Saving and Using Saved Models

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2026-03-03

Saving a trained CISS-VAE model

library(reticulate)
library(rCISSVAE)

# Train a model
res <- run_cissvae(data)

# Save the trained model to disk
save_cissvae_model(res$model, "trained_vae.pt")

# IMPORTANT
# The Python environment must be active so 'torch' can be imported.

Loading a saved model and imputing data

library(rCISSVAE)
library(reticulate)

## Activate your Python environment 
reticulate::use_virtualenv("cissvae_environment", required = TRUE)

## Load full model object
model <- load_cissvae_model(
  file = "trained_vae.pt"
)

## Perform imputation on new data
# Make sure your `data` has valid NAs and `clusters` vector is ready
imputed_df <- impute_with_cissvae(
  model_py = model,
  data = data,
  index_col = "index",
  columns_ignore = NULL,
  clusters = clusters,
  imputable_matrix = NULL,
  binary_feature_mask = NULL,
  val_proportion = 0.1,
  replacement_value = 0,
  batch_size = 4000L,
  seed = 42
)

# `imputed_df` is returned to R as a data.frame

If you have binary variables in your dataset, make sure to define the binary_feature_mask and convert the probabilities for the binary variables into {0, 1} values using desired thresholding.

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