feat: add `dimensionless_constants_only` with new backend

docs/examples.md

@@ -520,6 +520,8 @@ a constant `"2.6353e-22[m s⁻²]"`.
 
 Note that this expression has a large dynamic range so may be difficult to find. Consider searching with a larger `niterations` if needed.
 
+Note that you can also search for exclusively dimensionless constants by settings
+`dimensionless_constants_only` to `true`.
 
 ## 11. Additional features
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pysr"
-version = "0.18.3"
+version = "0.18.4"
 authors = [
     {name = "Miles Cranmer", email = "[email protected]"},
 ]

pysr/juliapkg.json

@@ -3,7 +3,7 @@
     "packages": {
         "SymbolicRegression": {
             "uuid": "8254be44-1295-4e6a-a16d-46603ac705cb",
-            "version": "=0.24.3"
+            "version": "=0.24.4"
         },
         "Serialization": {
             "uuid": "9e88b42a-f829-5b0c-bbe9-9e923198166b",

pysr/param_groupings.yml

@@ -14,6 +14,7 @@
     - loss_function
     - model_selection
     - dimensional_constraint_penalty
+    - dimensionless_constants_only
   - Working with Complexities:
     - parsimony
     - constraints

pysr/sr.py

@@ -328,6 +328,9 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
     dimensional_constraint_penalty : float
         Additive penalty for if dimensional analysis of an expression fails.
         By default, this is `1000.0`.
+    dimensionless_constants_only : bool
+        Whether to only search for dimensionless constants, if using units.
+        Default is `False`.
     use_frequency : bool
         Whether to measure the frequency of complexities, and use that
         instead of parsimony to explore equation space. Will naturally
@@ -688,6 +691,7 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
         complexity_of_variables: Union[int, float] = 1,
         parsimony: float = 0.0032,
         dimensional_constraint_penalty: Optional[float] = None,
+        dimensionless_constants_only: bool = False,
         use_frequency: bool = True,
         use_frequency_in_tournament: bool = True,
         adaptive_parsimony_scaling: float = 20.0,
@@ -783,6 +787,7 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
         self.complexity_of_variables = complexity_of_variables
         self.parsimony = parsimony
         self.dimensional_constraint_penalty = dimensional_constraint_penalty
+        self.dimensionless_constants_only = dimensionless_constants_only
         self.use_frequency = use_frequency
         self.use_frequency_in_tournament = use_frequency_in_tournament
         self.adaptive_parsimony_scaling = adaptive_parsimony_scaling
@@ -1654,6 +1659,7 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
             # These have the same name:
             parsimony=self.parsimony,
             dimensional_constraint_penalty=self.dimensional_constraint_penalty,
+            dimensionless_constants_only=self.dimensionless_constants_only,
             alpha=self.alpha,
             maxdepth=maxdepth,
             fast_cycle=self.fast_cycle,