module `onnxrt.validate.validate_difference`#

Short summary#

module mlprodict.onnxrt.validate.validate_difference

Validates runtime for many :scikit-learn: operators. The submodule relies on onnxconverter_common, sklearn-onnx.

Functions#

function	truncated documentation
`measure_relative_difference`	Measures the relative difference between predictions between two ways of computing them. The functions returns nan …

Documentation#

Validates runtime for many :scikit-learn: operators. The submodule relies on onnxconverter_common, sklearn-onnx.

source on GitHub

mlprodict.onnxrt.validate.validate_difference.measure_relative_difference(skl_pred, ort_pred, batch=True, abs_diff=False)#

Measures the relative difference between predictions between two ways of computing them. The functions returns nan if shapes are different.

Parameters

skl_pred – prediction from scikit-learn or any other way
ort_pred – prediction from an ONNX runtime or any other way
batch – predictions are processed in a batch, skl_pred and ort_pred should be arrays or tuple or list of arrays
abs_diff – return the absolute difference

Returns

relative max difference or nan if it does not make any sense

Because approximations get bigger when the vector is high, the function computes an adjusted relative differences. Let’s assume X and Y are two vectors, let’s denote $med(X)$ the median of X. The function returns the following metric: $\max_i(|X_i - Y_i| / \max(X_i, med(|X|))$ .

The function takes the fourth highest difference, not the three first which may happen after a conversion into float32.

source on GitHub

module onnxrt.validate.validate_benchmark_replay

module onnxrt.validate.validate_helper

module onnxrt.validate.validate_difference#

Short summary#

Functions#

Documentation#

module `onnxrt.validate.validate_difference`#