Single Objective Optimization Auto-Tuning

import paradance as para

Load the offline dataset used for Bayesian automatic tuning.

FILE_PATH is the path where the training data is stored. FILE_NAME is the filename.

loader = para.CSVLoader(
    file_path=FILE_PATH,
    file_name=FILE_NAME,
)

Each individual optimization target is stored sequentially in the targets list. The fusion form can be very flexible, using eval() to convert the string into a valid fusion target expression.

If there is only one target, we can use

formula="targets[0]"

Select the features to be used for hyperparameter search

selected_columns=['pxtr_0', 'pxtr_1', 'pxtr_2', 'pxtr_3', 'pxtr_4']

We should construct a Calculator, to calculate the composite score based on the selected features.

The calculation method for the composite score can be chosen as either sum or product, corresponding to the additive and multiplicative forms of the formulas.

cal = para.Calculator(
    df=loader.df,
    selected_columns=selected_columns,
    equation_type="product",
)

Determine the Bayesian search space: whether to search for the power term; whether to search for the first-order term of the features.

power = True
first_order = False

Next, construct the optimization target based on the above settings, and determine the direction of optimization, whether to maximize or minimize the fusion target expression.

The optimization process will be recorded in the LOG_FILE file.

ob = para.MultipleObjective(
    direction="minimize",
    weights_num=len(selected_columns),
    formula=formula,
    power=power,
    first_order=first_order,
    log_file=LOG_FILE,
)

Add an appropriate optimizer to the optimization target, such as the “portfolio” optimizer in this case, and set its coverage ratio to 0.95.

target_column is the sub-target that this optimizer is used to optimize, such as “price”.

ob.add_calculator(
    calculator=cal,
    flag="portfolio",
    hyperparameter=0.95,
    target_column=TARGET,
)

Begin optimization, and save the tuning log. n_trials represents the number of rounds for hyperparameter search.

ob.optimize(n_trials=600)