import itertools

import numpy as np

def transform_pairwise(X, y, blocks=None):

"""Transforms data into pairs with balanced labels for ranking

Transforms a n-class ranking problem into a two-class classification

problem.

In this method only pairs from the same block value are selected,

except for those that have the

same target value. The output is an array of balanced classes, i.e.

there are the same number of -1 as +1

Parameters

----------

X : array, shape (n_samples, n_features)

The data

y : array, shape (n_samples,)

Target labels.

b : array, shape (n_samples,), optional

Returns

-------

X_trans : array, shape (k, n_feaures)

Data as pairs

y_trans : array, shape (k,)

Output class labels, where classes have values {-1, +1}

diff: array

target difference for each considered samples

"""

X_new = []

y_new = []

diff = []

if blocks is None:

blocks = np.ones(len(y))

y = np.asarray(y)

comb = itertools.combinations(range(X.shape[0]), 2)

k = 0

for (i, j) in comb:

if y[i] == y[j] or blocks[i] != blocks[j]:

# skip if same target or different group

continue

X_new.append(X[i] - X[j])

diff.append(y[i] - y[j])

y_new.append(np.sign(diff[-1]))

# output balanced classes

if y_new[-1] != (-1) ** k:

y_new[-1] = - y_new[-1]

X_new[-1] = - X_new[-1]

diff[-1] = - diff[-1]

k += 1

return np.asarray(X_new), np.asarray(y_new).ravel(), np.array(diff).ravel()