Class Resolver

Lookup and instantiate classes with style.

💪 Getting Started

```python from class_resolver import ClassResolver from dataclasses import dataclass

class Base: pass

@dataclass class A(Base): name: str

@dataclass class B(Base): name: str

Index

resolver = ClassResolver([A, B], base=Base)

Lookup

assert A == resolver.lookup('A')

Instantiate with a dictionary

assert A(name='hi') == resolver.make('A', {'name': 'hi'})

Instantiate with kwargs

assert A(name='hi') == resolver.make('A', name='hi')

A pre-instantiated class will simply be passed through

assert A(name='hi') == resolver.make(A(name='hi')) ```

🤖 Writing Extensible Machine Learning Models with class-resolver

Assume you've implemented a simple multi-layer perceptron in PyTorch:

```python from itertools import chain

from more_itertools import pairwise from torch import nn

class MLP(nn.Sequential): def init(self, dims: list[int]): super().init(chain.fromiterable( ( nn.Linear(infeatures, outfeatures), nn.ReLU(), ) for infeatures, out_features in pairwise(dims) )) ```

This MLP uses a hard-coded rectified linear unit as the non-linear activation function between layers. We can generalize this MLP to use a variety of non-linear activation functions by adding an argument to its __init__() function like in:

```python from itertools import chain

from more_itertools import pairwise from torch import nn

class MLP(nn.Sequential): def init(self, dims: list[int], activation: str = "relu"): if activation == "relu": activation = nn.ReLU() elif activation == "tanh": activation = nn.Tanh() elif activation == "hardtanh": activation = nn.Hardtanh() else: raise KeyError(f"Unsupported activation: {activation}") super().init(chain.fromiterable( ( nn.Linear(infeatures, outfeatures), activation, ) for infeatures, out_features in pairwise(dims) )) ```

The first issue with this implementation is it relies on a hard-coded set of conditional statements and is therefore hard to extend. It can be improved by using a dictionary lookup:

```python from itertools import chain

from more_itertools import pairwise from torch import nn

activation_lookup: dict[str, nn.Module] = { "relu": nn.ReLU(), "tanh": nn.Tanh(), "hardtanh": nn.Hardtanh(), }

class MLP(nn.Sequential): def init(self, dims: list[int], activation: str = "relu"): activation = activationlookup[activation] super().init(chain.fromiterable( ( nn.Linear(infeatures, outfeatures), activation, ) for infeatures, outfeatures in pairwise(dims) )) ```

This approach is rigid because it requires pre-instantiation of the activations. If we needed to vary the arguments to the nn.HardTanh class, the previous approach wouldn't work. We can change the implementation to lookup on the class before instantiation then optionally pass some arguments:

```python from itertools import chain

from more_itertools import pairwise from torch import nn

activation_lookup: dict[str, type[nn.Module]] = { "relu": nn.ReLU, "tanh": nn.Tanh, "hardtanh": nn.Hardtanh, }

class MLP(nn.Sequential): def init( self, dims: list[int], activation: str = "relu", activationkwargs: None | dict[str, any] = None, ): activationcls = activationlookup[activation] activation = activationcls(**(activationkwargs or {})) super().init(chain.fromiterable( ( nn.Linear(infeatures, outfeatures), activation, ) for infeatures, outfeatures in pairwise(dims) )) ```

This is pretty good, but it still has a few issues:

1. you have to manually maintain the activation_lookup dictionary,
2. you can't pass an instance or class through the activation keyword
3. you have to get the casing just right
4. the default is hard-coded as a string, which means this has to get copied (error-prone) in any place that creates an MLP
5. you have to re-write this logic for all of your classes

Enter the class_resolver package, which takes care of all of these things using the following:

```python from itertools import chain

from classresolver import ClassResolver, Hint from moreitertools import pairwise from torch import nn

activation_resolver = ClassResolver( [nn.ReLU, nn.Tanh, nn.Hardtanh], base=nn.Module, default=nn.ReLU, )

class MLP(nn.Sequential): def init( self, dims: list[int], activation: Hint[nn.Module] = None, # Hint = Union[None, str, nn.Module, type[nn.Module]] activationkwargs: None | dict[str, any] = None, ): super().init(chain.fromiterable( ( nn.Linear(infeatures, outfeatures), activationresolver.make(activation, activationkwargs), ) for infeatures, outfeatures in pairwise(dims) )) ```

Because this is such a common pattern, we've made it available through contrib module in class_resolver.contrib.torch:

```python from itertools import chain

from classresolver import Hint from classresolver.contrib.torch import activationresolver from moreitertools import pairwise from torch import nn

class MLP(nn.Sequential): def init( self, dims: list[int], activation: Hint[nn.Module] = None, activationkwargs: None | dict[str, any] = None, ): super().init(chain.fromiterable( ( nn.Linear(infeatures, outfeatures), activationresolver.make(activation, activationkwargs), ) for infeatures, outfeatures in pairwise(dims) )) ```

Now, you can instantiate the MLP with any of the following:

```python MLP(dims=[10, 200, 40]) # uses default, which is ReLU MLP(dims=[10, 200, 40], activation="relu") # uses lowercase MLP(dims=[10, 200, 40], activation="ReLU") # uses stylized MLP(dims=[10, 200, 40], activation=nn.ReLU) # uses class MLP(dims=[10, 200, 40], activation=nn.ReLU()) # uses instance

MLP(dims=[10, 200, 40], activation="hardtanh", activationkwargs={"minval": 0.0, "maxvalue": 6.0}) # uses kwargs MLP(dims=[10, 200, 40], activation=nn.HardTanh, activationkwargs={"minval": 0.0, "maxvalue": 6.0}) # uses kwargs MLP(dims=[10, 200, 40], activation=nn.HardTanh(0.0, 6.0)) # uses instance ```

In practice, it makes sense to stick to using the strings in combination with hyper-parameter optimization libraries like Optuna.

🚀 Installation

The most recent release can be installed from PyPI with uv:

console $ uv pip install class_resolver

or with pip:

console $ python3 -m pip install class_resolver

The most recent code and data can be installed directly from GitHub with uv:

console $ uv pip install git+https://github.com/cthoyt/class-resolver.git

or with pip:

console $ python3 -m pip install git+https://github.com/cthoyt/class-resolver.git

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

See developer instructions

The final section of the README is for if you want to get involved by making a code contribution. ### Development Installation To install in development mode, use the following: ```console $ git clone git+https://github.com/cthoyt/class-resolver.git $ cd class-resolver $ uv pip install -e . ``` Alternatively, install using pip: ```console $ python3 -m pip install -e . ``` ### 🥼 Testing After cloning the repository and installing `tox` with `uv tool install tox --with tox-uv` or `python3 -m pip install tox tox-uv`, the unit tests in the `tests/` folder can be run reproducibly with: ```console $ tox -e py ``` Additionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/cthoyt/class-resolver/actions?query=workflow%3ATests). ### 📖 Building the Documentation The documentation can be built locally using the following: ```console $ git clone git+https://github.com/cthoyt/class-resolver.git $ cd class-resolver $ tox -e docs $ open docs/build/html/index.html ``` The documentation automatically installs the package as well as the `docs` extra specified in the [`pyproject.toml`](pyproject.toml). `sphinx` plugins like `texext` can be added there. Additionally, they need to be added to the `extensions` list in [`docs/source/conf.py`](docs/source/conf.py). The documentation can be deployed to [ReadTheDocs](https://readthedocs.io) using [this guide](https://docs.readthedocs.io/en/stable/intro/import-guide.html). The [`.readthedocs.yml`](.readthedocs.yml) YAML file contains all the configuration you'll need. You can also set up continuous integration on GitHub to check not only that Sphinx can build the documentation in an isolated environment (i.e., with `tox -e docs-test`) but also that [ReadTheDocs can build it too](https://docs.readthedocs.io/en/stable/pull-requests.html).

🧑‍💻 For Maintainers

See maintainer instructions

### Initial Configuration #### Configuring ReadTheDocs [ReadTheDocs](https://readthedocs.org) is an external documentation hosting service that integrates with GitHub's CI/CD. Do the following for each repository: 1. Log in to ReadTheDocs with your GitHub account to install the integration at https://readthedocs.org/accounts/login/?next=/dashboard/ 2. Import your project by navigating to https://readthedocs.org/dashboard/import then clicking the plus icon next to your repository 3. You can rename the repository on the next screen using a more stylized name (i.e., with spaces and capital letters) 4. Click next, and you're good to go! #### Configuring Archival on Zenodo [Zenodo](https://zenodo.org) is a long-term archival system that assigns a DOI to each release of your package. Do the following for each repository: 1. Log in to Zenodo via GitHub with this link: https://zenodo.org/oauth/login/github/?next=%2F. This brings you to a page that lists all of your organizations and asks you to approve installing the Zenodo app on GitHub. Click "grant" next to any organizations you want to enable the integration for, then click the big green "approve" button. This step only needs to be done once. 2. Navigate to https://zenodo.org/account/settings/github/, which lists all of your GitHub repositories (both in your username and any organizations you enabled). Click the on/off toggle for any relevant repositories. When you make a new repository, you'll have to come back to this After these steps, you're ready to go! After you make "release" on GitHub (steps for this are below), you can navigate to https://zenodo.org/account/settings/github/repository/cthoyt/class-resolver to see the DOI for the release and link to the Zenodo record for it. #### Registering with the Python Package Index (PyPI) The [Python Package Index (PyPI)](https://pypi.org) hosts packages so they can be easily installed with `pip`, `uv`, and equivalent tools. 1. Register for an account [here](https://pypi.org/account/register) 2. Navigate to https://pypi.org/manage/account and make sure you have verified your email address. A verification email might not have been sent by default, so you might have to click the "options" dropdown next to your address to get to the "re-send verification email" button 3. 2-Factor authentication is required for PyPI since the end of 2023 (see this [blog post from PyPI](https://blog.pypi.org/posts/2023-05-25-securing-pypi-with-2fa/)). This means you have to first issue account recovery codes, then set up 2-factor authentication 4. Issue an API token from https://pypi.org/manage/account/token This only needs to be done once per developer. #### Configuring your machine's connection to PyPI This needs to be done once per machine. ```console $ uv tool install keyring $ keyring set https://upload.pypi.org/legacy/ __token__ $ keyring set https://test.pypi.org/legacy/ __token__ ``` Note that this deprecates previous workflows using `.pypirc`. ### 📦 Making a Release #### Uploading to PyPI After installing the package in development mode and installing `tox` with `uv tool install tox --with tox-uv` or `python3 -m pip install tox tox-uv`, run the following from the console: ```console $ tox -e finish ``` This script does the following: 1. Uses [bump-my-version](https://github.com/callowayproject/bump-my-version) to switch the version number in the `pyproject.toml`, `CITATION.cff`, `src/class_resolver/version.py`, and [`docs/source/conf.py`](docs/source/conf.py) to not have the `-dev` suffix 2. Packages the code in both a tar archive and a wheel using [`uv build`](https://docs.astral.sh/uv/guides/publish/#building-your-package) 3. Uploads to PyPI using [`uv publish`](https://docs.astral.sh/uv/guides/publish/#publishing-your-package). 4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped. 5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use `tox -e bumpversion -- minor` after. #### Releasing on GitHub 1. Navigate to https://github.com/cthoyt/class-resolver/releases/new to draft a new release 2. Click the "Choose a Tag" dropdown and select the tag corresponding to the release you just made 3. Click the "Generate Release Notes" button to get a quick outline of recent changes. Modify the title and description as you see fit 4. Click the big green "Publish Release" button This will trigger Zenodo to assign a DOI to your release as well. ### Updating Package Boilerplate This project uses `cruft` to keep boilerplate (i.e., configuration, contribution guidelines, documentation configuration) up-to-date with the upstream cookiecutter package. Install cruft with either `uv tool install cruft` or `python3 -m pip install cruft` then run: ```console $ cruft update ``` More info on Cruft's update command is available [here](https://github.com/cruft/cruft?tab=readme-ov-file#updating-a-project).