Class: Rumale::Clustering::KMedoids

Inherits:
Base::Estimator show all
Includes:
Base::ClusterAnalyzer
Defined in:
rumale-clustering/lib/rumale/clustering/k_medoids.rb

Overview

KMedoids is a class that implements K-Medoids cluster analysis.

Reference

  • Arthur, D., and Vassilvitskii, S., “k-means++: the advantages of careful seeding,” Proc. SODA’07, pp. 1027–1035, 2007.

Examples:

require 'rumale/clustering/k_medoids'

analyzer = Rumale::Clustering::KMedoids.new(n_clusters: 10, max_iter: 50)
cluster_labels = analyzer.fit_predict(samples)

Instance Attribute Summary collapse

Attributes inherited from Base::Estimator

#params

Instance Method Summary collapse

Methods included from Base::ClusterAnalyzer

#score

Constructor Details

#initialize(n_clusters: 8, metric: 'euclidean', init: 'k-means++', max_iter: 50, tol: 1.0e-4, random_seed: nil) ⇒ KMedoids

Create a new cluster analyzer with K-Medoids method.

Parameters:

  • n_clusters (Integer) (defaults to: 8) —

    The number of clusters.

  • metric (String) (defaults to: 'euclidean') —

    The metric to calculate the distances. If metric is ‘euclidean’, Euclidean distance is calculated for distance between points. If metric is ‘precomputed’, the fit and fit_transform methods expect to be given a distance matrix.

  • init (String) (defaults to: 'k-means++') —

    The initialization method for centroids (‘random’ or ‘k-means++’).

  • max_iter (Integer) (defaults to: 50) —

    The maximum number of iterations.

  • tol (Float) (defaults to: 1.0e-4) —

    The tolerance of termination criterion.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator.



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# File 'rumale-clustering/lib/rumale/clustering/k_medoids.rb', line 40

def initialize(n_clusters: 8, metric: 'euclidean', init: 'k-means++', max_iter: 50, tol: 1.0e-4, random_seed: nil)
  super()
  @params = {
    n_clusters: n_clusters,
    metric: (metric == 'precomputed' ? 'precomputed' : 'euclidean'),
    init: (init == 'random' ? 'random' : 'k-means++'),
    max_iter: max_iter,
    tol: tol,
    random_seed: random_seed || srand
  }
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#medoid_ids ⇒ Numo::Int32 (readonly)

Return the indices of medoids.

Returns:

  • (Numo::Int32) —

    (shape: [n_clusters])



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# File 'rumale-clustering/lib/rumale/clustering/k_medoids.rb', line 24

def medoid_ids
  @medoid_ids
end

#rng ⇒ Random (readonly)

Return the random generator.

Returns:

  • (Random) 


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# File 'rumale-clustering/lib/rumale/clustering/k_medoids.rb', line 28

def rng
  @rng
end

Instance Method Details

#fit(x) ⇒ KMedoids

Analysis clusters with given training data.

Returns The learned cluster analyzer itself.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for fitting the model. If the metric is ‘precomputed’, x must be a square distance matrix (shape: [n_samples, n_samples]).

Returns:

  • (KMedoids) —

    The learned cluster analyzer itself.

Raises:

  • (ArgumentError) 


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# File 'rumale-clustering/lib/rumale/clustering/k_medoids.rb', line 59

def fit(x, _y = nil)
  x = ::Rumale::Validation.check_convert_sample_array(x)
  raise ArgumentError, 'the input distance matrix should be square' if check_invalid_array_shape(x)

  # initialize some varibales.
  distance_mat = @params[:metric] == 'precomputed' ? x : ::Rumale::PairwiseMetric.euclidean_distance(x)
  init_cluster_centers(distance_mat)
  error = distance_mat[true, @medoid_ids].mean
  @params[:max_iter].times do |_t|
    cluster_labels = assign_cluster(distance_mat[true, @medoid_ids])
    @params[:n_clusters].times do |n|
      assigned_ids = cluster_labels.eq(n).where
      @medoid_ids[n] = assigned_ids[distance_mat[assigned_ids, assigned_ids].sum(axis: 1).min_index]
    end
    new_error = distance_mat[true, @medoid_ids].mean
    break if (error - new_error).abs <= @params[:tol]

    error = new_error
  end
  @cluster_centers = x[@medoid_ids, true].dup if @params[:metric] == 'euclidean'
  self
end

#fit_predict(x) ⇒ Numo::Int32

Analysis clusters and assign samples to clusters.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for cluster analysis. If the metric is ‘precomputed’, x must be a square distance matrix (shape: [n_samples, n_samples]).

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted cluster label per sample.

Raises:

  • (ArgumentError) 


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# File 'rumale-clustering/lib/rumale/clustering/k_medoids.rb', line 103

def fit_predict(x)
  x = ::Rumale::Validation.check_convert_sample_array(x)
  raise ArgumentError, 'the input distance matrix should be square' if check_invalid_array_shape(x)

  fit(x)
  if @params[:metric] == 'precomputed'
    predict(x[true, @medoid_ids])
  else
    predict(x)
  end
end

#predict(x) ⇒ Numo::Int32

Predict cluster labels for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the cluster label. If the metric is ‘precomputed’, x must be distances between samples and medoids (shape: [n_samples, n_clusters]).

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted cluster label per sample.



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# File 'rumale-clustering/lib/rumale/clustering/k_medoids.rb', line 87

def predict(x)
  x = ::Rumale::Validation.check_convert_sample_array(x)

  distance_mat = @params[:metric] == 'precomputed' ? x : ::Rumale::PairwiseMetric.euclidean_distance(x, @cluster_centers)
  if @params[:metric] == 'precomputed' && distance_mat.shape[1] != @medoid_ids.size
    raise ArgumentError, 'the shape of input matrix should be n_samples-by-n_clusters'
  end

  assign_cluster(distance_mat)
end