Issue with Max_depth in tfdf

[lukebor]

    df_and_nn_model = tfdf.keras.GradientBoostedTreesModel(preprocessing=regmodel_wo_head,
                                                task=tfdf.keras.Task.REGRESSION,
                                                num_trees=500,
                                                max_depth=2,
                                                max_num_nodes=-1,
                                                min_examples=5,
                                                validation_ratio=0.2,
                                                subsample=0.9,
                                                early_stopping='MIN_LOSS_FINAL',
                                                shrinkage=0.001)

and after

df_and_nn_model.compile(metrics=[tf.keras.metrics.RootMeanSquaredError()])
with sys_pipes():
    df_and_nn_model.fit(train_dataset, validation_data=val_dataset)

[INFO kernel.cc:772] Configure learner
[FATAL hyper_parameters.cc:49] Already consumed hyper-parameter "max_depth".

This was working yesterday morning but i made updates on kaggle and it throws this exception, i have no idea what it means.

[achoum]

Hi,

Thanks for the bug report :).

The wrong error message is displayed. Instead, the message should read TensorFlow: INVALID_ARGUMENT: "max_num_nodes" is only available with "growing_strategy=BEST_FIRST_GLOBAL". [Op:SimpleMLModelTrainer].

Regarding the (correct) error message, you should remove the line max_num_nodes=-1 (or replace it with max_num_nodes=None).

[rstz]

Cleanup: This has been fixed a long time ago, closing.