Constrained optimization

Constrained Bayesian Optimization¶

In this tutorial we demonstrate the use of Xopt to perform Bayesian Optimization on a simple test problem subject to a single constraint.

Define the test problem¶

Here we define a simple optimization problem, where we attempt to minimize the sin function in the domian [0,2*pi], subject to a cos constraining function.

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from xopt.evaluator import Evaluator
from xopt.generators.bayesian import ExpectedImprovementGenerator
from xopt import Xopt
from xopt.vocs import VOCS

import time
import math
import numpy as np

# Ignore all warnings
import warnings

warnings.filterwarnings("ignore")

# define variables, function objective and constraining function
vocs = VOCS(
    variables={"x": [0, 2 * math.pi]},
    objectives={"f": "MINIMIZE"},
    constraints={"c": ["LESS_THAN", 0]},
)

from xopt.evaluator import Evaluator from xopt.generators.bayesian import ExpectedImprovementGenerator from xopt import Xopt from xopt.vocs import VOCS import time import math import numpy as np # Ignore all warnings import warnings warnings.filterwarnings("ignore") # define variables, function objective and constraining function vocs = VOCS( variables={"x": [0, 2 * math.pi]}, objectives={"f": "MINIMIZE"}, constraints={"c": ["LESS_THAN", 0]}, )

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# define a test function to optimize


def test_function(input_dict):
    return {"f": np.sin(input_dict["x"]), "c": np.cos(input_dict["x"])}

# define a test function to optimize def test_function(input_dict): return {"f": np.sin(input_dict["x"]), "c": np.cos(input_dict["x"])}

Create Xopt objects¶

Create the evaluator to evaluate our test function and create a generator that uses the Expected Improvement acquisition function to perform Bayesian Optimization.

In [ ]:

evaluator = Evaluator(function=test_function)
generator = ExpectedImprovementGenerator(vocs=vocs)
X = Xopt(evaluator=evaluator, generator=generator, vocs=vocs)

evaluator = Evaluator(function=test_function) generator = ExpectedImprovementGenerator(vocs=vocs) X = Xopt(evaluator=evaluator, generator=generator, vocs=vocs)

Generate and evaluate initial points¶

To begin optimization, we must generate some random initial data points. The first call to X.step() will generate and evaluate a number of randomly points specified by the generator. Note that if we add data to xopt before calling X.step() by assigning the data to X.data, calls to X.step() will ignore the random generation and proceed to generating points via Bayesian optimization.

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# call X.random_evaluate(n_samples) to generate + evaluate initial points
X.random_evaluate(n_samples=2)

# inspect the gathered data
X.data

# call X.random_evaluate(n_samples) to generate + evaluate initial points X.random_evaluate(n_samples=2) # inspect the gathered data X.data

Do bayesian optimization steps¶

To perform optimization we simply call X.step() in a loop. This allows us to do intermediate tasks in between optimization steps, such as examining the model and acquisition function at each step (as we demonstrate here).

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n_steps = 5

# test points for plotting
test_x = np.linspace(*X.vocs.bounds.flatten(), 50)

for i in range(n_steps):
    start = time.perf_counter()
    model = X.generator.train_model()
    fig, ax = X.generator.visualize_model(n_grid=100)
    print(time.perf_counter() - start)

    # add ground truth functions to plots
    out = test_function({"x": test_x})
    ax[0].plot(test_x, out["f"], "C0-.")
    ax[1].plot(test_x, out["c"], "C2-.")

    # do the optimization step
    X.step()

n_steps = 5 # test points for plotting test_x = np.linspace(*X.vocs.bounds.flatten(), 50) for i in range(n_steps): start = time.perf_counter() model = X.generator.train_model() fig, ax = X.generator.visualize_model(n_grid=100) print(time.perf_counter() - start) # add ground truth functions to plots out = test_function({"x": test_x}) ax[0].plot(test_x, out["f"], "C0-.") ax[1].plot(test_x, out["c"], "C2-.") # do the optimization step X.step()

In [ ]:

# access the collected data
X.data

# access the collected data X.data