Reference

MELD density estimation

class meld.meld.MELD(beta=60, offset=0, order=1, filter='heat', solver='chebyshev', chebyshev_order=50, lap_type='combinatorial', sample_normalize=True, anisotropy=1, n_landmark=None, **kwargs)[source]

MELD operator for filtering signals over a graph.

Parameters
  • beta (int, optional, Default: 60) – Amount of smoothing to apply. Default value of 60 determined through analysis of simulated data using Splatter.

  • offset (float, optional, Default: 0) – Amount to shift the MELD filter in the eigenvalue spectrum. Recommend using an eigenvalue from the graph based on the spectral distribution. Should be in interval [0,1]

  • order (int, optional, Default: 1) – Falloff and smoothness of the filter. High order leads to square-like filters.

  • filter (str, optional, Default: 'heat') – Filter type to use. Should be in [‘heat’, ‘laplacian’]

  • solver (string, optional, Default: 'chebyshev') – Method to solve convex problem. ‘chebyshev’ uses a chebyshev polynomial approximation of the corresponding filter. ‘exact’ uses the eigenvalue solution to the problem

  • chebyshev_order (int, optional, Default: 50) – Order of chebyshev approximation to use.

  • lap_type (('combinatorial', 'normalized'), Default: 'combinatorial') – The kind of Laplacian to calculate

  • sample_normalize (boolean, optional, Default: True) – If True, the sample indicator vectors are column normalized to sum to 1

property beta

Amount of smoothing to apply. Default value of 60 determined throughanalysis of simulated data using Splatter

property chebyshev_order

Order of chebyshev approximation to use.

property filter

Filter type to use. Should be in [‘heat’, ‘laplacian’]

fit_transform(X, sample_labels, **kwargs)[source]

Builds the MELD filter over a graph built on data X and estimates density of each sample in sample_labels

Parameters
  • X (array-like, shape=[n_samples, m_features]) – Data on which to build graph to perform data smoothing over.

  • sample_labels (array-like, shape=[n_samples, p_signals]) – 1- or 2-dimensional array of non-numerics indicating the sample origin for each cell.

  • kwargs (additional arguments for graphtools.Graph) –

Returns

sample_densities – Density estimate for each sample over a graph built from X

Return type

ndarray, shape=[n_samples, p_signals]

property lap_type

The kind of Laplacian to calculate

property offset

Amount to shift the MELD filter in the eigenvalue spectrum.Recommend using an eigenvalue from the graph based on thespectral distribution. Should be in interval [0,1]

property order

Falloff and smoothness of the filter.High order leads to square-like filters.

property sample_densities

Density associated with each sample

property solver

Method to solve convex problem.’chebyshev’ uses a chebyshev polynomial approximation of the correspondingfilter. ‘exact’ uses the eigenvalue solution to the problem

transform(sample_labels)[source]

Filters a collection of sample_indicators over the data graph.

Parameters

sample_indicators (ndarray [n, p]) – 1- or 2-dimensional sample indicator array to filter.

Returns

sample_densities – A density estimate for each sample.

Return type

ndarray [n, p]

Vertex Frequency Clustering

class meld.cluster.VertexFrequencyCluster(n_clusters=10, likelihood_bias=1, window_count=9, window_sizes=None, sparse=False, suppress=False, random_state=None, **kwargs)[source]

Bases: sklearn.base.BaseEstimator

Performs Vertex Frequency clustering for data given a

raw experimental signal and enhanced experimental signal.

Parameters
  • n_clusters (int, optional, default: 10) – The number of clusters to form.

  • likelihood_bias (float, optional, default: 1) – A normalization term that biases clustering towards the likelihood (higher values) or towards the spectrogram (lower values)

  • window_count (int, optional, default: 9) – Number of windows to use if window_sizes = None

  • window_sizes (None, optional, default: None) – ndarray of integer window sizes to supply to t

  • sparse (bool, optional, default: False) – Use sparse matrices. This is significantly slower, but will use less memory

  • suppress (bool, optional) – Suppress warnings

  • random_state (int or None, optional (default: None)) – Random seed for clustering

  • **kwargs – Description

Raises

NotImplementedError – Window functions are not implemented

Examples

fit(G)[source]

Sets eigenvectors and windows.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(n_clusters=None, **kwargs)[source]

Runs KMeans on the spectrogram.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

transform(sample_indicator, likelihood=None, center=True)[source]

Calculates the spectrogram of the graph using the sample_indicator