Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
lightning
package.scikit-learn
package.return
statements in the generated code.scikit-learn
package.string.Template
.numpy
dependency is no longer required at runtime for the generated Python code.statsmodels
package.abs
, tanh
, sqrt
, exp
, sigmoid
and softmax
.Kudos to @StrikerRUS who's responsible for all these amazing updates 💪
base_score
parameter became None (https://github.com/BayesWitnesses/m2cgen/issues/182).statsmodels
package are now supported. The list of added models includes: GLS, GLSAR, OLS, ProcessMLE, QuantReg and WLS.lightning
package: AdaGradRegressor/AdaGradClassifier, CDRegressor/CDClassifier, FistaRegressor/FistaClassifier, SAGARegressor/SAGAClassifier, SAGRegressor/SAGClassifier, SDCARegressor/SDCAClassifier, SGDClassifier, LinearSVR/LinearSVC and KernelSVC.scikit-learn
package.SubroutineExpr
expression has been removed from AST. The logic of how to split the generated code into subroutines is now focused in interpreters and was completely removed from assemblers.SubroutineExpr
from AST.Quite a few awesome updates in this release. Many thanks to @StrikerRUS and @chris-smith-zocdoc for making this release happen.
numpy
dependency is no longer required for generated Python code when no linear algebra is involved. Thanks @StrikerRUS for this update.scikit-learn
: SVC
, NuSVC
, SVR
and NuSVR
.best_ntree_limit
attribute to limit the number of estimators used during prediction. Thanks @arshamg for helping with that.