Metadata-Version: 2.1
Name: mlprodict
Version: 0.8.1697
Summary: Python Runtime for ONNX models, other helpers to convert machine learned models in C++.
Home-page: http://www.xavierdupre.fr/app/mlprodict/helpsphinx/index.html
Author: Xavier Dupré
Author-email: xavier.dupre@gmail.com
License: MIT
Download-URL: https://github.com/sdpython/mlprodict/
Description: 
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        .. _l-README:
        
        mlprodict
        =========
        
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        *mlprodict* was initially started to help implementing converters
        to *ONNX*. The main features is a python runtime for
        *ONNX* (class `OnnxInference
        <http://www.xavierdupre.fr/app/mlprodict/helpsphinx/mlprodict/onnxrt/onnx_inference.html>`_),
        visualization tools
        (see `Visualization
        <http://www.xavierdupre.fr/app/mlprodict/helpsphinx/api/tools.html#visualization>`_),
        and a `numpy API for ONNX
        <http://www.xavierdupre.fr/app/mlprodict/helpsphinx/tutorial/numpy_api_onnx.html>`_).
        The package also provides tools to compare
        predictions, to benchmark models converted with
        `sklearn-onnx <https://github.com/onnx/sklearn-onnx/tree/master/skl2onnx>`_.
        
        ::
        
            import numpy
            from sklearn.linear_model import LinearRegression
            from sklearn.datasets import load_iris
            from mlprodict.onnxrt import OnnxInference
            from mlprodict.onnxrt.validate.validate_difference import measure_relative_difference
            from mlprodict.tools import get_ir_version_from_onnx
        
            iris = load_iris()
            X = iris.data[:, :2]
            y = iris.target
            lr = LinearRegression()
            lr.fit(X, y)
        
            # Predictions with scikit-learn.
            expected = lr.predict(X[:5])
            print(expected)
        
            # Conversion into ONNX.
            from mlprodict.onnx_conv import to_onnx
            model_onnx = to_onnx(lr, X.astype(numpy.float32),
                                 black_op={'LinearRegressor'})
            print("ONNX:", str(model_onnx)[:200] + "\n...")
        
            # Predictions with onnxruntime
            model_onnx.ir_version = get_ir_version_from_onnx()
            oinf = OnnxInference(model_onnx, runtime='onnxruntime1')
            ypred = oinf.run({'X': X[:5].astype(numpy.float32)})
            print("ONNX output:", ypred)
        
            # Measuring the maximum difference.
            print("max abs diff:", measure_relative_difference(expected, ypred['variable']))
        
            # And the python runtime
            oinf = OnnxInference(model_onnx, runtime='python')
            ypred = oinf.run({'X': X[:5].astype(numpy.float32)},
                             verbose=1, fLOG=print)
            print("ONNX output:", ypred)
        
        **Installation**
        
        Installation from *pip* should work unless you need the latest
        development features.
        
        ::
        
            pip install mlprodict
        
        The package includes a runtime for *onnx*. That's why there
        is a limited number of dependencies. However, some features
        relies on *sklearn-onnx*, *onnxruntime*, *scikit-learn*.
        They can be installed with the following instructions:
        
        ::
        
            pip install mlprodict[all]
        
        The code is available at
        `GitHub/mlprodict <https://github.com/sdpython/mlprodict/>`_
        and has `online documentation <http://www.xavierdupre.fr/app/
        mlprodict/helpsphinx/index.html>`_.
        
Keywords: mlprodict,Xavier Dupré
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 5 - Production/Stable
Provides-Extra: npy
Provides-Extra: onnx_conv
Provides-Extra: onnx_val
Provides-Extra: sklapi
Provides-Extra: all
