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