%global _empty_manifest_terminate_build 0
Name:		python-m2cgen
Version:	0.10.0
Release:	1
Summary:	Code-generation for various ML models into native code.
License:	MIT
URL:		https://github.com/BayesWitnesses/m2cgen
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/72/92/eb4176f0e71bcd988a9df54e7103987a232082e5b6ef14a12d56b1c3bddf/m2cgen-0.10.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy

%description
# m2cgen

[![GitHub Actions Status](https://github.com/BayesWitnesses/m2cgen/workflows/GitHub%20Actions/badge.svg?branch=master)](https://github.com/BayesWitnesses/m2cgen/actions)
[![Coverage Status](https://codecov.io/gh/BayesWitnesses/m2cgen/branch/master/graph/badge.svg)](https://codecov.io/gh/BayesWitnesses/m2cgen)
[![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, Haskell, Ruby, F#, Rust, Elixir).

* [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 >= **3.7**.
```
pip install m2cgen
```


## Supported Languages

- C
- C#
- Dart
- F#
- Go
- Haskell
- Java
- JavaScript
- PHP
- PowerShell
- Python
- R
- Ruby
- Rust
- Visual Basic (VBA-compatible)
- Elixir

## 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>GammaRegressor</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>PoissonRegressor</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><li>TweedieRegressor</li></ul><li>StatsModels<ul><li>Generalized Least Squares (GLS)</li><li>Generalized Least Squares with AR Errors (GLSAR)</li><li>Generalized Linear Models (GLM)</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><li>SGDRegressor</li></ul></li></ul> |
| **SVM** | <ul><li>scikit-learn<ul><li>LinearSVC</li><li>NuSVC</li><li>OneClassSVM</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</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(including boosted forests)/gblinear booster only)</li><ul> | <ul><li>LGBMRegressor(gbdt/dart/goss booster only)</li><li>XGBRegressor(gbtree(including boosted forests)/gblinear booster only)</li></ul> |

You can find versions of packages with which compatibility is guaranteed by CI tests [here](https://github.com/BayesWitnesses/m2cgen/blob/master/requirements-test.txt#L1).
Other versions can also be supported but they are untested.

## 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
#### Outlier detection
Scalar value; signed distance of the sample to the separating hyperplane: positive for an inlier and negative for an outlier.
#### 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_diabetes
from sklearn import linear_model
import m2cgen as m2c

X, y = load_diabetes(return_X_y=True)

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 ((((((((((152.1334841628965) + ((input[0]) * (-10.012197817470472))) + ((input[1]) * (-239.81908936565458))) + ((input[2]) * (519.8397867901342))) + ((input[3]) * (324.39042768937657))) + ((input[4]) * (-792.1841616283054))) + ((input[5]) * (476.74583782366153))) + ((input[6]) * (101.04457032134408))) + ((input[7]) * (177.06417623225025))) + ((input[8]) * (751.2793210873945))) + ((input[9]) * (67.62538639104406));
    }
}
```

**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 `RecursionError: 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.

**Q: Generated by m2cgen code provides different results for some inputs compared to original Python model from which the code were obtained.**

A: Some models force input data to be particular type during prediction phase in their native Python libraries. Currently, m2cgen works only with ``float64`` (``double``) data type. You can try to cast your input data to another type manually and check results again. Also, some small differences can happen due to specific implementation of floating-point arithmetic in a target language.




%package -n python3-m2cgen
Summary:	Code-generation for various ML models into native code.
Provides:	python-m2cgen
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-m2cgen
# m2cgen

[![GitHub Actions Status](https://github.com/BayesWitnesses/m2cgen/workflows/GitHub%20Actions/badge.svg?branch=master)](https://github.com/BayesWitnesses/m2cgen/actions)
[![Coverage Status](https://codecov.io/gh/BayesWitnesses/m2cgen/branch/master/graph/badge.svg)](https://codecov.io/gh/BayesWitnesses/m2cgen)
[![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, Haskell, Ruby, F#, Rust, Elixir).

* [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 >= **3.7**.
```
pip install m2cgen
```


## Supported Languages

- C
- C#
- Dart
- F#
- Go
- Haskell
- Java
- JavaScript
- PHP
- PowerShell
- Python
- R
- Ruby
- Rust
- Visual Basic (VBA-compatible)
- Elixir

## 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>GammaRegressor</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>PoissonRegressor</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><li>TweedieRegressor</li></ul><li>StatsModels<ul><li>Generalized Least Squares (GLS)</li><li>Generalized Least Squares with AR Errors (GLSAR)</li><li>Generalized Linear Models (GLM)</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><li>SGDRegressor</li></ul></li></ul> |
| **SVM** | <ul><li>scikit-learn<ul><li>LinearSVC</li><li>NuSVC</li><li>OneClassSVM</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</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(including boosted forests)/gblinear booster only)</li><ul> | <ul><li>LGBMRegressor(gbdt/dart/goss booster only)</li><li>XGBRegressor(gbtree(including boosted forests)/gblinear booster only)</li></ul> |

You can find versions of packages with which compatibility is guaranteed by CI tests [here](https://github.com/BayesWitnesses/m2cgen/blob/master/requirements-test.txt#L1).
Other versions can also be supported but they are untested.

## 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
#### Outlier detection
Scalar value; signed distance of the sample to the separating hyperplane: positive for an inlier and negative for an outlier.
#### 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_diabetes
from sklearn import linear_model
import m2cgen as m2c

X, y = load_diabetes(return_X_y=True)

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 ((((((((((152.1334841628965) + ((input[0]) * (-10.012197817470472))) + ((input[1]) * (-239.81908936565458))) + ((input[2]) * (519.8397867901342))) + ((input[3]) * (324.39042768937657))) + ((input[4]) * (-792.1841616283054))) + ((input[5]) * (476.74583782366153))) + ((input[6]) * (101.04457032134408))) + ((input[7]) * (177.06417623225025))) + ((input[8]) * (751.2793210873945))) + ((input[9]) * (67.62538639104406));
    }
}
```

**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 `RecursionError: 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.

**Q: Generated by m2cgen code provides different results for some inputs compared to original Python model from which the code were obtained.**

A: Some models force input data to be particular type during prediction phase in their native Python libraries. Currently, m2cgen works only with ``float64`` (``double``) data type. You can try to cast your input data to another type manually and check results again. Also, some small differences can happen due to specific implementation of floating-point arithmetic in a target language.




%package help
Summary:	Development documents and examples for m2cgen
Provides:	python3-m2cgen-doc
%description help
# m2cgen

[![GitHub Actions Status](https://github.com/BayesWitnesses/m2cgen/workflows/GitHub%20Actions/badge.svg?branch=master)](https://github.com/BayesWitnesses/m2cgen/actions)
[![Coverage Status](https://codecov.io/gh/BayesWitnesses/m2cgen/branch/master/graph/badge.svg)](https://codecov.io/gh/BayesWitnesses/m2cgen)
[![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, Haskell, Ruby, F#, Rust, Elixir).

* [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 >= **3.7**.
```
pip install m2cgen
```


## Supported Languages

- C
- C#
- Dart
- F#
- Go
- Haskell
- Java
- JavaScript
- PHP
- PowerShell
- Python
- R
- Ruby
- Rust
- Visual Basic (VBA-compatible)
- Elixir

## 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>GammaRegressor</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>PoissonRegressor</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><li>TweedieRegressor</li></ul><li>StatsModels<ul><li>Generalized Least Squares (GLS)</li><li>Generalized Least Squares with AR Errors (GLSAR)</li><li>Generalized Linear Models (GLM)</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><li>SGDRegressor</li></ul></li></ul> |
| **SVM** | <ul><li>scikit-learn<ul><li>LinearSVC</li><li>NuSVC</li><li>OneClassSVM</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</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(including boosted forests)/gblinear booster only)</li><ul> | <ul><li>LGBMRegressor(gbdt/dart/goss booster only)</li><li>XGBRegressor(gbtree(including boosted forests)/gblinear booster only)</li></ul> |

You can find versions of packages with which compatibility is guaranteed by CI tests [here](https://github.com/BayesWitnesses/m2cgen/blob/master/requirements-test.txt#L1).
Other versions can also be supported but they are untested.

## 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
#### Outlier detection
Scalar value; signed distance of the sample to the separating hyperplane: positive for an inlier and negative for an outlier.
#### 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_diabetes
from sklearn import linear_model
import m2cgen as m2c

X, y = load_diabetes(return_X_y=True)

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 ((((((((((152.1334841628965) + ((input[0]) * (-10.012197817470472))) + ((input[1]) * (-239.81908936565458))) + ((input[2]) * (519.8397867901342))) + ((input[3]) * (324.39042768937657))) + ((input[4]) * (-792.1841616283054))) + ((input[5]) * (476.74583782366153))) + ((input[6]) * (101.04457032134408))) + ((input[7]) * (177.06417623225025))) + ((input[8]) * (751.2793210873945))) + ((input[9]) * (67.62538639104406));
    }
}
```

**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 `RecursionError: 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.

**Q: Generated by m2cgen code provides different results for some inputs compared to original Python model from which the code were obtained.**

A: Some models force input data to be particular type during prediction phase in their native Python libraries. Currently, m2cgen works only with ``float64`` (``double``) data type. You can try to cast your input data to another type manually and check results again. Also, some small differences can happen due to specific implementation of floating-point arithmetic in a target language.




%prep
%autosetup -n m2cgen-0.10.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-m2cgen -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.10.0-1
- Package Spec generated