Saving and Using Saved Models

library(reticulate)
res = run_cissvae(data)

# Import Python torch module
torch <- import("torch")

# Assume `model` is a Python object already available in the R session
# (e.g., created earlier via reticulate)
torch$save(res$model, "trained_vae.pt")

To load a saved CISS-VAE model and use it to impute new data (called data in example below, with cluster vector clusters),

library(rCISSVAE)
library(reticulate)

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

## Use CISSVAE to load the model
# Import the module so the class is registered (required for full-model loading)
import("ciss_vae.classes.vae")

# Load full model object
model <- torch$load("trained_vae.pt", map_location = "cpu", weights_only = FALSE)
model$eval()

# Optional: get imputed dataset
helpers <- import("ciss_vae.utils.helpers")
DataLoader <- import("torch.utils.data")$DataLoader

## Convert your dataset to python ClusterDataset object
  CD_mod   <- reticulate::import("ciss_vae.classes.cluster_dataset", 
  convert = FALSE)$ClusterDataset
  np       <- reticulate::import("numpy", convert = FALSE)
  pd       <- reticulate::import("pandas", convert = FALSE)

## make sure NAs are python compatible
  data[is.na(data)] <- NaN

## Convert data and clusters into python objects
  data_py <- pd$DataFrame(data = data, dtype = "float64")
  clusters_py <- np$array(as.integer(clusters), dtype = "int64")

## Make ClusterDataset and DataLoader
  dataset = CD_mod(
    data = data_py, 
    cluster_labels = clusters_py)
  data_loader <- DataLoader(dataset, batch_size = 4000L)

## Get Imputed Dataset
imputed_df <- helpers$get_imputed_df(model, data_loader)

Saving and Using Saved Models

2026-01-20