From 95f98119bcf34bc9a8eea1583b16ca407bfcc7cb Mon Sep 17 00:00:00 2001 From: sklearn-benchmark-bot Date: Sun, 5 Nov 2023 23:47:56 +0000 Subject: [PATCH] new result [f284ef2d] --- logs/log_f284ef2d | 622 +++++++++--------- ...s2.1.0-scipy1.11.2-threadpoolctl3.2.0.json | 2 +- 2 files changed, 322 insertions(+), 302 deletions(-) diff --git a/logs/log_f284ef2d b/logs/log_f284ef2d index d40e45a921..51a3e5b498 100644 --- a/logs/log_f284ef2d +++ b/logs/log_f284ef2d @@ -15,7 +15,7 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 103M 114M + dense lloyd 104M 114M dense elkan 137M 138M sparse lloyd 255M 255M sparse elkan 262M 261M @@ -27,8 +27,8 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 89.7M 89.7M - dense elkan 89.7M 89.6M + dense lloyd 90.1M 90.1M + dense elkan 90.1M 90.1M sparse lloyd 97.6M 97.6M sparse elkan 97.6M 97.6M ================ =========== ======== =========== @@ -51,23 +51,23 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 401±4ms 1.22±0.02s - dense elkan 2.18±0.01s 1.99±0.04s - sparse lloyd 1.67±0.05s 4.72±0.04s - sparse elkan 3.49±0.04s 5.27±0.06s + dense lloyd 408±10ms 1.16±0.02s + dense elkan 2.22±0.1s 2.05±0.1s + sparse lloyd 1.69±0.06s 4.60±0.1s + sparse elkan 3.69±0.2s 5.22±0.01s ================ =========== ============ ============ [ 4.35%] ··· cluster.KMeansBenchmark.time_predict ok -[ 4.35%] ··· ================ =========== ============ =========== - -- init - ---------------------------- ------------------------ - representation algorithm random k-means++ - ================ =========== ============ =========== - dense lloyd 8.26±0.2ms 6.09±1ms - dense elkan 5.27±0.2ms 7.22±1ms - sparse lloyd 26.7±4ms 26.6±4ms - sparse elkan 27.1±4ms 16.0±2ms - ================ =========== ============ =========== +[ 4.35%] ··· ================ =========== ============ ============ + -- init + ---------------------------- ------------------------- + representation algorithm random k-means++ + ================ =========== ============ ============ + dense lloyd 5.40±0.3ms 5.16±0.1ms + dense elkan 5.22±0.2ms 5.25±0.2ms + sparse lloyd 16.3±7ms 26.5±6ms + sparse elkan 27.0±6ms 15.2±0.9ms + ================ =========== ============ ============ [ 5.22%] ··· cluster.KMeansBenchmark.time_transform ok [ 5.22%] ··· ================ =========== ============ ============ @@ -75,19 +75,19 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 84.0±0.3ms 83.9±0.3ms - dense elkan 86.5±0.5ms 86.1±0.3ms - sparse lloyd 3.46±1s 3.49±1s - sparse elkan 3.77±1s 3.92±1s + dense lloyd 84.1±0.6ms 94.3±2ms + dense elkan 85.4±3ms 84.5±0.6ms + sparse lloyd 3.90±1s 3.85±1s + sparse elkan 3.81±1s 3.75±1s ================ =========== ============ ============ [ 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 -4.1098856925964355 + dense elkan random -4.109886169433594 dense elkan k-means++ -3.0753684043884277 sparse lloyd random -0.9266619682312012 sparse lloyd k-means++ -0.9249227643013 @@ -116,8 +116,8 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 91.1M 91.6M - sparse 175M 176M + dense 90.8M 91.4M + sparse 174M 176M ================ ======== =========== [ 8.70%] ··· ...ter.MiniBatchKMeansBenchmark.peakmem_predict ok @@ -126,7 +126,7 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 88.3M 88.3M + dense 88.2M 88.2M sparse 104M 104M ================ ======== =========== @@ -141,14 +141,14 @@ ================ ======== =========== [10.43%] ··· cluster.MiniBatchKMeansBenchmark.time_fit ok -[10.43%] ··· ================ ========== =========== - -- init - ---------------- ---------------------- - representation random k-means++ - ================ ========== =========== - dense 459±20ms 488±20ms - sparse 616±20ms 1.98±0.1s - ================ ========== =========== +[10.43%] ··· ================ ========== ============ + -- init + ---------------- ----------------------- + representation random k-means++ + ================ ========== ============ + dense 482±10ms 493±20ms + sparse 630±40ms 1.68±0.02s + ================ ========== ============ [11.30%] ··· cluster.MiniBatchKMeansBenchmark.time_predict ok [11.30%] ··· ================ ========== ============ @@ -156,8 +156,8 @@ ---------------- ----------------------- representation random k-means++ ================ ========== ============ - dense 6.90±1ms 5.21±0.2ms - sparse 37.1±2ms 23.9±7ms + dense 6.16±1ms 5.35±0.2ms + sparse 36.6±2ms 38.2±7ms ================ ========== ============ [12.17%] ··· cluster.MiniBatchKMeansBenchmark.time_transform ok @@ -166,8 +166,8 @@ ---------------- ------------------------- representation random k-means++ ================ ============ ============ - dense 95.1±1ms 95.0±1ms - sparse 7.01±0.05s 6.99±0.02s + dense 98.6±6ms 93.1±1ms + sparse 6.98±0.07s 7.04±0.02s ================ ============ ============ [13.04%] ··· ...er.MiniBatchKMeansBenchmark.track_test_score ok @@ -198,7 +198,7 @@ fit_algorithm 1 4 =============== ====== ====== lars 109M 130M - cd 103M 130M + cd 104M 130M =============== ====== ====== [15.65%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok @@ -217,19 +217,19 @@ --------------- ------------------------ fit_algorithm 1 4 =============== =========== ============ - lars 18.5±0.7s 10.4±0.09s - cd 752±2ms 3.28±0.4s + lars 16.7±0.3s 9.99±0.2s + cd 740±5ms 3.25±0.07s =============== =========== ============ [17.39%] ··· ...n.DictionaryLearningBenchmark.time_transform ok -[17.39%] ··· =============== =========== ========== - -- n_jobs - --------------- ---------------------- - fit_algorithm 1 4 - =============== =========== ========== - lars 276±20ms 295±20ms - cd 235±0.7ms 294±10ms - =============== =========== ========== +[17.39%] ··· =============== ========= ========== + -- n_jobs + --------------- -------------------- + fit_algorithm 1 4 + =============== ========= ========== + lars 272±2ms 311±10ms + cd 249±2ms 300±10ms + =============== ========= ========== [18.26%] ··· ...DictionaryLearningBenchmark.track_test_score ok [18.26%] ··· =============== ======== ====================== @@ -258,19 +258,19 @@ --------------- -------------- fit_algorithm 1 4 =============== ======= ====== - lars 97.6M 107M - cd 97.6M 108M + lars 97.9M 108M + cd 97.4M 108M =============== ======= ====== [20.87%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok -[20.87%] ··· =============== ======= ===== - -- n_jobs - --------------- ------------- - fit_algorithm 1 4 - =============== ======= ===== - lars 86.4M 88M - cd 86.2M 88M - =============== ======= ===== +[20.87%] ··· =============== ======= ======= + -- n_jobs + --------------- --------------- + fit_algorithm 1 4 + =============== ======= ======= + lars 86.3M 87.9M + cd 86.2M 87.9M + =============== ======= ======= [20.87%] ···· 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. @@ -296,8 +296,8 @@ --------------- ------------------------ fit_algorithm 1 4 =============== ============ =========== - lars 10.5±0.06s 18.0±1s - cd 3.03±0.01s 19.2±0.1s + lars 10.6±0.05s 20.7±1s + cd 3.08±0.02s 19.9±0.2s =============== ============ =========== [22.61%] ··· ...chDictionaryLearningBenchmark.time_transform ok @@ -306,8 +306,8 @@ --------------- -------------------- fit_algorithm 1 4 =============== ========= ========== - lars 218±6ms 305±10ms - cd 220±1ms 291±20ms + lars 252±4ms 302±10ms + cd 230±1ms 294±10ms =============== ========= ========== [22.61%] ···· For parameters: 'lars', 1 @@ -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. @@ -1093,6 +1091,24 @@ 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) + /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) 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. @@ -1487,6 +1503,10 @@ 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) + /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%] ··· =============== ======== ====================== @@ -1531,7 +1551,7 @@ [25.22%] ··· ============ ====== svd_solver ------------ ------ - full 907M + full 908M arpack 605M randomized 632M ============ ====== @@ -1540,7 +1560,7 @@ [26.09%] ··· ============ ====== svd_solver ------------ ------ - full 583M + full 582M arpack 583M randomized 583M ============ ====== @@ -1549,18 +1569,18 @@ [26.96%] ··· ============ ============ svd_solver ------------ ------------ - full 2.45±0.04s - arpack 1.09±0.01s - randomized 1.08±0.01s + full 2.42±0.03s + arpack 1.10±0.03s + randomized 1.07±0.02s ============ ============ [27.83%] ··· decomposition.PCABenchmark.time_transform ok [27.83%] ··· ============ =========== svd_solver ------------ ----------- - full 162±1ms - arpack 156±0.9ms - randomized 157±0.9ms + full 154±1ms + arpack 155±0.9ms + randomized 153±0.9ms ============ =========== [28.70%] ··· decomposition.PCABenchmark.track_test_score ok @@ -1599,26 +1619,26 @@ ================ ======= [32.17%] ··· ...GradientBoostingClassifierBenchmark.time_fit ok -[32.17%] ··· ================ ========= - representation - ---------------- --------- - dense 2.80±0s - sparse 2.25±0s - ================ ========= +[32.17%] ··· ================ ============ + representation + ---------------- ------------ + dense 2.74±0.01s + sparse 2.15±0.02s + ================ ============ [33.04%] ··· ...ientBoostingClassifierBenchmark.time_predict ok [33.04%] ··· ================ ============ representation ---------------- ------------ - dense 50.3±0.7ms - sparse 45.7±2ms + dense 43.5±0.2ms + sparse 40.6±0.8ms ================ ============ [33.91%] ··· ...BoostingClassifierBenchmark.track_test_score ok [33.91%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.5533298201822517 + dense 0.5591344614901367 sparse 0.10409974329281042 ================ ===================== @@ -1626,15 +1646,15 @@ [34.78%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.6223197570823495 + dense 0.6305070836938789 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 2.39±0.02s -[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 83.9±0.5ms +[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 91.2M +[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.38±0.05s +[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 85.2±0.8ms [39.13%] ··· ...BoostingClassifierBenchmark.track_test_score 0.7230709112942986 [40.00%] ··· ...oostingClassifierBenchmark.track_train_score 0.9812160155622751 [40.87%] ··· Setting up ensemble:24 ok @@ -1654,7 +1674,7 @@ ---------------- ------------- representation 1 4 ================ ====== ====== - dense 182M 189M + dense 182M 188M sparse 403M 403M ================ ====== ====== @@ -1664,8 +1684,8 @@ ---------------- ------------------------- representation 1 4 ================ ============ ============ - dense 7.87±0.03s 2.61±0.09s - sparse 11.5±0.05s 3.88±0.2s + dense 7.72±0.03s 2.58±0.07s + sparse 11.6±0.4s 4.14±0.09s ================ ============ ============ [43.48%] ··· ...RandomForestClassifierBenchmark.time_predict ok @@ -1674,8 +1694,8 @@ ---------------- ----------------------- representation 1 4 ================ ============ ========== - dense 234±0.6ms 154±10ms - sparse 2.21±0.05s 763±40ms + dense 236±1ms 155±5ms + sparse 2.25±0.01s 807±40ms ================ ============ ========== [44.35%] ··· ...omForestClassifierBenchmark.track_test_score ok @@ -1684,7 +1704,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.755838328432066 0.755838328432066 + dense 0.7594025567526377 0.7594025567526377 sparse 0.8656423941766682 0.8656423941766682 ================ ==================== ==================== @@ -1694,7 +1714,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.9974702083183716 0.9974702083183716 + dense 0.9968211515440704 0.9968211515440704 sparse 0.9996123288718864 0.9996123288718864 ================ ==================== ==================== @@ -1718,22 +1738,22 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 490M 490M - sparse 96.8M n/a + dense 489M 489M + sparse 96.4M 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 1.51±0s 1.83±0s - sparse 2.58±0.02s n/a - ================ ============ ========= +[47.83%] ··· ================ =========== ========= + -- precompute + ---------------- --------------------- + representation True False + ================ =========== ========= + dense 1.48±0s 1.80±0s + sparse 2.70±0.1s n/a + ================ =========== ========= [47.83%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1744,8 +1764,8 @@ ---------------- -------------------------- representation True False ================ ============= ============ - dense 48.8±2ms 49.3±0.1ms - sparse 2.31±0.01ms n/a + dense 47.2±0.4ms 47.2±0.4ms + sparse 2.31±0.05ms n/a ================ ============= ============ [48.70%] ···· For parameters: 'sparse', False @@ -1758,7 +1778,7 @@ representation True False ================ ==================== ==================== dense 0.9274010856209145 0.9274010850953214 - sparse 0.9478893102648059 n/a + sparse 0.9486717670516994 n/a ================ ==================== ==================== [49.57%] ···· For parameters: 'sparse', False @@ -1771,7 +1791,7 @@ representation True False ================ ==================== ==================== dense 0.9276022550495941 0.9276022552325599 - sparse 0.956157050950578 n/a + sparse 0.9559571560674978 n/a ================ ==================== ==================== [50.43%] ···· For parameters: 'sparse', False @@ -1797,35 +1817,35 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 490M 489M - sparse 96.8M n/a + dense 489M 490M + sparse 96.4M 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 1.52±0s 1.86±0.01s - sparse 2.69±0.09s n/a - ================ ============ ============ +[53.04%] ··· ================ =========== ========= + -- precompute + ---------------- --------------------- + representation True False + ================ =========== ========= + dense 1.49±0s 1.82±0s + sparse 2.40±0.2s n/a + ================ =========== ========= [53.04%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() [53.91%] ··· linear_model.LassoBenchmark.time_predict ok -[53.91%] ··· ================ ============= ============ +[53.91%] ··· ================ ============ ============= -- precompute ---------------- -------------------------- - representation True False - ================ ============= ============ - dense 48.8±0.2ms 48.7±0.2ms - sparse 2.30±0.02ms n/a - ================ ============= ============ + representation True False + ================ ============ ============= + dense 48.7±0.1ms 51.7±0.08ms + sparse 3.05±0.4ms n/a + ================ ============ ============= [53.91%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1837,7 +1857,7 @@ representation True False ================ ==================== ==================== dense 0.9274015024583205 0.9274015028138817 - sparse 0.9465391085023358 n/a + sparse 0.947276119181939 n/a ================ ==================== ==================== [54.78%] ···· For parameters: 'sparse', False @@ -1850,7 +1870,7 @@ representation True False ================ ==================== ==================== dense 0.92760249197518 0.9276024919395177 - sparse 0.9536712318349341 n/a + sparse 0.9535338818353631 n/a ================ ==================== ==================== [55.65%] ···· For parameters: 'sparse', False @@ -1877,16 +1897,16 @@ [58.26%] ··· ================ ============ representation ---------------- ------------ - dense 3.06±0.01s - sparse 1.12±0s + dense 3.08±0.02s + sparse 1.11±0s ================ ============ [59.13%] ··· ...model.LinearRegressionBenchmark.time_predict ok [59.13%] ··· ================ ============ representation ---------------- ------------ - dense 51.6±0.3ms - sparse 32.9±0.1ms + dense 52.1±0.5ms + sparse 33.4±0.3ms ================ ============ [60.00%] ··· ...l.LinearRegressionBenchmark.track_test_score ok @@ -1894,7 +1914,7 @@ representation ---------------- --------------------- dense 0.9274012651798128 - sparse 0.09967782612505727 + sparse 0.10598448412104489 ================ ===================== [60.87%] ··· ....LinearRegressionBenchmark.track_train_score ok @@ -1902,7 +1922,7 @@ representation ---------------- -------------------- dense 0.927602494829764 - sparse 0.9999999999963238 + sparse 0.9999999999963013 ================ ==================== [61.74%] ··· Setting up linear_model:28 ok @@ -1913,7 +1933,7 @@ representation solver 1 4 ================ ======== ======= ======= dense lbfgs 105M 98.8M - dense saga 83.5M 84.4M + dense saga 83.4M 84.4M sparse lbfgs 381M 125M sparse saga 104M 105M ================ ======== ======= ======= @@ -1924,23 +1944,23 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 99.1M 99.2M - dense saga 86.2M 86.1M - sparse lbfgs 100M 100M - sparse saga 88.1M 88M + dense lbfgs 99M 99.4M + dense saga 86.2M 85.8M + sparse lbfgs 101M 100M + sparse saga 88.3M 88.3M ================ ======== ======= ======= [63.48%] ··· ...r_model.LogisticRegressionBenchmark.time_fit ok -[63.48%] ··· ================ ======== ============ =========== - -- n_jobs - ------------------------- ------------------------ - representation solver 1 4 - ================ ======== ============ =========== - dense lbfgs 21.1±1ms 190±5ms - dense saga 5.42±0.1s 6.35±0.4s - sparse lbfgs 1.03±0.01s 2.92±0.2s - sparse saga 3.61±0.01s 4.88±0.1s - ================ ======== ============ =========== +[63.48%] ··· ================ ======== ============ ============ + -- n_jobs + ------------------------- ------------------------- + representation solver 1 4 + ================ ======== ============ ============ + dense lbfgs 21.4±1ms 190±0.8ms + dense saga 5.18±0.02s 5.19±0.3s + sparse lbfgs 1.11±0.02s 3.01±0.1s + sparse saga 4.07±0.02s 4.06±0.02s + ================ ======== ============ ============ [64.35%] ··· ...del.LogisticRegressionBenchmark.time_predict ok [64.35%] ··· ================ ======== ============= ============= @@ -1948,20 +1968,20 @@ ------------------------- --------------------------- representation solver 1 4 ================ ======== ============= ============= - dense lbfgs 3.39±0.2ms 3.16±0.09ms - dense saga 1.87±0.03ms 1.85±0.02ms - sparse lbfgs 7.59±0.03ms 7.57±0.02ms - sparse saga 6.42±0.04ms 6.41±0.6ms + dense lbfgs 3.57±0.2ms 3.02±0.03ms + dense saga 1.87±0.02ms 1.88±0.03ms + sparse lbfgs 6.97±0.05ms 6.96±0.04ms + sparse saga 4.57±0.01ms 4.57±0.02ms ================ ======== ============= ============= [65.22%] ··· ...LogisticRegressionBenchmark.track_test_score ok [65.22%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17227335753383094 - dense lbfgs 4 0.17227335753383094 - dense saga 1 0.7772169153138837 - dense saga 4 0.7772169153138837 + dense lbfgs 1 0.17400408054789931 + dense lbfgs 4 0.17400408054789931 + dense saga 1 0.7826896917597472 + dense saga 4 0.7826896917597472 sparse lbfgs 1 0.06538461538461539 sparse lbfgs 4 0.06538461538461539 sparse saga 1 0.5765140080078162 @@ -1972,10 +1992,10 @@ [66.09%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17817353876834482 - dense lbfgs 4 0.17817353876834482 - dense saga 1 0.7966489472863143 - dense saga 4 0.7966489472863143 + dense lbfgs 1 0.17902163696399911 + dense lbfgs 4 0.17902163696399911 + dense saga 1 0.7996936634968017 + dense saga 4 0.7996936634968017 sparse lbfgs 1 0.0681998556998557 sparse lbfgs 4 0.0681998556998557 sparse saga 1 0.6908414295256007 @@ -1987,7 +2007,7 @@ [66.96%] ··· ================ =========== ======= representation solver ---------------- ----------- ------- - dense auto 463M + dense auto 464M dense svd 825M dense cholesky 464M dense lsqr 472M @@ -2017,12 +2037,12 @@ dense sparse_cg 283M dense sag 283M dense saga 283M - sparse auto 120M + sparse auto 119M sparse svd n/a - sparse cholesky 120M - sparse lsqr 118M + sparse cholesky 119M + sparse lsqr 119M sparse sparse_cg 119M - sparse sag 120M + sparse sag 119M sparse saga 119M ================ =========== ====== @@ -2033,20 +2053,20 @@ [68.70%] ··· ================ =========== ============ representation solver ---------------- ----------- ------------ - dense auto 210±2ms - dense svd 1.72±0.02s - dense cholesky 210±2ms - dense lsqr 222±6ms - dense sparse_cg 265±2ms - dense sag 27.9±0.3s - dense saga 12.4±0.03s - sparse auto 152±0.6ms + dense auto 210±1ms + dense svd 1.70±0.03s + dense cholesky 214±4ms + dense lsqr 221±7ms + dense sparse_cg 262±2ms + dense sag 27.6±0.05s + dense saga 13.8±0.04s + sparse auto 151±0.8ms sparse svd n/a - sparse cholesky 5.76±0.08s - sparse lsqr 135±0.6ms - sparse sparse_cg 163±0.7ms - sparse sag 2.75±0.01s - sparse saga 2.06±0.03s + sparse cholesky 5.43±0.02s + sparse lsqr 135±0.7ms + sparse sparse_cg 153±0.5ms + sparse sag 2.47±0.01s + sparse saga 2.10±0.04s ================ =========== ============ [68.70%] ···· For parameters: 'sparse', 'svd' @@ -2056,20 +2076,20 @@ [69.57%] ··· ================ =========== ============= representation solver ---------------- ----------- ------------- - dense auto 25.7±0.2ms - dense svd 24.5±0.2ms - dense cholesky 24.4±0.08ms - dense lsqr 24.5±0.07ms - dense sparse_cg 25.7±0.1ms - dense sag 24.6±0.3ms - dense saga 24.4±0.3ms - sparse auto 6.99±0.03ms + dense auto 25.7±0.06ms + dense svd 25.8±0.1ms + dense cholesky 25.7±0.3ms + dense lsqr 25.1±0.6ms + dense sparse_cg 24.4±0.3ms + dense sag 24.8±0.4ms + dense saga 24.5±0.4ms + sparse auto 6.92±0.03ms sparse svd n/a - sparse cholesky 7.02±0.1ms - sparse lsqr 7.01±0.04ms - sparse sparse_cg 6.98±0.04ms - sparse sag 6.98±0.05ms - sparse saga 8.22±0.6ms + sparse cholesky 7.69±0.7ms + sparse lsqr 8.00±1ms + sparse sparse_cg 9.33±1ms + sparse sag 6.91±0.07ms + sparse saga 6.95±0.04ms ================ =========== ============= [69.57%] ···· For parameters: 'sparse', 'svd' @@ -2086,13 +2106,13 @@ dense sparse_cg 0.9433995989989826 dense sag 0.94339933719428 dense saga 0.9433995886080997 - sparse auto 0.9562942282558906 + sparse auto 0.9568826405790074 sparse svd n/a - sparse cholesky 0.9562941623880757 - sparse lsqr 0.9562942253900248 - sparse sparse_cg 0.9562942282558906 - sparse sag 0.9563049753873724 - sparse saga 0.9563047929543729 + sparse cholesky 0.9568824467371728 + sparse lsqr 0.9568826402267703 + sparse sparse_cg 0.9568826405790074 + sparse sag 0.9568873716288406 + sparse saga 0.956887186389341 ================ =========== ==================== [70.43%] ···· For parameters: 'sparse', 'svd' @@ -2109,13 +2129,13 @@ dense sparse_cg 0.9444001571192623 dense sag 0.9444001419121766 dense saga 0.9444001543688754 - sparse auto 0.9660353661056944 + sparse auto 0.9661476325951304 sparse svd n/a - sparse cholesky 0.9660353694471678 - sparse lsqr 0.9660353661706871 - sparse sparse_cg 0.9660353661056944 - sparse sag 0.9660318037275463 - sparse saga 0.9660317693369723 + sparse cholesky 0.9661476358449391 + sparse lsqr 0.966147633028373 + sparse sparse_cg 0.9661476325951304 + sparse sag 0.9661440822169587 + sparse saga 0.9661440468802306 ================ =========== ==================== [71.30%] ···· For parameters: 'sparse', 'svd' @@ -2127,7 +2147,7 @@ representation ---------------- ------- dense 159M - sparse 88.7M + sparse 87.6M ================ ======= [73.04%] ··· ..._model.SGDRegressorBenchmark.peakmem_predict ok @@ -2139,19 +2159,19 @@ ================ ======= [73.91%] ··· linear_model.SGDRegressorBenchmark.time_fit ok -[73.91%] ··· ================ ============ - representation - ---------------- ------------ - dense 5.35±0.02s - sparse 4.36±0.01s - ================ ============ +[73.91%] ··· ================ =========== + representation + ---------------- ----------- + dense 5.46±0.2s + sparse 4.71±0.3s + ================ =========== [74.78%] ··· linear_model.SGDRegressorBenchmark.time_predict ok [74.78%] ··· ================ ============= representation ---------------- ------------- - dense 10.4±0.1ms - sparse 2.44±0.02ms + dense 10.6±0.7ms + sparse 2.17±0.06ms ================ ============= [75.65%] ··· ...model.SGDRegressorBenchmark.track_test_score ok @@ -2159,7 +2179,7 @@ representation ---------------- -------------------- dense 0.9636293915848902 - sparse 0.9614746846749225 + sparse 0.9612704089373972 ================ ==================== [76.52%] ··· ...odel.SGDRegressorBenchmark.track_train_score ok @@ -2167,7 +2187,7 @@ representation ---------------- -------------------- dense 0.9641785427097553 - sparse 0.961954168147174 + sparse 0.9618178808682714 ================ ==================== [77.39%] ··· Setting up manifold:15 ok @@ -2175,17 +2195,17 @@ [77.39%] ··· ============ ======= method ------------ ------- - exact 89.4M - barnes_hut 97.1M + exact 88.8M + barnes_hut 97M ============ ======= [78.26%] ··· manifold.TSNEBenchmark.time_fit ok -[78.26%] ··· ============ ============ - method - ------------ ------------ - exact 7.55±0.2s - barnes_hut 3.11±0.05s - ============ ============ +[78.26%] ··· ============ =========== + method + ------------ ----------- + exact 6.44±0.3s + barnes_hut 3.37±0.2s + ============ =========== [79.13%] ··· manifold.TSNEBenchmark.track_test_score ok [79.13%] ··· ============ ==================== @@ -2209,13 +2229,13 @@ ------------------------------ --------------- representation metric 1 4 ================ ============= ======= ======= - dense cosine 669M 786M - dense euclidean 751M 1.08G - dense manhattan 254M 310M - dense correlation 247M 485M - sparse cosine 1.42G 1.42G - sparse euclidean 570M 955M - sparse manhattan 187M 207M + dense cosine 668M 786M + dense euclidean 752M 1.14G + dense manhattan 254M 339M + dense correlation 247M 484M + sparse cosine 1.42G 1.4G + sparse euclidean 570M 972M + sparse manhattan 187M 209M sparse correlation n/a n/a ================ ============= ======= ======= @@ -2255,13 +2275,13 @@ ------------------------------ ------------------------- representation metric 1 4 ================ ============= ============ ============ - dense cosine 1.11±0.01s 1.26±0.02s - dense euclidean 1.71±0s 2.98±0.07s - dense manhattan 6.27±0.03s 2.63±0.06s - dense correlation 3.40±0.01s 2.61±0.1s - sparse cosine 3.69±0.01s 2.62±0.2s - sparse euclidean 2.73±0.09s 2.06±0.1s - sparse manhattan 1.23±0.02s 1.32±0.03s + dense cosine 1.09±0s 1.25±0.03s + dense euclidean 1.73±0.04s 3.11±0.06s + dense manhattan 6.42±0.08s 2.68±0.07s + dense correlation 3.71±0.02s 2.57±0.05s + sparse cosine 3.76±0.03s 2.20±0.2s + sparse euclidean 2.83±0.03s 2.05±0.1s + sparse manhattan 1.02±0.05s 1.26±0.03s sparse correlation n/a n/a ================ ============= ============ ============ @@ -2286,12 +2306,12 @@ _____________________________________synth_classification_dataset - 0.6s, 0.0min [83.48%] ··· ...ction.CrossValidationBenchmark.time_crossval ok -[83.48%] ··· ======== =========== - n_jobs - -------- ----------- - 1 58.3±2s - 4 17.3±0.2s - ======== =========== +[83.48%] ··· ======== ============ + n_jobs + -------- ------------ + 1 56.7±0.09s + 4 16.6±0.02s + ======== ============ [84.35%] ··· ...tion.CrossValidationBenchmark.track_crossval ok [84.35%] ··· ======== ==================== @@ -2306,8 +2326,8 @@ [85.22%] ··· ======== ======= n_jobs -------- ------- - 1 95.5M - 4 93.1M + 1 95.3M + 4 92.9M ======== ======= [86.09%] ··· ...election.GridSearchBenchmark.peakmem_predict ok @@ -2319,21 +2339,21 @@ ======== ======= [86.96%] ··· model_selection.GridSearchBenchmark.time_fit ok -[86.96%] ··· ======== =========== - n_jobs - -------- ----------- - 1 5.95±0.2m - 4 1.71±0m - ======== =========== - -[87.83%] ··· ...l_selection.GridSearchBenchmark.time_predict ok -[87.83%] ··· ======== ============ +[86.96%] ··· ======== ============ n_jobs -------- ------------ - 1 71.4±0.1ms - 4 71.4±0.1ms + 1 5.77±0.05m + 4 1.71±0.01m ======== ============ +[87.83%] ··· ...l_selection.GridSearchBenchmark.time_predict ok +[87.83%] ··· ======== ========== + n_jobs + -------- ---------- + 1 53.9±2ms + 4 56.1±2ms + ======== ========== + [88.70%] ··· ...lection.GridSearchBenchmark.track_test_score ok [88.70%] ··· ======== ==================== n_jobs @@ -2357,9 +2377,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 77.5M 77.5M 80.7M 80.7M - kd_tree 79.8M 79.8M 88.4M 88.4M - ball_tree 79.9M 79.9M 88.3M 88.3M + brute 77.3M 77.3M 80.8M 80.8M + kd_tree 79.7M 79.7M 88.4M 88.4M + ball_tree 79.7M 79.7M 88.2M 88.2M =========== ========= ========= ========== ========== [91.30%] ··· ...NeighborsClassifierBenchmark.peakmem_predict ok @@ -2368,9 +2388,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 88.2M 88.4M 92.9M 93.1M - kd_tree 81.6M 83.7M 91.3M 91M - ball_tree 81.4M 83.6M 90.9M 90.7M + brute 88.1M 88.3M 93M 93.2M + kd_tree 81.5M 83.9M 91.1M 90.6M + ball_tree 81.4M 83.8M 90.7M 91M =========== ========= ========= ========== ========== [92.17%] ··· ...hbors.KNeighborsClassifierBenchmark.time_fit ok @@ -2379,12 +2399,12 @@ ----------------------- --------------------------- algorithm dimension 1 4 =========== =========== ============= ============= - brute low 1.24±0.1ms 1.21±0.02ms - brute high 1.40±0.01ms 1.75±0.03ms - kd_tree low 17.3±0.05ms 14.0±0.02ms - kd_tree high 59.7±0.3ms 59.8±0.4ms - ball_tree low 10.1±0.02ms 10.1±0.05ms - ball_tree high 38.7±0.3ms 38.7±0.2ms + brute low 1.49±0.2ms 1.21±0ms + brute high 1.72±0.02ms 1.75±0.02ms + kd_tree low 17.0±0.03ms 17.3±0.04ms + kd_tree high 59.6±1ms 59.7±1ms + ball_tree low 9.64±0.6ms 10.2±3ms + ball_tree high 35.4±0.9ms 35.6±0.8ms =========== =========== ============= ============= [93.04%] ··· ...s.KNeighborsClassifierBenchmark.time_predict ok @@ -2393,48 +2413,48 @@ ----------------------- ------------------------- algorithm dimension 1 4 =========== =========== ============ ============ - brute low 87.1±0.9ms 89.3±1ms - brute high 129±0.3ms 125±1ms - kd_tree low 1.25±0s 2.85±0.09s - kd_tree high 8.32±0.2s 7.68±0.2s - ball_tree low 2.06±0s 4.73±0.5s - ball_tree high 7.29±0.01s 9.89±0.6s + brute low 88.1±1ms 88.6±0.5ms + brute high 130±0.2ms 125±1ms + kd_tree low 1.49±0.04s 2.29±0.2s + kd_tree high 8.57±0.01s 8.08±0.3s + ball_tree low 2.21±0.08s 5.94±0.4s + ball_tree high 7.47±0.3s 10.6±0.2s =========== =========== ============ ============ [93.91%] ··· ...eighborsClassifierBenchmark.track_test_score ok [93.91%] ··· =========== =========== ======== ===================== algorithm dimension n_jobs ----------- ----------- -------- --------------------- - brute low 1 0.43304237472725493 - brute low 4 0.43304237472725493 - brute high 1 0.6673607688786586 - brute high 4 0.6673607688786586 - kd_tree low 1 0.43304237472725493 - kd_tree low 4 0.43304237472725493 - kd_tree high 1 0.6673607688786586 - kd_tree high 4 0.6673607688786586 - ball_tree low 1 0.43304237472725493 - ball_tree low 4 0.43304237472725493 - ball_tree high 1 0.6673607688786586 - ball_tree high 4 0.6673607688786586 + brute low 1 0.46142933549505083 + brute low 4 0.46142933549505083 + brute high 1 0.6749075737378727 + brute high 4 0.6749075737378727 + kd_tree low 1 0.46142933549505083 + kd_tree low 4 0.46142933549505083 + kd_tree high 1 0.6749075737378727 + kd_tree high 4 0.6749075737378727 + ball_tree low 1 0.46142933549505083 + ball_tree low 4 0.46142933549505083 + ball_tree high 1 0.6749075737378727 + ball_tree high 4 0.6749075737378727 =========== =========== ======== ===================== [94.78%] ··· ...ighborsClassifierBenchmark.track_train_score ok [94.78%] ··· =========== =========== ======== ==================== algorithm dimension n_jobs ----------- ----------- -------- -------------------- - brute low 1 0.6405296565460257 - brute low 4 0.6405296565460257 - brute high 1 0.7882956175266892 - brute high 4 0.7882956175266892 - kd_tree low 1 0.6405296565460257 - kd_tree low 4 0.6405296565460257 - kd_tree high 1 0.7882956175266892 - kd_tree high 4 0.7882956175266892 - ball_tree low 1 0.6405296565460257 - ball_tree low 4 0.6405296565460257 - ball_tree high 1 0.7882956175266892 - ball_tree high 4 0.7882956175266892 + brute low 1 0.6458356329950761 + brute low 4 0.6458356329950761 + brute high 1 0.7945182217311147 + brute high 4 0.7945182217311147 + kd_tree low 1 0.6458356329950761 + kd_tree low 4 0.6458356329950761 + kd_tree high 1 0.7945182217311147 + kd_tree high 4 0.7945182217311147 + ball_tree low 1 0.6458356329950761 + ball_tree low 4 0.6458356329950761 + ball_tree high 1 0.7945182217311147 + ball_tree high 4 0.7945182217311147 =========== =========== ======== ==================== [95.65%] ··· Setting up svm:14 ok @@ -2478,7 +2498,7 @@ [97.39%] ··· ========= ========= kernel --------- --------- - linear 1.75±0s + linear 1.76±0s poly 1.76±0s rbf 1.77±0s sigmoid 1.77±0s @@ -2640,10 +2660,10 @@ [98.26%] ··· ========= ============ kernel --------- ------------ - linear 666±5ms - poly 782±4ms - rbf 1.90±0.02s - sigmoid 788±3ms + linear 625±10ms + poly 727±10ms + rbf 1.94±0.05s + sigmoid 737±7ms ========= ============ [99.13%] ··· svm.SVCBenchmark.track_test_score ok @@ -2726,7 +2746,7 @@ scipy 1.11.2 py311h64a7726_1 conda-forge setuptools 68.2.2 pyhd8ed1ab_0 conda-forge six 1.16.0 pyh6c4a22f_0 conda-forge threadpoolctl 3.2.0 pyha21a80b_0 conda-forge -tk 8.6.13 h2797004_0 conda-forge +tk 8.6.13 noxft_h4845f30_101 conda-forge tzdata 2023c h71feb2d_0 conda-forge wheel 0.41.3 pyhd8ed1ab_0 conda-forge xz 5.2.6 h166bdaf_0 conda-forge diff --git a/results/sklearn-benchmark/f284ef2d-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/f284ef2d-conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json index 91da2dc922..8bb1a635e2 100644 --- a/results/sklearn-benchmark/f284ef2d-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/f284ef2d-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": "f284ef2dfe92f448bd2e8734c47071f04dbd734b", "env_name": "conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0", "date": 1699118804000, "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": [[103481344, 113729536, 137363456, 137695232, 254758912, 254799872, 261599232, 260747264], [["'dense'", "'sparse'"], ["'lloyd'", 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