public void SetupTrainingSpeedTests()
{
_dataPath_Digits = BaseTestClass.GetDataPath(TestDatasets.Digits.trainFilename);
if (!File.Exists(_dataPath_Digits))
{
throw new FileNotFoundException(string.Format(Errors.DatasetNotFound, _dataPath_Digits));
}
}
public static void Inicializar(TestContext testContext)
{
Initialize(testContext);
Usuario usuario = DefaultObjects.ObtemUsuarioPadrao();
usuario.AdicionarPerfil(Enumeradores.Perfil.AgendadorDeCargas);
DefaultPersistedObjects.PersistirUsuario(usuario);
BaseTestClass.SubstituirUsuarioConectado(new UsuarioConectado(usuario.Login, usuario.Nome, usuario.Perfis));
}
public void SetupTrainingSpeedTests()
{
_mslrWeb10k_Validate = BaseTestClass.GetDataPath(TestDatasets.MSLRWeb.validFilename);
_mslrWeb10k_Train = BaseTestClass.GetDataPath(TestDatasets.MSLRWeb.trainFilename);
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));
}
}
public void Component_DeserializeWithMissingFields_NoError()
{
//Arrange
BaseTestClass toSerialize = _fixture.Create <BaseTestClass>();
string schema = AvroConvert.GenerateSchema(typeof(BaseTestClass));
//Act
var result = AvroConvert.SerializeHeadless(toSerialize, schema);
var deserialized = AvroConvert.DeserializeHeadless <ReducedBaseTestClass>(result, schema);
//Assert
Assert.NotNull(result);
Assert.NotNull(deserialized);
Assert.Equal(toSerialize.justSomeProperty, deserialized.justSomeProperty);
}
public void TrainWithValidationSet()
{
var mlContext = new MLContext(seed: 1);
// Get the dataset.
var data = mlContext.Data.CreateTextLoader(TestDatasets.housing.GetLoaderColumns(),
hasHeader: TestDatasets.housing.fileHasHeader, separatorChar: TestDatasets.housing.fileSeparator)
.Load(BaseTestClass.GetDataPath(TestDatasets.housing.trainFilename));
var dataSplit = mlContext.Regression.TrainTestSplit(data, testFraction: 0.2);
var trainData = dataSplit.TrainSet;
var validData = dataSplit.TestSet;
// Create a pipeline to featurize the dataset.
var pipeline = mlContext.Transforms.Concatenate("Features", new string[] {
"CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", "RoomsPerDwelling",
"PercentPre40s", "EmploymentDistance", "HighwayDistance", "TaxRate", "TeacherRatio"
})
.Append(mlContext.Transforms.CopyColumns("Label", "MedianHomeValue"))
.AppendCacheCheckpoint(mlContext) as IEstimator <ITransformer>;
// Preprocess the datasets.
var preprocessor = pipeline.Fit(trainData);
var preprocessedTrainData = preprocessor.Transform(trainData);
var preprocessedValidData = preprocessor.Transform(validData);
// Train the model with a validation set.
var trainedModel = mlContext.Regression.Trainers.FastTree(new Trainers.FastTree.FastTreeRegressionTrainer.Options {
NumberOfTrees = 2,
EarlyStoppingMetric = EarlyStoppingMetric.L2Norm,
EarlyStoppingRule = new GeneralityLossRule()
})
.Fit(trainData: preprocessedTrainData, validationData: preprocessedValidData);
// Combine the model.
var model = preprocessor.Append(trainedModel);
// Score the data sets.
var scoredTrainData = model.Transform(trainData);
var scoredValidData = model.Transform(validData);
var trainMetrics = mlContext.Regression.Evaluate(scoredTrainData);
var validMetrics = mlContext.Regression.Evaluate(scoredValidData);
Common.AssertMetrics(trainMetrics);
Common.AssertMetrics(validMetrics);
}
public void Component_SerializeAndDeserializeClassesWithDifferentPropertyCases_NoError()
{
//Arrange
BaseTestClass toSerialize = _fixture.Create <BaseTestClass>();
//Act
var result = AvroConvert.Serialize(toSerialize);
var deserialized = AvroConvert.Deserialize <DifferentCaseBaseTestClass>(result);
//Assert
Assert.NotNull(result);
Assert.NotNull(deserialized);
Assert.Equal(toSerialize.justSomeProperty, deserialized.JustSomeProperty);
Assert.Equal(toSerialize.andLongProperty, deserialized.AndLongProperty);
}
public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = BaseTestClass.GetDataPath("iris.txt");
var env = new MLContext(seed: 1);
// Create text loader.
var options = new TextLoader.Options()
{
Columns = new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column("SepalLength", DataKind.Single, 1),
new TextLoader.Column("SepalWidth", DataKind.Single, 2),
new TextLoader.Column("PetalLength", DataKind.Single, 3),
new TextLoader.Column("PetalWidth", DataKind.Single, 4),
},
HasHeader = true,
};
var loader = new TextLoader(env, options: options);
IDataView data = loader.Load(_irisDataPath);
var pipeline = new ColumnConcatenatingEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
.Append(env.Transforms.Conversion.MapValueToKey("Label"))
.Append(env.MulticlassClassification.Trainers.SdcaCalibrated(
new SdcaCalibratedMulticlassTrainer.Options {
NumberOfThreads = 1, ConvergenceTolerance = 1e-2f,
}));
var model = pipeline.Fit(data);
_irisModel = env.Model.CreatePredictionEngine <IrisData, IrisPrediction>(model);
}
public void SetupScoringSpeedTests()
{
_dataPath_Wiki = BaseTestClass.GetDataPath(TestDatasets.WikiDetox.trainFilename);
if (!File.Exists(_dataPath_Wiki))
{
throw new FileNotFoundException(string.Format(Errors.DatasetNotFound, _dataPath_Wiki));
}
_modelPath_Wiki = Path.Combine(Path.GetDirectoryName(typeof(MulticlassClassificationTest).Assembly.Location), @"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, LinearBinaryModelParameters>();
cmd.ExecuteMamlCommand(environment);
}
public void SetupIrisPipeline()
{
_irisExample = new IrisData()
{
SepalLength = 3.3f,
SepalWidth = 1.6f,
PetalLength = 0.2f,
PetalWidth = 5.1f,
};
string _irisDataPath = BaseTestClass.GetDataPath("iris.txt");
var env = new MLContext(seed: 1, conc: 1);
var reader = new TextLoader(env,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("SepalLength", DataKind.R4, 1),
new TextLoader.Column("SepalWidth", DataKind.R4, 2),
new TextLoader.Column("PetalLength", DataKind.R4, 3),
new TextLoader.Column("PetalWidth", DataKind.R4, 4),
},
hasHeader: true
);
IDataView data = reader.Read(_irisDataPath);
var pipeline = new ColumnConcatenatingEstimator(env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
.Append(env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(
new SdcaMultiClassTrainer.Options {
NumThreads = 1, ConvergenceTolerance = 1e-2f,
}));
var model = pipeline.Fit(data);
_irisModel = model.CreatePredictionEngine <IrisData, IrisPrediction>(env);
}
public void SetupScoringSpeedTests()
{
_mslrWeb10k_Test = BaseTestClass.GetDataPath(TestDatasets.MSLRWeb.testFilename);
_mslrWeb10k_Validate = BaseTestClass.GetDataPath(TestDatasets.MSLRWeb.validFilename);
_mslrWeb10k_Train = BaseTestClass.GetDataPath(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(Path.GetDirectoryName(typeof(RankingTest).Assembly.Location), "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 + "}";
var environment = EnvironmentFactory.CreateRankingEnvironment <RankerEvaluator, TextLoader, HashingTransformer, FastTreeRankingTrainer, FastTreeRankingModelParameters>();
cmd.ExecuteMamlCommand(environment);
}
/// <summary>
/// Sets the BaseTestClass
/// </summary>
/// <param name="btc">Current BaseTestClass</param>
public void setBaseTestClass(BaseTestClass btc)
{
this.btc = btc;
//Sets the local driver using the driver from the BaseTeestClass
setDriver(this.btc.getDriver());
}
public firstPageView(BaseTestClass objBaseTestClass)
{
this.objBaseTestClass = objBaseTestClass;
}
/// <summary>
/// Constructor that defines page timeouts
/// </summary>
/// <param name="testClass">BaseTestClass object that contains the WebDriver</param>
public BasePageClass_BlueSource(BaseTestClass testClass)
{
setDefaultPageLoadTimeout(defaultPageTimeout);
setImplicitWaitTimeout(defaultImplicitWaitTimeout);
}
public void Is_ParameterValueIsInterfaceOfType_DoesNothing()
{
ITestInterface baseTestObject = new BaseTestClass();
Argument.Is((object)baseTestObject, typeof(ITestInterface), Option<string>.None);
Argument.Is((object)baseTestObject, typeof(BaseTestClass), Option<string>.None);
ITestInterface derivedTestObject = new DerivedTestClass();
Argument.Is((object)derivedTestObject, typeof(ITestInterface), Option<string>.None);
Argument.Is((object)derivedTestObject, typeof(BaseTestClass), Option<string>.None);
}
public static void Finalizar()
{
BaseTestClass.RestaurarUsuarioConectado();
Cleanup();
}
public void setBaseTestClass(BaseTestClass btc)
{
this.btc = btc;
}
public Page_LoginPage(BaseTestClass btc)
{
setBaseTestClass(btc);
verifyLoginPageURL();
}
