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Improve sklearnex relative perf in sklearn_example.json #209

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Improve sklearnex relative perf in sklearn_example.json #209

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Improve sklearnex relative perf in sklearn_example.json
ethanglaser Apr 23, 2026
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ethanglaser Apr 24, 2026
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42 changes: 22 additions & 20 deletions configs/sklearn_example.json
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
"source": "make_blobs",
"generation_kwargs": {
"centers": 2,
"n_samples": 1000,
"n_samples": 5000,
"n_features": [16, 64]
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@david-cortes-intel david-cortes-intel Apr 24, 2026

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Do we need the case with 16 features?

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@ethanglaser ethanglaser Apr 24, 2026 •
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I think this may have been originally added to demonstrate to users that configs can take lists instead of just individual values - so I'd be in favor of keeping it as is

},
"split_kwargs": { "ignore": true }
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Expand All @@ -33,53 +33,55 @@
}
]
},
"multi clsf data": {
"regression data": {
"data": {
"source": "make_classification",
"source": "make_regression",
"generation_kwargs": {
"n_classes": 4,
"n_samples": 1000,
"n_features": 10,
"n_informative": "[SPECIAL_VALUE]0.75"
"n_samples": 10000,
"n_features": 100,
"n_informative": 50,
"noise": 0.1
},
"split_kwargs": { "test_size": 0.1 }
"split_kwargs": { "test_size": 0.2 }
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}
},
"regression algorithms": {
"algorithm": [
{ "estimator": "LinearRegression" }
]
},
"unsupervised algorithms": {
"algorithm":
[
{
"estimator": "PCA",
"estimator_params": { "svd_solver": "auto", "n_components": 3 }
},
"algorithm": [
{
"estimator": "KMeans",
"estimator_params": { "n_init": 10, "n_clusters": "[SPECIAL_VALUE]auto" },
"estimator_methods": { "inference": "predict" }
},
{
"estimator": "DBSCAN",
"estimator_params": { "eps": 3, "min_samples": 5 }
}
]
},
"supervised algorithms": {
"algorithm": [
{
"estimator": ["KNeighborsClassifier", "KNeighborsRegressor"],
"estimator_params": { "n_neighbors": 5, "algorithm": "brute" }
"estimator_params": { "n_neighbors": 5, "algorithm": "kd_tree" }
},
{ "estimator": "ElasticNet" },
{ "estimator": "SVC" }
]
}
},
"TEMPLATES": {
"multi clsf": {
"SETS": ["common", "multi clsf data"],
"algorithm": { "estimator": "LogisticRegression" }
},
"supervised": {
"SETS": ["common", "binary clsf data", "supervised algorithms"]
},
"unsupervised": {
"SETS": ["common", "blobs data", "unsupervised algorithms"]
},
"regression": {
"SETS": ["common", "regression data", "regression algorithms"]
}
}
}
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