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test_optimisation_algorithm.py
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test_optimisation_algorithm.py
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"""
This file has nothing to do with the project, it is just a tool to test
the correctness of the realization of algorithms. Since the file is a
test and is intended for the implementation of algorithms, there are no
description of objects here.
If you run the file, you can see a progressive solution to the salesman
problem (on tkinter, no third-party libraries required). With a minimal
setting of the `main` function (choice of algorithm and number of
points) you can change the parameters.
The number of points tested less than 100, the parameters of the
algorithms were chosen for 50-100.
Implemented algorithms:
- SimulatedAnnealing
- GeneticAlgorithm
- AntColony
- BeesColony
"""
from __future__ import annotations
import random
import time
from abc import ABC, abstractmethod
from dataclasses import dataclass
from operator import attrgetter
from tkinter import Tk, Canvas
from typing import Iterable, Protocol, TypeVar, Any, Type
# === funcs ============================================================
def flip_coin() -> bool:
return random.random() > 0.5
class _Sortable_V(Protocol):
length: float
class _Sortable_P(Protocol):
@property
def length(self) -> float:
return 0.
SortableT = TypeVar("SortableT", bound=_Sortable_V | _Sortable_P)
Params = dict[str, Any]
# === travelling salesman problem and drawing of the result ============
class Point:
def __init__(self, number: int, x_min=20, x_max=520, y_min=20, y_max=520):
self.number = number
self.x = random.randint(x_min, x_max)
self.y = random.randint(y_min, y_max)
@property
def draw_coordinates(self) -> tuple[int, int, int, int]:
return (self.x - 2, self.y - 2, self.x + 2, self.y + 2)
class Path:
def __init__(self, point_number: int, _fake=False):
self.point_number = point_number
self._length = None
if _fake:
# for copying
return
self.points = [Point(num) for num in range(point_number)]
self.order_visits = list(range(point_number))
# cache shared between all paths
self.cache_len: dict[tuple[int, int], float] = dict()
for i in range(point_number):
for j in range(i, point_number):
self.update_line_length(i, j)
def update_line_length(self, i: int, j: int):
first_point = self.points[i]
second_point = self.points[j]
delta_x = first_point.x - second_point.x
delta_y = first_point.y - second_point.y
length = (delta_x**2 + delta_y**2) ** 0.5
self.cache_len[(i, j)] = length
self.cache_len[(j, i)] = length
@property
def length(self) -> float:
if self._length is None:
self._length = sum(self.cache_len[indexes] for indexes in self)
return self._length
def __iter__(self) -> Iterable[tuple[int, int]]:
for num in range(self.point_number):
yield (self.order_visits[num-1], self.order_visits[num])
def get_lines_coordinate(self) -> Iterable[tuple[int, int, int, int]]:
for indexes in self:
yield (
self.points[indexes[0]].x,
self.points[indexes[0]].y,
self.points[indexes[1]].x,
self.points[indexes[1]].y
)
def set_order(self, order: list[int]):
self._length = None
self.order_visits = order
def copy(self) -> Path:
path = Path(self.point_number, True)
path.points = self.points
path.set_order(self.order_visits.copy())
path.cache_len = self.cache_len
return path
def __eq__(self, other: Path) -> bool:
start_ind = other.order_visits.index(self.order_visits[0])
other_order = other.order_visits[start_ind:] + other.order_visits[:start_ind]
return self.order_visits == other_order
def shuffle(self):
random.shuffle(self.order_visits)
self.set_order(self.order_visits)
@staticmethod
def swap_points(path: Path):
i, j = random.sample(range(path.point_number), k=2)
points = path.order_visits
points[i], points[j] = points[j], points[i]
path.set_order(path.order_visits)
@staticmethod
def move_point(path: Path):
i, j = random.sample(range(path.point_number), k=2)
path.order_visits.insert(j, path.order_visits.pop(i))
path.set_order(path.order_visits)
modify_funcs = [swap_points, move_point]
class Tester:
def __init__(self, algorithm: AlgorithmAbstract):
self.algorithm = algorithm
@staticmethod
def plot(data: list[tuple[float, float, float]]):
import matplotlib.pyplot as plt
values, times, labels = tuple(zip(*data))
plt.plot(times, values, '-s')
plt.axis([0, max(times), 0, max(values)])
for i in range(len(values)):
plt.annotate(round(labels[i], 5), (times[i], values[i]))
plt.show()
def run(self) -> tuple[float, float]:
start_time = time.time()
while not self.algorithm.stop:
self.algorithm.do_one_step()
return (self.algorithm.path.length, time.time() - start_time)
@classmethod
def test(
cls,
algorithm_class: Type[AlgorithmAbstract],
params: Params = None,
*,
n: int = 10,
point_number: int = 100,
) -> tuple[float, float]:
if params is None:
params = algorithm_class.get_default_params(point_number)
value_sum, time_sum = 0, 0
for seed in range(0, n):
random.seed(seed)
algorithm = algorithm_class(point_number, params=params)
algorithm.disable_log()
tester = Tester(algorithm)
v, t = tester.run()
value_sum += v / n
time_sum += t / n
return (value_sum, time_sum)
class Runner:
def __init__(self, algorithm: AlgorithmAbstract):
self.algorithm = algorithm
algorithm.log(_counter="start")
self.root = Tk()
self.root.title("Test Algorithm")
self.canvas = Canvas(
self.root,
width=540,
height=540,
background="white",
)
self.canvas.pack()
self.line_ids = []
def set_points(self):
for point in self.algorithm.path.points:
self.canvas.create_rectangle(*point.draw_coordinates, fill="#f00")
def create_lines(self):
self.line_ids = [
self.canvas.create_line(*line_coordinates)
for line_coordinates in self.algorithm.path.get_lines_coordinate()
]
def drop_lines(self):
for line_id in self.line_ids:
self.canvas.delete(line_id)
def run(self):
self.algorithm.do_one_step()
self.drop_lines()
self.create_lines()
self.canvas.update()
if not self.algorithm.stop:
self.canvas.update()
self.canvas.after(0, self.run)
else:
self.root.title("Test Algorithm (stop)")
self.root.update()
print(f"result: {self.algorithm.path.length: <20}")
# === algorithms =======================================================
class AlgorithmAbstract(ABC):
"""
Adapter class between the task and the implementation of the
algorithm.
"""
point_number: int
path: Path
stop: bool
def __init__(self, point_number=100, *, params: Params = None):
self.point_number = point_number
self.path = Path(point_number)
self.stop = False
self.counter = 1
self._debug_info = []
self.log = self._log
if params is None:
params = self.get_default_params(self.point_number)
self._params = params
self.init_from_params_dct(params)
self.post_init_params()
def _log(self, *args, _counter: str = None):
counter = _counter or self.counter
info = [f"{counter: <6}", f"{self.path.length: <20}"]
if args:
info.extend(args)
if self._debug_info:
info.extend(self._debug_info)
self._debug_info = []
print(" | ".join(str(arg) for arg in info))
self.counter += 1
def _no_log(self, *args, **kwargs):
self._debug_info = []
self.counter += 1
def disable_log(self):
self.log = self._no_log
@staticmethod
def sort_paths(paths: list[SortableT]) -> list[SortableT]:
return sorted(paths, key=attrgetter("length"))
def init_from_params_dct(self, params: Params):
for (field, value) in params.items():
if callable(value):
setattr(self, field, value(self))
else:
setattr(self, field, value)
@staticmethod
@abstractmethod
def get_default_params(point_number: int) -> Params:
pass
@abstractmethod
def post_init_params(self):
pass
@abstractmethod
def stop_condition(self) -> bool:
pass
@abstractmethod
def algorithm_cycle(self) -> tuple | None:
pass
def do_one_step(self):
args = self.algorithm_cycle() or []
self.log(*args)
if self.stop_condition():
self.stop = True
class SimulatedAnnealing(AlgorithmAbstract):
temperature: float
cooling_coefficient: float
@staticmethod
def get_default_params(point_number):
return {
"temperature": 10,
"cooling_coefficient": 1 - (3.e-01 / point_number),
}
def post_init_params(self):
pass
def make_decision(self, new_len: float) -> bool:
if self.path.length > new_len:
return True
delta = (self.path.length - new_len) / self.path.length * 100
return random.random() < 2.71 ** (delta / self.temperature)
def algorithm_cycle(self):
for point in range(self.point_number):
for modify_func in Path.modify_funcs:
new_path = self.path.copy()
modify_func(new_path)
if self.make_decision(new_path.length):
self.path = new_path
self.temperature *= self.cooling_coefficient
return (format(self.temperature, '.9f'),)
def stop_condition(self):
return self.temperature < 1.0e-3
class GeneticAlgorithm(AlgorithmAbstract):
population_number_max: float
child_count: int
count_best: int
population_count: int
population: list[Path]
@staticmethod
def get_default_params(point_number):
return {
"population_number_max": (point_number * 1.2) ** 2,
"child_count": 7,
"count_best": 5,
"population_count": lambda s: s.child_count * s.count_best,
}
def post_init_params(self):
self.population = self.create_descendants(self.path, self.population_count)
for path in self.population:
path.shuffle()
@staticmethod
def create_descendants(parent_path: Path, count: int) -> list[Path]:
return [parent_path.copy() for _ in range(count)]
def filter_best_paths(self):
paths = self.sort_paths(self.population + [self.path])
best_paths = paths[:self.count_best]
self.path = best_paths[0]
new_population = []
for path in best_paths:
child = self.create_descendants(path, self.child_count)
new_population.extend(child)
self.population = new_population
def algorithm_cycle(self):
# how to crossbreed paths, I have not figured out, so only mutations
for path in self.population:
for modify_func in Path.modify_funcs:
if flip_coin():
modify_func(path)
self.filter_best_paths()
def stop_condition(self):
return self.counter >= self.population_number_max
class AntColony(AlgorithmAbstract):
class Ant:
order: list[int]
not_visited: list[int]
def __init__(self, colony: AntColony):
self.colony = colony
self.path = self.colony.path.copy()
self.order = list(range(self.colony.point_number))
def choose_point(self) -> int:
from_point = self.order[-1]
weights = [
self.colony.get_weight(from_point, to_point)
for to_point in self.not_visited
]
point_indexes = range(len(self.not_visited))
return random.choices(point_indexes, weights=weights, k=1)[0]
def hit_road(self) -> Path:
self.not_visited = self.order
self.order = []
self.order.append(self.not_visited.pop(-1))
while self.not_visited:
next_point_ind = self.choose_point()
self.order.append(self.not_visited.pop(next_point_ind))
self.path.set_order(self.order)
return self.path
# ====================
scout_count: int
ant_count: int
evaporation_coefficient: float
threshold: float
line_pheromones: dict[tuple[int, int], float]
ants: list[Ant]
@staticmethod
def get_default_params(point_number):
return {
"scout_count": point_number * 8,
"ant_count": point_number * 10,
"evaporation_coefficient": 0.6,
"threshold": 1 / point_number ** 1.6,
}
def post_init_params(self):
self.line_pheromones = {
(fr, to): 1
for fr in range(self.point_number)
for to in range(fr + 1, self.point_number)
}
self.ants = [self.Ant(self) for _ in range(self.ant_count)]
def get_weight(self, fr, to) -> float:
return self.line_pheromones[(fr, to) if fr < to else (to, fr)]
def update_pheromones(self, paths: list[Path]):
for key in self.line_pheromones.keys():
if self.line_pheromones[key] > self.threshold:
self.line_pheromones[key] *= self.evaporation_coefficient
if self.line_pheromones[key] < self.threshold:
self.line_pheromones[key] = self.threshold
best_length = self.path.length
deltas = [path.length - best_length for path in paths]
worst_delta = deltas[-1]
weights = [(1 - (delta / worst_delta)) ** 2 for delta in deltas]
paths.insert(0, self.path)
weights.insert(0, 1)
for (path, weight) in zip(paths, weights):
for (fr, to) in path:
self.line_pheromones[(fr, to) if fr < to else (to, fr)] += weight
def filter_paths(self, paths: list[Path]) -> list[Path]:
unique_paths = [self.path]
for new_path in paths:
if new_path.length >= self.path.length:
continue
for current_path in unique_paths:
if new_path == current_path:
break
else:
unique_paths.append(new_path)
sorted_paths = self.sort_paths(unique_paths)
return sorted_paths
def algorithm_cycle(self):
paths = [ant.hit_road() for ant in self.ants]
sorted_paths = self.filter_paths(paths)
if len(sorted_paths) > 1:
self.path = sorted_paths.pop(0).copy()
self.update_pheromones(sorted_paths)
def stop_condition(self):
return self.counter > self.scout_count
class BeesColony(AlgorithmAbstract):
@dataclass
class Source:
path: Path
nectar: int
@property
def length(self) -> float:
return self.path.length
class Bee:
source: BeesColony.Source
types = ["scout", "onlooker", "employee"]
def __init__(self, type: str, colony: BeesColony):
self.colony = colony
self.fly = self.get_fly_func(type)
def _fly_as_scout(self):
self.source = self.colony.get_random_source()
def _fly_as_onlooker(self):
best_source = self.colony.get_best_source()
self.source = self.colony.get_nearby_source(best_source)
def _fly_as_employee(self):
best_source = self.colony.get_best_source()
best_source.nectar -= 1
self.source = self.colony.get_nearby_source(best_source)
def get_fly_func(self, type: str):
fly_as_type = {
"scout": self._fly_as_scout,
"onlooker": self._fly_as_onlooker,
"employee": self._fly_as_employee,
}
return fly_as_type[type]
# ====================
scout_count: int
onlooker_count: int
employed_count: int
bee_count: int
source_count: int
nectar: int
max_change: int
decrement_counter: int
sources: list[Source]
bees: list[Bee]
@staticmethod
def get_default_params(point_number):
return {
"scout_count": point_number * 5,
"onlooker_count": point_number * 2,
"employed_count": point_number * 10,
"bee_count": lambda s: s.scout_count + s.onlooker_count + s.employed_count,
"source_count": lambda s: s.bee_count,
"nectar": lambda s: s.employed_count * 4,
"max_change": 4,
"decrement_counter": point_number * 4,
}
def post_init_params(self):
self.bees = [
*(
self.Bee("scout", colony=self)
for _ in range(self.scout_count)
),
*(
self.Bee("onlooker", colony=self)
for _ in range(self.onlooker_count)
),
*(
self.Bee("employee", colony=self)
for _ in range(self.employed_count)
),
]
self.sources = []
def get_random_source(self) -> Source:
source = BeesColony.Source(self.path.copy(), self.nectar)
source.path.shuffle()
return source
def get_best_source(self) -> Source:
for source in self.sources:
if source.nectar:
return source
return self.get_random_source()
def get_nearby_source(self, source: Source) -> Source:
nearby_path = source.path.copy()
for _ in range(self.max_change):
func = random.choice(Path.modify_funcs)
func(nearby_path)
return BeesColony.Source(nearby_path, self.nectar)
def algorithm_cycle(self):
for bee in self.bees:
bee.fly()
all_sources = self.sort_paths(self.sources + [bee.source for bee in self.bees])
if all_sources[0].length < self.path.length:
self.path = all_sources[0].path.copy()
active_sources = list(filter(attrgetter("nectar"), all_sources))
self.sources = active_sources[:self.source_count]
if not self.counter % self.decrement_counter:
self.max_change -= 1
def stop_condition(self):
return not self.max_change
# === main =============================================================
def main():
algorithm = AntColony(50)
runner = Runner(algorithm)
runner.set_points()
runner.run()
runner.root.mainloop()
def test_main(plot: bool = False):
Algorithm = GeneticAlgorithm
point_number = 70
field = "population_number_max"
variants = [
(point_number * 1.0) ** 2,
(point_number * 1.1) ** 2,
(point_number * 1.2) ** 2,
(point_number * 1.3) ** 2,
(point_number * 1.4) ** 2,
]
header = f"{'value': <20} | {'time': <20} | {field}"
print(header)
print("-" * len(header))
params = Algorithm.get_default_params(point_number)
data = []
for variant in variants:
params[field] = variant
value_sum, time_sum = Tester.test(Algorithm, params, point_number=point_number)
data.append((value_sum, time_sum, variant))
print(f"{value_sum: <20} | {time_sum: <20} | {variant}")
print("-" * len(header))
if plot:
try:
Tester.plot(data)
except ImportError:
msg = (
"The `matplotlib` package is not installed, it is not"
"possible to plot the chart."
)
print("\n" + msg)
# quite optimal results by the points count (depending on the position,
# the optimality varies in range of 10%):
#
# 20 - 1600-2000
# 30 - 2200-2500
# 50 - 3000
# 70 - 3700
# 100 - 4800
# 300 - 9000-10000
if __name__ == "__main__":
main()
# test_main(plot=True)