MAINT Parameter Validation for metrics.precision_score (#25708)

ZeeshanLone · jeremiedbb · web-flow · commit 34fd458ef63d · 2023-02-27T11:30:37.000Z
Co-authored-by: jeremie du boisberranger &lt;jeremiedbb@yahoo.fr&gt;
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -1904,6 +1904,23 @@ class after being classified as negative. This is the case when the
     return positive_likelihood_ratio, negative_likelihood_ratio
 
 
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0, 1}),
+            StrOptions({"warn"}),
+        ],
+    }
+)
 def precision_score(
     y_true,
     y_pred,
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -137,6 +137,7 @@ def _check_function_param_validation(
     "sklearn.metrics.pairwise.additive_chi2_kernel",
     "sklearn.metrics.precision_recall_curve",
     "sklearn.metrics.precision_recall_fscore_support",
+    "sklearn.metrics.precision_score",
     "sklearn.metrics.r2_score",
     "sklearn.metrics.roc_curve",
     "sklearn.metrics.zero_one_loss",
