Add hypothesis strategy for Partition

baybe/utils/interval.py

@@ -163,6 +163,8 @@ class Partition:
 
     @thresholds.validator
     def _validate_thresholds(self, _, value):
+        if not np.all(np.isfinite(value)):
+            raise ValueError("Thresholds must be finite.")
         if not all(x < y for x, y in pairwise(value)):
             raise ValueError("Thresholds must be strictly monotonically increasing.")
 

tests/hypothesis_strategies/utils.py

@@ -4,8 +4,9 @@
 
 import hypothesis.extra.numpy as hnp
 import hypothesis.strategies as st
+import numpy as np
 
-from baybe.utils.interval import Interval
+from baybe.utils.interval import Interval, Partition
 
 from .basic import finite_floats
 
@@ -65,3 +66,12 @@ def intervals(
         raise RuntimeError("This line should be unreachable.")
 
     return Interval.create(bounds)
+
+
+partitions = st.builds(
+    Partition,
+    thresholds=st.lists(finite_floats(), min_size=1)
+    .map(sorted)
+    .filter(lambda x: np.all(np.diff(x) > 0)),
+)
+"""Generate :class:`baybe.utils.interval.Partition`."""

tests/validation/utils/test_partition_validation.py

@@ -9,6 +9,8 @@
 @pytest.mark.parametrize(
     ("thresholds", "error", "match"),
     [
+        param([float("nan")], ValueError, "must be finite", id="nan"),
+        param([float("inf")], ValueError, "must be finite", id="inf"),
         param(1, TypeError, None, id="not_iterable"),
         param([], ValueError, None, id="too_short"),
         param([1, 0], ValueError, "monotonically increasing", id="decreasing"),