diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs
index c1583d56d1..b597c8e47b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs
@@ -31,12 +31,12 @@ public static void TextTransform()
// Another pipeline, that customizes the advanced settings of the FeaturizeText transformer.
string customizedColumnName = "CustomizedTextFeatures";
- var customized_pipeline = ml.Transforms.Text.FeaturizeText(customizedColumnName, "SentimentText", s =>
- {
- s.KeepPunctuations = false;
- s.KeepNumbers = false;
- s.OutputTokens = true;
- s.TextLanguage = TextFeaturizingEstimator.Language.English; // supports English, French, German, Dutch, Italian, Spanish, Japanese
+ var customized_pipeline = ml.Transforms.Text.FeaturizeText(customizedColumnName, new List<string> { "SentimentText" },
+ new TextFeaturizingEstimator.Options {
+ KeepPunctuations = false,
+ KeepNumbers = false,
+ OutputTokens = true,
+ TextLanguage = TextFeaturizingEstimator.Language.English, // supports English, French, German, Dutch, Italian, Spanish, Japanese
});
// The transformed data for both pipelines.
diff --git a/src/Microsoft.ML.StaticPipe/TransformsStatic.cs b/src/Microsoft.ML.StaticPipe/TransformsStatic.cs
index 4a30931b13..8ef109b9d6 100644
--- a/src/Microsoft.ML.StaticPipe/TransformsStatic.cs
+++ b/src/Microsoft.ML.StaticPipe/TransformsStatic.cs
@@ -1509,8 +1509,8 @@ internal sealed class OutPipelineColumn : Vector<float>
{
public readonly Scalar<string>[] Inputs;
- public OutPipelineColumn(IEnumerable<Scalar<string>> inputs, Action<Settings> advancedSettings)
- : base(new Reconciler(advancedSettings), inputs.ToArray())
+ public OutPipelineColumn(IEnumerable<Scalar<string>> inputs, Options options)
+ : base(new Reconciler(options), inputs.ToArray())
{
Inputs = inputs.ToArray();
}
@@ -1518,11 +1518,11 @@ public OutPipelineColumn(IEnumerable<Scalar<string>> inputs, Action<Settings> ad
private sealed class Reconciler : EstimatorReconciler
{
- private readonly Action<Settings> _settings;
+ private readonly Options _settings;
- public Reconciler(Action<Settings> advancedSettings)
+ public Reconciler(Options options)
{
- _settings = advancedSettings;
+ _settings = options;
}
public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
@@ -1543,14 +1543,14 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
/// </summary>
/// <param name="input">Input data.</param>
/// <param name="otherInputs">Additional data.</param>
- /// <param name="advancedSettings">Delegate which allows you to set transformation settings.</param>
+ /// <param name="options">Advanced transform settings.</param>
/// <returns></returns>
- public static Vector<float> FeaturizeText(this Scalar<string> input, Scalar<string>[] otherInputs = null, Action<TextFeaturizingEstimator.Settings> advancedSettings = null)
+ public static Vector<float> FeaturizeText(this Scalar<string> input, Scalar<string>[] otherInputs = null, TextFeaturizingEstimator.Options options = null)
{
Contracts.CheckValue(input, nameof(input));
Contracts.CheckValueOrNull(otherInputs);
otherInputs = otherInputs ?? new Scalar<string>[0];
- return new OutPipelineColumn(new[] { input }.Concat(otherInputs), advancedSettings);
+ return new OutPipelineColumn(new[] { input }.Concat(otherInputs), options);
}
}
diff --git a/src/Microsoft.ML.Transforms/Text/TextCatalog.cs b/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
index 57f56bfa64..5d5f0a0144 100644
--- a/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
+++ b/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
@@ -20,20 +20,11 @@ public static class TextCatalog
/// <param name="catalog">The text-related transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
- /// <param name="advancedSettings">Advanced transform settings</param>
- /// <example>
- /// <format type="text/markdown">
- /// <]
- /// ]]>
- /// </format>
- /// </example>
public static TextFeaturizingEstimator FeaturizeText(this TransformsCatalog.TextTransforms catalog,
string outputColumnName,
- string inputColumnName = null,
- Action<TextFeaturizingEstimator.Settings> advancedSettings = null)
+ string inputColumnName = null)
=> new TextFeaturizingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
- outputColumnName, inputColumnName, advancedSettings);
+ outputColumnName, inputColumnName);
/// <summary>
/// Transform several text columns into featurized float array that represents counts of ngrams and char-grams.
@@ -41,13 +32,20 @@ public static TextFeaturizingEstimator FeaturizeText(this TransformsCatalog.Text
/// <param name="catalog">The text-related transform's catalog.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnNames"/>.</param>
/// <param name="inputColumnNames">Name of the columns to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
- /// <param name="advancedSettings">Advanced transform settings</param>
+ /// <param name="options">Advanced options to the algorithm.</param>
+ /// <example>
+ /// <format type="text/markdown">
+ /// <]
+ /// ]]>
+ /// </format>
+ /// </example>
public static TextFeaturizingEstimator FeaturizeText(this TransformsCatalog.TextTransforms catalog,
string outputColumnName,
IEnumerable<string> inputColumnNames,
- Action<TextFeaturizingEstimator.Settings> advancedSettings = null)
+ TextFeaturizingEstimator.Options options)
=> new TextFeaturizingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(),
- outputColumnName, inputColumnNames, advancedSettings);
+ outputColumnName, inputColumnNames, options);
/// <summary>
/// Tokenize incoming text in <paramref name="inputColumnName"/> and output the tokens as <paramref name="outputColumnName"/>.
diff --git a/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs b/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs
index 5d4ed9ed89..985c6eb6fa 100644
--- a/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs
+++ b/src/Microsoft.ML.Transforms/Text/TextFeaturizingEstimator.cs
@@ -120,26 +120,59 @@ internal sealed class Arguments : TransformInputBase
public TextNormKind VectorNormalizer = TextNormKind.L2;
}
- public sealed class Settings
+ /// <summary>
+ /// Advanced options for the <see cref="TextFeaturizingEstimator"/>.
+ /// </summary>
+ public sealed class Options
{
#pragma warning disable MSML_NoInstanceInitializers // No initializers on instance fields or properties
+ /// <summary>
+ /// Dataset language.
+ /// </summary>
public Language TextLanguage { get; set; } = DefaultLanguage;
+ /// <summary>
+ /// Casing used for the text.
+ /// </summary>
public CaseNormalizationMode TextCase { get; set; } = CaseNormalizationMode.Lower;
+ /// <summary>
+ /// Whether to keep diacritical marks or remove them.
+ /// </summary>
public bool KeepDiacritics { get; set; } = false;
+ /// <summary>
+ /// Whether to keep punctuation marks or remove them.
+ /// </summary>
public bool KeepPunctuations { get; set; } = true;
+ /// <summary>
+ /// Whether to keep numbers or remove them.
+ /// </summary>
public bool KeepNumbers { get; set; } = true;
+ /// <summary>
+ /// Whether to output the transformed text tokens as an additional column.
+ /// </summary>
public bool OutputTokens { get; set; } = false;
+ /// <summary>
+ /// Vector Normalizer to use.
+ /// </summary>
public TextNormKind VectorNormalizer { get; set; } = TextNormKind.L2;
+ /// <summary>
+ /// Whether to use stop remover or not.
+ /// </summary>
public bool UseStopRemover { get; set; } = false;
+ /// <summary>
+ /// Whether to use char extractor or not.
+ /// </summary>
public bool UseCharExtractor { get; set; } = true;
+ /// <summary>
+ /// Whether to use word extractor or not.
+ /// </summary>
public bool UseWordExtractor { get; set; } = true;
#pragma warning restore MSML_NoInstanceInitializers // No initializers on instance fields or properties
}
- public readonly string OutputColumn;
+ internal readonly string OutputColumn;
private readonly string[] _inputColumns;
- public IReadOnlyCollection<string> InputColumns => _inputColumns.AsReadOnly();
- public Settings AdvancedSettings { get; }
+ internal IReadOnlyCollection<string> InputColumns => _inputColumns.AsReadOnly();
+ internal Options OptionalSettings { get; }
// These parameters are hardcoded for now.
// REVIEW: expose them once sub-transforms are estimators.
@@ -232,18 +265,18 @@ public bool NeedInitialSourceColumnConcatTransform
public TransformApplierParams(TextFeaturizingEstimator parent)
{
var host = parent._host;
- host.Check(Enum.IsDefined(typeof(Language), parent.AdvancedSettings.TextLanguage));
- host.Check(Enum.IsDefined(typeof(CaseNormalizationMode), parent.AdvancedSettings.TextCase));
+ host.Check(Enum.IsDefined(typeof(Language), parent.OptionalSettings.TextLanguage));
+ host.Check(Enum.IsDefined(typeof(CaseNormalizationMode), parent.OptionalSettings.TextCase));
WordExtractorFactory = parent._wordFeatureExtractor?.CreateComponent(host, parent._dictionary);
CharExtractorFactory = parent._charFeatureExtractor?.CreateComponent(host, parent._dictionary);
- VectorNormalizer = parent.AdvancedSettings.VectorNormalizer;
- Language = parent.AdvancedSettings.TextLanguage;
- UsePredefinedStopWordRemover = parent.AdvancedSettings.UseStopRemover;
- TextCase = parent.AdvancedSettings.TextCase;
- KeepDiacritics = parent.AdvancedSettings.KeepDiacritics;
- KeepPunctuations = parent.AdvancedSettings.KeepPunctuations;
- KeepNumbers = parent.AdvancedSettings.KeepNumbers;
- OutputTextTokens = parent.AdvancedSettings.OutputTokens;
+ VectorNormalizer = parent.OptionalSettings.VectorNormalizer;
+ Language = parent.OptionalSettings.TextLanguage;
+ UsePredefinedStopWordRemover = parent.OptionalSettings.UseStopRemover;
+ TextCase = parent.OptionalSettings.TextCase;
+ KeepDiacritics = parent.OptionalSettings.KeepDiacritics;
+ KeepPunctuations = parent.OptionalSettings.KeepPunctuations;
+ KeepNumbers = parent.OptionalSettings.KeepNumbers;
+ OutputTextTokens = parent.OptionalSettings.OutputTokens;
Dictionary = parent._dictionary;
}
}
@@ -254,18 +287,16 @@ public TransformApplierParams(TextFeaturizingEstimator parent)
internal const string UserName = "Text Transform";
internal const string LoaderSignature = "Text";
- public const Language DefaultLanguage = Language.English;
+ internal const Language DefaultLanguage = Language.English;
private const string TransformedTextColFormat = "{0}_TransformedText";
- public TextFeaturizingEstimator(IHostEnvironment env, string outputColumnName, string inputColumnName = null,
- Action<Settings> advancedSettings = null)
- : this(env, outputColumnName, new[] { inputColumnName ?? outputColumnName }, advancedSettings)
+ internal TextFeaturizingEstimator(IHostEnvironment env, string outputColumnName, string inputColumnName = null)
+ : this(env, outputColumnName, new[] { inputColumnName ?? outputColumnName })
{
}
- public TextFeaturizingEstimator(IHostEnvironment env, string name, IEnumerable<string> source,
- Action<Settings> advancedSettings = null)
+ internal TextFeaturizingEstimator(IHostEnvironment env, string name, IEnumerable<string> source, Options options = null)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(nameof(TextFeaturizingEstimator));
@@ -273,21 +304,25 @@ public TextFeaturizingEstimator(IHostEnvironment env, string name, IEnumerable<s
_host.CheckParam(source.Any(), nameof(source));
_host.CheckParam(!source.Any(string.IsNullOrWhiteSpace), nameof(source));
_host.CheckNonEmpty(name, nameof(name));
- _host.CheckValueOrNull(advancedSettings);
+ _host.CheckValueOrNull(options);
_inputColumns = source.ToArray();
OutputColumn = name;
- AdvancedSettings = new Settings();
- advancedSettings?.Invoke(AdvancedSettings);
+ OptionalSettings = new Options();
+ if (options != null)
+ OptionalSettings = options;
_dictionary = null;
- if (AdvancedSettings.UseWordExtractor)
+ if (OptionalSettings.UseWordExtractor)
_wordFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments();
- if (AdvancedSettings.UseCharExtractor)
+ if (OptionalSettings.UseCharExtractor)
_charFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments() { NgramLength = 3, AllLengths = false };
}
+ /// <summary>
+ /// Trains and returns a <see cref="Transformer"/>.
+ /// </summary>
public ITransformer Fit(IDataView input)
{
var h = _host;
@@ -463,7 +498,7 @@ public ITransformer Fit(IDataView input)
return new Transformer(_host, input, view);
}
- public static ITransformer Create(IHostEnvironment env, ModelLoadContext ctx)
+ private static ITransformer Create(IHostEnvironment env, ModelLoadContext ctx)
=> new Transformer(env, ctx);
private static string GenerateColumnName(Schema schema, string srcName, string xfTag)
@@ -471,6 +506,10 @@ private static string GenerateColumnName(Schema schema, string srcName, string x
return schema.GetTempColumnName(string.Format("{0}_{1}", srcName, xfTag));
}
+ /// <summary>
+ /// Returns the <see cref="SchemaShape"/> of the schema which will be produced by the transformer.
+ /// Used for schema propagation and verification in a pipeline.
+ /// </summary>
public SchemaShape GetOutputSchema(SchemaShape inputSchema)
{
_host.CheckValue(inputSchema, nameof(inputSchema));
@@ -485,12 +524,12 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
var metadata = new List<SchemaShape.Column>(2);
metadata.Add(new SchemaShape.Column(MetadataUtils.Kinds.SlotNames, SchemaShape.Column.VectorKind.Vector, TextType.Instance, false));
- if (AdvancedSettings.VectorNormalizer != TextNormKind.None)
+ if (OptionalSettings.VectorNormalizer != TextNormKind.None)
metadata.Add(new SchemaShape.Column(MetadataUtils.Kinds.IsNormalized, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false));
result[OutputColumn] = new SchemaShape.Column(OutputColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false,
new SchemaShape(metadata));
- if (AdvancedSettings.OutputTokens)
+ if (OptionalSettings.OutputTokens)
{
string name = string.Format(TransformedTextColFormat, OutputColumn);
result[name] = new SchemaShape.Column(name, SchemaShape.Column.VectorKind.VariableVector, TextType.Instance, false);
@@ -502,18 +541,18 @@ public SchemaShape GetOutputSchema(SchemaShape inputSchema)
// Factory method for SignatureDataTransform.
internal static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView data)
{
- Action<Settings> settings = s =>
+ var settings = new Options
{
- s.TextLanguage = args.Language;
- s.TextCase = args.TextCase;
- s.KeepDiacritics = args.KeepDiacritics;
- s.KeepPunctuations = args.KeepPunctuations;
- s.KeepNumbers = args.KeepNumbers;
- s.OutputTokens = args.OutputTokens;
- s.VectorNormalizer = args.VectorNormalizer;
- s.UseStopRemover = args.UsePredefinedStopWordRemover;
- s.UseWordExtractor = args.WordFeatureExtractor != null;
- s.UseCharExtractor = args.CharFeatureExtractor != null;
+ TextLanguage = args.Language,
+ TextCase = args.TextCase,
+ KeepDiacritics = args.KeepDiacritics,
+ KeepPunctuations = args.KeepPunctuations,
+ KeepNumbers = args.KeepNumbers,
+ OutputTokens = args.OutputTokens,
+ VectorNormalizer = args.VectorNormalizer,
+ UseStopRemover = args.UsePredefinedStopWordRemover,
+ UseWordExtractor = args.WordFeatureExtractor != null,
+ UseCharExtractor = args.CharFeatureExtractor != null,
};
var estimator = new TextFeaturizingEstimator(env, args.Columns.Name, args.Columns.Source ?? new[] { args.Columns.Name }, settings);
@@ -530,7 +569,7 @@ private sealed class Transformer : ITransformer, ICanSaveModel
private readonly IHost _host;
private readonly IDataView _xf;
- public Transformer(IHostEnvironment env, IDataView input, IDataView view)
+ internal Transformer(IHostEnvironment env, IDataView input, IDataView view)
{
_host = env.Register(nameof(Transformer));
_xf = ApplyTransformUtils.ApplyAllTransformsToData(_host, view, new EmptyDataView(_host, input.Schema), input);
diff --git a/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs b/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs
index d0d9ba7d0c..076fde30d1 100644
--- a/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs
+++ b/test/Microsoft.ML.Benchmarks/PredictionEngineBench.cs
@@ -72,8 +72,8 @@ public void SetupSentimentPipeline()
string _sentimentDataPath = BaseTestClass.GetDataPath("wikipedia-detox-250-line-data.tsv");
- var env = new MLContext(seed: 1, conc: 1);
- var reader = new TextLoader(env, columns: new[]
+ var mlContext = new MLContext(seed: 1, conc: 1);
+ var reader = new TextLoader(mlContext, columns: new[]
{
new TextLoader.Column("Label", DataKind.BL, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
@@ -83,13 +83,13 @@ public void SetupSentimentPipeline()
IDataView data = reader.Read(_sentimentDataPath);
- var pipeline = new TextFeaturizingEstimator(env, "Features", "SentimentText")
- .Append(env.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+ var pipeline = mlContext.Transforms.Text.FeaturizeText("Features", "SentimentText")
+ .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
new SdcaBinaryTrainer.Options {NumThreads = 1, ConvergenceTolerance = 1e-2f, }));
var model = pipeline.Fit(data);
- _sentimentModel = model.CreatePredictionEngine<SentimentData, SentimentPrediction>(env);
+ _sentimentModel = model.CreatePredictionEngine<SentimentData, SentimentPrediction>(mlContext);
}
[GlobalSetup(Target = nameof(MakeBreastCancerPredictions))]
diff --git a/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs b/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs
index 613dda6609..7c67d13ac4 100644
--- a/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs
+++ b/test/Microsoft.ML.Benchmarks/StochasticDualCoordinateAscentClassifierBench.cs
@@ -24,7 +24,7 @@ public class StochasticDualCoordinateAscentClassifierBench : WithExtraMetrics
private readonly string _sentimentDataPath = BaseTestClass.GetDataPath("wikipedia-detox-250-line-data.tsv");
private readonly Consumer _consumer = new Consumer(); // BenchmarkDotNet utility type used to prevent dead code elimination
- private readonly MLContext _env = new MLContext(seed: 1);
+ private readonly MLContext mlContext = new MLContext(seed: 1);
private readonly int[] _batchSizes = new int[] { 1, 2, 5 };
@@ -54,7 +54,7 @@ protected override IEnumerable<Metric> GetMetrics()
private TransformerChain<MulticlassPredictionTransformer<MulticlassLogisticRegressionModelParameters>> Train(string dataPath)
{
- var reader = new TextLoader(_env,
+ var reader = new TextLoader(mlContext,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
@@ -68,8 +68,8 @@ private TransformerChain<MulticlassPredictionTransformer<MulticlassLogisticRegre
IDataView data = reader.Read(dataPath);
- var pipeline = new ColumnConcatenatingEstimator(_env, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
- .Append(_env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent());
+ var pipeline = new ColumnConcatenatingEstimator(mlContext, "Features", new[] { "SepalLength", "SepalWidth", "PetalLength", "PetalWidth" })
+ .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent());
return pipeline.Fit(data);
}
@@ -100,20 +100,23 @@ public void TrainSentiment()
AllowQuoting = false,
AllowSparse = false
};
- var loader = _env.Data.ReadFromTextFile(_sentimentDataPath, arguments);
- var text = new TextFeaturizingEstimator(_env, "WordEmbeddings", "SentimentText", args =>
- {
- args.OutputTokens = true;
- args.KeepPunctuations = false;
- args.UseStopRemover = true;
- args.VectorNormalizer = TextFeaturizingEstimator.TextNormKind.None;
- args.UseCharExtractor = false;
- args.UseWordExtractor = false;
- }).Fit(loader).Transform(loader);
- var trans = _env.Transforms.Text.ExtractWordEmbeddings("Features", "WordEmbeddings_TransformedText",
+
+ var loader = mlContext.Data.ReadFromTextFile(_sentimentDataPath, arguments);
+ var text = mlContext.Transforms.Text.FeaturizeText("WordEmbeddings", new List<string> { "SentimentText" },
+ new TextFeaturizingEstimator.Options {
+ OutputTokens = true,
+ KeepPunctuations = false,
+ UseStopRemover = true,
+ VectorNormalizer = TextFeaturizingEstimator.TextNormKind.None,
+ UseCharExtractor = false,
+ UseWordExtractor = false,
+ }).Fit(loader).Transform(loader);
+
+ var trans = mlContext.Transforms.Text.ExtractWordEmbeddings("Features", "WordEmbeddings_TransformedText",
WordEmbeddingsExtractingEstimator.PretrainedModelKind.Sswe).Fit(text).Transform(text);
+
// Train
- var trainer = _env.MulticlassClassification.Trainers.StochasticDualCoordinateAscent();
+ var trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent();
var predicted = trainer.Fit(trans);
_consumer.Consume(predicted);
}
@@ -122,10 +125,10 @@ public void TrainSentiment()
public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
- _predictionEngine = _trainedModel.CreatePredictionEngine<IrisData, IrisPrediction>(_env);
+ _predictionEngine = _trainedModel.CreatePredictionEngine<IrisData, IrisPrediction>(mlContext);
_consumer.Consume(_predictionEngine.Predict(_example));
- var reader = new TextLoader(_env,
+ var reader = new TextLoader(mlContext,
columns: new[]
{
new TextLoader.Column("Label", DataKind.R4, 0),
@@ -139,7 +142,7 @@ public void SetupPredictBenchmarks()
IDataView testData = reader.Read(_dataPath);
IDataView scoredTestData = _trainedModel.Transform(testData);
- var evaluator = new MultiClassClassifierEvaluator(_env, new MultiClassClassifierEvaluator.Arguments());
+ var evaluator = new MultiClassClassifierEvaluator(mlContext, new MultiClassClassifierEvaluator.Arguments());
_metrics = evaluator.Evaluate(scoredTestData, DefaultColumnNames.Label, DefaultColumnNames.Score, DefaultColumnNames.PredictedLabel);
_batches = new IrisData[_batchSizes.Length][];
@@ -158,13 +161,13 @@ public void SetupPredictBenchmarks()
public float[] PredictIris() => _predictionEngine.Predict(_example).PredictedLabels;
[Benchmark]
- public void PredictIrisBatchOf1() => _trainedModel.Transform(_env.Data.ReadFromEnumerable(_batches[0]));
+ public void PredictIrisBatchOf1() => _trainedModel.Transform(mlContext.Data.ReadFromEnumerable(_batches[0]));
[Benchmark]
- public void PredictIrisBatchOf2() => _trainedModel.Transform(_env.Data.ReadFromEnumerable(_batches[1]));
+ public void PredictIrisBatchOf2() => _trainedModel.Transform(mlContext.Data.ReadFromEnumerable(_batches[1]));
[Benchmark]
- public void PredictIrisBatchOf5() => _trainedModel.Transform(_env.Data.ReadFromEnumerable(_batches[2]));
+ public void PredictIrisBatchOf5() => _trainedModel.Transform(mlContext.Data.ReadFromEnumerable(_batches[2]));
}
public class IrisData
diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
index e40435bfda..8fcb00842e 100644
--- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
+++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
@@ -998,10 +998,10 @@ public void EntryPointPipelineEnsembleText()
var data = splitOutput.TrainData[i];
if (i % 2 == 0)
{
- data = new TextFeaturizingEstimator(Env, "Features", "Text", args =>
- {
- args.UseStopRemover = true;
- }).Fit(data).Transform(data);
+ data = new TextFeaturizingEstimator(Env, "Features", new List<string> { "Text" },
+ new TextFeaturizingEstimator.Options {
+ UseStopRemover = true,
+ }).Fit(data).Transform(data);
}
else
{
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs
index 4b546475fd..34df7a6023 100644
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs
+++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Visibility.cs
@@ -2,6 +2,7 @@
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
+using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.Data;
using Microsoft.ML.RunTests;
@@ -24,7 +25,8 @@ void Visibility()
{
var ml = new MLContext(seed: 1, conc: 1);
var pipeline = ml.Data.CreateTextLoader(TestDatasets.Sentiment.GetLoaderColumns(), hasHeader: true)
- .Append(ml.Transforms.Text.FeaturizeText("Features", "SentimentText", s => s.OutputTokens = true));
+ .Append(ml.Transforms.Text.FeaturizeText("Features", new List<string> { "SentimentText" },
+ new Transforms.Text.TextFeaturizingEstimator.Options { OutputTokens = true }));
var src = new MultiFileSource(GetDataPath(TestDatasets.Sentiment.trainFilename));
var data = pipeline.Fit(src).Read(src);
diff --git a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
index 7c8ef48d22..26d18c4826 100644
--- a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
+++ b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
@@ -43,7 +43,7 @@ public void TextFeaturizerWorkout()
.AsDynamic;
var feat = data.MakeNewEstimator()
- .Append(row => row.text.FeaturizeText(advancedSettings: s => { s.OutputTokens = true; }));
+ .Append(row => row.text.FeaturizeText(options: new TextFeaturizingEstimator.Options { OutputTokens = true, }));
TestEstimatorCore(feat.AsDynamic, data.AsDynamic, invalidInput: invalidData);