Release Notes

23.1.1

Features and Improvements

• Unsupervised anomaly detection

  • Implemented N-1 experts for hyperparameter tuning

  • Added N-1 experts-based contamination factor identification

• Overhauled package documentation

Bug fixes

• Fixed a bug in feature importance explainers for when the dataset contains feature names that are numpy integers and an AutoML pipeline is being explained.

23.1.0

Features and Improvements

• Fairness metrics are now available to measure bias in both datasets and trained models. Fairness metrics can be imported from automl.fairness.metrics .

• Explanations can now be computed from custom user-defined metrics.

• Introduced max_tuning_trials option that controls maximum HPO trials per algorithm.

• New explainer (Counterfactual)

  • Added a model-agnostic counterfactual explainer for classification, regression, and anomaly detection tasks.

  • The explainer can find diverse counterfactuals for the desired prediction, while the user is able to choose which features to vary and their permitted range.

  • Counterfactual explanations can be visualized either with What-if explainer or dataframe.

• Added support of surrogate explainer for local text explanation.

• Code updated to comply with security checks with Python Bandit.

• Added catboost as a new classification model.

Bug fixes

• Fixed a bug on LIME’s explanation Bar Chart where annotations were misplaced for dataset stringified integers feature names.

• Fixed a bug where features would be placed incorrectly on plots’ axis when trying to visualize explanations for categorical features.

• Deleted internal state to reduce memory consumption in explanations

• Fixed a bug where dataset downcasting to int32 and float32 was only applied during training but not for doing the final fit or collecting predictions.

• Preprocessing of datetime columns is now much faster.

• Fixed a bug where dependencies of automl would on import initialize a rootLogger preventing subsequent applications from using logging.basicConfig() .

• Fixed a bug where the AutoTune step would override default params even if it did not find any better params than the default ones.

• Propogated dataset downcasting to all relevant pipeline stages, potentially reducing memory consumption for very large datasets.

• Changed AutoTune behavior to consider using default hyper-parameters scored at the end of feature selection step if they performed better than those AutoTune tried within timebudget. .

22.4.2

Features and Improvements

• Added support for explaining selected features in local and global permutation importance, as well as automatically detecting which features were selected by an AutoML model.

Bug fixes

• Fixed a bug in local perturbation-based feature attribution explainers for the n_iter='auto' option that caused the iterations to be set too high.

• Enhanced performance of local feature importance explainers to improve running times by batching inference calls together.

22.4.1

Features and Improvements

• Pipeline now accepts a min_class_instances input argument to manually specify the number of examples every class must have when doing classification. The value for min_class_instances must be at least 2.

Bug fixes

• Fixed a bug where IPython and ipywidgets are not properly guarded as an optional dependencies which makie them required.

• Fixed a bug introduced by last dependency update which caused fbprophet to not produce forecasts with correct index type, when fbprophet was installed manually.

22.4.0

Features and Improvements

• New feature dependence explainers

  • Added an Accumulated Local Effects (ALE) explainer

  • ALE explanations can be computed for up to two features if at least one is not categorical.

• New explainer (What-IF)

  • Added a What-IF explainer for classification and regression tasks

  • What-IF explanations include exploration of the behavior of an ML model on a single sample as well as on the entire dataset.

  • Sample exploration (edit a sample value and see how the model predictions changes) and relationships’ visualization (how a feature is related to predictions or other features) are supported.

• New feature importance aggregators

  • Added ALFI (Aggregate Local Feature Importance) that gives a visual summary of multiple local explanations.

• New local feature importance explainer

  • Added support for surrogate-based (LIME+) local feature importance explainers

Bug fixes

• Import failure due to CUDA: The package no longer crashes when imported on a machine with CUDA installed.

• Fixed a bug where TorchMLPClassifier would fail when trying to predict a single instance.

• Fixed a bug where OracleAutoMLx_Forecasting.ipynb would fail if visualization packages were not already installed.

• Fixed a bug that caused the pipeline.transform to raise an exception if a single row was passed.

• Explanation documentation

  • Our documentation website (http://automl.oraclecorp.com/) now includes documentation for the explanation objects returned by our explainers.

• Enhanced performance of local feature importance explainers to address long running times.

• Improved visualization of facet for the columns with cardinality equal to 1 by selecting the bars’ width and pads properly.

22.3.0

Features and Improvements

• New Explainer

  • Added support for KernelSHAP (a new feature importance tabulator), which provides fast approximations for the Shapley feature importance method.

• Support ARM architecture ( aarch64 )

  • Released platform-specific wheel file for ARM machines.

Miscellaneous

• Clarified documentation on the accepted data formats for input datasets and added a more meaningful corresponding error message.

22.2.0

Features and Improvements

• New profiler

  • Profiler tracks CPU and memory utilization

• Timeseries forecasting pipeline

  • Added the support for multivariate datasets

  • Added the support for exogenous variables

  • Enhanced heteroskedasticity detection technique

  • Applied Box-Cox transform-inverse_transform with params determined via MLE to handle heteroskedasticity

• Explainers / MLX integration

  • New global text explainer

    • Added support

  • New feature importance attribution explainers

    • Added several local and global feature importance explainers, including permutation importance, exactly Shapley, and SHAP-PI.

    • The explainers support for classification, regression and anomaly detection

    • The explainers can also be configured to explain the importance of features to any model (explanation_type=’observational’) as well as for a particular model (explanation_type=’interventional’).

    • Observational explanations are supported for all tasks; interventional explanations are only supported for classification and regression.

  • New feature dependence explainers

    • Added a partial dependence plot (PDP) and individual conditional expectations (ICE) explainer

    • PDP explanations include vizualization support for up to 4 dimensions. PDPs in higher dimension can be returned as dataframes.

• Unsupervised Anomaly Detection

  • Added N-1 Experts: a new experimental metric for UAD Model Selection

• Documentation

  • Added the description of init function of the automl to documentation

  • Cleaned up documentation for more consistency among different sections and added cross-references

Bug fixes

• Timeseries forecasting pipeline

  • Statsmodel exception for some frequencies, users are now able to pass in timeperiod as a parameter

• Preprocessing

  • Datetime preprocessor

    • Fixed the bug regarding column expansion and None/Null/Nan values

  • Standard preprocessor refitting

    • The standard preprocessor used to first be fit on a subsample of the training set, and then re-fit at the very end of the pipeline using the full training set. This occasionaly resulted in a different number of engineered features being produced. As a result, the features identified during the model selection module could no longer exist. The standard preprocessor is now fit only once.

• ONNX predictions inconsistency

  • Changed the ONNX conversion function to reduce the difference between the ONNX dumped model and the original pipeline object predictions

  • Improved ONNX conversion runtime

  • ONNX conversion now only requires a sample from the training or test set as input. This sample is used to infer the final types and shapes

Possibly breaking changes

• Removed matplotlib as a dependency of the AutoMLx package

  • Forecasting predictions can now instead be visualized only using plotly using the same interface as before, automl.utils.plot_forecast. The alternate visualizations that were provided with plotly using automl.utils.plot_forecast_interactive has been removed.

• Updated the AutoMLx package dependencies

  • All dependency versions have been reviewed and updated to address all known CVEs

  • A few unneeded dependencies have also been removed.