ShapPack

ShapPack is a Python package for interpretable machine learning based on Shapley values.

ShapPack is currently in beta and under active development!

Installation

$ pip install shappack

Usage

The usage of ShapPack is almost the same as that of slundberg/shap.

import shappack
import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
SEED = 123
np.random.seed(SEED)

# Prepare dataset
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston["data"], boston["target"], test_size=0.2, random_state=SEED)
scaler = StandardScaler()
X_train_std = scaler.fit_transform(X_train)
X_test_std = scaler.transform(X_test)

# Prepare model
model = SVR(kernel="rbf")
model.fit(X_train_std, y_train)

# Coumpute SHAP value
i = 2
explainer = shappack.KernelExplainer(model.predict, X_train_std[:100])
shap_value = explainer.shap_values(X_test_std[i], n_workers=-1)

For now, ShapPack does not have own visualization mechanism, so it is necessary to use slundberg/shap for visualization.

import shap
shap.initjs()
shap.force_plot(explainer.base_val[0], shap_value, X_test[i], boston.feature_names)

An important difference from slundberg/shap is that the shap_values function in ShapPack has three new arguments: n_workers, skip_features, and characteristic_func, which contribute to faster computation and scalability.

The usage of each of these arguments is described below.

n_workers: Multiprocessing

Specify the number of processes used for the calculation of SHAP values. n_workers=-1 means using all processors.

If the program is running on a multi-core server, we can expect a reduction in computation time.

shap_value = explainer.shap_values(X_test_std[i], n_workers=-1)

skip_features: Skip the calculation of SHAP values

We can skips the calculation of SHAP values for the features specified in skip_features. The features to be skipped can be specified by feature name or index number.

Note that we need to pass a list of feature names to KernelExplainer's feature_names argument when specifying skip_features by feature names.

explainer = shappack.KernelExplainer(model.predict, X_train_std[:100], feature_names=boston.feature_names)
skip_features=["PTRATIO", "TAX"]
shap_value = explainer.shap_values(X_test_std[i], skip_features=skip_features, n_workers=-1)
feature_names = np.delete(boston.feature_names, explainer.skip_idx)
x_test = np.delete(X_test[i], explainer.skip_idx)
shap.force_plot(explainer.base_val[0], shap_value, x_test, feature_names)

characteristic_func: Incorporate own characteristic function

We can incorporate own implemented characteristic functions into the characteristic_func argument.

The example below is a function that replaces the expected value calculation in the original Kernel SHAP's characteristic function with a minimum value calculation.

def my_characteristic_func(instance, subsets, model, data):
    n_subsets = subsets.shape[0]
    n_data = data.shape[0]
    synth_data = np.tile(data, (n_subsets, 1))
    for i, subset in enumerate(subsets):
        offset = i * n_data
        features_idx = np.where(subset == 1.0)[0]
        synth_data[offset : offset + n_data, features_idx] = instance[:, features_idx][0]
    model_preds = model(synth_data)
    ey = np.zeros(n_subsets)
    for i in range(n_subsets):
        ey[i] = np.min(model_preds[i * n_data : i * n_data + n_data])
    return ey

shap_value = explainer.shap_values(X_test_std[i], characteristic_func=my_characteristic_func)

License

This project is licensed under the terms of the MIT license, see LICENSE.