransform.Arguments(), testLoader);
}
else
{
cache = new CacheDataView(env, testLoader, new[] { 0, 1 });
}
//var testData = cache.AsEnumerable<SentimentDataBoolFloat>(env, false);
//var testDataArray = cache.AsEnumerable<SentimentDataBoolFloat>(env, false).ToArray();
int N = 1;
var model = ComponentCreation.CreatePredictionEngine <SentimentDataBoolFloat, SentimentPrediction>(env, trscorer);
var sw = new Stopwatch();
for (int call = 1; call <= ncall; ++call)
{
sw.Reset();
var pred = new List <float>();
sw.Start();
for (int i = 0; i < N; ++i)
{
if (strategy.Contains("array"))
{
/*
* foreach (var input in testDataArray)
* pred.Add(model.Predict(input).Score);
*/
}
else
{
/*
* foreach (var input in testData)
* pred.Add(model.Predict(input).Score);
*/
}
}
sw.Stop();
times.Add(new Tuple <int, TimeSpan, int, float[]>(N, sw.Elapsed, call, pred.ToArray()));
}
}
else if (engine == "scikit")
{
var args = new TextLoader.Options()
{
Separators = new[] { '\t' },
HasHeader = true,
Columns = new[] {
new TextLoader.Column("Label", DataKind.Boolean, 0),
new TextLoader.Column("SentimentText", DataKind.String, 1)
}
};
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, args).Load(new MultiFileSource(testFilename));
IDataView cache;
if (strategy.Contains("extcache"))
{
cache = new ExtendedCacheTransform(env, new ExtendedCacheTransform.Arguments(), testLoader);
}
else
{
cache = new CacheDataView(env, testLoader, new[] { 0, 1 });
}
//var testData = cache.AsEnumerable<SentimentDataBool>(env, false);
//var testDataArray = cache.AsEnumerable<SentimentDataBool>(env, false).ToArray();
int N = 1;
string allSchema = SchemaHelper.ToString(scorer.Schema);
Assert.IsTrue(allSchema.Contains("PredictedLabel:Bool:4; Score:R4:5; Probability:R4:6"));
var model = new ValueMapperPredictionEngine <SentimentDataBool>(env, scorer);
var output = new ValueMapperPredictionEngine <SentimentDataBool> .PredictionTypeForBinaryClassification();
var sw = new Stopwatch();
for (int call = 1; call <= ncall; ++call)
{
var pred = new List <float>();
sw.Reset();
sw.Start();
for (int i = 0; i < N; ++i)
{
if (strategy.Contains("array"))
{
/*
* foreach (var input in testDataArray)
* {
* model.Predict(input, ref output);
* pred.Add(output.Score);
* }
*/
}
else
{
/*
* foreach (var input in testData)
* {
* model.Predict(input, ref output);
* pred.Add(output.Score);
* }
*/
}
}
sw.Stop();
times.Add(new Tuple <int, TimeSpan, int, float[]>(N, sw.Elapsed, call, pred.ToArray()));
}
}
else
{
throw new NotImplementedException($"Unknown engine '{engine}'.");
}
}
return(times);
}
private static List <Tuple <int, TimeSpan, int> > _MeasureTime(int conc,
string engine, IDataScorerTransform scorer, ITransformer transformer,
int N, int ncall, bool cacheScikit)
{
var args = new TextLoader.Arguments()
{
Separator = "tab",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("Label", DataKind.BL, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
}
};
var testFilename = FileHelper.GetTestFile("wikipedia-detox-250-line-test.tsv");
var times = new List <Tuple <int, TimeSpan, int> >();
using (var env = EnvHelper.NewTestEnvironment(seed: 1, conc: conc))
{
// Take a couple examples out of the test data and run predictions on top.
var testLoader = TextLoader.ReadFile(env, args, new MultiFileSource(testFilename));
IDataView cache;
if (cacheScikit)
{
cache = new ExtendedCacheTransform(env, new ExtendedCacheTransform.Arguments(), testLoader);
}
else
{
cache = new CacheDataView(env, testLoader, new[] { 0, 1 });
}
var testData = cache.AsEnumerable <SentimentData>(env, false);
if (engine == "mlnet")
{
Console.WriteLine("engine={0} N={1} ncall={2} cacheScikit={3}", engine, N, ncall, cacheScikit);
var fct = transformer.MakePredictionFunction <SentimentData, SentimentPrediction>(env);
var sw = new Stopwatch();
for (int call = 1; call <= ncall; ++call)
{
sw.Reset();
sw.Start();
for (int i = 0; i < N; ++i)
{
foreach (var input in testData)
{
fct.Predict(input);
}
}
sw.Stop();
times.Add(new Tuple <int, TimeSpan, int>(N, sw.Elapsed, call));
}
}
else if (engine == "scikit")
{
Console.WriteLine("engine={0} N={1} ncall={2} cacheScikit={3}", engine, N, ncall, cacheScikit);
var model = new ValueMapperPredictionEngine <SentimentData>(env, scorer, conc: conc);
var output = new ValueMapperPredictionEngine <SentimentData> .PredictionTypeForBinaryClassification();
var sw = new Stopwatch();
for (int call = 1; call <= ncall; ++call)
{
sw.Reset();
sw.Start();
for (int i = 0; i < N; ++i)
{
foreach (var input in testData)
{
model.Predict(input, ref output);
}
}
sw.Stop();
times.Add(new Tuple <int, TimeSpan, int>(N, sw.Elapsed, call));
}
}
else
{
throw new NotImplementedException($"Unknown engine '{engine}'.");
}
}
return(times);
}
IDataTransform AppendToPipeline(IDataView input)
{
IDataView current = input;
if (_shuffleInput)
{
var args1 = new RowShufflingTransformer.Arguments()
{
ForceShuffle = false,
ForceShuffleSeed = _seedShuffle,
PoolRows = _poolRows,
PoolOnly = false,
};
current = new RowShufflingTransformer(Host, args1, current);
}
// We generate a random number.
var columnName = current.Schema.GetTempColumnName();
var args2 = new GenerateNumberTransform.Arguments()
{
Column = new GenerateNumberTransform.Column[] { new GenerateNumberTransform.Column()
{
Name = columnName
} },
Seed = _seed ?? 42
};
IDataTransform currentTr = new GenerateNumberTransform(Host, args2, current);
// We convert this random number into a part.
var cRatios = new float[_ratios.Length];
cRatios[0] = 0;
for (int i = 1; i < _ratios.Length; ++i)
{
cRatios[i] = cRatios[i - 1] + _ratios[i - 1];
}
ValueMapper <float, int> mapper = (in float src, ref int dst) =>
{
for (int i = cRatios.Length - 1; i > 0; --i)
{
if (src >= cRatios[i])
{
dst = i;
return;
}
}
dst = 0;
};
// Get location of columnName
int index;
currentTr.Schema.TryGetColumnIndex(columnName, out index);
var ct = currentTr.Schema.GetColumnType(index);
var view = LambdaColumnMapper.Create(Host, "Key to part mapper", currentTr,
columnName, _newColumn, ct, NumberType.I4, mapper);
// We cache the result to avoid the pipeline to change the random number.
var args3 = new ExtendedCacheTransform.Arguments()
{
inDataFrame = string.IsNullOrEmpty(_cacheFile),
numTheads = _numThreads,
cacheFile = _cacheFile,
reuse = _reuse,
};
currentTr = new ExtendedCacheTransform(Host, args3, view);
// Removing the temporary column.
var finalTr = ColumnSelectingTransformer.CreateDrop(Host, currentTr, new string[] { columnName });
var taggedViews = new List <Tuple <string, ITaggedDataView> >();
// filenames
if (_filenames != null || _tags != null)
{
int nbf = _filenames == null ? 0 : _filenames.Length;
if (nbf > 0 && nbf != _ratios.Length)
{
throw Host.Except("Differen number of filenames and ratios.");
}
int nbt = _tags == null ? 0 : _tags.Length;
if (nbt > 0 && nbt != _ratios.Length)
{
throw Host.Except("Differen number of filenames and ratios.");
}
int nb = Math.Max(nbf, nbt);
using (var ch = Host.Start("Split the datasets and stores each part."))
{
for (int i = 0; i < nb; ++i)
{
if (_filenames == null || !_filenames.Any())
{
ch.Info("Create part {0}: {1} (tag: {2})", i + 1, _ratios[i], _tags[i]);
}
else
{
ch.Info("Create part {0}: {1} (file: {2})", i + 1, _ratios[i], _filenames[i]);
}
var ar1 = new RangeFilter.Arguments()
{
Column = _newColumn, Min = i, Max = i, IncludeMax = true
};
int pardId = i;
var filtView = LambdaFilter.Create <int>(Host, string.Format("Select part {0}", i), currentTr,
_newColumn, NumberType.I4,
(in int part) => { return(part.Equals(pardId)); });
