ToggleSpecialistMethod: name output CSVs by kind and write time only

benchmarking/baselines/toggle_specialist.py

@@ -49,8 +49,8 @@
 sigma under-covers where the method is biased.
 
 Each run writes a per-run folder ``toggle_specialist_<test>_<upgradestart>_<lastdate>/``
-(v0-style naming) under ``out_dir`` (a temp dir by default), holding a per-segment data-stats CSV,
-a headline results CSV, the per-leg selection and reference-subset CSVs, and -- when
+under ``out_dir`` (a temp dir by default), holding CSVs named ``<kind>_<write time>.csv``: a
+per-segment data-stats CSV, a headline results CSV, the per-leg selection and reference-subset CSVs, and -- when
 ``save_plots`` -- four diagnostic plots (a test-vs-reference scatter, a per-segment daily-ratio
 timeseries, a per-segment used-data-coverage timeseries, and a pairing-gap histogram).
 The rich stats let a human confirm the right data was received and interpreted: each leg's uplift
@@ -1049,9 +1049,9 @@ def _write_outputs(
         )
         run_dir = out_root / run_name
         run_dir.mkdir(parents=True, exist_ok=True)
-        ts = pd.Timestamp.utcnow().strftime("%Y%m%d_%H%M%S_%f")
+        ts = pd.Timestamp.utcnow().strftime("%Y%m%d_%H%M%S")
 
-        stats.to_csv(run_dir / f"{run_name}_data_stats_{ts}.csv", index=False)
+        stats.to_csv(run_dir / f"data_stats_{ts}.csv", index=False)
 
         used_base = int(stats.loc[stats["segment"] == "baseline", "n_used_timestamps"].iloc[0])
         used_up = int(stats.loc[stats["segment"] == "upgraded", "n_used_timestamps"].iloc[0])
@@ -1080,17 +1080,17 @@ def _write_outputs(
                 }
             ]
         )
-        results.to_csv(run_dir / f"{run_name}_results_{ts}.csv", index=False)
+        results.to_csv(run_dir / f"results_{ts}.csv", index=False)
 
         if est.per_bin is not None:
-            est.per_bin.to_csv(run_dir / f"{run_name}_by_power_bin_{ts}.csv", index=False)
-        est.diagnostics.to_csv(run_dir / f"{run_name}_uncertainty_{ts}.csv", index=False)
+            est.per_bin.to_csv(run_dir / f"by_power_bin_{ts}.csv", index=False)
+        est.diagnostics.to_csv(run_dir / f"uncertainty_{ts}.csv", index=False)
         if subsets is not None:
-            subsets.to_csv(run_dir / f"{run_name}_reference_subsets_{ts}.csv", index=False)
+            subsets.to_csv(run_dir / f"reference_subsets_{ts}.csv", index=False)
         # Every leg's row selection, so the blend's two selections can be told apart.
         pd.concat(
             [component.accounting.assign(component=mode) for mode, component in components.items()], ignore_index=True
-        ).to_csv(run_dir / f"{run_name}_selection_{ts}.csv", index=False)
+        ).to_csv(run_dir / f"selection_{ts}.csv", index=False)
 
         if self.save_plots:
             # Scatter and ratio plots of one leg's own rows against its own reference.

tests/benchmarking/baselines/test_toggle_specialist.py

@@ -363,14 +363,14 @@ def test_per_bin_csv_is_written(self, tmp_path: Path) -> None:
             rated_power_kw=_RATED_KW,
             out_dir=tmp_path,
         ).estimate(MethodInput(scada_df=scada, test_wtg="T1", upgrade_timing=schedule, turbine_col=_TURBINE_COL))
-        assert list(tmp_path.rglob("*_by_power_bin_*.csv"))
+        assert list(tmp_path.rglob("by_power_bin_*.csv"))
 
     def test_no_per_bin_csv_when_conditioning_is_off(self, tmp_path: Path) -> None:
         scada, schedule, _ = _toggle_case(uplift=0.03)
         ToggleSpecialistMethod(columns=_COLUMNS, toggle_period=_CYCLE, toggle_datum=_DATUM, out_dir=tmp_path).estimate(
             MethodInput(scada_df=scada, test_wtg="T1", upgrade_timing=schedule, turbine_col=_TURBINE_COL)
         )
-        assert not list(tmp_path.rglob("*_by_power_bin_*.csv"))
+        assert not list(tmp_path.rglob("by_power_bin_*.csv"))
 
     def test_per_bin_plot_written_with_save_plots(self, tmp_path: Path) -> None:
         scada, schedule = _varying_rho_case(uplift=0.05)
@@ -587,7 +587,7 @@ def test_toggle_coverage_capped_near_duty_cycle(self) -> None:
 def _read_only_csv(folder: Path, kind: str) -> pd.DataFrame:
     run_dirs = [p for p in Path(folder).iterdir() if p.is_dir()]
     assert len(run_dirs) == 1, f"expected exactly one run dir, found {run_dirs}"
-    matches = list(run_dirs[0].glob(f"*_{kind}_*.csv"))
+    matches = list(run_dirs[0].glob(f"{kind}_*.csv"))
     assert len(matches) == 1, f"expected one {kind} csv, found {matches}"
     return pd.read_csv(matches[0])
 
@@ -851,15 +851,15 @@ class TestUncertaintyInTheWrittenOutputs:
     def test_results_csv_records_the_sigma_and_the_bootstrap_config(self, tmp_path: Path) -> None:
         scada, schedule = _noisy_toggle_case()
         out = _estimate(scada, schedule, out_dir=tmp_path, block_hours=24.0)
-        results = pd.concat([pd.read_csv(p) for p in tmp_path.glob("*/*_results_*.csv")])
+        results = pd.concat([pd.read_csv(p) for p in tmp_path.glob("*/results_*.csv")])
         assert results["uplift_sigma_frc"].iloc[0] == pytest.approx(out.sigma_overall)
         assert results["block_hours"].iloc[0] == 24.0
         assert results["n_resamples"].iloc[0] == 1000
 
     def test_an_uncertainty_csv_is_written(self, tmp_path: Path) -> None:
         scada, schedule = _noisy_toggle_case()
         _estimate(scada, schedule, out_dir=tmp_path, conditions=("power",), rated_power_kw=_RATED_KW)
-        written = list(tmp_path.glob("*/*_uncertainty_*.csv"))
+        written = list(tmp_path.glob("*/uncertainty_*.csv"))
         assert len(written) == 1
         frame = pd.read_csv(written[0])
         assert "n_upgraded_records" in frame.columns