Release history

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Version 5.0.1

Bug fixes

• Fixed installation issue.
• Updated package dependencies.

Version 5.0.0

New features

• Completely new hyperparameter tuning process.
• Completely reworked plotting interface.
• Accelerate your pipelines with sklearnex.
• New FeatureGrouper class to extract statistical features from similar groups.
• New create_app method to create a nice front-end for model predictions.
• New inverse_transform method for atom and models.
• New linear model: OrthogonalMatchingPursuit.
• The plot_results method now accepts time metrics.

API changes

• The gpu parameter is deprecated in favor of device and engine.
• Refactor of the Cleaner, Discretizer, Encoder and FeatureSelector classes.
• Refactor of all shap plots.
• Refactor of the apply method.
• The plot_scatter_matrix method is renamed to plot_relationships.
• The kSVM model is renamed to SVM.
• Multidimensional datasets are no longer supported. Check the deep learning section of the user guide for guidance with such datasets.
• The greater_is_better, needs_proba and needs_threshold parameters are deprecated. Metric functions are now created using make_scorer's default parameters.
• The drop method is removed from atom. Use the reworked apply method instead.
• The prediction methods can no longer be called from atom.
• The dashboard method for models is now called create_dashboard.

Enhancements

• New examples for plotting, automated feature scaling, pruning and advanced hyperparameter tuning.
• The Normalizer class can now be accelerated with GPU.
• The Scaler class now ignores binary columns (only 0s and 1s).
• The models parameter in plot and utility methods now accepts model indices.
• The transform method now also transforms only y when X has a default value.
• The prediction methods now return pandas objects.
• Dependency versions are checked with originals after unpickling.
• Automatic generation of documentation from docstrings.
• Improvements in documentation display for mobile phones.
• New feature_importance attribute for models.
• Added a visualization for automated feature scaling to plot_pipeline.

Bug fixes

• The FeatureExtractor class no longer raises a warning for highly fragmented dataframes.
• Fixed a bug where models could not call the score function.
• The Encoder class no longer fails when the user provides ordinal values that are not present during fitting.
• Fixed a bug with the max_nan_rows parameter in the Imputer class.
• Fixed a bug where Tokenizer could fail when no ngrams were found.