Patch release 2.2.2

This patch release incorporates two patches and is otherwise identical to 2.2.1:

• Ensure that c_api.h can be built with a C compiler (#348)
• Revert "Add MaxCategory() (#334)" (#349)

Patch release 2.2.1

This patch release incorporates a hotfix and is otherwise identical to 2.2.0:

• Fix PyBuffer serializer (#340)

2.2.0 Release

The new release will incorporate the following changes:

• Update deploy tutorial (#320)
• Add option to enable sanitizers in gtest (#317)
• Handle empty array inputs carefully, to avoid undefined behavior (#314)
• Support for isolation forests (#322, #327)
• When version mismatch happens, show versions (#325)
• Support importing directly from LightGBM model object (#332)
• Add weighted_n_node_samples field in sklearn importer (#330)
• Fix from_xgboost() for models with categorical splits (#333)
• Add MaxCategory() (#334)
• Fix handling of NaNs in categorial splits of LightGBM models (#304)
• Remove undefined behavior when predicting with invalid category value (#335)
• Add a test to prepare for integer default_left (#337)

2.1.0 Release

The new release incorporates the following changes:

• Work around a compiler bug in GCC 7 (#302)
• Fix errors reported by Python linter (#312)
• [CI] Upgrade to CentOS 7 + GCC 8 (#313)
• Improve vector leaf performance (#311)
• Handle the case where left_categories={} (#303)
• Use JSON serializer to enable model introspection (#296)
• Fix NativeLibLoader on Java 9+ (#315)

2.0.0 Release

The new release incorporates the following major changes:

• Remove dmlc-core dependency (#285, #287, #288, #289, #290, #291, #292, #293). #284 explains why we are jumping to a new major version.
• Rename CHECK and LOG macros to avoid name collisions (#295)
• Suppress warning about build time when parallel_comp is specified (#297)
• Handle LightGBM models with '=' in feature names (#298)
• Remove OpenMP dependency (#300)

1.3.0 release

This version incorporates one new major feature:

• [EXPERIMENTAL] Add General Tree Inference Library (GTIL), reference inference backend (#274). GTIL is useful in cases where it is infeasible to build the tree models as native shared libs.

It also contains the following bug fixes:

• Hotfix for serializer (#273)

1.2.0 release

This version incorporates two major new features:

• Faster model import for sklearn tree models (#264)
• Binary serializer to a file stream (#270)

Release 0.93

[CI] Use mainline branch

Release 0.92

Release 0.92

Treelite 0.32

Usability improvements

• Add __version__ attribute (#17)
• Shape results appropriately for multiclass classification (#16)
• Add default LOG handler, to make C API easier to use (#19)
• Allow batches smaller than nthread

Bug fixes

• Python 2.7 compatibility fix (#14)
• handle categorical splits with empty left_categories
• Fix create_shared() to be compatible with shells zsh and fish (#18)

New feature

• (Experimental) Export model as XGBoost model file, as per request in #14.