A library to parse PMML models into Scikit-learn estimators.
Project description
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
# 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.
Project details
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MD5 | f6c1d6759780ad69512c8235342a2734 |
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BLAKE2b-256 | 7b96d9388dc4db80e608140afb462b03064bdbc73a8f2e9d2ee99fae6d188320 |
Hashes for sklearn_pmml_model-1.0.0-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
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SHA256 | bceebf861900023d44f2405346034c7011261ab505b0f3ffce79369903cd69b1 |
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MD5 | 9d73f7714c4edf70d106dd3211d2bd23 |
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BLAKE2b-256 | da2e22d0b24c9de84f38187eb8786ff673fb90cee4d429d1efefce36e92a3a01 |
Hashes for sklearn_pmml_model-1.0.0-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | d41e015b5cfa498652345f27208ebc2f009f39be41ce779ea95652b8dff93f26 |
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MD5 | 7019e2c8677f8df1346ab11f1d1f9dcd |
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BLAKE2b-256 | 6422985fe1e8f7305585f06885f273ccc153e8e3f6d2655cd90e40afef3f6d30 |
Hashes for sklearn_pmml_model-1.0.0-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
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SHA256 | 152e424ae8189b8134bb2715df351a8b92832dd943cdd4e380dfa13247a294ea |
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MD5 | a61f369cc2d367be16a21230e055d092 |
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BLAKE2b-256 | 399d967ce255513d1ddbaea0b9da56028c481198fe801d0368fea10ce6dbaffc |
Hashes for sklearn_pmml_model-1.0.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | eaa682f0428a17c28288fc7ae1c8307c3a8ff80d30914a65a8db2e28f74530c1 |
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MD5 | 54ee166dd15a14db9d5dc6363c4fdf58 |
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BLAKE2b-256 | dc7f05a94974fa0902d350c8b202143ad1726d8fe7f130189ded1f847acaf2cc |
Hashes for sklearn_pmml_model-1.0.0-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | 281984dc06beb7ed4d33c38425b012a8d22d316b7c7937be718e914938bd4b09 |
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MD5 | b21c95073b81309c3f80859046d19e18 |
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BLAKE2b-256 | af1bc1048ec1b2349cfbff96c6cde435cf4f0b96671730c80869a4d23d864cdb |
Hashes for sklearn_pmml_model-1.0.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2be065f088124e32dc2f36c9d1c6d25ef49f454ad925f0ae4e5b6b1edaac04d1 |
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MD5 | 2a9e7fdfe7a41d622698065d6745e05e |
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BLAKE2b-256 | ae58b31123df939bb358b35c9c5e20adbc380b0d5d263c1568dfd1ef9fedecf9 |