gradient-free-optimizers 0.2.1

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Gradient-Free-Optimizers

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical search spaces.

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Latest versions:

Introduction

Gradient-Free-Optimizers provides a collection of easy to use optimization techniques, whose objective function only requires an arbitrary score that gets maximized.

This makes gradient-free optimization methods capable of performing hyperparameter-optimization of machine learning methods. The optimizers in this package only requires the score of the point to decide which point to evaluate next.

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Main features • Installation • Examples • API-info • Citation • License

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Main features

• Easy to use:

  • Simple API-design
  • Receive prepared information about ongoing and finished optimization runs

• High performance:

  • Modern optimization techniques
  • Lightweight backend
  • Save time with "short term memory"

• High reliability:

  • Extensive testing
  • Performance test for each optimizer

Installation

The most recent version of Gradient-Free-Optimizers is available on PyPi:

pip install gradient-free-optimizers

Examples

Convex function

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer


def parabola_function(para):
    loss = para["x"] * para["x"]
    return -loss


search_space = {"x": np.arange(-10, 10, 0.1)}

opt = RandomSearchOptimizer(search_space)
opt.search(parabola_function, n_iter=100000)

Non-convex function

import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer


def ackley_function(pos_new):
    x = pos_new["x1"]
    y = pos_new["x2"]

    a1 = -20 * np.exp(-0.2 * np.sqrt(0.5 * (x * x + y * y)))
    a2 = -np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
    score = a1 + a2 + 20
    return -score


search_space = {
    "x1": np.arange(-100, 101, 0.1),
    "x2": np.arange(-100, 101, 0.1),
}

opt = RandomSearchOptimizer(search_space)
opt.search(ackley_function, n_iter=30000)

Machine learning example

import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_wine

from gradient_free_optimizers import HillClimbingOptimizer


data = load_wine()
X, y = data.data, data.target


def model(para):
    gbc = GradientBoostingClassifier(
        n_estimators=para["n_estimators"],
        max_depth=para["max_depth"],
        min_samples_split=para["min_samples_split"],
        min_samples_leaf=para["min_samples_leaf"],
    )
    scores = cross_val_score(gbc, X, y, cv=3)

    return scores.mean()


search_space = {
    "n_estimators": np.arange(20, 120, 1),
    "max_depth": np.arange(2, 12, 1),
    "min_samples_split": np.arange(2, 12, 1),
    "min_samples_leaf": np.arange(1, 12, 1),
}

opt = HillClimbingOptimizer(search_space)
opt.search(model, n_iter=50)

Basic API-information

Optimization classes:

• HillClimbingOptimizer
• StochasticHillClimbingOptimizer
• TabuOptimizer
• RandomSearchOptimizer
• RandomRestartHillClimbingOptimizer
• RandomAnnealingOptimizer
• SimulatedAnnealingOptimizer
• StochasticTunnelingOptimizer
• ParallelTemperingOptimizer
• ParticleSwarmOptimizer
• EvolutionStrategyOptimizer
• BayesianOptimizer
• TreeStructuredParzenEstimators
• DecisionTreeOptimizer

Search method arguments:

• objective_function
• n_iter
• initialize
• warm_start
• max_time
• max_score
• memory
• memory_warm_start
• verbosity
• random_state

GFOs-design

This package was created as the optimization backend of the Hyperactive package. The separation of Gradient-Free-Optimizers from Hyperactive enables multiple advantages:

• Other developers can easily use GFOs as an optimizaton backend if desired
• Separate and more thorough testing
• Better isolation from the complex information flow in Hyperactive. GFOs only uses positions and scores in a N-dimensional search-space. It returns only the new position after each iteration.
• a smaller and cleaner code base, if you want to explore my implementation of these optimization techniques.

Citing Gradient-Free-Optimizers

@Misc{gfo2020,
  author =   {{Simon Blanke}},
  title =    {{Gradient-Free-Optimizers}: Simple and reliable optimization with local, global, population-based and sequential techniques in numerical search spaces.},
  howpublished = {\url{https://github.com/SimonBlanke}},
  year = {since 2020}
}

License