pyepo.func.rank

Learning to rank Losses

Module Contents

Classes

listwiseLTR

An autograd module for listwise learning to rank, where the goal is to learn

pairwiseLTR

An autograd module for pairwise learning to rank, where the goal is to learn

pointwiseLTR

An autograd module for pointwise learning to rank, where the goal is to
class pyepo.func.rank.listwiseLTR(optmodel, processes=1, solve_ratio=1, reduction='mean', dataset=None)

Bases: pyepo.func.abcmodule.optModule

An autograd module for listwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly.

For the listwise LTR, the cost vector needs to be predicted from the contextual data and the loss measures the scores of the whole ranked lists.

Thus, it allows us to design an algorithm based on stochastic gradient descent.

Reference: <https://proceedings.mlr.press/v162/mandi22a.html>

forward(pred_cost, true_cost)

Forward pass

class pyepo.func.rank.pairwiseLTR(optmodel, processes=1, solve_ratio=1, reduction='mean', dataset=None)

Bases: pyepo.func.abcmodule.optModule

An autograd module for pairwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly.

For the pairwise LTR, the cost vector needs to be predicted from the contextual data and the loss learns the relative ordering of pairs of items.

Thus, it allows us to design an algorithm based on stochastic gradient descent.

Reference: <https://proceedings.mlr.press/v162/mandi22a.html>

forward(pred_cost, true_cost)

Forward pass

class pyepo.func.rank.pointwiseLTR(optmodel, processes=1, solve_ratio=1, reduction='mean', dataset=None)

Bases: pyepo.func.abcmodule.optModule

An autograd module for pointwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly.

For the pointwise LTR, the cost vector needs to be predicted from contextual data, and calculates the ranking scores of the items.

Thus, it allows us to design an algorithm based on stochastic gradient descent.

Reference: <https://proceedings.mlr.press/v162/mandi22a.html>

forward(pred_cost, true_cost)

Forward pass