diff --git a/.gitattributes b/.gitattributes index fa13d022704805850cca5a6e16801a36a6f1f075..55faf3c13186eeada060fa01e88de1dfffbdef6b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1165,3 +1165,7 @@ evalkit_cambrian/lib/python3.10/site-packages/scipy/sparse/csgraph/_min_spanning evalkit_cambrian/lib/python3.10/site-packages/scipy/integrate/__pycache__/_lebedev.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text evalkit_cambrian/lib/python3.10/site-packages/scipy/linalg/_linalg_pythran.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text evalkit_cambrian/lib/python3.10/site-packages/scipy/cluster/_optimal_leaf_ordering.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +evalkit_cambrian/lib/python3.10/site-packages/scipy/sparse/linalg/_propack/_spropack.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/murmurhash.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/_vector_sentinel.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/_readonly_array_wrapper.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text diff --git a/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/INSTALLER b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/METADATA b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..58888f8eeaf24c3c9c7acd94730f51622683018f --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/METADATA @@ -0,0 +1,66 @@ +Metadata-Version: 2.4 +Name: google-crc32c +Version: 1.7.1 +Summary: A python wrapper of the C library 'Google CRC32C' +Home-page: https://github.com/googleapis/python-crc32c +Author: Google LLC +Author-email: googleapis-packages@google.com +License: Apache 2.0 +Platform: Posix +Platform: MacOS X +Platform: Windows +Classifier: Development Status :: 4 - Beta +Classifier: Intended Audience :: Developers +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: importlib_resources>=1.3; python_version < "3.9" and os_name == "nt" +Provides-Extra: testing +Requires-Dist: pytest; extra == "testing" +Dynamic: license-file +Dynamic: requires-dist + +# `google-crc32c` +![GA](https://img.shields.io/badge/support-GA-gold.svg) [](https://pypi.org/project/google-crc32c) ![Python Versions](https://img.shields.io/pypi/pyversions/google-crc32c) + +This package wraps the [`google/crc32c`](https://github.com/google/crc32c) +hardware-based implementation of the CRC32C hashing algorithm. Multiple wheels +are distributed as well as source. If a wheel is not published for the python +version and platform you are using, you will need to compile crc32c using a +C toolchain. + +# Currently Published Wheels + +Wheels are currently published for CPython 3.9, 3.10, 3.11, 3.12 and 3.13 +for multiple architectures. PyPy 3.9 and 3.10 are also supported for Linux. +For information on building your own wheels please view [BUILDING.md](BUILDING.md). + + +## Linux + +Wheels are published for the following platforms / architectures: + +- `manylinux2010` platform, `x86_64` and `1686` architectures +- `manylinux2014` platform, `aarch64` architecture + +### Unsupported Platforms + +- `manylinux1` platform, `x86_64` architecture support has ended. + +See https://github.com/pypa/manylinux/issues/994. + +## Mac OS + +Wheels are published for `x86_64` and `arm64` architectures. + + +## Windows + +Wheels are published for the `win_amd64` architecture. diff --git a/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/RECORD b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..60b5eb6130a16c72b6bffa416f366eebfb8b83b6 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/RECORD @@ -0,0 +1,20 @@ +google_crc32c-1.7.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +google_crc32c-1.7.1.dist-info/METADATA,sha256=2tF9Z0Cm8aOucOcMq0F1EGtwimEw0knKVjVh7qHU12s,2339 +google_crc32c-1.7.1.dist-info/RECORD,, +google_crc32c-1.7.1.dist-info/WHEEL,sha256=gU4NGWqXiV53MDl4ijY43V9IiGZhEk3E55ZEfMkaS-s,151 +google_crc32c-1.7.1.dist-info/licenses/LICENSE,sha256=z8d0m5b2O9McPEK1xHG_dWgUBT6EfBDz6wA0F7xSPTA,11358 +google_crc32c-1.7.1.dist-info/top_level.txt,sha256=r7PLPlKjfhMZLqeRsKXIQdIzbe3Frv_2_b8XmcvZ4FQ,14 +google_crc32c-1.7.1.dist-info/zip-safe,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1 +google_crc32c.libs/libcrc32c-7ebc40c5.so.1.1.0,sha256=oCcj2cmrPu8NKxM3ZVhY2golYzECJ9X7ilhsuWpel0o,25385 +google_crc32c/__config__.py,sha256=2i0KpKo-ypmMS_P9i3qObAW2eemCWtrmu_PJXktjHO8,1138 +google_crc32c/__init__.py,sha256=Dun6VhGxQ-8fydGR_hX4U9K3n8XPYXCDKmoIBAatpzM,1291 +google_crc32c/__pycache__/__config__.cpython-310.pyc,, +google_crc32c/__pycache__/__init__.cpython-310.pyc,, +google_crc32c/__pycache__/_checksum.cpython-310.pyc,, +google_crc32c/__pycache__/cext.cpython-310.pyc,, +google_crc32c/__pycache__/python.cpython-310.pyc,, +google_crc32c/_checksum.py,sha256=r_2PckK1pwWlW9E6e9xys4qdT4Z7JZe2WfYDFzNQbNI,2636 +google_crc32c/_crc32c.cpython-310-x86_64-linux-gnu.so,sha256=Qw5Xs5yn0tICAJG5KC-X81UseeZUpFI5jJhOIKFf1Ao,37705 +google_crc32c/cext.py,sha256=oF7NFpKWUOEoyYlbVGIAJVo8SNDiWRT_VYyCI14-5jo,1557 +google_crc32c/py.typed,sha256=2rT_wtizx2JwmnzAr6tXdmsEdE5PIp4kum7YsDhOw_w,76 +google_crc32c/python.py,sha256=9zy6ABAB_KM8JJRcZnE4b68VqnOEFRNmCGufrjcb7qc,5661 diff --git a/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/WHEEL b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..9d6be17c329ce8233870a40dd29f7a8af1bd4d3d --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/WHEEL @@ -0,0 +1,6 @@ +Wheel-Version: 1.0 +Generator: setuptools (78.1.0) +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64 +Tag: cp310-cp310-manylinux2014_x86_64 + diff --git a/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/licenses/LICENSE b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/google_crc32c-1.7.1.dist-info/licenses/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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of the date of their first contribution), except +those who explicitly didn't want to be mentioned. People with a * next +to their names are not found in the metadata of the git history. This +file is generated automatically by running `./bin/authors_update.py`. + +There are a total of 1310 authors. + +Ondřej Čertík +Fabian Pedregosa +Jurjen N.E. Bos +Mateusz Paprocki +*Marc-Etienne M.Leveille +Brian Jorgensen +Jason Gedge +Robert Schwarz +Pearu Peterson +Fredrik Johansson +Chris Wu +*Ulrich Hecht +Goutham Lakshminarayan +David Lawrence +Jaroslaw Tworek +David Marek +Bernhard R. Link +Andrej Tokarčík +Or Dvory +Saroj Adhikari +Pauli Virtanen +Robert Kern +James Aspnes +Nimish Telang +Abderrahim Kitouni +Pan Peng +Friedrich Hagedorn +Elrond der Elbenfuerst +Rizgar Mella +Felix Kaiser +Roberto Nobrega +David Roberts +Sebastian Krämer +Vinzent Steinberg +Riccardo Gori +Case Van Horsen +Stepan Roucka +Ali Raza Syed +Stefano Maggiolo +Robert Cimrman +Bastian Weber +Sebastian Krause +Sebastian Kreft +*Dan +Alan Bromborsky +Boris Timokhin +Robert +Andy R. 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Robert +Glenn Horton-Smith +Karan +Stefan Behnle <84378403+behnle@users.noreply.github.com> +Shreyash Mishra <72146041+Shreyash-cyber@users.noreply.github.com> +Arthur Milchior +NotWearingPants <26556598+NotWearingPants@users.noreply.github.com> +Ishan Pandhare +Carlos García Montoro +Parcly Taxel +Saicharan +Kunal Sheth +Biswadeep Purkayastha <98874428+metabiswadeep@users.noreply.github.com> +Jyn Spring 琴春 +Phil LeMaitre +Chris Kerr +José Senart +Uwe L. 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Kraus +Zac Hatfield-Dodds +platypus +codecruisader +James Whitehead +atharvParlikar +Ivan Petukhov +Augusto Borges +Han Wei Ang +Congxu Yang +Saicharan <62512681+saicharan2804@users.noreply.github.com> +Arnab Nandi +Harrison Oates <48871176+HarrisonOates@users.noreply.github.com> +Corey Cerovsek +Harsh Kasat +omahs <73983677+omahs@users.noreply.github.com> +Pascal Gitz +Ravindu-Hirimuthugoda +Sophia Pustova +George Pittock +Warren Jacinto +Sachin Singh +Zedmat <104870914+harshkasat@users.noreply.github.com> +Samith Karunathilake <55777141+samithkavishke@users.noreply.github.com> +Viraj Vekaria +Ankit Kumar Singh +Abhishek Kumar +Mohak Malviya +Matthias Liesenfeld <116307294+maliesen@users.noreply.github.com> +dodo +Mohamed Rezk +Tommaso Vaccari <05-gesto-follemente@icloud.com> +Alexis Schotte +Lauren Yim <31467609+cherryblossom000@users.noreply.github.com> +Prey Patel +Riccardo Di Girolamo +Abhishek kumar +Sam Lubelsky +Henrique Soares +Vladimir Sereda +Raj Sapale +Gerald Teschl +Richard Samuel <98638849+samuelard7@users.noreply.github.com> +HeeJae Chang +Hwayeon Kang +Nick Harder +Ethan DeGuire +Lorenz Winkler +Richard Rodenbusch +Zhenxu Zhu +Mark van Gelder +James A. Preiss +Emile Fourcini +Alberto Jiménez Ruiz +João Bravo +Dean Price +Hugo Kerstens +Jan Jancar +Matt Wang diff --git a/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/INSTALLER b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/LICENSE b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0744f229d697ca3ed1b1b257bfdb70e3eecf0b9e --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/LICENSE @@ -0,0 +1,153 @@ +Copyright (c) 2006-2023 SymPy Development Team + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of SymPy nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. + +-------------------------------------------------------------------------------- + +Patches that were taken from the Diofant project (https://github.com/diofant/diofant) +are licensed as: + +Copyright (c) 2006-2018 SymPy Development Team, + 2013-2023 Sergey B Kirpichev + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of Diofant or SymPy nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. 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Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of multipledispatch nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. + +-------------------------------------------------------------------------------- + +The files under the directory sympy/parsing/autolev/tests/pydy-example-repo +are directly copied from PyDy project and are licensed as: + +Copyright (c) 2009-2023, PyDy Authors +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +* Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. +* Neither the name of this project nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL PYDY AUTHORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +-------------------------------------------------------------------------------- + +The files under the directory sympy/parsing/latex +are directly copied from latex2sympy project and are licensed as: + +Copyright 2016, latex2sympy + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/METADATA b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..2ee82bf5e1020fe9b877981367a0d7d2ac6dd562 --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/METADATA @@ -0,0 +1,304 @@ +Metadata-Version: 2.1 +Name: sympy +Version: 1.13.3 +Summary: Computer algebra system (CAS) in Python +Home-page: https://sympy.org +Author: SymPy development team +Author-email: sympy@googlegroups.com +License: BSD +Project-URL: Source, https://github.com/sympy/sympy +Keywords: Math CAS +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Topic :: Scientific/Engineering +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Scientific/Engineering :: Physics +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Requires-Python: >=3.8 +Description-Content-Type: text/markdown +License-File: LICENSE +License-File: AUTHORS +Requires-Dist: mpmath <1.4,>=1.1.0 +Provides-Extra: dev +Requires-Dist: pytest >=7.1.0 ; extra == 'dev' +Requires-Dist: hypothesis >=6.70.0 ; extra == 'dev' + +# SymPy + +[![pypi version](https://img.shields.io/pypi/v/sympy.svg)](https://pypi.python.org/pypi/sympy) +[![Join the chat at https://gitter.im/sympy/sympy](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sympy/sympy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) +[![Zenodo Badge](https://zenodo.org/badge/18918/sympy/sympy.svg)](https://zenodo.org/badge/latestdoi/18918/sympy/sympy) +[![Downloads](https://pepy.tech/badge/sympy/month)](https://pepy.tech/project/sympy) +[![GitHub Issues](https://img.shields.io/badge/issue_tracking-github-blue.svg)](https://github.com/sympy/sympy/issues) +[![Git Tutorial](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?)](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) +[![Powered by NumFocus](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) +[![Commits since last release](https://img.shields.io/github/commits-since/sympy/sympy/latest.svg?longCache=true&style=flat-square&logo=git&logoColor=fff)](https://github.com/sympy/sympy/releases) + +[![SymPy Banner](https://github.com/sympy/sympy/raw/master/banner.svg)](https://sympy.org/) + + +See the [AUTHORS](AUTHORS) file for the list of authors. + +And many more people helped on the SymPy mailing list, reported bugs, +helped organize SymPy's participation in the Google Summer of Code, the +Google Highly Open Participation Contest, Google Code-In, wrote and +blogged about SymPy... + +License: New BSD License (see the [LICENSE](LICENSE) file for details) covers all +files in the sympy repository unless stated otherwise. + +Our mailing list is at +. + +We have a community chat at [Gitter](https://gitter.im/sympy/sympy). Feel +free to ask us anything there. We have a very welcoming and helpful +community. + +## Download + +The recommended installation method is through Anaconda, + + +You can also get the latest version of SymPy from + + +To get the git version do + + $ git clone https://github.com/sympy/sympy.git + +For other options (tarballs, debs, etc.), see +. + +## Documentation and Usage + +For in-depth instructions on installation and building the +documentation, see the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html). + +Everything is at: + + + +You can generate everything at the above site in your local copy of +SymPy by: + + $ cd doc + $ make html + +Then the docs will be in \_build/html. If +you don't want to read that, here is a short usage: + +From this directory, start Python and: + +``` python +>>> from sympy import Symbol, cos +>>> x = Symbol('x') +>>> e = 1/cos(x) +>>> print(e.series(x, 0, 10)) +1 + x**2/2 + 5*x**4/24 + 61*x**6/720 + 277*x**8/8064 + O(x**10) +``` + +SymPy also comes with a console that is a simple wrapper around the +classic python console (or IPython when available) that loads the SymPy +namespace and executes some common commands for you. + +To start it, issue: + + $ bin/isympy + +from this directory, if SymPy is not installed or simply: + + $ isympy + +if SymPy is installed. + +## Installation + +To install SymPy using PyPI, run the following command: + + $ pip install sympy + +To install SymPy using Anaconda, run the following command: + + $ conda install -c anaconda sympy + +To install SymPy from GitHub source, first clone SymPy using `git`: + + $ git clone https://github.com/sympy/sympy.git + +Then, in the `sympy` repository that you cloned, simply run: + + $ pip install . + +See for more information. + +## Contributing + +We welcome contributions from anyone, even if you are new to open +source. Please read our [Introduction to Contributing](https://docs.sympy.org/dev/contributing/introduction-to-contributing.html) +page and the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html). If you +are new and looking for some way to contribute, a good place to start is +to look at the issues tagged [Easy to Fix](https://github.com/sympy/sympy/issues?q=is%3Aopen+is%3Aissue+label%3A%22Easy+to+Fix%22). + +Please note that all participants in this project are expected to follow +our Code of Conduct. By participating in this project you agree to abide +by its terms. See [CODE\_OF\_CONDUCT.md](CODE_OF_CONDUCT.md). + +## Tests + +To execute all tests, run: + + $./setup.py test + +in the current directory. + +For the more fine-grained running of tests or doctests, use `bin/test` +or respectively `bin/doctest`. The master branch is automatically tested +by GitHub Actions. + +To test pull requests, use +[sympy-bot](https://github.com/sympy/sympy-bot). + +## Regenerate Experimental LaTeX Parser/Lexer + +The parser and lexer were generated with the [ANTLR4](http://antlr4.org) +toolchain in `sympy/parsing/latex/_antlr` and checked into the repo. +Presently, most users should not need to regenerate these files, but +if you plan to work on this feature, you will need the `antlr4` +command-line tool (and you must ensure that it is in your `PATH`). +One way to get it is: + + $ conda install -c conda-forge antlr=4.11.1 + +Alternatively, follow the instructions on the ANTLR website and download +the `antlr-4.11.1-complete.jar`. Then export the `CLASSPATH` as instructed +and instead of creating `antlr4` as an alias, make it an executable file +with the following contents: +``` bash +#!/bin/bash +java -jar /usr/local/lib/antlr-4.11.1-complete.jar "$@" +``` + +After making changes to `sympy/parsing/latex/LaTeX.g4`, run: + + $ ./setup.py antlr + +## Clean + +To clean everything (thus getting the same tree as in the repository): + + $ git clean -Xdf + +which will clear everything ignored by `.gitignore`, and: + + $ git clean -df + +to clear all untracked files. You can revert the most recent changes in +git with: + + $ git reset --hard + +WARNING: The above commands will all clear changes you may have made, +and you will lose them forever. Be sure to check things with `git +status`, `git diff`, `git clean -Xn`, and `git clean -n` before doing any +of those. + +## Bugs + +Our issue tracker is at . Please +report any bugs that you find. Or, even better, fork the repository on +GitHub and create a pull request. We welcome all changes, big or small, +and we will help you make the pull request if you are new to git (just +ask on our mailing list or Gitter Channel). If you further have any queries, you can find answers +on Stack Overflow using the [sympy](https://stackoverflow.com/questions/tagged/sympy) tag. + +## Brief History + +SymPy was started by Ondřej Čertík in 2005, he wrote some code during +the summer, then he wrote some more code during summer 2006. In February +2007, Fabian Pedregosa joined the project and helped fix many things, +contributed documentation, and made it alive again. 5 students (Mateusz +Paprocki, Brian Jorgensen, Jason Gedge, Robert Schwarz, and Chris Wu) +improved SymPy incredibly during summer 2007 as part of the Google +Summer of Code. Pearu Peterson joined the development during the summer +2007 and he has made SymPy much more competitive by rewriting the core +from scratch, which has made it from 10x to 100x faster. Jurjen N.E. Bos +has contributed pretty-printing and other patches. Fredrik Johansson has +written mpmath and contributed a lot of patches. + +SymPy has participated in every Google Summer of Code since 2007. You +can see for +full details. Each year has improved SymPy by bounds. Most of SymPy's +development has come from Google Summer of Code students. + +In 2011, Ondřej Čertík stepped down as lead developer, with Aaron +Meurer, who also started as a Google Summer of Code student, taking his +place. Ondřej Čertík is still active in the community but is too busy +with work and family to play a lead development role. + +Since then, a lot more people have joined the development and some +people have also left. You can see the full list in doc/src/aboutus.rst, +or online at: + + + +The git history goes back to 2007 when development moved from svn to hg. +To see the history before that point, look at +. + +You can use git to see the biggest developers. The command: + + $ git shortlog -ns + +will show each developer, sorted by commits to the project. The command: + + $ git shortlog -ns --since="1 year" + +will show the top developers from the last year. + +## Citation + +To cite SymPy in publications use + +> Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M, +> Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE, +> Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry +> MJ, Terrel AR, Roučka Š, Saboo A, Fernando I, Kulal S, Cimrman R, +> Scopatz A. (2017) SymPy: symbolic computing in Python. *PeerJ Computer +> Science* 3:e103 + +A BibTeX entry for LaTeX users is + +``` bibtex +@article{10.7717/peerj-cs.103, + title = {SymPy: symbolic computing in Python}, + author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, Amit and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony}, + year = 2017, + month = Jan, + keywords = {Python, Computer algebra system, Symbolics}, + abstract = { + SymPy is an open-source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provides additional examples and further outlines details of the architecture and features of SymPy. + }, + volume = 3, + pages = {e103}, + journal = {PeerJ Computer Science}, + issn = {2376-5992}, + url = {https://doi.org/10.7717/peerj-cs.103}, + doi = {10.7717/peerj-cs.103} +} +``` + +SymPy is BSD licensed, so you are free to use it whatever you like, be +it academic, commercial, creating forks or derivatives, as long as you +copy the BSD statement if you redistribute it (see the LICENSE file for +details). That said, although not required by the SymPy license, if it +is convenient for you, please cite SymPy when using it in your work and +also consider contributing all your changes back, so that we can +incorporate it and all of us will benefit in the end. diff --git a/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/RECORD b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..47cbb3a628fa09c143f3319ccfde338148758f1d --- /dev/null +++ b/evalkit_cambrian/lib/python3.10/site-packages/sympy-1.13.3.dist-info/RECORD @@ -0,0 +1,3076 @@ +../../../bin/isympy,sha256=S05SR_BqqLyCSUBTnXzFooBv4Un0W7JUUrA1RsJTGaU,227 +../../../share/man/man1/isympy.1,sha256=9DZdSOIQLikrATHlbkdDZ04LBQigZDUE0_oCXBDvdBs,6659 +__pycache__/isympy.cpython-310.pyc,, +isympy.py,sha256=gAoHa7OM0y9G5IBO7wO-uTpD-CPnd6sbmjJ_GGB0yzg,11207 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0000000000000000000000000000000000000000..eeb8319e074153b79c8d0f9c4b3afcec016ba2b6 --- /dev/null +++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_cython_templating.py @@ -0,0 +1,20 @@ +import pathlib +import pytest +import sklearn + + +def test_files_generated_by_templates_are_git_ignored(): + """Check the consistence of the files generated from template files.""" + gitignore_file = pathlib.Path(sklearn.__file__).parent.parent / ".gitignore" + if not gitignore_file.exists(): + pytest.skip("Tests are not run from the source folder") + + base_dir = pathlib.Path(sklearn.__file__).parent + ignored_files = gitignore_file.read_text().split("\n") + ignored_files = [pathlib.Path(line) for line in ignored_files] + + for filename in base_dir.glob("**/*.tp"): + filename = filename.relative_to(base_dir.parent) + # From "path/to/template.p??.tp" to "path/to/template.p??" + filename_wo_tempita_suffix = filename.with_suffix("") + assert filename_wo_tempita_suffix in ignored_files diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_deprecation.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_deprecation.py new file mode 100644 index 0000000000000000000000000000000000000000..b810cfb85d3f6eff0a74c37103982fa1abdf16ba --- /dev/null +++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_deprecation.py @@ -0,0 +1,67 @@ +# Authors: Raghav RV +# License: BSD 3 clause + + +import pickle + +from sklearn.utils.deprecation import _is_deprecated +from sklearn.utils.deprecation import deprecated +import pytest + + +@deprecated("qwerty") +class MockClass1: + pass + + +class MockClass2: + @deprecated("mockclass2_method") + def method(self): + pass + + @deprecated("n_features_ is deprecated") # type: ignore + @property + def n_features_(self): + """Number of input features.""" + return 10 + + +class MockClass3: + @deprecated() + def __init__(self): + pass + + +class MockClass4: + pass + + +@deprecated() +def mock_function(): + return 10 + + +def test_deprecated(): + with pytest.warns(FutureWarning, match="qwerty"): + MockClass1() + with pytest.warns(FutureWarning, match="mockclass2_method"): + MockClass2().method() + with pytest.warns(FutureWarning, match="deprecated"): + MockClass3() + with pytest.warns(FutureWarning, match="deprecated"): + val = mock_function() + assert val == 10 + + +def test_is_deprecated(): + # Test if _is_deprecated helper identifies wrapping via deprecated + # NOTE it works only for class methods and functions + assert _is_deprecated(MockClass1.__init__) + assert _is_deprecated(MockClass2().method) + assert _is_deprecated(MockClass3.__init__) + assert not _is_deprecated(MockClass4.__init__) + assert _is_deprecated(mock_function) + + +def test_pickle(): + pickle.loads(pickle.dumps(mock_function)) diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_encode.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_encode.py new file mode 100644 index 0000000000000000000000000000000000000000..083db25b7ca8045fe2ca2102e0ad8ed6ba483ab8 --- /dev/null +++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_encode.py @@ -0,0 +1,277 @@ +import pickle + +import numpy as np +import pytest +from numpy.testing import assert_array_equal + +from sklearn.utils._encode import _unique +from sklearn.utils._encode import _encode +from sklearn.utils._encode import _check_unknown +from sklearn.utils._encode import _get_counts + + +@pytest.mark.parametrize( + "values, expected", + [ + (np.array([2, 1, 3, 1, 3], dtype="int64"), np.array([1, 2, 3], dtype="int64")), + ( + np.array([2, 1, np.nan, 1, np.nan], dtype="float32"), + np.array([1, 2, np.nan], dtype="float32"), + ), + ( + np.array(["b", "a", "c", "a", "c"], dtype=object), + np.array(["a", "b", "c"], dtype=object), + ), + ( + np.array(["b", "a", None, "a", None], dtype=object), + np.array(["a", "b", None], dtype=object), + ), + (np.array(["b", "a", "c", "a", "c"]), np.array(["a", "b", "c"])), + ], + ids=["int64", "float32-nan", "object", "object-None", "str"], +) +def test_encode_util(values, expected): + uniques = _unique(values) + assert_array_equal(uniques, expected) + + result, encoded = _unique(values, return_inverse=True) + assert_array_equal(result, expected) + assert_array_equal(encoded, np.array([1, 0, 2, 0, 2])) + + encoded = _encode(values, uniques=uniques) + assert_array_equal(encoded, np.array([1, 0, 2, 0, 2])) + + result, counts = _unique(values, return_counts=True) + assert_array_equal(result, expected) + assert_array_equal(counts, np.array([2, 1, 2])) + + result, encoded, counts = _unique(values, return_inverse=True, return_counts=True) + assert_array_equal(result, expected) + assert_array_equal(encoded, np.array([1, 0, 2, 0, 2])) + assert_array_equal(counts, np.array([2, 1, 2])) + + +def test_encode_with_check_unknown(): + # test for the check_unknown parameter of _encode() + uniques = np.array([1, 2, 3]) + values = np.array([1, 2, 3, 4]) + + # Default is True, raise error + with pytest.raises(ValueError, match="y contains previously unseen labels"): + _encode(values, uniques=uniques, check_unknown=True) + + # dont raise error if False + _encode(values, uniques=uniques, check_unknown=False) + + # parameter is ignored for object dtype + uniques = np.array(["a", "b", "c"], dtype=object) + values = np.array(["a", "b", "c", "d"], dtype=object) + with pytest.raises(ValueError, match="y contains previously unseen labels"): + _encode(values, uniques=uniques, check_unknown=False) + + +def _assert_check_unknown(values, uniques, expected_diff, expected_mask): + diff = _check_unknown(values, uniques) + assert_array_equal(diff, expected_diff) + + diff, valid_mask = _check_unknown(values, uniques, return_mask=True) + assert_array_equal(diff, expected_diff) + assert_array_equal(valid_mask, expected_mask) + + +@pytest.mark.parametrize( + "values, uniques, expected_diff, expected_mask", + [ + (np.array([1, 2, 3, 4]), np.array([1, 2, 3]), [4], [True, True, True, False]), + (np.array([2, 1, 4, 5]), np.array([2, 5, 1]), [4], [True, True, False, True]), + (np.array([2, 1, np.nan]), np.array([2, 5, 1]), [np.nan], [True, True, False]), + ( + np.array([2, 1, 4, np.nan]), + np.array([2, 5, 1, np.nan]), + [4], + [True, True, False, True], + ), + ( + np.array([2, 1, 4, np.nan]), + np.array([2, 5, 1]), + [4, np.nan], + [True, True, False, False], + ), + ( + np.array([2, 1, 4, 5]), + np.array([2, 5, 1, np.nan]), + [4], + [True, True, False, True], + ), + ( + np.array(["a", "b", "c", "d"], dtype=object), + np.array(["a", "b", "c"], dtype=object), + np.array(["d"], dtype=object), + [True, True, True, False], + ), + ( + np.array(["d", "c", "a", "b"], dtype=object), + np.array(["a", "c", "b"], dtype=object), + np.array(["d"], dtype=object), + [False, True, True, True], + ), + ( + np.array(["a", "b", "c", "d"]), + np.array(["a", "b", "c"]), + np.array(["d"]), + [True, True, True, False], + ), + ( + np.array(["d", "c", "a", "b"]), + np.array(["a", "c", "b"]), + np.array(["d"]), + [False, True, True, True], + ), + ], +) +def test_check_unknown(values, uniques, expected_diff, expected_mask): + _assert_check_unknown(values, uniques, expected_diff, expected_mask) + + +@pytest.mark.parametrize("missing_value", [None, np.nan, float("nan")]) +@pytest.mark.parametrize("pickle_uniques", [True, False]) +def test_check_unknown_missing_values(missing_value, pickle_uniques): + # check for check_unknown with missing values with object dtypes + values = np.array(["d", "c", "a", "b", missing_value], dtype=object) + uniques = np.array(["c", "a", "b", missing_value], dtype=object) + if pickle_uniques: + uniques = pickle.loads(pickle.dumps(uniques)) + + expected_diff = ["d"] + expected_mask = [False, True, True, True, True] + _assert_check_unknown(values, uniques, expected_diff, expected_mask) + + values = np.array(["d", "c", "a", "b", missing_value], dtype=object) + uniques = np.array(["c", "a", "b"], dtype=object) + if pickle_uniques: + uniques = pickle.loads(pickle.dumps(uniques)) + + expected_diff = ["d", missing_value] + + expected_mask = [False, True, True, True, False] + _assert_check_unknown(values, uniques, expected_diff, expected_mask) + + values = np.array(["a", missing_value], dtype=object) + uniques = np.array(["a", "b", "z"], dtype=object) + if pickle_uniques: + uniques = pickle.loads(pickle.dumps(uniques)) + + expected_diff = [missing_value] + expected_mask = [True, False] + _assert_check_unknown(values, uniques, expected_diff, expected_mask) + + +@pytest.mark.parametrize("missing_value", [np.nan, None, float("nan")]) +@pytest.mark.parametrize("pickle_uniques", [True, False]) +def test_unique_util_missing_values_objects(missing_value, pickle_uniques): + # check for _unique and _encode with missing values with object dtypes + values = np.array(["a", "c", "c", missing_value, "b"], dtype=object) + expected_uniques = np.array(["a", "b", "c", missing_value], dtype=object) + + uniques = _unique(values) + + if missing_value is None: + assert_array_equal(uniques, expected_uniques) + else: # missing_value == np.nan + assert_array_equal(uniques[:-1], expected_uniques[:-1]) + assert np.isnan(uniques[-1]) + + if pickle_uniques: + uniques = pickle.loads(pickle.dumps(uniques)) + + encoded = _encode(values, uniques=uniques) + assert_array_equal(encoded, np.array([0, 2, 2, 3, 1])) + + +def test_unique_util_missing_values_numeric(): + # Check missing values in numerical values + values = np.array([3, 1, np.nan, 5, 3, np.nan], dtype=float) + expected_uniques = np.array([1, 3, 5, np.nan], dtype=float) + expected_inverse = np.array([1, 0, 3, 2, 1, 3]) + + uniques = _unique(values) + assert_array_equal(uniques, expected_uniques) + + uniques, inverse = _unique(values, return_inverse=True) + assert_array_equal(uniques, expected_uniques) + assert_array_equal(inverse, expected_inverse) + + encoded = _encode(values, uniques=uniques) + assert_array_equal(encoded, expected_inverse) + + +def test_unique_util_with_all_missing_values(): + # test for all types of missing values for object dtype + values = np.array([np.nan, "a", "c", "c", None, float("nan"), None], dtype=object) + + uniques = _unique(values) + assert_array_equal(uniques[:-1], ["a", "c", None]) + # last value is nan + assert np.isnan(uniques[-1]) + + expected_inverse = [3, 0, 1, 1, 2, 3, 2] + _, inverse = _unique(values, return_inverse=True) + assert_array_equal(inverse, expected_inverse) + + +def test_check_unknown_with_both_missing_values(): + # test for both types of missing values for object dtype + values = np.array([np.nan, "a", "c", "c", None, np.nan, None], dtype=object) + + diff = _check_unknown(values, known_values=np.array(["a", "c"], dtype=object)) + assert diff[0] is None + assert np.isnan(diff[1]) + + diff, valid_mask = _check_unknown( + values, known_values=np.array(["a", "c"], dtype=object), return_mask=True + ) + + assert diff[0] is None + assert np.isnan(diff[1]) + assert_array_equal(valid_mask, [False, True, True, True, False, False, False]) + + +@pytest.mark.parametrize( + "values, uniques, expected_counts", + [ + (np.array([1] * 10 + [2] * 4 + [3] * 15), np.array([1, 2, 3]), [10, 4, 15]), + ( + np.array([1] * 10 + [2] * 4 + [3] * 15), + np.array([1, 2, 3, 5]), + [10, 4, 15, 0], + ), + ( + np.array([np.nan] * 10 + [2] * 4 + [3] * 15), + np.array([2, 3, np.nan]), + [4, 15, 10], + ), + ( + np.array(["b"] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object), + ["a", "b", "c"], + [16, 4, 20], + ), + ( + np.array(["b"] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object), + ["c", "b", "a"], + [20, 4, 16], + ), + ( + np.array([np.nan] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object), + ["c", np.nan, "a"], + [20, 4, 16], + ), + ( + np.array(["b"] * 4 + ["a"] * 16 + ["c"] * 20, dtype=object), + ["a", "b", "c", "e"], + [16, 4, 20, 0], + ), + ], +) +def test_get_counts(values, uniques, expected_counts): + counts = _get_counts(values, uniques) + assert_array_equal(counts, expected_counts) diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_estimator_html_repr.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_estimator_html_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..d5bc6aab6ab8b1dbc470f21671cb9d804fb3561c --- /dev/null +++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_estimator_html_repr.py @@ -0,0 +1,333 @@ +from contextlib import closing +import html +from io import StringIO + +import pytest + +from sklearn import config_context +from sklearn.linear_model import LogisticRegression +from sklearn.neural_network import MLPClassifier +from sklearn.impute import SimpleImputer +from sklearn.decomposition import PCA +from sklearn.decomposition import TruncatedSVD +from sklearn.pipeline import Pipeline +from sklearn.pipeline import FeatureUnion +from sklearn.compose import ColumnTransformer +from sklearn.ensemble import VotingClassifier +from sklearn.feature_selection import SelectPercentile +from sklearn.cluster import Birch +from sklearn.cluster import AgglomerativeClustering +from sklearn.preprocessing import OneHotEncoder +from sklearn.preprocessing import StandardScaler +from sklearn.svm import LinearSVC +from sklearn.svm import LinearSVR +from sklearn.tree import DecisionTreeClassifier +from sklearn.multiclass import OneVsOneClassifier +from sklearn.ensemble import StackingClassifier +from sklearn.ensemble import StackingRegressor +from sklearn.gaussian_process.kernels import ExpSineSquared +from sklearn.kernel_ridge import KernelRidge + +from sklearn.model_selection import RandomizedSearchCV +from sklearn.utils._estimator_html_repr import _write_label_html +from sklearn.utils._estimator_html_repr import _get_visual_block +from sklearn.utils._estimator_html_repr import estimator_html_repr + + +@pytest.mark.parametrize("checked", [True, False]) +def test_write_label_html(checked): + # Test checking logic and labeling + name = "LogisticRegression" + tool_tip = "hello-world" + + with closing(StringIO()) as out: + _write_label_html(out, name, tool_tip, checked=checked) + html_label = out.getvalue() + assert "LogisticRegression" in html_label + assert html_label.startswith('
') + assert "
hello-world
" in html_label + if checked: + assert "checked>" in html_label + + +@pytest.mark.parametrize("est", ["passthrough", "drop", None]) +def test_get_visual_block_single_str_none(est): + # Test estimators that are represented by strings + est_html_info = _get_visual_block(est) + assert est_html_info.kind == "single" + assert est_html_info.estimators == est + assert est_html_info.names == str(est) + assert est_html_info.name_details == str(est) + + +def test_get_visual_block_single_estimator(): + est = LogisticRegression(C=10.0) + est_html_info = _get_visual_block(est) + assert est_html_info.kind == "single" + assert est_html_info.estimators == est + assert est_html_info.names == est.__class__.__name__ + assert est_html_info.name_details == str(est) + + +def test_get_visual_block_pipeline(): + pipe = Pipeline( + [ + ("imputer", SimpleImputer()), + ("do_nothing", "passthrough"), + ("do_nothing_more", None), + ("classifier", LogisticRegression()), + ] + ) + est_html_info = _get_visual_block(pipe) + assert est_html_info.kind == "serial" + assert est_html_info.estimators == tuple(step[1] for step in pipe.steps) + assert est_html_info.names == [ + "imputer: SimpleImputer", + "do_nothing: passthrough", + "do_nothing_more: passthrough", + "classifier: LogisticRegression", + ] + assert est_html_info.name_details == [str(est) for _, est in pipe.steps] + + +def test_get_visual_block_feature_union(): + f_union = FeatureUnion([("pca", PCA()), ("svd", TruncatedSVD())]) + est_html_info = _get_visual_block(f_union) + assert est_html_info.kind == "parallel" + assert est_html_info.names == ("pca", "svd") + assert est_html_info.estimators == tuple( + trans[1] for trans in f_union.transformer_list + ) + assert est_html_info.name_details == (None, None) + + +def test_get_visual_block_voting(): + clf = VotingClassifier( + [("log_reg", LogisticRegression()), ("mlp", MLPClassifier())] + ) + est_html_info = _get_visual_block(clf) + assert est_html_info.kind == "parallel" + assert est_html_info.estimators == tuple(trans[1] for trans in clf.estimators) + assert est_html_info.names == ("log_reg", "mlp") + assert est_html_info.name_details == (None, None) + + +def test_get_visual_block_column_transformer(): + ct = ColumnTransformer( + [("pca", PCA(), ["num1", "num2"]), ("svd", TruncatedSVD, [0, 3])] + ) + est_html_info = _get_visual_block(ct) + assert est_html_info.kind == "parallel" + assert est_html_info.estimators == tuple(trans[1] for trans in ct.transformers) + assert est_html_info.names == ("pca", "svd") + assert est_html_info.name_details == (["num1", "num2"], [0, 3]) + + +def test_estimator_html_repr_pipeline(): + num_trans = Pipeline( + steps=[("pass", "passthrough"), ("imputer", SimpleImputer(strategy="median"))] + ) + + cat_trans = Pipeline( + steps=[ + ("imputer", SimpleImputer(strategy="constant", missing_values="empty")), + ("one-hot", OneHotEncoder(drop="first")), + ] + ) + + preprocess = ColumnTransformer( + [ + ("num", num_trans, ["a", "b", "c", "d", "e"]), + ("cat", cat_trans, [0, 1, 2, 3]), + ] + ) + + feat_u = FeatureUnion( + [ + ("pca", PCA(n_components=1)), + ( + "tsvd", + Pipeline( + [ + ("first", TruncatedSVD(n_components=3)), + ("select", SelectPercentile()), + ] + ), + ), + ] + ) + + clf = VotingClassifier( + [ + ("lr", LogisticRegression(solver="lbfgs", random_state=1)), + ("mlp", MLPClassifier(alpha=0.001)), + ] + ) + + pipe = Pipeline( + [("preprocessor", preprocess), ("feat_u", feat_u), ("classifier", clf)] + ) + html_output = estimator_html_repr(pipe) + + # top level estimators show estimator with changes + assert html.escape(str(pipe)) in html_output + for _, est in pipe.steps: + assert ( + '
' + html.escape(str(est))
+        ) in html_output
+
+    # low level estimators do not show changes
+    with config_context(print_changed_only=True):
+        assert html.escape(str(num_trans["pass"])) in html_output
+        assert "passthrough" in html_output
+        assert html.escape(str(num_trans["imputer"])) in html_output
+
+        for _, _, cols in preprocess.transformers:
+            assert f"
{html.escape(str(cols))}
" in html_output
+
+        # feature union
+        for name, _ in feat_u.transformer_list:
+            assert f"" in html_output
+
+        pca = feat_u.transformer_list[0][1]
+        assert f"
{html.escape(str(pca))}
" in html_output
+
+        tsvd = feat_u.transformer_list[1][1]
+        first = tsvd["first"]
+        select = tsvd["select"]
+        assert f"
{html.escape(str(first))}
" in html_output
+        assert f"
{html.escape(str(select))}
" in html_output
+
+        # voting classifier
+        for name, est in clf.estimators:
+            assert f"" in html_output
+            assert f"
{html.escape(str(est))}
" in html_output
+
+
+@pytest.mark.parametrize("final_estimator", [None, LinearSVC()])
+def test_stacking_classsifer(final_estimator):
+    estimators = [
+        ("mlp", MLPClassifier(alpha=0.001)),
+        ("tree", DecisionTreeClassifier()),
+    ]
+    clf = StackingClassifier(estimators=estimators, final_estimator=final_estimator)
+
+    html_output = estimator_html_repr(clf)
+
+    assert html.escape(str(clf)) in html_output
+    # If final_estimator's default changes from LogisticRegression
+    # this should be updated
+    if final_estimator is None:
+        assert "LogisticRegression(" in html_output
+    else:
+        assert final_estimator.__class__.__name__ in html_output
+
+
+@pytest.mark.parametrize("final_estimator", [None, LinearSVR()])
+def test_stacking_regressor(final_estimator):
+    reg = StackingRegressor(
+        estimators=[("svr", LinearSVR())], final_estimator=final_estimator
+    )
+    html_output = estimator_html_repr(reg)
+
+    assert html.escape(str(reg.estimators[0][0])) in html_output
+    assert "LinearSVR" in html_output
+    if final_estimator is None:
+        assert "RidgeCV" in html_output
+    else:
+        assert html.escape(final_estimator.__class__.__name__) in html_output
+
+
+def test_birch_duck_typing_meta():
+    # Test duck typing meta estimators with Birch
+    birch = Birch(n_clusters=AgglomerativeClustering(n_clusters=3))
+    html_output = estimator_html_repr(birch)
+
+    # inner estimators do not show changes
+    with config_context(print_changed_only=True):
+        assert f"
{html.escape(str(birch.n_clusters))}" in html_output
+        assert "AgglomerativeClustering" in html_output
+
+    # outer estimator contains all changes
+    assert f"
{html.escape(str(birch))}" in html_output
+
+
+def test_ovo_classifier_duck_typing_meta():
+    # Test duck typing metaestimators with OVO
+    ovo = OneVsOneClassifier(LinearSVC(penalty="l1"))
+    html_output = estimator_html_repr(ovo)
+
+    # inner estimators do not show changes
+    with config_context(print_changed_only=True):
+        assert f"
{html.escape(str(ovo.estimator))}" in html_output
+        assert "LinearSVC" in html_output
+
+    # outer estimator
+    assert f"
{html.escape(str(ovo))}" in html_output
+
+
+def test_duck_typing_nested_estimator():
+    # Test duck typing metaestimators with random search
+    kernel_ridge = KernelRidge(kernel=ExpSineSquared())
+    param_distributions = {"alpha": [1, 2]}
+
+    kernel_ridge_tuned = RandomizedSearchCV(
+        kernel_ridge,
+        param_distributions=param_distributions,
+    )
+    html_output = estimator_html_repr(kernel_ridge_tuned)
+    assert "estimator: KernelRidge" in html_output
+
+
+@pytest.mark.parametrize("print_changed_only", [True, False])
+def test_one_estimator_print_change_only(print_changed_only):
+    pca = PCA(n_components=10)
+
+    with config_context(print_changed_only=print_changed_only):
+        pca_repr = html.escape(str(pca))
+        html_output = estimator_html_repr(pca)
+        assert pca_repr in html_output
+
+
+def test_fallback_exists():
+    """Check that repr fallback is in the HTML."""
+    pca = PCA(n_components=10)
+    html_output = estimator_html_repr(pca)
+
+    assert (
+        f'
{html.escape(str(pca))}'
+        in html_output
+    )
+
+
+def test_show_arrow_pipeline():
+    """Show arrow in pipeline for top level in pipeline"""
+    pipe = Pipeline([("scale", StandardScaler()), ("log_Reg", LogisticRegression())])
+
+    html_output = estimator_html_repr(pipe)
+    assert (
+        'class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline'
+        in html_output
+    )
+
+
+def test_invalid_parameters_in_stacking():
+    """Invalidate stacking configuration uses default repr.
+
+    Non-regression test for #24009.
+    """
+    stacker = StackingClassifier(estimators=[])
+
+    html_output = estimator_html_repr(stacker)
+    assert html.escape(str(stacker)) in html_output
+
+
+def test_estimator_get_params_return_cls():
+    """Check HTML repr works where a value in get_params is a class."""
+
+    class MyEstimator:
+        def get_params(self, deep=False):
+            return {"inner_cls": LogisticRegression}
+
+    est = MyEstimator()
+    assert "MyEstimator" in estimator_html_repr(est)
diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_mocking.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_mocking.py
new file mode 100644
index 0000000000000000000000000000000000000000..a12c41256581a895e2ffd14cfb1cfa3e097dae9e
--- /dev/null
+++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_mocking.py
@@ -0,0 +1,183 @@
+import numpy as np
+import pytest
+from scipy import sparse
+
+from numpy.testing import assert_array_equal
+from numpy.testing import assert_allclose
+
+from sklearn.datasets import load_iris
+from sklearn.utils import check_array
+from sklearn.utils import _safe_indexing
+from sklearn.utils._testing import _convert_container
+
+from sklearn.utils._mocking import CheckingClassifier
+
+
+@pytest.fixture
+def iris():
+    return load_iris(return_X_y=True)
+
+
+def _success(x):
+    return True
+
+
+def _fail(x):
+    return False
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {},
+        {"check_X": _success},
+        {"check_y": _success},
+        {"check_X": _success, "check_y": _success},
+    ],
+)
+def test_check_on_fit_success(iris, kwargs):
+    X, y = iris
+    CheckingClassifier(**kwargs).fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"check_X": _fail},
+        {"check_y": _fail},
+        {"check_X": _success, "check_y": _fail},
+        {"check_X": _fail, "check_y": _success},
+        {"check_X": _fail, "check_y": _fail},
+    ],
+)
+def test_check_on_fit_fail(iris, kwargs):
+    X, y = iris
+    clf = CheckingClassifier(**kwargs)
+    with pytest.raises(AssertionError):
+        clf.fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "pred_func", ["predict", "predict_proba", "decision_function", "score"]
+)
+def test_check_X_on_predict_success(iris, pred_func):
+    X, y = iris
+    clf = CheckingClassifier(check_X=_success).fit(X, y)
+    getattr(clf, pred_func)(X)
+
+
+@pytest.mark.parametrize(
+    "pred_func", ["predict", "predict_proba", "decision_function", "score"]
+)
+def test_check_X_on_predict_fail(iris, pred_func):
+    X, y = iris
+    clf = CheckingClassifier(check_X=_success).fit(X, y)
+    clf.set_params(check_X=_fail)
+    with pytest.raises(AssertionError):
+        getattr(clf, pred_func)(X)
+
+
+@pytest.mark.parametrize("input_type", ["list", "array", "sparse", "dataframe"])
+def test_checking_classifier(iris, input_type):
+    # Check that the CheckingClassifier outputs what we expect
+    X, y = iris
+    X = _convert_container(X, input_type)
+    clf = CheckingClassifier()
+    clf.fit(X, y)
+
+    assert_array_equal(clf.classes_, np.unique(y))
+    assert len(clf.classes_) == 3
+    assert clf.n_features_in_ == 4
+
+    y_pred = clf.predict(X)
+    assert_array_equal(y_pred, np.zeros(y_pred.size, dtype=int))
+
+    assert clf.score(X) == pytest.approx(0)
+    clf.set_params(foo_param=10)
+    assert clf.fit(X, y).score(X) == pytest.approx(1)
+
+    y_proba = clf.predict_proba(X)
+    assert y_proba.shape == (150, 3)
+    assert_allclose(y_proba[:, 0], 1)
+    assert_allclose(y_proba[:, 1:], 0)
+
+    y_decision = clf.decision_function(X)
+    assert y_decision.shape == (150, 3)
+    assert_allclose(y_decision[:, 0], 1)
+    assert_allclose(y_decision[:, 1:], 0)
+
+    # check the shape in case of binary classification
+    first_2_classes = np.logical_or(y == 0, y == 1)
+    X = _safe_indexing(X, first_2_classes)
+    y = _safe_indexing(y, first_2_classes)
+    clf.fit(X, y)
+
+    y_proba = clf.predict_proba(X)
+    assert y_proba.shape == (100, 2)
+    assert_allclose(y_proba[:, 0], 1)
+    assert_allclose(y_proba[:, 1], 0)
+
+    y_decision = clf.decision_function(X)
+    assert y_decision.shape == (100,)
+    assert_allclose(y_decision, 0)
+
+
+def test_checking_classifier_with_params(iris):
+    X, y = iris
+    X_sparse = sparse.csr_matrix(X)
+
+    clf = CheckingClassifier(check_X=sparse.issparse)
+    with pytest.raises(AssertionError):
+        clf.fit(X, y)
+    clf.fit(X_sparse, y)
+
+    clf = CheckingClassifier(
+        check_X=check_array, check_X_params={"accept_sparse": False}
+    )
+    clf.fit(X, y)
+    with pytest.raises(TypeError, match="A sparse matrix was passed"):
+        clf.fit(X_sparse, y)
+
+
+def test_checking_classifier_fit_params(iris):
+    # check the error raised when the number of samples is not the one expected
+    X, y = iris
+    clf = CheckingClassifier(expected_sample_weight=True)
+    sample_weight = np.ones(len(X) // 2)
+
+    msg = f"sample_weight.shape == ({len(X) // 2},), expected ({len(X)},)!"
+    with pytest.raises(ValueError) as exc:
+        clf.fit(X, y, sample_weight=sample_weight)
+    assert exc.value.args[0] == msg
+
+
+def test_checking_classifier_missing_fit_params(iris):
+    X, y = iris
+    clf = CheckingClassifier(expected_sample_weight=True)
+    err_msg = "Expected sample_weight to be passed"
+    with pytest.raises(AssertionError, match=err_msg):
+        clf.fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "methods_to_check",
+    [["predict"], ["predict", "predict_proba"]],
+)
+@pytest.mark.parametrize(
+    "predict_method", ["predict", "predict_proba", "decision_function", "score"]
+)
+def test_checking_classifier_methods_to_check(iris, methods_to_check, predict_method):
+    # check that methods_to_check allows to bypass checks
+    X, y = iris
+
+    clf = CheckingClassifier(
+        check_X=sparse.issparse,
+        methods_to_check=methods_to_check,
+    )
+
+    clf.fit(X, y)
+    if predict_method in methods_to_check:
+        with pytest.raises(AssertionError):
+            getattr(clf, predict_method)(X)
+    else:
+        getattr(clf, predict_method)(X)
diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_param_validation.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_param_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce8f9cdf939fd9177ad8fab151e3cee3329fab52
--- /dev/null
+++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_param_validation.py
@@ -0,0 +1,671 @@
+from numbers import Integral, Real
+
+import numpy as np
+from scipy.sparse import csr_matrix
+import pytest
+
+from sklearn.base import BaseEstimator
+from sklearn.model_selection import LeaveOneOut
+from sklearn.utils import deprecated
+from sklearn.utils._param_validation import Hidden
+from sklearn.utils._param_validation import Interval
+from sklearn.utils._param_validation import Options
+from sklearn.utils._param_validation import StrOptions
+from sklearn.utils._param_validation import _ArrayLikes
+from sklearn.utils._param_validation import _Booleans
+from sklearn.utils._param_validation import _Callables
+from sklearn.utils._param_validation import _CVObjects
+from sklearn.utils._param_validation import _InstancesOf
+from sklearn.utils._param_validation import _MissingValues
+from sklearn.utils._param_validation import _PandasNAConstraint
+from sklearn.utils._param_validation import _IterablesNotString
+from sklearn.utils._param_validation import _NoneConstraint
+from sklearn.utils._param_validation import _RandomStates
+from sklearn.utils._param_validation import _SparseMatrices
+from sklearn.utils._param_validation import _VerboseHelper
+from sklearn.utils._param_validation import HasMethods
+from sklearn.utils._param_validation import make_constraint
+from sklearn.utils._param_validation import generate_invalid_param_val
+from sklearn.utils._param_validation import generate_valid_param
+from sklearn.utils._param_validation import validate_params
+from sklearn.utils._param_validation import InvalidParameterError
+
+
+# Some helpers for the tests
+@validate_params({"a": [Real], "b": [Real], "c": [Real], "d": [Real]})
+def _func(a, b=0, *args, c, d=0, **kwargs):
+    """A function to test the validation of functions."""
+
+
+class _Class:
+    """A class to test the _InstancesOf constraint and the validation of methods."""
+
+    @validate_params({"a": [Real]})
+    def _method(self, a):
+        """A validated method"""
+
+    @deprecated()
+    @validate_params({"a": [Real]})
+    def _deprecated_method(self, a):
+        """A deprecated validated method"""
+
+
+class _Estimator(BaseEstimator):
+    """An estimator to test the validation of estimator parameters."""
+
+    _parameter_constraints: dict = {"a": [Real]}
+
+    def __init__(self, a):
+        self.a = a
+
+    def fit(self, X=None, y=None):
+        self._validate_params()
+
+
+@pytest.mark.parametrize("interval_type", [Integral, Real])
+def test_interval_range(interval_type):
+    """Check the range of values depending on closed."""
+    interval = Interval(interval_type, -2, 2, closed="left")
+    assert -2 in interval and 2 not in interval
+
+    interval = Interval(interval_type, -2, 2, closed="right")
+    assert -2 not in interval and 2 in interval
+
+    interval = Interval(interval_type, -2, 2, closed="both")
+    assert -2 in interval and 2 in interval
+
+    interval = Interval(interval_type, -2, 2, closed="neither")
+    assert -2 not in interval and 2 not in interval
+
+
+def test_interval_inf_in_bounds():
+    """Check that inf is included iff a bound is closed and set to None.
+
+    Only valid for real intervals.
+    """
+    interval = Interval(Real, 0, None, closed="right")
+    assert np.inf in interval
+
+    interval = Interval(Real, None, 0, closed="left")
+    assert -np.inf in interval
+
+    interval = Interval(Real, None, None, closed="neither")
+    assert np.inf not in interval
+    assert -np.inf not in interval
+
+
+@pytest.mark.parametrize(
+    "interval",
+    [Interval(Real, 0, 1, closed="left"), Interval(Real, None, None, closed="both")],
+)
+def test_nan_not_in_interval(interval):
+    """Check that np.nan is not in any interval."""
+    assert np.nan not in interval
+
+
+@pytest.mark.parametrize(
+    "params, error, match",
+    [
+        (
+            {"type": Integral, "left": 1.0, "right": 2, "closed": "both"},
+            TypeError,
+            r"Expecting left to be an int for an interval over the integers",
+        ),
+        (
+            {"type": Integral, "left": 1, "right": 2.0, "closed": "neither"},
+            TypeError,
+            "Expecting right to be an int for an interval over the integers",
+        ),
+        (
+            {"type": Integral, "left": None, "right": 0, "closed": "left"},
+            ValueError,
+            r"left can't be None when closed == left",
+        ),
+        (
+            {"type": Integral, "left": 0, "right": None, "closed": "right"},
+            ValueError,
+            r"right can't be None when closed == right",
+        ),
+        (
+            {"type": Integral, "left": 1, "right": -1, "closed": "both"},
+            ValueError,
+            r"right can't be less than left",
+        ),
+    ],
+)
+def test_interval_errors(params, error, match):
+    """Check that informative errors are raised for invalid combination of parameters"""
+    with pytest.raises(error, match=match):
+        Interval(**params)
+
+
+def test_stroptions():
+    """Sanity check for the StrOptions constraint"""
+    options = StrOptions({"a", "b", "c"}, deprecated={"c"})
+    assert options.is_satisfied_by("a")
+    assert options.is_satisfied_by("c")
+    assert not options.is_satisfied_by("d")
+
+    assert "'c' (deprecated)" in str(options)
+
+
+def test_options():
+    """Sanity check for the Options constraint"""
+    options = Options(Real, {-0.5, 0.5, np.inf}, deprecated={-0.5})
+    assert options.is_satisfied_by(-0.5)
+    assert options.is_satisfied_by(np.inf)
+    assert not options.is_satisfied_by(1.23)
+
+    assert "-0.5 (deprecated)" in str(options)
+
+
+@pytest.mark.parametrize(
+    "type, expected_type_name",
+    [
+        (int, "int"),
+        (Integral, "int"),
+        (Real, "float"),
+        (np.ndarray, "numpy.ndarray"),
+    ],
+)
+def test_instances_of_type_human_readable(type, expected_type_name):
+    """Check the string representation of the _InstancesOf constraint."""
+    constraint = _InstancesOf(type)
+    assert str(constraint) == f"an instance of '{expected_type_name}'"
+
+
+def test_hasmethods():
+    """Check the HasMethods constraint."""
+    constraint = HasMethods(["a", "b"])
+
+    class _Good:
+        def a(self):
+            pass  # pragma: no cover
+
+        def b(self):
+            pass  # pragma: no cover
+
+    class _Bad:
+        def a(self):
+            pass  # pragma: no cover
+
+    assert constraint.is_satisfied_by(_Good())
+    assert not constraint.is_satisfied_by(_Bad())
+    assert str(constraint) == "an object implementing 'a' and 'b'"
+
+
+@pytest.mark.parametrize(
+    "constraint",
+    [
+        Interval(Real, None, 0, closed="left"),
+        Interval(Real, 0, None, closed="left"),
+        Interval(Real, None, None, closed="neither"),
+        StrOptions({"a", "b", "c"}),
+        _MissingValues(),
+        _VerboseHelper(),
+        HasMethods("fit"),
+        _IterablesNotString(),
+        _CVObjects(),
+    ],
+)
+def test_generate_invalid_param_val(constraint):
+    """Check that the value generated does not satisfy the constraint"""
+    bad_value = generate_invalid_param_val(constraint)
+    assert not constraint.is_satisfied_by(bad_value)
+
+
+@pytest.mark.parametrize(
+    "integer_interval, real_interval",
+    [
+        (
+            Interval(Integral, None, 3, closed="right"),
+            Interval(Real, -5, 5, closed="both"),
+        ),
+        (
+            Interval(Integral, None, 3, closed="right"),
+            Interval(Real, -5, 5, closed="neither"),
+        ),
+        (
+            Interval(Integral, None, 3, closed="right"),
+            Interval(Real, 4, 5, closed="both"),
+        ),
+        (
+            Interval(Integral, None, 3, closed="right"),
+            Interval(Real, 5, None, closed="left"),
+        ),
+        (
+            Interval(Integral, None, 3, closed="right"),
+            Interval(Real, 4, None, closed="neither"),
+        ),
+        (
+            Interval(Integral, 3, None, closed="left"),
+            Interval(Real, -5, 5, closed="both"),
+        ),
+        (
+            Interval(Integral, 3, None, closed="left"),
+            Interval(Real, -5, 5, closed="neither"),
+        ),
+        (
+            Interval(Integral, 3, None, closed="left"),
+            Interval(Real, 1, 2, closed="both"),
+        ),
+        (
+            Interval(Integral, 3, None, closed="left"),
+            Interval(Real, None, -5, closed="left"),
+        ),
+        (
+            Interval(Integral, 3, None, closed="left"),
+            Interval(Real, None, -4, closed="neither"),
+        ),
+        (
+            Interval(Integral, -5, 5, closed="both"),
+            Interval(Real, None, 1, closed="right"),
+        ),
+        (
+            Interval(Integral, -5, 5, closed="both"),
+            Interval(Real, 1, None, closed="left"),
+        ),
+        (
+            Interval(Integral, -5, 5, closed="both"),
+            Interval(Real, -10, -4, closed="neither"),
+        ),
+        (
+            Interval(Integral, -5, 5, closed="both"),
+            Interval(Real, -10, -4, closed="right"),
+        ),
+        (
+            Interval(Integral, -5, 5, closed="neither"),
+            Interval(Real, 6, 10, closed="neither"),
+        ),
+        (
+            Interval(Integral, -5, 5, closed="neither"),
+            Interval(Real, 6, 10, closed="left"),
+        ),
+        (
+            Interval(Integral, 2, None, closed="left"),
+            Interval(Real, 0, 1, closed="both"),
+        ),
+        (
+            Interval(Integral, 1, None, closed="left"),
+            Interval(Real, 0, 1, closed="both"),
+        ),
+    ],
+)
+def test_generate_invalid_param_val_2_intervals(integer_interval, real_interval):
+    """Check that the value generated for an interval constraint does not satisfy any of
+    the interval constraints.
+    """
+    bad_value = generate_invalid_param_val(
+        real_interval, constraints=[real_interval, integer_interval]
+    )
+    assert not real_interval.is_satisfied_by(bad_value)
+    assert not integer_interval.is_satisfied_by(bad_value)
+
+    bad_value = generate_invalid_param_val(
+        integer_interval, constraints=[real_interval, integer_interval]
+    )
+    assert not real_interval.is_satisfied_by(bad_value)
+    assert not integer_interval.is_satisfied_by(bad_value)
+
+
+@pytest.mark.parametrize(
+    "constraints",
+    [
+        [_ArrayLikes()],
+        [_InstancesOf(list)],
+        [_Callables()],
+        [_NoneConstraint()],
+        [_RandomStates()],
+        [_SparseMatrices()],
+        [_Booleans()],
+        [Interval(Real, None, None, closed="both")],
+        [
+            Interval(Integral, 0, None, closed="left"),
+            Interval(Real, None, 0, closed="neither"),
+        ],
+    ],
+)
+def test_generate_invalid_param_val_all_valid(constraints):
+    """Check that the function raises NotImplementedError when there's no invalid value
+    for the constraint.
+    """
+    with pytest.raises(NotImplementedError):
+        generate_invalid_param_val(constraints[0], constraints=constraints)
+
+
+@pytest.mark.parametrize(
+    "constraint",
+    [
+        _ArrayLikes(),
+        _Callables(),
+        _InstancesOf(list),
+        _NoneConstraint(),
+        _RandomStates(),
+        _SparseMatrices(),
+        _Booleans(),
+        _VerboseHelper(),
+        _MissingValues(),
+        StrOptions({"a", "b", "c"}),
+        Options(Integral, {1, 2, 3}),
+        Interval(Integral, None, None, closed="neither"),
+        Interval(Integral, 0, 10, closed="neither"),
+        Interval(Integral, 0, None, closed="neither"),
+        Interval(Integral, None, 0, closed="neither"),
+        Interval(Real, 0, 1, closed="neither"),
+        Interval(Real, 0, None, closed="both"),
+        Interval(Real, None, 0, closed="right"),
+        HasMethods("fit"),
+        _IterablesNotString(),
+        _CVObjects(),
+    ],
+)
+def test_generate_valid_param(constraint):
+    """Check that the value generated does satisfy the constraint."""
+    value = generate_valid_param(constraint)
+    assert constraint.is_satisfied_by(value)
+
+
+@pytest.mark.parametrize(
+    "constraint_declaration, value",
+    [
+        (Interval(Real, 0, 1, closed="both"), 0.42),
+        (Interval(Integral, 0, None, closed="neither"), 42),
+        (StrOptions({"a", "b", "c"}), "b"),
+        (Options(type, {np.float32, np.float64}), np.float64),
+        (callable, lambda x: x + 1),
+        (None, None),
+        ("array-like", [[1, 2], [3, 4]]),
+        ("array-like", np.array([[1, 2], [3, 4]])),
+        ("sparse matrix", csr_matrix([[1, 2], [3, 4]])),
+        ("random_state", 0),
+        ("random_state", np.random.RandomState(0)),
+        ("random_state", None),
+        (_Class, _Class()),
+        (int, 1),
+        (Real, 0.5),
+        ("boolean", False),
+        ("verbose", 1),
+        ("missing_values", -1),
+        ("missing_values", -1.0),
+        ("missing_values", None),
+        ("missing_values", float("nan")),
+        ("missing_values", np.nan),
+        ("missing_values", "missing"),
+        (HasMethods("fit"), _Estimator(a=0)),
+        ("cv_object", 5),
+    ],
+)
+def test_is_satisfied_by(constraint_declaration, value):
+    """Sanity check for the is_satisfied_by method"""
+    constraint = make_constraint(constraint_declaration)
+    assert constraint.is_satisfied_by(value)
+
+
+@pytest.mark.parametrize(
+    "constraint_declaration, expected_constraint_class",
+    [
+        (Interval(Real, 0, 1, closed="both"), Interval),
+        (StrOptions({"option1", "option2"}), StrOptions),
+        (Options(Real, {0.42, 1.23}), Options),
+        ("array-like", _ArrayLikes),
+        ("sparse matrix", _SparseMatrices),
+        ("random_state", _RandomStates),
+        (None, _NoneConstraint),
+        (callable, _Callables),
+        (int, _InstancesOf),
+        ("boolean", _Booleans),
+        ("verbose", _VerboseHelper),
+        ("missing_values", _MissingValues),
+        (HasMethods("fit"), HasMethods),
+        ("cv_object", _CVObjects),
+    ],
+)
+def test_make_constraint(constraint_declaration, expected_constraint_class):
+    """Check that make_constraint dispaches to the appropriate constraint class"""
+    constraint = make_constraint(constraint_declaration)
+    assert constraint.__class__ is expected_constraint_class
+
+
+def test_make_constraint_unknown():
+    """Check that an informative error is raised when an unknown constraint is passed"""
+    with pytest.raises(ValueError, match="Unknown constraint"):
+        make_constraint("not a valid constraint")
+
+
+def test_validate_params():
+    """Check that validate_params works no matter how the arguments are passed"""
+    with pytest.raises(
+        InvalidParameterError, match="The 'a' parameter of _func must be"
+    ):
+        _func("wrong", c=1)
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'b' parameter of _func must be"
+    ):
+        _func(*[1, "wrong"], c=1)
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'c' parameter of _func must be"
+    ):
+        _func(1, **{"c": "wrong"})
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'd' parameter of _func must be"
+    ):
+        _func(1, c=1, d="wrong")
+
+    # check in the presence of extra positional and keyword args
+    with pytest.raises(
+        InvalidParameterError, match="The 'b' parameter of _func must be"
+    ):
+        _func(0, *["wrong", 2, 3], c=4, **{"e": 5})
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'c' parameter of _func must be"
+    ):
+        _func(0, *[1, 2, 3], c="four", **{"e": 5})
+
+
+def test_validate_params_missing_params():
+    """Check that no error is raised when there are parameters without
+    constraints
+    """
+
+    @validate_params({"a": [int]})
+    def func(a, b):
+        pass
+
+    func(1, 2)
+
+
+def test_decorate_validated_function():
+    """Check that validate_params functions can be decorated"""
+    decorated_function = deprecated()(_func)
+
+    with pytest.warns(FutureWarning, match="Function _func is deprecated"):
+        decorated_function(1, 2, c=3)
+
+    # outer decorator does not interfer with validation
+    with pytest.warns(FutureWarning, match="Function _func is deprecated"):
+        with pytest.raises(
+            InvalidParameterError, match=r"The 'c' parameter of _func must be"
+        ):
+            decorated_function(1, 2, c="wrong")
+
+
+def test_validate_params_method():
+    """Check that validate_params works with methods"""
+    with pytest.raises(
+        InvalidParameterError, match="The 'a' parameter of _Class._method must be"
+    ):
+        _Class()._method("wrong")
+
+    # validated method can be decorated
+    with pytest.warns(FutureWarning, match="Function _deprecated_method is deprecated"):
+        with pytest.raises(
+            InvalidParameterError,
+            match="The 'a' parameter of _Class._deprecated_method must be",
+        ):
+            _Class()._deprecated_method("wrong")
+
+
+def test_validate_params_estimator():
+    """Check that validate_params works with Estimator instances"""
+    # no validation in init
+    est = _Estimator("wrong")
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'a' parameter of _Estimator must be"
+    ):
+        est.fit()
+
+
+def test_stroptions_deprecated_subset():
+    """Check that the deprecated parameter must be a subset of options."""
+    with pytest.raises(ValueError, match="deprecated options must be a subset"):
+        StrOptions({"a", "b", "c"}, deprecated={"a", "d"})
+
+
+def test_hidden_constraint():
+    """Check that internal constraints are not exposed in the error message."""
+
+    @validate_params({"param": [Hidden(list), dict]})
+    def f(param):
+        pass
+
+    # list and dict are valid params
+    f({"a": 1, "b": 2, "c": 3})
+    f([1, 2, 3])
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'param' parameter"
+    ) as exc_info:
+        f(param="bad")
+
+    # the list option is not exposed in the error message
+    err_msg = str(exc_info.value)
+    assert "an instance of 'dict'" in err_msg
+    assert "an instance of 'list'" not in err_msg
+
+
+def test_hidden_stroptions():
+    """Check that we can have 2 StrOptions constraints, one being hidden."""
+
+    @validate_params({"param": [StrOptions({"auto"}), Hidden(StrOptions({"warn"}))]})
+    def f(param):
+        pass
+
+    # "auto" and "warn" are valid params
+    f("auto")
+    f("warn")
+
+    with pytest.raises(
+        InvalidParameterError, match="The 'param' parameter"
+    ) as exc_info:
+        f(param="bad")
+
+    # the "warn" option is not exposed in the error message
+    err_msg = str(exc_info.value)
+    assert "auto" in err_msg
+    assert "warn" not in err_msg
+
+
+def test_validate_params_set_param_constraints_attribute():
+    """Check that the validate_params decorator properly sets the parameter constraints
+    as attribute of the decorated function/method.
+    """
+    assert hasattr(_func, "_skl_parameter_constraints")
+    assert hasattr(_Class()._method, "_skl_parameter_constraints")
+
+
+def test_boolean_constraint_deprecated_int():
+    """Check that validate_params raise a deprecation message but still passes
+    validation when using an int for a parameter accepting a boolean.
+    """
+
+    @validate_params({"param": ["boolean"]})
+    def f(param):
+        pass
+
+    # True/False and np.bool_(True/False) are valid params
+    f(True)
+    f(np.bool_(False))
+
+    # an int is also valid but deprecated
+    with pytest.warns(
+        FutureWarning, match="Passing an int for a boolean parameter is deprecated"
+    ):
+        f(1)
+
+
+def test_no_validation():
+    """Check that validation can be skipped for a parameter."""
+
+    @validate_params({"param1": [int, None], "param2": "no_validation"})
+    def f(param1=None, param2=None):
+        pass
+
+    # param1 is validated
+    with pytest.raises(InvalidParameterError, match="The 'param1' parameter"):
+        f(param1="wrong")
+
+    # param2 is not validated: any type is valid.
+    class SomeType:
+        pass
+
+    f(param2=SomeType)
+    f(param2=SomeType())
+
+
+def test_pandas_na_constraint_with_pd_na():
+    """Add a specific test for checking support for `pandas.NA`."""
+    pd = pytest.importorskip("pandas")
+
+    na_constraint = _PandasNAConstraint()
+    assert na_constraint.is_satisfied_by(pd.NA)
+    assert not na_constraint.is_satisfied_by(np.array([1, 2, 3]))
+
+
+def test_iterable_not_string():
+    """Check that a string does not satisfy the _IterableNotString constraint."""
+    constraint = _IterablesNotString()
+    assert constraint.is_satisfied_by([1, 2, 3])
+    assert constraint.is_satisfied_by(range(10))
+    assert not constraint.is_satisfied_by("some string")
+
+
+def test_cv_objects():
+    """Check that the _CVObjects constraint accepts all current ways
+    to pass cv objects."""
+    constraint = _CVObjects()
+    assert constraint.is_satisfied_by(5)
+    assert constraint.is_satisfied_by(LeaveOneOut())
+    assert constraint.is_satisfied_by([([1, 2], [3, 4]), ([3, 4], [1, 2])])
+    assert constraint.is_satisfied_by(None)
+    assert not constraint.is_satisfied_by("not a CV object")
+
+
+def test_third_party_estimator():
+    """Check that the validation from a scikit-learn estimator inherited by a third
+    party estimator does not impose a match between the dict of constraints and the
+    parameters of the estimator.
+    """
+
+    class ThirdPartyEstimator(_Estimator):
+        def __init__(self, b):
+            self.b = b
+            super().__init__(a=0)
+
+        def fit(self, X=None, y=None):
+            super().fit(X, y)
+
+    # does not raise, even though "b" is not in the constraints dict and "a" is not
+    # a parameter of the estimator.
+    ThirdPartyEstimator(b=0).fit()
+
+
+def test_interval_real_not_int():
+    """Check for the type "real_not_int" in the Interval constraint."""
+    constraint = Interval("real_not_int", 0, 1, closed="both")
+    assert constraint.is_satisfied_by(1.0)
+    assert not constraint.is_satisfied_by(1)
diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_pprint.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_pprint.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa1e2e03841e996f1d02dd41b612ed1ec7564c18
--- /dev/null
+++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_pprint.py
@@ -0,0 +1,680 @@
+import re
+from pprint import PrettyPrinter
+
+import numpy as np
+
+from sklearn.utils._pprint import _EstimatorPrettyPrinter
+from sklearn.linear_model import LogisticRegressionCV
+from sklearn.pipeline import make_pipeline
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.feature_selection import SelectKBest, chi2
+from sklearn import config_context
+
+
+# Ignore flake8 (lots of line too long issues)
+# flake8: noqa
+
+# Constructors excerpted to test pprinting
+class LogisticRegression(BaseEstimator):
+    def __init__(
+        self,
+        penalty="l2",
+        dual=False,
+        tol=1e-4,
+        C=1.0,
+        fit_intercept=True,
+        intercept_scaling=1,
+        class_weight=None,
+        random_state=None,
+        solver="warn",
+        max_iter=100,
+        multi_class="warn",
+        verbose=0,
+        warm_start=False,
+        n_jobs=None,
+        l1_ratio=None,
+    ):
+        self.penalty = penalty
+        self.dual = dual
+        self.tol = tol
+        self.C = C
+        self.fit_intercept = fit_intercept
+        self.intercept_scaling = intercept_scaling
+        self.class_weight = class_weight
+        self.random_state = random_state
+        self.solver = solver
+        self.max_iter = max_iter
+        self.multi_class = multi_class
+        self.verbose = verbose
+        self.warm_start = warm_start
+        self.n_jobs = n_jobs
+        self.l1_ratio = l1_ratio
+
+    def fit(self, X, y):
+        return self
+
+
+class StandardScaler(TransformerMixin, BaseEstimator):
+    def __init__(self, copy=True, with_mean=True, with_std=True):
+        self.with_mean = with_mean
+        self.with_std = with_std
+        self.copy = copy
+
+    def transform(self, X, copy=None):
+        return self
+
+
+class RFE(BaseEstimator):
+    def __init__(self, estimator, n_features_to_select=None, step=1, verbose=0):
+        self.estimator = estimator
+        self.n_features_to_select = n_features_to_select
+        self.step = step
+        self.verbose = verbose
+
+
+class GridSearchCV(BaseEstimator):
+    def __init__(
+        self,
+        estimator,
+        param_grid,
+        scoring=None,
+        n_jobs=None,
+        iid="warn",
+        refit=True,
+        cv="warn",
+        verbose=0,
+        pre_dispatch="2*n_jobs",
+        error_score="raise-deprecating",
+        return_train_score=False,
+    ):
+        self.estimator = estimator
+        self.param_grid = param_grid
+        self.scoring = scoring
+        self.n_jobs = n_jobs
+        self.iid = iid
+        self.refit = refit
+        self.cv = cv
+        self.verbose = verbose
+        self.pre_dispatch = pre_dispatch
+        self.error_score = error_score
+        self.return_train_score = return_train_score
+
+
+class CountVectorizer(BaseEstimator):
+    def __init__(
+        self,
+        input="content",
+        encoding="utf-8",
+        decode_error="strict",
+        strip_accents=None,
+        lowercase=True,
+        preprocessor=None,
+        tokenizer=None,
+        stop_words=None,
+        token_pattern=r"(?u)\b\w\w+\b",
+        ngram_range=(1, 1),
+        analyzer="word",
+        max_df=1.0,
+        min_df=1,
+        max_features=None,
+        vocabulary=None,
+        binary=False,
+        dtype=np.int64,
+    ):
+        self.input = input
+        self.encoding = encoding
+        self.decode_error = decode_error
+        self.strip_accents = strip_accents
+        self.preprocessor = preprocessor
+        self.tokenizer = tokenizer
+        self.analyzer = analyzer
+        self.lowercase = lowercase
+        self.token_pattern = token_pattern
+        self.stop_words = stop_words
+        self.max_df = max_df
+        self.min_df = min_df
+        self.max_features = max_features
+        self.ngram_range = ngram_range
+        self.vocabulary = vocabulary
+        self.binary = binary
+        self.dtype = dtype
+
+
+class Pipeline(BaseEstimator):
+    def __init__(self, steps, memory=None):
+        self.steps = steps
+        self.memory = memory
+
+
+class SVC(BaseEstimator):
+    def __init__(
+        self,
+        C=1.0,
+        kernel="rbf",
+        degree=3,
+        gamma="auto_deprecated",
+        coef0=0.0,
+        shrinking=True,
+        probability=False,
+        tol=1e-3,
+        cache_size=200,
+        class_weight=None,
+        verbose=False,
+        max_iter=-1,
+        decision_function_shape="ovr",
+        random_state=None,
+    ):
+        self.kernel = kernel
+        self.degree = degree
+        self.gamma = gamma
+        self.coef0 = coef0
+        self.tol = tol
+        self.C = C
+        self.shrinking = shrinking
+        self.probability = probability
+        self.cache_size = cache_size
+        self.class_weight = class_weight
+        self.verbose = verbose
+        self.max_iter = max_iter
+        self.decision_function_shape = decision_function_shape
+        self.random_state = random_state
+
+
+class PCA(BaseEstimator):
+    def __init__(
+        self,
+        n_components=None,
+        copy=True,
+        whiten=False,
+        svd_solver="auto",
+        tol=0.0,
+        iterated_power="auto",
+        random_state=None,
+    ):
+        self.n_components = n_components
+        self.copy = copy
+        self.whiten = whiten
+        self.svd_solver = svd_solver
+        self.tol = tol
+        self.iterated_power = iterated_power
+        self.random_state = random_state
+
+
+class NMF(BaseEstimator):
+    def __init__(
+        self,
+        n_components=None,
+        init=None,
+        solver="cd",
+        beta_loss="frobenius",
+        tol=1e-4,
+        max_iter=200,
+        random_state=None,
+        alpha=0.0,
+        l1_ratio=0.0,
+        verbose=0,
+        shuffle=False,
+    ):
+        self.n_components = n_components
+        self.init = init
+        self.solver = solver
+        self.beta_loss = beta_loss
+        self.tol = tol
+        self.max_iter = max_iter
+        self.random_state = random_state
+        self.alpha = alpha
+        self.l1_ratio = l1_ratio
+        self.verbose = verbose
+        self.shuffle = shuffle
+
+
+class SimpleImputer(BaseEstimator):
+    def __init__(
+        self,
+        missing_values=np.nan,
+        strategy="mean",
+        fill_value=None,
+        verbose=0,
+        copy=True,
+    ):
+        self.missing_values = missing_values
+        self.strategy = strategy
+        self.fill_value = fill_value
+        self.verbose = verbose
+        self.copy = copy
+
+
+def test_basic(print_changed_only_false):
+    # Basic pprint test
+    lr = LogisticRegression()
+    expected = """
+LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
+                   intercept_scaling=1, l1_ratio=None, max_iter=100,
+                   multi_class='warn', n_jobs=None, penalty='l2',
+                   random_state=None, solver='warn', tol=0.0001, verbose=0,
+                   warm_start=False)"""
+
+    expected = expected[1:]  # remove first \n
+    assert lr.__repr__() == expected
+
+
+def test_changed_only():
+    # Make sure the changed_only param is correctly used when True (default)
+    lr = LogisticRegression(C=99)
+    expected = """LogisticRegression(C=99)"""
+    assert lr.__repr__() == expected
+
+    # Check with a repr that doesn't fit on a single line
+    lr = LogisticRegression(
+        C=99, class_weight=0.4, fit_intercept=False, tol=1234, verbose=True
+    )
+    expected = """
+LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
+                   verbose=True)"""
+    expected = expected[1:]  # remove first \n
+    assert lr.__repr__() == expected
+
+    imputer = SimpleImputer(missing_values=0)
+    expected = """SimpleImputer(missing_values=0)"""
+    assert imputer.__repr__() == expected
+
+    # Defaults to np.NaN, trying with float('NaN')
+    imputer = SimpleImputer(missing_values=float("NaN"))
+    expected = """SimpleImputer()"""
+    assert imputer.__repr__() == expected
+
+    # make sure array parameters don't throw error (see #13583)
+    repr(LogisticRegressionCV(Cs=np.array([0.1, 1])))
+
+
+def test_pipeline(print_changed_only_false):
+    # Render a pipeline object
+    pipeline = make_pipeline(StandardScaler(), LogisticRegression(C=999))
+    expected = """
+Pipeline(memory=None,
+         steps=[('standardscaler',
+                 StandardScaler(copy=True, with_mean=True, with_std=True)),
+                ('logisticregression',
+                 LogisticRegression(C=999, class_weight=None, dual=False,
+                                    fit_intercept=True, intercept_scaling=1,
+                                    l1_ratio=None, max_iter=100,
+                                    multi_class='warn', n_jobs=None,
+                                    penalty='l2', random_state=None,
+                                    solver='warn', tol=0.0001, verbose=0,
+                                    warm_start=False))],
+         verbose=False)"""
+
+    expected = expected[1:]  # remove first \n
+    assert pipeline.__repr__() == expected
+
+
+def test_deeply_nested(print_changed_only_false):
+    # Render a deeply nested estimator
+    rfe = RFE(RFE(RFE(RFE(RFE(RFE(RFE(LogisticRegression())))))))
+    expected = """
+RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
+                                                                                                                     class_weight=None,
+                                                                                                                     dual=False,
+                                                                                                                     fit_intercept=True,
+                                                                                                                     intercept_scaling=1,
+                                                                                                                     l1_ratio=None,
+                                                                                                                     max_iter=100,
+                                                                                                                     multi_class='warn',
+                                                                                                                     n_jobs=None,
+                                                                                                                     penalty='l2',
+                                                                                                                     random_state=None,
+                                                                                                                     solver='warn',
+                                                                                                                     tol=0.0001,
+                                                                                                                     verbose=0,
+                                                                                                                     warm_start=False),
+                                                                                        n_features_to_select=None,
+                                                                                        step=1,
+                                                                                        verbose=0),
+                                                                          n_features_to_select=None,
+                                                                          step=1,
+                                                                          verbose=0),
+                                                            n_features_to_select=None,
+                                                            step=1, verbose=0),
+                                              n_features_to_select=None, step=1,
+                                              verbose=0),
+                                n_features_to_select=None, step=1, verbose=0),
+                  n_features_to_select=None, step=1, verbose=0),
+    n_features_to_select=None, step=1, verbose=0)"""
+
+    expected = expected[1:]  # remove first \n
+    assert rfe.__repr__() == expected
+
+
+def test_gridsearch(print_changed_only_false):
+    # render a gridsearch
+    param_grid = [
+        {"kernel": ["rbf"], "gamma": [1e-3, 1e-4], "C": [1, 10, 100, 1000]},
+        {"kernel": ["linear"], "C": [1, 10, 100, 1000]},
+    ]
+    gs = GridSearchCV(SVC(), param_grid, cv=5)
+
+    expected = """
+GridSearchCV(cv=5, error_score='raise-deprecating',
+             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
+                           decision_function_shape='ovr', degree=3,
+                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
+                           probability=False, random_state=None, shrinking=True,
+                           tol=0.001, verbose=False),
+             iid='warn', n_jobs=None,
+             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
+                          'kernel': ['rbf']},
+                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
+             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
+             scoring=None, verbose=0)"""
+
+    expected = expected[1:]  # remove first \n
+    assert gs.__repr__() == expected
+
+
+def test_gridsearch_pipeline(print_changed_only_false):
+    # render a pipeline inside a gridsearch
+    pp = _EstimatorPrettyPrinter(compact=True, indent=1, indent_at_name=True)
+
+    pipeline = Pipeline([("reduce_dim", PCA()), ("classify", SVC())])
+    N_FEATURES_OPTIONS = [2, 4, 8]
+    C_OPTIONS = [1, 10, 100, 1000]
+    param_grid = [
+        {
+            "reduce_dim": [PCA(iterated_power=7), NMF()],
+            "reduce_dim__n_components": N_FEATURES_OPTIONS,
+            "classify__C": C_OPTIONS,
+        },
+        {
+            "reduce_dim": [SelectKBest(chi2)],
+            "reduce_dim__k": N_FEATURES_OPTIONS,
+            "classify__C": C_OPTIONS,
+        },
+    ]
+    gspipline = GridSearchCV(pipeline, cv=3, n_jobs=1, param_grid=param_grid)
+    expected = """
+GridSearchCV(cv=3, error_score='raise-deprecating',
+             estimator=Pipeline(memory=None,
+                                steps=[('reduce_dim',
+                                        PCA(copy=True, iterated_power='auto',
+                                            n_components=None,
+                                            random_state=None,
+                                            svd_solver='auto', tol=0.0,
+                                            whiten=False)),
+                                       ('classify',
+                                        SVC(C=1.0, cache_size=200,
+                                            class_weight=None, coef0=0.0,
+                                            decision_function_shape='ovr',
+                                            degree=3, gamma='auto_deprecated',
+                                            kernel='rbf', max_iter=-1,
+                                            probability=False,
+                                            random_state=None, shrinking=True,
+                                            tol=0.001, verbose=False))]),
+             iid='warn', n_jobs=1,
+             param_grid=[{'classify__C': [1, 10, 100, 1000],
+                          'reduce_dim': [PCA(copy=True, iterated_power=7,
+                                             n_components=None,
+                                             random_state=None,
+                                             svd_solver='auto', tol=0.0,
+                                             whiten=False),
+                                         NMF(alpha=0.0, beta_loss='frobenius',
+                                             init=None, l1_ratio=0.0,
+                                             max_iter=200, n_components=None,
+                                             random_state=None, shuffle=False,
+                                             solver='cd', tol=0.0001,
+                                             verbose=0)],
+                          'reduce_dim__n_components': [2, 4, 8]},
+                         {'classify__C': [1, 10, 100, 1000],
+                          'reduce_dim': [SelectKBest(k=10,
+                                                     score_func=)],
+                          'reduce_dim__k': [2, 4, 8]}],
+             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
+             scoring=None, verbose=0)"""
+
+    expected = expected[1:]  # remove first \n
+    repr_ = pp.pformat(gspipline)
+    # Remove address of '' for reproducibility
+    repr_ = re.sub("function chi2 at 0x.*>", "function chi2 at some_address>", repr_)
+    assert repr_ == expected
+
+
+def test_n_max_elements_to_show(print_changed_only_false):
+
+    n_max_elements_to_show = 30
+    pp = _EstimatorPrettyPrinter(
+        compact=True,
+        indent=1,
+        indent_at_name=True,
+        n_max_elements_to_show=n_max_elements_to_show,
+    )
+
+    # No ellipsis
+    vocabulary = {i: i for i in range(n_max_elements_to_show)}
+    vectorizer = CountVectorizer(vocabulary=vocabulary)
+
+    expected = r"""
+CountVectorizer(analyzer='word', binary=False, decode_error='strict',
+                dtype=, encoding='utf-8', input='content',
+                lowercase=True, max_df=1.0, max_features=None, min_df=1,
+                ngram_range=(1, 1), preprocessor=None, stop_words=None,
+                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
+                tokenizer=None,
+                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
+                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
+                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
+                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
+                            27: 27, 28: 28, 29: 29})"""
+
+    expected = expected[1:]  # remove first \n
+    assert pp.pformat(vectorizer) == expected
+
+    # Now with ellipsis
+    vocabulary = {i: i for i in range(n_max_elements_to_show + 1)}
+    vectorizer = CountVectorizer(vocabulary=vocabulary)
+
+    expected = r"""
+CountVectorizer(analyzer='word', binary=False, decode_error='strict',
+                dtype=, encoding='utf-8', input='content',
+                lowercase=True, max_df=1.0, max_features=None, min_df=1,
+                ngram_range=(1, 1), preprocessor=None, stop_words=None,
+                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
+                tokenizer=None,
+                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
+                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
+                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
+                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
+                            27: 27, 28: 28, 29: 29, ...})"""
+
+    expected = expected[1:]  # remove first \n
+    assert pp.pformat(vectorizer) == expected
+
+    # Also test with lists
+    param_grid = {"C": list(range(n_max_elements_to_show))}
+    gs = GridSearchCV(SVC(), param_grid)
+    expected = """
+GridSearchCV(cv='warn', error_score='raise-deprecating',
+             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
+                           decision_function_shape='ovr', degree=3,
+                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
+                           probability=False, random_state=None, shrinking=True,
+                           tol=0.001, verbose=False),
+             iid='warn', n_jobs=None,
+             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
+                               27, 28, 29]},
+             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
+             scoring=None, verbose=0)"""
+
+    expected = expected[1:]  # remove first \n
+    assert pp.pformat(gs) == expected
+
+    # Now with ellipsis
+    param_grid = {"C": list(range(n_max_elements_to_show + 1))}
+    gs = GridSearchCV(SVC(), param_grid)
+    expected = """
+GridSearchCV(cv='warn', error_score='raise-deprecating',
+             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
+                           decision_function_shape='ovr', degree=3,
+                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
+                           probability=False, random_state=None, shrinking=True,
+                           tol=0.001, verbose=False),
+             iid='warn', n_jobs=None,
+             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
+                               27, 28, 29, ...]},
+             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
+             scoring=None, verbose=0)"""
+
+    expected = expected[1:]  # remove first \n
+    assert pp.pformat(gs) == expected
+
+
+def test_bruteforce_ellipsis(print_changed_only_false):
+    # Check that the bruteforce ellipsis (used when the number of non-blank
+    # characters exceeds N_CHAR_MAX) renders correctly.
+
+    lr = LogisticRegression()
+
+    # test when the left and right side of the ellipsis aren't on the same
+    # line.
+    expected = """
+LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
+                   in...
+                   multi_class='warn', n_jobs=None, penalty='l2',
+                   random_state=None, solver='warn', tol=0.0001, verbose=0,
+                   warm_start=False)"""
+
+    expected = expected[1:]  # remove first \n
+    assert expected == lr.__repr__(N_CHAR_MAX=150)
+
+    # test with very small N_CHAR_MAX
+    # Note that N_CHAR_MAX is not strictly enforced, but it's normal: to avoid
+    # weird reprs we still keep the whole line of the right part (after the
+    # ellipsis).
+    expected = """
+Lo...
+                   warm_start=False)"""
+
+    expected = expected[1:]  # remove first \n
+    assert expected == lr.__repr__(N_CHAR_MAX=4)
+
+    # test with N_CHAR_MAX == number of non-blank characters: In this case we
+    # don't want ellipsis
+    full_repr = lr.__repr__(N_CHAR_MAX=float("inf"))
+    n_nonblank = len("".join(full_repr.split()))
+    assert lr.__repr__(N_CHAR_MAX=n_nonblank) == full_repr
+    assert "..." not in full_repr
+
+    # test with N_CHAR_MAX == number of non-blank characters - 10: the left and
+    # right side of the ellispsis are on different lines. In this case we
+    # want to expend the whole line of the right side
+    expected = """
+LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
+                   intercept_scaling=1, l1_ratio=None, max_i...
+                   multi_class='warn', n_jobs=None, penalty='l2',
+                   random_state=None, solver='warn', tol=0.0001, verbose=0,
+                   warm_start=False)"""
+    expected = expected[1:]  # remove first \n
+    assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 10)
+
+    # test with N_CHAR_MAX == number of non-blank characters - 10: the left and
+    # right side of the ellispsis are on the same line. In this case we don't
+    # want to expend the whole line of the right side, just add the ellispsis
+    # between the 2 sides.
+    expected = """
+LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
+                   intercept_scaling=1, l1_ratio=None, max_iter...,
+                   multi_class='warn', n_jobs=None, penalty='l2',
+                   random_state=None, solver='warn', tol=0.0001, verbose=0,
+                   warm_start=False)"""
+    expected = expected[1:]  # remove first \n
+    assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 4)
+
+    # test with N_CHAR_MAX == number of non-blank characters - 2: the left and
+    # right side of the ellispsis are on the same line, but adding the ellipsis
+    # would actually make the repr longer. So we don't add the ellipsis.
+    expected = """
+LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
+                   intercept_scaling=1, l1_ratio=None, max_iter=100,
+                   multi_class='warn', n_jobs=None, penalty='l2',
+                   random_state=None, solver='warn', tol=0.0001, verbose=0,
+                   warm_start=False)"""
+    expected = expected[1:]  # remove first \n
+    assert expected == lr.__repr__(N_CHAR_MAX=n_nonblank - 2)
+
+
+def test_builtin_prettyprinter():
+    # non regression test than ensures we can still use the builtin
+    # PrettyPrinter class for estimators (as done e.g. by joblib).
+    # Used to be a bug
+
+    PrettyPrinter().pprint(LogisticRegression())
+
+
+def test_kwargs_in_init():
+    # Make sure the changed_only=True mode is OK when an argument is passed as
+    # kwargs.
+    # Non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/17206
+
+    class WithKWargs(BaseEstimator):
+        # Estimator with a kwargs argument. These need to hack around
+        # set_params and get_params. Here we mimic what LightGBM does.
+        def __init__(self, a="willchange", b="unchanged", **kwargs):
+            self.a = a
+            self.b = b
+            self._other_params = {}
+            self.set_params(**kwargs)
+
+        def get_params(self, deep=True):
+            params = super().get_params(deep=deep)
+            params.update(self._other_params)
+            return params
+
+        def set_params(self, **params):
+            for key, value in params.items():
+                setattr(self, key, value)
+                self._other_params[key] = value
+            return self
+
+    est = WithKWargs(a="something", c="abcd", d=None)
+
+    expected = "WithKWargs(a='something', c='abcd', d=None)"
+    assert expected == est.__repr__()
+
+    with config_context(print_changed_only=False):
+        expected = "WithKWargs(a='something', b='unchanged', c='abcd', d=None)"
+        assert expected == est.__repr__()
+
+
+def test_complexity_print_changed_only():
+    # Make sure `__repr__` is called the same amount of times
+    # whether `print_changed_only` is True or False
+    # Non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/18490
+
+    class DummyEstimator(TransformerMixin, BaseEstimator):
+        nb_times_repr_called = 0
+
+        def __init__(self, estimator=None):
+            self.estimator = estimator
+
+        def __repr__(self):
+            DummyEstimator.nb_times_repr_called += 1
+            return super().__repr__()
+
+        def transform(self, X, copy=None):  # pragma: no cover
+            return X
+
+    estimator = DummyEstimator(
+        make_pipeline(DummyEstimator(DummyEstimator()), DummyEstimator(), "passthrough")
+    )
+    with config_context(print_changed_only=False):
+        repr(estimator)
+        nb_repr_print_changed_only_false = DummyEstimator.nb_times_repr_called
+
+    DummyEstimator.nb_times_repr_called = 0
+    with config_context(print_changed_only=True):
+        repr(estimator)
+        nb_repr_print_changed_only_true = DummyEstimator.nb_times_repr_called
+
+    assert nb_repr_print_changed_only_false == nb_repr_print_changed_only_true
diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_show_versions.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_show_versions.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6590bfde15f54677767f63318526a421e388fb7
--- /dev/null
+++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_show_versions.py
@@ -0,0 +1,41 @@
+from sklearn.utils.fixes import threadpool_info
+from sklearn.utils._show_versions import _get_sys_info
+from sklearn.utils._show_versions import _get_deps_info
+from sklearn.utils._show_versions import show_versions
+from sklearn.utils._testing import ignore_warnings
+
+
+def test_get_sys_info():
+    sys_info = _get_sys_info()
+
+    assert "python" in sys_info
+    assert "executable" in sys_info
+    assert "machine" in sys_info
+
+
+def test_get_deps_info():
+    with ignore_warnings():
+        deps_info = _get_deps_info()
+
+    assert "pip" in deps_info
+    assert "setuptools" in deps_info
+    assert "sklearn" in deps_info
+    assert "numpy" in deps_info
+    assert "scipy" in deps_info
+    assert "Cython" in deps_info
+    assert "pandas" in deps_info
+    assert "matplotlib" in deps_info
+    assert "joblib" in deps_info
+
+
+def test_show_versions(capsys):
+    with ignore_warnings():
+        show_versions()
+        out, err = capsys.readouterr()
+
+    assert "python" in out
+    assert "numpy" in out
+
+    info = threadpool_info()
+    if info:
+        assert "threadpoolctl info:" in out
diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_stats.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdf679b99b7f296b6037fbc262bfddd49cd03c89
--- /dev/null
+++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_stats.py
@@ -0,0 +1,98 @@
+import numpy as np
+from numpy.testing import assert_allclose
+from pytest import approx
+
+from sklearn.utils.stats import _weighted_percentile
+
+
+def test_weighted_percentile():
+    y = np.empty(102, dtype=np.float64)
+    y[:50] = 0
+    y[-51:] = 2
+    y[-1] = 100000
+    y[50] = 1
+    sw = np.ones(102, dtype=np.float64)
+    sw[-1] = 0.0
+    score = _weighted_percentile(y, sw, 50)
+    assert approx(score) == 1
+
+
+def test_weighted_percentile_equal():
+    y = np.empty(102, dtype=np.float64)
+    y.fill(0.0)
+    sw = np.ones(102, dtype=np.float64)
+    sw[-1] = 0.0
+    score = _weighted_percentile(y, sw, 50)
+    assert score == 0
+
+
+def test_weighted_percentile_zero_weight():
+    y = np.empty(102, dtype=np.float64)
+    y.fill(1.0)
+    sw = np.ones(102, dtype=np.float64)
+    sw.fill(0.0)
+    score = _weighted_percentile(y, sw, 50)
+    assert approx(score) == 1.0
+
+
+def test_weighted_percentile_zero_weight_zero_percentile():
+    y = np.array([0, 1, 2, 3, 4, 5])
+    sw = np.array([0, 0, 1, 1, 1, 0])
+    score = _weighted_percentile(y, sw, 0)
+    assert approx(score) == 2
+
+    score = _weighted_percentile(y, sw, 50)
+    assert approx(score) == 3
+
+    score = _weighted_percentile(y, sw, 100)
+    assert approx(score) == 4
+
+
+def test_weighted_median_equal_weights():
+    # Checks weighted percentile=0.5 is same as median when weights equal
+    rng = np.random.RandomState(0)
+    # Odd size as _weighted_percentile takes lower weighted percentile
+    x = rng.randint(10, size=11)
+    weights = np.ones(x.shape)
+
+    median = np.median(x)
+    w_median = _weighted_percentile(x, weights)
+    assert median == approx(w_median)
+
+
+def test_weighted_median_integer_weights():
+    # Checks weighted percentile=0.5 is same as median when manually weight
+    # data
+    rng = np.random.RandomState(0)
+    x = rng.randint(20, size=10)
+    weights = rng.choice(5, size=10)
+    x_manual = np.repeat(x, weights)
+
+    median = np.median(x_manual)
+    w_median = _weighted_percentile(x, weights)
+
+    assert median == approx(w_median)
+
+
+def test_weighted_percentile_2d():
+    # Check for when array 2D and sample_weight 1D
+    rng = np.random.RandomState(0)
+    x1 = rng.randint(10, size=10)
+    w1 = rng.choice(5, size=10)
+
+    x2 = rng.randint(20, size=10)
+    x_2d = np.vstack((x1, x2)).T
+
+    w_median = _weighted_percentile(x_2d, w1)
+    p_axis_0 = [_weighted_percentile(x_2d[:, i], w1) for i in range(x_2d.shape[1])]
+    assert_allclose(w_median, p_axis_0)
+
+    # Check when array and sample_weight boht 2D
+    w2 = rng.choice(5, size=10)
+    w_2d = np.vstack((w1, w2)).T
+
+    w_median = _weighted_percentile(x_2d, w_2d)
+    p_axis_0 = [
+        _weighted_percentile(x_2d[:, i], w_2d[:, i]) for i in range(x_2d.shape[1])
+    ]
+    assert_allclose(w_median, p_axis_0)
diff --git a/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_weight_vector.py b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_weight_vector.py
new file mode 100644
index 0000000000000000000000000000000000000000..627d46d1fda067ca1803fd041a646d244eae3275
--- /dev/null
+++ b/evalkit_tf449/lib/python3.10/site-packages/sklearn/utils/tests/test_weight_vector.py
@@ -0,0 +1,24 @@
+import numpy as np
+import pytest
+from sklearn.utils._weight_vector import (
+    WeightVector32,
+    WeightVector64,
+)
+
+
+@pytest.mark.parametrize(
+    "dtype, WeightVector",
+    [
+        (np.float32, WeightVector32),
+        (np.float64, WeightVector64),
+    ],
+)
+def test_type_invariance(dtype, WeightVector):
+    """Check the `dtype` consistency of `WeightVector`."""
+    weights = np.random.rand(100).astype(dtype)
+    average_weights = np.random.rand(100).astype(dtype)
+
+    weight_vector = WeightVector(weights, average_weights)
+
+    assert np.asarray(weight_vector.w).dtype is np.dtype(dtype)
+    assert np.asarray(weight_vector.aw).dtype is np.dtype(dtype)