mojolearn.AffinityPropagation#

class mojolearn.AffinityPropagation(*, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False, random_state=None)[source]#

Bases: _XCluster

Affinity propagation clustering.

Follows scikit-learn’s AffinityPropagation: responsibility and availability messages are exchanged until the set of exemplars is stable for convergence_iter iterations. The similarity is minus the squared euclidean distance, or a precomputed matrix. The small noise that breaks ties is drawn from this library’s own seeded random stream rather than NumPy’s, so results agree with scikit-learn’s within a tolerance. Memory grows with the square of the number of rows. Input is converted to float32.

Parameters:

  • damping (float in [0.5, 1), default 0.5) – Damping of the message updates.

  • max_iter (int, default 200) – Maximum number of iterations; must be at least 1.

  • convergence_iter (int, default 15) – Number of iterations with no change in the exemplars that stops the fit; must be at least 1.

  • copy (bool, default True) – Accepted for scikit-learn compatibility; X is never modified.

  • preference (float, array-like of shape (n_samples,) or None, default None) – Preference of each row to be an exemplar. None uses the median of the similarities, as in scikit-learn.

  • affinity ({‘euclidean’, ‘precomputed’}, default ‘euclidean’) – With ‘precomputed’, X is a square similarity matrix.

  • verbose (bool, default False) – Accepted for scikit-learn compatibility; nothing is printed.

  • random_state (int or None, default None) – Non-negative seed of the tie-breaking noise. None means 0. A NumPy RandomState is not accepted.

  • numeric_mode (str or None, default None) – Numeric mode for this estimator. None uses the process default (see mojolearn.set_numeric_mode()).

Variables:

  • cluster_centers_indices_ (Array of shape (n_clusters,), int32) – Row indices of the exemplars.

  • cluster_centers_ (Array of shape (n_clusters, n_features), float32) – The exemplar rows. Not set with affinity='precomputed'.

  • labels_ (Array of shape (n_samples,), int32) – Cluster of each training row.

  • affinity_matrix_ (Array of shape (n_samples, n_samples), float32) – The similarity matrix used.

  • n_iter_ (int) – Number of iterations run.

  • n_features_in_ (int) – Number of features seen in fit.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).random((500, 4), dtype=np.float32)
>>> model = mojolearn.AffinityPropagation(random_state=0).fit(X)
>>> labels = model.predict(X)
__init__(*, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False, random_state=None)[source]#

Methods

fit(X[, y])

Find the exemplars of X.

fit_predict(X[, y])

Fit the model on X and return the training labels.

get_params([deep])

numeric_mode_used()

The numeric mode this estimator runs in.

predict(X)

Assign each row of X to its nearest exemplar.

set_params(**params)

Set constructor parameters.

vendor_used()

The GPU backend this estimator runs on.
fit(X, y=None)[source]#

Find the exemplars of X.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Training rows, or a square similarity matrix with affinity='precomputed'. Converted to float32; NaN or infinity raises ValueError.

  • y (ignored) – Present for scikit-learn compatibility.

Returns:

self (AffinityPropagation) – The fitted estimator.

Raises:

ValueError – If affinity, damping, max_iter, convergence_iter or preference is invalid, or a precomputed matrix is not square.

predict(X)[source]#

Assign each row of X to its nearest exemplar.

Parameters:

X (array-like of shape (n_samples, n_features)) – Rows to label, converted to float32.

Returns:

labels (Array of shape (n_samples,), int32) – Cluster of each row; every row gets -1 when the fit found no exemplars.

Raises:
fit_predict(X, y=None)[source]#

Fit the model on X and return the training labels.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Training rows, or a square similarity matrix with affinity='precomputed'.

  • y (ignored) – Present for scikit-learn compatibility.

Returns:

labels (Array of shape (n_samples,), int32) – The fitted labels_.