pyshapr 0.5.1

Python wrapper for the R package shapr (via rpy2)

pyshapr

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

Renamed: This package was previously published as shaprpy. It has been renamed to pyshapr. The old shaprpy package remains available on PyPI for a transition period and simply forwards to pyshapr. Please switch to pip install pyshapr and import pyshapr.

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

Requirement: Python 3.11 or later is required to use pyshapr.

Changelog

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

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Installation

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

• 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

pyshapr 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 pyshapr 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 pyshapr

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.

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Quick Demo

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

# Load example data
dfx_train, dfx_explain, dfy_train, dfy_explain = 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_explain,
    approach="gaussian",
    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

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Supported Models

pyshapr can explain predictions from models built with:

• scikit-learn
• keras (Sequential API)
• xgboost

For other model types, you can supply:

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

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Supported Approaches

pyshapr forwards all approach-specific arguments to shapr::explain(). Commonly used approaches include:

• "arf", "categorical", "copula", "ctree", "empirical", "gaussian", "regression_separate", "regression_surrogate", "vaeac"
• "independence" (not recommended)

"arf", "ctree", "regression_separate", "regression_surrogate" and "vaeac" support mixed numerical/categorical feature sets, "categorical" supports categorical features only, while "copula", "empirical", "gaussian" and "independence" support numerical features only.

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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
• ARF and VAEAC examples for both numerical and mixed categorical feature sets
• 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