Topic
eval
About: eval is a research topic. Over the lifetime, 10 publications have been published within this topic receiving 50 citations.
Papers
TL;DR: Pytrec_eval as discussed by the authors is a Python interface to the tree_eval information retrieval evaluation toolkit, which exposes the reference implementations of trec_eval within Python as a native extension.
Abstract: We introduce pytrec_eval, a Python interface to the tree_eval information retrieval evaluation toolkit. pytrec_eval exposes the reference implementations of trec_eval within Python as a native extension. We show that pytrec_eval is around one order of magnitude faster than invoking trec_eval as a sub process from within Python. Compared to a native Python implementation of NDCG, pytrec_eval is twice as fast for practically-sized rankings. Finally, we demonstrate its effectiveness in an application where pytrec_eval is combined with Pyndri and the OpenAI Gym where query expansion is learned using Q-learning.
32 citations
TL;DR: The 5-ASA permeability enhancement by the EVAL and the glycine-immobilized EVAL membrane in the neutral environment is ascribed to totally different mechanisms, which indicates that the significant increase in the 5- Asa permeability of the EVsAL is suitable for local treatment of ulcerative colitis.
16 citations
15 Oct 2021
TL;DR: In this paper, the authors analyze 49,296,059 calls to eval from 240,327 scripts extracted from 15,401 R packages and find that eval is indeed in widespread use; R's eval is more pervasive and arguably dangerous than what was previously reported for JavaScript.
Abstract: Most dynamic languages allow users to turn text into code using various functions, often named eval, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from performing optimizations. This paper aims to provide a better sense of why programmers use eval. Understanding why eval is used in practice is key to finding ways to mitigate its negative impact. We have reasons to believe that reflective feature usage is language and application domain-specific; we focus on data science code written in R and compare our results to previous work that analyzed web programming in JavaScript. We analyze 49,296,059 calls to eval from 240,327 scripts extracted from 15,401 R packages. We find that eval is indeed in widespread use; R’s eval is more pervasive and arguably dangerous than what was previously reported for JavaScript.
4 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2018 | 2 |
| 2017 | 1 |
| 2014 | 1 |
| 2013 | 1 |
| 2006 | 1 |