0.6: Legacy Entry point Transforms.TextFeaturizer not found

System information

OS version/distro: Windows 10
.NET Version (eg., dotnet --info): .NET Core SDK (reflecting any global.json): Version: 2.1.401 Commit: 91b1c13032

Issue

What did you do? Ran old sentiment analysis example, updated all using to use legacy namespace
What happened? Legacy Entry point Transforms.TextFeaturizer not found
What did you expect? Expected all entrypoints to work

Source code / logs

/* This template shows the building blocks for training a machine learning model with ML.NET (https://aka.ms/mlnet).

This model predicts whether a sentence has a positive or negative sentiment. It is based on a sample that can be
found at https://aka.ms/mlnetsentimentanalysis, which provides a more detailed introduction to ML.NET and the scenario.*/

using System; using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Legacy; using Microsoft.ML.Legacy.Data; using Microsoft.ML.Legacy.Models; using Microsoft.ML.Legacy.Trainers; using Microsoft.ML.Legacy.Transforms; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Trainers; using Microsoft.ML.Transforms;

namespace MLNETConsoleApp2 { class Program { static void Main() {

        //1. Build an ML.NET pipeline for training a sentiment analysis model
        Console.WriteLine("Training a model for Sentiment Analysis using ML.NET");
        var pipeline = new LearningPipeline
        {
            // 1a. Load the training data using a TextLoader.
            new TextLoader(@"Data\wikipedia-detox-250-line-data.tsv").CreateFrom<SentimentData>(useHeader: true),

            // 1b. Featurize the text into a numeric vector that can be used by the machine learning algorithm.
            new TextFeaturizer("Features", "SentimentText"),

            // 1c. Add AveragedPerceptron (a linear learner) to the pipeline.
            new AveragedPerceptronBinaryClassifier() { NumIterations = 10 }
        };

        // 1d. Get a model by training the pipeline that was built.
        PredictionModel<SentimentData, SentimentPrediction> model = pipeline.Train<SentimentData, SentimentPrediction>();

        // 2. Evaluate the model to see how well it performs on different data (output the percent of examples classified correctly).
        Console.WriteLine("Training of model is complete \nTesting the model with test data");
        var testData = new TextLoader(@"Data\wikipedia-detox-250-line-test.tsv").CreateFrom<SentimentData>(useHeader: true);
        var evaluator = new BinaryClassificationEvaluator();
        BinaryClassificationMetrics metrics = evaluator.Evaluate(model, testData);
        Console.WriteLine($"Accuracy of trained model for test data is: {metrics.Accuracy:P2}");

        // 3. Save the model to file so it can be used in another app.
        model.WriteAsync("sentiment_model.zip");

        // 4. Use the model for a single prediction.
        SentimentData testInput = new SentimentData { SentimentText = "ML.NET is fun, more samples at https://github.com/dotnet/machinelearning-samples" };
        var sentiment = model.Predict(testInput).Sentiment ? "Positive" : "Negative";

        /* This template uses a minimal dataset to build a sentiment analysis model which leads to relatively low accuracy. 
         * Building good Machine Learning models require large volumes of data. This template comes with a minimal dataset (Data/wikipedia-detox) for sentiment analysis. 
         * In order to build a sentiment analysis model with higher accuracy please follow the walkthrough at https://aka.ms/mlnetsentimentanalysis*/
        Console.WriteLine($"Predicted sentiment for \"{testInput.SentimentText}\" is: {sentiment}");
    }

    /// <summary>
    /// Input class that tells ML.NET how to read the dataset (which columns are included)
    /// </summary>
    public class SentimentData
    {
        [Column(ordinal: "0", name: "Label")]
        public float Sentiment;
        [Column(ordinal: "1")]
        public string SentimentText;
    }

    /// <summary>
    /// Output class for the prediction, in this case including only the predicted sentiment.
    /// </summary>
    public class SentimentPrediction
    {
        [ColumnName("PredictedLabel")]
        public bool Sentiment;
    }
}

}

Please paste or attach the code or logs or traces that would be helpful to diagnose the issue you are reporting.

dotnet / machinelearning