Errors in running notebook

[wershofe]

Hello
When trying to run the notebook I run into a few problems:
1)
cpa.CPA.setup_anndata(adata,
drug_key='condition',
dose_key='dose_val',
categorical_covariate_keys=['cell_type'],
control_key='control',
combinatorial=True,
)
TypeError: register_fields() got unexpected keyword arguments {'combinatorial': True} passed without a source_registry.

model = cpa.CPA(adata=adata,
n_latent=256,
loss_ae='gauss',
doser_type='logsigm',
split_key='split',
**ae_hparams,
)
TypeError: init() missing 1 required positional argument: 'n_cat_list'

Are you able to provide solutions?
Thank you

[yanwu2014]

I'm also running into this issue, seems like VanillaEncoder and DecoderNormal expect n_cat_list as a parameter with no default value specified?

[Naghipourfar]

Hi,
Just published a new version with this issue fixed recently. Tutorials are also updated with the latest API. Sorry for the inconvenience. Please let me know if anything goes wrong.

[yanwu2014]

Awesome it's working for me now!

[tuln128]

Hi, Just published a new version with this issue fixed recently. Tutorials are also updated with the latest API. Sorry for the inconvenience. Please let me know if anything goes wrong.

Hello @Naghipourfar,
In the updated tutorial notebooks, it seems the trainer_params was assigned with arbitrary values (following is the example from the GSM.ipynb file:

trainer_params = {
'n_epochs_warmup': 5,
'adversary_lr': 0.0008847032648856746,
'adversary_wd': 9.629190571404551e-07,
'adversary_steps': 3,
'autoencoder_lr': 0.0007208558788012054,
'autoencoder_wd': 1.2280838320404273e-07,
'dosers_lr': 0.0008835011062896268,
'dosers_wd': 5.886005123780177e-06,
'penalty_adversary': 63.44954424334805,
'reg_adversary': 48.73324753854268,
'cycle_coeff': 7.19539336141403,
'step_size_lr': 25,
}).

I am wondering how these hyperparameter values were decided (randomly or by optimized tuning)? If the latter is correct, could you please share how to conduct hyperparameter tuning for CPA model?

Thank you very much, and I am looking forward to your feedback.
Kind regards,

[ArianAmani]

Hyperparameter tuner added #46
https://github.com/theislab/cpa/#how-to-optmize-cpa-hyperparamters-for-your-data