8000 [WIP] Make LabelEncoder more friendly to new labels by mjbommar · Pull Request #3243 · scikit-learn/scikit-learn · GitHub
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[WIP] Make LabelEncoder more friendly to new labels #3243

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mjbommar
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@mjbommar mjbommar commented Jun 4, 2014

This PR intends to make preprocessing.LabelEncoder more friendly for production/pipeline usage by adding a new_labels constructor argument.

Instead of always raising ValueError for unseen/new labels in transform, LabelEncoder may be initialized with new_labels as:

  • "raise": current behavior, i.e., raise ValueError; to remain default behavior
  • "nan": return np.nan for unseen/new labels
  • "update": update classes_ with new IDs [N, ..., N+m-1] for m new labels and assign
  • "label": set newly seen labels to have fixed class new_label_class=-1

Tests and documentation updates included.

(edit: adding "label" to list for quick summary)

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jnothman commented Jun 5, 2014

What about selecting a fixed "default" label? I assume this would be at
least as common as wanting nan as a label.

On 5 June 2014 03:42, Michael Bommarito notifications@github.com wrote:

This PR intends to make preprocessing.LabelEncoder more friendly for
production/pipeline usage by adding a new_labels constructor argument.

Instead of always raising ValueError for unseen/new labels in transform,
LabelEncoder may be initialized with new_labels as:

  • "raise": current behavior, i.e., raise ValueError; to remain
    default behavior
  • "nan": return np.nan for unseen/new labels
  • "update": update classes_ with new IDs [N, ..., N+m-1] for m new
    labels and assign

Tests and documentation updates included.

You can merge this Pull Request by running

git pull https://github.com/mjbommar/scikit-learn label-encoder-unseen

Or view, comment on, or merge it at:

#3243
Commit Summary

  • Adding new_labels argument to LabelEncoder
  • Adding tests for new_labels argument.
  • Changing classes_ update strategy
  • Adding nan behavior, renaming to
  • Updating tests to include nan case and update name
  • Fixing docstring for test-doc pass
  • Fixing docstring for test-doc pass (for real)
  • Updating doctests
  • Updating constructor documentation

File Changes

Patch Links:

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#3243.

@mjbommar
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mjbommar commented Jun 5, 2014

@jnothman, good idea. I would normally hit it with pd.fillna after, but that would be even friendlier.

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mjbommar commented Jun 8, 2014

@jnothman, should I add a new example to preprocessing.rst that shows how to handle this? I think this issue of handling unseen categorical labels is a very common pitfall for people and I seem to run into it very often when teaching.

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jnothman commented Jun 8, 2014

Yes, I think an example would be helpful.

8000
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mjbommar commented Jun 8, 2014

@jnothman, another subtle point about np.nan here - if we allow for np.nan encodings, we need to make our returned array as float*, not int*.

Which do we want?

  • All encodings returned as np.float64 (handles np.nan)
  • new_labels="nan" returned as np.float64, else np.int64

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Coverage Status

Coverage decreased (-0.0%) when pulling ab788f7 on mjbommar:label-encoder-unseen into d298a37 on scikit-learn:master.

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jnothman commented Jun 8, 2014

As a categorical label, NaN seems a bit strange altogether, given that it is a float. Is the option needed?

But if it's there, yes, I'd say upcast to a float type (could use find_common_type).

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mjbommar commented Jun 8, 2014

Well, this is the same issues that pandas addresses more generally, and Wes seemed to find no better solution than upcasting for np.nan:
http://pandas.pydata.org/pandas-docs/stable/gotchas.html#na-type-promotions

I think we know deterministically that the indices will be integer unless upcast, so do we need to use find_common_type?

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jnothman commented Jun 8, 2014

Oh perhaps not. I was thinking of the case where a smaller float is
suitable.

On 9 June 2014 08:33, Michael Bommarito notifications@github.com wrote:

Well, this is the same issues that pandas addresses more generally, and
Wes seemed to find no better solution than upcasting for np.nan:
http://pandas.pydata.org/pandas-docs/stable/gotchas.html#na-type-promotions

I think we know deterministically that the indices will be integer unless
upcast, so do we need to use find_common_type?

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#3243 (comment)
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jnothman commented Jun 8, 2014

But I'm not sure find_common_type helps there anyway

On 9 June 2014 08:44, Joel Nothman jnothman@student.usyd.edu.au wrote:

Oh perhaps not. I was thinking of the case where a smaller float is
suitable.

On 9 June 2014 08:33, Michael Bommarito notifications@github.com wrote:

Well, this is the same issues that pandas addresses more generally, and
Wes seemed to find no better solution than upcasting for np.nan:

http://pandas.pydata.org/pandas-docs/stable/gotchas.html#na-type-promotions

I think we know deterministically that the indices will be integer unless
upcast, so do we need to use find_common_type?

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#3243 (comment)
.

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mjbommar commented Jun 8, 2014

OK, the version I have currently pushed has the proposed float/int logic.

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@jnothman, just wanted to see if you were waiting on anything from me on this. I think I've addressed your comments thus far but wanted to make sure.

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I've not got further than looking at the PR description! It's a busy week, and I'm overseas for the next two, so I'm avoiding promises to review atm.

mjbommar added 25 commits July 24, 2014 08:03
@mjbommar
Adding tests for new_labels argument.
d990207
@mjbommar
Changing classes_ update strategy
8000
a69840b
@mjbommar
Adding nan behavior, renaming to
fce9fb5
@mjbommar
Updating tests to include nan case and update name
76921e5
@mjbommar
Fixing docstring for test-doc pass
0e39a2a
@mjbommar
Fixing docstring for test-doc pass (for real)
1da2880
@mjbommar
Updating doctests
926b166
@mjbommar
Updating constructor documentation
5ef9b85
@mjbommar
Adding specific "label" option to new_labels
4dfb4cb
@mjbommar
Adding test for "label" option to new_labels
392e54b
@mjbommar
Updating docstring for new_labels="label"
6D40
e053635
@mjbommar
pep8
122a98f
@mjbommar
Autodoc fix
de18372
@mjbommar
Fixing rst docs
d735ca2
@mjbommar
Changing dtypes for new_labels
d276565
@mjbommar
Adding example for new_labels argument
a01f8b0
@mjbommar
Adding new_labels handling to fit/fit_transform
495347c
@mjbommar
Improving difficulty of test cases with non-increasing unseen labels
dee4ae0
@mjbommar
Moving ValueError check to fit
c297017
@mjbommar
Improving difficult for new_labels='update' test to include multiple …
f29800b 
…transform with new labels
@mjbommar
Fixing negative indexing, renamed z->out, failing approach for new_la…
74b7589 
…bels=update w/ searchsorted
@mjbommar
PEP8
3e1be5d
@mjbommar
Removing nan option and corresponding test
abf01cc
@mjbommar
Handling repeated transform calls with new_class_mapping_, refactorin…
f26a902 
…g, cleaning after removing np.nan.
@mjbommar
Rebase
0725d4c
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Cleanly rebased final PR pending.

@mjbommar mjbommar mentioned this pull request Jul 24, 2014
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@mjbommar
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Closing for PR #3483.

@mjbommar mjbommar closed this Jul 24, 2014
@mjbommar mjbommar mentioned this pull request Aug 27, 2014
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