Multilabel Classification With Ranking I have a dataset as below:

     Key Attr1 Attr2 Attr3 Attr4 Attr5 Attr6
     kd1 l1    l2     l3    l4   l5     l6
     kd1 l1    l7     l8    l9   l5     l10
     kd1 l11   l12    l13   l14  l5     l10
     kd1 ..................................
      .
      .
      .
     kd2 ..................................
     kd2 ..................................
      .
      .
     kd3 ..................................
      .
      .
      .


For each instance, I have multiple combinations of target outputs(Attr1-Attr6).
Whenever I use multilabel libraries, I get a single combination of outputs.

I want a ranked list(top 3) of target label classifications for each key given as input.

For example:

predict('kd1') should return the following:

res = [ [l1,l7,l8,l9,l10], [l1,l2,l3,l4,l5,l6], [l11,l12,l13,l14,l5,l10] ]

Here res[0] is the best combination, res[1]  is the second best combination and so on.

How do I go about that?
the task seems very hard to understand what you want to achieve as you say that there several equally good representation for each data key... In other words, you say following?

kd1 = l1 + l2 + l3 + l4 + l5 + l6
kd1 = l1 + l7 + l8 + l9   l5 + l10
kd1 = l11 + l12 + l13 + l14 + l5 + l10
kd1 = ...