def test_scoring_branch_mcts_tree_in_stock(shared_datadir, default_config,
mock_stock):
mock_stock(
default_config,
"CC(C)(C)CO",
"CC(C)(C)OC(=O)N(CCCl)CCCl",
"N#CCc1cccc(O)c1F",
"O=[N+]([O-])c1ccccc1F",
"O=C1CCC(=O)N1Br",
"O=C=Nc1csc(C(F)(F)F)n1",
"CCC[Sn](Cl)(CCC)CCC",
"COc1ccc2ncsc2c1",
)
search_tree = SearchTree.from_json(
shared_datadir / "tree_with_branching.json", default_config)
nodes = list(search_tree.graph())
assert pytest.approx(StateScorer(default_config)(nodes[-1]),
abs=1e-3) == 0.950
assert NumberOfReactionsScorer()(nodes[-1]) == 14
assert NumberOfPrecursorsScorer(default_config)(nodes[-1]) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(nodes[-1]) == 8
assert PriceSumScorer(default_config)(nodes[-1]) == 8
cost_score = RouteCostScorer(default_config)(nodes[-1])
assert pytest.approx(cost_score, abs=1e-3) == 77.4797
def test_state_scorer_tree(load_reaction_tree, default_config, mock_stock):
mock_stock(default_config, "N#Cc1cccc(N)c1F", "O=C(Cl)c1ccc(F)cc1",
"CN1CCC(Cl)CC1", "O")
tree = ReactionTree.from_dict(load_reaction_tree("sample_reaction.json"))
scorer = StateScorer(default_config)
assert round(scorer(tree), 4) == 0.994
def test_add_scorer_to_collection(default_config):
collection = ScorerCollection(default_config)
del collection["state score"]
collection.load(StateScorer(default_config))
assert "state score" in collection.names()
def test_state_scorer_trees(default_config, setup_linear_reaction_tree):
rt = setup_linear_reaction_tree()
scorer = StateScorer(default_config)
scores = scorer([rt, rt])
assert round(scores[0], 4) == 0.994
assert round(scores[1], 4) == 0.994
def test_scoring_branched_route(load_reaction_tree, default_config):
tree = ReactionTree.from_dict(load_reaction_tree("branched_route.json"))
assert pytest.approx(StateScorer(default_config)(tree),
abs=1e-6) == 0.00012363
assert NumberOfReactionsScorer(default_config)(tree) == 14
assert NumberOfPrecursorsScorer(default_config)(tree) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 0
def __init__(self, search_tree, scorer=None):
self.search_tree = search_tree
if scorer is None:
# Do import here to avoid circular imports
from aizynthfinder.context.scoring import StateScorer
self.scorer = StateScorer()
else:
self.scorer = scorer
def test_state_scorer_nodes(generate_root, default_config):
root = generate_root("CCCCOc1ccc(CC(=O)N(C)O)cc1")
scorer = StateScorer(default_config)
scores = scorer([root, root])
assert repr(scorer) == "state score"
assert round(scores[0], 4) == 0.0491
assert round(scores[1], 4) == 0.0491
def test_scoring_branched_mcts_tree(shared_datadir, default_config):
search_tree = SearchTree.from_json(
shared_datadir / "tree_with_branching.json", default_config)
nodes = list(search_tree.graph())
assert pytest.approx(StateScorer()(nodes[-1]), abs=1e-6) == 0.00012363
assert NumberOfReactionsScorer()(nodes[-1]) == 14
assert NumberOfPrecursorsScorer(default_config)(nodes[-1]) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(nodes[-1]) == 0
def test_sort(default_config, setup_linear_mcts, setup_branched_mcts):
_, node1 = setup_linear_mcts()
_, node2 = setup_branched_mcts()
scorer = StateScorer(default_config)
sorted_nodes, scores, _ = scorer.sort([node2, node1])
assert [np.round(score, 4) for score in scores] == [0.994, 0.9866]
assert sorted_nodes == [node1, node2]
def test_scorers_one_mcts_node(default_config):
tree = SearchTree(default_config, root_smiles="CCCCOc1ccc(CC(=O)N(C)O)cc1")
node = tree.root
assert pytest.approx(StateScorer(default_config)(node), abs=1e-3) == 0.0497
assert NumberOfReactionsScorer(default_config)(node) == 0
assert NumberOfPrecursorsScorer(default_config)(node) == 1
assert NumberOfPrecursorsInStockScorer(default_config)(node) == 0
assert PriceSumScorer(default_config)(node) == 10
assert RouteCostScorer(default_config)(node) == 10
def test_rescore_collection_for_trees(default_config, setup_linear_reaction_tree):
rt = setup_linear_reaction_tree()
routes = RouteCollection(reaction_trees=[rt])
routes.compute_scores(StateScorer(default_config))
routes.rescore(NumberOfReactionsScorer())
assert routes.scores[0] == 2
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
def test_scoring_branched_route(default_config, setup_branched_reaction_tree):
tree = setup_branched_reaction_tree()
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-4) == 0.9866
assert NumberOfReactionsScorer()(tree) == 4
assert NumberOfPrecursorsScorer(default_config)(tree) == 5
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 5
assert PriceSumScorer(default_config)(tree) == 5
cost_score = RouteCostScorer(default_config)(tree)
assert pytest.approx(cost_score, abs=1e-4) == 13.6563
def __init__(
self,
search_tree: Union[MctsSearchTree, AndOrSearchTreeBase],
scorer: Scorer = None,
) -> None:
self.search_tree = search_tree
if scorer is None:
self.scorer: Scorer = StateScorer(search_tree.config)
else:
self.scorer = scorer
def test_state_scorer_nodes(setup_linear_mcts, setup_branched_mcts,
default_config):
_, node1 = setup_linear_mcts()
_, node2 = setup_branched_mcts()
scorer = StateScorer(default_config)
scores = scorer([node1, node2])
assert repr(scorer) == "state score"
assert round(scores[0], 4) == 0.994
assert round(scores[1], 4) == 0.9866
def test_scoring_branched_route_not_in_stock(default_config,
setup_branched_reaction_tree):
tree = setup_branched_reaction_tree("O")
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-4) == 0.7966
assert NumberOfReactionsScorer()(tree) == 4
assert NumberOfPrecursorsScorer(default_config)(tree) == 5
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 4
assert PriceSumScorer(default_config)(tree) == 14
cost_score = RouteCostScorer(default_config)(tree)
assert pytest.approx(cost_score, abs=1e-4) == 31.2344
def test_scorers_tree_one_node_route(default_config):
tree = ReactionTree()
tree.root = UniqueMolecule(smiles="CCCCOc1ccc(CC(=O)N(C)O)cc1")
tree.graph.add_node(tree.root)
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-3) == 0.0497
assert NumberOfReactionsScorer(default_config)(tree) == 0
assert NumberOfPrecursorsScorer(default_config)(tree) == 1
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 0
assert PriceSumScorer(default_config)(tree) == 10
assert RouteCostScorer(default_config)(tree) == 10
def test_sort(shared_datadir, default_config, mock_stock):
mock_stock(default_config, "CCCO", "CC")
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
nodes = list(search_tree.graph())
scorer = StateScorer(default_config)
sorted_nodes, scores, _ = scorer.sort(nodes)
assert [np.round(score, 4) for score in scores] == [0.9976, 0.0491]
assert sorted_nodes == [nodes[1], nodes[0]]
def test_compute_new_score_for_trees(default_config, setup_linear_reaction_tree):
rt = setup_linear_reaction_tree()
routes = RouteCollection(reaction_trees=[rt])
assert routes.nodes[0] is None
assert routes.scores[0] is np.nan
assert routes.all_scores[0] == {}
routes.compute_scores(StateScorer(default_config), NumberOfReactionsScorer())
assert routes.scores[0] is np.nan
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
def test_rescore_collection_for_trees(default_config, mock_stock,
load_reaction_tree):
mock_stock(default_config, "N#Cc1cccc(N)c1F", "O=C(Cl)c1ccc(F)cc1",
"CN1CCC(Cl)CC1", "O")
rt = ReactionTree.from_dict(load_reaction_tree("sample_reaction.json"))
routes = RouteCollection(reaction_trees=[rt])
routes.compute_scores(StateScorer(default_config))
routes.rescore(NumberOfReactionsScorer())
assert routes.scores[0] == 2
assert np.round(routes.all_scores[0]["state score"], 3) == 0.994
assert routes.all_scores[0]["number of reactions"] == 2
def test_scoring_branched_route_in_stock(load_reaction_tree, default_config,
mock_stock):
mock_stock(
default_config,
"CC(C)(C)CO",
"CC(C)(C)OC(=O)N(CCCl)CCCl",
"N#CCc1cccc(O)c1F",
"O=[N+]([O-])c1ccccc1F",
"O=C1CCC(=O)N1Br",
"O=C=Nc1csc(C(F)(F)F)n1",
"CCC[Sn](Cl)(CCC)CCC",
"COc1ccc2ncsc2c1",
)
tree = ReactionTree.from_dict(load_reaction_tree("branched_route.json"))
assert pytest.approx(StateScorer(default_config)(tree), abs=1e-3) == 0.950
assert NumberOfReactionsScorer(default_config)(tree) == 14
assert NumberOfPrecursorsScorer(default_config)(tree) == 8
assert NumberOfPrecursorsInStockScorer(default_config)(tree) == 8
assert PriceSumScorer(default_config)(tree) == 8
cost_score = RouteCostScorer(default_config)(tree)
assert pytest.approx(cost_score, abs=1e-3) == 77.4797
def test_state_scorer_node(default_config, setup_linear_mcts):
_, node = setup_linear_mcts()
scorer = StateScorer(default_config)
assert repr(scorer) == "state score"
assert round(scorer(node), 4) == 0.994
def test_state_scorer_tree(default_config, setup_linear_reaction_tree):
tree = setup_linear_reaction_tree()
scorer = StateScorer(default_config)
assert round(scorer(tree), 4) == 0.994
def test_state_scorer_node(generate_root, default_config):
root = generate_root("CCCCOc1ccc(CC(=O)N(C)O)cc1")
scorer = StateScorer(default_config)
assert repr(scorer) == "state score"
assert round(scorer(root), 4) == 0.0491