Feature Extraction - Deepstash
Machine Learning With Google

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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 sequences, first, we apply common NL pre-processing techniques tokenization (by snake case ), stop word removal and lemmatization. Then, we employ the famous Word2Vec model to generate word embeddings of the extracted sequences in order to train the Type4Py model, which is described next.

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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 Type4...

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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...

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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...

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Overview

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...

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Roadmap

So far, I have described the current state of Type4Py. For future work, here is our roadmap:

  • Enabling the type-checking process for the Type4Py’s predictions using mypy , preferably at the client-side.
  • Releasing a local v...

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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...

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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...

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Model Architecture & Training

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...

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VSCode Extension

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 ...

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decebaldobrica

#engineering, #machinelearning and #crypto

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