From 0faae2b17845b241a044860de818e859feb3b0c2 Mon Sep 17 00:00:00 2001 From: Steffen Schneider Date: Sat, 24 Jun 2023 13:51:27 +0200 Subject: [PATCH] Fix docs --- cebra/integrations/sklearn/metrics.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/cebra/integrations/sklearn/metrics.py b/cebra/integrations/sklearn/metrics.py index c002665e..cc07e8df 100644 --- a/cebra/integrations/sklearn/metrics.py +++ b/cebra/integrations/sklearn/metrics.py @@ -163,8 +163,9 @@ def _consistency_datasets( labels: List of labels corresponding to each embedding and to use for alignment between them. num_discretization_bins: Number of values for the digitalized common labels. The discretized labels are used - for embedding alignment. Also see the ``n_bins`` argument in :py:mod:`~..helpers.align_embeddings` - for more information on how this parameter is used internally. This argument is only used if ``labels`` + for embedding alignment. Also see the ``n_bins`` argument in + :py:mod:`cebra.integrations.sklearn.helpers.align_embeddings` for more information on how this + parameter is used internally. This argument is only used if ``labels`` is not ``None`` and the given labels are continuous and not already discrete. Returns: @@ -327,8 +328,9 @@ def consistency_score( trained on the **same dataset**. *Consistency between datasets* means the consistency between embeddings obtained from models trained on **different datasets**, such as different animals, sessions, etc. num_discretization_bins: Number of values for the digitalized common labels. The discretized labels are used - for embedding alignment. Also see the ``n_bins`` argument in :py:mod:`~..helpers.align_embeddings` - for more information on how this parameter is used internally. This argument is only used if ``labels`` + for embedding alignment. Also see the ``n_bins`` argument in + :py:mod:`cebra.integrations.sklearn.helpers.align_embeddings` for more information on how this + parameter is used internally. This argument is only used if ``labels`` is not ``None``, alignment between datasets is used (``between = "datasets"``), and the given labels are continuous and not already discrete.