Revert method names in .mutators (#7)

src/mlrose_ky/algorithms/mutators/_mutator_base.py

@@ -22,21 +22,21 @@ class _MutatorBase(ABC):
 
     Attributes
     ----------
-    optimization_problem : Any
+    _opt_prob : Any
         The optimization problem instance associated with the mutation operations.
-    chromosome_length : int
+    _length : int
         The length of the chromosome in the genetic algorithm, derived from the optimization problem.
 
     Parameters
     ----------
-    optimization_problem : Any
+    opt_prob : Any
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
+    def __init__(self, opt_prob: Any) -> None:
         super().__init__()
-        self.optimization_problem = optimization_problem
-        self.chromosome_length = optimization_problem.length
+        self._opt_prob: Any = opt_prob
+        self._length: int = opt_prob.length
 
     @abstractmethod
     def mutate(self, child: np.ndarray, mutation_probability: float) -> Any:

src/mlrose_ky/algorithms/mutators/discrete_mutator.py

@@ -19,20 +19,20 @@ class DiscreteMutator(_MutatorBase):
 
     Attributes
     ----------
-    optimization_problem : Any
+    _opt_prob : Any
         The optimization problem instance associated with the mutation operations.
-    max_gene_value : int
+    _max_val : int
         The maximum allowable value for any gene in the chromosome.
 
     Parameters
     ----------
-    optimization_problem : Any
+    opt_prob : Any
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
-        super().__init__(optimization_problem)
-        self.max_gene_value = optimization_problem.max_val
+    def __init__(self, opt_prob: Any) -> None:
+        super().__init__(opt_prob)
+        self._max_val: int = opt_prob.max_val
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         """
@@ -53,14 +53,14 @@ def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         np.ndarray
             The mutated chromosome.
         """
-        random_thresholds = np.random.uniform(size=self.chromosome_length)
+        random_thresholds = np.random.uniform(size=self._length)
         mutation_indices = np.where(random_thresholds < mutation_probability)[0]
 
-        if self.max_gene_value == 2:
+        if self._max_val == 2:
             child[mutation_indices] = 1 - child[mutation_indices]
         else:
             for index in mutation_indices:
-                possible_values = list(range(self.max_gene_value))
+                possible_values = list(range(self._max_val))
                 possible_values.remove(child[index])
                 child[index] = np.random.choice(possible_values)
 

src/mlrose_ky/algorithms/mutators/gene_swap_mutator.py

@@ -19,17 +19,17 @@ class SwapMutator(_MutatorBase):
 
     Attributes
     ----------
-    optimization_problem : Any
+    _opt_prob : Any
         The optimization problem instance associated with the mutation operations.
 
     Parameters
     ----------
-    optimization_problem : OptimizationProblem
+    opt_prob : OptimizationProblem
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
-        super().__init__(optimization_problem)
+    def __init__(self, opt_prob: Any) -> None:
+        super().__init__(opt_prob)
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         """

src/mlrose_ky/algorithms/mutators/single_gene_mutator.py

@@ -18,20 +18,20 @@ class ChangeOneMutator(_MutatorBase):
 
     Attributes
     ----------
-    optimization_problem : Any
+    _opt_prob : Any
         The optimization problem instance associated with the mutation operations.
-    max_gene_value : int
+    _max_val : int
         The maximum allowable value for any gene in the chromosome.
 
     Parameters
     ----------
-    optimization_problem : Any
+    opt_prob : Any
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
-        super().__init__(optimization_problem)
-        self.max_gene_value = optimization_problem.max_val
+    def __init__(self, opt_prob: Any) -> None:
+        super().__init__(opt_prob)
+        self._max_val: int = opt_prob.max_val
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         """
@@ -61,6 +61,6 @@ def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
 
         if np.random.rand() < mutation_probability:
             mutation_index = np.random.randint(len(child))
-            child[mutation_index] = np.random.randint(self.max_gene_value)
+            child[mutation_index] = np.random.randint(self._max_val)
 
         return child

src/mlrose_ky/algorithms/mutators/single_shift_mutator.py

@@ -19,20 +19,20 @@ class ShiftOneMutator(_MutatorBase):
 
     Attributes
     ----------
-    optimization_problem : Any
+    _opt_prob : Any
         The optimization problem instance associated with the mutation operations.
-    max_gene_value : int
+    _max_val : int
         The maximum allowable value for any gene in the chromosome, used to enforce wrap-around.
 
     Parameters
     ----------
-    optimization_problem : Any
+    opt_prob : Any
         An instance of an optimization problem that the mutator will operate on.
     """
 
-    def __init__(self, optimization_problem: Any) -> None:
-        super().__init__(optimization_problem)
-        self.max_gene_value = optimization_problem.max_val
+    def __init__(self, opt_prob: Any) -> None:
+        super().__init__(opt_prob)
+        self._max_val: int = opt_prob.max_val
 
     def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         """
@@ -55,7 +55,7 @@ def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         if np.random.rand() < mutation_probability:
             mutation_index = np.random.randint(len(child))
             shift_direction = 1 if np.random.randint(2) == 0 else -1
-            new_value = (child[mutation_index] + shift_direction) % self.max_gene_value
+            new_value = (child[mutation_index] + shift_direction) % self._max_val
             child[mutation_index] = new_value
 
         return child