A GitHub action that suggests type annotations for Python using machine learning.

Overview

Typilus: Suggest Python Type Annotations

A GitHub action that suggests type annotations for Python using machine learning.

This action makes suggestions within each pull request as suggested edits. You can then directly apply these suggestions to your code or ignore them.

Sample Suggestion Sample Suggestion

What are Python type annotations? Introduced in Python 3.5, type hints (more traditionally called type annotations) allow users to annotate their code with the expected types. These annotations are optionally checked by external tools, such as mypy and pyright, to prevent type errors; they also facilitate code comprehension and navigation. The typing module provides the core types.

Why use machine learning? Given the dynamic nature of Python, type inference is challenging, especially over partial contexts. To tackle this challenge, we use a graph neural network model that predicts types by probabilistically reasoning over a program’s structure, names, and patterns. This allows us to make suggestions with only a partial context, at the cost of suggesting some false positives.

Install Action in your Repository

To use the GitHub action, create a workflow file. For example,

name: Typilus Type Annotation Suggestions

# Controls when the action will run. Triggers the workflow on push or pull request
# events but only for the master branch
on:
  pull_request:
    branches: [ master ]

jobs:
  suggest:
    # The type of runner that the job will run on
    runs-on: ubuntu-latest

    steps:
    # Checks-out your repository under $GITHUB_WORKSPACE, so that typilus can access it.
    - uses: actions/[email protected]
    - uses: typilus/[email protected]
      env:
        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        MODEL_PATH: path/to/model.pkl.gz   # Optional: provide the path of a custom model instead of the pre-trained model.
        SUGGESTION_CONFIDENCE_THRESHOLD: 0.8   # Configure this to limit the confidence of suggestions on un-annotated locations. A float in [0, 1]. Default 0.8
        DISAGREEMENT_CONFIDENCE_THRESHOLD: 0.95  # Configure this to limit the confidence of suggestions on annotated locations.  A float in [0, 1]. Default 0.95

The action uses the GITHUB_TOKEN to retrieve the diff of the pull request and to post comments on the analyzed pull request.

Technical Details & Internals

This GitHub action is a reimplementation of the Graph2Class model of Allamanis et al. PLDI 2020 using the ptgnn library. Internally, it uses a Graph Neural Network to predict likely type annotations for Python code.

This action uses a pre-trained neural network that has been trained on a corpus of open-source repositories that use Python's type annotations. At this point we do not support online adaptation of the model to each project.

Training your own model

You may wish to train your own model and use it in this action. To do so, please follow the steps in ptgnn. Then provide a path to the model in your GitHub action configuration, through the MODEL_PATH environment variable.

Contributing

We welcome external contributions and ideas. Please look at the issues in the repository for ideas and improvements.

You might also like...
 30 Days Of Machine Learning Using Pytorch
30 Days Of Machine Learning Using Pytorch

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

customer churn prediction prevention in telecom industry using machine learning and survival analysis

Telco Customer Churn Prediction - Plotly Dash Application Description This dash application allows you to predict telco customer churn using machine l

using Machine Learning Algorithm to classification AppleStore application

AppleStore-classification-with-Machine-learning-Algo- using Machine Learning Algorithm to classification AppleStore application. the first step : 1: p

CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning applications.

SmartSim Example Zoo This repository contains CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning appl

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.
Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

Trading Tesla with Machine Learning and Sentiment Analysis An interactive program to train a Random Forest Classifier to predict Tesla daily prices us

A machine learning web application for binary classification using streamlit
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning

Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning My

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

A data preprocessing package for time series data. Design for machine learning and deep learning.

A data preprocessing package for time series data. Design for machine learning and deep learning.

Comments
  • IndexError: list index out of range

    IndexError: list index out of range

    Diff GET Status Code:  200
    Traceback (most recent call last):
      File "/usr/src/entrypoint.py", line 81, in <module>
        changed_files = get_changed_files(diff_rq.text)
      File "/usr/src/changeutils.py", line 38, in get_changed_files
        assert file_diff_lines[3].startswith("---")
    IndexError: list index out of range
    

    logs_302.zip

    opened by ZdenekM 1
  • Several small fixes

    Several small fixes

    Here are couple of things I noticed trying Typilus inference using GH Action:

    • gracefully handle patches that include a file renames (\wo any content modifications) by skipping such files
    • extractor stats reporting only processed files
    opened by bzz 0
  • Create a ptgnn-based Typilus model

    Create a ptgnn-based Typilus model

    Create and use the full Typilus model instead of graph2class.

    • [ ] Implement it in ptgnn
    • [ ] Use action cache to store intermediate result
    • [ ] Auto-update type space "once in a while"
    enhancement 
    opened by mallamanis 0
Releases(v0.9)
Mortality risk prediction for COVID-19 patients using XGBoost models

Mortality risk prediction for COVID-19 patients using XGBoost models Using demographic and lab test data received from the HM Hospitales in Spain, I b

1 Jan 19, 2022
Add built-in support for quaternions to numpy

Quaternions in numpy This Python module adds a quaternion dtype to NumPy. The code was originally based on code by Martin Ling (which he wrote with he

Mike Boyle 531 Dec 28, 2022
Predicting Baseball Metric Clusters: Clustering Application in Python Using scikit-learn

Clustering Clustering Application in Python Using scikit-learn This repository contains the prediction of baseball metric clusters using MLB Statcast

Tom Weichle 2 Apr 18, 2022
Flightfare-Prediction - It is a Flightfare Prediction Web Application Using Machine learning,Python and flask

Flight_fare-Prediction It is a Flight_fare Prediction Web Application Using Machine learning,Python and flask Using Machine leaning i have created a F

1 Dec 06, 2022
Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)"

CRAN Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)" This code doesn't exa

4 Nov 11, 2021
Mesh TensorFlow: Model Parallelism Made Easier

Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

1.3k Dec 26, 2022
Python/Sage Tool for deriving Scattering Matrices for WDF R-Adaptors

R-Solver A Python tools for deriving R-Type adaptors for Wave Digital Filters. This code is not quite production-ready. If you are interested in contr

8 Sep 19, 2022
Factorization machines in python

Factorization Machines in Python This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive re

Corey Lynch 892 Jan 03, 2023
Upgini : data search library for your machine learning pipelines

Automated data search library for your machine learning pipelines → find & deliver relevant external data & features to boost ML accuracy :chart_with_upwards_trend:

Upgini 175 Jan 08, 2023
Scikit-Garden or skgarden is a garden for Scikit-Learn compatible decision trees and forests.

Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

260 Dec 21, 2022
Time series changepoint detection

changepy Changepoint detection in time series in pure python Install pip install changepy Examples from changepy import pelt from cha

Rui Gil 92 Nov 08, 2022
Optimal Randomized Canonical Correlation Analysis

ORCCA Optimal Randomized Canonical Correlation Analysis This project is for the python version of ORCCA algorithm. It depends on Numpy for matrix calc

Yinsong Wang 1 Nov 21, 2021
Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared

Feature-Engineering Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared. When the dataset

kemalgunay 5 Apr 21, 2022
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

neurodata 3 Dec 16, 2022
#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

30 Days Of Streamlit 🎈 This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

Streamlit 53 Jan 02, 2023
Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

(intron I nterrogator and C lassifier) intronIC is a program that can be used to classify intron sequences as minor (U12-type) or major (U2-type), usi

Graham Larue 4 Jul 26, 2022
Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft 366 Jan 03, 2023
A naive Bayes model for cancer classification using a set of documents

Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The

Alex W King 1 Nov 24, 2021
Dive into Machine Learning

Dive into Machine Learning Hi there! You might find this guide helpful if: You know Python or you're learning it 🐍 You're new to Machine Learning You

Michael Floering 11.1k Jan 03, 2023
Primitives for machine learning and data science.

An Open Source Project from the Data to AI Lab, at MIT MLPrimitives Pipelines and primitives for machine learning and data science. Documentation: htt

MLBazaar 65 Dec 29, 2022