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Update contents of book following pre-commit
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- book/content/_build/.doctrees/glue_cache.json +1 -1
- book/content/_build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css +1 -1
- book/content/_build/html/_sources/base.rst +2 -2
- book/content/_build/html/_sources/dissimilarity.rst +2 -2
- book/content/_build/html/_sources/installation.ipynb +12 -12
- book/content/_build/html/_sources/intro.md +2 -2
- book/content/_build/html/_sources/partition.rst +2 -2
- book/content/_build/html/_sources/starting.ipynb +5 -5
- book/content/_build/html/_sources/utils.rst +2 -2
- book/content/_build/html/_static/basic.css +1 -1
- book/content/_build/html/_static/clipboard.min.js +1 -1
- book/content/_build/html/_static/copybutton.js +1 -1
- book/content/_build/html/_static/css/blank.css +1 -1
- book/content/_build/html/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css +0 -0
- book/content/_build/html/_static/css/theme.css +1 -1
- book/content/_build/html/_static/documentation_options.js +1 -1
- book/content/_build/html/_static/js/index.be7d3bbb2ef33a8344ce.js +0 -0
- book/content/_build/html/_static/language_data.js +0 -2
- book/content/_build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css +1 -1
- book/content/_build/html/_static/pygments.css +1 -1
- book/content/_build/html/_static/togglebutton.css +1 -1
- book/content/_build/html/_static/underscore.js +1 -1
- book/content/_build/html/_static/vendor/fontawesome/5.13.0/css/all.min.css +0 -0
- book/content/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.svg +0 -0
- book/content/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-regular-400.svg +0 -0
- book/content/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.svg +0 -0
- book/content/_build/html/_static/webpack-macros.html +1 -1
- book/content/_build/html/base.html +49 -49
- book/content/_build/html/dissimilarity.html +56 -56
- book/content/_build/html/genindex.html +69 -69
- book/content/_build/html/installation.html +61 -61
- book/content/_build/html/intro.html +42 -42
- book/content/_build/html/partition.html +56 -56
- book/content/_build/html/py-modindex.html +43 -43
- book/content/_build/html/search.html +50 -50
- book/content/_build/html/searchindex.js +1 -1
- book/content/_build/html/starting.html +56 -56
- book/content/_build/html/utils.html +45 -45
- book/content/_build/jupyter_execute/installation.ipynb +13 -13
- book/content/_build/jupyter_execute/installation.py +52 -53
- book/content/_build/jupyter_execute/starting.ipynb +23 -23
- book/content/_build/jupyter_execute/starting.py +103 -98
- book/content/_config.yml +5 -5
- book/content/_toc.yml +1 -1
- book/content/base.rst +2 -2
- book/content/dissimilarity.rst +2 -2
- book/content/installation.ipynb +12 -12
- book/content/intro.md +2 -2
- book/content/partition.rst +2 -2
- book/content/references.bib +2 -2
book/content/_build/.doctrees/glue_cache.json
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book/content/_build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
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--tabs-color-overline: rgb(207, 236, 238);
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--tabs-color-underline: rgb(207, 236, 238);
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--tabs-size-label: 1rem;
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}
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--tabs-color-overline: rgb(207, 236, 238);
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}
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book/content/_build/html/_sources/base.rst
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Base class
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=============
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.. automodule::
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:members:
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Base class
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=============
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.. automodule:: selector.base
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:members:
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book/content/_build/html/_sources/dissimilarity.rst
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Dissimilarity based methods
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=============
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.. automodule::
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:members:
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Dissimilarity based methods
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=============
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.. automodule:: selector.dissimilarity
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book/content/_build/html/_sources/installation.ipynb
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"We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.\n",
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"</div>\n",
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"\n",
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"The following dependencies are required to run
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"\n",
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"* Python >= 3.6: http://www.python.org/ \n",
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"* NumPy >= 1.21.5: http://www.numpy.org/\n",
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"\n",
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"## Installation with Ana- or Miniconda:\n",
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"\n",
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"1. To install
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"\n",
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"```\n",
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"# Activate your main conda environment if it is not loaded in your .bashrc.\n",
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"\n",
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"## Latest git revision\n",
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"\n",
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"This section shows how one can install the latest revision of
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"\n",
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"There are two installation methods:\n",
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"\n",
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"1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.\n",
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"```\n",
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"# with env, correct PATH\n",
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-
"pip install git+https://github.com/theochem/
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"# with env, broken PATH\n",
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"python -m pip install git+https://github.com/theochem/
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"# without env, correct PATH\n",
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"pip install git+https://github.com/theochem/
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"# without env, broken PATH\n",
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"python -m pip install git+https://github.com/theochem/
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"```\n",
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"\n",
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"2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.\n",
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"# A) Clone git repo with https OR ssh:\n",
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"# The second one only works if you have ssh set up for Github\n",
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"# A1) https\n",
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"git clone https://github.com/theochem/
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"# A2) ssh\n",
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"git clone [email protected]:theochem/
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"# B) Optionally write the version string\n",
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"pip install roberto # or any of the three other ways of running pip, see above.\n",
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"rob write-version\n",
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"# Install pytest in your conda env.\n",
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"conda install pytest pytest-xdist\n",
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"# Then run the tests.\n",
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"pytest --pyargs
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"```\n",
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"\n",
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"With Pip:\n",
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"pip install pytest pytest-xdist\n",
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"# .. and refresh the virtual environment.\n",
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"# This is a venv quirk. Without it, pytest may not find IOData.\n",
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"deactivate && source ~/
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"\n",
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"# Alternatively, install pytest in your home directory.\n",
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"pip install pytest pytest-xdist --user\n",
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"\n",
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"# Finally, run the tests.\n",
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"pytest --pyargs
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"```"
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]
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}
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"We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.\n",
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"</div>\n",
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"\n",
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+
"The following dependencies are required to run selector properly,\n",
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"\n",
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"* Python >= 3.6: http://www.python.org/ \n",
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"* NumPy >= 1.21.5: http://www.numpy.org/\n",
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"\n",
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"## Installation with Ana- or Miniconda:\n",
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"\n",
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"1. To install selector using the conda package management system, install [miniconda](https://conda.io/miniconda.html) or [anaconda](https://www.anaconda.com/download) first, and then:\n",
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"\n",
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"```\n",
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"# Activate your main conda environment if it is not loaded in your .bashrc.\n",
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"\n",
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"## Latest git revision\n",
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"\n",
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"This section shows how one can install the latest revision of selector from the git repository. This kind of installation comes with some risks (sudden API changes, bugs, …) and so be prepared to accept them when using the following installation instructions.\n",
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"\n",
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"There are two installation methods:\n",
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"\n",
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"1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.\n",
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"```\n",
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"# with env, correct PATH\n",
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"pip install git+https://github.com/theochem/Selector.git\n",
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"# with env, broken PATH\n",
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"python -m pip install git+https://github.com/theochem/Selector.git\n",
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"# without env, correct PATH\n",
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"pip install git+https://github.com/theochem/Selector.git --user\n",
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"# without env, broken PATH\n",
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"python -m pip install git+https://github.com/theochem/Selector.git --user\n",
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"```\n",
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"\n",
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"2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.\n",
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"# A) Clone git repo with https OR ssh:\n",
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"# The second one only works if you have ssh set up for Github\n",
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"# A1) https\n",
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+
"git clone https://github.com/theochem/Selector.git\n",
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"# A2) ssh\n",
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"git clone [email protected]:theochem/Selector.git\n",
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"# B) Optionally write the version string\n",
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"pip install roberto # or any of the three other ways of running pip, see above.\n",
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"rob write-version\n",
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"# Install pytest in your conda env.\n",
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"conda install pytest pytest-xdist\n",
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"# Then run the tests.\n",
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"pytest --pyargs selector -n auto\n",
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"```\n",
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"\n",
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"With Pip:\n",
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"pip install pytest pytest-xdist\n",
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"# .. and refresh the virtual environment.\n",
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"# This is a venv quirk. Without it, pytest may not find IOData.\n",
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"deactivate && source ~/selector/activate\n",
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"\n",
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"# Alternatively, install pytest in your home directory.\n",
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"pip install pytest pytest-xdist --user\n",
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"\n",
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"# Finally, run the tests.\n",
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"pytest --pyargs selector -n auto\n",
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"```"
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]
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}
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book/content/_build/html/_sources/intro.md
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<!-- #region -->
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# Welcome to QC-Selector's Documentation!
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[
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**"QC-Selector: A Generic Python Package for Subset Selection"**, Fanwang Meng, Alireza Tehrani, Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh"
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The
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## Why QC-Selector?
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<!-- #region -->
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# Welcome to QC-Selector's Documentation!
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[selector](https://github.com/theochem/Selector) is a free, open-source, and cross-platform Python library designed to help you effortlessly identify the most diverse subset of molecules from your dataset. Please use the following citation in any publication using selector library:
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**"QC-Selector: A Generic Python Package for Subset Selection"**, Fanwang Meng, Alireza Tehrani, Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh"
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+
The selector source code is hosted on [GitHub](https://github.com/theochem/Selector) and is released under the [GNU General Public License v3.0](https://github.com/theochem/Selector/blob/main/LICENSE). We welcome any contributions to the selector library in accordance with our Code of Conduct; please see our [Contributing Guidelines](https://qcdevs.org/guidelines/QCDevsCodeOfConduct/). Please report any issues you encounter while using selector library on [GitHub Issues](https://github.com/theochem/Selector/issues). For further information and inquiries please contact us at [email protected].
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## Why QC-Selector?
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book/content/_build/html/_sources/partition.rst
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Partition based methods
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=============
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Partition based methods
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=============
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.. automodule:: selector.partition
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:members:
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book/content/_build/html/_sources/starting.ipynb
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"metadata": {},
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"outputs": [],
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"source": [
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"import
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"outputs": [],
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"source": [
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"# MaxMin method\n",
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"from
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"from
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"# diverse subset selection from randomly generated data points\n",
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"metadata": {},
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"source": [
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"# install the selector module\n",
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"!pip install git+https://github.com/theochem/Selector\n",
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"!pip install rdkit"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import selector\n",
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"import matplotlib.pyplot as plt\n",
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"outputs": [],
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"source": [
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"# MaxMin method\n",
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"from selector.methods.partition import *\n",
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"from selector.methods.dissimilarity import *\n",
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"\n",
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"# diverse subset selection from randomly generated data points\n",
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"selector = MaxMin()\n",
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book/content/_build/html/_sources/utils.rst
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Utilities
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=============
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.. automodule::
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:members:
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Utilities
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=============
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.. automodule:: selector.utils
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:members:
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book/content/_build/html/_static/basic.css
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}
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book/content/_build/html/_static/clipboard.min.js
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*
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* Licensed MIT © Zeno Rocha
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*/
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-
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t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return l(t,e)}},{key:"cut",value:function(t){return f(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===d(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
|
|
|
4 |
*
|
5 |
* Licensed MIT © Zeno Rocha
|
6 |
*/
|
7 |
+
!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. Please use "readonly" instead of "disabled" attribute');if("cut"===n&&(e.hasAttribute("readonly")||e.hasAttribute("disabled")))throw new Error('Invalid "target" attribute. You can\'t cut text from elements with "readonly" or "disabled" attributes')}return t?l(t,{container:o}):e?"cut"===n?f(e):l(e,{container:o}):void 0};function d(t){return(d="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function p(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}function y(t,e){return(y=Object.setPrototypeOf||function(t,e){return t.__proto__=e,t})(t,e)}function h(n){var o=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(t){return!1}}();return function(){var t,e=m(n);return t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return l(t,e)}},{key:"cut",value:function(t){return f(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===d(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
|
book/content/_build/html/_static/copybutton.js
CHANGED
@@ -194,4 +194,4 @@ var copyTargetText = (trigger) => {
|
|
194 |
})
|
195 |
}
|
196 |
|
197 |
-
runWhenDOMLoaded(addCopyButtonToCodeCells)
|
|
|
194 |
})
|
195 |
}
|
196 |
|
197 |
+
runWhenDOMLoaded(addCopyButtonToCodeCells)
|
book/content/_build/html/_static/css/blank.css
CHANGED
@@ -1,2 +1,2 @@
|
|
1 |
/* This file is intentionally left blank to override the stylesheet of the
|
2 |
-
parent theme via theme.conf. The parent style we import directly in theme.css */
|
|
|
1 |
/* This file is intentionally left blank to override the stylesheet of the
|
2 |
+
parent theme via theme.conf. The parent style we import directly in theme.css */
|
book/content/_build/html/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css
CHANGED
The diff for this file is too large to render.
See raw diff
|
|
book/content/_build/html/_static/css/theme.css
CHANGED
@@ -41,7 +41,7 @@
|
|
41 |
|
42 |
/*****************************************************************************
|
43 |
* Color
|
44 |
-
*
|
45 |
* Colors are defined in rgb string way, "red, green, blue"
|
46 |
**/
|
47 |
--pst-color-primary: 19, 6, 84;
|
|
|
41 |
|
42 |
/*****************************************************************************
|
43 |
* Color
|
44 |
+
*
|
45 |
* Colors are defined in rgb string way, "red, green, blue"
|
46 |
**/
|
47 |
--pst-color-primary: 19, 6, 84;
|
book/content/_build/html/_static/documentation_options.js
CHANGED
@@ -9,4 +9,4 @@ var DOCUMENTATION_OPTIONS = {
|
|
9 |
HAS_SOURCE: true,
|
10 |
SOURCELINK_SUFFIX: '',
|
11 |
NAVIGATION_WITH_KEYS: true
|
12 |
-
};
|
|
|
9 |
HAS_SOURCE: true,
|
10 |
SOURCELINK_SUFFIX: '',
|
11 |
NAVIGATION_WITH_KEYS: true
|
12 |
+
};
|
book/content/_build/html/_static/js/index.be7d3bbb2ef33a8344ce.js
CHANGED
The diff for this file is too large to render.
See raw diff
|
|
book/content/_build/html/_static/language_data.js
CHANGED
@@ -293,5 +293,3 @@ function splitQuery(query) {
|
|
293 |
}
|
294 |
return result;
|
295 |
}
|
296 |
-
|
297 |
-
|
|
|
293 |
}
|
294 |
return result;
|
295 |
}
|
|
|
|
book/content/_build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
CHANGED
@@ -4,4 +4,4 @@
|
|
4 |
--tabs-color-overline: rgb(207, 236, 238);
|
5 |
--tabs-color-underline: rgb(207, 236, 238);
|
6 |
--tabs-size-label: 1rem;
|
7 |
-
}
|
|
|
4 |
--tabs-color-overline: rgb(207, 236, 238);
|
5 |
--tabs-color-underline: rgb(207, 236, 238);
|
6 |
--tabs-size-label: 1rem;
|
7 |
+
}
|
book/content/_build/html/_static/pygments.css
CHANGED
@@ -71,4 +71,4 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
|
|
71 |
.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
|
72 |
.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
|
73 |
.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
|
74 |
-
.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
|
|
|
71 |
.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
|
72 |
.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
|
73 |
.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
|
74 |
+
.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
|
book/content/_build/html/_static/togglebutton.css
CHANGED
@@ -87,4 +87,4 @@ button.toggle-button {
|
|
87 |
|
88 |
.toggle-button-hidden .vertical {
|
89 |
transform: rotate(-90deg);
|
90 |
-
}
|
|
|
87 |
|
88 |
.toggle-button-hidden .vertical {
|
89 |
transform: rotate(-90deg);
|
90 |
+
}
|
book/content/_build/html/_static/underscore.js
CHANGED
@@ -3,4 +3,4 @@
|
|
3 |
// https://underscorejs.org
|
4 |
// (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
|
5 |
// Underscore may be freely distributed under the MIT license.
|
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3 |
// https://underscorejs.org
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4 |
// (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
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// Underscore may be freely distributed under the MIT license.
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6 |
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|
book/content/_build/html/_static/vendor/fontawesome/5.13.0/css/all.min.css
CHANGED
The diff for this file is too large to render.
See raw diff
|
|
book/content/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.svg
CHANGED
book/content/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-regular-400.svg
CHANGED
book/content/_build/html/_static/vendor/fontawesome/5.13.0/webfonts/fa-solid-900.svg
CHANGED
book/content/_build/html/_static/webpack-macros.html
CHANGED
@@ -22,4 +22,4 @@
|
|
22 |
|
23 |
{% macro body_post() %}
|
24 |
<script src="{{ pathto('_static/js/index.be7d3bbb2ef33a8344ce.js', 1) }}"></script>
|
25 |
-
{% endmacro %}
|
|
|
22 |
|
23 |
{% macro body_post() %}
|
24 |
<script src="{{ pathto('_static/js/index.be7d3bbb2ef33a8344ce.js', 1) }}"></script>
|
25 |
+
{% endmacro %}
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book/content/_build/html/base.html
CHANGED
@@ -7,11 +7,11 @@
|
|
7 |
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
|
8 |
|
9 |
<title>Base class — Ayers Lab</title>
|
10 |
-
|
11 |
<link href="_static/css/theme.css" rel="stylesheet">
|
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<link href="_static/css/index.ff1ffe594081f20da1ef19478df9384b.css" rel="stylesheet">
|
13 |
|
14 |
-
|
15 |
<link rel="stylesheet"
|
16 |
href="_static/vendor/fontawesome/5.13.0/css/all.min.css">
|
17 |
<link rel="preload" as="font" type="font/woff2" crossorigin
|
@@ -19,10 +19,10 @@
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|
19 |
<link rel="preload" as="font" type="font/woff2" crossorigin
|
20 |
href="_static/vendor/fontawesome/5.13.0/webfonts/fa-brands-400.woff2">
|
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|
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-
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-
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-
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<link rel="stylesheet" type="text/css" href="_static/sphinx-book-theme.css?digest=c3fdc42140077d1ad13ad2f1588a4309" />
|
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<link rel="stylesheet" type="text/css" href="_static/togglebutton.css" />
|
@@ -31,7 +31,7 @@
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|
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<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css" />
|
32 |
<link rel="stylesheet" type="text/css" href="_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
|
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<link rel="stylesheet" type="text/css" href="_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
|
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-
|
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<link rel="preload" as="script" href="_static/js/index.be7d3bbb2ef33a8344ce.js">
|
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|
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<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
@@ -57,34 +57,34 @@
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|
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<link rel="prev" title="Getting started" href="starting.html" />
|
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<meta name="viewport" content="width=device-width, initial-scale=1" />
|
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<meta name="docsearch:language" content="None">
|
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-
|
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|
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<!-- Google Analytics -->
|
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-
|
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</head>
|
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<body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
|
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-
|
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<div class="container-fluid" id="banner"></div>
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-
|
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<div class="container-xl">
|
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<div class="row">
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|
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<div class="col-12 col-md-3 bd-sidebar site-navigation show" id="site-navigation">
|
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-
|
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<div class="navbar-brand-box">
|
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<a class="navbar-brand text-wrap" href="index.html">
|
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-
|
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<!-- `logo` is deprecated in Sphinx 4.0, so remove this when we stop supporting 3 -->
|
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-
|
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-
|
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-
|
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<img src="_static/selector_logo.png" class="logo" alt="logo">
|
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-
|
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-
|
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<h1 class="site-logo" id="site-title">Ayers Lab</h1>
|
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-
|
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</a>
|
89 |
</div><form class="bd-search d-flex align-items-center" action="search.html" method="get">
|
90 |
<i class="icon fas fa-search"></i>
|
@@ -153,17 +153,17 @@
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|
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</div>
|
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|
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|
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<main class="col py-md-3 pl-md-4 bd-content overflow-auto" role="main">
|
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|
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<div class="topbar container-xl fixed-top">
|
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<div class="topbar-contents row">
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<div class="col-12 col-md-3 bd-topbar-whitespace site-navigation show"></div>
|
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<div class="col pl-md-4 topbar-main">
|
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-
|
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<button id="navbar-toggler" class="navbar-toggler ml-0" type="button" data-toggle="collapse"
|
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data-toggle="tooltip" data-placement="bottom" data-target=".site-navigation" aria-controls="navbar-menu"
|
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aria-expanded="true" aria-label="Toggle navigation" aria-controls="site-navigation"
|
@@ -172,15 +172,15 @@
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<i class="fas fa-arrow-left"></i>
|
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<i class="fas fa-arrow-up"></i>
|
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</button>
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-
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-
|
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<div class="dropdown-buttons-trigger">
|
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<button id="dropdown-buttons-trigger" class="btn btn-secondary topbarbtn" aria-label="Download this page"><i
|
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class="fas fa-download"></i></button>
|
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|
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<div class="dropdown-buttons">
|
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<!-- ipynb file if we had a myst markdown file -->
|
183 |
-
|
184 |
<!-- Download raw file -->
|
185 |
<a class="dropdown-buttons" href="_sources/base.rst"><button type="button"
|
186 |
class="btn btn-secondary topbarbtn" title="Download source file" data-toggle="tooltip"
|
@@ -198,14 +198,14 @@
|
|
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aria-label="Connect with source repository"><i class="fab fa-github"></i></button>
|
199 |
<div class="dropdown-buttons sourcebuttons">
|
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<a class="repository-button"
|
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-
href="https://github.com/theochem/
|
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data-toggle="tooltip" data-placement="left" title="Source repository"><i
|
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class="fab fa-github"></i>repository</button></a>
|
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<a class="issues-button"
|
205 |
-
href="https://github.com/theochem/
|
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type="button" class="btn btn-secondary topbarbtn" data-toggle="tooltip" data-placement="left"
|
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<dd><p>Return indices representing subset of sample points.</p>
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
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<span id="base-class"></span><h1>Base class<a class="headerlink" href="#module-selector.base" title="Permalink to this headline">¶</a></h1>
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<p>Base class for diversity based subset selection.</p>
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<dt class="sig sig-object py" id="selector.base.SelectionBase">
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<dd><p>Base class for selecting subset of sample points.</p>
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<dt class="sig sig-object py" id="selector.base.SelectionBase.select">
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<span class="sig-name descname"><span class="pre">select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="headerlink" href="#selector.base.SelectionBase.select" title="Permalink to this definition">¶</a></dt>
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<dd><p>Return indices representing subset of sample points.</p>
|
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
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<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">numpy.ndarray</span></span></span><a class="headerlink" href="#selector.base.SelectionBase.select_from_cluster" title="Permalink to this definition">¶</a></dt>
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<dd><p>Return indices representing subset of sample points from one cluster.</p>
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
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<section id="dissimilarity-based-methods">
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<h1>Dissimilarity based methods<a class="headerlink" href="#dissimilarity-based-methods" title="Permalink to this headline">¶</a></h1>
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-
<span class="target" id="module-
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<dd><p>Selecting samples using MaxMin algorithm.</p>
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<p>MaxMin is possibly the most widely used method for dissimilarity-based
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compound selection. When presented with a dataset of samples, the
|
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property‐based and fragment‐based molecular descriptions, Quantitative
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Structure‐Activity Relationships 21.6 (2002): 598-604.</p>
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<dl class="py method">
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-
<dt class="sig sig-object py" id="
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-
<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#
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<dd><p>Return selected samples from a cluster based on MaxMin algorithm.</p>
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<dl class="simple">
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
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<dd><p>Selecting samples using MaxSum algorithm.</p>
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<p>Whereas the goal of the MaxMin algorithm is to maximize the minimum distance
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between any pair of distinct elements in the selected subset of a dataset,
|
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submodular functions and dynamic updates, Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI
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symposium on Principles of Database Systems. 2012.</p>
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<dl class="py method">
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-
<dt class="sig sig-object py" id="
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#
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<dd><p>Return selected samples from a cluster based on MaxSum algorithm.</p>
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<dl class="simple">
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">
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<dd><p>Selecting samples using OptiSim algorithm.</p>
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<p>The OptiSim algorithm selects samples from a dataset by first choosing the medoid center as the
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initial point. Next, points are randomly chosen and added to a subsample if they exist
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repeated.</p>
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<p>[1] J. Chem. Inf. Comput. Sci. 1997, 37, 6, 1181–1188. <a class="reference external" href="https://doi.org/10.1021/ci970282v">https://doi.org/10.1021/ci970282v</a></p>
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<dd><p>Return selected samples based on OptiSim algorithm.</p>
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<dl class="simple">
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<dt>X<span class="classifier">np.ndarray</span></dt><dd><p>Coordinate array of samples.</p>
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<dd><p>Return selected samples from a cluster based on OptiSim algorithm.</p>
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<dl class="simple">
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<dt>X<span class="classifier">np.ndarray</span></dt><dd><p>Coordinate array of samples.</p>
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<section id="dissimilarity-based-methods">
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<h1>Dissimilarity based methods<a class="headerlink" href="#dissimilarity-based-methods" title="Permalink to this headline">¶</a></h1>
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+
<span class="target" id="module-selector.dissimilarity"></span><p>Module for Dissimilarity-Based Selection Methods.</p>
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<dl class="py class">
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<dt class="sig sig-object py" id="selector.dissimilarity.MaxMin">
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">selector.dissimilarity.</span></span><span class="sig-name descname"><span class="pre">MaxMin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.dissimilarity.MaxMin" title="Permalink to this definition">¶</a></dt>
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<dd><p>Selecting samples using MaxMin algorithm.</p>
|
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<p>MaxMin is possibly the most widely used method for dissimilarity-based
|
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compound selection. When presented with a dataset of samples, the
|
|
|
253 |
property‐based and fragment‐based molecular descriptions, Quantitative
|
254 |
Structure‐Activity Relationships 21.6 (2002): 598-604.</p>
|
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<dl class="py method">
|
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+
<dt class="sig sig-object py" id="selector.dissimilarity.MaxMin.select_from_cluster">
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.dissimilarity.MaxMin.select_from_cluster" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Return selected samples from a cluster based on MaxMin algorithm.</p>
|
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<dl class="simple">
|
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
|
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</dd></dl>
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<dl class="py class">
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<dt class="sig sig-object py" id="selector.dissimilarity.MaxSum">
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">selector.dissimilarity.</span></span><span class="sig-name descname"><span class="pre">MaxSum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func_distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.dissimilarity.MaxSum" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Selecting samples using MaxSum algorithm.</p>
|
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<p>Whereas the goal of the MaxMin algorithm is to maximize the minimum distance
|
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between any pair of distinct elements in the selected subset of a dataset,
|
|
|
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submodular functions and dynamic updates, Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI
|
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symposium on Principles of Database Systems. 2012.</p>
|
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<dl class="py method">
|
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+
<dt class="sig sig-object py" id="selector.dissimilarity.MaxSum.select_from_cluster">
|
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.dissimilarity.MaxSum.select_from_cluster" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Return selected samples from a cluster based on MaxSum algorithm.</p>
|
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<dl class="simple">
|
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<dt>X: ndarray of shape (n_samples, n_features) or (n_samples, n_samples)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional feature space.
|
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</dd></dl>
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<dt class="sig sig-object py" id="selector.dissimilarity.OptiSim">
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">selector.dissimilarity.</span></span><span class="sig-name descname"><span class="pre">OptiSim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">r0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.dissimilarity.OptiSim" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Selecting samples using OptiSim algorithm.</p>
|
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<p>The OptiSim algorithm selects samples from a dataset by first choosing the medoid center as the
|
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initial point. Next, points are randomly chosen and added to a subsample if they exist
|
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repeated.</p>
|
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<p>[1] J. Chem. Inf. Comput. Sci. 1997, 37, 6, 1181–1188. <a class="reference external" href="https://doi.org/10.1021/ci970282v">https://doi.org/10.1021/ci970282v</a></p>
|
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|
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|
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<span class="sig-name descname"><span class="pre">algorithm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_size</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">list</span></span></span><a class="headerlink" href="#selector.dissimilarity.OptiSim.algorithm" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Return selected samples based on OptiSim algorithm.</p>
|
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<dl class="simple">
|
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<dt>X<span class="classifier">np.ndarray</span></dt><dd><p>Coordinate array of samples.</p>
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|
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|
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|
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|
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<dt class="sig sig-object py" id="selector.dissimilarity.OptiSim.select_from_cluster">
|
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.dissimilarity.OptiSim.select_from_cluster" title="Permalink to this definition">¶</a></dt>
|
347 |
<dd><p>Return selected samples from a cluster based on OptiSim algorithm.</p>
|
348 |
<dl class="simple">
|
349 |
<dt>X<span class="classifier">np.ndarray</span></dt><dd><p>Coordinate array of samples.</p>
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|
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By The QC-Dev Community with the support of Digital Research Alliance of Canada and The Jupyter Book Community<br/>
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© Copyright 2022-2023.<br/>
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By The QC-Dev Community with the support of Digital Research Alliance of Canada and The Jupyter Book Community<br/>
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© Copyright 2022-2023.<br/>
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<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
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</b>
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<p>We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.</p>
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<p>The following dependencies are required to run
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<li><p>Python >= 3.6: <a class="reference external" href="http://www.python.org/">http://www.python.org/</a></p></li>
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<li><p>NumPy >= 1.21.5: <a class="reference external" href="http://www.numpy.org/">http://www.numpy.org/</a></p></li>
|
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|
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<section id="installation-with-ana-or-miniconda">
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<h2>Installation with Ana- or Miniconda:<a class="headerlink" href="#installation-with-ana-or-miniconda" title="Permalink to this headline">¶</a></h2>
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<ol class="simple">
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-
<li><p>To install
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Activate your main conda environment if it is not loaded in your .bashrc.</span>
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<span class="c1"># E.g. run the following if you have miniconda installed in e.g. ~/miniconda3</span>
|
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</section>
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<section id="latest-git-revision">
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<h2>Latest git revision<a class="headerlink" href="#latest-git-revision" title="Permalink to this headline">¶</a></h2>
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-
<p>This section shows how one can install the latest revision of
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<p>There are two installation methods:</p>
|
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<ol class="simple">
|
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<li><p><strong>Quick and dirty.</strong> Of this method, there are four variants, depending on the correctness of your <code class="docutils literal notranslate"><span class="pre">PATH</span></code> variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.</p></li>
|
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># with env, correct PATH</span>
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<span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">
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<span class="c1"># with env, broken PATH</span>
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<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">
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<span class="c1"># without env, correct PATH</span>
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<span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">
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<span class="c1"># without env, broken PATH</span>
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<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># A) Clone git repo with https OR ssh:</span>
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<span class="c1"># The second one only works if you have ssh set up for Github</span>
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<span class="c1"># A1) https</span>
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<span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">
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<span class="c1"># B) Optionally write the version string</span>
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<p>With Pip:</p>
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<span class="n">pip</span> <span class="n">install</span> <span class="n">pytest</span> <span class="n">pytest</span><span class="o">-</span><span class="n">xdist</span>
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<span class="n">deactivate</span> <span class="o">&&</span> <span class="n">source</span> <span class="o">~/</span><span class="n">
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<span class="c1"># Alternatively, install pytest in your home directory.</span>
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<span class="n">pip</span> <span class="n">install</span> <span class="n">pytest</span> <span class="n">pytest</span><span class="o">-</span><span class="n">xdist</span> <span class="o">--</span><span class="n">user</span>
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<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
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<section id="stable-releases">
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|
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</b>
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<p>We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.</p>
|
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</div>
|
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+
<p>The following dependencies are required to run selector properly,</p>
|
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<ul class="simple">
|
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<li><p>Python >= 3.6: <a class="reference external" href="http://www.python.org/">http://www.python.org/</a></p></li>
|
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<li><p>NumPy >= 1.21.5: <a class="reference external" href="http://www.numpy.org/">http://www.numpy.org/</a></p></li>
|
|
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<section id="installation-with-ana-or-miniconda">
|
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<h2>Installation with Ana- or Miniconda:<a class="headerlink" href="#installation-with-ana-or-miniconda" title="Permalink to this headline">¶</a></h2>
|
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<ol class="simple">
|
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+
<li><p>To install selector using the conda package management system, install <a class="reference external" href="https://conda.io/miniconda.html">miniconda</a> or <a class="reference external" href="https://www.anaconda.com/download">anaconda</a> first, and then:</p></li>
|
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</ol>
|
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Activate your main conda environment if it is not loaded in your .bashrc.</span>
|
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<span class="c1"># E.g. run the following if you have miniconda installed in e.g. ~/miniconda3</span>
|
|
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</section>
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<section id="latest-git-revision">
|
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<h2>Latest git revision<a class="headerlink" href="#latest-git-revision" title="Permalink to this headline">¶</a></h2>
|
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+
<p>This section shows how one can install the latest revision of selector from the git repository. This kind of installation comes with some risks (sudden API changes, bugs, …) and so be prepared to accept them when using the following installation instructions.</p>
|
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<p>There are two installation methods:</p>
|
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<ol class="simple">
|
381 |
<li><p><strong>Quick and dirty.</strong> Of this method, there are four variants, depending on the correctness of your <code class="docutils literal notranslate"><span class="pre">PATH</span></code> variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.</p></li>
|
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</ol>
|
383 |
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># with env, correct PATH</span>
|
384 |
+
<span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">selector</span><span class="o">.</span><span class="n">git</span>
|
385 |
<span class="c1"># with env, broken PATH</span>
|
386 |
+
<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">selector</span><span class="o">.</span><span class="n">git</span>
|
387 |
<span class="c1"># without env, correct PATH</span>
|
388 |
+
<span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">selector</span><span class="o">.</span><span class="n">git</span> <span class="o">--</span><span class="n">user</span>
|
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<span class="c1"># without env, broken PATH</span>
|
390 |
+
<span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">selector</span><span class="o">.</span><span class="n">git</span> <span class="o">--</span><span class="n">user</span>
|
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</pre></div>
|
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</div>
|
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<ol class="simple">
|
|
|
396 |
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># A) Clone git repo with https OR ssh:</span>
|
397 |
<span class="c1"># The second one only works if you have ssh set up for Github</span>
|
398 |
<span class="c1"># A1) https</span>
|
399 |
+
<span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">theochem</span><span class="o">/</span><span class="n">selector</span><span class="o">.</span><span class="n">git</span>
|
400 |
<span class="c1"># A2) ssh</span>
|
401 |
+
<span class="n">git</span> <span class="n">clone</span> <span class="n">git</span><span class="nd">@github</span><span class="o">.</span><span class="n">com</span><span class="p">:</span><span class="n">theochem</span><span class="o">/</span><span class="n">selector</span><span class="o">.</span><span class="n">git</span>
|
402 |
<span class="c1"># B) Optionally write the version string</span>
|
403 |
<span class="n">pip</span> <span class="n">install</span> <span class="n">roberto</span> <span class="c1"># or any of the three other ways of running pip, see above.</span>
|
404 |
<span class="n">rob</span> <span class="n">write</span><span class="o">-</span><span class="n">version</span>
|
|
|
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install pytest in your conda env.</span>
|
426 |
<span class="n">conda</span> <span class="n">install</span> <span class="n">pytest</span> <span class="n">pytest</span><span class="o">-</span><span class="n">xdist</span>
|
427 |
<span class="c1"># Then run the tests.</span>
|
428 |
+
<span class="n">pytest</span> <span class="o">--</span><span class="n">pyargs</span> <span class="n">selector</span> <span class="o">-</span><span class="n">n</span> <span class="n">auto</span>
|
429 |
</pre></div>
|
430 |
</div>
|
431 |
<p>With Pip:</p>
|
|
|
433 |
<span class="n">pip</span> <span class="n">install</span> <span class="n">pytest</span> <span class="n">pytest</span><span class="o">-</span><span class="n">xdist</span>
|
434 |
<span class="c1"># .. and refresh the virtual environment.</span>
|
435 |
<span class="c1"># This is a venv quirk. Without it, pytest may not find IOData.</span>
|
436 |
+
<span class="n">deactivate</span> <span class="o">&&</span> <span class="n">source</span> <span class="o">~/</span><span class="n">selector</span><span class="o">/</span><span class="n">activate</span>
|
437 |
|
438 |
<span class="c1"># Alternatively, install pytest in your home directory.</span>
|
439 |
<span class="n">pip</span> <span class="n">install</span> <span class="n">pytest</span> <span class="n">pytest</span><span class="o">-</span><span class="n">xdist</span> <span class="o">--</span><span class="n">user</span>
|
440 |
|
441 |
<span class="c1"># Finally, run the tests.</span>
|
442 |
+
<span class="n">pytest</span> <span class="o">--</span><span class="n">pyargs</span> <span class="n">selector</span> <span class="o">-</span><span class="n">n</span> <span class="n">auto</span>
|
443 |
</pre></div>
|
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</div>
|
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</section>
|
|
|
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{
|
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requestKernel: true,
|
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binderOptions: {
|
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+
repo: "theochem/Selector",
|
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ref: "main",
|
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},
|
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codeMirrorConfig: {
|
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|
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By The QC-Dev Community with the support of Digital Research Alliance of Canada and The Jupyter Book Community<br/>
|
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|
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© Copyright 2022-2023.<br/>
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</p>
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href="https://github.com/theochem/
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|
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|
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<section class="tex2jax_ignore mathjax_ignore" id="welcome-to-qc-selector-s-documentation">
|
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<h1>Welcome to QC-Selector’s Documentation!<a class="headerlink" href="#welcome-to-qc-selector-s-documentation" title="Permalink to this headline">¶</a></h1>
|
254 |
-
<p><a class="reference external" href="https://github.com/theochem/
|
255 |
<p><strong>“QC-Selector: A Generic Python Package for Subset Selection”</strong>, Fanwang Meng, Alireza Tehrani, Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh”</p>
|
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-
<p>The
|
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<section id="why-qc-selector">
|
258 |
<h2>Why QC-Selector?<a class="headerlink" href="#why-qc-selector" title="Permalink to this headline">¶</a></h2>
|
259 |
<p>In the world of chemistry, selecting the right subset of molecules is critical for a wide range of applications, including drug discovery, materials science, and molecular optimization. QC-Selector offers a cutting-edge solution to streamline this process, empowering researchers, scientists, and developers to make smarter decisions faster.</p>
|
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By The QC-Dev Community with the support of Digital Research Alliance of Canada and The Jupyter Book Community<br/>
|
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© Copyright 2022-2023.<br/>
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<title>Welcome to QC-Selector’s Documentation! — Ayers Lab</title>
|
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+
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<h1>Welcome to QC-Selector’s Documentation!<a class="headerlink" href="#welcome-to-qc-selector-s-documentation" title="Permalink to this headline">¶</a></h1>
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<p><a class="reference external" href="https://github.com/theochem/Selector">selector</a> is a free, open-source, and cross-platform Python library designed to help you effortlessly identify the most diverse subset of molecules from your dataset. Please use the following citation in any publication using selector library:</p>
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<p><strong>“QC-Selector: A Generic Python Package for Subset Selection”</strong>, Fanwang Meng, Alireza Tehrani, Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh”</p>
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<p>The selector source code is hosted on <a class="reference external" href="https://github.com/theochem/Selector">GitHub</a> and is released under the <a class="reference external" href="https://github.com/theochem/Selector/blob/main/LICENSE">GNU General Public License v3.0</a>. We welcome any contributions to the selector library in accordance with our Code of Conduct; please see our <a class="reference external" href="https://qcdevs.org/guidelines/QCDevsCodeOfConduct/">Contributing Guidelines</a>. Please report any issues you encounter while using selector library on <a class="reference external" href="https://github.com/theochem/Selector/issues">GitHub Issues</a>. For further information and inquiries please contact us at <a class="reference external" href="mailto:qcdevs%40gmail.com">qcdevs<span>@</span>gmail<span>.</span>com</a>.</p>
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<section id="why-qc-selector">
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<h2>Why QC-Selector?<a class="headerlink" href="#why-qc-selector" title="Permalink to this headline">¶</a></h2>
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<p>In the world of chemistry, selecting the right subset of molecules is critical for a wide range of applications, including drug discovery, materials science, and molecular optimization. QC-Selector offers a cutting-edge solution to streamline this process, empowering researchers, scientists, and developers to make smarter decisions faster.</p>
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<section id="partition-based-methods">
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<h1>Partition based methods<a class="headerlink" href="#partition-based-methods" title="Permalink to this headline">¶</a></h1>
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<dd><p>Select samples using Directed Sphere Exclusion (DISE) algorithm.</p>
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any already selected sample. The radius of the exclusion sphere is an adjustable parameter.
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Journal of Chemical Information and Computer Sciences,
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43(1), 317–323. <a class="reference external" href="https://doi.org/10.1021/ci025554v">https://doi.org/10.1021/ci025554v</a></p>
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<dt>X: ndarray of shape (n_samples, n_features)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional space.</p>
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#
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<dd><p>Return selected samples from a cluster based on directed sphere exclusion algorithm</p>
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<dd><p>Selecting points using the Grid Partitioning algorithm.</p>
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dependent). A point is selected from each of the grids while the number of selected points is
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greatest diversity.</p>
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<p>Adapted from <a class="reference external" href="https://doi.org/10.1016/S1093-3263(99)00016-9">https://doi.org/10.1016/S1093-3263(99)00016-9</a>.</p>
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<dd><p>Grid partitioning algorithm for selecting points from cluster.</p>
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<dt>arr: np.ndarray</dt><dd><p>Coordinate array of points</p>
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<dd><p>Selecting points using an algorithm adapted from KDTree.</p>
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upon selecting requested number of points or if all available points exhausted.</p>
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<p>Adapted from: <a class="reference external" href="https://en.wikipedia.org/wiki/K-d_tree#Construction">https://en.wikipedia.org/wiki/K-d_tree#Construction</a></p>
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<dd><p>Main function for selecting points using the KDTree algorithm.</p>
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<section id="partition-based-methods">
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<h1>Partition based methods<a class="headerlink" href="#partition-based-methods" title="Permalink to this headline">¶</a></h1>
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+
<span class="target" id="module-selector.partition"></span><p>Module for Partition-Based Selection Methods.</p>
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<dl class="py class">
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+
<dt class="sig sig-object py" id="selector.partition.DirectedSphereExclusion">
|
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+
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">selector.partition.</span></span><span class="sig-name descname"><span class="pre">DirectedSphereExclusion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">r0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.DirectedSphereExclusion" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Select samples using Directed Sphere Exclusion (DISE) algorithm.</p>
|
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<p>In a nutshell, this algorithm iteratively excludes any sample within a given radius from
|
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any already selected sample. The radius of the exclusion sphere is an adjustable parameter.
|
|
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Journal of Chemical Information and Computer Sciences,
|
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43(1), 317–323. <a class="reference external" href="https://doi.org/10.1021/ci025554v">https://doi.org/10.1021/ci025554v</a></p>
|
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<dl class="py method">
|
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+
<dt class="sig sig-object py" id="selector.partition.DirectedSphereExclusion.algorithm">
|
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+
<span class="sig-name descname"><span class="pre">algorithm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_size</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.DirectedSphereExclusion.algorithm" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Return selected samples based on directed sphere exclusion algorithm.</p>
|
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<dl class="simple">
|
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<dt>X: ndarray of shape (n_samples, n_features)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional space.</p>
|
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</dd></dl>
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<dl class="py method">
|
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<dt class="sig sig-object py" id="selector.partition.DirectedSphereExclusion.select_from_cluster">
|
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.DirectedSphereExclusion.select_from_cluster" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Return selected samples from a cluster based on directed sphere exclusion algorithm</p>
|
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<dl class="simple">
|
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<dt>X: ndarray of shape (n_samples, n_features)</dt><dd><p>Feature matrix of <cite>n_samples</cite> samples in <cite>n_features</cite> dimensional space.</p>
|
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</dd></dl>
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<dl class="py class">
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<dt class="sig sig-object py" id="selector.partition.GridPartitioning">
|
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">selector.partition.</span></span><span class="sig-name descname"><span class="pre">GridPartitioning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cells</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'equisized_independent'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.GridPartitioning" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Selecting points using the Grid Partitioning algorithm.</p>
|
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<p>Points are partitioned into grids using an algorithm (equisized independent or equisized
|
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dependent). A point is selected from each of the grids while the number of selected points is
|
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greatest diversity.</p>
|
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<p>Adapted from <a class="reference external" href="https://doi.org/10.1016/S1093-3263(99)00016-9">https://doi.org/10.1016/S1093-3263(99)00016-9</a>.</p>
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<dl class="py method">
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_selected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.GridPartitioning.select_from_cluster" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Grid partitioning algorithm for selecting points from cluster.</p>
|
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<dl class="simple">
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<dt>arr: np.ndarray</dt><dd><p>Coordinate array of points</p>
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</dd></dl>
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">selector.partition.</span></span><span class="sig-name descname"><span class="pre">Medoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">start_id=0</span></em>, <em class="sig-param"><span class="pre">func_distance=<function</span> <span class="pre">Medoid.<lambda>></span></em>, <em class="sig-param"><span class="pre">scaling=10</span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.Medoid" title="Permalink to this definition">¶</a></dt>
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<dd><p>Selecting points using an algorithm adapted from KDTree.</p>
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<p>Points are initially used to construct a KDTree. Eucleidean distances are used for this
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algorithm. The first point selected is based on the starting_idx provided and becomes the first
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upon selecting requested number of points or if all available points exhausted.</p>
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<p>Adapted from: <a class="reference external" href="https://en.wikipedia.org/wiki/K-d_tree#Construction">https://en.wikipedia.org/wiki/K-d_tree#Construction</a></p>
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<span class="sig-name descname"><span class="pre">select_from_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_selected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.partition.Medoid.select_from_cluster" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Main function for selecting points using the KDTree algorithm.</p>
|
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<dl class="simple">
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<dt>arr: np.ndarray</dt><dd><p>Coordinate array of points</p>
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<section class="tex2jax_ignore mathjax_ignore" id="getting-started">
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<h1>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h1>
|
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<section id="demo-dissimilarity-based-selection-synthetic-data">
|
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<h2>Generating Synthetic Data<a class="headerlink" href="#generating-synthetic-data" title="Permalink to this headline">¶</a></h2>
|
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<div class="cell docutils container">
|
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<div class="cell_input docutils container">
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+
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install the selector module</span>
|
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+
<span class="o">!</span>pip install git+https://github.com/theochem/Selector
|
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<span class="o">!</span>pip install rdkit
|
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">selector</span>
|
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
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<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># MaxMin method</span>
|
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<span class="kn">from</span> <span class="nn">selector.methods.partition</span> <span class="kn">import</span> <span class="o">*</span>
|
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<span class="kn">from</span> <span class="nn">selector.methods.dissimilarity</span> <span class="kn">import</span> <span class="o">*</span>
|
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<span class="c1"># diverse subset selection from randomly generated data points</span>
|
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<span class="n">selector</span> <span class="o">=</span> <span class="n">MaxMin</span><span class="p">()</span>
|
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># diverse subset selection from randomly generated data points</span>
|
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<span class="n">selected_id7</span> <span class="o">=</span> <span class="n">GridPartitioning</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">"equisized_independent"</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
|
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<span class="c1"># diverse subset selection from data points with obvious patterns (different clusters)</span>
|
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+
<span class="n">selected_id8</span> <span class="o">=</span> <span class="n">GridPartitioning</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="s2">"equisized_independent"</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">coords_cluster</span><span class="p">,</span>
|
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<span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
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<span class="n">labels</span><span class="o">=</span><span class="n">class_labels_cluster</span><span class="p">)</span>
|
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<span class="n">graph_data</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span><span class="n">selected_id7</span><span class="p">)</span>
|
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{
|
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requestKernel: true,
|
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<dt class="sig sig-object py" id="
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<dd><p>Algorithm that uses sphere exclusion for selecting points from cluster.</p>
|
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<p>Iteratively searches for the optimal radius to obtain the correct number
|
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of selected samples. If the radius cannot converge to return <cite>size</cite> points,
|
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<div>
|
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+
|
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+
<section id="module-selector.utils">
|
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+
<span id="utilities"></span><h1>Utilities<a class="headerlink" href="#module-selector.utils" title="Permalink to this headline">¶</a></h1>
|
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<p>Module for Selection Utilities.</p>
|
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<dl class="py function">
|
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+
<dt class="sig sig-object py" id="selector.utils.optimize_radius">
|
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+
<span class="sig-prename descclassname"><span class="pre">selector.utils.</span></span><span class="sig-name descname"><span class="pre">optimize_radius</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#selector.utils.optimize_radius" title="Permalink to this definition">¶</a></dt>
|
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<dd><p>Algorithm that uses sphere exclusion for selecting points from cluster.</p>
|
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<p>Iteratively searches for the optimal radius to obtain the correct number
|
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of selected samples. If the radius cannot converge to return <cite>size</cite> points,
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{
|
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requestKernel: true,
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binderOptions: {
|
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+
repo: "theochem/Selector",
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ref: "main",
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},
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codeMirrorConfig: {
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<script>kernelName = 'python3'</script>
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</div>
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+
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+
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<!-- Previous / next buttons -->
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<div class='prev-next-area'>
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<a class='left-prev' id="prev-link" href="partition.html" title="previous page">
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<i class="fas fa-angle-left"></i>
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<div class="prev-next-info">
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</div>
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</a>
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</div>
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</div>
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</div>
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<footer class="footer">
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<div class="container">
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<p>
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+
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By The QC-Dev Community with the support of Digital Research Alliance of Canada and The Jupyter Book Community<br/>
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+
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© Copyright 2022-2023.<br/>
|
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</p>
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</div>
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</div>
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</div>
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+
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<script src="_static/js/index.be7d3bbb2ef33a8344ce.js"></script>
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</body>
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+
</html>
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book/content/_build/jupyter_execute/installation.ipynb
CHANGED
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|
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"We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.\n",
|
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"</div>\n",
|
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"\n",
|
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-
"The following dependencies are required to run
|
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"\n",
|
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"* Python >= 3.6: http://www.python.org/ \n",
|
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"* NumPy >= 1.21.5: http://www.numpy.org/\n",
|
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|
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"\n",
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"## Installation with Ana- or Miniconda:\n",
|
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"\n",
|
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-
"1. To install
|
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"\n",
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"```\n",
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"# Activate your main conda environment if it is not loaded in your .bashrc.\n",
|
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|
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"\n",
|
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"## Latest git revision\n",
|
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"\n",
|
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-
"This section shows how one can install the latest revision of
|
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"\n",
|
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"There are two installation methods:\n",
|
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"\n",
|
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"1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.\n",
|
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"```\n",
|
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"# with env, correct PATH\n",
|
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-
"pip install git+https://github.com/theochem/
|
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"# with env, broken PATH\n",
|
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-
"python -m pip install git+https://github.com/theochem/
|
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"# without env, correct PATH\n",
|
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-
"pip install git+https://github.com/theochem/
|
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"# without env, broken PATH\n",
|
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-
"python -m pip install git+https://github.com/theochem/
|
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"```\n",
|
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"\n",
|
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"2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.\n",
|
@@ -117,9 +117,9 @@
|
|
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"# A) Clone git repo with https OR ssh:\n",
|
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"# The second one only works if you have ssh set up for Github\n",
|
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"# A1) https\n",
|
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-
"git clone https://github.com/theochem/
|
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"# A2) ssh\n",
|
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-
"git clone [email protected]:theochem/
|
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"# B) Optionally write the version string\n",
|
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"pip install roberto # or any of the three other ways of running pip, see above.\n",
|
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"rob write-version\n",
|
@@ -148,7 +148,7 @@
|
|
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"# Install pytest in your conda env.\n",
|
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"conda install pytest pytest-xdist\n",
|
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"# Then run the tests.\n",
|
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-
"pytest --pyargs
|
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"```\n",
|
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"\n",
|
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"With Pip:\n",
|
@@ -157,13 +157,13 @@
|
|
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"pip install pytest pytest-xdist\n",
|
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"# .. and refresh the virtual environment.\n",
|
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"# This is a venv quirk. Without it, pytest may not find IOData.\n",
|
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-
"deactivate && source ~/
|
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"\n",
|
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"# Alternatively, install pytest in your home directory.\n",
|
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"pip install pytest pytest-xdist --user\n",
|
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"\n",
|
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"# Finally, run the tests.\n",
|
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-
"pytest --pyargs
|
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"```"
|
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]
|
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}
|
@@ -189,4 +189,4 @@
|
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 5
|
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-
}
|
|
|
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"We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.\n",
|
17 |
"</div>\n",
|
18 |
"\n",
|
19 |
+
"The following dependencies are required to run selector properly,\n",
|
20 |
"\n",
|
21 |
"* Python >= 3.6: http://www.python.org/ \n",
|
22 |
"* NumPy >= 1.21.5: http://www.numpy.org/\n",
|
|
|
29 |
"\n",
|
30 |
"## Installation with Ana- or Miniconda:\n",
|
31 |
"\n",
|
32 |
+
"1. To install selector using the conda package management system, install [miniconda](https://conda.io/miniconda.html) or [anaconda](https://www.anaconda.com/download) first, and then:\n",
|
33 |
"\n",
|
34 |
"```\n",
|
35 |
"# Activate your main conda environment if it is not loaded in your .bashrc.\n",
|
|
|
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"\n",
|
97 |
"## Latest git revision\n",
|
98 |
"\n",
|
99 |
+
"This section shows how one can install the latest revision of selector from the git repository. This kind of installation comes with some risks (sudden API changes, bugs, …) and so be prepared to accept them when using the following installation instructions.\n",
|
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"\n",
|
101 |
"There are two installation methods:\n",
|
102 |
"\n",
|
103 |
"1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.\n",
|
104 |
"```\n",
|
105 |
"# with env, correct PATH\n",
|
106 |
+
"pip install git+https://github.com/theochem/Selector.git\n",
|
107 |
"# with env, broken PATH\n",
|
108 |
+
"python -m pip install git+https://github.com/theochem/Selector.git\n",
|
109 |
"# without env, correct PATH\n",
|
110 |
+
"pip install git+https://github.com/theochem/Selector.git --user\n",
|
111 |
"# without env, broken PATH\n",
|
112 |
+
"python -m pip install git+https://github.com/theochem/Selector.git --user\n",
|
113 |
"```\n",
|
114 |
"\n",
|
115 |
"2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.\n",
|
|
|
117 |
"# A) Clone git repo with https OR ssh:\n",
|
118 |
"# The second one only works if you have ssh set up for Github\n",
|
119 |
"# A1) https\n",
|
120 |
+
"git clone https://github.com/theochem/Selector.git\n",
|
121 |
"# A2) ssh\n",
|
122 |
+
"git clone [email protected]:theochem/Selector.git\n",
|
123 |
"# B) Optionally write the version string\n",
|
124 |
"pip install roberto # or any of the three other ways of running pip, see above.\n",
|
125 |
"rob write-version\n",
|
|
|
148 |
"# Install pytest in your conda env.\n",
|
149 |
"conda install pytest pytest-xdist\n",
|
150 |
"# Then run the tests.\n",
|
151 |
+
"pytest --pyargs selector -n auto\n",
|
152 |
"```\n",
|
153 |
"\n",
|
154 |
"With Pip:\n",
|
|
|
157 |
"pip install pytest pytest-xdist\n",
|
158 |
"# .. and refresh the virtual environment.\n",
|
159 |
"# This is a venv quirk. Without it, pytest may not find IOData.\n",
|
160 |
+
"deactivate && source ~/selector/activate\n",
|
161 |
"\n",
|
162 |
"# Alternatively, install pytest in your home directory.\n",
|
163 |
"pip install pytest pytest-xdist --user\n",
|
164 |
"\n",
|
165 |
"# Finally, run the tests.\n",
|
166 |
+
"pytest --pyargs selector -n auto\n",
|
167 |
"```"
|
168 |
]
|
169 |
}
|
|
|
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 5
|
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+
}
|
book/content/_build/jupyter_execute/installation.py
CHANGED
@@ -1,44 +1,43 @@
|
|
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#!/usr/bin/env python
|
2 |
-
# coding: utf-8
|
3 |
|
4 |
# # Installation
|
5 |
-
#
|
6 |
# ## Stable releases
|
7 |
-
#
|
8 |
# <div class="alert alert-block alert-warning">
|
9 |
# <b>Warning:
|
10 |
# </b>
|
11 |
-
#
|
12 |
# We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.
|
13 |
# </div>
|
14 |
-
#
|
15 |
-
# The following dependencies are required to run
|
16 |
-
#
|
17 |
-
# * Python >= 3.6: http://www.python.org/
|
18 |
# * NumPy >= 1.21.5: http://www.numpy.org/
|
19 |
# * SciPy >= 1.5.0: http://www.scipy.org/
|
20 |
# * PyTest >= 5.3.4: https://docs.pytest.org/
|
21 |
# * PyTest-Cov >= 2.8.0: https://pypi.org/project/pytest-cov
|
22 |
-
#
|
23 |
# Normally, you don’t need to install these dependencies manually. They will be installed automatically when you follow the instructions below.
|
24 |
-
#
|
25 |
-
#
|
26 |
# ## Installation with Ana- or Miniconda:
|
27 |
-
#
|
28 |
-
# 1. To install
|
29 |
-
#
|
30 |
# ```
|
31 |
# # Activate your main conda environment if it is not loaded in your .bashrc.
|
32 |
# # E.g. run the following if you have miniconda installed in e.g. ~/miniconda3
|
33 |
# source ~/miniconda3/bin/activate
|
34 |
-
#
|
35 |
# # Create a horton3 conda environment. (optional, recommended)
|
36 |
# conda create -n horton3
|
37 |
# source activate horton3
|
38 |
-
#
|
39 |
# # Install the stable release.
|
40 |
# conda install -c theochem qc-selector
|
41 |
-
#
|
42 |
# # Unstable releases
|
43 |
# # (Only do this if you understand the implications.)
|
44 |
# # Install the testing release. (beta)
|
@@ -46,76 +45,76 @@
|
|
46 |
# # Install the development release. (alpha)
|
47 |
# conda install -c theochem/label/dev qc-selector
|
48 |
# ```
|
49 |
-
#
|
50 |
# ## Installation with Pip
|
51 |
-
#
|
52 |
# 1. You can work in a [virtual environment](https://docs.python.org/3/tutorial/venv.html):
|
53 |
-
#
|
54 |
# ```
|
55 |
# # Create a virtual environment in ~/horton3
|
56 |
# # Feel free to change the path.
|
57 |
# python3 -m venv ~/horton3
|
58 |
-
#
|
59 |
# # Activate the virtual environemnt.
|
60 |
# source ~/horton3/bin/activate
|
61 |
-
#
|
62 |
# # Install the stable release in the venv horton3.
|
63 |
# pip3 install qc-selector
|
64 |
# # alternative: python3 -m pip install qc-selector
|
65 |
-
#
|
66 |
# # For developers, install a pre-release (alpha or beta).
|
67 |
# # (Only do this if you understand the implications.)
|
68 |
# pip3 install --pre qc-selector
|
69 |
# # alternative: python3 -m pip install --pre qc-selector
|
70 |
# ```
|
71 |
-
#
|
72 |
-
#
|
73 |
# 2. You can install into your `{$HOME}` directory without creating a virtual environment
|
74 |
-
#
|
75 |
# ```
|
76 |
# # Install the stable release in your home directory.
|
77 |
# pip3 install qc-selector --user
|
78 |
# # alternative: python3 -m pip install qc-selector --user
|
79 |
-
#
|
80 |
# # For developers, install a pre-release (alpha or beta).
|
81 |
# # (Only do this if you understand the implications.)
|
82 |
# pip3 install --pre qc-selector --user
|
83 |
# # alternative: python3 -m pip install --pre qc-selector --user
|
84 |
# ```
|
85 |
-
#
|
86 |
# This is by far the simplest method, ideal to get started, but you have only one home directory. If the installation breaks due to some experimentation, it is harder to make a clean start in comparison to the other options.
|
87 |
-
#
|
88 |
# In case the `pip3` executable is not found, pip may be installed in a directory which is not included in your `${PATH}` variable. This seems to be a common issue on macOS. A simple workaround is to replace `pip3` by `python3 -m pip`.
|
89 |
-
#
|
90 |
-
# In case Python and your operating system are up to date, you may also use `pip` instead of `pip3` or `python` instead of `python3`. The `3` is only used to avoid potential confusion with Python 2. Note that the `3` is only present in names of executables, not names of
|
91 |
# Python modules.
|
92 |
-
#
|
93 |
# ## Latest git revision
|
94 |
-
#
|
95 |
-
# This section shows how one can install the latest revision of
|
96 |
-
#
|
97 |
# There are two installation methods:
|
98 |
-
#
|
99 |
# 1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.
|
100 |
# ```
|
101 |
# # with env, correct PATH
|
102 |
-
# pip install git+https://github.com/theochem/
|
103 |
# # with env, broken PATH
|
104 |
-
# python -m pip install git+https://github.com/theochem/
|
105 |
# # without env, correct PATH
|
106 |
-
# pip install git+https://github.com/theochem/
|
107 |
# # without env, broken PATH
|
108 |
-
# python -m pip install git+https://github.com/theochem/
|
109 |
# ```
|
110 |
-
#
|
111 |
# 2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.
|
112 |
# ```
|
113 |
# # A) Clone git repo with https OR ssh:
|
114 |
# # The second one only works if you have ssh set up for Github
|
115 |
# # A1) https
|
116 |
-
# git clone https://github.com/theochem/
|
117 |
# # A2) ssh
|
118 |
-
# git clone [email protected]:theochem/
|
119 |
# # B) Optionally write the version string
|
120 |
# pip install roberto # or any of the three other ways of running pip, see above.
|
121 |
# rob write-version
|
@@ -133,31 +132,31 @@
|
|
133 |
# # C6) pip, without env, broken PATH
|
134 |
# python -m pip install . --user
|
135 |
# ```
|
136 |
-
#
|
137 |
-
#
|
138 |
# ## Testing
|
139 |
-
#
|
140 |
# The tests are automatically run when we build packages with conda, but you may try them again on your own machine after installation.
|
141 |
-
#
|
142 |
# With Ana- or Miniconda:
|
143 |
# ```
|
144 |
# # Install pytest in your conda env.
|
145 |
# conda install pytest pytest-xdist
|
146 |
# # Then run the tests.
|
147 |
-
# pytest --pyargs
|
148 |
# ```
|
149 |
-
#
|
150 |
# With Pip:
|
151 |
# ```
|
152 |
# # Install pytest in your conda env ...
|
153 |
# pip install pytest pytest-xdist
|
154 |
# # .. and refresh the virtual environment.
|
155 |
# # This is a venv quirk. Without it, pytest may not find IOData.
|
156 |
-
# deactivate && source ~/
|
157 |
-
#
|
158 |
# # Alternatively, install pytest in your home directory.
|
159 |
# pip install pytest pytest-xdist --user
|
160 |
-
#
|
161 |
# # Finally, run the tests.
|
162 |
-
# pytest --pyargs
|
163 |
# ```
|
|
|
1 |
#!/usr/bin/env python
|
|
|
2 |
|
3 |
# # Installation
|
4 |
+
#
|
5 |
# ## Stable releases
|
6 |
+
#
|
7 |
# <div class="alert alert-block alert-warning">
|
8 |
# <b>Warning:
|
9 |
# </b>
|
10 |
+
#
|
11 |
# We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest git revision” section below.
|
12 |
# </div>
|
13 |
+
#
|
14 |
+
# The following dependencies are required to run selector properly,
|
15 |
+
#
|
16 |
+
# * Python >= 3.6: http://www.python.org/
|
17 |
# * NumPy >= 1.21.5: http://www.numpy.org/
|
18 |
# * SciPy >= 1.5.0: http://www.scipy.org/
|
19 |
# * PyTest >= 5.3.4: https://docs.pytest.org/
|
20 |
# * PyTest-Cov >= 2.8.0: https://pypi.org/project/pytest-cov
|
21 |
+
#
|
22 |
# Normally, you don’t need to install these dependencies manually. They will be installed automatically when you follow the instructions below.
|
23 |
+
#
|
24 |
+
#
|
25 |
# ## Installation with Ana- or Miniconda:
|
26 |
+
#
|
27 |
+
# 1. To install selector using the conda package management system, install [miniconda](https://conda.io/miniconda.html) or [anaconda](https://www.anaconda.com/download) first, and then:
|
28 |
+
#
|
29 |
# ```
|
30 |
# # Activate your main conda environment if it is not loaded in your .bashrc.
|
31 |
# # E.g. run the following if you have miniconda installed in e.g. ~/miniconda3
|
32 |
# source ~/miniconda3/bin/activate
|
33 |
+
#
|
34 |
# # Create a horton3 conda environment. (optional, recommended)
|
35 |
# conda create -n horton3
|
36 |
# source activate horton3
|
37 |
+
#
|
38 |
# # Install the stable release.
|
39 |
# conda install -c theochem qc-selector
|
40 |
+
#
|
41 |
# # Unstable releases
|
42 |
# # (Only do this if you understand the implications.)
|
43 |
# # Install the testing release. (beta)
|
|
|
45 |
# # Install the development release. (alpha)
|
46 |
# conda install -c theochem/label/dev qc-selector
|
47 |
# ```
|
48 |
+
#
|
49 |
# ## Installation with Pip
|
50 |
+
#
|
51 |
# 1. You can work in a [virtual environment](https://docs.python.org/3/tutorial/venv.html):
|
52 |
+
#
|
53 |
# ```
|
54 |
# # Create a virtual environment in ~/horton3
|
55 |
# # Feel free to change the path.
|
56 |
# python3 -m venv ~/horton3
|
57 |
+
#
|
58 |
# # Activate the virtual environemnt.
|
59 |
# source ~/horton3/bin/activate
|
60 |
+
#
|
61 |
# # Install the stable release in the venv horton3.
|
62 |
# pip3 install qc-selector
|
63 |
# # alternative: python3 -m pip install qc-selector
|
64 |
+
#
|
65 |
# # For developers, install a pre-release (alpha or beta).
|
66 |
# # (Only do this if you understand the implications.)
|
67 |
# pip3 install --pre qc-selector
|
68 |
# # alternative: python3 -m pip install --pre qc-selector
|
69 |
# ```
|
70 |
+
#
|
71 |
+
#
|
72 |
# 2. You can install into your `{$HOME}` directory without creating a virtual environment
|
73 |
+
#
|
74 |
# ```
|
75 |
# # Install the stable release in your home directory.
|
76 |
# pip3 install qc-selector --user
|
77 |
# # alternative: python3 -m pip install qc-selector --user
|
78 |
+
#
|
79 |
# # For developers, install a pre-release (alpha or beta).
|
80 |
# # (Only do this if you understand the implications.)
|
81 |
# pip3 install --pre qc-selector --user
|
82 |
# # alternative: python3 -m pip install --pre qc-selector --user
|
83 |
# ```
|
84 |
+
#
|
85 |
# This is by far the simplest method, ideal to get started, but you have only one home directory. If the installation breaks due to some experimentation, it is harder to make a clean start in comparison to the other options.
|
86 |
+
#
|
87 |
# In case the `pip3` executable is not found, pip may be installed in a directory which is not included in your `${PATH}` variable. This seems to be a common issue on macOS. A simple workaround is to replace `pip3` by `python3 -m pip`.
|
88 |
+
#
|
89 |
+
# In case Python and your operating system are up to date, you may also use `pip` instead of `pip3` or `python` instead of `python3`. The `3` is only used to avoid potential confusion with Python 2. Note that the `3` is only present in names of executables, not names of
|
90 |
# Python modules.
|
91 |
+
#
|
92 |
# ## Latest git revision
|
93 |
+
#
|
94 |
+
# This section shows how one can install the latest revision of selector from the git repository. This kind of installation comes with some risks (sudden API changes, bugs, …) and so be prepared to accept them when using the following installation instructions.
|
95 |
+
#
|
96 |
# There are two installation methods:
|
97 |
+
#
|
98 |
# 1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.
|
99 |
# ```
|
100 |
# # with env, correct PATH
|
101 |
+
# pip install git+https://github.com/theochem/Selector.git
|
102 |
# # with env, broken PATH
|
103 |
+
# python -m pip install git+https://github.com/theochem/Selector.git
|
104 |
# # without env, correct PATH
|
105 |
+
# pip install git+https://github.com/theochem/Selector.git --user
|
106 |
# # without env, broken PATH
|
107 |
+
# python -m pip install git+https://github.com/theochem/Selector.git --user
|
108 |
# ```
|
109 |
+
#
|
110 |
# 2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.
|
111 |
# ```
|
112 |
# # A) Clone git repo with https OR ssh:
|
113 |
# # The second one only works if you have ssh set up for Github
|
114 |
# # A1) https
|
115 |
+
# git clone https://github.com/theochem/Selector.git
|
116 |
# # A2) ssh
|
117 |
+
# git clone [email protected]:theochem/Selector.git
|
118 |
# # B) Optionally write the version string
|
119 |
# pip install roberto # or any of the three other ways of running pip, see above.
|
120 |
# rob write-version
|
|
|
132 |
# # C6) pip, without env, broken PATH
|
133 |
# python -m pip install . --user
|
134 |
# ```
|
135 |
+
#
|
136 |
+
#
|
137 |
# ## Testing
|
138 |
+
#
|
139 |
# The tests are automatically run when we build packages with conda, but you may try them again on your own machine after installation.
|
140 |
+
#
|
141 |
# With Ana- or Miniconda:
|
142 |
# ```
|
143 |
# # Install pytest in your conda env.
|
144 |
# conda install pytest pytest-xdist
|
145 |
# # Then run the tests.
|
146 |
+
# pytest --pyargs selector -n auto
|
147 |
# ```
|
148 |
+
#
|
149 |
# With Pip:
|
150 |
# ```
|
151 |
# # Install pytest in your conda env ...
|
152 |
# pip install pytest pytest-xdist
|
153 |
# # .. and refresh the virtual environment.
|
154 |
# # This is a venv quirk. Without it, pytest may not find IOData.
|
155 |
+
# deactivate && source ~/selector/activate
|
156 |
+
#
|
157 |
# # Alternatively, install pytest in your home directory.
|
158 |
# pip install pytest pytest-xdist --user
|
159 |
+
#
|
160 |
# # Finally, run the tests.
|
161 |
+
# pytest --pyargs selector -n auto
|
162 |
# ```
|
book/content/_build/jupyter_execute/starting.ipynb
CHANGED
@@ -32,8 +32,8 @@
|
|
32 |
"metadata": {},
|
33 |
"outputs": [],
|
34 |
"source": [
|
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-
"# install the
|
36 |
-
"!pip install git+https://github.com/theochem/
|
37 |
"!pip install rdkit"
|
38 |
]
|
39 |
},
|
@@ -44,7 +44,7 @@
|
|
44 |
"metadata": {},
|
45 |
"outputs": [],
|
46 |
"source": [
|
47 |
-
"import
|
48 |
"import matplotlib.pyplot as plt\n",
|
49 |
"import numpy as np\n",
|
50 |
"from typing import Any, Tuple, Union\n",
|
@@ -170,7 +170,7 @@
|
|
170 |
},
|
171 |
"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
@@ -185,7 +185,7 @@
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
191 |
},
|
@@ -221,8 +221,8 @@
|
|
221 |
"outputs": [],
|
222 |
"source": [
|
223 |
"# MaxMin method\n",
|
224 |
-
"from
|
225 |
-
"from
|
226 |
"\n",
|
227 |
"# diverse subset selection from randomly generated data points\n",
|
228 |
"selector = MaxMin()\n",
|
@@ -248,7 +248,7 @@
|
|
248 |
},
|
249 |
"metadata": {
|
250 |
"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
252 |
},
|
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"needs_background": "light"
|
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},
|
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|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
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|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
@@ -317,7 +317,7 @@
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
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|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
@@ -376,7 +376,7 @@
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
@@ -420,7 +420,7 @@
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
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"needs_background": "light"
|
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},
|
@@ -435,7 +435,7 @@
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
440 |
"needs_background": "light"
|
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},
|
@@ -486,7 +486,7 @@
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
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},
|
491 |
"needs_background": "light"
|
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},
|
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|
|
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},
|
519 |
"metadata": {
|
520 |
"filenames": {
|
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-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
522 |
},
|
523 |
"needs_background": "light"
|
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},
|
@@ -593,7 +593,7 @@
|
|
593 |
},
|
594 |
"metadata": {
|
595 |
"filenames": {
|
596 |
-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
597 |
},
|
598 |
"needs_background": "light"
|
599 |
},
|
@@ -643,7 +643,7 @@
|
|
643 |
},
|
644 |
"metadata": {
|
645 |
"filenames": {
|
646 |
-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
647 |
},
|
648 |
"needs_background": "light"
|
649 |
},
|
@@ -693,7 +693,7 @@
|
|
693 |
},
|
694 |
"metadata": {
|
695 |
"filenames": {
|
696 |
-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
697 |
},
|
698 |
"needs_background": "light"
|
699 |
},
|
@@ -742,7 +742,7 @@
|
|
742 |
},
|
743 |
"metadata": {
|
744 |
"filenames": {
|
745 |
-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
746 |
},
|
747 |
"needs_background": "light"
|
748 |
},
|
@@ -791,7 +791,7 @@
|
|
791 |
},
|
792 |
"metadata": {
|
793 |
"filenames": {
|
794 |
-
"image/png": "/Users/valeriichuiko/Desktop/projects/
|
795 |
},
|
796 |
"needs_background": "light"
|
797 |
},
|
@@ -824,4 +824,4 @@
|
|
824 |
},
|
825 |
"nbformat": 4,
|
826 |
"nbformat_minor": 5
|
827 |
-
}
|
|
|
32 |
"metadata": {},
|
33 |
"outputs": [],
|
34 |
"source": [
|
35 |
+
"# install the selector module\n",
|
36 |
+
"!pip install git+https://github.com/theochem/Selector\n",
|
37 |
"!pip install rdkit"
|
38 |
]
|
39 |
},
|
|
|
44 |
"metadata": {},
|
45 |
"outputs": [],
|
46 |
"source": [
|
47 |
+
"import selector\n",
|
48 |
"import matplotlib.pyplot as plt\n",
|
49 |
"import numpy as np\n",
|
50 |
"from typing import Any, Tuple, Union\n",
|
|
|
170 |
},
|
171 |
"metadata": {
|
172 |
"filenames": {
|
173 |
+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_6_0.png"
|
174 |
},
|
175 |
"needs_background": "light"
|
176 |
},
|
|
|
185 |
},
|
186 |
"metadata": {
|
187 |
"filenames": {
|
188 |
+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_6_1.png"
|
189 |
},
|
190 |
"needs_background": "light"
|
191 |
},
|
|
|
221 |
"outputs": [],
|
222 |
"source": [
|
223 |
"# MaxMin method\n",
|
224 |
+
"from selector.methods.partition import *\n",
|
225 |
+
"from selector.methods.dissimilarity import *\n",
|
226 |
"\n",
|
227 |
"# diverse subset selection from randomly generated data points\n",
|
228 |
"selector = MaxMin()\n",
|
|
|
248 |
},
|
249 |
"metadata": {
|
250 |
"filenames": {
|
251 |
+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_10_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
263 |
},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_10_1.png"
|
267 |
},
|
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"needs_background": "light"
|
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},
|
|
|
302 |
},
|
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"metadata": {
|
304 |
"filenames": {
|
305 |
+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_12_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_12_1.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
361 |
},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_14_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_14_1.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_16_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_16_1.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_18_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_19_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_22_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
645 |
"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_25_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
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},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_28_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
742 |
},
|
743 |
"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_31_0.png"
|
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},
|
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"needs_background": "light"
|
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},
|
|
|
791 |
},
|
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"metadata": {
|
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"filenames": {
|
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+
"image/png": "/Users/valeriichuiko/Desktop/projects/selector/book/content/_build/jupyter_execute/starting_34_0.png"
|
795 |
},
|
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"needs_background": "light"
|
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},
|
|
|
824 |
},
|
825 |
"nbformat": 4,
|
826 |
"nbformat_minor": 5
|
827 |
+
}
|
book/content/_build/jupyter_execute/starting.py
CHANGED
@@ -1,5 +1,4 @@
|
|
1 |
#!/usr/bin/env python
|
2 |
-
# coding: utf-8
|
3 |
|
4 |
# # Getting started
|
5 |
# ## Demo - Dissimilarity Based Selection Synthetic Data
|
@@ -11,33 +10,35 @@
|
|
11 |
# In[ ]:
|
12 |
|
13 |
|
14 |
-
# install the
|
15 |
-
|
16 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
17 |
|
18 |
|
19 |
# In[1]:
|
20 |
|
21 |
|
22 |
-
|
23 |
-
|
24 |
-
|
25 |
-
|
26 |
-
|
27 |
-
|
28 |
-
|
29 |
-
|
30 |
-
|
31 |
-
|
32 |
-
|
33 |
-
|
34 |
-
metric: str = "euclidean",
|
35 |
-
shuffle: bool = True,
|
36 |
-
random_state: int = 42,
|
37 |
-
pairwise_dist: bool = False,
|
38 |
-
**kwargs: Any,
|
39 |
-
) -> Union[Tuple[np.ndarray, np.ndarray],
|
40 |
-
Tuple[np.ndarray, np.ndarray, np.ndarray]]:
|
41 |
"""Generate synthetic data.
|
42 |
|
43 |
Parameters
|
@@ -78,21 +79,23 @@ def generate_synthetic_data(n_samples: int = 100,
|
|
78 |
|
79 |
"""
|
80 |
# pylint: disable=W0632
|
81 |
-
syn_data, class_labels = make_blobs(
|
82 |
-
|
83 |
-
|
84 |
-
|
85 |
-
|
86 |
-
|
87 |
-
|
88 |
-
|
89 |
-
|
|
|
90 |
if pairwise_dist:
|
91 |
-
dist = pairwise_distances(
|
92 |
-
|
93 |
-
|
94 |
-
|
95 |
-
|
|
|
96 |
return syn_data, class_labels, dist
|
97 |
else:
|
98 |
return syn_data, class_labels
|
@@ -101,28 +104,34 @@ def generate_synthetic_data(n_samples: int = 100,
|
|
101 |
# In[2]:
|
102 |
|
103 |
|
104 |
-
coords, class_labels, arr_dist = generate_synthetic_data(
|
105 |
-
|
106 |
-
|
107 |
-
|
108 |
-
|
109 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
110 |
|
111 |
-
coords_cluster, class_labels_cluster, arr_dist_cluster = generate_synthetic_data(n_samples=100,
|
112 |
-
n_features=2,
|
113 |
-
n_clusters=3,
|
114 |
-
pairwise_dist=True,
|
115 |
-
metric="euclidean",
|
116 |
-
random_state=42)
|
117 |
def graph_data(coords, selected=None, reference=False):
|
118 |
plt.figure(dpi=150)
|
119 |
plt.scatter(coords[:, 0], coords[:, 1])
|
120 |
if selected:
|
121 |
for i, mol_id in enumerate(selected):
|
122 |
-
plt.scatter(coords[mol_id, 0], coords[mol_id, 1], c=
|
123 |
-
plt.text(coords[mol_id,0], coords[mol_id,1], str(i+1))
|
124 |
if reference:
|
125 |
-
plt.scatter(coords[0, 0], coords[0, 1], c=
|
126 |
plt.show()
|
127 |
|
128 |
|
@@ -141,8 +150,6 @@ graph_data(coords_cluster)
|
|
141 |
|
142 |
|
143 |
# MaxMin method
|
144 |
-
from DiverseSelector.methods.partition import *
|
145 |
-
from DiverseSelector.methods.dissimilarity import *
|
146 |
|
147 |
# diverse subset selection from randomly generated data points
|
148 |
selector = MaxMin()
|
@@ -156,8 +163,8 @@ selected_ids2 = selector.select(arr=arr_dist_cluster, labels=class_labels_cluste
|
|
156 |
# In[9]:
|
157 |
|
158 |
|
159 |
-
graph_data(coords,selected_ids1)
|
160 |
-
graph_data(coords_cluster,selected_ids2)
|
161 |
|
162 |
|
163 |
# ### Adapted Optimizable K-Dissimilarity Selection (OptiSim)
|
@@ -170,8 +177,8 @@ selected_id3 = OptiSim().select(coords, 12)
|
|
170 |
# diverse subset selection from data points with obvious patterns (different clusters)
|
171 |
selected_id4 = OptiSim().select(coords_cluster, size=12, labels=class_labels_cluster)
|
172 |
|
173 |
-
graph_data(coords,selected_id3)
|
174 |
-
graph_data(coords_cluster,selected_id4)
|
175 |
|
176 |
|
177 |
# ### Directed Sphere Exclusion
|
@@ -182,10 +189,12 @@ graph_data(coords_cluster,selected_id4)
|
|
182 |
# diverse subset selection from randomly generated data points
|
183 |
selected_id5 = DirectedSphereExclusion().select(coords, 12)
|
184 |
# diverse subset selection from data points with obvious patterns (different clusters)
|
185 |
-
selected_id6 = DirectedSphereExclusion().select(
|
|
|
|
|
186 |
|
187 |
-
graph_data(coords,selected_id5)
|
188 |
-
graph_data(coords_cluster,selected_id6)
|
189 |
|
190 |
|
191 |
# ### Grid Partitioning Method
|
@@ -196,12 +205,12 @@ graph_data(coords_cluster,selected_id6)
|
|
196 |
# diverse subset selection from randomly generated data points
|
197 |
selected_id7 = GridPartitioning(2, "equisized_independent").select(coords, 12)
|
198 |
# diverse subset selection from data points with obvious patterns (different clusters)
|
199 |
-
selected_id8 = GridPartitioning(2, "equisized_independent").select(
|
200 |
-
|
201 |
-
|
202 |
|
203 |
-
graph_data(coords,selected_id7)
|
204 |
-
graph_data(coords_cluster,selected_id8)
|
205 |
# 20, 5, 3, "equisized_independent"
|
206 |
|
207 |
|
@@ -218,18 +227,14 @@ graph_data(coords_cluster, selected_ids)
|
|
218 |
# In[12]:
|
219 |
|
220 |
|
221 |
-
import numpy as np
|
222 |
-
from sklearn.metrics import pairwise_distances
|
223 |
np.random.seed(42)
|
224 |
-
cluster_one = np.random.normal(0, 1, (3,2))
|
225 |
-
cluster_two = np.random.normal(10, 1, (6,2))
|
226 |
-
cluster_three = np.random.normal(20, 1, (7,2))
|
227 |
-
labels_mocked = np.hstack([[0 for i in range(3)],
|
228 |
-
[1 for i in range(6)],
|
229 |
-
[2 for i in range(7)]])
|
230 |
|
231 |
mocked_cluster_coords = np.vstack([cluster_one, cluster_two, cluster_three])
|
232 |
-
selector = MaxMin(lambda x: pairwise_distances(x, metric=
|
233 |
selected_mocked = selector.select(mocked_cluster_coords, size=15, labels=labels_mocked)
|
234 |
|
235 |
graph_data(mocked_cluster_coords, selected_mocked)
|
@@ -242,10 +247,9 @@ selected_mocked
|
|
242 |
# In[6]:
|
243 |
|
244 |
|
245 |
-
selector = DissimilaritySelection(
|
246 |
-
|
247 |
-
|
248 |
-
method="maxsum")
|
249 |
selector.starting_idx = 0
|
250 |
selected_ids2 = selector.select()
|
251 |
|
@@ -261,11 +265,13 @@ graph_data(selected_ids2)
|
|
261 |
# In[8]:
|
262 |
|
263 |
|
264 |
-
selector = DissimilaritySelection(
|
265 |
-
|
266 |
-
|
267 |
-
|
268 |
-
|
|
|
|
|
269 |
selector.starting_idx = 0
|
270 |
selected_ids3 = selector.select()
|
271 |
|
@@ -281,11 +287,13 @@ graph_data(selected_ids3)
|
|
281 |
# In[10]:
|
282 |
|
283 |
|
284 |
-
selector = DissimilaritySelection(
|
285 |
-
|
286 |
-
|
287 |
-
|
288 |
-
|
|
|
|
|
289 |
selector.starting_idx = 0
|
290 |
selected_ids4 = selector.select()
|
291 |
|
@@ -301,10 +309,9 @@ graph_data(selected_ids4)
|
|
301 |
# In[12]:
|
302 |
|
303 |
|
304 |
-
selector = DissimilaritySelection(
|
305 |
-
|
306 |
-
|
307 |
-
random_seed=42)
|
308 |
selector.starting_idx = 0
|
309 |
selected_ids5 = selector.select()
|
310 |
|
@@ -320,10 +327,9 @@ graph_data(selected_ids5, True)
|
|
320 |
# In[14]:
|
321 |
|
322 |
|
323 |
-
selector = DissimilaritySelection(
|
324 |
-
|
325 |
-
|
326 |
-
random_seed=42)
|
327 |
selector.starting_idx = 0
|
328 |
selected_ids6 = selector.select()
|
329 |
|
@@ -332,4 +338,3 @@ selected_ids6 = selector.select()
|
|
332 |
|
333 |
|
334 |
graph_data(selected_ids6)
|
335 |
-
|
|
|
1 |
#!/usr/bin/env python
|
|
|
2 |
|
3 |
# # Getting started
|
4 |
# ## Demo - Dissimilarity Based Selection Synthetic Data
|
|
|
10 |
# In[ ]:
|
11 |
|
12 |
|
13 |
+
# install the selector module
|
14 |
+
from selector.methods.dissimilarity import *
|
15 |
+
from selector.methods.partition import *
|
16 |
+
from sklearn.metrics import pairwise_distances
|
17 |
+
from sklearn.datasets import make_blobs
|
18 |
+
from typing import Any, Tuple, Union
|
19 |
+
import numpy as np
|
20 |
+
import matplotlib.pyplot as plt
|
21 |
+
import selector
|
22 |
+
|
23 |
+
get_ipython().system("pip install git+https://github.com/theochem/Selector")
|
24 |
+
get_ipython().system("pip install rdkit")
|
25 |
|
26 |
|
27 |
# In[1]:
|
28 |
|
29 |
|
30 |
+
def generate_synthetic_data(
|
31 |
+
n_samples: int = 100,
|
32 |
+
n_features: int = 2,
|
33 |
+
n_clusters: int = 2,
|
34 |
+
cluster_std: float = 1.0,
|
35 |
+
center_box: Tuple[float, float] = (-10.0, 10.0),
|
36 |
+
metric: str = "euclidean",
|
37 |
+
shuffle: bool = True,
|
38 |
+
random_state: int = 42,
|
39 |
+
pairwise_dist: bool = False,
|
40 |
+
**kwargs: Any,
|
41 |
+
) -> Union[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray, np.ndarray]]:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
42 |
"""Generate synthetic data.
|
43 |
|
44 |
Parameters
|
|
|
79 |
|
80 |
"""
|
81 |
# pylint: disable=W0632
|
82 |
+
syn_data, class_labels = make_blobs(
|
83 |
+
n_samples=n_samples,
|
84 |
+
n_features=n_features,
|
85 |
+
centers=n_clusters,
|
86 |
+
cluster_std=cluster_std,
|
87 |
+
center_box=center_box,
|
88 |
+
shuffle=shuffle,
|
89 |
+
random_state=random_state,
|
90 |
+
return_centers=False,
|
91 |
+
)
|
92 |
if pairwise_dist:
|
93 |
+
dist = pairwise_distances(
|
94 |
+
X=syn_data,
|
95 |
+
Y=None,
|
96 |
+
metric=metric,
|
97 |
+
**kwargs,
|
98 |
+
)
|
99 |
return syn_data, class_labels, dist
|
100 |
else:
|
101 |
return syn_data, class_labels
|
|
|
104 |
# In[2]:
|
105 |
|
106 |
|
107 |
+
coords, class_labels, arr_dist = generate_synthetic_data(
|
108 |
+
n_samples=100,
|
109 |
+
n_features=2,
|
110 |
+
n_clusters=1,
|
111 |
+
pairwise_dist=True,
|
112 |
+
metric="euclidean",
|
113 |
+
random_state=42,
|
114 |
+
)
|
115 |
+
|
116 |
+
coords_cluster, class_labels_cluster, arr_dist_cluster = generate_synthetic_data(
|
117 |
+
n_samples=100,
|
118 |
+
n_features=2,
|
119 |
+
n_clusters=3,
|
120 |
+
pairwise_dist=True,
|
121 |
+
metric="euclidean",
|
122 |
+
random_state=42,
|
123 |
+
)
|
124 |
+
|
125 |
|
|
|
|
|
|
|
|
|
|
|
|
|
126 |
def graph_data(coords, selected=None, reference=False):
|
127 |
plt.figure(dpi=150)
|
128 |
plt.scatter(coords[:, 0], coords[:, 1])
|
129 |
if selected:
|
130 |
for i, mol_id in enumerate(selected):
|
131 |
+
plt.scatter(coords[mol_id, 0], coords[mol_id, 1], c="r")
|
132 |
+
plt.text(coords[mol_id, 0], coords[mol_id, 1], str(i + 1))
|
133 |
if reference:
|
134 |
+
plt.scatter(coords[0, 0], coords[0, 1], c="black")
|
135 |
plt.show()
|
136 |
|
137 |
|
|
|
150 |
|
151 |
|
152 |
# MaxMin method
|
|
|
|
|
153 |
|
154 |
# diverse subset selection from randomly generated data points
|
155 |
selector = MaxMin()
|
|
|
163 |
# In[9]:
|
164 |
|
165 |
|
166 |
+
graph_data(coords, selected_ids1)
|
167 |
+
graph_data(coords_cluster, selected_ids2)
|
168 |
|
169 |
|
170 |
# ### Adapted Optimizable K-Dissimilarity Selection (OptiSim)
|
|
|
177 |
# diverse subset selection from data points with obvious patterns (different clusters)
|
178 |
selected_id4 = OptiSim().select(coords_cluster, size=12, labels=class_labels_cluster)
|
179 |
|
180 |
+
graph_data(coords, selected_id3)
|
181 |
+
graph_data(coords_cluster, selected_id4)
|
182 |
|
183 |
|
184 |
# ### Directed Sphere Exclusion
|
|
|
189 |
# diverse subset selection from randomly generated data points
|
190 |
selected_id5 = DirectedSphereExclusion().select(coords, 12)
|
191 |
# diverse subset selection from data points with obvious patterns (different clusters)
|
192 |
+
selected_id6 = DirectedSphereExclusion().select(
|
193 |
+
coords_cluster, size=12, labels=class_labels_cluster
|
194 |
+
)
|
195 |
|
196 |
+
graph_data(coords, selected_id5)
|
197 |
+
graph_data(coords_cluster, selected_id6)
|
198 |
|
199 |
|
200 |
# ### Grid Partitioning Method
|
|
|
205 |
# diverse subset selection from randomly generated data points
|
206 |
selected_id7 = GridPartitioning(2, "equisized_independent").select(coords, 12)
|
207 |
# diverse subset selection from data points with obvious patterns (different clusters)
|
208 |
+
selected_id8 = GridPartitioning(2, "equisized_independent").select(
|
209 |
+
coords_cluster, size=12, labels=class_labels_cluster
|
210 |
+
)
|
211 |
|
212 |
+
graph_data(coords, selected_id7)
|
213 |
+
graph_data(coords_cluster, selected_id8)
|
214 |
# 20, 5, 3, "equisized_independent"
|
215 |
|
216 |
|
|
|
227 |
# In[12]:
|
228 |
|
229 |
|
|
|
|
|
230 |
np.random.seed(42)
|
231 |
+
cluster_one = np.random.normal(0, 1, (3, 2))
|
232 |
+
cluster_two = np.random.normal(10, 1, (6, 2))
|
233 |
+
cluster_three = np.random.normal(20, 1, (7, 2))
|
234 |
+
labels_mocked = np.hstack([[0 for i in range(3)], [1 for i in range(6)], [2 for i in range(7)]])
|
|
|
|
|
235 |
|
236 |
mocked_cluster_coords = np.vstack([cluster_one, cluster_two, cluster_three])
|
237 |
+
selector = MaxMin(lambda x: pairwise_distances(x, metric="euclidean"))
|
238 |
selected_mocked = selector.select(mocked_cluster_coords, size=15, labels=labels_mocked)
|
239 |
|
240 |
graph_data(mocked_cluster_coords, selected_mocked)
|
|
|
247 |
# In[6]:
|
248 |
|
249 |
|
250 |
+
selector = DissimilaritySelection(
|
251 |
+
num_selected=12, arr_dist=arr_dist, random_seed=42, method="maxsum"
|
252 |
+
)
|
|
|
253 |
selector.starting_idx = 0
|
254 |
selected_ids2 = selector.select()
|
255 |
|
|
|
265 |
# In[8]:
|
266 |
|
267 |
|
268 |
+
selector = DissimilaritySelection(
|
269 |
+
features=coords,
|
270 |
+
num_selected=12,
|
271 |
+
arr_dist=arr_dist,
|
272 |
+
grid_method="grid_partioning",
|
273 |
+
random_seed=42,
|
274 |
+
)
|
275 |
selector.starting_idx = 0
|
276 |
selected_ids3 = selector.select()
|
277 |
|
|
|
287 |
# In[10]:
|
288 |
|
289 |
|
290 |
+
selector = DissimilaritySelection(
|
291 |
+
num_selected=12,
|
292 |
+
features=coords,
|
293 |
+
random_seed=42,
|
294 |
+
dissim_func="grid_partitioning",
|
295 |
+
grid_method="equisized_dependent",
|
296 |
+
)
|
297 |
selector.starting_idx = 0
|
298 |
selected_ids4 = selector.select()
|
299 |
|
|
|
309 |
# In[12]:
|
310 |
|
311 |
|
312 |
+
selector = DissimilaritySelection(
|
313 |
+
num_selected=12, features=coords, dissim_func="sphere_exclusion", random_seed=42
|
314 |
+
)
|
|
|
315 |
selector.starting_idx = 0
|
316 |
selected_ids5 = selector.select()
|
317 |
|
|
|
327 |
# In[14]:
|
328 |
|
329 |
|
330 |
+
selector = DissimilaritySelection(
|
331 |
+
num_selected=12, features=coords, dissim_func="optisim", random_seed=42
|
332 |
+
)
|
|
|
333 |
selector.starting_idx = 0
|
334 |
selected_ids6 = selector.select()
|
335 |
|
|
|
338 |
|
339 |
|
340 |
graph_data(selected_ids6)
|
|
book/content/_config.yml
CHANGED
@@ -20,7 +20,7 @@ sphinx:
|
|
20 |
- 'sphinx.ext.autodoc'
|
21 |
config:
|
22 |
mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
|
23 |
-
|
24 |
|
25 |
#Read LaTeX
|
26 |
parse:
|
@@ -46,7 +46,7 @@ bibtex_bibfiles:
|
|
46 |
|
47 |
# Information about where the book exists on the web
|
48 |
repository:
|
49 |
-
url: https://github.com/theochem/
|
50 |
path_to_book: book/content # Optional path to your book, relative to the repository root
|
51 |
branch: main # Which branch of the repository should be used when creating links (optional)
|
52 |
|
@@ -55,11 +55,11 @@ repository:
|
|
55 |
launch_buttons:
|
56 |
thebe : true
|
57 |
colab_url: "https://colab.research.google.com"
|
58 |
-
|
59 |
copyright: "2022-2023"
|
60 |
-
|
61 |
html:
|
62 |
use_issues_button: true
|
63 |
use_repository_button: true
|
64 |
favicon: "selector_logo.png"
|
65 |
-
use_multitoc_numbering: false
|
|
|
20 |
- 'sphinx.ext.autodoc'
|
21 |
config:
|
22 |
mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
|
23 |
+
|
24 |
|
25 |
#Read LaTeX
|
26 |
parse:
|
|
|
46 |
|
47 |
# Information about where the book exists on the web
|
48 |
repository:
|
49 |
+
url: https://github.com/theochem/Selector # Online location of your book
|
50 |
path_to_book: book/content # Optional path to your book, relative to the repository root
|
51 |
branch: main # Which branch of the repository should be used when creating links (optional)
|
52 |
|
|
|
55 |
launch_buttons:
|
56 |
thebe : true
|
57 |
colab_url: "https://colab.research.google.com"
|
58 |
+
|
59 |
copyright: "2022-2023"
|
60 |
+
|
61 |
html:
|
62 |
use_issues_button: true
|
63 |
use_repository_button: true
|
64 |
favicon: "selector_logo.png"
|
65 |
+
use_multitoc_numbering: false
|
book/content/_toc.yml
CHANGED
@@ -13,4 +13,4 @@ parts:
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- file: base.rst
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- file: dissimilarity.rst
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- file: partition.rst
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-
- file: utils.rst
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- file: base.rst
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- file: dissimilarity.rst
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- file: partition.rst
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+
- file: utils.rst
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book/content/base.rst
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@@ -1,4 +1,4 @@
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1 |
Base class
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=============
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-
.. automodule::
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-
:members:
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Base class
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=============
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+
.. automodule:: selector.base
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+
:members:
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book/content/dissimilarity.rst
CHANGED
@@ -1,4 +1,4 @@
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1 |
Dissimilarity based methods
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2 |
=============
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-
.. automodule::
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-
:members:
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1 |
Dissimilarity based methods
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=============
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+
.. automodule:: selector.dissimilarity
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+
:members:
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book/content/installation.ipynb
CHANGED
@@ -16,7 +16,7 @@
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"We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest Git Revision” section below.\n",
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"</div>\n",
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"\n",
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-
"The following dependencies are required to run
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"\n",
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"* Python >= 3.6: http://www.python.org/ \n",
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"* NumPy >= 1.21.5: http://www.numpy.org/\n",
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@@ -29,7 +29,7 @@
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"\n",
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"## Installation with Anaconda or Miniconda:\n",
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"\n",
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-
"1. To install
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"\n",
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"```\n",
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"# Activate your main conda environment if it is not loaded in your .bashrc.\n",
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@@ -98,20 +98,20 @@
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"\n",
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"## Latest git revision\n",
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"\n",
|
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-
"This section shows how one can install the latest revision of `
|
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"\n",
|
103 |
"There are two installation methods:\n",
|
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"\n",
|
105 |
"1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.\n",
|
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"```\n",
|
107 |
"# with env, correct PATH\n",
|
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-
"pip install git+https://github.com/theochem/
|
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"# with env, broken PATH\n",
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-
"python -m pip install git+https://github.com/theochem/
|
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"# without env, correct PATH\n",
|
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-
"pip install git+https://github.com/theochem/
|
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"# without env, broken PATH\n",
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-
"python -m pip install git+https://github.com/theochem/
|
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"```\n",
|
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"\n",
|
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"2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.\n",
|
@@ -119,9 +119,9 @@
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"# A) Clone git repo with https OR ssh:\n",
|
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"# The second one only works if you have ssh set up for Github\n",
|
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"# A1) https\n",
|
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-
"git clone https://github.com/theochem/
|
123 |
"# A2) ssh\n",
|
124 |
-
"git clone [email protected]:theochem/
|
125 |
"# B) Optionally write the version string\n",
|
126 |
"pip install roberto # or any of the three other ways of running pip, see above.\n",
|
127 |
"rob write-version\n",
|
@@ -150,7 +150,7 @@
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|
150 |
"# Install pytest in your conda env.\n",
|
151 |
"conda install pytest pytest-xdist\n",
|
152 |
"# Then run the tests.\n",
|
153 |
-
"pytest --pyargs
|
154 |
"```\n",
|
155 |
"\n",
|
156 |
"With Pip:\n",
|
@@ -159,13 +159,13 @@
|
|
159 |
"pip install pytest pytest-xdist\n",
|
160 |
"# .. and refresh the virtual environment.\n",
|
161 |
"# This is a venv quirk. Without it, pytest may not find IOData.\n",
|
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-
"deactivate && source ~/
|
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"\n",
|
164 |
"# Alternatively, install pytest in your home directory.\n",
|
165 |
"pip install pytest pytest-xdist --user\n",
|
166 |
"\n",
|
167 |
"# Finally, run the tests.\n",
|
168 |
-
"pytest --pyargs
|
169 |
"```"
|
170 |
]
|
171 |
},
|
|
|
16 |
"We are preparing a 1.0 release. Until then, these instructions for installing a stable release will not work yet. If you enjoy living on the edge, try the development release as explained in the “Latest Git Revision” section below.\n",
|
17 |
"</div>\n",
|
18 |
"\n",
|
19 |
+
"The following dependencies are required to run selector properly,\n",
|
20 |
"\n",
|
21 |
"* Python >= 3.6: http://www.python.org/ \n",
|
22 |
"* NumPy >= 1.21.5: http://www.numpy.org/\n",
|
|
|
29 |
"\n",
|
30 |
"## Installation with Anaconda or Miniconda:\n",
|
31 |
"\n",
|
32 |
+
"1. To install selector using the conda package management system, install [miniconda](https://conda.io/miniconda.html) or [anaconda](https://www.anaconda.com/download) first, and then:\n",
|
33 |
"\n",
|
34 |
"```\n",
|
35 |
"# Activate your main conda environment if it is not loaded in your .bashrc.\n",
|
|
|
98 |
"\n",
|
99 |
"## Latest git revision\n",
|
100 |
"\n",
|
101 |
+
"This section shows how one can install the latest revision of `selector` from the git repository. This kind of installation comes with some risks (sudden API changes, bugs, …) and so be prepared to accept them when using the following installation instructions.\n",
|
102 |
"\n",
|
103 |
"There are two installation methods:\n",
|
104 |
"\n",
|
105 |
"1. **Quick and dirty.** Of this method, there are four variants, depending on the correctness of your `PATH` variable and the presence of a virtual or conda environment. These different scenarios are explained in more detail in the previous section.\n",
|
106 |
"```\n",
|
107 |
"# with env, correct PATH\n",
|
108 |
+
"pip install git+https://github.com/theochem/Selector.git\n",
|
109 |
"# with env, broken PATH\n",
|
110 |
+
"python -m pip install git+https://github.com/theochem/Selector.git\n",
|
111 |
"# without env, correct PATH\n",
|
112 |
+
"pip install git+https://github.com/theochem/Selector.git --user\n",
|
113 |
"# without env, broken PATH\n",
|
114 |
+
"python -m pip install git+https://github.com/theochem/Selector.git --user\n",
|
115 |
"```\n",
|
116 |
"\n",
|
117 |
"2. **Slow and Smart.** In addition to the four variations in the quick and dirty method, the slow and smart can be used with `pip` or just with `setup.py`. You also have the options to use SSH or HTTPS protocols to clone the git repository. Pick whichever works best for you.\n",
|
|
|
119 |
"# A) Clone git repo with https OR ssh:\n",
|
120 |
"# The second one only works if you have ssh set up for Github\n",
|
121 |
"# A1) https\n",
|
122 |
+
"git clone https://github.com/theochem/Selector.git\n",
|
123 |
"# A2) ssh\n",
|
124 |
+
"git clone [email protected]:theochem/Selector.git\n",
|
125 |
"# B) Optionally write the version string\n",
|
126 |
"pip install roberto # or any of the three other ways of running pip, see above.\n",
|
127 |
"rob write-version\n",
|
|
|
150 |
"# Install pytest in your conda env.\n",
|
151 |
"conda install pytest pytest-xdist\n",
|
152 |
"# Then run the tests.\n",
|
153 |
+
"pytest --pyargs selector -n auto\n",
|
154 |
"```\n",
|
155 |
"\n",
|
156 |
"With Pip:\n",
|
|
|
159 |
"pip install pytest pytest-xdist\n",
|
160 |
"# .. and refresh the virtual environment.\n",
|
161 |
"# This is a venv quirk. Without it, pytest may not find IOData.\n",
|
162 |
+
"deactivate && source ~/selector/activate\n",
|
163 |
"\n",
|
164 |
"# Alternatively, install pytest in your home directory.\n",
|
165 |
"pip install pytest pytest-xdist --user\n",
|
166 |
"\n",
|
167 |
"# Finally, run the tests.\n",
|
168 |
+
"pytest --pyargs selector -n auto\n",
|
169 |
"```"
|
170 |
]
|
171 |
},
|
book/content/intro.md
CHANGED
@@ -1,11 +1,11 @@
|
|
1 |
<!-- #region -->
|
2 |
# Welcome to QC-Selector's Documentation!
|
3 |
|
4 |
-
[
|
5 |
|
6 |
**"QC-Selector: A Generic Python Package for Subset Selection"**, Fanwang Meng, Alireza Tehrani, Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh"
|
7 |
|
8 |
-
The
|
9 |
|
10 |
|
11 |
## Why QC-Selector?
|
|
|
1 |
<!-- #region -->
|
2 |
# Welcome to QC-Selector's Documentation!
|
3 |
|
4 |
+
[selector](https://github.com/theochem/Selector) is a free, open-source, and cross-platform Python library designed to help you effortlessly identify the most diverse subset of molecules from your dataset. Please use the following citation in any publication using selector library:
|
5 |
|
6 |
**"QC-Selector: A Generic Python Package for Subset Selection"**, Fanwang Meng, Alireza Tehrani, Valerii Chuiko, Abigail Broscius, Abdul, Hassan, Maximilian van Zyl, Marco Martínez González, Yang, Ramón Alain Miranda-Quintana, Paul W. Ayers, and Farnaz Heidar-Zadeh"
|
7 |
|
8 |
+
The selector source code is hosted on [GitHub](https://github.com/theochem/Selector) and is released under the [GNU General Public License v3.0](https://github.com/theochem/Selector/blob/main/LICENSE). We welcome any contributions to the selector library in accordance with our Code of Conduct; please see our [Contributing Guidelines](https://qcdevs.org/guidelines/QCDevsCodeOfConduct/). Please report any issues you encounter while using selector library on [GitHub Issues](https://github.com/theochem/Selector/issues). For further information and inquiries please contact us at [email protected].
|
9 |
|
10 |
|
11 |
## Why QC-Selector?
|
book/content/partition.rst
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
Partition based methods
|
2 |
=============
|
3 |
|
4 |
-
.. automodule::
|
5 |
-
:members:
|
|
|
1 |
Partition based methods
|
2 |
=============
|
3 |
|
4 |
+
.. automodule:: selector.partition
|
5 |
+
:members:
|
book/content/references.bib
CHANGED
@@ -1,7 +1,7 @@
|
|
1 |
---
|
2 |
---
|
3 |
-
@article{
|
4 |
-
title={
|
5 |
author={QC-Devs},
|
6 |
journal={Nature Chemistry},
|
7 |
year={2023},
|
|
|
1 |
---
|
2 |
---
|
3 |
+
@article{selector,
|
4 |
+
title={selector: A Novel Approach for Selecting Diverse Molecular Subsets},
|
5 |
author={QC-Devs},
|
6 |
journal={Nature Chemistry},
|
7 |
year={2023},
|