diff --git a/logs/log_4e825370 b/logs/log_4e825370 index a535e5233f..600c811c2b 100644 --- a/logs/log_4e825370 +++ b/logs/log_4e825370 @@ -15,9 +15,9 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 104M 115M + dense lloyd 105M 115M dense elkan 139M 139M - sparse lloyd 256M 255M + sparse lloyd 256M 256M sparse elkan 263M 262M ================ =========== ======== =========== @@ -27,10 +27,10 @@ ---------------------------- -------------------- representation algorithm random k-means++ ================ =========== ======== =========== - dense lloyd 91.7M 90.9M - dense elkan 91.6M 91.7M - sparse lloyd 98.9M 98.9M - sparse elkan 98.9M 98.9M + dense lloyd 90.8M 91M + dense elkan 91M 91M + sparse lloyd 98.8M 98.8M + sparse elkan 98.8M 98.8M ================ =========== ======== =========== [ 2.61%] ··· cluster.KMeansBenchmark.peakmem_transform ok @@ -51,35 +51,35 @@ ---------------------------- ------------------------- representation algorithm random k-means++ ================ =========== ============ ============ - dense lloyd 410±50ms 1.29±0.05s - dense elkan 2.14±0.04s 2.12±0.03s - sparse lloyd 1.65±0.07s 4.39±0.05s - sparse elkan 3.44±0.03s 5.10±0.02s + dense lloyd 397±9ms 1.23±0.01s + dense elkan 2.29±0.01s 2.06±0.02s + sparse lloyd 1.57±0.05s 4.26±0.07s + sparse elkan 3.42±0.09s 5.30±0.05s ================ =========== ============ ============ [ 4.35%] ··· cluster.KMeansBenchmark.time_predict ok -[ 4.35%] ··· ================ =========== ============ ============= - -- init - ---------------------------- -------------------------- - representation algorithm random k-means++ - ================ =========== ============ ============= - dense lloyd 7.04±1ms 6.42±1ms - dense elkan 5.39±0.1ms 5.14±0.07ms - sparse lloyd 27.0±4ms 28.2±6ms - sparse elkan 19.6±7ms 26.6±4ms - ================ =========== ============ ============= +[ 4.35%] ··· ================ =========== ============ ============ + -- init + ---------------------------- ------------------------- + representation algorithm random k-means++ + ================ =========== ============ ============ + dense lloyd 5.69±1ms 5.23±0.1ms + dense elkan 5.20±0.1ms 5.49±0.2ms + sparse lloyd 28.9±9ms 28.4±9ms + sparse elkan 17.9±4ms 28.1±8ms + ================ =========== ============ ============ [ 5.22%] ··· cluster.KMeansBenchmark.time_transform ok -[ 5.22%] ··· ================ =========== =========== ============ - -- init - ---------------------------- ------------------------ - representation algorithm random k-means++ - ================ =========== =========== ============ - dense lloyd 97.0±9ms 95.7±0.6ms - dense elkan 98.6±20ms 96.2±1ms - sparse lloyd 3.69±0.4s 3.52±0.02s - sparse elkan 3.81±0.4s 3.34±0.03s - ================ =========== =========== ============ +[ 5.22%] ··· ================ =========== ============ ============ + -- init + ---------------------------- ------------------------- + representation algorithm random k-means++ + ================ =========== ============ ============ + dense lloyd 87.8±1ms 87.3±0.7ms + dense elkan 87.4±4ms 86.1±0.6ms + sparse lloyd 3.64±0.05s 3.83±0.05s + sparse elkan 3.82±0.2s 3.36±0.06s + ================ =========== ============ ============ [ 6.09%] ··· cluster.KMeansBenchmark.track_test_score ok [ 6.09%] ··· ================ =========== =========== ===================== @@ -87,7 +87,7 @@ ---------------- ----------- ----------- --------------------- dense lloyd random -4.109886169433594 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++ -0.9249262809753418 @@ -116,8 +116,8 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 92.4M 93.2M - sparse 175M 177M + dense 92.7M 93M + sparse 176M 177M ================ ======== =========== [ 8.70%] ··· ...ter.MiniBatchKMeansBenchmark.peakmem_predict ok @@ -126,7 +126,7 @@ ---------------- -------------------- representation random k-means++ ================ ======== =========== - dense 89.6M 89.6M + dense 89M 89M sparse 105M 105M ================ ======== =========== @@ -146,8 +146,8 @@ ---------------- ----------------------- representation random k-means++ ================ ========== ============ - dense 493±40ms 485±30ms - sparse 626±50ms 1.62±0.03s + dense 475±20ms 490±20ms + sparse 629±40ms 1.68±0.02s ================ ========== ============ [11.30%] ··· cluster.MiniBatchKMeansBenchmark.time_predict ok @@ -156,19 +156,19 @@ ---------------- ------------------------- representation random k-means++ ================ ============ ============ - dense 7.73±0.5ms 5.52±0.2ms - sparse 37.7±8ms 37.1±4ms + dense 5.39±0.1ms 5.42±0.2ms + sparse 36.1±2ms 36.5±2ms ================ ============ ============ [12.17%] ··· cluster.MiniBatchKMeansBenchmark.time_transform ok -[12.17%] ··· ================ =========== ============ - -- init - ---------------- ------------------------ - representation random k-means++ - ================ =========== ============ - dense 94.6±1ms 93.8±5ms - sparse 7.17±0.4s 6.75±0.06s - ================ =========== ============ +[12.17%] ··· ================ ============ ============ + -- init + ---------------- ------------------------- + representation random k-means++ + ================ ============ ============ + dense 95.7±0.9ms 96.0±1ms + sparse 7.57±0.03s 7.54±0.05s + ================ ============ ============ [13.04%] ··· ...er.MiniBatchKMeansBenchmark.track_test_score ok [13.04%] ··· ================ =========== ===================== @@ -197,8 +197,8 @@ --------------- ------------- fit_algorithm 1 4 =============== ====== ====== - lars 111M 132M - cd 104M 131M + lars 111M 131M + cd 104M 132M =============== ====== ====== [15.65%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok @@ -207,19 +207,19 @@ --------------- --------------- fit_algorithm 1 4 =============== ======= ======= - lars 86.3M 88.6M - cd 86.3M 88.6M + lars 86.2M 88.6M + cd 86.2M 88.6M =============== ======= ======= [16.52%] ··· ...osition.DictionaryLearningBenchmark.time_fit ok -[16.52%] ··· =============== =========== =========== - -- n_jobs - --------------- ----------------------- - fit_algorithm 1 4 - =============== =========== =========== - lars 17.7±0.8s 10.9±0.2s - cd 767±8ms 3.09±0.5s - =============== =========== =========== +[16.52%] ··· =============== =========== ============ + -- n_jobs + --------------- ------------------------ + fit_algorithm 1 4 + =============== =========== ============ + lars 16.0±0.1s 10.8±0.4s + cd 764±10ms 3.37±0.06s + =============== =========== ============ [17.39%] ··· ...n.DictionaryLearningBenchmark.time_transform ok [17.39%] ··· =============== ========= ========== @@ -227,8 +227,8 @@ --------------- -------------------- fit_algorithm 1 4 =============== ========= ========== - lars 234±1ms 297±10ms - cd 280±1ms 310±10ms + lars 231±9ms 297±20ms + cd 224±1ms 301±10ms =============== ========= ========== [18.26%] ··· ...DictionaryLearningBenchmark.track_test_score ok @@ -258,8 +258,8 @@ --------------- -------------- fit_algorithm 1 4 =============== ======= ====== - lars 99.3M 103M - cd 98.6M 103M + lars 99.6M 103M + cd 99M 104M =============== ======= ====== [20.87%] ··· ...ictionaryLearningBenchmark.peakmem_transform ok @@ -268,8 +268,8 @@ --------------- --------------- fit_algorithm 1 4 =============== ======= ======= - lars 87.4M 89.3M - cd 87.4M 89.3M + lars 87.4M 89.4M + cd 87.4M 89.4M =============== ======= ======= [20.87%] ···· For parameters: 'lars', 1 @@ -294,19 +294,19 @@ --------------- ------------------------ fit_algorithm 1 4 =============== ============ =========== - lars 1.18±0.08s 2.17±0.2s - cd 373±1ms 1.75±0.1s + lars 1.21±0.09s 2.02±0.1s + cd 384±0.6ms 1.98±0.2s =============== ============ =========== [22.61%] ··· ...chDictionaryLearningBenchmark.time_transform ok -[22.61%] ··· =============== ========= ========== - -- n_jobs - --------------- -------------------- - fit_algorithm 1 4 - =============== ========= ========== - lars 236±3ms 293±10ms - cd 233±3ms 297±10ms - =============== ========= ========== +[22.61%] ··· =============== =========== ========== + -- n_jobs + --------------- ---------------------- + fit_algorithm 1 4 + =============== =========== ========== + lars 233±0.8ms 299±10ms + cd 269±8ms 303±9ms + =============== =========== ========== [22.61%] ···· For parameters: 'lars', 1 /home/ubuntu/scikit-learn/asv_benchmarks/env/bc2fc3e467e9bf6ae53344bc521e29ad/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. @@ -717,10 +717,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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. @@ -927,8 +923,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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', 4 /home/ubuntu/scikit-learn/asv_benchmarks/env/bc2fc3e467e9bf6ae53344bc521e29ad/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. @@ -1125,12 +1119,6 @@ return func(*args, **kwargs) /home/ubuntu/scikit-learn/asv_benchmarks/env/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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%] ··· =============== ======== ====================== @@ -1177,9 +1165,9 @@ [25.22%] ··· ============ ====== svd_solver ------------ ------ - full 909M + full 908M arpack 606M - randomized 634M + randomized 623M ============ ====== [26.09%] ··· decomposition.PCABenchmark.peakmem_transform ok @@ -1195,19 +1183,19 @@ [26.96%] ··· ============ ============ svd_solver ------------ ------------ - full 2.85±0.2s - arpack 1.47±0.1s - randomized 1.09±0.03s + full 2.51±0.1s + arpack 1.41±0.06s + randomized 1.14±0.01s ============ ============ [27.83%] ··· decomposition.PCABenchmark.time_transform ok -[27.83%] ··· ============ ========= - svd_solver - ------------ --------- - full 156±2ms - arpack 153±2ms - randomized 159±4ms - ============ ========= +[27.83%] ··· ============ ========== + svd_solver + ------------ ---------- + full 150±2ms + arpack 154±5ms + randomized 161±10ms + ============ ========== [28.70%] ··· decomposition.PCABenchmark.track_test_score ok [28.70%] ··· ============ ==================== @@ -1229,18 +1217,18 @@ [30.43%] ··· Setting up ensemble:64 ok [30.43%] ··· ...dientBoostingClassifierBenchmark.peakmem_fit ok -[30.43%] ··· ================ ======= - representation - ---------------- ------- - dense 94.2M - sparse 119M - ================ ======= +[30.43%] ··· ================ ====== + representation + ---------------- ------ + dense 95M + sparse 119M + ================ ====== [31.30%] ··· ...tBoostingClassifierBenchmark.peakmem_predict ok [31.30%] ··· ================ ======= representation ---------------- ------- - dense 90.3M + dense 90.8M sparse 100M ================ ======= @@ -1248,23 +1236,23 @@ [32.17%] ··· ================ ============ representation ---------------- ------------ - dense 2.74±0.5s - sparse 2.14±0.01s + dense 2.87±0.2s + sparse 2.35±0.02s ================ ============ [33.04%] ··· ...ientBoostingClassifierBenchmark.time_predict ok [33.04%] ··· ================ ============ representation ---------------- ------------ - dense 49.9±0.6ms - sparse 45.8±0.9ms + dense 47.9±0.2ms + sparse 44.4±0.9ms ================ ============ [33.91%] ··· ...BoostingClassifierBenchmark.track_test_score ok [33.91%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.5582241962799195 + dense 0.5499945533259637 sparse 0.10409974329281042 ================ ===================== @@ -1272,15 +1260,15 @@ [34.78%] ··· ================ ===================== representation ---------------- --------------------- - dense 0.6284291544954166 + dense 0.6177639293498621 sparse 0.15180008167538628 ================ ===================== [35.65%] ··· Setting up ensemble:103 ok -[35.65%] ··· ...dientBoostingClassifierBenchmark.peakmem_fit 105M -[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 93.3M -[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.26±0.02s -[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 90.3±8ms +[35.65%] ··· ...dientBoostingClassifierBenchmark.peakmem_fit 104M +[36.52%] ··· ...tBoostingClassifierBenchmark.peakmem_predict 92.8M +[37.39%] ··· ...GradientBoostingClassifierBenchmark.time_fit 2.19±0.05s +[38.26%] ··· ...ientBoostingClassifierBenchmark.time_predict 89.0±0.5ms [39.13%] ··· ...BoostingClassifierBenchmark.track_test_score 0.7230709112942986 [40.00%] ··· ...oostingClassifierBenchmark.track_train_score 0.9812160155622751 [40.87%] ··· Setting up ensemble:24 ok @@ -1305,32 +1293,32 @@ ================ ====== ====== [42.61%] ··· ...ble.RandomForestClassifierBenchmark.time_fit ok -[42.61%] ··· ================ ============ ============ - -- n_jobs - ---------------- ------------------------- - representation 1 4 - ================ ============ ============ - dense 7.78±0.01s 2.71±0.09s - sparse 11.6±0.4s 4.09±0.2s - ================ ============ ============ - -[43.48%] ··· ...RandomForestClassifierBenchmark.time_predict ok -[43.48%] ··· ================ =========== =========== +[42.61%] ··· ================ =========== =========== -- n_jobs ---------------- ----------------------- representation 1 4 ================ =========== =========== - dense 167±0.6ms 124±0.5ms - sparse 2.08±0.2s 748±30ms + dense 8.27±0.4s 2.60±0.1s + sparse 13.9±0.3s 4.03±0.2s ================ =========== =========== +[43.48%] ··· ...RandomForestClassifierBenchmark.time_predict ok +[43.48%] ··· ================ ============ ========== + -- n_jobs + ---------------- ----------------------- + representation 1 4 + ================ ============ ========== + dense 168±8ms 124±5ms + sparse 2.18±0.01s 793±40ms + ================ ============ ========== + [44.35%] ··· ...omForestClassifierBenchmark.track_test_score ok [44.35%] ··· ================ ==================== ==================== -- n_jobs ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.746480158593025 0.746480158593025 + dense 0.7457286575999849 0.7457286575999849 sparse 0.8656423941766682 0.8656423941766682 ================ ==================== ==================== @@ -1340,7 +1328,7 @@ ---------------- ----------------------------------------- representation 1 4 ================ ==================== ==================== - dense 0.9971919081758355 0.9971919081758355 + dense 0.9967706435806469 0.9967706435806469 sparse 0.9996123288718864 0.9996123288718864 ================ ==================== ==================== @@ -1359,41 +1347,41 @@ asv: skipped: NotImplementedError() [46.96%] ··· ...ar_model.ElasticNetBenchmark.peakmem_predict ok -[46.96%] ··· ================ ======= ======= - -- precompute - ---------------- --------------- - representation True False - ================ ======= ======= - dense 490M 490M - sparse 97.7M n/a - ================ ======= ======= +[46.96%] ··· ================ ====== ======= + -- precompute + ---------------- -------------- + representation True False + ================ ====== ======= + dense 490M 490M + sparse 98M 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±0.05s 1.84±0.1s - sparse 2.99±0.01s n/a - ================ ============ =========== - -[47.83%] ···· For parameters: 'sparse', False - asv: skipped: NotImplementedError() - -[48.70%] ··· linear_model.ElasticNetBenchmark.time_predict ok -[48.70%] ··· ================ ============ ============ +[47.83%] ··· ================ ============ ============ -- precompute ---------------- ------------------------- representation True False ================ ============ ============ - dense 50.6±0.6ms 50.3±0.4ms - sparse 3.08±0.2ms n/a + dense 1.47±0.02s 1.83±0.04s + sparse 3.03±0.01s 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 50.2±1ms 48.9±2ms + sparse 3.06±0.3ms n/a + ================ ============ ========== + [48.70%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1404,7 +1392,7 @@ representation True False ================ ==================== ==================== dense 0.9274010856209145 0.9274010850953214 - sparse 0.9484881540213588 n/a + sparse 0.9488594663420415 n/a ================ ==================== ==================== [49.57%] ···· For parameters: 'sparse', False @@ -1417,7 +1405,7 @@ representation True False ================ ==================== ==================== dense 0.9276022550495941 0.9276022552325599 - sparse 0.9562336351855395 n/a + sparse 0.9563408923277211 n/a ================ ==================== ==================== [50.43%] ···· For parameters: 'sparse', False @@ -1438,14 +1426,14 @@ asv: skipped: NotImplementedError() [52.17%] ··· linear_model.LassoBenchmark.peakmem_predict ok -[52.17%] ··· ================ ======= ======= - -- precompute - ---------------- --------------- - representation True False - ================ ======= ======= - dense 490M 490M - sparse 97.7M n/a - ================ ======= ======= +[52.17%] ··· ================ ====== ======= + -- precompute + ---------------- -------------- + representation True False + ================ ====== ======= + dense 490M 490M + sparse 98M n/a + ================ ====== ======= [52.17%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1456,22 +1444,22 @@ ---------------- ------------------------- representation True False ================ ============ ============ - dense 1.47±0.01s 1.81±0.01s - sparse 2.75±0.02s n/a + dense 1.42±0.02s 1.87±0.02s + sparse 2.41±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 50.7±1ms 50.1±0.6ms - sparse 3.05±0.01ms n/a - ================ ============= ============ +[53.91%] ··· ================ ============ ============ + -- precompute + ---------------- ------------------------- + representation True False + ================ ============ ============ + dense 50.2±0.4ms 50.2±0.3ms + sparse 3.12±0.4ms n/a + ================ ============ ============ [53.91%] ···· For parameters: 'sparse', False asv: skipped: NotImplementedError() @@ -1483,7 +1471,7 @@ representation True False ================ ==================== ==================== dense 0.9274015024583205 0.9274015028138817 - sparse 0.9471504588144197 n/a + sparse 0.9476374372956523 n/a ================ ==================== ==================== [54.78%] ···· For parameters: 'sparse', False @@ -1496,7 +1484,7 @@ representation True False ================ ==================== ==================== dense 0.92760249197518 0.9276024919395177 - sparse 0.9537773482178159 n/a + sparse 0.9539277457175132 n/a ================ ==================== ==================== [55.65%] ···· For parameters: 'sparse', False @@ -1516,23 +1504,23 @@ representation ---------------- ------ dense 490M - sparse 158M + sparse 157M ================ ====== [58.26%] ··· linear_model.LinearRegressionBenchmark.time_fit ok -[58.26%] ··· ================ ============ - representation - ---------------- ------------ - dense 3.67±0.2s - sparse 1.13±0.02s - ================ ============ +[58.26%] ··· ================ =========== + representation + ---------------- ----------- + dense 3.56±0.1s + sparse 1.11±0s + ================ =========== [59.13%] ··· ...model.LinearRegressionBenchmark.time_predict ok [59.13%] ··· ================ ============ representation ---------------- ------------ - dense 48.5±0.7ms - sparse 32.7±0.4ms + dense 50.3±0.3ms + sparse 31.1±0.7ms ================ ============ [60.00%] ··· ...l.LinearRegressionBenchmark.track_test_score ok @@ -1540,7 +1528,7 @@ representation ---------------- --------------------- dense 0.9274012651798128 - sparse 0.10006193376167472 + sparse 0.11150477709402773 ================ ===================== [60.87%] ··· ....LinearRegressionBenchmark.track_train_score ok @@ -1548,7 +1536,7 @@ representation ---------------- -------------------- dense 0.927602494829764 - sparse 0.9999999999963308 + sparse 0.9999999999963599 ================ ==================== [61.74%] ··· Setting up linear_model:28 ok @@ -1558,9 +1546,9 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 107M 100M - dense saga 84.5M 85.6M - sparse lbfgs 382M 126M + dense lbfgs 108M 100M + dense saga 84.7M 85.8M + sparse lbfgs 383M 126M sparse saga 105M 106M ================ ======== ======= ======= @@ -1570,10 +1558,10 @@ ------------------------- --------------- representation solver 1 4 ================ ======== ======= ======= - dense lbfgs 101M 100M - dense saga 87.2M 87.3M - sparse lbfgs 102M 102M - sparse saga 89.3M 89.3M + dense lbfgs 100M 100M + dense saga 87M 87M + sparse lbfgs 101M 101M + sparse saga 89.1M 89.1M ================ ======== ======= ======= [63.48%] ··· ...r_model.LogisticRegressionBenchmark.time_fit ok @@ -1582,10 +1570,10 @@ ------------------------- ------------------------ representation solver 1 4 ================ ======== ============ =========== - dense lbfgs 22.7±2ms 190±0.7ms - dense saga 4.51±0s 5.08±0.3s - sparse lbfgs 1.04±0.01s 2.97±0.2s - sparse saga 4.07±0.2s 4.07±0.2s + dense lbfgs 22.3±2ms 189±5ms + dense saga 4.16±0.05s 5.03±0.4s + sparse lbfgs 1.04±0.01s 2.93±0.1s + sparse saga 4.14±0.01s 4.49±0.3s ================ ======== ============ =========== [64.35%] ··· ...del.LogisticRegressionBenchmark.time_predict ok @@ -1594,20 +1582,20 @@ ------------------------- --------------------------- representation solver 1 4 ================ ======== ============= ============= - dense lbfgs 3.21±0.03ms 3.26±0.1ms - dense saga 1.86±0.01ms 1.94±0.01ms - sparse lbfgs 6.78±0.04ms 6.77±0.04ms - sparse saga 6.08±0.9ms 4.39±0.2ms + dense lbfgs 3.56±0.2ms 3.03±0.3ms + dense saga 2.00±0.05ms 1.96±0.03ms + sparse lbfgs 7.07±0.03ms 7.07±0.02ms + sparse saga 4.60±0.01ms 4.60±0ms ================ ======== ============= ============= [65.22%] ··· ...LogisticRegressionBenchmark.track_test_score ok [65.22%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17149793411424544 - dense lbfgs 4 0.17149793411424544 - dense saga 1 0.7878580540658169 - dense saga 4 0.7878580540658169 + dense lbfgs 1 0.1746012767114597 + dense lbfgs 4 0.1746012767114597 + dense saga 1 0.7837126339309991 + dense saga 4 0.7837126339309991 sparse lbfgs 1 0.06538461538461539 sparse lbfgs 4 0.06538461538461539 sparse saga 1 0.5765140080078162 @@ -1618,10 +1606,10 @@ [66.09%] ··· ================ ======== ======== ===================== representation solver n_jobs ---------------- -------- -------- --------------------- - dense lbfgs 1 0.17984577339034574 - dense lbfgs 4 0.17984577339034574 - dense saga 1 0.79444332653521 - dense saga 4 0.79444332653521 + dense lbfgs 1 0.17975303723459757 + dense lbfgs 4 0.17975303723459757 + dense saga 1 0.7973107253330092 + dense saga 4 0.7973107253330092 sparse lbfgs 1 0.0681998556998557 sparse lbfgs 4 0.0681998556998557 sparse saga 1 0.6908414295256007 @@ -1638,8 +1626,8 @@ dense cholesky 465M dense lsqr 474M dense sparse_cg 468M - dense sag 473M - dense saga 473M + dense sag 474M + dense saga 474M sparse auto 194M sparse svd n/a sparse cholesky 1.27G @@ -1663,13 +1651,13 @@ dense sparse_cg 284M dense sag 284M dense saga 284M - sparse auto 120M + sparse auto 119M sparse svd n/a - sparse cholesky 120M - sparse lsqr 120M - sparse sparse_cg 120M - sparse sag 120M - sparse saga 120M + sparse cholesky 119M + sparse lsqr 119M + sparse sparse_cg 119M + sparse sag 119M + sparse saga 119M ================ =========== ====== [67.83%] ···· For parameters: 'sparse', 'svd' @@ -1679,20 +1667,20 @@ [68.70%] ··· ================ =========== ============ representation solver ---------------- ----------- ------------ - dense auto 209±2ms - dense svd 1.78±0.06s - dense cholesky 214±1ms - dense lsqr 224±8ms - dense sparse_cg 283±8ms - dense sag 30.0±0.5s - dense saga 12.4±0.3s - sparse auto 148±3ms + dense auto 210±5ms + dense svd 1.86±0.07s + dense cholesky 211±1ms + dense lsqr 228±7ms + dense sparse_cg 279±6ms + dense sag 29.2±0.9s + dense saga 13.9±0.01s + sparse auto 159±2ms sparse svd n/a - sparse cholesky 6.03±0.4s - sparse lsqr 134±3ms - sparse sparse_cg 162±20ms - sparse sag 2.73±0.3s - sparse saga 1.90±0.05s + sparse cholesky 5.99±0.4s + sparse lsqr 140±2ms + sparse sparse_cg 157±3ms + sparse sag 2.77±0.01s + sparse saga 1.88±0.2s ================ =========== ============ [68.70%] ···· For parameters: 'sparse', 'svd' @@ -1702,20 +1690,20 @@ [69.57%] ··· ================ =========== ============= representation solver ---------------- ----------- ------------- - dense auto 24.5±0.2ms - dense svd 24.4±0.5ms - dense cholesky 25.6±0.1ms - dense lsqr 24.3±0.2ms - dense sparse_cg 24.4±0.3ms - dense sag 24.3±0.3ms - dense saga 25.9±0.3ms - sparse auto 7.16±1ms + dense auto 25.2±0.7ms + dense svd 24.1±0.2ms + dense cholesky 25.5±0.2ms + dense lsqr 25.7±0.4ms + dense sparse_cg 25.5±0.2ms + dense sag 25.5±0.2ms + dense saga 25.6±0.1ms + sparse auto 7.73±0.7ms sparse svd n/a - sparse cholesky 7.68±0.7ms - sparse lsqr 7.64±0.2ms - sparse sparse_cg 7.66±0.05ms - sparse sag 7.82±0.7ms - sparse saga 8.93±0.8ms + sparse cholesky 8.03±0.7ms + sparse lsqr 6.96±1ms + sparse sparse_cg 6.90±0.04ms + sparse sag 6.92±0.04ms + sparse saga 6.91±0.05ms ================ =========== ============= [69.57%] ···· For parameters: 'sparse', 'svd' @@ -1732,13 +1720,13 @@ dense sparse_cg 0.9433995989989826 dense sag 0.94339933719428 dense saga 0.9433995886080997 - sparse auto 0.9568265119396898 + sparse auto 0.9557665333088992 sparse svd n/a - sparse cholesky 0.9568267357510376 - sparse lsqr 0.9568265100859151 - sparse sparse_cg 0.9568265119396898 - sparse sag 0.9568296621040289 - sparse saga 0.9568300118934715 + sparse cholesky 0.9557666362686599 + sparse lsqr 0.9557665318827444 + sparse sparse_cg 0.9557665333088992 + sparse sag 0.9557724892293011 + sparse saga 0.9557723020772435 ================ =========== ==================== [70.43%] ···· For parameters: 'sparse', 'svd' @@ -1755,13 +1743,13 @@ dense sparse_cg 0.9444001571192623 dense sag 0.9444001419121766 dense saga 0.9444001543688754 - sparse auto 0.9658894804802586 + sparse auto 0.9657963803700107 sparse svd n/a - sparse cholesky 0.9658894835882773 - sparse lsqr 0.965889480461103 - sparse sparse_cg 0.9658894804802586 - sparse sag 0.9658859295853838 - sparse saga 0.9658858951934723 + sparse cholesky 0.965796383257664 + sparse lsqr 0.9657963805096654 + sparse sparse_cg 0.9657963803700107 + sparse sag 0.9657928222376866 + sparse saga 0.9657927878829292 ================ =========== ==================== [71.30%] ···· For parameters: 'sparse', 'svd' @@ -1769,35 +1757,35 @@ [72.17%] ··· Setting up linear_model:151 ok [72.17%] ··· linear_model.SGDRegressorBenchmark.peakmem_fit ok -[72.17%] ··· ================ ======= - representation - ---------------- ------- - dense 160M - sparse 88.9M - ================ ======= +[72.17%] ··· ================ ====== + representation + ---------------- ------ + dense 161M + sparse 89M + ================ ====== [73.04%] ··· ..._model.SGDRegressorBenchmark.peakmem_predict ok [73.04%] ··· ================ ======= representation ---------------- ------- dense 160M - sparse 87.8M + sparse 87.2M ================ ======= [73.91%] ··· linear_model.SGDRegressorBenchmark.time_fit ok [73.91%] ··· ================ ============ representation ---------------- ------------ - dense 5.15±0.02s - sparse 4.28±0.02s + dense 5.12±0.05s + sparse 4.24±0.02s ================ ============ [74.78%] ··· linear_model.SGDRegressorBenchmark.time_predict ok [74.78%] ··· ================ ============= representation ---------------- ------------- - dense 10.7±0.6ms - sparse 2.14±0.01ms + dense 10.5±0.05ms + sparse 2.13±0.01ms ================ ============= [75.65%] ··· ...model.SGDRegressorBenchmark.track_test_score ok @@ -1805,7 +1793,7 @@ representation ---------------- -------------------- dense 0.9636293915848902 - sparse 0.9617746238888805 + sparse 0.9612509705085853 ================ ==================== [76.52%] ··· ...odel.SGDRegressorBenchmark.track_train_score ok @@ -1813,7 +1801,7 @@ representation ---------------- -------------------- dense 0.9641785427097553 - sparse 0.9622544668134381 + sparse 0.961738125598452 ================ ==================== [77.39%] ··· Setting up manifold:15 ok @@ -1821,16 +1809,16 @@ [77.39%] ··· ============ ======= method ------------ ------- - exact 91.6M - barnes_hut 98.2M + exact 90.7M + barnes_hut 98.5M ============ ======= [78.26%] ··· manifold.TSNEBenchmark.time_fit ok [78.26%] ··· ============ ============ method ------------ ------------ - exact 6.25±0.02s - barnes_hut 3.16±0.2s + exact 6.30±0.01s + barnes_hut 3.41±0.2s ============ ============ [79.13%] ··· manifold.TSNEBenchmark.track_test_score ok @@ -1855,13 +1843,13 @@ ------------------------------ --------------- representation metric 1 4 ================ ============= ======= ======= - dense cosine 670M 787M - dense euclidean 753M 1.06G - dense manhattan 255M 314M - dense correlation 249M 485M - sparse cosine 1.42G 1.43G - sparse euclidean 571M 1.04G - sparse manhattan 188M 229M + dense cosine 670M 761M + dense euclidean 753M 1.09G + dense manhattan 256M 338M + dense correlation 249M 481M + sparse cosine 1.42G 1.3G + sparse euclidean 571M 826M + sparse manhattan 188M 235M sparse correlation n/a n/a ================ ============= ======= ======= @@ -1901,13 +1889,13 @@ ------------------------------ ------------------------- representation metric 1 4 ================ ============= ============ ============ - dense cosine 1.07±0.01s 1.27±0.05s - dense euclidean 1.69±0.01s 3.06±0.07s - dense manhattan 7.02±0.5s 2.67±0.1s - dense correlation 3.35±0.04s 2.66±0.1s - sparse cosine 3.72±0.3s 2.67±0.08s - sparse euclidean 2.56±0.01s 2.13±0.09s - sparse manhattan 1.22±0.01s 1.30±0.02s + dense cosine 1.09±0.01s 1.32±0.06s + dense euclidean 1.69±0.02s 3.06±0.08s + dense manhattan 6.42±0.03s 2.83±0.2s + dense correlation 3.27±0.3s 2.66±0.1s + sparse cosine 3.66±0.01s 2.68±0.08s + sparse euclidean 2.54±0.04s 2.10±0.1s + sparse manhattan 1.25±0.2s 1.29±0.04s sparse correlation n/a n/a ================ ============= ============ ============ @@ -1935,8 +1923,8 @@ [83.48%] ··· ======== =========== n_jobs -------- ----------- - 1 57.0±0.2s - 4 17.7±0.1s + 1 56.0±0.8s + 4 17.5±0.1s ======== =========== [84.35%] ··· ...tion.CrossValidationBenchmark.track_crossval ok @@ -1952,8 +1940,8 @@ [85.22%] ··· ======== ======= n_jobs -------- ------- - 1 96.7M - 4 94.1M + 1 96.9M + 4 94.3M ======== ======= [86.09%] ··· ...election.GridSearchBenchmark.peakmem_predict ok @@ -1965,20 +1953,20 @@ ======== ===== [86.96%] ··· model_selection.GridSearchBenchmark.time_fit ok -[86.96%] ··· ======== ============ - n_jobs - -------- ------------ - 1 5.76±0.01m - 4 1.77±0.01m - ======== ============ +[86.96%] ··· ======== =========== + n_jobs + -------- ----------- + 1 5.73±0.1m + 4 1.72±0m + ======== =========== [87.83%] ··· ...l_selection.GridSearchBenchmark.time_predict ok -[87.83%] ··· ======== ============ - n_jobs - -------- ------------ - 1 37.5±0.4ms - 4 37.4±0.3ms - ======== ============ +[87.83%] ··· ======== ============= + n_jobs + -------- ------------- + 1 37.0±0.05ms + 4 37.7±6ms + ======== ============= [88.70%] ··· ...lection.GridSearchBenchmark.track_test_score ok [88.70%] ··· ======== ==================== @@ -2003,9 +1991,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 78.6M 78.6M 82.1M 82M - kd_tree 81.3M 81.3M 89.8M 89.8M - ball_tree 81.2M 81.2M 89.5M 89.5M + brute 78.7M 78.7M 82.2M 82.2M + kd_tree 81.4M 81.4M 89.8M 89.8M + ball_tree 81.3M 81.3M 89.5M 89.5M =========== ========= ========= ========== ========== [91.30%] ··· ...NeighborsClassifierBenchmark.peakmem_predict ok @@ -2014,9 +2002,9 @@ ----------- ----------------------------------------- algorithm low / 1 low / 4 high / 1 high / 4 =========== ========= ========= ========== ========== - brute 89.6M 89.4M 94.6M 94.3M - kd_tree 82.9M 85.3M 92.5M 92.3M - ball_tree 82.8M 85.2M 92M 92.4M + brute 89.3M 89.4M 94.5M 94.3M + kd_tree 83M 85.3M 92.4M 91.9M + ball_tree 82.9M 85.2M 92.3M 92.2M =========== ========= ========= ========== ========== [92.17%] ··· ...hbors.KNeighborsClassifierBenchmark.time_fit ok @@ -2025,62 +2013,62 @@ ----------------------- --------------------------- algorithm dimension 1 4 =========== =========== ============= ============= - brute low 1.45±0.2ms 1.03±0ms - brute high 1.83±0.6ms 1.28±0.2ms - kd_tree low 16.9±0.02ms 16.9±0.02ms - kd_tree high 50.7±0.3ms 51.0±0.2ms - ball_tree low 12.5±0.3ms 12.4±0.01ms - ball_tree high 33.9±1ms 33.8±0.2ms + brute low 1.89±0.2ms 1.07±0.2ms + brute high 1.19±0.01ms 1.20±0.01ms + kd_tree low 11.9±0.01ms 11.9±0.02ms + kd_tree high 50.4±0.5ms 71.6±10ms + ball_tree low 10.1±0.01ms 10.1±0.2ms + ball_tree high 38.2±0.8ms 38.2±0.5ms =========== =========== ============= ============= [93.04%] ··· ...s.KNeighborsClassifierBenchmark.time_predict ok -[93.04%] ··· =========== =========== ============ ============ - -- n_jobs - ----------------------- ------------------------- - algorithm dimension 1 4 - =========== =========== ============ ============ - brute low 86.1±20ms 85.5±0.5ms - brute high 119±0.2ms 122±1ms - kd_tree low 1.18±0.04s 2.80±0.06s - kd_tree high 8.94±0.6s 7.38±0.2s - ball_tree low 2.07±0.3s 5.11±0.5s - ball_tree high 8.77±0.5s 10.9±0.1s - =========== =========== ============ ============ +[93.04%] ··· =========== =========== ============ =========== + -- n_jobs + ----------------------- ------------------------ + algorithm dimension 1 4 + =========== =========== ============ =========== + brute low 84.5±0.1ms 83.4±20ms + brute high 120±1ms 119±1ms + kd_tree low 1.23±0.02s 2.81±0.3s + kd_tree high 9.31±0.2s 8.29±0.3s + ball_tree low 2.38±0.1s 4.58±0.6s + ball_tree high 7.10±0.08s 10.4±0.3s + =========== =========== ============ =========== [93.91%] ··· ...eighborsClassifierBenchmark.track_test_score ok -[93.91%] ··· =========== =========== ======== ==================== - algorithm dimension n_jobs - ----------- ----------- -------- -------------------- - brute low 1 0.4474345434518404 - brute low 4 0.4474345434518404 - brute high 1 0.6720143210525781 - brute high 4 0.6720143210525781 - kd_tree low 1 0.4474345434518404 - kd_tree low 4 0.4474345434518404 - kd_tree high 1 0.6720143210525781 - kd_tree high 4 0.6720143210525781 - ball_tree low 1 0.4474345434518404 - ball_tree low 4 0.4474345434518404 - ball_tree high 1 0.6720143210525781 - ball_tree high 4 0.6720143210525781 - =========== =========== ======== ==================== +[93.91%] ··· =========== =========== ======== ===================== + algorithm dimension n_jobs + ----------- ----------- -------- --------------------- + brute low 1 0.42955272636857755 + brute low 4 0.42955272636857755 + brute high 1 0.6637361619360791 + brute high 4 0.6637361619360791 + kd_tree low 1 0.42955272636857755 + kd_tree low 4 0.42955272636857755 + kd_tree high 1 0.6637361619360791 + kd_tree high 4 0.6637361619360791 + ball_tree low 1 0.42955272636857755 + ball_tree low 4 0.42955272636857755 + ball_tree high 1 0.6637361619360791 + ball_tree high 4 0.6637361619360791 + =========== =========== ======== ===================== [94.78%] ··· ...ighborsClassifierBenchmark.track_train_score ok [94.78%] ··· =========== =========== ======== ==================== algorithm dimension n_jobs ----------- ----------- -------- -------------------- - brute low 1 0.6406923513526007 - brute low 4 0.6406923513526007 - brute high 1 0.7953969464796552 - brute high 4 0.7953969464796552 - kd_tree low 1 0.6406923513526007 - kd_tree low 4 0.6406923513526007 - kd_tree high 1 0.7953969464796552 - kd_tree high 4 0.7953969464796552 - ball_tree low 1 0.6406923513526007 - ball_tree low 4 0.6406923513526007 - ball_tree high 1 0.7953969464796552 - ball_tree high 4 0.7953969464796552 + brute low 1 0.634181895625006 + brute low 4 0.634181895625006 + brute high 1 0.7923519182600929 + brute high 4 0.7923519182600929 + kd_tree low 1 0.634181895625006 + kd_tree low 4 0.634181895625006 + kd_tree high 1 0.7923519182600929 + kd_tree high 4 0.7923519182600929 + ball_tree low 1 0.634181895625006 + ball_tree low 4 0.634181895625006 + ball_tree high 1 0.7923519182600929 + ball_tree high 4 0.7923519182600929 =========== =========== ======== ==================== [95.65%] ··· Setting up svm:14 ok @@ -2124,10 +2112,10 @@ [97.39%] ··· ========= ============ kernel --------- ------------ - linear 1.73±0.06s - poly 1.67±0.03s - rbf 1.70±0.1s - sigmoid 1.63±0.01s + linear 1.65±0.03s + poly 1.63±0.02s + rbf 1.68±0.02s + sigmoid 1.73±0.09s ========= ============ [97.39%] ···· For parameters: 'linear' @@ -2243,10 +2231,10 @@ warnings.warn( /home/ubuntu/scikit-learn/asv_benchmarks/env/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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/bc2fc3e467e9bf6ae53344bc521e29ad/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. @@ -2288,10 +2276,10 @@ [98.26%] ··· ========= ============ kernel --------- ------------ - linear 644±7ms - poly 657±10ms - rbf 1.81±0.03s - sigmoid 677±50ms + linear 693±5ms + poly 660±4ms + rbf 1.68±0.03s + sigmoid 618±1ms ========= ============ [99.13%] ··· svm.SVCBenchmark.track_test_score ok @@ -2444,4 +2432,4 @@ Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Unknown: Dependent on hypervisor status Vulnerability Tsx async abort: Not affected -MemTotal: 16384572 kB +MemTotal: 16384564 kB diff --git a/results/sklearn-benchmark/4e825370-conda-py3.11-cython3.0.8-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json b/results/sklearn-benchmark/4e825370-conda-py3.11-cython3.0.8-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json index 3140f5e5de..df41d769e9 100644 --- a/results/sklearn-benchmark/4e825370-conda-py3.11-cython3.0.8-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json +++ b/results/sklearn-benchmark/4e825370-conda-py3.11-cython3.0.8-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0.json @@ -1 +1 @@ -{"commit_hash": "4e8253703013b38da503e2354d82fb7fa43dd4ec", "env_name": "conda-py3.11-cython3.0.8-joblib1.3.2-numpy1.25.2-pandas2.1.0-scipy1.11.2-threadpoolctl3.2.0", "date": 1708730185000, "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": "3.0.8", "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": "3.0.8", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0"}, 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