Functions — github.com/MIRALab-USTC/AI4Sci-MiCaM

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Functions121 in github.com/MIRALab-USTC/AI4Sci-MiCaM

⨍ Functions 121◇ Types & classes 28

↓ 10 callersFunctionfragment2smiles

(mol: MolGraph, indices: List[int])

src/merging_operation_learning.py:78

↓ 8 callersMethodcuda

(self)

src/model/mydataclass.py:46

↓ 7 callersFunctionparse_arguments

()

src/arguments.py:5

↓ 5 callersMethodget_atom_features

(atom: Chem.rdchem.Atom=None, IsConn: bool=False, BondType: Chem.rdchem.BondType=None)

src/model/mol_graph.py:284

↓ 5 callersFunctionsmiles2mol

(smiles: str, sanitize: bool=False)

src/model/utils.py:10

↓ 4 callersMethodload_operations

(cls, operation_path: str, num_operations: int=500)

src/model/mol_graph.py:42

↓ 4 callersMethodload_vocab

(cls, vocab_path: str)

src/model/mol_graph.py:48

↓ 4 callersFunctionnetworkx2data

(G: nx.Graph)

src/model/utils.py:29

↓ 4 callersMethodstep

(self)

src/model/scheduler.py:15

↓ 3 callersFunctiongraph2smiles

(fragment_graph: nx.Graph, with_idx: bool=False)

src/model/utils.py:17

↓ 3 callersFunctionmerge_nodes

(graph: nx.Graph, node1: int, node2: int)

src/merging_operation_learning.py:82

↓ 2 callersMethodadd_motif

(self, motif_smiles: str, connection_order: Optional[int] = None)

src/model/decoder.py:69

↓ 2 callersFunctionget_accuracy

(scores: torch.Tensor, labels: torch.Tensor)

src/model/utils.py:140

↓ 2 callersFunctionget_conn_list

(motif: Chem.rdchem.Mol, use_Isotope: bool=False, symm: bool=False)

src/model/utils.py:106

↓ 2 callersMethodget_data

(self)

src/model/mol_graph.py:195

↓ 2 callersMethodload_generator

(model_params: ModelParams, paths: Paths)

src/model/MiCaM_VAE.py:161

↓ 2 callersFunctionmake_trainig_data

( mols_pkl_dir: str, valid_path: str, vocab_path: str, train_processed_dir: str, vali

src/make_training_data.py:36

↓ 2 callersMethodmerge_atoms

(self, atom1: int, atom2: int)

src/model/decoder.py:35

↓ 2 callersFunctionmerging_operation_learning

( train_path: str, operation_path: str, num_iters: int, min_frequency: int, num_workers: i

src/merging_operation_learning.py:217

↓ 2 callersFunctionmol_graph2smiles

(graph: nx.Graph, postprocessing: bool=True)

src/model/utils.py:53

↓ 2 callersMethodmotif_to_graph

(smiles: str, motif_list: Optional[List[str]] = None)

src/model/mol_graph.py:298

↓ 2 callersFunctionmotif_vocab_construction

( train_path: str, vocab_path: str, operation_path: str, num_operations: int, num_wor

src/motif_vocab_construction.py:29

↓ 2 callersMethodrsample

(self, z: torch.Tensor, perturb: bool=True)

src/model/MiCaM_VAE.py:114

↓ 2 callersFunctionsample_from_distribution

(distribution: torch.Tensor, greedy: bool=False, topk: int=0)

src/model/utils.py:148

↓ 2 callersFunctionsmarts2atom

(smarts: str)

src/model/utils.py:50

↓ 2 callersMethodstate_update

(self)

src/model/decoder.py:59

↓ 1 callersMethod__init__

( self, in_channels: Optional[int] = None, hidden_channels: Optional[int] = None,

src/model/nn.py:10

↓ 1 callersMethod__init__

(self, params: TrainingParams, init_beta: int, init_step: int)

src/model/scheduler.py:6

↓ 1 callersMethod__init__

(self, atom_embedding: Atom_Embedding, edge_embedding: nn.Embedding, GNN: nn.Module

src/model/encoder.py:31

↓ 1 callersFunctionapply_merging_operation

( motif: str, mols: List[MolGraph], stats: Dict[str, int], indices: Dict[str, Dict[int, int]],

src/merging_operation_learning.py:197

↓ 1 callersMethodapply_merging_operation

(self, motif: str, stats: Dict[str, int], indices: Dict[str, Dict[int, int]])

src/merging_operation_learning.py:29

↓ 1 callersFunctionapply_merging_operation_consumer

(mols: List[MolGraph], stats: Dict[str, int], indices: Dict[str, Dict[int, int]], q: Queue, num_workers: int)

src/merging_operation_learning.py:179

↓ 1 callersMethodapply_merging_operation_producer

(self, motif: str, q: Queue)

src/merging_operation_learning.py:45

↓ 1 callersMethodassess_model

(self, generator)

src/model/benchmarks.py:61

↓ 1 callersMethodbenchmark

(self, train_path: str)

src/model/MiCaM_VAE.py:136

↓ 1 callersMethodconnection_query_step

( self, query: torch.Tensor, decoder_state: DecoderState, scores_motif_con

src/model/decoder.py:245

↓ 1 callersMethodconnetion_query_steps_for_batch

( self, decoder_states: List[DecoderState], max_decode_step: int, greedy:

src/model/decoder.py:286

↓ 1 callersMethodcyclize

(self, connection_idx)

src/model/decoder.py:92

↓ 1 callersMethoddecode

(self, z: torch.Tensor, greedy: bool=True, beam_top: int=1, batch_size: int=1000)

src/model/MiCaM_VAE.py:127

↓ 1 callersMethodembed_graph

(self, x, edge_attr)

src/model/encoder.py:44

↓ 1 callersFunctionfragment2smiles

(mol: Chem.rdchem.Mol, indices: List[int])

src/model/utils.py:46

↓ 1 callersMethodfrom_conn_idx

(self, conn_idx: int)

src/model/vocab.py:68

↓ 1 callersMethodgenerate

(self, number_samples: int)

src/model/MiCaM_VAE.py:198

↓ 1 callersMethodgenerate

(self, number_samples: int)

src/model/benchmarks.py:45

↓ 1 callersMethodget_conn_label

(self, motif_idx: int, order_idx: int)

src/model/vocab.py:96

↓ 1 callersMethodget_conns_idx

(self)

src/model/vocab.py:65

↓ 1 callersMethodget_conns_idx

(self)

src/model/vocab.py:99

↓ 1 callersMethodget_merging_graph

(self)

src/model/mol_graph.py:84

↓ 1 callersMethodget_mol_graph

(self)

src/model/mol_graph.py:69

↓ 1 callersMethodget_motifs

(self)

src/model/mol_graph.py:147

↓ 1 callersMethodget_props

(self)

src/model/mol_graph.py:186

↓ 1 callersFunctionget_stats

(mols: List[MolGraph], num_workers: int)

src/merging_operation_learning.py:113

↓ 1 callersFunctionget_stats_consumer

(stats: Dict[str, int], indices: Dict[str, Dict[int, int]], q: Queue, num_workers: int)

src/merging_operation_learning.py:100

↓ 1 callersMethodload_model

(model_params: ModelParams, paths: Paths)

src/model/MiCaM_VAE.py:149

↓ 1 callersFunctionload_mols

(train_path: str, num_workers: int)

src/merging_operation_learning.py:63

↓ 1 callersMethodload_motifs_embed

(self, file)

src/model/decoder.py:223

↓ 1 callersMethodload_motifs_embed

(self, path)

src/model/MiCaM_VAE.py:145

↓ 1 callersMethodlog_tb_results

(self, total_step: int, tb:SummaryWriter, beta, lr)

src/model/mydataclass.py:239

↓ 1 callersMethodmotif_idx_in_sublist

(self, motif_idx: int)

src/model/vocab.py:93

↓ 1 callersMethodpick_fisrt_motifs_for_batch

( self, latent_reprs: torch.Tensor, decoder_states: List[DecoderState], gr

src/model/decoder.py:227

↓ 1 callersFunctionpostprocess

(smiles: str)

src/model/utils.py:66

↓ 1 callersMethodpreprocess_vocab

()

src/model/mol_graph.py:274

↓ 1 callersMethodprint_results

(self, total_step: int, lr:float, beta: float)

src/model/mydataclass.py:234

↓ 1 callersMethodrefragment

(self)

src/model/mol_graph.py:99

↓ 1 callersMethodrelabel

(self)

src/model/mol_graph.py:150

↓ 1 callersMethodreset_parameters

(self)

src/model/nn.py:50

↓ 1 callersMethodresult

(self, return_trace: bool=False)

src/model/decoder.py:97

↓ 1 callersMethodsample

(self, num_sample: int=100, greedy: bool=True, beam_top: int=1)

src/model/MiCaM_VAE.py:123

↓ 1 callersMethodsave_motifs_embed

(self, file)

src/model/decoder.py:210

↓ 1 callersMethodsize

(self)

src/model/vocab.py:20

↓ 1 callersFunctiontrain

(args: argparse.Namespace)

src/train.py:22

↓ 1 callersFunctionupdate_stats

(mol: MolGraph, graph: nx.Graph, new_graph: nx.Graph, node1: int, node2: int, stats: Dict[str, int], indices:

src/merging_operation_learning.py:134

↓ 1 callersFunctionupdate_stats_producer

(mol: MolGraph, graph: nx.Graph, new_graph: nx.Graph, node1: int, node2: int, q: Queue, i: int)

src/merging_operation_learning.py:156

Method__getitem__

(self, index)

src/model/dataset.py:21

Method__getitem__

(self, smiles)

src/model/vocab.py:14

Method__getitem__

(self, smiles: str)

src/model/vocab.py:57

Method__init__

(self, smiles: str, idx: int=0)

src/merging_operation_learning.py:22

Method__init__

( self, latent_repr: torch.Tensor, batch_idx: int, return_trace: bool = Fa

src/model/decoder.py:14

Method__init__

(self, atom_embedding: nn.Module, edge_embedding: nn.Module, decoder_gnn: nn.Module

src/model/decoder.py:107

Method__init__

(self, in_channels, out_channels, hidden_channels, edge_dim, depth, dropout, virtual: bool=True, pooling: str=

src/model/nn.py:71

Method__init__

(self, model_params: ModelParams)

src/model/MiCaM_VAE.py:22

Method__init__

(self, model: MiCaM)

src/model/MiCaM_VAE.py:195

Method__init__

(self, params: TrainingParams, init_beta: int, init_step: int)

src/model/scheduler.py:19

Method__init__

(self, params: TrainingParams, init_beta: int=0, init_step: int=0)

src/model/scheduler.py:35

Method__init__

(self, data_dir: str)

src/model/dataset.py:16

Method__init__

(self, generator: DistributionMatchingGenerator, number_samples: int=10000, max_tries: int=20)

src/model/benchmarks.py:17

Method__init__

(self, training_set: List[str], num_samples: int=10000)

src/model/benchmarks.py:54

Method__init__

(self, smiles: str, tokenizer: str="graph", )

src/model/mol_graph.py:53

Method__init__

(self, args: Namespace)

src/model/mydataclass.py:74

Method__init__

(self, args: Namespace)

src/model/mydataclass.py:122

Method__init__

(self, args: Namespace)

src/model/mydataclass.py:165

Method__init__

(self, vocab_list)

src/model/vocab.py:10

Method__init__

(self, pair_list: List[Tuple[str, str]])

src/model/vocab.py:25

Method__init__

(self, motif_vocab: MotifVocab, sublist: List[int])

src/model/vocab.py:73

Method__init__

(self, atom_embed_size: List[int] )

src/model/encoder.py:10

Method__len__

(self)

src/model/dataset.py:25

src/model/mydataclass.py:136

Method__repr__

(self)

src/model/mydataclass.py:178

Method__repr__

(self)

src/model/mydataclass.py:212

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