Daal4py

The release of **Intel® Extension for Scikit-learn 2023.0.1** introduces the following changes:

🚨 What's New

- Performance improvements for tSNE algorithm https://github.com/intel/scikit-learn-intelex/commit/5275ebac37c416ce110634c6cee7b56f872ed71b
- Fixes for balanced classes and number of iterations in SVM https://github.com/intel/scikit-learn-intelex/commit/14849ee7190f5701e4eab2ad923fce9125e904ff, https://github.com/intel/scikit-learn-intelex/commit/4872a8ea0afa22813f1a4446ef5fc0d608660283, https://github.com/intel/scikit-learn-intelex/commit/9d0a05b80c09aa3930107e2eca233cd7b872593c
- Fix for `gamma` parameter in KMeans https://github.com/intel/scikit-learn-intelex/commit/1dca20c3761197d082d548f27f698d6389e60fbe

The release of **Intel® Extension for Scikit-learn 2023.0** introduces the following changes:

🚨 What's New

- Introduced new **Intel® oneDAL** functionality:
- DPC++ interface for Linear Regression algorithm

❗ Known Issues

- **Intel® Extension for Scikit-learn** SVC.fit and KNN.fit do not support GPU
- Most Intel® Extension for Scikit-learn sycl examples fail when using GPU context
- Running the Random Forest algorithm with versions 2021.7.1 and 2023.0 of scikit-learn-intelex on the 2nd *Generation Intel®* Xeon® *Scalable* Processors, formerly *Cascade Lake* may result in an 'Illegal instruction' error.
- No workaround is currently available for this issue.
- Recommendation: Use an older version of scikit-learn-intelex until the issue is fixed in a future release.

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](https://www.kaggle.com/code/alex97andreev/tuning-of-elasticnet-hyperparameters)
- [Accelerated Random Forest for Rent Prediction](https://www.kaggle.com/code/alex97andreev/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

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

📚 Support Materials

Kaggle kernels:
- [Fast Feature Importance using scikit-learn-intelex](https://www.kaggle.com/lordozvlad/fast-feature-importance-using-scikit-learn-intelex)
- [[Tabular Playground Series - December 2021] Fast Feature Importance with sklearnex](https://www.kaggle.com/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex?scriptVersionId=82254284)
- [[Tabular Playground Series - December 2021] SVC with sklearnex 20x speedup](https://www.kaggle.com/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup)
- [[Tabular Playground Series - January 2022] Fast PyCaret with Scikit-learn-Intelex](https://www.kaggle.com/lordozvlad/tps-jan-fast-pycaret-with-scikit-learn-intelex)
- [[Tabular Playground Series - February 2022] KNN with sklearnex 13x speedup](https://www.kaggle.com/alexeykolobyanin/tps-feb-knn-with-sklearnex-13x-speedup)
- [Fast SVM for Sparse Data from NLP Problem](https://www.kaggle.com/alex97andreev/fast-svm-for-sparse-data-from-nlp-problem)
- [Introduction to scikit-learn-intelex](https://www.kaggle.com/lordozvlad/introduction-to-scikit-learn-intelex)
- [[Datasets] Fast Feature Importance using sklearnex](https://www.kaggle.com/lordozvlad/datasets-fast-feature-importance-using-sklearnex)
- [[Tabular Playground Series - March 2022] Fast workflow using scikit-learn-intelex](https://www.kaggle.com/lordozvlad/tps-mar-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

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](https://www.kaggle.com/alexeykolobyanin/tps-sep-ridge-with-sklearn-intelex-2x-speedup)
- [Tabular Playground Series - Oct 2021] [Fast AutoML with Intel Extension for Scikit-learn](https://www.kaggle.com/lordozvlad/fast-automl-with-intel-extension-for-scikit-learn/notebook)
- [Titanic – Machine Learning from Disaster] [AutoML with Intel Extension for Sklearn](https://www.kaggle.com/lordozvlad/titanic-automl-with-intel-extension-for-sklearn/notebook)
- [Tabular Playground Series - Nov 2021] [AutoML with Intel® Extension](https://www.kaggle.com/lordozvlad/tps-nov-automl-with-intel-extension)
- [Tabular Playground Series - Nov 2021] [Log Regression with sklearnex 17x speedup](https://www.kaggle.com/alexeykolobyanin/tps-nov-log-regression-with-sklearnex-17x-speedup)
- [Tabular Playground Series - Dec 2021] [SVC with sklearnex 20x speedup](https://www.kaggle.com/alexeykolobyanin/tps-dec-svc-with-sklearnex-20x-speedup)
- [Tabular Playground Series - Dec 2021] [Fast Feature Importance with sklearnex](https://www.kaggle.com/lordozvlad/tps-dec-fast-feature-importance-with-sklearnex)
- Added [demo samples](https://github.com/intel/scikit-learn-intelex/tree/rls/2021.5-rls/examples/notebooks) 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](https://github.com/intel/scikit-learn-intelex/tree/rls/2021.5-rls/examples/notebooks/NYCTaxi-E2E-Demo) 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

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](https://medium.com/intel-analytics-software/save-time-and-money-with-intel-extension-for-scikit-learn-33627425ae4)
- Anaconda blogs:
- [Scikit-learn Speed-up with Intel and Anaconda](https://www.anaconda.com/blog/scikit-learn-speed-up-with-intel-and-anaconda)
- Oracle blogs:
- [Accelerate your model build process with the Intel® Extension for Scikit-learn](https://blogs.oracle.com/ai-and-datascience/post/model-build-intel-extension-scikit-learn)
- Kaggle kernels:
- [Tabular Playground Series - Jun 2021] [Fast LogReg with scikit-learn-intelex](https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex)
- [Tabular Playground Series - Jun 2021] [AutoGluon with sklearnex](https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex)
- [Tabular Playground Series - Jul 2021] [Fast RandomForest with sklearnex](https://www.kaggle.com/pahandrovich/tps-jul-2021-fast-randomforest-with-sklearnex)
- [Tabular Playground Series - Jul 2021] [RF with Intel Extension for Scikit-learn](https://www.kaggle.com/alexeykolobyanin/tps-jul-rf-with-intel-extension-for-scikit-learn)
- [Tabular Playground Series - Jul 2021] [Stacking with scikit-learn-intelex](https://www.kaggle.com/alexeykolobyanin/tps-jul-stacking-with-scikit-learn-intelex)
- [Tabular Playground Series - Aug 2021] [NuSVR with Intel Extension for Sklearn](https://www.kaggle.com/alexeykolobyanin/tps-aug-nusvr-with-intel-extension-for-sklearn)
- [Predict Future Sales] [Stacking with scikit-learn-intelex](https://www.kaggle.com/alexeykolobyanin/predict-sales-stacking-with-scikit-learn-intelex)
- [House Prices - Advanced Regression Techniques] [NuSVR sklearn-intelex 4x speedup](https://www.kaggle.com/alexeykolobyanin/house-prices-nusvr-sklearn-intelex-4x-speedup)
- Added [demo samples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks) 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*](https://github.com/intel/scikit-learn-intelex):
- 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](https://intel.github.io/scikit-learn-intelex/index.html#usage).
- 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.