Validate Assignments in Population

src/paretobench/analyze_metrics.py

@@ -532,7 +532,7 @@ def val_counts_to_summary_str(counts):
         n_equal = int(counts.get("=", 0))
         if (n_minus + n_plus + n_equal) == 0:
             return " "
-        return " \multicolumn{1}{c}{%d/%d/%d} " % (n_plus, n_minus, n_equal)
+        return r" \multicolumn{1}{c}{%d/%d/%d} " % (n_plus, n_minus, n_equal)
 
     comparisons = df.map(lambda x: (x[-1] if len(x) > 4 else "")).apply(pd.Series.value_counts).fillna(0)
     comparisons = comparisons.apply(val_counts_to_summary_str)

src/paretobench/containers.py

@@ -33,7 +33,6 @@ class Population(BaseModel):
 
     # Total number of function evaluations performed during optimization after this population was completed
     fevals: int
-
     # Optional lists of names for decision variables, objectives, and constraints
     names_x: Optional[List[str]] = None
     names_f: Optional[List[str]] = None
@@ -47,7 +46,7 @@ class Population(BaseModel):
     constraint_targets: np.ndarray
 
     # Pydantic config
-    model_config = ConfigDict(arbitrary_types_allowed=True)
+    model_config = ConfigDict(arbitrary_types_allowed=True, validate_assignment=True)
 
     @model_validator(mode="before")
     @classmethod

src/paretobench/problem.py

@@ -46,6 +46,7 @@ def __call__(self, x: np.ndarray, check_bounds=True):
             if check_bounds and ((x > self.var_upper_bounds).all() or (x < self.var_lower_bounds).all()):
                 raise InputError("Input lies outside of problem bounds.")
             pop = self._call(x[None, :])
+            pop.x = np.reshape(x, (1, -1))
 
         # If batched input is used
         elif len(x.shape) == 2:
@@ -57,13 +58,13 @@ def __call__(self, x: np.ndarray, check_bounds=True):
             if check_bounds and ((x > self.var_upper_bounds).all() or (x < self.var_lower_bounds).all()):
                 raise InputError("Input lies outside of problem bounds.")
             pop = self._call(x)
+            pop.x = x
 
         # If user provided something not usable
         else:
             raise ValueError(f"Incompatible shape of input array x: {x.shape}")
 
         # Set the decision variables
-        pop.x = x
         return pop
 
     def _call(self, x: np.ndarray):

tests/test_containers.py

@@ -288,6 +288,12 @@ def test_population_invalid_dimensions():
         Population(x=x, f=f, g=g, fevals=5)
 
 
+def test_field_assignment_validation():
+    with pytest.raises(ValueError, match="Expected array with 2 dimensions for field 'x'"):
+        pop = Population(f=np.random.random((256, 2)))
+        pop.x = np.random.random((2))
+
+
 def test_overwrite():
     # Create a randomized Experiment object
     experiment1 = Experiment.from_random(

tests/test_problems.py

@@ -100,3 +100,13 @@ def test_pareto_front(problem_name, npoints=1000):
     # Make sure the right size array is returned and it doesn't give bad values
     assert p.n_objs == f.shape[1]
     assert not np.isnan(f).any()
+
+
+def test_unbatched_problem_evaluation():
+    """
+    Confirms that the population object is correctly formated on unbatched calls to problem
+    """
+    prob = pb.WFG1()
+    x = np.random.random((prob.n))
+    pop = prob(x)
+    assert len(pop.x.shape) == 2