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Author
Contributors
Last Commit
May. 18, 2019
Created
Jan. 13, 2019

m2cgen

Build Status Coverage Status License: MIT

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).

Installation

Supported Python version is >= 3.4.

pip install m2cgen

Supported Languages

  • Python
  • Java
  • C
  • Go

Supported Models

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)

Classification Output

Linear/Linear SVM

Binary

Scalar value; signed distance of the sample to the hyperplane for the second class.

Multiclass

Vector value; signed distance of the sample to the hyperplane per each class.

Comment

The output is consistent with the output of LinearClassifierMixin.decision_function.

SVM

Binary

Scalar value; signed distance of the sample to the hyperplane for the second class.

Multiclass

Vector value; one-vs-one score for each class, shape (n_samples, n_classes * (n_classes-1) / 2).

Comment

The output is consistent with the output of BaseSVC.decision_function when the decision_function_shape is set to ovo.

Tree/Random Forest/XGBoost/LightGBM

Binary

Vector value; class probabilities.

Multiclass

Vector value; class probabilities.

Comment

The output is consistent with the output of the predict_proba method of DecisionTreeClassifier/ForestClassifier/XGBClassifier/LGBMClassifier.

Usage

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

CLI

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>

FAQ

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>).

Latest Releases
v0.2.1
 Apr. 17 2019
v0.2.1
 Apr. 3 2019
v0.2.0
 Mar. 22 2019
v0.1.1
 Mar. 4 2019
v0.1.1
 Feb. 17 2019