refactor: move check_regression into ab_test.py

This functions was only used in ab_test.py

Signed-off-by: Egor Lazarchuk <yegorlz@amazon.co.uk>

Author: ShadowCurse

tests/framework/ab_test.py

@@ -21,12 +21,9 @@
 of both invocations is the same, the test passes (with us being alerted to this situtation via a special pipeline that
 does not block PRs). If not, it fails, preventing PRs from introducing new vulnerable dependencies.
 """
-import statistics
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Callable, List, Optional, TypeVar
-
-import scipy
+from typing import Callable, Optional, TypeVar
 
 from framework import utils
 from framework.defs import FC_WORKSPACE_DIR
@@ -170,32 +167,6 @@ def set_did_not_grow_comparator(
     )
 
 
-def check_regression(
-    a_samples: List[float], b_samples: List[float], *, n_resamples: int = 9999
-):
-    """Checks for a regression by performing a permutation test. A permutation test is a non-parametric test that takes
-    three parameters: Two populations (sets of samples) and a function computing a "statistic" based on two populations.
-    First, the test computes the statistic for the initial populations. It then randomly
-    permutes the two populations (e.g. merges them and then randomly splits them again). For each such permuted
-    population, the statistic is computed. Then, all the statistics are sorted, and the percentile of the statistic for the
-    initial populations is computed. We then look at the fraction of statistics that are larger/smaller than that of the
-    initial populations. The minimum of these two fractions will then become the p-value.
-
-    The idea is that if the two populations are indeed drawn from the same distribution (e.g. if performance did not
-    change), then permuting will not affect the statistic (indeed, it should be approximately normal-distributed, and
-    the statistic for the initial populations will be somewhere "in the middle").
-
-    Useful for performance tests.
-    """
-    return scipy.stats.permutation_test(
-        (a_samples, b_samples),
-        # Compute the difference of means, such that a positive different indicates potential for regression.
-        lambda x, y: statistics.mean(y) - statistics.mean(x),
-        vectorized=False,
-        n_resamples=n_resamples,
-    )
-
-
 @with_filelock
 def git_clone(clone_path, commitish):
     """Clone the repository at `commit`.

tools/ab_test.py

@@ -25,12 +25,15 @@
 import sys
 from collections import defaultdict
 from pathlib import Path
+from typing import List
+
+import scipy
 
 # Hack to be able to use our test framework code
 sys.path.append(str(Path(__file__).parent.parent / "tests"))
 
 # pylint:disable=wrong-import-position
-from framework.ab_test import binary_ab_test, check_regression
+from framework.ab_test import binary_ab_test
 from framework.properties import global_props
 from host_tools.metrics import get_metrics_logger
 
@@ -215,6 +218,32 @@ def collect_data(tag: str, binary_dir: Path, pytest_opts: str):
     return load_data_series(Path(test_path))
 
 
+def check_regression(
+    a_samples: List[float], b_samples: List[float], *, n_resamples: int = 9999
+):
+    """Checks for a regression by performing a permutation test. A permutation test is a non-parametric test that takes
+    three parameters: Two populations (sets of samples) and a function computing a "statistic" based on two populations.
+    First, the test computes the statistic for the initial populations. It then randomly
+    permutes the two populations (e.g. merges them and then randomly splits them again). For each such permuted
+    population, the statistic is computed. Then, all the statistics are sorted, and the percentile of the statistic for the
+    initial populations is computed. We then look at the fraction of statistics that are larger/smaller than that of the
+    initial populations. The minimum of these two fractions will then become the p-value.
+
+    The idea is that if the two populations are indeed drawn from the same distribution (e.g. if performance did not
+    change), then permuting will not affect the statistic (indeed, it should be approximately normal-distributed, and
+    the statistic for the initial populations will be somewhere "in the middle").
+
+    Useful for performance tests.
+    """
+    return scipy.stats.permutation_test(
+        (a_samples, b_samples),
+        # Compute the difference of means, such that a positive different indicates potential for regression.
+        lambda x, y: statistics.mean(y) - statistics.mean(x),
+        vectorized=False,
+        n_resamples=n_resamples,
+    )
+
+
 def analyze_data(
     data_a,
     data_b,