ML.NETのチュートリアルをやってみた ref: https://qiita.com/KuriharaJun/items/62a664d26342eda2c275

file0.txt

using Microsoft.ML;
using Microsoft.ML.Core.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading.Tasks;

namespace SentimentAnalysis
{
    class Program
    {
        static readonly string _trainDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "wikipedia-detox-250-line-data.tsv");
        static readonly string _testDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "wikipedia-detox-250-line-test.tsv");
        static readonly string _modelPath = Path.Combine(Environment.CurrentDirectory, "Data", "Model.zip");
        static TextLoader _textLoader;
        static async Task Main(string[] args)
        {
            MLContext mlContext = new MLContext(seed:0);
            _textLoader = mlContext.Data.TextReader(new TextLoader.Arguments()
            {
                Separator = "tab",
                HasHeader = true,
                Column = new[]
                {
                    new TextLoader.Column("Label", DataKind.Bool, 0),
                    new TextLoader.Column("SentimentText", DataKind.Text, 1)
                }
            });
            var model = Train(mlContext, _trainDataPath);
            Evaluate(mlContext, model);
            Predict(mlContext, model);
            PredictWithModelLoadedFromFile(mlContext);
            Console.WriteLine();

            Console.WriteLine("=============== End of process ===============");
            Console.ReadKey();
        }


        public static ITransformer Train(MLContext mlContext, string dataPath)
        {
            IDataView dataView = _textLoader.Read(dataPath);
            var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features").Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves: 50, numTrees: 50, minDatapointsInLeaves: 20));
            Console.WriteLine("=============== Create and Train the Model ===============");
            var model = pipeline.Fit(dataView);
            Console.WriteLine("=============== End of training ===============");
            Console.WriteLine();

            return model;
        }

        public static void Evaluate(MLContext mlContext, ITransformer model)
        {
            IDataView dataView = _textLoader.Read(_testDataPath);
            Console.WriteLine("=============== Evaluating Model accuracy with Test data===============");
            var predictions = model.Transform(dataView);
            var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
            Console.WriteLine();
            Console.WriteLine("Model quality metrics evaluation");
            Console.WriteLine("--------------------------------");
            Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
            Console.WriteLine($"Auc: {metrics.Auc:P2}");
            Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
            Console.WriteLine("=============== End of model evaluation ===============");
            SaveModelAsFile(mlContext, model);
        }

        public static void Predict(MLContext mlContext, ITransformer model)
        {
            var predictionFunction = model.MakePredictionFunction<SentimentData, SentimentPrediction>(mlContext);

            SentimentData sampleStatement = new SentimentData
            {
                SentimentText = "This is a very rude movie"
            };

            var resultprediction = predictionFunction.Predict(sampleStatement);
            Console.WriteLine();
            Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");

            Console.WriteLine();
            Console.WriteLine($"Sentiment: {sampleStatement.SentimentText} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Toxic" : "Not Toxic")} | Probability: {resultprediction.Probability} ");
            Console.WriteLine("=============== End of Predictions ===============");
            Console.WriteLine();


        }

        private static void PredictWithModelLoadedFromFile(MLContext mlContext)
        {
            IEnumerable<SentimentData> sentiments = new[]
            {
                new SentimentData
                {
                    SentimentText = "This is a very rude movie"
                },
                new SentimentData
                {

                    SentimentText = "He is the best, and the article should say that."
                }
            };
            ITransformer loadedModel;
            using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
            {
                loadedModel = mlContext.Model.Load(stream);
            }
            var sentimentStreamingDataView = mlContext.CreateStreamingDataView(sentiments);
            var predictions = loadedModel.Transform(sentimentStreamingDataView);
            var predictedResults = predictions.AsEnumerable<SentimentPrediction>(mlContext, reuseRowObject: false);
            Console.WriteLine();
            Console.WriteLine("=============== Prediction Test of loaded model with a multiple samples ===============");

            Console.WriteLine();

            var sentimentsAndPredictions = sentiments.Zip(predictedResults, (sentiment, prediction) => (sentiment, prediction));

            foreach (var item in sentimentsAndPredictions)
            {
                Console.WriteLine(
                    $"Sentiment: {item.sentiment.SentimentText} | Prediction: {(Convert.ToBoolean(item.prediction.Prediction) ? "Toxic" : "Not Toxic")} | Probability: {item.prediction.Probability} ");

            }

            Console.WriteLine("=============== End of predictions ===============");
        }

        private static void SaveModelAsFile(MLContext mlContext, ITransformer model)
        {
            using (var fs = new FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
            {
                mlContext.Model.Save(model, fs);
            }

            Console.WriteLine("The model is saved to {0}", _modelPath);
        }
    }
}

file1.txt

using Microsoft.ML.Runtime.Api;

namespace SentimentAnalysis
{
    public class SentimentData
    {
        [Column(ordinal: "0", name: "Label")]
        public float Sentiment;
        [Column(ordinal: "1")]
        public string SentimentText;
    }

    public class SentimentPrediction
    {
        [ColumnName("PredictedLabel")]
        public bool Prediction { get; set; }

        [ColumnName("Probability")]
        public float Probability { get; set; }

        [ColumnName("Score")]
        public float Score { get; set; }
    }
}

file2.txt

=============== Create and Train the Model ===============
=============== End of training ===============

=============== Evaluating Model accuracy with Test data===============

Model quality metrics evaluation
--------------------------------
Accuracy: 94.44%
Auc: 98.77%
F1Score: 94.74%
=============== End of model evaluation ===============
The model is saved to G:\workspace\SentimentAnalysis\SentimentAnalysis\bin\Debug\netcoreapp2.1\Data\Model.zip

=============== Prediction Test of model with a single sample and test dataset ===============

Sentiment: This is a very rude movie | Prediction: Toxic | Probability: 0.5297049
=============== End of Predictions ===============


=============== Prediction Test of loaded model with a multiple samples ===============

Sentiment: This is a very rude movie | Prediction: Toxic | Probability: 0.5297049
Sentiment: He is the best, and the article should say that. | Prediction: Toxic | Probability: 0.9918675
=============== End of predictions ===============

=============== End of process ===============