Converting http to https (2)... (#12292)

doc/developers/contributing.rst

@@ -226,7 +226,7 @@ mailing list for more visibility.
 
 If any of the above seems like magic to you, then look up the `Git documentation
 <https://git-scm.com/documentation>`_ and the `Git development workflow
-<http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ on the
+<https://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ on the
 web.
 
 If some conflicts arise between your branch and the ``master`` branch, you need
@@ -352,7 +352,7 @@ and Cython optimizations.
 
    For two very well documented and more detailed guides on development
    workflow, please pay a visit to the `Scipy Development Workflow
-   <http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ -
+   <https://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html>`_ -
    and the `Astropy Workflow for Developers
    <https://astropy.readthedocs.io/en/latest/development/workflow/development_workflow.html>`_
    sections.

doc/modules/computing.rst

@@ -254,7 +254,7 @@ Influence of the Input Data Representation
 Scipy provides sparse matrix data structures which are optimized for storing
 sparse data. The main feature of sparse formats is that you don't store zeros
 so if your data is sparse then you use much less memory. A non-zero value in
-a sparse (`CSR or CSC <http://docs.scipy.org/doc/scipy/reference/sparse.html>`_)
+a sparse (`CSR or CSC <https://docs.scipy.org/doc/scipy/reference/sparse.html>`_)
 representation will only take on average one 32bit integer position + the 64
 bit floating point value + an additional 32bit per row or column in the matrix.
 Using sparse input on a dense (or sparse) linear model can speedup prediction
@@ -277,7 +277,7 @@ Here is sample code to test the sparsity of your input::
 
 As a rule of thumb you can consider that if the sparsity ratio is greater
 than 90% you can probably benefit from sparse formats. Check Scipy's sparse
-matrix formats `documentation <http://docs.scipy.org/doc/scipy/reference/sparse.html>`_
+matrix formats `documentation <https://docs.scipy.org/doc/scipy/reference/sparse.html>`_
 for more information on how to build (or convert your data to) sparse matrix
 formats. Most of the time the ``CSR`` and ``CSC`` formats work best.
 
@@ -424,7 +424,7 @@ Optimized BLAS / LAPACK implementations include:
  - MKL
  - Apple Accelerate and vecLib frameworks (OSX only)
 
-More information can be found on the `Scipy install page <http://docs.scipy.org/doc/numpy/user/install.html>`_
+More information can be found on the `Scipy install page <https://docs.scipy.org/doc/numpy/user/install.html>`_
 and in this
 `blog post <http://danielnouri.org/notes/2012/12/19/libblas-and-liblapack-issues-and-speed,-with-scipy-and-ubuntu/>`_
 from Daniel Nouri which has some nice step by step install instructions for
@@ -519,7 +519,7 @@ Links
 ......
 
   - `scikit-learn developer performance documentation <../developers/performance.html>`_
-  - `Scipy sparse matrix formats documentation <http://docs.scipy.org/doc/scipy/reference/sparse.html>`_
+  - `Scipy sparse matrix formats documentation <https://docs.scipy.org/doc/scipy/reference/sparse.html>`_
 
 Parallelism, resource management, and configuration
 =====================================================

doc/modules/cross_validation.rst

@@ -435,7 +435,7 @@ fold cross validation should be preferred to LOO.
  * R. Kohavi, `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
    <http://web.cs.iastate.edu/~jtian/cs573/Papers/Kohavi-IJCAI-95.pdf>`_, Intl. Jnt. Conf. AI
  * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation
-   <http://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf>`_, SIAM 2008;
+   <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.
 

doc/modules/linear_model.rst

@@ -152,7 +152,7 @@ as GridSearchCV except that it defaults to Generalized Cross-Validation
     * "Notes on Regularized Least Squares", Rifkin & Lippert (`technical report
       <http://cbcl.mit.edu/publications/ps/MIT-CSAIL-TR-2007-025.pdf>`_,
       `course slides
-      <http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf>`_).
+      <https://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf>`_).
 
 
 .. _lasso:
@@ -751,7 +751,7 @@ are "liblinear", "newton-cg", "lbfgs", "sag" and "saga":
 
 The solver "liblinear" uses a coordinate descent (CD) algorithm, and relies
 on the excellent C++ `LIBLINEAR library
-<http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_, which is shipped with
+<https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_, which is shipped with
 scikit-learn. However, the CD algorithm implemented in liblinear cannot learn
 a true multinomial (multiclass) model; instead, the optimization problem is
 decomposed in a "one-vs-rest" fashion so separate binary classifiers are

doc/modules/model_evaluation.rst

@@ -486,10 +486,10 @@ or *informedness*.
 
   .. [Guyon2015] I. Guyon, K. Bennett, G. Cawley, H.J. Escalante, S. Escalera, T.K. Ho, N. Macià,
      B. Ray, M. Saeed, A.R. Statnikov, E. Viegas, `Design of the 2015 ChaLearn AutoML Challenge
-     <http://ieeexplore.ieee.org/document/7280767/>`_,
+     <https://ieeexplore.ieee.org/document/7280767>`_,
      IJCNN 2015.
   .. [Mosley2013] L. Mosley, `A balanced approach to the multi-class imbalance problem
-     <http://lib.dr.iastate.edu/etd/13537/>`_,
+     <https://lib.dr.iastate.edu/etd/13537/>`_,
      IJCV 2010.
   .. [Kelleher2015] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, `Fundamentals of
      Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples,

doc/modules/naive_bayes.rst

@@ -175,7 +175,7 @@ match.
 
  * Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003).
    `Tackling the poor assumptions of naive bayes text classifiers.
-   <http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf>`_
+   <https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf>`_
    In ICML (Vol. 3, pp. 616-623).
 
 .. _bernoulli_naive_bayes:

doc/modules/neural_networks_supervised.rst

@@ -223,7 +223,7 @@ L-BFGS is a solver that approximates the Hessian matrix which represents the
 second-order partial derivative of a function. Further it approximates the
 inverse of the Hessian matrix to perform parameter updates. The implementation
 uses the Scipy version of `L-BFGS
-<http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html>`_.
+<https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html>`_.
 
 If the selected solver is 'L-BFGS', training does not support online nor
 mini-batch learning.
@@ -368,10 +368,10 @@ or want to do additional monitoring, using ``warm_start=True`` and
 .. topic:: References:
 
     * `"Learning representations by back-propagating errors."
-      <http://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf>`_
+      <https://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf>`_
       Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.
 
-    * `"Stochastic Gradient Descent" <http://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
+    * `"Stochastic Gradient Descent" <https://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
 
     * `"Backpropagation" <http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm>`_
       Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011.

doc/modules/sgd.rst

@@ -215,7 +215,7 @@ Stochastic Gradient Descent for sparse data
 There is built-in support for sparse data given in any matrix in a format
 supported by `scipy.sparse <https://docs.scipy.org/doc/scipy/reference/sparse.html>`_. For maximum efficiency, however, use the CSR
 matrix format as defined in `scipy.sparse.csr_matrix
-<http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html>`_.
+<https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html>`_.
 
 .. topic:: Examples:
 
@@ -429,7 +429,7 @@ Implementation details
 ======================
 
 The implementation of SGD is influenced by the `Stochastic Gradient SVM
-<http://leon.bottou.org/projects/sgd>`_  of Léon Bottou. Similar to SvmSGD,
+<https://leon.bottou.org/projects/sgd>`_  of Léon Bottou. Similar to SvmSGD,
 the weight vector is represented as the product of a scalar and a vector
 which allows an efficient weight update in the case of L2 regularization.
 In the case of sparse feature vectors, the intercept is updated with a
@@ -444,14 +444,14 @@ The code is written in Cython.
 
 .. topic:: References:
 
- * `"Stochastic Gradient Descent" <http://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
+ * `"Stochastic Gradient Descent" <https://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
 
- * `"The Tradeoffs of Large Scale Machine Learning" <http://leon.bottou.org/slides/largescale/lstut.pdf>`_ L. Bottou - Website, 2011.
+ * `"The Tradeoffs of Large Scale Machine Learning" <https://leon.bottou.org/slides/largescale/lstut.pdf>`_ L. Bottou - Website, 2011.
 
  * `"Pegasos: Primal estimated sub-gradient solver for svm"
    <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_
    S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
 
  * `"Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty"
-   <http://www.aclweb.org/anthology/P/P09/P09-1054.pdf>`_
+   <https://www.aclweb.org/anthology/P/P09/P09-1054.pdf>`_
    Y. Tsuruoka, J. Tsujii, S. Ananiadou -  In Proceedings of the AFNLP/ACL '09.

doc/modules/svm.rst

@@ -239,13 +239,13 @@ and use ``decision_function`` instead of ``predict_proba``.
 
  * Wu, Lin and Weng,
    `"Probability estimates for multi-class classification by pairwise coupling"
-   <http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf>`_,
+   <https://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf>`_,
    JMLR 5:975-1005, 2004.
 
 
  * Platt
    `"Probabilistic outputs for SVMs and comparisons to regularized likelihood methods"
-   <http://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_.
+   <https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_.
 
 Unbalanced problems
 --------------------
@@ -637,7 +637,7 @@ term :math:`\rho` :
 
 
  * `"Support-vector networks"
-   <http://link.springer.com/article/10.1007%2FBF00994018>`_,
+   <https://link.springer.com/article/10.1007%2FBF00994018>`_,
    C. Cortes, V. Vapnik - Machine Learning, 20, 273-297 (1995).
 
 
@@ -712,18 +712,18 @@ Implementation details
 Internally, we use `libsvm`_ and `liblinear`_ to handle all
 computations. These libraries are wrapped using C and Cython.
 
-.. _`libsvm`: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
-.. _`liblinear`: http://www.csie.ntu.edu.tw/~cjlin/liblinear/
+.. _`libsvm`: https://www.csie.ntu.edu.tw/~cjlin/libsvm/
+.. _`liblinear`: https://www.csie.ntu.edu.tw/~cjlin/liblinear/
 
 .. topic:: References:
 
   For a description of the implementation and details of the algorithms
   used, please refer to
 
     - `LIBSVM: A Library for Support Vector Machines
-      <http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_.
+      <https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_.
 
     - `LIBLINEAR -- A Library for Large Linear Classification
-      <http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_.
+      <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_.
 
 

doc/related_projects.rst

@@ -260,20 +260,20 @@ Domain specific packages
 - `scikit-image <https://scikit-image.org/>`_ Image processing and computer
   vision in python.
 
-- `Natural language toolkit (nltk) <http://www.nltk.org/>`_ Natural language
+- `Natural language toolkit (nltk) <https://www.nltk.org/>`_ Natural language
   processing and some machine learning.
 
 - `gensim <https://radimrehurek.com/gensim/>`_  A library for topic modelling,
   document indexing and similarity retrieval
 
 - `NiLearn <https://nilearn.github.io/>`_ Machine learning for neuro-imaging.
 
-- `AstroML <http://www.astroml.org/>`_  Machine learning for astronomy.
+- `AstroML <https://www.astroml.org/>`_  Machine learning for astronomy.
 
 - `MSMBuilder <http://msmbuilder.org/>`_  Machine learning for protein
   conformational dynamics time series.
 
-- `scikit-surprise <http://surpriselib.com>`_ A scikit for building and
+- `scikit-surprise <https://surpriselib.com/>`_ A scikit for building and
   evaluating recommender systems.
 
 Snippets and tidbits

doc/testimonials/testimonials.rst

@@ -53,7 +53,7 @@ Stephen Simmons, VP, Athena Research, JPMorgan
 
 .. image:: images/spotify.png
     :width: 120pt
-    :target: http://www.spotify.com
+    :target: https://www.spotify.com
 
 .. raw:: html
 
@@ -75,7 +75,7 @@ Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spoti
 
    </span>
 
-`Inria <http://www.inria.fr>`_
+`Inria <https://www.inria.fr/>`_
 -------------------------------
 
 .. raw:: html
@@ -84,7 +84,7 @@ Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spoti
 
 .. image:: images/inria.png
     :width: 120pt
-    :target: http://www.inria.fr
+    :target: https://www.inria.fr/
 
 .. raw:: html
 
@@ -96,7 +96,7 @@ Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spoti
 
 At INRIA, we use scikit-learn to support leading-edge basic research in many
 teams: `Parietal <https://team.inria.fr/parietal/>`_ for neuroimaging, `Lear
-<http://lear.inrialpes.fr/>`_ for computer vision, `Visages
+<https://lear.inrialpes.fr/>`_ for computer vision, `Visages
 <https://team.inria.fr/visages/>`_ for medical image analysis, `Privatics
 <https://team.inria.fr/privatics>`_ for security. The project is a fantastic
 tool to address difficult applications of machine learning in an academic
@@ -221,7 +221,7 @@ Mark Ayzenshtat, VP, Augmented Intelligence
 
    </span>
 
-`Télécom ParisTech <http://www.telecom-paristech.fr>`_
+`Télécom ParisTech <https://www.telecom-paristech.fr/>`_
 --------------------------------------------------------
 
 .. raw:: html
@@ -230,7 +230,7 @@ Mark Ayzenshtat, VP, Augmented Intelligence
 
 .. image:: images/telecomparistech.jpg
     :width: 120pt
-    :target: https://www.telecom-paristech.fr
+    :target: https://www.telecom-paristech.fr/
 
 .. raw:: html
 
@@ -254,15 +254,15 @@ Alexandre Gramfort, Assistant Professor
    </span>
 
 
-`Booking.com <http://booking.com>`_
+`Booking.com <https://www.booking.com>`_
 -------------------------------------
 .. raw:: html
 
   <div class="logo">
 
 .. image:: images/booking.png
     :width: 120pt
-    :target: http://www.booking.com
+    :target: https://www.booking.com
 
 .. raw:: html
 
@@ -289,7 +289,7 @@ Melanie Mueller, Data Scientist
 
    </span>
 
-`AWeber <http://www.aweber.com>`_
+`AWeber <https://www.aweber.com/>`_
 ------------------------------------------
 
 .. raw:: html
@@ -298,7 +298,7 @@ Melanie Mueller, Data Scientist
 
 .. image:: images/aweber.png
     :width: 120pt
-    :target: http://www.aweber.com
+    :target: https://www.aweber.com/
 
 .. raw:: html
 
@@ -493,7 +493,7 @@ Vijay Ramesh, Software Engineer in Data/science at Change.org
 
    </span>
 
-`PHIMECA Engineering <http://www.phimeca.com/?lang=en>`_
+`PHIMECA Engineering <https://www.phimeca.com/?lang=en>`_
 ----------------------------------------------------------
 
 .. raw:: html
@@ -502,7 +502,7 @@ Vijay Ramesh, Software Engineer in Data/science at Change.org
 
 .. image:: images/phimeca.png
     :width: 120pt
-    :target: http://www.phimeca.com/?lang=en
+    :target: https://www.phimeca.com/?lang=en
 
 .. raw:: html
 
@@ -727,7 +727,7 @@ Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica
 
 
 
-`Machinalis <http://www.machinalis.com>`_
+`Machinalis <https://www.machinalis.com/>`_
 -----------------------------------------
 
 .. raw:: html
@@ -736,7 +736,7 @@ Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica
 
 .. image:: images/machinalis.png
     :width: 120pt
-    :target: http://www.machinalis.com
+    :target: https://www.machinalis.com/
 
 .. raw:: html
 
@@ -761,7 +761,7 @@ Scikit-learn in one word: Awesome.
 Rafael Carrascosa, Lead developer
 
 
-`solido <http://www.solidodesign.com/>`_
+`solido <https://www.solidodesign.com/>`_
 -----------------------------------------
 
 .. raw:: html
@@ -770,7 +770,7 @@ Rafael Carrascosa, Lead developer
 
 .. image:: images/solido_logo.png
     :width: 120pt
-    :target: http://www.solidodesign.com
+    :target: https://www.solidodesign.com/
 
 .. raw:: html
 
@@ -833,7 +833,7 @@ Thorsten Kranz, Data Scientist, Coma Soft AG.
   </span>
 
 
-`Dataiku <http://www.dataiku.com/>`_
+`Dataiku <https://www.dataiku.com/>`_
 -----------------------------------------
 
 .. raw:: html
@@ -842,7 +842,7 @@ Thorsten Kranz, Data Scientist, Coma Soft AG.
 
 .. image:: images/dataiku_logo.png
     :width: 120pt
-    :target: http://www.dataiku.com
+    :target: https://www.dataiku.com/
 
 .. raw:: html