GraphToolbox¶

GraphToolbox is a Python package designed for graph machine learning focused on electricity load forecasting. It provides tools for data handling, model building, training, evaluation, and visualization.

Features¶

Data handling and preprocessing for graph datasets.
Various graph neural network models including Graph Convolutional Networks (GCNs), GraphSAGE, and Graph Attention Networks (GATs).
Training and evaluation utilities for graph-based models.
Visualization tools for graph data and model results.

Installation¶

Clone the repository and install the package and dependencies:

git clone git@github.com:eloicampagne/GraphToolbox.git
cd GraphToolbox
pip install .

Usage¶

Basic example of how to use GraphToolbox:

from graphtoolbox.data.dataset import *
from graphtoolbox.training.trainer import Trainer
from graphtoolbox.utils.helper_functions import *
from torch_geometric.nn.models import *

# Load datasets
out_channels = 48
data = DataClass(path_train='./train.csv',
                 path_test='./test.csv',
                 data_kwargs=data_kwargs,
                 folder_config='.')

graph_dataset_train = GraphDataset(data=data, period='train',
                                   graph_folder='../graph_representations',
                                   dataset_kwargs=dataset_kwargs,
                                   out_channels=out_channels)
graph_dataset_val = GraphDataset(data=data, period='val',
                                 scalers_feat=graph_dataset_train.scalers_feat,
                                 scalers_target=graph_dataset_train.scalers_target,
                                 graph_folder='../graph_representations',
                                 dataset_kwargs=dataset_kwargs,
                                 out_channels=out_channels)
graph_dataset_test = GraphDataset(data=data, period='test',
                                  scalers_feat=graph_dataset_train.scalers_feat,
                                  scalers_target=graph_dataset_train.scalers_target,
                                  graph_folder='../graph_representations',
                                  dataset_kwargs=dataset_kwargs,
                                  out_channels=out_channels)

# Initialize model
conv_class = GATConv
conv_kwargs = {'heads': 2}
params = {'num_layers': 3,
          'hidden_channels': 364,
          'lr': 1e-3,
          'batch_size': 16,
          'adj_matrix': 'gl3sr',
          'lam_reg': 0}

model = myGNN(
    in_channels=graph_dataset_train.num_node_features,
    num_layers=params["num_layers"],
    hidden_channels=params["hidden_channels"],
    out_channels=out_channels,
    conv_class=conv_class,
    conv_kwargs=conv_kwargs
)

# Initialize trainer
trainer = Trainer(
    model=model,
    dataset_train=graph_dataset_train,
    dataset_val=graph_dataset_val,
    dataset_test=graph_dataset_test,
    batch_size=params["batch_size"],
    return_attention=False,
    model_kwargs={'lr': params["lr"], 'num_epochs': 200},
    lam_reg=params["lam_reg"]
)

# Train model
pred_model_test, target_test, edge_index, attention_weights = trainer.train(
    plot_loss=True,
    force_training=True,
    save=False,
    patience=75
)

# Evaluate model
trainer.evaluate()

Contributing¶

Contributions are welcome! Please fork the repository and submit a pull request.

Special thanks to all contributors of the GraphToolbox project:

Eloi Campagne
Itai Zehavi

License¶

This project is licensed under the GPL License — see the LICENSE file for details.

Documentation¶

graphtoolbox