From a07c6c3d151dbda275d2d87ed360b2086ac498fc Mon Sep 17 00:00:00 2001 From: Micky774 Date: Thu, 6 Jan 2022 19:30:47 -0500 Subject: [PATCH 1/4] DOC -- sklearn.metrics.pairwise.nan_euclidean_distances Brought into numpydoc compliance --- sklearn/metrics/pairwise.py | 18 +++++++++++------- sklearn/tests/test_docstrings.py | 1 - 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index a48aebf0415b5..b3c8202047448 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -412,8 +412,10 @@ def nan_euclidean_distances( Parameters ---------- X : array-like of shape=(n_samples_X, n_features) + Array of vectors. Y : array-like of shape=(n_samples_Y, n_features), default=None + Array of vectors. If `None`, uses `Y=X`. squared : bool, default=False Return squared Euclidean distances. @@ -427,11 +429,20 @@ def nan_euclidean_distances( Returns ------- distances : ndarray of shape (n_samples_X, n_samples_Y) + Returns the distances between the row vectors of `X` + and the row vectors of `Y`. See Also -------- paired_distances : Distances between pairs of elements of X and Y. + References + ---------- + * John K. Dixon, "Pattern Recognition with Partly Missing Data", + IEEE Transactions on Systems, Man, and Cybernetics, Volume: 9, Issue: + 10, pp. 617 - 621, Oct. 1979. + http://ieeexplore.ieee.org/abstract/document/4310090/ + Examples -------- >>> from sklearn.metrics.pairwise import nan_euclidean_distances @@ -445,13 +456,6 @@ def nan_euclidean_distances( >>> nan_euclidean_distances(X, [[0, 0]]) array([[1. ], [1.41421356]]) - - References - ---------- - * John K. Dixon, "Pattern Recognition with Partly Missing Data", - IEEE Transactions on Systems, Man, and Cybernetics, Volume: 9, Issue: - 10, pp. 617 - 621, Oct. 1979. - http://ieeexplore.ieee.org/abstract/document/4310090/ """ force_all_finite = "allow-nan" if is_scalar_nan(missing_values) else True diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py index 742a02d925be5..94b87d472b56e 100644 --- a/sklearn/tests/test_docstrings.py +++ b/sklearn/tests/test_docstrings.py @@ -128,7 +128,6 @@ "sklearn.metrics.pairwise.kernel_metrics", "sklearn.metrics.pairwise.laplacian_kernel", "sklearn.metrics.pairwise.manhattan_distances", - "sklearn.metrics.pairwise.nan_euclidean_distances", "sklearn.metrics.pairwise.paired_cosine_distances", "sklearn.metrics.pairwise.paired_distances", "sklearn.metrics.pairwise.paired_euclidean_distances", From 583fde7fd2f0deec1eface73834f9eb88afdeba4 Mon Sep 17 00:00:00 2001 From: Micky774 Date: Mon, 10 Jan 2022 15:11:28 -0500 Subject: [PATCH 2/4] Updated doc to reflect de-facto `pairwise.py` standard --- sklearn/metrics/pairwise.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index b3c8202047448..36449944eb5e9 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -411,11 +411,12 @@ def nan_euclidean_distances( Parameters ---------- - X : array-like of shape=(n_samples_X, n_features) - Array of vectors. + X : {array-like, sparse matrix} of shape (n_samples_X, n_features) + An array where each row is a sample and each column is a feature. Y : array-like of shape=(n_samples_Y, n_features), default=None - Array of vectors. If `None`, uses `Y=X`. + An array where each row is a sample and each column is a feature. + If `None`, method uses `Y=X`. squared : bool, default=False Return squared Euclidean distances. From 0863d3b87004a98a1043564de737efe365a5b0bb Mon Sep 17 00:00:00 2001 From: Micky774 Date: Mon, 10 Jan 2022 15:14:23 -0500 Subject: [PATCH 3/4] Fixed typo from copy --- sklearn/metrics/pairwise.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index 36449944eb5e9..b57b77c297373 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -411,7 +411,7 @@ def nan_euclidean_distances( Parameters ---------- - X : {array-like, sparse matrix} of shape (n_samples_X, n_features) + X : array-like of shape (n_samples_X, n_features) An array where each row is a sample and each column is a feature. Y : array-like of shape=(n_samples_Y, n_features), default=None From d849261aff808332f34541e4c3a726327ab96168 Mon Sep 17 00:00:00 2001 From: Micky774 Date: Mon, 10 Jan 2022 15:24:07 -0500 Subject: [PATCH 4/4] Removed trailing whitespaces --- sklearn/metrics/pairwise.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index b57b77c297373..e91a18faa7289 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -411,12 +411,12 @@ def nan_euclidean_distances( Parameters ---------- - X : array-like of shape (n_samples_X, n_features) - An array where each row is a sample and each column is a feature. + X : array-like of shape (n_samples_X, n_features) + An array where each row is a sample and each column is a feature. Y : array-like of shape=(n_samples_Y, n_features), default=None - An array where each row is a sample and each column is a feature. - If `None`, method uses `Y=X`. + An array where each row is a sample and each column is a feature. + If `None`, method uses `Y=X`. squared : bool, default=False Return squared Euclidean distances.