module `sklapi.onnx_speed_up`#

Short summary#

module mlprodict.sklapi.onnx_speed_up

Classes#

class	truncated documentation
`_OnnxPipelineStepSpeedup`	Speeds up inference by replacing methods transform or predict by a runtime for ONNX.
`OnnxSpeedupClassifier`	Trains with scikit-learn, transform with ONNX.
`OnnxSpeedupCluster`	Trains with scikit-learn, transform with ONNX.
`OnnxSpeedupRegressor`	Trains with scikit-learn, transform with ONNX.
`OnnxSpeedupTransformer`	Trains with scikit-learn, transform with ONNX.

Properties#

property	truncated documentation
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`op_version`	Returns the opset version.
`op_version`	Returns the opset version.
`op_version`	Returns the opset version.
`op_version`	Returns the opset version.
`op_version`	Returns the opset version.

Methods#

method	truncated documentation
`__getstate__`	pickle does not support functions. This method removes any link to function when the runtime is …
`__getstate__`	pickle does not support functions. This method removes any link to function when the runtime is …
`__getstate__`	pickle does not support functions. This method removes any link to function when the runtime is …
`__getstate__`	pickle does not support functions. This method removes any link to function when the runtime is …
`__getstate__`	pickle does not support functions. This method removes any link to function when the runtime is …
`__init__`
`__init__`
`__init__`
`__init__`
`__init__`
`__setstate__`	pickle does not support functions. This method restores the function created when the runtime is …
`__setstate__`	pickle does not support functions. This method restores the function created when the runtime is …
`__setstate__`	pickle does not support functions. This method restores the function created when the runtime is …
`__setstate__`	pickle does not support functions. This method restores the function created when the runtime is …
`__setstate__`	pickle does not support functions. This method restores the function created when the runtime is …
`_build_onnx_runtime`	Returns an instance of `OnnxTransformer` which executes the ONNX graph.
`_build_onnx_runtime`	Returns an instance of `OnnxTransformer` which executes the ONNX graph.
`_build_onnx_runtime`	Returns an instance of `OnnxTransformer` which executes the ONNX graph.
`_build_onnx_runtime`	Returns an instance of `OnnxTransformer` which executes the ONNX graph.
`_build_onnx_runtime`	Returns an instance of `OnnxTransformer` which executes the ONNX graph.
`_build_onnx_runtime_numpy`	Builds a runtime based on numpy. Exports the ONNX graph into python code based on numpy and then dynamically …
`_build_onnx_runtime_numpy`	Builds a runtime based on numpy. Exports the ONNX graph into python code based on numpy and then dynamically …
`_build_onnx_runtime_numpy`	Builds a runtime based on numpy. Exports the ONNX graph into python code based on numpy and then dynamically …
`_build_onnx_runtime_numpy`	Builds a runtime based on numpy. Exports the ONNX graph into python code based on numpy and then dynamically …
`_build_onnx_runtime_numpy`	Builds a runtime based on numpy. Exports the ONNX graph into python code based on numpy and then dynamically …
`_build_onnx_runtime_numpy_compile`	Second part of `_build_onnx_runtime_numpy()`.
`_build_onnx_runtime_numpy_compile`	Second part of `_build_onnx_runtime_numpy()`.
`_build_onnx_runtime_numpy_compile`	Second part of `_build_onnx_runtime_numpy()`.
`_build_onnx_runtime_numpy_compile`	Second part of `_build_onnx_runtime_numpy()`.
`_build_onnx_runtime_numpy_compile`	Second part of `_build_onnx_runtime_numpy()`.
`_check_fitted_`
`_check_fitted_`
`_check_fitted_`
`_check_fitted_`
`_check_fitted_`
`_to_onnx`	Converts an estimator inference into ONNX.
`_to_onnx`	Converts an estimator inference into ONNX.
`_to_onnx`	Converts an estimator inference into ONNX.
`_to_onnx`	Converts an estimator inference into ONNX.
`_to_onnx`	Converts an estimator inference into ONNX.
`assert_almost_equal`	Checks that ONNX and scikit-learn produces the same outputs.
`assert_almost_equal`	Checks that ONNX and scikit-learn produces the same outputs.
`assert_almost_equal`	Checks that ONNX and scikit-learn produces the same outputs.
`assert_almost_equal`	Checks that ONNX and scikit-learn produces the same outputs.
`fit`	Trains based estimator.
`fit`	Trains based estimator.
`fit`	Trains based estimator.
`fit`	Trains based estimator.
`fit`	Fits the estimator, converts to ONNX.
`onnx_converter`	Returns a converter for this transform.
`onnx_converter`	Returns a converter for this transform.
`onnx_converter`	Returns a converter for this transform.
`onnx_converter`	Returns a converter for this transform.
`onnx_converter`	Returns a converter for this transform.
`onnx_parser`	Returns a parser for this model.
`onnx_parser`	Returns a parser for this model.
`onnx_parser`	Returns a parser for this model.
`onnx_parser`	Returns a parser for this model.
`onnx_parser`	Returns a parser for this model.
`onnx_shape_calculator`	Returns a shape calculator for this transform.
`onnx_shape_calculator`	Returns a shape calculator for this transform.
`onnx_shape_calculator`	Returns a shape calculator for this transform.
`onnx_shape_calculator`	Returns a shape calculator for this transform.
`onnx_shape_calculator`	Returns a shape calculator for this transform.
`predict`	Transforms with ONNX.
`predict`	Transforms with ONNX.
`predict`	Transforms with ONNX.
`predict_proba`	Transforms with ONNX.
`raw_predict`	Transforms with scikit-learn.
`raw_predict`	Transforms with scikit-learn.
`raw_predict`	Transforms with scikit-learn.
`raw_predict_proba`	Transforms with scikit-learn.
`raw_transform`	Transforms with scikit-learn.
`raw_transform`	Transforms with scikit-learn.
`transform`	Transforms with ONNX.
`transform`	Transforms with ONNX.

Documentation#

Speeding up scikit-learn with onnx.

New in version 0.7.

source on GitHub

class mlprodict.sklapi.onnx_speed_up.OnnxSpeedupClassifier(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

Bases: ClassifierMixin, _OnnxPipelineStepSpeedup

Trains with scikit-learn, transform with ONNX.

Parameters:

estimator – estimator to train
enforce_float32 – boolean onnxruntime only supports float32, scikit-learn usually uses double floats, this parameter ensures that every array of double floats is converted into single floats
runtime – string, defined the runtime to use as described in OnnxInference.
target_opset – targetted ONNX opset
conv_options – conversion options, see to_onnx
nopython – used by numba jitter

Attributes created by method fit:

estimator_: cloned and trained version of estimator
onnxrt_: objet of type OnnxInference,
sklearn.preprocessing.FunctionTransformer
numpy_code_: python code equivalent to the inference
method if the runtime is ‘numpy’ or ‘numba’
onnx_io_names_: dictionary, additional information
if the runtime is ‘numpy’ or ‘numba’

New in version 0.7.

source on GitHub

__init__(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

assert_almost_equal(X, **kwargs)#

Checks that ONNX and scikit-learn produces the same outputs.

source on GitHub

fit(X, y, sample_weight=None)#

Trains based estimator.

source on GitHub

predict(X)#

Transforms with ONNX.

Parameters:: X – features
Returns:: transformed features

source on GitHub

predict_proba(X)#

Transforms with ONNX.

Parameters:: X – features
Returns:: transformed features

source on GitHub

raw_predict(X)#

Transforms with scikit-learn.

Parameters:: X – features
Returns:: transformed features

source on GitHub

raw_predict_proba(X)#

Transforms with scikit-learn.

Parameters:: X – features
Returns:: transformed features

source on GitHub

class mlprodict.sklapi.onnx_speed_up.OnnxSpeedupCluster(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

Bases: ClusterMixin, _OnnxPipelineStepSpeedup

Trains with scikit-learn, transform with ONNX.

Parameters:

estimator – estimator to train
enforce_float32 – boolean onnxruntime only supports float32, scikit-learn usually uses double floats, this parameter ensures that every array of double floats is converted into single floats
runtime – string, defined the runtime to use as described in OnnxInference.
target_opset – targetted ONNX opset
conv_options – conversion options, see to_onnx
nopython – used by numba jitter

Attributes created by method fit:

estimator_: cloned and trained version of estimator
onnxrt_: objet of type OnnxInference,
sklearn.preprocessing.FunctionTransformer
numpy_code_: python code equivalent to the inference
method if the runtime is ‘numpy’ or ‘numba’
onnx_io_names_: dictionary, additional information
if the runtime is ‘numpy’ or ‘numba’

New in version 0.7.

source on GitHub

__init__(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

assert_almost_equal(X, **kwargs)#

Checks that ONNX and scikit-learn produces the same outputs.

source on GitHub

fit(X, y, sample_weight=None)#

Trains based estimator.

source on GitHub

predict(X)#

Transforms with ONNX.

Parameters:: X – features
Returns:: transformed features

source on GitHub

raw_predict(X)#

Transforms with scikit-learn.

Parameters:: X – features
Returns:: transformed features

source on GitHub

raw_transform(X)#

Transforms with scikit-learn.

Parameters:: X – features
Returns:: transformed features

source on GitHub

transform(X)#

Transforms with ONNX.

Parameters:: X – features
Returns:: transformed features

source on GitHub

class mlprodict.sklapi.onnx_speed_up.OnnxSpeedupRegressor(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

Bases: RegressorMixin, _OnnxPipelineStepSpeedup

Trains with scikit-learn, transform with ONNX.

Parameters:

estimator – estimator to train
enforce_float32 – boolean onnxruntime only supports float32, scikit-learn usually uses double floats, this parameter ensures that every array of double floats is converted into single floats
runtime – string, defined the runtime to use as described in OnnxInference.
target_opset – targetted ONNX opset
conv_options – conversion options, see to_onnx
nopython – used by numba jitter

Attributes created by method fit:

estimator_: cloned and trained version of estimator
onnxrt_: objet of type OnnxInference,
sklearn.preprocessing.FunctionTransformer
numpy_code_: python code equivalent to the inference
method if the runtime is ‘numpy’ or ‘numba’
onnx_io_names_: dictionary, additional information
if the runtime is ‘numpy’ or ‘numba’

New in version 0.7.

source on GitHub

__init__(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

assert_almost_equal(X, **kwargs)#

Checks that ONNX and scikit-learn produces the same outputs.

source on GitHub

fit(X, y, sample_weight=None)#

Trains based estimator.

source on GitHub

predict(X)#

Transforms with ONNX.

Parameters:: X – features
Returns:: transformed features

source on GitHub

raw_predict(X)#

Transforms with scikit-learn.

Parameters:: X – features
Returns:: transformed features

source on GitHub

class mlprodict.sklapi.onnx_speed_up.OnnxSpeedupTransformer(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

Bases: TransformerMixin, _OnnxPipelineStepSpeedup

Trains with scikit-learn, transform with ONNX.

Parameters:

estimator – estimator to train
enforce_float32 – boolean onnxruntime only supports float32, scikit-learn usually uses double floats, this parameter ensures that every array of double floats is converted into single floats
runtime – string, defined the runtime to use as described in OnnxInference.
target_opset – targetted ONNX opset
conv_options – conversion options, see to_onnx
nopython – used by numba jitter

Attributes created by method fit:

estimator_: cloned and trained version of estimator
onnxrt_: objet of type OnnxInference,
sklearn.preprocessing.FunctionTransformer
numpy_code_: python code equivalent to the inference
method if the runtime is ‘numpy’ or ‘numba’
onnx_io_names_: dictionary, additional information
if the runtime is ‘numpy’ or ‘numba’

New in version 0.7.

source on GitHub

__init__(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

_sklearn_auto_wrap_output_keys = {'transform'}#

assert_almost_equal(X, **kwargs)#

Checks that ONNX and scikit-learn produces the same outputs.

source on GitHub

fit(X, y=None, sample_weight=None)#

Trains based estimator.

source on GitHub

raw_transform(X)#

Transforms with scikit-learn.

Parameters:: X – features
Returns:: transformed features

source on GitHub

transform(X)#

Transforms with ONNX.

Parameters:: X – features
Returns:: transformed features

source on GitHub

class mlprodict.sklapi.onnx_speed_up._OnnxPipelineStepSpeedup(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

Bases: BaseEstimator, OnnxOperatorMixin

Speeds up inference by replacing methods transform or predict by a runtime for ONNX.

Parameters:

estimator – estimator to train
enforce_float32 – boolean onnxruntime only supports float32, scikit-learn usually uses double floats, this parameter ensures that every array of double floats is converted into single floats
runtime – string, defined the runtime to use as described in OnnxInference.
target_opset – targetted ONNX opset
conv_options – options for conversions, see to_onnx
nopython – used by numba jitter

Attributes created by method fit:

estimator_: cloned and trained version of estimator
onnxrt_: objet of type OnnxInference,
sklearn.preprocessing.FunctionTransformer
numpy_code_: python code equivalent to the inference
method if the runtime is ‘numpy’ or ‘numba’
onnx_io_names_: dictionary, additional information
if the runtime is ‘numpy’ or ‘numba’

New in version 0.7.

source on GitHub

__getstate__()#

pickle does not support functions. This method removes any link to function when the runtime is ‘numpy’.

source on GitHub

__init__(estimator, runtime='python', enforce_float32=True, target_opset=None, conv_options=None, nopython=True)#

__setstate__(state)#

pickle does not support functions. This method restores the function created when the runtime is ‘numpy’.

source on GitHub

_build_onnx_runtime(onx)#

Returns an instance of OnnxTransformer which executes the ONNX graph.

Parameters:

onx – ONNX graph
runtime – runtime type (see OnnxInference)

Returns:

instance of OnnxInference

source on GitHub

_build_onnx_runtime_numpy(onx)#

Builds a runtime based on numpy. Exports the ONNX graph into python code based on numpy and then dynamically compiles it with method _build_onnx_runtime_numpy_compile().

source on GitHub

_build_onnx_runtime_numpy_compile(opsets)#

Second part of _build_onnx_runtime_numpy().

source on GitHub