mojolearn.MDS#

class mojolearn.MDS(n_components=2, *, metric_mds=True, n_init=1, init='random', max_iter=300, verbose=0, eps=1e-06, n_jobs=None, random_state=None, metric='euclidean', metric_params=None, normalized_stress='auto', numeric_mode=None)[source]#

Bases: _Base

Multidimensional scaling by SMACOF.

Follows scikit-learn’s MDS: minimizes stress with the Guttman transform from n_init starts and keeps the start with the lowest stress. metric_mds=False runs Kruskal’s non-metric SMACOF. Random starts come from mojolearn’s own generator.

Parameters:

  • n_components (int, default 2) – Embedding dimension.

  • metric_mds (bool, default True) – Metric (True) or non-metric (False) MDS.

  • n_init (int, default 1) – Number of random starts (with init="random").

  • init ({“random”, “classical_mds”} or array-like, default “random”) – Starting configuration: random, the ClassicalMDS embedding, or an array of shape (n_samples, n_components).

  • max_iter (int, default 300) – Maximum SMACOF iterations per start.

  • verbose (ignored)

  • eps (float, default 1e-6) – Convergence tolerance on the stress.

  • n_jobs (ignored)

  • random_state (int or None, default None) – Seed for random starts; None means 0.

  • metric (str, default “euclidean”) – "precomputed" (X is a dissimilarity matrix) or a distance metric: "euclidean", "l2", "minkowski" (p from metric_params), "manhattan" ("cityblock", "l1"), "chebyshev" ("infinity") or "cosine"; a callable or any other name raises NotImplementedError.

  • metric_params (dict or None, default None) – Only {"p": ...} is supported.

  • normalized_stress (bool or “auto”, default “auto”) – Report normalized stress (Kruskal’s stress-1). "auto" is True for non-metric MDS and False otherwise.

  • numeric_mode ({“identical”, “fast”} or None, default None) – Numeric mode for this estimator. None uses the package default ("identical" unless changed). "identical" gives the same bits on NVIDIA and AMD GPUs; "fast" applies on Apple GPUs.

Variables:

  • embedding_ (ndarray of shape (n_samples, n_components))

  • stress_ (float)

  • n_iter_ (int)

  • dissimilarity_matrix_ (ndarray of shape (n_samples, n_samples))

  • n_features_in_ (int)

Raises:

ValueError – For an unknown init, or with metric_mds=False when n_samples**2 exceeds 2**31 - 1.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((300, 8), dtype=np.float32)
>>> Y = mojolearn.MDS(n_components=2, random_state=0).fit_transform(X)
__init__(n_components=2, *, metric_mds=True, n_init=1, init='random', max_iter=300, verbose=0, eps=1e-06, n_jobs=None, random_state=None, metric='euclidean', metric_params=None, normalized_stress='auto', numeric_mode=None)[source]#

Methods

fit(X[, y, init])

Compute the embedding.

fit_transform(X[, y, init])

Compute the embedding and return it.

get_params([deep])

Return this estimator's constructor parameters.

set_params(**params)

Set constructor parameters by keyword.
fit_transform(X, y=None, init=None)[source]#

Compute the embedding and return it.

Parameters:

  • X (array-like of shape (n_samples, n_features) or (n_samples, n_samples)) – Data, or dissimilarities with metric="precomputed".

  • y (ignored)

  • init (array-like of shape (n_samples, n_components) or None, default None) – Starting configuration; overrides the init parameter.

Returns:

ndarray of shape (n_samples, n_components), float32

fit(X, y=None, init=None)[source]#

Compute the embedding.

Parameters:

  • X (array-like of shape (n_samples, n_features) or (n_samples, n_samples))

  • y (ignored)

  • init (array-like of shape (n_samples, n_components) or None, default None) – Starting configuration; overrides the init parameter.

Returns:

self