From c3fad9a82ba56be8acf3c478381718034fbbf7e7 Mon Sep 17 00:00:00 2001 From: sklearn-benchmark-bot Date: Sun, 28 Jan 2024 23:42:26 +0000 Subject: [PATCH] new result [cb836be0] --- logs/log_cb836be0 | 722 +++++++++--------- ...s2.1.0-scipy1.11.2-threadpoolctl3.2.0.json | 2 +- 2 files changed, 354 insertions(+), 370 deletions(-) diff --git a/logs/log_cb836be0 b/logs/log_cb836be0 index 3347fcdbba..59facb0842 100644 --- a/logs/log_cb836be0 +++ b/logs/log_cb836be0 @@ -27,10 +27,10 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 90.6M 90.6M - dense elkan 90.6M 90.5M - sparse lloyd 98.3M 98.3M - sparse elkan 98.3M 98.3M + dense lloyd 90.1M 90.1M + dense elkan 90.3M 90.2M + sparse lloyd 97.6M 97.6M + sparse elkan 97.6M 97.6M ================ =========== ======== =========== [ 2.61%] ··· cluster.KMeansBenchmark.peakmem_transform ok @@ -41,33 +41,33 @@ ================ =========== ======== =========== dense lloyd 121M 121M dense elkan 121M 121M - sparse lloyd 126M 126M - sparse elkan 126M 126M + sparse lloyd 125M 125M + sparse elkan 125M 125M ================ =========== ======== =========== [ 3.48%] ··· cluster.KMeansBenchmark.time_fit ok -[ 3.48%] ··· ================ =========== ============ =========== - -- init - ---------------------------- ------------------------ - representation algorithm random k-means++ - ================ =========== ============ =========== - dense lloyd 403±3ms 1.26±0s - dense elkan 2.21±0.02s 2.14±0.2s - sparse lloyd 1.96±0.2s 4.44±0.1s - sparse elkan 3.47±0.01s 5.37±0.1s - ================ =========== ============ =========== +[ 3.48%] ··· ================ =========== ============ ============ + -- init + ---------------------------- ------------------------- + representation algorithm random k-means++ + ================ =========== ============ ============ + dense lloyd 411±10ms 1.23±0.03s + dense elkan 2.22±0.01s 2.08±0.02s + sparse lloyd 1.71±0.1s 4.41±0.03s + sparse elkan 3.40±0.01s 5.29±0.07s + ================ =========== ============ ============ [ 4.35%] ··· cluster.KMeansBenchmark.time_predict ok -[ 4.35%] ··· ================ =========== ============= ============ - -- init - ---------------------------- -------------------------- - representation algorithm random k-means++ - ================ =========== ============= ============ - dense lloyd 5.42±0.4ms 5.32±0.2ms - dense elkan 5.32±0.09ms 5.24±0.2ms - sparse lloyd 15.8±6ms 27.1±6ms - sparse elkan 28.2±9ms 26.8±6ms - ================ =========== ============= ============ +[ 4.35%] ··· ================ =========== ============= ============= + -- init + ---------------------------- --------------------------- + representation algorithm random k-means++ + ================ =========== ============= ============= + dense lloyd 5.32±0.05ms 5.40±0.1ms + dense elkan 5.04±0.04ms 5.04±0.06ms + sparse lloyd 28.5±3ms 27.6±4ms + sparse elkan 27.4±4ms 22.3±6ms + ================ =========== ============= ============= [ 5.22%] ··· cluster.KMeansBenchmark.time_transform ok [ 5.22%] ··· ================ =========== ============ ============ @@ -75,10 +75,10 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 106±6ms 106±0.6ms - dense elkan 106±1ms 106±2ms - sparse lloyd 3.51±0.08s 3.44±0.04s - sparse elkan 3.47±0.06s 3.40±0.03s + dense lloyd 92.5±0.3ms 92.5±0.4ms + dense elkan 92.9±0.3ms 92.5±0.2ms + sparse lloyd 3.46±0s 3.80±0.04s + sparse elkan 3.76±0.03s 3.76±0.02s ================ =========== ============ ============ [ 6.09%] ··· cluster.KMeansBenchmark.track_test_score ok @@ -100,7 +100,7 @@ representation algorithm init ---------------- ----------- ----------- --------------------- dense lloyd random -4.1075520515441895 - dense lloyd k-means++ -3.0780563354492188 + dense lloyd k-means++ -3.0780560970306396 dense elkan random -4.1075520515441895 dense elkan k-means++ -3.0780560970306396 sparse lloyd random -0.9227071404457092 @@ -116,8 +116,8 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 91.9M 92.6M - sparse 175M 176M + dense 91.1M 91.9M + sparse 174M 175M ================ ======== =========== [ 8.70%] ··· ...ter.MiniBatchKMeansBenchmark.peakmem_predict ok @@ -126,7 +126,7 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 88.4M 89M + dense 88.2M 88.2M sparse 104M 104M ================ ======== =========== @@ -136,8 +136,8 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 120M 120M - sparse 125M 125M + dense 119M 119M + sparse 124M 124M ================ ======== =========== [10.43%] ··· cluster.MiniBatchKMeansBenchmark.time_fit ok @@ -146,19 +146,19 @@ ---------------- ----------------------- representation random k-means++ ================ ========== ============ - dense 503±20ms 491±20ms - sparse 630±20ms 1.65±0.05s + dense 484±30ms 484±20ms + sparse 627±20ms 1.63±0.01s ================ ========== ============ [11.30%] ··· cluster.MiniBatchKMeansBenchmark.time_predict ok -[11.30%] ··· ================ ============= ============= - -- init - ---------------- --------------------------- - representation random k-means++ - ================ ============= ============= - dense 5.35±0.06ms 5.44±0.06ms - sparse 31.0±3ms 33.0±8ms - ================ ============= ============= +[11.30%] ··· ================ ============ ============ + -- init + ---------------- ------------------------- + representation random k-means++ + ================ ============ ============ + dense 5.70±0.3ms 5.44±0.2ms + sparse 30.6±8ms 31.1±3ms + ================ ============ ============ [12.17%] ··· cluster.MiniBatchKMeansBenchmark.time_transform ok [12.17%] ··· ================ ============ ============ @@ -166,8 +166,8 @@ ---------------- ------------------------- representation random k-means++ ================ ============ ============ - dense 90.2±1ms 90.2±0.6ms - sparse 6.92±0.05s 6.95±0.1s + dense 102±1ms 104±6ms + sparse 7.64±0.03s 7.67±0.02s ================ ============ ============ [13.04%] ··· ...er.MiniBatchKMeansBenchmark.track_test_score ok @@ -198,7 +198,7 @@ fit_algorithm 1 4 =============== ====== ====== lars 110M 131M - cd 104M 131M + cd 103M 131M =============== ====== ====== [15.65%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok @@ -207,19 +207,19 @@ --------------- --------------- fit_algorithm 1 4 =============== ======= ======= - lars 85.9M 87.9M - cd 85.6M 87.9M + lars 85.3M 87.5M + cd 85.4M 87.5M =============== ======= ======= [16.52%] ··· ...osition.DictionaryLearningBenchmark.time_fit ok -[16.52%] ··· =============== ============ =========== - -- n_jobs - --------------- ------------------------ - fit_algorithm 1 4 - =============== ============ =========== - lars 17.8±0.05s 10.1±0.1s - cd 794±10ms 3.15±0.5s - =============== ============ =========== +[16.52%] ··· =============== =========== =========== + -- n_jobs + --------------- ----------------------- + fit_algorithm 1 4 + =============== =========== =========== + lars 17.1±0.4s 10.4±0.2s + cd 814±20ms 3.23±0.4s + =============== =========== =========== [17.39%] ··· ...n.DictionaryLearningBenchmark.time_transform ok [17.39%] ··· =============== =========== ========== @@ -227,8 +227,8 @@ --------------- ---------------------- fit_algorithm 1 4 =============== =========== ========== - lars 229±0.7ms 296±10ms - cd 214±0.8ms 296±10ms + lars 282±0.8ms 296±10ms + cd 239±2ms 307±20ms =============== =========== ========== [18.26%] ··· ...DictionaryLearningBenchmark.track_test_score ok @@ -258,8 +258,8 @@ --------------- -------------- fit_algorithm 1 4 =============== ======= ====== - lars 98.5M 102M - cd 98.2M 102M + lars 98.2M 103M + cd 98.1M 103M =============== ======= ====== [20.87%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok @@ -268,8 +268,8 @@ --------------- --------------- fit_algorithm 1 4 =============== ======= ======= - lars 86.8M 88.7M - cd 86.8M 88.7M + lars 86.6M 88.3M + cd 86.6M 88.3M =============== ======= ======= [20.87%] ···· For parameters: 'lars', 1 @@ -289,14 +289,14 @@ return func(*args, **kwargs) [21.74%] ··· ...iniBatchDictionaryLearningBenchmark.time_fit ok -[21.74%] ··· =============== ============ ============ - -- n_jobs - --------------- ------------------------- - fit_algorithm 1 4 - =============== ============ ============ - lars 1.20±0.01s 1.82±0.04s - cd 371±1ms 2.08±0.2s - =============== ============ ============ +[21.74%] ··· =============== =========== =========== + -- n_jobs + --------------- ----------------------- + fit_algorithm 1 4 + =============== =========== =========== + lars 1.23±0s 1.91±0.2s + cd 384±0.7ms 2.28±0.1s + =============== =========== =========== [22.61%] ··· ...chDictionaryLearningBenchmark.time_transform ok [22.61%] ··· =============== ========= ========== @@ -304,8 +304,8 @@ --------------- -------------------- fit_algorithm 1 4 =============== ========= ========== - lars 219±1ms 298±10ms - cd 219±1ms 289±10ms + lars 244±8ms 306±8ms + cd 246±1ms 306±10ms =============== ========= ========== [22.61%] ···· For parameters: 'lars', 1 @@ -517,8 +517,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:186: 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:186: 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:186: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. @@ -715,8 +713,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:186: 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:186: 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', 1 /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:186: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. @@ -1119,16 +1115,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/f6ff9b5b333fe13b488feffb65c69a2e/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:186: 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:186: 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:186: 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:186: 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:186: 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:186: 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%] ··· =============== ======== ====================== @@ -1175,9 +1161,9 @@ [25.22%] ··· ============ ====== svd_solver ------------ ------ - full 908M + full 907M arpack 606M - randomized 633M + randomized 632M ============ ====== [26.09%] ··· decomposition.PCABenchmark.peakmem_transform ok @@ -1193,19 +1179,19 @@ [26.96%] ··· ============ ============ svd_solver ------------ ------------ - full 2.49±0.04s - arpack 1.14±0.1s - randomized 1.10±0.02s + full 2.55±0.1s + arpack 1.11±0.03s + randomized 1.09±0.01s ============ ============ [27.83%] ··· decomposition.PCABenchmark.time_transform ok -[27.83%] ··· ============ =========== - svd_solver - ------------ ----------- - full 159±1ms - arpack 156±1ms - randomized 157±0.7ms - ============ =========== +[27.83%] ··· ============ ========= + svd_solver + ------------ --------- + full 160±1ms + arpack 157±2ms + randomized 156±2ms + ============ ========= [28.70%] ··· decomposition.PCABenchmark.track_test_score ok [28.70%] ··· ============ ==================== @@ -1230,39 +1216,39 @@ [30.43%] ··· ================ ======= representation ---------------- ------- - dense 93.3M - sparse 118M + dense 92.7M + sparse 117M ================ ======= [31.30%] ··· ...tBoostingClassifierBenchmark.peakmem_predict ok [31.30%] ··· ================ ======= representation ---------------- ------- - dense 89.4M - sparse 99M + dense 89.3M + sparse 98.9M ================ ======= [32.17%] ··· ...GradientBoostingClassifierBenchmark.time_fit ok [32.17%] ··· ================ ============ representation ---------------- ------------ - dense 2.76±0s - sparse 2.45±0.02s + dense 2.76±0.01s + sparse 2.45±0.01s ================ ============ [33.04%] ··· ...ientBoostingClassifierBenchmark.time_predict ok [33.04%] ··· ================ ============ representation ---------------- ------------ - dense 44.1±0.3ms - sparse 41.3±0.6ms + dense 47.9±0.7ms + sparse 44.6±1ms ================ ============ [33.91%] ··· ...BoostingClassifierBenchmark.track_test_score ok [33.91%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.532061601296477 + dense 0.5425714785409576 sparse 0.10409974329281042 ================ ===================== @@ -1270,15 +1256,15 @@ [34.78%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.6224363800368322 + dense 0.6342628904750912 sparse 0.15180008167538628 ================ ===================== [35.65%] ··· Setting up ensemble:103 ok [35.65%] ··· ...dientBoostingClassifierBenchmark.peakmem_fit 104M -[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 92.6M -[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.31±0.1s -[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 83.7±1ms +[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 92.3M +[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.26±0.05s +[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 83.9±0.3ms [39.13%] ··· ...BoostingClassifierBenchmark.track_test_score 0.7230709112942986 [40.00%] ··· ...oostingClassifierBenchmark.track_train_score 0.9812160155622751 [40.87%] ··· Setting up ensemble:24 ok @@ -1288,7 +1274,7 @@ ---------------- ------------- representation 1 4 ================ ====== ====== - dense 180M 180M + dense 180M 179M sparse 403M 403M ================ ====== ====== @@ -1308,8 +1294,8 @@ ---------------- ------------------------- representation 1 4 ================ ============ ============ - dense 8.06±0.5s 2.58±0.06s - sparse 11.4±0.06s 3.99±0.2s + dense 7.83±0.5s 2.59±0.09s + sparse 12.9±0.04s 3.86±0.1s ================ ============ ============ [43.48%] ··· ...RandomForestClassifierBenchmark.time_predict ok @@ -1318,8 +1304,8 @@ ---------------- ----------------------- representation 1 4 ================ ============ ========== - dense 184±2ms 132±5ms - sparse 2.19±0.01s 753±10ms + dense 181±0.7ms 133±5ms + sparse 2.02±0.01s 752±20ms ================ ============ ========== [44.35%] ··· ...omForestClassifierBenchmark.track_test_score ok @@ -1328,7 +1314,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.753119070614823 0.753119070614823 + dense 0.7450993477768667 0.7450993477768667 sparse 0.8656423941766682 0.8656423941766682 ================ ==================== ==================== @@ -1338,7 +1324,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.9973254513014052 0.9973254513014052 + dense 0.9971465639552678 0.9971465639552678 sparse 0.9996123288718864 0.9996123288718864 ================ ==================== ==================== @@ -1363,34 +1349,34 @@ representation True False ================ ======= ======= dense 489M 489M - sparse 97.2M n/a + sparse 96.7M 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 1.48±0s 1.81±0s - sparse 2.77±0.1s n/a - ================ =========== ========= +[47.83%] ··· ================ ============ ========= + -- precompute + ---------------- ---------------------- + representation True False + ================ ============ ========= + dense 1.49±0s 1.81±0s + sparse 2.92±0.02s 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 51.9±0.2ms 48.8±0.2ms - sparse 3.07±0.6ms n/a - ================ ============ ============ +[48.70%] ··· ================ ============= ============ + -- precompute + ---------------- -------------------------- + representation True False + ================ ============= ============ + dense 50.1±0.09ms 46.8±0.2ms + sparse 3.10±0.01ms n/a + ================ ============= ============ [48.70%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1402,7 +1388,7 @@ representation True False ================ ==================== ==================== dense 0.9274010856209145 0.9274010850953214 - sparse 0.9513490145041169 n/a + sparse 0.9500186293910091 n/a ================ ==================== ==================== [49.57%] ···· For parameters: 'sparse', False @@ -1415,7 +1401,7 @@ representation True False ================ ==================== ==================== dense 0.9276022550495941 0.9276022552325599 - sparse 0.9561656826629058 n/a + sparse 0.956361459914158 n/a ================ ==================== ==================== [50.43%] ···· For parameters: 'sparse', False @@ -1441,35 +1427,35 @@ ---------------- --------------- representation True False ================ ======= ======= - dense 489M 489M - sparse 97.3M n/a + dense 490M 489M + 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 1.82±0s - sparse 2.75±0s n/a - ================ ========= ========= +[53.04%] ··· ================ ============ ========= + -- precompute + ---------------- ---------------------- + representation True False + ================ ============ ========= + dense 1.48±0s 1.81±0s + sparse 2.42±0.05s 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 51.7±0.1ms 51.4±0.1ms - sparse 2.59±0.02ms n/a - ================ ============= ============ +[53.91%] ··· ================ ============ ============ + -- precompute + ---------------- ------------------------- + representation True False + ================ ============ ============ + dense 46.9±0.2ms 49.9±0.3ms + sparse 3.06±0.4ms n/a + ================ ============ ============ [53.91%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1481,7 +1467,7 @@ representation True False ================ ==================== ==================== dense 0.9274015024583205 0.9274015028138817 - sparse 0.9498696747957521 n/a + sparse 0.9485004015565667 n/a ================ ==================== ==================== [54.78%] ···· For parameters: 'sparse', False @@ -1494,7 +1480,7 @@ representation True False ================ ==================== ==================== dense 0.92760249197518 0.9276024919395177 - sparse 0.9537991312874283 n/a + sparse 0.9539083689393967 n/a ================ ==================== ==================== [55.65%] ···· For parameters: 'sparse', False @@ -1506,14 +1492,14 @@ representation ---------------- ------- dense 1.22G - sparse 237M + sparse 236M ================ ======= [57.39%] ··· ...el.LinearRegressionBenchmark.peakmem_predict ok [57.39%] ··· ================ ====== representation ---------------- ------ - dense 489M + dense 490M sparse 157M ================ ====== @@ -1521,34 +1507,34 @@ [58.26%] ··· ================ ============ representation ---------------- ------------ - dense 3.10±0.03s - sparse 1.12±0s + dense 3.02±0.01s + sparse 1.11±0s ================ ============ [59.13%] ··· ...model.LinearRegressionBenchmark.time_predict ok -[59.13%] ··· ================ ============= - representation - ---------------- ------------- - dense 50.5±0.2ms - sparse 34.2±0.09ms - ================ ============= +[59.13%] ··· ================ ============ + representation + ---------------- ------------ + dense 47.3±0.1ms + sparse 32.7±0.1ms + ================ ============ [60.00%] ··· ...l.LinearRegressionBenchmark.track_test_score ok -[60.00%] ··· ================ ==================== +[60.00%] ··· ================ ===================== + representation + ---------------- --------------------- + dense 0.9274012651798128 + sparse 0.09867472290109691 + ================ ===================== + +[60.87%] ··· ....LinearRegressionBenchmark.track_train_score ok +[60.87%] ··· ================ ==================== representation ---------------- -------------------- - dense 0.9274012651798128 - sparse 0.1116026504470633 + dense 0.927602494829764 + sparse 0.9999999999962731 ================ ==================== -[60.87%] ··· ....LinearRegressionBenchmark.track_train_score ok -[60.87%] ··· ================ =================== - representation - ---------------- ------------------- - dense 0.927602494829764 - sparse 0.999999999996288 - ================ =================== - [61.74%] ··· Setting up linear_model:28 ok [61.74%] ··· ...odel.LogisticRegressionBenchmark.peakmem_fit ok [61.74%] ··· ================ ======== ======= ======= @@ -1556,10 +1542,10 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 106M 99.4M - dense saga 84.1M 85.4M - sparse lbfgs 383M 126M - sparse saga 105M 105M + dense lbfgs 106M 99.1M + dense saga 83.7M 84.9M + sparse lbfgs 382M 125M + sparse saga 104M 105M ================ ======== ======= ======= [62.61%] ··· ....LogisticRegressionBenchmark.peakmem_predict ok @@ -1568,23 +1554,23 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 99.8M 99.7M - dense saga 86.5M 86.5M + dense lbfgs 99.3M 99.2M + dense saga 86.4M 86.3M sparse lbfgs 101M 101M - sparse saga 88.4M 88.6M + sparse saga 88.1M 88.1M ================ ======== ======= ======= [63.48%] ··· ...r_model.LogisticRegressionBenchmark.time_fit ok -[63.48%] ··· ================ ======== ============ ============ - -- n_jobs - ------------------------- ------------------------- - representation solver 1 4 - ================ ======== ============ ============ - dense lbfgs 22.9±2ms 190±2ms - dense saga 5.23±0.02s 5.96±0.01s - sparse lbfgs 1.03±0.01s 2.97±0.2s - sparse saga 3.88±0.02s 4.20±0.03s - ================ ======== ============ ============ +[63.48%] ··· ================ ======== ============ =========== + -- n_jobs + ------------------------- ------------------------ + representation solver 1 4 + ================ ======== ============ =========== + dense lbfgs 21.2±1ms 190±2ms + dense saga 4.31±0.3s 5.50±0.7s + sparse lbfgs 1.16±0.02s 3.03±0.2s + sparse saga 3.97±0.02s 3.64±0.3s + ================ ======== ============ =========== [64.35%] ··· ...del.LogisticRegressionBenchmark.time_predict ok [64.35%] ··· ================ ======== ============= ============= @@ -1592,20 +1578,20 @@ ------------------------- --------------------------- representation solver 1 4 ================ ======== ============= ============= - dense lbfgs 3.21±0.02ms 3.11±0.08ms - dense saga 1.97±0.07ms 1.97±0.07ms - sparse lbfgs 6.87±0.03ms 6.87±0.03ms - sparse saga 4.47±0.2ms 5.97±0.6ms + dense lbfgs 3.23±0.1ms 2.95±0.06ms + dense saga 1.86±0.02ms 1.92±0.02ms + sparse lbfgs 6.81±0.05ms 6.81±0.05ms + sparse saga 4.41±0.05ms 4.40±0.05ms ================ ======== ============= ============= [65.22%] ··· ...LogisticRegressionBenchmark.track_test_score ok [65.22%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17318286562109475 - dense lbfgs 4 0.17318286562109475 - dense saga 1 0.772841833051545 - dense saga 4 0.772841833051545 + dense lbfgs 1 0.17248308930992695 + dense lbfgs 4 0.17248308930992695 + dense saga 1 0.7727661671483629 + dense saga 4 0.7727661671483629 sparse lbfgs 1 0.06538461538461539 sparse lbfgs 4 0.06538461538461539 sparse saga 1 0.5765140080078162 @@ -1616,10 +1602,10 @@ [66.09%] ··· ================ ======== ======== ==================== representation solver n_jobs ---------------- -------- -------- -------------------- - dense lbfgs 1 0.1793018870206775 - dense lbfgs 4 0.1793018870206775 - dense saga 1 0.7956578069106479 - dense saga 4 0.7956578069106479 + dense lbfgs 1 0.1789125193422021 + dense lbfgs 4 0.1789125193422021 + dense saga 1 0.7994225582161085 + dense saga 4 0.7994225582161085 sparse lbfgs 1 0.0681998556998557 sparse lbfgs 4 0.0681998556998557 sparse saga 1 0.6908414295256007 @@ -1632,9 +1618,9 @@ representation solver ---------------- ----------- ------- dense auto 464M - dense svd 826M + dense svd 825M dense cholesky 464M - dense lsqr 473M + dense lsqr 472M dense sparse_cg 467M dense sag 473M dense saga 473M @@ -1654,19 +1640,19 @@ [67.83%] ··· ================ =========== ====== representation solver ---------------- ----------- ------ - dense auto 284M - dense svd 284M - dense cholesky 284M - dense lsqr 284M - dense sparse_cg 284M + dense auto 283M + dense svd 283M + dense cholesky 283M + dense lsqr 283M + dense sparse_cg 283M dense sag 283M - dense saga 284M + dense saga 283M sparse auto 119M sparse svd n/a sparse cholesky 119M sparse lsqr 119M sparse sparse_cg 119M - sparse sag 120M + sparse sag 119M sparse saga 119M ================ =========== ====== @@ -1677,20 +1663,20 @@ [68.70%] ··· ================ =========== ============ representation solver ---------------- ----------- ------------ - dense auto 206±2ms - dense svd 1.69±0.02s - dense cholesky 202±2ms - dense lsqr 213±10ms - dense sparse_cg 269±2ms - dense sag 27.9±0.7s - dense saga 14.0±0.05s - sparse auto 164±0.4ms + dense auto 211±1ms + dense svd 1.71±0.02s + dense cholesky 209±0.7ms + dense lsqr 221±3ms + dense sparse_cg 278±3ms + dense sag 31.4±0.3s + dense saga 12.4±0.02s + sparse auto 157±0.7ms sparse svd n/a - sparse cholesky 6.12±0.4s - sparse lsqr 134±0.7ms - sparse sparse_cg 152±0.6ms - sparse sag 2.80±0.02s - sparse saga 2.09±0.08s + sparse cholesky 5.70±0.03s + sparse lsqr 138±0.6ms + sparse sparse_cg 165±6ms + sparse sag 2.93±0.1s + sparse saga 2.14±0.04s ================ =========== ============ [68.70%] ···· For parameters: 'sparse', 'svd' @@ -1700,20 +1686,20 @@ [69.57%] ··· ================ =========== ============= representation solver ---------------- ----------- ------------- - dense auto 24.5±0.1ms - dense svd 24.4±0.09ms - dense cholesky 24.4±0.06ms - dense lsqr 25.5±0.07ms - dense sparse_cg 25.5±0.07ms - dense sag 24.7±0.4ms - dense saga 24.5±0.1ms - sparse auto 7.75±0.05ms + dense auto 24.6±0.4ms + dense svd 26.2±0.5ms + dense cholesky 26.2±0.1ms + dense lsqr 26.1±0.07ms + dense sparse_cg 26.3±0.2ms + dense sag 26.2±0.08ms + dense saga 26.2±0.3ms + sparse auto 7.82±0.06ms sparse svd n/a - sparse cholesky 7.16±1ms - sparse lsqr 7.00±0.07ms - sparse sparse_cg 6.99±0.04ms - sparse sag 8.21±0.7ms - sparse saga 7.51±1ms + sparse cholesky 7.59±0.2ms + sparse lsqr 7.81±0.08ms + sparse sparse_cg 7.83±0.04ms + sparse sag 7.83±0.04ms + sparse saga 8.24±0.7ms ================ =========== ============= [69.57%] ···· For parameters: 'sparse', 'svd' @@ -1730,13 +1716,13 @@ dense sparse_cg 0.9433995989989826 dense sag 0.94339933719428 dense saga 0.9433995886080997 - sparse auto 0.9552351956612838 + sparse auto 0.9559364983453127 sparse svd n/a - sparse cholesky 0.9552346320808599 - sparse lsqr 0.9552351980332413 - sparse sparse_cg 0.9552351956612838 - sparse sag 0.9552381406432416 - sparse saga 0.95523800136432 + sparse cholesky 0.9559363001009055 + sparse lsqr 0.9559364939537307 + sparse sparse_cg 0.9559364983453127 + sparse sag 0.9559403054081762 + sparse saga 0.9559410027834436 ================ =========== ==================== [70.43%] ···· For parameters: 'sparse', 'svd' @@ -1753,13 +1739,13 @@ dense sparse_cg 0.9444001571192623 dense sag 0.9444001419121766 dense saga 0.9444001543688754 - sparse auto 0.96598301907802 + sparse auto 0.9659603006930404 sparse svd n/a - sparse cholesky 0.965983021855513 - sparse lsqr 0.9659830190828992 - sparse sparse_cg 0.96598301907802 - sparse sag 0.9659794621973116 - sparse saga 0.9659794267267156 + sparse cholesky 0.9659603032918241 + sparse lsqr 0.9659603004966123 + sparse sparse_cg 0.9659603006930404 + sparse sag 0.9659567438859308 + sparse saga 0.9659567100162183 ================ =========== ==================== [71.30%] ···· For parameters: 'sparse', 'svd' @@ -1767,43 +1753,43 @@ [72.17%] ··· Setting up linear_model:151 ok [72.17%] ··· linear_model.SGDRegressorBenchmark.peakmem_fit ok -[72.17%] ··· ================ ======= - representation - ---------------- ------- - dense 160M - sparse 89.3M - ================ ======= +[72.17%] ··· ================ ====== + representation + ---------------- ------ + dense 160M + sparse 88M + ================ ====== [73.04%] ··· ..._model.SGDRegressorBenchmark.peakmem_predict ok [73.04%] ··· ================ ======= representation ---------------- ------- dense 159M - sparse 86.7M + sparse 86.5M ================ ======= [73.91%] ··· linear_model.SGDRegressorBenchmark.time_fit ok [73.91%] ··· ================ ============ representation ---------------- ------------ - dense 5.83±0s - sparse 4.82±0.02s + dense 5.39±0.01s + sparse 4.93±0.01s ================ ============ [74.78%] ··· linear_model.SGDRegressorBenchmark.time_predict ok -[74.78%] ··· ================ ============= - representation - ---------------- ------------- - dense 11.1±0.07ms - sparse 2.16±0.01ms - ================ ============= +[74.78%] ··· ================ ============ + representation + ---------------- ------------ + dense 10.9±0.1ms + sparse 2.93±0.4ms + ================ ============ [75.65%] ··· ...model.SGDRegressorBenchmark.track_test_score ok [75.65%] ··· ================ ==================== representation ---------------- -------------------- dense 0.9636293915848902 - sparse 0.9616150599927415 + sparse 0.9614971673099436 ================ ==================== [76.52%] ··· ...odel.SGDRegressorBenchmark.track_train_score ok @@ -1811,7 +1797,7 @@ representation ---------------- -------------------- dense 0.9641785427097553 - sparse 0.9622476543527854 + sparse 0.9621322913914441 ================ ==================== [77.39%] ··· Setting up manifold:15 ok @@ -1819,16 +1805,16 @@ [77.39%] ··· ============ ======= method ------------ ------- - exact 91M - barnes_hut 97.4M + exact 89.9M + barnes_hut 97.8M ============ ======= [78.26%] ··· manifold.TSNEBenchmark.time_fit ok [78.26%] ··· ============ ============ method ------------ ------------ - exact 6.56±0.03s - barnes_hut 3.38±0.2s + exact 6.52±0.01s + barnes_hut 3.11±0.03s ============ ============ [79.13%] ··· manifold.TSNEBenchmark.track_test_score ok @@ -1836,7 +1822,7 @@ method ------------ -------------------- exact 0.3218818006120378 - barnes_hut 0.7243015170097351 + barnes_hut 0.7243015766143799 ============ ==================== [80.00%] ··· manifold.TSNEBenchmark.track_train_score ok @@ -1844,7 +1830,7 @@ method ------------ -------------------- exact 0.3218818006120378 - barnes_hut 0.7243015170097351 + barnes_hut 0.7243015766143799 ============ ==================== [80.87%] ··· ...istancesBenchmark.peakmem_pairwise_distances ok @@ -1853,13 +1839,13 @@ ------------------------------ --------------- representation metric 1 4 ================ ============= ======= ======= - dense cosine 669M 786M - dense euclidean 752M 1.06G - dense manhattan 255M 340M - dense correlation 248M 485M + dense cosine 669M 785M + dense euclidean 752M 930M + dense manhattan 255M 338M + dense correlation 248M 484M sparse cosine 1.42G 1.41G - sparse euclidean 570M 783M - sparse manhattan 187M 237M + sparse euclidean 570M 893M + sparse manhattan 187M 232M sparse correlation n/a n/a ================ ============= ======= ======= @@ -1885,7 +1871,7 @@ ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._random_dataset... _random_dataset(n_samples=4000, representation='sparse') - ___________________________________________________random_dataset - 0.3s, 0.0min + ___________________________________________________random_dataset - 0.1s, 0.0min For parameters: 'sparse', 'correlation', 1 asv: skipped: NotImplementedError() @@ -1899,13 +1885,13 @@ ------------------------------ ------------------------- representation metric 1 4 ================ ============= ============ ============ - dense cosine 1.11±0.01s 1.28±0.05s - dense euclidean 1.70±0.01s 3.09±0.06s - dense manhattan 6.37±0.04s 2.72±0.1s - dense correlation 3.37±0.02s 2.50±0.2s - sparse cosine 3.68±0s 2.60±0.07s - sparse euclidean 2.56±0s 2.12±0.04s - sparse manhattan 1.22±0.01s 1.28±0.02s + dense cosine 1.11±0.01s 1.24±0.03s + dense euclidean 1.72±0s 3.08±0.06s + dense manhattan 7.38±0.1s 2.65±0.07s + dense correlation 3.27±0.01s 2.52±0.03s + sparse cosine 3.72±0.1s 2.61±0.2s + sparse euclidean 2.56±0.02s 2.13±0.08s + sparse manhattan 1.23±0.01s 1.32±0.01s sparse correlation n/a n/a ================ ============= ============ ============ @@ -1919,7 +1905,7 @@ [82.61%] ··· ======== ====== n_jobs -------- ------ - 1 219M + 1 218M 4 119M ======== ====== @@ -1927,14 +1913,14 @@ ________________________________________________________________________________ [Memory] Calling benchmarks.datasets._synth_classification_dataset... _synth_classification_dataset(n_samples=50000, n_features=100) - _____________________________________synth_classification_dataset - 0.4s, 0.0min + _____________________________________synth_classification_dataset - 0.6s, 0.0min [83.48%] ··· ...ction.CrossValidationBenchmark.time_crossval ok [83.48%] ··· ======== ============ n_jobs -------- ------------ - 1 56.0±0.07s - 4 16.7±0.1s + 1 1.07±0.01m + 4 17.2±0.07s ======== ============ [84.35%] ··· ...tion.CrossValidationBenchmark.track_crossval ok @@ -1950,33 +1936,33 @@ [85.22%] ··· ======== ======= n_jobs -------- ------- - 1 96.2M - 4 93.5M + 1 95.8M + 4 93.3M ======== ======= [86.09%] ··· ...election.GridSearchBenchmark.peakmem_predict ok [86.09%] ··· ======== ======= n_jobs -------- ------- - 1 88.2M - 4 88.2M + 1 88.1M + 4 88.1M ======== ======= [86.96%] ··· model_selection.GridSearchBenchmark.time_fit ok [86.96%] ··· ======== ============ n_jobs -------- ------------ - 1 5.80±0.05m - 4 1.70±0.01m + 1 5.68±0.01m + 4 1.69±0.01m ======== ============ [87.83%] ··· ...l_selection.GridSearchBenchmark.time_predict ok -[87.83%] ··· ======== ============= - n_jobs - -------- ------------- - 1 43.1±3ms - 4 43.1±0.09ms - ======== ============= +[87.83%] ··· ======== ============ + n_jobs + -------- ------------ + 1 43.0±2ms + 4 42.9±0.3ms + ======== ============ [88.70%] ··· ...lection.GridSearchBenchmark.track_test_score ok [88.70%] ··· ======== ==================== @@ -2001,9 +1987,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 77.8M 77.8M 81.4M 81.3M - kd_tree 80.9M 80.6M 89M 89M - ball_tree 80.6M 80.5M 88.8M 88.8M + brute 77.6M 77.8M 81.1M 81.1M + kd_tree 80.2M 80.1M 88.6M 88.6M + ball_tree 80.1M 80.1M 88.5M 88.5M =========== ========= ========= ========== ========== [91.30%] ··· ...NeighborsClassifierBenchmark.peakmem_predict ok @@ -2012,9 +1998,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 88.8M 88.8M 93.7M 93.7M - kd_tree 82.2M 84.6M 91.9M 91.9M - ball_tree 81.9M 84.5M 91.5M 91.5M + brute 88.4M 88.3M 93M 93.1M + kd_tree 81.7M 84.4M 91.5M 91.4M + ball_tree 81.5M 84.3M 91M 90.9M =========== ========= ========= ========== ========== [92.17%] ··· ...hbors.KNeighborsClassifierBenchmark.time_fit ok @@ -2023,12 +2009,12 @@ ----------------------- --------------------------- algorithm dimension 1 4 =========== =========== ============= ============= - brute low 1.48±0.2ms 1.04±0ms - brute high 1.20±0.01ms 1.20±0.01ms - kd_tree low 17.3±2ms 17.1±0.03ms - kd_tree high 50.8±0.2ms 50.8±0.2ms - ball_tree low 8.71±0.3ms 8.73±0.04ms - ball_tree high 33.0±0.05ms 33.0±0.07ms + brute low 1.06±0.2ms 1.05±0ms + brute high 1.74±0.03ms 1.27±0.2ms + kd_tree low 12.0±0.06ms 12.8±0.6ms + kd_tree high 59.0±2ms 56.4±3ms + ball_tree low 8.76±0.07ms 8.75±0.07ms + ball_tree high 33.9±0.2ms 34.0±0.3ms =========== =========== ============= ============= [93.04%] ··· ...s.KNeighborsClassifierBenchmark.time_predict ok @@ -2037,48 +2023,48 @@ ----------------------- ------------------------- algorithm dimension 1 4 =========== =========== ============ ============ - brute low 90.7±0.1ms 88.4±0.7ms - brute high 124±0.3ms 127±0.6ms - kd_tree low 1.48±0.01s 2.91±0.09s - kd_tree high 8.35±0.04s 8.26±0.3s - ball_tree low 2.46±0.02s 5.66±0.1s - ball_tree high 7.59±0.04s 12.0±0.8s + brute low 91.1±0.4ms 88.7±0.3ms + brute high 130±0.4ms 129±0.4ms + kd_tree low 1.41±0.04s 2.82±0.2s + kd_tree high 9.38±0.09s 8.29±0.3s + ball_tree low 2.44±0.3s 4.84±0.8s + ball_tree high 7.45±0.6s 11.1±0.5s =========== =========== ============ ============ [93.91%] ··· ...eighborsClassifierBenchmark.track_test_score ok -[93.91%] ··· =========== =========== ======== ===================== - algorithm dimension n_jobs - ----------- ----------- -------- --------------------- - brute low 1 0.45073697155004055 - brute low 4 0.45073697155004055 - brute high 1 0.6695426194967049 - brute high 4 0.6695426194967049 - kd_tree low 1 0.45073697155004055 - kd_tree low 4 0.45073697155004055 - kd_tree high 1 0.6695426194967049 - kd_tree high 4 0.6695426194967049 - ball_tree low 1 0.45073697155004055 - ball_tree low 4 0.45073697155004055 - ball_tree high 1 0.6695426194967049 - ball_tree high 4 0.6695426194967049 - =========== =========== ======== ===================== +[93.91%] ··· =========== =========== ======== ==================== + algorithm dimension n_jobs + ----------- ----------- -------- -------------------- + brute low 1 0.4429571912829343 + brute low 4 0.4429571912829343 + brute high 1 0.6740298313758913 + brute high 4 0.6740298313758913 + kd_tree low 1 0.4429571912829343 + kd_tree low 4 0.4429571912829343 + kd_tree high 1 0.6740298313758913 + kd_tree high 4 0.6740298313758913 + ball_tree low 1 0.4429571912829343 + ball_tree low 4 0.4429571912829343 + ball_tree high 1 0.6740298313758913 + ball_tree high 4 0.6740298313758913 + =========== =========== ======== ==================== [94.78%] ··· ...ighborsClassifierBenchmark.track_train_score ok [94.78%] ··· =========== =========== ======== ==================== algorithm dimension n_jobs ----------- ----------- -------- -------------------- - brute low 1 0.6379314438555614 - brute low 4 0.6379314438555614 - brute high 1 0.7954821505242015 - brute high 4 0.7954821505242015 - kd_tree low 1 0.6379314438555614 - kd_tree low 4 0.6379314438555614 - kd_tree high 1 0.7954821505242015 - kd_tree high 4 0.7954821505242015 - ball_tree low 1 0.6379314438555614 - ball_tree low 4 0.6379314438555614 - ball_tree high 1 0.7954821505242015 - ball_tree high 4 0.7954821505242015 + brute low 1 0.6385128876659937 + brute low 4 0.6385128876659937 + brute high 1 0.7891633631498848 + brute high 4 0.7891633631498848 + kd_tree low 1 0.6385128876659937 + kd_tree low 4 0.6385128876659937 + kd_tree high 1 0.7891633631498848 + kd_tree high 4 0.7891633631498848 + ball_tree low 1 0.6385128876659937 + ball_tree low 4 0.6385128876659937 + ball_tree high 1 0.7891633631498848 + ball_tree high 4 0.7891633631498848 =========== =========== ======== ==================== [95.65%] ··· Setting up svm:14 ok @@ -2113,19 +2099,19 @@ kernel --------- ------ linear 204M - poly 205M - rbf 205M - sigmoid 205M + poly 204M + rbf 204M + sigmoid 204M ========= ====== [97.39%] ··· svm.SVCBenchmark.time_fit ok [97.39%] ··· ========= ========= kernel --------- --------- - linear 1.71±0s - poly 1.69±0s - rbf 1.71±0s - sigmoid 1.70±0s + linear 1.76±0s + poly 1.76±0s + rbf 1.77±0s + sigmoid 1.77±0s ========= ========= [97.39%] ···· For parameters: 'linear' @@ -2163,8 +2149,6 @@ 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: 'poly' /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. @@ -2284,10 +2268,10 @@ [98.26%] ··· ========= ============ kernel --------- ------------ - linear 760±50ms - poly 798±4ms - rbf 1.93±0.03s - sigmoid 728±5ms + linear 676±4ms + poly 627±2ms + rbf 1.75±0.02s + sigmoid 633±2ms ========= ============ [99.13%] ··· svm.SVCBenchmark.track_test_score ok diff --git a/results/sklearn-benchmark/cb836be0-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/cb836be0-conda-py3.11-cython3.0.3-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json index 3415f94964..5897c0ffbb 100644 --- a/results/sklearn-benchmark/cb836be0-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/cb836be0-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": 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