piskvorky/gensim

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

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#1,576 建立於 2017年9月8日

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 (6 留言) (0 反應) (0 負責人)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)

貢獻者指南

技術棧
pythonnumpy
領域
backenddatamachine learning
議題類型
research
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
1-3 hours
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
mostly clear
前置要求
basic Pythonfamiliarity with numpytopic modeling concepts
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
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.