Added HTTP URLs to HTTPS URLs finder & converter Python scripts, and processed HTTP-->HTTPS URL changes

src/NativeBridge/DataViewInterop.h

@@ -16,7 +16,7 @@ typedef MANAGED_CALLBACK_PTR(bool, GETLABELS)(DataSourceBlock *source, int col,
 
 // REVIEW: boost_python is not updated at the same speed as swig or pybind11.
 // Both have a larger audience now, see about pybind11 https://github.com/davisking/dlib/issues/293
-// It handles csr_matrix: http://pybind11-rtdtest.readthedocs.io/en/stable/advanced.html#transparent-conversion-of-dense-and-sparse-eigen-data-types.
+// It handles csr_matrix: https://pybind11-rtdtest.readthedocs.io/en/stable/advanced.html#transparent-conversion-of-dense-and-sparse-eigen-data-types.
 using namespace boost::python;
 
 // The data source wrapper used for managed interop. Some of the fields of this are visible to managed code.

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/FactorizationMachineBinaryClassifier.txt

@@ -22,7 +22,7 @@
             `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>`_

src/python/docs/docstrings/FastForestBinaryClassifier.txt

@@ -33,7 +33,7 @@
         **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>`_

src/python/docs/docstrings/FastForestRegressor.txt

@@ -43,7 +43,7 @@
         **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>`_

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

@@ -36,7 +36,7 @@
 
             `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

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/SsaForecaster.txt

@@ -11,7 +11,7 @@
         input time-series where each component in the spectrum corresponds to a
         trend, seasonal or noise component in the time-series. For details of the
         Singular Spectrum Analysis (SSA), refer to `this document
-        <http://arxiv.org/pdf/1206.6910.pdf>`_.
+        <https://arxiv.org/pdf/1206.6910.pdf>`_.
 
     .. seealso::
         :py:func:`IIDChangePointDetector

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/ci_script/_static/mystyle.css

@@ -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/nimbusml/decomposition/factorizationmachinebinaryclassifier.py

@@ -44,7 +44,7 @@ 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>`_

src/python/nimbusml/decomposition/pcaanomalydetector.py

@@ -57,7 +57,7 @@ class PcaAnomalyDetector(core, BasePredictor, ClassifierMixin):
 
             `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

src/python/nimbusml/ensemble/fastforestbinaryclassifier.py

@@ -55,7 +55,7 @@ class FastForestBinaryClassifier(
         **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>`_

src/python/nimbusml/ensemble/fastforestregressor.py

@@ -64,7 +64,7 @@ class FastForestRegressor(core, BasePredictor, RegressorMixin):
         **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>`_

src/python/nimbusml/ensemble/fasttreesbinaryclassifier.py

@@ -81,7 +81,7 @@ class FastTreesBinaryClassifier(
             <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 feature: see `Columns </nimbusml/concepts/columns>`_.
 

src/python/nimbusml/ensemble/fasttreesregressor.py

@@ -83,7 +83,7 @@ class FastTreesRegressor(core, BasePredictor, RegressorMixin):
             <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 feature: see `Columns </nimbusml/concepts/columns>`_.
 

src/python/nimbusml/ensemble/fasttreestweedieregressor.py

@@ -38,7 +38,7 @@ class FastTreesTweedieRegressor(
             <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 feature: see `Columns </nimbusml/concepts/columns>`_.
 

src/python/nimbusml/ensemble/gambinaryclassifier.py

@@ -42,7 +42,7 @@ class GamBinaryClassifier(core, BasePredictor, ClassifierMixin):
         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
@@ -78,7 +78,7 @@ class GamBinaryClassifier(core, BasePredictor, ClassifierMixin):
             `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 feature: see `Columns </nimbusml/concepts/columns>`_.

src/python/nimbusml/ensemble/gamregressor.py

@@ -41,7 +41,7 @@ class GamRegressor(core, BasePredictor, RegressorMixin):
         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
@@ -77,7 +77,7 @@ class GamRegressor(core, BasePredictor, RegressorMixin):
             `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 feature: see `Columns </nimbusml/concepts/columns>`_.

src/python/nimbusml/feature_extraction/text/lightlda.py

@@ -30,7 +30,7 @@ class LightLda(core, BaseTransform, TransformerMixin):
         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