Package: scikit-lego
scikit-legoWe love scikit learn but very often we find ourselves writing
custom transformers, metrics and models. The goal of this project
is to attempt to consolidate these into a package that offers
code quality/testing. This project started as a collaboration between
multiple companies in the Netherlands but has since received contributions
from around the globe. It was initiated byMatthijs BrounsandVincent D. Warmerdamas a tool to teach people how
to contribute to open source.Note that we're not formally affiliated with the scikit-learn project at all,
but we aim to strictly adhere to their standards.The same holds with lego. LEGO® is a trademark of the LEGO Group of companies which does not sponsor, authorize or endorse this project.InstallationInstallscikit-legovia pip withpython-mpipinstallscikit-legoViacondawithcondainstall-cconda-forgescikit-legoAlternatively, to edit and contribute you can fork/clone and run:python-mpipinstall-e".[dev]"pythonsetup.pydevelopDocumentationThe documentation can be foundhere.UsageWe offer custom metrics, models and transformers. You can import them just like you would
in scikit-learn.# the scikit learn stuff we lovefromsklearn.preprocessingimportStandardScalerfromsklearn.pipelineimportPipeline# from scikit lego stuff we addfromsklego.preprocessingimportRandomAdderfromsklego.mixtureimportGMMClassifier...mod=Pipeline([("scale",StandardScaler()),("random_noise",RandomAdder()),("model",GMMClassifier())])...FeaturesHere's a list of features that this library currently offers:sklego.datasets.load_abaloneloads in the abalone datasetsklego.datasets.load_arrestsloads in a dataset with fairness concernssklego.datasets.load_chickenloads in the joyful chickweight datasetsklego.datasets.load_heroesloads a heroes of the storm datasetsklego.datasets.load_heartsloads a dataset about heartssklego.datasets.load_penguinsloads a lovely dataset about penguinssklego.datasets.fetch_creditcardfetch a fraud dataset from openmlsklego.datasets.make_simpleseriesmake a simulated timeseriessklego.pandas_utils.add_lagsadds lag values in a pandas dataframesklego.pandas_utils.log_stepa useful decorator to log your pipeline stepssklego.dummy.RandomRegressordummy benchmark that predicts random valuessklego.linear_model.DeadZoneRegressorexperimental feature that has a deadzone in the cost functionsklego.linear_model.DemographicParityClassifierlogistic classifier constrained on demographic paritysklego.linear_model.EqualOpportunityClassifierlogistic classifier constrained on equal opportunitysklego.linear_model.ProbWeightRegressionlinear model that treats coefficients as probabilistic weightssklego.linear_model.LowessRegressionlocally weighted linear regressionsklego.linear_model.LADRegressionleast absolute deviation regressionsklego.linear_model.QuantileRegressionlinear quantile regression, generalizes LADRegressionsklego.linear_model.ImbalancedLinearRegressionpunish over/under-estimation of a model directlysklego.naive_bayes.GaussianMixtureNBclassifies by training a 1D GMM per column per classsklego.naive_bayes.BayesianGaussianMixtureNBclassifies by training a bayesian 1D GMM per classsklego.mixture.BayesianGMMClassifierclassifies by training a bayesian GMM per classsklego.mixture.BayesianGMMOutlierDetectordetects outliers based on a trained bayesian GMMsklego.mixture.GMMClassifierclassifies by training a GMM per classsklego.mixture.GMMOutlierDetectordetects outliers based on a trained GMMsklego.meta.ConfusionBalancerexperimental feature that allows you to balance the confusion matrixsklego.meta.DecayEstimatoradds decay to the sample_weight that the model acceptssklego.meta.EstimatorTransformeradds a model output as a featuresklego.meta.OutlierClassifierturns outlier models into classifiers for gridsearchsklego.meta.GroupedPredictorcan split the data into runs and run a model on eachsklego.meta.GroupedTransformercan split the data into runs and run a transformer on eachsklego.meta.SubjectiveClassifierexperimental feature to add a prior to your classifiersklego.meta.Thresholdermeta model that allows you to gridsearch over the thresholdsklego.meta.RegressionOutlierDetectormeta model that finds outliers by adding a threshold to regressionsklego.meta.ZeroInflatedRegressorpredicts zero or applies a regression based on a classifiersklego.preprocessing.ColumnCapperlimits extreme values of the model featuressklego.preprocessing.ColumnDropperdrops a column from pandassklego.preprocessing.ColumnSelectorselects columns based on column namesklego.preprocessing.InformationFiltertransformer that can de-correlate featuressklego.preprocessing.IdentityTransformerreturns the same data, allows for concatenating pipelinessklego.preprocessing.OrthogonalTransformermakes all features linearly independentsklego.preprocessing.PandasTypeSelectorselects columns based on pandas typesklego.preprocessing.PatsyTransformerapplies apatsyformulasklego.preprocessing.RandomAdderadds randomness in trainingsklego.preprocessing.RepeatingBasisFunctionrepeating feature engineering, useful for timeseriessklego.preprocessing.DictMapperassign numeric values on categorical columnssklego.preprocessing.OutlierRemoverexperimental method to remove outliers during trainingsklego.model_selection.GroupTimeSeriesSplittimeseries Kfold for groups with different amount of observations per groupsklego.model_selection.KlusterFoldValidationexperimental feature that does K folds based on clusteringsklego.model_selection.TimeGapSplittimeseries Kfold with a gap between train/testsklego.pipeline.DebugPipelineadds debug information to make debugging easiersklego.pipeline.make_debug_pipelineshorthand function to create a debugable pipelinesklego.metrics.correlation_scorecalculates correlation between model output and featuresklego.metrics.equal_opportunity_scorecalculates equal opportunity metricsklego.metrics.p_percent_scoreproxy for model fairness with regards to sensitive attributesklego.metrics.subset_scorecalculate a score on a subset of your data (meant for fairness tracking)New FeaturesWe want to be rather open here in what we accept but we do demand three
things before they become added to the project:any new feature contributes towards a demonstrable real-world usecaseany new feature passes standard unit tests (we use the ones from scikit-learn)the feature has been discussed in the issue list beforehandWe automate all of our testing and use pre-commit hooks to keep the code working.