Intel(R) Extension for Scikit-learn 2021.7.1

The release Intel® Extension for Scikit-learn 2021.7.1 introduces the following changes:

📚 Support Materials

• [Tabular Playground Series - Sep 2022] Tuning of ElasticNet hyperparameters
• Accelerated Random Forest for Rent Prediction

🚨 What's New

• oneAPI interface for kNN regression
• Fix for wrong column names of pandas DataFrame in sklearn.model_selection.train_test_split patched function

Intel(R) Extension for Scikit-learn 2021.6

The release Intel® Extension for Scikit-learn 2021.6 introduces the following changes:

📚 Support Materials

Kaggle kernels:

• Fast Feature Importance using scikit-learn-intelex
• [Tabular Playground Series - December 2021] Fast Feature Importance with sklearnex
• [Tabular Playground Series - December 2021] SVC with sklearnex 20x speedup
• [Tabular Playground Series - January 2022] Fast PyCaret with Scikit-learn-Intelex
• [Tabular Playground Series - February 2022] KNN with sklearnex 13x speedup
• Fast SVM for Sparse Data from NLP Problem
• Introduction to scikit-learn-intelex
• [Datasets] Fast Feature Importance using sklearnex
• [Tabular Playground Series - March 2022] Fast workflow using scikit-learn-intelex

🛠️ Library Engineering

• Reduced the size of oneDAL python run-time package by approximately 8%
• Added Python 3.10 support for daal4py and Intel(R) Extension for Scikit-learn packages

🚨 What's new

• Improved performance for the following Intel® Extension for Scikit-learn algorithms:

  • t-SNE for “Burnes-Hut” algorithm

• Introduced new functionality for Intel® Extension for Scikit-learn:

  • Manhattan, Minkowski, Chebyshev and Cosine distances for KNeighborsClassifier and NearestNeighbors with “brute” algorithm

• Fixed the following issues in Intel® Extension for Scikit-learn:

  • An issue with the search of common data type in pandas DataFrame
  • Patching overhead of finiteness checker for specific small data sizes
  • Incorrect values in a tree visualization with plot_tree function in RandomForestClassifier
  • Unexpected error for device strings in {device}:{device_index} format while using config context

Intel(R) Extension for Scikit-learn 2021.5

The release Intel® Extension for Scikit-learn 2021.5 introduces the following changes:

📚 Support Materials

• Kaggle kernels:
  • [Tabular Playground Series - Sep 2021] Ridge with sklearn-intelex 2x speedup
  • [Tabular Playground Series - Oct 2021] Fast AutoML with Intel Extension for Scikit-learn
  • [Titanic – Machine Learning from Disaster] AutoML with Intel Extension for Sklearn
  • [Tabular Playground Series - Nov 2021] AutoML with Intel® Extension
  • [Tabular Playground Series - Nov 2021] Log Regression with sklearnex 17x speedup
  • [Tabular Playground Series - Dec 2021] SVC with sklearnex 20x speedup
  • [Tabular Playground Series - Dec 2021] Fast Feature Importance with sklearnex
• Added demo samples of the Intel® Extension for Scikit-learn usage with the performance comparison to original Scikit-learn for ElasticNet, K-means, Lasso Regression, Linear regression, and Ridge Regression
• Added demo samples of the Modin usage

🛠️ Library Engineering

• Reduced the size of oneDAL library by approximately ~15%, this is a required dependency of Intel® extension for scikit learn.

🚨 New Features

• Scikit-learn 1.0 support

🚀 ​Improved performance

• [GPU] KNN algorithm prediction
• [GPU] SVC and SVR algorithms training

🐛 Bug Fixes

• Stabilized the results of Linear Regression in oneDAL and Intel® Extension for Scikit-learn
• Fixed an issue with RPATH on MacOS

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.

Intel(R) Extension for Scikit-learn 2021.3

The release Intel(R) Extension for Scikit-learn 2021.3 introduces the following changes:

📚 Support Materials

• Medium blogs:
  • Superior Machine Learning Performance on the Latest Intel Xeon Scalable Processors
  • Leverage Intel Optimizations in Scikit-Learn (SVM Performance Training and Inference)
  • Optimizing CatBoost Performance
  • Performance Optimizations for End-to-End AI Pipelines
• Kaggle kernels:
  • [Tabular Playground Series - Apr 2021] RF with Intel Extension for Scikit-learn
  • [Tabular Playground Series - Apr 2021] SVM with Intel Extension for Scikit-learn
  • [Tabular Playground Series - Apr 2021] SVM with Intel(R) Extension for Scikit-learn
  • [Tabular Playground Series - Jun 2021] AutoGluon with Intel(R) Extension for Scikit-learn
  • [Tabular Playground Series - Jun 2021] Fast LogReg with Intel(R) Extension for Scikit-learn
  • [Tabular Playground Series - Jun 2021] Fast ML stack with Intel(R) Extension for Scikit-learn
  • [Tabular Playground Series - Jun 2021] Fast Stacking with Intel(R) Extension for Scikit-learn
• Samples that illustrate the usage of Intel Extension for Scikit-learn

🛠️ Library Engineering

• Introduced optional dependencies on DPC++ runtime to Intel Extension for Scikit-learn and daal4py. To enable DPC++ backend, install dpcpp_cpp_rt package. It reduces the default package size with all dependencies from 1.2GB to 400 MB.

🚨 New Features

• Introduced the support of scikit-learn 1.0 version in Intel(R) Extension for Scikit-learn. The 2021.3 release of Intel(R) Extension for Scikit-learn supports the latest scikit-learn releases: 0.22.X, 0.23.X, 0.24.X and 1.0.X.
• The support of patch_sklearn for several algorithms: patch_sklearn(["SVC", "DBSCAN"])
• [CPU] Acceleration of SVR estimator
• [CPU] Acceleration of NuSVC and NuSVR estimators
• [CPU] Polynomial kernel support in SVM algorithms

🚀 ​Improved performance

• [CPU] SVM algorithms training and prediction
• [CPU] Linear, Ridge, ElasticNet, and Lasso regressions prediction

🐛 Bug Fixes

• Fixed binary incompatibility for the versions of numpy earlier than 1.19.4
• Fixed an issue with a very large number of trees (> 7000) for Random Forest algorithm
• Fixed patch_sklearn to patch both fit and predict methods of Logistic Regression when the algorithm is given as a single parameter to patch_sklearn
• [CPU] Reduced the memory consumption of SVM prediction
• [GPU] Fixed an issue with kernel compilation on the platforms without hardware FP64 support

❗ Known Issues

• Intel(R) Extension for Scikit-learn package installed from PyPI repository can’t be found on Debian systems (including Google Collab). Mitigation: add “site-packages” folder into Python packages searching before importing the packages:

import sys 
import os 
import site 
sys.path.append(os.path.join(os.path.dirname(site.getsitepackages()[0]), "site-packages")) 

Intel(R) Extension for Scikit-learn 2021.2.3

🚨 New Features

• Added support of patching scikit-learn version 1.0. scikit-learn version 0.21. * is no longer supported

Intel(R) Extension for Scikit-learn 2021.2

⚡️ 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

Intel® daal4py 2020 Update 3 Patch 1

What's New

• Added support of patching scikit-learn version 0.24.

Intel® daal4py 2021.1

What's New

Introduced new daal4py functionality:

• GPU:
  • Batch algorithms: K-means, Covariance, PCA, Logistic Regression, Linear Regression, Random Forest Classification and Regression, Gradient Boosting Classification and Regression, kNN, SVM, DBSCAN and Low-order moments
  • Online algorithms: Covariance, PCA, Linear Regression and Low-order moments

Improved daal4py performance for the following algorithms:

• CPU:
  • Logistic Regression training and prediction
  • k-Nearest Neighbors prediction with Brute Force method
  • Logistic Loss and Cross Entropy objective functions

Introduced new functionality for scikit-learn patching through daal4py:

• CPU:
  • Acceleration of NearestNeighbors and KNeighborsRegressor scikit-learn estimators with Brute Force and K-D tree methods
  • Acceleration of TSNE scikit-learn estimator
• GPU:
  • Intel GPU support in scikit-learn for DBSCAN, K-means, Linear and Logistic Regression

Improved performance of the following scikit-learn estimators via scikit-learn patching:

• CPU:
  • LogisticRegression fit, predict and predict_proba methods
  • KNeighborsClassifier predict, predict_proba and kneighbors methods with “brute” method

Known Issues

• train_test_split in daal4py patches for Scikit-learn can produce incorrect shuffling on Windows*

Installation

To install this package with conda run the following:

conda install -c intel daal4py

Intel® daal4py 2020 Update 3

What's New in Intel® daal4py 2020 Update 3:

Introduced new daal4py functionality:

• Conversion of trained XGBoost* and LightGBM* models into a daal4py Gradient Boosted Trees model for fast prediction
• Support of Modin* DataFrame as an input
• Brute Force method for k-Nearest Neighbors classification algorithm, which for datasets with more than 13 features demonstrates a better performance than the existing K-D tree method
• k-Nearest Neighbors search for K-D tree and Brute Force methods with computation of distances to nearest neighbors and their indices

Extended existing daal4py functionality:

• Voting methods for prediction in k-Nearest Neighbors classification and search: based on inverse-distance and uniform weighting
• New parameters in Decision Forest classification and regression: minObservationsInSplitNode, minWeightFractionInLeafNode, minImpurityDecreaseInSplitNode, maxLeafNodes with best-first strategy and sample weights
• Support of Support Vector Machine (SVM) decision function for Multi-class Classifier

Improved daal4py performance for the following algorithms:

• SVM training and prediction
• Decision Forest classification training
• RBF and Linear kernel functions

Introduced new functionality for scikit-learn patching through daal4py:

• Acceleration of KNeighborsClassifier scikit-learn estimator with Brute Force and K-D tree methods
• Acceleration of RandomForestClassifier and RandomForestRegressor scikit-learn estimators
• Sparse input support for KMeans and Support Vector Classification (SVC) scikit-learn estimators
• Prediction of probabilities for SVC scikit-learn estimator
• Support of ‘normalize’ parameter for Lasso and ElasticNet scikit-learn estimators

Improved performance of the following functionality for scikit-learn patching through daal4py:

• train_test_split()
• Support Vector Classification (SVC) fit and prediction

To install this package with conda run the following:
conda install -c intel daal4py