Open liaowenyu opened 6 days ago
Hi there! So we have some usage instructions but in short, you can train a specific model on a specific dataset using the train
command. For example to train a linear model on the norman19 dataset, use the command train experiment=neurips2024/linear_best_params_norman19
. To use a different model or a different dataset, switch out the experiment config (you can see a list of available configs here). This will automatically evaluate your model on the val split at the end of training as well.
To generate predictions on a pretrained model, you can use the predict
command, we'll try and add an example experiment config for predictions soon. But for now it'll look something like this:
# @package _global_
# specify here default configuration
# order of defaults determines the order in which configs override each other
defaults:
- override /data: norman19 ## Specify data config
- model /model: linear ## Specify model config
# Provide checkpoint path to a trained linear model
ckpt_path: ???
# You need to include the exact model hparams used during training
model:
inject_covariates: false
lr: 0.004716813309487752
wd: 1.7588044643755207e-08
# task name, determines output directory path
task_name: "predict"
# Path to prediction dataframe (generate with notebooks/demos/generate_prediction_dataframe)
prediction_dataframe_path: ???
# seed for random number generators in pytorch, numpy and python.random
seed: null
# Path to save predictions
output_path: "${paths.output_dir}/predictions/"
# Number of perturbations to generate in memory at once
chunk_size: 50
If you save this config in the src/configs/experiments
directory as something like linear_model_predict.yaml
you can generate predictions with the command predict experiment=linear_model_predict
.
Let me know if that helps answer your question?
could you give me a tutorial on jupyter, so that I can easily visualize the results
Hi, could you give me a tutorial on how to use your proposed models to predict gene expression?