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ethanglaserethanglaser
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Add prettier json linter for uniformity (#211)
* Add prettier json linter for uniformity * linting * lint again
1 parent 6d977d8 commit baf8c60

30 files changed

Lines changed: 323 additions & 468 deletions

‎.pre-commit-config.yaml‎

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@@ -23,3 +23,9 @@ repos:
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rev: 5.13.2
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hooks:
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- id: isort
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- repo: https://github.com/pre-commit/mirrors-prettier
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rev: v3.1.0
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hooks:
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- id: prettier
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args: [--tab-width=4, --print-width=120]
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files: ^configs/.*\.json$

‎configs/common/svm.json‎

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{
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"algorithm": {
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"library": "cuml",
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"estimator_methods": {"inference": "predict"},
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"estimator_methods": { "inference": "predict" },
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"estimator_params": { "verbose": false, "multiclass_strategy": "ovr" }
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},
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"data": { "format": "cupy" }
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{
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"algorithm": {
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"library": "cuml",
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"estimator_methods": {"inference": "predict"},
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"estimator_methods": { "inference": "predict" },
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"estimator_params": { "multiclass_strategy": "ovr" }
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},
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"data": { "format": "cupy" }
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{
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"algorithm": {
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"library": "cuml",
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"estimator_methods": {"inference": "predict"}
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"estimator_methods": { "inference": "predict" }
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},
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"data": { "format": "cupy" }
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}

‎configs/common/xgboost.json‎

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"algorithm": {
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"library": "xgboost",
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"estimator": "XGBRegressor",
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"estimator_methods": {"inference": "predict"},
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"estimator_methods": { "inference": "predict" },
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"estimator_params": { "objective": "reg:squarederror" }
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}
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}

‎configs/regular/dbscan.json‎

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"PARAMETERS_SETS": {
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"dbscan datasets": {
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"data": [
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{ "dataset": "cifar", "split_kwargs": { "train_size": 15000 }, "preprocessing_kwargs": { "normalize": "mean" } },
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{
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"dataset": "cifar",
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"split_kwargs": { "train_size": 15000 },
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"preprocessing_kwargs": { "normalize": "mean" }
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},
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{ "dataset": "mnist", "split_kwargs": { "train_size": 40000 } },
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{ "dataset": "sensit", "split_kwargs": { "ignore": true } },
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{ "dataset": "susy", "split_kwargs": { "train_size": 100000 } },
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]
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},
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"cuml dbscan": {
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"SETS": [
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"cuml implementation",
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"common dbscan parameters",
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"cuml dbscan parameters",
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"dbscan datasets"
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]
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"SETS": ["cuml implementation", "common dbscan parameters", "cuml dbscan parameters", "dbscan datasets"]
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}
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}
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}

‎configs/regular/kmeans.json‎

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"split_kwargs": { "ignore": true }
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},
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{
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"dataset" : "gisette",
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"split_kwargs" : {"ignore" : true},
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"dataset": "gisette",
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"split_kwargs": { "ignore": true },
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"preprocessing_kwargs": { "normalize": "standard" }
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},
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{
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"split_kwargs": { "train_size": 0.2, "test_size": null }
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}
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}
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]
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},
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"TEMPLATES": {
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]
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},
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"cuml kmeans": {
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"SETS": [
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"cuml implementation",
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"common kmeans parameters",
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"cuml kmeans parameters",
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"kmeans datasets"
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]
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"SETS": ["cuml implementation", "common kmeans parameters", "cuml kmeans parameters", "kmeans datasets"]
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}
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}
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}

‎configs/regular/knn.json‎

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"source": "make_classification",
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"generation_kwargs": [
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{
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"n_classes": 5, "n_samples": 400000, "n_features": 4,
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"n_redundant": 0, "n_repeated": 0, "n_informative": 4
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"n_classes": 5,
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"n_samples": 400000,
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"n_features": 4,
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"n_redundant": 0,
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"n_repeated": 0,
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"n_informative": 4
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},
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{
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"n_classes": 5, "n_samples": 200000, "n_features": 8,
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"n_redundant": 2, "n_repeated": 2, "n_informative": 4
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"n_classes": 5,
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"n_samples": 200000,
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"n_features": 8,
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"n_redundant": 2,
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"n_repeated": 2,
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"n_informative": 4
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},
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{
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"n_classes": 5, "n_samples": 100000, "n_features": 16,
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"n_redundant": 6, "n_repeated": 6, "n_informative": 4
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"n_classes": 5,
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"n_samples": 100000,
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"n_features": 16,
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"n_redundant": 6,
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"n_repeated": 6,
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"n_informative": 4
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}
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],
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"split_kwargs": { "train_size": 0.5, "test_size": 0.5 }
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},
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{
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"source": "make_regression",
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"generation_kwargs":[
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"generation_kwargs": [
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{ "n_samples": 400000, "n_features": 4, "noise": 1.0 },
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{ "n_samples": 200000, "n_features": 8, "noise": 1.5 },
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{ "n_samples": 100000, "n_features": 16, "noise": 2.0 }

‎configs/regular/lightgbm.json‎

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{
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"INCLUDE": [
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"../common/lightgbm.json",
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"xgboost_binary.json",
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"xgboost_multi.json",
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"xgboost_regression.json"
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],
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"INCLUDE": ["../common/lightgbm.json", "xgboost_binary.json", "xgboost_multi.json", "xgboost_regression.json"],
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"PARAMETERS_SETS": {},
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"TEMPLATES": {
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"lightgbm binary classification": {
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"SETS": [
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"lightgbm binary classification",
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"lightgbm implementations",
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"gbt binary classification data"
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]
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"SETS": ["lightgbm binary classification", "lightgbm implementations", "gbt binary classification data"]
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},
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"lightgbm multi classification": {
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"SETS": [
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"lightgbm multi classification",
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"lightgbm implementations",
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"gbt multi classification data"
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]
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"SETS": ["lightgbm multi classification", "lightgbm implementations", "gbt multi classification data"]
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},
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"lightgbm regression": {
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"SETS": [
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"lightgbm regression",
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"lightgbm implementations",
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"gbt regression data"
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]
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"SETS": ["lightgbm regression", "lightgbm implementations", "gbt regression data"]
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}
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}
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}

‎configs/regular/linear_model.json‎

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"split_kwargs": { "train_size": 0.2, "test_size": 0.8 },
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"generation_kwargs": [
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{
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"n_samples": 5000000, "n_features": 50,
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"n_informative": 5, "noise": 20.0
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"n_samples": 5000000,
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"n_features": 50,
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"n_informative": 5,
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"noise": 20.0
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},
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{
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"n_samples": 500000, "n_features": 400,
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"n_informative": 5, "noise": 40.0
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"n_samples": 500000,
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"n_features": 400,
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"n_informative": 5,
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"noise": 40.0
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},
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{
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"n_samples": 100000, "n_features": 2000,
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"n_informative": 5, "noise": 60.0
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"n_samples": 100000,
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"n_features": 2000,
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"n_informative": 5,
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"noise": 60.0
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}
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]
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}
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]
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},
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"sklearn lasso": {
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"SETS": [
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"sklearn-ex[cpu] implementations",
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"common lasso parameters",
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"regression datasets"
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]
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"SETS": ["sklearn-ex[cpu] implementations", "common lasso parameters", "regression datasets"]
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},
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"sklearn elasticnet": {
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"SETS": [
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"sklearn-ex[cpu] implementations",
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"common elasticnet parameters",
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"regression datasets"
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]
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"SETS": ["sklearn-ex[cpu] implementations", "common elasticnet parameters", "regression datasets"]
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},
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"cuml linear": {
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"SETS": [
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"cuml implementation",
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"common linear parameters",
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"cuml L2 parameters",
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"regression datasets"
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]
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"SETS": ["cuml implementation", "common linear parameters", "cuml L2 parameters", "regression datasets"]
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},
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"cuml ridge": {
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"SETS": [
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"cuml implementation",
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"common ridge parameters",
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"cuml L2 parameters",
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"regression datasets"
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]
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"SETS": ["cuml implementation", "common ridge parameters", "cuml L2 parameters", "regression datasets"]
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},
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"cuml lasso": {
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"SETS": [
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"cuml implementation",
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"common lasso parameters",
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"cuml L1 parameters",
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"regression datasets"
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]
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"SETS": ["cuml implementation", "common lasso parameters", "cuml L1 parameters", "regression datasets"]
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},
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"cuml elasticnet": {
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"SETS": [
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"cuml implementation",
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"common elasticnet parameters",
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"cuml L1 parameters",
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"regression datasets"
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]
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"SETS": ["cuml implementation", "common elasticnet parameters", "cuml L1 parameters", "regression datasets"]
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}
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}
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}

‎configs/regular/logreg.json‎

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"test_size": 0.95
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}
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},
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"algorithm": {"estimator_params": {"C": 1e-6}}
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"algorithm": { "estimator_params": { "C": 1e-6 } }
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},
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{
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"data": { "dataset": "mnist", "split_kwargs": { "train_size": 10000, "test_size": null } },
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"algorithm": {"estimator_params": {"C": 1e-8}}
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"algorithm": { "estimator_params": { "C": 1e-8 } }
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},
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{
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"data": {
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"dataset": "susy",
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"split_kwargs": { "train_size": 0.1, "test_size": null }
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},
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"algorithm": { "estimator_params": {"C": 1e-2} }
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"algorithm": { "estimator_params": { "C": 1e-2 } }
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},
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{
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"data": {
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"dataset": "hepmass",
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"split_kwargs": { "train_size": 0.1, "test_size": null }
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},
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"algorithm": { "estimator_params": {"C": 1e-5} }
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"algorithm": { "estimator_params": { "C": 1e-5 } }
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},
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{
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"data": {
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"dataset": "cifar",
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"split_kwargs": { "train_size": 0.1, "test_size": null },
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"preprocessing_kwargs": { "normalize": "mean" }
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},
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"algorithm": { "estimator_params": {"C": 1e-9} }
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"algorithm": { "estimator_params": { "C": 1e-9 } }
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},
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{
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"data": {
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"dataset": "gisette",
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"split_kwargs": { "train_size": 2000, "test_size": null },
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"preprocessing_kwargs": { "normalize": "standard" }
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},
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"algorithm": { "estimator_params": {"C": 1e1} }
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"algorithm": { "estimator_params": { "C": 1e1 } }
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}
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]
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},
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]
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},
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"cuml logreg": {
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"SETS": [
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"cuml implementation",
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"common logreg parameters",
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"cuml logreg parameters",
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"logreg datasets"
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]
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"SETS": ["cuml implementation", "common logreg parameters", "cuml logreg parameters", "logreg datasets"]
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}
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}
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}

‎configs/regular/pca.json‎

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},
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"TEMPLATES": {
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"sklearn pca": {
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"SETS": [
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"sklearn-ex[cpu,gpu] implementations",
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"pca parameters",
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"pca datasets"
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]
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"SETS": ["sklearn-ex[cpu,gpu] implementations", "pca parameters", "pca datasets"]
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},
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"cuml pca": {
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"SETS": [
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"cuml implementation",
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"pca parameters",
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"cuml pca parameters",
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"pca datasets"
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]
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"SETS": ["cuml implementation", "pca parameters", "cuml pca parameters", "pca datasets"]
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}
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}
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}

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