DOC http:// → https:// (scikit-learn#27581)

neurodata · Oct 13, 2023 · ed82f78 · ed82f78
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diff --git 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
@@ -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 <lope@usal.es>

diff --git 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
-    <http://dx.doi.org/10.2139/ssrn.3164764>`_
+    <https://doi.org/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
@@ -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
-    <http://dx.doi.org/10.2139/ssrn.3164764>`_
+    <https://doi.org/10.2139/ssrn.3164764>`_
 """
 
 # Authors: Christian Lorentzen <lorentzen.ch@gmail.com>

diff --git 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
@@ -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"
-           <http://dx.doi.org/10.1080/01621459.1999.10473882>`_
+           <https://doi.org/10.1080/01621459.1999.10473882>`_
     .. [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/