API Documentation

Olympus

class olympus.Olympus(*args, **kwargs)[source]

Master class of the olympus package

creates empty object and loads defaults

Parameters

  • me (str) – arbitrary name to identify the object

  • indent (int) – number of spaces used in string representation

benchmark(dataset='alkox', planners='all', database=<Database (name=olympus_eb71d764, kind=sqlite)>, num_ind_runs=5, num_iter=3)[source]
Parameters

dataset (str) – the dataset to use

Datasets

class olympus.datasets.Dataset(kind=None, data=None, columns=None, target_ids=None, test_frac=0.2, num_folds=5, random_seed=None)[source]

A Dataset object stores the data of a dataset by wrapping a pandas.DataFrame in its data attribute, provides additional information on the dataset, and provides convenience methods to access features and targets as well as to generate training/validation/test splits.

Parameters

  • kind (str) – kind of the Olympus dataset to load.

  • data (array) – custom dataset. Same input as for pandas.DataFrame.

  • columns (list) – column names. Same input as for pandas.DataFrame.

  • target_ids (list) – list of column indices, or names if provided, that identify the targets for the predictions.

  • test_frac (float) – fraction of the data to be used as test set.

  • num_folds (int) – number of cross validation folds the training set will be split into.

  • random_seed (int) – random seed for numpy. Setting a seed makes the random splits reproducible.

Models

Planners