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/20220314T110428/"
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 | 1.521 | 0.022 | 0.053 | 0.0 | -1 | 1 | 0.054 | 0.009 | 28.285 | 28.698 | Download | Download |
| 3 | fit | 100000 | 100000 | 100 | 1.476 | 0.016 | 0.054 | 0.0 | -1 | 5 | 0.050 | 0.000 | 29.275 | 29.275 | Download | Download |
| 6 | fit | 100000 | 100000 | 100 | 1.465 | 0.020 | 0.055 | 0.0 | 1 | 100 | 0.051 | 0.000 | 28.607 | 28.608 | Download | Download |
| 9 | fit | 100000 | 100000 | 100 | 1.485 | 0.018 | 0.054 | 0.0 | -1 | 100 | 0.051 | 0.000 | 29.156 | 29.156 | Download | Download |
| 12 | fit | 100000 | 100000 | 100 | 1.478 | 0.020 | 0.054 | 0.0 | 1 | 5 | 0.051 | 0.000 | 29.236 | 29.237 | Download | Download |
| 15 | fit | 100000 | 100000 | 100 | 1.486 | 0.025 | 0.054 | 0.0 | 1 | 1 | 0.050 | 0.000 | 29.604 | 29.604 | Download | Download |
| 18 | fit | 100000 | 100000 | 2 | 0.058 | 0.001 | 0.028 | 0.0 | -1 | 1 | 0.010 | 0.000 | 5.699 | 5.700 | Download | Download |
| 21 | fit | 100000 | 100000 | 2 | 0.058 | 0.001 | 0.028 | 0.0 | -1 | 5 | 0.010 | 0.000 | 5.634 | 5.634 | Download | Download |
| 24 | fit | 100000 | 100000 | 2 | 0.057 | 0.001 | 0.028 | 0.0 | 1 | 100 | 0.010 | 0.000 | 5.651 | 5.652 | Download | Download |
| 27 | fit | 100000 | 100000 | 2 | 0.058 | 0.000 | 0.028 | 0.0 | -1 | 100 | 0.010 | 0.000 | 5.742 | 5.743 | Download | Download |
| 30 | fit | 100000 | 100000 | 2 | 0.058 | 0.001 | 0.027 | 0.0 | 1 | 5 | 0.010 | 0.000 | 5.812 | 5.814 | Download | Download |
| 33 | fit | 100000 | 100000 | 2 | 0.057 | 0.000 | 0.028 | 0.0 | 1 | 1 | 0.010 | 0.000 | 5.637 | 5.637 | 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 | 2.589 | 0.291 | 0.000 | 0.003 | -1 | 1 | 0.202 | 0.004 | 12.831 | 12.834 | Download | Download |
| 2 | predict | 100000 | 1 | 100 | 0.026 | 0.003 | 0.000 | 0.026 | -1 | 1 | 0.009 | 0.000 | 2.910 | 2.912 | Download | Download |
| 4 | predict | 100000 | 1000 | 100 | 3.098 | 0.034 | 0.000 | 0.003 | -1 | 5 | 0.203 | 0.003 | 15.295 | 15.296 | Download | Download |
| 5 | predict | 100000 | 1 | 100 | 0.026 | 0.002 | 0.000 | 0.026 | -1 | 5 | 0.009 | 0.000 | 2.951 | 2.951 | Download | Download |
| 7 | predict | 100000 | 1000 | 100 | 2.160 | 0.006 | 0.000 | 0.002 | 1 | 100 | 0.252 | 0.004 | 8.586 | 8.587 | Download | Download |
| 8 | predict | 100000 | 1 | 100 | 0.022 | 0.000 | 0.000 | 0.022 | 1 | 100 | 0.009 | 0.000 | 2.530 | 2.530 | Download | Download |
| 10 | predict | 100000 | 1000 | 100 | 2.976 | 0.066 | 0.000 | 0.003 | -1 | 100 | 0.247 | 0.002 | 12.058 | 12.058 | Download | Download |
| 11 | predict | 100000 | 1 | 100 | 0.026 | 0.001 | 0.000 | 0.026 | -1 | 100 | 0.009 | 0.000 | 2.905 | 2.905 | Download | Download |
| 13 | predict | 100000 | 1000 | 100 | 2.166 | 0.013 | 0.000 | 0.002 | 1 | 5 | 0.201 | 0.002 | 10.758 | 10.758 | Download | Download |
| 14 | predict | 100000 | 1 | 100 | 0.022 | 0.000 | 0.000 | 0.022 | 1 | 5 | 0.009 | 0.000 | 2.594 | 2.594 | Download | Download |
| 16 | predict | 100000 | 1000 | 100 | 1.297 | 0.007 | 0.001 | 0.001 | 1 | 1 | 0.204 | 0.013 | 6.349 | 6.362 | Download | Download |
| 17 | predict | 100000 | 1 | 100 | 0.025 | 0.006 | 0.000 | 0.025 | 1 | 1 | 0.009 | 0.000 | 2.842 | 2.843 | Download | Download |
| 19 | predict | 100000 | 1000 | 2 | 1.846 | 0.023 | 0.000 | 0.002 | -1 | 1 | 0.033 | 0.001 | 56.796 | 56.816 | Download | Download |
| 20 | predict | 100000 | 1 | 2 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 1 | 0.001 | 0.000 | 5.243 | 5.269 | Download | Download |
| 22 | predict | 100000 | 1000 | 2 | 2.791 | 0.065 | 0.000 | 0.003 | -1 | 5 | 0.033 | 0.000 | 83.582 | 83.584 | Download | Download |
| 23 | predict | 100000 | 1 | 2 | 0.007 | 0.003 | 0.000 | 0.007 | -1 | 5 | 0.001 | 0.000 | 7.490 | 7.517 | Download | Download |
| 25 | predict | 100000 | 1000 | 2 | 2.113 | 0.009 | 0.000 | 0.002 | 1 | 100 | 0.075 | 0.001 | 28.106 | 28.107 | Download | Download |
| 26 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.193 | 3.203 | Download | Download |
| 28 | predict | 100000 | 1000 | 2 | 2.798 | 0.037 | 0.000 | 0.003 | -1 | 100 | 0.074 | 0.001 | 37.553 | 37.554 | Download | Download |
| 29 | predict | 100000 | 1 | 2 | 0.007 | 0.004 | 0.000 | 0.007 | -1 | 100 | 0.001 | 0.000 | 7.342 | 7.362 | Download | Download |
| 31 | predict | 100000 | 1000 | 2 | 2.112 | 0.023 | 0.000 | 0.002 | 1 | 5 | 0.033 | 0.000 | 63.472 | 63.474 | Download | Download |
| 32 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 3.720 | 3.728 | Download | Download |
| 34 | predict | 100000 | 1000 | 2 | 1.199 | 0.016 | 0.000 | 0.001 | 1 | 1 | 0.033 | 0.001 | 36.765 | 36.784 | Download | Download |
| 35 | predict | 100000 | 1 | 2 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 1 | 0.001 | 0.000 | 3.028 | 3.037 | Download | Download |
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 | 3.164 | 0.058 | 0.025 | 0.0 | -1 | 1 | 0.802 | 0.015 | 3.944 | 3.944 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 10 | 3.277 | 0.047 | 0.024 | 0.0 | -1 | 5 | 0.797 | 0.012 | 4.112 | 4.113 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 10 | 3.242 | 0.027 | 0.025 | 0.0 | 1 | 100 | 0.798 | 0.015 | 4.062 | 4.063 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 10 | 3.243 | 0.022 | 0.025 | 0.0 | -1 | 100 | 0.796 | 0.005 | 4.076 | 4.076 | Download | Download |
| 12 | fit | 1000000 | 1000000 | 10 | 3.238 | 0.014 | 0.025 | 0.0 | 1 | 5 | 0.808 | 0.015 | 4.006 | 4.006 | Download | Download |
| 15 | fit | 1000000 | 1000000 | 10 | 3.224 | 0.031 | 0.025 | 0.0 | 1 | 1 | 0.803 | 0.015 | 4.015 | 4.016 | Download | Download |
| 18 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.026 | 0.0 | -1 | 1 | 0.001 | 0.000 | 0.525 | 0.536 | Download | Download |
| 21 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.021 | 0.0 | -1 | 5 | 0.001 | 0.000 | 0.748 | 0.749 | Download | Download |
| 24 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.021 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.717 | 0.718 | Download | Download |
| 27 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.584 | 0.585 | Download | Download |
| 30 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.024 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.638 | 0.639 | Download | Download |
| 33 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.025 | 0.0 | 1 | 1 | 0.001 | 0.001 | 0.468 | 0.576 | 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 | 0.463 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.105 | 0.001 | 4.421 | 4.421 | Download | Download |
| 2 | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.379 | 9.593 | Download | Download |
| 4 | predict | 1000000 | 1000 | 10 | 0.865 | 0.006 | 0.000 | 0.001 | -1 | 5 | 0.190 | 0.003 | 4.541 | 4.542 | Download | Download |
| 5 | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 9.748 | 10.067 | Download | Download |
| 7 | predict | 1000000 | 1000 | 10 | 4.840 | 0.017 | 0.000 | 0.005 | 1 | 100 | 0.570 | 0.003 | 8.492 | 8.492 | Download | Download |
| 8 | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.308 | 4.431 | Download | Download |
| 10 | predict | 1000000 | 1000 | 10 | 2.803 | 0.012 | 0.000 | 0.003 | -1 | 100 | 0.576 | 0.007 | 4.865 | 4.865 | Download | Download |
| 11 | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 7.610 | 7.834 | Download | Download |
| 13 | predict | 1000000 | 1000 | 10 | 1.450 | 0.007 | 0.000 | 0.001 | 1 | 5 | 0.190 | 0.001 | 7.650 | 7.650 | Download | Download |
| 14 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.948 | 4.113 | Download | Download |
| 16 | predict | 1000000 | 1000 | 10 | 0.764 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.106 | 0.001 | 7.220 | 7.221 | Download | Download |
| 17 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.757 | 3.890 | Download | Download |
| 19 | predict | 1000 | 1000 | 2 | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.001 | 0.000 | 32.511 | 32.650 | Download | Download |
| 20 | predict | 1000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 17.592 | 18.047 | Download | Download |
| 22 | predict | 1000 | 1000 | 2 | 0.028 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 33.987 | 34.040 | Download | Download |
| 23 | predict | 1000 | 1 | 2 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 17.970 | 18.713 | Download | Download |
| 25 | predict | 1000 | 1000 | 2 | 0.041 | 0.001 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 7.621 | 7.621 | Download | Download |
| 26 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 4.070 | 4.187 | Download | Download |
| 28 | predict | 1000 | 1000 | 2 | 0.041 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.000 | 7.613 | 7.613 | Download | Download |
| 29 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 14.719 | 15.131 | Download | Download |
| 31 | predict | 1000 | 1000 | 2 | 0.025 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 29.702 | 29.916 | Download | Download |
| 32 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.504 | 4.625 | Download | Download |
| 34 | predict | 1000 | 1000 | 2 | 0.023 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.001 | 0.000 | 43.907 | 44.064 | Download | Download |
| 35 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 4.398 | 4.508 | Download | Download |
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 | 0.567 | 0.004 | 0.028 | 0.0 | 30 | random | 0.445 | 0.019 | 1.275 | 1.276 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 2 | 0.652 | 0.018 | 0.025 | 0.0 | 30 | k-means++ | 0.470 | 0.023 | 1.386 | 1.388 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 100 | 5.348 | 0.451 | 0.150 | 0.0 | 30 | random | 2.928 | 0.039 | 1.826 | 1.826 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 100 | 5.279 | 0.039 | 0.152 | 0.0 | 30 | k-means++ | 3.102 | 0.060 | 1.702 | 1.702 | 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 | 0.000 | 0.0 | 0.061 | 0.0 | 30 | random | 0.0 | 0.0 | 1.065 | 1.141 | Download | Download |
| 2 | predict | 1000000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 30 | random | 0.0 | 0.0 | 1.291 | 1.338 | Download | Download |
| 4 | predict | 1000000 | 1000 | 2 | 0.000 | 0.0 | 0.061 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.122 | 1.198 | Download | Download |
| 5 | predict | 1000000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.298 | 1.339 | Download | Download |
| 7 | predict | 1000000 | 1000 | 100 | 0.001 | 0.0 | 1.465 | 0.0 | 30 | random | 0.0 | 0.0 | 1.434 | 1.510 | Download | Download |
| 8 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.004 | 0.0 | 30 | random | 0.0 | 0.0 | 1.042 | 1.067 | Download | Download |
| 10 | predict | 1000000 | 1000 | 100 | 0.001 | 0.0 | 1.515 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.554 | 1.645 | Download | Download |
| 11 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.004 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.329 | 1.366 | Download | Download |
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 | 0.001534 | 0.000096 | 0.521390 | 0.000002 | 20 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.293414 | 0.001319 | 0.000107 | 0.325483 | 1.163342 | 1.167189 |
| 10 | sklearn_KMeans_short | sklearn | 0.032656 | predict | 10000 | 1000 | 100 | 0.002627 | 0.002112 | 0.304549 | 0.000003 | 20 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.283247 | 0.001299 | 0.000094 | 0.315903 | 2.022165 | 2.027442 |
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 | 0.095 | 0.002 | 0.002 | 0.0 | 20 | random | 0.041 | 0.001 | 2.330 | 2.330 | Download | Download |
| 3 | fit | 10000 | 10000 | 2 | 0.235 | 0.002 | 0.001 | 0.0 | 20 | k-means++ | 0.106 | 0.002 | 2.213 | 2.214 | Download | Download |
| 6 | fit | 10000 | 10000 | 100 | 0.231 | 0.004 | 0.035 | 0.0 | 20 | random | 0.155 | 0.002 | 1.487 | 1.488 | Download | Download |
| 9 | fit | 10000 | 10000 | 100 | 0.657 | 0.007 | 0.012 | 0.0 | 20 | k-means++ | 0.372 | 0.002 | 1.763 | 1.763 | 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 | 0.000 | 0.021 | 0.0 | 20 | random | 0.001 | 0.0 | 1.087 | 1.089 | Download | Download |
| 2 | predict | 10000 | 1 | 2 | 0.000 | 0.000 | 0.000 | 0.0 | 20 | random | 0.000 | 0.0 | 1.215 | 1.237 | Download | Download |
| 4 | predict | 10000 | 1000 | 2 | 0.001 | 0.000 | 0.023 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 0.963 | 0.966 | Download | Download |
| 5 | predict | 10000 | 1 | 2 | 0.000 | 0.000 | 0.000 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.340 | 1.370 | Download | Download |
| 7 | predict | 10000 | 1000 | 100 | 0.002 | 0.000 | 0.521 | 0.0 | 20 | random | 0.001 | 0.0 | 1.163 | 1.167 | Download | Download |
| 8 | predict | 10000 | 1 | 100 | 0.000 | 0.000 | 0.003 | 0.0 | 20 | random | 0.000 | 0.0 | 1.239 | 1.254 | Download | Download |
| 10 | predict | 10000 | 1000 | 100 | 0.003 | 0.002 | 0.305 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 2.022 | 2.027 | Download | Download |
| 11 | predict | 10000 | 1 | 100 | 0.000 | 0.000 | 0.003 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.216 | 1.231 | Download | Download |
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 | 11.425 | 0.231 | 0.07 | 0.000 | [20] | 2.003 | 0.018 | 5.703 | 5.704 | Download | Download |
| 3 | fit | 1000 | 1000 | 10000 | 0.797 | 0.036 | 0.10 | 0.001 | [27] | 0.760 | 0.032 | 1.049 | 1.050 | 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 | 2.640 | 0.0 | [20] | 0.001 | 0.001 | 0.400 | 0.678 | Download | Download |
| 2 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.012 | 0.0 | [20] | 0.000 | 0.000 | 0.306 | 0.310 | Download | Download |
| 4 | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | 4.526 | 0.0 | [27] | 0.004 | 0.002 | 0.404 | 0.437 | Download | Download |
| 5 | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 0.915 | 0.0 | [27] | 0.001 | 0.000 | 0.109 | 0.110 | Download | Download |
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 | 1.413 | 0.004 | 0.057 | 0.001 | True | True | deprecated | False | 1.616 | 0.004 | 0.875 | 0.875 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 8.471 | 0.752 | 0.094 | 0.000 | True | True | deprecated | False | 0.210 | 0.004 | 40.266 | 40.275 | 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 | 0.01 | 0.0 | 7.755 | 0.0 | True | True | deprecated | False | 0.017 | 0.0 | 0.596 | 0.596 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.00 | 0.0 | 5.516 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.452 | 0.474 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.00 | 0.0 | 5.335 | 0.0 | True | True | deprecated | False | 0.001 | 0.0 | 0.291 | 0.324 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.00 | 0.0 | 0.134 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.371 | 0.382 | Download | Download |
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 | 0.187 | 0.002 | 0.429 | 0.0 | 0.194 | 0.001 | 0.960 | 0.960 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 1.133 | 0.004 | 0.706 | 0.0 | 0.249 | 0.001 | 4.544 | 4.544 | 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 | 0.01 | 0.0 | 7.745 | 0.0 | 0.017 | 0.000 | 0.592 | 0.593 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.00 | 0.0 | 5.214 | 0.0 | 0.001 | 0.001 | 0.301 | 0.456 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.00 | 0.0 | 5.344 | 0.0 | 0.000 | 0.000 | 0.483 | 0.510 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.00 | 0.0 | 0.126 | 0.0 | 0.000 | 0.000 | 0.384 | 0.401 | Download | Download |
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 | 4.122 | 0.228 | 0.0 | 0.004 | 1000 | 0.5 | 12.0 | warn | warn | barnes_hut | euclidean | 0.0 | 2 | 300 | 30.0 | legacy | 0 | 4.249 | 0.377 | 0.97 | 0.974 | Download | Download |
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 | 0.476 | 0.004 | 0.168 | 0.0 | 0.031 | 0.005 | 15.282 | 15.508 | Download | Download |
| 1 | fit | 10000 | 10000 | 1000 | 0.409 | 0.003 | 0.196 | 0.0 | 0.214 | 0.007 | 1.913 | 1.914 | Download | Download |
| 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 |