Fix comments.

src/pydvl/value/semivalues.py

@@ -72,6 +72,7 @@
 
 import logging
 import math
+import warnings
 from enum import Enum
 from itertools import islice
 from typing import Collection, List, Optional, Protocol, Tuple, Type, TypeVar, cast
@@ -172,7 +173,7 @@ def compute_generic_semivalues(
         u: Utility object with model, data, and scoring function.
         coefficient: The semi-value coefficient
         done: Stopping criterion.
-        batch_size: Number of marginal evaluations per (parallelized) task.
+        batch_size: Number of marginal evaluations per single parallel job.
         n_jobs: Number of parallel jobs to use.
         config: Object configuring parallel computation, with cluster
             address, number of cpus, etc.
@@ -191,6 +192,12 @@ def compute_generic_semivalues(
             "will be doubled wrt. a 'direct' implementation of permutation MC"
         )
 
+    if batch_size != 1:
+        warnings.warn(
+            "batch_size is deprecated and will be removed in future versions.",
+            DeprecationWarning,
+        )
+
     result = ValuationResult.zeros(
         algorithm=f"semivalue-{str(sampler)}-{coefficient.__name__}",  # type: ignore
         indices=u.data.indices,
@@ -298,7 +305,7 @@ def compute_shapley_semivalues(
         done: Stopping criterion.
         sampler_t: The sampler type to use. See :mod:`pydvl.value.sampler`
             for a list.
-        batch_size: Number of marginal evaluations per (parallelized) task.
+        batch_size: Number of marginal evaluations per single parallel job.
         n_jobs: Number of parallel jobs to use.
         config: Object configuring parallel computation, with cluster
             address, number of cpus, etc.
@@ -342,7 +349,7 @@ def compute_banzhaf_semivalues(
         done: Stopping criterion.
         sampler_t: The sampler type to use. See :mod:`pydvl.value.sampler` for a
             list.
-        batch_size: Number of marginal evaluations per (parallelized) task.
+        batch_size: Number of marginal evaluations per single parallel job.
         n_jobs: Number of parallel jobs to use.
         seed: Either an instance of a numpy random number generator or a seed for it.
         config: Object configuring parallel computation, with cluster address,

tests/value/shapley/test_montecarlo.py

@@ -17,7 +17,7 @@
 
 from .. import check_rank_correlation, check_total_value, check_values
 from ..conftest import polynomial_dataset
-from ..utils import call_fn_multiple_seeds
+from ..utils import call_with_seeds
 
 log = logging.getLogger(__name__)
 
@@ -94,7 +94,7 @@ def test_montecarlo_shapley_housing_dataset_reproducible(
     kwargs: dict,
     seed: Seed,
 ):
-    values_1, values_2 = call_fn_multiple_seeds(
+    values_1, values_2 = call_with_seeds(
         compute_shapley_values,
         Utility(LinearRegression(), data=housing_dataset, scorer="r2"),
         mode=fun,
@@ -121,7 +121,7 @@ def test_montecarlo_shapley_housing_dataset_stochastic(
     seed: Seed,
     seed_alt: Seed,
 ):
-    values_1, values_2 = call_fn_multiple_seeds(
+    values_1, values_2 = call_with_seeds(
         compute_shapley_values,
         Utility(LinearRegression(), data=housing_dataset, scorer="r2"),
         mode=fun,

tests/value/test_semivalues.py

@@ -24,7 +24,7 @@
 from pydvl.value.stopping import AbsoluteStandardError, MaxUpdates
 
 from . import check_values
-from .utils import measure_execution_time
+from .utils import timed
 
 
 @pytest.mark.parametrize("num_samples", [5])
@@ -76,9 +76,8 @@ def test_shapley_batch_size(
 ):
     u, exact_values = analytic_shapley
     criterion = AbsoluteStandardError(0.02, 1.0) | MaxUpdates(2 ** (num_samples * 2))
-    result_single_batch, total_seconds_single_batch = measure_execution_time(
-        compute_generic_semivalues
-    )(
+    timed_fn = timed(compute_generic_semivalues)
+    result_single_batch = timed_fn(
         sampler(u.data.indices, seed=seed),
         u,
         coefficient,
@@ -87,9 +86,8 @@ def test_shapley_batch_size(
         batch_size=1,
         config=parallel_config,
     )
-    result_multi_batch, total_seconds_multi_batch = measure_execution_time(
-        compute_generic_semivalues
-    )(
+    total_seconds_single_batch = timed_fn.execution_time
+    result_multi_batch = timed_fn(
         sampler(u.data.indices, seed=seed),
         u,
         coefficient,
@@ -98,8 +96,9 @@ def test_shapley_batch_size(
         batch_size=batch_size,
         config=parallel_config,
     )
+    total_seconds_multi_batch = timed_fn.execution_time
     assert total_seconds_multi_batch < total_seconds_single_batch
-    check_values(result_single_batch, result_multi_batch, rtol=0.0, atol=0.0)
+    check_values(result_single_batch, result_multi_batch, rtol=1e-2)
 
 
 @pytest.mark.parametrize("num_samples", [5])

tests/value/utils.py

@@ -4,43 +4,48 @@
 from copy import deepcopy
 from functools import wraps
 from logging import getLogger
-from typing import Callable, Optional, Tuple, TypeVar
+from typing import Callable, Optional, Protocol, Tuple, TypeVar
 
 from pydvl.utils.types import Seed
 
 logger = getLogger(__name__)
 
-ReturnType = TypeVar("ReturnType")
+ReturnT = TypeVar("ReturnT")
 
 
-def call_fn_multiple_seeds(
-    fn: Callable, *args, seeds: Tuple[Seed, ...], **kwargs
-) -> Tuple:
+def call_with_seeds(fun: Callable, *args, seeds: Tuple[Seed, ...], **kwargs) -> Tuple:
     """
     Execute a function multiple times with different seeds. It copies the arguments
     and keyword arguments before passing them to the function.
 
     Args:
-        fn: The function to execute.
+        fun: The function to execute.
         args: The arguments to pass to the function.
         seeds: The seeds to use.
         kwargs: The keyword arguments to pass to the function.
 
     Returns:
         A tuple of the results of the function.
     """
-    return tuple(fn(*deepcopy(args), **deepcopy(kwargs), seed=seed) for seed in seeds)
+    return tuple(fun(*deepcopy(args), **deepcopy(kwargs), seed=seed) for seed in seeds)
 
 
-def measure_execution_time(
-    func: Callable[..., ReturnType]
-) -> Callable[..., Tuple[Optional[ReturnType], float]]:
+class TimedCallable(Protocol):
+    """A callable that has an attribute to keep track of execution time."""
+
+    execution_time: float
+
+    def __call__(self, *args, **kwargs) -> ReturnT:
+        ...
+
+
+def timed(fun: Callable[..., ReturnT]) -> TimedCallable:
     """
     Takes a function `func` and returns a function with the same input arguments and
     the original return value along with the execution time.
 
     Args:
-        func: The function to be measured, accepting arbitrary arguments and returning
+        fun: The function to be measured, accepting arbitrary arguments and returning
             any type.
 
     Returns:
@@ -49,18 +54,16 @@ def measure_execution_time(
             will have the same input arguments and return type as the original function.
     """
 
-    @wraps(func)
-    def wrapper(*args, **kwargs) -> Tuple[Optional[ReturnType], float]:
-        result = None
+    wrapper: TimedCallable
+
+    @wraps(fun)
+    def wrapper(*args, **kwargs) -> ReturnT:
         start_time = time.time()
-        try:
-            result = func(*args, **kwargs)
-        except Exception as e:
-            logger.error(f"Error in {func.__name__}: {e}")
-        finally:
-            end_time = time.time()
-            execution_time = end_time - start_time
-            logger.info(f"{func.__name__} took {execution_time:.5f} seconds.")
-            return result, execution_time
+        result = fun(*args, **kwargs)
+        end_time = time.time()
+        wrapper.execution_time = end_time - start_time
+        logger.info(f"{fun.__name__} took {wrapper.execution_time:.5f} seconds.")
+        return result
 
+    wrapper.execution_time = 0.0
     return wrapper