Intel(R) Extension for Scikit-learn 2021.4

The release Intel(R) Extension for Scikit-learn 2021.4 introduces the following changes:

📚 Support Materials

• Medium blogs:
  • Save Time and Money with Intel Extension for Scikit-learn
• Anaconda blogs:
  • Scikit-learn Speed-up with Intel and Anaconda
• Oracle blogs:
  • Accelerate your model build process with the Intel® Extension for Scikit-learn
• Kaggle kernels:
  • [Tabular Playground Series - Jun 2021] Fast LogReg with scikit-learn-intelex
  • [Tabular Playground Series - Jun 2021] AutoGluon with sklearnex
  • [Tabular Playground Series - Jul 2021] Fast RandomForest with sklearnex
  • [Tabular Playground Series - Jul 2021] RF with Intel Extension for Scikit-learn
  • [Tabular Playground Series - Jul 2021] Stacking with scikit-learn-intelex
  • [Tabular Playground Series - Aug 2021] NuSVR with Intel Extension for Sklearn
  • [Predict Future Sales] Stacking with scikit-learn-intelex
  • [House Prices - Advanced Regression Techniques] NuSVR sklearn-intelex 4x speedup
• Added demo samples comparing the usage of Intel® Extension for Scikit-learn and the original Scikit-learn for KNN, Logistic Regression, SVM and Random Forest algorithms

🛠️ Library Engineering

• Introduced new functionality for Intel® Extension for Scikit-learn*:
  • Enabled patching for all Scikit-learn applications at once:
    • You can enable global patching via command line:
      • python -m sklearnex.glob patch_sklearn
    • Or via code:
      • from sklearnex import patch_sklearn
        patch_sklearn(global_patch=True)
    • Read more in Intel® Extension for Scikit-learn documentation.
  • Added the support of Python 3.9 for both Intel® Extension for Scikit-learn and daal4py. The packages are available from PyPI and the Intel Channel on Anaconda Cloud.

🚨 New Features

• Enabled the global patching of all Scikit-learn applications
• Provided an integration with dpctl for heterogeneous computing (the support of dpctl.tensor.usm_ndarray for input and output)
• Extended API with set_config and get_config methods. Added the support of target_offload and allow_fallback_to_host options for device offloading scenarios
• Added the support of predict_proba in RandomForestClassifier estimator
• [CPU] Added the support of Sigmoid kernel in SVM algorithms
• [GPU] Added binary SVC support with Linear and RBF kernels

🚀 ​Improved performance

• [CPU] SVR algorithm training
• [CPU] NuSVC and NuSVR algorithms training
• [CPU] RandomForestRegression and RandomForestClassifier algorithms training and prediction
• [CPU] KMeans algorithm training

🐛 Bug Fixes

• Fixed an incorrectly raised exception during the patching of Random Forest algorithm when the number of trees was more than 7000.
• [CPU] Fixed an accuracy issue in Random Forest algorithm caused by the exclusion of constant features.
• [CPU] Fixed an issue in NuSVC Multiclass.
• [CPU] Fixed an issue with KMeans convergence inconsistency.
• [CPU] Fixed incorrect work of train_test_split with specific subset sizes.
• [GPU] Fixed incorrect bias calculation in SVM.

❗ Known Issues

• [GPU] For most algorithms, performance degradations were observed when the 2021.4 version of Intel® oneAPI DPC++ Compiler was used.
• [GPU] Examples are failing when run with Visual Studio Solutions on hardware that does not support double precision floating-point operations.