A library to parse PMML models into Scikit-learn estimators.
Project description
sklearn-pmml-model
A library to parse PMML models into Scikit-learn estimators.
Installation
The easiest way is to use pip:
$ pip install sklearn-pmml-model
Status
This library is in beta, and currently not all models are supported. The library currently does support 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 | ✅ | ✅ |
Gaussian Naive Bayes | ✅ |
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.
The following part of the specification is covered:
- DataDictionary
- DataField (continuous, categorical, ordinal)
- Value
- Interval
- DataField (continuous, categorical, ordinal)
- TransformationDictionary / LocalTransformations
- DerivedField
- TreeModel
- SimplePredicate
- SimpleSetPredicate
- Segmentation ('majorityVote' for Random Forests, 'modelChain' and 'sum' for Gradient Boosting)
- Regression
- RegressionTable
- NumericPredictor
- CategoricalPredictor
- RegressionTable
- GeneralRegressionModel (only linear models)
- PPMatrix
- PPCell
- ParamMatrix
- PCell
- PPMatrix
- NaiveBayesModel
- BayesInputs
- BayesInput
- TargetValueStats
- TargetValueStat
- GaussianDistribution
- TargetValueStat
- PairCounts
- TargetValueCounts
- TargetValueCount
- TargetValueCounts
- TargetValueStats
- BayesInput
- BayesInputs
Example
A minimal working example 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 and naive_bayes.
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 details on the code of conduct, and the process for submitting pull requests.
License
This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.
Project details
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MD5 | 5c8f50efb0e72e40e02875865f769467 |
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BLAKE2b-256 | b24bb5230a4d2f2efbd60c02dea80d5346da894c68ac7e2e310e03c5b3e49d7c |
Hashes for sklearn_pmml_model-0.0.17-cp35-cp35m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 1e781b0016dbc9fde62449e580b1ea107bed9b51ef092ab98d63465201893767 |
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MD5 | 1c2687d17c76062c5ac3c3c9f9b66a7b |
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Hashes for sklearn_pmml_model-0.0.17-cp35-cp35m-manylinux1_i686.whl
Algorithm | Hash digest | |
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SHA256 | c37e67718435c5dd596c4df3197bb89304abc8fb918c9ca61b39b1ef5b852169 |
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MD5 | 03caea1b617a808cb7bdfb0beb2df912 |
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BLAKE2b-256 | 27c764322e6168258e3dc1be4c19750d678fb2b5f76f4c08421b928accb70c22 |
Hashes for sklearn_pmml_model-0.0.17-cp35-cp35m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
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SHA256 | 554abea4332a0097e555ce7355ae1b8f0bc0e41f7d07b3f740e05e1e41e7e1ac |
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MD5 | a0a37c3d5fb1430165cd0dee15edc8c2 |
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