Source code for NiaPy.runner
# encoding=utf8
"""Implementation of Runner utility class."""
from __future__ import print_function
import datetime
import os
import logging
import pandas as pd
from NiaPy.task import StoppingTask, OptimizationType
from NiaPy.algorithms import AlgorithmUtility
logging.basicConfig()
logger = logging.getLogger('NiaPy.runner.Runner')
logger.setLevel('INFO')
__all__ = ["Runner"]
[docs]class Runner:
r"""Runner utility feature.
Feature which enables running multiple algorithms with multiple benchmarks.
It also support exporting results in various formats (e.g. Pandas DataFrame, JSON, Excel)
Attributes:
D (int): Dimension of problem
NP (int): Population size
nFES (int): Number of function evaluations
nRuns (int): Number of repetitions
useAlgorithms (Union[List[str], List[Algorithm]]): List of algorithms to run
useBenchmarks (Union[List[str], List[Benchmark]]): List of benchmarks to run
Returns:
results (Dict[str, Dict]): Returns the results.
"""
[docs] def __init__(self, D=10, nFES=1000000, nRuns=1, useAlgorithms='ArtificialBeeColonyAlgorithm', useBenchmarks='Ackley', **kwargs):
r"""Initialize Runner.
Args:
D (int): Dimension of problem
nFES (int): Number of function evaluations
nRuns (int): Number of repetitions
useAlgorithms (List[Algorithm]): List of algorithms to run
useBenchmarks (List[Benchmarks]): List of benchmarks to run
"""
self.D = D
self.nFES = nFES
self.nRuns = nRuns
self.useAlgorithms = useAlgorithms
self.useBenchmarks = useBenchmarks
self.results = {}
[docs] def benchmark_factory(self, name):
r"""Create optimization task.
Args:
name (str): Benchmark name.
Returns:
Task: Optimization task to use.
"""
return StoppingTask(D=self.D, nFES=self.nFES, optType=OptimizationType.MINIMIZATION, benchmark=name)
@classmethod
def __create_export_dir(cls):
r"""Create export directory if not already createed."""
if not os.path.exists("export"):
os.makedirs("export")
@classmethod
def __generate_export_name(cls, extension):
r"""Generate export file name.
Args:
extension (str): File format.
Returns:
"""
Runner.__create_export_dir()
return "export/" + str(datetime.datetime.now()).replace(":", ".") + "." + extension
def __export_to_dataframe_pickle(self):
r"""Export the results in the pandas dataframe pickle.
See Also:
* :func:`NiaPy.Runner.__createExportDir`
* :func:`NiaPy.Runner.__generateExportName`
"""
dataframe = pd.DataFrame.from_dict(self.results)
dataframe.to_pickle(self.__generate_export_name("pkl"))
logger.info("Export to Pandas DataFrame pickle (pkl) completed!")
def __export_to_json(self):
r"""Export the results in the JSON file.
See Also:
* :func:`NiaPy.Runner.__createExportDir`
* :func:`NiaPy.Runner.__generateExportName`
"""
dataframe = pd.DataFrame.from_dict(self.results)
dataframe.to_json(self.__generate_export_name("json"))
logger.info("Export to JSON file completed!")
[docs] def _export_to_xls(self):
r"""Export the results in the xls file.
See Also:
* :func:`NiaPy.Runner.__createExportDir`
* :func:`NiaPy.Runner.__generateExportName`
"""
dataframe = pd.DataFrame.from_dict(self.results)
dataframe.to_excel(self.__generate_export_name("xls"))
logger.info("Export to XLS completed!")
def __export_to_xlsx(self):
r"""Export the results in the xlsx file.
See Also:
* :func:`NiaPy.Runner.__createExportDir`
* :func:`NiaPy.Runner.__generateExportName`
"""
dataframe = pd.DataFrame.from_dict(self.results)
dataframe.to_excel(self.__generate_export_name("xslx"))
logger.info("Export to XLSX file completed!")
[docs] def run(self, export="dataframe", verbose=False):
"""Execute runner.
Arguments:
export (str): Takes export type (e.g. dataframe, json, xls, xlsx) (default: "dataframe")
verbose (bool): Switch for verbose logging (default: {False})
Raises:
TypeError: Raises TypeError if export type is not supported
Returns:
dict: Returns dictionary of results
See Also:
* :func:`NiaPy.Runner.useAlgorithms`
* :func:`NiaPy.Runner.useBenchmarks`
* :func:`NiaPy.Runner.__algorithmFactory`
"""
for alg in self.useAlgorithms:
if not isinstance(alg, "".__class__):
alg_name = str(type(alg).__name__)
else:
alg_name = alg
self.results[alg_name] = {}
if verbose:
logger.info("Running %s...", alg_name)
for bench in self.useBenchmarks:
if not isinstance(bench, "".__class__):
bench_name = str(type(bench).__name__)
else:
bench_name = bench
if verbose:
logger.info("Running %s algorithm on %s benchmark...", alg_name, bench_name)
self.results[alg_name][bench_name] = []
for _ in range(self.nRuns):
algorithm = AlgorithmUtility().get_algorithm(alg)
benchmark_stopping_task = self.benchmark_factory(bench)
self.results[alg_name][bench_name].append(algorithm.run(benchmark_stopping_task))
if verbose:
logger.info("---------------------------------------------------")
if export == "dataframe":
self.__export_to_dataframe_pickle()
elif export == "json":
self.__export_to_json()
elif export == "xsl":
self._export_to_xls()
elif export == "xlsx":
self.__export_to_xlsx()
else:
raise TypeError("Passed export type %s is not supported!", export)
return self.results