- Fix (List <-> Tensor) casting when base types match
- Fix deserialization of legacy Spark 2 models
- Scala 2.11 is no longer supported due to Spark 3 upgrade
- Please use 0.18.1 mleap version if you have models serialized with both Spark 2.x and Spark 3.x
- MathBinaryModel now supports Logit operations
- TensorflowTransformerOp now supports serialization and deserialization using SavedModel format
Casting.castnow supports conversions between ListType and TensorType
- Fix OneHotEncoder Python serialization
- Upgrade to scikit-learn 0.22
- Upgrade to Spark 3.0.2
- Upgrade Tensorflow version to 2.4.1
- upgrade to xgboost 1.0.0 - using h2oai Predictor
- support for using xgboost predictor when using xgboost regressor
- MathBinaryModel now supports Min and Max operations
- fix Spark deserialization of random forest classifier to include numTrees
- scoring optimizations for Interacting and CountVectorizer
- fix MathBinary serialization/deserialization in pyspark
- Fix default ports when running grpc/http requests; default grpc port is 65328 and can be overridden via MLEAP_GRPC_PORT; default http port should be: 65327 and can be overridden via MLEAP_HTTP_PORT
- Upgrade to Spark version 2.4.5
- Support for a performant implementation of the XGboost runtime (XGboost Predictor)
- Scikit-learn support for MultinomialLogisticRegression
- Support for min/max values other than defaults (i.e. 0.0 and 1.0) in MinMaxScalerModel
- Support for custom transformers (StringMap, MathUnary, MathBinary) in Pyspark
- Support MLWritable/MLReadable for custom transformers (StringMap, MathUnary, MathBinary) and fix this for Imputer transformer
- Fixes support for loading/storing bundles from/to hdfs in Pyspark
- Improve importing mleap version for python modules
- Fix XGBoost sparse vector support
- Fix MinMaxScalerModel outputs different in Spark vs MLeap
- Fix Spark deserialization for CountVectorizer transformer
- Added support for HandleInvalid in Bucketizer, VectorIndexer
- Fix setting HandleInvalid by default to OneHotEncoder for backwards compatibility
- Fixes MLReader for Imputer mleap implementation of Spark transformer
- Minor documentation updates
- None
- Load models at start up in mleap-spring-boot
- Add support for Python 3
- StringMap transformer - add new optional parameters handleInvalid & defaultValue
- Add support for LinearSVC transformer/model
- Fix Tensorflow bundle writing when transform() method isn't necessarily called
- Fix FrameReader reading a very large mleap frame
- Update xgboost4j and fix databricks runtime
- Use openjdk:8-jre-slim as docker base image
- Bump urllib3 from 1.23 to 1.24.2 in python package
- Add default grpc port to docker config
- General documentation improvements
We make every effort for the serialization format to be backwards compatible between different versions of MLeap. Please note below some important notes regarding backwards compatibility.
- The deprecated OneHotEncoder unfortunately had breaking changes in a few releases. In releases 0.11.0 and 0.12.0, the deserialization into MLeap was broken for OneHotEncoder. When using releases 0.13.0, 0.14.0, and 0.15.0, please ensure that the model returns the same results as before the upgrade, by potentially changing dropLast and handleInvalid values after deserialization. Alternatively, please use MLeap version 0.16.0 or higher, in case you have models serialized with other versions of MLeap that use OneHotEncoder. If your model uses OneHotEncoderEstimator or no one hot encoding, then you should not encounter any of the issues above.