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jnothman opened this issue Jan 29, 2014 · 12 comments · Fixed by #18046
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DOC More docs needed on model and data persistence #2801

jnothman opened this issue Jan 29, 2014 · 12 comments · Fixed by #18046
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@jnothman
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It seems the only documentation of model persistence is in the Quick Start tutorial. I think it needs to be covered in the User Guide, mentioning security and forward-compatibility caveats pertaining to pickle. It might note the benefits of joblib (for large models at least) over Pickle. Most other ML toolkits (particularly command-line tools) treat persistence as a very basic operation of the package. (This was fixed in #3317)

Similarly, there should be some comment on saving and loading custom data (input and output). Indeed, I can't find a direct description of the supported data-types; users are unlikely to have played with scipy.sparse before (although the feature_extraction module means they may not need to). Noting the benefits of joblib (without which the user may dump a sparse matrix's data, indices, indptr using .tofile) and memmapping is worthwhile. So may be reference to Pandas which could help manipulate datasets before/after entering Scikit-learn, and provides import/export to a variety of formats (http://pandas.pydata.org/pandas-docs/dev/io.html).

I have recently discovered new users who think the way to import/export large sparse arrays is load_svmlight_format, but then note that the loading/saving takes much more time than the processing they're trying to do... Let's give them a hand.
@jnothman
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scipy.io also deserves a mention when discussing dataset loading.

@MattpSoftware
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A good issue for a novice to work on?

@jnothman
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Some of this was covered in #3317 (and it turns out this issue was a duplicate of #1332). But if you can make an effort to understand what's covered now and what's missing, yes, I think this question of import/export could still have better coverage in the documentation.

@MattpSoftware
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Is all the current persistence documentation in model_persistence.rst?

On Mon, Sep 29, 2014 at 5:19 PM, jnothman notifications@github.com wrote:

Some of this was covered in #3317
#3317 (and it turns
out this issue was a duplicate of #1332
#1332). But if you
can make an effort to understand what's covered now and what's missing,
yes, I think this question of import/export could still have better
coverage in the documentation.

—
Reply to this email directly or view it on GitHub
#2801 (comment)
.

@jnothman
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jnothman commented Oct 1, 2014

Basically, yes

On 1 October 2014 11:46, Joel Nothman joel.nothman@sydney.edu.au wrote:

On 1 October 2014 10:40, Matt Pico notifications@github.com wrote:

Is all the current persistence documentation in model_persistence.rst?

On Mon, Sep 29, 2014 at 5:19 PM, jnothman notifications@github.com
wrote:

Some of this was covered in #3317
#3317 (and it turns
out this issue was a duplicate of #1332
#1332). But if
you
can make an effort to understand what's covered now and what's missing,
yes, I think this question of import/export could still have better
coverage in the documentation.

—
Reply to this email directly or view it on GitHub
<
https://github.com/scikit-learn/scikit-learn/issues/2801#issuecomment-57250301>

.

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#2801 (comment)
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@MattpSoftware
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Starting review of model_persistence.rst …

@arjoly
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arjoly commented Oct 7, 2014

Thanks @MattpSoftware !

@jnothman jnothman mentioned this issue Oct 29, 2014
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@evanmiller29
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Hey @amueller/@jnothman - does this still need some work? I'm happy to open a PR and work on it.

@jnothman
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jnothman commented Aug 7, 2019

Well this is certainly and old issue, but it would be great if someone who knows both common and efficient ways to store data to review what our user guides and tutorials currently say on the topic

@evanmiller29
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evanmiller29 commented Aug 8, 2019 •
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Ha yeah the new bug issues are a little too competitive so I thought I'd start working on some of the older issues..

I'm not 100% sure about the efficiency side, but I'm wondering if there's scope to do a model persistence code tutorial?

Here's the overview of what I'd do:

  • Fit model on data (probably using house price dataset)
  • Dump model using joblib to .pkl
  • Programatically output requirements.txt
  • Save train, test to CSV
  • Reload .pkl file and create dummy predictions.

Keen to hear your thoughts @jnothman

@amueller
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amueller commented Aug 8, 2019

There's this: https://scikit-learn.org/dev/modules/model_persistence.html

@zioalex
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zioalex commented Oct 12, 2019

Hi guys, I was searching a good easy issue where to start. This seems already done to me. Can I help here some how?

@cmarmo cmarmo mentioned this issue Aug 3, 2020
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@cmarmo cmarmo removed the help wanted label Aug 5, 2020
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DOC revamp model persistence documentation cmarmo/scikit-learn
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@jnothman @amueller @arjoly @cmarmo @lesteve @zioalex @MattpSoftware @evanmiller29