Add detailed reproducibility instructions.

README.md

@@ -33,12 +33,15 @@ In order to train models, you can simply run the following command:
 python cmd/train.py 
 ```
 
-By default, this command will train a score model in the time domain with the `ecg` dataset. In order to modify this behaviour, you can use [hydra override syntax](https://hydra.cc/docs/advanced/override_grammar/basic/). The following hyperparameters are useful to modify:
+By default, this command will train a score model in the time domain with the `ecg` dataset. In order to modify this behaviour, you can use [hydra override syntax](https://hydra.cc/docs/advanced/override_grammar/basic/). The following hyperparameters can be modified to retrain all the models appearing in the paper:
 
 | Hyperparameter | Description | Values |
 |----------------|-------------|---------------|
-| datamodule | Name of the dataset to use. | ecg |
-| score_model.lr_max | Max learning rate of the score model reached by the cosine scheduling with warmup. | $\mathbb{R^+}$ |
+|fourier_transform | Whether or not to train a diffusion model in the frequency domain. | true, false |
+| datamodule | Name of the dataset to use. | ecg, mimiciii, nasa, nasdaq, usdroughts|
+| datamodule.subdataset | For the NASA dataset only. Selects between the charge and discharge subsets. | charge, discharge |
+| datamodule.smoother_width | For the ECG dataset only. Width of the Gaussian kernel smoother applied in the frequency domain. | $\mathbb{R}^+$
+| score_model | The backbone to use for the score model. | default, lstm |
 
 At the end of training, your model is stored in the `lightning_logs` directory, in a folder named after the current `run_id`. You can find the `run_id` in the logs of the training and in the [wandb dashboard](https://wandb.ai/) if you have correctly configured wandb.
 
@@ -50,7 +53,9 @@ In order to sample from a trained model, you can simply run the following comman
 python cmd/sample.py model_id=XYZ
 ```
 
-where `XYZ` is the `run_id` of the model you want to sample from. At the end of sampling, the samples are stored in the `lightning_logs` directory, in a folder named after the current `run_id`.
+where `XYZ` is the `run_id` of the model you want to sample from. At the end of sampling, the samples are stored in the `lightning_logs` directory, in a folder named after the current `run_id`. 
+
+One can then reproduce the plots in the paper by including the  `run_id` to the `run_list` list appearing in [this notebook](notebooks/results.ipynb) and running all cells.
 
 # 3. Contribute