SOMClustering

class susi.SOMClustering(n_rows: int = 10, n_columns: int = 10, *, init_mode_unsupervised: str = 'random', n_iter_unsupervised: int = 1000, train_mode_unsupervised: str = 'online', neighborhood_mode_unsupervised: str = 'linear', learn_mode_unsupervised: str = 'min', distance_metric: str = 'euclidean', learning_rate_start: float = 0.5, learning_rate_end: float = 0.05, nbh_dist_weight_mode: str = 'pseudo-gaussian', n_jobs: int | None = None, random_state=None, verbose: int | None = 0)[source]

Bases: object

Unsupervised self-organizing map for clustering.

Parameters:

  • n_rows (int, optional (default=10)) – Number of rows for the SOM grid

  • n_columns (int, optional (default=10)) – Number of columns for the SOM grid

  • init_mode_unsupervised (str, optional (default=”random”)) – Initialization mode of the unsupervised SOM

  • n_iter_unsupervised (int, optional (default=1000)) – Number of iterations for the unsupervised SOM

  • train_mode_unsupervised (str, optional (default=”online”)) – Training mode of the unsupervised SOM

  • neighborhood_mode_unsupervised (str, optional (default=”linear”)) – Neighborhood mode of the unsupervised SOM

  • learn_mode_unsupervised (str, optional (default=”min”)) – Learning mode of the unsupervised SOM

  • distance_metric (str, optional (default=”euclidean”)) – Distance metric to compare on feature level (not SOM grid). Possible metrics: {“euclidean”, “manhattan”, “mahalanobis”, “tanimoto”, “spectralangle”}. Note that “tanimoto” tends to be slow.

    New in version 1.1.1: Spectral angle metric.

  • learning_rate_start (float, optional (default=0.5)) – Learning rate start value

  • learning_rate_end (float, optional (default=0.05)) – Learning rate end value (only needed for some lr definitions)

  • nbh_dist_weight_mode (str, optional (default=”pseudo-gaussian”)) – Formula of the neighborhood distance weight. Possible formulas are: {“pseudo-gaussian”, “mexican-hat”}.

  • n_jobs (int or None, optional (default=None)) – The number of jobs to run in parallel.

  • random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

  • verbose (int, optional (default=0)) – Controls the verbosity.

Variables:

  • node_list (np.ndarray of (int, int) tuples) – List of 2-dimensional coordinates of SOM nodes

  • radius_max (float, int) – Maximum radius of the neighborhood function

  • radius_min (float, int) – Minimum radius of the neighborhood function

  • unsuper_som (np.ndarray) – Weight vectors of the unsupervised SOM shape = (self.n_rows, self.n_columns, X.shape[1])

  • X (np.ndarray) – Input data

  • fitted (boolean) – States if estimator is fitted to X

  • max_iterations (int) – Maximum number of iterations for the current training

  • bmus (list of (int, int) tuples) – List of best matching units (BMUs) of the dataset X

  • variances (array of float) – Standard deviations of every feature

fit(X: Sequence, y: Sequence | None = None)[source]

Fit unsupervised SOM to input data.

Parameters:

  • X (array-like matrix of shape = [n_samples, n_features]) – The training input samples.

  • y (None) – Not used in this class.

Returns:

self

Return type:

object

Examples

Load the SOM and fit it to your input data X with:

>>> import susi
>>> som = susi.SOMClustering()
>>> som.fit(X)
fit_transform(X: Sequence, y: Sequence | None = None) → ndarray[source]

Fit to the input data and transform it.

Parameters:

  • X (array-like matrix of shape = [n_samples, n_features]) – The training and prediction input samples.

  • y (None, optional) – Ignored.

Returns:

Predictions including the BMUs of each datapoint

Return type:

np.array of tuples (int, int)

Examples

Load the SOM, fit it to your input data X and transform your input data with:

>>> import susi
>>> som = susi.SOMClustering()
>>> X_transformed = som.fit_transform(X)
get_bmu(datapoint: ndarray, som_array: ndarray) → Tuple[int, int][source]

Get best matching unit (BMU) for datapoint.

Parameters:

  • datapoint (np.ndarray, shape=shape[1]) – Datapoint = one row of the dataset X

  • som_array (np.ndarray) – Weight vectors of the SOM shape = (self.n_rows, self.n_columns, X.shape[1])

Returns:

Position of best matching unit (row, column)

Return type:

tuple, shape = (int, int)

get_bmus(X: ndarray, som_array: ndarray | None = None) → List[Tuple[int, int]] | None[source]

Get Best Matching Units for big datalist.

Parameters:

  • X (np.ndarray) – List of datapoints

  • som_array (np.ndarray, optional (default=`None`)) – Weight vectors of the SOM shape = (self.n_rows, self.n_columns, X.shape[1])

Returns:

bmus – Position of best matching units (row, column) for each datapoint

Return type:

list of (int, int) tuples

Examples

Load the SOM, fit it to your input data X and transform your input data with:

>>> import susi
>>> import matplotlib.pyplot as plt
>>> som = susi.SOMClustering()
>>> som.fit(X)
>>> bmu_list = som.get_bmus(X)
>>> plt.hist2d([x[0] for x in bmu_list], [x[1] for x in bmu_list]
get_clusters(X: ndarray) → List[Tuple[int, int]] | None[source]

Calculate the SOM nodes on the unsupervised SOM grid per datapoint.

Parameters:

X (np.ndarray) – Input data

Returns:

List of SOM nodes, one for each input datapoint

Return type:

list of tuples (int, int)

get_datapoints_from_node(node: Tuple[int, int]) → List[int][source]

Get all datapoints of one node.

Parameters:

node (tuple, shape (int, int)) – Node for which the linked datapoints are calculated

Returns:

datapoints – List of indices of the datapoints that are linked to node

Return type:

list of int

get_quantization_error(X: Sequence | None = None) → float[source]

Get quantization error for X (or the training data).

Parameters:

X (array-like matrix, optional (default=True)) – Samples of shape = [n_samples, n_features]. If None, the training data is used for the calculation.

Returns:

Mean quantization error over all datapoints.

Return type:

float

Raises:

RuntimeError – Raised if the SOM is not fitted yet.

get_u_matrix(mode: str = 'mean') → ndarray[source]

Calculate unified distance matrix (u-matrix).

Parameters:

mode (str, optional (default=”mean)) – Choice of the averaging algorithm

Returns:

u_matrix – U-matrix containing the distances between all nodes of the unsupervised SOM. Shape = (n_rows*2-1, n_columns*2-1)

Return type:

np.ndarray

Examples

Fit your SOM to input data X and then calculate the u-matrix with get_u_matrix(). You can plot the u-matrix then with e.g. pyplot.imshow().

>>> import susi
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> som = susi.SOMClustering()
>>> som.fit(X)
>>> umat = som.get_u_matrix()
>>> plt.imshow(np.squeeze(umat))
transform(X: Sequence, y: Sequence | None = None) → ndarray[source]

Transform input data.

Parameters:

  • X (array-like matrix of shape = [n_samples, n_features]) – The prediction input samples.

  • y (None, optional) – Ignored.

Returns:

Predictions including the BMUs of each datapoint

Return type:

np.array of tuples (int, int)

Examples

Load the SOM, fit it to your input data X and transform your input data with:

>>> import susi
>>> som = susi.SOMClustering()
>>> som.fit(X)
>>> X_transformed = som.transform(X)