Domain reduction, Sphinx docs

.github/workflows/build_docs.yml

@@ -29,6 +29,8 @@ jobs:
           pip install nbsphinx
           pip install sphinx_rtd_theme
           pip install jupyter
+          pip install numpydoc
+          pip install myst-parser
       - name: Install package
         run: |
           pip install -e .

.gitignore

@@ -32,4 +32,6 @@ venv.bak/
 
 docs/*
 docsrc/.ipynb_checkpoints/*
-docsrc/*.ipynb
+docsrc/*.ipynb
+docsrc/static/*
+docsrc/README.md

README.md

@@ -1,16 +1,18 @@
 <div align="center">
-  <img src="https://github.com/fmfn/BayesianOptimization/blob/master/examples/func.png"><br><br>
+  <img src="https://raw.githubusercontent.com/bayesian-optimization/BayesianOptimization/master/static/func.png"><br><br>
 </div>
 
 # Bayesian Optimization
 
-![tests](https://github.com/fmfn/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg)
-[![Codecov](https://codecov.io/github/fmfn/BayesianOptimization/badge.svg?branch=master&service=github)](https://codecov.io/github/fmfn/BayesianOptimization?branch=master)
+![tests](https://github.com/bayesian-optimization/BayesianOptimization/actions/workflows/run_tests.yml/badge.svg)
+[![Codecov](https://codecov.io/github/bayesian-optimization/BayesianOptimization/badge.svg?branch=master&service=github)](https://codecov.io/github/bayesian-optimization/BayesianOptimization?branch=master)
 [![Pypi](https://img.shields.io/pypi/v/bayesian-optimization.svg)](https://pypi.python.org/pypi/bayesian-optimization)
 
 Pure Python implementation of bayesian global optimization with gaussian
 processes.
 
+## Installation
+
 * PyPI (pip):
 
 ```console
@@ -30,48 +32,40 @@ suited for optimization of high cost functions, situations where the balance
 between exploration and exploitation is important.
 
 ## Quick Start
-See below for a quick tour over the basics of the Bayesian Optimization package. More detailed information, other advanced features, and tips on usage/implementation can be found in the [examples](https://github.com/fmfn/BayesianOptimization/tree/master/examples) folder. I suggest that you:
-- Follow the
-[basic tour notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/basic-tour.ipynb)
-to learn how to use the package's most important features.
-- Take a look at the
-[advanced tour notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/advanced-tour.ipynb)
-to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more.
-- Check out this
-[notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/visualization.ipynb)
-with a step by step visualization of how this method works.
-- To understand how to use bayesian optimization when additional constraints are present, see the
-[constrained optimization notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/constraints.ipynb).
-- Explore this [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation_vs_exploration.ipynb)
+See below for a quick tour over the basics of the Bayesian Optimization package. More detailed information, other advanced features, and tips on usage/implementation can be found in the [examples](http://bayesian-optimization.github.io/BayesianOptimization/examples.html) folder. I suggest that you:
+- Follow the [basic tour notebook](http://bayesian-optimization.github.io/BayesianOptimization/basic-tour.html) to learn how to use the package's most important features.
+- Take a look at the [advanced tour notebook](http://bayesian-optimization.github.io/BayesianOptimization/advanced-tour.html) to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more.
+- Check out this [notebook](http://bayesian-optimization.github.io/BayesianOptimization/visualization.html) with a step by step visualization of how this method works.
+- To understand how to use bayesian optimization when additional constraints are present, see the [constrained optimization notebook](http://bayesian-optimization.github.io/BayesianOptimization/constraints.html).
+- Explore this [notebook](http://bayesian-optimization.github.io/BayesianOptimization/exploitation_vs_exploration.html)
 exemplifying the balance between exploration and exploitation and how to
 control it.
-- Go over this [script](https://github.com/fmfn/BayesianOptimization/blob/master/examples/sklearn_example.py)
+- Go over this [script](https://github.com/bayesian-optimization/BayesianOptimization/blob/master/examples/sklearn_example.py)
 for examples of how to tune parameters of Machine Learning models using cross validation and bayesian optimization.
-- Explore the [domain reduction notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/domain_reduction.ipynb) to learn more about how search can be sped up by dynamically changing parameters' bounds.
-- Finally, take a look at this [script](https://github.com/fmfn/BayesianOptimization/blob/master/examples/async_optimization.py)
+- Explore the [domain reduction notebook](http://bayesian-optimization.github.io/BayesianOptimization/domain_reduction.html) to learn more about how search can be sped up by dynamically changing parameters' bounds.
+- Finally, take a look at this [script](https://github.com/bayesian-optimization/BayesianOptimization/blob/master/examples/async_optimization.py)
 for ideas on how to implement bayesian optimization in a distributed fashion using this package.
 
 
 ## How does it work?
 
 Bayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and which are not, as seen in the picture below.
 
-![BayesianOptimization in action](./examples/bo_example.png)
+![BayesianOptimization in action](./static/bo_example.png)
 
 As you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function. At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution, combined with a exploration strategy (such as UCB (Upper Confidence Bound), or EI (Expected Improvement)), are used to determine the next point that should be explored (see the gif below).
 
-![BayesianOptimization in action](./examples/bayesian_optimization.gif)
+![BayesianOptimization in action](./static/bayesian_optimization.gif)
 
 This process is designed to minimize the number of steps required to find a combination of parameters that are close to the optimal combination. To do so, this method uses a proxy optimization problem (finding the maximum of the acquisition function) that, albeit still a hard problem, is cheaper (in the computational sense) and common tools can be employed. Therefore Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor. See the references for a proper discussion of this method.
 
 This project is under active development, if you find a bug, or anything that
 needs correction, please let me know.
 
 
-Basic tour of the Bayesian Optimization package
-===============================================
+## Basic tour of the Bayesian Optimization package
 
-## 1. Specifying the function to be optimized
+### 1. Specifying the function to be optimized
 
 This is a function optimization package, therefore the first and most important ingredient is, of course, the function to be optimized.
 
@@ -89,7 +83,7 @@ def black_box_function(x, y):
     return -x ** 2 - (y - 1) ** 2 + 1
 ```
 
-## 2. Getting Started
+### 2. Getting Started
 
 All we need to get started is to instantiate a `BayesianOptimization` object specifying a function to be optimized `f`, and its parameters with their corresponding bounds, `pbounds`. This is a constrained optimization technique, so you must specify the minimum and maximum values that can be probed for each parameter in order for it to work
 
@@ -160,7 +154,7 @@ for i, res in enumerate(optimizer.res):
 ```
 
 
-### 2.1 Changing bounds
+#### 2.1 Changing bounds
 
 During the optimization process you may realize the bounds chosen for some parameters are not adequate. For these situations you can invoke the method `set_bounds` to alter them. You can pass any combination of **existing** parameters and their associated new bounds.
 
@@ -183,17 +177,17 @@ optimizer.maximize(
     |  10       | -1.762    |  1.442    |  0.1735   |
     =================================================
 
-### 2.2 Sequential Domain Reduction
+#### 2.2 Sequential Domain Reduction
 
 Sometimes the initial boundaries specified for a problem are too wide, and adding points to improve the response surface in regions of the solution domain is extraneous. Other times the cost function is very expensive to compute, and minimizing the number of calls is extremely beneficial.
 
 When it's worthwhile to converge on an optimal point quickly rather than try to find the optimal point, contracting the domain around the current optimal value as the search progresses can speed up the search progress considerably. Using the `SequentialDomainReductionTransformer` the bounds of the problem can be panned and zoomed dynamically in an attempt to improve convergence.
 
-![sequential domain reduction](./examples/sdr.png)
+![sequential domain reduction](./static/sdr.png)
 
-An example of using the `SequentialDomainReductionTransformer` is shown in the [domain reduction notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/domain_reduction.ipynb). More information about this method can be found in the paper ["On the robustness of a simple domain reduction scheme for simulation‐based optimization"](http://www.truegrid.com/srsm_revised.pdf).
+An example of using the `SequentialDomainReductionTransformer` is shown in the [domain reduction notebook](http://bayesian-optimization.github.io/BayesianOptimization/domain_reduction.html). More information about this method can be found in the paper ["On the robustness of a simple domain reduction scheme for simulation‐based optimization"](http://www.truegrid.com/srsm_revised.pdf).
 
-## 3. Guiding the optimization
+### 3. Guiding the optimization
 
 It is often the case that we have an idea of regions of the parameter space where the maximum of our function might lie. For these situations the `BayesianOptimization` object allows the user to specify points to be probed. By default these will be explored lazily (`lazy=True`), meaning these points will be evaluated only the next time you call `maximize`. This probing process happens before the gaussian process takes over.
 
@@ -221,11 +215,11 @@ optimizer.maximize(init_points=0, n_iter=0)
     =================================================
 
 
-## 4. Saving, loading and restarting
+### 4. Saving, loading and restarting
 
 By default you can follow the progress of your optimization by setting `verbose>0` when instantiating the `BayesianOptimization` object. If you need more control over logging/alerting you will need to use an observer. For more information about observers checkout the advanced tour notebook. Here we will only see how to use the native `JSONLogger` object to save to and load progress from files.
 
-### 4.1 Saving progress
+#### 4.1 Saving progress
 
 
 ```python
@@ -255,7 +249,7 @@ optimizer.maximize(
 
 By default the previous data in the json file is removed. If you want to keep working with the same logger, the `reset` parameter in `JSONLogger` should be set to False.
 
-### 4.2 Loading progress
+#### 4.2 Loading progress
 
 Naturally, if you stored progress you will be able to load that onto a new instance of `BayesianOptimization`. The easiest way to do it is by invoking the `load_logs` function, from the `util` submodule.
 
@@ -277,54 +271,71 @@ load_logs(new_optimizer, logs=["./logs.log"]);
 
 ## Next Steps
 
-This introduction covered the most basic functionality of the package. Checkout the [basic-tour](https://github.com/fmfn/BayesianOptimization/blob/master/examples/basic-tour.ipynb) and [advanced-tour](https://github.com/fmfn/BayesianOptimization/blob/master/examples/advanced-tour.ipynb) notebooks in the example folder, where you will find detailed explanations and other more advanced functionality. Also, browse the examples folder for implementation tips and ideas.
-
-Installation
-============
-
-### Installation
-
-The latest release can be obtained by two ways:
-
-* With PyPI (pip):
-
-      pip install bayesian-optimization
+This introduction covered the most basic functionality of the package. Checkout the [basic-tour](http://bayesian-optimization.github.io/BayesianOptimization/basic-tour.html) and [advanced-tour](http://bayesian-optimization.github.io/BayesianOptimization/advanced-tour.html), where you will find detailed explanations and other more advanced functionality. Also, browse the [examples](http://bayesian-optimization.github.io/BayesianOptimization/examples.html) for implementation tips and ideas.
 
-* With conda (from conda-forge channel):
-
-      conda install -c conda-forge bayesian-optimization
-
-The bleeding edge version can be installed with:
-
-    pip install git+https://github.com/fmfn/BayesianOptimization.git
+## Installing from GitHub
+To install directly from master, simply run
+```
+pip install https://github.com/bayesian-optimization/BayesianOptimization/archive/master.zip
+```
+Please note that these builds are not necessarily stable.
 
-If you prefer, you can clone it and run the setup.py file. Use the following
-commands to get a copy from Github and install all dependencies:
+If you prefer, you can also clone the repository and run `setup.py`:
+```
+git clone https://github.com/bayesian-optimization/BayesianOptimization.git
+cd BayesianOptimization
+python setup.py install
+```
+
 
-    git clone https://github.com/fmfn/BayesianOptimization.git
-    cd BayesianOptimization
-    python setup.py install
+## Minutiae
 
-Citation
-============
+### Citation
 
-If you used this package in your research and is interested in citing it here's how you do it:
+If you used this package in your research, please cite it:
 
 ```
 @Misc{,
     author = {Fernando Nogueira},
     title = {{Bayesian Optimization}: Open source constrained global optimization tool for {Python}},
     year = {2014--},
-    url = " https://github.com/fmfn/BayesianOptimization"
+    url = " https://github.com/bayesian-optimization/BayesianOptimization"
+}
+```
+If you used any of the advanced functionalities, please additionally cite the corresponding publication:
+
+For the `SequentialDomainTransformer`:
+```
+@article{
+    author = {Stander, Nielen and Craig, Kenneth},
+    year = {2002},
+    month = {06},
+    pages = {},
+    title = {On the robustness of a simple domain reduction scheme for simulation-based optimization},
+    volume = {19},
+    journal = {International Journal for Computer-Aided Engineering and Software (Eng. Comput.)},
+    doi = {10.1108/02644400210430190}
+}
+```
+
+For constrained optimization:
+```
+@inproceedings{gardner2014bayesian,
+    title={Bayesian optimization with inequality constraints.},
+    author={Gardner, Jacob R and Kusner, Matt J and Xu, Zhixiang Eddie and Weinberger, Kilian Q and Cunningham, John P},
+    booktitle={ICML},
+    volume={2014},
+    pages={937--945},
+    year={2014}
 }
 ```
 
-# Dependencies
+### Dependencies
 * Numpy
 * Scipy
 * Scikit-learn
 
-# References:
+### References:
 * http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
 * http://arxiv.org/pdf/1012.2599v1.pdf
 * http://www.gaussianprocess.org/gpml/

bayes_opt/constraint.py

@@ -81,7 +81,7 @@ def eval(self, **kwargs):
         Raises
         ------
         TypeError
-            If the kwargs keys don't match the function argument names.
+            If the kwargs' keys don't match the function argument names.
         """
         try:
             return self.fun(**kwargs)