由于ML.Net似乎相对较新，因此似乎没有太多的文档。我一直在试图学习如何使用它的过程中遇到一个问题，最终我弄清楚了它足以使它至少运行而不会出错。但是，我的模型似乎有问题。它总是以50％的概率返回0。我在下面包含了我的代码。有谁知道我可以探索的最新版本的ML.Net的有用资源吗？下面的代码应该建立一个二进制分类模型，该模型可以预测球队是否将进入季后赛。该数据只是上个赛季的最终结果，大部分数据已删除，因此剩下的唯一列是平均年龄，胜利，失败和季后赛状态（1 =季后赛＆0 =无季后赛）。
Program.cs
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Core.Data;
using Microsoft.ML.Runtime.Api;
using Microsoft.ML.Runtime.Data;
namespace MachineLearning2
{
class Program
{
static readonly string _trainDataPath = Path.Combine(Environment.CurrentDirectory, "trainingNHL.txt");
static readonly string _testDataPath = Path.Combine(Environment.CurrentDirectory, "testingNHL.txt");
static readonly string _modelPath = Path.Combine(Environment.CurrentDirectory, "Model.zip");
static TextLoader _textLoader;
static void Main(string[] args)
{
MLContext mlContext = new MLContext(seed: 0);
_textLoader = mlContext.Data.TextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = false,
Column = new[]
{
new TextLoader.Column("Age", DataKind.R4, 0),
new TextLoader.Column("Wins", DataKind.R4, 1),
new TextLoader.Column("Losses", DataKind.R4, 2),
new TextLoader.Column("Label", DataKind.R4, 3)
}
});
var model = Train(mlContext, _trainDataPath);
Evaluate(mlContext, model);
Predict(mlContext, model);
PredictWithModelLoadedFromFile(mlContext);
}
public static ITransformer Train(MLContext mlContext, string dataPath)
{
IDataView dataView = _textLoader.Read(dataPath);
var pipeline = mlContext.Transforms.Concatenate("Features","Age", "Wins", "Losses")
.Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves: 50, numTrees: 50, minDatapointsInLeafs: 20));
Console.WriteLine("=============== Create and Train the Model ===============");
var model = pipeline.Fit(dataView);
Console.WriteLine("=============== End of training ===============");
Console.WriteLine();
return model;
}
public static void Evaluate(MLContext mlContext, ITransformer model)
{
IDataView dataView = _textLoader.Read(_testDataPath);
Console.WriteLine("=============== Evaluating Model accuracy with Test data===============");
var predictions = model.Transform(dataView);
var metrics = mlContext.BinaryClassification.Evaluate(predictions, "Label");
Console.WriteLine();
Console.WriteLine("Model quality metrics evaluation");
Console.WriteLine("--------------------------------");
Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Auc: {metrics.Auc:P2}");
Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
Console.WriteLine("=============== End of model evaluation ===============");
SaveModelAsFile(mlContext, model);
}
private static void SaveModelAsFile(MLContext mlContext, ITransformer model)
{
using (var fs = new FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
mlContext.Model.Save(model, fs);
Console.WriteLine("The model is saved to {0}", _modelPath);
}
public static void Predict(MLContext mlContext, ITransformer model)
{
var predictionFunction = model.MakePredictionFunction<NHLData, NHLPrediction>(mlContext);
NHLData sampleTeam = new NHLData
{
Age = 29,
Wins = 60,
Losses = 22
};
var resultprediction = predictionFunction.Predict(sampleTeam);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============");
Console.WriteLine();
Console.WriteLine($"Age: {sampleTeam.Age} | Wins: {sampleTeam.Wins} | Losses: {sampleTeam.Losses} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Yes" : "No")} | Probability: {resultprediction.Probability} ");
Console.WriteLine("=============== End of Predictions ===============");
Console.WriteLine();
}
public static void PredictWithModelLoadedFromFile(MLContext mlContext)
{
IEnumerable<NHLData> teams = new[]
{
new NHLData
{
Age = 29,
Wins = 30,
Losses = 52
},
new NHLData
{
Age = 35,
Wins = 80,
Losses = 2
}
};
ITransformer loadedModel;
using (var stream = new FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
{
loadedModel = mlContext.Model.Load(stream);
}
// Create prediction engine
var nhlStreamingDataView = mlContext.CreateStreamingDataView(teams);
var predictions = loadedModel.Transform(nhlStreamingDataView);
// Use the model to predict whether comment data is toxic (1) or nice (0).
var predictedResults = predictions.AsEnumerable<NHLPrediction>(mlContext, reuseRowObject: false);
Console.WriteLine();
Console.WriteLine("=============== Prediction Test of loaded model with a multiple samples ===============");
var teamsAndPredictions = teams.Zip(predictedResults, (team, prediction) => (team, prediction));
foreach (var item in teamsAndPredictions)
{
Console.WriteLine($"Age: {item.team.Age} | Wins: {item.team.Wins} | Losses: {item.team.Losses} | Prediction: {(Convert.ToBoolean(item.prediction.Prediction) ? "Yes" : "No")} | Probability: {item.prediction.Probability} ");
}
Console.WriteLine("=============== End of predictions ===============");
}
}
}
NHLData.cs
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.ML.Runtime.Api;
namespace MachineLearning2
{
public class NHLData
{
[Column(ordinal: "0")]
public float Age;
[Column(ordinal: "1")]
public float Wins;
[Column(ordinal: "2")]
public float Losses;
[Column(ordinal: "3", name: "Label")]
public float Playoffs;
}
public class NHLPrediction
{
[ColumnName("PredictedLabel")]
public bool Prediction { get; set; }
[ColumnName("Probability")]
public float Probability { get; set; }
[ColumnName("Score")]
public float Score { get; set; }
}
}
trainingNHL.txt （列：年龄，胜利，失败，季后赛）
28.4,53,18,1
27.5,54,23,1
28,51,24,1
28.3,49,26,1
29.5,45,26,1
28.8,45,27,1
29.1,45,29,1
27.7,44,29,1
26.4,43,30,1
28.5,42,32,0
27,36,35,0
26.8,36,40,0
28,33,39,0
30.2,30,39,0
26.5,29,41,0
27.1,25,45,0
testingNHL.txt （列：年龄，获胜，失利，季后赛）
26.8,52,20,1
28.6,50,20,1
28.4,49,26,1
28.7,44,25,1
27.7,47,29,1
27.4,42,26,1
26.4,45,30,1
27.8,44,30,0
28.5,44,32,0
28.4,37,35,0
28.4,35,37,0
28.7,34,39,0
28.2,31,40,0
27.8,29,40,0
29.3,28,43,0
答案 0 :(得分：2)
trainingNHL.txt是您正在使用的完整数据集还是仅是其样本？我刚刚尝试用FastTree对其进行训练，然后看到“警告：50个增强迭代无法生长一棵树。这通常是因为对于此数据集，叶超参数中的最小文档设置得太高。”
鉴于您在FastTree中设置的参数，您将需要更多数据来训练有意义的模型。当我将minDatapointsInLeafs更改为2时，我可以训练一个非平凡的模型（尽管由于数据量，结果仍然不是很值得信赖）。您也可以尝试使用AveragedPerceptron或SDCA之类的东西。