Я использую .NET-Framework 4.6.1
После обновления ML.NET до версии 0.10 я не могу запустить свой код.Я строю свой конвейер, и у меня появляется ошибка при выполнении Fit () - Method.
Message = "Метод не найден: \" System.Collections.Generic.IEnumerable 1<!!0> System.Linq.Enumerable.Append(System.Collections.Generic.IEnumerable 1, !! 0) \"."
using System.Collections.Generic;в моих директивах.
Я что-то упустил или я должен придерживаться v0.9 сейчас?
Спасибо
using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Core.Data;
using Microsoft.ML.Data;
using MulticlassClassification_Iris.DataStructures;
namespace MulticlassClassification_Iris
{
public static partial class Program
{
private static string AppPath => Path.GetDirectoryName(Environment.GetCommandLineArgs()[0]);
private static string TrainDataPath = @"..\machinelearning-samples\samples\csharp\getting-started\MulticlassClassification_Iris\IrisClassification\Data\iris-train.txt";
private static string TestDataPath = @"..\machinelearning-samples\samples\csharp\getting-started\MulticlassClassification_Iris\IrisClassification\Data\iris-test.txt";
private static string ModelPath = @"C:\Users\waldemar\Documents\model.txt";
private static void Main(string[] args)
{
// Create MLContext to be shared across the model creation workflow objects
// Set a random seed for repeatable/deterministic results across multiple trainings.
var mlContext = new MLContext(seed: 0);
//1.
BuildTrainEvaluateAndSaveModel(mlContext);
//2.
TestSomePredictions(mlContext);
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
private static void BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Common data loading configuration
var trainingDataView = mlContext.Data.ReadFromTextFile<IrisData>(TrainDataPath, hasHeader: true);
var testDataView = mlContext.Data.ReadFromTextFile<IrisData>(TestDataPath, hasHeader: true);
// STEP 2: Common data process configuration with pipeline data transformations
var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", "SepalLength",
"SepalWidth",
"PetalLength",
"PetalWidth").AppendCacheCheckpoint(mlContext);
// STEP 3: Set the training algorithm, then append the trainer to the pipeline
var trainer = mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn: "Label", featureColumn: "Features");
var trainingPipeline = dataProcessPipeline.Append(trainer);
// STEP 4: Train the model fitting to the DataSet
//Measure training time
var watch = System.Diagnostics.Stopwatch.StartNew();
Console.WriteLine("=============== Training the model ===============");
ITransformer trainedModel = trainingPipeline.Fit(trainingDataView);
//Stop measuring time
watch.Stop();
long elapsedMs = watch.ElapsedMilliseconds;
Console.WriteLine($"***** Training time: {elapsedMs/1000} seconds *****");
// STEP 5: Evaluate the model and show accuracy stats
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
var predictions = trainedModel.Transform(testDataView);
var metrics = mlContext.MulticlassClassification.Evaluate(predictions, "Label", "Score");
Common.ConsoleHelper.PrintMultiClassClassificationMetrics(trainer.ToString(), metrics);
// STEP 6: Save/persist the trained model to a .ZIP file
using (var fs = new FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(trainedModel, fs);
Console.WriteLine("The model is saved to {0}", ModelPath);
}
private static void TestSomePredictions(MLContext mlContext)
{
//Test Classification Predictions with some hard-coded samples
ITransformer trainedModel;
using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
trainedModel = mlContext.Model.Load(stream);
}
// Create prediction engine related to the loaded trained model
var predEngine = trainedModel.CreatePredictionEngine<IrisData, IrisPrediction>(mlContext);
//Score sample 1
var resultprediction1 = predEngine.Predict(SampleIrisData.Iris1);
Console.WriteLine($"Actual: setosa. Predicted probability: setosa: {resultprediction1.Score[0]:0.####}");
Console.WriteLine($" versicolor: {resultprediction1.Score[1]:0.####}");
Console.WriteLine($" virginica: {resultprediction1.Score[2]:0.####}");
Console.WriteLine();
//Score sample 2
var resultprediction2 = predEngine.Predict(SampleIrisData.Iris2);
Console.WriteLine($"Actual: setosa. Predicted probability: setosa: {resultprediction2.Score[0]:0.####}");
Console.WriteLine($" versicolor: {resultprediction2.Score[1]:0.####}");
Console.WriteLine($" virginica: {resultprediction2.Score[2]:0.####}");
Console.WriteLine();
//Score sample 3
var resultprediction3 = predEngine.Predict(SampleIrisData.Iris3);
Console.WriteLine($"Actual: setosa. Predicted probability: setosa: {resultprediction3.Score[0]:0.####}");
Console.WriteLine($" versicolor: {resultprediction3.Score[1]:0.####}");
Console.WriteLine($" virginica: {resultprediction3.Score[2]:0.####}");
Console.WriteLine();
}
}
}