Awesome
scikit-lego
<a href="https://koaning.github.io/scikit-lego/"><img src="images/logo.png" width="35%" height="35%" align="right" /></a>
We 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 by Matthijs Brouns and Vincent D. Warmerdam as 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.
Installation
Install scikit-lego
via pip with
python -m pip install scikit-lego
Via conda with
conda install -c conda-forge scikit-lego
Alternatively, to edit and contribute you can fork/clone and run:
python -m pip install -e ".[dev]"
python setup.py develop
Documentation
The documentation can be found here.
Usage
We offer custom metrics, models and transformers. You can import them just like you would in scikit-learn.
# the scikit learn stuff we love
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# from scikit lego stuff we add
from sklego.preprocessing import RandomAdder
from sklego.mixture import GMMClassifier
...
mod = Pipeline([
("scale", StandardScaler()),
("random_noise", RandomAdder()),
("model", GMMClassifier())
])
...
Features
Here's a list of features that this library currently offers:
sklego.datasets.load_abalone
loads in the abalone datasetsklego.datasets.load_arrests
loads in a dataset with fairness concernssklego.datasets.load_chicken
loads in the joyful chickweight datasetsklego.datasets.load_heroes
loads a heroes of the storm datasetsklego.datasets.load_hearts
loads a dataset about heartssklego.datasets.load_penguins
loads a lovely dataset about penguinssklego.datasets.fetch_creditcard
fetch a fraud dataset from openmlsklego.datasets.make_simpleseries
make a simulated timeseriessklego.pandas_utils.add_lags
adds lag values in a pandas dataframesklego.pandas_utils.log_step
a useful decorator to log your pipeline stepssklego.dummy.RandomRegressor
dummy benchmark that predicts random valuessklego.linear_model.DeadZoneRegressor
experimental feature that has a deadzone in the cost functionsklego.linear_model.DemographicParityClassifier
logistic classifier constrained on demographic paritysklego.linear_model.EqualOpportunityClassifier
logistic classifier constrained on equal opportunitysklego.linear_model.ProbWeightRegression
linear model that treats coefficients as probabilistic weightssklego.linear_model.LowessRegression
locally weighted linear regressionsklego.linear_model.LADRegression
least absolute deviation regressionsklego.linear_model.QuantileRegression
linear quantile regression, generalizes LADRegressionsklego.linear_model.ImbalancedLinearRegression
punish over/under-estimation of a model directlysklego.naive_bayes.GaussianMixtureNB
classifies by training a 1D GMM per column per classsklego.naive_bayes.BayesianGaussianMixtureNB
classifies by training a bayesian 1D GMM per classsklego.mixture.BayesianGMMClassifier
classifies by training a bayesian GMM per classsklego.mixture.BayesianGMMOutlierDetector
detects outliers based on a trained bayesian GMMsklego.mixture.GMMClassifier
classifies by training a GMM per classsklego.mixture.GMMOutlierDetector
detects outliers based on a trained GMMsklego.meta.ConfusionBalancer
experimental feature that allows you to balance the confusion matrixsklego.meta.DecayEstimator
adds decay to the sample_weight that the model acceptssklego.meta.EstimatorTransformer
adds a model output as a featuresklego.meta.OutlierClassifier
turns outlier models into classifiers for gridsearchsklego.meta.GroupedPredictor
can split the data into runs and run a model on eachsklego.meta.GroupedTransformer
can split the data into runs and run a transformer on eachsklego.meta.SubjectiveClassifier
experimental feature to add a prior to your classifiersklego.meta.Thresholder
meta model that allows you to gridsearch over the thresholdsklego.meta.RegressionOutlierDetector
meta model that finds outliers by adding a threshold to regressionsklego.meta.ZeroInflatedRegressor
predicts zero or applies a regression based on a classifiersklego.preprocessing.ColumnCapper
limits extreme values of the model featuressklego.preprocessing.ColumnDropper
drops a column from pandassklego.preprocessing.ColumnSelector
selects columns based on column namesklego.preprocessing.InformationFilter
transformer that can de-correlate featuressklego.preprocessing.IdentityTransformer
returns the same data, allows for concatenating pipelinessklego.preprocessing.OrthogonalTransformer
makes all features linearly independentsklego.preprocessing.TypeSelector
selects columns based on typesklego.preprocessing.RandomAdder
adds randomness in trainingsklego.preprocessing.RepeatingBasisFunction
repeating feature engineering, useful for timeseriessklego.preprocessing.DictMapper
assign numeric values on categorical columnssklego.preprocessing.OutlierRemover
experimental method to remove outliers during trainingsklego.model_selection.GroupTimeSeriesSplit
timeseries Kfold for groups with different amount of observations per groupsklego.model_selection.KlusterFoldValidation
experimental feature that does K folds based on clusteringsklego.model_selection.TimeGapSplit
timeseries Kfold with a gap between train/testsklego.pipeline.DebugPipeline
adds debug information to make debugging easiersklego.pipeline.make_debug_pipeline
shorthand function to create a debugable pipelinesklego.metrics.correlation_score
calculates correlation between model output and featuresklego.metrics.equal_opportunity_score
calculates equal opportunity metricsklego.metrics.p_percent_score
proxy for model fairness with regards to sensitive attributesklego.metrics.subset_score
calculate a score on a subset of your data (meant for fairness tracking)
New Features
We 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 usecase
- any new feature passes standard unit tests (we use the ones from scikit-learn)
- the feature has been discussed in the issue list beforehand
We automate all of our testing and use pre-commit hooks to keep the code working.