fix: prts version specification until CompML/PRTS#77 is merged and ad…

…d skiptests conditions if not installed

aeon/performance_metrics/anomaly_detection/_binary.py

@@ -55,9 +55,7 @@ def range_precision(
         1920–30. 2018.
         http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
-    _check_soft_dependencies(
-        "prts>=1.0.0.3", obj="range_precision", suppress_import_stdout=True
-    )
+    _check_soft_dependencies("prts", obj="range_precision", suppress_import_stdout=True)
 
     from prts import ts_precision
 
@@ -117,9 +115,7 @@ def range_recall(
         1920–30. 2018.
         http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
-    _check_soft_dependencies(
-        "prts>=1.0.0.3", obj="range_recall", suppress_import_stdout=True
-    )
+    _check_soft_dependencies("prts", obj="range_recall", suppress_import_stdout=True)
 
     from prts import ts_recall
 
@@ -187,9 +183,7 @@ def range_f_score(
         1920–30. 2018.
         http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
-    _check_soft_dependencies(
-        "prts>=1.0.0.3", obj="range_recall", suppress_import_stdout=True
-    )
+    _check_soft_dependencies("prts", obj="range_recall", suppress_import_stdout=True)
 
     from prts import ts_fscore
 

aeon/performance_metrics/anomaly_detection/_continuous.py

@@ -157,7 +157,7 @@ def f_score_at_k_ranges(
         Function used to find the threshold.
     """
     _check_soft_dependencies(
-        "prts>=1.0.0.3", obj="f_score_at_k_ranges", suppress_import_stdout=True
+        "prts", obj="f_score_at_k_ranges", suppress_import_stdout=True
     )
 
     from prts import ts_fscore
@@ -231,7 +231,7 @@ def rp_rr_auc_score(
         http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series.pdf.
     """
     _check_soft_dependencies(
-        "prts>=1.0.0.3", obj="f_score_at_k_ranges", suppress_import_stdout=True
+        "prts", obj="f_score_at_k_ranges", suppress_import_stdout=True
     )
 
     from prts import ts_precision, ts_recall

aeon/performance_metrics/anomaly_detection/tests/test_ad_metrics.py

@@ -18,36 +18,46 @@
     rp_rr_auc_score,
 )
 from aeon.testing.data_generation import make_example_1d_numpy
+from aeon.utils.validation._dependencies import _check_soft_dependencies
 
-pr_metrics = [pr_auc_score, rp_rr_auc_score]
+pr_metrics = [pr_auc_score]
 range_metrics = [
     range_roc_auc_score,
     range_pr_auc_score,
     range_roc_vus_score,
     range_pr_vus_score,
-    range_recall,
-    range_precision,
-    range_f_score,
 ]
 other_metrics = [
     roc_auc_score,
     f_score_at_k_points,
-    f_score_at_k_ranges,
-    rp_rr_auc_score,
 ]
+continuous_metrics = [*pr_metrics, *other_metrics, *range_metrics]
+binary_metrics = []
+
+if _check_soft_dependencies("prts", severity="none"):
+    pr_metrics.append(rp_rr_auc_score)
+    range_metrics.extend(
+        [
+            range_recall,
+            range_precision,
+            range_f_score,
+        ]
+    )
+    other_metrics.extend(
+        [
+            f_score_at_k_ranges,
+            rp_rr_auc_score,
+        ]
+    )
+    continuous_metrics.extend(
+        [
+            rp_rr_auc_score,
+            f_score_at_k_ranges,
+        ]
+    )
+    binary_metrics = [range_recall, range_precision, range_f_score]
+
 metrics = [*pr_metrics, *range_metrics, *other_metrics]
-continuous_metrics = [
-    *pr_metrics,
-    range_roc_auc_score,
-    range_pr_auc_score,
-    range_roc_vus_score,
-    range_pr_vus_score,
-    roc_auc_score,
-    rp_rr_auc_score,
-    f_score_at_k_points,
-    f_score_at_k_ranges,
-]
-binary_metrics = [range_recall, range_precision, range_f_score]
 
 
 @pytest.mark.parametrize("metric", metrics, ids=[m.__name__ for m in metrics])
@@ -140,6 +150,10 @@ def test_edge_cases_pr_metrics(metric):
     assert score <= 0.2, f"{metric.__name__}(y_true, y_inverted)={score} is not <= 0.2"
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_range_based_f1():
     """Test range-based F1 score."""
     y_pred = np.array([0, 1, 1, 0])
@@ -148,6 +162,10 @@ def test_range_based_f1():
     np.testing.assert_almost_equal(result, 0.66666, decimal=4)
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_range_based_precision():
     """Test range-based precision."""
     y_pred = np.array([0, 1, 1, 0])
@@ -156,6 +174,10 @@ def test_range_based_precision():
     assert result == 0.5
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_range_based_recall():
     """Test range-based recall."""
     y_pred = np.array([0, 1, 1, 0])
@@ -164,6 +186,10 @@ def test_range_based_recall():
     assert result == 1
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_rf1_value_error():
     """Test range-based F1 score raises ValueError on binary predictions."""
     y_pred = np.array([0, 0.2, 0.7, 0])
@@ -187,6 +213,10 @@ def test_pr_curve_auc():
 #     np.testing.assert_almost_equal(result, 0.8333, decimal=4)
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_range_based_p_range_based_r_curve_auc():
     """Test range-based precision-recall curve AUC."""
     y_pred = np.array([0, 0.1, 1.0, 0.5, 0.1, 0])
@@ -195,6 +225,10 @@ def test_range_based_p_range_based_r_curve_auc():
     np.testing.assert_almost_equal(result, 0.9792, decimal=4)
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_range_based_p_range_based_r_auc_perfect_hit():
     """Test range-based precision-recall curve AUC with perfect hit."""
     y_pred = np.array([0, 0, 0.5, 0.5, 0, 0])
@@ -203,6 +237,10 @@ def test_range_based_p_range_based_r_auc_perfect_hit():
     np.testing.assert_almost_equal(result, 1.0000, decimal=4)
 
 
+@pytest.mark.skipif(
+    not _check_soft_dependencies("prts", severity="none"),
+    reason="required soft dependency prts not available",
+)
 def test_f_score_at_k_ranges():
     """Test range-based F1 score at k ranges."""
     y_pred = np.array([0.4, 0.1, 1.0, 0.5, 0.1, 0, 0.4, 0.5])