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# Conflicts: # test/integration/test_genetic_schemes.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| import os.path | ||
| from datetime import timedelta | ||
| from pathlib import Path | ||
| from typing import Type, Optional, Sequence, List | ||
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| import numpy as np | ||
| from rdkit.Chem.rdchem import BondType | ||
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| from examples.molecule_search.experiment import visualize_results, get_methane, get_all_mol_metrics | ||
| from examples.molecule_search.mol_adapter import MolAdapter | ||
| from examples.molecule_search.mol_advisor import MolChangeAdvisor | ||
| from examples.molecule_search.mol_graph import MolGraph | ||
| from examples.molecule_search.mol_mutations import CHEMICAL_MUTATIONS | ||
| from golem.api.main import GOLEM | ||
| from golem.core.dag.verification_rules import has_no_self_cycled_nodes, has_no_isolated_components, \ | ||
| has_no_isolated_nodes | ||
| from golem.core.optimisers.adaptive.operator_agent import MutationAgentTypeEnum | ||
| from golem.core.optimisers.genetic.gp_optimizer import EvoGraphOptimizer | ||
| from golem.core.optimisers.genetic.operators.crossover import CrossoverTypesEnum | ||
| from golem.core.optimisers.genetic.operators.elitism import ElitismTypesEnum | ||
| from golem.core.optimisers.genetic.operators.inheritance import GeneticSchemeTypesEnum | ||
| from golem.core.optimisers.objective import Objective | ||
| from golem.core.optimisers.opt_history_objects.opt_history import OptHistory | ||
| from golem.core.optimisers.optimizer import GraphOptimizer | ||
| from golem.core.paths import project_root | ||
| from golem.visualisation.opt_history.multiple_fitness_line import MultipleFitnessLines | ||
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| def run_experiment(optimizer_cls: Type[GraphOptimizer] = EvoGraphOptimizer, | ||
| adaptive_kind: MutationAgentTypeEnum = MutationAgentTypeEnum.random, | ||
| max_heavy_atoms: int = 50, | ||
| atom_types: Optional[List[str]] = None, | ||
| bond_types: Sequence[BondType] = (BondType.SINGLE, BondType.DOUBLE, BondType.TRIPLE), | ||
| initial_molecules: Optional[Sequence[MolGraph]] = None, | ||
| pop_size: int = 20, | ||
| metrics: Optional[List[str]] = None, | ||
| num_trials: int = 1, | ||
| trial_timeout: Optional[int] = None, | ||
| trial_iterations: Optional[int] = None, | ||
| visualize: bool = False, | ||
| save_history: bool = True, | ||
| pretrain_dir: Optional[str] = None, | ||
| ): | ||
| metrics = metrics or ['qed_score'] | ||
| optimizer_id = optimizer_cls.__name__.lower()[:3] | ||
| experiment_id = f'Experiment [optimizer={optimizer_id} metrics={", ".join(metrics)} pop_size={pop_size}]' | ||
| exp_name = f'{optimizer_id}_{adaptive_kind.value}_popsize{pop_size}_min{trial_timeout}_{"_".join(metrics)}' | ||
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| atom_types = atom_types or ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br'] | ||
| trial_results = [] | ||
| trial_histories = [] | ||
| trial_timedelta = timedelta(minutes=trial_timeout) if trial_timeout else None | ||
| all_metrics = get_all_mol_metrics() | ||
| objective = Objective( | ||
| quality_metrics={metric_name: all_metrics[metric_name] for metric_name in metrics}, | ||
| is_multi_objective=len(metrics) > 1 | ||
| ) | ||
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| for trial in range(num_trials): | ||
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| metrics = metrics or ['qed_score'] | ||
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| initial_graphs = initial_molecules or [get_methane()] | ||
| initial_graphs = MolAdapter().adapt(initial_graphs) | ||
| golem = GOLEM( | ||
| n_jobs=1, | ||
| timeout=trial_timedelta, | ||
| objective=objective, | ||
| optimizer=optimizer_cls, | ||
| initial_graphs=initial_graphs, | ||
| pop_size=pop_size, | ||
| max_pop_size=pop_size, | ||
| multi_objective=True, | ||
| genetic_scheme_type=GeneticSchemeTypesEnum.steady_state, | ||
| elitism_type=ElitismTypesEnum.replace_worst, | ||
| mutation_types=CHEMICAL_MUTATIONS, | ||
| crossover_types=[CrossoverTypesEnum.none], | ||
| adaptive_mutation_type=adaptive_kind, | ||
| adapter=MolAdapter(), | ||
| rules_for_constraint=[has_no_self_cycled_nodes, has_no_isolated_components, has_no_isolated_nodes], | ||
| advisor=MolChangeAdvisor(), | ||
| max_heavy_atoms=max_heavy_atoms, | ||
| available_atom_types=atom_types or ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br'], | ||
| bond_types=bond_types, | ||
| early_stopping_timeout=np.inf, | ||
| early_stopping_iterations=np.inf, | ||
| keep_n_best=4, | ||
| num_of_generations=trial_iterations, | ||
| keep_history=True, | ||
| history_dir=None, | ||
| ) | ||
| found_graphs = golem.optimise() | ||
| history = golem.optimiser.history | ||
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| if visualize: | ||
| molecules = [MolAdapter().restore(graph) for graph in found_graphs] | ||
| save_dir = Path('visualisations') / exp_name / f'trial_{trial}' | ||
| visualize_results(set(molecules), objective, history, save_dir) | ||
| if save_history: | ||
| result_dir = Path('results') / exp_name | ||
| result_dir.mkdir(parents=True, exist_ok=True) | ||
| history.save(result_dir / f'history_trial_{trial}.json') | ||
| trial_results.extend(history.final_choices) | ||
| trial_histories.append(history) | ||
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| # Compute mean & std for metrics of trials | ||
| ff = objective.format_fitness | ||
| trial_metrics = np.array([ind.fitness.values for ind in trial_results]) | ||
| trial_metrics_mean = trial_metrics.mean(axis=0) | ||
| trial_metrics_std = trial_metrics.std(axis=0) | ||
| print(f'Experiment {experiment_id}\n' | ||
| f'finished with metrics:\n' | ||
| f'mean={ff(trial_metrics_mean)}\n' | ||
| f' std={ff(trial_metrics_std)}') | ||
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| def plot_experiment_comparison(experiment_ids: Sequence[str], metric_id: int = 0, results_dir='./results'): | ||
| root = Path(results_dir) | ||
| histories = {} | ||
| for exp_name in experiment_ids: | ||
| trials = [] | ||
| for history_filename in os.listdir(root / exp_name): | ||
| if history_filename.startswith('history'): | ||
| history = OptHistory.load(root / exp_name / history_filename) | ||
| trials.append(history) | ||
| histories[exp_name] = trials | ||
| print(f'Loaded {len(trials)} trial histories for experiment: {exp_name}') | ||
| # Visualize | ||
| MultipleFitnessLines.from_histories(histories).visualize(metric_id=metric_id) | ||
| return histories | ||
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| if __name__ == '__main__': | ||
| run_experiment(adaptive_kind=MutationAgentTypeEnum.bandit, | ||
| max_heavy_atoms=38, | ||
| trial_timeout=6, | ||
| pop_size=50, | ||
| visualize=True, | ||
| num_trials=5, | ||
| pretrain_dir=os.path.join(project_root(), 'examples', 'molecule_search', 'histories') | ||
| ) |
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