[#179] Simplify _aggregate_per_threshold_results()

By pulling the mean and stdev calculation code out into its own
function, we can reduce some of the duplication. And in this case
catching a StatisticsError seems simpler than checking for certain
conditions to be met before calling the statistics functions.

hlink/linking/model_exploration/link_step_train_test_models.py

@@ -805,6 +805,24 @@ def _combine_by_threshold_matrix_entry(
     return results
 
 
+def _compute_mean_and_stdev(values: list[float]) -> (float, float):
+    """
+    Given a list of floats, return a tuple (mean, stdev). If there aren't enough
+    values to compute the mean and/or stdev, return np.nan for that entry.
+    """
+    try:
+        mean = statistics.mean(values)
+    except statistics.StatisticsError:
+        mean = np.nan
+
+    try:
+        stdev = statistics.stdev(values)
+    except statistics.StatisticsError:
+        stdev = np.nan
+
+    return (mean, stdev)
+
+
 def _aggregate_per_threshold_results(
     thresholded_metrics_df: pd.DataFrame,
     prediction_results: list[ThresholdTestResult],
@@ -824,33 +842,10 @@ def _aggregate_per_threshold_results(
     pr_auc_test = [r.pr_auc for r in prediction_results if not math.isnan(r.pr_auc)]
     mcc_test = [r.mcc for r in prediction_results if not math.isnan(r.mcc)]
 
-    # # variance requires at least two values
-    precision_test_sd = (
-        statistics.stdev(precision_test) if len(precision_test) > 1 else np.nan
-    )
-    recall_test_sd = statistics.stdev(recall_test) if len(recall_test) > 1 else np.nan
-    pr_auc_test_sd = statistics.stdev(pr_auc_test) if len(pr_auc_test) > 1 else np.nan
-    mcc_test_sd = statistics.stdev(mcc_test) if len(mcc_test) > 1 else np.nan
-
-    # Deal with tiny test data. This should never arise in practice but if it did we ought
-    # to issue a warning.
-    if len(precision_test) < 1:
-        #        raise RuntimeError("Not enough training data to get any valid precision values.")
-        precision_test_mean = np.nan
-    else:
-        precision_test_mean = (
-            statistics.mean(precision_test)
-            if len(precision_test) > 1
-            else precision_test[0]
-        )
-
-    if len(recall_test) < 1:
-        # raise RuntimeError("Not enough training data to get any valid recall values.")
-        recall_test_mean = np.nan
-    else:
-        recall_test_mean = (
-            statistics.mean(recall_test) if len(recall_test) > 1 else recall_test[0]
-        )
+    (precision_test_mean, precision_test_sd) = _compute_mean_and_stdev(precision_test)
+    (recall_test_mean, recall_test_sd) = _compute_mean_and_stdev(recall_test)
+    (pr_auc_test_mean, pr_auc_test_sd) = _compute_mean_and_stdev(pr_auc_test)
+    (mcc_test_mean, mcc_test_sd) = _compute_mean_and_stdev(mcc_test)
 
     new_desc = pd.DataFrame(
         {
@@ -862,9 +857,9 @@ def _aggregate_per_threshold_results(
             "precision_test_sd": [precision_test_sd],
             "recall_test_mean": [recall_test_mean],
             "recall_test_sd": [recall_test_sd],
-            "pr_auc_test_mean": [statistics.mean(pr_auc_test)],
+            "pr_auc_test_mean": [pr_auc_test_mean],
             "pr_auc_test_sd": [pr_auc_test_sd],
-            "mcc_test_mean": [statistics.mean(mcc_test)],
+            "mcc_test_mean": [mcc_test_mean],
             "mcc_test_sd": [mcc_test_sd],
         },
     )