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0.4.4 - 2019-11-11

This release was mainly made to provide access to wheels <>_ for Windows and MacOS.


  • Added ensemble.AdaBoostClassifier.


  • Added a clip_gradient parameter to linear_model.LinearRegression and linear_model.LogisticRegression. Gradient clipping was already implemented, but the maximum absolute value can now be set by the user.
  • The intercept_lr parameter of linear_model.LinearRegression and linear_model.LogisticRegression can now be passed an instance of optim.schedulers.Scheduler as well as a float.


  • Fixed metrics.SMAPE, the implementation was missing a multiplication by 2.


  • Added optim.schedulers.Optimal produces results that are identical to sklearn.linear_model.SGDRegressor and sklearn.linear_model.SGDClassifier when setting their learning_rate parameter to 'optimal'.


  • Added time_series.SNARIMAX, a generic model which encompasses well-known time series models such as ARIMA and NARX.