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Set up the Python environment for triplet embeddings

setup_python_env.Rd

Call this function once the very first time you use the embedding pipeline. It will:

  1. Check whether Miniconda/Anaconda is available on the system.

  2. Create a self-contained conda environment named envname.

  3. Install all required Python packages listed in requirements.txt into that environment.

  4. Activate the environment for the current R session.

Usage

setup_python_env(envname = NULL, requirements = NULL)

Arguments

envname

Name of the conda environment to create. Defaults to "triplet-embeddings". Change this only if you need to keep multiple isolated environments on the same machine.

requirements

Path to a requirements.txt file listing the Python packages to install. Defaults to the copy bundled with the package (inst/requirements.txt).

Value

The environment name, invisibly.

Details

On future R sessions you do not need to call this function again. Loading the package with library() is sufficient — the environment is detected and activated automatically at that point.

Python dependencies

The following packages are installed into the conda environment: numpy, pandas, torch, scikit-learn, scipy, and skorch. PyTorch is installed via conda from the pytorch channel; all other packages come from conda-forge. No pip installs are used, which ensures DLL compatibility on Windows. PyTorch is a large download (~300–800 MB depending on platform), so the first-time installation may take several minutes.

Examples

if (FALSE) { # \dontrun{
# Run once after installing the package:
setup_python_env()

# On all subsequent sessions just load the package as normal:
library(tripletTools)
results <- run_embeddings(
  input_file           = "triplets.csv",
  additional_data_file = "item_labels.csv",
  output_dir           = "embeddings_output"
)
} # }