# encoding=utf8
"""Argparser class."""
import logging
import sys
from argparse import ArgumentParser
import numpy as np
import niapy.benchmarks as benchmarks
from niapy.task import OptimizationType
logging.basicConfig()
logger = logging.getLogger('niapy.util.argparse')
logger.setLevel('INFO')
__all__ = ['get_argparser', 'get_args', 'get_args_dict']
def get_benchmark_names():
r"""Get benchmark names."""
return benchmarks.__all__
def optimization_type(x):
r"""Get OptimizationType from string.
Args:
x (str): String representing optimization type.
Returns:
OptimizationType: Optimization type based on type that is defined as enum.
"""
if x not in ['min', 'max']:
logger.info('You can use only [min, max], using min')
return OptimizationType.MAXIMIZATION if x == 'max' else OptimizationType.MINIMIZATION
[docs]def get_argparser():
r"""Create/Make parser for parsing string.
Parser:
* `-a` or `--algorithm` (str):
Name of algorithm to use. Default value is `jDE`.
* `-b` or `--benchmark` (str):
Name of benchmark to use. Default values is `Benchmark`.
* `-d` or `--dimension` (int):
Number of dimensions/components usd by benchmark. Default values is `10`.
* `--max-evals` (int):
Number of maximum function evaluations. Default values is `inf`.
* `--max-iters` (int):
Number of maximum algorithm iterations/generations. Default values is `inf`.
* `-n` or `--population-size` (int):
Number of individuals in population. Default values is `43`.
* `-r` or `--run-type` (str);
Run type of run. Value can be:
* '': No output during the run. Output is shown only at the end of algorithm run.
* `log`: Output is shown every time new global best solution is found
* `plot`: Output is shown only at the end of run. Output is shown as graph plotted in matplotlib. Graph represents convergence of algorithm over run time of algorithm.
Default value is `''`.
* `--seed` (list of int or int):
Set the starting seed of algorithm run. If multiple runs, user can provide list of ints, where each int usd use at new run. Default values is `None`.
* `--opt-type` (str):
Optimization type of the run. Values can be:
* `min`: For minimization problems
* `max`: For maximization problems
Default value is `min`.
Returns:
ArgumentParser: Parser for parsing arguments from string.
See Also:
* :class:`ArgumentParser`
* :func:`ArgumentParser.add_argument`
"""
parser, benchmark_names = ArgumentParser(description='Runner example.'), get_benchmark_names()
parser.add_argument('-a', '--algorithm', dest='algo', default='jDE', type=str)
parser.add_argument('-b', '--benchmark', dest='benchmark', nargs='*', default=benchmark_names[0], choices=benchmark_names, type=str)
parser.add_argument('-d', '--dimension', dest='dimension', default=10, type=int)
parser.add_argument('--max-evals', dest='max_evals', default=np.inf, type=int)
parser.add_argument('--max-iters', dest='max_iters', default=np.inf, type=int)
parser.add_argument('-n', '--population-size', dest='population_size', default=43, type=int)
parser.add_argument('-r', '--run-type', dest='run_type', choices=['', 'log', 'plot'], default='', type=str)
parser.add_argument('--seed', dest='seed', nargs='+', default=[None], type=int)
parser.add_argument('--opt-type', dest='opt_type', default=optimization_type('min'), type=optimization_type)
return parser
[docs]def get_args(argv):
r"""Parse arguments form input string.
Args:
argv (List[str]): List to parse.
Returns:
Dict[str, Union[float, int, str, OptimizationType]]: Where key represents argument name and values it's value.
See Also:
* :func:`niapy.util.argparser.get_argparser`.
* :func:`ArgumentParser.parse_args`
"""
parser = get_argparser()
a = parser.parse_args(argv)
return a
[docs]def get_args_dict(argv):
r"""Parse input string.
Args:
argv (List[str]): Input string to parse for arguments
Returns:
dict: Parsed input string
See Also:
* :func:`niapy.utils.get_args`
"""
return vars(get_args(argv))
if __name__ == '__main__':
r"""Run the algorithms based on parameters from the command line interface."""
args = get_args(sys.argv[1:])
logger.info(str(args))
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