Smile Versions Save

• All new declarative data visualization
• TreeSHAP (contributed by Ray Ma @rayeaster)
• UMAP (contributed by Ray Ma @rayeaster)
• Levenberg-Marquardt algorithm
• The packages smile-cas and smile-vega are merged into scala-scala package
• Spark integration in smile-spark
• NLP in Kotlin
• Grid search and random search for hyperparameter tuning
• Bug fixes
• Smile Shell is based on Scala REPL (2.13.2) again
• DataFrame and Tuple -> JSON
• Kotlin and Clojure notebooks

Kudos to Ray Ma @rayeaster for great contributions!

• Kotlin API
• Clojure API
• smile.plot.swing API is redesigned. Leaner, simpler, and better headless support
• Bug fixes

Various minor improvements

The CAS module is a computer algebra system that has the ability to manipulate mathematical expressions in a way similar to the traditional manual computations of mathematicians and scientists.

The symbolic manipulations supported include:

• simplification to a smaller expression or some standard form, including automatic simplification with assumptions and simplification with constraints

• substitution of symbols or numeric values for certain expressions

• change of form of expressions: expanding products and powers, partial and full factorization, rewriting as partial fractions, constraint satisfaction, rewriting trigonometric functions as exponentials, transforming logic expressions, etc.

• partial and total differentiation

• matrix operations including products, inverses, etc.

1. Vega-lite based plot
2. Jupyter notebook examples
3. Bug fixes

Smile has been fully rewritten with more than 150,000 lines change.

• Switch to L-GPL license.
• Fully redesigned API. It is leaner, simpler and even more friendly.
• Faster implementation and memory optimization. Many algorithms are fully reimplemented. RandomForest is 8X faster than XGBoost on large benchmark data (10MM samples).
• New parallelism mechanism
• All new DataFrame and Formula
• New algorithms such as ICA, error reduction prune, quantile loss, TWCNB, etc.
• Support arbitrary class labels.
• Enhancement and harden numeric computations.
• Support Parquet, SAS, Arrow, Avro, etc.
• Bug fixes.

• ElasticNet
• GroupKFold
• Bug fixes

• K-Modes clustering
• Online learning with LogisticRegression by SGD
• MCC (Matthews correlation coefficient) metric
• Bug fixes

1. Performance improvement of hierarchical clustering
2. Bug fixes.

1. DataFrame
2. New Shell for Mac and Linux
3. Shell improvement for Windows
4. Out of box support of native LAPACK for Windows
5. Scala functions to export AttributeDataset, double[][], double[] to ARFF or CSV
6. Scala functions for validation measures
7. Refactor feature transformation and generation classes
8. NeuralNetwork for regression
9. Recursive least squares
10. Refactor Scala NLP API
11. Bug fixes