[#179] Create precision() and recall() functions in core.model_metrics

_get_aggregate_metrics() now calls these core library functions.

hlink/linking/core/model_metrics.py

@@ -18,3 +18,21 @@ def mcc(tp: int, tn: int, fp: int, fn: int) -> float:
     else:
         mcc = 0
     return mcc
+
+
+def precision(tp: int, fp: int) -> float:
+    if (tp + fp) == 0:
+        precision = np.nan
+    else:
+        precision = tp / (tp + fp)
+
+    return precision
+
+
+def recall(tp: int, fn: int) -> float:
+    if (tp + fn) == 0:
+        recall = np.nan
+    else:
+        recall = tp / (tp + fn)
+
+    return recall

hlink/linking/model_exploration/link_step_train_test_models.py

@@ -774,14 +774,8 @@ def _get_aggregate_metrics(
 
     Return a tuple of (precision, recall, Matthews Correlation Coefficient).
     """
-    if (true_positives + false_positives) == 0:
-        precision = np.nan
-    else:
-        precision = true_positives / (true_positives + false_positives)
-    if (true_positives + false_negatives) == 0:
-        recall = np.nan
-    else:
-        recall = true_positives / (true_positives + false_negatives)
+    precision = metrics_core.precision(true_positives, false_positives)
+    recall = metrics_core.recall(true_positives, false_negatives)
     mcc = metrics_core.mcc(
         true_positives, true_negatives, false_positives, false_negatives
     )