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
