├── .github
├── ISSUE_TEMPLATE
│ ├── bug_report.md
│ └── feature_request.md
├── actions
│ └── merge-branches
│ │ ├── Dockerfile
│ │ ├── README.md
│ │ ├── action.yml
│ │ └── entrypoint.sh
└── workflows
│ └── nightly-merge.yml
├── .gitignore
├── .vsts-ci.yml
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── PULL_REQUEST_TEMPLATE.md
├── README.md
├── THIRD-PARTY-NOTICES.txt
├── build.cmd
├── build.sh
├── build
├── ci
│ └── phase-template.yml
├── libs_linux.txt
├── libs_mac.txt
├── libs_win.txt
├── sign.csproj
├── signed_build_phase.yml
├── vsts-ci-nightly.yml
└── vsts-ci.yml
├── docs
├── README.md
├── developers
│ ├── developer-guide.md
│ ├── entrypoints.md
│ ├── linux-build.md
│ ├── mac-build.md
│ └── windows-build.md
├── project-docs
│ └── style-guide.md
└── release-notes
│ ├── release-1.2.0.md
│ ├── release-1.3.0.md
│ ├── release-1.4.0.md
│ ├── release-1.5.0.md
│ └── release-1.6.0.md
├── nimbusml.sln
├── nuget.config
├── release-next.md
├── src
├── CommonCpp.props
├── DotNetBridge
│ ├── Bridge.cs
│ ├── DotNetBridge.csproj
│ ├── DotNetBridge.dll.config
│ ├── DotNetBridge.snk
│ ├── Entrypoints.cs
│ ├── ManifestUtils.cs
│ ├── MessageValidator.cs
│ ├── NativeDataInterop.cs
│ ├── NativeDataView.cs
│ ├── Properties
│ │ └── AssemblyInfo.cs
│ ├── RmlEnvironment.cs
│ ├── RunGraph.cs
│ ├── ValueListBuilder.cs
│ ├── app.config
│ └── transforms
│ │ └── VariableColumnTransform.cs
├── ManifestGenerator
│ ├── ManifestGenerator.cs
│ ├── ManifestGenerator.csproj
│ ├── Properties
│ │ └── AssemblyInfo.cs
│ └── app.config
├── NativeBridge
│ ├── CMakeLists.txt
│ ├── DataViewInterop.cpp
│ ├── DataViewInterop.h
│ ├── ManagedInterop.cpp
│ ├── ManagedInterop.h
│ ├── NativeBridge.vcxproj
│ ├── PythonInterop.cpp
│ ├── PythonInterop.h
│ ├── UnixInterface.h
│ ├── WinInterface.h
│ ├── build.sh
│ ├── dllmain.cpp
│ ├── inc
│ │ ├── RCa04612
│ │ ├── asm_version.h
│ │ ├── buildnumber.h
│ │ ├── clrdata.h
│ │ ├── clrinternal.h
│ │ ├── clrprivbinding.h
│ │ ├── clrprivhosting.h
│ │ ├── clrprivruntimebinders.h
│ │ ├── cordebug.h
│ │ ├── corerror.h
│ │ ├── corprof.h
│ │ ├── corpub.h
│ │ ├── corsym.h
│ │ ├── fusion.h
│ │ ├── fxver.h
│ │ ├── fxver.rc
│ │ ├── fxverstrings.h
│ │ ├── gchost.h
│ │ ├── ivalidator.h
│ │ ├── ivehandler.h
│ │ ├── metahost.h
│ │ ├── mscoree.h
│ │ ├── mscorsvc.h
│ │ ├── ndpversion.h
│ │ ├── ndpversion_generated.h
│ │ ├── product_version.h
│ │ ├── sospriv.h
│ │ ├── version.h
│ │ ├── xclrdata.h
│ │ └── xcordebug.h
│ ├── stdafx.cpp
│ ├── stdafx.h
│ └── targetver.h
├── Platforms
│ ├── Program.cs
│ └── build.csproj
└── python
│ ├── MANIFEST.in
│ ├── README.md
│ ├── docs
│ ├── docstrings
│ │ ├── AveragedPerceptronBinaryClassifier.txt
│ │ ├── Binner.txt
│ │ ├── BootstrapSampler.txt
│ │ ├── CharTokenizer.txt
│ │ ├── ClassifierBestPerformanceSelector.txt
│ │ ├── ClassifierWeightedAverage.txt
│ │ ├── ColumnConcatenator.txt
│ │ ├── ColumnDropper.txt
│ │ ├── ColumnDuplicator.txt
│ │ ├── ColumnSelector.txt
│ │ ├── CountSelector.txt
│ │ ├── CustomStopWordsRemover.txt
│ │ ├── Dart.txt
│ │ ├── DnnFeaturizer.txt
│ │ ├── EnsembleClassifier.txt
│ │ ├── EnsembleRegressor.txt
│ │ ├── ExponentialAverage.txt
│ │ ├── Expression.txt
│ │ ├── FactorizationMachineBinaryClassifier.txt
│ │ ├── FastForestBinaryClassifier.txt
│ │ ├── FastForestRegressor.txt
│ │ ├── FastLinearBinaryClassifier.txt
│ │ ├── FastLinearClassifier.txt
│ │ ├── FastLinearRegressor.txt
│ │ ├── FastTreesBinaryClassifier.txt
│ │ ├── FastTreesRegressor.txt
│ │ ├── FastTreesTweedieRegressor.txt
│ │ ├── Filter.txt
│ │ ├── FromKey.txt
│ │ ├── GamBinaryClassifier.txt
│ │ ├── GamRegressor.txt
│ │ ├── Gbdt.txt
│ │ ├── GlobalContrastRowScaler.txt
│ │ ├── Goss.txt
│ │ ├── Handler.txt
│ │ ├── IIDChangePointDetector.txt
│ │ ├── IIDSpikeDetector.txt
│ │ ├── Indicator.txt
│ │ ├── KMeansPlusPlus.txt
│ │ ├── LightGbmBinaryClassifier.txt
│ │ ├── LightGbmClassifier.txt
│ │ ├── LightGbmRanker.txt
│ │ ├── LightGbmRegressor.txt
│ │ ├── LightLda.txt
│ │ ├── LinearKernel.txt
│ │ ├── LinearSvmBinaryClassifier.txt
│ │ ├── Loader.txt
│ │ ├── LocalDeepSvmBinaryClassifier.txt
│ │ ├── LogMeanVarianceScaler.txt
│ │ ├── LogisticRegressionBinaryClassifier.txt
│ │ ├── LogisticRegressionClassifier.txt
│ │ ├── MeanVarianceScaler.txt
│ │ ├── MinMaxScaler.txt
│ │ ├── MutualInformationSelector.txt
│ │ ├── NGram.txt
│ │ ├── NGramFeaturizer.txt
│ │ ├── NaiveBayesClassifier.txt
│ │ ├── NgramHash.txt
│ │ ├── OneClassSVMAnomalyDetector.txt
│ │ ├── OneHotHashVectorizer.txt
│ │ ├── OneHotVectorizer.txt
│ │ ├── OneVsRestClassifier.txt
│ │ ├── OnlineGradientDescentRegressor.txt
│ │ ├── OptionalScaler.txt
│ │ ├── OrdinaryLeastSquaresRegressor.txt
│ │ ├── PcaAnomalyDetector.txt
│ │ ├── PcaTransformer.txt
│ │ ├── PercentileThreshold.txt
│ │ ├── PixelExtractor.txt
│ │ ├── PoissonRegressionRegressor.txt
│ │ ├── PolynomialKernel.txt
│ │ ├── PredefinedStopWordsRemover.txt
│ │ ├── PrefixColumnConcatenator.txt
│ │ ├── Pvalue.txt
│ │ ├── RangeFilter.txt
│ │ ├── RbfKernel.txt
│ │ ├── RegressorBestPerformanceSelector.txt
│ │ ├── Resizer.txt
│ │ ├── Sentiment.txt
│ │ ├── SgdBinaryClassifier.txt
│ │ ├── SigmoidKernel.txt
│ │ ├── SkipFilter.txt
│ │ ├── SlidingWindow.txt
│ │ ├── SsaChangePointDetector.txt
│ │ ├── SsaForecaster.txt
│ │ ├── SsaSpikeDetector.txt
│ │ ├── SsweEmbedding.txt
│ │ ├── SupervisedBinner.txt
│ │ ├── SymSgdBinaryClassifier.txt
│ │ ├── TakeFilter.txt
│ │ ├── TensorFlowScorer.txt
│ │ ├── ToKey.txt
│ │ ├── TreeFeaturizer.txt
│ │ ├── TypeConverter.txt
│ │ ├── WordEmbedding.txt
│ │ └── loss
│ │ │ ├── Exp.txt
│ │ │ ├── Hinge.txt
│ │ │ ├── Log.txt
│ │ │ ├── Poisson.txt
│ │ │ ├── SmoothedHinge.txt
│ │ │ ├── Squared.txt
│ │ │ └── Tweedie.txt
│ └── sphinx
│ │ ├── apiguide.rst
│ │ ├── ci_script
│ │ ├── _static
│ │ │ ├── images
│ │ │ │ ├── 1.1.1.png
│ │ │ │ ├── 1.1.2.png
│ │ │ │ ├── 2.3.000.png
│ │ │ │ ├── 2.3.4.png
│ │ │ │ ├── 2.3.5.png
│ │ │ │ ├── 2.3.6.png
│ │ │ │ ├── 2.4.1.png
│ │ │ │ ├── 2.4.2.png
│ │ │ │ ├── DecisionTree.png
│ │ │ │ ├── FDFigure.png
│ │ │ │ ├── ani_1.1.gif
│ │ │ │ ├── customer.png
│ │ │ │ ├── examples.png
│ │ │ │ ├── examples1.png
│ │ │ │ ├── examples2.png
│ │ │ │ ├── examples3.png
│ │ │ │ ├── examples4.png
│ │ │ │ ├── supported_version.png
│ │ │ │ └── table_car.png
│ │ │ └── mystyle.css
│ │ ├── conf.py
│ │ ├── fix_apiguide.py
│ │ ├── gen_toc_yml.py
│ │ └── update_all_toc_yml.py
│ │ ├── concepts.rst
│ │ ├── concepts
│ │ ├── columns.rst
│ │ ├── datasources.rst
│ │ ├── experimentvspipeline.rst
│ │ ├── metrics.rst
│ │ ├── roles.rst
│ │ ├── schema.rst
│ │ └── types.rst
│ │ ├── conf.py
│ │ ├── index.rst
│ │ ├── installationguide.rst
│ │ ├── loadsavemodels.rst
│ │ ├── make.bat
│ │ ├── make_yaml.bat
│ │ ├── modules.rst
│ │ ├── modules
│ │ ├── DataSchema.rst
│ │ ├── FileDataStream.rst
│ │ ├── Pipeline.rst
│ │ ├── Role.rst
│ │ ├── basepredictor.rst
│ │ ├── basetransform.rst
│ │ ├── cluster.rst
│ │ ├── cluster
│ │ │ └── kmeansplusplus.rst
│ │ ├── data.rst
│ │ ├── data
│ │ │ ├── DataSchema.rst
│ │ │ ├── FileDataStream.rst
│ │ │ └── Role.rst
│ │ ├── decomposition.rst
│ │ ├── decomposition
│ │ │ ├── factorizationmachinebinaryclassifier.rst
│ │ │ ├── pcaanomalydetector.rst
│ │ │ └── pcatransformer.rst
│ │ ├── ensemble.rst
│ │ ├── ensemble
│ │ │ ├── booster.rst
│ │ │ ├── booster
│ │ │ │ ├── dart.rst
│ │ │ │ ├── gbdt.rst
│ │ │ │ └── goss.rst
│ │ │ ├── fastforestbinaryclassifier.rst
│ │ │ ├── fastforestregressor.rst
│ │ │ ├── fasttreesbinaryclassifier.rst
│ │ │ ├── fasttreesregressor.rst
│ │ │ ├── fasttreestweedieregressor.rst
│ │ │ ├── gambinaryclassifier.rst
│ │ │ ├── gamregressor.rst
│ │ │ ├── lightgbmbinaryclassifier.rst
│ │ │ ├── lightgbmclassifier.rst
│ │ │ ├── lightgbmranker.rst
│ │ │ └── lightgbmregressor.rst
│ │ ├── feature_extraction.rst
│ │ ├── feature_extraction
│ │ │ ├── categorical.rst
│ │ │ ├── categorical
│ │ │ │ ├── onehothashvectorizer.rst
│ │ │ │ └── onehotvectorizer.rst
│ │ │ ├── image.rst
│ │ │ ├── image
│ │ │ │ ├── dnnfeaturizer.rst
│ │ │ │ ├── loader.rst
│ │ │ │ ├── pixelextractor.rst
│ │ │ │ └── resizer.rst
│ │ │ ├── text.rst
│ │ │ ├── text
│ │ │ │ ├── extractor.rst
│ │ │ │ ├── extractor
│ │ │ │ │ ├── ngram.rst
│ │ │ │ │ └── ngramhash.rst
│ │ │ │ ├── lightlda.rst
│ │ │ │ ├── ngramfeaturizer.rst
│ │ │ │ ├── sentiment.rst
│ │ │ │ ├── ssweembedding.rst
│ │ │ │ ├── stopwords.rst
│ │ │ │ ├── stopwords
│ │ │ │ │ ├── customstopwordsremover.rst
│ │ │ │ │ └── predefinedstopwordsremover.rst
│ │ │ │ └── wordembedding.rst
│ │ │ └── treefeaturizer.rst
│ │ ├── feature_selection.rst
│ │ ├── feature_selection
│ │ │ ├── countselector.rst
│ │ │ └── mutualinformationselector.rst
│ │ ├── linear_model.rst
│ │ ├── linear_model
│ │ │ ├── averagedperceptronbinaryclassifier.rst
│ │ │ ├── fastlinearbinaryclassifier.rst
│ │ │ ├── fastlinearclassifier.rst
│ │ │ ├── fastlinearregressor.rst
│ │ │ ├── logisticregressionbinaryclassifier.rst
│ │ │ ├── logisticregressionclassifier.rst
│ │ │ ├── onlinegradientdescentregressor.rst
│ │ │ ├── ordinaryleastsquaresregressor.rst
│ │ │ ├── poissonregressionregressor.rst
│ │ │ ├── sgdbinaryclassifier.rst
│ │ │ └── symsgdbinaryclassifier.rst
│ │ ├── loss.rst
│ │ ├── loss
│ │ │ ├── exp.rst
│ │ │ ├── hinge.rst
│ │ │ ├── log.rst
│ │ │ ├── poisson.rst
│ │ │ ├── smoothedhinge.rst
│ │ │ ├── squared.rst
│ │ │ └── tweedie.rst
│ │ ├── model_selection.rst
│ │ ├── model_selection
│ │ │ └── cv.rst
│ │ ├── multiclass.rst
│ │ ├── multiclass
│ │ │ └── onevsrestclassifier.rst
│ │ ├── naive_bayes.rst
│ │ ├── naive_bayes
│ │ │ └── naivebayesclassifier.rst
│ │ ├── preprocessing.rst
│ │ ├── preprocessing
│ │ │ ├── expression.rst
│ │ │ ├── filter.rst
│ │ │ ├── filter
│ │ │ │ ├── bootstrapsampler.rst
│ │ │ │ ├── rangefilter.rst
│ │ │ │ ├── skipfilter.rst
│ │ │ │ └── takefilter.rst
│ │ │ ├── fromkey.rst
│ │ │ ├── missing_values.rst
│ │ │ ├── missing_values
│ │ │ │ ├── filter.rst
│ │ │ │ ├── handler.rst
│ │ │ │ └── indicator.rst
│ │ │ ├── normalization.rst
│ │ │ ├── normalization
│ │ │ │ ├── binner.rst
│ │ │ │ ├── globalcontrastrowscaler.rst
│ │ │ │ ├── logmeanvariancescaler.rst
│ │ │ │ ├── meanvariancescaler.rst
│ │ │ │ ├── minmaxscaler.rst
│ │ │ │ ├── optionalscaler.rst
│ │ │ │ └── supervisedbinner.rst
│ │ │ ├── schema.rst
│ │ │ ├── schema
│ │ │ │ ├── columnconcatenator.rst
│ │ │ │ ├── columndropper.rst
│ │ │ │ ├── columnduplicator.rst
│ │ │ │ ├── columnselector.rst
│ │ │ │ └── typeconverter.rst
│ │ │ ├── tensorflowscorer.rst
│ │ │ ├── text.rst
│ │ │ ├── text
│ │ │ │ ├── chartokenizer.rst
│ │ │ │ ├── keyphraseextractor.rst
│ │ │ │ └── lemmatizer.rst
│ │ │ ├── timeseries.rst
│ │ │ ├── timeseries
│ │ │ │ ├── exponentialaverage.rst
│ │ │ │ ├── iidchangepointdetector.rst
│ │ │ │ ├── iidspikedetector.rst
│ │ │ │ ├── percentilethreshold.rst
│ │ │ │ ├── pvalue.rst
│ │ │ │ ├── slidingwindow.rst
│ │ │ │ ├── ssachangepointdetector.rst
│ │ │ │ └── ssaspikedetector.rst
│ │ │ └── tokey.rst
│ │ ├── svm.rst
│ │ ├── svm
│ │ │ ├── kernel.rst
│ │ │ ├── kernel
│ │ │ │ ├── linearkernel.rst
│ │ │ │ ├── polynomialkernel.rst
│ │ │ │ ├── rbfkernel.rst
│ │ │ │ └── sigmoidkernel.rst
│ │ │ ├── localdeepsvmbinaryclassifier.rst
│ │ │ └── oneclasssvmanomalydetector.rst
│ │ └── utils.rst
│ │ ├── ms_scikit.rst
│ │ ├── overview.rst
│ │ └── toc.yml
│ ├── nimbusml.pyproj
│ ├── nimbusml
│ ├── __init__.py
│ ├── __init__.py.in
│ ├── __main__.py
│ ├── base_predictor.py
│ ├── base_transform.py
│ ├── cluster
│ │ ├── __init__.py
│ │ └── kmeansplusplus.py
│ ├── datasets
│ │ ├── __init__.py
│ │ ├── baseline
│ │ │ ├── __init__.py
│ │ │ ├── featurizeImage_alexnet_output.csv
│ │ │ ├── featurizeImage_resnet101_output.csv
│ │ │ ├── featurizeImage_resnet18_output.csv
│ │ │ ├── featurizeImage_resnet50_output.csv
│ │ │ └── magic.nn
│ │ ├── data
│ │ │ ├── __init__.py
│ │ │ ├── gplv2
│ │ │ │ ├── COPYING
│ │ │ │ ├── airquality.csv
│ │ │ │ └── infert.csv
│ │ │ ├── test-100.uciadult.sample.csv
│ │ │ ├── test-msltr.sample.csv
│ │ │ ├── test-ticketchoice.csv
│ │ │ ├── test-twitter.gen-sample.tsv
│ │ │ ├── test.wikipedia.sample.tsv
│ │ │ ├── test_fs.csv
│ │ │ ├── timeseries.csv
│ │ │ ├── topics.csv
│ │ │ ├── train-250.wikipedia.sample.tsv
│ │ │ ├── train-500.uciadult.sample.csv
│ │ │ ├── train-msltr.sample.csv
│ │ │ ├── train-ticketchoice.csv
│ │ │ ├── train-twitter.gen-sample.tsv
│ │ │ └── train_fs.csv
│ │ ├── datasets.py
│ │ ├── image.py
│ │ └── images
│ │ │ ├── Microsoftlogo.png
│ │ │ ├── RevolutionAnalyticslogo.png
│ │ │ └── __init__.py
│ ├── decomposition
│ │ ├── __init__.py
│ │ ├── factorizationmachinebinaryclassifier.py
│ │ ├── pcaanomalydetector.py
│ │ └── pcatransformer.py
│ ├── ensemble
│ │ ├── __init__.py
│ │ ├── booster
│ │ │ ├── __init__.py
│ │ │ ├── dart.py
│ │ │ ├── gbdt.py
│ │ │ └── goss.py
│ │ ├── ensembleclassifier.py
│ │ ├── ensembleregressor.py
│ │ ├── fastforestbinaryclassifier.py
│ │ ├── fastforestregressor.py
│ │ ├── fasttreesbinaryclassifier.py
│ │ ├── fasttreesregressor.py
│ │ ├── fasttreestweedieregressor.py
│ │ ├── feature_selector
│ │ │ ├── __init__.py
│ │ │ ├── allfeatureselector.py
│ │ │ └── randomfeatureselector.py
│ │ ├── gambinaryclassifier.py
│ │ ├── gamregressor.py
│ │ ├── lightgbmbinaryclassifier.py
│ │ ├── lightgbmclassifier.py
│ │ ├── lightgbmranker.py
│ │ ├── lightgbmregressor.py
│ │ ├── output_combiner
│ │ │ ├── __init__.py
│ │ │ ├── classifieraverage.py
│ │ │ ├── classifiermedian.py
│ │ │ ├── classifierstacking.py
│ │ │ ├── classifiervoting.py
│ │ │ ├── classifierweightedaverage.py
│ │ │ ├── regressoraverage.py
│ │ │ ├── regressormedian.py
│ │ │ └── regressorstacking.py
│ │ ├── sub_model_selector
│ │ │ ├── __init__.py
│ │ │ ├── classifierallselector.py
│ │ │ ├── classifierbestdiverseselector.py
│ │ │ ├── classifierbestperformanceselector.py
│ │ │ ├── diversity_measure
│ │ │ │ ├── __init__.py
│ │ │ │ ├── classifierdisagreement.py
│ │ │ │ └── regressordisagreement.py
│ │ │ ├── regressorallselector.py
│ │ │ ├── regressorbestdiverseselector.py
│ │ │ └── regressorbestperformanceselector.py
│ │ ├── subset_selector
│ │ │ ├── __init__.py
│ │ │ ├── allinstanceselector.py
│ │ │ ├── bootstrapselector.py
│ │ │ └── randompartitionselector.py
│ │ └── votingensemble.py
│ ├── examples
│ │ ├── AveragedPerceptronBinaryClassifier.py
│ │ ├── Binner.py
│ │ ├── BootStrapSample.py
│ │ ├── CV.py
│ │ ├── CharTokenizer.py
│ │ ├── ColumnConcatenator.py
│ │ ├── ColumnDropper.py
│ │ ├── ColumnDuplicator.py
│ │ ├── ColumnSelector.py
│ │ ├── CountSelector.py
│ │ ├── DateTimeSplitter.py
│ │ ├── EnsembleClassifier.py
│ │ ├── EnsembleRegressor.py
│ │ ├── Exp.py
│ │ ├── FactorizationMachineBinaryClassifier.py
│ │ ├── FastForestBinaryClassifier.py
│ │ ├── FastForestRegressor.py
│ │ ├── FastLinearBinaryClassifier.py
│ │ ├── FastLinearClassifier.py
│ │ ├── FastLinearRegressor.py
│ │ ├── FastTreesBinaryClassifier.py
│ │ ├── FastTreesRegressor.py
│ │ ├── FastTreesTweedieRegressor.py
│ │ ├── Filter.py
│ │ ├── FromKey.py
│ │ ├── GamBinaryClassifier.py
│ │ ├── GamRegressor.py
│ │ ├── GlobalContrastRowScaler.py
│ │ ├── Handler.py
│ │ ├── Hinge.py
│ │ ├── IidChangePointDetector.py
│ │ ├── IidSpikeDetector.py
│ │ ├── Image.py
│ │ ├── Indicator.py
│ │ ├── KMeansPlusPlus.py
│ │ ├── LightGbmBinaryClassifier.py
│ │ ├── LightGbmClassifier.py
│ │ ├── LightGbmRanker.py
│ │ ├── LightGbmRegressor.py
│ │ ├── LightLda.py
│ │ ├── LinearSvmBinaryClassifier.py
│ │ ├── Log.py
│ │ ├── LogMeanVarianceScaler.py
│ │ ├── LogisticRegressionBinaryClassifier.py
│ │ ├── LogisticRegressionClassifier.py
│ │ ├── LpScaler.py
│ │ ├── MeanVarianceScaler.py
│ │ ├── MinMaxScaler.py
│ │ ├── MutualInformationSelector.py
│ │ ├── NGramExtractor.py
│ │ ├── NGramFeaturizer.py
│ │ ├── NGramFeaturizer2.py
│ │ ├── NGramFeaturizer3.py
│ │ ├── NaiveBayesClassifier.py
│ │ ├── OneHotHashVectorizer.py
│ │ ├── OneHotVectorizer.py
│ │ ├── OneVsRestClassifier.py
│ │ ├── OnlineGradientDescentRegressor.py
│ │ ├── OrdinaryLeastSquaresRegressor.py
│ │ ├── PcaAnomalyDetector.py
│ │ ├── PcaTransformer.py
│ │ ├── PermutationFeatureImportance.py
│ │ ├── PipelineWithFeatureContributions.py
│ │ ├── PipelineWithGridSearchCV1.py
│ │ ├── PipelineWithGridSearchCV2.py
│ │ ├── Poisson.py
│ │ ├── PoissonRegressionRegressor.py
│ │ ├── PrefixColumnConcatenator.py
│ │ ├── RangeFilter.py
│ │ ├── RobustScaler.py
│ │ ├── Schema.py
│ │ ├── Sentiment.py
│ │ ├── SgdBinaryClassifier.py
│ │ ├── SkipFilter.py
│ │ ├── SmoothedHinge.py
│ │ ├── Squared.py
│ │ ├── SsaChangePointDetector.py
│ │ ├── SsaForecaster.py
│ │ ├── SsaSpikeDetector.py
│ │ ├── SymSgdBinaryClassifier.py
│ │ ├── TensorFlowScorer.py
│ │ ├── ToKey.py
│ │ ├── ToKeyImputer.py
│ │ ├── ToString.py
│ │ ├── Tweedie.py
│ │ ├── TypeConverter.py
│ │ ├── VotingRegressor.py
│ │ ├── WordEmbedding.py
│ │ ├── WordTokenizer.py
│ │ ├── _Load_Save_Models.py
│ │ ├── _Load_Save_Pipeline.py
│ │ ├── _Load_Save_transforms.py
│ │ ├── __init__.py
│ │ ├── examples_from_dataframe
│ │ │ ├── AveragedPerceptronBinaryClassifier_infert_df.py
│ │ │ ├── Binner_df.py
│ │ │ ├── BootStrapSample_df.py
│ │ │ ├── CharTokenizer_df.py
│ │ │ ├── ColumnConcatenator_df.py
│ │ │ ├── ColumnDuplicator_df.py
│ │ │ ├── Concat_Drop_Select_Columns_df.py
│ │ │ ├── DateTimeSplitter_df.py
│ │ │ ├── EnsembleClassifier_iris_df.py
│ │ │ ├── EnsembleRegressor_airquality_df.py
│ │ │ ├── FactorizationMachineBinaryClassifier_infert_df.py
│ │ │ ├── FastForestBinaryClassifier_infert_df.py
│ │ │ ├── FastForestRegressor_airquality_df.py
│ │ │ ├── FastLinearBinaryClassifier_infert_df.py
│ │ │ ├── FastLinearClassifier_iris_df.py
│ │ │ ├── FastLinearRegressor_airquality_df.py
│ │ │ ├── FastTreesBinaryClassifier_infert_df.py
│ │ │ ├── FastTreesRegressor_airquality_df.py
│ │ │ ├── FastTreesTweedieRegressor_airquality_df.py
│ │ │ ├── Filter_df.py
│ │ │ ├── FromKey_df.py
│ │ │ ├── GamBinaryClassifier_infert_df.py
│ │ │ ├── GamRegressor_airquality_df.py
│ │ │ ├── GlobalContrastRowScaler_df.py
│ │ │ ├── Handler_df.py
│ │ │ ├── IidChangePointDetector_df.py
│ │ │ ├── IidSpikeDetector_df.py
│ │ │ ├── Image_df.py
│ │ │ ├── Indicator_df.py
│ │ │ ├── KMeansPlusPlus_df.py
│ │ │ ├── LightGbmBinaryClassifier_infert_df.py
│ │ │ ├── LightGbmClassifier_iris_df.py
│ │ │ ├── LightGbmRanker_sampleinputextraction_df.py
│ │ │ ├── LightGbmRegressor_airquality_df.py
│ │ │ ├── LightLda_df.py
│ │ │ ├── LinearSvmBinaryClassifier_df.py
│ │ │ ├── LogMeanVarianceScaler_df.py
│ │ │ ├── LogisticRegressionBinaryClassifier_infert_df.py
│ │ │ ├── LogisticRegressionClassifier_iris_df.py
│ │ │ ├── LpScaler_df.py
│ │ │ ├── MeanVarianceScaler_df.py
│ │ │ ├── MinMaxScaler_df.py
│ │ │ ├── MutualInformationSelector_df.py
│ │ │ ├── NGramExtractor_df.py
│ │ │ ├── NGramFeaturizer_df.py
│ │ │ ├── NaiveBayesClassifier_df.py
│ │ │ ├── OneHotHashVectorizer_df.py
│ │ │ ├── OneHotVectorizer_df.py
│ │ │ ├── OnlineGradientDescentRegressor_df.py
│ │ │ ├── OnnxRunner_df.py
│ │ │ ├── OrdinaryLeastSquaresRegressor_df.py
│ │ │ ├── PcaAnomalyDetector_df.py
│ │ │ ├── PcaTransformer_df.py
│ │ │ ├── PrefixColumnConcatenator_df.py
│ │ │ ├── RangeFilter_df.py
│ │ │ ├── RobustScaler_df.py
│ │ │ ├── SgdBinaryClassifier_infert_df.py
│ │ │ ├── SkipFilter_df.py
│ │ │ ├── SsaChangePointDetector_df.py
│ │ │ ├── SsaForecaster_df.py
│ │ │ ├── SsaSpikeDetector_df.py
│ │ │ ├── SymSgdBinaryClassifier_infert_df.py
│ │ │ ├── TimeSeriesImputer_df.py
│ │ │ ├── ToKeyImputer_df.py
│ │ │ ├── ToKey_df.py
│ │ │ ├── ToString_df.py
│ │ │ ├── VotingRegressor.py
│ │ │ ├── WordEmbedding_df.py
│ │ │ ├── WordTokenizer_df.py
│ │ │ ├── __init__.py
│ │ │ └── tmpfile_with_nans.csv
│ │ ├── frozen_saved_model.pb
│ │ └── pipeline.py
│ ├── feature_extraction
│ │ ├── __init__.py
│ │ ├── categorical
│ │ │ ├── __init__.py
│ │ │ ├── onehothashvectorizer.py
│ │ │ └── onehotvectorizer.py
│ │ ├── image
│ │ │ ├── __init__.py
│ │ │ ├── loader.py
│ │ │ ├── pixelextractor.py
│ │ │ └── resizer.py
│ │ ├── text
│ │ │ ├── __init__.py
│ │ │ ├── extractor
│ │ │ │ ├── __init__.py
│ │ │ │ ├── ngram.py
│ │ │ │ └── ngramhash.py
│ │ │ ├── lightlda.py
│ │ │ ├── ngramextractor.py
│ │ │ ├── ngramfeaturizer.py
│ │ │ ├── sentiment.py
│ │ │ ├── stopwords
│ │ │ │ ├── __init__.py
│ │ │ │ ├── customstopwordsremover.py
│ │ │ │ └── predefinedstopwordsremover.py
│ │ │ └── wordembedding.py
│ │ └── treefeaturizer.py
│ ├── feature_selection
│ │ ├── __init__.py
│ │ ├── countselector.py
│ │ └── mutualinformationselector.py
│ ├── internal
│ │ ├── __init__.py
│ │ ├── core
│ │ │ ├── __init__.py
│ │ │ ├── base_pipeline_item.py
│ │ │ ├── cluster
│ │ │ │ ├── __init__.py
│ │ │ │ └── kmeansplusplus.py
│ │ │ ├── decomposition
│ │ │ │ ├── __init__.py
│ │ │ │ ├── factorizationmachinebinaryclassifier.py
│ │ │ │ ├── pcaanomalydetector.py
│ │ │ │ └── pcatransformer.py
│ │ │ ├── ensemble
│ │ │ │ ├── __init__.py
│ │ │ │ ├── booster
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── dart.py
│ │ │ │ │ ├── gbdt.py
│ │ │ │ │ └── goss.py
│ │ │ │ ├── ensembleclassifier.py
│ │ │ │ ├── ensembleregressor.py
│ │ │ │ ├── fastforestbinaryclassifier.py
│ │ │ │ ├── fastforestregressor.py
│ │ │ │ ├── fasttreesbinaryclassifier.py
│ │ │ │ ├── fasttreesregressor.py
│ │ │ │ ├── fasttreestweedieregressor.py
│ │ │ │ ├── feature_selector
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── allfeatureselector.py
│ │ │ │ │ └── randomfeatureselector.py
│ │ │ │ ├── gambinaryclassifier.py
│ │ │ │ ├── gamregressor.py
│ │ │ │ ├── lightgbmbinaryclassifier.py
│ │ │ │ ├── lightgbmclassifier.py
│ │ │ │ ├── lightgbmranker.py
│ │ │ │ ├── lightgbmregressor.py
│ │ │ │ ├── output_combiner
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── classifieraverage.py
│ │ │ │ │ ├── classifiermedian.py
│ │ │ │ │ ├── classifierstacking.py
│ │ │ │ │ ├── classifiervoting.py
│ │ │ │ │ ├── classifierweightedaverage.py
│ │ │ │ │ ├── regressoraverage.py
│ │ │ │ │ ├── regressormedian.py
│ │ │ │ │ └── regressorstacking.py
│ │ │ │ ├── sub_model_selector
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── classifierallselector.py
│ │ │ │ │ ├── classifierbestdiverseselector.py
│ │ │ │ │ ├── classifierbestperformanceselector.py
│ │ │ │ │ ├── diversity_measure
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── classifierdisagreement.py
│ │ │ │ │ │ └── regressordisagreement.py
│ │ │ │ │ ├── regressorallselector.py
│ │ │ │ │ ├── regressorbestdiverseselector.py
│ │ │ │ │ └── regressorbestperformanceselector.py
│ │ │ │ └── subset_selector
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── allinstanceselector.py
│ │ │ │ │ ├── bootstrapselector.py
│ │ │ │ │ └── randompartitionselector.py
│ │ │ ├── feature_extraction
│ │ │ │ ├── __init__.py
│ │ │ │ ├── categorical
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── onehothashvectorizer.py
│ │ │ │ │ └── onehotvectorizer.py
│ │ │ │ ├── image
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── loader.py
│ │ │ │ │ ├── pixelextractor.py
│ │ │ │ │ └── resizer.py
│ │ │ │ ├── text
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── extractor
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── ngram.py
│ │ │ │ │ │ └── ngramhash.py
│ │ │ │ │ ├── lightlda.py
│ │ │ │ │ ├── ngramextractor.py
│ │ │ │ │ ├── ngramfeaturizer.py
│ │ │ │ │ ├── sentiment.py
│ │ │ │ │ ├── stopwords
│ │ │ │ │ │ ├── __init__.py
│ │ │ │ │ │ ├── customstopwordsremover.py
│ │ │ │ │ │ └── predefinedstopwordsremover.py
│ │ │ │ │ └── wordembedding.py
│ │ │ │ └── treefeaturizer.py
│ │ │ ├── feature_selection
│ │ │ │ ├── __init__.py
│ │ │ │ ├── countselector.py
│ │ │ │ └── mutualinformationselector.py
│ │ │ ├── linear_model
│ │ │ │ ├── __init__.py
│ │ │ │ ├── averagedperceptronbinaryclassifier.py
│ │ │ │ ├── fastlinearbinaryclassifier.py
│ │ │ │ ├── fastlinearclassifier.py
│ │ │ │ ├── fastlinearregressor.py
│ │ │ │ ├── linearsvmbinaryclassifier.py
│ │ │ │ ├── logisticregressionbinaryclassifier.py
│ │ │ │ ├── logisticregressionclassifier.py
│ │ │ │ ├── onlinegradientdescentregressor.py
│ │ │ │ ├── ordinaryleastsquaresregressor.py
│ │ │ │ ├── poissonregressionregressor.py
│ │ │ │ ├── sgdbinaryclassifier.py
│ │ │ │ └── symsgdbinaryclassifier.py
│ │ │ ├── loss
│ │ │ │ ├── __init__.py
│ │ │ │ ├── loss_factory.py
│ │ │ │ └── loss_table_json.py
│ │ │ ├── multiclass
│ │ │ │ ├── __init__.py
│ │ │ │ └── onevsrestclassifier.py
│ │ │ ├── naive_bayes
│ │ │ │ ├── __init__.py
│ │ │ │ └── naivebayesclassifier.py
│ │ │ ├── preprocessing
│ │ │ │ ├── __init__.py
│ │ │ │ ├── datasettransformer.py
│ │ │ │ ├── datetimesplitter.py
│ │ │ │ ├── filter
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── bootstrapsampler.py
│ │ │ │ │ ├── rangefilter.py
│ │ │ │ │ ├── skipfilter.py
│ │ │ │ │ └── takefilter.py
│ │ │ │ ├── fromkey.py
│ │ │ │ ├── missing_values
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── filter.py
│ │ │ │ │ ├── handler.py
│ │ │ │ │ └── indicator.py
│ │ │ │ ├── normalization
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── binner.py
│ │ │ │ │ ├── globalcontrastrowscaler.py
│ │ │ │ │ ├── logmeanvariancescaler.py
│ │ │ │ │ ├── lpscaler.py
│ │ │ │ │ ├── meanvariancescaler.py
│ │ │ │ │ ├── minmaxscaler.py
│ │ │ │ │ └── robustscaler.py
│ │ │ │ ├── onnxrunner.py
│ │ │ │ ├── schema
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── columnconcatenator.py
│ │ │ │ │ ├── columndropper.py
│ │ │ │ │ ├── columnduplicator.py
│ │ │ │ │ ├── columnselector.py
│ │ │ │ │ ├── prefixcolumnconcatenator.py
│ │ │ │ │ └── typeconverter.py
│ │ │ │ ├── tensorflowscorer.py
│ │ │ │ ├── text
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ ├── chartokenizer.py
│ │ │ │ │ └── wordtokenizer.py
│ │ │ │ ├── tokey.py
│ │ │ │ ├── tokeyimputer.py
│ │ │ │ └── tostring.py
│ │ │ └── timeseries
│ │ │ │ ├── __init__.py
│ │ │ │ ├── iidchangepointdetector.py
│ │ │ │ ├── iidspikedetector.py
│ │ │ │ ├── ssachangepointdetector.py
│ │ │ │ ├── ssaforecaster.py
│ │ │ │ ├── ssaspikedetector.py
│ │ │ │ └── timeseriesimputer.py
│ │ ├── entrypoints
│ │ │ ├── __init__.py
│ │ │ ├── _boosterparameterfunction_dart.py
│ │ │ ├── _boosterparameterfunction_gbdt.py
│ │ │ ├── _boosterparameterfunction_goss.py
│ │ │ ├── _calibratortrainer_fixedplattcalibrator.py
│ │ │ ├── _calibratortrainer_naivecalibrator.py
│ │ │ ├── _calibratortrainer_pavcalibrator.py
│ │ │ ├── _calibratortrainer_plattcalibrator.py
│ │ │ ├── _classificationlossfunction_exploss.py
│ │ │ ├── _classificationlossfunction_hingeloss.py
│ │ │ ├── _classificationlossfunction_logloss.py
│ │ │ ├── _classificationlossfunction_smoothedhingeloss.py
│ │ │ ├── _earlystoppingcriterion_gl.py
│ │ │ ├── _earlystoppingcriterion_lp.py
│ │ │ ├── _earlystoppingcriterion_pq.py
│ │ │ ├── _earlystoppingcriterion_tr.py
│ │ │ ├── _earlystoppingcriterion_up.py
│ │ │ ├── _ensemblebinarydiversitymeasure_disagreementdiversitymeasure.py
│ │ │ ├── _ensemblebinaryoutputcombiner_average.py
│ │ │ ├── _ensemblebinaryoutputcombiner_median.py
│ │ │ ├── _ensemblebinaryoutputcombiner_stacking.py
│ │ │ ├── _ensemblebinaryoutputcombiner_voting.py
│ │ │ ├── _ensemblebinaryoutputcombiner_weightedaverage.py
│ │ │ ├── _ensemblebinarysubmodelselector_allselector.py
│ │ │ ├── _ensemblebinarysubmodelselector_bestperformanceselector.py
│ │ │ ├── _ensemblefeatureselector_allfeatureselector.py
│ │ │ ├── _ensemblefeatureselector_randomfeatureselector.py
│ │ │ ├── _ensemblemulticlassdiversitymeasure_multidisagreementdiversitymeasure.py
│ │ │ ├── _ensemblemulticlassoutputcombiner_multiaverage.py
│ │ │ ├── _ensemblemulticlassoutputcombiner_multimedian.py
│ │ │ ├── _ensemblemulticlassoutputcombiner_multistacking.py
│ │ │ ├── _ensemblemulticlassoutputcombiner_multivoting.py
│ │ │ ├── _ensemblemulticlassoutputcombiner_multiweightedaverage.py
│ │ │ ├── _ensemblemulticlasssubmodelselector_allselectormulticlass.py
│ │ │ ├── _ensemblemulticlasssubmodelselector_bestdiverseselectormulticlass.py
│ │ │ ├── _ensemblemulticlasssubmodelselector_bestperformanceselectormulticlass.py
│ │ │ ├── _ensembleregressiondiversitymeasure_regressiondisagreementdiversitymeasure.py
│ │ │ ├── _ensembleregressionoutputcombiner_average.py
│ │ │ ├── _ensembleregressionoutputcombiner_median.py
│ │ │ ├── _ensembleregressionoutputcombiner_regressionstacking.py
│ │ │ ├── _ensembleregressionsubmodelselector_allselector.py
│ │ │ ├── _ensembleregressionsubmodelselector_bestdiverseselectorregression.py
│ │ │ ├── _ensembleregressionsubmodelselector_bestperformanceregressionselector.py
│ │ │ ├── _ensemblesubsetselector_allinstanceselector.py
│ │ │ ├── _ensemblesubsetselector_bootstrapselector.py
│ │ │ ├── _ensemblesubsetselector_randompartitionselector.py
│ │ │ ├── _fasttreetrainer_fasttreebinaryclassification.py
│ │ │ ├── _fasttreetrainer_fasttreeranking.py
│ │ │ ├── _fasttreetrainer_fasttreeregression.py
│ │ │ ├── _fasttreetrainer_fasttreetweedieregression.py
│ │ │ ├── _ngramextractor_ngram.py
│ │ │ ├── _ngramextractor_ngramhash.py
│ │ │ ├── _parallellightgbm_single.py
│ │ │ ├── _paralleltraining_single.py
│ │ │ ├── _regressionlossfunction_poissonloss.py
│ │ │ ├── _regressionlossfunction_squaredloss.py
│ │ │ ├── _regressionlossfunction_tweedieloss.py
│ │ │ ├── _sdcaclassificationlossfunction_hingeloss.py
│ │ │ ├── _sdcaclassificationlossfunction_logloss.py
│ │ │ ├── _sdcaclassificationlossfunction_smoothedhingeloss.py
│ │ │ ├── _sdcaregressionlossfunction_squaredloss.py
│ │ │ ├── _stopwordsremover_custom.py
│ │ │ ├── _stopwordsremover_predefined.py
│ │ │ ├── data_customtextloader.py
│ │ │ ├── data_dataviewreference.py
│ │ │ ├── data_idataviewarrayconverter.py
│ │ │ ├── data_predictormodelarrayconverter.py
│ │ │ ├── data_textloader.py
│ │ │ ├── models_anomalydetectionevaluator.py
│ │ │ ├── models_anomalypipelineensemble.py
│ │ │ ├── models_binaryclassificationevaluator.py
│ │ │ ├── models_binaryensemble.py
│ │ │ ├── models_binarypipelineensemble.py
│ │ │ ├── models_classificationevaluator.py
│ │ │ ├── models_clusterevaluator.py
│ │ │ ├── models_crossvalidationresultscombiner.py
│ │ │ ├── models_crossvalidator.py
│ │ │ ├── models_crossvalidatordatasetsplitter.py
│ │ │ ├── models_datasettransformer.py
│ │ │ ├── models_ensemblesummary.py
│ │ │ ├── models_fixedplattcalibrator.py
│ │ │ ├── models_multiclasspipelineensemble.py
│ │ │ ├── models_multioutputregressionevaluator.py
│ │ │ ├── models_naivecalibrator.py
│ │ │ ├── models_oneversusall.py
│ │ │ ├── models_onnxconverter.py
│ │ │ ├── models_onnxtransformer.py
│ │ │ ├── models_ovamodelcombiner.py
│ │ │ ├── models_pavcalibrator.py
│ │ │ ├── models_plattcalibrator.py
│ │ │ ├── models_quantileregressionevaluator.py
│ │ │ ├── models_rankingevaluator.py
│ │ │ ├── models_regressionensemble.py
│ │ │ ├── models_regressionevaluator.py
│ │ │ ├── models_regressionpipelineensemble.py
│ │ │ ├── models_schema.py
│ │ │ ├── models_summarizer.py
│ │ │ ├── models_traintestevaluator.py
│ │ │ ├── timeseriesprocessingentrypoints_exponentialaverage.py
│ │ │ ├── timeseriesprocessingentrypoints_iidchangepointdetector.py
│ │ │ ├── timeseriesprocessingentrypoints_iidspikedetector.py
│ │ │ ├── timeseriesprocessingentrypoints_percentilethresholdtransform.py
│ │ │ ├── timeseriesprocessingentrypoints_pvaluetransform.py
│ │ │ ├── timeseriesprocessingentrypoints_slidingwindowtransform.py
│ │ │ ├── timeseriesprocessingentrypoints_ssachangepointdetector.py
│ │ │ ├── timeseriesprocessingentrypoints_ssaforecasting.py
│ │ │ ├── timeseriesprocessingentrypoints_ssaspikedetector.py
│ │ │ ├── trainers_averagedperceptronbinaryclassifier.py
│ │ │ ├── trainers_ensembleclassification.py
│ │ │ ├── trainers_ensembleregression.py
│ │ │ ├── trainers_fastforestbinaryclassifier.py
│ │ │ ├── trainers_fastforestregressor.py
│ │ │ ├── trainers_fasttreebinaryclassifier.py
│ │ │ ├── trainers_fasttreeranker.py
│ │ │ ├── trainers_fasttreeregressor.py
│ │ │ ├── trainers_fasttreetweedieregressor.py
│ │ │ ├── trainers_fieldawarefactorizationmachinebinaryclassifier.py
│ │ │ ├── trainers_generalizedadditivemodelbinaryclassifier.py
│ │ │ ├── trainers_generalizedadditivemodelregressor.py
│ │ │ ├── trainers_kmeansplusplusclusterer.py
│ │ │ ├── trainers_lightgbmbinaryclassifier.py
│ │ │ ├── trainers_lightgbmclassifier.py
│ │ │ ├── trainers_lightgbmranker.py
│ │ │ ├── trainers_lightgbmregressor.py
│ │ │ ├── trainers_linearsvmbinaryclassifier.py
│ │ │ ├── trainers_localdeepsvmbinaryclassifier.py
│ │ │ ├── trainers_logisticregressionbinaryclassifier.py
│ │ │ ├── trainers_logisticregressionclassifier.py
│ │ │ ├── trainers_naivebayesclassifier.py
│ │ │ ├── trainers_onlinegradientdescentregressor.py
│ │ │ ├── trainers_ordinaryleastsquaresregressor.py
│ │ │ ├── trainers_pcaanomalydetector.py
│ │ │ ├── trainers_poissonregressor.py
│ │ │ ├── trainers_stochasticdualcoordinateascentbinaryclassifier.py
│ │ │ ├── trainers_stochasticdualcoordinateascentclassifier.py
│ │ │ ├── trainers_stochasticdualcoordinateascentregressor.py
│ │ │ ├── trainers_stochasticgradientdescentbinaryclassifier.py
│ │ │ ├── trainers_symsgdbinaryclassifier.py
│ │ │ ├── transforms_approximatebootstrapsampler.py
│ │ │ ├── transforms_binarypredictionscorecolumnsrenamer.py
│ │ │ ├── transforms_binnormalizer.py
│ │ │ ├── transforms_categoricalhashonehotvectorizer.py
│ │ │ ├── transforms_categoricalonehotvectorizer.py
│ │ │ ├── transforms_categoryimputer.py
│ │ │ ├── transforms_charactertokenizer.py
│ │ │ ├── transforms_columnconcatenator.py
│ │ │ ├── transforms_columncopier.py
│ │ │ ├── transforms_columnselector.py
│ │ │ ├── transforms_columntypeconverter.py
│ │ │ ├── transforms_combinerbycontiguousgroupid.py
│ │ │ ├── transforms_conditionalnormalizer.py
│ │ │ ├── transforms_datacache.py
│ │ │ ├── transforms_datasetscorer.py
│ │ │ ├── transforms_datasetscorerex.py
│ │ │ ├── transforms_datasettransformscorer.py
│ │ │ ├── transforms_datetimesplitter.py
│ │ │ ├── transforms_dictionarizer.py
│ │ │ ├── transforms_featurecombiner.py
│ │ │ ├── transforms_featurecontributioncalculationtransformer.py
│ │ │ ├── transforms_featureselectorbycount.py
│ │ │ ├── transforms_featureselectorbymutualinformation.py
│ │ │ ├── transforms_globalcontrastnormalizer.py
│ │ │ ├── transforms_hashconverter.py
│ │ │ ├── transforms_imagegrayscale.py
│ │ │ ├── transforms_imageloader.py
│ │ │ ├── transforms_imagepixelextractor.py
│ │ │ ├── transforms_imageresizer.py
│ │ │ ├── transforms_keytotextconverter.py
│ │ │ ├── transforms_labelcolumnkeybooleanconverter.py
│ │ │ ├── transforms_labelindicator.py
│ │ │ ├── transforms_labeltofloatconverter.py
│ │ │ ├── transforms_lightlda.py
│ │ │ ├── transforms_logmeanvariancenormalizer.py
│ │ │ ├── transforms_lpnormalizer.py
│ │ │ ├── transforms_manyheterogeneousmodelcombiner.py
│ │ │ ├── transforms_meanvariancenormalizer.py
│ │ │ ├── transforms_minmaxnormalizer.py
│ │ │ ├── transforms_missingvaluehandler.py
│ │ │ ├── transforms_missingvalueindicator.py
│ │ │ ├── transforms_missingvaluesdropper.py
│ │ │ ├── transforms_missingvaluesrowdropper.py
│ │ │ ├── transforms_missingvaluesubstitutor.py
│ │ │ ├── transforms_modelcombiner.py
│ │ │ ├── transforms_ngramtranslator.py
│ │ │ ├── transforms_nooperation.py
│ │ │ ├── transforms_optionalcolumncreator.py
│ │ │ ├── transforms_pcacalculator.py
│ │ │ ├── transforms_permutationfeatureimportance.py
│ │ │ ├── transforms_predictedlabelcolumnoriginalvalueconverter.py
│ │ │ ├── transforms_prefixcolumnconcatenator.py
│ │ │ ├── transforms_randomnumbergenerator.py
│ │ │ ├── transforms_robustscaler.py
│ │ │ ├── transforms_rowrangefilter.py
│ │ │ ├── transforms_rowskipandtakefilter.py
│ │ │ ├── transforms_rowskipfilter.py
│ │ │ ├── transforms_rowtakefilter.py
│ │ │ ├── transforms_scorecolumnselector.py
│ │ │ ├── transforms_scorer.py
│ │ │ ├── transforms_segregator.py
│ │ │ ├── transforms_sentimentanalyzer.py
│ │ │ ├── transforms_tensorflowscorer.py
│ │ │ ├── transforms_textfeaturizer.py
│ │ │ ├── transforms_texttokeyconverter.py
│ │ │ ├── transforms_timeseriesimputer.py
│ │ │ ├── transforms_tostring.py
│ │ │ ├── transforms_traintestdatasetsplitter.py
│ │ │ ├── transforms_treeleaffeaturizer.py
│ │ │ ├── transforms_twoheterogeneousmodelcombiner.py
│ │ │ ├── transforms_variablecolumntransform.py
│ │ │ ├── transforms_vectortoimage.py
│ │ │ ├── transforms_wordembeddings.py
│ │ │ └── transforms_wordtokenizer.py
│ │ └── utils
│ │ │ ├── __init__.py
│ │ │ ├── data_roles.py
│ │ │ ├── data_schema.py
│ │ │ ├── data_stream.py
│ │ │ ├── dataframes.py
│ │ │ ├── entrypoints.py
│ │ │ ├── schema_helper.py
│ │ │ ├── stubs.py
│ │ │ └── utils.py
│ ├── linear_model
│ │ ├── __init__.py
│ │ ├── averagedperceptronbinaryclassifier.py
│ │ ├── fastlinearbinaryclassifier.py
│ │ ├── fastlinearclassifier.py
│ │ ├── fastlinearregressor.py
│ │ ├── linearsvmbinaryclassifier.py
│ │ ├── logisticregressionbinaryclassifier.py
│ │ ├── logisticregressionclassifier.py
│ │ ├── onlinegradientdescentregressor.py
│ │ ├── ordinaryleastsquaresregressor.py
│ │ ├── poissonregressionregressor.py
│ │ ├── sgdbinaryclassifier.py
│ │ └── symsgdbinaryclassifier.py
│ ├── loss.py
│ ├── model_selection
│ │ ├── __init__.py
│ │ └── cv.py
│ ├── multiclass
│ │ ├── __init__.py
│ │ └── onevsrestclassifier.py
│ ├── naive_bayes
│ │ ├── __init__.py
│ │ └── naivebayesclassifier.py
│ ├── pipeline.py
│ ├── preprocessing
│ │ ├── __init__.py
│ │ ├── datasettransformer.py
│ │ ├── datetimesplitter.py
│ │ ├── filter
│ │ │ ├── __init__.py
│ │ │ ├── bootstrapsampler.py
│ │ │ ├── rangefilter.py
│ │ │ ├── skipfilter.py
│ │ │ └── takefilter.py
│ │ ├── fromkey.py
│ │ ├── missing_values
│ │ │ ├── __init__.py
│ │ │ ├── filter.py
│ │ │ ├── handler.py
│ │ │ └── indicator.py
│ │ ├── normalization
│ │ │ ├── __init__.py
│ │ │ ├── binner.py
│ │ │ ├── globalcontrastrowscaler.py
│ │ │ ├── logmeanvariancescaler.py
│ │ │ ├── lpscaler.py
│ │ │ ├── meanvariancescaler.py
│ │ │ ├── minmaxscaler.py
│ │ │ └── robustscaler.py
│ │ ├── onnxrunner.py
│ │ ├── schema
│ │ │ ├── __init__.py
│ │ │ ├── columnconcatenator.py
│ │ │ ├── columndropper.py
│ │ │ ├── columnduplicator.py
│ │ │ ├── columnselector.py
│ │ │ ├── prefixcolumnconcatenator.py
│ │ │ └── typeconverter.py
│ │ ├── tensorflowscorer.py
│ │ ├── text
│ │ │ ├── __init__.py
│ │ │ ├── chartokenizer.py
│ │ │ └── wordtokenizer.py
│ │ ├── tokey.py
│ │ ├── tokeyimputer.py
│ │ └── tostring.py
│ ├── tests
│ │ ├── __init__.py
│ │ ├── cluster
│ │ │ ├── __init__.py
│ │ │ └── test_kmeansplusplus.py
│ │ ├── data_type
│ │ │ ├── __init__.py
│ │ │ ├── test_datetime.py
│ │ │ ├── test_numeric.py
│ │ │ ├── test_text.py
│ │ │ └── test_text_label.py
│ │ ├── decomposition
│ │ │ ├── __init__.py
│ │ │ ├── test_factorizationmachine.py
│ │ │ ├── test_pcaanomalydetector.py
│ │ │ └── test_pcatransformer.py
│ │ ├── dprep
│ │ │ ├── __init__.py
│ │ │ └── test_dprep.py
│ │ ├── ensemble
│ │ │ ├── __init__.py
│ │ │ ├── test_ensembleclassifier.py
│ │ │ ├── test_ensembleregressor.py
│ │ │ ├── test_fasttreesbinaryclassifier.py
│ │ │ ├── test_fasttreestweedieregressor.py
│ │ │ ├── test_gambinaryclassifier.py
│ │ │ ├── test_lightgbmbinaryclassifier.py
│ │ │ ├── test_lightgbmclassifier.py
│ │ │ ├── test_lightgbmranker.py
│ │ │ ├── test_lightgbmregressor.py
│ │ │ └── test_votingregressor.py
│ │ ├── feature_extraction
│ │ │ ├── __init__.py
│ │ │ ├── categorical
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_onehothashvectorizer.py
│ │ │ │ └── test_onehotvectorizer.py
│ │ │ └── text
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_lightlda.py
│ │ │ │ ├── test_ngramextractor.py
│ │ │ │ ├── test_ngramfeaturizer.py
│ │ │ │ ├── test_sentiment.py
│ │ │ │ └── test_wordembedding.py
│ │ ├── feature_selection
│ │ │ ├── __init__.py
│ │ │ └── test_mutualinformationselector.py
│ │ ├── idv
│ │ │ ├── __init__.py
│ │ │ └── test_idv.py
│ │ ├── linear_model
│ │ │ ├── __init__.py
│ │ │ ├── test_averagedperceptronbinaryclassifier.py
│ │ │ ├── test_fastlinearclassifier.py
│ │ │ ├── test_linearsvmbinaryclassifier.py
│ │ │ ├── test_poissonregressionregressor.py
│ │ │ ├── test_sgdbinaryclassifier.py
│ │ │ └── test_symsgdbinaryclassifier.py
│ │ ├── metrics
│ │ │ ├── __init__.py
│ │ │ └── test_metrics.py
│ │ ├── model_selection
│ │ │ ├── __init__.py
│ │ │ ├── test_cv.py
│ │ │ └── test_sweep.py
│ │ ├── model_summary
│ │ │ ├── __init__.py
│ │ │ └── test_model_summary.py
│ │ ├── multiclass
│ │ │ ├── __init__.py
│ │ │ └── test_onevsrestclassifier.py
│ │ ├── naive_bayes
│ │ │ ├── __init__.py
│ │ │ └── test_naivebayesclassifier.py
│ │ ├── pipeline
│ │ │ ├── __init__.py
│ │ │ ├── test_clone.py
│ │ │ ├── test_csr_input.py
│ │ │ ├── test_load_save.py
│ │ │ ├── test_permutation_feature_importance.py
│ │ │ ├── test_pipeline_combining.py
│ │ │ ├── test_pipeline_get_schema.py
│ │ │ ├── test_pipeline_roles.py
│ │ │ ├── test_pipeline_split_models.py
│ │ │ ├── test_pipeline_subclassing.py
│ │ │ ├── test_pipeline_syntax.py
│ │ │ ├── test_pipeline_transform_method.py
│ │ │ ├── test_predict_proba_decision_function.py
│ │ │ ├── test_score_method.py
│ │ │ └── test_uci_adult.py
│ │ ├── preprocessing
│ │ │ ├── __init__.py
│ │ │ ├── filter
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_rangefilter.py
│ │ │ │ ├── test_skipfilter.py
│ │ │ │ └── test_takefilter.py
│ │ │ ├── missing_values
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_data_with_missing.py
│ │ │ │ └── test_filter.py
│ │ │ ├── normalization
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_globalcontrastrowscaler.py
│ │ │ │ ├── test_lpscaler.py
│ │ │ │ ├── test_meanvariancescaler.py
│ │ │ │ ├── test_minmaxscaler.py
│ │ │ │ └── test_robustscaler.py
│ │ │ ├── schema
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_columnconcatenator.py
│ │ │ │ ├── test_columnduplicator.py
│ │ │ │ └── test_prefixcolumnconcatenator.py
│ │ │ ├── test_datasettransformer.py
│ │ │ ├── test_datetimesplitter.py
│ │ │ ├── test_fromkey.py
│ │ │ ├── test_tokeyimputer.py
│ │ │ ├── test_tostring.py
│ │ │ └── text
│ │ │ │ ├── __init__.py
│ │ │ │ ├── test_ngramfeaturizer.py
│ │ │ │ └── test_wordtokenizer.py
│ │ ├── scikit
│ │ │ ├── __init__.py
│ │ │ ├── test_lightgbmranker_scikit.py
│ │ │ └── test_uci_adult_scikit.py
│ │ ├── test_csr_matrix_output.py
│ │ ├── test_data_schema.py
│ │ ├── test_data_schema_syntax.py
│ │ ├── test_data_stream.py
│ │ ├── test_data_types.py
│ │ ├── test_entrypoints.py
│ │ ├── test_errors.py
│ │ ├── test_fit_graph.py
│ │ ├── test_syntax.py
│ │ ├── test_syntax_expected_failures.py
│ │ ├── test_syntax_learner.py
│ │ ├── test_syntax_onehotvectorizer.py
│ │ ├── test_utils.py
│ │ ├── test_variable_column.py
│ │ ├── timeseries
│ │ │ ├── __init__.py
│ │ │ ├── test_iidchangepointdetector.py
│ │ │ ├── test_iidspikedetector.py
│ │ │ ├── test_ssachangepointdetector.py
│ │ │ ├── test_ssaforecaster.py
│ │ │ ├── test_ssaspikedetector.py
│ │ │ └── test_timeseriesimputer.py
│ │ └── utils
│ │ │ ├── __init__.py
│ │ │ ├── data
│ │ │ └── pipe.png
│ │ │ ├── test_exports.py
│ │ │ ├── test_exports_graphviz.py
│ │ │ └── test_pipeline_exports_complex.csv
│ ├── timeseries
│ │ ├── __init__.py
│ │ ├── iidchangepointdetector.py
│ │ ├── iidspikedetector.py
│ │ ├── ssachangepointdetector.py
│ │ ├── ssaforecaster.py
│ │ ├── ssaspikedetector.py
│ │ └── timeseriesimputer.py
│ └── utils
│ │ ├── __init__.py
│ │ ├── exports.py
│ │ ├── pipe.gv.png
│ │ └── utils.py
│ ├── setup.cfg
│ ├── setup.py
│ ├── setup.py.in
│ ├── tests
│ ├── test_copyright.py
│ ├── test_estimator_checks.py
│ └── test_pyproj.py
│ ├── tests_extended
│ ├── data_frame_tool.py
│ ├── test_automl_scenario.py
│ ├── test_docs_example.py
│ ├── test_docs_notebooks.py
│ └── test_export_to_onnx.py
│ └── tools
│ ├── change_to_https.py
│ ├── code_fixer.py
│ ├── codegen_checker.py
│ ├── compiler_utils.py
│ ├── doc_builder.py
│ ├── entrypoint_compiler.py
│ ├── find_http_urls.py
│ ├── fix_line_widths.py
│ ├── loss_processor.py
│ ├── manifest.json
│ ├── manifest_diff.json
│ ├── manifest_diff_parser.py
│ ├── temp_docs_updater.py
│ └── update_nimbusml_version.py
└── version.txt
/.github/ISSUE_TEMPLATE/bug_report.md:
--------------------------------------------------------------------------------
1 | ---
2 | name: Bug report
3 | about: Create a report to help us improve
4 |
5 | ---
6 |
7 | **Describe the bug**
8 | A clear and concise description of what the bug is.
9 |
10 | **To Reproduce**
11 | Steps to reproduce the behavior:
12 | 1. Go to '...'
13 | 2. Click on '....'
14 | 3. Scroll down to '....'
15 | 4. See error
16 |
17 | **Expected behavior**
18 | A clear and concise description of what you expected to happen.
19 |
20 | **Screenshots**
21 | If applicable, add screenshots to help explain your problem.
22 |
23 | **Desktop (please complete the following information):**
24 | - OS: [e.g. iOS]
25 | - Browser [e.g. chrome, safari]
26 | - Version [e.g. 22]
27 |
28 | **Smartphone (please complete the following information):**
29 | - Device: [e.g. iPhone6]
30 | - OS: [e.g. iOS8.1]
31 | - Browser [e.g. stock browser, safari]
32 | - Version [e.g. 22]
33 |
34 | **Additional context**
35 | Add any other context about the problem here.
36 |
--------------------------------------------------------------------------------
/.github/ISSUE_TEMPLATE/feature_request.md:
--------------------------------------------------------------------------------
1 | ---
2 | name: Feature request
3 | about: Suggest an idea for this project
4 |
5 | ---
6 |
7 | **Is your feature request related to a problem? Please describe.**
8 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
9 |
10 | **Describe the solution you'd like**
11 | A clear and concise description of what you want to happen.
12 |
13 | **Describe alternatives you've considered**
14 | A clear and concise description of any alternative solutions or features you've considered.
15 |
16 | **Additional context**
17 | Add any other context or screenshots about the feature request here.
18 |
--------------------------------------------------------------------------------
/.github/actions/merge-branches/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM alpine:latest
2 |
3 | LABEL repository="http://github.com/microsoft/NimbusML"
4 | LABEL "com.github.actions.name"="Merge Branches"
5 | LABEL "com.github.actions.description"="Automatically merge from one branch to another."
6 | LABEL "com.github.actions.icon"="git-merge"
7 | LABEL "com.github.actions.color"="orange"
8 |
9 | RUN apk --no-cache add bash curl git jq
10 |
11 | ADD entrypoint.sh /entrypoint.sh
12 |
13 | ENTRYPOINT ["/entrypoint.sh"]
14 |
--------------------------------------------------------------------------------
/.github/workflows/nightly-merge.yml:
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1 | name: 'Nightly Merge'
2 |
3 | on:
4 | push:
5 | branches:
6 | - master
7 |
8 | jobs:
9 | nightly-merge:
10 |
11 | runs-on: ubuntu-latest
12 |
13 | steps:
14 | - name: Checkout
15 | uses: actions/checkout@v1
16 |
17 | - name: Nightly Merge
18 | uses: ./.github/actions/merge-branches
19 | with:
20 | source_branch: 'master'
21 | target_branch: 'nightly'
22 | allow_ff: true
23 | env:
24 | GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
25 |
--------------------------------------------------------------------------------
/PULL_REQUEST_TEMPLATE.md:
--------------------------------------------------------------------------------
1 | We are excited to review your PR.
2 |
3 | So we can do the best job, please check:
4 |
5 | - [ ] There's a descriptive title that will make sense to other developers some time from now.
6 | - [ ] There's associated issues. All PR's should have issue(s) associated - unless a trivial self-evident change such as fixing a typo. You can use the format `Fixes #nnnn` in your description to cause GitHub to automatically close the issue(s) when your PR is merged.
7 | - [ ] Your change description explains what the change does, why you chose your approach, and anything else that reviewers should know.
8 | - [ ] You have included any necessary tests in the same PR.
9 |
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/build/libs_linux.txt:
--------------------------------------------------------------------------------
1 | Google.Protobuf.dll
2 | Newtonsoft.Json.dll
3 | libCpuMathNative.so
4 | libFastTreeNative.so
5 | libFeaturizers.so
6 | libLdaNative.so
7 | libMklImports.so
8 | libMklProxyNative.so
9 | libSymSgdNative.so
10 | lib_lightgbm.so
11 | libtensorflow.so
12 | libtensorflow_framework.so.1
13 | libonnxruntime.so
14 | System.Drawing.Common.dll
15 | TensorFlow.NET.dll
16 | NumSharp.Core.dll
17 | Microsoft.ML.*
18 |
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/build/libs_mac.txt:
--------------------------------------------------------------------------------
1 | Google.Protobuf.dll
2 | Newtonsoft.Json.dll
3 | libCpuMathNative.dylib
4 | libFastTreeNative.dylib
5 | libFeaturizers.dylib
6 | libLdaNative.dylib
7 | libMklImports.dylib
8 | libMklProxyNative.dylib
9 | libSymSgdNative.dylib
10 | lib_lightgbm.dylib
11 | libtensorflow.dylib
12 | libonnxruntime.dylib
13 | libtensorflow_framework.1.dylib
14 | System.Drawing.Common.dll
15 | TensorFlow.NET.dll
16 | NumSharp.Core.dll
17 | Microsoft.ML.*
18 |
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/build/libs_win.txt:
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1 | Google.Protobuf.dll
2 | Newtonsoft.Json.dll
3 | CpuMathNative.dll
4 | FastTreeNative.dll
5 | LdaNative.dll
6 | lib_lightgbm.dll
7 | libiomp5md.dll
8 | MklImports.dll
9 | MklProxyNative.dll
10 | SymSgdNative.dll
11 | Featurizers.dll
12 | tensorflow.dll
13 | TensorFlow.NET.dll
14 | NumSharp.Core.dll
15 | System.Drawing.Common.dll
16 | Microsoft.ML.*
17 | onnxruntime.dll
18 |
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/build/sign.csproj:
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1 |
2 |
3 |
4 | net461
5 | ../x64/
6 |
7 |
8 |
9 |
10 | all
11 | runtime; build; native; contentfiles; analyzers
12 |
13 |
14 |
15 |
16 |
17 |
18 | Microsoft400
19 |
20 |
21 |
22 |
23 |
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/docs/README.md:
--------------------------------------------------------------------------------
1 | Documents Index
2 | ===============
3 |
4 | Intro to NimbusML
5 | ===============
6 |
7 | NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices.
8 |
9 | Project Docs
10 | ============
11 |
12 | - [API](https://docs.microsoft.com/en-us/nimbusml/overview)
13 | - [Tutorials](https://docs.microsoft.com/en-us/nimbusml/tutorials)
14 | - [Developer Guide](developers/developer-guide.md)
15 | - [Contributing to ML.NET](CONTRIBUTING.md)
16 |
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/docs/developers/developer-guide.md:
--------------------------------------------------------------------------------
1 | Developer Guide
2 | ===============
3 |
4 | NimbusML runs on Windows, Linux, and macOS and supports Python 3.7, 3.6, 3.5, and 2.7, 64 bit versions only. It has been tested on Windows 10, MacOS 10.13, Ubuntu 14.04, Ubuntu 16.04, Ubuntu 18.04, CentOS 7, and RHEL 7.
5 |
6 | Building the repository
7 | =======================
8 |
9 | The NimbusML repo can be built directly from a terminal or cmd prompt. See the platform-specific build instructions for your dev environment:
10 |
11 | | [Windows](windows-build.md) | [Linux](linux-build.md) | [Mac](mac-build.md) |
12 |
13 | Nimbus official builds are produced in Azure Dev Ops, as specified by the file `.vsts-ci.yml`.
--------------------------------------------------------------------------------
/docs/developers/linux-build.md:
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1 | Building NimbusML from source on Linux
2 | ==========================================
3 | ## Prerequisites
4 | 1. gcc >= 5.4
5 | 2. cmake
6 | 3. curl
7 | 4. libunwind8*
8 | 5. libicu*
9 |
10 | *These are already included in most distros. If you need them and you have trouble finding them in your package repo, they can be gathered by installing the [.NET SDK](https://www.microsoft.com/net/download).
11 |
12 | ## Build
13 | Run `./build.sh`
14 |
15 | This downloads dependencies (.NET SDK, specific versions of Python and Boost), builds native code and managed code, and packages NimbusML into a pip-installable wheel. This produces debug binaries by default, and release versions can be specified by `./build.sh --configuration RlsLinPy3.7` for example.
16 |
17 | For additional options including running tests and building components independently, see `./build.sh -h`.
18 |
19 | ### Known Issues
20 | The LightGBM estimator fails on Linux when building from source. The official NimbusML Linux wheel package on Pypi.org has a working version of LightGBM.
21 |
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/docs/developers/mac-build.md:
--------------------------------------------------------------------------------
1 | Building NimbusML from source on Mac
2 | ==========================================
3 | ## Prerequisites
4 | 1. Xcode Command Line Tools (for Clang compiler)
5 | 2. cmake
6 |
7 | ## Build
8 | Run `./build.sh`
9 |
10 | This downloads dependencies (.NET SDK, specific versions of Python and Boost), builds native code and managed code, and packages NimbusML into a pip-installable wheel. This produces debug binaries by default, and release versions can be specified by `./build.sh --configuration RlsMacPy3.7` for examle.
11 |
12 | For additional options including running tests and building components independently, see `./build.sh -h`.
13 |
14 | ### Notes
15 | The LightGBM estimator currently has a runtime dependency on Gnu OpenMP libs on Mac. These can be obtained by installing gcc 4.2 or later, which can be done through homebrew with:
16 | ```brew gcc```
17 | Running LightGBM without this will give the following error:
18 | ```System.DllNotFoundException: 'Unable to load DLL 'lib_lightgbm': The specified module or one of its dependencies could not be found.'```
--------------------------------------------------------------------------------
/docs/developers/windows-build.md:
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1 | Building NimbusML from source on Windows
2 | ==========================================
3 | ## Prerequisites
4 | 1. Visual Studio 2015 or higher
5 | - Select C++ development tools from installer
6 |
7 | ## Build
8 | Run `build.cmd`
9 |
10 | This downloads dependencies (.NET SDK, specific versions of Python and Boost), builds native code and managed code, and packages NimbusML into a pip-installable wheel. This produces debug binaries by default, and release versions can be specified by `build.cmd --configuration RlsWinPy3.7` for example.
11 |
12 | For additional options including running tests and building components independently, see `build.cmd -?`.
13 |
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/nuget.config:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
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/release-next.md:
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1 | # [NimbusML](https://docs.microsoft.com/en-us/nimbusml/overview) Next
2 |
3 | ## **New Features**
4 |
5 | None.
6 |
7 | ## **Bug Fixes**
8 |
9 | None.
10 |
11 | ## **Enhancements**
12 |
13 | None.
14 |
15 | ## **Documentation and Samples**
16 |
17 | None.
18 |
19 | ## **Remarks**
20 |
21 | None.
22 |
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/src/DotNetBridge/DotNetBridge.dll.config:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
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/src/DotNetBridge/DotNetBridge.snk:
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/src/DotNetBridge/app.config:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
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/src/ManifestGenerator/app.config:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
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/src/NativeBridge/inc/asm_version.h:
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1 | // Licensed to the .NET Foundation under one or more agreements.
2 | // The .NET Foundation licenses this file to you under the MIT license.
3 | // See the LICENSE file in the project root for more information.
4 | #if defined(SILVERLIGHT)
5 | #if defined(FEATURE_CORESYSTEM)
6 | #define asm_rmj 4
7 | #define asm_rmm 0
8 | #define asm_rup 0
9 | #define asm_rpt 0
10 | #else
11 | #define asm_rmj 5
12 | #define asm_rmm 0
13 | #define asm_rup 5
14 | #define asm_rpt 0
15 | #endif
16 | #else
17 | #define asm_rmj 4
18 | #define asm_rmm 0
19 | #define asm_rup 0
20 | #define asm_rpt 0
21 | #endif
22 |
23 |
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/src/NativeBridge/inc/fxverstrings.h:
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1 | // Licensed to the .NET Foundation under one or more agreements.
2 | // The .NET Foundation licenses this file to you under the MIT license.
3 | // See the LICENSE file in the project root for more information.
4 |
5 | #ifndef VER_PRODUCTNAME_STR
6 | #define VER_PRODUCTNAME_STR L"Microsoft\256 .NET Core"
7 | #endif
8 |
9 | #ifndef VER_LEGALCOPYRIGHT_STR
10 | #define VER_LEGALCOPYRIGHT_STR "\251 Microsoft Corporation. All rights reserved."
11 | #define VER_LEGALCOPYRIGHT_STR_L L"\251 Microsoft Corporation. All rights reserved."
12 | #endif
13 |
14 | #ifndef VER_LEGALCOPYRIGHT_LOGO_STR
15 | #define VER_LEGALCOPYRIGHT_LOGO_STR "Copyright (c) Microsoft Corporation. All rights reserved."
16 | #define VER_LEGALCOPYRIGHT_LOGO_STR_L L"Copyright (c) Microsoft Corporation. All rights reserved."
17 | #endif
18 |
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/src/NativeBridge/inc/ndpversion.h:
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1 | // Licensed to the .NET Foundation under one or more agreements.
2 | // The .NET Foundation licenses this file to you under the MIT license.
3 | // See the LICENSE file in the project root for more information.
4 | #include
5 |
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/src/NativeBridge/inc/ndpversion_generated.h:
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1 | // Licensed to the .NET Foundation under one or more agreements.
2 | // The .NET Foundation licenses this file to you under the MIT license.
3 | // See the LICENSE file in the project root for more information.
4 |
5 | #if 0
6 | /**** Generated Based on d:\ProjectK\src\InternalApis\Version\buildnumber.settings.targets
7 | One can't put comments in this file (without the #if)
8 | because this header is preprocessed in non-C++ context (xml, perl, etc.). *****/
9 | #endif
10 | #define NDPVersionNumberMajor 4
11 | #define NDPVersionNumberMinor 0
12 | #define NDPVersionNumberMajor_A "4"
13 | #define NDPVersionNumberMinor_A "00"
14 | #define NDPVersionNumbers_A "4.00"
15 | #include "buildnumber.h"
16 |
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/src/NativeBridge/inc/version.h:
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1 | // Licensed to the .NET Foundation under one or more agreements.
2 | // The .NET Foundation licenses this file to you under the MIT license.
3 | // See the LICENSE file in the project root for more information.
4 |
5 | #include
6 |
7 | #define rmj NDPVersionNumberMajor
8 | #define rmm NDPVersionNumberMinor
9 | #define rup NDPBuildNumberMajor
10 | #define rpt NDPBuildNumberMinor
11 |
12 | #define fvn NDPFileVersionMinor
13 | #define fvb NDPFileVersionBuild
14 | #define fvr NDPFileVersionRevision
15 |
16 | #define szVerName ""
17 | #define szVerUser ""
18 |
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/src/NativeBridge/stdafx.cpp:
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1 | // Copyright (c) Microsoft Corporation. All rights reserved.
2 | // Licensed under the MIT license.
3 |
4 | #include "stdafx.h"
5 |
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/src/NativeBridge/targetver.h:
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1 | // Copyright (c) Microsoft Corporation. All rights reserved.
2 | // Licensed under the MIT license.
3 |
4 | #pragma once
5 |
6 | // Including SDKDDKVer.h defines the highest available Windows platform.
7 |
8 | // If you wish to build your application for a previous Windows platform, include WinSDKVer.h and
9 | // set the _WIN32_WINNT macro to the platform you wish to support before including SDKDDKVer.h.
10 |
11 | #include
12 |
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/src/Platforms/Program.cs:
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1 | namespace build { class Program { static void Main(string[] args) {} } }
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/src/python/MANIFEST.in:
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1 | recursive-include nimbusml/internal/libs *
2 | recursive-include nimbusml/datasets *.csv *.tsv *.png
3 | recursive-include nimbusml/examples *.py
4 |
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/src/python/docs/docstrings/ColumnDropper.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Specified columns to drop from the dataset.
4 |
5 | :param columns: a list of strings representing the column names to
6 | perform the transformation on.
7 |
8 | The << operator can be used to set this value (see
9 | `Column Operator `_)
10 |
11 | For example
12 | * ColumnDropper(columns=['education', 'age'])
13 | * ColumnDropper() << ['education', 'age']
14 |
15 | For more details see `Columns `_.
16 |
17 | .. seealso::
18 | :py:class:`ColumnConcatenator
19 | `,
20 | :py:class:`ColumnSelector
21 | `.
22 |
23 | .. index:: transform, schema
24 |
25 | Example:
26 | .. literalinclude:: /../nimbusml/examples/ColumnDropper.py
27 | :language: python
28 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/ColumnSelector.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Selects a set of columns to retrain, dropping all others.
4 |
5 | :param columns: a list of strings representing the column names to
6 | perform the transformation on.
7 |
8 | The << operator can be used to set this value (see
9 | `Column Operator `_)
10 |
11 | For example
12 | * ColumnSelector(columns=['education', 'age'])
13 | * ColumnSelector() << ['education', 'age']
14 |
15 | For more details see `Columns `_.
16 |
17 | .. seealso::
18 | :py:class:`ColumnConcatenator
19 | `,
20 | :py:class:`ColumnDropper
21 | `.
22 |
23 | .. index:: transform, schema
24 |
25 | Example:
26 | .. literalinclude:: /../nimbusml/examples/ColumnSelector.py
27 | :language: python
28 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/CustomStopWordsRemover.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Remover with list of stopwords specified by the user.
4 |
5 | .. remarks::
6 | The :py:class:`NGramFeaturizer
7 | ` transform produces a
8 | bag of counts of
9 | sequences of consecutive words from a given corpus of text.
10 | It also offers stopwords removing. A user-defined list of stopwords.
11 | It accepts the following option: ``stopword``.
12 |
13 | .. seealso::
14 | :py:class:`NGramFeaturizer
15 | `,
16 | :py:class:`PredefinedStopWordsRemover
17 | `,
18 |
19 | .. index:: transform, featurizer, text
20 |
21 | Example:
22 | .. literalinclude:: /../nimbusml/examples/NGramFeaturizer2.py
23 | :language: python
24 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/ExponentialAverage.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Applies a `Exponential average
4 | `_ on a time
5 | series.
6 |
7 | :param decay: Coefficient d in: m(y_t) = d * y_t + (1-d) * m(y_(t-1)), it
8 | should be in [0, 1], where m(y_t) is the output.
9 |
10 | .. seealso::
11 | :py:func:`PercentileThreshold
12 | `,
13 | :py:func:`Pvalue `,
14 | :py:func:`SlidingWindow
15 | `.
16 |
17 | .. index:: models, timeseries, transform
18 |
19 | Example:
20 | .. literalinclude:: /../nimbusml/examples/ExponentialAverage.py
21 | :language: python
22 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/FromKey.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Converts the key types back to their original values.
4 |
5 | .. remarks::
6 | The ``FromKey`` transform converts a column of keys, generated using
7 | :py:class:`ToKey `, to their original
8 | values.
9 |
10 | .. seealso::
11 | :py:class:`ToKey `,
12 | :py:class:`OneHotHashVectorizer
13 | `,
14 | :py:class:`OneHotVectorizer
15 | `,
16 | :py:class:`NGramFeaturizer
17 | `,
18 |
19 | .. index:: transform, preprocessing, text
20 |
21 | Example:
22 | .. literalinclude:: /../nimbusml/examples/FromKey.py
23 | :language: python
24 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/Gbdt.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Traditional Gradient Boosting Decision Tree.
4 |
5 | .. seealso::
6 | :py:func:`Dart `,
7 | :py:func:`Goss `,
8 | :py:func:`LightGbmBinaryClassifier
9 | `,
10 | :py:func:`LightGbmClassifier `,
11 | :py:func:`LightGbmRanker `,
12 | :py:func:`LightGbmRegressor `
13 |
14 | .. index:: ensemble, booster
15 |
16 | Example:
17 | .. literalinclude:: /../nimbusml/examples/LightGbmRegressor.py
18 | :language: python
19 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/LinearKernel.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Apply scalar product. .
4 |
5 | .. remarks::
6 | `LinearKernel `_ is a
7 | kernel function
8 | that computes the similarity between two features, calculated as the
9 | sum of the elementwise
10 | products of the two.
11 |
12 | .. seealso::
13 | :py:func:`PolynomialKernel `,
14 | :py:func:`RbfKernel `,
15 | :py:func:`SigmoidKernel `,
16 | :py:func:`OneClassSvmAnomalyDetector
17 | `
18 |
19 | .. index:: svm, kernel
20 |
21 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/PercentileThreshold.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Detects the values of time-series that are in the top percentile of
4 | the sliding window.
5 |
6 | .. seealso::
7 | :py:func:`ExponentialAverage
8 | `,
9 | :py:func:`Pvalue `,
10 | :py:func:`SlidingWindow
11 | `.
12 |
13 | .. index:: models, timeseries, transform
14 |
15 | Example:
16 | .. literalinclude:: /../nimbusml/examples/PercentileThresholdTransform.py
17 | :language: python
18 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/PolynomialKernel.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Apply polinomial function. a*+b)^d.
4 |
5 | .. remarks::
6 | `PolynomialKernel `_
7 | is a kernel function
8 | that computes the similarity between two features. It is commonly
9 | used in support vector machines.
10 |
11 | .. seealso::
12 | :py:func:`LinearKernel `,
13 | :py:func:`RbfKernel `,
14 | :py:func:`SigmoidKernel `,
15 | :py:func:`OneClassSvmAnomalyDetector
16 | `
17 |
18 | .. index:: svm, kernel
19 |
20 | Example:
21 | .. literalinclude:: /../nimbusml/examples/OneClassSVMAnomalyDetector.py
22 | :language: python
23 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/PredefinedStopWordsRemover.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Remover with predefined list of stop words.
4 |
5 | .. remarks::
6 | The :py:class:`NGramFeaturizer
7 | ` transform produces a
8 | bag of counts of
9 | sequences of consecutive words from a given corpus of text.
10 | It also offers stopwords removing. A precompiled language-specific
11 | lists of stop words is used in this class that includes the most
12 | common words from Microsoft Office.
13 |
14 | .. seealso::
15 | :py:class:`NGramFeaturizer
16 | `,
17 | :py:class:`CustomStopWordsRemover
18 | `,
19 |
20 | .. index:: transform, featurizer, text
21 |
22 | Example:
23 | .. literalinclude:: /../nimbusml/examples/NGramFeaturizer.py
24 | :language: python
25 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/Pvalue.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | This P-Value transform calculates the p-value of the current input in
4 | the sequence with regard to the values in the sliding window.
5 |
6 | .. seealso::
7 | :py:func:`PercentileThreshold
8 | `,
9 | :py:func:`ExponentialAverage
10 | `,
11 | :py:func:`SlidingWindow
12 | `.
13 |
14 | .. index:: models, timeseries, transform
15 |
16 | Example:
17 | .. literalinclude:: /../nimbusml/examples/PvalueTransform.py
18 | :language: python
19 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/RbfKernel.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Apply radial basis function. exp(-gamma*|x-y|^2).
4 |
5 | .. remarks::
6 | `Radial basis function kernel (RBF kernel)
7 | `_ is a
8 | kernel function
9 | that computes the similarity between two features. It is the most
10 | commonly used kernel in support vector machines.
11 |
12 | .. seealso::
13 | :py:func:`LinearKernel `,
14 | :py:func:`PolynomialKernel `,
15 | :py:func:`SigmoidKernel `,
16 | :py:func:`OneClassSvmAnomalyDetector
17 | `
18 |
19 | .. index:: svm, kernel
20 |
21 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/RegressorBestPerformanceSelector.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | **Description**
4 | Computes the weighted average of the outputs of the trained models
5 |
6 |
7 | :param metric_name: the metric type to be used to find the weights for
8 | each model. Can be ``"L1"``, ``"L2"``, ``"Rms"``, or ``"Loss"``, or
9 | ``"RSquared"``.
10 |
11 |
12 | .. seealso::
13 | :py:class:`EnsembleRegressor
14 | `
15 |
16 | * Submodel selectors:
17 | :py:class:`RegressorAllSelector
18 | `,
19 | :py:class:`RegressorBestDiverseSelector
20 | `
21 |
22 | * Output combiners:
23 | :py:class:`RegressorAverage
24 | `,
25 | :py:class:`RegressorMedian
26 | `,
27 | :py:class:`RegressorStacking
28 | `
29 |
30 |
31 | .. index:: models, ensemble, regression
32 |
33 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/SigmoidKernel.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Apply sigmoid function. tanh(gamma*+c).
4 |
5 | .. remarks::
6 | `SigmoidKernel `_ is a
7 | kernel function
8 | that computes the similarity between two features.
9 |
10 | .. seealso::
11 | :py:func:`LinearKernel `,
12 | :py:func:`PolynomialKernel `,
13 | :py:func:`RbfKernel `,
14 | :py:func:`OneClassSvmAnomalyDetector
15 | `
16 |
17 | .. index:: svm, kernel
18 |
19 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/SkipFilter.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Skip the first N rows of the dataset, allowing limiting input to a
4 | subset of rows.
5 |
6 | :param columns: a string representing the column name to perform the
7 | transformation on.
8 |
9 | * Input column type: numeric.
10 | * Output column type: numeric.
11 |
12 | The << operator can be used to set this value (see
13 | `Column Operator `_)
14 |
15 | For example
16 | * SkipFilter(columns='age')
17 | * SkipFilter() << {'age'}
18 |
19 | For more details see `Columns `_.
20 |
21 | :param count: number of rows to skip from the beginning of the dataset.
22 |
23 | .. index:: transform, random
24 |
25 | Example:
26 | .. literalinclude:: /../nimbusml/examples/SkipFilter.py
27 | :language: python
28 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/SlidingWindow.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Returns the last values for a time series [y(t-d-l+1), y(t-d-l+2),
4 | ..., y(t-l-1), y(t-l)] where d is the size of the window, l the lag
5 | and y is a Float.
6 |
7 | .. seealso::
8 | :py:func:`PercentileThreshold
9 | `,
10 | :py:func:`Pvalue `,
11 | :py:func:`ExponentialAverage
12 | `.
13 |
14 | .. index:: models, timeseries, transform
15 |
16 | Example:
17 | .. literalinclude:: /../nimbusml/examples/SlidingWindowTransform.py
18 | :language: python
19 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/TakeFilter.txt:
--------------------------------------------------------------------------------
1 | """
2 |
3 | Take N first rows of the dataset, allowing limiting input to a subset
4 | of rows.
5 |
6 | :param columns: a string representing the column name to perform the
7 | transformation on.
8 |
9 | * Input column type: numeric.
10 | * Output column type: numeric.
11 |
12 | The << operator can be used to set this value (see
13 | `Column Operator `_)
14 |
15 | For example
16 | * TakeFilter(columns='age')
17 | * TakeFilter() << {'age'}
18 |
19 | For more details see `Columns `_.
20 |
21 | :param count: number of rows to keep from the beginning of the dataset.
22 |
23 | .. index:: transform, random
24 |
25 | Example:
26 | .. literalinclude:: /../nimbusml/examples/SkipFilter.py
27 | :language: python
28 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/TreeFeaturizer.txt:
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1 | """
2 |
3 | TreeFeaturizer.
4 |
5 | .. remarks::
6 | Trains a tree ensemble, or loads it from a file, then maps a numeric
7 | feature vector to three outputs:
8 |
9 | * A vector containing the individual tree outputs of the tree
10 | ensemble.
11 | * A vector indicating the leaves that the feature vector falls on in
12 | the tree ensemble.
13 | * A vector indicating the paths that the feature vector falls on in
14 | the
15 | tree ensemble. If a both a model file and a trainer are specified,
16 | will use the model file. If neither are specified, will train a
17 | default FastTree model. This can handle key labels by training a
18 | regression model towards their optionally permuted indices.
19 |
20 | .. seealso::
21 | :py:class:`TensorFlowScorer
22 | `
23 | """
--------------------------------------------------------------------------------
/src/python/docs/docstrings/TypeConverter.txt:
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1 | """
2 |
3 | Converts a column to a different type, using standard conversions.
4 |
5 | :param result_type: The result type, e.g. 'R4', 'TX'. For more details
6 | see `Types `_.
7 | Note that the converted type to should
8 | compatible with the origin.
9 |
10 | .. index:: transform, schema
11 |
12 | Example:
13 | .. literalinclude:: /../nimbusml/examples/TypeConverter.py
14 | :language: python
15 | """
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/src/python/docs/sphinx/concepts.rst:
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1 | .. rxtitle:: ML.NET for Python Concepts
2 | .. rxdescription:: Introduction of different important nimbusml concepts
3 |
4 | ==========================
5 | ML.NET for Python Concepts
6 | ==========================
7 |
8 | .. toctree::
9 | :maxdepth: 1
10 | :caption: Contents:
11 |
12 | concepts/datasources
13 | concepts/types
14 | concepts/schema
15 | concepts/columns
16 | concepts/roles
17 | concepts/experimentvspipeline
18 |
19 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules.rst:
--------------------------------------------------------------------------------
1 | =============
2 | API/Reference
3 | =============
4 |
5 | .. toctree::
6 | ms_scikit
7 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/DataSchema.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.DataSchema`
2 | ===============================
3 |
4 | .. autoclass:: nimbusml.DataSchema
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/FileDataStream.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.FileDataStream`
2 | ===================================
3 |
4 | .. autoclass:: nimbusml.FileDataStream
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/Pipeline.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.Pipeline`
2 | =========================
3 |
4 | .. autoclass:: nimbusml.Pipeline
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/Role.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.Role`
2 | =========================
3 |
4 | .. autoclass:: nimbusml.Role
5 | :no-inherited-members:
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/basepredictor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.BasePredictor`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.BasePredictor
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/basetransform.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.BaseTransform`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.BaseTransform
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/cluster.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | cluster/kmeansplusplus
4 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/cluster/kmeansplusplus.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.cluster.KMeansPlusPlus`
2 | ===========================================
3 |
4 | .. autoclass:: nimbusml.cluster.KMeansPlusPlus
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/data.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | data/DataSchema
4 | data/FileDataStream
5 | data/Role
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/data/DataSchema.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.DataSchema`
2 | ===============================
3 |
4 | .. autoclass:: nimbusml.DataSchema
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/data/FileDataStream.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.FileDataStream`
2 | ===================================
3 |
4 | .. autoclass:: nimbusml.FileDataStream
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/data/Role.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.Role`
2 | =========================
3 |
4 | .. autoclass:: nimbusml.Role
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/decomposition.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | decomposition/factorizationmachinebinaryclassifier
4 | decomposition/pcaanomalydetector
5 | decomposition/pcatransformer
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/decomposition/factorizationmachinebinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.decomposition.FactorizationMachineBinaryClassifier`
2 | =======================================================================
3 |
4 | .. autoclass:: nimbusml.decomposition.FactorizationMachineBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/decomposition/pcaanomalydetector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.decomposition.PcaAnomalyDetector`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.decomposition.PcaAnomalyDetector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/decomposition/pcatransformer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.decomposition.PcaTransformer`
2 | =================================================
3 |
4 | .. autoclass:: nimbusml.decomposition.PcaTransformer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble.rst:
--------------------------------------------------------------------------------
1 | booster
2 | --------------
3 | .. toctree::
4 | :maxdepth: 1
5 |
6 | ensemble/booster
7 |
8 |
9 |
10 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/booster.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | booster/dart
4 | booster/gbdt
5 | booster/goss
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/booster/dart.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.booster.Dart`
2 | ======================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.booster.Dart
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/booster/gbdt.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.booster.Gbdt`
2 | ======================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.booster.Gbdt
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/booster/goss.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.booster.Goss`
2 | ======================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.booster.Goss
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/fastforestbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.FastForestBinaryClassifier`: RandomForest
2 | ======================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.FastForestBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/fastforestregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.FastForestRegressor`: RandomForest regression
2 | ==========================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.FastForestRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/fasttreesbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.FastTreesBinaryClassifier`: Decision Tree
2 | ======================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.FastTreesBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/fasttreesregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.FastTreesRegressor`: Decision Tree Regression
2 | ==========================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.FastTreesRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/fasttreestweedieregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.FastTreesTweedieRegressor`: Decision Tree Regression
2 | =================================================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.FastTreesTweedieRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/gambinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.GamBinaryClassifier`
2 | =================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.GamBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/gamregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.GamRegressor`
2 | ==========================================
3 |
4 | .. autoclass:: nimbusml.ensemble.GamRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/lightgbmbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.LightGbmBinaryClassifier`
2 | ======================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.LightGbmBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/lightgbmclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.LightGbmClassifier`
2 | ================================================
3 |
4 | .. autoclass:: nimbusml.ensemble.LightGbmClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/lightgbmranker.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.LightGbmRanker`
2 | ============================================
3 |
4 | .. autoclass:: nimbusml.ensemble.LightGbmRanker
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/ensemble/lightgbmregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.ensemble.LightGbmRegressor`
2 | ===============================================
3 |
4 | .. autoclass:: nimbusml.ensemble.LightGbmRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction.rst:
--------------------------------------------------------------------------------
1 | categorical
2 | -----------
3 | .. toctree::
4 |
5 | feature_extraction/categorical
6 |
7 |
8 | image
9 | -----
10 | .. toctree::
11 |
12 | feature_extraction/image
13 |
14 |
15 | text
16 | ----
17 | .. toctree::
18 |
19 | feature_extraction/text
20 | feature_extraction/text/lightlda
21 | feature_extraction/text/ngramfeaturizer
22 | feature_extraction/text/sentiment
23 | feature_extraction/text/wordembedding
24 |
25 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/categorical.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | categorical/onehotvectorizer
4 | categorical/onehothashvectorizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/categorical/onehothashvectorizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.categorical.OneHotHashVectorizer`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.categorical.OneHotHashVectorizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/categorical/onehotvectorizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.categorical.OneHotVectorizer`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.categorical.OneHotVectorizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/image.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | image/loader
4 | image/pixelextractor
5 | image/resizer
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/image/dnnfeaturizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.image.DnnFeaturizer`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.image.DnnFeaturizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/image/loader.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.image.Loader`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.image.Loader
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/image/pixelextractor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.image.PixelExtractor`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.image.PixelExtractor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/image/resizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.image.Resizer`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.image.Resizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text.rst:
--------------------------------------------------------------------------------
1 | extractor
2 | -----------
3 | .. toctree::
4 |
5 | text/extractor
6 |
7 | stopwords
8 | -----------
9 | .. toctree::
10 |
11 | text/stopwords
12 |
13 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/extractor.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | extractor/ngram
4 | extractor/ngramhash
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/extractor/ngram.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.extractor.Ngram`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.extractor.Ngram
5 | :no-inherited-members:
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/extractor/ngramhash.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.extractor.NgramHash`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.extractor.NgramHash
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/lightlda.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.LightLda`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.LightLda
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/ngramfeaturizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.NGramFeaturizer`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.NGramFeaturizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/sentiment.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.Sentiment`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.Sentiment
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/ssweembedding.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.SsweEmbedding`
2 | ==========================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.SsweEmbedding
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/stopwords.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | stopwords/customstopwordsremover
4 | stopwords/predefinedstopwordsremover
5 |
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/stopwords/customstopwordsremover.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.stopwords.CustomStopWordsRemover`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.stopwords.CustomStopWordsRemover
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/stopwords/predefinedstopwordsremover.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.stopwords.PredefinedStopWordsRemover`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.stopwords.PredefinedStopWordsRemover
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/text/wordembedding.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.text.WordEmbedding`
2 | ==========================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.text.WordEmbedding
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_extraction/treefeaturizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_extraction.TreeFeaturizer`
2 | ======================================================
3 |
4 | .. autoclass:: nimbusml.feature_extraction.TreeFeaturizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_selection.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | feature_selection/countselector
4 | feature_selection/mutualinformationselector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_selection/countselector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_selection.CountSelector`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.feature_selection.CountSelector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/feature_selection/mutualinformationselector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.feature_selection.MutualInformationSelector`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.feature_selection.MutualInformationSelector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | linear_model/averagedperceptronbinaryclassifier
4 | linear_model/fastlinearbinaryclassifier
5 | linear_model/fastlinearclassifier
6 | linear_model/fastlinearregressor
7 | linear_model/logisticregressionbinaryclassifier
8 | linear_model/logisticregressionclassifier
9 | linear_model/onlinegradientdescentregressor
10 | linear_model/ordinaryleastsquaresregressor
11 | linear_model/poissonregressionregressor
12 | linear_model/sgdbinaryclassifier
13 | linear_model/symsgdbinaryclassifier
14 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/averagedperceptronbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.AveragedPerceptronBinaryClassifier`
2 | ====================================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.AveragedPerceptronBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/fastlinearbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.FastLinearBinaryClassifier`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.FastLinearBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/fastlinearclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.FastLinearClassifier`
2 | ======================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.FastLinearClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/fastlinearregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.FastLinearRegressor`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.FastLinearRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/logisticregressionbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.LogisticRegressionBinaryClassifier`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.LogisticRegressionBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/logisticregressionclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.LogisticRegressionClassifier`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.LogisticRegressionClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/onlinegradientdescentregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.OnlineGradientDescentRegressor`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.OnlineGradientDescentRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/ordinaryleastsquaresregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.OrdinaryLeastSquaresRegressor`
2 | ===============================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.OrdinaryLeastSquaresRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/poissonregressionregressor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.PoissonRegressionRegressor`
2 | ============================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.PoissonRegressionRegressor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/sgdbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.SgdBinaryClassifier`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.SgdBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/linear_model/symsgdbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.linear_model.SymSgdBinaryClassifier`
2 | ========================================================
3 |
4 | .. autoclass:: nimbusml.linear_model.SymSgdBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss.rst:
--------------------------------------------------------------------------------
1 | .. _loss_intro:
2 |
3 | .. toctree::
4 |
5 | loss/exp
6 | loss/hinge
7 | loss/log
8 | loss/poisson
9 | loss/smoothedhinge
10 | loss/squared
11 | loss/tweedie
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/exp.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.Exp`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.Exp
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/hinge.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.Hinge`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.Hinge
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/log.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.Log`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.Log
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/poisson.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.Poisson`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.Poisson
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/smoothedhinge.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.SmoothedHinge`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.SmoothedHinge
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/squared.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.Squared`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.Squared
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/loss/tweedie.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.loss.Tweedie`
2 | ==================================
3 |
4 | .. autoclass:: nimbusml.loss.Tweedie
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/model_selection.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | model_selection/cv
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/model_selection/cv.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.model_selection.CV`
2 | ======================================================
3 |
4 | .. autoclass:: nimbusml.model_selection.CV
5 | :members:
6 | :no-inherited-members:
7 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/multiclass.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | multiclass/onevsrestclassifier
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/multiclass/onevsrestclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.multiclass.OneVsRestClassifier`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.multiclass.OneVsRestClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/naive_bayes.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | naive_bayes/naivebayesclassifier
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/naive_bayes/naivebayesclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.naive_bayes.NaiveBayesClassifier`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.naive_bayes.NaiveBayesClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing.rst:
--------------------------------------------------------------------------------
1 | filter
2 | ------
3 | .. toctree::
4 |
5 | preprocessing/filter
6 |
7 |
8 | missing_values
9 | --------------
10 | .. toctree::
11 |
12 | preprocessing/missing_values
13 |
14 |
15 | normalization
16 | -------------
17 | .. toctree::
18 |
19 | preprocessing/normalization
20 |
21 |
22 | schema
23 | ------
24 | .. toctree::
25 |
26 | preprocessing/schema
27 |
28 |
29 | text
30 | ----
31 | .. toctree::
32 |
33 | preprocessing/text
34 |
35 |
36 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/expression.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.Expression`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.Expression
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/filter.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | filter/bootstrapsampler
4 | filter/rangefilter
5 | filter/skipfilter
6 | filter/takefilter
7 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/filter/bootstrapsampler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.filter.BootstrapSampler`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.filter.BootstrapSampler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/filter/rangefilter.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.filter.RangeFilter`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.filter.RangeFilter
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/filter/skipfilter.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.filter.SkipFilter`
2 | ====================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.filter.SkipFilter
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/filter/takefilter.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.filter.TakeFilter`
2 | ====================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.filter.TakeFilter
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/fromkey.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.FromKey`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.FromKey
5 | :show-inheritance:
6 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/missing_values.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | missing_values/filter
4 | missing_values/handler
5 | missing_values/indicator
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/missing_values/filter.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.missing_values.Filter`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.missing_values.Filter
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/missing_values/handler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.missing_values.Handler`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.missing_values.Handler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/missing_values/indicator.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.missing_values.Indicator`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.missing_values.Indicator
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | normalization/binner
4 | normalization/globalcontrastrowscaler
5 | normalization/logmeanvariancescaler
6 | normalization/meanvariancescaler
7 | normalization/minmaxscaler
8 | normalization/supervisedbinner
9 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/binner.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.Binner`
2 | =======================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.Binner
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/globalcontrastrowscaler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.GlobalContrastRowScaler`
2 | ========================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.GlobalContrastRowScaler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/logmeanvariancescaler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.LogMeanVarianceScaler`
2 | ======================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.LogMeanVarianceScaler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/meanvariancescaler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.MeanVarianceScaler`
2 | ===================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.MeanVarianceScaler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/minmaxscaler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.MinMaxScaler`
2 | =============================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.MinMaxScaler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/optionalscaler.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.OptionalScaler`
2 | ===============================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.OptionalScaler
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/normalization/supervisedbinner.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.normalization.SupervisedBinner`
2 | =================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.normalization.SupervisedBinner
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/schema.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | schema/columnconcatenator
4 | schema/columndropper
5 | schema/columnduplicator
6 | schema/columnselector
7 | schema/typeconverter
8 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/schema/columnconcatenator.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.schema.ColumnConcatenator`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.schema.ColumnConcatenator
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/schema/columndropper.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.schema.ColumnDropper`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.schema.ColumnDropper
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/schema/columnduplicator.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.schema.ColumnDuplicator`
2 | ==========================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.schema.ColumnDuplicator
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/schema/columnselector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.schema.ColumnSelector`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.schema.ColumnSelector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/schema/typeconverter.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.schema.TypeConverter`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.schema.TypeConverter
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/tensorflowscorer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.TensorFlowScorer`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.TensorFlowScorer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/text.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | text/chartokenizer
4 |
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/text/chartokenizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.text.CharTokenizer`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.text.CharTokenizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/text/keyphraseextractor.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.text.KeyPhraseExtractor`
2 | ==========================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.text.KeyPhraseExtractor
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/text/lemmatizer.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.text.Lemmatizer`
2 | ==================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.text.Lemmatizer
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | timeseries/exponentialaverage
4 | timeseries/iidchangepointdetector
5 | timeseries/iidspikedetector
6 | timeseries/percentilethreshold
7 | timeseries/pvalue
8 | timeseries/slidingwindow
9 | timeseries/ssachangepointdetector
10 | timeseries/ssaspikedetector
11 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/exponentialaverage.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.ExponentialAverage`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.ExponentialAverage
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/iidchangepointdetector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.IIDChangePointDetector`
2 | =========================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.IIDChangePointDetector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/iidspikedetector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.IIDSpikeDetector`
2 | ===================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.IIDSpikeDetector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/percentilethreshold.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.PercentileThreshold`
2 | =================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.PercentileThreshold
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/pvalue.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.Pvalue`
2 | ====================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.Pvalue
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/slidingwindow.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.SlidingWindow`
2 | ===========================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.SlidingWindow
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/ssachangepointdetector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.SsaChangePointDetector`
2 | ====================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.SsaChangePointDetector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/timeseries/ssaspikedetector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.timeseries.SsaSpikeDetector`
2 | ==============================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.timeseries.SsaSpikeDetector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/preprocessing/tokey.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.preprocessing.ToKey`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.preprocessing.ToKey
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm.rst:
--------------------------------------------------------------------------------
1 | kernel
2 | -----------
3 | .. toctree::
4 |
5 | svm/kernel
6 |
7 |
8 |
9 |
10 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/kernel.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
3 | kernel/linearkernel
4 | kernel/polynomialkernel
5 | kernel/rbfkernel
6 | kernel/sigmoidkernel
7 |
8 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/kernel/linearkernel.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.svm.kernel.LinearKernel`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.svm.kernel.LinearKernel
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/kernel/polynomialkernel.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.svm.kernel.PolynomialKernel`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.svm.kernel.PolynomialKernel
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/kernel/rbfkernel.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.svm.kernel.RbfKernel`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.svm.kernel.RbfKernel
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/kernel/sigmoidkernel.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.svm.kernel.SigmoidKernel`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.svm.kernel.SigmoidKernel
5 | :no-inherited-members:
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/localdeepsvmbinaryclassifier.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.svm.LocalDeepSvmBinaryClassifier`
2 | =====================================================
3 |
4 | .. autoclass:: nimbusml.svm.LocalDeepSvmBinaryClassifier
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/svm/oneclasssvmanomalydetector.rst:
--------------------------------------------------------------------------------
1 | `nimbusml.svm.OneClassSvmAnomalyDetector`
2 | ================================================================
3 |
4 | .. autoclass:: nimbusml.svm.OneClassSvmAnomalyDetector
5 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/modules/utils.rst:
--------------------------------------------------------------------------------
1 | .. toctree::
2 |
--------------------------------------------------------------------------------
/src/python/docs/sphinx/overview.rst:
--------------------------------------------------------------------------------
1 | ========
2 | Overview
3 | ========
4 |
5 | NimbusML provides state-of-the-art ML algorithms, transforms and components,
6 | aiming to make them useful for all developers, data scientists, and information
7 | workers and helpful in all products, services and devices. The components are
8 | authored by the team members, as well as numerous contributors from MSR, CISL,
9 | Bing and other teams at Microsoft.
10 |
--------------------------------------------------------------------------------
/src/python/nimbusml/__main__.py:
--------------------------------------------------------------------------------
1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 | # The contents of this file will be executed when the module is
6 | # run with -m.
7 | import this
8 |
--------------------------------------------------------------------------------
/src/python/nimbusml/cluster/__init__.py:
--------------------------------------------------------------------------------
1 | from .kmeansplusplus import KMeansPlusPlus
2 |
3 | __all__ = [
4 | 'KMeansPlusPlus'
5 | ]
6 |
--------------------------------------------------------------------------------
/src/python/nimbusml/datasets/__init__.py:
--------------------------------------------------------------------------------
1 | from .datasets import get_dataset, available_datasets, \
2 | DataSetIris, DataSetInfert, Topics, Timeseries, \
3 | DataSetAirQuality, WikiDetox_Train, WikiDetox_Test, \
4 | Generated_Twitter_Train, Generated_Twitter_Test, \
5 | Generated_Ticket_Train, Generated_Ticket_Test, \
6 | Uci_Train, Uci_Test, MSLTR_Train, MSLTR_Test, \
7 | FS_Train, FS_Test
8 |
9 | __all__ = [
10 | 'get_dataset',
11 | 'available_datasets',
12 | 'DataSetIris',
13 | 'DataSetInfert', 'Topics', 'Timeseries',
14 | 'DataSetAirQuality', 'WikiDetox_Train', 'WikiDetox_Test',
15 | 'Generated_Twitter_Train', 'Generated_Twitter_Test',
16 | 'Generated_Ticket_Train', 'Generated_Ticket_Test',
17 | 'Uci_Train', 'Uci_Test', 'MSLTR_Train', 'MSLTR_Test',
18 | 'FS_Train', 'FS_Test'
19 | ]
20 |
--------------------------------------------------------------------------------
/src/python/nimbusml/datasets/baseline/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/baseline/__init__.py
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/src/python/nimbusml/datasets/baseline/magic.nn:
--------------------------------------------------------------------------------
1 | input i [3];
2 | output o [3] linear { // Depth 0
3 | Biases = Biases_0;
4 | from i where (s,d) => s[0] == d[0] { Weights = Weights_0; }
5 | }
6 |
7 | const Weights_0 = [
8 | 1, 1, 1
9 | ];
10 |
11 | const Biases_0 = [
12 | 0, 0, 0
13 | ];
14 |
15 |
--------------------------------------------------------------------------------
/src/python/nimbusml/datasets/data/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/data/__init__.py
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/src/python/nimbusml/datasets/data/test-ticketchoice.csv:
--------------------------------------------------------------------------------
1 | rank,group,carrier,price,Class,dep_day,nbr_stops,duration
2 | 0,10,AA,200,1,0,2,10
3 | 0,10,AA,250,1,0,1,8
4 | 1,10,AA,400,2,0,2,10
5 | 1,10,AA,450,2,0,1,8
6 | 2,10,AA,700,3,1,0,6
7 | 2,10,Delta,300,1,2,0,6
8 | 1,10,Delta,600,3,0,2,10
9 | 1,10,Multiple,300,1,0,0,6
10 | 1,10,Multiple,650,3,0,1,8
11 | 1,10,Multiple,645,3,0,1,8
12 | 0,10,United,250,1,0,1,8
13 | 1,10,United,300,1,0,0,6
14 | 2,10,United,300,1,1,0,6
15 | 1,10,United,450,2,0,1,8
16 | 2,10,United,500,2,0,0,6
17 |
--------------------------------------------------------------------------------
/src/python/nimbusml/datasets/data/timeseries.csv:
--------------------------------------------------------------------------------
1 | t1,t2,t3
2 | .01,.01,.01
3 | .02,.02,.02
4 | .03,.03,.02
5 | .03,.03,.025
6 | .03,.03,.0005
7 | .03,.05,.01
8 | .05,.07,.05
9 | .07,.09,.09
10 | .09,99,99
11 | 1.1,.10,.1
--------------------------------------------------------------------------------
/src/python/nimbusml/datasets/data/topics.csv:
--------------------------------------------------------------------------------
1 | review,review_reverse,label
2 | "animals birds cats dogs fish horse","radiation galaxy universe duck",1
3 | "horse birds house fish duck cats","space galaxy universe radiation",0
4 | "car truck driver bus pickup","bus pickup",1
5 | "car truck driver bus pickup horse","car truck",0
6 | "car truck","car truck driver bus pickup horse",1
7 | "bus pickup","car truck driver bus pickup",1
8 | "space galaxy universe radiation","horse birds house fish duck cats",1
9 | "radiation galaxy universe duck","animals birds cats dogs fish horse",1
10 |
11 |
--------------------------------------------------------------------------------
/src/python/nimbusml/datasets/image.py:
--------------------------------------------------------------------------------
1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 | """
6 | Data about images.
7 | """
8 | import os
9 |
10 |
11 | def get_RevolutionAnalyticslogo():
12 | """
13 | Return a path to *RevolutionAnalyticslogo.png*.
14 |
15 | .. image:: images/RevolutionAnalyticslogo.png
16 | """
17 | this = os.path.abspath(os.path.dirname(__file__))
18 | return os.path.join(this, "images", "RevolutionAnalyticslogo.png")
19 |
20 |
21 | def get_Microsoftlogo():
22 | """
23 | Return a path to *Microsoftlogo.png*.
24 |
25 | .. image:: images/Microsoftlogo.png
26 | """
27 | this = os.path.abspath(os.path.dirname(__file__))
28 | return os.path.join(this, "images", "Microsoftlogo.png")
29 |
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/src/python/nimbusml/datasets/images/Microsoftlogo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/images/Microsoftlogo.png
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/src/python/nimbusml/datasets/images/RevolutionAnalyticslogo.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/images/RevolutionAnalyticslogo.png
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/src/python/nimbusml/datasets/images/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/images/__init__.py
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/src/python/nimbusml/decomposition/__init__.py:
--------------------------------------------------------------------------------
1 | from .factorizationmachinebinaryclassifier import \
2 | FactorizationMachineBinaryClassifier
3 | from .pcaanomalydetector import PcaAnomalyDetector
4 | from .pcatransformer import PcaTransformer
5 |
6 | __all__ = [
7 | 'FactorizationMachineBinaryClassifier',
8 | 'PcaAnomalyDetector',
9 | 'PcaTransformer'
10 | ]
11 |
--------------------------------------------------------------------------------
/src/python/nimbusml/ensemble/booster/__init__.py:
--------------------------------------------------------------------------------
1 | from .dart import Dart
2 | from .gbdt import Gbdt
3 | from .goss import Goss
4 |
5 | __all__ = [
6 | 'Dart',
7 | 'Gbdt',
8 | 'Goss'
9 | ]
10 |
--------------------------------------------------------------------------------
/src/python/nimbusml/ensemble/feature_selector/__init__.py:
--------------------------------------------------------------------------------
1 | from .allfeatureselector import AllFeatureSelector
2 | from .randomfeatureselector import RandomFeatureSelector
3 |
4 | __all__ = [
5 | 'AllFeatureSelector',
6 | 'RandomFeatureSelector'
7 | ]
8 |
--------------------------------------------------------------------------------
/src/python/nimbusml/ensemble/output_combiner/__init__.py:
--------------------------------------------------------------------------------
1 | from .classifieraverage import ClassifierAverage
2 | from .classifiermedian import ClassifierMedian
3 | from .classifierstacking import ClassifierStacking
4 | from .classifiervoting import ClassifierVoting
5 | from .classifierweightedaverage import ClassifierWeightedAverage
6 | from .regressoraverage import RegressorAverage
7 | from .regressormedian import RegressorMedian
8 | from .regressorstacking import RegressorStacking
9 |
10 | __all__ = [
11 | 'ClassifierAverage',
12 | 'ClassifierMedian',
13 | 'ClassifierStacking',
14 | 'ClassifierVoting',
15 | 'ClassifierWeightedAverage',
16 | 'RegressorAverage',
17 | 'RegressorMedian',
18 | 'RegressorStacking'
19 | ]
20 |
--------------------------------------------------------------------------------
/src/python/nimbusml/ensemble/sub_model_selector/__init__.py:
--------------------------------------------------------------------------------
1 | from .classifierallselector import ClassifierAllSelector
2 | from .classifierbestdiverseselector import ClassifierBestDiverseSelector
3 | from .classifierbestperformanceselector import ClassifierBestPerformanceSelector
4 | from .regressorallselector import RegressorAllSelector
5 | from .regressorbestdiverseselector import RegressorBestDiverseSelector
6 | from .regressorbestperformanceselector import RegressorBestPerformanceSelector
7 |
8 | __all__ = [
9 | 'ClassifierAllSelector',
10 | 'ClassifierBestDiverseSelector',
11 | 'ClassifierBestPerformanceSelector',
12 | 'RegressorAllSelector',
13 | 'RegressorBestDiverseSelector',
14 | 'RegressorBestPerformanceSelector'
15 | ]
--------------------------------------------------------------------------------
/src/python/nimbusml/ensemble/sub_model_selector/diversity_measure/__init__.py:
--------------------------------------------------------------------------------
1 | from .classifierdisagreement import ClassifierDisagreement
2 | from .regressordisagreement import RegressorDisagreement
3 |
4 | __all__ = [
5 | 'ClassifierDisagreement',
6 | 'RegressorDisagreement'
7 | ]
8 |
--------------------------------------------------------------------------------
/src/python/nimbusml/ensemble/subset_selector/__init__.py:
--------------------------------------------------------------------------------
1 | from .allinstanceselector import AllInstanceSelector
2 | from .bootstrapselector import BootstrapSelector
3 | from .randompartitionselector import RandomPartitionSelector
4 |
5 | __all__ = [
6 | 'AllInstanceSelector',
7 | 'BootstrapSelector',
8 | 'RandomPartitionSelector'
9 | ]
10 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/BootStrapSample.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # BootstrapSampler
3 | from nimbusml import FileDataStream
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.preprocessing.filter import BootstrapSampler
6 |
7 | # data input (as a FileDataStream)
8 | path = get_dataset('infert').as_filepath()
9 |
10 | data = FileDataStream.read_csv(path, sep=',')
11 |
12 | # transform usage
13 | xf = BootstrapSampler()
14 |
15 | # fit and transform
16 | features = xf.fit_transform(data)
17 |
18 | # print features
19 | print(features.head())
20 | # age case education induced parity ... row_num spontaneous stratum
21 | # 0 27 1 12+ yrs 1 3 ... 53 1 53
22 | # 1 34 0 6-11yrs 0 2 ... 197 0 32
23 | # 2 31 0 6-11yrs 1 2 ... 180 0 15
24 | # 3 25 0 12+ yrs 1 1 ... 227 0 62
25 | # 4 28 1 6-11yrs 0 2 ... 23 2 23
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/ColumnDropper.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ColumnDropper
3 | import numpy
4 | from nimbusml import FileDataStream
5 | from nimbusml.datasets import get_dataset
6 | from nimbusml.preprocessing.schema import ColumnDropper
7 |
8 | # data input (as a FileDataStream)
9 | path = get_dataset('infert').as_filepath()
10 |
11 | data = FileDataStream.read_csv(path, sep=',', numeric_dtype=numpy.float32)
12 |
13 | # transform usage
14 | xf = ColumnDropper(columns=['education', 'age'])
15 |
16 | # fit and transform
17 | features = xf.fit_transform(data)
18 |
19 | # print features
20 | print(features.head())
21 | # case induced parity pooled.stratum row_num spontaneous stratum
22 | # 0 1.0 1.0 6.0 3.0 1.0 2.0 1.0
23 | # 1 1.0 1.0 1.0 1.0 2.0 0.0 2.0
24 | # 2 1.0 2.0 6.0 4.0 3.0 0.0 3.0
25 | # 3 1.0 2.0 4.0 2.0 4.0 0.0 4.0
26 | # 4 1.0 1.0 3.0 32.0 5.0 1.0 5.0
27 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/ColumnDuplicator.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ColumnDuplicator
3 | from nimbusml import FileDataStream
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.preprocessing.schema import ColumnDuplicator
6 |
7 | # data input (as a FileDataStream)
8 | path = get_dataset('infert').as_filepath()
9 |
10 | data = FileDataStream.read_csv(path, sep=',')
11 |
12 | # transform usage
13 | xf = ColumnDuplicator(
14 | columns={
15 | 'education_copy': 'education',
16 | 'age_copy': 'age'})
17 |
18 | # fit and transform
19 | features = xf.fit_transform(data)
20 |
21 | # print features
22 | print(features.head())
23 | # age age_copy case education education_copy induced parity ...
24 | # 0 26 26 1 0-5yrs 0-5yrs 1 6 ...
25 | # 1 42 42 1 0-5yrs 0-5yrs 1 1 ...
26 | # 2 39 39 1 0-5yrs 0-5yrs 2 6 ...
27 | # 3 34 34 1 0-5yrs 0-5yrs 2 4 ...
28 | # 4 35 35 1 6-11yrs 6-11yrs 1 3 ...
29 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/ColumnSelector.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ColumnSelector
3 | from nimbusml import FileDataStream
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.preprocessing.schema import ColumnSelector
6 |
7 | # data input (as a FileDataStream)
8 | path = get_dataset('infert').as_filepath()
9 |
10 | data = FileDataStream.read_csv(path, sep=',')
11 |
12 | # transform usage
13 | xf = ColumnSelector(columns=['education', 'age'])
14 |
15 | # fit and transform
16 | features = xf.fit_transform(data)
17 |
18 | # print features
19 | print(features.head())
20 | # age education
21 | # 0 26 0-5yrs
22 | # 1 42 0-5yrs
23 | # 2 39 0-5yrs
24 | # 3 34 0-5yrs
25 | # 4 35 6-11yrs
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Exp.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Exponential Loss
3 | from nimbusml.linear_model import SgdBinaryClassifier
4 | from nimbusml.loss import Exp
5 |
6 | # specify loss function using string keyword
7 | trainer1 = SgdBinaryClassifier(loss='exp')
8 |
9 | # can also use the loss class instead of string.
10 |
11 | trainer1 = SgdBinaryClassifier(loss=Exp()) # equivalent to loss='exp'
12 | trainer2 = SgdBinaryClassifier(loss=Exp(beta=0.4))
13 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Filter.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Filter
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml import FileDataStream
6 | from nimbusml.preprocessing.missing_values import Filter
7 |
8 | with_nans = pd.DataFrame(
9 | data=dict(
10 | Sepal_Length=[2.5, np.nan, 2.1, 1.0],
11 | Sepal_Width=[.75, .9, .8, .76],
12 | Petal_Length=[np.nan, 2.5, 2.6, 2.4],
13 | Petal_Width=[.8, .7, .9, 0.7]))
14 |
15 | # write NaNs to file to show how this transform work
16 | tmpfile = 'tmpfile_with_nans.csv'
17 | with_nans.to_csv(tmpfile, index=False)
18 |
19 | data = FileDataStream.read_csv(tmpfile, sep=',', numeric_dtype=np.float32)
20 |
21 | # transform usage
22 | xf = Filter(
23 | columns=[
24 | 'Petal_Length',
25 | 'Petal_Width',
26 | 'Sepal_Length',
27 | 'Sepal_Width'])
28 |
29 | # fit and transform
30 | features = xf.fit_transform(data)
31 |
32 | # print features
33 | print(features.head())
34 | # Petal_Length Petal_Width Sepal_Length Sepal_Width
35 | # 0 2.4 0.7 1.0 0.76
36 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/FromKey.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FromKey
3 | from nimbusml import FileDataStream, Pipeline
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.preprocessing import FromKey, ToKey
6 |
7 | # data input (as a FileDataStream)
8 | path = get_dataset('topics').as_filepath()
9 |
10 | # load data
11 | data = FileDataStream.read_csv(path, sep=',')
12 |
13 | # transform usage
14 | pipeline = Pipeline([
15 | ToKey(columns=['review_reverse']),
16 | FromKey(columns=['review_reverse'])
17 | ])
18 |
19 | # fit and transform
20 | output = pipeline.fit_transform(data)
21 | print(output.head())
22 | # label review review_reverse
23 | # 0 1 animals birds cats dogs fish horse radiation galaxy universe duck
24 | # 1 0 horse birds house fish duck cats space galaxy universe radiation
25 | # 2 1 car truck driver bus pickup bus pickup
26 | # 3 0 car truck driver bus pickup horse car truck
27 | # 4 1 car truck car truck driver bus pickup horse
28 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Hinge.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Hinge Loss
3 | from nimbusml.linear_model import AveragedPerceptronBinaryClassifier
4 | from nimbusml.loss import Hinge
5 |
6 | # specify the loss function as a string keyword
7 | trainer1 = AveragedPerceptronBinaryClassifier(loss='hinge')
8 |
9 | # can also use the loss class instead of string
10 |
11 | trainer1 = AveragedPerceptronBinaryClassifier(
12 | loss=Hinge()) # equivalent to loss='hinge'
13 | trainer2 = AveragedPerceptronBinaryClassifier(loss=Hinge(margin=2.0))
14 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Log.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Log Loss
3 | from nimbusml.linear_model import FastLinearBinaryClassifier
4 | from nimbusml.loss import Log
5 |
6 | # specifying the loss function as a string keyword
7 | trainer1 = FastLinearBinaryClassifier(loss='log')
8 |
9 | # can also use loss class instead of string
10 |
11 | trainer1 = FastLinearBinaryClassifier(loss=Log()) # equivalent to loss='log'
12 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/PcaTransformer.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # PcaTransformer
3 | import numpy
4 | from nimbusml import FileDataStream
5 | from nimbusml.datasets import get_dataset
6 | from nimbusml.decomposition import PcaTransformer
7 |
8 | # data input (as a FileDataStream)
9 | path = get_dataset('infert').as_filepath()
10 |
11 | data = FileDataStream.read_csv(path, sep=',', numeric_dtype=numpy.float32)
12 |
13 | # transform data
14 | feature_columns = ['age', 'parity', 'induced', 'spontaneous']
15 |
16 | pipe = PcaTransformer(rank=3, columns={'features': feature_columns})
17 |
18 | print(pipe.fit_transform(data).head())
19 | # age case education features.0 features.1 features.2 induced ...
20 | # 0 26.0 1.0 0-5yrs -5.675901 -3.964389 -1.031570 1.0 ...
21 | # 1 42.0 1.0 0-5yrs 10.364552 0.875251 0.773911 1.0 ...
22 | # 2 39.0 1.0 0-5yrs 7.336117 -4.073389 1.128798 2.0 ...
23 | # 3 34.0 1.0 0-5yrs 2.340584 -2.130528 1.248973 2.0 ...
24 | # 4 35.0 1.0 6-11yrs 3.343876 -1.088401 -0.100063 1.0 ...
25 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Poisson.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Poisson Loss
3 | from nimbusml.linear_model import OnlineGradientDescentRegressor
4 | from nimbusml.loss import Poisson
5 |
6 | # specifying the loss function as a string keyword
7 | trainer1 = OnlineGradientDescentRegressor(loss='poisson')
8 |
9 | # can also use loss class instead of string
10 |
11 | trainer2 = OnlineGradientDescentRegressor(
12 | loss=Poisson()) # equivalent to loss='tweedie'
13 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/PrefixColumnConcatenator.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # PrefixColumnConcatenator
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml.preprocessing.schema import PrefixColumnConcatenator
6 |
7 | data = pd.DataFrame(
8 | data=dict(
9 | PrefixA=[2.5, np.nan, 2.1, 1.0],
10 | PrefixB=[.75, .9, .8, .76],
11 | AnotherColumn=[np.nan, 2.5, 2.6, 2.4]))
12 |
13 | # transform usage
14 | xf = PrefixColumnConcatenator(columns={'combined': 'Prefix'})
15 |
16 | # fit and transform
17 | features = xf.fit_transform(data)
18 |
19 | # print features
20 | print(features.head())
21 | # PrefixA PrefixB AnotherColumn combined.PrefixA combined.PrefixB
22 | #0 2.5 0.75 NaN 2.5 0.75
23 | #1 NaN 0.90 2.5 NaN 0.90
24 | #2 2.1 0.80 2.6 2.1 0.80
25 | #3 1.0 0.76 2.4 1.0 0.76
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/SkipFilter.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | from nimbusml import FileDataStream
3 | from nimbusml.datasets import get_dataset
4 | from nimbusml.preprocessing.filter import SkipFilter, TakeFilter
5 |
6 | # data input (as a FileDataStream)
7 | path = get_dataset('infert').as_filepath()
8 | data = FileDataStream.read_csv(
9 | path, sep=',', names={
10 | 0: 'id'}, dtype={
11 | 'id': str, 'age': np.float32})
12 | print(data.head())
13 | # age case education id induced parity pooled.stratum spontaneous ...
14 | # 0 26.0 1 0-5yrs 1 1 6 3 2 ...
15 | # 1 42.0 1 0-5yrs 2 1 1 1 0 ...
16 | # 2 39.0 1 0-5yrs 3 2 6 4 0 ...
17 | # 3 34.0 1 0-5yrs 4 2 4 2 0 ...
18 | # 4 35.0 1 6-11yrs 5 1 3 32 1 ...
19 |
20 | # fit and transform
21 | print(TakeFilter(count=100).fit_transform(data).shape)
22 | # (100, 9), first 100 rows are preserved
23 |
24 | print(SkipFilter(count=100).fit_transform(data).shape)
25 | # (148, 9), first 100 rows are deleted
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/SmoothedHinge.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Smoothed Hinge Loss
3 | from nimbusml.linear_model import FastLinearBinaryClassifier
4 | # can also use loss class instead of string
5 | from nimbusml.loss import SmoothedHinge
6 |
7 | # specifying the loss function as a string keyword
8 | trainer1 = FastLinearBinaryClassifier(loss='smoothed_hinge')
9 |
10 | # equivalent to loss='smoothed_hinge'
11 | trainer2 = FastLinearBinaryClassifier(loss=SmoothedHinge())
12 | trainer3 = FastLinearBinaryClassifier(loss=SmoothedHinge(smoothing_const=0.5))
13 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Squared.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Squared Loss
3 | from nimbusml.linear_model import FastLinearRegressor
4 | # can also use loss class instead of string
5 | from nimbusml.loss import Squared
6 |
7 | # specifying the loss function as a string keyword
8 | trainer1 = FastLinearRegressor(loss='squared')
9 |
10 | trainer2 = FastLinearRegressor(loss=Squared()) # equivalent to loss='squared'
11 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/Tweedie.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Tweedie Loss
3 | from nimbusml.linear_model import OnlineGradientDescentRegressor
4 | # can also use loss class instead of string
5 | from nimbusml.loss import Tweedie
6 |
7 | # specifying the loss function as a string keyword
8 | trainer1 = OnlineGradientDescentRegressor(loss='tweedie')
9 |
10 | trainer2 = OnlineGradientDescentRegressor(
11 | loss=Tweedie()) # equivalent to loss='tweedie'
12 | trainer3 = OnlineGradientDescentRegressor(loss=Tweedie(index=3.0))
13 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/__init__.py:
--------------------------------------------------------------------------------
1 |
2 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/Binner_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # Binner
3 | import pandas as pd
4 | from nimbusml.preprocessing.normalization import Binner
5 |
6 | in_df = pd.DataFrame(
7 | data=dict(
8 | Sepal_Length=[
9 | 2.5, 1, 2.1, 1.0, 0.8, 1.1], Sepal_Width=[
10 | .75, .9, .8, .76, .85, 0.76], Petal_Length=[
11 | 0, 2.5, 2.6, 2.4, 2.1, 2.2], Species=[
12 | "setosa", "viginica", "setosa", "versicolor", "viginica",
13 | "versicolor"]))
14 |
15 | # generate two new Columns - Petal_Normed and Sepal_Normed
16 | # bin into 5 bins using equal width binning (.0, .25, .50, .75, 1.0)
17 | normed = Binner(
18 | num_bins=5) << {
19 | 'Petal_Normed': 'Petal_Length',
20 | 'Sepal_Normed': 'Sepal_Width'}
21 | out_df = normed.fit(in_df).transform(in_df)
22 |
23 | print('Binner\n', (out_df))
24 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/BootStrapSample_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # BootstrapSampler
3 | # text.
4 | import pandas
5 | from nimbusml.preprocessing.filter import BootstrapSampler
6 |
7 | # create the data
8 | customer_reviews = pandas.DataFrame(data=dict(review=[
9 | "I really did not like the taste of it",
10 | "It was surprisingly quite good!",
11 | "I will never ever ever go to that place again!!",
12 | "The best ever!! It was amazingly good and super fast",
13 | "I wish I had gone earlier, it was that great",
14 | "somewhat dissapointing. I'd probably wont try again",
15 | "Never visit again... rascals!"]))
16 |
17 | sample = BootstrapSampler(pool_size=20, complement=True)
18 |
19 | y = sample.fit_transform(customer_reviews)
20 |
21 | # view the sentiment scores!!
22 | print(y)
23 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/ColumnDuplicator_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ColumnDuplicator
3 | import pandas
4 | from nimbusml.preprocessing.schema import ColumnDuplicator
5 |
6 | df = pandas.DataFrame(data=dict(
7 | tokens1=['one_' + str(i) for i in range(8)],
8 | tokens2=['two_' + str(i) for i in range(8)]
9 | ))
10 |
11 | # duplicate a column
12 | cd = ColumnDuplicator() << {'tokens3': 'tokens1'}
13 | y = cd.fit_transform(df)
14 |
15 | # view the three columns
16 | print(y)
17 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/FastForestRegressor_airquality_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FastForestRegressor
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.ensemble import FastForestRegressor
6 | from sklearn.metrics import r2_score
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use the built-in data set 'airquality' to create test and train data
10 | # Unnamed: 0 Ozone Solar_R Wind Temp Month Day
11 | # 0 1 41.0 190.0 7.4 67 5 1
12 | # 1 2 36.0 118.0 8.0 72 5 2
13 |
14 | np.random.seed(0)
15 |
16 | df = get_dataset("airquality").as_df().fillna(0)
17 | df = df[df.Ozone.notnull()]
18 |
19 | X_train, X_test, y_train, y_test = train_test_split(
20 | df.loc[:, df.columns != 'Ozone'], df['Ozone'])
21 |
22 | # train a model and score
23 | fforest = FastForestRegressor().fit(X_train, y_train)
24 | scores = fforest.predict(X_test)
25 |
26 | # evaluate the model
27 | print('R-squared fit:', r2_score(y_test, scores))
28 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/FastLinearClassifier_iris_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FastLinearClassifier
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml.datasets import get_dataset
6 | from nimbusml.linear_model import FastLinearClassifier
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use 'iris' data set to create test and train data
10 | # Sepal_Length Sepal_Width Petal_Length Petal_Width Label Species Setosa
11 | # 0 5.1 3.5 1.4 0.2 0 setosa 1.0
12 | # 1 4.9 3.0 1.4 0.2 0 setosa 1.0
13 | np.random.seed(0)
14 |
15 | df = get_dataset("iris").as_df()
16 | df.drop(['Species'], inplace=True, axis=1)
17 |
18 | X_train, X_test, y_train, y_test = \
19 | train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
20 | lr = FastLinearClassifier().fit(X_train, y_train)
21 |
22 | scores = lr.predict(X_test)
23 | scores = pd.to_numeric(scores)
24 |
25 | # evaluate the model
26 | print('Accuracy:', np.mean(y_test == [i for i in scores]))
27 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/FastLinearRegressor_airquality_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FastLinearRegressor
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.linear_model import FastLinearRegressor
6 | from sklearn.metrics import r2_score
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use the built-in data set 'airquality' to create test and train data
10 | # Unnamed: 0 Ozone Solar_R Wind Temp Month Day
11 | # 0 1 41.0 190.0 7.4 67 5 1
12 | # 1 2 36.0 118.0 8.0 72 5 2
13 | np.random.seed(0)
14 |
15 | df = get_dataset("airquality").as_df().fillna(0)
16 | df = df[df.Ozone.notnull()]
17 |
18 | X_train, X_test, y_train, y_test = train_test_split(
19 | df.loc[:, df.columns != 'Ozone'], df['Ozone'])
20 |
21 | # train a model and score
22 | flinear = FastLinearRegressor().fit(X_train, y_train)
23 | scores = flinear.predict(X_test)
24 |
25 | # evaluate the model
26 | print('R-squared fit:', r2_score(y_test, scores, ))
27 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/FastTreesRegressor_airquality_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FastTreesRegressor
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.ensemble import FastTreesRegressor
6 | from sklearn.metrics import r2_score
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use the built-in data set 'airquality' to create test and train data
10 | # Unnamed: 0 Ozone Solar_R Wind Temp Month Day
11 | # 0 1 41.0 190.0 7.4 67 5 1
12 | # 1 2 36.0 118.0 8.0 72 5 2
13 | np.random.seed(0)
14 |
15 | df = get_dataset("airquality").as_df().fillna(0)
16 | df = df[df.Ozone.notnull()]
17 |
18 | X_train, X_test, y_train, y_test = train_test_split(
19 | df.loc[:, df.columns != 'Ozone'], df['Ozone'])
20 |
21 | # train a model and score
22 | ftree = FastTreesRegressor().fit(X_train, y_train)
23 | scores = ftree.predict(X_test)
24 |
25 | # evaluate the model
26 | print('R-squared fit:', r2_score(y_test, scores))
27 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/FastTreesTweedieRegressor_airquality_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FastTreesRegressor
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.ensemble import FastTreesTweedieRegressor
6 | from sklearn.metrics import r2_score
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use the built-in data set 'airquality' to create test and train data
10 | # Unnamed: 0 Ozone Solar_R Wind Temp Month Day
11 | # 0 1 41.0 190.0 7.4 67 5 1
12 | # 1 2 36.0 118.0 8.0 72 5 2
13 | np.random.seed(0)
14 |
15 | df = get_dataset("airquality").as_df().fillna(0)
16 | df = df[df.Ozone.notnull()]
17 |
18 | X_train, X_test, y_train, y_test = train_test_split(
19 | df.loc[:, df.columns != 'Ozone'], df['Ozone'])
20 |
21 | # train a model and score
22 | ftree = FastTreesTweedieRegressor().fit(X_train, y_train)
23 | scores = ftree.predict(X_test)
24 |
25 | # evaluate the model
26 | print('R-squared fit:', r2_score(y_test, scores))
27 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/FromKey_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # FromKey
3 |
4 | import pandas
5 | from nimbusml.preprocessing import FromKey, ToKey
6 | from pandas import Categorical
7 |
8 | # Create the data
9 | categorical_df = pandas.DataFrame(data=dict(
10 | key=Categorical.from_codes([0, 1, 2, 1, 2, 0], categories=['a', 'b', 'c']),
11 | text=['b', 'c', 'a', 'b', 'a', 'c']))
12 |
13 | fromkey = FromKey(columns='key')
14 | y = fromkey.fit_transform(categorical_df)
15 | print(y)
16 |
17 | tokey = ToKey(columns='text')
18 | y = tokey.fit_transform(categorical_df)
19 | y2 = fromkey.clone().fit_transform(y)
20 | print(y2['text'] == categorical_df['text'])
21 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/GamRegressor_airquality_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # GamRegressor
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.ensemble import GamRegressor
6 | from sklearn.metrics import r2_score
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use the built-in data set 'airquality' to create test and train data
10 | # Unnamed: 0 Ozone Solar_R Wind Temp Month Day
11 | # 0 1 41.0 190.0 7.4 67 5 1
12 | # 1 2 36.0 118.0 8.0 72 5 2
13 | np.random.seed(0)
14 |
15 | df = get_dataset("airquality").as_df().fillna(0)
16 | df = df[df.Ozone.notnull()]
17 |
18 | X_train, X_test, y_train, y_test = train_test_split(
19 | df.loc[:, df.columns != 'Ozone'], df['Ozone'])
20 |
21 | # train a model and score
22 | ftree = GamRegressor().fit(X_train, y_train)
23 | scores = ftree.predict(X_test)
24 |
25 | # evaluate the model
26 | print('R-squared fit:', r2_score(y_test, scores))
27 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/KMeansPlusPlus_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # KMeansPlusPlus
3 | import pandas
4 | from nimbusml import Pipeline
5 | from nimbusml.cluster import KMeansPlusPlus
6 |
7 | # define 3 clusters with centroids (1,1,1), (11,11,11) and (-11,-11,-11)
8 | X_train = pandas.DataFrame(data=dict(
9 | x=[0, 1, 2, 10, 11, 12, -10, -11, -12],
10 | y=[0, 1, 2, 10, 11, 12, -10, -11, -12],
11 | z=[0, 1, 2, 10, 11, 12, -10, -11, -12]))
12 |
13 | # these should clearly belong to just 1 of the 3 clusters
14 | X_test = pandas.DataFrame(data=dict(
15 | x=[-1, 3, 9, 13, -13, -20],
16 | y=[-1, 3, 9, 13, -13, -20],
17 | z=[-1, 3, 9, 13, -13, -20]))
18 |
19 | y_test = pandas.DataFrame(data=dict(
20 | clusterid=[2, 2, 1, 1, 0, 0]))
21 |
22 | pipe = Pipeline([
23 | KMeansPlusPlus(n_clusters=3)
24 | ]).fit(X_train)
25 |
26 | metrics, predictions = pipe.test(X_test, y_test, output_scores=True)
27 |
28 | # print predictions
29 | print(predictions.head())
30 |
31 | # print evaluation metrics
32 | print(metrics)
33 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/LightGbmClassifier_iris_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # LightGbmClassifier
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml.datasets import get_dataset
6 | from nimbusml.ensemble import LightGbmClassifier
7 | from sklearn.model_selection import train_test_split
8 |
9 | np.random.seed(0)
10 |
11 | # use 'iris' data set to create test and train data
12 | df = get_dataset("iris").as_df()
13 | print(df.head())
14 | # Sepal_Length Sepal_Width Petal_Length Petal_Width Label Species Setosa
15 | # 0 5.1 3.5 1.4 0.2 0 setosa 1.0
16 | # 1 4.9 3.0 1.4 0.2 0 setosa 1.0
17 |
18 | df.drop(['Species'], inplace=True, axis=1)
19 |
20 | X_train, X_test, y_train, y_test = \
21 | train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
22 | lr = LightGbmClassifier().fit(X_train, y_train)
23 |
24 | scores = lr.predict(X_test)
25 | scores = pd.to_numeric(scores)
26 |
27 | # evaluate the model
28 | print('Accuracy:', np.mean(y_test == [i for i in scores]))
29 |
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/src/python/nimbusml/examples/examples_from_dataframe/LightGbmRanker_sampleinputextraction_df.py:
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1 | ###############################################################################
2 | # LightGbmRanker
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml import Pipeline, Role
6 | from nimbusml.datasets import get_dataset
7 | from nimbusml.ensemble import LightGbmRanker
8 |
9 | np.random.seed(0)
10 | file_path = get_dataset("gen_tickettrain").as_filepath()
11 |
12 | df = pd.read_csv(file_path)
13 | df['group'] = df['group'].astype(np.uint32)
14 |
15 | X = df.drop(['rank'], axis=1)
16 | y = df['rank']
17 |
18 | e = Pipeline([LightGbmRanker() << {Role.Feature: [
19 | 'Class', 'dep_day', 'duration'], Role.Label: 'rank',
20 | Role.GroupId: 'group'}])
21 |
22 | e.fit(df)
23 |
24 | # test
25 | metrics, scores = e.test(X, y, evaltype='ranking',
26 | group_id='group', output_scores=True)
27 | print(metrics)
28 |
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/src/python/nimbusml/examples/examples_from_dataframe/LightGbmRegressor_airquality_df.py:
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1 | ###############################################################################
2 | # LightGbmRegressor
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.ensemble import LightGbmRegressor
6 | from sklearn.metrics import r2_score
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use the built-in data set 'airquality' to create test and train data
10 | # Unnamed: 0 Ozone Solar_R Wind Temp Month Day
11 | # 0 1 41.0 190.0 7.4 67 5 1
12 | # 1 2 36.0 118.0 8.0 72 5 2
13 | np.random.seed(0)
14 |
15 | df = get_dataset("airquality").as_df().fillna(0)
16 | df = df[df.Ozone.notnull()]
17 |
18 | X_train, X_test, y_train, y_test = train_test_split(
19 | df.loc[:, df.columns != 'Ozone'], df['Ozone'])
20 |
21 | # train a model and score
22 | ftree = LightGbmRegressor().fit(X_train, y_train)
23 | scores = ftree.predict(X_test)
24 |
25 | # evaluate the model
26 | print('R-squared fit:', r2_score(y_test, scores))
27 |
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/src/python/nimbusml/examples/examples_from_dataframe/LightLda_df.py:
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1 | ###############################################################################
2 | # LightLda: cluster topics
3 | import pandas
4 | from nimbusml import Pipeline
5 | from nimbusml.feature_extraction.text import LightLda, NGramFeaturizer
6 | from nimbusml.feature_extraction.text.extractor import Ngram
7 |
8 | # create the data
9 | topics = pandas.DataFrame(data=dict(review=[
10 | "animals birds cats dogs fish horse",
11 | "horse birds house fish duck cats",
12 | "car truck driver bus pickup",
13 | "car truck driver bus pickup horse ",
14 | "car truck",
15 | "bus pickup",
16 | "space galaxy universe radiation",
17 | "radiation galaxy universe duck"]))
18 |
19 | # there are three main topics in our data. set num_topic=3
20 | # and see if LightLDA vectors for topics look similar
21 | pipeline = Pipeline([NGramFeaturizer(word_feature_extractor=Ngram(
22 | ), vector_normalizer='None') << 'review', LightLda(num_topic=3)])
23 | y = pipeline.fit_transform(topics)
24 |
25 | # view the LDA topic vectors
26 | print(y)
27 |
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/src/python/nimbusml/examples/examples_from_dataframe/LogMeanVarianceScaler_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # LogMeanVarianceScaler
3 | import pandas as pd
4 | from nimbusml.preprocessing.normalization import LogMeanVarianceScaler
5 |
6 | in_df = pd.DataFrame(
7 | data=dict(
8 | Sepal_Length=[
9 | 2.5, 1, 2.1, 1.0], Sepal_Width=[
10 | .75, .9, .8, .76], Petal_Length=[
11 | 0, 2.5, 2.6, 2.4], Species=[
12 | "setosa", "viginica", "setosa", 'versicolor']))
13 |
14 | # generate two new Columns - Petal_Normed and Sepal_Normed
15 | normed = LogMeanVarianceScaler() << {
16 | 'Petal_Normed': 'Petal_Length',
17 | 'Sepal_Normed': 'Sepal_Width'}
18 | out_df = normed.fit_transform(in_df)
19 |
20 | print('LogMeanVarianceScaler\n', (out_df))
21 |
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/src/python/nimbusml/examples/examples_from_dataframe/LogisticRegressionClassifier_iris_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # LogisticRegressionClassifier
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml.datasets import get_dataset
6 | from nimbusml.linear_model import LogisticRegressionClassifier
7 | from sklearn.model_selection import train_test_split
8 |
9 | # use 'iris' data set to create test and train data
10 | # Sepal_Length Sepal_Width Petal_Length Petal_Width Label Species Setosa
11 | # 0 5.1 3.5 1.4 0.2 0 setosa 1.0
12 | # 1 4.9 3.0 1.4 0.2 0 setosa 1.0
13 | np.random.seed(0)
14 |
15 | df = get_dataset("iris").as_df()
16 | df.drop(['Species'], inplace=True, axis=1)
17 |
18 | X_train, X_test, y_train, y_test = \
19 | train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
20 | lr = LogisticRegressionClassifier().fit(X_train, y_train)
21 |
22 | scores = lr.predict(X_test)
23 | scores = pd.to_numeric(scores)
24 |
25 | # evaluate the model
26 | print('Accuracy:', np.mean(y_test == [i for i in scores]))
27 |
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/src/python/nimbusml/examples/examples_from_dataframe/MeanVarianceScaler_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # MeanVarianceScaler
3 | import pandas as pd
4 | from nimbusml.preprocessing.normalization import MeanVarianceScaler
5 |
6 | in_df = pd.DataFrame(
7 | data=dict(
8 | Sepal_Length=[
9 | 2.5, 1, 2.1, 1.0], Sepal_Width=[
10 | .75, .9, .8, .76], Petal_Length=[
11 | 0, 2.5, 2.6, 2.4], Species=[
12 | "setosa", "viginica", "setosa", 'versicolor']))
13 |
14 | # generate two new Columns - Petal_Normed and Sepal_Normed
15 | normed = MeanVarianceScaler() << {
16 | 'Petal_Normed': 'Petal_Length',
17 | 'Sepal_Normed': 'Sepal_Width'}
18 | out_df = normed.fit_transform(in_df)
19 |
20 | print('MeanVarianceScaler\n', (out_df))
21 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/MinMaxScaler_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # MinMaxScaler
3 | import pandas as pd
4 | from nimbusml.preprocessing.normalization import MinMaxScaler
5 |
6 | in_df = pd.DataFrame(
7 | data=dict(
8 | Sepal_Length=[
9 | 2.5, 1, 2.1, 1.0], Sepal_Width=[
10 | .75, .9, .8, .76], Petal_Length=[
11 | 0, 2.5, 2.6, 2.4], Species=[
12 | "setosa", "viginica", "setosa", 'versicolor']))
13 |
14 | # generate two new Columns - Petal_Normed and Sepal_Normed
15 | normed = MinMaxScaler() << {
16 | 'Petal_Normed': 'Petal_Length',
17 | 'Sepal_Normed': 'Sepal_Width'}
18 | out_df = normed.fit_transform(in_df)
19 |
20 | print('MinMaxScaler\n', (out_df))
21 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/RangeFilter_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # RangeFilter
3 | import numpy as np
4 | from nimbusml.datasets import get_dataset
5 | from nimbusml.preprocessing.filter import RangeFilter
6 | from sklearn.model_selection import train_test_split
7 |
8 | # use 'iris' data set to create test and train data
9 | # Sepal_Length Sepal_Width Petal_Length Petal_Width Label Species Setosa
10 | # 0 5.1 3.5 1.4 0.2 0 setosa 1.0
11 | # 1 4.9 3.0 1.4 0.2 0 setosa 1.0
12 | np.random.seed(0)
13 | df = get_dataset("iris").as_df()
14 |
15 | X_train, X_test, y_train, y_test = \
16 | train_test_split(df.loc[:, df.columns != 'Label'], df['Label'])
17 |
18 | # select rows where 5.0 <= Sepal_Length <= 5.1
19 | filter = RangeFilter(min=5.0, max=5.1) << 'Sepal_Length'
20 | print(filter.fit_transform(X_train))
21 |
22 | # select rows where Sepal_Length <= 4.5 or Sepal_Length >= 7.5
23 | filter = RangeFilter(min=4.5, max=7.5, complement=True) << 'Sepal_Length'
24 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/RobustScaler_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # RobustScaler
3 | import pandas as pd
4 | from nimbusml import Pipeline
5 | from nimbusml.preprocessing.normalization import RobustScaler
6 |
7 |
8 | df = pd.DataFrame(data=dict(c0=[1, 3, 5, 7, 9]))
9 |
10 | xf = RobustScaler(columns='c0', center=True, scale=True)
11 | pipeline = Pipeline([xf])
12 | result = pipeline.fit_transform(df)
13 |
14 | print(result)
15 | # c0
16 | # 0 -1.0
17 | # 1 -0.5
18 | # 2 0.0
19 | # 3 0.5
20 | # 4 1.0
21 |
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/src/python/nimbusml/examples/examples_from_dataframe/SkipFilter_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # SkipFilter: skip first N rows in a dataset
3 | # TakeFilter: take first N rows in a dataset
4 | import pandas
5 | from nimbusml import Pipeline
6 | from nimbusml.preprocessing.filter import SkipFilter, TakeFilter
7 |
8 | df = pandas.DataFrame(data=dict(
9 | review=['row' + str(i) for i in range(10)]))
10 |
11 | # skip the first 5 rows
12 | print(SkipFilter(count=5).fit_transform(df))
13 |
14 | # take the first 5 rows
15 | print(TakeFilter(count=5).fit_transform(df))
16 |
17 | # skip 3 then take 5 rows
18 | pipe = Pipeline([SkipFilter(count=3), TakeFilter(count=5)])
19 | print(pipe.fit_transform(df))
20 |
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/src/python/nimbusml/examples/examples_from_dataframe/TimeSeriesImputer_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # DateTimeSplitter
3 | import pandas
4 | from nimbusml.timeseries import TimeSeriesImputer
5 |
6 | df = pandas.DataFrame(data=dict(
7 | ts=[1, 2, 3, 5],
8 | grain=[1970, 1970, 1970, 1970],
9 | c3=[10, 13, 15, 20],
10 | c4=[19, 12, 16, 19]
11 | ))
12 |
13 | print(df)
14 |
15 | tsi = TimeSeriesImputer(time_series_column='ts',
16 | grain_columns=['grain'],
17 | filter_columns=['c3', 'c4'],
18 | impute_mode='ForwardFill',
19 | filter_mode='Include')
20 | result = tsi.fit_transform(df)
21 |
22 | print(result)
23 | # ts grain c3 c4 IsRowImputed
24 | # 0 0 0 0 0 False
25 | # 1 1 1970 10 19 False
26 | # 2 2 1970 13 12 False
27 | # 3 3 1970 15 16 False
28 | # 4 4 1970 15 16 True <== New row added
29 | # 5 5 1970 20 19 False
30 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/ToKeyImputer_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ToKeyImputer
3 |
4 | import pandas
5 | from nimbusml.preprocessing import ToKeyImputer
6 |
7 | # Create the data
8 | text_df = pandas.DataFrame(
9 | data=dict(
10 | text=[
11 | "cat",
12 | "dog",
13 | "fish",
14 | "orange",
15 | "cat orange",
16 | "dog",
17 | "fish",
18 | None,
19 | "spider"]))
20 |
21 | tokey = ToKeyImputer() << 'text'
22 | y = tokey.fit_transform(text_df)
23 | print(y)
24 |
25 | # text
26 | # 0 cat
27 | # 1 dog
28 | # 2 fish
29 | # 3 orange
30 | # 4 cat orange
31 | # 5 dog
32 | # 6 fish
33 | # 7 dog <== Missing value has been replaced
34 | # 8 spider
35 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/ToKey_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ToKey
3 |
4 | import pandas
5 | from nimbusml.preprocessing import ToKey
6 |
7 | # Create the data
8 | text_df = pandas.DataFrame(
9 | data=dict(
10 | text=[
11 | "cat",
12 | "dog",
13 | "fish",
14 | "orange",
15 | "cat orange",
16 | "dog",
17 | "fish",
18 | "spider"]))
19 |
20 | tokey = ToKey() << 'text'
21 | y = tokey.fit_transform(text_df)
22 | print(y)
23 |
--------------------------------------------------------------------------------
/src/python/nimbusml/examples/examples_from_dataframe/ToString_df.py:
--------------------------------------------------------------------------------
1 | ###############################################################################
2 | # ToString
3 |
4 | import pandas
5 | from nimbusml.preprocessing import ToString, ToKey
6 | from pandas import Categorical
7 |
8 | # Create the data
9 | categorical_df = pandas.DataFrame(data=dict(
10 | key=Categorical.from_codes([0, 1, 2, 1, 2, 0], categories=['a', 'b', 'c']),
11 | text=['b', 'c', 'a', 'b', 'a', 'c']))
12 |
13 | print(categorical_df.dtypes)
14 | # key category
15 | # text object
16 | # dtype: object
17 |
18 | tostring = ToString(columns='key')
19 | y = tostring.fit_transform(categorical_df)
20 | print(y)
21 | # key text
22 | # 0 1 b
23 | # 1 2 c
24 | # 2 3 a
25 | # 3 2 b
26 | # 4 3 a
27 | # 5 1 c
28 |
29 | print(y.dtypes)
30 | # key object <== converted to string
31 | # text object
32 | # dtype: object
33 |
34 | tokey = ToKey(columns='text')
35 | y = tokey.fit_transform(categorical_df)
36 | y2 = tostring.clone().fit_transform(y)
37 | print(y2['text'] == categorical_df['text'])
38 | # 0 True
39 | # 1 True
40 | # 2 True
41 | # 3 True
42 | # 4 True
43 | # 5 True
44 |
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/src/python/nimbusml/examples/examples_from_dataframe/__init__.py:
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1 |
2 |
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/src/python/nimbusml/examples/examples_from_dataframe/tmpfile_with_nans.csv:
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1 | Petal_Length,Petal_Width,Sepal_Length,Sepal_Width,Species
2 | ,0.8,2.5,0.75,setosa
3 | 2.5,0.7,,0.9,viginica
4 | 2.6,0.9,2.1,0.8,
5 | 2.4,0.7,1.0,0.76,versicolor
6 |
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/src/python/nimbusml/examples/frozen_saved_model.pb:
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/src/python/nimbusml/examples/pipeline.py:
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1 | ###############################################################################
2 | # Pipeline
3 | import numpy as np
4 | import pandas as pd
5 | from nimbusml import Pipeline, FileDataStream
6 | from nimbusml.linear_model import FastLinearRegressor
7 | from nimbusml.preprocessing.normalization import MeanVarianceScaler
8 |
9 | X = np.array([[1, 2.0], [2, 4], [3, 0.7]])
10 | Y = np.array([2, 3, 1.5])
11 |
12 | df = pd.DataFrame(dict(y=Y, x1=X[:, 0], x2=X[:, 1]))
13 |
14 | pipe = Pipeline([
15 | MeanVarianceScaler(),
16 | FastLinearRegressor()
17 | ])
18 |
19 | # fit with pandas dataframe
20 | pipe.fit(X, Y)
21 |
22 | # Fit with FileDataStream
23 | df.to_csv('data.csv', index=False)
24 | ds = FileDataStream.read_csv('data.csv', sep=',')
25 |
26 | pipe = Pipeline([
27 | MeanVarianceScaler(),
28 | FastLinearRegressor()
29 | ])
30 | pipe.fit(ds, 'y')
31 | print(pipe.summary())
32 | # Bias Weights.x1 Weights.x2
33 | # 0 1.032946 0.111758 1.210791
34 |
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/src/python/nimbusml/feature_extraction/__init__.py:
--------------------------------------------------------------------------------
1 | from .treefeaturizer import TreeFeaturizer
2 |
3 | __all__ = [
4 | 'TreeFeaturizer'
5 | ]
6 |
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/src/python/nimbusml/feature_extraction/categorical/__init__.py:
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1 | from .onehothashvectorizer import OneHotHashVectorizer
2 | from .onehotvectorizer import OneHotVectorizer
3 |
4 | __all__ = [
5 | 'OneHotHashVectorizer',
6 | 'OneHotVectorizer'
7 | ]
8 |
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/src/python/nimbusml/feature_extraction/image/__init__.py:
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1 | from .loader import Loader
2 | from .pixelextractor import PixelExtractor
3 | from .resizer import Resizer
4 |
5 | __all__ = [
6 | 'Loader',
7 | 'PixelExtractor',
8 | 'Resizer'
9 | ]
10 |
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/src/python/nimbusml/feature_extraction/text/__init__.py:
--------------------------------------------------------------------------------
1 | from .lightlda import LightLda
2 | from .ngramextractor import NGramExtractor
3 | from .ngramfeaturizer import NGramFeaturizer
4 | from .sentiment import Sentiment
5 | from .wordembedding import WordEmbedding
6 |
7 | __all__ = [
8 | 'LightLda',
9 | 'NGramExtractor',
10 | 'NGramFeaturizer',
11 | 'Sentiment',
12 | 'WordEmbedding'
13 | ]
14 |
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/src/python/nimbusml/feature_extraction/text/extractor/__init__.py:
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1 | from .ngram import Ngram
2 | from .ngramhash import NgramHash
3 |
4 | __all__ = [
5 | 'Ngram',
6 | 'NgramHash'
7 | ]
8 |
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/src/python/nimbusml/feature_extraction/text/stopwords/__init__.py:
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1 | from .customstopwordsremover import CustomStopWordsRemover
2 | from .predefinedstopwordsremover import PredefinedStopWordsRemover
3 |
4 | __all__ = [
5 | 'CustomStopWordsRemover',
6 | 'PredefinedStopWordsRemover'
7 | ]
8 |
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/src/python/nimbusml/feature_selection/__init__.py:
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1 | from .countselector import CountSelector
2 | from .mutualinformationselector import MutualInformationSelector
3 |
4 | __all__ = [
5 | 'CountSelector',
6 | 'MutualInformationSelector'
7 | ]
8 |
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/src/python/nimbusml/internal/entrypoints/_calibratortrainer_naivecalibrator.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | NaiveCalibrator
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def naive_calibrator(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'NaiveCalibrator'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='CalibratorTrainer')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_calibratortrainer_pavcalibrator.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | PavCalibrator
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def pav_calibrator(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'PavCalibrator'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='CalibratorTrainer')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_calibratortrainer_plattcalibrator.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | PlattCalibrator
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def platt_calibrator(
11 | **params):
12 | """
13 | **Description**
14 | Platt calibration.
15 |
16 | """
17 |
18 | entrypoint_name = 'PlattCalibrator'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='CalibratorTrainer')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_classificationlossfunction_exploss.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | ExpLoss
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def exp_loss(
13 | beta=1.0,
14 | **params):
15 | """
16 | **Description**
17 | Exponential loss.
18 |
19 | :param beta: Beta (dilation) (settings).
20 | """
21 |
22 | entrypoint_name = 'ExpLoss'
23 | settings = {}
24 |
25 | if beta is not None:
26 | settings['Beta'] = try_set(
27 | obj=beta,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real)
30 |
31 | component = Component(
32 | name=entrypoint_name,
33 | settings=settings,
34 | kind='ClassificationLossFunction')
35 | return component
36 |
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/src/python/nimbusml/internal/entrypoints/_classificationlossfunction_hingeloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | HingeLoss
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def hinge_loss(
13 | margin=1.0,
14 | **params):
15 | """
16 | **Description**
17 | Hinge loss.
18 |
19 | :param margin: Margin value (settings).
20 | """
21 |
22 | entrypoint_name = 'HingeLoss'
23 | settings = {}
24 |
25 | if margin is not None:
26 | settings['Margin'] = try_set(
27 | obj=margin,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real)
30 |
31 | component = Component(
32 | name=entrypoint_name,
33 | settings=settings,
34 | kind='ClassificationLossFunction')
35 | return component
36 |
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/src/python/nimbusml/internal/entrypoints/_classificationlossfunction_logloss.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | LogLoss
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def log_loss(
11 | **params):
12 | """
13 | **Description**
14 | Log loss.
15 |
16 | """
17 |
18 | entrypoint_name = 'LogLoss'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='ClassificationLossFunction')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_classificationlossfunction_smoothedhingeloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | SmoothedHingeLoss
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def smoothed_hinge_loss(
13 | smoothing_const=1.0,
14 | **params):
15 | """
16 | **Description**
17 | Smoothed Hinge loss.
18 |
19 | :param smoothing_const: Smoothing constant (settings).
20 | """
21 |
22 | entrypoint_name = 'SmoothedHingeLoss'
23 | settings = {}
24 |
25 | if smoothing_const is not None:
26 | settings['SmoothingConst'] = try_set(
27 | obj=smoothing_const,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real)
30 |
31 | component = Component(
32 | name=entrypoint_name,
33 | settings=settings,
34 | kind='ClassificationLossFunction')
35 | return component
36 |
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/src/python/nimbusml/internal/entrypoints/_earlystoppingcriterion_gl.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | GL
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def gl(
13 | threshold=0.01,
14 | **params):
15 | """
16 | **Description**
17 | Stop in case of loss of generality.
18 |
19 | :param threshold: Threshold in range [0,1]. (settings).
20 | """
21 |
22 | entrypoint_name = 'GL'
23 | settings = {}
24 |
25 | if threshold is not None:
26 | settings['Threshold'] = try_set(
27 | obj=threshold,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real,
30 | valid_range={
31 | 'Max': 1.0,
32 | 'Min': 0.0})
33 |
34 | component = Component(
35 | name=entrypoint_name,
36 | settings=settings,
37 | kind='EarlyStoppingCriterion')
38 | return component
39 |
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/src/python/nimbusml/internal/entrypoints/_earlystoppingcriterion_tr.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | TR
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def tr(
13 | threshold=0.01,
14 | **params):
15 | """
16 | **Description**
17 | Stop if validation score exceeds threshold value.
18 |
19 | :param threshold: Tolerance threshold. (Non negative value)
20 | (settings).
21 | """
22 |
23 | entrypoint_name = 'TR'
24 | settings = {}
25 |
26 | if threshold is not None:
27 | settings['Threshold'] = try_set(
28 | obj=threshold,
29 | none_acceptable=True,
30 | is_of_type=numbers.Real, valid_range={'Min': 0.0})
31 |
32 | component = Component(
33 | name=entrypoint_name,
34 | settings=settings,
35 | kind='EarlyStoppingCriterion')
36 | return component
37 |
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/src/python/nimbusml/internal/entrypoints/_earlystoppingcriterion_up.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | UP
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def up(
13 | window_size=5,
14 | **params):
15 | """
16 | **Description**
17 | Stops in case of consecutive loss in generality.
18 |
19 | :param window_size: The window size. (settings).
20 | """
21 |
22 | entrypoint_name = 'UP'
23 | settings = {}
24 |
25 | if window_size is not None:
26 | settings['WindowSize'] = try_set(
27 | obj=window_size,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real, valid_range={'Inf': 0})
30 |
31 | component = Component(
32 | name=entrypoint_name,
33 | settings=settings,
34 | kind='EarlyStoppingCriterion')
35 | return component
36 |
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/src/python/nimbusml/internal/entrypoints/_ensemblebinarydiversitymeasure_disagreementdiversitymeasure.py:
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1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | DisagreementDiversityMeasure
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def disagreement_diversity_measure(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'DisagreementDiversityMeasure'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleBinaryDiversityMeasure')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_ensemblebinaryoutputcombiner_average.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Average
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def average(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'Average'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleBinaryOutputCombiner')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblebinaryoutputcombiner_median.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Median
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def median(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'Median'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleBinaryOutputCombiner')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblebinaryoutputcombiner_stacking.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Stacking
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def stacking(
13 | validation_dataset_proportion=0.3,
14 | **params):
15 | """
16 | **Description**
17 | None
18 |
19 | :param validation_dataset_proportion: The proportion of instances
20 | to be selected to test the individual base learner. If it is
21 | 0, it uses training set (settings).
22 | """
23 |
24 | entrypoint_name = 'Stacking'
25 | settings = {}
26 |
27 | if validation_dataset_proportion is not None:
28 | settings['ValidationDatasetProportion'] = try_set(
29 | obj=validation_dataset_proportion,
30 | none_acceptable=True,
31 | is_of_type=numbers.Real)
32 |
33 | component = Component(
34 | name=entrypoint_name,
35 | settings=settings,
36 | kind='EnsembleBinaryOutputCombiner')
37 | return component
38 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblebinaryoutputcombiner_voting.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Voting
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def voting(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'Voting'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleBinaryOutputCombiner')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblebinarysubmodelselector_allselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | AllSelector
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def all_selector(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'AllSelector'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleBinarySubModelSelector')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblefeatureselector_allfeatureselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | AllFeatureSelector
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def all_feature_selector(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'AllFeatureSelector'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleFeatureSelector')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblefeatureselector_randomfeatureselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | RandomFeatureSelector
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def random_feature_selector(
13 | features_selection_proportion=0.8,
14 | **params):
15 | """
16 | **Description**
17 | None
18 |
19 | :param features_selection_proportion: The proportion of features
20 | to be selected. The range is 0.0-1.0 (settings).
21 | """
22 |
23 | entrypoint_name = 'RandomFeatureSelector'
24 | settings = {}
25 |
26 | if features_selection_proportion is not None:
27 | settings['FeaturesSelectionProportion'] = try_set(
28 | obj=features_selection_proportion,
29 | none_acceptable=True,
30 | is_of_type=numbers.Real)
31 |
32 | component = Component(
33 | name=entrypoint_name,
34 | settings=settings,
35 | kind='EnsembleFeatureSelector')
36 | return component
37 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblemulticlassdiversitymeasure_multidisagreementdiversitymeasure.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | MultiDisagreementDiversityMeasure
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def multi_disagreement_diversity_measure(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'MultiDisagreementDiversityMeasure'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleMulticlassDiversityMeasure')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblemulticlassoutputcombiner_multiaverage.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | MultiAverage
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 | from ..utils.utils import try_set
9 |
10 |
11 | def multi_average(
12 | normalize=True,
13 | **params):
14 | """
15 | **Description**
16 | None
17 |
18 | :param normalize: Whether to normalize the output of base models
19 | before combining them (settings).
20 | """
21 |
22 | entrypoint_name = 'MultiAverage'
23 | settings = {}
24 |
25 | if normalize is not None:
26 | settings['Normalize'] = try_set(
27 | obj=normalize, none_acceptable=True, is_of_type=bool)
28 |
29 | component = Component(
30 | name=entrypoint_name,
31 | settings=settings,
32 | kind='EnsembleMulticlassOutputCombiner')
33 | return component
34 |
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/src/python/nimbusml/internal/entrypoints/_ensemblemulticlassoutputcombiner_multimedian.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | MultiMedian
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 | from ..utils.utils import try_set
9 |
10 |
11 | def multi_median(
12 | normalize=True,
13 | **params):
14 | """
15 | **Description**
16 | None
17 |
18 | :param normalize: Whether to normalize the output of base models
19 | before combining them (settings).
20 | """
21 |
22 | entrypoint_name = 'MultiMedian'
23 | settings = {}
24 |
25 | if normalize is not None:
26 | settings['Normalize'] = try_set(
27 | obj=normalize, none_acceptable=True, is_of_type=bool)
28 |
29 | component = Component(
30 | name=entrypoint_name,
31 | settings=settings,
32 | kind='EnsembleMulticlassOutputCombiner')
33 | return component
34 |
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/src/python/nimbusml/internal/entrypoints/_ensemblemulticlassoutputcombiner_multistacking.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | MultiStacking
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def multi_stacking(
13 | validation_dataset_proportion=0.3,
14 | **params):
15 | """
16 | **Description**
17 | None
18 |
19 | :param validation_dataset_proportion: The proportion of instances
20 | to be selected to test the individual base learner. If it is
21 | 0, it uses training set (settings).
22 | """
23 |
24 | entrypoint_name = 'MultiStacking'
25 | settings = {}
26 |
27 | if validation_dataset_proportion is not None:
28 | settings['ValidationDatasetProportion'] = try_set(
29 | obj=validation_dataset_proportion,
30 | none_acceptable=True,
31 | is_of_type=numbers.Real)
32 |
33 | component = Component(
34 | name=entrypoint_name,
35 | settings=settings,
36 | kind='EnsembleMulticlassOutputCombiner')
37 | return component
38 |
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/src/python/nimbusml/internal/entrypoints/_ensemblemulticlassoutputcombiner_multivoting.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | MultiVoting
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def multi_voting(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'MultiVoting'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleMulticlassOutputCombiner')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_ensemblemulticlasssubmodelselector_allselectormulticlass.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | AllSelectorMultiClass
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def all_selector_multi_class(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'AllSelectorMultiClass'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleMulticlassSubModelSelector')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_ensembleregressiondiversitymeasure_regressiondisagreementdiversitymeasure.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | RegressionDisagreementDiversityMeasure
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def regression_disagreement_diversity_measure(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'RegressionDisagreementDiversityMeasure'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleRegressionDiversityMeasure')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_ensembleregressionoutputcombiner_average.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Average
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def average(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'Average'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleRegressionOutputCombiner')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_ensembleregressionoutputcombiner_median.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Median
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def median(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'Median'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleRegressionOutputCombiner')
25 | return component
26 |
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/src/python/nimbusml/internal/entrypoints/_ensembleregressionoutputcombiner_regressionstacking.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | RegressionStacking
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def regression_stacking(
13 | validation_dataset_proportion=0.3,
14 | **params):
15 | """
16 | **Description**
17 | None
18 |
19 | :param validation_dataset_proportion: The proportion of instances
20 | to be selected to test the individual base learner. If it is
21 | 0, it uses training set (settings).
22 | """
23 |
24 | entrypoint_name = 'RegressionStacking'
25 | settings = {}
26 |
27 | if validation_dataset_proportion is not None:
28 | settings['ValidationDatasetProportion'] = try_set(
29 | obj=validation_dataset_proportion,
30 | none_acceptable=True,
31 | is_of_type=numbers.Real)
32 |
33 | component = Component(
34 | name=entrypoint_name,
35 | settings=settings,
36 | kind='EnsembleRegressionOutputCombiner')
37 | return component
38 |
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/src/python/nimbusml/internal/entrypoints/_ensembleregressionsubmodelselector_allselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | AllSelector
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def all_selector(
11 | **params):
12 | """
13 | **Description**
14 | None
15 |
16 | """
17 |
18 | entrypoint_name = 'AllSelector'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='EnsembleRegressionSubModelSelector')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblesubsetselector_allinstanceselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | AllInstanceSelector
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 | from ..utils.utils import try_set
9 |
10 |
11 | def all_instance_selector(
12 | feature_selector=None,
13 | **params):
14 | """
15 | **Description**
16 | None
17 |
18 | :param feature_selector: The Feature selector (settings).
19 | """
20 |
21 | entrypoint_name = 'AllInstanceSelector'
22 | settings = {}
23 |
24 | if feature_selector is not None:
25 | settings['FeatureSelector'] = try_set(
26 | obj=feature_selector, none_acceptable=True, is_of_type=dict)
27 |
28 | component = Component(
29 | name=entrypoint_name,
30 | settings=settings,
31 | kind='EnsembleSubsetSelector')
32 | return component
33 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblesubsetselector_bootstrapselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | BootstrapSelector
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 | from ..utils.utils import try_set
9 |
10 |
11 | def bootstrap_selector(
12 | feature_selector=None,
13 | **params):
14 | """
15 | **Description**
16 | None
17 |
18 | :param feature_selector: The Feature selector (settings).
19 | """
20 |
21 | entrypoint_name = 'BootstrapSelector'
22 | settings = {}
23 |
24 | if feature_selector is not None:
25 | settings['FeatureSelector'] = try_set(
26 | obj=feature_selector, none_acceptable=True, is_of_type=dict)
27 |
28 | component = Component(
29 | name=entrypoint_name,
30 | settings=settings,
31 | kind='EnsembleSubsetSelector')
32 | return component
33 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_ensemblesubsetselector_randompartitionselector.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | RandomPartitionSelector
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 | from ..utils.utils import try_set
9 |
10 |
11 | def random_partition_selector(
12 | feature_selector=None,
13 | **params):
14 | """
15 | **Description**
16 | None
17 |
18 | :param feature_selector: The Feature selector (settings).
19 | """
20 |
21 | entrypoint_name = 'RandomPartitionSelector'
22 | settings = {}
23 |
24 | if feature_selector is not None:
25 | settings['FeatureSelector'] = try_set(
26 | obj=feature_selector, none_acceptable=True, is_of_type=dict)
27 |
28 | component = Component(
29 | name=entrypoint_name,
30 | settings=settings,
31 | kind='EnsembleSubsetSelector')
32 | return component
33 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_parallellightgbm_single.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Single
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def single(
11 | **params):
12 | """
13 | **Description**
14 | Single node machine learning process.
15 |
16 | """
17 |
18 | entrypoint_name = 'Single'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='ParallelLightGBM')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_paralleltraining_single.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Single
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def single(
11 | **params):
12 | """
13 | **Description**
14 | Single node machine learning process.
15 |
16 | """
17 |
18 | entrypoint_name = 'Single'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='ParallelTraining')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_regressionlossfunction_poissonloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | PoissonLoss
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def poisson_loss(
11 | **params):
12 | """
13 | **Description**
14 | Poisson loss.
15 |
16 | """
17 |
18 | entrypoint_name = 'PoissonLoss'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='RegressionLossFunction')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_regressionlossfunction_squaredloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | SquaredLoss
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def squared_loss(
11 | **params):
12 | """
13 | **Description**
14 | Squared loss.
15 |
16 | """
17 |
18 | entrypoint_name = 'SquaredLoss'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='RegressionLossFunction')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_regressionlossfunction_tweedieloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | TweedieLoss
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def tweedie_loss(
13 | index=1.5,
14 | **params):
15 | """
16 | **Description**
17 | Tweedie loss.
18 |
19 | :param index: Index parameter for the Tweedie distribution, in
20 | the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and
21 | intermediate values are compound Poisson loss. (settings).
22 | """
23 |
24 | entrypoint_name = 'TweedieLoss'
25 | settings = {}
26 |
27 | if index is not None:
28 | settings['Index'] = try_set(
29 | obj=index,
30 | none_acceptable=True,
31 | is_of_type=numbers.Real)
32 |
33 | component = Component(
34 | name=entrypoint_name,
35 | settings=settings,
36 | kind='RegressionLossFunction')
37 | return component
38 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_sdcaclassificationlossfunction_hingeloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | HingeLoss
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def hinge_loss(
13 | margin=1.0,
14 | **params):
15 | """
16 | **Description**
17 | Hinge loss.
18 |
19 | :param margin: Margin value (settings).
20 | """
21 |
22 | entrypoint_name = 'HingeLoss'
23 | settings = {}
24 |
25 | if margin is not None:
26 | settings['Margin'] = try_set(
27 | obj=margin,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real)
30 |
31 | component = Component(
32 | name=entrypoint_name,
33 | settings=settings,
34 | kind='SDCAClassificationLossFunction')
35 | return component
36 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_sdcaclassificationlossfunction_logloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | LogLoss
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def log_loss(
11 | **params):
12 | """
13 | **Description**
14 | Log loss.
15 |
16 | """
17 |
18 | entrypoint_name = 'LogLoss'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='SDCAClassificationLossFunction')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_sdcaclassificationlossfunction_smoothedhingeloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | SmoothedHingeLoss
4 | """
5 |
6 | import numbers
7 |
8 | from ..utils.entrypoints import Component
9 | from ..utils.utils import try_set
10 |
11 |
12 | def smoothed_hinge_loss(
13 | smoothing_const=1.0,
14 | **params):
15 | """
16 | **Description**
17 | Smoothed Hinge loss.
18 |
19 | :param smoothing_const: Smoothing constant (settings).
20 | """
21 |
22 | entrypoint_name = 'SmoothedHingeLoss'
23 | settings = {}
24 |
25 | if smoothing_const is not None:
26 | settings['SmoothingConst'] = try_set(
27 | obj=smoothing_const,
28 | none_acceptable=True,
29 | is_of_type=numbers.Real)
30 |
31 | component = Component(
32 | name=entrypoint_name,
33 | settings=settings,
34 | kind='SDCAClassificationLossFunction')
35 | return component
36 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_sdcaregressionlossfunction_squaredloss.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | SquaredLoss
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def squared_loss(
11 | **params):
12 | """
13 | **Description**
14 | Squared loss.
15 |
16 | """
17 |
18 | entrypoint_name = 'SquaredLoss'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='SDCARegressionLossFunction')
25 | return component
26 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_stopwordsremover_custom.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Custom
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 | from ..utils.utils import try_set
9 |
10 |
11 | def custom(
12 | stopword=None,
13 | **params):
14 | """
15 | **Description**
16 | Remover with list of stopwords specified by the user.
17 |
18 | :param stopword: List of stopwords (settings).
19 | """
20 |
21 | entrypoint_name = 'Custom'
22 | settings = {}
23 |
24 | if stopword is not None:
25 | settings['Stopword'] = try_set(
26 | obj=stopword, none_acceptable=True, is_of_type=list)
27 |
28 | component = Component(
29 | name=entrypoint_name,
30 | settings=settings,
31 | kind='StopWordsRemover')
32 | return component
33 |
--------------------------------------------------------------------------------
/src/python/nimbusml/internal/entrypoints/_stopwordsremover_predefined.py:
--------------------------------------------------------------------------------
1 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
2 | """
3 | Predefined
4 | """
5 |
6 |
7 | from ..utils.entrypoints import Component
8 |
9 |
10 | def predefined(
11 | **params):
12 | """
13 | **Description**
14 | Remover with predefined list of stop words.
15 |
16 | """
17 |
18 | entrypoint_name = 'Predefined'
19 | settings = {}
20 |
21 | component = Component(
22 | name=entrypoint_name,
23 | settings=settings,
24 | kind='StopWordsRemover')
25 | return component
26 |
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/src/python/nimbusml/internal/utils/__init__.py:
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/src/python/nimbusml/internal/utils/stubs.py:
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1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 | from functools import wraps
6 |
7 |
8 | def telemetry_pause():
9 | pass
10 |
11 |
12 | def telemetry_unpause():
13 | pass
14 |
15 |
16 | def telemetry_capture_call(name=None):
17 | pass
18 |
19 |
20 | def telemetry_transform(func):
21 | """
22 | Decorator for sending telemetry for nimbusml data transforms.
23 | """
24 |
25 | @wraps(func)
26 | def wrapper(*args, **kargs):
27 | """
28 | wrapped function call
29 | """
30 | params = func(*args, **kargs)
31 |
32 | # telemetry_info = func.__name__
33 | # nimbusml_bridge(telemetry_info=telemetry_info,
34 | # capture_telemetry_only=True)
35 |
36 | return params
37 |
38 | return wrapper
39 |
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/src/python/nimbusml/model_selection/__init__.py:
--------------------------------------------------------------------------------
1 | from .cv import CV
2 |
3 | __all__ = [
4 | 'CV'
5 | ]
6 |
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/src/python/nimbusml/multiclass/__init__.py:
--------------------------------------------------------------------------------
1 | from .onevsrestclassifier import OneVsRestClassifier
2 |
3 | __all__ = [
4 | 'OneVsRestClassifier'
5 | ]
6 |
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/src/python/nimbusml/naive_bayes/__init__.py:
--------------------------------------------------------------------------------
1 | from .naivebayesclassifier import NaiveBayesClassifier
2 |
3 | __all__ = [
4 | 'NaiveBayesClassifier'
5 | ]
6 |
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/src/python/nimbusml/preprocessing/__init__.py:
--------------------------------------------------------------------------------
1 | from .fromkey import FromKey
2 | from .tokey import ToKey
3 | from .tensorflowscorer import TensorFlowScorer
4 | from .datasettransformer import DatasetTransformer
5 | from .onnxrunner import OnnxRunner
6 | from .datetimesplitter import DateTimeSplitter
7 | from .tokeyimputer import ToKeyImputer
8 | from .tostring import ToString
9 |
10 | __all__ = [
11 | 'DateTimeSplitter',
12 | 'FromKey',
13 | 'ToKey',
14 | 'ToKeyImputer',
15 | 'ToString',
16 | 'TensorFlowScorer',
17 | 'DatasetTransformer',
18 | 'OnnxRunner'
19 | ]
20 |
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/src/python/nimbusml/preprocessing/filter/__init__.py:
--------------------------------------------------------------------------------
1 | from .bootstrapsampler import BootstrapSampler
2 | from .rangefilter import RangeFilter
3 | from .skipfilter import SkipFilter
4 | from .takefilter import TakeFilter
5 |
6 | __all__ = [
7 | 'BootstrapSampler',
8 | 'RangeFilter',
9 | 'SkipFilter',
10 | 'TakeFilter'
11 | ]
12 |
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/src/python/nimbusml/preprocessing/missing_values/__init__.py:
--------------------------------------------------------------------------------
1 | from .filter import Filter
2 | from .handler import Handler
3 | from .indicator import Indicator
4 |
5 | __all__ = [
6 | 'Filter',
7 | 'Handler',
8 | 'Indicator'
9 | ]
10 |
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/src/python/nimbusml/preprocessing/normalization/__init__.py:
--------------------------------------------------------------------------------
1 | from .binner import Binner
2 | from .globalcontrastrowscaler import GlobalContrastRowScaler
3 | from .logmeanvariancescaler import LogMeanVarianceScaler
4 | from .lpscaler import LpScaler
5 | from .meanvariancescaler import MeanVarianceScaler
6 | from .minmaxscaler import MinMaxScaler
7 | from .robustscaler import RobustScaler
8 |
9 | __all__ = [
10 | 'Binner',
11 | 'GlobalContrastRowScaler',
12 | 'LogMeanVarianceScaler',
13 | 'LpScaler',
14 | 'MeanVarianceScaler',
15 | 'MinMaxScaler',
16 | 'RobustScaler'
17 | ]
18 |
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/src/python/nimbusml/preprocessing/schema/__init__.py:
--------------------------------------------------------------------------------
1 | from .columnconcatenator import ColumnConcatenator
2 | from .columndropper import ColumnDropper
3 | from .columnduplicator import ColumnDuplicator
4 | from .columnselector import ColumnSelector
5 | from .prefixcolumnconcatenator import PrefixColumnConcatenator
6 | from .typeconverter import TypeConverter
7 |
8 | __all__ = [
9 | 'ColumnConcatenator',
10 | 'ColumnDropper',
11 | 'ColumnDuplicator',
12 | 'ColumnSelector',
13 | 'PrefixColumnConcatenator',
14 | 'TypeConverter'
15 | ]
16 |
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/src/python/nimbusml/preprocessing/text/__init__.py:
--------------------------------------------------------------------------------
1 | from .chartokenizer import CharTokenizer
2 | from .wordtokenizer import WordTokenizer
3 |
4 | __all__ = [
5 | 'CharTokenizer',
6 | 'WordTokenizer'
7 | ]
8 |
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/src/python/nimbusml/tests/pipeline/__init__.py:
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/src/python/nimbusml/tests/pipeline/test_pipeline_transform_method.py:
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1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 | import unittest
6 |
7 | import pandas
8 | from nimbusml import Pipeline, FileDataStream
9 | from nimbusml.datasets import get_dataset
10 | from nimbusml.feature_extraction.text import NGramFeaturizer
11 |
12 | path = get_dataset("wiki_detox_train").as_filepath()
13 | data = FileDataStream.read_csv(path, sep='\t')
14 | df = data.to_df().head()
15 | X = df['SentimentText']
16 |
17 | class TestPipelineTransformMethod(unittest.TestCase):
18 |
19 | def test_transform_only_pipeline_transform_method(self):
20 | p = Pipeline([NGramFeaturizer(char_feature_extractor=None) << 'SentimentText'])
21 | p.fit(X)
22 | xf = p.transform(X)
23 | assert 'SentimentText.==rude==' in xf.columns
24 |
25 | if __name__ == '__main__':
26 | unittest.main()
27 |
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/src/python/nimbusml/tests/preprocessing/__init__.py:
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/src/python/nimbusml/tests/preprocessing/filter/__init__.py:
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/src/python/nimbusml/tests/preprocessing/filter/test_rangefilter.py:
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1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 |
6 | import unittest
7 |
8 | import pandas as pd
9 | from nimbusml.preprocessing.filter import RangeFilter
10 |
11 |
12 | class TestRangeFilter(unittest.TestCase):
13 |
14 | def test_reange_filter_short_example(self):
15 | d = pd.DataFrame([[1., 1.9, 3.], [2., 3., 4.], [2., 3., 4.]])
16 | d.columns = ['aa', 'bb', 'cc']
17 |
18 | hdl = RangeFilter(min=0, max=2) << 'aa'
19 | res1 = hdl.fit_transform(d)
20 | assert res1 is not None
21 | assert res1.shape == (1, 3)
22 |
23 | hdl = RangeFilter(min=0, max=2) << 'bb'
24 | res2 = hdl.fit_transform(d)
25 | assert res2 is not None
26 | assert res2.shape == (1, 3)
27 | assert res1.values.ravel().tolist() == res2.values.ravel().tolist()
28 |
29 |
30 | if __name__ == '__main__':
31 | unittest.main()
32 |
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/src/python/nimbusml/tests/preprocessing/filter/test_skipfilter.py:
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1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 |
6 | import unittest
7 |
8 | import pandas
9 | from nimbusml.preprocessing.filter import SkipFilter
10 | from sklearn.utils.testing import assert_equal
11 |
12 |
13 | class TestSkipFilter(unittest.TestCase):
14 |
15 | def test_SkipFilter(self):
16 | df = pandas.DataFrame(data=dict(
17 | review=['row' + str(i) for i in range(10)]))
18 | y = SkipFilter(count=2).fit_transform(df)
19 | print(len(y))
20 | assert_equal(len(y), 8)
21 |
22 |
23 | if __name__ == '__main__':
24 | unittest.main()
25 |
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/src/python/nimbusml/tests/preprocessing/filter/test_takefilter.py:
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1 | # --------------------------------------------------------------------------------------------
2 | # Copyright (c) Microsoft Corporation. All rights reserved.
3 | # Licensed under the MIT License.
4 | # --------------------------------------------------------------------------------------------
5 |
6 | import unittest
7 |
8 | import pandas
9 | from nimbusml.preprocessing.filter import TakeFilter
10 | from sklearn.utils.testing import assert_equal
11 |
12 |
13 | class TestTakeFilter(unittest.TestCase):
14 |
15 | def test_TakeFilter(self):
16 | df = pandas.DataFrame(data=dict(
17 | review=['row' + str(i) for i in range(10)]))
18 | y = TakeFilter(count=8).fit_transform(df)
19 | print(len(y))
20 | assert_equal(len(y), 8)
21 |
22 |
23 | if __name__ == '__main__':
24 | unittest.main()
25 |
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/src/python/nimbusml/tests/preprocessing/missing_values/__init__.py:
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/src/python/nimbusml/tests/preprocessing/normalization/__init__.py:
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/src/python/nimbusml/tests/preprocessing/schema/__init__.py:
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/src/python/nimbusml/tests/preprocessing/text/__init__.py:
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/src/python/nimbusml/tests/scikit/__init__.py:
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/src/python/nimbusml/tests/timeseries/__init__.py:
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/src/python/nimbusml/tests/utils/__init__.py:
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/src/python/nimbusml/tests/utils/data/pipe.png:
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/src/python/nimbusml/tests/utils/test_pipeline_exports_complex.csv:
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1 | "ItemID" "Sentiment" "SentimentSource" "SentimentText" "RowNum" "Positive" "Train" "Small"
2 | 1 0 "Sentiment140" "is so sad for my APL friend............." 1 FALSE TRUE FALSE
3 | 2 0 "Sentiment140" "I missed the New Moon trailer..." 2 FALSE TRUE FALSE
4 | 3 1 "Sentiment140" "omg its already 7:30 :O" 3 TRUE TRUE FALSE
5 | 4 0 "Sentiment140" ".. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on ( 30mins)..." 4 FALSE TRUE FALSE
6 | 5 0 "Sentiment140" "i think mi bf is cheating on me!!! T_T" 5 FALSE TRUE FALSE
7 | 6 0 "Sentiment140" "or i just worry too much?" 6 FALSE TRUE FALSE
8 | 7 1 "Sentiment140" "Juuuuuuuuuuuuuuuuussssst Chillin!!" 7 TRUE TRUE FALSE
9 | 8 0 "Sentiment140" "Sunny Again Work Tomorrow :-| TV Tonight" 8 FALSE TRUE FALSE
10 | 9 1 "Sentiment140" "handed in my uniform today . i miss you already" 9 TRUE TRUE FALSE
11 |
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/src/python/nimbusml/timeseries/__init__.py:
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1 | from .iidspikedetector import IidSpikeDetector
2 | from .iidchangepointdetector import IidChangePointDetector
3 | from .ssaspikedetector import SsaSpikeDetector
4 | from .ssachangepointdetector import SsaChangePointDetector
5 | from .ssaforecaster import SsaForecaster
6 | from .timeseriesimputer import TimeSeriesImputer
7 |
8 | __all__ = [
9 | 'IidSpikeDetector',
10 | 'IidChangePointDetector',
11 | 'SsaSpikeDetector',
12 | 'SsaChangePointDetector',
13 | 'SsaForecaster',
14 | 'TimeSeriesImputer'
15 | ]
16 |
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/src/python/nimbusml/utils/__init__.py:
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1 | from .utils import get_X_y, evaluate_binary_classifier, load_img, ColumnSelector
2 |
3 | try:
4 | from inspect import signature
5 | except ImportError:
6 | from funcsigs import signature
7 |
8 | __all__ = [
9 | 'get_X_y',
10 | 'evaluate_binary_classifier',
11 | 'load_img',
12 | 'ColumnSelector',
13 | 'signature'
14 | ]
15 |
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/src/python/nimbusml/utils/pipe.gv.png:
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/src/python/setup.cfg:
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1 | [bdist_wheel]
2 | # Set universal = 1 for wheels that support both py2 and py3, otherwise set to 0
3 | universal = 0
4 |
5 | [easy_install]
6 |
7 |
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/version.txt:
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1 | 1.8.0
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