r"""
Two-opt Optimizer.
"""
# Copyright (c) 2024 Thinklab@SJTU
# ML4CO-Kit is licensed under Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
# See the Mulan PSL v2 for more details.
from typing import List
from ml4co_kit.task.base import TaskBase, TASK_TYPE
from ml4co_kit.optimizer.base import OptimizerBase, OPTIMIZER_TYPE, IMPL_TYPE
from ml4co_kit.optimizer.lib.two_opt.atsp.ctypes_atsp_2opt import ctypes_atsp_2opt_ls
from ml4co_kit.optimizer.lib.two_opt.tsp.torch_tsp_2opt import torch_tsp_2opt_ls
from ml4co_kit.optimizer.lib.two_opt.tsp.torch_tsp_2opt import torch_tsp_2opt_batch_ls
from ml4co_kit.optimizer.lib.two_opt.tsp.pybind11_tsp_2opt import pybind11_tsp_2opt_ls
from ml4co_kit.optimizer.lib.two_opt.tsp.pybind11_tsp_2opt import pybind11_tsp_2opt_batch_ls
[docs]class TwoOptOptimizer(OptimizerBase):
def __init__(
self,
impl_type: IMPL_TYPE = IMPL_TYPE.AUTO,
max_iters: int = 5000,
torch_device: str = "cpu",
type_2opt: int = 2,
):
# Super Initialization
super(TwoOptOptimizer, self).__init__(
optimizer_type=OPTIMIZER_TYPE.TWO_OPT,
impl_type=impl_type
)
# Set Attributes
self.max_iters = max_iters
self.torch_device = torch_device
self.type_2opt = type_2opt
#######################################
# Single Optimization Methods #
#######################################
def _auto_optimize(self, task_data: TaskBase, return_sol: bool = False):
"""Optimize the task data using auto implementation."""
if task_data.task_type == TASK_TYPE.ATSP:
return self._ctypes_optimize(task_data, return_sol)
elif task_data.task_type == TASK_TYPE.TSP:
return self._pybind11_optimize(task_data, return_sol)
else:
raise ValueError(
f"Optimizer {self.optimizer_type} ({self.impl_type})"
f"is not supported for {task_data.task_type}."
)
def _ctypes_optimize(self, task_data: TaskBase, return_sol: bool = False):
"""Optimize the task data using 2-opt local search (ctypes)."""
# Optimize
task_type = task_data.task_type
if task_type == TASK_TYPE.ATSP:
ctypes_atsp_2opt_ls(
task_data=task_data,
max_iters=self.max_iters
)
else:
raise self._get_not_implemented_error(task_type, False)
# Return the solution if needed
if return_sol:
return task_data.sol
def _pybind11_optimize(self, task_data: TaskBase, return_sol: bool = False):
"""Optimize the task data using 2-opt local search (pybind11)."""
# Optimize
task_type = task_data.task_type
if task_type == TASK_TYPE.TSP:
pybind11_tsp_2opt_ls(
task_data=task_data,
max_iters=self.max_iters,
type_2opt=self.type_2opt
)
else:
raise self._get_not_implemented_error(task_type, False)
# Return the solution if needed
if return_sol:
return task_data.sol
def _torch_optimize(self, task_data: TaskBase, return_sol: bool = False):
"""Optimize the task data using 2-opt local search (torch)."""
# Optimize
task_type = task_data.task_type
if task_type == TASK_TYPE.TSP:
torch_tsp_2opt_ls(
task_data=task_data,
max_iters=self.max_iters,
device=self.torch_device
)
else:
raise self._get_not_implemented_error(task_type, False)
# Return the solution if needed
if return_sol:
return task_data.sol
#######################################
# Batch Optimization Methods #
#######################################
def _auto_batch_optimize(self, batch_task_data: List[TaskBase]):
"""Optimize the (batch) task data using auto implementation."""
task_type = batch_task_data[0].task_type
if task_type == TASK_TYPE.ATSP:
return self._ctypes_batch_optimize(batch_task_data)
elif task_type == TASK_TYPE.TSP:
return self._torch_batch_optimize(batch_task_data)
else:
raise NotImplementedError(
f"Optimizer {self.optimizer_type} is not supported for {task_type}."
)
def _ctypes_batch_optimize(self, batch_task_data: List[TaskBase]):
"""Optimize the (batch) task data using 2-opt local search (ctypes)."""
task_type = batch_task_data[0].task_type
if task_type == TASK_TYPE.ATSP:
return self._pool_optimize(
batch_task_data=batch_task_data,
single_func=self._ctypes_optimize
)
else:
raise self._get_not_implemented_error(task_type, True)
def _pybind11_batch_optimize(self, batch_task_data: List[TaskBase]):
"""Optimize the (batch) task data using 2-opt local search (pybind11)."""
task_type = batch_task_data[0].task_type
if task_type == TASK_TYPE.TSP:
return pybind11_tsp_2opt_batch_ls(
batch_task_data=batch_task_data,
max_iters=self.max_iters,
type_2opt=self.type_2opt
)
else:
raise self._get_not_implemented_error(task_type, True)
def _torch_batch_optimize(self, batch_task_data: List[TaskBase]):
"""Optimize the (batch) task data using 2-opt local search (torch)."""
task_type = batch_task_data[0].task_type
if task_type == TASK_TYPE.TSP:
return torch_tsp_2opt_batch_ls(
batch_task_data=batch_task_data,
max_iters=self.max_iters,
device=self.torch_device
)
else:
raise self._get_not_implemented_error(task_type, True)