[SC 16234] Two small fixes to built-in ValidMind tests

tests/unit_tests/data_validation/test_IQROutliersBarPlot.py

@@ -81,3 +81,20 @@ def test_binary_exclusion(self):
         # Check that binary column is not included in figures
         figure_titles = [fig.layout.title.text for fig in results[:-1]]
         self.assertNotIn("binary", figure_titles)
+
+    def test_boolean_dtype_excluded_from_raw_data(self):
+        n_samples = 100
+        df = pd.DataFrame(
+            {
+                "numeric": np.random.randn(n_samples),
+                "flag": np.random.choice([True, False], n_samples),
+            }
+        )
+        vm_dataset = vm.init_dataset(
+            input_id="test_boolean_dataset", dataset=df, __log=False
+        )
+
+        results = IQROutliersBarPlot(vm_dataset)
+        raw_data = results[-1]
+
+        self.assertNotIn("flag", raw_data.outlier_counts_by_feature.index)

tests/unit_tests/model_validation/sklearn/test_WeakspotsDiagnosis.py

@@ -0,0 +1,31 @@
+import unittest
+
+from validmind.tests.model_validation.sklearn.WeakspotsDiagnosis import (
+    _prepare_metrics_and_thresholds,
+)
+
+
+class TestWeakspotsDiagnosisThresholds(unittest.TestCase):
+    def test_partial_thresholds_use_defaults_for_plotting(self):
+        _, plot_thresholds, pass_thresholds = _prepare_metrics_and_thresholds(
+            metrics=None,
+            thresholds={"accuracy": 0.65},
+        )
+
+        self.assertEqual(pass_thresholds, {"Accuracy": 0.65})
+        self.assertEqual(plot_thresholds["Accuracy"], 0.65)
+        self.assertEqual(plot_thresholds["Precision"], 0.5)
+        self.assertEqual(plot_thresholds["Recall"], 0.5)
+        self.assertEqual(plot_thresholds["F1"], 0.7)
+
+    def test_partial_thresholds_subset_for_pass_fail(self):
+        _, _, pass_thresholds = _prepare_metrics_and_thresholds(
+            metrics=None,
+            thresholds={"accuracy": 0.75, "f1": 0.55},
+        )
+
+        self.assertEqual(set(pass_thresholds.keys()), {"Accuracy", "F1"})
+
+
+if __name__ == "__main__":
+    unittest.main()

validmind/tests/data_validation/IQROutliersBarPlot.py

@@ -76,12 +76,14 @@ def IQROutliersBarPlot(
     """
     df = dataset.df
 
+    # Exclude binary/boolean features (IQR is not meaningful and quantile fails on bool)
+    eligible_columns = [
+        col for col in dataset.feature_columns_numeric if len(df[col].unique()) > 2
+    ]
+
     figures = []
 
-    for col in dataset.feature_columns_numeric:
-        # Skip binary features
-        if len(df[col].unique()) <= 2:
-            continue
+    for col in eligible_columns:
 
         outliers = compute_outliers(df[col], threshold)
         if outliers.empty:
@@ -121,8 +123,10 @@ def IQROutliersBarPlot(
         )
         figures.append(fig)
 
-    outliers_by_feature = df[dataset.feature_columns_numeric].apply(
-        lambda col: compute_outliers(col, threshold)
+    outliers_by_feature = (
+        df[eligible_columns].apply(lambda col: compute_outliers(col, threshold))
+        if eligible_columns
+        else df.iloc[:, 0:0]
     )
 
     return (

validmind/tests/model_validation/sklearn/WeakspotsDiagnosis.py

@@ -27,6 +27,36 @@
 }
 
 
+def _normalize_dict_keys(d: Dict) -> Dict:
+    """Normalize metric/threshold keys to title case (e.g. 'f1' -> 'F1')."""
+    return {k.title(): v for k, v in d.items()}
+
+
+def _prepare_metrics_and_thresholds(
+    metrics: Optional[Dict[str, Callable]],
+    thresholds: Optional[Dict[str, float]],
+) -> Tuple[Dict[str, Callable], Dict[str, float], Dict[str, float]]:
+    """
+    Prepare metrics and threshold dicts for plotting and pass/fail checks.
+
+    Custom thresholds may specify only a subset of metrics (e.g. accuracy only).
+    Plotting uses default thresholds for any metric without an explicit value so
+    charts always show a reference line; pass/fail uses only the user-provided
+    thresholds when a custom dict is supplied.
+    """
+    normalized_metrics = _normalize_dict_keys(metrics or DEFAULT_METRICS)
+    default_thresholds = _normalize_dict_keys(DEFAULT_THRESHOLDS)
+
+    if thresholds is not None:
+        pass_thresholds = _normalize_dict_keys(thresholds)
+        plot_thresholds = {**default_thresholds, **pass_thresholds}
+    else:
+        pass_thresholds = default_thresholds
+        plot_thresholds = default_thresholds
+
+    return normalized_metrics, plot_thresholds, pass_thresholds
+
+
 def _compute_metrics(
     results: dict,
     metrics: Dict[str, Callable],
@@ -230,11 +260,9 @@ def WeakspotsDiagnosis(
             "Column(s) provided in features_columns do not exist in the dataset"
         )
 
-    metrics = metrics or DEFAULT_METRICS
-    metrics = {k.title(): v for k, v in metrics.items()}
-
-    thresholds = thresholds or DEFAULT_THRESHOLDS
-    thresholds = {k.title(): v for k, v in thresholds.items()}
+    metrics, plot_thresholds, pass_thresholds = _prepare_metrics_and_thresholds(
+        metrics, thresholds
+    )
 
     results_headers = ["Slice", "Number of Records", "Feature"]
     results_headers.extend(metrics.keys())
@@ -290,14 +318,18 @@ def WeakspotsDiagnosis(
                 results_2=r2,
                 feature_column=feature,
                 metric=metric,
-                threshold=thresholds[metric],
+                threshold=plot_thresholds[metric],
             )
 
             figures.append(fig)
 
         # For simplicity, test has failed if any of the metrics is below the threshold. We will
         # rely on visual assessment for this test for now.
-        if not df[df[list(thresholds.keys())].lt(thresholds).any(axis=1)].empty:
+        pass_columns = [c for c in pass_thresholds if c in metrics]
+        if (
+            pass_columns
+            and not df[df[pass_columns].lt(pass_thresholds).any(axis=1)].empty
+        ):
             passed = False
         results_1 = pd.concat([results_1, pd.DataFrame(r1)])
         results_2 = pd.concat([results_2, pd.DataFrame(r2)])