piskvorky/gensim

Check what's the reason to use double-precision in topic models

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#1,576 创建于 2017年9月8日

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 (6 评论) (0 反应) (0 负责人)Python (15,144 star) (4,349 fork)batch import
Hacktoberfestbugdifficulty easygood first issueperformance

描述

Our TMs return vectors with double-precision float64, it looks like very suspicious, because float32 is enough for all. Need to check, what's a reason of this behavior and what's a concrete method.

The first step - look at this line in the test, after it - collect all TMs, that depends on this tests and check, where and why float64 happened.

Result - detailed description (where and why), and fixing this behavior after discussion (if needed)

贡献者指南

技术栈
pythonnumpy
领域
backenddatamachine learning
议题类型
research
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-3 hours
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
mostly clear
前置要求
basic Pythonfamiliarity with numpytopic modeling concepts
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
60
研究方向
Start by examining the test file at gensim/test/basetmtests.py line 51 (linked in the issue) to understand the expected precision. Then survey all topic model implementations in gensim (e.g., LdaModel, LsiModel, etc.) to identify where float64 is introduced. Check if the float64 comes from underlying numpy operations or external libraries. Document each instance with its location and reasoning. Finally, suggest a discussion on whether float32 can safely replace float64.