interdim.reduce

Functions

reduce_dimensionality(data[, method, ...])

Perform dimensionality reduction on the input data.

Classes

FastICA([n_components, algorithm, whiten, ...])	FastICA: a fast algorithm for Independent Component Analysis.
GaussianRandomProjection([n_components, ...])	Reduce dimensionality through Gaussian random projection.
Isomap(*[, n_neighbors, radius, ...])	Isomap Embedding.
LocallyLinearEmbedding(*[, n_neighbors, ...])	Locally Linear Embedding.
MDS([n_components, metric, n_init, ...])	Multidimensional scaling.
NMF([n_components, init, solver, beta_loss, ...])	Non-Negative Matrix Factorization (NMF).
PCA([n_components, copy, whiten, ...])	Principal component analysis (PCA).
SparseRandomProjection([n_components, ...])	Reduce dimensionality through sparse random projection.
SpectralEmbedding([n_components, affinity, ...])	Spectral embedding for non-linear dimensionality reduction.
TSNE([n_components, perplexity, ...])	T-distributed Stochastic Neighbor Embedding.
TruncatedSVD([n_components, algorithm, ...])	Dimensionality reduction using truncated SVD (aka LSA).

interdim.reduce.reduce_dimensionality(data: ndarray, method: Literal['pca', 'tsne', 'umap', 'truncated_svd', 'fast_ica', 'nmf', 'isomap', 'lle', 'mds', 'spectral_embedding', 'gaussian_random_projection', 'sparse_random_projection'] = 'tsne', n_components: int = 2, **kwargs) → ndarray

Perform dimensionality reduction on the input data.

Parameters:

• data – Input data to reduce.

• method – Reduction method to use.

• n_components – Number of dimensions to reduce to.

• **kwargs – Additional arguments to pass to the reduction method.

Returns:

Reduced data.

Raises:

• ValueError – If an unsupported reduction method is specified.

• ImportError – If UMAP is not installed when ‘umap’ method is selected.