dasf.ml.cluster.som

Kohonen’s Self-Organized Map (SOM) algorithm module.

Classes

SOM

Initializes a Self Organizing Maps.

Module Contents

class dasf.ml.cluster.som.SOM(x, y, input_len, num_epochs=100, sigma=0, sigmaN=1, learning_rate=0.5, learning_rateN=0.01, decay_function='exponential', neighborhood_function='gaussian', std_coeff=0.5, topology='rectangular', activation_distance='euclidean', random_seed=None, n_parallel=0, compact_support=False, **kwargs)[source]

Bases: dasf.ml.cluster.classifier.ClusterClassifier

Initializes a Self Organizing Maps.

A rule of thumb to set the size of the grid for a dimensionality reduction task is that it should contain 5*sqrt(N) neurons where N is the number of samples in the dataset to analyze.

E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23 hence a map 8-by-8 should perform well.

Parameters

xint: x dimension of the SOM.
yint: y dimension of the SOM.
input_lenint: Number of the elements of the vectors in input.
sigmafloat, default=min(x,y)/2: Spread of the neighborhood function, needs to be adequate to the dimensions of the map.
sigmaNfloat, default=0.01: Spread of the neighborhood function at last iteration.
learning_ratefloat, default=0.5: initial learning rate.
learning_rateNfloat, default=0.01: final learning rate
decay_functionstring, default=’exponential’: Function that reduces learning_rate and sigma at each iteration. Possible values: ‘exponential’, ‘linear’, ‘aymptotic’
neighborhood_functionstring, default=’gaussian’: Function that weights the neighborhood of a position in the map. Possible values: ‘gaussian’, ‘mexican_hat’, ‘bubble’, ‘triangle’
topologystring, default=’rectangular’: Topology of the map. Possible values: ‘rectangular’, ‘hexagonal’
activation_distancestring, default=’euclidean’: Distance used to activate the map. Possible values: ‘euclidean’, ‘cosine’, ‘manhattan’
random_seedint, default=None: Random seed to use.
n_paralleluint, default=#max_CUDA_threads or 500*#CPUcores: Number of samples to be processed at a time. Setting a too low value may drastically lower performance due to under-utilization, setting a too high value increases memory usage without granting any significant performance benefit.
xpnumpy or cupy, default=cupy if can be imported else numpy: Use numpy (CPU) or cupy (GPU) for computations.
std_coeff: float, default=0.5: Used to calculate gausssian exponent denominator: d = 2*std_coeff**2*sigma**2
compact_support: bool, default=False: Cut the neighbor function to 0 beyond neighbor radius sigma

Examples

>>> from dasf.ml.cluster import SOM
>>> import numpy as np
>>> X = np.array([[1, 1], [2, 1], [1, 0],
...               [4, 7], [3, 5], [3, 6]])
>>> som = SOM(x=3, y=2, input_len=2,
...           num_epochs=100).fit(X)
>>> som
SOM(x=3, y=2, input_len=2, num_epochs=100)

Constructor of the class SOM.

x

y

input_len

num_epochs

sigma

sigmaN

learning_rate

learning_rateN

decay_function

neighborhood_function

std_coeff

topology

activation_distance

random_seed

n_parallel

compact_support

__som_cpu

__som_mcpu

_lazy_fit_cpu(X, y=None, sample_weight=None)[source]

Fit SOM method using Dask with CPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_lazy_fit_gpu(X, y=None, sample_weight=None)[source]

Fit SOM method using Dask with GPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_fit_cpu(X, y=None, sample_weight=None)[source]

Fit SOM method using CPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_fit_gpu(X, y=None, sample_weight=None)[source]

Fit SOM method using GPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_lazy_fit_predict_cpu(X, y=None, sample_weight=None)[source]

Fit SOM and select the winner neurons for the input using Dask with CPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

y{array-like, sparse matrix} of shape (n_samples).: This is just a placeholder to keep the compatibility with other fit_predict methods. SOM does not use labels to verify the input.
sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit_predict methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_lazy_fit_predict_gpu(X, y=None, sample_weight=None)[source]

Fit SOM and select the winner neurons for the input using Dask with GPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

y{array-like, sparse matrix} of shape (n_samples).: This is just a placeholder to keep the compatibility with other fit_predict methods. SOM does not use labels to verify the input.
sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit_predict methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_fit_predict_cpu(X, y=None, sample_weight=None)[source]

Fit SOM and select the winner neurons for the input using CPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

y{array-like, sparse matrix} of shape (n_samples).: This is just a placeholder to keep the compatibility with other fit_predict methods. SOM does not use labels to verify the input.
sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit_predict methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_fit_predict_gpu(X, y=None, sample_weight=None)[source]

Fit SOM and select the winner neurons for the input using GPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

y{array-like, sparse matrix} of shape (n_samples).: This is just a placeholder to keep the compatibility with other fit_predict methods. SOM does not use labels to verify the input.
sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit_predict methods. This is not used by SOM.

Returns

selfobject: Returns a fitted instance of self.

_lazy_predict_cpu(X, sample_weight=None)[source]

Predict the input using a fitted SOM using Dask with CPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

_lazy_predict_gpu(X, sample_weight=None)[source]

Predict the input using a fitted SOM using Dask with GPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

_predict_cpu(X, sample_weight=None)[source]

Predict the input using a fitted SOM using CPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

_predict_gpu(X, sample_weight=None)[source]

Predict the input using a fitted SOM using GPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

sample_weightarray-like of shape (n_samples,), default=None: This is just a placeholder to keep the compatibility with other fit methods. This is not used by SOM.

Returns

labelsndarray of shape (n_samples,): Cluster labels. Noisy samples are given the label -1.

_lazy_quantization_error_cpu(X)[source]

Returns the quantization error computed as the average distance between each input sample and its best matching unit using Dask with CPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

Returns

errorfloat: The quantization error of the trained SOM.

_lazy_quantization_error_gpu(X)[source]

Returns the quantization error computed as the average distance between each input sample and its best matching unit using Dask with GPUs only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

Returns

errorfloat: The quantization error of the trained SOM.

_quantization_error_cpu(X)[source]

Returns the quantization error computed as the average distance between each input sample and its best matching unit using CPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

Returns

errorfloat: The quantization error of the trained SOM.

_quantization_error_gpu(X)[source]

Returns the quantization error computed as the average distance between each input sample and its best matching unit using GPU only.

Parameters

X : {array-like, sparse matrix} of shape (n_samples, n_features).

Returns

errorfloat: The quantization error of the trained SOM.

quantization_error(X)[source]: Generic quantization_error funtion according executor (for SOM method only).