diff --git a/.flake8 b/.flake8 deleted file mode 100644 index d3bb141..0000000 --- a/.flake8 +++ /dev/null @@ -1,3 +0,0 @@ -[flake8] -ignore = E203, E731, W503 -max-line-length = 120 \ No newline at end of file diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 148047b..f7306ba 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -2,42 +2,46 @@ name: Master Deploy on: push: - branches: - - 'master' + branches: + - "master" permissions: contents: write jobs: deploy: - - runs-on: ubuntu-latest - + runs-on: ubuntu-24.04 steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v3 - with: - python-version: '3.7' - - name: Install dependencies and test - run: | - python -m pip install pip==22.2.2 setuptools==65.3.0 wheel==0.37.1 - pip install poetry==1.1.15 - poetry install - poetry run coverage run -m pytest - - name: Generate Documentation - run: | - poetry run coverage html -d docs/coverage - poetry run pdoc --html --force deeprecsys -o docs/api - - name: Version Bump - run: | - poetry version patch - git config --global user.name 'Github Actions' - git config --global user.email 'luksfarris@users.noreply.github.com' - git commit -am "Bump to version $(poetry version -s)" - git push - - name: Build package and publish - run: | - poetry build - poetry config pypi-token.pypi ${{ secrets.PYPI_KEY }} - poetry publish + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: "3.11" + - name: Install Task + uses: arduino/setup-task@v2 + with: + version: 3.37.1 + - name: Install dependencies and test + run: | + task setup + venv/bin/poetry run pre-commit uninstall + task test + - name: Generate Documentation + run: | + task docs + - name: Version Bump + run: | + task bump + git push + git push origin v$(task version) + - name: Build package and publish + env: + PYPI_KEY: ${{ secrets.PYPI_KEY }} + run: | + task publish + - name: Publish Release + env: + GH_TOKEN: ${{ github.token }} + REPO_NAME: ${{ github.repository }} + TAG: $(task version) + run: gh release create "$TAG" --repo="$REPO_NAME" --title="$TAG" --generate-notes \ No newline at end of file diff --git a/.github/workflows/website.yml b/.github/workflows/website.yml new file mode 100644 index 0000000..d409867 --- /dev/null +++ b/.github/workflows/website.yml @@ -0,0 +1,44 @@ +# Simple workflow for deploying static content to GitHub Pages +name: Deploy static content to Pages + +on: + # Runs on pushes targeting the default branch that affect the docs folder + push: + branches: ["master"] + paths: ['docs/**'] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + # Single deploy job since we're just deploying + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Pages + uses: actions/configure-pages@v5 + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + # Upload entire repository + path: './docs' + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index e69de29..0000000 diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 26d3352..0000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml diff --git a/.idea/deeprecsys.iml b/.idea/deeprecsys.iml deleted file mode 100644 index 798f2f9..0000000 --- a/.idea/deeprecsys.iml +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml deleted file mode 100644 index 092eb8d..0000000 --- a/.idea/inspectionProfiles/Project_Default.xml +++ /dev/null @@ -1,32 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2d..0000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index 654090a..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,4 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml deleted file mode 100644 index 5ca9079..0000000 --- a/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index 49d3c22..0000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4343c37..13f0601 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,31 +1,12 @@ repos: - - repo: https://github.com/ambv/black - rev: 21.9b0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.4.4 hooks: - - id: black - language_version: python3.6 - - repo: https://github.com/PyCQA/flake8 - rev: 6.0.0 - hooks: - - id: flake8 - additional_dependencies: ["flake8-bugbear", mccabe, "pep8-naming"] - args: ["--max-complexity=5"] - - repo: https://github.com/pycqa/isort - rev: 5.8.0 - hooks: - - id: isort - name: isort (python) - args: ["--profile=black"] - - repo: https://github.com/PyCQA/pydocstyle - rev: 6.1.1 - hooks: - - id: pydocstyle - args: - [ - "--ignore=D100,D104,D203,D205,D209,D213,D400,D415", - "--match=deeprecsys/rl/.*", - ] - additional_dependencies: [toml] + - id: ruff + args: [--fix] + types_or: [python, jupyter] + - id: ruff-format + types_or: [python, jupyter] - repo: https://github.com/pre-commit/mirrors-mypy rev: v0.930 hooks: diff --git a/LICENSE b/LICENSE index 9f7aa0a..f288702 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,674 @@ -MIT License - -Copyright (c) 2021 Lucas Farris - -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. + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to +your programs, too. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you +these rights or asking you to surrender the rights. Therefore, you have +certain responsibilities if you distribute copies of the software, or if +you modify it: responsibilities to respect the freedom of others. + + For example, if you distribute copies of such a program, whether +gratis or for a fee, you must pass on to the recipients the same +freedoms that you received. You must make sure that they, too, receive +or can get the source code. And you must show them these terms so they +know their rights. + + Developers that use the GNU GPL protect your rights with two steps: +(1) assert copyright on the software, and (2) offer you this License +giving you legal permission to copy, distribute and/or modify it. + + For the developers' and authors' protection, the GPL clearly explains +that there is no warranty for this free software. For both users' and +authors' sake, the GPL requires that modified versions be marked as +changed, so that their problems will not be attributed erroneously to +authors of previous versions. + + Some devices are designed to deny users access to install or run +modified versions of the software inside them, although the manufacturer +can do so. This is fundamentally incompatible with the aim of +protecting users' freedom to change the software. The systematic +pattern of such abuse occurs in the area of products for individuals to +use, which is precisely where it is most unacceptable. Therefore, we +have designed this version of the GPL to prohibit the practice for those +products. If such problems arise substantially in other domains, we +stand ready to extend this provision to those domains in future versions +of the GPL, as needed to protect the freedom of users. + + Finally, every program is threatened constantly by software patents. +States should not allow patents to restrict development and use of +software on general-purpose computers, but in those that do, we wish to +avoid the special danger that patents applied to a free program could +make it effectively proprietary. To prevent this, the GPL assures that +patents cannot be used to render the program non-free. + + The precise terms and conditions for copying, distribution and +modification follow. + + TERMS AND CONDITIONS + + 0. Definitions. + + "This License" refers to version 3 of the GNU General Public License. + + "Copyright" also means copyright-like laws that apply to other kinds of +works, such as semiconductor masks. + + "The Program" refers to any copyrightable work licensed under this +License. Each licensee is addressed as "you". "Licensees" and +"recipients" may be individuals or organizations. + + To "modify" a work means to copy from or adapt all or part of the work +in a fashion requiring copyright permission, other than the making of an +exact copy. The resulting work is called a "modified version" of the +earlier work or a work "based on" the earlier work. + + A "covered work" means either the unmodified Program or a work based +on the Program. + + To "propagate" a work means to do anything with it that, without +permission, would make you directly or secondarily liable for +infringement under applicable copyright law, except executing it on a +computer or modifying a private copy. Propagation includes copying, +distribution (with or without modification), making available to the +public, and in some countries other activities as well. + + To "convey" a work means any kind of propagation that enables other +parties to make or receive copies. Mere interaction with a user through +a computer network, with no transfer of a copy, is not conveying. + + An interactive user interface displays "Appropriate Legal Notices" +to the extent that it includes a convenient and prominently visible +feature that (1) displays an appropriate copyright notice, and (2) +tells the user that there is no warranty for the work (except to the +extent that warranties are provided), that licensees may convey the +work under this License, and how to view a copy of this License. If +the interface presents a list of user commands or options, such as a +menu, a prominent item in the list meets this criterion. + + 1. Source Code. + + The "source code" for a work means the preferred form of the work +for making modifications to it. "Object code" means any non-source +form of a work. + + A "Standard Interface" means an interface that either is an official +standard defined by a recognized standards body, or, in the case of +interfaces specified for a particular programming language, one that +is widely used among developers working in that language. + + The "System Libraries" of an executable work include anything, other +than the work as a whole, that (a) is included in the normal form of +packaging a Major Component, but which is not part of that Major +Component, and (b) serves only to enable use of the work with that +Major Component, or to implement a Standard Interface for which an +implementation is available to the public in source code form. A +"Major Component", in this context, means a major essential component +(kernel, window system, and so on) of the specific operating system +(if any) on which the executable work runs, or a compiler used to +produce the work, or an object code interpreter used to run it. + + The "Corresponding Source" for a work in object code form means all +the source code needed to generate, install, and (for an executable +work) run the object code and to modify the work, including scripts to +control those activities. However, it does not include the work's +System Libraries, or general-purpose tools or generally available free +programs which are used unmodified in performing those activities but +which are not part of the work. For example, Corresponding Source +includes interface definition files associated with source files for +the work, and the source code for shared libraries and dynamically +linked subprograms that the work is specifically designed to require, +such as by intimate data communication or control flow between those +subprograms and other parts of the work. + + The Corresponding Source need not include anything that users +can regenerate automatically from other parts of the Corresponding +Source. + + The Corresponding Source for a work in source code form is that +same work. + + 2. Basic Permissions. + + All rights granted under this License are granted for the term of +copyright on the Program, and are irrevocable provided the stated +conditions are met. This License explicitly affirms your unlimited +permission to run the unmodified Program. The output from running a +covered work is covered by this License only if the output, given its +content, constitutes a covered work. This License acknowledges your +rights of fair use or other equivalent, as provided by copyright law. + + You may make, run and propagate covered works that you do not +convey, without conditions so long as your license otherwise remains +in force. You may convey covered works to others for the sole purpose +of having them make modifications exclusively for you, or provide you +with facilities for running those works, provided that you comply with +the terms of this License in conveying all material for which you do +not control copyright. Those thus making or running the covered works +for you must do so exclusively on your behalf, under your direction +and control, on terms that prohibit them from making any copies of +your copyrighted material outside their relationship with you. + + Conveying under any other circumstances is permitted solely under +the conditions stated below. Sublicensing is not allowed; section 10 +makes it unnecessary. + + 3. Protecting Users' Legal Rights From Anti-Circumvention Law. + + No covered work shall be deemed part of an effective technological +measure under any applicable law fulfilling obligations under article +11 of the WIPO copyright treaty adopted on 20 December 1996, or +similar laws prohibiting or restricting circumvention of such +measures. + + When you convey a covered work, you waive any legal power to forbid +circumvention of technological measures to the extent such circumvention +is effected by exercising rights under this License with respect to +the covered work, and you disclaim any intention to limit operation or +modification of the work as a means of enforcing, against the work's +users, your or third parties' legal rights to forbid circumvention of +technological measures. + + 4. Conveying Verbatim Copies. + + You may convey verbatim copies of the Program's source code as you +receive it, in any medium, provided that you conspicuously and +appropriately publish on each copy an appropriate copyright notice; +keep intact all notices stating that this License and any +non-permissive terms added in accord with section 7 apply to the code; +keep intact all notices of the absence of any warranty; and give all +recipients a copy of this License along with the Program. + + You may charge any price or no price for each copy that you convey, +and you may offer support or warranty protection for a fee. + + 5. Conveying Modified Source Versions. + + You may convey a work based on the Program, or the modifications to +produce it from the Program, in the form of source code under the +terms of section 4, provided that you also meet all of these conditions: + + a) The work must carry prominent notices stating that you modified + it, and giving a relevant date. + + b) The work must carry prominent notices stating that it is + released under this License and any conditions added under section + 7. This requirement modifies the requirement in section 4 to + "keep intact all notices". + + c) You must license the entire work, as a whole, under this + License to anyone who comes into possession of a copy. This + License will therefore apply, along with any applicable section 7 + additional terms, to the whole of the work, and all its parts, + regardless of how they are packaged. This License gives no + permission to license the work in any other way, but it does not + invalidate such permission if you have separately received it. + + d) If the work has interactive user interfaces, each must display + Appropriate Legal Notices; however, if the Program has interactive + interfaces that do not display Appropriate Legal Notices, your + work need not make them do so. + + A compilation of a covered work with other separate and independent +works, which are not by their nature extensions of the covered work, +and which are not combined with it such as to form a larger program, +in or on a volume of a storage or distribution medium, is called an +"aggregate" if the compilation and its resulting copyright are not +used to limit the access or legal rights of the compilation's users +beyond what the individual works permit. Inclusion of a covered work +in an aggregate does not cause this License to apply to the other +parts of the aggregate. + + 6. Conveying Non-Source Forms. + + You may convey a covered work in object code form under the terms +of sections 4 and 5, provided that you also convey the +machine-readable Corresponding Source under the terms of this License, +in one of these ways: + + a) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by the + Corresponding Source fixed on a durable physical medium + customarily used for software interchange. + + b) Convey the object code in, or embodied in, a physical product + (including a physical distribution medium), accompanied by a + written offer, valid for at least three years and valid for as + long as you offer spare parts or customer support for that product + model, to give anyone who possesses the object code either (1) a + copy of the Corresponding Source for all the software in the + product that is covered by this License, on a durable physical + medium customarily used for software interchange, for a price no + more than your reasonable cost of physically performing this + conveying of source, or (2) access to copy the + Corresponding Source from a network server at no charge. + + c) Convey individual copies of the object code with a copy of the + written offer to provide the Corresponding Source. This + alternative is allowed only occasionally and noncommercially, and + only if you received the object code with such an offer, in accord + with subsection 6b. + + d) Convey the object code by offering access from a designated + place (gratis or for a charge), and offer equivalent access to the + Corresponding Source in the same way through the same place at no + further charge. You need not require recipients to copy the + Corresponding Source along with the object code. If the place to + copy the object code is a network server, the Corresponding Source + may be on a different server (operated by you or a third party) + that supports equivalent copying facilities, provided you maintain + clear directions next to the object code saying where to find the + Corresponding Source. Regardless of what server hosts the + Corresponding Source, you remain obligated to ensure that it is + available for as long as needed to satisfy these requirements. + + e) Convey the object code using peer-to-peer transmission, provided + you inform other peers where the object code and Corresponding + Source of the work are being offered to the general public at no + charge under subsection 6d. + + A separable portion of the object code, whose source code is excluded +from the Corresponding Source as a System Library, need not be +included in conveying the object code work. + + A "User Product" is either (1) a "consumer product", which means any +tangible personal property which is normally used for personal, family, +or household purposes, or (2) anything designed or sold for incorporation +into a dwelling. In determining whether a product is a consumer product, +doubtful cases shall be resolved in favor of coverage. For a particular +product received by a particular user, "normally used" refers to a +typical or common use of that class of product, regardless of the status +of the particular user or of the way in which the particular user +actually uses, or expects or is expected to use, the product. A product +is a consumer product regardless of whether the product has substantial +commercial, industrial or non-consumer uses, unless such uses represent +the only significant mode of use of the product. + + "Installation Information" for a User Product means any methods, +procedures, authorization keys, or other information required to install +and execute modified versions of a covered work in that User Product from +a modified version of its Corresponding Source. The information must +suffice to ensure that the continued functioning of the modified object +code is in no case prevented or interfered with solely because +modification has been made. + + If you convey an object code work under this section in, or with, or +specifically for use in, a User Product, and the conveying occurs as +part of a transaction in which the right of possession and use of the +User Product is transferred to the recipient in perpetuity or for a +fixed term (regardless of how the transaction is characterized), the +Corresponding Source conveyed under this section must be accompanied +by the Installation Information. But this requirement does not apply +if neither you nor any third party retains the ability to install +modified object code on the User Product (for example, the work has +been installed in ROM). + + The requirement to provide Installation Information does not include a +requirement to continue to provide support service, warranty, or updates +for a work that has been modified or installed by the recipient, or for +the User Product in which it has been modified or installed. Access to a +network may be denied when the modification itself materially and +adversely affects the operation of the network or violates the rules and +protocols for communication across the network. + + Corresponding Source conveyed, and Installation Information provided, +in accord with this section must be in a format that is publicly +documented (and with an implementation available to the public in +source code form), and must require no special password or key for +unpacking, reading or copying. + + 7. Additional Terms. + + "Additional permissions" are terms that supplement the terms of this +License by making exceptions from one or more of its conditions. +Additional permissions that are applicable to the entire Program shall +be treated as though they were included in this License, to the extent +that they are valid under applicable law. If additional permissions +apply only to part of the Program, that part may be used separately +under those permissions, but the entire Program remains governed by +this License without regard to the additional permissions. + + When you convey a copy of a covered work, you may at your option +remove any additional permissions from that copy, or from any part of +it. (Additional permissions may be written to require their own +removal in certain cases when you modify the work.) You may place +additional permissions on material, added by you to a covered work, +for which you have or can give appropriate copyright permission. + + Notwithstanding any other provision of this License, for material you +add to a covered work, you may (if authorized by the copyright holders of +that material) supplement the terms of this License with terms: + + a) Disclaiming warranty or limiting liability differently from the + terms of sections 15 and 16 of this License; or + + b) Requiring preservation of specified reasonable legal notices or + author attributions in that material or in the Appropriate Legal + Notices displayed by works containing it; or + + c) Prohibiting misrepresentation of the origin of that material, or + requiring that modified versions of such material be marked in + reasonable ways as different from the original version; or + + d) Limiting the use for publicity purposes of names of licensors or + authors of the material; or + + e) Declining to grant rights under trademark law for use of some + trade names, trademarks, or service marks; or + + f) Requiring indemnification of licensors and authors of that + material by anyone who conveys the material (or modified versions of + it) with contractual assumptions of liability to the recipient, for + any liability that these contractual assumptions directly impose on + those licensors and authors. + + All other non-permissive additional terms are considered "further +restrictions" within the meaning of section 10. If the Program as you +received it, or any part of it, contains a notice stating that it is +governed by this License along with a term that is a further +restriction, you may remove that term. If a license document contains +a further restriction but permits relicensing or conveying under this +License, you may add to a covered work material governed by the terms +of that license document, provided that the further restriction does +not survive such relicensing or conveying. + + If you add terms to a covered work in accord with this section, you +must place, in the relevant source files, a statement of the +additional terms that apply to those files, or a notice indicating +where to find the applicable terms. + + Additional terms, permissive or non-permissive, may be stated in the +form of a separately written license, or stated as exceptions; +the above requirements apply either way. + + 8. Termination. + + You may not propagate or modify a covered work except as expressly +provided under this License. Any attempt otherwise to propagate or +modify it is void, and will automatically terminate your rights under +this License (including any patent licenses granted under the third +paragraph of section 11). + + However, if you cease all violation of this License, then your +license from a particular copyright holder is reinstated (a) +provisionally, unless and until the copyright holder explicitly and +finally terminates your license, and (b) permanently, if the copyright +holder fails to notify you of the violation by some reasonable means +prior to 60 days after the cessation. + + Moreover, your license from a particular copyright holder is +reinstated permanently if the copyright holder notifies you of the +violation by some reasonable means, this is the first time you have +received notice of violation of this License (for any work) from that +copyright holder, and you cure the violation prior to 30 days after +your receipt of the notice. + + Termination of your rights under this section does not terminate the +licenses of parties who have received copies or rights from you under +this License. If your rights have been terminated and not permanently +reinstated, you do not qualify to receive new licenses for the same +material under section 10. + + 9. Acceptance Not Required for Having Copies. + + You are not required to accept this License in order to receive or +run a copy of the Program. Ancillary propagation of a covered work +occurring solely as a consequence of using peer-to-peer transmission +to receive a copy likewise does not require acceptance. However, +nothing other than this License grants you permission to propagate or +modify any covered work. These actions infringe copyright if you do +not accept this License. Therefore, by modifying or propagating a +covered work, you indicate your acceptance of this License to do so. + + 10. Automatic Licensing of Downstream Recipients. + + Each time you convey a covered work, the recipient automatically +receives a license from the original licensors, to run, modify and +propagate that work, subject to this License. You are not responsible +for enforcing compliance by third parties with this License. + + An "entity transaction" is a transaction transferring control of an +organization, or substantially all assets of one, or subdividing an +organization, or merging organizations. If propagation of a covered +work results from an entity transaction, each party to that +transaction who receives a copy of the work also receives whatever +licenses to the work the party's predecessor in interest had or could +give under the previous paragraph, plus a right to possession of the +Corresponding Source of the work from the predecessor in interest, if +the predecessor has it or can get it with reasonable efforts. + + You may not impose any further restrictions on the exercise of the +rights granted or affirmed under this License. For example, you may +not impose a license fee, royalty, or other charge for exercise of +rights granted under this License, and you may not initiate litigation +(including a cross-claim or counterclaim in a lawsuit) alleging that +any patent claim is infringed by making, using, selling, offering for +sale, or importing the Program or any portion of it. + + 11. Patents. + + A "contributor" is a copyright holder who authorizes use under this +License of the Program or a work on which the Program is based. The +work thus licensed is called the contributor's "contributor version". + + A contributor's "essential patent claims" are all patent claims +owned or controlled by the contributor, whether already acquired or +hereafter acquired, that would be infringed by some manner, permitted +by this License, of making, using, or selling its contributor version, +but do not include claims that would be infringed only as a +consequence of further modification of the contributor version. For +purposes of this definition, "control" includes the right to grant +patent sublicenses in a manner consistent with the requirements of +this License. + + Each contributor grants you a non-exclusive, worldwide, royalty-free +patent license under the contributor's essential patent claims, to +make, use, sell, offer for sale, import and otherwise run, modify and +propagate the contents of its contributor version. + + In the following three paragraphs, a "patent license" is any express +agreement or commitment, however denominated, not to enforce a patent +(such as an express permission to practice a patent or covenant not to +sue for patent infringement). To "grant" such a patent license to a +party means to make such an agreement or commitment not to enforce a +patent against the party. + + If you convey a covered work, knowingly relying on a patent license, +and the Corresponding Source of the work is not available for anyone +to copy, free of charge and under the terms of this License, through a +publicly available network server or other readily accessible means, +then you must either (1) cause the Corresponding Source to be so +available, or (2) arrange to deprive yourself of the benefit of the +patent license for this particular work, or (3) arrange, in a manner +consistent with the requirements of this License, to extend the patent +license to downstream recipients. "Knowingly relying" means you have +actual knowledge that, but for the patent license, your conveying the +covered work in a country, or your recipient's use of the covered work +in a country, would infringe one or more identifiable patents in that +country that you have reason to believe are valid. + + If, pursuant to or in connection with a single transaction or +arrangement, you convey, or propagate by procuring conveyance of, a +covered work, and grant a patent license to some of the parties +receiving the covered work authorizing them to use, propagate, modify +or convey a specific copy of the covered work, then the patent license +you grant is automatically extended to all recipients of the covered +work and works based on it. + + A patent license is "discriminatory" if it does not include within +the scope of its coverage, prohibits the exercise of, or is +conditioned on the non-exercise of one or more of the rights that are +specifically granted under this License. You may not convey a covered +work if you are a party to an arrangement with a third party that is +in the business of distributing software, under which you make payment +to the third party based on the extent of your activity of conveying +the work, and under which the third party grants, to any of the +parties who would receive the covered work from you, a discriminatory +patent license (a) in connection with copies of the covered work +conveyed by you (or copies made from those copies), or (b) primarily +for and in connection with specific products or compilations that +contain the covered work, unless you entered into that arrangement, +or that patent license was granted, prior to 28 March 2007. + + Nothing in this License shall be construed as excluding or limiting +any implied license or other defenses to infringement that may +otherwise be available to you under applicable patent law. + + 12. No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under this +License and any other pertinent obligations, then as a consequence you may +not convey it at all. For example, if you agree to terms that obligate you +to collect a royalty for further conveying from those to whom you convey +the Program, the only way you could satisfy both those terms and this +License would be to refrain entirely from conveying the Program. + + 13. Use with the GNU Affero General Public License. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU Affero General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the special requirements of the GNU Affero General Public License, +section 13, concerning interaction through a network will apply to the +combination as such. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of +the GNU General Public License from time to time. Such new versions will +be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + + Each version is given a distinguishing version number. If the +Program specifies that a certain numbered version of the GNU General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 7810c64..be7a2fc 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,36 @@ -# PyDeepRecSys +# Deep RecSys -**pydeeprecsys** is maily an environment that simulates a Reinforcement Learning environment, using realistic Recommender System data. It includes a set of tools and agents. +`deeprecsys` is an open tool belt to speed up the development of modern data science projects at an enterprise level. -### Getting Started +These words were chosen very carefully, and by them we mean: +- **Open**: we rely on OSS and distribute openly with [a GNU GPLv3 license](./LICENSE) that won't change in the future. The official distribution channels are pypi ([see deeprecsys at pypi](https://pypi.org/project/deeprecsys/)) and GitHub ([see deeprecsys at Github](https://github.com/luksfarris/deeprecsys)). +- **Tool belt**: this project contains code that may extract, process, analyse, aggregate, test, and present data. +- **Modern**: the code will be updated as much as possible to the newest versions, as long as they are stable and don't break pre-existing functionality. +- **Data Science**: This project will contain a mixture of data engineering, machine learning engineering, data analysis, and data visualization. +- **Enterprise**: The code deployed here will likely have been battle-tested by large organizations with millions of customers. Unless stated, it is production-ready. All code including dependencies is audited and secure. -For instructions on setting up, check out the [Getting Started Wiki Page](https://github.com/luksfarris/pydeeprecsys/wiki/Getting-Started). +## Historical Note -This project is a WIP. It is provided "as is" under the MIT license. +If you're here from the research piece [Optimized Recommender Systems with Deep Reinforcement Learning](https://arxiv.org/abs/2110.03039), please checkout the old branch `origin/thesis` for reproducibility. The README should contain instructions to get you started. +## Installation and usage -### Dev Setup +Installation depends on your framework, so you may need to adapt this. Here's an example using pip: -Similar to what's in the CI workflow: ``` -python3 -m venv venv -python -m pip install pip==22.2.2 setuptools==65.3.0 wheel==0.37.1 -pip install poetry==1.1.15 -poetry install -``` \ No newline at end of file +pip install deeprecsys +``` + +## For Developers + +### Source Control + +All source control is done in `git`, via GitHub. Make sure you have a modern version of git installed. For instance, you can checkout the project using SSH with: + +``` +git clone git@github.com:luksfarris/deeprecsys.git +``` + +### Automation + +All scripts are written using Taskfile. You can install it following [Task's instructions](https://taskfile.dev/installation/). The file with all the tasks is `Taskfile.yml`. \ No newline at end of file diff --git a/Taskfile.yml b/Taskfile.yml new file mode 100644 index 0000000..7e626bb --- /dev/null +++ b/Taskfile.yml @@ -0,0 +1,66 @@ +version: "3" + +env: + VENV_FOLDER: venv + EXEC_FOLDER: bin + +tasks: + setup: + desc: Sets up the virtual environment and builds the dependencies + cmds: + - python3.11 -m venv ${VENV_FOLDER} + - ${VENV_FOLDER}/${EXEC_FOLDER}/pip install setuptools==68.0.0 wheel==0.43.0 pip==24.0 + - ${VENV_FOLDER}/${EXEC_FOLDER}/pip install poetry==1.8.3 + - task: dependencies + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry run pre-commit install + + version: + desc: Shows the version of the package + silent: true + cmds: + - echo $(${VENV_FOLDER}/${EXEC_FOLDER}/poetry version -s) + + dependencies: + internal: true + cmds: + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry install --all-extras + + lint: + desc: Runs all the pre-commit hooks + cmds: + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry run pre-commit run --all-files + + test: + desc: Runs all the tests + cmds: + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry run coverage run -m pytest tests + + docs: + desc: Generates the documentation + cmds: + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry run coverage html -d docs/coverage + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry run pdoc --html --force deeprecsys -o docs/api + + bump: + desc: Bumps the version of the package + env: + VERSION: $(task version) + cmds: + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry version patch + - git config --global user.name 'Github Actions' + - git config --global user.email 'luksfarris@users.noreply.github.com' + - git commit -am "Bump to version ${VERSION}" + - git tag -a v${VERSION} -m "Release version ${VERSION}" + + build: + desc: Builds the package + internal: true + cmds: + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry build + + publish: + desc: Builds the package and publishes it to PyPI + cmds: + - task: build + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry config pypi-token.pypi ${PYPI_KEY} + - ${VENV_FOLDER}/${EXEC_FOLDER}/poetry publish \ No newline at end of file diff --git a/deeprecsys/movielens_fairness_env.py b/deeprecsys/movielens_fairness_env.py deleted file mode 100644 index 93d7e5a..0000000 --- a/deeprecsys/movielens_fairness_env.py +++ /dev/null @@ -1,199 +0,0 @@ -import functools -import math -import os -from typing import Any, Dict, List, Optional, Tuple, Union - -import attr -import numpy as np -from gym import Env, Space -from gym.envs.registration import register -from gym.spaces import Box, Discrete -from mlfairnessgym.environments.recommenders import movie_lens_dynamic as movie_lens -from mlfairnessgym.environments.recommenders import movie_lens_utils, recsim_samplers -from numpy.core.multiarray import ndarray -from recsim.simulator import recsim_gym -from recsim.simulator.recsim_gym import RecSimGymEnv - -_env_specs = { - "id": "MovieLensFairness-v0", - "entry_point": "deeprecsys.movielens_fairness_env:MovieLensFairness", - "max_episode_steps": 50, -} -register(**_env_specs) - - -class MovieLensFairness(Env): - """MovieLens + MLFairnessGym + Recsim + Gym environment""" - - def __init__(self, slate_size: int = 1, seed: Optional[int] = None): - self.slate_size = slate_size - self.internal_env = self.prepare_environment() - self._rng = np.random.RandomState(seed=seed) - self.ndcg: List[float] = [] - self.dcg: List[float] = [] - - def _get_product_relevance(self, product_id: int) -> float: - """Relevance in range (0,1)""" - topic_affinity = ( - self.internal_env.environment.user_model._user_state.topic_affinity - ) - movie_vector = [ - d.movie_vec - for d in self.internal_env.environment._document_sampler._corpus - if d._doc_id == product_id - ][0] - return np.clip( - np.dot(movie_vector, topic_affinity), - movie_lens.User.MIN_SCORE, - movie_lens.User.MAX_SCORE, - ) - - def _get_dcg(self, relevances: List[float]) -> float: - return sum([relevances[i] / math.log(i + 2, 2) for i in range(len(relevances))]) - - def _calculate_ndcg(self, slate_product_ids: List[int]) -> None: - relevances = [self._get_product_relevance(p) for p in slate_product_ids] - dcg = self._get_dcg(relevances) - self.dcg.append(dcg) - ideal_relevances = [movie_lens.User.MAX_SCORE for _ in range(len(relevances))] - idcg = self._get_dcg(ideal_relevances) - self.ndcg.append(dcg / idcg) - - def step(self, action: Union[int, List[int]]) -> Tuple: - """Normalize reward and flattens/normalizes state""" - if isinstance(action, (list, np.ndarray)): - self._calculate_ndcg(action) - state, reward, done, info = self.internal_env.step(action) - encoded_state, info = self.movielens_state_encoder(state, action, info) - return encoded_state, reward / 5, done, info - else: - state, reward, done, info = self.internal_env.step([action]) - encoded_state, info = self.movielens_state_encoder(state, [action], info) - return encoded_state, reward / 5, done, info - - def reset(self) -> List: - """flattens/normalizes state""" - state = self.internal_env.reset() - self.ndcg = [] - self.dcg = [] - encoded_state, _ = self.movielens_state_encoder(state, [], {}) - return encoded_state - - def render(self, mode: str = "human", close: bool = False) -> Any: - return self.internal_env.render(mode) - - @property - def action_space(self) -> Space: - if self.slate_size == 1: - return Discrete(self.internal_env.action_space.nvec[0]) - else: - return self.internal_env.action_space - - @property - def reward_range(self) -> Tuple: - return self.internal_env.reward_range - - @property - def observation_space(self) -> Space: - return Box(low=0, high=1.0, shape=(25,), dtype=np.float32) - - def movielens_state_encoder( - self, state: dict, action_slate: List[int], info: dict - ) -> Tuple[ndarray, Dict]: - """if the slate size is > 1, we need to guarantee the Single choice (SC) - assumption, as described in the paper `SLATEQ: A Tractable Decomposition - for Reinforcement Learning withRecommendation Sets` - TODO: by randomly selecting one of the interactions? - """ - user_features = state["user"] - response_features = state["response"] - doc_features = [ - state["doc"][str(action_slate[i])]["genres"] - for i in range(len(action_slate)) - ] - if self.slate_size > 1: - if response_features: - chosen_action = self._rng.choice(self.slate_size) - response_features = (response_features[chosen_action],) - info["chosen_action"] = chosen_action - if doc_features: - doc_features = [doc_features[self._rng.choice(self.slate_size)]] - - refined_state: Dict[Union[str, Tuple], Any] = { - "user": user_features, - "response": response_features, - "slate_docs": doc_features, - } - # flattens the state - flat_state = np.array( - [ - refined_state["user"]["sex"], - refined_state["user"]["age"], - refined_state["user"]["occupation"], - refined_state["user"]["zip_code"], - *( - refined_state["slate_docs"][0] - if refined_state["slate_docs"] - else ([0] * 19) - ), - (refined_state.get("response") or ({},))[0].get("rating", 0), # type: ignore - (refined_state.get("response") or ({},))[0].get("violence_score", 0), # type: ignore - ] - ) - return flat_state, info - - def slate_action_selector(self, q_vals: List[float]) -> List[float]: - """Gets the index of the top N highest elements in the predictor array.""" - return np.argsort(q_vals)[-self.slate_size :][::-1] - - def prepare_environment(self) -> RecSimGymEnv: - current_path = os.path.dirname(__file__) - data_dir = os.path.join(current_path, "../output") - embeddings_path = os.path.join( - current_path, - "../mlfairnessgym/environments/recommenders/movielens_factorization.json", - ) - env_config = movie_lens.EnvConfig( - seeds=movie_lens.Seeds(0, 0), - data_dir=data_dir, - embeddings_path=embeddings_path, - ) - initial_embeddings = movie_lens_utils.load_embeddings(env_config) - # user constructor - user_ctor = functools.partial( - movie_lens.User, **attr.asdict(env_config.user_config) - ) - dataset = movie_lens_utils.Dataset( - env_config.data_dir, - user_ctor=user_ctor, - movie_ctor=movie_lens.Movie, - response_ctor=movie_lens.Response, - embeddings=initial_embeddings, - ) - # the SingletonSampler will sample each movie once sequentially - document_sampler = recsim_samplers.SingletonSampler( - dataset.get_movies(), movie_lens.Movie - ) - user_sampler = recsim_samplers.UserPoolSampler( - seed=env_config.seeds.user_sampler, - users=dataset.get_users(), - user_ctor=user_ctor, - ) - user_model = movie_lens.UserModel( - user_sampler=user_sampler, - seed=env_config.seeds.user_model, - ) - env = movie_lens.MovieLensEnvironment( - user_model, - document_sampler, - num_candidates=document_sampler.size(), - slate_size=self.slate_size, - resample_documents=False, - ) - _ = env.reset() - reward_aggregator = functools.partial( - movie_lens.multiobjective_reward, - lambda_non_violent=env_config.lambda_non_violent, - ) - recsim_env = recsim_gym.RecSimGymEnv(env, reward_aggregator) - return recsim_env diff --git a/deeprecsys/neural_networks/base_network.py b/deeprecsys/neural_networks/base_network.py index f6236cd..314c152 100644 --- a/deeprecsys/neural_networks/base_network.py +++ b/deeprecsys/neural_networks/base_network.py @@ -27,21 +27,21 @@ def load(self, path: str) -> None: """Read the model's parameters from the given path.""" self.load_state_dict(load(path)) - def soft_parameter_update( - self, source_network: Module, update_rate: float = 0.0 - ) -> None: + def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0) -> None: """When using target networks, this method updates the parameters of the current network using the parameters of the given source network. The update_rate is a float in range (0,1) and controls how the update affects the target (self). update_rate=0 means a full deep copy, and update_rate=1 means the target does not update at all. This parameter is usually called Tau. This method is usually called - an exponential moving average update.""" - for t, s in zip(self.parameters(), source_network.parameters()): + an exponential moving average update. + """ + for t, s in zip(self.parameters(), source_network.parameters(), strict=False): t.data.copy_(t.data * (1.0 - update_rate) + s.data * update_rate) def run_backpropagation(self, loss: Tensor) -> None: """Run backward on the given loss, and step the optimizer. - Requires an optimizer property.""" + Requires an optimizer property. + """ self.optimizer.zero_grad() loss.backward() self.optimizer.step() diff --git a/deeprecsys/neural_networks/deep_q_network.py b/deeprecsys/neural_networks/deep_q_network.py index a84154a..fc0b6e2 100644 --- a/deeprecsys/neural_networks/deep_q_network.py +++ b/deeprecsys/neural_networks/deep_q_network.py @@ -21,7 +21,8 @@ def sequential_architecture(layers: List[int], bias: bool = True) -> Module: class DeepQNetwork(BaseNetwork): """Implementation of a Deep Q Network with a Sequential arquitecture. Layers are - supposed to be provided as a list of torch modules.""" + supposed to be provided as a list of torch modules. + """ def __init__( self, @@ -66,16 +67,12 @@ def _calculate_loss(self, experiences: List[Tuple]) -> Tensor: state_tensors = FloatTensor(states).to(device=self.device) next_state_tensors = FloatTensor(next_states).to(device=self.device) reward_tensors = FloatTensor(rewards).to(device=self.device).reshape(-1, 1) - action_tensors = ( - LongTensor(array(actions)).reshape(-1, 1).to(device=self.device) - ) + action_tensors = LongTensor(array(actions)).reshape(-1, 1).to(device=self.device) done_tensors = BoolTensor(dones).to(device=self.device) actions_for_states = self.model(state_tensors) q_vals = gather(actions_for_states, 1, action_tensors) next_actions = [self.best_action_for_state(s) for s in next_states] - next_action_tensors = ( - LongTensor(next_actions).reshape(-1, 1).to(device=self.device) - ) + next_action_tensors = LongTensor(next_actions).reshape(-1, 1).to(device=self.device) q_vals_next = gather(self.model(next_state_tensors), 1, next_action_tensors) q_vals_next[done_tensors] = 0 expected_q_vals = self.discount_factor * q_vals_next + reward_tensors diff --git a/deeprecsys/neural_networks/dueling.py b/deeprecsys/neural_networks/dueling.py index 373b56f..b5bf235 100644 --- a/deeprecsys/neural_networks/dueling.py +++ b/deeprecsys/neural_networks/dueling.py @@ -35,11 +35,10 @@ def __init__( self.optimizer = Adam(self.parameters(), lr=learning_rate) self.statistics = statistics - def _build_network( - self, n_input: int, n_output: int, noise_sigma: float, hidden_layers: List[int] - ) -> None: + def _build_network(self, n_input: int, n_output: int, noise_sigma: float, hidden_layers: List[int]) -> None: """Build the dueling network with noisy layers, the value - subnet and the advantage subnet. TODO: add `.to_device()` to Modules""" + subnet and the advantage subnet. TODO: add `.to_device()` to Modules + """ if len(hidden_layers) != 4: raise ValueError("Unexpected amount of layers") fc_1, fc_2, value_size, advantage_size = hidden_layers @@ -65,9 +64,7 @@ def forward(self, state: Tensor) -> Tensor: # This is the Dueling DQN part # Combines V and A to get Q: Q(s,a) = V(s) + (A(s,a) - 1/|A| * sum A(s,a')) if len(state.shape) == 2: - q_values = value_of_state + ( - advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True) - ) + q_values = value_of_state + (advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True)) else: q_values = value_of_state + (advantage_of_state - advantage_of_state.mean()) return q_values @@ -85,69 +82,51 @@ def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]: _, top_indices = q_values.topk(k=k) return [int(v) for v in top_indices.detach().numpy()] # TODO: cpu() ? - def learn_with( - self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module - ) -> None: + def learn_with(self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module) -> None: """Train the target network using the replay buffer.""" experiences = buffer.sample_batch() self.optimizer.zero_grad() - td_error, weights = self._calculate_td_error_and_weigths( - experiences, target_network - ) + td_error, weights = self._calculate_td_error_and_weigths(experiences, target_network) loss = (td_error.pow(2) * weights).mean().to(self.device) loss.backward() self.optimizer.step() # store loss in statistics if self.statistics: if self.device == "cuda": - self.statistics.append_metric( - "loss", float(loss.detach().cpu().numpy()) - ) + self.statistics.append_metric("loss", float(loss.detach().cpu().numpy())) else: self.statistics.append_metric("loss", float(loss.detach().numpy())) # update buffer priorities - errors_from_batch = td_error.detach().cpu().numpy() + errors_from_batch = td_error.detach().cpu().numpy().flatten() buffer.update_priorities(experiences, errors_from_batch) def _calculate_td_error_and_weigths( self, experiences: List[Tuple], target_network: Module ) -> Tuple[Tensor, Tensor]: - states, actions, rewards, dones, next_states, weights, samples = [ - i for i in experiences - ] + states, actions, rewards, dones, next_states, weights, samples = [i for i in experiences] # convert to tensors state_tensors = FloatTensor(states).to(device=self.device) next_state_tensors = FloatTensor(next_states).to(device=self.device) reward_tensors = FloatTensor(rewards).to(device=self.device).reshape(-1, 1) - action_tensors = ( - LongTensor(array(actions)).reshape(-1, 1).to(device=self.device) - ) + action_tensors = LongTensor(array(actions)).reshape(-1, 1).to(device=self.device) done_tensors = BoolTensor(dones).to(device=self.device) weight_tensors = FloatTensor(weights).to(device=self.device) # the following logic is the DDQN update # Then we get the predicted actions for the states that came next # (using the main network) - actions_for_next_states = [ - self.top_k_actions_for_state(s)[0] for s in next_state_tensors - ] - actions_for_next_states_tensor = ( - LongTensor(actions_for_next_states).reshape(-1, 1).to(device=self.device) - ) + actions_for_next_states = [self.top_k_actions_for_state(s)[0] for s in next_state_tensors] + actions_for_next_states_tensor = LongTensor(actions_for_next_states).reshape(-1, 1).to(device=self.device) # Then we use them to get the estimated Q Values for these next states/actions, # according to the target network. Remember that the target network is a copy # of this one taken some steps ago next_q_values = target_network.forward(next_state_tensors) # now we get the q values for the actions that were predicted for the next state # we call detach() so no gradient will be backpropagated along this variable - next_q_values_for_actions = gather( - next_q_values, 1, actions_for_next_states_tensor - ).detach() + next_q_values_for_actions = gather(next_q_values, 1, actions_for_next_states_tensor).detach() # zero value for done timesteps next_q_values_for_actions[done_tensors] = 0 # bellman equation - expected_q_values = ( - self.discount_factor * next_q_values_for_actions + reward_tensors - ) + expected_q_values = self.discount_factor * next_q_values_for_actions + reward_tensors # Then get the Q-Values of the main network for the selected actions q_values = gather(self.forward(state_tensors), 1, action_tensors) # And compare them (this is the time-difference or TD error) diff --git a/deeprecsys/neural_networks/noisy_layer.py b/deeprecsys/neural_networks/noisy_layer.py index 12cb559..cf27d98 100644 --- a/deeprecsys/neural_networks/noisy_layer.py +++ b/deeprecsys/neural_networks/noisy_layer.py @@ -5,7 +5,8 @@ class NoisyLayer(Linear): """Special type of layer that adds random gaussian noise to the signal The gaussian noise parameters are - registered, and therefore the noise decreases over time. This is a better alternative to e-greedy exploration.""" + registered, and therefore the noise decreases over time. This is a better alternative to e-greedy exploration. + """ def __init__( self, @@ -15,7 +16,8 @@ def __init__( sigma: float = 0.017, ) -> None: """Create the layer with the given sigma weight. Registers epsilon as a parameter so that the network will - learn to reduce the noise.""" + learn to reduce the noise. + """ super().__init__(in_features, out_features, bias=bias) self.sigma_weight = Parameter(torch.full((out_features, in_features), sigma)) self.register_buffer("epsilon_weight", torch.zeros(out_features, in_features)) @@ -27,12 +29,11 @@ def forward(self, input: Tensor) -> Tensor: """At every forward operation, feeds the weights and biases with normally distributed random variables with mean zero and std deviation 1. This means the bias and the weights will have a noise of: - sigma (constant) * epsilon (random in range(-1,1))""" + sigma (constant) * epsilon (random in range(-1,1)) + """ self.epsilon_weight.normal_() bias = self.bias if bias is not None: self.epsilon_bias.normal_() bias = bias + self.sigma_bias * self.epsilon_bias.clone() - return functional.linear( - input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias - ) + return functional.linear(input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias) diff --git a/deeprecsys/neural_networks/policy_estimator.py b/deeprecsys/neural_networks/policy_estimator.py index 3d0feb0..414e822 100644 --- a/deeprecsys/neural_networks/policy_estimator.py +++ b/deeprecsys/neural_networks/policy_estimator.py @@ -12,7 +12,8 @@ class PolicyEstimator(BaseNetwork): """Estimates the policy function: the probability of each action being the - best decision in a particular state.""" + best decision in a particular state. + """ def __init__( self, @@ -37,12 +38,14 @@ def __init__( def action_probabilities(self, state: Any) -> Tensor: """Return a map of each possible action, and the probability that that's the best action to take at - this step.""" + this step. + """ return self.model(FloatTensor(state)) def predict(self, state: Any, k: int = 1) -> List[int]: """Given a state, uses the network output to choose the `k` best next actions according to the probability - distribution trained so far.""" + distribution trained so far. + """ probabilities = self.action_probabilities(state) prediction = multinomial(probabilities, num_samples=k, replacement=False) if self.device == "cuda": @@ -50,14 +53,10 @@ def predict(self, state: Any, k: int = 1) -> List[int]: else: return prediction.detach().numpy() - def update( - self, state: np.array, reward_baseline: Tensor, action: np.array - ) -> np.ndarray: + def update(self, state: np.array, reward_baseline: Tensor, action: np.array) -> np.ndarray: """Update the network with the given state, reward, and action taken.""" state_tensor = FloatTensor(state).to(device=self.device) - action_tensor = FloatTensor(np.array(action, dtype=np.float32)).to( - device=self.device - ) + action_tensor = FloatTensor(np.array(action, dtype=np.float32)).to(device=self.device) """ Update logic from the Policy Gradient theorem. """ action_probabilities = self.model(state_tensor) action_distribution = Categorical(action_probabilities) diff --git a/deeprecsys/neural_networks/q_value_estimator.py b/deeprecsys/neural_networks/q_value_estimator.py index ad95a29..b4995ea 100644 --- a/deeprecsys/neural_networks/q_value_estimator.py +++ b/deeprecsys/neural_networks/q_value_estimator.py @@ -23,16 +23,15 @@ def __init__(self, inputs: int, outputs: int, learning_rate: float = 1e-3): def predict(self, states: Tensor, actions: Tensor) -> Tensor: """Given a state and an action, return the estimated Q-Value""" - inputs = torch.cat([states, actions.type(FloatTensor)], dim=1).to( - device=self.device - ) + inputs = torch.cat([states, actions.type(FloatTensor)], dim=1).to(device=self.device) return self.model(inputs) class TwinnedQValueEstimator(BaseNetwork): """Estimate the Q-value (expected return) of each (state,action) pair, using 2 independent estimators, and predicting with the minimum estimated Q-value. - This is the "critic" part of the Actor-Critic model.""" + This is the "critic" part of the Actor-Critic model. + """ def __init__(self, inputs: int, outputs: int = 1, learning_rate: float = 1e-3): """Create the two estimators with the provided parameters.""" diff --git a/deeprecsys/neural_networks/value_estimator.py b/deeprecsys/neural_networks/value_estimator.py index f82d250..95f3261 100644 --- a/deeprecsys/neural_networks/value_estimator.py +++ b/deeprecsys/neural_networks/value_estimator.py @@ -11,7 +11,8 @@ class ValueEstimator(BaseNetwork): """Estimates the value function: the expected return of being in a - particular state""" + particular state + """ def __init__( self, @@ -22,9 +23,7 @@ def __init__( ): """Create the network with the given parameters. The output should always be one.""" super().__init__() - self.model = sequential_architecture( - [input_size] + hidden_layers + [output_size] - ) + self.model = sequential_architecture([input_size] + hidden_layers + [output_size]) self.optimizer = Adam(self.parameters(), lr=learning_rate) if self.device == "cuda": self.model.cuda() diff --git a/deeprecsys/rl/agents/actor_critic.py b/deeprecsys/rl/agents/actor_critic.py index c21a7cf..89f7261 100644 --- a/deeprecsys/rl/agents/actor_critic.py +++ b/deeprecsys/rl/agents/actor_critic.py @@ -13,7 +13,8 @@ class ActorCriticAgent(ReinforcementLearning): """Policy estimator using a value estimator as a baseline. It's on-policy, for discrete action spaces, and episodic environments. This implementation uses stochastic policies. - TODO: could be a sub class of reinforce""" + TODO: could be a sub class of reinforce + """ buffer: ExperienceReplayBuffer @@ -51,9 +52,7 @@ def __init__( def reset_buffer(self) -> None: """Clear all the experiences from the buffer""" - self.buffer = ExperienceReplayBuffer( - ExperienceReplayBufferParameters(10000, 1, 1) - ) + self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1)) def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]: """Return the next best K action""" @@ -63,9 +62,7 @@ def action_for_state(self, state: Any) -> int: """Return the next best action""" return self.top_k_actions_for_state(state)[0] - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """Store the experience in the experience buffer""" state_flat = state.flatten() new_state_flat = new_state.flatten() @@ -81,7 +78,7 @@ def learn_from_experiences(self) -> None: for timestep, experience in enumerate(experiences): total_return = 0 for i, t in enumerate(experiences[timestep:]): - total_return += (self.discount_factor ** i) * t.reward + total_return += (self.discount_factor**i) * t.reward # Calculate baseline/advantage baseline_value = self.value_estimator.predict(experience.state).detach() diff --git a/deeprecsys/rl/agents/agent.py b/deeprecsys/rl/agents/agent.py index 02b471d..96a2d9b 100644 --- a/deeprecsys/rl/agents/agent.py +++ b/deeprecsys/rl/agents/agent.py @@ -1,7 +1,7 @@ from abc import ABC, abstractmethod from typing import Any -from gym import Space +from gymnasium import Space class ReinforcementLearning(ABC): @@ -16,15 +16,14 @@ def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any: """Retrieve the next K best actions for this state.""" @abstractmethod - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """Store an experience (used in case of experience replay buffers)""" class RandomAgent(ReinforcementLearning): """An agent that randomly samples actions, regardless of the - environment's state.""" + environment's state. + """ action_space: Space @@ -42,8 +41,6 @@ def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any: """Randomly sample K actions from the action space.""" return self.action_space.sample() - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """Ignore the experience because this agent doesn't have any experience replay.""" pass diff --git a/deeprecsys/rl/agents/dqn.py b/deeprecsys/rl/agents/dqn.py index b78fbf6..c2055de 100644 --- a/deeprecsys/rl/agents/dqn.py +++ b/deeprecsys/rl/agents/dqn.py @@ -43,9 +43,7 @@ def __init__( random_state, ) - architecture = sequential_architecture( - [input_size] + hidden_layers + [output_size] - ) + architecture = sequential_architecture([input_size] + hidden_layers + [output_size]) self.network = DeepQNetwork(learning_rate, architecture, discount_factor) self.buffer = ExperienceReplayBuffer( ExperienceReplayBufferParameters( @@ -88,9 +86,7 @@ def exploit(self, state: Any) -> float: """TODO""" return self.network.best_action_for_state(state) - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """TODO""" if done and self.buffer.ready_to_predict(): self._decay() diff --git a/deeprecsys/rl/agents/epsilon_greedy.py b/deeprecsys/rl/agents/epsilon_greedy.py index 266c7c1..4889991 100644 --- a/deeprecsys/rl/agents/epsilon_greedy.py +++ b/deeprecsys/rl/agents/epsilon_greedy.py @@ -26,7 +26,8 @@ def __init__( def action_for_state(self, state: Any) -> Any: """With probability epsilon, we explore by sampling one of the random available actions. - Otherwise we exploit by chosing the action with the highest Q value.""" + Otherwise we exploit by chosing the action with the highest Q value. + """ if self.random_state.random() < self.epsilon: action = self.explore() else: @@ -35,9 +36,7 @@ def action_for_state(self, state: Any) -> Any: def _decay(self) -> None: """Slowly decrease the exploration probability.""" - self.epsilon = max( - self.epsilon * self.decay_rate, self.minimum_exploration_probability - ) + self.epsilon = max(self.epsilon * self.decay_rate, self.minimum_exploration_probability) @abstractmethod def explore(self) -> Any: diff --git a/deeprecsys/rl/agents/rainbow.py b/deeprecsys/rl/agents/rainbow.py index 4b7ba43..786d1d0 100644 --- a/deeprecsys/rl/agents/rainbow.py +++ b/deeprecsys/rl/agents/rainbow.py @@ -20,7 +20,8 @@ class RainbowDQNAgent(ReinforcementLearning): """Instead of sampling randomly from the buffer we prioritize experiences with PER Instead of epsilon-greedy we use gaussian noisy layers for exploration Instead of the Q value we calculate Value and Advantage (Dueling DQN). - This implementation does not include the Categorical DQN part (yet).""" + This implementation does not include the Categorical DQN part (yet). + """ def __init__( self, @@ -98,9 +99,7 @@ def action_for_state(self, state: Any) -> Any: """Get the next best action for the given state.""" return self.top_k_actions_for_state(state, k=1)[0] - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """Store the experience in the buffer""" state_flat = state.flatten() new_state_flat = new_state.flatten() diff --git a/deeprecsys/rl/agents/reinforce.py b/deeprecsys/rl/agents/reinforce.py index 27d3512..ba76619 100644 --- a/deeprecsys/rl/agents/reinforce.py +++ b/deeprecsys/rl/agents/reinforce.py @@ -13,7 +13,8 @@ class ReinforceAgent(ReinforcementLearning): """REINFORCE: Policy estimator using a value estimator as a baseline. - It's on-policy, for discrete action spaces, and episodic environments.""" + It's on-policy, for discrete action spaces, and episodic environments. + """ buffer: ExperienceReplayBuffer @@ -26,7 +27,8 @@ def __init__( learning_rate: float = 1e-3, ): """Start the network with the parameters provided. - The discount factor is commonly known as gamma.""" + The discount factor is commonly known as gamma. + """ self.episode_count = 0 if not hidden_layers: hidden_layers = [state_size * 2, state_size * 2] @@ -42,10 +44,9 @@ def __init__( def reset_buffer(self) -> None: """Recreate the experience buffer, effectively forgetting all the experiences - collected so far.""" - self.buffer = ExperienceReplayBuffer( - ExperienceReplayBufferParameters(10000, 1, 1) - ) + collected so far. + """ + self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1)) def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]: """Return the k next best actions for the given state.""" @@ -55,9 +56,7 @@ def action_for_state(self, state: Any) -> int: """Return the best action for the given state.""" return self.top_k_actions_for_state(state)[0] - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """Store the experience in the buffer and run the backpropagation if the buffer is ready.""" state_flat = state.flatten() new_state_flat = new_state.flatten() @@ -70,7 +69,8 @@ def store_experience( def discounted_rewards(self, rewards: np.array) -> np.array: """From a list of rewards obtained in an episode, we calculate the return minus the baseline. The baseline is the list of discounted - rewards minus the mean, divided by the standard deviation.""" + rewards minus the mean, divided by the standard deviation. + """ discount_r = np.zeros_like(rewards) timesteps = range(len(rewards)) reward_sum = 0 @@ -85,9 +85,7 @@ def discounted_rewards(self, rewards: np.array) -> np.array: def learn_from_experiences(self) -> None: """Train the policy estimator with all the experiences collected so far.""" experiences = list(self.buffer.experience_queue) - states, actions, rewards, dones, next_states = zip(*experiences) + states, actions, rewards, dones, next_states = zip(*experiences, strict=False) advantages = self.discounted_rewards(rewards) - advantages_tensor = FloatTensor(advantages).to( - device=self.policy_estimator.device - ) + advantages_tensor = FloatTensor(advantages).to(device=self.policy_estimator.device) self.policy_estimator.update(states, advantages_tensor, actions) diff --git a/deeprecsys/rl/agents/soft_actor_critic.py b/deeprecsys/rl/agents/soft_actor_critic.py index 491c69b..e49dba5 100644 --- a/deeprecsys/rl/agents/soft_actor_critic.py +++ b/deeprecsys/rl/agents/soft_actor_critic.py @@ -2,7 +2,7 @@ from typing import Any, Optional import torch -from gym.spaces import Discrete +from gymnasium.spaces import Discrete from torch import BoolTensor, FloatTensor from deeprecsys.neural_networks.gaussian_actor import GaussianActor @@ -21,7 +21,8 @@ class SoftActorCritic(ReinforcementLearning): """TODO: there's things to fix in this agent. It needs temperature optimization, and replace the current q-value estimator with the Q-value + value + value_target estimators, like described here - https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html""" + https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html + """ def __init__( self, @@ -50,9 +51,7 @@ def __init__( entropy_coefficient=entropy_coefficient, discount_factor=discount_factor, ) - self.critic = TwinnedQValueEstimator( - inputs=state_size + 1, learning_rate=learning_rate - ) + self.critic = TwinnedQValueEstimator(inputs=state_size + 1, learning_rate=learning_rate) self.target_critic = deepcopy(self.critic) self.buffer = PrioritizedExperienceReplayBuffer( buffer_parameters=buffer_parameters, @@ -72,8 +71,7 @@ def __init__( def should_update_network(self) -> bool: """Check if the buffer is ready to predict and if enough timesteps have passed.""" return ( - self.timesteps >= self.timesteps_to_start_predicting - and self.buffer.ready_to_predict() # noqa + self.timesteps >= self.timesteps_to_start_predicting and self.buffer.ready_to_predict() # noqa ) def action_for_state(self, state: Any) -> Any: @@ -88,9 +86,7 @@ def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any: """TODO""" pass - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: """Store the experience in the buffer.""" self.timesteps += 1 state_flat = state.flatten() @@ -127,9 +123,7 @@ def learn(self) -> None: # batch with indices and priority weights batch = self.buffer.sample_batch() - states, actions, rewards, dones, next_states, weights, samples = [ - i for i in batch - ] + states, actions, rewards, dones, next_states, weights, samples = [i for i in batch] # convert to tensors device = self.critic.device state_tensors = FloatTensor(states).to(device=device) diff --git a/deeprecsys/rl/experience_replay/buffer_parameters.py b/deeprecsys/rl/experience_replay/buffer_parameters.py index 4e52914..af1819c 100644 --- a/deeprecsys/rl/experience_replay/buffer_parameters.py +++ b/deeprecsys/rl/experience_replay/buffer_parameters.py @@ -17,16 +17,15 @@ def __init__( if minimum_experiences_to_start_predicting < batch_size: raise ValueError("The batch size mus the larger than the burn in") self.max_experiences = max_experiences - self.minimum_experiences_to_start_predicting = ( - minimum_experiences_to_start_predicting - ) + self.minimum_experiences_to_start_predicting = minimum_experiences_to_start_predicting self.batch_size = batch_size self.random_state = random_state class PERBufferParameters: """Parameters to configure the prioritization of experiences in a - Prioritized-Experience Replay Buffer""" + Prioritized-Experience Replay Buffer + """ def __init__( self, diff --git a/deeprecsys/rl/experience_replay/experience_buffer.py b/deeprecsys/rl/experience_replay/experience_buffer.py index ae4592c..b3a2c75 100644 --- a/deeprecsys/rl/experience_replay/experience_buffer.py +++ b/deeprecsys/rl/experience_replay/experience_buffer.py @@ -16,22 +16,21 @@ class ExperienceBuffer(ABC): @abstractmethod def ready_to_predict(self) -> bool: - """Whether or not enough experiences were collected""" + """Whether enough experiences were collected""" @abstractmethod def sample_batch(self) -> List[Tuple]: """Sample a batch of experiences from the buffer.""" @abstractmethod - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, next_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None: """Store an experience in the buffer.""" class ExperienceReplayBuffer(ExperienceBuffer): """Traditional experience replay buffer. Experiences are sampled randomly without - replacements within batches. Different batches may contain the same experience.""" + replacements within batches. Different batches may contain the same experience. + """ def __init__( self, @@ -40,9 +39,7 @@ def __init__( """Initialize the buffer with the provided parameters.""" if not parameters: parameters = ExperienceReplayBufferParameters() - self.minimum_experiences_to_start_predicting = ( - parameters.minimum_experiences_to_start_predicting - ) + self.minimum_experiences_to_start_predicting = parameters.minimum_experiences_to_start_predicting self.random_state = parameters.random_state # create double ended queue to store the experiences self.experience_queue: List = list(deque(maxlen=parameters.max_experiences)) @@ -51,17 +48,13 @@ def __init__( def sample_batch(self) -> List[Tuple]: """Sample a given number of experiences from the queue""" # samples the index of `batch_size` different experiences from the replay memory - samples = self.random_state.choice( - len(self.experience_queue), self.batch_size, replace=False - ) + samples = self.random_state.choice(len(self.experience_queue), self.batch_size, replace=False) # get the experiences experiences = [self.experience_queue[i] for i in samples] # returns a flattened list of the samples - return zip(*experiences) # type: ignore + return zip(*experiences, strict=False) # type: ignore - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, next_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None: """Store a new experience in the queue""" experience = Experience(state, action, reward, done, next_state) # type: ignore # append to the right (end) of the queue @@ -69,7 +62,6 @@ def store_experience( def ready_to_predict(self) -> bool: """Return true only if we had enough experiences to start predicting - (measured by the burn in)""" - return ( - len(self.experience_queue) >= self.minimum_experiences_to_start_predicting - ) + (measured by the burn in) + """ + return len(self.experience_queue) >= self.minimum_experiences_to_start_predicting diff --git a/deeprecsys/rl/experience_replay/priority_replay_buffer.py b/deeprecsys/rl/experience_replay/priority_replay_buffer.py index 7ead20f..cbc6da6 100644 --- a/deeprecsys/rl/experience_replay/priority_replay_buffer.py +++ b/deeprecsys/rl/experience_replay/priority_replay_buffer.py @@ -20,7 +20,8 @@ class PrioritizedExperienceReplayBuffer(ExperienceReplayBuffer): """Experience Replay Buffer that gives priority to experiences that the network learns more from. We can tell this - using the loss. We use importance sampling to avoid bias towards those experiences.""" + using the loss. We use importance sampling to avoid bias towards those experiences. + """ def __init__( self, @@ -45,15 +46,12 @@ def __init__( def priorities(self) -> numpy.array: """Get the priority of each experience in the queue""" - return numpy.array( - [e.priority for e in self.experience_queue], dtype=numpy.float32 - ) + return numpy.array([e.priority for e in self.experience_queue], dtype=numpy.float32) - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, next_state: Any - ) -> None: + def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None: """We include a priority to the experience. if the queue is empty, priority is 1 (max), - otherwise we check the maximum priority in the queue""" + otherwise we check the maximum priority in the queue + """ priorities = self.priorities() priority = priorities.max() if len(priorities) > 0 else 1.0 if not np.isnan(priority): @@ -64,41 +62,40 @@ def store_experience( def update_beta(self) -> None: """We want to grow the beta value slowly and linearly, starting at a value - close to zero, and stopping at 1.0. This is for the Importance Sampling""" + close to zero, and stopping at 1.0. This is for the Importance Sampling + """ if self.beta < 1.0: self.beta += self.beta_growth - def update_priorities( - self, batch: List[Tuple], errors_from_batch: List[float] - ) -> None: + def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]) -> None: """We want the priority of elements to be the TD error of plus an epsilon constant. The epsilon constant makes sure that no experience ever gets a priority zero. This prioritization strategy gives more importance to - elements that bring more learning to the network.""" - experience_indexes = [b[-1] for b in numpy.array(batch, dtype=numpy.object).T] + elements that bring more learning to the network. + """ + experience_indexes = [b[-1] for b in numpy.array(batch, dtype="object").T] for i in range(len(experience_indexes)): error = abs(errors_from_batch[i]) + self.epsilon if not np.isnan(error): - self.experience_queue[experience_indexes[i]] = self.experience_queue[ - experience_indexes[i] - ]._replace(priority=error) + experience = self.experience_queue[experience_indexes[i]] + experience._replace(priority=error) + self.experience_queue[experience_indexes[i]] = experience def sample_batch(self) -> List[Tuple]: """We sample experiences using their priorities as weights for sampling. The effect of the priorities is controlled by the alpha parameter. This is - already an advantage but it can introduce bias in a network by always + already an advantage, but it can introduce bias in a network by always choosing the same type of experiences for training. In order to fight this, we compute the weight of the experience (this is called Importance Sampling, or IP). We want the weights to decrease over time, this is controlled by - the beta parameter.""" + the beta parameter. + """ # calculate probabilities (alpha) probabilities = self.priorities() ** self.alpha p = probabilities / probabilities.sum() # sample experiences buffer_size = len(self.experience_queue) - samples = numpy.random.choice( - a=buffer_size, size=self.batch_size, p=p, replace=False - ) + samples = numpy.random.choice(a=buffer_size, size=self.batch_size, p=p, replace=False) experiences = [self.experience_queue[i].experience for i in samples] # importance Sampling # w_i = (1/N * 1/P_i) ^ beta @@ -106,4 +103,4 @@ def sample_batch(self) -> List[Tuple]: weights = weights / weights.max() self.update_beta() # return experiences with weights - return list(zip(*experiences)) + [tuple(weights)] + [tuple(samples)] + return list(zip(*experiences, strict=False)) + [tuple(weights)] + [tuple(samples)] diff --git a/deeprecsys/rl/learning_statistics.py b/deeprecsys/rl/learning_statistics.py index d6422a1..c312831 100644 --- a/deeprecsys/rl/learning_statistics.py +++ b/deeprecsys/rl/learning_statistics.py @@ -11,9 +11,7 @@ class LearningStatistics: """Special class to store and aggregate learning parameters.""" - def __init__( - self, model_name: Optional[str] = None, env_name: Optional[str] = None - ): + def __init__(self, model_name: Optional[str] = None, env_name: Optional[str] = None): """Start the collector for the given model and environment name.""" self.collected_metrics: List[Dict] = [] self.model_name = model_name diff --git a/deeprecsys/rl/manager.py b/deeprecsys/rl/manager.py index eaa6b83..b0a7634 100644 --- a/deeprecsys/rl/manager.py +++ b/deeprecsys/rl/manager.py @@ -2,14 +2,12 @@ from collections import defaultdict, namedtuple from typing import Any, Generator, List, Optional -import highway_env # noqa: F401 import numpy as np import torch -from gym import Env, make, spec +from gymnasium import Env, make, spec from numpy.core.multiarray import ndarray from numpy.random import RandomState -import deeprecsys.movielens_fairness_env # noqa: F401 from deeprecsys.rl import Logger from deeprecsys.rl.agents.agent import ReinforcementLearning from deeprecsys.rl.learning_statistics import LearningStatistics @@ -22,11 +20,13 @@ logger = Logger.create() -class Manager(object): +class Manager: """Class for learning from gym environments with some convenience methods.""" env_name: str env: Any + seed: int | None = None + random_state: RandomState | None = None def __init__( self, @@ -38,19 +38,13 @@ def __init__( **kwargs: Any, ) -> None: """Start the manager""" - if any( - [env_name is None and env is None, env_name is not None and env is not None] - ): + if any([env_name is None and env is None, env_name is not None and env is not None]): raise ValueError("Must specify exactly one of [env_name, env]") if env_name is not None: self.env_name = env_name # extract some parameters from the environment - self.max_episode_steps = ( - spec(self.env_name).max_episode_steps or max_episode_steps - ) - self.reward_threshold = ( - spec(self.env_name).reward_threshold or reward_threshold - ) + self.max_episode_steps = spec(self.env_name).max_episode_steps or max_episode_steps + self.reward_threshold = spec(self.env_name).reward_threshold or reward_threshold # create the environment self.env = make(env_name, **kwargs) # we seed the environment so that results are reproducible @@ -77,10 +71,11 @@ def execute_episodes( should_render: bool = False, ) -> List[EpisodeOutput]: """Execute any number of episodes with the given agent. - Returns the number of timesteps and sum of rewards per episode.""" + Returns the number of timesteps and sum of rewards per episode. + """ episode_outputs = [] for episode in range(n_episodes): - t, reward_sum, done, state = 0, 0, False, self.env.reset() + t, reward_sum, done, (state, _) = 0, 0, False, self.env.reset(seed=self.seed) logger.info(f"Running episode {episode}, starting at state {state}") while not done and t < self.max_episode_steps: if should_render: @@ -106,9 +101,7 @@ def _train_update_timestep(statistics: LearningStatistics) -> None: statistics.timestep += 1 @staticmethod - def _train_add_statistics( - statistics: LearningStatistics, rewards: List, moving_average: ndarray - ) -> None: + def _train_add_statistics(statistics: LearningStatistics, rewards: List, moving_average: ndarray) -> None: if statistics: statistics.append_metric("episode_rewards", sum(rewards)) statistics.append_metric("timestep_rewards", rewards) @@ -130,13 +123,13 @@ def train( logger.info("Training...") episode_rewards = [] for episode in range(max_episodes): - state = self.env.reset() + state, info = self.env.reset(seed=self.seed) rewards = [] self._train_start_new_episode(statistics, episode) done = False while done is False: action = self._train_get_step_action(rl, state) - new_state, reward, done, info = self.env.step(action) + new_state, reward, done, _, info = self.env.step(action) if "chosen_action" in info: action = action[info["chosen_action"]] rl.store_experience(state, action, reward, done, new_state) @@ -189,7 +182,7 @@ def hyperparameter_search( for the given number of episodes, and will run the determined number of times. """ combination_results = defaultdict(lambda: []) - for (p_name, p_value) in params.items(): + for p_name, p_value in params.items(): if len(p_value) < 2: continue for value in p_value: @@ -208,9 +201,7 @@ def hyperparameter_search( return combination_results - def setup_reproducibility( - self, seed: Optional[int] = None - ) -> Optional[RandomState]: + def setup_reproducibility(self, seed: Optional[int] = None) -> Optional[RandomState]: """Seeds the project's libraries: numpy, torch, gym""" if seed: # seed pytorch @@ -220,7 +211,7 @@ def setup_reproducibility( # seed numpy np.random.seed(seed) # seed gym - self.env.seed(seed) + self.seed = seed self.random_state = RandomState(seed) return self.random_state return None @@ -241,7 +232,7 @@ class CartpoleManager(Manager): def __init__(self, seed: Optional[int] = None): """Start the manager""" - super().__init__(env_name="CartPole-v0", seed=seed) + super().__init__(env_name="CartPole-v1", seed=seed) self.reward_threshold = 50 @@ -258,6 +249,4 @@ class MovieLensFairnessManager(Manager): def __init__(self, seed: Optional[int] = None, slate_size: int = 1): """Start the manager""" - super().__init__( - env_name="MovieLensFairness-v0", seed=seed, slate_size=slate_size - ) + super().__init__(env_name="MovieLensFairness-v0", seed=seed, slate_size=slate_size) diff --git a/deeprecsys/visit_simulator/__init__.py b/deeprecsys/visit_simulator/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/deeprecsys/visit_simulator/simulator_env.py b/deeprecsys/visit_simulator/simulator_env.py deleted file mode 100644 index b13117a..0000000 --- a/deeprecsys/visit_simulator/simulator_env.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import Any, List, Tuple - -import numpy -import pandas -from torch import FloatTensor -from torch.utils.data import DataLoader, Dataset - -from deeprecsys.neural_networks.binary_classifier import BinaryClassifier - - -class HomelikeDataset(Dataset): - def __init__(self) -> None: - base_path = "/home/farris/Developer/hl-ranking-algorithm/hl_ranking_algorithm/live_ranking/offline_training/" - self.users = ( - pandas.read_feather(base_path + "users.feather") - .set_index("session_id") - .dropna() - ) - self.history = pandas.read_feather(base_path + "history.feather") - self.inventory = ( - pandas.read_feather(base_path + "inventory.feather") - .set_index("pg_id") - .astype("float64") - ) - self.length = self.history.action.apply(len).sum() - self.user_index = 0 - self.user_history: List[Any] = [] - self.user_features = None - self.num_features = self.users.shape[1] + self.inventory.shape[1] - - def __getitem__(self, *_: Any) -> Tuple[FloatTensor, FloatTensor]: - if len(self.user_history) == 0: - self.user_features = self.users.iloc[self.user_index] - self.user_index += 1 - user_history = self.history.query( - f"session_id == '{self.users.index[0]}'" - ).explode("action") - user_history["reward"] = user_history.apply( - lambda df: df.action in df.reward, axis="columns" - ) - self.user_history = user_history[["action", "reward"]].values.tolist() - action, reward = self.user_history.pop() - item_features = self.inventory.loc[action].values - return ( - FloatTensor(numpy.concatenate((item_features, self.user_features), axis=0)), - FloatTensor([reward]), - ) - - def __len__(self) -> int: - return self.length - - -def generate_network_parameters(batch_size: int = 256) -> None: - dataset = HomelikeDataset() - data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False) - classifier = BinaryClassifier(input_shape=dataset.num_features) - for epoch in range(1): - for ix, data in enumerate(data_loader, 0): - features, targets = data - loss = classifier.update(features, targets) - print( - f"Epoch {epoch + 1} Batch {ix}/{int(dataset.length/batch_size)} Loss {loss}" - ) - classifier.save("visit_simulator.pch") - - -if __name__ == "__main__": - generate_network_parameters() - - -# class HomelikeApartmentSearch(Env): -# def __init__(self, seed: Optional[int] = 42, steps_per_episode: int = 30): -# self._rng = np.random.RandomState(seed=seed) -# self.user_history = [] -# self.steps_per_episode = steps_per_episode -# -# @staticmethod -# def _get_user_selection_probabilities(user_id: str): -# sessions = history.query(f"session_id == '{user_id}'")[["action", "reward"]] -# sessions_unnested = sessions.explode("action") -# sessions_unnested["reward"] = sessions_unnested.apply( -# lambda df: df.action in df.reward, axis="columns" -# ) -# return ( -# sessions_unnested.reset_index(drop=True) -# .groupby("action") -# .mean() -# .rename(columns={"reward": "select_probability"}) -# .squeeze(axis="columns") -# ) -# -# def reset(self) -> Dict: -# random_user = users.session_id.sample(1).values[0] -# self.selection_probabilities = self._get_user_selection_probabilities( -# random_user -# ) -# self.step = 0 -# return {"user": random_user, "step": self.step} -# -# def step(self, action: str) -> Tuple: -# simulate_selection = ... -# return encoded_state, reward / 5, done, info -# -# -# HomelikeApartmentSearch().reset() diff --git a/docs/CNAME b/docs/CNAME deleted file mode 100644 index 8b5a25a..0000000 --- a/docs/CNAME +++ /dev/null @@ -1 +0,0 @@ -deeprecsys.com \ No newline at end of file diff --git a/docs/api/deeprecsys/index.html b/docs/api/deeprecsys/index.html index b940320..52dd46e 100644 --- a/docs/api/deeprecsys/index.html +++ b/docs/api/deeprecsys/index.html @@ -3,7 +3,7 @@ - + deeprecsys API documentation @@ -26,7 +26,7 @@

Package deeprecsys

Sub-modules

-
deeprecsys.movielens_fairness_env
+
deeprecsys.neural_networks
@@ -34,10 +34,6 @@

Sub-modules

-
deeprecsys.tests
-
-
-
@@ -55,16 +51,15 @@

Index

\ No newline at end of file diff --git a/docs/api/deeprecsys/movielens_fairness_env.html b/docs/api/deeprecsys/movielens_fairness_env.html deleted file mode 100644 index d21c1d9..0000000 --- a/docs/api/deeprecsys/movielens_fairness_env.html +++ /dev/null @@ -1,734 +0,0 @@ - - - - - - -deeprecsys.movielens_fairness_env API documentation - - - - - - - - - - - -
-
-
-

Module deeprecsys.movielens_fairness_env

-
-
-
- -Expand source code - -
import os
-import functools
-import attr
-from mlfairnessgym.environments.recommenders import movie_lens_utils
-from mlfairnessgym.environments.recommenders import recsim_samplers
-from mlfairnessgym.environments.recommenders import movie_lens_dynamic as movie_lens
-from recsim.simulator import recsim_gym
-from gym.envs.registration import register
-from gym.spaces import Box, Discrete
-from gym import Env
-from typing import List, Union, Optional
-import numpy as np
-import math
-
-_env_specs = {
-    "id": "MovieLensFairness-v0",
-    "entry_point": "deeprecsys.movielens_fairness_env:MovieLensFairness",
-    "max_episode_steps": 50,
-}
-register(**_env_specs)
-
-
-class MovieLensFairness(Env):
-    """ MovieLens + MLFairnessGym + Recsim + Gym environment """
-
-    def __init__(self, slate_size: int = 1, seed: Optional[int] = None):
-        self.slate_size = slate_size
-        self.internal_env = self.prepare_environment()
-        self._rng = np.random.RandomState(seed=seed)
-        self.ndcg = []
-        self.dcg = []
-
-    def _get_product_relevance(self, product_id: int) -> float:
-        """ Relevance in range (0,1) """
-        topic_affinity = (
-            self.internal_env.environment.user_model._user_state.topic_affinity
-        )
-        movie_vector = [
-            d.movie_vec
-            for d in self.internal_env.environment._document_sampler._corpus
-            if d._doc_id == product_id
-        ][0]
-        return np.clip(
-            np.dot(movie_vector, topic_affinity),
-            movie_lens.User.MIN_SCORE,
-            movie_lens.User.MAX_SCORE,
-        )
-
-    def _get_dcg(self, relevances: List[float]) -> float:
-        return sum([relevances[i] / math.log(i + 2, 2) for i in range(len(relevances))])
-
-    def _calculate_ndcg(self, slate_product_ids: List[int]) -> float:
-        relevances = [self._get_product_relevance(p) for p in slate_product_ids]
-        dcg = self._get_dcg(relevances)
-        self.dcg.append(dcg)
-        ideal_relevances = [movie_lens.User.MAX_SCORE for _ in range(len(relevances))]
-        idcg = self._get_dcg(ideal_relevances)
-        self.ndcg.append(dcg / idcg)
-
-    def step(self, action: Union[int, List[int]]):
-        """ Normalize reward and flattens/normalizes state """
-        if type(action) in [list, np.ndarray, np.array]:
-            self._calculate_ndcg(action)
-            state, reward, done, info = self.internal_env.step(action)
-            encoded_state, info = self.movielens_state_encoder(state, action, info)
-            return encoded_state, reward / 5, done, info
-        else:
-            state, reward, done, info = self.internal_env.step([action])
-            encoded_state, info = self.movielens_state_encoder(state, [action], info)
-            return encoded_state, reward / 5, done, info
-
-    def reset(self):
-        """ flattens/normalizes state """
-        state = self.internal_env.reset()
-        self.ndcg = []
-        self.dcg = []
-        encoded_state, _ = self.movielens_state_encoder(state, [], {})
-        return encoded_state
-
-    def render(self, mode="human", close=False):
-        return self.internal_env.render(mode)
-
-    @property
-    def action_space(self):
-        if self.slate_size == 1:
-            return Discrete(self.internal_env.action_space.nvec[0])
-        else:
-            return self.internal_env.action_space
-
-    @property
-    def reward_range(self):
-        return self.internal_env.reward_range
-
-    @property
-    def observation_space(self):
-        return Box(low=0, high=1.0, shape=(25,), dtype=np.float32)
-
-    def movielens_state_encoder(
-        self, state: dict, action_slate: List[int], info: dict
-    ) -> List[int]:
-        """if the slate size is > 1, we need to guarantee the Single choice (SC)
-        assumption, as described in the paper `SLATEQ: A Tractable Decomposition
-        for Reinforcement Learning withRecommendation Sets`
-        TODO: by randomly selecting one of the interactions?
-        """
-        user_features = state["user"]
-        response_features = state["response"]
-        doc_features = [
-            state["doc"][str(action_slate[i])]["genres"]
-            for i in range(len(action_slate))
-        ]
-        if self.slate_size > 1:
-            if response_features:
-                chosen_action = self._rng.choice(self.slate_size)
-                response_features = (response_features[chosen_action],)
-                info["chosen_action"] = chosen_action
-            if doc_features:
-                doc_features = [doc_features[self._rng.choice(self.slate_size)]]
-
-        refined_state = {
-            "user": user_features,
-            "response": response_features,
-            "slate_docs": doc_features,
-        }
-        # flattens the state
-        flat_state = np.array(
-            [
-                refined_state["user"]["sex"],
-                refined_state["user"]["age"],
-                refined_state["user"]["occupation"],
-                refined_state["user"]["zip_code"],
-                *(
-                    refined_state["slate_docs"][0]
-                    if refined_state["slate_docs"]
-                    else ([0] * 19)
-                ),
-                (refined_state.get("response") or ({},))[0].get("rating", 0),
-                (refined_state.get("responsse") or ({},))[0].get("violence_score", 0),
-            ]
-        )
-        return flat_state, info
-
-    def slate_action_selector(self, qvals: List[float]) -> List[float]:
-        """Gets the index of the top N highest elements in the predictor array."""
-        return np.argsort(qvals)[-self.slate_size :][::-1]
-
-    def prepare_environment(self):
-        current_path = os.path.dirname(__file__)
-        data_dir = os.path.join(current_path, "../output")
-        embeddings_path = os.path.join(
-            current_path,
-            "../mlfairnessgym/environments/recommenders/movielens_factorization.json",
-        )
-        env_config = movie_lens.EnvConfig(
-            seeds=movie_lens.Seeds(0, 0),
-            data_dir=data_dir,
-            embeddings_path=embeddings_path,
-        )
-        initial_embeddings = movie_lens_utils.load_embeddings(env_config)
-        # user constructor
-        user_ctor = functools.partial(
-            movie_lens.User, **attr.asdict(env_config.user_config)
-        )
-        dataset = movie_lens_utils.Dataset(
-            env_config.data_dir,
-            user_ctor=user_ctor,
-            movie_ctor=movie_lens.Movie,
-            response_ctor=movie_lens.Response,
-            embeddings=initial_embeddings,
-        )
-        # the SingletonSampler will sample each movie once sequentially
-        document_sampler = recsim_samplers.SingletonSampler(
-            dataset.get_movies(), movie_lens.Movie
-        )
-        user_sampler = recsim_samplers.UserPoolSampler(
-            seed=env_config.seeds.user_sampler,
-            users=dataset.get_users(),
-            user_ctor=user_ctor,
-        )
-        user_model = movie_lens.UserModel(
-            user_sampler=user_sampler,
-            seed=env_config.seeds.user_model,
-        )
-        env = movie_lens.MovieLensEnvironment(
-            user_model,
-            document_sampler,
-            num_candidates=document_sampler.size(),
-            slate_size=self.slate_size,
-            resample_documents=False,
-        )
-        _ = env.reset()
-        reward_aggregator = functools.partial(
-            movie_lens.multiobjective_reward,
-            lambda_non_violent=env_config.lambda_non_violent,
-        )
-        recsim_env = recsim_gym.RecSimGymEnv(env, reward_aggregator)
-        return recsim_env
-
-
-
-
-
-
-
-
-
-

Classes

-
-
-class MovieLensFairness -(slate_size: int = 1, seed: Union[int, NoneType] = None) -
-
-

MovieLens + MLFairnessGym + Recsim + Gym environment

-
- -Expand source code - -
class MovieLensFairness(Env):
-    """ MovieLens + MLFairnessGym + Recsim + Gym environment """
-
-    def __init__(self, slate_size: int = 1, seed: Optional[int] = None):
-        self.slate_size = slate_size
-        self.internal_env = self.prepare_environment()
-        self._rng = np.random.RandomState(seed=seed)
-        self.ndcg = []
-        self.dcg = []
-
-    def _get_product_relevance(self, product_id: int) -> float:
-        """ Relevance in range (0,1) """
-        topic_affinity = (
-            self.internal_env.environment.user_model._user_state.topic_affinity
-        )
-        movie_vector = [
-            d.movie_vec
-            for d in self.internal_env.environment._document_sampler._corpus
-            if d._doc_id == product_id
-        ][0]
-        return np.clip(
-            np.dot(movie_vector, topic_affinity),
-            movie_lens.User.MIN_SCORE,
-            movie_lens.User.MAX_SCORE,
-        )
-
-    def _get_dcg(self, relevances: List[float]) -> float:
-        return sum([relevances[i] / math.log(i + 2, 2) for i in range(len(relevances))])
-
-    def _calculate_ndcg(self, slate_product_ids: List[int]) -> float:
-        relevances = [self._get_product_relevance(p) for p in slate_product_ids]
-        dcg = self._get_dcg(relevances)
-        self.dcg.append(dcg)
-        ideal_relevances = [movie_lens.User.MAX_SCORE for _ in range(len(relevances))]
-        idcg = self._get_dcg(ideal_relevances)
-        self.ndcg.append(dcg / idcg)
-
-    def step(self, action: Union[int, List[int]]):
-        """ Normalize reward and flattens/normalizes state """
-        if type(action) in [list, np.ndarray, np.array]:
-            self._calculate_ndcg(action)
-            state, reward, done, info = self.internal_env.step(action)
-            encoded_state, info = self.movielens_state_encoder(state, action, info)
-            return encoded_state, reward / 5, done, info
-        else:
-            state, reward, done, info = self.internal_env.step([action])
-            encoded_state, info = self.movielens_state_encoder(state, [action], info)
-            return encoded_state, reward / 5, done, info
-
-    def reset(self):
-        """ flattens/normalizes state """
-        state = self.internal_env.reset()
-        self.ndcg = []
-        self.dcg = []
-        encoded_state, _ = self.movielens_state_encoder(state, [], {})
-        return encoded_state
-
-    def render(self, mode="human", close=False):
-        return self.internal_env.render(mode)
-
-    @property
-    def action_space(self):
-        if self.slate_size == 1:
-            return Discrete(self.internal_env.action_space.nvec[0])
-        else:
-            return self.internal_env.action_space
-
-    @property
-    def reward_range(self):
-        return self.internal_env.reward_range
-
-    @property
-    def observation_space(self):
-        return Box(low=0, high=1.0, shape=(25,), dtype=np.float32)
-
-    def movielens_state_encoder(
-        self, state: dict, action_slate: List[int], info: dict
-    ) -> List[int]:
-        """if the slate size is > 1, we need to guarantee the Single choice (SC)
-        assumption, as described in the paper `SLATEQ: A Tractable Decomposition
-        for Reinforcement Learning withRecommendation Sets`
-        TODO: by randomly selecting one of the interactions?
-        """
-        user_features = state["user"]
-        response_features = state["response"]
-        doc_features = [
-            state["doc"][str(action_slate[i])]["genres"]
-            for i in range(len(action_slate))
-        ]
-        if self.slate_size > 1:
-            if response_features:
-                chosen_action = self._rng.choice(self.slate_size)
-                response_features = (response_features[chosen_action],)
-                info["chosen_action"] = chosen_action
-            if doc_features:
-                doc_features = [doc_features[self._rng.choice(self.slate_size)]]
-
-        refined_state = {
-            "user": user_features,
-            "response": response_features,
-            "slate_docs": doc_features,
-        }
-        # flattens the state
-        flat_state = np.array(
-            [
-                refined_state["user"]["sex"],
-                refined_state["user"]["age"],
-                refined_state["user"]["occupation"],
-                refined_state["user"]["zip_code"],
-                *(
-                    refined_state["slate_docs"][0]
-                    if refined_state["slate_docs"]
-                    else ([0] * 19)
-                ),
-                (refined_state.get("response") or ({},))[0].get("rating", 0),
-                (refined_state.get("responsse") or ({},))[0].get("violence_score", 0),
-            ]
-        )
-        return flat_state, info
-
-    def slate_action_selector(self, qvals: List[float]) -> List[float]:
-        """Gets the index of the top N highest elements in the predictor array."""
-        return np.argsort(qvals)[-self.slate_size :][::-1]
-
-    def prepare_environment(self):
-        current_path = os.path.dirname(__file__)
-        data_dir = os.path.join(current_path, "../output")
-        embeddings_path = os.path.join(
-            current_path,
-            "../mlfairnessgym/environments/recommenders/movielens_factorization.json",
-        )
-        env_config = movie_lens.EnvConfig(
-            seeds=movie_lens.Seeds(0, 0),
-            data_dir=data_dir,
-            embeddings_path=embeddings_path,
-        )
-        initial_embeddings = movie_lens_utils.load_embeddings(env_config)
-        # user constructor
-        user_ctor = functools.partial(
-            movie_lens.User, **attr.asdict(env_config.user_config)
-        )
-        dataset = movie_lens_utils.Dataset(
-            env_config.data_dir,
-            user_ctor=user_ctor,
-            movie_ctor=movie_lens.Movie,
-            response_ctor=movie_lens.Response,
-            embeddings=initial_embeddings,
-        )
-        # the SingletonSampler will sample each movie once sequentially
-        document_sampler = recsim_samplers.SingletonSampler(
-            dataset.get_movies(), movie_lens.Movie
-        )
-        user_sampler = recsim_samplers.UserPoolSampler(
-            seed=env_config.seeds.user_sampler,
-            users=dataset.get_users(),
-            user_ctor=user_ctor,
-        )
-        user_model = movie_lens.UserModel(
-            user_sampler=user_sampler,
-            seed=env_config.seeds.user_model,
-        )
-        env = movie_lens.MovieLensEnvironment(
-            user_model,
-            document_sampler,
-            num_candidates=document_sampler.size(),
-            slate_size=self.slate_size,
-            resample_documents=False,
-        )
-        _ = env.reset()
-        reward_aggregator = functools.partial(
-            movie_lens.multiobjective_reward,
-            lambda_non_violent=env_config.lambda_non_violent,
-        )
-        recsim_env = recsim_gym.RecSimGymEnv(env, reward_aggregator)
-        return recsim_env
-
-

Ancestors

-
    -
  • gym.core.Env
    • -
-

Instance variables

-
-
var action_space
-
-
-
- -Expand source code - -
@property
-def action_space(self):
-    if self.slate_size == 1:
-        return Discrete(self.internal_env.action_space.nvec[0])
-    else:
-        return self.internal_env.action_space
-
-
-
var observation_space
-
-
-
- -Expand source code - -
@property
-def observation_space(self):
-    return Box(low=0, high=1.0, shape=(25,), dtype=np.float32)
-
-
-
var reward_range
-
-

tuple() -> empty tuple -tuple(iterable) -> tuple initialized from iterable's items

-

If the argument is a tuple, the return value is the same object.

-
- -Expand source code - -
@property
-def reward_range(self):
-    return self.internal_env.reward_range
-
-
-
-

Methods

-
-
-def movielens_state_encoder(self, state: dict, action_slate: List[int], info: dict) ‑> List[int] -
-
-

if the slate size is > 1, we need to guarantee the Single choice (SC) -assumption, as described in the paper SLATEQ: A Tractable Decomposition -for Reinforcement Learning withRecommendation Sets -TODO: by randomly selecting one of the interactions?

-
- -Expand source code - -
def movielens_state_encoder(
-    self, state: dict, action_slate: List[int], info: dict
-) -> List[int]:
-    """if the slate size is > 1, we need to guarantee the Single choice (SC)
-    assumption, as described in the paper `SLATEQ: A Tractable Decomposition
-    for Reinforcement Learning withRecommendation Sets`
-    TODO: by randomly selecting one of the interactions?
-    """
-    user_features = state["user"]
-    response_features = state["response"]
-    doc_features = [
-        state["doc"][str(action_slate[i])]["genres"]
-        for i in range(len(action_slate))
-    ]
-    if self.slate_size > 1:
-        if response_features:
-            chosen_action = self._rng.choice(self.slate_size)
-            response_features = (response_features[chosen_action],)
-            info["chosen_action"] = chosen_action
-        if doc_features:
-            doc_features = [doc_features[self._rng.choice(self.slate_size)]]
-
-    refined_state = {
-        "user": user_features,
-        "response": response_features,
-        "slate_docs": doc_features,
-    }
-    # flattens the state
-    flat_state = np.array(
-        [
-            refined_state["user"]["sex"],
-            refined_state["user"]["age"],
-            refined_state["user"]["occupation"],
-            refined_state["user"]["zip_code"],
-            *(
-                refined_state["slate_docs"][0]
-                if refined_state["slate_docs"]
-                else ([0] * 19)
-            ),
-            (refined_state.get("response") or ({},))[0].get("rating", 0),
-            (refined_state.get("responsse") or ({},))[0].get("violence_score", 0),
-        ]
-    )
-    return flat_state, info
-
-
-
-def prepare_environment(self) -
-
-
-
- -Expand source code - -
def prepare_environment(self):
-    current_path = os.path.dirname(__file__)
-    data_dir = os.path.join(current_path, "../output")
-    embeddings_path = os.path.join(
-        current_path,
-        "../mlfairnessgym/environments/recommenders/movielens_factorization.json",
-    )
-    env_config = movie_lens.EnvConfig(
-        seeds=movie_lens.Seeds(0, 0),
-        data_dir=data_dir,
-        embeddings_path=embeddings_path,
-    )
-    initial_embeddings = movie_lens_utils.load_embeddings(env_config)
-    # user constructor
-    user_ctor = functools.partial(
-        movie_lens.User, **attr.asdict(env_config.user_config)
-    )
-    dataset = movie_lens_utils.Dataset(
-        env_config.data_dir,
-        user_ctor=user_ctor,
-        movie_ctor=movie_lens.Movie,
-        response_ctor=movie_lens.Response,
-        embeddings=initial_embeddings,
-    )
-    # the SingletonSampler will sample each movie once sequentially
-    document_sampler = recsim_samplers.SingletonSampler(
-        dataset.get_movies(), movie_lens.Movie
-    )
-    user_sampler = recsim_samplers.UserPoolSampler(
-        seed=env_config.seeds.user_sampler,
-        users=dataset.get_users(),
-        user_ctor=user_ctor,
-    )
-    user_model = movie_lens.UserModel(
-        user_sampler=user_sampler,
-        seed=env_config.seeds.user_model,
-    )
-    env = movie_lens.MovieLensEnvironment(
-        user_model,
-        document_sampler,
-        num_candidates=document_sampler.size(),
-        slate_size=self.slate_size,
-        resample_documents=False,
-    )
-    _ = env.reset()
-    reward_aggregator = functools.partial(
-        movie_lens.multiobjective_reward,
-        lambda_non_violent=env_config.lambda_non_violent,
-    )
-    recsim_env = recsim_gym.RecSimGymEnv(env, reward_aggregator)
-    return recsim_env
-
-
-
-def render(self, mode='human', close=False) -
-
-

Renders the environment.

-

The set of supported modes varies per environment. (And some -environments do not support rendering at all.) By convention, -if mode is:

-
    -
  • human: render to the current display or terminal and -return nothing. Usually for human consumption.
    • -
  • rgb_array: Return an numpy.ndarray with shape (x, y, 3), -representing RGB values for an x-by-y pixel image, suitable -for turning into a video.
    • -
  • ansi: Return a string (str) or StringIO.StringIO containing a -terminal-style text representation. The text can include newlines -and ANSI escape sequences (e.g. for colors).
    • -
-

Note

-

Make sure that your class's metadata 'render.modes' key includes -the list of supported modes. It's recommended to call super() -in implementations to use the functionality of this method.

-

Args

-
-
mode : str
-
the mode to render with
-
-

Example:

-

class MyEnv(Env): -metadata = {'render.modes': ['human', 'rgb_array']}

-
def render(self, mode='human'):
-    if mode == 'rgb_array':
-        return np.array(...) # return RGB frame suitable for video
-    elif mode == 'human':
-        ... # pop up a window and render
-    else:
-        super(MyEnv, self).render(mode=mode) # just raise an exception
-
-
- -Expand source code - -
def render(self, mode="human", close=False):
-    return self.internal_env.render(mode)
-
-
-
-def reset(self) -
-
-

flattens/normalizes state

-
- -Expand source code - -
def reset(self):
-    """ flattens/normalizes state """
-    state = self.internal_env.reset()
-    self.ndcg = []
-    self.dcg = []
-    encoded_state, _ = self.movielens_state_encoder(state, [], {})
-    return encoded_state
-
-
-
-def slate_action_selector(self, qvals: List[float]) ‑> List[float] -
-
-

Gets the index of the top N highest elements in the predictor array.

-
- -Expand source code - -
def slate_action_selector(self, qvals: List[float]) -> List[float]:
-    """Gets the index of the top N highest elements in the predictor array."""
-    return np.argsort(qvals)[-self.slate_size :][::-1]
-
-
-
-def step(self, action: Union[int, List[int]]) -
-
-

Normalize reward and flattens/normalizes state

-
- -Expand source code - -
def step(self, action: Union[int, List[int]]):
-    """ Normalize reward and flattens/normalizes state """
-    if type(action) in [list, np.ndarray, np.array]:
-        self._calculate_ndcg(action)
-        state, reward, done, info = self.internal_env.step(action)
-        encoded_state, info = self.movielens_state_encoder(state, action, info)
-        return encoded_state, reward / 5, done, info
-    else:
-        state, reward, done, info = self.internal_env.step([action])
-        encoded_state, info = self.movielens_state_encoder(state, [action], info)
-        return encoded_state, reward / 5, done, info
-
-
-
-
-
-
-
- -
- - - \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/neural_networks/base_network.html b/docs/api/deeprecsys/neural_networks/base_network.html similarity index 59% rename from docs/api/deeprecsys/rl/neural_networks/base_network.html rename to docs/api/deeprecsys/neural_networks/base_network.html index e8fec99..bb5f589 100644 --- a/docs/api/deeprecsys/rl/neural_networks/base_network.html +++ b/docs/api/deeprecsys/neural_networks/base_network.html @@ -3,8 +3,8 @@ - -deeprecsys.rl.neural_networks.base_network API documentation + +deeprecsys.neural_networks.base_network API documentation @@ -19,64 +19,72 @@
-

Module deeprecsys.rl.neural_networks.base_network

+

Module deeprecsys.neural_networks.base_network

Expand source code -
from torch.nn import Module
-from torch import save, load
+
import numpy as np
 import torch
+from torch import Tensor, device, load, save
+from torch.nn import Module
 from torch.utils.tensorboard import SummaryWriter
-import numpy as np
 from torchviz import make_dot
 
 
 class BaseNetwork(Module):
-    def __init__(self):
+    """Base class representing a neural network, contains utility and common methods"""
+
+    def __init__(self) -> None:
+        """Automatically detects if the device is CUDA or CPU"""
         super().__init__()
         self.device = self._auto_detect_device()
 
     @staticmethod
-    def _auto_detect_device():
+    def _auto_detect_device() -> device:
         has_cuda = torch.cuda.is_available() and torch.cuda.is_initialized()
         return torch.device("cuda" if has_cuda else "cpu")
 
-    def save(self, path: str):
-        """ Writes the model's parameters to the given path. """
+    def save(self, path: str) -> None:
+        """Write the model's parameters to the given path."""
         save(self.state_dict(), path)
 
-    def load(self, path: str):
-        """ Reads the model's parameters from the given path. """
+    def load(self, path: str) -> None:
+        """Read the model's parameters from the given path."""
         self.load_state_dict(load(path))
 
-    def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0):
+    def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0) -> None:
         """When using target networks, this method updates the parameters of the current network
         using the parameters of the given source network. The update_rate is a float in
         range (0,1) and controls how the update affects the target (self). update_rate=0
         means a full deep copy, and update_rate=1 means the target does not update
         at all. This parameter is usually called Tau. This method is usually called
-        an exponential moving average update."""
-        for t, s in zip(self.parameters(), source_network.parameters()):
+        an exponential moving average update.
+        """
+        for t, s in zip(self.parameters(), source_network.parameters(), strict=False):
             t.data.copy_(t.data * (1.0 - update_rate) + s.data * update_rate)
 
-    def run_backpropagation(self, loss):
-        """Requires an optimizer property. Runs backward on the given loss, and
-        steps the optimizer."""
+    def run_backpropagation(self, loss: Tensor) -> None:
+        """Run backward on the given loss, and step the optimizer.
+        Requires an optimizer property.
+        """
         self.optimizer.zero_grad()
         loss.backward()
         self.optimizer.step()
 
-    def disable_learning(self):
+    def disable_learning(self) -> None:
+        """Turn off the `requires_grad` parameter to stop the learning."""
         for param in self.parameters():
             param.requires_grad = False
 
-    def forward(self, *input):
+    def forward(self, *input: Tensor) -> Module:
+        """Return the output of the network for the provided input"""
         return self.model(*input)
 
-    def add_to_tensorboard(self, input_example: np.array):
+    def add_to_tensorboard(self, input_example: np.array) -> None:
+        """Add the input to the tensor board and renders the network graph to pdf"""
         writer = SummaryWriter(f"output/writer/{type(self).__name__}")
         tensor = torch.FloatTensor(input_example)
         writer.add_graph(self, tensor, verbose=True)
@@ -100,77 +108,67 @@ 
Module deeprecsys.rl.neural_networks.base_network
 

 
Classes

 

-

+

 class BaseNetwork
 

 

-
Base class for all neural network modules.

-
Your models should also subclass this class.

-
Modules can also contain other Modules, allowing to nest them in
-a tree structure. You can assign the submodules as regular attributes::

-
import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super(Model, self).__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-

-
Submodules assigned in this way will be registered, and will have their
-parameters converted too when you call :meth:to, etc.

-
Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
Base class representing a neural network, contains utility and common methods

+
Automatically detects if the device is CUDA or CPU

 

 
 Expand source code
 
 
class BaseNetwork(Module):
-    def __init__(self):
+    """Base class representing a neural network, contains utility and common methods"""
+
+    def __init__(self) -> None:
+        """Automatically detects if the device is CUDA or CPU"""
         super().__init__()
         self.device = self._auto_detect_device()
 
     @staticmethod
-    def _auto_detect_device():
+    def _auto_detect_device() -> device:
         has_cuda = torch.cuda.is_available() and torch.cuda.is_initialized()
         return torch.device("cuda" if has_cuda else "cpu")
 
-    def save(self, path: str):
-        """ Writes the model's parameters to the given path. """
+    def save(self, path: str) -> None:
+        """Write the model's parameters to the given path."""
         save(self.state_dict(), path)
 
-    def load(self, path: str):
-        """ Reads the model's parameters from the given path. """
+    def load(self, path: str) -> None:
+        """Read the model's parameters from the given path."""
         self.load_state_dict(load(path))
 
-    def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0):
+    def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0) -> None:
         """When using target networks, this method updates the parameters of the current network
         using the parameters of the given source network. The update_rate is a float in
         range (0,1) and controls how the update affects the target (self). update_rate=0
         means a full deep copy, and update_rate=1 means the target does not update
         at all. This parameter is usually called Tau. This method is usually called
-        an exponential moving average update."""
-        for t, s in zip(self.parameters(), source_network.parameters()):
+        an exponential moving average update.
+        """
+        for t, s in zip(self.parameters(), source_network.parameters(), strict=False):
             t.data.copy_(t.data * (1.0 - update_rate) + s.data * update_rate)
 
-    def run_backpropagation(self, loss):
-        """Requires an optimizer property. Runs backward on the given loss, and
-        steps the optimizer."""
+    def run_backpropagation(self, loss: Tensor) -> None:
+        """Run backward on the given loss, and step the optimizer.
+        Requires an optimizer property.
+        """
         self.optimizer.zero_grad()
         loss.backward()
         self.optimizer.step()
 
-    def disable_learning(self):
+    def disable_learning(self) -> None:
+        """Turn off the `requires_grad` parameter to stop the learning."""
         for param in self.parameters():
             param.requires_grad = False
 
-    def forward(self, *input):
+    def forward(self, *input: Tensor) -> Module:
+        """Return the output of the network for the provided input"""
         return self.model(*input)
 
-    def add_to_tensorboard(self, input_example: np.array):
+    def add_to_tensorboard(self, input_example: np.array) -> None:
+        """Add the input to the tensor board and renders the network graph to pdf"""
         writer = SummaryWriter(f"output/writer/{type(self).__name__}")
         tensor = torch.FloatTensor(input_example)
         writer.add_graph(self, tensor, verbose=True)
@@ -190,26 +188,28 @@ 
Ancestors

 
 
Subclasses

 
 
Methods

 

-

-def add_to_tensorboard(self, input_example: )
+

+def add_to_tensorboard(self, input_example: ) ‑> None
 

 

-

+
Add the input to the tensor board and renders the network graph to pdf

 

 
 Expand source code
 
-
def add_to_tensorboard(self, input_example: np.array):
+
def add_to_tensorboard(self, input_example: np.array) -> None:
+    """Add the input to the tensor board and renders the network graph to pdf"""
     writer = SummaryWriter(f"output/writer/{type(self).__name__}")
     tensor = torch.FloatTensor(input_example)
     writer.add_graph(self, tensor, verbose=True)
@@ -224,89 +224,84 @@ 
Methods

     graph.render(f"output/graphs/{type(self).__name__}")

 

 

-

-def disable_learning(self)
+

+def disable_learning(self) ‑> None
 

 

-

+
Turn off the requires_grad parameter to stop the learning.

 

 
 Expand source code
 
-
def disable_learning(self):
+
def disable_learning(self) -> None:
+    """Turn off the `requires_grad` parameter to stop the learning."""
     for param in self.parameters():
         param.requires_grad = False

 

 

-

-def forward(self, *input)
+

+def forward(self, *input: torch.Tensor) ‑> torch.nn.modules.module.Module
 

 

-
Defines the computation performed at every call.

-
Should be overridden by all subclasses.

-

-
Note

-
Although the recipe for forward pass needs to be defined within
-this function, one should call the :class:Module instance afterwards
-instead of this since the former takes care of running the
-registered hooks while the latter silently ignores them.

-

+
Return the output of the network for the provided input

 

 
 Expand source code
 
-
def forward(self, *input):
+
def forward(self, *input: Tensor) -> Module:
+    """Return the output of the network for the provided input"""
     return self.model(*input)

 

 

-

-def load(self, path: str)
+

+def load(self, path: str) ‑> None
 

 

-
Reads the model's parameters from the given path.

+
Read the model's parameters from the given path.

 

 
 Expand source code
 
-
def load(self, path: str):
-    """ Reads the model's parameters from the given path. """
+
def load(self, path: str) -> None:
+    """Read the model's parameters from the given path."""
     self.load_state_dict(load(path))

 

 

-

-def run_backpropagation(self, loss)
+

+def run_backpropagation(self, loss: torch.Tensor) ‑> None
 

 

-
Requires an optimizer property. Runs backward on the given loss, and
-steps the optimizer.

+
Run backward on the given loss, and step the optimizer.
+Requires an optimizer property.

 

 
 Expand source code
 
-
def run_backpropagation(self, loss):
-    """Requires an optimizer property. Runs backward on the given loss, and
-    steps the optimizer."""
+
def run_backpropagation(self, loss: Tensor) -> None:
+    """Run backward on the given loss, and step the optimizer.
+    Requires an optimizer property.
+    """
     self.optimizer.zero_grad()
     loss.backward()
     self.optimizer.step()

 

 

-

-def save(self, path: str)
+

+def save(self, path: str) ‑> None
 

 

-
Writes the model's parameters to the given path.

+
Write the model's parameters to the given path.

 

 
 Expand source code
 
-
def save(self, path: str):
-    """ Writes the model's parameters to the given path. """
+
def save(self, path: str) -> None:
+    """Write the model's parameters to the given path."""
     save(self.state_dict(), path)

 

 

-

-def soft_parameter_update(self, source_network: torch.nn.modules.module.Module, update_rate: float = 0.0)
+

+def soft_parameter_update(self, source_network: torch.nn.modules.module.Module, update_rate: float = 0.0) ‑> None
 

 

 
When using target networks, this method updates the parameters of the current network
@@ -319,14 +314,15 @@ 
Methods

 
 Expand source code
 
-
def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0):
+
def soft_parameter_update(self, source_network: Module, update_rate: float = 0.0) -> None:
     """When using target networks, this method updates the parameters of the current network
     using the parameters of the given source network. The update_rate is a float in
     range (0,1) and controls how the update affects the target (self). update_rate=0
     means a full deep copy, and update_rate=1 means the target does not update
     at all. This parameter is usually called Tau. This method is usually called
-    an exponential moving average update."""
-    for t, s in zip(self.parameters(), source_network.parameters()):
+    an exponential moving average update.
+    """
+    for t, s in zip(self.parameters(), source_network.parameters(), strict=False):
         t.data.copy_(t.data * (1.0 - update_rate) + s.data * update_rate)

 

 

@@ -343,21 +339,21 @@ 
Index
\ No newline at end of file diff --git a/docs/api/deeprecsys/neural_networks/binary_classifier.html b/docs/api/deeprecsys/neural_networks/binary_classifier.html new file mode 100644 index 0000000..39233ad --- /dev/null +++ b/docs/api/deeprecsys/neural_networks/binary_classifier.html @@ -0,0 +1,177 @@ + + + + + + +deeprecsys.neural_networks.binary_classifier API documentation + + + + + + + + + + + +
+
+
+

Module deeprecsys.neural_networks.binary_classifier

+
+
+
+ +Expand source code + +
from torch import Tensor
+from torch.nn import BCELoss, Linear, ReLU, Sequential, Sigmoid
+from torch.optim import Adam
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
+
+
+class BinaryClassifier(BaseNetwork):
+    def __init__(self, input_shape: int, learning_rate: float = 0.0025):
+        super().__init__()
+        layers = [
+            Linear(in_features=input_shape, out_features=256, bias=True),
+            ReLU(),
+            Linear(in_features=256, out_features=64, bias=True),
+            ReLU(),
+            Linear(in_features=64, out_features=16, bias=True),
+            ReLU(),
+            Linear(in_features=16, out_features=1, bias=True),
+            Sigmoid(),
+        ]
+        self.model = Sequential(*layers)
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        self.loss_function = BCELoss()
+        if self.device == "cuda":
+            self.model.cuda()
+
+    def update(self, features_batch: Tensor, targets_batch: Tensor) -> float:
+        predicted_selection = self.forward(features_batch)
+        loss = self.loss_function(predicted_selection, targets_batch)  # .reshape(-1,1))
+        self.run_backpropagation(loss)
+        return loss.item()
+
+
+
+
+
+
+
+
+
+

Classes

+
+
+class BinaryClassifier +(input_shape: int, learning_rate: float = 0.0025) +
+
+

Base class representing a neural network, contains utility and common methods

+

Automatically detects if the device is CUDA or CPU

+
+ +Expand source code + +
class BinaryClassifier(BaseNetwork):
+    def __init__(self, input_shape: int, learning_rate: float = 0.0025):
+        super().__init__()
+        layers = [
+            Linear(in_features=input_shape, out_features=256, bias=True),
+            ReLU(),
+            Linear(in_features=256, out_features=64, bias=True),
+            ReLU(),
+            Linear(in_features=64, out_features=16, bias=True),
+            ReLU(),
+            Linear(in_features=16, out_features=1, bias=True),
+            Sigmoid(),
+        ]
+        self.model = Sequential(*layers)
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        self.loss_function = BCELoss()
+        if self.device == "cuda":
+            self.model.cuda()
+
+    def update(self, features_batch: Tensor, targets_batch: Tensor) -> float:
+        predicted_selection = self.forward(features_batch)
+        loss = self.loss_function(predicted_selection, targets_batch)  # .reshape(-1,1))
+        self.run_backpropagation(loss)
+        return loss.item()
+
+

Ancestors

+ +

Methods

+
+
+def update(self, features_batch: torch.Tensor, targets_batch: torch.Tensor) ‑> float +
+
+
+
+ +Expand source code + +
def update(self, features_batch: Tensor, targets_batch: Tensor) -> float:
+    predicted_selection = self.forward(features_batch)
+    loss = self.loss_function(predicted_selection, targets_batch)  # .reshape(-1,1))
+    self.run_backpropagation(loss)
+    return loss.item()
+
+
+
+

Inherited members

+ +
+
+
+
+ +
+ + + \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/neural_networks/deep_q_network.html b/docs/api/deeprecsys/neural_networks/deep_q_network.html similarity index 57% rename from docs/api/deeprecsys/rl/neural_networks/deep_q_network.html rename to docs/api/deeprecsys/neural_networks/deep_q_network.html index 9608060..41cbd24 100644 --- a/docs/api/deeprecsys/rl/neural_networks/deep_q_network.html +++ b/docs/api/deeprecsys/neural_networks/deep_q_network.html @@ -3,8 +3,8 @@ - -deeprecsys.rl.neural_networks.deep_q_network API documentation + +deeprecsys.neural_networks.deep_q_network API documentation @@ -19,24 +19,26 @@
-

Module deeprecsys.rl.neural_networks.deep_q_network

+

Module deeprecsys.neural_networks.deep_q_network

Expand source code -
from torch import FloatTensor, max, LongTensor, BoolTensor, gather, Tensor
+
from typing import Any, List, Optional, Tuple
+
 from numpy import array, ravel
-from torch.nn import Sequential, Linear, ReLU, MSELoss, Module
+from torch import BoolTensor, FloatTensor, LongTensor, Tensor, gather, max
+from torch.nn import Linear, Module, MSELoss, ReLU, Sequential
 from torch.optim import Adam
-from typing import List, Any, Tuple, Optional
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
 from deeprecsys.rl.learning_statistics import LearningStatistics
-from deeprecsys.rl.neural_networks.base_network import BaseNetwork
 
 
 def sequential_architecture(layers: List[int], bias: bool = True) -> Module:
-    """ Fully connected layers, with bias, and ReLU activation"""
+    """Fully connected layers, with bias, and ReLU activation"""
     architecture = []
     for i in range(len(layers) - 2):
         architecture.append(Linear(layers[i], layers[i + 1], bias=bias))
@@ -47,7 +49,8 @@ 
Module deeprecsys.rl.neural_networks.deep_q_networkModule deeprecsys.rl.neural_networks.deep_q_networkModule deeprecsys.rl.neural_networks.deep_q_networkModule deeprecsys.rl.neural_networks.deep_q_networkModule deeprecsys.rl.neural_networks.deep_q_networkModule deeprecsys.rl.neural_networks.deep_q_network
 
Functions

 

-

-def gather(input, dim, index, out=None, sparse_grad=False) ‑> torch.Tensor
-

-

-
Gathers values along an axis specified by dim.

-
For a 3-D tensor the output is specified by::

-
out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
-out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
-out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
-

-
If :attr:input is an n-dimensional tensor with size
-:math:(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})
-and dim = i, then :attr:index must be an :math:n-dimensional tensor with
-size :math:(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1}) where :math:y \geq 1
-and :attr:out will have the same size as :attr:index.

-
Args

-

-
input : Tensor

-
the source tensor

-
dim : int

-
the axis along which to index

-
index : LongTensor

-
the indices of elements to gather

-
out : Tensor, optional

-
the destination tensor

-

-
sparse_grad(bool,optional): If True, gradient w.r.t. :attr:input will be a sparse tensor.
-Example::

-
>>> t = torch.tensor([[1,2],[3,4]])
->>> torch.gather(t, 1, torch.tensor([[0,0],[1,0]]))
-tensor([[ 1,  1],
-        [ 4,  3]])
-

-

-

-def max(...)
-

-

-

-
Function: max(input) -> Tensor

-

-
Returns the maximum value of all elements in the :attr:input tensor.

-
Args

-
{input}
-Example::

-
>>> a = torch.randn(1, 3)
->>> a
-tensor([[ 0.6763,  0.7445, -2.2369]])
->>> torch.max(a)
-tensor(0.7445)
-

-

-
Function: max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)

-

-
Returns a namedtuple (values, indices) where values is the maximum
-value of each row of the :attr:input tensor in the given dimension
-:attr:dim. And indices is the index location of each maximum value found
-(argmax).

-
If :attr:keepdim is True, the output tensors are of the same size
-as :attr:input except in the dimension :attr:dim where they are of size 1.
-Otherwise, :attr:dim is squeezed (see :func:torch.squeeze), resulting
-in the output tensors having 1 fewer dimension than :attr:input.

-
Args

-

-
{input}

-
{dim}

-
{keepdim} Default: False.

-
out : tuple, optional

-
the result tuple of two output tensors (max, max_indices)

-

-
Example::

-
>>> a = torch.randn(4, 4)
->>> a
-tensor([[-1.2360, -0.2942, -0.1222,  0.8475],
-        [ 1.1949, -1.1127, -2.2379, -0.6702],
-        [ 1.5717, -0.9207,  0.1297, -1.8768],
-        [-0.6172,  1.0036, -0.6060, -0.2432]])
->>> torch.max(a, 1)
-torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1]))
-

-

-
Function: max(input, other, out=None) -> Tensor

-

-
Each element of the tensor :attr:input is compared with the corresponding
-element of the tensor :attr:other and an element-wise maximum is taken.

-
The shapes of :attr:input and :attr:other don't need to match,
-but they must be :ref:broadcastable <broadcasting-semantics>.

-
[ \text{out}_i = \max(\text{tensor}_i, \text{other}_i) ]

-

-
Note: When the shapes do not match, the shape of the returned output tensor

-
follows the :ref:broadcasting rules <broadcasting-semantics>.

-

-
Args

-

-
input : Tensor

-
the input tensor.

-
other : Tensor

-
the second input tensor

-
out : Tensor, optional

-
the output tensor.

-

-
Example::

-
>>> a = torch.randn(4)
->>> a
-tensor([ 0.2942, -0.7416,  0.2653, -0.1584])
->>> b = torch.randn(4)
->>> b
-tensor([ 0.8722, -1.7421, -0.4141, -0.5055])
->>> torch.max(a, b)
-tensor([ 0.8722, -0.7416,  0.2653, -0.1584])
-

-

-

+

 def sequential_architecture(layers: List[int], bias: bool = True) ‑> torch.nn.modules.module.Module
 

 

@@ -234,7 +124,7 @@ 
Args

 Expand source code
 
 
def sequential_architecture(layers: List[int], bias: bool = True) -> Module:
-    """ Fully connected layers, with bias, and ReLU activation"""
+    """Fully connected layers, with bias, and ReLU activation"""
     architecture = []
     for i in range(len(layers) - 2):
         architecture.append(Linear(layers[i], layers[i + 1], bias=bias))
@@ -248,21 +138,22 @@ 
Args

 

 
Classes

 

-

+

 class DeepQNetwork
-(learning_rate: float, architecture: torch.nn.modules.module.Module, discount_factor: float = 0.99, statistics: Union[LearningStatistics, NoneType] = None)
+(learning_rate: float, architecture: torch.nn.modules.module.Module, discount_factor: float = 0.99, statistics: Optional[LearningStatistics] = None)
 

 

 
Implementation of a Deep Q Network with a Sequential arquitecture. Layers are
 supposed to be provided as a list of torch modules.

-
Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
Create the deep-Q network with the provided parameters.

 

 
 Expand source code
 
 
class DeepQNetwork(BaseNetwork):
     """Implementation of a Deep Q Network with a Sequential arquitecture. Layers are
-    supposed to be provided as a list of torch modules."""
+    supposed to be provided as a list of torch modules.
+    """
 
     def __init__(
         self,
@@ -271,6 +162,7 @@ 
Classes

         discount_factor: float = 0.99,
         statistics: Optional[LearningStatistics] = None,
     ):
+        """Create the deep-Q network with the provided parameters."""
         super().__init__()
         self.model = architecture
         self.discount_factor = discount_factor
@@ -280,6 +172,7 @@ 
Classes

             self.model.cuda()
 
     def best_action_for_state(self, state: Any) -> Any:
+        """Return the action with the highest estimated Q-value for the given state"""
         if type(state) is tuple:
             state = array([ravel(s) for s in state])
         state_tensor = FloatTensor(state).to(device=self.device)
@@ -287,7 +180,8 @@ 
Classes

         best_action = max(q_values, dim=-1)[1].item()
         return best_action
 
-    def learn_from(self, experiences: List[Tuple]):
+    def learn_from(self, experiences: List[Tuple]) -> None:
+        """Run the backpropagation from a batch of experiences"""
         self.optimizer.zero_grad()
         loss = self._calculate_loss(experiences)
         loss.backward()
@@ -304,16 +198,12 @@ 
Classes

         state_tensors = FloatTensor(states).to(device=self.device)
         next_state_tensors = FloatTensor(next_states).to(device=self.device)
         reward_tensors = FloatTensor(rewards).to(device=self.device).reshape(-1, 1)
-        action_tensors = (
-            LongTensor(array(actions)).reshape(-1, 1).to(device=self.device)
-        )
+        action_tensors = LongTensor(array(actions)).reshape(-1, 1).to(device=self.device)
         done_tensors = BoolTensor(dones).to(device=self.device)
         actions_for_states = self.model(state_tensors)
         q_vals = gather(actions_for_states, 1, action_tensors)
         next_actions = [self.best_action_for_state(s) for s in next_states]
-        next_action_tensors = (
-            LongTensor(next_actions).reshape(-1, 1).to(device=self.device)
-        )
+        next_action_tensors = LongTensor(next_actions).reshape(-1, 1).to(device=self.device)
         q_vals_next = gather(self.model(next_state_tensors), 1, next_action_tensors)
         q_vals_next[done_tensors] = 0
         expected_q_vals = self.discount_factor * q_vals_next + reward_tensors
@@ -321,21 +211,22 @@ 
Classes

 

 
Ancestors

 
 
Methods

 

-

+

 def best_action_for_state(self, state: Any) ‑> Any
 

 

-

+
Return the action with the highest estimated Q-value for the given state

 

 
 Expand source code
 
 
def best_action_for_state(self, state: Any) -> Any:
+    """Return the action with the highest estimated Q-value for the given state"""
     if type(state) is tuple:
         state = array([ravel(s) for s in state])
     state_tensor = FloatTensor(state).to(device=self.device)
@@ -344,16 +235,17 @@ 
Methods

     return best_action

 

 

-

-def learn_from(self, experiences: List[Tuple])
+

+def learn_from(self, experiences: List[Tuple]) ‑> None
 

 

-

+
Run the backpropagation from a batch of experiences

 

 
 Expand source code
 
-
def learn_from(self, experiences: List[Tuple]):
+
def learn_from(self, experiences: List[Tuple]) -> None:
+    """Run the backpropagation from a batch of experiences"""
     self.optimizer.zero_grad()
     loss = self._calculate_loss(experiences)
     loss.backward()
@@ -369,13 +261,15 @@ 
Methods

 

 
Inherited members

 
@@ -391,23 +285,21 @@ 
Index
\ No newline at end of file diff --git a/docs/api/deeprecsys/rl/neural_networks/dueling.html b/docs/api/deeprecsys/neural_networks/dueling.html similarity index 64% rename from docs/api/deeprecsys/rl/neural_networks/dueling.html rename to docs/api/deeprecsys/neural_networks/dueling.html index 648a120..0eb3a95 100644 --- a/docs/api/deeprecsys/rl/neural_networks/dueling.html +++ b/docs/api/deeprecsys/neural_networks/dueling.html @@ -3,8 +3,8 @@ - -deeprecsys.rl.neural_networks.dueling API documentation + +deeprecsys.neural_networks.dueling API documentation @@ -19,28 +19,30 @@
-

Module deeprecsys.rl.neural_networks.dueling

+

Module deeprecsys.neural_networks.dueling

Expand source code -
from torch import FloatTensor, LongTensor, BoolTensor, gather, Tensor
+
from typing import Any, List, Optional, Tuple
+
 from numpy import array, ravel
-from torch.nn import Module, ReLU, Linear, Sequential, functional
+from torch import BoolTensor, FloatTensor, LongTensor, Tensor, gather
+from torch.nn import Linear, Module, ReLU, Sequential, functional
 from torch.optim import Adam
-from typing import List, Any, Tuple, Optional
-from deeprecsys.rl.neural_networks.noisy_layer import NoisyLayer
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
+from deeprecsys.neural_networks.noisy_layer import NoisyLayer
 from deeprecsys.rl.experience_replay.priority_replay_buffer import (
     PrioritizedExperienceReplayBuffer,
 )
 from deeprecsys.rl.learning_statistics import LearningStatistics
-from deeprecsys.rl.neural_networks.base_network import BaseNetwork
 
 
 class DuelingDDQN(BaseNetwork):
-    """ Dueling DQN with Double DQN and Noisy Networks """
+    """Dueling DQN with Double DQN and Noisy Networks"""
 
     def __init__(
         self,
@@ -52,6 +54,7 @@ 
Module deeprecsys.rl.neural_networks.dueling

         discount_factor: float = 0.99,
         statistics: Optional[LearningStatistics] = None,
     ):
+        """Initialize the network with the provided parameters"""
         super().__init__()
         if not hidden_layers:
             hidden_layers = [256, 256, 64, 64]
@@ -60,12 +63,12 @@ 
Module deeprecsys.rl.neural_networks.dueling

         self.optimizer = Adam(self.parameters(), lr=learning_rate)
         self.statistics = statistics
 
-    def _build_network(
-        self, n_input: int, n_output: int, noise_sigma: float, hidden_layers: List[int]
-    ):
-        """Builds the dueling network with noisy layers, the value
-        subnet and the advantage subnet. TODO: add `.to_device()` to Modules"""
-        assert len(hidden_layers) == 4
+    def _build_network(self, n_input: int, n_output: int, noise_sigma: float, hidden_layers: List[int]) -> None:
+        """Build the dueling network with noisy layers, the value
+        subnet and the advantage subnet. TODO: add `.to_device()` to Modules
+        """
+        if len(hidden_layers) != 4:
+            raise ValueError("Unexpected amount of layers")
         fc_1, fc_2, value_size, advantage_size = hidden_layers
         self.fully_connected_1 = Linear(n_input, fc_1, bias=True)
         self.fully_connected_2 = NoisyLayer(fc_1, fc_2, bias=True, sigma=noise_sigma)
@@ -80,8 +83,8 @@ 
Module deeprecsys.rl.neural_networks.dueling

             Linear(advantage_size, n_output, bias=True),
         )
 
-    def forward(self, state):
-        """Calculates the forward between the layers"""
+    def forward(self, state: Tensor) -> Tensor:
+        """Calculate the forward between the layers"""
         layer_1_out = functional.relu(self.fully_connected_1(state))
         layer_2_out = functional.relu(self.fully_connected_2(layer_1_out))
         value_of_state = self.value_subnet(layer_2_out)
@@ -89,86 +92,69 @@ 
Module deeprecsys.rl.neural_networks.dueling

         # This is the Dueling DQN part
         # Combines V and A to get Q: Q(s,a) = V(s) + (A(s,a) - 1/|A| * sum A(s,a'))
         if len(state.shape) == 2:
-            q_values = value_of_state + (
-                advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True)
-            )
+            q_values = value_of_state + (advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True))
         else:
             q_values = value_of_state + (advantage_of_state - advantage_of_state.mean())
         return q_values
 
     def get_q_values(self, state: Any) -> Tensor:
+        """Run the state through the network and return the Q-value for each action."""
         if type(state) is tuple:
             state = array([ravel(s) for s in state])
         state_tensor = FloatTensor(state).to(device=self.device)
         return self.forward(state_tensor)
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+        """Get the top K actions ranked by their estimated Q-value."""
         q_values = self.get_q_values(state)
         _, top_indices = q_values.topk(k=k)
         return [int(v) for v in top_indices.detach().numpy()]  # TODO: cpu() ?
 
-    def learn_with(
-        self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module
-    ):
+    def learn_with(self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module) -> None:
+        """Train the target network using the replay buffer."""
         experiences = buffer.sample_batch()
         self.optimizer.zero_grad()
-        td_error, weights = self._calculate_td_error_and_weigths(
-            experiences, target_network
-        )
+        td_error, weights = self._calculate_td_error_and_weigths(experiences, target_network)
         loss = (td_error.pow(2) * weights).mean().to(self.device)
         loss.backward()
         self.optimizer.step()
         # store loss in statistics
         if self.statistics:
             if self.device == "cuda":
-                self.statistics.append_metric(
-                    "loss", float(loss.detach().cpu().numpy())
-                )
+                self.statistics.append_metric("loss", float(loss.detach().cpu().numpy()))
             else:
                 self.statistics.append_metric("loss", float(loss.detach().numpy()))
         # update buffer priorities
-        errors_from_batch = td_error.detach().cpu().numpy()
+        errors_from_batch = td_error.detach().cpu().numpy().flatten()
         buffer.update_priorities(experiences, errors_from_batch)
 
     def _calculate_td_error_and_weigths(
         self, experiences: List[Tuple], target_network: Module
     ) -> Tuple[Tensor, Tensor]:
-        states, actions, rewards, dones, next_states, weights, samples = [
-            i for i in experiences
-        ]
+        states, actions, rewards, dones, next_states, weights, samples = [i for i in experiences]
         # convert to tensors
         state_tensors = FloatTensor(states).to(device=self.device)
         next_state_tensors = FloatTensor(next_states).to(device=self.device)
         reward_tensors = FloatTensor(rewards).to(device=self.device).reshape(-1, 1)
-        action_tensors = (
-            LongTensor(array(actions)).reshape(-1, 1).to(device=self.device)
-        )
+        action_tensors = LongTensor(array(actions)).reshape(-1, 1).to(device=self.device)
         done_tensors = BoolTensor(dones).to(device=self.device)
         weight_tensors = FloatTensor(weights).to(device=self.device)
         # the following logic is the DDQN update
         # Then we get the predicted actions for the states that came next
         # (using the main network)
-        actions_for_next_states = [
-            self.top_k_actions_for_state(s)[0] for s in next_state_tensors
-        ]
-        actions_for_next_states_tensor = (
-            LongTensor(actions_for_next_states).reshape(-1, 1).to(device=self.device)
-        )
+        actions_for_next_states = [self.top_k_actions_for_state(s)[0] for s in next_state_tensors]
+        actions_for_next_states_tensor = LongTensor(actions_for_next_states).reshape(-1, 1).to(device=self.device)
         # Then we use them to get the estimated Q Values for these next states/actions,
         # according to the target network. Remember that the target network is a copy
         # of this one taken some steps ago
         next_q_values = target_network.forward(next_state_tensors)
         # now we get the q values for the actions that were predicted for the next state
         # we call detach() so no gradient will be backpropagated along this variable
-        next_q_values_for_actions = gather(
-            next_q_values, 1, actions_for_next_states_tensor
-        ).detach()
+        next_q_values_for_actions = gather(next_q_values, 1, actions_for_next_states_tensor).detach()
         # zero value for done timesteps
         next_q_values_for_actions[done_tensors] = 0
         # bellman equation
-        expected_q_values = (
-            self.discount_factor * next_q_values_for_actions + reward_tensors
-        )
+        expected_q_values = self.discount_factor * next_q_values_for_actions + reward_tensors
         # Then get the Q-Values of the main network for the selected actions
         q_values = gather(self.forward(state_tensors), 1, action_tensors)
         # And compare them (this is the time-difference or TD error)
@@ -181,60 +167,23 @@ 
Module deeprecsys.rl.neural_networks.dueling

 

 

 

-
Functions

-

-

-def gather(input, dim, index, out=None, sparse_grad=False) ‑> torch.Tensor
-

-

-
Gathers values along an axis specified by dim.

-
For a 3-D tensor the output is specified by::

-
out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
-out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
-out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2
-

-
If :attr:input is an n-dimensional tensor with size
-:math:(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})
-and dim = i, then :attr:index must be an :math:n-dimensional tensor with
-size :math:(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1}) where :math:y \geq 1
-and :attr:out will have the same size as :attr:index.

-
Args

-

-
input : Tensor

-
the source tensor

-
dim : int

-
the axis along which to index

-
index : LongTensor

-
the indices of elements to gather

-
out : Tensor, optional

-
the destination tensor

-

-
sparse_grad(bool,optional): If True, gradient w.r.t. :attr:input will be a sparse tensor.
-Example::

-
>>> t = torch.tensor([[1,2],[3,4]])
->>> torch.gather(t, 1, torch.tensor([[0,0],[1,0]]))
-tensor([[ 1,  1],
-        [ 4,  3]])
-

-

-

 

 

 
Classes

 

-

+

 class DuelingDDQN
-(n_input: int, n_output: int, learning_rate: float, hidden_layers: List[int] = None, noise_sigma: float = 0.17, discount_factor: float = 0.99, statistics: Union[LearningStatistics, NoneType] = None)
+(n_input: int, n_output: int, learning_rate: float, hidden_layers: List[int] = None, noise_sigma: float = 0.17, discount_factor: float = 0.99, statistics: Optional[LearningStatistics] = None)
 

 

-
Dueling DQN with Double DQN and Noisy Networks 

-
Initializes internal Module state, shared by both nn.Module and ScriptModule.

+
Dueling DQN with Double DQN and Noisy Networks

+
Initialize the network with the provided parameters

 

 
 Expand source code
 
 
class DuelingDDQN(BaseNetwork):
-    """ Dueling DQN with Double DQN and Noisy Networks """
+    """Dueling DQN with Double DQN and Noisy Networks"""
 
     def __init__(
         self,
@@ -246,6 +195,7 @@ 
Classes

         discount_factor: float = 0.99,
         statistics: Optional[LearningStatistics] = None,
     ):
+        """Initialize the network with the provided parameters"""
         super().__init__()
         if not hidden_layers:
             hidden_layers = [256, 256, 64, 64]
@@ -254,12 +204,12 @@ 
Classes

         self.optimizer = Adam(self.parameters(), lr=learning_rate)
         self.statistics = statistics
 
-    def _build_network(
-        self, n_input: int, n_output: int, noise_sigma: float, hidden_layers: List[int]
-    ):
-        """Builds the dueling network with noisy layers, the value
-        subnet and the advantage subnet. TODO: add `.to_device()` to Modules"""
-        assert len(hidden_layers) == 4
+    def _build_network(self, n_input: int, n_output: int, noise_sigma: float, hidden_layers: List[int]) -> None:
+        """Build the dueling network with noisy layers, the value
+        subnet and the advantage subnet. TODO: add `.to_device()` to Modules
+        """
+        if len(hidden_layers) != 4:
+            raise ValueError("Unexpected amount of layers")
         fc_1, fc_2, value_size, advantage_size = hidden_layers
         self.fully_connected_1 = Linear(n_input, fc_1, bias=True)
         self.fully_connected_2 = NoisyLayer(fc_1, fc_2, bias=True, sigma=noise_sigma)
@@ -274,8 +224,8 @@ 
Classes

             Linear(advantage_size, n_output, bias=True),
         )
 
-    def forward(self, state):
-        """Calculates the forward between the layers"""
+    def forward(self, state: Tensor) -> Tensor:
+        """Calculate the forward between the layers"""
         layer_1_out = functional.relu(self.fully_connected_1(state))
         layer_2_out = functional.relu(self.fully_connected_2(layer_1_out))
         value_of_state = self.value_subnet(layer_2_out)
@@ -283,86 +233,69 @@ 
Classes

         # This is the Dueling DQN part
         # Combines V and A to get Q: Q(s,a) = V(s) + (A(s,a) - 1/|A| * sum A(s,a'))
         if len(state.shape) == 2:
-            q_values = value_of_state + (
-                advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True)
-            )
+            q_values = value_of_state + (advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True))
         else:
             q_values = value_of_state + (advantage_of_state - advantage_of_state.mean())
         return q_values
 
     def get_q_values(self, state: Any) -> Tensor:
+        """Run the state through the network and return the Q-value for each action."""
         if type(state) is tuple:
             state = array([ravel(s) for s in state])
         state_tensor = FloatTensor(state).to(device=self.device)
         return self.forward(state_tensor)
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+        """Get the top K actions ranked by their estimated Q-value."""
         q_values = self.get_q_values(state)
         _, top_indices = q_values.topk(k=k)
         return [int(v) for v in top_indices.detach().numpy()]  # TODO: cpu() ?
 
-    def learn_with(
-        self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module
-    ):
+    def learn_with(self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module) -> None:
+        """Train the target network using the replay buffer."""
         experiences = buffer.sample_batch()
         self.optimizer.zero_grad()
-        td_error, weights = self._calculate_td_error_and_weigths(
-            experiences, target_network
-        )
+        td_error, weights = self._calculate_td_error_and_weigths(experiences, target_network)
         loss = (td_error.pow(2) * weights).mean().to(self.device)
         loss.backward()
         self.optimizer.step()
         # store loss in statistics
         if self.statistics:
             if self.device == "cuda":
-                self.statistics.append_metric(
-                    "loss", float(loss.detach().cpu().numpy())
-                )
+                self.statistics.append_metric("loss", float(loss.detach().cpu().numpy()))
             else:
                 self.statistics.append_metric("loss", float(loss.detach().numpy()))
         # update buffer priorities
-        errors_from_batch = td_error.detach().cpu().numpy()
+        errors_from_batch = td_error.detach().cpu().numpy().flatten()
         buffer.update_priorities(experiences, errors_from_batch)
 
     def _calculate_td_error_and_weigths(
         self, experiences: List[Tuple], target_network: Module
     ) -> Tuple[Tensor, Tensor]:
-        states, actions, rewards, dones, next_states, weights, samples = [
-            i for i in experiences
-        ]
+        states, actions, rewards, dones, next_states, weights, samples = [i for i in experiences]
         # convert to tensors
         state_tensors = FloatTensor(states).to(device=self.device)
         next_state_tensors = FloatTensor(next_states).to(device=self.device)
         reward_tensors = FloatTensor(rewards).to(device=self.device).reshape(-1, 1)
-        action_tensors = (
-            LongTensor(array(actions)).reshape(-1, 1).to(device=self.device)
-        )
+        action_tensors = LongTensor(array(actions)).reshape(-1, 1).to(device=self.device)
         done_tensors = BoolTensor(dones).to(device=self.device)
         weight_tensors = FloatTensor(weights).to(device=self.device)
         # the following logic is the DDQN update
         # Then we get the predicted actions for the states that came next
         # (using the main network)
-        actions_for_next_states = [
-            self.top_k_actions_for_state(s)[0] for s in next_state_tensors
-        ]
-        actions_for_next_states_tensor = (
-            LongTensor(actions_for_next_states).reshape(-1, 1).to(device=self.device)
-        )
+        actions_for_next_states = [self.top_k_actions_for_state(s)[0] for s in next_state_tensors]
+        actions_for_next_states_tensor = LongTensor(actions_for_next_states).reshape(-1, 1).to(device=self.device)
         # Then we use them to get the estimated Q Values for these next states/actions,
         # according to the target network. Remember that the target network is a copy
         # of this one taken some steps ago
         next_q_values = target_network.forward(next_state_tensors)
         # now we get the q values for the actions that were predicted for the next state
         # we call detach() so no gradient will be backpropagated along this variable
-        next_q_values_for_actions = gather(
-            next_q_values, 1, actions_for_next_states_tensor
-        ).detach()
+        next_q_values_for_actions = gather(next_q_values, 1, actions_for_next_states_tensor).detach()
         # zero value for done timesteps
         next_q_values_for_actions[done_tensors] = 0
         # bellman equation
-        expected_q_values = (
-            self.discount_factor * next_q_values_for_actions + reward_tensors
-        )
+        expected_q_values = self.discount_factor * next_q_values_for_actions + reward_tensors
         # Then get the Q-Values of the main network for the selected actions
         q_values = gather(self.forward(state_tensors), 1, action_tensors)
         # And compare them (this is the time-difference or TD error)
@@ -371,22 +304,22 @@ 
Classes

 

 
Ancestors

 
 
Methods

 

-

-def forward(self, state)
+

+def forward(self, state: torch.Tensor) ‑> torch.Tensor
 

 

-
Calculates the forward between the layers

+
Calculate the forward between the layers

 

 
 Expand source code
 
-
def forward(self, state):
-    """Calculates the forward between the layers"""
+
def forward(self, state: Tensor) -> Tensor:
+    """Calculate the forward between the layers"""
     layer_1_out = functional.relu(self.fully_connected_1(state))
     layer_2_out = functional.relu(self.fully_connected_2(layer_1_out))
     value_of_state = self.value_subnet(layer_2_out)
@@ -394,73 +327,68 @@ 
Methods

     # This is the Dueling DQN part
     # Combines V and A to get Q: Q(s,a) = V(s) + (A(s,a) - 1/|A| * sum A(s,a'))
     if len(state.shape) == 2:
-        q_values = value_of_state + (
-            advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True)
-        )
+        q_values = value_of_state + (advantage_of_state - advantage_of_state.mean(dim=1, keepdim=True))
     else:
         q_values = value_of_state + (advantage_of_state - advantage_of_state.mean())
     return q_values

 

 

-

+

 def get_q_values(self, state: Any) ‑> torch.Tensor
 

 

-

+
Run the state through the network and return the Q-value for each action.

 

 
 Expand source code
 
 
def get_q_values(self, state: Any) -> Tensor:
+    """Run the state through the network and return the Q-value for each action."""
     if type(state) is tuple:
         state = array([ravel(s) for s in state])
     state_tensor = FloatTensor(state).to(device=self.device)
     return self.forward(state_tensor)

 

 

-

-def learn_with(self, buffer: PrioritizedExperienceReplayBuffer, target_network: torch.nn.modules.module.Module)
+

+def learn_with(self, buffer: PrioritizedExperienceReplayBuffer, target_network: torch.nn.modules.module.Module) ‑> None
 

 

-

+
Train the target network using the replay buffer.

 

 
 Expand source code
 
-
def learn_with(
-    self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module
-):
+
def learn_with(self, buffer: PrioritizedExperienceReplayBuffer, target_network: Module) -> None:
+    """Train the target network using the replay buffer."""
     experiences = buffer.sample_batch()
     self.optimizer.zero_grad()
-    td_error, weights = self._calculate_td_error_and_weigths(
-        experiences, target_network
-    )
+    td_error, weights = self._calculate_td_error_and_weigths(experiences, target_network)
     loss = (td_error.pow(2) * weights).mean().to(self.device)
     loss.backward()
     self.optimizer.step()
     # store loss in statistics
     if self.statistics:
         if self.device == "cuda":
-            self.statistics.append_metric(
-                "loss", float(loss.detach().cpu().numpy())
-            )
+            self.statistics.append_metric("loss", float(loss.detach().cpu().numpy()))
         else:
             self.statistics.append_metric("loss", float(loss.detach().numpy()))
     # update buffer priorities
-    errors_from_batch = td_error.detach().cpu().numpy()
+    errors_from_batch = td_error.detach().cpu().numpy().flatten()
     buffer.update_priorities(experiences, errors_from_batch)

 

 

-

+

 def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> List[int]
 

 

-

+
Get the top K actions ranked by their estimated Q-value.

 

 
 Expand source code
 
 
def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+    """Get the top K actions ranked by their estimated Q-value."""
     q_values = self.get_q_values(state)
     _, top_indices = q_values.topk(k=k)
     return [int(v) for v in top_indices.detach().numpy()]  # TODO: cpu() ?

@@ -469,12 +397,14 @@ 
Methods

 

 
Inherited members

 
@@ -490,23 +420,18 @@ 
Index

 
    
 
  • Super-module
    
 
-
    • 
-
  • Functions
    
-
 
    • 
 
  • Classes
    
 
@@ -515,7 +440,7 @@ 
    
-
    Generated by pdoc 0.9.2.
    
+
    Generated by pdoc 0.10.0.
    
 
 
 
\ No newline at end of file
diff --git a/docs/api/deeprecsys/rl/neural_networks/gaussian_actor.html b/docs/api/deeprecsys/neural_networks/gaussian_actor.html
similarity index 67%
rename from docs/api/deeprecsys/rl/neural_networks/gaussian_actor.html
rename to docs/api/deeprecsys/neural_networks/gaussian_actor.html
index c39507b..4ca6325 100644
--- a/docs/api/deeprecsys/rl/neural_networks/gaussian_actor.html
+++ b/docs/api/deeprecsys/neural_networks/gaussian_actor.html
@@ -3,8 +3,8 @@
 
 
 
-
-deeprecsys.rl.neural_networks.gaussian_actor API documentation
+
+deeprecsys.neural_networks.gaussian_actor API documentation
 
 
 
@@ -19,21 +19,24 @@
 
    
 
    
 
    
-
    Module deeprecsys.rl.neural_networks.gaussian_actor
    
+
    Module deeprecsys.neural_networks.gaussian_actor
    
 
    
 
    
 
    
 
 Expand source code
 
-
    from deeprecsys.rl.neural_networks.base_network import BaseNetwork
-from deeprecsys.rl.neural_networks.deep_q_network import sequential_architecture
+
    from typing import Tuple
+
 import torch
-from torch.distributions import Normal
-import numpy as np
+from numpy import ndarray
 from torch import FloatTensor, Tensor
+from torch.distributions import Normal
 from torch.optim import Adam
-from deeprecsys.rl.neural_networks.q_value_estimator import TwinnedQValueEstimator
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
+from deeprecsys.neural_networks.deep_q_network import sequential_architecture
+from deeprecsys.neural_networks.q_value_estimator import TwinnedQValueEstimator
 
 LOG_STD_MAX = 2
 LOG_STD_MIN = -20
@@ -41,6 +44,8 @@ 
    Module deeprecsys.rl.neural_networks.gaussian_actorModule deeprecsys.rl.neural_networks.gaussian_actorModule deeprecsys.rl.neural_networks.gaussian_actorModule deeprecsys.rl.neural_networks.gaussian_actorModule deeprecsys.rl.neural_networks.gaussian_actorModule deeprecsys.rl.neural_networks.gaussian_actor
 
    Classes
    
 
    
-
    
+
    
 class GaussianActor
 (inputs: int, outputs: int, learning_rate: float = 0.001, entropy_coefficient: float = 0.2, discount_factor: float = 0.99)
 
    
 
    
-
    Base class for all neural network modules.
    
-
    Your models should also subclass this class.
    
-
    Modules can also contain other Modules, allowing to nest them in
-a tree structure. You can assign the submodules as regular attributes::
    
-
    import torch.nn as nn
-import torch.nn.functional as F
-
-class Model(nn.Module):
-    def __init__(self):
-        super(Model, self).__init__()
-        self.conv1 = nn.Conv2d(1, 20, 5)
-        self.conv2 = nn.Conv2d(20, 20, 5)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        return F.relu(self.conv2(x))
-
    
-
    Submodules assigned in this way will be registered, and will have their
-parameters converted too when you call :meth:to, etc.
    
-
    Initializes internal Module state, shared by both nn.Module and ScriptModule.
    
+
    Actor network for the soft-actor-critic agent.
    
+
    Create the actor network with the provided parameters.
    
 
    
 
 Expand source code
 
 
    class GaussianActor(BaseNetwork):
+    """Actor network for the soft-actor-critic agent."""
+
     def __init__(
         self,
         inputs: int,
@@ -166,6 +159,7 @@ 
    Classes
    
         entropy_coefficient: float = 0.2,
         discount_factor: float = 0.99,
     ):
+        """Create the actor network with the provided parameters."""
         super().__init__()
         network_output = outputs * 2  # estimation of means and standard deviations
         layers = [inputs] + [inputs * 2, inputs * 2] + [network_output]
@@ -175,21 +169,23 @@ 
    Classes
    
         self.alpha = torch.tensor(entropy_coefficient).to(self.device)
         self.gamma = discount_factor
 
-    def forward(self, states: FloatTensor):
+    def forward(self, states: FloatTensor) -> Tuple:
+        """Forward the given state in the network and return the output"""
         mean, log_std = torch.chunk(self.model(states), 2, dim=-1)
         log_std = torch.clamp(log_std, min=LOG_STD_MIN, max=LOG_STD_MAX)
         return mean, log_std
 
-    def predict(self, states: np.array):
+    def predict(self, states: ndarray) -> Tuple:
+        """Predict the next best actions for the given state."""
         states_tensor = FloatTensor(states).to(device=self.device)
-        # calculate Gaussian distribusion of (mean, std)
+        # calculate Gaussian distribution of (mean, std)
         means, log_stds = self.forward(states_tensor)
         stds = log_stds.exp()
         normals = Normal(means, stds)
         # sample actions
         xs = normals.rsample()
         actions = torch.tanh(xs)
-        # calculate entropies
+        # calculate entropy
         log_probs = normals.log_prob(xs) - torch.log(1 - actions.pow(2) + EPSILON)
         entropies = -log_probs.sum(dim=1, keepdim=True)
         return actions, entropies, torch.tanh(means)
@@ -204,7 +200,7 @@ 
    Classes
    
         weights: Tensor,
         critic: TwinnedQValueEstimator,
     ) -> Tensor:
-        """ Calculates the loss, backpropagates, and returns the entropy. """
+        """Calculate the loss, backpropagates, and returns the entropy."""
         # We re-sample actions to calculate expectations of Q.
         sampled_action, entropy, _ = self.predict(states)
         # expectations of Q with clipped double Q technique
@@ -225,6 +221,7 @@ 
    Classes
    
         dones: Tensor,
         target_critic: TwinnedQValueEstimator,
     ) -> Tensor:
+        """Estimate the Q-value for the next states"""
         with torch.no_grad():
             # actor samples next actions
             next_actions, next_entropies, _ = self.predict(next_states)
@@ -237,16 +234,16 @@ 
    Classes
    
 
    
 
    Ancestors
    
 
 
    Methods
    
 
    
-
    
-def calculate_loss(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, dones: torch.Tensor, next_states: torch.Tensor, weights: torch.Tensor, critic: TwinnedQValueEstimator) ‑> torch.Tensor
+
    
+def calculate_loss(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, dones: torch.Tensor, next_states: torch.Tensor, weights: torch.Tensor, critic: TwinnedQValueEstimator) ‑> torch.Tensor
 
    
 
    
-
    Calculates the loss, backpropagates, and returns the entropy.
    
+
    Calculate the loss, backpropagates, and returns the entropy.
    
 
    
 
 Expand source code
@@ -261,7 +258,7 @@ 
    Methods
    
     weights: Tensor,
     critic: TwinnedQValueEstimator,
 ) -> Tensor:
-    """ Calculates the loss, backpropagates, and returns the entropy. """
+    """Calculate the loss, backpropagates, and returns the entropy."""
     # We re-sample actions to calculate expectations of Q.
     sampled_action, entropy, _ = self.predict(states)
     # expectations of Q with clipped double Q technique
@@ -274,11 +271,11 @@ 
    Methods
    
     return entropy
    
 
    
 

-

-def calculate_target_q(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, next_states: torch.Tensor, dones: torch.Tensor, target_critic: TwinnedQValueEstimator) ‑> torch.Tensor
+

+def calculate_target_q(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, next_states: torch.Tensor, dones: torch.Tensor, target_critic: TwinnedQValueEstimator) ‑> torch.Tensor
 

 

-

+
Estimate the Q-value for the next states

 

 
 Expand source code
@@ -292,6 +289,7 @@ 
Methods

     dones: Tensor,
     target_critic: TwinnedQValueEstimator,
 ) -> Tensor:
+    """Estimate the Q-value for the next states"""
     with torch.no_grad():
         # actor samples next actions
         next_actions, next_entropies, _ = self.predict(next_states)
@@ -303,25 +301,42 @@ 
Methods

     return target_q
-
-def predict(self, states: ) +
+def forward(self, states: torch.FloatTensor) ‑> Tuple +
+
+

Forward the given state in the network and return the output

+
+ +Expand source code + +
def forward(self, states: FloatTensor) -> Tuple:
+    """Forward the given state in the network and return the output"""
+    mean, log_std = torch.chunk(self.model(states), 2, dim=-1)
+    log_std = torch.clamp(log_std, min=LOG_STD_MIN, max=LOG_STD_MAX)
+    return mean, log_std
+
+
+
+def predict(self, states: numpy.ndarray) ‑> Tuple
-
+

Predict the next best actions for the given state.

Expand source code -
def predict(self, states: np.array):
+
def predict(self, states: ndarray) -> Tuple:
+    """Predict the next best actions for the given state."""
     states_tensor = FloatTensor(states).to(device=self.device)
-    # calculate Gaussian distribusion of (mean, std)
+    # calculate Gaussian distribution of (mean, std)
     means, log_stds = self.forward(states_tensor)
     stds = log_stds.exp()
     normals = Normal(means, stds)
     # sample actions
     xs = normals.rsample()
     actions = torch.tanh(xs)
-    # calculate entropies
+    # calculate entropy
     log_probs = normals.log_prob(xs) - torch.log(1 - actions.pow(2) + EPSILON)
     entropies = -log_probs.sum(dim=1, keepdim=True)
     return actions, entropies, torch.tanh(means)

@@ -330,13 +345,14 @@ 
Methods

 
 
Inherited members

 
@@ -352,17 +368,18 @@ 
Index
\ No newline at end of file diff --git a/docs/api/deeprecsys/rl/neural_networks/index.html b/docs/api/deeprecsys/neural_networks/index.html similarity index 66% rename from docs/api/deeprecsys/rl/neural_networks/index.html rename to docs/api/deeprecsys/neural_networks/index.html index 4d1530e..1be6b98 100644 --- a/docs/api/deeprecsys/rl/neural_networks/index.html +++ b/docs/api/deeprecsys/neural_networks/index.html @@ -3,8 +3,8 @@ - -deeprecsys.rl.neural_networks API documentation + +deeprecsys.neural_networks API documentation @@ -19,42 +19,46 @@
-

Namespace deeprecsys.rl.neural_networks

+

Namespace deeprecsys.neural_networks

Sub-modules

-
deeprecsys.rl.neural_networks.base_network
+
deeprecsys.neural_networks.base_network
-
deeprecsys.rl.neural_networks.deep_q_network
+
deeprecsys.neural_networks.binary_classifier
-
deeprecsys.rl.neural_networks.dueling
+
deeprecsys.neural_networks.deep_q_network
-
deeprecsys.rl.neural_networks.gaussian_actor
+
deeprecsys.neural_networks.dueling
-
deeprecsys.rl.neural_networks.noisy_layer
+
deeprecsys.neural_networks.gaussian_actor
-
deeprecsys.rl.neural_networks.policy_estimator
+
deeprecsys.neural_networks.noisy_layer
-
deeprecsys.rl.neural_networks.q_value_estimator
+
deeprecsys.neural_networks.policy_estimator
-
deeprecsys.rl.neural_networks.value_estimator
+
deeprecsys.neural_networks.q_value_estimator
+
+
+
+
deeprecsys.neural_networks.value_estimator
@@ -75,26 +79,27 @@

Index

\ No newline at end of file diff --git a/docs/api/deeprecsys/rl/neural_networks/noisy_layer.html b/docs/api/deeprecsys/neural_networks/noisy_layer.html similarity index 69% rename from docs/api/deeprecsys/rl/neural_networks/noisy_layer.html rename to docs/api/deeprecsys/neural_networks/noisy_layer.html index d5ef861..59471bf 100644 --- a/docs/api/deeprecsys/rl/neural_networks/noisy_layer.html +++ b/docs/api/deeprecsys/neural_networks/noisy_layer.html @@ -3,8 +3,8 @@ - -deeprecsys.rl.neural_networks.noisy_layer API documentation + +deeprecsys.neural_networks.noisy_layer API documentation @@ -19,21 +19,33 @@
-

Module deeprecsys.rl.neural_networks.noisy_layer

+

Module deeprecsys.neural_networks.noisy_layer

Expand source code -
from torch.nn import Linear, Parameter, functional
-import torch
+
import torch
+from torch import Tensor
+from torch.nn import Linear, Parameter, functional
 
 
 class NoisyLayer(Linear):
+    """Special type of layer that adds random gaussian noise to the signal The gaussian noise parameters are
+    registered, and therefore the noise decreases over time. This is a better alternative to e-greedy exploration.
+    """
+
     def __init__(
-        self, in_features: int, out_features: int, bias=True, sigma: float = 0.017
-    ):
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        sigma: float = 0.017,
+    ) -> None:
+        """Create the layer with the given sigma weight. Registers epsilon as a parameter so that the network will
+        learn to reduce the noise.
+        """
         super().__init__(in_features, out_features, bias=bias)
         self.sigma_weight = Parameter(torch.full((out_features, in_features), sigma))
         self.register_buffer("epsilon_weight", torch.zeros(out_features, in_features))
@@ -41,19 +53,18 @@ 
Module deeprecsys.rl.neural_networks.noisy_layer<
             self.sigma_bias = Parameter(torch.full((out_features,), sigma))
             self.register_buffer("epsilon_bias", torch.zeros(out_features))
 
-    def forward(self, input):
+    def forward(self, input: Tensor) -> Tensor:
         """At every forward operation, feeds the weights and biases with normally
         distributed random variables with mean zero and std deviation 1. This means
         the bias and the weights will have a noise of:
-        sigma (constant) * epsilon (random in range(-1,1))"""
+        sigma (constant) * epsilon (random in range(-1,1))
+        """
         self.epsilon_weight.normal_()
         bias = self.bias
         if bias is not None:
             self.epsilon_bias.normal_()
             bias = bias + self.sigma_bias * self.epsilon_bias.clone()
-        return functional.linear(
-            input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias
-        )

+        return functional.linear(input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias)
@@ -65,58 +76,34 @@

Module deeprecsys.rl.neural_networks.noisy_layer<

Classes

-
+
class NoisyLayer -(in_features: int, out_features: int, bias=True, sigma: float = 0.017) +(in_features: int, out_features: int, bias: bool = True, sigma: float = 0.017)
-

Applies a linear transformation to the incoming data: :math:y = xA^T + b

-

Args

-
-
in_features
-
size of each input sample
-
out_features
-
size of each output sample
-
bias
-
If set to False, the layer will not learn an additive bias. -Default: True
-
-

Shape

-
    -
  • Input: :math:(N, *, H_{in}) where :math:* means any number of -additional dimensions and :math:H_{in} = \text{in\_features}
    • -
  • Output: :math:(N, *, H_{out}) where all but the last dimension -are the same shape as the input and :math:H_{out} = \text{out\_features}.
    • -
-

Attributes

-
-
weight
-
the learnable weights of the module of shape -:math:(\text{out\_features}, \text{in\_features}). The values are -initialized from :math:\mathcal{U}(-\sqrt{k}, \sqrt{k}), where -:math:k = \frac{1}{\text{in\_features}}
-
bias
-
the learnable bias of the module of shape :math:(\text{out\_features}). -If :attr:bias is True, the values are initialized from -:math:\mathcal{U}(-\sqrt{k}, \sqrt{k}) where -:math:k = \frac{1}{\text{in\_features}}
-
-

Examples::

-
>>> m = nn.Linear(20, 30)
->>> input = torch.randn(128, 20)
->>> output = m(input)
->>> print(output.size())
-torch.Size([128, 30])
-
-

Initializes internal Module state, shared by both nn.Module and ScriptModule.

+

Special type of layer that adds random gaussian noise to the signal The gaussian noise parameters are +registered, and therefore the noise decreases over time. This is a better alternative to e-greedy exploration.

+

Create the layer with the given sigma weight. Registers epsilon as a parameter so that the network will +learn to reduce the noise.

Expand source code 
class NoisyLayer(Linear):
+    """Special type of layer that adds random gaussian noise to the signal The gaussian noise parameters are
+    registered, and therefore the noise decreases over time. This is a better alternative to e-greedy exploration.
+    """
+
     def __init__(
-        self, in_features: int, out_features: int, bias=True, sigma: float = 0.017
-    ):
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        sigma: float = 0.017,
+    ) -> None:
+        """Create the layer with the given sigma weight. Registers epsilon as a parameter so that the network will
+        learn to reduce the noise.
+        """
         super().__init__(in_features, out_features, bias=bias)
         self.sigma_weight = Parameter(torch.full((out_features, in_features), sigma))
         self.register_buffer("epsilon_weight", torch.zeros(out_features, in_features))
@@ -124,19 +111,18 @@ 
Attributes

             self.sigma_bias = Parameter(torch.full((out_features,), sigma))
             self.register_buffer("epsilon_bias", torch.zeros(out_features))
 
-    def forward(self, input):
+    def forward(self, input: Tensor) -> Tensor:
         """At every forward operation, feeds the weights and biases with normally
         distributed random variables with mean zero and std deviation 1. This means
         the bias and the weights will have a noise of:
-        sigma (constant) * epsilon (random in range(-1,1))"""
+        sigma (constant) * epsilon (random in range(-1,1))
+        """
         self.epsilon_weight.normal_()
         bias = self.bias
         if bias is not None:
             self.epsilon_bias.normal_()
             bias = bias + self.sigma_bias * self.epsilon_bias.clone()
-        return functional.linear(
-            input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias
-        )
+ return functional.linear(input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias)

Ancestors

    @@ -145,8 +131,8 @@

    Ancestors

Methods

-
-def forward(self, input) +
+def forward(self, input: torch.Tensor) ‑> torch.Tensor

At every forward operation, feeds the weights and biases with normally @@ -157,19 +143,18 @@

Methods

Expand source code -
def forward(self, input):
+
def forward(self, input: Tensor) -> Tensor:
     """At every forward operation, feeds the weights and biases with normally
     distributed random variables with mean zero and std deviation 1. This means
     the bias and the weights will have a noise of:
-    sigma (constant) * epsilon (random in range(-1,1))"""
+    sigma (constant) * epsilon (random in range(-1,1))
+    """
     self.epsilon_weight.normal_()
     bias = self.bias
     if bias is not None:
         self.epsilon_bias.normal_()
         bias = bias + self.sigma_bias * self.epsilon_bias.clone()
-    return functional.linear(
-        input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias
-    )

+    return functional.linear(input, self.weight + self.sigma_weight * self.epsilon_weight.clone(), bias)
@@ -185,15 +170,15 @@

Index

  • Super-module

  • Classes

    @@ -202,7 +187,7 @@

    -

    Generated by pdoc 0.9.2.

    +

    Generated by pdoc 0.10.0.

    \ No newline at end of file diff --git a/docs/api/deeprecsys/neural_networks/policy_estimator.html b/docs/api/deeprecsys/neural_networks/policy_estimator.html new file mode 100644 index 0000000..156ce4f --- /dev/null +++ b/docs/api/deeprecsys/neural_networks/policy_estimator.html @@ -0,0 +1,305 @@ + + + + + + +deeprecsys.neural_networks.policy_estimator API documentation + + + + + + + + + + + +
    +
    +
    +

    Module deeprecsys.neural_networks.policy_estimator

    +
    +
    +
    + +Expand source code + +
    from typing import Any, List
+
+import numpy as np
+from torch import FloatTensor, Tensor, multinomial
+from torch import sum as torch_sum
+from torch.distributions import Categorical
+from torch.nn import Linear, Sequential, Softmax, Tanh
+from torch.optim import Adam
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
+
+
+class PolicyEstimator(BaseNetwork):
+    """Estimates the policy function: the probability of each action being the
+    best decision in a particular state.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_layers: List[int],
+        output_size: int,
+        learning_rate: float = 1e-2,
+    ):
+        """Create the neural network architecture for the policy estimator with the provided values."""
+        super().__init__()
+        layers = [input_size] + hidden_layers + [output_size]
+        architecture = []
+        for i in range(len(layers) - 2):
+            architecture.append(Linear(layers[i], layers[i + 1]))
+            architecture.append(Tanh())
+        architecture.append(Linear(layers[-2], layers[-1]))
+        architecture.append(Softmax(dim=-1))
+        self.model = Sequential(*architecture)
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        if self.device == "cuda":
+            self.model.cuda()
+
+    def action_probabilities(self, state: Any) -> Tensor:
+        """Return a map of each possible action, and the probability that that's the best action to take at
+        this step.
+        """
+        return self.model(FloatTensor(state))
+
+    def predict(self, state: Any, k: int = 1) -> List[int]:
+        """Given a state, uses the network output to choose the `k` best next actions according to the probability
+        distribution trained so far.
+        """
+        probabilities = self.action_probabilities(state)
+        prediction = multinomial(probabilities, num_samples=k, replacement=False)
+        if self.device == "cuda":
+            return prediction.detach().cpu().numpy()
+        else:
+            return prediction.detach().numpy()
+
+    def update(self, state: np.array, reward_baseline: Tensor, action: np.array) -> np.ndarray:
+        """Update the network with the given state, reward, and action taken."""
+        state_tensor = FloatTensor(state).to(device=self.device)
+        action_tensor = FloatTensor(np.array(action, dtype=np.float32)).to(device=self.device)
+        """ Update logic from the Policy Gradient theorem. """
+        action_probabilities = self.model(state_tensor)
+        action_distribution = Categorical(action_probabilities)
+        selected_log_probabilities = action_distribution.log_prob(action_tensor)
+        loss = torch_sum(-selected_log_probabilities * reward_baseline)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        if self.device == "cuda":
+            return loss.detach().cpu().numpy()
+        else:
+            return loss.detach().numpy()
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Classes

    +
    +
    +class PolicyEstimator +(input_size: int, hidden_layers: List[int], output_size: int, learning_rate: float = 0.01) +
    +
    +

    Estimates the policy function: the probability of each action being the +best decision in a particular state.

    +

    Create the neural network architecture for the policy estimator with the provided values.

    +
    + +Expand source code + +
    class PolicyEstimator(BaseNetwork):
+    """Estimates the policy function: the probability of each action being the
+    best decision in a particular state.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_layers: List[int],
+        output_size: int,
+        learning_rate: float = 1e-2,
+    ):
+        """Create the neural network architecture for the policy estimator with the provided values."""
+        super().__init__()
+        layers = [input_size] + hidden_layers + [output_size]
+        architecture = []
+        for i in range(len(layers) - 2):
+            architecture.append(Linear(layers[i], layers[i + 1]))
+            architecture.append(Tanh())
+        architecture.append(Linear(layers[-2], layers[-1]))
+        architecture.append(Softmax(dim=-1))
+        self.model = Sequential(*architecture)
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        if self.device == "cuda":
+            self.model.cuda()
+
+    def action_probabilities(self, state: Any) -> Tensor:
+        """Return a map of each possible action, and the probability that that's the best action to take at
+        this step.
+        """
+        return self.model(FloatTensor(state))
+
+    def predict(self, state: Any, k: int = 1) -> List[int]:
+        """Given a state, uses the network output to choose the `k` best next actions according to the probability
+        distribution trained so far.
+        """
+        probabilities = self.action_probabilities(state)
+        prediction = multinomial(probabilities, num_samples=k, replacement=False)
+        if self.device == "cuda":
+            return prediction.detach().cpu().numpy()
+        else:
+            return prediction.detach().numpy()
+
+    def update(self, state: np.array, reward_baseline: Tensor, action: np.array) -> np.ndarray:
+        """Update the network with the given state, reward, and action taken."""
+        state_tensor = FloatTensor(state).to(device=self.device)
+        action_tensor = FloatTensor(np.array(action, dtype=np.float32)).to(device=self.device)
+        """ Update logic from the Policy Gradient theorem. """
+        action_probabilities = self.model(state_tensor)
+        action_distribution = Categorical(action_probabilities)
+        selected_log_probabilities = action_distribution.log_prob(action_tensor)
+        loss = torch_sum(-selected_log_probabilities * reward_baseline)
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+        if self.device == "cuda":
+            return loss.detach().cpu().numpy()
+        else:
+            return loss.detach().numpy()
    +
    +

    Ancestors

    + +

    Methods

    +
    +
    +def action_probabilities(self, state: Any) ‑> torch.Tensor +
    +
    +

    Return a map of each possible action, and the probability that that's the best action to take at +this step.

    +
    + +Expand source code + +
    def action_probabilities(self, state: Any) -> Tensor:
+    """Return a map of each possible action, and the probability that that's the best action to take at
+    this step.
+    """
+    return self.model(FloatTensor(state))
    +
    +
    +
    +def predict(self, state: Any, k: int = 1) ‑> List[int] +
    +
    +

    Given a state, uses the network output to choose the k best next actions according to the probability +distribution trained so far.

    +
    + +Expand source code + +
    def predict(self, state: Any, k: int = 1) -> List[int]:
+    """Given a state, uses the network output to choose the `k` best next actions according to the probability
+    distribution trained so far.
+    """
+    probabilities = self.action_probabilities(state)
+    prediction = multinomial(probabilities, num_samples=k, replacement=False)
+    if self.device == "cuda":
+        return prediction.detach().cpu().numpy()
+    else:
+        return prediction.detach().numpy()
    +
    +
    +
    +def update(self, state: , reward_baseline: torch.Tensor, action: ) ‑> numpy.ndarray +
    +
    +

    Update the network with the given state, reward, and action taken.

    +
    + +Expand source code + +
    def update(self, state: np.array, reward_baseline: Tensor, action: np.array) -> np.ndarray:
+    """Update the network with the given state, reward, and action taken."""
+    state_tensor = FloatTensor(state).to(device=self.device)
+    action_tensor = FloatTensor(np.array(action, dtype=np.float32)).to(device=self.device)
+    """ Update logic from the Policy Gradient theorem. """
+    action_probabilities = self.model(state_tensor)
+    action_distribution = Categorical(action_probabilities)
+    selected_log_probabilities = action_distribution.log_prob(action_tensor)
+    loss = torch_sum(-selected_log_probabilities * reward_baseline)
+    self.optimizer.zero_grad()
+    loss.backward()
+    self.optimizer.step()
+    if self.device == "cuda":
+        return loss.detach().cpu().numpy()
+    else:
+        return loss.detach().numpy()
    +
    +
    +
    +

    Inherited members

    + +
    +
    +
    +
    + +
    + + + \ No newline at end of file diff --git a/docs/api/deeprecsys/neural_networks/q_value_estimator.html b/docs/api/deeprecsys/neural_networks/q_value_estimator.html new file mode 100644 index 0000000..19513eb --- /dev/null +++ b/docs/api/deeprecsys/neural_networks/q_value_estimator.html @@ -0,0 +1,385 @@ + + + + + + +deeprecsys.neural_networks.q_value_estimator API documentation + + + + + + + + + + + +
    +
    +
    +

    Module deeprecsys.neural_networks.q_value_estimator

    +
    +
    +
    + +Expand source code + +
    from typing import Tuple
+
+import torch
+from torch import FloatTensor, Tensor
+from torch.nn import Module
+from torch.optim import Adam
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
+from deeprecsys.neural_networks.deep_q_network import sequential_architecture
+
+
+class QValueEstimator(BaseNetwork):
+    """Estimate the Q-value (expected return) of each (state,action) pair"""
+
+    def __init__(self, inputs: int, outputs: int, learning_rate: float = 1e-3):
+        """Create the network architecture with the provided parameters."""
+        super().__init__()
+        layers = [inputs] + [inputs * 2, inputs * 2] + [outputs]
+        self.model = sequential_architecture(layers)
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        if self.device == "cuda":
+            self.model.cuda()
+
+    def predict(self, states: Tensor, actions: Tensor) -> Tensor:
+        """Given a state and an action, return the estimated Q-Value"""
+        inputs = torch.cat([states, actions.type(FloatTensor)], dim=1).to(device=self.device)
+        return self.model(inputs)
+
+
+class TwinnedQValueEstimator(BaseNetwork):
+    """Estimate the Q-value (expected return) of each (state,action) pair,
+    using 2 independent estimators, and predicting with the minimum estimated Q-value.
+    This is the "critic" part of the Actor-Critic model.
+    """
+
+    def __init__(self, inputs: int, outputs: int = 1, learning_rate: float = 1e-3):
+        """Create the two estimators with the provided parameters."""
+        super().__init__()
+        self.Q1 = QValueEstimator(inputs, outputs, learning_rate=learning_rate)
+        self.Q2 = QValueEstimator(inputs, outputs, learning_rate=learning_rate)
+
+    def predict(self, states: Tensor, actions: Tensor) -> Tensor:
+        """Given a (state, action) pair return the smaller Q-value of the two networks."""
+        q1, q2 = self.forward(states, actions)
+        return torch.min(q1, q2)
+
+    def forward(self, states: Tensor, actions: Tensor) -> Tuple:
+        """Calculate the output weighs for the given (state, action) pair"""
+        q1 = self.Q1.predict(states, actions)
+        q2 = self.Q2.predict(states, actions)
+        return q1, q2
+
+    def calculate_loss(
+        self,
+        states: Tensor,
+        actions: Tensor,
+        rewards: Tensor,
+        dones: Tensor,
+        next_states: Tensor,
+        weights: Tensor,
+        actor: Module,
+        target: "TwinnedQValueEstimator",
+    ) -> Tensor:
+        """Train the network and return the loss."""
+        curr_q1, curr_q2 = self(states, actions)
+        target_q = actor.calculate_target_q(
+            states,
+            actions,
+            rewards,
+            next_states,
+            dones,
+            target_critic=target,
+        )
+        # TD errors for updating priority weights
+        errors = torch.abs(curr_q1.detach() - target_q)
+        # Critic loss is mean squared TD errors with priority weights.
+        q1_loss = torch.mean((curr_q1 - target_q).pow(2) * weights)
+        q2_loss = torch.mean((curr_q2 - target_q).pow(2) * weights)
+        self.Q1.run_backpropagation(q1_loss)
+        self.Q2.run_backpropagation(q2_loss)
+        return errors
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Classes

    +
    +
    +class QValueEstimator +(inputs: int, outputs: int, learning_rate: float = 0.001) +
    +
    +

    Estimate the Q-value (expected return) of each (state,action) pair

    +

    Create the network architecture with the provided parameters.

    +
    + +Expand source code + +
    class QValueEstimator(BaseNetwork):
+    """Estimate the Q-value (expected return) of each (state,action) pair"""
+
+    def __init__(self, inputs: int, outputs: int, learning_rate: float = 1e-3):
+        """Create the network architecture with the provided parameters."""
+        super().__init__()
+        layers = [inputs] + [inputs * 2, inputs * 2] + [outputs]
+        self.model = sequential_architecture(layers)
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        if self.device == "cuda":
+            self.model.cuda()
+
+    def predict(self, states: Tensor, actions: Tensor) -> Tensor:
+        """Given a state and an action, return the estimated Q-Value"""
+        inputs = torch.cat([states, actions.type(FloatTensor)], dim=1).to(device=self.device)
+        return self.model(inputs)
    +
    +

    Ancestors

    + +

    Methods

    +
    +
    +def predict(self, states: torch.Tensor, actions: torch.Tensor) ‑> torch.Tensor +
    +
    +

    Given a state and an action, return the estimated Q-Value

    +
    + +Expand source code + +
    def predict(self, states: Tensor, actions: Tensor) -> Tensor:
+    """Given a state and an action, return the estimated Q-Value"""
+    inputs = torch.cat([states, actions.type(FloatTensor)], dim=1).to(device=self.device)
+    return self.model(inputs)
    +
    +
    +
    +

    Inherited members

    + +
    +
    +class TwinnedQValueEstimator +(inputs: int, outputs: int = 1, learning_rate: float = 0.001) +
    +
    +

    Estimate the Q-value (expected return) of each (state,action) pair, +using 2 independent estimators, and predicting with the minimum estimated Q-value. +This is the "critic" part of the Actor-Critic model.

    +

    Create the two estimators with the provided parameters.

    +
    + +Expand source code + +
    class TwinnedQValueEstimator(BaseNetwork):
+    """Estimate the Q-value (expected return) of each (state,action) pair,
+    using 2 independent estimators, and predicting with the minimum estimated Q-value.
+    This is the "critic" part of the Actor-Critic model.
+    """
+
+    def __init__(self, inputs: int, outputs: int = 1, learning_rate: float = 1e-3):
+        """Create the two estimators with the provided parameters."""
+        super().__init__()
+        self.Q1 = QValueEstimator(inputs, outputs, learning_rate=learning_rate)
+        self.Q2 = QValueEstimator(inputs, outputs, learning_rate=learning_rate)
+
+    def predict(self, states: Tensor, actions: Tensor) -> Tensor:
+        """Given a (state, action) pair return the smaller Q-value of the two networks."""
+        q1, q2 = self.forward(states, actions)
+        return torch.min(q1, q2)
+
+    def forward(self, states: Tensor, actions: Tensor) -> Tuple:
+        """Calculate the output weighs for the given (state, action) pair"""
+        q1 = self.Q1.predict(states, actions)
+        q2 = self.Q2.predict(states, actions)
+        return q1, q2
+
+    def calculate_loss(
+        self,
+        states: Tensor,
+        actions: Tensor,
+        rewards: Tensor,
+        dones: Tensor,
+        next_states: Tensor,
+        weights: Tensor,
+        actor: Module,
+        target: "TwinnedQValueEstimator",
+    ) -> Tensor:
+        """Train the network and return the loss."""
+        curr_q1, curr_q2 = self(states, actions)
+        target_q = actor.calculate_target_q(
+            states,
+            actions,
+            rewards,
+            next_states,
+            dones,
+            target_critic=target,
+        )
+        # TD errors for updating priority weights
+        errors = torch.abs(curr_q1.detach() - target_q)
+        # Critic loss is mean squared TD errors with priority weights.
+        q1_loss = torch.mean((curr_q1 - target_q).pow(2) * weights)
+        q2_loss = torch.mean((curr_q2 - target_q).pow(2) * weights)
+        self.Q1.run_backpropagation(q1_loss)
+        self.Q2.run_backpropagation(q2_loss)
+        return errors
    +
    +

    Ancestors

    + +

    Methods

    +
    +
    +def calculate_loss(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, dones: torch.Tensor, next_states: torch.Tensor, weights: torch.Tensor, actor: torch.nn.modules.module.Module, target: TwinnedQValueEstimator) ‑> torch.Tensor +
    +
    +

    Train the network and return the loss.

    +
    + +Expand source code + +
    def calculate_loss(
+    self,
+    states: Tensor,
+    actions: Tensor,
+    rewards: Tensor,
+    dones: Tensor,
+    next_states: Tensor,
+    weights: Tensor,
+    actor: Module,
+    target: "TwinnedQValueEstimator",
+) -> Tensor:
+    """Train the network and return the loss."""
+    curr_q1, curr_q2 = self(states, actions)
+    target_q = actor.calculate_target_q(
+        states,
+        actions,
+        rewards,
+        next_states,
+        dones,
+        target_critic=target,
+    )
+    # TD errors for updating priority weights
+    errors = torch.abs(curr_q1.detach() - target_q)
+    # Critic loss is mean squared TD errors with priority weights.
+    q1_loss = torch.mean((curr_q1 - target_q).pow(2) * weights)
+    q2_loss = torch.mean((curr_q2 - target_q).pow(2) * weights)
+    self.Q1.run_backpropagation(q1_loss)
+    self.Q2.run_backpropagation(q2_loss)
+    return errors
    +
    +
    +
    +def forward(self, states: torch.Tensor, actions: torch.Tensor) ‑> Tuple +
    +
    +

    Calculate the output weighs for the given (state, action) pair

    +
    + +Expand source code + +
    def forward(self, states: Tensor, actions: Tensor) -> Tuple:
+    """Calculate the output weighs for the given (state, action) pair"""
+    q1 = self.Q1.predict(states, actions)
+    q2 = self.Q2.predict(states, actions)
+    return q1, q2
    +
    +
    +
    +def predict(self, states: torch.Tensor, actions: torch.Tensor) ‑> torch.Tensor +
    +
    +

    Given a (state, action) pair return the smaller Q-value of the two networks.

    +
    + +Expand source code + +
    def predict(self, states: Tensor, actions: Tensor) -> Tensor:
+    """Given a (state, action) pair return the smaller Q-value of the two networks."""
+    q1, q2 = self.forward(states, actions)
+    return torch.min(q1, q2)
    +
    +
    +
    +

    Inherited members

    + +
    +
    +
    +
    + +
    + + + \ No newline at end of file diff --git a/docs/api/deeprecsys/neural_networks/value_estimator.html b/docs/api/deeprecsys/neural_networks/value_estimator.html new file mode 100644 index 0000000..9444d84 --- /dev/null +++ b/docs/api/deeprecsys/neural_networks/value_estimator.html @@ -0,0 +1,219 @@ + + + + + + +deeprecsys.neural_networks.value_estimator API documentation + + + + + + + + + + + +
    +
    +
    +

    Module deeprecsys.neural_networks.value_estimator

    +
    +
    +
    + +Expand source code + +
    from typing import List
+
+import numpy as np
+from torch import FloatTensor
+from torch.nn import MSELoss
+from torch.optim import Adam
+
+from deeprecsys.neural_networks.base_network import BaseNetwork
+from deeprecsys.neural_networks.deep_q_network import sequential_architecture
+
+
+class ValueEstimator(BaseNetwork):
+    """Estimates the value function: the expected return of being in a
+    particular state
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_layers: List[int],
+        output_size: int,
+        learning_rate: float = 0.1,
+    ):
+        """Create the network with the given parameters. The output should always be one."""
+        super().__init__()
+        self.model = sequential_architecture([input_size] + hidden_layers + [output_size])
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        if self.device == "cuda":
+            self.model.cuda()
+        self.loss_function = MSELoss()
+
+    def predict(self, state: np.array) -> float:
+        """Estimate the expected return of being in the given state."""
+        state_tensor = FloatTensor(state).to(device=self.device)
+        return self.model(state_tensor)
+
+    def update(self, state: np.array, return_value: float) -> None:
+        """Run backpropagation on the given state and return."""
+        expected_return = FloatTensor(np.array([return_value])).to(device=self.device)
+        predicted_return = self.predict(state)
+        self.optimizer.zero_grad()
+        loss = self.loss_function(predicted_return, expected_return)
+        loss.backward()
+        self.optimizer.step()
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Classes

    +
    +
    +class ValueEstimator +(input_size: int, hidden_layers: List[int], output_size: int, learning_rate: float = 0.1) +
    +
    +

    Estimates the value function: the expected return of being in a +particular state

    +

    Create the network with the given parameters. The output should always be one.

    +
    + +Expand source code + +
    class ValueEstimator(BaseNetwork):
+    """Estimates the value function: the expected return of being in a
+    particular state
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_layers: List[int],
+        output_size: int,
+        learning_rate: float = 0.1,
+    ):
+        """Create the network with the given parameters. The output should always be one."""
+        super().__init__()
+        self.model = sequential_architecture([input_size] + hidden_layers + [output_size])
+        self.optimizer = Adam(self.parameters(), lr=learning_rate)
+        if self.device == "cuda":
+            self.model.cuda()
+        self.loss_function = MSELoss()
+
+    def predict(self, state: np.array) -> float:
+        """Estimate the expected return of being in the given state."""
+        state_tensor = FloatTensor(state).to(device=self.device)
+        return self.model(state_tensor)
+
+    def update(self, state: np.array, return_value: float) -> None:
+        """Run backpropagation on the given state and return."""
+        expected_return = FloatTensor(np.array([return_value])).to(device=self.device)
+        predicted_return = self.predict(state)
+        self.optimizer.zero_grad()
+        loss = self.loss_function(predicted_return, expected_return)
+        loss.backward()
+        self.optimizer.step()
    +
    +

    Ancestors

    + +

    Methods

    +
    +
    +def predict(self, state: ) ‑> float +
    +
    +

    Estimate the expected return of being in the given state.

    +
    + +Expand source code + +
    def predict(self, state: np.array) -> float:
+    """Estimate the expected return of being in the given state."""
+    state_tensor = FloatTensor(state).to(device=self.device)
+    return self.model(state_tensor)
    +
    +
    +
    +def update(self, state: , return_value: float) ‑> None +
    +
    +

    Run backpropagation on the given state and return.

    +
    + +Expand source code + +
    def update(self, state: np.array, return_value: float) -> None:
+    """Run backpropagation on the given state and return."""
+    expected_return = FloatTensor(np.array([return_value])).to(device=self.device)
+    predicted_return = self.predict(state)
+    self.optimizer.zero_grad()
+    loss = self.loss_function(predicted_return, expected_return)
+    loss.backward()
+    self.optimizer.step()
    +
    +
    +
    +

    Inherited members

    + +
    +
    +
    +
    + +
    + + + \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/agents/actor_critic.html b/docs/api/deeprecsys/rl/agents/actor_critic.html index 752af1b..df28e64 100644 --- a/docs/api/deeprecsys/rl/agents/actor_critic.html +++ b/docs/api/deeprecsys/rl/agents/actor_critic.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.agents.actor_critic API documentation @@ -26,32 +26,37 @@

    Module deeprecsys.rl.agents.actor_critic

    Expand source code -
    from deeprecsys.rl.agents.agent import ReinforcementLearning
-from typing import Any, List, Optional
-from deeprecsys.rl.experience_replay.experience_buffer import ExperienceReplayBuffer
+
    from typing import Any, List, Optional
+
+from deeprecsys.neural_networks.policy_estimator import PolicyEstimator
+from deeprecsys.neural_networks.value_estimator import ValueEstimator
+from deeprecsys.rl.agents.agent import ReinforcementLearning
 from deeprecsys.rl.experience_replay.buffer_parameters import (
     ExperienceReplayBufferParameters,
 )
-from deeprecsys.rl.neural_networks.policy_estimator import PolicyEstimator
-from deeprecsys.rl.neural_networks.value_estimator import ValueEstimator
+from deeprecsys.rl.experience_replay.experience_buffer import ExperienceReplayBuffer
 
 
 class ActorCriticAgent(ReinforcementLearning):
     """Policy estimator using a value estimator as a baseline.
     It's on-policy, for discrete action spaces, and episodic environments.
     This implementation uses stochastic policies.
-    TODO: could be a sub class of reinforce"""
+    TODO: could be a sub class of reinforce
+    """
+
+    buffer: ExperienceReplayBuffer
 
     def __init__(
         self,
         n_actions: int,
         state_size: int,
-        discount_factor: int = 0.99,
+        discount_factor: float = 0.99,
         actor_hidden_layers: Optional[List[int]] = None,
         critic_hidden_layers: Optional[List[int]] = None,
-        actor_learning_rate=1e-3,
-        critic_learning_rate=1e-3,
-    ):
+        actor_learning_rate: float = 1e-3,
+        critic_learning_rate: float = 1e-3,
+    ) -> None:
+        """Create the actor and critic networks"""
         if not actor_hidden_layers:
             actor_hidden_layers = [state_size * 2, state_size * 2]
         if not critic_hidden_layers:
@@ -73,20 +78,20 @@ 
    Module deeprecsys.rl.agents.actor_critic
    
         # starts the buffer
         self.reset_buffer()
 
-    def reset_buffer(self):
-        self.buffer = ExperienceReplayBuffer(
-            ExperienceReplayBufferParameters(10000, 1, 1)
-        )
+    def reset_buffer(self) -> None:
+        """Clear all the experiences from the buffer"""
+        self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1))
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+        """Return the next best K action"""
         return self.policy_estimator.predict(state, k=k)
 
     def action_for_state(self, state: Any) -> int:
+        """Return the next best action"""
         return self.top_k_actions_for_state(state)[0]
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store the experience in the experience buffer"""
         state_flat = state.flatten()
         new_state_flat = new_state.flatten()
         self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
@@ -95,12 +100,13 @@ 
    Module deeprecsys.rl.agents.actor_critic
    
             self.learn_from_experiences()
             self.reset_buffer()
 
-    def learn_from_experiences(self):
+    def learn_from_experiences(self) -> None:
+        """Backpropagate the actor and critic networks"""
         experiences = list(self.buffer.experience_queue)
         for timestep, experience in enumerate(experiences):
             total_return = 0
             for i, t in enumerate(experiences[timestep:]):
-                total_return += (self.discount_factor ** i) * t.reward
+                total_return += (self.discount_factor**i) * t.reward
 
             # Calculate baseline/advantage
             baseline_value = self.value_estimator.predict(experience.state).detach()
@@ -122,13 +128,14 @@ 
    Classes
    
 
    
 
    
 class ActorCriticAgent
-(n_actions: int, state_size: int, discount_factor: int = 0.99, actor_hidden_layers: Union[List[int], NoneType] = None, critic_hidden_layers: Union[List[int], NoneType] = None, actor_learning_rate=0.001, critic_learning_rate=0.001)
+(n_actions: int, state_size: int, discount_factor: float = 0.99, actor_hidden_layers: Optional[List[int]] = None, critic_hidden_layers: Optional[List[int]] = None, actor_learning_rate: float = 0.001, critic_learning_rate: float = 0.001)
 
    
 
    
 
    Policy estimator using a value estimator as a baseline.
 It's on-policy, for discrete action spaces, and episodic environments.
 This implementation uses stochastic policies.
-TODO: could be a sub class of reinforce
    
+TODO: could be a sub class of reinforce
    
+
    Create the actor and critic networks
    
 
    
 
 Expand source code
@@ -137,18 +144,22 @@ 
    Classes
    
     """Policy estimator using a value estimator as a baseline.
     It's on-policy, for discrete action spaces, and episodic environments.
     This implementation uses stochastic policies.
-    TODO: could be a sub class of reinforce"""
+    TODO: could be a sub class of reinforce
+    """
+
+    buffer: ExperienceReplayBuffer
 
     def __init__(
         self,
         n_actions: int,
         state_size: int,
-        discount_factor: int = 0.99,
+        discount_factor: float = 0.99,
         actor_hidden_layers: Optional[List[int]] = None,
         critic_hidden_layers: Optional[List[int]] = None,
-        actor_learning_rate=1e-3,
-        critic_learning_rate=1e-3,
-    ):
+        actor_learning_rate: float = 1e-3,
+        critic_learning_rate: float = 1e-3,
+    ) -> None:
+        """Create the actor and critic networks"""
         if not actor_hidden_layers:
             actor_hidden_layers = [state_size * 2, state_size * 2]
         if not critic_hidden_layers:
@@ -170,20 +181,20 @@ 
    Classes
    
         # starts the buffer
         self.reset_buffer()
 
-    def reset_buffer(self):
-        self.buffer = ExperienceReplayBuffer(
-            ExperienceReplayBufferParameters(10000, 1, 1)
-        )
+    def reset_buffer(self) -> None:
+        """Clear all the experiences from the buffer"""
+        self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1))
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+        """Return the next best K action"""
         return self.policy_estimator.predict(state, k=k)
 
     def action_for_state(self, state: Any) -> int:
+        """Return the next best action"""
         return self.top_k_actions_for_state(state)[0]
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store the experience in the experience buffer"""
         state_flat = state.flatten()
         new_state_flat = new_state.flatten()
         self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
@@ -192,12 +203,13 @@ 
    Classes
    
             self.learn_from_experiences()
             self.reset_buffer()
 
-    def learn_from_experiences(self):
+    def learn_from_experiences(self) -> None:
+        """Backpropagate the actor and critic networks"""
         experiences = list(self.buffer.experience_queue)
         for timestep, experience in enumerate(experiences):
             total_return = 0
             for i, t in enumerate(experiences[timestep:]):
-                total_return += (self.discount_factor ** i) * t.reward
+                total_return += (self.discount_factor**i) * t.reward
 
             # Calculate baseline/advantage
             baseline_value = self.value_estimator.predict(experience.state).detach()
@@ -212,36 +224,45 @@ 
    Ancestors
    
 
  • ReinforcementLearning
    • 
 
  • abc.ABC
    •

+

Class variables

+
+
var buffer : ExperienceReplayBuffer
+
+
+
+

Methods

def action_for_state(self, state: Any) ‑> int
-
+

Return the next best action

Expand source code 
def action_for_state(self, state: Any) -> int:
+    """Return the next best action"""
     return self.top_k_actions_for_state(state)[0]
-def learn_from_experiences(self) +def learn_from_experiences(self) ‑> None
-
+

Backpropagate the actor and critic networks

Expand source code -
def learn_from_experiences(self):
+
def learn_from_experiences(self) -> None:
+    """Backpropagate the actor and critic networks"""
     experiences = list(self.buffer.experience_queue)
     for timestep, experience in enumerate(experiences):
         total_return = 0
         for i, t in enumerate(experiences[timestep:]):
-            total_return += (self.discount_factor ** i) * t.reward
+            total_return += (self.discount_factor**i) * t.reward
 
         # Calculate baseline/advantage
         baseline_value = self.value_estimator.predict(experience.state).detach()
@@ -253,32 +274,30 @@ 
Methods
-def reset_buffer(self) +def reset_buffer(self) ‑> None
-
+

Clear all the experiences from the buffer

Expand source code -
def reset_buffer(self):
-    self.buffer = ExperienceReplayBuffer(
-        ExperienceReplayBufferParameters(10000, 1, 1)
-    )
+
def reset_buffer(self) -> None:
+    """Clear all the experiences from the buffer"""
+    self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1))
-def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) +def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
-
+

Store the experience in the experience buffer

Expand source code -
def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
+
def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+    """Store the experience in the experience buffer"""
     state_flat = state.flatten()
     new_state_flat = new_state.flatten()
     self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
@@ -292,12 +311,13 @@ 
Methods

 def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> List[int]
 
 

-

+
Return the next best K action

 

 
 Expand source code
 
 
def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+    """Return the next best K action"""
     return self.policy_estimator.predict(state, k=k)

 

 

@@ -323,6 +343,7 @@ 
Index

 
ActorCriticAgent

 
    
 
  • action_for_state
    • 
+
  • buffer
    • 
 
  • learn_from_experiences
    • 
 
  • reset_buffer
    • 
 
  • store_experience
    • 
@@ -335,7 +356,7 @@ 
    
-
    Generated by pdoc 0.9.2.
    
+
    Generated by pdoc 0.10.0.
    
 
 
 
\ No newline at end of file
diff --git a/docs/api/deeprecsys/rl/agents/agent.html b/docs/api/deeprecsys/rl/agents/agent.html
index e95c28f..c22d979 100644
--- a/docs/api/deeprecsys/rl/agents/agent.html
+++ b/docs/api/deeprecsys/rl/agents/agent.html
@@ -3,7 +3,7 @@
 
 
 
-
+
 deeprecsys.rl.agents.agent API documentation
 
 
@@ -27,46 +27,50 @@ 
    Module deeprecsys.rl.agents.agent
    
 Expand source code
 
 
    from abc import ABC, abstractmethod
-from gym import Space
 from typing import Any
 
+from gymnasium import Space
+
 
 class ReinforcementLearning(ABC):
+    """Abstract class that encapsulates the behavior of RL models."""
+
     @abstractmethod
     def action_for_state(self, state: Any) -> Any:
-        pass
+        """Given a state, return the next predicted action."""
 
     @abstractmethod
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
-        pass
+        """Retrieve the next K best actions for this state."""
 
     @abstractmethod
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
-        pass
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store an experience (used in case of experience replay buffers)"""
 
 
 class RandomAgent(ReinforcementLearning):
     """An agent that randomly samples actions, regardless of the
-    environment's state."""
+    environment's state.
+    """
 
     action_space: Space
 
-    def __init__(self, action_space: Space, random_state=42):
+    def __init__(self, action_space: Space, random_state: Any = 42):
+        """Start the agent with the provided action space and seed."""
         self.action_space = action_space
         # we seed the state so actions are reproducible
         self.action_space.seed(random_state)
 
     def action_for_state(self, state: Any) -> Any:
+        """Sample a random action from the action space."""
         return self.action_space.sample()
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+        """Randomly sample K actions from the action space."""
         return self.action_space.sample()
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Ignore the experience because this agent doesn't have any experience replay."""
         pass
@@ -81,35 +85,39 @@

Classes

class RandomAgent -(action_space: gym.spaces.space.Space, random_state=42) +(action_space: gymnasium.spaces.space.Space, random_state: Any = 42)

An agent that randomly samples actions, regardless of the -environment's state.

+environment's state.

+

Start the agent with the provided action space and seed.

Expand source code 
class RandomAgent(ReinforcementLearning):
     """An agent that randomly samples actions, regardless of the
-    environment's state."""
+    environment's state.
+    """
 
     action_space: Space
 
-    def __init__(self, action_space: Space, random_state=42):
+    def __init__(self, action_space: Space, random_state: Any = 42):
+        """Start the agent with the provided action space and seed."""
         self.action_space = action_space
         # we seed the state so actions are reproducible
         self.action_space.seed(random_state)
 
     def action_for_state(self, state: Any) -> Any:
+        """Sample a random action from the action space."""
         return self.action_space.sample()
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+        """Randomly sample K actions from the action space."""
         return self.action_space.sample()
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Ignore the experience because this agent doesn't have any experience replay."""
         pass

Ancestors

@@ -119,7 +127,7 @@

Ancestors

Class variables

-
var action_space : gym.spaces.space.Space
+
var action_space : gymnasium.spaces.space.Space
@@ -130,27 +138,27 @@

Methods

def action_for_state(self, state: Any) ‑> Any
-
+

Sample a random action from the action space.

Expand source code 
def action_for_state(self, state: Any) -> Any:
+    """Sample a random action from the action space."""
     return self.action_space.sample()
-def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) +def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
-
+

Ignore the experience because this agent doesn't have any experience replay.

Expand source code -
def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
+
def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+    """Ignore the experience because this agent doesn't have any experience replay."""
     pass
@@ -158,12 +166,13 @@

Methods

def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> Any
-
+

Randomly sample K actions from the action space.

Expand source code 
def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+    """Randomly sample K actions from the action space."""
     return self.action_space.sample()
@@ -173,26 +182,25 @@

Methods

class ReinforcementLearning
-

Helper class that provides a standard way to create an ABC using -inheritance.

+

Abstract class that encapsulates the behavior of RL models.

Expand source code 
class ReinforcementLearning(ABC):
+    """Abstract class that encapsulates the behavior of RL models."""
+
     @abstractmethod
     def action_for_state(self, state: Any) -> Any:
-        pass
+        """Given a state, return the next predicted action."""
 
     @abstractmethod
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
-        pass
+        """Retrieve the next K best actions for this state."""
 
     @abstractmethod
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
-        pass
+ def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: + """Store an experience (used in case of experience replay buffers)"""

Ancestors

    @@ -213,44 +221,42 @@

    Methods

    def action_for_state(self, state: Any) ‑> Any
    -
    +

    Given a state, return the next predicted action.

    Expand source code 
    @abstractmethod
 def action_for_state(self, state: Any) -> Any:
-    pass
    + """Given a state, return the next predicted action."""
    -def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) +def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
    -
    +

    Store an experience (used in case of experience replay buffers)

    Expand source code 
    @abstractmethod
-def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
-    pass
    +def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: + """Store an experience (used in case of experience replay buffers)"""
    def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> Any
    -
    +

    Retrieve the next K best actions for this state.

    Expand source code 
    @abstractmethod
 def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
-    pass
    + """Retrieve the next K best actions for this state."""
@@ -294,7 +300,7 @@

-

Generated by pdoc 0.9.2.

+

Generated by pdoc 0.10.0.

\ No newline at end of file diff --git a/docs/api/deeprecsys/rl/agents/dqn.html b/docs/api/deeprecsys/rl/agents/dqn.html index 6edc300..ea3f4ec 100644 --- a/docs/api/deeprecsys/rl/agents/dqn.html +++ b/docs/api/deeprecsys/rl/agents/dqn.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.agents.dqn API documentation @@ -26,22 +26,24 @@

Module deeprecsys.rl.agents.dqn

Expand source code -
from numpy.random import RandomState
-from typing import List, Any
+
from typing import Any, List, Optional
+
 from numpy import arange
-from deeprecsys.rl.experience_replay.experience_buffer import ExperienceReplayBuffer
-from deeprecsys.rl.experience_replay.buffer_parameters import (
-    ExperienceReplayBufferParameters,
-)
-from deeprecsys.rl.agents.epsilon_greedy import DecayingEpsilonGreedy
-from deeprecsys.rl.neural_networks.deep_q_network import (
+from numpy.random import RandomState
+
+from deeprecsys.neural_networks.deep_q_network import (
     DeepQNetwork,
     sequential_architecture,
 )
+from deeprecsys.rl.agents.epsilon_greedy import DecayingEpsilonGreedy
+from deeprecsys.rl.experience_replay.buffer_parameters import (
+    ExperienceReplayBufferParameters,
+)
+from deeprecsys.rl.experience_replay.experience_buffer import ExperienceReplayBuffer
 
 
 class DQNAgent(DecayingEpsilonGreedy):
-    """ TODO: This agent needs to be fixed"""
+    """TODO: This agent needs to be fixed"""
 
     def __init__(
         self,
@@ -51,14 +53,17 @@ 
Module deeprecsys.rl.agents.dqn

         network_update_frequency: int = 3,
         initial_exploration_probability: float = 1.0,
         decay_rate: float = 0.99,
-        minimum_exploration_probability=0.05,
+        minimum_exploration_probability: float = 0.05,
         buffer_size: int = 10000,
         buffer_burn_in: int = 1000,
         batch_size: int = 32,
         discount_factor: float = 0.99,
         learning_rate: float = 0.99,
-        random_state: RandomState = RandomState(),
-    ):
+        random_state: Optional[RandomState] = None,
+    ) -> None:
+        """TODO"""
+        if random_state is None:
+            random_state = RandomState()
         super().__init__(
             initial_exploration_probability,
             decay_rate,
@@ -66,9 +71,7 @@ 
Module deeprecsys.rl.agents.dqn

             random_state,
         )
 
-        architecture = sequential_architecture(
-            [input_size] + hidden_layers + [output_size]
-        )
+        architecture = sequential_architecture([input_size] + hidden_layers + [output_size])
         self.network = DeepQNetwork(learning_rate, architecture, discount_factor)
         self.buffer = ExperienceReplayBuffer(
             ExperienceReplayBufferParameters(
@@ -82,7 +85,7 @@ 
Module deeprecsys.rl.agents.dqn

         self.network_update_frequency = network_update_frequency
         self.actions = arange(output_size)
 
-    def _check_update_network(self):
+    def _check_update_network(self) -> None:
         if self.buffer.ready_to_predict():
             self.step_count += 1
             if self.step_count == self.network_update_frequency:
@@ -91,6 +94,7 @@ 
Module deeprecsys.rl.agents.dqn

                 self.network.learn_from(batch)
 
     def action_for_state(self, state: Any) -> Any:
+        """TODO"""
         state_flat = state.flatten()
         if self.buffer.ready_to_predict():
             action = super().action_for_state(state_flat)
@@ -100,18 +104,18 @@ 
Module deeprecsys.rl.agents.dqn

         return action
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
-        # TODO:
-        pass
+        """TODO"""
 
-    def explore(self):
+    def explore(self) -> float:
+        """TODO"""
         return self.random_state.choice(self.actions)
 
-    def exploit(self, state: Any):
+    def exploit(self, state: Any) -> float:
+        """TODO"""
         return self.network.best_action_for_state(state)
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """TODO"""
         if done and self.buffer.ready_to_predict():
             self._decay()
         state_flat = state.flatten()
@@ -130,16 +134,17 @@ 
Classes

 

 

 class DQNAgent
-(input_size: int, output_size: int, hidden_layers: List, network_update_frequency: int = 3, initial_exploration_probability: float = 1.0, decay_rate: float = 0.99, minimum_exploration_probability=0.05, buffer_size: int = 10000, buffer_burn_in: int = 1000, batch_size: int = 32, discount_factor: float = 0.99, learning_rate: float = 0.99, random_state: numpy.random.mtrand.RandomState = RandomState(MT19937))
+(input_size: int, output_size: int, hidden_layers: List, network_update_frequency: int = 3, initial_exploration_probability: float = 1.0, decay_rate: float = 0.99, minimum_exploration_probability: float = 0.05, buffer_size: int = 10000, buffer_burn_in: int = 1000, batch_size: int = 32, discount_factor: float = 0.99, learning_rate: float = 0.99, random_state: Optional[numpy.random.mtrand.RandomState] = None)
 

 

-
TODO: This agent needs to be fixed

+
TODO: This agent needs to be fixed

+
TODO

 

 
 Expand source code
 
 
class DQNAgent(DecayingEpsilonGreedy):
-    """ TODO: This agent needs to be fixed"""
+    """TODO: This agent needs to be fixed"""
 
     def __init__(
         self,
@@ -149,14 +154,17 @@ 
Classes

         network_update_frequency: int = 3,
         initial_exploration_probability: float = 1.0,
         decay_rate: float = 0.99,
-        minimum_exploration_probability=0.05,
+        minimum_exploration_probability: float = 0.05,
         buffer_size: int = 10000,
         buffer_burn_in: int = 1000,
         batch_size: int = 32,
         discount_factor: float = 0.99,
         learning_rate: float = 0.99,
-        random_state: RandomState = RandomState(),
-    ):
+        random_state: Optional[RandomState] = None,
+    ) -> None:
+        """TODO"""
+        if random_state is None:
+            random_state = RandomState()
         super().__init__(
             initial_exploration_probability,
             decay_rate,
@@ -164,9 +172,7 @@ 
Classes

             random_state,
         )
 
-        architecture = sequential_architecture(
-            [input_size] + hidden_layers + [output_size]
-        )
+        architecture = sequential_architecture([input_size] + hidden_layers + [output_size])
         self.network = DeepQNetwork(learning_rate, architecture, discount_factor)
         self.buffer = ExperienceReplayBuffer(
             ExperienceReplayBufferParameters(
@@ -180,7 +186,7 @@ 
Classes

         self.network_update_frequency = network_update_frequency
         self.actions = arange(output_size)
 
-    def _check_update_network(self):
+    def _check_update_network(self) -> None:
         if self.buffer.ready_to_predict():
             self.step_count += 1
             if self.step_count == self.network_update_frequency:
@@ -189,6 +195,7 @@ 
Classes

                 self.network.learn_from(batch)
 
     def action_for_state(self, state: Any) -> Any:
+        """TODO"""
         state_flat = state.flatten()
         if self.buffer.ready_to_predict():
             action = super().action_for_state(state_flat)
@@ -198,18 +205,18 @@ 
Classes

         return action
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
-        # TODO:
-        pass
+        """TODO"""
 
-    def explore(self):
+    def explore(self) -> float:
+        """TODO"""
         return self.random_state.choice(self.actions)
 
-    def exploit(self, state: Any):
+    def exploit(self, state: Any) -> float:
+        """TODO"""
         return self.network.best_action_for_state(state)
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """TODO"""
         if done and self.buffer.ready_to_predict():
             self._decay()
         state_flat = state.flatten()
@@ -224,18 +231,65 @@ 
Ancestors

 
 
Methods

 

+

+def action_for_state(self, state: Any) ‑> Any
+

+

+
TODO

+

+
+Expand source code
+
+
def action_for_state(self, state: Any) -> Any:
+    """TODO"""
+    state_flat = state.flatten()
+    if self.buffer.ready_to_predict():
+        action = super().action_for_state(state_flat)
+    else:
+        action = self.explore()
+    self._check_update_network()
+    return action

+

+

+

+def exploit(self, state: Any) ‑> float
+

+

+
TODO

+

+
+Expand source code
+
+
def exploit(self, state: Any) -> float:
+    """TODO"""
+    return self.network.best_action_for_state(state)

+

+

+

+def explore(self) ‑> float
+

+

+
TODO

+

+
+Expand source code
+
+
def explore(self) -> float:
+    """TODO"""
+    return self.random_state.choice(self.actions)

+

+

 

-def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any)
+def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
 

 

-

+
TODO

 

 
 Expand source code
 
-
def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
+
def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+    """TODO"""
     if done and self.buffer.ready_to_predict():
         self._decay()
     state_flat = state.flatten()
@@ -247,27 +301,16 @@ 
Methods

 def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> Any
 
 

-

+
TODO

 

 
 Expand source code
 
 
def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
-    # TODO:
-    pass

+    """TODO"""

 

 

 

-
Inherited members

-

@@ -288,6 +331,9 @@

Index

  • DQNAgent

    @@ -298,7 +344,7 @@

    -

    Generated by pdoc 0.9.2.

    +

    Generated by pdoc 0.10.0.

    \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/agents/epsilon_greedy.html b/docs/api/deeprecsys/rl/agents/epsilon_greedy.html index ff7b1f0..7eb3217 100644 --- a/docs/api/deeprecsys/rl/agents/epsilon_greedy.html +++ b/docs/api/deeprecsys/rl/agents/epsilon_greedy.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.agents.epsilon_greedy API documentation @@ -26,20 +26,27 @@

    Module deeprecsys.rl.agents.epsilon_greedy

    Expand source code -
    from .agent import ReinforcementLearning
-from abc import ABC, abstractmethod
-from numpy.random import RandomState
+
    from abc import ABC, abstractmethod
 from typing import Any
 
+from numpy.random import RandomState
+
+from deeprecsys.rl.agents.agent import ReinforcementLearning
+
 
 class DecayingEpsilonGreedy(ReinforcementLearning, ABC):
+    """A reinforcement learning model that reduces exploration over time."""
+
     def __init__(
         self,
         initial_exploration_probability: float = 0.2,
         decay_rate: float = 1,
-        minimum_exploration_probability=0.01,
-        random_state: RandomState = RandomState(),
+        minimum_exploration_probability: float = 0.01,
+        random_state: RandomState = None,
     ):
+        """To set up a classic e-greedy model, the decay rate needs to be 1."""
+        if random_state is None:
+            random_state = RandomState()
         self.random_state = random_state
         self.epsilon = initial_exploration_probability
         self.minimum_exploration_probability = minimum_exploration_probability
@@ -47,27 +54,26 @@ 
    Module deeprecsys.rl.agents.epsilon_greedy
    
 
     def action_for_state(self, state: Any) -> Any:
         """With probability epsilon, we explore by sampling one of the random available actions.
-        Otherwise we exploit by chosing the action with the highest Q value."""
+        Otherwise we exploit by chosing the action with the highest Q value.
+        """
         if self.random_state.random() < self.epsilon:
             action = self.explore()
         else:
             action = self.exploit(state)
         return action
 
-    def _decay(self):
-        """ Slowly decrease the exploration probability. """
-        self.epsilon = max(
-            self.epsilon * self.decay_rate, self.minimum_exploration_probability
-        )
+    def _decay(self) -> None:
+        """Slowly decrease the exploration probability."""
+        self.epsilon = max(self.epsilon * self.decay_rate, self.minimum_exploration_probability)
 
     @abstractmethod
     def explore(self) -> Any:
-        """ Randomly selects an action"""
+        """Randomly selects an action"""
         pass
 
     @abstractmethod
     def exploit(self, state: Any) -> Any:
-        """ Selects the best action known for the given state """
+        """Select the best action known for the given state"""
         pass

    • @@ -82,23 +88,28 @@

    Classes

    class DecayingEpsilonGreedy -(initial_exploration_probability: float = 0.2, decay_rate: float = 1, minimum_exploration_probability=0.01, random_state: numpy.random.mtrand.RandomState = RandomState(MT19937)) +(initial_exploration_probability: float = 0.2, decay_rate: float = 1, minimum_exploration_probability: float = 0.01, random_state: numpy.random.mtrand.RandomState = None)
    -

    Helper class that provides a standard way to create an ABC using -inheritance.

    +

    A reinforcement learning model that reduces exploration over time.

    +

    To set up a classic e-greedy model, the decay rate needs to be 1.

    Expand source code 
    class DecayingEpsilonGreedy(ReinforcementLearning, ABC):
+    """A reinforcement learning model that reduces exploration over time."""
+
     def __init__(
         self,
         initial_exploration_probability: float = 0.2,
         decay_rate: float = 1,
-        minimum_exploration_probability=0.01,
-        random_state: RandomState = RandomState(),
+        minimum_exploration_probability: float = 0.01,
+        random_state: RandomState = None,
     ):
+        """To set up a classic e-greedy model, the decay rate needs to be 1."""
+        if random_state is None:
+            random_state = RandomState()
         self.random_state = random_state
         self.epsilon = initial_exploration_probability
         self.minimum_exploration_probability = minimum_exploration_probability
@@ -106,27 +117,26 @@ 
    Classes
    
 
     def action_for_state(self, state: Any) -> Any:
         """With probability epsilon, we explore by sampling one of the random available actions.
-        Otherwise we exploit by chosing the action with the highest Q value."""
+        Otherwise we exploit by chosing the action with the highest Q value.
+        """
         if self.random_state.random() < self.epsilon:
             action = self.explore()
         else:
             action = self.exploit(state)
         return action
 
-    def _decay(self):
-        """ Slowly decrease the exploration probability. """
-        self.epsilon = max(
-            self.epsilon * self.decay_rate, self.minimum_exploration_probability
-        )
+    def _decay(self) -> None:
+        """Slowly decrease the exploration probability."""
+        self.epsilon = max(self.epsilon * self.decay_rate, self.minimum_exploration_probability)
 
     @abstractmethod
     def explore(self) -> Any:
-        """ Randomly selects an action"""
+        """Randomly selects an action"""
         pass
 
     @abstractmethod
     def exploit(self, state: Any) -> Any:
-        """ Selects the best action known for the given state """
+        """Select the best action known for the given state"""
         pass

    Ancestors

    @@ -152,7 +162,8 @@

    Methods

    def action_for_state(self, state: Any) -> Any:
     """With probability epsilon, we explore by sampling one of the random available actions.
-    Otherwise we exploit by chosing the action with the highest Q value."""
+    Otherwise we exploit by chosing the action with the highest Q value.
+    """
     if self.random_state.random() < self.epsilon:
         action = self.explore()
     else:
@@ -164,14 +175,14 @@ 
    Methods
    
 def exploit(self, state: Any) ‑> Any
     
 
    
-
    Selects the best action known for the given state
    
+
    Select the best action known for the given state
    
 
    
 
 Expand source code
 
 
    @abstractmethod
 def exploit(self, state: Any) -> Any:
-    """ Selects the best action known for the given state """
+    """Select the best action known for the given state"""
     pass
    
 
    
 
    
@@ -186,11 +197,20 @@ 
    Methods
    
 
 
    @abstractmethod
 def explore(self) -> Any:
-    """ Randomly selects an action"""
+    """Randomly selects an action"""
     pass
    +

    Inherited members

    + @@ -222,7 +242,7 @@

    \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/agents/index.html b/docs/api/deeprecsys/rl/agents/index.html index 977c086..d39aa96 100644 --- a/docs/api/deeprecsys/rl/agents/index.html +++ b/docs/api/deeprecsys/rl/agents/index.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.agents API documentation @@ -89,7 +89,7 @@

    Index

    \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/agents/rainbow.html b/docs/api/deeprecsys/rl/agents/rainbow.html index a7dcf35..2dc4c4d 100644 --- a/docs/api/deeprecsys/rl/agents/rainbow.html +++ b/docs/api/deeprecsys/rl/agents/rainbow.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.agents.rainbow API documentation @@ -26,28 +26,30 @@

    Module deeprecsys.rl.agents.rainbow

    Expand source code -
    from numpy.random import RandomState
-from typing import Any, Optional, List
+
    from copy import deepcopy
+from typing import Any, List, Optional
+
 from numpy import arange
-from copy import deepcopy
-from deeprecsys.rl.neural_networks.dueling import DuelingDDQN
-from deeprecsys.rl.experience_replay.priority_replay_buffer import (
-    PrioritizedExperienceReplayBuffer,
-)
+from numpy.random import RandomState
+
+from deeprecsys.neural_networks.dueling import DuelingDDQN
+from deeprecsys.rl.agents.agent import ReinforcementLearning
 from deeprecsys.rl.experience_replay.buffer_parameters import (
-    PERBufferParameters,
     ExperienceReplayBufferParameters,
+    PERBufferParameters,
+)
+from deeprecsys.rl.experience_replay.priority_replay_buffer import (
+    PrioritizedExperienceReplayBuffer,
 )
-from deeprecsys.rl.agents.agent import ReinforcementLearning
 from deeprecsys.rl.learning_statistics import LearningStatistics
 
 
 class RainbowDQNAgent(ReinforcementLearning):
-
     """Instead of sampling randomly from the buffer we prioritize experiences with PER
     Instead of epsilon-greedy we use gaussian noisy layers for exploration
     Instead of the Q value we calculate Value and Advantage (Dueling DQN).
-    This implementation does not include the Categorical DQN part (yet)."""
+    This implementation does not include the Categorical DQN part (yet).
+    """
 
     def __init__(
         self,
@@ -64,10 +66,12 @@ 
    Module deeprecsys.rl.agents.rainbow
    
         discount_factor: float = 0.99,
         learning_rate: float = 0.0001,
         hidden_layers: List[int] = None,
-        random_state: RandomState = RandomState(),
+        random_state: RandomState = None,
         statistics: Optional[LearningStatistics] = None,
     ):
-
+        """Start the network and the buffer with the provided parameters."""
+        if random_state is None:
+            random_state = RandomState()
         self.network = DuelingDDQN(
             n_input=input_size,
             n_output=output_size,
@@ -97,7 +101,7 @@ 
    Module deeprecsys.rl.agents.rainbow
    
         self.actions = arange(output_size)
         self.random_state = random_state
 
-    def _check_update_network(self):
+    def _check_update_network(self) -> None:
         # we only start training the network once the buffer is ready
         # (the burn in is filled)
         if self.buffer.ready_to_predict():
@@ -110,6 +114,7 @@ 
    Module deeprecsys.rl.agents.rainbow
    
                 self.target_network.load_state_dict(self.network.state_dict())
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+        """Get the next k best actions for the given state"""
         state_flat = state.flatten()
         if self.buffer.ready_to_predict():
             actions = self.target_network.top_k_actions_for_state(state_flat, k=k)
@@ -119,11 +124,11 @@ 
    Module deeprecsys.rl.agents.rainbow
    
         return actions
 
     def action_for_state(self, state: Any) -> Any:
+        """Get the next best action for the given state."""
         return self.top_k_actions_for_state(state, k=1)[0]
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store the experience in the buffer"""
         state_flat = state.flatten()
         new_state_flat = new_state.flatten()
         self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
    
@@ -140,23 +145,24 @@ 
    Classes
    
 
    
 
    
 class RainbowDQNAgent
-(input_size: int, output_size: int, network_update_frequency: int = 5, network_sync_frequency: int = 200, priority_importance: float = 0.6, priority_weigth_growth: float = 0.001, buffer_size: int = 10000, buffer_burn_in: int = 1000, batch_size: int = 32, noise_sigma: float = 0.017, discount_factor: float = 0.99, learning_rate: float = 0.0001, hidden_layers: List[int] = None, random_state: numpy.random.mtrand.RandomState = RandomState(MT19937), statistics: Union[LearningStatistics, NoneType] = None)
+(input_size: int, output_size: int, network_update_frequency: int = 5, network_sync_frequency: int = 200, priority_importance: float = 0.6, priority_weigth_growth: float = 0.001, buffer_size: int = 10000, buffer_burn_in: int = 1000, batch_size: int = 32, noise_sigma: float = 0.017, discount_factor: float = 0.99, learning_rate: float = 0.0001, hidden_layers: List[int] = None, random_state: numpy.random.mtrand.RandomState = None, statistics: Optional[LearningStatistics] = None)
 
    
 
    
 
    Instead of sampling randomly from the buffer we prioritize experiences with PER
 Instead of epsilon-greedy we use gaussian noisy layers for exploration
 Instead of the Q value we calculate Value and Advantage (Dueling DQN).
-This implementation does not include the Categorical DQN part (yet).
    
+This implementation does not include the Categorical DQN part (yet).
    
+
    Start the network and the buffer with the provided parameters.
    
 
    
 
 Expand source code
 
 
    class RainbowDQNAgent(ReinforcementLearning):
-
     """Instead of sampling randomly from the buffer we prioritize experiences with PER
     Instead of epsilon-greedy we use gaussian noisy layers for exploration
     Instead of the Q value we calculate Value and Advantage (Dueling DQN).
-    This implementation does not include the Categorical DQN part (yet)."""
+    This implementation does not include the Categorical DQN part (yet).
+    """
 
     def __init__(
         self,
@@ -173,10 +179,12 @@ 
    Classes
    
         discount_factor: float = 0.99,
         learning_rate: float = 0.0001,
         hidden_layers: List[int] = None,
-        random_state: RandomState = RandomState(),
+        random_state: RandomState = None,
         statistics: Optional[LearningStatistics] = None,
     ):
-
+        """Start the network and the buffer with the provided parameters."""
+        if random_state is None:
+            random_state = RandomState()
         self.network = DuelingDDQN(
             n_input=input_size,
             n_output=output_size,
@@ -206,7 +214,7 @@ 
    Classes
    
         self.actions = arange(output_size)
         self.random_state = random_state
 
-    def _check_update_network(self):
+    def _check_update_network(self) -> None:
         # we only start training the network once the buffer is ready
         # (the burn in is filled)
         if self.buffer.ready_to_predict():
@@ -219,6 +227,7 @@ 
    Classes
    
                 self.target_network.load_state_dict(self.network.state_dict())
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+        """Get the next k best actions for the given state"""
         state_flat = state.flatten()
         if self.buffer.ready_to_predict():
             actions = self.target_network.top_k_actions_for_state(state_flat, k=k)
@@ -228,11 +237,11 @@ 
    Classes
    
         return actions
 
     def action_for_state(self, state: Any) -> Any:
+        """Get the next best action for the given state."""
         return self.top_k_actions_for_state(state, k=1)[0]
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store the experience in the buffer"""
         state_flat = state.flatten()
         new_state_flat = new_state.flatten()
         self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
    
@@ -248,27 +257,27 @@ 
    Methods
    
 def action_for_state(self, state: Any) ‑> Any
     
 
    
-
    
+
    Get the next best action for the given state.
    
 
    
 
 Expand source code
 
 
    def action_for_state(self, state: Any) -> Any:
+    """Get the next best action for the given state."""
     return self.top_k_actions_for_state(state, k=1)[0]
    
 
    
 
    
 
    
-def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any)
+def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
 
    
 
    
-
    
+
    Store the experience in the buffer
    
 
    
 
 Expand source code
 
-
    def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
+
    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+    """Store the experience in the buffer"""
     state_flat = state.flatten()
     new_state_flat = new_state.flatten()
     self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
    
@@ -278,12 +287,13 @@ 
    Methods
    
 def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> Any
     
 
    
-
    
+
    Get the next k best actions for the given state
    
 
    
 
 Expand source code
 
 
    def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+    """Get the next k best actions for the given state"""
     state_flat = state.flatten()
     if self.buffer.ready_to_predict():
         actions = self.target_network.top_k_actions_for_state(state_flat, k=k)
@@ -325,7 +335,7 @@ 
    
-
    Generated by pdoc 0.9.2.
    
+
    Generated by pdoc 0.10.0.
    
 
 
 
\ No newline at end of file
diff --git a/docs/api/deeprecsys/rl/agents/reinforce.html b/docs/api/deeprecsys/rl/agents/reinforce.html
index 9e9e9a6..f06276f 100644
--- a/docs/api/deeprecsys/rl/agents/reinforce.html
+++ b/docs/api/deeprecsys/rl/agents/reinforce.html
@@ -3,7 +3,7 @@
 
 
 
-
+
 deeprecsys.rl.agents.reinforce API documentation
 
 
@@ -26,29 +26,37 @@ 
    Module deeprecsys.rl.agents.reinforce
    
 
 Expand source code
 
-
    import numpy as np
+
    from typing import Any, List, Optional
+
+import numpy as np
+from torch import FloatTensor
+
+from deeprecsys.neural_networks.policy_estimator import PolicyEstimator
 from deeprecsys.rl.agents.agent import ReinforcementLearning
-from typing import Any, List, Optional
-from deeprecsys.rl.experience_replay.experience_buffer import ExperienceReplayBuffer
 from deeprecsys.rl.experience_replay.buffer_parameters import (
     ExperienceReplayBufferParameters,
 )
-from deeprecsys.rl.neural_networks.policy_estimator import PolicyEstimator
-from torch import FloatTensor
+from deeprecsys.rl.experience_replay.experience_buffer import ExperienceReplayBuffer
 
 
 class ReinforceAgent(ReinforcementLearning):
-    """Policy estimator using a value estimator as a baseline.
-    It's on-policy, for discrete action spaces, and episodic environments."""
+    """REINFORCE: Policy estimator using a value estimator as a baseline.
+    It's on-policy, for discrete action spaces, and episodic environments.
+    """
+
+    buffer: ExperienceReplayBuffer
 
     def __init__(
         self,
         n_actions: int,
         state_size: int,
         hidden_layers: Optional[List[int]] = None,
-        discount_factor: int = 0.99,  # a.k.a gamma
-        learning_rate=1e-3,
+        discount_factor: float = 0.99,
+        learning_rate: float = 1e-3,
     ):
+        """Start the network with the parameters provided.
+        The discount factor is commonly known as gamma.
+        """
         self.episode_count = 0
         if not hidden_layers:
             hidden_layers = [state_size * 2, state_size * 2]
@@ -62,20 +70,22 @@ 
    Module deeprecsys.rl.agents.reinforce
    
         # starts the buffer
         self.reset_buffer()
 
-    def reset_buffer(self):
-        self.buffer = ExperienceReplayBuffer(
-            ExperienceReplayBufferParameters(10000, 1, 1)
-        )
+    def reset_buffer(self) -> None:
+        """Recreate the experience buffer, effectively forgetting all the experiences
+        collected so far.
+        """
+        self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1))
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+        """Return the k next best actions for the given state."""
         return self.policy_estimator.predict(state, k=k)
 
     def action_for_state(self, state: Any) -> int:
+        """Return the best action for the given state."""
         return self.top_k_actions_for_state(state)[0]
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store the experience in the buffer and run the backpropagation if the buffer is ready."""
         state_flat = state.flatten()
         new_state_flat = new_state.flatten()
         self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
@@ -87,7 +97,8 @@ 
    Module deeprecsys.rl.agents.reinforce
    
     def discounted_rewards(self, rewards: np.array) -> np.array:
         """From a list of rewards obtained in an episode, we calculate
         the return minus the baseline. The baseline is the list of discounted
-        rewards minus the mean, divided by the standard deviation."""
+        rewards minus the mean, divided by the standard deviation.
+        """
         discount_r = np.zeros_like(rewards)
         timesteps = range(len(rewards))
         reward_sum = 0
@@ -99,13 +110,12 @@ 
    Module deeprecsys.rl.agents.reinforce
    
         baseline = (discount_r - return_mean) / return_std
         return baseline
 
-    def learn_from_experiences(self):
+    def learn_from_experiences(self) -> None:
+        """Train the policy estimator with all the experiences collected so far."""
         experiences = list(self.buffer.experience_queue)
-        states, actions, rewards, dones, next_states = zip(*experiences)
+        states, actions, rewards, dones, next_states = zip(*experiences, strict=False)
         advantages = self.discounted_rewards(rewards)
-        advantages_tensor = FloatTensor(advantages).to(
-            device=self.policy_estimator.device
-        )
+        advantages_tensor = FloatTensor(advantages).to(device=self.policy_estimator.device)
         self.policy_estimator.update(states, advantages_tensor, actions)
    
 
    
 
@@ -120,27 +130,35 @@ 
    Classes
    
 
    
 
    
 class ReinforceAgent
-(n_actions: int, state_size: int, hidden_layers: Union[List[int], NoneType] = None, discount_factor: int = 0.99, learning_rate=0.001)
+(n_actions: int, state_size: int, hidden_layers: Optional[List[int]] = None, discount_factor: float = 0.99, learning_rate: float = 0.001)
 
    
 
    
-
    Policy estimator using a value estimator as a baseline.
-It's on-policy, for discrete action spaces, and episodic environments.
    
+
    REINFORCE: Policy estimator using a value estimator as a baseline.
+It's on-policy, for discrete action spaces, and episodic environments.
    
+
    Start the network with the parameters provided.
+The discount factor is commonly known as gamma.
    
 
    
 
 Expand source code
 
 
    class ReinforceAgent(ReinforcementLearning):
-    """Policy estimator using a value estimator as a baseline.
-    It's on-policy, for discrete action spaces, and episodic environments."""
+    """REINFORCE: Policy estimator using a value estimator as a baseline.
+    It's on-policy, for discrete action spaces, and episodic environments.
+    """
+
+    buffer: ExperienceReplayBuffer
 
     def __init__(
         self,
         n_actions: int,
         state_size: int,
         hidden_layers: Optional[List[int]] = None,
-        discount_factor: int = 0.99,  # a.k.a gamma
-        learning_rate=1e-3,
+        discount_factor: float = 0.99,
+        learning_rate: float = 1e-3,
     ):
+        """Start the network with the parameters provided.
+        The discount factor is commonly known as gamma.
+        """
         self.episode_count = 0
         if not hidden_layers:
             hidden_layers = [state_size * 2, state_size * 2]
@@ -154,20 +172,22 @@ 
    Classes
    
         # starts the buffer
         self.reset_buffer()
 
-    def reset_buffer(self):
-        self.buffer = ExperienceReplayBuffer(
-            ExperienceReplayBufferParameters(10000, 1, 1)
-        )
+    def reset_buffer(self) -> None:
+        """Recreate the experience buffer, effectively forgetting all the experiences
+        collected so far.
+        """
+        self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1))
 
     def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+        """Return the k next best actions for the given state."""
         return self.policy_estimator.predict(state, k=k)
 
     def action_for_state(self, state: Any) -> int:
+        """Return the best action for the given state."""
         return self.top_k_actions_for_state(state)[0]
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+        """Store the experience in the buffer and run the backpropagation if the buffer is ready."""
         state_flat = state.flatten()
         new_state_flat = new_state.flatten()
         self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
@@ -179,7 +199,8 @@ 
    Classes
    
     def discounted_rewards(self, rewards: np.array) -> np.array:
         """From a list of rewards obtained in an episode, we calculate
         the return minus the baseline. The baseline is the list of discounted
-        rewards minus the mean, divided by the standard deviation."""
+        rewards minus the mean, divided by the standard deviation.
+        """
         discount_r = np.zeros_like(rewards)
         timesteps = range(len(rewards))
         reward_sum = 0
@@ -191,13 +212,12 @@ 
    Classes
    
         baseline = (discount_r - return_mean) / return_std
         return baseline
 
-    def learn_from_experiences(self):
+    def learn_from_experiences(self) -> None:
+        """Train the policy estimator with all the experiences collected so far."""
         experiences = list(self.buffer.experience_queue)
-        states, actions, rewards, dones, next_states = zip(*experiences)
+        states, actions, rewards, dones, next_states = zip(*experiences, strict=False)
         advantages = self.discounted_rewards(rewards)
-        advantages_tensor = FloatTensor(advantages).to(
-            device=self.policy_estimator.device
-        )
+        advantages_tensor = FloatTensor(advantages).to(device=self.policy_estimator.device)
         self.policy_estimator.update(states, advantages_tensor, actions)
    
 
    
 
    Ancestors
    
@@ -205,18 +225,26 @@ 
    Ancestors
    
 
  • ReinforcementLearning
    • 
 
  • abc.ABC
    • 
 
+
    Class variables
    
+
    
+
    var buffer : ExperienceReplayBuffer
    
+
    
+
    
+
    
+
    
 
    Methods
    
 
    
 
    
 def action_for_state(self, state: Any) ‑> int
 
    
 
    
-
    
+
    Return the best action for the given state.
    
 
    
 
 Expand source code
 
 
    def action_for_state(self, state: Any) -> int:
+    """Return the best action for the given state."""
     return self.top_k_actions_for_state(state)[0]
    
 
    
 
    
@@ -234,7 +262,8 @@ 
    Methods
    
 
    def discounted_rewards(self, rewards: np.array) -> np.array:
     """From a list of rewards obtained in an episode, we calculate
     the return minus the baseline. The baseline is the list of discounted
-    rewards minus the mean, divided by the standard deviation."""
+    rewards minus the mean, divided by the standard deviation.
+    """
     discount_r = np.zeros_like(rewards)
     timesteps = range(len(rewards))
     reward_sum = 0
@@ -248,51 +277,51 @@ 
    Methods
    
 
    
 
    
 
    
-def learn_from_experiences(self)
+def learn_from_experiences(self) ‑> None
 
    
 
    
-
    
+
    Train the policy estimator with all the experiences collected so far.
    
 
    
 
 Expand source code
 
-
    def learn_from_experiences(self):
+
    def learn_from_experiences(self) -> None:
+    """Train the policy estimator with all the experiences collected so far."""
     experiences = list(self.buffer.experience_queue)
-    states, actions, rewards, dones, next_states = zip(*experiences)
+    states, actions, rewards, dones, next_states = zip(*experiences, strict=False)
     advantages = self.discounted_rewards(rewards)
-    advantages_tensor = FloatTensor(advantages).to(
-        device=self.policy_estimator.device
-    )
+    advantages_tensor = FloatTensor(advantages).to(device=self.policy_estimator.device)
     self.policy_estimator.update(states, advantages_tensor, actions)
    
 
    
 
    
 
    
-def reset_buffer(self)
+def reset_buffer(self) ‑> None
 
    
 
    
-
    
+
    Recreate the experience buffer, effectively forgetting all the experiences
+collected so far.
    
 
    
 
 Expand source code
 
-
    def reset_buffer(self):
-    self.buffer = ExperienceReplayBuffer(
-        ExperienceReplayBufferParameters(10000, 1, 1)
-    )
    
+
    def reset_buffer(self) -> None:
+    """Recreate the experience buffer, effectively forgetting all the experiences
+    collected so far.
+    """
+    self.buffer = ExperienceReplayBuffer(ExperienceReplayBufferParameters(10000, 1, 1))
    
 
    
 
    
 
    
-def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any)
+def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
 
    
 
    
-
    
+
    Store the experience in the buffer and run the backpropagation if the buffer is ready.
    
 
    
 
 Expand source code
 
-
    def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
+
    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+    """Store the experience in the buffer and run the backpropagation if the buffer is ready."""
     state_flat = state.flatten()
     new_state_flat = new_state.flatten()
     self.buffer.store_experience(state_flat, action, reward, done, new_state_flat)
@@ -306,12 +335,13 @@ 
    Methods
    
 def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> List[int]
     
 
    
-
    
+
    Return the k next best actions for the given state.
    
 
    
 
 Expand source code
 
 
    def top_k_actions_for_state(self, state: Any, k: int = 1) -> List[int]:
+    """Return the k next best actions for the given state."""
     return self.policy_estimator.predict(state, k=k)
    
 
    
 
    
@@ -337,6 +367,7 @@ 
    Index
    
 
    ReinforceAgent
    
 
      
 
    • action_for_state
      • 
+
    • buffer
      • 
 
    • discounted_rewards
      • 
 
    • learn_from_experiences
      • 
 
    • reset_buffer
      • 
@@ -350,7 +381,7 @@ 
      
-
      Generated by pdoc 0.9.2.
      
+
      Generated by pdoc 0.10.0.
      
 
 
 
\ No newline at end of file
diff --git a/docs/api/deeprecsys/rl/agents/soft_actor_critic.html b/docs/api/deeprecsys/rl/agents/soft_actor_critic.html
index 6e6eae6..92addfa 100644
--- a/docs/api/deeprecsys/rl/agents/soft_actor_critic.html
+++ b/docs/api/deeprecsys/rl/agents/soft_actor_critic.html
@@ -3,7 +3,7 @@
 
 
 
-
+
 deeprecsys.rl.agents.soft_actor_critic API documentation
 
 
@@ -27,31 +27,34 @@ 
      Module deeprecsys.rl.agents.soft_actor_critic
      Expand source code
 
 
      from copy import deepcopy
+from typing import Any, Optional
+
 import torch
-from torch import FloatTensor, BoolTensor
-from typing import Any
-from gym.spaces import Space
-from deeprecsys.rl.neural_networks.gaussian_actor import GaussianActor
-from deeprecsys.rl.neural_networks.q_value_estimator import TwinnedQValueEstimator
+from gymnasium.spaces import Discrete
+from torch import BoolTensor, FloatTensor
+
+from deeprecsys.neural_networks.gaussian_actor import GaussianActor
+from deeprecsys.neural_networks.q_value_estimator import TwinnedQValueEstimator
 from deeprecsys.rl.agents.agent import ReinforcementLearning
-from deeprecsys.rl.experience_replay.priority_replay_buffer import (
-    PrioritizedExperienceReplayBuffer,
-)
 from deeprecsys.rl.experience_replay.buffer_parameters import (
     ExperienceReplayBufferParameters,
     PERBufferParameters,
 )
+from deeprecsys.rl.experience_replay.priority_replay_buffer import (
+    PrioritizedExperienceReplayBuffer,
+)
 
 
 class SoftActorCritic(ReinforcementLearning):
     """TODO: there's things to fix in this agent. It needs temperature
     optimization, and replace the current q-value estimator with the
     Q-value + value + value_target estimators, like described here
-    https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html"""
+    https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html
+    """
 
     def __init__(
         self,
-        action_space: Space,
+        action_space: Discrete,
         state_size: int,
         timesteps_to_start_predicting: int = 256,
         learning_rate: float = 0.0001,
@@ -59,10 +62,14 @@ 
      Module deeprecsys.rl.agents.soft_actor_critic
      Module deeprecsys.rl.agents.soft_actor_critic
      Module deeprecsys.rl.agents.soft_actor_critic

    Module deeprecsys.rl.agents.soft_actor_criticModule deeprecsys.rl.agents.soft_actor_criticModule deeprecsys.rl.agents.soft_actor_criticClasses
    class SoftActorCritic -(action_space: gym.spaces.space.Space, state_size: int, timesteps_to_start_predicting: int = 256, learning_rate: float = 0.0001, soft_target_update_rate: float = 0.005, entropy_coefficient: float = 0.2, target_update_interval: int = 2, discount_factor: float = 0.99, buffer_parameters=<deeprecsys.rl.experience_replay.buffer_parameters.ExperienceReplayBufferParameters object>, per_parameters=<deeprecsys.rl.experience_replay.buffer_parameters.PERBufferParameters object>) +(action_space: gymnasium.spaces.discrete.Discrete, state_size: int, timesteps_to_start_predicting: int = 256, learning_rate: float = 0.0001, soft_target_update_rate: float = 0.005, entropy_coefficient: float = 0.2, target_update_interval: int = 2, discount_factor: float = 0.99, buffer_parameters: Optional[ExperienceReplayBufferParameters] = None, per_parameters: Optional[PERBufferParameters] = None)

    TODO: there's things to fix in this agent. It needs temperature optimization, and replace the current q-value estimator with the Q-value + value + value_target estimators, like described here -https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html

    +https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html

    +

    Initialize the agent and the buffer with the given parameters.

    Expand source code @@ -207,11 +211,12 @@

    Classes

    """TODO: there's things to fix in this agent. It needs temperature optimization, and replace the current q-value estimator with the Q-value + value + value_target estimators, like described here - https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html""" + https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html + """ def __init__( self, - action_space: Space, + action_space: Discrete, state_size: int, timesteps_to_start_predicting: int = 256, learning_rate: float = 0.0001, @@ -219,10 +224,14 @@

    Classes

    entropy_coefficient: float = 0.2, target_update_interval: int = 2, discount_factor: float = 0.99, - buffer_parameters=ExperienceReplayBufferParameters(), - per_parameters=PERBufferParameters(), + buffer_parameters: Optional[ExperienceReplayBufferParameters] = None, + per_parameters: Optional[PERBufferParameters] = None, ): - + """Initialize the agent and the buffer with the given parameters.""" + if buffer_parameters is None: + buffer_parameters = ExperienceReplayBufferParameters() + if per_parameters is None: + per_parameters = PERBufferParameters() self.action_space = action_space n_actions = 1 # TODO: slate size self.actor = GaussianActor( @@ -232,13 +241,11 @@

    Classes

    entropy_coefficient=entropy_coefficient, discount_factor=discount_factor, ) - self.critic = TwinnedQValueEstimator( - inputs=state_size + 1, learning_rate=learning_rate - ) + self.critic = TwinnedQValueEstimator(inputs=state_size + 1, learning_rate=learning_rate) self.target_critic = deepcopy(self.critic) self.buffer = PrioritizedExperienceReplayBuffer( - buffer_parameters=ExperienceReplayBufferParameters(), - per_parameters=PERBufferParameters(), + buffer_parameters=buffer_parameters, + per_parameters=per_parameters, ) # disable gradient calculations of the target network @@ -251,26 +258,26 @@

    Classes

    self.target_update_interval = target_update_interval self.gamma = discount_factor - def should_update_network(self): + def should_update_network(self) -> bool: + """Check if the buffer is ready to predict and if enough timesteps have passed.""" return ( - self.timesteps >= self.timesteps_to_start_predicting - and self.buffer.ready_to_predict() # noqa + self.timesteps >= self.timesteps_to_start_predicting and self.buffer.ready_to_predict() # noqa ) def action_for_state(self, state: Any) -> Any: + """Before we're ready to start predicting, we return random actions (explore).""" if self.timesteps < self.timesteps_to_start_predicting: action = self.action_space.sample() else: action = self.explore(state) return int(action) - def top_k_actions_for_state(self, state, k): - # TODO: + def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any: + """TODO""" pass - def store_experience( - self, state: Any, action: Any, reward: float, done: bool, new_state: Any - ): + def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None: + """Store the experience in the buffer.""" self.timesteps += 1 state_flat = state.flatten() new_state_flat = new_state.flatten() @@ -279,7 +286,7 @@

    Classes

    self.learn() def explore(self, state: Any) -> Any: - # act with gaussian randomness + """Act with gaussian randomness""" with torch.no_grad(): action, _, _ = self.actor.predict(state.reshape(1, -1)) action_array = action.cpu().numpy().reshape(-1) @@ -287,16 +294,16 @@

    Classes

    return action_array[0].clip(0, n_actions - 1).round() def exploit(self, state: Any) -> Any: - # act without randomness + """Act without randomness""" with torch.no_grad(): _, _, action = self.actor.predict(state.reshape(1, -1)) action_array = action.cpu().numpy().reshape(-1) n_actions = self.action_space.n return action_array[0].clip(0, n_actions - 1).round() - def learn(self): + def learn(self) -> None: + """Run the backpropagation and update the buffer priorities""" self.learning_steps += 1 - if self.learning_steps % self.target_update_interval == 0: # instead of updating the target network "the hard way", we use a Tau # parameter as a weighting factor to update the weights as an @@ -306,9 +313,7 @@

    Classes

    # batch with indices and priority weights batch = self.buffer.sample_batch() - states, actions, rewards, dones, next_states, weights, samples = [ - i for i in batch - ] + states, actions, rewards, dones, next_states, weights, samples = [i for i in batch] # convert to tensors device = self.critic.device state_tensors = FloatTensor(states).to(device=device) @@ -351,12 +356,13 @@

    Methods

    def action_for_state(self, state: Any) ‑> Any
    -
    +

    Before we're ready to start predicting, we return random actions (explore).

    Expand source code 
    def action_for_state(self, state: Any) -> Any:
+    """Before we're ready to start predicting, we return random actions (explore)."""
     if self.timesteps < self.timesteps_to_start_predicting:
         action = self.action_space.sample()
     else:
@@ -368,13 +374,13 @@ 
    Methods
    
 def exploit(self, state: Any) ‑> Any
     
 
    
-
    
+
    Act without randomness
    
 
    
 
 Expand source code
 
 
    def exploit(self, state: Any) -> Any:
-    # act without randomness
+    """Act without randomness"""
     with torch.no_grad():
         _, _, action = self.actor.predict(state.reshape(1, -1))
     action_array = action.cpu().numpy().reshape(-1)
@@ -386,13 +392,13 @@ 
    Methods
    
 def explore(self, state: Any) ‑> Any
     
 
    
-
    
+
    Act with gaussian randomness
    
 
    
 
 Expand source code
 
 
    def explore(self, state: Any) -> Any:
-    # act with gaussian randomness
+    """Act with gaussian randomness"""
     with torch.no_grad():
         action, _, _ = self.actor.predict(state.reshape(1, -1))
     action_array = action.cpu().numpy().reshape(-1)
@@ -401,17 +407,17 @@ 
    Methods
    
 
    
 
    
 
    
-def learn(self)
+def learn(self) ‑> None
 
    
 
    
-
    
+
    Run the backpropagation and update the buffer priorities
    
 
    
 
 Expand source code
 
-
    def learn(self):
+
    def learn(self) -> None:
+    """Run the backpropagation and update the buffer priorities"""
     self.learning_steps += 1
-
     if self.learning_steps % self.target_update_interval == 0:
         # instead of updating the target network "the hard way", we use a Tau
         # parameter as a weighting factor to update the weights as an
@@ -421,9 +427,7 @@ 
    Methods
    
 
     # batch with indices and priority weights
     batch = self.buffer.sample_batch()
-    states, actions, rewards, dones, next_states, weights, samples = [
-        i for i in batch
-    ]
+    states, actions, rewards, dones, next_states, weights, samples = [i for i in batch]
     # convert to tensors
     device = self.critic.device
     state_tensors = FloatTensor(states).to(device=device)
@@ -457,33 +461,32 @@ 
    Methods
    
 
    
 
    
 
    
-def should_update_network(self)
+def should_update_network(self) ‑> bool
 
    
 
    
-
    
+
    Check if the buffer is ready to predict and if enough timesteps have passed.
    
 
    
 
 Expand source code
 
-
    def should_update_network(self):
+
    def should_update_network(self) -> bool:
+    """Check if the buffer is ready to predict and if enough timesteps have passed."""
     return (
-        self.timesteps >= self.timesteps_to_start_predicting
-        and self.buffer.ready_to_predict()  # noqa
+        self.timesteps >= self.timesteps_to_start_predicting and self.buffer.ready_to_predict()  # noqa
     )
    
 
    
 
    
 
    
-def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any)
+def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) ‑> None
 
    
 
    
-
    
+
    Store the experience in the buffer.
    
 
    
 
 Expand source code
 
-
    def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, new_state: Any
-):
+
    def store_experience(self, state: Any, action: Any, reward: float, done: bool, new_state: Any) -> None:
+    """Store the experience in the buffer."""
     self.timesteps += 1
     state_flat = state.flatten()
     new_state_flat = new_state.flatten()
@@ -493,16 +496,16 @@ 
    Methods
    
 
    
 
    
 
    
-def top_k_actions_for_state(self, state, k)
+def top_k_actions_for_state(self, state: Any, k: int = 1) ‑> Any
 
    
 
    
-
    
+
    TODO
    
 
    
 
 Expand source code
 
-
    def top_k_actions_for_state(self, state, k):
-    # TODO:
+
    def top_k_actions_for_state(self, state: Any, k: int = 1) -> Any:
+    """TODO"""
     pass
    
 
    
 
    
@@ -542,7 +545,7 @@ 
    
 
 
 
 
\ No newline at end of file
diff --git a/docs/api/deeprecsys/rl/experience_replay/buffer_parameters.html b/docs/api/deeprecsys/rl/experience_replay/buffer_parameters.html
index 5baf425..ab5f746 100644
--- a/docs/api/deeprecsys/rl/experience_replay/buffer_parameters.html
+++ b/docs/api/deeprecsys/rl/experience_replay/buffer_parameters.html
@@ -3,7 +3,7 @@
 
 
 
-
+
 deeprecsys.rl.experience_replay.buffer_parameters API documentation
 
 
@@ -30,28 +30,30 @@ 
    Module deeprecsys.rl.experience_replay.buffer_parameters
 
 
 class ExperienceReplayBufferParameters:
-    """ Parameters to configure an experience replay buffer. """
+    """Parameters to configure an experience replay buffer."""
 
     def __init__(
         self,
         max_experiences: int = 50,
         minimum_experiences_to_start_predicting: int = 32,
         batch_size: int = 32,
-        random_state: RandomState = RandomState(),
+        random_state: RandomState = None,
     ):
+        """Initialize the buffer parameters."""
+        if random_state is None:
+            random_state = RandomState()
         if minimum_experiences_to_start_predicting < batch_size:
             raise ValueError("The batch size mus the larger than the burn in")
         self.max_experiences = max_experiences
-        self.minimum_experiences_to_start_predicting = (
-            minimum_experiences_to_start_predicting
-        )
+        self.minimum_experiences_to_start_predicting = minimum_experiences_to_start_predicting
         self.batch_size = batch_size
         self.random_state = random_state
 
 
 class PERBufferParameters:
-    """Parameters to configure the priorititization of experiences in a
-    Prioritized-Experience Replay Buffer"""
+    """Parameters to configure the prioritization of experiences in a
+    Prioritized-Experience Replay Buffer
+    """
 
     def __init__(
         self,
@@ -60,6 +62,7 @@ 
    Module deeprecsys.rl.experience_replay.buffer_parameters
         alpha: float = 0.6,
         epsilon: float = 0.01,
     ):
+        """Store the parameters referring to the importance sampling."""
         self.beta = beta
         self.beta_growth = beta_growth
         self.alpha = alpha
@@ -77,30 +80,32 @@ 
    Classes
    
 
    
 
    
 class ExperienceReplayBufferParameters
-(max_experiences: int = 50, minimum_experiences_to_start_predicting: int = 32, batch_size: int = 32, random_state: numpy.random.mtrand.RandomState = RandomState(MT19937))
+(max_experiences: int = 50, minimum_experiences_to_start_predicting: int = 32, batch_size: int = 32, random_state: numpy.random.mtrand.RandomState = None)
 
    
 
    
-
    Parameters to configure an experience replay buffer.
    
+
    Parameters to configure an experience replay buffer.
    
+
    Initialize the buffer parameters.
    
 
    
 
 Expand source code
 
 
    class ExperienceReplayBufferParameters:
-    """ Parameters to configure an experience replay buffer. """
+    """Parameters to configure an experience replay buffer."""
 
     def __init__(
         self,
         max_experiences: int = 50,
         minimum_experiences_to_start_predicting: int = 32,
         batch_size: int = 32,
-        random_state: RandomState = RandomState(),
+        random_state: RandomState = None,
     ):
+        """Initialize the buffer parameters."""
+        if random_state is None:
+            random_state = RandomState()
         if minimum_experiences_to_start_predicting < batch_size:
             raise ValueError("The batch size mus the larger than the burn in")
         self.max_experiences = max_experiences
-        self.minimum_experiences_to_start_predicting = (
-            minimum_experiences_to_start_predicting
-        )
+        self.minimum_experiences_to_start_predicting = minimum_experiences_to_start_predicting
         self.batch_size = batch_size
         self.random_state = random_state
    
 
    
@@ -110,15 +115,17 @@ 
    Classes
    
 (beta: float = 0.01, beta_growth: float = 0.001, alpha: float = 0.6, epsilon: float = 0.01)
     
 
    
-
    Parameters to configure the priorititization of experiences in a
-Prioritized-Experience Replay Buffer
    
+
    Parameters to configure the prioritization of experiences in a
+Prioritized-Experience Replay Buffer
    
+
    Store the parameters referring to the importance sampling.
    
 
    
 
 Expand source code
 
 
    class PERBufferParameters:
-    """Parameters to configure the priorititization of experiences in a
-    Prioritized-Experience Replay Buffer"""
+    """Parameters to configure the prioritization of experiences in a
+    Prioritized-Experience Replay Buffer
+    """
 
     def __init__(
         self,
@@ -127,6 +134,7 @@ 
    Classes
    
         alpha: float = 0.6,
         epsilon: float = 0.01,
     ):
+        """Store the parameters referring to the importance sampling."""
         self.beta = beta
         self.beta_growth = beta_growth
         self.alpha = alpha
@@ -161,7 +169,7 @@ 
    
-
    Generated by pdoc 0.9.2.
    
+
    Generated by pdoc 0.10.0.
    
 
 
 
\ No newline at end of file
diff --git a/docs/api/deeprecsys/rl/experience_replay/experience_buffer.html b/docs/api/deeprecsys/rl/experience_replay/experience_buffer.html
index 68172de..e4c429c 100644
--- a/docs/api/deeprecsys/rl/experience_replay/experience_buffer.html
+++ b/docs/api/deeprecsys/rl/experience_replay/experience_buffer.html
@@ -3,7 +3,7 @@
 
 
 
-
+
 deeprecsys.rl.experience_replay.experience_buffer API documentation
 
 
@@ -27,71 +27,72 @@ 
    Module deeprecsys.rl.experience_replay.experience_buffer
 Expand source code
 
    from abc import ABC, abstractmethod
-from collections import namedtuple, deque
-from typing import List, Tuple, Any
+from collections import deque, namedtuple
+from typing import Any, List, Tuple
+
 from deeprecsys.rl.experience_replay.buffer_parameters import (
     ExperienceReplayBufferParameters,
 )
 
-Experience = namedtuple(
+Experience = namedtuple(  # type: ignore
     "Experience", field_names=["state", "action", "reward", "done", "next_state"]
 )
 
 
 class ExperienceBuffer(ABC):
+    """Abstract class encapsulating common aspects of an experience replay buffer."""
+
     @abstractmethod
     def ready_to_predict(self) -> bool:
-        pass
+        """Whether enough experiences were collected"""
 
     @abstractmethod
     def sample_batch(self) -> List[Tuple]:
-        pass
+        """Sample a batch of experiences from the buffer."""
 
     @abstractmethod
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-    ):
-        pass
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
+        """Store an experience in the buffer."""
 
 
 class ExperienceReplayBuffer(ExperienceBuffer):
+    """Traditional experience replay buffer. Experiences are sampled randomly without
+    replacements within batches. Different batches may contain the same experience.
+    """
+
     def __init__(
         self,
-        parameters=ExperienceReplayBufferParameters(),
+        parameters: ExperienceReplayBufferParameters = None,
     ):
-        self.minimum_experiences_to_start_predicting = (
-            parameters.minimum_experiences_to_start_predicting
-        )
+        """Initialize the buffer with the provided parameters."""
+        if not parameters:
+            parameters = ExperienceReplayBufferParameters()
+        self.minimum_experiences_to_start_predicting = parameters.minimum_experiences_to_start_predicting
         self.random_state = parameters.random_state
         # create double ended queue to store the experiences
-        self.experience_queue = deque(maxlen=parameters.max_experiences)
+        self.experience_queue: List = list(deque(maxlen=parameters.max_experiences))
         self.batch_size = parameters.batch_size
 
     def sample_batch(self) -> List[Tuple]:
-        """ Samples a given number of experiences from the queue """
+        """Sample a given number of experiences from the queue"""
         # samples the index of `batch_size` different experiences from the replay memory
-        samples = self.random_state.choice(
-            len(self.experience_queue), self.batch_size, replace=False
-        )
+        samples = self.random_state.choice(len(self.experience_queue), self.batch_size, replace=False)
         # get the experiences
         experiences = [self.experience_queue[i] for i in samples]
         # returns a flattened list of the samples
-        return zip(*experiences)
+        return zip(*experiences, strict=False)  # type: ignore
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-    ):
-        """ Stores a new experience in the queue """
-        experience = Experience(state, action, reward, done, next_state)
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
+        """Store a new experience in the queue"""
+        experience = Experience(state, action, reward, done, next_state)  # type: ignore
         # append to the right (end) of the queue
         self.experience_queue.append(experience)
 
-    def ready_to_predict(self):
-        """Returns true only if we had enough experiences to start predicting
-        (measured by the burn in)"""
-        return (
-            len(self.experience_queue) >= self.minimum_experiences_to_start_predicting
-        )
    + def ready_to_predict(self) -> bool: + """Return true only if we had enough experiences to start predicting + (measured by the burn in) + """ + return len(self.experience_queue) >= self.minimum_experiences_to_start_predicting
    @@ -141,26 +142,25 @@

    Instance variables

    class ExperienceBuffer
    -

    Helper class that provides a standard way to create an ABC using -inheritance.

    +

    Abstract class encapsulating common aspects of an experience replay buffer.

    Expand source code 
    class ExperienceBuffer(ABC):
+    """Abstract class encapsulating common aspects of an experience replay buffer."""
+
     @abstractmethod
     def ready_to_predict(self) -> bool:
-        pass
+        """Whether enough experiences were collected"""
 
     @abstractmethod
     def sample_batch(self) -> List[Tuple]:
-        pass
+        """Sample a batch of experiences from the buffer."""
 
     @abstractmethod
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-    ):
-        pass
    + def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None: + """Store an experience in the buffer."""

    Ancestors

      @@ -176,97 +176,96 @@

      Methods

      def ready_to_predict(self) ‑> bool
      -
      +

      Whether enough experiences were collected

      Expand source code 
      @abstractmethod
 def ready_to_predict(self) -> bool:
-    pass
      + """Whether enough experiences were collected"""
      def sample_batch(self) ‑> List[Tuple]
      -
      +

      Sample a batch of experiences from the buffer.

      Expand source code 
      @abstractmethod
 def sample_batch(self) -> List[Tuple]:
-    pass
      + """Sample a batch of experiences from the buffer."""
      -def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) +def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) ‑> None
      -
      +

      Store an experience in the buffer.

      Expand source code 
      @abstractmethod
-def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-):
-    pass
      +def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None: + """Store an experience in the buffer."""
    class ExperienceReplayBuffer -(parameters=<deeprecsys.rl.experience_replay.buffer_parameters.ExperienceReplayBufferParameters object>) +(parameters: ExperienceReplayBufferParameters = None)
    -

    Helper class that provides a standard way to create an ABC using -inheritance.

    +

    Traditional experience replay buffer. Experiences are sampled randomly without +replacements within batches. Different batches may contain the same experience.

    +

    Initialize the buffer with the provided parameters.

    Expand source code 
    class ExperienceReplayBuffer(ExperienceBuffer):
+    """Traditional experience replay buffer. Experiences are sampled randomly without
+    replacements within batches. Different batches may contain the same experience.
+    """
+
     def __init__(
         self,
-        parameters=ExperienceReplayBufferParameters(),
+        parameters: ExperienceReplayBufferParameters = None,
     ):
-        self.minimum_experiences_to_start_predicting = (
-            parameters.minimum_experiences_to_start_predicting
-        )
+        """Initialize the buffer with the provided parameters."""
+        if not parameters:
+            parameters = ExperienceReplayBufferParameters()
+        self.minimum_experiences_to_start_predicting = parameters.minimum_experiences_to_start_predicting
         self.random_state = parameters.random_state
         # create double ended queue to store the experiences
-        self.experience_queue = deque(maxlen=parameters.max_experiences)
+        self.experience_queue: List = list(deque(maxlen=parameters.max_experiences))
         self.batch_size = parameters.batch_size
 
     def sample_batch(self) -> List[Tuple]:
-        """ Samples a given number of experiences from the queue """
+        """Sample a given number of experiences from the queue"""
         # samples the index of `batch_size` different experiences from the replay memory
-        samples = self.random_state.choice(
-            len(self.experience_queue), self.batch_size, replace=False
-        )
+        samples = self.random_state.choice(len(self.experience_queue), self.batch_size, replace=False)
         # get the experiences
         experiences = [self.experience_queue[i] for i in samples]
         # returns a flattened list of the samples
-        return zip(*experiences)
+        return zip(*experiences, strict=False)  # type: ignore
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-    ):
-        """ Stores a new experience in the queue """
-        experience = Experience(state, action, reward, done, next_state)
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
+        """Store a new experience in the queue"""
+        experience = Experience(state, action, reward, done, next_state)  # type: ignore
         # append to the right (end) of the queue
         self.experience_queue.append(experience)
 
-    def ready_to_predict(self):
-        """Returns true only if we had enough experiences to start predicting
-        (measured by the burn in)"""
-        return (
-            len(self.experience_queue) >= self.minimum_experiences_to_start_predicting
-        )
    + def ready_to_predict(self) -> bool: + """Return true only if we had enough experiences to start predicting + (measured by the burn in) + """ + return len(self.experience_queue) >= self.minimum_experiences_to_start_predicting

    Ancestors

      @@ -280,58 +279,53 @@

      Subclasses

      Methods

      -def ready_to_predict(self) +def ready_to_predict(self) ‑> bool
      -

      Returns true only if we had enough experiences to start predicting +

      Return true only if we had enough experiences to start predicting (measured by the burn in)

      Expand source code -
      def ready_to_predict(self):
-    """Returns true only if we had enough experiences to start predicting
-    (measured by the burn in)"""
-    return (
-        len(self.experience_queue) >= self.minimum_experiences_to_start_predicting
-    )
      +
      def ready_to_predict(self) -> bool:
+    """Return true only if we had enough experiences to start predicting
+    (measured by the burn in)
+    """
+    return len(self.experience_queue) >= self.minimum_experiences_to_start_predicting
      def sample_batch(self) ‑> List[Tuple]
      -

      Samples a given number of experiences from the queue

      +

      Sample a given number of experiences from the queue

      Expand source code 
      def sample_batch(self) -> List[Tuple]:
-    """ Samples a given number of experiences from the queue """
+    """Sample a given number of experiences from the queue"""
     # samples the index of `batch_size` different experiences from the replay memory
-    samples = self.random_state.choice(
-        len(self.experience_queue), self.batch_size, replace=False
-    )
+    samples = self.random_state.choice(len(self.experience_queue), self.batch_size, replace=False)
     # get the experiences
     experiences = [self.experience_queue[i] for i in samples]
     # returns a flattened list of the samples
-    return zip(*experiences)
      + return zip(*experiences, strict=False) # type: ignore
      -def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) +def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) ‑> None
      -

      Stores a new experience in the queue

      +

      Store a new experience in the queue

      Expand source code -
      def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-):
-    """ Stores a new experience in the queue """
-    experience = Experience(state, action, reward, done, next_state)
+
      def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
+    """Store a new experience in the queue"""
+    experience = Experience(state, action, reward, done, next_state)  # type: ignore
     # append to the right (end) of the queue
     self.experience_queue.append(experience)
      @@ -386,7 +380,7 @@

      -

      Generated by pdoc 0.9.2.

      +

      Generated by pdoc 0.10.0.

      \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/experience_replay/index.html b/docs/api/deeprecsys/rl/experience_replay/index.html index e89eb74..4aee7a0 100644 --- a/docs/api/deeprecsys/rl/experience_replay/index.html +++ b/docs/api/deeprecsys/rl/experience_replay/index.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.experience_replay API documentation @@ -69,7 +69,7 @@

      Index

      \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/experience_replay/priority_replay_buffer.html b/docs/api/deeprecsys/rl/experience_replay/priority_replay_buffer.html index dd9f728..eeb9985 100644 --- a/docs/api/deeprecsys/rl/experience_replay/priority_replay_buffer.html +++ b/docs/api/deeprecsys/rl/experience_replay/priority_replay_buffer.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.experience_replay.priority_replay_buffer API documentation @@ -28,28 +28,39 @@

      Module deeprecsys.rl.experience_replay.priority_replay_b 
      from collections import namedtuple
 from typing import Any, List, Tuple
+
 import numpy
+import numpy as np
+
+from deeprecsys.rl.experience_replay.buffer_parameters import (
+    ExperienceReplayBufferParameters,
+    PERBufferParameters,
+)
 from deeprecsys.rl.experience_replay.experience_buffer import (
     Experience,
     ExperienceReplayBuffer,
 )
-from deeprecsys.rl.experience_replay.buffer_parameters import (
-    PERBufferParameters,
-    ExperienceReplayBufferParameters,
-)
-import numpy as np
 
-PriorityExperience = namedtuple(
+PriorityExperience = namedtuple(  # type: ignore
     "PriorityExperience", field_names=["experience", "priority"]
 )
 
 
 class PrioritizedExperienceReplayBuffer(ExperienceReplayBuffer):
+    """Experience Replay Buffer that gives priority to experiences that the network learns more from. We can tell this
+    using the loss. We use importance sampling to avoid bias towards those experiences.
+    """
+
     def __init__(
         self,
-        buffer_parameters=ExperienceReplayBufferParameters(),
-        per_parameters=PERBufferParameters(),
+        buffer_parameters: ExperienceReplayBufferParameters = None,
+        per_parameters: PERBufferParameters = None,
     ):
+        """Start the buffer with the provided parameters."""
+        if not buffer_parameters:
+            buffer_parameters = ExperienceReplayBufferParameters()
+        if not per_parameters:
+            per_parameters = PERBufferParameters()
         super().__init__(buffer_parameters)
         # beta controls the effect of the weights (how much to learn from each
         # experience in the batch)
@@ -62,59 +73,57 @@ 
      Module deeprecsys.rl.experience_replay.priority_replay_b
         self.epsilon = per_parameters.epsilon
 
     def priorities(self) -> numpy.array:
-        """ Gets the priority for each experience in the queue """
-        return numpy.array(
-            [e.priority for e in self.experience_queue], dtype=numpy.float32
-        )
+        """Get the priority of each experience in the queue"""
+        return numpy.array([e.priority for e in self.experience_queue], dtype=numpy.float32)
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
         """We include a priority to the experience. if the queue is empty, priority is 1 (max),
-        otherwise we check the maximum priority in the queue"""
+        otherwise we check the maximum priority in the queue
+        """
         priorities = self.priorities()
         priority = priorities.max() if len(priorities) > 0 else 1.0
         if not np.isnan(priority):
             experience = Experience(state, action, reward, done, next_state)
-            priority_experience = PriorityExperience(experience, priority)
+            priority_experience = PriorityExperience(experience, priority)  # type: ignore
             # append to the right (end) of the queue
             self.experience_queue.append(priority_experience)
 
-    def update_beta(self):
+    def update_beta(self) -> None:
         """We want to grow the beta value slowly and linearly, starting at a value
-        close to zero, and stopping at 1.0. This is for the Importance Sampling"""
+        close to zero, and stopping at 1.0. This is for the Importance Sampling
+        """
         if self.beta < 1.0:
             self.beta += self.beta_growth
 
-    def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]):
+    def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]) -> None:
         """We want the priority of elements to be the TD error of plus an epsilon
         constant. The epsilon constant makes sure that no experience ever gets a
         priority zero. This prioritization strategy gives more importance to
-        elements that bring more learning to the network."""
-        experience_indexes = [b[-1] for b in numpy.array(batch, dtype=numpy.object).T]
+        elements that bring more learning to the network.
+        """
+        experience_indexes = [b[-1] for b in numpy.array(batch, dtype="object").T]
         for i in range(len(experience_indexes)):
             error = abs(errors_from_batch[i]) + self.epsilon
             if not np.isnan(error):
-                self.experience_queue[experience_indexes[i]] = self.experience_queue[
-                    experience_indexes[i]
-                ]._replace(priority=error)
+                experience = self.experience_queue[experience_indexes[i]]
+                experience._replace(priority=error)
+                self.experience_queue[experience_indexes[i]] = experience
 
     def sample_batch(self) -> List[Tuple]:
         """We sample experiences using their priorities as weights for sampling. The
         effect of the priorities is controlled by the alpha parameter. This is
-        already an advantage but it can introduce bias in a network by always
+        already an advantage, but it can introduce bias in a network by always
         choosing the same type of experiences for training. In order to fight this, we
         compute the weight of the experience (this is called Importance Sampling,
         or IP). We want the weights to decrease over time, this is controlled by
-        the beta parameter."""
+        the beta parameter.
+        """
         # calculate probabilities (alpha)
         probabilities = self.priorities() ** self.alpha
         p = probabilities / probabilities.sum()
         # sample experiences
         buffer_size = len(self.experience_queue)
-        samples = numpy.random.choice(
-            a=buffer_size, size=self.batch_size, p=p, replace=False
-        )
+        samples = numpy.random.choice(a=buffer_size, size=self.batch_size, p=p, replace=False)
         experiences = [self.experience_queue[i].experience for i in samples]
         # importance Sampling
         # w_i = (1/N * 1/P_i) ^ beta
@@ -122,7 +131,7 @@ 
      Module deeprecsys.rl.experience_replay.priority_replay_b
         weights = weights / weights.max()
         self.update_beta()
         # return experiences with weights
-        return list(zip(*experiences)) + [tuple(weights)] + [tuple(samples)]
      + return list(zip(*experiences, strict=False)) + [tuple(weights)] + [tuple(samples)]
      @@ -136,21 +145,31 @@

      Classes

      class PrioritizedExperienceReplayBuffer -(buffer_parameters=<deeprecsys.rl.experience_replay.buffer_parameters.ExperienceReplayBufferParameters object>, per_parameters=<deeprecsys.rl.experience_replay.buffer_parameters.PERBufferParameters object>) +(buffer_parameters: ExperienceReplayBufferParameters = None, per_parameters: PERBufferParameters = None)
      -

      Helper class that provides a standard way to create an ABC using -inheritance.

      +

      Experience Replay Buffer that gives priority to experiences that the network learns more from. We can tell this +using the loss. We use importance sampling to avoid bias towards those experiences.

      +

      Start the buffer with the provided parameters.

      Expand source code 
      class PrioritizedExperienceReplayBuffer(ExperienceReplayBuffer):
+    """Experience Replay Buffer that gives priority to experiences that the network learns more from. We can tell this
+    using the loss. We use importance sampling to avoid bias towards those experiences.
+    """
+
     def __init__(
         self,
-        buffer_parameters=ExperienceReplayBufferParameters(),
-        per_parameters=PERBufferParameters(),
+        buffer_parameters: ExperienceReplayBufferParameters = None,
+        per_parameters: PERBufferParameters = None,
     ):
+        """Start the buffer with the provided parameters."""
+        if not buffer_parameters:
+            buffer_parameters = ExperienceReplayBufferParameters()
+        if not per_parameters:
+            per_parameters = PERBufferParameters()
         super().__init__(buffer_parameters)
         # beta controls the effect of the weights (how much to learn from each
         # experience in the batch)
@@ -163,59 +182,57 @@ 
      Classes
      
         self.epsilon = per_parameters.epsilon
 
     def priorities(self) -> numpy.array:
-        """ Gets the priority for each experience in the queue """
-        return numpy.array(
-            [e.priority for e in self.experience_queue], dtype=numpy.float32
-        )
+        """Get the priority of each experience in the queue"""
+        return numpy.array([e.priority for e in self.experience_queue], dtype=numpy.float32)
 
-    def store_experience(
-        self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-    ):
+    def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
         """We include a priority to the experience. if the queue is empty, priority is 1 (max),
-        otherwise we check the maximum priority in the queue"""
+        otherwise we check the maximum priority in the queue
+        """
         priorities = self.priorities()
         priority = priorities.max() if len(priorities) > 0 else 1.0
         if not np.isnan(priority):
             experience = Experience(state, action, reward, done, next_state)
-            priority_experience = PriorityExperience(experience, priority)
+            priority_experience = PriorityExperience(experience, priority)  # type: ignore
             # append to the right (end) of the queue
             self.experience_queue.append(priority_experience)
 
-    def update_beta(self):
+    def update_beta(self) -> None:
         """We want to grow the beta value slowly and linearly, starting at a value
-        close to zero, and stopping at 1.0. This is for the Importance Sampling"""
+        close to zero, and stopping at 1.0. This is for the Importance Sampling
+        """
         if self.beta < 1.0:
             self.beta += self.beta_growth
 
-    def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]):
+    def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]) -> None:
         """We want the priority of elements to be the TD error of plus an epsilon
         constant. The epsilon constant makes sure that no experience ever gets a
         priority zero. This prioritization strategy gives more importance to
-        elements that bring more learning to the network."""
-        experience_indexes = [b[-1] for b in numpy.array(batch, dtype=numpy.object).T]
+        elements that bring more learning to the network.
+        """
+        experience_indexes = [b[-1] for b in numpy.array(batch, dtype="object").T]
         for i in range(len(experience_indexes)):
             error = abs(errors_from_batch[i]) + self.epsilon
             if not np.isnan(error):
-                self.experience_queue[experience_indexes[i]] = self.experience_queue[
-                    experience_indexes[i]
-                ]._replace(priority=error)
+                experience = self.experience_queue[experience_indexes[i]]
+                experience._replace(priority=error)
+                self.experience_queue[experience_indexes[i]] = experience
 
     def sample_batch(self) -> List[Tuple]:
         """We sample experiences using their priorities as weights for sampling. The
         effect of the priorities is controlled by the alpha parameter. This is
-        already an advantage but it can introduce bias in a network by always
+        already an advantage, but it can introduce bias in a network by always
         choosing the same type of experiences for training. In order to fight this, we
         compute the weight of the experience (this is called Importance Sampling,
         or IP). We want the weights to decrease over time, this is controlled by
-        the beta parameter."""
+        the beta parameter.
+        """
         # calculate probabilities (alpha)
         probabilities = self.priorities() ** self.alpha
         p = probabilities / probabilities.sum()
         # sample experiences
         buffer_size = len(self.experience_queue)
-        samples = numpy.random.choice(
-            a=buffer_size, size=self.batch_size, p=p, replace=False
-        )
+        samples = numpy.random.choice(a=buffer_size, size=self.batch_size, p=p, replace=False)
         experiences = [self.experience_queue[i].experience for i in samples]
         # importance Sampling
         # w_i = (1/N * 1/P_i) ^ beta
@@ -223,7 +240,7 @@ 
      Classes
      
         weights = weights / weights.max()
         self.update_beta()
         # return experiences with weights
-        return list(zip(*experiences)) + [tuple(weights)] + [tuple(samples)]
      + return list(zip(*experiences, strict=False)) + [tuple(weights)] + [tuple(samples)]

      Ancestors

        @@ -237,16 +254,14 @@

        Methods

        def priorities(self) ‑> 
        -

        Gets the priority for each experience in the queue

        +

        Get the priority of each experience in the queue

        Expand source code 
        def priorities(self) -> numpy.array:
-    """ Gets the priority for each experience in the queue """
-    return numpy.array(
-        [e.priority for e in self.experience_queue], dtype=numpy.float32
-    )
        + """Get the priority of each experience in the queue""" + return numpy.array([e.priority for e in self.experience_queue], dtype=numpy.float32)
        @@ -255,7 +270,7 @@

        Methods

        We sample experiences using their priorities as weights for sampling. The effect of the priorities is controlled by the alpha parameter. This is -already an advantage but it can introduce bias in a network by always +already an advantage, but it can introduce bias in a network by always choosing the same type of experiences for training. In order to fight this, we compute the weight of the experience (this is called Importance Sampling, or IP). We want the weights to decrease over time, this is controlled by @@ -267,19 +282,18 @@

        Methods

        def sample_batch(self) -> List[Tuple]:
     """We sample experiences using their priorities as weights for sampling. The
     effect of the priorities is controlled by the alpha parameter. This is
-    already an advantage but it can introduce bias in a network by always
+    already an advantage, but it can introduce bias in a network by always
     choosing the same type of experiences for training. In order to fight this, we
     compute the weight of the experience (this is called Importance Sampling,
     or IP). We want the weights to decrease over time, this is controlled by
-    the beta parameter."""
+    the beta parameter.
+    """
     # calculate probabilities (alpha)
     probabilities = self.priorities() ** self.alpha
     p = probabilities / probabilities.sum()
     # sample experiences
     buffer_size = len(self.experience_queue)
-    samples = numpy.random.choice(
-        a=buffer_size, size=self.batch_size, p=p, replace=False
-    )
+    samples = numpy.random.choice(a=buffer_size, size=self.batch_size, p=p, replace=False)
     experiences = [self.experience_queue[i].experience for i in samples]
     # importance Sampling
     # w_i = (1/N * 1/P_i) ^ beta
@@ -287,11 +301,11 @@ 
        Methods
        
     weights = weights / weights.max()
     self.update_beta()
     # return experiences with weights
-    return list(zip(*experiences)) + [tuple(weights)] + [tuple(samples)]
        + return list(zip(*experiences, strict=False)) + [tuple(weights)] + [tuple(samples)]
        -def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) +def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) ‑> None

        We include a priority to the experience. if the queue is empty, priority is 1 (max), @@ -300,22 +314,21 @@

        Methods

        Expand source code -
        def store_experience(
-    self, state: Any, action: Any, reward: float, done: bool, next_state: Any
-):
+
        def store_experience(self, state: Any, action: Any, reward: float, done: bool, next_state: Any) -> None:
     """We include a priority to the experience. if the queue is empty, priority is 1 (max),
-    otherwise we check the maximum priority in the queue"""
+    otherwise we check the maximum priority in the queue
+    """
     priorities = self.priorities()
     priority = priorities.max() if len(priorities) > 0 else 1.0
     if not np.isnan(priority):
         experience = Experience(state, action, reward, done, next_state)
-        priority_experience = PriorityExperience(experience, priority)
+        priority_experience = PriorityExperience(experience, priority)  # type: ignore
         # append to the right (end) of the queue
         self.experience_queue.append(priority_experience)
        -def update_beta(self) +def update_beta(self) ‑> None

        We want to grow the beta value slowly and linearly, starting at a value @@ -324,15 +337,16 @@

        Methods

        Expand source code -
        def update_beta(self):
+
        def update_beta(self) -> None:
     """We want to grow the beta value slowly and linearly, starting at a value
-    close to zero, and stopping at 1.0. This is for the Importance Sampling"""
+    close to zero, and stopping at 1.0. This is for the Importance Sampling
+    """
     if self.beta < 1.0:
         self.beta += self.beta_growth
        -def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]) +def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]) ‑> None

        We want the priority of elements to be the TD error of plus an epsilon @@ -343,18 +357,19 @@

        Methods

        Expand source code -
        def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]):
+
        def update_priorities(self, batch: List[Tuple], errors_from_batch: List[float]) -> None:
     """We want the priority of elements to be the TD error of plus an epsilon
     constant. The epsilon constant makes sure that no experience ever gets a
     priority zero. This prioritization strategy gives more importance to
-    elements that bring more learning to the network."""
-    experience_indexes = [b[-1] for b in numpy.array(batch, dtype=numpy.object).T]
+    elements that bring more learning to the network.
+    """
+    experience_indexes = [b[-1] for b in numpy.array(batch, dtype="object").T]
     for i in range(len(experience_indexes)):
         error = abs(errors_from_batch[i]) + self.epsilon
         if not np.isnan(error):
-            self.experience_queue[experience_indexes[i]] = self.experience_queue[
-                experience_indexes[i]
-            ]._replace(priority=error)
        
+            experience = self.experience_queue[experience_indexes[i]]
+            experience._replace(priority=error)
+            self.experience_queue[experience_indexes[i]] = experience
      @@ -428,7 +443,7 @@

      -

      Generated by pdoc 0.9.2.

      +

      Generated by pdoc 0.10.0.

      \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/index.html b/docs/api/deeprecsys/rl/index.html index 7266e1e..5c1ec68 100644 --- a/docs/api/deeprecsys/rl/index.html +++ b/docs/api/deeprecsys/rl/index.html @@ -3,7 +3,7 @@ - + deeprecsys.rl API documentation @@ -22,6 +22,38 @@

      Module deeprecsys.rl

      +
      + +Expand source code + +
      from logging import Logger as DefaultLogger
+from typing import Any
+
+
+class Logger(DefaultLogger):
+    """Shared logger that only prints when verbose is true"""
+
+    verbose: bool = True
+    instance: "Logger" = None
+
+    @classmethod
+    def create(cls) -> "Logger":
+        """Create or reuse a shared logger class"""
+        if not cls.instance:
+            cls.instance = cls("deeprecsys")
+        return cls.instance
+
+    def _log(self, *args: Any, **kwargs: Any) -> None:
+        if Logger.verbose:
+            # noinspection PyProtectedMember
+            super(Logger, self)._log(*args, **kwargs)
+
+    @staticmethod
+    def print(*args: Any, **kwargs: Any) -> None:
+        """Print wrapper that only prints when verbose is True"""
+        if Logger.verbose:
+            print(*args, **kwargs)
      +

      Sub-modules

      @@ -42,10 +74,6 @@

      Sub-modules

      -
      deeprecsys.rl.neural_networks
      -
      -
      -

      @@ -53,6 +81,97 @@

      Sub-modules

      +

      Classes

      +
      +
      +class Logger +(name, level=0) +
      +
      +

      Shared logger that only prints when verbose is true

      +

      Initialize the logger with a name and an optional level.

      +
      + +Expand source code + +
      class Logger(DefaultLogger):
+    """Shared logger that only prints when verbose is true"""
+
+    verbose: bool = True
+    instance: "Logger" = None
+
+    @classmethod
+    def create(cls) -> "Logger":
+        """Create or reuse a shared logger class"""
+        if not cls.instance:
+            cls.instance = cls("deeprecsys")
+        return cls.instance
+
+    def _log(self, *args: Any, **kwargs: Any) -> None:
+        if Logger.verbose:
+            # noinspection PyProtectedMember
+            super(Logger, self)._log(*args, **kwargs)
+
+    @staticmethod
+    def print(*args: Any, **kwargs: Any) -> None:
+        """Print wrapper that only prints when verbose is True"""
+        if Logger.verbose:
+            print(*args, **kwargs)
      +
      +

      Ancestors

      +
        +
      • logging.Logger
        • +
      • logging.Filterer
        • +
      +

      Class variables

      +
      +
      var instance : Logger
      +
      +
      +
      +
      var verbose : bool
      +
      +
      +
      +
      +

      Static methods

      +
      +
      +def create() ‑> Logger +
      +
      +

      Create or reuse a shared logger class

      +
      + +Expand source code + +
      @classmethod
+def create(cls) -> "Logger":
+    """Create or reuse a shared logger class"""
+    if not cls.instance:
+        cls.instance = cls("deeprecsys")
+    return cls.instance
      +
      +
      +
      +def print(*args: Any, **kwargs: Any) ‑> None +
      +
      +

      Print wrapper that only prints when verbose is True

      +
      + +Expand source code + +
      @staticmethod
+def print(*args: Any, **kwargs: Any) -> None:
+    """Print wrapper that only prints when verbose is True"""
+    if Logger.verbose:
+        print(*args, **kwargs)
      +
      +
      +
      +
      +
    + +

  • Classes

    +
    • \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/learning_statistics.html b/docs/api/deeprecsys/rl/learning_statistics.html index d960ecf..1b8bc4c 100644 --- a/docs/api/deeprecsys/rl/learning_statistics.html +++ b/docs/api/deeprecsys/rl/learning_statistics.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.learning_statistics API documentation @@ -26,26 +26,29 @@

    Module deeprecsys.rl.learning_statistics

    Expand source code -
    import matplotlib.pyplot as plt
+
    from typing import Any, Dict, List, Optional
+
+import matplotlib.pyplot as plt
 import pandas
 import seaborn as sns
-from typing import Any, List, Dict, Optional
 
 sns.set_theme()
 sns.set_context("paper")
 
 
 class LearningStatistics:
-    def __init__(
-        self, model_name: Optional[str] = None, env_name: Optional[str] = None
-    ):
+    """Special class to store and aggregate learning parameters."""
+
+    def __init__(self, model_name: Optional[str] = None, env_name: Optional[str] = None):
+        """Start the collector for the given model and environment name."""
         self.collected_metrics: List[Dict] = []
         self.model_name = model_name
         self.env_name = env_name
         self.timestep = 0
         self.episode = 0
 
-    def append_metric(self, metric_name: str, metric_value: Any):
+    def append_metric(self, metric_name: str, metric_value: Any) -> None:
+        """Store the metric with the given name and value."""
         self.collected_metrics.append(
             {
                 "metric": metric_name,
@@ -60,6 +63,7 @@ 
    Module deeprecsys.rl.learning_statistics
    
     def get_metrics(
         self, metric_name: str, model: Optional[str] = None, env: Optional[str] = None
     ) -> Optional[pandas.Series]:
+        """Get all the collected metrics for the given name, model, and environment."""
         measurements = [
             v["measurement"]
             for v in self.collected_metrics
@@ -67,28 +71,36 @@ 
    Module deeprecsys.rl.learning_statistics
    
         ]
         if measurements:
             return pandas.Series(measurements)
+        else:
+            return None
 
     @property
     def moving_rewards(self) -> Optional[pandas.Series]:
+        """Get the moving average of the rewards observed so far."""
         return self.get_metrics("moving_rewards")
 
     @property
     def episode_rewards(self) -> Optional[pandas.Series]:
+        """Get the reward values stored so far."""
         return self.get_metrics("episode_rewards")
 
     @property
     def epsilon_values(self) -> Optional[pandas.Series]:
+        """Get the epsilon values stored so far."""
         return self.get_metrics("epsilon_values")
 
     @property
     def loss_values(self) -> Optional[pandas.Series]:
+        """Get the loss values stored so far."""
         return self.get_metrics("loss")
 
-    def plot_rewards(self):
+    def plot_rewards(self) -> None:
+        """Plot the rewards obtaining so far."""
         self.episode_rewards.plot()
         self.moving_rewards.plot()
 
-    def plot_learning_stats(self):
+    def plot_learning_stats(self) -> None:
+        """Plot the relevant reinforcement learning metrics."""
         # generate subplots
         fig, axs = plt.subplots(2, 2)
         fig.suptitle("Agent learning metrics")
@@ -115,25 +127,28 @@ 
    Classes
    
 
    
 
    
 class LearningStatistics
-(model_name: Union[str, NoneType] = None, env_name: Union[str, NoneType] = None)
+(model_name: Optional[str] = None, env_name: Optional[str] = None)
 
    
 
    
-
    
+
    Special class to store and aggregate learning parameters.
    
+
    Start the collector for the given model and environment name.
    
 
    
 
 Expand source code
 
 
    class LearningStatistics:
-    def __init__(
-        self, model_name: Optional[str] = None, env_name: Optional[str] = None
-    ):
+    """Special class to store and aggregate learning parameters."""
+
+    def __init__(self, model_name: Optional[str] = None, env_name: Optional[str] = None):
+        """Start the collector for the given model and environment name."""
         self.collected_metrics: List[Dict] = []
         self.model_name = model_name
         self.env_name = env_name
         self.timestep = 0
         self.episode = 0
 
-    def append_metric(self, metric_name: str, metric_value: Any):
+    def append_metric(self, metric_name: str, metric_value: Any) -> None:
+        """Store the metric with the given name and value."""
         self.collected_metrics.append(
             {
                 "metric": metric_name,
@@ -148,6 +163,7 @@ 
    Classes
    
     def get_metrics(
         self, metric_name: str, model: Optional[str] = None, env: Optional[str] = None
     ) -> Optional[pandas.Series]:
+        """Get all the collected metrics for the given name, model, and environment."""
         measurements = [
             v["measurement"]
             for v in self.collected_metrics
@@ -155,28 +171,36 @@ 
    Classes
    
         ]
         if measurements:
             return pandas.Series(measurements)
+        else:
+            return None
 
     @property
     def moving_rewards(self) -> Optional[pandas.Series]:
+        """Get the moving average of the rewards observed so far."""
         return self.get_metrics("moving_rewards")
 
     @property
     def episode_rewards(self) -> Optional[pandas.Series]:
+        """Get the reward values stored so far."""
         return self.get_metrics("episode_rewards")
 
     @property
     def epsilon_values(self) -> Optional[pandas.Series]:
+        """Get the epsilon values stored so far."""
         return self.get_metrics("epsilon_values")
 
     @property
     def loss_values(self) -> Optional[pandas.Series]:
+        """Get the loss values stored so far."""
         return self.get_metrics("loss")
 
-    def plot_rewards(self):
+    def plot_rewards(self) -> None:
+        """Plot the rewards obtaining so far."""
         self.episode_rewards.plot()
         self.moving_rewards.plot()
 
-    def plot_learning_stats(self):
+    def plot_learning_stats(self) -> None:
+        """Plot the relevant reinforcement learning metrics."""
         # generate subplots
         fig, axs = plt.subplots(2, 2)
         fig.suptitle("Agent learning metrics")
@@ -193,51 +217,55 @@ 
    Classes
    
 
    
 
    Instance variables
    
 
    
-
    var episode_rewards : Union[pandas.core.series.Series, NoneType]
    
+
    var episode_rewards : Optional[pandas.core.series.Series]
    
 
    
-
    
+
    Get the reward values stored so far.
    
 
    
 
 Expand source code
 
 
    @property
 def episode_rewards(self) -> Optional[pandas.Series]:
+    """Get the reward values stored so far."""
     return self.get_metrics("episode_rewards")
    
 
    
 
    
-
    var epsilon_values : Union[pandas.core.series.Series, NoneType]
    
+
    var epsilon_values : Optional[pandas.core.series.Series]
    
 
    
-
    
+
    Get the epsilon values stored so far.
    
 
    
 
 Expand source code
 
 
    @property
 def epsilon_values(self) -> Optional[pandas.Series]:
+    """Get the epsilon values stored so far."""
     return self.get_metrics("epsilon_values")
    
 
    
 
    
-
    var loss_values : Union[pandas.core.series.Series, NoneType]
    
+
    var loss_values : Optional[pandas.core.series.Series]
    
 
    
-
    
+
    Get the loss values stored so far.
    
 
    
 
 Expand source code
 
 
    @property
 def loss_values(self) -> Optional[pandas.Series]:
+    """Get the loss values stored so far."""
     return self.get_metrics("loss")
    
 
    
 
    
-
    var moving_rewards : Union[pandas.core.series.Series, NoneType]
    
+
    var moving_rewards : Optional[pandas.core.series.Series]
    
 
    
-
    
+
    Get the moving average of the rewards observed so far.
    
 
    
 
 Expand source code
 
 
    @property
 def moving_rewards(self) -> Optional[pandas.Series]:
+    """Get the moving average of the rewards observed so far."""
     return self.get_metrics("moving_rewards")
    
 
    
 
    
@@ -245,15 +273,16 @@ 
    Instance variables
    
 
    Methods
    
 
    
 
    
-def append_metric(self, metric_name: str, metric_value: Any)
+def append_metric(self, metric_name: str, metric_value: Any) ‑> None
 
    
 
    
-
    
+
    Store the metric with the given name and value.
    
 
    
 
 Expand source code
 
-
    def append_metric(self, metric_name: str, metric_value: Any):
+
    def append_metric(self, metric_name: str, metric_value: Any) -> None:
+    """Store the metric with the given name and value."""
     self.collected_metrics.append(
         {
             "metric": metric_name,
@@ -267,10 +296,10 @@ 
    Methods
    
 
    
 
    
 
    
-def get_metrics(self, metric_name: str, model: Union[str, NoneType] = None, env: Union[str, NoneType] = None) ‑> Union[pandas.core.series.Series, NoneType]
+def get_metrics(self, metric_name: str, model: Optional[str] = None, env: Optional[str] = None) ‑> Optional[pandas.core.series.Series]
 
    
 
    
-
    
+
    Get all the collected metrics for the given name, model, and environment.
    
 
    
 
 Expand source code
@@ -278,25 +307,29 @@ 
    Methods
    
 
    def get_metrics(
     self, metric_name: str, model: Optional[str] = None, env: Optional[str] = None
 ) -> Optional[pandas.Series]:
+    """Get all the collected metrics for the given name, model, and environment."""
     measurements = [
         v["measurement"]
         for v in self.collected_metrics
         if (v["metric"] == metric_name and v["model"] == model and v["env"] == env)
     ]
     if measurements:
-        return pandas.Series(measurements)
    
+        return pandas.Series(measurements)
+    else:
+        return None
    -def plot_learning_stats(self) +def plot_learning_stats(self) ‑> None
    -
    +

    Plot the relevant reinforcement learning metrics.

    Expand source code -
    def plot_learning_stats(self):
+
    def plot_learning_stats(self) -> None:
+    """Plot the relevant reinforcement learning metrics."""
     # generate subplots
     fig, axs = plt.subplots(2, 2)
     fig.suptitle("Agent learning metrics")
@@ -313,15 +346,16 @@ 
    Methods
    -def plot_rewards(self) +def plot_rewards(self) ‑> None
    -
    +

    Plot the rewards obtaining so far.

    Expand source code -
    def plot_rewards(self):
+
    def plot_rewards(self) -> None:
+    """Plot the rewards obtaining so far."""
     self.episode_rewards.plot()
     self.moving_rewards.plot()
    @@ -363,7 +397,7 @@

    -

    Generated by pdoc 0.9.2.

    +

    Generated by pdoc 0.10.0.

    \ No newline at end of file diff --git a/docs/api/deeprecsys/rl/manager.html b/docs/api/deeprecsys/rl/manager.html index 7fb41aa..e356493 100644 --- a/docs/api/deeprecsys/rl/manager.html +++ b/docs/api/deeprecsys/rl/manager.html @@ -3,7 +3,7 @@ - + deeprecsys.rl.manager API documentation @@ -26,53 +26,53 @@

    Module deeprecsys.rl.manager

    Expand source code -
    from gym import make, spec, Env
-from collections import namedtuple, defaultdict
-from typing import Any, List, Optional
-import math
-from numpy.random import RandomState
+
    import math
+from collections import defaultdict, namedtuple
+from typing import Any, Generator, List, Optional
+
 import numpy as np
-import highway_env  # noqa: F401
-import deeprecsys.movielens_fairness_env  # noqa: F401
-from deeprecsys.rl.agents.agent import ReinforcementLearning
-from deeprecsys.rl.learning_statistics import LearningStatistics
 import torch
+from gymnasium import Env, make, spec
+from numpy.core.multiarray import ndarray
+from numpy.random import RandomState
 
+from deeprecsys.rl import Logger
+from deeprecsys.rl.agents.agent import ReinforcementLearning
+from deeprecsys.rl.learning_statistics import LearningStatistics
 
 # An episode output is a data model to represent 3 things: how many timesteps the
 # episode took to finish, the total sum of rewards, and the average reward sum of the
 # last 100 episodes.
 EpisodeOutput = namedtuple("EpisodeOutput", "timesteps,reward_sum")
 
+logger = Logger.create()
 
-class Manager(object):
-    """ Class for learning from gym environments with some convenience methods. """
+
+class Manager:
+    """Class for learning from gym environments with some convenience methods."""
 
     env_name: str
     env: Any
+    seed: int | None = None
+    random_state: RandomState | None = None
 
     def __init__(
         self,
         env_name: Optional[str] = None,
         seed: Optional[int] = None,
         env: Optional[Env] = None,
-        max_episode_steps: int = math.inf,
+        max_episode_steps: float = math.inf,
         reward_threshold: float = math.inf,
-        **kwargs,
-    ):
-        if any(
-            [env_name is None and env is None, env_name is not None and env is not None]
-        ):
+        **kwargs: Any,
+    ) -> None:
+        """Start the manager"""
+        if any([env_name is None and env is None, env_name is not None and env is not None]):
             raise ValueError("Must specify exactly one of [env_name, env]")
         if env_name is not None:
             self.env_name = env_name
             # extract some parameters from the environment
-            self.max_episode_steps = (
-                spec(self.env_name).max_episode_steps or max_episode_steps
-            )
-            self.reward_threshold = (
-                spec(self.env_name).reward_threshold or reward_threshold
-            )
+            self.max_episode_steps = spec(self.env_name).max_episode_steps or max_episode_steps
+            self.reward_threshold = spec(self.env_name).reward_threshold or reward_threshold
             # create the environment
             self.env = make(env_name, **kwargs)
             # we seed the environment so that results are reproducible
@@ -84,91 +84,119 @@ 
    Module deeprecsys.rl.manager
    
         self.setup_reproducibility(seed)
         self.slate_size: int = kwargs["slate_size"] if "slate_size" in kwargs else 1
 
-    def print_overview(self):
-        """ Prints the most important variables of the environment. """
-        print("Reward threshold: {} ".format(self.reward_threshold))
-        print("Reward signal range: {} ".format(self.env.reward_range))
-        print("Maximum episode steps: {} ".format(self.max_episode_steps))
-        print("Action apace size: {}".format(self.env.action_space))
-        print("Observation space size {} ".format(self.env.observation_space))
+    def print_overview(self) -> None:
+        """Print the most important variables of the environment."""
+        logger.info("Reward threshold: {} ".format(self.reward_threshold))
+        logger.info("Reward signal range: {} ".format(self.env.reward_range))
+        logger.info("Maximum episode steps: {} ".format(self.max_episode_steps))
+        logger.info("Action apace size: {}".format(self.env.action_space))
+        logger.info("Observation space size {} ".format(self.env.observation_space))
 
     def execute_episodes(
         self,
         rl: ReinforcementLearning,
         n_episodes: int = 1,
         should_render: bool = False,
-        should_print: bool = False,
     ) -> List[EpisodeOutput]:
         """Execute any number of episodes with the given agent.
-        Returns the number of timesteps and sum of rewards per episode."""
+        Returns the number of timesteps and sum of rewards per episode.
+        """
         episode_outputs = []
         for episode in range(n_episodes):
-            t, reward_sum, done, state = 0, 0, False, self.env.reset()
-            if should_print:
-                print(f"Running episode {episode}, starting at state {state}")
+            t, reward_sum, done, (state, _) = 0, 0, False, self.env.reset(seed=self.seed)
+            logger.info(f"Running episode {episode}, starting at state {state}")
             while not done and t < self.max_episode_steps:
                 if should_render:
                     self.env.render()
                 action = rl.action_for_state(state)
                 state, reward, done, _ = self.env.step(action)
-                if should_print:
-                    print(f"t={t} a={action} r={reward} s={state}")
+                logger.info(f"t={t} a={action} r={reward} s={state}")
                 reward_sum += reward
                 t += 1
             episode_outputs.append(EpisodeOutput(t, reward_sum))
             self.env.close()
         return episode_outputs
 
+    @staticmethod
+    def _train_start_new_episode(statistics: LearningStatistics, episode: int) -> None:
+        if statistics:
+            statistics.episode = episode
+            statistics.timestep = 0
+
+    @staticmethod
+    def _train_update_timestep(statistics: LearningStatistics) -> None:
+        if statistics:
+            statistics.timestep += 1
+
+    @staticmethod
+    def _train_add_statistics(statistics: LearningStatistics, rewards: List, moving_average: ndarray) -> None:
+        if statistics:
+            statistics.append_metric("episode_rewards", sum(rewards))
+            statistics.append_metric("timestep_rewards", rewards)
+            statistics.append_metric("moving_rewards", moving_average)
+
+    def _train_get_step_action(self, rl: ReinforcementLearning, state: Any) -> Any:
+        if self.slate_size == 1:
+            return rl.action_for_state(state)
+        else:
+            return rl.top_k_actions_for_state(state, k=self.slate_size)
+
     def train(
         self,
         rl: ReinforcementLearning,
         statistics: Optional[LearningStatistics] = None,
-        max_episodes=50,
-        should_print: bool = True,
-    ):
-        if should_print is True:
-            print("Training...")
+        max_episodes: int = 50,
+    ) -> None:
+        """Train the agent for the given amount of episodes."""
+        logger.info("Training...")
         episode_rewards = []
         for episode in range(max_episodes):
-            state = self.env.reset()
+            state, info = self.env.reset(seed=self.seed)
             rewards = []
-            if statistics:
-                statistics.episode = episode
-                statistics.timestep = 0
+            self._train_start_new_episode(statistics, episode)
             done = False
             while done is False:
-                if self.slate_size == 1:
-                    action = rl.action_for_state(state)
-                else:
-                    action = rl.top_k_actions_for_state(state, k=self.slate_size)
-                new_state, reward, done, info = self.env.step(action)
+                action = self._train_get_step_action(rl, state)
+                new_state, reward, done, _, info = self.env.step(action)
                 if "chosen_action" in info:
                     action = action[info["chosen_action"]]
-                rl.store_experience(
-                    state, action, reward, done, new_state
-                )  # guardar experiencia en el buffer
+                rl.store_experience(state, action, reward, done, new_state)
                 rewards.append(reward)
                 state = new_state.copy()
-                if statistics:
-                    statistics.timestep += 1
+                self._train_update_timestep(statistics)
             episode_rewards.append(sum(rewards))
             moving_average = np.mean(episode_rewards[-100:])
-            if statistics:
-                statistics.append_metric("episode_rewards", sum(rewards))
-                statistics.append_metric("timestep_rewards", rewards)
-                statistics.append_metric("moving_rewards", moving_average)
-            if should_print is True:
-                print(
-                    "\rEpisode {:d} Mean Rewards {:.2f} Last Reward {:.2f}\t\t".format(
-                        episode, moving_average, sum(rewards)
-                    ),
-                    end="",
-                )
+            self._train_add_statistics(statistics, rewards, moving_average)
+
+            logger.print(
+                f"\rEpisode {episode:d} Mean Rewards {moving_average:.2f} Last Reward {rewards[-1]:.2f}\t\t",
+                end="",
+            )
             if moving_average >= self.reward_threshold:
-                if should_print is True:
-                    print("Reward threshold reached")
+                logger.info("Reward threshold reached")
                 break
 
+    def _hyperparameter_search_run_combinations(
+        self,
+        runs_per_combination: int,
+        rl: ReinforcementLearning,
+        episodes: int,
+        learning_statistics: LearningStatistics,
+        parameter_name: str,
+        parameter_value: Any,
+    ) -> Generator:
+        for run in range(runs_per_combination):
+            self.train(
+                rl=rl,
+                max_episodes=episodes,
+                statistics=learning_statistics,
+            )
+            yield learning_statistics.moving_rewards.iloc[-1]
+            logger.print(
+                f"\rTested combination {parameter_name}={parameter_value} round {run} result was {learning_statistics.moving_rewards.iloc[-1]} \t\t",  # noqa: E501
+                end="",
+            )
+
     def hyperparameter_search(
         self,
         agent: type,
@@ -176,43 +204,33 @@ 
    Module deeprecsys.rl.manager
    
         default_params: dict,
         episodes: int = 100,
         runs_per_combination: int = 3,
-        verbose: bool = True,
     ) -> dict:
         """Given an agent class, and a dictionary of hyperparameter names and values,
-        will try all combinations, and return the mean reward of each combinatio
-        for the given number of episods, and will run the determined number of times."""
+        will try all combinations, and return the mean reward of each combination
+        for the given number of episodes, and will run the determined number of times.
+        """
         combination_results = defaultdict(lambda: [])
-        for (p_name, p_value) in params.items():
+        for p_name, p_value in params.items():
             if len(p_value) < 2:
                 continue
             for value in p_value:
                 rl = agent(**{**default_params, p_name: value})
                 learning_statistics = LearningStatistics()
                 combination_key = f"{p_name}={value}"
-                for run in range(runs_per_combination):
-                    self.train(
-                        rl=rl,
-                        max_episodes=episodes,
-                        should_print=False,
-                        statistics=learning_statistics,
-                    )
-                    combination_results[combination_key].append(
-                        learning_statistics.moving_rewards.iloc[-1]
-                    )
-                    if verbose:
-                        print(
-                            f"\rTested combination {p_name}={value} round {run} "
-                            f"result was {learning_statistics.moving_rewards.iloc[-1]}"
-                            "\t\t",
-                            end="",
-                        )
+                for result in self._hyperparameter_search_run_combinations(
+                    runs_per_combination,
+                    rl,
+                    episodes,
+                    learning_statistics,
+                    p_name,
+                    value,
+                ):
+                    combination_results[combination_key].append(result)
 
         return combination_results
 
-    def setup_reproducibility(
-        self, seed: Optional[int] = None
-    ) -> Optional[RandomState]:
-        """ Seeds the project's libraries: numpy, torch, gym """
+    def setup_reproducibility(self, seed: Optional[int] = None) -> Optional[RandomState]:
+        """Seeds the project's libraries: numpy, torch, gym"""
         if seed:
             # seed pytorch
             torch.manual_seed(seed)
@@ -221,34 +239,45 @@ 
    Module deeprecsys.rl.manager
    
             # seed numpy
             np.random.seed(seed)
             # seed gym
-            self.env.seed(seed)
+            self.seed = seed
             self.random_state = RandomState(seed)
             return self.random_state
+        return None
 
 
 class HighwayManager(Manager):
+    """Manager for the highway environment"""
+
     def __init__(self, seed: Optional[int] = None, vehicles: int = 50):
+        """Start the manager"""
         super().__init__(env_name="highway-v0", seed=seed)
         self.env.configure({"vehicles_count": vehicles})
         self.max_episode_steps = self.env.config["duration"]
 
 
 class CartpoleManager(Manager):
+    """Manager for the cart pole environment"""
+
     def __init__(self, seed: Optional[int] = None):
-        super().__init__(env_name="CartPole-v0", seed=seed)
+        """Start the manager"""
+        super().__init__(env_name="CartPole-v1", seed=seed)
         self.reward_threshold = 50
 
 
 class LunarLanderManager(Manager):
+    """Manager for the lunar lander environment"""
+
     def __init__(self, seed: Optional[int] = None):
+        """Start the manager"""
         super().__init__(env_name="LunarLander-v2", seed=seed)
 
 
 class MovieLensFairnessManager(Manager):
+    """Manager for the movie lens environment"""
+
     def __init__(self, seed: Optional[int] = None, slate_size: int = 1):
-        super().__init__(
-            env_name="MovieLensFairness-v0", seed=seed, slate_size=slate_size
-        )
    
+        """Start the manager"""
+        super().__init__(env_name="MovieLensFairness-v0", seed=seed, slate_size=slate_size)
    @@ -262,34 +291,27 @@

    Classes

    class CartpoleManager -(seed: Union[int, NoneType] = None) +(seed: Optional[int] = None)
    -

    Class for learning from gym environments with some convenience methods.

    +

    Manager for the cart pole environment

    +

    Start the manager

    Expand source code 
    class CartpoleManager(Manager):
+    """Manager for the cart pole environment"""
+
     def __init__(self, seed: Optional[int] = None):
-        super().__init__(env_name="CartPole-v0", seed=seed)
+        """Start the manager"""
+        super().__init__(env_name="CartPole-v1", seed=seed)
         self.reward_threshold = 50

    Ancestors

    -

    Class variables

    -
    -
    var env : Any
    -
    -
    -
    -
    var env_name : str
    -
    -
    -
    -

    Inherited members

    @@ -326,16 +349,20 @@

    Instance variables

    class HighwayManager -(seed: Union[int, NoneType] = None, vehicles: int = 50) +(seed: Optional[int] = None, vehicles: int = 50)
    -

    Class for learning from gym environments with some convenience methods.

    +

    Manager for the highway environment

    +

    Start the manager

    Expand source code 
    class HighwayManager(Manager):
+    """Manager for the highway environment"""
+
     def __init__(self, seed: Optional[int] = None, vehicles: int = 50):
+        """Start the manager"""
         super().__init__(env_name="highway-v0", seed=seed)
         self.env.configure({"vehicles_count": vehicles})
         self.max_episode_steps = self.env.config["duration"]
    @@ -344,17 +371,6 @@

    Ancestors

    -

    Class variables

    -
    -
    var env : Any
    -
    -
    -
    -
    var env_name : str
    -
    -
    -
    -

    Inherited members

    class LunarLanderManager -(seed: Union[int, NoneType] = None) +(seed: Optional[int] = None)
    -

    Class for learning from gym environments with some convenience methods.

    +

    Manager for the lunar lander environment

    +

    Start the manager

    Expand source code 
    class LunarLanderManager(Manager):
+    """Manager for the lunar lander environment"""
+
     def __init__(self, seed: Optional[int] = None):
+        """Start the manager"""
         super().__init__(env_name="LunarLander-v2", seed=seed)

    Ancestors

    -

    Class variables

    -
    -
    var env : Any
    -
    -
    -
    -
    var env_name : str
    -
    -
    -
    -

    Inherited members

    class Manager -(env_name: Union[str, NoneType] = None, seed: Union[int, NoneType] = None, env: Union[gym.core.Env, NoneType] = None, max_episode_steps: int = inf, reward_threshold: float = inf, **kwargs) +(env_name: Optional[str] = None, seed: Optional[int] = None, env: Optional[gymnasium.core.Env] = None, max_episode_steps: float = inf, reward_threshold: float = inf, **kwargs: Any)
    -

    Class for learning from gym environments with some convenience methods.

    +

    Class for learning from gym environments with some convenience methods.

    +

    Start the manager

    Expand source code -
    class Manager(object):
-    """ Class for learning from gym environments with some convenience methods. """
+
    class Manager:
+    """Class for learning from gym environments with some convenience methods."""
 
     env_name: str
     env: Any
+    seed: int | None = None
+    random_state: RandomState | None = None
 
     def __init__(
         self,
         env_name: Optional[str] = None,
         seed: Optional[int] = None,
         env: Optional[Env] = None,
-        max_episode_steps: int = math.inf,
+        max_episode_steps: float = math.inf,
         reward_threshold: float = math.inf,
-        **kwargs,
-    ):
-        if any(
-            [env_name is None and env is None, env_name is not None and env is not None]
-        ):
+        **kwargs: Any,
+    ) -> None:
+        """Start the manager"""
+        if any([env_name is None and env is None, env_name is not None and env is not None]):
             raise ValueError("Must specify exactly one of [env_name, env]")
         if env_name is not None:
             self.env_name = env_name
             # extract some parameters from the environment
-            self.max_episode_steps = (
-                spec(self.env_name).max_episode_steps or max_episode_steps
-            )
-            self.reward_threshold = (
-                spec(self.env_name).reward_threshold or reward_threshold
-            )
+            self.max_episode_steps = spec(self.env_name).max_episode_steps or max_episode_steps
+            self.reward_threshold = spec(self.env_name).reward_threshold or reward_threshold
             # create the environment
             self.env = make(env_name, **kwargs)
             # we seed the environment so that results are reproducible
@@ -457,91 +466,119 @@ 
    Inherited members
    
         self.setup_reproducibility(seed)
         self.slate_size: int = kwargs["slate_size"] if "slate_size" in kwargs else 1
 
-    def print_overview(self):
-        """ Prints the most important variables of the environment. """
-        print("Reward threshold: {} ".format(self.reward_threshold))
-        print("Reward signal range: {} ".format(self.env.reward_range))
-        print("Maximum episode steps: {} ".format(self.max_episode_steps))
-        print("Action apace size: {}".format(self.env.action_space))
-        print("Observation space size {} ".format(self.env.observation_space))
+    def print_overview(self) -> None:
+        """Print the most important variables of the environment."""
+        logger.info("Reward threshold: {} ".format(self.reward_threshold))
+        logger.info("Reward signal range: {} ".format(self.env.reward_range))
+        logger.info("Maximum episode steps: {} ".format(self.max_episode_steps))
+        logger.info("Action apace size: {}".format(self.env.action_space))
+        logger.info("Observation space size {} ".format(self.env.observation_space))
 
     def execute_episodes(
         self,
         rl: ReinforcementLearning,
         n_episodes: int = 1,
         should_render: bool = False,
-        should_print: bool = False,
     ) -> List[EpisodeOutput]:
         """Execute any number of episodes with the given agent.
-        Returns the number of timesteps and sum of rewards per episode."""
+        Returns the number of timesteps and sum of rewards per episode.
+        """
         episode_outputs = []
         for episode in range(n_episodes):
-            t, reward_sum, done, state = 0, 0, False, self.env.reset()
-            if should_print:
-                print(f"Running episode {episode}, starting at state {state}")
+            t, reward_sum, done, (state, _) = 0, 0, False, self.env.reset(seed=self.seed)
+            logger.info(f"Running episode {episode}, starting at state {state}")
             while not done and t < self.max_episode_steps:
                 if should_render:
                     self.env.render()
                 action = rl.action_for_state(state)
                 state, reward, done, _ = self.env.step(action)
-                if should_print:
-                    print(f"t={t} a={action} r={reward} s={state}")
+                logger.info(f"t={t} a={action} r={reward} s={state}")
                 reward_sum += reward
                 t += 1
             episode_outputs.append(EpisodeOutput(t, reward_sum))
             self.env.close()
         return episode_outputs
 
+    @staticmethod
+    def _train_start_new_episode(statistics: LearningStatistics, episode: int) -> None:
+        if statistics:
+            statistics.episode = episode
+            statistics.timestep = 0
+
+    @staticmethod
+    def _train_update_timestep(statistics: LearningStatistics) -> None:
+        if statistics:
+            statistics.timestep += 1
+
+    @staticmethod
+    def _train_add_statistics(statistics: LearningStatistics, rewards: List, moving_average: ndarray) -> None:
+        if statistics:
+            statistics.append_metric("episode_rewards", sum(rewards))
+            statistics.append_metric("timestep_rewards", rewards)
+            statistics.append_metric("moving_rewards", moving_average)
+
+    def _train_get_step_action(self, rl: ReinforcementLearning, state: Any) -> Any:
+        if self.slate_size == 1:
+            return rl.action_for_state(state)
+        else:
+            return rl.top_k_actions_for_state(state, k=self.slate_size)
+
     def train(
         self,
         rl: ReinforcementLearning,
         statistics: Optional[LearningStatistics] = None,
-        max_episodes=50,
-        should_print: bool = True,
-    ):
-        if should_print is True:
-            print("Training...")
+        max_episodes: int = 50,
+    ) -> None:
+        """Train the agent for the given amount of episodes."""
+        logger.info("Training...")
         episode_rewards = []
         for episode in range(max_episodes):
-            state = self.env.reset()
+            state, info = self.env.reset(seed=self.seed)
             rewards = []
-            if statistics:
-                statistics.episode = episode
-                statistics.timestep = 0
+            self._train_start_new_episode(statistics, episode)
             done = False
             while done is False:
-                if self.slate_size == 1:
-                    action = rl.action_for_state(state)
-                else:
-                    action = rl.top_k_actions_for_state(state, k=self.slate_size)
-                new_state, reward, done, info = self.env.step(action)
+                action = self._train_get_step_action(rl, state)
+                new_state, reward, done, _, info = self.env.step(action)
                 if "chosen_action" in info:
                     action = action[info["chosen_action"]]
-                rl.store_experience(
-                    state, action, reward, done, new_state
-                )  # guardar experiencia en el buffer
+                rl.store_experience(state, action, reward, done, new_state)
                 rewards.append(reward)
                 state = new_state.copy()
-                if statistics:
-                    statistics.timestep += 1
+                self._train_update_timestep(statistics)
             episode_rewards.append(sum(rewards))
             moving_average = np.mean(episode_rewards[-100:])
-            if statistics:
-                statistics.append_metric("episode_rewards", sum(rewards))
-                statistics.append_metric("timestep_rewards", rewards)
-                statistics.append_metric("moving_rewards", moving_average)
-            if should_print is True:
-                print(
-                    "\rEpisode {:d} Mean Rewards {:.2f} Last Reward {:.2f}\t\t".format(
-                        episode, moving_average, sum(rewards)
-                    ),
-                    end="",
-                )
+            self._train_add_statistics(statistics, rewards, moving_average)
+
+            logger.print(
+                f"\rEpisode {episode:d} Mean Rewards {moving_average:.2f} Last Reward {rewards[-1]:.2f}\t\t",
+                end="",
+            )
             if moving_average >= self.reward_threshold:
-                if should_print is True:
-                    print("Reward threshold reached")
+                logger.info("Reward threshold reached")
                 break
 
+    def _hyperparameter_search_run_combinations(
+        self,
+        runs_per_combination: int,
+        rl: ReinforcementLearning,
+        episodes: int,
+        learning_statistics: LearningStatistics,
+        parameter_name: str,
+        parameter_value: Any,
+    ) -> Generator:
+        for run in range(runs_per_combination):
+            self.train(
+                rl=rl,
+                max_episodes=episodes,
+                statistics=learning_statistics,
+            )
+            yield learning_statistics.moving_rewards.iloc[-1]
+            logger.print(
+                f"\rTested combination {parameter_name}={parameter_value} round {run} result was {learning_statistics.moving_rewards.iloc[-1]} \t\t",  # noqa: E501
+                end="",
+            )
+
     def hyperparameter_search(
         self,
         agent: type,
@@ -549,43 +586,33 @@ 
    Inherited members
    
         default_params: dict,
         episodes: int = 100,
         runs_per_combination: int = 3,
-        verbose: bool = True,
     ) -> dict:
         """Given an agent class, and a dictionary of hyperparameter names and values,
-        will try all combinations, and return the mean reward of each combinatio
-        for the given number of episods, and will run the determined number of times."""
+        will try all combinations, and return the mean reward of each combination
+        for the given number of episodes, and will run the determined number of times.
+        """
         combination_results = defaultdict(lambda: [])
-        for (p_name, p_value) in params.items():
+        for p_name, p_value in params.items():
             if len(p_value) < 2:
                 continue
             for value in p_value:
                 rl = agent(**{**default_params, p_name: value})
                 learning_statistics = LearningStatistics()
                 combination_key = f"{p_name}={value}"
-                for run in range(runs_per_combination):
-                    self.train(
-                        rl=rl,
-                        max_episodes=episodes,
-                        should_print=False,
-                        statistics=learning_statistics,
-                    )
-                    combination_results[combination_key].append(
-                        learning_statistics.moving_rewards.iloc[-1]
-                    )
-                    if verbose:
-                        print(
-                            f"\rTested combination {p_name}={value} round {run} "
-                            f"result was {learning_statistics.moving_rewards.iloc[-1]}"
-                            "\t\t",
-                            end="",
-                        )
+                for result in self._hyperparameter_search_run_combinations(
+                    runs_per_combination,
+                    rl,
+                    episodes,
+                    learning_statistics,
+                    p_name,
+                    value,
+                ):
+                    combination_results[combination_key].append(result)
 
         return combination_results
 
-    def setup_reproducibility(
-        self, seed: Optional[int] = None
-    ) -> Optional[RandomState]:
-        """ Seeds the project's libraries: numpy, torch, gym """
+    def setup_reproducibility(self, seed: Optional[int] = None) -> Optional[RandomState]:
+        """Seeds the project's libraries: numpy, torch, gym"""
         if seed:
             # seed pytorch
             torch.manual_seed(seed)
@@ -594,9 +621,10 @@ 
    Inherited members
    
             # seed numpy
             np.random.seed(seed)
             # seed gym
-            self.env.seed(seed)
+            self.seed = seed
             self.random_state = RandomState(seed)
-            return self.random_state
    
+            return self.random_state
+        return None

    Subclasses

      @@ -615,11 +643,19 @@

      Class variables

      +
      var random_state : Optional[numpy.random.mtrand.RandomState]
      +
      +
      +
      +
      var seed : Optional[int]
      +
      +
      +

    Methods

    -def execute_episodes(self, rl: ReinforcementLearning, n_episodes: int = 1, should_render: bool = False, should_print: bool = False) ‑> List[EpisodeOutput] +def execute_episodes(self, rl: ReinforcementLearning, n_episodes: int = 1, should_render: bool = False) ‑> List[EpisodeOutput]

    Execute any number of episodes with the given agent. @@ -633,22 +669,20 @@

    Methods

    rl: ReinforcementLearning, n_episodes: int = 1, should_render: bool = False, - should_print: bool = False, ) -> List[EpisodeOutput]: """Execute any number of episodes with the given agent. - Returns the number of timesteps and sum of rewards per episode.""" + Returns the number of timesteps and sum of rewards per episode. + """ episode_outputs = [] for episode in range(n_episodes): - t, reward_sum, done, state = 0, 0, False, self.env.reset() - if should_print: - print(f"Running episode {episode}, starting at state {state}") + t, reward_sum, done, (state, _) = 0, 0, False, self.env.reset(seed=self.seed) + logger.info(f"Running episode {episode}, starting at state {state}") while not done and t < self.max_episode_steps: if should_render: self.env.render() action = rl.action_for_state(state) state, reward, done, _ = self.env.step(action) - if should_print: - print(f"t={t} a={action} r={reward} s={state}") + logger.info(f"t={t} a={action} r={reward} s={state}") reward_sum += reward t += 1 episode_outputs.append(EpisodeOutput(t, reward_sum)) @@ -657,12 +691,12 @@

    Methods

    Given an agent class, and a dictionary of hyperparameter names and values, -will try all combinations, and return the mean reward of each combinatio -for the given number of episods, and will run the determined number of times.

    +will try all combinations, and return the mean reward of each combination +for the given number of episodes, and will run the determined number of times.

    Expand source code @@ -674,60 +708,52 @@

    Methods

    default_params: dict, episodes: int = 100, runs_per_combination: int = 3, - verbose: bool = True, ) -> dict: """Given an agent class, and a dictionary of hyperparameter names and values, - will try all combinations, and return the mean reward of each combinatio - for the given number of episods, and will run the determined number of times.""" + will try all combinations, and return the mean reward of each combination + for the given number of episodes, and will run the determined number of times. + """ combination_results = defaultdict(lambda: []) - for (p_name, p_value) in params.items(): + for p_name, p_value in params.items(): if len(p_value) < 2: continue for value in p_value: rl = agent(**{**default_params, p_name: value}) learning_statistics = LearningStatistics() combination_key = f"{p_name}={value}" - for run in range(runs_per_combination): - self.train( - rl=rl, - max_episodes=episodes, - should_print=False, - statistics=learning_statistics, - ) - combination_results[combination_key].append( - learning_statistics.moving_rewards.iloc[-1] - ) - if verbose: - print( - f"\rTested combination {p_name}={value} round {run} " - f"result was {learning_statistics.moving_rewards.iloc[-1]}" - "\t\t", - end="", - ) + for result in self._hyperparameter_search_run_combinations( + runs_per_combination, + rl, + episodes, + learning_statistics, + p_name, + value, + ): + combination_results[combination_key].append(result) return combination_results
    -def print_overview(self) +def print_overview(self) ‑> None
    -

    Prints the most important variables of the environment.

    +

    Print the most important variables of the environment.

    Expand source code -
    def print_overview(self):
-    """ Prints the most important variables of the environment. """
-    print("Reward threshold: {} ".format(self.reward_threshold))
-    print("Reward signal range: {} ".format(self.env.reward_range))
-    print("Maximum episode steps: {} ".format(self.max_episode_steps))
-    print("Action apace size: {}".format(self.env.action_space))
-    print("Observation space size {} ".format(self.env.observation_space))
    +
    def print_overview(self) -> None:
+    """Print the most important variables of the environment."""
+    logger.info("Reward threshold: {} ".format(self.reward_threshold))
+    logger.info("Reward signal range: {} ".format(self.env.reward_range))
+    logger.info("Maximum episode steps: {} ".format(self.max_episode_steps))
+    logger.info("Action apace size: {}".format(self.env.action_space))
+    logger.info("Observation space size {} ".format(self.env.observation_space))
    -def setup_reproducibility(self, seed: Union[int, NoneType] = None) ‑> Union[numpy.random.mtrand.RandomState, NoneType] +def setup_reproducibility(self, seed: Optional[int] = None) ‑> Optional[numpy.random.mtrand.RandomState]

    Seeds the project's libraries: numpy, torch, gym

    @@ -735,10 +761,8 @@

    Methods

    Expand source code -
    def setup_reproducibility(
-    self, seed: Optional[int] = None
-) -> Optional[RandomState]:
-    """ Seeds the project's libraries: numpy, torch, gym """
+
    def setup_reproducibility(self, seed: Optional[int] = None) -> Optional[RandomState]:
+    """Seeds the project's libraries: numpy, torch, gym"""
     if seed:
         # seed pytorch
         torch.manual_seed(seed)
@@ -747,16 +771,17 @@ 
    Methods
    
         # seed numpy
         np.random.seed(seed)
         # seed gym
-        self.env.seed(seed)
+        self.seed = seed
         self.random_state = RandomState(seed)
-        return self.random_state
    
+        return self.random_state
+    return None
    -def train(self, rl: ReinforcementLearning, statistics: Union[LearningStatistics, NoneType] = None, max_episodes=50, should_print: bool = True) +def train(self, rl: ReinforcementLearning, statistics: Optional[LearningStatistics] = None, max_episodes: int = 50) ‑> None
    -
    +

    Train the agent for the given amount of episodes.

    Expand source code @@ -765,50 +790,35 @@

    Methods

    self, rl: ReinforcementLearning, statistics: Optional[LearningStatistics] = None, - max_episodes=50, - should_print: bool = True, -): - if should_print is True: - print("Training...") + max_episodes: int = 50, +) -> None: + """Train the agent for the given amount of episodes.""" + logger.info("Training...") episode_rewards = [] for episode in range(max_episodes): - state = self.env.reset() + state, info = self.env.reset(seed=self.seed) rewards = [] - if statistics: - statistics.episode = episode - statistics.timestep = 0 + self._train_start_new_episode(statistics, episode) done = False while done is False: - if self.slate_size == 1: - action = rl.action_for_state(state) - else: - action = rl.top_k_actions_for_state(state, k=self.slate_size) - new_state, reward, done, info = self.env.step(action) + action = self._train_get_step_action(rl, state) + new_state, reward, done, _, info = self.env.step(action) if "chosen_action" in info: action = action[info["chosen_action"]] - rl.store_experience( - state, action, reward, done, new_state - ) # guardar experiencia en el buffer + rl.store_experience(state, action, reward, done, new_state) rewards.append(reward) state = new_state.copy() - if statistics: - statistics.timestep += 1 + self._train_update_timestep(statistics) episode_rewards.append(sum(rewards)) moving_average = np.mean(episode_rewards[-100:]) - if statistics: - statistics.append_metric("episode_rewards", sum(rewards)) - statistics.append_metric("timestep_rewards", rewards) - statistics.append_metric("moving_rewards", moving_average) - if should_print is True: - print( - "\rEpisode {:d} Mean Rewards {:.2f} Last Reward {:.2f}\t\t".format( - episode, moving_average, sum(rewards) - ), - end="", - ) + self._train_add_statistics(statistics, rewards, moving_average) + + logger.print( + f"\rEpisode {episode:d} Mean Rewards {moving_average:.2f} Last Reward {rewards[-1]:.2f}\t\t", + end="", + ) if moving_average >= self.reward_threshold: - if should_print is True: - print("Reward threshold reached") + logger.info("Reward threshold reached") break
    @@ -816,35 +826,26 @@

    Methods

    class MovieLensFairnessManager -(seed: Union[int, NoneType] = None, slate_size: int = 1) +(seed: Optional[int] = None, slate_size: int = 1)
    -

    Class for learning from gym environments with some convenience methods.

    +

    Manager for the movie lens environment

    +

    Start the manager

    Expand source code 
    class MovieLensFairnessManager(Manager):
+    """Manager for the movie lens environment"""
+
     def __init__(self, seed: Optional[int] = None, slate_size: int = 1):
-        super().__init__(
-            env_name="MovieLensFairness-v0", seed=seed, slate_size=slate_size
-        )
    + """Start the manager""" + super().__init__(env_name="MovieLensFairness-v0", seed=seed, slate_size=slate_size)

    Ancestors

    -

    Class variables

    -
    -
    var env : Any
    -
    -
    -
    -
    var env_name : str
    -
    -
    -
    -

    Inherited members

    @@ -875,10 +877,6 @@

    Index

  • MovieLensFairnessManager

    -
    • @@ -926,7 +914,7 @@

    -

    Generated by pdoc 0.9.2.

    +

    Generated by pdoc 0.10.0.

    \ No newline at end of file diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..1d68ce0 --- /dev/null +++ b/docs/index.html @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 6b94bb3..74a8c00 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,1902 +1,1951 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + [[package]] name = "absl-py" -version = "0.15.0" +version = "2.1.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -six = "*" - -[[package]] -name = "accelerate" -version = "0.12.0" -description = "Accelerate" -category = "main" -optional = false -python-versions = ">=3.7.0" - -[package.dependencies] -numpy = ">=1.17" -packaging = ">=20.0" -psutil = "*" -pyyaml = "*" -torch = ">=1.4.0" - -[package.extras] -dev = ["black (>=22.0,<23.0)", "datasets", "deepspeed (<0.7.0)", "evaluate", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scipy", "sklearn", "tqdm", "transformers"] -quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)"] -sagemaker = ["sagemaker"] -test_dev = ["datasets", "deepspeed (<0.7.0)", "evaluate", "scipy", "sklearn", "tqdm", "transformers"] -test_prod = ["parameterized", "pytest", "pytest-subtests", "pytest-xdist"] -test_trackers = ["comet-ml", "tensorboard", "wandb"] -testing = ["datasets", "deepspeed (<0.7.0)", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scipy", "sklearn", "tqdm", "transformers"] - -[[package]] -name = "astunparse" -version = "1.6.3" -description = "An AST unparser for Python" -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -six = ">=1.6.1,<2.0" - -[[package]] -name = "cached-property" -version = "1.5.2" -description = "A decorator for caching properties in classes." -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "cachetools" -version = "3.1.1" -description = "Extensible memoizing collections and decorators" -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "catalyst" -version = "22.4" -description = "Catalyst. Accelerated deep learning R&D with PyTorch." -category = "main" -optional = false -python-versions = ">=3.7.0" - -[package.dependencies] -accelerate = ">=0.5.1" -hydra-slayer = ">=0.4.0" -numpy = ">=1.18" -tensorboardX = ">=2.1.0" -torch = ">=1.4.0" -tqdm = ">=4.33.0" - -[package.extras] -all = ["Pillow (>=6.1)", "imageio (>=2.5.0)", "matplotlib (>=3.1.0)", "opencv-python-headless (>=4.2.0.32)", "optuna (>=2.0.0)", "pandas (>=1.0.0)", "requests", "scikit-image (<0.19.0)", "scikit-learn (>=1.0)", "scipy (>=1.4.1)", "torchvision (>=0.5.0)"] -comet = ["comet-ml"] -cv = ["Pillow (>=6.1)", "imageio (>=2.5.0)", "opencv-python-headless (>=4.2.0.32)", "requests", "scikit-image (<0.19.0)", "torchvision (>=0.5.0)"] -deepspeed = ["deepspeed (>=0.4.0)"] -dev = ["Jinja2 (<=3.0.3)", "black (==21.8b0)", "catalyst-codestyle (==21.09.2)", "catalyst-sphinx-theme (==1.2.0)", "click (<=8.0.4)", "docutils (==0.17.1)", "mock (==3.0.5)", "path", "pre-commit (==2.13.0)", "pytest", "sphinx (==2.2.1)", "tomlkit (==0.7.2)"] -ml = ["matplotlib (>=3.1.0)", "pandas (>=1.0.0)", "scikit-learn (>=1.0)", "scipy (>=1.4.1)"] -mlflow = ["mlflow"] -neptune = ["neptune-client (>=0.9.8)"] -onnx = ["onnx", "onnxruntime"] -onnx-gpu = ["onnx", "onnxruntime-gpu"] -optuna = ["optuna (>=2.0.0)"] -profiler = ["torch-tb-profiler"] -wandb = ["wandb"] - -[[package]] -name = "certifi" -version = "2022.6.15" -description = "Python package for providing Mozilla's CA Bundle." -category = "main" -optional = false -python-versions = ">=3.6" +optional = true +python-versions = ">=3.7" +files = [ + {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"}, + {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"}, +] [[package]] name = "cfgv" version = "3.3.1" description = "Validate configuration and produce human readable error messages." -category = "dev" optional = false python-versions = ">=3.6.1" - -[[package]] -name = "charset-normalizer" -version = "2.0.12" -description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." -category = "main" -optional = false -python-versions = ">=3.5.0" - -[package.extras] -unicode_backport = ["unicodedata2"] - -[[package]] -name = "clang" -version = "5.0" -description = "libclang python bindings" -category = "main" -optional = false -python-versions = "*" +files = [ + {file = "cfgv-3.3.1-py2.py3-none-any.whl", hash = "sha256:c6a0883f3917a037485059700b9e75da2464e6c27051014ad85ba6aaa5884426"}, + {file = "cfgv-3.3.1.tar.gz", hash = "sha256:f5a830efb9ce7a445376bb66ec94c638a9787422f96264c98edc6bdeed8ab736"}, +] [[package]] name = "cloudpickle" -version = "2.1.0" -description = "Extended pickling support for Python objects" -category = "main" -optional = false -python-versions = ">=3.6" +version = "3.0.0" +description = "Pickler class to extend the standard pickle.Pickler functionality" +optional = true +python-versions = ">=3.8" +files = [ + {file = "cloudpickle-3.0.0-py3-none-any.whl", hash = "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7"}, + {file = "cloudpickle-3.0.0.tar.gz", hash = "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"}, +] [[package]] name = "colorama" -version = "0.4.5" +version = "0.4.6" description = "Cross-platform colored terminal text." -category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +files = [ + {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, + {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, +] + +[[package]] +name = "contourpy" +version = "1.2.1" +description = "Python library for calculating contours of 2D quadrilateral grids" +optional = false +python-versions = ">=3.9" +files = [ + {file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"}, + {file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b"}, + {file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd"}, + {file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619"}, + {file = "contourpy-1.2.1-cp310-cp310-win32.whl", hash = "sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8"}, + {file = "contourpy-1.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9"}, + {file = "contourpy-1.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6022cecf8f44e36af10bd9118ca71f371078b4c168b6e0fab43d4a889985dbb5"}, + {file = "contourpy-1.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6150ffa5c767bc6332df27157d95442c379b7dce3a38dff89c0f39b63275696f"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4c863140fafc615c14a4bf4efd0f4425c02230eb8ef02784c9a156461e62c965"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df"}, + {file = "contourpy-1.2.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:49e70d111fee47284d9dd867c9bb9a7058a3c617274900780c43e38d90fe1205"}, + {file = "contourpy-1.2.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:b59c0ffceff8d4d3996a45f2bb6f4c207f94684a96bf3d9728dbb77428dd8cb8"}, + {file = "contourpy-1.2.1-cp311-cp311-win32.whl", hash = "sha256:7b4182299f251060996af5249c286bae9361fa8c6a9cda5efc29fe8bfd6062ec"}, + {file = "contourpy-1.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2855c8b0b55958265e8b5888d6a615ba02883b225f2227461aa9127c578a4922"}, + {file = "contourpy-1.2.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:62828cada4a2b850dbef89c81f5a33741898b305db244904de418cc957ff05dc"}, + {file = "contourpy-1.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:309be79c0a354afff9ff7da4aaed7c3257e77edf6c1b448a779329431ee79d7e"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e785e0f2ef0d567099b9ff92cbfb958d71c2d5b9259981cd9bee81bd194c9a4"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1cac0a8f71a041aa587410424ad46dfa6a11f6149ceb219ce7dd48f6b02b87a7"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af3f4485884750dddd9c25cb7e3915d83c2db92488b38ccb77dd594eac84c4a0"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ce6889abac9a42afd07a562c2d6d4b2b7134f83f18571d859b25624a331c90b"}, + {file = "contourpy-1.2.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:a1eea9aecf761c661d096d39ed9026574de8adb2ae1c5bd7b33558af884fb2ce"}, + {file = "contourpy-1.2.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:187fa1d4c6acc06adb0fae5544c59898ad781409e61a926ac7e84b8f276dcef4"}, + {file = "contourpy-1.2.1-cp312-cp312-win32.whl", hash = "sha256:c2528d60e398c7c4c799d56f907664673a807635b857df18f7ae64d3e6ce2d9f"}, + {file = "contourpy-1.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:1a07fc092a4088ee952ddae19a2b2a85757b923217b7eed584fdf25f53a6e7ce"}, + {file = "contourpy-1.2.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bb6834cbd983b19f06908b45bfc2dad6ac9479ae04abe923a275b5f48f1a186b"}, + {file = "contourpy-1.2.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1d59e739ab0e3520e62a26c60707cc3ab0365d2f8fecea74bfe4de72dc56388f"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bd3db01f59fdcbce5b22afad19e390260d6d0222f35a1023d9adc5690a889364"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a12a813949e5066148712a0626895c26b2578874e4cc63160bb007e6df3436fe"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fe0ccca550bb8e5abc22f530ec0466136379c01321fd94f30a22231e8a48d985"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445"}, + {file = "contourpy-1.2.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02"}, + {file = "contourpy-1.2.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d31a63bc6e6d87f77d71e1abbd7387ab817a66733734883d1fc0021ed9bfa083"}, + {file = "contourpy-1.2.1-cp39-cp39-win32.whl", hash = "sha256:ddcb8581510311e13421b1f544403c16e901c4e8f09083c881fab2be80ee31ba"}, + {file = "contourpy-1.2.1-cp39-cp39-win_amd64.whl", hash = "sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9"}, + {file = "contourpy-1.2.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609"}, + {file = "contourpy-1.2.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3"}, + {file = "contourpy-1.2.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f"}, + {file = "contourpy-1.2.1.tar.gz", hash = "sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c"}, +] + +[package.dependencies] +numpy = ">=1.20" + +[package.extras] +bokeh = ["bokeh", "selenium"] +docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"] +mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"] +test = ["Pillow", "contourpy[test-no-images]", "matplotlib"] +test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"] + +[[package]] +name = "coverage" +version = "7.5.1" +description = "Code coverage measurement for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "coverage-7.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0884920835a033b78d1c73b6d3bbcda8161a900f38a488829a83982925f6c2e"}, + {file = "coverage-7.5.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:39afcd3d4339329c5f58de48a52f6e4e50f6578dd6099961cf22228feb25f38f"}, + {file = "coverage-7.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a7b0ceee8147444347da6a66be737c9d78f3353b0681715b668b72e79203e4a"}, + {file = "coverage-7.5.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a9ca3f2fae0088c3c71d743d85404cec8df9be818a005ea065495bedc33da35"}, + {file = "coverage-7.5.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5fd215c0c7d7aab005221608a3c2b46f58c0285a819565887ee0b718c052aa4e"}, + {file = "coverage-7.5.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:4bf0655ab60d754491004a5efd7f9cccefcc1081a74c9ef2da4735d6ee4a6223"}, + {file = "coverage-7.5.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:61c4bf1ba021817de12b813338c9be9f0ad5b1e781b9b340a6d29fc13e7c1b5e"}, + {file = "coverage-7.5.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:db66fc317a046556a96b453a58eced5024af4582a8dbdc0c23ca4dbc0d5b3146"}, + {file = "coverage-7.5.1-cp310-cp310-win32.whl", hash = "sha256:b016ea6b959d3b9556cb401c55a37547135a587db0115635a443b2ce8f1c7228"}, + {file = "coverage-7.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:df4e745a81c110e7446b1cc8131bf986157770fa405fe90e15e850aaf7619bc8"}, + {file = "coverage-7.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:796a79f63eca8814ca3317a1ea443645c9ff0d18b188de470ed7ccd45ae79428"}, + {file = "coverage-7.5.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4fc84a37bfd98db31beae3c2748811a3fa72bf2007ff7902f68746d9757f3746"}, + {file = "coverage-7.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6175d1a0559986c6ee3f7fccfc4a90ecd12ba0a383dcc2da30c2b9918d67d8a3"}, + {file = "coverage-7.5.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fc81d5878cd6274ce971e0a3a18a8803c3fe25457165314271cf78e3aae3aa2"}, + {file = "coverage-7.5.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:556cf1a7cbc8028cb60e1ff0be806be2eded2daf8129b8811c63e2b9a6c43bca"}, + {file = "coverage-7.5.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:9981706d300c18d8b220995ad22627647be11a4276721c10911e0e9fa44c83e8"}, + {file = "coverage-7.5.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:d7fed867ee50edf1a0b4a11e8e5d0895150e572af1cd6d315d557758bfa9c057"}, + {file = "coverage-7.5.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:ef48e2707fb320c8f139424a596f5b69955a85b178f15af261bab871873bb987"}, + {file = "coverage-7.5.1-cp311-cp311-win32.whl", hash = "sha256:9314d5678dcc665330df5b69c1e726a0e49b27df0461c08ca12674bcc19ef136"}, + {file = "coverage-7.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:5fa567e99765fe98f4e7d7394ce623e794d7cabb170f2ca2ac5a4174437e90dd"}, + {file = "coverage-7.5.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b6cf3764c030e5338e7f61f95bd21147963cf6aa16e09d2f74f1fa52013c1206"}, + {file = "coverage-7.5.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2ec92012fefebee89a6b9c79bc39051a6cb3891d562b9270ab10ecfdadbc0c34"}, + {file = "coverage-7.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16db7f26000a07efcf6aea00316f6ac57e7d9a96501e990a36f40c965ec7a95d"}, + {file = "coverage-7.5.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:beccf7b8a10b09c4ae543582c1319c6df47d78fd732f854ac68d518ee1fb97fa"}, + {file = "coverage-7.5.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8748731ad392d736cc9ccac03c9845b13bb07d020a33423fa5b3a36521ac6e4e"}, + {file = "coverage-7.5.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7352b9161b33fd0b643ccd1f21f3a3908daaddf414f1c6cb9d3a2fd618bf2572"}, + {file = "coverage-7.5.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:7a588d39e0925f6a2bff87154752481273cdb1736270642aeb3635cb9b4cad07"}, + {file = "coverage-7.5.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:68f962d9b72ce69ea8621f57551b2fa9c70509af757ee3b8105d4f51b92b41a7"}, + {file = "coverage-7.5.1-cp312-cp312-win32.whl", hash = "sha256:f152cbf5b88aaeb836127d920dd0f5e7edff5a66f10c079157306c4343d86c19"}, + {file = "coverage-7.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:5a5740d1fb60ddf268a3811bcd353de34eb56dc24e8f52a7f05ee513b2d4f596"}, + {file = "coverage-7.5.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e2213def81a50519d7cc56ed643c9e93e0247f5bbe0d1247d15fa520814a7cd7"}, + {file = "coverage-7.5.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5037f8fcc2a95b1f0e80585bd9d1ec31068a9bcb157d9750a172836e98bc7a90"}, + {file = "coverage-7.5.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c3721c2c9e4c4953a41a26c14f4cef64330392a6d2d675c8b1db3b645e31f0e"}, + {file = "coverage-7.5.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca498687ca46a62ae590253fba634a1fe9836bc56f626852fb2720f334c9e4e5"}, + {file = "coverage-7.5.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0cdcbc320b14c3e5877ee79e649677cb7d89ef588852e9583e6b24c2e5072661"}, + {file = "coverage-7.5.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:57e0204b5b745594e5bc14b9b50006da722827f0b8c776949f1135677e88d0b8"}, + {file = "coverage-7.5.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:8fe7502616b67b234482c3ce276ff26f39ffe88adca2acf0261df4b8454668b4"}, + {file = "coverage-7.5.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:9e78295f4144f9dacfed4f92935fbe1780021247c2fabf73a819b17f0ccfff8d"}, + {file = "coverage-7.5.1-cp38-cp38-win32.whl", hash = "sha256:1434e088b41594baa71188a17533083eabf5609e8e72f16ce8c186001e6b8c41"}, + {file = "coverage-7.5.1-cp38-cp38-win_amd64.whl", hash = "sha256:0646599e9b139988b63704d704af8e8df7fa4cbc4a1f33df69d97f36cb0a38de"}, + {file = "coverage-7.5.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4cc37def103a2725bc672f84bd939a6fe4522310503207aae4d56351644682f1"}, + {file = "coverage-7.5.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:fc0b4d8bfeabd25ea75e94632f5b6e047eef8adaed0c2161ada1e922e7f7cece"}, + {file = "coverage-7.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d0a0f5e06881ecedfe6f3dd2f56dcb057b6dbeb3327fd32d4b12854df36bf26"}, + {file = "coverage-7.5.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9735317685ba6ec7e3754798c8871c2f49aa5e687cc794a0b1d284b2389d1bd5"}, + {file = "coverage-7.5.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d21918e9ef11edf36764b93101e2ae8cc82aa5efdc7c5a4e9c6c35a48496d601"}, + {file = "coverage-7.5.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:c3e757949f268364b96ca894b4c342b41dc6f8f8b66c37878aacef5930db61be"}, + {file = "coverage-7.5.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:79afb6197e2f7f60c4824dd4b2d4c2ec5801ceb6ba9ce5d2c3080e5660d51a4f"}, + {file = "coverage-7.5.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d1d0d98d95dd18fe29dc66808e1accf59f037d5716f86a501fc0256455219668"}, + {file = "coverage-7.5.1-cp39-cp39-win32.whl", hash = "sha256:1cc0fe9b0b3a8364093c53b0b4c0c2dd4bb23acbec4c9240b5f284095ccf7981"}, + {file = "coverage-7.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:dde0070c40ea8bb3641e811c1cfbf18e265d024deff6de52c5950677a8fb1e0f"}, + {file = "coverage-7.5.1-pp38.pp39.pp310-none-any.whl", hash = "sha256:6537e7c10cc47c595828b8a8be04c72144725c383c4702703ff4e42e44577312"}, + {file = "coverage-7.5.1.tar.gz", hash = "sha256:54de9ef3a9da981f7af93eafde4ede199e0846cd819eb27c88e2b712aae9708c"}, +] + +[package.dependencies] +tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""} + +[package.extras] +toml = ["tomli"] [[package]] name = "cycler" -version = "0.11.0" +version = "0.12.1" description = "Composable style cycles" -category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.8" +files = [ + {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"}, + {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"}, +] -[[package]] -name = "decorator" -version = "5.1.1" -description = "Decorators for Humans" -category = "main" -optional = false -python-versions = ">=3.5" +[package.extras] +docs = ["ipython", "matplotlib", "numpydoc", "sphinx"] +tests = ["pytest", "pytest-cov", "pytest-xdist"] [[package]] name = "distlib" -version = "0.3.6" +version = "0.3.8" description = "Distribution utilities" -category = "dev" -optional = false -python-versions = "*" - -[[package]] -name = "dm-tree" -version = "0.1.7" -description = "Tree is a library for working with nested data structures." -category = "main" optional = false python-versions = "*" +files = [ + {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"}, + {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"}, +] [[package]] -name = "edward2" -version = "0.0.2" -description = "Edward2" -category = "main" +name = "exceptiongroup" +version = "1.2.1" +description = "Backport of PEP 654 (exception groups)" optional = false -python-versions = "*" +python-versions = ">=3.7" +files = [ + {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"}, + {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"}, +] [package.extras] -numpy = ["numpy (>=1.7)", "scipy (>=1.0.0)"] -tensorflow = ["tensorflow (>=2.0.0a0)", "tensorflow-probability (>=0.8.0)"] -tests = ["absl-py (>=0.5.0)", "matplotlib (>=2.0.0)", "pylint (>=1.9.0)", "tensorflow-datasets (>=1.3.0)"] -tf-nightly = ["tf-nightly", "tfp-nightly"] +test = ["pytest (>=6)"] [[package]] -name = "etils" -version = "0.7.1" -description = "Collection of common python utils" -category = "main" -optional = false -python-versions = ">=3.7" - -[package.dependencies] -etils = {version = "*", extras = ["epy"], optional = true, markers = "extra == \"epath\""} -importlib_resources = {version = "*", optional = true, markers = "extra == \"epath\""} -typing_extensions = {version = "*", optional = true, markers = "extra == \"epath\""} -zipp = {version = "*", optional = true, markers = "extra == \"epath\""} - -[package.extras] -all = ["etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils", "etils"] -array-types = ["etils"] -dev = ["chex", "pylint (>=2.6.0)", "pytest", "pytest-subtests", "pytest-xdist", "yapf"] -ecolab = ["etils", "etils", "jupyter", "mediapy", "numpy"] -edc = ["etils"] -enp = ["etils", "numpy"] -epath = ["etils", "importlib-resources", "typing-extensions", "zipp"] -epy = ["typing-extensions"] -etqdm = ["absl-py", "etils", "tqdm"] -etree = ["etils", "etils", "etils", "etils"] -etree-dm = ["dm-tree", "etils"] -etree-jax = ["etils", "jax"] -etree-tf = ["etils", "tf-nightly"] +name = "farama-notifications" +version = "0.0.4" +description = "Notifications for all Farama Foundation maintained libraries." +optional = true +python-versions = "*" +files = [ + {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"}, + {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"}, +] [[package]] name = "filelock" -version = "3.8.0" +version = "3.12.2" description = "A platform independent file lock." -category = "dev" optional = false python-versions = ">=3.7" +files = [ + {file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"}, + {file = "filelock-3.12.2.tar.gz", hash = "sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81"}, +] [package.extras] -docs = ["furo (>=2022.6.21)", "sphinx (>=5.1.1)", "sphinx-autodoc-typehints (>=1.19.1)"] -testing = ["covdefaults (>=2.2)", "coverage (>=6.4.2)", "pytest (>=7.1.2)", "pytest-cov (>=3)", "pytest-timeout (>=2.1)"] - -[[package]] -name = "flatbuffers" -version = "1.12" -description = "The FlatBuffers serialization format for Python" -category = "main" -optional = false -python-versions = "*" +docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4.1)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"] [[package]] name = "fonttools" -version = "4.37.1" +version = "4.51.0" description = "Tools to manipulate font files" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "fonttools-4.51.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:84d7751f4468dd8cdd03ddada18b8b0857a5beec80bce9f435742abc9a851a74"}, + {file = "fonttools-4.51.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8b4850fa2ef2cfbc1d1f689bc159ef0f45d8d83298c1425838095bf53ef46308"}, + {file = "fonttools-4.51.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5b48a1121117047d82695d276c2af2ee3a24ffe0f502ed581acc2673ecf1037"}, + {file = "fonttools-4.51.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:180194c7fe60c989bb627d7ed5011f2bef1c4d36ecf3ec64daec8302f1ae0716"}, + {file = "fonttools-4.51.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:96a48e137c36be55e68845fc4284533bda2980f8d6f835e26bca79d7e2006438"}, + {file = "fonttools-4.51.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:806e7912c32a657fa39d2d6eb1d3012d35f841387c8fc6cf349ed70b7c340039"}, + {file = "fonttools-4.51.0-cp310-cp310-win32.whl", hash = "sha256:32b17504696f605e9e960647c5f64b35704782a502cc26a37b800b4d69ff3c77"}, + {file = "fonttools-4.51.0-cp310-cp310-win_amd64.whl", hash = "sha256:c7e91abdfae1b5c9e3a543f48ce96013f9a08c6c9668f1e6be0beabf0a569c1b"}, + {file = "fonttools-4.51.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a8feca65bab31479d795b0d16c9a9852902e3a3c0630678efb0b2b7941ea9c74"}, + {file = "fonttools-4.51.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8ac27f436e8af7779f0bb4d5425aa3535270494d3bc5459ed27de3f03151e4c2"}, + {file = "fonttools-4.51.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0e19bd9e9964a09cd2433a4b100ca7f34e34731e0758e13ba9a1ed6e5468cc0f"}, + {file = "fonttools-4.51.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2b92381f37b39ba2fc98c3a45a9d6383bfc9916a87d66ccb6553f7bdd129097"}, + {file = "fonttools-4.51.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:5f6bc991d1610f5c3bbe997b0233cbc234b8e82fa99fc0b2932dc1ca5e5afec0"}, + {file = "fonttools-4.51.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9696fe9f3f0c32e9a321d5268208a7cc9205a52f99b89479d1b035ed54c923f1"}, + {file = "fonttools-4.51.0-cp311-cp311-win32.whl", hash = "sha256:3bee3f3bd9fa1d5ee616ccfd13b27ca605c2b4270e45715bd2883e9504735034"}, + {file = "fonttools-4.51.0-cp311-cp311-win_amd64.whl", hash = "sha256:0f08c901d3866a8905363619e3741c33f0a83a680d92a9f0e575985c2634fcc1"}, + {file = "fonttools-4.51.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:4060acc2bfa2d8e98117828a238889f13b6f69d59f4f2d5857eece5277b829ba"}, + {file = "fonttools-4.51.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1250e818b5f8a679ad79660855528120a8f0288f8f30ec88b83db51515411fcc"}, + {file = "fonttools-4.51.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76f1777d8b3386479ffb4a282e74318e730014d86ce60f016908d9801af9ca2a"}, + {file = "fonttools-4.51.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b5ad456813d93b9c4b7ee55302208db2b45324315129d85275c01f5cb7e61a2"}, + {file = "fonttools-4.51.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:68b3fb7775a923be73e739f92f7e8a72725fd333eab24834041365d2278c3671"}, + {file = "fonttools-4.51.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8e2f1a4499e3b5ee82c19b5ee57f0294673125c65b0a1ff3764ea1f9db2f9ef5"}, + {file = "fonttools-4.51.0-cp312-cp312-win32.whl", hash = "sha256:278e50f6b003c6aed19bae2242b364e575bcb16304b53f2b64f6551b9c000e15"}, + {file = "fonttools-4.51.0-cp312-cp312-win_amd64.whl", hash = "sha256:b3c61423f22165541b9403ee39874dcae84cd57a9078b82e1dce8cb06b07fa2e"}, + {file = "fonttools-4.51.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:1621ee57da887c17312acc4b0e7ac30d3a4fb0fec6174b2e3754a74c26bbed1e"}, + {file = "fonttools-4.51.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e9d9298be7a05bb4801f558522adbe2feea1b0b103d5294ebf24a92dd49b78e5"}, + {file = "fonttools-4.51.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ee1af4be1c5afe4c96ca23badd368d8dc75f611887fb0c0dac9f71ee5d6f110e"}, + {file = "fonttools-4.51.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c18b49adc721a7d0b8dfe7c3130c89b8704baf599fb396396d07d4aa69b824a1"}, + {file = "fonttools-4.51.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:de7c29bdbdd35811f14493ffd2534b88f0ce1b9065316433b22d63ca1cd21f14"}, + {file = "fonttools-4.51.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:cadf4e12a608ef1d13e039864f484c8a968840afa0258b0b843a0556497ea9ed"}, + {file = "fonttools-4.51.0-cp38-cp38-win32.whl", hash = "sha256:aefa011207ed36cd280babfaa8510b8176f1a77261833e895a9d96e57e44802f"}, + {file = "fonttools-4.51.0-cp38-cp38-win_amd64.whl", hash = "sha256:865a58b6e60b0938874af0968cd0553bcd88e0b2cb6e588727117bd099eef836"}, + {file = "fonttools-4.51.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:60a3409c9112aec02d5fb546f557bca6efa773dcb32ac147c6baf5f742e6258b"}, + {file = "fonttools-4.51.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f7e89853d8bea103c8e3514b9f9dc86b5b4120afb4583b57eb10dfa5afbe0936"}, + {file = "fonttools-4.51.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56fc244f2585d6c00b9bcc59e6593e646cf095a96fe68d62cd4da53dd1287b55"}, + {file = "fonttools-4.51.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0d145976194a5242fdd22df18a1b451481a88071feadf251221af110ca8f00ce"}, + {file = "fonttools-4.51.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:c5b8cab0c137ca229433570151b5c1fc6af212680b58b15abd797dcdd9dd5051"}, + {file = "fonttools-4.51.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:54dcf21a2f2d06ded676e3c3f9f74b2bafded3a8ff12f0983160b13e9f2fb4a7"}, + {file = "fonttools-4.51.0-cp39-cp39-win32.whl", hash = "sha256:0118ef998a0699a96c7b28457f15546815015a2710a1b23a7bf6c1be60c01636"}, + {file = "fonttools-4.51.0-cp39-cp39-win_amd64.whl", hash = "sha256:599bdb75e220241cedc6faebfafedd7670335d2e29620d207dd0378a4e9ccc5a"}, + {file = "fonttools-4.51.0-py3-none-any.whl", hash = "sha256:15c94eeef6b095831067f72c825eb0e2d48bb4cea0647c1b05c981ecba2bf39f"}, + {file = "fonttools-4.51.0.tar.gz", hash = "sha256:dc0673361331566d7a663d7ce0f6fdcbfbdc1f59c6e3ed1165ad7202ca183c68"}, +] [package.extras] -all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres", "scipy"] -lxml = ["lxml (>=4.0,<5)"] +interpolatable = ["munkres", "pycairo", "scipy"] +lxml = ["lxml (>=4.0)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] repacker = ["uharfbuzz (>=0.23.0)"] symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] -unicode = ["unicodedata2 (>=14.0.0)"] +unicode = ["unicodedata2 (>=15.1.0)"] woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] -name = "gast" -version = "0.4.0" -description = "Python AST that abstracts the underlying Python version" -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - -[[package]] -name = "gin-config" -version = "0.5.0" -description = "Gin-Config: A lightweight configuration library for Python" -category = "main" -optional = false -python-versions = "*" - -[package.extras] -pytorch-nightly = ["pytorch-nightly"] -tensorflow = ["tensorflow (>=1.13.0)"] -tensorflow-gpu = ["tensorflow-gpu (>=1.13.0)"] -testing = ["absl-py (>=0.1.6)", "mock (>=3.0.5)", "nose"] -tf-nightly = ["tf-nightly"] -torch = ["torch (>=1.3.0)"] - -[[package]] -name = "google-auth" -version = "1.35.0" -description = "Google Authentication Library" -category = "main" -optional = false -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*" - -[package.dependencies] -cachetools = ">=2.0.0,<5.0" -pyasn1-modules = ">=0.2.1" -rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""} -six = ">=1.9.0" - -[package.extras] -aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "requests (>=2.20.0,<3.0.0dev)"] -pyopenssl = ["pyopenssl (>=20.0.0)"] -reauth = ["pyu2f (>=0.1.5)"] - -[[package]] -name = "google-auth-oauthlib" -version = "0.4.6" -description = "Google Authentication Library" -category = "main" -optional = false -python-versions = ">=3.6" - -[package.dependencies] -google-auth = ">=1.0.0" -requests-oauthlib = ">=0.7.0" +name = "fsspec" +version = "2024.3.1" +description = "File-system specification" +optional = true +python-versions = ">=3.8" +files = [ + {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"}, + {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"}, +] [package.extras] -tool = ["click (>=6.0.0)"] - -[[package]] -name = "google-pasta" -version = "0.2.0" -description = "pasta is an AST-based Python refactoring library" -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -six = "*" +abfs = ["adlfs"] +adl = ["adlfs"] +arrow = ["pyarrow (>=1)"] +dask = ["dask", "distributed"] +devel = ["pytest", "pytest-cov"] +dropbox = ["dropbox", "dropboxdrivefs", "requests"] +full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"] +fuse = ["fusepy"] +gcs = ["gcsfs"] +git = ["pygit2"] +github = ["requests"] +gs = ["gcsfs"] +gui = ["panel"] +hdfs = ["pyarrow (>=1)"] +http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"] +libarchive = ["libarchive-c"] +oci = ["ocifs"] +s3 = ["s3fs"] +sftp = ["paramiko"] +smb = ["smbprotocol"] +ssh = ["paramiko"] +tqdm = ["tqdm"] [[package]] name = "graphviz" -version = "0.20.1" +version = "0.20.3" description = "Simple Python interface for Graphviz" -category = "main" -optional = false -python-versions = ">=3.7" +optional = true +python-versions = ">=3.8" +files = [ + {file = "graphviz-0.20.3-py3-none-any.whl", hash = "sha256:81f848f2904515d8cd359cc611faba817598d2feaac4027b266aa3eda7b3dde5"}, + {file = "graphviz-0.20.3.zip", hash = "sha256:09d6bc81e6a9fa392e7ba52135a9d49f1ed62526f96499325930e87ca1b5925d"}, +] [package.extras] dev = ["flake8", "pep8-naming", "tox (>=3)", "twine", "wheel"] -docs = ["sphinx (>=5)", "sphinx-autodoc-typehints", "sphinx-rtd-theme"] -test = ["coverage", "mock (>=4)", "pytest (>=7)", "pytest-cov", "pytest-mock (>=3)"] +docs = ["sphinx (>=5,<7)", "sphinx-autodoc-typehints", "sphinx-rtd-theme"] +test = ["coverage", "pytest (>=7,<8.1)", "pytest-cov", "pytest-mock (>=3)"] [[package]] name = "grpcio" -version = "1.48.0" +version = "1.63.0" description = "HTTP/2-based RPC framework" -category = "main" -optional = false -python-versions = ">=3.6" - -[package.dependencies] -six = ">=1.5.2" +optional = true +python-versions = ">=3.8" +files = [ + {file = "grpcio-1.63.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:2e93aca840c29d4ab5db93f94ed0a0ca899e241f2e8aec6334ab3575dc46125c"}, + {file = "grpcio-1.63.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:91b73d3f1340fefa1e1716c8c1ec9930c676d6b10a3513ab6c26004cb02d8b3f"}, + {file = "grpcio-1.63.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:b3afbd9d6827fa6f475a4f91db55e441113f6d3eb9b7ebb8fb806e5bb6d6bd0d"}, + {file = "grpcio-1.63.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8f3f6883ce54a7a5f47db43289a0a4c776487912de1a0e2cc83fdaec9685cc9f"}, + {file = "grpcio-1.63.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cf8dae9cc0412cb86c8de5a8f3be395c5119a370f3ce2e69c8b7d46bb9872c8d"}, + {file = "grpcio-1.63.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:08e1559fd3b3b4468486b26b0af64a3904a8dbc78d8d936af9c1cf9636eb3e8b"}, + {file = "grpcio-1.63.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:5c039ef01516039fa39da8a8a43a95b64e288f79f42a17e6c2904a02a319b357"}, + {file = "grpcio-1.63.0-cp310-cp310-win32.whl", hash = "sha256:ad2ac8903b2eae071055a927ef74121ed52d69468e91d9bcbd028bd0e554be6d"}, + {file = "grpcio-1.63.0-cp310-cp310-win_amd64.whl", hash = "sha256:b2e44f59316716532a993ca2966636df6fbe7be4ab6f099de6815570ebe4383a"}, + {file = "grpcio-1.63.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:f28f8b2db7b86c77916829d64ab21ff49a9d8289ea1564a2b2a3a8ed9ffcccd3"}, + {file = "grpcio-1.63.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:65bf975639a1f93bee63ca60d2e4951f1b543f498d581869922910a476ead2f5"}, + {file = "grpcio-1.63.0-cp311-cp311-manylinux_2_17_aarch64.whl", hash = "sha256:b5194775fec7dc3dbd6a935102bb156cd2c35efe1685b0a46c67b927c74f0cfb"}, + {file = "grpcio-1.63.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e4cbb2100ee46d024c45920d16e888ee5d3cf47c66e316210bc236d5bebc42b3"}, + {file = "grpcio-1.63.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ff737cf29b5b801619f10e59b581869e32f400159e8b12d7a97e7e3bdeee6a2"}, + {file = "grpcio-1.63.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:cd1e68776262dd44dedd7381b1a0ad09d9930ffb405f737d64f505eb7f77d6c7"}, + {file = "grpcio-1.63.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:93f45f27f516548e23e4ec3fbab21b060416007dbe768a111fc4611464cc773f"}, + {file = "grpcio-1.63.0-cp311-cp311-win32.whl", hash = "sha256:878b1d88d0137df60e6b09b74cdb73db123f9579232c8456f53e9abc4f62eb3c"}, + {file = "grpcio-1.63.0-cp311-cp311-win_amd64.whl", hash = "sha256:756fed02dacd24e8f488f295a913f250b56b98fb793f41d5b2de6c44fb762434"}, + {file = "grpcio-1.63.0-cp312-cp312-linux_armv7l.whl", hash = "sha256:93a46794cc96c3a674cdfb59ef9ce84d46185fe9421baf2268ccb556f8f81f57"}, + {file = "grpcio-1.63.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:a7b19dfc74d0be7032ca1eda0ed545e582ee46cd65c162f9e9fc6b26ef827dc6"}, + {file = "grpcio-1.63.0-cp312-cp312-manylinux_2_17_aarch64.whl", hash = "sha256:8064d986d3a64ba21e498b9a376cbc5d6ab2e8ab0e288d39f266f0fca169b90d"}, + {file = "grpcio-1.63.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:219bb1848cd2c90348c79ed0a6b0ea51866bc7e72fa6e205e459fedab5770172"}, + {file = "grpcio-1.63.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2d60cd1d58817bc5985fae6168d8b5655c4981d448d0f5b6194bbcc038090d2"}, + {file = "grpcio-1.63.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:9e350cb096e5c67832e9b6e018cf8a0d2a53b2a958f6251615173165269a91b0"}, + {file = "grpcio-1.63.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:56cdf96ff82e3cc90dbe8bac260352993f23e8e256e063c327b6cf9c88daf7a9"}, + {file = "grpcio-1.63.0-cp312-cp312-win32.whl", hash = "sha256:3a6d1f9ea965e750db7b4ee6f9fdef5fdf135abe8a249e75d84b0a3e0c668a1b"}, + {file = "grpcio-1.63.0-cp312-cp312-win_amd64.whl", hash = "sha256:d2497769895bb03efe3187fb1888fc20e98a5f18b3d14b606167dacda5789434"}, + {file = "grpcio-1.63.0-cp38-cp38-linux_armv7l.whl", hash = "sha256:fdf348ae69c6ff484402cfdb14e18c1b0054ac2420079d575c53a60b9b2853ae"}, + {file = "grpcio-1.63.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:a3abfe0b0f6798dedd2e9e92e881d9acd0fdb62ae27dcbbfa7654a57e24060c0"}, + {file = "grpcio-1.63.0-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:6ef0ad92873672a2a3767cb827b64741c363ebaa27e7f21659e4e31f4d750280"}, + {file = "grpcio-1.63.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b416252ac5588d9dfb8a30a191451adbf534e9ce5f56bb02cd193f12d8845b7f"}, + {file = "grpcio-1.63.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e3b77eaefc74d7eb861d3ffbdf91b50a1bb1639514ebe764c47773b833fa2d91"}, + {file = "grpcio-1.63.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:b005292369d9c1f80bf70c1db1c17c6c342da7576f1c689e8eee4fb0c256af85"}, + {file = "grpcio-1.63.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:cdcda1156dcc41e042d1e899ba1f5c2e9f3cd7625b3d6ebfa619806a4c1aadda"}, + {file = "grpcio-1.63.0-cp38-cp38-win32.whl", hash = "sha256:01799e8649f9e94ba7db1aeb3452188048b0019dc37696b0f5ce212c87c560c3"}, + {file = "grpcio-1.63.0-cp38-cp38-win_amd64.whl", hash = "sha256:6a1a3642d76f887aa4009d92f71eb37809abceb3b7b5a1eec9c554a246f20e3a"}, + {file = "grpcio-1.63.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:75f701ff645858a2b16bc8c9fc68af215a8bb2d5a9b647448129de6e85d52bce"}, + {file = "grpcio-1.63.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:cacdef0348a08e475a721967f48206a2254a1b26ee7637638d9e081761a5ba86"}, + {file = "grpcio-1.63.0-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:0697563d1d84d6985e40ec5ec596ff41b52abb3fd91ec240e8cb44a63b895094"}, + {file = "grpcio-1.63.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6426e1fb92d006e47476d42b8f240c1d916a6d4423c5258ccc5b105e43438f61"}, + {file = "grpcio-1.63.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e48cee31bc5f5a31fb2f3b573764bd563aaa5472342860edcc7039525b53e46a"}, + {file = "grpcio-1.63.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:50344663068041b34a992c19c600236e7abb42d6ec32567916b87b4c8b8833b3"}, + {file = "grpcio-1.63.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:259e11932230d70ef24a21b9fb5bb947eb4703f57865a404054400ee92f42f5d"}, + {file = "grpcio-1.63.0-cp39-cp39-win32.whl", hash = "sha256:a44624aad77bf8ca198c55af811fd28f2b3eaf0a50ec5b57b06c034416ef2d0a"}, + {file = "grpcio-1.63.0-cp39-cp39-win_amd64.whl", hash = "sha256:166e5c460e5d7d4656ff9e63b13e1f6029b122104c1633d5f37eaea348d7356d"}, + {file = "grpcio-1.63.0.tar.gz", hash = "sha256:f3023e14805c61bc439fb40ca545ac3d5740ce66120a678a3c6c2c55b70343d1"}, +] [package.extras] -protobuf = ["grpcio-tools (>=1.48.0)"] +protobuf = ["grpcio-tools (>=1.63.0)"] [[package]] -name = "gym" -version = "0.25.2" -description = "Gym: A universal API for reinforcement learning environments" -category = "main" -optional = false -python-versions = ">=3.6" +name = "gymnasium" +version = "0.29.1" +description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)." +optional = true +python-versions = ">=3.8" +files = [ + {file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"}, + {file = "gymnasium-0.29.1.tar.gz", hash = "sha256:1a532752efcb7590478b1cc7aa04f608eb7a2fdad5570cd217b66b6a35274bb1"}, +] [package.dependencies] cloudpickle = ">=1.2.0" -gym_notices = ">=0.0.4" -importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""} -numpy = ">=1.18.0" +farama-notifications = ">=0.0.1" +importlib-metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""} +numpy = ">=1.21.0" +typing-extensions = ">=4.3.0" [package.extras] accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"] -all = ["ale-py (>=0.7.5,<0.8.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (>=4.0.0,<5.0.0)"] -atari = ["ale-py (>=0.7.5,<0.8.0)"] -box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (>=4.0.0,<5.0.0)"] -classic_control = ["pygame (==2.1.0)"] -mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"] -mujoco_py = ["mujoco_py (>=2.1,<2.2)"] -other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"] -testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (>=4.0.0,<5.0.0)"] -toy_text = ["pygame (==2.1.0)"] - -[[package]] -name = "gym-notices" -version = "0.0.8" -description = "Notices for gym" -category = "main" -optional = false -python-versions = "*" +all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"] +atari = ["shimmy[atari] (>=0.1.0,<1.0)"] +box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"] +classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"] +jax = ["jax (>=0.4.0)", "jaxlib (>=0.4.0)"] +mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.3.3)"] +mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"] +other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "torch (>=1.0.0)"] +testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"] +toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"] [[package]] name = "h5py" -version = "3.1.0" +version = "3.11.0" description = "Read and write HDF5 files from Python" -category = "main" -optional = false -python-versions = ">=3.6" +optional = true +python-versions = ">=3.8" +files = [ + {file = "h5py-3.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1625fd24ad6cfc9c1ccd44a66dac2396e7ee74940776792772819fc69f3a3731"}, + {file = "h5py-3.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c072655ad1d5fe9ef462445d3e77a8166cbfa5e599045f8aa3c19b75315f10e5"}, + {file = "h5py-3.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77b19a40788e3e362b54af4dcf9e6fde59ca016db2c61360aa30b47c7b7cef00"}, + {file = "h5py-3.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:ef4e2f338fc763f50a8113890f455e1a70acd42a4d083370ceb80c463d803972"}, + {file = "h5py-3.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:bbd732a08187a9e2a6ecf9e8af713f1d68256ee0f7c8b652a32795670fb481ba"}, + {file = "h5py-3.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:75bd7b3d93fbeee40860fd70cdc88df4464e06b70a5ad9ce1446f5f32eb84007"}, + {file = "h5py-3.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:52c416f8eb0daae39dabe71415cb531f95dce2d81e1f61a74537a50c63b28ab3"}, + {file = "h5py-3.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:083e0329ae534a264940d6513f47f5ada617da536d8dccbafc3026aefc33c90e"}, + {file = "h5py-3.11.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:a76cae64080210389a571c7d13c94a1a6cf8cb75153044fd1f822a962c97aeab"}, + {file = "h5py-3.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f3736fe21da2b7d8a13fe8fe415f1272d2a1ccdeff4849c1421d2fb30fd533bc"}, + {file = "h5py-3.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aa6ae84a14103e8dc19266ef4c3e5d7c00b68f21d07f2966f0ca7bdb6c2761fb"}, + {file = "h5py-3.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:21dbdc5343f53b2e25404673c4f00a3335aef25521bd5fa8c707ec3833934892"}, + {file = "h5py-3.11.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:754c0c2e373d13d6309f408325343b642eb0f40f1a6ad21779cfa9502209e150"}, + {file = "h5py-3.11.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:731839240c59ba219d4cb3bc5880d438248533366f102402cfa0621b71796b62"}, + {file = "h5py-3.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8ec9df3dd2018904c4cc06331951e274f3f3fd091e6d6cc350aaa90fa9b42a76"}, + {file = "h5py-3.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:55106b04e2c83dfb73dc8732e9abad69d83a436b5b82b773481d95d17b9685e1"}, + {file = "h5py-3.11.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f4e025e852754ca833401777c25888acb96889ee2c27e7e629a19aee288833f0"}, + {file = "h5py-3.11.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6c4b760082626120031d7902cd983d8c1f424cdba2809f1067511ef283629d4b"}, + {file = "h5py-3.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:67462d0669f8f5459529de179f7771bd697389fcb3faab54d63bf788599a48ea"}, + {file = "h5py-3.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:d9c944d364688f827dc889cf83f1fca311caf4fa50b19f009d1f2b525edd33a3"}, + {file = "h5py-3.11.0.tar.gz", hash = "sha256:7b7e8f78072a2edec87c9836f25f34203fd492a4475709a18b417a33cfb21fa9"}, +] [package.dependencies] -cached-property = {version = "*", markers = "python_version < \"3.8\""} -numpy = [ - {version = ">=1.14.5", markers = "python_version == \"3.7\""}, - {version = ">=1.17.5", markers = "python_version == \"3.8\""}, - {version = ">=1.19.3", markers = "python_version >= \"3.9\""}, -] +numpy = ">=1.17.3" [[package]] -name = "hydra-slayer" -version = "0.4.0" -description = "A framework for elegantly configuring complex applications" -category = "main" -optional = false -python-versions = ">=3.6,<4.0" +name = "highway-env" +version = "1.8.2" +description = "An environment for simulated highway driving tasks." +optional = true +python-versions = "*" +files = [ + {file = "highway-env-1.8.2.tar.gz", hash = "sha256:0347e648a539fb0dc4313a06b16f359bdff3e17aa05bc603521c3b17a09e195f"}, + {file = "highway_env-1.8.2-py3-none-any.whl", hash = "sha256:2ad239cfb8b04494b2860890c71997a9aaa2f00367dbc638a6e93a44b616ee9d"}, +] + +[package.dependencies] +gymnasium = ">=0.27" +matplotlib = "*" +numpy = "*" +pandas = "*" +pygame = ">=2.0.2" +scipy = "*" + +[package.extras] +deploy = ["pytest-runner", "sphinx (<1.7.3)", "sphinx-rtd-theme"] [[package]] name = "identify" -version = "2.5.4" +version = "2.5.24" description = "File identification library for Python" -category = "dev" optional = false python-versions = ">=3.7" +files = [ + {file = "identify-2.5.24-py2.py3-none-any.whl", hash = "sha256:986dbfb38b1140e763e413e6feb44cd731faf72d1909543178aa79b0e258265d"}, + {file = "identify-2.5.24.tar.gz", hash = "sha256:0aac67d5b4812498056d28a9a512a483f5085cc28640b02b258a59dac34301d4"}, +] [package.extras] license = ["ukkonen"] -[[package]] -name = "idna" -version = "3.3" -description = "Internationalized Domain Names in Applications (IDNA)" -category = "main" -optional = false -python-versions = ">=3.5" - [[package]] name = "importlib-metadata" -version = "4.12.0" +version = "7.1.0" description = "Read metadata from Python packages" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"}, + {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"}, +] [package.dependencies] -typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""} zipp = ">=0.5" [package.extras] -docs = ["jaraco.packaging (>=9)", "rst.linker (>=1.9)", "sphinx"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] perf = ["ipython"] -testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] +testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"] [[package]] name = "importlib-resources" -version = "5.9.0" +version = "6.4.0" description = "Read resources from Python packages" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "importlib_resources-6.4.0-py3-none-any.whl", hash = "sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c"}, + {file = "importlib_resources-6.4.0.tar.gz", hash = "sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145"}, +] [package.dependencies] zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx"] -testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"] [[package]] -name = "jax" -version = "0.3.15" -description = "Differentiate, compile, and transform Numpy code." -category = "main" +name = "iniconfig" +version = "2.0.0" +description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.7" - -[package.dependencies] -absl-py = "*" -etils = {version = "*", extras = ["epath"]} -numpy = ">=1.19" -opt_einsum = "*" -scipy = ">=1.5" -typing_extensions = "*" - -[package.extras] -ci = ["jaxlib (==0.3.14)"] -cpu = ["jaxlib (==0.3.15)"] -cuda = ["jaxlib (==0.3.15+cuda11.cudnn82)"] -cuda11_cudnn805 = ["jaxlib (==0.3.15+cuda11.cudnn805)"] -cuda11_cudnn82 = ["jaxlib (==0.3.15+cuda11.cudnn82)"] -minimum-jaxlib = ["jaxlib (==0.3.14)"] -tpu = ["jaxlib (==0.3.15)", "libtpu-nightly (==0.1.dev20220722)", "requests"] +files = [ + {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, + {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, +] [[package]] -name = "keras" -version = "2.6.0" -description = "TensorFlow Keras." -category = "main" -optional = false +name = "intel-openmp" +version = "2021.4.0" +description = "Intel OpenMP* Runtime Library" +optional = true python-versions = "*" +files = [ + {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"}, +] [[package]] -name = "keras-preprocessing" -version = "1.1.2" -description = "Easy data preprocessing and data augmentation for deep learning models" -category = "main" -optional = false -python-versions = "*" +name = "jinja2" +version = "3.1.4" +description = "A very fast and expressive template engine." +optional = true +python-versions = ">=3.7" +files = [ + {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"}, + {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"}, +] [package.dependencies] -numpy = ">=1.9.1" -six = ">=1.9.0" +MarkupSafe = ">=2.0" [package.extras] -image = ["Pillow (>=5.2.0)", "scipy (>=0.14)"] -pep8 = ["flake8"] -tests = ["keras", "pandas", "pillow", "pytest", "pytest-cov", "pytest-xdist", "tensorflow"] +i18n = ["Babel (>=2.7)"] + +[[package]] +name = "keras" +version = "3.3.3" +description = "Multi-backend Keras." +optional = true +python-versions = ">=3.9" +files = [ + {file = "keras-3.3.3-py3-none-any.whl", hash = "sha256:260df9ef71c6b89eb6816ce1c60f139c38ccdddd16f24e7005d2be127cdef8e4"}, + {file = "keras-3.3.3.tar.gz", hash = "sha256:f2fdffc8434fd77045cf8fb21816dbaa2308d5f76974ca924b2f60b40433b1a0"}, +] + +[package.dependencies] +absl-py = "*" +h5py = "*" +ml-dtypes = "*" +namex = "*" +numpy = "*" +optree = "*" +rich = "*" [[package]] name = "kiwisolver" -version = "1.4.4" +version = "1.4.5" description = "A fast implementation of the Cassowary constraint solver" -category = "main" optional = false python-versions = ">=3.7" +files = [ + {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"}, + {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"}, + {file = "kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ef7afcd2d281494c0a9101d5c571970708ad911d028137cd558f02b851c08b4"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:9eaa8b117dc8337728e834b9c6e2611f10c79e38f65157c4c38e9400286f5cb1"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ec20916e7b4cbfb1f12380e46486ec4bcbaa91a9c448b97023fde0d5bbf9e4ff"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39b42c68602539407884cf70d6a480a469b93b81b7701378ba5e2328660c847a"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa12042de0171fad672b6c59df69106d20d5596e4f87b5e8f76df757a7c399aa"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a40773c71d7ccdd3798f6489aaac9eee213d566850a9533f8d26332d626b82c"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:19df6e621f6d8b4b9c4d45f40a66839294ff2bb235e64d2178f7522d9170ac5b"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:83d78376d0d4fd884e2c114d0621624b73d2aba4e2788182d286309ebdeed770"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e391b1f0a8a5a10ab3b9bb6afcfd74f2175f24f8975fb87ecae700d1503cdee0"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:852542f9481f4a62dbb5dd99e8ab7aedfeb8fb6342349a181d4036877410f525"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:59edc41b24031bc25108e210c0def6f6c2191210492a972d585a06ff246bb79b"}, + {file = "kiwisolver-1.4.5-cp310-cp310-win32.whl", hash = "sha256:a6aa6315319a052b4ee378aa171959c898a6183f15c1e541821c5c59beaa0238"}, + {file = "kiwisolver-1.4.5-cp310-cp310-win_amd64.whl", hash = "sha256:d0ef46024e6a3d79c01ff13801cb19d0cad7fd859b15037aec74315540acc276"}, + {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:11863aa14a51fd6ec28688d76f1735f8f69ab1fabf388851a595d0721af042f5"}, + {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8ab3919a9997ab7ef2fbbed0cc99bb28d3c13e6d4b1ad36e97e482558a91be90"}, + {file = "kiwisolver-1.4.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fcc700eadbbccbf6bc1bcb9dbe0786b4b1cb91ca0dcda336eef5c2beed37b797"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dfdd7c0b105af050eb3d64997809dc21da247cf44e63dc73ff0fd20b96be55a9"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76c6a5964640638cdeaa0c359382e5703e9293030fe730018ca06bc2010c4437"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bbea0db94288e29afcc4c28afbf3a7ccaf2d7e027489c449cf7e8f83c6346eb9"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ceec1a6bc6cab1d6ff5d06592a91a692f90ec7505d6463a88a52cc0eb58545da"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f91de7223d4c7b793867797bacd1ee53bfe7359bd70d27b7b58a04efbb9436c8"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:faae4860798c31530dd184046a900e652c95513796ef51a12bc086710c2eec4d"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:b0157420efcb803e71d1b28e2c287518b8808b7cf1ab8af36718fd0a2c453eb0"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:06f54715b7737c2fecdbf140d1afb11a33d59508a47bf11bb38ecf21dc9ab79f"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fdb7adb641a0d13bdcd4ef48e062363d8a9ad4a182ac7647ec88f695e719ae9f"}, + {file = "kiwisolver-1.4.5-cp311-cp311-win32.whl", hash = "sha256:bb86433b1cfe686da83ce32a9d3a8dd308e85c76b60896d58f082136f10bffac"}, + {file = "kiwisolver-1.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:6c08e1312a9cf1074d17b17728d3dfce2a5125b2d791527f33ffbe805200a355"}, + {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:32d5cf40c4f7c7b3ca500f8985eb3fb3a7dfc023215e876f207956b5ea26632a"}, + {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f846c260f483d1fd217fe5ed7c173fb109efa6b1fc8381c8b7552c5781756192"}, + {file = "kiwisolver-1.4.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5ff5cf3571589b6d13bfbfd6bcd7a3f659e42f96b5fd1c4830c4cf21d4f5ef45"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7269d9e5f1084a653d575c7ec012ff57f0c042258bf5db0954bf551c158466e7"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da802a19d6e15dffe4b0c24b38b3af68e6c1a68e6e1d8f30148c83864f3881db"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3aba7311af82e335dd1e36ffff68aaca609ca6290c2cb6d821a39aa075d8e3ff"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:763773d53f07244148ccac5b084da5adb90bfaee39c197554f01b286cf869228"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2270953c0d8cdab5d422bee7d2007f043473f9d2999631c86a223c9db56cbd16"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d099e745a512f7e3bbe7249ca835f4d357c586d78d79ae8f1dcd4d8adeb9bda9"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:74db36e14a7d1ce0986fa104f7d5637aea5c82ca6326ed0ec5694280942d1162"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:7e5bab140c309cb3a6ce373a9e71eb7e4873c70c2dda01df6820474f9889d6d4"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:0f114aa76dc1b8f636d077979c0ac22e7cd8f3493abbab152f20eb8d3cda71f3"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:88a2df29d4724b9237fc0c6eaf2a1adae0cdc0b3e9f4d8e7dc54b16812d2d81a"}, + {file = "kiwisolver-1.4.5-cp312-cp312-win32.whl", hash = "sha256:72d40b33e834371fd330fb1472ca19d9b8327acb79a5821d4008391db8e29f20"}, + {file = "kiwisolver-1.4.5-cp312-cp312-win_amd64.whl", hash = "sha256:2c5674c4e74d939b9d91dda0fae10597ac7521768fec9e399c70a1f27e2ea2d9"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:3a2b053a0ab7a3960c98725cfb0bf5b48ba82f64ec95fe06f1d06c99b552e130"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3cd32d6c13807e5c66a7cbb79f90b553642f296ae4518a60d8d76243b0ad2898"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:59ec7b7c7e1a61061850d53aaf8e93db63dce0c936db1fda2658b70e4a1be709"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:da4cfb373035def307905d05041c1d06d8936452fe89d464743ae7fb8371078b"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2400873bccc260b6ae184b2b8a4fec0e4082d30648eadb7c3d9a13405d861e89"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:1b04139c4236a0f3aff534479b58f6f849a8b351e1314826c2d230849ed48985"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:4e66e81a5779b65ac21764c295087de82235597a2293d18d943f8e9e32746265"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:7931d8f1f67c4be9ba1dd9c451fb0eeca1a25b89e4d3f89e828fe12a519b782a"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b3f7e75f3015df442238cca659f8baa5f42ce2a8582727981cbfa15fee0ee205"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:bbf1d63eef84b2e8c89011b7f2235b1e0bf7dacc11cac9431fc6468e99ac77fb"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:4c380469bd3f970ef677bf2bcba2b6b0b4d5c75e7a020fb863ef75084efad66f"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-win32.whl", hash = "sha256:9408acf3270c4b6baad483865191e3e582b638b1654a007c62e3efe96f09a9a3"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-win_amd64.whl", hash = "sha256:5b94529f9b2591b7af5f3e0e730a4e0a41ea174af35a4fd067775f9bdfeee01a"}, + {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:11c7de8f692fc99816e8ac50d1d1aef4f75126eefc33ac79aac02c099fd3db71"}, + {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:53abb58632235cd154176ced1ae8f0d29a6657aa1aa9decf50b899b755bc2b93"}, + {file = "kiwisolver-1.4.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:88b9f257ca61b838b6f8094a62418421f87ac2a1069f7e896c36a7d86b5d4c29"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3195782b26fc03aa9c6913d5bad5aeb864bdc372924c093b0f1cebad603dd712"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fc579bf0f502e54926519451b920e875f433aceb4624a3646b3252b5caa9e0b6"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5a580c91d686376f0f7c295357595c5a026e6cbc3d77b7c36e290201e7c11ecb"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cfe6ab8da05c01ba6fbea630377b5da2cd9bcbc6338510116b01c1bc939a2c18"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d2e5a98f0ec99beb3c10e13b387f8db39106d53993f498b295f0c914328b1333"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:a51a263952b1429e429ff236d2f5a21c5125437861baeed77f5e1cc2d2c7c6da"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3edd2fa14e68c9be82c5b16689e8d63d89fe927e56debd6e1dbce7a26a17f81b"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:74d1b44c6cfc897df648cc9fdaa09bc3e7679926e6f96df05775d4fb3946571c"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:76d9289ed3f7501012e05abb8358bbb129149dbd173f1f57a1bf1c22d19ab7cc"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:92dea1ffe3714fa8eb6a314d2b3c773208d865a0e0d35e713ec54eea08a66250"}, + {file = "kiwisolver-1.4.5-cp38-cp38-win32.whl", hash = "sha256:5c90ae8c8d32e472be041e76f9d2f2dbff4d0b0be8bd4041770eddb18cf49a4e"}, + {file = "kiwisolver-1.4.5-cp38-cp38-win_amd64.whl", hash = "sha256:c7940c1dc63eb37a67721b10d703247552416f719c4188c54e04334321351ced"}, + {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:9407b6a5f0d675e8a827ad8742e1d6b49d9c1a1da5d952a67d50ef5f4170b18d"}, + {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:15568384086b6df3c65353820a4473575dbad192e35010f622c6ce3eebd57af9"}, + {file = "kiwisolver-1.4.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0dc9db8e79f0036e8173c466d21ef18e1befc02de8bf8aa8dc0813a6dc8a7046"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:cdc8a402aaee9a798b50d8b827d7ecf75edc5fb35ea0f91f213ff927c15f4ff0"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:955e8513d07a283056b1396e9a57ceddbd272d9252c14f154d450d227606eb54"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:346f5343b9e3f00b8db8ba359350eb124b98c99efd0b408728ac6ebf38173958"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b9098e0049e88c6a24ff64545cdfc50807818ba6c1b739cae221bbbcbc58aad3"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7b8b454bac16428b22560d0a1cf0a09875339cab69df61d7805bf48919415901"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:f1d072c2eb0ad60d4c183f3fb44ac6f73fb7a8f16a2694a91f988275cbf352f9"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:31a82d498054cac9f6d0b53d02bb85811185bcb477d4b60144f915f3b3126342"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6512cb89e334e4700febbffaaa52761b65b4f5a3cf33f960213d5656cea36a77"}, + {file = "kiwisolver-1.4.5-cp39-cp39-win32.whl", hash = "sha256:9db8ea4c388fdb0f780fe91346fd438657ea602d58348753d9fb265ce1bca67f"}, + {file = "kiwisolver-1.4.5-cp39-cp39-win_amd64.whl", hash = "sha256:59415f46a37f7f2efeec758353dd2eae1b07640d8ca0f0c42548ec4125492635"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:5c7b3b3a728dc6faf3fc372ef24f21d1e3cee2ac3e9596691d746e5a536de920"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:620ced262a86244e2be10a676b646f29c34537d0d9cc8eb26c08f53d98013390"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:378a214a1e3bbf5ac4a8708304318b4f890da88c9e6a07699c4ae7174c09a68d"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaf7be1207676ac608a50cd08f102f6742dbfc70e8d60c4db1c6897f62f71523"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ba55dce0a9b8ff59495ddd050a0225d58bd0983d09f87cfe2b6aec4f2c1234e4"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5e7139af55d1688f8b960ee9ad5adafc4ac17c1c473fe07133ac092310d76544"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dced8146011d2bc2e883f9bd68618b8247387f4bbec46d7392b3c3b032640126"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9bf3325c47b11b2e51bca0824ea217c7cd84491d8ac4eefd1e409705ef092bd"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5794cf59533bc3f1b1c821f7206a3617999db9fbefc345360aafe2e067514929"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e368f200bbc2e4f905b8e71eb38b3c04333bddaa6a2464a6355487b02bb7fb09"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5d706eba36b4c4d5bc6c6377bb6568098765e990cfc21ee16d13963fab7b3e7"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85267bd1aa8880a9c88a8cb71e18d3d64d2751a790e6ca6c27b8ccc724bcd5ad"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee"}, + {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"}, +] + +[[package]] +name = "mako" +version = "1.3.5" +description = "A super-fast templating language that borrows the best ideas from the existing templating languages." +optional = false +python-versions = ">=3.8" +files = [ + {file = "Mako-1.3.5-py3-none-any.whl", hash = "sha256:260f1dbc3a519453a9c856dedfe4beb4e50bd5a26d96386cb6c80856556bb91a"}, + {file = "Mako-1.3.5.tar.gz", hash = "sha256:48dbc20568c1d276a2698b36d968fa76161bf127194907ea6fc594fa81f943bc"}, +] [package.dependencies] -typing-extensions = {version = "*", markers = "python_version < \"3.8\""} +MarkupSafe = ">=0.9.2" + +[package.extras] +babel = ["Babel"] +lingua = ["lingua"] +testing = ["pytest"] [[package]] name = "markdown" -version = "3.4.1" -description = "Python implementation of Markdown." -category = "main" +version = "3.6" +description = "Python implementation of John Gruber's Markdown." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"}, + {file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"}, +] [package.dependencies] importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""} [package.extras] +docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"] testing = ["coverage", "pyyaml"] +[[package]] +name = "markdown-it-py" +version = "3.0.0" +description = "Python port of markdown-it. Markdown parsing, done right!" +optional = true +python-versions = ">=3.8" +files = [ + {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, + {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, +] + +[package.dependencies] +mdurl = ">=0.1,<1.0" + +[package.extras] +benchmarking = ["psutil", "pytest", "pytest-benchmark"] +code-style = ["pre-commit (>=3.0,<4.0)"] +compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"] +linkify = ["linkify-it-py (>=1,<3)"] +plugins = ["mdit-py-plugins"] +profiling = ["gprof2dot"] +rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + [[package]] name = "markupsafe" -version = "2.1.1" +version = "2.1.5" description = "Safely add untrusted strings to HTML/XML markup." -category = "main" optional = false python-versions = ">=3.7" +files = [ + {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e61659ba32cf2cf1481e575d0462554625196a1f2fc06a1c777d3f48e8865d46"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2174c595a0d73a3080ca3257b40096db99799265e1c27cc5a610743acd86d62f"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ae2ad8ae6ebee9d2d94b17fb62763125f3f374c25618198f40cbb8b525411900"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:075202fa5b72c86ad32dc7d0b56024ebdbcf2048c0ba09f1cde31bfdd57bcfff"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:598e3276b64aff0e7b3451b72e94fa3c238d452e7ddcd893c3ab324717456bad"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fce659a462a1be54d2ffcacea5e3ba2d74daa74f30f5f143fe0c58636e355fdd"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-win32.whl", hash = "sha256:d9fad5155d72433c921b782e58892377c44bd6252b5af2f67f16b194987338a4"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-win_amd64.whl", hash = "sha256:bf50cd79a75d181c9181df03572cdce0fbb75cc353bc350712073108cba98de5"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:629ddd2ca402ae6dbedfceeba9c46d5f7b2a61d9749597d4307f943ef198fc1f"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5b7b716f97b52c5a14bffdf688f971b2d5ef4029127f1ad7a513973cfd818df2"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ec585f69cec0aa07d945b20805be741395e28ac1627333b1c5b0105962ffced"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7502934a33b54030eaf1194c21c692a534196063db72176b0c4028e140f8f32c"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0e397ac966fdf721b2c528cf028494e86172b4feba51d65f81ffd65c63798f3f"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c061bb86a71b42465156a3ee7bd58c8c2ceacdbeb95d05a99893e08b8467359a"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3a57fdd7ce31c7ff06cdfbf31dafa96cc533c21e443d57f5b1ecc6cdc668ec7f"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-win32.whl", hash = "sha256:397081c1a0bfb5124355710fe79478cdbeb39626492b15d399526ae53422b906"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-win_amd64.whl", hash = "sha256:2b7c57a4dfc4f16f7142221afe5ba4e093e09e728ca65c51f5620c9aaeb9a617"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:8dec4936e9c3100156f8a2dc89c4b88d5c435175ff03413b443469c7c8c5f4d1"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:3c6b973f22eb18a789b1460b4b91bf04ae3f0c4234a0a6aa6b0a92f6f7b951d4"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac07bad82163452a6884fe8fa0963fb98c2346ba78d779ec06bd7a6262132aee"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ea3d8a3d18833cf4304cd2fc9cbb1efe188ca9b5efef2bdac7adc20594a0e46b"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d050b3361367a06d752db6ead6e7edeb0009be66bc3bae0ee9d97fb326badc2a"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:bec0a414d016ac1a18862a519e54b2fd0fc8bbfd6890376898a6c0891dd82e9f"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:58c98fee265677f63a4385256a6d7683ab1832f3ddd1e66fe948d5880c21a169"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-win32.whl", hash = "sha256:8590b4ae07a35970728874632fed7bd57b26b0102df2d2b233b6d9d82f6c62ad"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-win_amd64.whl", hash = "sha256:823b65d8706e32ad2df51ed89496147a42a2a6e01c13cfb6ffb8b1e92bc910bb"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c8b29db45f8fe46ad280a7294f5c3ec36dbac9491f2d1c17345be8e69cc5928f"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec6a563cff360b50eed26f13adc43e61bc0c04d94b8be985e6fb24b81f6dcfdf"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a549b9c31bec33820e885335b451286e2969a2d9e24879f83fe904a5ce59d70a"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4f11aa001c540f62c6166c7726f71f7573b52c68c31f014c25cc7901deea0b52"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:7b2e5a267c855eea6b4283940daa6e88a285f5f2a67f2220203786dfa59b37e9"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:2d2d793e36e230fd32babe143b04cec8a8b3eb8a3122d2aceb4a371e6b09b8df"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ce409136744f6521e39fd8e2a24c53fa18ad67aa5bc7c2cf83645cce5b5c4e50"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-win32.whl", hash = "sha256:4096e9de5c6fdf43fb4f04c26fb114f61ef0bf2e5604b6ee3019d51b69e8c371"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-win_amd64.whl", hash = "sha256:4275d846e41ecefa46e2015117a9f491e57a71ddd59bbead77e904dc02b1bed2"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:656f7526c69fac7f600bd1f400991cc282b417d17539a1b228617081106feb4a"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:97cafb1f3cbcd3fd2b6fbfb99ae11cdb14deea0736fc2b0952ee177f2b813a46"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f3fbcb7ef1f16e48246f704ab79d79da8a46891e2da03f8783a5b6fa41a9532"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fa9db3f79de01457b03d4f01b34cf91bc0048eb2c3846ff26f66687c2f6d16ab"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ffee1f21e5ef0d712f9033568f8344d5da8cc2869dbd08d87c84656e6a2d2f68"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5dedb4db619ba5a2787a94d877bc8ffc0566f92a01c0ef214865e54ecc9ee5e0"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:30b600cf0a7ac9234b2638fbc0fb6158ba5bdcdf46aeb631ead21248b9affbc4"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8dd717634f5a044f860435c1d8c16a270ddf0ef8588d4887037c5028b859b0c3"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-win32.whl", hash = "sha256:daa4ee5a243f0f20d528d939d06670a298dd39b1ad5f8a72a4275124a7819eff"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-win_amd64.whl", hash = "sha256:619bc166c4f2de5caa5a633b8b7326fbe98e0ccbfacabd87268a2b15ff73a029"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7a68b554d356a91cce1236aa7682dc01df0edba8d043fd1ce607c49dd3c1edcf"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:db0b55e0f3cc0be60c1f19efdde9a637c32740486004f20d1cff53c3c0ece4d2"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e53af139f8579a6d5f7b76549125f0d94d7e630761a2111bc431fd820e163b8"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:17b950fccb810b3293638215058e432159d2b71005c74371d784862b7e4683f3"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c31f53cdae6ecfa91a77820e8b151dba54ab528ba65dfd235c80b086d68a465"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:bff1b4290a66b490a2f4719358c0cdcd9bafb6b8f061e45c7a2460866bf50c2e"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:bc1667f8b83f48511b94671e0e441401371dfd0f0a795c7daa4a3cd1dde55bea"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5049256f536511ee3f7e1b3f87d1d1209d327e818e6ae1365e8653d7e3abb6a6"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-win32.whl", hash = "sha256:00e046b6dd71aa03a41079792f8473dc494d564611a8f89bbbd7cb93295ebdcf"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-win_amd64.whl", hash = "sha256:fa173ec60341d6bb97a89f5ea19c85c5643c1e7dedebc22f5181eb73573142c5"}, + {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"}, +] [[package]] name = "matplotlib" -version = "3.5.3" +version = "3.8.4" description = "Python plotting package" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.9" +files = [ + {file = "matplotlib-3.8.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:abc9d838f93583650c35eca41cfcec65b2e7cb50fd486da6f0c49b5e1ed23014"}, + {file = "matplotlib-3.8.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f65c9f002d281a6e904976007b2d46a1ee2bcea3a68a8c12dda24709ddc9106"}, + {file = "matplotlib-3.8.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce1edd9f5383b504dbc26eeea404ed0a00656c526638129028b758fd43fc5f10"}, + {file = "matplotlib-3.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ecd79298550cba13a43c340581a3ec9c707bd895a6a061a78fa2524660482fc0"}, + {file = "matplotlib-3.8.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:90df07db7b599fe7035d2f74ab7e438b656528c68ba6bb59b7dc46af39ee48ef"}, + {file = "matplotlib-3.8.4-cp310-cp310-win_amd64.whl", hash = "sha256:ac24233e8f2939ac4fd2919eed1e9c0871eac8057666070e94cbf0b33dd9c338"}, + {file = "matplotlib-3.8.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:72f9322712e4562e792b2961971891b9fbbb0e525011e09ea0d1f416c4645661"}, + {file = "matplotlib-3.8.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:232ce322bfd020a434caaffbd9a95333f7c2491e59cfc014041d95e38ab90d1c"}, + {file = "matplotlib-3.8.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6addbd5b488aedb7f9bc19f91cd87ea476206f45d7116fcfe3d31416702a82fa"}, + {file = "matplotlib-3.8.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc4ccdc64e3039fc303defd119658148f2349239871db72cd74e2eeaa9b80b71"}, + {file = "matplotlib-3.8.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:b7a2a253d3b36d90c8993b4620183b55665a429da8357a4f621e78cd48b2b30b"}, + {file = "matplotlib-3.8.4-cp311-cp311-win_amd64.whl", hash = "sha256:8080d5081a86e690d7688ffa542532e87f224c38a6ed71f8fbed34dd1d9fedae"}, + {file = "matplotlib-3.8.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:6485ac1f2e84676cff22e693eaa4fbed50ef5dc37173ce1f023daef4687df616"}, + {file = "matplotlib-3.8.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c89ee9314ef48c72fe92ce55c4e95f2f39d70208f9f1d9db4e64079420d8d732"}, + {file = "matplotlib-3.8.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50bac6e4d77e4262c4340d7a985c30912054745ec99756ce213bfbc3cb3808eb"}, + {file = "matplotlib-3.8.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f51c4c869d4b60d769f7b4406eec39596648d9d70246428745a681c327a8ad30"}, + {file = "matplotlib-3.8.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:b12ba985837e4899b762b81f5b2845bd1a28f4fdd1a126d9ace64e9c4eb2fb25"}, + {file = "matplotlib-3.8.4-cp312-cp312-win_amd64.whl", hash = "sha256:7a6769f58ce51791b4cb8b4d7642489df347697cd3e23d88266aaaee93b41d9a"}, + {file = "matplotlib-3.8.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:843cbde2f0946dadd8c5c11c6d91847abd18ec76859dc319362a0964493f0ba6"}, + {file = "matplotlib-3.8.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1c13f041a7178f9780fb61cc3a2b10423d5e125480e4be51beaf62b172413b67"}, + {file = "matplotlib-3.8.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb44f53af0a62dc80bba4443d9b27f2fde6acfdac281d95bc872dc148a6509cc"}, + {file = "matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:606e3b90897554c989b1e38a258c626d46c873523de432b1462f295db13de6f9"}, + {file = "matplotlib-3.8.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:9bb0189011785ea794ee827b68777db3ca3f93f3e339ea4d920315a0e5a78d54"}, + {file = "matplotlib-3.8.4-cp39-cp39-win_amd64.whl", hash = "sha256:6209e5c9aaccc056e63b547a8152661324404dd92340a6e479b3a7f24b42a5d0"}, + {file = "matplotlib-3.8.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:c7064120a59ce6f64103c9cefba8ffe6fba87f2c61d67c401186423c9a20fd35"}, + {file = "matplotlib-3.8.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0e47eda4eb2614300fc7bb4657fced3e83d6334d03da2173b09e447418d499f"}, + {file = "matplotlib-3.8.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:493e9f6aa5819156b58fce42b296ea31969f2aab71c5b680b4ea7a3cb5c07d94"}, + {file = "matplotlib-3.8.4.tar.gz", hash = "sha256:8aac397d5e9ec158960e31c381c5ffc52ddd52bd9a47717e2a694038167dffea"}, +] [package.dependencies] +contourpy = ">=1.0.1" cycler = ">=0.10" fonttools = ">=4.22.0" -kiwisolver = ">=1.0.1" -numpy = ">=1.17" +importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""} +kiwisolver = ">=1.3.1" +numpy = ">=1.21" packaging = ">=20.0" -pillow = ">=6.2.0" -pyparsing = ">=2.2.1" +pillow = ">=8" +pyparsing = ">=2.3.1" python-dateutil = ">=2.7" -setuptools_scm = ">=4,<7" [[package]] -name = "nodeenv" -version = "1.7.0" -description = "Node.js virtual environment builder" -category = "dev" -optional = false -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*" +name = "mdurl" +version = "0.1.2" +description = "Markdown URL utilities" +optional = true +python-versions = ">=3.7" +files = [ + {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, + {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, +] [[package]] -name = "numpy" -version = "1.19.5" -description = "NumPy is the fundamental package for array computing with Python." -category = "main" -optional = false -python-versions = ">=3.6" +name = "mkl" +version = "2021.4.0" +description = "Intel® oneAPI Math Kernel Library" +optional = true +python-versions = "*" +files = [ + {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"}, + {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"}, + {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"}, + {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"}, + {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"}, +] + +[package.dependencies] +intel-openmp = "==2021.*" +tbb = "==2021.*" [[package]] -name = "oauthlib" -version = "3.2.0" -description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic" -category = "main" -optional = false -python-versions = ">=3.6" +name = "ml-dtypes" +version = "0.4.0" +description = "" +optional = true +python-versions = ">=3.9" +files = [ + {file = "ml_dtypes-0.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:93afe37f3a879d652ec9ef1fc47612388890660a2657fbb5747256c3b818fd81"}, + {file = "ml_dtypes-0.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2bb83fd064db43e67e67d021e547698af4c8d5c6190f2e9b1c53c09f6ff5531d"}, + {file = "ml_dtypes-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03e7cda6ef164eed0abb31df69d2c00c3a5ab3e2610b6d4c42183a43329c72a5"}, + {file = "ml_dtypes-0.4.0-cp310-cp310-win_amd64.whl", hash = "sha256:a15d96d090aebb55ee85173d1775ae325a001aab607a76c8ea0b964ccd6b5364"}, + {file = "ml_dtypes-0.4.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:bdf689be7351cc3c95110c910c1b864002f113e682e44508910c849e144f3df1"}, + {file = "ml_dtypes-0.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c83e4d443962d891d51669ff241d5aaad10a8d3d37a81c5532a45419885d591c"}, + {file = "ml_dtypes-0.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1e2f4237b459a63c97c2c9f449baa637d7e4c20addff6a9bac486f22432f3b6"}, + {file = "ml_dtypes-0.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:75b4faf99d0711b81f393db36d210b4255fd419f6f790bc6c1b461f95ffb7a9e"}, + {file = "ml_dtypes-0.4.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:ee9f91d4c4f9959a7e1051c141dc565f39e54435618152219769e24f5e9a4d06"}, + {file = "ml_dtypes-0.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ad6849a2db386b38e4d54fe13eb3293464561780531a918f8ef4c8169170dd49"}, + {file = "ml_dtypes-0.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaa32979ebfde3a0d7c947cafbf79edc1ec77ac05ad0780ee86c1d8df70f2259"}, + {file = "ml_dtypes-0.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:3b67ec73a697c88c1122038e0de46520e48dc2ec876d42cf61bc5efe3c0b7675"}, + {file = "ml_dtypes-0.4.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:41affb38fdfe146e3db226cf2953021184d6f0c4ffab52136613e9601706e368"}, + {file = "ml_dtypes-0.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:43cf4356a0fe2eeac6d289018d0734e17a403bdf1fd911953c125dd0358edcc0"}, + {file = "ml_dtypes-0.4.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f1724ddcdf5edbaf615a62110af47407f1719b8d02e68ccee60683acb5f74da1"}, + {file = "ml_dtypes-0.4.0-cp39-cp39-win_amd64.whl", hash = "sha256:723af6346447268a3cf0b7356e963d80ecb5732b5279b2aa3fa4b9fc8297c85e"}, + {file = "ml_dtypes-0.4.0.tar.gz", hash = "sha256:eaf197e72f4f7176a19fe3cb8b61846b38c6757607e7bf9cd4b1d84cd3e74deb"}, +] + +[package.dependencies] +numpy = [ + {version = ">=1.21.2", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, + {version = ">1.20", markers = "python_version < \"3.10\""}, + {version = ">=1.23.3", markers = "python_version >= \"3.11\" and python_version < \"3.12\""}, + {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, +] + +[package.extras] +dev = ["absl-py", "pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"] + +[[package]] +name = "mpmath" +version = "1.3.0" +description = "Python library for arbitrary-precision floating-point arithmetic" +optional = true +python-versions = "*" +files = [ + {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, + {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, +] + +[package.extras] +develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] +docs = ["sphinx"] +gmpy = ["gmpy2 (>=2.1.0a4)"] +tests = ["pytest (>=4.6)"] + +[[package]] +name = "namex" +version = "0.0.8" +description = "A simple utility to separate the implementation of your Python package and its public API surface." +optional = true +python-versions = "*" +files = [ + {file = "namex-0.0.8-py3-none-any.whl", hash = "sha256:7ddb6c2bb0e753a311b7590f84f6da659dd0c05e65cb89d519d54c0a250c0487"}, + {file = "namex-0.0.8.tar.gz", hash = "sha256:32a50f6c565c0bb10aa76298c959507abdc0e850efe085dc38f3440fcb3aa90b"}, +] + +[[package]] +name = "networkx" +version = "3.2.1" +description = "Python package for creating and manipulating graphs and networks" +optional = true +python-versions = ">=3.9" +files = [ + {file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"}, + {file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"}, +] [package.extras] -rsa = ["cryptography (>=3.0.0)"] -signals = ["blinker (>=1.4.0)"] -signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"] +default = ["matplotlib (>=3.5)", "numpy (>=1.22)", "pandas (>=1.4)", "scipy (>=1.9,!=1.11.0,!=1.11.1)"] +developer = ["changelist (==0.4)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"] +doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.14)", "sphinx (>=7)", "sphinx-gallery (>=0.14)", "texext (>=0.6.7)"] +extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"] +test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"] [[package]] -name = "opt-einsum" -version = "3.3.0" -description = "Optimizing numpys einsum function" -category = "main" +name = "nodeenv" +version = "1.8.0" +description = "Node.js virtual environment builder" optional = false -python-versions = ">=3.5" +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*" +files = [ + {file = "nodeenv-1.8.0-py2.py3-none-any.whl", hash = "sha256:df865724bb3c3adc86b3876fa209771517b0cfe596beff01a92700e0e8be4cec"}, + {file = "nodeenv-1.8.0.tar.gz", hash = "sha256:d51e0c37e64fbf47d017feac3145cdbb58836d7eee8c6f6d3b6880c5456227d2"}, +] + +[package.dependencies] +setuptools = "*" + +[[package]] +name = "numpy" +version = "1.26.4" +description = "Fundamental package for array computing in Python" +optional = false +python-versions = ">=3.9" +files = [ + {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"}, + {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"}, + {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"}, + {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"}, + {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"}, + {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"}, + {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"}, + {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"}, + {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"}, + {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"}, + {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"}, + {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"}, + {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"}, + {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"}, + {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"}, + {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"}, + {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"}, + {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"}, + {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"}, + {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"}, + {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"}, + {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"}, + {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"}, + {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"}, + {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"}, + {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"}, + {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"}, + {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"}, + {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"}, + {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"}, + {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"}, + {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"}, + {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"}, + {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"}, + {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"}, + {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"}, +] + +[[package]] +name = "optree" +version = "0.11.0" +description = "Optimized PyTree Utilities." +optional = true +python-versions = ">=3.7" +files = [ + {file = "optree-0.11.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fa9ed745d4cbac5e15df70339b30867ba033542b87f7b734f4cacae5ec73ba00"}, + {file = "optree-0.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f53951bfb640417558568284a8949d67bcdbf21fa0113107e20bd9403aa20b2b"}, + {file = "optree-0.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0df9a3923725aabb112ec7f10c74fa96b6c640da1cd30e7bc62fd4b03ef02875"}, + {file = "optree-0.11.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:979ffc2b96f16595c219fb7a89597dd2fa00ac47a3b411fdcf8ae6821da52290"}, + {file = "optree-0.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:228b97e8c991739b10c8548c118747ba32ee765f88236342e492bf9648afc0bc"}, + {file = "optree-0.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:a91840f9d45e7c01f151ba1815ae32b4c3c21e4290298772ee4b13314f729856"}, + {file = "optree-0.11.0-cp310-cp310-win_arm64.whl", hash = "sha256:31d444684ebd8c9f09a3d806fb3277843138ef9952b7a2954908e440e3b22519"}, + {file = "optree-0.11.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a5f37bcfe4e363e3bb8d36c5698fb829546956b2fe88951994387162a1859625"}, + {file = "optree-0.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6e8c3757088cd7fce666f2a5e031b65d7898e210452380d2657c0fc0a7ec9932"}, + {file = "optree-0.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:39bed744a61e2f795e172d2853779ac59b8dea236982dc160ea22063afc99ca3"}, + {file = "optree-0.11.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e250144eacdd5813dec0b18d91df0229197e3be402db42fd8e254ec90ea343d"}, + {file = "optree-0.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc17f9d085cd75a2de4f299a9c5e3c3520138eac7596061e581230b03862b44d"}, + {file = "optree-0.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:a64df43fce2d8eeafd7db6e27447c56b3fa64842df847819684b3b1cc254c016"}, + {file = "optree-0.11.0-cp311-cp311-win_arm64.whl", hash = "sha256:d666099a78f7bf31bf3a520d6871ddcae65484bcff095fc4271a391553b09c75"}, + {file = "optree-0.11.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:9bf322ad14f907ad4660ca286e731e750546d54934a94cc5ba7efe8860c60ab4"}, + {file = "optree-0.11.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:64c2e00fe508f50a42c50838df0d1f5be0dce5b4bef2373db8ad72b860211015"}, + {file = "optree-0.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:738e8bf4158e9c11cd051d89c2e453aeacf80ff8719ebc3251069015646554d0"}, + {file = "optree-0.11.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0db6968394096223881053dffdcaf2b8e220fd85db904f14aa931e4dc422c046"}, + {file = "optree-0.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e5df0e8aaca124cc1ffca311786cc909810f3c046de090729cdafbf910082f8"}, + {file = "optree-0.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:ee208f0bec6436085a9fa3ae98af54bfcb8822086894fc1ade283e80a6f11fd7"}, + {file = "optree-0.11.0-cp312-cp312-win_arm64.whl", hash = "sha256:26b1230f9b75b579923a4f837c7c13db8b8d815cf68ce5af31dda5d818a877b2"}, + {file = "optree-0.11.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6cdd625dab2dff5374ff9c6792e8702fced8f0ea713ce959fc8f95499b5ecb2f"}, + {file = "optree-0.11.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:234a4f8f97a1217f13390df7ac416771689749d9a1c8eda31bf8622cd333219e"}, + {file = "optree-0.11.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6a406eee5acd3fd4875fa44c3972d29ae6d4329e7296e9219986fe6ff8e92ea0"}, + {file = "optree-0.11.0-cp37-cp37m-win_amd64.whl", hash = "sha256:63e020a34b7168b5d0701a265c7c95b07984ff699d4894b20fa601282be88f20"}, + {file = "optree-0.11.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e2d47bd28eff690eb2f7432e490265a291b04d6d346cf7b586491b2e2337bf97"}, + {file = "optree-0.11.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2bc08fb9691f43afc3a01119dead6b823ce3d7239e42fc3e47d4028eed50a6a2"}, + {file = "optree-0.11.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3cdc9fac9888d9eff11128ccfc4d4c10309163e372f312f7942ecee8df3d7824"}, + {file = "optree-0.11.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2b3bb59324d635f2015bb3e237fd772b1fd548eee6cc80e008fbe0f092e9228d"}, + {file = "optree-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b201a9405e250cf5770955863af2a236e382bdf5e4e086897ff03c41418c39da"}, + {file = "optree-0.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:162ed3ff2eb3f1c358e131e72c025f2b93d69b906e9057a811d014032ec71dc8"}, + {file = "optree-0.11.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:00a63f10d4a476e8e9aa2988daba9b2e88cb369c5aacc12545957d7d00bcd1a7"}, + {file = "optree-0.11.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:418850ceff364f51a6d81f32a1efd06a4e2d8df79a162e892685bc20c0aedd72"}, + {file = "optree-0.11.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b8126d81ecb2c9e3554420834014ba343251f564c905ee3bef09d205b924b0c0"}, + {file = "optree-0.11.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4144126dd3c2ece2d2dd1d5e0b39fb91adf1c46f660c2c5a2df7f80666989d5d"}, + {file = "optree-0.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9d236bc1491a5e366921b95fecc05aa6ff55989a81f2242cd11121b82c24503"}, + {file = "optree-0.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:b26ac807d8993b7e43081b4b7bbb0378b4e5f3e6525daf923c470bc176cc3327"}, + {file = "optree-0.11.0-cp39-cp39-win_arm64.whl", hash = "sha256:9d9d644e5448db9f32e2497487aca3bb2d3f92cbb50429a411ccda3f1f0968f3"}, + {file = "optree-0.11.0.tar.gz", hash = "sha256:8e6a46e95c3ea8546055087d6fe52a1dcd56de5182365f1469106cc72cdf3307"}, +] [package.dependencies] -numpy = ">=1.7" +typing-extensions = ">=4.0.0" [package.extras] -docs = ["numpydoc", "sphinx (==1.2.3)", "sphinx-rtd-theme", "sphinxcontrib-napoleon"] -tests = ["pytest", "pytest-cov", "pytest-pep8"] +benchmark = ["dm-tree (>=0.1,<0.2.0a0)", "jax[cpu] (>=0.4.6,<0.5.0a0)", "pandas", "tabulate", "termcolor", "torch (>=2.0,<2.1.0a0)", "torchvision"] +docs = ["docutils", "jax[cpu]", "numpy", "sphinx (>=5.2.1)", "sphinx-autoapi", "sphinx-autobuild", "sphinx-autodoc-typehints (>=1.19.2)", "sphinx-copybutton", "sphinx-rtd-theme", "sphinxcontrib-bibtex", "torch"] +jax = ["jax"] +lint = ["black (>=22.6.0)", "cpplint", "doc8 (<1.0.0a0)", "flake8", "flake8-bugbear", "flake8-comprehensions", "flake8-docstrings", "flake8-pyi", "flake8-simplify", "isort (>=5.11.0)", "mypy (>=0.990)", "pre-commit", "pydocstyle", "pyenchant", "pylint[spelling] (>=2.15.0)", "ruff", "xdoctest"] +numpy = ["numpy"] +test = ["pytest", "pytest-cov", "pytest-xdist"] +torch = ["torch"] [[package]] name = "packaging" -version = "21.3" +version = "24.0" description = "Core utilities for Python packages" -category = "main" optional = false -python-versions = ">=3.6" - -[package.dependencies] -pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" +python-versions = ">=3.7" +files = [ + {file = "packaging-24.0-py3-none-any.whl", hash = "sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5"}, + {file = "packaging-24.0.tar.gz", hash = "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"}, +] [[package]] name = "pandas" -version = "1.1.5" +version = "2.2.2" description = "Powerful data structures for data analysis, time series, and statistics" -category = "main" optional = false -python-versions = ">=3.6.1" +python-versions = ">=3.9" +files = [ + {file = "pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce"}, + {file = "pandas-2.2.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238"}, + {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4abfe0be0d7221be4f12552995e58723c7422c80a659da13ca382697de830c08"}, + {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8635c16bf3d99040fdf3ca3db669a7250ddf49c55dc4aa8fe0ae0fa8d6dcc1f0"}, + {file = "pandas-2.2.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:40ae1dffb3967a52203105a077415a86044a2bea011b5f321c6aa64b379a3f51"}, + {file = "pandas-2.2.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:8e5a0b00e1e56a842f922e7fae8ae4077aee4af0acb5ae3622bd4b4c30aedf99"}, + {file = "pandas-2.2.2-cp310-cp310-win_amd64.whl", hash = "sha256:ddf818e4e6c7c6f4f7c8a12709696d193976b591cc7dc50588d3d1a6b5dc8772"}, + {file = "pandas-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:696039430f7a562b74fa45f540aca068ea85fa34c244d0deee539cb6d70aa288"}, + {file = "pandas-2.2.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8e90497254aacacbc4ea6ae5e7a8cd75629d6ad2b30025a4a8b09aa4faf55151"}, + {file = "pandas-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:58b84b91b0b9f4bafac2a0ac55002280c094dfc6402402332c0913a59654ab2b"}, + {file = "pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d2123dc9ad6a814bcdea0f099885276b31b24f7edf40f6cdbc0912672e22eee"}, + {file = "pandas-2.2.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:2925720037f06e89af896c70bca73459d7e6a4be96f9de79e2d440bd499fe0db"}, + {file = "pandas-2.2.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0cace394b6ea70c01ca1595f839cf193df35d1575986e484ad35c4aeae7266c1"}, + {file = "pandas-2.2.2-cp311-cp311-win_amd64.whl", hash = "sha256:873d13d177501a28b2756375d59816c365e42ed8417b41665f346289adc68d24"}, + {file = "pandas-2.2.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:9dfde2a0ddef507a631dc9dc4af6a9489d5e2e740e226ad426a05cabfbd7c8ef"}, + {file = "pandas-2.2.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e9b79011ff7a0f4b1d6da6a61aa1aa604fb312d6647de5bad20013682d1429ce"}, + {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1cb51fe389360f3b5a4d57dbd2848a5f033350336ca3b340d1c53a1fad33bcad"}, + {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad"}, + {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3e374f59e440d4ab45ca2fffde54b81ac3834cf5ae2cdfa69c90bc03bde04d76"}, + {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:43498c0bdb43d55cb162cdc8c06fac328ccb5d2eabe3cadeb3529ae6f0517c32"}, + {file = "pandas-2.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:d187d355ecec3629624fccb01d104da7d7f391db0311145817525281e2804d23"}, + {file = "pandas-2.2.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2"}, + {file = "pandas-2.2.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9057e6aa78a584bc93a13f0a9bf7e753a5e9770a30b4d758b8d5f2a62a9433cd"}, + {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863"}, + {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921"}, + {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:a77e9d1c386196879aa5eb712e77461aaee433e54c68cf253053a73b7e49c33a"}, + {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:92fd6b027924a7e178ac202cfbe25e53368db90d56872d20ffae94b96c7acc57"}, + {file = "pandas-2.2.2-cp39-cp39-win_amd64.whl", hash = "sha256:640cef9aa381b60e296db324337a554aeeb883ead99dc8f6c18e81a93942f5f4"}, + {file = "pandas-2.2.2.tar.gz", hash = "sha256:9e79019aba43cb4fda9e4d983f8e88ca0373adbb697ae9c6c43093218de28b54"}, +] [package.dependencies] -numpy = ">=1.15.4" -python-dateutil = ">=2.7.3" -pytz = ">=2017.2" +numpy = [ + {version = ">=1.22.4", markers = "python_version < \"3.11\""}, + {version = ">=1.23.2", markers = "python_version == \"3.11\""}, + {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, +] +python-dateutil = ">=2.8.2" +pytz = ">=2020.1" +tzdata = ">=2022.7" [package.extras] -test = ["hypothesis (>=3.58)", "pytest (>=4.0.2)", "pytest-xdist"] +all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"] +aws = ["s3fs (>=2022.11.0)"] +clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"] +compression = ["zstandard (>=0.19.0)"] +computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"] +consortium-standard = ["dataframe-api-compat (>=0.1.7)"] +excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"] +feather = ["pyarrow (>=10.0.1)"] +fss = ["fsspec (>=2022.11.0)"] +gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"] +hdf5 = ["tables (>=3.8.0)"] +html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"] +mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"] +output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"] +parquet = ["pyarrow (>=10.0.1)"] +performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"] +plot = ["matplotlib (>=3.6.3)"] +postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"] +pyarrow = ["pyarrow (>=10.0.1)"] +spss = ["pyreadstat (>=1.2.0)"] +sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"] +test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"] +xml = ["lxml (>=4.9.2)"] + +[[package]] +name = "pdoc3" +version = "0.10.0" +description = "Auto-generate API documentation for Python projects." +optional = false +python-versions = ">= 3.6" +files = [ + {file = "pdoc3-0.10.0-py3-none-any.whl", hash = "sha256:ba45d1ada1bd987427d2bf5cdec30b2631a3ff5fb01f6d0e77648a572ce6028b"}, + {file = "pdoc3-0.10.0.tar.gz", hash = "sha256:5f22e7bcb969006738e1aa4219c75a32f34c2d62d46dc9d2fb2d3e0b0287e4b7"}, +] + +[package.dependencies] +mako = "*" +markdown = ">=3.0" [[package]] name = "pillow" -version = "9.2.0" +version = "10.3.0" description = "Python Imaging Library (Fork)" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "pillow-10.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:90b9e29824800e90c84e4022dd5cc16eb2d9605ee13f05d47641eb183cd73d45"}, + {file = "pillow-10.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a2c405445c79c3f5a124573a051062300936b0281fee57637e706453e452746c"}, + {file = "pillow-10.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78618cdbccaa74d3f88d0ad6cb8ac3007f1a6fa5c6f19af64b55ca170bfa1edf"}, + {file = "pillow-10.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:261ddb7ca91fcf71757979534fb4c128448b5b4c55cb6152d280312062f69599"}, + {file = "pillow-10.3.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:ce49c67f4ea0609933d01c0731b34b8695a7a748d6c8d186f95e7d085d2fe475"}, + {file = "pillow-10.3.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:b14f16f94cbc61215115b9b1236f9c18403c15dd3c52cf629072afa9d54c1cbf"}, + {file = "pillow-10.3.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d33891be6df59d93df4d846640f0e46f1a807339f09e79a8040bc887bdcd7ed3"}, + {file = "pillow-10.3.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b50811d664d392f02f7761621303eba9d1b056fb1868c8cdf4231279645c25f5"}, + {file = "pillow-10.3.0-cp310-cp310-win32.whl", hash = "sha256:ca2870d5d10d8726a27396d3ca4cf7976cec0f3cb706debe88e3a5bd4610f7d2"}, + {file = "pillow-10.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:f0d0591a0aeaefdaf9a5e545e7485f89910c977087e7de2b6c388aec32011e9f"}, + {file = "pillow-10.3.0-cp310-cp310-win_arm64.whl", hash = "sha256:ccce24b7ad89adb5a1e34a6ba96ac2530046763912806ad4c247356a8f33a67b"}, + {file = "pillow-10.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:5f77cf66e96ae734717d341c145c5949c63180842a545c47a0ce7ae52ca83795"}, + {file = "pillow-10.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e4b878386c4bf293578b48fc570b84ecfe477d3b77ba39a6e87150af77f40c57"}, + {file = "pillow-10.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fdcbb4068117dfd9ce0138d068ac512843c52295ed996ae6dd1faf537b6dbc27"}, + {file = "pillow-10.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9797a6c8fe16f25749b371c02e2ade0efb51155e767a971c61734b1bf6293994"}, + {file = "pillow-10.3.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:9e91179a242bbc99be65e139e30690e081fe6cb91a8e77faf4c409653de39451"}, + {file = "pillow-10.3.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:1b87bd9d81d179bd8ab871603bd80d8645729939f90b71e62914e816a76fc6bd"}, + {file = "pillow-10.3.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:81d09caa7b27ef4e61cb7d8fbf1714f5aec1c6b6c5270ee53504981e6e9121ad"}, + {file = "pillow-10.3.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:048ad577748b9fa4a99a0548c64f2cb8d672d5bf2e643a739ac8faff1164238c"}, + {file = "pillow-10.3.0-cp311-cp311-win32.whl", hash = "sha256:7161ec49ef0800947dc5570f86568a7bb36fa97dd09e9827dc02b718c5643f09"}, + {file = "pillow-10.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:8eb0908e954d093b02a543dc963984d6e99ad2b5e36503d8a0aaf040505f747d"}, + {file = "pillow-10.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:4e6f7d1c414191c1199f8996d3f2282b9ebea0945693fb67392c75a3a320941f"}, + {file = "pillow-10.3.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:e46f38133e5a060d46bd630faa4d9fa0202377495df1f068a8299fd78c84de84"}, + {file = "pillow-10.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:50b8eae8f7334ec826d6eeffaeeb00e36b5e24aa0b9df322c247539714c6df19"}, + {file = "pillow-10.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9d3bea1c75f8c53ee4d505c3e67d8c158ad4df0d83170605b50b64025917f338"}, + {file = "pillow-10.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19aeb96d43902f0a783946a0a87dbdad5c84c936025b8419da0a0cd7724356b1"}, + {file = "pillow-10.3.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:74d28c17412d9caa1066f7a31df8403ec23d5268ba46cd0ad2c50fb82ae40462"}, + {file = "pillow-10.3.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ff61bfd9253c3915e6d41c651d5f962da23eda633cf02262990094a18a55371a"}, + {file = "pillow-10.3.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d886f5d353333b4771d21267c7ecc75b710f1a73d72d03ca06df49b09015a9ef"}, + {file = "pillow-10.3.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4b5ec25d8b17217d635f8935dbc1b9aa5907962fae29dff220f2659487891cd3"}, + {file = "pillow-10.3.0-cp312-cp312-win32.whl", hash = "sha256:51243f1ed5161b9945011a7360e997729776f6e5d7005ba0c6879267d4c5139d"}, + {file = "pillow-10.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:412444afb8c4c7a6cc11a47dade32982439925537e483be7c0ae0cf96c4f6a0b"}, + {file = "pillow-10.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:798232c92e7665fe82ac085f9d8e8ca98826f8e27859d9a96b41d519ecd2e49a"}, + {file = "pillow-10.3.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:4eaa22f0d22b1a7e93ff0a596d57fdede2e550aecffb5a1ef1106aaece48e96b"}, + {file = "pillow-10.3.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:cd5e14fbf22a87321b24c88669aad3a51ec052eb145315b3da3b7e3cc105b9a2"}, + {file = "pillow-10.3.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1530e8f3a4b965eb6a7785cf17a426c779333eb62c9a7d1bbcf3ffd5bf77a4aa"}, + {file = "pillow-10.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d512aafa1d32efa014fa041d38868fda85028e3f930a96f85d49c7d8ddc0383"}, + {file = "pillow-10.3.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:339894035d0ede518b16073bdc2feef4c991ee991a29774b33e515f1d308e08d"}, + {file = "pillow-10.3.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:aa7e402ce11f0885305bfb6afb3434b3cd8f53b563ac065452d9d5654c7b86fd"}, + {file = "pillow-10.3.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:0ea2a783a2bdf2a561808fe4a7a12e9aa3799b701ba305de596bc48b8bdfce9d"}, + {file = "pillow-10.3.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:c78e1b00a87ce43bb37642c0812315b411e856a905d58d597750eb79802aaaa3"}, + {file = "pillow-10.3.0-cp38-cp38-win32.whl", hash = "sha256:72d622d262e463dfb7595202d229f5f3ab4b852289a1cd09650362db23b9eb0b"}, + {file = "pillow-10.3.0-cp38-cp38-win_amd64.whl", hash = "sha256:2034f6759a722da3a3dbd91a81148cf884e91d1b747992ca288ab88c1de15999"}, + {file = "pillow-10.3.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:2ed854e716a89b1afcedea551cd85f2eb2a807613752ab997b9974aaa0d56936"}, + {file = "pillow-10.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:dc1a390a82755a8c26c9964d457d4c9cbec5405896cba94cf51f36ea0d855002"}, + {file = "pillow-10.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4203efca580f0dd6f882ca211f923168548f7ba334c189e9eab1178ab840bf60"}, + {file = "pillow-10.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3102045a10945173d38336f6e71a8dc71bcaeed55c3123ad4af82c52807b9375"}, + {file = "pillow-10.3.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:6fb1b30043271ec92dc65f6d9f0b7a830c210b8a96423074b15c7bc999975f57"}, + {file = "pillow-10.3.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:1dfc94946bc60ea375cc39cff0b8da6c7e5f8fcdc1d946beb8da5c216156ddd8"}, + {file = "pillow-10.3.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:b09b86b27a064c9624d0a6c54da01c1beaf5b6cadfa609cf63789b1d08a797b9"}, + {file = "pillow-10.3.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d3b2348a78bc939b4fed6552abfd2e7988e0f81443ef3911a4b8498ca084f6eb"}, + {file = "pillow-10.3.0-cp39-cp39-win32.whl", hash = "sha256:45ebc7b45406febf07fef35d856f0293a92e7417ae7933207e90bf9090b70572"}, + {file = "pillow-10.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:0ba26351b137ca4e0db0342d5d00d2e355eb29372c05afd544ebf47c0956ffeb"}, + {file = "pillow-10.3.0-cp39-cp39-win_arm64.whl", hash = "sha256:50fd3f6b26e3441ae07b7c979309638b72abc1a25da31a81a7fbd9495713ef4f"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:6b02471b72526ab8a18c39cb7967b72d194ec53c1fd0a70b050565a0f366d355"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:8ab74c06ffdab957d7670c2a5a6e1a70181cd10b727cd788c4dd9005b6a8acd9"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:048eeade4c33fdf7e08da40ef402e748df113fd0b4584e32c4af74fe78baaeb2"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e2ec1e921fd07c7cda7962bad283acc2f2a9ccc1b971ee4b216b75fad6f0463"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:4c8e73e99da7db1b4cad7f8d682cf6abad7844da39834c288fbfa394a47bbced"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:16563993329b79513f59142a6b02055e10514c1a8e86dca8b48a893e33cf91e3"}, + {file = "pillow-10.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:dd78700f5788ae180b5ee8902c6aea5a5726bac7c364b202b4b3e3ba2d293170"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:aff76a55a8aa8364d25400a210a65ff59d0168e0b4285ba6bf2bd83cf675ba32"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:b7bc2176354defba3edc2b9a777744462da2f8e921fbaf61e52acb95bafa9828"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:793b4e24db2e8742ca6423d3fde8396db336698c55cd34b660663ee9e45ed37f"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d93480005693d247f8346bc8ee28c72a2191bdf1f6b5db469c096c0c867ac015"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c83341b89884e2b2e55886e8fbbf37c3fa5efd6c8907124aeb72f285ae5696e5"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1a1d1915db1a4fdb2754b9de292642a39a7fb28f1736699527bb649484fb966a"}, + {file = "pillow-10.3.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a0eaa93d054751ee9964afa21c06247779b90440ca41d184aeb5d410f20ff591"}, + {file = "pillow-10.3.0.tar.gz", hash = "sha256:9d2455fbf44c914840c793e89aa82d0e1763a14253a000743719ae5946814b2d"}, +] [package.extras] -docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"] +fpx = ["olefile"] +mic = ["olefile"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] +typing = ["typing-extensions"] +xmp = ["defusedxml"] [[package]] name = "platformdirs" -version = "2.5.2" -description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." -category = "dev" +version = "4.0.0" +description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." optional = false python-versions = ">=3.7" +files = [ + {file = "platformdirs-4.0.0-py3-none-any.whl", hash = "sha256:118c954d7e949b35437270383a3f2531e99dd93cf7ce4dc8340d3356d30f173b"}, + {file = "platformdirs-4.0.0.tar.gz", hash = "sha256:cb633b2bcf10c51af60beb0ab06d2f1d69064b43abf4c185ca6b28865f3f9731"}, +] + +[package.extras] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"] +test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"] + +[[package]] +name = "pluggy" +version = "1.5.0" +description = "plugin and hook calling mechanisms for python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"}, + {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"}, +] [package.extras] -docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx (>=4)", "sphinx-autodoc-typehints (>=1.12)"] -test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"] +dev = ["pre-commit", "tox"] +testing = ["pytest", "pytest-benchmark"] [[package]] name = "pre-commit" -version = "2.20.0" +version = "3.7.1" description = "A framework for managing and maintaining multi-language pre-commit hooks." -category = "dev" optional = false -python-versions = ">=3.7" +python-versions = ">=3.9" +files = [ + {file = "pre_commit-3.7.1-py2.py3-none-any.whl", hash = "sha256:fae36fd1d7ad7d6a5a1c0b0d5adb2ed1a3bda5a21bf6c3e5372073d7a11cd4c5"}, + {file = "pre_commit-3.7.1.tar.gz", hash = "sha256:8ca3ad567bc78a4972a3f1a477e94a79d4597e8140a6e0b651c5e33899c3654a"}, +] [package.dependencies] cfgv = ">=2.0.0" identify = ">=1.0.0" -importlib-metadata = {version = "*", markers = "python_version < \"3.8\""} nodeenv = ">=0.11.1" pyyaml = ">=5.1" -toml = "*" -virtualenv = ">=20.0.8" +virtualenv = ">=20.10.0" [[package]] name = "protobuf" -version = "3.19.4" -description = "Protocol Buffers" -category = "main" -optional = false -python-versions = ">=3.5" - -[[package]] -name = "psutil" -version = "5.9.1" -description = "Cross-platform lib for process and system monitoring in Python." -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - -[package.extras] -test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"] +version = "5.26.1" +description = "" +optional = true +python-versions = ">=3.8" +files = [ + {file = "protobuf-5.26.1-cp310-abi3-win32.whl", hash = "sha256:3c388ea6ddfe735f8cf69e3f7dc7611e73107b60bdfcf5d0f024c3ccd3794e23"}, + {file = "protobuf-5.26.1-cp310-abi3-win_amd64.whl", hash = "sha256:e6039957449cb918f331d32ffafa8eb9255769c96aa0560d9a5bf0b4e00a2a33"}, + {file = "protobuf-5.26.1-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:38aa5f535721d5bb99861166c445c4105c4e285c765fbb2ac10f116e32dcd46d"}, + {file = "protobuf-5.26.1-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:fbfe61e7ee8c1860855696e3ac6cfd1b01af5498facc6834fcc345c9684fb2ca"}, + {file = "protobuf-5.26.1-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:f7417703f841167e5a27d48be13389d52ad705ec09eade63dfc3180a959215d7"}, + {file = "protobuf-5.26.1-cp38-cp38-win32.whl", hash = "sha256:d693d2504ca96750d92d9de8a103102dd648fda04540495535f0fec7577ed8fc"}, + {file = "protobuf-5.26.1-cp38-cp38-win_amd64.whl", hash = "sha256:9b557c317ebe6836835ec4ef74ec3e994ad0894ea424314ad3552bc6e8835b4e"}, + {file = "protobuf-5.26.1-cp39-cp39-win32.whl", hash = "sha256:b9ba3ca83c2e31219ffbeb9d76b63aad35a3eb1544170c55336993d7a18ae72c"}, + {file = "protobuf-5.26.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ee014c2c87582e101d6b54260af03b6596728505c79f17c8586e7523aaa8f8c"}, + {file = "protobuf-5.26.1-py3-none-any.whl", hash = "sha256:da612f2720c0183417194eeaa2523215c4fcc1a1949772dc65f05047e08d5932"}, + {file = "protobuf-5.26.1.tar.gz", hash = "sha256:8ca2a1d97c290ec7b16e4e5dff2e5ae150cc1582f55b5ab300d45cb0dfa90e51"}, +] [[package]] name = "pyarrow" -version = "9.0.0" +version = "16.1.0" description = "Python library for Apache Arrow" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "pyarrow-16.1.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:17e23b9a65a70cc733d8b738baa6ad3722298fa0c81d88f63ff94bf25eaa77b9"}, + {file = "pyarrow-16.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4740cc41e2ba5d641071d0ab5e9ef9b5e6e8c7611351a5cb7c1d175eaf43674a"}, + {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:98100e0268d04e0eec47b73f20b39c45b4006f3c4233719c3848aa27a03c1aef"}, + {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f68f409e7b283c085f2da014f9ef81e885d90dcd733bd648cfba3ef265961848"}, + {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:a8914cd176f448e09746037b0c6b3a9d7688cef451ec5735094055116857580c"}, + {file = "pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:48be160782c0556156d91adbdd5a4a7e719f8d407cb46ae3bb4eaee09b3111bd"}, + {file = "pyarrow-16.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:9cf389d444b0f41d9fe1444b70650fea31e9d52cfcb5f818b7888b91b586efff"}, + {file = "pyarrow-16.1.0-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:d0ebea336b535b37eee9eee31761813086d33ed06de9ab6fc6aaa0bace7b250c"}, + {file = "pyarrow-16.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2e73cfc4a99e796727919c5541c65bb88b973377501e39b9842ea71401ca6c1c"}, + {file = "pyarrow-16.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bf9251264247ecfe93e5f5a0cd43b8ae834f1e61d1abca22da55b20c788417f6"}, + {file = "pyarrow-16.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ddf5aace92d520d3d2a20031d8b0ec27b4395cab9f74e07cc95edf42a5cc0147"}, + {file = "pyarrow-16.1.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:25233642583bf658f629eb230b9bb79d9af4d9f9229890b3c878699c82f7d11e"}, + {file = "pyarrow-16.1.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a33a64576fddfbec0a44112eaf844c20853647ca833e9a647bfae0582b2ff94b"}, + {file = "pyarrow-16.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:185d121b50836379fe012753cf15c4ba9638bda9645183ab36246923875f8d1b"}, + {file = "pyarrow-16.1.0-cp312-cp312-macosx_10_15_x86_64.whl", hash = "sha256:2e51ca1d6ed7f2e9d5c3c83decf27b0d17bb207a7dea986e8dc3e24f80ff7d6f"}, + {file = "pyarrow-16.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:06ebccb6f8cb7357de85f60d5da50e83507954af617d7b05f48af1621d331c9a"}, + {file = "pyarrow-16.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b04707f1979815f5e49824ce52d1dceb46e2f12909a48a6a753fe7cafbc44a0c"}, + {file = "pyarrow-16.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0d32000693deff8dc5df444b032b5985a48592c0697cb6e3071a5d59888714e2"}, + {file = "pyarrow-16.1.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:8785bb10d5d6fd5e15d718ee1d1f914fe768bf8b4d1e5e9bf253de8a26cb1628"}, + {file = "pyarrow-16.1.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:e1369af39587b794873b8a307cc6623a3b1194e69399af0efd05bb202195a5a7"}, + {file = "pyarrow-16.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:febde33305f1498f6df85e8020bca496d0e9ebf2093bab9e0f65e2b4ae2b3444"}, + {file = "pyarrow-16.1.0-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:b5f5705ab977947a43ac83b52ade3b881eb6e95fcc02d76f501d549a210ba77f"}, + {file = "pyarrow-16.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:0d27bf89dfc2576f6206e9cd6cf7a107c9c06dc13d53bbc25b0bd4556f19cf5f"}, + {file = "pyarrow-16.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d07de3ee730647a600037bc1d7b7994067ed64d0eba797ac74b2bc77384f4c2"}, + {file = "pyarrow-16.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fbef391b63f708e103df99fbaa3acf9f671d77a183a07546ba2f2c297b361e83"}, + {file = "pyarrow-16.1.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:19741c4dbbbc986d38856ee7ddfdd6a00fc3b0fc2d928795b95410d38bb97d15"}, + {file = "pyarrow-16.1.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:f2c5fb249caa17b94e2b9278b36a05ce03d3180e6da0c4c3b3ce5b2788f30eed"}, + {file = "pyarrow-16.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:e6b6d3cd35fbb93b70ade1336022cc1147b95ec6af7d36906ca7fe432eb09710"}, + {file = "pyarrow-16.1.0-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:18da9b76a36a954665ccca8aa6bd9f46c1145f79c0bb8f4f244f5f8e799bca55"}, + {file = "pyarrow-16.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:99f7549779b6e434467d2aa43ab2b7224dd9e41bdde486020bae198978c9e05e"}, + {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f07fdffe4fd5b15f5ec15c8b64584868d063bc22b86b46c9695624ca3505b7b4"}, + {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ddfe389a08ea374972bd4065d5f25d14e36b43ebc22fc75f7b951f24378bf0b5"}, + {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:3b20bd67c94b3a2ea0a749d2a5712fc845a69cb5d52e78e6449bbd295611f3aa"}, + {file = "pyarrow-16.1.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:ba8ac20693c0bb0bf4b238751d4409e62852004a8cf031c73b0e0962b03e45e3"}, + {file = "pyarrow-16.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:31a1851751433d89a986616015841977e0a188662fcffd1a5677453f1df2de0a"}, + {file = "pyarrow-16.1.0.tar.gz", hash = "sha256:15fbb22ea96d11f0b5768504a3f961edab25eaf4197c341720c4a387f6c60315"}, +] [package.dependencies] numpy = ">=1.16.6" [[package]] -name = "pyasn1" -version = "0.4.8" -description = "ASN.1 types and codecs" -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "pyasn1-modules" -version = "0.2.8" -description = "A collection of ASN.1-based protocols modules." -category = "main" -optional = false -python-versions = "*" +name = "pygame" +version = "2.5.2" +description = "Python Game Development" +optional = true +python-versions = ">=3.6" +files = [ + {file = "pygame-2.5.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a0769eb628c818761755eb0a0ca8216b95270ea8cbcbc82227e39ac9644643da"}, + {file = "pygame-2.5.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed9a3d98adafa0805ccbaaff5d2996a2b5795381285d8437a4a5d248dbd12b4a"}, + {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f30d1618672a55e8c6669281ba264464b3ab563158e40d89e8c8b3faa0febebd"}, + {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:39690e9be9baf58b7359d1f3b2336e1fd6f92fedbbce42987be5df27f8d30718"}, + {file = "pygame-2.5.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03879ec299c9f4ba23901b2649a96b2143f0a5d787f0b6c39469989e2320caf1"}, + {file = "pygame-2.5.2-cp310-cp310-win32.whl", hash = "sha256:74e1d6284100e294f445832e6f6343be4fe4748decc4f8a51131ae197dae8584"}, + {file = "pygame-2.5.2-cp310-cp310-win_amd64.whl", hash = "sha256:485239c7d32265fd35b76ae8f64f34b0637ae11e69d76de15710c4b9edcc7c8d"}, + {file = "pygame-2.5.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:34646ca20e163dc6f6cf8170f1e12a2e41726780112594ac061fa448cf7ccd75"}, + {file = "pygame-2.5.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3b8a6e351665ed26ea791f0e1fd649d3f483e8681892caef9d471f488f9ea5ee"}, + {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc346965847aef00013fa2364f41a64f068cd096dcc7778fc306ca3735f0eedf"}, + {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35632035fd81261f2d797fa810ea8c46111bd78ceb6089d52b61ed7dc3c5d05f"}, + {file = "pygame-2.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0e24d05184e4195fe5ebcdce8b18ecb086f00182b9ae460a86682d312ce8d31f"}, + {file = "pygame-2.5.2-cp311-cp311-win32.whl", hash = "sha256:f02c1c7505af18d426d355ac9872bd5c916b27f7b0fe224749930662bea47a50"}, + {file = "pygame-2.5.2-cp311-cp311-win_amd64.whl", hash = "sha256:6d58c8cf937815d3b7cdc0fa9590c5129cb2c9658b72d00e8a4568dea2ff1d42"}, + {file = "pygame-2.5.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1a2a43802bb5e89ce2b3b775744e78db4f9a201bf8d059b946c61722840ceea8"}, + {file = "pygame-2.5.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1c289f2613c44fe70a1e40769de4a49c5ab5a29b9376f1692bb1a15c9c1c9bfa"}, + {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:074aa6c6e110c925f7f27f00c7733c6303407edc61d738882985091d1eb2ef17"}, + {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fe0228501ec616779a0b9c4299e837877783e18df294dd690b9ab0eed3d8aaab"}, + {file = "pygame-2.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:31648d38ecdc2335ffc0e38fb18a84b3339730521505dac68514f83a1092e3f4"}, + {file = "pygame-2.5.2-cp312-cp312-win32.whl", hash = "sha256:224c308856334bc792f696e9278e50d099a87c116f7fc314cd6aa3ff99d21592"}, + {file = "pygame-2.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:dd2d2650faf54f9a0f5bd0db8409f79609319725f8f08af6507a0609deadcad4"}, + {file = "pygame-2.5.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:9b30bc1220c457169571aac998e54b013aaeb732d2fd8744966cb1cfab1f61d1"}, + {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78fcd7643358b886a44127ff7dec9041c056c212b3a98977674f83f99e9b12d3"}, + {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:35cf093a51cb294ede56c29d4acf41538c00f297fcf78a9b186fb7d23c0577b6"}, + {file = "pygame-2.5.2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6fe323acbf53a0195c8c98b1b941eba7ac24e3e2b28ae48e8cda566f15fc4945"}, + {file = "pygame-2.5.2-cp36-cp36m-win32.whl", hash = "sha256:5697528266b4716d9cdd44a5a1d210f4d86ef801d0f64ca5da5d0816704009d9"}, + {file = "pygame-2.5.2-cp36-cp36m-win_amd64.whl", hash = "sha256:edda1f7cff4806a4fa39e0e8ccd75f38d1d340fa5fc52d8582ade87aca247d92"}, + {file = "pygame-2.5.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9bd738fd4ecc224769d0b4a719f96900a86578e26e0105193658a32966df2aae"}, + {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:30a8d7cf12363b4140bf2f93b5eec4028376ca1d0fe4b550588f836279485308"}, + {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bc12e4dea3e88ea8a553de6d56a37b704dbe2aed95105889f6afeb4b96e62097"}, + {file = "pygame-2.5.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b34c73cb328024f8db3cb6487a37e54000148988275d8d6e5adf99d9323c937"}, + {file = "pygame-2.5.2-cp37-cp37m-win32.whl", hash = "sha256:7d0a2794649defa57ef50b096a99f7113d3d0c2e32d1426cafa7d618eadce4c7"}, + {file = "pygame-2.5.2-cp37-cp37m-win_amd64.whl", hash = "sha256:41f8779f52e0f6e6e6ccb8f0b5536e432bf386ee29c721a1c22cada7767b0cef"}, + {file = "pygame-2.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:677e37bc0ea7afd89dde5a88ced4458aa8656159c70a576eea68b5622ee1997b"}, + {file = "pygame-2.5.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:47a8415d2bd60e6909823b5643a1d4ef5cc29417d817f2a214b255f6fa3a1e4c"}, + {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ff21201df6278b8ca2e948fb148ffe88f5481fd03760f381dd61e45954c7dff"}, + {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d29a84b2e02814b9ba925357fd2e1df78efe5e1aa64dc3051eaed95d2b96eafd"}, + {file = "pygame-2.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d78485c4d21133d6b2fbb504cd544ca655e50b6eb551d2995b3aa6035928adda"}, + {file = "pygame-2.5.2-cp38-cp38-win32.whl", hash = "sha256:d851247239548aa357c4a6840fb67adc2d570ce7cb56988d036a723d26b48bff"}, + {file = "pygame-2.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:88d1cdacc2d3471eceab98bf0c93c14d3a8461f93e58e3d926f20d4de3a75554"}, + {file = "pygame-2.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4f1559e7efe4efb9dc19d2d811d702f325d9605f9f6f9ececa39ee6890c798f5"}, + {file = "pygame-2.5.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cf2191b756ceb0e8458a761d0c665b0c70b538570449e0d39b75a5ba94ac5cf0"}, + {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6cf2257447ce7f2d6de37e5fb019d2bbe32ed05a5721ace8bc78c2d9beaf3aee"}, + {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d75cbbfaba2b81434d62631d0b08b85fab16cf4a36e40b80298d3868927e1299"}, + {file = "pygame-2.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:daca456d5b9f52e088e06a127dec182b3638a775684fb2260f25d664351cf1ae"}, + {file = "pygame-2.5.2-cp39-cp39-win32.whl", hash = "sha256:3b3e619e33d11c297d7a57a82db40681f9c2c3ae1d5bf06003520b4fe30c435d"}, + {file = "pygame-2.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:1822d534bb7fe756804647b6da2c9ea5d7a62d8796b2e15d172d3be085de28c6"}, + {file = "pygame-2.5.2-pp36-pypy36_pp73-win32.whl", hash = "sha256:e708fc8f709a0fe1d1876489345f2e443d47f3976d33455e2e1e937f972f8677"}, + {file = "pygame-2.5.2-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c13edebc43c240fb0532969e914f0ccefff5ae7e50b0b788d08ad2c15ef793e4"}, + {file = "pygame-2.5.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:263b4a7cbfc9fe2055abc21b0251cc17dea6dff750f0e1c598919ff350cdbffe"}, + {file = "pygame-2.5.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e58e2b0c791041e4bccafa5bd7650623ba1592b8fe62ae0a276b7d0ecb314b6c"}, + {file = "pygame-2.5.2-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a0bd67426c02ffe6c9827fc4bcbda9442fbc451d29b17c83a3c088c56fef2c90"}, + {file = "pygame-2.5.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9dcff6cbba1584cf7732ce1dbdd044406cd4f6e296d13bcb7fba963fb4aeefc9"}, + {file = "pygame-2.5.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:ce4b6c0bfe44d00bb0998a6517bd0cf9455f642f30f91bc671ad41c05bf6f6ae"}, + {file = "pygame-2.5.2-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:68c4e8e60b725ffc7a6c6ecd9bb5fcc5ed2d6e0e2a2c4a29a8454856ef16ad63"}, + {file = "pygame-2.5.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f3849f97372a3381c66955f99a0d58485ccd513c3d00c030b869094ce6997a6"}, + {file = "pygame-2.5.2.tar.gz", hash = "sha256:c1b89eb5d539e7ac5cf75513125fb5f2f0a2d918b1fd6e981f23bf0ac1b1c24a"}, +] + +[[package]] +name = "pygments" +version = "2.18.0" +description = "Pygments is a syntax highlighting package written in Python." +optional = true +python-versions = ">=3.8" +files = [ + {file = "pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a"}, + {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"}, +] -[package.dependencies] -pyasn1 = ">=0.4.6,<0.5.0" +[package.extras] +windows-terminal = ["colorama (>=0.4.6)"] [[package]] name = "pyparsing" -version = "3.0.9" +version = "3.1.2" description = "pyparsing module - Classes and methods to define and execute parsing grammars" -category = "main" optional = false python-versions = ">=3.6.8" +files = [ + {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"}, + {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"}, +] [package.extras] diagrams = ["jinja2", "railroad-diagrams"] +[[package]] +name = "pytest" +version = "8.2.0" +description = "pytest: simple powerful testing with Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pytest-8.2.0-py3-none-any.whl", hash = "sha256:1733f0620f6cda4095bbf0d9ff8022486e91892245bb9e7d5542c018f612f233"}, + {file = "pytest-8.2.0.tar.gz", hash = "sha256:d507d4482197eac0ba2bae2e9babf0672eb333017bcedaa5fb1a3d42c1174b3f"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "sys_platform == \"win32\""} +exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""} +iniconfig = "*" +packaging = "*" +pluggy = ">=1.5,<2.0" +tomli = {version = ">=1", markers = "python_version < \"3.11\""} + +[package.extras] +dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"] + [[package]] name = "python-dateutil" -version = "2.8.2" +version = "2.9.0.post0" description = "Extensions to the standard Python datetime module" -category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +files = [ + {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, + {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, +] [package.dependencies] six = ">=1.5" [[package]] name = "pytz" -version = "2022.2.1" +version = "2024.1" description = "World timezone definitions, modern and historical" -category = "main" optional = false python-versions = "*" +files = [ + {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"}, + {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, +] [[package]] name = "pyyaml" -version = "6.0" +version = "6.0.1" description = "YAML parser and emitter for Python" -category = "main" -optional = false -python-versions = ">=3.6" - -[[package]] -name = "recsim-ng" -version = "0.1.2" -description = "RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems" -category = "main" optional = false python-versions = ">=3.6" +files = [ + {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"}, + {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"}, + {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"}, + {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"}, + {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"}, + {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"}, + {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"}, + {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"}, + {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"}, + {file = "PyYAML-6.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f003ed9ad21d6a4713f0a9b5a7a0a79e08dd0f221aff4525a2be4c346ee60aab"}, + {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"}, + {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"}, + {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"}, + {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"}, + {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"}, + {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, + {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, + {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, + {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, + {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, + {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"}, + {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"}, + {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"}, + {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"}, + {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afd7e57eddb1a54f0f1a974bc4391af8bcce0b444685d936840f125cf046d5bd"}, + {file = "PyYAML-6.0.1-cp36-cp36m-win32.whl", hash = "sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585"}, + {file = "PyYAML-6.0.1-cp36-cp36m-win_amd64.whl", hash = "sha256:f22ac1c3cac4dbc50079e965eba2c1058622631e526bd9afd45fedd49ba781fa"}, + {file = "PyYAML-6.0.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b1275ad35a5d18c62a7220633c913e1b42d44b46ee12554e5fd39c70a243d6a3"}, + {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18aeb1bf9a78867dc38b259769503436b7c72f7a1f1f4c93ff9a17de54319b27"}, + {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:596106435fa6ad000c2991a98fa58eeb8656ef2325d7e158344fb33864ed87e3"}, + {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa90d3f661d43131ca170712d903e6295d1f7a0f595074f151c0aed377c9b9c"}, + {file = "PyYAML-6.0.1-cp37-cp37m-win32.whl", hash = "sha256:9046c58c4395dff28dd494285c82ba00b546adfc7ef001486fbf0324bc174fba"}, + {file = "PyYAML-6.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:4fb147e7a67ef577a588a0e2c17b6db51dda102c71de36f8549b6816a96e1867"}, + {file = "PyYAML-6.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1d4c7e777c441b20e32f52bd377e0c409713e8bb1386e1099c2415f26e479595"}, + {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"}, + {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"}, + {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"}, + {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"}, + {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"}, + {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"}, + {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"}, + {file = "PyYAML-6.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c8098ddcc2a85b61647b2590f825f3db38891662cfc2fc776415143f599bb859"}, + {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"}, + {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"}, + {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"}, + {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"}, + {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"}, + {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"}, + {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"}, +] + +[[package]] +name = "rich" +version = "13.7.1" +description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" +optional = true +python-versions = ">=3.7.0" +files = [ + {file = "rich-13.7.1-py3-none-any.whl", hash = "sha256:4edbae314f59eb482f54e9e30bf00d33350aaa94f4bfcd4e9e3110e64d0d7222"}, + {file = "rich-13.7.1.tar.gz", hash = "sha256:9be308cb1fe2f1f57d67ce99e95af38a1e2bc71ad9813b0e247cf7ffbcc3a432"}, +] [package.dependencies] -absl-py = "*" -edward2 = "*" -gin-config = "*" -gym = "*" -jax = "*" -matplotlib = "*" -numpy = "*" -tensorflow = ">=2.3.0" -tensorflow-probability = "*" +markdown-it-py = ">=2.2.0" +pygments = ">=2.13.0,<3.0.0" + +[package.extras] +jupyter = ["ipywidgets (>=7.5.1,<9)"] [[package]] -name = "requests" -version = "2.27.1" -description = "Python HTTP for Humans." -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*" +name = "scipy" +version = "1.13.0" +description = "Fundamental algorithms for scientific computing in Python" +optional = true +python-versions = ">=3.9" +files = [ + {file = "scipy-1.13.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba419578ab343a4e0a77c0ef82f088238a93eef141b2b8017e46149776dfad4d"}, + {file = "scipy-1.13.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:22789b56a999265431c417d462e5b7f2b487e831ca7bef5edeb56efe4c93f86e"}, + {file = "scipy-1.13.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05f1432ba070e90d42d7fd836462c50bf98bd08bed0aa616c359eed8a04e3922"}, + {file = "scipy-1.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8434f6f3fa49f631fae84afee424e2483289dfc30a47755b4b4e6b07b2633a4"}, + {file = "scipy-1.13.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:dcbb9ea49b0167de4167c40eeee6e167caeef11effb0670b554d10b1e693a8b9"}, + {file = "scipy-1.13.0-cp310-cp310-win_amd64.whl", hash = "sha256:1d2f7bb14c178f8b13ebae93f67e42b0a6b0fc50eba1cd8021c9b6e08e8fb1cd"}, + {file = "scipy-1.13.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0fbcf8abaf5aa2dc8d6400566c1a727aed338b5fe880cde64907596a89d576fa"}, + {file = "scipy-1.13.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5e4a756355522eb60fcd61f8372ac2549073c8788f6114449b37e9e8104f15a5"}, + {file = "scipy-1.13.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5acd8e1dbd8dbe38d0004b1497019b2dbbc3d70691e65d69615f8a7292865d7"}, + {file = "scipy-1.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ff7dad5d24a8045d836671e082a490848e8639cabb3dbdacb29f943a678683d"}, + {file = "scipy-1.13.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:4dca18c3ffee287ddd3bc8f1dabaf45f5305c5afc9f8ab9cbfab855e70b2df5c"}, + {file = "scipy-1.13.0-cp311-cp311-win_amd64.whl", hash = "sha256:a2f471de4d01200718b2b8927f7d76b5d9bde18047ea0fa8bd15c5ba3f26a1d6"}, + {file = "scipy-1.13.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d0de696f589681c2802f9090fff730c218f7c51ff49bf252b6a97ec4a5d19e8b"}, + {file = "scipy-1.13.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:b2a3ff461ec4756b7e8e42e1c681077349a038f0686132d623fa404c0bee2551"}, + {file = "scipy-1.13.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6bf9fe63e7a4bf01d3645b13ff2aa6dea023d38993f42aaac81a18b1bda7a82a"}, + {file = "scipy-1.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e7626dfd91cdea5714f343ce1176b6c4745155d234f1033584154f60ef1ff42"}, + {file = "scipy-1.13.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:109d391d720fcebf2fbe008621952b08e52907cf4c8c7efc7376822151820820"}, + {file = "scipy-1.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:8930ae3ea371d6b91c203b1032b9600d69c568e537b7988a3073dfe4d4774f21"}, + {file = "scipy-1.13.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5407708195cb38d70fd2d6bb04b1b9dd5c92297d86e9f9daae1576bd9e06f602"}, + {file = "scipy-1.13.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:ac38c4c92951ac0f729c4c48c9e13eb3675d9986cc0c83943784d7390d540c78"}, + {file = "scipy-1.13.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09c74543c4fbeb67af6ce457f6a6a28e5d3739a87f62412e4a16e46f164f0ae5"}, + {file = "scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28e286bf9ac422d6beb559bc61312c348ca9b0f0dae0d7c5afde7f722d6ea13d"}, + {file = "scipy-1.13.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:33fde20efc380bd23a78a4d26d59fc8704e9b5fd9b08841693eb46716ba13d86"}, + {file = "scipy-1.13.0-cp39-cp39-win_amd64.whl", hash = "sha256:45c08bec71d3546d606989ba6e7daa6f0992918171e2a6f7fbedfa7361c2de1e"}, + {file = "scipy-1.13.0.tar.gz", hash = "sha256:58569af537ea29d3f78e5abd18398459f195546bb3be23d16677fb26616cc11e"}, +] [package.dependencies] -certifi = ">=2017.4.17" -charset-normalizer = {version = ">=2.0.0,<2.1.0", markers = "python_version >= \"3\""} -idna = {version = ">=2.5,<4", markers = "python_version >= \"3\""} -urllib3 = ">=1.21.1,<1.27" +numpy = ">=1.22.4,<2.3" [package.extras] -socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"] -use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"] +dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"] +doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.12.0)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"] +test = ["array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] -name = "requests-oauthlib" -version = "1.3.1" -description = "OAuthlib authentication support for Requests." -category = "main" +name = "seaborn" +version = "0.13.2" +description = "Statistical data visualization" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +python-versions = ">=3.8" +files = [ + {file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"}, + {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"}, +] [package.dependencies] -oauthlib = ">=3.0.0" -requests = ">=2.0.0" +matplotlib = ">=3.4,<3.6.1 || >3.6.1" +numpy = ">=1.20,<1.24.0 || >1.24.0" +pandas = ">=1.2" [package.extras] -rsa = ["oauthlib[signedtoken] (>=3.0.0)"] +dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"] +docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"] +stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"] [[package]] -name = "rsa" -version = "4.4" -description = "Pure-Python RSA implementation" -category = "main" +name = "setuptools" +version = "68.0.0" +description = "Easily download, build, install, upgrade, and uninstall Python packages" optional = false -python-versions = "*" +python-versions = ">=3.7" +files = [ + {file = "setuptools-68.0.0-py3-none-any.whl", hash = "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f"}, + {file = "setuptools-68.0.0.tar.gz", hash = "sha256:baf1fdb41c6da4cd2eae722e135500da913332ab3f2f5c7d33af9b492acb5235"}, +] -[package.dependencies] -pyasn1 = ">=0.1.3" +[package.extras] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] -name = "scipy" -version = "1.6.1" -description = "SciPy: Scientific Library for Python" -category = "main" +name = "six" +version = "1.16.0" +description = "Python 2 and 3 compatibility utilities" optional = false -python-versions = ">=3.7" - -[package.dependencies] -numpy = ">=1.16.5" +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +files = [ + {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"}, + {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, +] [[package]] -name = "setuptools-scm" -version = "6.4.2" -description = "the blessed package to manage your versions by scm tags" -category = "main" -optional = false -python-versions = ">=3.6" +name = "sympy" +version = "1.12" +description = "Computer algebra system (CAS) in Python" +optional = true +python-versions = ">=3.8" +files = [ + {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"}, + {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"}, +] [package.dependencies] -packaging = ">=20.0" -tomli = ">=1.0.0" - -[package.extras] -test = ["pytest (>=6.2)", "virtualenv (>20)"] -toml = ["setuptools (>=42)"] +mpmath = ">=0.19" [[package]] -name = "six" -version = "1.15.0" -description = "Python 2 and 3 compatibility utilities" -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +name = "tbb" +version = "2021.12.0" +description = "Intel® oneAPI Threading Building Blocks (oneTBB)" +optional = true +python-versions = "*" +files = [ + {file = "tbb-2021.12.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:f2cc9a7f8ababaa506cbff796ce97c3bf91062ba521e15054394f773375d81d8"}, + {file = "tbb-2021.12.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:a925e9a7c77d3a46ae31c34b0bb7f801c4118e857d137b68f68a8e458fcf2bd7"}, + {file = "tbb-2021.12.0-py3-none-win32.whl", hash = "sha256:b1725b30c174048edc8be70bd43bb95473f396ce895d91151a474d0fa9f450a8"}, + {file = "tbb-2021.12.0-py3-none-win_amd64.whl", hash = "sha256:fc2772d850229f2f3df85f1109c4844c495a2db7433d38200959ee9265b34789"}, +] [[package]] name = "tensorboard" -version = "2.6.0" +version = "2.16.2" description = "TensorBoard lets you watch Tensors Flow" -category = "main" -optional = false -python-versions = ">=3.6" +optional = true +python-versions = ">=3.9" +files = [ + {file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"}, +] [package.dependencies] absl-py = ">=0.4" -google-auth = ">=1.6.3,<2" -google-auth-oauthlib = ">=0.4.1,<0.5" -grpcio = ">=1.24.3" +grpcio = ">=1.48.2" markdown = ">=2.6.8" numpy = ">=1.12.0" -protobuf = ">=3.6.0" -requests = ">=2.21.0,<3" -tensorboard-data-server = ">=0.6.0,<0.7.0" -tensorboard-plugin-wit = ">=1.6.0" -werkzeug = ">=0.11.15" +protobuf = ">=3.19.6,<4.24.0 || >4.24.0" +setuptools = ">=41.0.0" +six = ">1.9" +tensorboard-data-server = ">=0.7.0,<0.8.0" +werkzeug = ">=1.0.1" [[package]] name = "tensorboard-data-server" -version = "0.6.1" +version = "0.7.2" description = "Fast data loading for TensorBoard" -category = "main" -optional = false -python-versions = ">=3.6" - -[[package]] -name = "tensorboard-plugin-wit" -version = "1.8.1" -description = "What-If Tool TensorBoard plugin." -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "tensorboardx" -version = "2.5.1" -description = "TensorBoardX lets you watch Tensors Flow without Tensorflow" -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -numpy = "*" -protobuf = ">=3.8.0,<=3.20.1" - -[[package]] -name = "tensorflow" -version = "2.6.5" -description = "TensorFlow is an open source machine learning framework for everyone." -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -absl-py = ">=0.10,<1.0" -astunparse = ">=1.6.3,<1.7.0" -clang = ">=5.0,<6.0" -flatbuffers = ">=1.12.0,<1.13.0" -gast = "0.4.0" -google-pasta = ">=0.2,<1.0" -grpcio = ">=1.37.0,<2.0" -h5py = ">=3.1.0,<3.2.0" -keras = ">=2.6.0,<2.7" -keras-preprocessing = ">=1.1.2,<1.2.0" -numpy = ">=1.19.2,<1.20.0" -opt-einsum = ">=3.3.0,<3.4.0" -protobuf = ">=3.9.2,<3.20" -six = ">=1.15.0,<1.16.0" -tensorboard = ">=2.6.0,<2.7" -tensorflow-estimator = ">=2.6.0,<2.7" -termcolor = ">=1.1.0,<1.2.0" -typing-extensions = ">=3.7,<3.11" -wrapt = ">=1.12.1,<1.13.0" - -[[package]] -name = "tensorflow-estimator" -version = "2.6.0" -description = "TensorFlow Estimator." -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "tensorflow-probability" -version = "0.17.0" -description = "Probabilistic modeling and statistical inference in TensorFlow" -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -absl-py = "*" -cloudpickle = ">=1.3" -decorator = "*" -dm-tree = "*" -gast = ">=0.3.2" -numpy = ">=1.13.3" -six = ">=1.10.0" - -[package.extras] -jax = ["jax", "jaxlib"] -tfds = ["tensorflow-datasets (>=2.2.0)"] - -[[package]] -name = "termcolor" -version = "1.1.0" -description = "ANSII Color formatting for output in terminal." -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "toml" -version = "0.10.2" -description = "Python Library for Tom's Obvious, Minimal Language" -category = "dev" -optional = false -python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +optional = true +python-versions = ">=3.7" +files = [ + {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"}, + {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"}, + {file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"}, +] [[package]] name = "tomli" version = "2.0.1" description = "A lil' TOML parser" -category = "main" optional = false python-versions = ">=3.7" +files = [ + {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"}, + {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, +] [[package]] name = "torch" -version = "1.12.1" +version = "2.3.0+cpu" description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" -category = "main" -optional = false -python-versions = ">=3.7.0" +optional = true +python-versions = ">=3.8.0" +files = [ + {file = "torch-2.3.0+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:e3c220702d82c7596924150e0499fbbffcf62a88a59adc860fa357cd8dc1c302"}, + {file = "torch-2.3.0+cpu-cp310-cp310-win_amd64.whl", hash = "sha256:ab0c05525195b8fecdf2ea75968ed32ccd87dff16381b6e13249babb4a9596ff"}, + {file = "torch-2.3.0+cpu-cp311-cp311-linux_x86_64.whl", hash = "sha256:97a38b25ee0e3d020691e7846efbca62a3d8a57645c027dcb5ba0adfec36fe55"}, + {file = "torch-2.3.0+cpu-cp311-cp311-win_amd64.whl", hash = "sha256:a8ac195974be6f067245bae8156b8c06fb0a723b0eed8f2e244b5dd58c7e2a49"}, + {file = "torch-2.3.0+cpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:a8982e52185771591dad577a124a7770f72f288f8ae5833317b1e329c0d2f07e"}, + {file = "torch-2.3.0+cpu-cp312-cp312-win_amd64.whl", hash = "sha256:483131a7997995d867313ee902743084e844e830ab2a0c5e079c61ec2da3cd17"}, + {file = "torch-2.3.0+cpu-cp38-cp38-linux_x86_64.whl", hash = "sha256:8c52484880d5fbe511cffc255dd34847ddeced3f94334c6bf7eb2b0445f10cb4"}, + {file = "torch-2.3.0+cpu-cp38-cp38-win_amd64.whl", hash = "sha256:28a11bcc0d709b397d675cff689707019b8cc122e6bf328b57b900f47c36f156"}, + {file = "torch-2.3.0+cpu-cp39-cp39-linux_x86_64.whl", hash = "sha256:1e86e225e472392440ace378ba3165b5e87648e8b5fbf16adc41c0df881c38b8"}, + {file = "torch-2.3.0+cpu-cp39-cp39-win_amd64.whl", hash = "sha256:5c2afdff80203eaabf4c223a294c2f465020b3360e8e87f76b52ace9c5801ebe"}, +] [package.dependencies] -typing-extensions = "*" +filelock = "*" +fsspec = "*" +jinja2 = "*" +mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""} +networkx = "*" +sympy = "*" +typing-extensions = ">=4.8.0" + +[package.extras] +opt-einsum = ["opt-einsum (>=3.3)"] +optree = ["optree (>=0.9.1)"] + +[package.source] +type = "legacy" +url = "https://download.pytorch.org/whl/cpu" +reference = "cputorch" [[package]] name = "torchviz" version = "0.0.2" description = "A small package to create visualizations of PyTorch execution graphs" -category = "main" -optional = false +optional = true python-versions = "*" +files = [ + {file = "torchviz-0.0.2.tar.gz", hash = "sha256:c790b4c993f783433604bf610cfa58bb0a031260be4ee5196a00c0884e768051"}, +] [package.dependencies] graphviz = "*" torch = "*" -[[package]] -name = "tqdm" -version = "4.64.0" -description = "Fast, Extensible Progress Meter" -category = "main" -optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7" - -[package.dependencies] -colorama = {version = "*", markers = "platform_system == \"Windows\""} - -[package.extras] -dev = ["py-make (>=0.1.0)", "twine", "wheel"] -notebook = ["ipywidgets (>=6)"] -slack = ["slack-sdk"] -telegram = ["requests"] - [[package]] name = "typing-extensions" -version = "3.10.0.2" -description = "Backported and Experimental Type Hints for Python 3.5+" -category = "main" -optional = false -python-versions = "*" +version = "4.11.0" +description = "Backported and Experimental Type Hints for Python 3.8+" +optional = true +python-versions = ">=3.8" +files = [ + {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"}, + {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"}, +] [[package]] -name = "urllib3" -version = "1.22" -description = "HTTP library with thread-safe connection pooling, file post, and more." -category = "main" +name = "tzdata" +version = "2024.1" +description = "Provider of IANA time zone data" optional = false -python-versions = "*" - -[package.extras] -secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)"] -socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] +python-versions = ">=2" +files = [ + {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"}, + {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"}, +] [[package]] name = "virtualenv" -version = "20.16.4" +version = "20.26.2" description = "Virtual Python Environment builder" -category = "dev" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" +files = [ + {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"}, + {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"}, +] [package.dependencies] -distlib = ">=0.3.5,<1" -filelock = ">=3.4.1,<4" -importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.8\""} -platformdirs = ">=2.4,<3" +distlib = ">=0.3.7,<1" +filelock = ">=3.12.2,<4" +platformdirs = ">=3.9.1,<5" [package.extras] -docs = ["proselint (>=0.13)", "sphinx (>=5.1.1)", "sphinx-argparse (>=0.3.1)", "sphinx-rtd-theme (>=1)", "towncrier (>=21.9)"] -testing = ["coverage (>=6.2)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=21.3)", "pytest (>=7.0.1)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.6.1)", "pytest-randomly (>=3.10.3)", "pytest-timeout (>=2.1)"] +docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] [[package]] name = "werkzeug" -version = "2.2.2" +version = "3.0.3" description = "The comprehensive WSGI web application library." -category = "main" -optional = false -python-versions = ">=3.7" +optional = true +python-versions = ">=3.8" +files = [ + {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"}, + {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"}, +] [package.dependencies] MarkupSafe = ">=2.1.1" [package.extras] -watchdog = ["watchdog"] - -[[package]] -name = "wrapt" -version = "1.12.1" -description = "Module for decorators, wrappers and monkey patching." -category = "main" -optional = false -python-versions = "*" +watchdog = ["watchdog (>=2.3)"] [[package]] name = "zipp" -version = "3.8.1" +version = "3.18.1" description = "Backport of pathlib-compatible object wrapper for zip files" -category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +files = [ + {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, + {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, +] [package.extras] -docs = ["jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx"] -testing = ["func-timeout", "jaraco.itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] -[metadata] -lock-version = "1.1" -python-versions = ">=3.7,<4" -content-hash = "72599da18ef3167a6206e5fa0bc9d2a97b4cd605375c1903c009a1480e21a60f" +[extras] +recsys = ["gymnasium", "highway-env", "keras", "tensorboard", "torch", "torchviz"] -[metadata.files] -absl-py = [ - {file = "absl-py-0.15.0.tar.gz", hash = "sha256:72d782fbeafba66ba3e525d46bccac949b9a174dbf66233e50ece09ee688dc81"}, - {file = "absl_py-0.15.0-py3-none-any.whl", hash = "sha256:ea907384af023a7e681368bedb896159ab100c7db593efbbd5cde22af11270cd"}, -] -accelerate = [ - {file = "accelerate-0.12.0-py3-none-any.whl", hash = "sha256:7742ca5c9f15dd1e0a283305599c196e260af4717a561d1f544aeab27d828af6"}, - {file = "accelerate-0.12.0.tar.gz", hash = "sha256:e8b119c94fac31877620d5f9de311164ec81fa9dc9e175f0d0d4f50fc8d79473"}, -] -astunparse = [ - {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"}, - {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"}, -] -cached-property = [ - {file = "cached-property-1.5.2.tar.gz", hash = "sha256:9fa5755838eecbb2d234c3aa390bd80fbd3ac6b6869109bfc1b499f7bd89a130"}, - {file = "cached_property-1.5.2-py2.py3-none-any.whl", hash = "sha256:df4f613cf7ad9a588cc381aaf4a512d26265ecebd5eb9e1ba12f1319eb85a6a0"}, -] -cachetools = [ - {file = "cachetools-3.1.1-py2.py3-none-any.whl", hash = "sha256:428266a1c0d36dc5aca63a2d7c5942e88c2c898d72139fca0e97fdd2380517ae"}, - {file = "cachetools-3.1.1.tar.gz", hash = "sha256:8ea2d3ce97850f31e4a08b0e2b5e6c34997d7216a9d2c98e0f3978630d4da69a"}, -] -catalyst = [ - {file = "catalyst-22.4-py2.py3-none-any.whl", hash = "sha256:2ae086d653bb3e5becaaac90349ba72ee76f884f5be74f0917a42c5826c66583"}, - {file = "catalyst-22.4.tar.gz", hash = "sha256:e226b6d68376b12a6956debe3448e8a87409259c0125317163826f0665b17c6b"}, -] -certifi = [ - {file = "certifi-2022.6.15-py3-none-any.whl", hash = "sha256:fe86415d55e84719d75f8b69414f6438ac3547d2078ab91b67e779ef69378412"}, - {file = "certifi-2022.6.15.tar.gz", hash = "sha256:84c85a9078b11105f04f3036a9482ae10e4621616db313fe045dd24743a0820d"}, -] -cfgv = [ - {file = "cfgv-3.3.1-py2.py3-none-any.whl", hash = "sha256:c6a0883f3917a037485059700b9e75da2464e6c27051014ad85ba6aaa5884426"}, - {file = "cfgv-3.3.1.tar.gz", hash = "sha256:f5a830efb9ce7a445376bb66ec94c638a9787422f96264c98edc6bdeed8ab736"}, -] -charset-normalizer = [ - {file = "charset-normalizer-2.0.12.tar.gz", hash = "sha256:2857e29ff0d34db842cd7ca3230549d1a697f96ee6d3fb071cfa6c7393832597"}, - {file = "charset_normalizer-2.0.12-py3-none-any.whl", hash = "sha256:6881edbebdb17b39b4eaaa821b438bf6eddffb4468cf344f09f89def34a8b1df"}, -] -clang = [ - {file = "clang-5.0-py2-none-any.whl", hash = "sha256:b9301dff507041b5019b30ae710b78b0552c1ca1d4441b8dfa93c2e85078a5f8"}, - {file = "clang-5.0.tar.gz", hash = "sha256:ceccae97eda0225a5b44d42ffd61102e248325c2865ca53e4407746464a5333a"}, -] -cloudpickle = [ - {file = "cloudpickle-2.1.0-py3-none-any.whl", hash = "sha256:b5c434f75c34624eedad3a14f2be5ac3b5384774d5b0e3caf905c21479e6c4b1"}, - {file = "cloudpickle-2.1.0.tar.gz", hash = "sha256:bb233e876a58491d9590a676f93c7a5473a08f747d5ab9df7f9ce564b3e7938e"}, -] -colorama = [ - {file = "colorama-0.4.5-py2.py3-none-any.whl", hash = "sha256:854bf444933e37f5824ae7bfc1e98d5bce2ebe4160d46b5edf346a89358e99da"}, - {file = "colorama-0.4.5.tar.gz", hash = "sha256:e6c6b4334fc50988a639d9b98aa429a0b57da6e17b9a44f0451f930b6967b7a4"}, -] -cycler = [ - {file = "cycler-0.11.0-py3-none-any.whl", hash = "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3"}, - {file = "cycler-0.11.0.tar.gz", hash = "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"}, -] -decorator = [ - {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"}, - {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"}, -] -distlib = [ - {file = "distlib-0.3.6-py2.py3-none-any.whl", hash = "sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e"}, - {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"}, -] -dm-tree = [ - {file = "dm-tree-0.1.7.tar.gz", hash = "sha256:30fec8aca5b92823c0e796a2f33b875b4dccd470b57e91e6c542405c5f77fd2a"}, - {file = "dm_tree-0.1.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3fae437135b6cbbdd51e96488a35e78c3617defa0b65265e7e8752d506f933fd"}, - {file = "dm_tree-0.1.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d377bd621b485db42c4aeea0eabbd8f6274b89a9c338c2c1bf69a40c3b86a1fd"}, - {file = "dm_tree-0.1.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1410fa2f2cc8dc7c01386f4e93ddeeb56765574ffafb632a9b6bd96496195b10"}, - {file = "dm_tree-0.1.7-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:57edb6fbd88fcdd9908547cbf21045a9d663c0d9e5983dca7e6f9cf8b6584bb5"}, - {file = "dm_tree-0.1.7-cp310-cp310-win_amd64.whl", hash = "sha256:9edc1783a08d87c4e130781f55cbd904d6a564f7cce7dfb63f9ef3bee8e38209"}, - {file = "dm_tree-0.1.7-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:98fce150ceebb0a818f0eace1616004031cfa5e3375f50599ad790ff52414ba9"}, - {file = "dm_tree-0.1.7-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:b4364fc9a5721a2b840ac8ea75b8f58b430bec9fdc8b99304d2aecb3cfe46b1b"}, - {file = "dm_tree-0.1.7-cp36-cp36m-win_amd64.whl", hash = "sha256:a085f500b295a6bf439c538e9058c7798ecb8c7d0dc916291f3d8d79d6124d17"}, - {file = "dm_tree-0.1.7-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:f3e2bd9b9c05d1a0039f7c128d8b055c8a05708ef569cdbbeec0a2946e425bd4"}, - {file = "dm_tree-0.1.7-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:91c6240e47c9d80dbd7de5a29a2ca663143717a72c613130ba8ac4354fa741a9"}, - {file = "dm_tree-0.1.7-cp37-cp37m-win_amd64.whl", hash = "sha256:0f01743cc2247170e64798c6b4b31853717054bf9ceec47a1b1b8c2a4baf5792"}, - {file = "dm_tree-0.1.7-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:4992ac5c42af1d73042cd2d3af4e7892d3750e6c1bb8e5a4f81534aa6515f350"}, - {file = "dm_tree-0.1.7-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:20f24cad4decbf4c1f176a959d16e877c73df33b07d7d1f078a5b8abe72f79f8"}, - {file = "dm_tree-0.1.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:3166304411d14c50a5da1c583e24d6069b44de0c9e06479cb36cdf048a466945"}, - {file = "dm_tree-0.1.7-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:3b00885c21267934a3d3c68660811d3f891c9539fd53712f5b2423c6d74bf1e6"}, - {file = "dm_tree-0.1.7-cp38-cp38-win_amd64.whl", hash = "sha256:7f1f3dca9d669f3c09654ff6d69cfafd86a7f967c3095405b2692ee8d8ef3cfd"}, - {file = "dm_tree-0.1.7-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:51b9bdf1109b47cc22884b1919e6fe38edf28b5aa02e7c661bb760a0e7cf0157"}, - {file = "dm_tree-0.1.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2a843608e078d1622ebb5e50962a8c718d3fa1ab9461b95a12395a803545b2f5"}, - {file = "dm_tree-0.1.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:7fa0740b7fbae2c3a43a3114a514891b5d6c383050828f36aa1816cf40f73a6a"}, - {file = "dm_tree-0.1.7-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:1379a02df36e2bbff9819ceafa55ccd436b15af398803f781f372f8ead7ed871"}, - {file = "dm_tree-0.1.7-cp39-cp39-win_amd64.whl", hash = "sha256:3ca0a58e219b7b0bc201fea4679971188d0a9028a2543c16803a84e8f8c7eb2c"}, -] -edward2 = [ - {file = "edward2-0.0.2-py2.py3-none-any.whl", hash = "sha256:fab43ab87554409b713918bf0f1068f1209db10ea341879235503e0e7f734c6d"}, - {file = "edward2-0.0.2.tar.gz", hash = "sha256:b27f3ba5a08598e34473250addf1ce71e9288c5065bc034904877e23393520b1"}, -] -etils = [ - {file = "etils-0.7.1-py3-none-any.whl", hash = "sha256:9b5140835372e3db8b6e8d7da9e16beb2390ae6f24c253107c8127e4d2205189"}, - {file = "etils-0.7.1.tar.gz", hash = "sha256:207c097dd4180d5e5ab1c7b7ee78b7bf9471e12534deace23b1d392debd292f3"}, -] -filelock = [] -flatbuffers = [] -fonttools = [ - {file = "fonttools-4.37.1-py3-none-any.whl", hash = "sha256:fff6b752e326c15756c819fe2fe7ceab69f96a1dbcfe8911d0941cdb49905007"}, - {file = "fonttools-4.37.1.zip", hash = "sha256:4606e1a88ee1f6699d182fea9511bd9a8a915d913eab4584e5226da1180fcce7"}, -] -gast = [ - {file = "gast-0.4.0-py3-none-any.whl", hash = "sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4"}, - {file = "gast-0.4.0.tar.gz", hash = "sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1"}, -] -gin-config = [ - {file = "gin-config-0.5.0.tar.gz", hash = "sha256:0c6ea5026ded927c8c93c990b01c695257c1df446e45e549a158cfbc79e19ed6"}, - {file = "gin_config-0.5.0-py3-none-any.whl", hash = "sha256:bddb7ca221ea2b46cdb59321e79fecf02d6e3b728906047fcd4076c297609fd6"}, -] -google-auth = [ - {file = "google-auth-1.35.0.tar.gz", hash = "sha256:b7033be9028c188ee30200b204ea00ed82ea1162e8ac1df4aa6ded19a191d88e"}, - {file = "google_auth-1.35.0-py2.py3-none-any.whl", hash = "sha256:997516b42ecb5b63e8d80f5632c1a61dddf41d2a4c2748057837e06e00014258"}, -] -google-auth-oauthlib = [ - {file = "google-auth-oauthlib-0.4.6.tar.gz", hash = "sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a"}, - {file = "google_auth_oauthlib-0.4.6-py2.py3-none-any.whl", hash = "sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73"}, -] -google-pasta = [ - {file = "google-pasta-0.2.0.tar.gz", hash = "sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e"}, - {file = "google_pasta-0.2.0-py2-none-any.whl", hash = "sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954"}, - {file = "google_pasta-0.2.0-py3-none-any.whl", hash = "sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed"}, -] -graphviz = [ - {file = "graphviz-0.20.1-py3-none-any.whl", hash = "sha256:587c58a223b51611c0cf461132da386edd896a029524ca61a1462b880bf97977"}, - {file = "graphviz-0.20.1.zip", hash = "sha256:8c58f14adaa3b947daf26c19bc1e98c4e0702cdc31cf99153e6f06904d492bf8"}, -] -grpcio = [ - {file = "grpcio-1.48.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:4a049a032144641ed5d073535c0dc69eb6029187cc729a66946c86dcc8eec3a1"}, - {file = "grpcio-1.48.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:f8bc76f5cd95f5476e5285fe5d3704a9332586a569fbbccef551b0b6f7a270f9"}, - {file = "grpcio-1.48.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:448d397fe88e9fef8170f019b86abdc4d554ae311aaf4dbff1532fde227d3308"}, - {file = "grpcio-1.48.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8f9b6b6f7c83869d2316c5d13f953381881a16741275a34ec5ed5762f11b206e"}, - {file = "grpcio-1.48.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5bd8541c4b6b43c9024496d30b4a12346325d3a17a1f3c80ad8924caed1e35c3"}, - {file = "grpcio-1.48.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:877d33aeba05ae0b9e81761a694914ed33613f655c35f6bbcf4ebbcb984e0167"}, - {file = "grpcio-1.48.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:cd01a8201fd8ab2ce496f7e65975da1f1e629eac8eea84ead0fd77e32e4350cd"}, - {file = "grpcio-1.48.0-cp310-cp310-win32.whl", hash = "sha256:0388da923dff58ba7f711233e41c2b749b5817b8e0f137a107672d9c15a1009c"}, - {file = "grpcio-1.48.0-cp310-cp310-win_amd64.whl", hash = "sha256:8dcffdb8921fd88857ae350fd579277a5f9315351e89ed9094ef28927a46d40d"}, - {file = "grpcio-1.48.0-cp36-cp36m-linux_armv7l.whl", hash = "sha256:2138c50331232f56178c2b36dcfa6ad67aad705fe410955f3b2a53d722191b89"}, - {file = "grpcio-1.48.0-cp36-cp36m-macosx_10_10_x86_64.whl", hash = "sha256:af2d80f142da2a6af45204a5ca2374e2747af07a99de54a1164111e169a761ff"}, - {file = "grpcio-1.48.0-cp36-cp36m-manylinux_2_17_aarch64.whl", hash = "sha256:59284bd4cdf47c147c26d91aca693765318d524328f6ece2a1a0b85a12a362af"}, - {file = "grpcio-1.48.0-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cc3ebfe356c0c6750379cd194bf2b7e5d1d2f29db1832358f05a73e9290db98c"}, - {file = "grpcio-1.48.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dc2619a31339e1c53731f54761f1a2cb865d3421f690e00ef3e92f90d2a0c5ae"}, - {file = "grpcio-1.48.0-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:7df637405de328a54c1c8c08a3206f974c7a577730f90644af4c3400b7bfde2d"}, - {file = "grpcio-1.48.0-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:9e73b95969a579798bfbeb85d376695cce5172357fb52e450467ceb8e7365152"}, - {file = "grpcio-1.48.0-cp36-cp36m-win32.whl", hash = "sha256:059e9d58b5aba7fb9eabe3a4d2ac49e1dcbc2b54b0f166f6475e40b7f4435343"}, - {file = "grpcio-1.48.0-cp36-cp36m-win_amd64.whl", hash = "sha256:7cebcf645170f0c82ef71769544f9ac4515993a4d367f5900aba2eb4ecd2a32f"}, - {file = "grpcio-1.48.0-cp37-cp37m-linux_armv7l.whl", hash = "sha256:8af3a8845df35b838104d6fb1ae7f4969d248cf037fa2794916d31e917346f72"}, - {file = "grpcio-1.48.0-cp37-cp37m-macosx_10_10_x86_64.whl", hash = "sha256:a1ef40975ec9ced6c17ce7fbec9825823da782fa606f0b92392646ff3886f198"}, - {file = "grpcio-1.48.0-cp37-cp37m-manylinux_2_17_aarch64.whl", hash = "sha256:7cccbf6db31f2a78e1909047ff69620f94a4e6e53251858e9502fbbff5714b48"}, - {file = "grpcio-1.48.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f3f142579f58def64c0850f0bb0eb1b425ae885f5669dda5b73ade64ad2b753"}, - {file = "grpcio-1.48.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:656c6f6f7b815bca3054780b8cdfa1e4e37cd36c887a48558d00c2cf85f31697"}, - {file = "grpcio-1.48.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:cba4538e8a2ef123ea570e7b1d62162e158963c2471e35d79eb9690c971a10c0"}, - {file = "grpcio-1.48.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:9daa67820fafceec6194ed1686c1783816e62d6756ff301ba93e682948836846"}, - {file = "grpcio-1.48.0-cp37-cp37m-win32.whl", hash = "sha256:7ec264a7fb413e0c804a7a48a6f7d7212742955a60724c44d793da35a8f30873"}, - {file = "grpcio-1.48.0-cp37-cp37m-win_amd64.whl", hash = "sha256:a2b1b33b92359388b8164807313dcbb3317101b038a5d54342982560329d958f"}, - {file = "grpcio-1.48.0-cp38-cp38-linux_armv7l.whl", hash = "sha256:7b820696a5ce7b98f459f234698cb323f89b355373789188efa126d7f47a2a92"}, - {file = "grpcio-1.48.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:e4dfae66ebc165c46c5b7048eb554472ee72fbaab2c2c2da7f9b1621c81e077c"}, - {file = "grpcio-1.48.0-cp38-cp38-manylinux_2_17_aarch64.whl", hash = "sha256:f7115038edce33b494e0138b0bd31a2eb6595d45e2eed23be46bc32886feb741"}, - {file = "grpcio-1.48.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b4e996282238943ca114628255be61980e38b25f73a08ae2ffd02b63eaf70d3a"}, - {file = "grpcio-1.48.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13dad31f5155fa555d393511cc8108c41b1b5b54dc4c24c27d4694ddd7a78fad"}, - {file = "grpcio-1.48.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:c84b9d90b2641963de98b35bb7a2a51f78119fe5bd00ef27246ba9f4f0835e36"}, - {file = "grpcio-1.48.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:41b65166779d7dafac4c98380ac19f690f1c5fe18083a71d370df87b24dd30ff"}, - {file = "grpcio-1.48.0-cp38-cp38-win32.whl", hash = "sha256:b890e5f5fbc21cb994894f73ecb2faaa66697d8debcb228a5adb0622b9bec3b2"}, - {file = "grpcio-1.48.0-cp38-cp38-win_amd64.whl", hash = "sha256:5fe3af539d2f50891ed93aed3064ffbcc38bf848aa3f7ed1fbedcce139c57302"}, - {file = "grpcio-1.48.0-cp39-cp39-linux_armv7l.whl", hash = "sha256:a4ed57f4e3d91259551e6765782b22d9e8b8178fec43ebf8e1b2c392c4ced37b"}, - {file = "grpcio-1.48.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:60843d8184e171886dd7a93d6672e2ef0b08dfd4f88da7421c10b46b6e031ac4"}, - {file = "grpcio-1.48.0-cp39-cp39-manylinux_2_17_aarch64.whl", hash = "sha256:0ecba22f25ccde2442be7e7dd7fa746905d628f03312b4a0c9961f0d99771f53"}, - {file = "grpcio-1.48.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34f5917f0c49a04633dc12d483c8aee6f6d9f69133b700214d3703f72a72f501"}, - {file = "grpcio-1.48.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f4c4ad8ad7e2cf3a272cbc96734d56635e6543939022f17e0c4487f7d2a45bf9"}, - {file = "grpcio-1.48.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:111fb2f5f4a069f331ae23106145fd16dd4e1112ca223858a922068614dac6d2"}, - {file = "grpcio-1.48.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:beb0573daa49889efcfea0a6e995b4f39d481aa1b94e1257617406ef417b56a6"}, - {file = "grpcio-1.48.0-cp39-cp39-win32.whl", hash = "sha256:ce70254a082cb767217b2fdee374cc79199d338d46140753438cd6d67c609b2f"}, - {file = "grpcio-1.48.0-cp39-cp39-win_amd64.whl", hash = "sha256:ae3fd135666448058fe277d93c10e0f18345fbcbb015c4642de2fa3db6f0c205"}, - {file = "grpcio-1.48.0.tar.gz", hash = "sha256:eaf4bb73819863440727195411ab3b5c304f6663625e66f348e91ebe0a039306"}, -] -gym = [ - {file = "gym-0.25.2.tar.gz", hash = "sha256:c8323f4c6f37363710b519742a0094316deddc9d8fc9e3f6deac08d6da47150c"}, -] -gym-notices = [ - {file = "gym-notices-0.0.8.tar.gz", hash = "sha256:ad25e200487cafa369728625fe064e88ada1346618526102659b4640f2b4b911"}, - {file = "gym_notices-0.0.8-py3-none-any.whl", hash = "sha256:e5f82e00823a166747b4c2a07de63b6560b1acb880638547e0cabf825a01e463"}, -] -h5py = [ - {file = "h5py-3.1.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:1cd367f89a5441236bdbb795e9fb9a9e3424929c00b4a54254ca760437f83d69"}, - {file = "h5py-3.1.0-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:fea05349f63625a8fb808e57e42bb4c76930cf5d50ac58b678c52f913a48a89b"}, - {file = "h5py-3.1.0-cp36-cp36m-win_amd64.whl", hash = "sha256:2e37352ddfcf9d77a2a47f7c8f7e125c6d20cc06c2995edeb7be222d4e152636"}, - {file = "h5py-3.1.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e33f61d3eb862614c0f273a1f993a64dc2f093e1a3094932c50ada9d2db2170f"}, - {file = "h5py-3.1.0-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:236ac8d943be30b617ab615c3d4a4bf4a438add2be87e54af3687ab721a18fac"}, - {file = "h5py-3.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:02c391fdb980762a1cc03a4bcaecd03dc463994a9a63a02264830114a96e111f"}, - {file = "h5py-3.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f89a3dae38843ffa49d17a31a3509a8129e9b46ece602a0138e1ed79e685c361"}, - {file = "h5py-3.1.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:ba71f6229d2013fbb606476ecc29c6223fc16b244d35fcd8566ad9dbaf910857"}, - {file = "h5py-3.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:dccb89358bc84abcd711363c3e138f9f4eccfdf866f2139a8e72308328765b2c"}, - {file = "h5py-3.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cb74df83709d6d03d11e60b9480812f58da34f194beafa8c8314dbbeeedfe0a6"}, - {file = "h5py-3.1.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:80c623be10479e81b64fa713b7ed4c0bbe9f02e8e7d2a2e5382336087b615ce4"}, - {file = "h5py-3.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:1cdfd1c5449ca1329d152f0b66830e93226ebce4f5e07dd8dc16bfc2b1a49d7b"}, - {file = "h5py-3.1.0.tar.gz", hash = "sha256:1e2516f190652beedcb8c7acfa1c6fa92d99b42331cbef5e5c7ec2d65b0fc3c2"}, -] -hydra-slayer = [ - {file = "hydra-slayer-0.4.0.tar.gz", hash = "sha256:2e7ef0b99e5e11504d80996f689ed5ed7ae8766edb8e76b6933b306966159604"}, - {file = "hydra_slayer-0.4.0-py3-none-any.whl", hash = "sha256:6c8510811607b9a01d7386d7c09c28d3ea9bf20c945816f17d18fa88d57e8ffd"}, -] -identify = [ - {file = "identify-2.5.4-py2.py3-none-any.whl", hash = "sha256:962d9bec27ccd1fcceff9b11f8c635afeb163ea3d8b6b30d8f1eee37ec7fac47"}, - {file = "identify-2.5.4.tar.gz", hash = "sha256:b020e876cec2b11dadb3324fa0427eb744b7d66ef19ac579a748dfff774b6dcf"}, -] -idna = [ - {file = "idna-3.3-py3-none-any.whl", hash = "sha256:84d9dd047ffa80596e0f246e2eab0b391788b0503584e8945f2368256d2735ff"}, - {file = "idna-3.3.tar.gz", hash = "sha256:9d643ff0a55b762d5cdb124b8eaa99c66322e2157b69160bc32796e824360e6d"}, -] -importlib-metadata = [ - {file = "importlib_metadata-4.12.0-py3-none-any.whl", hash = "sha256:7401a975809ea1fdc658c3aa4f78cc2195a0e019c5cbc4c06122884e9ae80c23"}, - {file = "importlib_metadata-4.12.0.tar.gz", hash = "sha256:637245b8bab2b6502fcbc752cc4b7a6f6243bb02b31c5c26156ad103d3d45670"}, -] -importlib-resources = [ - {file = "importlib_resources-5.9.0-py3-none-any.whl", hash = "sha256:f78a8df21a79bcc30cfd400bdc38f314333de7c0fb619763f6b9dabab8268bb7"}, - {file = "importlib_resources-5.9.0.tar.gz", hash = "sha256:5481e97fb45af8dcf2f798952625591c58fe599d0735d86b10f54de086a61681"}, -] -jax = [ - {file = "jax-0.3.15.tar.gz", hash = "sha256:7ab82cb617dfa3ca94e3e8cc22ad51fcdd2e59391182635e7fcd570aafa9a6c2"}, -] -keras = [ - {file = "keras-2.6.0-py2.py3-none-any.whl", hash = "sha256:504af5656a9829fe803ce48a8580ef16916e89906aceddad9e098614269437e7"}, -] -keras-preprocessing = [ - {file = "Keras_Preprocessing-1.1.2-py2.py3-none-any.whl", hash = "sha256:7b82029b130ff61cc99b55f3bd27427df4838576838c5b2f65940e4fcec99a7b"}, - {file = "Keras_Preprocessing-1.1.2.tar.gz", hash = "sha256:add82567c50c8bc648c14195bf544a5ce7c1f76761536956c3d2978970179ef3"}, -] -kiwisolver = [ - {file = "kiwisolver-1.4.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2f5e60fabb7343a836360c4f0919b8cd0d6dbf08ad2ca6b9cf90bf0c76a3c4f6"}, - {file = "kiwisolver-1.4.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:10ee06759482c78bdb864f4109886dff7b8a56529bc1609d4f1112b93fe6423c"}, - {file = "kiwisolver-1.4.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c79ebe8f3676a4c6630fd3f777f3cfecf9289666c84e775a67d1d358578dc2e3"}, - {file = "kiwisolver-1.4.4-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:abbe9fa13da955feb8202e215c4018f4bb57469b1b78c7a4c5c7b93001699938"}, - {file = "kiwisolver-1.4.4-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:7577c1987baa3adc4b3c62c33bd1118c3ef5c8ddef36f0f2c950ae0b199e100d"}, - {file = "kiwisolver-1.4.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8ad8285b01b0d4695102546b342b493b3ccc6781fc28c8c6a1bb63e95d22f09"}, - {file = "kiwisolver-1.4.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8ed58b8acf29798b036d347791141767ccf65eee7f26bde03a71c944449e53de"}, - {file = "kiwisolver-1.4.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a68b62a02953b9841730db7797422f983935aeefceb1679f0fc85cbfbd311c32"}, - {file = "kiwisolver-1.4.4-cp310-cp310-win32.whl", hash = "sha256:e92a513161077b53447160b9bd8f522edfbed4bd9759e4c18ab05d7ef7e49408"}, - {file = "kiwisolver-1.4.4-cp310-cp310-win_amd64.whl", hash = "sha256:3fe20f63c9ecee44560d0e7f116b3a747a5d7203376abeea292ab3152334d004"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:62ac9cc684da4cf1778d07a89bf5f81b35834cb96ca523d3a7fb32509380cbf6"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:41dae968a94b1ef1897cb322b39360a0812661dba7c682aa45098eb8e193dbdf"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:02f79693ec433cb4b5f51694e8477ae83b3205768a6fb48ffba60549080e295b"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d0611a0a2a518464c05ddd5a3a1a0e856ccc10e67079bb17f265ad19ab3c7597"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:db5283d90da4174865d520e7366801a93777201e91e79bacbac6e6927cbceede"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:1041feb4cda8708ce73bb4dcb9ce1ccf49d553bf87c3954bdfa46f0c3f77252c"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-win32.whl", hash = "sha256:a553dadda40fef6bfa1456dc4be49b113aa92c2a9a9e8711e955618cd69622e3"}, - {file = "kiwisolver-1.4.4-cp37-cp37m-win_amd64.whl", hash = "sha256:03baab2d6b4a54ddbb43bba1a3a2d1627e82d205c5cf8f4c924dc49284b87166"}, - {file = "kiwisolver-1.4.4-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:841293b17ad704d70c578f1f0013c890e219952169ce8a24ebc063eecf775454"}, - {file = "kiwisolver-1.4.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f4f270de01dd3e129a72efad823da90cc4d6aafb64c410c9033aba70db9f1ff0"}, - {file = "kiwisolver-1.4.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f9f39e2f049db33a908319cf46624a569b36983c7c78318e9726a4cb8923b26c"}, - {file = "kiwisolver-1.4.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c97528e64cb9ebeff9701e7938653a9951922f2a38bd847787d4a8e498cc83ae"}, - {file = "kiwisolver-1.4.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d1573129aa0fd901076e2bfb4275a35f5b7aa60fbfb984499d661ec950320b0"}, - {file = "kiwisolver-1.4.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ad881edc7ccb9d65b0224f4e4d05a1e85cf62d73aab798943df6d48ab0cd79a1"}, - {file = "kiwisolver-1.4.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b428ef021242344340460fa4c9185d0b1f66fbdbfecc6c63eff4b7c29fad429d"}, - {file = "kiwisolver-1.4.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:2e407cb4bd5a13984a6c2c0fe1845e4e41e96f183e5e5cd4d77a857d9693494c"}, - {file = "kiwisolver-1.4.4-cp38-cp38-win32.whl", hash = "sha256:75facbe9606748f43428fc91a43edb46c7ff68889b91fa31f53b58894503a191"}, - {file = "kiwisolver-1.4.4-cp38-cp38-win_amd64.whl", hash = "sha256:5bce61af018b0cb2055e0e72e7d65290d822d3feee430b7b8203d8a855e78766"}, - {file = "kiwisolver-1.4.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:8c808594c88a025d4e322d5bb549282c93c8e1ba71b790f539567932722d7bd8"}, - {file = "kiwisolver-1.4.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f0a71d85ecdd570ded8ac3d1c0f480842f49a40beb423bb8014539a9f32a5897"}, - {file = "kiwisolver-1.4.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b533558eae785e33e8c148a8d9921692a9fe5aa516efbdff8606e7d87b9d5824"}, - {file = "kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:efda5fc8cc1c61e4f639b8067d118e742b812c930f708e6667a5ce0d13499e29"}, - {file = "kiwisolver-1.4.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:7c43e1e1206cd421cd92e6b3280d4385d41d7166b3ed577ac20444b6995a445f"}, - {file = "kiwisolver-1.4.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bc8d3bd6c72b2dd9decf16ce70e20abcb3274ba01b4e1c96031e0c4067d1e7cd"}, - {file = "kiwisolver-1.4.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4ea39b0ccc4f5d803e3337dd46bcce60b702be4d86fd0b3d7531ef10fd99a1ac"}, - {file = "kiwisolver-1.4.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:968f44fdbf6dd757d12920d63b566eeb4d5b395fd2d00d29d7ef00a00582aac9"}, - {file = "kiwisolver-1.4.4-cp39-cp39-win32.whl", hash = "sha256:da7e547706e69e45d95e116e6939488d62174e033b763ab1496b4c29b76fabea"}, - {file = "kiwisolver-1.4.4-cp39-cp39-win_amd64.whl", hash = "sha256:ba59c92039ec0a66103b1d5fe588fa546373587a7d68f5c96f743c3396afc04b"}, - {file = "kiwisolver-1.4.4-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:91672bacaa030f92fc2f43b620d7b337fd9a5af28b0d6ed3f77afc43c4a64b5a"}, - {file = "kiwisolver-1.4.4-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:787518a6789009c159453da4d6b683f468ef7a65bbde796bcea803ccf191058d"}, - {file = "kiwisolver-1.4.4-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da152d8cdcab0e56e4f45eb08b9aea6455845ec83172092f09b0e077ece2cf7a"}, - {file = "kiwisolver-1.4.4-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ecb1fa0db7bf4cff9dac752abb19505a233c7f16684c5826d1f11ebd9472b871"}, - {file = "kiwisolver-1.4.4.tar.gz", hash = "sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955"}, -] -markdown = [ - {file = "Markdown-3.4.1-py3-none-any.whl", hash = "sha256:08fb8465cffd03d10b9dd34a5c3fea908e20391a2a90b88d66362cb05beed186"}, - {file = "Markdown-3.4.1.tar.gz", hash = "sha256:3b809086bb6efad416156e00a0da66fe47618a5d6918dd688f53f40c8e4cfeff"}, -] -markupsafe = [ - {file = "MarkupSafe-2.1.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-win32.whl", hash = "sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6"}, - {file = "MarkupSafe-2.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-win32.whl", hash = "sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff"}, - {file = "MarkupSafe-2.1.1-cp37-cp37m-win_amd64.whl", hash = "sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-win32.whl", hash = "sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1"}, - {file = "MarkupSafe-2.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-win32.whl", hash = "sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c"}, - {file = "MarkupSafe-2.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247"}, - {file = "MarkupSafe-2.1.1.tar.gz", hash = "sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b"}, -] -matplotlib = [ - {file = "matplotlib-3.5.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a206a1b762b39398efea838f528b3a6d60cdb26fe9d58b48265787e29cd1d693"}, - {file = "matplotlib-3.5.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cd45a6f3e93a780185f70f05cf2a383daed13c3489233faad83e81720f7ede24"}, - {file = "matplotlib-3.5.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d62880e1f60e5a30a2a8484432bcb3a5056969dc97258d7326ad465feb7ae069"}, - {file = "matplotlib-3.5.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9ab29589cef03bc88acfa3a1490359000c18186fc30374d8aa77d33cc4a51a4a"}, - {file = "matplotlib-3.5.3-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2886cc009f40e2984c083687251821f305d811d38e3df8ded414265e4583f0c5"}, - {file = "matplotlib-3.5.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c995f7d9568f18b5db131ab124c64e51b6820a92d10246d4f2b3f3a66698a15b"}, - {file = "matplotlib-3.5.3-cp310-cp310-win32.whl", hash = "sha256:6bb93a0492d68461bd458eba878f52fdc8ac7bdb6c4acdfe43dba684787838c2"}, - {file = "matplotlib-3.5.3-cp310-cp310-win_amd64.whl", hash = "sha256:2e6d184ebe291b9e8f7e78bbab7987d269c38ea3e062eace1fe7d898042ef804"}, - {file = "matplotlib-3.5.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6ea6aef5c4338e58d8d376068e28f80a24f54e69f09479d1c90b7172bad9f25b"}, - {file = "matplotlib-3.5.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:839d47b8ead7ad9669aaacdbc03f29656dc21f0d41a6fea2d473d856c39c8b1c"}, - {file = "matplotlib-3.5.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3b4fa56159dc3c7f9250df88f653f085068bcd32dcd38e479bba58909254af7f"}, - {file = "matplotlib-3.5.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:94ff86af56a3869a4ae26a9637a849effd7643858a1a04dd5ee50e9ab75069a7"}, - {file = "matplotlib-3.5.3-cp37-cp37m-win32.whl", hash = "sha256:35a8ad4dddebd51f94c5d24bec689ec0ec66173bf614374a1244c6241c1595e0"}, - {file = "matplotlib-3.5.3-cp37-cp37m-win_amd64.whl", hash = "sha256:43e9d3fa077bf0cc95ded13d331d2156f9973dce17c6f0c8b49ccd57af94dbd9"}, - {file = "matplotlib-3.5.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:22227c976ad4dc8c5a5057540421f0d8708c6560744ad2ad638d48e2984e1dbc"}, - {file = "matplotlib-3.5.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bf618a825deb6205f015df6dfe6167a5d9b351203b03fab82043ae1d30f16511"}, - {file = "matplotlib-3.5.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:9befa5954cdbc085e37d974ff6053da269474177921dd61facdad8023c4aeb51"}, - {file = "matplotlib-3.5.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3840c280ebc87a48488a46f760ea1c0c0c83fcf7abbe2e6baf99d033fd35fd8"}, - {file = "matplotlib-3.5.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dacddf5bfcec60e3f26ec5c0ae3d0274853a258b6c3fc5ef2f06a8eb23e042be"}, - {file = "matplotlib-3.5.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:b428076a55fb1c084c76cb93e68006f27d247169f056412607c5c88828d08f88"}, - {file = "matplotlib-3.5.3-cp38-cp38-win32.whl", hash = "sha256:874df7505ba820e0400e7091199decf3ff1fde0583652120c50cd60d5820ca9a"}, - {file = "matplotlib-3.5.3-cp38-cp38-win_amd64.whl", hash = "sha256:b28de401d928890187c589036857a270a032961411934bdac4cf12dde3d43094"}, - {file = "matplotlib-3.5.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:3211ba82b9f1518d346f6309df137b50c3dc4421b4ed4815d1d7eadc617f45a1"}, - {file = "matplotlib-3.5.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6fe807e8a22620b4cd95cfbc795ba310dc80151d43b037257250faf0bfcd82bc"}, - {file = "matplotlib-3.5.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5c096363b206a3caf43773abebdbb5a23ea13faef71d701b21a9c27fdcef72f4"}, - {file = "matplotlib-3.5.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0bcdfcb0f976e1bac6721d7d457c17be23cf7501f977b6a38f9d38a3762841f7"}, - {file = "matplotlib-3.5.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1e64ac9be9da6bfff0a732e62116484b93b02a0b4d4b19934fb4f8e7ad26ad6a"}, - {file = "matplotlib-3.5.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:73dd93dc35c85dece610cca8358003bf0760d7986f70b223e2306b4ea6d1406b"}, - {file = "matplotlib-3.5.3-cp39-cp39-win32.whl", hash = "sha256:879c7e5fce4939c6aa04581dfe08d57eb6102a71f2e202e3314d5fbc072fd5a0"}, - {file = "matplotlib-3.5.3-cp39-cp39-win_amd64.whl", hash = "sha256:ab8d26f07fe64f6f6736d635cce7bfd7f625320490ed5bfc347f2cdb4fae0e56"}, - {file = "matplotlib-3.5.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:99482b83ebf4eb6d5fc6813d7aacdefdd480f0d9c0b52dcf9f1cc3b2c4b3361a"}, - {file = "matplotlib-3.5.3-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f814504e459c68118bf2246a530ed953ebd18213dc20e3da524174d84ed010b2"}, - {file = "matplotlib-3.5.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:57f1b4e69f438a99bb64d7f2c340db1b096b41ebaa515cf61ea72624279220ce"}, - {file = "matplotlib-3.5.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:d2484b350bf3d32cae43f85dcfc89b3ed7bd2bcd781ef351f93eb6fb2cc483f9"}, - {file = "matplotlib-3.5.3.tar.gz", hash = "sha256:339cac48b80ddbc8bfd05daae0a3a73414651a8596904c2a881cfd1edb65f26c"}, -] -nodeenv = [] -numpy = [ - {file = "numpy-1.19.5-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:cc6bd4fd593cb261332568485e20a0712883cf631f6f5e8e86a52caa8b2b50ff"}, - {file = "numpy-1.19.5-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:aeb9ed923be74e659984e321f609b9ba54a48354bfd168d21a2b072ed1e833ea"}, - {file = "numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:8b5e972b43c8fc27d56550b4120fe6257fdc15f9301914380b27f74856299fea"}, - {file = "numpy-1.19.5-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:43d4c81d5ffdff6bae58d66a3cd7f54a7acd9a0e7b18d97abb255defc09e3140"}, - {file = "numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:a4646724fba402aa7504cd48b4b50e783296b5e10a524c7a6da62e4a8ac9698d"}, - {file = "numpy-1.19.5-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:2e55195bc1c6b705bfd8ad6f288b38b11b1af32f3c8289d6c50d47f950c12e76"}, - {file = "numpy-1.19.5-cp36-cp36m-win32.whl", hash = "sha256:39b70c19ec771805081578cc936bbe95336798b7edf4732ed102e7a43ec5c07a"}, - {file = "numpy-1.19.5-cp36-cp36m-win_amd64.whl", hash = "sha256:dbd18bcf4889b720ba13a27ec2f2aac1981bd41203b3a3b27ba7a33f88ae4827"}, - {file = "numpy-1.19.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:603aa0706be710eea8884af807b1b3bc9fb2e49b9f4da439e76000f3b3c6ff0f"}, - {file = "numpy-1.19.5-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:cae865b1cae1ec2663d8ea56ef6ff185bad091a5e33ebbadd98de2cfa3fa668f"}, - {file = "numpy-1.19.5-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:36674959eed6957e61f11c912f71e78857a8d0604171dfd9ce9ad5cbf41c511c"}, - {file = "numpy-1.19.5-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:06fab248a088e439402141ea04f0fffb203723148f6ee791e9c75b3e9e82f080"}, - {file = "numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:6149a185cece5ee78d1d196938b2a8f9d09f5a5ebfbba66969302a778d5ddd1d"}, - {file = "numpy-1.19.5-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:50a4a0ad0111cc1b71fa32dedd05fa239f7fb5a43a40663269bb5dc7877cfd28"}, - {file = "numpy-1.19.5-cp37-cp37m-win32.whl", hash = "sha256:d051ec1c64b85ecc69531e1137bb9751c6830772ee5c1c426dbcfe98ef5788d7"}, - {file = "numpy-1.19.5-cp37-cp37m-win_amd64.whl", hash = "sha256:a12ff4c8ddfee61f90a1633a4c4afd3f7bcb32b11c52026c92a12e1325922d0d"}, - {file = "numpy-1.19.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:cf2402002d3d9f91c8b01e66fbb436a4ed01c6498fffed0e4c7566da1d40ee1e"}, - {file = "numpy-1.19.5-cp38-cp38-manylinux1_i686.whl", hash = "sha256:1ded4fce9cfaaf24e7a0ab51b7a87be9038ea1ace7f34b841fe3b6894c721d1c"}, - {file = "numpy-1.19.5-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:012426a41bc9ab63bb158635aecccc7610e3eff5d31d1eb43bc099debc979d94"}, - {file = "numpy-1.19.5-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:759e4095edc3c1b3ac031f34d9459fa781777a93ccc633a472a5468587a190ff"}, - {file = "numpy-1.19.5-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:a9d17f2be3b427fbb2bce61e596cf555d6f8a56c222bd2ca148baeeb5e5c783c"}, - {file = "numpy-1.19.5-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:99abf4f353c3d1a0c7a5f27699482c987cf663b1eac20db59b8c7b061eabd7fc"}, - {file = "numpy-1.19.5-cp38-cp38-win32.whl", hash = "sha256:384ec0463d1c2671170901994aeb6dce126de0a95ccc3976c43b0038a37329c2"}, - {file = "numpy-1.19.5-cp38-cp38-win_amd64.whl", hash = "sha256:811daee36a58dc79cf3d8bdd4a490e4277d0e4b7d103a001a4e73ddb48e7e6aa"}, - {file = "numpy-1.19.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c843b3f50d1ab7361ca4f0b3639bf691569493a56808a0b0c54a051d260b7dbd"}, - {file = "numpy-1.19.5-cp39-cp39-manylinux1_i686.whl", hash = "sha256:d6631f2e867676b13026e2846180e2c13c1e11289d67da08d71cacb2cd93d4aa"}, - {file = "numpy-1.19.5-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7fb43004bce0ca31d8f13a6eb5e943fa73371381e53f7074ed21a4cb786c32f8"}, - {file = "numpy-1.19.5-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:2ea52bd92ab9f768cc64a4c3ef8f4b2580a17af0a5436f6126b08efbd1838371"}, - {file = "numpy-1.19.5-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:400580cbd3cff6ffa6293df2278c75aef2d58d8d93d3c5614cd67981dae68ceb"}, - {file = "numpy-1.19.5-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:df609c82f18c5b9f6cb97271f03315ff0dbe481a2a02e56aeb1b1a985ce38e60"}, - {file = "numpy-1.19.5-cp39-cp39-win32.whl", hash = "sha256:ab83f24d5c52d60dbc8cd0528759532736b56db58adaa7b5f1f76ad551416a1e"}, - {file = "numpy-1.19.5-cp39-cp39-win_amd64.whl", hash = "sha256:0eef32ca3132a48e43f6a0f5a82cb508f22ce5a3d6f67a8329c81c8e226d3f6e"}, - {file = "numpy-1.19.5-pp36-pypy36_pp73-manylinux2010_x86_64.whl", hash = "sha256:a0d53e51a6cb6f0d9082decb7a4cb6dfb33055308c4c44f53103c073f649af73"}, - {file = "numpy-1.19.5.zip", hash = "sha256:a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4"}, -] -oauthlib = [ - {file = "oauthlib-3.2.0-py3-none-any.whl", hash = "sha256:6db33440354787f9b7f3a6dbd4febf5d0f93758354060e802f6c06cb493022fe"}, - {file = "oauthlib-3.2.0.tar.gz", hash = "sha256:23a8208d75b902797ea29fd31fa80a15ed9dc2c6c16fe73f5d346f83f6fa27a2"}, -] -opt-einsum = [ - {file = "opt_einsum-3.3.0-py3-none-any.whl", hash = "sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147"}, - {file = "opt_einsum-3.3.0.tar.gz", hash = "sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549"}, -] -packaging = [ - {file = "packaging-21.3-py3-none-any.whl", hash = "sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522"}, - {file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"}, -] -pandas = [ - {file = "pandas-1.1.5-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:bf23a3b54d128b50f4f9d4675b3c1857a688cc6731a32f931837d72effb2698d"}, - {file = "pandas-1.1.5-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:5a780260afc88268a9d3ac3511d8f494fdcf637eece62fb9eb656a63d53eb7ca"}, - {file = "pandas-1.1.5-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:b61080750d19a0122469ab59b087380721d6b72a4e7d962e4d7e63e0c4504814"}, - {file = "pandas-1.1.5-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:0de3ddb414d30798cbf56e642d82cac30a80223ad6fe484d66c0ce01a84d6f2f"}, - {file = "pandas-1.1.5-cp36-cp36m-win32.whl", hash = "sha256:70865f96bb38fec46f7ebd66d4b5cfd0aa6b842073f298d621385ae3898d28b5"}, - {file = "pandas-1.1.5-cp36-cp36m-win_amd64.whl", hash = "sha256:19a2148a1d02791352e9fa637899a78e371a3516ac6da5c4edc718f60cbae648"}, - {file = "pandas-1.1.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:26fa92d3ac743a149a31b21d6f4337b0594b6302ea5575b37af9ca9611e8981a"}, - {file = "pandas-1.1.5-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:c16d59c15d946111d2716856dd5479221c9e4f2f5c7bc2d617f39d870031e086"}, - {file = "pandas-1.1.5-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:3be7a7a0ca71a2640e81d9276f526bca63505850add10206d0da2e8a0a325dae"}, - {file = "pandas-1.1.5-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:573fba5b05bf2c69271a32e52399c8de599e4a15ab7cec47d3b9c904125ab788"}, - {file = "pandas-1.1.5-cp37-cp37m-win32.whl", hash = "sha256:21b5a2b033380adbdd36b3116faaf9a4663e375325831dac1b519a44f9e439bb"}, - {file = "pandas-1.1.5-cp37-cp37m-win_amd64.whl", hash = "sha256:24c7f8d4aee71bfa6401faeba367dd654f696a77151a8a28bc2013f7ced4af98"}, - {file = "pandas-1.1.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2860a97cbb25444ffc0088b457da0a79dc79f9c601238a3e0644312fcc14bf11"}, - {file = "pandas-1.1.5-cp38-cp38-manylinux1_i686.whl", hash = "sha256:5008374ebb990dad9ed48b0f5d0038124c73748f5384cc8c46904dace27082d9"}, - {file = "pandas-1.1.5-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:2c2f7c670ea4e60318e4b7e474d56447cf0c7d83b3c2a5405a0dbb2600b9c48e"}, - {file = "pandas-1.1.5-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:0a643bae4283a37732ddfcecab3f62dd082996021b980f580903f4e8e01b3c5b"}, - {file = "pandas-1.1.5-cp38-cp38-win32.whl", hash = "sha256:5447ea7af4005b0daf695a316a423b96374c9c73ffbd4533209c5ddc369e644b"}, - {file = "pandas-1.1.5-cp38-cp38-win_amd64.whl", hash = "sha256:4c62e94d5d49db116bef1bd5c2486723a292d79409fc9abd51adf9e05329101d"}, - {file = "pandas-1.1.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:731568be71fba1e13cae212c362f3d2ca8932e83cb1b85e3f1b4dd77d019254a"}, - {file = "pandas-1.1.5-cp39-cp39-manylinux1_i686.whl", hash = "sha256:c61c043aafb69329d0f961b19faa30b1dab709dd34c9388143fc55680059e55a"}, - {file = "pandas-1.1.5-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:2b1c6cd28a0dfda75c7b5957363333f01d370936e4c6276b7b8e696dd500582a"}, - {file = "pandas-1.1.5-cp39-cp39-win32.whl", hash = "sha256:c94ff2780a1fd89f190390130d6d36173ca59fcfb3fe0ff596f9a56518191ccb"}, - {file = "pandas-1.1.5-cp39-cp39-win_amd64.whl", hash = "sha256:edda9bacc3843dfbeebaf7a701763e68e741b08fccb889c003b0a52f0ee95782"}, - {file = "pandas-1.1.5.tar.gz", hash = "sha256:f10fc41ee3c75a474d3bdf68d396f10782d013d7f67db99c0efbfd0acb99701b"}, -] -pillow = [ - {file = "Pillow-9.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:a9c9bc489f8ab30906d7a85afac4b4944a572a7432e00698a7239f44a44e6efb"}, - {file = "Pillow-9.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:510cef4a3f401c246cfd8227b300828715dd055463cdca6176c2e4036df8bd4f"}, - {file = "Pillow-9.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7888310f6214f19ab2b6df90f3f06afa3df7ef7355fc025e78a3044737fab1f5"}, - {file = "Pillow-9.2.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:831e648102c82f152e14c1a0938689dbb22480c548c8d4b8b248b3e50967b88c"}, - {file = "Pillow-9.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1cc1d2451e8a3b4bfdb9caf745b58e6c7a77d2e469159b0d527a4554d73694d1"}, - {file = "Pillow-9.2.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:136659638f61a251e8ed3b331fc6ccd124590eeff539de57c5f80ef3a9594e58"}, - {file = "Pillow-9.2.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:6e8c66f70fb539301e064f6478d7453e820d8a2c631da948a23384865cd95544"}, - {file = "Pillow-9.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:37ff6b522a26d0538b753f0b4e8e164fdada12db6c6f00f62145d732d8a3152e"}, - {file = "Pillow-9.2.0-cp310-cp310-win32.whl", hash = "sha256:c79698d4cd9318d9481d89a77e2d3fcaeff5486be641e60a4b49f3d2ecca4e28"}, - {file = "Pillow-9.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:254164c57bab4b459f14c64e93df11eff5ded575192c294a0c49270f22c5d93d"}, - {file = "Pillow-9.2.0-cp311-cp311-macosx_10_10_universal2.whl", hash = "sha256:408673ed75594933714482501fe97e055a42996087eeca7e5d06e33218d05aa8"}, - {file = "Pillow-9.2.0-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:727dd1389bc5cb9827cbd1f9d40d2c2a1a0c9b32dd2261db522d22a604a6eec9"}, - {file = "Pillow-9.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50dff9cc21826d2977ef2d2a205504034e3a4563ca6f5db739b0d1026658e004"}, - {file = "Pillow-9.2.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cb6259196a589123d755380b65127ddc60f4c64b21fc3bb46ce3a6ea663659b0"}, - {file = "Pillow-9.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b0554af24df2bf96618dac71ddada02420f946be943b181108cac55a7a2dcd4"}, - {file = "Pillow-9.2.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:15928f824870535c85dbf949c09d6ae7d3d6ac2d6efec80f3227f73eefba741c"}, - {file = "Pillow-9.2.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:bdd0de2d64688ecae88dd8935012c4a72681e5df632af903a1dca8c5e7aa871a"}, - {file = "Pillow-9.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d5b87da55a08acb586bad5c3aa3b86505f559b84f39035b233d5bf844b0834b1"}, - {file = "Pillow-9.2.0-cp311-cp311-win32.whl", hash = "sha256:b6d5e92df2b77665e07ddb2e4dbd6d644b78e4c0d2e9272a852627cdba0d75cf"}, - {file = "Pillow-9.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:6bf088c1ce160f50ea40764f825ec9b72ed9da25346216b91361eef8ad1b8f8c"}, - {file = "Pillow-9.2.0-cp37-cp37m-macosx_10_10_x86_64.whl", hash = "sha256:2c58b24e3a63efd22554c676d81b0e57f80e0a7d3a5874a7e14ce90ec40d3069"}, - {file = "Pillow-9.2.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eef7592281f7c174d3d6cbfbb7ee5984a671fcd77e3fc78e973d492e9bf0eb3f"}, - {file = "Pillow-9.2.0-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dcd7b9c7139dc8258d164b55696ecd16c04607f1cc33ba7af86613881ffe4ac8"}, - {file = "Pillow-9.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a138441e95562b3c078746a22f8fca8ff1c22c014f856278bdbdd89ca36cff1b"}, - {file = "Pillow-9.2.0-cp37-cp37m-manylinux_2_28_aarch64.whl", hash = "sha256:93689632949aff41199090eff5474f3990b6823404e45d66a5d44304e9cdc467"}, - {file = "Pillow-9.2.0-cp37-cp37m-manylinux_2_28_x86_64.whl", hash = "sha256:f3fac744f9b540148fa7715a435d2283b71f68bfb6d4aae24482a890aed18b59"}, - {file = "Pillow-9.2.0-cp37-cp37m-win32.whl", hash = "sha256:fa768eff5f9f958270b081bb33581b4b569faabf8774726b283edb06617101dc"}, - {file = "Pillow-9.2.0-cp37-cp37m-win_amd64.whl", hash = "sha256:69bd1a15d7ba3694631e00df8de65a8cb031911ca11f44929c97fe05eb9b6c1d"}, - {file = "Pillow-9.2.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:030e3460861488e249731c3e7ab59b07c7853838ff3b8e16aac9561bb345da14"}, - {file = "Pillow-9.2.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:74a04183e6e64930b667d321524e3c5361094bb4af9083db5c301db64cd341f3"}, - {file = "Pillow-9.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2d33a11f601213dcd5718109c09a52c2a1c893e7461f0be2d6febc2879ec2402"}, - {file = "Pillow-9.2.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fd6f5e3c0e4697fa7eb45b6e93996299f3feee73a3175fa451f49a74d092b9f"}, - {file = "Pillow-9.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a647c0d4478b995c5e54615a2e5360ccedd2f85e70ab57fbe817ca613d5e63b8"}, - {file = "Pillow-9.2.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:4134d3f1ba5f15027ff5c04296f13328fecd46921424084516bdb1b2548e66ff"}, - {file = "Pillow-9.2.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:bc431b065722a5ad1dfb4df354fb9333b7a582a5ee39a90e6ffff688d72f27a1"}, - {file = "Pillow-9.2.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:1536ad017a9f789430fb6b8be8bf99d2f214c76502becc196c6f2d9a75b01b76"}, - {file = "Pillow-9.2.0-cp38-cp38-win32.whl", hash = "sha256:2ad0d4df0f5ef2247e27fc790d5c9b5a0af8ade9ba340db4a73bb1a4a3e5fb4f"}, - {file = "Pillow-9.2.0-cp38-cp38-win_amd64.whl", hash = "sha256:ec52c351b35ca269cb1f8069d610fc45c5bd38c3e91f9ab4cbbf0aebc136d9c8"}, - {file = "Pillow-9.2.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:0ed2c4ef2451de908c90436d6e8092e13a43992f1860275b4d8082667fbb2ffc"}, - {file = "Pillow-9.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4ad2f835e0ad81d1689f1b7e3fbac7b01bb8777d5a985c8962bedee0cc6d43da"}, - {file = "Pillow-9.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ea98f633d45f7e815db648fd7ff0f19e328302ac36427343e4432c84432e7ff4"}, - {file = "Pillow-9.2.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7761afe0126d046974a01e030ae7529ed0ca6a196de3ec6937c11df0df1bc91c"}, - {file = "Pillow-9.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a54614049a18a2d6fe156e68e188da02a046a4a93cf24f373bffd977e943421"}, - {file = "Pillow-9.2.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:5aed7dde98403cd91d86a1115c78d8145c83078e864c1de1064f52e6feb61b20"}, - {file = "Pillow-9.2.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:13b725463f32df1bfeacbf3dd197fb358ae8ebcd8c5548faa75126ea425ccb60"}, - {file = "Pillow-9.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:808add66ea764ed97d44dda1ac4f2cfec4c1867d9efb16a33d158be79f32b8a4"}, - {file = "Pillow-9.2.0-cp39-cp39-win32.whl", hash = "sha256:337a74fd2f291c607d220c793a8135273c4c2ab001b03e601c36766005f36885"}, - {file = "Pillow-9.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:fac2d65901fb0fdf20363fbd345c01958a742f2dc62a8dd4495af66e3ff502a4"}, - {file = "Pillow-9.2.0-pp37-pypy37_pp73-macosx_10_10_x86_64.whl", hash = "sha256:ad2277b185ebce47a63f4dc6302e30f05762b688f8dc3de55dbae4651872cdf3"}, - {file = "Pillow-9.2.0-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7c7b502bc34f6e32ba022b4a209638f9e097d7a9098104ae420eb8186217ebbb"}, - {file = "Pillow-9.2.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d1f14f5f691f55e1b47f824ca4fdcb4b19b4323fe43cc7bb105988cad7496be"}, - {file = "Pillow-9.2.0-pp37-pypy37_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:dfe4c1fedfde4e2fbc009d5ad420647f7730d719786388b7de0999bf32c0d9fd"}, - {file = "Pillow-9.2.0-pp38-pypy38_pp73-macosx_10_10_x86_64.whl", hash = "sha256:f07f1f00e22b231dd3d9b9208692042e29792d6bd4f6639415d2f23158a80013"}, - {file = "Pillow-9.2.0-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1802f34298f5ba11d55e5bb09c31997dc0c6aed919658dfdf0198a2fe75d5490"}, - {file = "Pillow-9.2.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:17d4cafe22f050b46d983b71c707162d63d796a1235cdf8b9d7a112e97b15bac"}, - {file = "Pillow-9.2.0-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:96b5e6874431df16aee0c1ba237574cb6dff1dcb173798faa6a9d8b399a05d0e"}, - {file = "Pillow-9.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:0030fdbd926fb85844b8b92e2f9449ba89607231d3dd597a21ae72dc7fe26927"}, - {file = "Pillow-9.2.0.tar.gz", hash = "sha256:75e636fd3e0fb872693f23ccb8a5ff2cd578801251f3a4f6854c6a5d437d3c04"}, -] -platformdirs = [ - {file = "platformdirs-2.5.2-py3-none-any.whl", hash = "sha256:027d8e83a2d7de06bbac4e5ef7e023c02b863d7ea5d079477e722bb41ab25788"}, - {file = "platformdirs-2.5.2.tar.gz", hash = "sha256:58c8abb07dcb441e6ee4b11d8df0ac856038f944ab98b7be6b27b2a3c7feef19"}, -] -pre-commit = [] -protobuf = [ - {file = "protobuf-3.19.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f51d5a9f137f7a2cec2d326a74b6e3fc79d635d69ffe1b036d39fc7d75430d37"}, - {file = "protobuf-3.19.4-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:09297b7972da685ce269ec52af761743714996b4381c085205914c41fcab59fb"}, - {file = "protobuf-3.19.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:072fbc78d705d3edc7ccac58a62c4c8e0cec856987da7df8aca86e647be4e35c"}, - {file = "protobuf-3.19.4-cp310-cp310-win32.whl", hash = "sha256:7bb03bc2873a2842e5ebb4801f5c7ff1bfbdf426f85d0172f7644fcda0671ae0"}, - {file = "protobuf-3.19.4-cp310-cp310-win_amd64.whl", hash = "sha256:f358aa33e03b7a84e0d91270a4d4d8f5df6921abe99a377828839e8ed0c04e07"}, - {file = "protobuf-3.19.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:1c91ef4110fdd2c590effb5dca8fdbdcb3bf563eece99287019c4204f53d81a4"}, - {file = "protobuf-3.19.4-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c438268eebb8cf039552897d78f402d734a404f1360592fef55297285f7f953f"}, - {file = "protobuf-3.19.4-cp36-cp36m-win32.whl", hash = "sha256:835a9c949dc193953c319603b2961c5c8f4327957fe23d914ca80d982665e8ee"}, - {file = "protobuf-3.19.4-cp36-cp36m-win_amd64.whl", hash = "sha256:4276cdec4447bd5015453e41bdc0c0c1234eda08420b7c9a18b8d647add51e4b"}, - {file = "protobuf-3.19.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6cbc312be5e71869d9d5ea25147cdf652a6781cf4d906497ca7690b7b9b5df13"}, - {file = "protobuf-3.19.4-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:54a1473077f3b616779ce31f477351a45b4fef8c9fd7892d6d87e287a38df368"}, - {file = "protobuf-3.19.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:435bb78b37fc386f9275a7035fe4fb1364484e38980d0dd91bc834a02c5ec909"}, - {file = "protobuf-3.19.4-cp37-cp37m-win32.whl", hash = "sha256:16f519de1313f1b7139ad70772e7db515b1420d208cb16c6d7858ea989fc64a9"}, - {file = "protobuf-3.19.4-cp37-cp37m-win_amd64.whl", hash = "sha256:cdc076c03381f5c1d9bb1abdcc5503d9ca8b53cf0a9d31a9f6754ec9e6c8af0f"}, - {file = "protobuf-3.19.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:69da7d39e39942bd52848438462674c463e23963a1fdaa84d88df7fbd7e749b2"}, - {file = "protobuf-3.19.4-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:48ed3877fa43e22bcacc852ca76d4775741f9709dd9575881a373bd3e85e54b2"}, - {file = "protobuf-3.19.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bd95d1dfb9c4f4563e6093a9aa19d9c186bf98fa54da5252531cc0d3a07977e7"}, - {file = "protobuf-3.19.4-cp38-cp38-win32.whl", hash = "sha256:b38057450a0c566cbd04890a40edf916db890f2818e8682221611d78dc32ae26"}, - {file = "protobuf-3.19.4-cp38-cp38-win_amd64.whl", hash = "sha256:7ca7da9c339ca8890d66958f5462beabd611eca6c958691a8fe6eccbd1eb0c6e"}, - {file = "protobuf-3.19.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:36cecbabbda242915529b8ff364f2263cd4de7c46bbe361418b5ed859677ba58"}, - {file = "protobuf-3.19.4-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:c1068287025f8ea025103e37d62ffd63fec8e9e636246b89c341aeda8a67c934"}, - {file = "protobuf-3.19.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:96bd766831596d6014ca88d86dc8fe0fb2e428c0b02432fd9db3943202bf8c5e"}, - {file = "protobuf-3.19.4-cp39-cp39-win32.whl", hash = "sha256:84123274d982b9e248a143dadd1b9815049f4477dc783bf84efe6250eb4b836a"}, - {file = "protobuf-3.19.4-cp39-cp39-win_amd64.whl", hash = "sha256:3112b58aac3bac9c8be2b60a9daf6b558ca3f7681c130dcdd788ade7c9ffbdca"}, - {file = "protobuf-3.19.4-py2.py3-none-any.whl", hash = "sha256:8961c3a78ebfcd000920c9060a262f082f29838682b1f7201889300c1fbe0616"}, - {file = "protobuf-3.19.4.tar.gz", hash = "sha256:9df0c10adf3e83015ced42a9a7bd64e13d06c4cf45c340d2c63020ea04499d0a"}, -] -psutil = [ - {file = "psutil-5.9.1-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:799759d809c31aab5fe4579e50addf84565e71c1dc9f1c31258f159ff70d3f87"}, - {file = "psutil-5.9.1-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:9272167b5f5fbfe16945be3db475b3ce8d792386907e673a209da686176552af"}, - {file = "psutil-5.9.1-cp27-cp27m-win32.whl", hash = "sha256:0904727e0b0a038830b019551cf3204dd48ef5c6868adc776e06e93d615fc5fc"}, - {file = "psutil-5.9.1-cp27-cp27m-win_amd64.whl", hash = "sha256:e7e10454cb1ab62cc6ce776e1c135a64045a11ec4c6d254d3f7689c16eb3efd2"}, - {file = "psutil-5.9.1-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:56960b9e8edcca1456f8c86a196f0c3d8e3e361320071c93378d41445ffd28b0"}, - {file = "psutil-5.9.1-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:44d1826150d49ffd62035785a9e2c56afcea66e55b43b8b630d7706276e87f22"}, - {file = "psutil-5.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c7be9d7f5b0d206f0bbc3794b8e16fb7dbc53ec9e40bbe8787c6f2d38efcf6c9"}, - {file = "psutil-5.9.1-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abd9246e4cdd5b554a2ddd97c157e292ac11ef3e7af25ac56b08b455c829dca8"}, - {file = "psutil-5.9.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:29a442e25fab1f4d05e2655bb1b8ab6887981838d22effa2396d584b740194de"}, - {file = "psutil-5.9.1-cp310-cp310-win32.whl", hash = "sha256:20b27771b077dcaa0de1de3ad52d22538fe101f9946d6dc7869e6f694f079329"}, - {file = "psutil-5.9.1-cp310-cp310-win_amd64.whl", hash = "sha256:58678bbadae12e0db55186dc58f2888839228ac9f41cc7848853539b70490021"}, - {file = "psutil-5.9.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:3a76ad658641172d9c6e593de6fe248ddde825b5866464c3b2ee26c35da9d237"}, - {file = "psutil-5.9.1-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a6a11e48cb93a5fa606306493f439b4aa7c56cb03fc9ace7f6bfa21aaf07c453"}, - {file = "psutil-5.9.1-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:068935df39055bf27a29824b95c801c7a5130f118b806eee663cad28dca97685"}, - {file = "psutil-5.9.1-cp36-cp36m-win32.whl", hash = "sha256:0f15a19a05f39a09327345bc279c1ba4a8cfb0172cc0d3c7f7d16c813b2e7d36"}, - {file = "psutil-5.9.1-cp36-cp36m-win_amd64.whl", hash = "sha256:db417f0865f90bdc07fa30e1aadc69b6f4cad7f86324b02aa842034efe8d8c4d"}, - {file = "psutil-5.9.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:91c7ff2a40c373d0cc9121d54bc5f31c4fa09c346528e6a08d1845bce5771ffc"}, - {file = "psutil-5.9.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fea896b54f3a4ae6f790ac1d017101252c93f6fe075d0e7571543510f11d2676"}, - {file = "psutil-5.9.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3054e923204b8e9c23a55b23b6df73a8089ae1d075cb0bf711d3e9da1724ded4"}, - {file = "psutil-5.9.1-cp37-cp37m-win32.whl", hash = "sha256:d2d006286fbcb60f0b391741f520862e9b69f4019b4d738a2a45728c7e952f1b"}, - {file = "psutil-5.9.1-cp37-cp37m-win_amd64.whl", hash = "sha256:b14ee12da9338f5e5b3a3ef7ca58b3cba30f5b66f7662159762932e6d0b8f680"}, - {file = "psutil-5.9.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:19f36c16012ba9cfc742604df189f2f28d2720e23ff7d1e81602dbe066be9fd1"}, - {file = "psutil-5.9.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:944c4b4b82dc4a1b805329c980f270f170fdc9945464223f2ec8e57563139cf4"}, - {file = "psutil-5.9.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b6750a73a9c4a4e689490ccb862d53c7b976a2a35c4e1846d049dcc3f17d83b"}, - {file = "psutil-5.9.1-cp38-cp38-win32.whl", hash = "sha256:a8746bfe4e8f659528c5c7e9af5090c5a7d252f32b2e859c584ef7d8efb1e689"}, - {file = "psutil-5.9.1-cp38-cp38-win_amd64.whl", hash = "sha256:79c9108d9aa7fa6fba6e668b61b82facc067a6b81517cab34d07a84aa89f3df0"}, - {file = "psutil-5.9.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:28976df6c64ddd6320d281128817f32c29b539a52bdae5e192537bc338a9ec81"}, - {file = "psutil-5.9.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b88f75005586131276634027f4219d06e0561292be8bd6bc7f2f00bdabd63c4e"}, - {file = "psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:645bd4f7bb5b8633803e0b6746ff1628724668681a434482546887d22c7a9537"}, - {file = "psutil-5.9.1-cp39-cp39-win32.whl", hash = "sha256:32c52611756096ae91f5d1499fe6c53b86f4a9ada147ee42db4991ba1520e574"}, - {file = "psutil-5.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:f65f9a46d984b8cd9b3750c2bdb419b2996895b005aefa6cbaba9a143b1ce2c5"}, - {file = "psutil-5.9.1.tar.gz", hash = "sha256:57f1819b5d9e95cdfb0c881a8a5b7d542ed0b7c522d575706a80bedc848c8954"}, -] -pyarrow = [ - {file = "pyarrow-9.0.0-cp310-cp310-macosx_10_13_universal2.whl", hash = "sha256:767cafb14278165ad539a2918c14c1b73cf20689747c21375c38e3fe62884902"}, - {file = "pyarrow-9.0.0-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:0238998dc692efcb4e41ae74738d7c1234723271ccf520bd8312dca07d49ef8d"}, - {file = "pyarrow-9.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:55328348b9139c2b47450d512d716c2248fd58e2f04e2fc23a65e18726666d42"}, - {file = "pyarrow-9.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fc856628acd8d281652c15b6268ec7f27ebcb015abbe99d9baad17f02adc51f1"}, - {file = "pyarrow-9.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29eb3e086e2b26202f3a4678316b93cfb15d0e2ba20f3ec12db8fd9cc07cde63"}, - {file = "pyarrow-9.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e753f8fcf07d8e3a0efa0c8bd51fef5c90281ffd4c5637c08ce42cd0ac297de"}, - {file = "pyarrow-9.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:3eef8a981f45d89de403e81fb83b8119c20824caddf1404274e41a5d66c73806"}, - {file = "pyarrow-9.0.0-cp37-cp37m-macosx_10_13_x86_64.whl", hash = "sha256:7fa56cbd415cef912677270b8e41baad70cde04c6d8a8336eeb2aba85aa93706"}, - {file = "pyarrow-9.0.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:f8c46bde1030d704e2796182286d1c56846552c50a39ad5bf5a20c0d8159fc35"}, - {file = "pyarrow-9.0.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8ad430cee28ebc4d6661fc7315747c7a18ae2a74e67498dcb039e1c762a2fb67"}, - {file = "pyarrow-9.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:81a60bb291a964f63b2717fb1b28f6615ffab7e8585322bfb8a6738e6b321282"}, - {file = "pyarrow-9.0.0-cp37-cp37m-win_amd64.whl", hash = "sha256:9cef618159567d5f62040f2b79b1c7b38e3885f4ffad0ec97cd2d86f88b67cef"}, - {file = "pyarrow-9.0.0-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:5526a3bfb404ff6d31d62ea582cf2466c7378a474a99ee04d1a9b05de5264541"}, - {file = "pyarrow-9.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:da3e0f319509a5881867effd7024099fb06950a0768dad0d6873668bb88cfaba"}, - {file = "pyarrow-9.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2c715eca2092273dcccf6f08437371e04d112f9354245ba2fbe6c801879450b7"}, - {file = "pyarrow-9.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f11a645a41ee531c3a5edda45dea07c42267f52571f818d388971d33fc7e2d4a"}, - {file = "pyarrow-9.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5b390bdcfb8c5b900ef543f911cdfec63e88524fafbcc15f83767202a4a2491"}, - {file = "pyarrow-9.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:d9eb04db626fa24fdfb83c00f76679ca0d98728cdbaa0481b6402bf793a290c0"}, - {file = "pyarrow-9.0.0-cp39-cp39-macosx_10_13_universal2.whl", hash = "sha256:4eebdab05afa23d5d5274b24c1cbeb1ba017d67c280f7d39fd8a8f18cbad2ec9"}, - {file = "pyarrow-9.0.0-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:02b820ecd1da02012092c180447de449fc688d0c3f9ff8526ca301cdd60dacd0"}, - {file = "pyarrow-9.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:92f3977e901db1ef5cba30d6cc1d7942b8d94b910c60f89013e8f7bb86a86eef"}, - {file = "pyarrow-9.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f241bd488c2705df930eedfe304ada71191dcf67d6b98ceda0cc934fd2a8388e"}, - {file = "pyarrow-9.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c5a073a930c632058461547e0bc572da1e724b17b6b9eb31a97da13f50cb6e0"}, - {file = "pyarrow-9.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f59bcd5217a3ae1e17870792f82b2ff92df9f3862996e2c78e156c13e56ff62e"}, - {file = "pyarrow-9.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:fe2ce795fa1d95e4e940fe5661c3c58aee7181c730f65ac5dd8794a77228de59"}, - {file = "pyarrow-9.0.0.tar.gz", hash = "sha256:7fb02bebc13ab55573d1ae9bb5002a6d20ba767bf8569b52fce5301d42495ab7"}, -] -pyasn1 = [ - {file = "pyasn1-0.4.8-py2.4.egg", hash = "sha256:fec3e9d8e36808a28efb59b489e4528c10ad0f480e57dcc32b4de5c9d8c9fdf3"}, - {file = "pyasn1-0.4.8-py2.5.egg", hash = "sha256:0458773cfe65b153891ac249bcf1b5f8f320b7c2ce462151f8fa74de8934becf"}, - {file = "pyasn1-0.4.8-py2.6.egg", hash = "sha256:5c9414dcfede6e441f7e8f81b43b34e834731003427e5b09e4e00e3172a10f00"}, - {file = "pyasn1-0.4.8-py2.7.egg", hash = "sha256:6e7545f1a61025a4e58bb336952c5061697da694db1cae97b116e9c46abcf7c8"}, - {file = "pyasn1-0.4.8-py2.py3-none-any.whl", hash = "sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d"}, - {file = "pyasn1-0.4.8-py3.1.egg", hash = "sha256:78fa6da68ed2727915c4767bb386ab32cdba863caa7dbe473eaae45f9959da86"}, - {file = "pyasn1-0.4.8-py3.2.egg", hash = "sha256:08c3c53b75eaa48d71cf8c710312316392ed40899cb34710d092e96745a358b7"}, - {file = "pyasn1-0.4.8-py3.3.egg", hash = "sha256:03840c999ba71680a131cfaee6fab142e1ed9bbd9c693e285cc6aca0d555e576"}, - {file = "pyasn1-0.4.8-py3.4.egg", hash = "sha256:7ab8a544af125fb704feadb008c99a88805126fb525280b2270bb25cc1d78a12"}, - {file = "pyasn1-0.4.8-py3.5.egg", hash = "sha256:e89bf84b5437b532b0803ba5c9a5e054d21fec423a89952a74f87fa2c9b7bce2"}, - {file = "pyasn1-0.4.8-py3.6.egg", hash = "sha256:014c0e9976956a08139dc0712ae195324a75e142284d5f87f1a87ee1b068a359"}, - {file = "pyasn1-0.4.8-py3.7.egg", hash = "sha256:99fcc3c8d804d1bc6d9a099921e39d827026409a58f2a720dcdb89374ea0c776"}, - {file = "pyasn1-0.4.8.tar.gz", hash = "sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba"}, -] -pyasn1-modules = [ - {file = "pyasn1-modules-0.2.8.tar.gz", hash = "sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e"}, - {file = "pyasn1_modules-0.2.8-py2.4.egg", hash = "sha256:0fe1b68d1e486a1ed5473f1302bd991c1611d319bba158e98b106ff86e1d7199"}, - {file = "pyasn1_modules-0.2.8-py2.5.egg", hash = "sha256:fe0644d9ab041506b62782e92b06b8c68cca799e1a9636ec398675459e031405"}, - {file = "pyasn1_modules-0.2.8-py2.6.egg", hash = "sha256:a99324196732f53093a84c4369c996713eb8c89d360a496b599fb1a9c47fc3eb"}, - {file = "pyasn1_modules-0.2.8-py2.7.egg", hash = "sha256:0845a5582f6a02bb3e1bde9ecfc4bfcae6ec3210dd270522fee602365430c3f8"}, - {file = "pyasn1_modules-0.2.8-py2.py3-none-any.whl", hash = "sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74"}, - {file = "pyasn1_modules-0.2.8-py3.1.egg", hash = "sha256:f39edd8c4ecaa4556e989147ebf219227e2cd2e8a43c7e7fcb1f1c18c5fd6a3d"}, - {file = "pyasn1_modules-0.2.8-py3.2.egg", hash = "sha256:b80486a6c77252ea3a3e9b1e360bc9cf28eaac41263d173c032581ad2f20fe45"}, - {file = "pyasn1_modules-0.2.8-py3.3.egg", hash = "sha256:65cebbaffc913f4fe9e4808735c95ea22d7a7775646ab690518c056784bc21b4"}, - {file = "pyasn1_modules-0.2.8-py3.4.egg", hash = "sha256:15b7c67fabc7fc240d87fb9aabf999cf82311a6d6fb2c70d00d3d0604878c811"}, - {file = "pyasn1_modules-0.2.8-py3.5.egg", hash = "sha256:426edb7a5e8879f1ec54a1864f16b882c2837bfd06eee62f2c982315ee2473ed"}, - {file = "pyasn1_modules-0.2.8-py3.6.egg", hash = "sha256:cbac4bc38d117f2a49aeedec4407d23e8866ea4ac27ff2cf7fb3e5b570df19e0"}, - {file = "pyasn1_modules-0.2.8-py3.7.egg", hash = "sha256:c29a5e5cc7a3f05926aff34e097e84f8589cd790ce0ed41b67aed6857b26aafd"}, -] -pyparsing = [ - {file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"}, - {file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"}, -] -python-dateutil = [ - {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"}, - {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"}, -] -pytz = [] -pyyaml = [ - {file = "PyYAML-6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53"}, - {file = "PyYAML-6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c"}, - {file = "PyYAML-6.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc"}, - {file = "PyYAML-6.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b"}, - {file = "PyYAML-6.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5"}, - {file = "PyYAML-6.0-cp310-cp310-win32.whl", hash = "sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513"}, - {file = "PyYAML-6.0-cp310-cp310-win_amd64.whl", hash = "sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a"}, - {file = "PyYAML-6.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86"}, - {file = "PyYAML-6.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f"}, - {file = "PyYAML-6.0-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92"}, - {file = "PyYAML-6.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4"}, - {file = "PyYAML-6.0-cp36-cp36m-win32.whl", hash = "sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293"}, - {file = "PyYAML-6.0-cp36-cp36m-win_amd64.whl", hash = "sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57"}, - {file = "PyYAML-6.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c"}, - {file = "PyYAML-6.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0"}, - {file = "PyYAML-6.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4"}, - {file = "PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9"}, - {file = "PyYAML-6.0-cp37-cp37m-win32.whl", hash = "sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737"}, - {file = "PyYAML-6.0-cp37-cp37m-win_amd64.whl", hash = "sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d"}, - {file = "PyYAML-6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b"}, - {file = "PyYAML-6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba"}, - {file = "PyYAML-6.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34"}, - {file = "PyYAML-6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287"}, - {file = "PyYAML-6.0-cp38-cp38-win32.whl", hash = "sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78"}, - {file = "PyYAML-6.0-cp38-cp38-win_amd64.whl", hash = "sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07"}, - {file = "PyYAML-6.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b"}, - {file = "PyYAML-6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174"}, - {file = "PyYAML-6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803"}, - {file = "PyYAML-6.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3"}, - {file = "PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0"}, - {file = "PyYAML-6.0-cp39-cp39-win32.whl", hash = "sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb"}, - {file = "PyYAML-6.0-cp39-cp39-win_amd64.whl", hash = "sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c"}, - {file = "PyYAML-6.0.tar.gz", hash = "sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2"}, -] -recsim-ng = [ - {file = "recsim_ng-0.1.2-py3-none-any.whl", hash = "sha256:91d69de851d462f6834cb16e667344d0153bd0e1370c5abfe6dc990513016f5b"}, - {file = "recsim_ng-0.1.2.tar.gz", hash = "sha256:8e8d8e9e98ef404f0147c52f242e372c490749ddaae2639d651ea08c6b20a668"}, -] -requests = [ - {file = "requests-2.27.1-py2.py3-none-any.whl", hash = "sha256:f22fa1e554c9ddfd16e6e41ac79759e17be9e492b3587efa038054674760e72d"}, - {file = "requests-2.27.1.tar.gz", hash = "sha256:68d7c56fd5a8999887728ef304a6d12edc7be74f1cfa47714fc8b414525c9a61"}, -] -requests-oauthlib = [ - {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"}, - {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"}, -] -rsa = [ - {file = "rsa-4.4-py2.py3-none-any.whl", hash = "sha256:4afbaaecc3e9550c7351fdf0ab3fea1857ff616b85bab59215f00fb42e0e9582"}, - {file = "rsa-4.4.tar.gz", hash = "sha256:5d95293bbd0fbee1dd9cb4b72d27b723942eb50584abc8c4f5f00e4bcfa55307"}, -] -scipy = [ - {file = "scipy-1.6.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a15a1f3fc0abff33e792d6049161b7795909b40b97c6cc2934ed54384017ab76"}, - {file = "scipy-1.6.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:e79570979ccdc3d165456dd62041d9556fb9733b86b4b6d818af7a0afc15f092"}, - {file = "scipy-1.6.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:a423533c55fec61456dedee7b6ee7dce0bb6bfa395424ea374d25afa262be261"}, - {file = "scipy-1.6.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:33d6b7df40d197bdd3049d64e8e680227151673465e5d85723b3b8f6b15a6ced"}, - {file = "scipy-1.6.1-cp37-cp37m-win32.whl", hash = "sha256:6725e3fbb47da428794f243864f2297462e9ee448297c93ed1dcbc44335feb78"}, - {file = "scipy-1.6.1-cp37-cp37m-win_amd64.whl", hash = "sha256:5fa9c6530b1661f1370bcd332a1e62ca7881785cc0f80c0d559b636567fab63c"}, - {file = "scipy-1.6.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bd50daf727f7c195e26f27467c85ce653d41df4358a25b32434a50d8870fc519"}, - {file = "scipy-1.6.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:f46dd15335e8a320b0fb4685f58b7471702234cba8bb3442b69a3e1dc329c345"}, - {file = "scipy-1.6.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0e5b0ccf63155d90da576edd2768b66fb276446c371b73841e3503be1d63fb5d"}, - {file = "scipy-1.6.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:2481efbb3740977e3c831edfd0bd9867be26387cacf24eb5e366a6a374d3d00d"}, - {file = "scipy-1.6.1-cp38-cp38-win32.whl", hash = "sha256:68cb4c424112cd4be886b4d979c5497fba190714085f46b8ae67a5e4416c32b4"}, - {file = "scipy-1.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:5f331eeed0297232d2e6eea51b54e8278ed8bb10b099f69c44e2558c090d06bf"}, - {file = "scipy-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0c8a51d33556bf70367452d4d601d1742c0e806cd0194785914daf19775f0e67"}, - {file = "scipy-1.6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:83bf7c16245c15bc58ee76c5418e46ea1811edcc2e2b03041b804e46084ab627"}, - {file = "scipy-1.6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:794e768cc5f779736593046c9714e0f3a5940bc6dcc1dba885ad64cbfb28e9f0"}, - {file = "scipy-1.6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:5da5471aed911fe7e52b86bf9ea32fb55ae93e2f0fac66c32e58897cfb02fa07"}, - {file = "scipy-1.6.1-cp39-cp39-win32.whl", hash = "sha256:8e403a337749ed40af60e537cc4d4c03febddcc56cd26e774c9b1b600a70d3e4"}, - {file = "scipy-1.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:a5193a098ae9f29af283dcf0041f762601faf2e595c0db1da929875b7570353f"}, - {file = "scipy-1.6.1.tar.gz", hash = "sha256:c4fceb864890b6168e79b0e714c585dbe2fd4222768ee90bc1aa0f8218691b11"}, -] -setuptools-scm = [ - {file = "setuptools_scm-6.4.2-py3-none-any.whl", hash = "sha256:acea13255093849de7ccb11af9e1fb8bde7067783450cee9ef7a93139bddf6d4"}, - {file = "setuptools_scm-6.4.2.tar.gz", hash = "sha256:6833ac65c6ed9711a4d5d2266f8024cfa07c533a0e55f4c12f6eff280a5a9e30"}, -] -six = [] -tensorboard = [ - {file = "tensorboard-2.6.0-py3-none-any.whl", hash = "sha256:f7dac4cdfb52d14c9e3f74585ce2aaf8e6203620a864e51faf84988b09f7bbdb"}, -] -tensorboard-data-server = [ - {file = "tensorboard_data_server-0.6.1-py3-none-any.whl", hash = "sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7"}, - {file = "tensorboard_data_server-0.6.1-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee"}, - {file = "tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl", hash = "sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a"}, -] -tensorboard-plugin-wit = [ - {file = "tensorboard_plugin_wit-1.8.1-py3-none-any.whl", hash = "sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe"}, -] -tensorboardx = [ - {file = "tensorboardX-2.5.1-py2.py3-none-any.whl", hash = "sha256:8808133ccca673cd04076f6f2a85cf2d39bb2d0393a0f20d0f9cbb06d472b57e"}, - {file = "tensorboardX-2.5.1.tar.gz", hash = "sha256:ea85a3446f22ce8a917fe4fa4d8a7a96222ef84ac835267d038c34bb99f6d61b"}, -] -tensorflow = [ - {file = "tensorflow-2.6.5-cp37-cp37m-macosx_10_11_x86_64.whl", hash = "sha256:e4e29e760d5d46737eda0e01078d15564a5433292e286945aa3d150c96547238"}, - {file = "tensorflow-2.6.5-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:a3dcb2fa4feb70f745455bd0e4d63df22efa0761901e424be8fac6b068513045"}, - {file = "tensorflow-2.6.5-cp37-cp37m-win_amd64.whl", hash = "sha256:4828c436e1b26e5bb21e650c05aa33a5705af27b3a1f52bc2af6c6a1e2ae5721"}, - {file = "tensorflow-2.6.5-cp38-cp38-macosx_10_11_x86_64.whl", hash = "sha256:fb88297fb1cb046fb7952f8aef5b34fd144070f9f556268c83bfa5b2bc76f2f9"}, - {file = "tensorflow-2.6.5-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:94ce4af01c487d80157f0c8c2ca03477c70d0eeb983520c0927ca06f61089278"}, - {file = "tensorflow-2.6.5-cp38-cp38-win_amd64.whl", hash = "sha256:bf9aa12002eea7f886aa4b0f224c9d5a566d99e08cd703b7467d38ba3b74d1bb"}, - {file = "tensorflow-2.6.5-cp39-cp39-macosx_10_11_x86_64.whl", hash = "sha256:b47d1b49e79b5146003732afed7213d11e4d24caa7d0458a76532f5593227ae0"}, - {file = "tensorflow-2.6.5-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:9d1c677ea56e8448c0c99119e97825bc5f13dc5989aab579f8b02da8ff82aa2b"}, - {file = "tensorflow-2.6.5-cp39-cp39-win_amd64.whl", hash = "sha256:65e7dffbaa3af99c3d9f02c61a63c1000708ac3f16b1a5417696c54c1c2eadf4"}, -] -tensorflow-estimator = [ - {file = "tensorflow_estimator-2.6.0-py2.py3-none-any.whl", hash = "sha256:cf78528998efdb637ac0abaf525c929bf192767544eb24ae20d9266effcf5afd"}, -] -tensorflow-probability = [ - {file = "tensorflow_probability-0.17.0-py2.py3-none-any.whl", hash = "sha256:4cb04201c2fb92593c9a73514f4da3897554f927305c38f6aa6cc4caa14a7357"}, -] -termcolor = [ - {file = "termcolor-1.1.0.tar.gz", hash = "sha256:1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b"}, -] -toml = [ - {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"}, - {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"}, -] -tomli = [ - {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"}, - {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, -] -torch = [ - {file = "torch-1.12.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:9c038662db894a23e49e385df13d47b2a777ffd56d9bcd5b832593fab0a7e286"}, - {file = "torch-1.12.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:4e1b9c14cf13fd2ab8d769529050629a0e68a6fc5cb8e84b4a3cc1dd8c4fe541"}, - {file = "torch-1.12.1-cp310-cp310-win_amd64.whl", hash = "sha256:e9c8f4a311ac29fc7e8e955cfb7733deb5dbe1bdaabf5d4af2765695824b7e0d"}, - {file = "torch-1.12.1-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:976c3f997cea38ee91a0dd3c3a42322785414748d1761ef926b789dfa97c6134"}, - {file = "torch-1.12.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:68104e4715a55c4bb29a85c6a8d57d820e0757da363be1ba680fa8cc5be17b52"}, - {file = "torch-1.12.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:743784ccea0dc8f2a3fe6a536bec8c4763bd82c1352f314937cb4008d4805de1"}, - {file = "torch-1.12.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:b5dbcca369800ce99ba7ae6dee3466607a66958afca3b740690d88168752abcf"}, - {file = "torch-1.12.1-cp37-cp37m-win_amd64.whl", hash = "sha256:f3b52a634e62821e747e872084ab32fbcb01b7fa7dbb7471b6218279f02a178a"}, - {file = "torch-1.12.1-cp37-none-macosx_10_9_x86_64.whl", hash = "sha256:8a34a2fbbaa07c921e1b203f59d3d6e00ed379f2b384445773bd14e328a5b6c8"}, - {file = "torch-1.12.1-cp37-none-macosx_11_0_arm64.whl", hash = "sha256:42f639501928caabb9d1d55ddd17f07cd694de146686c24489ab8c615c2871f2"}, - {file = "torch-1.12.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0b44601ec56f7dd44ad8afc00846051162ef9c26a8579dda0a02194327f2d55e"}, - {file = "torch-1.12.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:cd26d8c5640c3a28c526d41ccdca14cf1cbca0d0f2e14e8263a7ac17194ab1d2"}, - {file = "torch-1.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:42e115dab26f60c29e298559dbec88444175528b729ae994ec4c65d56fe267dd"}, - {file = "torch-1.12.1-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:a8320ba9ad87e80ca5a6a016e46ada4d1ba0c54626e135d99b2129a4541c509d"}, - {file = "torch-1.12.1-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:03e31c37711db2cd201e02de5826de875529e45a55631d317aadce2f1ed45aa8"}, - {file = "torch-1.12.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9b356aea223772cd754edb4d9ecf2a025909b8615a7668ac7d5130f86e7ec421"}, - {file = "torch-1.12.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:6cf6f54b43c0c30335428195589bd00e764a6d27f3b9ba637aaa8c11aaf93073"}, - {file = "torch-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:f00c721f489089dc6364a01fd84906348fe02243d0af737f944fddb36003400d"}, - {file = "torch-1.12.1-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:bfec2843daa654f04fda23ba823af03e7b6f7650a873cdb726752d0e3718dada"}, - {file = "torch-1.12.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:69fe2cae7c39ccadd65a123793d30e0db881f1c1927945519c5c17323131437e"}, -] -torchviz = [] -tqdm = [ - {file = "tqdm-4.64.0-py2.py3-none-any.whl", hash = "sha256:74a2cdefe14d11442cedf3ba4e21a3b84ff9a2dbdc6cfae2c34addb2a14a5ea6"}, - {file = "tqdm-4.64.0.tar.gz", hash = "sha256:40be55d30e200777a307a7585aee69e4eabb46b4ec6a4b4a5f2d9f11e7d5408d"}, -] -typing-extensions = [ - {file = "typing_extensions-3.10.0.2-py2-none-any.whl", hash = "sha256:d8226d10bc02a29bcc81df19a26e56a9647f8b0a6d4a83924139f4a8b01f17b7"}, - {file = "typing_extensions-3.10.0.2-py3-none-any.whl", hash = "sha256:f1d25edafde516b146ecd0613dabcc61409817af4766fbbcfb8d1ad4ec441a34"}, - {file = "typing_extensions-3.10.0.2.tar.gz", hash = "sha256:49f75d16ff11f1cd258e1b988ccff82a3ca5570217d7ad8c5f48205dd99a677e"}, -] -urllib3 = [ - {file = "urllib3-1.22-py2.py3-none-any.whl", hash = "sha256:06330f386d6e4b195fbfc736b297f58c5a892e4440e54d294d7004e3a9bbea1b"}, - {file = "urllib3-1.22.tar.gz", hash = "sha256:cc44da8e1145637334317feebd728bd869a35285b93cbb4cca2577da7e62db4f"}, -] -virtualenv = [ - {file = "virtualenv-20.16.4-py3-none-any.whl", hash = "sha256:035ed57acce4ac35c82c9d8802202b0e71adac011a511ff650cbcf9635006a22"}, - {file = "virtualenv-20.16.4.tar.gz", hash = "sha256:014f766e4134d0008dcaa1f95bafa0fb0f575795d07cae50b1bee514185d6782"}, -] -werkzeug = [] -wrapt = [ - {file = "wrapt-1.12.1.tar.gz", hash = "sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7"}, -] -zipp = [ - {file = "zipp-3.8.1-py3-none-any.whl", hash = "sha256:47c40d7fe183a6f21403a199b3e4192cca5774656965b0a4988ad2f8feb5f009"}, - {file = "zipp-3.8.1.tar.gz", hash = "sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2"}, -] +[metadata] +lock-version = "2.0" +python-versions = ">=3.9,<4" +content-hash = "e423dbfce30b4c65d839bc5c7856ba476957f608a28e4a2fb3fca5d384da2006" diff --git a/pyproject.toml b/pyproject.toml index e037cc0..459052c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,64 +1,67 @@ [tool.poetry] name = "deeprecsys" -version = "0.1.1" -description = "Python Recommender System based on Deep Reinforcement Learning" +version = "0.2.1" +description = "deeprecsys is an open tool belt to speed up the development of modern data science projects at an enterprise level" authors = ["Lucas Farris "] license = "MIT" +[[tool.poetry.source]] +name = "cputorch" +url = "https://download.pytorch.org/whl/cpu" +priority = "supplemental" + + [tool.poetry.dependencies] -python = ">=3.7,<4" -#absl-py = "0.12.0" -#tensorflow = "^2.4.1" -#pandas = "0.25.3" -#gym = "0.18.0" -#torch = "1.4.0" -#more-itertools = "^8.7.0" -#networkx = "^2.5.1" -#simplejson = "^3.17.2" -#scikit-learn = "^0.24.1" -recsim-ng = "0.1.2" -catalyst = "22.4" -torchviz = "^0.0.2" -pandas = "<1.4.0" -pyarrow = "^9.0.0" +python = ">=3.9,<4" +pandas = "^2.2.2" +pyarrow = "^16.1.0" +torch = { version = "^2.3.0+cpu", source = "cputorch", optional = true } +keras = { version = "^3.3.3", optional = true } +gymnasium = { version = "^0.29.1", optional = true } +tensorboard = {version = "^2.16.2", optional = true} +torchviz = {version = "^0.0.2", optional = true} +highway-env = {version = "^1.8.2", optional = true} +seaborn = "^0.13.2" + + +[tool.poetry.extras] +recsys = ["torch", "keras", "gymnasium", "tensorboard", "torchviz", "highway-env"] -[tool.poetry.dev-dependencies] -#black = "20.8b1" -#flake8 = "3.9.0" -#notebook = "6.1.5" -#jupyter = "1.0.0" -#pytest = "6.2.3" -#coverage = {extras = ["toml"], version = "^5.5"} -#pdoc3 = "0.9.2" -#build = "^0.3.1" -#recsim = "0.2.4" -#matplotlib = "2.2.5" -#seaborn = "0.11.1" -#rl-agents = {git = "https://github.com/eleurent/rl-agents"} -#highway-env = "^1.1" -#tqdm = "^4.60.0" -#box2d-py = "^2.3.8" -#tensorboardX = "^2.2" -#torchviz = "^0.0.2" -#pre-commit = "2.9.2" -pre-commit = "^2.20.0" + +[tool.poetry.group.dev.dependencies] +pre-commit = ">=2.20.0" +pytest = "^8.2.0" +coverage = { extras = ["toml"], version = "^7.5.1" } +pdoc3 = "^0.10.0" [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] -testpaths = [ - "deeprecsys/tests" -] +testpaths = ["deeprecsys/tests"] +addopts = "--maxfail=2" [tool.coverage.run] -omit= ['venv/*', 'tests/*'] +omit = ['venv/*', 'tests/*'] [tool.coverage.report] -omit= ['venv/*'] -exclude_lines = [ - 'raise NotImplementedError', - 'pass' -] +omit = ['venv/*'] +exclude_lines = ['raise NotImplementedError', 'pass'] + + +[tool.ruff] +lint.select = ["F", "E", "W", "C90", "I", "N", "D", "S"] +lint.ignore = ["D100", "D104", "D203", "D205", "D213", "D400", "D415"] +line-length = 120 +indent-width = 4 +target-version = "py311" + +[tool.ruff.lint.mccabe] +max-complexity = 5 +[tool.ruff.lint.per-file-ignores] +"tests/**/*.py" = [ + "S101", # assertions + "D103" # function docstrings +] \ No newline at end of file diff --git a/tests/unit/test_base_networks.py b/tests/unit/test_base_networks.py index e2558ac..a7b53f5 100644 --- a/tests/unit/test_base_networks.py +++ b/tests/unit/test_base_networks.py @@ -1,10 +1,6 @@ import numpy as np -from deeprecsys.movielens_fairness_env import MovieLensFairness # noqa: F401 -from deeprecsys.rl.agents.actor_critic import ActorCriticAgent -from deeprecsys.rl.agents.rainbow import RainbowDQNAgent -from deeprecsys.rl.agents.reinforce import ReinforceAgent -from deeprecsys.rl.neural_networks.value_estimator import ValueEstimator +from deeprecsys.neural_networks.value_estimator import ValueEstimator def test_save_load(tmp_file_cleanup: str) -> None: @@ -27,16 +23,12 @@ def test_save_load(tmp_file_cleanup: str) -> None: assert network.predict(inputs).detach().cpu().numpy()[0] == predicted_value -def test_tensorboard_writer_reinforce() -> None: - env = MovieLensFairness(slate_size=1) - reinforce_agent = ReinforceAgent( - state_size=env.observation_space.shape[0], n_actions=env.action_space.n - ) - reinforce_agent.policy_estimator.add_to_tensorboard(env.reset()) - ac_agent = ActorCriticAgent( - state_size=env.observation_space.shape[0], n_actions=env.action_space.n - ) - ac_agent.value_estimator.add_to_tensorboard(env.reset()) - dqn_agent = RainbowDQNAgent(env.observation_space.shape[0], env.action_space.n) - dqn_agent.network.add_to_tensorboard(env.reset()) - # if no errors were raised, we're good +# def test_tensorboard_writer_reinforce() -> None: +# env = MovieLensFairness(slate_size=1) +# reinforce_agent = ReinforceAgent(state_size=env.observation_space.shape[0], n_actions=env.action_space.n) +# reinforce_agent.policy_estimator.add_to_tensorboard(env.reset()) +# ac_agent = ActorCriticAgent(state_size=env.observation_space.shape[0], n_actions=env.action_space.n) +# ac_agent.value_estimator.add_to_tensorboard(env.reset()) +# dqn_agent = RainbowDQNAgent(env.observation_space.shape[0], env.action_space.n) +# dqn_agent.network.add_to_tensorboard(env.reset()) +# # if no errors were raised, we're good diff --git a/tests/unit/test_manager.py b/tests/unit/test_manager.py index afa5a89..3042f4f 100644 --- a/tests/unit/test_manager.py +++ b/tests/unit/test_manager.py @@ -1,14 +1,14 @@ from deeprecsys.rl.agents.reinforce import ReinforceAgent -from deeprecsys.rl.manager import MovieLensFairnessManager +from deeprecsys.rl.manager import CartpoleManager -def test_movie_lens_manager() -> None: - manager = MovieLensFairnessManager() +def test_manager_init() -> None: + manager = CartpoleManager() assert manager.env is not None def test_hyperparameter_search() -> None: - manager = MovieLensFairnessManager() + manager = CartpoleManager() agent = ReinforceAgent default_params = { diff --git a/tests/unit/test_movielens_fairness_env.py b/tests/unit/test_movielens_fairness_env.py deleted file mode 100644 index 60e7594..0000000 --- a/tests/unit/test_movielens_fairness_env.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -import pytest -from gym import make - -from deeprecsys.movielens_fairness_env import MovieLensFairness # noqa: F401 - - -def test_env_generation() -> None: - env = make("MovieLensFairness-v0") - assert env is not None - - -def test_reset_state() -> None: - env = make("MovieLensFairness-v0") - state = env.reset() - assert type(state) is np.ndarray - - -def test_step() -> None: - env = make("MovieLensFairness-v0") - env.reset() - state, reward, done, info = env.step(123) - assert type(state) is np.ndarray - assert len(state) == 25 - assert 0 <= reward <= 1 - assert done in [True, False] - assert type(info) is dict - - -def test_slate_environment() -> None: - env = MovieLensFairness(slate_size=5) - obs = env.reset() - assert len(obs) == 25 - next_action = env.action_space.sample() - assert len(next_action) == 5 - state, reward, done, info = env.step(next_action) - assert len(state) == 25 - assert 0 <= reward <= 1 - assert done is False - assert type(info) == dict - - -def test_ndcg() -> None: - env = MovieLensFairness(slate_size=5) - env.reset() - expected_ndcg = pytest.approx(0.279, abs=1e-2) - env._calculate_ndcg([1, 2, 3, 4, 5]) - assert env.ndcg[0] == expected_ndcg - env.step([1, 2, 3, 4, 5]) - assert env.ndcg[0] == expected_ndcg diff --git a/tests/unit/test_random_agent.py b/tests/unit/test_random_agent.py index fc34d84..e4e7e00 100644 --- a/tests/unit/test_random_agent.py +++ b/tests/unit/test_random_agent.py @@ -1,8 +1,8 @@ import numpy as np import pytest -from gym import Space -from gym.spaces.box import Box -from gym.spaces.discrete import Discrete +from gymnasium import Space +from gymnasium.spaces.box import Box +from gymnasium.spaces.discrete import Discrete from deeprecsys.rl.agents.agent import RandomAgent @@ -24,11 +24,9 @@ def test_random_agent(action_space: Space, random_seed: None) -> None: # then the action is valid assert action is not None # when we generate another action from a new agent but the same seed - new_action = RandomAgent(action_space, random_state=random_seed).action_for_state( - None - ) + new_action = RandomAgent(action_space, random_state=random_seed).action_for_state(None) # then the actions are identical - if type(action) == int: + if isinstance(action, int): assert action == new_action else: assert action.flatten().tolist() == new_action.flatten().tolist() diff --git a/tests/unit/test_reinforce_agent.py b/tests/unit/test_reinforce_agent.py index 72388ba..e74dd58 100644 --- a/tests/unit/test_reinforce_agent.py +++ b/tests/unit/test_reinforce_agent.py @@ -10,9 +10,7 @@ def test_reinforce_init() -> None: def test_reinforce_interaction() -> None: manager = CartpoleManager() - agent = ReinforceAgent( - n_actions=2, state_size=4, discount_factor=0.95, learning_rate=0.001 - ) + agent = ReinforceAgent(n_actions=2, state_size=4, discount_factor=0.95, learning_rate=0.001) learning_statistics = LearningStatistics() manager.train(agent, statistics=learning_statistics, max_episodes=500) assert learning_statistics.episode_rewards.tolist()[-1] > 30 diff --git a/tests/unit/test_reproducibility.py b/tests/unit/test_reproducibility.py index 218bf84..b63da6a 100644 --- a/tests/unit/test_reproducibility.py +++ b/tests/unit/test_reproducibility.py @@ -5,29 +5,27 @@ ExperienceReplayBuffer, ExperienceReplayBufferParameters, ) -from deeprecsys.rl.manager import CartpoleManager, MovieLensFairnessManager +from deeprecsys.rl.manager import CartpoleManager SEED = 42 def test_environment_seed() -> None: # given an environment - manager = MovieLensFairnessManager(seed=SEED) + manager = CartpoleManager(seed=SEED) # and an initial state - state = manager.env.reset() + state, _ = manager.env.reset(seed=SEED) # when we try to restart it several times for _ in range(3): # and we recreate the environment - manager = MovieLensFairnessManager(seed=SEED) + manager = CartpoleManager(seed=SEED) # then the initial state is always the same - assert (manager.env.reset() == state).all() + assert (manager.env.reset(seed=SEED)[0] == state).all() def _create_buffer(random_state: RandomState) -> ExperienceReplayBuffer: buffer = ExperienceReplayBuffer( - parameters=ExperienceReplayBufferParameters( - random_state=random_state, batch_size=1, max_experiences=200 - ) + parameters=ExperienceReplayBufferParameters(random_state=random_state, batch_size=1, max_experiences=200) ) for i in range(200): buffer.store_experience(i, 0, 1, False, i + 1) diff --git a/tests/unit/test_sac_agent.py b/tests/unit/test_sac_agent.py index d9e543f..9c13eb7 100644 --- a/tests/unit/test_sac_agent.py +++ b/tests/unit/test_sac_agent.py @@ -1,6 +1,6 @@ from deeprecsys.rl.agents.soft_actor_critic import SoftActorCritic from deeprecsys.rl.learning_statistics import LearningStatistics -from deeprecsys.rl.manager import CartpoleManager, MovieLensFairnessManager +from deeprecsys.rl.manager import CartpoleManager def test_sac_init() -> None: @@ -12,23 +12,6 @@ def test_sac_init() -> None: assert agent is not None -def test_sac_recommendation_env() -> None: - # given the recsys env - manager = MovieLensFairnessManager() - # and a SAC agent - agent = SoftActorCritic( - action_space=manager.env.action_space, - state_size=manager.env.observation_space.shape[0], - discount_factor=0.95, - learning_rate=0.001, - timesteps_to_start_predicting=64, - target_update_interval=1, - ) - # when we train the agent - manager.train(agent, max_episodes=5) - # then no errors are raised - - def test_sac_interaction() -> None: # given an environment manager = CartpoleManager() @@ -47,5 +30,6 @@ def test_sac_interaction() -> None: # then it is able to learn assert learning_statistics.episode_rewards.tolist()[-1] > 30 # and it is able to make predictions - exploit_action = agent.exploit(manager.env.reset()) + state, info = manager.env.reset() + exploit_action = agent.exploit(state) assert exploit_action in [0, 1] diff --git a/tests/unit/test_slate_training.py b/tests/unit/test_slate_training.py index 3463fbc..1899523 100644 --- a/tests/unit/test_slate_training.py +++ b/tests/unit/test_slate_training.py @@ -5,6 +5,7 @@ from deeprecsys.rl.manager import MovieLensFairnessManager +@pytest.mark.skip(reason="Find another slated env") @pytest.mark.parametrize("slate_size", [5, 10, 30]) def test_slate_interaction(slate_size: int) -> None: manager = MovieLensFairnessManager(slate_size=slate_size, seed=42) @@ -15,6 +16,7 @@ def test_slate_interaction(slate_size: int) -> None: assert env.step(example_action) is not None +@pytest.mark.skip(reason="Find another slated env") @pytest.mark.parametrize("slate_size", [5, 10, 30]) def test_slate_random_training(slate_size: int) -> None: manager = MovieLensFairnessManager(slate_size=slate_size, seed=42) @@ -22,6 +24,7 @@ def test_slate_random_training(slate_size: int) -> None: manager.train(agent, max_episodes=30) +@pytest.mark.skip(reason="Find another slated env") @pytest.mark.parametrize("slate_size", [5, 10, 30]) def test_slate_reinforce_training(slate_size: int) -> None: manager = MovieLensFairnessManager(slate_size=slate_size, seed=42)