Added kmeans++ initialization to clustering API

tensorflow_model_optimization/python/core/clustering/keras/cluster_config.py

@@ -29,7 +29,9 @@ class CentroidInitialization(str, enum.Enum):
        axis is evenly spaced into as many regions as many clusters we want to
        have. After this the corresponding X values are obtained and used to
        initialize the clusters centroids.
+  * `KMEANS_PLUS_PLUS`: cluster centroids using the kmeans++ algorithm
   """
   LINEAR = "LINEAR"
   RANDOM = "RANDOM"
   DENSITY_BASED = "DENSITY_BASED"
+  KMEANS_PLUS_PLUS = "KMEANS_PLUS_PLUS"

tensorflow_model_optimization/python/core/clustering/keras/clustering_centroids.py

@@ -17,12 +17,11 @@
 import abc
 import six
 import tensorflow as tf
-
+from tensorflow.python.ops import clustering_ops
 from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
 
 k = tf.keras.backend
 CentroidInitialization = cluster_config.CentroidInitialization
-
 @six.add_metaclass(abc.ABCMeta)
 class AbstractCentroidsInitialisation:
   """
@@ -53,6 +52,20 @@ def get_cluster_centroids(self):
                                     self.number_of_clusters)
     return cluster_centroids
 
+class KmeansPlusPlusCentroidsInitialisation(AbstractCentroidsInitialisation):
+  """
+  Cluster centroids based on kmeans++ algorithm
+  """
+  def get_cluster_centroids(self):
+
+    weights = tf.reshape(self.weights, [-1, 1])
+
+    cluster_centroids = clustering_ops.kmeans_plus_plus_initialization(weights,
+                                                                       self.number_of_clusters,
+                                                                       seed=9,
+                                                                       num_retries_per_sample=-1)
+
+    return cluster_centroids
 
 class RandomCentroidsInitialisation(AbstractCentroidsInitialisation):
   """
@@ -192,7 +205,9 @@ class CentroidsInitializerFactory:
       CentroidInitialization.LINEAR : LinearCentroidsInitialisation,
       CentroidInitialization.RANDOM : RandomCentroidsInitialisation,
       CentroidInitialization.DENSITY_BASED :
-          DensityBasedCentroidsInitialisation
+          DensityBasedCentroidsInitialisation,
+      CentroidInitialization.KMEANS_PLUS_PLUS :
+          KmeansPlusPlusCentroidsInitialisation,
   }
 
   @classmethod

tensorflow_model_optimization/python/core/clustering/keras/clustering_centroids_test.py

@@ -40,6 +40,7 @@ def setUp(self):
       (CentroidInitialization.LINEAR),
       (CentroidInitialization.RANDOM),
       (CentroidInitialization.DENSITY_BASED),
+      (CentroidInitialization.KMEANS_PLUS_PLUS),
   )
   def testExistingInitsAreSupported(self, init_type):
     """
@@ -63,6 +64,10 @@ def testNonExistingInitIsNotSupported(self):
           CentroidInitialization.DENSITY_BASED,
           clustering_centroids.DensityBasedCentroidsInitialisation
       ),
+      (
+          CentroidInitialization.KMEANS_PLUS_PLUS,
+          clustering_centroids.KmeansPlusPlusCentroidsInitialisation
+       ),
   )
   def testReturnsMethodForExistingInit(self, init_type, method):
     """
@@ -177,6 +182,30 @@ def testClusterCentroids(self, weights, number_of_clusters, centroids):
     calc_centroids = K.batch_get_value([dbci.get_cluster_centroids()])[0]
     self.assertSequenceAlmostEqual(centroids, calc_centroids, places=4)
 
+  @parameterized.parameters(
+    (
+            [0, 1, 2, 3, 3.1, 3.2, 3.3, 3.4, 3.5],
+            5,
+            [3.1, 0., 2., 1., 3.4]
+    ),
+    (
+            [0, 1, 2, 3, 3.1, 3.2, 3.3, 3.4, 3.5],
+            3,
+            [3.1, 0., 2.]
+    ),
+    (
+            [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.],
+            3,
+            [6., 1., 8.]
+    )
+  )
+  def testKmeanPlusPlusValues(self, weights, number_of_clusters, centroids):
+    kmci = clustering_centroids.KmeansPlusPlusCentroidsInitialisation(
+        weights,
+        number_of_clusters
+    )
+    calc_centroids = K.batch_get_value([kmci.get_cluster_centroids()])[0]
+    self.assertSequenceAlmostEqual(centroids, calc_centroids, places=4)
 
 if __name__ == '__main__':
   test.main()