Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Releasing
We have two different environments for development and production as it is common in software development. In the development env., we test, debug, and profile Type4Py’s server-side components before releasing new features/fixes into the production code.
Also, whenever we train a new Type4Py neural model, we test it against its evaluation metrics (see its paper ) and run integration tests to ensure that it produces expected predctions for given Python source files. Finally, the VSCode extension uses the development env. when testing new featues/fixes before releasing a new extension version.
4
6 reads
MORE IDEAS ON THIS
Feature Extraction
As with most ML tasks, we need to find a set of relevant features in order to predict type annotations. Here, we consider features as type hints. Specifically, we extract three kinds of type hints, namely, identifiers, code context, and visible type hints (VTHs).
To learn from the extracted...
4
14 reads
Implementation
To extract type hints, we first extract Abstract Syntax Trees (ASTs) and perform light-weight static analysis using our LibSA4Py package. NLP tasks are applied using NLTK. To train the Word2Vec model, w...
4
7 reads
Development and Release of Type4Py: Machine Learning-based Type Auto-completion for Python
Dynamic programming languages like Python and TypeScript allows developers to optionally define type annotations and benefit from the advantages of static typing such as better code completion, early bug detection.
However, retrofitting types is a cumbersome and error-prone process. To addr...
4
47 reads
Overview
Before going into the detail of the Type4Py’s model and its implementation, it would be helpful to see the overview of Type4Py. In general, there is a VSCode extension at the client-side (developers) and the Type4Py model and its pipeline are deployed on our servers. Simply, the extension sends a...
4
28 reads
Dataset
For an ML model to learn and generalize, there is a need for a large and high-quality dataset. Therefore, we created the ManyTypes4Py dataset which contains 5.2K Python projects and 4.2M type annotations. To avoid data leakage fr...
4
25 reads
Deployment
To deploy the Type4Py model for the production environment, we convert the pre-trained PyTorch model to an ONNX model which allows us to query the model on both GPUs and CPUs with very fast inference speed and low...
4
5 reads
Model Architecture & Training
The basic idea is that two RNN captures different aspects of input sequences from both identifiers and code context.
Next, the output of two RNNs is concatenated into a single vector, which is passed through a fully-connected linear layer.
The final linear layer maps the learned typ...
4
12 reads
VSCode Extension
The Type4Py’s VSCode extension is small and simple.
Type slots are functions parameters, return types, and variables, which are located based on the line and column numbers. Currently, type prediction can be triggered via Command Pallete or by enabling the AutoInfer setting, which predicts ...
4
14 reads
Read & Learn
20x Faster
without
deepstash
with
deepstash
with
deepstash
Access to 200,000+ ideas
—
Access to the mobile app
—
Unlimited idea saving & library
—
—
Unlimited history
—
—
Unlimited listening to ideas
—
—
Downloading & offline access
—
—
Personalized recommendations
—
—
Supercharge your mind with one idea per day
Enter your email and spend 1 minute every day to learn something new.
I agree to receive email updates