Release Notes

Release 0.2.1

Major Features and Improvements

• The theory glossary has been improved to include the notions of vectorization, kernel and amplitude for persistence diagrams.

• The ripser function in gtda.externals.python.ripser_interface no longer uses scikit-learn’s pairwise_distances when metric is 'precomputed', thus allowing square arrays with negative entries or infinities to be passed.

• check_point_clouds in gtda.utils.validation now checks for square array input when the input should be a collection of distance-type matrices. Warnings guide the user to correctly setting the distance_matrices parameter. force_all_finite=False no longer means accepting NaN input (only infinite input is accepted).

• VietorisRipsPersistence in gtda.homology.simplicial no longer masks out infinite entries in the input to be fed to ripser.

• The docstrings for check_point_clouds and VietorisRipsPersistence have been improved to reflect these changes and the extra level of generality for ripser.

Bug Fixes

• The variable used to indicate the location of Boost headers has been renamed from Boost_INCLUDE_DIR to Boost_INCLUDE_DIRS to address developer installation issues in some Linux systems.

Backwards-Incompatible Changes

• The keyword parameter distance_matrix in check_point_clouds has been renamed to distance_matrices.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Anibal Medina-Mardones, Julian Burella Pérez, Guillaume Tauzin, and Wojciech Reise.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.2.0

Major Features and Improvements

This is a major release which substantially broadens the scope of giotto-tda and introduces several improvements. The library’s documentation has been greatly improved and is now hosted via GitHub pages. It includes rendered jupyter notebooks from the repository’s examples folder, as well as an improved theory glossary, more detailed installation instructions, improved guidelines for contributing, and an FAQ.

Plotting functions and plotting API

This version introduces built-in plotting capabilities to giotto-tda. These come in the form of:

• a new plotting subpackage populated with plotting functions for common data structures;

• a new PlotterMixin and a class-level plotting API based on newly introduced plot, transform_plot and fit_transform_plot methods which are now available in several of giotto-tda’s transformers.

Changes and additions to gtda.homology

The internal structure of this subpackage has been changed. ConsistentRescaling has been moved to a new point_clouds subpackage (see below), and gtda.homology no longer contains a point_clouds submodule. Instead, it contains two submodules, simplicial and cubical. simplicial contains the VietorisRipsPersistence class as well as the following new classes:

• SparseRipsPersistence,

• EuclideanCechPersistence.

The cubical submodule contains CubicalPersistence, a new class for computing persistent homology of filtered cubical complexes such as those coming from 2D or 3D greyscale images.

New images subpackage

The new gtda.images subpackage contains classes which, together with gtda.homology.CubicalPersistence, extend the capabilities of giotto-tda to computer vision, by handling input representing binary or greyscale 2D/3D images represented as arrays.

The classes in gtda.images.filtrations are responsible for converting binary image input into greyscale images in a variety of ways. The greyscale output can then be fed to gtda.homology.CubicalPersistence to extract topological signatures in the form of persistence diagrams. These classes are:

• HeightFiltration,

• RadialFiltration,

• DilationFiltration,

• ErosionFiltration,

• SignedDistanceFiltration.

The classes in gtda.images.preprocessing perform a variety of preprocessing steps on either binary or greyscale image input, as well as conversion to point cloud format. They are:

• Binarizer,

• Inverter,

• Padder,

• ImageToPointCloud.

New point_clouds subpackage

ConsistentRescaling is no longer placed in gtda.homology. Instead, it is now in a point_clouds subpackage containing classes which process or modify the geometry of point cloud data. gtda.point_clouds also contains the new class ConsecutiveRescaling, written with time series applications in mind.

List of point cloud input

All classes in the homology subpackage (VietorisRipsPersistence, SparseRipsPersistence, and EuclideanCechPersistence) can now take as inputs to the fit and transform methods lists of 2D arrays instead of simply 3D arrays. In this way, collections of point clouds with varying numbers of points can be processed.

Changes and additions to gtda.diagrams

The diagrams subpackage contains the following new classes:

• PersistenceImage

• Silhouette

Additionally, the subpackage has been reorganised as follows:

• The features submodule now only contains the scalar feature generation classes Amplitude (moved there from distance) and PersistenceEntropy.

• Classes which produce vector representations from persistence diagrams have been moved to the new representations submodule.

Changes and additions to gtda.utils

• validate_params has been thoroughly refactored, documented and exposed for the benefit of developers.

• check_diagrams has been modified, documented and exposed for the benefit of developers.

• The new check_point_clouds performs validation of inputs consisting of collections of point clouds of distance matrices. It accepts both lists of 2D ndarrays and 3D ndarrays, and is used in the fit and transform methods of classes in gtda.homology.simplicial to allow for list input (see above).

External modules and HPC improvements

A substantial effort has been put in improving the quality of the high-performance components contained in gtda.externals. The end result is a cleaner packaging as well as faster execution of C++ functions due to improved bindings. In particular:

• Two binaries are now shipped for ripser, one of them being optimised for calculations with mod 2 coefficients.

• Recent improvements by the authors of the hera C++ library have been integrated in giotto-tda.

• Compiler optimisations for Windows-based systems have been added.

• The integration of pybind11 has been improved and several issues arising with CMake and boost during developer installations have been addressed.

Bug Fixes

• Fixed a bug with TakensEmbedding’s algorithm for search of optimal parameters.

• Inconsistencies in between the meaning of “bottleneck amplitude” in the theory and in the code have been ironed out. The code has been modified to agree with the theory glossary. The outputs of the gtda.diagrams classes Amplitude, Scaler and Filtering is affected.

• Fixed bugs affecting color normalization in Mapper graph plots.

Backwards-Incompatible Changes

• Python 3.5 is no longer supported.

• Mac OS X versions below 10.14 are no longer supported by the wheels shipped via PyPI.

• ConsistentRescaling is no longer found in gtda.homology and is now part of gtda.point_clouds.

• The outputs of the gtda.diagrams classes Amplitude, Scaler and Filtering have changed due to sqrt(2) factors (see Bug Fixes).

• The meta_transformers module has been removed.

• The plotting module has been removed from the examples folder of the repository.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Guillaume Tauzin, Wojciech Reise, Julian Burella Pérez, Roman Yurchak, Lewis Tunstall, Anibal Medina-Mardones, and Adélie Garin.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.4

Library name change

The library and GitHub repository have been renamed to giotto-tda! While the new name is meant to better convey the library’s focus on Topology-powered machine learning and Data Analysis, the commitment to seamless integration with scikit-learn will remain just as strong and a defining feature of the project. Concurrently, the main module has been renamed from giotto to gtda in this version. giotto-learn will remain on PyPI as a legacy package (stuck at v0.1.3) until we have ensured that users and developers have fully migrated. The new PyPI package giotto-tda will start at v0.1.4 for project continuity.

Short summary: install via

pip install -U giotto-tda

and import gtda in your scripts or notebooks!

Change of license

The license changes from Apache 2.0 to GNU AGPLv3 from this release on.

Major Features and Improvements

• Added a mapper submodule implementing the Mapper algorithm of Singh, Mémoli and Carlsson. The main tools are the functions make_mapper_pipeline, plot_static_mapper_graph and plot_interactive_mapper_graph. The first creates an object of class MapperPipeline which can be fit-transformed to data to create a Mapper graph in the form of an igraph.Graph object (see below). The MapperPipeline class itself is a simple subclass of scikit-learn’s Pipeline which is adapted to the precise structure of the Mapper algorithm, so that a MapperPipeline object can be used as part of even larger scikit-learn pipelines, inside a meta-estimator, in a grid search, etc. One also has access to other important features of scikit-learn’s Pipeline, such as memory caching to avoid unnecessary recomputation of early steps when parameters involved in later steps are changed. The clustering step can be parallelised over the pullback cover sets via joblib – though this can actually lower performance in small- and medium-size datasets. A range of pre-defined filter functions are also included, as well as covers in one and several dimensions, agglomerative clustering algorithms based on stopping rules to create flat cuts, and utilities for making transformers out of callables or out of other classes which have no transform method. plot_static_mapper_graph allows the user to visualise (in 2D or 3D) the Mapper graph arising from fit-transforming a MapperPipeline to data, and offers a range of colouring options to correlate the graph’s structure with exogenous or endogenous information. It relies on plotly for plotting and displaying metadata. plot_interactive_mapper_graph adds interactivity to this, via ipywidgets: specifically, the user can fine-tune some parameters involved in the definition of the Mapper pipeline, and observe in real time how the structure of the graph changes as a result. In this release, all hyperparameters involved in the covering and clustering steps are supported. The ability to fine-tune other hyperparameters will be considered for future versions.

• Added support for Python 3.8.

Bug Fixes

• Fixed consistently incorrect documentation for the fit_transform methods. This has been achieved by introducing a class decorator adapt_fit_transform_docs which is defined in the newly introduced gtda.utils._docs.py.

Backwards-Incompatible Changes

• The library name change and the change in the name of the main module giotto are important major changes.

• There are now additional dependencies in the python-igraph, matplotlib, plotly, and ipywidgets libraries.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Lewis Tunstall, Guillaume Tauzin, Philipp Weiler, Julian Burella Pérez.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.3

Major Features and Improvements

None

Bug Fixes

• Fixed a bug in diagrams.Amplitude causing the transformed array to be wrongly filled and added adequate test.

Backwards-Incompatible Changes

None.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Major Features and Improvements

• Added support for Python 3.5.

Bug Fixes

None.

Backwards-Incompatible Changes

None.

Thanks to our Contributors

This release contains contributions from many people:

Matteo Caorsi, Henry Tom (@henrytomsf), Guillaume Tauzin.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.1

Major Features and Improvements

• Improved documentation.

• Improved features of class Labeller.

• Improved features of class PearsonDissimilarities.

• Improved GitHub files.

• Improved CI.

Bug Fixes

Fixed minor bugs from the first release.

Backwards-Incompatible Changes

The following class were renamed: - class PearsonCorrelation was renamed to class PearsonDissimilarities

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Guillaume Tauzin, Matteo Caorsi, Olivier Morel.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.0

Major Features and Improvements

The following submodules where added:

• giotto.homology implements transformers to modify metric spaces or generate persistence diagrams.

• giotto.diagrams implements transformers to preprocess persistence diagrams or extract features from them.

• giotto.time_series implements transformers to preprocess time series or embed them in a higher dimensional space for persistent homology.

• giotto.graphs implements transformers to create graphs or extract metric spaces from graphs.

• giotto.meta_transformers implements convenience giotto.Pipeline transformers for direct topological feature generation.

• giotto.utils implements hyperparameters and input validation functions.

• giotto.base implements a TransformerResamplerMixin for transformers that have a resample method.

• giotto.pipeline extends scikit-learn’s module by defining Pipelines that include TransformerResamplers.

Bug Fixes

None

Backwards-Incompatible Changes

None

Thanks to our Contributors

This release contains contributions from many people:

Guillaume Tauzin, Umberto Lupo, Philippe Nguyen, Matteo Caorsi, Julian Burella Pérez, Alessio Ghiraldello.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions. In particular, we would like to thank Martino Milani, who worked on an early prototype of a Mapper implementation; although very different from the current one, it adopted an early form of caching to avoid recomputation in refitting, which was an inspiration for this implementation.

Release 0.1a.0

Initial release of the library, original named giotto-learn.