gt4sd.algorithms.conditional_generation.guacamol.implementation.graph_ga module¶

Graph GA implementation.

Summary¶

Classes:

class GraphGA(smi_file, mutation_rate, population_size, offspring_size, n_jobs, random_start, generations, patience)[source]¶

Bases: object

__init__(smi_file, mutation_rate, population_size, offspring_size, n_jobs, random_start, generations, patience)[source]¶

Initialize SMILESGA.

Parameters

smi_file – path where to load hypothesis, candidate labels and, optionally, the smiles file.
population_size (int) – used with n_mutations for the initial generation of smiles within the population.
n_jobs (int) – number of concurrently running jobs.
random_start (bool) – set to True to randomly choose list of SMILES for generating optimizied molecules.
generations (int) – number of evolutionary generations.
patience (int) – used for early stopping if population scores remains the same after generating molecules.
mutation_rate (float) – frequency of the new mutations in a single gene or organism over time.
offspring_size (int) – number of molecules to select for new population.

get_generator()[source]¶

Create an instance of the GB_GA_Generator.

__dict__ = mappingproxy({'__module__': 'gt4sd.algorithms.conditional_generation.guacamol.implementation.graph_ga', '__init__': <function GraphGA.__init__>, 'get_generator': <function GraphGA.get_generator>, '__dict__': <attribute '__dict__' of 'GraphGA' objects>, '__weakref__': <attribute '__weakref__' of 'GraphGA' objects>, '__doc__': None, '__annotations__': {}})¶

__module__ = 'gt4sd.algorithms.conditional_generation.guacamol.implementation.graph_ga'¶