Ann
ANN (Approximate Nearest Neighbor)
An approximate nearest neighbor search algorithm is allowed to return points, whose distance from the query is at most c times the distance from the query to its nearest points.
The appeal of this approach is that, in many cases, an approximate nearest neighbor is almost as good as the exact one. In particular, if the distance measure accurately captures the notion of user quality, then small differences in the distance should not matter.
The ANN algorithm is able to solve multiclass classification tasks. The Apache Ignite implementation is a heuristic algorithm based upon searching of small limited size N
of candidate points (internally it uses a distributed KMeans clustering algorithm to find centroids) that can vote for class labels like a KNN algorithm.
The difference between KNN and ANN is that in the prediction phase, all training points are involved in searching knearest neighbors in the KNN algorithm, but in ANN this search starts only on a small subset of candidates points.
Model
ANN classification output represents a class membership. An object is classified by the majority votes of its neighbors. The object is assigned to a particular class that is most common among its k
nearest neighbors. k
is a positive integer, typically small. There is a special case when k
is 1, then the object is simply assigned to the class of that single nearest neighbor.
At present, Ignite supports the following parameters for the ANN classification algorithm:

k
 a number of nearest neighbors. 
distanceMeasure
 one of the distance metrics provided by the Machine Learning (ML) framework, such as Euclidean, Hamming, or Manhattan. 
KNNStrategy
 could be SIMPLE or WEIGHTED (it enables a weighted KNN algorithm).
NNClassificationModel knnMdl = trainer.fit(
...
).withK(5)
.withDistanceMeasure(new EuclideanDistance())
.withStrategy(NNStrategy.WEIGHTED);
Trainer
The trainer of the ANN model is based upon KMeans to calculate the candidate subset and this is the reason that it has the same parameters as the KMeans algorithm to tune its hyperparameters. It builds not only the set of candidates but also their classlabel distributions to vote for the class label during the prediction phase.
At present, Ignite supports the following parameters for ANNClassificationTrainer
:

k
 a number of possible clusters. 
maxIterations
 one stop criteria (the other one is epsilon). 
epsilon
 delta of convergence (delta between old and new centroid values). 
distance
 one of the distance metrics provided by the ML framework, such as Euclidean, Hamming or Manhattan. 
seed
 one of initialization parameters which helps to reproduce models (trainer has a random initialization step to get the first centroids).
// Set up the trainer
ANNClassificationTrainer trainer = new ANNClassificationTrainer()
.withDistance(new ManhattanDistance())
.withK(50)
.withMaxIterations(1000)
.withSeed(1234L)
.withEpsilon(1e2);
// Build the model
NNClassificationModel knnMdl = trainer.fit(
ignite,
dataCache,
(k, v) > VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
(k, v) > v[0]
).withK(5)
.withDistanceMeasure(new EuclideanDistance())
.withStrategy(NNStrategy.WEIGHTED);
Example
To see how ANNClassificationModel can be used in practice, try this example, available on GitHub and delivered with every Apache Ignite distribution. The training dataset is the Iris dataset that can be loaded from the UCI Machine Learning Repository.
© 2020 GridGain Systems, Inc. All Rights Reserved. Privacy Policy  Legal Notices. GridGain® is a registered trademark of GridGain Systems, Inc.
Apache, Apache Ignite, the Apache feather and the Apache Ignite logo are either registered trademarks or trademarks of The Apache Software Foundation.