+{% endblock %}
+{% block content %}
+
+ {% block logo %}
+ {% if logo %}
+ {% set logo_link = "https://github.com/thierrymoudiki/thierrymoudiki.github.io/blob/master/images/t-logo2.png" %}
+ {% if logo_link %}{% endif %}
+
+ {% if logo_link %}{% endif %}
+ {% else %}
+
+
+
+ {% endif %}
+ {% endblock %}
+ {% if search %}
+
+ {% endif %}
+
+
+ {% if search %}
+ {% include "search.html.jinja2" %}
+ {% endif %}
+{% endblock %}
+{% endif %}
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 509abe3..0000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,281 +0,0 @@
-@ECHO OFF
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
- set SPHINXBUILD=sphinx-build
-)
-set BUILDDIR=build
-set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
-set I18NSPHINXOPTS=%SPHINXOPTS% source
-if NOT "%PAPER%" == "" (
- set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
- set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
-)
-
-if "%1" == "" goto help
-
-if "%1" == "help" (
- :help
- echo.Please use `make ^` where ^ is one of
- echo. html to make standalone HTML files
- echo. dirhtml to make HTML files named index.html in directories
- echo. singlehtml to make a single large HTML file
- echo. pickle to make pickle files
- echo. json to make JSON files
- echo. htmlhelp to make HTML files and a HTML help project
- echo. qthelp to make HTML files and a qthelp project
- echo. devhelp to make HTML files and a Devhelp project
- echo. epub to make an epub
- echo. epub3 to make an epub3
- echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
- echo. text to make text files
- echo. man to make manual pages
- echo. texinfo to make Texinfo files
- echo. gettext to make PO message catalogs
- echo. changes to make an overview over all changed/added/deprecated items
- echo. xml to make Docutils-native XML files
- echo. pseudoxml to make pseudoxml-XML files for display purposes
- echo. linkcheck to check all external links for integrity
- echo. doctest to run all doctests embedded in the documentation if enabled
- echo. coverage to run coverage check of the documentation if enabled
- echo. dummy to check syntax errors of document sources
- goto end
-)
-
-if "%1" == "clean" (
- for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
- del /q /s %BUILDDIR%\*
- goto end
-)
-
-
-REM Check if sphinx-build is available and fallback to Python version if any
-%SPHINXBUILD% 1>NUL 2>NUL
-if errorlevel 9009 goto sphinx_python
-goto sphinx_ok
-
-:sphinx_python
-
-set SPHINXBUILD=python -m sphinx.__init__
-%SPHINXBUILD% 2> nul
-if errorlevel 9009 (
- echo.
- echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
- echo.installed, then set the SPHINXBUILD environment variable to point
- echo.to the full path of the 'sphinx-build' executable. Alternatively you
- echo.may add the Sphinx directory to PATH.
- echo.
- echo.If you don't have Sphinx installed, grab it from
- echo.http://sphinx-doc.org/
- exit /b 1
-)
-
-:sphinx_ok
-
-
-if "%1" == "html" (
- %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The HTML pages are in %BUILDDIR%/html.
- goto end
-)
-
-if "%1" == "dirhtml" (
- %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
- goto end
-)
-
-if "%1" == "singlehtml" (
- %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
- goto end
-)
-
-if "%1" == "pickle" (
- %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished; now you can process the pickle files.
- goto end
-)
-
-if "%1" == "json" (
- %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished; now you can process the JSON files.
- goto end
-)
-
-if "%1" == "htmlhelp" (
- %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished; now you can run HTML Help Workshop with the ^
-.hhp project file in %BUILDDIR%/htmlhelp.
- goto end
-)
-
-if "%1" == "qthelp" (
- %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished; now you can run "qcollectiongenerator" with the ^
-.qhcp project file in %BUILDDIR%/qthelp, like this:
- echo.^> qcollectiongenerator %BUILDDIR%\qthelp\twitterpandas.qhcp
- echo.To view the help file:
- echo.^> assistant -collectionFile %BUILDDIR%\qthelp\twitterpandas.ghc
- goto end
-)
-
-if "%1" == "devhelp" (
- %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished.
- goto end
-)
-
-if "%1" == "epub" (
- %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The epub file is in %BUILDDIR%/epub.
- goto end
-)
-
-if "%1" == "epub3" (
- %SPHINXBUILD% -b epub3 %ALLSPHINXOPTS% %BUILDDIR%/epub3
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The epub3 file is in %BUILDDIR%/epub3.
- goto end
-)
-
-if "%1" == "latex" (
- %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
- goto end
-)
-
-if "%1" == "latexpdf" (
- %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
- cd %BUILDDIR%/latex
- make all-pdf
- cd %~dp0
- echo.
- echo.Build finished; the PDF files are in %BUILDDIR%/latex.
- goto end
-)
-
-if "%1" == "latexpdfja" (
- %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
- cd %BUILDDIR%/latex
- make all-pdf-ja
- cd %~dp0
- echo.
- echo.Build finished; the PDF files are in %BUILDDIR%/latex.
- goto end
-)
-
-if "%1" == "text" (
- %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The text files are in %BUILDDIR%/text.
- goto end
-)
-
-if "%1" == "man" (
- %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The manual pages are in %BUILDDIR%/man.
- goto end
-)
-
-if "%1" == "texinfo" (
- %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
- goto end
-)
-
-if "%1" == "gettext" (
- %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
- goto end
-)
-
-if "%1" == "changes" (
- %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
- if errorlevel 1 exit /b 1
- echo.
- echo.The overview file is in %BUILDDIR%/changes.
- goto end
-)
-
-if "%1" == "linkcheck" (
- %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
- if errorlevel 1 exit /b 1
- echo.
- echo.Link check complete; look for any errors in the above output ^
-or in %BUILDDIR%/linkcheck/output.txt.
- goto end
-)
-
-if "%1" == "doctest" (
- %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
- if errorlevel 1 exit /b 1
- echo.
- echo.Testing of doctests in the sources finished, look at the ^
-results in %BUILDDIR%/doctest/output.txt.
- goto end
-)
-
-if "%1" == "coverage" (
- %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage
- if errorlevel 1 exit /b 1
- echo.
- echo.Testing of coverage in the sources finished, look at the ^
-results in %BUILDDIR%/coverage/python.txt.
- goto end
-)
-
-if "%1" == "xml" (
- %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The XML files are in %BUILDDIR%/xml.
- goto end
-)
-
-if "%1" == "pseudoxml" (
- %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
- goto end
-)
-
-if "%1" == "dummy" (
- %SPHINXBUILD% -b dummy %ALLSPHINXOPTS% %BUILDDIR%/dummy
- if errorlevel 1 exit /b 1
- echo.
- echo.Build finished. Dummy builder generates no files.
- goto end
-)
-
-:end
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
deleted file mode 100644
index d6cfd09..0000000
--- a/docs/mkdocs.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-site_name: Techtonique/GPopt
-theme:
- name: darkly # sandstone # slate # lux # darkly
- nav_style: dark
- #name: null
- #custom_dir: 'custom_theme/'
-
-google_analytics: ['UA-146345113-2', 'https://techtonique.github.io/gpopt/']
-
-docs_dir: sources
-# repo_url: https://github.com/Techtonique/GPopt
-edit_uri: ""
-site_description: 'Documentation for GPopt.'
-
-nav:
- - Home: index.md
- - Documentation:
- - GPopt: documentation/gpopt.md
- - Contributing: CONTRIBUTING.md
- - License: LICENSE.md
- - Techtonique: ../
\ No newline at end of file
diff --git a/docs/module.html.jinja2 b/docs/module.html.jinja2
new file mode 100644
index 0000000..cb7ef5d
--- /dev/null
+++ b/docs/module.html.jinja2
@@ -0,0 +1,304 @@
+{% extends "frame.html.jinja2" %}
+{% block title %}{{ module.modulename }} API documentation{% endblock %}
+{% block nav %}
+ {% block module_list_link %}
+ {% set parentmodule = ".".join(module.modulename.split(".")[:-1]) %}
+ {% if parentmodule and parentmodule in all_modules %}
+
+ {% include "resources/box-arrow-in-left.svg" %}
+
+ {{- parentmodule -}}
+
+ {% elif not root_module_name %}
+
+ {% include "resources/box-arrow-in-left.svg" %}
+
+ Module Index
+
+ {% endif %}
+ {% endblock %}
+
+ {% block nav_title %}
+ {% if logo %}
+ {% set logo_link = "https://github.com/thierrymoudiki/thierrymoudiki.github.io/blob/master/images/t-logo2.png" %}
+ {% if logo_link %}{% endif %}
+
+ {% if logo_link %}{% endif %}
+ {% endif %}
+ {% endblock %}
+
+ {% block search_box %}
+ {% if search and all_modules|length > 1 %}
+ {# we set a pattern here so that we can use the :valid CSS selector #}
+
+ {% endif %}
+ {% endblock %}
+
+ {% block nav_index %}
+ {% set index = module.docstring | to_markdown | to_html | attr("toc_html") %}
+ {% if index %}
+
+{% enddefaultmacro %}
+{% defaultmacro is_public(doc) %}
+ {#
+ This macro is a bit unconventional in that its output is not rendered, but treated as a boolean:
+ Returning no text is interpreted as false, returning any other text is iterpreted as true.
+ Implementing this as a macro makes it very easy to override with a custom template, see
+ https://github.com/mitmproxy/pdoc/tree/main/examples/custom-template.
+ #}
+ {% if "@private" in doc.docstring %}
+ {# hide members explicitly marked as @private #}
+ {% elif "@public" in doc.docstring %}
+ {# show members explicitly marked as @public #}
+ true
+ {% elif not include_undocumented and not doc.docstring %}
+ {# hide members that are undocumented if include_undocumented has been toggled off. #}
+ {% elif doc.name == "__init__" and (doc.docstring or (doc.kind == "function" and doc.signature_without_self.parameters)) %}
+ {# show constructors that have a docstring or at least one extra argument #}
+ true
+ {% elif doc.name == "__doc__" %}
+ {# We don't want to document __doc__ itself, https://github.com/mitmproxy/pdoc/issues/235 #}
+ {% elif doc.kind == "variable" and doc.is_typevar and not doc.docstring %}
+ {# do not document TypeVars, that only clutters the docs. #}
+ {% elif doc.kind == "module" and doc.fullname not in all_modules %}
+ {# Skip modules that were manually excluded, https://github.com/mitmproxy/pdoc/issues/334 #}
+ {% elif (doc.qualname or doc.name) is in(module.obj.__all__ or []) %}
+ {# members starting with an underscore are still public if mentioned in __all__ #}
+ true
+ {% elif not doc.name.startswith("_") %}
+ {# members not starting with an underscore are considered public by default #}
+ true
+ {% endif %}
+{% enddefaultmacro %}
+{# fmt: off #}
+{% defaultmacro inherited(cls) %}
+ {% for base, members in cls.inherited_members.items() %}
+ {% set m = None %}{# workaround for https://github.com/pallets/jinja/issues/1427 #}
+ {% set member_html %}
+ {% for m in members if is_public(m) | trim %}
+
+ {% set parts = module.modulename.split(".") %}
+ {% for part in parts %}
+ {%- set fullname = ".".join(parts[:loop.index]) -%}
+ {%- if fullname in all_modules and fullname != module.modulename -%}
+ {{ part }}
+ {%- else -%}
+ {{ part }}
+ {%- endif -%}
+ {%- if loop.nextitem -%}
+ .
+ {%- endif -%}
+ {% endfor %}
+
A primary goal of this project is to be inclusive to the largest number of contributors, and most importantly with the most varied and diverse backgrounds possible. As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion, or lack of religion thereof.
-
This code of conduct outlines our expectations for all those who participate to the project, as well as the consequences for unacceptable behavior.
-
We invite all those who participate in, to help us create safe and positive experiences for everyone.
-
2. Open [Source/Culture/Tech] Citizenship
-
A supplemental goal of this Code of Conduct is to encourage participants to recognize and strengthen the relationships between our actions and their effects on other participants.
-
Communities mirror the societies in which they exist, and positive action is essential to counteract the many forms of inequality and abuses of power that exist in society.
-
3. Expected Behavior
-
The following behaviors are expected and requested of all contributors:
-
-
Attempt collaboration before conflict.
-
Participate in an authentic and active way. In doing so, you contribute to the health and longevity of this project.
-
Exercise consideration and respect in your speech and actions.
-
Refrain from demeaning, discriminatory, or harassing behavior and speech.
-
Be mindful of your surroundings and of your fellow participants.
-
-
4. Unacceptable Behavior
-
The following behaviors are considered harassment and are unacceptable:
-
-
Violence, threats of violence or violent language directed against another person.
-
Sexist, racist, homophobic, transphobic, ableist or otherwise discriminatory jokes and language.
-
Posting or displaying sexually explicit or violent material.
-
Posting or threatening to post other people's personally identifying information ("doxing").
-
Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability.
-
Inappropriate photography or recording.
-
Unwelcome sexual attention. This includes, sexualized comments or jokes.
-
Deliberate intimidation, stalking or following (online or in person).
-
Advocating for, or encouraging, any of the above behavior.
-
-
5. Consequences of Unacceptable Behavior
-
Unacceptable behavior from any contributor will not be tolerated.
-
Anyone asked to stop unacceptable behavior is expected to comply immediately.
-
If a contributor engages in unacceptable behavior, appropriate action will be taken, up to and including a temporary ban or permanent expulsion without warning.
-
6. Scope
-
We expect all contributors to abide by this Code of Conduct in all venues, online and in-person.
Copyright (c) [2019] [Thierry Moudiki]
-All rights reserved.
-
Redistribution and use in source and binary forms, with or without
-modification, are permitted (subject to the limitations in the disclaimer
-below) provided that the following conditions are met:
-
* Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-
- * Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
- * Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from this
- software without specific prior written permission.
-
-
NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
-THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
-BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
-IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Search
-
-
-
-
From here you can search these documents. Enter your search terms below.
lower_bound: a numpy array;
- lower bound for researched minimum
-
upper_bound: a numpy array;
- upper bound for researched minimum
-
objective_func: a function;
- the objective function to be minimized
-
x_init:
- initial setting of points where objective_func is evaluated (optional)
-
y_init:
- initial setting values at points where objective_func is evaluated (optional)
-
n_init: an integer;
- number of points in the initial setting, when x_init and y_init are not provided
-
n_choices: an integer;
- number of points for the calculation of expected improvement
-
n_iter: an integer;
- number of iterations of the minimization algorithm
-
alpha: a float;
- Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
-
n_restarts_optimizer: an integer;
- The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
-
seed: an integer;
- reproducibility seed
-
save: a string;
- Specifies where to save the optimizer in its current state
-
n_jobs: an integer;
- number of jobs for parallel computing on initial setting (can be -1)
-
per_second: a boolean;
- experimental, default is False (leave to default for now)
-
log_scale: a boolean;
- experimental, default is False (leave to default for now)
verbose: an integer;
- verbose = 0: nothing is printed,
- verbose = 1: a progress bar is printed (longer than 0),
- verbose = 2: information about each iteration is printed (longer than 1)
-
n_more_iter: an integer;
- additional number of iterations for the optimizer (which has been run once)
-
abs_tol: a float;
- tolerance for convergence of the optimizer (early stopping based on expected improvement)
-
min_budget: an integer (default is 50);
- minimum number of iterations before early stopping controlled by abs_tol
-
func_args: a list;
- additional parameters for the objective function (if necessary)
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- var tokenMetadata = lunr.utils.clone(metadata) || {}
- tokenMetadata["position"] = [sliceStart, sliceLength]
- tokenMetadata["index"] = tokens.length
-
- tokens.push(
- new lunr.Token (
- str.slice(sliceStart, sliceEnd),
- tokenMetadata
- )
- )
- }
-
- sliceStart = sliceEnd + 1
- }
-
- }
-
- return tokens
-}
-
-/**
- * The separator used to split a string into tokens. Override this property to change the behaviour of
- * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.
- *
- * @static
- * @see lunr.tokenizer
- */
-lunr.tokenizer.separator = /[\s\-]+/
-/*!
- * lunr.Pipeline
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * lunr.Pipelines maintain an ordered list of functions to be applied to all
- * tokens in documents entering the search index and queries being ran against
- * the index.
- *
- * An instance of lunr.Index created with the lunr shortcut will contain a
- * pipeline with a stop word filter and an English language stemmer. Extra
- * functions can be added before or after either of these functions or these
- * default functions can be removed.
- *
- * When run the pipeline will call each function in turn, passing a token, the
- * index of that token in the original list of all tokens and finally a list of
- * all the original tokens.
- *
- * The output of functions in the pipeline will be passed to the next function
- * in the pipeline. To exclude a token from entering the index the function
- * should return undefined, the rest of the pipeline will not be called with
- * this token.
- *
- * For serialisation of pipelines to work, all functions used in an instance of
- * a pipeline should be registered with lunr.Pipeline. Registered functions can
- * then be loaded. If trying to load a serialised pipeline that uses functions
- * that are not registered an error will be thrown.
- *
- * If not planning on serialising the pipeline then registering pipeline functions
- * is not necessary.
- *
- * @constructor
- */
-lunr.Pipeline = function () {
- this._stack = []
-}
-
-lunr.Pipeline.registeredFunctions = Object.create(null)
-
-/**
- * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token
- * string as well as all known metadata. A pipeline function can mutate the token string
- * or mutate (or add) metadata for a given token.
- *
- * A pipeline function can indicate that the passed token should be discarded by returning
- * null, undefined or an empty string. This token will not be passed to any downstream pipeline
- * functions and will not be added to the index.
- *
- * Multiple tokens can be returned by returning an array of tokens. Each token will be passed
- * to any downstream pipeline functions and all will returned tokens will be added to the index.
- *
- * Any number of pipeline functions may be chained together using a lunr.Pipeline.
- *
- * @interface lunr.PipelineFunction
- * @param {lunr.Token} token - A token from the document being processed.
- * @param {number} i - The index of this token in the complete list of tokens for this document/field.
- * @param {lunr.Token[]} tokens - All tokens for this document/field.
- * @returns {(?lunr.Token|lunr.Token[])}
- */
-
-/**
- * Register a function with the pipeline.
- *
- * Functions that are used in the pipeline should be registered if the pipeline
- * needs to be serialised, or a serialised pipeline needs to be loaded.
- *
- * Registering a function does not add it to a pipeline, functions must still be
- * added to instances of the pipeline for them to be used when running a pipeline.
- *
- * @param {lunr.PipelineFunction} fn - The function to check for.
- * @param {String} label - The label to register this function with
- */
-lunr.Pipeline.registerFunction = function (fn, label) {
- if (label in this.registeredFunctions) {
- lunr.utils.warn('Overwriting existing registered function: ' + label)
- }
-
- fn.label = label
- lunr.Pipeline.registeredFunctions[fn.label] = fn
-}
-
-/**
- * Warns if the function is not registered as a Pipeline function.
- *
- * @param {lunr.PipelineFunction} fn - The function to check for.
- * @private
- */
-lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {
- var isRegistered = fn.label && (fn.label in this.registeredFunctions)
-
- if (!isRegistered) {
- lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn)
- }
-}
-
-/**
- * Loads a previously serialised pipeline.
- *
- * All functions to be loaded must already be registered with lunr.Pipeline.
- * If any function from the serialised data has not been registered then an
- * error will be thrown.
- *
- * @param {Object} serialised - The serialised pipeline to load.
- * @returns {lunr.Pipeline}
- */
-lunr.Pipeline.load = function (serialised) {
- var pipeline = new lunr.Pipeline
-
- serialised.forEach(function (fnName) {
- var fn = lunr.Pipeline.registeredFunctions[fnName]
-
- if (fn) {
- pipeline.add(fn)
- } else {
- throw new Error('Cannot load unregistered function: ' + fnName)
- }
- })
-
- return pipeline
-}
-
-/**
- * Adds new functions to the end of the pipeline.
- *
- * Logs a warning if the function has not been registered.
- *
- * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.
- */
-lunr.Pipeline.prototype.add = function () {
- var fns = Array.prototype.slice.call(arguments)
-
- fns.forEach(function (fn) {
- lunr.Pipeline.warnIfFunctionNotRegistered(fn)
- this._stack.push(fn)
- }, this)
-}
-
-/**
- * Adds a single function after a function that already exists in the
- * pipeline.
- *
- * Logs a warning if the function has not been registered.
- *
- * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
- * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
- */
-lunr.Pipeline.prototype.after = function (existingFn, newFn) {
- lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
-
- var pos = this._stack.indexOf(existingFn)
- if (pos == -1) {
- throw new Error('Cannot find existingFn')
- }
-
- pos = pos + 1
- this._stack.splice(pos, 0, newFn)
-}
-
-/**
- * Adds a single function before a function that already exists in the
- * pipeline.
- *
- * Logs a warning if the function has not been registered.
- *
- * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.
- * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.
- */
-lunr.Pipeline.prototype.before = function (existingFn, newFn) {
- lunr.Pipeline.warnIfFunctionNotRegistered(newFn)
-
- var pos = this._stack.indexOf(existingFn)
- if (pos == -1) {
- throw new Error('Cannot find existingFn')
- }
-
- this._stack.splice(pos, 0, newFn)
-}
-
-/**
- * Removes a function from the pipeline.
- *
- * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.
- */
-lunr.Pipeline.prototype.remove = function (fn) {
- var pos = this._stack.indexOf(fn)
- if (pos == -1) {
- return
- }
-
- this._stack.splice(pos, 1)
-}
-
-/**
- * Runs the current list of functions that make up the pipeline against the
- * passed tokens.
- *
- * @param {Array} tokens The tokens to run through the pipeline.
- * @returns {Array}
- */
-lunr.Pipeline.prototype.run = function (tokens) {
- var stackLength = this._stack.length
-
- for (var i = 0; i < stackLength; i++) {
- var fn = this._stack[i]
- var memo = []
-
- for (var j = 0; j < tokens.length; j++) {
- var result = fn(tokens[j], j, tokens)
-
- if (result === null || result === void 0 || result === '') continue
-
- if (Array.isArray(result)) {
- for (var k = 0; k < result.length; k++) {
- memo.push(result[k])
- }
- } else {
- memo.push(result)
- }
- }
-
- tokens = memo
- }
-
- return tokens
-}
-
-/**
- * Convenience method for passing a string through a pipeline and getting
- * strings out. This method takes care of wrapping the passed string in a
- * token and mapping the resulting tokens back to strings.
- *
- * @param {string} str - The string to pass through the pipeline.
- * @param {?object} metadata - Optional metadata to associate with the token
- * passed to the pipeline.
- * @returns {string[]}
- */
-lunr.Pipeline.prototype.runString = function (str, metadata) {
- var token = new lunr.Token (str, metadata)
-
- return this.run([token]).map(function (t) {
- return t.toString()
- })
-}
-
-/**
- * Resets the pipeline by removing any existing processors.
- *
- */
-lunr.Pipeline.prototype.reset = function () {
- this._stack = []
-}
-
-/**
- * Returns a representation of the pipeline ready for serialisation.
- *
- * Logs a warning if the function has not been registered.
- *
- * @returns {Array}
- */
-lunr.Pipeline.prototype.toJSON = function () {
- return this._stack.map(function (fn) {
- lunr.Pipeline.warnIfFunctionNotRegistered(fn)
-
- return fn.label
- })
-}
-/*!
- * lunr.Vector
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * A vector is used to construct the vector space of documents and queries. These
- * vectors support operations to determine the similarity between two documents or
- * a document and a query.
- *
- * Normally no parameters are required for initializing a vector, but in the case of
- * loading a previously dumped vector the raw elements can be provided to the constructor.
- *
- * For performance reasons vectors are implemented with a flat array, where an elements
- * index is immediately followed by its value. E.g. [index, value, index, value]. This
- * allows the underlying array to be as sparse as possible and still offer decent
- * performance when being used for vector calculations.
- *
- * @constructor
- * @param {Number[]} [elements] - The flat list of element index and element value pairs.
- */
-lunr.Vector = function (elements) {
- this._magnitude = 0
- this.elements = elements || []
-}
-
-
-/**
- * Calculates the position within the vector to insert a given index.
- *
- * This is used internally by insert and upsert. If there are duplicate indexes then
- * the position is returned as if the value for that index were to be updated, but it
- * is the callers responsibility to check whether there is a duplicate at that index
- *
- * @param {Number} insertIdx - The index at which the element should be inserted.
- * @returns {Number}
- */
-lunr.Vector.prototype.positionForIndex = function (index) {
- // For an empty vector the tuple can be inserted at the beginning
- if (this.elements.length == 0) {
- return 0
- }
-
- var start = 0,
- end = this.elements.length / 2,
- sliceLength = end - start,
- pivotPoint = Math.floor(sliceLength / 2),
- pivotIndex = this.elements[pivotPoint * 2]
-
- while (sliceLength > 1) {
- if (pivotIndex < index) {
- start = pivotPoint
- }
-
- if (pivotIndex > index) {
- end = pivotPoint
- }
-
- if (pivotIndex == index) {
- break
- }
-
- sliceLength = end - start
- pivotPoint = start + Math.floor(sliceLength / 2)
- pivotIndex = this.elements[pivotPoint * 2]
- }
-
- if (pivotIndex == index) {
- return pivotPoint * 2
- }
-
- if (pivotIndex > index) {
- return pivotPoint * 2
- }
-
- if (pivotIndex < index) {
- return (pivotPoint + 1) * 2
- }
-}
-
-/**
- * Inserts an element at an index within the vector.
- *
- * Does not allow duplicates, will throw an error if there is already an entry
- * for this index.
- *
- * @param {Number} insertIdx - The index at which the element should be inserted.
- * @param {Number} val - The value to be inserted into the vector.
- */
-lunr.Vector.prototype.insert = function (insertIdx, val) {
- this.upsert(insertIdx, val, function () {
- throw "duplicate index"
- })
-}
-
-/**
- * Inserts or updates an existing index within the vector.
- *
- * @param {Number} insertIdx - The index at which the element should be inserted.
- * @param {Number} val - The value to be inserted into the vector.
- * @param {function} fn - A function that is called for updates, the existing value and the
- * requested value are passed as arguments
- */
-lunr.Vector.prototype.upsert = function (insertIdx, val, fn) {
- this._magnitude = 0
- var position = this.positionForIndex(insertIdx)
-
- if (this.elements[position] == insertIdx) {
- this.elements[position + 1] = fn(this.elements[position + 1], val)
- } else {
- this.elements.splice(position, 0, insertIdx, val)
- }
-}
-
-/**
- * Calculates the magnitude of this vector.
- *
- * @returns {Number}
- */
-lunr.Vector.prototype.magnitude = function () {
- if (this._magnitude) return this._magnitude
-
- var sumOfSquares = 0,
- elementsLength = this.elements.length
-
- for (var i = 1; i < elementsLength; i += 2) {
- var val = this.elements[i]
- sumOfSquares += val * val
- }
-
- return this._magnitude = Math.sqrt(sumOfSquares)
-}
-
-/**
- * Calculates the dot product of this vector and another vector.
- *
- * @param {lunr.Vector} otherVector - The vector to compute the dot product with.
- * @returns {Number}
- */
-lunr.Vector.prototype.dot = function (otherVector) {
- var dotProduct = 0,
- a = this.elements, b = otherVector.elements,
- aLen = a.length, bLen = b.length,
- aVal = 0, bVal = 0,
- i = 0, j = 0
-
- while (i < aLen && j < bLen) {
- aVal = a[i], bVal = b[j]
- if (aVal < bVal) {
- i += 2
- } else if (aVal > bVal) {
- j += 2
- } else if (aVal == bVal) {
- dotProduct += a[i + 1] * b[j + 1]
- i += 2
- j += 2
- }
- }
-
- return dotProduct
-}
-
-/**
- * Calculates the similarity between this vector and another vector.
- *
- * @param {lunr.Vector} otherVector - The other vector to calculate the
- * similarity with.
- * @returns {Number}
- */
-lunr.Vector.prototype.similarity = function (otherVector) {
- return this.dot(otherVector) / this.magnitude() || 0
-}
-
-/**
- * Converts the vector to an array of the elements within the vector.
- *
- * @returns {Number[]}
- */
-lunr.Vector.prototype.toArray = function () {
- var output = new Array (this.elements.length / 2)
-
- for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {
- output[j] = this.elements[i]
- }
-
- return output
-}
-
-/**
- * A JSON serializable representation of the vector.
- *
- * @returns {Number[]}
- */
-lunr.Vector.prototype.toJSON = function () {
- return this.elements
-}
-/* eslint-disable */
-/*!
- * lunr.stemmer
- * Copyright (C) 2020 Oliver Nightingale
- * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
- */
-
-/**
- * lunr.stemmer is an english language stemmer, this is a JavaScript
- * implementation of the PorterStemmer taken from http://tartarus.org/~martin
- *
- * @static
- * @implements {lunr.PipelineFunction}
- * @param {lunr.Token} token - The string to stem
- * @returns {lunr.Token}
- * @see {@link lunr.Pipeline}
- * @function
- */
-lunr.stemmer = (function(){
- var step2list = {
- "ational" : "ate",
- "tional" : "tion",
- "enci" : "ence",
- "anci" : "ance",
- "izer" : "ize",
- "bli" : "ble",
- "alli" : "al",
- "entli" : "ent",
- "eli" : "e",
- "ousli" : "ous",
- "ization" : "ize",
- "ation" : "ate",
- "ator" : "ate",
- "alism" : "al",
- "iveness" : "ive",
- "fulness" : "ful",
- "ousness" : "ous",
- "aliti" : "al",
- "iviti" : "ive",
- "biliti" : "ble",
- "logi" : "log"
- },
-
- step3list = {
- "icate" : "ic",
- "ative" : "",
- "alize" : "al",
- "iciti" : "ic",
- "ical" : "ic",
- "ful" : "",
- "ness" : ""
- },
-
- c = "[^aeiou]", // consonant
- v = "[aeiouy]", // vowel
- C = c + "[^aeiouy]*", // consonant sequence
- V = v + "[aeiou]*", // vowel sequence
-
- mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0
- meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1
- mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1
- s_v = "^(" + C + ")?" + v; // vowel in stem
-
- var re_mgr0 = new RegExp(mgr0);
- var re_mgr1 = new RegExp(mgr1);
- var re_meq1 = new RegExp(meq1);
- var re_s_v = new RegExp(s_v);
-
- var re_1a = /^(.+?)(ss|i)es$/;
- var re2_1a = /^(.+?)([^s])s$/;
- var re_1b = /^(.+?)eed$/;
- var re2_1b = /^(.+?)(ed|ing)$/;
- var re_1b_2 = /.$/;
- var re2_1b_2 = /(at|bl|iz)$/;
- var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$");
- var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-
- var re_1c = /^(.+?[^aeiou])y$/;
- var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-
- var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-
- var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- var re2_4 = /^(.+?)(s|t)(ion)$/;
-
- var re_5 = /^(.+?)e$/;
- var re_5_1 = /ll$/;
- var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-
- var porterStemmer = function porterStemmer(w) {
- var stem,
- suffix,
- firstch,
- re,
- re2,
- re3,
- re4;
-
- if (w.length < 3) { return w; }
-
- firstch = w.substr(0,1);
- if (firstch == "y") {
- w = firstch.toUpperCase() + w.substr(1);
- }
-
- // Step 1a
- re = re_1a
- re2 = re2_1a;
-
- if (re.test(w)) { w = w.replace(re,"$1$2"); }
- else if (re2.test(w)) { w = w.replace(re2,"$1$2"); }
-
- // Step 1b
- re = re_1b;
- re2 = re2_1b;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = re_mgr0;
- if (re.test(fp[1])) {
- re = re_1b_2;
- w = w.replace(re,"");
- }
- } else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = re_s_v;
- if (re2.test(stem)) {
- w = stem;
- re2 = re2_1b_2;
- re3 = re3_1b_2;
- re4 = re4_1b_2;
- if (re2.test(w)) { w = w + "e"; }
- else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); }
- else if (re4.test(w)) { w = w + "e"; }
- }
- }
-
- // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)
- re = re_1c;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- w = stem + "i";
- }
-
- // Step 2
- re = re_2;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = re_mgr0;
- if (re.test(stem)) {
- w = stem + step2list[suffix];
- }
- }
-
- // Step 3
- re = re_3;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = re_mgr0;
- if (re.test(stem)) {
- w = stem + step3list[suffix];
- }
- }
-
- // Step 4
- re = re_4;
- re2 = re2_4;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = re_mgr1;
- if (re.test(stem)) {
- w = stem;
- }
- } else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = re_mgr1;
- if (re2.test(stem)) {
- w = stem;
- }
- }
-
- // Step 5
- re = re_5;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = re_mgr1;
- re2 = re_meq1;
- re3 = re3_5;
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {
- w = stem;
- }
- }
-
- re = re_5_1;
- re2 = re_mgr1;
- if (re.test(w) && re2.test(w)) {
- re = re_1b_2;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
-
- if (firstch == "y") {
- w = firstch.toLowerCase() + w.substr(1);
- }
-
- return w;
- };
-
- return function (token) {
- return token.update(porterStemmer);
- }
-})();
-
-lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')
-/*!
- * lunr.stopWordFilter
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * lunr.generateStopWordFilter builds a stopWordFilter function from the provided
- * list of stop words.
- *
- * The built in lunr.stopWordFilter is built using this generator and can be used
- * to generate custom stopWordFilters for applications or non English languages.
- *
- * @function
- * @param {Array} token The token to pass through the filter
- * @returns {lunr.PipelineFunction}
- * @see lunr.Pipeline
- * @see lunr.stopWordFilter
- */
-lunr.generateStopWordFilter = function (stopWords) {
- var words = stopWords.reduce(function (memo, stopWord) {
- memo[stopWord] = stopWord
- return memo
- }, {})
-
- return function (token) {
- if (token && words[token.toString()] !== token.toString()) return token
- }
-}
-
-/**
- * lunr.stopWordFilter is an English language stop word list filter, any words
- * contained in the list will not be passed through the filter.
- *
- * This is intended to be used in the Pipeline. If the token does not pass the
- * filter then undefined will be returned.
- *
- * @function
- * @implements {lunr.PipelineFunction}
- * @params {lunr.Token} token - A token to check for being a stop word.
- * @returns {lunr.Token}
- * @see {@link lunr.Pipeline}
- */
-lunr.stopWordFilter = lunr.generateStopWordFilter([
- 'a',
- 'able',
- 'about',
- 'across',
- 'after',
- 'all',
- 'almost',
- 'also',
- 'am',
- 'among',
- 'an',
- 'and',
- 'any',
- 'are',
- 'as',
- 'at',
- 'be',
- 'because',
- 'been',
- 'but',
- 'by',
- 'can',
- 'cannot',
- 'could',
- 'dear',
- 'did',
- 'do',
- 'does',
- 'either',
- 'else',
- 'ever',
- 'every',
- 'for',
- 'from',
- 'get',
- 'got',
- 'had',
- 'has',
- 'have',
- 'he',
- 'her',
- 'hers',
- 'him',
- 'his',
- 'how',
- 'however',
- 'i',
- 'if',
- 'in',
- 'into',
- 'is',
- 'it',
- 'its',
- 'just',
- 'least',
- 'let',
- 'like',
- 'likely',
- 'may',
- 'me',
- 'might',
- 'most',
- 'must',
- 'my',
- 'neither',
- 'no',
- 'nor',
- 'not',
- 'of',
- 'off',
- 'often',
- 'on',
- 'only',
- 'or',
- 'other',
- 'our',
- 'own',
- 'rather',
- 'said',
- 'say',
- 'says',
- 'she',
- 'should',
- 'since',
- 'so',
- 'some',
- 'than',
- 'that',
- 'the',
- 'their',
- 'them',
- 'then',
- 'there',
- 'these',
- 'they',
- 'this',
- 'tis',
- 'to',
- 'too',
- 'twas',
- 'us',
- 'wants',
- 'was',
- 'we',
- 'were',
- 'what',
- 'when',
- 'where',
- 'which',
- 'while',
- 'who',
- 'whom',
- 'why',
- 'will',
- 'with',
- 'would',
- 'yet',
- 'you',
- 'your'
-])
-
-lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')
-/*!
- * lunr.trimmer
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * lunr.trimmer is a pipeline function for trimming non word
- * characters from the beginning and end of tokens before they
- * enter the index.
- *
- * This implementation may not work correctly for non latin
- * characters and should either be removed or adapted for use
- * with languages with non-latin characters.
- *
- * @static
- * @implements {lunr.PipelineFunction}
- * @param {lunr.Token} token The token to pass through the filter
- * @returns {lunr.Token}
- * @see lunr.Pipeline
- */
-lunr.trimmer = function (token) {
- return token.update(function (s) {
- return s.replace(/^\W+/, '').replace(/\W+$/, '')
- })
-}
-
-lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')
-/*!
- * lunr.TokenSet
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * A token set is used to store the unique list of all tokens
- * within an index. Token sets are also used to represent an
- * incoming query to the index, this query token set and index
- * token set are then intersected to find which tokens to look
- * up in the inverted index.
- *
- * A token set can hold multiple tokens, as in the case of the
- * index token set, or it can hold a single token as in the
- * case of a simple query token set.
- *
- * Additionally token sets are used to perform wildcard matching.
- * Leading, contained and trailing wildcards are supported, and
- * from this edit distance matching can also be provided.
- *
- * Token sets are implemented as a minimal finite state automata,
- * where both common prefixes and suffixes are shared between tokens.
- * This helps to reduce the space used for storing the token set.
- *
- * @constructor
- */
-lunr.TokenSet = function () {
- this.final = false
- this.edges = {}
- this.id = lunr.TokenSet._nextId
- lunr.TokenSet._nextId += 1
-}
-
-/**
- * Keeps track of the next, auto increment, identifier to assign
- * to a new tokenSet.
- *
- * TokenSets require a unique identifier to be correctly minimised.
- *
- * @private
- */
-lunr.TokenSet._nextId = 1
-
-/**
- * Creates a TokenSet instance from the given sorted array of words.
- *
- * @param {String[]} arr - A sorted array of strings to create the set from.
- * @returns {lunr.TokenSet}
- * @throws Will throw an error if the input array is not sorted.
- */
-lunr.TokenSet.fromArray = function (arr) {
- var builder = new lunr.TokenSet.Builder
-
- for (var i = 0, len = arr.length; i < len; i++) {
- builder.insert(arr[i])
- }
-
- builder.finish()
- return builder.root
-}
-
-/**
- * Creates a token set from a query clause.
- *
- * @private
- * @param {Object} clause - A single clause from lunr.Query.
- * @param {string} clause.term - The query clause term.
- * @param {number} [clause.editDistance] - The optional edit distance for the term.
- * @returns {lunr.TokenSet}
- */
-lunr.TokenSet.fromClause = function (clause) {
- if ('editDistance' in clause) {
- return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)
- } else {
- return lunr.TokenSet.fromString(clause.term)
- }
-}
-
-/**
- * Creates a token set representing a single string with a specified
- * edit distance.
- *
- * Insertions, deletions, substitutions and transpositions are each
- * treated as an edit distance of 1.
- *
- * Increasing the allowed edit distance will have a dramatic impact
- * on the performance of both creating and intersecting these TokenSets.
- * It is advised to keep the edit distance less than 3.
- *
- * @param {string} str - The string to create the token set from.
- * @param {number} editDistance - The allowed edit distance to match.
- * @returns {lunr.Vector}
- */
-lunr.TokenSet.fromFuzzyString = function (str, editDistance) {
- var root = new lunr.TokenSet
-
- var stack = [{
- node: root,
- editsRemaining: editDistance,
- str: str
- }]
-
- while (stack.length) {
- var frame = stack.pop()
-
- // no edit
- if (frame.str.length > 0) {
- var char = frame.str.charAt(0),
- noEditNode
-
- if (char in frame.node.edges) {
- noEditNode = frame.node.edges[char]
- } else {
- noEditNode = new lunr.TokenSet
- frame.node.edges[char] = noEditNode
- }
-
- if (frame.str.length == 1) {
- noEditNode.final = true
- }
-
- stack.push({
- node: noEditNode,
- editsRemaining: frame.editsRemaining,
- str: frame.str.slice(1)
- })
- }
-
- if (frame.editsRemaining == 0) {
- continue
- }
-
- // insertion
- if ("*" in frame.node.edges) {
- var insertionNode = frame.node.edges["*"]
- } else {
- var insertionNode = new lunr.TokenSet
- frame.node.edges["*"] = insertionNode
- }
-
- if (frame.str.length == 0) {
- insertionNode.final = true
- }
-
- stack.push({
- node: insertionNode,
- editsRemaining: frame.editsRemaining - 1,
- str: frame.str
- })
-
- // deletion
- // can only do a deletion if we have enough edits remaining
- // and if there are characters left to delete in the string
- if (frame.str.length > 1) {
- stack.push({
- node: frame.node,
- editsRemaining: frame.editsRemaining - 1,
- str: frame.str.slice(1)
- })
- }
-
- // deletion
- // just removing the last character from the str
- if (frame.str.length == 1) {
- frame.node.final = true
- }
-
- // substitution
- // can only do a substitution if we have enough edits remaining
- // and if there are characters left to substitute
- if (frame.str.length >= 1) {
- if ("*" in frame.node.edges) {
- var substitutionNode = frame.node.edges["*"]
- } else {
- var substitutionNode = new lunr.TokenSet
- frame.node.edges["*"] = substitutionNode
- }
-
- if (frame.str.length == 1) {
- substitutionNode.final = true
- }
-
- stack.push({
- node: substitutionNode,
- editsRemaining: frame.editsRemaining - 1,
- str: frame.str.slice(1)
- })
- }
-
- // transposition
- // can only do a transposition if there are edits remaining
- // and there are enough characters to transpose
- if (frame.str.length > 1) {
- var charA = frame.str.charAt(0),
- charB = frame.str.charAt(1),
- transposeNode
-
- if (charB in frame.node.edges) {
- transposeNode = frame.node.edges[charB]
- } else {
- transposeNode = new lunr.TokenSet
- frame.node.edges[charB] = transposeNode
- }
-
- if (frame.str.length == 1) {
- transposeNode.final = true
- }
-
- stack.push({
- node: transposeNode,
- editsRemaining: frame.editsRemaining - 1,
- str: charA + frame.str.slice(2)
- })
- }
- }
-
- return root
-}
-
-/**
- * Creates a TokenSet from a string.
- *
- * The string may contain one or more wildcard characters (*)
- * that will allow wildcard matching when intersecting with
- * another TokenSet.
- *
- * @param {string} str - The string to create a TokenSet from.
- * @returns {lunr.TokenSet}
- */
-lunr.TokenSet.fromString = function (str) {
- var node = new lunr.TokenSet,
- root = node
-
- /*
- * Iterates through all characters within the passed string
- * appending a node for each character.
- *
- * When a wildcard character is found then a self
- * referencing edge is introduced to continually match
- * any number of any characters.
- */
- for (var i = 0, len = str.length; i < len; i++) {
- var char = str[i],
- final = (i == len - 1)
-
- if (char == "*") {
- node.edges[char] = node
- node.final = final
-
- } else {
- var next = new lunr.TokenSet
- next.final = final
-
- node.edges[char] = next
- node = next
- }
- }
-
- return root
-}
-
-/**
- * Converts this TokenSet into an array of strings
- * contained within the TokenSet.
- *
- * This is not intended to be used on a TokenSet that
- * contains wildcards, in these cases the results are
- * undefined and are likely to cause an infinite loop.
- *
- * @returns {string[]}
- */
-lunr.TokenSet.prototype.toArray = function () {
- var words = []
-
- var stack = [{
- prefix: "",
- node: this
- }]
-
- while (stack.length) {
- var frame = stack.pop(),
- edges = Object.keys(frame.node.edges),
- len = edges.length
-
- if (frame.node.final) {
- /* In Safari, at this point the prefix is sometimes corrupted, see:
- * https://github.com/olivernn/lunr.js/issues/279 Calling any
- * String.prototype method forces Safari to "cast" this string to what
- * it's supposed to be, fixing the bug. */
- frame.prefix.charAt(0)
- words.push(frame.prefix)
- }
-
- for (var i = 0; i < len; i++) {
- var edge = edges[i]
-
- stack.push({
- prefix: frame.prefix.concat(edge),
- node: frame.node.edges[edge]
- })
- }
- }
-
- return words
-}
-
-/**
- * Generates a string representation of a TokenSet.
- *
- * This is intended to allow TokenSets to be used as keys
- * in objects, largely to aid the construction and minimisation
- * of a TokenSet. As such it is not designed to be a human
- * friendly representation of the TokenSet.
- *
- * @returns {string}
- */
-lunr.TokenSet.prototype.toString = function () {
- // NOTE: Using Object.keys here as this.edges is very likely
- // to enter 'hash-mode' with many keys being added
- //
- // avoiding a for-in loop here as it leads to the function
- // being de-optimised (at least in V8). From some simple
- // benchmarks the performance is comparable, but allowing
- // V8 to optimize may mean easy performance wins in the future.
-
- if (this._str) {
- return this._str
- }
-
- var str = this.final ? '1' : '0',
- labels = Object.keys(this.edges).sort(),
- len = labels.length
-
- for (var i = 0; i < len; i++) {
- var label = labels[i],
- node = this.edges[label]
-
- str = str + label + node.id
- }
-
- return str
-}
-
-/**
- * Returns a new TokenSet that is the intersection of
- * this TokenSet and the passed TokenSet.
- *
- * This intersection will take into account any wildcards
- * contained within the TokenSet.
- *
- * @param {lunr.TokenSet} b - An other TokenSet to intersect with.
- * @returns {lunr.TokenSet}
- */
-lunr.TokenSet.prototype.intersect = function (b) {
- var output = new lunr.TokenSet,
- frame = undefined
-
- var stack = [{
- qNode: b,
- output: output,
- node: this
- }]
-
- while (stack.length) {
- frame = stack.pop()
-
- // NOTE: As with the #toString method, we are using
- // Object.keys and a for loop instead of a for-in loop
- // as both of these objects enter 'hash' mode, causing
- // the function to be de-optimised in V8
- var qEdges = Object.keys(frame.qNode.edges),
- qLen = qEdges.length,
- nEdges = Object.keys(frame.node.edges),
- nLen = nEdges.length
-
- for (var q = 0; q < qLen; q++) {
- var qEdge = qEdges[q]
-
- for (var n = 0; n < nLen; n++) {
- var nEdge = nEdges[n]
-
- if (nEdge == qEdge || qEdge == '*') {
- var node = frame.node.edges[nEdge],
- qNode = frame.qNode.edges[qEdge],
- final = node.final && qNode.final,
- next = undefined
-
- if (nEdge in frame.output.edges) {
- // an edge already exists for this character
- // no need to create a new node, just set the finality
- // bit unless this node is already final
- next = frame.output.edges[nEdge]
- next.final = next.final || final
-
- } else {
- // no edge exists yet, must create one
- // set the finality bit and insert it
- // into the output
- next = new lunr.TokenSet
- next.final = final
- frame.output.edges[nEdge] = next
- }
-
- stack.push({
- qNode: qNode,
- output: next,
- node: node
- })
- }
- }
- }
- }
-
- return output
-}
-lunr.TokenSet.Builder = function () {
- this.previousWord = ""
- this.root = new lunr.TokenSet
- this.uncheckedNodes = []
- this.minimizedNodes = {}
-}
-
-lunr.TokenSet.Builder.prototype.insert = function (word) {
- var node,
- commonPrefix = 0
-
- if (word < this.previousWord) {
- throw new Error ("Out of order word insertion")
- }
-
- for (var i = 0; i < word.length && i < this.previousWord.length; i++) {
- if (word[i] != this.previousWord[i]) break
- commonPrefix++
- }
-
- this.minimize(commonPrefix)
-
- if (this.uncheckedNodes.length == 0) {
- node = this.root
- } else {
- node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child
- }
-
- for (var i = commonPrefix; i < word.length; i++) {
- var nextNode = new lunr.TokenSet,
- char = word[i]
-
- node.edges[char] = nextNode
-
- this.uncheckedNodes.push({
- parent: node,
- char: char,
- child: nextNode
- })
-
- node = nextNode
- }
-
- node.final = true
- this.previousWord = word
-}
-
-lunr.TokenSet.Builder.prototype.finish = function () {
- this.minimize(0)
-}
-
-lunr.TokenSet.Builder.prototype.minimize = function (downTo) {
- for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {
- var node = this.uncheckedNodes[i],
- childKey = node.child.toString()
-
- if (childKey in this.minimizedNodes) {
- node.parent.edges[node.char] = this.minimizedNodes[childKey]
- } else {
- // Cache the key for this node since
- // we know it can't change anymore
- node.child._str = childKey
-
- this.minimizedNodes[childKey] = node.child
- }
-
- this.uncheckedNodes.pop()
- }
-}
-/*!
- * lunr.Index
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * An index contains the built index of all documents and provides a query interface
- * to the index.
- *
- * Usually instances of lunr.Index will not be created using this constructor, instead
- * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be
- * used to load previously built and serialized indexes.
- *
- * @constructor
- * @param {Object} attrs - The attributes of the built search index.
- * @param {Object} attrs.invertedIndex - An index of term/field to document reference.
- * @param {Object} attrs.fieldVectors - Field vectors
- * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.
- * @param {string[]} attrs.fields - The names of indexed document fields.
- * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.
- */
-lunr.Index = function (attrs) {
- this.invertedIndex = attrs.invertedIndex
- this.fieldVectors = attrs.fieldVectors
- this.tokenSet = attrs.tokenSet
- this.fields = attrs.fields
- this.pipeline = attrs.pipeline
-}
-
-/**
- * A result contains details of a document matching a search query.
- * @typedef {Object} lunr.Index~Result
- * @property {string} ref - The reference of the document this result represents.
- * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.
- * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.
- */
-
-/**
- * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple
- * query language which itself is parsed into an instance of lunr.Query.
- *
- * For programmatically building queries it is advised to directly use lunr.Query, the query language
- * is best used for human entered text rather than program generated text.
- *
- * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported
- * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'
- * or 'world', though those that contain both will rank higher in the results.
- *
- * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can
- * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding
- * wildcards will increase the number of documents that will be found but can also have a negative
- * impact on query performance, especially with wildcards at the beginning of a term.
- *
- * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term
- * hello in the title field will match this query. Using a field not present in the index will lead
- * to an error being thrown.
- *
- * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term
- * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported
- * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.
- * Avoid large values for edit distance to improve query performance.
- *
- * Each term also supports a presence modifier. By default a term's presence in document is optional, however
- * this can be changed to either required or prohibited. For a term's presence to be required in a document the
- * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and
- * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not
- * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.
- *
- * To escape special characters the backslash character '\' can be used, this allows searches to include
- * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead
- * of attempting to apply a boost of 2 to the search term "foo".
- *
- * @typedef {string} lunr.Index~QueryString
- * @example
Simple single term query
- * hello
- * @example
Multiple term query
- * hello world
- * @example
term scoped to a field
- * title:hello
- * @example
term with a boost of 10
- * hello^10
- * @example
term with an edit distance of 2
- * hello~2
- * @example
terms with presence modifiers
- * -foo +bar baz
- */
-
-/**
- * Performs a search against the index using lunr query syntax.
- *
- * Results will be returned sorted by their score, the most relevant results
- * will be returned first. For details on how the score is calculated, please see
- * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.
- *
- * For more programmatic querying use lunr.Index#query.
- *
- * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.
- * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.
- * @returns {lunr.Index~Result[]}
- */
-lunr.Index.prototype.search = function (queryString) {
- return this.query(function (query) {
- var parser = new lunr.QueryParser(queryString, query)
- parser.parse()
- })
-}
-
-/**
- * A query builder callback provides a query object to be used to express
- * the query to perform on the index.
- *
- * @callback lunr.Index~queryBuilder
- * @param {lunr.Query} query - The query object to build up.
- * @this lunr.Query
- */
-
-/**
- * Performs a query against the index using the yielded lunr.Query object.
- *
- * If performing programmatic queries against the index, this method is preferred
- * over lunr.Index#search so as to avoid the additional query parsing overhead.
- *
- * A query object is yielded to the supplied function which should be used to
- * express the query to be run against the index.
- *
- * Note that although this function takes a callback parameter it is _not_ an
- * asynchronous operation, the callback is just yielded a query object to be
- * customized.
- *
- * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.
- * @returns {lunr.Index~Result[]}
- */
-lunr.Index.prototype.query = function (fn) {
- // for each query clause
- // * process terms
- // * expand terms from token set
- // * find matching documents and metadata
- // * get document vectors
- // * score documents
-
- var query = new lunr.Query(this.fields),
- matchingFields = Object.create(null),
- queryVectors = Object.create(null),
- termFieldCache = Object.create(null),
- requiredMatches = Object.create(null),
- prohibitedMatches = Object.create(null)
-
- /*
- * To support field level boosts a query vector is created per
- * field. An empty vector is eagerly created to support negated
- * queries.
- */
- for (var i = 0; i < this.fields.length; i++) {
- queryVectors[this.fields[i]] = new lunr.Vector
- }
-
- fn.call(query, query)
-
- for (var i = 0; i < query.clauses.length; i++) {
- /*
- * Unless the pipeline has been disabled for this term, which is
- * the case for terms with wildcards, we need to pass the clause
- * term through the search pipeline. A pipeline returns an array
- * of processed terms. Pipeline functions may expand the passed
- * term, which means we may end up performing multiple index lookups
- * for a single query term.
- */
- var clause = query.clauses[i],
- terms = null,
- clauseMatches = lunr.Set.empty
-
- if (clause.usePipeline) {
- terms = this.pipeline.runString(clause.term, {
- fields: clause.fields
- })
- } else {
- terms = [clause.term]
- }
-
- for (var m = 0; m < terms.length; m++) {
- var term = terms[m]
-
- /*
- * Each term returned from the pipeline needs to use the same query
- * clause object, e.g. the same boost and or edit distance. The
- * simplest way to do this is to re-use the clause object but mutate
- * its term property.
- */
- clause.term = term
-
- /*
- * From the term in the clause we create a token set which will then
- * be used to intersect the indexes token set to get a list of terms
- * to lookup in the inverted index
- */
- var termTokenSet = lunr.TokenSet.fromClause(clause),
- expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()
-
- /*
- * If a term marked as required does not exist in the tokenSet it is
- * impossible for the search to return any matches. We set all the field
- * scoped required matches set to empty and stop examining any further
- * clauses.
- */
- if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {
- for (var k = 0; k < clause.fields.length; k++) {
- var field = clause.fields[k]
- requiredMatches[field] = lunr.Set.empty
- }
-
- break
- }
-
- for (var j = 0; j < expandedTerms.length; j++) {
- /*
- * For each term get the posting and termIndex, this is required for
- * building the query vector.
- */
- var expandedTerm = expandedTerms[j],
- posting = this.invertedIndex[expandedTerm],
- termIndex = posting._index
-
- for (var k = 0; k < clause.fields.length; k++) {
- /*
- * For each field that this query term is scoped by (by default
- * all fields are in scope) we need to get all the document refs
- * that have this term in that field.
- *
- * The posting is the entry in the invertedIndex for the matching
- * term from above.
- */
- var field = clause.fields[k],
- fieldPosting = posting[field],
- matchingDocumentRefs = Object.keys(fieldPosting),
- termField = expandedTerm + "/" + field,
- matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)
-
- /*
- * if the presence of this term is required ensure that the matching
- * documents are added to the set of required matches for this clause.
- *
- */
- if (clause.presence == lunr.Query.presence.REQUIRED) {
- clauseMatches = clauseMatches.union(matchingDocumentsSet)
-
- if (requiredMatches[field] === undefined) {
- requiredMatches[field] = lunr.Set.complete
- }
- }
-
- /*
- * if the presence of this term is prohibited ensure that the matching
- * documents are added to the set of prohibited matches for this field,
- * creating that set if it does not yet exist.
- */
- if (clause.presence == lunr.Query.presence.PROHIBITED) {
- if (prohibitedMatches[field] === undefined) {
- prohibitedMatches[field] = lunr.Set.empty
- }
-
- prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)
-
- /*
- * Prohibited matches should not be part of the query vector used for
- * similarity scoring and no metadata should be extracted so we continue
- * to the next field
- */
- continue
- }
-
- /*
- * The query field vector is populated using the termIndex found for
- * the term and a unit value with the appropriate boost applied.
- * Using upsert because there could already be an entry in the vector
- * for the term we are working with. In that case we just add the scores
- * together.
- */
- queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })
-
- /**
- * If we've already seen this term, field combo then we've already collected
- * the matching documents and metadata, no need to go through all that again
- */
- if (termFieldCache[termField]) {
- continue
- }
-
- for (var l = 0; l < matchingDocumentRefs.length; l++) {
- /*
- * All metadata for this term/field/document triple
- * are then extracted and collected into an instance
- * of lunr.MatchData ready to be returned in the query
- * results
- */
- var matchingDocumentRef = matchingDocumentRefs[l],
- matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),
- metadata = fieldPosting[matchingDocumentRef],
- fieldMatch
-
- if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {
- matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)
- } else {
- fieldMatch.add(expandedTerm, field, metadata)
- }
-
- }
-
- termFieldCache[termField] = true
- }
- }
- }
-
- /**
- * If the presence was required we need to update the requiredMatches field sets.
- * We do this after all fields for the term have collected their matches because
- * the clause terms presence is required in _any_ of the fields not _all_ of the
- * fields.
- */
- if (clause.presence === lunr.Query.presence.REQUIRED) {
- for (var k = 0; k < clause.fields.length; k++) {
- var field = clause.fields[k]
- requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)
- }
- }
- }
-
- /**
- * Need to combine the field scoped required and prohibited
- * matching documents into a global set of required and prohibited
- * matches
- */
- var allRequiredMatches = lunr.Set.complete,
- allProhibitedMatches = lunr.Set.empty
-
- for (var i = 0; i < this.fields.length; i++) {
- var field = this.fields[i]
-
- if (requiredMatches[field]) {
- allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])
- }
-
- if (prohibitedMatches[field]) {
- allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])
- }
- }
-
- var matchingFieldRefs = Object.keys(matchingFields),
- results = [],
- matches = Object.create(null)
-
- /*
- * If the query is negated (contains only prohibited terms)
- * we need to get _all_ fieldRefs currently existing in the
- * index. This is only done when we know that the query is
- * entirely prohibited terms to avoid any cost of getting all
- * fieldRefs unnecessarily.
- *
- * Additionally, blank MatchData must be created to correctly
- * populate the results.
- */
- if (query.isNegated()) {
- matchingFieldRefs = Object.keys(this.fieldVectors)
-
- for (var i = 0; i < matchingFieldRefs.length; i++) {
- var matchingFieldRef = matchingFieldRefs[i]
- var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)
- matchingFields[matchingFieldRef] = new lunr.MatchData
- }
- }
-
- for (var i = 0; i < matchingFieldRefs.length; i++) {
- /*
- * Currently we have document fields that match the query, but we
- * need to return documents. The matchData and scores are combined
- * from multiple fields belonging to the same document.
- *
- * Scores are calculated by field, using the query vectors created
- * above, and combined into a final document score using addition.
- */
- var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),
- docRef = fieldRef.docRef
-
- if (!allRequiredMatches.contains(docRef)) {
- continue
- }
-
- if (allProhibitedMatches.contains(docRef)) {
- continue
- }
-
- var fieldVector = this.fieldVectors[fieldRef],
- score = queryVectors[fieldRef.fieldName].similarity(fieldVector),
- docMatch
-
- if ((docMatch = matches[docRef]) !== undefined) {
- docMatch.score += score
- docMatch.matchData.combine(matchingFields[fieldRef])
- } else {
- var match = {
- ref: docRef,
- score: score,
- matchData: matchingFields[fieldRef]
- }
- matches[docRef] = match
- results.push(match)
- }
- }
-
- /*
- * Sort the results objects by score, highest first.
- */
- return results.sort(function (a, b) {
- return b.score - a.score
- })
-}
-
-/**
- * Prepares the index for JSON serialization.
- *
- * The schema for this JSON blob will be described in a
- * separate JSON schema file.
- *
- * @returns {Object}
- */
-lunr.Index.prototype.toJSON = function () {
- var invertedIndex = Object.keys(this.invertedIndex)
- .sort()
- .map(function (term) {
- return [term, this.invertedIndex[term]]
- }, this)
-
- var fieldVectors = Object.keys(this.fieldVectors)
- .map(function (ref) {
- return [ref, this.fieldVectors[ref].toJSON()]
- }, this)
-
- return {
- version: lunr.version,
- fields: this.fields,
- fieldVectors: fieldVectors,
- invertedIndex: invertedIndex,
- pipeline: this.pipeline.toJSON()
- }
-}
-
-/**
- * Loads a previously serialized lunr.Index
- *
- * @param {Object} serializedIndex - A previously serialized lunr.Index
- * @returns {lunr.Index}
- */
-lunr.Index.load = function (serializedIndex) {
- var attrs = {},
- fieldVectors = {},
- serializedVectors = serializedIndex.fieldVectors,
- invertedIndex = Object.create(null),
- serializedInvertedIndex = serializedIndex.invertedIndex,
- tokenSetBuilder = new lunr.TokenSet.Builder,
- pipeline = lunr.Pipeline.load(serializedIndex.pipeline)
-
- if (serializedIndex.version != lunr.version) {
- lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'")
- }
-
- for (var i = 0; i < serializedVectors.length; i++) {
- var tuple = serializedVectors[i],
- ref = tuple[0],
- elements = tuple[1]
-
- fieldVectors[ref] = new lunr.Vector(elements)
- }
-
- for (var i = 0; i < serializedInvertedIndex.length; i++) {
- var tuple = serializedInvertedIndex[i],
- term = tuple[0],
- posting = tuple[1]
-
- tokenSetBuilder.insert(term)
- invertedIndex[term] = posting
- }
-
- tokenSetBuilder.finish()
-
- attrs.fields = serializedIndex.fields
-
- attrs.fieldVectors = fieldVectors
- attrs.invertedIndex = invertedIndex
- attrs.tokenSet = tokenSetBuilder.root
- attrs.pipeline = pipeline
-
- return new lunr.Index(attrs)
-}
-/*!
- * lunr.Builder
- * Copyright (C) 2020 Oliver Nightingale
- */
-
-/**
- * lunr.Builder performs indexing on a set of documents and
- * returns instances of lunr.Index ready for querying.
- *
- * All configuration of the index is done via the builder, the
- * fields to index, the document reference, the text processing
- * pipeline and document scoring parameters are all set on the
- * builder before indexing.
- *
- * @constructor
- * @property {string} _ref - Internal reference to the document reference field.
- * @property {string[]} _fields - Internal reference to the document fields to index.
- * @property {object} invertedIndex - The inverted index maps terms to document fields.
- * @property {object} documentTermFrequencies - Keeps track of document term frequencies.
- * @property {object} documentLengths - Keeps track of the length of documents added to the index.
- * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.
- * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.
- * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.
- * @property {number} documentCount - Keeps track of the total number of documents indexed.
- * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.
- * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.
- * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.
- * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.
- */
-lunr.Builder = function () {
- this._ref = "id"
- this._fields = Object.create(null)
- this._documents = Object.create(null)
- this.invertedIndex = Object.create(null)
- this.fieldTermFrequencies = {}
- this.fieldLengths = {}
- this.tokenizer = lunr.tokenizer
- this.pipeline = new lunr.Pipeline
- this.searchPipeline = new lunr.Pipeline
- this.documentCount = 0
- this._b = 0.75
- this._k1 = 1.2
- this.termIndex = 0
- this.metadataWhitelist = []
-}
-
-/**
- * Sets the document field used as the document reference. Every document must have this field.
- * The type of this field in the document should be a string, if it is not a string it will be
- * coerced into a string by calling toString.
- *
- * The default ref is 'id'.
- *
- * The ref should _not_ be changed during indexing, it should be set before any documents are
- * added to the index. Changing it during indexing can lead to inconsistent results.
- *
- * @param {string} ref - The name of the reference field in the document.
- */
-lunr.Builder.prototype.ref = function (ref) {
- this._ref = ref
-}
-
-/**
- * A function that is used to extract a field from a document.
- *
- * Lunr expects a field to be at the top level of a document, if however the field
- * is deeply nested within a document an extractor function can be used to extract
- * the right field for indexing.
- *
- * @callback fieldExtractor
- * @param {object} doc - The document being added to the index.
- * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.
- * @example
Extracting a nested field
- * function (doc) { return doc.nested.field }
- */
-
-/**
- * Adds a field to the list of document fields that will be indexed. Every document being
- * indexed should have this field. Null values for this field in indexed documents will
- * not cause errors but will limit the chance of that document being retrieved by searches.
- *
- * All fields should be added before adding documents to the index. Adding fields after
- * a document has been indexed will have no effect on already indexed documents.
- *
- * Fields can be boosted at build time. This allows terms within that field to have more
- * importance when ranking search results. Use a field boost to specify that matches within
- * one field are more important than other fields.
- *
- * @param {string} fieldName - The name of a field to index in all documents.
- * @param {object} attributes - Optional attributes associated with this field.
- * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.
- * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.
- * @throws {RangeError} fieldName cannot contain unsupported characters '/'
- */
-lunr.Builder.prototype.field = function (fieldName, attributes) {
- if (/\//.test(fieldName)) {
- throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'")
- }
-
- this._fields[fieldName] = attributes || {}
-}
-
-/**
- * A parameter to tune the amount of field length normalisation that is applied when
- * calculating relevance scores. A value of 0 will completely disable any normalisation
- * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b
- * will be clamped to the range 0 - 1.
- *
- * @param {number} number - The value to set for this tuning parameter.
- */
-lunr.Builder.prototype.b = function (number) {
- if (number < 0) {
- this._b = 0
- } else if (number > 1) {
- this._b = 1
- } else {
- this._b = number
- }
-}
-
-/**
- * A parameter that controls the speed at which a rise in term frequency results in term
- * frequency saturation. The default value is 1.2. Setting this to a higher value will give
- * slower saturation levels, a lower value will result in quicker saturation.
- *
- * @param {number} number - The value to set for this tuning parameter.
- */
-lunr.Builder.prototype.k1 = function (number) {
- this._k1 = number
-}
-
-/**
- * Adds a document to the index.
- *
- * Before adding fields to the index the index should have been fully setup, with the document
- * ref and all fields to index already having been specified.
- *
- * The document must have a field name as specified by the ref (by default this is 'id') and
- * it should have all fields defined for indexing, though null or undefined values will not
- * cause errors.
- *
- * Entire documents can be boosted at build time. Applying a boost to a document indicates that
- * this document should rank higher in search results than other documents.
- *
- * @param {object} doc - The document to add to the index.
- * @param {object} attributes - Optional attributes associated with this document.
- * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.
- */
-lunr.Builder.prototype.add = function (doc, attributes) {
- var docRef = doc[this._ref],
- fields = Object.keys(this._fields)
-
- this._documents[docRef] = attributes || {}
- this.documentCount += 1
-
- for (var i = 0; i < fields.length; i++) {
- var fieldName = fields[i],
- extractor = this._fields[fieldName].extractor,
- field = extractor ? extractor(doc) : doc[fieldName],
- tokens = this.tokenizer(field, {
- fields: [fieldName]
- }),
- terms = this.pipeline.run(tokens),
- fieldRef = new lunr.FieldRef (docRef, fieldName),
- fieldTerms = Object.create(null)
-
- this.fieldTermFrequencies[fieldRef] = fieldTerms
- this.fieldLengths[fieldRef] = 0
-
- // store the length of this field for this document
- this.fieldLengths[fieldRef] += terms.length
-
- // calculate term frequencies for this field
- for (var j = 0; j < terms.length; j++) {
- var term = terms[j]
-
- if (fieldTerms[term] == undefined) {
- fieldTerms[term] = 0
- }
-
- fieldTerms[term] += 1
-
- // add to inverted index
- // create an initial posting if one doesn't exist
- if (this.invertedIndex[term] == undefined) {
- var posting = Object.create(null)
- posting["_index"] = this.termIndex
- this.termIndex += 1
-
- for (var k = 0; k < fields.length; k++) {
- posting[fields[k]] = Object.create(null)
- }
-
- this.invertedIndex[term] = posting
- }
-
- // add an entry for this term/fieldName/docRef to the invertedIndex
- if (this.invertedIndex[term][fieldName][docRef] == undefined) {
- this.invertedIndex[term][fieldName][docRef] = Object.create(null)
- }
-
- // store all whitelisted metadata about this token in the
- // inverted index
- for (var l = 0; l < this.metadataWhitelist.length; l++) {
- var metadataKey = this.metadataWhitelist[l],
- metadata = term.metadata[metadataKey]
-
- if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {
- this.invertedIndex[term][fieldName][docRef][metadataKey] = []
- }
-
- this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)
- }
- }
-
- }
-}
-
-/**
- * Calculates the average document length for this index
- *
- * @private
- */
-lunr.Builder.prototype.calculateAverageFieldLengths = function () {
-
- var fieldRefs = Object.keys(this.fieldLengths),
- numberOfFields = fieldRefs.length,
- accumulator = {},
- documentsWithField = {}
-
- for (var i = 0; i < numberOfFields; i++) {
- var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
- field = fieldRef.fieldName
-
- documentsWithField[field] || (documentsWithField[field] = 0)
- documentsWithField[field] += 1
-
- accumulator[field] || (accumulator[field] = 0)
- accumulator[field] += this.fieldLengths[fieldRef]
- }
-
- var fields = Object.keys(this._fields)
-
- for (var i = 0; i < fields.length; i++) {
- var fieldName = fields[i]
- accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]
- }
-
- this.averageFieldLength = accumulator
-}
-
-/**
- * Builds a vector space model of every document using lunr.Vector
- *
- * @private
- */
-lunr.Builder.prototype.createFieldVectors = function () {
- var fieldVectors = {},
- fieldRefs = Object.keys(this.fieldTermFrequencies),
- fieldRefsLength = fieldRefs.length,
- termIdfCache = Object.create(null)
-
- for (var i = 0; i < fieldRefsLength; i++) {
- var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),
- fieldName = fieldRef.fieldName,
- fieldLength = this.fieldLengths[fieldRef],
- fieldVector = new lunr.Vector,
- termFrequencies = this.fieldTermFrequencies[fieldRef],
- terms = Object.keys(termFrequencies),
- termsLength = terms.length
-
-
- var fieldBoost = this._fields[fieldName].boost || 1,
- docBoost = this._documents[fieldRef.docRef].boost || 1
-
- for (var j = 0; j < termsLength; j++) {
- var term = terms[j],
- tf = termFrequencies[term],
- termIndex = this.invertedIndex[term]._index,
- idf, score, scoreWithPrecision
-
- if (termIdfCache[term] === undefined) {
- idf = lunr.idf(this.invertedIndex[term], this.documentCount)
- termIdfCache[term] = idf
- } else {
- idf = termIdfCache[term]
- }
-
- score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)
- score *= fieldBoost
- score *= docBoost
- scoreWithPrecision = Math.round(score * 1000) / 1000
- // Converts 1.23456789 to 1.234.
- // Reducing the precision so that the vectors take up less
- // space when serialised. Doing it now so that they behave
- // the same before and after serialisation. Also, this is
- // the fastest approach to reducing a number's precision in
- // JavaScript.
-
- fieldVector.insert(termIndex, scoreWithPrecision)
- }
-
- fieldVectors[fieldRef] = fieldVector
- }
-
- this.fieldVectors = fieldVectors
-}
-
-/**
- * Creates a token set of all tokens in the index using lunr.TokenSet
- *
- * @private
- */
-lunr.Builder.prototype.createTokenSet = function () {
- this.tokenSet = lunr.TokenSet.fromArray(
- Object.keys(this.invertedIndex).sort()
- )
-}
-
-/**
- * Builds the index, creating an instance of lunr.Index.
- *
- * This completes the indexing process and should only be called
- * once all documents have been added to the index.
- *
- * @returns {lunr.Index}
- */
-lunr.Builder.prototype.build = function () {
- this.calculateAverageFieldLengths()
- this.createFieldVectors()
- this.createTokenSet()
-
- return new lunr.Index({
- invertedIndex: this.invertedIndex,
- fieldVectors: this.fieldVectors,
- tokenSet: this.tokenSet,
- fields: Object.keys(this._fields),
- pipeline: this.searchPipeline
- })
-}
-
-/**
- * Applies a plugin to the index builder.
- *
- * A plugin is a function that is called with the index builder as its context.
- * Plugins can be used to customise or extend the behaviour of the index
- * in some way. A plugin is just a function, that encapsulated the custom
- * behaviour that should be applied when building the index.
- *
- * The plugin function will be called with the index builder as its argument, additional
- * arguments can also be passed when calling use. The function will be called
- * with the index builder as its context.
- *
- * @param {Function} plugin The plugin to apply.
- */
-lunr.Builder.prototype.use = function (fn) {
- var args = Array.prototype.slice.call(arguments, 1)
- args.unshift(this)
- fn.apply(this, args)
-}
-/**
- * Contains and collects metadata about a matching document.
- * A single instance of lunr.MatchData is returned as part of every
- * lunr.Index~Result.
- *
- * @constructor
- * @param {string} term - The term this match data is associated with
- * @param {string} field - The field in which the term was found
- * @param {object} metadata - The metadata recorded about this term in this field
- * @property {object} metadata - A cloned collection of metadata associated with this document.
- * @see {@link lunr.Index~Result}
- */
-lunr.MatchData = function (term, field, metadata) {
- var clonedMetadata = Object.create(null),
- metadataKeys = Object.keys(metadata || {})
-
- // Cloning the metadata to prevent the original
- // being mutated during match data combination.
- // Metadata is kept in an array within the inverted
- // index so cloning the data can be done with
- // Array#slice
- for (var i = 0; i < metadataKeys.length; i++) {
- var key = metadataKeys[i]
- clonedMetadata[key] = metadata[key].slice()
- }
-
- this.metadata = Object.create(null)
-
- if (term !== undefined) {
- this.metadata[term] = Object.create(null)
- this.metadata[term][field] = clonedMetadata
- }
-}
-
-/**
- * An instance of lunr.MatchData will be created for every term that matches a
- * document. However only one instance is required in a lunr.Index~Result. This
- * method combines metadata from another instance of lunr.MatchData with this
- * objects metadata.
- *
- * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.
- * @see {@link lunr.Index~Result}
- */
-lunr.MatchData.prototype.combine = function (otherMatchData) {
- var terms = Object.keys(otherMatchData.metadata)
-
- for (var i = 0; i < terms.length; i++) {
- var term = terms[i],
- fields = Object.keys(otherMatchData.metadata[term])
-
- if (this.metadata[term] == undefined) {
- this.metadata[term] = Object.create(null)
- }
-
- for (var j = 0; j < fields.length; j++) {
- var field = fields[j],
- keys = Object.keys(otherMatchData.metadata[term][field])
-
- if (this.metadata[term][field] == undefined) {
- this.metadata[term][field] = Object.create(null)
- }
-
- for (var k = 0; k < keys.length; k++) {
- var key = keys[k]
-
- if (this.metadata[term][field][key] == undefined) {
- this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]
- } else {
- this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])
- }
-
- }
- }
- }
-}
-
-/**
- * Add metadata for a term/field pair to this instance of match data.
- *
- * @param {string} term - The term this match data is associated with
- * @param {string} field - The field in which the term was found
- * @param {object} metadata - The metadata recorded about this term in this field
- */
-lunr.MatchData.prototype.add = function (term, field, metadata) {
- if (!(term in this.metadata)) {
- this.metadata[term] = Object.create(null)
- this.metadata[term][field] = metadata
- return
- }
-
- if (!(field in this.metadata[term])) {
- this.metadata[term][field] = metadata
- return
- }
-
- var metadataKeys = Object.keys(metadata)
-
- for (var i = 0; i < metadataKeys.length; i++) {
- var key = metadataKeys[i]
-
- if (key in this.metadata[term][field]) {
- this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])
- } else {
- this.metadata[term][field][key] = metadata[key]
- }
- }
-}
-/**
- * A lunr.Query provides a programmatic way of defining queries to be performed
- * against a {@link lunr.Index}.
- *
- * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method
- * so the query object is pre-initialized with the right index fields.
- *
- * @constructor
- * @property {lunr.Query~Clause[]} clauses - An array of query clauses.
- * @property {string[]} allFields - An array of all available fields in a lunr.Index.
- */
-lunr.Query = function (allFields) {
- this.clauses = []
- this.allFields = allFields
-}
-
-/**
- * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.
- *
- * This allows wildcards to be added to the beginning and end of a term without having to manually do any string
- * concatenation.
- *
- * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.
- *
- * @constant
- * @default
- * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour
- * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists
- * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists
- * @see lunr.Query~Clause
- * @see lunr.Query#clause
- * @see lunr.Query#term
- * @example
- * query.term('foo', {
- * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING
- * })
- */
-
-lunr.Query.wildcard = new String ("*")
-lunr.Query.wildcard.NONE = 0
-lunr.Query.wildcard.LEADING = 1
-lunr.Query.wildcard.TRAILING = 2
-
-/**
- * Constants for indicating what kind of presence a term must have in matching documents.
- *
- * @constant
- * @enum {number}
- * @see lunr.Query~Clause
- * @see lunr.Query#clause
- * @see lunr.Query#term
- * @example
query term with required presence
- * query.term('foo', { presence: lunr.Query.presence.REQUIRED })
- */
-lunr.Query.presence = {
- /**
- * Term's presence in a document is optional, this is the default value.
- */
- OPTIONAL: 1,
-
- /**
- * Term's presence in a document is required, documents that do not contain
- * this term will not be returned.
- */
- REQUIRED: 2,
-
- /**
- * Term's presence in a document is prohibited, documents that do contain
- * this term will not be returned.
- */
- PROHIBITED: 3
-}
-
-/**
- * A single clause in a {@link lunr.Query} contains a term and details on how to
- * match that term against a {@link lunr.Index}.
- *
- * @typedef {Object} lunr.Query~Clause
- * @property {string[]} fields - The fields in an index this clause should be matched against.
- * @property {number} [boost=1] - Any boost that should be applied when matching this clause.
- * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.
- * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.
- * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.
- * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.
- */
-
-/**
- * Adds a {@link lunr.Query~Clause} to this query.
- *
- * Unless the clause contains the fields to be matched all fields will be matched. In addition
- * a default boost of 1 is applied to the clause.
- *
- * @param {lunr.Query~Clause} clause - The clause to add to this query.
- * @see lunr.Query~Clause
- * @returns {lunr.Query}
- */
-lunr.Query.prototype.clause = function (clause) {
- if (!('fields' in clause)) {
- clause.fields = this.allFields
- }
-
- if (!('boost' in clause)) {
- clause.boost = 1
- }
-
- if (!('usePipeline' in clause)) {
- clause.usePipeline = true
- }
-
- if (!('wildcard' in clause)) {
- clause.wildcard = lunr.Query.wildcard.NONE
- }
-
- if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {
- clause.term = "*" + clause.term
- }
-
- if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {
- clause.term = "" + clause.term + "*"
- }
-
- if (!('presence' in clause)) {
- clause.presence = lunr.Query.presence.OPTIONAL
- }
-
- this.clauses.push(clause)
-
- return this
-}
-
-/**
- * A negated query is one in which every clause has a presence of
- * prohibited. These queries require some special processing to return
- * the expected results.
- *
- * @returns boolean
- */
-lunr.Query.prototype.isNegated = function () {
- for (var i = 0; i < this.clauses.length; i++) {
- if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {
- return false
- }
- }
-
- return true
-}
-
-/**
- * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}
- * to the list of clauses that make up this query.
- *
- * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion
- * to a token or token-like string should be done before calling this method.
- *
- * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an
- * array, each term in the array will share the same options.
- *
- * @param {object|object[]} term - The term(s) to add to the query.
- * @param {object} [options] - Any additional properties to add to the query clause.
- * @returns {lunr.Query}
- * @see lunr.Query#clause
- * @see lunr.Query~Clause
- * @example
adding a single term to a query
- * query.term("foo")
- * @example
adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard
';
-}
-
-function displayResults (results) {
- var search_results = document.getElementById("mkdocs-search-results");
- while (search_results.firstChild) {
- search_results.removeChild(search_results.firstChild);
- }
- if (results.length > 0){
- for (var i=0; i < results.length; i++){
- var result = results[i];
- var html = formatResult(result.location, result.title, result.summary);
- search_results.insertAdjacentHTML('beforeend', html);
- }
- } else {
- var noResultsText = search_results.getAttribute('data-no-results-text');
- if (!noResultsText) {
- noResultsText = "No results found";
- }
- search_results.insertAdjacentHTML('beforeend', '
' + noResultsText + '
');
- }
-}
-
-function doSearch () {
- var query = document.getElementById('mkdocs-search-query').value;
- if (query.length > min_search_length) {
- if (!window.Worker) {
- displayResults(search(query));
- } else {
- searchWorker.postMessage({query: query});
- }
- } else {
- // Clear results for short queries
- displayResults([]);
- }
-}
-
-function initSearch () {
- var search_input = document.getElementById('mkdocs-search-query');
- if (search_input) {
- search_input.addEventListener("keyup", doSearch);
- }
- var term = getSearchTermFromLocation();
- if (term) {
- search_input.value = term;
- doSearch();
- }
-}
-
-function onWorkerMessage (e) {
- if (e.data.allowSearch) {
- initSearch();
- } else if (e.data.results) {
- var results = e.data.results;
- displayResults(results);
- } else if (e.data.config) {
- min_search_length = e.data.config.min_search_length-1;
- }
-}
-
-if (!window.Worker) {
- console.log('Web Worker API not supported');
- // load index in main thread
- $.getScript(joinUrl(base_url, "search/worker.js")).done(function () {
- console.log('Loaded worker');
- init();
- window.postMessage = function (msg) {
- onWorkerMessage({data: msg});
- };
- }).fail(function (jqxhr, settings, exception) {
- console.error('Could not load worker.js');
- });
-} else {
- // Wrap search in a web worker
- var searchWorker = new Worker(joinUrl(base_url, "search/worker.js"));
- searchWorker.postMessage({init: true});
- searchWorker.onmessage = onWorkerMessage;
-}
diff --git a/docs/site/search/search_index.json b/docs/site/search/search_index.json
deleted file mode 100644
index 914c4bf..0000000
--- a/docs/site/search/search_index.json
+++ /dev/null
@@ -1 +0,0 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"GPopt | Star Welcome to GPopt 's website. GPopt does Bayesian optimization using Gaussian Process regression Installing 1st method : by using pip at the command line for the stable version pip install mlsauce Quickstart Bayesian Optimization with GPopt Bayesian Optimization with GPopt Part 2 (save and resume) Hyperparameters tuning with GPopt Documentation The documentation can be found (work in progress) here . Contributing Want to contribute to mlsauce 's development on Github, read this !","title":"Home"},{"location":"#gpopt-star","text":"Welcome to GPopt 's website. GPopt does Bayesian optimization using Gaussian Process regression","title":"GPopt | Star"},{"location":"#installing","text":"1st method : by using pip at the command line for the stable version pip install mlsauce","title":"Installing"},{"location":"#quickstart","text":"Bayesian Optimization with GPopt Bayesian Optimization with GPopt Part 2 (save and resume) Hyperparameters tuning with GPopt","title":"Quickstart"},{"location":"#documentation","text":"The documentation can be found (work in progress) here .","title":"Documentation"},{"location":"#contributing","text":"Want to contribute to mlsauce 's development on Github, read this !","title":"Contributing"},{"location":"CONTRIBUTING/","text":"GPopt 's Code of Conduct 1. Purpose A primary goal of this project is to be inclusive to the largest number of contributors, and most importantly with the most varied and diverse backgrounds possible . As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion, or lack of religion thereof. This code of conduct outlines our expectations for all those who participate to the project, as well as the consequences for unacceptable behavior. We invite all those who participate in, to help us create safe and positive experiences for everyone. 2. Open [Source/Culture/Tech] Citizenship A supplemental goal of this Code of Conduct is to encourage participants to recognize and strengthen the relationships between our actions and their effects on other participants. Communities mirror the societies in which they exist, and positive action is essential to counteract the many forms of inequality and abuses of power that exist in society. 3. Expected Behavior The following behaviors are expected and requested of all contributors: Attempt collaboration before conflict . Participate in an authentic and active way. In doing so, you contribute to the health and longevity of this project. Exercise consideration and respect in your speech and actions. Refrain from demeaning, discriminatory, or harassing behavior and speech. Be mindful of your surroundings and of your fellow participants. 4. Unacceptable Behavior The following behaviors are considered harassment and are unacceptable: Violence, threats of violence or violent language directed against another person. Sexist, racist, homophobic, transphobic, ableist or otherwise discriminatory jokes and language. Posting or displaying sexually explicit or violent material. Posting or threatening to post other people's personally identifying information (\"doxing\"). Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability. Inappropriate photography or recording. Unwelcome sexual attention. This includes, sexualized comments or jokes. Deliberate intimidation, stalking or following (online or in person). Advocating for, or encouraging, any of the above behavior. 5. Consequences of Unacceptable Behavior Unacceptable behavior from any contributor will not be tolerated. Anyone asked to stop unacceptable behavior is expected to comply immediately. If a contributor engages in unacceptable behavior, appropriate action will be taken, up to and including a temporary ban or permanent expulsion without warning. 6. Scope We expect all contributors to abide by this Code of Conduct in all venues, online and in-person. 7. Contact info thierry.moudiki AT gmail.com 8. License and attribution Portions of text derived from the Citizen Code of Conduct .","title":"Contributing"},{"location":"CONTRIBUTING/#gpopts-code-of-conduct","text":"","title":"GPopt's Code of Conduct"},{"location":"CONTRIBUTING/#1-purpose","text":"A primary goal of this project is to be inclusive to the largest number of contributors, and most importantly with the most varied and diverse backgrounds possible . As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion, or lack of religion thereof. This code of conduct outlines our expectations for all those who participate to the project, as well as the consequences for unacceptable behavior. We invite all those who participate in, to help us create safe and positive experiences for everyone.","title":"1. Purpose"},{"location":"CONTRIBUTING/#2-open-sourceculturetech-citizenship","text":"A supplemental goal of this Code of Conduct is to encourage participants to recognize and strengthen the relationships between our actions and their effects on other participants. Communities mirror the societies in which they exist, and positive action is essential to counteract the many forms of inequality and abuses of power that exist in society.","title":"2. Open [Source/Culture/Tech] Citizenship"},{"location":"CONTRIBUTING/#3-expected-behavior","text":"The following behaviors are expected and requested of all contributors: Attempt collaboration before conflict . Participate in an authentic and active way. In doing so, you contribute to the health and longevity of this project. Exercise consideration and respect in your speech and actions. Refrain from demeaning, discriminatory, or harassing behavior and speech. Be mindful of your surroundings and of your fellow participants.","title":"3. Expected Behavior"},{"location":"CONTRIBUTING/#4-unacceptable-behavior","text":"The following behaviors are considered harassment and are unacceptable: Violence, threats of violence or violent language directed against another person. Sexist, racist, homophobic, transphobic, ableist or otherwise discriminatory jokes and language. Posting or displaying sexually explicit or violent material. Posting or threatening to post other people's personally identifying information (\"doxing\"). Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability. Inappropriate photography or recording. Unwelcome sexual attention. This includes, sexualized comments or jokes. Deliberate intimidation, stalking or following (online or in person). Advocating for, or encouraging, any of the above behavior.","title":"4. Unacceptable Behavior"},{"location":"CONTRIBUTING/#5-consequences-of-unacceptable-behavior","text":"Unacceptable behavior from any contributor will not be tolerated. Anyone asked to stop unacceptable behavior is expected to comply immediately. If a contributor engages in unacceptable behavior, appropriate action will be taken, up to and including a temporary ban or permanent expulsion without warning.","title":"5. Consequences of Unacceptable Behavior"},{"location":"CONTRIBUTING/#6-scope","text":"We expect all contributors to abide by this Code of Conduct in all venues, online and in-person.","title":"6. Scope"},{"location":"CONTRIBUTING/#7-contact-info","text":"thierry.moudiki AT gmail.com","title":"7. Contact info"},{"location":"CONTRIBUTING/#8-license-and-attribution","text":"Portions of text derived from the Citizen Code of Conduct .","title":"8. License and attribution"},{"location":"LICENSE/","text":"The Clear BSD License Copyright (c) [2019] [Thierry Moudiki] All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.","title":"License"},{"location":"documentation/gpopt/","text":"GPopt Bayesian optimization using Gaussian Process Regression [source] GPOpt GPopt.GPOpt.GPOpt.GPOpt( lower_bound, upper_bound, objective_func=None, x_init=None, y_init=None, n_init=10, n_choices=25000, n_iter=190, alpha=1e-06, n_restarts_optimizer=25, seed=123, save=None, n_jobs=1, per_second=False, log_scale=False, ) Class GPOpt. Arguments: lower_bound: a numpy array; lower bound for researched minimum upper_bound: a numpy array; upper bound for researched minimum objective_func: a function; the objective function to be minimized x_init: initial setting of points where objective_func is evaluated (optional) y_init: initial setting values at points where objective_func is evaluated (optional) n_init: an integer; number of points in the initial setting, when x_init and y_init are not provided n_choices: an integer; number of points for the calculation of expected improvement n_iter: an integer; number of iterations of the minimization algorithm alpha: a float; Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel) n_restarts_optimizer: an integer; The number of restarts of the optimizer for finding the kernel\u2019s parameters which maximize the log-marginal likelihood. seed: an integer; reproducibility seed save: a string; Specifies where to save the optimizer in its current state n_jobs: an integer; number of jobs for parallel computing on initial setting (can be -1) per_second: a boolean; experimental , default is False (leave to default for now) log_scale: a boolean; experimental , default is False (leave to default for now) see also Bayesian Optimization with GPopt and Hyperparameters tuning with GPopt [source] optimize GPOpt.optimize(verbose=1, n_more_iter=None, abs_tol=None, min_budget=50, func_args=None) Launch optimization loop. Arguments: verbose: an integer; verbose = 0: nothing is printed, verbose = 1: a progress bar is printed (longer than 0), verbose = 2: information about each iteration is printed (longer than 1) n_more_iter: an integer; additional number of iterations for the optimizer (which has been run once) abs_tol: a float; tolerance for convergence of the optimizer (early stopping based on expected improvement) min_budget: an integer (default is 50); minimum number of iterations before early stopping controlled by abs_tol func_args: a list; additional parameters for the objective function (if necessary) see also Bayesian Optimization with GPopt and Hyperparameters tuning with GPopt [source] load GPOpt.load(path) load data from stored shelve. Arguments path : a string; path to stored shelve. See also: Bayesian Optimization with GPopt Part 2 (save and resume) [source] close_shelve GPOpt.close_shelve() Close shelve. Arguments No argument. See also: Bayesian Optimization with GPopt Part 2 (save and resume)","title":"GPopt"},{"location":"documentation/gpopt/#gpopt","text":"Bayesian optimization using Gaussian Process Regression [source]","title":"GPopt"},{"location":"documentation/gpopt/#gpopt_1","text":"GPopt.GPOpt.GPOpt.GPOpt( lower_bound, upper_bound, objective_func=None, x_init=None, y_init=None, n_init=10, n_choices=25000, n_iter=190, alpha=1e-06, n_restarts_optimizer=25, seed=123, save=None, n_jobs=1, per_second=False, log_scale=False, ) Class GPOpt. Arguments: lower_bound: a numpy array; lower bound for researched minimum upper_bound: a numpy array; upper bound for researched minimum objective_func: a function; the objective function to be minimized x_init: initial setting of points where objective_func is evaluated (optional) y_init: initial setting values at points where objective_func is evaluated (optional) n_init: an integer; number of points in the initial setting, when x_init and y_init are not provided n_choices: an integer; number of points for the calculation of expected improvement n_iter: an integer; number of iterations of the minimization algorithm alpha: a float; Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel) n_restarts_optimizer: an integer; The number of restarts of the optimizer for finding the kernel\u2019s parameters which maximize the log-marginal likelihood. seed: an integer; reproducibility seed save: a string; Specifies where to save the optimizer in its current state n_jobs: an integer; number of jobs for parallel computing on initial setting (can be -1) per_second: a boolean; experimental , default is False (leave to default for now) log_scale: a boolean; experimental , default is False (leave to default for now) see also Bayesian Optimization with GPopt and Hyperparameters tuning with GPopt [source]","title":"GPOpt"},{"location":"documentation/gpopt/#optimize","text":"GPOpt.optimize(verbose=1, n_more_iter=None, abs_tol=None, min_budget=50, func_args=None) Launch optimization loop. Arguments: verbose: an integer; verbose = 0: nothing is printed, verbose = 1: a progress bar is printed (longer than 0), verbose = 2: information about each iteration is printed (longer than 1) n_more_iter: an integer; additional number of iterations for the optimizer (which has been run once) abs_tol: a float; tolerance for convergence of the optimizer (early stopping based on expected improvement) min_budget: an integer (default is 50); minimum number of iterations before early stopping controlled by abs_tol func_args: a list; additional parameters for the objective function (if necessary) see also Bayesian Optimization with GPopt and Hyperparameters tuning with GPopt [source]","title":"optimize"},{"location":"documentation/gpopt/#load","text":"GPOpt.load(path) load data from stored shelve. Arguments path : a string; path to stored shelve. See also: Bayesian Optimization with GPopt Part 2 (save and resume) [source]","title":"load"},{"location":"documentation/gpopt/#close_shelve","text":"GPOpt.close_shelve() Close shelve. Arguments No argument. See also: Bayesian Optimization with GPopt Part 2 (save and resume)","title":"close_shelve"}]}
\ No newline at end of file
diff --git a/docs/site/search/worker.js b/docs/site/search/worker.js
deleted file mode 100644
index 8628dbc..0000000
--- a/docs/site/search/worker.js
+++ /dev/null
@@ -1,133 +0,0 @@
-var base_path = 'function' === typeof importScripts ? '.' : '/search/';
-var allowSearch = false;
-var index;
-var documents = {};
-var lang = ['en'];
-var data;
-
-function getScript(script, callback) {
- console.log('Loading script: ' + script);
- $.getScript(base_path + script).done(function () {
- callback();
- }).fail(function (jqxhr, settings, exception) {
- console.log('Error: ' + exception);
- });
-}
-
-function getScriptsInOrder(scripts, callback) {
- if (scripts.length === 0) {
- callback();
- return;
- }
- getScript(scripts[0], function() {
- getScriptsInOrder(scripts.slice(1), callback);
- });
-}
-
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- if( 'function' === typeof importScripts ) {
- importScripts.apply(null, urls);
- callback();
- } else {
- getScriptsInOrder(urls, callback);
- }
-}
-
-function onJSONLoaded () {
- data = JSON.parse(this.responseText);
- var scriptsToLoad = ['lunr.js'];
- if (data.config && data.config.lang && data.config.lang.length) {
- lang = data.config.lang;
- }
- if (lang.length > 1 || lang[0] !== "en") {
- scriptsToLoad.push('lunr.stemmer.support.js');
- if (lang.length > 1) {
- scriptsToLoad.push('lunr.multi.js');
- }
- if (lang.includes("ja") || lang.includes("jp")) {
- scriptsToLoad.push('tinyseg.js');
- }
- for (var i=0; i < lang.length; i++) {
- if (lang[i] != 'en') {
- scriptsToLoad.push(['lunr', lang[i], 'js'].join('.'));
- }
- }
- }
- loadScripts(scriptsToLoad, onScriptsLoaded);
-}
-
-function onScriptsLoaded () {
- console.log('All search scripts loaded, building Lunr index...');
- if (data.config && data.config.separator && data.config.separator.length) {
- lunr.tokenizer.separator = new RegExp(data.config.separator);
- }
-
- if (data.index) {
- index = lunr.Index.load(data.index);
- data.docs.forEach(function (doc) {
- documents[doc.location] = doc;
- });
- console.log('Lunr pre-built index loaded, search ready');
- } else {
- index = lunr(function () {
- if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) {
- this.use(lunr[lang[0]]);
- } else if (lang.length > 1) {
- this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility
- }
- this.field('title');
- this.field('text');
- this.ref('location');
-
- for (var i=0; i < data.docs.length; i++) {
- var doc = data.docs[i];
- this.add(doc);
- documents[doc.location] = doc;
- }
- });
- console.log('Lunr index built, search ready');
- }
- allowSearch = true;
- postMessage({config: data.config});
- postMessage({allowSearch: allowSearch});
-}
-
-function init () {
- var oReq = new XMLHttpRequest();
- oReq.addEventListener("load", onJSONLoaded);
- var index_path = base_path + '/search_index.json';
- if( 'function' === typeof importScripts ){
- index_path = 'search_index.json';
- }
- oReq.open("GET", index_path);
- oReq.send();
-}
-
-function search (query) {
- if (!allowSearch) {
- console.error('Assets for search still loading');
- return;
- }
-
- var resultDocuments = [];
- var results = index.search(query);
- for (var i=0; i < results.length; i++){
- var result = results[i];
- doc = documents[result.ref];
- doc.summary = doc.text.substring(0, 200);
- resultDocuments.push(doc);
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- onmessage = function (e) {
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- init();
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- postMessage({ results: search(e.data.query) });
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diff --git a/docs/site/sitemap.xml b/docs/site/sitemap.xml
deleted file mode 100644
index 0f8724e..0000000
--- a/docs/site/sitemap.xml
+++ /dev/null
@@ -1,3 +0,0 @@
-
-
-
\ No newline at end of file
diff --git a/docs/site/sitemap.xml.gz b/docs/site/sitemap.xml.gz
deleted file mode 100644
index e89e4e3..0000000
Binary files a/docs/site/sitemap.xml.gz and /dev/null differ
diff --git a/docs/sources/CONTRIBUTING.md b/docs/sources/CONTRIBUTING.md
deleted file mode 100644
index 1bbdb68..0000000
--- a/docs/sources/CONTRIBUTING.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# `GPopt`'s Code of Conduct
-
-## 1. Purpose
-
-A primary goal of this project is to be __inclusive__ to the largest number of contributors, and most importantly __with the most varied and diverse backgrounds possible__. As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion, or lack of religion thereof.
-
-This code of conduct outlines our expectations for all those who participate to the project, as well as the consequences for unacceptable behavior.
-
-We invite all those who participate in, to help us create safe and positive experiences for everyone.
-
-## 2. Open [Source/Culture/Tech] Citizenship
-
-A supplemental goal of this Code of Conduct is to encourage participants to recognize and strengthen the relationships between our actions and their effects on other participants.
-
-Communities mirror the societies in which they exist, and positive action is essential to counteract the many forms of inequality and abuses of power that exist in society.
-
-## 3. Expected Behavior
-
-The following behaviors are expected and requested of all contributors:
-
- * __Attempt collaboration before conflict__.
- * Participate in an __authentic__ and active way. In doing so, you contribute to the health and longevity of this project.
- * Exercise consideration and respect in your speech and actions.
- * Refrain from demeaning, discriminatory, or harassing behavior and speech.
- * Be mindful of your surroundings and of your fellow participants.
-
-## 4. Unacceptable Behavior
-
-The following behaviors are considered harassment and are unacceptable:
-
- * Violence, threats of violence or violent language directed against another person.
- * Sexist, racist, homophobic, transphobic, ableist or otherwise discriminatory jokes and language.
- * Posting or displaying sexually explicit or violent material.
- * Posting or threatening to post other people's personally identifying information ("doxing").
- * Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability.
- * Inappropriate photography or recording.
- * Unwelcome sexual attention. This includes, sexualized comments or jokes.
- * Deliberate intimidation, stalking or following (online or in person).
- * Advocating for, or encouraging, any of the above behavior.
-
-## 5. Consequences of Unacceptable Behavior
-
-Unacceptable behavior from any contributor will not be tolerated.
-
-Anyone asked to stop unacceptable behavior is expected to comply immediately.
-
-If a contributor engages in unacceptable behavior, appropriate action will be taken, up to and including a temporary ban or permanent expulsion without warning.
-
-## 6. Scope
-
-We expect all contributors to abide by this Code of Conduct in all venues, online and in-person.
-
-## 7. Contact info
-
-thierry.moudiki AT gmail.com
-
-## 8. License and attribution
-
-Portions of text derived from the [Citizen Code of Conduct](http://citizencodeofconduct.org/).
diff --git a/docs/sources/LICENSE.md b/docs/sources/LICENSE.md
deleted file mode 100644
index 5e88b1a..0000000
--- a/docs/sources/LICENSE.md
+++ /dev/null
@@ -1,32 +0,0 @@
-The Clear BSD License
-
-Copyright (c) [2019] [Thierry Moudiki]
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted (subject to the limitations in the disclaimer
-below) provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-
- * Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
- * Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from this
- software without specific prior written permission.
-
-NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
-THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
-BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
-IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/docs/sources/documentation/gpopt.md b/docs/sources/documentation/gpopt.md
deleted file mode 100644
index 345b398..0000000
--- a/docs/sources/documentation/gpopt.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# GPopt
-
-_Bayesian optimization using Gaussian Process Regression_
-
-[[source]](https://github.com/Techtonique/GPopt/blob/main/GPopt/GPOpt/GPOpt.py#L22)
-
-### GPOpt
-
-
-```python
-GPopt.GPOpt.GPOpt.GPOpt(
- lower_bound,
- upper_bound,
- objective_func=None,
- x_init=None,
- y_init=None,
- n_init=10,
- n_choices=25000,
- n_iter=190,
- alpha=1e-06,
- n_restarts_optimizer=25,
- seed=123,
- save=None,
- n_jobs=1,
- per_second=False,
- log_scale=False,
-)
-```
-
-
-Class GPOpt.
-
-__Arguments:__
-
-lower_bound: a numpy array;
- lower bound for researched minimum
-
-upper_bound: a numpy array;
- upper bound for researched minimum
-
-objective_func: a function;
- the objective function to be minimized
-
-x_init:
- initial setting of points where `objective_func` is evaluated (optional)
-
-y_init:
- initial setting values at points where `objective_func` is evaluated (optional)
-
-n_init: an integer;
- number of points in the initial setting, when `x_init` and `y_init` are not provided
-
-n_choices: an integer;
- number of points for the calculation of expected improvement
-
-n_iter: an integer;
- number of iterations of the minimization algorithm
-
-alpha: a float;
- Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
-
-n_restarts_optimizer: an integer;
- The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
-
-seed: an integer;
- reproducibility seed
-
-save: a string;
- Specifies where to save the optimizer in its current state
-
-n_jobs: an integer;
- number of jobs for parallel computing on initial setting (can be -1)
-
-per_second: a boolean;
- __experimental__, default is False (leave to default for now)
-
-log_scale: a boolean;
- __experimental__, default is False (leave to default for now)
-
-see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
- and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
-
-
-----
-
-[[source]](https://github.com/Techtonique/GPopt/blob/main/GPopt/GPOpt/GPOpt.py#L337)
-
-### optimize
-
-
-```python
-GPOpt.optimize(verbose=1, n_more_iter=None, abs_tol=None, min_budget=50, func_args=None)
-```
-
-
-Launch optimization loop.
-
-__Arguments:__
-
-verbose: an integer;
- verbose = 0: nothing is printed,
- verbose = 1: a progress bar is printed (longer than 0),
- verbose = 2: information about each iteration is printed (longer than 1)
-
-n_more_iter: an integer;
- additional number of iterations for the optimizer (which has been run once)
-
-abs_tol: a float;
- tolerance for convergence of the optimizer (early stopping based on expected improvement)
-
-min_budget: an integer (default is 50);
- minimum number of iterations before early stopping controlled by `abs_tol`
-
-func_args: a list;
- additional parameters for the objective function (if necessary)
-
-see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
-
-
-----
-
-[[source]](https://github.com/Techtonique/GPopt/blob/main/GPopt/GPOpt/GPOpt.py#L219)
-
-### load
-
-
-```python
-GPOpt.load(path)
-```
-
-
-load data from stored shelve.
-
-__Arguments__
-
-path : a string; path to stored shelve.
-
-See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-
-
-----
-
-[[source]](https://github.com/Techtonique/GPopt/blob/main/GPopt/GPOpt/GPOpt.py#L241)
-
-### close_shelve
-
-
-```python
-GPOpt.close_shelve()
-```
-
-
-Close shelve.
-
-__Arguments__
-
-No argument.
-
-See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-
-
-----
-
diff --git a/docs/sources/index.md b/docs/sources/index.md
deleted file mode 100644
index 82857ff..0000000
--- a/docs/sources/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
-# GPopt | Star
-
- [](./LICENSE.md) [](https://pepy.tech/project/gpopt) [](https://github.com/Techtonique/gpopt)
-
-Welcome to __GPopt__'s website.
-
-__GPopt__ does Bayesian optimization using Gaussian Process regression
-
-## Installing
-
-- __1st method__: by using `pip` at the command line for the stable version
-
-```bash
-pip install mlsauce
-```
-
-## Quickstart
-
-- [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-- [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-- [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
-
-## Documentation
-
-The documentation can be found (work in progress) [here](documentation/gpopt.md).
-
-
-## Contributing
-
-Want to contribute to __mlsauce__'s development on Github, [read this](CONTRIBUTING.md)!
-
-
\ No newline at end of file
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+.pdoc-code .vg { color: #f8f8f2 } /* Name.Variable.Global */
+.pdoc-code .vi { color: #f8f8f2 } /* Name.Variable.Instance */
+.pdoc-code .vm { color: #f8f8f2 } /* Name.Variable.Magic */
\ No newline at end of file
diff --git a/docs/static/theme.css b/docs/static/theme.css
new file mode 100644
index 0000000..6c7f72d
--- /dev/null
+++ b/docs/static/theme.css
@@ -0,0 +1,20 @@
+:root {
+ --pdoc-background: #212529;
+}
+
+.pdoc {
+ --text: #f7f7f7;
+ --muted: #9d9d9d;
+ --link: #58a6ff;
+ --link-hover: #3989ff;
+ --code: #333;
+ --active: #555;
+
+ --accent: #343434;
+ --accent2: #555;
+
+ --nav-hover: rgba(0, 0, 0, 0.1);
+ --name: #77C1FF;
+ --def: #0cdd0c;
+ --annotation: #00c037;
+}
\ No newline at end of file
diff --git a/docs/t-logo.png b/docs/t-logo.png
new file mode 100644
index 0000000..14342d0
Binary files /dev/null and b/docs/t-logo.png differ
diff --git a/docs/t-logo2.png b/docs/t-logo2.png
new file mode 100644
index 0000000..b60c56b
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diff --git a/docs/templates/CONTRIBUTING.md b/docs/templates/CONTRIBUTING.md
deleted file mode 100644
index 1bbdb68..0000000
--- a/docs/templates/CONTRIBUTING.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# `GPopt`'s Code of Conduct
-
-## 1. Purpose
-
-A primary goal of this project is to be __inclusive__ to the largest number of contributors, and most importantly __with the most varied and diverse backgrounds possible__. As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion, or lack of religion thereof.
-
-This code of conduct outlines our expectations for all those who participate to the project, as well as the consequences for unacceptable behavior.
-
-We invite all those who participate in, to help us create safe and positive experiences for everyone.
-
-## 2. Open [Source/Culture/Tech] Citizenship
-
-A supplemental goal of this Code of Conduct is to encourage participants to recognize and strengthen the relationships between our actions and their effects on other participants.
-
-Communities mirror the societies in which they exist, and positive action is essential to counteract the many forms of inequality and abuses of power that exist in society.
-
-## 3. Expected Behavior
-
-The following behaviors are expected and requested of all contributors:
-
- * __Attempt collaboration before conflict__.
- * Participate in an __authentic__ and active way. In doing so, you contribute to the health and longevity of this project.
- * Exercise consideration and respect in your speech and actions.
- * Refrain from demeaning, discriminatory, or harassing behavior and speech.
- * Be mindful of your surroundings and of your fellow participants.
-
-## 4. Unacceptable Behavior
-
-The following behaviors are considered harassment and are unacceptable:
-
- * Violence, threats of violence or violent language directed against another person.
- * Sexist, racist, homophobic, transphobic, ableist or otherwise discriminatory jokes and language.
- * Posting or displaying sexually explicit or violent material.
- * Posting or threatening to post other people's personally identifying information ("doxing").
- * Personal insults, particularly those related to gender, sexual orientation, race, religion, or disability.
- * Inappropriate photography or recording.
- * Unwelcome sexual attention. This includes, sexualized comments or jokes.
- * Deliberate intimidation, stalking or following (online or in person).
- * Advocating for, or encouraging, any of the above behavior.
-
-## 5. Consequences of Unacceptable Behavior
-
-Unacceptable behavior from any contributor will not be tolerated.
-
-Anyone asked to stop unacceptable behavior is expected to comply immediately.
-
-If a contributor engages in unacceptable behavior, appropriate action will be taken, up to and including a temporary ban or permanent expulsion without warning.
-
-## 6. Scope
-
-We expect all contributors to abide by this Code of Conduct in all venues, online and in-person.
-
-## 7. Contact info
-
-thierry.moudiki AT gmail.com
-
-## 8. License and attribution
-
-Portions of text derived from the [Citizen Code of Conduct](http://citizencodeofconduct.org/).
diff --git a/docs/templates/LICENSE.md b/docs/templates/LICENSE.md
deleted file mode 100644
index 5e88b1a..0000000
--- a/docs/templates/LICENSE.md
+++ /dev/null
@@ -1,32 +0,0 @@
-The Clear BSD License
-
-Copyright (c) [2019] [Thierry Moudiki]
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted (subject to the limitations in the disclaimer
-below) provided that the following conditions are met:
-
- * Redistributions of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-
- * Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in the
- documentation and/or other materials provided with the distribution.
-
- * Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from this
- software without specific prior written permission.
-
-NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
-THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
-PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
-BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
-IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/docs/templates/documentation/gpopt.md b/docs/templates/documentation/gpopt.md
deleted file mode 100644
index e93e901..0000000
--- a/docs/templates/documentation/gpopt.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# GPopt
-
-_Bayesian optimization using Gaussian Process Regression_
-
-{{autogenerated}}
\ No newline at end of file
diff --git a/docs/templates/index.md b/docs/templates/index.md
deleted file mode 100644
index 82857ff..0000000
--- a/docs/templates/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
-# GPopt | Star
-
- [](./LICENSE.md) [](https://pepy.tech/project/gpopt) [](https://github.com/Techtonique/gpopt)
-
-Welcome to __GPopt__'s website.
-
-__GPopt__ does Bayesian optimization using Gaussian Process regression
-
-## Installing
-
-- __1st method__: by using `pip` at the command line for the stable version
-
-```bash
-pip install mlsauce
-```
-
-## Quickstart
-
-- [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-- [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-- [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
-
-## Documentation
-
-The documentation can be found (work in progress) [here](documentation/gpopt.md).
-
-
-## Contributing
-
-Want to contribute to __mlsauce__'s development on Github, [read this](CONTRIBUTING.md)!
-
-
\ No newline at end of file
diff --git a/examples/branin_hart.py b/examples/branin_hart.py
index 922760a..3eeb782 100644
--- a/examples/branin_hart.py
+++ b/examples/branin_hart.py
@@ -93,13 +93,13 @@ def hart6(xx):
upper_bound = np.array([10, 15]),
n_init=10, n_iter=10)
gp_opt3.optimize(verbose=1)
-plt.plot(np.diff(gp_opt3.max_ei))
+#plt.plot(np.diff(gp_opt3.max_ei))
print(gp_opt3.y_min)
gp_opt3.optimize(verbose=1, n_more_iter=10)
print(gp_opt3.y_min)
-plt.plot(np.diff(gp_opt3.max_ei))
+#plt.plot(np.diff(gp_opt3.max_ei))
gp_opt3.optimize(verbose=1, n_more_iter=50)
-plt.plot(np.diff(gp_opt3.max_ei))
+#plt.plot(np.diff(gp_opt3.max_ei))
print(gp_opt3.y_min)
print("\n")
diff --git a/examples/lazy_hart6D.py b/examples/lazy_hart6D.py
new file mode 100644
index 0000000..e2b2fad
--- /dev/null
+++ b/examples/lazy_hart6D.py
@@ -0,0 +1,107 @@
+import os
+import GPopt as gp
+from time import time
+import numpy as np
+import matplotlib.pyplot as plt
+from os import chdir
+from scipy.optimize import minimize
+
+print(f"\n ----- Running: {os.path.basename(__file__)}... ----- \n")
+
+print(f"\n Hartmann 6D function:")
+
+# [0, 1]^6
+def hart6(xx):
+
+ alpha = np.array([1.0, 1.2, 3.0, 3.2])
+
+ A = np.array([10, 3, 17, 3.5, 1.7, 8,
+ 0.05, 10, 17, 0.1, 8, 14,
+ 3, 3.5, 1.7, 10, 17, 8,
+ 17, 8, 0.05, 10, 0.1, 14]).reshape(4, 6)
+
+ P = 1e-4 * np.array([1312, 1696, 5569, 124, 8283, 5886,
+ 2329, 4135, 8307, 3736, 1004, 9991,
+ 2348, 1451, 3522, 2883, 3047, 6650,
+ 4047, 8828, 8732, 5743, 1091, 381]).reshape(4, 6)
+
+ xxmat = np.tile(xx,4).reshape(4, 6)
+
+ inner = np.sum(A*(xxmat-P)**2, axis = 1)
+ outer = np.sum(alpha * np.exp(-inner))
+
+ return(-outer)
+
+# "True" minimum
+print("\n")
+res = minimize(hart6, x0=[0, 0, 0, 0, 0, 0], method='Nelder-Mead', tol=1e-6)
+print("0 - hart6 minimize ----------")
+print(res.x)
+print(hart6(res.x))
+
+
+print("---------- \n")
+print("2 - Hartmann 6D")
+# hart6
+gp_opt1 = gp.GPOpt(objective_func=hart6,
+ lower_bound = np.repeat(0, 6),
+ upper_bound = np.repeat(1, 6),
+ n_init=10, n_iter=90)
+
+
+print("\n 2 - 1 type_exec = 'independent' sequential")
+gp_opt1.lazyoptimize(method = "mc", verbose=2, abs_tol=1e-4,
+ type_exec = "independent",
+ estimators = ["LinearRegression",
+ "RidgeCV",
+ "LassoCV",
+ "ElasticNetCV",
+ "KNeighborsRegressor",
+ "BaggingRegressor",
+ "ExtraTreesRegressor",
+ "RandomForestRegressor",
+ ]
+ )
+print(gp_opt1.best_surrogate, gp_opt1.x_min, gp_opt1.y_min)
+print("\n")
+
+print("\n 2 - 1 type_exec = 'independent' parallel")
+gp_opt3 = gp.GPOpt(objective_func=hart6,
+ lower_bound = np.repeat(0, 6),
+ upper_bound = np.repeat(1, 6),
+ n_init=10, n_iter=190,
+ n_jobs=-1)
+
+print("\n 2 - 1 type_exec = 'independent'")
+start = time()
+gp_opt3.lazyoptimize(method = "mc",
+ verbose=0, abs_tol=1e-2,
+ type_exec = "independent",
+ estimators = ["LinearRegression",
+ "Ridge"
+ "ElasticNet",
+ "Lasso",
+ "BaggingRegressor",
+ "ExtraTreesRegressor",
+ ]
+ )
+print(gp_opt3.best_surrogate, gp_opt3.x_min, gp_opt3.y_min)
+print("Elapsed (total): ", time() - start)
+print("\n")
+
+
+print("\n 2 - 2 type_exec = 'queue'")
+
+gp_opt2 = gp.GPOpt(objective_func=hart6,
+ lower_bound = np.repeat(0, 6),
+ upper_bound = np.repeat(1, 6),
+ n_init=10, n_iter=90)
+
+gp_opt2.lazyoptimize(method = "mc", verbose=2, abs_tol=1e-4,
+ type_exec = "queue",
+ estimators = [ "BaggingRegressor",
+ "ExtraTreesRegressor",
+ ]
+ )
+print(gp_opt2.x_min, gp_opt2.y_min)
+print("\n")
\ No newline at end of file
diff --git a/examples/lazybranin.py b/examples/lazybranin.py
new file mode 100644
index 0000000..aa03783
--- /dev/null
+++ b/examples/lazybranin.py
@@ -0,0 +1,56 @@
+import os
+import GPopt as gp
+import numpy as np
+from os import chdir
+from scipy.optimize import minimize
+
+print(f"\n ----- Running: {os.path.basename(__file__)}... ----- \n")
+
+# branin
+def branin(x):
+ x1 = x[0]
+ x2 = x[1]
+ term1 = (x2 - (5.1*x1**2)/(4*np.pi**2) + (5*x1)/np.pi - 6)**2
+ term2 = 10*(1-1/(8*np.pi))*np.cos(x1)
+ return (term1 + term2 + 10)
+
+
+# "True" minimum
+print("\n")
+res = minimize(branin, x0=[0, 0], method='Nelder-Mead', tol=1e-6)
+print("0 - branin minimize ----------")
+print(res.x)
+print(branin(res.x))
+
+
+X_init = np.asarray([[2.5000, 2.5000],
+[10.0, 3.7500],
+[-1.2500, 6.2500],
+[5.6250, 5.6250],
+[8.1250, 8.1250],
+[9.3750, 1.8750],
+[-3.1250, 4.3750],
+[2.8125, 4.6875],
+[5.3125, 7.1875],
+[10, 0.9375]])
+Y_init = np.asarray([branin(X_init[i,:]) for i in range(10)])
+
+print("\n")
+print("X_init ---------- \n")
+print(X_init)
+print("\n")
+print("Y_init ---------- \n")
+print(Y_init)
+print("\n")
+
+gp_opt1 = gp.GPOpt(x_init = X_init,
+ y_init = Y_init,
+ lower_bound = np.array([-5, 0]),
+ upper_bound = np.array([10, 15]),
+ objective_func=branin,
+ n_choices=25000,
+ n_init=10,
+ n_iter=190,
+ seed=4327)
+
+gp_opt1.lazyoptimize(method = "mc", verbose=2, abs_tol=1e-4)
diff --git a/gpopt-docs/GPopt/GPOpt.html b/gpopt-docs/GPopt/GPOpt.html
index 35b29ff..0d7cbc6 100644
--- a/gpopt-docs/GPopt/GPOpt.html
+++ b/gpopt-docs/GPopt/GPOpt.html
@@ -38,6 +38,9 @@
21classGPOpt:
- 22"""Class GPOpt.
- 23
- 24 # Arguments:
- 25
- 26 lower_bound: a numpy array;
- 27 lower bound for researched minimum
- 28
- 29 upper_bound: a numpy array;
- 30 upper bound for researched minimum
- 31
- 32 objective_func: a function;
- 33 the objective function to be minimized
- 34
- 35 gp_obj: a GaussianProcessRegressor object;
- 36 An ML model for estimating the uncertainty around the objective function
- 37
- 38 x_init:
- 39 initial setting of points where `objective_func` is evaluated (optional)
- 40
- 41 y_init:
- 42 initial setting values at points where `objective_func` is evaluated (optional)
- 43
- 44 n_init: an integer;
- 45 number of points in the initial setting, when `x_init` and `y_init` are not provided
- 46
- 47 n_choices: an integer;
- 48 number of points for the calculation of expected improvement
- 49
- 50 n_iter: an integer;
- 51 number of iterations of the minimization algorithm
- 52
- 53 alpha: a float;
- 54 Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
- 55
- 56 n_restarts_optimizer: an integer;
- 57 The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
- 58
- 59 seed: an integer;
- 60 reproducibility seed
- 61
- 62 save: a string;
- 63 Specifies where to save the optimizer in its current state
- 64
- 65 n_jobs: an integer;
- 66 number of jobs for parallel computing on initial setting (can be -1)
- 67
- 68 per_second: a boolean;
- 69 __experimental__, default is False (leave to default for now)
- 70
- 71 log_scale: a boolean;
- 72 __experimental__, default is False (leave to default for now)
- 73
- 74 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
- 75 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
- 76
- 77 """
- 78
- 79def__init__(
- 80self,
- 81lower_bound,
- 82upper_bound,
- 83objective_func=None,
- 84gp_obj=None,
- 85x_init=None,
- 86y_init=None,
- 87n_init=10,
- 88n_choices=25000,
- 89n_iter=190,
- 90alpha=1e-6,
- 91n_restarts_optimizer=25,
- 92seed=123,
- 93save=None,
- 94n_jobs=1,
- 95per_second=False,# /!\ very experimental
- 96log_scale=False,# /!\ experimental
- 97):
- 98
- 99n_dims=len(lower_bound)
-100
-101assertn_dims==len(
-102upper_bound
-103),"'upper_bound' and 'lower_bound' must have the same dimensions"
-104
-105self.objective_func=objective_func
-106self.lower_bound=lower_bound
-107self.upper_bound=upper_bound
-108self.y_init=y_init
-109self.log_scale=log_scale
-110self.n_dims=n_dims
-111self.n_init=n_init
-112self.n_choices=n_choices
-113self.n_iter=n_iter
-114self.alpha=alpha
-115self.n_restarts_optimizer=n_restarts_optimizer
-116self.seed=seed
-117self.save=save
-118self.per_second=per_second
-119self.x_min=None
-120self.y_min=None
-121self.y_mean=None
-122self.y_std=None
-123self.ei=np.array([])
-124self.max_ei=[]
-125ifgp_objisNone:
-126self.gp_obj=GaussianProcessRegressor(
-127kernel=Matern(nu=2.5),
-128alpha=self.alpha,
-129normalize_y=True,
-130n_restarts_optimizer=self.n_restarts_optimizer,
-131random_state=self.seed,
-132)
-133else:
-134self.gp_obj=gp_obj
-135
-136# Sobol seqs for initial design and choices
-137sobol_seq_init=np.transpose(
-138generate_sobol2(
-139n_dims=self.n_dims,
-140n_points=self.n_init,
-141skip=2,
-142)
-143)
-144sobol_seq_choices=np.transpose(
-145generate_sobol2(
-146n_dims=self.n_dims,
-147n_points=self.n_choices,
-148skip=self.n_init+2,
-149)
-150)
-151
-152# Sobol seqs for initial design and choices with bounds
-153ifself.log_scale==False:
-154
-155bounds_range=upper_bound-lower_bound
-156self.x_init=(
-157bounds_range*sobol_seq_init+lower_bound
-158ifx_initisNone
-159elsex_init
-160)
-161self.x_choices=bounds_range*sobol_seq_choices+lower_bound
-162
-163else:# (!) experimental
-164
-165assert(
-166lower_bound>0
-167).all(),"all elements of `lower_bound` must be > 0"
-168assert(
-169upper_bound>0
-170).all(),"all elements of `upper_bound` must be > 0"
-171
-172log_lower_bound=np.log(lower_bound)
-173log_upper_bound=np.log(upper_bound)
-174log_bounds_range=log_upper_bound-log_lower_bound
-175self.x_init=(
-176np.minimum(
-177np.exp(log_bounds_range*sobol_seq_init+log_lower_bound),
-1781.7976931348623157e308,
-179)
-180ifx_initisNone
-181elsex_init
-182)
-183self.x_choices=np.minimum(
-184np.exp(log_bounds_range*sobol_seq_choices+log_lower_bound),
-1851.7976931348623157e308,
-186)
-187
-188# shelve for saving (not for loading)
-189ifself.saveisnotNone:
-190self.sh=shelve.open(filename=save,flag="c",writeback=True)
-191
-192ifself.per_second:
-193self.timings=[]
-194self.rf_obj=RandomForestRegressor(
-195n_estimators=250,random_state=self.seed
-196)
+
22classGPOpt:
+ 23"""Class GPOpt.
+ 24
+ 25 # Arguments:
+ 26
+ 27 lower_bound: a numpy array;
+ 28 lower bound for researched minimum
+ 29
+ 30 upper_bound: a numpy array;
+ 31 upper bound for researched minimum
+ 32
+ 33 objective_func: a function;
+ 34 the objective function to be minimized
+ 35
+ 36 params_names: a list;
+ 37 names of the parameters of the objective function (optional)
+ 38
+ 39 gp_obj: a GaussianProcessRegressor object;
+ 40 An ML model for estimating the uncertainty around the objective function
+ 41
+ 42 x_init:
+ 43 initial setting of points where `objective_func` is evaluated (optional)
+ 44
+ 45 y_init:
+ 46 initial setting values at points where `objective_func` is evaluated (optional)
+ 47
+ 48 n_init: an integer;
+ 49 number of points in the initial setting, when `x_init` and `y_init` are not provided
+ 50
+ 51 n_choices: an integer;
+ 52 number of points for the calculation of expected improvement
+ 53
+ 54 n_iter: an integer;
+ 55 number of iterations of the minimization algorithm
+ 56
+ 57 alpha: a float;
+ 58 Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
+ 59
+ 60 n_restarts_optimizer: an integer;
+ 61 The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
+ 62
+ 63 seed: an integer;
+ 64 reproducibility seed
+ 65
+ 66 save: a string;
+ 67 Specifies where to save the optimizer in its current state
+ 68
+ 69 n_jobs: an integer;
+ 70 number of jobs for parallel computing on initial setting (can be -1)
+ 71
+ 72 per_second: a boolean;
+ 73 __experimental__, default is False (leave to default for now)
+ 74
+ 75 log_scale: a boolean;
+ 76 __experimental__, default is False (leave to default for now)
+ 77
+ 78 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+ 79 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+ 80
+ 81 """
+ 82
+ 83def__init__(
+ 84self,
+ 85lower_bound,
+ 86upper_bound,
+ 87objective_func=None,
+ 88params_names=None,
+ 89gp_obj=None,
+ 90x_init=None,
+ 91y_init=None,
+ 92n_init=10,
+ 93n_choices=25000,
+ 94n_iter=190,
+ 95alpha=1e-6,
+ 96n_restarts_optimizer=25,
+ 97seed=123,
+ 98save=None,
+ 99n_jobs=1,
+100per_second=False,# /!\ very experimental
+101log_scale=False,# /!\ experimental
+102):
+103
+104n_dims=len(lower_bound)
+105
+106assertn_dims==len(
+107upper_bound
+108),"'upper_bound' and 'lower_bound' must have the same dimensions"
+109
+110self.objective_func=objective_func
+111self.params_names=params_names
+112self.lower_bound=lower_bound
+113self.upper_bound=upper_bound
+114self.y_init=y_init
+115self.log_scale=log_scale
+116self.n_dims=n_dims
+117self.n_init=n_init
+118self.n_choices=n_choices
+119self.n_iter=n_iter
+120self.alpha=alpha
+121self.n_restarts_optimizer=n_restarts_optimizer
+122self.seed=seed
+123self.save=save
+124self.per_second=per_second
+125self.x_min=None
+126self.y_min=None
+127self.y_mean=None
+128self.y_std=None
+129self.ei=np.array([])
+130self.max_ei=[]
+131ifgp_objisNone:
+132self.gp_obj=GaussianProcessRegressor(
+133kernel=Matern(nu=2.5),
+134alpha=self.alpha,
+135normalize_y=True,
+136n_restarts_optimizer=self.n_restarts_optimizer,
+137random_state=self.seed,
+138)
+139else:
+140self.gp_obj=gp_obj
+141
+142# Sobol seqs for initial design and choices
+143sobol_seq_init=np.transpose(
+144generate_sobol2(
+145n_dims=self.n_dims,
+146n_points=self.n_init,
+147skip=2,
+148)
+149)
+150sobol_seq_choices=np.transpose(
+151generate_sobol2(
+152n_dims=self.n_dims,
+153n_points=self.n_choices,
+154skip=self.n_init+2,
+155)
+156)
+157
+158# Sobol seqs for initial design and choices with bounds
+159ifself.log_scale==False:
+160
+161bounds_range=upper_bound-lower_bound
+162self.x_init=(
+163bounds_range*sobol_seq_init+lower_bound
+164ifx_initisNone
+165elsex_init
+166)
+167self.x_choices=bounds_range*sobol_seq_choices+lower_bound
+168
+169else:# (!) experimental
+170
+171assert(
+172lower_bound>0
+173).all(),"all elements of `lower_bound` must be > 0"
+174assert(
+175upper_bound>0
+176).all(),"all elements of `upper_bound` must be > 0"
+177
+178log_lower_bound=np.log(lower_bound)
+179log_upper_bound=np.log(upper_bound)
+180log_bounds_range=log_upper_bound-log_lower_bound
+181self.x_init=(
+182np.minimum(
+183np.exp(log_bounds_range*sobol_seq_init+log_lower_bound),
+1841.7976931348623157e308,
+185)
+186ifx_initisNone
+187elsex_init
+188)
+189self.x_choices=np.minimum(
+190np.exp(log_bounds_range*sobol_seq_choices+log_lower_bound),
+1911.7976931348623157e308,
+192)
+193
+194# shelve for saving (not for loading)
+195ifself.saveisnotNone:
+196self.sh=shelve.open(filename=save,flag="c",writeback=True)197
-198self.n_jobs=n_jobs
-199
-200# from sklearn.base
-201defget_params(self):
-202"""Get object attributes.
+198ifself.per_second:
+199self.timings=[]
+200self.rf_obj=RandomForestRegressor(
+201n_estimators=250,random_state=self.seed
+202)203
-204 Returns
-205 -------
-206 params : mapping of string to any
-207 Parameter names mapped to their values.
-208 """
-209out=dict()
-210param_names=dir(self)
-211forkeyinparam_names:
-212ifkey.startswith("_")isFalse:
-213out[key]=getattr(self,key,None)
-214
-215returnout
-216
-217# for parallel case on initial design
-218defeval_objective(self,arg):
-219try:
-220returnself.objective_func(self.x_init[arg,:])
-221except:
-222return1e06
-223
-224# load data from stored shelve
-225defload(self,path):
-226"""load data from stored shelve.
-227
-228 # Arguments
+204self.n_jobs=n_jobs
+205
+206# from sklearn.base
+207defget_params(self):
+208"""Get object attributes.
+209
+210 Returns
+211 -------
+212 params : mapping of string to any
+213 Parameter names mapped to their values.
+214 """
+215out=dict()
+216param_names=dir(self)
+217forkeyinparam_names:
+218ifkey.startswith("_")isFalse:
+219out[key]=getattr(self,key,None)
+220
+221returnout
+222
+223# for parallel case on initial design
+224defeval_objective(self,arg):
+225try:
+226returnself.objective_func(self.x_init[arg,:])
+227except:
+228return1e06229
-230 path : a string; path to stored shelve.
-231
-232 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-233 """
-234
-235self.sh=shelve.open(filename=path)
-236forkey,valueinself.sh.items():
-237setattr(self,key,value)
-238
-239# update shelve in optimization loop
-240defupdate_shelve(self):
-241forkey,valueinself.get_params().items():
-242if(callable(value)isFalse)&(key!="sh"):
-243self.sh[key]=value
-244self.sh.sync()
-245
-246# closing shelve (can't be modified after)
-247defclose_shelve(self):
-248"""Close shelve.
-249
-250 # Arguments
+230# load data from stored shelve
+231defload(self,path):
+232"""load data from stored shelve.
+233
+234 # Arguments
+235
+236 path : a string; path to stored shelve.
+237
+238 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+239 """
+240
+241self.sh=shelve.open(filename=path)
+242forkey,valueinself.sh.items():
+243setattr(self,key,value)
+244
+245# update shelve in optimization loop
+246defupdate_shelve(self):
+247forkey,valueinself.get_params().items():
+248if(callable(value)isFalse)&(key!="sh"):
+249self.sh[key]=value
+250self.sh.sync()251
-252 No argument.
-253
-254 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-255 """
-256
-257self.sh.close()
-258
-259# fit predict
-260defgp_fit_predict(self,X_train,y_train,X_test):
-261
-262iflen(X_train.shape)==1:
-263X_train=X_train.reshape((-1,1))
-264X_test=X_test.reshape((-1,1))
-265
-266# Get mean and standard deviation
-267returnself.gp_obj.fit(X_train,y_train).predict(
-268X_test,return_std=True
-269)
-270
-271# fit predict timings
-272deftimings_fit_predict(self,X_train,y_train,X_test):
-273
-274iflen(X_train.shape)==1:
-275X_train=X_train.reshape((-1,1))
-276X_test=X_test.reshape((-1,1))
-277
-278# Get mean preds for timings
-279returnself.rf_obj.fit(X_train,y_train).predict(X_test)
-280
-281# find next parameter by using expected improvement (ei)
-282defnext_parameter_by_ei(self,seed,i):
+252# closing shelve (can't be modified after)
+253defclose_shelve(self):
+254"""Close shelve.
+255
+256 # Arguments
+257
+258 No argument.
+259
+260 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+261 """
+262
+263self.sh.close()
+264
+265# fit predict
+266defgp_fit_predict(self,X_train,y_train,X_test):
+267
+268iflen(X_train.shape)==1:
+269X_train=X_train.reshape((-1,1))
+270X_test=X_test.reshape((-1,1))
+271
+272# Get mean and standard deviation
+273returnself.gp_obj.fit(X_train,y_train).predict(
+274X_test,return_std=True
+275)
+276
+277# fit predict timings
+278deftimings_fit_predict(self,X_train,y_train,X_test):
+279
+280iflen(X_train.shape)==1:
+281X_train=X_train.reshape((-1,1))
+282X_test=X_test.reshape((-1,1))283
-284gamma_hat=(self.y_min-self.y_mean)/self.y_std
-285
-286self.ei=-self.y_std*(
-287gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
-288)
+284# Get mean preds for timings
+285returnself.rf_obj.fit(X_train,y_train).predict(X_test)
+286
+287# find next parameter by using expected improvement (ei)
+288defnext_parameter_by_ei(self,seed,i):289
-290# find max index -----
+290gamma_hat=(self.y_min-self.y_mean)/self.y_std291
-292ifself.per_secondisFalse:
-293
-294# find index for max. ei
-295max_index=self.ei.argmin()
-296
-297else:# self.per_second is True
-298
-299# predict timings on self.x_choices
-300# train on X = self.parameters and y = self.timings
-301# (must have same shape[0])
-302timing_preds=self.timings_fit_predict(
-303X_train=np.asarray(self.parameters),
-304y_train=np.asarray(self.timings),
-305X_test=self.x_choices,
-306)
-307
-308# find index for max. ei (and min. timings)
-309max_index=(-self.ei/timing_preds).argmax()
-310
-311self.max_ei.append(np.abs(self.ei[max_index]))
-312
-313# Select next choice
-314next_param=self.x_choices[max_index,:]
-315
-316ifnext_paraminnp.asarray(self.parameters):
-317
-318ifself.log_scale==False:
-319
-320np.random.seed(self.seed*i+1000)
-321next_param=(
-322self.upper_bound-self.lower_bound
-323)*np.random.rand(self.n_dims)+self.lower_bound
-324
-325else:# /!\ very... experimental
-326
-327np.random.seed(self.seed)
-328log_upper_bound=np.log(self.upper_bound)
-329log_lower_bound=np.log(self.lower_bound)
-330log_bounds_range=log_upper_bound-log_lower_bound
-331
-332next_param=np.minimum(
-333np.exp(
-334log_bounds_range*np.random.rand(self.n_dims)
-335+log_lower_bound
-336),
-3371.7976931348623157e308,
-338)
-339
-340returnnext_param
-341
-342# optimize the objective
-343defoptimize(
-344self,
-345verbose=1,
-346n_more_iter=None,
-347abs_tol=None,# suggested 1e-4, for n_iter = 200
-348min_budget=50,# minimum budget for early stopping
-349func_args=None,
-350):
-351"""Launch optimization loop.
-352
-353 # Arguments:
-354
-355 verbose: an integer;
-356 verbose = 0: nothing is printed,
-357 verbose = 1: a progress bar is printed (longer than 0),
-358 verbose = 2: information about each iteration is printed (longer than 1)
-359
-360 n_more_iter: an integer;
-361 additional number of iterations for the optimizer (which has been run once)
-362
-363 abs_tol: a float;
-364 tolerance for convergence of the optimizer (early stopping based on expected improvement)
+292self.ei=-self.y_std*(
+293gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
+294)
+295
+296# find max index -----
+297
+298ifself.per_secondisFalse:
+299
+300# find index for max. ei
+301max_index=self.ei.argmin()
+302
+303else:# self.per_second is True
+304
+305# predict timings on self.x_choices
+306# train on X = self.parameters and y = self.timings
+307# (must have same shape[0])
+308timing_preds=self.timings_fit_predict(
+309X_train=np.asarray(self.parameters),
+310y_train=np.asarray(self.timings),
+311X_test=self.x_choices,
+312)
+313
+314# find index for max. ei (and min. timings)
+315max_index=(-self.ei/timing_preds).argmax()
+316
+317self.max_ei.append(np.abs(self.ei[max_index]))
+318
+319# Select next choice
+320next_param=self.x_choices[max_index,:]
+321
+322ifnext_paraminnp.asarray(self.parameters):
+323
+324ifself.log_scale==False:
+325
+326np.random.seed(self.seed*i+1000)
+327next_param=(
+328self.upper_bound-self.lower_bound
+329)*np.random.rand(self.n_dims)+self.lower_bound
+330
+331else:# /!\ very... experimental
+332
+333np.random.seed(self.seed)
+334log_upper_bound=np.log(self.upper_bound)
+335log_lower_bound=np.log(self.lower_bound)
+336log_bounds_range=log_upper_bound-log_lower_bound
+337
+338next_param=np.minimum(
+339np.exp(
+340log_bounds_range*np.random.rand(self.n_dims)
+341+log_lower_bound
+342),
+3431.7976931348623157e308,
+344)
+345
+346returnnext_param
+347
+348# optimize the objective
+349defoptimize(
+350self,
+351verbose=1,
+352n_more_iter=None,
+353abs_tol=None,# suggested 1e-4, for n_iter = 200
+354min_budget=50,# minimum budget for early stopping
+355func_args=None,
+356):
+357"""Launch optimization loop.
+358
+359 # Arguments:
+360
+361 verbose: an integer;
+362 verbose = 0: nothing is printed,
+363 verbose = 1: a progress bar is printed (longer than 0),
+364 verbose = 2: information about each iteration is printed (longer than 1)365
-366 min_budget: an integer (default is 50);
-367 minimum number of iterations before early stopping controlled by `abs_tol`
+366 n_more_iter: an integer;
+367 additional number of iterations for the optimizer (which has been run once)368
-369 func_args: a list;
-370 additional parameters for the objective function (if necessary)
+369 abs_tol: a float;
+370 tolerance for convergence of the optimizer (early stopping based on expected improvement)371
-372 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-373 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+372 min_budget: an integer (default is 50);
+373 minimum number of iterations before early stopping controlled by `abs_tol`374
-375 """
-376
-377# verbose = 0: nothing is printed
-378# verbose = 1: a progress bar is printed (longer than 0)
-379# verbose = 2: information about each iteration is printed (longer than 1)
-380iffunc_argsisNone:
-381func_args=[]
+375 func_args: a list;
+376 additional parameters for the objective function (if necessary)
+377
+378 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+379 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+380
+381 """382
-383if(
-384n_more_iterisNone
-385):# initial optimization, before more iters are requested
-386
-387n_iter=self.n_iter
-388# stopping iter for early stopping (default is total budget)
-389iter_stop=n_iter# potentially # got to check this
-390
-391# initial design ----------
+383# verbose = 0: nothing is printed
+384# verbose = 1: a progress bar is printed (longer than 0)
+385# verbose = 2: information about each iteration is printed (longer than 1)
+386iffunc_argsisNone:
+387func_args=[]
+388
+389if(
+390n_more_iterisNone
+391):# initial optimization, before more iters are requested392
-393if(verbose==1)|(verbose==2):
-394print(f"\n Creating initial design... \n")
-395
-396ifverbose==1:
-397progbar=Progbar(target=self.n_init)
+393n_iter=self.n_iter
+394# stopping iter for early stopping (default is total budget)
+395iter_stop=n_iter# potentially # got to check this
+396
+397# initial design ----------398
-399self.parameters=self.x_init.tolist()
-400self.scores=[]
+399if(verbose==1)|(verbose==2):
+400print(f"\n Creating initial design... \n")401
-402ifself.saveisnotNone:
-403self.update_shelve()
+402ifverbose==1:
+403progbar=Progbar(target=self.n_init)404
-405ifself.y_initisNone:# calculate scores on initial design
-406
-407assert(
-408self.objective_funcisnotNone
-409),"self.y_init is None: must have 'objective_func' not None"
+405self.parameters=self.x_init.tolist()
+406self.scores=[]
+407
+408ifself.saveisnotNone:
+409self.update_shelve()410
-411ifself.n_jobs==1:
+411ifself.y_initisNone:# calculate scores on initial design412
-413foriinrange(self.n_init):
-414
-415x_next=self.x_init[i,:]
+413assert(
+414self.objective_funcisnotNone
+415),"self.y_init is None: must have 'objective_func' not None"416
-417try:
+417ifself.n_jobs==1:418
-419ifself.per_secondisTrue:
+419foriinrange(self.n_init):420
-421start=time()
-422score=self.objective_func(x_next,*func_args)
-423if(np.isfinite(score)==False)or(
-424np.isnan(score)==True
-425):
-426continue
-427self.timings.append(np.log(time()-start))
-428
-429else:# self.per_second is False
-430
-431score=self.objective_func(x_next,*func_args)
-432if(np.isfinite(score)==False)or(
-433np.isnan(score)==True
-434):
-435continue
+421x_next=self.x_init[i,:]
+422
+423try:
+424
+425ifself.per_secondisTrue:
+426
+427start=time()
+428score=self.objective_func(x_next,*func_args)
+429if(np.isfinite(score)==False)or(
+430np.isnan(score)==True
+431):
+432continue
+433self.timings.append(np.log(time()-start))
+434
+435else:# self.per_second is False436
-437self.scores.append(score)
-438
-439ifself.saveisnotNone:
-440self.update_shelve()
-441
-442except:
-443
-444continue
-445
-446ifverbose==1:
-447progbar.update(i)# update progress bar
-448
-449ifverbose==2:
-450print(f"point: {x_next}; score: {score}")
-451# end loop # calculate scores on initial design
-452
-453ifverbose==1:
-454progbar.update(self.n_init)
-455
-456else:# self.n_jobs != 1
-457
-458assert(
-459self.per_secondisFalse
-460),"timings not calculated here"
+437score=self.objective_func(x_next,*func_args)
+438if(np.isfinite(score)==False)or(
+439np.isnan(score)==True
+440):
+441continue
+442
+443self.scores.append(score)
+444
+445ifself.saveisnotNone:
+446self.update_shelve()
+447
+448except:
+449
+450continue
+451
+452ifverbose==1:
+453progbar.update(i)# update progress bar
+454
+455ifverbose==2:
+456print(f"point: {x_next}; score: {score}")
+457# end loop # calculate scores on initial design
+458
+459ifverbose==1:
+460progbar.update(self.n_init)461
-462scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
-463delayed(self.objective_func)(self.x_init[i,:])
-464foriinrange(self.n_init)
-465)
-466
-467self.scores=scores
-468
-469ifself.saveisnotNone:
-470self.update_shelve()
-471
-472else:# if self.y_init is None:
-473
-474assertself.x_init.shape[0]==len(
-475self.y_init
-476),"must have: self.x_init.shape[0] == len(self.y_init)"
+462else:# self.n_jobs != 1
+463
+464assert(
+465self.per_secondisFalse
+466),"timings not calculated here"
+467
+468scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
+469delayed(self.objective_func)(self.x_init[i,:])
+470foriinrange(self.n_init)
+471)
+472
+473self.scores=scores
+474
+475ifself.saveisnotNone:
+476self.update_shelve()477
-478self.scores=pickle.loads(
-479pickle.dumps(self.y_init.tolist(),-1)
-480)
-481
-482# current best score on initial design
-483min_index=(np.asarray(self.scores)).argmin()
-484self.y_min=self.scores[min_index]
-485self.x_min=self.x_init[min_index,:]
-486
-487# current gp mean and std on initial design
-488y_mean,y_std=self.gp_fit_predict(
-489np.asarray(self.parameters),
-490np.asarray(self.scores),
-491self.x_choices,
-492)
-493self.y_mean=y_mean
-494self.y_std=np.maximum(2.220446049250313e-16,y_std)
-495
-496# saving after initial design computation
-497ifself.saveisnotNone:
-498self.update_shelve()
-499
-500else:# if n_more_iter is not None
+478else:# if self.y_init is None:
+479
+480assertself.x_init.shape[0]==len(
+481self.y_init
+482),"must have: self.x_init.shape[0] == len(self.y_init)"
+483
+484self.scores=pickle.loads(
+485pickle.dumps(self.y_init.tolist(),-1)
+486)
+487
+488# current best score on initial design
+489min_index=(np.asarray(self.scores)).argmin()
+490self.y_min=self.scores[min_index]
+491self.x_min=self.x_init[min_index,:]
+492
+493# current gp mean and std on initial design
+494y_mean,y_std=self.gp_fit_predict(
+495np.asarray(self.parameters),
+496np.asarray(self.scores),
+497self.x_choices,
+498)
+499self.y_mean=y_mean
+500self.y_std=np.maximum(2.220446049250313e-16,y_std)501
-502assertself.n_iter>5,"you must have n_iter > 5"
-503n_iter=n_more_iter
-504iter_stop=len(self.max_ei)+n_more_iter# potentially
+502# saving after initial design computation
+503ifself.saveisnotNone:
+504self.update_shelve()505
-506if(verbose==1)|(verbose==2):
-507print(f"\n ...Done. \n")
-508try:
-509print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
-510except:
-511pass
-512
-513# end init design ----------
-514
-515# if n_more_iter is None: # initial optimization, before more iters are requested
-516
-517if(verbose==1)|(verbose==2):
-518print(f"\n Optimization loop... \n")
-519
-520# early stopping?
-521ifabs_tolisnotNone:
-522assert(
-523min_budget>20
-524),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+506else:# if n_more_iter is not None
+507
+508assertself.n_iter>5,"you must have n_iter > 5"
+509n_iter=n_more_iter
+510iter_stop=len(self.max_ei)+n_more_iter# potentially
+511
+512if(verbose==1)|(verbose==2):
+513print(f"\n ...Done. \n")
+514try:
+515print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
+516except:
+517pass
+518
+519# end init design ----------
+520
+521# if n_more_iter is None: # initial optimization, before more iters are requested
+522
+523if(verbose==1)|(verbose==2):
+524print(f"\n Optimization loop... \n")525
-526ifverbose==1:
-527progbar=Progbar(target=n_iter)
-528
-529# main loop ----------
-530
-531foriinrange(n_iter):
-532
-533# find next set of parameters (vector), maximizing ei
-534next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)
-535
-536try:
-537
-538ifself.per_secondisTrue:
-539
-540start=time()
+526# early stopping?
+527ifabs_tolisnotNone:
+528assert(
+529min_budget>20
+530),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+531
+532ifverbose==1:
+533progbar=Progbar(target=n_iter)
+534
+535# main loop ----------
+536
+537foriinrange(n_iter):
+538
+539# find next set of parameters (vector), maximizing ei
+540next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)541
-542ifself.objective_funcisnotNone:
+542try:543
-544score_next_param=self.objective_func(
-545next_param,*func_args
-546)
+544ifself.per_secondisTrue:
+545
+546start=time()547
-548if(np.isfinite(score_next_param)==False)or(
-549np.isnan(score_next_param)==True
-550):
-551continue
-552
-553else:
-554
-555assert(self.x_initisnotNone)and(
-556self.y_initisnotNone
-557),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+548ifself.objective_funcisnotNone:
+549
+550score_next_param=self.objective_func(
+551next_param,*func_args
+552)
+553
+554if(np.isfinite(score_next_param)==False)or(
+555np.isnan(score_next_param)==True
+556):
+557continue558
-559print(f"\n next param: {next_param}\n")
-560score_next_param=float(
-561input("get new score: \n")
-562)# or an API response
-563
-564if(np.isfinite(score_next_param)==False)or(
-565np.isnan(score_next_param)==True
-566):
-567continue
-568
-569self.timings.append(np.log(time()-start))
-570
-571else:# self.per_second is False:
-572
-573ifself.objective_funcisnotNone:
+559else:
+560
+561assert(self.x_initisnotNone)and(
+562self.y_initisnotNone
+563),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+564
+565print(f"\n next param: {next_param}\n")
+566score_next_param=float(
+567input("get new score: \n")
+568)# or an API response
+569
+570if(np.isfinite(score_next_param)==False)or(
+571np.isnan(score_next_param)==True
+572):
+573continue574
-575score_next_param=self.objective_func(
-576next_param,*func_args
-577)
+575self.timings.append(np.log(time()-start))
+576
+577else:# self.per_second is False:578
-579if(np.isfinite(score_next_param)==False)or(
-580np.isnan(score_next_param)==True
-581):
-582continue
-583
-584else:
-585
-586assert(self.x_initisnotNone)and(
-587self.y_initisnotNone
-588),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+579ifself.objective_funcisnotNone:
+580
+581score_next_param=self.objective_func(
+582next_param,*func_args
+583)
+584
+585if(np.isfinite(score_next_param)==False)or(
+586np.isnan(score_next_param)==True
+587):
+588continue589
-590print(f"\n next param: {next_param}\n")
-591score_next_param=float(
-592input("get new score: \n")
-593)# or an API response
-594
-595if(np.isfinite(score_next_param)==False)or(
-596np.isnan(score_next_param)==True
-597):
-598continue
-599
-600except:
-601
-602continue
-603
-604self.parameters.append(next_param.tolist())
+590else:
+591
+592assert(self.x_initisnotNone)and(
+593self.y_initisnotNone
+594),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+595
+596print(f"\n next param: {next_param}\n")
+597score_next_param=float(
+598input("get new score: \n")
+599)# or an API response
+600
+601if(np.isfinite(score_next_param)==False)or(
+602np.isnan(score_next_param)==True
+603):
+604continue605
-606self.scores.append(score_next_param)
+606except:607
-608ifself.saveisnotNone:
-609self.update_shelve()
-610
-611ifverbose==2:
-612print(f"iteration {i+1} -----")
-613print(f"current minimum: {self.x_min}")
-614print(f"current minimum score: {self.y_min}")
-615print(f"next parameter: {next_param}")
-616print(f"score for next parameter: {score_next_param}\n")
-617
-618ifscore_next_param<self.y_min:
-619self.x_min=next_param
-620self.y_min=score_next_param
-621ifself.saveisnotNone:
-622self.update_shelve()
+608continue
+609
+610self.parameters.append(next_param.tolist())
+611
+612self.scores.append(score_next_param)
+613
+614ifself.saveisnotNone:
+615self.update_shelve()
+616
+617ifverbose==2:
+618print(f"iteration {i+1} -----")
+619print(f"current minimum: {self.x_min}")
+620print(f"current minimum score: {self.y_min}")
+621print(f"next parameter: {next_param}")
+622print(f"score for next parameter: {score_next_param}\n")623
-624self.y_mean,self.y_std=self.gp_fit_predict(
-625np.asarray(self.parameters),
-626np.asarray(self.scores),
-627self.x_choices,
-628)
+624ifscore_next_param<self.y_min:
+625self.x_min=next_param
+626self.y_min=score_next_param
+627ifself.saveisnotNone:
+628self.update_shelve()629
-630ifself.saveisnotNone:
-631self.update_shelve()
-632
-633ifverbose==1:
-634progbar.update(i+1)# update progress bar
+630self.y_mean,self.y_std=self.gp_fit_predict(
+631np.asarray(self.parameters),
+632np.asarray(self.scores),
+633self.x_choices,
+634)635
-636# early stopping
-637
-638ifabs_tolisnotNone:
-639
-640# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
-641iflen(self.max_ei)>min_budget:
-642
-643diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))
-644
-645ifdiff_max_ei[-1]<=abs_tol:
-646
-647iter_stop=len(self.max_ei)# index i starts at 0
+636ifself.saveisnotNone:
+637self.update_shelve()
+638
+639ifverbose==1:
+640progbar.update(i+1)# update progress bar
+641
+642# early stopping
+643
+644ifabs_tolisnotNone:
+645
+646# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
+647iflen(self.max_ei)>min_budget:648
-649break
+649diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))650
-651# end main loop ----------
+651ifdiff_max_ei[-1]<=abs_tol:652
-653if(verbose==1)&(i<(n_iter-1)):
-654progbar.update(n_iter)
-655
-656self.n_iter=iter_stop
-657ifself.saveisnotNone:
-658self.update_shelve()
-659
-660return(self.x_min,self.y_min)
+653iter_stop=len(self.max_ei)# index i starts at 0
+654
+655break
+656
+657# end main loop ----------
+658
+659if(verbose==1)&(i<(n_iter-1)):
+660progbar.update(n_iter)
+661
+662self.n_iter=iter_stop
+663ifself.saveisnotNone:
+664self.update_shelve()
+665
+666DescribeResult=namedtuple(
+667"DescribeResult",("best_params","best_score")
+668)
+669
+670ifself.params_namesisNone:
+671
+672returnDescribeResult(self.x_min,self.y_min)
+673
+674else:
+675
+676returnDescribeResult(
+677dict(zip(self.params_names,self.x_min)),self.y_min
+678)
@@ -823,6 +843,9 @@
Arguments:
objective_func: a function;
the objective function to be minimized
+params_names: a list;
+ names of the parameters of the objective function (optional)
+
gp_obj: a GaussianProcessRegressor object;
An ML model for estimating the uncertainty around the objective function
@@ -872,132 +895,134 @@
225defload(self,path):
-226"""load data from stored shelve.
-227
-228 # Arguments
-229
-230 path : a string; path to stored shelve.
-231
-232 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-233 """
-234
-235self.sh=shelve.open(filename=path)
-236forkey,valueinself.sh.items():
-237setattr(self,key,value)
+
231defload(self,path):
+232"""load data from stored shelve.
+233
+234 # Arguments
+235
+236 path : a string; path to stored shelve.
+237
+238 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+239 """
+240
+241self.sh=shelve.open(filename=path)
+242forkey,valueinself.sh.items():
+243setattr(self,key,value)
247defclose_shelve(self):
-248"""Close shelve.
-249
-250 # Arguments
-251
-252 No argument.
-253
-254 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-255 """
-256
-257self.sh.close()
+
253defclose_shelve(self):
+254"""Close shelve.
+255
+256 # Arguments
+257
+258 No argument.
+259
+260 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+261 """
+262
+263self.sh.close()
@@ -1403,16 +1439,16 @@
Arguments
-
260defgp_fit_predict(self,X_train,y_train,X_test):
-261
-262iflen(X_train.shape)==1:
-263X_train=X_train.reshape((-1,1))
-264X_test=X_test.reshape((-1,1))
-265
-266# Get mean and standard deviation
-267returnself.gp_obj.fit(X_train,y_train).predict(
-268X_test,return_std=True
-269)
+
266defgp_fit_predict(self,X_train,y_train,X_test):
+267
+268iflen(X_train.shape)==1:
+269X_train=X_train.reshape((-1,1))
+270X_test=X_test.reshape((-1,1))
+271
+272# Get mean and standard deviation
+273returnself.gp_obj.fit(X_train,y_train).predict(
+274X_test,return_std=True
+275)
@@ -1430,14 +1466,14 @@
Arguments
-
272deftimings_fit_predict(self,X_train,y_train,X_test):
-273
-274iflen(X_train.shape)==1:
-275X_train=X_train.reshape((-1,1))
-276X_test=X_test.reshape((-1,1))
-277
-278# Get mean preds for timings
-279returnself.rf_obj.fit(X_train,y_train).predict(X_test)
+
278deftimings_fit_predict(self,X_train,y_train,X_test):
+279
+280iflen(X_train.shape)==1:
+281X_train=X_train.reshape((-1,1))
+282X_test=X_test.reshape((-1,1))
+283
+284# Get mean preds for timings
+285returnself.rf_obj.fit(X_train,y_train).predict(X_test)
288defnext_parameter_by_ei(self,seed,i):289
-290# find max index -----
+290gamma_hat=(self.y_min-self.y_mean)/self.y_std291
-292ifself.per_secondisFalse:
-293
-294# find index for max. ei
-295max_index=self.ei.argmin()
-296
-297else:# self.per_second is True
-298
-299# predict timings on self.x_choices
-300# train on X = self.parameters and y = self.timings
-301# (must have same shape[0])
-302timing_preds=self.timings_fit_predict(
-303X_train=np.asarray(self.parameters),
-304y_train=np.asarray(self.timings),
-305X_test=self.x_choices,
-306)
-307
-308# find index for max. ei (and min. timings)
-309max_index=(-self.ei/timing_preds).argmax()
-310
-311self.max_ei.append(np.abs(self.ei[max_index]))
-312
-313# Select next choice
-314next_param=self.x_choices[max_index,:]
-315
-316ifnext_paraminnp.asarray(self.parameters):
-317
-318ifself.log_scale==False:
-319
-320np.random.seed(self.seed*i+1000)
-321next_param=(
-322self.upper_bound-self.lower_bound
-323)*np.random.rand(self.n_dims)+self.lower_bound
-324
-325else:# /!\ very... experimental
-326
-327np.random.seed(self.seed)
-328log_upper_bound=np.log(self.upper_bound)
-329log_lower_bound=np.log(self.lower_bound)
-330log_bounds_range=log_upper_bound-log_lower_bound
-331
-332next_param=np.minimum(
-333np.exp(
-334log_bounds_range*np.random.rand(self.n_dims)
-335+log_lower_bound
-336),
-3371.7976931348623157e308,
-338)
-339
-340returnnext_param
+292self.ei=-self.y_std*(
+293gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
+294)
+295
+296# find max index -----
+297
+298ifself.per_secondisFalse:
+299
+300# find index for max. ei
+301max_index=self.ei.argmin()
+302
+303else:# self.per_second is True
+304
+305# predict timings on self.x_choices
+306# train on X = self.parameters and y = self.timings
+307# (must have same shape[0])
+308timing_preds=self.timings_fit_predict(
+309X_train=np.asarray(self.parameters),
+310y_train=np.asarray(self.timings),
+311X_test=self.x_choices,
+312)
+313
+314# find index for max. ei (and min. timings)
+315max_index=(-self.ei/timing_preds).argmax()
+316
+317self.max_ei.append(np.abs(self.ei[max_index]))
+318
+319# Select next choice
+320next_param=self.x_choices[max_index,:]
+321
+322ifnext_paraminnp.asarray(self.parameters):
+323
+324ifself.log_scale==False:
+325
+326np.random.seed(self.seed*i+1000)
+327next_param=(
+328self.upper_bound-self.lower_bound
+329)*np.random.rand(self.n_dims)+self.lower_bound
+330
+331else:# /!\ very... experimental
+332
+333np.random.seed(self.seed)
+334log_upper_bound=np.log(self.upper_bound)
+335log_lower_bound=np.log(self.lower_bound)
+336log_bounds_range=log_upper_bound-log_lower_bound
+337
+338next_param=np.minimum(
+339np.exp(
+340log_bounds_range*np.random.rand(self.n_dims)
+341+log_lower_bound
+342),
+3431.7976931348623157e308,
+344)
+345
+346returnnext_param
@@ -1531,324 +1567,336 @@
Arguments
-
343defoptimize(
-344self,
-345verbose=1,
-346n_more_iter=None,
-347abs_tol=None,# suggested 1e-4, for n_iter = 200
-348min_budget=50,# minimum budget for early stopping
-349func_args=None,
-350):
-351"""Launch optimization loop.
-352
-353 # Arguments:
-354
-355 verbose: an integer;
-356 verbose = 0: nothing is printed,
-357 verbose = 1: a progress bar is printed (longer than 0),
-358 verbose = 2: information about each iteration is printed (longer than 1)
-359
-360 n_more_iter: an integer;
-361 additional number of iterations for the optimizer (which has been run once)
-362
-363 abs_tol: a float;
-364 tolerance for convergence of the optimizer (early stopping based on expected improvement)
+
349defoptimize(
+350self,
+351verbose=1,
+352n_more_iter=None,
+353abs_tol=None,# suggested 1e-4, for n_iter = 200
+354min_budget=50,# minimum budget for early stopping
+355func_args=None,
+356):
+357"""Launch optimization loop.
+358
+359 # Arguments:
+360
+361 verbose: an integer;
+362 verbose = 0: nothing is printed,
+363 verbose = 1: a progress bar is printed (longer than 0),
+364 verbose = 2: information about each iteration is printed (longer than 1)365
-366 min_budget: an integer (default is 50);
-367 minimum number of iterations before early stopping controlled by `abs_tol`
+366 n_more_iter: an integer;
+367 additional number of iterations for the optimizer (which has been run once)368
-369 func_args: a list;
-370 additional parameters for the objective function (if necessary)
+369 abs_tol: a float;
+370 tolerance for convergence of the optimizer (early stopping based on expected improvement)371
-372 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-373 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+372 min_budget: an integer (default is 50);
+373 minimum number of iterations before early stopping controlled by `abs_tol`374
-375 """
-376
-377# verbose = 0: nothing is printed
-378# verbose = 1: a progress bar is printed (longer than 0)
-379# verbose = 2: information about each iteration is printed (longer than 1)
-380iffunc_argsisNone:
-381func_args=[]
+375 func_args: a list;
+376 additional parameters for the objective function (if necessary)
+377
+378 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+379 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+380
+381 """382
-383if(
-384n_more_iterisNone
-385):# initial optimization, before more iters are requested
-386
-387n_iter=self.n_iter
-388# stopping iter for early stopping (default is total budget)
-389iter_stop=n_iter# potentially # got to check this
-390
-391# initial design ----------
+383# verbose = 0: nothing is printed
+384# verbose = 1: a progress bar is printed (longer than 0)
+385# verbose = 2: information about each iteration is printed (longer than 1)
+386iffunc_argsisNone:
+387func_args=[]
+388
+389if(
+390n_more_iterisNone
+391):# initial optimization, before more iters are requested392
-393if(verbose==1)|(verbose==2):
-394print(f"\n Creating initial design... \n")
-395
-396ifverbose==1:
-397progbar=Progbar(target=self.n_init)
+393n_iter=self.n_iter
+394# stopping iter for early stopping (default is total budget)
+395iter_stop=n_iter# potentially # got to check this
+396
+397# initial design ----------398
-399self.parameters=self.x_init.tolist()
-400self.scores=[]
+399if(verbose==1)|(verbose==2):
+400print(f"\n Creating initial design... \n")401
-402ifself.saveisnotNone:
-403self.update_shelve()
+402ifverbose==1:
+403progbar=Progbar(target=self.n_init)404
-405ifself.y_initisNone:# calculate scores on initial design
-406
-407assert(
-408self.objective_funcisnotNone
-409),"self.y_init is None: must have 'objective_func' not None"
+405self.parameters=self.x_init.tolist()
+406self.scores=[]
+407
+408ifself.saveisnotNone:
+409self.update_shelve()410
-411ifself.n_jobs==1:
+411ifself.y_initisNone:# calculate scores on initial design412
-413foriinrange(self.n_init):
-414
-415x_next=self.x_init[i,:]
+413assert(
+414self.objective_funcisnotNone
+415),"self.y_init is None: must have 'objective_func' not None"416
-417try:
+417ifself.n_jobs==1:418
-419ifself.per_secondisTrue:
+419foriinrange(self.n_init):420
-421start=time()
-422score=self.objective_func(x_next,*func_args)
-423if(np.isfinite(score)==False)or(
-424np.isnan(score)==True
-425):
-426continue
-427self.timings.append(np.log(time()-start))
-428
-429else:# self.per_second is False
-430
-431score=self.objective_func(x_next,*func_args)
-432if(np.isfinite(score)==False)or(
-433np.isnan(score)==True
-434):
-435continue
+421x_next=self.x_init[i,:]
+422
+423try:
+424
+425ifself.per_secondisTrue:
+426
+427start=time()
+428score=self.objective_func(x_next,*func_args)
+429if(np.isfinite(score)==False)or(
+430np.isnan(score)==True
+431):
+432continue
+433self.timings.append(np.log(time()-start))
+434
+435else:# self.per_second is False436
-437self.scores.append(score)
-438
-439ifself.saveisnotNone:
-440self.update_shelve()
-441
-442except:
-443
-444continue
-445
-446ifverbose==1:
-447progbar.update(i)# update progress bar
-448
-449ifverbose==2:
-450print(f"point: {x_next}; score: {score}")
-451# end loop # calculate scores on initial design
-452
-453ifverbose==1:
-454progbar.update(self.n_init)
-455
-456else:# self.n_jobs != 1
-457
-458assert(
-459self.per_secondisFalse
-460),"timings not calculated here"
+437score=self.objective_func(x_next,*func_args)
+438if(np.isfinite(score)==False)or(
+439np.isnan(score)==True
+440):
+441continue
+442
+443self.scores.append(score)
+444
+445ifself.saveisnotNone:
+446self.update_shelve()
+447
+448except:
+449
+450continue
+451
+452ifverbose==1:
+453progbar.update(i)# update progress bar
+454
+455ifverbose==2:
+456print(f"point: {x_next}; score: {score}")
+457# end loop # calculate scores on initial design
+458
+459ifverbose==1:
+460progbar.update(self.n_init)461
-462scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
-463delayed(self.objective_func)(self.x_init[i,:])
-464foriinrange(self.n_init)
-465)
-466
-467self.scores=scores
-468
-469ifself.saveisnotNone:
-470self.update_shelve()
-471
-472else:# if self.y_init is None:
-473
-474assertself.x_init.shape[0]==len(
-475self.y_init
-476),"must have: self.x_init.shape[0] == len(self.y_init)"
+462else:# self.n_jobs != 1
+463
+464assert(
+465self.per_secondisFalse
+466),"timings not calculated here"
+467
+468scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
+469delayed(self.objective_func)(self.x_init[i,:])
+470foriinrange(self.n_init)
+471)
+472
+473self.scores=scores
+474
+475ifself.saveisnotNone:
+476self.update_shelve()477
-478self.scores=pickle.loads(
-479pickle.dumps(self.y_init.tolist(),-1)
-480)
-481
-482# current best score on initial design
-483min_index=(np.asarray(self.scores)).argmin()
-484self.y_min=self.scores[min_index]
-485self.x_min=self.x_init[min_index,:]
-486
-487# current gp mean and std on initial design
-488y_mean,y_std=self.gp_fit_predict(
-489np.asarray(self.parameters),
-490np.asarray(self.scores),
-491self.x_choices,
-492)
-493self.y_mean=y_mean
-494self.y_std=np.maximum(2.220446049250313e-16,y_std)
-495
-496# saving after initial design computation
-497ifself.saveisnotNone:
-498self.update_shelve()
-499
-500else:# if n_more_iter is not None
+478else:# if self.y_init is None:
+479
+480assertself.x_init.shape[0]==len(
+481self.y_init
+482),"must have: self.x_init.shape[0] == len(self.y_init)"
+483
+484self.scores=pickle.loads(
+485pickle.dumps(self.y_init.tolist(),-1)
+486)
+487
+488# current best score on initial design
+489min_index=(np.asarray(self.scores)).argmin()
+490self.y_min=self.scores[min_index]
+491self.x_min=self.x_init[min_index,:]
+492
+493# current gp mean and std on initial design
+494y_mean,y_std=self.gp_fit_predict(
+495np.asarray(self.parameters),
+496np.asarray(self.scores),
+497self.x_choices,
+498)
+499self.y_mean=y_mean
+500self.y_std=np.maximum(2.220446049250313e-16,y_std)501
-502assertself.n_iter>5,"you must have n_iter > 5"
-503n_iter=n_more_iter
-504iter_stop=len(self.max_ei)+n_more_iter# potentially
+502# saving after initial design computation
+503ifself.saveisnotNone:
+504self.update_shelve()505
-506if(verbose==1)|(verbose==2):
-507print(f"\n ...Done. \n")
-508try:
-509print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
-510except:
-511pass
-512
-513# end init design ----------
-514
-515# if n_more_iter is None: # initial optimization, before more iters are requested
-516
-517if(verbose==1)|(verbose==2):
-518print(f"\n Optimization loop... \n")
-519
-520# early stopping?
-521ifabs_tolisnotNone:
-522assert(
-523min_budget>20
-524),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+506else:# if n_more_iter is not None
+507
+508assertself.n_iter>5,"you must have n_iter > 5"
+509n_iter=n_more_iter
+510iter_stop=len(self.max_ei)+n_more_iter# potentially
+511
+512if(verbose==1)|(verbose==2):
+513print(f"\n ...Done. \n")
+514try:
+515print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
+516except:
+517pass
+518
+519# end init design ----------
+520
+521# if n_more_iter is None: # initial optimization, before more iters are requested
+522
+523if(verbose==1)|(verbose==2):
+524print(f"\n Optimization loop... \n")525
-526ifverbose==1:
-527progbar=Progbar(target=n_iter)
-528
-529# main loop ----------
-530
-531foriinrange(n_iter):
-532
-533# find next set of parameters (vector), maximizing ei
-534next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)
-535
-536try:
-537
-538ifself.per_secondisTrue:
-539
-540start=time()
+526# early stopping?
+527ifabs_tolisnotNone:
+528assert(
+529min_budget>20
+530),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+531
+532ifverbose==1:
+533progbar=Progbar(target=n_iter)
+534
+535# main loop ----------
+536
+537foriinrange(n_iter):
+538
+539# find next set of parameters (vector), maximizing ei
+540next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)541
-542ifself.objective_funcisnotNone:
+542try:543
-544score_next_param=self.objective_func(
-545next_param,*func_args
-546)
+544ifself.per_secondisTrue:
+545
+546start=time()547
-548if(np.isfinite(score_next_param)==False)or(
-549np.isnan(score_next_param)==True
-550):
-551continue
-552
-553else:
-554
-555assert(self.x_initisnotNone)and(
-556self.y_initisnotNone
-557),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+548ifself.objective_funcisnotNone:
+549
+550score_next_param=self.objective_func(
+551next_param,*func_args
+552)
+553
+554if(np.isfinite(score_next_param)==False)or(
+555np.isnan(score_next_param)==True
+556):
+557continue558
-559print(f"\n next param: {next_param}\n")
-560score_next_param=float(
-561input("get new score: \n")
-562)# or an API response
-563
-564if(np.isfinite(score_next_param)==False)or(
-565np.isnan(score_next_param)==True
-566):
-567continue
-568
-569self.timings.append(np.log(time()-start))
-570
-571else:# self.per_second is False:
-572
-573ifself.objective_funcisnotNone:
+559else:
+560
+561assert(self.x_initisnotNone)and(
+562self.y_initisnotNone
+563),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+564
+565print(f"\n next param: {next_param}\n")
+566score_next_param=float(
+567input("get new score: \n")
+568)# or an API response
+569
+570if(np.isfinite(score_next_param)==False)or(
+571np.isnan(score_next_param)==True
+572):
+573continue574
-575score_next_param=self.objective_func(
-576next_param,*func_args
-577)
+575self.timings.append(np.log(time()-start))
+576
+577else:# self.per_second is False:578
-579if(np.isfinite(score_next_param)==False)or(
-580np.isnan(score_next_param)==True
-581):
-582continue
-583
-584else:
-585
-586assert(self.x_initisnotNone)and(
-587self.y_initisnotNone
-588),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+579ifself.objective_funcisnotNone:
+580
+581score_next_param=self.objective_func(
+582next_param,*func_args
+583)
+584
+585if(np.isfinite(score_next_param)==False)or(
+586np.isnan(score_next_param)==True
+587):
+588continue589
-590print(f"\n next param: {next_param}\n")
-591score_next_param=float(
-592input("get new score: \n")
-593)# or an API response
-594
-595if(np.isfinite(score_next_param)==False)or(
-596np.isnan(score_next_param)==True
-597):
-598continue
-599
-600except:
-601
-602continue
-603
-604self.parameters.append(next_param.tolist())
+590else:
+591
+592assert(self.x_initisnotNone)and(
+593self.y_initisnotNone
+594),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+595
+596print(f"\n next param: {next_param}\n")
+597score_next_param=float(
+598input("get new score: \n")
+599)# or an API response
+600
+601if(np.isfinite(score_next_param)==False)or(
+602np.isnan(score_next_param)==True
+603):
+604continue605
-606self.scores.append(score_next_param)
+606except:607
-608ifself.saveisnotNone:
-609self.update_shelve()
-610
-611ifverbose==2:
-612print(f"iteration {i+1} -----")
-613print(f"current minimum: {self.x_min}")
-614print(f"current minimum score: {self.y_min}")
-615print(f"next parameter: {next_param}")
-616print(f"score for next parameter: {score_next_param}\n")
-617
-618ifscore_next_param<self.y_min:
-619self.x_min=next_param
-620self.y_min=score_next_param
-621ifself.saveisnotNone:
-622self.update_shelve()
+608continue
+609
+610self.parameters.append(next_param.tolist())
+611
+612self.scores.append(score_next_param)
+613
+614ifself.saveisnotNone:
+615self.update_shelve()
+616
+617ifverbose==2:
+618print(f"iteration {i+1} -----")
+619print(f"current minimum: {self.x_min}")
+620print(f"current minimum score: {self.y_min}")
+621print(f"next parameter: {next_param}")
+622print(f"score for next parameter: {score_next_param}\n")623
-624self.y_mean,self.y_std=self.gp_fit_predict(
-625np.asarray(self.parameters),
-626np.asarray(self.scores),
-627self.x_choices,
-628)
+624ifscore_next_param<self.y_min:
+625self.x_min=next_param
+626self.y_min=score_next_param
+627ifself.saveisnotNone:
+628self.update_shelve()629
-630ifself.saveisnotNone:
-631self.update_shelve()
-632
-633ifverbose==1:
-634progbar.update(i+1)# update progress bar
+630self.y_mean,self.y_std=self.gp_fit_predict(
+631np.asarray(self.parameters),
+632np.asarray(self.scores),
+633self.x_choices,
+634)635
-636# early stopping
-637
-638ifabs_tolisnotNone:
-639
-640# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
-641iflen(self.max_ei)>min_budget:
-642
-643diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))
-644
-645ifdiff_max_ei[-1]<=abs_tol:
-646
-647iter_stop=len(self.max_ei)# index i starts at 0
+636ifself.saveisnotNone:
+637self.update_shelve()
+638
+639ifverbose==1:
+640progbar.update(i+1)# update progress bar
+641
+642# early stopping
+643
+644ifabs_tolisnotNone:
+645
+646# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
+647iflen(self.max_ei)>min_budget:648
-649break
+649diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))650
-651# end main loop ----------
+651ifdiff_max_ei[-1]<=abs_tol:652
-653if(verbose==1)&(i<(n_iter-1)):
-654progbar.update(n_iter)
-655
-656self.n_iter=iter_stop
-657ifself.saveisnotNone:
-658self.update_shelve()
-659
-660return(self.x_min,self.y_min)
+653iter_stop=len(self.max_ei)# index i starts at 0
+654
+655break
+656
+657# end main loop ----------
+658
+659if(verbose==1)&(i<(n_iter-1)):
+660progbar.update(n_iter)
+661
+662self.n_iter=iter_stop
+663ifself.saveisnotNone:
+664self.update_shelve()
+665
+666DescribeResult=namedtuple(
+667"DescribeResult",("best_params","best_score")
+668)
+669
+670ifself.params_namesisNone:
+671
+672returnDescribeResult(self.x_min,self.y_min)
+673
+674else:
+675
+676returnDescribeResult(
+677dict(zip(self.params_names,self.x_min)),self.y_min
+678)
7importnumpyasnp 8importpickle 9importshelve
- 10fromsklearn.gaussian_processimportGaussianProcessRegressor
- 11fromsklearn.ensembleimportRandomForestRegressor
- 12fromsklearn.gaussian_process.kernelsimportMatern
- 13importscipy.statsasst
- 14fromjoblibimportParallel,delayed
- 15fromtimeimporttime
- 16from..utilsimportgenerate_sobol2
- 17from..utilsimportProgbar
- 18
+ 10fromcollectionsimportnamedtuple
+ 11fromsklearn.gaussian_processimportGaussianProcessRegressor
+ 12fromsklearn.ensembleimportRandomForestRegressor
+ 13fromsklearn.gaussian_process.kernelsimportMatern
+ 14importscipy.statsasst
+ 15fromjoblibimportParallel,delayed
+ 16fromtimeimporttime
+ 17from..utilsimportgenerate_sobol2
+ 18from..utilsimportProgbar 19
- 20classGPOpt:
- 21"""Class GPOpt.
- 22
- 23 # Arguments:
- 24
- 25 lower_bound: a numpy array;
- 26 lower bound for researched minimum
- 27
- 28 upper_bound: a numpy array;
- 29 upper bound for researched minimum
- 30
- 31 objective_func: a function;
- 32 the objective function to be minimized
- 33
- 34 gp_obj: a GaussianProcessRegressor object;
- 35 An ML model for estimating the uncertainty around the objective function
- 36
- 37 x_init:
- 38 initial setting of points where `objective_func` is evaluated (optional)
- 39
- 40 y_init:
- 41 initial setting values at points where `objective_func` is evaluated (optional)
- 42
- 43 n_init: an integer;
- 44 number of points in the initial setting, when `x_init` and `y_init` are not provided
- 45
- 46 n_choices: an integer;
- 47 number of points for the calculation of expected improvement
- 48
- 49 n_iter: an integer;
- 50 number of iterations of the minimization algorithm
- 51
- 52 alpha: a float;
- 53 Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
- 54
- 55 n_restarts_optimizer: an integer;
- 56 The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
- 57
- 58 seed: an integer;
- 59 reproducibility seed
- 60
- 61 save: a string;
- 62 Specifies where to save the optimizer in its current state
- 63
- 64 n_jobs: an integer;
- 65 number of jobs for parallel computing on initial setting (can be -1)
- 66
- 67 per_second: a boolean;
- 68 __experimental__, default is False (leave to default for now)
- 69
- 70 log_scale: a boolean;
- 71 __experimental__, default is False (leave to default for now)
- 72
- 73 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
- 74 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
- 75
- 76 """
- 77
- 78def__init__(
- 79self,
- 80lower_bound,
- 81upper_bound,
- 82objective_func=None,
- 83gp_obj=None,
- 84x_init=None,
- 85y_init=None,
- 86n_init=10,
- 87n_choices=25000,
- 88n_iter=190,
- 89alpha=1e-6,
- 90n_restarts_optimizer=25,
- 91seed=123,
- 92save=None,
- 93n_jobs=1,
- 94per_second=False,# /!\ very experimental
- 95log_scale=False,# /!\ experimental
- 96):
- 97
- 98n_dims=len(lower_bound)
- 99
-100assertn_dims==len(
-101upper_bound
-102),"'upper_bound' and 'lower_bound' must have the same dimensions"
-103
-104self.objective_func=objective_func
-105self.lower_bound=lower_bound
-106self.upper_bound=upper_bound
-107self.y_init=y_init
-108self.log_scale=log_scale
-109self.n_dims=n_dims
-110self.n_init=n_init
-111self.n_choices=n_choices
-112self.n_iter=n_iter
-113self.alpha=alpha
-114self.n_restarts_optimizer=n_restarts_optimizer
-115self.seed=seed
-116self.save=save
-117self.per_second=per_second
-118self.x_min=None
-119self.y_min=None
-120self.y_mean=None
-121self.y_std=None
-122self.ei=np.array([])
-123self.max_ei=[]
-124ifgp_objisNone:
-125self.gp_obj=GaussianProcessRegressor(
-126kernel=Matern(nu=2.5),
-127alpha=self.alpha,
-128normalize_y=True,
-129n_restarts_optimizer=self.n_restarts_optimizer,
-130random_state=self.seed,
-131)
-132else:
-133self.gp_obj=gp_obj
-134
-135# Sobol seqs for initial design and choices
-136sobol_seq_init=np.transpose(
-137generate_sobol2(
-138n_dims=self.n_dims,
-139n_points=self.n_init,
-140skip=2,
-141)
-142)
-143sobol_seq_choices=np.transpose(
-144generate_sobol2(
-145n_dims=self.n_dims,
-146n_points=self.n_choices,
-147skip=self.n_init+2,
-148)
-149)
-150
-151# Sobol seqs for initial design and choices with bounds
-152ifself.log_scale==False:
-153
-154bounds_range=upper_bound-lower_bound
-155self.x_init=(
-156bounds_range*sobol_seq_init+lower_bound
-157ifx_initisNone
-158elsex_init
-159)
-160self.x_choices=bounds_range*sobol_seq_choices+lower_bound
-161
-162else:# (!) experimental
-163
-164assert(
-165lower_bound>0
-166).all(),"all elements of `lower_bound` must be > 0"
-167assert(
-168upper_bound>0
-169).all(),"all elements of `upper_bound` must be > 0"
-170
-171log_lower_bound=np.log(lower_bound)
-172log_upper_bound=np.log(upper_bound)
-173log_bounds_range=log_upper_bound-log_lower_bound
-174self.x_init=(
-175np.minimum(
-176np.exp(log_bounds_range*sobol_seq_init+log_lower_bound),
-1771.7976931348623157e308,
-178)
-179ifx_initisNone
-180elsex_init
-181)
-182self.x_choices=np.minimum(
-183np.exp(log_bounds_range*sobol_seq_choices+log_lower_bound),
-1841.7976931348623157e308,
-185)
-186
-187# shelve for saving (not for loading)
-188ifself.saveisnotNone:
-189self.sh=shelve.open(filename=save,flag="c",writeback=True)
-190
-191ifself.per_second:
-192self.timings=[]
-193self.rf_obj=RandomForestRegressor(
-194n_estimators=250,random_state=self.seed
-195)
+ 20
+ 21classGPOpt:
+ 22"""Class GPOpt.
+ 23
+ 24 # Arguments:
+ 25
+ 26 lower_bound: a numpy array;
+ 27 lower bound for researched minimum
+ 28
+ 29 upper_bound: a numpy array;
+ 30 upper bound for researched minimum
+ 31
+ 32 objective_func: a function;
+ 33 the objective function to be minimized
+ 34
+ 35 params_names: a list;
+ 36 names of the parameters of the objective function (optional)
+ 37
+ 38 gp_obj: a GaussianProcessRegressor object;
+ 39 An ML model for estimating the uncertainty around the objective function
+ 40
+ 41 x_init:
+ 42 initial setting of points where `objective_func` is evaluated (optional)
+ 43
+ 44 y_init:
+ 45 initial setting values at points where `objective_func` is evaluated (optional)
+ 46
+ 47 n_init: an integer;
+ 48 number of points in the initial setting, when `x_init` and `y_init` are not provided
+ 49
+ 50 n_choices: an integer;
+ 51 number of points for the calculation of expected improvement
+ 52
+ 53 n_iter: an integer;
+ 54 number of iterations of the minimization algorithm
+ 55
+ 56 alpha: a float;
+ 57 Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
+ 58
+ 59 n_restarts_optimizer: an integer;
+ 60 The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
+ 61
+ 62 seed: an integer;
+ 63 reproducibility seed
+ 64
+ 65 save: a string;
+ 66 Specifies where to save the optimizer in its current state
+ 67
+ 68 n_jobs: an integer;
+ 69 number of jobs for parallel computing on initial setting (can be -1)
+ 70
+ 71 per_second: a boolean;
+ 72 __experimental__, default is False (leave to default for now)
+ 73
+ 74 log_scale: a boolean;
+ 75 __experimental__, default is False (leave to default for now)
+ 76
+ 77 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+ 78 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+ 79
+ 80 """
+ 81
+ 82def__init__(
+ 83self,
+ 84lower_bound,
+ 85upper_bound,
+ 86objective_func=None,
+ 87params_names=None,
+ 88gp_obj=None,
+ 89x_init=None,
+ 90y_init=None,
+ 91n_init=10,
+ 92n_choices=25000,
+ 93n_iter=190,
+ 94alpha=1e-6,
+ 95n_restarts_optimizer=25,
+ 96seed=123,
+ 97save=None,
+ 98n_jobs=1,
+ 99per_second=False,# /!\ very experimental
+100log_scale=False,# /!\ experimental
+101):
+102
+103n_dims=len(lower_bound)
+104
+105assertn_dims==len(
+106upper_bound
+107),"'upper_bound' and 'lower_bound' must have the same dimensions"
+108
+109self.objective_func=objective_func
+110self.params_names=params_names
+111self.lower_bound=lower_bound
+112self.upper_bound=upper_bound
+113self.y_init=y_init
+114self.log_scale=log_scale
+115self.n_dims=n_dims
+116self.n_init=n_init
+117self.n_choices=n_choices
+118self.n_iter=n_iter
+119self.alpha=alpha
+120self.n_restarts_optimizer=n_restarts_optimizer
+121self.seed=seed
+122self.save=save
+123self.per_second=per_second
+124self.x_min=None
+125self.y_min=None
+126self.y_mean=None
+127self.y_std=None
+128self.ei=np.array([])
+129self.max_ei=[]
+130ifgp_objisNone:
+131self.gp_obj=GaussianProcessRegressor(
+132kernel=Matern(nu=2.5),
+133alpha=self.alpha,
+134normalize_y=True,
+135n_restarts_optimizer=self.n_restarts_optimizer,
+136random_state=self.seed,
+137)
+138else:
+139self.gp_obj=gp_obj
+140
+141# Sobol seqs for initial design and choices
+142sobol_seq_init=np.transpose(
+143generate_sobol2(
+144n_dims=self.n_dims,
+145n_points=self.n_init,
+146skip=2,
+147)
+148)
+149sobol_seq_choices=np.transpose(
+150generate_sobol2(
+151n_dims=self.n_dims,
+152n_points=self.n_choices,
+153skip=self.n_init+2,
+154)
+155)
+156
+157# Sobol seqs for initial design and choices with bounds
+158ifself.log_scale==False:
+159
+160bounds_range=upper_bound-lower_bound
+161self.x_init=(
+162bounds_range*sobol_seq_init+lower_bound
+163ifx_initisNone
+164elsex_init
+165)
+166self.x_choices=bounds_range*sobol_seq_choices+lower_bound
+167
+168else:# (!) experimental
+169
+170assert(
+171lower_bound>0
+172).all(),"all elements of `lower_bound` must be > 0"
+173assert(
+174upper_bound>0
+175).all(),"all elements of `upper_bound` must be > 0"
+176
+177log_lower_bound=np.log(lower_bound)
+178log_upper_bound=np.log(upper_bound)
+179log_bounds_range=log_upper_bound-log_lower_bound
+180self.x_init=(
+181np.minimum(
+182np.exp(log_bounds_range*sobol_seq_init+log_lower_bound),
+1831.7976931348623157e308,
+184)
+185ifx_initisNone
+186elsex_init
+187)
+188self.x_choices=np.minimum(
+189np.exp(log_bounds_range*sobol_seq_choices+log_lower_bound),
+1901.7976931348623157e308,
+191)
+192
+193# shelve for saving (not for loading)
+194ifself.saveisnotNone:
+195self.sh=shelve.open(filename=save,flag="c",writeback=True)196
-197self.n_jobs=n_jobs
-198
-199# from sklearn.base
-200defget_params(self):
-201"""Get object attributes.
+197ifself.per_second:
+198self.timings=[]
+199self.rf_obj=RandomForestRegressor(
+200n_estimators=250,random_state=self.seed
+201)202
-203 Returns
-204 -------
-205 params : mapping of string to any
-206 Parameter names mapped to their values.
-207 """
-208out=dict()
-209param_names=dir(self)
-210forkeyinparam_names:
-211ifkey.startswith("_")isFalse:
-212out[key]=getattr(self,key,None)
-213
-214returnout
-215
-216# for parallel case on initial design
-217defeval_objective(self,arg):
-218try:
-219returnself.objective_func(self.x_init[arg,:])
-220except:
-221return1e06
-222
-223# load data from stored shelve
-224defload(self,path):
-225"""load data from stored shelve.
-226
-227 # Arguments
+203self.n_jobs=n_jobs
+204
+205# from sklearn.base
+206defget_params(self):
+207"""Get object attributes.
+208
+209 Returns
+210 -------
+211 params : mapping of string to any
+212 Parameter names mapped to their values.
+213 """
+214out=dict()
+215param_names=dir(self)
+216forkeyinparam_names:
+217ifkey.startswith("_")isFalse:
+218out[key]=getattr(self,key,None)
+219
+220returnout
+221
+222# for parallel case on initial design
+223defeval_objective(self,arg):
+224try:
+225returnself.objective_func(self.x_init[arg,:])
+226except:
+227return1e06228
-229 path : a string; path to stored shelve.
-230
-231 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-232 """
-233
-234self.sh=shelve.open(filename=path)
-235forkey,valueinself.sh.items():
-236setattr(self,key,value)
-237
-238# update shelve in optimization loop
-239defupdate_shelve(self):
-240forkey,valueinself.get_params().items():
-241if(callable(value)isFalse)&(key!="sh"):
-242self.sh[key]=value
-243self.sh.sync()
-244
-245# closing shelve (can't be modified after)
-246defclose_shelve(self):
-247"""Close shelve.
-248
-249 # Arguments
+229# load data from stored shelve
+230defload(self,path):
+231"""load data from stored shelve.
+232
+233 # Arguments
+234
+235 path : a string; path to stored shelve.
+236
+237 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+238 """
+239
+240self.sh=shelve.open(filename=path)
+241forkey,valueinself.sh.items():
+242setattr(self,key,value)
+243
+244# update shelve in optimization loop
+245defupdate_shelve(self):
+246forkey,valueinself.get_params().items():
+247if(callable(value)isFalse)&(key!="sh"):
+248self.sh[key]=value
+249self.sh.sync()250
-251 No argument.
-252
-253 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-254 """
-255
-256self.sh.close()
-257
-258# fit predict
-259defgp_fit_predict(self,X_train,y_train,X_test):
-260
-261iflen(X_train.shape)==1:
-262X_train=X_train.reshape((-1,1))
-263X_test=X_test.reshape((-1,1))
-264
-265# Get mean and standard deviation
-266returnself.gp_obj.fit(X_train,y_train).predict(
-267X_test,return_std=True
-268)
-269
-270# fit predict timings
-271deftimings_fit_predict(self,X_train,y_train,X_test):
-272
-273iflen(X_train.shape)==1:
-274X_train=X_train.reshape((-1,1))
-275X_test=X_test.reshape((-1,1))
-276
-277# Get mean preds for timings
-278returnself.rf_obj.fit(X_train,y_train).predict(X_test)
-279
-280# find next parameter by using expected improvement (ei)
-281defnext_parameter_by_ei(self,seed,i):
+251# closing shelve (can't be modified after)
+252defclose_shelve(self):
+253"""Close shelve.
+254
+255 # Arguments
+256
+257 No argument.
+258
+259 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+260 """
+261
+262self.sh.close()
+263
+264# fit predict
+265defgp_fit_predict(self,X_train,y_train,X_test):
+266
+267iflen(X_train.shape)==1:
+268X_train=X_train.reshape((-1,1))
+269X_test=X_test.reshape((-1,1))
+270
+271# Get mean and standard deviation
+272returnself.gp_obj.fit(X_train,y_train).predict(
+273X_test,return_std=True
+274)
+275
+276# fit predict timings
+277deftimings_fit_predict(self,X_train,y_train,X_test):
+278
+279iflen(X_train.shape)==1:
+280X_train=X_train.reshape((-1,1))
+281X_test=X_test.reshape((-1,1))282
-283gamma_hat=(self.y_min-self.y_mean)/self.y_std
-284
-285self.ei=-self.y_std*(
-286gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
-287)
+283# Get mean preds for timings
+284returnself.rf_obj.fit(X_train,y_train).predict(X_test)
+285
+286# find next parameter by using expected improvement (ei)
+287defnext_parameter_by_ei(self,seed,i):288
-289# find max index -----
+289gamma_hat=(self.y_min-self.y_mean)/self.y_std290
-291ifself.per_secondisFalse:
-292
-293# find index for max. ei
-294max_index=self.ei.argmin()
-295
-296else:# self.per_second is True
-297
-298# predict timings on self.x_choices
-299# train on X = self.parameters and y = self.timings
-300# (must have same shape[0])
-301timing_preds=self.timings_fit_predict(
-302X_train=np.asarray(self.parameters),
-303y_train=np.asarray(self.timings),
-304X_test=self.x_choices,
-305)
-306
-307# find index for max. ei (and min. timings)
-308max_index=(-self.ei/timing_preds).argmax()
-309
-310self.max_ei.append(np.abs(self.ei[max_index]))
-311
-312# Select next choice
-313next_param=self.x_choices[max_index,:]
-314
-315ifnext_paraminnp.asarray(self.parameters):
-316
-317ifself.log_scale==False:
-318
-319np.random.seed(self.seed*i+1000)
-320next_param=(
-321self.upper_bound-self.lower_bound
-322)*np.random.rand(self.n_dims)+self.lower_bound
-323
-324else:# /!\ very... experimental
-325
-326np.random.seed(self.seed)
-327log_upper_bound=np.log(self.upper_bound)
-328log_lower_bound=np.log(self.lower_bound)
-329log_bounds_range=log_upper_bound-log_lower_bound
-330
-331next_param=np.minimum(
-332np.exp(
-333log_bounds_range*np.random.rand(self.n_dims)
-334+log_lower_bound
-335),
-3361.7976931348623157e308,
-337)
-338
-339returnnext_param
-340
-341# optimize the objective
-342defoptimize(
-343self,
-344verbose=1,
-345n_more_iter=None,
-346abs_tol=None,# suggested 1e-4, for n_iter = 200
-347min_budget=50,# minimum budget for early stopping
-348func_args=None,
-349):
-350"""Launch optimization loop.
-351
-352 # Arguments:
-353
-354 verbose: an integer;
-355 verbose = 0: nothing is printed,
-356 verbose = 1: a progress bar is printed (longer than 0),
-357 verbose = 2: information about each iteration is printed (longer than 1)
-358
-359 n_more_iter: an integer;
-360 additional number of iterations for the optimizer (which has been run once)
-361
-362 abs_tol: a float;
-363 tolerance for convergence of the optimizer (early stopping based on expected improvement)
+291self.ei=-self.y_std*(
+292gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
+293)
+294
+295# find max index -----
+296
+297ifself.per_secondisFalse:
+298
+299# find index for max. ei
+300max_index=self.ei.argmin()
+301
+302else:# self.per_second is True
+303
+304# predict timings on self.x_choices
+305# train on X = self.parameters and y = self.timings
+306# (must have same shape[0])
+307timing_preds=self.timings_fit_predict(
+308X_train=np.asarray(self.parameters),
+309y_train=np.asarray(self.timings),
+310X_test=self.x_choices,
+311)
+312
+313# find index for max. ei (and min. timings)
+314max_index=(-self.ei/timing_preds).argmax()
+315
+316self.max_ei.append(np.abs(self.ei[max_index]))
+317
+318# Select next choice
+319next_param=self.x_choices[max_index,:]
+320
+321ifnext_paraminnp.asarray(self.parameters):
+322
+323ifself.log_scale==False:
+324
+325np.random.seed(self.seed*i+1000)
+326next_param=(
+327self.upper_bound-self.lower_bound
+328)*np.random.rand(self.n_dims)+self.lower_bound
+329
+330else:# /!\ very... experimental
+331
+332np.random.seed(self.seed)
+333log_upper_bound=np.log(self.upper_bound)
+334log_lower_bound=np.log(self.lower_bound)
+335log_bounds_range=log_upper_bound-log_lower_bound
+336
+337next_param=np.minimum(
+338np.exp(
+339log_bounds_range*np.random.rand(self.n_dims)
+340+log_lower_bound
+341),
+3421.7976931348623157e308,
+343)
+344
+345returnnext_param
+346
+347# optimize the objective
+348defoptimize(
+349self,
+350verbose=1,
+351n_more_iter=None,
+352abs_tol=None,# suggested 1e-4, for n_iter = 200
+353min_budget=50,# minimum budget for early stopping
+354func_args=None,
+355):
+356"""Launch optimization loop.
+357
+358 # Arguments:
+359
+360 verbose: an integer;
+361 verbose = 0: nothing is printed,
+362 verbose = 1: a progress bar is printed (longer than 0),
+363 verbose = 2: information about each iteration is printed (longer than 1)364
-365 min_budget: an integer (default is 50);
-366 minimum number of iterations before early stopping controlled by `abs_tol`
+365 n_more_iter: an integer;
+366 additional number of iterations for the optimizer (which has been run once)367
-368 func_args: a list;
-369 additional parameters for the objective function (if necessary)
+368 abs_tol: a float;
+369 tolerance for convergence of the optimizer (early stopping based on expected improvement)370
-371 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-372 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+371 min_budget: an integer (default is 50);
+372 minimum number of iterations before early stopping controlled by `abs_tol`373
-374 """
-375
-376# verbose = 0: nothing is printed
-377# verbose = 1: a progress bar is printed (longer than 0)
-378# verbose = 2: information about each iteration is printed (longer than 1)
-379iffunc_argsisNone:
-380func_args=[]
+374 func_args: a list;
+375 additional parameters for the objective function (if necessary)
+376
+377 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+378 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+379
+380 """381
-382if(
-383n_more_iterisNone
-384):# initial optimization, before more iters are requested
-385
-386n_iter=self.n_iter
-387# stopping iter for early stopping (default is total budget)
-388iter_stop=n_iter# potentially # got to check this
-389
-390# initial design ----------
+382# verbose = 0: nothing is printed
+383# verbose = 1: a progress bar is printed (longer than 0)
+384# verbose = 2: information about each iteration is printed (longer than 1)
+385iffunc_argsisNone:
+386func_args=[]
+387
+388if(
+389n_more_iterisNone
+390):# initial optimization, before more iters are requested391
-392if(verbose==1)|(verbose==2):
-393print(f"\n Creating initial design... \n")
-394
-395ifverbose==1:
-396progbar=Progbar(target=self.n_init)
+392n_iter=self.n_iter
+393# stopping iter for early stopping (default is total budget)
+394iter_stop=n_iter# potentially # got to check this
+395
+396# initial design ----------397
-398self.parameters=self.x_init.tolist()
-399self.scores=[]
+398if(verbose==1)|(verbose==2):
+399print(f"\n Creating initial design... \n")400
-401ifself.saveisnotNone:
-402self.update_shelve()
+401ifverbose==1:
+402progbar=Progbar(target=self.n_init)403
-404ifself.y_initisNone:# calculate scores on initial design
-405
-406assert(
-407self.objective_funcisnotNone
-408),"self.y_init is None: must have 'objective_func' not None"
+404self.parameters=self.x_init.tolist()
+405self.scores=[]
+406
+407ifself.saveisnotNone:
+408self.update_shelve()409
-410ifself.n_jobs==1:
+410ifself.y_initisNone:# calculate scores on initial design411
-412foriinrange(self.n_init):
-413
-414x_next=self.x_init[i,:]
+412assert(
+413self.objective_funcisnotNone
+414),"self.y_init is None: must have 'objective_func' not None"415
-416try:
+416ifself.n_jobs==1:417
-418ifself.per_secondisTrue:
+418foriinrange(self.n_init):419
-420start=time()
-421score=self.objective_func(x_next,*func_args)
-422if(np.isfinite(score)==False)or(
-423np.isnan(score)==True
-424):
-425continue
-426self.timings.append(np.log(time()-start))
-427
-428else:# self.per_second is False
-429
-430score=self.objective_func(x_next,*func_args)
-431if(np.isfinite(score)==False)or(
-432np.isnan(score)==True
-433):
-434continue
+420x_next=self.x_init[i,:]
+421
+422try:
+423
+424ifself.per_secondisTrue:
+425
+426start=time()
+427score=self.objective_func(x_next,*func_args)
+428if(np.isfinite(score)==False)or(
+429np.isnan(score)==True
+430):
+431continue
+432self.timings.append(np.log(time()-start))
+433
+434else:# self.per_second is False435
-436self.scores.append(score)
-437
-438ifself.saveisnotNone:
-439self.update_shelve()
-440
-441except:
-442
-443continue
-444
-445ifverbose==1:
-446progbar.update(i)# update progress bar
-447
-448ifverbose==2:
-449print(f"point: {x_next}; score: {score}")
-450# end loop # calculate scores on initial design
-451
-452ifverbose==1:
-453progbar.update(self.n_init)
-454
-455else:# self.n_jobs != 1
-456
-457assert(
-458self.per_secondisFalse
-459),"timings not calculated here"
+436score=self.objective_func(x_next,*func_args)
+437if(np.isfinite(score)==False)or(
+438np.isnan(score)==True
+439):
+440continue
+441
+442self.scores.append(score)
+443
+444ifself.saveisnotNone:
+445self.update_shelve()
+446
+447except:
+448
+449continue
+450
+451ifverbose==1:
+452progbar.update(i)# update progress bar
+453
+454ifverbose==2:
+455print(f"point: {x_next}; score: {score}")
+456# end loop # calculate scores on initial design
+457
+458ifverbose==1:
+459progbar.update(self.n_init)460
-461scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
-462delayed(self.objective_func)(self.x_init[i,:])
-463foriinrange(self.n_init)
-464)
-465
-466self.scores=scores
-467
-468ifself.saveisnotNone:
-469self.update_shelve()
-470
-471else:# if self.y_init is None:
-472
-473assertself.x_init.shape[0]==len(
-474self.y_init
-475),"must have: self.x_init.shape[0] == len(self.y_init)"
+461else:# self.n_jobs != 1
+462
+463assert(
+464self.per_secondisFalse
+465),"timings not calculated here"
+466
+467scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
+468delayed(self.objective_func)(self.x_init[i,:])
+469foriinrange(self.n_init)
+470)
+471
+472self.scores=scores
+473
+474ifself.saveisnotNone:
+475self.update_shelve()476
-477self.scores=pickle.loads(
-478pickle.dumps(self.y_init.tolist(),-1)
-479)
-480
-481# current best score on initial design
-482min_index=(np.asarray(self.scores)).argmin()
-483self.y_min=self.scores[min_index]
-484self.x_min=self.x_init[min_index,:]
-485
-486# current gp mean and std on initial design
-487y_mean,y_std=self.gp_fit_predict(
-488np.asarray(self.parameters),
-489np.asarray(self.scores),
-490self.x_choices,
-491)
-492self.y_mean=y_mean
-493self.y_std=np.maximum(2.220446049250313e-16,y_std)
-494
-495# saving after initial design computation
-496ifself.saveisnotNone:
-497self.update_shelve()
-498
-499else:# if n_more_iter is not None
+477else:# if self.y_init is None:
+478
+479assertself.x_init.shape[0]==len(
+480self.y_init
+481),"must have: self.x_init.shape[0] == len(self.y_init)"
+482
+483self.scores=pickle.loads(
+484pickle.dumps(self.y_init.tolist(),-1)
+485)
+486
+487# current best score on initial design
+488min_index=(np.asarray(self.scores)).argmin()
+489self.y_min=self.scores[min_index]
+490self.x_min=self.x_init[min_index,:]
+491
+492# current gp mean and std on initial design
+493y_mean,y_std=self.gp_fit_predict(
+494np.asarray(self.parameters),
+495np.asarray(self.scores),
+496self.x_choices,
+497)
+498self.y_mean=y_mean
+499self.y_std=np.maximum(2.220446049250313e-16,y_std)500
-501assertself.n_iter>5,"you must have n_iter > 5"
-502n_iter=n_more_iter
-503iter_stop=len(self.max_ei)+n_more_iter# potentially
+501# saving after initial design computation
+502ifself.saveisnotNone:
+503self.update_shelve()504
-505if(verbose==1)|(verbose==2):
-506print(f"\n ...Done. \n")
-507try:
-508print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
-509except:
-510pass
-511
-512# end init design ----------
-513
-514# if n_more_iter is None: # initial optimization, before more iters are requested
-515
-516if(verbose==1)|(verbose==2):
-517print(f"\n Optimization loop... \n")
-518
-519# early stopping?
-520ifabs_tolisnotNone:
-521assert(
-522min_budget>20
-523),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+505else:# if n_more_iter is not None
+506
+507assertself.n_iter>5,"you must have n_iter > 5"
+508n_iter=n_more_iter
+509iter_stop=len(self.max_ei)+n_more_iter# potentially
+510
+511if(verbose==1)|(verbose==2):
+512print(f"\n ...Done. \n")
+513try:
+514print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
+515except:
+516pass
+517
+518# end init design ----------
+519
+520# if n_more_iter is None: # initial optimization, before more iters are requested
+521
+522if(verbose==1)|(verbose==2):
+523print(f"\n Optimization loop... \n")524
-525ifverbose==1:
-526progbar=Progbar(target=n_iter)
-527
-528# main loop ----------
-529
-530foriinrange(n_iter):
-531
-532# find next set of parameters (vector), maximizing ei
-533next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)
-534
-535try:
-536
-537ifself.per_secondisTrue:
-538
-539start=time()
+525# early stopping?
+526ifabs_tolisnotNone:
+527assert(
+528min_budget>20
+529),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+530
+531ifverbose==1:
+532progbar=Progbar(target=n_iter)
+533
+534# main loop ----------
+535
+536foriinrange(n_iter):
+537
+538# find next set of parameters (vector), maximizing ei
+539next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)540
-541ifself.objective_funcisnotNone:
+541try:542
-543score_next_param=self.objective_func(
-544next_param,*func_args
-545)
+543ifself.per_secondisTrue:
+544
+545start=time()546
-547if(np.isfinite(score_next_param)==False)or(
-548np.isnan(score_next_param)==True
-549):
-550continue
-551
-552else:
-553
-554assert(self.x_initisnotNone)and(
-555self.y_initisnotNone
-556),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+547ifself.objective_funcisnotNone:
+548
+549score_next_param=self.objective_func(
+550next_param,*func_args
+551)
+552
+553if(np.isfinite(score_next_param)==False)or(
+554np.isnan(score_next_param)==True
+555):
+556continue557
-558print(f"\n next param: {next_param}\n")
-559score_next_param=float(
-560input("get new score: \n")
-561)# or an API response
-562
-563if(np.isfinite(score_next_param)==False)or(
-564np.isnan(score_next_param)==True
-565):
-566continue
-567
-568self.timings.append(np.log(time()-start))
-569
-570else:# self.per_second is False:
-571
-572ifself.objective_funcisnotNone:
+558else:
+559
+560assert(self.x_initisnotNone)and(
+561self.y_initisnotNone
+562),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+563
+564print(f"\n next param: {next_param}\n")
+565score_next_param=float(
+566input("get new score: \n")
+567)# or an API response
+568
+569if(np.isfinite(score_next_param)==False)or(
+570np.isnan(score_next_param)==True
+571):
+572continue573
-574score_next_param=self.objective_func(
-575next_param,*func_args
-576)
+574self.timings.append(np.log(time()-start))
+575
+576else:# self.per_second is False:577
-578if(np.isfinite(score_next_param)==False)or(
-579np.isnan(score_next_param)==True
-580):
-581continue
-582
-583else:
-584
-585assert(self.x_initisnotNone)and(
-586self.y_initisnotNone
-587),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+578ifself.objective_funcisnotNone:
+579
+580score_next_param=self.objective_func(
+581next_param,*func_args
+582)
+583
+584if(np.isfinite(score_next_param)==False)or(
+585np.isnan(score_next_param)==True
+586):
+587continue588
-589print(f"\n next param: {next_param}\n")
-590score_next_param=float(
-591input("get new score: \n")
-592)# or an API response
-593
-594if(np.isfinite(score_next_param)==False)or(
-595np.isnan(score_next_param)==True
-596):
-597continue
-598
-599except:
-600
-601continue
-602
-603self.parameters.append(next_param.tolist())
+589else:
+590
+591assert(self.x_initisnotNone)and(
+592self.y_initisnotNone
+593),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+594
+595print(f"\n next param: {next_param}\n")
+596score_next_param=float(
+597input("get new score: \n")
+598)# or an API response
+599
+600if(np.isfinite(score_next_param)==False)or(
+601np.isnan(score_next_param)==True
+602):
+603continue604
-605self.scores.append(score_next_param)
+605except:606
-607ifself.saveisnotNone:
-608self.update_shelve()
-609
-610ifverbose==2:
-611print(f"iteration {i+1} -----")
-612print(f"current minimum: {self.x_min}")
-613print(f"current minimum score: {self.y_min}")
-614print(f"next parameter: {next_param}")
-615print(f"score for next parameter: {score_next_param}\n")
-616
-617ifscore_next_param<self.y_min:
-618self.x_min=next_param
-619self.y_min=score_next_param
-620ifself.saveisnotNone:
-621self.update_shelve()
+607continue
+608
+609self.parameters.append(next_param.tolist())
+610
+611self.scores.append(score_next_param)
+612
+613ifself.saveisnotNone:
+614self.update_shelve()
+615
+616ifverbose==2:
+617print(f"iteration {i+1} -----")
+618print(f"current minimum: {self.x_min}")
+619print(f"current minimum score: {self.y_min}")
+620print(f"next parameter: {next_param}")
+621print(f"score for next parameter: {score_next_param}\n")622
-623self.y_mean,self.y_std=self.gp_fit_predict(
-624np.asarray(self.parameters),
-625np.asarray(self.scores),
-626self.x_choices,
-627)
+623ifscore_next_param<self.y_min:
+624self.x_min=next_param
+625self.y_min=score_next_param
+626ifself.saveisnotNone:
+627self.update_shelve()628
-629ifself.saveisnotNone:
-630self.update_shelve()
-631
-632ifverbose==1:
-633progbar.update(i+1)# update progress bar
+629self.y_mean,self.y_std=self.gp_fit_predict(
+630np.asarray(self.parameters),
+631np.asarray(self.scores),
+632self.x_choices,
+633)634
-635# early stopping
-636
-637ifabs_tolisnotNone:
-638
-639# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
-640iflen(self.max_ei)>min_budget:
-641
-642diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))
-643
-644ifdiff_max_ei[-1]<=abs_tol:
-645
-646iter_stop=len(self.max_ei)# index i starts at 0
+635ifself.saveisnotNone:
+636self.update_shelve()
+637
+638ifverbose==1:
+639progbar.update(i+1)# update progress bar
+640
+641# early stopping
+642
+643ifabs_tolisnotNone:
+644
+645# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
+646iflen(self.max_ei)>min_budget:647
-648break
+648diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))649
-650# end main loop ----------
+650ifdiff_max_ei[-1]<=abs_tol:651
-652if(verbose==1)&(i<(n_iter-1)):
-653progbar.update(n_iter)
-654
-655self.n_iter=iter_stop
-656ifself.saveisnotNone:
-657self.update_shelve()
-658
-659return(self.x_min,self.y_min)
+652iter_stop=len(self.max_ei)# index i starts at 0
+653
+654break
+655
+656# end main loop ----------
+657
+658if(verbose==1)&(i<(n_iter-1)):
+659progbar.update(n_iter)
+660
+661self.n_iter=iter_stop
+662ifself.saveisnotNone:
+663self.update_shelve()
+664
+665DescribeResult=namedtuple(
+666"DescribeResult",("best_params","best_score")
+667)
+668
+669ifself.params_namesisNone:
+670
+671returnDescribeResult(self.x_min,self.y_min)
+672
+673else:
+674
+675returnDescribeResult(
+676dict(zip(self.params_names,self.x_min)),self.y_min
+677)
@@ -826,646 +847,663 @@
-
21classGPOpt:
- 22"""Class GPOpt.
- 23
- 24 # Arguments:
- 25
- 26 lower_bound: a numpy array;
- 27 lower bound for researched minimum
- 28
- 29 upper_bound: a numpy array;
- 30 upper bound for researched minimum
- 31
- 32 objective_func: a function;
- 33 the objective function to be minimized
- 34
- 35 gp_obj: a GaussianProcessRegressor object;
- 36 An ML model for estimating the uncertainty around the objective function
- 37
- 38 x_init:
- 39 initial setting of points where `objective_func` is evaluated (optional)
- 40
- 41 y_init:
- 42 initial setting values at points where `objective_func` is evaluated (optional)
- 43
- 44 n_init: an integer;
- 45 number of points in the initial setting, when `x_init` and `y_init` are not provided
- 46
- 47 n_choices: an integer;
- 48 number of points for the calculation of expected improvement
- 49
- 50 n_iter: an integer;
- 51 number of iterations of the minimization algorithm
- 52
- 53 alpha: a float;
- 54 Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
- 55
- 56 n_restarts_optimizer: an integer;
- 57 The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
- 58
- 59 seed: an integer;
- 60 reproducibility seed
- 61
- 62 save: a string;
- 63 Specifies where to save the optimizer in its current state
- 64
- 65 n_jobs: an integer;
- 66 number of jobs for parallel computing on initial setting (can be -1)
- 67
- 68 per_second: a boolean;
- 69 __experimental__, default is False (leave to default for now)
- 70
- 71 log_scale: a boolean;
- 72 __experimental__, default is False (leave to default for now)
- 73
- 74 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
- 75 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
- 76
- 77 """
- 78
- 79def__init__(
- 80self,
- 81lower_bound,
- 82upper_bound,
- 83objective_func=None,
- 84gp_obj=None,
- 85x_init=None,
- 86y_init=None,
- 87n_init=10,
- 88n_choices=25000,
- 89n_iter=190,
- 90alpha=1e-6,
- 91n_restarts_optimizer=25,
- 92seed=123,
- 93save=None,
- 94n_jobs=1,
- 95per_second=False,# /!\ very experimental
- 96log_scale=False,# /!\ experimental
- 97):
- 98
- 99n_dims=len(lower_bound)
-100
-101assertn_dims==len(
-102upper_bound
-103),"'upper_bound' and 'lower_bound' must have the same dimensions"
-104
-105self.objective_func=objective_func
-106self.lower_bound=lower_bound
-107self.upper_bound=upper_bound
-108self.y_init=y_init
-109self.log_scale=log_scale
-110self.n_dims=n_dims
-111self.n_init=n_init
-112self.n_choices=n_choices
-113self.n_iter=n_iter
-114self.alpha=alpha
-115self.n_restarts_optimizer=n_restarts_optimizer
-116self.seed=seed
-117self.save=save
-118self.per_second=per_second
-119self.x_min=None
-120self.y_min=None
-121self.y_mean=None
-122self.y_std=None
-123self.ei=np.array([])
-124self.max_ei=[]
-125ifgp_objisNone:
-126self.gp_obj=GaussianProcessRegressor(
-127kernel=Matern(nu=2.5),
-128alpha=self.alpha,
-129normalize_y=True,
-130n_restarts_optimizer=self.n_restarts_optimizer,
-131random_state=self.seed,
-132)
-133else:
-134self.gp_obj=gp_obj
-135
-136# Sobol seqs for initial design and choices
-137sobol_seq_init=np.transpose(
-138generate_sobol2(
-139n_dims=self.n_dims,
-140n_points=self.n_init,
-141skip=2,
-142)
-143)
-144sobol_seq_choices=np.transpose(
-145generate_sobol2(
-146n_dims=self.n_dims,
-147n_points=self.n_choices,
-148skip=self.n_init+2,
-149)
-150)
-151
-152# Sobol seqs for initial design and choices with bounds
-153ifself.log_scale==False:
-154
-155bounds_range=upper_bound-lower_bound
-156self.x_init=(
-157bounds_range*sobol_seq_init+lower_bound
-158ifx_initisNone
-159elsex_init
-160)
-161self.x_choices=bounds_range*sobol_seq_choices+lower_bound
-162
-163else:# (!) experimental
-164
-165assert(
-166lower_bound>0
-167).all(),"all elements of `lower_bound` must be > 0"
-168assert(
-169upper_bound>0
-170).all(),"all elements of `upper_bound` must be > 0"
-171
-172log_lower_bound=np.log(lower_bound)
-173log_upper_bound=np.log(upper_bound)
-174log_bounds_range=log_upper_bound-log_lower_bound
-175self.x_init=(
-176np.minimum(
-177np.exp(log_bounds_range*sobol_seq_init+log_lower_bound),
-1781.7976931348623157e308,
-179)
-180ifx_initisNone
-181elsex_init
-182)
-183self.x_choices=np.minimum(
-184np.exp(log_bounds_range*sobol_seq_choices+log_lower_bound),
-1851.7976931348623157e308,
-186)
-187
-188# shelve for saving (not for loading)
-189ifself.saveisnotNone:
-190self.sh=shelve.open(filename=save,flag="c",writeback=True)
-191
-192ifself.per_second:
-193self.timings=[]
-194self.rf_obj=RandomForestRegressor(
-195n_estimators=250,random_state=self.seed
-196)
+
22classGPOpt:
+ 23"""Class GPOpt.
+ 24
+ 25 # Arguments:
+ 26
+ 27 lower_bound: a numpy array;
+ 28 lower bound for researched minimum
+ 29
+ 30 upper_bound: a numpy array;
+ 31 upper bound for researched minimum
+ 32
+ 33 objective_func: a function;
+ 34 the objective function to be minimized
+ 35
+ 36 params_names: a list;
+ 37 names of the parameters of the objective function (optional)
+ 38
+ 39 gp_obj: a GaussianProcessRegressor object;
+ 40 An ML model for estimating the uncertainty around the objective function
+ 41
+ 42 x_init:
+ 43 initial setting of points where `objective_func` is evaluated (optional)
+ 44
+ 45 y_init:
+ 46 initial setting values at points where `objective_func` is evaluated (optional)
+ 47
+ 48 n_init: an integer;
+ 49 number of points in the initial setting, when `x_init` and `y_init` are not provided
+ 50
+ 51 n_choices: an integer;
+ 52 number of points for the calculation of expected improvement
+ 53
+ 54 n_iter: an integer;
+ 55 number of iterations of the minimization algorithm
+ 56
+ 57 alpha: a float;
+ 58 Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)
+ 59
+ 60 n_restarts_optimizer: an integer;
+ 61 The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood.
+ 62
+ 63 seed: an integer;
+ 64 reproducibility seed
+ 65
+ 66 save: a string;
+ 67 Specifies where to save the optimizer in its current state
+ 68
+ 69 n_jobs: an integer;
+ 70 number of jobs for parallel computing on initial setting (can be -1)
+ 71
+ 72 per_second: a boolean;
+ 73 __experimental__, default is False (leave to default for now)
+ 74
+ 75 log_scale: a boolean;
+ 76 __experimental__, default is False (leave to default for now)
+ 77
+ 78 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+ 79 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+ 80
+ 81 """
+ 82
+ 83def__init__(
+ 84self,
+ 85lower_bound,
+ 86upper_bound,
+ 87objective_func=None,
+ 88params_names=None,
+ 89gp_obj=None,
+ 90x_init=None,
+ 91y_init=None,
+ 92n_init=10,
+ 93n_choices=25000,
+ 94n_iter=190,
+ 95alpha=1e-6,
+ 96n_restarts_optimizer=25,
+ 97seed=123,
+ 98save=None,
+ 99n_jobs=1,
+100per_second=False,# /!\ very experimental
+101log_scale=False,# /!\ experimental
+102):
+103
+104n_dims=len(lower_bound)
+105
+106assertn_dims==len(
+107upper_bound
+108),"'upper_bound' and 'lower_bound' must have the same dimensions"
+109
+110self.objective_func=objective_func
+111self.params_names=params_names
+112self.lower_bound=lower_bound
+113self.upper_bound=upper_bound
+114self.y_init=y_init
+115self.log_scale=log_scale
+116self.n_dims=n_dims
+117self.n_init=n_init
+118self.n_choices=n_choices
+119self.n_iter=n_iter
+120self.alpha=alpha
+121self.n_restarts_optimizer=n_restarts_optimizer
+122self.seed=seed
+123self.save=save
+124self.per_second=per_second
+125self.x_min=None
+126self.y_min=None
+127self.y_mean=None
+128self.y_std=None
+129self.ei=np.array([])
+130self.max_ei=[]
+131ifgp_objisNone:
+132self.gp_obj=GaussianProcessRegressor(
+133kernel=Matern(nu=2.5),
+134alpha=self.alpha,
+135normalize_y=True,
+136n_restarts_optimizer=self.n_restarts_optimizer,
+137random_state=self.seed,
+138)
+139else:
+140self.gp_obj=gp_obj
+141
+142# Sobol seqs for initial design and choices
+143sobol_seq_init=np.transpose(
+144generate_sobol2(
+145n_dims=self.n_dims,
+146n_points=self.n_init,
+147skip=2,
+148)
+149)
+150sobol_seq_choices=np.transpose(
+151generate_sobol2(
+152n_dims=self.n_dims,
+153n_points=self.n_choices,
+154skip=self.n_init+2,
+155)
+156)
+157
+158# Sobol seqs for initial design and choices with bounds
+159ifself.log_scale==False:
+160
+161bounds_range=upper_bound-lower_bound
+162self.x_init=(
+163bounds_range*sobol_seq_init+lower_bound
+164ifx_initisNone
+165elsex_init
+166)
+167self.x_choices=bounds_range*sobol_seq_choices+lower_bound
+168
+169else:# (!) experimental
+170
+171assert(
+172lower_bound>0
+173).all(),"all elements of `lower_bound` must be > 0"
+174assert(
+175upper_bound>0
+176).all(),"all elements of `upper_bound` must be > 0"
+177
+178log_lower_bound=np.log(lower_bound)
+179log_upper_bound=np.log(upper_bound)
+180log_bounds_range=log_upper_bound-log_lower_bound
+181self.x_init=(
+182np.minimum(
+183np.exp(log_bounds_range*sobol_seq_init+log_lower_bound),
+1841.7976931348623157e308,
+185)
+186ifx_initisNone
+187elsex_init
+188)
+189self.x_choices=np.minimum(
+190np.exp(log_bounds_range*sobol_seq_choices+log_lower_bound),
+1911.7976931348623157e308,
+192)
+193
+194# shelve for saving (not for loading)
+195ifself.saveisnotNone:
+196self.sh=shelve.open(filename=save,flag="c",writeback=True)197
-198self.n_jobs=n_jobs
-199
-200# from sklearn.base
-201defget_params(self):
-202"""Get object attributes.
+198ifself.per_second:
+199self.timings=[]
+200self.rf_obj=RandomForestRegressor(
+201n_estimators=250,random_state=self.seed
+202)203
-204 Returns
-205 -------
-206 params : mapping of string to any
-207 Parameter names mapped to their values.
-208 """
-209out=dict()
-210param_names=dir(self)
-211forkeyinparam_names:
-212ifkey.startswith("_")isFalse:
-213out[key]=getattr(self,key,None)
-214
-215returnout
-216
-217# for parallel case on initial design
-218defeval_objective(self,arg):
-219try:
-220returnself.objective_func(self.x_init[arg,:])
-221except:
-222return1e06
-223
-224# load data from stored shelve
-225defload(self,path):
-226"""load data from stored shelve.
-227
-228 # Arguments
+204self.n_jobs=n_jobs
+205
+206# from sklearn.base
+207defget_params(self):
+208"""Get object attributes.
+209
+210 Returns
+211 -------
+212 params : mapping of string to any
+213 Parameter names mapped to their values.
+214 """
+215out=dict()
+216param_names=dir(self)
+217forkeyinparam_names:
+218ifkey.startswith("_")isFalse:
+219out[key]=getattr(self,key,None)
+220
+221returnout
+222
+223# for parallel case on initial design
+224defeval_objective(self,arg):
+225try:
+226returnself.objective_func(self.x_init[arg,:])
+227except:
+228return1e06229
-230 path : a string; path to stored shelve.
-231
-232 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-233 """
-234
-235self.sh=shelve.open(filename=path)
-236forkey,valueinself.sh.items():
-237setattr(self,key,value)
-238
-239# update shelve in optimization loop
-240defupdate_shelve(self):
-241forkey,valueinself.get_params().items():
-242if(callable(value)isFalse)&(key!="sh"):
-243self.sh[key]=value
-244self.sh.sync()
-245
-246# closing shelve (can't be modified after)
-247defclose_shelve(self):
-248"""Close shelve.
-249
-250 # Arguments
+230# load data from stored shelve
+231defload(self,path):
+232"""load data from stored shelve.
+233
+234 # Arguments
+235
+236 path : a string; path to stored shelve.
+237
+238 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+239 """
+240
+241self.sh=shelve.open(filename=path)
+242forkey,valueinself.sh.items():
+243setattr(self,key,value)
+244
+245# update shelve in optimization loop
+246defupdate_shelve(self):
+247forkey,valueinself.get_params().items():
+248if(callable(value)isFalse)&(key!="sh"):
+249self.sh[key]=value
+250self.sh.sync()251
-252 No argument.
-253
-254 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-255 """
-256
-257self.sh.close()
-258
-259# fit predict
-260defgp_fit_predict(self,X_train,y_train,X_test):
-261
-262iflen(X_train.shape)==1:
-263X_train=X_train.reshape((-1,1))
-264X_test=X_test.reshape((-1,1))
-265
-266# Get mean and standard deviation
-267returnself.gp_obj.fit(X_train,y_train).predict(
-268X_test,return_std=True
-269)
-270
-271# fit predict timings
-272deftimings_fit_predict(self,X_train,y_train,X_test):
-273
-274iflen(X_train.shape)==1:
-275X_train=X_train.reshape((-1,1))
-276X_test=X_test.reshape((-1,1))
-277
-278# Get mean preds for timings
-279returnself.rf_obj.fit(X_train,y_train).predict(X_test)
-280
-281# find next parameter by using expected improvement (ei)
-282defnext_parameter_by_ei(self,seed,i):
+252# closing shelve (can't be modified after)
+253defclose_shelve(self):
+254"""Close shelve.
+255
+256 # Arguments
+257
+258 No argument.
+259
+260 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+261 """
+262
+263self.sh.close()
+264
+265# fit predict
+266defgp_fit_predict(self,X_train,y_train,X_test):
+267
+268iflen(X_train.shape)==1:
+269X_train=X_train.reshape((-1,1))
+270X_test=X_test.reshape((-1,1))
+271
+272# Get mean and standard deviation
+273returnself.gp_obj.fit(X_train,y_train).predict(
+274X_test,return_std=True
+275)
+276
+277# fit predict timings
+278deftimings_fit_predict(self,X_train,y_train,X_test):
+279
+280iflen(X_train.shape)==1:
+281X_train=X_train.reshape((-1,1))
+282X_test=X_test.reshape((-1,1))283
-284gamma_hat=(self.y_min-self.y_mean)/self.y_std
-285
-286self.ei=-self.y_std*(
-287gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
-288)
+284# Get mean preds for timings
+285returnself.rf_obj.fit(X_train,y_train).predict(X_test)
+286
+287# find next parameter by using expected improvement (ei)
+288defnext_parameter_by_ei(self,seed,i):289
-290# find max index -----
+290gamma_hat=(self.y_min-self.y_mean)/self.y_std291
-292ifself.per_secondisFalse:
-293
-294# find index for max. ei
-295max_index=self.ei.argmin()
-296
-297else:# self.per_second is True
-298
-299# predict timings on self.x_choices
-300# train on X = self.parameters and y = self.timings
-301# (must have same shape[0])
-302timing_preds=self.timings_fit_predict(
-303X_train=np.asarray(self.parameters),
-304y_train=np.asarray(self.timings),
-305X_test=self.x_choices,
-306)
-307
-308# find index for max. ei (and min. timings)
-309max_index=(-self.ei/timing_preds).argmax()
-310
-311self.max_ei.append(np.abs(self.ei[max_index]))
-312
-313# Select next choice
-314next_param=self.x_choices[max_index,:]
-315
-316ifnext_paraminnp.asarray(self.parameters):
-317
-318ifself.log_scale==False:
-319
-320np.random.seed(self.seed*i+1000)
-321next_param=(
-322self.upper_bound-self.lower_bound
-323)*np.random.rand(self.n_dims)+self.lower_bound
-324
-325else:# /!\ very... experimental
-326
-327np.random.seed(self.seed)
-328log_upper_bound=np.log(self.upper_bound)
-329log_lower_bound=np.log(self.lower_bound)
-330log_bounds_range=log_upper_bound-log_lower_bound
-331
-332next_param=np.minimum(
-333np.exp(
-334log_bounds_range*np.random.rand(self.n_dims)
-335+log_lower_bound
-336),
-3371.7976931348623157e308,
-338)
-339
-340returnnext_param
-341
-342# optimize the objective
-343defoptimize(
-344self,
-345verbose=1,
-346n_more_iter=None,
-347abs_tol=None,# suggested 1e-4, for n_iter = 200
-348min_budget=50,# minimum budget for early stopping
-349func_args=None,
-350):
-351"""Launch optimization loop.
-352
-353 # Arguments:
-354
-355 verbose: an integer;
-356 verbose = 0: nothing is printed,
-357 verbose = 1: a progress bar is printed (longer than 0),
-358 verbose = 2: information about each iteration is printed (longer than 1)
-359
-360 n_more_iter: an integer;
-361 additional number of iterations for the optimizer (which has been run once)
-362
-363 abs_tol: a float;
-364 tolerance for convergence of the optimizer (early stopping based on expected improvement)
+292self.ei=-self.y_std*(
+293gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
+294)
+295
+296# find max index -----
+297
+298ifself.per_secondisFalse:
+299
+300# find index for max. ei
+301max_index=self.ei.argmin()
+302
+303else:# self.per_second is True
+304
+305# predict timings on self.x_choices
+306# train on X = self.parameters and y = self.timings
+307# (must have same shape[0])
+308timing_preds=self.timings_fit_predict(
+309X_train=np.asarray(self.parameters),
+310y_train=np.asarray(self.timings),
+311X_test=self.x_choices,
+312)
+313
+314# find index for max. ei (and min. timings)
+315max_index=(-self.ei/timing_preds).argmax()
+316
+317self.max_ei.append(np.abs(self.ei[max_index]))
+318
+319# Select next choice
+320next_param=self.x_choices[max_index,:]
+321
+322ifnext_paraminnp.asarray(self.parameters):
+323
+324ifself.log_scale==False:
+325
+326np.random.seed(self.seed*i+1000)
+327next_param=(
+328self.upper_bound-self.lower_bound
+329)*np.random.rand(self.n_dims)+self.lower_bound
+330
+331else:# /!\ very... experimental
+332
+333np.random.seed(self.seed)
+334log_upper_bound=np.log(self.upper_bound)
+335log_lower_bound=np.log(self.lower_bound)
+336log_bounds_range=log_upper_bound-log_lower_bound
+337
+338next_param=np.minimum(
+339np.exp(
+340log_bounds_range*np.random.rand(self.n_dims)
+341+log_lower_bound
+342),
+3431.7976931348623157e308,
+344)
+345
+346returnnext_param
+347
+348# optimize the objective
+349defoptimize(
+350self,
+351verbose=1,
+352n_more_iter=None,
+353abs_tol=None,# suggested 1e-4, for n_iter = 200
+354min_budget=50,# minimum budget for early stopping
+355func_args=None,
+356):
+357"""Launch optimization loop.
+358
+359 # Arguments:
+360
+361 verbose: an integer;
+362 verbose = 0: nothing is printed,
+363 verbose = 1: a progress bar is printed (longer than 0),
+364 verbose = 2: information about each iteration is printed (longer than 1)365
-366 min_budget: an integer (default is 50);
-367 minimum number of iterations before early stopping controlled by `abs_tol`
+366 n_more_iter: an integer;
+367 additional number of iterations for the optimizer (which has been run once)368
-369 func_args: a list;
-370 additional parameters for the objective function (if necessary)
+369 abs_tol: a float;
+370 tolerance for convergence of the optimizer (early stopping based on expected improvement)371
-372 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-373 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+372 min_budget: an integer (default is 50);
+373 minimum number of iterations before early stopping controlled by `abs_tol`374
-375 """
-376
-377# verbose = 0: nothing is printed
-378# verbose = 1: a progress bar is printed (longer than 0)
-379# verbose = 2: information about each iteration is printed (longer than 1)
-380iffunc_argsisNone:
-381func_args=[]
+375 func_args: a list;
+376 additional parameters for the objective function (if necessary)
+377
+378 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+379 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+380
+381 """382
-383if(
-384n_more_iterisNone
-385):# initial optimization, before more iters are requested
-386
-387n_iter=self.n_iter
-388# stopping iter for early stopping (default is total budget)
-389iter_stop=n_iter# potentially # got to check this
-390
-391# initial design ----------
+383# verbose = 0: nothing is printed
+384# verbose = 1: a progress bar is printed (longer than 0)
+385# verbose = 2: information about each iteration is printed (longer than 1)
+386iffunc_argsisNone:
+387func_args=[]
+388
+389if(
+390n_more_iterisNone
+391):# initial optimization, before more iters are requested392
-393if(verbose==1)|(verbose==2):
-394print(f"\n Creating initial design... \n")
-395
-396ifverbose==1:
-397progbar=Progbar(target=self.n_init)
+393n_iter=self.n_iter
+394# stopping iter for early stopping (default is total budget)
+395iter_stop=n_iter# potentially # got to check this
+396
+397# initial design ----------398
-399self.parameters=self.x_init.tolist()
-400self.scores=[]
+399if(verbose==1)|(verbose==2):
+400print(f"\n Creating initial design... \n")401
-402ifself.saveisnotNone:
-403self.update_shelve()
+402ifverbose==1:
+403progbar=Progbar(target=self.n_init)404
-405ifself.y_initisNone:# calculate scores on initial design
-406
-407assert(
-408self.objective_funcisnotNone
-409),"self.y_init is None: must have 'objective_func' not None"
+405self.parameters=self.x_init.tolist()
+406self.scores=[]
+407
+408ifself.saveisnotNone:
+409self.update_shelve()410
-411ifself.n_jobs==1:
+411ifself.y_initisNone:# calculate scores on initial design412
-413foriinrange(self.n_init):
-414
-415x_next=self.x_init[i,:]
+413assert(
+414self.objective_funcisnotNone
+415),"self.y_init is None: must have 'objective_func' not None"416
-417try:
+417ifself.n_jobs==1:418
-419ifself.per_secondisTrue:
+419foriinrange(self.n_init):420
-421start=time()
-422score=self.objective_func(x_next,*func_args)
-423if(np.isfinite(score)==False)or(
-424np.isnan(score)==True
-425):
-426continue
-427self.timings.append(np.log(time()-start))
-428
-429else:# self.per_second is False
-430
-431score=self.objective_func(x_next,*func_args)
-432if(np.isfinite(score)==False)or(
-433np.isnan(score)==True
-434):
-435continue
+421x_next=self.x_init[i,:]
+422
+423try:
+424
+425ifself.per_secondisTrue:
+426
+427start=time()
+428score=self.objective_func(x_next,*func_args)
+429if(np.isfinite(score)==False)or(
+430np.isnan(score)==True
+431):
+432continue
+433self.timings.append(np.log(time()-start))
+434
+435else:# self.per_second is False436
-437self.scores.append(score)
-438
-439ifself.saveisnotNone:
-440self.update_shelve()
-441
-442except:
-443
-444continue
-445
-446ifverbose==1:
-447progbar.update(i)# update progress bar
-448
-449ifverbose==2:
-450print(f"point: {x_next}; score: {score}")
-451# end loop # calculate scores on initial design
-452
-453ifverbose==1:
-454progbar.update(self.n_init)
-455
-456else:# self.n_jobs != 1
-457
-458assert(
-459self.per_secondisFalse
-460),"timings not calculated here"
+437score=self.objective_func(x_next,*func_args)
+438if(np.isfinite(score)==False)or(
+439np.isnan(score)==True
+440):
+441continue
+442
+443self.scores.append(score)
+444
+445ifself.saveisnotNone:
+446self.update_shelve()
+447
+448except:
+449
+450continue
+451
+452ifverbose==1:
+453progbar.update(i)# update progress bar
+454
+455ifverbose==2:
+456print(f"point: {x_next}; score: {score}")
+457# end loop # calculate scores on initial design
+458
+459ifverbose==1:
+460progbar.update(self.n_init)461
-462scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
-463delayed(self.objective_func)(self.x_init[i,:])
-464foriinrange(self.n_init)
-465)
-466
-467self.scores=scores
-468
-469ifself.saveisnotNone:
-470self.update_shelve()
-471
-472else:# if self.y_init is None:
-473
-474assertself.x_init.shape[0]==len(
-475self.y_init
-476),"must have: self.x_init.shape[0] == len(self.y_init)"
+462else:# self.n_jobs != 1
+463
+464assert(
+465self.per_secondisFalse
+466),"timings not calculated here"
+467
+468scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
+469delayed(self.objective_func)(self.x_init[i,:])
+470foriinrange(self.n_init)
+471)
+472
+473self.scores=scores
+474
+475ifself.saveisnotNone:
+476self.update_shelve()477
-478self.scores=pickle.loads(
-479pickle.dumps(self.y_init.tolist(),-1)
-480)
-481
-482# current best score on initial design
-483min_index=(np.asarray(self.scores)).argmin()
-484self.y_min=self.scores[min_index]
-485self.x_min=self.x_init[min_index,:]
-486
-487# current gp mean and std on initial design
-488y_mean,y_std=self.gp_fit_predict(
-489np.asarray(self.parameters),
-490np.asarray(self.scores),
-491self.x_choices,
-492)
-493self.y_mean=y_mean
-494self.y_std=np.maximum(2.220446049250313e-16,y_std)
-495
-496# saving after initial design computation
-497ifself.saveisnotNone:
-498self.update_shelve()
-499
-500else:# if n_more_iter is not None
+478else:# if self.y_init is None:
+479
+480assertself.x_init.shape[0]==len(
+481self.y_init
+482),"must have: self.x_init.shape[0] == len(self.y_init)"
+483
+484self.scores=pickle.loads(
+485pickle.dumps(self.y_init.tolist(),-1)
+486)
+487
+488# current best score on initial design
+489min_index=(np.asarray(self.scores)).argmin()
+490self.y_min=self.scores[min_index]
+491self.x_min=self.x_init[min_index,:]
+492
+493# current gp mean and std on initial design
+494y_mean,y_std=self.gp_fit_predict(
+495np.asarray(self.parameters),
+496np.asarray(self.scores),
+497self.x_choices,
+498)
+499self.y_mean=y_mean
+500self.y_std=np.maximum(2.220446049250313e-16,y_std)501
-502assertself.n_iter>5,"you must have n_iter > 5"
-503n_iter=n_more_iter
-504iter_stop=len(self.max_ei)+n_more_iter# potentially
+502# saving after initial design computation
+503ifself.saveisnotNone:
+504self.update_shelve()505
-506if(verbose==1)|(verbose==2):
-507print(f"\n ...Done. \n")
-508try:
-509print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
-510except:
-511pass
-512
-513# end init design ----------
-514
-515# if n_more_iter is None: # initial optimization, before more iters are requested
-516
-517if(verbose==1)|(verbose==2):
-518print(f"\n Optimization loop... \n")
-519
-520# early stopping?
-521ifabs_tolisnotNone:
-522assert(
-523min_budget>20
-524),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+506else:# if n_more_iter is not None
+507
+508assertself.n_iter>5,"you must have n_iter > 5"
+509n_iter=n_more_iter
+510iter_stop=len(self.max_ei)+n_more_iter# potentially
+511
+512if(verbose==1)|(verbose==2):
+513print(f"\n ...Done. \n")
+514try:
+515print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
+516except:
+517pass
+518
+519# end init design ----------
+520
+521# if n_more_iter is None: # initial optimization, before more iters are requested
+522
+523if(verbose==1)|(verbose==2):
+524print(f"\n Optimization loop... \n")525
-526ifverbose==1:
-527progbar=Progbar(target=n_iter)
-528
-529# main loop ----------
-530
-531foriinrange(n_iter):
-532
-533# find next set of parameters (vector), maximizing ei
-534next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)
-535
-536try:
-537
-538ifself.per_secondisTrue:
-539
-540start=time()
+526# early stopping?
+527ifabs_tolisnotNone:
+528assert(
+529min_budget>20
+530),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+531
+532ifverbose==1:
+533progbar=Progbar(target=n_iter)
+534
+535# main loop ----------
+536
+537foriinrange(n_iter):
+538
+539# find next set of parameters (vector), maximizing ei
+540next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)541
-542ifself.objective_funcisnotNone:
+542try:543
-544score_next_param=self.objective_func(
-545next_param,*func_args
-546)
+544ifself.per_secondisTrue:
+545
+546start=time()547
-548if(np.isfinite(score_next_param)==False)or(
-549np.isnan(score_next_param)==True
-550):
-551continue
-552
-553else:
-554
-555assert(self.x_initisnotNone)and(
-556self.y_initisnotNone
-557),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+548ifself.objective_funcisnotNone:
+549
+550score_next_param=self.objective_func(
+551next_param,*func_args
+552)
+553
+554if(np.isfinite(score_next_param)==False)or(
+555np.isnan(score_next_param)==True
+556):
+557continue558
-559print(f"\n next param: {next_param}\n")
-560score_next_param=float(
-561input("get new score: \n")
-562)# or an API response
-563
-564if(np.isfinite(score_next_param)==False)or(
-565np.isnan(score_next_param)==True
-566):
-567continue
-568
-569self.timings.append(np.log(time()-start))
-570
-571else:# self.per_second is False:
-572
-573ifself.objective_funcisnotNone:
+559else:
+560
+561assert(self.x_initisnotNone)and(
+562self.y_initisnotNone
+563),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+564
+565print(f"\n next param: {next_param}\n")
+566score_next_param=float(
+567input("get new score: \n")
+568)# or an API response
+569
+570if(np.isfinite(score_next_param)==False)or(
+571np.isnan(score_next_param)==True
+572):
+573continue574
-575score_next_param=self.objective_func(
-576next_param,*func_args
-577)
+575self.timings.append(np.log(time()-start))
+576
+577else:# self.per_second is False:578
-579if(np.isfinite(score_next_param)==False)or(
-580np.isnan(score_next_param)==True
-581):
-582continue
-583
-584else:
-585
-586assert(self.x_initisnotNone)and(
-587self.y_initisnotNone
-588),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+579ifself.objective_funcisnotNone:
+580
+581score_next_param=self.objective_func(
+582next_param,*func_args
+583)
+584
+585if(np.isfinite(score_next_param)==False)or(
+586np.isnan(score_next_param)==True
+587):
+588continue589
-590print(f"\n next param: {next_param}\n")
-591score_next_param=float(
-592input("get new score: \n")
-593)# or an API response
-594
-595if(np.isfinite(score_next_param)==False)or(
-596np.isnan(score_next_param)==True
-597):
-598continue
-599
-600except:
-601
-602continue
-603
-604self.parameters.append(next_param.tolist())
+590else:
+591
+592assert(self.x_initisnotNone)and(
+593self.y_initisnotNone
+594),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+595
+596print(f"\n next param: {next_param}\n")
+597score_next_param=float(
+598input("get new score: \n")
+599)# or an API response
+600
+601if(np.isfinite(score_next_param)==False)or(
+602np.isnan(score_next_param)==True
+603):
+604continue605
-606self.scores.append(score_next_param)
+606except:607
-608ifself.saveisnotNone:
-609self.update_shelve()
-610
-611ifverbose==2:
-612print(f"iteration {i+1} -----")
-613print(f"current minimum: {self.x_min}")
-614print(f"current minimum score: {self.y_min}")
-615print(f"next parameter: {next_param}")
-616print(f"score for next parameter: {score_next_param}\n")
-617
-618ifscore_next_param<self.y_min:
-619self.x_min=next_param
-620self.y_min=score_next_param
-621ifself.saveisnotNone:
-622self.update_shelve()
+608continue
+609
+610self.parameters.append(next_param.tolist())
+611
+612self.scores.append(score_next_param)
+613
+614ifself.saveisnotNone:
+615self.update_shelve()
+616
+617ifverbose==2:
+618print(f"iteration {i+1} -----")
+619print(f"current minimum: {self.x_min}")
+620print(f"current minimum score: {self.y_min}")
+621print(f"next parameter: {next_param}")
+622print(f"score for next parameter: {score_next_param}\n")623
-624self.y_mean,self.y_std=self.gp_fit_predict(
-625np.asarray(self.parameters),
-626np.asarray(self.scores),
-627self.x_choices,
-628)
+624ifscore_next_param<self.y_min:
+625self.x_min=next_param
+626self.y_min=score_next_param
+627ifself.saveisnotNone:
+628self.update_shelve()629
-630ifself.saveisnotNone:
-631self.update_shelve()
-632
-633ifverbose==1:
-634progbar.update(i+1)# update progress bar
+630self.y_mean,self.y_std=self.gp_fit_predict(
+631np.asarray(self.parameters),
+632np.asarray(self.scores),
+633self.x_choices,
+634)635
-636# early stopping
-637
-638ifabs_tolisnotNone:
-639
-640# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
-641iflen(self.max_ei)>min_budget:
-642
-643diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))
-644
-645ifdiff_max_ei[-1]<=abs_tol:
-646
-647iter_stop=len(self.max_ei)# index i starts at 0
+636ifself.saveisnotNone:
+637self.update_shelve()
+638
+639ifverbose==1:
+640progbar.update(i+1)# update progress bar
+641
+642# early stopping
+643
+644ifabs_tolisnotNone:
+645
+646# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
+647iflen(self.max_ei)>min_budget:648
-649break
+649diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))650
-651# end main loop ----------
+651ifdiff_max_ei[-1]<=abs_tol:652
-653if(verbose==1)&(i<(n_iter-1)):
-654progbar.update(n_iter)
-655
-656self.n_iter=iter_stop
-657ifself.saveisnotNone:
-658self.update_shelve()
-659
-660return(self.x_min,self.y_min)
+653iter_stop=len(self.max_ei)# index i starts at 0
+654
+655break
+656
+657# end main loop ----------
+658
+659if(verbose==1)&(i<(n_iter-1)):
+660progbar.update(n_iter)
+661
+662self.n_iter=iter_stop
+663ifself.saveisnotNone:
+664self.update_shelve()
+665
+666DescribeResult=namedtuple(
+667"DescribeResult",("best_params","best_score")
+668)
+669
+670ifself.params_namesisNone:
+671
+672returnDescribeResult(self.x_min,self.y_min)
+673
+674else:
+675
+676returnDescribeResult(
+677dict(zip(self.params_names,self.x_min)),self.y_min
+678)
@@ -1482,6 +1520,9 @@
Arguments:
objective_func: a function;
the objective function to be minimized
+params_names: a list;
+ names of the parameters of the objective function (optional)
+
gp_obj: a GaussianProcessRegressor object;
An ML model for estimating the uncertainty around the objective function
@@ -1531,132 +1572,134 @@
225defload(self,path):
-226"""load data from stored shelve.
-227
-228 # Arguments
-229
-230 path : a string; path to stored shelve.
-231
-232 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-233 """
-234
-235self.sh=shelve.open(filename=path)
-236forkey,valueinself.sh.items():
-237setattr(self,key,value)
+
231defload(self,path):
+232"""load data from stored shelve.
+233
+234 # Arguments
+235
+236 path : a string; path to stored shelve.
+237
+238 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+239 """
+240
+241self.sh=shelve.open(filename=path)
+242forkey,valueinself.sh.items():
+243setattr(self,key,value)
247defclose_shelve(self):
-248"""Close shelve.
-249
-250 # Arguments
-251
-252 No argument.
-253
-254 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
-255 """
-256
-257self.sh.close()
+
253defclose_shelve(self):
+254"""Close shelve.
+255
+256 # Arguments
+257
+258 No argument.
+259
+260 See also: [Bayesian Optimization with GPopt Part 2 (save and resume)](https://thierrymoudiki.github.io/blog/2021/04/30/python/misc/gpopt)
+261 """
+262
+263self.sh.close()
@@ -2062,16 +2116,16 @@
Arguments
-
260defgp_fit_predict(self,X_train,y_train,X_test):
-261
-262iflen(X_train.shape)==1:
-263X_train=X_train.reshape((-1,1))
-264X_test=X_test.reshape((-1,1))
-265
-266# Get mean and standard deviation
-267returnself.gp_obj.fit(X_train,y_train).predict(
-268X_test,return_std=True
-269)
+
266defgp_fit_predict(self,X_train,y_train,X_test):
+267
+268iflen(X_train.shape)==1:
+269X_train=X_train.reshape((-1,1))
+270X_test=X_test.reshape((-1,1))
+271
+272# Get mean and standard deviation
+273returnself.gp_obj.fit(X_train,y_train).predict(
+274X_test,return_std=True
+275)
@@ -2089,14 +2143,14 @@
Arguments
-
272deftimings_fit_predict(self,X_train,y_train,X_test):
-273
-274iflen(X_train.shape)==1:
-275X_train=X_train.reshape((-1,1))
-276X_test=X_test.reshape((-1,1))
-277
-278# Get mean preds for timings
-279returnself.rf_obj.fit(X_train,y_train).predict(X_test)
+
278deftimings_fit_predict(self,X_train,y_train,X_test):
+279
+280iflen(X_train.shape)==1:
+281X_train=X_train.reshape((-1,1))
+282X_test=X_test.reshape((-1,1))
+283
+284# Get mean preds for timings
+285returnself.rf_obj.fit(X_train,y_train).predict(X_test)
288defnext_parameter_by_ei(self,seed,i):289
-290# find max index -----
+290gamma_hat=(self.y_min-self.y_mean)/self.y_std291
-292ifself.per_secondisFalse:
-293
-294# find index for max. ei
-295max_index=self.ei.argmin()
-296
-297else:# self.per_second is True
-298
-299# predict timings on self.x_choices
-300# train on X = self.parameters and y = self.timings
-301# (must have same shape[0])
-302timing_preds=self.timings_fit_predict(
-303X_train=np.asarray(self.parameters),
-304y_train=np.asarray(self.timings),
-305X_test=self.x_choices,
-306)
-307
-308# find index for max. ei (and min. timings)
-309max_index=(-self.ei/timing_preds).argmax()
-310
-311self.max_ei.append(np.abs(self.ei[max_index]))
-312
-313# Select next choice
-314next_param=self.x_choices[max_index,:]
-315
-316ifnext_paraminnp.asarray(self.parameters):
-317
-318ifself.log_scale==False:
-319
-320np.random.seed(self.seed*i+1000)
-321next_param=(
-322self.upper_bound-self.lower_bound
-323)*np.random.rand(self.n_dims)+self.lower_bound
-324
-325else:# /!\ very... experimental
-326
-327np.random.seed(self.seed)
-328log_upper_bound=np.log(self.upper_bound)
-329log_lower_bound=np.log(self.lower_bound)
-330log_bounds_range=log_upper_bound-log_lower_bound
-331
-332next_param=np.minimum(
-333np.exp(
-334log_bounds_range*np.random.rand(self.n_dims)
-335+log_lower_bound
-336),
-3371.7976931348623157e308,
-338)
-339
-340returnnext_param
+292self.ei=-self.y_std*(
+293gamma_hat*st.norm.cdf(gamma_hat)+st.norm.pdf(gamma_hat)
+294)
+295
+296# find max index -----
+297
+298ifself.per_secondisFalse:
+299
+300# find index for max. ei
+301max_index=self.ei.argmin()
+302
+303else:# self.per_second is True
+304
+305# predict timings on self.x_choices
+306# train on X = self.parameters and y = self.timings
+307# (must have same shape[0])
+308timing_preds=self.timings_fit_predict(
+309X_train=np.asarray(self.parameters),
+310y_train=np.asarray(self.timings),
+311X_test=self.x_choices,
+312)
+313
+314# find index for max. ei (and min. timings)
+315max_index=(-self.ei/timing_preds).argmax()
+316
+317self.max_ei.append(np.abs(self.ei[max_index]))
+318
+319# Select next choice
+320next_param=self.x_choices[max_index,:]
+321
+322ifnext_paraminnp.asarray(self.parameters):
+323
+324ifself.log_scale==False:
+325
+326np.random.seed(self.seed*i+1000)
+327next_param=(
+328self.upper_bound-self.lower_bound
+329)*np.random.rand(self.n_dims)+self.lower_bound
+330
+331else:# /!\ very... experimental
+332
+333np.random.seed(self.seed)
+334log_upper_bound=np.log(self.upper_bound)
+335log_lower_bound=np.log(self.lower_bound)
+336log_bounds_range=log_upper_bound-log_lower_bound
+337
+338next_param=np.minimum(
+339np.exp(
+340log_bounds_range*np.random.rand(self.n_dims)
+341+log_lower_bound
+342),
+3431.7976931348623157e308,
+344)
+345
+346returnnext_param
@@ -2190,324 +2244,336 @@
Arguments
-
343defoptimize(
-344self,
-345verbose=1,
-346n_more_iter=None,
-347abs_tol=None,# suggested 1e-4, for n_iter = 200
-348min_budget=50,# minimum budget for early stopping
-349func_args=None,
-350):
-351"""Launch optimization loop.
-352
-353 # Arguments:
-354
-355 verbose: an integer;
-356 verbose = 0: nothing is printed,
-357 verbose = 1: a progress bar is printed (longer than 0),
-358 verbose = 2: information about each iteration is printed (longer than 1)
-359
-360 n_more_iter: an integer;
-361 additional number of iterations for the optimizer (which has been run once)
-362
-363 abs_tol: a float;
-364 tolerance for convergence of the optimizer (early stopping based on expected improvement)
+
349defoptimize(
+350self,
+351verbose=1,
+352n_more_iter=None,
+353abs_tol=None,# suggested 1e-4, for n_iter = 200
+354min_budget=50,# minimum budget for early stopping
+355func_args=None,
+356):
+357"""Launch optimization loop.
+358
+359 # Arguments:
+360
+361 verbose: an integer;
+362 verbose = 0: nothing is printed,
+363 verbose = 1: a progress bar is printed (longer than 0),
+364 verbose = 2: information about each iteration is printed (longer than 1)365
-366 min_budget: an integer (default is 50);
-367 minimum number of iterations before early stopping controlled by `abs_tol`
+366 n_more_iter: an integer;
+367 additional number of iterations for the optimizer (which has been run once)368
-369 func_args: a list;
-370 additional parameters for the objective function (if necessary)
+369 abs_tol: a float;
+370 tolerance for convergence of the optimizer (early stopping based on expected improvement)371
-372 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
-373 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+372 min_budget: an integer (default is 50);
+373 minimum number of iterations before early stopping controlled by `abs_tol`374
-375 """
-376
-377# verbose = 0: nothing is printed
-378# verbose = 1: a progress bar is printed (longer than 0)
-379# verbose = 2: information about each iteration is printed (longer than 1)
-380iffunc_argsisNone:
-381func_args=[]
+375 func_args: a list;
+376 additional parameters for the objective function (if necessary)
+377
+378 see also [Bayesian Optimization with GPopt](https://thierrymoudiki.github.io/blog/2021/04/16/python/misc/gpopt)
+379 and [Hyperparameters tuning with GPopt](https://thierrymoudiki.github.io/blog/2021/06/11/python/misc/hyperparam-tuning-gpopt)
+380
+381 """382
-383if(
-384n_more_iterisNone
-385):# initial optimization, before more iters are requested
-386
-387n_iter=self.n_iter
-388# stopping iter for early stopping (default is total budget)
-389iter_stop=n_iter# potentially # got to check this
-390
-391# initial design ----------
+383# verbose = 0: nothing is printed
+384# verbose = 1: a progress bar is printed (longer than 0)
+385# verbose = 2: information about each iteration is printed (longer than 1)
+386iffunc_argsisNone:
+387func_args=[]
+388
+389if(
+390n_more_iterisNone
+391):# initial optimization, before more iters are requested392
-393if(verbose==1)|(verbose==2):
-394print(f"\n Creating initial design... \n")
-395
-396ifverbose==1:
-397progbar=Progbar(target=self.n_init)
+393n_iter=self.n_iter
+394# stopping iter for early stopping (default is total budget)
+395iter_stop=n_iter# potentially # got to check this
+396
+397# initial design ----------398
-399self.parameters=self.x_init.tolist()
-400self.scores=[]
+399if(verbose==1)|(verbose==2):
+400print(f"\n Creating initial design... \n")401
-402ifself.saveisnotNone:
-403self.update_shelve()
+402ifverbose==1:
+403progbar=Progbar(target=self.n_init)404
-405ifself.y_initisNone:# calculate scores on initial design
-406
-407assert(
-408self.objective_funcisnotNone
-409),"self.y_init is None: must have 'objective_func' not None"
+405self.parameters=self.x_init.tolist()
+406self.scores=[]
+407
+408ifself.saveisnotNone:
+409self.update_shelve()410
-411ifself.n_jobs==1:
+411ifself.y_initisNone:# calculate scores on initial design412
-413foriinrange(self.n_init):
-414
-415x_next=self.x_init[i,:]
+413assert(
+414self.objective_funcisnotNone
+415),"self.y_init is None: must have 'objective_func' not None"416
-417try:
+417ifself.n_jobs==1:418
-419ifself.per_secondisTrue:
+419foriinrange(self.n_init):420
-421start=time()
-422score=self.objective_func(x_next,*func_args)
-423if(np.isfinite(score)==False)or(
-424np.isnan(score)==True
-425):
-426continue
-427self.timings.append(np.log(time()-start))
-428
-429else:# self.per_second is False
-430
-431score=self.objective_func(x_next,*func_args)
-432if(np.isfinite(score)==False)or(
-433np.isnan(score)==True
-434):
-435continue
+421x_next=self.x_init[i,:]
+422
+423try:
+424
+425ifself.per_secondisTrue:
+426
+427start=time()
+428score=self.objective_func(x_next,*func_args)
+429if(np.isfinite(score)==False)or(
+430np.isnan(score)==True
+431):
+432continue
+433self.timings.append(np.log(time()-start))
+434
+435else:# self.per_second is False436
-437self.scores.append(score)
-438
-439ifself.saveisnotNone:
-440self.update_shelve()
-441
-442except:
-443
-444continue
-445
-446ifverbose==1:
-447progbar.update(i)# update progress bar
-448
-449ifverbose==2:
-450print(f"point: {x_next}; score: {score}")
-451# end loop # calculate scores on initial design
-452
-453ifverbose==1:
-454progbar.update(self.n_init)
-455
-456else:# self.n_jobs != 1
-457
-458assert(
-459self.per_secondisFalse
-460),"timings not calculated here"
+437score=self.objective_func(x_next,*func_args)
+438if(np.isfinite(score)==False)or(
+439np.isnan(score)==True
+440):
+441continue
+442
+443self.scores.append(score)
+444
+445ifself.saveisnotNone:
+446self.update_shelve()
+447
+448except:
+449
+450continue
+451
+452ifverbose==1:
+453progbar.update(i)# update progress bar
+454
+455ifverbose==2:
+456print(f"point: {x_next}; score: {score}")
+457# end loop # calculate scores on initial design
+458
+459ifverbose==1:
+460progbar.update(self.n_init)461
-462scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
-463delayed(self.objective_func)(self.x_init[i,:])
-464foriinrange(self.n_init)
-465)
-466
-467self.scores=scores
-468
-469ifself.saveisnotNone:
-470self.update_shelve()
-471
-472else:# if self.y_init is None:
-473
-474assertself.x_init.shape[0]==len(
-475self.y_init
-476),"must have: self.x_init.shape[0] == len(self.y_init)"
+462else:# self.n_jobs != 1
+463
+464assert(
+465self.per_secondisFalse
+466),"timings not calculated here"
+467
+468scores=Parallel(n_jobs=self.n_jobs,prefer="threads")(
+469delayed(self.objective_func)(self.x_init[i,:])
+470foriinrange(self.n_init)
+471)
+472
+473self.scores=scores
+474
+475ifself.saveisnotNone:
+476self.update_shelve()477
-478self.scores=pickle.loads(
-479pickle.dumps(self.y_init.tolist(),-1)
-480)
-481
-482# current best score on initial design
-483min_index=(np.asarray(self.scores)).argmin()
-484self.y_min=self.scores[min_index]
-485self.x_min=self.x_init[min_index,:]
-486
-487# current gp mean and std on initial design
-488y_mean,y_std=self.gp_fit_predict(
-489np.asarray(self.parameters),
-490np.asarray(self.scores),
-491self.x_choices,
-492)
-493self.y_mean=y_mean
-494self.y_std=np.maximum(2.220446049250313e-16,y_std)
-495
-496# saving after initial design computation
-497ifself.saveisnotNone:
-498self.update_shelve()
-499
-500else:# if n_more_iter is not None
+478else:# if self.y_init is None:
+479
+480assertself.x_init.shape[0]==len(
+481self.y_init
+482),"must have: self.x_init.shape[0] == len(self.y_init)"
+483
+484self.scores=pickle.loads(
+485pickle.dumps(self.y_init.tolist(),-1)
+486)
+487
+488# current best score on initial design
+489min_index=(np.asarray(self.scores)).argmin()
+490self.y_min=self.scores[min_index]
+491self.x_min=self.x_init[min_index,:]
+492
+493# current gp mean and std on initial design
+494y_mean,y_std=self.gp_fit_predict(
+495np.asarray(self.parameters),
+496np.asarray(self.scores),
+497self.x_choices,
+498)
+499self.y_mean=y_mean
+500self.y_std=np.maximum(2.220446049250313e-16,y_std)501
-502assertself.n_iter>5,"you must have n_iter > 5"
-503n_iter=n_more_iter
-504iter_stop=len(self.max_ei)+n_more_iter# potentially
+502# saving after initial design computation
+503ifself.saveisnotNone:
+504self.update_shelve()505
-506if(verbose==1)|(verbose==2):
-507print(f"\n ...Done. \n")
-508try:
-509print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
-510except:
-511pass
-512
-513# end init design ----------
-514
-515# if n_more_iter is None: # initial optimization, before more iters are requested
-516
-517if(verbose==1)|(verbose==2):
-518print(f"\n Optimization loop... \n")
-519
-520# early stopping?
-521ifabs_tolisnotNone:
-522assert(
-523min_budget>20
-524),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+506else:# if n_more_iter is not None
+507
+508assertself.n_iter>5,"you must have n_iter > 5"
+509n_iter=n_more_iter
+510iter_stop=len(self.max_ei)+n_more_iter# potentially
+511
+512if(verbose==1)|(verbose==2):
+513print(f"\n ...Done. \n")
+514try:
+515print(np.hstack((self.x_init,self.y_init.reshape(-1,1))))
+516except:
+517pass
+518
+519# end init design ----------
+520
+521# if n_more_iter is None: # initial optimization, before more iters are requested
+522
+523if(verbose==1)|(verbose==2):
+524print(f"\n Optimization loop... \n")525
-526ifverbose==1:
-527progbar=Progbar(target=n_iter)
-528
-529# main loop ----------
-530
-531foriinrange(n_iter):
-532
-533# find next set of parameters (vector), maximizing ei
-534next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)
-535
-536try:
-537
-538ifself.per_secondisTrue:
-539
-540start=time()
+526# early stopping?
+527ifabs_tolisnotNone:
+528assert(
+529min_budget>20
+530),"With 'abs_tol' provided, you must have 'min_budget' > 20"
+531
+532ifverbose==1:
+533progbar=Progbar(target=n_iter)
+534
+535# main loop ----------
+536
+537foriinrange(n_iter):
+538
+539# find next set of parameters (vector), maximizing ei
+540next_param=self.next_parameter_by_ei(seed=len(self.max_ei),i=i)541
-542ifself.objective_funcisnotNone:
+542try:543
-544score_next_param=self.objective_func(
-545next_param,*func_args
-546)
+544ifself.per_secondisTrue:
+545
+546start=time()547
-548if(np.isfinite(score_next_param)==False)or(
-549np.isnan(score_next_param)==True
-550):
-551continue
-552
-553else:
-554
-555assert(self.x_initisnotNone)and(
-556self.y_initisnotNone
-557),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+548ifself.objective_funcisnotNone:
+549
+550score_next_param=self.objective_func(
+551next_param,*func_args
+552)
+553
+554if(np.isfinite(score_next_param)==False)or(
+555np.isnan(score_next_param)==True
+556):
+557continue558
-559print(f"\n next param: {next_param}\n")
-560score_next_param=float(
-561input("get new score: \n")
-562)# or an API response
-563
-564if(np.isfinite(score_next_param)==False)or(
-565np.isnan(score_next_param)==True
-566):
-567continue
-568
-569self.timings.append(np.log(time()-start))
-570
-571else:# self.per_second is False:
-572
-573ifself.objective_funcisnotNone:
+559else:
+560
+561assert(self.x_initisnotNone)and(
+562self.y_initisnotNone
+563),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+564
+565print(f"\n next param: {next_param}\n")
+566score_next_param=float(
+567input("get new score: \n")
+568)# or an API response
+569
+570if(np.isfinite(score_next_param)==False)or(
+571np.isnan(score_next_param)==True
+572):
+573continue574
-575score_next_param=self.objective_func(
-576next_param,*func_args
-577)
+575self.timings.append(np.log(time()-start))
+576
+577else:# self.per_second is False:578
-579if(np.isfinite(score_next_param)==False)or(
-580np.isnan(score_next_param)==True
-581):
-582continue
-583
-584else:
-585
-586assert(self.x_initisnotNone)and(
-587self.y_initisnotNone
-588),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+579ifself.objective_funcisnotNone:
+580
+581score_next_param=self.objective_func(
+582next_param,*func_args
+583)
+584
+585if(np.isfinite(score_next_param)==False)or(
+586np.isnan(score_next_param)==True
+587):
+588continue589
-590print(f"\n next param: {next_param}\n")
-591score_next_param=float(
-592input("get new score: \n")
-593)# or an API response
-594
-595if(np.isfinite(score_next_param)==False)or(
-596np.isnan(score_next_param)==True
-597):
-598continue
-599
-600except:
-601
-602continue
-603
-604self.parameters.append(next_param.tolist())
+590else:
+591
+592assert(self.x_initisnotNone)and(
+593self.y_initisnotNone
+594),"self.objective_func is not None: must have (self.x_init is not None) and (self.y_init is not None)"
+595
+596print(f"\n next param: {next_param}\n")
+597score_next_param=float(
+598input("get new score: \n")
+599)# or an API response
+600
+601if(np.isfinite(score_next_param)==False)or(
+602np.isnan(score_next_param)==True
+603):
+604continue605
-606self.scores.append(score_next_param)
+606except:607
-608ifself.saveisnotNone:
-609self.update_shelve()
-610
-611ifverbose==2:
-612print(f"iteration {i+1} -----")
-613print(f"current minimum: {self.x_min}")
-614print(f"current minimum score: {self.y_min}")
-615print(f"next parameter: {next_param}")
-616print(f"score for next parameter: {score_next_param}\n")
-617
-618ifscore_next_param<self.y_min:
-619self.x_min=next_param
-620self.y_min=score_next_param
-621ifself.saveisnotNone:
-622self.update_shelve()
+608continue
+609
+610self.parameters.append(next_param.tolist())
+611
+612self.scores.append(score_next_param)
+613
+614ifself.saveisnotNone:
+615self.update_shelve()
+616
+617ifverbose==2:
+618print(f"iteration {i+1} -----")
+619print(f"current minimum: {self.x_min}")
+620print(f"current minimum score: {self.y_min}")
+621print(f"next parameter: {next_param}")
+622print(f"score for next parameter: {score_next_param}\n")623
-624self.y_mean,self.y_std=self.gp_fit_predict(
-625np.asarray(self.parameters),
-626np.asarray(self.scores),
-627self.x_choices,
-628)
+624ifscore_next_param<self.y_min:
+625self.x_min=next_param
+626self.y_min=score_next_param
+627ifself.saveisnotNone:
+628self.update_shelve()629
-630ifself.saveisnotNone:
-631self.update_shelve()
-632
-633ifverbose==1:
-634progbar.update(i+1)# update progress bar
+630self.y_mean,self.y_std=self.gp_fit_predict(
+631np.asarray(self.parameters),
+632np.asarray(self.scores),
+633self.x_choices,
+634)635
-636# early stopping
-637
-638ifabs_tolisnotNone:
-639
-640# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
-641iflen(self.max_ei)>min_budget:
-642
-643diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))
-644
-645ifdiff_max_ei[-1]<=abs_tol:
-646
-647iter_stop=len(self.max_ei)# index i starts at 0
+636ifself.saveisnotNone:
+637self.update_shelve()
+638
+639ifverbose==1:
+640progbar.update(i+1)# update progress bar
+641
+642# early stopping
+643
+644ifabs_tolisnotNone:
+645
+646# if self.max_ei.size > (self.n_init + self.n_iter * min_budget_pct):
+647iflen(self.max_ei)>min_budget:648
-649break
+649diff_max_ei=np.abs(np.diff(np.asarray(self.max_ei)))650
-651# end main loop ----------
+651ifdiff_max_ei[-1]<=abs_tol:652
-653if(verbose==1)&(i<(n_iter-1)):
-654progbar.update(n_iter)
-655
-656self.n_iter=iter_stop
-657ifself.saveisnotNone:
-658self.update_shelve()
-659
-660return(self.x_min,self.y_min)
+653iter_stop=len(self.max_ei)# index i starts at 0
+654
+655break
+656
+657# end main loop ----------
+658
+659if(verbose==1)&(i<(n_iter-1)):
+660progbar.update(n_iter)
+661
+662self.n_iter=iter_stop
+663ifself.saveisnotNone:
+664self.update_shelve()
+665
+666DescribeResult=namedtuple(
+667"DescribeResult",("best_params","best_score")
+668)
+669
+670ifself.params_namesisNone:
+671
+672returnDescribeResult(self.x_min,self.y_min)
+673
+674else:
+675
+676returnDescribeResult(
+677dict(zip(self.params_names,self.x_min)),self.y_min
+678)
diff --git a/gpopt-docs/search.js b/gpopt-docs/search.js
index 5180332..2740d26 100644
--- a/gpopt-docs/search.js
+++ b/gpopt-docs/search.js
@@ -1,6 +1,6 @@
window.pdocSearch = (function(){
/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oGPOpt class.\n"}, {"fullname": "GPopt.GPOpt.GPOpt.GPOpt", "modulename": "GPopt.GPOpt.GPOpt", "qualname": "GPOpt", "kind": "class", "doc": "
Class GPOpt.
\n\n
Arguments:
\n\n
lower_bound: a numpy array;\n lower bound for researched minimum\n\nupper_bound: a numpy array;\n upper bound for researched minimum\n\nobjective_func: a function;\n the objective function to be minimized\n\ngp_obj: a GaussianProcessRegressor object;\n An ML model for estimating the uncertainty around the objective function \n\nx_init:\n initial setting of points where `objective_func` is evaluated (optional)\n\ny_init:\n initial setting values at points where `objective_func` is evaluated (optional)\n\nn_init: an integer;\n number of points in the initial setting, when `x_init` and `y_init` are not provided\n\nn_choices: an integer;\n number of points for the calculation of expected improvement\n\nn_iter: an integer;\n number of iterations of the minimization algorithm\n\nalpha: a float;\n Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)\n\nn_restarts_optimizer: an integer;\n The number of restarts of the optimizer for finding the kernel\u2019s parameters which maximize the log-marginal likelihood.\n\nseed: an integer;\n reproducibility seed\n\nsave: a string;\n Specifies where to save the optimizer in its current state\n\nn_jobs: an integer;\n number of jobs for parallel computing on initial setting (can be -1)\n\nper_second: a boolean;\n __experimental__, default is False (leave to default for now)\n\nlog_scale: a boolean;\n __experimental__, default is False (leave to default for now)\n
verbose: an integer;\n verbose = 0: nothing is printed,\n verbose = 1: a progress bar is printed (longer than 0),\n verbose = 2: information about each iteration is printed (longer than 1)\n\nn_more_iter: an integer;\n additional number of iterations for the optimizer (which has been run once)\n\nabs_tol: a float;\n tolerance for convergence of the optimizer (early stopping based on expected improvement)\n\nmin_budget: an integer (default is 50);\n minimum number of iterations before early stopping controlled by `abs_tol`\n\nfunc_args: a list;\n additional parameters for the objective function (if necessary)\n
lower_bound: a numpy array;\n lower bound for researched minimum\n\nupper_bound: a numpy array;\n upper bound for researched minimum\n\nobjective_func: a function;\n the objective function to be minimized\n\ngp_obj: a GaussianProcessRegressor object;\n An ML model for estimating the uncertainty around the objective function \n\nx_init:\n initial setting of points where `objective_func` is evaluated (optional)\n\ny_init:\n initial setting values at points where `objective_func` is evaluated (optional)\n\nn_init: an integer;\n number of points in the initial setting, when `x_init` and `y_init` are not provided\n\nn_choices: an integer;\n number of points for the calculation of expected improvement\n\nn_iter: an integer;\n number of iterations of the minimization algorithm\n\nalpha: a float;\n Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)\n\nn_restarts_optimizer: an integer;\n The number of restarts of the optimizer for finding the kernel\u2019s parameters which maximize the log-marginal likelihood.\n\nseed: an integer;\n reproducibility seed\n\nsave: a string;\n Specifies where to save the optimizer in its current state\n\nn_jobs: an integer;\n number of jobs for parallel computing on initial setting (can be -1)\n\nper_second: a boolean;\n __experimental__, default is False (leave to default for now)\n\nlog_scale: a boolean;\n __experimental__, default is False (leave to default for now)\n
verbose: an integer;\n verbose = 0: nothing is printed,\n verbose = 1: a progress bar is printed (longer than 0),\n verbose = 2: information about each iteration is printed (longer than 1)\n\nn_more_iter: an integer;\n additional number of iterations for the optimizer (which has been run once)\n\nabs_tol: a float;\n tolerance for convergence of the optimizer (early stopping based on expected improvement)\n\nmin_budget: an integer (default is 50);\n minimum number of iterations before early stopping controlled by `abs_tol`\n\nfunc_args: a list;\n additional parameters for the objective function (if necessary)\n
lower_bound: a numpy array;\n lower bound for researched minimum\n\nupper_bound: a numpy array;\n upper bound for researched minimum\n\nobjective_func: a function;\n the objective function to be minimized\n\nparams_names: a list;\n names of the parameters of the objective function (optional)\n\ngp_obj: a GaussianProcessRegressor object;\n An ML model for estimating the uncertainty around the objective function \n\nx_init:\n initial setting of points where `objective_func` is evaluated (optional)\n\ny_init:\n initial setting values at points where `objective_func` is evaluated (optional)\n\nn_init: an integer;\n number of points in the initial setting, when `x_init` and `y_init` are not provided\n\nn_choices: an integer;\n number of points for the calculation of expected improvement\n\nn_iter: an integer;\n number of iterations of the minimization algorithm\n\nalpha: a float;\n Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)\n\nn_restarts_optimizer: an integer;\n The number of restarts of the optimizer for finding the kernel\u2019s parameters which maximize the log-marginal likelihood.\n\nseed: an integer;\n reproducibility seed\n\nsave: a string;\n Specifies where to save the optimizer in its current state\n\nn_jobs: an integer;\n number of jobs for parallel computing on initial setting (can be -1)\n\nper_second: a boolean;\n __experimental__, default is False (leave to default for now)\n\nlog_scale: a boolean;\n __experimental__, default is False (leave to default for now)\n
verbose: an integer;\n verbose = 0: nothing is printed,\n verbose = 1: a progress bar is printed (longer than 0),\n verbose = 2: information about each iteration is printed (longer than 1)\n\nn_more_iter: an integer;\n additional number of iterations for the optimizer (which has been run once)\n\nabs_tol: a float;\n tolerance for convergence of the optimizer (early stopping based on expected improvement)\n\nmin_budget: an integer (default is 50);\n minimum number of iterations before early stopping controlled by `abs_tol`\n\nfunc_args: a list;\n additional parameters for the objective function (if necessary)\n
lower_bound: a numpy array;\n lower bound for researched minimum\n\nupper_bound: a numpy array;\n upper bound for researched minimum\n\nobjective_func: a function;\n the objective function to be minimized\n\nparams_names: a list;\n names of the parameters of the objective function (optional)\n\ngp_obj: a GaussianProcessRegressor object;\n An ML model for estimating the uncertainty around the objective function \n\nx_init:\n initial setting of points where `objective_func` is evaluated (optional)\n\ny_init:\n initial setting values at points where `objective_func` is evaluated (optional)\n\nn_init: an integer;\n number of points in the initial setting, when `x_init` and `y_init` are not provided\n\nn_choices: an integer;\n number of points for the calculation of expected improvement\n\nn_iter: an integer;\n number of iterations of the minimization algorithm\n\nalpha: a float;\n Value added to the diagonal of the kernel matrix during fitting (for Matern 5/2 kernel)\n\nn_restarts_optimizer: an integer;\n The number of restarts of the optimizer for finding the kernel\u2019s parameters which maximize the log-marginal likelihood.\n\nseed: an integer;\n reproducibility seed\n\nsave: a string;\n Specifies where to save the optimizer in its current state\n\nn_jobs: an integer;\n number of jobs for parallel computing on initial setting (can be -1)\n\nper_second: a boolean;\n __experimental__, default is False (leave to default for now)\n\nlog_scale: a boolean;\n __experimental__, default is False (leave to default for now)\n
verbose: an integer;\n verbose = 0: nothing is printed,\n verbose = 1: a progress bar is printed (longer than 0),\n verbose = 2: information about each iteration is printed (longer than 1)\n\nn_more_iter: an integer;\n additional number of iterations for the optimizer (which has been run once)\n\nabs_tol: a float;\n tolerance for convergence of the optimizer (early stopping based on expected improvement)\n\nmin_budget: an integer (default is 50);\n minimum number of iterations before early stopping controlled by `abs_tol`\n\nfunc_args: a list;\n additional parameters for the objective function (if necessary)\n
\n", "signature": "(\tself,\tverbose=1,\tn_more_iter=None,\tabs_tol=None,\tmin_budget=50,\tfunc_args=None):", "funcdef": "def"}];
// mirrored in build-search-index.js (part 1)
// Also split on html tags. this is a cheap heuristic, but good enough.
diff --git a/requirements.txt b/requirements.txt
index 9f9430b..43ece8a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,9 @@
joblib
matplotlib
+nnetsauce
numpy
pandas
scipy
scikit-learn
-threadpoolctl
\ No newline at end of file
+threadpoolctl
+tqdm
\ No newline at end of file
diff --git a/save.db b/save.db
new file mode 100644
index 0000000..06208da
Binary files /dev/null and b/save.db differ
diff --git a/setup.py b/setup.py
index 4dc4545..19a09ad 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
from codecs import open
from os import path
-__version__ = "0.4.1"
+__version__ = "0.6.0"
subprocess.call("pip install -r requirements.txt", shell=True)
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ "
+ 
+ 
