ranklab 0.1.0

Learning to Rank: differentiable ranking, LTR losses (RankNet, LambdaRank, ApproxNDCG, ListNet, ListMLE), and IR evaluation metrics.

ranklab

Python bindings for rankit -- differentiable ranking, LTR losses, and IR evaluation metrics.

Install

pip install ranklab

Usage

import ranklab

# Differentiable soft ranking
scores = [5.0, 1.0, 2.0, 4.0, 3.0]
ranks = ranklab.soft_rank(scores, temperature=1.0)

# RankNet pairwise loss
predictions = [0.8, 0.3, 0.6]
relevance = [2.0, 0.0, 1.0]
loss = ranklab.ranknet_loss(predictions, relevance)

# NDCG evaluation
ranked = [("doc1", 0.9), ("doc2", 0.8), ("doc3", 0.7)]
qrels = {"doc1": 2, "doc2": 1, "doc3": 0}
score = ranklab.ndcg(ranked, qrels, k=3)

API

Differentiable ranking: soft_rank, soft_rank_neural_sort, soft_rank_sigmoid, soft_rank_smooth_i, differentiable_topk

LTR losses: ranknet_loss, approx_ndcg, lambda_loss, listnet_loss, listmle_loss

Gradients: compute_lambdarank_gradients, compute_ranking_svm_gradients

Eval metrics: ndcg, map_score, mrr, precision_at_k, recall_at_k

License

MIT OR Apache-2.0