RFAE (version 0.1.0)

predict.encode: Predict Spectral Embeddings

Description

Projects test data into the forest embedding space using a pre-trained encoding map.

Usage

# S3 method for encode
predict(object, rf, x, parallel = TRUE, ...)

Value

A matrix of embeddings, with nrow(x) rows and k columns, the latter argument used to learn the eigenmap.

Arguments

object

Spectral embedding for the rf learned via eigenmap.

rf

Pre-trained random forest object of class ranger.

x

Data to be embedded.

parallel

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

...

Additional arguments passed to methods.

Details

This function uses the weights learned via eigenmap to project new data into the low-dimensional embedding space using the Nyström formula. For details, see Bengio et al. (2004).

References

Bengio, Y., Delalleau, O., Le Roux, N., Paiement, J., Vincent, P., & Ouimet, M. (2004). Learning eigenfunctions links spectral embedding and kernel PCA. Neural Computation, 16(10): 2197-2219.

See Also

adversarial_rf

Examples

# Set seed
set.seed(1)

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

# Train RF. You can also use RF variants, such as the Adversarial Random
# Forests (ARF).
rf <- ranger::ranger(Species ~ ., data = iris[trn, ], num.trees=50)

# Learn the encodings, which are found using diffusion maps.
emap <- encode(rf, iris[trn, ], k=2)

# Embed test points
emb <- predict(emap, rf, iris[tst, ])