Make hypothesis respect floating point precision

tests/hypothesis_strategies/acquisition.py

@@ -17,20 +17,18 @@
     qUpperConfidenceBound,
 )
 
+from ..hypothesis_strategies.basic import finite_floats
+
 # These acqfs are ordered roughly according to increasing complexity
 acquisition_functions = st.one_of(
     st.builds(ExpectedImprovement),
     st.builds(ProbabilityOfImprovement),
-    st.builds(
-        UpperConfidenceBound, beta=st.floats(min_value=0.0, allow_infinity=False)
-    ),
+    st.builds(UpperConfidenceBound, beta=finite_floats(min_value=0.0)),
     st.builds(PosteriorMean),
     st.builds(LogExpectedImprovement),
     st.builds(qExpectedImprovement),
     st.builds(qProbabilityOfImprovement),
-    st.builds(
-        qUpperConfidenceBound, beta=st.floats(min_value=0.0, allow_infinity=False)
-    ),
+    st.builds(qUpperConfidenceBound, beta=finite_floats(min_value=0.0)),
     st.builds(qSimpleRegret),
     st.builds(qLogExpectedImprovement),
     st.builds(qNoisyExpectedImprovement),

tests/hypothesis_strategies/alternative_creation/test_searchspace.py

@@ -115,7 +115,7 @@ def test_searchspace_creation_from_dataframe(df, parameters, expected):
 )
 def test_discrete_space_creation_from_simplex_inner(parameters, boundary_only):
     """Candidates from a simplex space satisfy the simplex constraint."""
-    tolerance = 1e-6
+    tolerance = 1e-2
     max_possible = sum(max(p.values) for p in parameters)
     min_possible = sum(min(p.values) for p in parameters)
 

tests/hypothesis_strategies/basic.py

@@ -3,6 +3,14 @@
 from functools import partial
 
 import hypothesis.strategies as st
+import numpy as np
 
-finite_floats = partial(st.floats, allow_infinity=False, allow_nan=False)
+from baybe.utils.numerical import DTypeFloatNumpy
+
+finite_floats = partial(
+    st.floats,
+    allow_infinity=False,
+    allow_nan=False,
+    width=32 if DTypeFloatNumpy == np.float32 else 64,
+)
 """A strategy producing finite (i.e., non-nan and non-infinite) floats."""

tests/hypothesis_strategies/constraints.py

@@ -27,6 +27,8 @@
 from baybe.parameters.base import DiscreteParameter
 from baybe.parameters.numerical import NumericalDiscreteParameter
 
+from ..hypothesis_strategies.basic import finite_floats
+
 
 def sub_selection_conditions(superset: Optional[list[Any]] = None):
     """Generate :class:`baybe.constraints.conditions.SubSelectionCondition`."""
@@ -39,9 +41,7 @@ def sub_selection_conditions(superset: Optional[list[Any]] = None):
 
 def threshold_conditions():
     """Generate :class:`baybe.constraints.conditions.ThresholdCondition`."""
-    return st.builds(
-        ThresholdCondition, threshold=st.floats(allow_infinity=False, allow_nan=False)
-    )
+    return st.builds(ThresholdCondition, threshold=finite_floats())
 
 
 @st.composite
@@ -232,12 +232,12 @@ def _continuous_linear_constraints(
 
     coefficients = draw(
         st.lists(
-            st.floats(allow_nan=False),
+            finite_floats(),
             min_size=len(parameter_names),
             max_size=len(parameter_names),
         )
     )
-    rhs = draw(st.floats(allow_nan=False))
+    rhs = draw(finite_floats())
     return constraint_type(parameter_names, coefficients, rhs)
 
 

tests/hypothesis_strategies/dataframes.py

@@ -3,11 +3,13 @@
 from hypothesis import strategies as st
 from hypothesis.extra.pandas import column, data_frames, indexes
 
+from ..hypothesis_strategies.basic import finite_floats
+
 
 @st.composite
 def random_dataframes(draw: st.DrawFn):
     """Generate pandas dataframes of random shape and content."""
-    index_elements = st.one_of(st.text(), st.integers(), st.floats())
+    index_elements = st.one_of(st.text(), st.integers(), finite_floats())
     cols = st.builds(
         column, name=index_elements, dtype=st.sampled_from([int, float, str])
     )

tests/hypothesis_strategies/objectives.py

@@ -6,6 +6,7 @@
 from baybe.objectives.enum import Scalarizer
 from baybe.objectives.single import SingleTargetObjective
 
+from ..hypothesis_strategies.basic import finite_floats
 from ..hypothesis_strategies.targets import numerical_targets
 from ..hypothesis_strategies.utils import intervals as st_intervals
 
@@ -24,7 +25,7 @@ def desirability_objectives(draw: st.DrawFn):
     )
     weights = draw(
         st.lists(
-            st.floats(min_value=0.0, exclude_min=True),
+            finite_floats(min_value=0.0, exclude_min=True),
             min_size=len(targets),
             max_size=len(targets),
         )

tests/hypothesis_strategies/parameters.py

@@ -19,11 +19,12 @@
 from baybe.parameters.substance import SubstanceEncoding, SubstanceParameter
 from baybe.utils.numerical import DTypeFloatNumpy
 
+from ..hypothesis_strategies.basic import finite_floats
 from .utils import intervals
 
 decorrelations = st.one_of(
     st.booleans(),
-    st.floats(min_value=0.0, max_value=1.0, exclude_min=True, exclude_max=True),
+    finite_floats(min_value=0.0, max_value=1.0, exclude_min=True, exclude_max=True),
 )
 """A strategy that generates decorrelation settings."""
 
@@ -68,7 +69,7 @@ def custom_descriptors(draw: st.DrawFn):
     index = st.lists(st.text(min_size=1), min_size=2, max_size=10, unique=True)
     cols = columns(
         names_or_number=10,
-        elements=st.floats(allow_nan=False, allow_infinity=False),
+        elements=finite_floats(),
         unique=True,
         dtype=DTypeFloatNumpy,
     )
@@ -85,9 +86,7 @@ def numerical_discrete_parameters(
     name = draw(parameter_names)
     values = draw(
         st.lists(
-            st.floats(
-                allow_infinity=False,
-                allow_nan=False,
+            finite_floats(
                 min_value=min_value,
                 max_value=max_value,
             ),
@@ -100,10 +99,9 @@ def numerical_discrete_parameters(
         tolerance = 0.0
     else:
         tolerance = draw(
-            st.floats(
+            finite_floats(
                 min_value=0.0,
                 max_value=max_tolerance,
-                allow_nan=False,
                 exclude_max=True,
             )
         )