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https://github.com/iamdecode/sklearn-pmml-model

A library to parse and convert PMML models into Scikit-learn estimators.
https://github.com/iamdecode/sklearn-pmml-model

machine-learning pmml scikit-learn sklearn

Last synced: about 19 hours ago
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A library to parse and convert PMML models into Scikit-learn estimators.

Host: GitHub
URL: https://github.com/iamdecode/sklearn-pmml-model
Owner: iamDecode
License: bsd-2-clause
Created: 2018-06-08T17:38:21.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2024-09-24T15:22:03.000Z (3 months ago)
Last Synced: 2024-12-18T12:07:08.275Z (8 days ago)
Topics: machine-learning, pmml, scikit-learn, sklearn
Language: Python
Homepage:
Size: 597 KB
Stars: 77
Watchers: 4
Forks: 15
Open Issues: 13
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        

# 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)              | ✅             | ✅         | ✅¹        |

| [Random Forests](sklearn_pmml_model/ensemble)          | ✅             | ✅         | ✅¹        |

| [Gradient Boosting](sklearn_pmml_model/ensemble)       | ✅             | ✅         | ✅¹        |

| [Linear Regression](sklearn_pmml_model/linear_model)   | ✅             | ✅         | ✅³        |

| [Ridge](sklearn_pmml_model/linear_model)               | ✅² | ✅         | ✅³        |

| [Lasso](sklearn_pmml_model/linear_model)               | ✅² | ✅         | ✅³        |

| [ElasticNet](sklearn_pmml_model/linear_model)          | ✅² | ✅         | ✅³        |

| [Gaussian Naive Bayes](sklearn_pmml_model/naive_bayes) | ✅             |            | ✅³        |

| [Support Vector Machines](sklearn_pmml_model/svm)      | ✅             | ✅         | ✅³        |

| [Nearest Neighbors](sklearn_pmml_model/neighbors)      | ✅             | ✅         |                      |

| [Neural Networks](sklearn_pmml_model/neural_network)   | ✅             | ✅         |                      |

_{¹ Categorical feature support using slightly modified internals, based on [scikit-learn#12866](https://github.com/scikit-learn/scikit-learn/pull/12866).}

_{² 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.}

_{³ By one-hot encoding categorical features automatically.}

  

## 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.