Release 1.1

New features

• Multiquantile regression

  Now it's possible to train models with shared tree structure and multiple predicted quantile values in each leaf. Currently this approach doesn't give a strong guarantee for predicted quantile values consistency, but it still provides more consistency than training multiple independent models for each quantile. You can read short description in the documentation. Short example for Python: loss_function='MultiQuantile:alpha=0.2,0.4'. Supported only on CPU for now.

• Support text and embedding features for regression and ranking.

• Spark: Read/write Spark's Dataset-like API for Pool. #2030

• Support HashedCateg column type. This allows to use externally prehashed categorical features both in training and prediction.

• New option plot_file in Python functions with plot parameter allows to save plots to file. #758

• Add eval_fraction parameter. #1500

• Non-symmetric trees model summation.

• init_model parameter now works with non-symmetric trees.

• Partial support for Apache Spark 3.3 (only for Scala 2.12 and without PySpark).

Speedups

• 2x speedup DCG, nDCG and FilteredDCG metrics calculation for groups with >= 50 objects and with top=-1 (all objects from each group, default value)
• Fixed 2x slowdown of PairLogit and other ranking losses on CPU introduced in release 0.23

Bugfixes

• Fix for pandas integer array. #2096
• Save feature names to json format. #2102
• Fix feature weights on CPU
• Use feature weights on GPU
• Fix gradient calculation for QueryRMSE on GPU
• Fix ranking metrics with group weights in calc_metrics
• Fix JVM applier on data with text features. #2132