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Project description
Chytorch [kʌɪtɔːrtʃ]
Library for modeling molecules and reactions in torch way.
Installation
Use pip install chytorch
to install release version.
Or pip install .
in source code directory to install DEV version.
Pretrained models
Chytorch main package doesn't include models zoo.
Each model has its own named package and can be installed separately.
Installed models can be imported from chytorch.zoo.<model_name>.Model
.
Usage
chytorch.nn.MoleculeEncoder
and chytorch.nn.ReactionEncoder
- core graphormer layers for molecules and reactions.
API is combination of torch.nn.TransformerEncoderLayer
with torch.nn.TransformerEncoder
.
Batch preparation:
chytorch.utils.data.MoleculeDataset
and chytorch.utils.data.ReactionDataset
- Map-like on-the-fly dataset generators for molecules and reactions.
Supported chython.MoleculeContainer
and chython.ReactionContainer
objects.
chytorch.utils.data.collate_molecules
and chytorch.utils.data.collate_reactions
- collate functions for torch.utils.data.DataLoader
.
Example:
from chython import SMILESRead
data = []
for r in SMILESRead('data.smi'):
r.canonicalize() # fix aromaticity and functional groups
data.append(r)
ds = chytorch.utils.data.MoleculeDataset(data)
dl = torch.utils.data.DataLoader(ds, collate_fn=chytorch.utils.data.collate_molecules, batch_size=10)
Forward call:
Molecules coded as tensors of:
- atoms numbers shifted by 2 (e.g. hydrogen = 3). 0 - reserved for padding, 1 - reserved for CLS token, 2 - for MLM task.
- neighbors count, including implicit hydrogens shifted by 2 (e.g. CO = CH3OH = [6, 4]). 0 - reserved for padding, 1 - for MLM task, 2 - no-neighbors, 3 - one neighbor.
- topological distances' matrix shifted by 2 with upper limit. 0 - reserved for padding, 1 - reserved for not-connected graph components coding, 2 - self-loop, 3 - connected atoms.
Reactions coded in similar way. Molecules atoms and neighbors matrices just stacked. Distance matrices stacked on diagonal. Reactions include additional tensor with reaction role codes for each token. 0 - padding, 1 - reaction CLS, 2 - reactants, 3 - products.
encoder = chytorch.nn.MoleculeEncoder()
for b in dl:
encoder(*b)
Combine molecules and labels:
chytorch.utils.data.chained_collate
- helper for combining different data parts.
dl = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(chytorch.utils.data.MoleculeDataset(molecules_list), properties_tensor),
collate_fn=chytorch.utils.data.chained_collate(chytorch.utils.data.collate_molecules, torch.stack))
Scheduler:
chytorch.optim.lr_scheduler.WarmUpCosine
- Linear warmup followed with cosine-function for 0-pi range rescaled to lr_rate - decrease_coef * lr_rate interval.
Voting NN with single hidden layer:
chytorch.nn.VotingClassifier
and chytorch.nn.VotingRegressor
- speed optimized multiple heads for ensemble predictions.
Caching:
chytorch.utils.cache.SequencedFileCache
, chytorch.utils.cache.SequencedDBCache
, chytorch.utils.cache.SequencedDtypeCompressedCache
, chytorch.utils.cache.CycleDataLoader
- helpers for caching slow dataset generators output.
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