Then provide a path to the model in your GitHub action configuration, through the You may wish to train your own model and use it in this action. This action uses a pre-trained neural network that has been trained onĪ corpus of open-source repositories that use Python's type annotations.Īt this point we do not support online adaptation of the model to each project. Uses a Graph Neural Network to predict likely type annotations for Python This GitHub action is a reimplementation of the Graph2Class model of The action uses the GITHUB_TOKEN to retrieve the diff of the pull requestĪnd to post comments on the analyzed pull request. Default 0.8 DISAGREEMENT_CONFIDENCE_THRESHOLD: 0.95 # Configure this to limit the confidence of suggestions on annotated locations. SUGGESTION_CONFIDENCE_THRESHOLD: 0.8 # Configure this to limit the confidence of suggestions on un-annotated locations. GITHUB_TOKEN: $ MODEL_PATH: path/to/ # Optional: provide the path of a custom model instead of the pre-trained model. # Checks-out your repository under $GITHUB_WORKSPACE, so that typilus can access it. # The type of runner that the job will run on runs-on: ubuntu-latest steps: Triggers the workflow on push or pull request # events but only for the master branch on: Getting started is easy if you know Python. Name: Typilus Type Annotation Suggestions # Controls when the action will run. Mypy type checks programs that have type annotations conforming to PEP 484. To use the GitHub action, create a workflow file. Suggestions with only a partial context, at the cost of suggesting some false Network model that predicts types by probabilistically reasoning overĪ program’s structure, names, and patterns. To tackle this challenge, we use a graph neural Given the dynamic nature of Python, type inference is challenging,Įspecially over partial contexts. Python type annotations code#To prevent type errors they also facilitate code comprehension and navigation. Optionally checked by external tools, such as mypy and pyright, To annotate their code with the expected types. (more traditionally called type annotations) allow users You can then directly apply these suggestions to your code or ignore them. This action makes suggestions within each pull request as A GitHub action that suggests type annotations for Python using machine learning.
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