LIPO is a package for derivative-free, global optimization. Is based on the dlib package and provides wrappers around its optimization routine.

The algorithm outperforms random search - sometimes by margins as large as 10000x. It is often preferable to Bayesian optimization which requires "tuning of the tuner". Performance is on par with moderately to well tuned Bayesian optimization.

The provided implementation has the option to automatically enlarge the search space if bounds are found to be too restrictive (i.e. the optimum being to close to one of them).

See the LIPO algorithm implementation for details.

A great blog post by the author of dlib exists, describing how it works.

Installation

Execute

pip install lipo

Usage

from lipo import GlobalOptimizer

def function(x, y, z):
    zdict = {"a": 1, "b": 2}
    return -((x - 1.23) ** 6) + -((y - 0.3) ** 4) * zdict[z]

pre_eval_x = dict(x=2.3, y=13, z="b")
evaluations = [(pre_eval_x, function(**pre_eval_x))]

search = GlobalOptimizer(
    function,
    lower_bounds={"x": -10.0, "y": -10},
    upper_bounds={"x": 10.0, "y": -3},
    categories={"z": ["a", "b"]},
    evaluations=evaluations,
    maximize=True,
)

num_function_calls = 1000
search.run(num_function_calls)

The optimizer will automatically extend the search bounds if necessary.

Further, the package provides an implementation of the scikit-learn interface for hyperparamter search.

from lipo import LIPOSearchCV

search = LIPOSearchCV(
    estimator,
    param_space={"param_1": [0.1, 100], "param_2": ["category_1", "category_2"]},
    n_iter=100
)
search.fit(X, y)
print(search.best_params_)