Stabilize time-dependent performance-data column order

src/rtichoke/performance_data/performance_data_times.py

@@ -21,6 +21,35 @@
 import numpy as np
 
 
+_PERFORMANCE_DATA_TIMES_COLUMNS = [
+    "reference_group",
+    "fixed_time_horizon",
+    "censoring_heuristic",
+    "competing_heuristic",
+    "stratified_by",
+    "chosen_cutoff",
+    "excluded",
+    "true_positives",
+    "true_negatives",
+    "false_positives",
+    "false_negatives",
+    "predicted_positives",
+    "predicted_negatives",
+    "real_positives",
+    "real_negatives",
+    "n",
+    "sensitivity",
+    "specificity",
+    "ppv",
+    "npv",
+    "false_positive_rate",
+    "lift",
+    "net_benefit",
+    "net_benefit_interventions_avoided",
+    "ppcr",
+]
+
+
 def prepare_performance_data_times(
     probs: Dict[str, np.ndarray],
     reals: Union[np.ndarray, Dict[str, np.ndarray]],
@@ -131,6 +160,7 @@ def prepare_performance_data_times(
             "_fixed_time_horizon_order",
             "_heuristic_order",
         )
+        .select(_PERFORMANCE_DATA_TIMES_COLUMNS)
     )
 
 

tests/test_time_performance_schema_order.py

@@ -0,0 +1,44 @@
+import numpy as np
+import polars as pl
+from polars.testing import assert_frame_equal
+
+from rtichoke import prepare_performance_data_times
+
+
+PROBS = {"model": np.array([0.1, 0.2, 0.5, 0.5, 0.8, 0.9])}
+REALS = np.array([0, 0, 1, 0, 1, 1])
+TIMES = np.array([8.0, 7.0, 3.0, 6.0, 2.0, 1.0])
+
+
+def _prepare(stratified_by):
+    return prepare_performance_data_times(
+        probs=PROBS,
+        reals=REALS,
+        times=TIMES,
+        fixed_time_horizons=[5.0],
+        stratified_by=stratified_by,
+        by=0.5,
+    )
+
+
+def test_time_performance_schema_order_is_stable_across_stratification():
+    threshold_only = _prepare(("probability_threshold",))
+    ppcr_only = _prepare(("ppcr",))
+    combined = _prepare(("probability_threshold", "ppcr"))
+
+    assert ppcr_only.columns == threshold_only.columns
+    assert combined.columns == threshold_only.columns
+
+
+def test_combined_time_ppcr_values_match_ppcr_only():
+    ppcr_only = _prepare(("ppcr",)).sort(
+        ["reference_group", "fixed_time_horizon", "chosen_cutoff"]
+    )
+    combined_ppcr = (
+        _prepare(("probability_threshold", "ppcr"))
+        .filter(pl.col("stratified_by") == "ppcr")
+        .select(ppcr_only.columns)
+        .sort(["reference_group", "fixed_time_horizon", "chosen_cutoff"])
+    )
+
+    assert_frame_equal(combined_ppcr, ppcr_only)