Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/rishit-dagli/uoft-csc311-project

Last synced: 14 days ago
JSON representation

Host: GitHub
URL: https://github.com/rishit-dagli/uoft-csc311-project
Owner: Rishit-dagli
License: apache-2.0
Created: 2024-03-17T15:03:15.000Z (8 months ago)
Default Branch: main
Last Pushed: 2024-04-03T23:47:44.000Z (7 months ago)
Last Synced: 2024-10-08T18:21:38.033Z (about 1 month ago)
Language: Python
Size: 2.6 MB
Stars: 1
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# CSC311 Final Project

We give instructions on how to run the code for Part B of the project. The code resides in the `part_b` directory. Currently, our code is tested with Python 3.10.x, hiwever we believe it should work well with Python 3.9.x and above.

Assuming you have cloned this repository, and you are in the `part_b` directory of the repository, you can run the following command to setup the environment:

```bash
pip install -r requirements.txt
```

## Evaluation code for Part B

All our evaluation code for the models in part A which we use for evaluation in part B resides in the `part_b/evaluate/` directory. Due to size constraints on Markus, we do not include the trained parameters for the random forest model.

## Training the model

To train the model, you can use the `main.py` script. The script can be used as follows:

```bash
python main.py [OPTIONS]
```

The following options are available:

- `--project`
- Type: `str`
- Default: `"test"`
- Description: Name of the project.

- `--custom_loss`
- Action: `store_true`
- Description: Use custom loss function.

- `--model_type`
- Type: `str`
- Default: `"original"`
- Choices: `["original", "mhsa"]`
- Description: Model architecture type: 'original' or 'mhsa'.

- `--optimizer`
- Type: `str`
- Default: `"adam"`
- Description: Optimizer to use.

- `--lr_scheduler`
- Type: `str`
- Default: `"none"`
- Choices: `["none", "steplr", "cosineannealing", "reducelronplateau", "cosinedecay"]`
- Description: Type of LR scheduler: steplr, cosineannealing, reducelronplateau, cosinedecay, none.

- `--step_size`
- Type: `int`
- Default: `10`
- Description: Step size for StepLR and T_max for CosineAnnealing.

- `--gamma`
- Type: `float`
- Default: `0.1`
- Description: Gamma for StepLR, ReduceLROnPlateau, and factor for CosineAnnealing.

- `--patience`
- Type: `int`
- Default: `10`
- Description: Patience for ReduceLROnPlateau.

- `--lr`
- Type: `float`
- Default: `1e-3`
- Description: Initial learning rate.

- `--base_path`
- Type: `str`
- Default: `"../../data"`
- Description: Base path for the dataset.

- `--gpu`
- Action: `store_true`
- Description: Use GPU for training.

- `--num_devices`
- Type: `int`
- Default: `1`
- Description: Number of devices to use for training.

- `--epochs`
- Type: `int`
- Default: `10`
- Description: Number of epochs.

- `--batch_size`
- Type: `int`
- Default: `32`
- Description: Batch size.

- `--save_model`
- Type: `str`
- Default: `"model.pth"`
- Description: Path to save the model.

- `--checkpoint_path`
- Type: `str`
- Default: `None`
- Description: Path to a checkpoint to resume training from.

- `--seed`
- Type: `int`
- Default: `3047`
- Description: Random seed.

### Run our model configuration

To run our best performing model configuration for each of the two model architectures we try out (Embeddings and Multi-Head Self-Attention), you can use the following commands:

```bash
# Original model
python main.py --project education_model --model_type original --optimizer adamw --lr_scheduler none --lr 1e-1 --base_path ../data --epochs 8 --batch_size 64 --save_model model.pth --seed 3047

# Multi-Head Self-Attention model
python main.py --project education_model --model_type mhsa --optimizer adamw --lr_scheduler reducelronplateau --patience 15 --lr 1e-1 --base_path ../data --epochs 250 --batch_size 32 --save_model model.pth --seed 3047
```

You should run each of these commands with `--num_devices [NUMBER_OF_DEVICES]` if you want to use more than 1 device for the training and `--gpu` if you want to use GPU for training.

### Viewing the training logs

You can view the training logs using TensorBoard. To do this, you can run the following command:

```bash
tensorboard --logdir=logs
```

This will start a TensorBoard server that you can access by visiting `http://localhost:6006` in your browser.

## Evaluating the model

To evaluate the model on thew entire test set, the script can be used as follows:

```bash
python main.py --model_type original --base_path ../../data --batch_size 3543 --epochs 0 --checkpoint_path model.pth

python main.py --model_type mhsa --base_path ../../data --batch_size 3543 --epochs 0 --checkpoint_path model.pth
```