Add support to multilabel

[glemaitre]

We should add support to multilabel when y can be converted back to multiclass.
It means that the sum of each row should be one.

[chkoar]

Are we talking about multilabel or multioutput/multiclass?

[glemaitre]

those are always confusing. an example will speak for itself (but it should a multilabel case encoding a multiclass)

[[0 0 1]
 [1 0 0]
 [0 1 0]]

is a multilabel-indicator type encoding the following:

[[2]
 [0]
 [1]]

[chkoar]

I wouldn't call it multilabel. It is a binarized version of the target, right?
I am -1 for adding that logic inside the algorithms. We could use the LabelBinirizer for that, no?

[massich]

@chkoar I think that @glemaitre refers to provide the same support for y as scikit-learn does ( see here )

[MarcoNiemann]

Well, shouldn't multi-label be:

[[0,1,1],
 [1,0,0],
 [0,1,0],
 [1,0,1],
 [1,0,1],
 ...]

Because the version mentioned by @glemaitre appears - as stated by @chkoar - to be a binarized version of a multi-class problem. But the difference between multi-class and multi-label is that multi-class only allows the assignment of a single class to the target instance, whereas in a multi-label case it can be an arbitrary amount of class assignments.

For an implementation one might consider the label powerset transformation of multi-label data into a multiclass data set. So e.g. for the data set above one might apply the following transformation:

[[1],
 [2],
 [3],
 [4],
 [4],
 ...]

For all people searching for a quick and dirty solution I appear to have some success with the following solution:

from skmultilearn.problem_transformation import LabelPowerset
from imblearn.over_sampling import RandomOverSampler

# Import a dataset with X and multi-label y

lp = LabelPowerset()
ros = RandomOverSampler(random_state=42)

# Applies the above stated multi-label (ML) to multi-class (MC) transformation.
yt = lp.transform(y)

X_resampled, y_resampled = ros.fit_sample(X, yt)

# Inverts the ML-MC transformation to recreate the ML set
y_resampled = lp.inverse_transform(y_resampled)

(Use of the skmultilearn package for convenience sake to avoid custom transformation!)

[glemaitre]

imblearn accept by default one-vs-all enconding from now on

[j-greer]

@MarcoNiemann your solution works well when the imbalance occurs across the i^th dimension of y rather than the j^th.

Expanding upon your example:

[
[0,1,1],
[0,1,1],
[1,1,1],
[1,1,1],
[1,1,1],
[1,1,1],

 ...
]

Can be considered imbalanced along rows but take the following example:

[
[0,0,1],
[1,0,0],
[1,0,0],
[1,1,0],
[1,1,0],
 ...
]

This is imbalanced in the sense that y_i3 is mostly zero. Do you know of a way of addressing this type of imbalance problem using imbalanced-learn? @glemaitre

[rjurney]

@glenmaitre This seems an unsolved problem in the Python space. Support for this would be amazing.

[glemaitre]

@rjurney The issue is that the literature does not address this problem. So I am not really sure how we could go forward. It would be cool to have an overview of the full literature. It is a while I did not look at it.

[HabeebullahEbrahemi]

#just correcting the import part for my case python 3.7
from skmultilearn.problem_transform import LabelPowerset