v0.6.2

What's Changed

• Add Python 3.12 by @raphaelreinauer in #699

Full Changelog: v0.6.1...v0.6.2

v0.6.1

What's Changed

• Updating pybind11 by @rballeba in #689
• CI use GitHub actions by @MonkeyBreaker in #615
• Add support for building Python 3.11 wheels by @raphaelreinauer in #697
• Replace sklearn.utils.metaestimators.if_delegate_has_method with available_if by @allispaul in #693
• Allow NumPy functions to pass type checks by @allispaul in #692
• Fixes simple error in _validate_clusterer by @pujaltes in #668
• make sure to fix the CI/CD by @matteocao in #698

New Contributors

• @rballeba made their first contribution in #689
• @raphaelreinauer made their first contribution in #697
• @allispaul made their first contribution in #693
• @pujaltes made their first contribution in #668

Full Changelog: v0.6.0...v0.6.1

giotto-tda version 0.6.0

This is a major release including a new local homology subpackage, a new backend for computing Vietoris–Rips barcodes, wheels for Python 3.10 and Apple Silicon systems, and end of support for Python 3.6.

Major Features and Improvements

• A new local_homology subpackage containing scikit-learn–compatible transformers for the extraction of local homology features has been added (#602). A tutorial and an example notebooks explain it.
• Wheels for Python 3.10 are now available (#644 and #646).
• Wheels for Apple Silicon systems are now available for Python versions 3.8, 3.9 and 3.10 (#646).
• giotto-ph is now the backend for the computation of Vietoris–Rips barcodes, replacing ripser.py (#614).
• The documentation has been improved (#609).

Bug Fixes

• A bug involving tests for the mapper subpackage has been fixed (#638).

Backwards-Incompatible Changes

• Python 3.6 is no longer supported, and the manylinux standard has been bumped from manylinux2010 to manylinux2014 (#644 and #646).
• The python-igraph requirement has been replaced with igraph >= 0.9.8 (#616).

Thanks to our Contributors

This release contains contributions from:

Umberto Lupo, Jacob Bamberger, Wojciech Reise, Julián Burella Pérez, and Anibal Medina-Mardones

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

giotto-tda version 0.5.1

Major Features and Improvements

None.

Bug Fixes

A bug preventing Mapper pipelines from working with memory caching has been fixed (#597).

Backwards-Incompatible Changes

None.

Thanks to our Contributors

This release contains contributions from:

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.

giotto-tda version 0.5.0

Major Features and Improvements

• An object-oriented API for interactive plotting of Mapper graphs has been added with the MapperInteractivePlotter (#586). This is intended to supersede plot_interactive_mapper graph as it allows for inspection of the current state of the objects change by interactivity. See also "Backwards-Incompatible Changes" below.
• Further citations have been added to the mathematical glossary (#564).

Bug Fixes

• A bug preventing EuclideanCechPersistence from working correctly on point clouds in more than 2 dimensions has been fixed (#588).
• A validation bug preventing VietorisRipsPersistence and WeightedRipsPersistence from accepting non-empty dictionaries as metric_params has been fixed (#590).
• A bug causing an exception to be raised when node_color_statistic was passed as a numpy array in plot_static_mapper_graph has been fixed (#576).

Backwards-Incompatible Changes

• A major change to the behaviour of the (static and interactive) Mapper plotting functions plot_static_mapper_graph and plot_interactive_mapper_graph was introduced in #584. The new MapperInteractivePlotter class (see "Major Features and Improvements" above) also follows this new API. The main changes are as follows:

  • color_by_columns_dropdown has been eliminated.
  • color_variable has been renamed to color_features (but cannot be an array).
  • An additional keyword argument color_data has been added to more clearly separate the input data to the Mapper pipeline from the data to be used for coloring.
  • node_color_statistic is now applied column by column -- previously it could end up being applied to 2d arrays as a whole.
  • The defaults for color-related arguments lead to index values instead of the mean of the data.

• The default for weight_params in WeightedRipsPersistence is now the empty dictionary, and None is no longer allowed (#595).

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Wojciech Reise, Julian Burella Pérez, Sean Law, Anibal Medina-Mardones, and Lewis Tunstall

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

giotto-tda version 0.4.0

Major Features and Improvements

• Wheels for Python 3.9 have been added (#528).
• Weighted Rips filtrations, and in particular distance-to-measure (DTM) based filtrations, are now supported in ripser and by the new WeightedRipsPersistence transformer (#541).
• See "Backwards-Incompatible Changes" for major improvements to ParallelClustering and therefore make_mapper_pipeline which are also major breaking changes.
• GraphGeodesicDistance can now take rectangular input (the number of vertices is inferred to be max(x.shape)), and KNeighborsGraph can now take sparse input (#537).
• VietorisRipsPersistence now takes a metric_params parameter (#541).

Bug Fixes

• A documentation bug affecting plots from DensityFiltration has been fixed (#540).
• A bug affecting the bindings for GUDHI's edge collapser, which incorrectly did not ignore lower diagonal entries, has been fixed (#538).
• Symmetry conflicts in the case of sparse input to ripser and VietorisRipsPersistence are now handled in a way true to the documentation, i.e. by favouring upper diagonal entries if different values in transpose positions are also stored (#537).

Backwards-Incompatible Changes

• The minimum required version of pyflagser is now 0.4.3 (#537).
• ParallelClustering.fit_transform now outputs one array of cluster labels per sample, bringing it closer to scikit-learn convention for clusterers, and the fitted single clusterers are no longer stored in the clusterers_ attribute of the fitted object (#535 and #552).

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Julian Burella Pérez, 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.

giotto-tda version 0.3.1

Major Features and Improvements

• The latest changes made to the ripser.py submodule have been pulled (#530, see also #532). This includes in particular the performance improvements to the C++ backend submitted by Julian Burella Pérez via scikit-tda/ripser.py#106. The developer installation now includes a new dependency in robinhood hashmap. These changes do not affect functionality.
• The example notebook classifying_shapes.ipynb has been modified and improved (#523).
• The tutorial previously called time_series_classification.ipynb has been split into an introductory tutorial on the Takens embedding ideas (topology_time_series.ipynb) and an example notebook on gravitational wave detection (gravitational_waves_detection.ipynb) which presents a time series classification task (#529).
• The documentation for PairwiseDistance has been improved (#525).

Bug Fixes

• Timeout deadlines for some of the hypothesis tests have been increased to make them less flaky (#531).

Backwards-Incompatible Changes

• Due to poor support for brew in the macOS 10.14 virtual machines by Azure, the CI for macOS systems is now run on 10.15 virtual machines and 10.14 is no longer supported by the wheels (#527)

Thanks to our Contributors

This release contains contributions from many people:

Julian Burella Pérez, Umberto Lupo, Lewis Tunstall, Wojciech Reise, and Rayna Andreeva.

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

giotto-tda version 0.3.0

Major Features and Improvements

This is a major release which adds substantial new functionality and introduces several improvements.

Persistent homology of directed flag complexes via pyflagser

• The pyflagser package (source, docs) is now an official dependency of giotto-tda.
• The FlagserPersistence transformer has been added to gtda.homology (#339). It wraps pyflagser.flagser_weighted to allow for computations of persistence diagrams from directed or undirected weighted graphs. A new notebook demonstrates its use.

Edge collapsing and performance improvements for persistent homology

• GUDHI C++ components have been updated to the state of GUDHI v3.3.0, yielding performance improvements in SparseRipsPersistence, EuclideanCechPersistence and CubicalPersistence (#468).
• Bindings for GUDHI's edge collapser have been created and can now be used as an optional preprocessing step via the optional keyword argument collapse_edges in VietorisRipsPersistence and in gtda.externals.ripser (#469 and #483). When collapse_edges=True, and the input data and/or number of required homology dimensions is sufficiently large, the resulting runtimes for Vietoris–Rips persistent homology are state of the art.
• The performance of the Ripser bindings has otherwise been improved by avoiding unnecessary data copies, better managing the memory, and using more efficient matrix routines (#501 and #507).

New transformers and functionality in gtda.homology

• The WeakAlphaPersistence transformer has been added to gtda.homology (#464). Like VietorisRipsPersistence, SparseRipsPersistence and EuclideanCechPersistence, it computes persistent homology from point clouds, but its runtime can scale much better with size in low dimensions.
• VietorisRipsPersistence now accepts sparse input when metric="precomputed" (#424).
• CubicalPersistence now accepts lists of 2D arrays (#503).
• A reduced_homology parameter has been added to all persistent homology transformers. When True, one infinite bar in the H0 barcode is removed for the user automatically. Previously, it was not possible to keep these bars in the simplicial homology transformers. The default is always True, which implies a breaking change in the case of CubicalPersistence (#467).

Persistence diagrams

• A ComplexPolynomial feature extraction transformer has been added (#479).
• A NumberOfPoints feature extraction transformer has been added (#496).
• An option to normalize the entropy in PersistenceEntropy according to a heuristic has been added, and a nan_fill_value parameter allows to replace any NaN produced by the entropy calculation with a fixed constant (#450).
• The computations in HeatKernel, PersistenceImage and in the pairwise distances and amplitudes related to them has been changed to yield the continuum limit when n_bins tends to infinity; sigma is now measured in the same units as the filtration parameter and defaults to 0.1 (#454).

New curves subpackage

A new curves subpackage has been added to preprocess, and extract features from, collections of multi-channel curves such as returned by BettiCurve, PersistenceLandscape and Silhouette (#480). It contains:

• A StandardFeatures transformer that can extract features channel-wise in a generic way.
• A Derivative transformer that computes channel-wise derivatives of any order by discrete differences (#492).

New metaestimators subpackage

A new metaestimator subpackage has been added with a CollectionTransformer meta-estimator which converts any transformer instance into a fit-transformer acting on collections (#495).

Images

• A DensityFiltration for collections of binary images has been added (#473).
• Padder and Inverter have been extended to greyscale images (#489).

Time series

• TakensEmbedding is now a new transformer acting on collections of time series (#460).
• The former TakensEmbedding acting on a single time series has been renamed to SingleTakensEmbedding transformer, and the internal logic employed in its fit for computing optimal hyperparameters is now available via a takens_embedding_optimal_parameters convenience function (#460).
• The _slice_windows method of SlidingWindow has been made public and renamed into slice_windows (#460).

Graphs

• GraphGeodesicDistance has been improved as follows (#422):

  • The new parameters directed, unweighted and method have been added.
  • The rules on the role of zero entries, infinity entries, and non-stored values have been made clearer.
  • Masked arrays are now supported.

• A mode parameter has been added to KNeighborsGraph; as in scikit-learn, it can be set to either "distance" or "connectivity" (#478).

• List input is now accepted by all transformers in gtda.graphs, and outputs are consistently either lists or 3D arrays (#478).

• Sparse matrices returned by KNeighborsGraph and TransitionGraph now have int dtype (0-1 adjacency matrices), and are necessarily symmetric (#478).

Mapper

• Pullback cover set labels and partial cluster labels have been added to Mapper node hovertexts (#445).

• The functionality of Nerve and make_mapper_pipeline has been greatly extended (#447 and #456):

  • Node and edge metadata are now accessible in output igraph.Graph objects by means of the VertexSeq and EdgeSeq attributes vs and es (respectively). Graph-level dictionaries are no longer used.
  • Available node metadata can be accessed by graph.vs[attr_name] where for attr_name is one of "pullback_set_label", "partial_cluster_label", or "node_elements".
  • Sizes of intersections are automatically stored as edge weights, accessible by graph.es["weight"].
  • A "store_intersections" keyword argument has been added to Nerve and make_mapper_pipeline to allow to store the indices defining node intersections as edge attributes, accessible via graph.es["edge_elements"].
  • A contract_nodes optional parameter has been added to both Nerve and make_mapper_pipeline; nodes which are subsets of other nodes are thrown away from the graph when this parameter is set to True.
  • A graph_ attribute is stored during Nerve.fit.

• Two of the Nerve parameters (min_intersection and the new contract_nodes) are now available in the widgets generated by plot_interactive_mapper_graph, and the layout of these widgets has been improved (#456).

• ParallelClustering and Nerve have been exposed in the documentation and in gtda.mapper's __init__ (#447).

Plotting

• A plot_params kwarg is available in plotting functions and methods throughout to allow user customisability of output figures. The user must pass a dictionary with keys "layout" and/or "trace" (or "traces" in some cases) (#441).
• Several plots produced by plot class methods now have default titles (#453).
• Infinite deaths are now plotted by plot_diagrams (#461).
• Possible multiplicities of persistence pairs in persistence diagram plots are now indicated in the hovertext (#454).
• plot_heatmap now accepts boolean array input (#444).

New tutorials and examples

The following new tutorials have been added:

• Topology of time series, which explains the theory of the Takens time-delay embedding and its use with persistent homology, demonstrates the new API of sever...

giotto-tda version 0.2.2

Major Features and Improvements

• The documentation for gtda.mapper.utils.decorators.method_to_transform has been improved.

• A table of contents has been added to the theory glossary.

• The theory glossary has been restructured by including a section titled "Analysis". Entries for l^p norms, L^p norms and heat vectorization have been added.

• The project's Azure CI for Windows versions has been sped-up by ensuring that the locally installed boost version is detected.

• Several python bindings to external code from GUDHI, ripser.py and Hera have been made public: specifically, from gtda.externals import * now gives power users access to:

  • bottleneck_distance,
  • wasserstein_distance,
  • ripser,
  • SparseRipsComplex,
  • CechComplex,
  • CubicalComplex,
  • PeriodicCubicalComplex,
  • SimplexTree,
  • WitnessComplex,
  • StrongWitnessComplex.

  However, these functionalities are still undocumented.

• The gtda.mapper.visualisation and gtda.mapper.utils._visualisation modules have been thoroughly refactored to improve code clarity, add functionality, change behaviour and fix bugs. Specifically, in figures generated by both plot_static_mapper_graph and plot_interactive_mapper_graph:

  • The colorbar no longer shows values rescaled to the interval [0, 1]. Instead, it always shows the true range of node summary statistics.
  • The values of the node summary statistics are now displayed in the hovertext boxes. A a new keyword argument n_sig_figs controls their rounding (3 is the default).
  • plotly_kwargs has been renamed to plotly_params (see "Backwards-Incompatible Changes" below).
  • The dependency on matplotlib's rgb2hex and get_cmap functions has been removed. As no other component in giotto-tda required matplotlib, the dependency on this library has been removed completely.
  • A node_scale keyword argument has been added which can be used to controls the size of nodes (see "Backwards-Incompatible Changes" below).
  • The overall look of Mapper graphs has been improved by increasing the opacity of node colors so that edges do not hide them, and by reducing the thickness of marker lines.

  Furthermore, a clone_pipeline keyword argument has been added to plot_interactive_mapper_graph, which when set to False allows the user to mutate the input pipeline via the interactive widget.

• The docstrings of plot_static_mapper_graph, plot_interactive_mapper_graph and make_mapper_pipeline have been improved.

Bug Fixes

• A CI bug introduced by an update to the XCode compiler installed on the Azure Mac machines has been fixed.
• A bug afflicting Mapper colors, which was due to an incorrect rescaling to [0, 1], has been fixed.

Backwards-Incompatible Changes

• The keyword parameter plotly_kwargs in plot_static_mapper_graph and plot_interactive_mapper_graph has been renamed to plotly_params and has now slightly different specifications. A new logic controls how the information contained in plotly_params is used to update plotly figures.
• The function get_node_sizeref in gtda.mapper.utils.visualization has been hidden by renaming it to _get_node_sizeref. Its main intended use is subsumed by the new node_scale parameter of plot_static_mapper_graph and plot_interactive_mapper_graph.

Thanks to our Contributors

This release contains contributions from many people:

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

giotto-tda version 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=Falseno 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.