Daal4py

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](https://medium.com/intel-analytics-software/superior-machine-learning-performance-on-the-latest-intel-xeon-scalable-processor-efdec279f5a3)
- [Leverage Intel Optimizations in Scikit-Learn (SVM Performance Training and Inference)](https://medium.com/intel-analytics-software/leverage-intel-optimizations-in-scikit-learn-f562cb9d5544)
- [Optimizing CatBoost Performance](https://medium.com/intel-analytics-software/optimizing-catboost-performance-4f73f0593071)
- [Performance Optimizations for End-to-End AI Pipelines](https://medium.com/intel-analytics-software/performance-optimizations-for-end-to-end-ai-pipelines-231e0966505a)
- Kaggle kernels:
- [Tabular Playground Series - Apr 2021] [RF with Intel Extension for Scikit-learn](https://www.kaggle.com/andreyrus/tps-apr-rf-with-intel-extension-for-scikit-learn)
- [Tabular Playground Series - Apr 2021] [SVM with Intel Extension for Scikit-learn](https://www.kaggle.com/napetrov/tps04-svm-with-intel-extension-for-scikit-learn)
- [Tabular Playground Series - Apr 2021] [SVM with Intel(R) Extension for Scikit-learn](https://www.kaggle.com/napetrov/tps04-svm-with-scikit-learn-intelex)
- [Tabular Playground Series - Jun 2021] [AutoGluon with Intel(R) Extension for Scikit-learn](https://www.kaggle.com/alex97andreev/tps-jun-autogluon-with-sklearnex)
- [Tabular Playground Series - Jun 2021] [Fast LogReg with Intel(R) Extension for Scikit-learn](https://www.kaggle.com/kppetrov/tps-jun-fast-logreg-with-scikit-learn-intelex)
- [Tabular Playground Series - Jun 2021] [Fast ML stack with Intel(R) Extension for Scikit-learn](https://www.kaggle.com/masdevas/fast-ml-stack-with-scikit-learn-intelex)
- [Tabular Playground Series - Jun 2021] [Fast Stacking with Intel(R) Extension for Scikit-learn](https://www.kaggle.com/owerbat/tps-jun-fast-stacking-with-scikit-learn-intelex)
- [Samples](https://github.com/intel/scikit-learn-intelex/tree/master/examples/notebooks) 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](https://github.com/intel/scikit-learn-intelex#-installation). 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:
python
import sys
import os
import site
sys.path.append(os.path.join(os.path.dirname(site.getsitepackages()[0]), "site-packages"))

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

⚡️ 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](https://medium.com/intel-analytics-software/intel-gives-scikit-learn-the-performance-boost-data-scientists-need-42eb47c80b18)
- [From Hours to Minutes: 600x Faster SVM](https://medium.com/intel-analytics-software/from-hours-to-minutes-600x-faster-svm-647f904c31ae)
- [Improve the Performance of XGBoost and LightGBM Inference](https://medium.com/intel-analytics-software/improving-the-performance-of-xgboost-and-lightgbm-inference-3b542c03447e)
- [Accelerate Kaggle Challenges Using Intel AI Analytics Toolkit](https://medium.com/intel-analytics-software/accelerate-kaggle-challenges-using-intel-ai-analytics-toolkit-beb148f66d5a)
- Kaggle kernels:
- [Accelerate sklearn algorithms using sklearnex](https://www.kaggle.com/kppetrov/accelerate-sklearn-algorithms-using-sklearnex)
- [Fast SVC using scikit-learn-intelex for NLP](https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp)
- [Fast SVC using scikit-learn-intelex for MNIST](https://www.kaggle.com/kppetrov/fast-svc-using-scikit-learn-intelex-for-mnist)
- [Fast KNN using  scikit-learn-intelex for MNIST](https://www.kaggle.com/kppetrov/fast-knn-using-scikit-learn-intelex-for-mnist)
- [Using scikit-learn-intelex for What's Cooking](https://www.kaggle.com/kppetrov/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`

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:
bash
conda install -c intel daal4py

What's New
- Added support of patching scikit-learn version 0.24.

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