m2cgen (Model 2 Code Generator) - is a lightweight library which provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go, JavaScript).
Supported Python version is >= 3.4.
pip install m2cgen
- Python
- Java
- C
- Go
- JavaScript
| Classification | Regression | |
|---|---|---|
| Linear | LogisticRegression, LogisticRegressionCV, RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier | LinearRegression, HuberRegressor, ElasticNet, ElasticNetCV, TheilSenRegressor, Lars, LarsCV, Lasso, LassoCV, LassoLars, LassoLarsIC, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, Ridge, RidgeCV, BayesianRidge, ARDRegression, SGDRegressor, PassiveAggressiveRegressor |
| SVM | SVC, NuSVC, LinearSVC | SVR, NuSVR, LinearSVR |
| Tree | DecisionTreeClassifier, ExtraTreeClassifier | DecisionTreeRegressor, ExtraTreeRegressor |
| Random Forest | RandomForestClassifier, ExtraTreesClassifier | RandomForestRegressor, ExtraTreesRegressor |
| Boosting | XGBClassifier(gbtree/dart booster only), LGBMClassifier(gbdt/dart booster only) | XGBRegressor(gbtree/dart booster only), LGBMRegressor(gbdt/dart booster only) |
Scalar value; signed distance of the sample to the hyperplane for the second class.
Vector value; signed distance of the sample to the hyperplane per each class.
The output is consistent with the output of LinearClassifierMixin.decision_function.
Scalar value; signed distance of the sample to the hyperplane for the second class.
Vector value; one-vs-one score for each class, shape (n_samples, n_classes * (n_classes-1) / 2).
The output is consistent with the output of BaseSVC.decision_function when the decision_function_shape is set to ovo.
Vector value; class probabilities.
Vector value; class probabilities.
The output is consistent with the output of the predict_proba method of DecisionTreeClassifier/ForestClassifier/XGBClassifier/LGBMClassifier.
Here's a simple example of how a linear model trained in Python environment can be represented in Java code:
from sklearn.datasets import load_boston
from sklearn import linear_model
import m2cgen as m2c
boston = load_boston()
X, y = boston.data, boston.target
estimator = linear_model.LinearRegression()
estimator.fit(X, y)
code = m2c.export_to_java(estimator)Generated Java code:
public class Model {
public static double score(double[] input) {
return (((((((((((((36.45948838508965) + ((input[0]) * (-0.10801135783679647))) + ((input[1]) * (0.04642045836688297))) + ((input[2]) * (0.020558626367073608))) + ((input[3]) * (2.6867338193449406))) + ((input[4]) * (-17.76661122830004))) + ((input[5]) * (3.8098652068092163))) + ((input[6]) * (0.0006922246403454562))) + ((input[7]) * (-1.475566845600257))) + ((input[8]) * (0.30604947898516943))) + ((input[9]) * (-0.012334593916574394))) + ((input[10]) * (-0.9527472317072884))) + ((input[11]) * (0.009311683273794044))) + ((input[12]) * (-0.5247583778554867));
}
}You can find more examples of generated code for different models/languages here.
m2cgen can be used as a CLI tool to generate code using serialized model objects (pickle protocol):
$ m2cgen <pickle_file> --language <language> [--indent <indent>]
[--class_name <class_name>] [--package_name <package_name>]
[--recursion-limit <recursion_limit>]
Piping is also supported:
$ cat <pickle_file> | m2cgen --language <language>
Q: Generation fails with RuntimeError: maximum recursion depth exceeded error.
A: If this error occurs while generating code using an ensemble model, try to reduce the number of trained estimators within that model. Alternatively you can increase the maximum recursion depth with sys.setrecursionlimit(<new_depth>).