Release history

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Version 5.1.2

API changes

• The default strategy for the encode method has changed from "LeaveOneOut" to "Target"-encoding. LeaveOneOut is no longer a supported strategy.

Bug fixes

• Fixed a bug where stratification failed for datasets where the target column was not placed last.
• Fixed a bug where transformers with no get_feature_names_out method could fail.
• Fixed a bug where the FeatureSelector class could fail when transforming a dataset with different column order than seen at fit time.

Version 5.1.1

API changes

• The infrequent_to_value parameter in the Encoder class is replaced with infrequent_to_value to be consistent with sklearn's naming convention.

Enhancements

• Added the kwargs parameter to the save_data method.

Bug fixes

• Fixed an installation issue for systems without an x86 architecture.
• Fixed a bug where Voting would fail for certain metrics.
• Fixed a bug where the automl method would fail for some transformers.
• Fixed a bug where the time metric in mlflow was always zero.
• Fixed a bug where shap plots wouldn't display the full column names.
• Fixed a bug where column names where not properly propagated during transformation.

Version 5.1.0

New features

• Support for multilabel classification, multiclass-multilabel classification and multioutput regression tasks. Read more in the user guide.
• New backend parameter to choose a parallel execution backend.
• New parallel parameter to train multiple models simultaneously.
• Integration with DAGsHub to store your mlflow experiments. Read more in the user guide.
• New serve method to deploy models to a rest API endpoint.
• New get_best_threshold method to calculate the optimal threshold for binary and multilabel tasks.
• New get_sample_weight method to calculate the sample weights for a balanced data set.

API changes

• The report method is renamed to eda.
• The ATOMLoader class is deprecated in favor of the load method.
• The errors attribute for runners is deprecated.

Enhancements

• Added three new notebook examples.
• Added the drop_chars parameter to the Cleaner class.
• Added the errors parameter to the trainers.
• Rework of the dependencies, making the base package more lightweight.
• The logging entries for external libraries are redirected to atom's file handler.

Bug fixes

• Fixed multiple errors that appeared after sklearn's 1.2 update.
• Fixed a bug where hyperparameter tuning could fail for multi-metric runs.
• Fixed a bug where trials would try to report multiple times the same step.
• Fixed a bug where custom models could skip in-training validation.
• Fixed an issue where the bootstrapping estimators were trained using partial_fit.

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.