A high-performance topological machine learning toolbox in Python
giotto-tda is a high performance topological machine learning toolbox in Python built on top of
scikit-learn and is distributed under the GNU AGPLv3 license. It is part of the Giotto family of open-source projects.
- Seamless integration with
scikit-learnStrictly adhere to the
scikit-learnAPI and development guidelines, inherit the strengths of that framework.
- Code modularityTopological feature creation steps as transformers. Allow for the creation of a large number of topologically-powered machine learning pipelines.
- StandardisationImplement the most successful techniques from the literature into a generic framework with a consistent API.
- InnovationImprove on existing algorithms, and make new ones available in open source.
- PerformanceFor the most demanding computations, fall back to state-of-the-art C++ implementations, bound efficiently to Python. Vectorized code and implements multi-core parallelism (with
- Data structuresSupport for tabular data, time series, graphs, and images.
30s guide to
For installation instructions, see the installation instructions.
The functionalities of
giotto-tda are provided in
This allows you to generate topological features from your data in a familiar way. Here is an example with the
from gtda.homology import VietorisRipsPersistence VR = VietorisRipsPersistence()
diagrams = VR.fit_transform(point_clouds)
A plotting API allows for quick visual inspection of the outputs of many of
giotto-tda’s transformers. To visualize the i-th output sample, run
diagrams = VR.plot(diagrams, sample=i)
You can create scalar or vector features from persistence diagrams using
giotto-tda’s dedicated transformers. Here is an example with the
from gtda.diagrams import PersistenceEntropy PE = PersistenceEntropy() features = PE.fit_transform(diagrams)
features is a two-dimensional
numpy array. This is important to making this type of topological feature generation fit into a typical machine learning workflow from
In particular, topological feature creation steps can be fed to or used alongside models from
scikit-learn, creating end-to-end pipelines which can be evaluated in cross-validation,
optimised via grid-searches, etc.:
from sklearn.ensemble import RandomForestClassifier from gtda.pipeline import make_pipeline from sklearn.model_selection import train_test_split X_train, X_valid, y_train, y_valid = train_test_split(point_clouds, labels) RFC = RandomForestClassifier() model = make_pipeline(VR, PE, RFC) model.fit(X_train, y_train) model.score(X_valid, y_valid)
giotto-tda also implements the Mapper algorithm as a highly customisable
Pipeline, and provides simple plotting functions
for visualizing output Mapper graphs and have real-time interaction with the pipeline parameters:
from gtda.mapper import make_mapper_pipeline from sklearn.decomposition import PCA from sklearn.cluster import DBSCAN pipe = make_mapper_pipeline(filter_func=PCA(), clusterer=DBSCAN()) plot_interactive_mapper_graph(pipe, data)
Tutorials and examples¶
We provide a number of tutorials and examples, which offer:
quick start guides to the API;
in-depth examples showcasing more of the library’s features;
intuitive explanations of topological techniques.
Major Features and Improvements¶
The documentation for
gtda.mapper.utils.decorators.method_to_transformhas 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:
However, these functionalities are still undocumented.
gtda.mapper.utils._visualisationmodules have been thoroughly refactored to improve code clarity, add functionality, change behaviour and fix bugs. Specifically, in figures generated by both
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_figscontrols their rounding (3 is the default).
plotly_kwargshas been renamed to
plotly_params(see “Backwards-Incompatible Changes” below).
The dependency on
get_cmapfunctions has been removed. As no other component in
matplotlib, the dependency on this library has been removed completely.
node_scalekeyword 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.
clone_pipelinekeyword argument has been added to
plot_interactive_mapper_graph, which when set to
Falseallows the user to mutate the input pipeline via the interactive widget.
The docstrings of
make_mapper_pipelinehave been improved.
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.
The keyword parameter
plot_interactive_mapper_graphhas been renamed to
plotly_paramsand has now slightly different specifications. A new logic controls how the information contained in
plotly_paramsis used to update plotly figures.
gtda.mapper.utils.visualizationhas been hidden by renaming it to
_get_node_sizeref. Its main intended use is subsumed by the new
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.