From 54146546f708294f853e41d8abde6013020b9616 Mon Sep 17 00:00:00 2001 From: sklearn-benchmark-bot Date: Sun, 29 Oct 2023 23:50:56 +0000 Subject: [PATCH] new result [5fc67aeb] --- logs/log_5fc67aeb | 644 +++++++++--------- ...s2.1.0-scipy1.11.2-threadpoolctl3.2.0.json | 2 +- 2 files changed, 323 insertions(+), 323 deletions(-) diff --git a/logs/log_5fc67aeb b/logs/log_5fc67aeb index 3ec2ca47e1..02b3f6de16 100644 --- a/logs/log_5fc67aeb +++ b/logs/log_5fc67aeb @@ -16,8 +16,8 @@ representation algorithm random k-means++ ================ =========== ======== =========== dense lloyd 103M 114M - dense elkan 137M 137M - sparse lloyd 254M 254M + dense elkan 137M 138M + sparse lloyd 254M 255M sparse elkan 261M 261M ================ =========== ======== =========== @@ -27,10 +27,10 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 89.3M 89.2M - dense elkan 89.2M 89.2M - sparse lloyd 97M 97M - sparse elkan 97M 97M + dense lloyd 89.2M 89.2M + dense elkan 89.6M 89.7M + sparse lloyd 97.2M 97.2M + sparse elkan 97.2M 97.2M ================ =========== ======== =========== [ 2.61%] ··· cluster.KMeansBenchmark.peakmem_transform ok @@ -39,8 +39,8 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 120M 120M - dense elkan 120M 120M + dense lloyd 121M 121M + dense elkan 121M 121M sparse lloyd 125M 125M sparse elkan 125M 125M ================ =========== ======== =========== @@ -51,23 +51,23 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 405±10ms 1.15±0.02s - dense elkan 2.21±0.02s 1.98±0.04s - sparse lloyd 1.82±0.1s 4.57±0.08s - sparse elkan 3.48±0.2s 5.29±0.02s + dense lloyd 398±7ms 1.23±0.01s + dense elkan 2.24±0.02s 2.07±0.02s + sparse lloyd 1.71±0.03s 4.49±0.08s + sparse elkan 3.46±0.02s 5.27±0.05s ================ =========== ============ ============ [ 4.35%] ··· cluster.KMeansBenchmark.time_predict ok -[ 4.35%] ··· ================ =========== ============= ============ - -- init - ---------------------------- -------------------------- - representation algorithm random k-means++ - ================ =========== ============= ============ - dense lloyd 5.36±0.06ms 5.12±0.1ms - dense elkan 5.31±0.2ms 5.34±0.1ms - sparse lloyd 19.0±7ms 29.1±3ms - sparse elkan 29.4±3ms 27.4±3ms - ================ =========== ============= ============ +[ 4.35%] ··· ================ =========== ============ ============ + -- init + ---------------------------- ------------------------- + representation algorithm random k-means++ + ================ =========== ============ ============ + dense lloyd 5.43±0.1ms 5.17±0.1ms + dense elkan 5.18±0.2ms 5.24±0.2ms + sparse lloyd 27.2±6ms 26.9±6ms + sparse elkan 27.3±6ms 29.1±6ms + ================ =========== ============ ============ [ 5.22%] ··· cluster.KMeansBenchmark.time_transform ok [ 5.22%] ··· ================ =========== ============ ============ @@ -75,10 +75,10 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 93.0±2ms 89.6±3ms - dense elkan 92.7±1ms 92.6±2ms - sparse lloyd 6.92±0.9s 6.31±0.04s - sparse elkan 6.47±0.01s 6.14±0.04s + dense lloyd 98.9±1ms 88.1±3ms + dense elkan 86.9±1ms 87.2±1ms + sparse lloyd 6.65±0.09s 6.63±0.03s + sparse elkan 6.33±0.03s 6.66±0.4s ================ =========== ============ ============ [ 6.09%] ··· cluster.KMeansBenchmark.track_test_score ok @@ -87,7 +87,7 @@ ---------------- ----------- ----------- --------------------- dense lloyd random -4.109885215759277 dense lloyd k-means++ -3.0753684043884277 - dense elkan random -4.109886169433594 + dense elkan random -4.109885215759277 dense elkan k-means++ -3.0753684043884277 sparse lloyd random -0.9266619682312012 sparse lloyd k-means++ -0.9249227643013 @@ -102,7 +102,7 @@ dense lloyd random -4.1075520515441895 dense lloyd k-means++ -3.0780563354492188 dense elkan random -4.1075520515441895 - dense elkan k-means++ -3.0780563354492188 + dense elkan k-means++ -3.0780560970306396 sparse lloyd random -0.9227071404457092 sparse lloyd k-means++ -0.922096312046051 sparse elkan random -0.9227071404457092 @@ -116,8 +116,8 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 90.6M 91.6M - sparse 174M 176M + dense 91M 91.6M + sparse 174M 175M ================ ======== =========== [ 8.70%] ··· ...ter.MiniBatchKMeansBenchmark.peakmem_predict ok @@ -126,7 +126,7 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 88M 87.2M + dense 87.8M 87.8M sparse 103M 103M ================ ======== =========== @@ -141,24 +141,24 @@ ================ ======== =========== [10.43%] ··· cluster.MiniBatchKMeansBenchmark.time_fit ok -[10.43%] ··· ================ ========== =========== - -- init - ---------------- ---------------------- - representation random k-means++ - ================ ========== =========== - dense 476±10ms 477±20ms - sparse 557±7ms 1.86±0.1s - ================ ========== =========== +[10.43%] ··· ================ ========== ============ + -- init + ---------------- ----------------------- + representation random k-means++ + ================ ========== ============ + dense 494±30ms 485±20ms + sparse 549±10ms 1.65±0.04s + ================ ========== ============ [11.30%] ··· cluster.MiniBatchKMeansBenchmark.time_predict ok -[11.30%] ··· ================ ============ ============ - -- init - ---------------- ------------------------- - representation random k-means++ - ================ ============ ============ - dense 6.28±0.6ms 5.12±0.2ms - sparse 36.9±4ms 24.6±7ms - ================ ============ ============ +[11.30%] ··· ================ ============= ============ + -- init + ---------------- -------------------------- + representation random k-means++ + ================ ============= ============ + dense 5.38±0.06ms 5.45±0.2ms + sparse 35.8±2ms 26.7±8ms + ================ ============= ============ [12.17%] ··· cluster.MiniBatchKMeansBenchmark.time_transform ok [12.17%] ··· ================ ============ ============ @@ -166,8 +166,8 @@ ---------------- ------------------------- representation random k-means++ ================ ============ ============ - dense 81.4±0.7ms 81.1±0.9ms - sparse 6.76±0.05s 6.74±0.05s + dense 87.5±0.9ms 87.6±0.8ms + sparse 7.02±0.04s 7.08±0.04s ================ ============ ============ [13.04%] ··· ...er.MiniBatchKMeansBenchmark.track_test_score ok @@ -198,18 +198,18 @@ fit_algorithm 1 4 =============== ====== ====== lars 109M 130M - cd 102M 130M + cd 103M 130M =============== ====== ====== [15.65%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok -[15.65%] ··· =============== ======= ======= - -- n_jobs - --------------- --------------- - fit_algorithm 1 4 - =============== ======= ======= - lars 84.9M 86.9M - cd 84.9M 86.9M - =============== ======= ======= +[15.65%] ··· =============== ===== ======= + -- n_jobs + --------------- ------------- + fit_algorithm 1 4 + =============== ===== ======= + lars 85M 87.1M + cd 85M 87.1M + =============== ===== ======= [16.52%] ··· ...osition.DictionaryLearningBenchmark.time_fit ok [16.52%] ··· =============== ============ ============ @@ -217,8 +217,8 @@ --------------- ------------------------- fit_algorithm 1 4 =============== ============ ============ - lars 15.7±0.01s 10.2±0.09s - cd 737±8ms 3.32±0.05s + lars 17.0±0.04s 10.2±0.1s + cd 781±9ms 3.24±0.09s =============== ============ ============ [17.39%] ··· ...n.DictionaryLearningBenchmark.time_transform ok @@ -227,8 +227,8 @@ --------------- ---------------------- fit_algorithm 1 4 =============== =========== ========== - lars 233±0.9ms 283±10ms - cd 230±0.6ms 300±10ms + lars 248±2ms 288±10ms + cd 237±0.9ms 298±20ms =============== =========== ========== [18.26%] ··· ...DictionaryLearningBenchmark.track_test_score ok @@ -259,7 +259,7 @@ fit_algorithm 1 4 =============== ======= ====== lars 97.8M 108M - cd 97.5M 108M + cd 97.6M 108M =============== ======= ====== [20.87%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok @@ -268,8 +268,8 @@ --------------- --------------- fit_algorithm 1 4 =============== ======= ======= - lars 86M 87.6M - cd 85.9M 87.6M + lars 86.2M 87.9M + cd 86.1M 87.9M =============== ======= ======= [20.87%] ···· For parameters: 'lars', 1 @@ -296,19 +296,19 @@ --------------- ------------------------ fit_algorithm 1 4 =============== ============ =========== - lars 10.3±0.04s 20.8±2s - cd 3.07±0.01s 17.3±0.1s + lars 10.7±0.05s 16.4±0.5s + cd 3.07±0.01s 19.5±0.4s =============== ============ =========== [22.61%] ··· ...chDictionaryLearningBenchmark.time_transform ok -[22.61%] ··· =============== ========= ========== - -- n_jobs - --------------- -------------------- - fit_algorithm 1 4 - =============== ========= ========== - lars 226±1ms 302±10ms - cd 220±1ms 302±10ms - =============== ========= ========== +[22.61%] ··· =============== =========== ========== + -- n_jobs + --------------- ---------------------- + fit_algorithm 1 4 + =============== =========== ========== + lars 241±1ms 304±20ms + cd 239±0.6ms 294±20ms + =============== =========== ========== [22.61%] ···· For parameters: 'lars', 1 /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. @@ -519,8 +519,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. return func(*args, **kwargs) - /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. - return func(*args, **kwargs) For parameters: 'lars', 4 /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. @@ -1489,6 +1487,14 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. return func(*args, **kwargs) + /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. + return func(*args, **kwargs) + /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. + return func(*args, **kwargs) + /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. + return func(*args, **kwargs) + /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:187: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. + return func(*args, **kwargs) [23.48%] ··· ...DictionaryLearningBenchmark.track_test_score ok [23.48%] ··· =============== ======== ====================== @@ -1533,7 +1539,7 @@ [25.22%] ··· ============ ====== svd_solver ------------ ------ - full 906M + full 907M arpack 605M randomized 632M ============ ====== @@ -1551,18 +1557,18 @@ [26.96%] ··· ============ ============ svd_solver ------------ ------------ - full 2.46±0.08s + full 2.51±0.1s arpack 1.10±0s - randomized 1.08±0.02s + randomized 1.08±0.01s ============ ============ [27.83%] ··· decomposition.PCABenchmark.time_transform ok [27.83%] ··· ============ ========= svd_solver ------------ --------- - full 156±2ms - arpack 156±1ms - randomized 155±2ms + full 160±1ms + arpack 158±1ms + randomized 160±1ms ============ ========= [28.70%] ··· decomposition.PCABenchmark.track_test_score ok @@ -1588,39 +1594,39 @@ [30.43%] ··· ================ ======= representation ---------------- ------- - dense 90.9M - sparse 116M + dense 92.7M + sparse 117M ================ ======= [31.30%] ··· ...tBoostingClassifierBenchmark.peakmem_predict ok [31.30%] ··· ================ ======= representation ---------------- ------- - dense 88.5M - sparse 98.1M + dense 88.7M + sparse 98.4M ================ ======= [32.17%] ··· ...GradientBoostingClassifierBenchmark.time_fit ok [32.17%] ··· ================ ============ representation ---------------- ------------ - dense 3.16±0s - sparse 2.31±0.01s + dense 2.89±0.02s + sparse 2.22±0s ================ ============ [33.04%] ··· ...ientBoostingClassifierBenchmark.time_predict ok [33.04%] ··· ================ ============ representation ---------------- ------------ - dense 48.6±5ms - sparse 45.2±0.9ms + dense 45.5±0.9ms + sparse 41.0±1ms ================ ============ [33.91%] ··· ...BoostingClassifierBenchmark.track_test_score ok [33.91%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.55301913301601 + dense 0.5348830259941878 sparse 0.10409974329281042 ================ ===================== @@ -1628,15 +1634,15 @@ [34.78%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.6253525646732084 + dense 0.6220449352320004 sparse 0.15180008167538628 ================ ===================== [35.65%] ··· Setting up ensemble:103 ok [35.65%] ··· ...dientBoostingClassifierBenchmark.peakmem_fit 103M -[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 91M -[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.39±0.04s -[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 82.8±0.6ms +[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 90.5M +[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.49±0.09s +[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 85.1±3ms [39.13%] ··· ...BoostingClassifierBenchmark.track_test_score 0.7230709112942986 [40.00%] ··· ...oostingClassifierBenchmark.track_train_score 0.9812160155622751 [40.87%] ··· Setting up ensemble:24 ok @@ -1647,7 +1653,7 @@ representation 1 4 ================ ====== ====== dense 179M 179M - sparse 402M 402M + sparse 403M 403M ================ ====== ====== [41.74%] ··· ...domForestClassifierBenchmark.peakmem_predict ok @@ -1657,7 +1663,7 @@ representation 1 4 ================ ====== ====== dense 182M 188M - sparse 402M 402M + sparse 403M 403M ================ ====== ====== [42.61%] ··· ...ble.RandomForestClassifierBenchmark.time_fit ok @@ -1666,8 +1672,8 @@ ---------------- ------------------------- representation 1 4 ================ ============ ============ - dense 7.76±0.03s 2.58±0.1s - sparse 13.0±0.05s 4.18±0.02s + dense 9.11±0.02s 2.58±0.05s + sparse 13.2±0.2s 4.09±0.1s ================ ============ ============ [43.48%] ··· ...RandomForestClassifierBenchmark.time_predict ok @@ -1676,8 +1682,8 @@ ---------------- ----------------------- representation 1 4 ================ ============ ========== - dense 235±0.6ms 160±5ms - sparse 2.12±0.01s 783±30ms + dense 267±0.8ms 164±6ms + sparse 2.20±0.02s 784±20ms ================ ============ ========== [44.35%] ··· ...omForestClassifierBenchmark.track_test_score ok @@ -1686,7 +1692,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.7431096932058513 0.7431096932058513 + dense 0.7488514083455955 0.7488514083455955 sparse 0.8656423941766682 0.8656423941766682 ================ ==================== ==================== @@ -1696,7 +1702,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.996787236878439 0.996787236878439 + dense 0.9968132197915482 0.9968132197915482 sparse 0.9996123288718864 0.9996123288718864 ================ ==================== ==================== @@ -1708,7 +1714,7 @@ representation True False ================ ====== ======= dense 852M 1.21G - sparse 123M n/a + sparse 124M n/a ================ ====== ======= [46.09%] ···· For parameters: 'sparse', False @@ -1720,22 +1726,22 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 488M 488M - sparse 96.7M n/a + dense 489M 489M + sparse 96.3M n/a ================ ======= ======= [46.96%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() [47.83%] ··· linear_model.ElasticNetBenchmark.time_fit ok -[47.83%] ··· ================ ============ ========= - -- precompute - ---------------- ---------------------- - representation True False - ================ ============ ========= - dense 1.52±0s 1.84±0s - sparse 2.98±0.03s n/a - ================ ============ ========= +[47.83%] ··· ================ ============ ============ + -- precompute + ---------------- ------------------------- + representation True False + ================ ============ ============ + dense 1.50±0s 1.84±0.01s + sparse 2.93±0.03s n/a + ================ ============ ============ [47.83%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1746,8 +1752,8 @@ ---------------- ------------------------ representation True False ================ ============= ========== - dense 51.3±2ms 53.8±1ms - sparse 3.09±0.01ms n/a + dense 52.2±0.2ms 49.4±2ms + sparse 3.11±0.01ms n/a ================ ============= ========== [48.70%] ···· For parameters: 'sparse', False @@ -1760,7 +1766,7 @@ representation True False ================ ==================== ==================== dense 0.9274010856209145 0.9274010850953214 - sparse 0.9489950487365856 n/a + sparse 0.9492849961902541 n/a ================ ==================== ==================== [49.57%] ···· For parameters: 'sparse', False @@ -1773,7 +1779,7 @@ representation True False ================ ==================== ==================== dense 0.9276022550495941 0.9276022552325599 - sparse 0.9566208756361017 n/a + sparse 0.9566045497340457 n/a ================ ==================== ==================== [50.43%] ···· For parameters: 'sparse', False @@ -1787,7 +1793,7 @@ representation True False ================ ====== ======= dense 852M 1.21G - sparse 123M n/a + sparse 124M n/a ================ ====== ======= [51.30%] ···· For parameters: 'sparse', False @@ -1799,8 +1805,8 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 488M 488M - sparse 96.7M n/a + dense 489M 489M + sparse 96.3M n/a ================ ======= ======= [52.17%] ···· For parameters: 'sparse', False @@ -1812,8 +1818,8 @@ ---------------- ---------------------- representation True False ================ ============ ========= - dense 1.54±0s 1.87±0s - sparse 2.73±0.01s n/a + dense 1.50±0.03s 1.81±0s + sparse 2.36±0.01s n/a ================ ============ ========= [53.04%] ···· For parameters: 'sparse', False @@ -1825,8 +1831,8 @@ ---------------- -------------------------- representation True False ================ ============= ============ - dense 54.5±0.1ms 54.5±0.1ms - sparse 3.09±0.01ms n/a + dense 48.6±0.4ms 51.5±0.5ms + sparse 2.61±0.01ms n/a ================ ============= ============ [53.91%] ···· For parameters: 'sparse', False @@ -1839,7 +1845,7 @@ representation True False ================ ==================== ==================== dense 0.9274015024583205 0.9274015028138817 - sparse 0.9473379934657499 n/a + sparse 0.9479341767297718 n/a ================ ==================== ==================== [54.78%] ···· For parameters: 'sparse', False @@ -1852,7 +1858,7 @@ representation True False ================ ==================== ==================== dense 0.92760249197518 0.9276024919395177 - sparse 0.9541392686500457 n/a + sparse 0.9541565916975006 n/a ================ ==================== ==================== [55.65%] ···· For parameters: 'sparse', False @@ -1871,24 +1877,24 @@ [57.39%] ··· ================ ====== representation ---------------- ------ - dense 488M + dense 489M sparse 156M ================ ====== [58.26%] ··· linear_model.LinearRegressionBenchmark.time_fit ok -[58.26%] ··· ================ ========= - representation - ---------------- --------- - dense 3.13±0s - sparse 1.12±0s - ================ ========= +[58.26%] ··· ================ ============ + representation + ---------------- ------------ + dense 3.05±0.02s + sparse 1.11±0s + ================ ============ [59.13%] ··· ...model.LinearRegressionBenchmark.time_predict ok [59.13%] ··· ================ ============= representation ---------------- ------------- - dense 50.6±0.08ms - sparse 33.4±0.07ms + dense 51.8±0.1ms + sparse 33.7±0.06ms ================ ============= [60.00%] ··· ...l.LinearRegressionBenchmark.track_test_score ok @@ -1896,7 +1902,7 @@ representation ---------------- --------------------- dense 0.9274012651798128 - sparse 0.10318049470166679 + sparse 0.10634161838836309 ================ ===================== [60.87%] ··· ....LinearRegressionBenchmark.track_train_score ok @@ -1904,7 +1910,7 @@ representation ---------------- -------------------- dense 0.927602494829764 - sparse 0.9999999999962513 + sparse 0.9999999999962699 ================ ==================== [61.74%] ··· Setting up linear_model:28 ok @@ -1915,8 +1921,8 @@ representation solver 1 4 ================ ======== ======= ======= dense lbfgs 105M 98.6M - dense saga 83.1M 84.3M - sparse lbfgs 382M 125M + dense saga 83.4M 84.5M + sparse lbfgs 381M 125M sparse saga 104M 104M ================ ======== ======= ======= @@ -1926,10 +1932,10 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 98.8M 98.8M - dense saga 85.9M 86M + dense lbfgs 98.8M 98.9M + dense saga 85.9M 85.8M sparse lbfgs 100M 100M - sparse saga 87.7M 87.6M + sparse saga 87.8M 87.8M ================ ======== ======= ======= [63.48%] ··· ...r_model.LogisticRegressionBenchmark.time_fit ok @@ -1938,10 +1944,10 @@ ------------------------- ------------------------- representation solver 1 4 ================ ======== ============ ============ - dense lbfgs 21.7±2ms 190±6ms - dense saga 5.01±0.06s 5.08±0.09s - sparse lbfgs 1.01±0.01s 2.95±0.1s - sparse saga 4.13±0.02s 4.12±0.3s + dense lbfgs 22.7±2ms 189±6ms + dense saga 4.81±0.02s 5.11±0.4s + sparse lbfgs 1.03±0.01s 3.03±0.1s + sparse saga 3.87±0.2s 4.13±0.01s ================ ======== ============ ============ [64.35%] ··· ...del.LogisticRegressionBenchmark.time_predict ok @@ -1950,20 +1956,20 @@ ------------------------- --------------------------- representation solver 1 4 ================ ======== ============= ============= - dense lbfgs 3.48±0.08ms 2.97±0.1ms - dense saga 1.90±0.02ms 1.91±0.01ms - sparse lbfgs 7.00±0.03ms 9.72±0.04ms - sparse saga 6.15±0.01ms 6.15±0.01ms + dense lbfgs 3.22±0.03ms 2.99±0.04ms + dense saga 1.93±0.01ms 1.85±0.01ms + sparse lbfgs 7.11±0.04ms 7.09±0.04ms + sparse saga 4.63±0.01ms 4.63±0.01ms ================ ======== ============= ============= [65.22%] ··· ...LogisticRegressionBenchmark.track_test_score ok [65.22%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17694223144036997 - dense lbfgs 4 0.17694223144036997 - dense saga 1 0.7801291666834305 - dense saga 4 0.7801291666834305 + dense lbfgs 1 0.17258548459073092 + dense lbfgs 4 0.17258548459073092 + dense saga 1 0.7897838712645264 + dense saga 4 0.7897838712645264 sparse lbfgs 1 0.06538461538461539 sparse lbfgs 4 0.06538461538461539 sparse saga 1 0.5765140080078162 @@ -1971,18 +1977,18 @@ ================ ======== ======== ===================== [66.09%] ··· ...ogisticRegressionBenchmark.track_train_score ok -[66.09%] ··· ================ ======== ======== ==================== - representation solver n_jobs - ---------------- -------- -------- -------------------- - dense lbfgs 1 0.1789146275513357 - dense lbfgs 4 0.1789146275513357 - dense saga 1 0.8001024137196161 - dense saga 4 0.8001024137196161 - sparse lbfgs 1 0.0681998556998557 - sparse lbfgs 4 0.0681998556998557 - sparse saga 1 0.6908414295256007 - sparse saga 4 0.6908414295256007 - ================ ======== ======== ==================== +[66.09%] ··· ================ ======== ======== ===================== + representation solver n_jobs + ---------------- -------- -------- --------------------- + dense lbfgs 1 0.17891424041559628 + dense lbfgs 4 0.17891424041559628 + dense saga 1 0.8011235145936236 + dense saga 4 0.8011235145936236 + sparse lbfgs 1 0.0681998556998557 + sparse lbfgs 4 0.0681998556998557 + sparse saga 1 0.6908414295256007 + sparse saga 4 0.6908414295256007 + ================ ======== ======== ===================== [66.96%] ··· Setting up linear_model:78 ok [66.96%] ··· linear_model.RidgeBenchmark.peakmem_fit ok @@ -1991,7 +1997,7 @@ ---------------- ----------- ------- dense auto 463M dense svd 825M - dense cholesky 463M + dense cholesky 464M dense lsqr 472M dense sparse_cg 467M dense sag 472M @@ -2013,19 +2019,19 @@ representation solver ---------------- ----------- ------ dense auto 283M - dense svd 283M - dense cholesky 283M - dense lsqr 283M - dense sparse_cg 283M - dense sag 283M - dense saga 283M - sparse auto 117M + dense svd 282M + dense cholesky 282M + dense lsqr 282M + dense sparse_cg 282M + dense sag 282M + dense saga 282M + sparse auto 118M sparse svd n/a - sparse cholesky 119M - sparse lsqr 119M - sparse sparse_cg 117M - sparse sag 117M - sparse saga 119M + sparse cholesky 118M + sparse lsqr 118M + sparse sparse_cg 118M + sparse sag 118M + sparse saga 118M ================ =========== ====== [67.83%] ···· For parameters: 'sparse', 'svd' @@ -2035,20 +2041,20 @@ [68.70%] ··· ================ =========== ============ representation solver ---------------- ----------- ------------ - dense auto 209±2ms - dense svd 1.72±0.06s - dense cholesky 211±1ms - dense lsqr 225±8ms - dense sparse_cg 257±2ms - dense sag 28.2±0.09s - dense saga 12.3±0.03s - sparse auto 152±0.5ms + dense auto 212±2ms + dense svd 1.72±0.05s + dense cholesky 219±1ms + dense lsqr 229±6ms + dense sparse_cg 268±3ms + dense sag 28.7±0.6s + dense saga 12.3±0.1s + sparse auto 166±0.7ms sparse svd n/a - sparse cholesky 5.36±0.03s - sparse lsqr 144±0.7ms - sparse sparse_cg 165±0.9ms - sparse sag 2.84±0.05s - sparse saga 2.11±0.02s + sparse cholesky 5.44±0.01s + sparse lsqr 136±0.5ms + sparse sparse_cg 155±0.6ms + sparse sag 2.79±0.05s + sparse saga 1.84±0.01s ================ =========== ============ [68.70%] ···· For parameters: 'sparse', 'svd' @@ -2058,20 +2064,20 @@ [69.57%] ··· ================ =========== ============= representation solver ---------------- ----------- ------------- - dense auto 25.7±0.1ms - dense svd 25.6±0.09ms - dense cholesky 25.7±0.2ms - dense lsqr 25.7±0.2ms - dense sparse_cg 25.9±0.09ms - dense sag 24.4±0.06ms - dense saga 25.6±0.09ms - sparse auto 6.98±0.3ms + dense auto 24.3±0.06ms + dense svd 24.3±0.08ms + dense cholesky 24.4±0.08ms + dense lsqr 24.3±0.1ms + dense sparse_cg 24.3±0.1ms + dense sag 24.5±0.1ms + dense saga 24.5±0.06ms + sparse auto 6.91±0.03ms sparse svd n/a - sparse cholesky 9.32±1ms - sparse lsqr 6.91±0.03ms - sparse sparse_cg 6.98±1ms - sparse sag 6.93±0.03ms - sparse saga 6.98±0.04ms + sparse cholesky 7.15±1ms + sparse lsqr 7.82±0.7ms + sparse sparse_cg 7.51±1ms + sparse sag 7.83±0.7ms + sparse saga 7.79±0.04ms ================ =========== ============= [69.57%] ···· For parameters: 'sparse', 'svd' @@ -2088,13 +2094,13 @@ dense sparse_cg 0.9433995989989826 dense sag 0.94339933719428 dense saga 0.9433995886080997 - sparse auto 0.9559941698932061 + sparse auto 0.956000762270701 sparse svd n/a - sparse cholesky 0.9559944346330396 - sparse lsqr 0.9559941684408819 - sparse sparse_cg 0.9559941698932061 - sparse sag 0.9559983947836294 - sparse saga 0.9559981694514983 + sparse cholesky 0.9560002318461847 + sparse lsqr 0.9560007638654375 + sparse sparse_cg 0.956000762270701 + sparse sag 0.9560027017570925 + sparse saga 0.9560022118821598 ================ =========== ==================== [70.43%] ···· For parameters: 'sparse', 'svd' @@ -2111,13 +2117,13 @@ dense sparse_cg 0.9444001571192623 dense sag 0.9444001419121766 dense saga 0.9444001543688754 - sparse auto 0.965958230743709 + sparse auto 0.9659279324318244 sparse svd n/a - sparse cholesky 0.9659582338021545 - sparse lsqr 0.9659582305541862 - sparse sparse_cg 0.965958230743709 - sparse sag 0.9659546816725302 - sparse saga 0.9659546455031128 + sparse cholesky 0.9659279352049591 + sparse lsqr 0.9659279325722477 + sparse sparse_cg 0.9659279324318244 + sparse sag 0.9659243773776127 + sparse saga 0.9659243429642638 ================ =========== ==================== [71.30%] ···· For parameters: 'sparse', 'svd' @@ -2128,8 +2134,8 @@ [72.17%] ··· ================ ======= representation ---------------- ------- - dense 160M - sparse 87.5M + dense 159M + sparse 88.3M ================ ======= [73.04%] ··· ..._model.SGDRegressorBenchmark.peakmem_predict ok @@ -2144,16 +2150,16 @@ [73.91%] ··· ================ ============ representation ---------------- ------------ - dense 5.32±0s - sparse 4.81±0.01s + dense 5.50±0.01s + sparse 4.38±0s ================ ============ [74.78%] ··· linear_model.SGDRegressorBenchmark.time_predict ok [74.78%] ··· ================ ============= representation ---------------- ------------- - dense 10.7±0.7ms - sparse 2.45±0.01ms + dense 10.9±0.06ms + sparse 2.14±0.01ms ================ ============= [75.65%] ··· ...model.SGDRegressorBenchmark.track_test_score ok @@ -2161,7 +2167,7 @@ representation ---------------- -------------------- dense 0.9636293915848902 - sparse 0.9610050955140929 + sparse 0.9608295557379729 ================ ==================== [76.52%] ··· ...odel.SGDRegressorBenchmark.track_train_score ok @@ -2169,7 +2175,7 @@ representation ---------------- -------------------- dense 0.9641785427097553 - sparse 0.9622836756531341 + sparse 0.9617597737672584 ================ ==================== [77.39%] ··· Setting up manifold:15 ok @@ -2177,7 +2183,7 @@ [77.39%] ··· ============ ======= method ------------ ------- - exact 89M + exact 89.6M barnes_hut 96.6M ============ ======= @@ -2185,8 +2191,8 @@ [78.26%] ··· ============ ============ method ------------ ------------ - exact 6.87±0.03s - barnes_hut 3.15±0.08s + exact 6.44±0.04s + barnes_hut 3.15±0.1s ============ ============ [79.13%] ··· manifold.TSNEBenchmark.track_test_score ok @@ -2194,7 +2200,7 @@ method ------------ -------------------- exact 0.3218818006120378 - barnes_hut 0.7243015766143799 + barnes_hut 0.7243016362190247 ============ ==================== [80.00%] ··· manifold.TSNEBenchmark.track_train_score ok @@ -2202,7 +2208,7 @@ method ------------ -------------------- exact 0.3218818006120378 - barnes_hut 0.7243015766143799 + barnes_hut 0.7243016362190247 ============ ==================== [80.87%] ··· ...istancesBenchmark.peakmem_pairwise_distances ok @@ -2211,13 +2217,13 @@ ------------------------------ --------------- representation metric 1 4 ================ ============= ======= ======= - dense cosine 669M 785M - dense euclidean 751M 1.16G - dense manhattan 254M 326M - dense correlation 247M 483M - sparse cosine 1.42G 1.43G - sparse euclidean 570M 886M - sparse manhattan 187M 227M + dense cosine 669M 784M + dense euclidean 751M 933M + dense manhattan 254M 339M + dense correlation 247M 484M + sparse cosine 1.42G 1.37G + sparse euclidean 570M 946M + sparse manhattan 187M 229M sparse correlation n/a n/a ================ ============= ======= ======= @@ -2237,7 +2243,7 @@ ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._random_dataset... _random_dataset(n_samples=12000, representation='sparse') - ___________________________________________________random_dataset - 0.6s, 0.0min + ___________________________________________________random_dataset - 0.9s, 0.0min For parameters: 'sparse', 'manhattan', 1 ________________________________________________________________________________ @@ -2257,13 +2263,13 @@ ------------------------------ ------------------------- representation metric 1 4 ================ ============= ============ ============ - dense cosine 1.11±0.01s 1.28±0.06s - dense euclidean 1.69±0s 3.03±0.04s - dense manhattan 6.35±0.07s 2.59±0.08s - dense correlation 3.25±0.01s 2.61±0.1s - sparse cosine 4.13±0.01s 2.36±0.3s - sparse euclidean 2.53±0.01s 1.98±0.2s - sparse manhattan 1.23±0.02s 1.32±0.02s + dense cosine 1.10±0.01s 1.23±0.02s + dense euclidean 1.75±0.01s 3.10±0.02s + dense manhattan 6.30±0.09s 2.63±0.06s + dense correlation 3.72±0.03s 2.50±0.2s + sparse cosine 4.13±0.06s 2.63±0.09s + sparse euclidean 2.81±0.02s 2.07±0.07s + sparse manhattan 1.22±0.02s 1.31±0.02s sparse correlation n/a n/a ================ ============= ============ ============ @@ -2285,14 +2291,14 @@ ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._synth_classification_dataset... _synth_classification_dataset(n_samples=50000, n_features=100) - _____________________________________synth_classification_dataset - 0.6s, 0.0min + _____________________________________synth_classification_dataset - 0.5s, 0.0min [83.48%] ··· ...ction.CrossValidationBenchmark.time_crossval ok [83.48%] ··· ======== ============ n_jobs -------- ------------ - 1 1.06±0m - 4 16.7±0.06s + 1 1.07±0.01m + 4 16.8±0.2s ======== ============ [84.35%] ··· ...tion.CrossValidationBenchmark.track_crossval ok @@ -2308,32 +2314,32 @@ [85.22%] ··· ======== ======= n_jobs -------- ------- - 1 95.2M - 4 92.7M + 1 95.4M + 4 92.9M ======== ======= [86.09%] ··· ...election.GridSearchBenchmark.peakmem_predict ok [86.09%] ··· ======== ======= n_jobs -------- ------- - 1 87.6M - 4 87.6M + 1 87.7M + 4 87.7M ======== ======= [86.96%] ··· model_selection.GridSearchBenchmark.time_fit ok [86.96%] ··· ======== ============ n_jobs -------- ------------ - 1 5.66±0.07m - 4 1.72±0m + 1 6.43±0.03m + 4 1.70±0m ======== ============ [87.83%] ··· ...l_selection.GridSearchBenchmark.time_predict ok [87.83%] ··· ======== ============ n_jobs -------- ------------ - 1 70.5±0.1ms - 4 70.5±0.1ms + 1 71.2±0.1ms + 4 71.4±0.1ms ======== ============ [88.70%] ··· ...lection.GridSearchBenchmark.track_test_score ok @@ -2359,9 +2365,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 77.2M 77.2M 80.7M 80.7M - kd_tree 80.1M 80M 88.2M 88.2M - ball_tree 80.1M 80.1M 88M 88M + brute 77.3M 77.3M 80.8M 80.8M + kd_tree 80M 79.8M 88.4M 88.4M + ball_tree 79.9M 79.9M 88.2M 88.2M =========== ========= ========= ========== ========== [91.30%] ··· ...NeighborsClassifierBenchmark.peakmem_predict ok @@ -2370,9 +2376,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 87.8M 88M 92.8M 93M - kd_tree 81.3M 83.6M 90.8M 90.9M - ball_tree 81.1M 83.5M 90.6M 90.6M + brute 88.2M 87.8M 92.6M 92.7M + kd_tree 81.5M 84.3M 91.2M 90.9M + ball_tree 81.3M 84.1M 90.7M 90.8M =========== ========= ========= ========== ========== [92.17%] ··· ...hbors.KNeighborsClassifierBenchmark.time_fit ok @@ -2381,12 +2387,12 @@ ----------------------- --------------------------- algorithm dimension 1 4 =========== =========== ============= ============= - brute low 1.49±0.03ms 1.41±0.2ms - brute high 1.37±0.01ms 1.35±0.08ms - kd_tree low 13.9±0.04ms 13.8±0.2ms - kd_tree high 58.9±1ms 59.0±2ms - ball_tree low 9.96±0.08ms 9.96±0.02ms - ball_tree high 38.2±0.2ms 34.0±7ms + brute low 1.48±0ms 1.20±0.2ms + brute high 1.72±0.02ms 1.73±0.03ms + kd_tree low 14.0±2ms 17.0±0.03ms + kd_tree high 58.9±0.5ms 58.9±0.5ms + ball_tree low 12.3±0.01ms 12.2±0.02ms + ball_tree high 33.8±7ms 33.7±0.1ms =========== =========== ============= ============= [93.04%] ··· ...s.KNeighborsClassifierBenchmark.time_predict ok @@ -2395,48 +2401,48 @@ ----------------------- ------------------------- algorithm dimension 1 4 =========== =========== ============ ============ - brute low 90.9±6ms 88.6±0.3ms - brute high 129±0.3ms 130±0.4ms - kd_tree low 1.18±0s 2.75±0.07s - kd_tree high 8.28±0.01s 8.11±0.2s - ball_tree low 2.27±0.01s 5.59±0.7s - ball_tree high 7.17±0.02s 10.4±0.6s + brute low 91.0±0.4ms 89.0±0.9ms + brute high 125±0.4ms 130±0.3ms + kd_tree low 1.26±0s 2.92±0.2s + kd_tree high 8.54±0.03s 8.23±0.3s + ball_tree low 2.30±0.05s 5.88±0.08s + ball_tree high 7.17±0.02s 9.11±0.3s =========== =========== ============ ============ [93.91%] ··· ...eighborsClassifierBenchmark.track_test_score ok -[93.91%] ··· =========== =========== ======== ==================== - algorithm dimension n_jobs - ----------- ----------- -------- -------------------- - brute low 1 0.4246889943953359 - brute low 4 0.4246889943953359 - brute high 1 0.6672111427203136 - brute high 4 0.6672111427203136 - kd_tree low 1 0.4246889943953359 - kd_tree low 4 0.4246889943953359 - kd_tree high 1 0.6672111427203136 - kd_tree high 4 0.6672111427203136 - ball_tree low 1 0.4246889943953359 - ball_tree low 4 0.4246889943953359 - ball_tree high 1 0.6672111427203136 - ball_tree high 4 0.6672111427203136 - =========== =========== ======== ==================== +[93.91%] ··· =========== =========== ======== ===================== + algorithm dimension n_jobs + ----------- ----------- -------- --------------------- + brute low 1 0.45100683086646176 + brute low 4 0.45100683086646176 + brute high 1 0.6658270696568748 + brute high 4 0.6658270696568748 + kd_tree low 1 0.45100683086646176 + kd_tree low 4 0.45100683086646176 + kd_tree high 1 0.6658270696568748 + kd_tree high 4 0.6658270696568748 + ball_tree low 1 0.45100683086646176 + ball_tree low 4 0.45100683086646176 + ball_tree high 1 0.6658270696568748 + ball_tree high 4 0.6658270696568748 + =========== =========== ======== ===================== [94.78%] ··· ...ighborsClassifierBenchmark.track_train_score ok [94.78%] ··· =========== =========== ======== ==================== algorithm dimension n_jobs ----------- ----------- -------- -------------------- - brute low 1 0.6396430691659004 - brute low 4 0.6396430691659004 - brute high 1 0.7977845579910865 - brute high 4 0.7977845579910865 - kd_tree low 1 0.6396430691659004 - kd_tree low 4 0.6396430691659004 - kd_tree high 1 0.7977845579910865 - kd_tree high 4 0.7977845579910865 - ball_tree low 1 0.6396430691659004 - ball_tree low 4 0.6396430691659004 - ball_tree high 1 0.7977845579910865 - ball_tree high 4 0.7977845579910865 + brute low 1 0.6361749374159553 + brute low 4 0.6361749374159553 + brute high 1 0.7952355516807968 + brute high 4 0.7952355516807968 + kd_tree low 1 0.6361749374159553 + kd_tree low 4 0.6361749374159553 + kd_tree high 1 0.7952355516807968 + kd_tree high 4 0.7952355516807968 + ball_tree low 1 0.6361749374159553 + ball_tree low 4 0.6361749374159553 + ball_tree high 1 0.7952355516807968 + ball_tree high 4 0.7952355516807968 =========== =========== ======== ==================== [95.65%] ··· Setting up svm:14 ok @@ -2477,14 +2483,14 @@ ========= ====== [97.39%] ··· svm.SVCBenchmark.time_fit ok -[97.39%] ··· ========= ============ - kernel - --------- ------------ - linear 1.64±0.01s - poly 1.65±0s - rbf 1.66±0s - sigmoid 1.66±0s - ========= ============ +[97.39%] ··· ========= ========= + kernel + --------- --------- + linear 1.70±0s + poly 1.70±0s + rbf 1.71±0s + sigmoid 1.70±0s + ========= ========= [97.39%] ···· For parameters: 'linear' /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. @@ -2561,8 +2567,6 @@ warnings.warn( /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. warnings.warn( - /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. - warnings.warn( For parameters: 'rbf' /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. @@ -2601,8 +2605,6 @@ warnings.warn( /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. warnings.warn( - /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. - warnings.warn( For parameters: 'sigmoid' /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. @@ -2641,17 +2643,15 @@ warnings.warn( /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. warnings.warn( - /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/svm/_base.py:297: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler. - warnings.warn( [98.26%] ··· svm.SVCBenchmark.time_predict ok [98.26%] ··· ========= ============ kernel --------- ------------ - linear 610±2ms - poly 667±6ms - rbf 2.01±0.04s - sigmoid 676±5ms + linear 632±9ms + poly 615±7ms + rbf 1.75±0.01s + sigmoid 638±10ms ========= ============ [99.13%] ··· svm.SVCBenchmark.track_test_score ok diff --git a/results/sklearn-benchmark/5fc67aeb-conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json b/results/sklearn-benchmark/5fc67aeb-conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json index fc8db15733..cfde7bfd74 100644 --- a/results/sklearn-benchmark/5fc67aeb-conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json +++ b/results/sklearn-benchmark/5fc67aeb-conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json 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