Repository: predict-idlab/powershap (language: python)

Issue:
Missing XGBoost support
Here is my code to get XGBoost working (from a notebook):

```
from powershap import PowerShap
import powershap.shap_wrappers.shap_explainer as se
import powershap.shap_wrappers.shap_explainer_factory as sef
import shap
import xgboost as xgb
from typing import Callable


class XGBoostExplainer(se.ShapExplainer):
    @staticmethod
    def supports_model(model) -> bool:
        supported_models = [xgb.XGBRegressor, xgb.XGBClassifier]
        return isinstance(model, tuple(supported_models))

    def _validate_data(self, validate_data: Callable, X, y, **kwargs):
        kwargs["force_all_finite"] = False  # xgboost allows NaNs and infs in X
        kwargs["dtype"] = None  # allow non-numeric data
        return super()._validate_data(validate_data, X, y, **kwargs)

    def _fit_get_shap(
        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
    ) -> np.array:
        # Fit the model
        params = self.model.get_params()
        #PowerShap_model = self.model.copy().set_params(random_seed=random_seed)
        PowerShap_model = self.model.__class__(**{**params, 'random_seed':random_seed})
        PowerShap_model.fit(X_train, Y_train)#, eval_set=(X_val, Y_val))
        # Calculate the shap values
        C_explainer = shap.TreeExplainer(PowerShap_model)
        return C_explainer.shap_values(X_val)

    def _get_more_tags(self):
        return {"allow_nan": True}

sef.ShapExplainerFactory._explainer_models.append(XGBoostExplainer)    
```


Discussion / hints:
Hey @mattharrison,

It would be great to see support for XGBoost added to this library :+1:
If you'd like to contribute the code for this, I'd be happy to review a pull request. If not, I can submit a pull request myself.

Thanks, Jeroen

Fix patch:
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..a45ae8d
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,13 @@
+isort = isort powershap tests
+black = black powershap tests
+
+.PHONY: format
+format:
+	$(isort)
+	$(black)
+
+.PHONY: lint
+lint:
+	ruff powershap tests
+	$(isort) --check-only --df
+	$(black) --check --diff
diff --git a/poetry.lock b/poetry.lock
index 04418ba..ea37f7e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -20,17 +20,41 @@ wheel = ">=0.23.0,<1.0"
 
 [[package]]
 name = "attrs"
-version = "22.1.0"
+version = "22.2.0"
 description = "Classes Without Boilerplate"
 category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
+
+[package.extras]
+cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
+dev = ["attrs[docs,tests]"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
+tests = ["attrs[tests-no-zope]", "zope.interface"]
+tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"]
+
+[[package]]
+name = "black"
+version = "22.12.0"
+description = "The uncompromising code formatter."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+typed-ast = {version = ">=1.4.2", markers = "python_version < \"3.8\" and implementation_name == \"cpython\""}
+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
-dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"]
-docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"]
-tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"]
-tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "cachetools"
@@ -59,7 +83,7 @@ six = "*"
 
 [[package]]
 name = "certifi"
-version = "2022.9.24"
+version = "2022.12.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "dev"
 optional = false
@@ -76,6 +100,18 @@ python-versions = ">=3.6.0"
 [package.extras]
 unicode-backport = ["unicodedata2"]
 
+[[package]]
+name = "click"
+version = "8.1.3"
+description = "Composable command line interface toolkit"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
+
 [[package]]
 name = "cloudpickle"
 version = "2.2.0"
@@ -94,7 +130,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7
 
 [[package]]
 name = "coverage"
-version = "6.5.0"
+version = "7.0.2"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
@@ -116,7 +152,7 @@ python-versions = ">=3.6"
 
 [[package]]
 name = "exceptiongroup"
-version = "1.0.4"
+version = "1.1.0"
 description = "Backport of PEP 654 (exception groups)"
 category = "dev"
 optional = false
@@ -127,7 +163,7 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "flatbuffers"
-version = "22.11.23"
+version = "22.12.6"
 description = "The FlatBuffers serialization format for Python"
 category = "dev"
 optional = false
@@ -165,7 +201,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "google-auth"
-version = "2.14.1"
+version = "2.15.0"
 description = "Google Authentication Library"
 category = "dev"
 optional = false
@@ -224,17 +260,14 @@ test = ["coverage", "mock (>=4)", "pytest (>=7)", "pytest-cov", "pytest-mock (>=
 
 [[package]]
 name = "grpcio"
-version = "1.50.0"
+version = "1.51.1"
 description = "HTTP/2-based RPC framework"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 
-[package.dependencies]
-six = ">=1.5.2"
-
 [package.extras]
-protobuf = ["grpcio-tools (>=1.50.0)"]
+protobuf = ["grpcio-tools (>=1.51.1)"]
 
 [[package]]
 name = "h5py"
@@ -257,7 +290,7 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "importlib-metadata"
-version = "5.1.0"
+version = "6.0.0"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
@@ -268,7 +301,7 @@ typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""}
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
 testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
 
@@ -280,6 +313,20 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "isort"
+version = "5.11.4"
+description = "A Python utility / library to sort Python imports."
+category = "dev"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.extras]
+colors = ["colorama (>=0.4.3,<0.5.0)"]
+pipfile-deprecated-finder = ["pipreqs", "requirementslib"]
+plugins = ["setuptools"]
+requirements-deprecated-finder = ["pip-api", "pipreqs"]
+
 [[package]]
 name = "joblib"
 version = "1.2.0"
@@ -381,6 +428,14 @@ pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
 setuptools_scm = ">=4,<7"
 
+[[package]]
+name = "mypy-extensions"
+version = "0.4.3"
+description = "Experimental type system extensions for programs checked with the mypy typechecker."
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "numba"
 version = "0.56.4"
@@ -433,14 +488,11 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"]
 
 [[package]]
 name = "packaging"
-version = "21.3"
+version = "22.0"
 description = "Core utilities for Python packages"
 category = "main"
 optional = false
-python-versions = ">=3.6"
-
-[package.dependencies]
-pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
+python-versions = ">=3.7"
 
 [[package]]
 name = "pandas"
@@ -463,6 +515,14 @@ pytz = ">=2017.3"
 [package.extras]
 test = ["hypothesis (>=3.58)", "pytest (>=6.0)", "pytest-xdist"]
 
+[[package]]
+name = "pathspec"
+version = "0.10.3"
+description = "Utility library for gitignore style pattern matching of file paths."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "patsy"
 version = "0.5.3"
@@ -480,16 +540,31 @@ test = ["pytest", "pytest-cov", "scipy"]
 
 [[package]]
 name = "pillow"
-version = "9.3.0"
+version = "9.4.0"
 description = "Python Imaging Library (Fork)"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 
 [package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "platformdirs"
+version = "2.6.2"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+typing-extensions = {version = ">=4.4", markers = "python_version < \"3.8\""}
+
+[package.extras]
+docs = ["furo (>=2022.12.7)", "proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.5)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.2.2)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
+
 [[package]]
 name = "plotly"
 version = "5.11.0"
@@ -603,7 +678,7 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2022.6"
+version = "2022.7"
 description = "World timezone definitions, modern and historical"
 category = "main"
 optional = false
@@ -653,6 +728,14 @@ python-versions = ">=3.6,<4"
 [package.dependencies]
 pyasn1 = ">=0.1.3"
 
+[[package]]
+name = "ruff"
+version = "0.0.219"
+description = "An extremely fast Python linter, written in Rust."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "scikit-learn"
 version = "1.0.2"
@@ -891,11 +974,11 @@ python-versions = ">=3.7"
 
 [[package]]
 name = "tensorflow-io-gcs-filesystem"
-version = "0.28.0"
+version = "0.29.0"
 description = "TensorFlow IO"
 category = "dev"
 optional = false
-python-versions = ">=3.7, <3.11"
+python-versions = ">=3.7, <3.12"
 
 [package.extras]
 tensorflow = ["tensorflow (>=2.11.0,<2.12.0)"]
@@ -948,6 +1031,14 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "typed-ast"
+version = "1.5.4"
+description = "a fork of Python 2 and 3 ast modules with type comment support"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "typing-extensions"
 version = "4.4.0"
@@ -1002,6 +1093,25 @@ category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 
+[[package]]
+name = "xgboost"
+version = "1.6.2"
+description = "XGBoost Python Package"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = "*"
+scipy = "*"
+
+[package.extras]
+dask = ["dask", "distributed", "pandas"]
+datatable = ["datatable"]
+pandas = ["pandas"]
+plotting = ["graphviz", "matplotlib"]
+scikit-learn = ["scikit-learn"]
+
 [[package]]
 name = "zipp"
 version = "3.11.0"
@@ -1017,7 +1127,7 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools"
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.7.1,<3.11"
-content-hash = "a163991807ce011c4247dc2ac20c9fa16bbb09ad5d9534ef4a4476a8d03da850"
+content-hash = "fd72c4281371489e89e1a41eaa48136a123a10437fc98ad0a386b10a87bf0b1d"
 
 [metadata.files]
 absl-py = [
@@ -1029,8 +1139,22 @@ astunparse = [
     {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
 ]
 attrs = [
-    {file = "attrs-22.1.0-py2.py3-none-any.whl", hash = "sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c"},
-    {file = "attrs-22.1.0.tar.gz", hash = "sha256:29adc2665447e5191d0e7c568fde78b21f9672d344281d0c6e1ab085429b22b6"},
+    {file = "attrs-22.2.0-py3-none-any.whl", hash = "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836"},
+    {file = "attrs-22.2.0.tar.gz", hash = "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"},
+]
+black = [
+    {file = "black-22.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9eedd20838bd5d75b80c9f5487dbcb06836a43833a37846cf1d8c1cc01cef59d"},
+    {file = "black-22.12.0-cp310-cp310-win_amd64.whl", hash = "sha256:159a46a4947f73387b4d83e87ea006dbb2337eab6c879620a3ba52699b1f4351"},
+    {file = "black-22.12.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d30b212bffeb1e252b31dd269dfae69dd17e06d92b87ad26e23890f3efea366f"},
+    {file = "black-22.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:7412e75863aa5c5411886804678b7d083c7c28421210180d67dfd8cf1221e1f4"},
+    {file = "black-22.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c116eed0efb9ff870ded8b62fe9f28dd61ef6e9ddd28d83d7d264a38417dcee2"},
+    {file = "black-22.12.0-cp37-cp37m-win_amd64.whl", hash = "sha256:1f58cbe16dfe8c12b7434e50ff889fa479072096d79f0a7f25e4ab8e94cd8350"},
+    {file = "black-22.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77d86c9f3db9b1bf6761244bc0b3572a546f5fe37917a044e02f3166d5aafa7d"},
+    {file = "black-22.12.0-cp38-cp38-win_amd64.whl", hash = "sha256:82d9fe8fee3401e02e79767016b4907820a7dc28d70d137eb397b92ef3cc5bfc"},
+    {file = "black-22.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:101c69b23df9b44247bd88e1d7e90154336ac4992502d4197bdac35dd7ee3320"},
+    {file = "black-22.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:559c7a1ba9a006226f09e4916060982fd27334ae1998e7a38b3f33a37f7a2148"},
+    {file = "black-22.12.0-py3-none-any.whl", hash = "sha256:436cc9167dd28040ad90d3b404aec22cedf24a6e4d7de221bec2730ec0c97bcf"},
+    {file = "black-22.12.0.tar.gz", hash = "sha256:229351e5a18ca30f447bf724d007f890f97e13af070bb6ad4c0a441cd7596a2f"},
 ]
 cachetools = [
     {file = "cachetools-5.2.0-py3-none-any.whl", hash = "sha256:f9f17d2aec496a9aa6b76f53e3b614c965223c061982d434d160f930c698a9db"},
@@ -1054,13 +1178,17 @@ catboost = [
     {file = "catboost-1.1.1-cp39-none-win_amd64.whl", hash = "sha256:156264dbe9e841cb0b6333383e928cb8f65df4d00429a9771eb8b06b9bcfa17c"},
 ]
 certifi = [
-    {file = "certifi-2022.9.24-py3-none-any.whl", hash = "sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382"},
-    {file = "certifi-2022.9.24.tar.gz", hash = "sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14"},
+    {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"},
+    {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"},
 ]
 charset-normalizer = [
     {file = "charset-normalizer-2.1.1.tar.gz", hash = "sha256:5a3d016c7c547f69d6f81fb0db9449ce888b418b5b9952cc5e6e66843e9dd845"},
     {file = "charset_normalizer-2.1.1-py3-none-any.whl", hash = "sha256:83e9a75d1911279afd89352c68b45348559d1fc0506b054b346651b5e7fee29f"},
 ]
+click = [
+    {file = "click-8.1.3-py3-none-any.whl", hash = "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48"},
+    {file = "click-8.1.3.tar.gz", hash = "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e"},
+]
 cloudpickle = [
     {file = "cloudpickle-2.2.0-py3-none-any.whl", hash = "sha256:7428798d5926d8fcbfd092d18d01a2a03daf8237d8fcdc8095d256b8490796f0"},
     {file = "cloudpickle-2.2.0.tar.gz", hash = "sha256:3f4219469c55453cfe4737e564b67c2a149109dabf7f242478948b895f61106f"},
@@ -1070,68 +1198,69 @@ colorama = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 coverage = [
-    {file = "coverage-6.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ef8674b0ee8cc11e2d574e3e2998aea5df5ab242e012286824ea3c6970580e53"},
-    {file = "coverage-6.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:784f53ebc9f3fd0e2a3f6a78b2be1bd1f5575d7863e10c6e12504f240fd06660"},
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@@ -1915,6 +2084,32 @@ tqdm = [
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 ]
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@@ -1997,6 +2192,14 @@ wrapt = [
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 ]
+xgboost = [
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diff --git a/powershap/__init__.py b/powershap/__init__.py
index 84ad1c9..64ad462 100644
--- a/powershap/__init__.py
+++ b/powershap/__init__.py
@@ -1,6 +1,8 @@
 # The init file for PowerShap
 
 __author__ = "Jarne Verhaeghe, Jeroen Van Der Donckt"
-__version__ = '0.0.8'
+__version__ = "0.0.8"
 
 from .powershap import PowerShap
+
+__all__ = ["PowerShap"]
diff --git a/powershap/powershap.py b/powershap/powershap.py
index 59615d6..2d8c762 100644
--- a/powershap/powershap.py
+++ b/powershap/powershap.py
@@ -1,17 +1,16 @@
 __author__ = "Jarne Verhaeghe, Jeroen Van Der Donckt"
 
 import warnings
-import sklearn
+
 import numpy as np
 import pandas as pd
-
+import sklearn
 from sklearn.base import BaseEstimator
 from sklearn.feature_selection import SelectorMixin
-from sklearn.utils.validation import check_is_fitted
 from sklearn.model_selection import BaseCrossValidator
+from sklearn.utils.validation import check_is_fitted
 
 from .shap_wrappers import ShapExplainerFactory
-
 from .utils import powerSHAP_statistical_analysis
 
 
@@ -83,9 +82,9 @@ def __init__(
             This is especially useful in high-dimensional datasets
         limit_convergence_its: int, optional
             The number of maximum allowed recursions when `force_convergence` is True. By
-            default 0, meaning that no limit is applied. A limit_convergence_its of 1 suggests 
-            only executing one convergence recursion 
-            after a single full automatic PowerShap execution. 
+            default 0, meaning that no limit is applied. A limit_convergence_its of 1 suggests
+            only executing one convergence recursion
+            after a single full automatic PowerShap execution.
         limit_automatic: int, optional
             The number of maximum allowed iterations when `automatic` is True. By
             default None, meaning that no limit is applied.
@@ -147,9 +146,11 @@ def _infinite_splitter(cv):
             If the splitter is exhausted, it will be reset and restarted.
             """
             from copy import deepcopy
+
             cv = deepcopy(cv)
-            splitter = None 
+            splitter = None
             random_state = 0
+
             def split(X, y=None, groups=None):
                 nonlocal splitter, random_state
                 if splitter is None:
@@ -166,8 +167,9 @@ def split(X, y=None, groups=None):
                             random_state += 1
                         splitter = cv.split(X, y=y, groups=groups)
                         yield next(splitter)
+
             return split
-     
+
         if cv is not None:
             self.cv = _infinite_splitter(cv)
         else:
@@ -184,16 +186,11 @@ def _get_default_model(y: np.ndarray):
         dtype = y.dtype
         if np.issubdtype(dtype, np.number) and not np.issubdtype(dtype, np.integer):
             return CatBoostRegressor(
-                n_estimators=250,
-                od_type="Iter",
-                od_wait=25,
-                use_best_model=True,
-                verbose=0,
+                n_estimators=250, od_type="Iter", od_wait=25, use_best_model=True, verbose=0
             )
         if np.issubdtype(dtype, np.integer) and len(np.unique(y.ravel())) >= 5:
             warnings.warn(
-                "Classifying although there are >= 5 integers in the labels.",
-                UserWarning,
+                "Classifying although there are >= 5 integers in the labels.", UserWarning
             )
         return CatBoostClassifier(
             n_estimators=250, od_type="Iter", od_wait=25, use_best_model=True, verbose=0
@@ -208,10 +205,7 @@ def _log_feature_names_sklean_v0(self, X):
             types = sorted(t.__qualname__ for t in set(type(v) for v in feature_names))
             if len(types) > 1 or types[0] != "str":
                 feature_names = None
-                warnings.warn(
-                    "Feature names only support names that are all strings.",
-                    UserWarning,
-                )
+                warnings.warn("Feature names only support names that are all strings.", UserWarning)
 
         if feature_names is not None and len(feature_names) > 0:
             self.feature_names_in_ = feature_names
@@ -282,9 +276,7 @@ def _automatic_fit(
                     **kwargs,
                 )
 
-                max_iterations_old = (
-                    max_iterations_old + self.limit_incremental_iterations
-                )
+                max_iterations_old = max_iterations_old + self.limit_incremental_iterations
 
             else:
                 self._print(
@@ -311,10 +303,7 @@ def _automatic_fit(
             shaps_df = pd.concat([shaps_df, shaps_df_recursive])
 
             processed_shaps_df = powerSHAP_statistical_analysis(
-                shaps_df,
-                self.power_alpha,
-                self.power_req_iterations,
-                include_all=self.include_all,
+                shaps_df, self.power_alpha, self.power_req_iterations, include_all=self.include_all
             )
 
             if not any(processed_shaps_df.p_value < self.power_alpha):
@@ -359,7 +348,7 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
         if stratify is None and self.stratify:
             # Set stratify to y, if no stratify is given and self.stratify is True
             stratify = y
-        
+
         # kwargs take precedence over fit_kwargs
         kwargs = {**self.fit_kwargs, **kwargs}
 
@@ -374,9 +363,7 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
             self._log_feature_names_sklean_v0(X)
         # Perform the necessary sklearn checks -> X and y are both ndarray
         # Logs the feature names as well (in self.feature_names_in_ in sklearn 1.x)
-        X, y = self._explainer._validate_data(
-            self._validate_data, X, y, multi_output=True
-        )
+        X, y = self._explainer._validate_data(self._validate_data, X, y, multi_output=True)
 
         self._print("Starting powershap")
 
@@ -403,10 +390,7 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
         )
 
         processed_shaps_df = powerSHAP_statistical_analysis(
-            shaps_df,
-            self.power_alpha,
-            self.power_req_iterations,
-            include_all=self.include_all,
+            shaps_df, self.power_alpha, self.power_req_iterations, include_all=self.include_all
         )
 
         if self.automatic:
@@ -432,20 +416,22 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
                     converge_df[converge_df.p_value < self.power_alpha].index.values
                 )
 
-                # If limit_convergence_its is zero, the convergence mode does not have a limit. If not, the 
-                # While loop condition is recalculated every while loop iteration.
+                # If limit_convergence_its is zero, the convergence mode does not have a limit.
+                # If not, the while loop condition is recalculated every while loop iteration.
                 if self.limit_convergence_its > 0:
                     current_converge_recursions = 0
-                    while_convergence_bool = current_converge_recursions < self.limit_convergence_its
+                    while_convergence_bool = (
+                        current_converge_recursions < self.limit_convergence_its
+                    )
                 else:
                     while_convergence_bool = True
 
-                while((len(converge_df[converge_df.p_value < self.power_alpha]) > 0) & (while_convergence_bool)):
+                while (len(converge_df[converge_df.p_value < self.power_alpha]) > 0) & (
+                    while_convergence_bool
+                ):
                     self._print("Rerunning powershap for convergence. ")
                     converge_shaps_df = self._explainer.explain(
-                        X=X.drop(
-                            columns=X.columns.values[significant_cols.astype(np.int32)]
-                        ),
+                        X=X.drop(columns=X.columns.values[significant_cols.astype(np.int32)]),
                         y=y,
                         loop_its=loop_its,
                         val_size=self.val_size,
@@ -464,9 +450,7 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
                     )
 
                     converge_df = self._automatic_fit(
-                        X=X.drop(
-                            columns=X.columns.values[significant_cols.astype(np.int32)]
-                        ),
+                        X=X.drop(columns=X.columns.values[significant_cols.astype(np.int32)]),
                         y=y,
                         processed_shaps_df=converge_df,
                         loop_its=loop_its,
@@ -479,35 +463,30 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
 
                     significant_cols = np.append(
                         significant_cols,
-                        converge_df[
-                            converge_df.p_value < self.power_alpha
-                        ].index.values,
+                        converge_df[converge_df.p_value < self.power_alpha].index.values,
                     )
 
                     processed_shaps_df.loc[
                         converge_df[converge_df.p_value < self.power_alpha].index.values
                     ] = converge_df[converge_df.p_value < self.power_alpha]
 
-                    
                     if self.limit_convergence_its > 0:
                         current_converge_recursions += 1
                         print(current_converge_recursions)
-                        while_convergence_bool = current_converge_recursions < self.limit_convergence_its
+                        while_convergence_bool = (
+                            current_converge_recursions < self.limit_convergence_its
+                        )
 
                         if not while_convergence_bool:
                             self._print("Convergence limit reached: Stopping convergence mode.")
 
-
                 processed_shaps_df.loc[converge_df.index.values] = converge_df
 
-
         self._print("Done!")
 
         ## Set the p-values property (used in the transform function)
         # Remove the random feature (legit features have int index)
-        sub_df = processed_shaps_df[
-            processed_shaps_df.index.map(lambda x: isinstance(x, int))
-        ]
+        sub_df = processed_shaps_df[processed_shaps_df.index.map(lambda x: isinstance(x, int))]
         # Sort to have original order again
         sub_df = sub_df.sort_index()
         self._p_values = sub_df.p_value.values
@@ -534,8 +513,7 @@ def transform(self, X):
         if hasattr(self, "feature_names_in_") and isinstance(X, pd.DataFrame):
             assert np.all(X.columns.values == self.feature_names_in_)
             return pd.DataFrame(
-                super().transform(X),
-                columns=self.feature_names_in_[self._get_support_mask()],
+                super().transform(X), columns=self.feature_names_in_[self._get_support_mask()]
             )
         return super().transform(X)
 
diff --git a/powershap/shap_wrappers/__init__.py b/powershap/shap_wrappers/__init__.py
index 35e6544..e513f33 100644
--- a/powershap/shap_wrappers/__init__.py
+++ b/powershap/shap_wrappers/__init__.py
@@ -1,1 +1,3 @@
-from .shap_explainer_factory import ShapExplainerFactory
\ No newline at end of file
+from .shap_explainer_factory import ShapExplainerFactory
+
+__all__ = ["ShapExplainerFactory"]
diff --git a/powershap/shap_wrappers/shap_explainer.py b/powershap/shap_wrappers/shap_explainer.py
index a75b327..b373600 100644
--- a/powershap/shap_wrappers/shap_explainer.py
+++ b/powershap/shap_wrappers/shap_explainer.py
@@ -1,25 +1,22 @@
 __author__ = "Jarne Verhaeghe, Jeroen Van Der Donckt"
 
 import warnings
-import shap
-import pandas as pd
-import numpy as np
+from abc import ABC
+from copy import copy
+from typing import Any, Callable
 
-from tqdm.auto import tqdm
+import numpy as np
+import pandas as pd
+import shap
 from numpy.random import RandomState
 from sklearn.model_selection import train_test_split
-from abc import ABC
-
-from typing import Any, Callable
+from tqdm.auto import tqdm
 
 
 class ShapExplainer(ABC):
     """Interface class for a (POWERshap explainer class."""
 
-    def __init__(
-        self,
-        model: Any,
-    ):
+    def __init__(self, model: Any):
         """Create a POWERshap explainer instance.
 
         Parameters
@@ -33,9 +30,7 @@ def __init__(
         self.model = model
 
     # Should be implemented by subclass
-    def _fit_get_shap(
-        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
-    ) -> np.array:
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         raise NotImplementedError
 
     def _validate_data(self, validate_data: Callable, X, y, **kwargs):
@@ -81,8 +76,7 @@ def explain(
         y: np.array
             The labels.
         loop_its: int
-            The number of iterations to fit the model with random state and random
-            feature.
+            The number of iterations to fit the model with random state and random feature.
         val_size: float
             The fractional size of the validation set. Should be a float between ]0,1[.
         stratify: np.array, optional
@@ -100,7 +94,7 @@ def explain(
             The keyword arguments for the fit method.
         """
         random_col_name = "random_uniform_feature"
-        assert not random_col_name in X.columns
+        assert random_col_name not in X.columns
 
         X = X.copy(deep=True)
 
@@ -127,21 +121,16 @@ def explain(
             elif groups is None:
                 # stratify may be None or not None
                 train_idx, val_idx = train_test_split(
-                    np.arange(len(X)),
-                    test_size=val_size,
-                    random_state=i,
-                    stratify=stratify,
+                    np.arange(len(X)), test_size=val_size, random_state=i, stratify=stratify
                 )
             elif stratify is None:
                 # groups may be None or not None
                 from sklearn.model_selection import GroupShuffleSplit
 
                 train_idx, val_idx = next(
-                    GroupShuffleSplit(
-                        random_state=i,
-                        n_splits=1,
-                        test_size=val_size,
-                    ).split(X, y, groups=groups)
+                    GroupShuffleSplit(random_state=i, n_splits=1, test_size=val_size).split(
+                        X, y, groups=groups
+                    )
                 )
             else:
                 # stratify and groups are both not None
@@ -150,22 +139,17 @@ def explain(
 
                     train_idx, val_idx = next(
                         StratifiedGroupKFold(
-                            shuffle=True,
-                            random_state=i,
-                            n_splits=int(1 / val_size),
+                            shuffle=True, random_state=i, n_splits=int(1 / val_size)
                         ).split(X, y, groups=groups)
                     )
-                except:
+                except Exception:
                     warnings.warn(
                         "Did not find StratifiedGroupKFold in sklearn install, "
                         + "this is only supported in sklearn 1.x.",
                         UserWarning,
                     )
                     train_idx, val_idx = train_test_split(
-                        np.arange(len(X)),
-                        test_size=val_size,
-                        random_state=i,
-                        stratify=stratify,
+                        np.arange(len(X)), test_size=val_size, random_state=i, stratify=stratify
                     )
 
             X_train = X.iloc[np.sort(train_idx)]
@@ -200,7 +184,7 @@ def _get_more_tags(self):
 
 ### CATBOOST
 
-from catboost import CatBoostRegressor, CatBoostClassifier
+from catboost import CatBoostClassifier, CatBoostRegressor
 
 
 class CatboostExplainer(ShapExplainer):
@@ -214,9 +198,7 @@ def _validate_data(self, validate_data: Callable, X, y, **kwargs):
         kwargs["dtype"] = None  # allow non-numeric data
         return super()._validate_data(validate_data, X, y, **kwargs)
 
-    def _fit_get_shap(
-        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
-    ) -> np.array:
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         # Fit the model
         PowerShap_model = self.model.copy().set_params(random_seed=random_seed)
         PowerShap_model.fit(X_train, Y_train, eval_set=(X_val, Y_val))
@@ -228,10 +210,14 @@ def _get_more_tags(self):
         return {"allow_nan": True}
 
 
+### LGBM
+
+
 class LGBMExplainer(ShapExplainer):
     @staticmethod
     def supports_model(model) -> bool:
         from lightgbm import LGBMClassifier, LGBMRegressor
+
         supported_models = [LGBMClassifier, LGBMRegressor]
         return isinstance(model, tuple(supported_models))
 
@@ -239,15 +225,10 @@ def _validate_data(self, validate_data: Callable, X, y, **kwargs):
         kwargs["force_all_finite"] = False  # lgbm allows NaNs and infs in X
         return super()._validate_data(validate_data, X, y, **kwargs)
 
-    def _fit_get_shap(
-        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
-    ) -> np.array:
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         # Fit the model
-
         # Why we need to use deepcopy and delete LGBM __deepcopy__
         # https://github.com/microsoft/LightGBM/issues/4085
-        from copy import copy
-
         PowerShap_model = copy(self.model).set_params(random_seed=random_seed)
         PowerShap_model.fit(X_train, Y_train, eval_set=(X_val, Y_val))
         # Calculate the shap values
@@ -258,6 +239,33 @@ def _get_more_tags(self):
         return {"allow_nan": True}
 
 
+### XGBOOST
+
+
+class XGBoostExplainer(ShapExplainer):
+    @staticmethod
+    def supports_model(model) -> bool:
+        from xgboost import XGBClassifier, XGBRegressor
+
+        supported_models = [XGBClassifier, XGBRegressor]
+        return isinstance(model, tuple(supported_models))
+
+    def _validate_data(self, validate_data: Callable, X, y, **kwargs):
+        kwargs["force_all_finite"] = False  # xgboost allows NaNs and infs in X
+        kwargs["dtype"] = None  # allow non-numeric data
+        return super()._validate_data(validate_data, X, y, **kwargs)
+
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
+        # Fit the model
+        PowerShap_model = copy(self.model).set_params(random_seed=random_seed)
+        PowerShap_model.fit(X_train, Y_train)  # , eval_set=(X_val, Y_val))
+        # Calculate the shap values
+        C_explainer = shap.TreeExplainer(PowerShap_model)
+        return C_explainer.shap_values(X_val)
+
+    def _get_more_tags(self):
+        return {"allow_nan": True}
+
 
 ### RANDOMFOREST
 
@@ -268,16 +276,15 @@ def supports_model(model) -> bool:
         # TODO: these first 2 require extra checks on the base_estimator
         # from sklearn.ensemble._weight_boosting import BaseWeightBoosting
         # from sklearn.ensemble._bagging import BaseBagging
-        from sklearn.ensemble._forest import ForestRegressor, ForestClassifier
+        from sklearn.ensemble._forest import ForestClassifier, ForestRegressor
         from sklearn.ensemble._gb import BaseGradientBoosting
+
         # from sklearn.ensemble._hist_gradient_boosting import BaseHistGradientBoosting
 
         supported_models = [ForestRegressor, ForestClassifier, BaseGradientBoosting]
         return issubclass(type(model), tuple(supported_models))
 
-    def _fit_get_shap(
-        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
-    ) -> np.array:
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         from sklearn.base import clone
 
         # Fit the model
@@ -300,15 +307,13 @@ def supports_model(model) -> bool:
         supported_models = [LinearClassifierMixin, LinearModel, BaseSGD]
         return issubclass(type(model), tuple(supported_models))
 
-    def _fit_get_shap(
-        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
-    ) -> np.array:
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         from sklearn.base import clone
 
         # Fit the model
         try:
             PowerShap_model = clone(self.model).set_params(random_state=random_seed)
-        except:
+        except Exception:
             PowerShap_model = clone(self.model)
         PowerShap_model.fit(X_train, Y_train)
 
@@ -330,9 +335,7 @@ def supports_model(model) -> bool:
         supported_models = [tf.keras.Model]  # , torch.nn.Module]
         return isinstance(model, tuple(supported_models))
 
-    def _fit_get_shap(
-        self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs
-    ) -> np.array:
+    def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         import tensorflow as tf
 
         tf.compat.v1.disable_v2_behavior()  # https://github.com/slundberg/shap/issues/2189
diff --git a/powershap/shap_wrappers/shap_explainer_factory.py b/powershap/shap_wrappers/shap_explainer_factory.py
index 01a81de..c82f01f 100644
--- a/powershap/shap_wrappers/shap_explainer_factory.py
+++ b/powershap/shap_wrappers/shap_explainer_factory.py
@@ -1,16 +1,17 @@
 __author__ = "Jarne Verhaeghe, Jeroen Van Der Donckt"
 
+from typing import Any
+
 from .shap_explainer import (
-    ShapExplainer,
     CatboostExplainer,
-    LGBMExplainer,
+    DeepLearningExplainer,
     EnsembleExplainer,
+    LGBMExplainer,
     LinearExplainer,
-    DeepLearningExplainer,
+    ShapExplainer,
+    XGBoostExplainer,
 )
 
-from typing import Any
-
 
 class ShapExplainerFactory:
     """Factory class for creating the appropriate ShapExplainer."""
@@ -18,6 +19,7 @@ class ShapExplainerFactory:
     _explainer_models = [
         CatboostExplainer,
         LGBMExplainer,
+        XGBoostExplainer,
         EnsembleExplainer,
         LinearExplainer,
         DeepLearningExplainer,
@@ -42,8 +44,6 @@ def get_explainer(cls, model: Any) -> ShapExplainer:
             try:  # To avoid errors when the library is not installed
                 if explainer_class.supports_model(model):
                     return explainer_class(model)
-            except:
+            except Exception:
                 pass
-        raise ValueError(
-            f"Given model ({model}) is not yet supported by our explainer models"
-        )
+        raise ValueError(f"Given model ({model}) is not yet supported by our explainer models")
diff --git a/powershap/utils.py b/powershap/utils.py
index c0b9e43..68c243e 100644
--- a/powershap/utils.py
+++ b/powershap/utils.py
@@ -1,20 +1,17 @@
 __author__ = "Jarne Verhaeghe"
 
-import pandas as pd
 import numpy as np
-
+import pandas as pd
 from scipy import stats
 from statsmodels.stats.power import TTestPower
 
+
 def p_values_arg_coef(coefficients, arg):
     return stats.percentileofscore(coefficients, arg)
 
 
 def powerSHAP_statistical_analysis(
-    shaps_df: pd.DataFrame,
-    power_alpha: float,
-    power_req_iterations: float,
-    include_all: bool,
+    shaps_df: pd.DataFrame, power_alpha: float, power_req_iterations: float, include_all: bool
 ):
     p_values = []
     effect_size = []
@@ -23,25 +20,20 @@ def powerSHAP_statistical_analysis(
     n_samples = len(shaps_df["random_uniform_feature"].values)
     mean_random_uniform = shaps_df["random_uniform_feature"].mean()
     for i in range(len(shaps_df.columns)):
-        p_value = (
-            p_values_arg_coef(
-                np.array(shaps_df.values[:, i]), mean_random_uniform)
-            / 100
-        )
+        p_value = p_values_arg_coef(np.array(shaps_df.values[:, i]), mean_random_uniform) / 100
 
         p_values.append(p_value)
 
         if include_all or p_value < power_alpha:
             pooled_standard_deviation = np.sqrt(
                 (
-                    (shaps_df.std().values[i] ** 2) 
+                    (shaps_df.std().values[i] ** 2)
                     + (shaps_df["random_uniform_feature"].values.std() ** 2)
                 )
                 / (2)
             )
             effect_size.append(
-                (mean_random_uniform - shaps_df.mean().values[i])
-                / pooled_standard_deviation
+                (mean_random_uniform - shaps_df.mean().values[i]) / pooled_standard_deviation
             )
             power_list.append(
                 TTestPower().power(
@@ -77,9 +69,7 @@ def powerSHAP_statistical_analysis(
                 np.reshape(np.array(p_values), (len(p_values), 1)),
                 np.reshape(np.array(effect_size), (len(effect_size), 1)),
                 np.reshape(np.array(power_list), (len(power_list), 1)),
-                np.reshape(
-                    np.array(required_iterations), (len(required_iterations), 1)
-                ),
+                np.reshape(np.array(required_iterations), (len(required_iterations), 1)),
             ]
         ),
         columns=[
diff --git a/pyproject.toml b/pyproject.toml
index 4f5f858..304ba8e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,7 +22,37 @@ pytest = "^7.1.1"
 pytest-cov = "^3.0.0"
 tensorflow = "^2.8.0"
 lightgbm = "^3.3.2"
+xgboost = "^1.6.0"
+black = "^22.12.0"
+isort = "^5.11.4"
+ruff = "^0.0.219"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
+
+## Formatting configuration
+
+# Linting
+[tool.ruff]
+line-length = 100
+extend-select = ["Q"]
+ignore = ["E402", "F403", "F811", "F401"]
+
+# Formatting
+[tool.black]
+color = true
+line-length = 100
+skip-string-normalization = true
+skip-magic-trailing-comma = true
+
+# Sort imports
+[tool.isort]
+line_length = 100
+known_first_party = ["tsdownsample"]
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+combine_as_imports = true
+color_output = true
+skip = "tests/toml_test.py"
\ No newline at end of file


Test patch:
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 36e5e1a..6a2fa4d 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ['ubuntu-latest']
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v2
diff --git a/tests/conftest.py b/tests/conftest.py
index c092563..61170e0 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,8 +1,7 @@
 __author__ = "Jeroen Van Der Donckt"
 
-import pytest
 import pandas as pd
-
+import pytest
 from sklearn.datasets import make_classification, make_regression
 
 
@@ -18,9 +17,7 @@ def dummy_classification() -> pd.DataFrame:
         shuffle=False,
     )
     X = pd.DataFrame(
-        X,
-        columns=[f"informative_{i}" for i in range(2)]
-        + [f"random_{i}" for i in range(8)],
+        X, columns=[f"informative_{i}" for i in range(2)] + [f"random_{i}" for i in range(8)]
     )
     return X, y
 
@@ -28,15 +25,9 @@ def dummy_classification() -> pd.DataFrame:
 @pytest.fixture
 def dummy_regression() -> pd.DataFrame:
     X, y = make_regression(
-        n_samples=500,
-        n_features=10,
-        n_informative=2,
-        random_state=42,
-        shuffle=False,
+        n_samples=500, n_features=10, n_informative=2, random_state=42, shuffle=False
     )
     X = pd.DataFrame(
-        X,
-        columns=[f"informative_{i}" for i in range(2)]
-        + [f"random_{i}" for i in range(8)],
+        X, columns=[f"informative_{i}" for i in range(2)] + [f"random_{i}" for i in range(8)]
     )
     return X, y
diff --git a/tests/test_catboost_powershap.py b/tests/test_catboost_powershap.py
index d12197c..e04aeee 100644
--- a/tests/test_catboost_powershap.py
+++ b/tests/test_catboost_powershap.py
@@ -2,11 +2,11 @@
 
 import numpy as np
 import pandas as pd
+from catboost import CatBoostClassifier, CatBoostRegressor
 
 from powershap import PowerShap
-from .conftest import dummy_classification, dummy_regression
 
-from catboost import CatBoostClassifier, CatBoostRegressor
+from .conftest import dummy_classification, dummy_regression
 
 
 def test_catboost_class_powershap(dummy_classification):
@@ -15,9 +15,7 @@ def test_catboost_class_powershap(dummy_classification):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=250, verbose=0),
-        power_iterations=15,
-        automatic=False,
+        model=CatBoostClassifier(n_estimators=250, verbose=0), power_iterations=15, automatic=False
     )
 
     selector.fit(X, y)
@@ -33,9 +31,7 @@ def test_catboost_regr_powershap(dummy_regression):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     selector = PowerShap(
-        model=CatBoostRegressor(n_estimators=250, verbose=0),
-        power_iterations=15,
-        automatic=False,
+        model=CatBoostRegressor(n_estimators=250, verbose=0), power_iterations=15, automatic=False
     )
 
     selector.fit(X, y)
@@ -53,10 +49,7 @@ def test_catboost_handle_nans(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=10, verbose=0),
-        power_iterations=15,
-    )
+    selector = PowerShap(model=CatBoostClassifier(n_estimators=10, verbose=0), power_iterations=15)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
@@ -64,6 +57,7 @@ def test_catboost_handle_nans(dummy_classification):
     assert len(selected_feats.columns) == n_informative
     assert all([c.startswith("informative") for c in selected_feats.columns])
 
+
 def test_catboost_handle_infs(dummy_classification):
     X, y = dummy_classification
     X.iloc[:5] = np.Inf
@@ -72,10 +66,7 @@ def test_catboost_handle_infs(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=10, verbose=0),
-        power_iterations=15,
-    )
+    selector = PowerShap(model=CatBoostClassifier(n_estimators=10, verbose=0), power_iterations=15)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
@@ -83,6 +74,7 @@ def test_catboost_handle_infs(dummy_classification):
     assert len(selected_feats.columns) == n_informative
     assert all([c.startswith("informative") for c in selected_feats.columns])
 
+
 def test_catboost_handle_infs_nans(dummy_classification):
     X, y = dummy_classification
     X.iloc[:5] = np.Inf
@@ -94,10 +86,7 @@ def test_catboost_handle_infs_nans(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=10, verbose=0),
-        power_iterations=15,
-    )
+    selector = PowerShap(model=CatBoostClassifier(n_estimators=10, verbose=0), power_iterations=15)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
diff --git a/tests/test_deep_learning_powershap.py b/tests/test_deep_learning_powershap.py
index 2106a1d..75e8a48 100644
--- a/tests/test_deep_learning_powershap.py
+++ b/tests/test_deep_learning_powershap.py
@@ -1,9 +1,10 @@
 __author__ = "Jeroen Van Der Donckt"
 
+import tensorflow as tf
+
 from powershap import PowerShap
-from .conftest import dummy_classification, dummy_regression
 
-import tensorflow as tf
+from .conftest import dummy_classification, dummy_regression
 
 
 def test_deep_learning_class_powershap(dummy_classification):
@@ -12,20 +13,12 @@ def test_deep_learning_class_powershap(dummy_classification):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     model = tf.keras.Sequential()
-    model.add(
-        tf.keras.layers.Dense(5, input_shape=(X.shape[1] + 1,), activation="relu")
-    )
+    model.add(tf.keras.layers.Dense(5, input_shape=(X.shape[1] + 1,), activation="relu"))
     model.add(tf.keras.layers.Dense(1, activation="sigmoid"))
 
-    selector = PowerShap(
-        model=model,
-        power_iterations=5,
-        automatic=False,
-    )
+    selector = PowerShap(model=model, power_iterations=5, automatic=False)
 
-    selector.fit(
-        X, y, loss="binary_crossentropy", optimizer="adam", batch_size=16, epochs=5
-    )
+    selector.fit(X, y, loss="binary_crossentropy", optimizer="adam", batch_size=16, epochs=5)
     _ = selector.transform(X)
 
 
@@ -35,16 +28,10 @@ def test_deep_learning_regr_powershap(dummy_regression):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     model = tf.keras.Sequential()
-    model.add(
-        tf.keras.layers.Dense(5, input_shape=(X.shape[1] + 1,), activation="linear")
-    )
+    model.add(tf.keras.layers.Dense(5, input_shape=(X.shape[1] + 1,), activation="linear"))
     model.add(tf.keras.layers.Dense(1, activation="linear"))
 
-    selector = PowerShap(
-        model=model,
-        power_iterations=5,
-        automatic=False,
-    )
+    selector = PowerShap(model=model, power_iterations=5, automatic=False)
 
     selector.fit(X, y, loss="mse", optimizer="adam", batch_size=16, epochs=5)
     _ = selector.transform(X)
diff --git a/tests/test_ensemble_powershap.py b/tests/test_ensemble_powershap.py
index 1d3c76a..934a4fd 100644
--- a/tests/test_ensemble_powershap.py
+++ b/tests/test_ensemble_powershap.py
@@ -1,9 +1,10 @@
 __author__ = "Jeroen Van Der Donckt"
 
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+
 from powershap import PowerShap
-from .conftest import dummy_classification, dummy_regression
 
-from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from .conftest import dummy_classification, dummy_regression
 
 
 def test_ensemble_class_powershap(dummy_classification):
@@ -12,19 +13,14 @@ def test_ensemble_class_powershap(dummy_classification):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     selector = PowerShap(
-        model=RandomForestClassifier(n_estimators=25),
-        power_iterations=15,
-        automatic=False,
+        model=RandomForestClassifier(n_estimators=25), power_iterations=15, automatic=False
     )
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
 
 
 def test_ensemble_regr_powershap(dummy_regression):
@@ -33,16 +29,11 @@ def test_ensemble_regr_powershap(dummy_regression):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     selector = PowerShap(
-        model=RandomForestRegressor(n_estimators=25),
-        power_iterations=15,
-        automatic=False,
+        model=RandomForestRegressor(n_estimators=25), power_iterations=15, automatic=False
     )
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
diff --git a/tests/test_lgbm_powershap.py b/tests/test_lgbm_powershap.py
index 9d5b636..f6c9e80 100644
--- a/tests/test_lgbm_powershap.py
+++ b/tests/test_lgbm_powershap.py
@@ -2,11 +2,11 @@
 
 import numpy as np
 import pandas as pd
+from lightgbm import LGBMClassifier, LGBMRegressor
 
 from powershap import PowerShap
-from .conftest import dummy_classification, dummy_regression
 
-from lightgbm import LGBMClassifier, LGBMRegressor
+from .conftest import dummy_classification, dummy_regression
 
 
 def test_lgbm_class_powershap(dummy_classification):
@@ -15,9 +15,7 @@ def test_lgbm_class_powershap(dummy_classification):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     selector = PowerShap(
-        model=LGBMClassifier(n_estimators=250, verbose=0),
-        power_iterations=50,
-        automatic=False,
+        model=LGBMClassifier(n_estimators=250, verbose=0), power_iterations=50, automatic=False
     )
 
     selector.fit(X, y)
@@ -33,9 +31,7 @@ def test_lgbm_regr_powershap(dummy_regression):
     assert n_informative > 0, "No informative columns in the dummy data!"
 
     selector = PowerShap(
-        model=LGBMRegressor(n_estimators=250, verbose=0),
-        power_iterations=15,
-        automatic=False,
+        model=LGBMRegressor(n_estimators=250, verbose=0), power_iterations=15, automatic=False
     )
 
     selector.fit(X, y)
@@ -53,10 +49,7 @@ def test_lgbm_handle_nans(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=LGBMClassifier(n_estimators=10, verbose=0),
-        power_iterations=15,
-    )
+    selector = PowerShap(model=LGBMClassifier(n_estimators=10, verbose=0), power_iterations=15)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
@@ -73,10 +66,7 @@ def test_lgbm_handle_infs(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=LGBMClassifier(n_estimators=10, verbose=0),
-        power_iterations=15,
-    )
+    selector = PowerShap(model=LGBMClassifier(n_estimators=10, verbose=0), power_iterations=15)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
@@ -96,10 +86,7 @@ def test_lgbm_handle_infs_nans(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=LGBMClassifier(n_estimators=10, verbose=0),
-        power_iterations=15,
-    )
+    selector = PowerShap(model=LGBMClassifier(n_estimators=10, verbose=0), power_iterations=15)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
@@ -115,10 +102,7 @@ def test_lgbm_handle_cats(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=LGBMClassifier(n_estimators=10, verbose=0),
-        power_iterations=50,
-    )
+    selector = PowerShap(model=LGBMClassifier(n_estimators=10, verbose=0), power_iterations=50)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
diff --git a/tests/test_linear_powershap.py b/tests/test_linear_powershap.py
index cb09b56..5780aa7 100644
--- a/tests/test_linear_powershap.py
+++ b/tests/test_linear_powershap.py
@@ -1,9 +1,10 @@
 __author__ = "Jeroen Van Der Donckt"
 
+from sklearn.linear_model import LinearRegression, LogisticRegression
+
 from powershap import PowerShap
-from .conftest import dummy_classification, dummy_regression
 
-from sklearn.linear_model import LogisticRegression, LinearRegression
+from .conftest import dummy_classification, dummy_regression
 
 
 def test_logistic_regr_powershap(dummy_classification):
@@ -11,20 +12,13 @@ def test_logistic_regr_powershap(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=LogisticRegression(),
-        power_iterations=15,
-        automatic=False,
-    )
+    selector = PowerShap(model=LogisticRegression(), power_iterations=15, automatic=False)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
 
 
 def test_linear_regr_powershap(dummy_regression):
@@ -32,17 +26,10 @@ def test_linear_regr_powershap(dummy_regression):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        model=LinearRegression(),
-        power_iterations=15,
-        automatic=False,
-    )
+    selector = PowerShap(model=LinearRegression(), power_iterations=15, automatic=False)
 
     selector.fit(X, y)
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
diff --git a/tests/test_powershap.py b/tests/test_powershap.py
index 6a252e2..143dff3 100644
--- a/tests/test_powershap.py
+++ b/tests/test_powershap.py
@@ -2,11 +2,11 @@
 
 import numpy as np
 import pandas as pd
+from catboost import CatBoostClassifier, CatBoostRegressor
 
 from powershap import PowerShap
-from .conftest import dummy_classification, dummy_regression
 
-from catboost import CatBoostClassifier, CatBoostRegressor
+from .conftest import dummy_classification, dummy_regression
 
 ### DEFAULT MODEL & AUTOMATIC MODE
 
@@ -16,10 +16,7 @@ def test_default_class_powershap(dummy_classification):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        power_iterations=15,
-        automatic=False,
-    )
+    selector = PowerShap(power_iterations=15, automatic=False)
     assert selector.model is None
 
     selector.fit(X, y)
@@ -27,10 +24,7 @@ def test_default_class_powershap(dummy_classification):
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
 
 
 def test_default_regr_powershap(dummy_regression):
@@ -38,10 +32,7 @@ def test_default_regr_powershap(dummy_regression):
     n_informative = sum([c.startswith("informative") for c in X.columns])
     assert n_informative > 0, "No informative columns in the dummy data!"
 
-    selector = PowerShap(
-        power_iterations=15,
-        automatic=False,
-    )
+    selector = PowerShap(power_iterations=15, automatic=False)
     assert selector.model is None
 
     selector.fit(X, y)
@@ -49,10 +40,7 @@ def test_default_regr_powershap(dummy_regression):
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
 
 
 def test_default_class_automatic_powershap(dummy_classification):
@@ -68,10 +56,7 @@ def test_default_class_automatic_powershap(dummy_classification):
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
 
 
 def test_default_regr_automatic_powershap(dummy_regression):
@@ -87,10 +72,7 @@ def test_default_regr_automatic_powershap(dummy_regression):
     selected_feats = selector.transform(X)
 
     assert len(selected_feats.columns) >= n_informative
-    assert (
-        sum([c.startswith("informative") for c in selected_feats.columns])
-        == n_informative
-    )
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
 
 
 ### INPUT FEATURE NAMES
@@ -102,7 +84,8 @@ def test_powershap_dataframe(dummy_classification):
     selector = PowerShap(
         model=CatBoostClassifier(n_estimators=10, verbose=0),
         power_iterations=5,
-        automatic=False, show_progress=False,
+        automatic=False,
+        show_progress=False,
     )
 
     assert isinstance(X, pd.DataFrame)
@@ -118,9 +101,7 @@ def test_powershap_array(dummy_classification):
     X = X.values
 
     selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=10, verbose=0),
-        power_iterations=5,
-        automatic=False,
+        model=CatBoostClassifier(n_estimators=10, verbose=0), power_iterations=5, automatic=False
     )
 
     assert isinstance(X, np.ndarray)
@@ -130,7 +111,6 @@ def test_powershap_array(dummy_classification):
     assert isinstance(selected_feats, np.ndarray)
 
 
-
 ### STRATIFY & GROUPS
 
 
@@ -144,7 +124,7 @@ def test_powershap_stratify_constructor(dummy_classification):
         stratify=True,
     )
 
-    assert selector.stratify == True
+    assert selector.stratify is True
     assert selector.cv is None
 
     selector.fit(X, y)
@@ -154,9 +134,7 @@ def test_powershap_stratify_fit(dummy_classification):
     X, y = dummy_classification
 
     selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=10, verbose=0),
-        power_iterations=5,
-        automatic=False,
+        model=CatBoostClassifier(n_estimators=10, verbose=0), power_iterations=5, automatic=False
     )
 
     assert selector.cv is None
@@ -168,9 +146,7 @@ def test_powershap_groups_fit(dummy_classification):
     X, y = dummy_classification
 
     selector = PowerShap(
-        model=CatBoostClassifier(n_estimators=10, verbose=0),
-        power_iterations=5,
-        automatic=False,
+        model=CatBoostClassifier(n_estimators=10, verbose=0), power_iterations=5, automatic=False
     )
 
     assert selector.cv is None
@@ -195,8 +171,10 @@ def test_powershap_stratify_constructor_groups_fit(dummy_classification):
 
 ### CROSS-VALIDATION
 
+
 def test_powershap_cv_kfold(dummy_classification):
     from sklearn.model_selection import KFold
+
     X, y = dummy_classification
 
     cv = KFold(3)
@@ -215,6 +193,7 @@ def test_powershap_cv_kfold(dummy_classification):
 
 def test_powershap_cv_groupkfold(dummy_classification):
     from sklearn.model_selection import GroupKFold
+
     X, y = dummy_classification
 
     cv = GroupKFold(3)
@@ -233,6 +212,7 @@ def test_powershap_cv_groupkfold(dummy_classification):
 
 def test_powershap_cv_stratifiedgroupkfold(dummy_classification):
     from sklearn.model_selection import StratifiedGroupKFold
+
     X, y = dummy_classification
 
     cv = StratifiedGroupKFold(3)
@@ -251,6 +231,7 @@ def test_powershap_cv_stratifiedgroupkfold(dummy_classification):
 
 def test_powershap_cv_groupshufflesplit(dummy_classification):
     from sklearn.model_selection import GroupShuffleSplit
+
     X, y = dummy_classification
 
     cv = GroupShuffleSplit(3)
diff --git a/tests/test_shap_explainer.py b/tests/test_shap_explainer.py
index 1514aef..6ff0f05 100644
--- a/tests/test_shap_explainer.py
+++ b/tests/test_shap_explainer.py
@@ -4,26 +4,27 @@
 
 from powershap.shap_wrappers import ShapExplainerFactory
 from powershap.shap_wrappers.shap_explainer import (
-    LinearExplainer,
     CatboostExplainer,
-    LGBMExplainer,
-    EnsembleExplainer,
     DeepLearningExplainer,
+    EnsembleExplainer,
+    LGBMExplainer,
+    LinearExplainer,
+    XGBoostExplainer,
 )
 
 
 def test_get_linear_explainer():
     from sklearn.linear_model import (
+        LinearRegression,
         LogisticRegression,
         LogisticRegressionCV,
         PassiveAggressiveClassifier,
         Perceptron,
+        Ridge,
         RidgeClassifier,
         RidgeClassifierCV,
-        SGDClassifier,
-        LinearRegression,
-        Ridge,
         RidgeCV,
+        SGDClassifier,
         SGDRegressor,
     )
 
@@ -68,18 +69,28 @@ def test_get_lightgbm_explainer():
         assert isinstance(explainer, LGBMExplainer)
 
 
+def test_get_xgboost_explainer():
+    from xgboost import XGBClassifier, XGBRegressor
+
+    model_classes = [XGBClassifier, XGBRegressor]
+
+    for model_class in model_classes:
+        explainer = ShapExplainerFactory.get_explainer(model_class())
+        assert isinstance(explainer, XGBoostExplainer)
+
+
 def test_get_ensemble_explainer():
     from sklearn.ensemble import (
-        RandomForestClassifier,
         AdaBoostClassifier,
-        GradientBoostingClassifier,
-        ExtraTreesClassifier,
-        # HistGradientBoostingClassifier,
-        RandomForestRegressor,
         AdaBoostRegressor,
-        GradientBoostingRegressor,
+        ExtraTreesClassifier,
         ExtraTreesRegressor,
-        # HistGradientBoostingRegressor,
+        GradientBoostingClassifier,
+        GradientBoostingRegressor,
+        HistGradientBoostingClassifier,
+        HistGradientBoostingRegressor,
+        RandomForestClassifier,
+        RandomForestRegressor,
     )
 
     model_classes = [
diff --git a/tests/test_xgboost_powershap.py b/tests/test_xgboost_powershap.py
new file mode 100644
index 0000000..a8b467f
--- /dev/null
+++ b/tests/test_xgboost_powershap.py
@@ -0,0 +1,60 @@
+__author__ = "Jeroen Van Der Donckt, Jarne Verhaeghe"
+
+import numpy as np
+import pandas as pd
+from xgboost import XGBClassifier, XGBRegressor
+
+from powershap import PowerShap
+
+from .conftest import dummy_classification, dummy_regression
+
+
+def test_xgboost_class_powershap(dummy_classification):
+    X, y = dummy_classification
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    selector = PowerShap(
+        model=XGBClassifier(n_estimators=250, verbosity=0), power_iterations=50, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_xgboost_regr_powershap(dummy_regression):
+    X, y = dummy_regression
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    selector = PowerShap(
+        model=XGBRegressor(n_estimators=250, verbosity=0), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_xgboost_handle_nans(dummy_classification):
+    X, y = dummy_classification
+    X.iloc[:2] = None
+    X["nan_col"] = None
+    assert np.any(pd.isna(X))
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    selector = PowerShap(
+        model=XGBClassifier(n_estimators=250, verbosity=0, learning_rate=2), power_iterations=10
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])