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Fix SDCA sample runtime exception. #3259

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Merged
merged 3 commits into from
Apr 9, 2019
Merged

Fix SDCA sample runtime exception. #3259

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762750d
Fix SDCA sample runtime exception.
codemzs Apr 9, 2019
44e01d0
PR feedback.
codemzs Apr 9, 2019
38a75f4
More fixes.
codemzs Apr 9, 2019
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8 changes: 4 additions & 4 deletions ...rosoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscent.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -47,9 +47,9 @@ public static void Example()
// Featurize the text column through the FeaturizeText API.
// Then append a binary classifier, setting the "Label" column as the label of the dataset, and
// the "Features" column produced by FeaturizeText as the features column.
var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText")
.AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
.Append(mlContext.BinaryClassification.Trainers.SdcaNonCalibrated(labelColumnName: "Sentiment", featureColumnName: "Features", l2Regularization: 0.001f));
.Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Sentiment", featureColumnName: "Features", l2Regularization: 0.001f));

// Step 3: Run Cross-Validation on this pipeline.
var cvResults = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumnName: "Sentiment");
Expand All @@ -59,7 +59,7 @@ public static void Example()

// If we wanted to specify more advanced parameters for the algorithm,
// we could do so by tweaking the 'advancedSetting'.
var advancedPipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
var advancedPipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText")
.Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(
new SdcaLogisticRegressionBinaryTrainer.Options {
LabelColumnName = "Sentiment",
Expand All @@ -69,7 +69,7 @@ public static void Example()
}));

// Run Cross-Validation on this second pipeline.
var cvResults_advancedPipeline = mlContext.BinaryClassification.CrossValidate(data, pipeline, labelColumnName: "Sentiment", numberOfFolds: 3);
var cvResults_advancedPipeline = mlContext.BinaryClassification.CrossValidate(data, advancedPipeline, labelColumnName: "Sentiment", numberOfFolds: 3);
accuracies = cvResults_advancedPipeline.Select(r => r.Metrics.Accuracy);
Console.WriteLine(accuracies.Average());

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