gt4sd.algorithms.conditional_generation.guacamol.implementation.graph_mcts module¶
Graph MCTS implementation.
Summary¶
Classes:
Reference¶
- class GraphMCTS(init_smiles, population_size, n_jobs, generations, patience, num_sims, max_children, max_atoms, pickle_directory='/usr/share/miniconda/envs/gt4sd/lib/python3.10/site-packages/guacamol_baselines/graph_mcts')[source]¶
Bases:
object- __init__(init_smiles, population_size, n_jobs, generations, patience, num_sims, max_children, max_atoms, pickle_directory='/usr/share/miniconda/envs/gt4sd/lib/python3.10/site-packages/guacamol_baselines/graph_mcts')[source]¶
Initialize SMILESGA.
- Parameters
init_smiles (
str) – 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.generations (
int) – number of evolutionary generations.patience (
int) – used for early stopping if population scores remains the same after generating molecules.num_sims (
float) – number of times to traverse the tree.max_children (
int) – maximum number of childerns a node could have.max_atoms (
int) – maximum number of atoms to explore to terminal the node state.pickle_directory (
str) – path from where to load pickle files.
- get_generator()[source]¶
Create an instance of the GB_MCTS_Generator.
- Return type
GB_MCTS_Generator- Returns
an instance of GB_MCTS_Generator.
- __dict__ = mappingproxy({'__module__': 'gt4sd.algorithms.conditional_generation.guacamol.implementation.graph_mcts', '__init__': <function GraphMCTS.__init__>, 'get_generator': <function GraphMCTS.get_generator>, '__dict__': <attribute '__dict__' of 'GraphMCTS' objects>, '__weakref__': <attribute '__weakref__' of 'GraphMCTS' objects>, '__doc__': None, '__annotations__': {}})¶
- __doc__ = None¶
- __module__ = 'gt4sd.algorithms.conditional_generation.guacamol.implementation.graph_mcts'¶
- __weakref__¶
list of weak references to the object (if defined)