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shaprpy

Source: .github/shaprpy.md

shaprpy is a Python wrapper for the R package shapr, using the rpy2 Python library to access R from within Python.

Note: This wrapper is not as comprehensively tested as the R package. rpy2 has limited support on Windows, and the same therefore applies to shaprpy. shaprpy has only been tested on Linux (and WSL - Windows Subsystem for Linux), and the below instructions assume a Linux environment.

Requirement: Python 3.10 or later is required to use shaprpy.

Changelog

For a list of changes and updates to the shaprpy package, see the shaprpy CHANGELOG.

Installation

These instructions assume you already have pip and R installed and available to the Python environment in which you want to run shaprpy.

  • Official instructions for installing pip can be found here.
  • Official instructions for installing R can be found here.

On Debian/Ubuntu-based systems, R can also be installed via:

sudo apt update
sudo apt install r-base r-base-dev -y

1. Install the R package shapr

shaprpy requires the R package shapr (version 1.0.5 or newer). In your R environment, install the latest version from CRAN using:

Rscript -e 'install.packages("shapr", repos="https://cran.rstudio.com")'

2. Ensure R is discoverable (R_HOME and PATH)

Sometimes rpy2 (which shaprpy relies on) cannot automatically locate your R installation. To ensure proper detection, verify that:

  • R is available in your system PATH, or
  • The R_HOME environment variable is set to your R installation directory.

Example:

export R_HOME=$(R RHOME)
export PATH=$PATH:$(R RHOME)/bin

3. Install the Python wrapper

Install directly from PyPI with:

pip install shaprpy

Local development install (for contributors)

If you have cloned the repository and want to install in development mode for local changes, navigate to the ./python directory and run:

pip install -e .

The -e flag installs in editable mode, allowing local code changes to be reflected immediately.

Quick Demo

from sklearn.ensemble import RandomForestRegressor
from shaprpy import explain
from shaprpy.datasets import load_california_housing

# Load example data
dfx_train, dfx_test, dfy_train, dfy_test = load_california_housing()

# Fit a model
model = RandomForestRegressor()
model.fit(dfx_train, dfy_train.values.flatten())

# Explain predictions
explanation = explain(
    model=model,
    x_train=dfx_train,
    x_explain=dfx_test,
    approach="empirical",
    phi0=dfy_train.mean().item(),
    seed=1
)

explanation.print() # Print the Shapley values

# Get a summary object with computation details
summary = explanation.summary()
print(summary)  # Displays a formatted summary (also available directly via explanation.summary())

# Access specific summary attributes (available with tab-completion in Jupyter)
summary['approach']     # Approach used
summary['timing_summary']['total_time_secs']  # Total computation time

# Extract one or more specific result objects directly
explanation.get_results("proglang") # Programming language used (Python/R)
explanation.get_results("approach") # Approach used
explanation.get_results().keys()  # All available result objects

# Plotting (requires the 'shap' library)
# Convert to a SHAP Explanation object
shap_exp = explanation.to_shap()

import shap
shap.plots.waterfall(shap_exp[0]) # Plot the first observation

Supported Models

shaprpy can explain predictions from models built with:

For other model types, you can supply:

  • A custom predict_model function
  • (Optionally) a custom get_model_specs function to shaprpy.explain.

Examples

See the examples folder on GitHub for runnable examples, including:

  • Basic usage with scikit-learn models
  • Usage with xgboost models
  • Usage with keras models
  • A custom PyTorch model
  • Usage of the Shapr class and associated ShaprSummary class for exploration and extraction of explanation results.
  • Plotting functionality for the Shapley values through the shap package
  • The regression paradigm described in Olsen et al. (2024), which shows:
    • How to specify the regression model
    • How to enable automatic cross-validation of hyperparameters
    • How to apply pre-processing steps before fitting regression models