Change http to https

src/python/docs/docstrings/AveragedPerceptronBinaryClassifier.txt

@@ -45,10 +45,10 @@
             <https://en.wikipedia.org/wiki/Perceptron>`_
 
             `Large Margin Classification Using the Perceptron Algorithm
-            <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8200>`_
+            <https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8200>`_
 
             `Discriminative Training Methods for Hidden Markov Models
-            <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.6725>`_
+            <https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.6725>`_
 
 
     :param loss: The default is :py:class:`'hinge' <nimbusml.loss.Hinge>`. Other

src/python/docs/docstrings/DnnFeaturizer.txt

@@ -36,7 +36,7 @@
         * ``"Alexnet"``
 
         The default value is ``"Resnet18"``.
-        See `Deep Residual Learning for Image Recognition <http://www.cv-
+        See `Deep Residual Learning for Image Recognition <https://www.cv-
         foundation.org/openaccess/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html>`_
         for details about ResNet.
 

src/python/docs/docstrings/FactorizationMachineBinaryClassifier.txt

@@ -22,10 +22,10 @@
             `Field Aware Factorization Machines
             <https://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf>`_,
             `Field-aware Factorization Machines for CTR Prediction
-            <http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf>`_,
+            <https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf>`_,
             `Adaptive Subgradient Methods for Online Learning and Stochastic
             Optimization
-            <http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_
+            <https://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_
 
 
     :param feature: see `Columns </nimbusml/concepts/columns>`_.

src/python/docs/docstrings/FastForestBinaryClassifier.txt

@@ -33,10 +33,10 @@
         **Reference**
 
             `Wikipedia: Random forest
-            <http://en.wikipedia.org/wiki/Random_forest>`_
+            <https://en.wikipedia.org/wiki/Random_forest>`_
 
             `Quantile regression forest
-            <http://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>`_
+            <https://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>`_
 
             `From Stumps to Trees to Forests
             <https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-

src/python/docs/docstrings/FastForestRegressor.txt

@@ -43,10 +43,10 @@
         **Reference**
 
             `Wikipedia: Random forest
-            <http://en.wikipedia.org/wiki/Random_forest>`_
+            <https://en.wikipedia.org/wiki/Random_forest>`_
 
             `Quantile regression forest
-            <http://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>`_
+            <https://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>`_
 
             `From Stumps to Trees to Forests
             <https://blogs.technet.microsoft.com/machinelearning/2014/09/10/from-

src/python/docs/docstrings/FastLinearBinaryClassifier.txt

@@ -58,7 +58,7 @@
             content/uploads/2016/06/main-3.pdf>`_
 
             `Stochastic Dual Coordinate Ascent Methods for Regularized Loss
-            Minimization <http://www.jmlr.org/papers/volume14/shalev-
+            Minimization <https://www.jmlr.org/papers/volume14/shalev-
             shwartz13a/shalev-shwartz13a.pdf>`_
 
 

src/python/docs/docstrings/FastLinearClassifier.txt

@@ -56,7 +56,7 @@
             content/uploads/2016/06/main-3.pdf>`_
 
             `Stochastic Dual Coordinate Ascent Methods for Regularized Loss
-            Minimization <http://www.jmlr.org/papers/volume14/shalev-
+            Minimization <https://www.jmlr.org/papers/volume14/shalev-
             shwartz13a/shalev-shwartz13a.pdf>`_
 
 

src/python/docs/docstrings/FastLinearRegressor.txt

@@ -56,7 +56,7 @@
             content/uploads/2016/06/main-3.pdf>`_
 
             `Stochastic Dual Coordinate Ascent Methods for Regularized Loss
-            Minimization <http://www.jmlr.org/papers/volume14/shalev-
+            Minimization <https://www.jmlr.org/papers/volume14/shalev-
             shwartz13a/shalev-shwartz13a.pdf>`_
 
 

src/python/docs/docstrings/FastTreesBinaryClassifier.txt

@@ -57,7 +57,7 @@
             <https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
 
             `Greedy function approximation: A gradient boosting machine.
-            <http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451>`_
+            <https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451>`_
 
     :param optimizer: Default is ``sgd``.
 

src/python/docs/docstrings/FastTreesRegressor.txt

@@ -62,7 +62,7 @@
             <https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
 
             `Greedy function approximation: A gradient boosting machine.
-            <http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451>`_
+            <https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451>`_
 
     :param optimizer: Default is ``sgd``.
 

src/python/docs/docstrings/FastTreesTweedieRegressor.txt

@@ -14,7 +14,7 @@
             <https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting>`_
 
             `Greedy function approximation: A gradient boosting machine.
-            <http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451>`_
+            <https://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451>`_
 
     :param optimizer: Default is ``sgd``.
 

src/python/docs/docstrings/GamBinaryClassifier.txt

@@ -21,7 +21,7 @@
         functions learned will step between the discretization boundaries.
 
         This implementation is based on the this `paper
-        <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_,
+        <https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_,
         but diverges from it in several important respects: most
         significantly,
         in each round of boosting, rather than do one feature at a time, it
@@ -57,7 +57,7 @@
             `Generalized additive models
             <https://en.wikipedia.org/wiki/Generalized_additive_model>`_,
             `Intelligible Models for Classification and Regression
-            <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_
+            <https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_
 
 
     :param normalize: Specifies the type of automatic normalization used:

src/python/docs/docstrings/GamRegressor.txt

@@ -21,7 +21,7 @@
         functions learned will step between the discretization boundaries.
 
         This implementation is based on the this `paper
-        <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_,
+        <https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_,
         but diverges from it in several important respects: most
         significantly,
         in each round of boosting, rather than do one feature at a time, it
@@ -57,7 +57,7 @@
             `Generalized additive models
             <https://en.wikipedia.org/wiki/Generalized_additive_model>`_,
             `Intelligible Models for Classification and Regression
-            <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_
+            <https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.7619>`_
 
 
     :param normalize: Specifies the type of automatic normalization used:

src/python/docs/docstrings/LightLda.txt

@@ -10,7 +10,7 @@
         topical vectors. LightLDA is an extremely
         efficient implementation of LDA developed in MSR-Asia that
         incorporates a number of optimization techniques
-        `(http://arxiv.org/abs/1412.1576) <http://arxiv.org/abs/1412.1576>`_.
+        `(https://arxiv.org/abs/1412.1576) <https://arxiv.org/abs/1412.1576>`_.
         With the LDA transform, we can
         train a topic model to produce 1 million topics with 1 million
         vocabulary on a 1-billion-token document set one

src/python/docs/docstrings/LocalDeepSvmBinaryClassifier.txt

@@ -39,14 +39,14 @@
         More details about LD-SVM can be found in this paper `Local deep
         kernel
         learning for efficient non-linear SVM prediction
-        <http://research.microsoft.com/en-
+        <https://research.microsoft.com/en-
         us/um/people/manik/pubs/Jose13.pdf>`_.
 
 
         **Reference**
 
             `Local deep kernel learning for efficient non-linear SVM prediction
-            <http://research.microsoft.com/en-
+            <https://research.microsoft.com/en-
             us/um/people/manik/pubs/Jose13.pdf>`_
 
 

src/python/docs/docstrings/LogisticRegressionBinaryClassifier.txt

@@ -69,14 +69,14 @@
 
         **Reference**
 
-            `Wikipedia: L-BFGS <http://en.wikipedia.org/wiki/L-BFGS>`_
+            `Wikipedia: L-BFGS <https://en.wikipedia.org/wiki/L-BFGS>`_
 
             `Wikipedia: Logistic
-            regression <http://en.wikipedia.org/wiki/Logistic_regression>`_
+            regression <https://en.wikipedia.org/wiki/Logistic_regression>`_
 
             `Scalable
             Training of L1-Regularized Log-Linear Models
-            <http://research.microsoft.com/apps/pubs/default.aspx?id=78900>`_
+            <https://research.microsoft.com/apps/pubs/default.aspx?id=78900>`_
 
             `Test Run - L1
             and L2 Regularization for Machine Learning

src/python/docs/docstrings/LogisticRegressionClassifier.txt

@@ -70,14 +70,14 @@
 
         **Reference**
 
-            `Wikipedia: L-BFGS <http://en.wikipedia.org/wiki/L-BFGS>`_
+            `Wikipedia: L-BFGS <https://en.wikipedia.org/wiki/L-BFGS>`_
 
             `Wikipedia: Logistic
-            regression <http://en.wikipedia.org/wiki/Logistic_regression>`_
+            regression <https://en.wikipedia.org/wiki/Logistic_regression>`_
 
             `Scalable
             Training of L1-Regularized Log-Linear Models
-            <http://research.microsoft.com/apps/pubs/default.aspx?id=78900>`_
+            <https://research.microsoft.com/apps/pubs/default.aspx?id=78900>`_
 
             `Test Run - L1
             and L2 Regularization for Machine Learning

src/python/docs/docstrings/OneClassSVMAnomalyDetector.txt

@@ -29,10 +29,10 @@
             us/library/azure/dn913103.aspx>`_
 
             `Estimating the Support of a High-Dimensional Distribution
-            <http://research.microsoft.com/pubs/69731/tr-99-87.pdf>`_
+            <https://research.microsoft.com/pubs/69731/tr-99-87.pdf>`_
 
             `New Support Vector Algorithms
-            <http://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf>`_
+            <https://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf>`_
 
             `LIBSVM: A Library for Support Vector Machines
             <https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_

src/python/docs/docstrings/PcaAnomalyDetector.txt

@@ -36,12 +36,12 @@
 
             `Randomized Methods for Computing the Singular Value Decomposition
             (SVD) of very large matrices
-            <http://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf>`_
+            <https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf>`_
             `A randomized algorithm for principal component analysis
             <https://arxiv.org/abs/0809.2274>`_,
             `Finding Structure with Randomness: Probabilistic Algorithms for
             Constructing Approximate Matrix Decompositions
-            <http://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-
+            <https://users.cms.caltech.edu/~jtropp/papers/HMT11-Finding-Structure-
             SIREV.pdf>`_
 
 

src/python/docs/docstrings/SgdBinaryClassifier.txt

@@ -13,14 +13,14 @@
         associated optimization problem is sparse, then Hogwild SGD achieves
         a
         nearly optimal rate of convergence. For a detailed reference, please
-        refer to `http://arxiv.org/pdf/1106.5730v2.pdf
-        <http://arxiv.org/pdf/1106.5730v2.pdf>`_.
+        refer to `https://arxiv.org/pdf/1106.5730v2.pdf
+        <https://arxiv.org/pdf/1106.5730v2.pdf>`_.
 
 
         **Reference**
 
-            `http://arxiv.org/pdf/1106.5730v2.pdf
-            <http://arxiv.org/pdf/1106.5730v2.pdf>`_
+            `https://arxiv.org/pdf/1106.5730v2.pdf
+            <https://arxiv.org/pdf/1106.5730v2.pdf>`_
 
 
     :param normalize: Specifies the type of automatic normalization used:

src/python/docs/docstrings/SigmoidKernel.txt

@@ -3,7 +3,7 @@
     Apply sigmoid function. tanh(gamma*<x,y>+c).
 
     .. remarks::
-        `SigmoidKernel <http://www2.spsc.tugraz.at/www-
+        `SigmoidKernel <https://www2.spsc.tugraz.at/www-
         archive/AdvancedSignalProcessing/WS05-Mistral/advances.pdf>`_ is a
         kernel function
         that computes the similarity between two features.

src/python/docs/docstrings/SsweEmbedding.txt

@@ -7,12 +7,12 @@
     versions of `GloVe Models
     <https://nlp.stanford.edu/projects/glove/>`_, `FastText
     <https://en.wikipedia.org/wiki/FastText>`_, and `Sswe
-    <http://anthology.aclweb.org/P/P14/P14-1146.pdf>`_.
+    <https://anthology.aclweb.org/P/P14/P14-1146.pdf>`_.
 
     .. remarks::
         Sentiment-specific word embedding (SSWE) is a DNN featurizer
         developed
-        by MSRA (`paper <http://anthology.aclweb.org/P/P14/P14-1146.pdf>`_).
+        by MSRA (`paper <https://anthology.aclweb.org/P/P14/P14-1146.pdf>`_).
         It
         incorporates sentiment information into the neural network to learn
         sentiment specific word embedding. It proves to be useful in various

src/python/docs/docstrings/SupervisedBinner.txt

@@ -24,7 +24,7 @@
         the default is to normalize features before training.
 
         ``SupervisedBinner`` implements the `Entropy-Based Discretization
-        <http://www.aaai.org/Papers/KDD/1996/KDD96-019.pdf>`_.
+        <https://www.aaai.org/Papers/KDD/1996/KDD96-019.pdf>`_.
         Partition of the data is performed recursively to select the split
         with highest entropy gain with respect to the label.
         Therefore, the final binned features will have high correlation with

src/python/docs/docstrings/WordEmbedding.txt

@@ -10,7 +10,7 @@
         available options are various versions of `GloVe Models
         <https://nlp.stanford.edu/projects/glove/>`_, `FastText
         <https://en.wikipedia.org/wiki/FastText>`_, and `Sswe
-        <http://anthology.aclweb.org/P/P14/P14-1146.pdf>`_.
+        <https://anthology.aclweb.org/P/P14/P14-1146.pdf>`_.
 
 
     :param model_kind: Pre-trained model used to create the vocabulary.

src/python/docs/sphinx/_static/mystyle.css

@@ -6726,8 +6726,8 @@ button.close {
 *
 */
 /*!
- *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
- *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
+ *  Font Awesome 4.2.0 by @davegandy - https://fontawesome.io - @fontawesome
+ *  License - https://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
  */
 /* FONT PATH
  * -------------------------- */
@@ -8432,7 +8432,7 @@ label {
   padding: 0px;
 }
 /* Flexible box model classes */
-/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
+/* Taken from Alex Russell https://infrequently.org/2009/08/css-3-progress/ */
 /* This file is a compatability layer.  It allows the usage of flexible box 
 model layouts accross multiple browsers, including older browsers.  The newest,
 universal implementation of the flexible box model is used when available (see

src/python/docs/sphinx/ci_script/conf.py

@@ -128,8 +128,8 @@
     'relative': True,
     'reference_url': {
         'nimbusml': None,
-        'matplotlib': 'http://matplotlib.org',
-        'numpy': 'http://www.numpy.org/',
+        'matplotlib': 'https://matplotlib.org',
+        'numpy': 'https://www.numpy.org/',
         'scipy': 'https://www.scipy.org/'},
 }
 

src/python/docs/sphinx/conf.py

@@ -145,8 +145,8 @@ def install_and_import(package):
     'relative': True,
     'reference_url': {
         'nimbusml': None,
-        'matplotlib': 'http://matplotlib.org',
-        'numpy': 'http://www.numpy.org/',
+        'matplotlib': 'https://matplotlib.org',
+        'numpy': 'https://www.numpy.org/',
         'scipy': 'https://www.scipy.org/'},
 }
 

src/python/docs/sphinx/make.bat

@@ -38,7 +38,7 @@ if errorlevel 9009 (
 	echo.Sphinx directory to PATH.
 	echo.
 	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
+	echo.https://sphinx-doc.org/
 	exit /b 1
 )
 

src/python/docs/sphinx/metrics.rst

@@ -39,7 +39,7 @@ This corresponds to evaltype='binary'.
 
 **Negative Recall** -  see  `Precision and Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_
 
-**Log-loss** - see `Log Loss <http://wiki.fast.ai/index.php/Log_Loss>`_
+**Log-loss** - see `Log Loss <https://wiki.fast.ai/index.php/Log_Loss>`_
 
 **Log-loss reduction** - RIG(Y|X) * 100 = (H(Y) - H(Y|X)) / H(Y) * 100. Ranges from [-inf, 100], where
 100 is perfect predictions and 0 indicates mean predictions.
@@ -48,7 +48,7 @@ This corresponds to evaltype='binary'.
 
 **F1 Score** - see `Precision and Recall <https://en.wikipedia.org/wiki/Precision_and_recall>`_
 
-**AUPRC** - see `Area under Precision-Recall Curve <http://pages.cs.wisc.edu/~boyd/aucpr_final.pdf>`_
+**AUPRC** - see `Area under Precision-Recall Curve <https://pages.cs.wisc.edu/~boyd/aucpr_final.pdf>`_
 
 .. note:: Note about ROC
 
@@ -74,7 +74,7 @@ This corresponds to evaltype='multiclass'.
 **Accuracy(macro-avg)** - Every class contributes equally to the accuracy metric. Minority classes are
 given equal weight as the larger classes.
 
-**Log-loss** - see `Log Loss <http://wiki.fast.ai/index.php/Log_Loss>`_
+**Log-loss** - see `Log Loss <https://wiki.fast.ai/index.php/Log_Loss>`_
 
 **Log-loss reduction** - RIG(Y|X) * 100 = (H(Y) - H(Y|X)) / H(Y) * 100. Ranges from [-inf, 100], where
 100 is perfect predictions and 0 indicates mean predictions.

src/python/nimbusml.pyproj

@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="utf-8"?>
-<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" DefaultTargets="Build">
+<Project ToolsVersion="4.0" xmlns="https://schemas.microsoft.com/developer/msbuild/2003" DefaultTargets="Build">
   <PropertyGroup>
     <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
     <SchemaVersion>2.0</SchemaVersion>

src/python/nimbusml/decomposition/factorizationmachinebinaryclassifier.py

@@ -44,10 +44,10 @@ class FactorizationMachineBinaryClassifier(
             `Field Aware Factorization Machines
             <https://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf>`_,
             `Field-aware Factorization Machines for CTR Prediction
-            <http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf>`_,
+            <https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf>`_,
             `Adaptive Subgradient Methods for Online Learning and Stochastic
             Optimization
-            <http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_
+            <https://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf>`_
 
 
     :param feature: see `Columns </nimbusml/concepts/columns>`_.