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Package rename (#75)
* Renaming GPflowOpt package to gpflowopt * Revert some changes in comments
1 parent cc36cfe commit 13de6f4

38 files changed

Lines changed: 387 additions & 378 deletions

‎doc/source/conf.py‎

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@@ -18,7 +18,7 @@
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# documentation root, use os.path.abspath to make it absolute, like shown here.
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#
2020
import os
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from GPflowOpt import __version__
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from gpflowopt import __version__
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# on_rtd is whether we are on readthedocs.org, this line of code grabbed from docs.readthedocs.org
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on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
@@ -151,7 +151,7 @@
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# (source start file, target name, title,
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# author, documentclass [howto, manual, or own class]).
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latex_documents = [
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(master_doc, 'GPflowOpt.tex', 'GPflowOpt Documentation',
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(master_doc, 'gpflowopt.tex', 'GPflowOpt Documentation',
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'Joachim van der Herten', 'manual'),
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]
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@@ -161,7 +161,7 @@
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# One entry per manual page. List of tuples
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# (source start file, name, description, authors, manual section).
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man_pages = [
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(master_doc, 'gpflowopt', 'GPflowOpt Documentation',
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(master_doc, 'GPflowOpt', 'GPflowOpt Documentation',
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[author], 1)
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]
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‎doc/source/notebooks/constrained_bo.ipynb‎

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@@ -28,8 +28,8 @@
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import GPflow\n",
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"import GPflowOpt\n",
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"import gpflow\n",
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"import gpflowopt\n",
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"import numpy as np"
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]
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},
@@ -67,12 +67,12 @@
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" return -(-np.cos(1.5*X[:,0]+np.pi)*np.cos(1.5*X[:,1])+np.sin(1.5*X[:,0]+np.pi)*np.sin(1.5*X[:,1]))[:,None]\n",
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"\n",
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"# Setup input domain\n",
70-
"domain = GPflowOpt.domain.ContinuousParameter('x1', -2.25, 2.5) + \\\n",
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" GPflowOpt.domain.ContinuousParameter('x2', -2.5, 1.75)\n",
70+
"domain = gpflowopt.domain.ContinuousParameter('x1', -2.25, 2.5) + \\\n",
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" gpflowopt.domain.ContinuousParameter('x2', -2.5, 1.75)\n",
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"\n",
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"# Plot\n",
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"def plotfx(): \n",
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" X = GPflowOpt.design.FactorialDesign(101, domain).generate()\n",
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" X = gpflowopt.design.FactorialDesign(101, domain).generate()\n",
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" Zo = townsend(X)\n",
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" Zc = constraint(X)\n",
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" mask = Zc>=0\n",
@@ -111,22 +111,22 @@
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"outputs": [],
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"source": [
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"# Initial evaluations\n",
114-
"design = GPflowOpt.design.LatinHyperCube(11, domain)\n",
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"design = gpflowopt.design.LatinHyperCube(11, domain)\n",
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"X = design.generate()\n",
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"Yo = townsend(X)\n",
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"Yc = constraint(X)\n",
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"\n",
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"# Models\n",
120-
"objective_model = GPflow.gpr.GPR(X, Yo, GPflow.kernels.Matern52(2, ARD=True))\n",
120+
"objective_model = gpflow.gpr.GPR(X, Yo, gpflow.kernels.Matern52(2, ARD=True))\n",
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"objective_model.likelihood.variance = 0.01\n",
122-
"constraint_model = GPflow.gpr.GPR(np.copy(X), Yc, GPflow.kernels.Matern52(2, ARD=True))\n",
123-
"constraint_model.kern.lengthscales.transform = GPflow.transforms.Log1pe(1e-3)\n",
122+
"constraint_model = gpflow.gpr.GPR(np.copy(X), Yc, gpflow.kernels.Matern52(2, ARD=True))\n",
123+
"constraint_model.kern.lengthscales.transform = gpflow.transforms.Log1pe(1e-3)\n",
124124
"constraint_model.likelihood.variance = 0.01\n",
125-
"constraint_model.likelihood.variance.prior = GPflow.priors.Gamma(1./4.,1.0)\n",
125+
"constraint_model.likelihood.variance.prior = gpflow.priors.Gamma(1./4.,1.0)\n",
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"\n",
127127
"# Setup\n",
128-
"ei = GPflowOpt.acquisition.ExpectedImprovement(objective_model)\n",
129-
"pof = GPflowOpt.acquisition.ProbabilityOfFeasibility(constraint_model)\n",
128+
"ei = gpflowopt.acquisition.ExpectedImprovement(objective_model)\n",
129+
"pof = gpflowopt.acquisition.ProbabilityOfFeasibility(constraint_model)\n",
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"joint = ei * pof"
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]
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},
@@ -172,7 +172,7 @@
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],
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"source": [
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"def plot():\n",
175-
" Xeval = GPflowOpt.design.FactorialDesign(101, domain).generate()\n",
175+
" Xeval = gpflowopt.design.FactorialDesign(101, domain).generate()\n",
176176
" Yevala,_ = joint.operands[0].models[0].predict_f(Xeval)\n",
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" Yevalb,_ = joint.operands[1].models[0].predict_f(Xeval)\n",
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" Yevalc = np.maximum(ei.evaluate(Xeval), 0)\n",
@@ -237,11 +237,11 @@
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"source": [
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"# First setup the optimization strategy for the acquisition function\n",
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"# Combining MC step followed by L-BFGS-B\n",
240-
"acquisition_opt = GPflowOpt.optim.StagedOptimizer([GPflowOpt.optim.MCOptimizer(domain, 200), \n",
241-
" GPflowOpt.optim.SciPyOptimizer(domain)])\n",
240+
"acquisition_opt = gpflowopt.optim.StagedOptimizer([gpflowopt.optim.MCOptimizer(domain, 200), \n",
241+
" gpflowopt.optim.SciPyOptimizer(domain)])\n",
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"\n",
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"# Then run the BayesianOptimizer for 50 iterations\n",
244-
"optimizer = GPflowOpt.BayesianOptimizer(domain, joint, optimizer=acquisition_opt)\n",
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"optimizer = gpflowopt.BayesianOptimizer(domain, joint, optimizer=acquisition_opt)\n",
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"with optimizer.silent():\n",
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" result = optimizer.optimize([townsend, constraint], n_iter=50)\n",
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" \n",

‎doc/source/notebooks/firststeps.ipynb‎

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@@ -53,7 +53,7 @@
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],
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"source": [
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"import numpy as np\n",
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"from GPflowOpt.domain import ContinuousParameter\n",
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"from gpflowopt.domain import ContinuousParameter\n",
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"\n",
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"\n",
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"def fx(X):\n",
@@ -93,18 +93,18 @@
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}
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],
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"source": [
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"import GPflow\n",
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"from GPflowOpt.bo import BayesianOptimizer\n",
98-
"from GPflowOpt.design import LatinHyperCube\n",
99-
"from GPflowOpt.acquisition import ExpectedImprovement\n",
100-
"from GPflowOpt.optim import SciPyOptimizer\n",
96+
"import gpflow\n",
97+
"from gpflowopt.bo import BayesianOptimizer\n",
98+
"from gpflowopt.design import LatinHyperCube\n",
99+
"from gpflowopt.acquisition import ExpectedImprovement\n",
100+
"from gpflowopt.optim import SciPyOptimizer\n",
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"\n",
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"# Use standard Gaussian process Regression\n",
103103
"lhd = LatinHyperCube(21, domain)\n",
104104
"X = lhd.generate()\n",
105105
"Y = fx(X)\n",
106-
"model = GPflow.gpr.GPR(X, Y, GPflow.kernels.Matern52(2, ARD=True))\n",
107-
"model.kern.lengthscales.transform = GPflow.transforms.Log1pe(1e-3)\n",
106+
"model = gpflow.gpr.GPR(X, Y, gpflow.kernels.Matern52(2, ARD=True))\n",
107+
"model.kern.lengthscales.transform = gpflow.transforms.Log1pe(1e-3)\n",
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"\n",
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"# Now create the Bayesian Optimizer\n",
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"alpha = ExpectedImprovement(model)\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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"version": "3.5.2"
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}
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},
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"nbformat": 4,

‎doc/source/notebooks/hyperopt.ipynb‎

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@@ -101,9 +101,9 @@
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}
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],
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"source": [
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"from GPflow.kernels import RBF, Cosine, Linear, Bias, Matern52\n",
105-
"from GPflow import transforms\n",
106-
"from GPflow.gpr import GPR\n",
104+
"from gpflow.kernels import RBF, Cosine, Linear, Bias, Matern52\n",
105+
"from gpflow import transforms\n",
106+
"from gpflow.gpr import GPR\n",
107107
"\n",
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"Q = 10 # nr of terms in the sum\n",
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"max_iters = 1000\n",
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}
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],
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"source": [
186-
"from GPflowOpt.domain import ContinuousParameter\n",
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"from GPflowOpt.objective import batch_apply\n",
186+
"from gpflowopt.domain import ContinuousParameter\n",
187+
"from gpflowopt.objective import batch_apply\n",
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"\n",
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"# Objective function for our optimization\n",
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"# Input: N x 2Q ndarray, output: N x 1.\n",
@@ -239,9 +239,9 @@
239239
}
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],
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"source": [
242-
"from GPflowOpt.design import LatinHyperCube\n",
243-
"from GPflowOpt.acquisition import ExpectedImprovement\n",
244-
"from GPflowOpt import optim, BayesianOptimizer\n",
242+
"from gpflowopt.design import LatinHyperCube\n",
243+
"from gpflowopt.acquisition import ExpectedImprovement\n",
244+
"from gpflowopt import optim, BayesianOptimizer\n",
245245
"design = LatinHyperCube(6, domain)\n",
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"X = design.generate()\n",
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"\n",

‎doc/source/notebooks/multiobjective.ipynb‎

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‎doc/source/notebooks/new_acquisition.ipynb‎

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"\n",
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"import matplotlib.pyplot as plt \n",
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"import copy\n",
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"import GPflow\n",
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"import GPflowOpt\n",
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"import gpflow\n",
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"import gpflowopt\n",
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"import tensorflow as tf"
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]
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},
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" return f[:,None] + rng.rand(X.shape[0], 1) * 0.25\n",
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"\n",
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"# Setup input domain\n",
52-
"domain = GPflowOpt.domain.ContinuousParameter('x1', -3, 3) + \\\n",
53-
" GPflowOpt.domain.ContinuousParameter('x2', -2, 2)"
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"domain = gpflowopt.domain.ContinuousParameter('x1', -3, 3) + \\\n",
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" gpflowopt.domain.ContinuousParameter('x2', -2, 2)"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"class AugmentedEI(GPflowOpt.acquisition.ExpectedImprovement):\n",
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"class AugmentedEI(gpflowopt.acquisition.ExpectedImprovement):\n",
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" def __init__(self, model):\n",
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" super(AugmentedEI, self).__init__(model)\n",
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"\n",
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}
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],
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"source": [
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"design = GPflowOpt.design.LatinHyperCube(9, domain)\n",
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"design = gpflowopt.design.LatinHyperCube(9, domain)\n",
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"X = design.generate()\n",
120120
"Y = camelback(X)\n",
121-
"m = GPflow.gpr.GPR(X, Y, GPflow.kernels.Matern52(2, ARD=True, lengthscales=[10,10], variance=10000))\n",
121+
"m = gpflow.gpr.GPR(X, Y, gpflow.kernels.Matern52(2, ARD=True, lengthscales=[10,10], variance=10000))\n",
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"m.likelihood.variance = 1\n",
123123
"m.likelihood.variance.fixed = True\n",
124-
"ei = GPflowOpt.acquisition.ExpectedImprovement(m)\n",
125-
"m = GPflow.gpr.GPR(X, Y, GPflow.kernels.Matern52(2, ARD=True, lengthscales=[10,10], variance=10000))\n",
124+
"ei = gpflowopt.acquisition.ExpectedImprovement(m)\n",
125+
"m = gpflow.gpr.GPR(X, Y, gpflow.kernels.Matern52(2, ARD=True, lengthscales=[10,10], variance=10000))\n",
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"m.likelihood.variance = 1\n",
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"m.likelihood.variance.fixed = False\n",
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"aei = AugmentedEI(m)\n",
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"\n",
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"opt = GPflowOpt.optim.StagedOptimizer([GPflowOpt.optim.MCOptimizer(domain, 200), \n",
131-
" GPflowOpt.optim.SciPyOptimizer(domain)])\n",
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"opt = gpflowopt.optim.StagedOptimizer([gpflowopt.optim.MCOptimizer(domain, 200), \n",
131+
" gpflowopt.optim.SciPyOptimizer(domain)])\n",
132132
"\n",
133-
"bopt1 = GPflowOpt.BayesianOptimizer(domain, ei, optimizer=opt)\n",
133+
"bopt1 = gpflowopt.BayesianOptimizer(domain, ei, optimizer=opt)\n",
134134
"with bopt1.silent():\n",
135135
" bopt1.optimize(camelback, n_iter=50)\n",
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"\n",
137-
"bopt2 = GPflowOpt.BayesianOptimizer(domain, aei, optimizer=opt)\n",
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"bopt2 = gpflowopt.BayesianOptimizer(domain, aei, optimizer=opt)\n",
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"with bopt2.silent():\n",
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" bopt2.optimize(camelback, n_iter=50)\n",
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"\n",
141141
"f, axes = plt.subplots(1,2, figsize=(14,7))\n",
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"\n",
143-
"Xeval = GPflowOpt.design.FactorialDesign(101, domain).generate()\n",
143+
"Xeval = gpflowopt.design.FactorialDesign(101, domain).generate()\n",
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"Yeval = camelback(Xeval)\n",
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"titles = ['EI', 'AEI']\n",
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"shape = (101, 101)\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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"version": "3.5.2"
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}
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},
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"nbformat": 4,

‎doc/source/notebooks/structure.ipynb‎

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}
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],
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"source": [
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"from GPflowOpt.domain import ContinuousParameter\n",
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"from gpflowopt.domain import ContinuousParameter\n",
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"domain = ContinuousParameter('x1', -2, 2) + ContinuousParameter('x2', -1, 2)\n",
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"domain"
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]
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}
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],
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"source": [
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"from GPflowOpt.optim import SciPyOptimizer\n",
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"from gpflowopt.optim import SciPyOptimizer\n",
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"\n",
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"optimizer = SciPyOptimizer(domain, method='SLSQP')\n",
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"optimizer.set_initial([-1,-1])\n",
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}
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],
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"source": [
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"from GPflowOpt.optim import MCOptimizer\n",
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"from gpflowopt.optim import MCOptimizer\n",
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"optimizer = MCOptimizer(domain, 200)\n",
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"optimizer.optimize(fx)"
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]
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}
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],
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"source": [
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"from GPflowOpt.bo import BayesianOptimizer\n",
211-
"from GPflowOpt.design import FactorialDesign\n",
212-
"from GPflowOpt.acquisition import ExpectedImprovement\n",
213-
"import GPflow\n",
210+
"from gpflowopt.bo import BayesianOptimizer\n",
211+
"from gpflowopt.design import FactorialDesign\n",
212+
"from gpflowopt.acquisition import ExpectedImprovement\n",
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"import gpflow\n",
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"\n",
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"# The Bayesian Optimizer does not expect gradients to be returned\n",
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"def fx(X):\n",
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"\n",
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"# initializing a standard BO model, Gaussian Process Regression with\n",
226226
"# Matern52 ARD Kernel\n",
227-
"model = GPflow.gpr.GPR(X,Y,GPflow.kernels.Matern52(2, ARD=True))\n",
227+
"model = gpflow.gpr.GPR(X, Y, gpflow.kernels.Matern52(2, ARD=True))\n",
228228
"alpha = ExpectedImprovement(model)\n",
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"\n",
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"# Now we must specify an optimization algorithm to optimize the acquisition \n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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"version": "3.5.2"
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}
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},
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"nbformat": 4,

GPflowOpt/__init__.py renamed to gpflowopt/__init__.py

File renamed without changes.

GPflowOpt/_version.py renamed to gpflowopt/_version.py

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GPflowOpt/acquisition/__init__.py renamed to gpflowopt/acquisition/__init__.py

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