1) was indeed a design decision. Your design is certainly an alternative
design, that might be more convenient in some situations,
but requires adding this feature to all transformers, which basically
just adds a bunch of boilerplate code everywhere.
So you could argue our design decision was more driven by ease of
maintenance than ease of use.
There might be some transformers in your package that we could add to
scikit-learn in some form, but several are already available,
SimpleImputer implements MedianMeanImputer, CategoricalVariableImputer
and FrequentCategoryImputer
We don't currently have RandomSampleImputer and EndTailImputer, I think.
AddNaNBinaryImputer is "MissingIndicator" in sklearn.
OneHotCategoricalEncoder and OrdinalEncoder exist,
CountFrequencyCategoricalEncoder and MeanCategoriclaEncoder are in the
works,
though there are some arguments about the details. These are also in the
categorical-encoding package:
http://contrib.scikit-learn.org/categorical-encoding/
RareLabelCategoricalEncoder is something I definitely want in
OneHotEncoder, not sure if there's a PR yet.
Do you have examples of WoERatioCategoricalEncoder or Windsorizer or any
of the discretizers actually working well in practice?
I have not seen them used much, they seemed to be popular in Weka, though.
BoxCoxTransformer is implemented in PowerTransformer, and
LogTransformer, ReciprocalTransformer and ExponentialTransformer can be
implemented as FunctionTransformer(np.log), FunctionTransformer(lambda
x: 1/x) and FunctionTransformer(lambda x: x ** exp) I believe.
It might be interesting to add your package to scikit-learn-contrib:
https://github.com/scikit-learn-contrib
We are struggling a bit with how to best organize that, though.
Cheers,
Andy
On 4/10/19 2:13 PM, Sole Galli wrote:
Hi Nicolas,
You are right, I am just checking this in the source code.
Sorry for the confusion and thanks for the quick response
Cheers
Sole
On Wed, 10 Apr 2019 at 18:43, Nicolas Goix <goix.nico...@gmail.com
<mailto:goix.nico...@gmail.com>> wrote:
Hi Sole,
I'm not sure the 2 limitations you mentioned are correct.
1) in your example, using the ColumnTransformer you can impute
different values for different columns.
2) the sklearn transformers do learn on the training set and are
able to perpetuate the values learnt from the train set to unseen
data.
Nicolas
On Wed, Apr 10, 2019, 18:25 Sole Galli <solegal...@gmail.com
<mailto:solegal...@gmail.com>> wrote:
Dear Scikit-Learn team,
Feature engineering is a big task ahead of building
machine learning models. It involves imputation of missing
values, encoding of categorical variables, discretisation,
variable transformation etc.
Sklearn includes some functionality for feature
engineering, which is useful, but it has a few limitations:
1) it does not allow for feature specification - it will
do the same process on all variables, for example
SimpleImputer. Typically, we want to impute different
columns with different values.
2) It does not capture information from the training set,
this is it does not learn, therefore, it is not able to
perpetuate the values learnt from the train set, to unseen
data.
The 2 limitations above apply to all the feature
transformers in sklearn, I believe.
Therefore, if these transformers are used as part of a
pipeline, we could end up doing different transformations
to train and test, depending on the characteristics of the
datasets. For business purposes, this is not a desired option.
I think that building transformers that learn from the
train set would be of much use for the community.
To this end, I built a python package called feature
engine <https://pypi.org/project/feature-engine/> which
expands the sklearn-api with additional feature
engineering techniques, and the functionality that allows
the transformer to learn from data and store the
parameters learnt.
The techniques included have been used worldwide, both in
business and in data competitions, and reported in kdd
reports and other articles. I also cover them in an udemy
course
<https://www.udemy.com/feature-engineering-for-machine-learning>
which has enrolled several thousand students.
The package capitalises on the use of pandas to capture
the features, but I am confident that the columns names
could be captured and the df transformed to a numpy array
to comply with sklearn requirements.
I wondered whether it would be of interest to include the
functionality of this package within sklearn?
If you would consider extending the sklearn api to include
these transformers, I would be happy to help.
Alternatively, would you consider to add the package to
your website? where you mention the libaries that extend
sklearn functionality?
All feedback is welcome.
Many thanks and I look forward to hearing from you
Thank you so much fur such an awesome contribution through
the sklearn api
Kind regards
Sole
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