Expression fitting

.github/copilot-instructions.md

@@ -17,7 +17,6 @@ Bingo is an open-source Python package developed by NASA for symbolic regression
 - `sympy`: Symbolic mathematics
 - `scikit-learn>=1.1`: Machine learning utilities
 - `dill>=0.2.9`: Serialization
-- `smcpy>=0.1.4`: Statistical methods
 - `pybind11`: Python-C++ bindings
 
 ### Build System
@@ -34,7 +33,6 @@ bingo/
 │   ├── evaluation/                 # Fitness evaluation
 │   ├── evolutionary_algorithms/    # EA implementations
 │   ├── evolutionary_optimizers/    # Optimization strategies (Island, Archipelago)
-│   ├── local_optimizers/           # Local optimization methods
 │   ├── selection/                  # Selection operators (Tournament, etc.)
 │   ├── stats/                      # Statistics and tracking
 │   ├── expressions/                # Expression representations and variation
@@ -74,7 +72,7 @@ bingo/
 - **Variation strategies**: 
   - `VarOr`: Mutation OR crossover OR replication
   - `VarAnd`: Mutation AND crossover
-- **Fitness evaluation**: Vector-based with aggregation metrics (MAE, RMSE, MSE, etc.)
+- **Fitness evaluation**: Fitness functions return scalar lower-is-better values
 
 ### Code Organization Principles
 - Each module should have a clear, single responsibility
@@ -216,8 +214,8 @@ pytest tests
 6. Add usage examples if applicable
 
 ### Adding a New Fitness Function
-1. Create class inheriting from `FitnessFunction` or `VectorBasedFunction`
-2. Implement `evaluate_fitness_vector(self, individual)` method
+1. Create a class inheriting from `FitnessFunction`
+2. Implement `__call__(self, individual)` to return a scalar lower-is-better value
 3. Add docstring explaining the metric
 4. Add tests in `tests/unit/evaluation/`
 

.github/workflows/pypi.yml

@@ -104,6 +104,26 @@ jobs:
       - name: Clean-install and import source distribution
         run: python -m pip install --upgrade pip && python -m pip install dist/*.tar.gz && python -c "import bingo"
 
+  smoke-test-evidence-extra:
+    name: Smoke test Evidence extra
+    needs: build
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          name: distributions
+          path: dist
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.13"
+      - name: Clean-install and import Evidence extra
+        shell: bash
+        run: |
+          python -m pip install --upgrade pip
+          wheel=$(printf '%s\n' dist/*cp313-*manylinux*.whl)
+          python -m pip install --only-binary=:all: "${wheel}[evidence]"
+          python -c "import bingo.symbolic_regression; import smcpy"
+
   installed-tests:
     name: Installed artifact tests (Python ${{ matrix.python-version }})
     needs: build
@@ -114,16 +134,18 @@ jobs:
         include:
           - python-version: "3.13"
             wheel-tag: cp313
+            dependencies: ".[MPI,ONNX,TESTS,evidence]"
           - python-version: "3.14"
             wheel-tag: cp314
+            dependencies: ".[MPI,ONNX,TESTS,evidence]"
     steps:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
       - uses: ./.github/actions/setup-bingo
         with:
           python-version: ${{ matrix.python-version }}
-          dependencies: ".[MPI,ONNX,TESTS]"
+          dependencies: ${{ matrix.dependencies }}
       - uses: actions/download-artifact@v4
         with:
           name: distributions
@@ -138,7 +160,7 @@ jobs:
 
   release-gate:
     name: Verify release tag and artifact version
-    needs: [metadata, smoke-test-wheel, smoke-test-sdist, installed-tests]
+    needs: [metadata, smoke-test-wheel, smoke-test-sdist, smoke-test-evidence-extra, installed-tests]
     if: startsWith(github.ref, 'refs/tags/')
     runs-on: ubuntu-latest
     outputs:

README.md

@@ -10,7 +10,7 @@ Bingo is an open source package for performing symbolic regression, though it
 can be used as a general purpose evolutionary optimization package.  
 
 ## Key Features
-*   Integrated local optimization strategies
+*   Expression-specific symbolic-regression fitting
 *   Parallel island evolution strategy implemented with mpi4py
 *   Coevolution of fitness predictors
 

bingo/chromosomes/chromosome.py

@@ -103,41 +103,3 @@ def distance(self, other):
             Distance from self to other
         """
         raise NotImplementedError
-
-    def needs_local_optimization(self):
-        """Does the `Chromosome` need local optimization
-
-        Returns
-        -------
-        bool
-            Whether `Chromosome` needs optimization
-        """
-        raise NotImplementedError(
-            "This Chromosome cannot be used in local "
-            "optimization until its local optimization "
-            "interface has been implemented"
-        )
-
-    def get_number_local_optimization_params(self):
-        """Get number of parameters in local optimization
-
-        Returns
-        -------
-        int
-            Number of parameters to be optimized
-        """
-        return 0
-
-    def set_local_optimization_params(self, params):
-        """Set local optimization parameters
-
-        Parameters
-        ----------
-        params : list-like of numeric
-            Values to set the parameters to
-        """
-        raise NotImplementedError(
-            "This Chromosome cannot be used in local "
-            "optimization until its local optimization "
-            "interface has been implemented"
-        )

bingo/chromosomes/multiple_floats.py

@@ -14,60 +14,7 @@ class MultipleFloatChromosome(MultipleValueChromosome):
     ----------
     values : list of floats
         The genetic information stored in an individual chromosome.
-    needs_opt_list : list of ints
-        (optional) The indices of the `individual_list` in a
-        `chromosomes` object that are subject local optimization.
-        This list may be empty
     """
-    def __init__(self, values, needs_opt_list=None):
-        super().__init__(values)
-        if needs_opt_list is None:
-            needs_opt_list = []
-        self._needs_opt_list = needs_opt_list
-
-    def needs_local_optimization(self):
-        """Does the individual need local optimization
-
-        Returns
-        -------
-        bool
-            Individual needs optimization
-        """
-        if not self._needs_opt_list:
-            return False
-        return True
-
-    def get_number_local_optimization_params(self):
-        """Get number of parameters in local optimization
-
-        Returns
-        -------
-        int
-            number of parameters to be optimized
-        """
-        return len(self._needs_opt_list)
-
-    def set_local_optimization_params(self, params):
-        """Set local optimization parameters
-
-        Parameters
-        ----------
-        params : list-like of numeric
-                 Values to set the parameters
-        """
-        for param, index in zip(params, self._needs_opt_list):
-            self.values[index] = param
-
-    def get_local_optimization_params(self):
-        """Gets local optimization_params
-
-        Returns
-        -------
-        list
-        """
-        return [self.values[i] for i in self._needs_opt_list]
-
-
 class MultipleFloatChromosomeGenerator(MultipleValueChromosomeGenerator):
     """Generation of a population of Multi-Value chromosomes
 
@@ -77,18 +24,7 @@ class MultipleFloatChromosomeGenerator(MultipleValueChromosomeGenerator):
         A function that returns a randomly generated float value.
     values_per_chromosome : int
         The number of values that each chromosome will hold
-    needs_opt_list : list of ints
-        The indices of the `individual_list` in a  `chromosomes` object
-        that are subject local optimization. This list may be empty
     """
-    def __init__(self, random_value_function, values_per_chromosome,
-                 needs_opt_list=None):
-        super().__init__(random_value_function, values_per_chromosome)
-        if needs_opt_list is None:
-            needs_opt_list = []
-        self._check_opt_list_contains_feasible_values(needs_opt_list)
-        self._needs_opt_list = self._remove_duplicates(needs_opt_list)
-
     def __call__(self):
         """Generation of a population of size `population_size`
         of Multi-Value chromosomes with lists that contain
@@ -99,21 +35,7 @@ def __call__(self):
         list of chromosomes :
             The chromosomes which their values are generated by
             `random_value_function` with the optimization list
-            `needs_opt_list`.
+            generated by `random_value_function`.
         """
         random_list = self._generate_list(self._values_per_chromosome)
-        return MultipleFloatChromosome(random_list, self._needs_opt_list)
-
-    def _check_opt_list_contains_feasible_values(self, list_of_indices):
-        if not all(isinstance(x, int) for x in list_of_indices):
-            raise ValueError("The list of optimization indices must be \
-                              unsigned integers.")
-        if list_of_indices and (min(list_of_indices) < 0 or
-               max(list_of_indices) >= self._values_per_chromosome):
-            raise ValueError("The list of optimization indices must be within \
-                              the length of the list of values.")
-
-    @staticmethod
-    def _remove_duplicates(list_of_ints):
-        set_of_ints = set(list_of_ints)
-        return sorted(list(set_of_ints))
+        return MultipleFloatChromosome(random_list)

bingo/chromosomes/multiple_values.py

@@ -55,16 +55,6 @@ def distance(self, other):
         dist = sum(v1 != v2 for v1, v2 in zip(self.values, other.values))
         return dist
 
-    def get_number_local_optimization_params(self):
-        raise NotImplementedError
-
-    def needs_local_optimization(self):
-        raise NotImplementedError
-
-    def set_local_optimization_params(self, params):
-        raise NotImplementedError
-
-
 class MultipleValueChromosomeGenerator(Generator):
     """Generation of a population of Multi-Value chromosomes