module external.onnxruntime_perf_regression

Inheritance diagram of pymlbenchmark.external.onnxruntime_perf_regression

Short summary

module pymlbenchmark.external.onnxruntime_perf_regression

Implements a benchmark for a single regression about performance for onnxruntime.

source on GitHub

Classes

class

truncated documentation

OnnxRuntimeBenchPerfTestRegression

Specific test to compare computing time predictions with scikit-learn and onnxruntime for a binary …

Methods

method

truncated documentation

_get_random_dataset

Returns a random datasets.

fcts

Returns a few functions, tests methods perdict, predict_proba for both scikit-learn and OnnxInference

Documentation

Implements a benchmark for a single regression about performance for onnxruntime.

source on GitHub

class pymlbenchmark.external.onnxruntime_perf_regression.OnnxRuntimeBenchPerfTestRegression(estimator, dim=None, N_fit=100000, runtimes=('python_compiled', 'onnxruntime1'), onnx_options=None, dtype=<class 'numpy.float32'>, **opts)

Bases: OnnxRuntimeBenchPerfTest

Specific test to compare computing time predictions with scikit-learn and onnxruntime for a binary classification. See example l-example-onnxruntime-linreg. The class requires the following modules to be installed: onnx, onnxruntime, skl2onnx, mlprodict.

source on GitHub

Parameters:
  • estimator – estimator class

  • dim – number of features

  • N_fit – number of observations to fit an estimator

  • runtimes – runtimes to test for class OnnxInference

  • opts – training settings

  • onnx_options – ONNX conversion options

  • dtype – dtype (float32 or float64)

source on GitHub

_get_random_dataset(N, dim)

Returns a random datasets.

source on GitHub

fcts(dim=None, **kwargs)

Returns a few functions, tests methods perdict, predict_proba for both scikit-learn and OnnxInference multiplied by the number of runtime to test.

source on GitHub