module `onnxrt.validate.validate_helper`#

Short summary#

module mlprodict.onnxrt.validate.validate_helper

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

Classes#

class	truncated documentation
`RuntimeBadResultsError`	Raised when the results are too different from scikit-learn.

Functions#

function	truncated documentation
`_dictionary2str`
`_dispsimple`
`_get_problem_data`
`_measure_time`	Measures the execution time for a function.
`_merge_options`
`_multiply_time_kwargs`	Multiplies values in time_kwargs following strategy time_kwargs_fact for a given model inst.
`_shape_exc`
`default_time_kwargs`	Returns default values number and repeat to measure the execution of a function.
`dump_into_folder`	Dumps information when an error was detected using pickle.
`measure_time`	Measures a statement and returns the results as a dictionary.
`modules_list`	Returns modules and versions currently used.
`sklearn_operators`	Builds the list of operators from scikit-learn. The function goes through the list of submodule and get …

Methods#

method	truncated documentation
`__init__`

Documentation#

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

source on GitHub

exception mlprodict.onnxrt.validate.validate_helper.RuntimeBadResultsError(msg, obs)#

Bases: RuntimeError

Raised when the results are too different from scikit-learn.

source on GitHub

Parameters:

msg – to display
obs – observations

source on GitHub

__init__(msg, obs)#

Parameters:

msg – to display
obs – observations

source on GitHub

mlprodict.onnxrt.validate.validate_helper._dictionary2str(di)#

mlprodict.onnxrt.validate.validate_helper._dispsimple(arr, fLOG)#

mlprodict.onnxrt.validate.validate_helper._get_problem_data(prob, n_features)#

mlprodict.onnxrt.validate.validate_helper._measure_time(fct, repeat=1, number=1, first_run=True)#

Measures the execution time for a function.

Parameters:

fct – function to measure
repeat – number of times to repeat
number – number of times between two measures
first_run – if True, runs the function once before measuring

Returns:

last result, average, values

source on GitHub

mlprodict.onnxrt.validate.validate_helper._merge_options(all_conv_options, aoptions)#

mlprodict.onnxrt.validate.validate_helper._multiply_time_kwargs(time_kwargs, time_kwargs_fact, inst)#

Multiplies values in time_kwargs following strategy time_kwargs_fact for a given model inst.

Parameters:

time_kwargs – see below
time_kwargs_fact – see below
inst – scikit-learn model

Returns:

new time_kwargs

Possible values for time_kwargs_fact:

a integer: multiplies number by this number
‘lin’: multiplies value number for linear models depending on the number of rows to process ( $\propto 1/\log_{10}(n)$ )

<<<

from pprint import pprint
from sklearn.linear_model import LinearRegression
from mlprodict.onnxrt.validate.validate_helper import (
    default_time_kwargs, _multiply_time_kwargs)

lr = LinearRegression()
kw = default_time_kwargs()
pprint(kw)

kw2 = _multiply_time_kwargs(kw, 'lin', lr)
pprint(kw2)

>>>

    {1: {'number': 15, 'repeat': 20},
{'number': 10, 'repeat': 20},
{'number': 4, 'repeat': 10},
{'number': 4, 'repeat': 4},
{'number': 2, 'repeat': 2}}
    {1: {'number': 150, 'repeat': 20},
{'number': 100, 'repeat': 20},
{'number': 20, 'repeat': 10},
{'number': 12, 'repeat': 4},
{'number': 6, 'repeat': 2}}

source on GitHub

mlprodict.onnxrt.validate.validate_helper._shape_exc(obj)#

mlprodict.onnxrt.validate.validate_helper.default_time_kwargs()#

Returns default values number and repeat to measure the execution of a function.

<<<

from mlprodict.onnxrt.validate.validate_helper import default_time_kwargs
import pprint
pprint.pprint(default_time_kwargs())

>>>

    {1: {'number': 15, 'repeat': 20},
{'number': 10, 'repeat': 20},
{'number': 4, 'repeat': 10},
{'number': 4, 'repeat': 4},
{'number': 2, 'repeat': 2}}

keys define the number of rows, values defines number and repeat.

source on GitHub

mlprodict.onnxrt.validate.validate_helper.dump_into_folder(dump_folder, obs_op=None, is_error=True, **kwargs)#

Dumps information when an error was detected using pickle.

Parameters:

dump_folder – dump_folder
obs_op – obs_op (information)
is_error – is it an error or not?
kwargs – additional parameters :return: name

source on GitHub

mlprodict.onnxrt.validate.validate_helper.measure_time(stmt, x, repeat=10, number=50, div_by_number=False, first_run=True, max_time=None)#

Measures a statement and returns the results as a dictionary.

Parameters:

stmt – string
x – matrix
repeat – average over repeat experiment
number – number of executions in one row
div_by_number – divide by the number of executions
first_run – if True, runs the function once before measuring
max_time – execute the statement until the total goes beyond this time (approximatively), repeat is ignored, div_by_number must be set to True

Returns:

dictionary

See Timer.repeat for a better understanding of parameter repeat and number. The function returns a duration corresponding to number times the execution of the main statement.

source on GitHub

mlprodict.onnxrt.validate.validate_helper.modules_list()#

Returns modules and versions currently used.

<<<

from mlprodict.onnxrt.validate.validate_helper import modules_list
from pyquickhelper.pandashelper import df2rst
from pandas import DataFrame
print(df2rst(DataFrame(modules_list())))

>>>

name	version
mlprodict	0.9.1887
numpy	1.23.5
onnx	1.13.0
onnxmltools	1.11.1
onnxruntime	1.13.1
pandas	1.5.3
scipy	1.10.0
skl2onnx	1.13.1
sklearn	1.2.1

source on GitHub

mlprodict.onnxrt.validate.validate_helper.sklearn_operators(subfolder=None, extended=False, experimental=True)#

Builds the list of operators from scikit-learn. The function goes through the list of submodule and get the list of class which inherit from :epkg:`scikit-learn:base:BaseEstimator`.

Parameters:

subfolder – look into only one subfolder
extended – extends the list to the list of operators this package implements a converter for
experimental – includes experimental module from scikit-learn (see sklearn.experimental)

Returns:

the list of found operators

source on GitHub

module onnxrt.validate.validate_helper#

Short summary#

Classes#

Functions#

Methods#

Documentation#

module `onnxrt.validate.validate_helper`#