ADLM API#

ADLM Parameter Configuration#

Note

Currently,ADLM is not fully integrated into ESIDLM, and you can configure ADLM parameters through double_angle.yaml files.

Global#

  • seed: the random seed in model.

  • train_data_path: the file path of your train data.

  • valid_data_path: the file path of your valid data.

  • test_data_path: the file path of your test data.

  • output_folder_path: the output_folder path.

  • input_angle_cont_cols: the columns of continuous variable corresponding to the Angle in the dataset.

  • input_angle_data_cols: the columns of angle variable in the dataset.

  • input_cont_cols: the columns name of continuous variable in the dataset.

  • input_cate_cols: the columns name of categorical variable in the dataset.

  • task_target_cols: the columns name of prediction in dataset.

Hyperparameter configuration#

Dataloader#

  • batch_size: The number of data samples captured in one step training. (default=64)

  • num_workers: The number of process created when used dataloader. (default=4)

Model#

  • d_embed: The number of dimensions used to represent each input feature in the embedding for categorical. (default=32)

  • d_model: The number of hidden layer nodes. (default=256)

  • n_layers: The number of hidden layer. (default=1)

  • p_drop: The percent of neurons are temporarily removed from the network during training. (default=0.3)

  • act_fn: Activation function. (default=relu)

  • loss: The dictionary configuring the loss function for model training.

  • lr: Learning rate. (default=3e-4)

  • weight_decay: The value for penalizes large weights in the model during training to prevent overfitting. (default=1e-5)

Model_Callback#

  • save_top_k: Specifies the number of best models to keep based on a given metric during training on validation accuracy. (default=1, save the best)

  • monitor: Specifies the metric to monitor during training. (default=valid_loss)

  • mode: Specifies whether the monitored metric should be minimized (MAE) or maximized (R). (default=min)

  • verbose: Determines whether to print information about the saving process to the console. (default=True)

  • patience: Specifies the number of epochs to wait for improvement in the monitored metric before stopping training. (default=10)

Trainer#

  • max_epochs: Specifies the maximum number of epochs to train for. (default=5)

  • accelerator: Specifies the hardware accelerator to use during training. (default=”gpu”)

  • devices: Specifies the number of devices to use during training. (default=1)

  • deterministic: Ensures reproducibility of the training results. (default=True)