[#176] Lowercase tp/fp/fn/tn variable names

hlink/linking/model_exploration/link_step_train_test_models.py

@@ -652,13 +652,13 @@ def _capture_prediction_results(
         predictions.createOrReplaceTempView(f"{table_prefix}predictions")
 
         (
-            test_TP_count,
-            test_FP_count,
-            test_FN_count,
-            test_TN_count,
+            tp_count,
+            fp_count,
+            fn_count,
+            tn_count,
         ) = _get_confusion_matrix(predictions, dep_var)
         test_precision, test_recall, test_mcc = _get_aggregate_metrics(
-            test_TP_count, test_FP_count, test_FN_count, test_TN_count
+            tp_count, fp_count, fn_count, tn_count
         )
 
         result = ThresholdTestResult(
@@ -690,15 +690,15 @@ def _save_training_results(
             # )
 
 
-def _calc_mcc(TP: int, TN: int, FP: int, FN: int) -> float:
+def _calc_mcc(tp: int, tn: int, fp: int, fn: int) -> float:
     """
-    Given the counts of true positives (TP), true negatives (TN), false
-    positives (FP), and false negatives (FN) for a model run, compute the
+    Given the counts of true positives (tp), true negatives (tn), false
+    positives (fp), and false negatives (fn) for a model run, compute the
     Matthews Correlation Coefficient (MCC).
     """
-    if (math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))) != 0:
-        mcc = ((TP * TN) - (FP * FN)) / (
-            math.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))
+    if (math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))) != 0:
+        mcc = ((tp * tn) - (fp * fn)) / (
+            math.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
         )
     else:
         mcc = 0
@@ -779,7 +779,7 @@ def _get_confusion_matrix(
 
 
 def _get_aggregate_metrics(
-    TP_count: int, FP_count: int, FN_count: int, TN_count: int
+    true_positives: int, false_positives: int, false_negatives: int, true_negatives: int
 ) -> tuple[float, float, float]:
     """
     Given the counts of true positives, false positives, false negatives, and
@@ -788,15 +788,15 @@ def _get_aggregate_metrics(
 
     Return a tuple of (precision, recall, Matthews Correlation Coefficient).
     """
-    if (TP_count + FP_count) == 0:
+    if (true_positives + false_positives) == 0:
         precision = np.nan
     else:
-        precision = TP_count / (TP_count + FP_count)
-    if (TP_count + FN_count) == 0:
+        precision = true_positives / (true_positives + false_positives)
+    if (true_positives + false_negatives) == 0:
         recall = np.nan
     else:
-        recall = TP_count / (TP_count + FN_count)
-    mcc = _calc_mcc(TP_count, TN_count, FP_count, FN_count)
+        recall = true_positives / (true_positives + false_negatives)
+    mcc = _calc_mcc(true_positives, true_negatives, false_positives, false_negatives)
     return precision, recall, mcc