UMAP

Instance Constructors

1. new UMAP(nNeighborsIn: Int = 15, nComponents: Int = 2, metric: Distance = new Euclidean, nEpochs: Option[Int] = None, learningRate: Double = 1.0, init: UMAPInitialization = RandomInit, minDist: Double = 0.1, spread: Double = 1.0, setOPMixRatio: Double = 1.0, localConnectivity: Double = 1.0, repulsionStrength: Double = 1.0, negativeSampleRate: Int = 5, transformQueueSize: Double = 4.0, a: Option[Double] = None, b: Option[Double] = None, angularRPForest: Boolean = false, targetNNeighbors: Int = 1, targetMetric: Option[Distance] = None, targetWeight: Double = 0.5, transformSeed: Int = 42, verbose: Boolean = false)

   
   nComponents

   int (optional, default 2) The dimension of the space to embed into. This defaults to 2 to provide easy visualization, but can reasonably be set to any integer value in the range 2 to 100.

   metric

   function (optional, default 'euclidean') The metric to use to compute distances in high dimensional space.

   nEpochs

   int (optional, default None) The number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If None is specified a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small).

   learningRate

   double (optional, default 1.0) The initial learning rate for the embedding optimization.

   init

   string (optional, default 'random') How to initialize the low dimensional embedding. Options are: * 'spectral': use a spectral embedding of the fuzzy 1-skeleton * 'random': assign initial embedding positions at random. * A numpy array of initial embedding positions.

   minDist

   double (optional, default 0.1) The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out.

   spread

   double (optional, default 1.0) The effective scale of embedded points. In combination with min_dist this determines how clustered/clumped the embedded points are.

   setOPMixRatio

   double (optional, default 1.0) Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy set operations use the product t-norm. The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection.

   localConnectivity

   int (optional, default 1) The local connectivity required -- i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally. In practice this should be not more than the local intrinsic dimension of the manifold.

   repulsionStrength

   double (optional, default 1.0) Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.

   negativeSampleRate

   int (optional, default 5) The number of negative samples to select per positive sample in the optimization process. Increasing this value will result in greater repulsive force being applied, greater optimization cost, but slightly more accuracy.

   transformQueueSize

   double (optional, default 4.0) For transform operations (embedding new points using a trained model_ this will control how aggressively to search for nearest neighbors. Larger values will result in slower performance but more accurate nearest neighbor evaluation.

   a

   double (optional, default None) More specific parameters controlling the embedding. If None these values are set automatically as determined by min_dist and spread.

   b

   double (optional, default None) More specific parameters controlling the embedding. If None these values are set automatically as determined by min_dist and spread.

   angularRPForest

   bool (optional, default False) Whether to use an angular random projection forest to initialise the approximate nearest neighbor search. This can be faster, but is mostly on useful for metric that use an angular style distance such as cosine, correlation etc. In the case of those metrics angular forests will be chosen automatically.

   transformSeed

   int (optional, default 42) Random seed used for the stochastic aspects of the transform operation. This ensures consistency in transform operations.

   verbose

   bool (optional, default False) Controls verbosity of logging.