piskvorky/gensim

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

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#1576 opened on Sep 8, 2017

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 (6 comments) (0 reactions) (0 assignees)Python (15,144 stars) (4,349 forks)batch import
Hacktoberfestbugdifficulty easygood first issueperformance

Description

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)

Contributor guide

Tech stack
pythonnumpy
Domain
backenddatamachine learning
Issue type
research
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
basic Pythonfamiliarity with numpytopic modeling concepts
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60
Research direction
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.