diff --git a/CHANGELOG.md b/CHANGELOG.md
index d548ff43d..c990ca1d4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -31,6 +31,9 @@ randomness.
   `compute_beta_shapley_semivalues`, `compute_shapley_semivalues` and
   `compute_generic_semivalues`.
   [PR #428](https://github.com/aai-institute/pyDVL/pull/428)
+- Added classwise Shapley as proposed by (Schoch et al. 2021)
+  [https://arxiv.org/abs/2211.06800]
+  [PR #338](https://github.com/aai-institute/pyDVL/pull/338)
 
 ### Changed
 
diff --git a/src/pydvl/utils/numeric.py b/src/pydvl/utils/numeric.py
index dd22dad9a..84487614f 100644
--- a/src/pydvl/utils/numeric.py
+++ b/src/pydvl/utils/numeric.py
@@ -4,10 +4,6 @@
 """
 from __future__ import annotations
 
-import logging
-import os
-import random
-import time
 from itertools import chain, combinations
 from typing import (
     Collection,
@@ -17,8 +13,6 @@
     Optional,
     Tuple,
     TypeVar,
-    Union,
-    cast,
     overload,
 )
 
@@ -39,10 +33,6 @@
     "top_k_value_accuracy",
 ]
 
-
-logger = logging.getLogger(__name__)
-
-
 T = TypeVar("T", bound=np.generic)
 
 
@@ -184,7 +174,8 @@ def random_powerset_label_min(
 
     if min_elements_per_label < 0:
         raise ValueError(
-            f"Parameter min_elements={min_elements_per_label} needs to be bigger or equal to 0."
+            f"Parameter min_elements={min_elements_per_label} needs to be bigger or "
+            f"equal to 0."
         )
 
     rng = np.random.default_rng(seed)
diff --git a/src/pydvl/value/shapley/classwise.py b/src/pydvl/value/shapley/classwise.py
index 1470ca385..cfc6226d3 100644
--- a/src/pydvl/value/shapley/classwise.py
+++ b/src/pydvl/value/shapley/classwise.py
@@ -71,17 +71,19 @@
 from numpy.typing import NDArray
 from tqdm import tqdm
 
+from pydvl.parallel import (
+    ParallelConfig,
+    effective_n_jobs,
+    init_executor,
+    init_parallel_backend,
+)
 from pydvl.utils import (
     Dataset,
-    ParallelConfig,
     Scorer,
     Seed,
     SupervisedModel,
     Utility,
-    effective_n_jobs,
     ensure_seed_sequence,
-    init_executor,
-    init_parallel_backend,
     random_powerset_label_min,
 )
 from pydvl.value.result import ValuationResult