From 6e103483cce088570fb7b1cc6a39e5cc832f12c0 Mon Sep 17 00:00:00 2001 From: sklearn-benchmark-bot Date: Sat, 28 Oct 2023 22:48:17 +0000 Subject: [PATCH] new result [5fc67aeb] --- logs/log_5fc67aeb | 722 +++++++++--------- ...s2.1.0-scipy1.11.2-threadpoolctl3.2.0.json | 2 +- 2 files changed, 362 insertions(+), 362 deletions(-) diff --git a/logs/log_5fc67aeb b/logs/log_5fc67aeb index e70b0c3ca3..3ec2ca47e1 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 138M - sparse lloyd 255M 255M + dense elkan 137M 137M + sparse lloyd 254M 254M sparse elkan 261M 261M ================ =========== ======== =========== @@ -27,10 +27,10 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 90M 89.9M - dense elkan 89.9M 89.9M - sparse lloyd 97.4M 97.4M - sparse elkan 97.4M 97.4M + dense lloyd 89.3M 89.2M + dense elkan 89.2M 89.2M + sparse lloyd 97M 97M + sparse elkan 97M 97M ================ =========== ======== =========== [ 2.61%] ··· cluster.KMeansBenchmark.peakmem_transform ok @@ -39,8 +39,8 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 121M 121M - dense elkan 121M 121M + dense lloyd 120M 120M + dense elkan 120M 120M sparse lloyd 125M 125M sparse elkan 125M 125M ================ =========== ======== =========== @@ -51,10 +51,10 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 405±9ms 1.15±0s - dense elkan 2.19±0.04s 2.01±0.02s - sparse lloyd 1.75±0.06s 4.51±0.3s - sparse elkan 3.63±0.1s 5.50±0.08s + 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 ================ =========== ============ ============ [ 4.35%] ··· cluster.KMeansBenchmark.time_predict ok @@ -63,29 +63,29 @@ ---------------------------- -------------------------- representation algorithm random k-means++ ================ =========== ============= ============ - dense lloyd 5.30±0.06ms 8.04±1ms - dense elkan 5.32±0.2ms 5.28±0.2ms - sparse lloyd 14.9±7ms 27.2±6ms - sparse elkan 27.3±6ms 26.8±6ms + 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 ================ =========== ============= ============ [ 5.22%] ··· cluster.KMeansBenchmark.time_transform ok -[ 5.22%] ··· ================ =========== ============ =========== - -- init - ---------------------------- ------------------------ - representation algorithm random k-means++ - ================ =========== ============ =========== - dense lloyd 92.9±1ms 93.2±2ms - dense elkan 85.8±0.5ms 93.4±2ms - sparse lloyd 4.76±1s 5.33±1s - sparse elkan 3.83±1s 3.85±1s - ================ =========== ============ =========== +[ 5.22%] ··· ================ =========== ============ ============ + -- init + ---------------------------- ------------------------- + 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 + ================ =========== ============ ============ [ 6.09%] ··· cluster.KMeansBenchmark.track_test_score ok [ 6.09%] ··· ================ =========== =========== ===================== representation algorithm init ---------------- ----------- ----------- --------------------- - dense lloyd random -4.109886169433594 + dense lloyd random -4.109885215759277 dense lloyd k-means++ -3.0753684043884277 dense elkan random -4.109886169433594 dense elkan k-means++ -3.0753684043884277 @@ -100,9 +100,9 @@ representation algorithm init ---------------- ----------- ----------- --------------------- dense lloyd random -4.1075520515441895 - dense lloyd k-means++ -3.0780560970306396 + dense lloyd k-means++ -3.0780563354492188 dense elkan random -4.1075520515441895 - dense elkan k-means++ -3.0780560970306396 + dense elkan k-means++ -3.0780563354492188 sparse lloyd random -0.9227071404457092 sparse lloyd k-means++ -0.922096312046051 sparse elkan random -0.9227071404457092 @@ -116,7 +116,7 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 90.6M 91.5M + dense 90.6M 91.6M sparse 174M 176M ================ ======== =========== @@ -126,7 +126,7 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 88M 88M + dense 88M 87.2M sparse 103M 103M ================ ======== =========== @@ -141,24 +141,24 @@ ================ ======== =========== [10.43%] ··· cluster.MiniBatchKMeansBenchmark.time_fit ok -[10.43%] ··· ================ ========== ============ - -- init - ---------------- ----------------------- - representation random k-means++ - ================ ========== ============ - dense 477±20ms 464±20ms - sparse 623±30ms 1.59±0.04s - ================ ========== ============ +[10.43%] ··· ================ ========== =========== + -- init + ---------------- ---------------------- + representation random k-means++ + ================ ========== =========== + dense 476±10ms 477±20ms + sparse 557±7ms 1.86±0.1s + ================ ========== =========== [11.30%] ··· cluster.MiniBatchKMeansBenchmark.time_predict ok -[11.30%] ··· ================ ========== ============ - -- init - ---------------- ----------------------- - representation random k-means++ - ================ ========== ============ - dense 6.19±1ms 5.33±0.2ms - sparse 36.6±2ms 24.0±9ms - ================ ========== ============ +[11.30%] ··· ================ ============ ============ + -- init + ---------------- ------------------------- + representation random k-means++ + ================ ============ ============ + dense 6.28±0.6ms 5.12±0.2ms + sparse 36.9±4ms 24.6±7ms + ================ ============ ============ [12.17%] ··· cluster.MiniBatchKMeansBenchmark.time_transform ok [12.17%] ··· ================ ============ ============ @@ -166,8 +166,8 @@ ---------------- ------------------------- representation random k-means++ ================ ============ ============ - dense 92.3±0.9ms 83.6±1ms - sparse 6.75±0.03s 7.48±0.02s + dense 81.4±0.7ms 81.1±0.9ms + sparse 6.76±0.05s 6.74±0.05s ================ ============ ============ [13.04%] ··· ...er.MiniBatchKMeansBenchmark.track_test_score ok @@ -198,28 +198,28 @@ fit_algorithm 1 4 =============== ====== ====== lars 109M 130M - cd 103M 130M + cd 102M 130M =============== ====== ====== [15.65%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok -[15.65%] ··· =============== ===== ===== - -- n_jobs - --------------- ----------- - fit_algorithm 1 4 - =============== ===== ===== - lars 85M 87M - cd 85M 87M - =============== ===== ===== +[15.65%] ··· =============== ======= ======= + -- n_jobs + --------------- --------------- + fit_algorithm 1 4 + =============== ======= ======= + lars 84.9M 86.9M + cd 84.9M 86.9M + =============== ======= ======= [16.52%] ··· ...osition.DictionaryLearningBenchmark.time_fit ok -[16.52%] ··· =============== ========= ============ - -- n_jobs - --------------- ---------------------- - fit_algorithm 1 4 - =============== ========= ============ - lars 18.0±0s 9.80±0.06s - cd 788±6ms 3.37±0.04s - =============== ========= ============ +[16.52%] ··· =============== ============ ============ + -- n_jobs + --------------- ------------------------- + fit_algorithm 1 4 + =============== ============ ============ + lars 15.7±0.01s 10.2±0.09s + cd 737±8ms 3.32±0.05s + =============== ============ ============ [17.39%] ··· ...n.DictionaryLearningBenchmark.time_transform ok [17.39%] ··· =============== =========== ========== @@ -227,8 +227,8 @@ --------------- ---------------------- fit_algorithm 1 4 =============== =========== ========== - lars 235±0.9ms 301±10ms - cd 228±1ms 300±10ms + lars 233±0.9ms 283±10ms + cd 230±0.6ms 300±10ms =============== =========== ========== [18.26%] ··· ...DictionaryLearningBenchmark.track_test_score ok @@ -258,7 +258,7 @@ --------------- -------------- fit_algorithm 1 4 =============== ======= ====== - lars 97.6M 108M + lars 97.8M 108M cd 97.5M 108M =============== ======= ====== @@ -268,8 +268,8 @@ --------------- --------------- fit_algorithm 1 4 =============== ======= ======= - lars 86.2M 87.8M - cd 86M 87.8M + lars 86M 87.6M + cd 85.9M 87.6M =============== ======= ======= [20.87%] ···· For parameters: 'lars', 1 @@ -296,8 +296,8 @@ --------------- ------------------------ fit_algorithm 1 4 =============== ============ =========== - lars 11.2±0.05s 21.4±2s - cd 3.03±0.02s 19.6±0.7s + lars 10.3±0.04s 20.8±2s + cd 3.07±0.01s 17.3±0.1s =============== ============ =========== [22.61%] ··· ...chDictionaryLearningBenchmark.time_transform ok @@ -306,8 +306,8 @@ --------------- -------------------- fit_algorithm 1 4 =============== ========= ========== - lars 233±7ms 301±10ms - cd 232±2ms 305±10ms + lars 226±1ms 302±10ms + cd 220±1ms 302±10ms =============== ========= ========== [22.61%] ···· For parameters: 'lars', 1 @@ -519,16 +519,10 @@ 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. - 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) + + 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. 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. @@ -1105,12 +1099,12 @@ 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: 'cd', 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. 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: 'cd', 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. 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. @@ -1539,7 +1533,7 @@ [25.22%] ··· ============ ====== svd_solver ------------ ------ - full 907M + full 906M arpack 605M randomized 632M ============ ====== @@ -1557,18 +1551,18 @@ [26.96%] ··· ============ ============ svd_solver ------------ ------------ - full 2.44±0.04s - arpack 1.09±0s - randomized 1.12±0.01s + full 2.46±0.08s + arpack 1.10±0s + randomized 1.08±0.02s ============ ============ [27.83%] ··· decomposition.PCABenchmark.time_transform ok [27.83%] ··· ============ ========= svd_solver ------------ --------- - full 161±1ms + full 156±2ms arpack 156±1ms - randomized 160±1ms + randomized 155±2ms ============ ========= [28.70%] ··· decomposition.PCABenchmark.track_test_score ok @@ -1594,39 +1588,39 @@ [30.43%] ··· ================ ======= representation ---------------- ------- - dense 91.2M - sparse 117M + dense 90.9M + sparse 116M ================ ======= [31.30%] ··· ...tBoostingClassifierBenchmark.peakmem_predict ok [31.30%] ··· ================ ======= representation ---------------- ------- - dense 88.8M - sparse 98.4M + dense 88.5M + sparse 98.1M ================ ======= [32.17%] ··· ...GradientBoostingClassifierBenchmark.time_fit ok [32.17%] ··· ================ ============ representation ---------------- ------------ - dense 2.80±0.01s - sparse 2.23±0.01s + dense 3.16±0s + sparse 2.31±0.01s ================ ============ [33.04%] ··· ...ientBoostingClassifierBenchmark.time_predict ok -[33.04%] ··· ================ ========== - representation - ---------------- ---------- - dense 49.4±6ms - sparse 45.9±1ms - ================ ========== +[33.04%] ··· ================ ============ + representation + ---------------- ------------ + dense 48.6±5ms + sparse 45.2±0.9ms + ================ ============ [33.91%] ··· ...BoostingClassifierBenchmark.track_test_score ok [33.91%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.5542371517925532 + dense 0.55301913301601 sparse 0.10409974329281042 ================ ===================== @@ -1634,15 +1628,15 @@ [34.78%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.6330727844153277 + dense 0.6253525646732084 sparse 0.15180008167538628 ================ ===================== [35.65%] ··· Setting up ensemble:103 ok [35.65%] ··· ...dientBoostingClassifierBenchmark.peakmem_fit 103M -[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 91.1M -[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.41±0.1s -[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 84.6±4ms +[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 91M +[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.39±0.04s +[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 82.8±0.6ms [39.13%] ··· ...BoostingClassifierBenchmark.track_test_score 0.7230709112942986 [40.00%] ··· ...oostingClassifierBenchmark.track_train_score 0.9812160155622751 [40.87%] ··· Setting up ensemble:24 ok @@ -1653,7 +1647,7 @@ representation 1 4 ================ ====== ====== dense 179M 179M - sparse 402M 403M + sparse 402M 402M ================ ====== ====== [41.74%] ··· ...domForestClassifierBenchmark.peakmem_predict ok @@ -1663,7 +1657,7 @@ representation 1 4 ================ ====== ====== dense 182M 188M - sparse 402M 403M + sparse 402M 402M ================ ====== ====== [42.61%] ··· ...ble.RandomForestClassifierBenchmark.time_fit ok @@ -1672,8 +1666,8 @@ ---------------- ------------------------- representation 1 4 ================ ============ ============ - dense 9.09±0.03s 2.60±0.07s - sparse 12.9±0.04s 3.92±0.1s + dense 7.76±0.03s 2.58±0.1s + sparse 13.0±0.05s 4.18±0.02s ================ ============ ============ [43.48%] ··· ...RandomForestClassifierBenchmark.time_predict ok @@ -1682,8 +1676,8 @@ ---------------- ----------------------- representation 1 4 ================ ============ ========== - dense 234±1ms 164±6ms - sparse 2.26±0.02s 774±30ms + dense 235±0.6ms 160±5ms + sparse 2.12±0.01s 783±30ms ================ ============ ========== [44.35%] ··· ...omForestClassifierBenchmark.track_test_score ok @@ -1692,7 +1686,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.7514125134095717 0.7514125134095717 + dense 0.7431096932058513 0.7431096932058513 sparse 0.8656423941766682 0.8656423941766682 ================ ==================== ==================== @@ -1702,7 +1696,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.9976925608991845 0.9976925608991845 + dense 0.996787236878439 0.996787236878439 sparse 0.9996123288718864 0.9996123288718864 ================ ==================== ==================== @@ -1714,7 +1708,7 @@ representation True False ================ ====== ======= dense 852M 1.21G - sparse 124M n/a + sparse 123M n/a ================ ====== ======= [46.09%] ···· For parameters: 'sparse', False @@ -1726,8 +1720,8 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 489M 489M - sparse 96.8M n/a + dense 488M 488M + sparse 96.7M n/a ================ ======= ======= [46.96%] ···· For parameters: 'sparse', False @@ -1739,22 +1733,22 @@ ---------------- ---------------------- representation True False ================ ============ ========= - dense 1.49±0s 1.81±0s - sparse 3.00±0.02s n/a + dense 1.52±0s 1.84±0s + sparse 2.98±0.03s n/a ================ ============ ========= [47.83%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() [48.70%] ··· linear_model.ElasticNetBenchmark.time_predict ok -[48.70%] ··· ================ ============= ============= - -- precompute - ---------------- --------------------------- - representation True False - ================ ============= ============= - dense 50.3±0.07ms 50.3±0.08ms - sparse 2.61±0.02ms n/a - ================ ============= ============= +[48.70%] ··· ================ ============= ========== + -- precompute + ---------------- ------------------------ + representation True False + ================ ============= ========== + dense 51.3±2ms 53.8±1ms + sparse 3.09±0.01ms n/a + ================ ============= ========== [48.70%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1766,7 +1760,7 @@ representation True False ================ ==================== ==================== dense 0.9274010856209145 0.9274010850953214 - sparse 0.9503669656848922 n/a + sparse 0.9489950487365856 n/a ================ ==================== ==================== [49.57%] ···· For parameters: 'sparse', False @@ -1779,7 +1773,7 @@ representation True False ================ ==================== ==================== dense 0.9276022550495941 0.9276022552325599 - sparse 0.9557095101976094 n/a + sparse 0.9566208756361017 n/a ================ ==================== ==================== [50.43%] ···· For parameters: 'sparse', False @@ -1793,7 +1787,7 @@ representation True False ================ ====== ======= dense 852M 1.21G - sparse 124M n/a + sparse 123M n/a ================ ====== ======= [51.30%] ···· For parameters: 'sparse', False @@ -1805,35 +1799,35 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 489M 489M - sparse 96.8M n/a + dense 488M 488M + sparse 96.7M n/a ================ ======= ======= [52.17%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() [53.04%] ··· linear_model.LassoBenchmark.time_fit ok -[53.04%] ··· ================ =========== ========= - -- precompute - ---------------- --------------------- - representation True False - ================ =========== ========= - dense 1.49±0s 1.81±0s - sparse 2.51±0.1s n/a - ================ =========== ========= +[53.04%] ··· ================ ============ ========= + -- precompute + ---------------- ---------------------- + representation True False + ================ ============ ========= + dense 1.54±0s 1.87±0s + sparse 2.73±0.01s n/a + ================ ============ ========= [53.04%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() [53.91%] ··· linear_model.LassoBenchmark.time_predict ok -[53.91%] ··· ================ ============ ============ - -- precompute - ---------------- ------------------------- - representation True False - ================ ============ ============ - dense 46.9±0.2ms 50.0±0.2ms - sparse 3.11±0.4ms n/a - ================ ============ ============ +[53.91%] ··· ================ ============= ============ + -- precompute + ---------------- -------------------------- + representation True False + ================ ============= ============ + dense 54.5±0.1ms 54.5±0.1ms + sparse 3.09±0.01ms n/a + ================ ============= ============ [53.91%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1845,7 +1839,7 @@ representation True False ================ ==================== ==================== dense 0.9274015024583205 0.9274015028138817 - sparse 0.9490962488661691 n/a + sparse 0.9473379934657499 n/a ================ ==================== ==================== [54.78%] ···· For parameters: 'sparse', False @@ -1858,7 +1852,7 @@ representation True False ================ ==================== ==================== dense 0.92760249197518 0.9276024919395177 - sparse 0.9533432474207427 n/a + sparse 0.9541392686500457 n/a ================ ==================== ==================== [55.65%] ···· For parameters: 'sparse', False @@ -1877,32 +1871,32 @@ [57.39%] ··· ================ ====== representation ---------------- ------ - dense 489M + dense 488M sparse 156M ================ ====== [58.26%] ··· linear_model.LinearRegressionBenchmark.time_fit ok -[58.26%] ··· ================ =========== - representation - ---------------- ----------- - dense 3.19±0.2s - sparse 1.11±0s - ================ =========== +[58.26%] ··· ================ ========= + representation + ---------------- --------- + dense 3.13±0s + sparse 1.12±0s + ================ ========= [59.13%] ··· ...model.LinearRegressionBenchmark.time_predict ok -[59.13%] ··· ================ ============ - representation - ---------------- ------------ - dense 49.6±0.8ms - sparse 33.1±3ms - ================ ============ +[59.13%] ··· ================ ============= + representation + ---------------- ------------- + dense 50.6±0.08ms + sparse 33.4±0.07ms + ================ ============= [60.00%] ··· ...l.LinearRegressionBenchmark.track_test_score ok [60.00%] ··· ================ ===================== representation ---------------- --------------------- dense 0.9274012651798128 - sparse 0.10550304492260665 + sparse 0.10318049470166679 ================ ===================== [60.87%] ··· ....LinearRegressionBenchmark.track_train_score ok @@ -1910,7 +1904,7 @@ representation ---------------- -------------------- dense 0.927602494829764 - sparse 0.9999999999962889 + sparse 0.9999999999962513 ================ ==================== [61.74%] ··· Setting up linear_model:28 ok @@ -1921,9 +1915,9 @@ representation solver 1 4 ================ ======== ======= ======= dense lbfgs 105M 98.6M - dense saga 83.5M 84.4M - sparse lbfgs 381M 125M - sparse saga 104M 105M + dense saga 83.1M 84.3M + sparse lbfgs 382M 125M + sparse saga 104M 104M ================ ======== ======= ======= [62.61%] ··· ....LogisticRegressionBenchmark.peakmem_predict ok @@ -1932,10 +1926,10 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 98.7M 98.7M - dense saga 86.1M 86M + dense lbfgs 98.8M 98.8M + dense saga 85.9M 86M sparse lbfgs 100M 100M - sparse saga 87.9M 87.9M + sparse saga 87.7M 87.6M ================ ======== ======= ======= [63.48%] ··· ...r_model.LogisticRegressionBenchmark.time_fit ok @@ -1944,10 +1938,10 @@ ------------------------- ------------------------- representation solver 1 4 ================ ======== ============ ============ - dense lbfgs 22.0±1ms 190±3ms - dense saga 5.44±0.01s 5.82±0.2s - sparse lbfgs 1.13±0.01s 2.89±0.2s - sparse saga 4.13±0.1s 3.53±0.08s + 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 ================ ======== ============ ============ [64.35%] ··· ...del.LogisticRegressionBenchmark.time_predict ok @@ -1956,20 +1950,20 @@ ------------------------- --------------------------- representation solver 1 4 ================ ======== ============= ============= - dense lbfgs 3.27±0.3ms 3.07±0.04ms - dense saga 1.92±0.03ms 1.92±0.03ms - sparse lbfgs 7.06±0.1ms 7.03±0.08ms - sparse saga 4.62±0.01ms 6.13±0.01ms + 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 ================ ======== ============= ============= [65.22%] ··· ...LogisticRegressionBenchmark.track_test_score ok [65.22%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17421675376024606 - dense lbfgs 4 0.17421675376024606 - dense saga 1 0.7813306063625303 - dense saga 4 0.7813306063625303 + dense lbfgs 1 0.17694223144036997 + dense lbfgs 4 0.17694223144036997 + dense saga 1 0.7801291666834305 + dense saga 4 0.7801291666834305 sparse lbfgs 1 0.06538461538461539 sparse lbfgs 4 0.06538461538461539 sparse saga 1 0.5765140080078162 @@ -1977,18 +1971,18 @@ ================ ======== ======== ===================== [66.09%] ··· ...ogisticRegressionBenchmark.track_train_score ok -[66.09%] ··· ================ ======== ======== ===================== - representation solver n_jobs - ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17947291893890438 - dense lbfgs 4 0.17947291893890438 - dense saga 1 0.7992028148325083 - dense saga 4 0.7992028148325083 - 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 0.1789146275513357 + 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 + ================ ======== ======== ==================== [66.96%] ··· Setting up linear_model:78 ok [66.96%] ··· linear_model.RidgeBenchmark.peakmem_fit ok @@ -2006,7 +2000,7 @@ sparse svd n/a sparse cholesky 1.27G sparse lsqr 194M - sparse sparse_cg 193M + sparse sparse_cg 192M sparse sag 158M sparse saga 158M ================ =========== ======= @@ -2018,20 +2012,20 @@ [67.83%] ··· ================ =========== ====== representation solver ---------------- ----------- ------ - dense auto 284M + dense auto 283M dense svd 283M - dense cholesky 284M - dense lsqr 284M - dense sparse_cg 282M + dense cholesky 283M + dense lsqr 283M + dense sparse_cg 283M dense sag 283M - dense saga 282M - sparse auto 118M + dense saga 283M + sparse auto 117M sparse svd n/a - sparse cholesky 118M - sparse lsqr 118M - sparse sparse_cg 118M - sparse sag 118M - sparse saga 118M + sparse cholesky 119M + sparse lsqr 119M + sparse sparse_cg 117M + sparse sag 117M + sparse saga 119M ================ =========== ====== [67.83%] ···· For parameters: 'sparse', 'svd' @@ -2041,20 +2035,20 @@ [68.70%] ··· ================ =========== ============ representation solver ---------------- ----------- ------------ - dense auto 208±1ms - dense svd 1.71±0.02s - dense cholesky 205±1ms - dense lsqr 214±7ms - dense sparse_cg 242±7ms - dense sag 28.6±0.6s - dense saga 13.8±0.5s - sparse auto 161±6ms + 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 sparse svd n/a - sparse cholesky 5.43±0.01s - sparse lsqr 131±0.4ms - sparse sparse_cg 148±0.5ms - sparse sag 2.50±0.01s - sparse saga 1.86±0.02s + 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 ================ =========== ============ [68.70%] ···· For parameters: 'sparse', 'svd' @@ -2064,20 +2058,20 @@ [69.57%] ··· ================ =========== ============= representation solver ---------------- ----------- ------------- - dense auto 23.5±0.2ms - dense svd 23.4±0.06ms - dense cholesky 23.5±0.08ms - dense lsqr 23.6±2ms - dense sparse_cg 23.5±0.09ms - dense sag 24.7±0.7ms - dense saga 24.9±0.2ms - sparse auto 6.92±0.08ms + 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 sparse svd n/a - sparse cholesky 6.88±0.03ms - sparse lsqr 6.87±0.04ms - sparse sparse_cg 6.87±0.05ms - sparse sag 6.87±0.04ms - sparse saga 7.71±0.7ms + 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 ================ =========== ============= [69.57%] ···· For parameters: 'sparse', 'svd' @@ -2094,13 +2088,13 @@ dense sparse_cg 0.9433995989989826 dense sag 0.94339933719428 dense saga 0.9433995886080997 - sparse auto 0.9566293314237935 + sparse auto 0.9559941698932061 sparse svd n/a - sparse cholesky 0.9566294205918099 - sparse lsqr 0.9566293294813514 - sparse sparse_cg 0.9566293314237935 - sparse sag 0.9566329028965642 - sparse saga 0.9566330257922844 + sparse cholesky 0.9559944346330396 + sparse lsqr 0.9559941684408819 + sparse sparse_cg 0.9559941698932061 + sparse sag 0.9559983947836294 + sparse saga 0.9559981694514983 ================ =========== ==================== [70.43%] ···· For parameters: 'sparse', 'svd' @@ -2117,13 +2111,13 @@ dense sparse_cg 0.9444001571192623 dense sag 0.9444001419121766 dense saga 0.9444001543688754 - sparse auto 0.9657393826014592 + sparse auto 0.965958230743709 sparse svd n/a - sparse cholesky 0.9657393858884632 - sparse lsqr 0.9657393826835505 - sparse sparse_cg 0.9657393826014592 - sparse sag 0.9657358295811385 - sparse saga 0.9657357928601014 + sparse cholesky 0.9659582338021545 + sparse lsqr 0.9659582305541862 + sparse sparse_cg 0.965958230743709 + sparse sag 0.9659546816725302 + sparse saga 0.9659546455031128 ================ =========== ==================== [71.30%] ···· For parameters: 'sparse', 'svd' @@ -2131,43 +2125,43 @@ [72.17%] ··· Setting up linear_model:151 ok [72.17%] ··· linear_model.SGDRegressorBenchmark.peakmem_fit ok -[72.17%] ··· ================ ====== - representation - ---------------- ------ - dense 160M - sparse 88M - ================ ====== +[72.17%] ··· ================ ======= + representation + ---------------- ------- + dense 160M + sparse 87.5M + ================ ======= [73.04%] ··· ..._model.SGDRegressorBenchmark.peakmem_predict ok [73.04%] ··· ================ ======= representation ---------------- ------- dense 158M - sparse 85.9M + sparse 86.1M ================ ======= [73.91%] ··· linear_model.SGDRegressorBenchmark.time_fit ok [73.91%] ··· ================ ============ representation ---------------- ------------ - dense 5.74±0.01s - sparse 4.74±0.03s + dense 5.32±0s + sparse 4.81±0.01s ================ ============ [74.78%] ··· linear_model.SGDRegressorBenchmark.time_predict ok -[74.78%] ··· ================ ============ - representation - ---------------- ------------ - dense 10.3±0.3ms - sparse 2.45±0ms - ================ ============ +[74.78%] ··· ================ ============= + representation + ---------------- ------------- + dense 10.7±0.7ms + sparse 2.45±0.01ms + ================ ============= [75.65%] ··· ...model.SGDRegressorBenchmark.track_test_score ok [75.65%] ··· ================ ==================== representation ---------------- -------------------- dense 0.9636293915848902 - sparse 0.962268737808276 + sparse 0.9610050955140929 ================ ==================== [76.52%] ··· ...odel.SGDRegressorBenchmark.track_train_score ok @@ -2175,7 +2169,7 @@ representation ---------------- -------------------- dense 0.9641785427097553 - sparse 0.9621822550639929 + sparse 0.9622836756531341 ================ ==================== [77.39%] ··· Setting up manifold:15 ok @@ -2183,16 +2177,16 @@ [77.39%] ··· ============ ======= method ------------ ------- - exact 88.5M - barnes_hut 96M + exact 89M + barnes_hut 96.6M ============ ======= [78.26%] ··· manifold.TSNEBenchmark.time_fit ok [78.26%] ··· ============ ============ method ------------ ------------ - exact 6.50±0.01s - barnes_hut 3.17±0.03s + exact 6.87±0.03s + barnes_hut 3.15±0.08s ============ ============ [79.13%] ··· manifold.TSNEBenchmark.track_test_score ok @@ -2200,7 +2194,7 @@ method ------------ -------------------- exact 0.3218818006120378 - barnes_hut 0.7243016362190247 + barnes_hut 0.7243015766143799 ============ ==================== [80.00%] ··· manifold.TSNEBenchmark.track_train_score ok @@ -2208,7 +2202,7 @@ method ------------ -------------------- exact 0.3218818006120378 - barnes_hut 0.7243016362190247 + barnes_hut 0.7243015766143799 ============ ==================== [80.87%] ··· ...istancesBenchmark.peakmem_pairwise_distances ok @@ -2217,13 +2211,13 @@ ------------------------------ --------------- representation metric 1 4 ================ ============= ======= ======= - dense cosine 668M 785M - dense euclidean 751M 1.14G - dense manhattan 254M 339M - dense correlation 247M 484M - sparse cosine 1.42G 1.42G - sparse euclidean 569M 917M - sparse manhattan 186M 236M + 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 sparse correlation n/a n/a ================ ============= ======= ======= @@ -2243,13 +2237,13 @@ ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._random_dataset... _random_dataset(n_samples=12000, representation='sparse') - ___________________________________________________random_dataset - 0.8s, 0.0min + ___________________________________________________random_dataset - 0.6s, 0.0min For parameters: 'sparse', 'manhattan', 1 ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._random_dataset... _random_dataset(n_samples=4000, representation='sparse') - ___________________________________________________random_dataset - 0.1s, 0.0min + ___________________________________________________random_dataset - 0.3s, 0.0min For parameters: 'sparse', 'correlation', 1 asv: skipped: NotImplementedError() @@ -2263,13 +2257,13 @@ ------------------------------ ------------------------- representation metric 1 4 ================ ============= ============ ============ - dense cosine 1.11±0.06s 1.24±0.04s - dense euclidean 1.73±0.01s 3.13±0.1s - dense manhattan 6.32±0.02s 2.66±0.09s - dense correlation 3.74±0.01s 2.48±0.4s - sparse cosine 3.79±0.01s 2.48±0.2s - sparse euclidean 2.76±0.03s 2.12±0.07s - sparse manhattan 1.22±0.02s 1.32±0.01s + 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 sparse correlation n/a n/a ================ ============= ============ ============ @@ -2291,15 +2285,15 @@ ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._synth_classification_dataset... _synth_classification_dataset(n_samples=50000, n_features=100) - _____________________________________synth_classification_dataset - 0.5s, 0.0min + _____________________________________synth_classification_dataset - 0.6s, 0.0min [83.48%] ··· ...ction.CrossValidationBenchmark.time_crossval ok -[83.48%] ··· ======== =========== - n_jobs - -------- ----------- - 1 57.0±0.5s - 4 16.8±0.1s - ======== =========== +[83.48%] ··· ======== ============ + n_jobs + -------- ------------ + 1 1.06±0m + 4 16.7±0.06s + ======== ============ [84.35%] ··· ...tion.CrossValidationBenchmark.track_crossval ok [84.35%] ··· ======== ==================== @@ -2314,32 +2308,32 @@ [85.22%] ··· ======== ======= n_jobs -------- ------- - 1 95.1M - 4 92.6M + 1 95.2M + 4 92.7M ======== ======= [86.09%] ··· ...election.GridSearchBenchmark.peakmem_predict ok [86.09%] ··· ======== ======= n_jobs -------- ------- - 1 87.4M - 4 87.4M + 1 87.6M + 4 87.6M ======== ======= [86.96%] ··· model_selection.GridSearchBenchmark.time_fit ok [86.96%] ··· ======== ============ n_jobs -------- ------------ - 1 5.76±0.04m - 4 1.69±0.01m + 1 5.66±0.07m + 4 1.72±0m ======== ============ [87.83%] ··· ...l_selection.GridSearchBenchmark.time_predict ok [87.83%] ··· ======== ============ n_jobs -------- ------------ - 1 70.8±0.2ms - 4 70.8±0.2ms + 1 70.5±0.1ms + 4 70.5±0.1ms ======== ============ [88.70%] ··· ...lection.GridSearchBenchmark.track_test_score ok @@ -2365,9 +2359,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 76.9M 77.1M 80.4M 80.4M - kd_tree 79.6M 79.5M 88M 88M - ball_tree 79.6M 79.5M 87.8M 87.8M + 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 =========== ========= ========= ========== ========== [91.30%] ··· ...NeighborsClassifierBenchmark.peakmem_predict ok @@ -2376,9 +2370,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 87.5M 87.7M 92.9M 92.6M - kd_tree 81.3M 83.7M 91M 90.9M - ball_tree 81.1M 83.6M 90.5M 90.6M + 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 =========== ========= ========= ========== ========== [92.17%] ··· ...hbors.KNeighborsClassifierBenchmark.time_fit ok @@ -2387,62 +2381,62 @@ ----------------------- --------------------------- algorithm dimension 1 4 =========== =========== ============= ============= - brute low 1.19±0.2ms 1.04±0.08ms - brute high 1.20±0.01ms 1.20±0.01ms - kd_tree low 11.9±0.03ms 11.9±0.03ms - kd_tree high 50.8±0.2ms 50.9±0.2ms - ball_tree low 8.58±0.05ms 8.58±0.03ms - ball_tree high 32.9±0.2ms 32.8±0.1ms + 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 =========== =========== ============= ============= [93.04%] ··· ...s.KNeighborsClassifierBenchmark.time_predict ok -[93.04%] ··· =========== =========== ============ ============= - -- n_jobs - ----------------------- -------------------------- - algorithm dimension 1 4 - =========== =========== ============ ============= - brute low 90.3±0.3ms 88.5±0.07ms - brute high 130±0.2ms 131±0.2ms - kd_tree low 1.38±0.01s 2.82±0.3s - kd_tree high 9.00±0.01s 8.11±0.2s - ball_tree low 2.41±0.05s 6.05±0.2s - ball_tree high 7.31±0.02s 11.3±0.2s - =========== =========== ============ ============= +[93.04%] ··· =========== =========== ============ ============ + -- n_jobs + ----------------------- ------------------------- + 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 + =========== =========== ============ ============ [93.91%] ··· ...eighborsClassifierBenchmark.track_test_score ok -[93.91%] ··· =========== =========== ======== ===================== - algorithm dimension n_jobs - ----------- ----------- -------- --------------------- - brute low 1 0.43764350424799614 - brute low 4 0.43764350424799614 - brute high 1 0.6681073740828299 - brute high 4 0.6681073740828299 - kd_tree low 1 0.43764350424799614 - kd_tree low 4 0.43764350424799614 - kd_tree high 1 0.6681073740828299 - kd_tree high 4 0.6681073740828299 - ball_tree low 1 0.43764350424799614 - ball_tree low 4 0.43764350424799614 - ball_tree high 1 0.6681073740828299 - ball_tree high 4 0.6681073740828299 - =========== =========== ======== ===================== +[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 + =========== =========== ======== ==================== [94.78%] ··· ...ighborsClassifierBenchmark.track_train_score ok [94.78%] ··· =========== =========== ======== ==================== algorithm dimension n_jobs ----------- ----------- -------- -------------------- - brute low 1 0.6417771228408591 - brute low 4 0.6417771228408591 - brute high 1 0.7930458884952338 - brute high 4 0.7930458884952338 - kd_tree low 1 0.6417771228408591 - kd_tree low 4 0.6417771228408591 - kd_tree high 1 0.7930458884952338 - kd_tree high 4 0.7930458884952338 - ball_tree low 1 0.6417771228408591 - ball_tree low 4 0.6417771228408591 - ball_tree high 1 0.7930458884952338 - ball_tree high 4 0.7930458884952338 + 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 =========== =========== ======== ==================== [95.65%] ··· Setting up svm:14 ok @@ -2486,10 +2480,10 @@ [97.39%] ··· ========= ============ kernel --------- ------------ - linear 1.70±0s - poly 1.70±0s - rbf 1.71±0s - sigmoid 1.71±0.02s + linear 1.64±0.01s + poly 1.65±0s + rbf 1.66±0s + sigmoid 1.66±0s ========= ============ [97.39%] ···· For parameters: 'linear' @@ -2567,6 +2561,8 @@ 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. @@ -2605,6 +2601,8 @@ 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. @@ -2643,15 +2641,17 @@ 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 745±50ms - poly 677±20ms - rbf 1.79±0.02s - sigmoid 695±10ms + linear 610±2ms + poly 667±6ms + rbf 2.01±0.04s + sigmoid 676±5ms ========= ============ [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 56577ccdc0..fc8db15733 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 @@ -1 +1 @@ -{"commit_hash": "5fc67aeb092d636895b599921283221a68c7a2ad", "env_name": "conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0", "date": 1698408361000, "params": {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "3.0.3", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0"}, "python": "3.11", "requirements": {"numpy": "1.25.2", "scipy": "1.11.2", "cython": "3.0.3", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0"}, "env_vars": {}, "result_columns": ["result", "params", "version", "started_at", "duration", "stats_ci_99_a", "stats_ci_99_b", "stats_q_25", "stats_q_75", "stats_number", "stats_repeat", "samples", "profile"], "results": {"cluster.KMeansBenchmark.peakmem_fit": [[103178240, 113532928, 137244672, 137580544, 254705664, 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