Adapt test case to check if a small batch size has a performance impr…

…ovement compared to a singular batch size.

tests/value/test_semivalues.py

@@ -1,10 +1,11 @@
 import math
+import time
 from typing import Type
 
 import numpy as np
 import pytest
 
-from pydvl.utils import ParallelConfig
+from pydvl.utils import ParallelConfig, Seed
 from pydvl.value.sampler import (
     AntitheticSampler,
     DeterministicPermutationSampler,
@@ -23,6 +24,7 @@
 from pydvl.value.stopping import AbsoluteStandardError, MaxUpdates
 
 from . import check_values
+from .utils import measure_execution_time
 
 
 @pytest.mark.parametrize("num_samples", [5])
@@ -60,29 +62,44 @@ def test_shapley(
 
 @pytest.mark.parametrize(
     "num_samples,sampler,coefficient,batch_size",
-    [(5, PermutationSampler, beta_coefficient(1, 1), 2)],
+    [(5, PermutationSampler, beta_coefficient(1, 1), 5)],
 )
 def test_shapley_batch_size(
     num_samples: int,
     analytic_shapley,
-    sampler: Type[PowersetSampler],
+    sampler: Type[PermutationSampler],
     coefficient: SVCoefficient,
     batch_size: int,
     n_jobs: int,
     parallel_config: ParallelConfig,
+    seed: Seed,
 ):
     u, exact_values = analytic_shapley
     criterion = AbsoluteStandardError(0.02, 1.0) | MaxUpdates(2 ** (num_samples * 2))
-    values = compute_generic_semivalues(
-        sampler(u.data.indices),
+    result_single_batch, total_seconds_single_batch = measure_execution_time(
+        compute_generic_semivalues
+    )(
+        sampler(u.data.indices, seed=seed),
+        u,
+        coefficient,
+        criterion,
+        n_jobs=n_jobs,
+        batch_size=1,
+        config=parallel_config,
+    )
+    result_multi_batch, total_seconds_multi_batch = measure_execution_time(
+        compute_generic_semivalues
+    )(
+        sampler(u.data.indices, seed=seed),
         u,
         coefficient,
         criterion,
         n_jobs=n_jobs,
         batch_size=batch_size,
         config=parallel_config,
     )
-    check_values(values, exact_values, rtol=0.2)
+    assert total_seconds_multi_batch < total_seconds_single_batch
+    check_values(result_single_batch, result_multi_batch, rtol=0.0, atol=0.0)
 
 
 @pytest.mark.parametrize("num_samples", [5])