RFAE (version 0.1.0)

reconstruction_error: Mixed-type Reconstruction Error

Description

Computes the reconstruction error of a decoded dataset compared to the original.

Usage

reconstruction_error(Xhat, X)

Value

A list containing column-wise reconstruction error, and the average reconstruction error for categorical and numeric variables. Values lie between 0-1, where 0 represents perfect reconstruction, and 1 represents no reconstruction.

Arguments

Xhat

Reconstructed dataset

X

Ground truth dataset

Details

In standard AEs, reconstruction error is generally estimated via \(L_2\) loss. This is not sensible with a mix of continuous and categorical data, so we devise a measure that evaluates distortion on continuous variables as \(1 - R^2\), and categorical variables as prediction error.

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

# Compute the reconstruction error
error <- reconstruction_error(out, iris[tst, ])