KMeansClusteringExtensions.KMeans Método
Definición
Importante
Parte de la información hace referencia a la versión preliminar del producto, que puede haberse modificado sustancialmente antes de lanzar la versión definitiva. Microsoft no otorga ninguna garantía, explícita o implícita, con respecto a la información proporcionada aquí.
Sobrecargas
| KMeans(ClusteringCatalog+ClusteringTrainers, KMeansTrainer+Options) |
Entrene un algoritmo de agrupación en clústeres de KMeans++ mediante KMeansTrainer. |
| KMeans(ClusteringCatalog+ClusteringTrainers, String, String, Int32) |
Entrene un algoritmo de agrupación en clústeres de KMeans++ mediante KMeansTrainer. |
KMeans(ClusteringCatalog+ClusteringTrainers, KMeansTrainer+Options)
Entrene un algoritmo de agrupación en clústeres de KMeans++ mediante 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 KMeansTrainerParámetros
Objeto instructor del catálogo de agrupación en clústeres.
- options
- KMeansTrainer.Options
Opciones avanzadas de algoritmo.
Devoluciones
Ejemplos
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}");
}
}
}
Se aplica a
KMeans(ClusteringCatalog+ClusteringTrainers, String, String, Int32)
Entrene un algoritmo de agrupación en clústeres de KMeans++ mediante 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 KMeansTrainerParámetros
Objeto instructor del catálogo de agrupación en clústeres.
- featureColumnName
- String
Nombre de la columna de característica.
- exampleWeightColumnName
- String
Nombre de la columna de peso de ejemplo (opcional).
- numberOfClusters
- Int32
Número de clústeres que se van a usar para KMeans.
Devoluciones
Ejemplos
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}");
}
}
}
Se aplica a
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