Feat/revert all method names for backwards compatibility

src/mlrose_ky/__init__.py

@@ -25,26 +25,15 @@
 from .algorithms.decay import GeomDecay, ArithDecay, ExpDecay, CustomSchedule
 
 # noinspection PyUnresolvedReferences
-from .algorithms.crossovers import OnePointCrossover, UniformCrossover, TSPCrossover
+from .algorithms.crossovers import OnePointCrossOver, UniformCrossOver, TSPCrossOver
 
 # noinspection PyUnresolvedReferences
 from .algorithms.mutators import ChangeOneMutator, DiscreteMutator, SwapMutator, ShiftOneMutator
 
 # noinspection PyUnresolvedReferences
-from .fitness import (
-    OneMax,
-    FlipFlop,
-    FourPeaks,
-    SixPeaks,
-    ContinuousPeaks,
-    Knapsack,
-    TravellingSales,
-    Queens,
-    MaxKColor,
-    CustomFitness,
-)
+from .fitness import OneMax, FlipFlop, FourPeaks, SixPeaks, ContinuousPeaks, Knapsack, TravellingSales, Queens, MaxKColor, CustomFitness
 
-# noinspection PyUnresolvedReferences
+# noinspection PyUnresolvedReferences,PyProtectedMember
 from .neural import NeuralNetwork, LinearRegression, LogisticRegression, NNClassifier, _nn_core
 
 # noinspection PyUnresolvedReferences

src/mlrose_ky/algorithms/__init__.py

@@ -3,12 +3,15 @@
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
 
-from .crossovers import UniformCrossover, TSPCrossover, OnePointCrossover
-from .decay import ArithDecay, CustomSchedule, ExpDecay, GeomDecay
 from .ga import genetic_alg
 from .gd import gradient_descent
 from .hc import hill_climb
 from .mimic import mimic
-from .mutators import ChangeOneMutator, DiscreteMutator, ShiftOneMutator, SwapMutator
 from .rhc import random_hill_climb
 from .sa import simulated_annealing
+
+from .crossovers import UniformCrossOver, TSPCrossOver, OnePointCrossOver
+
+from .decay import ArithDecay, CustomSchedule, ExpDecay, GeomDecay
+
+from .mutators import ChangeOneMutator, DiscreteMutator, ShiftOneMutator, SwapMutator

src/mlrose_ky/algorithms/crossovers/__init__.py

@@ -3,6 +3,6 @@
 # Author: Genevieve Hayes
 # License: BSD 3-clause
 
-from .one_point_crossover import OnePointCrossover
-from .tsp_crossover import TSPCrossover
-from .uniform_crossover import UniformCrossover
+from .one_point_crossover import OnePointCrossOver
+from .tsp_crossover import TSPCrossOver
+from .uniform_crossover import UniformCrossOver

src/mlrose_ky/algorithms/crossovers/_crossover_base.py

@@ -8,10 +8,12 @@
 # License: BSD 3-clause
 
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Sequence
 
+import numpy as np
 
-class _CrossoverBase(ABC):
+
+class _CrossOverBase(ABC):
     """
     Base class for crossover operations in a genetic algorithm.
 
@@ -20,7 +22,7 @@ class _CrossoverBase(ABC):
 
     Parameters
     ----------
-    optimization_problem : Any
+    opt_prob : Any
         An instance of the optimization problem related to the genetic algorithm.
         This problem instance should provide necessary properties like 'length'
         that might be needed for the crossover operation.
@@ -34,21 +36,21 @@ class _CrossoverBase(ABC):
         'length' property.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the CrossoverBase with the given optimization problem.
+        Initialize the _CrossOverBase with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the GA.
         """
         super().__init__()
-        self._opt_prob: Any = optimization_problem
-        self._length: int = optimization_problem.length
+        self._opt_prob: Any = opt_prob
+        self._length: int = opt_prob.length
 
     @abstractmethod
-    def mate(self, parent1: Any, parent2: Any) -> Any:
+    def mate(self, p1: Sequence[int | float], p2: Sequence[int | float]) -> np.ndarray:
         """
         Perform the crossover (mating) between two parents to produce offspring.
 
@@ -57,14 +59,14 @@ def mate(self, parent1: Any, parent2: Any) -> Any:
 
         Parameters
         ----------
-        parent1 : Any
+        p1 : Sequence[int | float]
             The first parent participating in the crossover.
-        parent2 : Any
+        p2 : Sequence[int | float]
             The second parent participating in the crossover.
 
         Returns
         -------
-        Any
+        np.ndarray
             The offspring resulting from the crossover. The type of this result
             can vary depending on the specific GA implementation.
         """

src/mlrose_ky/algorithms/crossovers/one_point_crossover.py

@@ -11,10 +11,10 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers._crossover_base import _CrossoverBase
+from mlrose_ky.algorithms.crossovers._crossover_base import _CrossOverBase
 
 
-class OnePointCrossover(_CrossoverBase):
+class OnePointCrossOver(_CrossOverBase):
     """
     One-point crossover for genetic algorithms.
 
@@ -23,21 +23,21 @@ class OnePointCrossover(_CrossoverBase):
     is exchanged to create a new offspring.
 
     Inherits from:
-    _CrossoverBase : Abstract base class for crossover operations.
+    _CrossOverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the OnePointCrossover with the given optimization problem.
+        Initialize the OnePointCrossOver with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the genetic algorithm.
         """
-        super().__init__(optimization_problem)
+        super().__init__(opt_prob)
 
-    def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray:
+    def mate(self, p1: Sequence[float], p2: Sequence[float]) -> np.ndarray:
         """
         Perform the one-point crossover between two parent sequences to produce offspring.
 
@@ -46,9 +46,9 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
 
         Parameters
         ----------
-        parent1 : Sequence[float]
+        p1 : Sequence[float]
             The first parent chromosome sequence.
-        parent2 : Sequence[float]
+        p2 : Sequence[float]
             The second parent chromosome sequence.
 
         Returns
@@ -57,4 +57,4 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
             The offspring chromosome resulting from the crossover.
         """
         crossover_point = 1 + np.random.randint(self._length - 1)
-        return np.array([*parent1[:crossover_point], *parent2[crossover_point:]])
+        return np.array([*p1[:crossover_point], *p2[crossover_point:]])

src/mlrose_ky/algorithms/crossovers/tsp_crossover.py

@@ -12,10 +12,10 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers._crossover_base import _CrossoverBase
+from mlrose_ky.algorithms.crossovers._crossover_base import _CrossOverBase
 
 
-class TSPCrossover(_CrossoverBase):
+class TSPCrossOver(_CrossOverBase):
     """
     Crossover operation tailored for the Travelling Salesperson Problem (TSP) in genetic algorithms.
 
@@ -24,21 +24,21 @@ class TSPCrossover(_CrossoverBase):
     logic to combine parental genes.
 
     Inherits from:
-    _CrossoverBase : Abstract base class for crossover operations.
+    _CrossOverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the TSPCrossover with the given optimization problem.
+        Initialize the TSPCrossOver with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the genetic algorithm.
         """
-        super().__init__(optimization_problem)
+        super().__init__(opt_prob)
 
-    def mate(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
+    def mate(self, p1: Sequence[int], p2: Sequence[int]) -> np.ndarray:
         """
         Perform the crossover (mating) between two parent sequences to produce offspring.
 
@@ -47,28 +47,28 @@ def mate(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
 
         Parameters
         ----------
-        parent1 : Sequence[int]
+        p1 : Sequence[int]
             The first parent representing a TSP route.
-        parent2 : Sequence[int]
+        p2 : Sequence[int]
             The second parent representing a TSP route.
 
         Returns
         -------
         np.ndarray
             The offspring representing a new TSP route.
         """
-        return self._mate_fill(parent1, parent2)
+        return self._mate_fill(p1, p2)
 
-    def _mate_fill(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
+    def _mate_fill(self, p1: Sequence[int], p2: Sequence[int]) -> np.ndarray:
         """
         Perform a fill-based crossover using a segment of the first parent and filling
         the rest with non-repeated cities from the second parent.
 
         Parameters
         ----------
-        parent1 : Sequence[int]
+        p1 : Sequence[int]
             The first parent representing a TSP route.
-        parent2 : Sequence[int]
+        p2 : Sequence[int]
             The second parent representing a TSP route.
 
         Returns
@@ -79,15 +79,15 @@ def _mate_fill(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarr
         if self._length > 1:
             n = 1 + np.random.randint(self._length - 1)
             child = np.array([0] * self._length)
-            child[:n] = parent1[:n]
-            unvisited = [city for city in parent2 if city not in parent1[:n]]
+            child[:n] = p1[:n]
+            unvisited = [city for city in p2 if city not in p1[:n]]
             child[n:] = unvisited
         else:
-            child = np.copy(parent1 if np.random.randint(2) == 0 else parent2)
+            child = np.copy(p1 if np.random.randint(2) == 0 else p2)
 
         return child
 
-    def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.ndarray:
+    def _mate_traverse(self, parent_1: Sequence[int], parent_2: Sequence[int]) -> np.ndarray:
         """
         Perform a traversal-based crossover using city adjacency considerations from
         both parents to construct a viable TSP route.
@@ -98,9 +98,9 @@ def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.n
 
         Parameters
         ----------
-        parent1 : Sequence[int]
+        parent_1 : Sequence[int]
             The first parent representing a TSP route.
-        parent2 : Sequence[int]
+        parent_2 : Sequence[int]
             The second parent representing a TSP route.
 
         Returns
@@ -109,13 +109,13 @@ def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.n
             The offspring TSP route.
         """
         if self._length > 1:
-            next_city_parent1 = np.append(parent1[1:], parent1[0])
-            next_city_parent2 = np.append(parent2[1:], parent2[0])
+            next_city_parent1 = np.append(parent_1[1:], parent_1[0])
+            next_city_parent2 = np.append(parent_2[1:], parent_2[0])
 
             visited_cities = [False] * self._length
             offspring_route = np.array([0] * self._length)
 
-            starting_city = np.random.randint(len(parent1))
+            starting_city = np.random.randint(len(parent_1))
             offspring_route[0] = starting_city
             visited_cities[starting_city] = True
 
@@ -137,13 +137,13 @@ def _mate_traverse(self, parent1: Sequence[int], parent2: Sequence[int]) -> np.n
                     next_city = next_city2 if fitness1 > fitness2 else next_city1  # Choose the smaller distance
                 else:
                     while True:
-                        next_city = np.random.randint(len(parent1))
+                        next_city = np.random.randint(len(parent_1))
                         if not visited_cities[next_city]:
                             break
 
                 offspring_route[index] = next_city
                 visited_cities[next_city] = True
         else:
-            offspring_route = np.copy(parent1 if np.random.randint(2) == 0 else parent2)
+            offspring_route = np.copy(parent_1 if np.random.randint(2) == 0 else parent_2)
 
         return offspring_route

src/mlrose_ky/algorithms/crossovers/uniform_crossover.py

@@ -12,10 +12,10 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers._crossover_base import _CrossoverBase
+from mlrose_ky.algorithms.crossovers._crossover_base import _CrossOverBase
 
 
-class UniformCrossover(_CrossoverBase):
+class UniformCrossOver(_CrossOverBase):
     """
     Uniform crossover for genetic algorithms.
 
@@ -24,21 +24,21 @@ class UniformCrossover(_CrossoverBase):
     method is often used when no prior knowledge about the problem structure is known.
 
     Inherits from:
-    _CrossoverBase : Abstract base class for crossover operations.
+    _CrossOverBase : Abstract base class for crossover operations.
     """
 
-    def __init__(self, optimization_problem: Any):
+    def __init__(self, opt_prob: Any):
         """
-        Initialize the UniformCrossover with the given optimization problem.
+        Initialize the UniformCrossOver with the given optimization problem.
 
         Parameters
         ----------
-        optimization_problem : Any
+        opt_prob : Any
             An instance of the optimization problem related to the genetic algorithm.
         """
-        super().__init__(optimization_problem)
+        super().__init__(opt_prob)
 
-    def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray:
+    def mate(self, p1: Sequence[float], p2: Sequence[float]) -> np.ndarray:
         """
         Perform the uniform crossover between two parent sequences to produce offspring.
 
@@ -47,9 +47,9 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
 
         Parameters
         ----------
-        parent1 : Sequence[float]
+        p1 : Sequence[float]
             The first parent chromosome sequence.
-        parent2 : Sequence[float]
+        p2 : Sequence[float]
             The second parent chromosome sequence.
 
         Returns
@@ -58,5 +58,5 @@ def mate(self, parent1: Sequence[float], parent2: Sequence[float]) -> np.ndarray
             The offspring chromosome resulting from the crossover.
         """
         gene_selector = np.random.randint(2, size=self._length)
-        stacked_parents = np.vstack((parent1, parent2))
+        stacked_parents = np.vstack((p1, p2))
         return stacked_parents[gene_selector, np.arange(self._length)]