What is the configs/kt_config.json? about pykt-toolkit HOT 3 OPEN

Thanks for the clarification.
For the issue, I searched all the cases with Table 7 for the dkt and concluded that dropout 0.5 for dkt is the best result, and that was right. :)
If you don't mind, can you share your best parameters for other models?

At least, it would be a great help if you share the values for the models : AKT, DKT, DKVMN, GKT, SAINT, SAKT

Thanks a lot.

from pykt-toolkit.

Thank you for your advice.
We will open the optimal parameters of all models for all data sets in the near future.

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Thank you for your attention to our work.

Neither the parameters in "configs/kt_config.json" nor "wandb/wandb_{model_name}_train.py" are the best parameters.
The best parameters of each fold for each dataset are obtained by hyperparameter search.

For example, for dataset assist2015, the best parameters are(dkt):
fold 0: {"dataset_name": "assist2015", "model_name": "dkt", "emb_type": "qid", "save_dir": "dkt_tiaocan_assist2015_rerun", "seed": 3407, "fold": 0, "dropout": 0.5, "emb_size": 200, "learning_rate": 0.001}
fold 1: {"dataset_name": "assist2015", "model_name": "dkt", "emb_type": "qid", "save_dir": "dkt_tiaocan_assist2015_rerun", "seed": 224, "fold": 1, "dropout": 0.5, "emb_size": 200, "learning_rate": 0.001}
fold 2: {"dataset_name": "assist2015", "model_name": "dkt", "emb_type": "qid", "save_dir": "dkt_tiaocan_assist2015_rerun", "seed": 42, "fold": 2, "dropout": 0.5, "emb_size": 200, "learning_rate": 0.001}
fold 3: {"dataset_name": "assist2015", "model_name": "dkt", "emb_type": "qid", "save_dir": "dkt_tiaocan_assist2015_rerun", "seed": 42, "fold": 3, "dropout": 0.5, "emb_size": 200, "learning_rate": 0.001}
fold 4: {"dataset_name": "assist2015", "model_name": "dkt", "emb_type": "qid", "save_dir": "dkt_tiaocan_assist2015_rerun", "seed": 42, "fold": 4, "dropout": 0.5, "emb_size": 200, "learning_rate": 0.001}

The best parameters above are obtained on Tesla V100.

from pykt-toolkit.