{
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        "author": "Dennis Collaris",
        "author_email": "d.collaris@me.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
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            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
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        "description": "<img src=\"https://user-images.githubusercontent.com/1223300/41346080-c2c910a0-6f05-11e8-89e9-71a72bb9543f.png\" width=\"300\">\n\n# sklearn-pmml-model\n\n[![PyPI version](https://badge.fury.io/py/sklearn-pmml-model.svg)](https://badge.fury.io/py/sklearn-pmml-model)\n[![CircleCI](https://circleci.com/gh/iamDecode/sklearn-pmml-model.svg?style=shield)](https://circleci.com/gh/iamDecode/sklearn-pmml-model)\n[![codecov](https://codecov.io/gh/iamDecode/sklearn-pmml-model/branch/master/graph/badge.svg?token=CGbbgziGwn)](https://codecov.io/gh/iamDecode/sklearn-pmml-model)\n[![ReadTheDocs](https://readthedocs.org/projects/sklearn-pmml-model/badge/?version=latest&style=flat)](https://sklearn-pmml-model.readthedocs.io/en/latest/)\n\nA library to parse PMML models into Scikit-learn estimators.\n\n## Installation\n\nThe easiest way is to use pip:\n\n```\n$ pip install sklearn-pmml-model\n```\n\n## Status\nThis library is very alpha, and currently only supports a limited number of models. The library currently supports the following models:\n- [Decision Trees](sklearn_pmml_model/tree) (`sklearn_pmml_model.tree.PMMLTreeClassifier`)\n- [Random Forests](sklearn_pmml_model/ensemble) (`sklearn_pmml_model.ensemble.PMMLForestClassifier`)\n- [Linear Regression](sklearn_pmml_model/linear_model) (`sklearn_pmml_model.linear_model.PMMLLinearRegression`)\n- [Ridge](sklearn_pmml_model/linear_model) (`sklearn_pmml_model.linear_model.PMMLRidge`)\n- [Lasso](sklearn_pmml_model/linear_model) (`sklearn_pmml_model.linear_model.PMMLLasso`)\n- [ElasticNet](sklearn_pmml_model/linear_model) (`sklearn_pmml_model.linear_model.PMMLElasticNet`)\n\nA small part of the [specification](http://dmg.org/pmml/v4-3/GeneralStructure.html) is covered:\n- DataDictionary\n  - DataField (continuous, categorical, ordinal)\n    - Value\n    - Interval\n- TransformationDictionary\n  - DerivedField\n- TreeModel\n  - SimplePredicate\n  - SimpleSetPredicate\n- Segmentation ('majorityVote' only, for Random Forests)\n- Regression\n  - RegressionTable\n    - NumericPredictor\n    - CategoricalPredictor\n- GeneralRegressionModel (only linear models)\n  - PPMatrix\n    - PPCell\n  - ParamMatrix\n    - PCell\n  \n## Example\nA minimal working example is shown below:\n\n```python\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nimport numpy as np\nfrom sklearn_pmml_model.ensemble import PMMLForestClassifier\n\n# Prepare data\niris = load_iris()\nX = pd.DataFrame(iris.data)\nX.columns = np.array(iris.feature_names)\ny = pd.Series(np.array(iris.target_names)[iris.target])\ny.name = \"Class\"\nXtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.33, random_state=123)\n\nclf = PMMLForestClassifier(pmml=\"models/randomForest.pmml\")\nclf.predict(Xte)\nclf.score(Xte, yte)\n```\n\nMore examples can be found in the subsequent packages: [tree](sklearn_pmml_model/tree), [ensemble](sklearn_pmml_model/ensemble) and [linear_model](sklearn_pmml_model/linear_model).\n\n## Development\n\n### Prerequisites\n\nTests can be run using Py.test. Grab a local copy of the source:\n\n```\n$ git clone http://github.com/iamDecode/sklearn-pmml-model\n$ cd sklearn-pmml-model\n```\n\ncreate a virtual environment and activating it:\n```\n$ python3 -m venv venv\n$ source venv/bin/activate\n```\n\nand install the dependencies:\n\n```\n$ pip install -r requirements.txt\n```\n\nThe final step is to build the Cython extensions:\n\n```\n$ python setup.py build_ext --inplace\n```\n\n### Testing\n\nYou can execute tests with py.test by running:\n```\n$ python setup.py pytest\n```\n\n## Contributing\n\nFeel free to make a contribution. Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on the code of conduct, and the process for submitting pull requests.\n\n## License\n\nThis project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details.",
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