scikit-learn-intelex 2021.2.2

Intel(R) Extension for Scikit-learn 2021.2

on Python PyPI

⚡️ New package - Intel(R) Extension for Scikit-learn*

• Intel(R) Extension for Scikit-learn* contains scikit-learn patching functionality originally available in daal4py package. All future updates for the patching will be available in Intel(R) Extension for Scikit-learn only. Please use the package instead of daal4py.

⚠️ Deprecations

• Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package - Intel(R) Extension for Scikit-learn*. All future updates for the patching will be available in Intel(R) Extension for Scikit-learn only. Please use the package instead of daal4py for the Scikit-learn acceleration.

📚 Support Materials

• Medium blogs:
  • Intel Gives Scikit-Learn the Performance Boost Data Scientists Need
  • From Hours to Minutes: 600x Faster SVM
  • Improve the Performance of XGBoost and LightGBM Inference
  • Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit
• Kaggle kernels:
  • Accelerate sklearn algorithms using sklearnex
  • Fast SVC using scikit-learn-intelex for NLP
  • Fast SVC using scikit-learn-intelex for MNIST
  • Fast KNN using  scikit-learn-intelex for MNIST
  • Using scikit-learn-intelex for What's Cooking

🛠️ Library Engineering

• Enabled new PyPI distribution channel for Intel(R) Extension for Scikit-learn and daal4py:
  • Four latest Python versions (3.6, 3.7, 3.8) are supported on Linux, Windows and MacOS.
  • Support of both CPU and GPU is included in the package.
  • You can download daal4py using the following command: pip install daal4py
  • You can download Intel(R) Extension for Scikit-learn using the following command: pip install scikit-learn-intelex

🚨 New Features

• Patches for four latest scikit-learn releases: 0.21.X, 0.22.X, 0.23.X and 0.24.X
• [CPU] Acceleration of roc_auc_score function
• [CPU] Bit-to-bit results reproducibility for: LinearRegression, Ridge, SVC, KMeans, PCA, Lasso, ElasticNet, tSNE, KNeighborsClassifier, KNeighborsRegressor, NearestNeighbors, RandomForestClassifier, RandomForestRegressor

🚀 ​Improved performance

• [CPU] RandomForestClassifier and RandomForestRegressor scikit-learn estimators: training and prediction
• [CPU] Principal Component Analysis (PCA) scikit-learn estimator: training
• [CPU] Support Vector Classification (SVC) scikit-learn estimators: training and prediction
• [CPU] Support Vector Classification (SVC) scikit-learn estimator with the probability==True parameter: training and prediction

🐛 Bug Fixes

• [CPU] Improved accuracy of RandomForestClassifier and RandomForestRegressor scikit-learn estimators
• [CPU] Fixed patching issues with pairwise_distances
• [CPU] Fixed the behavior of the patch_sklearn and unpatch_sklearn functions
• [CPU] Fixed unexpected behavior that made accelerated functionality unavailable through scikit-learn patching if the input was not of float32 or float64 data types. Scikit-learn patching now works with all numpy data types.
• [CPU] Fixed a memory leak that appeared when DataFrame from pandas was used as an input type
• [CPU] Fixed performance issue for interoperability with Modin