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/20220314T000704/"
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 | 1.288 | 0.014 | 0.062 | 0.0 | -1 | 1 | 0.046 | 0.006 | 27.987 | 28.245 | Download | Download |
| 3 | fit | 100000 | 100000 | 100 | 1.258 | 0.013 | 0.064 | 0.0 | -1 | 5 | 0.044 | 0.000 | 28.452 | 28.453 | Download | Download |
| 6 | fit | 100000 | 100000 | 100 | 1.253 | 0.009 | 0.064 | 0.0 | 1 | 100 | 0.044 | 0.000 | 28.227 | 28.227 | Download | Download |
| 9 | fit | 100000 | 100000 | 100 | 1.255 | 0.011 | 0.064 | 0.0 | -1 | 100 | 0.044 | 0.000 | 28.469 | 28.471 | Download | Download |
| 12 | fit | 100000 | 100000 | 100 | 1.256 | 0.014 | 0.064 | 0.0 | 1 | 5 | 0.044 | 0.000 | 28.433 | 28.434 | Download | Download |
| 15 | fit | 100000 | 100000 | 100 | 1.256 | 0.028 | 0.064 | 0.0 | 1 | 1 | 0.044 | 0.000 | 28.610 | 28.611 | Download | Download |
| 18 | fit | 100000 | 100000 | 2 | 0.048 | 0.001 | 0.033 | 0.0 | -1 | 1 | 0.009 | 0.000 | 5.679 | 5.680 | Download | Download |
| 21 | fit | 100000 | 100000 | 2 | 0.048 | 0.000 | 0.034 | 0.0 | -1 | 5 | 0.009 | 0.000 | 5.583 | 5.584 | Download | Download |
| 24 | fit | 100000 | 100000 | 2 | 0.048 | 0.000 | 0.034 | 0.0 | 1 | 100 | 0.009 | 0.000 | 5.587 | 5.588 | Download | Download |
| 27 | fit | 100000 | 100000 | 2 | 0.048 | 0.001 | 0.033 | 0.0 | -1 | 100 | 0.009 | 0.000 | 5.636 | 5.637 | Download | Download |
| 30 | fit | 100000 | 100000 | 2 | 0.048 | 0.000 | 0.033 | 0.0 | 1 | 5 | 0.009 | 0.000 | 5.599 | 5.600 | Download | Download |
| 33 | fit | 100000 | 100000 | 2 | 0.048 | 0.000 | 0.033 | 0.0 | 1 | 1 | 0.009 | 0.000 | 5.598 | 5.598 | 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 | 2.244 | 0.228 | 0.000 | 0.002 | -1 | 1 | 0.171 | 0.004 | 13.112 | 13.116 | Download | Download |
| 2 | predict | 100000 | 1 | 100 | 0.021 | 0.002 | 0.000 | 0.021 | -1 | 1 | 0.008 | 0.000 | 2.682 | 2.683 | Download | Download |
| 4 | predict | 100000 | 1000 | 100 | 2.865 | 0.089 | 0.000 | 0.003 | -1 | 5 | 0.172 | 0.001 | 16.658 | 16.658 | Download | Download |
| 5 | predict | 100000 | 1 | 100 | 0.021 | 0.002 | 0.000 | 0.021 | -1 | 5 | 0.008 | 0.000 | 2.765 | 2.765 | Download | Download |
| 7 | predict | 100000 | 1000 | 100 | 1.861 | 0.003 | 0.000 | 0.002 | 1 | 100 | 0.211 | 0.003 | 8.814 | 8.815 | Download | Download |
| 8 | predict | 100000 | 1 | 100 | 0.018 | 0.000 | 0.000 | 0.018 | 1 | 100 | 0.008 | 0.000 | 2.326 | 2.327 | Download | Download |
| 10 | predict | 100000 | 1000 | 100 | 2.621 | 0.048 | 0.000 | 0.003 | -1 | 100 | 0.210 | 0.001 | 12.471 | 12.472 | Download | Download |
| 11 | predict | 100000 | 1 | 100 | 0.021 | 0.002 | 0.000 | 0.021 | -1 | 100 | 0.008 | 0.000 | 2.695 | 2.696 | Download | Download |
| 13 | predict | 100000 | 1000 | 100 | 1.843 | 0.007 | 0.000 | 0.002 | 1 | 5 | 0.173 | 0.002 | 10.674 | 10.675 | Download | Download |
| 14 | predict | 100000 | 1 | 100 | 0.018 | 0.000 | 0.000 | 0.018 | 1 | 5 | 0.008 | 0.000 | 2.441 | 2.441 | Download | Download |
| 16 | predict | 100000 | 1000 | 100 | 1.140 | 0.004 | 0.001 | 0.001 | 1 | 1 | 0.170 | 0.002 | 6.690 | 6.690 | Download | Download |
| 17 | predict | 100000 | 1 | 100 | 0.020 | 0.004 | 0.000 | 0.020 | 1 | 1 | 0.008 | 0.000 | 2.615 | 2.616 | Download | Download |
| 19 | predict | 100000 | 1000 | 2 | 1.691 | 0.030 | 0.000 | 0.002 | -1 | 1 | 0.026 | 0.000 | 63.901 | 63.908 | Download | Download |
| 20 | predict | 100000 | 1 | 2 | 0.005 | 0.004 | 0.000 | 0.005 | -1 | 1 | 0.001 | 0.000 | 7.424 | 7.464 | Download | Download |
| 22 | predict | 100000 | 1000 | 2 | 2.447 | 0.024 | 0.000 | 0.002 | -1 | 5 | 0.028 | 0.000 | 88.900 | 88.904 | Download | Download |
| 23 | predict | 100000 | 1 | 2 | 0.006 | 0.004 | 0.000 | 0.006 | -1 | 5 | 0.001 | 0.000 | 7.867 | 7.937 | Download | Download |
| 25 | predict | 100000 | 1000 | 2 | 1.799 | 0.002 | 0.000 | 0.002 | 1 | 100 | 0.063 | 0.001 | 28.658 | 28.661 | Download | Download |
| 26 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.308 | 3.321 | Download | Download |
| 28 | predict | 100000 | 1000 | 2 | 2.465 | 0.021 | 0.000 | 0.002 | -1 | 100 | 0.062 | 0.000 | 39.653 | 39.654 | Download | Download |
| 29 | predict | 100000 | 1 | 2 | 0.009 | 0.005 | 0.000 | 0.009 | -1 | 100 | 0.001 | 0.000 | 10.433 | 10.482 | Download | Download |
| 31 | predict | 100000 | 1000 | 2 | 1.788 | 0.003 | 0.000 | 0.002 | 1 | 5 | 0.028 | 0.000 | 64.990 | 64.991 | Download | Download |
| 32 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 3.507 | 3.524 | Download | Download |
| 34 | predict | 100000 | 1000 | 2 | 1.029 | 0.002 | 0.000 | 0.001 | 1 | 1 | 0.027 | 0.000 | 38.439 | 38.442 | Download | Download |
| 35 | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.377 | 2.389 | 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 | 2.654 | 0.035 | 0.030 | 0.0 | -1 | 1 | 0.687 | 0.003 | 3.865 | 3.865 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 10 | 2.650 | 0.037 | 0.030 | 0.0 | -1 | 5 | 0.700 | 0.018 | 3.788 | 3.789 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 10 | 2.671 | 0.045 | 0.030 | 0.0 | 1 | 100 | 0.696 | 0.009 | 3.839 | 3.839 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 10 | 2.662 | 0.043 | 0.030 | 0.0 | -1 | 100 | 0.696 | 0.008 | 3.822 | 3.822 | Download | Download |
| 12 | fit | 1000000 | 1000000 | 10 | 2.644 | 0.033 | 0.030 | 0.0 | 1 | 5 | 0.695 | 0.016 | 3.806 | 3.807 | Download | Download |
| 15 | fit | 1000000 | 1000000 | 10 | 2.639 | 0.032 | 0.030 | 0.0 | 1 | 1 | 0.693 | 0.011 | 3.806 | 3.807 | Download | Download |
| 18 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 1 | 0.001 | 0.000 | 0.583 | 0.584 | Download | Download |
| 21 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 5 | 0.001 | 0.000 | 0.566 | 0.568 | Download | Download |
| 24 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.608 | 0.609 | Download | Download |
| 27 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.609 | 0.609 | Download | Download |
| 30 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.580 | 0.580 | Download | Download |
| 33 | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.032 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.554 | 0.554 | 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 | 0.375 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.096 | 0.001 | 3.897 | 3.897 | Download | Download |
| 2 | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 9.477 | 9.860 | Download | Download |
| 4 | predict | 1000000 | 1000 | 10 | 0.691 | 0.010 | 0.000 | 0.001 | -1 | 5 | 0.172 | 0.002 | 4.027 | 4.027 | Download | Download |
| 5 | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 9.747 | 10.098 | Download | Download |
| 7 | predict | 1000000 | 1000 | 10 | 4.187 | 0.020 | 0.000 | 0.004 | 1 | 100 | 0.520 | 0.006 | 8.058 | 8.059 | Download | Download |
| 8 | predict | 1000000 | 1 | 10 | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 100 | 0.001 | 0.000 | 4.362 | 4.552 | Download | Download |
| 10 | predict | 1000000 | 1000 | 10 | 2.250 | 0.016 | 0.000 | 0.002 | -1 | 100 | 0.518 | 0.013 | 4.346 | 4.347 | Download | Download |
| 11 | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 100 | 0.001 | 0.000 | 7.176 | 7.384 | Download | Download |
| 13 | predict | 1000000 | 1000 | 10 | 1.264 | 0.017 | 0.000 | 0.001 | 1 | 5 | 0.171 | 0.001 | 7.375 | 7.376 | Download | Download |
| 14 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.079 | 4.243 | Download | Download |
| 16 | predict | 1000000 | 1000 | 10 | 0.666 | 0.009 | 0.000 | 0.001 | 1 | 1 | 0.097 | 0.002 | 6.854 | 6.855 | Download | Download |
| 17 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.647 | 3.849 | Download | Download |
| 19 | predict | 1000 | 1000 | 2 | 0.022 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.001 | 0.000 | 35.542 | 35.780 | Download | Download |
| 20 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 16.646 | 17.063 | Download | Download |
| 22 | predict | 1000 | 1000 | 2 | 0.023 | 0.000 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 33.227 | 33.273 | Download | Download |
| 23 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 16.603 | 17.012 | Download | Download |
| 25 | predict | 1000 | 1000 | 2 | 0.034 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 7.551 | 7.551 | Download | Download |
| 26 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 4.254 | 4.346 | Download | Download |
| 28 | predict | 1000 | 1000 | 2 | 0.034 | 0.000 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.000 | 7.508 | 7.508 | Download | Download |
| 29 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 15.092 | 15.635 | Download | Download |
| 31 | predict | 1000 | 1000 | 2 | 0.021 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 31.307 | 31.358 | Download | Download |
| 32 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.224 | 4.368 | Download | Download |
| 34 | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 45.202 | 45.447 | Download | Download |
| 35 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 4.287 | 4.444 | 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 | 0.471 | 0.006 | 0.034 | 0.0 | 30 | random | 0.384 | 0.020 | 1.226 | 1.228 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 2 | 0.539 | 0.013 | 0.030 | 0.0 | 30 | k-means++ | 0.405 | 0.017 | 1.332 | 1.333 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 100 | 4.607 | 0.376 | 0.174 | 0.0 | 30 | random | 2.443 | 0.010 | 1.886 | 1.886 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 100 | 4.547 | 0.025 | 0.176 | 0.0 | 30 | k-means++ | 2.601 | 0.006 | 1.748 | 1.748 | 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 | 0.073 | 0.0 | 30 | random | 0.0 | 0.0 | 1.124 | 1.205 | Download | Download |
| 2 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | random | 0.0 | 0.0 | 1.316 | 1.361 | Download | Download |
| 4 | predict | 1000000 | 1000 | 2 | 0.0 | 0.0 | 0.074 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.120 | 1.213 | Download | Download |
| 5 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.337 | 1.376 | Download | Download |
| 7 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 1.999 | 0.0 | 30 | random | 0.0 | 0.0 | 1.373 | 1.454 | Download | Download |
| 8 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.005 | 0.0 | 30 | random | 0.0 | 0.0 | 1.298 | 1.353 | Download | Download |
| 10 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 1.975 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.478 | 1.579 | Download | Download |
| 11 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.005 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.250 | 1.289 | 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 | 0.001237 | 0.000138 | 0.646465 | 0.000001 | 20 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.293414 | 0.001080 | 0.000102 | 0.325483 | 1.145791 | 1.150860 |
| 10 | sklearn_KMeans_short | sklearn | 0.032656 | predict | 10000 | 1000 | 100 | 0.001991 | 0.001764 | 0.401870 | 0.000002 | 20 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.283247 | 0.001059 | 0.000088 | 0.315903 | 1.879897 | 1.886312 |
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 | 0.081 | 0.002 | 0.002 | 0.0 | 20 | random | 0.033 | 0.001 | 2.429 | 2.430 | Download | Download |
| 3 | fit | 10000 | 10000 | 2 | 0.199 | 0.002 | 0.001 | 0.0 | 20 | k-means++ | 0.091 | 0.003 | 2.189 | 2.190 | Download | Download |
| 6 | fit | 10000 | 10000 | 100 | 0.197 | 0.003 | 0.041 | 0.0 | 20 | random | 0.131 | 0.003 | 1.505 | 1.505 | Download | Download |
| 9 | fit | 10000 | 10000 | 100 | 0.554 | 0.004 | 0.014 | 0.0 | 20 | k-means++ | 0.314 | 0.005 | 1.762 | 1.762 | 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 | 0.026 | 0.0 | 20 | random | 0.001 | 0.0 | 1.040 | 1.042 | Download | Download |
| 2 | predict | 10000 | 1 | 2 | 0.000 | 0.000 | 0.000 | 0.0 | 20 | random | 0.000 | 0.0 | 1.249 | 1.286 | Download | Download |
| 4 | predict | 10000 | 1000 | 2 | 0.001 | 0.000 | 0.026 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 1.000 | 1.005 | Download | Download |
| 5 | predict | 10000 | 1 | 2 | 0.000 | 0.000 | 0.000 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.267 | 1.305 | Download | Download |
| 7 | predict | 10000 | 1000 | 100 | 0.001 | 0.000 | 0.646 | 0.0 | 20 | random | 0.001 | 0.0 | 1.146 | 1.151 | Download | Download |
| 8 | predict | 10000 | 1 | 100 | 0.000 | 0.000 | 0.004 | 0.0 | 20 | random | 0.000 | 0.0 | 1.209 | 1.232 | Download | Download |
| 10 | predict | 10000 | 1000 | 100 | 0.002 | 0.002 | 0.402 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 1.880 | 1.886 | Download | Download |
| 11 | predict | 10000 | 1 | 100 | 0.000 | 0.000 | 0.004 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.273 | 1.287 | 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 | 10.009 | 0.147 | 0.080 | 0.000 | [20] | 1.72 | 0.015 | 5.818 | 5.818 | Download | Download |
| 3 | fit | 1000 | 1000 | 10000 | 0.721 | 0.034 | 0.111 | 0.001 | [27] | 0.70 | 0.029 | 1.029 | 1.030 | 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 | 2.915 | 0.0 | [20] | 0.001 | 0.001 | 0.450 | 0.773 | Download | Download |
| 2 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.013 | 0.0 | [20] | 0.000 | 0.000 | 0.336 | 0.339 | Download | Download |
| 4 | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | 5.239 | 0.0 | [27] | 0.005 | 0.001 | 0.323 | 0.327 | Download | Download |
| 5 | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 0.967 | 0.0 | [27] | 0.001 | 0.000 | 0.056 | 0.057 | 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 | 1.237 | 0.004 | 0.065 | 0.001 | True | True | deprecated | False | 1.403 | 0.011 | 0.882 | 0.882 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 7.599 | 0.537 | 0.105 | 0.000 | True | True | deprecated | False | 0.185 | 0.003 | 41.183 | 41.187 | 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 | 8.306 | 0.0 | True | True | deprecated | False | 0.016 | 0.0 | 0.584 | 0.584 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.00 | 0.0 | 6.286 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.430 | 0.440 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.00 | 0.0 | 5.751 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.310 | 0.343 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.00 | 0.0 | 0.137 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.396 | 0.403 | 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 | 0.164 | 0.001 | 0.487 | 0.0 | 0.169 | 0.001 | 0.973 | 0.974 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 1.005 | 0.007 | 0.796 | 0.0 | 0.214 | 0.001 | 4.703 | 4.703 | 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 | 8.110 | 0.0 | 0.016 | 0.0 | 0.602 | 0.602 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.00 | 0.0 | 5.913 | 0.0 | 0.000 | 0.0 | 0.331 | 0.499 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.00 | 0.0 | 6.127 | 0.0 | 0.000 | 0.0 | 0.454 | 0.476 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.00 | 0.0 | 0.131 | 0.0 | 0.000 | 0.0 | 0.415 | 0.424 | 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 | 3.427 | 0.172 | 0.0 | 0.003 | 1000 | 0.5 | 12.0 | warn | warn | barnes_hut | euclidean | 0.0 | 2 | 300 | 30.0 | legacy | 0 | 3.504 | 0.321 | 0.978 | 0.982 | 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 | 0.421 | 0.010 | 0.190 | 0.0 | 0.027 | 0.004 | 15.771 | 15.944 | Download | Download |
| 1 | fit | 10000 | 10000 | 1000 | 0.368 | 0.002 | 0.217 | 0.0 | 0.181 | 0.003 | 2.032 | 2.033 | 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 |