style: black-format 5 files with pre-existing drift

src/spotforecast2/plots/diagnostics.py

@@ -185,23 +185,19 @@ def plot_feature_importance_by_family(
         print(type(fig).__name__)
         ```
     """
-    ranking = sorted(
-        zip(names, importances), key=lambda kv: kv[1], reverse=True
-    )[:top_n]
+    ranking = sorted(zip(names, importances), key=lambda kv: kv[1], reverse=True)[
+        :top_n
+    ]
     labels = [n for n, _ in ranking][::-1]
     values = [v for _, v in ranking][::-1]
     colors = [_FAMILY_COLOR.get(feature_family(n), "#888888") for n in labels]
 
     fig, ax = plt.subplots(figsize=(8.5, 5.5))
     ax.barh(labels, values, color=colors)
     ax.set_xlabel("split count (feature importance)")
-    ax.set_title(
-        f"Top-{top_n} feature importances (coloured by family; lags in red)"
-    )
+    ax.set_title(f"Top-{top_n} feature importances (coloured by family; lags in red)")
     ax.grid(True, axis="x", color="#E5E5E5", linewidth=0.5)
-    handles = [
-        plt.Rectangle((0, 0), 1, 1, color=c) for c in _FAMILY_COLOR.values()
-    ]
+    handles = [plt.Rectangle((0, 0), 1, 1, color=c) for c in _FAMILY_COLOR.values()]
     ax.legend(handles, _FAMILY_COLOR.keys(), fontsize=7, loc="lower right")
     for spine in ("top", "right"):
         ax.spines[spine].set_visible(False)

tests/test_model_selection_boundary.py

@@ -13,7 +13,6 @@
     suggest_bounds,
 )
 
-
 # ---------------------------------------------------------------------------
 # Shared fixtures / helpers
 # ---------------------------------------------------------------------------
@@ -38,6 +37,7 @@
 # report_boundary_positions
 # ---------------------------------------------------------------------------
 
+
 class TestReportBoundaryPositions:
     def test_interior_no_flag(self):
         """An optimum well inside all bounds returns an empty list."""
@@ -72,6 +72,7 @@ def test_log10_dim_near_lower(self):
         # learning_rate = 0.006 in (0.005, 0.3) log10:
         # pos = (log10(0.006) - log10(0.005)) / (log10(0.3) - log10(0.005))
         import math
+
         low, high, val = 0.005, 0.3, 0.006
         pos = (math.log10(val) - math.log10(low)) / (math.log10(high) - math.log10(low))
         assert pos < 0.10  # confirm this is a near-lower case
@@ -131,9 +132,7 @@ def test_logger_injection(self, caplog):
         with caplog.at_level(logging.INFO, logger="test_custom_boundary_logger"):
             report_boundary_positions(params, space, logger=custom_logger)
         # The custom logger should have emitted at least one INFO message
-        assert any(
-            r.name == "test_custom_boundary_logger" for r in caplog.records
-        )
+        assert any(r.name == "test_custom_boundary_logger" for r in caplog.records)
 
     def test_multiple_flags(self):
         """Multiple near-boundary dims all appear in the returned list."""
@@ -157,6 +156,7 @@ def test_log_invalid_val_skipped(self):
 # boundary_report (DataFrame form)
 # ---------------------------------------------------------------------------
 
+
 class TestBoundaryReport:
     def test_returns_dataframe(self):
         best = {"estimator__reg_alpha": 9.89, "estimator__learning_rate": 0.069}
@@ -165,7 +165,15 @@ def test_returns_dataframe(self):
             "estimator__learning_rate": (0.005, 0.3, "log10"),
         }
         df = boundary_report(best, space)
-        assert set(df.columns) == {"param", "low", "high", "value", "scale", "position", "flag"}
+        assert set(df.columns) == {
+            "param",
+            "low",
+            "high",
+            "value",
+            "scale",
+            "position",
+            "flag",
+        }
 
     def test_flagged_near_upper(self):
         best = {"estimator__reg_alpha": 9.89}
@@ -191,8 +199,8 @@ def test_categorical_skipped(self):
 
     def test_sorted_descending_position(self):
         best = {
-            "estimator__reg_alpha": 9.89,   # near upper → high position
-            "estimator__num_leaves": 300,    # interior
+            "estimator__reg_alpha": 9.89,  # near upper → high position
+            "estimator__num_leaves": 300,  # interior
         }
         space = {
             "estimator__reg_alpha": (0.001, 10.0),
@@ -213,6 +221,7 @@ def test_prefix_stripped_in_param_column(self):
 # suggest_bounds
 # ---------------------------------------------------------------------------
 
+
 class TestSuggestBounds:
     def test_interior_unchanged(self):
         best = {"estimator__num_leaves": 300}
@@ -277,7 +286,9 @@ def test_all_keys_preserved(self):
         # The near-upper-boundary integer dim must have been widened upward
         assert new_space["estimator__num_leaves"][1] > 1024
         # Interior log10 dim is unchanged
-        assert new_space["estimator__learning_rate"] == space["estimator__learning_rate"]
+        assert (
+            new_space["estimator__learning_rate"] == space["estimator__learning_rate"]
+        )
 
     def test_widen_factor_parameter(self):
         """Different widen_factor values produce different bounds."""

tests/test_plot_with_outliers_bounds.py

@@ -323,9 +323,9 @@ def test_no_targets_kwarg_emits_no_deprecation_warning(self):
             for w in caught
             if issubclass(w.category, (DeprecationWarning, FutureWarning))
         ]
-        assert deprecation_warnings == [], (
-            f"Unexpected deprecation warnings: {[str(w.message) for w in deprecation_warnings]}"
-        )
+        assert (
+            deprecation_warnings == []
+        ), f"Unexpected deprecation warnings: {[str(w.message) for w in deprecation_warnings]}"
 
     def test_explicit_targets_wins_over_config_targets(self):
         """When explicit targets= differs from config.targets, explicit wins."""

tests/test_plots_diagnostics.py

@@ -26,7 +26,6 @@
     plot_shap_summary,
 )
 
-
 # ---------------------------------------------------------------------------
 # Teardown
 # ---------------------------------------------------------------------------
@@ -174,8 +173,14 @@ def test_importance_plot_top_n_respected():
 
 def test_importance_plot_family_colors_distinct():
     """Known feature names from different families get different bar colors."""
-    names = ["lag_1", "holiday_DE", "poly_hour_2", "window_mean_72",
-             "sin_hour", "wind_speed"]
+    names = [
+        "lag_1",
+        "holiday_DE",
+        "poly_hour_2",
+        "window_mean_72",
+        "sin_hour",
+        "wind_speed",
+    ]
     scores = [100, 90, 80, 70, 60, 50]
     fig = plot_feature_importance_by_family(names, scores)
     ax = fig.axes[0]
@@ -205,9 +210,7 @@ def test_shap_summary_returns_figure():
     from lightgbm import LGBMRegressor
 
     rng = np.random.default_rng(7)
-    X = pd.DataFrame(
-        rng.standard_normal((120, 4)), columns=["f0", "f1", "f2", "f3"]
-    )
+    X = pd.DataFrame(rng.standard_normal((120, 4)), columns=["f0", "f1", "f2", "f3"])
     y = X["f0"] * 2 + rng.standard_normal(120) * 0.1
     est = LGBMRegressor(n_estimators=10, verbose=-1, random_state=0)
     est.fit(X, y)
@@ -228,9 +231,7 @@ def test_shap_summary_subsamples_max_samples():
     from lightgbm import LGBMRegressor
 
     rng = np.random.default_rng(9)
-    X = pd.DataFrame(
-        rng.standard_normal((500, 3)), columns=["a", "b", "c"]
-    )
+    X = pd.DataFrame(rng.standard_normal((500, 3)), columns=["a", "b", "c"])
     y = X["a"] + rng.standard_normal(500) * 0.2
     est = LGBMRegressor(n_estimators=10, verbose=-1, random_state=0)
     est.fit(X, y)
@@ -254,8 +255,7 @@ def test_forecast_vs_reference_returns_figure_with_overlap():
     forecast = _make_forecast(24)
     reference = _make_forecast(24, seed=1)  # same index -> full overlap
     fig = plot_forecast_vs_reference(
-        forecast, reference,
-        forecast_label="team_4", reference_label="ENTSO-E"
+        forecast, reference, forecast_label="team_4", reference_label="ENTSO-E"
     )
     assert isinstance(fig, Figure)
 
@@ -297,9 +297,7 @@ def test_forecast_vs_reference_unit_scale_applied(caplog):
     forecast = _make_forecast(24)
     reference = _make_forecast(24, seed=5)
     with caplog.at_level(logging.INFO, logger="spotforecast2.plots.diagnostics"):
-        fig = plot_forecast_vs_reference(
-            forecast, reference, unit_scale=1.0, unit="MW"
-        )
+        fig = plot_forecast_vs_reference(forecast, reference, unit_scale=1.0, unit="MW")
     ax = fig.axes[0]
     # y-axis upper limit should be in the MW range (>1000), not GW (<100)
     ymin, ymax = ax.get_ylim()
@@ -339,6 +337,5 @@ def test_forecast_vs_reference_mad_logged_in_display_unit(caplog):
     assert match, f"Could not parse GW value from log message: {msg!r}"
     logged_value = float(match.group(1))
     assert logged_value == pytest.approx(1.0, abs=0.05), (
-        f"Logged MAD should be 1.0 GW, got {logged_value}. "
-        f"Full message: {msg!r}"
+        f"Logged MAD should be 1.0 GW, got {logged_value}. " f"Full message: {msg!r}"
     )

tests/test_stats_select_pacf_lags.py

@@ -11,11 +11,11 @@
 
 from spotforecast2.stats.autocorrelation import select_pacf_lags
 
-
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
 
+
 def _make_daily_ar_series(n_days: int = 120, seed: int = 42) -> pd.Series:
     """AR(1) with moderate coefficient for general lag selection tests."""
     rng = np.random.default_rng(seed)
@@ -40,6 +40,7 @@ def _make_ar24_series(n_days: int = 200, seed: int = 42) -> pd.Series:
 # Happy-path tests
 # ---------------------------------------------------------------------------
 
+
 class TestSelectPacfLagsHappyPath:
     def test_returns_list_of_ints(self):
         series = _make_daily_ar_series()
@@ -89,6 +90,7 @@ def test_n_lags_limits_search(self):
 # Degenerate / edge-case tests
 # ---------------------------------------------------------------------------
 
+
 class TestSelectPacfLagsDegenerate:
     def test_constant_series_fallback_returned(self):
         series = pd.Series([1.0] * 50)
@@ -139,6 +141,7 @@ def test_fallback_none_is_default(self):
 # Parameter variation
 # ---------------------------------------------------------------------------
 
+
 class TestSelectPacfLagsParams:
     def test_default_n_lags_200_top_k_8(self):
         """Default parameters work on a long series without error."""