hugegraph-ml/src/hugegraph_ml/examples/bgnn_example.py

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# pylint: disable=C0103

from hugegraph_ml.data.hugegraph2dgl import HugeGraph2DGL
from hugegraph_ml.models.bgnn import (
    BGNNPredictor,
    GNNModelDGL,
    convert_data,
    encode_cat_features,
    replace_na,
)


def bgnn_example():
    hg2d = HugeGraph2DGL()
    g = hg2d.convert_hetero_graph_bgnn(vertex_labels=["AVAZU__N_v"], edge_labels=["AVAZU__E_e"])
    X, y, cat_features, train_mask, val_mask, test_mask = convert_data(g)
    encoded_X = X.copy()
    encoded_X = encode_cat_features(encoded_X, y, cat_features, train_mask, val_mask, test_mask)
    encoded_X = replace_na(encoded_X, train_mask)
    gnn_model = GNNModelDGL(in_dim=y.shape[1], hidden_dim=128, out_dim=y.shape[1])
    bgnn = BGNNPredictor(
        gnn_model,
        task="regression",
        loss_fn=None,
        trees_per_epoch=5,
        backprop_per_epoch=5,
        lr=0.1,
        append_gbdt_pred=False,
        gbdt_depth=6,
        gbdt_lr=0.1,
    )
    metrics = bgnn.fit(
        g,
        encoded_X,
        y,
        train_mask,
        val_mask,
        test_mask,
        original_X=X,
        cat_features=cat_features,
        num_epochs=100,
        patience=10,
        metric_name="loss",
    )
    print(metrics)


if __name__ == "__main__":
    bgnn_example()