├── .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: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /build/libs_win.txt: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /build/sign.csproj: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /nuget.config: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | -------------------------------------------------------------------------------- /release-next.md: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/DotNetBridge/DotNetBridge.dll.config: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /src/DotNetBridge/DotNetBridge.snk: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/DotNetBridge/DotNetBridge.snk -------------------------------------------------------------------------------- /src/DotNetBridge/app.config: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /src/ManifestGenerator/app.config: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | -------------------------------------------------------------------------------- /src/NativeBridge/inc/asm_version.h: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/NativeBridge/inc/fxverstrings.h: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/NativeBridge/inc/ndpversion.h: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/NativeBridge/inc/ndpversion_generated.h: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/NativeBridge/inc/version.h: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/NativeBridge/stdafx.cpp: -------------------------------------------------------------------------------- 1 | // Copyright (c) Microsoft Corporation. All rights reserved. 2 | // Licensed under the MIT license. 3 | 4 | #include "stdafx.h" 5 | -------------------------------------------------------------------------------- /src/NativeBridge/targetver.h: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/Platforms/Program.cs: -------------------------------------------------------------------------------- 1 | namespace build { class Program { static void Main(string[] args) {} } } -------------------------------------------------------------------------------- /src/python/MANIFEST.in: -------------------------------------------------------------------------------- 1 | recursive-include nimbusml/internal/libs * 2 | recursive-include nimbusml/datasets *.csv *.tsv *.png 3 | recursive-include nimbusml/examples *.py 4 | -------------------------------------------------------------------------------- /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: -------------------------------------------------------------------------------- 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: -------------------------------------------------------------------------------- 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 | """ -------------------------------------------------------------------------------- /src/python/docs/sphinx/ci_script/_static/images/1.1.1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/docs/sphinx/ci_script/_static/images/1.1.1.png -------------------------------------------------------------------------------- /src/python/docs/sphinx/ci_script/_static/images/1.1.2.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /src/python/docs/sphinx/concepts.rst: -------------------------------------------------------------------------------- 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 -------------------------------------------------------------------------------- /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 -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/datasets/images/Microsoftlogo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/images/Microsoftlogo.png -------------------------------------------------------------------------------- /src/python/nimbusml/datasets/images/RevolutionAnalyticslogo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/images/RevolutionAnalyticslogo.png -------------------------------------------------------------------------------- /src/python/nimbusml/datasets/images/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/datasets/images/__init__.py -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/examples/examples_from_dataframe/LightGbmRanker_sampleinputextraction_df.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/examples/examples_from_dataframe/LightGbmRegressor_airquality_df.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/examples/examples_from_dataframe/LightLda_df.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/examples/examples_from_dataframe/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /src/python/nimbusml/examples/examples_from_dataframe/tmpfile_with_nans.csv: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/examples/frozen_saved_model.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/examples/frozen_saved_model.pb -------------------------------------------------------------------------------- /src/python/nimbusml/examples/pipeline.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/feature_extraction/__init__.py: -------------------------------------------------------------------------------- 1 | from .treefeaturizer import TreeFeaturizer 2 | 3 | __all__ = [ 4 | 'TreeFeaturizer' 5 | ] 6 | -------------------------------------------------------------------------------- /src/python/nimbusml/feature_extraction/categorical/__init__.py: -------------------------------------------------------------------------------- 1 | from .onehothashvectorizer import OneHotHashVectorizer 2 | from .onehotvectorizer import OneHotVectorizer 3 | 4 | __all__ = [ 5 | 'OneHotHashVectorizer', 6 | 'OneHotVectorizer' 7 | ] 8 | -------------------------------------------------------------------------------- /src/python/nimbusml/feature_extraction/image/__init__.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/feature_extraction/text/extractor/__init__.py: -------------------------------------------------------------------------------- 1 | from .ngram import Ngram 2 | from .ngramhash import NgramHash 3 | 4 | __all__ = [ 5 | 'Ngram', 6 | 'NgramHash' 7 | ] 8 | -------------------------------------------------------------------------------- /src/python/nimbusml/feature_extraction/text/stopwords/__init__.py: -------------------------------------------------------------------------------- 1 | from .customstopwordsremover import CustomStopWordsRemover 2 | from .predefinedstopwordsremover import PredefinedStopWordsRemover 3 | 4 | __all__ = [ 5 | 'CustomStopWordsRemover', 6 | 'PredefinedStopWordsRemover' 7 | ] 8 | -------------------------------------------------------------------------------- /src/python/nimbusml/feature_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from .countselector import CountSelector 2 | from .mutualinformationselector import MutualInformationSelector 3 | 4 | __all__ = [ 5 | 'CountSelector', 6 | 'MutualInformationSelector' 7 | ] 8 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/internal/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/internal/core/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/internal/core/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/internal/core/cluster/__init__.py: -------------------------------------------------------------------------------- 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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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_calibratortrainer_plattcalibrator.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_classificationlossfunction_exploss.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_classificationlossfunction_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='ClassificationLossFunction') 25 | return component 26 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_earlystoppingcriterion_gl.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_earlystoppingcriterion_tr.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_earlystoppingcriterion_up.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/entrypoints/_ensemblebinarydiversitymeasure_disagreementdiversitymeasure.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /src/python/nimbusml/internal/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/internal/utils/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/internal/utils/stubs.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/model_selection/__init__.py: -------------------------------------------------------------------------------- 1 | from .cv import CV 2 | 3 | __all__ = [ 4 | 'CV' 5 | ] 6 | -------------------------------------------------------------------------------- /src/python/nimbusml/multiclass/__init__.py: -------------------------------------------------------------------------------- 1 | from .onevsrestclassifier import OneVsRestClassifier 2 | 3 | __all__ = [ 4 | 'OneVsRestClassifier' 5 | ] 6 | -------------------------------------------------------------------------------- /src/python/nimbusml/naive_bayes/__init__.py: -------------------------------------------------------------------------------- 1 | from .naivebayesclassifier import NaiveBayesClassifier 2 | 3 | __all__ = [ 4 | 'NaiveBayesClassifier' 5 | ] 6 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /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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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 | -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/preprocessing/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/filter/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/preprocessing/filter/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/filter/test_rangefilter.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/filter/test_skipfilter.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/filter/test_takefilter.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/missing_values/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/preprocessing/missing_values/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/normalization/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/preprocessing/normalization/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/schema/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/preprocessing/schema/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/preprocessing/text/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/preprocessing/text/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/scikit/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/scikit/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/timeseries/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/timeseries/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/utils/__init__.py -------------------------------------------------------------------------------- /src/python/nimbusml/tests/utils/data/pipe.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/tests/utils/data/pipe.png -------------------------------------------------------------------------------- /src/python/nimbusml/tests/utils/test_pipeline_exports_complex.csv: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/timeseries/__init__.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/utils/__init__.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /src/python/nimbusml/utils/pipe.gv.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/NimbusML/f6be39ce9359786976429bab0ccd837e849b4ba5/src/python/nimbusml/utils/pipe.gv.png -------------------------------------------------------------------------------- /src/python/setup.cfg: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /version.txt: -------------------------------------------------------------------------------- 1 | 1.8.0 --------------------------------------------------------------------------------