lambo
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Published
1 year ago
•
samuelstanton
samuelstanton
'NoneType' object
I have run the current version of the code with the below command:
python scripts/black_box_opt.py optimizer=lambo optimizer.encoder_obj=mlm task=proxy_rfp tokenizer=protein surrogate=multi_task_exact_gp acquisition=nehvi
and there is the full stack trace of the code with TypeError: 'NoneType' object is not iterable error.
wandb: Run `wandb offline` to turn off syncing.
logger:
_target_: upcycle.logging.DataFrameLogger
log_dir: data/experiments/test/summer-puddle-110/2023-05-01_06-34-09
task:
_target_: lambo.tasks.proxy_rfp.proxy_rfp.ProxyRFPTask
obj_dim: 2
log_prefix: proxy_rfp
batch_size: 16
max_len: 244
max_num_edits: null
max_ngram_size: 1
allow_len_change: false
num_start_examples: 512
acquisition:
_target_: lambo.acquisitions.ehvi.NoisyEHVI
num_samples: 2
batch_size: 16
encoder:
_target_: lambo.models.lm_elements.LanguageModel
name: mlm_cnn
model:
_target_: lambo.models.shared_elements.mCNN
tokenizer:
_target_: lambo.utils.ResidueTokenizer
max_len: 244
embed_dim: 64
latent_dim: 16
out_dim: 16
kernel_size: 5
p: 0.0
layernorm: true
max_len_delta: 0
batch_size: 32
num_epochs: 128
patience: 32
lr: 0.001
max_shift: 0
mask_ratio: 0.125
optimizer:
_target_: lambo.optimizers.lambo.LaMBO
_recursive_: false
num_rounds: 64
num_gens: 16
num_opt_steps: 32
patience: 32
lr: 0.1
concentrate_pool: 1
mask_ratio: 0.125
resampling_weight: 1.0
encoder_obj: mlm
optimize_latent: true
position_sampler: uniform
entropy_penalty: 0.01
window_size: 1
latent_init: null
algorithm:
_target_: pymoo.algorithms.soo.nonconvex.ga.GA
pop_size: 16
n_offsprings: null
sampling:
_target_: lambo.optimizers.sampler.BatchSampler
batch_size: 16
crossover:
_target_: lambo.optimizers.crossover.BatchCrossover
prob: 0.25
prob_per_query: 0.25
mutation:
_target_: lambo.optimizers.mutation.LocalMutation
prob: 1.0
eta: 16
safe_mut: false
eliminate_duplicates: true
tokenizer:
_target_: lambo.utils.ResidueTokenizer
surrogate:
_target_: lambo.models.gp_models.MultiTaskExactGP
max_shift: 0
mask_size: 0
bootstrap_ratio: null
min_num_train: 128
task_noise_init: 0.25
gp_lr: 0.005
enc_lr: 0.005
bs: 32
eval_bs: 16
num_epochs: 256
holdout_ratio: 0.2
early_stopping: true
patience: 32
eval_period: 2
out_dim: 2
feature_dim: 16
encoder_wd: 0.0001
rank: null
task_covar_prior:
_target_: gpytorch.priors.LKJCovariancePrior
'n': 2
eta: 2.0
sd_prior:
_target_: gpytorch.priors.SmoothedBoxPrior
a: 0.0001
b: 1.0
data_covar_module:
_target_: gpytorch.kernels.MaternKernel
ard_num_dims: 16
lengthscale_prior:
_target_: gpytorch.priors.NormalPrior
loc: 0.7
scale: 0.01
likelihood:
_target_: gpytorch.likelihoods.MultitaskGaussianLikelihood
num_tasks: 2
has_global_noise: false
noise_constraint:
_target_: gpytorch.constraints.GreaterThan
lower_bound: 0.0001
seed: 0
trial_id: 0
project_name: lambo
version: v0.2.1
data_dir: data/experiments
exp_name: test
job_name: summer-puddle-110
timestamp: 2023-05-01_06-34-09
log_dir: data/experiments/test
wandb_mode: online
wandb_host: https://api.wandb.ai
GPU available: True
AdRed is non-dominated, adding to start pool
AdRed, [<lambo.utils.FoldxMutation object at 0x2aab699c5c70>]
DsRed.M1 is non-dominated, adding to start pool
DsRed.M1, [<lambo.utils.FoldxMutation object at 0x2aab69545a60>]
DsRed.T4 is non-dominated, adding to start pool
DsRed.T4, [<lambo.utils.FoldxMutation object at 0x2aab699fe580>]
RFP630 is non-dominated, adding to start pool
RFP630, [<lambo.utils.FoldxMutation object at 0x2aab6a1f4b20>]
mRouge is non-dominated, adding to start pool
mRouge, [<lambo.utils.FoldxMutation object at 0x2aab6a1f4b80>]
mScarlet is non-dominated, adding to start pool
mScarlet, [<lambo.utils.FoldxMutation object at 0x2aab6a1f4b80>]
[2023-05-01 06:34:37,890][root][ERROR] - 'NoneType' object is not iterable
Traceback (most recent call last):
File "scripts/black_box_opt.py", line 56, in main
metrics = optimizer.optimize(
File "/storage/hpc/data/nmn5x/LAMBO/lambo/lambo/optimizers/lambo.py", line 75, in optimize
is_feasible = self.bb_task.is_feasible(candidate_pool)
File "/storage/hpc/data/nmn5x/LAMBO/lambo/lambo/tasks/base_task.py", line 72, in is_feasible
is_feasible = np.array([len(cand) <= self.max_len for cand in candidates]).reshape(-1)
File "/storage/hpc/data/nmn5x/LAMBO/lambo/lambo/tasks/base_task.py", line 72, in <listcomp>
is_feasible = np.array([len(cand) <= self.max_len for cand in candidates]).reshape(-1)
File "/storage/hpc/data/nmn5x/LAMBO/lambo/lambo/candidate.py", line 147, in __len__
tok_idxs = self.tokenizer.encode(self.mutant_residue_seq)
File "/storage/hpc/data/nmn5x/miniconda/envs/lambo-env-lewis/lib/python3.8/site-packages/cachetools/__init__.py", line 642, in wrapper
v = func(*args, **kwargs)
File "/storage/hpc/data/nmn5x/LAMBO/lambo/lambo/utils.py", line 63, in encode
for char in seq:
TypeError: 'NoneType' object is not iterable
I would be very grateful if you guide me in this order. Thank you!
I ran the same command and got different output. Most likely this is the result of a misconfigured environment or FoldX wasn't properly installed
logger:
_target_: upcycle.logging.DataFrameLogger
log_dir: data/experiments/test/comfy-hill-96/2023-05-01_08-08-06
task:
_target_: lambo.tasks.proxy_rfp.proxy_rfp.ProxyRFPTask
obj_dim: 2
log_prefix: proxy_rfp
batch_size: 16
max_len: 244
max_num_edits: null
max_ngram_size: 1
allow_len_change: false
num_start_examples: 512
acquisition:
_target_: lambo.acquisitions.ehvi.NoisyEHVI
num_samples: 2
batch_size: 16
encoder:
_target_: lambo.models.lm_elements.LanguageModel
name: mlm_cnn
model:
_target_: lambo.models.shared_elements.mCNN
tokenizer:
_target_: lambo.utils.ResidueTokenizer
max_len: 244
embed_dim: 64
latent_dim: 16
out_dim: 16
kernel_size: 5
p: 0.0
layernorm: true
max_len_delta: 0
batch_size: 32
num_epochs: 128
patience: 32
lr: 0.001
max_shift: 0
mask_ratio: 0.125
optimizer:
_target_: lambo.optimizers.lambo.LaMBO
_recursive_: false
num_rounds: 64
num_gens: 16
num_opt_steps: 32
patience: 32
lr: 0.1
concentrate_pool: 1
mask_ratio: 0.125
resampling_weight: 1.0
encoder_obj: mlm
optimize_latent: true
position_sampler: uniform
entropy_penalty: 0.01
window_size: 1
latent_init: null
algorithm:
_target_: pymoo.algorithms.soo.nonconvex.ga.GA
pop_size: 16
n_offsprings: null
sampling:
_target_: lambo.optimizers.sampler.BatchSampler
batch_size: 16
crossover:
_target_: lambo.optimizers.crossover.BatchCrossover
prob: 0.25
prob_per_query: 0.25
mutation:
_target_: lambo.optimizers.mutation.LocalMutation
prob: 1.0
eta: 16
safe_mut: false
eliminate_duplicates: true
tokenizer:
_target_: lambo.utils.ResidueTokenizer
surrogate:
_target_: lambo.models.gp_models.MultiTaskExactGP
max_shift: 0
mask_size: 0
bootstrap_ratio: null
min_num_train: 128
task_noise_init: 0.25
gp_lr: 0.005
enc_lr: 0.005
bs: 32
eval_bs: 16
num_epochs: 256
holdout_ratio: 0.2
early_stopping: true
patience: 32
eval_period: 2
out_dim: 2
feature_dim: 16
encoder_wd: 0.0001
rank: null
task_covar_prior:
_target_: gpytorch.priors.LKJCovariancePrior
'n': 2
eta: 2.0
sd_prior:
_target_: gpytorch.priors.SmoothedBoxPrior
a: 0.0001
b: 1.0
data_covar_module:
_target_: gpytorch.kernels.MaternKernel
ard_num_dims: 16
lengthscale_prior:
_target_: gpytorch.priors.NormalPrior
loc: 0.7
scale: 0.01
likelihood:
_target_: gpytorch.likelihoods.MultitaskGaussianLikelihood
num_tasks: 2
has_global_noise: false
noise_constraint:
_target_: gpytorch.constraints.GreaterThan
lower_bound: 0.0001
seed: 0
trial_id: 0
project_name: lambo
version: v0.2.1
data_dir: data/experiments
exp_name: test
job_name: comfy-hill-96
timestamp: 2023-05-01_08-08-06
log_dir: data/experiments/test
wandb_mode: online
wandb_host: https://api.wandb.ai
GPU available: True
AdRed is non-dominated, adding to start pool
DsRed.M1 is non-dominated, adding to start pool
DsRed.T4 is non-dominated, adding to start pool
RFP630 is non-dominated, adding to start pool
mRouge is non-dominated, adding to start pool
mScarlet is non-dominated, adding to start pool
[-0.11583198 0.46189176]
| | round_idx | hypervol_abs | hypervol_rel | num_bb_evals | time_elapsed |
|---:|------------:|---------------:|---------------:|---------------:|---------------:|
| 0 | 0.0000 | 0.6751 | 1.0000 | 0.0000 | 0.0253 |
best candidates
| | obj_val_0 | obj_val_1 |
|---:|------------:|------------:|
| 0 | -11558.6276 | -71.4708 |
active set contracted to 6 pareto points
active set augmented with 0 random points
[-10258.66136409 39.81466 ]
[[-11189.00587946 -39.8155 ]
[-10376.84011515 -71.4708 ]
[-10820.91136186 -55.6143 ]
[-11558.62762577 29.6978 ]
[-11445.82982225 -27.9617 ]
[-10591.87684184 -61.8757 ]]
LSKHGLTKDMTMKYRMEGCVDGHKFVITGHGNGSPFEGKQTINLCVVEGGPLPFSEDILSAVFNRVFTDYPQGMVDFFKNSCPAGYTWQRSLLFEDGAVCTASADITVSVEENCFYHESKFHGVNFPADGPVMKKMTINWEPCCEKIIPVPRQGILKGDVAMYLLLKDGGRYRCQFDTVYKAKTDSKKMPEWHFIQHKLTREDRSDAKNQKWQLAEHSVASRSALA
TEDVIKEFMQFKVRMEGSVNGHYFEIEGEGEGKPYEGTQTAKLQVTKGGPLPFAWDILSPQFSKAYVKHPADIPDYMKLSFPEGFTWERSMNFEDGGVVEVQQDSSLQDGTFIYKVKFKGVNFPADGPVMQKKTAGWEPSTEKLYPQDGVLKGEISHALKLKDGGHYTCDFKTVYKAKKPVQLPGNHYVDSKLDITNHNEDYTVVEQYEHAEARHSG
VIKEFMRFKVRMEGSVNGHEFEIEGEGEGRPYEGTQTAKLKVTKGGPLPFAWDILSPQFSKVYVKHPADIPDYKKLSFPEGFKWERVMNFEDGGVVTVTQDSSLQDGCFIYKVKFIGVNFPSDGPVMQKKTMGWEPSTERLYPRDGVLKGEIHKALKLKDGGHYLVEFKSIYMAKKPVQLPGYYYVDSKLDITSHNEDYTIVEQYERAEGRHHLFL
MNSLIKENMRMMVVEGSVNGYQFKCTGEGDGNPYMGTQTMRIKVVEGGPLPFAFDILATSFSKTFIKHTKGIPDFFKQSFPEGFTWERVTRYEDGGVFTVMQDTSLEDGCLVYHAKVRGVNFPSNGAVMQKKTKGWEPSTEMLYPADGGLRGYSQMALNVDGGGYLSCSFETTYRSKKTVENFKMPGFHFVDHRLERLEESDKEMFVVQHEHAVAKFCDLPSKLGRL
EEDNMAIIKEFMRFKTHMEGSVNGHEFEIEGEGEGRPYEGTQTAKLKVTKGGPLPFAWDILSPQFSKAYVKHPADIPDYLKLSFPEGFKWERVMNFEDGGVVTVTQDSSLQDGEFIYKVKLRGTNFPSDGPVMQKKTMGWEACSERMYPEDGALKGEMKMRLKLKDGGHYDAEVKTTYKAKKPVQLPGAYNTNTKLDITSHNEDYTIVEQYERNEGRHSTGGMDELYK
AVIKEFMRFKVHMEGSMNGHEFEIEGEGEGRPYEGTQTAKLKVTKGGPLPFSWDILSPQFSRAFTKHPADIPDYYKQSFPEGFKWERVMNFEDGGAVTVTQDTSLEDGTLIYKVKLRGTNFPPDGPVMQKKTMGWEASTERLYPEDGVLKGDIKMALRLKDGGRYLADFKTTYKAKKPVQMPGAYNVDRKLDITSHNEDYTVVEQYERSEGRHSTG
---- fitting surrogate model ----
440 train, 53 val, 64 test
---- preparing checkpoint ----
starting val NLL: 0.3491
---- fitting all params ----
