What’s New

v0.4.0 (June 18, 2020)

Breaking changes

  • Python <3.6 is no longer officially supported. The package might still work, but we don’t test against these versions anymore.

Enhancements

  • The package can now be installed via conda:

    conda install -c phausamann -c conda-forge sklearn-xarray

v0.3.0 (November 5, 2018)

Breaking changes

  • wrap now returns a new class CompatEstimatorWrapper when compat=True.

  • The standard EstimatorWrapper directly reflects the parameters of the underlying estimator as instance attributes, regardless of the value of compat (which is deprecated and has no effect).

Enhancements

  • EstimatorWrapper now directly reflects both the parameters and the fitted attributes (e.g. mean_) of the underlying estimator. The estimator attribute is still an instance of the actual estimator but is treated mostly as just the type of the instance (It’s not stored as the type for compatibility with clone).

  • Added the CompatEstimatorWrapper which acts like a standard sklearn estimator (with the wrapped estimator as nested) and does not present the attributes of the underlying estimator.

  • Added inverse_transform to preprocessing.Concatenator.

Bug fixes

  • Fixed failing tests with sklearn 0.20.

v0.2.0 (April 9, 2018)

Breaking changes

  • wrap now returns a decorated EstimatorWrapper instead of an estimator-specific wrapper class.

  • Removed the common.decorators module, because the decorated estimators could not be pickled and therefore didn’t pass the usual sklearn estimator checks.

  • Removed the dataset and dataarray modules. Wrappers have to be directly imported from sklearn_xarray.

  • Removed the data module (now called datasets).

Enhancements

  • Added wrappers for fit_transform, partial_fit, predict_proba, predict_log_proba and decision_function.

v0.1.4 (March 15, 2018)

Enhancements

  • preprocessing.Transposer now also accepts a subset of X.dims for the order parameter.

  • preprocessing.Splitter and preprocessing.Segmenter now accept an axis argument that specifies where to insert the new dimension.

  • Huge performance improvements for preprocessing.Segmenter by using numpy.lib.stride_tricks.as_strided instead of a loop. The general-purpose backend for segmenting can be found in utils.segment_array.

Deprecations

  • The data module containing different example datasets is being renamed to datasets according to the scikit-learn standards. Since the dataset module will be removed, there will no longer be confusion due to similar naming.

v0.1.3 (January 9, 2018)

Enhancements

The wrapper now passes the DataArray’s data attribute to the wrapped estimator, making it possible to wrap estimators from dask-ml and use dask-backed DataArrays and Datasets as inputs.

v0.1.2 (December 10, 2017)

Enhancements

The wrapping mechanism has been changed to work with both DataArrays and Datasets. From now on, you can use from sklearn_xarray import wrap which will automatically determine the type of xarray object when calling fit. Note that a wrapper fitted for DataArrays cannot be used for Datasets and vice-versa.

The wrappers now also support passing an estimator type rather than an instance and passing the parameters of the wrapped estimator directly to the wrapper.