NOTES.md

Notes

Pre-training an autoencoder for support-matching

Run support-matching without the discriminator and save the weights

Turn off the discriminator

alg:
    ga_steps: 1
    num_disc_updates: 0
    twoway_disc_loss: false
    prior_loss_w: 0
    pred_y_loss_w: 0
    pred_s_loss_w: 0
    warmup_steps: 0
    disc_loss_w: 0

Set artifact_name (it’s a top-level config value) to a string.

Use the weight artifact

Select ae_arch=artifact and then set ae_arch.artifact_name to whatever you chose in step 1.

Saving pre-defined dataset splits

>>> import numpy as np
>>> import torch
>>> from conduit.data.datasets.vision import NICOPP
>>> from src.data.splitter import save_split_inds_as_artifact
>>> import wandb
>>> run = wandb.init(project="support-matching", entity= "predictive-analytics-lab", dir="local_logging")
>>> ds = NICOPP(root="/srv/galene0/shared/data")
>>> train_inds = torch.as_tensor(np.nonzero(ds.metadata["split"] == NICOPP.Split.TRAIN.value)[0])
>>> test_inds = torch.as_tensor(np.nonzero(ds.metadata["split"] == NICOPP.Split.TEST.value)[0])
>>> dep_inds = torch.as_tensor(np.nonzero(ds.metadata["split"] == NICOPP.Split.VAL.value)[0])
>>> save_split_inds_as_artifact(
...   run=run,
...   train_inds=train_inds,
...   test_inds=test_inds,
...   dep_inds=dep_inds,
...   ds=ds,
...   seed=0,
...   artifact_name="nicopp_change_is_hard_split",
... )
>>> run.finish()