def test_find_repetetive_patterns_created_tree_no_patterns(
default_config, mock_stock, shared_datadir):
mock_stock(default_config, Molecule(smiles="CC"), Molecule(smiles="CCCO"))
# Try with a short tree (3 nodes, 1 reaction)
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
analysis = TreeAnalysis(search_tree)
rt = ReactionTree.from_analysis(analysis)
assert not rt.has_repeating_patterns
hidden_nodes = [
node for node in rt.graph if rt.graph.nodes[node].get("hide", False)
]
assert len(hidden_nodes) == 0
# Try with something longer
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition_longer.json", default_config)
analysis = TreeAnalysis(search_tree)
rt = ReactionTree.from_analysis(analysis)
assert not rt.has_repeating_patterns
def test_find_repetetive_patterns_created_tree(default_config, mock_stock,
shared_datadir):
mock_stock(default_config, Molecule(smiles="CC"), Molecule(smiles="C"))
# Try one with 2 repetetive units
search_tree = SearchTree.from_json(
shared_datadir / "tree_with_repetition.json", default_config)
analysis = TreeAnalysis(search_tree)
rt = ReactionTree.from_analysis(analysis)
assert rt.has_repeating_patterns
hidden_nodes = [
node for node in rt.graph if rt.graph.nodes[node].get("hide", False)
]
assert len(hidden_nodes) == 5
# Try one with 3 repetetive units
search_tree = SearchTree.from_json(
shared_datadir / "tree_with_3_repetitions.json", default_config)
analysis = TreeAnalysis(search_tree)
rt = ReactionTree.from_analysis(analysis)
assert rt.has_repeating_patterns
hidden_nodes = [
node for node in rt.graph if rt.graph.nodes[node].get("hide", False)
]
assert len(hidden_nodes) == 10
def test_template_occurence_scorer_no_metadata(shared_datadir, default_config):
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
nodes = list(search_tree.graph())
scorer = AverageTemplateOccurenceScorer()
assert scorer(nodes[1]) == 0
def test_number_of_reaction_scorer_node(shared_datadir, default_config):
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
nodes = list(search_tree.graph())
scorer = NumberOfReactionsScorer()
assert scorer(nodes[1]) == 1
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_create_combine_tree_dict_from_tree(mock_stock, default_config,
load_reaction_tree,
shared_datadir):
mock_stock(
default_config,
"Nc1ccc(NC(=S)Nc2ccccc2)cc1",
"Cc1ccc2nc3ccccc3c(Cl)c2c1",
"Nc1ccc(N)cc1",
"S=C=Nc1ccccc1",
"Cc1ccc2nc3ccccc3c(N)c2c1",
"Nc1ccc(Br)cc1",
)
search_tree = SearchTree.from_json(
shared_datadir / "tree_for_clustering.json", default_config)
analysis = TreeAnalysis(search_tree)
collection = RouteCollection.from_analysis(analysis, 3)
expected = load_reaction_tree("combined_example_tree.json")
combined_dict = collection.combined_reaction_trees().to_dict()
assert len(combined_dict["children"]) == 2
assert combined_dict["children"][0]["is_reaction"]
assert len(combined_dict["children"][0]["children"]) == 2
assert len(combined_dict["children"][1]["children"]) == 2
assert len(combined_dict["children"][1]["children"][0]["children"]) == 2
assert combined_dict["children"][1]["children"][0]["children"][0][
"is_reaction"]
assert combined_dict == expected
def test_template_occurence_scorer(shared_datadir, default_config):
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
nodes = list(search_tree.graph())
nodes[0][nodes[1]]["action"].metadata["library_occurence"] = 5
scorer = AverageTemplateOccurenceScorer()
assert scorer(nodes[0]) == 0
assert scorer(nodes[1]) == 5
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_serialize_deserialize_tree(
fresh_tree,
generate_root,
simple_actions,
mock_expansion_policy,
default_config,
mocker,
tmpdir,
):
serializer = MoleculeSerializer()
root = generate_root("CCCCOc1ccc(CC(=O)N(C)O)cc1")
fresh_tree.root = root
action_list, prior_list = mock_expansion_policy(root.state.mols[0])
root.expand()
child = root.promising_child()
mocked_json_dump = mocker.patch("aizynthfinder.mcts.mcts.json.dump")
serializer = MoleculeSerializer()
filename = str(tmpdir / "dummy.json")
# Test serialization
fresh_tree.serialize(filename)
expected_dict = {
"tree": root.serialize(serializer),
"molecules": serializer.store
}
mocked_json_dump.assert_called_once_with(expected_dict,
mocker.ANY,
indent=mocker.ANY)
# Test deserialization
mocker.patch("aizynthfinder.mcts.mcts.json.load",
return_value=expected_dict)
new_tree = SearchTree.from_json(filename, default_config)
root_new = new_tree.root
assert len(root_new.children()) == 1
new_child = root_new.children()[0]
assert root_new.children_view()["values"] == root.children_view()["values"]
assert root_new.children_view()["priors"] == root.children_view()["priors"]
assert (root_new.children_view()["visitations"] == root.children_view()
["visitations"])
assert root_new.is_expanded
assert new_child.children_view()["values"] == child.children_view(
)["values"]
assert new_child.children_view()["priors"] == child.children_view(
)["priors"]
assert (new_child.children_view()["visitations"] == child.children_view()
["visitations"])
assert not new_child.is_expanded
assert str(root_new.state) == str(root.state)
assert str(new_child.state) == str(child.state)
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 setup_analysis(default_config, shared_datadir, tmpdir, mock_stock):
mock_stock(
default_config, "N#Cc1cccc(N)c1F", "O=C(Cl)c1ccc(F)cc1", "CN1CCC(Cl)CC1", "O"
)
with gzip.open(shared_datadir / "full_search_tree.json.gz", "rb") as gzip_obj:
with open(tmpdir / "full_search_tree.json", "wb") as fileobj:
fileobj.write(gzip_obj.read())
tree = SearchTree.from_json(tmpdir / "full_search_tree.json", default_config)
nodes = list(tree.graph())
def wrapper(scorer=None):
return TreeAnalysis(tree, scorer=scorer), nodes
return wrapper
def test_route_node_depth_from_analysis(default_config, mock_stock,
shared_datadir):
mock_stock(default_config, Molecule(smiles="CC"), Molecule(smiles="CCCO"))
search_tree = SearchTree.from_json(
shared_datadir / "tree_without_repetition.json", default_config)
analysis = TreeAnalysis(search_tree)
rt = ReactionTree.from_analysis(analysis)
mols = list(rt.molecules())
assert rt.depth(mols[0]) == 0
assert rt.depth(mols[1]) == 2
assert rt.depth(mols[2]) == 2
rxns = list(rt.reactions())
assert rt.depth(rxns[0]) == 1
for mol in rt.molecules():
assert rt.depth(mol) == 2 * rt.graph.nodes[mol]["transform"]