Converting http to https (3)... (scikit-learn#12302)

benchmarks/bench_covertype.py

@@ -34,10 +34,10 @@
    S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
 
  * `"Training Linear SVMs in Linear Time"
-   <www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_
+   <https://www.cs.cornell.edu/people/tj/publications/joachims_06a.pdf>`_
    T. Joachims - In SIGKDD '06
 
-[1] http://archive.ics.uci.edu/ml/datasets/Covertype
+[1] https://archive.ics.uci.edu/ml/datasets/Covertype
 
 """
 from __future__ import division, print_function

doc/about.rst

@@ -99,14 +99,14 @@ full-time. It also hosts coding sprints and other events.
    :align: center
    :target: https://www.inria.fr
 
-`Paris-Saclay Center for Data Science <http://www.datascience-paris-saclay.fr>`_
+`Paris-Saclay Center for Data Science <https://www.datascience-paris-saclay.fr/>`_
 funded one year for a developer to work on the project full-time
 (2014-2015) and 50% of the time of Guillaume Lemaitre (2016-2017).
 
 .. image:: images/cds-logo.png
    :width: 200pt
    :align: center
-   :target: http://www.datascience-paris-saclay.fr
+   :target: https://www.datascience-paris-saclay.fr/
 
 `NYU Moore-Sloan Data Science Environment <https://cds.nyu.edu/mooresloan/>`_
 funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan Data Science
@@ -118,14 +118,14 @@ Environment also funds several students to work on the project part-time.
    :target: https://cds.nyu.edu/mooresloan/
 
 
-`Télécom Paristech <http://www.telecom-paristech.com>`_ funded Manoj Kumar (2014),
+`Télécom Paristech <https://www.telecom-paristech.fr/>`_ funded Manoj Kumar (2014),
 Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot (2016-2017)
 and Albert Thomas (2017) to work on scikit-learn.
 
 .. image:: themes/scikit-learn/static/img/telecom.png
    :width: 100pt
    :align: center
-   :target: http://www.telecom-paristech.fr/
+   :target: https://www.telecom-paristech.fr/
 
 
 `Columbia University <https://columbia.edu/>`_ funds Andreas Müller since 2016.

doc/glossary.rst

@@ -170,7 +170,7 @@ General Concepts
         :class:`~sklearn.preprocessing.OneHotEncoder` can be used to
         one-hot encode categorical features.
         See also :ref:`preprocessing_categorical_features` and the
-        `http://contrib.scikit-learn.org/categorical-encoding
+        `https://contrib.scikit-learn.org/categorical-encoding/
         <category_encoders>`_ package for tools related to encoding
         categorical features.
 

doc/modules/clustering.rst

@@ -286,7 +286,7 @@ small, as shown in the example and cited reference.
 .. topic:: References:
 
  * `"Web Scale K-Means clustering"
-   <http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf>`_
+   <https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf>`_
    D. Sculley, *Proceedings of the 19th international conference on World
    wide web* (2010)
 
@@ -445,7 +445,7 @@ works well for a small number of clusters but is not advised when using
 many clusters.
 
 For two clusters, it solves a convex relaxation of the `normalised
-cuts <http://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf>`_ problem on
+cuts <https://people.eecs.berkeley.edu/~malik/papers/SM-ncut.pdf>`_ problem on
 the similarity graph: cutting the graph in two so that the weight of the
 edges cut is small compared to the weights of the edges inside each
 cluster. This criteria is especially interesting when working on images:
@@ -1008,7 +1008,7 @@ the user is advised
 
  * Tian Zhang, Raghu Ramakrishnan, Maron Livny
    BIRCH: An efficient data clustering method for large databases.
-   http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
+   https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
 
  * Roberto Perdisci
    JBirch - Java implementation of BIRCH clustering algorithm
@@ -1144,7 +1144,7 @@ random labelings by defining the adjusted Rand index as follows:
 .. topic:: References
 
  * `Comparing Partitions
-   <http://link.springer.com/article/10.1007%2FBF01908075>`_
+   <https://link.springer.com/article/10.1007%2FBF01908075>`_
    L. Hubert and P. Arabie, Journal of Classification 1985
 
  * `Wikipedia entry for the adjusted Rand index
@@ -1483,7 +1483,7 @@ mean of homogeneity and completeness**:
 .. topic:: References
 
  * `V-Measure: A conditional entropy-based external cluster evaluation
-   measure <http://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
+   measure <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
    Andrew Rosenberg and Julia Hirschberg, 2007
 
  .. [B2011] `Identication and Characterization of Events in Social Media

doc/modules/compose.rst

@@ -380,7 +380,7 @@ ColumnTransformer for heterogeneous data
 Many datasets contain features of different types, say text, floats, and dates,
 where each type of feature requires separate preprocessing or feature
 extraction steps.  Often it is easiest to preprocess data before applying
-scikit-learn methods, for example using `pandas <http://pandas.pydata.org/>`__.
+scikit-learn methods, for example using `pandas <https://pandas.pydata.org/>`__.
 Processing your data before passing it to scikit-learn might be problematic for
 one of the following reasons:
 
@@ -395,7 +395,7 @@ transformations for different columns of the data, within a
 :class:`~sklearn.pipeline.Pipeline` that is safe from data leakage and that can
 be parametrized. :class:`~sklearn.compose.ColumnTransformer` works on
 arrays, sparse matrices, and
-`pandas DataFrames <http://pandas.pydata.org/pandas-docs/stable/>`__.
+`pandas DataFrames <https://pandas.pydata.org/pandas-docs/stable/>`__.
 
 To each column, a different transformation can be applied, such as
 preprocessing or a specific feature extraction method::

doc/modules/covariance.rst

@@ -264,7 +264,7 @@ paper. It is the same algorithm as in the R ``glasso`` package.
 .. topic:: References:
 
    * Friedman et al, `"Sparse inverse covariance estimation with the
-     graphical lasso" <http://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
+     graphical lasso" <https://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
      Biostatistics 9, pp 432, 2008
 
 .. _robust_covariance:

doc/modules/cross_validation.rst

@@ -422,7 +422,7 @@ fold cross validation should be preferred to LOO.
  * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation
    <https://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf>`_, SIAM 2008;
  * G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to
-   Statistical Learning <http://www-bcf.usc.edu/~gareth/ISL>`_, Springer 2013.
+   Statistical Learning <https://www-bcf.usc.edu/~gareth/ISL/>`_, Springer 2013.
 
 
 Leave P Out (LPO)

doc/modules/decomposition.rst

@@ -270,10 +270,10 @@ factorization, while larger values shrink many coefficients to zero.
 .. topic:: References:
 
   .. [Mrl09] `"Online Dictionary Learning for Sparse Coding"
-     <http://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
+     <https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
      J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
   .. [Jen09] `"Structured Sparse Principal Component Analysis"
-     <www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf>`_
+     <https://www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf>`_
      R. Jenatton, G. Obozinski, F. Bach, 2009
 
 
@@ -289,7 +289,7 @@ where :math:`k` is a user-specified parameter.
 When truncated SVD is applied to term-document matrices
 (as returned by ``CountVectorizer`` or ``TfidfVectorizer``),
 this transformation is known as
-`latent semantic analysis <http://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
+`latent semantic analysis <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
 (LSA), because it transforms such matrices
 to a "semantic" space of low dimensionality.
 In particular, LSA is known to combat the effects of synonymy and polysemy
@@ -354,7 +354,7 @@ compensating for LSA's erroneous assumptions about textual data.
   * Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008),
     *Introduction to Information Retrieval*, Cambridge University Press,
     chapter 18: `Matrix decompositions & latent semantic indexing
-    <http://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
+    <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
 
 
 .. _DictionaryLearning:
@@ -495,7 +495,7 @@ extracted from part of the image of a raccoon face looks like.
 .. topic:: References:
 
   * `"Online dictionary learning for sparse coding"
-    <http://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
+    <https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
     J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
 
 .. _MiniBatchDictionaryLearning:

doc/modules/feature_extraction.rst

@@ -207,7 +207,7 @@ otherwise the features will not be mapped evenly to the columns.
 
  * Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and
    Josh Attenberg (2009). `Feature hashing for large scale multitask learning
-   <http://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
+   <https://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
 
  * `MurmurHash3 <https://github.com/aappleby/smhasher>`_.
 
@@ -409,7 +409,7 @@ identify and warn about some kinds of inconsistencies.
 
     .. [NQY18] J. Nothman, H. Qin and R. Yurchak (2018).
                `"Stop Word Lists in Free Open-source Software Packages"
-               <http://aclweb.org/anthology/W18-2502>`__.
+               <https://aclweb.org/anthology/W18-2502>`__.
                In *Proc. Workshop for NLP Open Source Software*.
 
 .. _tfidf:
@@ -673,7 +673,7 @@ The output is not shown here.
 
 For an introduction to Unicode and character encodings in general,
 see Joel Spolsky's `Absolute Minimum Every Software Developer Must Know
-About Unicode <http://www.joelonsoftware.com/articles/Unicode.html>`_.
+About Unicode <https://www.joelonsoftware.com/articles/Unicode.html>`_.
 
 .. _`ftfy`: https://github.com/LuminosoInsight/python-ftfy
 
@@ -932,7 +932,7 @@ Some tips and tricks:
     scikit-learn codebase, but can be added by customizing either the
     tokenizer or the analyzer.
     Here's a ``CountVectorizer`` with a tokenizer and lemmatizer using
-    `NLTK <http://www.nltk.org>`_::
+    `NLTK <https://www.nltk.org/>`_::
 
         >>> from nltk import word_tokenize          # doctest: +SKIP
         >>> from nltk.stem import WordNetLemmatizer # doctest: +SKIP

doc/modules/kernel_approximation.rst

@@ -194,7 +194,7 @@ or store training examples.
 .. topic:: References:
 
     .. [RR2007] `"Random features for large-scale kernel machines"
-      <http://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf>`_
+      <https://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf>`_
       Rahimi, A. and Recht, B. - Advances in neural information processing 2007,
     .. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels"
       <http://www.maths.lth.se/matematiklth/personal/sminchis/papers/lis_dagm10.pdf>`_

doc/modules/linear_model.rst

@@ -508,7 +508,7 @@ column is always zero.
 .. topic:: References:
 
  * Original Algorithm is detailed in the paper `Least Angle Regression
-   <http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
+   <https://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
    by Hastie et al.
 
 
@@ -547,7 +547,7 @@ previously chosen dictionary elements.
 
 .. topic:: References:
 
- * http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
+ * https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
 
  * `Matching pursuits with time-frequency dictionaries
    <http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf>`_,
@@ -710,7 +710,7 @@ ARD is also known in the literature as *Sparse Bayesian Learning* and
 
     .. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1
 
-    .. [2] David Wipf and Srikantan Nagarajan: `A new view of automatic relevance determination <http://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
+    .. [2] David Wipf and Srikantan Nagarajan: `A new view of automatic relevance determination <https://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
 
     .. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine <http://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_
 

doc/modules/manifold.rst

@@ -343,7 +343,7 @@ The overall complexity of spectral embedding is
 
    * `"Laplacian Eigenmaps for Dimensionality Reduction
      and Data Representation"
-     <http://web.cse.ohio-state.edu/~mbelkin/papers/LEM_NC_03.pdf>`_
+     <https://web.cse.ohio-state.edu/~mbelkin/papers/LEM_NC_03.pdf>`_
      M. Belkin, P. Niyogi, Neural Computation, June 2003; 15 (6):1373-1396
 
 
@@ -461,15 +461,15 @@ order to avoid that, the disparities :math:`\hat{d}_{ij}` are normalized.
 .. topic:: References:
 
   * `"Modern Multidimensional Scaling - Theory and Applications"
-    <http://www.springer.com/fr/book/9780387251509>`_
+    <https://www.springer.com/fr/book/9780387251509>`_
     Borg, I.; Groenen P. Springer Series in Statistics (1997)
 
   * `"Nonmetric multidimensional scaling: a numerical method"
-    <http://link.springer.com/article/10.1007%2FBF02289694>`_
+    <https://link.springer.com/article/10.1007%2FBF02289694>`_
     Kruskal, J. Psychometrika, 29 (1964)
 
   * `"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis"
-    <http://link.springer.com/article/10.1007%2FBF02289565>`_
+    <https://link.springer.com/article/10.1007%2FBF02289565>`_
     Kruskal, J. Psychometrika, 29, (1964)
 
 .. _t_sne:
@@ -561,7 +561,7 @@ is a tradeoff between performance and accuracy. Larger angles imply that we
 can approximate larger regions by a single point, leading to better speed
 but less accurate results.
 
-`"How to Use t-SNE Effectively" <http://distill.pub/2016/misread-tsne/>`_
+`"How to Use t-SNE Effectively" <https://distill.pub/2016/misread-tsne/>`_
 provides a good discussion of the effects of the various parameters, as well
 as interactive plots to explore the effects of different parameters.
 

doc/modules/metrics.rst

@@ -63,7 +63,7 @@ is equivalent to :func:`linear_kernel`, only slower.)
 
     * C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to
       Information Retrieval. Cambridge University Press.
-      http://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html
+      https://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html
 
 .. _linear_kernel:
 
@@ -149,7 +149,7 @@ Manhattan distance between the input vectors.
 
 It has proven useful in ML applied to noiseless data.
 See e.g. `Machine learning for quantum mechanics in a nutshell
-<http://onlinelibrary.wiley.com/doi/10.1002/qua.24954/abstract/>`_.
+<https://onlinelibrary.wiley.com/doi/10.1002/qua.24954/abstract/>`_.
 
 .. _chi2_kernel:
 

doc/modules/model_evaluation.rst

@@ -775,7 +775,7 @@ binary classification and multilabel indicator format.
 .. topic:: References:
 
   .. [Manning2008] C.D. Manning, P. Raghavan, H. Schütze, `Introduction to Information Retrieval
-     <http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html>`_,
+     <https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html>`_,
      2008.
   .. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman,
      `The Pascal Visual Object Classes (VOC) Challenge
@@ -785,7 +785,7 @@ binary classification and multilabel indicator format.
      <http://www.machinelearning.org/proceedings/icml2006/030_The_Relationship_Bet.pdf>`_,
      ICML 2006.
   .. [Flach2015] P.A. Flach, M. Kull, `Precision-Recall-Gain Curves: PR Analysis Done Right
-     <http://papers.nips.cc/paper/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf>`_,
+     <https://papers.nips.cc/paper/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf>`_,
      NIPS 2015.
 
 

doc/modules/model_persistence.rst

@@ -87,4 +87,4 @@ another architecture is not supported.
 
 If you want to know more about these issues and explore other possible
 serialization methods, please refer to this
-`talk by Alex Gaynor <http://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.
+`talk by Alex Gaynor <https://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.

doc/modules/naive_bayes.rst

@@ -72,7 +72,7 @@ it is known to be a bad estimator, so the probability outputs from
 .. topic:: References:
 
  * H. Zhang (2004). `The optimality of Naive Bayes.
-   <http://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
+   <https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
    Proc. FLAIRS.
 
 .. _gaussian_naive_bayes:

doc/modules/neighbors.rst

@@ -307,7 +307,7 @@ keyword ``algorithm = 'kd_tree'``, and are computed using the class
 .. topic:: References:
 
    * `"Multidimensional binary search trees used for associative searching"
-     <http://dl.acm.org/citation.cfm?doid=361002.361007>`_,
+     <https://dl.acm.org/citation.cfm?doid=361002.361007>`_,
      Bentley, J.L., Communications of the ACM (1975)
 
 

doc/modules/neural_networks_unsupervised.rst

@@ -152,10 +152,10 @@ explore the space more thoroughly.
 .. topic:: References:
 
     * `"A fast learning algorithm for deep belief nets"
-      <http://www.cs.toronto.edu/~hinton/absps/fastnc.pdf>`_
+      <https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf>`_
       G. Hinton, S. Osindero, Y.-W. Teh, 2006
 
     * `"Training Restricted Boltzmann Machines using Approximations to
       the Likelihood Gradient"
-      <http://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf>`_
+      <https://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf>`_
       T. Tieleman, 2008

doc/modules/outlier_detection.rst

@@ -153,7 +153,7 @@ but regular, observation outside the frontier.
 .. topic:: References:
 
     * `Estimating the support of a high-dimensional distribution
-      <http://dl.acm.org/citation.cfm?id=1119749>`_ Schölkopf,
+      <https://dl.acm.org/citation.cfm?id=1119749>`_ Schölkopf,
       Bernhard, et al. Neural computation 13.7 (2001): 1443-1471.
 
 .. topic:: Examples:

doc/modules/random_projection.rst

@@ -22,7 +22,7 @@ technique for distance based method.
 .. topic:: References:
 
  * Sanjoy Dasgupta. 2000.
-   `Experiments with random projection. <http://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf>`_
+   `Experiments with random projection. <https://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf>`_
    In Proceedings of the Sixteenth conference on Uncertainty in artificial
    intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan
    Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.

doc/modules/tree.rst

@@ -125,8 +125,8 @@ Using the Iris dataset, we can construct a tree as follows::
     >>> clf = clf.fit(iris.data, iris.target)
 
 Once trained, we can export the tree in `Graphviz
-<http://www.graphviz.org/>`_ format using the :func:`export_graphviz`
-exporter. If you use the `conda <http://conda.io>`_ package manager, the graphviz binaries  
+<https://www.graphviz.org/>`_ format using the :func:`export_graphviz`
+exporter. If you use the `conda <https://conda.io/>`_ package manager, the graphviz binaries  
 and the python package can be installed with 
 
     conda install python-graphviz