pyepo.func.rank

Learning to rank Losses

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

Module Contents

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>

solpool

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>

relu

solpool

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>

solpool

forward(pred_cost, true_cost)

Forward pass