Metadata-Version: 2.1
Name: bore
Version: 1.0.0
Summary: Bayesian Optimization by Density-Ratio Estimation
Home-page: https://github.com/ltiao/bore
Author: Louis C. Tiao
Author-email: louistiao@gmail.com
License: MIT license
Description: =======================================================
        BORE: Bayesian Optimization as Density-Ratio Estimation
        =======================================================
        
        
        .. image:: https://img.shields.io/pypi/v/bore.svg
                :target: https://pypi.python.org/pypi/bore
        
        .. image:: https://img.shields.io/travis/ltiao/bore.svg
                :target: https://travis-ci.org/ltiao/bore
        
        .. image:: https://readthedocs.org/projects/bore/badge/?version=latest
                :target: https://bore.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        
        .. image:: https://pyup.io/repos/github/ltiao/bore/shield.svg
             :target: https://pyup.io/repos/github/ltiao/bore/
             :alt: Updates
        
        A minimalistic implementation of BORE: Bayesian Optimization as Density-Ratio Estimation [1]_
        in Python 3 and TensorFlow 2.
        
        Getting Started
        ---------------
        
        Install my-project with npm
        
        .. code-block:: bash
        
          $ pip install bore[tf]
        
        With GPU acceleration:
        
        .. code-block:: bash
        
          $ pip install bore[tf-gpu]
        
        With support for HpBandSter plugin 
        
        .. code-block:: bash
        
          $ pip install bore[tf,hpbandster]
        
        Usage/Examples
        --------------
        
        .. code-block:: python
        
          from bore.models import MaximizableSequential
          from tensorflow.keras.layers import Dense
        
          # build model
          classifier = MaximizableSequential()
          classifier.add(Dense(16, activation="relu"))
          classifier.add(Dense(16, activation="relu"))
          classifier.add(Dense(1, activation="sigmoid"))
        
          # compile model
          classifier.compile(optimizer="adam", loss="binary_crossentropy")
        
        The optimization loop can be implemented as follows:
        
        .. code-block:: python
        
          import numpy as np
        
          features = []
          targets = []
        
          # initial design
          features.extend(features_init)
          targets.extend(targets_init)
        
          for i in range(num_iterations):
        
              # construct classification problem
              X = np.vstack(features)
              y = np.hstack(targets)
        
              tau = np.quantile(y, q=0.25)
              z = np.less(y, tau)
        
              # update classifier
              classifier.fit(X, z, epochs=200, batch_size=64)
        
              # suggest new candidate
              x_next = classifier.argmax(method="L-BFGS-B", num_start_points=3, bounds=bounds)
        
              # evaluate blackbox function
              y_next = blackbox.evaluate(x_next)
        
              # update dataset
              features.append(x_next)
              targets.append(y_next)
        
        Features
        --------
        
        * BORE-MLP: BORE based on a multi-layer perceptron (MLP) (i.e. a fully-connected neural network) classifier
        
        Roadmap
        -------
        
        * Integration with the `Optuna <https://optuna.org/>`_ framework by `Sampler <https://optuna.readthedocs.io/en/stable/reference/generated/optuna.samplers.BaseSampler.html#optuna.samplers.BaseSampler>`_ plugin implementation.
        
        Authors
        -------
        
        Lead Developers:
        ++++++++++++++++
        
        +------------------+----------------------------+
        | |tiao|           | |klein|                    |
        +------------------+----------------------------+
        | Louis Tiao       | Aaron Klein                |
        +------------------+----------------------------+
        | https://tiao.io/ | https://aaronkl.github.io/ |
        +------------------+----------------------------+
        
        .. |tiao| image:: http://gravatar.com/avatar/d8b59298191057fa164edf80f0743fcc?s=120
           :align: middle
        .. |klein| image:: https://via.placeholder.com/120
           :align: middle
        
        Reference
        ---------
        
        .. [1] L. Tiao, A. Klein, C. Archambeau, E. V. Bonilla, M. Seeger, and F. Ramos. 
          `BORE: Bayesian Optimization by Density-Ratio Estimation <https://arxiv.org/abs/2102.09009>`_. 
          In Proceedings of the 38th International Conference on Machine Learning (ICML2021), 
          Virtual (Online), July 2021.
        
        Cite:
        +++++
        
        .. code-block::
        
          @inproceedings{tiao2021-bore,
            title={{B}ayesian {O}ptimization by {D}ensity-{R}atio {E}stimation},
            author={Tiao, Louis and Klein, Aaron and Archambeau, C\'{e}dric and Bonilla, Edwin V and Seeger, Matthias and Ramos, Fabio},
            booktitle={Proceedings of the 38th International Conference on Machine Learning (ICML2021)},
            address={Virtual (Online)},
            year={2021},
            month={July}
          }
        
        License
        -------
        
        MIT License
        
        Copyright (c) 2021, Louis C. Tiao
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
        
        =======
        History
        =======
        
        0.1.0 (2019-12-27)
        ------------------
        
        * First release on PyPI.
        
Keywords: bore
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Provides-Extra: hpbandster
Provides-Extra: tf
Provides-Extra: tf-gpu
