# Use pip-tools to manage dependencies
$ pip install pip-tools
# Install pinned dependencies
$ pip-sync
# Install package in editable mode
$ pip install -e .
Before you run anything, log into your W&B account with wandb login
. Then you can run
model training, for example, as follows.
./train.py data=example model=knn
To submit jobs to the SLURM cluster, set the launcher to slurm
.
./train.py hydra/launcher=slurm # ... other arguments
model=
GAT | Vanilla Graph Attention Model |
GnnRes | Vanilla GNNRes with 1 timeslot as input |
GnnRes-HF | GNNRes with variable number of feature channels |
GnnRes-High-Input | GNNRes with multiple timeslots as input |
GnnRes-Avg | GNNRes with global average over training sample |
Unet | Vanilla U-Net |
For GNNRes-High-Input:
- input_size: Number of input time slots
- n_channels: Number of features channels insides residual blocks
- inout_skipconn: If there should be an additional GCN on the input and output of the model
- depth: Number of residual blocks
- activation: Activation function (ReLU, Sigmoid)
data=
t4c | Traffic Forecasting Dataset |
t4c_avg | Traffic Forecasting Dataset including global average |
- data_dir: Directory of data files
- batch_size: Batchsize during training
- input_size: Number of input time slots
- city: City of data files
- threshold: Threshold used for mask creation
- include_timestamps: If timestamps should be included as circular features
task=
temporal_forecasting | Temporal Forecasting Task |
temporal_forecasting_avg | Temporal Forecating Task including global average |
temporal_forecasting_unet | Traffic Forecasting Task adapted to U-Net |
- lr: Learning rate used during training