From 75b441491024359402f17d593db55a10438790c7 Mon Sep 17 00:00:00 2001
From: rileyh <rileyh@umn.edu>
Date: Wed, 11 Dec 2024 15:33:16 -0600
Subject: [PATCH] [#179] Unify variable and argument names

- tp, tn, fp, fn are easy to type but look a little too similar to be
  easily readable.
- true_positives, true_negatives, false_positives, false_negatives are
  really explicit but difficult to type.
---
 hlink/linking/core/model_metrics.py    | 36 +++++++++++++++++---------
 hlink/tests/core/model_metrics_test.py | 22 ++++++++--------
 2 files changed, 35 insertions(+), 23 deletions(-)

diff --git a/hlink/linking/core/model_metrics.py b/hlink/linking/core/model_metrics.py
index 18af2dc..46533bb 100644
--- a/hlink/linking/core/model_metrics.py
+++ b/hlink/linking/core/model_metrics.py
@@ -11,34 +11,46 @@ def f_measure(true_pos: int, false_pos: int, false_neg: int) -> float:
     return 2 * true_pos / (2 * true_pos + false_pos + false_neg)
 
 
-def mcc(tp: int, tn: int, fp: int, fn: int) -> float:
+def mcc(true_pos: int, true_neg: int, false_pos: int, false_neg: 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 (true_pos), true negatives (true_neg), false
+    positives (false_pos), and false negatives (false_neg) 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(
+            (true_pos + false_pos)
+            * (true_pos + false_neg)
+            * (true_neg + false_pos)
+            * (true_neg + false_neg)
+        )
+    ) != 0:
+        mcc = ((true_pos * true_neg) - (false_pos * false_neg)) / (
+            math.sqrt(
+                (true_pos + false_pos)
+                * (true_pos + false_neg)
+                * (true_neg + false_pos)
+                * (true_neg + false_neg)
+            )
         )
     else:
         mcc = 0
     return mcc
 
 
-def precision(tp: int, fp: int) -> float:
-    if (tp + fp) == 0:
+def precision(true_pos: int, false_pos: int) -> float:
+    if (true_pos + false_pos) == 0:
         precision = np.nan
     else:
-        precision = tp / (tp + fp)
+        precision = true_pos / (true_pos + false_pos)
 
     return precision
 
 
-def recall(tp: int, fn: int) -> float:
-    if (tp + fn) == 0:
+def recall(true_pos: int, false_neg: int) -> float:
+    if (true_pos + false_neg) == 0:
         recall = np.nan
     else:
-        recall = tp / (tp + fn)
+        recall = true_pos / (true_pos + false_neg)
 
     return recall
diff --git a/hlink/tests/core/model_metrics_test.py b/hlink/tests/core/model_metrics_test.py
index 4fc56a3..2cb1d33 100644
--- a/hlink/tests/core/model_metrics_test.py
+++ b/hlink/tests/core/model_metrics_test.py
@@ -53,20 +53,20 @@ def test_f_measure_is_harmonic_mean_of_precision_and_recall(
 
 
 def test_mcc_example() -> None:
-    tp = 3112
-    fp = 205
-    fn = 1134
-    tn = 33259
+    true_pos = 3112
+    false_pos = 205
+    false_neg = 1134
+    true_neg = 33259
 
-    mcc_score = mcc(tp, tn, fp, fn)
+    mcc_score = mcc(true_pos, true_neg, false_pos, false_neg)
     assert abs(mcc_score - 0.8111208) < 0.0001, "expected MCC to be near 0.8111208"
 
 
 def test_precision_example() -> None:
-    tp = 3112
-    fp = 205
+    true_pos = 3112
+    false_pos = 205
 
-    precision_score = precision(tp, fp)
+    precision_score = precision(true_pos, false_pos)
     assert (
         abs(precision_score - 0.9381972) < 0.0001
     ), "expected precision to be near 0.9381972"
@@ -93,10 +93,10 @@ def test_precision_no_positive_predictions() -> None:
 
 
 def test_recall_example() -> None:
-    tp = 3112
-    fn = 1134
+    true_pos = 3112
+    false_neg = 1134
 
-    recall_score = recall(tp, fn)
+    recall_score = recall(true_pos, false_neg)
     assert (
         abs(recall_score - 0.7329251) < 0.0001
     ), "expected recall to be near 0.7329251"