r"""
Fast 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.fast_2opt.tsp_fast_2opt import pybind11_tsp_fast_2opt_ls
[docs]class FastTwoOptOptimizer(OptimizerBase):
def __init__(
self,
impl_type: IMPL_TYPE = IMPL_TYPE.AUTO,
num_steps: int = -1,
knn: int = 50,
seed: int = 1234,
num_workers: int = 1
):
# Super Initialization
super(FastTwoOptOptimizer, self).__init__(
optimizer_type=OPTIMIZER_TYPE.FAST_2OPT,
impl_type=impl_type
)
# Set Attributes
self.num_steps = num_steps
self.knn = knn
self.seed = seed
self.num_workers = num_workers
#######################################
# 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.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 _pybind11_optimize(self, task_data: TaskBase, return_sol: bool = False):
"""Optimize the task data using DC 2-opt (pybind11)."""
# Optimize
task_type = task_data.task_type
if task_type == TASK_TYPE.TSP:
pybind11_tsp_fast_2opt_ls(
task_data=task_data,
num_steps=self.num_steps,
knn=self.knn,
seed=self.seed,
)
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.TSP:
return self._pybind11_batch_optimize(batch_task_data)
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 DC 2-opt (pybind11)."""
task_type = batch_task_data[0].task_type
if task_type == TASK_TYPE.TSP:
return self._pool_optimize(
batch_task_data=batch_task_data,
single_func=self._pybind11_optimize
)
else:
raise self._get_not_implemented_error(task_type, True)