static void Main(string[] args)
{
var xt = new Maml<TemplateData>(t =>
html => html.Set(
div => div.Set(
new { attribute_name = "value", stuff = "cool", number = 2 },
p => p.Set(string.Format("Item 1 = {0}", t.TheString)),
p => p.Set(new { data = "some stuff" }, "Item \" 2"),
ul => ul.Set(Enumerable.Range(1, t.TheNumber).Select(
i => (Action<Maml<TemplateData>>)
(li => li.Set(string.Format("list item {0}", i))))
)
),
div => div.Set((CData)"this is some cdata"),
script => script.Set(new
{
src = "http://www.test.com/test.js", type = "text/javascript"
})
)
);
Console.WriteLine(
xt.ToString(
new TemplateData { TheNumber = 5, TheString = "Hello" }, true
)
);
Console.WriteLine();
Console.WriteLine(
xt.ToString(
new TemplateData { TheNumber = 2, TheString = "BLAH BLAH" }
)
);
Console.ReadLine();
}
public void Test_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking()
{
// This benchmark is profiling bulk scoring speed and not training speed.
string cmd = @"Test data=" + _mslrWeb10k_Test + " in=" + _modelPath_MSLR;
using (var environment = EnvironmentFactory.CreateRankingEnvironment <RankerEvaluator, TextLoader, HashTransformer, FastTreeRankingTrainer>())
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void Test_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron()
{
// This benchmark is profiling bulk scoring speed and not training speed.
string modelpath = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.fold000.zip");
string cmd = @"Test data=" + _dataPath_Wiki + " in=" + modelpath;
var environment = EnvironmentFactory.CreateClassificationEnvironment <TextLoader, OneHotEncodingTransformer, AveragedPerceptronTrainer>();
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
public void Test_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking()
{
// This benchmark is profiling bulk scoring speed and not training speed.
string cmd = @"Test data=" + _mslrWeb10k_Test + " in=" + _modelPath_MSLR;
using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void SetupScoringSpeedTests()
{
SetupTrainingSpeedTests();
_modelPath_Wiki = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.zip");
string cmd = @"CV k=5 data=" + _dataPath_Wiki + " loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}} xf=Concat{col=Features:FeaturesText,logged_in,ns} tr=OVA{p=AveragedPerceptron{iter=10}} out={" + _modelPath_Wiki + "}";
using (var tlc = new TlcEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false);
}
}
public void Test_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron()
{
// This benchmark is profiling bulk scoring speed and not training speed.
string modelpath = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.fold000.zip");
string cmd = @"Test data=" + _dataPath_Wiki + " in=" + modelpath;
using (var tlc = new TlcEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false);
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // x86 output differs from Baseline
void TestCommandLine()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
var env = new MLContext();
var x = Maml.Main(new[] { @"showschema loader=Text{col=data_0:R4:0-150527} xf=Onnx{InputColumns={data_0} OutputColumns={softmaxout_1} model={squeezenet/00000001/model.onnx}}" });
Assert.Equal(0, x);
}
public void TestCSGeneratorHelp()
{
var cmd = "? CSGenerator";
using (var std = new Scikit.ML.DocHelperMlExt.StdCapture())
{
Maml.MainAll(cmd);
if (std.StdOut.Length == 0)
{
Assert.Inconclusive("Not accurate on a remote machine.");
}
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // x86 output differs from Baseline
void TestCommandLine()
{
if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
return;
}
using (var env = new ConsoleEnvironment())
{
var x = Maml.Main(new[] { @"showschema loader=Text{col=data_0:R4:0-150527} xf=Onnx{InputColumn=data_0 OutputColumn=softmaxout_1 model={squeezenet/00000001/model.onnx}}" });
Assert.Equal(0, x);
}
}
public void TrainTest_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking()
{
string cmd = @"TrainTest test=" + _mslrWeb10k_Validate +
" eval=RankingEvaluator{t=10}" +
" data=" + _mslrWeb10k_Train +
" loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" +
" xf=HashTransform{col=GroupId} xf=NAHandleTransform{col=Features}" +
" tr=FastTreeRanking{}";
var environment = EnvironmentFactory.CreateRankingEnvironment <RankerEvaluator, TextLoader, HashingTransformer, FastTreeRankingTrainer>();
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
public void CV_Multiclass_WikiDetox_BigramsAndTrichar_LightGBMMulticlass()
{
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" +
" xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" +
" xf=Concat{col=Features:FeaturesText,logged_in,ns}" +
" tr=LightGBMMulticlass{iter=10}";
var environment = EnvironmentFactory.CreateClassificationEnvironment <TextLoader, OneHotEncodingTransformer, LightGbmMulticlassTrainer>();
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
public void CV_Multiclass_WikiDetox_BigramsAndTrichar_LightGBMMulticlass()
{
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" +
" xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" +
" xf=Concat{col=Features:FeaturesText,logged_in,ns} tr=LightGBMMulticlass{}";
using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void TrainTest_Ranking_MSLRWeb10K_RawNumericFeatures_FastTreeRanking()
{
string cmd = @"TrainTest test=" + _mslrWeb10k_Validate +
" eval=RankingEvaluator{t=10}" +
" data=" + _mslrWeb10k_Train +
" loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" +
" xf=HashTransform{col=GroupId} xf=NAHandleTransform{col=Features}" +
" tr=FastTreeRanking{}";
using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void CV_Multiclass_WikiDetox_WordEmbeddings_SDCAMC()
{
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" tr=SDCAMC" +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" +
" xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor={} charExtractor={}}" +
" xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D}" +
" xf=Concat{col=Features:FeaturesWordEmbedding,logged_in,ns}";
var environment = EnvironmentFactory.CreateClassificationEnvironment <TextLoader, OneHotEncodingTransformer, SdcaMultiClassTrainer>();
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
public void CV_Multiclass_WikiDetox_WordEmbeddings_SDCAMC()
{
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" tr=SDCAMC" +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" +
" xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor={} charExtractor={}}" +
" xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D}" +
" xf=Concat{col=Features:FeaturesWordEmbedding,logged_in,ns}";
using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void CV_Multiclass_WikiDetox_WordEmbeddings_OVAAveragedPerceptron()
{
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" tr=OVA{p=AveragedPerceptron{iter=10}}" +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+}" +
" xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor=NGramExtractorTransform{ngram=2}}" +
" xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D}" +
" xf=Concat{col=Features:FeaturesText,FeaturesWordEmbedding,logged_in,ns}";
using (var environment = EnvironmentFactory.CreateClassificationEnvironment <TextLoader, CategoricalTransform, AveragedPerceptronTrainer>())
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void TestCSGenerator()
{
var methodName = System.Reflection.MethodBase.GetCurrentMethod().Name;
var basePath = FileHelper.GetOutputFile("CSharpApiExt.cs", methodName);
var cmd = $"? generator=cs{{csFilename={basePath} exclude=System.CodeDom.dll}}";
using (var std = new StdCapture())
{
Maml.Main(new[] { cmd });
Assert.IsTrue(std.StdOut.Length > 0);
Assert.IsTrue(std.StdErr.Length == 0);
Assert.IsFalse(std.StdOut.ToLower().Contains("usage"));
}
var text = File.ReadAllText(basePath);
// TODO: this tests fails because when ML.net is used
// as a nuget, buget binaries and custom binaries
// are not in the same folder. The command looks into
// its folder and fetches every DLL to look into exposed
// learner and transforms. XGBoostWrapper is in another folder and does not
// appear it.
//Assert.IsTrue(text.ToLower().Contains("xgb"));
}
public void SetupScoringSpeedTests()
{
_dataPath_Wiki = Path.GetFullPath(TestDatasets.WikiDetox.trainFilename);
if (!File.Exists(_dataPath_Wiki))
{
throw new FileNotFoundException(string.Format(Errors.DatasetNotFound, _dataPath_Wiki));
}
_modelPath_Wiki = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.zip");
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" +
" xf=Concat{col=Features:FeaturesText,logged_in,ns}" +
" tr=OVA{p=AveragedPerceptron{iter=10}}" +
" out={" + _modelPath_Wiki + "}";
var environment = EnvironmentFactory.CreateClassificationEnvironment <TextLoader, OneHotEncodingTransformer, AveragedPerceptronTrainer>();
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
public void SetupScoringSpeedTests()
{
_mslrWeb10k_Test = Path.GetFullPath(TestDatasets.MSLRWeb.testFilename);
_mslrWeb10k_Validate = Path.GetFullPath(TestDatasets.MSLRWeb.validFilename);
_mslrWeb10k_Train = Path.GetFullPath(TestDatasets.MSLRWeb.trainFilename);
if (!File.Exists(_mslrWeb10k_Test))
{
throw new FileNotFoundException(string.Format(Errors.DatasetNotFound, _mslrWeb10k_Test));
}
if (!File.Exists(_mslrWeb10k_Validate))
{
throw new FileNotFoundException(string.Format(Errors.DatasetNotFound, _mslrWeb10k_Validate));
}
if (!File.Exists(_mslrWeb10k_Train))
{
throw new FileNotFoundException(string.Format(Errors.DatasetNotFound, _mslrWeb10k_Train));
}
_modelPath_MSLR = Path.Combine(Directory.GetCurrentDirectory(), @"FastTreeRankingModel.zip");
string cmd = @"TrainTest test=" + _mslrWeb10k_Validate +
" eval=RankingEvaluator{t=10}" +
" data=" + _mslrWeb10k_Train +
" loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" +
" xf=HashTransform{col=GroupId}" +
" xf=NAHandleTransform{col=Features}" +
" tr=FastTreeRanking{}" +
" out={" + _modelPath_MSLR + "}";
using (var environment = EnvironmentFactory.CreateRankingEnvironment <RankerEvaluator, TextLoader, HashTransformer, FastTreeRankingTrainer>())
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void SetupScoringSpeedTests()
{
_mslrWeb10k_Test = Path.GetFullPath(TestDatasets.MSLRWeb.testFilename);
_mslrWeb10k_Validate = Path.GetFullPath(TestDatasets.MSLRWeb.validFilename);
_mslrWeb10k_Train = Path.GetFullPath(TestDatasets.MSLRWeb.trainFilename);
if (!File.Exists(_mslrWeb10k_Test))
{
throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _mslrWeb10k_Test));
}
if (!File.Exists(_mslrWeb10k_Validate))
{
throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _mslrWeb10k_Validate));
}
if (!File.Exists(_mslrWeb10k_Train))
{
throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _mslrWeb10k_Train));
}
_modelPath_MSLR = Path.Combine(Directory.GetCurrentDirectory(), @"FastTreeRankingModel.zip");
string cmd = @"TrainTest test=" + _mslrWeb10k_Validate +
" eval=RankingEvaluator{t=10}" +
" data=" + _mslrWeb10k_Train +
" loader=TextLoader{col=Label:R4:0 col=GroupId:TX:1 col=Features:R4:2-138}" +
" xf=HashTransform{col=GroupId}" +
" xf=NAHandleTransform{col=Features}" +
" tr=FastTreeRanking{}" +
" out={" + _modelPath_MSLR + "}";
using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void SetupScoringSpeedTests()
{
_dataPath_Wiki = Path.GetFullPath(TestDatasets.WikiDetox.trainFilename);
if (!File.Exists(_dataPath_Wiki))
{
throw new FileNotFoundException(string.Format(Helpers.DatasetNotFound, _dataPath_Wiki));
}
_modelPath_Wiki = Path.Combine(Directory.GetCurrentDirectory(), @"WikiModel.zip");
string cmd = @"CV k=5 data=" + _dataPath_Wiki +
" loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} xf=Convert{col=logged_in type=R4}" +
" xf=CategoricalTransform{col=ns}" +
" xf=TextTransform{col=FeaturesText:comment wordExtractor=NGramExtractorTransform{ngram=2}}" +
" xf=Concat{col=Features:FeaturesText,logged_in,ns}" +
" tr=OVA{p=AveragedPerceptron{iter=10}}" +
" out={" + _modelPath_Wiki + "}";
using (var environment = new ConsoleEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
Maml.MainCore(environment, cmd, alwaysPrintStacktrace: false);
}
}
public void TestCommandLine()
{
Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0-100} xf=Rff{col=B:A dim=4 useSin+ kernel=LaplacianRandom} in=f:\2.txt" }));
}
public void TestLdaCommandLine()
{
Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0-10} xf=lda{col=B:A} in=f:\2.txt" }), (int)0);
}
void TestCommandLine()
{
var x = Maml.Main(new[] { @"showschema loader=Text{col=data_0:R4:0-150527} xf=Onnx{InputColumns={data_0} OutputColumns={softmaxout_1} model={squeezenet/00000001/model.onnx}}" });
Assert.Equal(0, x);
}
public void TestGcnNormCommandLine()
{
Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0-10} xf=GcnTransform{col=B:A} in=f:\2.txt" }), (int)0);
}
public void TestCommandLine()
{
Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} xf=KeyToBinary{col=C:B} in=f:\2.txt" }));
}
public void TestCommandLine()
{
Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Term{col=B:A} xf=KeyToVector{col=C:B col={name=D source=B bag+}} in=f:\2.txt" }));
}
public void TestCommandLine()
{
Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:TX:0} xf=Convert{col=B:A type=R4} in=f:\2.txt" }), (int)0);
}
public void TestCommandLine()
{
Assert.Equal(Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=NAReplace{col=C:A} in=f:\2.txt" }), (int)0);
}
public void TestCommandLine()
{
Assert.Equal(0, Maml.Main(new[] { @"showschema loader=Text{col=A:R4:0} xf=Cat{col=B:A} in=f:\2.txt" }));
}
/// <summary>
/// Invoke MAML with specified arguments without output baseline.
/// This method is used in unit tests when the output is not baselined.
/// If the output is to be baselined and compared, the other overload should be used.
/// </summary>
protected int MainForTest(string args)
{
return(Maml.MainCore(ML, args, false));
}
