discrete_optimization package

Subpackages

Submodules

discrete_optimization.datasets module

Fetch datasets for examples and tests.

discrete_optimization.datasets.decompress_gz_to_folder(input_file, output_folder, url)[source]

discrete_optimization.datasets.fetch_all_datasets(data_home: str | None = None)[source]

Fetch data used by examples for all packages.

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_fjsp(data_home: str | None = None)[source]

discrete_optimization.datasets.fetch_data_for_mis(data_home: str | None = None)[source]

discrete_optimization.datasets.fetch_data_from_coursera(data_home: str | None = None)[source]

Fetch data from coursera repo.

https://github.com/discreteoptimization/assignment

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_from_imopse(data_home: str | None = None)[source]

Fetch data from iMOPSE repo for rcpsp_multiskill examples.

https://github.com/imopse/iMOPSE

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_from_jsplib_repo(data_home: str | None = None)[source]

Fetch data from jsplib repo. (for jobshop problems)

https://github.com/tamy0612/JSPLIB

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_from_mslib(data_home: str | None = None)[source]

Fetch data from MSLIB for rcpsp_multiskill examples. cf https://www.projectmanagement.ugent.be/research/project_scheduling/MSRCPSP Params:

data_home: Specify the cache folder for the datasets. By default
all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_from_mspsplib_repo(data_home: str | None = None)[source]

Fetch data from youngkd repo. (for multiskill rcpsp)

https://github.com/youngkd/MSPSP-InstLib

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_from_psplib(data_home: str | None = None)[source]

Fetch data for rcpsp examples from psplib.

cf https://www.om-db.wi.tum.de/psplib/data.html

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.fetch_data_from_solutionsupdate(data_home: str | None = None)[source]

Fetch data for rcpsp examples from solutionsupdate.

cf http://solutionsupdate.ugent.be/index.php/solutions-update

Params:

data_home: Specify the cache folder for the datasets. By default: all discrete-optimization data is stored in ‘~/discrete_optimization_data’ subfolders.

discrete_optimization.datasets.get_data_home(data_home: str | None = None) → str[source]

Return the path of the discrete-optimization data directory.

This folder is used by some large dataset loaders to avoid downloading the data several times. By default the data dir is set to a folder named ‘discrete_optimization_data’ in the user home folder. Alternatively, it can be set by the ‘DISCRETE_OPTIMIZATION_DATA’ environment variable or programmatically by giving an explicit folder path. The ‘~’ symbol is expanded to the user home folder. If the folder does not already exist, it is automatically created.

Params:: data_home : The path to discrete-optimization data directory. If None, the default path is ~/discrete_optimization_data.

discrete_optimization package

Subpackages

Submodules

discrete_optimization.datasets module

Module contents