sklearn-pmml-model
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 (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 | |||
| Random Forests | |||
| Gradient Boosting | |||
| Linear Regression | |||
| Ridge | |||
| Lasso | |||
| ElasticNet | |||
| Gaussian Naive Bayes | |||
| Support Vector Machines | |||
| Nearest Neighbors | |||
| Neural Networks |
1 Categorical feature support using slightly modified internals, based on scikit-learn#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) is shown below:
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
# Prepare 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)
clf = PMMLForestClassifier(pmml="models/randomForest.pmml")
clf.predict(Xte)
clf.score(Xte, yte)More examples can be found in the subsequent packages: tree, ensemble, linear_model, naive_bayes, svm, neighbors and neural_network.
Benchmark
Depending on the data set and model, sklearn-pmml-model is between 5 and a 1000 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.
Running times (load + predict, in seconds)
| Linear model | Naive Bayes | Decision tree | Random Forest | Gradient boosting | ||
|---|---|---|---|---|---|---|
| Wine | PyPMML |
0.773291 | 0.77384 | 0.777425 | 0.895204 | 0.902355 |
sklearn-pmml-model |
0.005813 | 0.006357 | 0.002693 | 0.108882 | 0.121823 | |
| Breast cancer | PyPMML |
3.849855 | 3.878448 | 3.83623 | 4.16358 | 4.13766 |
sklearn-pmml-model |
0.015723 | 0.011278 | 0.002807 | 0.146234 | 0.044016 |
Improvement
| Linear model | Naive Bayes | Decision tree | Random Forest | Gradient boosting | ||
|---|---|---|---|---|---|---|
| Wine | Improvement | 133× | 122× | 289× | 8× | 7× |
| Breast cancer | Improvement | 245× | 344× | 1,367× | 28× | 94× |
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 for more details.
License
This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.