RFAE (version 0.1.0)

decode_knn: Decode RF Embeddings

Description

Maps the low-dimensional KPCA embedding of a random forest back to the input space via iterative k-nearest neighbors.

Usage

decode_knn(rf, emap, z, x_tilde = NULL, k = 5, parallel = TRUE)

Value

Decoded dataset.

Arguments

rf

Pre-trained random forest object of class ranger.

emap

Spectral embedding learned via eigenmap.

z

Matrix of embedded data to map back to the input space.

x_tilde

Supplied training data, if none supplied then the RF is used to generate synthetic training data according to the eForest scheme. Default is NULL.

k

Number of nearest neighbors to evaluate.

parallel

Compute in parallel? Must register backend beforehand, e.g. via doParallel.

Details

decode_knn decodes the embedded data back to the original input space using a k-nearest neighbors (kNN) (Cover & Hart, 1967) approach. For a given embedding vector, decoding works by first finding the k nearest embeddings within the training set. Then, x_tilde is either supplied or generated from the RF (if generated, using the 'eForest' scheme (Feng & Zhou, 2018)), which provides a proxy for the training samples associated with these embeddings, to avoid needing to retain training data. Finally, data is reconstructed by weighted averaging for numerical features, and the most likely value for categorical features.

References

Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

Feng, J., & Zhou, Z. H. (2018, April). Autoencoder by forest. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32 , No. 1).

Examples

# Set seed
set.seed(1)

# Split training and test
trn <- sample(1:nrow(iris), 100)
tst <- setdiff(1:nrow(iris), trn)

# Train RF, learn the encodings and project test points.
rf <- ranger::ranger(Species ~ ., data = iris[trn, ], num.trees=50)
emap <- encode(rf, iris[trn, ], k=2)
emb <- predict(emap, rf, iris[tst, ])

# Decode test samples back to the input space
out <- decode_knn(rf, emap, emb, k=5)$x_hat