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sklearn-pmml-model

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A library to parse and convert PMML models into Scikit-learn estimators.

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최종 버전 다운로드 (.zip)

- config.yml
- - bug_report.md
  - feature_request.md
- - build.yaml
  - codeql.yml
  - lint.yaml
- - custom.css
- conf.py
- contributing.md
- index.rst
- install.rst
- Makefile
- release_notes.rst
- categorical-test.csv
- gb-gbm-cat-pima-regression.pmml
- gb-gbm-cat-pima.pmml
- gb-xgboost-iris.pmml
- knn-clf-pima.pmml
- knn-reg-pima.pmml
- linear-model-glm.pmml
- linear-model-lasso.pmml
- linear-model-lm.pmml
- linear-model-lmc.pmml
- linear-model-ridge.pmml
- linear-model-ridgec.pmml
- nb-cat-pima.pmml
- nn-iris.pmml
- nn-pima-regression.pmml
- randomForest.pmml
- rf-cat-pima-regression.pmml
- rf-cat-pima.pmml
- rf-iris.pmml
- svc-cat-pima.pmml
- svr-cat-pima.pmml
- tree-cat-pima-regression.pmml
- tree-cat-pima.pmml
- tree-cat.pmml
- tree-digits.pmml
- tree-iris.pmml
- - __init__.py
  - base.py
- - __init__.py
  - _gradient_boosting.pyx
  - forest.py
  - gb.py
  - README.md
- - __init__.py
  - base.py
  - implementations.py
  - README.md
- - __init__.py
  - implementations.py
  - README.md
- - __init__.py
  - _base.py
  - _classes.py
  - README.md
- - __init__.py
  - _base.py
  - _classes.py
  - README.md
- - __init__.py
  - _base.py
  - _classes.py
  - README.md
- - __init__.py
  - _criterion.pxd
  - _criterion.pyx
  - _splitter.pxd
  - _splitter.pyx
  - _tree.pxd
  - _tree.pyx
  - _utils.pxd
  - _utils.pyx
  - quad_tree.pxd
  - quad_tree.pyx
  - README.md
  - tree.py
- __init__.py
- base.py
- datatypes.py
- - test_auto_detect.py
- - test_forest.py
  - test_gb.py
- - test_linear_model.py
- - test_naive_bayes.py
- - test_knn.py
- - test_neural_network.py
- - test_svm.py
- - test_tree.py
- __init__.py
- test_base.py
- test_datatypes.py
.flake8
.gitignore
.readthedocs.yaml
benchmark.ipynb
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
pyproject.toml
README.md
requirements.txt
setup.py

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# sklearn-pmml-model

[![PyPI version](https://badge.fury.io/py/sklearn-pmml-model.svg)](https://badge.fury.io/py/sklearn-pmml-model)
[![codecov](https://codecov.io/gh/iamDecode/sklearn-pmml-model/branch/master/graph/badge.svg?token=CGbbgziGwn)](https://codecov.io/gh/iamDecode/sklearn-pmml-model)
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[![ReadTheDocs](https://readthedocs.org/projects/sklearn-pmml-model/badge/?version=latest&style=flat)](https://sklearn-pmml-model.readthedocs.io/en/latest/)

A library to effortlessly import models trained on different platforms and with programming languages into scikit-learn in Python. First export your model to [PMML](http://dmg.org/pmml/v4-3/GeneralStructure.html) (widely supported). Next, load the exported PMML file with this library, and use the class as any other scikit-learn estimator.

## Installation

The easiest way is to use pip:

```
$ pip install sklearn-pmml-model
```

## Status
The library currently supports the following models:

| Model                                                  | Classification | Regression | Categorical features |
|--------------------------------------------------------|----------------|------------|----------------------|
| [Decision Trees](sklearn_pmml_model/tree)              | ✅             | ✅         | ✅<sup>1</sup>        |
| [Random Forests](sklearn_pmml_model/ensemble)          | ✅             | ✅         | ✅<sup>1</sup>        |
| [Gradient Boosting](sklearn_pmml_model/ensemble)       | ✅             | ✅         | ✅<sup>1</sup>        |
| [Linear Regression](sklearn_pmml_model/linear_model)   | ✅             | ✅         | ✅<sup>3</sup>        |
| [Ridge](sklearn_pmml_model/linear_model)               | ✅<sup>2</sup> | ✅         | ✅<sup>3</sup>        |
| [Lasso](sklearn_pmml_model/linear_model)               | ✅<sup>2</sup> | ✅         | ✅<sup>3</sup>        |
| [ElasticNet](sklearn_pmml_model/linear_model)          | ✅<sup>2</sup> | ✅         | ✅<sup>3</sup>        |
| [Gaussian Naive Bayes](sklearn_pmml_model/naive_bayes) | ✅             |            | ✅<sup>3</sup>        |
| [Support Vector Machines](sklearn_pmml_model/svm)      | ✅             | ✅         | ✅<sup>3</sup>        |
| [Nearest Neighbors](sklearn_pmml_model/neighbors)      | ✅             | ✅         |                      |
| [Neural Networks](sklearn_pmml_model/neural_network)   | ✅             | ✅         |                      |

<sub><sup>1</sup> Categorical feature support using slightly modified internals, based on [scikit-learn#12866](https://github.com/scikit-learn/scikit-learn/pull/12866).</sub>

<sub><sup>2</sup> These models differ only in training characteristics, the resulting model is of the same form. Classification is supported using `PMMLLogisticRegression` for regression models and `PMMLRidgeClassifier` for general regression models.</sub>

<sub><sup>3</sup> By one-hot encoding categorical features automatically.</sub>
  
## Example
A minimal working example (using [this PMML file](https://github.com/iamDecode/sklearn-pmml-model/blob/master/models/randomForest.pmml)) is shown below:

```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn_pmml_model.ensemble import PMMLForestClassifier
from sklearn_pmml_model.auto_detect import auto_detect_estimator

# Prepare the data
iris = load_iris()
X = pd.DataFrame(iris.data)
X.columns = np.array(iris.feature_names)
y = pd.Series(np.array(iris.target_names)[iris.target])
y.name = "Class"
Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.33, random_state=123)

# Specify the model type for the least overhead...
#clf = PMMLForestClassifier(pmml="models/randomForest.pmml")

# ...or simply let the library auto-detect the model type
clf = auto_detect_estimator(pmml="models/randomForest.pmml")

# Use the model as any other scikit-learn model
clf.predict(Xte)
clf.score(Xte, yte)
```

More examples can be found in the subsequent packages: [tree](sklearn_pmml_model/tree), [ensemble](sklearn_pmml_model/ensemble), [linear_model](sklearn_pmml_model/linear_model), [naive_bayes](sklearn_pmml_model/naive_bayes), [svm](sklearn_pmml_model/svm), [neighbors](sklearn_pmml_model/neighbors) and [neural_network](sklearn_pmml_model/neural_network).

## Benchmark

Depending on the data set and model, `sklearn-pmml-model` is between 1 and 10 times faster than competing libraries, by leveraging the optimization and industry-tested robustness of `sklearn`. Source code for this benchmark can be found in the corresponding [jupyter notebook](benchmark.ipynb).

### Running times (load + predict, in seconds)

|               |                     | Linear model | Naive Bayes | Decision tree | Random Forest | Gradient boosting |
|---------------|---------------------|--------------|-------------|---------------|---------------|-------------------|
| Wine          | `PyPMML`            | 0.013038     | 0.005674    | 0.005587      | 0.032734      | 0.034649          |
|               | `sklearn-pmml-model`| 0.00404      | 0.004059    | 0.000964      | 0.030008      | 0.032949          |
| Breast cancer | `PyPMML`            | 0.009838     | 0.01153     | 0.009367      | 0.058941      | 0.031196          |
|               | `sklearn-pmml-model`| 0.010749     | 0.008481    | 0.001106      | 0.044021      | 0.013411          |

### Improvement

|               |                    | Linear model | Naive Bayes | Decision tree | Random Forest | Gradient boosting |
|---------------|--------------------|--------------|-------------|---------------|---------------|-------------------|
| Wine          | Improvement        | 3.23×        | 1.40×       | 5.80×         | 1.09×         | 1.05×             |
| Breast cancer | Improvement        | 0.91×        | 1.36×       | **8.47×**     | 1.34×         | 2.33×             |

*Benchmark ran on: 24 september 2024 17:19*

## Development

### Prerequisites

Tests can be run using Py.test. Grab a local copy of the source:

```
$ git clone http://github.com/iamDecode/sklearn-pmml-model
$ cd sklearn-pmml-model
```

create a virtual environment and activating it:
```
$ python3 -m venv venv
$ source venv/bin/activate
```

and install the dependencies:

```
$ pip install -r requirements.txt
```

The final step is to build the Cython extensions:

```
$ python setup.py build_ext --inplace
```

### Testing

You can execute tests with py.test by running:
```
$ python setup.py pytest
```

## Contributing

Feel free to make a contribution. Please read [CONTRIBUTING.md](CONTRIBUTING.md) for more details.

## License

This project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details.