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Add EmpiricalCovariance algorithm #226

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bcda511
Add EmpiricalCovariance algorithm
Vika-F Oct 16, 2026
a603f0e
Formatting
Vika-F Oct 16, 2026
f6ca63b
Move EmpiricalCovariance config to 'experiments'
Vika-F Oct 16, 2026
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15 changes: 15 additions & 0 deletions configs/common/covariance.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,15 @@
{
"INCLUDE": ["../common/sklearn.json"],
"PARAMETERS_SETS": {
"covariance parameters": {
"algorithm": {
"estimator": "EmpiricalCovariance",
"estimator_params": {
"store_precision": [true, false],
"assume_centered": false
},
"estimator_methods": { "training": "fit" }
}
}
}
}
18 changes: 18 additions & 0 deletions configs/common/sklearn.json
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Expand Up @@ -18,6 +18,24 @@
"data": { "format": "dpnp", "order": "C" }
}
],
"sklearn-ex[cpu] preview implementations": {
"algorithm": [
{ "library": "sklearn", "device": "cpu" },
{ "library": "sklearnex.preview", "device": "cpu" }
]
},
"sklearn-ex[cpu,gpu] preview implementations": [
{ "algorithm": { "library": "sklearn", "device": "cpu" } },
{ "algorithm": { "library": "sklearnex.preview", "device": "cpu" } },
{
"algorithm": {
"library": "sklearnex.preview",
"device": "gpu",
"sklearn_context": { "array_api_dispatch": true }
},
"data": { "format": "dpnp", "order": "C" }
}
],
"sklearnex spmd implementation": {
"algorithm": {
"library": "sklearnex.spmd",
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29 changes: 29 additions & 0 deletions configs/experiments/covariance.json
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{
"INCLUDE": ["../common/sklearn.json", "../common/covariance.json"],
"PARAMETERS_SETS": {
"covariance datasets": [
{
"data": {
"source": "make_blobs",
"generation_kwargs": [
{ "n_samples": 10000000, "n_features": 50, "centers": 1 },
{ "n_samples": 2000000, "n_features": 500, "centers": 1 },
{ "n_samples": 200000, "n_features": 2000, "centers": 1 }
],
"split_kwargs": { "ignore": true }
}
},
{
"data": {
"dataset": ["susy", "higgs"],
"split_kwargs": { "ignore": true }
}
}
]
},
"TEMPLATES": {
"sklearn covariance": {
"SETS": ["sklearn-ex[cpu,gpu] preview implementations", "covariance parameters", "covariance datasets"]
}
}
}
33 changes: 33 additions & 0 deletions configs/weekly/covariance.json
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{
"INCLUDE": ["../common/sklearn.json", "../common/covariance.json"],
"PARAMETERS_SETS": {
"high-load covariance datasets": [
{
"data": {
"source": "make_blobs",
"generation_kwargs": [
{ "n_samples": 50000000, "n_features": 50, "centers": 1 },
{ "n_samples": 10000000, "n_features": 500, "centers": 1 },
{ "n_samples": 1000000, "n_features": 5000, "centers": 1 }

@david-cortes-intel david-cortes-intel Oct 19, 2026

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This one is quite big. Are you sure it runs in a reasonable amount of time compared to the rest of the benchmarks?

],
"split_kwargs": { "ignore": true }
}
},
{
"data": {
"dataset": ["airline_depdelay", "epsilon"],
"split_kwargs": { "ignore": true }
}
}
]
},
"TEMPLATES": {
"sklearn covariance": {
"SETS": [
"sklearn-ex[cpu,gpu] preview implementations",
"covariance parameters",
"high-load covariance datasets"
]
}
}
}
9 changes: 9 additions & 0 deletions sklbench/benchmarks/sklearn_estimator.py
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Expand Up @@ -190,6 +190,15 @@ def get_subset_metrics_of_estimator(
)
}
)
elif task == "utility":
# SPMD estimators are skipped: their score triggers an additional collective fit
if (
"Covariance" in str(estimator_instance)
and stage == "training"
and hasattr(estimator_instance, "score")
and "spmd" not in type(estimator_instance).__module__
):
metrics.update({"average log-likelihood": float(estimator_instance.score(x))})
elif task == "clustering":
if hasattr(estimator_instance, "inertia_"):
# compute inertia manually using distances to cluster centers
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2 changes: 1 addition & 1 deletion sklbench/report/implementation.py
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Expand Up @@ -74,7 +74,7 @@
"indifferent": [
# SVM
"support vectors",
# PCA
# PCA, EmpiricalCovariance
"average log-likelihood",
"1st component variance ratio",
# DBSCAN
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