A Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs suitable for training machine learning models. Our library admits the construction of control-flow graphs, data-flow graphs, and composite ``program graphs'' that combine control-flow, data-flow, syntactic, and lexical information about a program. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs
This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can iden…
Anomaly DetectionTime SeriesTime Series AnalysisTerra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Imperative programming allows users to implement their deep neural networks (DNNs) easily and has become an essential part of recent deep learning (DL) frameworks. Recently, several systems have been proposed to combine …
Deep LearningWAX-ML: A Python library for machine learning and feedback loops on streaming data
Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and fee…
BIG-bench Machine Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2QuForge: A Library for Qudits Simulation
Quantum computing with qudits, an extension of qubits to multiple levels, is a research field less mature than qubit-based quantum computing. However, qudits can offer some advantages over qubits, by representing informa…
Quantum Machine LearningArtificial chemistry experiments with chemlambda, lambda calculus, interaction combinators
Given a graph rewrite system, a graph G is a quine graph if it has a non-void maximal collection of non-conflicting matches of left patterns of graphs rewrites, such that after the parallel application of the rewrites we…