Stochastic Universal Sampling

class StochasticUniversalSampling(num_batches=None, batch_size=1, duplicate_molecules=True, duplicate_batches=True, key_maker=Inchi(), fitness_modifier=None, random_seed=None)

Bases: Selector

Yields batches of molecules through stochastic universal sampling.

Stochastic universal sampling lays out batches along a line, with each batch taking up length proportional to its fitness. It then creates a set of evenly spaced pointers to different points on the line, each of which is occupied by a batch. Batches which are pointed to are yielded.

This approach means weaker members of the population are given a greater chance to be chosen than in Roulette selection [1].

References

[1]

https://en.wikipedia.org/wiki/Stochastic_universal_sampling

Examples

Yielding Single Molecule Batches

Yielding molecules one at a time. For example, if molecules need to be selected for mutation or the next generation.

import stk

# Make the selector.
stochastic_sampling = stk.StochasticUniversalSampling(5)

population = tuple(
    stk.MoleculeRecord(
        topology_graph=stk.polymer.Linear(
            building_blocks=(
                stk.BuildingBlock(
                    smiles='BrCCBr',
                    functional_groups=[stk.BromoFactory()],
                ),
            ),
            repeating_unit='A',
            num_repeating_units=2,
        ),
    ).with_fitness_value(i)
    for i in range(100)
)

# Select the molecules.
for selected, in stochastic_sampling.select(population):
    # Do stuff with each selected molecule.
    pass

Methods

select(population[, included_batches, ...])

Yield batches of molecule records from population.

__init__(num_batches=None, batch_size=1, duplicate_molecules=True, duplicate_batches=True, key_maker=Inchi(), fitness_modifier=None, random_seed=None)

Initialize a StochasticUniversalSampling instance.

Parameters:

• num_batches (int, optional) – The number of batches to yield. If None then yielding will continue forever or until the generator is exhausted, whichever comes first.

• batch_size (int, optional) – The number of molecules yielded at once.

• duplicate_molecules (bool, optional) – If True the same molecule can be yielded in more than one batch.

• duplicate_batches (bool, optional) – If True the same batch can be yielded more than once.

• key_maker (MoleculeKeyMaker, optional) – Used to get the keys of molecules. If two molecules have the same key, they are considered duplicates.

• fitness_modifier (callable, optional) – Takes the population on which select() is called and returns a dict, which maps records in the population to the fitness values the Selector should use. If None, the regular fitness values of the records are used.

• random_seed (int, optional) – The random seed to use.

select(population, included_batches=None, excluded_batches=None)

Yield batches of molecule records from population.

Parameters:

• population (tuple of MoleculeRecord) – A collection of molecules from which batches are selected.

• included_batches (set, optional) – The identity keys of batches which are allowed to be yielded, if None all batches can be yielded. If not None only batches included_batches will be yielded.

• excluded_batches (class:set, optional) – The identity keys of batches which are not allowed to be yielded. If None, no batch is forbidden from being yielded.

Yields:

Batch of MoleculeRecord – A batch of selected molecule records.