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 | ✅ | ✅ | ✅1 |
| Random Forests | ✅ | ✅ | ✅1 |
| Gradient Boosting | ✅ | ✅ | ✅1 |
| Linear Regression | ✅ | ✅ | ✅3 |
| Ridge | ✅2 | ✅ | ✅3 |
| Lasso | ✅2 | ✅ | ✅3 |
| ElasticNet | ✅2 | ✅ | ✅3 |
| Gaussian Naive Bayes | ✅ | ✅3 | |
| Support Vector Machines | ✅ | ✅ | ✅3 |
| 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
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, ensemble, linear_model, naive_bayes, svm, neighbors and 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.
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 for more details.
License
This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.
1 maintainer
Metadata
1 maintainer
Release files for sklearn-pmml-model 1.0.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| sklearn_pmml_model-1.0.8.tar.gz | 985.1 kB | Without provenance | Details |
Built distributions (wheels)
Total release size: 31.5 MB
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Release files / sklearn_pmml_model-1.0.8-cp311-cp311-win_amd64.whl
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Release files / sklearn_pmml_model-1.0.8-cp311-cp311-win32.whl
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Release files / sklearn_pmml_model-1.0.8-cp311-cp311-macosx_11_0_arm64.whl
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Release files / sklearn_pmml_model-1.0.8-cp311-cp311-macosx_10_9_x86_64.whl
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