Revert method, prop, attr names: .algorithms (#7)

src/mlrose_ky/__init__.py

@@ -25,24 +25,13 @@
 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
 from .neural import NeuralNetwork, LinearRegression, LogisticRegression, NNClassifier, _nn_core

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)]

src/mlrose_ky/algorithms/decay/__init__.py

@@ -3,7 +3,7 @@
 # Author: Genevieve Hayes
 # License: BSD 3-clause
 
-from .arithmetic_decay import ArithDecay
-from .custom_decay import CustomSchedule
-from .exponential_decay import ExpDecay
-from .geometric_decay import GeomDecay
+from .arith_decay import ArithDecay
+from .custom_schedule import CustomSchedule
+from .expl_decay import ExpDecay
+from .geom_decay import GeomDecay

src/mlrose_ky/algorithms/decay/arithmetic_decay.py → src/mlrose_ky/algorithms/decay/arith_decay.py

@@ -64,29 +64,29 @@ def __eq__(self, other: object) -> bool:
             return False
         return self.init_temp == other.init_temp and self.decay == other.decay and self.min_temp == other.min_temp
 
-    def evaluate(self, time: int) -> float:
+    def evaluate(self, t: int) -> float:
         """
         Calculate and return the temperature parameter at the given time.
 
         Parameters
         ----------
-        time : int
+        t : int
             The time at which to evaluate the temperature parameter.
 
         Returns
         -------
         float
             The temperature parameter at the given time, respecting the minimum temperature.
         """
-        return max(self.init_temp - (self.decay * time), self.min_temp)
+        return max(self.init_temp - (self.decay * t), self.min_temp)
 
-    def get_info(self, time: int = None, prefix: str = "") -> dict:
+    def get_info__(self, t: int = None, prefix: str = "") -> dict:
         """
         Generate a dictionary containing the decay schedule's settings and optionally its current value.
 
         Parameters
         ----------
-        time : int, optional
+        t : int, optional
             The time at which to evaluate the current temperature value.
             If provided, the current value is included in the returned dictionary.
         prefix : str, optional
@@ -106,7 +106,7 @@ def get_info(self, time: int = None, prefix: str = "") -> dict:
             f"{info_prefix}min_temp": self.min_temp,
         }
 
-        if time is not None:
-            info[f"{info_prefix}current_value"] = self.evaluate(time)
+        if t is not None:
+            info[f"{info_prefix}current_value"] = self.evaluate(t)
 
         return info

src/mlrose_ky/algorithms/decay/exponential_decay.py → src/mlrose_ky/algorithms/decay/expl_decay.py

@@ -6,9 +6,6 @@
 import numpy as np
 
 
-# temp comment
-
-
 class ExpDecay:
     """
     Defines an exponential decay schedule for the temperature parameter T in simulated annealing,

src/mlrose_ky/algorithms/ga.py

@@ -110,7 +110,7 @@ def genetic_alg(
     max_iters: int | float = np.inf,
     curve: bool = False,
     random_state: int = None,
-    state_fitness_callback: Callable[..., Any] = None,
+    state_fitness_callback: Callable = None,
     callback_user_info: Any = None,
     hamming_factor: float = 0.0,
     hamming_decay_factor: float = None,

src/mlrose_ky/algorithms/hc.py

@@ -13,7 +13,7 @@
 @short_name("hc")
 def hill_climb(
     problem: Any,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     restarts: int = 0,
     init_state: np.ndarray = None,
     curve: bool = False,

src/mlrose_ky/algorithms/mimic.py

@@ -17,7 +17,7 @@ def mimic(
     keep_pct: float = 0.2,
     max_attempts: int = 10,
     noise: float = 0.0,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     curve: bool = False,
     random_state: int = None,
     state_fitness_callback: Callable = None,

src/mlrose_ky/algorithms/mutators/__init__.py

@@ -3,7 +3,7 @@
 # Authors: Genevieve Hayes (modified by Andrew Rollings, Kyle Nakamura)
 # License: BSD 3-clause
 
+from .change_one_mutator import ChangeOneMutator
 from .discrete_mutator import DiscreteMutator
-from .gene_swap_mutator import SwapMutator
-from .single_gene_mutator import ChangeOneMutator
-from .single_shift_mutator import ShiftOneMutator
+from .shift_one_mutator import ShiftOneMutator
+from .swap_mutator import SwapMutator

src/mlrose_ky/algorithms/mutators/_mutator_base.py

@@ -40,7 +40,7 @@ def __init__(self, opt_prob: Any):
         self._length: int = opt_prob.length
 
     @abstractmethod
-    def mutate(self, child: np.ndarray, mutation_probability: float) -> Any:
+    def mutate(self, child: np.ndarray, mutation_probability: float) -> np.ndarray:
         """
         Apply mutation operation to a given child chromosome based on a mutation probability.
 

src/mlrose_ky/algorithms/rhc.py

@@ -14,7 +14,7 @@
 def random_hill_climb(
     problem: Any,
     max_attempts: int = 10,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     restarts: int = 0,
     init_state: np.ndarray = None,
     curve: bool = False,

src/mlrose_ky/algorithms/sa.py

@@ -16,7 +16,7 @@ def simulated_annealing(
     problem: Any,
     schedule: Any = GeomDecay(),
     max_attempts: int = 10,
-    max_iters: int = np.inf,
+    max_iters: int | float = np.inf,
     init_state: np.ndarray = None,
     curve: bool = False,
     random_state: int = None,

src/mlrose_ky/decorators/short_name_decorator.py

@@ -29,12 +29,12 @@ def short_name(expr: str) -> Callable:
         A decorator that assigns the provided short name to a function and returns the function.
     """
 
-    def decorator(func: Callable) -> Callable:
+    def short_name_func_applicator(func: Callable) -> Callable:
         """Assign a short name to the given function."""
         func.__short_name__ = expr
         return func
 
-    return decorator
+    return short_name_func_applicator
 
 
 def get_short_name(v: Any) -> str:

src/mlrose_ky/gridsearch/grid_search_mixin.py

@@ -40,7 +40,7 @@ def __init__(self, scorer_method: Callable = None):
         self._params: inspect.Signature = inspect.signature(self._scorer_method)
         self._get_y_argmax: bool = False
 
-    def perform_grid_search(
+    def _perform_grid_search(
         self, classifier: Any, x_train: np.ndarray, y_train: np.ndarray, cv: int, parameters: dict, n_jobs: int = 1, verbose: bool = False
     ) -> skms.GridSearchCV:
         """

src/mlrose_ky/opt_probs/discrete_opt.py

@@ -10,7 +10,7 @@
 from scipy.sparse.csgraph import minimum_spanning_tree, depth_first_tree
 from sklearn.metrics import mutual_info_score
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover, TSPCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver, TSPCrossOver
 from mlrose_ky.algorithms.mutators import SwapMutator
 from mlrose_ky.opt_probs.opt_prob import _OptProb
 
@@ -34,8 +34,8 @@ class DiscreteOpt(_OptProb):
         Number of unique values that each element in the state vector can take.
         Assumes values are integers in the range 0 to (max_val - 1), inclusive.
 
-    crossover : UniformCrossover | TSPCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver | TSPCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : SwapMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `SwapMutator`.
@@ -54,7 +54,7 @@ class DiscreteOpt(_OptProb):
         Problem type; always 'discrete' for this class.
     noise : float
         Noise factor for probability density estimation.
-    _crossover : UniformCrossover
+    _crossover : UniformCrossOver
         Crossover operation for reproduction.
     _mutator : SwapMutator
         Mutation operation for reproduction.
@@ -70,7 +70,7 @@ def __init__(
         fitness_fn: Any,
         maximize: bool = True,
         max_val: int = 2,
-        crossover: UniformCrossover | TSPCrossover = None,
+        crossover: UniformCrossOver | TSPCrossOver = None,
         mutator: "SwapMutator" = None,
     ):
         self._get_mutual_info_impl = self._get_mutual_info_slow
@@ -100,7 +100,7 @@ def __init__(
         self.prob_type: str = "discrete"
         self.noise: float = 0
 
-        self._crossover: UniformCrossover | TSPCrossover = UniformCrossover(self) if crossover is None else crossover
+        self._crossover: UniformCrossOver | TSPCrossOver = UniformCrossOver(self) if crossover is None else crossover
         self._mutator: SwapMutator = SwapMutator(self) if mutator is None else mutator
 
         self._mut_mask: np.ndarray | None = None

src/mlrose_ky/opt_probs/flip_flop_opt.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import OnePointCrossover
+from mlrose_ky.algorithms.crossovers import OnePointCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness import FlipFlop
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -27,8 +27,8 @@ class FlipFlopOpt(DiscreteOpt):
     maximize : bool, default=True
         Whether to maximize the fitness function. Set :code:`False` for minimization problems.
 
-    crossover : OnePointCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `OnePointCrossover`.
+    crossover : OnePointCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `OnePointCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -56,7 +56,7 @@ def __init__(
         length: int = None,
         fitness_fn: Any = None,
         maximize: bool = True,
-        crossover: "OnePointCrossover" = None,
+        crossover: "OnePointCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
     ):
         if (fitness_fn is None) and (length is None):
@@ -71,7 +71,7 @@ def __init__(
             fitness_fn = FlipFlop()
 
         self.max_val: int = 2
-        crossover = OnePointCrossover(self) if crossover is None else crossover
+        crossover = OnePointCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
 
         super().__init__(length, fitness_fn, maximize, crossover=crossover, mutator=mutator)

src/mlrose_ky/opt_probs/knapsack_opt.py

@@ -5,7 +5,7 @@
 
 from typing import Any
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness.knapsack import Knapsack
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -37,8 +37,8 @@ class KnapsackOpt(DiscreteOpt):
     max_weight_pct : float, default=0.35
         Maximum allowable weight as a percentage of the total weight.
 
-    crossover : UniformCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -67,7 +67,7 @@ def __init__(
         weights: list[float] = None,
         values: list[float] = None,
         max_weight_pct: float = 0.35,
-        crossover: "UniformCrossover" = None,
+        crossover: "UniformCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
         multiply_by_max_item_count: bool = False,
     ):
@@ -94,7 +94,7 @@ def __init__(
                 multiply_by_max_item_count=multiply_by_max_item_count,
             )
 
-        crossover = UniformCrossover(self) if crossover is None else crossover
+        crossover = UniformCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
 
         super().__init__(length, fitness_fn, maximize, max_val, crossover, mutator)

src/mlrose_ky/opt_probs/max_k_color_opt.py

@@ -8,7 +8,7 @@
 import networkx as nx
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness import MaxKColor
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -34,8 +34,8 @@ class MaxKColorOpt(DiscreteOpt):
     max_colors : int, default=None
         Maximum number of colors to use for coloring the graph.
 
-    crossover : UniformCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -68,7 +68,7 @@ def __init__(
         fitness_fn: Any = None,
         maximize: bool = False,
         max_colors: int = None,
-        crossover: "UniformCrossover" = None,
+        crossover: "UniformCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
         source_graph: nx.Graph = None,
     ):
@@ -126,7 +126,7 @@ def __init__(
         self.max_val: int = max_colors
 
         # Use default crossover and mutator if none are provided
-        crossover = UniformCrossover(self) if crossover is None else crossover
+        crossover = UniformCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
         super().__init__(length, fitness_fn, maximize, max_colors, crossover, mutator)
 

src/mlrose_ky/opt_probs/queens_opt.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import UniformCrossover
+from mlrose_ky.algorithms.crossovers import UniformCrossOver
 from mlrose_ky.algorithms.mutators import ChangeOneMutator
 from mlrose_ky.fitness.queens import Queens
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -27,8 +27,8 @@ class QueensOpt(DiscreteOpt):
     maximize : bool, default=False
         Whether to maximize the fitness function. Set :code:`False` for minimization problems.
 
-    crossover : UniformCrossover, default=None
-        Crossover operation used for reproduction. If None, defaults to `UniformCrossover`.
+    crossover : UniformCrossOver, default=None
+        Crossover operation used for reproduction. If None, defaults to `UniformCrossOver`.
 
     mutator : ChangeOneMutator, default=None
         Mutation operation used for reproduction. If None, defaults to `ChangeOneMutator`.
@@ -53,7 +53,7 @@ def __init__(
         length: int = None,
         fitness_fn: Any = None,
         maximize: bool = False,
-        crossover: "UniformCrossover" = None,
+        crossover: "UniformCrossOver" = None,
         mutator: "ChangeOneMutator" = None,
     ):
         # Ensure that either fitness_fn or length is provided
@@ -77,7 +77,7 @@ def __init__(
         self.max_val: int = length
 
         # Use default crossover and mutator if none are provided
-        crossover = UniformCrossover(self) if crossover is None else crossover
+        crossover = UniformCrossOver(self) if crossover is None else crossover
         mutator = ChangeOneMutator(self) if mutator is None else mutator
         super().__init__(length, fitness_fn, maximize, length, crossover, mutator)
 

src/mlrose_ky/opt_probs/tsp_opt.py

@@ -7,7 +7,7 @@
 
 import numpy as np
 
-from mlrose_ky.algorithms.crossovers import TSPCrossover
+from mlrose_ky.algorithms.crossovers import TSPCrossOver
 from mlrose_ky.algorithms.mutators import SwapMutator
 from mlrose_ky.fitness import TravellingSales
 from mlrose_ky.opt_probs.discrete_opt import DiscreteOpt
@@ -69,8 +69,8 @@ def __init__(
 
         self.length: int = length
 
-        # Initialize the parent class with the TSPCrossover and SwapMutator
-        super().__init__(length, fitness_fn, maximize, max_val=length, crossover=TSPCrossover(self), mutator=SwapMutator(self))
+        # Initialize the parent class with the TSPCrossOver and SwapMutator
+        super().__init__(length, fitness_fn, maximize, max_val=length, crossover=TSPCrossOver(self), mutator=SwapMutator(self))
 
         # Ensure that the fitness function type is 'tsp'
         if self.fitness_fn.get_prob_type() != "tsp":

src/mlrose_ky/runners/_nn_runner_base.py

@@ -150,17 +150,6 @@ def __init__(
         self.cv_results_df: pd.DataFrame | None = None
         self.best_params: dict | None = None
 
-    def dynamic_runner_name(self) -> str:
-        """
-        Generates a dynamic name for the runner based on the class name and experiment name.
-
-        Returns
-        -------
-        str
-            The dynamic name for the runner.
-        """
-        return f"{self.__class__.__name__}_{self._experiment_name}"
-
     def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame | None, Any | None]:
         """
         Executes the runner, performing grid search and handling the results.
@@ -182,7 +171,7 @@ def run(self) -> tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame |
                     search_results = pk.load(pickle_file)
             else:
                 run_start = time.perf_counter()
-                search_results = self.perform_grid_search(
+                search_results = self._perform_grid_search(
                     classifier=self.classifier,
                     parameters=self.grid_search_parameters,
                     x_train=self.x_train,

src/mlrose_ky/runners/_runner_base.py

@@ -53,20 +53,20 @@ class _RunnerBase(ABC):
     _sigint_params: tuple[int, Any] | None = None
 
     @classmethod
-    def get_runner_name(cls) -> str:
+    def runner_name(cls) -> str:
         """Get a short name for the runner class."""
         return get_short_name(cls)
 
-    def get_dynamic_runner_name(self) -> str:
+    def dynamic_runner_name(self) -> str:
         """Get the dynamic name of the runner, if set, otherwise return the default runner name."""
-        dynamic_runner_name = self._dynamic_short_name or self.get_runner_name()
+        dynamic_runner_name = self._dynamic_short_name or self.runner_name()
 
         if not dynamic_runner_name:
             raise ValueError("dynamic_runner_name is None")
 
         return dynamic_runner_name
 
-    def set_dynamic_runner_name(self, name: str):
+    def _set_dynamic_runner_name(self, name: str):
         """Set a dynamic runner name."""
         self._dynamic_short_name = name
 
@@ -285,7 +285,7 @@ def run_experiment(self, algorithm: Any, **kwargs: Any) -> tuple[pd.DataFrame |
 
         value_sets = list(itertools.product(*values))
 
-        logging.info(f"Running {self.get_dynamic_runner_name()}")
+        logging.info(f"Running {self.dynamic_runner_name()}")
         run_start = time.perf_counter()
 
         for value_set in value_sets:
@@ -380,7 +380,7 @@ def _get_pickle_filename_root(self, name: str) -> str:
         """Generate the root filename for the pickle file based on experiment metadata."""
         return build_data_filename(
             output_directory=self._output_directory,
-            runner_name=self.get_dynamic_runner_name(),
+            runner_name=self.dynamic_runner_name(),
             experiment_name=self._experiment_name,
             df_name=name,
         )
@@ -598,7 +598,7 @@ def save_state(
             logging.debug(data_desc)
 
         logging.debug(
-            f"runner_name:[{self.get_dynamic_runner_name()}], experiment_name:[{self._experiment_name}], "
+            f"runner_name:[{self.dynamic_runner_name()}], experiment_name:[{self._experiment_name}], "
             + ("" if attempt is None else f"attempt:[{attempt}], ")
             + f"iteration:[{iteration}], done:[{done}], "
             f"time:[{t:.2f}], fitness:[{fitness:.4f}]"

src/mlrose_ky/runners/nngs_runner.py

@@ -157,7 +157,7 @@ def __init__(
         )
 
         # Update short name based on the algorithm
-        self.set_dynamic_runner_name(f"{get_short_name(self)}_{get_short_name(algorithm)}")
+        self._set_dynamic_runner_name(f"{get_short_name(self)}_{get_short_name(algorithm)}")
 
     def run_one_experiment_(self, algorithm: Any, total_args: dict, **params: dict) -> tuple | None:
         """

tests/test_gridsearch.py

@@ -66,7 +66,7 @@ def test_perform_grid_search(self, grid_search_mixin, sample_data, dummy_classif
         """Should perform grid search with a classifier and return GridSearchCV object"""
         X_train, X_test, y_train, y_test = sample_data
 
-        search_results = grid_search_mixin.perform_grid_search(
+        search_results = grid_search_mixin._perform_grid_search(
             classifier=dummy_classifier, x_train=X_train, y_train=y_train, cv=3, parameters=param_grid
         )
 
@@ -79,7 +79,7 @@ def test_perform_grid_search(self, grid_search_mixin, sample_data, dummy_classif
     def test_handle_empty_input(self, grid_search_mixin):
         """Should handle empty input data gracefully"""
         with pytest.raises(ValueError):
-            grid_search_mixin.perform_grid_search(
+            grid_search_mixin._perform_grid_search(
                 classifier=DummyClassifier(), x_train=np.array([]), y_train=np.array([]), cv=3, parameters={"strategy": ["most_frequent"]}
             )
 
@@ -172,7 +172,7 @@ def test_perform_grid_search_with_verbose(self, grid_search_mixin, sample_data,
         X_train, X_test, y_train, y_test = sample_data
 
         # Perform grid search with verbose output enabled
-        search_results = grid_search_mixin.perform_grid_search(
+        search_results = grid_search_mixin._perform_grid_search(
             dummy_classifier, X_train, y_train, cv=3, parameters=grid_search_parameters, verbose=True
         )
 
@@ -187,7 +187,7 @@ def test_perform_grid_search_with_multiple_jobs(self, grid_search_mixin, sample_
         X_train, X_test, y_train, y_test = sample_data
 
         # Perform grid search with n_jobs set to 2
-        search_results = grid_search_mixin.perform_grid_search(
+        search_results = grid_search_mixin._perform_grid_search(
             dummy_classifier, X_train, y_train, cv=3, parameters=grid_search_parameters, n_jobs=2
         )
 

tests/test_opt_probs/test_discrete_opt.py

@@ -8,7 +8,7 @@
 import mlrose_ky
 from mlrose_ky.opt_probs import DiscreteOpt
 from mlrose_ky.fitness import OneMax
-from mlrose_ky.algorithms import OnePointCrossover
+from mlrose_ky.algorithms import OnePointCrossOver
 
 
 class TestDiscreteOpt:
@@ -164,7 +164,7 @@ def test_reproduce_mut1_max2(self):
     def test_reproduce_mut1_max_gt2(self):
         """Test reproduce method when mutation_prob is 1 and max_val is greater than 2"""
         problem = DiscreteOpt(5, OneMax(), max_val=3)
-        problem._crossover = OnePointCrossover(problem)
+        problem._crossover = OnePointCrossOver(problem)
         father = np.zeros(5)
         mother = np.ones(5) * 2
         child = problem.reproduce(father, mother, mutation_prob=1)

tests/test_runners/test_ga_runner.py

@@ -102,7 +102,7 @@ def test_garunner_initialization_with_additional_kwargs(self, problem, runner_kw
         runner = GARunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "ga"
+        assert runner.runner_name() == "ga"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_mimic_runner.py

@@ -97,7 +97,7 @@ def test_mimicrunner_initialization_with_additional_kwargs(self, problem, runner
         runner = MIMICRunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "mimic"
+        assert runner.runner_name() == "mimic"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_nn_runner_base.py

@@ -96,7 +96,7 @@ def test_nn_runner_base_run_method(self):
 
         with (
             patch.object(runner, "_setup", return_value=None) as mock_setup,
-            patch.object(runner, "perform_grid_search", return_value=mock_grid_search_result) as mock_grid_search,
+            patch.object(runner, "_perform_grid_search", return_value=mock_grid_search_result) as mock_grid_search,
             patch.object(runner, "_tear_down", return_value=None) as mock_tear_down,
             patch.object(runner, "_print_banner", return_value=None) as mock_print_banner,
         ):
@@ -127,7 +127,7 @@ def test_nn_runner_base_teardown_removes_files(self):
                 output_directory="test_output",
             )
 
-            runner.get_runner_name = MagicMock(return_value="TestRunner")
+            runner.runner_name = MagicMock(return_value="TestRunner")
             runner.best_params = {"param1": 0.1, "param2": 1}
             runner._output_directory = "test_output"
             runner.replay_mode = MagicMock(return_value=False)

tests/test_runners/test_rhc_runner.py

@@ -69,7 +69,7 @@ def test_rhc_runner_initialization_with_additional_kwargs(self, problem, runner_
         runner = RHCRunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "rhc"
+        assert runner.runner_name() == "rhc"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_sa_runner.py

@@ -84,7 +84,7 @@ def test_sarunner_initialization_with_additional_kwargs(self, problem, runner_kw
         runner = SARunner(**runner_kwargs, **additional_kwargs)
 
         assert runner.problem == problem
-        assert runner.get_runner_name() == "sa"
+        assert runner.runner_name() == "sa"
         assert runner._experiment_name == runner_kwargs["experiment_name"]
         assert runner.seed == runner_kwargs["seed"]
         assert runner.iteration_list == runner_kwargs["iteration_list"]

tests/test_runners/test_skmlp_runner.py

@@ -83,7 +83,7 @@ def test_dynamic_runner_name(self, runner_kwargs):
         """Test that the runner name is set dynamically based on the algorithm."""
         runner = SKMLPRunner(**runner_kwargs)
         expected_name = "skmlp"
-        assert runner.get_runner_name() == expected_name
+        assert runner.runner_name() == expected_name
 
     def test_grid_search_scorer_method(self, runner):
         """Test that the grid search scorer method is set correctly."""
@@ -102,6 +102,6 @@ def test_skmlp_runner_initialization_with_additional_kwargs(self, runner_kwargs)
         additional_kwargs = {"custom_arg": "custom_value"}
         runner = SKMLPRunner(**runner_kwargs, **additional_kwargs)
 
-        assert runner.get_runner_name() == "skmlp"
+        assert runner.runner_name() == "skmlp"
         assert runner.classifier.mlp.early_stopping == runner_kwargs["early_stopping"]
         assert runner.classifier.mlp.random_state == SEED