Fastshap: A fast, approximate shap kernel

fastshap: A fast, approximate shap kernel

fastshap was designed to be:

Fast Calculating shap values can take an extremely long time.
fastshap utilizes inner and outer batch assignments to keep the
calculations inside vectorized operations as often as it can.
Used on Tabular Data Can accept numpy arrays or pandas
DataFrames, and can handle categorical variables natively. As of
right now, only 1 dimensional outputs are accepted.

WARNING This package specifically offers a kernel explainer, which
can calculate approximate shap values of f(X) towards y for any function
f. Much faster shap solutions are available specifically for gradient
boosted trees.

Installation

This package can be installed using either pip or conda, through
conda-forge:

# Using pip
$ pip install fastshap --no-cache-dir

You can also download the latest development version from this
repository. If you want to install from github with conda, you must
first run conda install pip git.

$ pip install git+https://github.com/AnotherSamWilson/fastshap.git

Basic Usage

We will use the iris dataset for this example. Here, we load the data
and train a simple lightgbm model on the dataset:

from sklearn.datasets import load_iris
import pandas as pd
import lightgbm as lgb
import numpy as np

# Define our dataset and target variable
data = pd.concat(load_iris(as_frame=True,return_X_y=True),axis=1)
data.rename({"target": "species"}, inplace=True, axis=1)
data["species"] = data["species"].astype("category")
target = data.pop("sepal length (cm)")

# Train our model
dtrain = lgb.Dataset(data=data, label=target)
lgbmodel = lgb.train(
    params={"seed": 1, "verbose": -1},
    train_set=dtrain,
    num_boost_round=10
)

# Define the function we wish to build shap values for.
model = lgbmodel.predict

preds = model(data)

We now have a model which takes a Pandas dataframe, and returns
predictions. We can create an explainer that will use data as a
background dataset to calculate the shap values of any dataset we wish:

import fastshap

ke = fastshap.KernelExplainer(model, data)
sv = ke.calculate_shap_values(data, verbose=False)

print(all(preds == sv.sum(1)))

## True

Stratifying the Background Set

We can select a subset of our data to act as a background set. By
stratifying the background set on the results of the model output, we
will usually get very similar results, while decreasing the caculation
time drastically.

ke.stratify_background_set(5)
sv2 = ke.calculate_shap_values(
  data, 
  background_fold_to_use=0,
  verbose=False
)

print(np.abs(sv2 - sv).mean(0))

## [1.74764532e-03 1.61829094e-02 1.99534408e-03 4.02640884e-16
##  1.71084747e-02]

What we did is break up our background set into 10 different sets,
stratified by the model output. We then used the first of these sets as
our background set. We then compared the average difference between
these shap values, and the shap values we obtained from using the entire
dataset.

Choosing Batch Sizes

If the entire process was vectorized, it would require an array of size
(# Samples * # Coalitions * # Background samples, # Columns). Where
# Coalitions is the sum of the total number of coalitions that are
going to be run. Even for small datasets, this becomes enormous.
fastshap breaks this array up into chunks by splitting the process
into a series of batches.

This is a list of the large arrays and their maximum size:

Global
- Mask Matrix (# Coalitions, # Columns) dtype = int8
Outer Batch
- Linear Targets (Total Coalition Combinations, Outer Batch Size) dtype = adaptive
Inner Batch
- Model Evaluation Features (Inner Batch Size, # background samples) dtype = adaptive

The adaptive datatypes of the arrays above will be matched to the data
types of the model output. Therefore, if your model returns float32,
these arrays will be stored as float32. The final, returned shap
values will also be returned as the datatype returned by the model.

These theoretical sizes can be calculated directly so that the user can
determine appropriate batch sizes for their machine:

# Combines our background data back into 1 DataFrame
ke.stratify_background_set(1)
(
    mask_matrix_size, 
    linear_target_size, 
    inner_model_eval_set_size
) = ke.get_theoretical_array_expansion_sizes(
    outer_batch_size=150,
    inner_batch_size=150,
    n_coalition_sizes=3,
    background_fold_to_use=None,
)

print(
  np.product(linear_target_size) + np.product(inner_model_eval_set_size)
)

## 92100

For the iris dataset, even if we sent the entire set (150 rows) through
as one batch, we only need 92100 elements stored in arrays. This is
manageable on most machines. However, this number grows extremely
quickly with the samples and number of columns. It is highly advised
to determine a good batch scheme before running this process.

Specifying a Custom Linear Model

Any linear model available from sklearn.linear_model can be used to
calculate the shap values. If you wish for some sparsity in the shap
values, you can use Lasso regression:

from sklearn.linear_model import Lasso

# Use our entire background set
ke.stratify_background_set(1)
sv_lasso = ke.calculate_shap_values(
  data, 
  background_fold_to_use=0,
  linear_model=Lasso(alpha=0.1),
  verbose=False
)

print(sv_lasso[0,:])

## [-0.         -0.33797832 -0.         -0.14634971  5.84333333]

The default model used is sklearn.linear_model.LinearRegression.

GitHub

View Github

Fastshap: A fast, approximate shap kernel