def _softmax_ranknet():
pairwise_predicted_scores = scores.get_softmax_pairwise_scores(predicted_scores)
pairwise_label_scores = scores.get_pairwise_label_scores(labels)
loss = loss_fns.get_pair_loss(pairwise_label_scores, pairwise_predicted_scores,
params)
return loss
def _lambdarank():
pairwise_predicted_scores = scores.get_pairwise_scores(predicted_scores)
pairwise_label_scores = scores.get_pairwise_label_scores(labels)
n_data = tf.shape(labels)[0]
swapped_ndcg = math_fns.cal_swapped_ndcg(labels,
predicted_scores, top_k_int=n_data)
loss = loss_fns.get_lambda_pair_loss(pairwise_label_scores, pairwise_predicted_scores,
params, swapped_ndcg)
return loss
def _ranksvm():
pairwise_predicted_scores = scores.get_pairwise_scores(
predicted_scores)
pairwise_label_scores = scores.get_pairwise_label_scores(labels)
return loss_fns.get_hinge_loss(pairwise_label_scores,
pairwise_predicted_scores, params)