From e15d61b916cdb29815bc53497fa4949a7e988b56 Mon Sep 17 00:00:00 2001 From: Fabi <117525608+fabfabi@users.noreply.github.com> Date: Mon, 1 Apr 2024 04:14:40 +0200 Subject: [PATCH] docs: fix bug in tutorial (#10143) --- doc/tutorials/learning_to_rank.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/tutorials/learning_to_rank.rst b/doc/tutorials/learning_to_rank.rst index bfc727ed7..15a611bd0 100644 --- a/doc/tutorials/learning_to_rank.rst +++ b/doc/tutorials/learning_to_rank.rst @@ -52,7 +52,7 @@ Notice that the samples are sorted based on their query index in a non-decreasin X, y = make_classification(random_state=seed) rng = np.random.default_rng(seed) n_query_groups = 3 - qid = rng.integers(0, 3, size=X.shape[0]) + qid = rng.integers(0, n_query_groups, size=X.shape[0]) # Sort the inputs based on query index sorted_idx = np.argsort(qid) @@ -65,14 +65,14 @@ The simplest way to train a ranking model is by using the scikit-learn estimator .. code-block:: python ranker = xgb.XGBRanker(tree_method="hist", lambdarank_num_pair_per_sample=8, objective="rank:ndcg", lambdarank_pair_method="topk") - ranker.fit(X, y, qid=qid) + ranker.fit(X, y, qid=qid[sorted_idx]) Please note that, as of writing, there's no learning-to-rank interface in scikit-learn. As a result, the :py:class:`xgboost.XGBRanker` class does not fully conform the scikit-learn estimator guideline and can not be directly used with some of its utility functions. For instances, the ``auc_score`` and ``ndcg_score`` in scikit-learn don't consider query group information nor the pairwise loss. Most of the metrics are implemented as part of XGBoost, but to use scikit-learn utilities like :py:func:`sklearn.model_selection.cross_validation`, we need to make some adjustments in order to pass the ``qid`` as an additional parameter for :py:meth:`xgboost.XGBRanker.score`. Given a data frame ``X`` (either pandas or cuDF), add the column ``qid`` as follows: .. code-block:: python df = pd.DataFrame(X, columns=[str(i) for i in range(X.shape[1])]) - df["qid"] = qid + df["qid"] = qid[sorted_idx] ranker.fit(df, y) # No need to pass qid as a separate argument from sklearn.model_selection import StratifiedGroupKFold, cross_val_score