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H2O v3.22.0.3 Release Notes

  • 🚀 Download at: http://h2o-release.s3.amazonaws.com/h2o/rel-xia/3/index.html

    Bug

    🛠 [PUBDEV-5829] - Fixed an issue with the REST API. Calling "get model" no longer returns 0 for the timestamp of the model. [PUBDEV-5959] - The PySparking client no longer hangs after re-connecting to the H2O external backend. 🛠 [PUBDEV-5990] - Fixed an OOM issue in h2o.arrange. 🛠 [PUBDEV-6059] - Fixed an issue that caused importing Pargue files with large Double data to fail. [PUBDEV-6076] - After applying group_by to a time stamped column, the original time stamp format is now retained. 0️⃣ [PUBDEV-6079] - In AutoML, cross-validation metrics are now used for early stopping by default. Because of this, the validation_frame argument is now ignored unless nfolds==0 and, in that case, will be used for early stopping. 🛠 [PUBDEV-6098] - Fixed an issue that caused the MOJO visualizer to fail for Isolation Forest models. [PUBDEV-6101] - StackedEnsembleMojoModel is now serializable. 🛠 [PUBDEV-6107] - In the R client, fixed an error that occurrred when running getModelTree. 🚀 [PUBDEV-6109] - In Flow, fixed an issue that caused POJOs, MOJOs, and genmodel.jar to fail to download. This occurred when Flow was launched via Enterprise Steam and in any deployment where user_context was specified. 🛠 [PUBDEV-6111] - Fixed the formula used for calculating L2 distance. [PUBDEV-6117] - The Python client now allows users to enable XGBoost compare with any H2O frame. The convert_H2OFrame_2_DMatrix method accepts any H2O frame and can convert it to valid data for native XGBoost. [PUBDEV-6120] - H2O XGBoost now reports correct variable importances. The variable importances are computed from the gains of their respective loss functions during tree construction. [PUBDEV-6122] - Users can now save PDP plots. 🛠 [PUBDEV-6123] - Fixed an issue that resulted in a SQL exception when connecting H2O to a SQL server and importing a table. 🛠 [PUBDEV-6137] - Fixed an issue with GCS support on Hadoop environments.

    New Feature

    [PUBDEV-1984] - Added monotonic variables for GBM. [PUBDEV-6030] - EasyPredictModelWrapper now calculates reconstruction errors for AutoEncoder. [PUBDEV-6091] - When running a grid search, a timesteamp column was added that shows when each model was added to the grid summary table.

    Improvement

    [PUBDEV-5865] - In GBM, users can now specify the monotone_constraints parameter. [PUBDEV-6106] - Prediction contributions from each tree from MOJO to easywrapper are now exposed. ⚡️ [PUBDEV-6110] - Updated Gradle to version 5.0. 🛠 [PUBDEV-6115] - Fixed the output of rankTsv in the AutoML leaderboard.

    📄 Docs

    ⚡️ [PUBDEV-4377] - Updated the Prediction section to include information on how the prediction threshold is selected for classification problems. ⚡️ [PUBDEV-6105] - Updated the description of enum_limited to indicate that T=1024. [PUBDEV-6148] - In the GBM chapter, added monotone_constraints to list of available parameters.