KMeansClusteringExtensions.KMeans Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.KMeansClustering.dll

Pacchetto:

Microsoft.ML v3.0.1

Overload

KMeans(ClusteringCatalog+ClusteringTrainers, KMeansTrainer+Options)	Eseguire il training di un algoritmo di clustering KMeans++ usando KMeansTrainer.
KMeans(ClusteringCatalog+ClusteringTrainers, String, String, Int32)	Eseguire il training di un algoritmo di clustering KMeans++ usando KMeansTrainer.

KMeans(ClusteringCatalog+ClusteringTrainers, KMeansTrainer+Options)

Eseguire il training di un algoritmo di clustering KMeans++ usando KMeansTrainer.

public static Microsoft.ML.Trainers.KMeansTrainer KMeans (this Microsoft.ML.ClusteringCatalog.ClusteringTrainers catalog, Microsoft.ML.Trainers.KMeansTrainer.Options options);

static member KMeans : Microsoft.ML.ClusteringCatalog.ClusteringTrainers * Microsoft.ML.Trainers.KMeansTrainer.Options -> Microsoft.ML.Trainers.KMeansTrainer

<Extension()>
Public Function KMeans (catalog As ClusteringCatalog.ClusteringTrainers, options As KMeansTrainer.Options) As KMeansTrainer

Parametri

catalog

ClusteringCatalog.ClusteringTrainers

Oggetto di training del catalogo clustering.

options

KMeansTrainer.Options

Opzioni avanzate dell'algoritmo.

Restituisce

KMeansTrainer

Esempio

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers;

namespace Samples.Dynamic.Trainers.Clustering
{
    public static class KMeansWithOptions
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000, 0);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            IDataView trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define trainer options.
            var options = new KMeansTrainer.Options
            {
                NumberOfClusters = 2,
                OptimizationTolerance = 1e-6f,
                NumberOfThreads = 1
            };

            // Define the trainer.
            var pipeline = mlContext.Clustering.Trainers.KMeans(options);

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use a different random seed to make it different
            // from the training data.
            var testData = mlContext.Data.LoadFromEnumerable(
                GenerateRandomDataPoints(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                transformedTestData, reuseRowObject: false).ToList();

            // Print 5 predictions. Note that the label is only used as a comparison
            // with the predicted label. It is not used during training.
            foreach (var p in predictions.Take(2))
                Console.WriteLine(
                    $"Label: {p.Label}, Prediction: {p.PredictedLabel}");

            foreach (var p in predictions.TakeLast(3))
                Console.WriteLine(
                    $"Label: {p.Label}, Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 2, Prediction: 2
            //   Label: 2, Prediction: 2

            // Evaluate the overall metrics
            var metrics = mlContext.Clustering.Evaluate(
                transformedTestData, "Label", "Score", "Features");

            PrintMetrics(metrics);

            // Expected output:
            //   Normalized Mutual Information: 0.92
            //   Average Distance: 4.18
            //   Davies Bouldin Index: 2.87

            // Get the cluster centroids and the number of clusters k from
            // KMeansModelParameters.
            VBuffer<float>[] centroids = default;

            var modelParams = model.Model;
            modelParams.GetClusterCentroids(ref centroids, out int k);
            Console.WriteLine(
                $"The first 3 coordinates of the first centroid are: " +
                string.Join(", ", centroids[0].GetValues().ToArray().Take(3)));

            Console.WriteLine(
                $"The first 3 coordinates of the second centroid are: " +
                string.Join(", ", centroids[1].GetValues().ToArray().Take(3)));

            // Expected output:
            //   The first 3 coordinates of the first centroid are: (0.5840713, 0.5678288, 0.6221277)
            //   The first 3 coordinates of the second centroid are: (0.3705794, 0.4289133, 0.4001645)
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                int label = i < count / 2 ? 0 : 1;
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features with two clusters.
                    // The first half has feature values centered around 0.6, while
                    // the second half has values centered around 0.4.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(index => label == 0 ? randomFloat() + 0.1f :
                            randomFloat() - 0.1f).ToArray()
                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            // The label is not used during training, just for comparison with the
            // predicted label.
            [KeyType(2)]
            public uint Label { get; set; }

            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label (not used during training, just for comparison).
            public uint Label { get; set; }
            // Predicted label from the trainer.
            public uint PredictedLabel { get; set; }
        }

        // Pretty-print of ClusteringMetrics object.
        private static void PrintMetrics(ClusteringMetrics metrics)
        {
            Console.WriteLine($"Normalized Mutual Information: " +
                $"{metrics.NormalizedMutualInformation:F2}");

            Console.WriteLine($"Average Distance: " +
                $"{metrics.AverageDistance:F2}");

            Console.WriteLine($"Davies Bouldin Index: " +
                $"{metrics.DaviesBouldinIndex:F2}");
        }
    }
}

KMeans(ClusteringCatalog+ClusteringTrainers, String, String, Int32)

Eseguire il training di un algoritmo di clustering KMeans++ usando KMeansTrainer.

public static Microsoft.ML.Trainers.KMeansTrainer KMeans (this Microsoft.ML.ClusteringCatalog.ClusteringTrainers catalog, string featureColumnName = "Features", string exampleWeightColumnName = default, int numberOfClusters = 5);

static member KMeans : Microsoft.ML.ClusteringCatalog.ClusteringTrainers * string * string * int -> Microsoft.ML.Trainers.KMeansTrainer

<Extension()>
Public Function KMeans (catalog As ClusteringCatalog.ClusteringTrainers, Optional featureColumnName As String = "Features", Optional exampleWeightColumnName As String = Nothing, Optional numberOfClusters As Integer = 5) As KMeansTrainer

Parametri

catalog

ClusteringCatalog.ClusteringTrainers

Oggetto di training del catalogo clustering.

featureColumnName

String

Nome della colonna di funzionalità.

exampleWeightColumnName

String

Nome della colonna di peso di esempio (facoltativo).

numberOfClusters

Int32

Numero di cluster da usare per KMeans.

Restituisce

KMeansTrainer

Esempio

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic.Trainers.Clustering
{
    public static class KMeans
    {
        public static void Example()
        {
            // Create a new context for ML.NET operations. It can be used for
            // exception tracking and logging, as a catalog of available operations
            // and as the source of randomness. Setting the seed to a fixed number
            // in this example to make outputs deterministic.
            var mlContext = new MLContext(seed: 0);

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(1000, 123);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            IDataView trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Define the trainer.
            var pipeline = mlContext.Clustering.Trainers.KMeans(
                numberOfClusters: 2);

            // Train the model.
            var model = pipeline.Fit(trainingData);

            // Create testing data. Use a different random seed to make it different
            // from the training data.
            var testData = mlContext.Data.LoadFromEnumerable(
                GenerateRandomDataPoints(500, seed: 123));

            // Run the model on test data set.
            var transformedTestData = model.Transform(testData);

            // Convert IDataView object to a list.
            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
                transformedTestData, reuseRowObject: false).ToList();

            // Print 5 predictions. Note that the label is only used as a comparison
            // with the predicted label. It is not used during training.
            foreach (var p in predictions.Take(2))
                Console.WriteLine(
                    $"Label: {p.Label}, Prediction: {p.PredictedLabel}");

            foreach (var p in predictions.TakeLast(3))
                Console.WriteLine(
                    $"Label: {p.Label}, Prediction: {p.PredictedLabel}");

            // Expected output:
            //   Label: 1, Prediction: 1
            //   Label: 1, Prediction: 1
            //   Label: 2, Prediction: 2
            //   Label: 2, Prediction: 2
            //   Label: 2, Prediction: 2

            // Evaluate the overall metrics
            var metrics = mlContext.Clustering.Evaluate(
                transformedTestData, "Label", "Score", "Features");

            PrintMetrics(metrics);

            // Expected output:
            //   Normalized Mutual Information: 0.95
            //   Average Distance: 4.17
            //   Davies Bouldin Index: 2.87

            // Get the cluster centroids and the number of clusters k from
            // KMeansModelParameters.
            VBuffer<float>[] centroids = default;

            var modelParams = model.Model;
            modelParams.GetClusterCentroids(ref centroids, out int k);
            Console.WriteLine(
                $"The first 3 coordinates of the first centroid are: " +
                string.Join(", ", centroids[0].GetValues().ToArray().Take(3)));

            Console.WriteLine(
                $"The first 3 coordinates of the second centroid are: " +
                string.Join(", ", centroids[1].GetValues().ToArray().Take(3)));

            // Expected output similar to:
            //   The first 3 coordinates of the first centroid are: (0.6035213, 0.6017533, 0.5964218)
            //   The first 3 coordinates of the second centroid are: (0.4031044, 0.4175443, 0.4082336)
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);
            float randomFloat() => (float)random.NextDouble();
            for (int i = 0; i < count; i++)
            {
                int label = i < count / 2 ? 0 : 1;
                yield return new DataPoint
                {
                    Label = (uint)label,
                    // Create random features with two clusters.
                    // The first half has feature values centered around 0.6, while
                    // the second half has values centered around 0.4.
                    Features = Enumerable.Repeat(label, 50)
                        .Select(index => label == 0 ? randomFloat() + 0.1f :
                            randomFloat() - 0.1f).ToArray()
                };
            }
        }

        // Example with label and 50 feature values. A data set is a collection of
        // such examples.
        private class DataPoint
        {
            // The label is not used during training, just for comparison with the
            // predicted label.
            [KeyType(2)]
            public uint Label { get; set; }

            [VectorType(50)]
            public float[] Features { get; set; }
        }

        // Class used to capture predictions.
        private class Prediction
        {
            // Original label (not used during training, just for comparison).
            public uint Label { get; set; }
            // Predicted label from the trainer.
            public uint PredictedLabel { get; set; }
        }

        // Pretty-print of ClusteringMetrics object.
        private static void PrintMetrics(ClusteringMetrics metrics)
        {
            Console.WriteLine($"Normalized Mutual Information: " +
                $"{metrics.NormalizedMutualInformation:F2}");

            Console.WriteLine($"Average Distance: " +
                $"{metrics.AverageDistance:F2}");

            Console.WriteLine($"Davies Bouldin Index: " +
                $"{metrics.DaviesBouldinIndex:F2}");
        }
    }
}