.. _api_and_modules: API and Modules =============== .. currentmodule:: torchdr .. automodule:: torchdr :no-members: :no-inherited-members: Dimensionality Reduction ``sklearn`` Compatible Estimators ----------------------------------------------------------- TorchDR provides a set of classes that are compatible with the ``sklearn`` API. For example, running :class:`TSNE ` can be done in the exact same way as running :class:`sklearn.manifold.TSNE ` with the same parameters. Note that the TorchDR classes work seamlessly with both Numpy and PyTorch tensors. For all methods, TorchDR provides the ability to use GPU acceleration using ``device='cuda'`` as well as LazyTensor objects that allows to fit large scale models directly on the GPU memory without overflows using ``keops=True``. TorchDR supports a variety of dimensionality reduction methods. They are presented in the following sections. Spectral Embedding ^^^^^^^^^^^^^^^^^^ Those classes are used to perform classical spectral embedding from a :class:`torchdr.Affinity` object defined on the input data. They give the same output as using :class:`torchdr.AffinityMatcher` with this same :class:`torchdr.Affinity` in input space and a :class:`torchdr.ScalarProductAffinity` in the embedding space. However, :class:`torchdr.AffinityMatcher` relies on a gradient-based solver while the spectral embedding classes rely on the eigendecomposition of the affinity matrix. .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst PCA KernelPCA IncrementalPCA Neighbor Embedding ^^^^^^^^^^^^^^^^^^ TorchDR supports the following neighbor embedding methods. .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst SNE TSNE TSNEkhorn InfoTSNE LargeVis UMAP Advanced Dimensionality Reduction with TorchDR ----------------------------------------------- TorchDR provides a set of generic classes that can be used to implement new dimensionality reduction methods. These classes provide a modular and extensible framework that allows you to focus on the core components of your method. Base Classes ^^^^^^^^^^^^ The :class:`torchdr.DRModule` class is the base class for a dimensionality reduction estimator. It is the base class for all the DR classes in TorchDR. :class:`torchdr.AffinityMatcher` is the base class for all the DR methods that use gradient-based optimization to minimize a loss function constructed from two affinities in input and embedding spaces. .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst DRModule AffinityMatcher Base Neighbor Embedding Modules """"""""""""""""""""""""""""""" Neighbor embedding base modules inherit from the :class:`torchdr.AffinityMatcher` class and implement specific strategies that are common to all neighbor embedding methods such as early exaggeration. In particular, :class:`torchdr.SparseNeighborEmbedding` relies on the sparsity of the input affinity to compute the attractive term in linear time. :class:`torchdr.SampledNeighborEmbedding` inherits from this class and adds the possibility to approximate the repulsive term of the loss via negative samples. .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst NeighborEmbedding SparseNeighborEmbedding SampledNeighborEmbedding Affinity Classes ^^^^^^^^^^^^^^^^ The following classes are used to compute the affinities between the data points. Broadly speaking, they define a notion of similarity between samples. Simple Affinities """"""""""""""""" .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst GaussianAffinity StudentAffinity ScalarProductAffinity NormalizedGaussianAffinity NormalizedStudentAffinity Affinities Normalized by kNN Distances """"""""""""""""""""""""""""""""""""""" .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst SelfTuningAffinity MAGICAffinity Entropic Affinities """"""""""""""""""" .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst SinkhornAffinity EntropicAffinity SymmetricEntropicAffinity Quadratic Affinities """"""""""""""""""""" .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst DoublyStochasticQuadraticAffinity UMAP Affinities """"""""""""""" .. autosummary:: :toctree: gen_modules/ :template: myclass_template.rst UMAPAffinityIn UMAPAffinityOut Scores ^^^^^^ The following classes are used to evaluate the embeddings. .. autosummary:: :toctree: gen_modules/ :template: myfunc_template.rst silhouette_score Utils ^^^^^ The following classes are used to perform various operations such as computing the pairwise distances between the data points as well as solving root search problems. .. autosummary:: :toctree: gen_modules/ :template: myfunc_template.rst pairwise_distances binary_search false_position