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 squarelike 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 (arraylike, shape=[n_samples, m_features]) – Data on which to build graph to perform data smoothing over.
sample_labels (arraylike, shape=[n_samples, p_signals]) – 1 or 2dimensional array of nonnumerics 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 squarelike 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
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

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
mapping of string to any

set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
object