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/20220313T221146/"
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 | 1.187 | 0.014 | 0.067 | 0.0 | -1 | 1 | 0.038 | 0.006 | 30.883 | 31.207 | Download | Download |
| 3 | fit | 100000 | 100000 | 100 | 1.152 | 0.010 | 0.069 | 0.0 | -1 | 5 | 0.037 | 0.000 | 31.476 | 31.477 | Download | Download |
| 6 | fit | 100000 | 100000 | 100 | 1.167 | 0.014 | 0.069 | 0.0 | 1 | 100 | 0.037 | 0.001 | 31.479 | 31.482 | Download | Download |
| 9 | fit | 100000 | 100000 | 100 | 1.156 | 0.011 | 0.069 | 0.0 | -1 | 100 | 0.037 | 0.001 | 31.073 | 31.078 | Download | Download |
| 12 | fit | 100000 | 100000 | 100 | 1.144 | 0.015 | 0.070 | 0.0 | 1 | 5 | 0.037 | 0.001 | 30.964 | 30.967 | Download | Download |
| 15 | fit | 100000 | 100000 | 100 | 1.154 | 0.015 | 0.069 | 0.0 | 1 | 1 | 0.037 | 0.001 | 31.455 | 31.458 | Download | Download |
| 18 | fit | 100000 | 100000 | 2 | 0.046 | 0.001 | 0.035 | 0.0 | -1 | 1 | 0.008 | 0.000 | 5.704 | 5.709 | Download | Download |
| 21 | fit | 100000 | 100000 | 2 | 0.045 | 0.000 | 0.035 | 0.0 | -1 | 5 | 0.008 | 0.000 | 5.598 | 5.599 | Download | Download |
| 24 | fit | 100000 | 100000 | 2 | 0.046 | 0.000 | 0.035 | 0.0 | 1 | 100 | 0.008 | 0.000 | 5.633 | 5.633 | Download | Download |
| 27 | fit | 100000 | 100000 | 2 | 0.045 | 0.000 | 0.035 | 0.0 | -1 | 100 | 0.008 | 0.000 | 5.607 | 5.608 | Download | Download |
| 30 | fit | 100000 | 100000 | 2 | 0.046 | 0.000 | 0.035 | 0.0 | 1 | 5 | 0.008 | 0.000 | 5.649 | 5.650 | Download | Download |
| 33 | fit | 100000 | 100000 | 2 | 0.045 | 0.000 | 0.035 | 0.0 | 1 | 1 | 0.008 | 0.000 | 5.579 | 5.580 | 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 | 1.711 | 0.261 | 0.000 | 0.002 | -1 | 1 | 0.191 | 0.004 | 8.951 | 8.953 | Download | Download |
| 2 | predict | 100000 | 1 | 100 | 0.018 | 0.001 | 0.000 | 0.018 | -1 | 1 | 0.006 | 0.000 | 2.993 | 2.997 | Download | Download |
| 4 | predict | 100000 | 1000 | 100 | 2.444 | 0.026 | 0.000 | 0.002 | -1 | 5 | 0.192 | 0.001 | 12.745 | 12.745 | Download | Download |
| 5 | predict | 100000 | 1 | 100 | 0.020 | 0.001 | 0.000 | 0.020 | -1 | 5 | 0.006 | 0.000 | 3.184 | 3.187 | Download | Download |
| 7 | predict | 100000 | 1000 | 100 | 1.960 | 0.003 | 0.000 | 0.002 | 1 | 100 | 0.231 | 0.007 | 8.470 | 8.473 | Download | Download |
| 8 | predict | 100000 | 1 | 100 | 0.017 | 0.000 | 0.000 | 0.017 | 1 | 100 | 0.006 | 0.000 | 2.724 | 2.724 | Download | Download |
| 10 | predict | 100000 | 1000 | 100 | 2.436 | 0.023 | 0.000 | 0.002 | -1 | 100 | 0.229 | 0.001 | 10.645 | 10.645 | Download | Download |
| 11 | predict | 100000 | 1 | 100 | 0.019 | 0.001 | 0.000 | 0.019 | -1 | 100 | 0.006 | 0.000 | 2.944 | 2.945 | Download | Download |
| 13 | predict | 100000 | 1000 | 100 | 1.941 | 0.011 | 0.000 | 0.002 | 1 | 5 | 0.191 | 0.000 | 10.160 | 10.160 | Download | Download |
| 14 | predict | 100000 | 1 | 100 | 0.016 | 0.000 | 0.000 | 0.016 | 1 | 5 | 0.006 | 0.000 | 2.637 | 2.638 | Download | Download |
| 16 | predict | 100000 | 1000 | 100 | 1.088 | 0.003 | 0.001 | 0.001 | 1 | 1 | 0.190 | 0.000 | 5.730 | 5.730 | Download | Download |
| 17 | predict | 100000 | 1 | 100 | 0.017 | 0.002 | 0.000 | 0.017 | 1 | 1 | 0.006 | 0.000 | 2.750 | 2.751 | Download | Download |
| 19 | predict | 100000 | 1000 | 2 | 1.412 | 0.015 | 0.000 | 0.001 | -1 | 1 | 0.029 | 0.001 | 49.064 | 49.088 | Download | Download |
| 20 | predict | 100000 | 1 | 2 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 1 | 0.001 | 0.000 | 5.663 | 5.688 | Download | Download |
| 22 | predict | 100000 | 1000 | 2 | 2.323 | 0.024 | 0.000 | 0.002 | -1 | 5 | 0.030 | 0.000 | 78.304 | 78.304 | Download | Download |
| 23 | predict | 100000 | 1 | 2 | 0.006 | 0.003 | 0.000 | 0.006 | -1 | 5 | 0.001 | 0.000 | 7.492 | 7.638 | Download | Download |
| 25 | predict | 100000 | 1000 | 2 | 1.899 | 0.005 | 0.000 | 0.002 | 1 | 100 | 0.066 | 0.000 | 28.667 | 28.667 | Download | Download |
| 26 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.740 | 3.758 | Download | Download |
| 28 | predict | 100000 | 1000 | 2 | 2.331 | 0.026 | 0.000 | 0.002 | -1 | 100 | 0.066 | 0.000 | 35.192 | 35.193 | Download | Download |
| 29 | predict | 100000 | 1 | 2 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.306 | 6.348 | Download | Download |
| 31 | predict | 100000 | 1000 | 2 | 1.884 | 0.002 | 0.000 | 0.002 | 1 | 5 | 0.030 | 0.000 | 63.323 | 63.323 | Download | Download |
| 32 | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.154 | 4.180 | Download | Download |
| 34 | predict | 100000 | 1000 | 2 | 0.961 | 0.004 | 0.000 | 0.001 | 1 | 1 | 0.029 | 0.000 | 33.703 | 33.704 | Download | Download |
| 35 | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.626 | 2.640 | 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 | 2.102 | 0.032 | 0.038 | 0.0 | -1 | 1 | 0.629 | 0.020 | 3.341 | 3.343 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 10 | 2.085 | 0.029 | 0.038 | 0.0 | -1 | 5 | 0.651 | 0.026 | 3.202 | 3.205 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 10 | 2.096 | 0.011 | 0.038 | 0.0 | 1 | 100 | 0.615 | 0.008 | 3.409 | 3.409 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 10 | 2.087 | 0.011 | 0.038 | 0.0 | -1 | 100 | 0.632 | 0.026 | 3.303 | 3.306 | Download | Download |
| 12 | fit | 1000000 | 1000000 | 10 | 2.090 | 0.030 | 0.038 | 0.0 | 1 | 5 | 0.633 | 0.030 | 3.303 | 3.307 | Download | Download |
| 15 | fit | 1000000 | 1000000 | 10 | 2.085 | 0.023 | 0.038 | 0.0 | 1 | 1 | 0.618 | 0.015 | 3.374 | 3.375 | Download | Download |
| 18 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | -1 | 1 | 0.001 | 0.000 | 0.588 | 0.590 | Download | Download |
| 21 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.034 | 0.0 | -1 | 5 | 0.001 | 0.000 | 0.605 | 0.606 | Download | Download |
| 24 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.609 | 0.611 | Download | Download |
| 27 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.609 | 0.610 | Download | Download |
| 30 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.032 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.606 | 0.607 | Download | Download |
| 33 | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.600 | 0.601 | 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 | 0.361 | 0.003 | 0.000 | 0.000 | -1 | 1 | 0.074 | 0.000 | 4.876 | 4.877 | Download | Download |
| 2 | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 9.754 | 10.107 | Download | Download |
| 4 | predict | 1000000 | 1000 | 10 | 0.675 | 0.004 | 0.000 | 0.001 | -1 | 5 | 0.133 | 0.002 | 5.074 | 5.075 | Download | Download |
| 5 | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 9.016 | 9.327 | Download | Download |
| 7 | predict | 1000000 | 1000 | 10 | 3.688 | 0.040 | 0.000 | 0.004 | 1 | 100 | 0.404 | 0.004 | 9.139 | 9.139 | Download | Download |
| 8 | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 100 | 0.001 | 0.000 | 4.402 | 4.469 | Download | Download |
| 10 | predict | 1000000 | 1000 | 10 | 2.212 | 0.008 | 0.000 | 0.002 | -1 | 100 | 0.405 | 0.003 | 5.466 | 5.467 | Download | Download |
| 11 | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 100 | 0.001 | 0.000 | 6.949 | 7.068 | Download | Download |
| 13 | predict | 1000000 | 1000 | 10 | 1.105 | 0.012 | 0.000 | 0.001 | 1 | 5 | 0.133 | 0.002 | 8.317 | 8.318 | Download | Download |
| 14 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.927 | 4.119 | Download | Download |
| 16 | predict | 1000000 | 1000 | 10 | 0.596 | 0.009 | 0.000 | 0.001 | 1 | 1 | 0.073 | 0.001 | 8.172 | 8.172 | Download | Download |
| 17 | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.895 | 4.124 | Download | Download |
| 19 | predict | 1000 | 1000 | 2 | 0.019 | 0.000 | 0.001 | 0.000 | -1 | 1 | 0.001 | 0.000 | 35.637 | 35.778 | Download | Download |
| 20 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 16.615 | 17.193 | Download | Download |
| 22 | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 30.024 | 30.130 | Download | Download |
| 23 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 16.313 | 16.820 | Download | Download |
| 25 | predict | 1000 | 1000 | 2 | 0.032 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 6.569 | 6.569 | Download | Download |
| 26 | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 4.132 | 4.245 | Download | Download |
| 28 | predict | 1000 | 1000 | 2 | 0.032 | 0.000 | 0.001 | 0.000 | -1 | 100 | 0.005 | 0.000 | 6.551 | 6.551 | Download | Download |
| 29 | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 16.266 | 16.888 | Download | Download |
| 31 | predict | 1000 | 1000 | 2 | 0.019 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 28.384 | 28.560 | Download | Download |
| 32 | predict | 1000 | 1 | 2 | 0.000 | 0.000 | 0.000 | 0.000 | 1 | 5 | 0.000 | 0.000 | 4.452 | 4.622 | Download | Download |
| 34 | predict | 1000 | 1000 | 2 | 0.018 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 44.023 | 44.673 | Download | Download |
| 35 | predict | 1000 | 1 | 2 | 0.000 | 0.000 | 0.000 | 0.000 | 1 | 1 | 0.000 | 0.000 | 4.464 | 4.633 | 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 | 0.457 | 0.002 | 0.035 | 0.0 | 30 | random | 0.426 | 0.020 | 1.072 | 1.073 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 2 | 0.531 | 0.015 | 0.030 | 0.0 | 30 | k-means++ | 0.460 | 0.023 | 1.154 | 1.155 | Download | Download |
| 6 | fit | 1000000 | 1000000 | 100 | 4.370 | 0.383 | 0.183 | 0.0 | 30 | random | 2.293 | 0.007 | 1.905 | 1.905 | Download | Download |
| 9 | fit | 1000000 | 1000000 | 100 | 4.256 | 0.016 | 0.188 | 0.0 | 30 | k-means++ | 2.458 | 0.011 | 1.731 | 1.731 | 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 | 0.085 | 0.0 | 30 | random | 0.0 | 0.0 | 1.271 | 1.402 | Download | Download |
| 2 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | random | 0.0 | 0.0 | 1.230 | 1.269 | Download | Download |
| 4 | predict | 1000000 | 1000 | 2 | 0.0 | 0.0 | 0.085 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.277 | 1.415 | Download | Download |
| 5 | predict | 1000000 | 1 | 2 | 0.0 | 0.0 | 0.000 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.244 | 1.287 | Download | Download |
| 7 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 2.148 | 0.0 | 30 | random | 0.0 | 0.0 | 1.455 | 1.533 | Download | Download |
| 8 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.005 | 0.0 | 30 | random | 0.0 | 0.0 | 1.214 | 1.246 | Download | Download |
| 10 | predict | 1000000 | 1000 | 100 | 0.0 | 0.0 | 2.187 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.393 | 1.469 | Download | Download |
| 11 | predict | 1000000 | 1 | 100 | 0.0 | 0.0 | 0.005 | 0.0 | 30 | k-means++ | 0.0 | 0.0 | 1.244 | 1.279 | 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 | 0.001574 | 0.000448 | 0.508115 | 0.000002 | 20 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.293414 | 0.001060 | 0.000076 | 0.325483 | 1.485241 | 1.489065 |
| 10 | sklearn_KMeans_short | sklearn | 0.032656 | predict | 10000 | 1000 | 100 | 0.001472 | 0.000134 | 0.543638 | 0.000001 | 20 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.283247 | 0.001096 | 0.000141 | 0.315903 | 1.342217 | 1.353242 |
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 | 0.115 | 0.009 | 0.001 | 0.0 | 20 | random | 0.030 | 0.002 | 3.812 | 3.819 | Download | Download |
| 3 | fit | 10000 | 10000 | 2 | 0.233 | 0.002 | 0.001 | 0.0 | 20 | k-means++ | 0.085 | 0.001 | 2.743 | 2.743 | Download | Download |
| 6 | fit | 10000 | 10000 | 100 | 0.242 | 0.002 | 0.033 | 0.0 | 20 | random | 0.146 | 0.001 | 1.659 | 1.659 | Download | Download |
| 9 | fit | 10000 | 10000 | 100 | 0.631 | 0.013 | 0.013 | 0.0 | 20 | k-means++ | 0.423 | 0.002 | 1.489 | 1.489 | 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 | 1.266 | 1.267 | Download | Download |
| 2 | predict | 10000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 20 | random | 0.000 | 0.0 | 1.240 | 1.273 | Download | Download |
| 4 | predict | 10000 | 1000 | 2 | 0.001 | 0.0 | 0.023 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 1.193 | 1.197 | Download | Download |
| 5 | predict | 10000 | 1 | 2 | 0.000 | 0.0 | 0.000 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.208 | 1.243 | Download | Download |
| 7 | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 0.508 | 0.0 | 20 | random | 0.001 | 0.0 | 1.485 | 1.489 | Download | Download |
| 8 | predict | 10000 | 1 | 100 | 0.000 | 0.0 | 0.004 | 0.0 | 20 | random | 0.000 | 0.0 | 1.315 | 1.344 | Download | Download |
| 10 | predict | 10000 | 1000 | 100 | 0.001 | 0.0 | 0.544 | 0.0 | 20 | k-means++ | 0.001 | 0.0 | 1.342 | 1.353 | Download | Download |
| 11 | predict | 10000 | 1 | 100 | 0.000 | 0.0 | 0.004 | 0.0 | 20 | k-means++ | 0.000 | 0.0 | 1.276 | 1.314 | 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 | 9.448 | 0.139 | 0.085 | 0.000 | [20] | 1.636 | 0.009 | 5.774 | 5.774 | Download | Download |
| 3 | fit | 1000 | 1000 | 10000 | 0.658 | 0.026 | 0.122 | 0.001 | [27] | 0.526 | 0.040 | 1.252 | 1.255 | 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 | 3.452 | 0.0 | [20] | 0.001 | 0.001 | 0.398 | 0.717 | Download | Download |
| 2 | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.015 | 0.0 | [20] | 0.000 | 0.000 | 0.333 | 0.345 | Download | Download |
| 4 | predict | 1000 | 100 | 10000 | 0.001 | 0.0 | 5.510 | 0.0 | [27] | 0.004 | 0.000 | 0.368 | 0.369 | Download | Download |
| 5 | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 1.242 | 0.0 | [27] | 0.001 | 0.000 | 0.113 | 0.113 | 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 | 1.100 | 0.007 | 0.073 | 0.001 | True | True | deprecated | False | 1.101 | 0.005 | 0.999 | 0.999 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 6.196 | 0.415 | 0.129 | 0.000 | True | True | deprecated | False | 0.198 | 0.004 | 31.288 | 31.294 | 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 | 0.008 | 0.0 | 10.371 | 0.0 | True | True | deprecated | False | 0.013 | 0.0 | 0.609 | 0.609 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.000 | 0.0 | 6.555 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.484 | 0.496 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 7.024 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.289 | 0.324 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.000 | 0.0 | 0.161 | 0.0 | True | True | deprecated | False | 0.000 | 0.0 | 0.373 | 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 | 0.168 | 0.005 | 0.478 | 0.0 | 0.170 | 0.001 | 0.986 | 0.986 | Download | Download |
| 3 | fit | 1000000 | 1000000 | 100 | 0.883 | 0.007 | 0.906 | 0.0 | 0.226 | 0.003 | 3.909 | 3.909 | 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 | 0.008 | 0.0 | 10.365 | 0.0 | 0.013 | 0.000 | 0.598 | 0.598 | Download | Download |
| 2 | predict | 1000 | 10 | 10000 | 0.000 | 0.0 | 6.954 | 0.0 | 0.001 | 0.001 | 0.169 | 0.332 | Download | Download |
| 4 | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 7.067 | 0.0 | 0.000 | 0.000 | 0.439 | 0.467 | Download | Download |
| 5 | predict | 1000000 | 10 | 100 | 0.000 | 0.0 | 0.164 | 0.0 | 0.000 | 0.000 | 0.358 | 0.368 | 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 | 3.277 | 0.147 | 0.0 | 0.003 | 1000 | 0.5 | 12.0 | warn | warn | barnes_hut | euclidean | 0.0 | 2 | 300 | 30.0 | legacy | 0 | 3.381 | 0.297 | 0.969 | 0.973 | 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 | 0.373 | 0.009 | 0.214 | 0.0 | 0.025 | 0.004 | 14.667 | 14.870 | Download | Download |
| 1 | fit | 10000 | 10000 | 1000 | 0.325 | 0.007 | 0.246 | 0.0 | 0.192 | 0.003 | 1.687 | 1.688 | 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 |