// Licensed to the .NET Foundation under one or more agreements.
// 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.Linq;
using Microsoft.ML.RunTests;
using Microsoft.ML.TestFramework.Attributes;
using Microsoft.ML.TestFrameworkCommon;
using Microsoft.ML.Trainers;
using Xunit;
 
namespace Microsoft.ML.Tests.Scenarios.Api
{
    public partial class ApiScenariosTests
    {
        /// <summary>
        /// Start with a dataset in a text file. Run text featurization on text values. 
        /// Train a linear model over that. (I am thinking sentiment classification.) 
        /// Out of the result, produce some structure over which you can get predictions programmatically 
        /// (for example, the prediction does not happen over a file as it did during training).
        /// </summary>
        [Fact]
        public void SimpleTrainAndPredict()
        {
            var ml = new MLContext(seed: 1);
            var data = ml.Data.LoadFromTextFile<SentimentData>(GetDataPath(TestDatasets.Sentiment.trainFilename), hasHeader: true);
 
            // Pipeline.
            var pipeline = ml.Transforms.Text.FeaturizeText("Features", "SentimentText")
                .AppendCacheCheckpoint(ml)
                .Append(ml.BinaryClassification.Trainers.SdcaNonCalibrated(
                    new SdcaNonCalibratedBinaryTrainer.Options { NumberOfThreads = 1 }));
 
            // Train.
            var model = pipeline.Fit(data);
 
            // Create prediction engine and test predictions.
            var engine = ml.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
 
            // Take a couple examples out of the test data and run predictions on top.
            var testData = ml.Data.CreateEnumerable<SentimentData>(
                ml.Data.LoadFromTextFile<SentimentData>(GetDataPath(TestDatasets.Sentiment.testFilename), hasHeader: true), false);
            foreach (var input in testData.Take(5))
            {
                var prediction = engine.Predict(input);
                // Verify that predictions match and scores are separated from zero.
                Assert.Equal(input.Sentiment, prediction.Sentiment);
                Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1);
            }
        }
 
        /// <summary>
        /// Start with a dataset in a text file. Run text featurization on text values. 
        /// Train a linear model over that. (I am thinking sentiment classification.) 
        /// Out of the result, produce some structure over which you can get predictions programmatically 
        /// (for example, the prediction does not happen over a file as it did during training).
        /// Uses Symbolic SGD Trainer.
        /// </summary>
        [NativeDependencyFact("MklImports")]
        public void SimpleTrainAndPredictSymSGD()
        {
            var ml = new MLContext(seed: 1);
            var data = ml.Data.LoadFromTextFile<SentimentData>(GetDataPath(TestDatasets.Sentiment.trainFilename), hasHeader: true);
 
            // Pipeline.
            var pipeline = ml.Transforms.Text.FeaturizeText("Features", "SentimentText")
                .AppendCacheCheckpoint(ml)
                .Append(ml.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(new SymbolicSgdLogisticRegressionBinaryTrainer.Options
                {
                    NumberOfThreads = 1
                }));
 
            // Train.
            var model = pipeline.Fit(data);
 
            // Create prediction engine and test predictions.
            var engine = ml.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
 
            // Take a couple examples out of the test data and run predictions on top.
            var testData = ml.Data.CreateEnumerable<SentimentData>(
                ml.Data.LoadFromTextFile<SentimentData>(GetDataPath(TestDatasets.Sentiment.testFilename), hasHeader: true), false);
            foreach (var input in testData.Take(5))
            {
                var prediction = engine.Predict(input);
                // Verify that predictions match and scores are separated from zero.
                Assert.Equal(input.Sentiment, prediction.Sentiment);
                Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1);
            }
        }
    }
}