// 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.Collections.Generic;
using Microsoft.ML.Data;
using Microsoft.ML.RunTests;
using Microsoft.ML.TestFramework.Attributes;
using Microsoft.ML.Transforms.TimeSeries;
using Xunit;
using Xunit.Abstractions;
 
namespace Microsoft.ML.Tests
{
    public class TimeSeriesEstimatorTests : TestDataPipeBase
    {
        private const int InputSize = 150528;
 
        private class Data
        {
            public float Value;
 
            public Data(float value)
            {
                Value = value;
            }
        }
 
        private class TestDataXY
        {
            [VectorType(InputSize)]
            public float[] A;
        }
        private class TestDataDifferentType
        {
            [VectorType(InputSize)]
            public string[] data_0;
        }
 
        public TimeSeriesEstimatorTests(ITestOutputHelper output) : base(output)
        {
        }
 
        [NativeDependencyFact("MklImports")]
        public void TestSsaChangePointEstimator()
        {
            int confidence = 95;
            int changeHistorySize = 10;
            int seasonalitySize = 10;
            int numberOfSeasonsInTraining = 5;
            int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize;
 
            List<Data> data = new List<Data>();
            var dataView = ML.Data.LoadFromEnumerable(data);
 
            for (int j = 0; j < numberOfSeasonsInTraining; j++)
                for (int i = 0; i < seasonalitySize; i++)
                    data.Add(new Data(i));
 
            for (int i = 0; i < changeHistorySize; i++)
                data.Add(new Data(i * 100));
 
            var pipe = new SsaChangePointEstimator(Env, "Change",
                confidence, changeHistorySize, maxTrainingSize, seasonalitySize, "Value");
 
            var xyData = new List<TestDataXY> { new TestDataXY() { A = new float[InputSize] } };
            var stringData = new List<TestDataDifferentType> { new TestDataDifferentType() { data_0 = new string[InputSize] } };
 
            var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
            var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
 
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
 
            Done();
        }
 
        [NativeDependencyFact("MklImports")]
        public void TestSsaForecastingEstimator()
        {
            const int changeHistorySize = 10;
            const int seasonalitySize = 10;
            const int numberOfSeasonsInTraining = 5;
 
            List<Data> data = new List<Data>();
 
            var ml = new MLContext(seed: 1);
            var dataView = ml.Data.LoadFromEnumerable(data);
 
            for (int j = 0; j < numberOfSeasonsInTraining; j++)
                for (int i = 0; i < seasonalitySize; i++)
                    data.Add(new Data(i));
 
            for (int i = 0; i < changeHistorySize; i++)
                data.Add(new Data(i * 100));
 
            // Train
            var pipe = new SsaForecastingEstimator(Env, "Forecast", "Value", 10, 11, 22, 4,
                    confidenceLowerBoundColumn: "ConfidenceLowerBound",
                    confidenceUpperBoundColumn: "ConfidenceUpperBound");
 
            var xyData = new List<TestDataXY> { new TestDataXY() { A = new float[InputSize] } };
            var stringData = new List<TestDataDifferentType> { new TestDataDifferentType() { data_0 = new string[InputSize] } };
 
            var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
            var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
 
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
 
            Done();
        }
 
        [NativeDependencyFact("MklImports")]
        public void TestSsaSpikeEstimator()
        {
            int confidence = 95;
            int pValueHistorySize = 10;
            int seasonalitySize = 10;
            int numberOfSeasonsInTraining = 5;
            int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize;
 
            List<Data> data = new List<Data>();
            var dataView = ML.Data.LoadFromEnumerable(data);
 
            for (int j = 0; j < numberOfSeasonsInTraining; j++)
                for (int i = 0; i < seasonalitySize; i++)
                    data.Add(new Data(i));
 
            for (int i = 0; i < pValueHistorySize; i++)
                data.Add(new Data(i * 100));
 
            var pipe = new SsaSpikeEstimator(Env, "Change",
                confidence, pValueHistorySize, maxTrainingSize, seasonalitySize, "Value");
 
            var xyData = new List<TestDataXY> { new TestDataXY() { A = new float[InputSize] } };
            var stringData = new List<TestDataDifferentType> { new TestDataDifferentType() { data_0 = new string[InputSize] } };
 
            var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
            var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
 
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
 
            Done();
        }
 
        [Fact]
        public void TestIidChangePointEstimator()
        {
            int confidence = 95;
            int changeHistorySize = 10;
 
            List<Data> data = new List<Data>();
            var dataView = ML.Data.LoadFromEnumerable(data);
 
            for (int i = 0; i < changeHistorySize; i++)
                data.Add(new Data(i * 100));
 
            var pipe = new IidChangePointEstimator(Env,
                "Change", confidence, changeHistorySize, "Value");
 
            var xyData = new List<TestDataXY> { new TestDataXY() { A = new float[InputSize] } };
            var stringData = new List<TestDataDifferentType> { new TestDataDifferentType() { data_0 = new string[InputSize] } };
 
            var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
            var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
 
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
 
            Done();
        }
 
        [Fact]
        public void TestIidSpikeEstimator()
        {
            int confidence = 95;
            int pValueHistorySize = 10;
 
            List<Data> data = new List<Data>();
            var dataView = ML.Data.LoadFromEnumerable(data);
 
            for (int i = 0; i < pValueHistorySize; i++)
                data.Add(new Data(i * 100));
 
            var pipe = new IidSpikeEstimator(Env,
                "Change", confidence, pValueHistorySize, "Value");
 
            var xyData = new List<TestDataXY> { new TestDataXY() { A = new float[InputSize] } };
            var stringData = new List<TestDataDifferentType> { new TestDataDifferentType() { data_0 = new string[InputSize] } };
 
            var invalidDataWrongNames = ML.Data.LoadFromEnumerable(xyData);
            var invalidDataWrongTypes = ML.Data.LoadFromEnumerable(stringData);
 
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongTypes);
            TestEstimatorCore(pipe, dataView, invalidInput: invalidDataWrongNames);
 
            Done();
        }
    }
}