API Make IterativeImputer experimental#13824
Merged
jnothman merged 12 commits intoscikit-learn:masterfrom May 8, 2019
Merged
Conversation
Member
|
You'll also need to import |
thomasjpfan
approved these changes
May 8, 2019
Member
Author
|
I take it from
#13789 (comment)
that @glemaitre notionally approves. Anyone else?
|
Merged
glemaitre
reviewed
May 8, 2019
| repeated for ``max_iter`` imputation rounds. The results of the final | ||
| imputation round are returned. | ||
|
|
||
| .. note:: |
Member
There was a problem hiding this comment.
Could you make a small change in l. 25 and 27 of this file. The hyperlinks does not work:
- :class:
impute.SimpleImputer-> :class:SimpleImputer - :class:
impute.IterativeImputer-> :class:IterativeImputer
TomDLT
approved these changes
May 8, 2019
Member
TomDLT
left a comment
There was a problem hiding this comment.
LGTM except for some copy-paste left-overs.
sklearn/impute/_iterative.py
Outdated
|
|
||
| This estimator is still **experimental** for now: the predictions | ||
| and the API might change without any deprecation cycle. To use it, | ||
| you need to explicitly import ``enable_hist_gradient_boosting``:: |
sklearn/impute/_iterative.py
Outdated
|
|
||
| >>> # explicitly require this experimental feature | ||
| >>> from sklearn.experimental import enable_iterative_imputer # noqa | ||
| >>> # now you can import normally from ensemble |
|
|
||
| >>> # explicitly require this experimental feature | ||
| >>> from sklearn.experimental import enable_iterative_imputer # noqa | ||
| >>> # now you can import normally from ensemble |
Member
Author
|
Thanks for finding those. I did this in a rush
|
Member
Author
|
Sorry for the merge conflicts on KNNImputer, @thomasjpfan! |
jnothman
added a commit
to jnothman/scikit-learn
that referenced
this pull request
May 8, 2019
ogrisel
reviewed
May 10, 2019
| # enable experimental module so that the new GBDTs estimators can be | ||
| # enable experimental module so that experimental estimators can be | ||
| # discovered properly by sphinx | ||
| from sklearn.experimental import enable_hist_gradient_boosting # noqa |
Member
There was a problem hiding this comment.
@jnothman I believe this does not work as expected:
>>> from sklearn.experimental import *
>>> from sklearn.ensemble import HistGradientBoostingRegressor
Traceback (most recent call last):
File "<ipython-input-5-47b8aeb492fe>", line 1, in <module>
from sklearn.ensemble import HistGradientBoostingRegressor
ImportError: cannot import name 'HistGradientBoostingRegressor' from 'sklearn.ensemble' (/home/ogrisel/code/scikit-learn/sklearn/ensemble/__init__.py)
>>> from sklearn.experimental import enable_hist_gradient_boosting
>>> from sklearn.ensemble import HistGradientBoostingRegressor
>>>
koenvandevelde
pushed a commit
to koenvandevelde/scikit-learn
that referenced
this pull request
Jul 12, 2019
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Fixes #13789 .
See #13773. The point is that we've made some design and algorithmic choices that may deserve refinement over the next release or two without requiring backwards compatibility.
We need to either accept or reject this change before release of 0.21.0