ribs.archives.ArchiveDataFrame¶

class ribs.archives.ArchiveDataFrame(*args, **kwargs)[source]¶

A modified DataFrame for archive data.

As this class inherits from DataFrame, it has the same methods, attributes, and arguments (even though the arguments shown here are *args and **kwargs). However, this class adds methods that make it convenient to work with elites. This documentation only lists these additional methods and attributes.

Example

This object is created by as_pandas() (i.e. users typically do not create it on their own):

df = archive.as_pandas()

To iterate through every Elite, use:

for elite in df.iterelites():
    elite.solution
    elite.objective
    ...

There are also methods to access the solutions, objectives, etc. of all elites in the archive. For instance, the following is an array where entry i contains the measures of the i’th elite in the DataFrame:

df.measures_batch()

Warning

Accessing batch methods (e.g. measures_batch()) always creates a copy, so the following will copy the measures 3 times:

df.measures_batch()[0]
df.measures_batch().mean()
df.measures_batch().median()

Thus, if you need to use the method several times, we recommend storing it first, like so:

measures_batch = df.measures_batch()
measures_batch[0]
measures_batch.mean()
measures_batch.median()

Note

After saving an ArchiveDataFrame to a CSV, loading it with pandas.read_csv() will load a DataFrame. To load a CSV as an ArchiveDataFrame, pass the DataFrame from read_csv to ArchiveDataFrame:

df = ArchiveDataFrame(pd.read_csv("file.csv"))

Note

All the batch methods “align” with each other – i.e. measures_batch()[i] corresponds to index_batch()[i], metadata_batch()[i], objective_batch()[i], and solution_batch()[i].

Methods

`solution_batch`()	Array with solutions of all elites.
`objective_batch`()	Array with objective values of all elites.
`measures_batch`()	Array with measures of all elites.
`index_batch`()	Array with indices of all elites.
`metadata_batch`()	Array with metadata of all elites.
`iterelites`()	Iterator which outputs every `Elite` in the ArchiveDataFrame.

Attributes

index_batch()[source]¶

Array with indices of all elites.

None if there are no indices in the ArchiveDataFrame.

Returns: See above.
Return type: (n,) numpy.ndarray

iterelites()[source]¶

Iterator which outputs every Elite in the ArchiveDataFrame.

Data which is unavailable will be turned into None. For example, if there are no solution columns, then elite.solution will be None.

measures_batch()[source]¶

Array with measures of all elites.

None if there are no measures in the ArchiveDataFrame.

Returns: See above.
Return type: (n, measure_dim) numpy.ndarray

metadata_batch()[source]¶

Array with metadata of all elites.

None if there is no metadata (e.g. if include_metadata=False in as_pandas()).

Returns: See above.
Return type: (n,) numpy.ndarray

objective_batch()[source]¶

Array with objective values of all elites.

None if there are no objectives in the ArchiveDataFrame.

Returns: See above.
Return type: (n,) numpy.ndarray

solution_batch()[source]¶

Array with solutions of all elites.

None if there are no solutions (e.g. if include_solutions=False in as_pandas()).

Returns: See above.
Return type: (n, solution_dim) numpy.ndarray