Benchmark (ONNX) for GradientBoostingRegressor#
Overview#
(Source code, png, hires.png, pdf)
(Source code, png, hires.png, pdf)
Detailed graphs#
(Source code, png, hires.png, pdf)
Configuration#
<<<
from pyquickhelper.pandashelper import df2rst
import pandas
name = os.path.join(
__WD__, "../../onnx/results/bench_plot_onnxruntime_gbr.time.csv")
df = pandas.read_csv(name)
print(df2rst(df, number_format=4))
>>>
name |
version |
value |
---|---|---|
date |
2020-01-05 |
|
python |
3.7.2 (default, Mar 1 2019, 18:34:21) [GCC 6.3.0 20170516] |
|
platform |
linux |
|
OS |
Linux-4.9.0-8-amd64-x86_64-with-debian-9.6 |
|
machine |
x86_64 |
|
processor |
||
release |
4.9.0-8-amd64 |
|
architecture |
(‘64bit’, ‘’) |
|
mlprodict |
0.3 |
|
numpy |
1.17.5 |
openblas, language=c |
onnx |
1.6.36 |
opset=12 |
onnxruntime |
1.1.995 |
CPU-DNNL-MKL-ML |
pandas |
0.25.3 |
|
skl2onnx |
1.6.994 |
|
sklearn |
0.22.1 |
Raw results#
bench_plot_onnxruntime_gbr.csv
<<<
from pyquickhelper.pandashelper import df2rst
from pymlbenchmark.benchmark.bench_helper import bench_pivot
import pandas
name = os.path.join(
__WD__, "../../onnx/results/bench_plot_onnxruntime_gbr.perf.csv")
df = pandas.read_csv(name)
piv = bench_pivot(df).reset_index(drop=False)
piv['speedup_py'] = piv['skl'] / piv['onxpython_compiled']
piv['speedup_ort'] = piv['skl'] / piv['onxonnxruntime1']
print(df2rst(piv, number_format=4))
method |
skl_nb_base_estimators |
N |
dim |
max_depth |
n_estimators |
number |
count |
error_c |
onnx_opset |
onxonnxruntime1 |
onxpython_compiled |
skl |
speedup_py |
speedup_ort |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
predict |
-1 |
1 |
1 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
2 |
1 |
1 |
10 |
0 |
12 |
5.692e-05 |
4.261e-05 |
|||
predict |
-1 |
1 |
1 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
2 |
10 |
1 |
10 |
0 |
12 |
3.129e-05 |
1.864e-05 |
|||
predict |
-1 |
1 |
1 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
2 |
100 |
1 |
10 |
0 |
12 |
3.252e-05 |
1.884e-05 |
|||
predict |
-1 |
1 |
1 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
5 |
1 |
1 |
10 |
0 |
12 |
3.865e-05 |
2.09e-05 |
|||
predict |
-1 |
1 |
1 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
5 |
10 |
1 |
10 |
0 |
12 |
3.888e-05 |
2.854e-05 |
|||
predict |
-1 |
1 |
1 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
5 |
100 |
1 |
10 |
0 |
12 |
3.839e-05 |
2.23e-05 |
|||
predict |
-1 |
1 |
1 |
10 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
10 |
1 |
1 |
10 |
0 |
12 |
6.305e-05 |
3.179e-05 |
|||
predict |
-1 |
1 |
1 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
10 |
10 |
1 |
10 |
0 |
12 |
6.583e-05 |
7.123e-05 |
|||
predict |
-1 |
1 |
1 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
1 |
10 |
100 |
1 |
10 |
0 |
12 |
6.413e-05 |
5.312e-05 |
|||
predict |
-1 |
1 |
5 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
2 |
1 |
1 |
10 |
0 |
12 |
3.614e-05 |
7.232e-05 |
|||
predict |
-1 |
1 |
5 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
2 |
10 |
1 |
10 |
0 |
12 |
3.283e-05 |
1.902e-05 |
|||
predict |
-1 |
1 |
5 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
2 |
100 |
1 |
10 |
0 |
12 |
3.261e-05 |
1.891e-05 |
|||
predict |
-1 |
1 |
5 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
5 |
1 |
1 |
10 |
0 |
12 |
4.066e-05 |
2.219e-05 |
|||
predict |
-1 |
1 |
5 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
5 |
10 |
1 |
10 |
0 |
12 |
4.122e-05 |
2.263e-05 |
|||
predict |
-1 |
1 |
5 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
5 |
100 |
1 |
10 |
0 |
12 |
4.076e-05 |
2.201e-05 |
|||
predict |
-1 |
1 |
5 |
10 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
10 |
1 |
1 |
10 |
0 |
12 |
7.003e-05 |
3.211e-05 |
|||
predict |
-1 |
1 |
5 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
10 |
10 |
1 |
10 |
0 |
12 |
7.062e-05 |
0.0001198 |
|||
predict |
-1 |
1 |
5 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
5 |
10 |
100 |
1 |
10 |
0 |
12 |
7.25e-05 |
0.000142 |
|||
predict |
-1 |
1 |
10 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
2 |
1 |
1 |
10 |
0 |
12 |
3.819e-05 |
0.000165 |
|||
predict |
-1 |
1 |
10 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
2 |
10 |
1 |
10 |
0 |
12 |
3.335e-05 |
1.918e-05 |
|||
predict |
-1 |
1 |
10 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
2 |
100 |
1 |
10 |
0 |
12 |
3.488e-05 |
1.943e-05 |
|||
predict |
-1 |
1 |
10 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
5 |
1 |
1 |
10 |
0 |
12 |
4.057e-05 |
2.362e-05 |
|||
predict |
-1 |
1 |
10 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
5 |
10 |
1 |
10 |
0 |
12 |
4.218e-05 |
2.361e-05 |
|||
predict |
-1 |
1 |
10 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
5 |
100 |
1 |
10 |
0 |
12 |
4.097e-05 |
2.345e-05 |
|||
predict |
-1 |
1 |
10 |
10 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
10 |
1 |
1 |
10 |
0 |
12 |
7.396e-05 |
3.193e-05 |
|||
predict |
-1 |
1 |
10 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
10 |
10 |
1 |
10 |
0 |
12 |
7.633e-05 |
0.0001357 |
|||
predict |
-1 |
1 |
10 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
10 |
10 |
100 |
1 |
10 |
0 |
12 |
7.3e-05 |
0.0001978 |
|||
predict |
-1 |
1 |
20 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
2 |
1 |
1 |
10 |
0 |
12 |
3.606e-05 |
0.0002188 |
|||
predict |
-1 |
1 |
20 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
2 |
10 |
1 |
10 |
0 |
12 |
3.289e-05 |
1.884e-05 |
|||
predict |
-1 |
1 |
20 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
2 |
100 |
1 |
10 |
0 |
12 |
3.415e-05 |
2.02e-05 |
|||
predict |
-1 |
1 |
20 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
5 |
1 |
1 |
10 |
0 |
12 |
4.06e-05 |
2.328e-05 |
|||
predict |
-1 |
1 |
20 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
5 |
10 |
1 |
10 |
0 |
12 |
4.245e-05 |
2.371e-05 |
|||
predict |
-1 |
1 |
20 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
5 |
100 |
1 |
10 |
0 |
12 |
4.245e-05 |
2.35e-05 |
|||
predict |
-1 |
1 |
20 |
10 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
10 |
1 |
1 |
10 |
0 |
12 |
7.302e-05 |
3.52e-05 |
|||
predict |
-1 |
1 |
20 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
10 |
10 |
1 |
10 |
0 |
12 |
9.714e-05 |
3.402e-05 |
|||
predict |
-1 |
1 |
20 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
1 |
20 |
10 |
100 |
1 |
10 |
0 |
12 |
8.523e-05 |
0.0002023 |
|||
predict |
-1 |
10 |
1 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
2 |
1 |
1 |
10 |
0 |
12 |
8.291e-05 |
4.624e-05 |
|||
predict |
-1 |
10 |
1 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
2 |
10 |
1 |
10 |
0 |
12 |
4.112e-05 |
2.272e-05 |
|||
predict |
-1 |
10 |
1 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
2 |
100 |
1 |
10 |
0 |
12 |
4.073e-05 |
2.235e-05 |
|||
predict |
-1 |
10 |
1 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
5 |
1 |
1 |
10 |
0 |
12 |
6.456e-05 |
4.323e-05 |
|||
predict |
-1 |
10 |
1 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
5 |
10 |
1 |
10 |
0 |
12 |
6.863e-05 |
4.449e-05 |
|||
predict |
-1 |
10 |
1 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
5 |
100 |
1 |
10 |
0 |
12 |
6.414e-05 |
4.455e-05 |
|||
predict |
-1 |
10 |
1 |
10 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
10 |
1 |
1 |
10 |
0 |
12 |
0.0001413 |
0.000107 |
|||
predict |
-1 |
10 |
1 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
10 |
10 |
1 |
10 |
0 |
12 |
0.0001468 |
0.0001148 |
|||
predict |
-1 |
10 |
1 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
1 |
10 |
100 |
1 |
10 |
0 |
12 |
0.0001448 |
0.0001127 |
|||
predict |
-1 |
10 |
5 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
2 |
1 |
1 |
10 |
0 |
12 |
4.761e-05 |
2.996e-05 |
|||
predict |
-1 |
10 |
5 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
2 |
10 |
1 |
10 |
0 |
12 |
4.835e-05 |
3.004e-05 |
|||
predict |
-1 |
10 |
5 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
2 |
100 |
1 |
10 |
0 |
12 |
4.854e-05 |
2.97e-05 |
|||
predict |
-1 |
10 |
5 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
5 |
1 |
1 |
10 |
0 |
12 |
7.82e-05 |
5.965e-05 |
|||
predict |
-1 |
10 |
5 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
5 |
10 |
1 |
10 |
0 |
12 |
8.416e-05 |
5.816e-05 |
|||
predict |
-1 |
10 |
5 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
5 |
100 |
1 |
10 |
0 |
12 |
7.797e-05 |
5.955e-05 |
|||
predict |
-1 |
10 |
5 |
10 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
10 |
1 |
1 |
10 |
0 |
12 |
0.0002118 |
0.0001581 |
|||
predict |
-1 |
10 |
5 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
10 |
10 |
1 |
10 |
0 |
12 |
0.0002138 |
0.0001689 |
|||
predict |
-1 |
10 |
5 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
5 |
10 |
100 |
1 |
10 |
0 |
12 |
0.000215 |
0.0001609 |
|||
predict |
-1 |
10 |
10 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
2 |
1 |
1 |
10 |
0 |
12 |
4.979e-05 |
3.044e-05 |
|||
predict |
-1 |
10 |
10 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
2 |
10 |
1 |
10 |
0 |
12 |
4.879e-05 |
3.028e-05 |
|||
predict |
-1 |
10 |
10 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
2 |
100 |
1 |
10 |
0 |
12 |
4.951e-05 |
3.056e-05 |
|||
predict |
-1 |
10 |
10 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
5 |
1 |
1 |
10 |
0 |
12 |
8.372e-05 |
6.678e-05 |
|||
predict |
-1 |
10 |
10 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
5 |
10 |
1 |
10 |
0 |
12 |
9.125e-05 |
6.666e-05 |
|||
predict |
-1 |
10 |
10 |
5 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
5 |
100 |
1 |
10 |
0 |
12 |
8.296e-05 |
6.606e-05 |
|||
predict |
-1 |
10 |
10 |
10 |
1 |
1 |
10 |
0 |
-1 |
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predict |
-1 |
10 |
10 |
10 |
1 |
1 |
10 |
0 |
12 |
0.0002347 |
0.0001619 |
|||
predict |
-1 |
10 |
10 |
10 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
10 |
10 |
1 |
10 |
0 |
12 |
0.0002375 |
0.0001821 |
|||
predict |
-1 |
10 |
10 |
10 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
10 |
10 |
100 |
1 |
10 |
0 |
12 |
0.0002544 |
0.0001954 |
|||
predict |
-1 |
10 |
20 |
2 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
20 |
2 |
1 |
1 |
10 |
0 |
12 |
4.994e-05 |
3.043e-05 |
|||
predict |
-1 |
10 |
20 |
2 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
20 |
2 |
10 |
1 |
10 |
0 |
12 |
4.931e-05 |
3.065e-05 |
|||
predict |
-1 |
10 |
20 |
2 |
100 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
20 |
2 |
100 |
1 |
10 |
0 |
12 |
5.042e-05 |
3.291e-05 |
|||
predict |
-1 |
10 |
20 |
5 |
1 |
1 |
10 |
0 |
-1 |
|||||
predict |
-1 |
10 |
20 |
5 |
1 |
1 |
10 |
0 |
12 |
8.6e-05 |
6.967e-05 |
|||
predict |
-1 |
10 |
20 |
5 |
10 |
1 |
10 |
0 |
-1 |
|||||
predict |
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10 |
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|||||
predict |
2 |
1000 |
10 |
5 |
1 |
1 |
10 |
0 |
-1 |
0.002712 |
||||
predict |
2 |
1000 |
10 |
5 |
1 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
10 |
5 |
10 |
1 |
10 |
0 |
-1 |
0.002704 |
||||
predict |
2 |
1000 |
10 |
5 |
10 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
10 |
5 |
100 |
1 |
10 |
0 |
-1 |
0.00269 |
||||
predict |
2 |
1000 |
10 |
5 |
100 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
10 |
10 |
1 |
1 |
10 |
0 |
-1 |
0.005577 |
||||
predict |
2 |
1000 |
10 |
10 |
1 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
10 |
10 |
10 |
1 |
10 |
0 |
-1 |
0.005538 |
||||
predict |
2 |
1000 |
10 |
10 |
10 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
10 |
10 |
100 |
1 |
10 |
0 |
-1 |
0.00564 |
||||
predict |
2 |
1000 |
10 |
10 |
100 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
20 |
2 |
1 |
1 |
10 |
0 |
-1 |
0.001443 |
||||
predict |
2 |
1000 |
20 |
2 |
1 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
20 |
2 |
10 |
1 |
10 |
0 |
-1 |
0.001442 |
||||
predict |
2 |
1000 |
20 |
2 |
10 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
20 |
2 |
100 |
1 |
10 |
0 |
-1 |
0.001449 |
||||
predict |
2 |
1000 |
20 |
2 |
100 |
1 |
10 |
0 |
12 |
|||||
predict |
2 |
1000 |
20 |
5 |
1 |
1 |
10 |
0 |
-1 |
0.003043 |
||||
predict |
2 |
1000 |
20 |
5 |
1 |
1 |
10 |
0 |
12 |
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predict |
2 |
1000 |
20 |
5 |
10 |
1 |
10 |
0 |
-1 |
0.003048 |
||||
predict |
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2 |
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1 |
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||||
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20 |
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1 |
10 |
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0.006949 |
||||
predict |
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20 |
10 |
1 |
1 |
10 |
0 |
12 |
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predict |
2 |
1000 |
20 |
10 |
10 |
1 |
10 |
0 |
-1 |
0.006991 |
||||
predict |
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1000 |
20 |
10 |
10 |
1 |
10 |
0 |
12 |
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predict |
2 |
1000 |
20 |
10 |
100 |
1 |
10 |
0 |
-1 |
0.006965 |
||||
predict |
2 |
1000 |
20 |
10 |
100 |
1 |
10 |
0 |
12 |
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predict |
2 |
10000 |
1 |
2 |
1 |
1 |
10 |
0 |
-1 |
0.01003 |
||||
predict |
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1 |
1 |
10 |
0 |
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0.008316 |
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0.008209 |
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1 |
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20 |
10 |
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predict |
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12 |
<<<
from pyquickhelper.pandashelper import df2rst
import pandas
name = os.path.join(
__WD__, "../../onnx/results/bench_plot_onnxruntime_gbr.perf.csv")
df = pandas.read_csv(name)
df = df[df['lib'] == 'skl']
print(df2rst(df, number_format=4))
method |
lib |
skl_nb_base_estimators |
N |
dim |
max_depth |
n_estimators |
onnx_options |
repeat |
number |
min |
max |
min3 |
max3 |
mean |
lower |
upper |
count |
median |
error_c |
onnx_nodes |
onnx_opset |
ort_size |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
predict |
skl |
2 |
1 |
1 |
2 |
1 |
10 |
1 |
0.0002212 |
0.0003848 |
0.0002222 |
0.0002339 |
0.0002427 |
0.0002212 |
0.000337 |
10 |
0.000223 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
2 |
1 |
10 |
1 |
0.0002202 |
0.000269 |
0.0002249 |
0.000243 |
0.0002345 |
0.0002202 |
0.0002673 |
10 |
0.0002256 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
2 |
1 |
10 |
1 |
0.0003392 |
0.0004124 |
0.0003467 |
0.0003553 |
0.000356 |
0.0003392 |
0.0003957 |
10 |
0.000348 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
2 |
1 |
10 |
1 |
0.001538 |
0.001796 |
0.001611 |
0.001637 |
0.001631 |
0.001538 |
0.001755 |
10 |
0.001625 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
2 |
1 |
10 |
1 |
0.008873 |
0.0114 |
0.009384 |
0.01068 |
0.01003 |
0.008873 |
0.0114 |
10 |
0.01015 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
2 |
10 |
10 |
1 |
9.897e-05 |
0.0001692 |
9.97e-05 |
0.0001047 |
0.0001084 |
9.897e-05 |
0.0001488 |
10 |
0.0001002 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
2 |
10 |
10 |
1 |
0.0001056 |
0.0001297 |
0.0001063 |
0.0001107 |
0.0001105 |
0.0001056 |
0.0001244 |
10 |
0.0001074 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
2 |
10 |
10 |
1 |
0.0001754 |
0.0002022 |
0.000178 |
0.0001801 |
0.0001814 |
0.0001754 |
0.0001964 |
10 |
0.0001788 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
2 |
10 |
10 |
1 |
0.0009102 |
0.0009679 |
0.0009228 |
0.0009347 |
0.0009294 |
0.0009102 |
0.0009592 |
10 |
0.0009255 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
2 |
10 |
10 |
1 |
0.008275 |
0.008361 |
0.008314 |
0.008322 |
0.008316 |
0.008275 |
0.008359 |
10 |
0.008317 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
2 |
100 |
10 |
1 |
0.0001015 |
0.0001819 |
0.0001026 |
0.0001093 |
0.0001128 |
0.0001015 |
0.0001587 |
10 |
0.0001036 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
2 |
100 |
10 |
1 |
0.0001075 |
0.0001319 |
0.0001085 |
0.0001126 |
0.0001122 |
0.0001075 |
0.0001264 |
10 |
0.000109 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
2 |
100 |
10 |
1 |
0.0001769 |
0.0002059 |
0.0001798 |
0.0001821 |
0.0001829 |
0.0001769 |
0.0001986 |
10 |
0.0001807 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
2 |
100 |
10 |
1 |
0.0009147 |
0.000948 |
0.0009171 |
0.0009244 |
0.0009219 |
0.0009147 |
0.0009401 |
10 |
0.0009183 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
2 |
100 |
10 |
1 |
0.008174 |
0.008263 |
0.008195 |
0.008227 |
0.008209 |
0.008174 |
0.008261 |
10 |
0.008198 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
5 |
1 |
10 |
1 |
0.0001054 |
0.0002208 |
0.0001101 |
0.0001203 |
0.0001244 |
0.0001054 |
0.0001894 |
10 |
0.0001129 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
5 |
1 |
10 |
1 |
0.0001241 |
0.0001534 |
0.0001253 |
0.0001295 |
0.0001301 |
0.0001241 |
0.0001474 |
10 |
0.0001268 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
5 |
1 |
10 |
1 |
0.0003067 |
0.0003463 |
0.0003087 |
0.0003184 |
0.0003144 |
0.0003067 |
0.0003369 |
10 |
0.0003094 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
5 |
1 |
10 |
1 |
0.00207 |
0.002103 |
0.002074 |
0.00209 |
0.002083 |
0.00207 |
0.002103 |
10 |
0.002084 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
5 |
1 |
10 |
1 |
0.01926 |
0.01931 |
0.0193 |
0.0193 |
0.01929 |
0.01926 |
0.01931 |
10 |
0.0193 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
5 |
10 |
10 |
1 |
0.0001084 |
0.0002242 |
0.0001112 |
0.0001233 |
0.0001267 |
0.0001084 |
0.0001922 |
10 |
0.0001122 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
5 |
10 |
10 |
1 |
0.0001253 |
0.0001561 |
0.0001284 |
0.0001348 |
0.0001333 |
0.0001253 |
0.0001503 |
10 |
0.0001297 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
5 |
10 |
10 |
1 |
0.0003103 |
0.0003435 |
0.0003122 |
0.0003181 |
0.0003176 |
0.0003103 |
0.0003367 |
10 |
0.0003128 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
5 |
10 |
10 |
1 |
0.002074 |
0.002144 |
0.002091 |
0.002108 |
0.0021 |
0.002074 |
0.002135 |
10 |
0.002097 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
5 |
10 |
10 |
1 |
0.01937 |
0.01944 |
0.01939 |
0.01941 |
0.0194 |
0.01937 |
0.01944 |
10 |
0.0194 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
5 |
100 |
10 |
1 |
0.0001077 |
0.000228 |
0.0001082 |
0.000121 |
0.0001253 |
0.0001077 |
0.0001942 |
10 |
0.00011 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
5 |
100 |
10 |
1 |
0.0001247 |
0.0001621 |
0.0001276 |
0.000133 |
0.0001327 |
0.0001247 |
0.0001534 |
10 |
0.0001296 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
5 |
100 |
10 |
1 |
0.0003073 |
0.0003451 |
0.0003116 |
0.0003173 |
0.0003159 |
0.0003073 |
0.0003361 |
10 |
0.0003128 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
5 |
100 |
10 |
1 |
0.002077 |
0.002123 |
0.002084 |
0.002093 |
0.002091 |
0.002077 |
0.002116 |
10 |
0.002086 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
5 |
100 |
10 |
1 |
0.01937 |
0.01947 |
0.0194 |
0.01944 |
0.01941 |
0.01937 |
0.01947 |
10 |
0.0194 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
10 |
1 |
10 |
1 |
0.0001129 |
0.0002591 |
0.0001183 |
0.000135 |
0.0001405 |
0.0001129 |
0.0002236 |
10 |
0.0001249 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
10 |
1 |
10 |
1 |
0.0001655 |
0.0002204 |
0.0001678 |
0.0001853 |
0.0001783 |
0.0001655 |
0.0002113 |
10 |
0.0001704 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
10 |
1 |
10 |
1 |
0.0005552 |
0.0006282 |
0.0005601 |
0.0005675 |
0.0005705 |
0.0005552 |
0.0006111 |
10 |
0.0005643 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
10 |
1 |
10 |
1 |
0.004081 |
0.004253 |
0.004097 |
0.004111 |
0.004117 |
0.004081 |
0.004208 |
10 |
0.004104 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
10 |
1 |
10 |
1 |
0.03719 |
0.03762 |
0.03725 |
0.0373 |
0.03729 |
0.03719 |
0.03751 |
10 |
0.03726 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
10 |
10 |
10 |
1 |
0.0001208 |
0.0002938 |
0.0001299 |
0.0001588 |
0.0001533 |
0.0001208 |
0.0002491 |
10 |
0.0001332 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
10 |
10 |
10 |
1 |
0.0001668 |
0.0002526 |
0.0001748 |
0.0002057 |
0.0001904 |
0.0001668 |
0.0002412 |
10 |
0.0001777 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
10 |
10 |
10 |
1 |
0.0005603 |
0.0006175 |
0.0005655 |
0.0005718 |
0.0005723 |
0.0005603 |
0.0006042 |
10 |
0.0005656 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
10 |
10 |
10 |
1 |
0.004086 |
0.004263 |
0.004111 |
0.004135 |
0.004131 |
0.004086 |
0.004222 |
10 |
0.004118 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
10 |
10 |
10 |
1 |
0.03741 |
0.03795 |
0.03751 |
0.03774 |
0.03762 |
0.03741 |
0.03795 |
10 |
0.03752 |
0 |
||||
predict |
skl |
2 |
1 |
1 |
10 |
100 |
10 |
1 |
0.0001164 |
0.0002713 |
0.0001191 |
0.0001414 |
0.0001452 |
0.0001164 |
0.0002358 |
10 |
0.0001223 |
0 |
||||
predict |
skl |
2 |
10 |
1 |
10 |
100 |
10 |
1 |
0.0001661 |
0.0002428 |
0.0001694 |
0.0001917 |
0.0001824 |
0.0001661 |
0.0002265 |
10 |
0.0001709 |
0 |
||||
predict |
skl |
2 |
100 |
1 |
10 |
100 |
10 |
1 |
0.0005543 |
0.0006321 |
0.0005611 |
0.0005685 |
0.0005701 |
0.0005543 |
0.0006119 |
10 |
0.0005624 |
0 |
||||
predict |
skl |
2 |
1000 |
1 |
10 |
100 |
10 |
1 |
0.004072 |
0.004251 |
0.004084 |
0.004109 |
0.004107 |
0.004072 |
0.004204 |
10 |
0.00409 |
0 |
||||
predict |
skl |
2 |
10000 |
1 |
10 |
100 |
10 |
1 |
0.03719 |
0.03759 |
0.03725 |
0.03737 |
0.03732 |
0.03719 |
0.03755 |
10 |
0.03731 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
2 |
1 |
10 |
1 |
0.0001004 |
0.0002436 |
0.0001012 |
0.000108 |
0.0001175 |
0.0001004 |
0.0002001 |
10 |
0.0001024 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
2 |
1 |
10 |
1 |
0.000111 |
0.0001556 |
0.0001125 |
0.0001175 |
0.0001186 |
0.000111 |
0.0001438 |
10 |
0.0001138 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
2 |
1 |
10 |
1 |
0.0002097 |
0.0002438 |
0.000214 |
0.0002223 |
0.0002193 |
0.0002097 |
0.0002373 |
10 |
0.0002185 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
2 |
1 |
10 |
1 |
0.001158 |
0.001191 |
0.00116 |
0.001168 |
0.001166 |
0.001158 |
0.001184 |
10 |
0.001162 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
2 |
1 |
10 |
1 |
0.01012 |
0.01026 |
0.01013 |
0.01016 |
0.01015 |
0.01012 |
0.01023 |
10 |
0.01015 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
2 |
10 |
10 |
1 |
0.0001012 |
0.0001825 |
0.0001021 |
0.0001078 |
0.0001122 |
0.0001012 |
0.0001588 |
10 |
0.0001029 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
2 |
10 |
10 |
1 |
0.0001112 |
0.0001367 |
0.0001142 |
0.0001211 |
0.000118 |
0.0001112 |
0.0001321 |
10 |
0.0001148 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
2 |
10 |
10 |
1 |
0.0002112 |
0.0002503 |
0.0002155 |
0.0002179 |
0.0002196 |
0.0002112 |
0.0002408 |
10 |
0.0002157 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
2 |
10 |
10 |
1 |
0.001148 |
0.001194 |
0.001154 |
0.001165 |
0.001161 |
0.001148 |
0.001185 |
10 |
0.001159 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
2 |
10 |
10 |
1 |
0.01011 |
0.01023 |
0.01013 |
0.01016 |
0.01014 |
0.01011 |
0.01021 |
10 |
0.01014 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
2 |
100 |
10 |
1 |
0.0001013 |
0.0001779 |
0.0001031 |
0.0001131 |
0.0001141 |
0.0001013 |
0.0001571 |
10 |
0.0001057 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
2 |
100 |
10 |
1 |
0.0001124 |
0.0001371 |
0.0001142 |
0.0001205 |
0.0001184 |
0.0001124 |
0.0001325 |
10 |
0.000115 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
2 |
100 |
10 |
1 |
0.0002126 |
0.0002414 |
0.0002142 |
0.0002187 |
0.0002188 |
0.0002126 |
0.0002353 |
10 |
0.0002151 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
2 |
100 |
10 |
1 |
0.001153 |
0.001191 |
0.001155 |
0.001159 |
0.00116 |
0.001153 |
0.00118 |
10 |
0.001157 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
2 |
100 |
10 |
1 |
0.01009 |
0.01025 |
0.01011 |
0.01016 |
0.01013 |
0.01009 |
0.01022 |
10 |
0.01012 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
5 |
1 |
10 |
1 |
0.0001059 |
0.0002272 |
0.0001085 |
0.0001172 |
0.0001227 |
0.0001059 |
0.0001916 |
10 |
0.00011 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
5 |
1 |
10 |
1 |
0.0001345 |
0.0001649 |
0.0001362 |
0.0001437 |
0.0001419 |
0.0001345 |
0.000159 |
10 |
0.0001386 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
5 |
1 |
10 |
1 |
0.0003604 |
0.0004039 |
0.0003643 |
0.000376 |
0.0003708 |
0.0003604 |
0.0003947 |
10 |
0.0003673 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
5 |
1 |
10 |
1 |
0.002307 |
0.002368 |
0.002328 |
0.002348 |
0.002336 |
0.002307 |
0.002366 |
10 |
0.002335 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
5 |
1 |
10 |
1 |
0.02093 |
0.0211 |
0.021 |
0.02102 |
0.02101 |
0.02093 |
0.02109 |
10 |
0.02101 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
5 |
10 |
10 |
1 |
0.0001073 |
0.0002248 |
0.0001093 |
0.000117 |
0.0001241 |
0.0001073 |
0.0001906 |
10 |
0.0001135 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
5 |
10 |
10 |
1 |
0.0001335 |
0.0001665 |
0.0001369 |
0.0001431 |
0.0001418 |
0.0001335 |
0.0001597 |
10 |
0.0001379 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
5 |
10 |
10 |
1 |
0.0003617 |
0.0004124 |
0.0003651 |
0.000376 |
0.0003734 |
0.0003617 |
0.0004019 |
10 |
0.0003677 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
5 |
10 |
10 |
1 |
0.002349 |
0.002416 |
0.002354 |
0.002372 |
0.002368 |
0.002349 |
0.002405 |
10 |
0.002366 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
5 |
10 |
10 |
1 |
0.02118 |
0.0213 |
0.02119 |
0.02124 |
0.02121 |
0.02118 |
0.02129 |
10 |
0.02119 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
5 |
100 |
10 |
1 |
0.0001062 |
0.000232 |
0.0001085 |
0.0001163 |
0.0001302 |
0.0001062 |
0.000209 |
10 |
0.0001127 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
5 |
100 |
10 |
1 |
0.0001349 |
0.0001694 |
0.000138 |
0.0001424 |
0.000143 |
0.0001349 |
0.0001619 |
10 |
0.000139 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
5 |
100 |
10 |
1 |
0.0003611 |
0.0004015 |
0.000364 |
0.0003701 |
0.0003695 |
0.0003611 |
0.000392 |
10 |
0.0003655 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
5 |
100 |
10 |
1 |
0.002337 |
0.002407 |
0.002351 |
0.002355 |
0.002355 |
0.002337 |
0.002392 |
10 |
0.002351 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
5 |
100 |
10 |
1 |
0.02107 |
0.02125 |
0.02114 |
0.02116 |
0.02115 |
0.02107 |
0.02124 |
10 |
0.02114 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
10 |
1 |
10 |
1 |
0.0001213 |
0.0002759 |
0.0001288 |
0.0001348 |
0.0001445 |
0.0001213 |
0.000231 |
10 |
0.00013 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
10 |
1 |
10 |
1 |
0.0001986 |
0.0002682 |
0.0002045 |
0.00023 |
0.000217 |
0.0001986 |
0.0002605 |
10 |
0.0002054 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
10 |
1 |
10 |
1 |
0.0006833 |
0.0009495 |
0.0006984 |
0.000734 |
0.0007338 |
0.0006833 |
0.000882 |
10 |
0.0007002 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
10 |
1 |
10 |
1 |
0.00507 |
0.005555 |
0.00509 |
0.005124 |
0.00515 |
0.00507 |
0.005423 |
10 |
0.005096 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
10 |
1 |
10 |
1 |
0.0465 |
0.04725 |
0.04665 |
0.04669 |
0.04672 |
0.0465 |
0.04709 |
10 |
0.04666 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
10 |
10 |
10 |
1 |
0.0001233 |
0.0003089 |
0.0001311 |
0.0001403 |
0.0001506 |
0.0001233 |
0.0002549 |
10 |
0.0001322 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
10 |
10 |
10 |
1 |
0.0002005 |
0.0003387 |
0.0002098 |
0.0002408 |
0.0002314 |
0.0002005 |
0.0003078 |
10 |
0.0002143 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
10 |
10 |
10 |
1 |
0.0006987 |
0.0009714 |
0.000709 |
0.0007401 |
0.0007467 |
0.0006987 |
0.000899 |
10 |
0.0007185 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
10 |
10 |
10 |
1 |
0.005094 |
0.005565 |
0.005115 |
0.005137 |
0.005172 |
0.005094 |
0.005437 |
10 |
0.005124 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
10 |
10 |
10 |
1 |
0.04662 |
0.04728 |
0.04678 |
0.04692 |
0.04686 |
0.04662 |
0.04723 |
10 |
0.0468 |
0 |
||||
predict |
skl |
2 |
1 |
5 |
10 |
100 |
10 |
1 |
0.0001243 |
0.0003086 |
0.0001276 |
0.0001442 |
0.0001577 |
0.0001243 |
0.0002657 |
10 |
0.0001362 |
0 |
||||
predict |
skl |
2 |
10 |
5 |
10 |
100 |
10 |
1 |
0.0002026 |
0.0003604 |
0.0002132 |
0.0002224 |
0.0002328 |
0.0002026 |
0.0003212 |
10 |
0.0002159 |
0 |
||||
predict |
skl |
2 |
100 |
5 |
10 |
100 |
10 |
1 |
0.0006971 |
0.0009783 |
0.0006987 |
0.0007269 |
0.0007401 |
0.0006971 |
0.0009025 |
10 |
0.0007071 |
0 |
||||
predict |
skl |
2 |
1000 |
5 |
10 |
100 |
10 |
1 |
0.005077 |
0.00561 |
0.005094 |
0.005127 |
0.005167 |
0.005077 |
0.005469 |
10 |
0.005113 |
0 |
||||
predict |
skl |
2 |
10000 |
5 |
10 |
100 |
10 |
1 |
0.04675 |
0.04721 |
0.04676 |
0.04697 |
0.04689 |
0.04675 |
0.04717 |
10 |
0.04685 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
2 |
1 |
10 |
1 |
0.0001036 |
0.0002578 |
0.0001077 |
0.0001165 |
0.0001258 |
0.0001036 |
0.0002133 |
10 |
0.0001091 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
2 |
1 |
10 |
1 |
0.0001131 |
0.0001458 |
0.0001163 |
0.000122 |
0.0001205 |
0.0001131 |
0.0001388 |
10 |
0.0001167 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
2 |
1 |
10 |
1 |
0.0002266 |
0.0002604 |
0.0002283 |
0.0002295 |
0.0002321 |
0.0002266 |
0.0002509 |
10 |
0.000229 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
2 |
1 |
10 |
1 |
0.001297 |
0.001325 |
0.001304 |
0.00131 |
0.001307 |
0.001297 |
0.001321 |
10 |
0.001306 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
2 |
1 |
10 |
1 |
0.01148 |
0.01171 |
0.0115 |
0.01153 |
0.01153 |
0.01148 |
0.01165 |
10 |
0.01151 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
2 |
10 |
10 |
1 |
0.000102 |
0.0001893 |
0.0001034 |
0.0001101 |
0.0001142 |
0.000102 |
0.0001639 |
10 |
0.0001045 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
2 |
10 |
10 |
1 |
0.0001152 |
0.0001389 |
0.0001158 |
0.0001213 |
0.0001206 |
0.0001152 |
0.0001343 |
10 |
0.0001188 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
2 |
10 |
10 |
1 |
0.0002242 |
0.0002566 |
0.0002276 |
0.0002298 |
0.0002313 |
0.0002242 |
0.0002487 |
10 |
0.0002284 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
2 |
10 |
10 |
1 |
0.001293 |
0.001327 |
0.001301 |
0.001307 |
0.001305 |
0.001293 |
0.001322 |
10 |
0.001302 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
2 |
10 |
10 |
1 |
0.01145 |
0.01165 |
0.01146 |
0.01149 |
0.01149 |
0.01145 |
0.0116 |
10 |
0.01147 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
2 |
100 |
10 |
1 |
0.0001044 |
0.0001957 |
0.0001066 |
0.0001223 |
0.0001212 |
0.0001044 |
0.0001748 |
10 |
0.0001081 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
2 |
100 |
10 |
1 |
0.0001146 |
0.0001412 |
0.0001168 |
0.0001231 |
0.0001212 |
0.0001146 |
0.0001363 |
10 |
0.0001176 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
2 |
100 |
10 |
1 |
0.0002279 |
0.0002584 |
0.0002301 |
0.0002382 |
0.0002346 |
0.0002279 |
0.0002518 |
10 |
0.0002305 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
2 |
100 |
10 |
1 |
0.001298 |
0.001327 |
0.001304 |
0.001308 |
0.001306 |
0.001298 |
0.001321 |
10 |
0.001304 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
2 |
100 |
10 |
1 |
0.01152 |
0.01171 |
0.01153 |
0.01156 |
0.01156 |
0.01152 |
0.01166 |
10 |
0.01155 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
5 |
1 |
10 |
1 |
0.0001075 |
0.0002283 |
0.0001105 |
0.0001162 |
0.0001245 |
0.0001075 |
0.000193 |
10 |
0.000112 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
5 |
1 |
10 |
1 |
0.0001392 |
0.0001725 |
0.0001412 |
0.0001467 |
0.000146 |
0.0001392 |
0.0001649 |
10 |
0.0001417 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
5 |
1 |
10 |
1 |
0.0003987 |
0.0004384 |
0.000402 |
0.0004074 |
0.0004072 |
0.0003987 |
0.0004284 |
10 |
0.0004041 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
5 |
1 |
10 |
1 |
0.00269 |
0.002743 |
0.002701 |
0.002724 |
0.002712 |
0.00269 |
0.002743 |
10 |
0.002705 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
5 |
1 |
10 |
1 |
0.0243 |
0.02454 |
0.02435 |
0.02439 |
0.02438 |
0.0243 |
0.0245 |
10 |
0.02437 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
5 |
10 |
10 |
1 |
0.0001078 |
0.0002267 |
0.0001095 |
0.0001153 |
0.0001237 |
0.0001078 |
0.0001915 |
10 |
0.0001117 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
5 |
10 |
10 |
1 |
0.0001397 |
0.0001754 |
0.0001417 |
0.0001474 |
0.0001473 |
0.0001397 |
0.0001673 |
10 |
0.0001439 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
5 |
10 |
10 |
1 |
0.000398 |
0.0004465 |
0.0004032 |
0.0004099 |
0.0004097 |
0.000398 |
0.0004356 |
10 |
0.0004059 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
5 |
10 |
10 |
1 |
0.002689 |
0.002752 |
0.0027 |
0.002702 |
0.002704 |
0.002689 |
0.002738 |
10 |
0.002701 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
5 |
10 |
10 |
1 |
0.02437 |
0.02476 |
0.02441 |
0.02445 |
0.02447 |
0.02437 |
0.02471 |
10 |
0.02442 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
5 |
100 |
10 |
1 |
0.0001083 |
0.0002308 |
0.0001109 |
0.0001258 |
0.0001335 |
0.0001083 |
0.0002142 |
10 |
0.0001134 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
5 |
100 |
10 |
1 |
0.0001407 |
0.0001709 |
0.0001439 |
0.0001486 |
0.0001483 |
0.0001407 |
0.0001647 |
10 |
0.0001456 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
5 |
100 |
10 |
1 |
0.0003957 |
0.0004573 |
0.0004037 |
0.0004154 |
0.0004115 |
0.0003957 |
0.0004459 |
10 |
0.0004053 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
5 |
100 |
10 |
1 |
0.00268 |
0.002727 |
0.002683 |
0.002695 |
0.00269 |
0.00268 |
0.002717 |
10 |
0.002685 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
5 |
100 |
10 |
1 |
0.0242 |
0.02453 |
0.02426 |
0.02432 |
0.0243 |
0.0242 |
0.02447 |
10 |
0.02428 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
10 |
1 |
10 |
1 |
0.000123 |
0.0002804 |
0.0001313 |
0.0001366 |
0.0001475 |
0.000123 |
0.0002352 |
10 |
0.0001328 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
10 |
1 |
10 |
1 |
0.0002112 |
0.0002871 |
0.0002225 |
0.0002346 |
0.0002312 |
0.0002112 |
0.0002742 |
10 |
0.000226 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
10 |
1 |
10 |
1 |
0.0007549 |
0.001047 |
0.0007576 |
0.0008148 |
0.0008058 |
0.0007549 |
0.0009769 |
10 |
0.000765 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
10 |
1 |
10 |
1 |
0.005465 |
0.006026 |
0.005507 |
0.005552 |
0.005577 |
0.005465 |
0.005887 |
10 |
0.005527 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
10 |
1 |
10 |
1 |
0.04935 |
0.04989 |
0.04945 |
0.04953 |
0.04951 |
0.04935 |
0.04978 |
10 |
0.04948 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
10 |
10 |
10 |
1 |
0.0001206 |
0.000313 |
0.0001315 |
0.0001486 |
0.0001534 |
0.0001206 |
0.0002591 |
10 |
0.0001358 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
10 |
10 |
10 |
1 |
0.0002154 |
0.0003723 |
0.0002203 |
0.0002488 |
0.0002454 |
0.0002154 |
0.000333 |
10 |
0.0002287 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
10 |
10 |
10 |
1 |
0.0007487 |
0.001075 |
0.0007582 |
0.0008009 |
0.0008034 |
0.0007487 |
0.0009891 |
10 |
0.0007628 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
10 |
10 |
10 |
1 |
0.005434 |
0.005996 |
0.005483 |
0.005493 |
0.005538 |
0.005434 |
0.005848 |
10 |
0.005489 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
10 |
10 |
10 |
1 |
0.04934 |
0.04978 |
0.04937 |
0.04939 |
0.04941 |
0.04934 |
0.04966 |
10 |
0.04938 |
0 |
||||
predict |
skl |
2 |
1 |
10 |
10 |
100 |
10 |
1 |
0.0001218 |
0.0003186 |
0.0001319 |
0.0001401 |
0.0001509 |
0.0001218 |
0.0002611 |
10 |
0.0001333 |
0 |
||||
predict |
skl |
2 |
10 |
10 |
10 |
100 |
10 |
1 |
0.0002219 |
0.0004579 |
0.0002328 |
0.0002499 |
0.0002635 |
0.0002219 |
0.0003958 |
10 |
0.0002375 |
0 |
||||
predict |
skl |
2 |
100 |
10 |
10 |
100 |
10 |
1 |
0.0007635 |
0.001088 |
0.0007714 |
0.0008146 |
0.0008189 |
0.0007635 |
0.001005 |
10 |
0.000776 |
0 |
||||
predict |
skl |
2 |
1000 |
10 |
10 |
100 |
10 |
1 |
0.005535 |
0.006083 |
0.005559 |
0.005644 |
0.00564 |
0.005535 |
0.005943 |
10 |
0.005589 |
0 |
||||
predict |
skl |
2 |
10000 |
10 |
10 |
100 |
10 |
1 |
0.0497 |
0.05059 |
0.04978 |
0.05027 |
0.05002 |
0.0497 |
0.05059 |
10 |
0.0499 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
2 |
1 |
10 |
1 |
0.0001004 |
0.0002543 |
0.0001029 |
0.0001132 |
0.0001289 |
0.0001004 |
0.0002261 |
10 |
0.0001048 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
2 |
1 |
10 |
1 |
0.0001137 |
0.0001812 |
0.0001147 |
0.0001178 |
0.0001227 |
0.0001137 |
0.0001613 |
10 |
0.0001155 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
2 |
1 |
10 |
1 |
0.0002359 |
0.0002716 |
0.0002368 |
0.0002397 |
0.000241 |
0.0002359 |
0.0002613 |
10 |
0.0002371 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
2 |
1 |
10 |
1 |
0.001433 |
0.001466 |
0.001439 |
0.001443 |
0.001443 |
0.001433 |
0.00146 |
10 |
0.001441 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
2 |
1 |
10 |
1 |
0.01301 |
0.01324 |
0.01302 |
0.01305 |
0.01305 |
0.01301 |
0.01318 |
10 |
0.01303 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
2 |
10 |
10 |
1 |
0.0001012 |
0.0001844 |
0.0001021 |
0.000108 |
0.0001124 |
0.0001012 |
0.00016 |
10 |
0.000103 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
2 |
10 |
10 |
1 |
0.000115 |
0.000139 |
0.000117 |
0.0001234 |
0.0001212 |
0.000115 |
0.0001346 |
10 |
0.0001188 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
2 |
10 |
10 |
1 |
0.0002331 |
0.0002669 |
0.0002363 |
0.0002405 |
0.0002402 |
0.0002331 |
0.0002583 |
10 |
0.0002368 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
2 |
10 |
10 |
1 |
0.001433 |
0.001468 |
0.001437 |
0.001444 |
0.001442 |
0.001433 |
0.00146 |
10 |
0.001441 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
2 |
10 |
10 |
1 |
0.01298 |
0.01322 |
0.013 |
0.01302 |
0.01303 |
0.01298 |
0.01316 |
10 |
0.01301 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
2 |
100 |
10 |
1 |
0.000103 |
0.0001948 |
0.0001041 |
0.00011 |
0.0001156 |
0.000103 |
0.0001681 |
10 |
0.0001055 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
2 |
100 |
10 |
1 |
0.000116 |
0.0001414 |
0.000117 |
0.0001215 |
0.0001211 |
0.000116 |
0.0001358 |
10 |
0.0001181 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
2 |
100 |
10 |
1 |
0.0002358 |
0.0002735 |
0.000238 |
0.0002415 |
0.000243 |
0.0002358 |
0.0002637 |
10 |
0.0002395 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
2 |
100 |
10 |
1 |
0.001442 |
0.001475 |
0.001445 |
0.001448 |
0.001449 |
0.001442 |
0.001467 |
10 |
0.001447 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
2 |
100 |
10 |
1 |
0.01299 |
0.01324 |
0.01301 |
0.01303 |
0.01304 |
0.01299 |
0.01318 |
10 |
0.01302 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
5 |
1 |
10 |
1 |
0.0001078 |
0.0002301 |
0.0001125 |
0.0001258 |
0.0001274 |
0.0001078 |
0.0001958 |
10 |
0.0001149 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
5 |
1 |
10 |
1 |
0.0001414 |
0.0001732 |
0.0001434 |
0.0001493 |
0.0001484 |
0.0001414 |
0.0001661 |
10 |
0.0001445 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
5 |
1 |
10 |
1 |
0.0004257 |
0.0004686 |
0.0004292 |
0.0004338 |
0.0004352 |
0.0004257 |
0.0004585 |
10 |
0.0004315 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
5 |
1 |
10 |
1 |
0.003025 |
0.003091 |
0.003032 |
0.003052 |
0.003043 |
0.003025 |
0.003082 |
10 |
0.003033 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
5 |
1 |
10 |
1 |
0.02764 |
0.02805 |
0.02771 |
0.02774 |
0.02775 |
0.02764 |
0.02796 |
10 |
0.02773 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
5 |
10 |
10 |
1 |
0.0001102 |
0.0002339 |
0.0001134 |
0.0001279 |
0.0001302 |
0.0001102 |
0.0001991 |
10 |
0.0001186 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
5 |
10 |
10 |
1 |
0.0001431 |
0.0001747 |
0.0001446 |
0.0001494 |
0.0001496 |
0.0001431 |
0.0001675 |
10 |
0.0001466 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
5 |
10 |
10 |
1 |
0.0004286 |
0.000476 |
0.00043 |
0.00044 |
0.0004366 |
0.0004286 |
0.0004636 |
10 |
0.0004301 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
5 |
10 |
10 |
1 |
0.003021 |
0.003147 |
0.00303 |
0.003045 |
0.003048 |
0.003021 |
0.003115 |
10 |
0.00304 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
5 |
10 |
10 |
1 |
0.0277 |
0.02803 |
0.02773 |
0.02779 |
0.02777 |
0.0277 |
0.02795 |
10 |
0.02774 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
5 |
100 |
10 |
1 |
0.0001087 |
0.0002332 |
0.0001123 |
0.0001236 |
0.0001278 |
0.0001087 |
0.0001976 |
10 |
0.0001151 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
5 |
100 |
10 |
1 |
0.0001434 |
0.0001749 |
0.0001453 |
0.0001518 |
0.0001503 |
0.0001434 |
0.000168 |
10 |
0.0001462 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
5 |
100 |
10 |
1 |
0.000428 |
0.0004789 |
0.0004309 |
0.0004372 |
0.0004375 |
0.000428 |
0.0004656 |
10 |
0.0004324 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
5 |
100 |
10 |
1 |
0.003017 |
0.003093 |
0.003024 |
0.003042 |
0.003035 |
0.003017 |
0.003076 |
10 |
0.003028 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
5 |
100 |
10 |
1 |
0.02764 |
0.02798 |
0.02765 |
0.02768 |
0.02769 |
0.02764 |
0.02788 |
10 |
0.02765 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
10 |
1 |
10 |
1 |
0.0001336 |
0.0002761 |
0.0001387 |
0.0001622 |
0.0001616 |
0.0001336 |
0.000245 |
10 |
0.0001443 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
10 |
1 |
10 |
1 |
0.0002343 |
0.0003294 |
0.0002475 |
0.0002758 |
0.0002638 |
0.0002343 |
0.0003191 |
10 |
0.0002572 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
10 |
1 |
10 |
1 |
0.0009259 |
0.001355 |
0.0009355 |
0.0009746 |
0.0009967 |
0.0009259 |
0.001244 |
10 |
0.0009424 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
10 |
1 |
10 |
1 |
0.006866 |
0.007288 |
0.006886 |
0.006928 |
0.006949 |
0.006866 |
0.007193 |
10 |
0.006895 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
10 |
1 |
10 |
1 |
0.05947 |
0.05982 |
0.05955 |
0.05969 |
0.05963 |
0.05947 |
0.05982 |
10 |
0.05962 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
10 |
10 |
10 |
1 |
0.0001332 |
0.0003209 |
0.0001404 |
0.0001475 |
0.0001601 |
0.0001332 |
0.0002659 |
10 |
0.0001415 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
10 |
10 |
10 |
1 |
0.0002308 |
0.0004878 |
0.0002431 |
0.0002825 |
0.0002827 |
0.0002308 |
0.000424 |
10 |
0.0002536 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
10 |
10 |
10 |
1 |
0.0009171 |
0.001347 |
0.0009412 |
0.0009757 |
0.001002 |
0.0009171 |
0.001244 |
10 |
0.0009537 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
10 |
10 |
10 |
1 |
0.006846 |
0.007302 |
0.006944 |
0.006993 |
0.006991 |
0.006846 |
0.007228 |
10 |
0.006967 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
10 |
10 |
10 |
1 |
0.05968 |
0.05996 |
0.05971 |
0.05977 |
0.05977 |
0.05968 |
0.05995 |
10 |
0.05973 |
0 |
||||
predict |
skl |
2 |
1 |
20 |
10 |
100 |
10 |
1 |
0.0001319 |
0.0003168 |
0.0001389 |
0.000196 |
0.0001732 |
0.0001319 |
0.0002836 |
10 |
0.0001438 |
0 |
||||
predict |
skl |
2 |
10 |
20 |
10 |
100 |
10 |
1 |
0.0002361 |
0.0004428 |
0.0002433 |
0.0002806 |
0.0002767 |
0.0002361 |
0.0003925 |
10 |
0.0002581 |
0 |
||||
predict |
skl |
2 |
100 |
20 |
10 |
100 |
10 |
1 |
0.0009316 |
0.001362 |
0.0009469 |
0.001013 |
0.001013 |
0.0009316 |
0.001255 |
10 |
0.0009712 |
0 |
||||
predict |
skl |
2 |
1000 |
20 |
10 |
100 |
10 |
1 |
0.006726 |
0.00729 |
0.00691 |
0.007028 |
0.006965 |
0.006726 |
0.007237 |
10 |
0.006957 |
0 |
||||
predict |
skl |
2 |
10000 |
20 |
10 |
100 |
10 |
1 |
0.05972 |
0.05994 |
0.0598 |
0.05988 |
0.05983 |
0.05972 |
0.05994 |
10 |
0.05983 |
0 |
Benchmark code#
# coding: utf-8
"""
Benchmark of :epkg:`onnxruntime` on RandomForest.
"""
# Authors: Xavier Dupré (benchmark)
# License: MIT
import matplotlib
matplotlib.use('Agg')
import os
from time import perf_counter as time
import numpy
import pandas
import matplotlib.pyplot as plt
import sklearn
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.utils._testing import ignore_warnings
from sklearn.utils.extmath import softmax
from scipy.special import expit
from pymlbenchmark.context import machine_information
from pymlbenchmark.benchmark import BenchPerf
from pymlbenchmark.external import OnnxRuntimeBenchPerfTestRegression
from pymlbenchmark.plotting import plot_bench_results
model_name = "GradientBoostingRegressor"
filename = os.path.splitext(os.path.split(__file__)[-1])[0]
@ignore_warnings(category=FutureWarning)
def run_bench(repeat=10, verbose=False):
pbefore = dict(dim=[1, 5, 10, 15],
max_depth=[2, 5, 10],
n_estimators=[1, 10, 50],
onnx_options=[None])
pafter = dict(N=[1, 10, 100, 1000, 10000])
test = lambda dim=None, **opts: OnnxRuntimeBenchPerfTestRegression(
GradientBoostingRegressor, dim=dim, **opts)
bp = BenchPerf(pbefore, pafter, test)
with sklearn.config_context(assume_finite=True):
start = time()
results = list(bp.enumerate_run_benchs(repeat=repeat, verbose=verbose,
stop_if_error=False))
end = time()
results_df = pandas.DataFrame(results)
print("Total time = %0.3f sec\n" % (end - start))
return results_df
#########################
# Runs the benchmark
# ++++++++++++++++++
df = run_bench(verbose=True)
df.to_csv("%s.perf.csv" % filename, index=False)
print(df.head())
#########################
# Extract information about the machine used
# ++++++++++++++++++++++++++++++++++++++++++
pkgs = ['numpy', 'pandas', 'sklearn', 'skl2onnx',
'onnxruntime', 'onnx', 'mlprodict']
dfi = pandas.DataFrame(machine_information(pkgs))
dfi.to_csv("%s.time.csv" % filename, index=False)
print(dfi)
#############################
# Plot the results
# ++++++++++++++++
def label_fct(la):
la = la.replace("onxpython_compiled", "opy")
la = la.replace("onxpython", "opy")
la = la.replace("onxonnxruntime1", "ort")
la = la.replace("fit_intercept", "fi")
la = la.replace("True", "1")
la = la.replace("False", "0")
la = la.replace("max_depth", "mxd")
return la
plot_bench_results(df, row_cols=['N', 'max_depth', 'onnx_options'], col_cols='method',
x_value='dim', hue_cols=['n_estimators'],
title="%s\nBenchmark scikit-learn / onnxruntime" % model_name,
label_fct=label_fct)
plt.savefig("%s.png" % filename)
import sys
if "--quiet" not in sys.argv:
plt.show()