FIX precommit fixes

Author: dantegd

python/cuml/cuml/benchmark/config.py

@@ -219,7 +219,9 @@ def validate_config(raw_config: dict[str, Any]) -> None:
     defaults = raw_config.get("defaults", {})
     if not isinstance(defaults, dict):
         raise BenchmarkConfigError("Config field 'defaults' must be a mapping")
-    _validate_default_or_entry(defaults, context="defaults", require_algorithm=False)
+    _validate_default_or_entry(
+        defaults, context="defaults", require_algorithm=False
+    )
 
     profiles = raw_config.get("profiles", {})
     if not isinstance(profiles, dict):
@@ -375,7 +377,9 @@ def _validate_default_or_entry(
         )
 
     for numeric_field in ("n_reps", "random_state"):
-        if numeric_field in entry and not isinstance(entry[numeric_field], int):
+        if numeric_field in entry and not isinstance(
+            entry[numeric_field], int
+        ):
             raise BenchmarkConfigError(
                 f"{context} field '{numeric_field}' must be an integer"
             )
@@ -389,7 +393,9 @@ def _validate_default_or_entry(
     if "rows" in entry:
         _normalize_int_list(entry["rows"], field_name=f"{context}.rows")
     if "features" in entry:
-        _normalize_int_list(entry["features"], field_name=f"{context}.features")
+        _normalize_int_list(
+            entry["features"], field_name=f"{context}.features"
+        )
     if "shapes" in entry:
         _normalize_shapes(entry["shapes"], field_name=f"{context}.shapes")
 
@@ -454,7 +460,9 @@ def _apply_defaults(
 
 def _validate_post_defaults_entry(entry: dict[str, Any]) -> None:
     _validate_default_or_entry(
-        entry, context=f"benchmark '{entry.get('id', entry['algorithm'])}'", require_algorithm=True
+        entry,
+        context=f"benchmark '{entry.get('id', entry['algorithm'])}'",
+        require_algorithm=True,
     )
 
     benchmark_name = entry.get("id", entry["algorithm"])
@@ -600,7 +608,8 @@ def _apply_algorithm_filter(
 
 
 def _build_override_list(
-    fixed_values: dict[str, Any] | None, grid_values: dict[str, list[Any]] | None
+    fixed_values: dict[str, Any] | None,
+    grid_values: dict[str, list[Any]] | None,
 ) -> list[dict[str, Any]]:
     fixed = deepcopy(fixed_values or {})
     grid = deepcopy(grid_values or {})
@@ -645,9 +654,7 @@ def _normalize_int_list(
     return value
 
 
-def _normalize_shapes(
-    value: Any, *, field_name: str
-) -> list[dict[str, int]]:
+def _normalize_shapes(value: Any, *, field_name: str) -> list[dict[str, int]]:
     if not isinstance(value, list) or not value:
         raise BenchmarkConfigError(
             f"Field '{field_name}' must be a non-empty list"

python/cuml/cuml/benchmark/configs/single_gpu.yaml

@@ -1557,4 +1557,3 @@ benchmarks:
     id: multinomialnb_fit_wide_nightly
     rows: [8000000]
     tags: [nightly-only, wide, naive-bayes]
-

python/cuml/cuml/benchmark/run_benchmarks.py

@@ -264,8 +264,7 @@ def _validate_benchmark_inputs(test_split, input_type, run_gpu):
     """Validate per-benchmark inputs and normalize CPU-only input types."""
     if not 0.0 <= test_split <= 1.0:
         raise ValueError(
-            "test_split: got %f, want a value between 0.0 and 1.0"
-            % test_split
+            "test_split: got %f, want a value between 0.0 and 1.0" % test_split
         )
 
     if run_gpu:
@@ -343,7 +342,12 @@ def _resolved_entry_dimensions(entry, args, explicit_options):
     if "--default-size" in explicit_options:
         return None, [0], [0]
 
-    row_override_flags = {"--min-rows", "--max-rows", "--num-sizes", "--num-rows"}
+    row_override_flags = {
+        "--min-rows",
+        "--max-rows",
+        "--num-sizes",
+        "--num-rows",
+    }
     if "--num-rows" in explicit_options:
         base_rows = [args.num_rows]
     elif row_override_flags.intersection(explicit_options):
@@ -383,7 +387,9 @@ def _run_config_benchmarks(args, explicit_options):
             "profile and filters."
         )
 
-    allow_gpu_runs = is_gpu_available() and "--skip-gpu" not in explicit_options
+    allow_gpu_runs = (
+        is_gpu_available() and "--skip-gpu" not in explicit_options
+    )
     if allow_gpu_runs and any(entry["run_gpu"] for entry in benchmark_entries):
         setup_rmm_allocator(args.rmm_allocator)
 

python/cuml/cuml/benchmark/runners.py

@@ -15,12 +15,13 @@
 # Supports both package and standalone execution
 try:
     from cuml.benchmark import datagen
-    from cuml.benchmark.gpu_check import is_cuml_available, is_gpu_available
+    from cuml.benchmark.gpu_check import is_gpu_available
 except ImportError:
     if not any("cuml/benchmark" in p for p in sys.path):
         raise
     import datagen  # noqa: E402
-    from gpu_check import is_cuml_available, is_gpu_available  # noqa: E402
+    from gpu_check import is_gpu_available  # noqa: E402
+
 
 def _metric_array_to_numpy(data):
     """Convert metric inputs to NumPy for sklearn compatibility."""
@@ -350,9 +351,7 @@ def _run_one_size(
                     y_pred_cpu = cpu_model.transform(X_test)
                 y_test = _metric_array_to_numpy(y_test)
                 y_pred_cpu = _metric_array_to_numpy(y_pred_cpu)
-                cpu_accuracy = algo_pair.accuracy_function(
-                    y_test, y_pred_cpu
-                )
+                cpu_accuracy = algo_pair.accuracy_function(y_test, y_pred_cpu)
 
         if n_samples == 0:
             # Update n_samples = training samples + testing samples

python/cuml/tests/test_benchmark_config.py

@@ -52,15 +52,15 @@ def test_load_and_resolve_config_default_profile_filters_single_gpu_manifest():
         / "configs"
         / "single_gpu.yaml"
     )
-    resolved = load_and_resolve_config(
-        str(config_path)
-    )
+    resolved = load_and_resolve_config(str(config_path))
 
     benchmark_ids = {entry["benchmark_id"] for entry in resolved["benchmarks"]}
 
     assert resolved["suite_name"] == "single_gpu"
     assert resolved["profile"] == "default"
-    assert all(entry["input_type"] == "cupy" for entry in resolved["benchmarks"])
+    assert all(
+        entry["input_type"] == "cupy" for entry in resolved["benchmarks"]
+    )
     assert "logreg_fit_narrow_default" in benchmark_ids
     assert "logreg_fit_medium_default" in benchmark_ids
     assert "logreg_fit_wide_default" in benchmark_ids
@@ -79,11 +79,15 @@ def test_load_and_resolve_config_single_gpu_profiles_preserve_algorithm_set():
         / "single_gpu.yaml"
     )
 
-    default_resolved = load_and_resolve_config(str(config_path), profile="default")
+    default_resolved = load_and_resolve_config(
+        str(config_path), profile="default"
+    )
     extended_resolved = load_and_resolve_config(
         str(config_path), profile="extended"
     )
-    nightly_resolved = load_and_resolve_config(str(config_path), profile="nightly")
+    nightly_resolved = load_and_resolve_config(
+        str(config_path), profile="nightly"
+    )
 
     default_algorithms = {
         entry["algorithm"] for entry in default_resolved["benchmarks"]
@@ -270,12 +274,14 @@ def test_run_config_benchmarks_uses_shape_pairs_without_cartesian_product(
         ([250], [16]),
     ]
     assert list(results["benchmark_id"]) == ["shape-bench", "shape-bench"]
-    assert set(["config_path", "suite_name", "suite_tier", "profile"]).issubset(
-        results.columns
-    )
+    assert set(
+        ["config_path", "suite_name", "suite_tier", "profile"]
+    ).issubset(results.columns)
 
 
-def test_run_config_benchmarks_applies_only_explicit_cli_overrides(monkeypatch):
+def test_run_config_benchmarks_applies_only_explicit_cli_overrides(
+    monkeypatch,
+):
     calls = []
     setup_calls = []
 
@@ -396,9 +402,9 @@ def test_main_runs_config_smoke_manifest_end_to_end(monkeypatch, tmp_path):
     assert [kwargs["bench_dims"] for _, kwargs in calls] == [[8], [8]]
 
     results = pd.read_csv(csv_path)
-    assert set(["benchmark_id", "config_path", "suite_name", "suite_tier", "profile"]).issubset(
-        results.columns
-    )
+    assert set(
+        ["benchmark_id", "config_path", "suite_name", "suite_tier", "profile"]
+    ).issubset(results.columns)
     assert set(results["benchmark_id"]) == {
         "test_logreg_fit",
         "test_scaler_fittransform",

python/cuml/tests/test_benchmark_runners.py

@@ -45,7 +45,9 @@ def test_accuracy_runner_converts_cupy_metric_inputs(monkeypatch):
     monkeypatch.setattr(
         "cuml.benchmark.runners.datagen.gen_data", lambda *args, **kwargs: data
     )
-    monkeypatch.setattr("cuml.benchmark.runners.is_gpu_available", lambda: True)
+    monkeypatch.setattr(
+        "cuml.benchmark.runners.is_gpu_available", lambda: True
+    )
 
     result = runner._run_one_size(
         _MockAlgoPair(),