v0.9.0

New features

• New RandomSVD algorithm
• New LanczosSVD algorithm
• New distributed versions of Random Forest Classifier and Random Forest Regressor
• New nested versions of Random Forest Classifier and Random Forest Regressor
• Included a version of TeraSort algorithm

Changed

• New documentation for SVD algorithm, RF and TeraSort

Fixed

• Fix bugs & tests

v0.8.0

New features

• save and load methods for all models
• Adding Muliclass CSVM
• Adding TS-QR (Tall Skinny QR)
• New in-place operations for ds-arrays:
  add iadd isub
• Matrix-Subtraction and Matrix-Addition
• Concatenating two ds-arrays by columns
• Save ds-array to npy file
• Load ds-array from several npy files
• Create ds-arrays from blocks
• GridSearch for simulations & improvements
• Inverse transformation in Scalers
• Train-Test-Split functionality
• Add KNN Classifier
• Better SVD columns pairing
• GPU Support using CUDA/CuPy for algorithms: Kmeans, KNN, SVD, PCA, Matmul, Addition, Subtraction, QR, Kronecker

Changed

• New documentation for GPU, RandomForest, Scalers

Fixed

• Fix bug Scalers & tests

v0.7.1

What's Changed

0.7.0 + documentation fix

Full Changelog: v0.7.0...v0.7.1

v0.7.0

New features

• QR decomposition
• Random Forest regressor
• MinMax scaler
• Matrix multiplication with transposed arguments
• several utility functions to pad matrices, or to remove last rows/columns

Improvements

• improved performance of SVD
• computing units for each task

v0.6.4

Dependencies

• PyCOMPSs >= 2.7
• Scikit-learn >= 0.19.2
• NumPy >= 1.15.4
• Scipy >= 1.0.0
• cvxpy>=1.1.5

Improvements

• SVD doc example fixed.
• LR example fixed.
• Warn when cvxpy dependency missing (for mn4 installation).
• Added link to Contributing guide in docs

v0.6.3

Dependencies

• PyCOMPSs >= 2.7
• Scikit-learn >= 0.19.2
• NumPy >= 1.15.4
• Scipy >= 1.0.0
• cvxpy>=1.1.5

Improvements

• PyPI long_description shortened.

v0.6.2

Dependencies

• PyCOMPSs >= 2.7
• Scikit-learn >= 0.19.2
• NumPy >= 1.15.4
• Scipy >= 1.0.0
• cvxpy>=1.1.5

Improvements

• Added extra info for PyPI

v0.6.1

Dependencies

• PyCOMPSs >= 2.7
• Scikit-learn >= 0.19.2
• NumPy >= 1.15.4
• Scipy >= 1.0.0
• cvxpy>=1.1.5

Improvements

• Documentation fixes.

v0.6.0

Dependencies

• PyCOMPSs >= 2.7
• Scikit-learn >= 0.19.2
• NumPy >= 1.15.4
• Scipy >= 1.0.0
• cvxpy>=1.1.5

Upgrade Steps

If using docker, just use the new image.

If you have a local installation, upgrade to COMPSs 2.7 (see COMPSs doc) before upgrading to dislib 0.6.0. Also, install the Python cvxpy module in order to use the regression algorithms: pip install cvxpy.

Breaking Changes

• ds-array doesn't accept a chunk_size bigger than the array.
• Moved data loading routines to a different file as array.py was getting too big.
• apply_along_axis for sparse data now returns sparse ds-arrays.
• Some PyCOMPSs log messages have changed.

New Features

• User guide and glossary
• Method to read from npy files
• Support for one-dimensional data in ds-array
• Parametrized ds-array tests
• identity, full and zeros methods that generate ds-arrays filled with a value
• ds-array operators: subtraction, division, conjugate, transpose, item setting, etc.
• matmul, kronecker product and rechunk methods for of ds-arrays
• Automatic deletion of ds-arrays when the GC is called.
• Multivariate linear regression.
• SVD (Singular Value Decomposition)
• PCA using SVD
• ADMM Lasso algorithm
• Daura clustering algorithm

Bug Fixes

• Some bugs in the ds-array
• Internal inconsistencies in transformed_array of PCA

Improvements

• Improved performance testing scripts and added new tests
• Allow executing applications with params using dislib exec
• Extended and improved the tutorial notebook
• Updated dislib-base docker image
• Replaced COLLECTION_INOUT parameters with COLLECTION_OUT when possible for improving performance

v0.5.0

Dependencies

• PyCOMPSs == 2.5
• Scikit-learn >= 0.19.2
• NumPy >= 1.15.4
• Scipy >= 1.0.0

New Features

• Added grid search and randomized search with cross-validation
• Added K-fold splitter
• dislib command line can now run jupyter notebooks

Bug Fixes

• Fixed various bugs in fancy indexing of ds-arrays
• dislib command line now works on MacOS
• Fixed "source" links in the documentation to point to the appropriate version of the source code
• dislib command line now works even if PyCOMPSs is not installed

Improvements

• Added a new notebook and improved the existing one
• PCA now supports sparse data
• Estimators now extend scikit-learn's base estimator for greater integration