difference between evaluate_preds and model.evaluate about keras-gcn HOT 3 CLOSED

Thanks for your question. Note that the evaluate_preds function takes an additional index/mask array, e.g. idx_train which is not the case in your keras-based implementation. This explains the difference in measured accuracy.

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Sorry for late response. I think I solved this issue. I mistakenly thought that the argument sample_weight is enough to apply weight to the input data. In fact we also need to use weighted_metrics but not metrics. I found that evaluate_pred can be replaced by mode.evaluate therefore we don't need to implement metrics categorical_crossentropy and accuracy. The codes can be simplified to keras-based implementation without changing the results. Here are my solutions:

1. add weighted_metrics categorical_crossentropy and accuracy to model.compile:

model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.01), weighted_metrics=['categorical_crossentropy', 'accuracy'])

2. evaluate the train data using mode.evaluate:

_, train_loss, train_acc = model.evaluate(
        graph, y_train, sample_weight=train_mask, batch_size=X.shape[0], verbose=0)

To be more simplified, we can also get rid of loops over epochs and use earlystopping callbacks.

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This sounds good, thanks for looking into this! Feel free to make a pull request if you think this might be helpful for other users as well.

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