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initial alignment of configs to final results #176

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merged 12 commits into from
Mar 21, 2025
Merged

initial alignment of configs to final results #176

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fc29011
initial alignment of configs to final results
ethanglaser Mar 19, 2025
fca41a9
logic updates
ethanglaser Mar 20, 2025
3e94f60
fix large scale CI
ethanglaser Mar 20, 2025
3af87d7
black
ethanglaser Mar 21, 2025
66b6834
minimize incremental
ethanglaser Mar 21, 2025
3f25ddb
Merge branch 'dev/eglaser-large-scale-ci-fix' into dev/eglaser-large-…
ethanglaser Mar 21, 2025
d3f0b85
black
ethanglaser Mar 21, 2025
7796f95
align custom function with skl estimator
ethanglaser Mar 21, 2025
f6c6967
Update sklbench/utils/measurement.py
ethanglaser Mar 21, 2025
646f7a5
Update sklbench/utils/measurement.py
ethanglaser Mar 21, 2025
e684c8d
Update sklbench/utils/measurement.py
ethanglaser Mar 21, 2025
878d31d
Update sklbench/utils/measurement.py
ethanglaser Mar 21, 2025
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100 changes: 0 additions & 100 deletions configs/incremental.json
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15 changes: 8 additions & 7 deletions configs/spmd/large_scale/basic_stats.json
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Expand Up @@ -4,16 +4,17 @@
"spmd basicstats parameters": {
"algorithm": {
"estimator": "BasicStatistics",
"estimator_methods": { "training": "fit" }
"estimator_methods": { "training": "fit" },
"sklearnex_context": { "use_raw_input": true }
},
"data": {
"split_kwargs": { "test_size": 0.0001 }
}
"data": {
"split_kwargs": { "test_size": 0.0001 }
}
},
"synthetic data": {
"data": [
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 10000000, "n_features": 10, "centers": 1 } },
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 100000, "n_features": 1000, "centers": 1 } }
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 100000000, "n_features": 10, "centers": 1 } },
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 1000000, "n_features": 1000, "centers": 1 } }
]
}
},
Expand All @@ -22,7 +23,7 @@
"SETS": [
"sklearnex spmd implementation",
"large scale 2k parameters",
"synthetic data",
"synthetic data",
"spmd basicstats parameters"
]
}
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13 changes: 7 additions & 6 deletions configs/spmd/large_scale/basic_stats_strong.json
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Expand Up @@ -4,11 +4,12 @@
"spmd basicstats parameters": {
"algorithm": {
"estimator": "BasicStatistics",
"estimator_methods": { "training": "fit" }
"estimator_methods": { "training": "fit" },
"sklearnex_context": { "use_raw_input": true }
},
"data": {
"split_kwargs": { "test_size": 0.0001 }
}
"data": {
"split_kwargs": { "test_size": 0.0001 }
}
},
"synthetic data": {
"data": [
Expand All @@ -20,8 +21,8 @@
"basicstats": {
"SETS": [
"sklearnex spmd implementation",
"large scale strong 2k parameters",
"synthetic data",
"large scale strong <=64 parameters",
"synthetic data",
"spmd basicstats parameters"
]
}
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9 changes: 5 additions & 4 deletions configs/spmd/large_scale/covariance.json
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Expand Up @@ -4,16 +4,17 @@
"spmd basicstats parameters": {
"algorithm": {
"estimator": "EmpiricalCovariance",
"estimator_methods": { "training": "fit" }
"estimator_methods": { "training": "fit" },
"sklearnex_context": { "use_raw_input": true }
},
"data": {
"split_kwargs": { "test_size": 0.0001 }
}
},
"synthetic data": {
"data": [
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 10000000, "n_features": 10, "centers": 1 } },
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 100000, "n_features": 1000, "centers": 1 } }
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 100000000, "n_features": 10, "centers": 1 } },
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 1000000, "n_features": 1000, "centers": 1 } }
]
}
},
Expand All @@ -22,7 +23,7 @@
"SETS": [
"sklearnex spmd implementation",
"large scale 2k parameters",
"synthetic data",
"synthetic data",
"spmd basicstats parameters"
]
}
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7 changes: 4 additions & 3 deletions configs/spmd/large_scale/covariance_strong.json
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Expand Up @@ -4,7 +4,8 @@
"spmd basicstats parameters": {
"algorithm": {
"estimator": "EmpiricalCovariance",
"estimator_methods": { "training": "fit" }
"estimator_methods": { "training": "fit" },
"sklearnex_context": { "use_raw_input": true }
},
"data": {
"split_kwargs": { "test_size": 0.0001 }
Expand All @@ -20,8 +21,8 @@
"covariance": {
"SETS": [
"sklearnex spmd implementation",
"large scale strong 2k parameters",
"synthetic data",
"large scale strong <=64 parameters",
"synthetic data",
"spmd basicstats parameters"
]
}
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5 changes: 3 additions & 2 deletions configs/spmd/large_scale/dbscan.json
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Expand Up @@ -9,15 +9,16 @@
},
"estimator_params" : {
"eps": 10, "min_samples": 5
}
},
"sklearnex_context": { "use_raw_input": true }
},
"data": {
"dtype": "float64"
}
},
"synthetic dataset": {
"data": [
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 100000, "n_features": 100, "centers": 10 } }
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 40000, "n_features": 100, "centers": 10 } }
]
}
},
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15 changes: 8 additions & 7 deletions configs/spmd/large_scale/dbscan_strong.json
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Expand Up @@ -3,21 +3,22 @@
"PARAMETERS_SETS": {
"spmd dbscan parameters": {
"algorithm": {
"estimator": "DBSCAN",
"estimator_methods": {
"training": "fit"
"estimator": "DBSCAN",
"estimator_methods": {
"training": "fit"
},
"estimator_params" : {
"eps": 10, "min_samples": 5
}
"eps": 15, "min_samples": 50
},
"sklearnex_context": { "use_raw_input": true }
},
"data": {
"dtype": "float64"
}
},
"synthetic dataset": {
"data": [
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 500000, "n_features": 100, "centers": 10 } }
{ "source": "make_blobs", "generation_kwargs": { "n_samples": 4000000, "n_features": 100, "centers": 10 } }
]
}
},
Expand All @@ -27,7 +28,7 @@
"common dbscan parameters",
"synthetic dataset",
"sklearnex spmd implementation",
"large scale strong <=64 parameters",
"large scale strong <=64 parameters",
"spmd dbscan parameters"
]
}
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28 changes: 28 additions & 0 deletions configs/spmd/large_scale/forest_max_samples.json
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@@ -0,0 +1,28 @@
{
"INCLUDE": ["../../common/sklearn.json", "large_scale.json"],
"PARAMETERS_SETS": {
"spmd forest classification parameters": {
"algorithm": {
"estimator": "RandomForestClassifier",
"estimator_methods": { "training": "fit" },
"estimator_params": { "n_estimators": 20, "max_depth": 10 },
"sklearnex_context": { "use_raw_input": true }
}
},
"synthetic data": {
"data": [
{ "source": "make_classification", "split_kwargs": { "train_size": 1000000, "test_size": 1000 }, "generation_kwargs": { "n_samples": 1001000, "n_features": 100, "n_classes": 2 } }
]
}
},
"TEMPLATES": {
"forestCls": {
"SETS": [
"sklearnex spmd implementation",
"large scale 32 parameters",
"synthetic data",
"spmd forest classification parameters"
]
}
}
}
9 changes: 4 additions & 5 deletions configs/spmd/large_scale/forest.json → ...md/large_scale/forest_no_max_samples.json
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Expand Up @@ -4,14 +4,13 @@
"spmd forest classification parameters": {
"algorithm": {
"estimator": "RandomForestClassifier",
"estimator_methods": { "training": "fit" },
"estimator_params": { "n_estimators": 20, "max_depth": 4 }
"estimator_params": { "n_estimators": 100, "max_depth": 7 },
"sklearnex_context": { "use_raw_input": true }
}
},
"synthetic data": {
"data": [
{ "source": "make_classification", "split_kwargs": { "train_size": 500000, "test_size": 1000 }, "generation_kwargs": { "n_samples": 501000, "n_features": 10, "n_classes": 2 } },
{ "source": "make_classification", "split_kwargs": { "train_size": 10000, "test_size": 1000 }, "generation_kwargs": { "n_samples": 11000, "n_features": 1000, "n_classes": 2 } }
{ "source": "make_classification", "split_kwargs": { "train_size": 1000000, "test_size": 1000 }, "generation_kwargs": { "n_samples": 1001000, "n_features": 100, "n_classes": 2 }, "n_informative": "[SPECIAL_VALUE]0.5" }
]
}
},
Expand All @@ -20,7 +19,7 @@
"SETS": [
"sklearnex spmd implementation",
"large scale 2k parameters",
"synthetic data",
"synthetic data",
"spmd forest classification parameters"
]
}
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11 changes: 6 additions & 5 deletions configs/spmd/large_scale/forest_strong.json
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Expand Up @@ -4,22 +4,23 @@
"spmd forest classification parameters": {
"algorithm": {
"estimator": "RandomForestClassifier",
"estimator_methods": { "training": "fit" },
"estimator_params": { "n_estimators": 20, "max_depth": 4 }
"estimator_methods": { "training": "fit" },
"estimator_params": { "n_estimators": 100, "max_depth": 8 },
"sklearnex_context": {"use_raw_input": true}
}
},
"synthetic data": {
"data": [
{ "source": "make_classification", "split_kwargs": { "train_size": 10000000, "test_size": 1000 }, "generation_kwargs": { "n_samples": 10001000, "n_features": 100, "n_classes": 2 } }
{ "source": "make_classification", "split_kwargs": { "train_size": 20000, "test_size": 1000 }, "generation_kwargs": { "n_samples": 21000, "n_features": 200, "n_classes": 2 } }
]
}
},
"TEMPLATES": {
"forestCls": {
"SETS": [
"sklearnex spmd implementation",
"large scale strong 2k parameters",
"synthetic data",
"large scale strong <=64 parameters",
"synthetic data",
"spmd forest classification parameters"
]
}
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