Initial commit

CONTRIBUTORS.md

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+Code written by:
+- David Eriksson <[email protected]>

LICENSE.md

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README.md

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+## Overview	
+
+This is the code-release for the TuRBO algorithm from ***Scalable Global Optimization via Local Bayesian Optimization*** appearing in NeurIPS 2019. This is an implementation for the noise-free case and may not work well if observations are noisy as the center of the trust region should be chosen based on the posterior mean in this case.
+
+Note that TuRBO is a **minimization** algorithm, so please make sure you reformulate potential maximization problems.
+
+## Benchmark functions
+
+### Robot pushing
+The original code for the robot pushing problem is available at https://github.com/zi-w/Ensemble-Bayesian-Optimization. We have made the following changes to the code when running our experiments:
+
+1. We turned off the visualization, which speeds up the function evaluations. 
+2. We replaced all instances of ```np.random.normal(0, 0.01)``` by ```np.random.normal(0, 1e-6)``` in ```push_utils.py```. This makes the function close to noise-free. Another option is to average over several evaluations using the original code
+3. We flipped the sign of the objective function to turn this into a minimization problem.
+
+Dependencies: ```numpy ```, ```pygame```, ```box2d-py```
+
+### Rover
+The original code for the robot pushing problem is available at https://github.com/zi-w/Ensemble-Bayesian-Optimization. We used the large version of the problem, which has 60 dimensions. We have flipped the sign of the objective function to turn this into a minimization problem.
+
+Dependencies: ```numpy```, ```scipy```
+
+### Lunar
+
+The lunar code is available in the OpenAI gym: https://github.com/openai/gym. The goal of the problem is to learn the parameter values of a controller for the lunar lander. The controller we learn is a modification of the original heuristic controller which takes the form:
+
+```
+def heuristic_Controller(s, w):
+    angle_targ = s[0] * w[0] + s[2] * w[1]
+    if angle_targ > w[2]:
+        angle_targ = w[2]
+    if angle_targ < -w[2]:
+        angle_targ = -w[2]
+    hover_targ = w[3] * np.abs(s[0])
+
+    angle_todo = (angle_targ - s[4]) * w[4] - (s[5]) * w[5]
+    hover_todo = (hover_targ - s[1]) * w[6] - (s[3]) * w[7]
+
+    if s[6] or s[7]:
+        angle_todo = w[8]
+        hover_todo = -(s[3]) * w[9]
+
+    a = 0
+    if hover_todo > np.abs(angle_todo) and hover_todo > w[10]:
+        a = 2
+    elif angle_todo < -w[11]:
+        a = 3
+    elif angle_todo > +w[11]:
+        a = 1
+    return a
+```
+
+We use the constraints 0 <= w_i <= 2 for all parameters.
+
+For more information about the logic behind this controller and how to integrate it with ```gym```, take a look at the original heuristic controller source code: https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py#L364
+
+Dependencies: ```gym```, ```box2d-py```
+
+### Cosmological constant
+The code for the cosmological constant problem is available here: https://ascl.net/1306.012. You need to follow the instructions and compile the FORTRAN code. This gives you an executable ```CAMB``` that you can call to run the simulation.
+
+The parameter names and bounds that we tune are the following:
+
+```
+ombh2:           [0.01, 0.25]
+omch2:           [0.01, 0.25]
+omnuh2:          [0.01, 0.25]
+omk:             [0.01, 0.25]
+hubble:          [52.5, 100]
+temp_cmb:        [2.7, 2.8]
+hefrac:          [0.2, 0.3]
+mneu:            [2.9, 3.09]
+scalar_amp:      [1.5e-9, 2.6e-8] 
+scalar_spec_ind: [0.72, 5]
+rf_fudge:        [0, 100]
+rf_fudge_he:     [0, 100]
+```
+
+## Examples
+Check the examples folder for two examples on how to use Turbo-1 and Turbo-n.
+
+## Citing us
+
+A pre-print of our paper is available at: https://arxiv.org/abs/1910.01739
+
+```
+@article{eriksson2019scalable,
+  title={Scalable Global Optimization via Local Bayesian Optimization},
+  author={Eriksson, David and Pearce, Michael and Gardner, Jacob R and Turner, Ryan and Poloczek, Matthias},
+  journal={arXiv preprint arXiv:1910.01739},
+  year={2019}
+}
+```
+
+The link and citation key will be updated when the camera-ready version of the paper is available.

requirements.txt

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+numpy==1.17.3
+torch==1.3.0
+gpytorch==0.3.6

setup.py

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+from setuptools import setup, find_packages
+
+setup(
+    name="turbo",
+    version="0.0.1",
+    packages=find_packages(),
+    install_requires=["numpy>=1.17.3", "torch>=1.3.0", "gpytorch>=0.3.6"],
+)

turbo/__init__.py

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+from .turbo_1 import Turbo1
+from .turbo_m import TurboM

turbo/gp.py

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+###############################################################################
+# Copyright (c) 2019 Uber Technologies, Inc.                                  #
+#                                                                             #
+# Licensed under the Uber Non-Commercial License (the "License");             #
+# you may not use this file except in compliance with the License.            #
+# You may obtain a copy of the License at the root directory of this project. #
+#                                                                             #
+# See the License for the specific language governing permissions and         #
+# limitations under the License.                                              #
+###############################################################################
+
+import math
+
+import gpytorch
+import numpy as np
+import torch
+from gpytorch.constraints.constraints import Interval
+from gpytorch.distributions import MultivariateNormal
+from gpytorch.kernels import MaternKernel, ScaleKernel
+from gpytorch.likelihoods import GaussianLikelihood
+from gpytorch.means import ConstantMean
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from gpytorch.models import ExactGP
+
+
+# GP Model
+class GP(ExactGP):
+    def __init__(self, train_x, train_y, likelihood, lengthscale_constraint, outputscale_constraint, ard_dims):
+        super(GP, self).__init__(train_x, train_y, likelihood)
+        self.ard_dims = ard_dims
+        self.mean_module = ConstantMean()
+        base_kernel = MaternKernel(lengthscale_constraint=lengthscale_constraint, ard_num_dims=ard_dims, nu=2.5)
+        self.covar_module = ScaleKernel(base_kernel, outputscale_constraint=outputscale_constraint)
+
+    def forward(self, x):
+        mean_x = self.mean_module(x)
+        covar_x = self.covar_module(x)
+        return MultivariateNormal(mean_x, covar_x)
+
+
+def train_gp(train_x, train_y, use_ard, num_steps, hypers={}):
+    """Fit a GP model where train_x is in [0, 1]^d and train_y is standardized."""
+    assert train_x.ndim == 2
+    assert train_y.ndim == 1
+    assert train_x.shape[0] == train_y.shape[0]
+
+    # Create hyper parameter bounds
+    noise_constraint = Interval(5e-4, 0.2)
+    if use_ard:
+        lengthscale_constraint = Interval(0.005, 2.0)
+    else:
+        lengthscale_constraint = Interval(0.005, math.sqrt(train_x.shape[1]))  # [0.005, sqrt(dim)]
+    outputscale_constraint = Interval(0.05, 20.0)
+
+    # Create models
+    likelihood = GaussianLikelihood(noise_constraint=noise_constraint).to(device=train_x.device, dtype=train_y.dtype)
+    ard_dims = train_x.shape[1] if use_ard else None
+    model = GP(
+        train_x=train_x,
+        train_y=train_y,
+        likelihood=likelihood,
+        lengthscale_constraint=lengthscale_constraint,
+        outputscale_constraint=outputscale_constraint,
+        ard_dims=ard_dims,
+    ).to(device=train_x.device, dtype=train_x.dtype)
+
+    # Find optimal model hyperparameters
+    model.train()
+    likelihood.train()
+
+    # "Loss" for GPs - the marginal log likelihood
+    mll = ExactMarginalLogLikelihood(likelihood, model)
+
+    # Initialize model hypers
+    if hypers:
+        model.load_state_dict(hypers)
+    else:
+        hypers = {}
+        hypers["covar_module.outputscale"] = 1.0
+        hypers["covar_module.base_kernel.lengthscale"] = 0.5
+        hypers["likelihood.noise"] = 0.005
+        model.initialize(**hypers)
+
+    # Use the adam optimizer
+    optimizer = torch.optim.Adam([{"params": model.parameters()}], lr=0.1)
+
+    for _ in range(num_steps):
+        optimizer.zero_grad()
+        output = model(train_x)
+        loss = -mll(output, train_y)
+        loss.backward()
+        optimizer.step()
+
+    # Switch to eval mode
+    model.eval()
+    likelihood.eval()
+
+    return model