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# Copyright 2020 The Trieste Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to...
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import tensorflow as tf from tests.util.models.gpflow.models import QuadraticMeanAndRBFKernel from trieste.models.gpflow import BatchReparametrizationSampler from trieste.types import TensorType from trieste.utils import DEFAULTS class PseudoBatchReparametrizationSampler(BatchReparametrizationSampler): """A Samp...
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# Copyright 2021 The Trieste Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to...
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# Copyright 2023 The Trieste Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to...
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trieste-develop
trieste-develop/tests/integration/models/multifidelity/test_multifidelity_models.py
import gpflow import numpy as np import numpy.testing as npt import tensorflow as tf from tensorflow.keras.metrics import mean_squared_error import trieste from trieste.data import ( Dataset, add_fidelity_column, check_and_extract_fidelity_query_points, split_dataset_by_fidelity, ) from trieste.models....
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trieste-develop
trieste-develop/tests/integration/models/keras/test_predictions.py
# Copyright 2021 The Trieste Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to...
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trieste-develop
trieste-develop/docs/conf.py
# Copyright 2020 The Trieste Contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to...
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trieste-develop
trieste-develop/docs/notebooks/asynchronous_greedy_multiprocessing.pct.py
# %% [markdown] # # Asynchronous Bayesian optimization with Trieste # # In this notebook we demonstrate Trieste's ability to perform asynchronous Bayesian optimisation, as is suitable for scenarios where the objective function can be run for several points in parallel but where observations might return back at differe...
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trieste-develop
trieste-develop/docs/notebooks/batch_optimization.pct.py
# %% [markdown] # # Batch Bayesian Optimization with Batch Expected Improvement, Local Penalization, Kriging Believer and GIBBON # %% [markdown] # Sometimes it is practically convenient to query several points at a time. This notebook demonstrates four ways to perfom batch Bayesian optimization with Trieste. # %% imp...
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trieste-develop
trieste-develop/docs/notebooks/rembo.pct.py
# %% [markdown] # # High-dimensional Bayesian optimization with Random EMbedding Bayesian Optimization (REMBO). # This notebook demonstrates a simple method for optimizing a high-dimensional (100-D) problem, where standard BO methods have trouble. # %% import math import gpflow import numpy as np import tensorflow as...
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trieste-develop
trieste-develop/docs/notebooks/explicit_constraints.pct.py
# %% [markdown] # # Explicit Constraints # %% [markdown] # This notebook demonstrates ways to perfom Bayesian optimization with Trieste in the presence of explicit input constraints. # %% import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import trieste np.random.seed(1234) tf.random.set_see...
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trieste-develop
trieste-develop/docs/notebooks/failure_ego.pct.py
# %% [markdown] # # EGO with a failure region # %% from __future__ import annotations import numpy as np import tensorflow as tf np.random.seed(1234) tf.random.set_seed(1234) # %% [markdown] # ## The problem # # This notebook is similar to the [EI notebook](expected_improvement.ipynb), where we look to find the min...
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trieste-develop
trieste-develop/docs/notebooks/inequality_constraints.pct.py
# %% [markdown] # # Inequality constraints # %% import numpy as np import tensorflow as tf np.random.seed(1799) tf.random.set_seed(1799) # %% [markdown] # ## The problem # # In this tutorial, we replicate one of the results of <cite data-cite="gardner14">[Gardner et al.](http://proceedings.mlr.press/v32/gardner14.ht...
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trieste-develop
trieste-develop/docs/notebooks/thompson_sampling.pct.py
# %% [markdown] # # Batch-sequential optimization with Thompson sampling # %% import numpy as np import tensorflow as tf np.random.seed(1793) tf.random.set_seed(1793) # %% [markdown] # ## Define the problem and model # # You can use Thompson sampling for Bayesian optimization in much the same way as we used EGO and ...
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trieste-develop
trieste-develop/docs/notebooks/feasible_sets.pct.py
# %% [markdown] # # Bayesian active learning of failure or feasibility regions # # When designing a system it is important to identify design parameters that may affect the reliability of the system and cause failures, or lead to unsatisfactory performance. Consider designing a communication network that for some desig...
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trieste-develop
trieste-develop/docs/notebooks/multi_objective_ehvi.pct.py
# -*- coding: utf-8 -*- # %% [markdown] # # Multi-objective optimization with Expected HyperVolume Improvement # %% import math import gpflow import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from trieste.experimental.plotting import ( plot_bo_points, plot_function_2d, plot_mobo_h...
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trieste-develop
trieste-develop/docs/notebooks/data_transformation.pct.py
# -*- coding: utf-8 -*- # %% [markdown] # # Data transformation with the help of Ask-Tell interface. # %% import os import gpflow import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import tensorflow_probability as tfp from trieste.experimental.plotting import plot_regret import trieste from t...
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trieste-develop
trieste-develop/docs/notebooks/active_learning.pct.py
# %% [markdown] # # Active Learning # %% [markdown] # Sometimes, we may just want to learn a black-box function, rather than optimizing it. This goal is known as active learning and corresponds to choosing query points that reduce our model uncertainty. This notebook demonstrates how to perform Bayesian active learnin...
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trieste-develop
trieste-develop/docs/notebooks/ask_tell_optimization.pct.py
# %% [markdown] # # Ask-Tell Optimization Interface # %% [markdown] # In this notebook we will illustrate the use of an Ask-Tell interface in Trieste. It is useful for cases where you want to have greater control of the optimization loop, or when letting Trieste manage this loop is impossible. # # First, some code to ...
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trieste-develop
trieste-develop/docs/notebooks/multifidelity_modelling.pct.py
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.14.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # %% import gpflow.kernels # %% [markdo...
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trieste-develop
trieste-develop/docs/notebooks/qhsri-tutorial.pct.py
# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.14.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # %% [markdown] # # Batch HSRI Tutorial ...
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trieste-develop
trieste-develop/docs/notebooks/visualizing_with_tensorboard.pct.py
# %% [markdown] # # Tracking and visualizing optimizations using Tensorboard # %% import numpy as np import tensorflow as tf np.random.seed(1793) tf.random.set_seed(1793) # %% [markdown] # We often wish to track or visualize the Bayesian optimization process, either during and following execution. This tutorial show...
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trieste-develop
trieste-develop/docs/notebooks/deep_ensembles.pct.py
# %% [markdown] # # Bayesian optimization with deep ensembles # # Gaussian processes as a surrogate models are hard to beat on smaller datasets and optimization budgets. However, they scale poorly with amount of data, cannot easily capture non-stationarities and they are rather slow at prediction time. Here we show how...
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trieste-develop
trieste-develop/docs/notebooks/openai_gym_lunar_lander.pct.py
# %% [markdown] # # Trieste meets OpenAI Gym # # This notebook demonstrates how to use Trieste to apply Bayesian optimization to a problem that is slightly more practical than classical optimization benchmarks shown used in other tutorials. We will use OpenAI Gym, which is a popular toolkit for reinforcement learning (...
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trieste-develop
trieste-develop/docs/notebooks/active_learning_for_binary_classification.pct.py
# %% [markdown] # # Active Learning for Gaussian Process Classification Model # %% import gpflow import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import trieste from trieste.acquisition.function import BayesianActiveLearningByDisagreement from trieste.acquisition.rule import OBJECTIVE from t...
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