from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
from sklearn_benchmarks.utils import default_results_directory
from pathlib import Path
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
results_dir = default_results_directory()
# Parameters
results_dir = "./results/local/20220315T132808/"
results_dir = Path(results_dir)
reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True, results_dir=results_dir)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speed-up
time scikit-learn / time sklearnex. For instance, a speed-up of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: algorithm=brute.
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 100000 | 100000 | 100 | 1.293 | 0.021 | 0.062 | 0.0 | -1 | 1 | 0.051 | 0.006 | 25.476 | 25.668 | Download | Download |
| 3 | fit | 100000 | 100000 | 100 | 1.303 | 0.023 | 0.061 | 0.0 | -1 | 5 | 0.049 | 0.000 | 26.637 | 26.637 | Download | Download |
| 6 | fit | 100000 | 100000 | 100 | 1.290 | 0.014 | 0.062 | 0.0 | 1 | 100 | 0.049 | 0.000 | 26.363 | 26.363 | Download | Download |
| 9 | fit | 100000 | 100000 | 100 | 1.293 | 0.012 | 0.062 | 0.0 | -1 | 100 | 0.049 | 0.000 | 26.486 | 26.486 | Download | Download |
| 12 | fit | 100000 | 100000 | 100 | 1.290 | 0.022 | 0.062 | 0.0 | 1 | 5 | 0.049 | 0.000 | 26.426 | 26.426 | Download | Download |
| 15 | fit | 100000 | 100000 | 100 | 1.302 | 0.022 | 0.061 | 0.0 | 1 | 1 | 0.049 | 0.000 | 26.594 | 26.594 | Download | Download |
| 18 | fit | 100000 | 100000 | 2 | 0.052 | 0.001 | 0.031 | 0.0 | -1 | 1 | 0.009 | 0.000 | 5.942 | 5.944 | Download | Download |
| 21 | fit | 100000 | 100000 | 2 | 0.052 | 0.001 | 0.031 | 0.0 | -1 | 5 | 0.009 | 0.000 | 5.857 | 5.859 | Download | Download |
| 24 | fit | 100000 | 100000 | 2 | 0.052 | 0.000 | 0.031 | 0.0 | 1 | 100 | 0.009 | 0.000 | 6.143 | 6.144 | Download | Download |
| 27 | fit | 100000 | 100000 | 2 | 0.051 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.009 | 0.000 | 5.713 | 5.714 | Download | Download |
| 30 | fit | 100000 | 100000 | 2 | 0.051 | 0.000 | 0.031 | 0.0 | 1 | 5 | 0.009 | 0.000 | 5.665 | 5.666 | Download | Download |
| 33 | fit | 100000 | 100000 | 2 | 0.055 | 0.003 | 0.029 | 0.0 | 1 | 1 | 0.009 | 0.000 | 6.323 | 6.324 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 100000 | 1000 | 100 | 2.471 | 0.234 | 0.000 | 0.002 | -1 | 1 | 0.178 | 0.007 | 13.916 | 13.926 | Download | Download |
| 2 | predict | 100000 | 1 | 100 | 0.023 | 0.003 | 0.000 | 0.023 | -1 | 1 | 0.009 | 0.000 | 2.535 | 2.535 | Download | Download |
| 4 | predict | 100000 | 1000 | 100 | 3.125 | 0.066 | 0.000 | 0.003 | -1 | 5 | 0.177 | 0.001 | 17.687 | 17.687 | Download | Download |
| 5 | predict | 100000 | 1 | 100 | 0.024 | 0.003 | 0.000 | 0.024 | -1 | 5 | 0.009 | 0.000 | 2.640 | 2.640 | Download | Download |
| 7 | predict | 100000 | 1000 | 100 | 1.939 | 0.006 | 0.000 | 0.002 | 1 | 100 | 0.212 | 0.001 | 9.134 | 9.134 | Download | Download |
| 8 | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 100 | 0.009 | 0.000 | 2.170 | 2.171 | Download | Download |
| 10 | predict | 100000 | 1000 | 100 | 2.830 | 0.049 | 0.000 | 0.003 | -1 | 100 | 0.216 | 0.011 | 13.098 | 13.114 | Download | Download |
| 11 | predict | 100000 | 1 | 100 | 0.025 | 0.004 | 0.000 | 0.025 | -1 | 100 | 0.009 | 0.000 | 2.648 | 2.649 | Download | Download |
| 13 | predict | 100000 | 1000 | 100 | 1.927 | 0.006 | 0.000 | 0.002 | 1 | 5 | 0.176 | 0.002 | 10.917 | 10.917 | Download | Download |
| 14 | predict | 100000 | 1 | 100 | 0.021 | 0.000 | 0.000 | 0.021 | 1 | 5 | 0.009 | 0.000 | 2.253 | 2.254 | Download | Download |
| 16 | predict | 100000 | 1000 | 100 | 1.227 | 0.004 | 0.001 | 0.001 | 1 | 1 | 0.175 | 0.001 | 7.017 | 7.017 | Download | Download |
| 17 | predict | 100000 | 1 | 100 | 0.023 | 0.006 | 0.000 | 0.023 | 1 | 1 | 0.009 | 0.000 | 2.419 | 2.419 | Download | Download |
| 19 | predict | 100000 | 1000 | 2 | 1.901 | 0.022 | 0.000 | 0.002 | -1 | 1 | 0.026 | 0.000 | 72.475 | 72.482 | Download | Download |
| 20 | predict | 100000 | 1 | 2 | 0.006 | 0.004 | 0.000 | 0.006 | -1 | 1 | 0.001 | 0.000 | 8.226 | 8.283 | Download | Download |
| 22 | predict | 100000 | 1000 | 2 | 2.698 | 0.038 | 0.000 | 0.003 | -1 | 5 | 0.027 | 0.000 | 99.309 | 99.311 | Download | Download |
| 23 | predict | 100000 | 1 | 2 | 0.005 | 0.000 | 0.000 | 0.005 | -1 | 5 | 0.001 | 0.000 | 7.298 | 7.352 | Download | Download |
| 25 | predict | 100000 | 1000 | 2 | 1.905 | 0.011 | 0.000 | 0.002 | 1 | 100 | 0.062 | 0.001 | 30.877 | 30.882 | Download | Download |
| 26 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.199 | 3.226 | Download | Download |
| 28 | predict | 100000 | 1000 | 2 | 2.660 | 0.025 | 0.000 | 0.003 | -1 | 100 | 0.061 | 0.001 | 43.318 | 43.319 | Download | Download |
| 29 | predict | 100000 | 1 | 2 | 0.007 | 0.004 | 0.000 | 0.007 | -1 | 100 | 0.001 | 0.000 | 7.586 | 7.674 | Download | Download |
| 31 | predict | 100000 | 1000 | 2 | 1.887 | 0.006 | 0.000 | 0.002 | 1 | 5 | 0.027 | 0.000 | 68.983 | 68.989 | Download | Download |
| 32 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 3.515 | 3.550 | Download | Download |
| 34 | predict | 100000 | 1000 | 2 | 1.125 | 0.007 | 0.000 | 0.001 | 1 | 1 | 0.026 | 0.000 | 42.609 | 42.610 | Download | Download |
| 35 | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.485 | 2.497 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: algorithm=kd_tree.
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 1000000 | 1000000 | 10 | 3.427 | 0.074 | 0.023 | 0.0 | -1 | 1 | 0.830 | 0.017 | 4.130 | 4.131 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 10 | 3.352 | 0.059 | 0.024 | 0.0 | -1 | 5 | 0.822 | 0.016 | 4.080 | 4.080 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 10 | 3.306 | 0.055 | 0.024 | 0.0 | 1 | 100 | 0.829 | 0.012 | 3.989 | 3.989 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 10 | 3.344 | 0.058 | 0.024 | 0.0 | -1 | 100 | 0.822 | 0.006 | 4.069 | 4.069 | Download | Download |
| 12 | fit | 1000000 | 1000000 | 10 | 3.493 | 0.060 | 0.023 | 0.0 | 1 | 5 | 0.814 | 0.008 | 4.289 | 4.289 | Download | Download |
| 15 | fit | 1000000 | 1000000 | 10 | 3.535 | 0.078 | 0.023 | 0.0 | 1 | 1 | 0.833 | 0.025 | 4.246 | 4.248 | Download | Download |
| 18 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 1 | 0.001 | 0.000 | 0.596 | 0.598 | Download | Download |
| 21 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.030 | 0.0 | -1 | 5 | 0.001 | 0.000 | 0.593 | 0.595 | Download | Download |
| 24 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.030 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.602 | 0.603 | Download | Download |
| 27 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.584 | 0.585 | Download | Download |
| 30 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.030 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.583 | 0.586 | Download | Download |
| 33 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.030 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.606 | 0.607 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 1000000 | 1000 | 10 | 0.469 | 0.007 | 0.000 | 0.000 | -1 | 1 | 0.132 | 0.001 | 3.558 | 3.558 | Download | Download |
| 2 | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.006 | 9.549 | Download | Download |
| 4 | predict | 1000000 | 1000 | 10 | 0.881 | 0.004 | 0.000 | 0.001 | -1 | 5 | 0.227 | 0.002 | 3.871 | 3.871 | Download | Download |
| 5 | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 8.736 | 9.467 | Download | Download |
| 7 | predict | 1000000 | 1000 | 10 | 5.254 | 0.023 | 0.000 | 0.005 | 1 | 100 | 0.687 | 0.013 | 7.642 | 7.643 | Download | Download |
| 8 | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.103 | 4.349 | Download | Download |
| 10 | predict | 1000000 | 1000 | 10 | 2.988 | 0.011 | 0.000 | 0.003 | -1 | 100 | 0.692 | 0.013 | 4.318 | 4.318 | Download | Download |
| 11 | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 8.053 | 8.579 | Download | Download |
| 13 | predict | 1000000 | 1000 | 10 | 1.593 | 0.012 | 0.000 | 0.002 | 1 | 5 | 0.227 | 0.003 | 7.005 | 7.006 | Download | Download |
| 14 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.914 | 4.154 | Download | Download |
| 16 | predict | 1000000 | 1000 | 10 | 0.848 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.130 | 0.002 | 6.518 | 6.518 | Download | Download |
| 17 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.982 | 4.276 | Download | Download |
| 19 | predict | 1000 | 1000 | 2 | 0.023 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.001 | 0.000 | 41.227 | 41.572 | Download | Download |
| 20 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 16.377 | 17.454 | Download | Download |
| 22 | predict | 1000 | 1000 | 2 | 0.024 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 34.509 | 34.696 | Download | Download |
| 23 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 16.327 | 17.423 | Download | Download |
| 25 | predict | 1000 | 1000 | 2 | 0.035 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 7.532 | 7.533 | Download | Download |
| 26 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 3.951 | 4.195 | Download | Download |
| 28 | predict | 1000 | 1000 | 2 | 0.036 | 0.000 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.000 | 7.764 | 7.764 | Download | Download |
| 29 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 13.956 | 15.719 | Download | Download |
| 31 | predict | 1000 | 1000 | 2 | 0.021 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 30.428 | 30.548 | Download | Download |
| 32 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.415 | 4.624 | Download | Download |
| 34 | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 44.405 | 44.752 | Download | Download |
| 35 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 4.166 | 4.459 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 1000000 | 1000000 | 2 | 0.516 | 0.006 | 0.031 | 0.0 | 30 | random | 0.385 | 0.019 | 1.342 | 1.343 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 2 | 0.593 | 0.011 | 0.027 | 0.0 | 30 | k-means++ | 0.407 | 0.017 | 1.458 | 1.459 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 100 | 5.043 | 0.525 | 0.159 | 0.0 | 30 | random | 2.873 | 0.020 | 1.755 | 1.755 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 100 | 5.113 | 0.269 | 0.156 | 0.0 | 30 | k-means++ | 3.114 | 0.055 | 1.642 | 1.642 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 1000000 | 1000 | 2 | 0.0 | 0.0 | 0.069 | 0.0 | 30 | random | 0.0 | 0.0 | 1.150 | 1.271 | Download | Download |
| 2 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | random | 0.0 | 0.0 | 1.248 | 1.290 | Download | Download |
| 4 | predict | 1000000 | 1000 | 2 | 0.0 | 0.0 | 0.070 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.093 | 1.204 | Download | Download |
| 5 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.317 | 1.388 | Download | Download |
| 7 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 1.796 | 0.0 | 30 | random | 0.0 | 0.0 | 1.438 | 1.545 | Download | Download |
| 8 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.004 | 0.0 | 30 | random | 0.0 | 0.0 | 1.203 | 1.233 | Download | Download |
| 10 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 1.901 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.369 | 1.501 | Download | Download |
| 11 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.004 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.225 | 1.290 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | library | diff_adjusted_rand_scores | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | sklearn_KMeans_short | sklearn | 0.032069 | predict | 10000 | 1000 | 100 | 0.001367 | 0.000150 | 0.585400 | 0.000001 | 20 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.293414 | 0.001206 | 0.000126 | 0.325483 | 1.133293 | 1.139459 |
| 10 | sklearn_KMeans_short | sklearn | 0.032656 | predict | 10000 | 1000 | 100 | 0.001475 | 0.000137 | 0.542234 | 0.000001 | 20 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.283247 | 0.001174 | 0.000127 | 0.315903 | 1.256556 | 1.263934 |
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 10000 | 10000 | 2 | 0.083 | 0.003 | 0.002 | 0.0 | 20 | random | 0.036 | 0.001 | 2.286 | 2.288 | Download | Download |
| 3 | fit | 10000 | 10000 | 2 | 0.203 | 0.003 | 0.001 | 0.0 | 20 | k-means++ | 0.090 | 0.001 | 2.260 | 2.260 | Download | Download |
| 6 | fit | 10000 | 10000 | 100 | 0.215 | 0.005 | 0.037 | 0.0 | 20 | random | 0.141 | 0.004 | 1.522 | 1.523 | Download | Download |
| 9 | fit | 10000 | 10000 | 100 | 0.571 | 0.007 | 0.014 | 0.0 | 20 | k-means++ | 0.365 | 0.006 | 1.564 | 1.564 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 10000 | 1000 | 2 | 0.001 | 0.0 | 0.024 | 0.0 | 20 | random | 0.001 | 0.0 | 1.008 | 1.013 | Download | Download |
| 2 | predict | 10000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 20 | random | 0.000 | 0.0 | 1.276 | 1.309 | Download | Download |
| 4 | predict | 10000 | 1000 | 2 | 0.001 | 0.0 | 0.026 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 0.925 | 0.931 | Download | Download |
| 5 | predict | 10000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.324 | 1.358 | Download | Download |
| 7 | predict | 10000 | 1000 | 100 | 0.001 | 0.0 | 0.585 | 0.0 | 20 | random | 0.001 | 0.0 | 1.133 | 1.139 | Download | Download |
| 8 | predict | 10000 | 1 | 100 | 0.000 | 0.0 | 0.003 | 0.0 | 20 | random | 0.000 | 0.0 | 1.220 | 1.245 | Download | Download |
| 10 | predict | 10000 | 1000 | 100 | 0.001 | 0.0 | 0.542 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 1.257 | 1.264 | Download | Download |
| 11 | predict | 10000 | 1 | 100 | 0.000 | 0.0 | 0.003 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.204 | 1.273 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1.0, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 1000000 | 1000000 | 100 | 11.343 | 0.128 | 0.071 | 0.000 | [20] | 2.046 | 0.026 | 5.543 | 5.543 | Download | Download |
| 3 | fit | 1000 | 1000 | 10000 | 0.832 | 0.053 | 0.096 | 0.001 | [27] | 0.975 | 0.051 | 0.854 | 0.855 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 3.219 | 0.0 | [20] | 0.001 | 0.001 | 0.381 | 0.692 | Download | Download |
| 2 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.014 | 0.0 | [20] | 0.000 | 0.000 | 0.286 | 0.291 | Download | Download |
| 4 | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | 4.789 | 0.0 | [27] | 0.003 | 0.000 | 0.535 | 0.536 | Download | Download |
| 5 | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 0.983 | 0.0 | [27] | 0.001 | 0.000 | 0.130 | 0.131 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters:
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | copy_X | fit_intercept | normalize | positive | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 1000 | 1000 | 10000 | 1.355 | 0.015 | 0.059 | 0.001 | True | True | deprecated | False | 1.580 | 0.025 | 0.857 | 0.857 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 9.422 | 0.643 | 0.085 | 0.000 | True | True | deprecated | False | 0.198 | 0.005 | 47.579 | 47.595 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | copy_X | fit_intercept | normalize | positive | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 1000 | 1000 | 10000 | 0.012 | 0.0 | 6.659 | 0.0 | True | True | deprecated | False | 0.019 | 0.0 | 0.633 | 0.633 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.000 | 0.0 | 6.334 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.430 | 0.446 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 5.866 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.290 | 0.329 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.000 | 0.0 | 0.146 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.415 | 0.429 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: alpha=1e-06.
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 1000 | 1000 | 10000 | 0.176 | 0.004 | 0.455 | 0.0 | 0.183 | 0.002 | 0.958 | 0.958 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 1.101 | 0.014 | 0.727 | 0.0 | 0.230 | 0.003 | 4.786 | 4.787 | Download | Download |
predict
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | predict | 1000 | 1000 | 10000 | 0.012 | 0.0 | 6.699 | 0.0 | 0.019 | 0.0 | 0.630 | 0.630 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.000 | 0.0 | 6.432 | 0.0 | 0.000 | 0.0 | 0.355 | 0.434 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 5.907 | 0.0 | 0.000 | 0.0 | 0.464 | 0.494 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.000 | 0.0 | 0.159 | 0.0 | 0.000 | 0.0 | 0.349 | 0.359 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters:
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_iter | angle | early_exaggeration | init | learning_rate | method | metric | min_grad_norm | n_components | n_iter_without_progress | perplexity | square_distances | verbose | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 1000 | 1000 | 3 | 3.422 | 0.17 | 0.0 | 0.003 | 1000 | 0.5 | 12.0 | warn | warn | barnes_hut | euclidean | 0.0 | 2 | 300 | 30.0 | legacy | 0 | 3.492 | 0.306 | 0.98 | 0.984 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
scikit-learn-intelex (2021.20220207.123530) vs. scikit-learn (1.0.2)
All estimators share the following parameters: n_components=10.
fit
| function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fit | 100000 | 100000 | 100 | 0.505 | 0.013 | 0.159 | 0.0 | 0.028 | 0.005 | 17.764 | 18.092 | Download | Download |
| 1 | fit | 10000 | 10000 | 1000 | 0.448 | 0.004 | 0.179 | 0.0 | 0.195 | 0.004 | 2.294 | 2.294 | Download | Download |
Profiling traces can be visualized using Perfetto UI.
| python | 3.8.12 | packaged by conda-forge | (default, Jan 30 2022, 23:42:07) [GCC 9.4.0] |
|---|---|
| executable | /usr/share/miniconda/envs/sklbench/bin/python |
| machine | Linux-5.11.0-1028-azure-x86_64-with-glibc2.10 |
| version | |
|---|---|
| pip | 22.0.4 |
| setuptools | 60.9.3 |
| sklearn | 1.0.2 |
| numpy | 1.22.3 |
| scipy | 1.8.0 |
| Cython | None |
| pandas | 1.4.1 |
| matplotlib | 3.5.1 |
| joblib | 1.1.0 |
| threadpoolctl | 3.1.0 |
| user_api | internal_api | prefix | filepath | version | threading_layer | architecture | num_threads | |
|---|---|---|---|---|---|---|---|---|
| 0 | blas | openblas | libopenblas | /usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.18.so | 0.3.18 | pthreads | SkylakeX | 2 |
| 1 | openmp | openmp | libgomp | /usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0 | None | NaN | NaN | 2 |
| cpu_count | 2 |
|---|---|
| physical_cpu_count | 2 |