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Updating OVA tests #2956

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Mar 14, 2019
Merged

Updating OVA tests #2956

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62 changes: 2 additions & 60 deletions test/Microsoft.ML.Functional.Tests/Training.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -451,23 +451,12 @@ public void MetacomponentsFunctionAsExpectedOva()
separatorChar: TestDatasets.iris.fileSeparator);

// Create a model training an OVA trainer with a binary classifier.
var anomalyDetectionTrainer = mlContext.AnomalyDetection.Trainers.RandomizedPca();
var anomalyDetectionPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(anomalyDetectionTrainer))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

// Fit the binary classification pipeline.
Assert.Throws<InvalidOperationException>(() => anomalyDetectionPipeline.Fit(data));

// Create a model training an OVA trainer with a binary classifier.
var binaryclassificationTrainer = mlContext.BinaryClassification.Trainers.LogisticRegression(
var binaryClassificationTrainer = mlContext.BinaryClassification.Trainers.LogisticRegression(
new LogisticRegressionBinaryClassificationTrainer.Options { MaximumNumberOfIterations = 10, NumberOfThreads = 1, });
var binaryClassificationPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryclassificationTrainer));
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryClassificationTrainer));

// Fit the binary classification pipeline.
var binaryClassificationModel = binaryClassificationPipeline.Fit(data);
Expand All @@ -477,53 +466,6 @@ public void MetacomponentsFunctionAsExpectedOva()

// Evaluate the model.
var binaryClassificationMetrics = mlContext.MulticlassClassification.Evaluate(binaryClassificationPredictions);

// Create a model training an OVA trainer with a clustering trainer.
var kmeansTrainer = mlContext.Clustering.Trainers.KMeans(
new KMeansTrainer.Options { MaximumNumberOfIterations = 10, NumberOfThreads = 1, });

Assert.Throws<ArgumentOutOfRangeException>(() =>
mlContext.Transforms.Concatenate("Features", Iris.Features)
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(kmeansTrainer))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel")));

// Create a model training an OVA trainer with a multiclass classification trainer.
var multiclassTrainer = mlContext.MulticlassClassification.Trainers.LogisticRegression(
new LogisticRegressionMulticlassClassificationTrainer.Options { MaximumNumberOfIterations = 10, NumberOfThreads = 1, });
Assert.Throws<ArgumentOutOfRangeException>(() =>
mlContext.Transforms.Concatenate("Features", Iris.Features)
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(multiclassTrainer))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel")));

// Create a model training an OVA trainer with a ranking trainer.
var rankingTrainer = mlContext.Ranking.Trainers.FastTree(
new FastTreeRankingTrainer.Options { NumberOfTrees = 2, NumberOfThreads = 1, });
// Todo #2920: Make this fail somehow.
var rankingPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(rankingTrainer))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

// Fit the invalid pipeline.
Assert.Throws<ArgumentOutOfRangeException>(() => rankingPipeline.Fit(data));

// Create a model training an OVA trainer with a regressor.
var regressionTrainer = mlContext.Regression.Trainers.PoissonRegression(
new PoissonRegressionTrainer.Options { MaximumNumberOfIterations = 10, NumberOfThreads = 1, });
// Todo #2920: Make this fail somehow.
var regressionPipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
.AppendCacheCheckpoint(mlContext)
.Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
.Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(regressionTrainer))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

// Fit the invalid pipeline.
Assert.Throws<ArgumentOutOfRangeException>(() => regressionPipeline.Fit(data));
}
}
}