Ladder VAE for molecular generation task.
A project for Deep Generative Models course in 2020 spring.
Tested with:
python 3.6.9
molsets 0.3.1
torch 1.4.0
rdkit 2019.03.4
Currently only support install manually:
- Clone the repository:
git clone https://github.com/wxx07/MolLVAE.git-
Install RDKit for running tests.
-
Install
mollvae:
cd MolLVAE/
pip install -e .cd MolLVAE/
python mollvae/train.py --device <device> \
--model_save <path to trained model> \
--log_path <path to training log> \
--train_load data/train.csv \
--valid_load data/valid.csv \
--lr_n_restarts 20 \
--ratio 0.05 \
--emb_sz 256 \
--enc_hidden_size 256 \
--enc_num_layers 1 \
--dec_hid_sz 512 \
--dec_n_layer 2 \
--ladder_d_size 256 128 64 \
--ladder_z_size 16 8 4 \
--ladder_z2z_layer_size 8 16 \
--dropout 0.2Train with hyperparameters obtained from random searching.
Run:
python mollvae/tests/test_set_reconstruct.py --device <device> \
--test_load data/test.csv \
--n_enc_zs 1 1 1 --n_dec_xs 1 --gen_bsz 128 \
--emb_sz 256 \
--enc_hidden_size 256 \
--enc_num_layers 1 \
--dec_hid_sz 512 \
--dec_n_layer 2 \
--ladder_d_size 256 128 64 \
--ladder_z_size 16 8 4 \
--ladder_z2z_layer_size 8 16 \
--dropout 0.2 \
--model_load res/exp.best_hyp_combo97/model_195.ptto get one-per-one reconstruction rate ( one input, one latent code, one decoding attempt)
Run:
python mollvae/tests/test_set_reconstruct.py --device cuda:0 \
--test_load data/test.csv \
--n_enc_zs 10 10 10 --n_dec_xs 10 --gen_bsz 128 \
--emb_sz 256 \
--enc_hidden_size 256 \
--enc_num_layers 1 \
--dec_hid_sz 512 \
--dec_n_layer 2 \
--ladder_d_size 256 128 64 \
--ladder_z_size 16 8 4 \
--ladder_z2z_layer_size 8 16 \
--dropout 0.2 \
--model_load res/exp.best_hyp_combo97/model_195.ptto get 100-per-1 reconstruction rate ( one input, ten latent codes, ten decoding attempts for each latent code)
Run:
python mollvae/sample.py --device cuda:1 \
--sample_type prior \
--n_enc_zs 1000 1000 1000 --n_dec_xs 10 --gen_bsz 128 \
--emb_sz 256 \
--enc_hidden_size 256 \
--enc_num_layers 1 \
--dec_hid_sz 512 \
--dec_n_layer 2 \
--ladder_d_size 256 128 64 \
--ladder_z_size 16 8 4 \
--ladder_z2z_layer_size 8 16 \
--dropout 0.2 \
--model_load res/exp.best_hyp_combo97/model_195.pt \
--sample_save <path to save sampled smiles>to sample at top z layer 1000 times and attempt to decode 10 times for each latent code.
Show validity and unique rate.
Run:
python mollvae/sample.py --device cuda:1 \
--sample_type prior \
--n_enc_zs 1000 100 10 --n_dec_xs 10 --gen_bsz 128 \
--emb_sz 256 \
--enc_hidden_size 256 \
--enc_num_layers 1 \
--dec_hid_sz 512 \
--dec_n_layer 2 \
--ladder_d_size 256 128 64 \
--ladder_z_size 16 8 4 \
--ladder_z2z_layer_size 8 16 \
--dropout 0.2 \
--model_load res/exp.best_hyp_combo97/model_195.pt \
--sample_save prior.10x10x10.dec_xs_10.csvto sample at top z layer 10 times, sample at next highest z layer 10 times for each z_top and so on. Attempts to decode 10 times for each latent code.
Show validity and unique rate.
Run
python mollvae/sample.py --device cuda:0 \
--sample_type control_z \
--sample_layer <z layer index> \
--n_enc_zs <n_enc_zs> --n_dec_xs 10 --gen_bsz 128 \
--emb_sz 256 \
--enc_hidden_size 256 \
--enc_num_layers 1 \
--dec_hid_sz 512 \
--dec_n_layer 2 \
--ladder_d_size 256 128 64 \
--ladder_z_size 16 8 4 \
--ladder_z2z_layer_size 8 16 \
--dropout 0.2 \
--model_load res/exp.best_hyp_combo97/model_195.pt \
--sample_save <path to save sampled smiles>to get molecules decode from latent codes varying at layer sample_layer
Similar pipeline to addtt's except skip connection.
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