C# 中的机器学习库 [关闭]

介绍

csharp
using Microsoft.ML;
using Microsoft.ML.Data;
// 定义数据结构
public class NewsData
{
    [LoadColumn(0)]
    public string Label { get; set; }
    
    [LoadColumn(1)]
    public string Text { get; set; }
}
// 定义预测结果结构
public class NewsPrediction
{
    [ColumnName("PredictedLabel")]
    public string Category { get; set; }
}
// 创建MLContext
var context = new MLContext();
// 加载数据
var data = context.Data.LoadFromTextFile("news_data.csv", separatorChar: ',');
// 划分数据集
var trainTestSplit = context.Data.TrainTestSplit(data, testFraction: 0.2);
// 定义数据处理管道
var dataPipeline = context.Transforms.Text.FeaturizeText("Features", nameof(NewsData.Text))
    .Append(context.Transforms.Conversion.MapValueToKey("Label"))
    .Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// 定义学习算法
var trainer = context.Transforms.Conversion.MapKeyToValue("PredictedLabel")
    .Append(context.Transforms.Text.NormalizeText("Text"))
    .Append(context.Transforms.Text.TokenizeIntoWords("Tokens"))
    .Append(context.Transforms.Text.RemoveDefaultStopWords("Tokens"))
    .Append(context.Transforms.Text.ApplyWordEmbedding("Features"))
    .Append(context.Transforms.Concatenate("Features"))
    .Append(context.Transforms.NormalizeMinMax("Features"))
    .Append(context.Transforms.Conversion.MapValueToKey("Label"))
    .Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel"))
    .Append(context.Transforms.NormalizeMinMax("Features"))
    .Append(context.Transforms.Conversion.MapValueToKey("Label"))
    .Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
// 定义评估指标
var metrics = context.MulticlassClassification.CrossValidate(trainTestSplit.TrainSet, trainer, numberOfFolds: 5);
// 输出评估结果
foreach (var fold in metrics)
{
    Console.WriteLine($"Accuracy: {fold.MacroAccuracy}");
    Console.WriteLine($"LogLoss: {fold.LogLoss}");
    Console.WriteLine($"LogLossReduction: {fold.LogLossReduction}");
}
// 训练模型
var model = trainer.Fit(trainTestSplit.TrainSet);
// 评估模型
var predictions = model.Transform(trainTestSplit.TestSet);
var metrics = context.MulticlassClassification.Evaluate(predictions);
// 输出评估结果
Console.WriteLine($"Accuracy: {metrics.MacroAccuracy}");
Console.WriteLine($"LogLoss: {metrics.LogLoss}");
Console.WriteLine($"LogLossReduction: {metrics.LogLossReduction}");
// 进行预测
var predictionEngine = context.Model.CreatePredictionEngine(model);
var prediction = predictionEngine.Predict(new NewsData { Text = "这是一篇体育新闻" });
Console.WriteLine($"预测结果：{prediction.Category}");