Release history

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Version 6.0.1

New features

• Support for Python 3.12

Enhancements

• Packaging license file.

Version 6.0.0

New features

• Completely new module for time series. Read more in the user guide.
• Support for Python 3.11 and drop support for Python 3.8 and Python 3.9.
• New data engines. Read more in the user guide.
• Added the dask parallelization backend.
• Improved memory optimizations. Read more in the user guide.
• Added the iterative strategy for numerical imputation.
• Added the hdbscan strategy to the Pruner class.
• Use the ignore parameter to ignore columns in the dataset.
• New update_traces method to further customize your plots.

API changes

• The plot_results method is divided into plot_results and plot_bootstrap and accepts any metric.
• The FeatureGrouper class no longer accepts a name parameter. Provide the group names directly through the group parameter as dict.
• Rework of the register method.
• The multioutput attribute is deprecated. Multioutput meta-estimators are now assigned automatically.
• Model tags have to be separated from the acronym by an underscore.
• The engine parameter is now a dict.
• The automl method is deprecated.

Enhancements

• Transformations only on y are now accepted, e.g., atom.scale(columns=-1).
• The Imputer class has many more strategies for numerical columns designed for time series.
• The evaluate method highlights the highest score per metric.
• Full support for pandas nullable dtypes.
• The dataset can now be provided as callable.
• The FeatureExtractor class can extract features from the dataset's index.
• Subplots can now share axes on the canvas.
• The save and save_data methods now accept pathlib.Path objects as filename.
• Cleaner representation on hover for the plot_timeline method.
• The cv key in ht_params now accepts a custom cross-validation generator.
• Improved error message for incorrect stratification of multioutput datasets.
• Rework of the shrink method.

Bug fixes

• Fixed a bug where the cross_validate method could fail for pipelines that changed the number of rows.
• Fixed a bug where the Pruner class didn't drop all outlier clusters.
• Fixed a bug where the pipeline could fail for transformers that returned a series.
• Fixed a bug where the pipeline could fail for transformers that reset its internal attributes during fitting.
• Fixed a bug where the register method failed in Databricks.
• Fixed a bug where tuning hyperparameter for a base_estimator inside a custom meta-estimator would fail.
• Fixed a bug where the data properties' @setter could fail for numpy arrays.
• Fixed a bug where reference lines for some plots didn't lie exactly on the unity line.