Detect anomalies in product sales with ML.NET

https://learn.microsoft.com/en-us/dotnet/machine-learning/tutorials/sales-anomaly-detection

Tutorial: Detect anomalies in product sales - ML.NET

 

https://github.com/dotnet/samples/tree/main/machine-learning/tutorials/ProductSalesAnomalyDetection

GitHub - dotnet/samples: Sample code referenced by the .NET documentation

 

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);
    }