using System;
using Microsoft.ML.Models;
using Microsoft.ML.Runtime;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Trainers;
using Microsoft.ML.Transforms;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using System.Threading.Tasks;

namespace MLNETTest
{
    public class SentimentData
    {
        [Column(ordinal: "0", name: "Label")]
        public float Sentiment;

        [Column(ordinal: "1")]
        public string SentimentText;
    }

    public class SentimentPrediction
    {
        [ColumnName("PredictedLabel")]
        public bool Sentiment;
    }

    class Program
    {
        const string _dataPath = @"wikipedia-detox-250-line-data.tsv";
        const string _testDataPath = @"wikipedia-detox-250-line-test.tsv";

        static async Task Main(string[] args)
        {
            var model = await TrainAsync();

            Evaluate(model);

            Predict(model);
        }

        public static async Task<PredictionModel<SentimentData, SentimentPrediction>> TrainAsync()
        {
            var pipeline = new LearningPipeline();

            pipeline.Add(new TextLoader(_dataPath).CreateFrom<SentimentData>(useHeader: true));

            pipeline.Add(new TextFeaturizer("Features", "SentimentText"));

            pipeline.Add(new FastForestBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });

            PredictionModel<SentimentData, SentimentPrediction> model = pipeline.Train<SentimentData, SentimentPrediction>();

            return model;
        }

        public static void Evaluate(PredictionModel<SentimentData, SentimentPrediction> model)
        {
            var testData = new TextLoader(_testDataPath).CreateFrom<SentimentData>(useHeader: true);

            var evaluator = new BinaryClassificationEvaluator();

            BinaryClassificationMetrics metrics = evaluator.Evaluate(model, testData);

            Console.WriteLine();
            Console.WriteLine("PredictionModel quality metrics evaluation");
            Console.WriteLine("-------------------------------------");
            Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
            Console.WriteLine($"Auc: {metrics.Auc:P2}");
            Console.WriteLine($"F1Score: {metrics.F1Score:P2}");

        }

        public static void Predict(PredictionModel<SentimentData, SentimentPrediction> model)
        {
            IEnumerable<SentimentData> sentiments = new[]
            {
                new SentimentData
                {
                    SentimentText = "Please refrain from adding nonsense to Wikipedia."
                },

                new SentimentData
                {
                    SentimentText = "He is the best, and the article should say that."
                }
            };

            IEnumerable<SentimentPrediction> predictions = model.Predict(sentiments);

            Console.WriteLine();
            Console.WriteLine("Sentiment Predictions");
            Console.WriteLine("---------------------");

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

            foreach (var item in sentimentsAndPredictions)
            {
                Console.WriteLine($"Sentiment: {item.sentiment.SentimentText} | Prediction: {(item.prediction.Sentiment ? "Positive" : "Negative")}");
            }
            Console.ReadLine();
        }
    }
}