daal4py 2020.2

Introduced new functionality:

• Thunder method for Support Vector Machine (SVM) training algorithm, which demonstrates better training time than the existing sequential minimal optimization method

Extended existing functionality:

• Training with the number of features greater than the number of observations for Linear Regression, Ridge Regression, and Principal Component Analysis
• New sample_weights parameter for SVM algorithm
• New parameter in K-Means algorithm, resultsToEvaluate, which controls computation of centroids, assignments, and exact objective function

Improved performance for the following:

• Support Vector Machine training and prediction, Elastic Net and LASSO training, Principal Component Analysis training and transform, K-D tree based k-Nearest Neighbors prediction
• K-Means algorithm in batch computation mode
• RBF kernel function

Deprecated 32-bit support:

• 2020 product line will be the last one to support 32-bit

Introduced improvements to daal4py library:

• Performance optimizations for pandas input format
• Scikit-learn compatible API for AdaBoost classifier, Decision Tree classifier, and Gradient Boosted Trees classifier and regressor

Improved performance of the following Intel Scikit-learn algorithms and functions:

• fit and prediction in K-Means and Support Vector Classification (SVC), fit in Elastic Net and LASSO, fit and transform in PCA
• Support Vector Classification (SVC) with non-default weights of samples and classes
• train_test_split() and assert_all_finite()

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

daal4py 2020.1

Introduced new functionality:

• Elastic Net algorithm with L1 and L2 regularization in batch computation mode. The algorithm supports various optimization solvers that handle non-smooth functions.
• Probabilistic classification for Decision Forest Classification algorithm with a choice voting method to calculate probabilities.

Extended existing functionality:

• Performance optimizations for distributed Spark samples, K-means algorithm for some input dimensions, Gradient Boosted Trees training stage for large datasets on multi-core platforms and Decision Forest prediction stage for datasets with a small number of observations on processors that support Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512)
• Performance optimizations across algorithms that use SOA (Structure Of Arrays) NumericTable as an input on processors that support Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

daal4py 2020.0

Added support for Brownboost, Logistboost as well as Stump regression and Stump classification algorithms to daal4py.
Added support for Adaboost classification algorithm, including support for method="SAMME" or "SAMMER" for multi-class data.
"Variable Importance" feature has been added in Gradient Boosting Trees.
Ability to compute class prediction probabilities has been added to appropriate classifiers, including logistic regression, tree-based classifiers, etc.

2019.5

Single node support for DBSCAN, LASSO, Coordinate Descent (CD) solver algorithms
Distributed model support for SVD, QR, K-means init++ and parallel++ algorithms

daal4py 2019.3

Product release with Intel(R) Parallel Studio 2019 Update 3