Overview¶

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.

Guiding principles¶

Seamless integration with scikit-learn

Strictly adhere to the scikit-learn API and development guidelines, inherit the strengths of that framework.

Code modularity

Topological feature creation steps as transformers. Allow for the creation of a large number of topologically-powered machine learning pipelines.

Standardisation

Implement the most successful techniques from the literature into a generic framework with a consistent API.

Innovation

Improve on existing algorithms, and make new ones available in open source.

Performance

For 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 joblib).

Data structures

Support for tabular data, time series, graphs, and images.

30s guide to `giotto-tda`¶

For installation instructions, see the installation instructions.

The functionalities of giotto-tda are provided in scikit-learn–style transformers. This allows you to generate topological features from your data in a familiar way. Here is an example with the VietorisRipsPersistence transformer:

from gtda.homology import VietorisRipsPersistence
VR = VietorisRipsPersistence()

which computes topological summaries, called persistence diagrams, from collections of point clouds or weighted graphs, as follows:

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 PersistenceEntropy transformer:

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 scikit-learn. 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 scikit-learn 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)

Resources¶

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.

Use cases¶

A selection of use cases for giotto-tda is collected at this page. Please note, however, that some of these were written for past versions of giotto-tda. In some cases, only small modifications are needed to run them on recent versions, while in others it is best to install the relevant past version of giotto-tda (preferably in a fresh environmnent). In a couple of cases, the legacy giotto-learn or giotto-learn-nightly will be needed.

What’s new¶

This release was made shortly after the release of version 0.5.0, to resolve an important bug. Please refer to the release notes for 0.5.0 to see the major improvements and backwards-incompatible changes to the Mapper subpackage which were introduced there.

Major Features and Improvements¶

None.

Bug Fixes¶

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

Backwards-Incompatible Changes¶