1 write to Categorical
Microsoft.ML.Data (1)
Transforms\TransformsCatalog.cs (1)
42Categorical = new CategoricalTransforms(this);
70 references to Categorical
Microsoft.ML.AutoML (4)
EstimatorExtensions\EstimatorExtensions.cs (2)
190return context.Transforms.Categorical.OneHotEncoding(cols); 221return context.Transforms.Categorical.OneHotHashEncoding(cols);
SweepableEstimator\Estimators\OneHotEncoding.cs (2)
12return context.Transforms.Categorical.OneHotEncoding(inputOutputPairs); 21return context.Transforms.Categorical.OneHotHashEncoding(inputOutputPairs);
Microsoft.ML.Fairlearn.Tests (1)
GridSearchTest.cs (1)
95var pipeline = context.Transforms.Categorical.OneHotHashEncoding("sensitiveFeature_encode", "sensitiveFeature")
Microsoft.ML.IntegrationTests (5)
IntrospectiveTraining.cs (1)
339.Append(mlContext.Transforms.Categorical.OneHotHashEncoding("CategoricalFeatures", numberOfBits: 8, // get collisions!
SchemaDefinitionTests.cs (4)
36var pipeline1 = _ml.Transforms.Categorical.OneHotEncoding("Cat", "Workclass", maximumNumberOfKeys: 3) 40var pipeline2 = _ml.Transforms.Categorical.OneHotEncoding("Cat", "Workclass", maximumNumberOfKeys: 4) 66var pipeline = _ml.Transforms.Categorical.OneHotEncoding("Categories") 67.Append(_ml.Transforms.Categorical.OneHotEncoding("Workclass"))
Microsoft.ML.PerformanceTests (1)
KMeansAndLogisticRegressionBench.cs (1)
36var estimatorPipeline = ml.Transforms.Categorical.OneHotEncoding("CatFeatures")
Microsoft.ML.Predictor.Tests (1)
TestPredictors.cs (1)
650var cat = ML.Transforms.Categorical.OneHotEncoding("Features", "Categories").Fit(dataView).Transform(dataView);
Microsoft.ML.Samples (13)
Dynamic\ModelOperations\OnnxConversion.cs (7)
51.Append(mlContext.Transforms.Categorical.OneHotEncoding("workclass")) 52.Append(mlContext.Transforms.Categorical.OneHotEncoding("education")) 53.Append(mlContext.Transforms.Categorical.OneHotEncoding("marital-status")) 54.Append(mlContext.Transforms.Categorical.OneHotEncoding("occupation")) 55.Append(mlContext.Transforms.Categorical.OneHotEncoding("relationship")) 56.Append(mlContext.Transforms.Categorical.OneHotEncoding("ethnicity")) 57.Append(mlContext.Transforms.Categorical.OneHotEncoding("native-country"))
Dynamic\Transforms\Categorical\OneHotEncoding.cs (2)
30var pipeline = mlContext.Transforms.Categorical.OneHotEncoding( 48var keyPipeline = mlContext.Transforms.Categorical.OneHotEncoding(
Dynamic\Transforms\Categorical\OneHotEncodingMultiColumn.cs (1)
30mlContext.Transforms.Categorical.OneHotEncoding(
Dynamic\Transforms\Categorical\OneHotHashEncoding.cs (2)
30var pipeline = mlContext.Transforms.Categorical.OneHotHashEncoding( 47var keyPipeline = mlContext.Transforms.Categorical.OneHotHashEncoding(
Dynamic\Transforms\Categorical\OneHotHashEncodingMultiColumn.cs (1)
30mlContext.Transforms.Categorical.OneHotHashEncoding(
Microsoft.ML.SamplesUtils (7)
SamplesDatasetUtils.cs (7)
93.Append(mlContext.Transforms.Categorical.OneHotEncoding("workclass")) 94.Append(mlContext.Transforms.Categorical.OneHotEncoding("education")) 95.Append(mlContext.Transforms.Categorical.OneHotEncoding("marital-status")) 96.Append(mlContext.Transforms.Categorical.OneHotEncoding("occupation")) 97.Append(mlContext.Transforms.Categorical.OneHotEncoding("relationship")) 98.Append(mlContext.Transforms.Categorical.OneHotEncoding("ethnicity")) 99.Append(mlContext.Transforms.Categorical.OneHotEncoding("native-country"))
Microsoft.ML.TensorFlow.Tests (1)
TensorflowTests.cs (1)
709var pipe = _mlContext.Transforms.Categorical.OneHotEncoding("OneHotLabel", "Label")
Microsoft.ML.Tests (37)
FeatureContributionTests.cs (3)
425.Append(ML.Transforms.Categorical.OneHotEncoding(outputColumnName: vendorIdEncoded, inputColumnName: nameof(TaxiTrip.VendorId))) 426.Append(ML.Transforms.Categorical.OneHotEncoding(outputColumnName: rateCodeEncoded, inputColumnName: nameof(TaxiTrip.RateCode))) 427.Append(ML.Transforms.Categorical.OneHotEncoding(outputColumnName: paymentTypeEncoded, inputColumnName: nameof(TaxiTrip.PaymentType)))
OnnxConversionTest.cs (4)
481var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("Vector", "Key", outputKind); 792var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("F2", "F2", Transforms.OneHotEncodingEstimator.OutputKind.Bag) 1005var pipeline = mlContext.Transforms.Categorical.OneHotHashEncoding(new[]{ 2026var pipe = ML.Transforms.Categorical.OneHotHashEncoding(new[]{
Scenarios\Api\CookbookSamples\CookbookSamplesDynamicApi.cs (3)
595mlContext.Transforms.Categorical.OneHotEncoding("CategoricalOneHot", "CategoricalFeatures") 597.Append(mlContext.Transforms.Categorical.OneHotEncoding("CategoricalBag", "CategoricalFeatures", OneHotEncodingEstimator.OutputKind.Bag)) 599.Append(mlContext.Transforms.Categorical.OneHotEncoding("WorkclassOneHot", "Workclass"))
Scenarios\RegressionTest.cs (3)
28.Append(context.Transforms.Categorical.OneHotEncoding(outputColumnName: "VendorIdEncoded", inputColumnName: "VendorId")) 29.Append(context.Transforms.Categorical.OneHotEncoding(outputColumnName: "RateCodeEncoded", inputColumnName: "RateCode")) 30.Append(context.Transforms.Categorical.OneHotEncoding(outputColumnName: "PaymentTypeEncoded", inputColumnName: "PaymentType"))
TrainerEstimators\TrainerEstimators.cs (1)
218var oneHotPipeline = pipeline.Append(ML.Transforms.Categorical.OneHotEncoding("LoggedIn"));
Transformers\CategoricalHashTests.cs (10)
52var pipe = ML.Transforms.Categorical.OneHotHashEncoding(new[]{ 90.Append(ML.Transforms.Categorical.OneHotHashEncoding("A", "ScalarString", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)) 91.Append(ML.Transforms.Categorical.OneHotHashEncoding("B", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)) 92.Append(ML.Transforms.Categorical.OneHotHashEncoding("C", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)) 93.Append(ML.Transforms.Categorical.OneHotHashEncoding("D", "ScalarString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)) 94.Append(ML.Transforms.Categorical.OneHotHashEncoding("E", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)) 95.Append(ML.Transforms.Categorical.OneHotHashEncoding("F", "VarVectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)) 97.Append(ML.Transforms.Categorical.OneHotHashEncoding("G", "SingleVectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)); 120var bagPipe = ML.Transforms.Categorical.OneHotHashEncoding( 225var pipe = ML.Transforms.Categorical.OneHotHashEncoding(new[]{
Transformers\CategoricalTests.cs (10)
71var pipe = ML.Transforms.Categorical.OneHotEncoding(new[]{ 114.Append(mlContext.Transforms.Categorical.OneHotEncoding("Features", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)); 138var pipe = mlContext.Transforms.Categorical.OneHotEncoding(new[] { ci }, sideData); 165.Append(ML.Transforms.Categorical.OneHotEncoding("A", "ScalarString", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)) 166.Append(ML.Transforms.Categorical.OneHotEncoding("B", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Indicator)) 167.Append(ML.Transforms.Categorical.OneHotEncoding("C", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Bag)) 168.Append(ML.Transforms.Categorical.OneHotEncoding("D", "ScalarString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)) 169.Append(ML.Transforms.Categorical.OneHotEncoding("E", "VectorString", outputKind: OneHotEncodingEstimator.OutputKind.Binary)); 193var pipe = ML.Transforms.Categorical.OneHotEncoding(new[] { 319var pipe = ML.Transforms.Categorical.OneHotEncoding(new[]{
Transformers\ConvertTests.cs (1)
338var pipe = ML.Transforms.Categorical.OneHotEncoding(new[] {
Transformers\KeyToVectorEstimatorTests.cs (1)
257.Append(mlContext.Transforms.Categorical.OneHotHashEncoding("ProblematicColumn"));
Transformers\NAIndicatorTests.cs (1)
138var pipe = ML.Transforms.Categorical.OneHotEncoding("CatA", "A");