def configure_ermlp_hpo_pipeline(model_name: str): config = get_config_dict(model_name) # Step 1: Query embedding dimensions print_hpo_embedding_dimensions_message() embedding_dimensions = select_positive_integer_values( EMBEDDING_DIMENSIONS_PRINT_MSG, EMBEDDING_DIMENSIONS_PROMPT_MSG, EMBEDDING_DIMENSIONS_ERROR_MSG) config[EMBEDDING_DIM] = embedding_dimensions print_section_divider() # Step 2: Query margin loss print_hpo_margin_losses_message() magin_loss = select_float_values(print_msg=MARGIN_LOSSES_PRINT_MSG, prompt_msg=MARGIN_LOSSES_PROMPT_MSG, error_msg=MARGIN_LOSSES_ERROR_MSG) config[MARGIN_LOSS] = magin_loss print_section_divider() # Step 3: Query learning rate print_hpo_learning_rates_message() learning_rate = select_float_values(print_msg=LEARNING_RATES_PRINT_MSG, prompt_msg=LEARNING_RATES_PROMPT_MSG, error_msg=LEARNING_RATES_ERROR_MSG) config[LEARNING_RATE] = learning_rate print_section_divider() # Step 4: Query batch size print_hpo_batch_sizes_message() batch_sizes = select_positive_integer_values( print_msg=BATCH_SIZES_PRINT_MSG, prompt_msg=BATCH_SIZES_PROMPT_MSG, error_msg=BATCH_SIZES_ERROR_MSG) config[BATCH_SIZE] = batch_sizes print_section_divider() # Step 5: Query number of epochs print_hpo_epochs_message() number_epochs = select_positive_integer_values( print_msg=EPOCHS_PRINT_MSG, prompt_msg=EPOCHS_PROMPT_MSG, error_msg=EPOCHS_ERROR_MSG) config[NUM_EPOCHS] = number_epochs print_section_divider() return config
def configure_um_hpo_pipeline(model_name): config = get_config_dict(model_name) # Step 1: Query embedding dimensions print_hpo_embedding_dimensions_message() embedding_dimensions = select_positive_integer_values(EMBEDDING_DIMENSIONS_PRINT_MSG, EMBEDDING_DIMENSIONS_PROMPT_MSG, EMBEDDING_DIMENSIONS_ERROR_MSG) config[EMBEDDING_DIM] = embedding_dimensions print_section_divider() # Step 2: Query margin loss print_hpo_margin_losses_message() magin_loss = select_float_values( print_msg=MARGIN_LOSSES_PRINT_MSG, prompt_msg=MARGIN_LOSSES_PROMPT_MSG, error_msg=MARGIN_LOSSES_ERROR_MSG) config[MARGIN_LOSS] = magin_loss print_section_divider() # Step 3: Query L_p norms to use as scoring function print_hpo_scoring_fcts_message() scoring_fct_norm = select_positive_integer_values( print_msg=NORMS_SCORING_FUNCTION_PRINT_MSG, prompt_msg=NORMS_SCORING_FUNCTION_PROMPT_MSG, error_msg=NORMS_SCORING_FUNCTION_ERROR_MSG ) config[SCORING_FUNCTION_NORM] = scoring_fct_norm print_section_divider() # Step 4: Query L_p norms for normalizing the entities print_hpo_entity_normalization_norms_message() entity_normalization_norm = select_positive_integer_values( print_msg=NORMS_FOR_NORMALIZATION_OF_ENTITIES_PRINT_MSG, prompt_msg=NORMS_FOR_NORMALIZATION_OF_ENTITIES_PROMPT_MSG, error_msg=NORMS_FOR_NORMALIZATION_OF_ENTITIES_ERROR_MSG ) config[NORM_FOR_NORMALIZATION_OF_ENTITIES] = entity_normalization_norm print_section_divider() # Step 5: Query learning rate print_hpo_learning_rates_message() learning_rate = select_float_values( print_msg=LEARNING_RATES_PRINT_MSG, prompt_msg=LEARNING_RATES_PROMPT_MSG, error_msg=LEARNING_RATES_ERROR_MSG) config[LEARNING_RATE] = learning_rate print_section_divider() # Step 6: Query batch size print_hpo_batch_sizes_message() batch_size = select_positive_integer_values( print_msg=BATCH_SIZES_PRINT_MSG, prompt_msg=BATCH_SIZES_PROMPT_MSG, error_msg=BATCH_SIZES_ERROR_MSG) config[BATCH_SIZE] = batch_size print_section_divider() # Step 7: Query number of epochs print_hpo_epochs_message() number_epochs = select_positive_integer_values( print_msg=EPOCHS_PRINT_MSG, prompt_msg=EPOCHS_PROMPT_MSG, error_msg=EPOCHS_ERROR_MSG) config[NUM_EPOCHS] = number_epochs print_section_divider() return config
def configure_trans_h_hpo_pipeline(model_name): config = get_config_dict(model_name) # Step 1: Query embedding dimensions print_hpo_embedding_dimensions_message() embedding_dimensions = select_positive_integer_values( EMBEDDING_DIMENSIONS_PRINT_MSG, EMBEDDING_DIMENSIONS_PROMPT_MSG, EMBEDDING_DIMENSIONS_ERROR_MSG, ) config[EMBEDDING_DIM] = embedding_dimensions print_section_divider() # Step 2: Query margin loss print_hpo_margin_losses_message() magin_loss = select_float_values( print_msg=MARGIN_LOSSES_PRINT_MSG, prompt_msg=MARGIN_LOSSES_PROMPT_MSG, error_msg=MARGIN_LOSSES_ERROR_MSG, ) config[MARGIN_LOSS] = magin_loss print_section_divider() # Step 3: Query L_p norms to use as scoring function print_hpo_scoring_fcts_message() scoring_fct_norm = select_positive_integer_values( print_msg=NORMS_SCORING_FUNCTION_PRINT_MSG, prompt_msg=NORMS_SCORING_FUNCTION_PROMPT_MSG, error_msg=NORMS_SCORING_FUNCTION_ERROR_MSG, ) config[SCORING_FUNCTION_NORM] = scoring_fct_norm print_section_divider() # Step 4: Query weight for the soft constraints print_hpo_trans_h_soft_constraints_weights_message() soft_constraints_weight = select_float_values( print_msg=WEIGHTS_SOFT_CONSTRAINT_TRANS_H_PRINT_MSG, prompt_msg=WEIGHTS_SOFT_CONSTRAINT_TRANS_H_PROMPT_MSG, error_msg=WEIGHTS_SOFT_CONSTRAINT_TRANS_H_ERROR_MSG, ) config[WEIGHT_SOFT_CONSTRAINT_TRANS_H] = soft_constraints_weight print_section_divider() # Step 5: Query learning rate print_hpo_learning_rates_message() learning_rate = select_float_values( print_msg=LEARNING_RATES_PRINT_MSG, prompt_msg=LEARNING_RATES_PROMPT_MSG, error_msg=LEARNING_RATES_ERROR_MSG, ) config[LEARNING_RATE] = learning_rate print_section_divider() # Step 6: Query batch size print_hpo_batch_sizes_message() batch_sizes = select_positive_integer_values( print_msg=BATCH_SIZES_PRINT_MSG, prompt_msg=BATCH_SIZES_PROMPT_MSG, error_msg=BATCH_SIZES_ERROR_MSG, ) config[BATCH_SIZE] = batch_sizes print_section_divider() # Step 7: Query number of epochs print_hpo_epochs_message() number_epochs = select_positive_integer_values( print_msg=EPOCHS_PRINT_MSG, prompt_msg=EPOCHS_PROMPT_MSG, error_msg=EPOCHS_ERROR_MSG, ) config[NUM_EPOCHS] = number_epochs print_section_divider() return config