SCIKIT-leaRn tests

AMD Ryzen 9 3900X 12-Core testing with a MSI X570-A PRO (MS-7C37) v3.0 (H.70 BIOS) and NVIDIA GeForce RTX 3060 on Ubuntu 24.04 via the Phoronix Test Suite. Noble python 3.12 performance vs. python compiled without frame pointers.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2405056-VPA1-MERGE7223
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Identifier
Performance Per
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Date
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  Test
  Duration
noble
May 02
  12 Hours, 54 Minutes
scikit-learn-python-disabled-fp
May 03
  10 Hours, 58 Minutes
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  11 Hours, 56 Minutes
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SCIKIT-leaRn tests Suite 1.0.0 System Test suite extracted from SCIKIT-leaRn tests. pts/scikit-learn-2.0.0 tree.py Benchmark: Tree pts/scikit-learn-2.0.0 sample_without_replacement.py Benchmark: Sample Without Replacement pts/scikit-learn-2.0.0 text_vectorizers.py Benchmark: Text Vectorizers pts/scikit-learn-2.0.0 isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset perturbed_logarithm Benchmark: Isotonic / Perturbed Logarithm pts/scikit-learn-2.0.0 tsne_mnist.py Benchmark: TSNE MNIST Dataset pts/scikit-learn-2.0.0 plot_polynomial_kernel_approximation.py Benchmark: Plot Polynomial Kernel Approximation pts/scikit-learn-2.0.0 random_projections.py --n-times 100 Benchmark: Sparse Random Projections / 100 Iterations pts/scikit-learn-2.0.0 kernel_pca_solvers_time_vs_n_components.py Benchmark: Kernel PCA Solvers / Time vs. N Components pts/scikit-learn-2.0.0 hist_gradient_boosting.py Benchmark: Hist Gradient Boosting pts/scikit-learn-2.0.0 plot_hierarchical.py Benchmark: Plot Hierarchical pts/scikit-learn-2.0.0 hist_gradient_boosting_categorical_only.py Benchmark: Hist Gradient Boosting Categorical Only pts/scikit-learn-2.0.0 hist_gradient_boosting_adult.py Benchmark: Hist Gradient Boosting Adult pts/scikit-learn-2.0.0 isolation_forest.py Benchmark: Isolation Forest pts/scikit-learn-2.0.0 saga.py Benchmark: SAGA pts/scikit-learn-2.0.0 glm.py Benchmark: GLM pts/scikit-learn-2.0.0 isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset logistic Benchmark: Isotonic / Logistic pts/scikit-learn-2.0.0 plot_svd.py Benchmark: Plot Singular Value Decomposition pts/scikit-learn-2.0.0 sparsify.py Benchmark: Sparsify pts/scikit-learn-2.0.0 lasso.py Benchmark: Lasso pts/scikit-learn-2.0.0 plot_incremental_pca.py Benchmark: Plot Incremental PCA pts/scikit-learn-2.0.0 mnist.py Benchmark: MNIST Dataset pts/scikit-learn-2.0.0 lof.py Benchmark: LocalOutlierFactor pts/scikit-learn-2.0.0 20newsgroups.py -e logistic_regression Benchmark: 20 Newsgroups / Logistic Regression pts/scikit-learn-2.0.0 plot_ward.py Benchmark: Plot Ward pts/scikit-learn-2.0.0 hist_gradient_boosting_threading.py Benchmark: Hist Gradient Boosting Threading pts/scikit-learn-2.0.0 feature_expansions.py Benchmark: Feature Expansions pts/scikit-learn-2.0.0 plot_omp_lars.py Benchmark: Plot OMP vs. LARS pts/scikit-learn-2.0.0 hist_gradient_boosting_higgsboson.py Benchmark: Hist Gradient Boosting Higgs Boson pts/scikit-learn-2.0.0 plot_fastkmeans.py Benchmark: Plot Fast KMeans pts/scikit-learn-2.0.0 covertype.py Benchmark: Covertype Dataset Benchmark pts/scikit-learn-2.0.0 plot_neighbors.py Benchmark: Plot Neighbors pts/scikit-learn-2.0.0 plot_lasso_path.py Benchmark: Plot Lasso Path pts/scikit-learn-2.0.0 kernel_pca_solvers_time_vs_n_samples.py Benchmark: Kernel PCA Solvers / Time vs. N Samples pts/scikit-learn-2.0.0 plot_nmf.py Benchmark: Plot Non-Negative Matrix Factorization pts/scikit-learn-2.0.0 rcv1_logreg_convergence.py Benchmark: RCV1 Logreg Convergencet pts/scikit-learn-2.0.0 isotonic.py --iterations 100 --log_min_problem_size 1 --log_max_problem_size 10 --dataset pathological Benchmark: Isotonic / Pathological pts/scikit-learn-2.0.0 plot_parallel_pairwise.py Benchmark: Plot Parallel Pairwise pts/scikit-learn-2.0.0 online_ocsvm.py Benchmark: SGDOneClassSVM pts/scikit-learn-2.0.0 sgd_regression.py Benchmark: SGD Regression pts/scikit-learn-2.0.0 glmnet.py Benchmark: Glmnet