diff --git a/src/python/docs/docstrings/AveragedPerceptronBinaryClassifier.txt b/src/python/docs/docstrings/AveragedPerceptronBinaryClassifier.txt index 45b12209..31314605 100644 --- a/src/python/docs/docstrings/AveragedPerceptronBinaryClassifier.txt +++ b/src/python/docs/docstrings/AveragedPerceptronBinaryClassifier.txt @@ -45,10 +45,10 @@ `_ `Large Margin Classification Using the Perceptron Algorithm - `_ + `_ `Discriminative Training Methods for Hidden Markov Models - `_ + `_ :param loss: The default is :py:class:`'hinge' `. Other diff --git a/src/python/docs/docstrings/DnnFeaturizer.txt b/src/python/docs/docstrings/DnnFeaturizer.txt index 861f9a9c..08e51ccd 100644 --- a/src/python/docs/docstrings/DnnFeaturizer.txt +++ b/src/python/docs/docstrings/DnnFeaturizer.txt @@ -36,7 +36,7 @@ * ``"Alexnet"`` The default value is ``"Resnet18"``. - See `Deep Residual Learning for Image Recognition `_ for details about ResNet. diff --git a/src/python/docs/docstrings/FactorizationMachineBinaryClassifier.txt b/src/python/docs/docstrings/FactorizationMachineBinaryClassifier.txt index 19139fe9..0577eb02 100644 --- a/src/python/docs/docstrings/FactorizationMachineBinaryClassifier.txt +++ b/src/python/docs/docstrings/FactorizationMachineBinaryClassifier.txt @@ -22,10 +22,10 @@ `Field Aware Factorization Machines `_, `Field-aware Factorization Machines for CTR Prediction - `_, + `_, `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/docs/docstrings/FastForestBinaryClassifier.txt b/src/python/docs/docstrings/FastForestBinaryClassifier.txt index 6ebc1938..89ac6110 100644 --- a/src/python/docs/docstrings/FastForestBinaryClassifier.txt +++ b/src/python/docs/docstrings/FastForestBinaryClassifier.txt @@ -33,10 +33,10 @@ **Reference** `Wikipedia: Random forest - `_ + `_ `Quantile regression forest - `_ + `_ `From Stumps to Trees to Forests `_ + `_ `Quantile regression forest - `_ + `_ `From Stumps to Trees to Forests `_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/docs/docstrings/FastLinearClassifier.txt b/src/python/docs/docstrings/FastLinearClassifier.txt index 6c741d22..48c54be7 100644 --- a/src/python/docs/docstrings/FastLinearClassifier.txt +++ b/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 `_ diff --git a/src/python/docs/docstrings/FastLinearRegressor.txt b/src/python/docs/docstrings/FastLinearRegressor.txt index a80eb8bc..5ede5a1c 100644 --- a/src/python/docs/docstrings/FastLinearRegressor.txt +++ b/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 `_ diff --git a/src/python/docs/docstrings/FastTreesBinaryClassifier.txt b/src/python/docs/docstrings/FastTreesBinaryClassifier.txt index 1789d738..15865149 100644 --- a/src/python/docs/docstrings/FastTreesBinaryClassifier.txt +++ b/src/python/docs/docstrings/FastTreesBinaryClassifier.txt @@ -57,7 +57,7 @@ `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param optimizer: Default is ``sgd``. diff --git a/src/python/docs/docstrings/FastTreesRegressor.txt b/src/python/docs/docstrings/FastTreesRegressor.txt index cd1f76b8..91a3622d 100644 --- a/src/python/docs/docstrings/FastTreesRegressor.txt +++ b/src/python/docs/docstrings/FastTreesRegressor.txt @@ -62,7 +62,7 @@ `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param optimizer: Default is ``sgd``. diff --git a/src/python/docs/docstrings/FastTreesTweedieRegressor.txt b/src/python/docs/docstrings/FastTreesTweedieRegressor.txt index 76cd6749..3c02e645 100644 --- a/src/python/docs/docstrings/FastTreesTweedieRegressor.txt +++ b/src/python/docs/docstrings/FastTreesTweedieRegressor.txt @@ -14,7 +14,7 @@ `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param optimizer: Default is ``sgd``. diff --git a/src/python/docs/docstrings/GamBinaryClassifier.txt b/src/python/docs/docstrings/GamBinaryClassifier.txt index 69484156..acd5f023 100644 --- a/src/python/docs/docstrings/GamBinaryClassifier.txt +++ b/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 - `_, + `_, 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 `_, `Intelligible Models for Classification and Regression - `_ + `_ :param normalize: Specifies the type of automatic normalization used: diff --git a/src/python/docs/docstrings/GamRegressor.txt b/src/python/docs/docstrings/GamRegressor.txt index 54d71d10..3e44a736 100644 --- a/src/python/docs/docstrings/GamRegressor.txt +++ b/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 - `_, + `_, 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 `_, `Intelligible Models for Classification and Regression - `_ + `_ :param normalize: Specifies the type of automatic normalization used: diff --git a/src/python/docs/docstrings/LightLda.txt b/src/python/docs/docstrings/LightLda.txt index 95736da9..aaec0162 100644 --- a/src/python/docs/docstrings/LightLda.txt +++ b/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) `_. + `(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 diff --git a/src/python/docs/docstrings/LocalDeepSvmBinaryClassifier.txt b/src/python/docs/docstrings/LocalDeepSvmBinaryClassifier.txt index cf028dcd..4863237a 100644 --- a/src/python/docs/docstrings/LocalDeepSvmBinaryClassifier.txt +++ b/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 - `_. **Reference** `Local deep kernel learning for efficient non-linear SVM prediction - `_ diff --git a/src/python/docs/docstrings/LogisticRegressionBinaryClassifier.txt b/src/python/docs/docstrings/LogisticRegressionBinaryClassifier.txt index 6fb1063d..b268dea2 100644 --- a/src/python/docs/docstrings/LogisticRegressionBinaryClassifier.txt +++ b/src/python/docs/docstrings/LogisticRegressionBinaryClassifier.txt @@ -69,14 +69,14 @@ **Reference** - `Wikipedia: L-BFGS `_ + `Wikipedia: L-BFGS `_ `Wikipedia: Logistic - regression `_ + regression `_ `Scalable Training of L1-Regularized Log-Linear Models - `_ + `_ `Test Run - L1 and L2 Regularization for Machine Learning diff --git a/src/python/docs/docstrings/LogisticRegressionClassifier.txt b/src/python/docs/docstrings/LogisticRegressionClassifier.txt index db6f386e..405c20f3 100644 --- a/src/python/docs/docstrings/LogisticRegressionClassifier.txt +++ b/src/python/docs/docstrings/LogisticRegressionClassifier.txt @@ -70,14 +70,14 @@ **Reference** - `Wikipedia: L-BFGS `_ + `Wikipedia: L-BFGS `_ `Wikipedia: Logistic - regression `_ + regression `_ `Scalable Training of L1-Regularized Log-Linear Models - `_ + `_ `Test Run - L1 and L2 Regularization for Machine Learning diff --git a/src/python/docs/docstrings/OneClassSVMAnomalyDetector.txt b/src/python/docs/docstrings/OneClassSVMAnomalyDetector.txt index 958bd389..44e9ef30 100644 --- a/src/python/docs/docstrings/OneClassSVMAnomalyDetector.txt +++ b/src/python/docs/docstrings/OneClassSVMAnomalyDetector.txt @@ -29,10 +29,10 @@ us/library/azure/dn913103.aspx>`_ `Estimating the Support of a High-Dimensional Distribution - `_ + `_ `New Support Vector Algorithms - `_ + `_ `LIBSVM: A Library for Support Vector Machines `_ diff --git a/src/python/docs/docstrings/PcaAnomalyDetector.txt b/src/python/docs/docstrings/PcaAnomalyDetector.txt index 5896c5c9..c55b5752 100644 --- a/src/python/docs/docstrings/PcaAnomalyDetector.txt +++ b/src/python/docs/docstrings/PcaAnomalyDetector.txt @@ -36,12 +36,12 @@ `Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices - `_ + `_ `A randomized algorithm for principal component analysis `_, `Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions - `_ diff --git a/src/python/docs/docstrings/SgdBinaryClassifier.txt b/src/python/docs/docstrings/SgdBinaryClassifier.txt index c1ed86ac..a585e088 100644 --- a/src/python/docs/docstrings/SgdBinaryClassifier.txt +++ b/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 - `_. + refer to `https://arxiv.org/pdf/1106.5730v2.pdf + `_. **Reference** - `http://arxiv.org/pdf/1106.5730v2.pdf - `_ + `https://arxiv.org/pdf/1106.5730v2.pdf + `_ :param normalize: Specifies the type of automatic normalization used: diff --git a/src/python/docs/docstrings/SigmoidKernel.txt b/src/python/docs/docstrings/SigmoidKernel.txt index 3a22d2cd..2d74c3fc 100644 --- a/src/python/docs/docstrings/SigmoidKernel.txt +++ b/src/python/docs/docstrings/SigmoidKernel.txt @@ -3,7 +3,7 @@ Apply sigmoid function. tanh(gamma*+c). .. remarks:: - `SigmoidKernel `_ is a kernel function that computes the similarity between two features. diff --git a/src/python/docs/docstrings/SsweEmbedding.txt b/src/python/docs/docstrings/SsweEmbedding.txt index 55897d9d..b7551fd9 100644 --- a/src/python/docs/docstrings/SsweEmbedding.txt +++ b/src/python/docs/docstrings/SsweEmbedding.txt @@ -7,12 +7,12 @@ versions of `GloVe Models `_, `FastText `_, and `Sswe - `_. + `_. .. remarks:: Sentiment-specific word embedding (SSWE) is a DNN featurizer developed - by MSRA (`paper `_). + by MSRA (`paper `_). It incorporates sentiment information into the neural network to learn sentiment specific word embedding. It proves to be useful in various diff --git a/src/python/docs/docstrings/SupervisedBinner.txt b/src/python/docs/docstrings/SupervisedBinner.txt index 963a560e..95317b75 100644 --- a/src/python/docs/docstrings/SupervisedBinner.txt +++ b/src/python/docs/docstrings/SupervisedBinner.txt @@ -24,7 +24,7 @@ the default is to normalize features before training. ``SupervisedBinner`` implements the `Entropy-Based Discretization - `_. + `_. 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 diff --git a/src/python/docs/docstrings/WordEmbedding.txt b/src/python/docs/docstrings/WordEmbedding.txt index 3ba1ffe8..54ea3908 100644 --- a/src/python/docs/docstrings/WordEmbedding.txt +++ b/src/python/docs/docstrings/WordEmbedding.txt @@ -10,7 +10,7 @@ available options are various versions of `GloVe Models `_, `FastText `_, and `Sswe - `_. + `_. :param model_kind: Pre-trained model used to create the vocabulary. diff --git a/src/python/docs/sphinx/_static/mystyle.css b/src/python/docs/sphinx/_static/mystyle.css index a69e381c..5f94d8af 100644 --- a/src/python/docs/sphinx/_static/mystyle.css +++ b/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 diff --git a/src/python/docs/sphinx/ci_script/conf.py b/src/python/docs/sphinx/ci_script/conf.py index f96889d1..1acb3312 100644 --- a/src/python/docs/sphinx/ci_script/conf.py +++ b/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/'}, } diff --git a/src/python/docs/sphinx/conf.py b/src/python/docs/sphinx/conf.py index 1f0cccfc..9fb1d4ab 100644 --- a/src/python/docs/sphinx/conf.py +++ b/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/'}, } diff --git a/src/python/docs/sphinx/make.bat b/src/python/docs/sphinx/make.bat index 248fa3fe..aaeee177 100644 --- a/src/python/docs/sphinx/make.bat +++ b/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 ) diff --git a/src/python/docs/sphinx/metrics.rst b/src/python/docs/sphinx/metrics.rst index 4efe0103..80f0d057 100644 --- a/src/python/docs/sphinx/metrics.rst +++ b/src/python/docs/sphinx/metrics.rst @@ -39,7 +39,7 @@ This corresponds to evaltype='binary'. **Negative Recall** - see `Precision and Recall `_ -**Log-loss** - see `Log Loss `_ +**Log-loss** - see `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 `_ -**AUPRC** - see `Area under Precision-Recall Curve `_ +**AUPRC** - see `Area under Precision-Recall Curve `_ .. 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 `_ +**Log-loss** - see `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. diff --git a/src/python/nimbusml.pyproj b/src/python/nimbusml.pyproj index 910b76ea..c099bbb7 100644 --- a/src/python/nimbusml.pyproj +++ b/src/python/nimbusml.pyproj @@ -1,5 +1,5 @@ - + Debug 2.0 diff --git a/src/python/nimbusml/decomposition/factorizationmachinebinaryclassifier.py b/src/python/nimbusml/decomposition/factorizationmachinebinaryclassifier.py index 7382dd10..28237444 100644 --- a/src/python/nimbusml/decomposition/factorizationmachinebinaryclassifier.py +++ b/src/python/nimbusml/decomposition/factorizationmachinebinaryclassifier.py @@ -44,10 +44,10 @@ class FactorizationMachineBinaryClassifier( `Field Aware Factorization Machines `_, `Field-aware Factorization Machines for CTR Prediction - `_, + `_, `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/decomposition/pcaanomalydetector.py b/src/python/nimbusml/decomposition/pcaanomalydetector.py index 57b21b90..2264eeab 100644 --- a/src/python/nimbusml/decomposition/pcaanomalydetector.py +++ b/src/python/nimbusml/decomposition/pcaanomalydetector.py @@ -57,12 +57,12 @@ class PcaAnomalyDetector(core, BasePredictor, ClassifierMixin): `Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices - `_ + `_ `A randomized algorithm for principal component analysis `_, `Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions - `_ diff --git a/src/python/nimbusml/ensemble/fastforestbinaryclassifier.py b/src/python/nimbusml/ensemble/fastforestbinaryclassifier.py index 09c7677f..c4b6be12 100644 --- a/src/python/nimbusml/ensemble/fastforestbinaryclassifier.py +++ b/src/python/nimbusml/ensemble/fastforestbinaryclassifier.py @@ -55,10 +55,10 @@ class FastForestBinaryClassifier( **Reference** `Wikipedia: Random forest - `_ + `_ `Quantile regression forest - `_ + `_ `From Stumps to Trees to Forests `_ + `_ `Quantile regression forest - `_ + `_ `From Stumps to Trees to Forests `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/ensemble/fasttreesregressor.py b/src/python/nimbusml/ensemble/fasttreesregressor.py index 3a55bb4c..33b31859 100644 --- a/src/python/nimbusml/ensemble/fasttreesregressor.py +++ b/src/python/nimbusml/ensemble/fasttreesregressor.py @@ -83,7 +83,7 @@ class FastTreesRegressor(core, BasePredictor, RegressorMixin): `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/ensemble/fasttreestweedieregressor.py b/src/python/nimbusml/ensemble/fasttreestweedieregressor.py index e9ac1750..14ba8e3c 100644 --- a/src/python/nimbusml/ensemble/fasttreestweedieregressor.py +++ b/src/python/nimbusml/ensemble/fasttreestweedieregressor.py @@ -38,7 +38,7 @@ class FastTreesTweedieRegressor( `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/ensemble/gambinaryclassifier.py b/src/python/nimbusml/ensemble/gambinaryclassifier.py index 2427c2ba..127c4d8a 100644 --- a/src/python/nimbusml/ensemble/gambinaryclassifier.py +++ b/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 - `_, + `_, 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 `_, `Intelligible Models for Classification and Regression - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/ensemble/gamregressor.py b/src/python/nimbusml/ensemble/gamregressor.py index 13587cd8..a2f2f7a2 100644 --- a/src/python/nimbusml/ensemble/gamregressor.py +++ b/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 - `_, + `_, 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 `_, `Intelligible Models for Classification and Regression - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/feature_extraction/text/lightlda.py b/src/python/nimbusml/feature_extraction/text/lightlda.py index ec016d5d..e9dcd510 100644 --- a/src/python/nimbusml/feature_extraction/text/lightlda.py +++ b/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) `_. + `(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 diff --git a/src/python/nimbusml/feature_extraction/text/wordembedding.py b/src/python/nimbusml/feature_extraction/text/wordembedding.py index 452c735e..1d585ee9 100644 --- a/src/python/nimbusml/feature_extraction/text/wordembedding.py +++ b/src/python/nimbusml/feature_extraction/text/wordembedding.py @@ -31,7 +31,7 @@ class WordEmbedding(core, BaseTransform, TransformerMixin): available options are various versions of `GloVe Models `_, `FastText `_, and `Sswe - `_. + `_. :param columns: a dictionary of key-value pairs, where key is the output diff --git a/src/python/nimbusml/internal/core/decomposition/factorizationmachinebinaryclassifier.py b/src/python/nimbusml/internal/core/decomposition/factorizationmachinebinaryclassifier.py index f0a7b9a5..7ecb088e 100644 --- a/src/python/nimbusml/internal/core/decomposition/factorizationmachinebinaryclassifier.py +++ b/src/python/nimbusml/internal/core/decomposition/factorizationmachinebinaryclassifier.py @@ -42,10 +42,10 @@ class FactorizationMachineBinaryClassifier( `Field Aware Factorization Machines `_, `Field-aware Factorization Machines for CTR Prediction - `_, + `_, `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization - `_ + `_ :param learning_rate: Initial learning rate. diff --git a/src/python/nimbusml/internal/core/decomposition/pcaanomalydetector.py b/src/python/nimbusml/internal/core/decomposition/pcaanomalydetector.py index 08da4e08..e6ec1783 100644 --- a/src/python/nimbusml/internal/core/decomposition/pcaanomalydetector.py +++ b/src/python/nimbusml/internal/core/decomposition/pcaanomalydetector.py @@ -57,12 +57,12 @@ class PcaAnomalyDetector( `Randomized Methods for Computing the Singular Value Decomposition (SVD) of very large matrices - `_ + `_ `A randomized algorithm for principal component analysis `_, `Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions - `_ diff --git a/src/python/nimbusml/internal/core/ensemble/fastforestbinaryclassifier.py b/src/python/nimbusml/internal/core/ensemble/fastforestbinaryclassifier.py index 3f351ef2..1e6e7820 100644 --- a/src/python/nimbusml/internal/core/ensemble/fastforestbinaryclassifier.py +++ b/src/python/nimbusml/internal/core/ensemble/fastforestbinaryclassifier.py @@ -54,10 +54,10 @@ class FastForestBinaryClassifier( **Reference** `Wikipedia: Random forest - `_ + `_ `Quantile regression forest - `_ + `_ `From Stumps to Trees to Forests `_ + `_ `Quantile regression forest - `_ + `_ `From Stumps to Trees to Forests `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param num_trees: Specifies the total number of decision trees to create in the ensemble. By creating more decision trees, you can potentially get diff --git a/src/python/nimbusml/internal/core/ensemble/fasttreesregressor.py b/src/python/nimbusml/internal/core/ensemble/fasttreesregressor.py index d041e9b8..705c9ea0 100644 --- a/src/python/nimbusml/internal/core/ensemble/fasttreesregressor.py +++ b/src/python/nimbusml/internal/core/ensemble/fasttreesregressor.py @@ -83,7 +83,7 @@ class FastTreesRegressor( `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param num_trees: Specifies the total number of decision trees to create in the ensemble. By creating more decision trees, you can potentially get diff --git a/src/python/nimbusml/internal/core/ensemble/fasttreestweedieregressor.py b/src/python/nimbusml/internal/core/ensemble/fasttreestweedieregressor.py index ccda9375..f604daf6 100644 --- a/src/python/nimbusml/internal/core/ensemble/fasttreestweedieregressor.py +++ b/src/python/nimbusml/internal/core/ensemble/fasttreestweedieregressor.py @@ -35,7 +35,7 @@ class FastTreesTweedieRegressor( `_ `Greedy function approximation: A gradient boosting machine. - `_ + `_ :param num_trees: Specifies the total number of decision trees to create in the ensemble. By creating more decision trees, you can potentially get diff --git a/src/python/nimbusml/internal/core/ensemble/gambinaryclassifier.py b/src/python/nimbusml/internal/core/ensemble/gambinaryclassifier.py index 1d0eecea..e8828d6d 100644 --- a/src/python/nimbusml/internal/core/ensemble/gambinaryclassifier.py +++ b/src/python/nimbusml/internal/core/ensemble/gambinaryclassifier.py @@ -42,7 +42,7 @@ class GamBinaryClassifier( functions learned will step between the discretization boundaries. This implementation is based on the this `paper - `_, + `_, 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( `Generalized additive models `_, `Intelligible Models for Classification and Regression - `_ + `_ :param num_iterations: Total number of iterations over all features. diff --git a/src/python/nimbusml/internal/core/ensemble/gamregressor.py b/src/python/nimbusml/internal/core/ensemble/gamregressor.py index 07a093c6..37db2d69 100644 --- a/src/python/nimbusml/internal/core/ensemble/gamregressor.py +++ b/src/python/nimbusml/internal/core/ensemble/gamregressor.py @@ -40,7 +40,7 @@ class GamRegressor(BasePipelineItem, DefaultSignatureWithRoles): functions learned will step between the discretization boundaries. This implementation is based on the this `paper - `_, + `_, but diverges from it in several important respects: most significantly, in each round of boosting, rather than do one feature at a time, it @@ -76,7 +76,7 @@ class GamRegressor(BasePipelineItem, DefaultSignatureWithRoles): `Generalized additive models `_, `Intelligible Models for Classification and Regression - `_ + `_ :param num_iterations: Total number of iterations over all features. diff --git a/src/python/nimbusml/internal/core/feature_extraction/text/lightlda.py b/src/python/nimbusml/internal/core/feature_extraction/text/lightlda.py index 98ba5dd3..d9663e20 100644 --- a/src/python/nimbusml/internal/core/feature_extraction/text/lightlda.py +++ b/src/python/nimbusml/internal/core/feature_extraction/text/lightlda.py @@ -28,7 +28,7 @@ class LightLda(BasePipelineItem, DefaultSignature): 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) `_. + `(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 diff --git a/src/python/nimbusml/internal/core/feature_extraction/text/wordembedding.py b/src/python/nimbusml/internal/core/feature_extraction/text/wordembedding.py index 691a79d3..3b6f43c9 100644 --- a/src/python/nimbusml/internal/core/feature_extraction/text/wordembedding.py +++ b/src/python/nimbusml/internal/core/feature_extraction/text/wordembedding.py @@ -28,7 +28,7 @@ class WordEmbedding(BasePipelineItem, DefaultSignature): available options are various versions of `GloVe Models `_, `FastText `_, and `Sswe - `_. + `_. :param model_kind: Pre-trained model used to create the vocabulary. diff --git a/src/python/nimbusml/internal/core/linear_model/averagedperceptronbinaryclassifier.py b/src/python/nimbusml/internal/core/linear_model/averagedperceptronbinaryclassifier.py index 0492a3c9..2ade57f0 100644 --- a/src/python/nimbusml/internal/core/linear_model/averagedperceptronbinaryclassifier.py +++ b/src/python/nimbusml/internal/core/linear_model/averagedperceptronbinaryclassifier.py @@ -67,10 +67,10 @@ class AveragedPerceptronBinaryClassifier( `_ `Large Margin Classification Using the Perceptron Algorithm - `_ + `_ `Discriminative Training Methods for Hidden Markov Models - `_ + `_ :param normalize: Specifies the type of automatic normalization used: diff --git a/src/python/nimbusml/internal/core/linear_model/fastlinearbinaryclassifier.py b/src/python/nimbusml/internal/core/linear_model/fastlinearbinaryclassifier.py index 8bf9c66d..d978bbd9 100644 --- a/src/python/nimbusml/internal/core/linear_model/fastlinearbinaryclassifier.py +++ b/src/python/nimbusml/internal/core/linear_model/fastlinearbinaryclassifier.py @@ -80,7 +80,7 @@ class FastLinearBinaryClassifier( content/uploads/2016/06/main-3.pdf>`_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/nimbusml/internal/core/linear_model/fastlinearclassifier.py b/src/python/nimbusml/internal/core/linear_model/fastlinearclassifier.py index 7e5066ed..2c568ee2 100644 --- a/src/python/nimbusml/internal/core/linear_model/fastlinearclassifier.py +++ b/src/python/nimbusml/internal/core/linear_model/fastlinearclassifier.py @@ -78,7 +78,7 @@ class FastLinearClassifier( content/uploads/2016/06/main-3.pdf>`_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/nimbusml/internal/core/linear_model/fastlinearregressor.py b/src/python/nimbusml/internal/core/linear_model/fastlinearregressor.py index baa67ddb..d14d6885 100644 --- a/src/python/nimbusml/internal/core/linear_model/fastlinearregressor.py +++ b/src/python/nimbusml/internal/core/linear_model/fastlinearregressor.py @@ -78,7 +78,7 @@ class FastLinearRegressor( content/uploads/2016/06/main-3.pdf>`_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/nimbusml/internal/core/linear_model/logisticregressionbinaryclassifier.py b/src/python/nimbusml/internal/core/linear_model/logisticregressionbinaryclassifier.py index f410b3cc..cb46200f 100644 --- a/src/python/nimbusml/internal/core/linear_model/logisticregressionbinaryclassifier.py +++ b/src/python/nimbusml/internal/core/linear_model/logisticregressionbinaryclassifier.py @@ -90,14 +90,14 @@ class LogisticRegressionBinaryClassifier( **Reference** - `Wikipedia: L-BFGS `_ + `Wikipedia: L-BFGS `_ `Wikipedia: Logistic - regression `_ + regression `_ `Scalable Training of L1-Regularized Log-Linear Models - `_ + `_ `Test Run - L1 and L2 Regularization for Machine Learning diff --git a/src/python/nimbusml/internal/core/linear_model/logisticregressionclassifier.py b/src/python/nimbusml/internal/core/linear_model/logisticregressionclassifier.py index eb58c4c2..3b7936b4 100644 --- a/src/python/nimbusml/internal/core/linear_model/logisticregressionclassifier.py +++ b/src/python/nimbusml/internal/core/linear_model/logisticregressionclassifier.py @@ -91,14 +91,14 @@ class LogisticRegressionClassifier( **Reference** - `Wikipedia: L-BFGS `_ + `Wikipedia: L-BFGS `_ `Wikipedia: Logistic - regression `_ + regression `_ `Scalable Training of L1-Regularized Log-Linear Models - `_ + `_ `Test Run - L1 and L2 Regularization for Machine Learning diff --git a/src/python/nimbusml/internal/core/linear_model/sgdbinaryclassifier.py b/src/python/nimbusml/internal/core/linear_model/sgdbinaryclassifier.py index 2af47365..3720d5e7 100644 --- a/src/python/nimbusml/internal/core/linear_model/sgdbinaryclassifier.py +++ b/src/python/nimbusml/internal/core/linear_model/sgdbinaryclassifier.py @@ -35,14 +35,14 @@ class SgdBinaryClassifier( 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 - `_. + refer to `https://arxiv.org/pdf/1106.5730v2.pdf + `_. **Reference** - `http://arxiv.org/pdf/1106.5730v2.pdf - `_ + `https://arxiv.org/pdf/1106.5730v2.pdf + `_ :param normalize: Specifies the type of automatic normalization used: diff --git a/src/python/nimbusml/linear_model/averagedperceptronbinaryclassifier.py b/src/python/nimbusml/linear_model/averagedperceptronbinaryclassifier.py index 02d48768..3e498670 100644 --- a/src/python/nimbusml/linear_model/averagedperceptronbinaryclassifier.py +++ b/src/python/nimbusml/linear_model/averagedperceptronbinaryclassifier.py @@ -67,10 +67,10 @@ class AveragedPerceptronBinaryClassifier( `_ `Large Margin Classification Using the Perceptron Algorithm - `_ + `_ `Discriminative Training Methods for Hidden Markov Models - `_ + `_ :param feature: see `Columns `_. diff --git a/src/python/nimbusml/linear_model/fastlinearbinaryclassifier.py b/src/python/nimbusml/linear_model/fastlinearbinaryclassifier.py index 9374edd6..ccfe86a1 100644 --- a/src/python/nimbusml/linear_model/fastlinearbinaryclassifier.py +++ b/src/python/nimbusml/linear_model/fastlinearbinaryclassifier.py @@ -80,7 +80,7 @@ class FastLinearBinaryClassifier( content/uploads/2016/06/main-3.pdf>`_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/nimbusml/linear_model/fastlinearclassifier.py b/src/python/nimbusml/linear_model/fastlinearclassifier.py index c9546c25..39818f49 100644 --- a/src/python/nimbusml/linear_model/fastlinearclassifier.py +++ b/src/python/nimbusml/linear_model/fastlinearclassifier.py @@ -77,7 +77,7 @@ class FastLinearClassifier(core, BasePredictor, ClassifierMixin): content/uploads/2016/06/main-3.pdf>`_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/nimbusml/linear_model/fastlinearregressor.py b/src/python/nimbusml/linear_model/fastlinearregressor.py index 7e180d1c..a344066b 100644 --- a/src/python/nimbusml/linear_model/fastlinearregressor.py +++ b/src/python/nimbusml/linear_model/fastlinearregressor.py @@ -77,7 +77,7 @@ class FastLinearRegressor(core, BasePredictor, RegressorMixin): content/uploads/2016/06/main-3.pdf>`_ `Stochastic Dual Coordinate Ascent Methods for Regularized Loss - Minimization `_ diff --git a/src/python/nimbusml/linear_model/logisticregressionbinaryclassifier.py b/src/python/nimbusml/linear_model/logisticregressionbinaryclassifier.py index 38df685b..48bb9e78 100644 --- a/src/python/nimbusml/linear_model/logisticregressionbinaryclassifier.py +++ b/src/python/nimbusml/linear_model/logisticregressionbinaryclassifier.py @@ -91,14 +91,14 @@ class LogisticRegressionBinaryClassifier( **Reference** - `Wikipedia: L-BFGS `_ + `Wikipedia: L-BFGS `_ `Wikipedia: Logistic - regression `_ + regression `_ `Scalable Training of L1-Regularized Log-Linear Models - `_ + `_ `Test Run - L1 and L2 Regularization for Machine Learning diff --git a/src/python/nimbusml/linear_model/logisticregressionclassifier.py b/src/python/nimbusml/linear_model/logisticregressionclassifier.py index f6ded82f..4b2af101 100644 --- a/src/python/nimbusml/linear_model/logisticregressionclassifier.py +++ b/src/python/nimbusml/linear_model/logisticregressionclassifier.py @@ -92,14 +92,14 @@ class LogisticRegressionClassifier( **Reference** - `Wikipedia: L-BFGS `_ + `Wikipedia: L-BFGS `_ `Wikipedia: Logistic - regression `_ + regression `_ `Scalable Training of L1-Regularized Log-Linear Models - `_ + `_ `Test Run - L1 and L2 Regularization for Machine Learning diff --git a/src/python/nimbusml/linear_model/sgdbinaryclassifier.py b/src/python/nimbusml/linear_model/sgdbinaryclassifier.py index b45e8bf2..6da73ca8 100644 --- a/src/python/nimbusml/linear_model/sgdbinaryclassifier.py +++ b/src/python/nimbusml/linear_model/sgdbinaryclassifier.py @@ -34,14 +34,14 @@ class SgdBinaryClassifier(core, BasePredictor, ClassifierMixin): 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 - `_. + refer to `https://arxiv.org/pdf/1106.5730v2.pdf + `_. **Reference** - `http://arxiv.org/pdf/1106.5730v2.pdf - `_ + `https://arxiv.org/pdf/1106.5730v2.pdf + `_ :param feature: see `Columns `_. diff --git a/src/python/setup.py b/src/python/setup.py index 213acaa2..2df60695 100644 --- a/src/python/setup.py +++ b/src/python/setup.py @@ -146,7 +146,7 @@ # Although 'package_data' is the preferred approach, in some case # you may need to place data files outside of your packages. See: - # http://docs.python.org/3.4/distutils/setupscript.html#installing + # https://docs.python.org/3.4/distutils/setupscript.html#installing # -additional-files # noqa # In this case, 'data_file' will be installed into # '/my_data' diff --git a/src/python/setup.py.in b/src/python/setup.py.in index 07f92fe1..d9b3f1a4 100644 --- a/src/python/setup.py.in +++ b/src/python/setup.py.in @@ -147,7 +147,7 @@ setup( # Although 'package_data' is the preferred approach, in some case # you may need to place data files outside of your packages. See: - # http://docs.python.org/3.4/distutils/setupscript.html#installing + # https://docs.python.org/3.4/distutils/setupscript.html#installing # -additional-files # noqa # In this case, 'data_file' will be installed into # '/my_data'