[#179] Factor away _get_aggregate_metrics()

This function is now simple enough that we can just inline it in the one
place where it's called.

hlink/linking/core/model_metrics.py

@@ -4,6 +4,8 @@
 #   https://github.com/ipums/hlink
 import math
 
+import numpy as np
+
 
 def mcc(tp: int, tn: int, fp: int, fn: int) -> float:
     """

hlink/linking/model_exploration/link_step_train_test_models.py

@@ -658,14 +658,14 @@ def _capture_prediction_results(
             fn_count,
             tn_count,
         ) = _get_confusion_matrix(predictions, dep_var)
-        test_precision, test_recall, test_mcc = _get_aggregate_metrics(
-            tp_count, fp_count, fn_count, tn_count
-        )
+        precision = metrics_core.precision(tp_count, fp_count)
+        recall = metrics_core.recall(tp_count, fn_count)
+        mcc = metrics_core.mcc(tp_count, tn_count, fp_count, fn_count)
 
         result = ThresholdTestResult(
-            precision=test_precision,
-            recall=test_recall,
-            mcc=test_mcc,
+            precision=precision,
+            recall=recall,
+            mcc=mcc,
             pr_auc=pr_auc,
             model_id=model,
             alpha_threshold=alpha_threshold,
@@ -764,24 +764,6 @@ def _get_confusion_matrix(
     )
 
 
-def _get_aggregate_metrics(
-    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
-    true negatives for a model run, compute several metrics to evaluate the
-    model's quality.
-
-    Return a tuple of (precision, recall, Matthews Correlation Coefficient).
-    """
-    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
-    )
-    return precision, recall, mcc
-
-
 # The outer list  entries hold results from each outer fold, the inner list has a ThresholdTestResult per threshold
 # matrix entry. We need to get data for each threshold entry together. Basically we need to invert the data.
 def _combine_by_threshold_matrix_entry(

hlink/tests/core/model_metrics_test.py

@@ -0,0 +1,36 @@
+# This file is part of the ISRDI's hlink.
+# For copyright and licensing information, see the NOTICE and LICENSE files
+# in this project's top-level directory, and also on-line at:
+#   https://github.com/ipums/hlink
+
+from hlink.linking.core.model_metrics import mcc, precision, recall
+
+
+def test_mcc_example() -> None:
+    tp = 3112
+    fp = 205
+    fn = 1134
+    tn = 33259
+
+    mcc_score = mcc(tp, tn, fp, fn)
+    assert abs(mcc_score - 0.8111208) < 0.0001, "expected MCC to be near 0.8111208"
+
+
+def test_precision_example() -> None:
+    tp = 3112
+    fp = 205
+
+    precision_score = precision(tp, fp)
+    assert (
+        abs(precision_score - 0.9381972) < 0.0001
+    ), "expected precision to be near 0.9381972"
+
+
+def test_recall_example() -> None:
+    tp = 3112
+    fn = 1134
+
+    recall_score = recall(tp, fn)
+    assert (
+        abs(recall_score - 0.7329251) < 0.0001
+    ), "expected recall to be near 0.7329251"

hlink/tests/model_exploration_test.py

@@ -13,7 +13,6 @@
     _custom_param_grid_builder,
     _get_model_parameters,
     _get_confusion_matrix,
-    _get_aggregate_metrics,
 )
 
 
@@ -1016,20 +1015,3 @@ def test_get_confusion_matrix(spark: SparkSession) -> None:
     assert false_positives == 3
     assert false_negatives == 2
     assert true_negatives == 1
-
-
-def test_get_aggregate_metrics() -> None:
-    true_positives = 3112
-    false_positives = 205
-    false_negatives = 1134
-    true_negatives = 33259
-
-    precision, recall, mcc = _get_aggregate_metrics(
-        true_positives, false_positives, false_negatives, true_negatives
-    )
-
-    assert (
-        abs(precision - 0.9381972) < 0.0001
-    ), "expected precision to be near 0.9381972"
-    assert abs(recall - 0.7329251) < 0.0001, "expected recall to be near 0.7329251"
-    assert abs(mcc - 0.8111208) < 0.0001, "expected MCC to be near 0.8111208"