Metadata-Version: 2.1
Name: m2cgen
Version: 0.7.0
Summary: Code-generation for various ML models into native code.
Home-page: https://github.com/BayesWitnesses/m2cgen
License: MIT
Description: # m2cgen
        
        [![Build Status](https://travis-ci.org/BayesWitnesses/m2cgen.svg?branch=master)](https://travis-ci.org/BayesWitnesses/m2cgen)
        [![Coverage Status](https://coveralls.io/repos/github/BayesWitnesses/m2cgen/badge.svg?branch=master)](https://coveralls.io/github/BayesWitnesses/m2cgen?branch=master)
        [![License: MIT](https://img.shields.io/github/license/BayesWitnesses/m2cgen.svg)](https://github.com/BayesWitnesses/m2cgen/blob/master/LICENSE)
        [![Python Versions](https://img.shields.io/pypi/pyversions/m2cgen.svg?logo=python&logoColor=white)](https://pypi.org/project/m2cgen)
        [![PyPI Version](https://img.shields.io/pypi/v/m2cgen.svg?logo=pypi&logoColor=white)](https://pypi.org/project/m2cgen)
        [![Downloads](https://pepy.tech/badge/m2cgen)](https://pepy.tech/project/m2cgen)
        
        **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, Visual Basic, C#, PowerShell, R, PHP, Dart).
        
        * [Installation](#installation)
        * [Supported Languages](#supported-languages)
        * [Supported Models](#supported-models)
        * [Classification Output](#classification-output)
        * [Usage](#usage)
        * [CLI](#cli)
        * [FAQ](#faq)
        
        ## Installation
        Supported Python version is >= **3.5**.
        ```
        pip install m2cgen
        ```
        
        
        ## Supported Languages
        
        - C
        - C#
        - Dart
        - Go
        - Java
        - JavaScript
        - PHP
        - PowerShell
        - Python
        - R
        - Visual Basic
        
        ## Supported Models
        
        |  | Classification | Regression |
        | --- | --- | --- |
        | **Linear** | <ul><li>scikit-learn<ul><li>LogisticRegression</li><li>LogisticRegressionCV</li><li>PassiveAggressiveClassifier</li><li>Perceptron</li><li>RidgeClassifier</li><li>RidgeClassifierCV</li><li>SGDClassifier</li></ul></li><li>lightning<ul><li>AdaGradClassifier</li><li>CDClassifier</li><li>FistaClassifier</li><li>SAGAClassifier</li><li>SAGClassifier</li><li>SDCAClassifier</li><li>SGDClassifier</li></ul></li></ul> | <ul><li>scikit-learn<ul><li>ARDRegression</li><li>BayesianRidge</li><li>ElasticNet</li><li>ElasticNetCV</li><li>HuberRegressor</li><li>Lars</li><li>LarsCV</li><li>Lasso</li><li>LassoCV</li><li>LassoLars</li><li>LassoLarsCV</li><li>LassoLarsIC</li><li>LinearRegression</li><li>OrthogonalMatchingPursuit</li><li>OrthogonalMatchingPursuitCV</li><li>PassiveAggressiveRegressor</li><li>RANSACRegressor(only supported regression estimators can be used as a base estimator)</li><li>Ridge</li><li>RidgeCV</li><li>SGDRegressor</li><li>TheilSenRegressor</li></ul><li>StatsModels<ul><li>Generalized Least Squares (GLS)</li><li>Generalized Least Squares with AR Errors (GLSAR)</li><li>Ordinary Least Squares (OLS)</li><li>[Gaussian] Process Regression Using Maximum Likelihood-based Estimation (ProcessMLE)</li><li>Quantile Regression (QuantReg)</li><li>Weighted Least Squares (WLS)</li></ul><li>lightning<ul><li>AdaGradRegressor</li><li>CDRegressor</li><li>FistaRegressor</li><li>SAGARegressor</li><li>SAGRegressor</li><li>SDCARegressor</li></ul></li></ul> |
        | **SVM** | <ul><li>scikit-learn<ul><li>LinearSVC</li><li>NuSVC</li><li>SVC</li></ul></li><li>lightning<ul><li>KernelSVC</li><li>LinearSVC</li></ul></li></ul> | <ul><li>scikit-learn<ul><li>LinearSVR</li><li>NuSVR</li><li>SVR</li></ul></li><li>lightning<ul><li>LinearSVR</li></ul></li></ul> |
        | **Tree** | <ul><li>DecisionTreeClassifier</li><li>ExtraTreeClassifier</li></ul> | <ul><li>DecisionTreeRegressor</li><li>ExtraTreeRegressor</li></ul> |
        | **Random Forest** | <ul><li>ExtraTreesClassifier</li><li>LGBMClassifier(rf booster only)</li><li>RandomForestClassifier</li><li>XGBRFClassifier(binary only, multiclass is not supported yet)</li></ul> | <ul><li>ExtraTreesRegressor</li><li>LGBMRegressor(rf booster only)</li><li>RandomForestRegressor</li><li>XGBRFRegressor</li></ul> |
        | **Boosting** | <ul><li>LGBMClassifier(gbdt/dart/goss booster only)</li><li>XGBClassifier(gbtree/gblinear booster only)</li><ul> | <ul><li>LGBMRegressor(gbdt/dart/goss booster only)</li><li>XGBRegressor(gbtree/gblinear booster only)</li></ul> |
        
        ## Classification Output
        ### Linear / Linear SVM / Kernel 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 / Boosting
        #### 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` / `ExtraTreeClassifier` / `ExtraTreesClassifier` / `RandomForestClassifier` / `XGBRFClassifier` / `XGBClassifier` / `LGBMClassifier`.
        
        ## Usage
        
        Here's a simple example of how a linear model trained in Python environment can be represented in Java code:
        ```python
        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:
        ```java
        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](https://github.com/BayesWitnesses/m2cgen/tree/master/generated_code_examples).**
        
        ## 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>] [--function_name <function_name>]
                 [--class_name <class_name>] [--module_name <module_name>] [--package_name <package_name>]
                 [--namespace <namespace>] [--recursion-limit <recursion_limit>]
        ```
        Don't forget that for unpickling serialized model objects their classes must be defined in the top level of an importable module in the unpickling environment.
        
        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>)`.
        
        **Q: Generation fails with `ImportError: No module named <module_name_here>` error while transpiling model from a serialized model object.**
        
        A: This error indicates that pickle protocol cannot deserialize model object. For unpickling serialized model objects, it is required that their classes must be defined in the top level of an importable module in the unpickling environment. So installation of package which provided model's class definition should solve the problem.
        
Keywords: sklearn ml code-generation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.5
Description-Content-Type: text/markdown
