piskvorky/gensim

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

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#1,576 opened on 2017年9月8日

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Hacktoberfestbugdifficulty easygood first issueperformance

説明

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)

コントリビューターガイド

技術スタック
pythonnumpy
領域
backenddatamachine learning
Issue 種別
research
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
3
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
1-3 hours
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
mostly clear
前提条件
basic Pythonfamiliarity with numpytopic modeling concepts
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
60
調査方針
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.