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add preprocessing benchmarking #216

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add preprocessing benchmarking
icfaust Jul 10, 2026
b42d645
Update preprocessing.json
icfaust Jul 10, 2026
4356292
Merge branch 'IntelPython:main' into dev/add_preprocessor_support
icfaust Jul 24, 2026
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37 changes: 37 additions & 0 deletions configs/regular/preprocessing.json
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{
"INCLUDE": ["../common/sklearn.json"],
"PARAMETERS_SETS": {
"preprocessing parameters": {
"algorithm": {
"estimator": "MaxAbsScaler",
"estimator_methods": { "training": "fit", "inference": "transform" }
}
},
"preprocessing datasets": [
{
"data": {
"source": "make_regression",
"generation_kwargs": [
{ "n_samples": 100000, "n_features": 5000, "noise": 1.0 },
{ "n_samples": 1000000, "n_features": 500, "noise": 1.0 },
{ "n_samples": 10000000, "n_features": 50, "noise": 1.0 }

@david-cortes-intel david-cortes-intel Jul 13, 2026

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Perhaps it could add cases with sparse data instead of something this large.

@icfaust icfaust Jul 24, 2026

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https://github.com/uxlfoundation/scikit-learn-intelex/blob/main/sklearnex/preview/preprocessing/_data.py#L83 unfortunately not something we handle in the current moment (due to IncrementalBasicStatistics not handling sparse data).

@icfaust icfaust Jul 24, 2026

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Also the reason for such big data is to match order of magnitude to linear regression sizing https://github.com/IntelPython/scikit-learn_bench/blob/main/configs/regular/linear_model.json#L11

],
"split_kwargs": { "train_size": 0.5, "test_size": 0.5 },
"dtype": ["float32", "float64"]
}
},
{
"data": {
"dataset": ["sift", "gist", "svhn"],
"split_kwargs": { "train_size": 0.5, "test_size": 0.5 },
"dtype": ["float32", "float64"]
}
}
]
},
"TEMPLATES": {
"sklearn preprocessing": {
"SETS": ["sklearn-ex[cpu,gpu] implementations", "preprocessing parameters", "preprocessing datasets"]
}
}
}
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