public void IncorrectLabelColumnThrows()
{
var dataPath = DatasetUtil.GetUciAdultDataset();
var context = new MLContext(1);
Assert.Throws <ArgumentException>(new System.Action(() => context.Auto().InferColumns(dataPath, "Junk", groupColumns: false)));
}
public void AutoFitImageClassificationTrainTest()
{
var context = new MLContext(seed: 1);
var datasetPath = DatasetUtil.GetFlowersDataset();
var columnInference = context.Auto().InferColumns(datasetPath, "Label");
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = context.Data.ShuffleRows(textLoader.Load(datasetPath), seed: 1);
var originalColumnNames = trainData.Schema.Select(c => c.Name);
TrainTestData trainTestData = context.Data.TrainTestSplit(trainData, testFraction: 0.2, seed: 1);
IDataView trainDataset = SplitUtil.DropAllColumnsExcept(context, trainTestData.TrainSet, originalColumnNames);
IDataView testDataset = SplitUtil.DropAllColumnsExcept(context, trainTestData.TestSet, originalColumnNames);
var result = context.Auto()
.CreateMulticlassClassificationExperiment(0)
.Execute(trainDataset, testDataset, columnInference.ColumnInformation);
//Known issue, where on Ubuntu there is degradation in accuracy.
if (!(RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ||
RuntimeInformation.IsOSPlatform(OSPlatform.OSX)))
{
Assert.Equal(0.778, result.BestRun.ValidationMetrics.MicroAccuracy, 3);
}
else
{
Assert.Equal(1, result.BestRun.ValidationMetrics.MicroAccuracy, 3);
}
var scoredData = result.BestRun.Model.Transform(trainData);
Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type);
}
public void GetNextPipeline()
{
var context = new MLContext(1);
var uciAdult = DatasetUtil.GetUciAdultDataView();
var columns = DatasetColumnInfoUtil.GetDatasetColumnInfo(context, uciAdult, new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
});
// get next pipeline
var pipeline = PipelineSuggester.GetNextPipeline(context, new List <PipelineScore>(), columns, TaskKind.BinaryClassification);
// serialize & deserialize pipeline
var serialized = JsonConvert.SerializeObject(pipeline);
Console.WriteLine(serialized);
var deserialized = JsonConvert.DeserializeObject <Pipeline>(serialized);
// run pipeline
var estimator = deserialized.ToEstimator(context);
var scoredData = estimator.Fit(uciAdult).Transform(uciAdult);
var score = context.BinaryClassification.EvaluateNonCalibrated(scoredData).Accuracy;
var result = new PipelineScore(deserialized, score, true);
Assert.NotNull(result);
}
public void AutoFitMaxExperimentTimeTest()
{
// A single binary classification experiment takes less than 5 seconds.
// System.OperationCanceledException is thrown when ongoing experiment
// is canceled and at least one model has been generated.
// BinaryClassificationExperiment includes LightGBM, which is not 32-bit
// compatible.
var context = new MLContext(1);
var dataPath = DatasetUtil.GetUciAdultDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var experiment = context.Auto()
.CreateBinaryClassificationExperiment(15)
.Execute(trainData, new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
});
// Ensure the (last) model that was training when maximum experiment time was reached has been stopped,
// and that its MLContext has been canceled. Sometimes during CI unit testing, the host machines can run slower than normal, which
// can increase the run time of unit tests, and may not produce multiple runs.
if (experiment.RunDetails.Select(r => r.Exception == null).Count() > 1 && experiment.RunDetails.Last().Exception != null)
{
Assert.True(experiment.RunDetails.Last().Exception.Message.Contains("Operation was canceled"),
"Training process was not successfully canceled after maximum experiment time was reached.");
// Ensure that the best found model can still run after maximum experiment time was reached.
IDataView predictions = experiment.BestRun.Model.Transform(trainData);
}
}
public void AutoFeaturizer_iris_test()
{
var context = new MLContext(1);
var dataset = DatasetUtil.GetIrisDataView();
var pipeline = context.Auto().Featurizer(dataset, excludeColumns: new[] { "Label" });
Approvals.Verify(JsonSerializer.Serialize(pipeline, _jsonSerializerOptions));
}
public void AutoFeaturizer_uci_adult_test()
{
var context = new MLContext(1);
var dataset = DatasetUtil.GetUciAdultDataView();
var pipeline = context.Auto().Featurizer(dataset, outputColumnName: "OutputFeature", excludeColumns: new[] { "Label" });
Approvals.Verify(JsonSerializer.Serialize(pipeline, _jsonSerializerOptions));
}
public void IdentifyLabelColumnThroughIndexWithHeader()
{
var result = new MLContext().Auto().InferColumns(DatasetUtil.DownloadUciAdultDataset(), 14, hasHeader: true);
Assert.True(result.TextLoaderOptions.HasHeader);
var labelCol = result.TextLoaderOptions.Columns.First(c => c.Source[0].Min == 14 && c.Source[0].Max == 14);
Assert.Equal("hours-per-week", labelCol.Name);
Assert.Equal("hours-per-week", result.ColumnInformation.LabelColumnName);
}
public void AutoFitRankingTest()
{
string labelColumnName = "Label";
string scoreColumnName = "Score";
string groupIdColumnName = "GroupId";
string featuresColumnVectorNameA = "FeatureVectorA";
string featuresColumnVectorNameB = "FeatureVectorB";
var mlContext = new MLContext(1);
// STEP 1: Load data
var reader = new TextLoader(mlContext, GetLoaderArgsRank(labelColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB));
var trainDataView = reader.Load(new MultiFileSource(DatasetUtil.GetMLSRDataset()));
var testDataView = mlContext.Data.TakeRows(trainDataView, 500);
trainDataView = mlContext.Data.SkipRows(trainDataView, 500);
// STEP 2: Run AutoML experiment
var experiment = mlContext.Auto()
.CreateRankingExperiment(5);
ExperimentResult <RankingMetrics>[] experimentResults =
{
experiment.Execute(trainDataView, labelColumnName, groupIdColumnName),
experiment.Execute(trainDataView, testDataView),
experiment.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = labelColumnName,
GroupIdColumnName = groupIdColumnName,
}),
experiment.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = labelColumnName,
GroupIdColumnName = groupIdColumnName,
SamplingKeyColumnName = groupIdColumnName
})
};
for (int i = 0; i < experimentResults.Length; i++)
{
RunDetail <RankingMetrics> bestRun = experimentResults[i].BestRun;
Assert.True(experimentResults[i].RunDetails.Count() > 0);
Assert.NotNull(bestRun.ValidationMetrics);
Assert.True(bestRun.ValidationMetrics.NormalizedDiscountedCumulativeGains.Last() > 0.4);
Assert.True(bestRun.ValidationMetrics.DiscountedCumulativeGains.Last() > 20);
var outputSchema = bestRun.Model.GetOutputSchema(trainDataView.Schema);
var expectedOutputNames = new string[] { labelColumnName, groupIdColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB,
"Features", scoreColumnName };
foreach (var col in outputSchema)
{
Assert.True(col.Name == expectedOutputNames[col.Index]);
}
}
}
public void IdentifyLabelColumnThroughIndexWithoutHeader()
{
var result = new MLContext().Auto().InferColumns(DatasetUtil.DownloadIrisDataset(), DatasetUtil.IrisDatasetLabelColIndex);
Assert.False(result.TextLoaderOptions.HasHeader);
var labelCol = result.TextLoaderOptions.Columns.First(c => c.Source[0].Min == DatasetUtil.IrisDatasetLabelColIndex &&
c.Source[0].Max == DatasetUtil.IrisDatasetLabelColIndex);
Assert.Equal(DefaultColumnNames.Label, labelCol.Name);
Assert.Equal(DefaultColumnNames.Label, result.ColumnInformation.LabelColumnName);
}
public void AutoFitMaxExperimentTimeTest()
{
// A single binary classification experiment takes less than 5 seconds.
// System.OperationCanceledException is thrown when ongoing experiment
// is canceled and at least one model has been generated.
// BinaryClassificationExperiment includes LightGBM, which is not 32-bit
// compatible.
var context = new MLContext(1);
var dataPath = DatasetUtil.GetUciAdultDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var experiment = context.Auto()
.CreateBinaryClassificationExperiment(15)
.Execute(trainData, new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
});
// Ensure the (last) model that was training when maximum experiment time was reached has been stopped,
// and that its MLContext has been canceled. Sometimes during CI unit testing, the host machines can run slower than normal, which
// can increase the run time of unit tests, and may not produce multiple runs.
if (experiment.RunDetails.Select(r => r.Exception == null).Count() > 1 && experiment.RunDetails.Last().Exception != null)
{
var expectedExceptionMessage = "Operation was canceled";
var lastException = experiment.RunDetails.Last().Exception;
var containsMessage = lastException.Message.Contains(expectedExceptionMessage);
if (lastException is AggregateException lastAggregateException)
{
// Sometimes multiple threads might throw the same "Operation was cancelled"
// exception and all of them are grouped inside an AggregateException
// Must check that all exceptions are the expected one.
containsMessage = true;
foreach (var ex in lastAggregateException.Flatten().InnerExceptions)
{
if (!ex.Message.Contains(expectedExceptionMessage))
{
containsMessage = false;
}
}
}
Assert.True(containsMessage,
$"Did not obtain '{expectedExceptionMessage}' error." +
$"Obtained unexpected error of type {lastException.GetType()} with message: {lastException.Message}");
// Ensure that the best found model can still run after maximum experiment time was reached.
IDataView predictions = experiment.BestRun.Model.Transform(trainData);
}
}
public void AutoFitRegressionTest(string culture)
{
var originalCulture = Thread.CurrentThread.CurrentCulture;
try
{
Thread.CurrentThread.CurrentCulture = new CultureInfo(culture);
// If users run AutoML with a different locale, sometimes
// the sweeper encounters problems when parsing some strings.
// So testing in another culture is necessary.
// Furthermore, these issues might only occur after ~70
// iterations, so more experiment time is needed for this to
// occur.
uint experimentTime = (uint)(culture == "en-US" ? 0 : 180);
var experimentSettings = new RegressionExperimentSettings {
MaxExperimentTimeInSeconds = experimentTime
};
if (!Environment.Is64BitProcess)
{
// LightGBM isn't available on x86 machines
experimentSettings.Trainers.Remove(RegressionTrainer.LightGbm);
}
var context = new MLContext(1);
var dataPath = DatasetUtil.GetMlNetGeneratedRegressionDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.MlNetGeneratedRegressionLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var validationData = context.Data.TakeRows(trainData, 20);
trainData = context.Data.SkipRows(trainData, 20);
var result = context.Auto()
.CreateRegressionExperiment(experimentSettings)
.Execute(trainData, validationData,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
});
Assert.True(result.RunDetails.Max(i => i.ValidationMetrics.RSquared > 0.9));
// Ensure experimentTime allows enough iterations to fully test the internationalization code
// If the below assertion fails, increase the experiment time so the number of iterations is met
Assert.True(culture == "en-US" || result.RunDetails.Count() >= 75, $"RunDetails.Count() = {result.RunDetails.Count()}, below 75");
}
finally
{
Thread.CurrentThread.CurrentCulture = originalCulture;
}
}
public void AutoFitRegressionTest(string culture)
{
var originalCulture = Thread.CurrentThread.CurrentCulture;
try
{
Thread.CurrentThread.CurrentCulture = new CultureInfo(culture);
// If users run AutoML with a different locale, sometimes
// the sweeper encounters problems when parsing some strings.
// So testing in another culture is necessary.
// Furthermore, these issues might only occur after ~70
// iterations, so setting the internal maxModels parameter.
int maxModels = culture == "en-US" ? 1 : 75;
var experimentSettings = new RegressionExperimentSettings {
MaxModels = maxModels
};
if (!Environment.Is64BitProcess)
{
// LightGBM isn't available on x86 machines
experimentSettings.Trainers.Remove(RegressionTrainer.LightGbm);
}
var context = new MLContext(1);
var dataPath = DatasetUtil.GetMlNetGeneratedRegressionDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.MlNetGeneratedRegressionLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var validationData = context.Data.TakeRows(trainData, 20);
trainData = context.Data.SkipRows(trainData, 20);
var result = context.Auto()
.CreateRegressionExperiment(experimentSettings)
.Execute(trainData, validationData,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
});
Assert.True(result.RunDetails.Max(i => i?.ValidationMetrics?.RSquared) > 0.99);
// Test the internal maxModels parameter
Assert.True(culture == "en-US" || result.RunDetails.Count() == 75, $"RunDetails.Count() = {result.RunDetails.Count()}, is not 75");
}
finally
{
Thread.CurrentThread.CurrentCulture = originalCulture;
}
}
public void AutoFit_UCI_Adult_CrossValidation_10_Test()
{
var context = new MLContext(1);
var dataPath = DatasetUtil.GetUciAdultDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var result = context.Auto()
.CreateBinaryClassificationExperiment(1)
.Execute(trainData, 10, DatasetUtil.UciAdultLabel);
Assert.True(result.BestRun.Results.Select(x => x.ValidationMetrics.Accuracy).Min() > 0.70);
Assert.NotNull(result.BestRun.Estimator);
Assert.NotNull(result.BestRun.TrainerName);
}
public void UnGroupReturnsMoreColumnsThanGroup()
{
var dataPath = DatasetUtil.DownloadUciAdultDataset();
var context = new MLContext();
var columnInferenceWithoutGrouping = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel, groupColumns: false);
foreach (var col in columnInferenceWithoutGrouping.TextLoaderOptions.Columns)
{
Assert.False(col.Source.Length > 1 || col.Source[0].Min != col.Source[0].Max);
}
var columnInferenceWithGrouping = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel, groupColumns: true);
Assert.True(columnInferenceWithGrouping.TextLoaderOptions.Columns.Count() < columnInferenceWithoutGrouping.TextLoaderOptions.Columns.Count());
}
public void InferColumnsColumnInfoParam()
{
var columnInfo = new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
};
var result = new MLContext().Auto().InferColumns(DatasetUtil.DownloadMlNetGeneratedRegressionDataset(),
columnInfo);
var labelCol = result.TextLoaderOptions.Columns.First(c => c.Name == DatasetUtil.MlNetGeneratedRegressionLabel);
Assert.Equal(DataKind.Single, labelCol.DataKind);
Assert.Equal(DatasetUtil.MlNetGeneratedRegressionLabel, result.ColumnInformation.LabelColumnName);
Assert.Single(result.ColumnInformation.NumericColumnNames);
Assert.Equal(DefaultColumnNames.Features, result.ColumnInformation.NumericColumnNames.First());
Assert.Null(result.ColumnInformation.ExampleWeightColumnName);
}
public void AutoFit_UCI_Adult_Train_Test_Split_Test()
{
var context = new MLContext(1);
var dataPath = DatasetUtil.GetUciAdultDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var dataTrainTest = context.Data.TrainTestSplit(trainData);
var result = context.Auto()
.CreateBinaryClassificationExperiment(1)
.Execute(dataTrainTest.TrainSet, dataTrainTest.TestSet, DatasetUtil.UciAdultLabel);
Assert.True(result.BestRun.ValidationMetrics.Accuracy > 0.70);
Assert.NotNull(result.BestRun.Estimator);
Assert.NotNull(result.BestRun.Model);
Assert.NotNull(result.BestRun.TrainerName);
}
public void GetNextPipelineMock()
{
var context = new MLContext(1);
var uciAdult = DatasetUtil.GetUciAdultDataView();
var columns = DatasetColumnInfoUtil.GetDatasetColumnInfo(context, uciAdult, new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
});
// Get next pipeline loop
var history = new List <PipelineScore>();
var task = TaskKind.BinaryClassification;
var maxIterations = 60;
for (var i = 0; i < maxIterations; i++)
{
// Get next pipeline
var pipeline = PipelineSuggester.GetNextPipeline(context, history, columns, task, ((IChannelProvider)context).Start("AutoMLTest"));
if (pipeline == null)
{
break;
}
var result = new PipelineScore(pipeline, AutoMlUtils.Random.Value.NextDouble(), true);
history.Add(result);
}
Assert.Equal(maxIterations, history.Count);
// Get all 'Stage 1' and 'Stage 2' runs from Pipeline Suggester
var allAvailableTrainers = RecipeInference.AllowedTrainers(context, task, new ColumnInformation(), null);
var stage1Runs = history.Take(allAvailableTrainers.Count());
var stage2Runs = history.Skip(allAvailableTrainers.Count());
// Get the trainer names from top 3 Stage 1 runs
var topStage1Runs = stage1Runs.OrderByDescending(r => r.Score).Take(3);
var topStage1TrainerNames = topStage1Runs.Select(r => r.Pipeline.Nodes.Last().Name);
// Get unique trainer names from Stage 2 runs
var stage2TrainerNames = stage2Runs.Select(r => r.Pipeline.Nodes.Last().Name).Distinct();
// Assert that are only 3 unique trainers used in stage 2
Assert.Equal(3, stage2TrainerNames.Count());
// Assert that all trainers in stage 2 were the top trainers from stage 1
Assert.False(topStage1TrainerNames.Except(stage2TrainerNames).Any());
}
public void AutoFitRankingTest()
{
string labelColumnName = "Label";
string scoreColumnName = "Score";
string groupIdColumnName = "CustomGroupId";
string featuresColumnVectorNameA = "FeatureVectorA";
string featuresColumnVectorNameB = "FeatureVectorB";
var mlContext = new MLContext(1);
// STEP 1: Load data
var reader = new TextLoader(mlContext, GetLoaderArgsRank(labelColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB));
var trainDataView = reader.Load(new MultiFileSource(DatasetUtil.GetMLSRDataset()));
var testDataView = mlContext.Data.TakeRows(trainDataView, 500);
trainDataView = mlContext.Data.SkipRows(trainDataView, 500);
// STEP 2: Run AutoML experiment
ExperimentResult <RankingMetrics> experimentResult = mlContext.Auto()
.CreateRankingExperiment(new RankingExperimentSettings()
{
GroupIdColumnName = "CustomGroupId", MaxExperimentTimeInSeconds = 5
})
.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = labelColumnName,
GroupIdColumnName = groupIdColumnName
});
RunDetail <RankingMetrics> bestRun = experimentResult.BestRun;
Assert.True(experimentResult.RunDetails.Count() > 0);
Assert.NotNull(bestRun.ValidationMetrics);
Assert.True(experimentResult.RunDetails.Max(i => i.ValidationMetrics.NormalizedDiscountedCumulativeGains.Max() > .5));
Assert.True(experimentResult.RunDetails.Max(i => i.ValidationMetrics.DiscountedCumulativeGains.Max() > 34));
var outputSchema = bestRun.Model.GetOutputSchema(trainDataView.Schema);
var expectedOutputNames = new string[] { labelColumnName, groupIdColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB,
"Features", scoreColumnName };
foreach (var col in outputSchema)
{
Assert.True(col.Name == expectedOutputNames[col.Index]);
}
}
public void AutoFitBinaryTest()
{
var context = new MLContext();
var dataPath = DatasetUtil.DownloadUciAdultDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var result = context.Auto()
.CreateBinaryClassificationExperiment(0)
.Execute(trainData, new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
});
Assert.True(result.BestRun.ValidationMetrics.Accuracy > 0.70);
Assert.NotNull(result.BestRun.Estimator);
Assert.NotNull(result.BestRun.Model);
Assert.NotNull(result.BestRun.TrainerName);
}
public void AutoFitRankingCVTest()
{
string labelColumnName = "Label";
string groupIdColumnName = "GroupIdCustom";
string featuresColumnVectorNameA = "FeatureVectorA";
string featuresColumnVectorNameB = "FeatureVectorB";
uint numFolds = 3;
var mlContext = new MLContext(1);
var reader = new TextLoader(mlContext, GetLoaderArgsRank(labelColumnName, groupIdColumnName,
featuresColumnVectorNameA, featuresColumnVectorNameB));
var trainDataView = reader.Load(DatasetUtil.GetMLSRDataset());
// Take less than 1500 rows of data to satisfy CrossValSummaryRunner's
// limit.
trainDataView = mlContext.Data.TakeRows(trainDataView, 1499);
var experiment = mlContext.Auto()
.CreateRankingExperiment(5);
CrossValidationExperimentResult <RankingMetrics>[] experimentResults =
{
experiment.Execute(trainDataView, numFolds,
new ColumnInformation()
{
LabelColumnName = labelColumnName,
GroupIdColumnName = groupIdColumnName
}),
experiment.Execute(trainDataView, numFolds,labelColumnName, groupIdColumnName)
};
for (int i = 0; i < experimentResults.Length; i++)
{
CrossValidationRunDetail <RankingMetrics> bestRun = experimentResults[i].BestRun;
Assert.True(experimentResults[i].RunDetails.Count() > 0);
var enumerator = bestRun.Results.GetEnumerator();
while (enumerator.MoveNext())
{
var model = enumerator.Current;
Assert.True(model.ValidationMetrics.NormalizedDiscountedCumulativeGains.Max() > 0.31);
Assert.True(model.ValidationMetrics.DiscountedCumulativeGains.Max() > 15);
}
}
}
public void AutoFitContextLogTest()
{
// This test confirms that logs produced from contexts made during AutoML experiment
// runs are correctly relayed to the main Experiment MLContext.
_markerAutoFitContextLogTest = false;
var context = new MLContext(1);
context.Log += MlContextLog;
var datasetPath = DatasetUtil.GetFlowersDataset();
var columnInference = context.Auto().InferColumns(datasetPath, "Label");
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(datasetPath);
var result = context.Auto()
.CreateMulticlassClassificationExperiment(15)
.Execute(trainData, columnInference.ColumnInformation);
Assert.True(_markerAutoFitContextLogTest, "Image classification trainer logs from Experiment's sub contexts" +
"were not relayed to the main MLContext.");
}
public void AutoFitRegressionTest()
{
var context = new MLContext();
var dataPath = DatasetUtil.DownloadMlNetGeneratedRegressionDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.MlNetGeneratedRegressionLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var validationData = context.Data.TakeRows(trainData, 20);
trainData = context.Data.SkipRows(trainData, 20);
var result = context.Auto()
.CreateRegressionExperiment(0)
.Execute(trainData, validationData,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
});
Assert.True(result.RunDetails.Max(i => i.ValidationMetrics.RSquared > 0.9));
}
public void AutoFitImageClassification()
{
// This test executes the code path that model builder code will take to get a model using image
// classification API.
var context = new MLContext(1);
context.Log += Context_Log;
var datasetPath = DatasetUtil.GetFlowersDataset();
var columnInference = context.Auto().InferColumns(datasetPath, "Label");
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(datasetPath);
var result = context.Auto()
.CreateMulticlassClassificationExperiment(0)
.Execute(trainData, columnInference.ColumnInformation);
Assert.InRange(result.BestRun.ValidationMetrics.MicroAccuracy, 0.80, 0.9);
var scoredData = result.BestRun.Model.Transform(trainData);
Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type);
}
public void AutoFitRegressionTest(string culture)
{
var originalCulture = Thread.CurrentThread.CurrentCulture;
try
{
Thread.CurrentThread.CurrentCulture = new CultureInfo(culture);
// If users run AutoML with a different locale, sometimes
// the sweeper encounters problems when parsing some strings.
// So testing in another culture is necessary.
// Furthermore, these issues might only occur after ~70
// iterations, so more experiment time is needed for this to
// occur.
uint experimentTime = (uint)(culture == "en-US" ? 0 : 180);
var experimentSettings = new RegressionExperimentSettings {
MaxExperimentTimeInSeconds = experimentTime
};
if (!Environment.Is64BitProcess)
{
// LightGBM isn't available on x86 machines
experimentSettings.Trainers.Remove(RegressionTrainer.LightGbm);
}
var context = new MLContext(1);
var dataPath = DatasetUtil.GetMlNetGeneratedRegressionDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.MlNetGeneratedRegressionLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var trainData = textLoader.Load(dataPath);
var validationData = context.Data.TakeRows(trainData, 20);
trainData = context.Data.SkipRows(trainData, 20);
var result = context.Auto()
.CreateRegressionExperiment(experimentSettings)
.Execute(trainData, validationData,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.MlNetGeneratedRegressionLabel
});
Assert.True(result.RunDetails.Max(i => i?.ValidationMetrics?.RSquared) > 0.9);
// Ensure experimentTime allows enough iterations to fully test the internationalization code
// If the below assertion fails, increase the experiment time so the number of iterations is met
Assert.True(culture == "en-US" || result.RunDetails.Count() >= 75, $"RunDetails.Count() = {result.RunDetails.Count()}, below 75");
}
catch (AggregateException ae)
{
// During CI unit testing, the host machines can run slower than normal, which
// can increase the run time of unit tests and throw OperationCanceledExceptions
// from multiple threads in the form of a single AggregateException.
foreach (var ex in ae.Flatten().InnerExceptions)
{
var ignoredExceptions = new List <Exception>();
if (ex is OperationCanceledException)
{
continue;
}
else
{
ignoredExceptions.Add(ex);
}
if (ignoredExceptions.Count > 0)
{
throw new AggregateException(ignoredExceptions);
}
}
}
finally
{
Thread.CurrentThread.CurrentCulture = originalCulture;
}
}
public void AutoFitWithPresplittedData()
{
// Models created in AutoML should work over the same data,
// no matter how that data is splitted before passing it to the experiment execution
// or to the model for prediction
var context = new MLContext(1);
var dataPath = DatasetUtil.GetUciAdultDataset();
var columnInference = context.Auto().InferColumns(dataPath, DatasetUtil.UciAdultLabel);
var textLoader = context.Data.CreateTextLoader(columnInference.TextLoaderOptions);
var dataFull = textLoader.Load(dataPath);
var dataTrainTest = context.Data.TrainTestSplit(dataFull);
var dataCV = context.Data.CrossValidationSplit(dataFull, numberOfFolds: 2);
var modelFull = context.Auto()
.CreateBinaryClassificationExperiment(0)
.Execute(dataFull,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
})
.BestRun
.Model;
var modelTrainTest = context.Auto()
.CreateBinaryClassificationExperiment(0)
.Execute(dataTrainTest.TrainSet,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
})
.BestRun
.Model;
var modelCV = context.Auto()
.CreateBinaryClassificationExperiment(0)
.Execute(dataCV.First().TrainSet,
new ColumnInformation()
{
LabelColumnName = DatasetUtil.UciAdultLabel
})
.BestRun
.Model;
var models = new[] { modelFull, modelTrainTest, modelCV };
foreach (var model in models)
{
var resFull = model.Transform(dataFull);
var resTrainTest = model.Transform(dataTrainTest.TrainSet);
var resCV = model.Transform(dataCV.First().TrainSet);
Assert.Equal(30, resFull.Schema.Count);
Assert.Equal(30, resTrainTest.Schema.Count);
Assert.Equal(30, resCV.Schema.Count);
foreach (var col in resFull.Schema)
{
Assert.Equal(col.Name, resTrainTest.Schema[col.Index].Name);
Assert.Equal(col.Name, resCV.Schema[col.Index].Name);
}
}
}
public void ValidateInferColsPath()
{
UserInputValidationUtil.ValidateInferColumnsArgs(DatasetUtil.GetUciAdultDataset());
}
public void LabelIndexOutOfBoundsThrows()
{
Assert.Throws <ArgumentOutOfRangeException>(() => new MLContext().Auto().InferColumns(DatasetUtil.DownloadUciAdultDataset(), 100));
}
