From ed82f78769fb29b5bfa79dec2ddb9d0e7babd0d7 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Fri, 13 Oct 2023 17:26:15 +0200 Subject: [PATCH] =?UTF-8?q?DOC=20http://=20=E2=86=92=20https://=20(#27581)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- benchmarks/bench_plot_nmf.py | 2 +- benchmarks/bench_plot_polynomial_kernel_approximation.py | 4 ++-- .../linear_model/plot_poisson_regression_non_normal_loss.py | 2 +- .../linear_model/plot_tweedie_regression_insurance_claims.py | 2 +- setup.py | 4 ++-- sklearn/metrics/_regression.py | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) diff --git a/benchmarks/bench_plot_nmf.py b/benchmarks/bench_plot_nmf.py index d23191df0fbc9..3484850011c1f 100644 --- a/benchmarks/bench_plot_nmf.py +++ b/benchmarks/bench_plot_nmf.py @@ -38,7 +38,7 @@ def _norm(x): """Dot product-based Euclidean norm implementation - See: http://fseoane.net/blog/2011/computing-the-vector-norm/ + See: https://fa.bianp.net/blog/2011/computing-the-vector-norm/ """ return np.sqrt(squared_norm(x)) diff --git a/benchmarks/bench_plot_polynomial_kernel_approximation.py b/benchmarks/bench_plot_polynomial_kernel_approximation.py index ad89d974f3d93..1cd9f70a38f44 100644 --- a/benchmarks/bench_plot_polynomial_kernel_approximation.py +++ b/benchmarks/bench_plot_polynomial_kernel_approximation.py @@ -30,12 +30,12 @@ [1] Pham, N., & Pagh, R. (2013, August). Fast and scalable polynomial kernels via explicit feature maps. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 239-247) -(http://chbrown.github.io/kdd-2013-usb/kdd/p239.pdf) +(https://chbrown.github.io/kdd-2013-usb/kdd/p239.pdf) [2] Charikar, M., Chen, K., & Farach-Colton, M. (2002, July). Finding frequent items in data streams. In International Colloquium on Automata, Languages, and Programming (pp. 693-703). Springer, Berlin, Heidelberg. -(http://www.vldb.org/pvldb/1/1454225.pdf) +(https://people.cs.rutgers.edu/~farach/pubs/FrequentStream.pdf) """ # Author: Daniel Lopez-Sanchez diff --git a/examples/linear_model/plot_poisson_regression_non_normal_loss.py b/examples/linear_model/plot_poisson_regression_non_normal_loss.py index d59284f525fe6..2c5d6c991c72a 100644 --- a/examples/linear_model/plot_poisson_regression_non_normal_loss.py +++ b/examples/linear_model/plot_poisson_regression_non_normal_loss.py @@ -32,7 +32,7 @@ .. [1] A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor Third-Party Liability Claims (November 8, 2018). `doi:10.2139/ssrn.3164764 - `_ + `_ """ diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py index a1894eaa88ed2..97f20a0ac6fe5 100644 --- a/examples/linear_model/plot_tweedie_regression_insurance_claims.py +++ b/examples/linear_model/plot_tweedie_regression_insurance_claims.py @@ -34,7 +34,7 @@ .. [1] A. Noll, R. Salzmann and M.V. Wuthrich, Case Study: French Motor Third-Party Liability Claims (November 8, 2018). `doi:10.2139/ssrn.3164764 - `_ + `_ """ # Authors: Christian Lorentzen diff --git a/setup.py b/setup.py index 87f5c717d7c11..14242d60c3f79 100755 --- a/setup.py +++ b/setup.py @@ -35,7 +35,7 @@ LONG_DESCRIPTION = f.read() MAINTAINER = "Andreas Mueller" MAINTAINER_EMAIL = "amueller@ais.uni-bonn.de" -URL = "http://scikit-learn.org" +URL = "https://scikit-learn.org" DOWNLOAD_URL = "https://pypi.org/project/scikit-learn/#files" LICENSE = "new BSD" PROJECT_URLS = { @@ -174,7 +174,7 @@ def check_package_status(package, min_version): instructions = ( "Installation instructions are available on the " "scikit-learn website: " - "http://scikit-learn.org/stable/install.html\n" + "https://scikit-learn.org/stable/install.html\n" ) if package_status["up_to_date"] is False: diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py index 77a93f4c17513..0259a3f41620c 100644 --- a/sklearn/metrics/_regression.py +++ b/sklearn/metrics/_regression.py @@ -1644,7 +1644,7 @@ def d2_pinball_score( ---------- .. [1] Eq. (7) of `Koenker, Roger; Machado, José A. F. (1999). "Goodness of Fit and Related Inference Processes for Quantile Regression" - `_ + `_ .. [2] Eq. (3.11) of Hastie, Trevor J., Robert Tibshirani and Martin J. Wainwright. "Statistical Learning with Sparsity: The Lasso and Generalizations." (2015). https://hastie.su.domains/StatLearnSparsity/