NiaPy

Python micro framework for building nature-inspired algorithms.

class NiaPy.Runner(D=10, nFES=1000000, nRuns=1, useAlgorithms='ArtificialBeeColonyAlgorithm', useBenchmarks='Ackley', **kwargs)[source]

Bases: object

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)

Variables

  • 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

Returns the results.

Return type

results (Dict[str, Dict])

Initialize Runner.

Parameters

  • 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

__init__(D=10, nFES=1000000, nRuns=1, useAlgorithms='ArtificialBeeColonyAlgorithm', useBenchmarks='Ackley', **kwargs)[source]

Initialize Runner.

Parameters

  • 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

_export_to_xls()[source]

Export the results in the xls file.

See also

  • NiaPy.Runner.__createExportDir()

  • NiaPy.Runner.__generateExportName()

benchmark_factory(name)[source]

Create optimization task.

Parameters

name (str) – Benchmark name.

Returns

Optimization task to use.

Return type

Task

run(export='dataframe', verbose=False)[source]

Execute runner.

Parameters

  • 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

Returns dictionary of results

Return type

dict

See also

  • NiaPy.Runner.useAlgorithms()

  • NiaPy.Runner.useBenchmarks()

  • NiaPy.Runner.__algorithmFactory()

class NiaPy.Runner(D=10, nFES=1000000, nRuns=1, useAlgorithms='ArtificialBeeColonyAlgorithm', useBenchmarks='Ackley', **kwargs)[source]

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)

Variables

  • 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

Returns the results.

Return type

results (Dict[str, Dict])

Initialize Runner.

Parameters

  • 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