/// <summary>
/// Computes the quality metrics for the multi-class classification PredictionModel
/// using the specified data set.
/// </summary>
/// <param name="model">
/// The trained multi-class classification PredictionModel to be evaluated.
/// </param>
/// <param name="testData">
/// The test data that will be predicted and used to evaluate the model.
/// </param>
/// <returns>
/// A ClassificationMetrics instance that describes how well the model performed against the test data.
/// </returns>
public ClassificationMetrics Evaluate(PredictionModel model, ILearningPipelineLoader testData)
{
var environment = new MLContext();
environment.CheckValue(model, nameof(model));
environment.CheckValue(testData, nameof(testData));
Experiment experiment = environment.CreateExperiment();
ILearningPipelineStep testDataStep = testData.ApplyStep(previousStep: null, experiment);
if (!(testDataStep is ILearningPipelineDataStep testDataOutput))
{
throw environment.Except($"The {nameof(ILearningPipelineLoader)} did not return a {nameof(ILearningPipelineDataStep)} from ApplyStep.");
}
var datasetScorer = new DatasetTransformScorer
{
Data = testDataOutput.Data,
};
DatasetTransformScorer.Output scoreOutput = experiment.Add(datasetScorer);
Data = scoreOutput.ScoredData;
Output evaluteOutput = experiment.Add(this);
experiment.Compile();
experiment.SetInput(datasetScorer.TransformModel, model.PredictorModel);
testData.SetInput(environment, experiment);
experiment.Run();
IDataView overallMetrics = experiment.GetOutput(evaluteOutput.OverallMetrics);
if (overallMetrics == null)
{
throw environment.Except($"Could not find OverallMetrics in the results returned in {nameof(ClassificationEvaluator)} Evaluate.");
}
IDataView confusionMatrix = experiment.GetOutput(evaluteOutput.ConfusionMatrix);
if (confusionMatrix == null)
{
throw environment.Except($"Could not find ConfusionMatrix in the results returned in {nameof(ClassificationEvaluator)} Evaluate.");
}
var metric = ClassificationMetrics.FromMetrics(environment, overallMetrics, confusionMatrix);
if (metric.Count != 1)
{
throw environment.Except($"Exactly one metric set was expected but found {metric.Count} metrics");
}
return(metric[0]);
}
TrainAndGetMetrics(ILearningPipelineLoader dataTrain, ILearningPipelineLoader dataTest, ILearningPipelineItem trainer)
{
var pipeline = new LearningPipeline();
pipeline.Add(dataTrain);
pipeline.Add(trainer);
var model = pipeline.Train <MLNetData, MLNetPredict>();
var evaluator = new BinaryClassificationEvaluator();
var metrics = evaluator.Evaluate(model, dataTest);
return(metrics.Accuracy, metrics.Auc, metrics.F1Score, model);
}
/// <summary>
/// Computes the quality metrics for the PredictionModel using the specified data set.
/// </summary>
/// <param name="model">
/// The trained PredictionModel to be evaluated.
/// </param>
/// <param name="testData">
/// The test data that will be predicted and used to evaulate the model.
/// </param>
/// <returns>
/// A RegressionMetrics instance that describes how well the model performed against the test data.
/// </returns>
public RegressionMetrics Evaluate(PredictionModel model, ILearningPipelineLoader testData)
{
using (var environment = new TlcEnvironment())
{
environment.CheckValue(model, nameof(model));
environment.CheckValue(testData, nameof(testData));
Experiment experiment = environment.CreateExperiment();
ILearningPipelineStep testDataStep = testData.ApplyStep(previousStep: null, experiment);
if (!(testDataStep is ILearningPipelineDataStep testDataOutput))
{
throw environment.Except($"The {nameof(ILearningPipelineLoader)} did not return a {nameof(ILearningPipelineDataStep)} from ApplyStep.");
}
var datasetScorer = new DatasetTransformScorer
{
Data = testDataOutput.Data,
};
DatasetTransformScorer.Output scoreOutput = experiment.Add(datasetScorer);
Data = scoreOutput.ScoredData;
Output evaluteOutput = experiment.Add(this);
experiment.Compile();
experiment.SetInput(datasetScorer.TransformModel, model.PredictorModel);
testData.SetInput(environment, experiment);
experiment.Run();
IDataView overallMetrics = experiment.GetOutput(evaluteOutput.OverallMetrics);
if (overallMetrics == null)
{
throw environment.Except($"Could not find OverallMetrics in the results returned in {nameof(RegressionEvaluator)} Evaluate.");
}
return(RegressionMetrics.FromOverallMetrics(environment, overallMetrics));
}
}
public NeualNetwork()
{
//初始化构建一个专用的pipline，用于传输操作
LearningPipeline pipeline = new LearningPipeline();
//1.加入数据
ILearningPipelineLoader loaderItem = CollectionDataSource.Create <LearningInput>(new List <LearningInput>()
{
new LearningInput()
{
Rawdata1 = 1.0,
Rawdata2 = 2.0,
Rawdata3 = 3.0,
Rawdata4 = 4.0,
Label = 1,
}
});
pipeline.Add(loaderItem);
//2.指定属性
pipeline.Add(new ColumnConcatenator("Features", "Rawdata1", "Rawdata2", "Rawdata3", "Rawdata4"));
//3.指定训练方法
pipeline.Add(new LogisticRegressionClassifier()
{
});
//4.指定prediction
pipeline.Add(new PredictedLabelColumnOriginalValueConverter()
{
PredictedLabelColumn = "PredictedLabel"
});
var model = pipeline.Train <LearningInput, LearningOutput>();
var pred = model.Predict(new LearningInput()
{
Rawdata1 = 1.0,
Rawdata2 = 2.0,
Rawdata3 = 3.0,
Rawdata4 = 4.0,
});
}
/// <summary>
/// Performs train-test on a pipeline.
/// </summary>
/// <typeparam name="TInput">Class type that represents input schema.</typeparam>
/// <typeparam name="TOutput">Class type that represents prediction schema.</typeparam>
/// <param name="pipeline">Machine learning pipeline that contains <see cref="ILearningPipelineLoader"/>,
/// transforms and at least one trainer.</param>
/// <param name="testData"><see cref="ILearningPipelineLoader"/> that represents the test dataset.</param>
/// <returns>Metrics and predictor model.</returns>
public TrainTestEvaluatorOutput <TInput, TOutput> TrainTestEvaluate <TInput, TOutput>(LearningPipeline pipeline, ILearningPipelineLoader testData)
where TInput : class
where TOutput : class, new()
{
using (var environment = new TlcEnvironment())
{
Experiment subGraph = environment.CreateExperiment();
ILearningPipelineStep step = null;
List <ILearningPipelineLoader> loaders = new List <ILearningPipelineLoader>();
List <Var <ITransformModel> > transformModels = new List <Var <ITransformModel> >();
Var <ITransformModel> lastTransformModel = null;
Var <IDataView> firstPipelineDataStep = null;
Var <IPredictorModel> firstModel = null;
ILearningPipelineItem firstTransform = null;
foreach (ILearningPipelineItem currentItem in pipeline)
{
if (currentItem is ILearningPipelineLoader loader)
{
loaders.Add(loader);
continue;
}
step = currentItem.ApplyStep(step, subGraph);
if (step is ILearningPipelineDataStep dataStep && dataStep.Model != null)
{
transformModels.Add(dataStep.Model);
if (firstPipelineDataStep == null)
{
firstPipelineDataStep = dataStep.Data;
firstTransform = currentItem;
}
}
