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/20220314T213129/"
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 | 1.365 | 0.023 | 0.059 | 0.0 | -1 | 1 | 0.056 | 0.008 | 24.514 | 24.752 | Download | Download |
| 3 | fit | 100000 | 100000 | 100 | 1.317 | 0.023 | 0.061 | 0.0 | -1 | 5 | 0.051 | 0.000 | 25.824 | 25.825 | Download | Download |
| 6 | fit | 100000 | 100000 | 100 | 1.312 | 0.018 | 0.061 | 0.0 | 1 | 100 | 0.049 | 0.000 | 26.795 | 26.796 | Download | Download |
| 9 | fit | 100000 | 100000 | 100 | 1.305 | 0.017 | 0.061 | 0.0 | -1 | 100 | 0.049 | 0.000 | 26.800 | 26.800 | Download | Download |
| 12 | fit | 100000 | 100000 | 100 | 1.306 | 0.006 | 0.061 | 0.0 | 1 | 5 | 0.049 | 0.000 | 26.791 | 26.791 | Download | Download |
| 15 | fit | 100000 | 100000 | 100 | 1.319 | 0.031 | 0.061 | 0.0 | 1 | 1 | 0.049 | 0.001 | 26.909 | 26.914 | Download | Download |
| 18 | fit | 100000 | 100000 | 2 | 0.051 | 0.001 | 0.031 | 0.0 | -1 | 1 | 0.008 | 0.000 | 6.463 | 6.466 | Download | Download |
| 21 | fit | 100000 | 100000 | 2 | 0.051 | 0.000 | 0.031 | 0.0 | -1 | 5 | 0.008 | 0.000 | 6.284 | 6.286 | Download | Download |
| 24 | fit | 100000 | 100000 | 2 | 0.051 | 0.001 | 0.031 | 0.0 | 1 | 100 | 0.008 | 0.000 | 6.338 | 6.340 | Download | Download |
| 27 | fit | 100000 | 100000 | 2 | 0.051 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.008 | 0.000 | 6.438 | 6.439 | Download | Download |
| 30 | fit | 100000 | 100000 | 2 | 0.045 | 0.000 | 0.035 | 0.0 | 1 | 5 | 0.008 | 0.000 | 5.652 | 5.653 | Download | Download |
| 33 | fit | 100000 | 100000 | 2 | 0.050 | 0.000 | 0.032 | 0.0 | 1 | 1 | 0.008 | 0.000 | 6.269 | 6.270 | 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 | 2.678 | 0.258 | 0.000 | 0.003 | -1 | 1 | 0.197 | 0.009 | 13.583 | 13.598 | Download | Download |
| 2 | predict | 100000 | 1 | 100 | 0.027 | 0.003 | 0.000 | 0.027 | -1 | 1 | 0.010 | 0.000 | 2.601 | 2.603 | Download | Download |
| 4 | predict | 100000 | 1000 | 100 | 3.108 | 0.102 | 0.000 | 0.003 | -1 | 5 | 0.201 | 0.011 | 15.453 | 15.474 | Download | Download |
| 5 | predict | 100000 | 1 | 100 | 0.029 | 0.003 | 0.000 | 0.029 | -1 | 5 | 0.010 | 0.000 | 2.866 | 2.867 | Download | Download |
| 7 | predict | 100000 | 1000 | 100 | 2.000 | 0.007 | 0.000 | 0.002 | 1 | 100 | 0.243 | 0.009 | 8.242 | 8.248 | Download | Download |
| 8 | predict | 100000 | 1 | 100 | 0.026 | 0.000 | 0.000 | 0.026 | 1 | 100 | 0.010 | 0.000 | 2.549 | 2.549 | Download | Download |
| 10 | predict | 100000 | 1000 | 100 | 2.824 | 0.044 | 0.000 | 0.003 | -1 | 100 | 0.236 | 0.002 | 11.946 | 11.947 | Download | Download |
| 11 | predict | 100000 | 1 | 100 | 0.031 | 0.004 | 0.000 | 0.031 | -1 | 100 | 0.010 | 0.000 | 3.105 | 3.105 | Download | Download |
| 13 | predict | 100000 | 1000 | 100 | 1.988 | 0.007 | 0.000 | 0.002 | 1 | 5 | 0.195 | 0.001 | 10.171 | 10.171 | Download | Download |
| 14 | predict | 100000 | 1 | 100 | 0.025 | 0.000 | 0.000 | 0.025 | 1 | 5 | 0.010 | 0.000 | 2.452 | 2.452 | Download | Download |
| 16 | predict | 100000 | 1000 | 100 | 1.289 | 0.008 | 0.001 | 0.001 | 1 | 1 | 0.196 | 0.002 | 6.583 | 6.584 | Download | Download |
| 17 | predict | 100000 | 1 | 100 | 0.024 | 0.000 | 0.000 | 0.024 | 1 | 1 | 0.010 | 0.000 | 2.447 | 2.448 | Download | Download |
| 19 | predict | 100000 | 1000 | 2 | 1.876 | 0.034 | 0.000 | 0.002 | -1 | 1 | 0.030 | 0.000 | 62.887 | 62.888 | Download | Download |
| 20 | predict | 100000 | 1 | 2 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 1 | 0.001 | 0.001 | 4.252 | 5.023 | Download | Download |
| 22 | predict | 100000 | 1000 | 2 | 2.640 | 0.026 | 0.000 | 0.003 | -1 | 5 | 0.031 | 0.000 | 85.082 | 85.090 | Download | Download |
| 23 | predict | 100000 | 1 | 2 | 0.009 | 0.004 | 0.000 | 0.009 | -1 | 5 | 0.001 | 0.000 | 10.545 | 10.640 | Download | Download |
| 25 | predict | 100000 | 1000 | 2 | 1.934 | 0.005 | 0.000 | 0.002 | 1 | 100 | 0.070 | 0.001 | 27.726 | 27.731 | Download | Download |
| 26 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.239 | 3.255 | Download | Download |
| 28 | predict | 100000 | 1000 | 2 | 2.677 | 0.025 | 0.000 | 0.003 | -1 | 100 | 0.068 | 0.000 | 39.217 | 39.217 | Download | Download |
| 29 | predict | 100000 | 1 | 2 | 0.007 | 0.004 | 0.000 | 0.007 | -1 | 100 | 0.001 | 0.000 | 7.121 | 7.148 | Download | Download |
| 31 | predict | 100000 | 1000 | 2 | 1.826 | 0.019 | 0.000 | 0.002 | 1 | 5 | 0.031 | 0.001 | 58.374 | 58.429 | Download | Download |
| 32 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 3.264 | 3.279 | Download | Download |
| 34 | predict | 100000 | 1000 | 2 | 1.160 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.030 | 0.000 | 39.255 | 39.257 | Download | Download |
| 35 | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.219 | 2.235 | 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 | 2.937 | 0.018 | 0.027 | 0.0 | -1 | 1 | 0.777 | 0.023 | 3.779 | 3.780 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 10 | 3.192 | 0.042 | 0.025 | 0.0 | -1 | 5 | 0.763 | 0.008 | 4.183 | 4.184 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 10 | 3.205 | 0.039 | 0.025 | 0.0 | 1 | 100 | 0.763 | 0.007 | 4.198 | 4.198 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 10 | 3.164 | 0.040 | 0.025 | 0.0 | -1 | 100 | 0.764 | 0.012 | 4.140 | 4.140 | Download | Download |
| 12 | fit | 1000000 | 1000000 | 10 | 3.109 | 0.041 | 0.026 | 0.0 | 1 | 5 | 0.780 | 0.018 | 3.987 | 3.988 | Download | Download |
| 15 | fit | 1000000 | 1000000 | 10 | 3.092 | 0.046 | 0.026 | 0.0 | 1 | 1 | 0.779 | 0.026 | 3.970 | 3.972 | Download | Download |
| 18 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 1 | 0.001 | 0.000 | 0.541 | 0.541 | Download | Download |
| 21 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.034 | 0.0 | -1 | 5 | 0.001 | 0.000 | 0.496 | 0.497 | Download | Download |
| 24 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.514 | 0.516 | Download | Download |
| 27 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.523 | 0.525 | Download | Download |
| 30 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.030 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.539 | 0.541 | Download | Download |
| 33 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.554 | 0.555 | 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 | 0.451 | 0.009 | 0.000 | 0.000 | -1 | 1 | 0.117 | 0.002 | 3.856 | 3.856 | Download | Download |
| 2 | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.555 | 10.163 | Download | Download |
| 4 | predict | 1000000 | 1000 | 10 | 0.855 | 0.007 | 0.000 | 0.001 | -1 | 5 | 0.206 | 0.005 | 4.145 | 4.146 | Download | Download |
| 5 | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 8.164 | 8.741 | Download | Download |
| 7 | predict | 1000000 | 1000 | 10 | 4.926 | 0.024 | 0.000 | 0.005 | 1 | 100 | 0.609 | 0.003 | 8.087 | 8.087 | Download | Download |
| 8 | predict | 1000000 | 1 | 10 | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 100 | 0.001 | 0.000 | 4.032 | 4.201 | Download | Download |
| 10 | predict | 1000000 | 1000 | 10 | 2.797 | 0.017 | 0.000 | 0.003 | -1 | 100 | 0.612 | 0.006 | 4.570 | 4.570 | Download | Download |
| 11 | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 100 | 0.001 | 0.000 | 7.509 | 7.896 | Download | Download |
| 13 | predict | 1000000 | 1000 | 10 | 1.444 | 0.013 | 0.000 | 0.001 | 1 | 5 | 0.207 | 0.003 | 6.966 | 6.966 | Download | Download |
| 14 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.554 | 3.808 | Download | Download |
| 16 | predict | 1000000 | 1000 | 10 | 0.763 | 0.012 | 0.000 | 0.001 | 1 | 1 | 0.122 | 0.008 | 6.263 | 6.277 | Download | Download |
| 17 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.567 | 3.794 | Download | Download |
| 19 | predict | 1000 | 1000 | 2 | 0.025 | 0.005 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 55.485 | 56.025 | Download | Download |
| 20 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 15.402 | 15.825 | Download | Download |
| 22 | predict | 1000 | 1000 | 2 | 0.023 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 28.613 | 28.847 | Download | Download |
| 23 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 15.341 | 15.837 | Download | Download |
| 25 | predict | 1000 | 1000 | 2 | 0.031 | 0.001 | 0.001 | 0.000 | 1 | 100 | 0.005 | 0.000 | 6.306 | 6.319 | Download | Download |
| 26 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 3.738 | 3.931 | Download | Download |
| 28 | predict | 1000 | 1000 | 2 | 0.033 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.000 | 6.734 | 6.752 | Download | Download |
| 29 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 14.048 | 14.976 | Download | Download |
| 31 | predict | 1000 | 1000 | 2 | 0.021 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 28.948 | 29.010 | Download | Download |
| 32 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.212 | 4.472 | Download | Download |
| 34 | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 41.872 | 42.621 | Download | Download |
| 35 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 4.236 | 4.406 | 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 | 0.497 | 0.002 | 0.032 | 0.0 | 30 | random | 0.417 | 0.019 | 1.194 | 1.195 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 2 | 0.560 | 0.013 | 0.029 | 0.0 | 30 | k-means++ | 0.443 | 0.019 | 1.265 | 1.266 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 100 | 5.381 | 0.358 | 0.149 | 0.0 | 30 | random | 3.087 | 0.030 | 1.743 | 1.743 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 100 | 5.405 | 0.064 | 0.148 | 0.0 | 30 | k-means++ | 3.271 | 0.033 | 1.652 | 1.652 | 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 | 0.071 | 0.0 | 30 | random | 0.0 | 0.0 | 1.037 | 1.133 | Download | Download |
| 2 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | random | 0.0 | 0.0 | 1.288 | 1.342 | Download | Download |
| 4 | predict | 1000000 | 1000 | 2 | 0.0 | 0.0 | 0.069 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.089 | 1.192 | Download | Download |
| 5 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.266 | 1.307 | Download | Download |
| 7 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 1.708 | 0.0 | 30 | random | 0.0 | 0.0 | 1.376 | 1.471 | Download | Download |
| 8 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.005 | 0.0 | 30 | random | 0.0 | 0.0 | 1.116 | 1.144 | Download | Download |
| 10 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 1.773 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.366 | 1.479 | Download | Download |
| 11 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.005 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.052 | 1.097 | 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 | 0.001474 | 0.000238 | 0.542790 | 0.000001 | 20 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.293414 | 0.001218 | 0.000108 | 0.325483 | 1.210159 | 1.214892 |
| 10 | sklearn_KMeans_short | sklearn | 0.032656 | predict | 10000 | 1000 | 100 | 0.001430 | 0.000113 | 0.559263 | 0.000001 | 20 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.283247 | 0.001235 | 0.000110 | 0.315903 | 1.157915 | 1.162495 |
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 | 0.090 | 0.004 | 0.002 | 0.0 | 20 | random | 0.038 | 0.002 | 2.373 | 2.375 | Download | Download |
| 3 | fit | 10000 | 10000 | 2 | 0.217 | 0.001 | 0.001 | 0.0 | 20 | k-means++ | 0.090 | 0.002 | 2.406 | 2.407 | Download | Download |
| 6 | fit | 10000 | 10000 | 100 | 0.220 | 0.003 | 0.036 | 0.0 | 20 | random | 0.152 | 0.003 | 1.447 | 1.448 | Download | Download |
| 9 | fit | 10000 | 10000 | 100 | 0.604 | 0.008 | 0.013 | 0.0 | 20 | k-means++ | 0.371 | 0.007 | 1.627 | 1.628 | 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 | 0.023 | 0.0 | 20 | random | 0.001 | 0.0 | 0.957 | 0.962 | Download | Download |
| 2 | predict | 10000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 20 | random | 0.000 | 0.0 | 1.330 | 1.359 | Download | Download |
| 4 | predict | 10000 | 1000 | 2 | 0.001 | 0.0 | 0.023 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 0.992 | 0.996 | Download | Download |
| 5 | predict | 10000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.552 | 1.586 | Download | Download |
| 7 | predict | 10000 | 1000 | 100 | 0.001 | 0.0 | 0.543 | 0.0 | 20 | random | 0.001 | 0.0 | 1.210 | 1.215 | Download | Download |
| 8 | predict | 10000 | 1 | 100 | 0.000 | 0.0 | 0.003 | 0.0 | 20 | random | 0.000 | 0.0 | 1.279 | 1.289 | Download | Download |
| 10 | predict | 10000 | 1000 | 100 | 0.001 | 0.0 | 0.559 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 1.158 | 1.162 | Download | Download |
| 11 | predict | 10000 | 1 | 100 | 0.000 | 0.0 | 0.003 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.297 | 1.306 | 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 | 10.944 | 0.038 | 0.073 | 0.000 | [20] | 2.161 | 0.016 | 5.066 | 5.066 | Download | Download |
| 3 | fit | 1000 | 1000 | 10000 | 0.816 | 0.047 | 0.098 | 0.001 | [27] | 0.889 | 0.050 | 0.918 | 0.919 | 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 | 2.821 | 0.0 | [20] | 0.001 | 0.001 | 0.321 | 0.507 | Download | Download |
| 2 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.014 | 0.0 | [20] | 0.000 | 0.000 | 0.244 | 0.256 | Download | Download |
| 4 | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | 4.589 | 0.0 | [27] | 0.005 | 0.001 | 0.345 | 0.351 | Download | Download |
| 5 | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 1.051 | 0.0 | [27] | 0.001 | 0.000 | 0.058 | 0.059 | 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 | 1.355 | 0.007 | 0.059 | 0.001 | True | True | deprecated | False | 1.546 | 0.014 | 0.876 | 0.876 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 9.268 | 0.656 | 0.086 | 0.000 | True | True | deprecated | False | 0.206 | 0.003 | 44.963 | 44.968 | 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 | 0.013 | 0.0 | 6.387 | 0.0 | True | True | deprecated | False | 0.020 | 0.0 | 0.629 | 0.629 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.000 | 0.0 | 5.429 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.487 | 0.502 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 5.130 | 0.0 | True | True | deprecated | False | 0.001 | 0.0 | 0.292 | 0.331 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.000 | 0.0 | 0.146 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.353 | 0.368 | 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 | 0.186 | 0.002 | 0.431 | 0.0 | 0.194 | 0.002 | 0.959 | 0.959 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 1.146 | 0.011 | 0.698 | 0.0 | 0.250 | 0.004 | 4.585 | 4.585 | 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 | 6.797 | 0.0 | 0.021 | 0.001 | 0.563 | 0.563 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.000 | 0.0 | 5.383 | 0.0 | 0.001 | 0.001 | 0.291 | 0.429 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 5.281 | 0.0 | 0.000 | 0.000 | 0.476 | 0.508 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.000 | 0.0 | 0.142 | 0.0 | 0.000 | 0.000 | 0.388 | 0.394 | 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 | 3.531 | 0.2 | 0.0 | 0.004 | 1000 | 0.5 | 12.0 | warn | warn | barnes_hut | euclidean | 0.0 | 2 | 300 | 30.0 | legacy | 0 | 3.624 | 0.334 | 0.974 | 0.978 | 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 | 0.502 | 0.008 | 0.159 | 0.0 | 0.032 | 0.004 | 15.673 | 15.810 | Download | Download |
| 1 | fit | 10000 | 10000 | 1000 | 0.432 | 0.004 | 0.185 | 0.0 | 0.205 | 0.005 | 2.104 | 2.104 | 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 |