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https://learn.microsoft.com/en-us/dotnet/machine-learning/tutorials/sales-anomaly-detection
Tutorial: Detect anomalies in product sales - ML.NET
Learn how to build an anomaly detection application for product sales data. This tutorial creates a .NET Core console application using C# in Visual Studio 2019.
learn.microsoft.com
https://github.com/dotnet/samples/tree/main/machine-learning/tutorials/ProductSalesAnomalyDetection
GitHub - dotnet/samples: Sample code referenced by the .NET documentation
Sample code referenced by the .NET documentation. Contribute to dotnet/samples development by creating an account on GitHub.
github.com
잘은 되는 거 같은데 원하는 지점 찾는 게 힘드네!
Console.WriteLine("Detect temporary changes in pattern");
// STEP 2: Set the training algorithm
// <SnippetAddSpikeTrainer>
var iidSpikeEstimator = mlContext.Transforms.DetectIidSpike(outputColumnName: nameof(ProductSalesPrediction.Prediction), inputColumnName: nameof(ProductSalesData.Given), confidence: 99.949, pvalueHistoryLength: docSize / 4);
// </SnippetAddSpikeTrainer>
// STEP 3: Create the transform
// Create the spike detection transform
Console.WriteLine("=============== Training the model ===============");
// <SnippetTrainModel1>
ITransformer iidSpikeTransform = iidSpikeEstimator.Fit(CreateEmptyDataView(mlContext));
// </SnippetTrainModel1>
Console.WriteLine("=============== End of training process ===============");
//Apply data transformation to create predictions.
// <SnippetTransformData1>
IDataView transformedData = iidSpikeTransform.Transform(productSales);
// </SnippetTransformData1>
// <SnippetCreateEnumerable1>
var predictions = mlContext.Data.CreateEnumerable<ProductSalesPrediction>(transformedData, reuseRowObject: false);
// </SnippetCreateEnumerable1>
// <SnippetDisplayHeader1>
Console.WriteLine("Alert\tScore\tP-Value");
// </SnippetDisplayHeader1>
// <SnippetDisplayResults1>
foreach (var p in predictions)
{
var results = $"{idx++}\t{p.Prediction[0]}\t{p.Prediction[1]:f2}\t{p.Prediction[2]:F2}\t";
if (p.Prediction[0] == 1)
{
Console.WriteLine(results);
}
Console.WriteLine(results);
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