bayesian-optimization/config.py at main · machine-learning-tutorial/bayesian-optimization · GitHub

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#!/usr/bin/env python

import warnings
from typing import Optional, Union

import botorch
import gpytorch
import gym
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
from botorch.acquisition import (
    ExpectedImprovement,
    ProbabilityOfImprovement,
    UpperConfidenceBound,
)
# from botorch.fit import fit_gpytorch_mll
from botorch.fit import fit_gpytorch_model
from botorch.models import SingleTaskGP
from botorch.models.transforms import Standardize
from botorch.optim import optimize_acqf
from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.mlls import ExactMarginalLogLikelihood
from gpytorch.models import ExactGP
from gym.spaces.utils import unflatten
from gym.wrappers import RescaleAction
from IPython.display import clear_output, display
from scipy.constants import c, e, m_p

from ares_ea.bo_utils import (
    ProximalAcquisitionFunction,
    get_new_bound,
    plot_acq_with_gp,
    sample_gp_posterior_plot,
    sample_gp_prior_plot,
    scale_action,
)
from ares_ea.env import ARESEA

np.random.seed(0)
warnings.filterwarnings("ignore")


# sns.set_context("talk", font_scale=1.2, rc={"lines.linewidth": 3})
# sns.set_style(
#     "ticks",
#     {
#         "grid.linestyle": "none",
#         "axes.edgecolor": "0",
#         "axes.linewidth": 1.2,
#         #        "legend.frameon": True,
#         #        "xtick.direction": "out",
#         #        "ytick.direction": "out",
#         #       "xtick.top": True,
#         #        "ytick.right": True,
#     },
# )
# sns.set(rc={'figure.figsize': (18, 20)})
plt.rcParams['figure.figsize'] = 12, 7
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['image.cmap'] = "viridis"
plt.rcParams['image.interpolation'] = "none"
plt.rcParams['savefig.bbox'] = "tight"

SMALL_SIZE = 20
MEDIUM_SIZE = 25
BIGGER_SIZE = 30

plt.rc('font', size=SMALL_SIZE)          # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title